From 010956049e51ffe567cff314b65ad044f7fb4ada Mon Sep 17 00:00:00 2001 From: Mathieu Guillame-Bert Date: Fri, 18 Aug 2023 22:08:54 +0200 Subject: [PATCH 1/3] Update M5 tuto with new tp merged plotting --- docs/src/tutorials/m5_competition.ipynb | 4158 ++++++++++++++++++----- 1 file changed, 3294 insertions(+), 864 deletions(-) diff --git a/docs/src/tutorials/m5_competition.ipynb b/docs/src/tutorials/m5_competition.ipynb index 11468ace9..046438f93 100644 --- a/docs/src/tutorials/m5_competition.ipynb +++ b/docs/src/tutorials/m5_competition.ipynb @@ -48,20 +48,19 @@ "cell_type": "code", "execution_count": 1, "id": "52cc838e-e565-4d86-9eae-8a686ad6b0c7", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:50:21.370161Z", - "iopub.status.busy": "2023-07-24T15:50:21.369911Z", - "iopub.status.idle": "2023-07-24T15:50:23.570967Z", - "shell.execute_reply": "2023-07-24T15:50:23.570490Z" - } - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ + "\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.1.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.2.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n", "Note: you may need to restart the kernel to use updated packages.\n", + "\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.1.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.2.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n", "Note: you may need to restart the kernel to use updated packages.\n" ] } @@ -78,14 +77,7 @@ "cell_type": "code", "execution_count": 2, "id": "4e0d9e3a-b105-48e1-a47d-295312604774", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:50:23.573520Z", - "iopub.status.busy": "2023-07-24T15:50:23.573332Z", - "iopub.status.idle": "2023-07-24T15:50:24.174748Z", - "shell.execute_reply": "2023-07-24T15:50:24.174387Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "import os\n", @@ -114,14 +106,7 @@ "cell_type": "code", "execution_count": 3, "id": "ddb18926-5aec-4f36-8b04-008488eade04", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:50:24.177184Z", - "iopub.status.busy": "2023-07-24T15:50:24.176812Z", - "iopub.status.idle": "2023-07-24T15:50:24.180479Z", - "shell.execute_reply": "2023-07-24T15:50:24.180152Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "# Directory to download the raw M5 dataset and to export the result.\n", @@ -145,25 +130,18 @@ "cell_type": "code", "execution_count": 4, "id": "105c5f4a-0ab1-4de1-b46b-5b80254a5ee1", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:50:24.182231Z", - "iopub.status.busy": "2023-07-24T15:50:24.182127Z", - "iopub.status.idle": "2023-07-24T15:50:24.306289Z", - "shell.execute_reply": "2023-07-24T15:50:24.305746Z" - } - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "total 879848\r\n", - "-rw-r--r-- 1 brauliorios staff 101K Jun 26 18:14 calendar.csv\r\n", - "-rw-r--r-- 1 brauliorios staff 116M Jun 26 18:14 sales_train_evaluation.csv\r\n", - "-rw-r--r-- 1 brauliorios staff 114M Jun 26 18:14 sales_train_validation.csv\r\n", - "-rw-r--r-- 1 brauliorios staff 5.0M Jun 26 18:14 sample_submission.csv\r\n", - "-rw-r--r-- 1 brauliorios staff 194M Jun 26 18:14 sell_prices.csv\r\n" + "total 430M\n", + "-rw-r----- 1 gbm primarygroup 102K Aug 18 21:48 calendar.csv\n", + "-rw-r----- 1 gbm primarygroup 117M Aug 18 21:48 sales_train_evaluation.csv\n", + "-rw-r----- 1 gbm primarygroup 115M Aug 18 21:48 sales_train_validation.csv\n", + "-rw-r----- 1 gbm primarygroup 5.0M Aug 18 21:48 sample_submission.csv\n", + "-rw-r----- 1 gbm primarygroup 194M Aug 18 21:48 sell_prices.csv\n" ] } ], @@ -186,14 +164,7 @@ "cell_type": "code", "execution_count": 5, "id": "e144ebe2-dc46-46e3-8d89-67355aed7000", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:50:24.309677Z", - "iopub.status.busy": "2023-07-24T15:50:24.309367Z", - "iopub.status.idle": "2023-07-24T15:50:28.170843Z", - "shell.execute_reply": "2023-07-24T15:50:28.170501Z" - } - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -283,14 +254,7 @@ "cell_type": "code", "execution_count": 6, "id": "be833bab-ef50-4809-8341-23938b1279f4", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:50:28.172923Z", - "iopub.status.busy": "2023-07-24T15:50:28.172627Z", - "iopub.status.idle": "2023-07-24T15:50:28.280421Z", - "shell.execute_reply": "2023-07-24T15:50:28.280094Z" - } - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -348,14 +312,7 @@ "cell_type": "code", "execution_count": 7, "id": "df78d002-e33d-4b02-aec0-d5deca909805", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:50:28.282332Z", - "iopub.status.busy": "2023-07-24T15:50:28.282195Z", - "iopub.status.idle": "2023-07-24T15:50:28.293206Z", - "shell.execute_reply": "2023-07-24T15:50:28.292904Z" - } - }, + "metadata": {}, "outputs": [ { "data": { @@ -590,14 +547,7 @@ "cell_type": "code", "execution_count": 8, "id": "6b55d1f4-cf72-4f62-92ee-c988b94fc01d", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:50:28.295097Z", - "iopub.status.busy": "2023-07-24T15:50:28.294840Z", - "iopub.status.idle": "2023-07-24T15:50:28.370472Z", - "shell.execute_reply": "2023-07-24T15:50:28.370022Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "cleaned_sales = pd.melt(\n", @@ -623,14 +573,7 @@ "cell_type": "code", "execution_count": 9, "id": "cfe44b50-afbc-40ab-bd64-7ec231baf123", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:50:28.372509Z", - "iopub.status.busy": "2023-07-24T15:50:28.372371Z", - "iopub.status.idle": "2023-07-24T15:50:28.479408Z", - "shell.execute_reply": "2023-07-24T15:50:28.478990Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "origin_date = datetime(2011, 1, 29, tzinfo=timezone.utc)\n", @@ -652,14 +595,7 @@ "cell_type": "code", "execution_count": 10, "id": "4688a3e7-adff-449c-a5dc-0e052b52c96d", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:50:28.482001Z", - "iopub.status.busy": "2023-07-24T15:50:28.481746Z", - "iopub.status.idle": "2023-07-24T15:50:28.484192Z", - "shell.execute_reply": "2023-07-24T15:50:28.483849Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "del cleaned_sales[\"id\"]" @@ -678,14 +614,7 @@ "cell_type": "code", "execution_count": 11, "id": "6fa03718-4050-400b-b5cb-f8402a95766a", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:50:28.486101Z", - "iopub.status.busy": "2023-07-24T15:50:28.485926Z", - "iopub.status.idle": "2023-07-24T15:50:28.491611Z", - "shell.execute_reply": "2023-07-24T15:50:28.491334Z" - } - }, + "metadata": {}, "outputs": [ { "data": { @@ -816,14 +745,7 @@ "cell_type": "code", "execution_count": 12, "id": "801c3ab0-de0b-400d-9416-a74917c507dc", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:50:28.493265Z", - "iopub.status.busy": "2023-07-24T15:50:28.493140Z", - "iopub.status.idle": "2023-07-24T15:50:29.286134Z", - "shell.execute_reply": "2023-07-24T15:50:29.285793Z" - } - }, + "metadata": {}, "outputs": [ { "name": "stderr", @@ -838,6 +760,9 @@ }, { "data": { + "text/html": [ + "
80 indexes × 2 features (memory usage: 3.8 MB)

Index: (item_id=FOODS_3_090, dept_id=FOODS_3, cat_id=FOODS, store_id=CA_1, state_id=CA)

1941 events
timestampdaysales
2011-01-29 00:00:00+00:001107
2011-01-30 00:00:00+00:002182
2011-01-31 00:00:00+00:00347
2011-02-01 00:00:00+00:00447
2011-02-02 00:00:00+00:00562
2011-02-03 00:00:00+00:00690
2011-02-04 00:00:00+00:00781
2011-02-05 00:00:00+00:008124
2011-02-06 00:00:00+00:009134
2011-02-07 00:00:00+00:001065
2011-02-08 00:00:00+00:00110
2011-02-09 00:00:00+00:00120
2011-02-10 00:00:00+00:00130
2011-02-11 00:00:00+00:00140
2011-02-12 00:00:00+00:00150
2011-02-13 00:00:00+00:00160
2011-02-14 00:00:00+00:00170
2011-02-15 00:00:00+00:00180
2011-02-16 00:00:00+00:00190
2011-02-17 00:00:00+00:00200
………

Index: (item_id=FOODS_3_090, dept_id=FOODS_3, cat_id=FOODS, store_id=CA_2, state_id=CA)

1941 events
timestampdaysales
2011-01-29 00:00:00+00:001116
2011-01-30 00:00:00+00:00290
2011-01-31 00:00:00+00:00335
2011-02-01 00:00:00+00:00433
2011-02-02 00:00:00+00:00526
2011-02-03 00:00:00+00:00638
2011-02-04 00:00:00+00:007100
2011-02-05 00:00:00+00:00889
2011-02-06 00:00:00+00:00971
2011-02-07 00:00:00+00:00100
2011-02-08 00:00:00+00:00110
2011-02-09 00:00:00+00:00120
2011-02-10 00:00:00+00:00130
2011-02-11 00:00:00+00:00140
2011-02-12 00:00:00+00:00150
2011-02-13 00:00:00+00:00160
2011-02-14 00:00:00+00:00170
2011-02-15 00:00:00+00:00180
2011-02-16 00:00:00+00:00190
2011-02-17 00:00:00+00:00200
………

Index: (item_id=FOODS_3_090, dept_id=FOODS_3, cat_id=FOODS, store_id=CA_3, state_id=CA)

1941 events
timestampdaysales
2011-01-29 00:00:00+00:001108
2011-01-30 00:00:00+00:002132
2011-01-31 00:00:00+00:003102
2011-02-01 00:00:00+00:004120
2011-02-02 00:00:00+00:005106
2011-02-03 00:00:00+00:006123
2011-02-04 00:00:00+00:007279
2011-02-05 00:00:00+00:008175
2011-02-06 00:00:00+00:009186
2011-02-07 00:00:00+00:0010120
2011-02-08 00:00:00+00:00110
2011-02-09 00:00:00+00:00120
2011-02-10 00:00:00+00:00130
2011-02-11 00:00:00+00:00140
2011-02-12 00:00:00+00:00150
2011-02-13 00:00:00+00:00160
2011-02-14 00:00:00+00:00170
2011-02-15 00:00:00+00:00180
2011-02-16 00:00:00+00:00190
2011-02-17 00:00:00+00:00200
………

Index: (item_id=FOODS_3_090, dept_id=FOODS_3, cat_id=FOODS, store_id=CA_4, state_id=CA)

1941 events
timestampdaysales
2011-01-29 00:00:00+00:00169
2011-01-30 00:00:00+00:00249
2011-01-31 00:00:00+00:00337
2011-02-01 00:00:00+00:00440
2011-02-02 00:00:00+00:00538
2011-02-03 00:00:00+00:00639
2011-02-04 00:00:00+00:00761
2011-02-05 00:00:00+00:00879
2011-02-06 00:00:00+00:009102
2011-02-07 00:00:00+00:001086
2011-02-08 00:00:00+00:00110
2011-02-09 00:00:00+00:00120
2011-02-10 00:00:00+00:00130
2011-02-11 00:00:00+00:00140
2011-02-12 00:00:00+00:00150
2011-02-13 00:00:00+00:00160
2011-02-14 00:00:00+00:00170
2011-02-15 00:00:00+00:00180
2011-02-16 00:00:00+00:00190
2011-02-17 00:00:00+00:00200
………

Index: (item_id=FOODS_3_090, dept_id=FOODS_3, cat_id=FOODS, store_id=TX_1, state_id=TX)

1941 events
timestampdaysales
2011-01-29 00:00:00+00:00175
2011-01-30 00:00:00+00:00254
2011-01-31 00:00:00+00:00374
2011-02-01 00:00:00+00:00462
2011-02-02 00:00:00+00:00560
2011-02-03 00:00:00+00:006106
2011-02-04 00:00:00+00:00796
2011-02-05 00:00:00+00:008118
2011-02-06 00:00:00+00:009177
2011-02-07 00:00:00+00:001051
2011-02-08 00:00:00+00:00110
2011-02-09 00:00:00+00:00120
2011-02-10 00:00:00+00:00130
2011-02-11 00:00:00+00:00140
2011-02-12 00:00:00+00:00150
2011-02-13 00:00:00+00:00160
2011-02-14 00:00:00+00:00170
2011-02-15 00:00:00+00:00180
2011-02-16 00:00:00+00:00190
2011-02-17 00:00:00+00:00200
………
… (75 more indexes not shown)
" + ], "text/plain": [ "indexes: [('item_id', str_), ('dept_id', str_), ('cat_id', str_), ('store_id', str_), ('state_id', str_)]\n", "features: [('day', int64), ('sales', int64)]\n", @@ -862,11 +787,7 @@ " timestamps: [1.2963e+09 1.2963e+09 1.2964e+09 ... 1.4637e+09 1.4638e+09 1.4639e+09]\n", " 'day': [ 1 2 3 ... 1939 1940 1941]\n", " 'sales': [75 54 74 ... 68 62 82]\n", - " item_id=b'FOODS_3_090' dept_id=b'FOODS_3' cat_id=b'FOODS' store_id=b'TX_2' state_id=b'TX' (1941 events):\n", - " timestamps: [1.2963e+09 1.2963e+09 1.2964e+09 ... 1.4637e+09 1.4638e+09 1.4639e+09]\n", - " 'day': [ 1 2 3 ... 1939 1940 1941]\n", - " 'sales': [164 215 94 ... 75 64 75]\n", - " ... (74 remaining)\n", + " ... (showing 5 of 80 indexes)\n", "memory usage: 3.8 MB" ] }, @@ -897,14 +818,7 @@ "cell_type": "code", "execution_count": 13, "id": "bb44fde1-d2b4-4ff8-b0c2-4367ee95e514", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:50:29.288073Z", - "iopub.status.busy": "2023-07-24T15:50:29.287934Z", - "iopub.status.idle": "2023-07-24T15:50:29.857210Z", - "shell.execute_reply": "2023-07-24T15:50:29.856827Z" - } - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -915,8 +829,285 @@ }, { "data": { - "application/javascript": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\nconst JS_MIME_TYPE = 'application/javascript';\n const HTML_MIME_TYPE = 'text/html';\n const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n const CLASS_NAME = 'output_bokeh rendered_html';\n\n /**\n * Render data to the DOM node\n */\n function render(props, node) {\n const script = document.createElement(\"script\");\n node.appendChild(script);\n }\n\n /**\n * Handle when an output is cleared or removed\n */\n function handleClearOutput(event, handle) {\n const cell = handle.cell;\n\n const id = cell.output_area._bokeh_element_id;\n const server_id = cell.output_area._bokeh_server_id;\n // Clean up Bokeh references\n if (id != null && id in Bokeh.index) {\n Bokeh.index[id].model.document.clear();\n delete Bokeh.index[id];\n }\n\n if (server_id !== undefined) {\n // Clean up Bokeh references\n const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n cell.notebook.kernel.execute(cmd_clean, {\n iopub: {\n output: function(msg) {\n const id = msg.content.text.trim();\n if (id in Bokeh.index) {\n Bokeh.index[id].model.document.clear();\n delete Bokeh.index[id];\n }\n }\n }\n });\n // Destroy server and session\n const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n cell.notebook.kernel.execute(cmd_destroy);\n }\n }\n\n /**\n * Handle when a new output is added\n */\n function handleAddOutput(event, handle) {\n const output_area = handle.output_area;\n const output = handle.output;\n\n // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n return\n }\n\n const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n\n if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n // store reference to embed id on output_area\n output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n }\n if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n const bk_div = document.createElement(\"div\");\n bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n const script_attrs = bk_div.children[0].attributes;\n for (let i = 0; i < script_attrs.length; i++) {\n toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n }\n // store reference to server id on output_area\n output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n }\n }\n\n function register_renderer(events, OutputArea) {\n\n function append_mime(data, metadata, element) {\n // create a DOM node to render to\n const toinsert = this.create_output_subarea(\n metadata,\n CLASS_NAME,\n EXEC_MIME_TYPE\n );\n this.keyboard_manager.register_events(toinsert);\n // Render to node\n const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n render(props, toinsert[toinsert.length - 1]);\n element.append(toinsert);\n return toinsert\n }\n\n /* Handle when an output is cleared or removed */\n events.on('clear_output.CodeCell', handleClearOutput);\n events.on('delete.Cell', handleClearOutput);\n\n /* Handle when a new output is added */\n events.on('output_added.OutputArea', handleAddOutput);\n\n /**\n * Register the mime type and append_mime function with output_area\n */\n OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n /* Is output safe? */\n safe: true,\n /* Index of renderer in `output_area.display_order` */\n index: 0\n });\n }\n\n // register the mime type if in Jupyter Notebook environment and previously unregistered\n if (root.Jupyter !== undefined) {\n const events = require('base/js/events');\n const OutputArea = require('notebook/js/outputarea').OutputArea;\n\n if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n register_renderer(events, OutputArea);\n }\n }\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n const el = document.getElementById(null);\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.1.1.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\n} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(null)).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));", - "application/vnd.bokehjs_load.v0+json": "" + "application/javascript": [ + "(function(root) {\n", + " function now() {\n", + " return new Date();\n", + " }\n", + "\n", + " const force = true;\n", + "\n", + " if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n", + " root._bokeh_onload_callbacks = [];\n", + " root._bokeh_is_loading = undefined;\n", + " }\n", + "\n", + "const JS_MIME_TYPE = 'application/javascript';\n", + " const HTML_MIME_TYPE = 'text/html';\n", + " const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", + " const CLASS_NAME = 'output_bokeh rendered_html';\n", + "\n", + " /**\n", + " * Render data to the DOM node\n", + " */\n", + " function render(props, node) {\n", + " const script = document.createElement(\"script\");\n", + " node.appendChild(script);\n", + " }\n", + "\n", + " /**\n", + " * Handle when an output is cleared or removed\n", + " */\n", + " function handleClearOutput(event, handle) {\n", + " const cell = handle.cell;\n", + "\n", + " const id = cell.output_area._bokeh_element_id;\n", + " const server_id = cell.output_area._bokeh_server_id;\n", + " // Clean up Bokeh references\n", + " if (id != null && id in Bokeh.index) {\n", + " Bokeh.index[id].model.document.clear();\n", + " delete Bokeh.index[id];\n", + " }\n", + "\n", + " if (server_id !== undefined) {\n", + " // Clean up Bokeh references\n", + " const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", + " cell.notebook.kernel.execute(cmd_clean, {\n", + " iopub: {\n", + " output: function(msg) {\n", + " const id = msg.content.text.trim();\n", + " if (id in Bokeh.index) {\n", + " Bokeh.index[id].model.document.clear();\n", + " delete Bokeh.index[id];\n", + " }\n", + " }\n", + " }\n", + " });\n", + " // Destroy server and session\n", + " const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", + " cell.notebook.kernel.execute(cmd_destroy);\n", + " }\n", + " }\n", + "\n", + " /**\n", + " * Handle when a new output is added\n", + " */\n", + " function handleAddOutput(event, handle) {\n", + " const output_area = handle.output_area;\n", + " const output = handle.output;\n", + "\n", + " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", + " if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n", + " return\n", + " }\n", + "\n", + " const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", + "\n", + " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", + " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n", + " // store reference to embed id on output_area\n", + " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", + " }\n", + " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", + " const bk_div = document.createElement(\"div\");\n", + " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", + " const script_attrs = bk_div.children[0].attributes;\n", + " for (let i = 0; i < script_attrs.length; i++) {\n", + " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", + " toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n", + " }\n", + " // store reference to server id on output_area\n", + " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", + " }\n", + " }\n", + "\n", + " function register_renderer(events, OutputArea) {\n", + "\n", + " function append_mime(data, metadata, element) {\n", + " // create a DOM node to render to\n", + " const toinsert = this.create_output_subarea(\n", + " metadata,\n", + " CLASS_NAME,\n", + " EXEC_MIME_TYPE\n", + " );\n", + " this.keyboard_manager.register_events(toinsert);\n", + " // Render to node\n", + " const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", + " render(props, toinsert[toinsert.length - 1]);\n", + " element.append(toinsert);\n", + " return toinsert\n", + " }\n", + "\n", + " /* Handle when an output is cleared or removed */\n", + " events.on('clear_output.CodeCell', handleClearOutput);\n", + " events.on('delete.Cell', handleClearOutput);\n", + "\n", + " /* Handle when a new output is added */\n", + " events.on('output_added.OutputArea', handleAddOutput);\n", + "\n", + " /**\n", + " * Register the mime type and append_mime function with output_area\n", + " */\n", + " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", + " /* Is output safe? */\n", + " safe: true,\n", + " /* Index of renderer in `output_area.display_order` */\n", + " index: 0\n", + " });\n", + " }\n", + "\n", + " // register the mime type if in Jupyter Notebook environment and previously unregistered\n", + " if (root.Jupyter !== undefined) {\n", + " const events = require('base/js/events');\n", + " const OutputArea = require('notebook/js/outputarea').OutputArea;\n", + "\n", + " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", + " register_renderer(events, OutputArea);\n", + " }\n", + " }\n", + " if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n", + " root._bokeh_timeout = Date.now() + 5000;\n", + " root._bokeh_failed_load = false;\n", + " }\n", + "\n", + " const NB_LOAD_WARNING = {'data': {'text/html':\n", + " \"
\\n\"+\n", + " \"

\\n\"+\n", + " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", + " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", + " \"

\\n\"+\n", + " \"\\n\"+\n", + " \"\\n\"+\n", + " \"from bokeh.resources import INLINE\\n\"+\n", + " \"output_notebook(resources=INLINE)\\n\"+\n", + " \"\\n\"+\n", + " \"
\"}};\n", + "\n", + " function display_loaded() {\n", + " const el = document.getElementById(null);\n", + " if (el != null) {\n", + " el.textContent = \"BokehJS is loading...\";\n", + " }\n", + " if (root.Bokeh !== undefined) {\n", + " if (el != null) {\n", + " el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n", + " }\n", + " } else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(display_loaded, 100)\n", + " }\n", + " }\n", + "\n", + " function run_callbacks() {\n", + " try {\n", + " root._bokeh_onload_callbacks.forEach(function(callback) {\n", + " if (callback != null)\n", + " callback();\n", + " });\n", + " } finally {\n", + " delete root._bokeh_onload_callbacks\n", + " }\n", + " console.debug(\"Bokeh: all callbacks have finished\");\n", + " }\n", + "\n", + " function load_libs(css_urls, js_urls, callback) {\n", + " if (css_urls == null) css_urls = [];\n", + " if (js_urls == null) js_urls = [];\n", + "\n", + " root._bokeh_onload_callbacks.push(callback);\n", + " if (root._bokeh_is_loading > 0) {\n", + " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", + " return null;\n", + " }\n", + " if (js_urls == null || js_urls.length === 0) {\n", + " run_callbacks();\n", + " return null;\n", + " }\n", + " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", + " root._bokeh_is_loading = css_urls.length + js_urls.length;\n", + "\n", + " function on_load() {\n", + " root._bokeh_is_loading--;\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", + " run_callbacks()\n", + " }\n", + " }\n", + "\n", + " function on_error(url) {\n", + " console.error(\"failed to load \" + url);\n", + " }\n", + "\n", + " for (let i = 0; i < css_urls.length; i++) {\n", + " const url = css_urls[i];\n", + " const element = document.createElement(\"link\");\n", + " element.onload = on_load;\n", + " element.onerror = on_error.bind(null, url);\n", + " element.rel = \"stylesheet\";\n", + " element.type = \"text/css\";\n", + " element.href = url;\n", + " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", + " document.body.appendChild(element);\n", + " }\n", + "\n", + " for (let i = 0; i < js_urls.length; i++) {\n", + " const url = js_urls[i];\n", + " const element = document.createElement('script');\n", + " element.onload = on_load;\n", + " element.onerror = on_error.bind(null, url);\n", + " element.async = false;\n", + " element.src = url;\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.head.appendChild(element);\n", + " }\n", + " };\n", + "\n", + " function inject_raw_css(css) {\n", + " const element = document.createElement(\"style\");\n", + " element.appendChild(document.createTextNode(css));\n", + " document.body.appendChild(element);\n", + " }\n", + "\n", + " const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.1.1.min.js\"];\n", + " const css_urls = [];\n", + "\n", + " const inline_js = [ function(Bokeh) {\n", + " Bokeh.set_log_level(\"info\");\n", + " },\n", + "function(Bokeh) {\n", + " }\n", + " ];\n", + "\n", + " function run_inline_js() {\n", + " if (root.Bokeh !== undefined || force === true) {\n", + " for (let i = 0; i < inline_js.length; i++) {\n", + " inline_js[i].call(root, root.Bokeh);\n", + " }\n", + "} else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(run_inline_js, 100);\n", + " } else if (!root._bokeh_failed_load) {\n", + " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", + " root._bokeh_failed_load = true;\n", + " } else if (force !== true) {\n", + " const cell = $(document.getElementById(null)).parents('.cell').data().cell;\n", + " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", + " }\n", + " }\n", + "\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", + " run_inline_js();\n", + " } else {\n", + " load_libs(css_urls, js_urls, function() {\n", + " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", + " run_inline_js();\n", + " });\n", + " }\n", + "}(window));" + ], + "application/vnd.bokehjs_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n const el = document.getElementById(null);\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.1.1.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\n} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(null)).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" }, "metadata": {}, "output_type": "display_data" @@ -925,7 +1116,7 @@ "data": { "text/html": [ "\n", - "
\n" + "
\n" ] }, "metadata": {}, @@ -933,7 +1124,32 @@ }, { "data": { - "application/javascript": "(function(root) {\n function embed_document(root) {\n const docs_json = {\"44ea058d-5d87-4eb5-b682-05838e2e59fb\":{\"version\":\"3.1.1\",\"title\":\"Bokeh 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+ " const render_items = [{\"docid\":\"6d249099-a36e-47d4-92ec-a02b68729229\",\"roots\":{\"p1560\":\"ca59431f-3475-47f2-9b6a-ef73bad0d849\"},\"root_ids\":[\"p1560\"]}];\n", + " root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n", + " }\n", + " if (root.Bokeh !== undefined) {\n", + " embed_document(root);\n", + " } else {\n", + " let attempts = 0;\n", + " const timer = setInterval(function(root) {\n", + " if (root.Bokeh !== undefined) {\n", + " clearInterval(timer);\n", + " embed_document(root);\n", + " } else {\n", + " attempts++;\n", + " if (attempts > 100) {\n", + " clearInterval(timer);\n", + " console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n", + " }\n", + " }\n", + " }, 10, root)\n", + " }\n", + "})(window);" + ], "application/vnd.bokehjs_exec.v0+json": "" }, "metadata": { @@ -965,14 +1181,7 @@ "cell_type": "code", "execution_count": 14, "id": "f9adcbde-35b6-4a61-a12d-4e0bd2ae62fa", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:50:29.859086Z", - "iopub.status.busy": "2023-07-24T15:50:29.858921Z", - "iopub.status.idle": "2023-07-24T15:50:29.864720Z", - "shell.execute_reply": "2023-07-24T15:50:29.864446Z" - } - }, + "metadata": {}, "outputs": [ { "data": { @@ -1130,14 +1339,7 @@ "cell_type": "code", "execution_count": 15, "id": "4530d2e8-2c12-4a05-b3cc-ef2eed90fe5f", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:50:29.866408Z", - "iopub.status.busy": "2023-07-24T15:50:29.866276Z", - "iopub.status.idle": "2023-07-24T15:50:29.880995Z", - "shell.execute_reply": "2023-07-24T15:50:29.880685Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "cleaned_calendar = raw_calendar.copy()\n", @@ -1175,14 +1377,7 @@ "cell_type": "code", "execution_count": 16, "id": "59b58a95-86e1-4eae-87b7-fe18f95a2d50", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:50:29.882711Z", - "iopub.status.busy": "2023-07-24T15:50:29.882611Z", - "iopub.status.idle": "2023-07-24T15:50:29.887151Z", - "shell.execute_reply": "2023-07-24T15:50:29.886844Z" - } - }, + "metadata": {}, "outputs": [ { "data": { @@ -1313,14 +1508,7 @@ "cell_type": "code", "execution_count": 17, "id": "e667774f-c040-4006-b214-ae841d9e6d88", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:50:29.888849Z", - "iopub.status.busy": "2023-07-24T15:50:29.888719Z", - "iopub.status.idle": "2023-07-24T15:50:29.893636Z", - "shell.execute_reply": "2023-07-24T15:50:29.893338Z" - } - }, + "metadata": {}, "outputs": [ { "name": "stderr", @@ -1334,6 +1522,9 @@ }, { "data": { + "text/html": [ + "
1 indexes × 7 features (memory usage: 164.6 kB)

Index: ()

1969 events
timestampevent_name_1event_type_1event_name_2event_type_2snap_CAsnap_TXsnap_WI
2011-01-29 00:00:00+00:00no_eventno_eventno_eventno_event000
2011-01-30 00:00:00+00:00no_eventno_eventno_eventno_event000
2011-01-31 00:00:00+00:00no_eventno_eventno_eventno_event000
2011-02-01 00:00:00+00:00no_eventno_eventno_eventno_event110
2011-02-02 00:00:00+00:00no_eventno_eventno_eventno_event101
2011-02-03 00:00:00+00:00no_eventno_eventno_eventno_event111
2011-02-04 00:00:00+00:00no_eventno_eventno_eventno_event100
2011-02-05 00:00:00+00:00no_eventno_eventno_eventno_event111
2011-02-06 00:00:00+00:00SuperBowlSportingno_eventno_event111
2011-02-07 00:00:00+00:00no_eventno_eventno_eventno_event110
2011-02-08 00:00:00+00:00no_eventno_eventno_eventno_event101
2011-02-09 00:00:00+00:00no_eventno_eventno_eventno_event111
2011-02-10 00:00:00+00:00no_eventno_eventno_eventno_event100
2011-02-11 00:00:00+00:00no_eventno_eventno_eventno_event011
2011-02-12 00:00:00+00:00no_eventno_eventno_eventno_event011
2011-02-13 00:00:00+00:00no_eventno_eventno_eventno_event010
2011-02-14 00:00:00+00:00ValentinesDayCulturalno_eventno_event001
2011-02-15 00:00:00+00:00no_eventno_eventno_eventno_event011
2011-02-16 00:00:00+00:00no_eventno_eventno_eventno_event000
2011-02-17 00:00:00+00:00no_eventno_eventno_eventno_event000
……………………
" + ], "text/plain": [ "indexes: []\n", "features: [('event_name_1', str_), ('event_type_1', str_), ('event_name_2', str_), ('event_type_2', str_), ('snap_CA', int64), ('snap_TX', int64), ('snap_WI', int64)]\n", @@ -1380,14 +1571,7 @@ "cell_type": "code", "execution_count": 18, "id": "1ce4218a-594b-41d6-b7de-418a9414b089", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:50:29.895541Z", - "iopub.status.busy": "2023-07-24T15:50:29.895376Z", - "iopub.status.idle": "2023-07-24T15:50:29.900014Z", - "shell.execute_reply": "2023-07-24T15:50:29.899641Z" - } - }, + "metadata": {}, "outputs": [ { "data": { @@ -1488,14 +1672,7 @@ "cell_type": "code", "execution_count": 19, "id": "6279d6dc-06f7-4c06-b329-40d58ec69144", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:50:29.902107Z", - "iopub.status.busy": "2023-07-24T15:50:29.901903Z", - "iopub.status.idle": "2023-07-24T15:50:31.710217Z", - "shell.execute_reply": "2023-07-24T15:50:31.709818Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "wm_yr_wk_to_date = wm_yr_wk_map[wm_yr_wk_map[\"weekday\"] == \"Saturday\"][\n", @@ -1527,14 +1704,7 @@ "cell_type": "code", "execution_count": 20, "id": "f3dc338e-6d84-4be6-aac2-416d1acabd99", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:50:31.712304Z", - "iopub.status.busy": "2023-07-24T15:50:31.712163Z", - "iopub.status.idle": "2023-07-24T15:50:31.716534Z", - "shell.execute_reply": "2023-07-24T15:50:31.716224Z" - } - }, + "metadata": {}, "outputs": [ { "data": { @@ -1634,14 +1804,7 @@ "cell_type": "code", "execution_count": 21, "id": "7fa7a3ed-f033-4e5c-b41f-6cf5fa71bb52", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:50:31.718289Z", - "iopub.status.busy": "2023-07-24T15:50:31.718177Z", - "iopub.status.idle": "2023-07-24T15:50:35.435218Z", - "shell.execute_reply": "2023-07-24T15:50:35.434784Z" - } - }, + "metadata": {}, "outputs": [ { "name": "stderr", @@ -1653,6 +1816,9 @@ }, { "data": { + "text/html": [ + "
30490 indexes × 1 features (memory usage: 119.3 MB)

Index: (store_id=CA_1, item_id=FOODS_1_001)

282 events
timestampsell_price
2011-01-29 00:00:00+00:002
2011-02-05 00:00:00+00:002
2011-02-12 00:00:00+00:002
2011-02-19 00:00:00+00:002
2011-02-26 00:00:00+00:002
2011-03-05 00:00:00+00:002
2011-03-12 00:00:00+00:002
2011-03-19 00:00:00+00:002
2011-03-26 00:00:00+00:002
2011-04-02 00:00:00+00:002
2011-04-09 00:00:00+00:002
2011-04-16 00:00:00+00:002
2011-04-23 00:00:00+00:002
2011-04-30 00:00:00+00:002
2011-05-07 00:00:00+00:002
2011-05-14 00:00:00+00:002
2011-05-21 00:00:00+00:002
2011-05-28 00:00:00+00:002
2011-06-04 00:00:00+00:002
2011-06-11 00:00:00+00:002
……

Index: (store_id=CA_1, item_id=FOODS_1_002)

282 events
timestampsell_price
2011-01-29 00:00:00+00:007.88
2011-02-05 00:00:00+00:007.88
2011-02-12 00:00:00+00:007.88
2011-02-19 00:00:00+00:007.88
2011-02-26 00:00:00+00:007.88
2011-03-05 00:00:00+00:007.88
2011-03-12 00:00:00+00:007.88
2011-03-19 00:00:00+00:007.88
2011-03-26 00:00:00+00:007.88
2011-04-02 00:00:00+00:007.88
2011-04-09 00:00:00+00:007.88
2011-04-16 00:00:00+00:007.88
2011-04-23 00:00:00+00:007.88
2011-04-30 00:00:00+00:007.88
2011-05-07 00:00:00+00:007.88
2011-05-14 00:00:00+00:007.88
2011-05-21 00:00:00+00:007.88
2011-05-28 00:00:00+00:007.88
2011-06-04 00:00:00+00:007.88
2011-06-11 00:00:00+00:007.88
……

Index: (store_id=CA_1, item_id=FOODS_1_003)

282 events
timestampsell_price
2011-01-29 00:00:00+00:002.88
2011-02-05 00:00:00+00:002.88
2011-02-12 00:00:00+00:002.88
2011-02-19 00:00:00+00:002.88
2011-02-26 00:00:00+00:002.88
2011-03-05 00:00:00+00:002.88
2011-03-12 00:00:00+00:002.88
2011-03-19 00:00:00+00:002.88
2011-03-26 00:00:00+00:002.88
2011-04-02 00:00:00+00:002.88
2011-04-09 00:00:00+00:002.88
2011-04-16 00:00:00+00:002.88
2011-04-23 00:00:00+00:002.88
2011-04-30 00:00:00+00:002.88
2011-05-07 00:00:00+00:002.88
2011-05-14 00:00:00+00:002.88
2011-05-21 00:00:00+00:002.88
2011-05-28 00:00:00+00:002.88
2011-06-04 00:00:00+00:002.88
2011-06-11 00:00:00+00:002.88
……

Index: (store_id=CA_1, item_id=FOODS_1_004)

225 events
timestampsell_price
2012-03-03 00:00:00+00:001.78
2012-03-10 00:00:00+00:001.78
2012-03-17 00:00:00+00:001.78
2012-03-24 00:00:00+00:001.78
2012-03-31 00:00:00+00:001.78
2012-04-07 00:00:00+00:001.78
2012-04-14 00:00:00+00:001.78
2012-04-21 00:00:00+00:001.78
2012-04-28 00:00:00+00:001.78
2012-05-05 00:00:00+00:001.78
2012-05-12 00:00:00+00:001.78
2012-05-19 00:00:00+00:001.78
2012-05-26 00:00:00+00:001.78
2012-06-02 00:00:00+00:001.78
2012-06-09 00:00:00+00:001.78
2012-06-16 00:00:00+00:001.78
2012-06-23 00:00:00+00:001.78
2012-06-30 00:00:00+00:001.78
2012-07-07 00:00:00+00:001.78
2012-07-14 00:00:00+00:001.78
……

Index: (store_id=CA_1, item_id=FOODS_1_005)

282 events
timestampsell_price
2011-01-29 00:00:00+00:002.94
2011-02-05 00:00:00+00:002.94
2011-02-12 00:00:00+00:002.94
2011-02-19 00:00:00+00:002.94
2011-02-26 00:00:00+00:002.94
2011-03-05 00:00:00+00:002.94
2011-03-12 00:00:00+00:002.94
2011-03-19 00:00:00+00:002.94
2011-03-26 00:00:00+00:002.94
2011-04-02 00:00:00+00:002.94
2011-04-09 00:00:00+00:002.94
2011-04-16 00:00:00+00:002.94
2011-04-23 00:00:00+00:002.94
2011-04-30 00:00:00+00:002.94
2011-05-07 00:00:00+00:002.94
2011-05-14 00:00:00+00:002.94
2011-05-21 00:00:00+00:002.94
2011-05-28 00:00:00+00:002.94
2011-06-04 00:00:00+00:002.94
2011-06-11 00:00:00+00:002.94
……
… (30485 more indexes not shown)
" + ], "text/plain": [ "indexes: [('store_id', str_), ('item_id', str_)]\n", "features: [('sell_price', float64)]\n", @@ -1672,10 +1838,7 @@ " store_id=b'CA_1' item_id=b'FOODS_1_005' (282 events):\n", " timestamps: [1.2963e+09 1.2969e+09 1.2975e+09 ... 1.4650e+09 1.4656e+09 1.4662e+09]\n", " 'sell_price': [2.94 2.94 2.94 ... 3.54 3.54 3.54]\n", - " store_id=b'CA_1' item_id=b'FOODS_1_006' (282 events):\n", - " timestamps: [1.2963e+09 1.2969e+09 1.2975e+09 ... 1.4650e+09 1.4656e+09 1.4662e+09]\n", - " 'sell_price': [1.97 1.97 1.97 ... 2.48 2.48 2.48]\n", - " ... (30484 remaining)\n", + " ... (showing 5 of 30490 indexes)\n", "memory usage: 119.3 MB" ] }, @@ -1705,14 +1868,7 @@ "cell_type": "code", "execution_count": 22, "id": "f2c824aa-7aa4-4ce6-a0cb-17a6d69ef084", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:50:35.437630Z", - "iopub.status.busy": "2023-07-24T15:50:35.437466Z", - "iopub.status.idle": "2023-07-24T15:50:35.439511Z", - "shell.execute_reply": "2023-07-24T15:50:35.439216Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "del raw_sales\n", @@ -1736,20 +1892,13 @@ "cell_type": "code", "execution_count": 23, "id": "0447be99-3c1c-4fd5-9b25-47a8f3fc9eca", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:50:35.441347Z", - "iopub.status.busy": "2023-07-24T15:50:35.441206Z", - "iopub.status.idle": "2023-07-24T15:50:35.443388Z", - "shell.execute_reply": "2023-07-24T15:50:35.443081Z" - } - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Selected product (selected_index): (b'FOODS_3_714', b'FOODS_3', b'FOODS', b'WI_3', b'WI')\n" + "Selected product (selected_index): (b'FOODS_3_694', b'FOODS_3', b'FOODS', b'WI_2', b'WI')\n" ] } ], @@ -1773,14 +1922,7 @@ "cell_type": "code", "execution_count": 24, "id": "396f6d0e-4555-4835-8e84-712a6e892e64", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:50:35.445198Z", - "iopub.status.busy": "2023-07-24T15:50:35.445062Z", - "iopub.status.idle": "2023-07-24T15:50:35.447259Z", - "shell.execute_reply": "2023-07-24T15:50:35.446936Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "plot_options = {\n", @@ -1809,14 +1951,7 @@ "cell_type": "code", "execution_count": 25, "id": "d8cec8ef-62a0-4d99-b46c-a217ae012f9c", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:50:35.449069Z", - "iopub.status.busy": "2023-07-24T15:50:35.448934Z", - "iopub.status.idle": "2023-07-24T15:50:35.451434Z", - "shell.execute_reply": "2023-07-24T15:50:35.451118Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "all_features = [\n", @@ -1840,21 +1975,14 @@ "cell_type": "code", "execution_count": 26, "id": "07e74746-76f7-4992-a9c6-2d68d06bbaa2", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:50:35.453286Z", - "iopub.status.busy": "2023-07-24T15:50:35.453143Z", - "iopub.status.idle": "2023-07-24T15:50:35.484948Z", - "shell.execute_reply": "2023-07-24T15:50:35.484574Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "label_list: List[tp.EventSet] = []\n", "\n", "# Prediction horizon. In the M5 competition, participants should forecast the next 28 days.\n", "# You can predict less days to make the training section of this notebook run faster.\n", - "horizons = list(range(1, 14))\n", + "horizons = list(range(1, 28))\n", "\n", "# For each of the horizons.\n", "for horizon in horizons:\n", @@ -1884,26 +2012,296 @@ "cell_type": "code", "execution_count": 27, "id": "2fa783ac-4c30-4d0e-9dfa-2dd05c6bd696", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:50:35.487485Z", - "iopub.status.busy": "2023-07-24T15:50:35.487056Z", - "iopub.status.idle": "2023-07-24T15:50:35.809336Z", - "shell.execute_reply": "2023-07-24T15:50:35.808984Z" - } - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "The number of plots (13) is larger than \"options.max_num_plots=4\". Only the first plots will be printed.\n" + "The number of plots (27) is larger than \"options.max_num_plots=4\". Only the first plots will be printed.\n" ] }, { "data": { - "application/javascript": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\nconst JS_MIME_TYPE = 'application/javascript';\n const HTML_MIME_TYPE = 'text/html';\n const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n const CLASS_NAME = 'output_bokeh rendered_html';\n\n /**\n * Render data to the DOM node\n */\n function render(props, node) {\n const script = document.createElement(\"script\");\n node.appendChild(script);\n }\n\n /**\n * Handle when an output is cleared or removed\n */\n function handleClearOutput(event, handle) {\n const cell = handle.cell;\n\n const id = cell.output_area._bokeh_element_id;\n const server_id = cell.output_area._bokeh_server_id;\n // Clean up Bokeh references\n if (id != null && id in Bokeh.index) {\n Bokeh.index[id].model.document.clear();\n delete Bokeh.index[id];\n }\n\n if (server_id !== undefined) {\n // Clean up Bokeh references\n const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n cell.notebook.kernel.execute(cmd_clean, {\n iopub: {\n output: function(msg) {\n const id = msg.content.text.trim();\n if (id in Bokeh.index) {\n Bokeh.index[id].model.document.clear();\n delete Bokeh.index[id];\n }\n }\n }\n });\n // Destroy server and session\n const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n cell.notebook.kernel.execute(cmd_destroy);\n }\n }\n\n /**\n * Handle when a new output is added\n */\n function handleAddOutput(event, handle) {\n const output_area = handle.output_area;\n const output = handle.output;\n\n // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n return\n }\n\n const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n\n if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n // store reference to embed id on output_area\n output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n }\n if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n const bk_div = document.createElement(\"div\");\n bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n const script_attrs = bk_div.children[0].attributes;\n for (let i = 0; i < script_attrs.length; i++) {\n toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n }\n // store reference to server id on output_area\n output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n }\n }\n\n function register_renderer(events, OutputArea) {\n\n function append_mime(data, metadata, element) {\n // create a DOM node to render to\n const toinsert = this.create_output_subarea(\n metadata,\n CLASS_NAME,\n EXEC_MIME_TYPE\n );\n this.keyboard_manager.register_events(toinsert);\n // Render to node\n const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n render(props, toinsert[toinsert.length - 1]);\n element.append(toinsert);\n return toinsert\n }\n\n /* Handle when an output is cleared or removed */\n events.on('clear_output.CodeCell', handleClearOutput);\n events.on('delete.Cell', handleClearOutput);\n\n /* Handle when a new output is added */\n events.on('output_added.OutputArea', handleAddOutput);\n\n /**\n * Register the mime type and append_mime function with output_area\n */\n OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n /* Is output safe? */\n safe: true,\n /* Index of renderer in `output_area.display_order` */\n index: 0\n });\n }\n\n // register the mime type if in Jupyter Notebook environment and previously unregistered\n if (root.Jupyter !== undefined) {\n const events = require('base/js/events');\n const OutputArea = require('notebook/js/outputarea').OutputArea;\n\n if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n register_renderer(events, OutputArea);\n }\n }\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n const el = document.getElementById(null);\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.1.1.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\n} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(null)).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));", - "application/vnd.bokehjs_load.v0+json": "" + "application/javascript": [ + "(function(root) {\n", + " function now() {\n", + " return new Date();\n", + " }\n", + "\n", + " const force = true;\n", + "\n", + " if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n", + " root._bokeh_onload_callbacks = [];\n", + " root._bokeh_is_loading = undefined;\n", + " }\n", + "\n", + "const JS_MIME_TYPE = 'application/javascript';\n", + " const HTML_MIME_TYPE = 'text/html';\n", + " const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", + " const CLASS_NAME = 'output_bokeh rendered_html';\n", + "\n", + " /**\n", + " * Render data to the DOM node\n", + " */\n", + " function render(props, node) {\n", + " const script = document.createElement(\"script\");\n", + " node.appendChild(script);\n", + " }\n", + "\n", + " /**\n", + " * Handle when an output is cleared or removed\n", + " */\n", + " function handleClearOutput(event, handle) {\n", + " const cell = handle.cell;\n", + "\n", + " const id = cell.output_area._bokeh_element_id;\n", + " const server_id = cell.output_area._bokeh_server_id;\n", + " // Clean up Bokeh references\n", + " if (id != null && id in Bokeh.index) {\n", + " Bokeh.index[id].model.document.clear();\n", + " delete Bokeh.index[id];\n", + " }\n", + "\n", + " if (server_id !== undefined) {\n", + " // Clean up Bokeh references\n", + " const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", + " cell.notebook.kernel.execute(cmd_clean, {\n", + " iopub: {\n", + " output: function(msg) {\n", + " const id = msg.content.text.trim();\n", + " if (id in Bokeh.index) {\n", + " Bokeh.index[id].model.document.clear();\n", + " delete Bokeh.index[id];\n", + " }\n", + " }\n", + " }\n", + " });\n", + " // Destroy server and session\n", + " const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", + " cell.notebook.kernel.execute(cmd_destroy);\n", + " }\n", + " }\n", + "\n", + " /**\n", + " * Handle when a new output is added\n", + " */\n", + " function handleAddOutput(event, handle) {\n", + " const output_area = handle.output_area;\n", + " const output = handle.output;\n", + "\n", + " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", + " if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n", + " return\n", + " }\n", + "\n", + " const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", + "\n", + " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", + " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n", + " // store reference to embed id on output_area\n", + " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", + " }\n", + " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", + " const bk_div = document.createElement(\"div\");\n", + " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", + " const script_attrs = bk_div.children[0].attributes;\n", + " for (let i = 0; i < script_attrs.length; i++) {\n", + " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", + " toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n", + " }\n", + " // store reference to server id on output_area\n", + " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", + " }\n", + " }\n", + "\n", + " function register_renderer(events, OutputArea) {\n", + "\n", + " function append_mime(data, metadata, element) {\n", + " // create a DOM node to render to\n", + " const toinsert = this.create_output_subarea(\n", + " metadata,\n", + " CLASS_NAME,\n", + " EXEC_MIME_TYPE\n", + " );\n", + " this.keyboard_manager.register_events(toinsert);\n", + " // Render to node\n", + " const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", + " render(props, toinsert[toinsert.length - 1]);\n", + " element.append(toinsert);\n", + " return toinsert\n", + " }\n", + "\n", + " /* Handle when an output is cleared or removed */\n", + " events.on('clear_output.CodeCell', handleClearOutput);\n", + " events.on('delete.Cell', handleClearOutput);\n", + "\n", + " /* Handle when a new output is added */\n", + " events.on('output_added.OutputArea', handleAddOutput);\n", + "\n", + " /**\n", + " * Register the mime type and append_mime function with output_area\n", + " */\n", + " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", + " /* Is output safe? */\n", + " safe: true,\n", + " /* Index of renderer in `output_area.display_order` */\n", + " index: 0\n", + " });\n", + " }\n", + "\n", + " // register the mime type if in Jupyter Notebook environment and previously unregistered\n", + " if (root.Jupyter !== undefined) {\n", + " const events = require('base/js/events');\n", + " const OutputArea = require('notebook/js/outputarea').OutputArea;\n", + "\n", + " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", + " register_renderer(events, OutputArea);\n", + " }\n", + " }\n", + " if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n", + " root._bokeh_timeout = Date.now() + 5000;\n", + " root._bokeh_failed_load = false;\n", + " }\n", + "\n", + " const NB_LOAD_WARNING = {'data': {'text/html':\n", + " \"
\\n\"+\n", + " \"

\\n\"+\n", + " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", + " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", + " \"

\\n\"+\n", + " \"\\n\"+\n", + " \"\\n\"+\n", + " \"from bokeh.resources import INLINE\\n\"+\n", + " \"output_notebook(resources=INLINE)\\n\"+\n", + " \"\\n\"+\n", + " \"
\"}};\n", + "\n", + " function display_loaded() {\n", + " const el = document.getElementById(null);\n", + " if (el != null) {\n", + " el.textContent = \"BokehJS is loading...\";\n", + " }\n", + " if (root.Bokeh !== undefined) {\n", + " if (el != null) {\n", + " el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n", + " }\n", + " } else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(display_loaded, 100)\n", + " }\n", + " }\n", + "\n", + " function run_callbacks() {\n", + " try {\n", + " root._bokeh_onload_callbacks.forEach(function(callback) {\n", + " if (callback != null)\n", + " callback();\n", + " });\n", + " } finally {\n", + " delete root._bokeh_onload_callbacks\n", + " }\n", + " console.debug(\"Bokeh: all callbacks have finished\");\n", + " }\n", + "\n", + " function load_libs(css_urls, js_urls, callback) {\n", + " if (css_urls == null) css_urls = [];\n", + " if (js_urls == null) js_urls = [];\n", + "\n", + " root._bokeh_onload_callbacks.push(callback);\n", + " if (root._bokeh_is_loading > 0) {\n", + " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", + " return null;\n", + " }\n", + " if (js_urls == null || js_urls.length === 0) {\n", + " run_callbacks();\n", + " return null;\n", + " }\n", + " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", + " root._bokeh_is_loading = css_urls.length + js_urls.length;\n", + "\n", + " function on_load() {\n", + " root._bokeh_is_loading--;\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", + " run_callbacks()\n", + " }\n", + " }\n", + "\n", + " function on_error(url) {\n", + " console.error(\"failed to load \" + url);\n", + " }\n", + "\n", + " for (let i = 0; i < css_urls.length; i++) {\n", + " const url = css_urls[i];\n", + " const element = document.createElement(\"link\");\n", + " element.onload = on_load;\n", + " element.onerror = on_error.bind(null, url);\n", + " element.rel = \"stylesheet\";\n", + " element.type = \"text/css\";\n", + " element.href = url;\n", + " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", + " document.body.appendChild(element);\n", + " }\n", + "\n", + " for (let i = 0; i < js_urls.length; i++) {\n", + " const url = js_urls[i];\n", + " const element = document.createElement('script');\n", + " element.onload = on_load;\n", + " element.onerror = on_error.bind(null, url);\n", + " element.async = false;\n", + " element.src = url;\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.head.appendChild(element);\n", + " }\n", + " };\n", + "\n", + " function inject_raw_css(css) {\n", + " const element = document.createElement(\"style\");\n", + " element.appendChild(document.createTextNode(css));\n", + " document.body.appendChild(element);\n", + " }\n", + "\n", + " const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.1.1.min.js\"];\n", + " const css_urls = [];\n", + "\n", + " const inline_js = [ function(Bokeh) {\n", + " Bokeh.set_log_level(\"info\");\n", + " },\n", + "function(Bokeh) {\n", + " }\n", + " ];\n", + "\n", + " function run_inline_js() {\n", + " if (root.Bokeh !== undefined || force === true) {\n", + " for (let i = 0; i < inline_js.length; i++) {\n", + " inline_js[i].call(root, root.Bokeh);\n", + " }\n", + "} else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(run_inline_js, 100);\n", + " } else if (!root._bokeh_failed_load) {\n", + " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", + " root._bokeh_failed_load = true;\n", + " } else if (force !== true) {\n", + " const cell = $(document.getElementById(null)).parents('.cell').data().cell;\n", + " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", + " }\n", + " }\n", + "\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", + " run_inline_js();\n", + " } else {\n", + " load_libs(css_urls, js_urls, function() {\n", + " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", + " run_inline_js();\n", + " });\n", + " }\n", + "}(window));" + ], + "application/vnd.bokehjs_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n const el = document.getElementById(null);\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.1.1.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\n} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(null)).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" }, "metadata": {}, "output_type": "display_data" @@ -1912,7 +2310,7 @@ "data": { "text/html": [ "\n", - "
\n" + "
\n" ] }, "metadata": {}, @@ -1920,12 +2318,37 @@ }, { "data": { - "application/javascript": "(function(root) {\n function embed_document(root) {\n const docs_json = {\"4055edae-02a1-4d2a-ba5d-d425ece66791\":{\"version\":\"3.1.1\",\"title\":\"Bokeh Application\",\"defs\":[],\"roots\":[{\"type\":\"object\",\"name\":\"GridPlot\",\"id\":\"p3041\",\"attributes\":{\"rows\":null,\"cols\":null,\"toolbar\":{\"type\":\"object\",\"name\":\"Toolbar\",\"id\":\"p3040\",\"attributes\":{\"logo\":null,\"tools\":[{\"type\":\"object\",\"name\":\"ToolProxy\",\"id\":\"p3031\",\"attributes\":{\"tools\":[{\"type\":\"object\",\"name\":\"PanTool\",\"id\":\"p2546\",\"attributes\":{\"dimensions\":\"width\"}},{\"type\":\"object\",\"name\":\"PanTool\",\"id\":\"p2644\",\"attributes\":{\"dimensions\":\"width\"}},{\"type\":\"object\",\"name\":\"PanTool\",\"id\":\"p2742\",\"attributes\":{\"dimensions\":\"width\"}},{\"type\":\"object\",\"name\":\"PanTool\",\"id\":\"p2840\",\"attributes\":{\"dimensions\":\"width\"}}]}},{\"type\":\"object\",\"name\":\"ToolProxy\",\"id\":\"p3032\",\"attributes\":{\"tools\":[{\"type\":\"object\",\"name\":\"PanTool\",\"id\":\"p2547\"},{\"type\":\"object\",\"name\":\"PanTool\",\"id\":\"p2645\"},{\"type\":\"object\",\"name\":\"PanTool\",\"id\":\"p2743\"},{\"type\":\"object\",\"name\":\"PanTool\",\"id\":\"p2841\"}]}},{\"type\":\"object\",\"name\":\"ToolProxy\",\"id\":\"p3033\",\"attributes\":{\"tools\":[{\"type\":\"object\",\"name\":\"WheelZoomTool\",\"id\":\"p2548\",\"attributes\":{\"dimensions\":\"width\"}},{\"type\":\"object\",\"name\":\"WheelZoomTool\",\"id\":\"p2646\",\"attributes\":{\"dimensions\":\"width\"}},{\"type\":\"object\",\"name\":\"WheelZoomTool\",\"id\":\"p2744\",\"attributes\":{\"dimensions\":\"width\"}},{\"type\":\"object\",\"name\":\"WheelZoomTool\",\"id\":\"p2842\",\"attributes\":{\"dimensions\":\"width\"}}]}},{\"type\":\"object\",\"name\":\"ToolProxy\",\"id\":\"p3034\",\"attributes\":{\"tools\":[{\"type\":\"object\",\"name\":\"WheelZoomTool\",\"id\":\"p2549\",\"attributes\":{\"dimensions\":\"height\"}},{\"type\":\"object\",\"name\":\"WheelZoomTool\",\"id\":\"p2647\",\"attributes\":{\"dimensions\":\"height\"}},{\"type\":\"object\",\"name\":\"WheelZoomTool\",\"id\":\"p2745\",\"attributes\":{\"dimensions\":\"height\"}},{\"type\":\"object\",\"name\":\"WheelZoomTool\",\"id\":\"p2843\",\"attributes\":{\"dimensions\":\"height\"}}]}},{\"type\":\"object\",\"name\":\"ToolProxy\",\"id\":\"p3035\",\"attributes\":{\"tools\":[{\"type\":\"object\",\"name\":\"BoxZoomTool\",\"id\":\"p2550\",\"attributes\":{\"overlay\":{\"type\":\"object\",\"name\":\"BoxAnnotation\",\"id\":\"p2551\",\"attributes\":{\"syncable\":false,\"level\":\"overlay\",\"visible\":false,\"left_units\":\"canvas\",\"right_units\":\"canvas\",\"bottom_units\":\"canvas\",\"top_units\":\"canvas\",\"line_color\":\"black\",\"line_alpha\":1.0,\"line_width\":2,\"line_dash\":[4,4],\"fill_color\":\"lightgrey\",\"fill_alpha\":0.5}}}},{\"type\":\"object\",\"name\":\"BoxZoomTool\",\"id\":\"p2648\",\"attributes\":{\"overlay\":{\"type\":\"object\",\"name\":\"BoxAnnotation\",\"id\":\"p2649\",\"attributes\":{\"syncable\":false,\"level\":\"overlay\",\"visible\":false,\"left_units\":\"canvas\",\"right_units\":\"canvas\",\"bottom_units\":\"canvas\",\"top_units\":\"canvas\",\"line_color\":\"black\",\"line_alpha\":1.0,\"line_width\":2,\"line_dash\":[4,4],\"fill_color\":\"lightgrey\",\"fill_alpha\":0.5}}}},{\"type\":\"object\",\"name\":\"BoxZoomTool\",\"id\":\"p2746\",\"attributes\":{\"overlay\":{\"type\":\"object\",\"name\":\"BoxAnnotation\",\"id\":\"p2747\",\"attributes\":{\"syncable\":false,\"level\":\"overlay\",\"visible\":false,\"left_units\":\"canvas\",\"right_units\":\"canvas\",\"bottom_units\":\"canvas\",\"top_units\":\"canvas\",\"line_color\":\"black\",\"line_alpha\":1.0,\"line_width\":2,\"line_dash\":[4,4],\"fill_color\":\"lightgrey\",\"fill_alpha\":0.5}}}},{\"type\":\"object\",\"name\":\"BoxZoomTool\",\"id\":\"p2844\",\"attributes\":{\"overlay\":{\"type\":\"object\",\"name\":\"BoxAnnotation\",\"id\":\"p2845\",\"attributes\":{\"syncable\":false,\"level\":\"overlay\",\"visible\":false,\"left_units\":\"canvas\",\"right_units\":\"canvas\",\"bottom_units\":\"canvas\",\"top_units\":\"canvas\",\"line_color\":\"black\",\"line_alpha\":1.0,\"line_width\":2,\"line_dash\":[4,4],\"fill_color\":\"lightgrey\",\"fill_alpha\":0.5}}}}]}},{\"type\":\"object\",\"name\":\"ToolProxy\",\"id\":\"p3036\",\"attributes\":{\"tools\":[{\"type\":\"object\",\"name\":\"ResetTool\",\"id\":\"p2552\"},{\"type\":\"object\",\"name\":\"ResetTool\",\"id\":\"p2650\"},{\"type\":\"object\",\"name\":\"ResetTool\",\"id\":\"p2748\"},{\"type\":\"object\",\"name\":\"ResetTool\",\"id\":\"p2846\"}]}},{\"type\":\"object\",\"name\":\"ToolProxy\",\"id\":\"p3037\",\"attributes\":{\"tools\":[{\"type\":\"object\",\"name\":\"UndoTool\",\"id\":\"p2553\"},{\"type\":\"object\",\"name\":\"UndoTool\",\"id\":\"p2651\"},{\"type\":\"object\",\"name\":\"UndoTool\",\"id\":\"p2749\"},{\"type\":\"object\",\"name\":\"UndoTool\",\"id\":\"p2847\"}]}},{\"type\":\"object\",\"name\":\"SaveTool\",\"id\":\"p3038\"},{\"type\":\"object\",\"name\":\"ToolProxy\",\"id\":\"p3039\",\"attributes\":{\"tools\":[{\"type\":\"object\",\"name\":\"HoverTool\",\"id\":\"p2555\",\"attributes\":{\"renderers\":\"auto\"}},{\"type\":\"object\",\"name\":\"HoverTool\",\"id\":\"p2653\",\"attributes\":{\"renderers\":\"auto\"}},{\"type\":\"object\",\"name\":\"HoverTool\",\"id\":\"p2751\",\"attributes\":{\"renderers\":\"auto\"}},{\"type\":\"object\",\"name\":\"HoverTool\",\"id\":\"p2849\",\"attributes\":{\"renderers\":\"auto\"}}]}}]}},\"toolbar_location\":\"right\",\"children\":[[{\"type\":\"object\",\"name\":\"Figure\",\"id\":\"p2478\",\"attributes\":{\"width\":500,\"height\":150,\"x_range\":{\"type\":\"object\",\"name\":\"Range1d\",\"id\":\"p2567\",\"attributes\":{\"js_property_callbacks\":{\"type\":\"map\",\"entries\":[[\"change:start\",[{\"type\":\"object\",\"name\":\"CustomJS\",\"id\":\"p2871\",\"attributes\":{\"args\":{\"type\":\"map\",\"entries\":[[\"p1_x_range\",{\"id\":\"p2567\"}],[\"p2_x_range\",{\"type\":\"object\",\"name\":\"Range1d\",\"id\":\"p2665\",\"attributes\":{\"js_property_callbacks\":{\"type\":\"map\",\"entries\":[[\"change:start\",[{\"id\":\"p2871\"}]],[\"change:end\",[{\"id\":\"p2871\"}]]]},\"start\":1420077600000.0,\"end\":1451530800000.0}}],[\"p3_x_range\",{\"type\":\"object\",\"name\":\"Range1d\",\"id\":\"p2763\",\"attributes\":{\"js_property_callbacks\":{\"type\":\"map\",\"entries\":[[\"change:start\",[{\"id\":\"p2871\"}]],[\"change:end\",[{\"id\":\"p2871\"}]]]},\"start\":1420077600000.0,\"end\":1451530800000.0}}],[\"p4_x_range\",{\"type\":\"object\",\"name\":\"Range1d\",\"id\":\"p2861\",\"attributes\":{\"js_property_callbacks\":{\"type\":\"map\",\"entries\":[[\"change:start\",[{\"id\":\"p2871\"}]],[\"change:end\",[{\"id\":\"p2871\"}]]]},\"start\":1420077600000.0,\"end\":1451530800000.0}}]]},\"code\":\"\\n 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+ " const render_items = [{\"docid\":\"168e8175-c285-4a4c-a162-bd8fc8e78c96\",\"roots\":{\"p3037\":\"eb549b3a-cfa6-40dd-aea1-3a997940e037\"},\"root_ids\":[\"p3037\"]}];\n", + " root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n", + " }\n", + " if (root.Bokeh !== undefined) {\n", + " embed_document(root);\n", + " } else {\n", + " let attempts = 0;\n", + " const timer = setInterval(function(root) {\n", + " if (root.Bokeh !== undefined) {\n", + " clearInterval(timer);\n", + " embed_document(root);\n", + " } else {\n", + " attempts++;\n", + " if (attempts > 100) {\n", + " clearInterval(timer);\n", + " console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n", + " }\n", + " }\n", + " }, 10, root)\n", + " }\n", + "})(window);" + ], "application/vnd.bokehjs_exec.v0+json": "" }, "metadata": { "application/vnd.bokehjs_exec.v0+json": { - "id": "p3041" + "id": "p3037" } }, "output_type": "display_data" @@ -1952,14 +2375,7 @@ "cell_type": "code", "execution_count": 28, "id": "609439b4-a424-4fad-8291-c94cc4de668b", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:50:35.812055Z", - "iopub.status.busy": "2023-07-24T15:50:35.811906Z", - "iopub.status.idle": "2023-07-24T15:50:35.825793Z", - "shell.execute_reply": "2023-07-24T15:50:35.825430Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "lagged_sales_list: List[tp.EventSet] = []\n", @@ -1979,14 +2395,7 @@ "cell_type": "code", "execution_count": 29, "id": "83888d9a-2e94-4d62-ae30-4815896ad4cf", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:50:35.827563Z", - "iopub.status.busy": "2023-07-24T15:50:35.827455Z", - "iopub.status.idle": "2023-07-24T15:50:36.169763Z", - "shell.execute_reply": "2023-07-24T15:50:36.169372Z" - } - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1997,35 +2406,337 @@ }, { "data": { - "application/javascript": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\nconst JS_MIME_TYPE = 'application/javascript';\n const HTML_MIME_TYPE = 'text/html';\n const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n const CLASS_NAME = 'output_bokeh rendered_html';\n\n /**\n * Render data to the DOM node\n */\n function render(props, node) {\n const script = document.createElement(\"script\");\n node.appendChild(script);\n }\n\n /**\n * Handle when an output is cleared or removed\n */\n function handleClearOutput(event, handle) {\n const cell = handle.cell;\n\n const id = cell.output_area._bokeh_element_id;\n const server_id = cell.output_area._bokeh_server_id;\n // Clean up Bokeh references\n if (id != null && id in Bokeh.index) {\n Bokeh.index[id].model.document.clear();\n delete Bokeh.index[id];\n }\n\n if (server_id !== undefined) {\n // Clean up Bokeh references\n const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n cell.notebook.kernel.execute(cmd_clean, {\n iopub: {\n output: function(msg) {\n const id = msg.content.text.trim();\n if (id in Bokeh.index) {\n Bokeh.index[id].model.document.clear();\n delete Bokeh.index[id];\n }\n }\n }\n });\n // Destroy server and session\n const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n cell.notebook.kernel.execute(cmd_destroy);\n }\n }\n\n /**\n * Handle when a new output is added\n */\n function handleAddOutput(event, handle) {\n const output_area = handle.output_area;\n const output = handle.output;\n\n // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n return\n }\n\n const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n\n if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n // store reference to embed id on output_area\n output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n }\n if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n const bk_div = document.createElement(\"div\");\n bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n const script_attrs = bk_div.children[0].attributes;\n for (let i = 0; i < script_attrs.length; i++) {\n toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n }\n // store reference to server id on output_area\n output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n }\n }\n\n function register_renderer(events, OutputArea) {\n\n function append_mime(data, metadata, element) {\n // create a DOM node to render to\n const toinsert = this.create_output_subarea(\n metadata,\n CLASS_NAME,\n EXEC_MIME_TYPE\n );\n this.keyboard_manager.register_events(toinsert);\n // Render to node\n const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n render(props, toinsert[toinsert.length - 1]);\n element.append(toinsert);\n return toinsert\n }\n\n /* Handle when an output is cleared or removed */\n events.on('clear_output.CodeCell', handleClearOutput);\n events.on('delete.Cell', handleClearOutput);\n\n /* Handle when a new output is added */\n events.on('output_added.OutputArea', handleAddOutput);\n\n /**\n * Register the mime type and append_mime function with output_area\n */\n OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n /* Is output safe? */\n safe: true,\n /* Index of renderer in `output_area.display_order` */\n index: 0\n });\n }\n\n // register the mime type if in Jupyter Notebook environment and previously unregistered\n if (root.Jupyter !== undefined) {\n const events = require('base/js/events');\n const OutputArea = require('notebook/js/outputarea').OutputArea;\n\n if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n register_renderer(events, OutputArea);\n }\n }\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n const el = document.getElementById(null);\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.1.1.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\n} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(null)).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));", - "application/vnd.bokehjs_load.v0+json": "" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ + "application/javascript": [ + "(function(root) {\n", + " function now() {\n", + " return new Date();\n", + " }\n", "\n", - "
\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": "(function(root) {\n function embed_document(root) {\n const docs_json = {\"239f86fd-c43f-4d02-b69b-4c14d9581d9c\":{\"version\":\"3.1.1\",\"title\":\"Bokeh Application\",\"defs\":[],\"roots\":[{\"type\":\"object\",\"name\":\"GridPlot\",\"id\":\"p4678\",\"attributes\":{\"rows\":null,\"cols\":null,\"toolbar\":{\"type\":\"object\",\"name\":\"Toolbar\",\"id\":\"p4677\",\"attributes\":{\"logo\":null,\"tools\":[{\"type\":\"object\",\"name\":\"ToolProxy\",\"id\":\"p4668\",\"attributes\":{\"tools\":[{\"type\":\"object\",\"name\":\"PanTool\",\"id\":\"p4183\",\"attributes\":{\"dimensions\":\"width\"}},{\"type\":\"object\",\"name\":\"PanTool\",\"id\":\"p4281\",\"attributes\":{\"dimensions\":\"width\"}},{\"type\":\"object\",\"name\":\"PanTool\",\"id\":\"p4379\",\"attributes\":{\"dimensions\":\"width\"}},{\"type\":\"object\",\"name\":\"PanTool\",\"id\":\"p4477\",\"attributes\":{\"dimensions\":\"width\"}}]}},{\"type\":\"object\",\"name\":\"ToolProxy\",\"id\":\"p4669\",\"attributes\":{\"tools\":[{\"type\":\"object\",\"name\":\"PanTool\",\"id\":\"p4184\"},{\"type\":\"object\",\"name\":\"PanTool\",\"id\":\"p4282\"},{\"type\":\"object\",\"name\":\"PanTool\",\"id\":\"p4380\"},{\"type\":\"object\",\"name\":\"PanTool\",\"id\":\"p4478\"}]}},{\"type\":\"object\",\"name\":\"ToolProxy\",\"id\":\"p4670\",\"attributes\":{\"tools\":[{\"type\":\"object\",\"name\":\"WheelZoomTool\",\"id\":\"p4185\",\"attributes\":{\"dimensions\":\"width\"}},{\"type\":\"object\",\"name\":\"WheelZoomTool\",\"id\":\"p4283\",\"attributes\":{\"dimensions\":\"width\"}},{\"type\":\"object\",\"name\":\"WheelZoomTool\",\"id\":\"p4381\",\"attributes\":{\"dimensions\":\"width\"}},{\"type\":\"object\",\"name\":\"WheelZoomTool\",\"id\":\"p4479\",\"attributes\":{\"dimensions\":\"width\"}}]}},{\"type\":\"object\",\"name\":\"ToolProxy\",\"id\":\"p4671\",\"attributes\":{\"tools\":[{\"type\":\"object\",\"name\":\"WheelZoomTool\",\"id\":\"p4186\",\"attributes\":{\"dimensions\":\"height\"}},{\"type\":\"object\",\"name\":\"WheelZoomTool\",\"id\":\"p4284\",\"attributes\":{\"dimensions\":\"height\"}},{\"type\":\"object\",\"name\":\"WheelZoomTool\",\"id\":\"p4382\",\"attributes\":{\"dimensions\":\"height\"}},{\"type\":\"object\",\"name\":\"WheelZoomTool\",\"id\":\"p4480\",\"attributes\":{\"dimensions\":\"height\"}}]}},{\"type\":\"object\",\"name\":\"ToolProxy\",\"id\":\"p4672\",\"attributes\":{\"tools\":[{\"type\":\"object\",\"name\":\"BoxZoomTool\",\"id\":\"p4187\",\"attributes\":{\"overlay\":{\"type\":\"object\",\"name\":\"BoxAnnotation\",\"id\":\"p4188\",\"attributes\":{\"syncable\":false,\"level\":\"overlay\",\"visible\":false,\"left_units\":\"canvas\",\"right_units\":\"canvas\",\"bottom_units\":\"canvas\",\"top_units\":\"canvas\",\"line_color\":\"black\",\"line_alpha\":1.0,\"line_width\":2,\"line_dash\":[4,4],\"fill_color\":\"lightgrey\",\"fill_alpha\":0.5}}}},{\"type\":\"object\",\"name\":\"BoxZoomTool\",\"id\":\"p4285\",\"attributes\":{\"overlay\":{\"type\":\"object\",\"name\":\"BoxAnnotation\",\"id\":\"p4286\",\"attributes\":{\"syncable\":false,\"level\":\"overlay\",\"visible\":false,\"left_units\":\"canvas\",\"right_units\":\"canvas\",\"bottom_units\":\"canvas\",\"top_units\":\"canvas\",\"line_color\":\"black\",\"line_alpha\":1.0,\"line_width\":2,\"line_dash\":[4,4],\"fill_color\":\"lightgrey\",\"fill_alpha\":0.5}}}},{\"type\":\"object\",\"name\":\"BoxZoomTool\",\"id\":\"p4383\",\"attributes\":{\"overlay\":{\"type\":\"object\",\"name\":\"BoxAnnotation\",\"id\":\"p4384\",\"attributes\":{\"syncable\":false,\"level\":\"overlay\",\"visible\":false,\"left_units\":\"canvas\",\"right_units\":\"canvas\",\"bottom_units\":\"canvas\",\"top_units\":\"canvas\",\"line_color\":\"black\",\"line_alpha\":1.0,\"line_width\":2,\"line_dash\":[4,4],\"fill_color\":\"lightgrey\",\"fill_alpha\":0.5}}}},{\"type\":\"object\",\"name\":\"BoxZoomTool\",\"id\":\"p4481\",\"attributes\":{\"overlay\":{\"type\":\"object\",\"name\":\"BoxAnnotation\",\"id\":\"p4482\",\"attributes\":{\"syncable\":false,\"level\":\"overlay\",\"visible\":false,\"left_units\":\"canvas\",\"right_units\":\"canvas\",\"bottom_units\":\"canvas\",\"top_units\":\"canvas\",\"line_color\":\"black\",\"line_alpha\":1.0,\"line_width\":2,\"line_dash\":[4,4],\"fill_color\":\"lightgrey\",\"fill_alpha\":0.5}}}}]}},{\"type\":\"object\",\"name\":\"ToolProxy\",\"id\":\"p4673\",\"attributes\":{\"tools\":[{\"type\":\"object\",\"name\":\"ResetTool\",\"id\":\"p4189\"},{\"type\":\"object\",\"name\":\"ResetTool\",\"id\":\"p4287\"},{\"type\":\"object\",\"name\":\"ResetTool\",\"id\":\"p4385\"},{\"type\":\"object\",\"name\":\"ResetTool\",\"id\":\"p4483\"}]}},{\"type\":\"object\",\"name\":\"ToolProxy\",\"id\":\"p4674\",\"attributes\":{\"tools\":[{\"type\":\"object\",\"name\":\"UndoTool\",\"id\":\"p4190\"},{\"type\":\"object\",\"name\":\"UndoTool\",\"id\":\"p4288\"},{\"type\":\"object\",\"name\":\"UndoTool\",\"id\":\"p4386\"},{\"type\":\"object\",\"name\":\"UndoTool\",\"id\":\"p4484\"}]}},{\"type\":\"object\",\"name\":\"SaveTool\",\"id\":\"p4675\"},{\"type\":\"object\",\"name\":\"ToolProxy\",\"id\":\"p4676\",\"attributes\":{\"tools\":[{\"type\":\"object\",\"name\":\"HoverTool\",\"id\":\"p4192\",\"attributes\":{\"renderers\":\"auto\"}},{\"type\":\"object\",\"name\":\"HoverTool\",\"id\":\"p4290\",\"attributes\":{\"renderers\":\"auto\"}},{\"type\":\"object\",\"name\":\"HoverTool\",\"id\":\"p4388\",\"attributes\":{\"renderers\":\"auto\"}},{\"type\":\"object\",\"name\":\"HoverTool\",\"id\":\"p4486\",\"attributes\":{\"renderers\":\"auto\"}}]}}]}},\"toolbar_location\":\"right\",\"children\":[[{\"type\":\"object\",\"name\":\"Figure\",\"id\":\"p4115\",\"attributes\":{\"width\":500,\"height\":150,\"x_range\":{\"type\":\"object\",\"name\":\"Range1d\",\"id\":\"p4204\",\"attributes\":{\"js_property_callbacks\":{\"type\":\"map\",\"entries\":[[\"change:start\",[{\"type\":\"object\",\"name\":\"CustomJS\",\"id\":\"p4508\",\"attributes\":{\"args\":{\"type\":\"map\",\"entries\":[[\"p1_x_range\",{\"id\":\"p4204\"}],[\"p2_x_range\",{\"type\":\"object\",\"name\":\"Range1d\",\"id\":\"p4302\",\"attributes\":{\"js_property_callbacks\":{\"type\":\"map\",\"entries\":[[\"change:start\",[{\"id\":\"p4508\"}]],[\"change:end\",[{\"id\":\"p4508\"}]]]},\"start\":1420077600000.0,\"end\":1451530800000.0}}],[\"p3_x_range\",{\"type\":\"object\",\"name\":\"Range1d\",\"id\":\"p4400\",\"attributes\":{\"js_property_callbacks\":{\"type\":\"map\",\"entries\":[[\"change:start\",[{\"id\":\"p4508\"}]],[\"change:end\",[{\"id\":\"p4508\"}]]]},\"start\":1420077600000.0,\"end\":1451530800000.0}}],[\"p4_x_range\",{\"type\":\"object\",\"name\":\"Range1d\",\"id\":\"p4498\",\"attributes\":{\"js_property_callbacks\":{\"type\":\"map\",\"entries\":[[\"change:start\",[{\"id\":\"p4508\"}]],[\"change:end\",[{\"id\":\"p4508\"}]]]},\"start\":1420077600000.0,\"end\":1451530800000.0}}]]},\"code\":\"\\n 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events.on('delete.Cell', handleClearOutput);\n", + "\n", + " /* Handle when a new output is added */\n", + " events.on('output_added.OutputArea', handleAddOutput);\n", + "\n", + " /**\n", + " * Register the mime type and append_mime function with output_area\n", + " */\n", + " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", + " /* Is output safe? */\n", + " safe: true,\n", + " /* Index of renderer in `output_area.display_order` */\n", + " index: 0\n", + " });\n", + " }\n", + "\n", + " // register the mime type if in Jupyter Notebook environment and previously unregistered\n", + " if (root.Jupyter !== undefined) {\n", + " const events = require('base/js/events');\n", + " const OutputArea = require('notebook/js/outputarea').OutputArea;\n", + "\n", + " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", + " register_renderer(events, OutputArea);\n", + " }\n", + " }\n", + " if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n", + " root._bokeh_timeout = Date.now() + 5000;\n", + " root._bokeh_failed_load = false;\n", + " }\n", + "\n", + " const NB_LOAD_WARNING = {'data': {'text/html':\n", + " \"
\\n\"+\n", + " \"

\\n\"+\n", + " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", + " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", + " \"

\\n\"+\n", + " \"\\n\"+\n", + " \"\\n\"+\n", + " \"from bokeh.resources import INLINE\\n\"+\n", + " \"output_notebook(resources=INLINE)\\n\"+\n", + " \"\\n\"+\n", + " \"
\"}};\n", + "\n", + " function display_loaded() {\n", + " const el = document.getElementById(null);\n", + " if (el != null) {\n", + " el.textContent = \"BokehJS is loading...\";\n", + " }\n", + " if (root.Bokeh !== undefined) {\n", + " if (el != null) {\n", + " el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n", + " }\n", + " } else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(display_loaded, 100)\n", + " }\n", + " }\n", + "\n", + " function run_callbacks() {\n", + " try {\n", + " root._bokeh_onload_callbacks.forEach(function(callback) {\n", + " if (callback != null)\n", + " callback();\n", + " });\n", + " } finally {\n", + " delete root._bokeh_onload_callbacks\n", + " }\n", + " console.debug(\"Bokeh: all callbacks have finished\");\n", + " }\n", + "\n", + " function load_libs(css_urls, js_urls, callback) {\n", + " if (css_urls == null) css_urls = [];\n", + " if (js_urls == null) js_urls = [];\n", + "\n", + " root._bokeh_onload_callbacks.push(callback);\n", + " if (root._bokeh_is_loading > 0) {\n", + " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", + " return null;\n", + " }\n", + " if (js_urls == null || js_urls.length === 0) {\n", + " run_callbacks();\n", + " return null;\n", + " }\n", + " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", + " root._bokeh_is_loading = css_urls.length + js_urls.length;\n", + "\n", + " function on_load() {\n", + " root._bokeh_is_loading--;\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", + " run_callbacks()\n", + " }\n", + " }\n", + "\n", + " function on_error(url) {\n", + " console.error(\"failed to load \" + url);\n", + " }\n", + "\n", + " for (let i = 0; i < css_urls.length; i++) {\n", + " const url = css_urls[i];\n", + " const element = document.createElement(\"link\");\n", + " element.onload = on_load;\n", + " element.onerror = on_error.bind(null, url);\n", + " element.rel = \"stylesheet\";\n", + " element.type = \"text/css\";\n", + " element.href = url;\n", + " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", + " document.body.appendChild(element);\n", + " }\n", + "\n", + " for (let i = 0; i < js_urls.length; i++) {\n", + " const url = js_urls[i];\n", + " const element = document.createElement('script');\n", + " element.onload = on_load;\n", + " element.onerror = on_error.bind(null, url);\n", + " element.async = false;\n", + " element.src = url;\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.head.appendChild(element);\n", + " }\n", + " };\n", + "\n", + " function inject_raw_css(css) {\n", + " const element = document.createElement(\"style\");\n", + " element.appendChild(document.createTextNode(css));\n", + " document.body.appendChild(element);\n", + " }\n", + "\n", + " const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.1.1.min.js\"];\n", + " const css_urls = [];\n", + "\n", + " const inline_js = [ function(Bokeh) {\n", + " Bokeh.set_log_level(\"info\");\n", + " },\n", + "function(Bokeh) {\n", + " }\n", + " ];\n", + "\n", + " function run_inline_js() {\n", + " if (root.Bokeh !== undefined || force === true) {\n", + " for (let i = 0; i < inline_js.length; i++) {\n", + " inline_js[i].call(root, root.Bokeh);\n", + " }\n", + "} else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(run_inline_js, 100);\n", + " } else if (!root._bokeh_failed_load) {\n", + " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", + " root._bokeh_failed_load = true;\n", + " } else if (force !== true) {\n", + " const cell = $(document.getElementById(null)).parents('.cell').data().cell;\n", + " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", + " }\n", + " }\n", + "\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", + " run_inline_js();\n", + " } else {\n", + " load_libs(css_urls, js_urls, function() {\n", + " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", + " run_inline_js();\n", + " });\n", + " }\n", + "}(window));" + ], + "application/vnd.bokehjs_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n const el = document.getElementById(null);\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.1.1.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\n} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(null)).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "
\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": [ + "(function(root) {\n", + " function embed_document(root) {\n", + " const docs_json = {\"74d0b9ba-fdd6-40df-9efc-ed6b81a867e9\":{\"version\":\"3.1.1\",\"title\":\"Bokeh Application\",\"defs\":[],\"roots\":[{\"type\":\"object\",\"name\":\"GridPlot\",\"id\":\"p4670\",\"attributes\":{\"rows\":null,\"cols\":null,\"toolbar\":{\"type\":\"object\",\"name\":\"Toolbar\",\"id\":\"p4669\",\"attributes\":{\"logo\":null,\"tools\":[{\"type\":\"object\",\"name\":\"ToolProxy\",\"id\":\"p4660\",\"attributes\":{\"tools\":[{\"type\":\"object\",\"name\":\"PanTool\",\"id\":\"p4179\",\"attributes\":{\"dimensions\":\"width\"}},{\"type\":\"object\",\"name\":\"PanTool\",\"id\":\"p4276\",\"attributes\":{\"dimensions\":\"width\"}},{\"type\":\"object\",\"name\":\"PanTool\",\"id\":\"p4373\",\"attributes\":{\"dimensions\":\"width\"}},{\"type\":\"object\",\"name\":\"PanTool\",\"id\":\"p4470\",\"attributes\":{\"dimensions\":\"width\"}}]}},{\"type\":\"object\",\"name\":\"ToolProxy\",\"id\":\"p4661\",\"attributes\":{\"tools\":[{\"type\":\"object\",\"name\":\"PanTool\",\"id\":\"p4180\"},{\"type\":\"object\",\"name\":\"PanTool\",\"id\":\"p4277\"},{\"type\":\"object\",\"name\":\"PanTool\",\"id\":\"p4374\"},{\"type\":\"object\",\"name\":\"PanTool\",\"id\":\"p4471\"}]}},{\"type\":\"object\",\"name\":\"ToolProxy\",\"id\":\"p4662\",\"attributes\":{\"tools\":[{\"type\":\"object\",\"name\":\"WheelZoomTool\",\"id\":\"p4181\",\"attributes\":{\"dimensions\":\"width\"}},{\"type\":\"object\",\"name\":\"WheelZoomTool\",\"id\":\"p4278\",\"attributes\":{\"dimensions\":\"width\"}},{\"type\":\"object\",\"name\":\"WheelZoomTool\",\"id\":\"p4375\",\"attributes\":{\"dimensions\":\"width\"}},{\"type\":\"object\",\"name\":\"WheelZoomTool\",\"id\":\"p4472\",\"attributes\":{\"dimensions\":\"width\"}}]}},{\"type\":\"object\",\"name\":\"ToolProxy\",\"id\":\"p4663\",\"attributes\":{\"tools\":[{\"type\":\"object\",\"name\":\"WheelZoomTool\",\"id\":\"p4182\",\"attributes\":{\"dimensions\":\"height\"}},{\"type\":\"object\",\"name\":\"WheelZoomTool\",\"id\":\"p4279\",\"attributes\":{\"dimensions\":\"height\"}},{\"type\":\"object\",\"name\":\"WheelZoomTool\",\"id\":\"p4376\",\"attributes\":{\"dimensions\":\"height\"}},{\"type\":\"object\",\"name\":\"WheelZoomTool\",\"id\":\"p4473\",\"attributes\":{\"dimensions\":\"height\"}}]}},{\"type\":\"object\",\"name\":\"ToolProxy\",\"id\":\"p4664\",\"attributes\":{\"tools\":[{\"type\":\"object\",\"name\":\"BoxZoomTool\",\"id\":\"p4183\",\"attributes\":{\"overlay\":{\"type\":\"object\",\"name\":\"BoxAnnotation\",\"id\":\"p4184\",\"attributes\":{\"syncable\":false,\"level\":\"overlay\",\"visible\":false,\"left_units\":\"canvas\",\"right_units\":\"canvas\",\"bottom_units\":\"canvas\",\"top_units\":\"canvas\",\"line_color\":\"black\",\"line_alpha\":1.0,\"line_width\":2,\"line_dash\":[4,4],\"fill_color\":\"lightgrey\",\"fill_alpha\":0.5}}}},{\"type\":\"object\",\"name\":\"BoxZoomTool\",\"id\":\"p4280\",\"attributes\":{\"overlay\":{\"type\":\"object\",\"name\":\"BoxAnnotation\",\"id\":\"p4281\",\"attributes\":{\"syncable\":false,\"level\":\"overlay\",\"visible\":false,\"left_units\":\"canvas\",\"right_units\":\"canvas\",\"bottom_units\":\"canvas\",\"top_units\":\"canvas\",\"line_color\":\"black\",\"line_alpha\":1.0,\"line_width\":2,\"line_dash\":[4,4],\"fill_color\":\"lightgrey\",\"fill_alpha\":0.5}}}},{\"type\":\"object\",\"name\":\"BoxZoomTool\",\"id\":\"p4377\",\"attributes\":{\"overlay\":{\"type\":\"object\",\"name\":\"BoxAnnotation\",\"id\":\"p4378\",\"attributes\":{\"syncable\":false,\"level\":\"overlay\",\"visible\":false,\"left_units\":\"canvas\",\"right_units\":\"canvas\",\"bottom_units\":\"canvas\",\"top_units\":\"canvas\",\"line_color\":\"black\",\"line_alpha\":1.0,\"line_width\":2,\"line_dash\":[4,4],\"fill_color\":\"lightgrey\",\"fill_alpha\":0.5}}}},{\"type\":\"object\",\"name\":\"BoxZoomTool\",\"id\":\"p4474\",\"attributes\":{\"overlay\":{\"type\":\"object\",\"name\":\"BoxAnnotation\",\"id\":\"p4475\",\"attributes\":{\"syncable\":false,\"level\":\"overlay\",\"visible\":false,\"left_units\":\"canvas\",\"right_units\":\"canvas\",\"bottom_units\":\"canvas\",\"top_units\":\"canvas\",\"line_color\":\"black\",\"line_alpha\":1.0,\"line_width\":2,\"line_dash\":[4,4],\"fill_color\":\"lightgrey\",\"fill_alpha\":0.5}}}}]}},{\"type\":\"object\",\"name\":\"ToolProxy\",\"id\":\"p4665\",\"attributes\":{\"tools\":[{\"type\":\"object\",\"name\":\"ResetTool\",\"id\":\"p4185\"},{\"type\":\"object\",\"name\":\"ResetTool\",\"id\":\"p4282\"},{\"type\":\"object\",\"name\":\"ResetTool\",\"id\":\"p4379\"},{\"type\":\"object\",\"name\":\"ResetTool\",\"id\":\"p4476\"}]}},{\"type\":\"object\",\"name\":\"ToolProxy\",\"id\":\"p4666\",\"attributes\":{\"tools\":[{\"type\":\"object\",\"name\":\"UndoTool\",\"id\":\"p4186\"},{\"type\":\"object\",\"name\":\"UndoTool\",\"id\":\"p4283\"},{\"type\":\"object\",\"name\":\"UndoTool\",\"id\":\"p4380\"},{\"type\":\"object\",\"name\":\"UndoTool\",\"id\":\"p4477\"}]}},{\"type\":\"object\",\"name\":\"SaveTool\",\"id\":\"p4667\"},{\"type\":\"object\",\"name\":\"ToolProxy\",\"id\":\"p4668\",\"attributes\":{\"tools\":[{\"type\":\"object\",\"name\":\"HoverTool\",\"id\":\"p4188\",\"attributes\":{\"renderers\":\"auto\"}},{\"type\":\"object\",\"name\":\"HoverTool\",\"id\":\"p4285\",\"attributes\":{\"renderers\":\"auto\"}},{\"type\":\"object\",\"name\":\"HoverTool\",\"id\":\"p4382\",\"attributes\":{\"renderers\":\"auto\"}},{\"type\":\"object\",\"name\":\"HoverTool\",\"id\":\"p4479\",\"attributes\":{\"renderers\":\"auto\"}}]}}]}},\"toolbar_location\":\"right\",\"children\":[[{\"type\":\"object\",\"name\":\"Figure\",\"id\":\"p4112\",\"attributes\":{\"width\":500,\"height\":150,\"x_range\":{\"type\":\"object\",\"name\":\"Range1d\",\"id\":\"p4111\",\"attributes\":{\"js_property_callbacks\":{\"type\":\"map\",\"entries\":[[\"change:start\",[{\"type\":\"object\",\"name\":\"CustomJS\",\"id\":\"p4500\",\"attributes\":{\"args\":{\"type\":\"map\",\"entries\":[[\"p1_x_range\",{\"id\":\"p4111\"}],[\"p2_x_range\",{\"type\":\"object\",\"name\":\"Range1d\",\"id\":\"p4209\",\"attributes\":{\"js_property_callbacks\":{\"type\":\"map\",\"entries\":[[\"change:start\",[{\"id\":\"p4500\"}]],[\"change:end\",[{\"id\":\"p4500\"}]]]},\"start\":1420066800000.0,\"end\":1451516400000.0}}],[\"p3_x_range\",{\"type\":\"object\",\"name\":\"Range1d\",\"id\":\"p4306\",\"attributes\":{\"js_property_callbacks\":{\"type\":\"map\",\"entries\":[[\"change:start\",[{\"id\":\"p4500\"}]],[\"change:end\",[{\"id\":\"p4500\"}]]]},\"start\":1420066800000.0,\"end\":1451516400000.0}}],[\"p4_x_range\",{\"type\":\"object\",\"name\":\"Range1d\",\"id\":\"p4403\",\"attributes\":{\"js_property_callbacks\":{\"type\":\"map\",\"entries\":[[\"change:start\",[{\"id\":\"p4500\"}]],[\"change:end\",[{\"id\":\"p4500\"}]]]},\"start\":1420066800000.0,\"end\":1451516400000.0}}]]},\"code\":\"\\n 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"feature_lagged_sales.plot(**plot_options, max_num_plots=4)" ] @@ -2045,14 +2756,7 @@ "cell_type": "code", "execution_count": 30, "id": "995ea19e-d39a-4f94-a010-6cd130e623c9", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:50:36.172304Z", - "iopub.status.busy": "2023-07-24T15:50:36.172130Z", - "iopub.status.idle": "2023-07-24T15:50:36.193953Z", - "shell.execute_reply": "2023-07-24T15:50:36.193563Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "moving_stats_list: List[tp.EventSet] = []\n", @@ -2083,14 +2787,7 @@ "cell_type": "code", "execution_count": 31, "id": "70161a5f-9e63-4e01-ab98-c5e4a96f59e1", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:50:36.199622Z", - "iopub.status.busy": "2023-07-24T15:50:36.199319Z", - "iopub.status.idle": "2023-07-24T15:50:36.655488Z", - "shell.execute_reply": "2023-07-24T15:50:36.655149Z" - } - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -2101,8 +2798,285 @@ }, { "data": { - 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delete Bokeh.index[id];\n }\n\n if (server_id !== undefined) {\n // Clean up Bokeh references\n const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n cell.notebook.kernel.execute(cmd_clean, {\n iopub: {\n output: function(msg) {\n const id = msg.content.text.trim();\n if (id in Bokeh.index) {\n Bokeh.index[id].model.document.clear();\n delete Bokeh.index[id];\n }\n }\n }\n });\n // Destroy server and session\n const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n cell.notebook.kernel.execute(cmd_destroy);\n }\n }\n\n /**\n * Handle when a new output is added\n */\n function handleAddOutput(event, handle) {\n const output_area = handle.output_area;\n const output = handle.output;\n\n // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n return\n }\n\n const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n\n if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n // store reference to embed id on output_area\n output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n }\n if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n const bk_div = document.createElement(\"div\");\n bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n const script_attrs = bk_div.children[0].attributes;\n for (let i = 0; i < script_attrs.length; i++) {\n toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n }\n // store reference to server id on output_area\n output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n }\n }\n\n function register_renderer(events, OutputArea) {\n\n function append_mime(data, metadata, element) {\n // create a DOM node to render to\n const toinsert = this.create_output_subarea(\n metadata,\n CLASS_NAME,\n EXEC_MIME_TYPE\n );\n this.keyboard_manager.register_events(toinsert);\n // Render to node\n const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n render(props, toinsert[toinsert.length - 1]);\n element.append(toinsert);\n return toinsert\n }\n\n /* Handle when an output is cleared or removed */\n events.on('clear_output.CodeCell', handleClearOutput);\n events.on('delete.Cell', handleClearOutput);\n\n /* Handle when a new output is added */\n events.on('output_added.OutputArea', handleAddOutput);\n\n /**\n * Register the mime type and append_mime function with output_area\n */\n OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n /* Is output safe? */\n safe: true,\n /* Index of renderer in `output_area.display_order` */\n index: 0\n });\n }\n\n // register the mime type if in Jupyter Notebook environment and previously unregistered\n if (root.Jupyter !== undefined) {\n const events = require('base/js/events');\n const OutputArea = require('notebook/js/outputarea').OutputArea;\n\n if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n register_renderer(events, OutputArea);\n }\n }\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n const el = document.getElementById(null);\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.1.1.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\n} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(null)).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));", - "application/vnd.bokehjs_load.v0+json": "" + "application/javascript": [ + "(function(root) {\n", + " function now() {\n", + " return new Date();\n", + " }\n", + "\n", + " const force = true;\n", + "\n", + " if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n", + " root._bokeh_onload_callbacks = [];\n", + " root._bokeh_is_loading = undefined;\n", + " }\n", + "\n", + "const JS_MIME_TYPE = 'application/javascript';\n", + " const HTML_MIME_TYPE = 'text/html';\n", + " const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", + " const CLASS_NAME = 'output_bokeh rendered_html';\n", + "\n", + " /**\n", + " * Render data to the DOM node\n", + " */\n", + " function render(props, node) {\n", + " const script = document.createElement(\"script\");\n", + " node.appendChild(script);\n", + " }\n", + "\n", + " /**\n", + " * Handle when an output is cleared or removed\n", + " */\n", + " function handleClearOutput(event, handle) {\n", + " const cell = handle.cell;\n", + "\n", + " const id = cell.output_area._bokeh_element_id;\n", + " const server_id = cell.output_area._bokeh_server_id;\n", + " // Clean up Bokeh references\n", + " if (id != null && id in Bokeh.index) {\n", + " Bokeh.index[id].model.document.clear();\n", + " delete Bokeh.index[id];\n", + " }\n", + "\n", + " if (server_id !== undefined) {\n", + " // Clean up Bokeh references\n", + " const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", + " cell.notebook.kernel.execute(cmd_clean, {\n", + " iopub: {\n", + " output: function(msg) {\n", + " const id = msg.content.text.trim();\n", + " if (id in Bokeh.index) {\n", + " Bokeh.index[id].model.document.clear();\n", + " delete Bokeh.index[id];\n", + " }\n", + " }\n", + " }\n", + " });\n", + " // Destroy server and session\n", + " const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", + " cell.notebook.kernel.execute(cmd_destroy);\n", + " }\n", + " }\n", + "\n", + " /**\n", + " * Handle when a new output is added\n", + " */\n", + " function handleAddOutput(event, handle) {\n", + " const output_area = handle.output_area;\n", + " const output = handle.output;\n", + "\n", + " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", + " if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n", + " return\n", + " }\n", + "\n", + " const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", + "\n", + " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", + " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n", + " // store reference to embed id on output_area\n", + " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", + " }\n", + " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", + " const bk_div = document.createElement(\"div\");\n", + " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", + " const script_attrs = bk_div.children[0].attributes;\n", + " for (let i = 0; i < script_attrs.length; i++) {\n", + " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", + " toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n", + " }\n", + " // store reference to server id on output_area\n", + " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", + " }\n", + " }\n", + "\n", + " function register_renderer(events, OutputArea) {\n", + "\n", + " function append_mime(data, metadata, element) {\n", + " // create a DOM node to render to\n", + " const toinsert = this.create_output_subarea(\n", + " metadata,\n", + " CLASS_NAME,\n", + " EXEC_MIME_TYPE\n", + " );\n", + " this.keyboard_manager.register_events(toinsert);\n", + " // Render to node\n", + " const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", + " render(props, toinsert[toinsert.length - 1]);\n", + " element.append(toinsert);\n", + " return toinsert\n", + " }\n", + "\n", + " /* Handle when an output is cleared or removed */\n", + " events.on('clear_output.CodeCell', handleClearOutput);\n", + " events.on('delete.Cell', handleClearOutput);\n", + "\n", + " /* Handle when a new output is added */\n", + " events.on('output_added.OutputArea', handleAddOutput);\n", + "\n", + " /**\n", + " * Register the mime type and append_mime function with output_area\n", + " */\n", + " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", + " /* Is output safe? */\n", + " safe: true,\n", + " /* Index of renderer in `output_area.display_order` */\n", + " index: 0\n", + " });\n", + " }\n", + "\n", + " // register the mime type if in Jupyter Notebook environment and previously unregistered\n", + " if (root.Jupyter !== undefined) {\n", + " const events = require('base/js/events');\n", + " const OutputArea = require('notebook/js/outputarea').OutputArea;\n", + "\n", + " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", + " register_renderer(events, OutputArea);\n", + " }\n", + " }\n", + " if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n", + " root._bokeh_timeout = Date.now() + 5000;\n", + " root._bokeh_failed_load = false;\n", + " }\n", + "\n", + " const NB_LOAD_WARNING = {'data': {'text/html':\n", + " \"
\\n\"+\n", + " \"

\\n\"+\n", + " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", + " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", + " \"

\\n\"+\n", + " \"\\n\"+\n", + " \"\\n\"+\n", + " \"from bokeh.resources import INLINE\\n\"+\n", + " \"output_notebook(resources=INLINE)\\n\"+\n", + " \"\\n\"+\n", + " \"
\"}};\n", + "\n", + " function display_loaded() {\n", + " const el = document.getElementById(null);\n", + " if (el != null) {\n", + " el.textContent = \"BokehJS is loading...\";\n", + " }\n", + " if (root.Bokeh !== undefined) {\n", + " if (el != null) {\n", + " el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n", + " }\n", + " } else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(display_loaded, 100)\n", + " }\n", + " }\n", + "\n", + " function run_callbacks() {\n", + " try {\n", + " root._bokeh_onload_callbacks.forEach(function(callback) {\n", + " if (callback != null)\n", + " callback();\n", + " });\n", + " } finally {\n", + " delete root._bokeh_onload_callbacks\n", + " }\n", + " console.debug(\"Bokeh: all callbacks have finished\");\n", + " }\n", + "\n", + " function load_libs(css_urls, js_urls, callback) {\n", + " if (css_urls == null) css_urls = [];\n", + " if (js_urls == null) js_urls = [];\n", + "\n", + " root._bokeh_onload_callbacks.push(callback);\n", + " if (root._bokeh_is_loading > 0) {\n", + " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", + " return null;\n", + " }\n", + " if (js_urls == null || js_urls.length === 0) {\n", + " run_callbacks();\n", + " return null;\n", + " }\n", + " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", + " root._bokeh_is_loading = css_urls.length + js_urls.length;\n", + "\n", + " function on_load() {\n", + " root._bokeh_is_loading--;\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", + " run_callbacks()\n", + " }\n", + " }\n", + "\n", + " function on_error(url) {\n", + " console.error(\"failed to load \" + url);\n", + " }\n", + "\n", + " for (let i = 0; i < css_urls.length; i++) {\n", + " const url = css_urls[i];\n", + " const element = document.createElement(\"link\");\n", + " element.onload = on_load;\n", + " element.onerror = on_error.bind(null, url);\n", + " element.rel = \"stylesheet\";\n", + " element.type = \"text/css\";\n", + " element.href = url;\n", + " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", + " document.body.appendChild(element);\n", + " }\n", + "\n", + " for (let i = 0; i < js_urls.length; i++) {\n", + " const url = js_urls[i];\n", + " const element = document.createElement('script');\n", + " element.onload = on_load;\n", + " element.onerror = on_error.bind(null, url);\n", + " element.async = false;\n", + " element.src = url;\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.head.appendChild(element);\n", + " }\n", + " };\n", + "\n", + " function inject_raw_css(css) {\n", + " const element = document.createElement(\"style\");\n", + " element.appendChild(document.createTextNode(css));\n", + " document.body.appendChild(element);\n", + " }\n", + "\n", + " const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.1.1.min.js\"];\n", + " const css_urls = [];\n", + "\n", + " const inline_js = [ function(Bokeh) {\n", + " Bokeh.set_log_level(\"info\");\n", + " },\n", + "function(Bokeh) {\n", + " }\n", + " ];\n", + "\n", + " function run_inline_js() {\n", + " if (root.Bokeh !== undefined || force === true) {\n", + " for (let i = 0; i < inline_js.length; i++) {\n", + " inline_js[i].call(root, root.Bokeh);\n", + " }\n", + "} else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(run_inline_js, 100);\n", + " } else if (!root._bokeh_failed_load) {\n", + " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", + " root._bokeh_failed_load = true;\n", + " } else if (force !== true) {\n", + " const cell = $(document.getElementById(null)).parents('.cell').data().cell;\n", + " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", + " }\n", + " }\n", + "\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", + " run_inline_js();\n", + " } else {\n", + " load_libs(css_urls, js_urls, function() {\n", + " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", + " run_inline_js();\n", + " });\n", + " }\n", + "}(window));" + ], + "application/vnd.bokehjs_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n const el = document.getElementById(null);\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.1.1.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\n} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(null)).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" }, "metadata": {}, "output_type": "display_data" @@ -2111,7 +3085,7 @@ "data": { "text/html": [ "\n", - "
\n" + "
\n" ] }, "metadata": {}, @@ -2119,12 +3093,37 @@ }, { "data": { - "application/javascript": "(function(root) {\n function embed_document(root) {\n const docs_json = {\"592cbcb0-039b-424e-88ce-54375cbb620b\":{\"version\":\"3.1.1\",\"title\":\"Bokeh 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+ " const render_items = [{\"docid\":\"47b109f1-5cb7-434f-add2-1e64e25efb10\",\"roots\":{\"p6591\":\"d8078942-dcab-481d-a7e7-c6cebda60bf4\"},\"root_ids\":[\"p6591\"]}];\n", + " root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n", + " }\n", + " if (root.Bokeh !== undefined) {\n", + " embed_document(root);\n", + " } else {\n", + " let attempts = 0;\n", + " const timer = setInterval(function(root) {\n", + " if (root.Bokeh !== undefined) {\n", + " clearInterval(timer);\n", + " embed_document(root);\n", + " } else {\n", + " attempts++;\n", + " if (attempts > 100) {\n", + " clearInterval(timer);\n", + " console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n", + " }\n", + " }\n", + " }, 10, root)\n", + " }\n", + "})(window);" + ], "application/vnd.bokehjs_exec.v0+json": "" }, "metadata": { "application/vnd.bokehjs_exec.v0+json": { - "id": "p6604" + "id": "p6591" } }, "output_type": "display_data" @@ -2149,14 +3148,7 @@ "cell_type": "code", "execution_count": 32, "id": "5e67fff6-d75a-4d04-991c-649e0c5374a7", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:50:36.657560Z", - "iopub.status.busy": "2023-07-24T15:50:36.657410Z", - "iopub.status.idle": "2023-07-24T15:50:37.121970Z", - "shell.execute_reply": "2023-07-24T15:50:37.121566Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "calendar_list: List[tp.EventSet] = []\n", @@ -2172,19 +3164,289 @@ "cell_type": "code", "execution_count": 33, "id": "63294d29-6e78-4e01-b098-fcb704065ca3", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:50:37.123838Z", - "iopub.status.busy": "2023-07-24T15:50:37.123700Z", - "iopub.status.idle": "2023-07-24T15:50:37.470260Z", - "shell.execute_reply": "2023-07-24T15:50:37.469898Z" - } - }, + "metadata": {}, "outputs": [ { "data": { - "application/javascript": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\nconst JS_MIME_TYPE = 'application/javascript';\n const HTML_MIME_TYPE = 'text/html';\n const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n const CLASS_NAME = 'output_bokeh rendered_html';\n\n /**\n * Render data to the DOM node\n */\n function render(props, node) {\n const script = document.createElement(\"script\");\n node.appendChild(script);\n }\n\n /**\n * Handle when an output is cleared or removed\n */\n function handleClearOutput(event, handle) {\n const cell = handle.cell;\n\n const id = cell.output_area._bokeh_element_id;\n const server_id = cell.output_area._bokeh_server_id;\n // Clean up Bokeh references\n if (id != null && id in Bokeh.index) {\n Bokeh.index[id].model.document.clear();\n delete Bokeh.index[id];\n }\n\n if (server_id !== undefined) {\n // Clean up Bokeh references\n const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n cell.notebook.kernel.execute(cmd_clean, {\n iopub: {\n output: function(msg) {\n const id = msg.content.text.trim();\n if (id in Bokeh.index) {\n Bokeh.index[id].model.document.clear();\n delete Bokeh.index[id];\n }\n }\n }\n });\n // Destroy server and session\n const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n cell.notebook.kernel.execute(cmd_destroy);\n }\n }\n\n /**\n * Handle when a new output is added\n */\n function handleAddOutput(event, handle) {\n const output_area = handle.output_area;\n const output = handle.output;\n\n // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n return\n }\n\n const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n\n if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n // store reference to embed id on output_area\n output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n }\n if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n const bk_div = document.createElement(\"div\");\n bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n const script_attrs = bk_div.children[0].attributes;\n for (let i = 0; i < script_attrs.length; i++) {\n toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n }\n // store reference to server id on output_area\n output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n }\n }\n\n function register_renderer(events, OutputArea) {\n\n function append_mime(data, metadata, element) {\n // create a DOM node to render to\n const toinsert = this.create_output_subarea(\n metadata,\n CLASS_NAME,\n EXEC_MIME_TYPE\n );\n this.keyboard_manager.register_events(toinsert);\n // Render to node\n const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n render(props, toinsert[toinsert.length - 1]);\n element.append(toinsert);\n return toinsert\n }\n\n /* Handle when an output is cleared or removed */\n events.on('clear_output.CodeCell', handleClearOutput);\n events.on('delete.Cell', handleClearOutput);\n\n /* Handle when a new output is added */\n events.on('output_added.OutputArea', handleAddOutput);\n\n /**\n * Register the mime type and append_mime function with output_area\n */\n OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n /* Is output safe? */\n safe: true,\n /* Index of renderer in `output_area.display_order` */\n index: 0\n });\n }\n\n // register the mime type if in Jupyter Notebook environment and previously unregistered\n if (root.Jupyter !== undefined) {\n const events = require('base/js/events');\n const OutputArea = require('notebook/js/outputarea').OutputArea;\n\n if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n register_renderer(events, OutputArea);\n }\n }\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n const el = document.getElementById(null);\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.1.1.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\n} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(null)).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));", - "application/vnd.bokehjs_load.v0+json": "" + "application/javascript": [ + "(function(root) {\n", + " function now() {\n", + " return new Date();\n", + " }\n", + "\n", + " const force = true;\n", + "\n", + " if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n", + " root._bokeh_onload_callbacks = [];\n", + " root._bokeh_is_loading = undefined;\n", + " }\n", + "\n", + "const JS_MIME_TYPE = 'application/javascript';\n", + " const HTML_MIME_TYPE = 'text/html';\n", + " const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", + " const CLASS_NAME = 'output_bokeh rendered_html';\n", + "\n", + " /**\n", + " * Render data to the DOM node\n", + " */\n", + " function render(props, node) {\n", + " const script = document.createElement(\"script\");\n", + " node.appendChild(script);\n", + " }\n", + "\n", + " /**\n", + " * Handle when an output is cleared or removed\n", + " */\n", + " function handleClearOutput(event, handle) {\n", + " const cell = handle.cell;\n", + "\n", + " const id = cell.output_area._bokeh_element_id;\n", + " const server_id = cell.output_area._bokeh_server_id;\n", + " // Clean up Bokeh references\n", + " if (id != null && id in Bokeh.index) {\n", + " Bokeh.index[id].model.document.clear();\n", + " delete Bokeh.index[id];\n", + " }\n", + "\n", + " if (server_id !== undefined) {\n", + " // Clean up Bokeh references\n", + " const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", + " cell.notebook.kernel.execute(cmd_clean, {\n", + " iopub: {\n", + " output: function(msg) {\n", + " const id = msg.content.text.trim();\n", + " if (id in Bokeh.index) {\n", + " Bokeh.index[id].model.document.clear();\n", + " delete Bokeh.index[id];\n", + " }\n", + " }\n", + " }\n", + " });\n", + " // Destroy server and session\n", + " const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", + " cell.notebook.kernel.execute(cmd_destroy);\n", + " }\n", + " }\n", + "\n", + " /**\n", + " * Handle when a new output is added\n", + " */\n", + " function handleAddOutput(event, handle) {\n", + " const output_area = handle.output_area;\n", + " const output = handle.output;\n", + "\n", + " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", + " if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n", + " return\n", + " }\n", + "\n", + " const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", + "\n", + " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", + " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n", + " // store reference to embed id on output_area\n", + " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", + " }\n", + " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", + " const bk_div = document.createElement(\"div\");\n", + " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", + " const script_attrs = bk_div.children[0].attributes;\n", + " for (let i = 0; i < script_attrs.length; i++) {\n", + " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", + " toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n", + " }\n", + " // store reference to server id on output_area\n", + " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", + " }\n", + " }\n", + "\n", + " function register_renderer(events, OutputArea) {\n", + "\n", + " function append_mime(data, metadata, element) {\n", + " // create a DOM node to render to\n", + " const toinsert = this.create_output_subarea(\n", + " metadata,\n", + " CLASS_NAME,\n", + " EXEC_MIME_TYPE\n", + " );\n", + " this.keyboard_manager.register_events(toinsert);\n", + " // Render to node\n", + " const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", + " render(props, toinsert[toinsert.length - 1]);\n", + " element.append(toinsert);\n", + " return toinsert\n", + " }\n", + "\n", + " /* Handle when an output is cleared or removed */\n", + " events.on('clear_output.CodeCell', handleClearOutput);\n", + " events.on('delete.Cell', handleClearOutput);\n", + "\n", + " /* Handle when a new output is added */\n", + " events.on('output_added.OutputArea', handleAddOutput);\n", + "\n", + " /**\n", + " * Register the mime type and append_mime function with output_area\n", + " */\n", + " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", + " /* Is output safe? */\n", + " safe: true,\n", + " /* Index of renderer in `output_area.display_order` */\n", + " index: 0\n", + " });\n", + " }\n", + "\n", + " // register the mime type if in Jupyter Notebook environment and previously unregistered\n", + " if (root.Jupyter !== undefined) {\n", + " const events = require('base/js/events');\n", + " const OutputArea = require('notebook/js/outputarea').OutputArea;\n", + "\n", + " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", + " register_renderer(events, OutputArea);\n", + " }\n", + " }\n", + " if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n", + " root._bokeh_timeout = Date.now() + 5000;\n", + " root._bokeh_failed_load = false;\n", + " }\n", + "\n", + " const NB_LOAD_WARNING = {'data': {'text/html':\n", + " \"
\\n\"+\n", + " \"

\\n\"+\n", + " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", + " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", + " \"

\\n\"+\n", + " \"\\n\"+\n", + " \"\\n\"+\n", + " \"from bokeh.resources import INLINE\\n\"+\n", + " \"output_notebook(resources=INLINE)\\n\"+\n", + " \"\\n\"+\n", + " \"
\"}};\n", + "\n", + " function display_loaded() {\n", + " const el = document.getElementById(null);\n", + " if (el != null) {\n", + " el.textContent = \"BokehJS is loading...\";\n", + " }\n", + " if (root.Bokeh !== undefined) {\n", + " if (el != null) {\n", + " el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n", + " }\n", + " } else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(display_loaded, 100)\n", + " }\n", + " }\n", + "\n", + " function run_callbacks() {\n", + " try {\n", + " root._bokeh_onload_callbacks.forEach(function(callback) {\n", + " if (callback != null)\n", + " callback();\n", + " });\n", + " } finally {\n", + " delete root._bokeh_onload_callbacks\n", + " }\n", + " console.debug(\"Bokeh: all callbacks have finished\");\n", + " }\n", + "\n", + " function load_libs(css_urls, js_urls, callback) {\n", + " if (css_urls == null) css_urls = [];\n", + " if (js_urls == null) js_urls = [];\n", + "\n", + " root._bokeh_onload_callbacks.push(callback);\n", + " if (root._bokeh_is_loading > 0) {\n", + " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", + " return null;\n", + " }\n", + " if (js_urls == null || js_urls.length === 0) {\n", + " run_callbacks();\n", + " return null;\n", + " }\n", + " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", + " root._bokeh_is_loading = css_urls.length + js_urls.length;\n", + "\n", + " function on_load() {\n", + " root._bokeh_is_loading--;\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", + " run_callbacks()\n", + " }\n", + " }\n", + "\n", + " function on_error(url) {\n", + " console.error(\"failed to load \" + url);\n", + " }\n", + "\n", + " for (let i = 0; i < css_urls.length; i++) {\n", + " const url = css_urls[i];\n", + " const element = document.createElement(\"link\");\n", + " element.onload = on_load;\n", + " element.onerror = on_error.bind(null, url);\n", + " element.rel = \"stylesheet\";\n", + " element.type = \"text/css\";\n", + " element.href = url;\n", + " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", + " document.body.appendChild(element);\n", + " }\n", + "\n", + " for (let i = 0; i < js_urls.length; i++) {\n", + " const url = js_urls[i];\n", + " const element = document.createElement('script');\n", + " element.onload = on_load;\n", + " element.onerror = on_error.bind(null, url);\n", + " element.async = false;\n", + " element.src = url;\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.head.appendChild(element);\n", + " }\n", + " };\n", + "\n", + " function inject_raw_css(css) {\n", + " const element = document.createElement(\"style\");\n", + " element.appendChild(document.createTextNode(css));\n", + " document.body.appendChild(element);\n", + " }\n", + "\n", + " const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.1.1.min.js\"];\n", + " const css_urls = [];\n", + "\n", + " const inline_js = [ function(Bokeh) {\n", + " Bokeh.set_log_level(\"info\");\n", + " },\n", + "function(Bokeh) {\n", + " }\n", + " ];\n", + "\n", + " function run_inline_js() {\n", + " if (root.Bokeh !== undefined || force === true) {\n", + " for (let i = 0; i < inline_js.length; i++) {\n", + " inline_js[i].call(root, root.Bokeh);\n", + " }\n", + "} else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(run_inline_js, 100);\n", + " } else if (!root._bokeh_failed_load) {\n", + " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", + " root._bokeh_failed_load = true;\n", + " } else if (force !== true) {\n", + " const cell = $(document.getElementById(null)).parents('.cell').data().cell;\n", + " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", + " }\n", + " }\n", + "\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", + " run_inline_js();\n", + " } else {\n", + " load_libs(css_urls, js_urls, function() {\n", + " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", + " run_inline_js();\n", + " });\n", + " }\n", + "}(window));" + ], + "application/vnd.bokehjs_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n const el = document.getElementById(null);\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.1.1.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\n} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(null)).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" }, "metadata": {}, "output_type": "display_data" @@ -2193,7 +3455,7 @@ "data": { "text/html": [ "\n", - "
\n" + "
\n" ] }, "metadata": {}, @@ -2201,12 +3463,37 @@ }, { "data": { - "application/javascript": "(function(root) {\n function embed_document(root) {\n const docs_json = {\"0ac8e564-e1c0-4085-8ffb-82a64c2646f9\":{\"version\":\"3.1.1\",\"title\":\"Bokeh 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+ " const render_items = [{\"docid\":\"b8cd1fed-dfd3-49f8-923f-61cf3c792978\",\"roots\":{\"p8621\":\"de383b3d-24b4-4619-a47d-591d9f8b0d9e\"},\"root_ids\":[\"p8621\"]}];\n", + " root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n", + " }\n", + " if (root.Bokeh !== undefined) {\n", + " embed_document(root);\n", + " } else {\n", + " let attempts = 0;\n", + " const timer = setInterval(function(root) {\n", + " if (root.Bokeh !== undefined) {\n", + " clearInterval(timer);\n", + " embed_document(root);\n", + " } else {\n", + " attempts++;\n", + " if (attempts > 100) {\n", + " clearInterval(timer);\n", + " console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n", + " }\n", + " }\n", + " }, 10, root)\n", + " }\n", + "})(window);" + ], "application/vnd.bokehjs_exec.v0+json": "" }, "metadata": { "application/vnd.bokehjs_exec.v0+json": { - "id": "p8637" + "id": "p8621" } }, "output_type": "display_data" @@ -2233,14 +3520,7 @@ "cell_type": "code", "execution_count": 34, "id": "48a0b18c-0eb8-4ebc-85d1-a928ac7355d5", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:50:37.472272Z", - "iopub.status.busy": "2023-07-24T15:50:37.472117Z", - "iopub.status.idle": "2023-07-24T15:50:37.480027Z", - "shell.execute_reply": "2023-07-24T15:50:37.479733Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "# `sales_per_dept` contains all the sales indexed by department / category / store / state.\n", @@ -2271,14 +3551,7 @@ "cell_type": "code", "execution_count": 35, "id": "8d9f3d61-3aa3-4a15-9065-9da27b747107", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:50:37.481894Z", - "iopub.status.busy": "2023-07-24T15:50:37.481785Z", - "iopub.status.idle": "2023-07-24T15:50:37.487630Z", - "shell.execute_reply": "2023-07-24T15:50:37.487213Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "# Create a daily sampling.\n", @@ -2298,14 +3571,7 @@ "cell_type": "code", "execution_count": 36, "id": "fc6bd2ff-8383-43e1-9202-e42fcec713bd", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:50:37.489632Z", - "iopub.status.busy": "2023-07-24T15:50:37.489470Z", - "iopub.status.idle": "2023-07-24T15:50:38.135052Z", - "shell.execute_reply": "2023-07-24T15:50:38.134532Z" - } - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -2316,7 +3582,7 @@ }, { "data": { - "image/png": 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", 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", 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" ] @@ -2344,14 +3610,7 @@ "cell_type": "code", "execution_count": 37, "id": "6d6b8394-de68-4ce6-898b-02f5d2baed2c", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:50:38.137179Z", - "iopub.status.busy": "2023-07-24T15:50:38.136979Z", - "iopub.status.idle": "2023-07-24T15:50:38.141257Z", - "shell.execute_reply": "2023-07-24T15:50:38.140910Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "sum28_sales_per_dept = sum28_sales_per_dept.propagate(sales_evset, resample=True)\n", @@ -2363,14 +3622,7 @@ "cell_type": "code", "execution_count": 38, "id": "44dfa773-1cff-47c9-babc-a003bd2c9315", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:50:38.143050Z", - "iopub.status.busy": "2023-07-24T15:50:38.142934Z", - "iopub.status.idle": "2023-07-24T15:50:38.479042Z", - "shell.execute_reply": "2023-07-24T15:50:38.478616Z" - } - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -2381,7 +3633,7 @@ }, { "data": { - "image/png": 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", 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", 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" ] @@ -2409,14 +3661,7 @@ "cell_type": "code", "execution_count": 39, "id": "14f08bd5-5c88-424e-9db4-42e241fb51bd", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:50:38.481111Z", - "iopub.status.busy": "2023-07-24T15:50:38.480960Z", - "iopub.status.idle": "2023-07-24T15:50:38.508299Z", - "shell.execute_reply": "2023-07-24T15:50:38.507907Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "# Propagate the global special event to each product.\n", @@ -2446,14 +3691,7 @@ "cell_type": "code", "execution_count": 40, "id": "af507fc1-a9ea-45c3-86ea-d37661847a7c", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:50:38.510419Z", - "iopub.status.busy": "2023-07-24T15:50:38.510270Z", - "iopub.status.idle": "2023-07-24T15:50:38.892582Z", - "shell.execute_reply": "2023-07-24T15:50:38.892244Z" - } - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -2464,42 +3702,344 @@ }, { "data": { - "application/javascript": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\nconst JS_MIME_TYPE = 'application/javascript';\n const HTML_MIME_TYPE = 'text/html';\n const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n const CLASS_NAME = 'output_bokeh rendered_html';\n\n /**\n * Render data to the DOM node\n */\n function render(props, node) {\n const script = document.createElement(\"script\");\n node.appendChild(script);\n }\n\n /**\n * Handle when an output is cleared or removed\n */\n function handleClearOutput(event, handle) {\n const cell = handle.cell;\n\n const id = cell.output_area._bokeh_element_id;\n const server_id = cell.output_area._bokeh_server_id;\n // Clean up Bokeh references\n if (id != null && id in Bokeh.index) {\n Bokeh.index[id].model.document.clear();\n delete Bokeh.index[id];\n }\n\n if (server_id !== undefined) {\n // Clean up Bokeh references\n const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n cell.notebook.kernel.execute(cmd_clean, {\n iopub: {\n output: function(msg) {\n const id = msg.content.text.trim();\n if (id in Bokeh.index) {\n Bokeh.index[id].model.document.clear();\n delete Bokeh.index[id];\n }\n }\n }\n });\n // Destroy server and session\n const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n cell.notebook.kernel.execute(cmd_destroy);\n }\n }\n\n /**\n * Handle when a new output is added\n */\n function handleAddOutput(event, handle) {\n const output_area = handle.output_area;\n const output = handle.output;\n\n // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n return\n }\n\n const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n\n if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n // store reference to embed id on output_area\n output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n }\n if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n const bk_div = document.createElement(\"div\");\n bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n const script_attrs = bk_div.children[0].attributes;\n for (let i = 0; i < script_attrs.length; i++) {\n toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n }\n // store reference to server id on output_area\n output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n }\n }\n\n function register_renderer(events, OutputArea) {\n\n function append_mime(data, metadata, element) {\n // create a DOM node to render to\n const toinsert = this.create_output_subarea(\n metadata,\n CLASS_NAME,\n EXEC_MIME_TYPE\n );\n this.keyboard_manager.register_events(toinsert);\n // Render to node\n const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n render(props, toinsert[toinsert.length - 1]);\n element.append(toinsert);\n return toinsert\n }\n\n /* Handle when an output is cleared or removed */\n events.on('clear_output.CodeCell', handleClearOutput);\n events.on('delete.Cell', handleClearOutput);\n\n /* Handle when a new output is added */\n events.on('output_added.OutputArea', handleAddOutput);\n\n /**\n * Register the mime type and append_mime function with output_area\n */\n OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n /* Is output safe? */\n safe: true,\n /* Index of renderer in `output_area.display_order` */\n index: 0\n });\n }\n\n // register the mime type if in Jupyter Notebook environment and previously unregistered\n if (root.Jupyter !== undefined) {\n const events = require('base/js/events');\n const OutputArea = require('notebook/js/outputarea').OutputArea;\n\n if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n register_renderer(events, OutputArea);\n }\n }\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n const el = document.getElementById(null);\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.1.1.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\n} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(null)).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));", - "application/vnd.bokehjs_load.v0+json": "" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ + "application/javascript": [ + "(function(root) {\n", + " function now() {\n", + " return new Date();\n", + " }\n", "\n", - "
\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": "(function(root) {\n function embed_document(root) {\n const docs_json = {\"83f23353-e0ba-4332-8390-3fb0af390cea\":{\"version\":\"3.1.1\",\"title\":\"Bokeh 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]},\"start\":1420077600000.0,\"end\":1451530800000.0}}],[\"p3_x_range\",{\"type\":\"object\",\"name\":\"Range1d\",\"id\":\"p10252\",\"attributes\":{\"js_property_callbacks\":{\"type\":\"map\",\"entries\":[[\"change:start\",[{\"id\":\"p10262\"}]],[\"change:end\",[{\"id\":\"p10262\"}]]]},\"start\":1420077600000.0,\"end\":1451530800000.0}}]]},\"code\":\"\\n 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const render_items = [{\"docid\":\"83f23353-e0ba-4332-8390-3fb0af390cea\",\"roots\":{\"p10392\":\"f009a0db-c8d3-470c-9698-c455163b6bef\"},\"root_ids\":[\"p10392\"]}];\n root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n }\n if (root.Bokeh !== undefined) {\n embed_document(root);\n } else {\n let attempts = 0;\n const timer = setInterval(function(root) {\n if (root.Bokeh !== undefined) {\n clearInterval(timer);\n embed_document(root);\n } else {\n attempts++;\n if (attempts > 100) {\n clearInterval(timer);\n console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n }\n }\n }, 10, root)\n }\n})(window);", - "application/vnd.bokehjs_exec.v0+json": "" - }, - "metadata": { - "application/vnd.bokehjs_exec.v0+json": { - "id": "p10392" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "feature_special_events.plot(**plot_options, max_num_plots=3)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", + " const force = true;\n", + "\n", + " if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n", + " root._bokeh_onload_callbacks = [];\n", + " root._bokeh_is_loading = undefined;\n", + " }\n", + "\n", + "const JS_MIME_TYPE = 'application/javascript';\n", + " const HTML_MIME_TYPE = 'text/html';\n", + " const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", + " const CLASS_NAME = 'output_bokeh rendered_html';\n", + "\n", + " /**\n", + " * Render data to the DOM node\n", + " */\n", + " function render(props, node) {\n", + " const script = document.createElement(\"script\");\n", + " node.appendChild(script);\n", + " }\n", + "\n", + " /**\n", + " * Handle when an output is cleared or removed\n", + " */\n", + " function handleClearOutput(event, handle) {\n", + " const cell = handle.cell;\n", + "\n", + " const id = cell.output_area._bokeh_element_id;\n", + " const server_id = cell.output_area._bokeh_server_id;\n", + " // Clean up Bokeh references\n", + " if (id != null && id in Bokeh.index) {\n", + " Bokeh.index[id].model.document.clear();\n", + " delete Bokeh.index[id];\n", + " }\n", + "\n", + " if (server_id !== undefined) {\n", + " // Clean up Bokeh references\n", + " const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", + " cell.notebook.kernel.execute(cmd_clean, {\n", + " iopub: {\n", + " output: function(msg) {\n", + " const id = msg.content.text.trim();\n", + " if (id in Bokeh.index) {\n", + " Bokeh.index[id].model.document.clear();\n", + " delete Bokeh.index[id];\n", + " }\n", + " }\n", + " }\n", + " });\n", + " // Destroy server and session\n", + " const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", + " cell.notebook.kernel.execute(cmd_destroy);\n", + " }\n", + " }\n", + "\n", + " /**\n", + " * Handle when a new output is added\n", + " */\n", + " function handleAddOutput(event, handle) {\n", + " const output_area = handle.output_area;\n", + " const output = handle.output;\n", + "\n", + " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", + " if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n", + " return\n", + " }\n", + "\n", + " const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", + "\n", + " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", + " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n", + " // store reference to embed id on output_area\n", + " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", + " }\n", + " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", + " const bk_div = document.createElement(\"div\");\n", + " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", + " const script_attrs = bk_div.children[0].attributes;\n", + " for (let i = 0; i < script_attrs.length; i++) {\n", + " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", + " toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n", + " }\n", + " // store reference to server id on output_area\n", + " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", + " }\n", + " }\n", + "\n", + " function register_renderer(events, OutputArea) {\n", + "\n", + " function append_mime(data, metadata, element) {\n", + " // create a DOM node to render to\n", + " const toinsert = this.create_output_subarea(\n", + " metadata,\n", + " CLASS_NAME,\n", + " EXEC_MIME_TYPE\n", + " );\n", + " this.keyboard_manager.register_events(toinsert);\n", + " // Render to node\n", + " const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", + " render(props, toinsert[toinsert.length - 1]);\n", + " element.append(toinsert);\n", + " return toinsert\n", + " }\n", + "\n", + " /* Handle when an output is cleared or removed */\n", + " events.on('clear_output.CodeCell', handleClearOutput);\n", + " events.on('delete.Cell', handleClearOutput);\n", + "\n", + " /* Handle when a new output is added */\n", + " events.on('output_added.OutputArea', handleAddOutput);\n", + "\n", + " /**\n", + " * Register the mime type and append_mime function with output_area\n", + " */\n", + " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", + " /* Is output safe? */\n", + " safe: true,\n", + " /* Index of renderer in `output_area.display_order` */\n", + " index: 0\n", + " });\n", + " }\n", + "\n", + " // register the mime type if in Jupyter Notebook environment and previously unregistered\n", + " if (root.Jupyter !== undefined) {\n", + " const events = require('base/js/events');\n", + " const OutputArea = require('notebook/js/outputarea').OutputArea;\n", + "\n", + " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", + " register_renderer(events, OutputArea);\n", + " }\n", + " }\n", + " if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n", + " root._bokeh_timeout = Date.now() + 5000;\n", + " root._bokeh_failed_load = false;\n", + " }\n", + "\n", + " const NB_LOAD_WARNING = {'data': {'text/html':\n", + " \"
\\n\"+\n", + " \"

\\n\"+\n", + " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", + " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", + " \"

\\n\"+\n", + " \"
    \\n\"+\n", + " \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n", + " \"
  • use INLINE resources instead, as so:
    • \\n\"+\n", + " \"
\\n\"+\n", + " \"\\n\"+\n", + " \"from bokeh.resources import INLINE\\n\"+\n", + " \"output_notebook(resources=INLINE)\\n\"+\n", + " \"\\n\"+\n", + " \"
\"}};\n", + "\n", + " function display_loaded() {\n", + " const el = document.getElementById(null);\n", + " if (el != null) {\n", + " el.textContent = \"BokehJS is loading...\";\n", + " }\n", + " if (root.Bokeh !== undefined) {\n", + " if (el != null) {\n", + " el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n", + " }\n", + " } else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(display_loaded, 100)\n", + " }\n", + " }\n", + "\n", + " function run_callbacks() {\n", + " try {\n", + " root._bokeh_onload_callbacks.forEach(function(callback) {\n", + " if (callback != null)\n", + " callback();\n", + " });\n", + " } finally {\n", + " delete root._bokeh_onload_callbacks\n", + " }\n", + " console.debug(\"Bokeh: all callbacks have finished\");\n", + " }\n", + "\n", + " function load_libs(css_urls, js_urls, callback) {\n", + " if (css_urls == null) css_urls = [];\n", + " if (js_urls == null) js_urls = [];\n", + "\n", + " root._bokeh_onload_callbacks.push(callback);\n", + " if (root._bokeh_is_loading > 0) {\n", + " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", + " return null;\n", + " }\n", + " if (js_urls == null || js_urls.length === 0) {\n", + " run_callbacks();\n", + " return null;\n", + " }\n", + " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", + " root._bokeh_is_loading = css_urls.length + js_urls.length;\n", + "\n", + " function on_load() {\n", + " root._bokeh_is_loading--;\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", + " run_callbacks()\n", + " }\n", + " }\n", + "\n", + " function on_error(url) {\n", + " console.error(\"failed to load \" + url);\n", + " }\n", + "\n", + " for (let i = 0; i < css_urls.length; i++) {\n", + " const url = css_urls[i];\n", + " const element = document.createElement(\"link\");\n", + " element.onload = on_load;\n", + " element.onerror = on_error.bind(null, url);\n", + " element.rel = \"stylesheet\";\n", + " element.type = \"text/css\";\n", + " element.href = url;\n", + " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", + " document.body.appendChild(element);\n", + " }\n", + "\n", + " for (let i = 0; i < js_urls.length; i++) {\n", + " const url = js_urls[i];\n", + " const element = document.createElement('script');\n", + " element.onload = on_load;\n", + " element.onerror = on_error.bind(null, url);\n", + " element.async = false;\n", + " element.src = url;\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.head.appendChild(element);\n", + " }\n", + " };\n", + "\n", + " function inject_raw_css(css) {\n", + " const element = document.createElement(\"style\");\n", + " element.appendChild(document.createTextNode(css));\n", + " document.body.appendChild(element);\n", + " }\n", + "\n", + " const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.1.1.min.js\"];\n", + " const css_urls = [];\n", + "\n", + " const inline_js = [ function(Bokeh) {\n", + " Bokeh.set_log_level(\"info\");\n", + " },\n", + "function(Bokeh) {\n", + " }\n", + " ];\n", + "\n", + " function run_inline_js() {\n", + " if (root.Bokeh !== undefined || force === true) {\n", + " for (let i = 0; i < inline_js.length; i++) {\n", + " inline_js[i].call(root, root.Bokeh);\n", + " }\n", + "} else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(run_inline_js, 100);\n", + " } else if (!root._bokeh_failed_load) {\n", + " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", + " root._bokeh_failed_load = true;\n", + " } else if (force !== true) {\n", + " const cell = $(document.getElementById(null)).parents('.cell').data().cell;\n", + " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", + " }\n", + " }\n", + "\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", + " run_inline_js();\n", + " } else {\n", + " load_libs(css_urls, js_urls, function() {\n", + " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", + " run_inline_js();\n", + " });\n", + " }\n", + "}(window));" + ], + "application/vnd.bokehjs_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n const el = document.getElementById(null);\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.1.1.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\n} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(null)).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "
\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": [ + "(function(root) {\n", + " function embed_document(root) {\n", + " const docs_json = {\"8e7ef4ac-0508-4332-9515-2ce902250c9c\":{\"version\":\"3.1.1\",\"title\":\"Bokeh Application\",\"defs\":[],\"roots\":[{\"type\":\"object\",\"name\":\"GridPlot\",\"id\":\"p10373\",\"attributes\":{\"rows\":null,\"cols\":null,\"toolbar\":{\"type\":\"object\",\"name\":\"Toolbar\",\"id\":\"p10372\",\"attributes\":{\"logo\":null,\"tools\":[{\"type\":\"object\",\"name\":\"ToolProxy\",\"id\":\"p10363\",\"attributes\":{\"tools\":[{\"type\":\"object\",\"name\":\"PanTool\",\"id\":\"p10021\",\"attributes\":{\"dimensions\":\"width\"}},{\"type\":\"object\",\"name\":\"PanTool\",\"id\":\"p10117\",\"attributes\":{\"dimensions\":\"width\"}},{\"type\":\"object\",\"name\":\"PanTool\",\"id\":\"p10213\",\"attributes\":{\"dimensions\":\"width\"}}]}},{\"type\":\"object\",\"name\":\"ToolProxy\",\"id\":\"p10364\",\"attributes\":{\"tools\":[{\"type\":\"object\",\"name\":\"PanTool\",\"id\":\"p10022\"},{\"type\":\"object\",\"name\":\"PanTool\",\"id\":\"p10118\"},{\"type\":\"object\",\"name\":\"PanTool\",\"id\":\"p10214\"}]}},{\"type\":\"object\",\"name\":\"ToolProxy\",\"id\":\"p10365\",\"attributes\":{\"tools\":[{\"type\":\"object\",\"name\":\"WheelZoomTool\",\"id\":\"p10023\",\"attributes\":{\"dimensions\":\"width\"}},{\"type\":\"object\",\"name\":\"WheelZoomTool\",\"id\":\"p10119\",\"attributes\":{\"dimensions\":\"width\"}},{\"type\":\"object\",\"name\":\"WheelZoomTool\",\"id\":\"p10215\",\"attributes\":{\"dimensions\":\"width\"}}]}},{\"type\":\"object\",\"name\":\"ToolProxy\",\"id\":\"p10366\",\"attributes\":{\"tools\":[{\"type\":\"object\",\"name\":\"WheelZoomTool\",\"id\":\"p10024\",\"attributes\":{\"dimensions\":\"height\"}},{\"type\":\"object\",\"name\":\"WheelZoomTool\",\"id\":\"p10120\",\"attributes\":{\"dimensions\":\"height\"}},{\"type\":\"object\",\"name\":\"WheelZoomTool\",\"id\":\"p10216\",\"attributes\":{\"dimensions\":\"height\"}}]}},{\"type\":\"object\",\"name\":\"ToolProxy\",\"id\":\"p10367\",\"attributes\":{\"tools\":[{\"type\":\"object\",\"name\":\"BoxZoomTool\",\"id\":\"p10025\",\"attributes\":{\"overlay\":{\"type\":\"object\",\"name\":\"BoxAnnotation\",\"id\":\"p10026\",\"attributes\":{\"syncable\":false,\"level\":\"overlay\",\"visible\":false,\"left_units\":\"canvas\",\"right_units\":\"canvas\",\"bottom_units\":\"canvas\",\"top_units\":\"canvas\",\"line_color\":\"black\",\"line_alpha\":1.0,\"line_width\":2,\"line_dash\":[4,4],\"fill_color\":\"lightgrey\",\"fill_alpha\":0.5}}}},{\"type\":\"object\",\"name\":\"BoxZoomTool\",\"id\":\"p10121\",\"attributes\":{\"overlay\":{\"type\":\"object\",\"name\":\"BoxAnnotation\",\"id\":\"p10122\",\"attributes\":{\"syncable\":false,\"level\":\"overlay\",\"visible\":false,\"left_units\":\"canvas\",\"right_units\":\"canvas\",\"bottom_units\":\"canvas\",\"top_units\":\"canvas\",\"line_color\":\"black\",\"line_alpha\":1.0,\"line_width\":2,\"line_dash\":[4,4],\"fill_color\":\"lightgrey\",\"fill_alpha\":0.5}}}},{\"type\":\"object\",\"name\":\"BoxZoomTool\",\"id\":\"p10217\",\"attributes\":{\"overlay\":{\"type\":\"object\",\"name\":\"BoxAnnotation\",\"id\":\"p10218\",\"attributes\":{\"syncable\":false,\"level\":\"overlay\",\"visible\":false,\"left_units\":\"canvas\",\"right_units\":\"canvas\",\"bottom_units\":\"canvas\",\"top_units\":\"canvas\",\"line_color\":\"black\",\"line_alpha\":1.0,\"line_width\":2,\"line_dash\":[4,4],\"fill_color\":\"lightgrey\",\"fill_alpha\":0.5}}}}]}},{\"type\":\"object\",\"name\":\"ToolProxy\",\"id\":\"p10368\",\"attributes\":{\"tools\":[{\"type\":\"object\",\"name\":\"ResetTool\",\"id\":\"p10027\"},{\"type\":\"object\",\"name\":\"ResetTool\",\"id\":\"p10123\"},{\"type\":\"object\",\"name\":\"ResetTool\",\"id\":\"p10219\"}]}},{\"type\":\"object\",\"name\":\"ToolProxy\",\"id\":\"p10369\",\"attributes\":{\"tools\":[{\"type\":\"object\",\"name\":\"UndoTool\",\"id\":\"p10028\"},{\"type\":\"object\",\"name\":\"UndoTool\",\"id\":\"p10124\"},{\"type\":\"object\",\"name\":\"UndoTool\",\"id\":\"p10220\"}]}},{\"type\":\"object\",\"name\":\"SaveTool\",\"id\":\"p10370\"},{\"type\":\"object\",\"name\":\"ToolProxy\",\"id\":\"p10371\",\"attributes\":{\"tools\":[{\"type\":\"object\",\"name\":\"HoverTool\",\"id\":\"p10030\",\"attributes\":{\"renderers\":\"auto\"}},{\"type\":\"object\",\"name\":\"HoverTool\",\"id\":\"p10126\",\"attributes\":{\"renderers\":\"auto\"}},{\"type\":\"object\",\"name\":\"HoverTool\",\"id\":\"p10222\",\"attributes\":{\"renderers\":\"auto\"}}]}}]}},\"toolbar_location\":\"right\",\"children\":[[{\"type\":\"object\",\"name\":\"Figure\",\"id\":\"p9955\",\"attributes\":{\"width\":500,\"height\":150,\"x_range\":{\"type\":\"object\",\"name\":\"Range1d\",\"id\":\"p9954\",\"attributes\":{\"js_property_callbacks\":{\"type\":\"map\",\"entries\":[[\"change:start\",[{\"type\":\"object\",\"name\":\"CustomJS\",\"id\":\"p10243\",\"attributes\":{\"args\":{\"type\":\"map\",\"entries\":[[\"p1_x_range\",{\"id\":\"p9954\"}],[\"p2_x_range\",{\"type\":\"object\",\"name\":\"Range1d\",\"id\":\"p10051\",\"attributes\":{\"js_property_callbacks\":{\"type\":\"map\",\"entries\":[[\"change:start\",[{\"id\":\"p10243\"}]],[\"change:end\",[{\"id\":\"p10243\"}]]]},\"start\":1420066800000.0,\"end\":1451516400000.0}}],[\"p3_x_range\",{\"type\":\"object\",\"name\":\"Range1d\",\"id\":\"p10147\",\"attributes\":{\"js_property_callbacks\":{\"type\":\"map\",\"entries\":[[\"change:start\",[{\"id\":\"p10243\"}]],[\"change:end\",[{\"id\":\"p10243\"}]]]},\"start\":1420066800000.0,\"end\":1451516400000.0}}]]},\"code\":\"\\n 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+ " const render_items = [{\"docid\":\"8e7ef4ac-0508-4332-9515-2ce902250c9c\",\"roots\":{\"p10373\":\"fd01f2b7-444a-4311-8f70-8b8ec8b6f5bb\"},\"root_ids\":[\"p10373\"]}];\n", + " root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n", + " }\n", + " if (root.Bokeh !== undefined) {\n", + " embed_document(root);\n", + " } else {\n", + " let attempts = 0;\n", + " const timer = setInterval(function(root) {\n", + " if (root.Bokeh !== undefined) {\n", + " clearInterval(timer);\n", + " embed_document(root);\n", + " } else {\n", + " attempts++;\n", + " if (attempts > 100) {\n", + " clearInterval(timer);\n", + " console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n", + " }\n", + " }\n", + " }, 10, root)\n", + " }\n", + "})(window);" + ], + "application/vnd.bokehjs_exec.v0+json": "" + }, + "metadata": { + "application/vnd.bokehjs_exec.v0+json": { + "id": "p10373" + } + }, + "output_type": "display_data" + } + ], + "source": [ + "feature_special_events.plot(**plot_options, max_num_plots=3)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", "id": "e3a6532f-e01f-477e-80e2-90f0a6cd75eb", "metadata": {}, "source": [ @@ -2513,19 +4053,12 @@ "cell_type": "code", "execution_count": 41, "id": "7ed92623-3363-4313-9e9b-5e803a4202f2", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:50:38.894504Z", - "iopub.status.busy": "2023-07-24T15:50:38.894365Z", - "iopub.status.idle": "2023-07-24T15:50:38.898509Z", - "shell.execute_reply": "2023-07-24T15:50:38.898222Z" - } - }, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "features: [('f_sales', int64), ('f_sales_lag_1_d', int64), ('f_sales_lag_2_d', int64), ('f_sales_lag_3_d', int64), ('f_sales_lag_7_d', int64), ('f_sales_lag_14_d', int64), ('f_sma_7_sales', float32), ('f_sd_7_sales', float32), ('f_sum_7_sales', float32), ('f_sma_14_sales', float32), ('f_sd_14_sales', float32), ('f_sum_14_sales', float32), ('f_sma_28_sales', float32), ('f_sd_28_sales', float32), ('f_sum_28_sales', float32), ('f_sma_84_sales', float32), ('f_sd_84_sales', float32), ('f_sum_84_sales', float32), ('f_calendar_day_of_month', int32), ('f_calendar_day_of_week', int32), ('f_calendar_month', int32), ('f_sum28_per_dep_sales', int64), ('f_event_name_1', str_), ('f_event_type_1', str_), ('f_event_name_2', str_), ('f_event_type_2', str_), ('f_snap_CA', int64), ('f_snap_TX', int64), ('f_snap_WI', int64), ('f_leak5_event_name_1', str_), ('f_leak5_event_type_1', str_), ('f_leak5_event_name_2', str_), ('f_leak5_event_type_2', str_), ('f_leak5_snap_CA', int64), ('f_leak5_snap_TX', int64), ('f_leak5_snap_WI', int64), ('day', int64), ('label_horizon_1_days', int64), ('label_horizon_2_days', int64), ('label_horizon_3_days', int64), ('label_horizon_4_days', int64), ('label_horizon_5_days', int64), ('label_horizon_6_days', int64), ('label_horizon_7_days', int64), ('label_horizon_8_days', int64), ('label_horizon_9_days', int64), ('label_horizon_10_days', int64), ('label_horizon_11_days', int64), ('label_horizon_12_days', int64), ('label_horizon_13_days', int64)]\n", + "features: [('f_sales', int64), ('f_sales_lag_1_d', int64), ('f_sales_lag_2_d', int64), ('f_sales_lag_3_d', int64), ('f_sales_lag_7_d', int64), ('f_sales_lag_14_d', int64), ('f_sma_7_sales', float32), ('f_sd_7_sales', float32), ('f_sum_7_sales', float32), ('f_sma_14_sales', float32), ('f_sd_14_sales', float32), ('f_sum_14_sales', float32), ('f_sma_28_sales', float32), ('f_sd_28_sales', float32), ('f_sum_28_sales', float32), ('f_sma_84_sales', float32), ('f_sd_84_sales', float32), ('f_sum_84_sales', float32), ('f_calendar_day_of_month', int32), ('f_calendar_day_of_week', int32), ('f_calendar_month', int32), ('f_sum28_per_dep_sales', int64), ('f_event_name_1', str_), ('f_event_type_1', str_), ('f_event_name_2', str_), ('f_event_type_2', str_), ('f_snap_CA', int64), ('f_snap_TX', int64), ('f_snap_WI', int64), ('f_leak5_event_name_1', str_), ('f_leak5_event_type_1', str_), ('f_leak5_event_name_2', str_), ('f_leak5_event_type_2', str_), ('f_leak5_snap_CA', int64), ('f_leak5_snap_TX', int64), ('f_leak5_snap_WI', int64), ('day', int64), ('label_horizon_1_days', int64), ('label_horizon_2_days', int64), ('label_horizon_3_days', int64), ('label_horizon_4_days', int64), ('label_horizon_5_days', int64), ('label_horizon_6_days', int64), ('label_horizon_7_days', int64), ('label_horizon_8_days', int64), ('label_horizon_9_days', int64), ('label_horizon_10_days', int64), ('label_horizon_11_days', int64), ('label_horizon_12_days', int64), ('label_horizon_13_days', int64), ('label_horizon_14_days', int64), ('label_horizon_15_days', int64), ('label_horizon_16_days', int64), ('label_horizon_17_days', int64), ('label_horizon_18_days', int64), ('label_horizon_19_days', int64), ('label_horizon_20_days', int64), ('label_horizon_21_days', int64), ('label_horizon_22_days', int64), ('label_horizon_23_days', int64), ('label_horizon_24_days', int64), ('label_horizon_25_days', int64), ('label_horizon_26_days', int64), ('label_horizon_27_days', int64)]\n", "indexes: [('item_id', str_), ('dept_id', str_), ('cat_id', str_), ('store_id', str_), ('state_id', str_)]\n", "is_unix_timestamp: True" ] @@ -2560,14 +4093,7 @@ "cell_type": "code", "execution_count": 42, "id": "d8c0e672-7a16-42df-82cd-994132ee0fcc", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:50:38.900202Z", - "iopub.status.busy": "2023-07-24T15:50:38.900100Z", - "iopub.status.idle": "2023-07-24T15:50:38.925195Z", - "shell.execute_reply": "2023-07-24T15:50:38.924801Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "tabular_test = tabular.filter(tp.equal_scalar(tabular[\"day\"], 1914))\n", @@ -2589,14 +4115,7 @@ "cell_type": "code", "execution_count": 43, "id": "a08b642e-6750-44b6-b4eb-a26b2bcdcf98", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:50:38.927226Z", - "iopub.status.busy": "2023-07-24T15:50:38.927077Z", - "iopub.status.idle": "2023-07-24T15:50:38.938445Z", - "shell.execute_reply": "2023-07-24T15:50:38.938147Z" - } - }, + "metadata": {}, "outputs": [ { "data": { @@ -2630,179 +4149,179 @@ " f_sales_lag_3_d\n", " f_sales_lag_7_d\n", " ...\n", - " label_horizon_5_days\n", - " label_horizon_6_days\n", - " label_horizon_7_days\n", - " label_horizon_8_days\n", - " label_horizon_9_days\n", - " label_horizon_10_days\n", - " label_horizon_11_days\n", - " label_horizon_12_days\n", - " label_horizon_13_days\n", + " label_horizon_19_days\n", + " label_horizon_20_days\n", + " label_horizon_21_days\n", + " label_horizon_22_days\n", + " label_horizon_23_days\n", + " label_horizon_24_days\n", + " label_horizon_25_days\n", + " label_horizon_26_days\n", + " label_horizon_27_days\n", " timestamp\n", " \n", " \n", " \n", " \n", " 0\n", - " FOODS_3_714\n", + " FOODS_3_694\n", " FOODS_3\n", " FOODS\n", - " WI_3\n", + " WI_2\n", " WI\n", - " 22\n", - " 20\n", - " 36\n", - " 26\n", - " 20\n", + " 24\n", + " 29\n", + " 33\n", + " 31\n", + " 38\n", " ...\n", - " 27\n", - " 22\n", - " 26\n", " 31\n", - " 19\n", - " 21\n", - " 13\n", - " 18\n", - " 25\n", + " 31\n", + " 34\n", + " 24\n", + " 36\n", + " 30\n", + " 44\n", + " 37\n", + " 42\n", " 1.461542e+09\n", " \n", " \n", " 1\n", - " FOODS_3_714\n", + " FOODS_3_694\n", " FOODS_3\n", " FOODS\n", " WI_1\n", " WI\n", - " 17\n", - " 15\n", - " 30\n", + " 13\n", + " 24\n", + " 28\n", " 18\n", - " 16\n", + " 27\n", " ...\n", - " 28\n", - " 8\n", - " 14\n", - " 12\n", - " 13\n", - " 21\n", - " 16\n", - " 13\n", + " 10\n", + " 27\n", + " 20\n", + " 20\n", " 16\n", + " 22\n", + " 31\n", + " 30\n", + " 32\n", " 1.461542e+09\n", " \n", " \n", " 2\n", - " FOODS_3_714\n", + " FOODS_3_694\n", " FOODS_3\n", " FOODS\n", " TX_3\n", " TX\n", - " 32\n", - " 19\n", - " 30\n", + " 21\n", " 17\n", - " 13\n", - " ...\n", " 31\n", - " 29\n", + " 20\n", + " 16\n", + " ...\n", " 21\n", - " 34\n", - " 38\n", - " 25\n", - " 27\n", + " 26\n", + " 17\n", + " 35\n", " 19\n", - " 24\n", + " 23\n", + " 22\n", + " 14\n", + " 21\n", " 1.461542e+09\n", " \n", " \n", " 3\n", - " FOODS_3_714\n", + " FOODS_3_694\n", " FOODS_3\n", " FOODS\n", - " CA_4\n", - " CA\n", - " 6\n", - " 8\n", - " 5\n", - " 10\n", - " 8\n", - " ...\n", - " 9\n", + " TX_2\n", + " TX\n", + " 26\n", + " 21\n", + " 17\n", + " 22\n", " 9\n", - " 10\n", - " 10\n", + " ...\n", + " 26\n", + " 33\n", " 12\n", - " 9\n", - " 4\n", - " 5\n", - " 5\n", + " 19\n", + " 13\n", + " 11\n", + " 18\n", + " 38\n", + " 25\n", " 1.461542e+09\n", " \n", " \n", " 4\n", - " FOODS_3_714\n", + " FOODS_3_694\n", " FOODS_3\n", " FOODS\n", - " CA_1\n", - " CA\n", - " 10\n", - " 27\n", - " 22\n", - " 18\n", - " 15\n", + " WI_3\n", + " WI\n", + " 42\n", + " 48\n", + " 46\n", + " 46\n", + " 54\n", " ...\n", + " 64\n", + " 57\n", + " 57\n", " 37\n", - " 7\n", - " 16\n", - " 38\n", - " 44\n", - " 21\n", - " 16\n", - " 13\n", - " 20\n", + " 30\n", + " 45\n", + " 51\n", + " 46\n", + " 48\n", " 1.461542e+09\n", " \n", " \n", "\n", - "

5 rows × 56 columns

\n", + "

5 rows × 70 columns

\n", "" ], "text/plain": [ " item_id dept_id cat_id store_id state_id f_sales f_sales_lag_1_d \\\n", - "0 FOODS_3_714 FOODS_3 FOODS WI_3 WI 22 20 \n", - "1 FOODS_3_714 FOODS_3 FOODS WI_1 WI 17 15 \n", - "2 FOODS_3_714 FOODS_3 FOODS TX_3 TX 32 19 \n", - "3 FOODS_3_714 FOODS_3 FOODS CA_4 CA 6 8 \n", - "4 FOODS_3_714 FOODS_3 FOODS CA_1 CA 10 27 \n", + "0 FOODS_3_694 FOODS_3 FOODS WI_2 WI 24 29 \n", + "1 FOODS_3_694 FOODS_3 FOODS WI_1 WI 13 24 \n", + "2 FOODS_3_694 FOODS_3 FOODS TX_3 TX 21 17 \n", + "3 FOODS_3_694 FOODS_3 FOODS TX_2 TX 26 21 \n", + "4 FOODS_3_694 FOODS_3 FOODS WI_3 WI 42 48 \n", "\n", " f_sales_lag_2_d f_sales_lag_3_d f_sales_lag_7_d ... \\\n", - "0 36 26 20 ... \n", - "1 30 18 16 ... \n", - "2 30 17 13 ... \n", - "3 5 10 8 ... \n", - "4 22 18 15 ... \n", - "\n", - " label_horizon_5_days label_horizon_6_days label_horizon_7_days \\\n", - "0 27 22 26 \n", - "1 28 8 14 \n", - "2 31 29 21 \n", - "3 9 9 10 \n", - "4 37 7 16 \n", - "\n", - " label_horizon_8_days label_horizon_9_days label_horizon_10_days \\\n", - "0 31 19 21 \n", - "1 12 13 21 \n", - "2 34 38 25 \n", - "3 10 12 9 \n", - "4 38 44 21 \n", - "\n", - " label_horizon_11_days label_horizon_12_days label_horizon_13_days \\\n", - "0 13 18 25 \n", - "1 16 13 16 \n", - "2 27 19 24 \n", - "3 4 5 5 \n", - "4 16 13 20 \n", + "0 33 31 38 ... \n", + "1 28 18 27 ... \n", + "2 31 20 16 ... \n", + "3 17 22 9 ... \n", + "4 46 46 54 ... \n", + "\n", + " label_horizon_19_days label_horizon_20_days label_horizon_21_days \\\n", + "0 31 31 34 \n", + "1 10 27 20 \n", + "2 21 26 17 \n", + "3 26 33 12 \n", + "4 64 57 57 \n", + "\n", + " label_horizon_22_days label_horizon_23_days label_horizon_24_days \\\n", + "0 24 36 30 \n", + "1 20 16 22 \n", + "2 35 19 23 \n", + "3 19 13 11 \n", + "4 37 30 45 \n", + "\n", + " label_horizon_25_days label_horizon_26_days label_horizon_27_days \\\n", + "0 44 37 42 \n", + "1 31 30 32 \n", + "2 22 14 21 \n", + "3 18 38 25 \n", + "4 51 46 48 \n", "\n", " timestamp \n", "0 1.461542e+09 \n", @@ -2811,7 +4330,7 @@ "3 1.461542e+09 \n", "4 1.461542e+09 \n", "\n", - "[5 rows x 56 columns]" + "[5 rows x 70 columns]" ] }, "execution_count": 43, @@ -2838,14 +4357,7 @@ "cell_type": "code", "execution_count": 44, "id": "799a0b07-50a8-4af3-8ed4-9131b5a102ad", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:50:38.940179Z", - "iopub.status.busy": "2023-07-24T15:50:38.940048Z", - "iopub.status.idle": "2023-07-24T15:50:39.334059Z", - "shell.execute_reply": "2023-07-24T15:50:39.333688Z" - } - }, + "metadata": {}, "outputs": [ { "data": { @@ -2879,179 +4391,179 @@ " f_sales_lag_3_d\n", " f_sales_lag_7_d\n", " ...\n", - " label_horizon_5_days\n", - " label_horizon_6_days\n", - " label_horizon_7_days\n", - " label_horizon_8_days\n", - " label_horizon_9_days\n", - " label_horizon_10_days\n", - " label_horizon_11_days\n", - " label_horizon_12_days\n", - " label_horizon_13_days\n", + " label_horizon_19_days\n", + " label_horizon_20_days\n", + " label_horizon_21_days\n", + " label_horizon_22_days\n", + " label_horizon_23_days\n", + " label_horizon_24_days\n", + " label_horizon_25_days\n", + " label_horizon_26_days\n", + " label_horizon_27_days\n", " timestamp\n", " \n", " \n", " \n", " \n", " 0\n", - " FOODS_3_714\n", + " FOODS_3_694\n", " FOODS_3\n", " FOODS\n", - " WI_3\n", + " WI_2\n", " WI\n", + " 48\n", + " 47\n", " 31\n", - " 37\n", - " 36\n", - " 29\n", - " 14\n", + " 46\n", + " 38\n", " ...\n", - " 44\n", " 34\n", " 32\n", - " 34\n", - " 63\n", - " 47\n", - " 25\n", - " 27\n", - " 27\n", + " 29\n", + " 32\n", + " 18\n", + " 24\n", + " 41\n", + " 30\n", + " 44\n", " 1.298765e+09\n", " \n", " \n", " 1\n", - " FOODS_3_714\n", + " FOODS_3_694\n", " FOODS_3\n", " FOODS\n", - " WI_3\n", + " WI_2\n", " WI\n", - " 43\n", + " 30\n", + " 48\n", + " 47\n", " 31\n", - " 37\n", " 36\n", - " 14\n", " ...\n", - " 47\n", " 32\n", - " 34\n", - " 63\n", - " 47\n", - " 25\n", - " 27\n", - " 27\n", - " 27\n", + " 29\n", + " 32\n", + " 18\n", + " 24\n", + " 41\n", + " 30\n", + " 44\n", + " 36\n", " 1.298851e+09\n", " \n", " \n", " 2\n", - " FOODS_3_714\n", + " FOODS_3_694\n", " FOODS_3\n", " FOODS\n", - " WI_3\n", + " WI_2\n", " WI\n", - " 15\n", - " 43\n", - " 31\n", - " 37\n", - " 33\n", - " ...\n", - " 38\n", - " 34\n", - " 63\n", + " 42\n", + " 30\n", + " 48\n", " 47\n", - " 25\n", - " 27\n", - " 27\n", - " 27\n", - " 49\n", + " 29\n", + " ...\n", + " 29\n", + " 32\n", + " 18\n", + " 24\n", + " 41\n", + " 30\n", + " 44\n", + " 36\n", + " 15\n", " 1.298938e+09\n", " \n", " \n", " 3\n", - " FOODS_3_714\n", + " FOODS_3_694\n", " FOODS_3\n", " FOODS\n", - " WI_3\n", + " WI_2\n", " WI\n", - " 34\n", - " 15\n", - " 43\n", - " 31\n", - " 27\n", + " 39\n", + " 42\n", + " 30\n", + " 48\n", + " 44\n", " ...\n", - " 22\n", - " 63\n", - " 47\n", - " 25\n", - " 27\n", - " 27\n", - " 27\n", - " 49\n", + " 32\n", + " 18\n", + " 24\n", + " 41\n", + " 30\n", " 44\n", + " 36\n", + " 15\n", + " 26\n", " 1.299024e+09\n", " \n", " \n", " 4\n", - " FOODS_3_714\n", + " FOODS_3_694\n", " FOODS_3\n", " FOODS\n", - " WI_3\n", + " WI_2\n", " WI\n", - " 32\n", - " 34\n", - " 15\n", - " 43\n", - " 29\n", - " ...\n", - " 38\n", - " 47\n", " 25\n", - " 27\n", - " 27\n", - " 27\n", - " 49\n", + " 39\n", + " 42\n", + " 30\n", + " 46\n", + " ...\n", + " 18\n", + " 24\n", + " 41\n", + " 30\n", " 44\n", - " 47\n", + " 36\n", + " 15\n", + " 26\n", + " 33\n", " 1.299110e+09\n", " \n", " \n", "\n", - "

5 rows × 56 columns

\n", + "

5 rows × 70 columns

\n", "" ], "text/plain": [ " item_id dept_id cat_id store_id state_id f_sales f_sales_lag_1_d \\\n", - "0 FOODS_3_714 FOODS_3 FOODS WI_3 WI 31 37 \n", - "1 FOODS_3_714 FOODS_3 FOODS WI_3 WI 43 31 \n", - "2 FOODS_3_714 FOODS_3 FOODS WI_3 WI 15 43 \n", - "3 FOODS_3_714 FOODS_3 FOODS WI_3 WI 34 15 \n", - "4 FOODS_3_714 FOODS_3 FOODS WI_3 WI 32 34 \n", + "0 FOODS_3_694 FOODS_3 FOODS WI_2 WI 48 47 \n", + "1 FOODS_3_694 FOODS_3 FOODS WI_2 WI 30 48 \n", + "2 FOODS_3_694 FOODS_3 FOODS WI_2 WI 42 30 \n", + "3 FOODS_3_694 FOODS_3 FOODS WI_2 WI 39 42 \n", + "4 FOODS_3_694 FOODS_3 FOODS WI_2 WI 25 39 \n", "\n", " f_sales_lag_2_d f_sales_lag_3_d f_sales_lag_7_d ... \\\n", - "0 36 29 14 ... \n", - "1 37 36 14 ... \n", - "2 31 37 33 ... \n", - "3 43 31 27 ... \n", - "4 15 43 29 ... \n", - "\n", - " label_horizon_5_days label_horizon_6_days label_horizon_7_days \\\n", - "0 44 34 32 \n", - "1 47 32 34 \n", - "2 38 34 63 \n", - "3 22 63 47 \n", - "4 38 47 25 \n", - "\n", - " label_horizon_8_days label_horizon_9_days label_horizon_10_days \\\n", - "0 34 63 47 \n", - "1 63 47 25 \n", - "2 47 25 27 \n", - "3 25 27 27 \n", - "4 27 27 27 \n", - "\n", - " label_horizon_11_days label_horizon_12_days label_horizon_13_days \\\n", - "0 25 27 27 \n", - "1 27 27 27 \n", - "2 27 27 49 \n", - "3 27 49 44 \n", - "4 49 44 47 \n", + "0 31 46 38 ... \n", + "1 47 31 36 ... \n", + "2 48 47 29 ... \n", + "3 30 48 44 ... \n", + "4 42 30 46 ... \n", + "\n", + " label_horizon_19_days label_horizon_20_days label_horizon_21_days \\\n", + "0 34 32 29 \n", + "1 32 29 32 \n", + "2 29 32 18 \n", + "3 32 18 24 \n", + "4 18 24 41 \n", + "\n", + " label_horizon_22_days label_horizon_23_days label_horizon_24_days \\\n", + "0 32 18 24 \n", + "1 18 24 41 \n", + "2 24 41 30 \n", + "3 41 30 44 \n", + "4 30 44 36 \n", + "\n", + " label_horizon_25_days label_horizon_26_days label_horizon_27_days \\\n", + "0 41 30 44 \n", + "1 30 44 36 \n", + "2 44 36 15 \n", + "3 36 15 26 \n", + "4 15 26 33 \n", "\n", " timestamp \n", "0 1.298765e+09 \n", @@ -3060,7 +4572,7 @@ "3 1.299024e+09 \n", "4 1.299110e+09 \n", "\n", - "[5 rows x 56 columns]" + "[5 rows x 70 columns]" ] }, "execution_count": 44, @@ -3087,14 +4599,7 @@ "cell_type": "code", "execution_count": 45, "id": "c936ac9a-ef09-4243-bf69-4b9ff73a2efe", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:50:39.335993Z", - "iopub.status.busy": "2023-07-24T15:50:39.335857Z", - "iopub.status.idle": "2023-07-24T15:50:41.194575Z", - "shell.execute_reply": "2023-07-24T15:50:41.194213Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "test_dataset.to_csv(os.path.join(work_directory, \"test.csv\"), index=False)\n", @@ -3106,12 +4611,6 @@ "execution_count": 46, "id": "a4f231d2-499e-4a77-b635-21e469f1dce6", "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:50:41.196584Z", - "iopub.status.busy": "2023-07-24T15:50:41.196446Z", - "iopub.status.idle": "2023-07-24T15:50:41.325314Z", - "shell.execute_reply": "2023-07-24T15:50:41.324657Z" - }, "scrolled": true }, "outputs": [ @@ -3119,19 +4618,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "==> tmp/temporian_m5/train.csv <==\r\n", - "item_id,dept_id,cat_id,store_id,state_id,f_sales,f_sales_lag_1_d,f_sales_lag_2_d,f_sales_lag_3_d,f_sales_lag_7_d,f_sales_lag_14_d,f_sma_7_sales,f_sd_7_sales,f_sum_7_sales,f_sma_14_sales,f_sd_14_sales,f_sum_14_sales,f_sma_28_sales,f_sd_28_sales,f_sum_28_sales,f_sma_84_sales,f_sd_84_sales,f_sum_84_sales,f_calendar_day_of_month,f_calendar_day_of_week,f_calendar_month,f_sum28_per_dep_sales,f_event_name_1,f_event_type_1,f_event_name_2,f_event_type_2,f_snap_CA,f_snap_TX,f_snap_WI,f_leak5_event_name_1,f_leak5_event_type_1,f_leak5_event_name_2,f_leak5_event_type_2,f_leak5_snap_CA,f_leak5_snap_TX,f_leak5_snap_WI,day,label_horizon_1_days,label_horizon_2_days,label_horizon_3_days,label_horizon_4_days,label_horizon_5_days,label_horizon_6_days,label_horizon_7_days,label_horizon_8_days,label_horizon_9_days,label_horizon_10_days,label_horizon_11_days,label_horizon_12_days,label_horizon_13_days,timestamp\r\n", - "FOODS_3_714,FOODS_3,FOODS,WI_3,WI,31,37,36,29,14,52,29.571428,7.168525,11.017965,952.0,207.0,27.714285,9.779195,388.0,30.964285,10.942976,867.0,31.733334,27,6,2,9092,no_event,no_event,no_event,no_event,0,0,0,no_event,no_event,no_event,no_event,1,0,0,30,43,15,27,49,44,34,32,34,63,47,25,27,27,1298764800.0\r\n", - "FOODS_3_714,FOODS_3,FOODS,WI_3,WI,43,31,37,36,14,45,33.714287,5.0345745,11.020083,995.0,236.0,27.571428,9.537252,386.0,31.5,11.149952,882.0,32.096775,28,0,2,8983,no_event,no_event,no_event,no_event,0,0,0,no_event,no_event,no_event,no_event,1,1,1,31,15,34,49,44,47,32,34,63,47,25,27,27,27,1298851200.0\r\n", - "FOODS_3_714,FOODS_3,FOODS,WI_3,WI,15,43,31,37,33,37,31.142857,8.288177,11.247048,1010.0,218.0,26.0,9.665846,364.0,31.071428,11.538533,870.0,31.5625,1,1,3,8891,no_event,no_event,no_event,no_event,1,1,0,no_event,no_event,no_event,no_event,1,1,1,32,34,32,44,47,38,34,63,47,25,27,27,27,49,1298937600.0\r\n", - "FOODS_3_714,FOODS_3,FOODS,WI_3,WI,34,15,43,31,27,24,32.142857,8.14912,11.083206,1044.0,225.0,26.714285,9.859213,374.0,31.464285,11.443907,881.0,31.636364,2,2,3,8929,no_event,no_event,no_event,no_event,1,0,1,no_event,no_event,no_event,no_event,1,1,0,33,32,34,47,38,22,63,47,25,27,27,27,49,44,1299024000.0\r\n", - "FOODS_3_714,FOODS_3,FOODS,WI_3,WI,32,34,15,43,29,14,32.57143,8.0508585,10.919174,1076.0,228.0,28.0,9.273619,392.0,31.464285,11.443907,881.0,31.647058,3,3,3,8743,no_event,no_event,no_event,no_event,1,1,1,no_event,no_event,no_event,no_event,1,0,1,34,34,63,38,22,38,47,25,27,27,27,49,44,47,1299110400.0\r\n", - "FOODS_3_714,FOODS_3,FOODS,WI_3,WI,34,32,34,15,36,15,32.285713,7.959079,10.769193,1110.0,226.0,29.357143,8.640495,411.0,31.5,11.450203,882.0,31.714285,4,4,3,8582,no_event,no_event,no_event,no_event,1,0,0,LentStart,Religious,no_event,no_event,1,1,1,35,63,47,22,38,35,25,27,27,27,49,44,47,38,1299196800.0\r\n", - "FOODS_3_714,FOODS_3,FOODS,WI_3,WI,63,34,32,34,37,32,36.0,13.458932,11.797775,1173.0,252.0,31.571428,12.251614,442.0,31.892857,12.303714,893.0,32.583332,5,5,3,8273,no_event,no_event,no_event,no_event,1,1,1,no_event,no_event,no_event,no_event,1,0,0,36,47,25,38,35,29,27,27,27,49,44,47,38,22,1299283200.0\r\n", - "FOODS_3_714,FOODS_3,FOODS,WI_3,WI,47,63,34,32,31,14,38.285713,13.770718,11.869759,1220.0,268.0,33.92857,11.810796,475.0,32.392857,12.618975,907.0,32.972973,6,6,3,7988,no_event,no_event,no_event,no_event,1,1,1,no_event,no_event,no_event,no_event,0,1,1,37,25,27,35,29,34,27,27,49,44,47,38,22,38,1299369600.0\r\n", - "FOODS_3_714,FOODS_3,FOODS,WI_3,WI,25,47,63,34,43,14,35.714287,14.319959,11.781864,1245.0,250.0,34.714287,10.779799,486.0,32.107143,12.692347,899.0,32.763157,7,0,3,7831,no_event,no_event,no_event,no_event,1,1,0,no_event,no_event,no_event,no_event,0,1,1,38,27,27,29,34,26,27,49,44,47,38,22,38,35,1299456000.0\r\n", - "head: -n: No such file or directory\r\n", - "head: 5: No such file or directory\r\n" + "item_id,dept_id,cat_id,store_id,state_id,f_sales,f_sales_lag_1_d,f_sales_lag_2_d,f_sales_lag_3_d,f_sales_lag_7_d,f_sales_lag_14_d,f_sma_7_sales,f_sd_7_sales,f_sum_7_sales,f_sma_14_sales,f_sd_14_sales,f_sum_14_sales,f_sma_28_sales,f_sd_28_sales,f_sum_28_sales,f_sma_84_sales,f_sd_84_sales,f_sum_84_sales,f_calendar_day_of_month,f_calendar_day_of_week,f_calendar_month,f_sum28_per_dep_sales,f_event_name_1,f_event_type_1,f_event_name_2,f_event_type_2,f_snap_CA,f_snap_TX,f_snap_WI,f_leak5_event_name_1,f_leak5_event_type_1,f_leak5_event_name_2,f_leak5_event_type_2,f_leak5_snap_CA,f_leak5_snap_TX,f_leak5_snap_WI,day,label_horizon_1_days,label_horizon_2_days,label_horizon_3_days,label_horizon_4_days,label_horizon_5_days,label_horizon_6_days,label_horizon_7_days,label_horizon_8_days,label_horizon_9_days,label_horizon_10_days,label_horizon_11_days,label_horizon_12_days,label_horizon_13_days,label_horizon_14_days,label_horizon_15_days,label_horizon_16_days,label_horizon_17_days,label_horizon_18_days,label_horizon_19_days,label_horizon_20_days,label_horizon_21_days,label_horizon_22_days,label_horizon_23_days,label_horizon_24_days,label_horizon_25_days,label_horizon_26_days,label_horizon_27_days,timestamp\n", + "FOODS_3_694,FOODS_3,FOODS,WI_2,WI,48,47,31,46,38,53,40.142857,7.3955307,281.0,42.357143,7.393116,593.0,42.82143,11.796045,1199.0,43.333332,12.070166,1300.0,27,6,2,5313,no_event,no_event,no_event,no_event,0,0,0,no_event,no_event,no_event,no_event,1,0,0,30,30,42,39,25,27,43,34,29,29,19,7,42,30,32,46,27,33,36,34,32,29,32,18,24,41,30,44,1298764800.0\n", + "FOODS_3_694,FOODS_3,FOODS,WI_2,WI,30,48,47,31,36,41,39.285713,8.136589,275.0,41.57143,8.0508585,582.0,42.607143,11.971213,1193.0,42.903225,12.10533,1330.0,28,0,2,5298,no_event,no_event,no_event,no_event,0,0,0,no_event,no_event,no_event,no_event,1,1,1,31,42,39,25,27,43,34,29,29,19,7,42,30,32,46,27,33,36,34,32,29,32,18,24,41,30,44,36,1298851200.0\n", + "FOODS_3_694,FOODS_3,FOODS,WI_2,WI,42,30,48,47,29,45,41.142857,6.9781,288.0,41.357143,7.9964914,579.0,42.392857,11.926379,1187.0,42.875,11.91572,1372.0,1,1,3,5254,no_event,no_event,no_event,no_event,1,1,0,no_event,no_event,no_event,no_event,1,1,1,32,39,25,27,43,34,29,29,19,7,42,30,32,46,27,33,36,34,32,29,32,18,24,41,30,44,36,15,1298937600.0\n", + "FOODS_3_694,FOODS_3,FOODS,WI_2,WI,39,42,30,48,44,34,40.42857,6.9045978,283.0,41.714287,7.768329,584.0,42.67857,11.744462,1195.0,42.757576,11.752576,1411.0,2,2,3,5319,no_event,no_event,no_event,no_event,1,0,1,no_event,no_event,no_event,no_event,1,1,0,33,25,27,43,34,29,29,19,7,42,30,32,46,27,33,36,34,32,29,32,18,24,41,30,44,36,15,26,1299024000.0\n" ] } ], @@ -3154,21 +4645,29 @@ "cell_type": "code", "execution_count": 47, "id": "18d6d3b1-32d0-4d4c-b829-814277353ad2", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:50:41.329029Z", - "iopub.status.busy": "2023-07-24T15:50:41.328774Z", - "iopub.status.idle": "2023-07-24T15:50:45.464466Z", - "shell.execute_reply": "2023-07-24T15:50:45.464083Z" - } - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ + "\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.1.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.2.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n", "Note: you may need to restart the kernel to use updated packages.\n" ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-08-18 21:58:32.832959: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n", + "2023-08-18 21:58:32.834353: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\n", + "2023-08-18 21:58:32.865036: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\n", + "2023-08-18 21:58:32.865713: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", + "To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", + "2023-08-18 21:58:33.681504: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n" + ] } ], "source": [ @@ -3182,20 +4681,13 @@ "cell_type": "code", "execution_count": 48, "id": "f6666033-3849-4418-bfc8-5a791221e5d1", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:50:45.466824Z", - "iopub.status.busy": "2023-07-24T15:50:45.466461Z", - "iopub.status.idle": "2023-07-24T15:50:45.469422Z", - "shell.execute_reply": "2023-07-24T15:50:45.469121Z" - } - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "label_names: ['label_horizon_1_days', 'label_horizon_2_days', 'label_horizon_3_days', 'label_horizon_4_days', 'label_horizon_5_days', 'label_horizon_6_days', 'label_horizon_7_days', 'label_horizon_8_days', 'label_horizon_9_days', 'label_horizon_10_days', 'label_horizon_11_days', 'label_horizon_12_days', 'label_horizon_13_days']\n", + "label_names: ['label_horizon_1_days', 'label_horizon_2_days', 'label_horizon_3_days', 'label_horizon_4_days', 'label_horizon_5_days', 'label_horizon_6_days', 'label_horizon_7_days', 'label_horizon_8_days', 'label_horizon_9_days', 'label_horizon_10_days', 'label_horizon_11_days', 'label_horizon_12_days', 'label_horizon_13_days', 'label_horizon_14_days', 'label_horizon_15_days', 'label_horizon_16_days', 'label_horizon_17_days', 'label_horizon_18_days', 'label_horizon_19_days', 'label_horizon_20_days', 'label_horizon_21_days', 'label_horizon_22_days', 'label_horizon_23_days', 'label_horizon_24_days', 'label_horizon_25_days', 'label_horizon_26_days', 'label_horizon_27_days']\n", "feature_names: ['f_sales', 'f_sales_lag_1_d', 'f_sales_lag_2_d', 'f_sales_lag_3_d', 'f_sales_lag_7_d', 'f_sales_lag_14_d', 'f_sma_7_sales', 'f_sd_7_sales', 'f_sum_7_sales', 'f_sma_14_sales', 'f_sd_14_sales', 'f_sum_14_sales', 'f_sma_28_sales', 'f_sd_28_sales', 'f_sum_28_sales', 'f_sma_84_sales', 'f_sd_84_sales', 'f_sum_84_sales', 'f_calendar_day_of_month', 'f_calendar_day_of_week', 'f_calendar_month', 'f_sum28_per_dep_sales', 'f_event_name_1', 'f_event_type_1', 'f_event_name_2', 'f_event_type_2', 'f_snap_CA', 'f_snap_TX', 'f_snap_WI', 'f_leak5_event_name_1', 'f_leak5_event_type_1', 'f_leak5_event_name_2', 'f_leak5_event_type_2', 'f_leak5_snap_CA', 'f_leak5_snap_TX', 'f_leak5_snap_WI', 'dept_id', 'cat_id', 'store_id', 'state_id']\n" ] } @@ -3228,14 +4720,7 @@ "cell_type": "code", "execution_count": 49, "id": "6a46a286-1825-49ec-a147-f539987da590", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:50:45.471183Z", - "iopub.status.busy": "2023-07-24T15:50:45.471056Z", - "iopub.status.idle": "2023-07-24T15:50:45.662410Z", - "shell.execute_reply": "2023-07-24T15:50:45.661895Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "def dataset_pandas_to_tensorflow(df):\n", @@ -3259,49 +4744,1078 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 54, "id": "4a7580bf-bd81-4806-9dde-1465b0a6fb00", "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:50:45.664537Z", - "iopub.status.busy": "2023-07-24T15:50:45.664385Z", - "iopub.status.idle": "2023-07-24T15:50:51.724389Z", - "shell.execute_reply": "2023-07-24T15:50:51.723855Z" - }, "scrolled": true }, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Use /tmp/tmplcypdssc as temporary training directory\n", + "Reading training dataset...\n", + "Training tensor examples:\n", + "Features: {'f_sales': , 'f_sales_lag_1_d': , 'f_sales_lag_2_d': , 'f_sales_lag_3_d': , 'f_sales_lag_7_d': , 'f_sales_lag_14_d': , 'f_sma_7_sales': , 'f_sd_7_sales': , 'f_sum_7_sales': , 'f_sma_14_sales': , 'f_sd_14_sales': , 'f_sum_14_sales': , 'f_sma_28_sales': , 'f_sd_28_sales': , 'f_sum_28_sales': , 'f_sma_84_sales': , 'f_sd_84_sales': , 'f_sum_84_sales': , 'f_calendar_day_of_month': , 'f_calendar_day_of_week': , 'f_calendar_month': , 'f_sum28_per_dep_sales': , 'f_event_name_1': , 'f_event_type_1': , 'f_event_name_2': , 'f_event_type_2': , 'f_snap_CA': , 'f_snap_TX': , 'f_snap_WI': , 'f_leak5_event_name_1': , 'f_leak5_event_type_1': , 'f_leak5_event_name_2': , 'f_leak5_event_type_2': , 'f_leak5_snap_CA': , 'f_leak5_snap_TX': , 'f_leak5_snap_WI': , 'dept_id': , 'cat_id': , 'store_id': , 'state_id': }\n", + "Label: {'label_horizon_1_days': , 'label_horizon_2_days': , 'label_horizon_3_days': , 'label_horizon_4_days': , 'label_horizon_5_days': , 'label_horizon_6_days': , 'label_horizon_7_days': , 'label_horizon_8_days': , 'label_horizon_9_days': , 'label_horizon_10_days': , 'label_horizon_11_days': , 'label_horizon_12_days': , 'label_horizon_13_days': , 'label_horizon_14_days': , 'label_horizon_15_days': , 'label_horizon_16_days': , 'label_horizon_17_days': , 'label_horizon_18_days': , 'label_horizon_19_days': , 'label_horizon_20_days': , 'label_horizon_21_days': , 'label_horizon_22_days': , 'label_horizon_23_days': , 'label_horizon_24_days': , 'label_horizon_25_days': , 'label_horizon_26_days': , 'label_horizon_27_days': }\n", + "Weights: None\n", + "Normalized tensor features:\n", + " {'f_sales': SemanticTensor(semantic=, tensor=), 'f_sales_lag_1_d': SemanticTensor(semantic=, tensor=), 'f_sales_lag_2_d': SemanticTensor(semantic=, tensor=), 'f_sales_lag_3_d': SemanticTensor(semantic=, tensor=), 'f_sales_lag_7_d': SemanticTensor(semantic=, tensor=), 'f_sales_lag_14_d': SemanticTensor(semantic=, tensor=), 'f_sma_7_sales': SemanticTensor(semantic=, tensor=), 'f_sd_7_sales': SemanticTensor(semantic=, tensor=), 'f_sum_7_sales': SemanticTensor(semantic=, tensor=), 'f_sma_14_sales': SemanticTensor(semantic=, tensor=), 'f_sd_14_sales': SemanticTensor(semantic=, tensor=), 'f_sum_14_sales': SemanticTensor(semantic=, tensor=), 'f_sma_28_sales': SemanticTensor(semantic=, tensor=), 'f_sd_28_sales': SemanticTensor(semantic=, tensor=), 'f_sum_28_sales': SemanticTensor(semantic=, tensor=), 'f_sma_84_sales': SemanticTensor(semantic=, tensor=), 'f_sd_84_sales': SemanticTensor(semantic=, tensor=), 'f_sum_84_sales': SemanticTensor(semantic=, tensor=), 'f_calendar_day_of_month': SemanticTensor(semantic=, tensor=), 'f_calendar_day_of_week': SemanticTensor(semantic=, tensor=), 'f_calendar_month': SemanticTensor(semantic=, tensor=), 'f_sum28_per_dep_sales': SemanticTensor(semantic=, tensor=), 'f_event_name_1': SemanticTensor(semantic=, tensor=), 'f_event_type_1': SemanticTensor(semantic=, tensor=), 'f_event_name_2': SemanticTensor(semantic=, tensor=), 'f_event_type_2': SemanticTensor(semantic=, tensor=), 'f_snap_CA': SemanticTensor(semantic=, tensor=), 'f_snap_TX': SemanticTensor(semantic=, tensor=), 'f_snap_WI': SemanticTensor(semantic=, tensor=), 'f_leak5_event_name_1': SemanticTensor(semantic=, tensor=), 'f_leak5_event_type_1': SemanticTensor(semantic=, tensor=), 'f_leak5_event_name_2': SemanticTensor(semantic=, tensor=), 'f_leak5_event_type_2': SemanticTensor(semantic=, tensor=), 'f_leak5_snap_CA': SemanticTensor(semantic=, tensor=), 'f_leak5_snap_TX': SemanticTensor(semantic=, tensor=), 'f_leak5_snap_WI': SemanticTensor(semantic=, tensor=), 'dept_id': SemanticTensor(semantic=, tensor=), 'cat_id': SemanticTensor(semantic=, tensor=), 'store_id': SemanticTensor(semantic=, tensor=), 'state_id': SemanticTensor(semantic=, tensor=)}\n", + "Training dataset read in 0:00:04.394148. Found 150720 examples.\n", + "Training model...\n", + "Standard output detected as not visible to the user e.g. running in a notebook. Creating a training log redirection. If training gets stuck, try calling tfdf.keras.set_training_logs_redirection(False).\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[INFO 23-08-18 22:00:46.8128 CEST kernel.cc:773] Start Yggdrasil model training\n", + "[INFO 23-08-18 22:00:46.8128 CEST kernel.cc:774] Collect training examples\n", + "[INFO 23-08-18 22:00:46.8128 CEST kernel.cc:787] Dataspec guide:\n", + "column_guides {\n", + " column_name_pattern: \"^label_horizon_1_days$\"\n", + " type: NUMERICAL\n", + "}\n", + "column_guides {\n", + " column_name_pattern: \"^label_horizon_2_days$\"\n", + " type: NUMERICAL\n", + "}\n", + "column_guides {\n", + " column_name_pattern: \"^label_horizon_3_days$\"\n", + " type: NUMERICAL\n", + "}\n", + "column_guides {\n", + " column_name_pattern: \"^label_horizon_4_days$\"\n", + " type: NUMERICAL\n", + "}\n", + "column_guides {\n", + " column_name_pattern: \"^label_horizon_5_days$\"\n", + " type: NUMERICAL\n", + "}\n", + "column_guides {\n", + " column_name_pattern: \"^label_horizon_6_days$\"\n", + " type: NUMERICAL\n", + "}\n", + "column_guides {\n", + " column_name_pattern: \"^label_horizon_7_days$\"\n", + " type: NUMERICAL\n", + "}\n", + "column_guides {\n", + " column_name_pattern: \"^label_horizon_8_days$\"\n", + " type: NUMERICAL\n", + "}\n", + "column_guides {\n", + " column_name_pattern: \"^label_horizon_9_days$\"\n", + " type: NUMERICAL\n", + "}\n", + "column_guides {\n", + " column_name_pattern: \"^label_horizon_10_days$\"\n", + " type: NUMERICAL\n", + "}\n", + "column_guides {\n", + " column_name_pattern: \"^label_horizon_11_days$\"\n", + " type: NUMERICAL\n", + "}\n", + "column_guides {\n", + " column_name_pattern: \"^label_horizon_12_days$\"\n", + " type: NUMERICAL\n", + "}\n", + "column_guides {\n", + " column_name_pattern: \"^label_horizon_13_days$\"\n", + " type: NUMERICAL\n", + "}\n", + "column_guides {\n", + " column_name_pattern: \"^label_horizon_14_days$\"\n", + " type: NUMERICAL\n", + "}\n", + "column_guides {\n", + " column_name_pattern: \"^label_horizon_15_days$\"\n", + " type: NUMERICAL\n", + "}\n", + "column_guides {\n", + " column_name_pattern: \"^label_horizon_16_days$\"\n", + " type: NUMERICAL\n", + "}\n", + "column_guides {\n", + " column_name_pattern: \"^label_horizon_17_days$\"\n", + " type: NUMERICAL\n", + "}\n", + "column_guides {\n", + " column_name_pattern: \"^label_horizon_18_days$\"\n", + " type: NUMERICAL\n", + "}\n", + "column_guides {\n", + " column_name_pattern: \"^label_horizon_19_days$\"\n", + " type: NUMERICAL\n", + "}\n", + "column_guides {\n", + " column_name_pattern: \"^label_horizon_20_days$\"\n", + " type: NUMERICAL\n", + "}\n", + "column_guides {\n", + " column_name_pattern: \"^label_horizon_21_days$\"\n", + " type: NUMERICAL\n", + "}\n", + "column_guides {\n", + " column_name_pattern: \"^label_horizon_22_days$\"\n", + " type: NUMERICAL\n", + "}\n", + "column_guides {\n", + " column_name_pattern: \"^label_horizon_23_days$\"\n", + " type: NUMERICAL\n", + "}\n", + "column_guides {\n", + " column_name_pattern: \"^label_horizon_24_days$\"\n", + " type: NUMERICAL\n", + "}\n", + "column_guides {\n", + " column_name_pattern: \"^label_horizon_25_days$\"\n", + " type: NUMERICAL\n", + "}\n", + "column_guides {\n", + " column_name_pattern: \"^label_horizon_26_days$\"\n", + " type: NUMERICAL\n", + "}\n", + "column_guides {\n", + " column_name_pattern: \"^label_horizon_27_days$\"\n", + " type: NUMERICAL\n", + "}\n", + "default_column_guide {\n", + " categorial {\n", + " max_vocab_count: 2000\n", + " }\n", + " discretized_numerical {\n", + " maximum_num_bins: 255\n", + " }\n", + "}\n", + "ignore_columns_without_guides: false\n", + "detect_numerical_as_discretized_numerical: false\n", + "\n", + "[INFO 23-08-18 22:00:46.8187 CEST kernel.cc:393] Number of batches: 1508\n", + "[INFO 23-08-18 22:00:46.8187 CEST kernel.cc:394] Number of examples: 150720\n", + "[INFO 23-08-18 22:00:46.9182 CEST kernel.cc:794] Training dataset:\n", + "Number of records: 150720\n", + "Number of columns: 67\n", + "\n", + "Number of columns by type:\n", + "\tNUMERICAL: 55 (82.0896%)\n", + "\tCATEGORICAL: 12 (17.9104%)\n", + "\n", + "Columns:\n", + "\n", + "NUMERICAL: 55 (82.0896%)\n", + "\t2: \"f_calendar_day_of_month\" NUMERICAL mean:15.7107 min:1 max:31 sd:8.79464\n", + "\t3: \"f_calendar_day_of_week\" NUMERICAL mean:3.00159 min:0 max:6 sd:2.00066\n", + "\t4: \"f_calendar_month\" NUMERICAL mean:6.42622 min:1 max:12 sd:3.44017\n", + "\t13: \"f_leak5_snap_CA\" NUMERICAL mean:0.327495 min:0 max:1 sd:0.469299\n", + "\t14: \"f_leak5_snap_TX\" NUMERICAL mean:0.328025 min:0 max:1 sd:0.469494\n", + "\t15: \"f_leak5_snap_WI\" NUMERICAL mean:0.328025 min:0 max:1 sd:0.469494\n", + "\t16: \"f_sales\" NUMERICAL mean:29.5872 min:0 max:763 sd:31.5277\n", + "\t17: \"f_sales_lag_14_d\" NUMERICAL mean:29.5573 min:0 max:763 sd:31.5369\n", + "\t18: \"f_sales_lag_1_d\" NUMERICAL mean:29.5861 min:0 max:763 sd:31.5303\n", + "\t19: \"f_sales_lag_2_d\" NUMERICAL mean:29.5797 min:0 max:763 sd:31.5265\n", + "\t20: \"f_sales_lag_3_d\" NUMERICAL mean:29.5746 min:0 max:763 sd:31.5255\n", + "\t21: \"f_sales_lag_7_d\" NUMERICAL mean:29.5707 min:0 max:763 sd:31.5324\n", + "\t22: \"f_sd_14_sales\" NUMERICAL mean:10.0323 min:0 max:203.329 sd:10.8368\n", + "\t23: \"f_sd_28_sales\" NUMERICAL mean:10.8908 min:0 max:192.891 sd:11.4905\n", + "\t24: \"f_sd_7_sales\" NUMERICAL mean:9.12405 min:0 max:235.264 sd:10.209\n", + "\t25: \"f_sd_84_sales\" NUMERICAL mean:12.2336 min:0 max:173.639 sd:12.8959\n", + "\t26: \"f_sma_14_sales\" NUMERICAL mean:29.5678 min:0 max:468.643 sd:27.8563\n", + "\t27: \"f_sma_28_sales\" NUMERICAL mean:29.5606 min:0 max:442.75 sd:27.2844\n", + "\t28: \"f_sma_7_sales\" NUMERICAL mean:29.5773 min:0 max:602.857 sd:28.3994\n", + "\t29: \"f_sma_84_sales\" NUMERICAL mean:29.5427 min:0 max:279.786 sd:26.147\n", + "\t30: \"f_snap_CA\" NUMERICAL mean:0.329087 min:0 max:1 sd:0.469882\n", + "\t31: \"f_snap_TX\" NUMERICAL mean:0.329087 min:0 max:1 sd:0.469882\n", + "\t32: \"f_snap_WI\" NUMERICAL mean:0.329087 min:0 max:1 sd:0.469882\n", + "\t33: \"f_sum28_per_dep_sales\" NUMERICAL mean:6621.58 min:1333 max:19255 sd:2895.7\n", + "\t34: \"f_sum_14_sales\" NUMERICAL mean:413.949 min:0 max:6561 sd:389.988\n", + "\t35: \"f_sum_28_sales\" NUMERICAL mean:827.698 min:0 max:12397 sd:763.964\n", + "\t36: \"f_sum_7_sales\" NUMERICAL mean:207.041 min:0 max:4220 sd:198.796\n", + "\t37: \"f_sum_84_sales\" NUMERICAL mean:2460.49 min:0 max:23502 sd:2194.56\n", + "\t38: \"label_horizon_10_days\" NUMERICAL mean:29.6007 min:0 max:763 sd:31.5209\n", + "\t39: \"label_horizon_11_days\" NUMERICAL mean:29.6038 min:0 max:763 sd:31.5202\n", + "\t40: \"label_horizon_12_days\" NUMERICAL mean:29.6097 min:0 max:763 sd:31.5218\n", + "\t41: \"label_horizon_13_days\" NUMERICAL mean:29.6176 min:0 max:763 sd:31.5275\n", + "\t42: \"label_horizon_14_days\" NUMERICAL mean:29.6194 min:0 max:763 sd:31.5271\n", + "\t43: \"label_horizon_15_days\" NUMERICAL mean:29.6189 min:0 max:763 sd:31.5249\n", + "\t44: \"label_horizon_16_days\" NUMERICAL mean:29.6204 min:0 max:763 sd:31.5241\n", + "\t45: \"label_horizon_17_days\" NUMERICAL mean:29.6229 min:0 max:763 sd:31.5229\n", + "\t46: \"label_horizon_18_days\" NUMERICAL mean:29.6257 min:0 max:763 sd:31.5219\n", + "\t47: \"label_horizon_19_days\" NUMERICAL mean:29.6304 min:0 max:763 sd:31.523\n", + "\t48: \"label_horizon_1_days\" NUMERICAL mean:29.5853 min:0 max:763 sd:31.5245\n", + "\t49: \"label_horizon_20_days\" NUMERICAL mean:29.6377 min:0 max:763 sd:31.5259\n", + "\t50: \"label_horizon_21_days\" NUMERICAL mean:29.6423 min:0 max:763 sd:31.5277\n", + "\t51: \"label_horizon_22_days\" NUMERICAL mean:29.6404 min:0 max:763 sd:31.5232\n", + "\t52: \"label_horizon_23_days\" NUMERICAL mean:29.6433 min:0 max:763 sd:31.5218\n", + "\t53: \"label_horizon_24_days\" NUMERICAL mean:29.6473 min:0 max:763 sd:31.5202\n", + "\t54: \"label_horizon_25_days\" NUMERICAL mean:29.6505 min:0 max:763 sd:31.518\n", + "\t55: \"label_horizon_26_days\" NUMERICAL mean:29.6582 min:0 max:763 sd:31.5185\n", + "\t56: \"label_horizon_27_days\" NUMERICAL mean:29.6661 min:0 max:763 sd:31.5204\n", + "\t57: \"label_horizon_2_days\" NUMERICAL mean:29.5869 min:0 max:763 sd:31.5242\n", + "\t58: \"label_horizon_3_days\" NUMERICAL mean:29.5863 min:0 max:763 sd:31.5234\n", + "\t59: \"label_horizon_4_days\" NUMERICAL mean:29.5873 min:0 max:763 sd:31.5224\n", + "\t60: \"label_horizon_5_days\" NUMERICAL mean:29.5916 min:0 max:763 sd:31.525\n", + "\t61: \"label_horizon_6_days\" NUMERICAL mean:29.5976 min:0 max:763 sd:31.5282\n", + "\t62: \"label_horizon_7_days\" NUMERICAL mean:29.5993 min:0 max:763 sd:31.5276\n", + "\t63: \"label_horizon_8_days\" NUMERICAL mean:29.5965 min:0 max:763 sd:31.5239\n", + "\t64: \"label_horizon_9_days\" NUMERICAL mean:29.5992 min:0 max:763 sd:31.5224\n", + "\n", + "CATEGORICAL: 12 (17.9104%)\n", + "\t0: \"cat_id\" CATEGORICAL has-dict vocab-size:2 zero-ood-items most-frequent:\"FOODS\" 150720 (100%)\n", + "\t1: \"dept_id\" CATEGORICAL has-dict vocab-size:2 zero-ood-items most-frequent:\"FOODS_3\" 150720 (100%)\n", + "\t5: \"f_event_name_1\" CATEGORICAL has-dict vocab-size:32 zero-ood-items most-frequent:\"no_event\" 138640 (91.9851%)\n", + "\t6: \"f_event_name_2\" CATEGORICAL has-dict vocab-size:6 zero-ood-items most-frequent:\"no_event\" 150400 (99.7877%)\n", + "\t7: \"f_event_type_1\" CATEGORICAL has-dict vocab-size:6 zero-ood-items most-frequent:\"no_event\" 138640 (91.9851%)\n", + "\t8: \"f_event_type_2\" CATEGORICAL has-dict vocab-size:4 zero-ood-items most-frequent:\"no_event\" 150400 (99.7877%)\n", + "\t9: \"f_leak5_event_name_1\" CATEGORICAL has-dict vocab-size:32 zero-ood-items most-frequent:\"no_event\" 138640 (91.9851%)\n", + "\t10: \"f_leak5_event_name_2\" CATEGORICAL has-dict vocab-size:6 zero-ood-items most-frequent:\"no_event\" 150400 (99.7877%)\n", + "\t11: \"f_leak5_event_type_1\" CATEGORICAL has-dict vocab-size:6 zero-ood-items most-frequent:\"no_event\" 138640 (91.9851%)\n", + "\t12: \"f_leak5_event_type_2\" CATEGORICAL has-dict vocab-size:4 zero-ood-items most-frequent:\"no_event\" 150400 (99.7877%)\n", + "\t65: \"state_id\" CATEGORICAL has-dict vocab-size:4 zero-ood-items most-frequent:\"CA\" 60288 (40%)\n", + "\t66: \"store_id\" CATEGORICAL has-dict vocab-size:11 zero-ood-items most-frequent:\"CA_1\" 15072 (10%)\n", + "\n", + "Terminology:\n", + "\tnas: Number of non-available (i.e. missing) values.\n", + "\tood: Out of dictionary.\n", + "\tmanually-defined: Attribute which type is manually defined by the user i.e. the type was not automatically inferred.\n", + "\ttokenized: The attribute value is obtained through tokenization.\n", + "\thas-dict: The attribute is attached to a string dictionary e.g. a categorical attribute stored as a string.\n", + "\tvocab-size: Number of unique values.\n", + "\n", + "[INFO 23-08-18 22:00:46.9183 CEST kernel.cc:810] Configure learner\n", + "[INFO 23-08-18 22:00:46.9183 CEST kernel.cc:824] Training config:\n", + "learner: \"MULTITASKER\"\n", + "features: \"^cat_id$\"\n", + "features: \"^dept_id$\"\n", + "features: \"^f_calendar_day_of_month$\"\n", + "features: \"^f_calendar_day_of_week$\"\n", + "features: \"^f_calendar_month$\"\n", + "features: \"^f_event_name_1$\"\n", + "features: \"^f_event_name_2$\"\n", + "features: \"^f_event_type_1$\"\n", + "features: \"^f_event_type_2$\"\n", + "features: \"^f_leak5_event_name_1$\"\n", + "features: \"^f_leak5_event_name_2$\"\n", + "features: \"^f_leak5_event_type_1$\"\n", + "features: \"^f_leak5_event_type_2$\"\n", + "features: \"^f_leak5_snap_CA$\"\n", + "features: \"^f_leak5_snap_TX$\"\n", + "features: \"^f_leak5_snap_WI$\"\n", + "features: \"^f_sales$\"\n", + "features: \"^f_sales_lag_14_d$\"\n", + "features: \"^f_sales_lag_1_d$\"\n", + "features: \"^f_sales_lag_2_d$\"\n", + "features: \"^f_sales_lag_3_d$\"\n", + "features: \"^f_sales_lag_7_d$\"\n", + "features: \"^f_sd_14_sales$\"\n", + "features: \"^f_sd_28_sales$\"\n", + "features: \"^f_sd_7_sales$\"\n", + "features: \"^f_sd_84_sales$\"\n", + "features: \"^f_sma_14_sales$\"\n", + "features: \"^f_sma_28_sales$\"\n", + "features: \"^f_sma_7_sales$\"\n", + "features: \"^f_sma_84_sales$\"\n", + "features: \"^f_snap_CA$\"\n", + "features: \"^f_snap_TX$\"\n", + "features: \"^f_snap_WI$\"\n", + "features: \"^f_sum28_per_dep_sales$\"\n", + "features: \"^f_sum_14_sales$\"\n", + "features: \"^f_sum_28_sales$\"\n", + "features: \"^f_sum_7_sales$\"\n", + "features: \"^f_sum_84_sales$\"\n", + "features: \"^state_id$\"\n", + "features: \"^store_id$\"\n", + "label: \"^label_horizon_1_days$\"\n", + "task: REGRESSION\n", + "metadata {\n", + " framework: \"TF Keras\"\n", + "}\n", + "[yggdrasil_decision_forests.model.multitasker.proto.multitasker_config] {\n", + " base_learner {\n", + " learner: \"GRADIENT_BOOSTED_TREES\"\n", + " }\n", + " subtasks {\n", + " primary: true\n", + " train_config {\n", + " label: \"^label_horizon_1_days$\"\n", + " task: REGRESSION\n", + " }\n", + " }\n", + " subtasks {\n", + " primary: true\n", + " train_config {\n", + " label: \"^label_horizon_2_days$\"\n", + " task: REGRESSION\n", + " }\n", + " }\n", + " subtasks {\n", + " primary: true\n", + " train_config {\n", + " label: \"^label_horizon_3_days$\"\n", + " task: REGRESSION\n", + " }\n", + " }\n", + " subtasks {\n", + " primary: true\n", + " train_config {\n", + " label: \"^label_horizon_4_days$\"\n", + " task: REGRESSION\n", + " }\n", + " }\n", + " subtasks {\n", + " primary: true\n", + " train_config {\n", + " label: \"^label_horizon_5_days$\"\n", + " task: REGRESSION\n", + " }\n", + " }\n", + " subtasks {\n", + " primary: true\n", + " train_config {\n", + " label: \"^label_horizon_6_days$\"\n", + " task: REGRESSION\n", + " }\n", + " }\n", + " subtasks {\n", + " primary: true\n", + " train_config {\n", + " label: \"^label_horizon_7_days$\"\n", + " task: REGRESSION\n", + " }\n", + " }\n", + " subtasks {\n", + " primary: true\n", + " train_config {\n", + " label: \"^label_horizon_8_days$\"\n", + " task: REGRESSION\n", + " }\n", + " }\n", + " subtasks {\n", + " primary: true\n", + " train_config {\n", + " label: \"^label_horizon_9_days$\"\n", + " task: REGRESSION\n", + " }\n", + " }\n", + " subtasks {\n", + " primary: true\n", + " train_config {\n", + " label: \"^label_horizon_10_days$\"\n", + " task: REGRESSION\n", + " }\n", + " }\n", + " subtasks {\n", + " primary: true\n", + " train_config {\n", + " label: \"^label_horizon_11_days$\"\n", + " task: REGRESSION\n", + " }\n", + " }\n", + " subtasks {\n", + " primary: true\n", + " train_config {\n", + " label: \"^label_horizon_12_days$\"\n", + " task: REGRESSION\n", + " }\n", + " }\n", + " subtasks {\n", + " primary: true\n", + " train_config {\n", + " label: \"^label_horizon_13_days$\"\n", + " task: REGRESSION\n", + " }\n", + " }\n", + " subtasks {\n", + " primary: true\n", + " train_config {\n", + " label: \"^label_horizon_14_days$\"\n", + " task: REGRESSION\n", + " }\n", + " }\n", + " subtasks {\n", + " primary: true\n", + " train_config {\n", + " label: \"^label_horizon_15_days$\"\n", + " task: REGRESSION\n", + " }\n", + " }\n", + " subtasks {\n", + " primary: true\n", + " train_config {\n", + " label: \"^label_horizon_16_days$\"\n", + " task: REGRESSION\n", + " }\n", + " }\n", + " subtasks {\n", + " primary: true\n", + " train_config {\n", + " label: \"^label_horizon_17_days$\"\n", + " task: REGRESSION\n", + " }\n", + " }\n", + " subtasks {\n", + " primary: true\n", + " train_config {\n", + " label: \"^label_horizon_18_days$\"\n", + " task: REGRESSION\n", + " }\n", + " }\n", + " subtasks {\n", + " primary: true\n", + " train_config {\n", + " label: \"^label_horizon_19_days$\"\n", + " task: REGRESSION\n", + " }\n", + " }\n", + " subtasks {\n", + " primary: true\n", + " train_config {\n", + " label: \"^label_horizon_20_days$\"\n", + " task: REGRESSION\n", + " }\n", + " }\n", + " subtasks {\n", + " primary: true\n", + " train_config {\n", + " label: \"^label_horizon_21_days$\"\n", + " task: REGRESSION\n", + " }\n", + " }\n", + " subtasks {\n", + " primary: true\n", + " train_config {\n", + " label: \"^label_horizon_22_days$\"\n", + " task: REGRESSION\n", + " }\n", + " }\n", + " subtasks {\n", + " primary: true\n", + " train_config {\n", + " label: \"^label_horizon_23_days$\"\n", + " task: REGRESSION\n", + " }\n", + " }\n", + " subtasks {\n", + " primary: true\n", + " train_config {\n", + " label: \"^label_horizon_24_days$\"\n", + " task: REGRESSION\n", + " }\n", + " }\n", + " subtasks {\n", + " primary: true\n", + " train_config {\n", + " label: \"^label_horizon_25_days$\"\n", + " task: REGRESSION\n", + " }\n", + " }\n", + " subtasks {\n", + " primary: true\n", + " train_config {\n", + " label: \"^label_horizon_26_days$\"\n", + " task: REGRESSION\n", + " }\n", + " }\n", + " subtasks {\n", + " primary: true\n", + " train_config {\n", + " label: \"^label_horizon_27_days$\"\n", + " task: REGRESSION\n", + " }\n", + " }\n", + "}\n", + "\n", + "[INFO 23-08-18 22:00:46.9186 CEST kernel.cc:827] Deployment config:\n", + "cache_path: \"/tmp/tmplcypdssc/working_cache\"\n", + "num_threads: 8\n", + "try_resume_training: true\n", + "\n", + "[INFO 23-08-18 22:00:46.9187 CEST kernel.cc:889] Train model\n", + "[INFO 23-08-18 22:00:46.9258 CEST multitasker.cc:286] Train multitasker primary tasks with 27 model(s)\n", + "[WARNING[WARNING[WARNING 23-08-18 22:00:46.9299 CEST gradient_boosted_trees.cc:[[WARNING 23-08-18 22:00:46.9299 CEST WARNINGgradient_boosted_trees.cc: 23-08-18 22:00:46.9299 CEST [ 23-08-18 22:00:46.9299 CEST 1818[WARNINGWARNINGgradient_boosted_trees.cc: 23-08-18 22:00:46.9299 CEST ] gradient_boosted_trees.cc:gradient_boosted_trees.cc 23-08-18 22:00:46.9299 CEST : 23-08-18 22:00:46.9299 CEST 18181818] \"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-18 22:00:46.9299 CEST gradient_boosted_trees.cc:1829] gradient_boosted_trees.cc:1818[WARNING1818 23-08-18 22:00:46.9299 CEST gradient_boosted_trees.ccgradient_boosted_trees.cc\"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".:\n", + "] ] :1818] 1818] \"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".] \"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "1818\"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "\n", + "[\n", + "[[WARNING[WARNING 23-08-18 22:00:46.9300 CEST [\n", + "WARNING[ 23-08-18 22:00:46.9300 CEST gradient_boosted_trees.ccgradient_boosted_trees.cc::] WARNINGWARNING 23-08-18 22:00:46.9300 CEST WARNING 23-08-18 22:00:46.9300 CEST 1829 23-08-18 22:00:46.9300 CEST 23-08-18 22:00:46.9300 CEST ] \"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "1829\n", + "] gradient_boosted_trees.cc\"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "gradient_boosted_trees.cc[WARNINGgradient_boosted_trees.ccgradient_boosted_trees.cc::1829\n", + "] 23-08-18 22:00:46.9300 CEST :[1829\"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".[WARNING[WARNING] 1843: 23-08-18 22:00:46.9301 CEST gradient_boosted_trees.cc:1843] 23-08-18 22:00:46.9301 CEST gradient_boosted_trees.cc:\"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".1829\n", + "] WARNING\n", + " 23-08-18 22:00:46.9301 CEST ] gradient_boosted_trees.cc:1829[] WARNING\"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".[\n", + "\"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".1829\"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".] \n", + "\n", + " 23-08-18 22:00:46.9301 CEST gradient_boosted_trees.cc:\n", + "1843] [WARNINGgradient_boosted_trees.cc\"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".[\n", + ":WARNING\"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", + " 23-08-18 22:00:46.9301 CEST 23-08-18 22:00:46.9301 CEST 1843gradient_boosted_trees.cc[] :1843gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".] \n", + "\"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".WARNING 23-08-18 22:00:46.9302 CEST gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", + "\n", + "\n", + "WARNING 23-08-18 22:00:46.9309 CEST gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", + "[INFO 23-08-18 22:00:47.7167 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", + "[INFO 23-08-18 22:00:47.7169 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", + "[INFO 23-08-18 22:00:47.7209 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", + "[INFO 23-08-18 22:00:47.7209 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", + "[INFO 23-08-18 22:00:47.7347 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", + "[INFO 23-08-18 22:00:47.7347 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", + "[INFO 23-08-18 22:00:47.7426 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", + "[INFO 23-08-18 22:00:47.7427 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", + "[INFO 23-08-18 22:00:47.7429 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", + "[INFO 23-08-18 22:00:47.7429 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", + "[INFO 23-08-18 22:00:47.7480 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", + "[INFO 23-08-18 22:00:47.7481 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", + "[INFO 23-08-18 22:00:47.7522 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", + "[INFO 23-08-18 22:00:47.7522 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", + "[INFO 23-08-18 22:00:47.7627 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", + "[INFO 23-08-18 22:00:47.7627 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", + "[INFO 23-08-18 22:00:47.8435 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", + "[INFO 23-08-18 22:00:47.8799 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", + "[INFO 23-08-18 22:00:47.8849 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", + "[INFO 23-08-18 22:00:47.9025 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", + "[INFO 23-08-18 22:00:48.0678 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", + "[INFO 23-08-18 22:00:48.0818 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", + "[INFO 23-08-18 22:00:48.1529 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", + "[INFO 23-08-18 22:00:48.2466 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", + "[INFO 23-08-18 22:00:48.5693 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.295769 train-rmse:29.295769 valid-loss:28.769020 valid-rmse:28.769020\n", + "[INFO 23-08-18 22:00:48.8418 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.335985 train-rmse:29.335985 valid-loss:29.099661 valid-rmse:29.099661\n", + "[INFO 23-08-18 22:00:48.9186 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.323404 train-rmse:29.323404 valid-loss:28.357626 valid-rmse:28.357626\n", + "[INFO 23-08-18 22:00:48.9865 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.214186 train-rmse:29.214186 valid-loss:29.899080 valid-rmse:29.899080\n", + "[INFO 23-08-18 22:00:49.2447 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.267696 train-rmse:29.267696 valid-loss:29.130789 valid-rmse:29.130789\n", + "[INFO 23-08-18 22:00:49.3179 CEST gradient_boosted_trees.cc[INFO 23-08-18 22:00:49.3209 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.399338 train-rmse:29.399338 valid-loss:29.028122 valid-rmse:29.028122\n", + ":1542] \tnum-trees:1 train-loss:29.124109 train-rmse:29.124109 valid-loss:29.133936 valid-rmse:29.133936\n", + "[INFO 23-08-18 22:00:49.3306 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.303213 train-rmse:29.303213 valid-loss:29.393198 valid-rmse:29.393198\n", + "[INFO 23-08-18 22:00:49.6147 CEST gradient_boosted_trees.cc:1544] \tnum-trees:2 train-loss:27.293285 train-rmse:27.293285 valid-loss:26.745028 valid-rmse:26.745028\n", + "[INFO 23-08-18 22:01:19.8681 CEST gradient_boosted_trees.cc:1544] \tnum-trees:24 train-loss:13.311581 train-rmse:13.311581 valid-loss:13.150414 valid-rmse:13.150414\n", + "[INFO 23-08-18 22:01:49.9277 CEST gradient_boosted_trees.cc:1544] \tnum-trees:42 train-loss:12.409389 train-rmse:12.409389 valid-loss:12.698593 valid-rmse:12.698593\n", + "[INFO 23-08-18 22:01:58.5746 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:13.866138 train-rmse:13.866138 valid-loss:15.359539 valid-rmse:15.359539\n", + "[INFO 23-08-18 22:01:58.5989 CEST gradient_boosted_trees.cc:247] Truncates the model to 48 tree(s) i.e. 48 iteration(s).\n", + "[INFO 23-08-18 22:01:58.5993 CEST gradient_boosted_trees.cc:310] Final model num-trees:48 valid-loss:15.355445 valid-rmse:15.355445\n", + "[WARNING 23-08-18 22:01:58.6182 CEST gradient_boosted_trees.cc:1818] \"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-18 22:01:58.6184 CEST gradient_boosted_trees.cc:1829] \"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-18 22:01:58.6187 CEST gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", + "[INFO 23-08-18 22:01:58.9735 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:13.070064 train-rmse:13.070064 valid-loss:13.403740 valid-rmse:13.403740\n", + "[INFO 23-08-18 22:01:58.9736 CEST gradient_boosted_trees.cc:247] Truncates the model to 50 tree(s) i.e. 50 iteration(s).\n", + "[INFO 23-08-18 22:01:58.9736 CEST gradient_boosted_trees.cc:310] Final model num-trees:50 valid-loss:13.403740 valid-rmse:13.403740\n", + "[WARNING 23-08-18 22:01:58.9858 CEST gradient_boosted_trees.cc:1818] \"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-18 22:01:58.9859 CEST gradient_boosted_trees.cc:1829] \"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-18 22:01:58.9859 CEST gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", + "[INFO 23-08-18 22:01:59.4701 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:13.575471 train-rmse:13.575471 valid-loss:15.029240 valid-rmse:15.029240\n", + "[INFO 23-08-18 22:01:59.4922 CEST gradient_boosted_trees.cc:247] Truncates the model to 50 tree(s) i.e. 50 iteration(s).\n", + "[INFO 23-08-18 22:01:59.4979 CEST gradient_boosted_trees.cc:310] Final model num-trees:50 valid-loss:15.029240 valid-rmse:15.029240\n", + "[WARNING 23-08-18 22:01:59.5213 CEST gradient_boosted_trees.cc:1818] \"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-18 22:01:59.5213 CEST gradient_boosted_trees.cc:1829] \"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-18 22:01:59.5213 CEST gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", + "[INFO 23-08-18 22:01:59.6596 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:14.514459 train-rmse:14.514459 valid-loss:14.994228 valid-rmse:14.994228\n", + "[INFO 23-08-18 22:01:59.6718 CEST gradient_boosted_trees.cc:247] Truncates the model to 50 tree(s) i.e. 50 iteration(s).\n", + "[INFO 23-08-18 22:01:59.6787 CEST gradient_boosted_trees.cc:310] Final model num-trees:50 valid-loss:14.994228 valid-rmse:14.994228\n", + "[WARNING 23-08-18 22:01:59.7168 CEST gradient_boosted_trees.cc:1818] \"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-18 22:01:59.7168 CEST gradient_boosted_trees.cc:1829] \"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-18 22:01:59.7169 CEST gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", + "[INFO 23-08-18 22:01:59.9275 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:13.423209 train-rmse:13.423209 valid-loss:13.453820 valid-rmse:13.453820\n", + "[INFO 23-08-18 22:01:59.9427 CEST gradient_boosted_trees.cc:247] Truncates the model to 50 tree(s) i.e. 50 iteration(s).\n", + "[INFO 23-08-18 22:01:59.9492 CEST gradient_boosted_trees.cc:310] Final model num-trees:50 valid-loss:13.453820 valid-rmse:13.453820\n", + "[WARNING 23-08-18 22:01:59.9585 CEST gradient_boosted_trees.cc:1818] \"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-18 22:01:59.9601 CEST gradient_boosted_trees.cc:1829] \"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-18 22:01:59.9602 CEST gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", + "[INFO 23-08-18 22:02:00.8021 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", + "[INFO 23-08-18 22:02:00.8022 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", + "[INFO 23-08-18 22:02:00.9806 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:14.071945 train-rmse:14.071945 valid-loss:14.601532 valid-rmse:14.601532\n", + "[INFO 23-08-18 22:02:01.0068 CEST gradient_boosted_trees.cc:247] Truncates the model to 47 tree(s) i.e. 47 iteration(s).\n", + "[INFO 23-08-18 22:02:01.0075 CEST gradient_boosted_trees.cc:310] Final model num-trees:47 valid-loss:14.593384 valid-rmse:14.593384\n", + "[INFO 23-08-18 22:02:01.0077 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", + "[WARNING 23-08-18 22:02:01.0236 CEST gradient_boosted_trees.cc:1818] \"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-18 22:02:01.0236 CEST gradient_boosted_trees.cc:1829] \"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-18 22:02:01.0236 CEST gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", + "[INFO 23-08-18 22:02:01.0882 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:13.252879 train-rmse:13.252879 valid-loss:13.566219 valid-rmse:13.566219\n", + "[INFO 23-08-18 22:02:01.0882 CEST gradient_boosted_trees.cc:247] Truncates the model to 50 tree(s) i.e. 50 iteration(s).\n", + "[INFO 23-08-18 22:02:01.0882 CEST gradient_boosted_trees.cc:310] Final model num-trees:50 valid-loss:13.566219 valid-rmse:13.566219\n", + "[WARNING 23-08-18 22:02:01.1082 CEST gradient_boosted_trees.cc:1818] \"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-18 22:02:01.1082 CEST gradient_boosted_trees.cc:1829] \"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-18 22:02:01.1082 CEST gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", + "[INFO 23-08-18 22:02:01.1418 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:12.181689 train-rmse:12.181689 valid-loss:12.686768 valid-rmse:12.686768\n", + "[INFO 23-08-18 22:02:01.1418 CEST gradient_boosted_trees.cc:247] Truncates the model to 41 tree(s) i.e. 41 iteration(s).\n", + "[INFO 23-08-18 22:02:01.1420 CEST gradient_boosted_trees.cc:310] Final model num-trees:41 valid-loss:12.684937 valid-rmse:12.684937\n", + "[WARNING 23-08-18 22:02:01.1508 CEST gradient_boosted_trees.cc:1818] \"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-18 22:02:01.1508 CEST gradient_boosted_trees.cc:1829] \"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-18 22:02:01.1508 CEST gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", + "[INFO 23-08-18 22:02:02.0549 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", + "[INFO 23-08-18 22:02:02.0910 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", + "[INFO 23-08-18 22:02:02.1008 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", + "[INFO 23-08-18 22:02:02.1008 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", + "[INFO 23-08-18 22:02:02.2184 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", + "[INFO 23-08-18 22:02:02.3325 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", + "[INFO 23-08-18 22:02:02.3987 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.497025 train-rmse:29.497025 valid-loss:28.542063 valid-rmse:28.542063\n", + "[INFO 23-08-18 22:02:02.7815 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", + "[INFO 23-08-18 22:02:02.7816 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", + "[INFO 23-08-18 22:02:03.0006 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", + "[INFO 23-08-18 22:02:03.2160 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", + "[INFO 23-08-18 22:02:03.2163 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", + "[INFO 23-08-18 22:02:03.3580 CEST gradient_boosted_trees.cc:459] [INFO 23-08-18 22:02:03.3747 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", + "Default loss set to SQUARED_ERROR\n", + "[INFO 23-08-18 22:02:03.3829 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", + "[INFO 23-08-18 22:02:03.4437 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", + "[INFO 23-08-18 22:02:03.4438 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", + "[INFO 23-08-18 22:02:03.6323 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.474899 train-rmse:29.474899 valid-loss:28.634254 valid-rmse:28.634254\n", + "[INFO 23-08-18 22:02:03.6838 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.487211 train-rmse:29.487211 valid-loss:28.607134 valid-rmse:28.607134\n", + "[[INFO 23-08-18 22:02:03.9859 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", + "[INFO 23-08-18 22:02:04.0069 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", + "INFO 23-08-18 22:02:04.0147 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", + "[INFO 23-08-18 22:02:04.2577 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", + "[INFO 23-08-18 22:02:04.5124 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", + "[INFO 23-08-18 22:02:04.7282 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.421598 train-rmse:29.421598 valid-loss:29.626032 valid-rmse:29.626032\n", + "[INFO 23-08-18 22:02:05.1650 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.453743 train-rmse:29.453743 valid-loss:29.037130 valid-rmse:29.037130\n", + "[INFO 23-08-18 22:02:06.0216 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.541935 train-rmse:29.541935 valid-loss:28.897583 valid-rmse:28.897583\n", + "[INFO 23-08-18 22:02:06.4533 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.470354 train-rmse:29.470354 valid-loss:28.912182 valid-rmse:28.912182\n", + "[INFO 23-08-18 22:02:06.8504 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.545298 train-rmse:29.545298 valid-loss:29.298836 valid-rmse:29.298836\n", + "[INFO 23-08-18 22:02:20.4704 CEST gradient_boosted_trees.cc:1544] \tnum-trees:12 train-loss:18.709484 train-rmse:18.709484 valid-loss:19.026440 valid-rmse:19.026440\n", + "[INFO 23-08-18 22:02:51.1193 CEST gradient_boosted_trees.cc:1544] \tnum-trees:32 train-loss:15.605031 train-rmse:15.605031 valid-loss:15.806738 valid-rmse:15.806738\n", + "[INFO 23-08-18 22:03:11.3780 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:14.750937 train-rmse:14.750937 valid-loss:15.528153 valid-rmse:15.528153\n", + "[INFO 23-08-18 22:03:11.3985 CEST gradient_boosted_trees.cc:247] Truncates the model to 50 tree(s) i.e. 50 iteration(s).\n", + "[INFO 23-08-18 22:03:11.4047 CEST gradient_boosted_trees.cc:310] Final model num-trees:50 valid-loss:15.528153 valid-rmse:15.528153\n", + "[WARNING 23-08-18 22:03:11.4755 CEST gradient_boosted_trees.cc:1818] \"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-18 22:03:11.4798 CEST gradient_boosted_trees.cc:1829] \"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-18 22:03:11.4800 CEST gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", + "[INFO 23-08-18 22:03:13.7410 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:14.620991 train-rmse:14.620991 valid-loss:15.723470 valid-rmse:15.723470\n", + "[INFO 23-08-18 22:03:13.7651 CEST gradient_boosted_trees.cc:247] Truncates the model to 50 tree(s) i.e. 50 iteration(s).\n", + "[INFO 23-08-18 22:03:13.7725 CEST gradient_boosted_trees.cc:310] Final model num-trees:50 valid-loss:15.723470 valid-rmse:15.723470\n", + "[WARNING 23-08-18 22:03:13.7748 CEST gradient_boosted_trees.cc:1818] \"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-18 22:03:13.7748 CEST gradient_boosted_trees.cc:1829] \"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-18 22:03:13.7748 CEST gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", + "[INFO 23-08-18 22:03:14.4094 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:14.713874 train-rmse:14.713874 valid-loss:15.431265 valid-rmse:15.431265\n", + "[INFO 23-08-18 22:03:14.4460 CEST gradient_boosted_trees.cc:247] Truncates the model to 50 tree(s) i.e. 50 iteration(s).\n", + "[INFO 23-08-18 22:03:14.4559 CEST gradient_boosted_trees.cc:310] Final model num-trees:50 valid-loss:15.431265 valid-rmse:15.431265\n", + "[WARNING 23-08-18 22:03:14.4602 CEST gradient_boosted_trees.cc:1818] \"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-18 22:03:14.4602 CEST gradient_boosted_trees.cc:1829] \"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-18 22:03:14.4602 CEST gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", + "[INFO 23-08-18 22:03:14.5655 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:15.277184 train-rmse:15.277184 valid-loss:15.470344 valid-rmse:15.470344\n", + "[INFO 23-08-18 22:03:14.5884 CEST gradient_boosted_trees.cc:247] Truncates the model to 50 tree(s) i.e. 50 iteration(s).\n", + "[INFO 23-08-18 22:03:14.5965 CEST gradient_boosted_trees.cc:310] Final model num-trees:50 valid-loss:15.470344 valid-rmse:15.470344\n", + "[WARNING 23-08-18 22:03:14.6399 CEST gradient_boosted_trees.cc:1818] \"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-18 22:03:14.6400 CEST gradient_boosted_trees.cc:1829] \"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-18 22:03:14.6400 CEST gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", + "[INFO 23-08-18 22:03:14.8590 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:14.870501 train-rmse:14.870501 valid-loss:15.861364 valid-rmse:15.861364\n", + "[INFO 23-08-18 22:03:14.8590 CEST gradient_boosted_trees.cc:247] Truncates the model to 50 tree(s) i.e. 50 iteration(s).\n", + "[INFO 23-08-18 22:03:14.8591 CEST gradient_boosted_trees.cc:310] Final model num-trees:50 valid-loss:15.861364 valid-rmse:15.861364\n", + "[WARNING 23-08-18 22:03:14.8685 CEST gradient_boosted_trees.cc:1818] \"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-18 22:03:14.8685 CEST gradient_boosted_trees.cc:1829] \"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-18 22:03:14.8685 CEST gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", + "[INFO 23-08-18 22:03:15.4962 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", + "[INFO 23-08-18 22:03:15.4962 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", + "[INFO 23-08-18 22:03:15.6724 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:15.393883 train-rmse:15.393883 valid-loss:15.816501 valid-rmse:15.816501\n", + "[INFO 23-08-18 22:03:15.6787 CEST gradient_boosted_trees.cc:247] Truncates the model to 50 tree(s) i.e. 50 iteration(s).\n", + "[INFO 23-08-18 22:03:15.6787 CEST gradient_boosted_trees.cc:310] Final model num-trees:50 valid-loss:15.816501 valid-rmse:15.816501\n", + "[WARNING 23-08-18 22:03:15.6809 CEST gradient_boosted_trees.cc:1818] \"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-18 22:03:15.6811 CEST gradient_boosted_trees.cc:1829] \"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-18 22:03:15.6813 CEST gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", + "[INFO 23-08-18 22:03:15.9065 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", + "[INFO 23-08-18 22:03:16.0331 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:14.769040 train-rmse:14.769040 valid-loss:15.246088 valid-rmse:15.246088\n", + "[INFO 23-08-18 22:03:16.0570 CEST gradient_boosted_trees.cc:247] Truncates the model to 50 tree(s) i.e. 50 iteration(s).\n", + "[INFO 23-08-18 22:03:16.0645 CEST gradient_boosted_trees.cc:310] Final model num-trees:50 valid-loss:15.246088 valid-rmse:15.246088\n", + "[INFO 23-08-18 22:03:16.0667 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:15.019258 train-rmse:15.019258 valid-loss:15.995070 valid-rmse:15.995070\n", + "[INFO 23-08-18 22:03:16.0687 CEST gradient_boosted_trees.cc:247] Truncates the model to 50 tree(s) i.e. 50 iteration(s).\n", + "[INFO 23-08-18 22:03:16.0687 CEST gradient_boosted_trees.cc:310] Final model num-trees:50 valid-loss:15.995070 valid-rmse:15.995070\n", + "[WARNING 23-08-18 22:03:16.0712 CEST gradient_boosted_trees.cc:1818] \"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-18 22:03:16.0714 CEST gradient_boosted_trees.cc:1829] \"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-18 22:03:16.0717 CEST gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", + "[WARNING 23-08-18 22:03:16.0725 CEST gradient_boosted_trees.cc:1818] \"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-18 22:03:16.0726 CEST gradient_boosted_trees.cc:1829] \"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-18 22:03:16.0726 CEST gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", + "[INFO 23-08-18 22:03:16.4936 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", + "[INFO 23-08-18 22:03:16.5082 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", + "[INFO 23-08-18 22:03:16.6764 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", + "[INFO 23-08-18 22:03:16.8860 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.561636 train-rmse:29.561636 valid-loss:28.802879 valid-rmse:28.802879\n", + "[INFO 23-08-18 22:03:16.9083 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", + "[INFO 23-08-18 22:03:16.9084 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", + "[INFO 23-08-18 22:03:16.9435 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", + "[INFO 23-08-18 22:03:16.9437 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", + "[INFO 23-08-18 22:03:17.0687 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", + "[INFO 23-08-18 22:03:17.1606 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", + "[INFO 23-08-18 22:03:17.4994 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", + "[INFO 23-08-18 22:03:17.5236 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", + "[INFO 23-08-18 22:03:17.8828 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", + "[INFO 23-08-18 22:03:17.9670 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.519100 train-rmse:29.519100 valid-loss:28.915615 valid-rmse:28.915615\n", + "[INFO 23-08-18 22:03:18.1788 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", + "[INFO 23-08-18 22:03:18.1789 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", + "[INFO 23-08-18 22:03:18.3775 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.555222 train-rmse:29.555222 valid-loss:29.081779 valid-rmse:29.081779\n", + "[INFO 23-08-18 22:03:18.4761 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", + "[INFO 23-08-18 22:03:18.5486 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.485874 train-rmse:29.485874 valid-loss:29.859213 valid-rmse:29.859213\n", + "[INFO 23-08-18 22:03:19.2257 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", + "[INFO 23-08-18 22:03:19.2542 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", + "[INFO 23-08-18 22:03:19.3422 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", + "[INFO 23-08-18 22:03:19.3506 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", + "[INFO 23-08-18 22:03:19.4901 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", + "[INFO 23-08-18 22:03:19.6664 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.545330 train-rmse:29.545330 valid-loss:28.827353 valid-rmse:28.827353\n", + "[INFO 23-08-18 22:03:19.7627 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", + "[INFO 23-08-18 22:03:20.2336 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.580265 train-rmse:29.580265 valid-loss:28.985928 valid-rmse:28.985928\n", + "[INFO 23-08-18 22:03:21.1593 CEST gradient_boosted_trees.cc:1544] \tnum-trees:5 train-loss:23.735891 train-rmse:23.735891 valid-loss:23.227442 valid-rmse:23.227442\n", + "[INFO 23-08-18 22:03:21.5350 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.592987 train-rmse:29.592987 valid-loss:29.220953 valid-rmse:29.220953\n", + "[INFO 23-08-18 22:03:21.7667 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.552290 train-rmse:29.552290 valid-loss:29.436209 valid-rmse:29.436209\n", + "[INFO 23-08-18 22:03:51.5950 CEST gradient_boosted_trees.cc:1544] \tnum-trees:27 train-loss:16.634533 train-rmse:16.634533 valid-loss:16.681488 valid-rmse:16.681488\n", + "[INFO 23-08-18 22:04:21.8185 CEST gradient_boosted_trees.cc:1544] \tnum-trees:45 train-loss:15.733476 train-rmse:15.733476 valid-loss:17.104557 valid-rmse:17.104557\n", + "[INFO 23-08-18 22:04:26.1068 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:15.488959 train-rmse:15.488959 valid-loss:16.052216 valid-rmse:16.052216\n", + "[INFO 23-08-18 22:04:26.1068 CEST gradient_boosted_trees.cc:247] Truncates the model to 50 tree(s) i.e. 50 iteration(s).\n", + "[INFO 23-08-18 22:04:26.1068 CEST gradient_boosted_trees.cc:310] Final model num-trees:50 valid-loss:16.052216 valid-rmse:16.052216\n", + "[WARNING 23-08-18 22:04:26.2555 CEST gradient_boosted_trees.cc:1818] \"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-18 22:04:26.3144 CEST gradient_boosted_trees.cc:1829] \"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-18 22:04:26.3147 CEST gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", + "[INFO 23-08-18 22:04:28.4457 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:15.436479 train-rmse:15.436479 valid-loss:16.489502 valid-rmse:16.489502\n", + "[INFO 23-08-18 22:04:28.4679 CEST gradient_boosted_trees.cc:247] Truncates the model to 50 tree(s) i.e. 50 iteration(s).\n", + "[INFO 23-08-18 22:04:28.4745 CEST gradient_boosted_trees.cc:310] Final model num-trees:50 valid-loss:16.489502 valid-rmse:16.489502\n", + "[WARNING 23-08-18 22:04:28.4768 CEST gradient_boosted_trees.cc:1818] \"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-18 22:04:28.4769 CEST gradient_boosted_trees.cc:1829] \"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-18 22:04:28.4769 CEST gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", + "[INFO 23-08-18 22:04:29.3600 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:15.585465 train-rmse:15.585465 valid-loss:16.446827 valid-rmse:16.446827\n", + "[INFO 23-08-18 22:04:29.3600 CEST gradient_boosted_trees.cc:247] Truncates the model to 50 tree(s) i.e. 50 iteration(s).\n", + "[INFO 23-08-18 22:04:29.3601 CEST gradient_boosted_trees.cc:310] Final model num-trees:50 valid-loss:16.446827 valid-rmse:16.446827\n", + "[WARNING 23-08-18 22:04:29.3640 CEST gradient_boosted_trees.cc:1818] \"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-18 22:04:29.3640 CEST gradient_boosted_trees.cc:1829] \"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-18 22:04:29.3640 CEST gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", + "[INFO 23-08-18 22:04:29.7111 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:15.498506 train-rmse:15.498506 valid-loss:16.925005 valid-rmse:16.925005\n", + "[INFO 23-08-18 22:04:29.7114 CEST gradient_boosted_trees.cc:247] Truncates the model to 50 tree(s) i.e. 50 iteration(s).\n", + "[INFO 23-08-18 22:04:29.7115 CEST gradient_boosted_trees.cc:310] Final model num-trees:50 valid-loss:16.925005 valid-rmse:16.925005\n", + "[INFO 23-08-18 22:04:29.8985 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", + "[INFO 23-08-18 22:04:29.9154 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:15.528732 train-rmse:15.528732 valid-loss:16.360813 valid-rmse:16.360813\n", + "[INFO 23-08-18 22:04:29.9154 CEST gradient_boosted_trees.cc:247] Truncates the model to 50 tree(s) i.e. 50 iteration(s).\n", + "[INFO 23-08-18 22:04:29.9155 CEST gradient_boosted_trees.cc:310] Final model num-trees:50 valid-loss:16.360813 valid-rmse:16.360813\n", + "[INFO 23-08-18 22:04:29.9267 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", + "[INFO 23-08-18 22:04:30.0825 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", + "[INFO 23-08-18 22:04:30.5422 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:15.795024 train-rmse:15.795024 valid-loss:17.031176 valid-rmse:17.031176\n", + "[INFO 23-08-18 22:04:30.6062 CEST gradient_boosted_trees.cc:247] Truncates the model to 50 tree(s) i.e. 50 iteration(s).\n", + "[INFO 23-08-18 22:04:30.6090 CEST gradient_boosted_trees.cc:310] Final model num-trees:50 valid-loss:17.031176 valid-rmse:17.031176\n", + "[INFO 23-08-18 22:04:30.9694 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", + "[INFO 23-08-18 22:04:30.9694 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", + "[INFO 23-08-18 22:04:31.0827 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", + "[INFO 23-08-18 22:04:31.2504 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:15.940226 train-rmse:15.940226 valid-loss:16.628620 valid-rmse:16.628620\n", + "[INFO 23-08-18 22:04:31.2506 CEST gradient_boosted_trees.cc:247] Truncates the model to 50 tree(s) i.e. 50 iteration(s).\n", + "[INFO 23-08-18 22:04:31.2510 CEST gradient_boosted_trees.cc:310] Final model num-trees:50 valid-loss:16.628620 valid-rmse:16.628620\n", + "[INFO 23-08-18 22:04:31.4163 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.473177 train-rmse:29.473177 valid-loss:30.028440 valid-rmse:30.028440\n", + "[INFO 23-08-18 22:04:31.4533 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:15.958979 train-rmse:15.958979 valid-loss:17.016514 valid-rmse:17.016514\n", + "[INFO 23-08-18 22:04:31.4533 CEST gradient_boosted_trees.cc:247] Truncates the model to 50 tree(s) i.e. 50 iteration(s).\n", + "[INFO 23-08-18 22:04:31.4534 CEST gradient_boosted_trees.cc:310] Final model num-trees:50 valid-loss:17.016514 valid-rmse:17.016514\n", + "[INFO 23-08-18 22:04:31.7284 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", + "[INFO 23-08-18 22:04:31.7285 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", + "[INFO 23-08-18 22:04:31.7701 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.512590 train-rmse:29.512590 valid-loss:29.749025 valid-rmse:29.749025\n", + "[INFO 23-08-18 22:04:31.8506 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", + "[INFO 23-08-18 22:04:32.5836 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.433599 train-rmse:29.433599 valid-loss:30.823900 valid-rmse:30.823900\n", + "[INFO 23-08-18 22:04:51.7864 CEST gradient_boosted_trees.cc:1544] \tnum-trees:34 train-loss:16.647221 train-rmse:16.647221 valid-loss:17.445215 valid-rmse:17.445215\n", + "[INFO 23-08-18 22:05:00.1259 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:15.972644 train-rmse:15.972644 valid-loss:17.476349 valid-rmse:17.476349\n", + "[INFO 23-08-18 22:05:00.1260 CEST gradient_boosted_trees.cc:247] Truncates the model to 50 tree(s) i.e. 50 iteration(s).\n", + "[INFO 23-08-18 22:05:00.1260 CEST gradient_boosted_trees.cc:310] Final model num-trees:50 valid-loss:17.476349 valid-rmse:17.476349\n", + "[INFO 23-08-18 22:05:00.3792 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:15.949216 train-rmse:15.949216 valid-loss:16.998175 valid-rmse:16.998175\n", + "[INFO 23-08-18 22:05:00.3793 CEST gradient_boosted_trees.cc:247] Truncates the model to 50 tree(s) i.e. 50 iteration(s).\n", + "[INFO 23-08-18 22:05:00.3793 CEST gradient_boosted_trees.cc:310] Final model num-trees:50 valid-loss:16.998175 valid-rmse:16.998175\n", + "[INFO 23-08-18 22:05:00.9771 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:15.933821 train-rmse:15.933821 valid-loss:18.116308 valid-rmse:18.116308\n", + "[INFO 23-08-18 22:05:01.0064 CEST gradient_boosted_trees.cc:247] Truncates the model to 50 tree(s) i.e. 50 iteration(s).\n", + "[INFO 23-08-18 22:05:01.0065 CEST gradient_boosted_trees.cc:310] Final model num-trees:50 valid-loss:18.116308 valid-rmse:18.116308\n", + "[INFO 23-08-18 22:05:01.0266 CEST kernel.cc:926] Export model in log directory: /tmp/tmplcypdssc with prefix 42d4958eb01341a8\n", + "[INFO 23-08-18 22:05:01.1251 CEST kernel.cc:944] Save model in resources\n", + "[WARNING 23-08-18 22:05:01.1283 CEST abstract_model.cc:1058] Validation evaluation not supported for MULTITASKER\n", + "[INFO 23-08-18 22:05:01.1292 CEST abstract_model.cc:849] Model self evaluation:\n", + "Number of predictions (with weights): 1\n", + "Task: REGRESSION\n", + "Loss (SQUARED_ERROR): 12.6849\n", + "\n", + "RMSE: 3.56159\n", + "Default RMSE: : 0\n", + "\n", + "[INFO 23-08-18 22:05:01.1298 CEST abstract_model.cc:849] Model self evaluation:\n", + "Number of predictions (with weights): 1\n", + "Task: REGRESSION\n", + "Loss (SQUARED_ERROR): 13.4037\n", + "\n", + "RMSE: 3.66111\n", + "Default RMSE: : 0\n", + "\n", + "[INFO 23-08-18 22:05:01.1304 CEST abstract_model.cc:849] Model self evaluation:\n", + "Number of predictions (with weights): 1\n", + "Task: REGRESSION\n", + "Loss (SQUARED_ERROR): 13.4538\n", + "\n", + "RMSE: 3.66794\n", + "Default RMSE: : 0\n", + "\n", + "[INFO 23-08-18 22:05:01.1310 CEST abstract_model.cc:849] Model self evaluation:\n", + "Number of predictions (with weights): 1\n", + "Task: REGRESSION\n", + "Loss (SQUARED_ERROR): 13.5662\n", + "\n", + "RMSE: 3.68323\n", + "Default RMSE: : 0\n", + "\n", + "[INFO 23-08-18 22:05:01.1315 CEST abstract_model.cc:849] Model self evaluation:\n", + "Number of predictions (with weights): 1\n", + "Task: REGRESSION\n", + "Loss (SQUARED_ERROR): 15.0292\n", + "\n", + "RMSE: 3.87676\n", + "Default RMSE: : 0\n", + "\n", + "[INFO 23-08-18 22:05:01.1319 CEST abstract_model.cc:849] Model self evaluation:\n", + "Number of predictions (with weights): 1\n", + "Task: REGRESSION\n", + "Loss (SQUARED_ERROR): 15.3554\n", + "\n", + "RMSE: 3.9186\n", + "Default RMSE: : 0\n", + "\n", + "[INFO 23-08-18 22:05:01.1323 CEST abstract_model.cc:849] Model self evaluation:\n", + "Number of predictions (with weights): 1\n", + "Task: REGRESSION\n", + "Loss (SQUARED_ERROR): 14.5934\n", + "\n", + "RMSE: 3.82013\n", + "Default RMSE: : 0\n", + "\n", + "[INFO 23-08-18 22:05:01.1327 CEST abstract_model.cc:849] Model self evaluation:\n", + "Number of predictions (with weights): 1\n", + "Task: REGRESSION\n", + "Loss (SQUARED_ERROR): 14.9942\n", + "\n", + "RMSE: 3.87224\n", + "Default RMSE: : 0\n", + "\n", + "[INFO 23-08-18 22:05:01.1331 CEST abstract_model.cc:849] Model self evaluation:\n", + "Number of predictions (with weights): 1\n", + "Task: REGRESSION\n", + "Loss (SQUARED_ERROR): 15.4313\n", + "\n", + "RMSE: 3.92826\n", + "Default RMSE: : 0\n", + "\n", + "[INFO 23-08-18 22:05:01.1335 CEST abstract_model.cc:849] Model self evaluation:\n", + "Number of predictions (with weights): 1\n", + "Task: REGRESSION\n", + "Loss (SQUARED_ERROR): 15.5282\n", + "\n", + "RMSE: 3.94058\n", + "Default RMSE: : 0\n", + "\n", + "[INFO 23-08-18 22:05:01.1340 CEST abstract_model.cc:849] Model self evaluation:\n", + "Number of predictions (with weights): 1\n", + "Task: REGRESSION\n", + "Loss (SQUARED_ERROR): 15.7235\n", + "\n", + "RMSE: 3.96528\n", + "Default RMSE: : 0\n", + "\n", + "[INFO 23-08-18 22:05:01.1345 CEST abstract_model.cc:849] Model self evaluation:\n", + "Number of predictions (with weights): 1\n", + "Task: REGRESSION\n", + "Loss (SQUARED_ERROR): 15.2461\n", + "\n", + "RMSE: 3.90462\n", + "Default RMSE: : 0\n", + "\n", + "[INFO 23-08-18 22:05:01.1350 CEST abstract_model.cc:849] Model self evaluation:\n", + "Number of predictions (with weights): 1\n", + "Task: REGRESSION\n", + "Loss (SQUARED_ERROR): 15.8614\n", + "\n", + "RMSE: 3.98263\n", + "Default RMSE: : 0\n", + "\n", + "[INFO 23-08-18 22:05:01.1356 CEST abstract_model.cc:849] Model self evaluation:\n", + "Number of predictions (with weights): 1\n", + "Task: REGRESSION\n", + "Loss (SQUARED_ERROR): 15.9951\n", + "\n", + "RMSE: 3.99938\n", + "Default RMSE: : 0\n", + "\n", + "[INFO 23-08-18 22:05:01.1360 CEST abstract_model.cc:849] Model self evaluation:\n", + "Number of predictions (with weights): 1\n", + "Task: REGRESSION\n", + "Loss (SQUARED_ERROR): 15.4703\n", + "\n", + "RMSE: 3.93324\n", + "Default RMSE: : 0\n", + "\n", + "[INFO 23-08-18 22:05:01.1364 CEST abstract_model.cc:849] Model self evaluation:\n", + "Number of predictions (with weights): 1\n", + "Task: REGRESSION\n", + "Loss (SQUARED_ERROR): 15.8165\n", + "\n", + "RMSE: 3.977\n", + "Default RMSE: : 0\n", + "\n", + "[INFO 23-08-18 22:05:01.1369 CEST abstract_model.cc:849] Model self evaluation:\n", + "Number of predictions (with weights): 1\n", + "Task: REGRESSION\n", + "Loss (SQUARED_ERROR): 16.0522\n", + "\n", + "RMSE: 4.00652\n", + "Default RMSE: : 0\n", + "\n", + "[INFO 23-08-18 22:05:01.1375 CEST abstract_model.cc:849] Model self evaluation:\n", + "Number of predictions (with weights): 1\n", + "Task: REGRESSION\n", + "Loss (SQUARED_ERROR): 16.4895\n", + "\n", + "RMSE: 4.06073\n", + "Default RMSE: : 0\n", + "\n", + "[INFO 23-08-18 22:05:01.1394 CEST abstract_model.cc:849] Model self evaluation:\n", + "Number of predictions (with weights): 1\n", + "Task: REGRESSION\n", + "Loss (SQUARED_ERROR): 16.3608\n", + "\n", + "RMSE: 4.04485\n", + "Default RMSE: : 0\n", + "\n", + "[INFO 23-08-18 22:05:01.1401 CEST abstract_model.cc:849] Model self evaluation:\n", + "Number of predictions (with weights): 1\n", + "Task: REGRESSION\n", + "Loss (SQUARED_ERROR): 16.925\n", + "\n", + "RMSE: 4.114\n", + "Default RMSE: : 0\n", + "\n", + "[INFO 23-08-18 22:05:01.1406 CEST abstract_model.cc:849] Model self evaluation:\n", + "Number of predictions (with weights): 1\n", + "Task: REGRESSION\n", + "Loss (SQUARED_ERROR): 16.4468\n", + "\n", + "RMSE: 4.05547\n", + "Default RMSE: : 0\n", + "\n", + "[INFO 23-08-18 22:05:01.1412 CEST abstract_model.cc:849] Model self evaluation:\n", + "Number of predictions (with weights): 1\n", + "Task: REGRESSION\n", + "Loss (SQUARED_ERROR): 17.0312\n", + "\n", + "RMSE: 4.12688\n", + "Default RMSE: : 0\n", + "\n", + "[INFO 23-08-18 22:05:01.1418 CEST abstract_model.cc:849] Model self evaluation:\n", + "Number of predictions (with weights): 1\n", + "Task: REGRESSION\n", + "Loss (SQUARED_ERROR): 16.6286\n", + "\n", + "RMSE: 4.07782\n", + "Default RMSE: : 0\n", + "\n", + "[INFO 23-08-18 22:05:01.1423 CEST abstract_model.cc:849] Model self evaluation:\n", + "Number of predictions (with weights): 1\n", + "Task: REGRESSION\n", + "Loss (SQUARED_ERROR): 17.0165\n", + "\n", + "RMSE: 4.12511\n", + "Default RMSE: : 0\n", + "\n", + "[INFO 23-08-18 22:05:01.1430 CEST abstract_model.cc:849] Model self 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kernel.cc:1075] Use fast generic engine\n", + "[INFO 23-08-18 22:05:01.2718 CEST kernel.cc:1243] Loading model from path /tmp/tmplcypdssc/model/ with prefix 42d4958eb01341a8_1\n", + "[INFO 23-08-18 22:05:01.2805 CEST kernel.cc:1075] Use fast generic engine\n", + "[INFO 23-08-18 22:05:01.3239 CEST kernel.cc:1243] Loading model from path /tmp/tmplcypdssc/model/ with prefix 42d4958eb01341a8_2\n", + "[INFO 23-08-18 22:05:01.3302 CEST kernel.cc:1075] Use fast generic engine\n", + "[INFO 23-08-18 22:05:01.3619 CEST kernel.cc:1243] Loading model from path /tmp/tmplcypdssc/model/ with prefix 42d4958eb01341a8_3\n", + "[INFO 23-08-18 22:05:01.3681 CEST kernel.cc:1075] Use fast generic engine\n", + "[INFO 23-08-18 22:05:01.3998 CEST kernel.cc:1243] Loading model from path /tmp/tmplcypdssc/model/ with prefix 42d4958eb01341a8_4\n", + "[INFO 23-08-18 22:05:01.4058 CEST kernel.cc:1075] Use fast generic engine\n", + "[INFO 23-08-18 22:05:01.4539 CEST kernel.cc:1243] Loading model from path 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engine\n", + "[INFO 23-08-18 22:05:01.6704 CEST kernel.cc:1243] Loading model from path /tmp/tmplcypdssc/model/ with prefix 42d4958eb01341a8_10\n", + "[INFO 23-08-18 22:05:01.6778 CEST kernel.cc:1075] Use fast generic engine\n", + "[INFO 23-08-18 22:05:01.7085 CEST kernel.cc:1243] Loading model from path /tmp/tmplcypdssc/model/ with prefix 42d4958eb01341a8_11\n", + "[INFO 23-08-18 22:05:01.7159 CEST kernel.cc:1075] Use fast generic engine\n", + "[INFO 23-08-18 22:05:01.7443 CEST kernel.cc:1243] Loading model from path /tmp/tmplcypdssc/model/ with prefix 42d4958eb01341a8_12\n", + "[INFO 23-08-18 22:05:01.7512 CEST kernel.cc:1075] Use fast generic engine\n", + "[INFO 23-08-18 22:05:01.7837 CEST kernel.cc:1243] Loading model from path /tmp/tmplcypdssc/model/ with prefix 42d4958eb01341a8_13\n", + "[INFO 23-08-18 22:05:01.7912 CEST kernel.cc:1075] Use fast generic engine\n", + "[INFO 23-08-18 22:05:01.8208 CEST kernel.cc:1243] Loading model from path /tmp/tmplcypdssc/model/ with prefix 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tmp/temporian_m5/gbm.model/assets/ with prefix b7284e4ab6264214_0\n", - "[INFO 23-07-24 12:50:50.4550 -03 abstract_model.cc:1312] Engine \"GradientBoostedTreesQuickScorerExtended\" built\n", - "[INFO 23-07-24 12:50:50.4550 -03 kernel.cc:1075] Use fast generic engine\n", - "[INFO 23-07-24 12:50:50.4624 -03 kernel.cc:1243] Loading model from path tmp/temporian_m5/gbm.model/assets/ with prefix b7284e4ab6264214_1\n", - "[INFO 23-07-24 12:50:50.4668 -03 kernel.cc:1075] Use fast generic engine\n", - "[INFO 23-07-24 12:50:50.4738 -03 kernel.cc:1243] Loading model from path tmp/temporian_m5/gbm.model/assets/ with prefix b7284e4ab6264214_2\n", - "[INFO 23-07-24 12:50:50.4784 -03 kernel.cc:1075] Use fast generic engine\n", - "[INFO 23-07-24 12:50:50.4860 -03 kernel.cc:1243] Loading model from path tmp/temporian_m5/gbm.model/assets/ with prefix b7284e4ab6264214_3\n", - "[INFO 23-07-24 12:50:50.4937 -03 kernel.cc:1075] Use fast generic engine\n", - "[INFO 23-07-24 12:50:50.5012 -03 kernel.cc:1243] Loading model from path tmp/temporian_m5/gbm.model/assets/ with prefix b7284e4ab6264214_4\n", - "[INFO 23-07-24 12:50:50.5058 -03 kernel.cc:1075] Use fast generic engine\n", - "[INFO 23-07-24 12:50:50.5129 -03 kernel.cc:1243] Loading model from path tmp/temporian_m5/gbm.model/assets/ with prefix b7284e4ab6264214_5\n", - "[INFO 23-07-24 12:50:50.5175 -03 kernel.cc:1075] Use fast generic engine\n", - "[INFO 23-07-24 12:50:50.5246 -03 kernel.cc:1243] Loading model from path tmp/temporian_m5/gbm.model/assets/ with prefix b7284e4ab6264214_6\n", - "[INFO 23-07-24 12:50:50.5309 -03 kernel.cc:1075] Use fast generic engine\n", - "[INFO 23-07-24 12:50:50.5386 -03 kernel.cc:1243] Loading model from path tmp/temporian_m5/gbm.model/assets/ with prefix b7284e4ab6264214_7\n", - "[INFO 23-07-24 12:50:50.5431 -03 kernel.cc:1075] Use fast generic engine\n", - "[INFO 23-07-24 12:50:50.5502 -03 kernel.cc:1243] Loading model from path tmp/temporian_m5/gbm.model/assets/ with prefix b7284e4ab6264214_8\n", - "[INFO 23-07-24 12:50:50.5548 -03 kernel.cc:1075] Use fast generic engine\n", - "[INFO 23-07-24 12:50:50.5616 -03 kernel.cc:1243] Loading model from path tmp/temporian_m5/gbm.model/assets/ with prefix b7284e4ab6264214_9\n", - "[INFO 23-07-24 12:50:50.5662 -03 kernel.cc:1075] Use fast generic engine\n", - "[INFO 23-07-24 12:50:50.5737 -03 kernel.cc:1243] Loading model from path tmp/temporian_m5/gbm.model/assets/ with prefix b7284e4ab6264214_10\n", - "[INFO 23-07-24 12:50:50.5784 -03 kernel.cc:1075] Use fast generic engine\n", - "[INFO 23-07-24 12:50:50.5858 -03 kernel.cc:1243] Loading model from path tmp/temporian_m5/gbm.model/assets/ with prefix b7284e4ab6264214_11\n", - "[INFO 23-07-24 12:50:50.5903 -03 kernel.cc:1075] Use fast generic engine\n", - "[INFO 23-07-24 12:50:50.5980 -03 kernel.cc:1243] Loading model from path tmp/temporian_m5/gbm.model/assets/ with prefix b7284e4ab6264214_12\n", - "[INFO 23-07-24 12:50:50.6026 -03 kernel.cc:1075] Use fast generic engine\n" + "WARNING:tensorflow:AutoGraph could not transform and will run it as-is.\n", + "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n", + "Cause: could not get source code\n", + "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: AutoGraph could not transform and will run it as-is.\n", + "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n", + "Cause: could not get source code\n", + "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n", + "Model compiled.\n" ] } ], @@ -3335,16 +5849,9 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 55, "id": "e985dba0-dc14-4341-b0c8-ad145fef7ca3", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:50:51.726531Z", - "iopub.status.busy": "2023-07-24T15:50:51.726385Z", - "iopub.status.idle": "2023-07-24T15:50:52.173560Z", - "shell.execute_reply": "2023-07-24T15:50:52.173204Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "tf_predictions = model.predict(tf_test_dataset, verbose=0)" @@ -3361,16 +5868,9 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 56, "id": "068ea997-52ae-40eb-b18e-a118a3d7b026", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:50:52.175654Z", - "iopub.status.busy": "2023-07-24T15:50:52.175511Z", - "iopub.status.idle": "2023-07-24T15:50:52.203847Z", - "shell.execute_reply": "2023-07-24T15:50:52.203517Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "index_names = tabular_test.schema.index_names()\n", @@ -3424,27 +5924,20 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 60, "id": "978fc1ff-7914-43df-af04-e5b054a9e9f2", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:50:52.205632Z", - "iopub.status.busy": "2023-07-24T15:50:52.205525Z", - "iopub.status.idle": "2023-07-24T15:50:52.857062Z", - "shell.execute_reply": "2023-07-24T15:50:52.856621Z" - } - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "The number of plots (240) is larger than \"options.max_num_plots=6\". Only the first plots will be printed.\n" + "The number of plots (80) is larger than \"options.max_num_plots=6\". Only the first plots will be printed.\n" ] }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -3454,7 +5947,7 @@ } ], "source": [ - "real_sales_evset = sales_evset[\"sales\"].filter(sales_evset[\"day\"] > 1914)\n", + "real_sales_evset = sales_evset[\"sales\"].filter(sales_evset[\"day\"] >= 1914)\n", "\n", "past_sales_evset = sales_evset[\"sales\"].filter((sales_evset[\"day\"] <= 1914) & (sales_evset[\"day\"] >= 1914 - 40))\n", "\n", @@ -3466,73 +5959,10 @@ "tp.plot(\n", " [real_sales_evset, prediction_evset, past_sales_evset],\n", " max_num_plots=3*2,\n", + " merge=True,\n", ")\n" ] }, - { - "cell_type": "markdown", - "id": "321bcd2c-624c-4909-a7be-3efe7ecd5f82", - "metadata": {}, - "source": [ - "#### Custom plots\n", - "\n", - "In order to show the predictions on top of real sales and compare them, we need to create our own custom plots." - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "id": "32bcf947-b3aa-4082-b453-3c4cacbb19da", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:50:52.859138Z", - "iopub.status.busy": "2023-07-24T15:50:52.859024Z", - "iopub.status.idle": "2023-07-24T15:50:53.421090Z", - "shell.execute_reply": "2023-07-24T15:50:53.420504Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "\n", - "import matplotlib.pyplot as plt\n", - "\n", - "def convert_timestamps_to_datetimes(timestamps):\n", - " return [datetime.fromtimestamp(t, tz=timezone.utc) for t in timestamps]\n", - "\n", - "num_plot = 4\n", - "fig, axs = plt.subplots(num_plot, figsize=(10, 10))\n", - "for plot_idx, index in enumerate(real_sales_evset.data):\n", - " if plot_idx >= num_plot:\n", - " break\n", - "\n", - " ax = axs[plot_idx]\n", - " ax.set_title(index)\n", - "\n", - " # Convert timestamps to dates\n", - " dates_real = convert_timestamps_to_datetimes(real_sales_evset.data[index].timestamps)\n", - " dates_pred = convert_timestamps_to_datetimes(prediction_evset.data[index].timestamps)\n", - " dates_past = convert_timestamps_to_datetimes(past_sales_evset.data[index].timestamps)\n", - "\n", - " # Plot past sales, test ground-truth (real) and predicted sales\n", - " ax.plot(dates_past, past_sales_evset.data[index].features[0], label=\"past\")\n", - " ax.plot(dates_real, real_sales_evset.data[index].features[0], label=\"real\")\n", - " ax.plot(dates_pred, prediction_evset.data[index].features[0], label=\"pred\")\n", - " ax.legend()\n", - "\n", - "fig.tight_layout()" - ] - }, { "attachments": {}, "cell_type": "markdown", @@ -3561,7 +5991,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.4" + "version": "3.10.9" } }, "nbformat": 4, From 2a5919ff73ebc56dfa7340f514dcc45a9ebac467 Mon Sep 17 00:00:00 2001 From: Mathieu Guillame-Bert Date: Fri, 18 Aug 2023 22:09:25 +0200 Subject: [PATCH 2/3] Update m5_competition.ipynb --- docs/src/tutorials/m5_competition.ipynb | 4782 +---------------------- 1 file changed, 83 insertions(+), 4699 deletions(-) diff --git a/docs/src/tutorials/m5_competition.ipynb b/docs/src/tutorials/m5_competition.ipynb index 046438f93..e11c7342f 100644 --- a/docs/src/tutorials/m5_competition.ipynb +++ b/docs/src/tutorials/m5_competition.ipynb @@ -46,25 +46,10 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "52cc838e-e565-4d86-9eae-8a686ad6b0c7", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.1.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.2.1\u001b[0m\n", - "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n", - "Note: you may need to restart the kernel to use updated packages.\n", - "\n", - "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.1.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.2.1\u001b[0m\n", - "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n", - "Note: you may need to restart the kernel to use updated packages.\n" - ] - } - ], + "outputs": [], "source": [ "%pip install temporian -q\n", "\n", @@ -75,7 +60,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "4e0d9e3a-b105-48e1-a47d-295312604774", "metadata": {}, "outputs": [], @@ -104,7 +89,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "ddb18926-5aec-4f36-8b04-008488eade04", "metadata": {}, "outputs": [], @@ -128,23 +113,10 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "105c5f4a-0ab1-4de1-b46b-5b80254a5ee1", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "total 430M\n", - "-rw-r----- 1 gbm primarygroup 102K Aug 18 21:48 calendar.csv\n", - "-rw-r----- 1 gbm primarygroup 117M Aug 18 21:48 sales_train_evaluation.csv\n", - "-rw-r----- 1 gbm primarygroup 115M Aug 18 21:48 sales_train_validation.csv\n", - "-rw-r----- 1 gbm primarygroup 5.0M Aug 18 21:48 sample_submission.csv\n", - "-rw-r----- 1 gbm primarygroup 194M Aug 18 21:48 sell_prices.csv\n" - ] - } - ], + "outputs": [], "source": [ "!ls -lh tmp/temporian_m5/raw" ] @@ -162,63 +134,10 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "e144ebe2-dc46-46e3-8d89-67355aed7000", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "==========\n", - "raw_sales\n", - "\n", - "\n", - "RangeIndex: 30490 entries, 0 to 30489\n", - "Columns: 1947 entries, id to d_1941\n", - "dtypes: int64(1941), object(6)\n", - "memory usage: 452.9+ MB\n", - "==========\n", - "raw_sell_prices\n", - "\n", - "\n", - "RangeIndex: 6841121 entries, 0 to 6841120\n", - "Data columns (total 4 columns):\n", - " # Column Dtype \n", - "--- ------ ----- \n", - " 0 store_id object \n", - " 1 item_id object \n", - " 2 wm_yr_wk int64 \n", - " 3 sell_price float64\n", - "dtypes: float64(1), int64(1), object(2)\n", - "memory usage: 208.8+ MB\n", - "==========\n", - "raw_calendar_raw\n", - "\n", - "\n", - "RangeIndex: 1969 entries, 0 to 1968\n", - "Data columns (total 14 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 date 1969 non-null object\n", - " 1 wm_yr_wk 1969 non-null int64 \n", - " 2 weekday 1969 non-null object\n", - " 3 wday 1969 non-null int64 \n", - " 4 month 1969 non-null int64 \n", - " 5 year 1969 non-null int64 \n", - " 6 d 1969 non-null object\n", - " 7 event_name_1 162 non-null object\n", - " 8 event_type_1 162 non-null object\n", - " 9 event_name_2 5 non-null object\n", - " 10 event_type_2 5 non-null object\n", - " 11 snap_CA 1969 non-null int64 \n", - " 12 snap_TX 1969 non-null int64 \n", - " 13 snap_WI 1969 non-null int64 \n", - "dtypes: int64(7), object(7)\n", - "memory usage: 215.5+ KB\n" - ] - } - ], + "outputs": [], "source": [ "raw_path = lambda x: os.path.join(raw_data_dir, x)\n", "\n", @@ -252,18 +171,10 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "be833bab-ef50-4809-8341-23938b1279f4", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of selected items: 80\n" - ] - } - ], + "outputs": [], "source": [ "downsample_dataset = True\n", "\n", @@ -310,210 +221,10 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "df78d002-e33d-4b02-aec0-d5deca909805", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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iditem_iddept_idcat_idstore_idstate_idd_1d_2d_3d_4...d_1932d_1933d_1934d_1935d_1936d_1937d_1938d_1939d_1940d_1941
2314FOODS_3_090_CA_1_evaluationFOODS_3_090FOODS_3FOODSCA_1CA1071824747...77477438423951696764
2450FOODS_3_226_CA_1_evaluationFOODS_3_226FOODS_3FOODSCA_1CA139210...51191543355
2476FOODS_3_252_CA_1_evaluationFOODS_3_252FOODS_3FOODSCA_1CA1914108...38616136343930325050
2779FOODS_3_555_CA_1_evaluationFOODS_3_555FOODS_3FOODSCA_1CA23161410...20262519192616153226
2810FOODS_3_586_CA_1_evaluationFOODS_3_586FOODS_3FOODSCA_1CA42363023...42457134463318334843
\n", - "

5 rows × 1947 columns

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" - ], - "text/plain": [ - " id item_id dept_id cat_id store_id \\\n", - "2314 FOODS_3_090_CA_1_evaluation FOODS_3_090 FOODS_3 FOODS CA_1 \n", - "2450 FOODS_3_226_CA_1_evaluation FOODS_3_226 FOODS_3 FOODS CA_1 \n", - "2476 FOODS_3_252_CA_1_evaluation FOODS_3_252 FOODS_3 FOODS CA_1 \n", - "2779 FOODS_3_555_CA_1_evaluation FOODS_3_555 FOODS_3 FOODS CA_1 \n", - "2810 FOODS_3_586_CA_1_evaluation FOODS_3_586 FOODS_3 FOODS CA_1 \n", - "\n", - " state_id d_1 d_2 d_3 d_4 ... d_1932 d_1933 d_1934 d_1935 \\\n", - "2314 CA 107 182 47 47 ... 77 47 74 38 \n", - "2450 CA 13 9 2 10 ... 5 11 9 1 \n", - "2476 CA 19 14 10 8 ... 38 61 61 36 \n", - "2779 CA 23 16 14 10 ... 20 26 25 19 \n", - "2810 CA 42 36 30 23 ... 42 45 71 34 \n", - "\n", - " d_1936 d_1937 d_1938 d_1939 d_1940 d_1941 \n", - "2314 42 39 51 69 67 64 \n", - "2450 5 4 3 3 5 5 \n", - "2476 34 39 30 32 50 50 \n", - "2779 19 26 16 15 32 26 \n", - "2810 46 33 18 33 48 43 \n", - "\n", - "[5 rows x 1947 columns]" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "raw_sales.head()" ] @@ -545,7 +256,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "6b55d1f4-cf72-4f62-92ee-c988b94fc01d", "metadata": {}, "outputs": [], @@ -571,7 +282,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "cfe44b50-afbc-40ab-bd64-7ec231baf123", "metadata": {}, "outputs": [], @@ -593,7 +304,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "4688a3e7-adff-449c-a5dc-0e052b52c96d", "metadata": {}, "outputs": [], @@ -612,122 +323,10 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "id": "6fa03718-4050-400b-b5cb-f8402a95766a", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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item_iddept_idcat_idstore_idstate_iddaysalestimestamp
0FOODS_3_090FOODS_3FOODSCA_1CA11072011-01-29 00:00:00+00:00
1FOODS_3_226FOODS_3FOODSCA_1CA1132011-01-29 00:00:00+00:00
2FOODS_3_252FOODS_3FOODSCA_1CA1192011-01-29 00:00:00+00:00
3FOODS_3_555FOODS_3FOODSCA_1CA1232011-01-29 00:00:00+00:00
4FOODS_3_586FOODS_3FOODSCA_1CA1422011-01-29 00:00:00+00:00
\n", - "
" - ], - "text/plain": [ - " item_id dept_id cat_id store_id state_id day sales \\\n", - "0 FOODS_3_090 FOODS_3 FOODS CA_1 CA 1 107 \n", - "1 FOODS_3_226 FOODS_3 FOODS CA_1 CA 1 13 \n", - "2 FOODS_3_252 FOODS_3 FOODS CA_1 CA 1 19 \n", - "3 FOODS_3_555 FOODS_3 FOODS CA_1 CA 1 23 \n", - "4 FOODS_3_586 FOODS_3 FOODS CA_1 CA 1 42 \n", - "\n", - " timestamp \n", - "0 2011-01-29 00:00:00+00:00 \n", - "1 2011-01-29 00:00:00+00:00 \n", - "2 2011-01-29 00:00:00+00:00 \n", - "3 2011-01-29 00:00:00+00:00 \n", - "4 2011-01-29 00:00:00+00:00 " - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "cleaned_sales.head()" ] @@ -743,59 +342,10 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "id": "801c3ab0-de0b-400d-9416-a74917c507dc", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:root:Feature \"item_id\" is an array of numpy.object_ and was casted to numpy.string_ (Note: numpy.string_ is equivalent to numpy.bytes_).\n", - "WARNING:root:Feature \"dept_id\" is an array of numpy.object_ and was casted to numpy.string_ (Note: numpy.string_ is equivalent to numpy.bytes_).\n", - "WARNING:root:Feature \"cat_id\" is an array of numpy.object_ and was casted to numpy.string_ (Note: numpy.string_ is equivalent to numpy.bytes_).\n", - "WARNING:root:Feature \"store_id\" is an array of numpy.object_ and was casted to numpy.string_ (Note: numpy.string_ is equivalent to numpy.bytes_).\n", - "WARNING:root:Feature \"state_id\" is an array of numpy.object_ and was casted to numpy.string_ (Note: numpy.string_ is equivalent to numpy.bytes_).\n" - ] - }, - { - "data": { - "text/html": [ - "
80 indexes × 2 features (memory usage: 3.8 MB)

Index: (item_id=FOODS_3_090, dept_id=FOODS_3, cat_id=FOODS, store_id=CA_1, state_id=CA)

1941 events
timestampdaysales
2011-01-29 00:00:00+00:001107
2011-01-30 00:00:00+00:002182
2011-01-31 00:00:00+00:00347
2011-02-01 00:00:00+00:00447
2011-02-02 00:00:00+00:00562
2011-02-03 00:00:00+00:00690
2011-02-04 00:00:00+00:00781
2011-02-05 00:00:00+00:008124
2011-02-06 00:00:00+00:009134
2011-02-07 00:00:00+00:001065
2011-02-08 00:00:00+00:00110
2011-02-09 00:00:00+00:00120
2011-02-10 00:00:00+00:00130
2011-02-11 00:00:00+00:00140
2011-02-12 00:00:00+00:00150
2011-02-13 00:00:00+00:00160
2011-02-14 00:00:00+00:00170
2011-02-15 00:00:00+00:00180
2011-02-16 00:00:00+00:00190
2011-02-17 00:00:00+00:00200
………

Index: (item_id=FOODS_3_090, dept_id=FOODS_3, cat_id=FOODS, store_id=CA_2, state_id=CA)

1941 events
timestampdaysales
2011-01-29 00:00:00+00:001116
2011-01-30 00:00:00+00:00290
2011-01-31 00:00:00+00:00335
2011-02-01 00:00:00+00:00433
2011-02-02 00:00:00+00:00526
2011-02-03 00:00:00+00:00638
2011-02-04 00:00:00+00:007100
2011-02-05 00:00:00+00:00889
2011-02-06 00:00:00+00:00971
2011-02-07 00:00:00+00:00100
2011-02-08 00:00:00+00:00110
2011-02-09 00:00:00+00:00120
2011-02-10 00:00:00+00:00130
2011-02-11 00:00:00+00:00140
2011-02-12 00:00:00+00:00150
2011-02-13 00:00:00+00:00160
2011-02-14 00:00:00+00:00170
2011-02-15 00:00:00+00:00180
2011-02-16 00:00:00+00:00190
2011-02-17 00:00:00+00:00200
………

Index: (item_id=FOODS_3_090, dept_id=FOODS_3, cat_id=FOODS, store_id=CA_3, state_id=CA)

1941 events
timestampdaysales
2011-01-29 00:00:00+00:001108
2011-01-30 00:00:00+00:002132
2011-01-31 00:00:00+00:003102
2011-02-01 00:00:00+00:004120
2011-02-02 00:00:00+00:005106
2011-02-03 00:00:00+00:006123
2011-02-04 00:00:00+00:007279
2011-02-05 00:00:00+00:008175
2011-02-06 00:00:00+00:009186
2011-02-07 00:00:00+00:0010120
2011-02-08 00:00:00+00:00110
2011-02-09 00:00:00+00:00120
2011-02-10 00:00:00+00:00130
2011-02-11 00:00:00+00:00140
2011-02-12 00:00:00+00:00150
2011-02-13 00:00:00+00:00160
2011-02-14 00:00:00+00:00170
2011-02-15 00:00:00+00:00180
2011-02-16 00:00:00+00:00190
2011-02-17 00:00:00+00:00200
………

Index: (item_id=FOODS_3_090, dept_id=FOODS_3, cat_id=FOODS, store_id=CA_4, state_id=CA)

1941 events
timestampdaysales
2011-01-29 00:00:00+00:00169
2011-01-30 00:00:00+00:00249
2011-01-31 00:00:00+00:00337
2011-02-01 00:00:00+00:00440
2011-02-02 00:00:00+00:00538
2011-02-03 00:00:00+00:00639
2011-02-04 00:00:00+00:00761
2011-02-05 00:00:00+00:00879
2011-02-06 00:00:00+00:009102
2011-02-07 00:00:00+00:001086
2011-02-08 00:00:00+00:00110
2011-02-09 00:00:00+00:00120
2011-02-10 00:00:00+00:00130
2011-02-11 00:00:00+00:00140
2011-02-12 00:00:00+00:00150
2011-02-13 00:00:00+00:00160
2011-02-14 00:00:00+00:00170
2011-02-15 00:00:00+00:00180
2011-02-16 00:00:00+00:00190
2011-02-17 00:00:00+00:00200
………

Index: (item_id=FOODS_3_090, dept_id=FOODS_3, cat_id=FOODS, store_id=TX_1, state_id=TX)

1941 events
timestampdaysales
2011-01-29 00:00:00+00:00175
2011-01-30 00:00:00+00:00254
2011-01-31 00:00:00+00:00374
2011-02-01 00:00:00+00:00462
2011-02-02 00:00:00+00:00560
2011-02-03 00:00:00+00:006106
2011-02-04 00:00:00+00:00796
2011-02-05 00:00:00+00:008118
2011-02-06 00:00:00+00:009177
2011-02-07 00:00:00+00:001051
2011-02-08 00:00:00+00:00110
2011-02-09 00:00:00+00:00120
2011-02-10 00:00:00+00:00130
2011-02-11 00:00:00+00:00140
2011-02-12 00:00:00+00:00150
2011-02-13 00:00:00+00:00160
2011-02-14 00:00:00+00:00170
2011-02-15 00:00:00+00:00180
2011-02-16 00:00:00+00:00190
2011-02-17 00:00:00+00:00200
………
… (75 more indexes not shown)
" - ], - "text/plain": [ - "indexes: [('item_id', str_), ('dept_id', str_), ('cat_id', str_), ('store_id', str_), ('state_id', str_)]\n", - "features: [('day', int64), ('sales', int64)]\n", - "events:\n", - " item_id=b'FOODS_3_090' dept_id=b'FOODS_3' cat_id=b'FOODS' store_id=b'CA_1' state_id=b'CA' (1941 events):\n", - " timestamps: [1.2963e+09 1.2963e+09 1.2964e+09 ... 1.4637e+09 1.4638e+09 1.4639e+09]\n", - " 'day': [ 1 2 3 ... 1939 1940 1941]\n", - " 'sales': [107 182 47 ... 69 67 64]\n", - " item_id=b'FOODS_3_090' dept_id=b'FOODS_3' cat_id=b'FOODS' store_id=b'CA_2' state_id=b'CA' (1941 events):\n", - " timestamps: [1.2963e+09 1.2963e+09 1.2964e+09 ... 1.4637e+09 1.4638e+09 1.4639e+09]\n", - " 'day': [ 1 2 3 ... 1939 1940 1941]\n", - " 'sales': [116 90 35 ... 66 63 59]\n", - " item_id=b'FOODS_3_090' dept_id=b'FOODS_3' cat_id=b'FOODS' store_id=b'CA_3' state_id=b'CA' (1941 events):\n", - " timestamps: [1.2963e+09 1.2963e+09 1.2964e+09 ... 1.4637e+09 1.4638e+09 1.4639e+09]\n", - " 'day': [ 1 2 3 ... 1939 1940 1941]\n", - " 'sales': [108 132 102 ... 110 156 99]\n", - " item_id=b'FOODS_3_090' dept_id=b'FOODS_3' cat_id=b'FOODS' store_id=b'CA_4' state_id=b'CA' (1941 events):\n", - " timestamps: [1.2963e+09 1.2963e+09 1.2964e+09 ... 1.4637e+09 1.4638e+09 1.4639e+09]\n", - " 'day': [ 1 2 3 ... 1939 1940 1941]\n", - " 'sales': [69 49 37 ... 22 16 38]\n", - " item_id=b'FOODS_3_090' dept_id=b'FOODS_3' cat_id=b'FOODS' store_id=b'TX_1' state_id=b'TX' (1941 events):\n", - " timestamps: [1.2963e+09 1.2963e+09 1.2964e+09 ... 1.4637e+09 1.4638e+09 1.4639e+09]\n", - " 'day': [ 1 2 3 ... 1939 1940 1941]\n", - " 'sales': [75 54 74 ... 68 62 82]\n", - " ... (showing 5 of 80 indexes)\n", - "memory usage: 3.8 MB" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "sales_evset = tp.from_pandas(\n", " cleaned_sales,\n", @@ -816,350 +366,10 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "id": "bb44fde1-d2b4-4ff8-b0c2-4367ee95e514", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The number of plots (160) is larger than \"options.max_num_plots=4\". Only the first plots will be printed.\n" - ] - }, - { - "data": { - "application/javascript": [ - "(function(root) {\n", - " function now() {\n", - " return new Date();\n", - " }\n", - "\n", - " const force = true;\n", - "\n", - " if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n", - " root._bokeh_onload_callbacks = [];\n", - " root._bokeh_is_loading = undefined;\n", - " }\n", - "\n", - "const JS_MIME_TYPE = 'application/javascript';\n", - " const HTML_MIME_TYPE = 'text/html';\n", - " const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", - " const CLASS_NAME = 'output_bokeh rendered_html';\n", - "\n", - " /**\n", - " * Render data to the DOM node\n", - " */\n", - " function render(props, node) {\n", - " const script = document.createElement(\"script\");\n", - " node.appendChild(script);\n", - " }\n", - "\n", - " /**\n", - " * Handle when an output is cleared or removed\n", - " */\n", - " function handleClearOutput(event, handle) {\n", - " const cell = handle.cell;\n", - "\n", - " const id = cell.output_area._bokeh_element_id;\n", - " const server_id = cell.output_area._bokeh_server_id;\n", - " // Clean up Bokeh references\n", - " if (id != null && id in Bokeh.index) {\n", - " Bokeh.index[id].model.document.clear();\n", - " delete Bokeh.index[id];\n", - " }\n", - "\n", - " if (server_id !== undefined) {\n", - " // Clean up Bokeh references\n", - " const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", - " cell.notebook.kernel.execute(cmd_clean, {\n", - " iopub: {\n", - " output: function(msg) {\n", - " const id = msg.content.text.trim();\n", - " if (id in Bokeh.index) {\n", - " Bokeh.index[id].model.document.clear();\n", - " delete Bokeh.index[id];\n", - " }\n", - " }\n", - " }\n", - " });\n", - " // Destroy server and session\n", - " const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", - " cell.notebook.kernel.execute(cmd_destroy);\n", - " }\n", - " }\n", - "\n", - " /**\n", - " * Handle when a new output is added\n", - " */\n", - " function handleAddOutput(event, handle) {\n", - " const output_area = handle.output_area;\n", - " const output = handle.output;\n", - "\n", - " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", - " if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n", - " return\n", - " }\n", - "\n", - " const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", - "\n", - " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", - " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n", - " // store reference to embed id on output_area\n", - " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", - " }\n", - " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", - " const bk_div = document.createElement(\"div\");\n", - " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", - " const script_attrs = bk_div.children[0].attributes;\n", - " for (let i = 0; i < script_attrs.length; i++) {\n", - " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", - " toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n", - " }\n", - " // store reference to server id on output_area\n", - " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", - " }\n", - " }\n", - "\n", - " function register_renderer(events, OutputArea) {\n", - "\n", - " function append_mime(data, metadata, element) {\n", - " // create a DOM node to render to\n", - " const toinsert = this.create_output_subarea(\n", - " metadata,\n", - " CLASS_NAME,\n", - " EXEC_MIME_TYPE\n", - " );\n", - " this.keyboard_manager.register_events(toinsert);\n", - " // Render to node\n", - " const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", - " render(props, toinsert[toinsert.length - 1]);\n", - " element.append(toinsert);\n", - " return toinsert\n", - " }\n", - "\n", - " /* Handle when an output is cleared or removed */\n", - " events.on('clear_output.CodeCell', handleClearOutput);\n", - " events.on('delete.Cell', handleClearOutput);\n", - "\n", - " /* Handle when a new output is added */\n", - " events.on('output_added.OutputArea', handleAddOutput);\n", - "\n", - " /**\n", - " * Register the mime type and append_mime function with output_area\n", - " */\n", - " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", - " /* Is output safe? */\n", - " safe: true,\n", - " /* Index of renderer in `output_area.display_order` */\n", - " index: 0\n", - " });\n", - " }\n", - "\n", - " // register the mime type if in Jupyter Notebook environment and previously unregistered\n", - " if (root.Jupyter !== undefined) {\n", - " const events = require('base/js/events');\n", - " const OutputArea = require('notebook/js/outputarea').OutputArea;\n", - "\n", - " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", - " register_renderer(events, OutputArea);\n", - " }\n", - " }\n", - " if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n", - " root._bokeh_timeout = Date.now() + 5000;\n", - " root._bokeh_failed_load = false;\n", - " }\n", - "\n", - " const NB_LOAD_WARNING = {'data': {'text/html':\n", - " \"
\\n\"+\n", - " \"

\\n\"+\n", - " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", - " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", - " \"

\\n\"+\n", - " \"
    \\n\"+\n", - " \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n", - " \"
  • use INLINE resources instead, as so:
    • \\n\"+\n", - " \"
\\n\"+\n", - " \"\\n\"+\n", - " \"from bokeh.resources import INLINE\\n\"+\n", - " \"output_notebook(resources=INLINE)\\n\"+\n", - " \"\\n\"+\n", - " \"
\"}};\n", - "\n", - " function display_loaded() {\n", - " const el = document.getElementById(null);\n", - " if (el != null) {\n", - " el.textContent = \"BokehJS is loading...\";\n", - " }\n", - " if (root.Bokeh !== undefined) {\n", - " if (el != null) {\n", - " el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n", - " }\n", - " } else if (Date.now() < root._bokeh_timeout) {\n", - " setTimeout(display_loaded, 100)\n", - " }\n", - " }\n", - "\n", - " function run_callbacks() {\n", - " try {\n", - " root._bokeh_onload_callbacks.forEach(function(callback) {\n", - " if (callback != null)\n", - " callback();\n", - " });\n", - " } finally {\n", - " delete root._bokeh_onload_callbacks\n", - " }\n", - " console.debug(\"Bokeh: all callbacks have finished\");\n", - " }\n", - "\n", - " function load_libs(css_urls, js_urls, callback) {\n", - " if (css_urls == null) css_urls = [];\n", - " if (js_urls == null) js_urls = [];\n", - "\n", - " root._bokeh_onload_callbacks.push(callback);\n", - " if (root._bokeh_is_loading > 0) {\n", - " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", - " return null;\n", - " }\n", - " if (js_urls == null || js_urls.length === 0) {\n", - " run_callbacks();\n", - " return null;\n", - " }\n", - " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", - " root._bokeh_is_loading = css_urls.length + js_urls.length;\n", - "\n", - " function on_load() {\n", - " root._bokeh_is_loading--;\n", - " if (root._bokeh_is_loading === 0) {\n", - " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", - " run_callbacks()\n", - " }\n", - " }\n", - "\n", - " function on_error(url) {\n", - " console.error(\"failed to load \" + url);\n", - " }\n", - "\n", - " for (let i = 0; i < css_urls.length; i++) {\n", - " const url = css_urls[i];\n", - " const element = document.createElement(\"link\");\n", - " element.onload = on_load;\n", - " element.onerror = on_error.bind(null, url);\n", - " element.rel = \"stylesheet\";\n", - " element.type = \"text/css\";\n", - " element.href = url;\n", - " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", - " document.body.appendChild(element);\n", - " }\n", - "\n", - " for (let i = 0; i < js_urls.length; i++) {\n", - " const url = js_urls[i];\n", - " const element = document.createElement('script');\n", - " element.onload = on_load;\n", - " element.onerror = on_error.bind(null, url);\n", - " element.async = false;\n", - " element.src = url;\n", - " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", - " document.head.appendChild(element);\n", - " }\n", - " };\n", - "\n", - " function inject_raw_css(css) {\n", - " const element = document.createElement(\"style\");\n", - " element.appendChild(document.createTextNode(css));\n", - " document.body.appendChild(element);\n", - " }\n", - "\n", - " const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.1.1.min.js\"];\n", - " const css_urls = [];\n", - "\n", - " const inline_js = [ function(Bokeh) {\n", - " Bokeh.set_log_level(\"info\");\n", - " },\n", - "function(Bokeh) {\n", - " }\n", - " ];\n", - "\n", - " function run_inline_js() {\n", - " if (root.Bokeh !== undefined || force === true) {\n", - " for (let i = 0; i < inline_js.length; i++) {\n", - " inline_js[i].call(root, root.Bokeh);\n", - " }\n", - "} else if (Date.now() < root._bokeh_timeout) {\n", - " setTimeout(run_inline_js, 100);\n", - " } else if (!root._bokeh_failed_load) {\n", - " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", - " root._bokeh_failed_load = true;\n", - " } else if (force !== true) {\n", - " const cell = $(document.getElementById(null)).parents('.cell').data().cell;\n", - " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", - " }\n", - " }\n", - "\n", - " if (root._bokeh_is_loading === 0) {\n", - " console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", - " run_inline_js();\n", - " } else {\n", - " load_libs(css_urls, js_urls, function() {\n", - " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", - " run_inline_js();\n", - " });\n", - " }\n", - "}(window));" - ], - "application/vnd.bokehjs_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
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1 indexes × 7 features (memory usage: 164.6 kB)

Index: ()

1969 events
timestampevent_name_1event_type_1event_name_2event_type_2snap_CAsnap_TXsnap_WI
2011-01-29 00:00:00+00:00no_eventno_eventno_eventno_event000
2011-01-30 00:00:00+00:00no_eventno_eventno_eventno_event000
2011-01-31 00:00:00+00:00no_eventno_eventno_eventno_event000
2011-02-01 00:00:00+00:00no_eventno_eventno_eventno_event110
2011-02-02 00:00:00+00:00no_eventno_eventno_eventno_event101
2011-02-03 00:00:00+00:00no_eventno_eventno_eventno_event111
2011-02-04 00:00:00+00:00no_eventno_eventno_eventno_event100
2011-02-05 00:00:00+00:00no_eventno_eventno_eventno_event111
2011-02-06 00:00:00+00:00SuperBowlSportingno_eventno_event111
2011-02-07 00:00:00+00:00no_eventno_eventno_eventno_event110
2011-02-08 00:00:00+00:00no_eventno_eventno_eventno_event101
2011-02-09 00:00:00+00:00no_eventno_eventno_eventno_event111
2011-02-10 00:00:00+00:00no_eventno_eventno_eventno_event100
2011-02-11 00:00:00+00:00no_eventno_eventno_eventno_event011
2011-02-12 00:00:00+00:00no_eventno_eventno_eventno_event011
2011-02-13 00:00:00+00:00no_eventno_eventno_eventno_event010
2011-02-14 00:00:00+00:00ValentinesDayCulturalno_eventno_event001
2011-02-15 00:00:00+00:00no_eventno_eventno_eventno_event011
2011-02-16 00:00:00+00:00no_eventno_eventno_eventno_event000
2011-02-17 00:00:00+00:00no_eventno_eventno_eventno_event000
……………………
" - ], - "text/plain": [ - "indexes: []\n", - "features: [('event_name_1', str_), ('event_type_1', str_), ('event_name_2', str_), ('event_type_2', str_), ('snap_CA', int64), ('snap_TX', int64), ('snap_WI', int64)]\n", - "events:\n", - " (1969 events):\n", - " timestamps: [1.2963e+09 1.2963e+09 1.2964e+09 ... 1.4661e+09 1.4662e+09 1.4663e+09]\n", - " 'event_name_1': [b'no_event' b'no_event' b'no_event' ... b'no_event' b'no_event'\n", - " b'NBAFinalsEnd']\n", - " 'event_type_1': [b'no_event' b'no_event' b'no_event' ... b'no_event' b'no_event'\n", - " b'Sporting']\n", - " 'event_name_2': [b'no_event' b'no_event' b'no_event' ... b'no_event' b'no_event'\n", - " b\"Father's day\"]\n", - " 'event_type_2': [b'no_event' b'no_event' b'no_event' ... b'no_event' b'no_event'\n", - " b'Cultural']\n", - " 'snap_CA': [0 0 0 ... 0 0 0]\n", - " 'snap_TX': [0 0 0 ... 0 0 0]\n", - " 'snap_WI': [0 0 0 ... 0 0 0]\n", - "memory usage: 164.6 kB" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "calendar_evset = tp.from_pandas(cleaned_calendar)\n", "\n", @@ -1569,91 +479,10 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "id": "1ce4218a-594b-41d6-b7de-418a9414b089", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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store_iditem_idwm_yr_wksell_price
0CA_1HOBBIES_1_001113259.58
1CA_1HOBBIES_1_001113269.58
2CA_1HOBBIES_1_001113278.26
3CA_1HOBBIES_1_001113288.26
4CA_1HOBBIES_1_001113298.26
\n", - "
" - ], - "text/plain": [ - " store_id item_id wm_yr_wk sell_price\n", - "0 CA_1 HOBBIES_1_001 11325 9.58\n", - "1 CA_1 HOBBIES_1_001 11326 9.58\n", - "2 CA_1 HOBBIES_1_001 11327 8.26\n", - "3 CA_1 HOBBIES_1_001 11328 8.26\n", - "4 CA_1 HOBBIES_1_001 11329 8.26" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "raw_sell_prices.head()" ] @@ -1670,7 +499,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "id": "6279d6dc-06f7-4c06-b329-40d58ec69144", "metadata": {}, "outputs": [], @@ -1702,91 +531,10 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "id": "f3dc338e-6d84-4be6-aac2-416d1acabd99", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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store_iditem_idsell_pricetimestamp
0CA_1HOBBIES_1_0019.582013-07-13
1CA_1HOBBIES_1_0019.582013-07-20
2CA_1HOBBIES_1_0018.262013-07-27
3CA_1HOBBIES_1_0018.262013-08-03
4CA_1HOBBIES_1_0018.262013-08-10
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" - ], - "text/plain": [ - " store_id item_id sell_price timestamp\n", - "0 CA_1 HOBBIES_1_001 9.58 2013-07-13\n", - "1 CA_1 HOBBIES_1_001 9.58 2013-07-20\n", - "2 CA_1 HOBBIES_1_001 8.26 2013-07-27\n", - "3 CA_1 HOBBIES_1_001 8.26 2013-08-03\n", - "4 CA_1 HOBBIES_1_001 8.26 2013-08-10" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "cleaned_sell_prices.head()" ] @@ -1802,51 +550,10 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "id": "7fa7a3ed-f033-4e5c-b41f-6cf5fa71bb52", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:root:Feature \"store_id\" is an array of numpy.object_ and was casted to numpy.string_ (Note: numpy.string_ is equivalent to numpy.bytes_).\n", - "WARNING:root:Feature \"item_id\" is an array of numpy.object_ and was casted to numpy.string_ (Note: numpy.string_ is equivalent to numpy.bytes_).\n" - ] - }, - { - "data": { - "text/html": [ - "
30490 indexes × 1 features (memory usage: 119.3 MB)

Index: (store_id=CA_1, item_id=FOODS_1_001)

282 events
timestampsell_price
2011-01-29 00:00:00+00:002
2011-02-05 00:00:00+00:002
2011-02-12 00:00:00+00:002
2011-02-19 00:00:00+00:002
2011-02-26 00:00:00+00:002
2011-03-05 00:00:00+00:002
2011-03-12 00:00:00+00:002
2011-03-19 00:00:00+00:002
2011-03-26 00:00:00+00:002
2011-04-02 00:00:00+00:002
2011-04-09 00:00:00+00:002
2011-04-16 00:00:00+00:002
2011-04-23 00:00:00+00:002
2011-04-30 00:00:00+00:002
2011-05-07 00:00:00+00:002
2011-05-14 00:00:00+00:002
2011-05-21 00:00:00+00:002
2011-05-28 00:00:00+00:002
2011-06-04 00:00:00+00:002
2011-06-11 00:00:00+00:002
……

Index: (store_id=CA_1, item_id=FOODS_1_002)

282 events
timestampsell_price
2011-01-29 00:00:00+00:007.88
2011-02-05 00:00:00+00:007.88
2011-02-12 00:00:00+00:007.88
2011-02-19 00:00:00+00:007.88
2011-02-26 00:00:00+00:007.88
2011-03-05 00:00:00+00:007.88
2011-03-12 00:00:00+00:007.88
2011-03-19 00:00:00+00:007.88
2011-03-26 00:00:00+00:007.88
2011-04-02 00:00:00+00:007.88
2011-04-09 00:00:00+00:007.88
2011-04-16 00:00:00+00:007.88
2011-04-23 00:00:00+00:007.88
2011-04-30 00:00:00+00:007.88
2011-05-07 00:00:00+00:007.88
2011-05-14 00:00:00+00:007.88
2011-05-21 00:00:00+00:007.88
2011-05-28 00:00:00+00:007.88
2011-06-04 00:00:00+00:007.88
2011-06-11 00:00:00+00:007.88
……

Index: (store_id=CA_1, item_id=FOODS_1_003)

282 events
timestampsell_price
2011-01-29 00:00:00+00:002.88
2011-02-05 00:00:00+00:002.88
2011-02-12 00:00:00+00:002.88
2011-02-19 00:00:00+00:002.88
2011-02-26 00:00:00+00:002.88
2011-03-05 00:00:00+00:002.88
2011-03-12 00:00:00+00:002.88
2011-03-19 00:00:00+00:002.88
2011-03-26 00:00:00+00:002.88
2011-04-02 00:00:00+00:002.88
2011-04-09 00:00:00+00:002.88
2011-04-16 00:00:00+00:002.88
2011-04-23 00:00:00+00:002.88
2011-04-30 00:00:00+00:002.88
2011-05-07 00:00:00+00:002.88
2011-05-14 00:00:00+00:002.88
2011-05-21 00:00:00+00:002.88
2011-05-28 00:00:00+00:002.88
2011-06-04 00:00:00+00:002.88
2011-06-11 00:00:00+00:002.88
……

Index: (store_id=CA_1, item_id=FOODS_1_004)

225 events
timestampsell_price
2012-03-03 00:00:00+00:001.78
2012-03-10 00:00:00+00:001.78
2012-03-17 00:00:00+00:001.78
2012-03-24 00:00:00+00:001.78
2012-03-31 00:00:00+00:001.78
2012-04-07 00:00:00+00:001.78
2012-04-14 00:00:00+00:001.78
2012-04-21 00:00:00+00:001.78
2012-04-28 00:00:00+00:001.78
2012-05-05 00:00:00+00:001.78
2012-05-12 00:00:00+00:001.78
2012-05-19 00:00:00+00:001.78
2012-05-26 00:00:00+00:001.78
2012-06-02 00:00:00+00:001.78
2012-06-09 00:00:00+00:001.78
2012-06-16 00:00:00+00:001.78
2012-06-23 00:00:00+00:001.78
2012-06-30 00:00:00+00:001.78
2012-07-07 00:00:00+00:001.78
2012-07-14 00:00:00+00:001.78
……

Index: (store_id=CA_1, item_id=FOODS_1_005)

282 events
timestampsell_price
2011-01-29 00:00:00+00:002.94
2011-02-05 00:00:00+00:002.94
2011-02-12 00:00:00+00:002.94
2011-02-19 00:00:00+00:002.94
2011-02-26 00:00:00+00:002.94
2011-03-05 00:00:00+00:002.94
2011-03-12 00:00:00+00:002.94
2011-03-19 00:00:00+00:002.94
2011-03-26 00:00:00+00:002.94
2011-04-02 00:00:00+00:002.94
2011-04-09 00:00:00+00:002.94
2011-04-16 00:00:00+00:002.94
2011-04-23 00:00:00+00:002.94
2011-04-30 00:00:00+00:002.94
2011-05-07 00:00:00+00:002.94
2011-05-14 00:00:00+00:002.94
2011-05-21 00:00:00+00:002.94
2011-05-28 00:00:00+00:002.94
2011-06-04 00:00:00+00:002.94
2011-06-11 00:00:00+00:002.94
……
… (30485 more indexes not shown)
" - ], - "text/plain": [ - "indexes: [('store_id', str_), ('item_id', str_)]\n", - "features: [('sell_price', float64)]\n", - "events:\n", - " store_id=b'CA_1' item_id=b'FOODS_1_001' (282 events):\n", - " timestamps: [1.2963e+09 1.2969e+09 1.2975e+09 ... 1.4650e+09 1.4656e+09 1.4662e+09]\n", - " 'sell_price': [2. 2. 2. ... 2.24 2.24 2.24]\n", - " store_id=b'CA_1' item_id=b'FOODS_1_002' (282 events):\n", - " timestamps: [1.2963e+09 1.2969e+09 1.2975e+09 ... 1.4650e+09 1.4656e+09 1.4662e+09]\n", - " 'sell_price': [7.88 7.88 7.88 ... 9.48 9.48 9.48]\n", - " store_id=b'CA_1' item_id=b'FOODS_1_003' (282 events):\n", - " timestamps: [1.2963e+09 1.2969e+09 1.2975e+09 ... 1.4650e+09 1.4656e+09 1.4662e+09]\n", - " 'sell_price': [2.88 2.88 2.88 ... 3.23 3.23 3.23]\n", - " store_id=b'CA_1' item_id=b'FOODS_1_004' (225 events):\n", - " timestamps: [1.3307e+09 1.3313e+09 1.3319e+09 ... 1.4650e+09 1.4656e+09 1.4662e+09]\n", - " 'sell_price': [1.78 1.78 1.78 ... 1.96 1.96 1.96]\n", - " store_id=b'CA_1' item_id=b'FOODS_1_005' (282 events):\n", - " timestamps: [1.2963e+09 1.2969e+09 1.2975e+09 ... 1.4650e+09 1.4656e+09 1.4662e+09]\n", - " 'sell_price': [2.94 2.94 2.94 ... 3.54 3.54 3.54]\n", - " ... (showing 5 of 30490 indexes)\n", - "memory usage: 119.3 MB" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "sell_prices_evset = tp.from_pandas(\n", " cleaned_sell_prices, indexes=[\"store_id\", \"item_id\"]\n", @@ -1866,7 +573,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "id": "f2c824aa-7aa4-4ce6-a0cb-17a6d69ef084", "metadata": {}, "outputs": [], @@ -1890,18 +597,10 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "id": "0447be99-3c1c-4fd5-9b25-47a8f3fc9eca", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Selected product (selected_index): (b'FOODS_3_694', b'FOODS_3', b'FOODS', b'WI_2', b'WI')\n" - ] - } - ], + "outputs": [], "source": [ "# Select a product.\n", "selected_index = sales_evset.get_arbitrary_index_key()\n", @@ -1920,7 +619,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "id": "396f6d0e-4555-4835-8e84-712a6e892e64", "metadata": {}, "outputs": [], @@ -1949,7 +648,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "id": "d8cec8ef-62a0-4d99-b46c-a217ae012f9c", "metadata": {}, "outputs": [], @@ -1973,7 +672,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "id": "07e74746-76f7-4992-a9c6-2d68d06bbaa2", "metadata": {}, "outputs": [], @@ -2010,350 +709,10 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "id": "2fa783ac-4c30-4d0e-9dfa-2dd05c6bd696", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The number of plots (27) is larger than \"options.max_num_plots=4\". 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\\n\"+\n", - " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", - " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", - " \"

\\n\"+\n", - " \"
    \\n\"+\n", - " \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n", - " \"
  • use INLINE resources instead, as so:
    • \\n\"+\n", - " \"
\\n\"+\n", - " \"\\n\"+\n", - " \"from bokeh.resources import INLINE\\n\"+\n", - " \"output_notebook(resources=INLINE)\\n\"+\n", - " \"\\n\"+\n", - " \"
\"}};\n", - "\n", - " function display_loaded() {\n", - " const el = document.getElementById(null);\n", - " if (el != null) {\n", - " el.textContent = \"BokehJS is loading...\";\n", - " }\n", - " if (root.Bokeh !== undefined) {\n", - " if (el != null) {\n", - " el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n", - " }\n", - " } else if (Date.now() < root._bokeh_timeout) {\n", - " setTimeout(display_loaded, 100)\n", - " }\n", - " }\n", - "\n", - " function run_callbacks() {\n", - " try {\n", - " root._bokeh_onload_callbacks.forEach(function(callback) {\n", - " if (callback != null)\n", - " callback();\n", - " });\n", - " } finally {\n", - " delete root._bokeh_onload_callbacks\n", - " }\n", - " console.debug(\"Bokeh: all callbacks have finished\");\n", - " }\n", - "\n", - " function load_libs(css_urls, js_urls, callback) {\n", - " if (css_urls == null) css_urls = [];\n", - " if (js_urls == null) js_urls = [];\n", - "\n", - " root._bokeh_onload_callbacks.push(callback);\n", - " if (root._bokeh_is_loading > 0) {\n", - " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", - " return null;\n", - " }\n", - " if (js_urls == null || js_urls.length === 0) {\n", - " run_callbacks();\n", - " return null;\n", - " }\n", - " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", - " root._bokeh_is_loading = css_urls.length + js_urls.length;\n", - "\n", - " function on_load() {\n", - " root._bokeh_is_loading--;\n", - " if (root._bokeh_is_loading === 0) {\n", - " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", - " run_callbacks()\n", - " }\n", - " }\n", - "\n", - " function on_error(url) {\n", - " console.error(\"failed to load \" + url);\n", - " }\n", - "\n", - " for (let i = 0; i < css_urls.length; i++) {\n", - " const url = css_urls[i];\n", - " const element = document.createElement(\"link\");\n", - " element.onload = on_load;\n", - " element.onerror = on_error.bind(null, url);\n", - " element.rel = \"stylesheet\";\n", - " element.type = \"text/css\";\n", - " element.href = url;\n", - " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", - " document.body.appendChild(element);\n", - " }\n", - "\n", - " for (let i = 0; i < js_urls.length; i++) {\n", - " const url = js_urls[i];\n", - " const element = document.createElement('script');\n", - " element.onload = on_load;\n", - " element.onerror = on_error.bind(null, url);\n", - " element.async = false;\n", - " element.src = url;\n", - " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", - " document.head.appendChild(element);\n", - " }\n", - " };\n", - "\n", - " function inject_raw_css(css) {\n", - " const element = document.createElement(\"style\");\n", - " element.appendChild(document.createTextNode(css));\n", - " document.body.appendChild(element);\n", - " }\n", - "\n", - " const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.1.1.min.js\"];\n", - " const css_urls = [];\n", - "\n", - " const inline_js = [ function(Bokeh) {\n", - " Bokeh.set_log_level(\"info\");\n", - " },\n", - "function(Bokeh) {\n", - " }\n", - " ];\n", - "\n", - " function run_inline_js() {\n", - " if (root.Bokeh !== undefined || force === true) {\n", - " for (let i = 0; i < inline_js.length; i++) {\n", - " inline_js[i].call(root, root.Bokeh);\n", - " }\n", - "} else if (Date.now() < root._bokeh_timeout) {\n", - " setTimeout(run_inline_js, 100);\n", - " } else if (!root._bokeh_failed_load) {\n", - " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", - " root._bokeh_failed_load = true;\n", - " } else if (force !== true) {\n", - " const cell = $(document.getElementById(null)).parents('.cell').data().cell;\n", - " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", - " }\n", - " }\n", - "\n", - " if (root._bokeh_is_loading === 0) {\n", - " console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", - " run_inline_js();\n", - " } else {\n", - " load_libs(css_urls, js_urls, function() {\n", - " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", - " run_inline_js();\n", - " });\n", - " }\n", - "}(window));" - ], - "application/vnd.bokehjs_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n const el = document.getElementById(null);\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.1.1.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\n} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(null)).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - "
\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": [ - "(function(root) {\n", - " function embed_document(root) {\n", - " const docs_json = {\"168e8175-c285-4a4c-a162-bd8fc8e78c96\":{\"version\":\"3.1.1\",\"title\":\"Bokeh Application\",\"defs\":[],\"roots\":[{\"type\":\"object\",\"name\":\"GridPlot\",\"id\":\"p3037\",\"attributes\":{\"rows\":null,\"cols\":null,\"toolbar\":{\"type\":\"object\",\"name\":\"Toolbar\",\"id\":\"p3036\",\"attributes\":{\"logo\":null,\"tools\":[{\"type\":\"object\",\"name\":\"ToolProxy\",\"id\":\"p3027\",\"attributes\":{\"tools\":[{\"type\":\"object\",\"name\":\"PanTool\",\"id\":\"p2546\",\"attributes\":{\"dimensions\":\"width\"}},{\"type\":\"object\",\"name\":\"PanTool\",\"id\":\"p2643\",\"attributes\":{\"dimensions\":\"width\"}},{\"type\":\"object\",\"name\":\"PanTool\",\"id\":\"p2740\",\"attributes\":{\"dimensions\":\"width\"}},{\"type\":\"object\",\"name\":\"PanTool\",\"id\":\"p2837\",\"attributes\":{\"dimensions\":\"width\"}}]}},{\"type\":\"object\",\"name\":\"ToolProxy\",\"id\":\"p3028\",\"attributes\":{\"tools\":[{\"type\":\"object\",\"name\":\"PanTool\",\"id\":\"p2547\"},{\"type\":\"object\",\"name\":\"PanTool\",\"id\":\"p2644\"},{\"type\":\"object\",\"name\":\"PanTool\",\"id\":\"p2741\"},{\"type\":\"object\",\"name\":\"PanTool\",\"id\":\"p2838\"}]}},{\"type\":\"object\",\"name\":\"ToolProxy\",\"id\":\"p3029\",\"attributes\":{\"tools\":[{\"type\":\"object\",\"name\":\"WheelZoomTool\",\"id\":\"p2548\",\"attributes\":{\"dimensions\":\"width\"}},{\"type\":\"object\",\"name\":\"WheelZoomTool\",\"id\":\"p2645\",\"attributes\":{\"dimensions\":\"width\"}},{\"type\":\"object\",\"name\":\"WheelZoomTool\",\"id\":\"p2742\",\"attributes\":{\"dimensions\":\"width\"}},{\"type\":\"object\",\"name\":\"WheelZoomTool\",\"id\":\"p2839\",\"attributes\":{\"dimensions\":\"width\"}}]}},{\"type\":\"object\",\"name\":\"ToolProxy\",\"id\":\"p3030\",\"attributes\":{\"tools\":[{\"type\":\"object\",\"name\":\"WheelZoomTool\",\"id\":\"p2549\",\"attributes\":{\"dimensions\":\"height\"}},{\"type\":\"object\",\"name\":\"WheelZoomTool\",\"id\":\"p2646\",\"attributes\":{\"dimensions\":\"height\"}},{\"type\":\"object\",\"name\":\"WheelZoomTool\",\"id\":\"p2743\",\"attributes\":{\"dimensions\":\"height\"}},{\"type\":\"object\",\"name\":\"WheelZoomTool\",\"id\":\"p2840\",\"attributes\":{\"dimensions\":\"height\"}}]}},{\"type\":\"object\",\"name\":\"ToolProxy\",\"id\":\"p3031\",\"attributes\":{\"tools\":[{\"type\":\"object\",\"name\":\"BoxZoomTool\",\"id\":\"p2550\",\"attributes\":{\"overlay\":{\"type\":\"object\",\"name\":\"BoxAnnotation\",\"id\":\"p2551\",\"attributes\":{\"syncable\":false,\"level\":\"overlay\",\"visible\":false,\"left_units\":\"canvas\",\"right_units\":\"canvas\",\"bottom_units\":\"canvas\",\"top_units\":\"canvas\",\"line_color\":\"black\",\"line_alpha\":1.0,\"line_width\":2,\"line_dash\":[4,4],\"fill_color\":\"lightgrey\",\"fill_alpha\":0.5}}}},{\"type\":\"object\",\"name\":\"BoxZoomTool\",\"id\":\"p2647\",\"attributes\":{\"overlay\":{\"type\":\"object\",\"name\":\"BoxAnnotation\",\"id\":\"p2648\",\"attributes\":{\"syncable\":false,\"level\":\"overlay\",\"visible\":false,\"left_units\":\"canvas\",\"right_units\":\"canvas\",\"bottom_units\":\"canvas\",\"top_units\":\"canvas\",\"line_color\":\"black\",\"line_alpha\":1.0,\"line_width\":2,\"line_dash\":[4,4],\"fill_color\":\"lightgrey\",\"fill_alpha\":0.5}}}},{\"type\":\"object\",\"name\":\"BoxZoomTool\",\"id\":\"p2744\",\"attributes\":{\"overlay\":{\"type\":\"object\",\"name\":\"BoxAnnotation\",\"id\":\"p2745\",\"attributes\":{\"syncable\":false,\"level\":\"overlay\",\"visible\":false,\"left_units\":\"canvas\",\"right_units\":\"canvas\",\"bottom_units\":\"canvas\",\"top_units\":\"canvas\",\"line_color\":\"black\",\"line_alpha\":1.0,\"line_width\":2,\"line_dash\":[4,4],\"fill_color\":\"lightgrey\",\"fill_alpha\":0.5}}}},{\"type\":\"object\",\"name\":\"BoxZoomTool\",\"id\":\"p2841\",\"attributes\":{\"overlay\":{\"type\":\"object\",\"name\":\"BoxAnnotation\",\"id\":\"p2842\",\"attributes\":{\"syncable\":false,\"level\":\"overlay\",\"visible\":false,\"left_units\":\"canvas\",\"right_units\":\"canvas\",\"bottom_units\":\"canvas\",\"top_units\":\"canvas\",\"line_color\":\"black\",\"line_alpha\":1.0,\"line_width\":2,\"line_dash\":[4,4],\"fill_color\":\"lightgrey\",\"fill_alpha\":0.5}}}}]}},{\"type\":\"object\",\"name\":\"ToolProxy\",\"id\":\"p3032\",\"attributes\":{\"tools\":[{\"type\":\"object\",\"name\":\"ResetTool\",\"id\":\"p2552\"},{\"type\":\"object\",\"name\":\"ResetTool\",\"id\":\"p2649\"},{\"type\":\"object\",\"name\":\"ResetTool\",\"id\":\"p2746\"},{\"type\":\"object\",\"name\":\"ResetTool\",\"id\":\"p2843\"}]}},{\"type\":\"object\",\"name\":\"ToolProxy\",\"id\":\"p3033\",\"attributes\":{\"tools\":[{\"type\":\"object\",\"name\":\"UndoTool\",\"id\":\"p2553\"},{\"type\":\"object\",\"name\":\"UndoTool\",\"id\":\"p2650\"},{\"type\":\"object\",\"name\":\"UndoTool\",\"id\":\"p2747\"},{\"type\":\"object\",\"name\":\"UndoTool\",\"id\":\"p2844\"}]}},{\"type\":\"object\",\"name\":\"SaveTool\",\"id\":\"p3034\"},{\"type\":\"object\",\"name\":\"ToolProxy\",\"id\":\"p3035\",\"attributes\":{\"tools\":[{\"type\":\"object\",\"name\":\"HoverTool\",\"id\":\"p2555\",\"attributes\":{\"renderers\":\"auto\"}},{\"type\":\"object\",\"name\":\"HoverTool\",\"id\":\"p2652\",\"attributes\":{\"renderers\":\"auto\"}},{\"type\":\"object\",\"name\":\"HoverTool\",\"id\":\"p2749\",\"attributes\":{\"renderers\":\"auto\"}},{\"type\":\"object\",\"name\":\"HoverTool\",\"id\":\"p2846\",\"attributes\":{\"renderers\":\"auto\"}}]}}]}},\"toolbar_location\":\"right\",\"children\":[[{\"type\":\"object\",\"name\":\"Figure\",\"id\":\"p2479\",\"attributes\":{\"width\":500,\"height\":150,\"x_range\":{\"type\":\"object\",\"name\":\"Range1d\",\"id\":\"p2478\",\"attributes\":{\"js_property_callbacks\":{\"type\":\"map\",\"entries\":[[\"change:start\",[{\"type\":\"object\",\"name\":\"CustomJS\",\"id\":\"p2867\",\"attributes\":{\"args\":{\"type\":\"map\",\"entries\":[[\"p1_x_range\",{\"id\":\"p2478\"}],[\"p2_x_range\",{\"type\":\"object\",\"name\":\"Range1d\",\"id\":\"p2576\",\"attributes\":{\"js_property_callbacks\":{\"type\":\"map\",\"entries\":[[\"change:start\",[{\"id\":\"p2867\"}]],[\"change:end\",[{\"id\":\"p2867\"}]]]},\"start\":1420066800000.0,\"end\":1451516400000.0}}],[\"p3_x_range\",{\"type\":\"object\",\"name\":\"Range1d\",\"id\":\"p2673\",\"attributes\":{\"js_property_callbacks\":{\"type\":\"map\",\"entries\":[[\"change:start\",[{\"id\":\"p2867\"}]],[\"change:end\",[{\"id\":\"p2867\"}]]]},\"start\":1420066800000.0,\"end\":1451516400000.0}}],[\"p4_x_range\",{\"type\":\"object\",\"name\":\"Range1d\",\"id\":\"p2770\",\"attributes\":{\"js_property_callbacks\":{\"type\":\"map\",\"entries\":[[\"change:start\",[{\"id\":\"p2867\"}]],[\"change:end\",[{\"id\":\"p2867\"}]]]},\"start\":1420066800000.0,\"end\":1451516400000.0}}]]},\"code\":\"\\n 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- " const render_items = [{\"docid\":\"168e8175-c285-4a4c-a162-bd8fc8e78c96\",\"roots\":{\"p3037\":\"eb549b3a-cfa6-40dd-aea1-3a997940e037\"},\"root_ids\":[\"p3037\"]}];\n", - " root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n", - " }\n", - " if (root.Bokeh !== undefined) {\n", - " embed_document(root);\n", - " } else {\n", - " let attempts = 0;\n", - " const timer = setInterval(function(root) {\n", - " if (root.Bokeh !== undefined) {\n", - " clearInterval(timer);\n", - " embed_document(root);\n", - " } else {\n", - " attempts++;\n", - " if (attempts > 100) {\n", - " clearInterval(timer);\n", - " console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n", - " }\n", - " }\n", - " }, 10, root)\n", - " }\n", - "})(window);" - ], - "application/vnd.bokehjs_exec.v0+json": "" - }, - "metadata": { - "application/vnd.bokehjs_exec.v0+json": { - "id": "p3037" - } - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "labels.plot(**plot_options, max_num_plots=4)" ] @@ -2373,7 +732,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "id": "609439b4-a424-4fad-8291-c94cc4de668b", "metadata": {}, "outputs": [], @@ -2393,350 +752,10 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": null, "id": "83888d9a-2e94-4d62-ae30-4815896ad4cf", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The number of plots (5) is larger than \"options.max_num_plots=4\". Only the first plots will be printed.\n" - ] - }, - { - "data": { - "application/javascript": [ - "(function(root) {\n", - " function now() {\n", - " return new Date();\n", - " }\n", - "\n", - " const force = true;\n", - "\n", - " if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n", - " root._bokeh_onload_callbacks = [];\n", - " root._bokeh_is_loading = undefined;\n", - " }\n", - "\n", - "const JS_MIME_TYPE = 'application/javascript';\n", - " const HTML_MIME_TYPE = 'text/html';\n", - " const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", - " const CLASS_NAME = 'output_bokeh rendered_html';\n", - "\n", - " /**\n", - " * Render data to the DOM node\n", - " */\n", - " function render(props, node) {\n", - " const script = document.createElement(\"script\");\n", - " node.appendChild(script);\n", - " }\n", - "\n", - " /**\n", - " * Handle when an output is cleared or removed\n", - " */\n", - " function handleClearOutput(event, handle) {\n", - " const cell = handle.cell;\n", - "\n", - " const id = cell.output_area._bokeh_element_id;\n", - " const server_id = cell.output_area._bokeh_server_id;\n", - " // Clean up Bokeh references\n", - " if (id != null && id in Bokeh.index) {\n", - " Bokeh.index[id].model.document.clear();\n", - " delete Bokeh.index[id];\n", - " }\n", - "\n", - " if (server_id !== undefined) {\n", - " // Clean up Bokeh references\n", - " const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", - " cell.notebook.kernel.execute(cmd_clean, {\n", - " iopub: {\n", - " output: function(msg) {\n", - " const id = msg.content.text.trim();\n", - " if (id in Bokeh.index) {\n", - " Bokeh.index[id].model.document.clear();\n", - " delete Bokeh.index[id];\n", - " }\n", - " }\n", - " }\n", - " });\n", - " // Destroy server and session\n", - " const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", - " cell.notebook.kernel.execute(cmd_destroy);\n", - " }\n", - " }\n", - "\n", - " /**\n", - " * Handle when a new output is added\n", - " */\n", - " function handleAddOutput(event, handle) {\n", - " const output_area = handle.output_area;\n", - " const output = handle.output;\n", - "\n", - " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", - " if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n", - " return\n", - " }\n", - "\n", - " const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", - "\n", - " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", - " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n", - " // store reference to embed id on output_area\n", - " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", - " }\n", - " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", - " const bk_div = document.createElement(\"div\");\n", - " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", - " const script_attrs = bk_div.children[0].attributes;\n", - " for (let i = 0; i < script_attrs.length; i++) {\n", - " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", - " toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n", - " }\n", - " // store reference to server id on output_area\n", - " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", - " }\n", - " }\n", - "\n", - " function register_renderer(events, OutputArea) {\n", - "\n", - " function append_mime(data, metadata, element) {\n", - " // create a DOM node to render to\n", - " const toinsert = this.create_output_subarea(\n", - " metadata,\n", - " CLASS_NAME,\n", - " EXEC_MIME_TYPE\n", - " );\n", - " this.keyboard_manager.register_events(toinsert);\n", - " // Render to node\n", - " const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", - " render(props, toinsert[toinsert.length - 1]);\n", - " element.append(toinsert);\n", - " return toinsert\n", - " }\n", - "\n", - " /* Handle when an output is cleared or removed */\n", - " events.on('clear_output.CodeCell', handleClearOutput);\n", - " events.on('delete.Cell', handleClearOutput);\n", - "\n", - " /* Handle when a new output is added */\n", - " events.on('output_added.OutputArea', handleAddOutput);\n", - "\n", - " /**\n", - " * Register the mime type and append_mime function with output_area\n", - " */\n", - " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", - " /* Is output safe? */\n", - " safe: true,\n", - " /* Index of renderer in `output_area.display_order` */\n", - " index: 0\n", - " });\n", - " }\n", - "\n", - " // register the mime type if in Jupyter Notebook environment and previously unregistered\n", - " if (root.Jupyter !== undefined) {\n", - " const events = require('base/js/events');\n", - " const OutputArea = require('notebook/js/outputarea').OutputArea;\n", - "\n", - " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", - " register_renderer(events, OutputArea);\n", - " }\n", - " }\n", - " if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n", - " root._bokeh_timeout = Date.now() + 5000;\n", - " root._bokeh_failed_load = false;\n", - " }\n", - "\n", - " const NB_LOAD_WARNING = {'data': {'text/html':\n", - " \"
\\n\"+\n", - " \"

\\n\"+\n", - " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", - " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", - " \"

\\n\"+\n", - " \"
    \\n\"+\n", - " \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n", - " \"
  • use INLINE resources instead, as so:
    • \\n\"+\n", - " \"
\\n\"+\n", - " \"\\n\"+\n", - " \"from bokeh.resources import INLINE\\n\"+\n", - " \"output_notebook(resources=INLINE)\\n\"+\n", - " \"\\n\"+\n", - " \"
\"}};\n", - "\n", - " function display_loaded() {\n", - " const el = document.getElementById(null);\n", - " if (el != null) {\n", - " el.textContent = \"BokehJS is loading...\";\n", - " }\n", - " if (root.Bokeh !== undefined) {\n", - " if (el != null) {\n", - " el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n", - " }\n", - " } else if (Date.now() < root._bokeh_timeout) {\n", - " setTimeout(display_loaded, 100)\n", - " }\n", - " }\n", - "\n", - " function run_callbacks() {\n", - " try {\n", - " root._bokeh_onload_callbacks.forEach(function(callback) {\n", - " if (callback != null)\n", - " callback();\n", - " });\n", - " } finally {\n", - " delete root._bokeh_onload_callbacks\n", - " }\n", - " console.debug(\"Bokeh: all callbacks have finished\");\n", - " }\n", - "\n", - " function load_libs(css_urls, js_urls, callback) {\n", - " if (css_urls == null) css_urls = [];\n", - " if (js_urls == null) js_urls = [];\n", - "\n", - " root._bokeh_onload_callbacks.push(callback);\n", - " if (root._bokeh_is_loading > 0) {\n", - " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", - " return null;\n", - " }\n", - " if (js_urls == null || js_urls.length === 0) {\n", - " run_callbacks();\n", - " return null;\n", - " }\n", - " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", - " root._bokeh_is_loading = css_urls.length + js_urls.length;\n", - "\n", - " function on_load() {\n", - " root._bokeh_is_loading--;\n", - " if (root._bokeh_is_loading === 0) {\n", - " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", - " run_callbacks()\n", - " }\n", - " }\n", - "\n", - " function on_error(url) {\n", - " console.error(\"failed to load \" + url);\n", - " }\n", - "\n", - " for (let i = 0; i < css_urls.length; i++) {\n", - " const url = css_urls[i];\n", - " const element = document.createElement(\"link\");\n", - " element.onload = on_load;\n", - " element.onerror = on_error.bind(null, url);\n", - " element.rel = \"stylesheet\";\n", - " element.type = \"text/css\";\n", - " element.href = url;\n", - " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", - " document.body.appendChild(element);\n", - " }\n", - "\n", - " for (let i = 0; i < js_urls.length; i++) {\n", - " const url = js_urls[i];\n", - " const element = document.createElement('script');\n", - " element.onload = on_load;\n", - " element.onerror = on_error.bind(null, url);\n", - " element.async = false;\n", - " element.src = url;\n", - " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", - " document.head.appendChild(element);\n", - " }\n", - " };\n", - "\n", - " function inject_raw_css(css) {\n", - " const element = document.createElement(\"style\");\n", - " element.appendChild(document.createTextNode(css));\n", - " document.body.appendChild(element);\n", - " }\n", - "\n", - " const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.1.1.min.js\"];\n", - " const css_urls = [];\n", - "\n", - " const inline_js = [ function(Bokeh) {\n", - " Bokeh.set_log_level(\"info\");\n", - " },\n", - "function(Bokeh) {\n", - " }\n", - " ];\n", - "\n", - " function run_inline_js() {\n", - " if (root.Bokeh !== undefined || force === true) {\n", - " for (let i = 0; i < inline_js.length; i++) {\n", - " inline_js[i].call(root, root.Bokeh);\n", - " }\n", - "} else if (Date.now() < root._bokeh_timeout) {\n", - " setTimeout(run_inline_js, 100);\n", - " } else if (!root._bokeh_failed_load) {\n", - " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", - " root._bokeh_failed_load = true;\n", - " } else if (force !== true) {\n", - " const cell = $(document.getElementById(null)).parents('.cell').data().cell;\n", - " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", - " }\n", - " }\n", - "\n", - " if (root._bokeh_is_loading === 0) {\n", - " console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", - " run_inline_js();\n", - " } else {\n", - " load_libs(css_urls, js_urls, function() {\n", - " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", - " run_inline_js();\n", - " });\n", - " }\n", - "}(window));" - ], - "application/vnd.bokehjs_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n const el = document.getElementById(null);\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.1.1.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\n} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(null)).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - "
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- " const render_items = [{\"docid\":\"74d0b9ba-fdd6-40df-9efc-ed6b81a867e9\",\"roots\":{\"p4670\":\"e6eee782-cbc7-4744-80f7-4a6d6ba94eab\"},\"root_ids\":[\"p4670\"]}];\n", - " root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n", - " }\n", - " if (root.Bokeh !== undefined) {\n", - " embed_document(root);\n", - " } else {\n", - " let attempts = 0;\n", - " const timer = setInterval(function(root) {\n", - " if (root.Bokeh !== undefined) {\n", - " clearInterval(timer);\n", - " embed_document(root);\n", - " } else {\n", - " attempts++;\n", - " if (attempts > 100) {\n", - " clearInterval(timer);\n", - " console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n", - " }\n", - " }\n", - " }, 10, root)\n", - " }\n", - "})(window);" - ], - "application/vnd.bokehjs_exec.v0+json": "" - }, - "metadata": { - "application/vnd.bokehjs_exec.v0+json": { - "id": "p4670" - } - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "feature_lagged_sales.plot(**plot_options, max_num_plots=4)" ] @@ -2754,7 +773,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "id": "995ea19e-d39a-4f94-a010-6cd130e623c9", "metadata": {}, "outputs": [], @@ -2785,350 +804,10 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, "id": "70161a5f-9e63-4e01-ab98-c5e4a96f59e1", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The number of plots (12) is larger than \"options.max_num_plots=5\". Only the first plots will be printed.\n" - ] - }, - { - "data": { - "application/javascript": [ - "(function(root) {\n", - " function now() {\n", - " return new Date();\n", - " }\n", - "\n", - " const force = true;\n", - "\n", - " if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n", - " root._bokeh_onload_callbacks = [];\n", - " root._bokeh_is_loading = undefined;\n", - " }\n", - "\n", - "const JS_MIME_TYPE = 'application/javascript';\n", - " const HTML_MIME_TYPE = 'text/html';\n", - " const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", - " const CLASS_NAME = 'output_bokeh rendered_html';\n", - "\n", - " /**\n", - " * Render data to the DOM node\n", - " */\n", - " function render(props, node) {\n", - " const script = document.createElement(\"script\");\n", - " node.appendChild(script);\n", - " }\n", - "\n", - " /**\n", - " * Handle when an output is cleared or removed\n", - " */\n", - " function handleClearOutput(event, handle) {\n", - " const cell = handle.cell;\n", - "\n", - " const id = cell.output_area._bokeh_element_id;\n", - " const server_id = cell.output_area._bokeh_server_id;\n", - " // Clean up Bokeh references\n", - " if (id != null && id in Bokeh.index) {\n", - " Bokeh.index[id].model.document.clear();\n", - " delete Bokeh.index[id];\n", - " }\n", - "\n", - " if (server_id !== undefined) {\n", - " // Clean up Bokeh references\n", - " const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", - " cell.notebook.kernel.execute(cmd_clean, {\n", - " iopub: {\n", - " output: function(msg) {\n", - " const id = msg.content.text.trim();\n", - " if (id in Bokeh.index) {\n", - " Bokeh.index[id].model.document.clear();\n", - " delete Bokeh.index[id];\n", - " }\n", - " }\n", - " }\n", - " });\n", - " // Destroy server and session\n", - " const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", - " cell.notebook.kernel.execute(cmd_destroy);\n", - " }\n", - " }\n", - "\n", - " /**\n", - " * Handle when a new output is added\n", - " */\n", - " function handleAddOutput(event, handle) {\n", - " const output_area = handle.output_area;\n", - " const output = handle.output;\n", - "\n", - " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", - " if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n", - " return\n", - " }\n", - "\n", - " const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", - "\n", - " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", - " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n", - " // store reference to embed id on output_area\n", - " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", - " }\n", - " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", - " const bk_div = document.createElement(\"div\");\n", - " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", - " const script_attrs = bk_div.children[0].attributes;\n", - " for (let i = 0; i < script_attrs.length; i++) {\n", - " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", - " toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n", - " }\n", - " // store reference to server id on output_area\n", - " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", - " }\n", - " }\n", - "\n", - " function register_renderer(events, OutputArea) {\n", - "\n", - " function append_mime(data, metadata, element) {\n", - " // create a DOM node to render to\n", - " const toinsert = this.create_output_subarea(\n", - " metadata,\n", - " CLASS_NAME,\n", - " EXEC_MIME_TYPE\n", - " );\n", - " this.keyboard_manager.register_events(toinsert);\n", - " // Render to node\n", - " const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", - " render(props, toinsert[toinsert.length - 1]);\n", - " element.append(toinsert);\n", - " return toinsert\n", - " }\n", - "\n", - " /* Handle when an output is cleared or removed */\n", - " events.on('clear_output.CodeCell', handleClearOutput);\n", - " events.on('delete.Cell', handleClearOutput);\n", - "\n", - " /* Handle when a new output is added */\n", - " events.on('output_added.OutputArea', handleAddOutput);\n", - "\n", - " /**\n", - " * Register the mime type and append_mime function with output_area\n", - " */\n", - " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", - " /* Is output safe? */\n", - " safe: true,\n", - " /* Index of renderer in `output_area.display_order` */\n", - " index: 0\n", - " });\n", - " }\n", - "\n", - " // register the mime type if in Jupyter Notebook environment and previously unregistered\n", - " if (root.Jupyter !== undefined) {\n", - " const events = require('base/js/events');\n", - " const OutputArea = require('notebook/js/outputarea').OutputArea;\n", - "\n", - " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", - " register_renderer(events, OutputArea);\n", - " }\n", - " }\n", - " if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n", - " root._bokeh_timeout = Date.now() + 5000;\n", - " root._bokeh_failed_load = false;\n", - " }\n", - "\n", - " const NB_LOAD_WARNING = {'data': {'text/html':\n", - " \"
\\n\"+\n", - " \"

\\n\"+\n", - " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", - " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", - " \"

\\n\"+\n", - " \"
    \\n\"+\n", - " \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n", - " \"
  • use INLINE resources instead, as so:
    • \\n\"+\n", - " \"
\\n\"+\n", - " \"\\n\"+\n", - " \"from bokeh.resources import INLINE\\n\"+\n", - " \"output_notebook(resources=INLINE)\\n\"+\n", - " \"\\n\"+\n", - " \"
\"}};\n", - "\n", - " function display_loaded() {\n", - " const el = document.getElementById(null);\n", - " if (el != null) {\n", - " el.textContent = \"BokehJS is loading...\";\n", - " }\n", - " if (root.Bokeh !== undefined) {\n", - " if (el != null) {\n", - " el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n", - " }\n", - " } else if (Date.now() < root._bokeh_timeout) {\n", - " setTimeout(display_loaded, 100)\n", - " }\n", - " }\n", - "\n", - " function run_callbacks() {\n", - " try {\n", - " root._bokeh_onload_callbacks.forEach(function(callback) {\n", - " if (callback != null)\n", - " callback();\n", - " });\n", - " } finally {\n", - " delete root._bokeh_onload_callbacks\n", - " }\n", - " console.debug(\"Bokeh: all callbacks have finished\");\n", - " }\n", - "\n", - " function load_libs(css_urls, js_urls, callback) {\n", - " if (css_urls == null) css_urls = [];\n", - " if (js_urls == null) js_urls = [];\n", - "\n", - " root._bokeh_onload_callbacks.push(callback);\n", - " if (root._bokeh_is_loading > 0) {\n", - " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", - " return null;\n", - " }\n", - " if (js_urls == null || js_urls.length === 0) {\n", - " run_callbacks();\n", - " return null;\n", - " }\n", - " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", - " root._bokeh_is_loading = css_urls.length + js_urls.length;\n", - "\n", - " function on_load() {\n", - " root._bokeh_is_loading--;\n", - " if (root._bokeh_is_loading === 0) {\n", - " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", - " run_callbacks()\n", - " }\n", - " }\n", - "\n", - " function on_error(url) {\n", - " console.error(\"failed to load \" + url);\n", - " }\n", - "\n", - " for (let i = 0; i < css_urls.length; i++) {\n", - " const url = css_urls[i];\n", - " const element = document.createElement(\"link\");\n", - " element.onload = on_load;\n", - " element.onerror = on_error.bind(null, url);\n", - " element.rel = \"stylesheet\";\n", - " element.type = \"text/css\";\n", - " element.href = url;\n", - " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", - " document.body.appendChild(element);\n", - " }\n", - "\n", - " for (let i = 0; i < js_urls.length; i++) {\n", - " const url = js_urls[i];\n", - " const element = document.createElement('script');\n", - " element.onload = on_load;\n", - " element.onerror = on_error.bind(null, url);\n", - " element.async = false;\n", - " element.src = url;\n", - " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", - " document.head.appendChild(element);\n", - " }\n", - " };\n", - "\n", - " function inject_raw_css(css) {\n", - " const element = document.createElement(\"style\");\n", - " element.appendChild(document.createTextNode(css));\n", - " document.body.appendChild(element);\n", - " }\n", - "\n", - " const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.1.1.min.js\"];\n", - " const css_urls = [];\n", - "\n", - " const inline_js = [ function(Bokeh) {\n", - " Bokeh.set_log_level(\"info\");\n", - " },\n", - "function(Bokeh) {\n", - " }\n", - " ];\n", - "\n", - " function run_inline_js() {\n", - " if (root.Bokeh !== undefined || force === true) {\n", - " for (let i = 0; i < inline_js.length; i++) {\n", - " inline_js[i].call(root, root.Bokeh);\n", - " }\n", - "} else if (Date.now() < root._bokeh_timeout) {\n", - " setTimeout(run_inline_js, 100);\n", - " } else if (!root._bokeh_failed_load) {\n", - " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", - " root._bokeh_failed_load = true;\n", - " } else if (force !== true) {\n", - " const cell = $(document.getElementById(null)).parents('.cell').data().cell;\n", - " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", - " }\n", - " }\n", - "\n", - " if (root._bokeh_is_loading === 0) {\n", - " console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", - " run_inline_js();\n", - " } else {\n", - " load_libs(css_urls, js_urls, function() {\n", - " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", - " run_inline_js();\n", - " });\n", - " }\n", - "}(window));" - ], - "application/vnd.bokehjs_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n const el = document.getElementById(null);\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.1.1.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\n} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(null)).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - "
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- " const render_items = [{\"docid\":\"47b109f1-5cb7-434f-add2-1e64e25efb10\",\"roots\":{\"p6591\":\"d8078942-dcab-481d-a7e7-c6cebda60bf4\"},\"root_ids\":[\"p6591\"]}];\n", - " root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n", - " }\n", - " if (root.Bokeh !== undefined) {\n", - " embed_document(root);\n", - " } else {\n", - " let attempts = 0;\n", - " const timer = setInterval(function(root) {\n", - " if (root.Bokeh !== undefined) {\n", - " clearInterval(timer);\n", - " embed_document(root);\n", - " } else {\n", - " attempts++;\n", - " if (attempts > 100) {\n", - " clearInterval(timer);\n", - " console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n", - " }\n", - " }\n", - " }, 10, root)\n", - " }\n", - "})(window);" - ], - "application/vnd.bokehjs_exec.v0+json": "" - }, - "metadata": { - "application/vnd.bokehjs_exec.v0+json": { - "id": "p6591" - } - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "feature_moving_stats.plot(**plot_options, max_num_plots=5)" ] @@ -3146,7 +825,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": null, "id": "5e67fff6-d75a-4d04-991c-649e0c5374a7", "metadata": {}, "outputs": [], @@ -3162,343 +841,10 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": null, "id": "63294d29-6e78-4e01-b098-fcb704065ca3", "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "(function(root) {\n", - " function now() {\n", - " return new Date();\n", - " }\n", - "\n", - " const force = true;\n", - "\n", - " if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n", - " root._bokeh_onload_callbacks = [];\n", - " root._bokeh_is_loading = undefined;\n", - " }\n", - "\n", - "const JS_MIME_TYPE = 'application/javascript';\n", - " const HTML_MIME_TYPE = 'text/html';\n", - " const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", - " const CLASS_NAME = 'output_bokeh rendered_html';\n", - "\n", - " /**\n", - " * Render data to the DOM node\n", - " */\n", - " function render(props, node) {\n", - " const script = document.createElement(\"script\");\n", - " node.appendChild(script);\n", - " }\n", - "\n", - " /**\n", - " * Handle when an output is cleared or removed\n", - " */\n", - " function handleClearOutput(event, handle) {\n", - " const cell = handle.cell;\n", - "\n", - " const id = cell.output_area._bokeh_element_id;\n", - " const server_id = cell.output_area._bokeh_server_id;\n", - " // Clean up Bokeh references\n", - " if (id != null && id in Bokeh.index) {\n", - " Bokeh.index[id].model.document.clear();\n", - " delete Bokeh.index[id];\n", - " }\n", - "\n", - " if (server_id !== undefined) {\n", - " // Clean up Bokeh references\n", - " const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", - " cell.notebook.kernel.execute(cmd_clean, {\n", - " iopub: {\n", - " output: function(msg) {\n", - " const id = msg.content.text.trim();\n", - " if (id in Bokeh.index) {\n", - " Bokeh.index[id].model.document.clear();\n", - " delete Bokeh.index[id];\n", - " }\n", - " }\n", - " }\n", - " });\n", - " // Destroy server and session\n", - " const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", - " cell.notebook.kernel.execute(cmd_destroy);\n", - " }\n", - " }\n", - "\n", - " /**\n", - " * Handle when a new output is added\n", - " */\n", - " function handleAddOutput(event, handle) {\n", - " const output_area = handle.output_area;\n", - " const output = handle.output;\n", - "\n", - " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", - " if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n", - " return\n", - " }\n", - "\n", - " const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", - "\n", - " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", - " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n", - " // store reference to embed id on output_area\n", - " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", - " }\n", - " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", - " const bk_div = document.createElement(\"div\");\n", - " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", - " const script_attrs = bk_div.children[0].attributes;\n", - " for (let i = 0; i < script_attrs.length; i++) {\n", - " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", - " toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n", - " }\n", - " // store reference to server id on output_area\n", - " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", - " }\n", - " }\n", - "\n", - " function register_renderer(events, OutputArea) {\n", - "\n", - " function append_mime(data, metadata, element) {\n", - " // create a DOM node to render to\n", - " const toinsert = this.create_output_subarea(\n", - " metadata,\n", - " CLASS_NAME,\n", - " EXEC_MIME_TYPE\n", - " );\n", - " this.keyboard_manager.register_events(toinsert);\n", - " // Render to node\n", - " const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", - " render(props, toinsert[toinsert.length - 1]);\n", - " element.append(toinsert);\n", - " return toinsert\n", - " }\n", - "\n", - " /* Handle when an output is cleared or removed */\n", - " events.on('clear_output.CodeCell', handleClearOutput);\n", - " events.on('delete.Cell', handleClearOutput);\n", - "\n", - " /* Handle when a new output is added */\n", - " events.on('output_added.OutputArea', handleAddOutput);\n", - "\n", - " /**\n", - " * Register the mime type and append_mime function with output_area\n", - " */\n", - " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", - " /* Is output safe? */\n", - " safe: true,\n", - " /* Index of renderer in `output_area.display_order` */\n", - " index: 0\n", - " });\n", - " }\n", - "\n", - " // register the mime type if in Jupyter Notebook environment and previously unregistered\n", - " if (root.Jupyter !== undefined) {\n", - " const events = require('base/js/events');\n", - " const OutputArea = require('notebook/js/outputarea').OutputArea;\n", - "\n", - " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", - " register_renderer(events, OutputArea);\n", - " }\n", - " }\n", - " if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n", - " root._bokeh_timeout = Date.now() + 5000;\n", - " root._bokeh_failed_load = false;\n", - " }\n", - "\n", - " const NB_LOAD_WARNING = {'data': {'text/html':\n", - " \"
\\n\"+\n", - " \"

\\n\"+\n", - " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", - " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", - " \"

\\n\"+\n", - " \"
    \\n\"+\n", - " \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n", - " \"
  • use INLINE resources instead, as so:
    • \\n\"+\n", - " \"
\\n\"+\n", - " \"\\n\"+\n", - " \"from bokeh.resources import INLINE\\n\"+\n", - " \"output_notebook(resources=INLINE)\\n\"+\n", - " \"\\n\"+\n", - " \"
\"}};\n", - "\n", - " function display_loaded() {\n", - " const el = document.getElementById(null);\n", - " if (el != null) {\n", - " el.textContent = \"BokehJS is loading...\";\n", - " }\n", - " if (root.Bokeh !== undefined) {\n", - " if (el != null) {\n", - " el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n", - " }\n", - " } else if (Date.now() < root._bokeh_timeout) {\n", - " setTimeout(display_loaded, 100)\n", - " }\n", - " }\n", - "\n", - " function run_callbacks() {\n", - " try {\n", - " root._bokeh_onload_callbacks.forEach(function(callback) {\n", - " if (callback != null)\n", - " callback();\n", - " });\n", - " } finally {\n", - " delete root._bokeh_onload_callbacks\n", - " }\n", - " console.debug(\"Bokeh: all callbacks have finished\");\n", - " }\n", - "\n", - " function load_libs(css_urls, js_urls, callback) {\n", - " if (css_urls == null) css_urls = [];\n", - " if (js_urls == null) js_urls = [];\n", - "\n", - " root._bokeh_onload_callbacks.push(callback);\n", - " if (root._bokeh_is_loading > 0) {\n", - " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", - " return null;\n", - " }\n", - " if (js_urls == null || js_urls.length === 0) {\n", - " run_callbacks();\n", - " return null;\n", - " }\n", - " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", - " root._bokeh_is_loading = css_urls.length + js_urls.length;\n", - "\n", - " function on_load() {\n", - " root._bokeh_is_loading--;\n", - " if (root._bokeh_is_loading === 0) {\n", - " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", - " run_callbacks()\n", - " }\n", - " }\n", - "\n", - " function on_error(url) {\n", - " console.error(\"failed to load \" + url);\n", - " }\n", - "\n", - " for (let i = 0; i < css_urls.length; i++) {\n", - " const url = css_urls[i];\n", - " const element = document.createElement(\"link\");\n", - " element.onload = on_load;\n", - " element.onerror = on_error.bind(null, url);\n", - " element.rel = \"stylesheet\";\n", - " element.type = \"text/css\";\n", - " element.href = url;\n", - " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", - " document.body.appendChild(element);\n", - " }\n", - "\n", - " for (let i = 0; i < js_urls.length; i++) {\n", - " const url = js_urls[i];\n", - " const element = document.createElement('script');\n", - " element.onload = on_load;\n", - " element.onerror = on_error.bind(null, url);\n", - " element.async = false;\n", - " element.src = url;\n", - " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", - " document.head.appendChild(element);\n", - " }\n", - " };\n", - "\n", - " function inject_raw_css(css) {\n", - " const element = document.createElement(\"style\");\n", - " element.appendChild(document.createTextNode(css));\n", - " document.body.appendChild(element);\n", - " }\n", - "\n", - " const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.1.1.min.js\"];\n", - " const css_urls = [];\n", - "\n", - " const inline_js = [ function(Bokeh) {\n", - " Bokeh.set_log_level(\"info\");\n", - " },\n", - "function(Bokeh) {\n", - " }\n", - " ];\n", - "\n", - " function run_inline_js() {\n", - " if (root.Bokeh !== undefined || force === true) {\n", - " for (let i = 0; i < inline_js.length; i++) {\n", - " inline_js[i].call(root, root.Bokeh);\n", - " }\n", - "} else if (Date.now() < root._bokeh_timeout) {\n", - " setTimeout(run_inline_js, 100);\n", - " } else if (!root._bokeh_failed_load) {\n", - " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", - " root._bokeh_failed_load = true;\n", - " } else if (force !== true) {\n", - " const cell = $(document.getElementById(null)).parents('.cell').data().cell;\n", - " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", - " }\n", - " }\n", - "\n", - " if (root._bokeh_is_loading === 0) {\n", - " console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", - " run_inline_js();\n", - " } else {\n", - " load_libs(css_urls, js_urls, function() {\n", - " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", - " run_inline_js();\n", - " });\n", - " }\n", - "}(window));" - ], - "application/vnd.bokehjs_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n const el = document.getElementById(null);\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.1.1.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\n} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(null)).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - "
\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": [ - "(function(root) {\n", - " function embed_document(root) {\n", - " const docs_json = {\"b8cd1fed-dfd3-49f8-923f-61cf3c792978\":{\"version\":\"3.1.1\",\"title\":\"Bokeh 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"feature_calendar.plot(**plot_options, max_num_plots=5)" ] @@ -3518,7 +864,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": null, "id": "48a0b18c-0eb8-4ebc-85d1-a928ac7355d5", "metadata": {}, "outputs": [], @@ -3549,7 +895,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": null, "id": "8d9f3d61-3aa3-4a15-9065-9da27b747107", "metadata": {}, "outputs": [], @@ -3569,28 +915,10 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": null, "id": "fc6bd2ff-8383-43e1-9202-e42fcec713bd", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The number of plots (10) is larger than \"options.max_num_plots=3\". Only the first plots will be printed.\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "sum28_sales_per_dept.plot(max_num_plots=3)" ] @@ -3608,7 +936,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": null, "id": "6d6b8394-de68-4ce6-898b-02f5d2baed2c", "metadata": {}, "outputs": [], @@ -3620,28 +948,10 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": null, "id": "44dfa773-1cff-47c9-babc-a003bd2c9315", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The number of plots (80) is larger than \"options.max_num_plots=3\". Only the first plots will be printed.\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "sum28_sales_per_dept.plot(max_num_plots=3)" ] @@ -3659,7 +969,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": null, "id": "14f08bd5-5c88-424e-9db4-42e241fb51bd", "metadata": {}, "outputs": [], @@ -3689,350 +999,10 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": null, "id": "af507fc1-a9ea-45c3-86ea-d37661847a7c", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The number of plots (14) is larger than \"options.max_num_plots=3\". Only the first plots will be printed.\n" - ] - }, - { - "data": { - "application/javascript": [ - "(function(root) {\n", - " function now() {\n", - " return new Date();\n", - " }\n", - "\n", - " const force = true;\n", - "\n", - " if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n", - " root._bokeh_onload_callbacks = [];\n", - " root._bokeh_is_loading = undefined;\n", - " }\n", - "\n", - "const JS_MIME_TYPE = 'application/javascript';\n", - " const HTML_MIME_TYPE = 'text/html';\n", - " const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", - " const CLASS_NAME = 'output_bokeh rendered_html';\n", - "\n", - " /**\n", - " * Render data to the DOM node\n", - " */\n", - " function render(props, node) {\n", - " const script = document.createElement(\"script\");\n", - " node.appendChild(script);\n", - " }\n", - "\n", - " /**\n", - " * Handle when an output is cleared or removed\n", - " */\n", - " function handleClearOutput(event, handle) {\n", - " const cell = handle.cell;\n", - "\n", - " const id = cell.output_area._bokeh_element_id;\n", - " const server_id = cell.output_area._bokeh_server_id;\n", - " // Clean up Bokeh references\n", - " if (id != null && id in Bokeh.index) {\n", - " Bokeh.index[id].model.document.clear();\n", - " delete Bokeh.index[id];\n", - " }\n", - "\n", - " if (server_id !== undefined) {\n", - " // Clean up Bokeh references\n", - " const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", - " cell.notebook.kernel.execute(cmd_clean, {\n", - " iopub: {\n", - " output: function(msg) {\n", - " const id = msg.content.text.trim();\n", - " if (id in Bokeh.index) {\n", - " Bokeh.index[id].model.document.clear();\n", - " delete Bokeh.index[id];\n", - " }\n", - " }\n", - " }\n", - " });\n", - " // Destroy server and session\n", - " const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", - " cell.notebook.kernel.execute(cmd_destroy);\n", - " }\n", - " }\n", - "\n", - " /**\n", - " * Handle when a new output is added\n", - " */\n", - " function handleAddOutput(event, handle) {\n", - " const output_area = handle.output_area;\n", - " const output = handle.output;\n", - "\n", - " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", - " if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n", - " return\n", - " }\n", - "\n", - " const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", - "\n", - " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", - " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n", - " // store reference to embed id on output_area\n", - " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", - " }\n", - " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", - " const bk_div = document.createElement(\"div\");\n", - " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", - " const script_attrs = bk_div.children[0].attributes;\n", - " for (let i = 0; i < script_attrs.length; i++) {\n", - " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", - " toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n", - " }\n", - " // store reference to server id on output_area\n", - " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", - " }\n", - " }\n", - "\n", - " function register_renderer(events, OutputArea) {\n", - "\n", - " function append_mime(data, metadata, element) {\n", - " // create a DOM node to render to\n", - " const toinsert = this.create_output_subarea(\n", - " metadata,\n", - " CLASS_NAME,\n", - " EXEC_MIME_TYPE\n", - " );\n", - " this.keyboard_manager.register_events(toinsert);\n", - " // Render to node\n", - " const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", - " render(props, toinsert[toinsert.length - 1]);\n", - " element.append(toinsert);\n", - " return toinsert\n", - " }\n", - "\n", - " /* Handle when an output is cleared or removed */\n", - " events.on('clear_output.CodeCell', handleClearOutput);\n", - " events.on('delete.Cell', handleClearOutput);\n", - "\n", - " /* Handle when a new output is added */\n", - " events.on('output_added.OutputArea', handleAddOutput);\n", - "\n", - " /**\n", - " * Register the mime type and append_mime function with output_area\n", - " */\n", - " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", - " /* Is output safe? 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\\n\"+\n", - " \"

\\n\"+\n", - " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", - " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", - " \"

\\n\"+\n", - " \"
    \\n\"+\n", - " \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n", - " \"
  • use INLINE resources instead, as so:
    • \\n\"+\n", - " \"
\\n\"+\n", - " \"\\n\"+\n", - " \"from bokeh.resources import INLINE\\n\"+\n", - " \"output_notebook(resources=INLINE)\\n\"+\n", - " \"\\n\"+\n", - " \"
\"}};\n", - "\n", - " function display_loaded() {\n", - " const el = document.getElementById(null);\n", - " if (el != null) {\n", - " el.textContent = \"BokehJS is loading...\";\n", - " }\n", - " if (root.Bokeh !== undefined) {\n", - " if (el != null) {\n", - " el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n", - " }\n", - " } else if (Date.now() < root._bokeh_timeout) {\n", - " setTimeout(display_loaded, 100)\n", - " }\n", - " }\n", - "\n", - " function run_callbacks() {\n", - " try {\n", - " root._bokeh_onload_callbacks.forEach(function(callback) {\n", - " if (callback != null)\n", - " callback();\n", - " });\n", - " } finally {\n", - " delete root._bokeh_onload_callbacks\n", - " }\n", - " console.debug(\"Bokeh: all callbacks have finished\");\n", - " }\n", - "\n", - " function load_libs(css_urls, js_urls, callback) {\n", - " if (css_urls == null) css_urls = [];\n", - " if (js_urls == null) js_urls = [];\n", - "\n", - " root._bokeh_onload_callbacks.push(callback);\n", - " if (root._bokeh_is_loading > 0) {\n", - " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", - " return null;\n", - " }\n", - " if (js_urls == null || js_urls.length === 0) {\n", - " run_callbacks();\n", - " return null;\n", - " }\n", - " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", - " root._bokeh_is_loading = css_urls.length + js_urls.length;\n", - "\n", - " function on_load() {\n", - " root._bokeh_is_loading--;\n", - " if (root._bokeh_is_loading === 0) {\n", - " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", - " run_callbacks()\n", - " }\n", - " }\n", - "\n", - " function on_error(url) {\n", - " console.error(\"failed to load \" + url);\n", - " }\n", - "\n", - " for (let i = 0; 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\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
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str_), ('f_event_type_2', str_), ('f_snap_CA', int64), ('f_snap_TX', int64), ('f_snap_WI', int64), ('f_leak5_event_name_1', str_), ('f_leak5_event_type_1', str_), ('f_leak5_event_name_2', str_), ('f_leak5_event_type_2', str_), ('f_leak5_snap_CA', int64), ('f_leak5_snap_TX', int64), ('f_leak5_snap_WI', int64), ('day', int64), ('label_horizon_1_days', int64), ('label_horizon_2_days', int64), ('label_horizon_3_days', int64), ('label_horizon_4_days', int64), ('label_horizon_5_days', int64), ('label_horizon_6_days', int64), ('label_horizon_7_days', int64), ('label_horizon_8_days', int64), ('label_horizon_9_days', int64), ('label_horizon_10_days', int64), ('label_horizon_11_days', int64), ('label_horizon_12_days', int64), ('label_horizon_13_days', int64), ('label_horizon_14_days', int64), ('label_horizon_15_days', int64), ('label_horizon_16_days', int64), ('label_horizon_17_days', int64), ('label_horizon_18_days', int64), ('label_horizon_19_days', int64), ('label_horizon_20_days', int64), 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5 rows × 70 columns

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item_iddept_idcat_idstore_idstate_idf_salesf_sales_lag_1_df_sales_lag_2_df_sales_lag_3_df_sales_lag_7_d...label_horizon_19_dayslabel_horizon_20_dayslabel_horizon_21_dayslabel_horizon_22_dayslabel_horizon_23_dayslabel_horizon_24_dayslabel_horizon_25_dayslabel_horizon_26_dayslabel_horizon_27_daystimestamp
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"1 30 44 36 \n", - "2 44 36 15 \n", - "3 36 15 26 \n", - "4 15 26 33 \n", - "\n", - " timestamp \n", - "0 1.298765e+09 \n", - "1 1.298851e+09 \n", - "2 1.298938e+09 \n", - "3 1.299024e+09 \n", - "4 1.299110e+09 \n", - "\n", - "[5 rows x 70 columns]" - ] - }, - "execution_count": 44, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "train_dataset = tp.to_pandas(tabular_train)\n", "\n", @@ -4597,7 +1112,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": null, "id": "c936ac9a-ef09-4243-bf69-4b9ff73a2efe", "metadata": {}, "outputs": [], @@ -4608,24 +1123,12 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": null, "id": "a4f231d2-499e-4a77-b635-21e469f1dce6", "metadata": { "scrolled": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "item_id,dept_id,cat_id,store_id,state_id,f_sales,f_sales_lag_1_d,f_sales_lag_2_d,f_sales_lag_3_d,f_sales_lag_7_d,f_sales_lag_14_d,f_sma_7_sales,f_sd_7_sales,f_sum_7_sales,f_sma_14_sales,f_sd_14_sales,f_sum_14_sales,f_sma_28_sales,f_sd_28_sales,f_sum_28_sales,f_sma_84_sales,f_sd_84_sales,f_sum_84_sales,f_calendar_day_of_month,f_calendar_day_of_week,f_calendar_month,f_sum28_per_dep_sales,f_event_name_1,f_event_type_1,f_event_name_2,f_event_type_2,f_snap_CA,f_snap_TX,f_snap_WI,f_leak5_event_name_1,f_leak5_event_type_1,f_leak5_event_name_2,f_leak5_event_type_2,f_leak5_snap_CA,f_leak5_snap_TX,f_leak5_snap_WI,day,label_horizon_1_days,label_horizon_2_days,label_horizon_3_days,label_horizon_4_days,label_horizon_5_days,label_horizon_6_days,label_horizon_7_days,label_horizon_8_days,label_horizon_9_days,label_horizon_10_days,label_horizon_11_days,label_horizon_12_days,label_horizon_13_days,label_horizon_14_days,label_horizon_15_days,label_horizon_16_days,label_horizon_17_days,label_horizon_18_days,label_horizon_19_days,label_horizon_20_days,label_horizon_21_days,label_horizon_22_days,label_horizon_23_days,label_horizon_24_days,label_horizon_25_days,label_horizon_26_days,label_horizon_27_days,timestamp\n", - "FOODS_3_694,FOODS_3,FOODS,WI_2,WI,48,47,31,46,38,53,40.142857,7.3955307,281.0,42.357143,7.393116,593.0,42.82143,11.796045,1199.0,43.333332,12.070166,1300.0,27,6,2,5313,no_event,no_event,no_event,no_event,0,0,0,no_event,no_event,no_event,no_event,1,0,0,30,30,42,39,25,27,43,34,29,29,19,7,42,30,32,46,27,33,36,34,32,29,32,18,24,41,30,44,1298764800.0\n", - "FOODS_3_694,FOODS_3,FOODS,WI_2,WI,30,48,47,31,36,41,39.285713,8.136589,275.0,41.57143,8.0508585,582.0,42.607143,11.971213,1193.0,42.903225,12.10533,1330.0,28,0,2,5298,no_event,no_event,no_event,no_event,0,0,0,no_event,no_event,no_event,no_event,1,1,1,31,42,39,25,27,43,34,29,29,19,7,42,30,32,46,27,33,36,34,32,29,32,18,24,41,30,44,36,1298851200.0\n", - "FOODS_3_694,FOODS_3,FOODS,WI_2,WI,42,30,48,47,29,45,41.142857,6.9781,288.0,41.357143,7.9964914,579.0,42.392857,11.926379,1187.0,42.875,11.91572,1372.0,1,1,3,5254,no_event,no_event,no_event,no_event,1,1,0,no_event,no_event,no_event,no_event,1,1,1,32,39,25,27,43,34,29,29,19,7,42,30,32,46,27,33,36,34,32,29,32,18,24,41,30,44,36,15,1298937600.0\n", - "FOODS_3_694,FOODS_3,FOODS,WI_2,WI,39,42,30,48,44,34,40.42857,6.9045978,283.0,41.714287,7.768329,584.0,42.67857,11.744462,1195.0,42.757576,11.752576,1411.0,2,2,3,5319,no_event,no_event,no_event,no_event,1,0,1,no_event,no_event,no_event,no_event,1,1,0,33,25,27,43,34,29,29,19,7,42,30,32,46,27,33,36,34,32,29,32,18,24,41,30,44,36,15,26,1299024000.0\n" - ] - } - ], + "outputs": [], "source": [ "!head tmp/temporian_m5/train.csv -n 5" ] @@ -4643,33 +1146,10 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": null, "id": "18d6d3b1-32d0-4d4c-b829-814277353ad2", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.1.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.2.1\u001b[0m\n", - "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n", - "Note: you may need to restart the kernel to use updated packages.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-08-18 21:58:32.832959: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n", - "2023-08-18 21:58:32.834353: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\n", - "2023-08-18 21:58:32.865036: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\n", - "2023-08-18 21:58:32.865713: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", - "To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", - "2023-08-18 21:58:33.681504: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n" - ] - } - ], + "outputs": [], "source": [ "%pip install tensorflow tensorflow_decision_forests -q\n", "\n", @@ -4679,19 +1159,10 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": null, "id": "f6666033-3849-4418-bfc8-5a791221e5d1", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "label_names: ['label_horizon_1_days', 'label_horizon_2_days', 'label_horizon_3_days', 'label_horizon_4_days', 'label_horizon_5_days', 'label_horizon_6_days', 'label_horizon_7_days', 'label_horizon_8_days', 'label_horizon_9_days', 'label_horizon_10_days', 'label_horizon_11_days', 'label_horizon_12_days', 'label_horizon_13_days', 'label_horizon_14_days', 'label_horizon_15_days', 'label_horizon_16_days', 'label_horizon_17_days', 'label_horizon_18_days', 'label_horizon_19_days', 'label_horizon_20_days', 'label_horizon_21_days', 'label_horizon_22_days', 'label_horizon_23_days', 'label_horizon_24_days', 'label_horizon_25_days', 'label_horizon_26_days', 'label_horizon_27_days']\n", - "feature_names: ['f_sales', 'f_sales_lag_1_d', 'f_sales_lag_2_d', 'f_sales_lag_3_d', 'f_sales_lag_7_d', 'f_sales_lag_14_d', 'f_sma_7_sales', 'f_sd_7_sales', 'f_sum_7_sales', 'f_sma_14_sales', 'f_sd_14_sales', 'f_sum_14_sales', 'f_sma_28_sales', 'f_sd_28_sales', 'f_sum_28_sales', 'f_sma_84_sales', 'f_sd_84_sales', 'f_sum_84_sales', 'f_calendar_day_of_month', 'f_calendar_day_of_week', 'f_calendar_month', 'f_sum28_per_dep_sales', 'f_event_name_1', 'f_event_type_1', 'f_event_name_2', 'f_event_type_2', 'f_snap_CA', 'f_snap_TX', 'f_snap_WI', 'f_leak5_event_name_1', 'f_leak5_event_type_1', 'f_leak5_event_name_2', 'f_leak5_event_type_2', 'f_leak5_snap_CA', 'f_leak5_snap_TX', 'f_leak5_snap_WI', 'dept_id', 'cat_id', 'store_id', 'state_id']\n" - ] - } - ], + "outputs": [], "source": [ "# The name of the labels and features.\n", "label_names = [x for x in test_dataset.columns if x.startswith(\"label_\")]\n", @@ -4718,7 +1189,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": null, "id": "6a46a286-1825-49ec-a147-f539987da590", "metadata": {}, "outputs": [], @@ -4744,1081 +1215,12 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": null, "id": "4a7580bf-bd81-4806-9dde-1465b0a6fb00", "metadata": { "scrolled": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Use /tmp/tmplcypdssc as temporary training directory\n", - "Reading training dataset...\n", - "Training tensor examples:\n", - "Features: {'f_sales': , 'f_sales_lag_1_d': , 'f_sales_lag_2_d': , 'f_sales_lag_3_d': , 'f_sales_lag_7_d': , 'f_sales_lag_14_d': , 'f_sma_7_sales': , 'f_sd_7_sales': , 'f_sum_7_sales': , 'f_sma_14_sales': , 'f_sd_14_sales': , 'f_sum_14_sales': , 'f_sma_28_sales': , 'f_sd_28_sales': , 'f_sum_28_sales': , 'f_sma_84_sales': , 'f_sd_84_sales': , 'f_sum_84_sales': , 'f_calendar_day_of_month': , 'f_calendar_day_of_week': , 'f_calendar_month': , 'f_sum28_per_dep_sales': , 'f_event_name_1': , 'f_event_type_1': , 'f_event_name_2': , 'f_event_type_2': , 'f_snap_CA': , 'f_snap_TX': , 'f_snap_WI': , 'f_leak5_event_name_1': , 'f_leak5_event_type_1': , 'f_leak5_event_name_2': , 'f_leak5_event_type_2': , 'f_leak5_snap_CA': , 'f_leak5_snap_TX': , 'f_leak5_snap_WI': , 'dept_id': , 'cat_id': , 'store_id': , 'state_id': }\n", - "Label: {'label_horizon_1_days': , 'label_horizon_2_days': , 'label_horizon_3_days': , 'label_horizon_4_days': , 'label_horizon_5_days': , 'label_horizon_6_days': , 'label_horizon_7_days': , 'label_horizon_8_days': , 'label_horizon_9_days': , 'label_horizon_10_days': , 'label_horizon_11_days': , 'label_horizon_12_days': , 'label_horizon_13_days': , 'label_horizon_14_days': , 'label_horizon_15_days': , 'label_horizon_16_days': , 'label_horizon_17_days': , 'label_horizon_18_days': , 'label_horizon_19_days': , 'label_horizon_20_days': , 'label_horizon_21_days': , 'label_horizon_22_days': , 'label_horizon_23_days': , 'label_horizon_24_days': , 'label_horizon_25_days': , 'label_horizon_26_days': , 'label_horizon_27_days': }\n", - "Weights: None\n", - "Normalized tensor features:\n", - " {'f_sales': SemanticTensor(semantic=, tensor=), 'f_sales_lag_1_d': SemanticTensor(semantic=, tensor=), 'f_sales_lag_2_d': SemanticTensor(semantic=, tensor=), 'f_sales_lag_3_d': SemanticTensor(semantic=, tensor=), 'f_sales_lag_7_d': SemanticTensor(semantic=, tensor=), 'f_sales_lag_14_d': SemanticTensor(semantic=, tensor=), 'f_sma_7_sales': SemanticTensor(semantic=, tensor=), 'f_sd_7_sales': SemanticTensor(semantic=, tensor=), 'f_sum_7_sales': SemanticTensor(semantic=, tensor=), 'f_sma_14_sales': SemanticTensor(semantic=, tensor=), 'f_sd_14_sales': SemanticTensor(semantic=, tensor=), 'f_sum_14_sales': SemanticTensor(semantic=, tensor=), 'f_sma_28_sales': SemanticTensor(semantic=, tensor=), 'f_sd_28_sales': SemanticTensor(semantic=, tensor=), 'f_sum_28_sales': SemanticTensor(semantic=, tensor=), 'f_sma_84_sales': SemanticTensor(semantic=, tensor=), 'f_sd_84_sales': SemanticTensor(semantic=, tensor=), 'f_sum_84_sales': SemanticTensor(semantic=, tensor=), 'f_calendar_day_of_month': SemanticTensor(semantic=, tensor=), 'f_calendar_day_of_week': SemanticTensor(semantic=, tensor=), 'f_calendar_month': SemanticTensor(semantic=, tensor=), 'f_sum28_per_dep_sales': SemanticTensor(semantic=, tensor=), 'f_event_name_1': SemanticTensor(semantic=, tensor=), 'f_event_type_1': SemanticTensor(semantic=, tensor=), 'f_event_name_2': SemanticTensor(semantic=, tensor=), 'f_event_type_2': SemanticTensor(semantic=, tensor=), 'f_snap_CA': SemanticTensor(semantic=, tensor=), 'f_snap_TX': SemanticTensor(semantic=, tensor=), 'f_snap_WI': SemanticTensor(semantic=, tensor=), 'f_leak5_event_name_1': SemanticTensor(semantic=, tensor=), 'f_leak5_event_type_1': SemanticTensor(semantic=, tensor=), 'f_leak5_event_name_2': SemanticTensor(semantic=, tensor=), 'f_leak5_event_type_2': SemanticTensor(semantic=, tensor=), 'f_leak5_snap_CA': SemanticTensor(semantic=, tensor=), 'f_leak5_snap_TX': SemanticTensor(semantic=, tensor=), 'f_leak5_snap_WI': SemanticTensor(semantic=, tensor=), 'dept_id': SemanticTensor(semantic=, tensor=), 'cat_id': SemanticTensor(semantic=, tensor=), 'store_id': SemanticTensor(semantic=, tensor=), 'state_id': SemanticTensor(semantic=, tensor=)}\n", - "Training dataset read in 0:00:04.394148. Found 150720 examples.\n", - "Training model...\n", - "Standard output detected as not visible to the user e.g. running in a notebook. Creating a training log redirection. If training gets stuck, try calling tfdf.keras.set_training_logs_redirection(False).\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[INFO 23-08-18 22:00:46.8128 CEST kernel.cc:773] Start Yggdrasil model training\n", - "[INFO 23-08-18 22:00:46.8128 CEST kernel.cc:774] Collect training examples\n", - "[INFO 23-08-18 22:00:46.8128 CEST kernel.cc:787] Dataspec guide:\n", - "column_guides {\n", - " column_name_pattern: \"^label_horizon_1_days$\"\n", - " type: NUMERICAL\n", - "}\n", - "column_guides {\n", - " column_name_pattern: \"^label_horizon_2_days$\"\n", - " type: NUMERICAL\n", - "}\n", - "column_guides {\n", - " column_name_pattern: \"^label_horizon_3_days$\"\n", - " type: NUMERICAL\n", - "}\n", - "column_guides {\n", - " column_name_pattern: \"^label_horizon_4_days$\"\n", - " type: NUMERICAL\n", - "}\n", - "column_guides {\n", - " column_name_pattern: \"^label_horizon_5_days$\"\n", - " type: NUMERICAL\n", - "}\n", - "column_guides {\n", - " column_name_pattern: \"^label_horizon_6_days$\"\n", - " type: NUMERICAL\n", - "}\n", - "column_guides {\n", - " column_name_pattern: \"^label_horizon_7_days$\"\n", - " type: NUMERICAL\n", - "}\n", - "column_guides {\n", - " column_name_pattern: \"^label_horizon_8_days$\"\n", - " type: NUMERICAL\n", - "}\n", - "column_guides {\n", - " column_name_pattern: \"^label_horizon_9_days$\"\n", - " type: NUMERICAL\n", - "}\n", - "column_guides {\n", - " column_name_pattern: \"^label_horizon_10_days$\"\n", - " type: NUMERICAL\n", - "}\n", - "column_guides {\n", - " column_name_pattern: \"^label_horizon_11_days$\"\n", - " type: NUMERICAL\n", - "}\n", - "column_guides {\n", - " column_name_pattern: \"^label_horizon_12_days$\"\n", - " type: NUMERICAL\n", - "}\n", - "column_guides {\n", - " column_name_pattern: \"^label_horizon_13_days$\"\n", - " type: NUMERICAL\n", - "}\n", - "column_guides {\n", - " column_name_pattern: \"^label_horizon_14_days$\"\n", - " type: NUMERICAL\n", - "}\n", - "column_guides {\n", - " column_name_pattern: \"^label_horizon_15_days$\"\n", - " type: NUMERICAL\n", - "}\n", - "column_guides {\n", - " column_name_pattern: \"^label_horizon_16_days$\"\n", - " type: NUMERICAL\n", - "}\n", - "column_guides {\n", - " column_name_pattern: \"^label_horizon_17_days$\"\n", - " type: NUMERICAL\n", - "}\n", - "column_guides {\n", - " column_name_pattern: \"^label_horizon_18_days$\"\n", - " type: NUMERICAL\n", - "}\n", - "column_guides {\n", - " column_name_pattern: \"^label_horizon_19_days$\"\n", - " type: NUMERICAL\n", - "}\n", - "column_guides {\n", - " column_name_pattern: \"^label_horizon_20_days$\"\n", - " type: NUMERICAL\n", - "}\n", - "column_guides {\n", - " column_name_pattern: \"^label_horizon_21_days$\"\n", - " type: NUMERICAL\n", - "}\n", - "column_guides {\n", - " column_name_pattern: \"^label_horizon_22_days$\"\n", - " type: NUMERICAL\n", - "}\n", - "column_guides {\n", - " column_name_pattern: \"^label_horizon_23_days$\"\n", - " type: NUMERICAL\n", - "}\n", - "column_guides {\n", - " column_name_pattern: \"^label_horizon_24_days$\"\n", - " type: NUMERICAL\n", - "}\n", - "column_guides {\n", - " column_name_pattern: \"^label_horizon_25_days$\"\n", - " type: NUMERICAL\n", - "}\n", - "column_guides {\n", - " column_name_pattern: \"^label_horizon_26_days$\"\n", - " type: NUMERICAL\n", - "}\n", - "column_guides {\n", - " column_name_pattern: \"^label_horizon_27_days$\"\n", - " type: NUMERICAL\n", - "}\n", - "default_column_guide {\n", - " categorial {\n", - " max_vocab_count: 2000\n", - " }\n", - " discretized_numerical {\n", - " maximum_num_bins: 255\n", - " }\n", - "}\n", - "ignore_columns_without_guides: false\n", - "detect_numerical_as_discretized_numerical: false\n", - "\n", - "[INFO 23-08-18 22:00:46.8187 CEST kernel.cc:393] Number of batches: 1508\n", - "[INFO 23-08-18 22:00:46.8187 CEST kernel.cc:394] Number of examples: 150720\n", - "[INFO 23-08-18 22:00:46.9182 CEST kernel.cc:794] Training dataset:\n", - "Number of records: 150720\n", - "Number of columns: 67\n", - "\n", - "Number of columns by type:\n", - "\tNUMERICAL: 55 (82.0896%)\n", - "\tCATEGORICAL: 12 (17.9104%)\n", - "\n", - "Columns:\n", - "\n", - "NUMERICAL: 55 (82.0896%)\n", - "\t2: \"f_calendar_day_of_month\" NUMERICAL mean:15.7107 min:1 max:31 sd:8.79464\n", - "\t3: \"f_calendar_day_of_week\" NUMERICAL mean:3.00159 min:0 max:6 sd:2.00066\n", - "\t4: \"f_calendar_month\" NUMERICAL mean:6.42622 min:1 max:12 sd:3.44017\n", - "\t13: \"f_leak5_snap_CA\" NUMERICAL mean:0.327495 min:0 max:1 sd:0.469299\n", - "\t14: \"f_leak5_snap_TX\" NUMERICAL mean:0.328025 min:0 max:1 sd:0.469494\n", - "\t15: \"f_leak5_snap_WI\" NUMERICAL mean:0.328025 min:0 max:1 sd:0.469494\n", - "\t16: \"f_sales\" NUMERICAL mean:29.5872 min:0 max:763 sd:31.5277\n", - "\t17: \"f_sales_lag_14_d\" NUMERICAL mean:29.5573 min:0 max:763 sd:31.5369\n", - "\t18: \"f_sales_lag_1_d\" NUMERICAL mean:29.5861 min:0 max:763 sd:31.5303\n", - "\t19: \"f_sales_lag_2_d\" NUMERICAL mean:29.5797 min:0 max:763 sd:31.5265\n", - "\t20: \"f_sales_lag_3_d\" NUMERICAL mean:29.5746 min:0 max:763 sd:31.5255\n", - "\t21: \"f_sales_lag_7_d\" NUMERICAL mean:29.5707 min:0 max:763 sd:31.5324\n", - "\t22: \"f_sd_14_sales\" NUMERICAL mean:10.0323 min:0 max:203.329 sd:10.8368\n", - "\t23: \"f_sd_28_sales\" NUMERICAL mean:10.8908 min:0 max:192.891 sd:11.4905\n", - "\t24: \"f_sd_7_sales\" NUMERICAL mean:9.12405 min:0 max:235.264 sd:10.209\n", - "\t25: \"f_sd_84_sales\" NUMERICAL mean:12.2336 min:0 max:173.639 sd:12.8959\n", - "\t26: \"f_sma_14_sales\" NUMERICAL mean:29.5678 min:0 max:468.643 sd:27.8563\n", - "\t27: \"f_sma_28_sales\" NUMERICAL mean:29.5606 min:0 max:442.75 sd:27.2844\n", - "\t28: \"f_sma_7_sales\" NUMERICAL mean:29.5773 min:0 max:602.857 sd:28.3994\n", - "\t29: \"f_sma_84_sales\" NUMERICAL mean:29.5427 min:0 max:279.786 sd:26.147\n", - "\t30: \"f_snap_CA\" NUMERICAL mean:0.329087 min:0 max:1 sd:0.469882\n", - "\t31: \"f_snap_TX\" NUMERICAL mean:0.329087 min:0 max:1 sd:0.469882\n", - "\t32: \"f_snap_WI\" NUMERICAL mean:0.329087 min:0 max:1 sd:0.469882\n", - "\t33: \"f_sum28_per_dep_sales\" NUMERICAL mean:6621.58 min:1333 max:19255 sd:2895.7\n", - "\t34: \"f_sum_14_sales\" NUMERICAL mean:413.949 min:0 max:6561 sd:389.988\n", - "\t35: \"f_sum_28_sales\" NUMERICAL mean:827.698 min:0 max:12397 sd:763.964\n", - "\t36: \"f_sum_7_sales\" NUMERICAL mean:207.041 min:0 max:4220 sd:198.796\n", - "\t37: \"f_sum_84_sales\" NUMERICAL mean:2460.49 min:0 max:23502 sd:2194.56\n", - "\t38: \"label_horizon_10_days\" NUMERICAL mean:29.6007 min:0 max:763 sd:31.5209\n", - "\t39: \"label_horizon_11_days\" NUMERICAL mean:29.6038 min:0 max:763 sd:31.5202\n", - "\t40: \"label_horizon_12_days\" NUMERICAL mean:29.6097 min:0 max:763 sd:31.5218\n", - "\t41: \"label_horizon_13_days\" NUMERICAL mean:29.6176 min:0 max:763 sd:31.5275\n", - "\t42: \"label_horizon_14_days\" NUMERICAL mean:29.6194 min:0 max:763 sd:31.5271\n", - "\t43: \"label_horizon_15_days\" NUMERICAL mean:29.6189 min:0 max:763 sd:31.5249\n", - "\t44: \"label_horizon_16_days\" NUMERICAL mean:29.6204 min:0 max:763 sd:31.5241\n", - "\t45: \"label_horizon_17_days\" NUMERICAL mean:29.6229 min:0 max:763 sd:31.5229\n", - "\t46: \"label_horizon_18_days\" NUMERICAL mean:29.6257 min:0 max:763 sd:31.5219\n", - "\t47: \"label_horizon_19_days\" NUMERICAL mean:29.6304 min:0 max:763 sd:31.523\n", - "\t48: \"label_horizon_1_days\" NUMERICAL mean:29.5853 min:0 max:763 sd:31.5245\n", - "\t49: \"label_horizon_20_days\" NUMERICAL mean:29.6377 min:0 max:763 sd:31.5259\n", - "\t50: \"label_horizon_21_days\" NUMERICAL mean:29.6423 min:0 max:763 sd:31.5277\n", - "\t51: \"label_horizon_22_days\" NUMERICAL mean:29.6404 min:0 max:763 sd:31.5232\n", - "\t52: \"label_horizon_23_days\" NUMERICAL mean:29.6433 min:0 max:763 sd:31.5218\n", - "\t53: \"label_horizon_24_days\" NUMERICAL mean:29.6473 min:0 max:763 sd:31.5202\n", - "\t54: \"label_horizon_25_days\" NUMERICAL mean:29.6505 min:0 max:763 sd:31.518\n", - "\t55: \"label_horizon_26_days\" NUMERICAL mean:29.6582 min:0 max:763 sd:31.5185\n", - "\t56: \"label_horizon_27_days\" NUMERICAL mean:29.6661 min:0 max:763 sd:31.5204\n", - "\t57: \"label_horizon_2_days\" NUMERICAL mean:29.5869 min:0 max:763 sd:31.5242\n", - "\t58: \"label_horizon_3_days\" NUMERICAL mean:29.5863 min:0 max:763 sd:31.5234\n", - "\t59: \"label_horizon_4_days\" NUMERICAL mean:29.5873 min:0 max:763 sd:31.5224\n", - "\t60: \"label_horizon_5_days\" NUMERICAL mean:29.5916 min:0 max:763 sd:31.525\n", - "\t61: \"label_horizon_6_days\" NUMERICAL mean:29.5976 min:0 max:763 sd:31.5282\n", - "\t62: \"label_horizon_7_days\" NUMERICAL mean:29.5993 min:0 max:763 sd:31.5276\n", - "\t63: \"label_horizon_8_days\" NUMERICAL mean:29.5965 min:0 max:763 sd:31.5239\n", - "\t64: \"label_horizon_9_days\" NUMERICAL mean:29.5992 min:0 max:763 sd:31.5224\n", - "\n", - "CATEGORICAL: 12 (17.9104%)\n", - "\t0: \"cat_id\" CATEGORICAL has-dict vocab-size:2 zero-ood-items most-frequent:\"FOODS\" 150720 (100%)\n", - "\t1: \"dept_id\" CATEGORICAL has-dict vocab-size:2 zero-ood-items most-frequent:\"FOODS_3\" 150720 (100%)\n", - "\t5: \"f_event_name_1\" CATEGORICAL has-dict vocab-size:32 zero-ood-items most-frequent:\"no_event\" 138640 (91.9851%)\n", - "\t6: \"f_event_name_2\" CATEGORICAL has-dict vocab-size:6 zero-ood-items most-frequent:\"no_event\" 150400 (99.7877%)\n", - "\t7: \"f_event_type_1\" CATEGORICAL has-dict vocab-size:6 zero-ood-items most-frequent:\"no_event\" 138640 (91.9851%)\n", - "\t8: \"f_event_type_2\" CATEGORICAL has-dict vocab-size:4 zero-ood-items most-frequent:\"no_event\" 150400 (99.7877%)\n", - "\t9: \"f_leak5_event_name_1\" CATEGORICAL has-dict vocab-size:32 zero-ood-items most-frequent:\"no_event\" 138640 (91.9851%)\n", - "\t10: \"f_leak5_event_name_2\" CATEGORICAL has-dict vocab-size:6 zero-ood-items most-frequent:\"no_event\" 150400 (99.7877%)\n", - "\t11: \"f_leak5_event_type_1\" CATEGORICAL has-dict vocab-size:6 zero-ood-items most-frequent:\"no_event\" 138640 (91.9851%)\n", - "\t12: \"f_leak5_event_type_2\" CATEGORICAL has-dict vocab-size:4 zero-ood-items most-frequent:\"no_event\" 150400 (99.7877%)\n", - "\t65: \"state_id\" CATEGORICAL has-dict vocab-size:4 zero-ood-items most-frequent:\"CA\" 60288 (40%)\n", - "\t66: \"store_id\" CATEGORICAL has-dict vocab-size:11 zero-ood-items most-frequent:\"CA_1\" 15072 (10%)\n", - "\n", - "Terminology:\n", - "\tnas: Number of non-available (i.e. missing) values.\n", - "\tood: Out of dictionary.\n", - "\tmanually-defined: Attribute which type is manually defined by the user i.e. the type was not automatically inferred.\n", - "\ttokenized: The attribute value is obtained through tokenization.\n", - "\thas-dict: The attribute is attached to a string dictionary e.g. a categorical attribute stored as a string.\n", - "\tvocab-size: Number of unique values.\n", - "\n", - "[INFO 23-08-18 22:00:46.9183 CEST kernel.cc:810] Configure learner\n", - "[INFO 23-08-18 22:00:46.9183 CEST kernel.cc:824] Training config:\n", - "learner: \"MULTITASKER\"\n", - "features: \"^cat_id$\"\n", - "features: \"^dept_id$\"\n", - "features: \"^f_calendar_day_of_month$\"\n", - "features: \"^f_calendar_day_of_week$\"\n", - "features: \"^f_calendar_month$\"\n", - "features: \"^f_event_name_1$\"\n", - "features: \"^f_event_name_2$\"\n", - "features: \"^f_event_type_1$\"\n", - "features: \"^f_event_type_2$\"\n", - "features: \"^f_leak5_event_name_1$\"\n", - "features: \"^f_leak5_event_name_2$\"\n", - "features: \"^f_leak5_event_type_1$\"\n", - "features: \"^f_leak5_event_type_2$\"\n", - "features: \"^f_leak5_snap_CA$\"\n", - "features: \"^f_leak5_snap_TX$\"\n", - "features: \"^f_leak5_snap_WI$\"\n", - "features: \"^f_sales$\"\n", - "features: \"^f_sales_lag_14_d$\"\n", - "features: \"^f_sales_lag_1_d$\"\n", - "features: \"^f_sales_lag_2_d$\"\n", - "features: \"^f_sales_lag_3_d$\"\n", - "features: \"^f_sales_lag_7_d$\"\n", - "features: \"^f_sd_14_sales$\"\n", - "features: \"^f_sd_28_sales$\"\n", - "features: \"^f_sd_7_sales$\"\n", - "features: \"^f_sd_84_sales$\"\n", - "features: \"^f_sma_14_sales$\"\n", - "features: \"^f_sma_28_sales$\"\n", - "features: \"^f_sma_7_sales$\"\n", - "features: \"^f_sma_84_sales$\"\n", - "features: \"^f_snap_CA$\"\n", - "features: \"^f_snap_TX$\"\n", - "features: \"^f_snap_WI$\"\n", - "features: \"^f_sum28_per_dep_sales$\"\n", - "features: \"^f_sum_14_sales$\"\n", - "features: \"^f_sum_28_sales$\"\n", - "features: \"^f_sum_7_sales$\"\n", - "features: \"^f_sum_84_sales$\"\n", - "features: \"^state_id$\"\n", - "features: \"^store_id$\"\n", - "label: \"^label_horizon_1_days$\"\n", - "task: REGRESSION\n", - "metadata {\n", - " framework: \"TF Keras\"\n", - "}\n", - "[yggdrasil_decision_forests.model.multitasker.proto.multitasker_config] {\n", - " base_learner {\n", - " learner: \"GRADIENT_BOOSTED_TREES\"\n", - " }\n", - " subtasks {\n", - " primary: true\n", - " train_config {\n", - " label: \"^label_horizon_1_days$\"\n", - " task: REGRESSION\n", - " }\n", - " }\n", - " subtasks {\n", - " primary: true\n", - " train_config {\n", - " label: \"^label_horizon_2_days$\"\n", - " task: REGRESSION\n", - " }\n", - " }\n", - " subtasks {\n", - " primary: true\n", - " train_config {\n", - " label: \"^label_horizon_3_days$\"\n", - " task: REGRESSION\n", - " }\n", - " }\n", - " subtasks {\n", - " primary: true\n", - " train_config {\n", - " label: \"^label_horizon_4_days$\"\n", - " task: REGRESSION\n", - " }\n", - " }\n", - " subtasks {\n", - " primary: true\n", - " train_config {\n", - " label: \"^label_horizon_5_days$\"\n", - " task: REGRESSION\n", - " }\n", - " }\n", - " subtasks {\n", - " primary: true\n", - " train_config {\n", - " label: \"^label_horizon_6_days$\"\n", - " task: REGRESSION\n", - " }\n", - " }\n", - " subtasks {\n", - " primary: true\n", - " train_config {\n", - " label: \"^label_horizon_7_days$\"\n", - " task: REGRESSION\n", - " }\n", - " }\n", - " subtasks {\n", - " primary: true\n", - " train_config {\n", - " label: \"^label_horizon_8_days$\"\n", - " task: REGRESSION\n", - " }\n", - " }\n", - " subtasks {\n", - " primary: true\n", - " train_config {\n", - " label: \"^label_horizon_9_days$\"\n", - " task: REGRESSION\n", - " }\n", - " }\n", - " subtasks {\n", - " primary: true\n", - " train_config {\n", - " label: \"^label_horizon_10_days$\"\n", - " task: REGRESSION\n", - " }\n", - " }\n", - " subtasks {\n", - " primary: true\n", - " train_config {\n", - " label: \"^label_horizon_11_days$\"\n", - " task: REGRESSION\n", - " }\n", - " }\n", - " subtasks {\n", - " primary: true\n", - " train_config {\n", - " label: \"^label_horizon_12_days$\"\n", - " task: REGRESSION\n", - " }\n", - " }\n", - " subtasks {\n", - " primary: true\n", - " train_config {\n", - " label: \"^label_horizon_13_days$\"\n", - " task: REGRESSION\n", - " }\n", - " }\n", - " subtasks {\n", - " primary: true\n", - " train_config {\n", - " label: \"^label_horizon_14_days$\"\n", - " task: REGRESSION\n", - " }\n", - " }\n", - " subtasks {\n", - " primary: true\n", - " train_config {\n", - " label: \"^label_horizon_15_days$\"\n", - " task: REGRESSION\n", - " }\n", - " }\n", - " subtasks {\n", - " primary: true\n", - " train_config {\n", - " label: \"^label_horizon_16_days$\"\n", - " task: REGRESSION\n", - " }\n", - " }\n", - " subtasks {\n", - " primary: true\n", - " train_config {\n", - " label: \"^label_horizon_17_days$\"\n", - " task: REGRESSION\n", - " }\n", - " }\n", - " subtasks {\n", - " primary: true\n", - " train_config {\n", - " label: \"^label_horizon_18_days$\"\n", - " task: REGRESSION\n", - " }\n", - " }\n", - " subtasks {\n", - " primary: true\n", - " train_config {\n", - " label: \"^label_horizon_19_days$\"\n", - " task: REGRESSION\n", - " }\n", - " }\n", - " subtasks {\n", - " primary: true\n", - " train_config {\n", - " label: \"^label_horizon_20_days$\"\n", - " task: REGRESSION\n", - " }\n", - " }\n", - " subtasks {\n", - " primary: true\n", - " train_config {\n", - " label: \"^label_horizon_21_days$\"\n", - " task: REGRESSION\n", - " }\n", - " }\n", - " subtasks {\n", - " primary: true\n", - " train_config {\n", - " label: \"^label_horizon_22_days$\"\n", - " task: REGRESSION\n", - " }\n", - " }\n", - " subtasks {\n", - " primary: true\n", - " train_config {\n", - " label: \"^label_horizon_23_days$\"\n", - " task: REGRESSION\n", - " }\n", - " }\n", - " subtasks {\n", - " primary: true\n", - " train_config {\n", - " label: \"^label_horizon_24_days$\"\n", - " task: REGRESSION\n", - " }\n", - " }\n", - " subtasks {\n", - " primary: true\n", - " train_config {\n", - " label: \"^label_horizon_25_days$\"\n", - " task: REGRESSION\n", - " }\n", - " }\n", - " subtasks {\n", - " primary: true\n", - " train_config {\n", - " label: \"^label_horizon_26_days$\"\n", - " task: REGRESSION\n", - " }\n", - " }\n", - " subtasks {\n", - " primary: true\n", - " train_config {\n", - " label: \"^label_horizon_27_days$\"\n", - " task: REGRESSION\n", - " }\n", - " }\n", - "}\n", - "\n", - "[INFO 23-08-18 22:00:46.9186 CEST kernel.cc:827] Deployment config:\n", - "cache_path: \"/tmp/tmplcypdssc/working_cache\"\n", - "num_threads: 8\n", - "try_resume_training: true\n", - "\n", - "[INFO 23-08-18 22:00:46.9187 CEST kernel.cc:889] Train model\n", - "[INFO 23-08-18 22:00:46.9258 CEST multitasker.cc:286] Train multitasker primary tasks with 27 model(s)\n", - "[WARNING[WARNING[WARNING 23-08-18 22:00:46.9299 CEST gradient_boosted_trees.cc:[[WARNING 23-08-18 22:00:46.9299 CEST WARNINGgradient_boosted_trees.cc: 23-08-18 22:00:46.9299 CEST [ 23-08-18 22:00:46.9299 CEST 1818[WARNINGWARNINGgradient_boosted_trees.cc: 23-08-18 22:00:46.9299 CEST ] gradient_boosted_trees.cc:gradient_boosted_trees.cc 23-08-18 22:00:46.9299 CEST : 23-08-18 22:00:46.9299 CEST 18181818] \"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", - "[WARNING 23-08-18 22:00:46.9299 CEST gradient_boosted_trees.cc:1829] gradient_boosted_trees.cc:1818[WARNING1818 23-08-18 22:00:46.9299 CEST gradient_boosted_trees.ccgradient_boosted_trees.cc\"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".:\n", - "] ] :1818] 1818] \"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".] \"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", - "1818\"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", - "\n", - "[\n", - "[[WARNING[WARNING 23-08-18 22:00:46.9300 CEST [\n", - "WARNING[ 23-08-18 22:00:46.9300 CEST gradient_boosted_trees.ccgradient_boosted_trees.cc::] WARNINGWARNING 23-08-18 22:00:46.9300 CEST WARNING 23-08-18 22:00:46.9300 CEST 1829 23-08-18 22:00:46.9300 CEST 23-08-18 22:00:46.9300 CEST ] \"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", - "1829\n", - "] gradient_boosted_trees.cc\"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\n", - "gradient_boosted_trees.cc[WARNINGgradient_boosted_trees.ccgradient_boosted_trees.cc::1829\n", - "] 23-08-18 22:00:46.9300 CEST :[1829\"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".[WARNING[WARNING] 1843: 23-08-18 22:00:46.9301 CEST gradient_boosted_trees.cc:1843] 23-08-18 22:00:46.9301 CEST gradient_boosted_trees.cc:\"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".1829\n", - "] WARNING\n", - " 23-08-18 22:00:46.9301 CEST ] gradient_boosted_trees.cc:1829[] WARNING\"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".[\n", - "\"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".1829\"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".] \n", - "\n", - " 23-08-18 22:00:46.9301 CEST gradient_boosted_trees.cc:\n", - "1843] [WARNINGgradient_boosted_trees.cc\"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".[\n", - ":WARNING\"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", - " 23-08-18 22:00:46.9301 CEST 23-08-18 22:00:46.9301 CEST 1843gradient_boosted_trees.cc[] :1843gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".] \n", - "\"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".WARNING 23-08-18 22:00:46.9302 CEST gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", - "\n", - "\n", - "WARNING 23-08-18 22:00:46.9309 CEST gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", - "[INFO 23-08-18 22:00:47.7167 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", - "[INFO 23-08-18 22:00:47.7169 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", - "[INFO 23-08-18 22:00:47.7209 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", - "[INFO 23-08-18 22:00:47.7209 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", - "[INFO 23-08-18 22:00:47.7347 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", - "[INFO 23-08-18 22:00:47.7347 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", - "[INFO 23-08-18 22:00:47.7426 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", - "[INFO 23-08-18 22:00:47.7427 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", - "[INFO 23-08-18 22:00:47.7429 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", - "[INFO 23-08-18 22:00:47.7429 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", - "[INFO 23-08-18 22:00:47.7480 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", - "[INFO 23-08-18 22:00:47.7481 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", - "[INFO 23-08-18 22:00:47.7522 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", - "[INFO 23-08-18 22:00:47.7522 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", - "[INFO 23-08-18 22:00:47.7627 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", - "[INFO 23-08-18 22:00:47.7627 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", - "[INFO 23-08-18 22:00:47.8435 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", - "[INFO 23-08-18 22:00:47.8799 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", - "[INFO 23-08-18 22:00:47.8849 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", - "[INFO 23-08-18 22:00:47.9025 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", - "[INFO 23-08-18 22:00:48.0678 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", - "[INFO 23-08-18 22:00:48.0818 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", - "[INFO 23-08-18 22:00:48.1529 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", - "[INFO 23-08-18 22:00:48.2466 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", - "[INFO 23-08-18 22:00:48.5693 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.295769 train-rmse:29.295769 valid-loss:28.769020 valid-rmse:28.769020\n", - "[INFO 23-08-18 22:00:48.8418 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.335985 train-rmse:29.335985 valid-loss:29.099661 valid-rmse:29.099661\n", - "[INFO 23-08-18 22:00:48.9186 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.323404 train-rmse:29.323404 valid-loss:28.357626 valid-rmse:28.357626\n", - "[INFO 23-08-18 22:00:48.9865 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.214186 train-rmse:29.214186 valid-loss:29.899080 valid-rmse:29.899080\n", - "[INFO 23-08-18 22:00:49.2447 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.267696 train-rmse:29.267696 valid-loss:29.130789 valid-rmse:29.130789\n", - "[INFO 23-08-18 22:00:49.3179 CEST gradient_boosted_trees.cc[INFO 23-08-18 22:00:49.3209 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.399338 train-rmse:29.399338 valid-loss:29.028122 valid-rmse:29.028122\n", - ":1542] \tnum-trees:1 train-loss:29.124109 train-rmse:29.124109 valid-loss:29.133936 valid-rmse:29.133936\n", - "[INFO 23-08-18 22:00:49.3306 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.303213 train-rmse:29.303213 valid-loss:29.393198 valid-rmse:29.393198\n", - "[INFO 23-08-18 22:00:49.6147 CEST gradient_boosted_trees.cc:1544] \tnum-trees:2 train-loss:27.293285 train-rmse:27.293285 valid-loss:26.745028 valid-rmse:26.745028\n", - "[INFO 23-08-18 22:01:19.8681 CEST gradient_boosted_trees.cc:1544] \tnum-trees:24 train-loss:13.311581 train-rmse:13.311581 valid-loss:13.150414 valid-rmse:13.150414\n", - "[INFO 23-08-18 22:01:49.9277 CEST gradient_boosted_trees.cc:1544] \tnum-trees:42 train-loss:12.409389 train-rmse:12.409389 valid-loss:12.698593 valid-rmse:12.698593\n", - "[INFO 23-08-18 22:01:58.5746 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:13.866138 train-rmse:13.866138 valid-loss:15.359539 valid-rmse:15.359539\n", - "[INFO 23-08-18 22:01:58.5989 CEST gradient_boosted_trees.cc:247] Truncates the model to 48 tree(s) i.e. 48 iteration(s).\n", - "[INFO 23-08-18 22:01:58.5993 CEST gradient_boosted_trees.cc:310] Final model num-trees:48 valid-loss:15.355445 valid-rmse:15.355445\n", - "[WARNING 23-08-18 22:01:58.6182 CEST gradient_boosted_trees.cc:1818] \"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", - "[WARNING 23-08-18 22:01:58.6184 CEST gradient_boosted_trees.cc:1829] \"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\n", - "[WARNING 23-08-18 22:01:58.6187 CEST gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", - "[INFO 23-08-18 22:01:58.9735 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:13.070064 train-rmse:13.070064 valid-loss:13.403740 valid-rmse:13.403740\n", - "[INFO 23-08-18 22:01:58.9736 CEST gradient_boosted_trees.cc:247] Truncates the model to 50 tree(s) i.e. 50 iteration(s).\n", - "[INFO 23-08-18 22:01:58.9736 CEST gradient_boosted_trees.cc:310] Final model num-trees:50 valid-loss:13.403740 valid-rmse:13.403740\n", - "[WARNING 23-08-18 22:01:58.9858 CEST gradient_boosted_trees.cc:1818] \"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", - "[WARNING 23-08-18 22:01:58.9859 CEST gradient_boosted_trees.cc:1829] \"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\n", - "[WARNING 23-08-18 22:01:58.9859 CEST gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", - "[INFO 23-08-18 22:01:59.4701 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:13.575471 train-rmse:13.575471 valid-loss:15.029240 valid-rmse:15.029240\n", - "[INFO 23-08-18 22:01:59.4922 CEST gradient_boosted_trees.cc:247] Truncates the model to 50 tree(s) i.e. 50 iteration(s).\n", - "[INFO 23-08-18 22:01:59.4979 CEST gradient_boosted_trees.cc:310] Final model num-trees:50 valid-loss:15.029240 valid-rmse:15.029240\n", - "[WARNING 23-08-18 22:01:59.5213 CEST gradient_boosted_trees.cc:1818] \"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", - "[WARNING 23-08-18 22:01:59.5213 CEST gradient_boosted_trees.cc:1829] \"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\n", - "[WARNING 23-08-18 22:01:59.5213 CEST gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", - "[INFO 23-08-18 22:01:59.6596 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:14.514459 train-rmse:14.514459 valid-loss:14.994228 valid-rmse:14.994228\n", - "[INFO 23-08-18 22:01:59.6718 CEST gradient_boosted_trees.cc:247] Truncates the model to 50 tree(s) i.e. 50 iteration(s).\n", - "[INFO 23-08-18 22:01:59.6787 CEST gradient_boosted_trees.cc:310] Final model num-trees:50 valid-loss:14.994228 valid-rmse:14.994228\n", - "[WARNING 23-08-18 22:01:59.7168 CEST gradient_boosted_trees.cc:1818] \"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", - "[WARNING 23-08-18 22:01:59.7168 CEST gradient_boosted_trees.cc:1829] \"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\n", - "[WARNING 23-08-18 22:01:59.7169 CEST gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", - "[INFO 23-08-18 22:01:59.9275 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:13.423209 train-rmse:13.423209 valid-loss:13.453820 valid-rmse:13.453820\n", - "[INFO 23-08-18 22:01:59.9427 CEST gradient_boosted_trees.cc:247] Truncates the model to 50 tree(s) i.e. 50 iteration(s).\n", - "[INFO 23-08-18 22:01:59.9492 CEST gradient_boosted_trees.cc:310] Final model num-trees:50 valid-loss:13.453820 valid-rmse:13.453820\n", - "[WARNING 23-08-18 22:01:59.9585 CEST gradient_boosted_trees.cc:1818] \"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", - "[WARNING 23-08-18 22:01:59.9601 CEST gradient_boosted_trees.cc:1829] \"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\n", - "[WARNING 23-08-18 22:01:59.9602 CEST gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", - "[INFO 23-08-18 22:02:00.8021 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", - "[INFO 23-08-18 22:02:00.8022 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", - "[INFO 23-08-18 22:02:00.9806 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:14.071945 train-rmse:14.071945 valid-loss:14.601532 valid-rmse:14.601532\n", - "[INFO 23-08-18 22:02:01.0068 CEST gradient_boosted_trees.cc:247] Truncates the model to 47 tree(s) i.e. 47 iteration(s).\n", - "[INFO 23-08-18 22:02:01.0075 CEST gradient_boosted_trees.cc:310] Final model num-trees:47 valid-loss:14.593384 valid-rmse:14.593384\n", - "[INFO 23-08-18 22:02:01.0077 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", - "[WARNING 23-08-18 22:02:01.0236 CEST gradient_boosted_trees.cc:1818] \"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", - "[WARNING 23-08-18 22:02:01.0236 CEST gradient_boosted_trees.cc:1829] \"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\n", - "[WARNING 23-08-18 22:02:01.0236 CEST gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", - "[INFO 23-08-18 22:02:01.0882 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:13.252879 train-rmse:13.252879 valid-loss:13.566219 valid-rmse:13.566219\n", - "[INFO 23-08-18 22:02:01.0882 CEST gradient_boosted_trees.cc:247] Truncates the model to 50 tree(s) i.e. 50 iteration(s).\n", - "[INFO 23-08-18 22:02:01.0882 CEST gradient_boosted_trees.cc:310] Final model num-trees:50 valid-loss:13.566219 valid-rmse:13.566219\n", - "[WARNING 23-08-18 22:02:01.1082 CEST gradient_boosted_trees.cc:1818] \"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", - "[WARNING 23-08-18 22:02:01.1082 CEST gradient_boosted_trees.cc:1829] \"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\n", - "[WARNING 23-08-18 22:02:01.1082 CEST gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", - "[INFO 23-08-18 22:02:01.1418 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:12.181689 train-rmse:12.181689 valid-loss:12.686768 valid-rmse:12.686768\n", - "[INFO 23-08-18 22:02:01.1418 CEST gradient_boosted_trees.cc:247] Truncates the model to 41 tree(s) i.e. 41 iteration(s).\n", - "[INFO 23-08-18 22:02:01.1420 CEST gradient_boosted_trees.cc:310] Final model num-trees:41 valid-loss:12.684937 valid-rmse:12.684937\n", - "[WARNING 23-08-18 22:02:01.1508 CEST gradient_boosted_trees.cc:1818] \"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", - "[WARNING 23-08-18 22:02:01.1508 CEST gradient_boosted_trees.cc:1829] \"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\n", - "[WARNING 23-08-18 22:02:01.1508 CEST gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", - "[INFO 23-08-18 22:02:02.0549 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", - "[INFO 23-08-18 22:02:02.0910 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", - "[INFO 23-08-18 22:02:02.1008 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", - "[INFO 23-08-18 22:02:02.1008 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", - "[INFO 23-08-18 22:02:02.2184 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", - "[INFO 23-08-18 22:02:02.3325 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", - "[INFO 23-08-18 22:02:02.3987 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.497025 train-rmse:29.497025 valid-loss:28.542063 valid-rmse:28.542063\n", - "[INFO 23-08-18 22:02:02.7815 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", - "[INFO 23-08-18 22:02:02.7816 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", - "[INFO 23-08-18 22:02:03.0006 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", - "[INFO 23-08-18 22:02:03.2160 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", - "[INFO 23-08-18 22:02:03.2163 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", - "[INFO 23-08-18 22:02:03.3580 CEST gradient_boosted_trees.cc:459] [INFO 23-08-18 22:02:03.3747 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", - "Default loss set to SQUARED_ERROR\n", - "[INFO 23-08-18 22:02:03.3829 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", - "[INFO 23-08-18 22:02:03.4437 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", - "[INFO 23-08-18 22:02:03.4438 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", - "[INFO 23-08-18 22:02:03.6323 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.474899 train-rmse:29.474899 valid-loss:28.634254 valid-rmse:28.634254\n", - "[INFO 23-08-18 22:02:03.6838 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.487211 train-rmse:29.487211 valid-loss:28.607134 valid-rmse:28.607134\n", - "[[INFO 23-08-18 22:02:03.9859 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", - "[INFO 23-08-18 22:02:04.0069 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", - "INFO 23-08-18 22:02:04.0147 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", - "[INFO 23-08-18 22:02:04.2577 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", - "[INFO 23-08-18 22:02:04.5124 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", - "[INFO 23-08-18 22:02:04.7282 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.421598 train-rmse:29.421598 valid-loss:29.626032 valid-rmse:29.626032\n", - "[INFO 23-08-18 22:02:05.1650 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.453743 train-rmse:29.453743 valid-loss:29.037130 valid-rmse:29.037130\n", - "[INFO 23-08-18 22:02:06.0216 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.541935 train-rmse:29.541935 valid-loss:28.897583 valid-rmse:28.897583\n", - "[INFO 23-08-18 22:02:06.4533 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.470354 train-rmse:29.470354 valid-loss:28.912182 valid-rmse:28.912182\n", - "[INFO 23-08-18 22:02:06.8504 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.545298 train-rmse:29.545298 valid-loss:29.298836 valid-rmse:29.298836\n", - "[INFO 23-08-18 22:02:20.4704 CEST gradient_boosted_trees.cc:1544] \tnum-trees:12 train-loss:18.709484 train-rmse:18.709484 valid-loss:19.026440 valid-rmse:19.026440\n", - "[INFO 23-08-18 22:02:51.1193 CEST gradient_boosted_trees.cc:1544] \tnum-trees:32 train-loss:15.605031 train-rmse:15.605031 valid-loss:15.806738 valid-rmse:15.806738\n", - "[INFO 23-08-18 22:03:11.3780 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:14.750937 train-rmse:14.750937 valid-loss:15.528153 valid-rmse:15.528153\n", - "[INFO 23-08-18 22:03:11.3985 CEST gradient_boosted_trees.cc:247] Truncates the model to 50 tree(s) i.e. 50 iteration(s).\n", - "[INFO 23-08-18 22:03:11.4047 CEST gradient_boosted_trees.cc:310] Final model num-trees:50 valid-loss:15.528153 valid-rmse:15.528153\n", - "[WARNING 23-08-18 22:03:11.4755 CEST gradient_boosted_trees.cc:1818] \"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", - "[WARNING 23-08-18 22:03:11.4798 CEST gradient_boosted_trees.cc:1829] \"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\n", - "[WARNING 23-08-18 22:03:11.4800 CEST gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", - "[INFO 23-08-18 22:03:13.7410 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:14.620991 train-rmse:14.620991 valid-loss:15.723470 valid-rmse:15.723470\n", - "[INFO 23-08-18 22:03:13.7651 CEST gradient_boosted_trees.cc:247] Truncates the model to 50 tree(s) i.e. 50 iteration(s).\n", - "[INFO 23-08-18 22:03:13.7725 CEST gradient_boosted_trees.cc:310] Final model num-trees:50 valid-loss:15.723470 valid-rmse:15.723470\n", - "[WARNING 23-08-18 22:03:13.7748 CEST gradient_boosted_trees.cc:1818] \"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", - "[WARNING 23-08-18 22:03:13.7748 CEST gradient_boosted_trees.cc:1829] \"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\n", - "[WARNING 23-08-18 22:03:13.7748 CEST gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", - "[INFO 23-08-18 22:03:14.4094 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:14.713874 train-rmse:14.713874 valid-loss:15.431265 valid-rmse:15.431265\n", - "[INFO 23-08-18 22:03:14.4460 CEST gradient_boosted_trees.cc:247] Truncates the model to 50 tree(s) i.e. 50 iteration(s).\n", - "[INFO 23-08-18 22:03:14.4559 CEST gradient_boosted_trees.cc:310] Final model num-trees:50 valid-loss:15.431265 valid-rmse:15.431265\n", - "[WARNING 23-08-18 22:03:14.4602 CEST gradient_boosted_trees.cc:1818] \"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", - "[WARNING 23-08-18 22:03:14.4602 CEST gradient_boosted_trees.cc:1829] \"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\n", - "[WARNING 23-08-18 22:03:14.4602 CEST gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", - "[INFO 23-08-18 22:03:14.5655 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:15.277184 train-rmse:15.277184 valid-loss:15.470344 valid-rmse:15.470344\n", - "[INFO 23-08-18 22:03:14.5884 CEST gradient_boosted_trees.cc:247] Truncates the model to 50 tree(s) i.e. 50 iteration(s).\n", - "[INFO 23-08-18 22:03:14.5965 CEST gradient_boosted_trees.cc:310] Final model num-trees:50 valid-loss:15.470344 valid-rmse:15.470344\n", - "[WARNING 23-08-18 22:03:14.6399 CEST gradient_boosted_trees.cc:1818] \"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", - "[WARNING 23-08-18 22:03:14.6400 CEST gradient_boosted_trees.cc:1829] \"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\n", - "[WARNING 23-08-18 22:03:14.6400 CEST gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", - "[INFO 23-08-18 22:03:14.8590 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:14.870501 train-rmse:14.870501 valid-loss:15.861364 valid-rmse:15.861364\n", - "[INFO 23-08-18 22:03:14.8590 CEST gradient_boosted_trees.cc:247] Truncates the model to 50 tree(s) i.e. 50 iteration(s).\n", - "[INFO 23-08-18 22:03:14.8591 CEST gradient_boosted_trees.cc:310] Final model num-trees:50 valid-loss:15.861364 valid-rmse:15.861364\n", - "[WARNING 23-08-18 22:03:14.8685 CEST gradient_boosted_trees.cc:1818] \"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", - "[WARNING 23-08-18 22:03:14.8685 CEST gradient_boosted_trees.cc:1829] \"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\n", - "[WARNING 23-08-18 22:03:14.8685 CEST gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", - "[INFO 23-08-18 22:03:15.4962 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", - "[INFO 23-08-18 22:03:15.4962 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", - "[INFO 23-08-18 22:03:15.6724 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:15.393883 train-rmse:15.393883 valid-loss:15.816501 valid-rmse:15.816501\n", - "[INFO 23-08-18 22:03:15.6787 CEST gradient_boosted_trees.cc:247] Truncates the model to 50 tree(s) i.e. 50 iteration(s).\n", - "[INFO 23-08-18 22:03:15.6787 CEST gradient_boosted_trees.cc:310] Final model num-trees:50 valid-loss:15.816501 valid-rmse:15.816501\n", - "[WARNING 23-08-18 22:03:15.6809 CEST gradient_boosted_trees.cc:1818] \"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", - "[WARNING 23-08-18 22:03:15.6811 CEST gradient_boosted_trees.cc:1829] \"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\n", - "[WARNING 23-08-18 22:03:15.6813 CEST gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", - "[INFO 23-08-18 22:03:15.9065 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", - "[INFO 23-08-18 22:03:16.0331 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:14.769040 train-rmse:14.769040 valid-loss:15.246088 valid-rmse:15.246088\n", - "[INFO 23-08-18 22:03:16.0570 CEST gradient_boosted_trees.cc:247] Truncates the model to 50 tree(s) i.e. 50 iteration(s).\n", - "[INFO 23-08-18 22:03:16.0645 CEST gradient_boosted_trees.cc:310] Final model num-trees:50 valid-loss:15.246088 valid-rmse:15.246088\n", - "[INFO 23-08-18 22:03:16.0667 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:15.019258 train-rmse:15.019258 valid-loss:15.995070 valid-rmse:15.995070\n", - "[INFO 23-08-18 22:03:16.0687 CEST gradient_boosted_trees.cc:247] Truncates the model to 50 tree(s) i.e. 50 iteration(s).\n", - "[INFO 23-08-18 22:03:16.0687 CEST gradient_boosted_trees.cc:310] Final model num-trees:50 valid-loss:15.995070 valid-rmse:15.995070\n", - "[WARNING 23-08-18 22:03:16.0712 CEST gradient_boosted_trees.cc:1818] \"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", - "[WARNING 23-08-18 22:03:16.0714 CEST gradient_boosted_trees.cc:1829] \"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\n", - "[WARNING 23-08-18 22:03:16.0717 CEST gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", - "[WARNING 23-08-18 22:03:16.0725 CEST gradient_boosted_trees.cc:1818] \"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", - "[WARNING 23-08-18 22:03:16.0726 CEST gradient_boosted_trees.cc:1829] \"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\n", - "[WARNING 23-08-18 22:03:16.0726 CEST gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", - "[INFO 23-08-18 22:03:16.4936 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", - "[INFO 23-08-18 22:03:16.5082 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", - "[INFO 23-08-18 22:03:16.6764 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", - "[INFO 23-08-18 22:03:16.8860 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.561636 train-rmse:29.561636 valid-loss:28.802879 valid-rmse:28.802879\n", - "[INFO 23-08-18 22:03:16.9083 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", - "[INFO 23-08-18 22:03:16.9084 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", - "[INFO 23-08-18 22:03:16.9435 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", - "[INFO 23-08-18 22:03:16.9437 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", - "[INFO 23-08-18 22:03:17.0687 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", - "[INFO 23-08-18 22:03:17.1606 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", - "[INFO 23-08-18 22:03:17.4994 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", - "[INFO 23-08-18 22:03:17.5236 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", - "[INFO 23-08-18 22:03:17.8828 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", - "[INFO 23-08-18 22:03:17.9670 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.519100 train-rmse:29.519100 valid-loss:28.915615 valid-rmse:28.915615\n", - "[INFO 23-08-18 22:03:18.1788 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", - "[INFO 23-08-18 22:03:18.1789 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", - "[INFO 23-08-18 22:03:18.3775 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.555222 train-rmse:29.555222 valid-loss:29.081779 valid-rmse:29.081779\n", - "[INFO 23-08-18 22:03:18.4761 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", - "[INFO 23-08-18 22:03:18.5486 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.485874 train-rmse:29.485874 valid-loss:29.859213 valid-rmse:29.859213\n", - "[INFO 23-08-18 22:03:19.2257 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", - "[INFO 23-08-18 22:03:19.2542 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", - "[INFO 23-08-18 22:03:19.3422 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", - "[INFO 23-08-18 22:03:19.3506 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", - "[INFO 23-08-18 22:03:19.4901 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", - "[INFO 23-08-18 22:03:19.6664 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.545330 train-rmse:29.545330 valid-loss:28.827353 valid-rmse:28.827353\n", - "[INFO 23-08-18 22:03:19.7627 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", - "[INFO 23-08-18 22:03:20.2336 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.580265 train-rmse:29.580265 valid-loss:28.985928 valid-rmse:28.985928\n", - "[INFO 23-08-18 22:03:21.1593 CEST gradient_boosted_trees.cc:1544] \tnum-trees:5 train-loss:23.735891 train-rmse:23.735891 valid-loss:23.227442 valid-rmse:23.227442\n", - "[INFO 23-08-18 22:03:21.5350 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.592987 train-rmse:29.592987 valid-loss:29.220953 valid-rmse:29.220953\n", - "[INFO 23-08-18 22:03:21.7667 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.552290 train-rmse:29.552290 valid-loss:29.436209 valid-rmse:29.436209\n", - "[INFO 23-08-18 22:03:51.5950 CEST gradient_boosted_trees.cc:1544] \tnum-trees:27 train-loss:16.634533 train-rmse:16.634533 valid-loss:16.681488 valid-rmse:16.681488\n", - "[INFO 23-08-18 22:04:21.8185 CEST gradient_boosted_trees.cc:1544] \tnum-trees:45 train-loss:15.733476 train-rmse:15.733476 valid-loss:17.104557 valid-rmse:17.104557\n", - "[INFO 23-08-18 22:04:26.1068 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:15.488959 train-rmse:15.488959 valid-loss:16.052216 valid-rmse:16.052216\n", - "[INFO 23-08-18 22:04:26.1068 CEST gradient_boosted_trees.cc:247] Truncates the model to 50 tree(s) i.e. 50 iteration(s).\n", - "[INFO 23-08-18 22:04:26.1068 CEST gradient_boosted_trees.cc:310] Final model num-trees:50 valid-loss:16.052216 valid-rmse:16.052216\n", - "[WARNING 23-08-18 22:04:26.2555 CEST gradient_boosted_trees.cc:1818] \"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", - "[WARNING 23-08-18 22:04:26.3144 CEST gradient_boosted_trees.cc:1829] \"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\n", - "[WARNING 23-08-18 22:04:26.3147 CEST gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", - "[INFO 23-08-18 22:04:28.4457 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:15.436479 train-rmse:15.436479 valid-loss:16.489502 valid-rmse:16.489502\n", - "[INFO 23-08-18 22:04:28.4679 CEST gradient_boosted_trees.cc:247] Truncates the model to 50 tree(s) i.e. 50 iteration(s).\n", - "[INFO 23-08-18 22:04:28.4745 CEST gradient_boosted_trees.cc:310] Final model num-trees:50 valid-loss:16.489502 valid-rmse:16.489502\n", - "[WARNING 23-08-18 22:04:28.4768 CEST gradient_boosted_trees.cc:1818] \"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", - "[WARNING 23-08-18 22:04:28.4769 CEST gradient_boosted_trees.cc:1829] \"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\n", - "[WARNING 23-08-18 22:04:28.4769 CEST gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", - "[INFO 23-08-18 22:04:29.3600 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:15.585465 train-rmse:15.585465 valid-loss:16.446827 valid-rmse:16.446827\n", - "[INFO 23-08-18 22:04:29.3600 CEST gradient_boosted_trees.cc:247] Truncates the model to 50 tree(s) i.e. 50 iteration(s).\n", - "[INFO 23-08-18 22:04:29.3601 CEST gradient_boosted_trees.cc:310] Final model num-trees:50 valid-loss:16.446827 valid-rmse:16.446827\n", - "[WARNING 23-08-18 22:04:29.3640 CEST gradient_boosted_trees.cc:1818] \"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", - "[WARNING 23-08-18 22:04:29.3640 CEST gradient_boosted_trees.cc:1829] \"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\n", - "[WARNING 23-08-18 22:04:29.3640 CEST gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", - "[INFO 23-08-18 22:04:29.7111 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:15.498506 train-rmse:15.498506 valid-loss:16.925005 valid-rmse:16.925005\n", - "[INFO 23-08-18 22:04:29.7114 CEST gradient_boosted_trees.cc:247] Truncates the model to 50 tree(s) i.e. 50 iteration(s).\n", - "[INFO 23-08-18 22:04:29.7115 CEST gradient_boosted_trees.cc:310] Final model num-trees:50 valid-loss:16.925005 valid-rmse:16.925005\n", - "[INFO 23-08-18 22:04:29.8985 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", - "[INFO 23-08-18 22:04:29.9154 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:15.528732 train-rmse:15.528732 valid-loss:16.360813 valid-rmse:16.360813\n", - "[INFO 23-08-18 22:04:29.9154 CEST gradient_boosted_trees.cc:247] Truncates the model to 50 tree(s) i.e. 50 iteration(s).\n", - "[INFO 23-08-18 22:04:29.9155 CEST gradient_boosted_trees.cc:310] Final model num-trees:50 valid-loss:16.360813 valid-rmse:16.360813\n", - "[INFO 23-08-18 22:04:29.9267 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", - "[INFO 23-08-18 22:04:30.0825 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", - "[INFO 23-08-18 22:04:30.5422 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:15.795024 train-rmse:15.795024 valid-loss:17.031176 valid-rmse:17.031176\n", - "[INFO 23-08-18 22:04:30.6062 CEST gradient_boosted_trees.cc:247] Truncates the model to 50 tree(s) i.e. 50 iteration(s).\n", - "[INFO 23-08-18 22:04:30.6090 CEST gradient_boosted_trees.cc:310] Final model num-trees:50 valid-loss:17.031176 valid-rmse:17.031176\n", - "[INFO 23-08-18 22:04:30.9694 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", - "[INFO 23-08-18 22:04:30.9694 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", - "[INFO 23-08-18 22:04:31.0827 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", - "[INFO 23-08-18 22:04:31.2504 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:15.940226 train-rmse:15.940226 valid-loss:16.628620 valid-rmse:16.628620\n", - "[INFO 23-08-18 22:04:31.2506 CEST gradient_boosted_trees.cc:247] Truncates the model to 50 tree(s) i.e. 50 iteration(s).\n", - "[INFO 23-08-18 22:04:31.2510 CEST gradient_boosted_trees.cc:310] Final model num-trees:50 valid-loss:16.628620 valid-rmse:16.628620\n", - "[INFO 23-08-18 22:04:31.4163 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.473177 train-rmse:29.473177 valid-loss:30.028440 valid-rmse:30.028440\n", - "[INFO 23-08-18 22:04:31.4533 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:15.958979 train-rmse:15.958979 valid-loss:17.016514 valid-rmse:17.016514\n", - "[INFO 23-08-18 22:04:31.4533 CEST gradient_boosted_trees.cc:247] Truncates the model to 50 tree(s) i.e. 50 iteration(s).\n", - "[INFO 23-08-18 22:04:31.4534 CEST gradient_boosted_trees.cc:310] Final model num-trees:50 valid-loss:17.016514 valid-rmse:17.016514\n", - "[INFO 23-08-18 22:04:31.7284 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", - "[INFO 23-08-18 22:04:31.7285 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", - "[INFO 23-08-18 22:04:31.7701 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.512590 train-rmse:29.512590 valid-loss:29.749025 valid-rmse:29.749025\n", - "[INFO 23-08-18 22:04:31.8506 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", - "[INFO 23-08-18 22:04:32.5836 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.433599 train-rmse:29.433599 valid-loss:30.823900 valid-rmse:30.823900\n", - "[INFO 23-08-18 22:04:51.7864 CEST gradient_boosted_trees.cc:1544] \tnum-trees:34 train-loss:16.647221 train-rmse:16.647221 valid-loss:17.445215 valid-rmse:17.445215\n", - "[INFO 23-08-18 22:05:00.1259 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:15.972644 train-rmse:15.972644 valid-loss:17.476349 valid-rmse:17.476349\n", - "[INFO 23-08-18 22:05:00.1260 CEST gradient_boosted_trees.cc:247] Truncates the model to 50 tree(s) i.e. 50 iteration(s).\n", - "[INFO 23-08-18 22:05:00.1260 CEST gradient_boosted_trees.cc:310] Final model num-trees:50 valid-loss:17.476349 valid-rmse:17.476349\n", - "[INFO 23-08-18 22:05:00.3792 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:15.949216 train-rmse:15.949216 valid-loss:16.998175 valid-rmse:16.998175\n", - "[INFO 23-08-18 22:05:00.3793 CEST gradient_boosted_trees.cc:247] Truncates the model to 50 tree(s) i.e. 50 iteration(s).\n", - "[INFO 23-08-18 22:05:00.3793 CEST gradient_boosted_trees.cc:310] Final model num-trees:50 valid-loss:16.998175 valid-rmse:16.998175\n", - "[INFO 23-08-18 22:05:00.9771 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:15.933821 train-rmse:15.933821 valid-loss:18.116308 valid-rmse:18.116308\n", - "[INFO 23-08-18 22:05:01.0064 CEST gradient_boosted_trees.cc:247] Truncates the model to 50 tree(s) i.e. 50 iteration(s).\n", - "[INFO 23-08-18 22:05:01.0065 CEST gradient_boosted_trees.cc:310] Final model num-trees:50 valid-loss:18.116308 valid-rmse:18.116308\n", - "[INFO 23-08-18 22:05:01.0266 CEST kernel.cc:926] Export model in log directory: /tmp/tmplcypdssc with prefix 42d4958eb01341a8\n", - "[INFO 23-08-18 22:05:01.1251 CEST kernel.cc:944] Save model in resources\n", - "[WARNING 23-08-18 22:05:01.1283 CEST abstract_model.cc:1058] Validation evaluation not supported for MULTITASKER\n", - "[INFO 23-08-18 22:05:01.1292 CEST abstract_model.cc:849] Model self evaluation:\n", - "Number of predictions (with weights): 1\n", - "Task: REGRESSION\n", - "Loss (SQUARED_ERROR): 12.6849\n", - "\n", - "RMSE: 3.56159\n", - "Default RMSE: : 0\n", - "\n", - "[INFO 23-08-18 22:05:01.1298 CEST abstract_model.cc:849] Model self evaluation:\n", - "Number of predictions (with weights): 1\n", - "Task: REGRESSION\n", - "Loss (SQUARED_ERROR): 13.4037\n", - "\n", - "RMSE: 3.66111\n", - "Default RMSE: : 0\n", - "\n", - "[INFO 23-08-18 22:05:01.1304 CEST abstract_model.cc:849] Model self evaluation:\n", - "Number of predictions (with weights): 1\n", - "Task: REGRESSION\n", - "Loss (SQUARED_ERROR): 13.4538\n", - "\n", - "RMSE: 3.66794\n", - "Default RMSE: : 0\n", - "\n", - "[INFO 23-08-18 22:05:01.1310 CEST abstract_model.cc:849] Model self evaluation:\n", - "Number of predictions (with weights): 1\n", - "Task: REGRESSION\n", - "Loss (SQUARED_ERROR): 13.5662\n", - "\n", - "RMSE: 3.68323\n", - "Default RMSE: : 0\n", - "\n", - "[INFO 23-08-18 22:05:01.1315 CEST abstract_model.cc:849] Model self evaluation:\n", - "Number of predictions (with weights): 1\n", - "Task: REGRESSION\n", - "Loss (SQUARED_ERROR): 15.0292\n", - "\n", - "RMSE: 3.87676\n", - "Default RMSE: : 0\n", - "\n", - "[INFO 23-08-18 22:05:01.1319 CEST abstract_model.cc:849] Model self evaluation:\n", - "Number of predictions (with weights): 1\n", - "Task: REGRESSION\n", - "Loss (SQUARED_ERROR): 15.3554\n", - "\n", - "RMSE: 3.9186\n", - "Default RMSE: : 0\n", - "\n", - "[INFO 23-08-18 22:05:01.1323 CEST abstract_model.cc:849] Model self evaluation:\n", - "Number of predictions (with weights): 1\n", - "Task: REGRESSION\n", - "Loss (SQUARED_ERROR): 14.5934\n", - "\n", - "RMSE: 3.82013\n", - "Default RMSE: : 0\n", - "\n", - "[INFO 23-08-18 22:05:01.1327 CEST abstract_model.cc:849] Model self evaluation:\n", - "Number of predictions (with weights): 1\n", - "Task: REGRESSION\n", - "Loss (SQUARED_ERROR): 14.9942\n", - "\n", - "RMSE: 3.87224\n", - "Default RMSE: : 0\n", - "\n", - "[INFO 23-08-18 22:05:01.1331 CEST abstract_model.cc:849] Model self evaluation:\n", - "Number of predictions (with weights): 1\n", - "Task: REGRESSION\n", - "Loss (SQUARED_ERROR): 15.4313\n", - "\n", - "RMSE: 3.92826\n", - "Default RMSE: : 0\n", - "\n", - "[INFO 23-08-18 22:05:01.1335 CEST abstract_model.cc:849] Model self evaluation:\n", - "Number of predictions (with weights): 1\n", - "Task: REGRESSION\n", - "Loss (SQUARED_ERROR): 15.5282\n", - "\n", - "RMSE: 3.94058\n", - "Default RMSE: : 0\n", - "\n", - "[INFO 23-08-18 22:05:01.1340 CEST abstract_model.cc:849] Model self evaluation:\n", - "Number of predictions (with weights): 1\n", - "Task: REGRESSION\n", - "Loss (SQUARED_ERROR): 15.7235\n", - "\n", - "RMSE: 3.96528\n", - "Default RMSE: : 0\n", - "\n", - "[INFO 23-08-18 22:05:01.1345 CEST abstract_model.cc:849] Model self evaluation:\n", - "Number of predictions (with weights): 1\n", - "Task: REGRESSION\n", - "Loss (SQUARED_ERROR): 15.2461\n", - "\n", - "RMSE: 3.90462\n", - "Default RMSE: : 0\n", - "\n", - "[INFO 23-08-18 22:05:01.1350 CEST abstract_model.cc:849] Model self evaluation:\n", - "Number of predictions (with weights): 1\n", - "Task: REGRESSION\n", - "Loss (SQUARED_ERROR): 15.8614\n", - "\n", - "RMSE: 3.98263\n", - "Default RMSE: : 0\n", - "\n", - "[INFO 23-08-18 22:05:01.1356 CEST abstract_model.cc:849] Model self evaluation:\n", - "Number of predictions (with weights): 1\n", - "Task: REGRESSION\n", - "Loss (SQUARED_ERROR): 15.9951\n", - "\n", - "RMSE: 3.99938\n", - "Default RMSE: : 0\n", - "\n", - "[INFO 23-08-18 22:05:01.1360 CEST abstract_model.cc:849] Model self evaluation:\n", - "Number of predictions (with weights): 1\n", - "Task: REGRESSION\n", - "Loss (SQUARED_ERROR): 15.4703\n", - "\n", - "RMSE: 3.93324\n", - "Default RMSE: : 0\n", - "\n", - "[INFO 23-08-18 22:05:01.1364 CEST abstract_model.cc:849] Model self evaluation:\n", - "Number of predictions (with weights): 1\n", - "Task: REGRESSION\n", - "Loss (SQUARED_ERROR): 15.8165\n", - "\n", - "RMSE: 3.977\n", - "Default RMSE: : 0\n", - "\n", - "[INFO 23-08-18 22:05:01.1369 CEST abstract_model.cc:849] Model self evaluation:\n", - "Number of predictions (with weights): 1\n", - "Task: REGRESSION\n", - "Loss (SQUARED_ERROR): 16.0522\n", - "\n", - "RMSE: 4.00652\n", - "Default RMSE: : 0\n", - "\n", - "[INFO 23-08-18 22:05:01.1375 CEST abstract_model.cc:849] Model self evaluation:\n", - "Number of predictions (with weights): 1\n", - "Task: REGRESSION\n", - "Loss (SQUARED_ERROR): 16.4895\n", - "\n", - "RMSE: 4.06073\n", - "Default RMSE: : 0\n", - "\n", - "[INFO 23-08-18 22:05:01.1394 CEST abstract_model.cc:849] Model self evaluation:\n", - "Number of predictions (with weights): 1\n", - "Task: REGRESSION\n", - "Loss (SQUARED_ERROR): 16.3608\n", - "\n", - "RMSE: 4.04485\n", - "Default RMSE: : 0\n", - "\n", - "[INFO 23-08-18 22:05:01.1401 CEST abstract_model.cc:849] Model self evaluation:\n", - "Number of predictions (with weights): 1\n", - "Task: REGRESSION\n", - "Loss (SQUARED_ERROR): 16.925\n", - "\n", - "RMSE: 4.114\n", - "Default RMSE: : 0\n", - "\n", - "[INFO 23-08-18 22:05:01.1406 CEST abstract_model.cc:849] Model self evaluation:\n", - "Number of predictions (with weights): 1\n", - "Task: REGRESSION\n", - "Loss (SQUARED_ERROR): 16.4468\n", - "\n", - "RMSE: 4.05547\n", - "Default RMSE: : 0\n", - "\n", - "[INFO 23-08-18 22:05:01.1412 CEST abstract_model.cc:849] Model self evaluation:\n", - "Number of predictions (with weights): 1\n", - "Task: REGRESSION\n", - "Loss (SQUARED_ERROR): 17.0312\n", - "\n", - "RMSE: 4.12688\n", - "Default RMSE: : 0\n", - "\n", - "[INFO 23-08-18 22:05:01.1418 CEST abstract_model.cc:849] Model self evaluation:\n", - "Number of predictions (with weights): 1\n", - "Task: REGRESSION\n", - "Loss (SQUARED_ERROR): 16.6286\n", - "\n", - "RMSE: 4.07782\n", - "Default RMSE: : 0\n", - "\n", - "[INFO 23-08-18 22:05:01.1423 CEST abstract_model.cc:849] Model self evaluation:\n", - "Number of predictions (with weights): 1\n", - "Task: REGRESSION\n", - "Loss (SQUARED_ERROR): 17.0165\n", - "\n", - "RMSE: 4.12511\n", - "Default RMSE: : 0\n", - "\n", - "[INFO 23-08-18 22:05:01.1430 CEST abstract_model.cc:849] Model self evaluation:\n", - "Number of predictions (with weights): 1\n", - "Task: REGRESSION\n", - "Loss (SQUARED_ERROR): 17.4763\n", - "\n", - "RMSE: 4.18047\n", - "Default RMSE: : 0\n", - "\n", - "[INFO 23-08-18 22:05:01.1436 CEST abstract_model.cc:849] Model self evaluation:\n", - "Number of predictions (with weights): 1\n", - "Task: REGRESSION\n", - "Loss (SQUARED_ERROR): 16.9982\n", - "\n", - "RMSE: 4.12288\n", - "Default RMSE: : 0\n", - "\n", - "[INFO 23-08-18 22:05:01.1448 CEST abstract_model.cc:849] Model self evaluation:\n", - "Number of predictions (with weights): 1\n", - "Task: REGRESSION\n", - "Loss (SQUARED_ERROR): 18.1163\n", - "\n", - "RMSE: 4.25633\n", - "Default RMSE: : 0\n", - "\n", - "[INFO 23-08-18 22:05:01.2297 CEST kernel.cc:1243] Loading model from path /tmp/tmplcypdssc/model/ with prefix 42d4958eb01341a8_0\n", - "[INFO 23-08-18 22:05:01.2350 CEST abstract_model.cc:1311] Engine \"GradientBoostedTreesQuickScorerExtended\" built\n", - "[INFO 23-08-18 22:05:01.2351 CEST kernel.cc:1075] Use fast generic engine\n", - "[INFO 23-08-18 22:05:01.2718 CEST kernel.cc:1243] Loading model from path /tmp/tmplcypdssc/model/ with prefix 42d4958eb01341a8_1\n", - "[INFO 23-08-18 22:05:01.2805 CEST kernel.cc:1075] Use fast generic engine\n", - "[INFO 23-08-18 22:05:01.3239 CEST kernel.cc:1243] Loading model from path /tmp/tmplcypdssc/model/ with prefix 42d4958eb01341a8_2\n", - "[INFO 23-08-18 22:05:01.3302 CEST kernel.cc:1075] Use fast generic engine\n", - "[INFO 23-08-18 22:05:01.3619 CEST kernel.cc:1243] Loading model from path /tmp/tmplcypdssc/model/ with prefix 42d4958eb01341a8_3\n", - "[INFO 23-08-18 22:05:01.3681 CEST kernel.cc:1075] Use fast generic engine\n", - "[INFO 23-08-18 22:05:01.3998 CEST kernel.cc:1243] Loading model from path /tmp/tmplcypdssc/model/ with prefix 42d4958eb01341a8_4\n", - "[INFO 23-08-18 22:05:01.4058 CEST kernel.cc:1075] Use fast generic engine\n", - "[INFO 23-08-18 22:05:01.4539 CEST kernel.cc:1243] Loading model from path /tmp/tmplcypdssc/model/ with prefix 42d4958eb01341a8_5\n", - "[INFO 23-08-18 22:05:01.4630 CEST kernel.cc:1075] Use fast generic engine\n", - "[INFO 23-08-18 22:05:01.5081 CEST kernel.cc:1243] Loading model from path /tmp/tmplcypdssc/model/ with prefix 42d4958eb01341a8_6\n", - "[INFO 23-08-18 22:05:01.5141 CEST kernel.cc:1075] Use fast generic engine\n", - "[INFO 23-08-18 22:05:01.5473 CEST kernel.cc:1243] Loading model from path /tmp/tmplcypdssc/model/ with prefix 42d4958eb01341a8_7\n", - "[INFO 23-08-18 22:05:01.5554 CEST kernel.cc:1075] Use fast generic engine\n", - "[INFO 23-08-18 22:05:01.5900 CEST kernel.cc:1243] Loading model from path /tmp/tmplcypdssc/model/ with prefix 42d4958eb01341a8_8\n", - "[INFO 23-08-18 22:05:01.5981 CEST kernel.cc:1075] Use fast generic engine\n", - "[INFO 23-08-18 22:05:01.6303 CEST kernel.cc:1243] Loading model from path /tmp/tmplcypdssc/model/ with prefix 42d4958eb01341a8_9\n", - "[INFO 23-08-18 22:05:01.6381 CEST kernel.cc:1075] Use fast generic engine\n", - "[INFO 23-08-18 22:05:01.6704 CEST kernel.cc:1243] Loading model from path /tmp/tmplcypdssc/model/ with prefix 42d4958eb01341a8_10\n", - "[INFO 23-08-18 22:05:01.6778 CEST kernel.cc:1075] Use fast generic engine\n", - "[INFO 23-08-18 22:05:01.7085 CEST kernel.cc:1243] Loading model from path /tmp/tmplcypdssc/model/ with prefix 42d4958eb01341a8_11\n", - "[INFO 23-08-18 22:05:01.7159 CEST kernel.cc:1075] Use fast generic engine\n", - "[INFO 23-08-18 22:05:01.7443 CEST kernel.cc:1243] Loading model from path /tmp/tmplcypdssc/model/ with prefix 42d4958eb01341a8_12\n", - "[INFO 23-08-18 22:05:01.7512 CEST kernel.cc:1075] Use fast generic engine\n", - "[INFO 23-08-18 22:05:01.7837 CEST kernel.cc:1243] Loading model from path /tmp/tmplcypdssc/model/ with prefix 42d4958eb01341a8_13\n", - "[INFO 23-08-18 22:05:01.7912 CEST kernel.cc:1075] Use fast generic engine\n", - "[INFO 23-08-18 22:05:01.8208 CEST kernel.cc:1243] Loading model from path /tmp/tmplcypdssc/model/ with prefix 42d4958eb01341a8_14\n", - "[INFO 23-08-18 22:05:01.8270 CEST kernel.cc:1075] Use fast generic engine\n", - "[INFO 23-08-18 22:05:01.8551 CEST kernel.cc:1243] Loading model from path /tmp/tmplcypdssc/model/ with prefix 42d4958eb01341a8_15\n", - "[INFO 23-08-18 22:05:01.8605 CEST kernel.cc:1075] Use fast generic engine\n", - "[INFO 23-08-18 22:05:01.8857 CEST kernel.cc:1243] Loading model from path /tmp/tmplcypdssc/model/ with prefix 42d4958eb01341a8_16\n", - "[INFO 23-08-18 22:05:01.8914 CEST kernel.cc:1075] Use fast generic engine\n", - "[INFO 23-08-18 22:05:01.9151 CEST kernel.cc:1243] Loading model from path /tmp/tmplcypdssc/model/ with prefix 42d4958eb01341a8_17\n", - "[INFO 23-08-18 22:05:01.9201 CEST kernel.cc:1075] Use fast generic engine\n", - "[INFO 23-08-18 22:05:01.9445 CEST kernel.cc:1243] Loading model from path /tmp/tmplcypdssc/model/ with prefix 42d4958eb01341a8_18\n", - "[INFO 23-08-18 22:05:01.9495 CEST kernel.cc:1075] Use fast generic engine\n", - "[INFO 23-08-18 22:05:01.9751 CEST kernel.cc:1243] Loading model from path /tmp/tmplcypdssc/model/ with prefix 42d4958eb01341a8_19\n", - "[INFO 23-08-18 22:05:01.9801 CEST kernel.cc:1075] Use fast generic engine\n", - "[INFO 23-08-18 22:05:02.0072 CEST kernel.cc:1243] Loading model from path /tmp/tmplcypdssc/model/ with prefix 42d4958eb01341a8_20\n", - "[INFO 23-08-18 22:05:02.0118 CEST kernel.cc:1075] Use fast generic engine\n", - "[INFO 23-08-18 22:05:02.0385 CEST kernel.cc:1243] Loading model from path /tmp/tmplcypdssc/model/ with prefix 42d4958eb01341a8_21\n", - "[INFO 23-08-18 22:05:02.0431 CEST kernel.cc:1075] Use fast generic engine\n", - "[INFO 23-08-18 22:05:02.0683 CEST kernel.cc:1243] Loading model from path /tmp/tmplcypdssc/model/ with prefix 42d4958eb01341a8_22\n", - "[INFO 23-08-18 22:05:02.0729 CEST kernel.cc:1075] Use fast generic engine\n", - "[INFO 23-08-18 22:05:02.0974 CEST kernel.cc:1243] Loading model from path /tmp/tmplcypdssc/model/ with prefix 42d4958eb01341a8_23\n", - "[INFO 23-08-18 22:05:02.1018 CEST kernel.cc:1075] Use fast generic engine\n", - "[INFO 23-08-18 22:05:02.1401 CEST kernel.cc:1243] Loading model from path /tmp/tmplcypdssc/model/ with prefix 42d4958eb01341a8_24\n", - "[INFO 23-08-18 22:05:02.1451 CEST kernel.cc:1075] Use fast generic engine\n", - "[INFO 23-08-18 22:05:02.1746 CEST kernel.cc:1243] Loading model from path /tmp/tmplcypdssc/model/ with prefix 42d4958eb01341a8_25\n", - "[INFO 23-08-18 22:05:02.1796 CEST kernel.cc:1075] Use fast generic engine\n", - "[INFO 23-08-18 22:05:02.2044 CEST kernel.cc:1243] Loading model from path /tmp/tmplcypdssc/model/ with prefix 42d4958eb01341a8_26\n", - "[INFO 23-08-18 22:05:02.2094 CEST kernel.cc:1075] Use fast generic engine\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Model trained in 0:04:15.405290\n", - "Compiling model...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:AutoGraph could not transform and will run it as-is.\n", - "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n", - "Cause: could not get source code\n", - "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: AutoGraph could not transform and will run it as-is.\n", - "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n", - "Cause: could not get source code\n", - "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n", - "Model compiled.\n" - ] - } - ], + "outputs": [], "source": [ "model_path = os.path.join(work_directory, \"gbm.model\")\n", "if os.path.exists(model_path):\n", @@ -5849,7 +1251,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": null, "id": "e985dba0-dc14-4341-b0c8-ad145fef7ca3", "metadata": {}, "outputs": [], @@ -5868,7 +1270,7 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": null, "id": "068ea997-52ae-40eb-b18e-a118a3d7b026", "metadata": {}, "outputs": [], @@ -5924,28 +1326,10 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": null, "id": "978fc1ff-7914-43df-af04-e5b054a9e9f2", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The number of plots (80) is larger than \"options.max_num_plots=6\". Only the first plots will be printed.\n" - ] - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "real_sales_evset = sales_evset[\"sales\"].filter(sales_evset[\"day\"] >= 1914)\n", "\n", From 8a6f2f1c089a5febab29eee9320eff3357a3c4d7 Mon Sep 17 00:00:00 2001 From: Mathieu Guillame-Bert Date: Tue, 22 Aug 2023 17:27:47 +0200 Subject: [PATCH 3/3] Update m5_competition.ipynb --- docs/src/tutorials/m5_competition.ipynb | 1769 ++++++++++++++++++----- 1 file changed, 1417 insertions(+), 352 deletions(-) diff --git a/docs/src/tutorials/m5_competition.ipynb b/docs/src/tutorials/m5_competition.ipynb index 79f1720e0..3c8f8d533 100644 --- a/docs/src/tutorials/m5_competition.ipynb +++ b/docs/src/tutorials/m5_competition.ipynb @@ -136,12 +136,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "total 879848\n", - "-rw-r--r--@ 1 ian staff 101K Aug 18 11:22 calendar.csv\n", - "-rw-r--r--@ 1 ian staff 116M Aug 18 11:22 sales_train_evaluation.csv\n", - "-rw-r--r--@ 1 ian staff 114M Aug 18 11:22 sales_train_validation.csv\n", - "-rw-r--r--@ 1 ian staff 5.0M Aug 18 11:22 sample_submission.csv\n", - "-rw-r--r--@ 1 ian staff 194M Aug 18 11:22 sell_prices.csv\n" + "total 430M\n", + "-rw-r----- 1 gbm primarygroup 102K Aug 18 21:48 calendar.csv\n", + "-rw-r----- 1 gbm primarygroup 117M Aug 18 21:48 sales_train_evaluation.csv\n", + "-rw-r----- 1 gbm primarygroup 115M Aug 18 21:48 sales_train_validation.csv\n", + "-rw-r----- 1 gbm primarygroup 5.0M Aug 18 21:48 sample_submission.csv\n", + "-rw-r----- 1 gbm primarygroup 194M Aug 18 21:48 sell_prices.csv\n" ] } ], @@ -1116,7 +1116,7 @@ "data": { "text/html": [ "\n", - "
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+ " const render_items = [{\"docid\":\"a080d758-2e00-4fab-b825-1066c4799484\",\"roots\":{\"p1560\":\"b857ad2f-86ad-4af8-a9fe-e48cd339deff\"},\"root_ids\":[\"p1560\"]}];\n", " root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n", " }\n", " if (root.Bokeh !== undefined) {\n", @@ -1161,7 +1161,7 @@ } ], "source": [ - "sales_evset.plot(max_num_plots=4, interactive=True, width_px=500)" + "sales_evset.plot(max_num_plots=4, interactive=True, width_px=600)" ] }, { @@ -1898,7 +1898,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Selected product (selected_index): (b'FOODS_3_714', b'FOODS_3', b'FOODS', b'WI_3', b'WI')\n" + "Selected product (selected_index): (b'FOODS_3_694', b'FOODS_3', b'FOODS', b'WI_2', b'WI')\n" ] } ], @@ -1931,7 +1931,7 @@ " # Make the plot interactive.\n", " \"interactive\": True,\n", " # Make sure the toolbar is visible\n", - " \"width_px\": 500,\n", + " \"width_px\": 600,\n", " # Only plot the data for the year 2015.\n", " \"min_time\": datetime(2015, 1, 1),\n", " \"max_time\": datetime(2015, 12, 31),\n", @@ -1982,7 +1982,7 @@ "\n", "# Prediction horizon. In the M5 competition, participants should forecast the next 28 days.\n", "# You can predict less days to make the training section of this notebook run faster.\n", - "horizons = list(range(1, 14))\n", + "horizons = list(range(1, 28))\n", "\n", "# For each of the horizons.\n", "for horizon in horizons:\n", @@ -2018,7 +2018,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "The number of plots (13) is larger than \"options.max_num_plots=4\". Only the first plots will be printed.\n" + "The number of plots (27) is larger than \"options.max_num_plots=4\". Only the first plots will be printed.\n" ] }, { @@ -2310,7 +2310,7 @@ "data": { "text/html": [ "\n", - "
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+ " const render_items = [{\"docid\":\"97172bdc-7f9c-4349-ae2e-eea385b6ed94\",\"roots\":{\"p8621\":\"fdaf7267-aa15-431e-9658-0c40c9fc68ec\"},\"root_ids\":[\"p8621\"]}];\n", " root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n", " }\n", " if (root.Bokeh !== undefined) {\n", @@ -3493,7 +3493,7 @@ }, "metadata": { "application/vnd.bokehjs_exec.v0+json": { - "id": "p8637" + "id": "p8621" } }, "output_type": "display_data" @@ -3582,7 +3582,7 @@ }, { "data": { - "image/png": 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", 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", 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" ] @@ -3592,7 +3592,7 @@ } ], "source": [ - "sum28_sales_per_dept.plot(max_num_plots=3)" + "sum28_sales_per_dept.plot(max_num_plots=3, width_px=600)" ] }, { @@ -3633,7 +3633,7 @@ }, { "data": { - "image/png": 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", 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+ " const render_items = [{\"docid\":\"0b61f21a-270c-4de3-a7ec-1d23bb7876d0\",\"roots\":{\"p10373\":\"f6c65aac-2e6a-40e8-b028-24ca0f8948af\"},\"root_ids\":[\"p10373\"]}];\n", " root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n", " }\n", " if (root.Bokeh !== undefined) {\n", @@ -4027,7 +4027,7 @@ }, "metadata": { "application/vnd.bokehjs_exec.v0+json": { - "id": "p10392" + "id": "p10373" } }, "output_type": "display_data" @@ -4058,7 +4058,7 @@ { "data": { "text/plain": [ - "features: [('f_sales', int64), ('f_sales_lag_1_d', int64), ('f_sales_lag_2_d', int64), ('f_sales_lag_3_d', int64), ('f_sales_lag_7_d', int64), ('f_sales_lag_14_d', int64), ('f_sma_7_sales', float32), ('f_sd_7_sales', float32), ('f_sum_7_sales', float32), ('f_sma_14_sales', float32), ('f_sd_14_sales', float32), ('f_sum_14_sales', float32), ('f_sma_28_sales', float32), ('f_sd_28_sales', float32), ('f_sum_28_sales', float32), ('f_sma_84_sales', float32), ('f_sd_84_sales', float32), ('f_sum_84_sales', float32), ('f_calendar_day_of_month', int32), ('f_calendar_day_of_week', int32), ('f_calendar_month', int32), ('f_sum28_per_dep_sales', int64), ('f_event_name_1', str_), ('f_event_type_1', str_), ('f_event_name_2', str_), ('f_event_type_2', str_), ('f_snap_CA', int64), ('f_snap_TX', int64), ('f_snap_WI', int64), ('f_leak5_event_name_1', str_), ('f_leak5_event_type_1', str_), ('f_leak5_event_name_2', str_), ('f_leak5_event_type_2', str_), ('f_leak5_snap_CA', int64), ('f_leak5_snap_TX', int64), ('f_leak5_snap_WI', int64), ('day', int64), ('label_horizon_1_days', int64), ('label_horizon_2_days', int64), ('label_horizon_3_days', int64), ('label_horizon_4_days', int64), ('label_horizon_5_days', int64), ('label_horizon_6_days', int64), ('label_horizon_7_days', int64), ('label_horizon_8_days', int64), ('label_horizon_9_days', int64), ('label_horizon_10_days', int64), ('label_horizon_11_days', int64), ('label_horizon_12_days', int64), ('label_horizon_13_days', int64)]\n", + "features: [('f_sales', int64), ('f_sales_lag_1_d', int64), ('f_sales_lag_2_d', int64), ('f_sales_lag_3_d', int64), ('f_sales_lag_7_d', int64), ('f_sales_lag_14_d', int64), ('f_sma_7_sales', float32), ('f_sd_7_sales', float32), ('f_sum_7_sales', float32), ('f_sma_14_sales', float32), ('f_sd_14_sales', float32), ('f_sum_14_sales', float32), ('f_sma_28_sales', float32), ('f_sd_28_sales', float32), ('f_sum_28_sales', float32), ('f_sma_84_sales', float32), ('f_sd_84_sales', float32), ('f_sum_84_sales', float32), ('f_calendar_day_of_month', int32), ('f_calendar_day_of_week', int32), ('f_calendar_month', int32), ('f_sum28_per_dep_sales', int64), ('f_event_name_1', str_), ('f_event_type_1', str_), ('f_event_name_2', str_), ('f_event_type_2', str_), ('f_snap_CA', int64), ('f_snap_TX', int64), ('f_snap_WI', int64), ('f_leak5_event_name_1', str_), ('f_leak5_event_type_1', str_), ('f_leak5_event_name_2', str_), ('f_leak5_event_type_2', str_), ('f_leak5_snap_CA', int64), ('f_leak5_snap_TX', int64), ('f_leak5_snap_WI', int64), ('day', int64), ('label_horizon_1_days', int64), ('label_horizon_2_days', int64), ('label_horizon_3_days', int64), ('label_horizon_4_days', int64), ('label_horizon_5_days', int64), ('label_horizon_6_days', int64), ('label_horizon_7_days', int64), ('label_horizon_8_days', int64), ('label_horizon_9_days', int64), ('label_horizon_10_days', int64), ('label_horizon_11_days', int64), ('label_horizon_12_days', int64), ('label_horizon_13_days', int64), ('label_horizon_14_days', int64), ('label_horizon_15_days', int64), ('label_horizon_16_days', int64), ('label_horizon_17_days', int64), ('label_horizon_18_days', int64), ('label_horizon_19_days', int64), ('label_horizon_20_days', int64), ('label_horizon_21_days', int64), ('label_horizon_22_days', int64), ('label_horizon_23_days', int64), ('label_horizon_24_days', int64), ('label_horizon_25_days', int64), ('label_horizon_26_days', int64), ('label_horizon_27_days', int64)]\n", "indexes: [('item_id', str_), ('dept_id', str_), ('cat_id', str_), ('store_id', str_), ('state_id', str_)]\n", "is_unix_timestamp: True" ] @@ -4149,179 +4149,179 @@ " f_sales_lag_3_d\n", " f_sales_lag_7_d\n", " ...\n", - " label_horizon_5_days\n", - " label_horizon_6_days\n", - " label_horizon_7_days\n", - " label_horizon_8_days\n", - " label_horizon_9_days\n", - " label_horizon_10_days\n", - " label_horizon_11_days\n", - " label_horizon_12_days\n", - " label_horizon_13_days\n", + " label_horizon_19_days\n", + " label_horizon_20_days\n", + " label_horizon_21_days\n", + " label_horizon_22_days\n", + " label_horizon_23_days\n", + " label_horizon_24_days\n", + " label_horizon_25_days\n", + " label_horizon_26_days\n", + " label_horizon_27_days\n", " timestamp\n", " \n", " \n", " \n", " \n", " 0\n", - " FOODS_3_714\n", + " FOODS_3_694\n", " FOODS_3\n", " FOODS\n", - " WI_3\n", + " WI_2\n", " WI\n", - " 22\n", - " 20\n", - " 36\n", - " 26\n", - " 20\n", + " 24\n", + " 29\n", + " 33\n", + " 31\n", + " 38\n", " ...\n", " 31\n", - " 19\n", - " 21\n", - " 13\n", - " 18\n", - " 25\n", - " 42\n", + " 31\n", + " 34\n", + " 24\n", + " 36\n", + " 30\n", + " 44\n", " 37\n", - " 27\n", + " 42\n", " 1.461542e+09\n", " \n", " \n", " 1\n", - " FOODS_3_714\n", + " FOODS_3_694\n", " FOODS_3\n", " FOODS\n", " WI_1\n", " WI\n", - " 17\n", - " 15\n", - " 30\n", + " 13\n", + " 24\n", + " 28\n", " 18\n", - " 16\n", + " 27\n", " ...\n", - " 12\n", - " 13\n", - " 21\n", - " 16\n", - " 13\n", + " 10\n", + " 27\n", + " 20\n", + " 20\n", " 16\n", - " 18\n", - " 26\n", - " 28\n", + " 22\n", + " 31\n", + " 30\n", + " 32\n", " 1.461542e+09\n", " \n", " \n", " 2\n", - " FOODS_3_714\n", + " FOODS_3_694\n", " FOODS_3\n", " FOODS\n", " TX_3\n", " TX\n", - " 32\n", - " 19\n", - " 30\n", + " 21\n", " 17\n", - " 13\n", + " 31\n", + " 20\n", + " 16\n", " ...\n", - " 34\n", - " 38\n", - " 25\n", - " 27\n", + " 21\n", + " 26\n", + " 17\n", + " 35\n", " 19\n", - " 24\n", - " 8\n", - " 27\n", - " 31\n", + " 23\n", + " 22\n", + " 14\n", + " 21\n", " 1.461542e+09\n", " \n", " \n", " 3\n", - " FOODS_3_714\n", + " FOODS_3_694\n", " FOODS_3\n", " FOODS\n", - " CA_4\n", - " CA\n", - " 6\n", - " 8\n", - " 5\n", - " 10\n", - " 8\n", + " TX_2\n", + " TX\n", + " 26\n", + " 21\n", + " 17\n", + " 22\n", + " 9\n", " ...\n", - " 10\n", + " 26\n", + " 33\n", " 12\n", - " 9\n", - " 4\n", - " 5\n", - " 5\n", - " 5\n", - " 7\n", - " 9\n", + " 19\n", + " 13\n", + " 11\n", + " 18\n", + " 38\n", + " 25\n", " 1.461542e+09\n", " \n", " \n", " 4\n", - " FOODS_3_714\n", + " FOODS_3_694\n", " FOODS_3\n", " FOODS\n", - " CA_1\n", - " CA\n", - " 10\n", - " 27\n", - " 22\n", - " 18\n", - " 15\n", + " WI_3\n", + " WI\n", + " 42\n", + " 48\n", + " 46\n", + " 46\n", + " 54\n", " ...\n", - " 38\n", - " 44\n", - " 21\n", - " 16\n", - " 13\n", - " 20\n", - " 16\n", - " 23\n", + " 64\n", + " 57\n", + " 57\n", " 37\n", + " 30\n", + " 45\n", + " 51\n", + " 46\n", + " 48\n", " 1.461542e+09\n", " \n", " \n", "\n", - "

5 rows × 56 columns

\n", + "

5 rows × 70 columns

\n", "" ], "text/plain": [ " item_id dept_id cat_id store_id state_id f_sales f_sales_lag_1_d \\\n", - "0 FOODS_3_714 FOODS_3 FOODS WI_3 WI 22 20 \n", - "1 FOODS_3_714 FOODS_3 FOODS WI_1 WI 17 15 \n", - "2 FOODS_3_714 FOODS_3 FOODS TX_3 TX 32 19 \n", - "3 FOODS_3_714 FOODS_3 FOODS CA_4 CA 6 8 \n", - "4 FOODS_3_714 FOODS_3 FOODS CA_1 CA 10 27 \n", + "0 FOODS_3_694 FOODS_3 FOODS WI_2 WI 24 29 \n", + "1 FOODS_3_694 FOODS_3 FOODS WI_1 WI 13 24 \n", + "2 FOODS_3_694 FOODS_3 FOODS TX_3 TX 21 17 \n", + "3 FOODS_3_694 FOODS_3 FOODS TX_2 TX 26 21 \n", + "4 FOODS_3_694 FOODS_3 FOODS WI_3 WI 42 48 \n", "\n", " f_sales_lag_2_d f_sales_lag_3_d f_sales_lag_7_d ... \\\n", - "0 36 26 20 ... \n", - "1 30 18 16 ... \n", - "2 30 17 13 ... \n", - "3 5 10 8 ... \n", - "4 22 18 15 ... \n", - "\n", - " label_horizon_5_days label_horizon_6_days label_horizon_7_days \\\n", - "0 31 19 21 \n", - "1 12 13 21 \n", - "2 34 38 25 \n", - "3 10 12 9 \n", - "4 38 44 21 \n", - "\n", - " label_horizon_8_days label_horizon_9_days label_horizon_10_days \\\n", - "0 13 18 25 \n", - "1 16 13 16 \n", - "2 27 19 24 \n", - "3 4 5 5 \n", - "4 16 13 20 \n", - "\n", - " label_horizon_11_days label_horizon_12_days label_horizon_13_days \\\n", - "0 42 37 27 \n", - "1 18 26 28 \n", - "2 8 27 31 \n", - "3 5 7 9 \n", - "4 16 23 37 \n", + "0 33 31 38 ... \n", + "1 28 18 27 ... \n", + "2 31 20 16 ... \n", + "3 17 22 9 ... \n", + "4 46 46 54 ... \n", + "\n", + " label_horizon_19_days label_horizon_20_days label_horizon_21_days \\\n", + "0 31 31 34 \n", + "1 10 27 20 \n", + "2 21 26 17 \n", + "3 26 33 12 \n", + "4 64 57 57 \n", + "\n", + " label_horizon_22_days label_horizon_23_days label_horizon_24_days \\\n", + "0 24 36 30 \n", + "1 20 16 22 \n", + "2 35 19 23 \n", + "3 19 13 11 \n", + "4 37 30 45 \n", + "\n", + " label_horizon_25_days label_horizon_26_days label_horizon_27_days \\\n", + "0 44 37 42 \n", + "1 31 30 32 \n", + "2 22 14 21 \n", + "3 18 38 25 \n", + "4 51 46 48 \n", "\n", " timestamp \n", "0 1.461542e+09 \n", @@ -4330,7 +4330,7 @@ "3 1.461542e+09 \n", "4 1.461542e+09 \n", "\n", - "[5 rows x 56 columns]" + "[5 rows x 70 columns]" ] }, "execution_count": 43, @@ -4391,179 +4391,179 @@ " f_sales_lag_3_d\n", " f_sales_lag_7_d\n", " ...\n", - " label_horizon_5_days\n", - " label_horizon_6_days\n", - " label_horizon_7_days\n", - " label_horizon_8_days\n", - " label_horizon_9_days\n", - " label_horizon_10_days\n", - " label_horizon_11_days\n", - " label_horizon_12_days\n", - " label_horizon_13_days\n", + " label_horizon_19_days\n", + " label_horizon_20_days\n", + " label_horizon_21_days\n", + " label_horizon_22_days\n", + " label_horizon_23_days\n", + " label_horizon_24_days\n", + " label_horizon_25_days\n", + " label_horizon_26_days\n", + " label_horizon_27_days\n", " timestamp\n", " \n", " \n", " \n", " \n", " 0\n", - " FOODS_3_714\n", + " FOODS_3_694\n", " FOODS_3\n", " FOODS\n", - " WI_3\n", + " WI_2\n", " WI\n", + " 48\n", + " 47\n", " 31\n", - " 37\n", - " 36\n", - " 29\n", - " 14\n", + " 46\n", + " 38\n", " ...\n", " 34\n", - " 63\n", - " 47\n", - " 25\n", - " 27\n", - " 27\n", - " 27\n", - " 49\n", + " 32\n", + " 29\n", + " 32\n", + " 18\n", + " 24\n", + " 41\n", + " 30\n", " 44\n", " 1.298765e+09\n", " \n", " \n", " 1\n", - " FOODS_3_714\n", + " FOODS_3_694\n", " FOODS_3\n", " FOODS\n", - " WI_3\n", + " WI_2\n", " WI\n", - " 43\n", + " 30\n", + " 48\n", + " 47\n", " 31\n", - " 37\n", " 36\n", - " 14\n", " ...\n", - " 63\n", - " 47\n", - " 25\n", - " 27\n", - " 27\n", - " 27\n", - " 49\n", + " 32\n", + " 29\n", + " 32\n", + " 18\n", + " 24\n", + " 41\n", + " 30\n", " 44\n", - " 47\n", + " 36\n", " 1.298851e+09\n", " \n", " \n", " 2\n", - " FOODS_3_714\n", + " FOODS_3_694\n", " FOODS_3\n", " FOODS\n", - " WI_3\n", + " WI_2\n", " WI\n", - " 15\n", - " 43\n", - " 31\n", - " 37\n", - " 33\n", - " ...\n", + " 42\n", + " 30\n", + " 48\n", " 47\n", - " 25\n", - " 27\n", - " 27\n", - " 27\n", - " 49\n", + " 29\n", + " ...\n", + " 29\n", + " 32\n", + " 18\n", + " 24\n", + " 41\n", + " 30\n", " 44\n", - " 47\n", - " 38\n", + " 36\n", + " 15\n", " 1.298938e+09\n", " \n", " \n", " 3\n", - " FOODS_3_714\n", + " FOODS_3_694\n", " FOODS_3\n", " FOODS\n", - " WI_3\n", + " WI_2\n", " WI\n", - " 34\n", - " 15\n", - " 43\n", - " 31\n", - " 27\n", + " 39\n", + " 42\n", + " 30\n", + " 48\n", + " 44\n", " ...\n", - " 25\n", - " 27\n", - " 27\n", - " 27\n", - " 49\n", + " 32\n", + " 18\n", + " 24\n", + " 41\n", + " 30\n", " 44\n", - " 47\n", - " 38\n", - " 22\n", + " 36\n", + " 15\n", + " 26\n", " 1.299024e+09\n", " \n", " \n", " 4\n", - " FOODS_3_714\n", + " FOODS_3_694\n", " FOODS_3\n", " FOODS\n", - " WI_3\n", + " WI_2\n", " WI\n", - " 32\n", - " 34\n", - " 15\n", - " 43\n", - " 29\n", + " 25\n", + " 39\n", + " 42\n", + " 30\n", + " 46\n", " ...\n", - " 27\n", - " 27\n", - " 27\n", - " 49\n", + " 18\n", + " 24\n", + " 41\n", + " 30\n", " 44\n", - " 47\n", - " 38\n", - " 22\n", - " 38\n", + " 36\n", + " 15\n", + " 26\n", + " 33\n", " 1.299110e+09\n", " \n", " \n", "\n", - "

5 rows × 56 columns

\n", + "

5 rows × 70 columns

\n", "" ], "text/plain": [ " item_id dept_id cat_id store_id state_id f_sales f_sales_lag_1_d \\\n", - "0 FOODS_3_714 FOODS_3 FOODS WI_3 WI 31 37 \n", - "1 FOODS_3_714 FOODS_3 FOODS WI_3 WI 43 31 \n", - "2 FOODS_3_714 FOODS_3 FOODS WI_3 WI 15 43 \n", - "3 FOODS_3_714 FOODS_3 FOODS WI_3 WI 34 15 \n", - "4 FOODS_3_714 FOODS_3 FOODS WI_3 WI 32 34 \n", + "0 FOODS_3_694 FOODS_3 FOODS WI_2 WI 48 47 \n", + "1 FOODS_3_694 FOODS_3 FOODS WI_2 WI 30 48 \n", + "2 FOODS_3_694 FOODS_3 FOODS WI_2 WI 42 30 \n", + "3 FOODS_3_694 FOODS_3 FOODS WI_2 WI 39 42 \n", + "4 FOODS_3_694 FOODS_3 FOODS WI_2 WI 25 39 \n", "\n", " f_sales_lag_2_d f_sales_lag_3_d f_sales_lag_7_d ... \\\n", - "0 36 29 14 ... \n", - "1 37 36 14 ... \n", - "2 31 37 33 ... \n", - "3 43 31 27 ... \n", - "4 15 43 29 ... \n", - "\n", - " label_horizon_5_days label_horizon_6_days label_horizon_7_days \\\n", - "0 34 63 47 \n", - "1 63 47 25 \n", - "2 47 25 27 \n", - "3 25 27 27 \n", - "4 27 27 27 \n", - "\n", - " label_horizon_8_days label_horizon_9_days label_horizon_10_days \\\n", - "0 25 27 27 \n", - "1 27 27 27 \n", - "2 27 27 49 \n", - "3 27 49 44 \n", - "4 49 44 47 \n", - "\n", - " label_horizon_11_days label_horizon_12_days label_horizon_13_days \\\n", - "0 27 49 44 \n", - "1 49 44 47 \n", - "2 44 47 38 \n", - "3 47 38 22 \n", - "4 38 22 38 \n", + "0 31 46 38 ... \n", + "1 47 31 36 ... \n", + "2 48 47 29 ... \n", + "3 30 48 44 ... \n", + "4 42 30 46 ... \n", + "\n", + " label_horizon_19_days label_horizon_20_days label_horizon_21_days \\\n", + "0 34 32 29 \n", + "1 32 29 32 \n", + "2 29 32 18 \n", + "3 32 18 24 \n", + "4 18 24 41 \n", + "\n", + " label_horizon_22_days label_horizon_23_days label_horizon_24_days \\\n", + "0 32 18 24 \n", + "1 18 24 41 \n", + "2 24 41 30 \n", + "3 41 30 44 \n", + "4 30 44 36 \n", + "\n", + " label_horizon_25_days label_horizon_26_days label_horizon_27_days \\\n", + "0 41 30 44 \n", + "1 30 44 36 \n", + "2 44 36 15 \n", + "3 36 15 26 \n", + "4 15 26 33 \n", "\n", " timestamp \n", "0 1.298765e+09 \n", @@ -4572,7 +4572,7 @@ "3 1.299024e+09 \n", "4 1.299110e+09 \n", "\n", - "[5 rows x 56 columns]" + "[5 rows x 70 columns]" ] }, "execution_count": 44, @@ -4618,19 +4618,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "==> tmp/temporian_m5/train.csv <==\n", - "item_id,dept_id,cat_id,store_id,state_id,f_sales,f_sales_lag_1_d,f_sales_lag_2_d,f_sales_lag_3_d,f_sales_lag_7_d,f_sales_lag_14_d,f_sma_7_sales,f_sd_7_sales,f_sum_7_sales,f_sma_14_sales,f_sd_14_sales,f_sum_14_sales,f_sma_28_sales,f_sd_28_sales,f_sum_28_sales,f_sma_84_sales,f_sd_84_sales,f_sum_84_sales,f_calendar_day_of_month,f_calendar_day_of_week,f_calendar_month,f_sum28_per_dep_sales,f_event_name_1,f_event_type_1,f_event_name_2,f_event_type_2,f_snap_CA,f_snap_TX,f_snap_WI,f_leak5_event_name_1,f_leak5_event_type_1,f_leak5_event_name_2,f_leak5_event_type_2,f_leak5_snap_CA,f_leak5_snap_TX,f_leak5_snap_WI,day,label_horizon_1_days,label_horizon_2_days,label_horizon_3_days,label_horizon_4_days,label_horizon_5_days,label_horizon_6_days,label_horizon_7_days,label_horizon_8_days,label_horizon_9_days,label_horizon_10_days,label_horizon_11_days,label_horizon_12_days,label_horizon_13_days,timestamp\n", - "FOODS_3_714,FOODS_3,FOODS,WI_3,WI,31,37,36,29,14,52,29.571428,7.168525,207.0,27.714285,9.779195,388.0,30.964285,10.942976,867.0,31.733334,11.017965,952.0,27,6,2,9092,no_event,no_event,no_event,no_event,0,0,0,no_event,no_event,no_event,no_event,1,0,0,30,43,15,34,32,34,63,47,25,27,27,27,49,44,1298764800.0\n", - "FOODS_3_714,FOODS_3,FOODS,WI_3,WI,43,31,37,36,14,45,33.714287,5.0345745,236.0,27.571428,9.537252,386.0,31.5,11.149952,882.0,32.096775,11.020083,995.0,28,0,2,8983,no_event,no_event,no_event,no_event,0,0,0,no_event,no_event,no_event,no_event,1,1,1,31,15,34,32,34,63,47,25,27,27,27,49,44,47,1298851200.0\n", - "FOODS_3_714,FOODS_3,FOODS,WI_3,WI,15,43,31,37,33,37,31.142857,8.288177,218.0,26.0,9.665846,364.0,31.071428,11.538533,870.0,31.5625,11.247048,1010.0,1,1,3,8891,no_event,no_event,no_event,no_event,1,1,0,no_event,no_event,no_event,no_event,1,1,1,32,34,32,34,63,47,25,27,27,27,49,44,47,38,1298937600.0\n", - "FOODS_3_714,FOODS_3,FOODS,WI_3,WI,34,15,43,31,27,24,32.142857,8.14912,225.0,26.714285,9.859213,374.0,31.464285,11.443907,881.0,31.636364,11.083206,1044.0,2,2,3,8929,no_event,no_event,no_event,no_event,1,0,1,no_event,no_event,no_event,no_event,1,1,0,33,32,34,63,47,25,27,27,27,49,44,47,38,22,1299024000.0\n", - "FOODS_3_714,FOODS_3,FOODS,WI_3,WI,32,34,15,43,29,14,32.57143,8.0508585,228.0,28.0,9.273619,392.0,31.464285,11.443907,881.0,31.647058,10.919174,1076.0,3,3,3,8743,no_event,no_event,no_event,no_event,1,1,1,no_event,no_event,no_event,no_event,1,0,1,34,34,63,47,25,27,27,27,49,44,47,38,22,38,1299110400.0\n", - "FOODS_3_714,FOODS_3,FOODS,WI_3,WI,34,32,34,15,36,15,32.285713,7.959079,226.0,29.357143,8.640495,411.0,31.5,11.450203,882.0,31.714285,10.769193,1110.0,4,4,3,8582,no_event,no_event,no_event,no_event,1,0,0,LentStart,Religious,no_event,no_event,1,1,1,35,63,47,25,27,27,27,49,44,47,38,22,38,35,1299196800.0\n", - "FOODS_3_714,FOODS_3,FOODS,WI_3,WI,63,34,32,34,37,32,36.0,13.458932,252.0,31.571428,12.251614,442.0,31.892857,12.303714,893.0,32.583332,11.797775,1173.0,5,5,3,8273,no_event,no_event,no_event,no_event,1,1,1,no_event,no_event,no_event,no_event,1,0,0,36,47,25,27,27,27,49,44,47,38,22,38,35,29,1299283200.0\n", - "FOODS_3_714,FOODS_3,FOODS,WI_3,WI,47,63,34,32,31,14,38.285713,13.770718,268.0,33.92857,11.810796,475.0,32.392857,12.618975,907.0,32.972973,11.869759,1220.0,6,6,3,7988,no_event,no_event,no_event,no_event,1,1,1,no_event,no_event,no_event,no_event,0,1,1,37,25,27,27,27,49,44,47,38,22,38,35,29,34,1299369600.0\n", - "FOODS_3_714,FOODS_3,FOODS,WI_3,WI,25,47,63,34,43,14,35.714287,14.319959,250.0,34.714287,10.779799,486.0,32.107143,12.692347,899.0,32.763157,11.781864,1245.0,7,0,3,7831,no_event,no_event,no_event,no_event,1,1,0,no_event,no_event,no_event,no_event,0,1,1,38,27,27,27,49,44,47,38,22,38,35,29,34,26,1299456000.0\n", - "head: -n: No such file or directory\n", - "head: 5: No such file or directory\n" + "item_id,dept_id,cat_id,store_id,state_id,f_sales,f_sales_lag_1_d,f_sales_lag_2_d,f_sales_lag_3_d,f_sales_lag_7_d,f_sales_lag_14_d,f_sma_7_sales,f_sd_7_sales,f_sum_7_sales,f_sma_14_sales,f_sd_14_sales,f_sum_14_sales,f_sma_28_sales,f_sd_28_sales,f_sum_28_sales,f_sma_84_sales,f_sd_84_sales,f_sum_84_sales,f_calendar_day_of_month,f_calendar_day_of_week,f_calendar_month,f_sum28_per_dep_sales,f_event_name_1,f_event_type_1,f_event_name_2,f_event_type_2,f_snap_CA,f_snap_TX,f_snap_WI,f_leak5_event_name_1,f_leak5_event_type_1,f_leak5_event_name_2,f_leak5_event_type_2,f_leak5_snap_CA,f_leak5_snap_TX,f_leak5_snap_WI,day,label_horizon_1_days,label_horizon_2_days,label_horizon_3_days,label_horizon_4_days,label_horizon_5_days,label_horizon_6_days,label_horizon_7_days,label_horizon_8_days,label_horizon_9_days,label_horizon_10_days,label_horizon_11_days,label_horizon_12_days,label_horizon_13_days,label_horizon_14_days,label_horizon_15_days,label_horizon_16_days,label_horizon_17_days,label_horizon_18_days,label_horizon_19_days,label_horizon_20_days,label_horizon_21_days,label_horizon_22_days,label_horizon_23_days,label_horizon_24_days,label_horizon_25_days,label_horizon_26_days,label_horizon_27_days,timestamp\n", + "FOODS_3_694,FOODS_3,FOODS,WI_2,WI,48,47,31,46,38,53,40.142857,7.3955307,281.0,42.357143,7.393116,593.0,42.82143,11.796045,1199.0,43.333332,12.070166,1300.0,27,6,2,5313,no_event,no_event,no_event,no_event,0,0,0,no_event,no_event,no_event,no_event,1,0,0,30,30,42,39,25,27,43,34,29,29,19,7,42,30,32,46,27,33,36,34,32,29,32,18,24,41,30,44,1298764800.0\n", + "FOODS_3_694,FOODS_3,FOODS,WI_2,WI,30,48,47,31,36,41,39.285713,8.136589,275.0,41.57143,8.0508585,582.0,42.607143,11.971213,1193.0,42.903225,12.10533,1330.0,28,0,2,5298,no_event,no_event,no_event,no_event,0,0,0,no_event,no_event,no_event,no_event,1,1,1,31,42,39,25,27,43,34,29,29,19,7,42,30,32,46,27,33,36,34,32,29,32,18,24,41,30,44,36,1298851200.0\n", + "FOODS_3_694,FOODS_3,FOODS,WI_2,WI,42,30,48,47,29,45,41.142857,6.9781,288.0,41.357143,7.9964914,579.0,42.392857,11.926379,1187.0,42.875,11.91572,1372.0,1,1,3,5254,no_event,no_event,no_event,no_event,1,1,0,no_event,no_event,no_event,no_event,1,1,1,32,39,25,27,43,34,29,29,19,7,42,30,32,46,27,33,36,34,32,29,32,18,24,41,30,44,36,15,1298937600.0\n", + "FOODS_3_694,FOODS_3,FOODS,WI_2,WI,39,42,30,48,44,34,40.42857,6.9045978,283.0,41.714287,7.768329,584.0,42.67857,11.744462,1195.0,42.757576,11.752576,1411.0,2,2,3,5319,no_event,no_event,no_event,no_event,1,0,1,no_event,no_event,no_event,no_event,1,1,0,33,25,27,43,34,29,29,19,7,42,30,32,46,27,33,36,34,32,29,32,18,24,41,30,44,36,15,26,1299024000.0\n" ] } ], @@ -4651,7 +4643,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 47, "id": "18d6d3b1-32d0-4d4c-b829-814277353ad2", "metadata": {}, "outputs": [ @@ -4664,6 +4656,18 @@ "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n", "Note: you may need to restart the kernel to use updated packages.\n" ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-08-22 17:19:07.938517: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n", + "2023-08-22 17:19:07.941786: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\n", + "2023-08-22 17:19:08.021257: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\n", + "2023-08-22 17:19:08.022287: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", + "To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", + "2023-08-22 17:19:09.593394: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n" + ] } ], "source": [ @@ -4675,10 +4679,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 48, "id": "f6666033-3849-4418-bfc8-5a791221e5d1", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "label_names: ['label_horizon_1_days', 'label_horizon_2_days', 'label_horizon_3_days', 'label_horizon_4_days', 'label_horizon_5_days', 'label_horizon_6_days', 'label_horizon_7_days', 'label_horizon_8_days', 'label_horizon_9_days', 'label_horizon_10_days', 'label_horizon_11_days', 'label_horizon_12_days', 'label_horizon_13_days', 'label_horizon_14_days', 'label_horizon_15_days', 'label_horizon_16_days', 'label_horizon_17_days', 'label_horizon_18_days', 'label_horizon_19_days', 'label_horizon_20_days', 'label_horizon_21_days', 'label_horizon_22_days', 'label_horizon_23_days', 'label_horizon_24_days', 'label_horizon_25_days', 'label_horizon_26_days', 'label_horizon_27_days']\n", + "feature_names: ['f_sales', 'f_sales_lag_1_d', 'f_sales_lag_2_d', 'f_sales_lag_3_d', 'f_sales_lag_7_d', 'f_sales_lag_14_d', 'f_sma_7_sales', 'f_sd_7_sales', 'f_sum_7_sales', 'f_sma_14_sales', 'f_sd_14_sales', 'f_sum_14_sales', 'f_sma_28_sales', 'f_sd_28_sales', 'f_sum_28_sales', 'f_sma_84_sales', 'f_sd_84_sales', 'f_sum_84_sales', 'f_calendar_day_of_month', 'f_calendar_day_of_week', 'f_calendar_month', 'f_sum28_per_dep_sales', 'f_event_name_1', 'f_event_type_1', 'f_event_name_2', 'f_event_type_2', 'f_snap_CA', 'f_snap_TX', 'f_snap_WI', 'f_leak5_event_name_1', 'f_leak5_event_type_1', 'f_leak5_event_name_2', 'f_leak5_event_type_2', 'f_leak5_snap_CA', 'f_leak5_snap_TX', 'f_leak5_snap_WI', 'dept_id', 'cat_id', 'store_id', 'state_id']\n" + ] + } + ], "source": [ "# The name of the labels and features.\n", "label_names = [x for x in test_dataset.columns if x.startswith(\"label_\")]\n", @@ -4705,7 +4718,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 49, "id": "6a46a286-1825-49ec-a147-f539987da590", "metadata": {}, "outputs": [], @@ -4731,29 +4744,1107 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 50, "id": "4a7580bf-bd81-4806-9dde-1465b0a6fb00", "metadata": { "scrolled": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Use /tmp/tmpfn9n19rs as temporary training directory\n", + "Reading training dataset...\n", + "Training tensor examples:\n", + "Features: {'f_sales': , 'f_sales_lag_1_d': , 'f_sales_lag_2_d': , 'f_sales_lag_3_d': , 'f_sales_lag_7_d': , 'f_sales_lag_14_d': , 'f_sma_7_sales': , 'f_sd_7_sales': , 'f_sum_7_sales': , 'f_sma_14_sales': , 'f_sd_14_sales': , 'f_sum_14_sales': , 'f_sma_28_sales': , 'f_sd_28_sales': , 'f_sum_28_sales': , 'f_sma_84_sales': , 'f_sd_84_sales': , 'f_sum_84_sales': , 'f_calendar_day_of_month': , 'f_calendar_day_of_week': , 'f_calendar_month': , 'f_sum28_per_dep_sales': , 'f_event_name_1': , 'f_event_type_1': , 'f_event_name_2': , 'f_event_type_2': , 'f_snap_CA': , 'f_snap_TX': , 'f_snap_WI': , 'f_leak5_event_name_1': , 'f_leak5_event_type_1': , 'f_leak5_event_name_2': , 'f_leak5_event_type_2': , 'f_leak5_snap_CA': , 'f_leak5_snap_TX': , 'f_leak5_snap_WI': , 'dept_id': , 'cat_id': , 'store_id': , 'state_id': }\n", + "Label: {'label_horizon_1_days': , 'label_horizon_2_days': , 'label_horizon_3_days': , 'label_horizon_4_days': , 'label_horizon_5_days': , 'label_horizon_6_days': , 'label_horizon_7_days': , 'label_horizon_8_days': , 'label_horizon_9_days': , 'label_horizon_10_days': , 'label_horizon_11_days': , 'label_horizon_12_days': , 'label_horizon_13_days': , 'label_horizon_14_days': , 'label_horizon_15_days': , 'label_horizon_16_days': , 'label_horizon_17_days': , 'label_horizon_18_days': , 'label_horizon_19_days': , 'label_horizon_20_days': , 'label_horizon_21_days': , 'label_horizon_22_days': , 'label_horizon_23_days': , 'label_horizon_24_days': , 'label_horizon_25_days': , 'label_horizon_26_days': , 'label_horizon_27_days': }\n", + "Weights: None\n", + "Normalized tensor features:\n", + " {'f_sales': SemanticTensor(semantic=, tensor=), 'f_sales_lag_1_d': SemanticTensor(semantic=, tensor=), 'f_sales_lag_2_d': SemanticTensor(semantic=, tensor=), 'f_sales_lag_3_d': SemanticTensor(semantic=, tensor=), 'f_sales_lag_7_d': SemanticTensor(semantic=, tensor=), 'f_sales_lag_14_d': SemanticTensor(semantic=, tensor=), 'f_sma_7_sales': SemanticTensor(semantic=, tensor=), 'f_sd_7_sales': SemanticTensor(semantic=, tensor=), 'f_sum_7_sales': SemanticTensor(semantic=, tensor=), 'f_sma_14_sales': SemanticTensor(semantic=, tensor=), 'f_sd_14_sales': SemanticTensor(semantic=, tensor=), 'f_sum_14_sales': SemanticTensor(semantic=, tensor=), 'f_sma_28_sales': SemanticTensor(semantic=, tensor=), 'f_sd_28_sales': SemanticTensor(semantic=, tensor=), 'f_sum_28_sales': SemanticTensor(semantic=, tensor=), 'f_sma_84_sales': SemanticTensor(semantic=, tensor=), 'f_sd_84_sales': SemanticTensor(semantic=, tensor=), 'f_sum_84_sales': SemanticTensor(semantic=, tensor=), 'f_calendar_day_of_month': SemanticTensor(semantic=, tensor=), 'f_calendar_day_of_week': SemanticTensor(semantic=, tensor=), 'f_calendar_month': SemanticTensor(semantic=, tensor=), 'f_sum28_per_dep_sales': SemanticTensor(semantic=, tensor=), 'f_event_name_1': SemanticTensor(semantic=, tensor=), 'f_event_type_1': SemanticTensor(semantic=, tensor=), 'f_event_name_2': SemanticTensor(semantic=, tensor=), 'f_event_type_2': SemanticTensor(semantic=, tensor=), 'f_snap_CA': SemanticTensor(semantic=, tensor=), 'f_snap_TX': SemanticTensor(semantic=, tensor=), 'f_snap_WI': SemanticTensor(semantic=, tensor=), 'f_leak5_event_name_1': SemanticTensor(semantic=, tensor=), 'f_leak5_event_type_1': SemanticTensor(semantic=, tensor=), 'f_leak5_event_name_2': SemanticTensor(semantic=, tensor=), 'f_leak5_event_type_2': SemanticTensor(semantic=, tensor=), 'f_leak5_snap_CA': SemanticTensor(semantic=, tensor=), 'f_leak5_snap_TX': SemanticTensor(semantic=, tensor=), 'f_leak5_snap_WI': SemanticTensor(semantic=, tensor=), 'dept_id': SemanticTensor(semantic=, tensor=), 'cat_id': SemanticTensor(semantic=, tensor=), 'store_id': SemanticTensor(semantic=, tensor=), 'state_id': SemanticTensor(semantic=, tensor=)}\n", + "Training dataset read in 0:00:15.636382. Found 150720 examples.\n", + "Training model...\n", + "Standard output detected as not visible to the user e.g. running in a notebook. Creating a training log redirection. If training gets stuck, try calling tfdf.keras.set_training_logs_redirection(False).\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[INFO 23-08-22 17:19:27.7980 CEST kernel.cc:773] Start Yggdrasil model training\n", + "[INFO 23-08-22 17:19:27.7980 CEST kernel.cc:774] Collect training examples\n", + "[INFO 23-08-22 17:19:27.7981 CEST kernel.cc:787] Dataspec guide:\n", + "column_guides {\n", + " column_name_pattern: \"^label_horizon_1_days$\"\n", + " type: NUMERICAL\n", + "}\n", + "column_guides {\n", + " column_name_pattern: \"^label_horizon_2_days$\"\n", + " type: NUMERICAL\n", + "}\n", + "column_guides {\n", + " column_name_pattern: \"^label_horizon_3_days$\"\n", + " type: NUMERICAL\n", + "}\n", + "column_guides {\n", + " column_name_pattern: \"^label_horizon_4_days$\"\n", + " type: NUMERICAL\n", + "}\n", + "column_guides {\n", + " column_name_pattern: \"^label_horizon_5_days$\"\n", + " type: NUMERICAL\n", + "}\n", + "column_guides {\n", + " column_name_pattern: \"^label_horizon_6_days$\"\n", + " type: NUMERICAL\n", + "}\n", + "column_guides {\n", + " column_name_pattern: \"^label_horizon_7_days$\"\n", + " type: NUMERICAL\n", + "}\n", + "column_guides {\n", + " column_name_pattern: \"^label_horizon_8_days$\"\n", + " type: NUMERICAL\n", + "}\n", + "column_guides {\n", + " column_name_pattern: \"^label_horizon_9_days$\"\n", + " type: NUMERICAL\n", + "}\n", + "column_guides {\n", + " column_name_pattern: \"^label_horizon_10_days$\"\n", + " type: NUMERICAL\n", + "}\n", + "column_guides {\n", + " column_name_pattern: \"^label_horizon_11_days$\"\n", + " type: NUMERICAL\n", + "}\n", + "column_guides {\n", + " column_name_pattern: \"^label_horizon_12_days$\"\n", + " type: NUMERICAL\n", + "}\n", + "column_guides {\n", + " column_name_pattern: \"^label_horizon_13_days$\"\n", + " type: NUMERICAL\n", + "}\n", + "column_guides {\n", + " column_name_pattern: \"^label_horizon_14_days$\"\n", + " type: NUMERICAL\n", + "}\n", + "column_guides {\n", + " column_name_pattern: \"^label_horizon_15_days$\"\n", + " type: NUMERICAL\n", + "}\n", + "column_guides {\n", + " column_name_pattern: \"^label_horizon_16_days$\"\n", + " type: NUMERICAL\n", + "}\n", + "column_guides {\n", + " column_name_pattern: \"^label_horizon_17_days$\"\n", + " type: NUMERICAL\n", + "}\n", + "column_guides {\n", + " column_name_pattern: \"^label_horizon_18_days$\"\n", + " type: NUMERICAL\n", + "}\n", + "column_guides {\n", + " column_name_pattern: \"^label_horizon_19_days$\"\n", + " type: NUMERICAL\n", + "}\n", + "column_guides {\n", + " column_name_pattern: \"^label_horizon_20_days$\"\n", + " type: NUMERICAL\n", + "}\n", + "column_guides {\n", + " column_name_pattern: \"^label_horizon_21_days$\"\n", + " type: NUMERICAL\n", + "}\n", + "column_guides {\n", + " column_name_pattern: \"^label_horizon_22_days$\"\n", + " type: NUMERICAL\n", + "}\n", + "column_guides {\n", + " column_name_pattern: \"^label_horizon_23_days$\"\n", + " type: NUMERICAL\n", + "}\n", + "column_guides {\n", + " column_name_pattern: \"^label_horizon_24_days$\"\n", + " type: NUMERICAL\n", + "}\n", + "column_guides {\n", + " column_name_pattern: \"^label_horizon_25_days$\"\n", + " type: NUMERICAL\n", + "}\n", + "column_guides {\n", + " column_name_pattern: \"^label_horizon_26_days$\"\n", + " type: NUMERICAL\n", + "}\n", + "column_guides {\n", + " column_name_pattern: \"^label_horizon_27_days$\"\n", + " type: NUMERICAL\n", + "}\n", + "default_column_guide {\n", + " categorial {\n", + " max_vocab_count: 2000\n", + " }\n", + " discretized_numerical {\n", + " maximum_num_bins: 255\n", + " }\n", + "}\n", + "ignore_columns_without_guides: false\n", + "detect_numerical_as_discretized_numerical: false\n", + "\n", + "[INFO 23-08-22 17:19:27.8163 CEST kernel.cc:393] Number of batches: 1508\n", + "[INFO 23-08-22 17:19:27.8163 CEST kernel.cc:394] Number of examples: 150720\n", + "[INFO 23-08-22 17:19:28.0986 CEST kernel.cc:794] Training dataset:\n", + "Number of records: 150720\n", + "Number of columns: 67\n", + "\n", + "Number of columns by type:\n", + "\tNUMERICAL: 55 (82.0896%)\n", + "\tCATEGORICAL: 12 (17.9104%)\n", + "\n", + "Columns:\n", + "\n", + "NUMERICAL: 55 (82.0896%)\n", + "\t2: \"f_calendar_day_of_month\" NUMERICAL mean:15.7107 min:1 max:31 sd:8.79464\n", + "\t3: \"f_calendar_day_of_week\" NUMERICAL mean:3.00159 min:0 max:6 sd:2.00066\n", + "\t4: \"f_calendar_month\" NUMERICAL mean:6.42622 min:1 max:12 sd:3.44017\n", + "\t13: \"f_leak5_snap_CA\" NUMERICAL mean:0.327495 min:0 max:1 sd:0.469299\n", + "\t14: \"f_leak5_snap_TX\" NUMERICAL mean:0.328025 min:0 max:1 sd:0.469494\n", + "\t15: \"f_leak5_snap_WI\" NUMERICAL mean:0.328025 min:0 max:1 sd:0.469494\n", + "\t16: \"f_sales\" NUMERICAL mean:29.5872 min:0 max:763 sd:31.5277\n", + "\t17: \"f_sales_lag_14_d\" NUMERICAL mean:29.5573 min:0 max:763 sd:31.5369\n", + "\t18: \"f_sales_lag_1_d\" NUMERICAL mean:29.5861 min:0 max:763 sd:31.5303\n", + "\t19: \"f_sales_lag_2_d\" NUMERICAL mean:29.5797 min:0 max:763 sd:31.5265\n", + "\t20: \"f_sales_lag_3_d\" NUMERICAL mean:29.5746 min:0 max:763 sd:31.5255\n", + "\t21: \"f_sales_lag_7_d\" NUMERICAL mean:29.5707 min:0 max:763 sd:31.5324\n", + "\t22: \"f_sd_14_sales\" NUMERICAL mean:10.0323 min:0 max:203.329 sd:10.8368\n", + "\t23: \"f_sd_28_sales\" NUMERICAL mean:10.8908 min:0 max:192.891 sd:11.4905\n", + "\t24: \"f_sd_7_sales\" NUMERICAL mean:9.12405 min:0 max:235.264 sd:10.209\n", + "\t25: \"f_sd_84_sales\" NUMERICAL mean:12.2336 min:0 max:173.639 sd:12.8959\n", + "\t26: \"f_sma_14_sales\" NUMERICAL mean:29.5678 min:0 max:468.643 sd:27.8563\n", + "\t27: \"f_sma_28_sales\" NUMERICAL mean:29.5606 min:0 max:442.75 sd:27.2844\n", + "\t28: \"f_sma_7_sales\" NUMERICAL mean:29.5773 min:0 max:602.857 sd:28.3994\n", + "\t29: \"f_sma_84_sales\" NUMERICAL mean:29.5427 min:0 max:279.786 sd:26.147\n", + "\t30: \"f_snap_CA\" NUMERICAL mean:0.329087 min:0 max:1 sd:0.469882\n", + "\t31: \"f_snap_TX\" NUMERICAL mean:0.329087 min:0 max:1 sd:0.469882\n", + "\t32: \"f_snap_WI\" NUMERICAL mean:0.329087 min:0 max:1 sd:0.469882\n", + "\t33: \"f_sum28_per_dep_sales\" NUMERICAL mean:6621.58 min:1333 max:19255 sd:2895.7\n", + "\t34: \"f_sum_14_sales\" NUMERICAL mean:413.949 min:0 max:6561 sd:389.988\n", + "\t35: \"f_sum_28_sales\" NUMERICAL mean:827.698 min:0 max:12397 sd:763.964\n", + "\t36: \"f_sum_7_sales\" NUMERICAL mean:207.041 min:0 max:4220 sd:198.796\n", + "\t37: \"f_sum_84_sales\" NUMERICAL mean:2460.49 min:0 max:23502 sd:2194.56\n", + "\t38: \"label_horizon_10_days\" NUMERICAL mean:29.6007 min:0 max:763 sd:31.5209\n", + "\t39: \"label_horizon_11_days\" NUMERICAL mean:29.6038 min:0 max:763 sd:31.5202\n", + "\t40: \"label_horizon_12_days\" NUMERICAL mean:29.6097 min:0 max:763 sd:31.5218\n", + "\t41: \"label_horizon_13_days\" NUMERICAL mean:29.6176 min:0 max:763 sd:31.5275\n", + "\t42: \"label_horizon_14_days\" NUMERICAL mean:29.6194 min:0 max:763 sd:31.5271\n", + "\t43: \"label_horizon_15_days\" NUMERICAL mean:29.6189 min:0 max:763 sd:31.5249\n", + "\t44: \"label_horizon_16_days\" NUMERICAL mean:29.6204 min:0 max:763 sd:31.5241\n", + "\t45: \"label_horizon_17_days\" NUMERICAL mean:29.6229 min:0 max:763 sd:31.5229\n", + "\t46: \"label_horizon_18_days\" NUMERICAL mean:29.6257 min:0 max:763 sd:31.5219\n", + "\t47: \"label_horizon_19_days\" NUMERICAL mean:29.6304 min:0 max:763 sd:31.523\n", + "\t48: \"label_horizon_1_days\" NUMERICAL mean:29.5853 min:0 max:763 sd:31.5245\n", + "\t49: \"label_horizon_20_days\" NUMERICAL mean:29.6377 min:0 max:763 sd:31.5259\n", + "\t50: \"label_horizon_21_days\" NUMERICAL mean:29.6423 min:0 max:763 sd:31.5277\n", + "\t51: \"label_horizon_22_days\" NUMERICAL mean:29.6404 min:0 max:763 sd:31.5232\n", + "\t52: \"label_horizon_23_days\" NUMERICAL mean:29.6433 min:0 max:763 sd:31.5218\n", + "\t53: \"label_horizon_24_days\" NUMERICAL mean:29.6473 min:0 max:763 sd:31.5202\n", + "\t54: \"label_horizon_25_days\" NUMERICAL mean:29.6505 min:0 max:763 sd:31.518\n", + "\t55: \"label_horizon_26_days\" NUMERICAL mean:29.6582 min:0 max:763 sd:31.5185\n", + "\t56: \"label_horizon_27_days\" NUMERICAL mean:29.6661 min:0 max:763 sd:31.5204\n", + "\t57: \"label_horizon_2_days\" NUMERICAL mean:29.5869 min:0 max:763 sd:31.5242\n", + "\t58: \"label_horizon_3_days\" NUMERICAL mean:29.5863 min:0 max:763 sd:31.5234\n", + "\t59: \"label_horizon_4_days\" NUMERICAL mean:29.5873 min:0 max:763 sd:31.5224\n", + "\t60: \"label_horizon_5_days\" NUMERICAL mean:29.5916 min:0 max:763 sd:31.525\n", + "\t61: \"label_horizon_6_days\" NUMERICAL mean:29.5976 min:0 max:763 sd:31.5282\n", + "\t62: \"label_horizon_7_days\" NUMERICAL mean:29.5993 min:0 max:763 sd:31.5276\n", + "\t63: \"label_horizon_8_days\" NUMERICAL mean:29.5965 min:0 max:763 sd:31.5239\n", + "\t64: \"label_horizon_9_days\" NUMERICAL mean:29.5992 min:0 max:763 sd:31.5224\n", + "\n", + "CATEGORICAL: 12 (17.9104%)\n", + "\t0: \"cat_id\" CATEGORICAL has-dict vocab-size:2 zero-ood-items most-frequent:\"FOODS\" 150720 (100%)\n", + "\t1: \"dept_id\" CATEGORICAL has-dict vocab-size:2 zero-ood-items most-frequent:\"FOODS_3\" 150720 (100%)\n", + "\t5: \"f_event_name_1\" CATEGORICAL has-dict vocab-size:32 zero-ood-items most-frequent:\"no_event\" 138640 (91.9851%)\n", + "\t6: \"f_event_name_2\" CATEGORICAL has-dict vocab-size:6 zero-ood-items most-frequent:\"no_event\" 150400 (99.7877%)\n", + "\t7: \"f_event_type_1\" CATEGORICAL has-dict vocab-size:6 zero-ood-items most-frequent:\"no_event\" 138640 (91.9851%)\n", + "\t8: \"f_event_type_2\" CATEGORICAL has-dict vocab-size:4 zero-ood-items most-frequent:\"no_event\" 150400 (99.7877%)\n", + "\t9: \"f_leak5_event_name_1\" CATEGORICAL has-dict vocab-size:32 zero-ood-items most-frequent:\"no_event\" 138640 (91.9851%)\n", + "\t10: \"f_leak5_event_name_2\" CATEGORICAL has-dict vocab-size:6 zero-ood-items most-frequent:\"no_event\" 150400 (99.7877%)\n", + "\t11: \"f_leak5_event_type_1\" CATEGORICAL has-dict vocab-size:6 zero-ood-items most-frequent:\"no_event\" 138640 (91.9851%)\n", + "\t12: \"f_leak5_event_type_2\" CATEGORICAL has-dict vocab-size:4 zero-ood-items most-frequent:\"no_event\" 150400 (99.7877%)\n", + "\t65: \"state_id\" CATEGORICAL has-dict vocab-size:4 zero-ood-items most-frequent:\"CA\" 60288 (40%)\n", + "\t66: \"store_id\" CATEGORICAL has-dict vocab-size:11 zero-ood-items most-frequent:\"CA_1\" 15072 (10%)\n", + "\n", + "Terminology:\n", + "\tnas: Number of non-available (i.e. missing) values.\n", + "\tood: Out of dictionary.\n", + "\tmanually-defined: Attribute which type is manually defined by the user i.e. the type was not automatically inferred.\n", + "\ttokenized: The attribute value is obtained through tokenization.\n", + "\thas-dict: The attribute is attached to a string dictionary e.g. a categorical attribute stored as a string.\n", + "\tvocab-size: Number of unique values.\n", + "\n", + "[INFO 23-08-22 17:19:28.1055 CEST kernel.cc:810] Configure learner\n", + "[INFO 23-08-22 17:19:28.1057 CEST kernel.cc:824] Training config:\n", + "learner: \"MULTITASKER\"\n", + "features: \"^cat_id$\"\n", + "features: \"^dept_id$\"\n", + "features: \"^f_calendar_day_of_month$\"\n", + "features: \"^f_calendar_day_of_week$\"\n", + "features: \"^f_calendar_month$\"\n", + "features: \"^f_event_name_1$\"\n", + "features: \"^f_event_name_2$\"\n", + "features: \"^f_event_type_1$\"\n", + "features: \"^f_event_type_2$\"\n", + "features: \"^f_leak5_event_name_1$\"\n", + "features: \"^f_leak5_event_name_2$\"\n", + "features: \"^f_leak5_event_type_1$\"\n", + "features: \"^f_leak5_event_type_2$\"\n", + "features: \"^f_leak5_snap_CA$\"\n", + "features: \"^f_leak5_snap_TX$\"\n", + "features: \"^f_leak5_snap_WI$\"\n", + "features: \"^f_sales$\"\n", + "features: \"^f_sales_lag_14_d$\"\n", + "features: \"^f_sales_lag_1_d$\"\n", + "features: \"^f_sales_lag_2_d$\"\n", + "features: \"^f_sales_lag_3_d$\"\n", + "features: \"^f_sales_lag_7_d$\"\n", + "features: \"^f_sd_14_sales$\"\n", + "features: \"^f_sd_28_sales$\"\n", + "features: \"^f_sd_7_sales$\"\n", + "features: \"^f_sd_84_sales$\"\n", + "features: \"^f_sma_14_sales$\"\n", + "features: \"^f_sma_28_sales$\"\n", + "features: \"^f_sma_7_sales$\"\n", + "features: \"^f_sma_84_sales$\"\n", + "features: \"^f_snap_CA$\"\n", + "features: \"^f_snap_TX$\"\n", + "features: \"^f_snap_WI$\"\n", + "features: \"^f_sum28_per_dep_sales$\"\n", + "features: \"^f_sum_14_sales$\"\n", + "features: \"^f_sum_28_sales$\"\n", + "features: \"^f_sum_7_sales$\"\n", + "features: \"^f_sum_84_sales$\"\n", + "features: \"^state_id$\"\n", + "features: \"^store_id$\"\n", + "label: \"^label_horizon_1_days$\"\n", + "task: REGRESSION\n", + "metadata {\n", + " framework: \"TF Keras\"\n", + "}\n", + "[yggdrasil_decision_forests.model.multitasker.proto.multitasker_config] {\n", + " base_learner {\n", + " learner: \"GRADIENT_BOOSTED_TREES\"\n", + " }\n", + " subtasks {\n", + " primary: true\n", + " train_config {\n", + " label: \"^label_horizon_1_days$\"\n", + " task: REGRESSION\n", + " }\n", + " }\n", + " subtasks {\n", + " primary: true\n", + " train_config {\n", + " label: \"^label_horizon_2_days$\"\n", + " task: REGRESSION\n", + " }\n", + " }\n", + " subtasks {\n", + " primary: true\n", + " train_config {\n", + " label: \"^label_horizon_3_days$\"\n", + " task: REGRESSION\n", + " }\n", + " }\n", + " subtasks {\n", + " primary: true\n", + " train_config {\n", + " label: \"^label_horizon_4_days$\"\n", + " task: REGRESSION\n", + " }\n", + " }\n", + " subtasks {\n", + " primary: true\n", + " train_config {\n", + " label: \"^label_horizon_5_days$\"\n", + " task: REGRESSION\n", + " }\n", + " }\n", + " subtasks {\n", + " primary: true\n", + " train_config {\n", + " label: \"^label_horizon_6_days$\"\n", + " task: REGRESSION\n", + " }\n", + " }\n", + " subtasks {\n", + " primary: true\n", + " train_config {\n", + " label: \"^label_horizon_7_days$\"\n", + " task: REGRESSION\n", + " }\n", + " }\n", + " subtasks {\n", + " primary: true\n", + " train_config {\n", + " label: \"^label_horizon_8_days$\"\n", + " task: REGRESSION\n", + " }\n", + " }\n", + " subtasks {\n", + " primary: true\n", + " train_config {\n", + " label: \"^label_horizon_9_days$\"\n", + " task: REGRESSION\n", + " }\n", + " }\n", + " subtasks {\n", + " primary: true\n", + " train_config {\n", + " label: \"^label_horizon_10_days$\"\n", + " task: REGRESSION\n", + " }\n", + " }\n", + " subtasks {\n", + " primary: true\n", + " train_config {\n", + " label: \"^label_horizon_11_days$\"\n", + " task: REGRESSION\n", + " }\n", + " }\n", + " subtasks {\n", + " primary: true\n", + " train_config {\n", + " label: \"^label_horizon_12_days$\"\n", + " task: REGRESSION\n", + " }\n", + " }\n", + " subtasks {\n", + " primary: true\n", + " train_config {\n", + " label: \"^label_horizon_13_days$\"\n", + " task: REGRESSION\n", + " }\n", + " }\n", + " subtasks {\n", + " primary: true\n", + " train_config {\n", + " label: \"^label_horizon_14_days$\"\n", + " task: REGRESSION\n", + " }\n", + " }\n", + " subtasks {\n", + " primary: true\n", + " train_config {\n", + " label: \"^label_horizon_15_days$\"\n", + " task: REGRESSION\n", + " }\n", + " }\n", + " subtasks {\n", + " primary: true\n", + " train_config {\n", + " label: \"^label_horizon_16_days$\"\n", + " task: REGRESSION\n", + " }\n", + " }\n", + " subtasks {\n", + " primary: true\n", + " train_config {\n", + " label: \"^label_horizon_17_days$\"\n", + " task: REGRESSION\n", + " }\n", + " }\n", + " subtasks {\n", + " primary: true\n", + " train_config {\n", + " label: \"^label_horizon_18_days$\"\n", + " task: REGRESSION\n", + " }\n", + " }\n", + " subtasks {\n", + " primary: true\n", + " train_config {\n", + " label: \"^label_horizon_19_days$\"\n", + " task: REGRESSION\n", + " }\n", + " }\n", + " subtasks {\n", + " primary: true\n", + " train_config {\n", + " label: \"^label_horizon_20_days$\"\n", + " task: REGRESSION\n", + " }\n", + " }\n", + " subtasks {\n", + " primary: true\n", + " train_config {\n", + " label: \"^label_horizon_21_days$\"\n", + " task: REGRESSION\n", + " }\n", + " }\n", + " subtasks {\n", + " primary: true\n", + " train_config {\n", + " label: \"^label_horizon_22_days$\"\n", + " task: REGRESSION\n", + " }\n", + " }\n", + " subtasks {\n", + " primary: true\n", + " train_config {\n", + " label: \"^label_horizon_23_days$\"\n", + " task: REGRESSION\n", + " }\n", + " }\n", + " subtasks {\n", + " primary: true\n", + " train_config {\n", + " label: \"^label_horizon_24_days$\"\n", + " task: REGRESSION\n", + " }\n", + " }\n", + " subtasks {\n", + " primary: true\n", + " train_config {\n", + " label: \"^label_horizon_25_days$\"\n", + " task: REGRESSION\n", + " }\n", + " }\n", + " subtasks {\n", + " primary: true\n", + " train_config {\n", + " label: \"^label_horizon_26_days$\"\n", + " task: REGRESSION\n", + " }\n", + " }\n", + " subtasks {\n", + " primary: true\n", + " train_config {\n", + " label: \"^label_horizon_27_days$\"\n", + " task: REGRESSION\n", + " }\n", + " }\n", + "}\n", + "\n", + "[INFO 23-08-22 17:19:28.1066 CEST kernel.cc:827] Deployment config:\n", + "cache_path: \"/tmp/tmpfn9n19rs/working_cache\"\n", + "num_threads: 8\n", + "try_resume_training: true\n", + "\n", + "[INFO 23-08-22 17:19:28.1118 CEST kernel.cc:889] Train model\n", + "[INFO 23-08-22 17:19:28.1495 CEST multitasker.cc:286] Train multitasker primary tasks with 27 model(s)\n", + "[WARNING[ 23-08-22 17:19:28.1574 CEST gradient_boosted_trees.cc:1818] \"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING[[WARNING[WARNINGWARNINGWARNING 23-08-22 17:19:28.1575 CEST gradient_boosted_trees.cc: 23-08-22 17:19:28.1575 CEST gradient_boosted_trees.cc:1818 23-08-22 17:19:28.1575 CEST gradient_boosted_trees.cc] 1818\"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "] 23-08-22 17:19:28.1575 CEST gradient_boosted_trees.cc:[WARNING 23-08-22 17:19:28.1576 CEST gradient_boosted_trees.cc:1829] \"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:19:28.1576 CEST gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", + " 23-08-22 17:19:28.1574 CEST gradient_boosted_trees.cc:1829] \"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:19:28.1577 CEST gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", + "[WARNING 23-08-22 17:19:28.1580 CEST gradient_boosted_trees.cc:1818] \"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:19:28.1581 CEST gradient_boosted_trees.cc:1829] \"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:19:28.1581 CEST gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", + "\"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:19:28.1581 CEST gradient_boosted_trees.cc:1829] [WARNING 23-08-22 17:19:28.1582 CEST gradient_boosted_trees.cc:1818] \"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:19:28.1582 CEST gradient_boosted_trees.cc:1829] \"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:19:28.1582 CEST gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", + "[WARNING 23-08-22 17:19:28.1584 CEST gradient_boosted_trees.cc:1818] \"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:19:28.1584 CEST gradient_boosted_trees.cc:1829] \"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:19:28.1584 CEST gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", + "\"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:19:28.1598 CEST gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", + "1818] \"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:19:28.1600 CEST gradient_boosted_trees.cc:1829] \"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:19:28.1600 CEST gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", + ":1818] \"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:19:28.1602 CEST gradient_boosted_trees.cc:1829] \"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:19:28.1602 CEST gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", + "[INFO 23-08-22 17:19:32.0507 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", + "[INFO 23-08-22 17:19:32.0614 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", + "[INFO 23-08-22 17:19:32.1276 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", + "[INFO 23-08-22 17:19:32.1455 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", + "[INFO 23-08-22 17:19:32.4159 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", + "[INFO 23-08-22 17:19:32.4160 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", + "[INFO 23-08-22 17:19:32.5365 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", + "[INFO 23-08-22 17:19:32.5365 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", + "[INFO 23-08-22 17:19:32.6083 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", + "[INFO 23-08-22 17:19:32.6084 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", + "[INFO 23-08-22 17:19:32.8171 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", + "[INFO 23-08-22 17:19:32.8182 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", + "[INFO 23-08-22 17:19:32.8183 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", + "[INFO 23-08-22 17:19:32.9276 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", + "[INFO 23-08-22 17:19:32.9277 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", + "[INFO 23-08-22 17:19:33.1349 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", + "[INFO 23-08-22 17:19:33.1350 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", + "[INFO 23-08-22 17:19:33.1581 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", + "[INFO 23-08-22 17:19:34.0283 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", + "[INFO 23-08-22 17:19:34.1116 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", + "[INFO 23-08-22 17:19:34.3145 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", + "[INFO 23-08-22 17:19:34.8990 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", + "[INFO 23-08-22 17:19:35.1325 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.267696 train-rmse:29.267696 valid-loss:29.130789 valid-rmse:29.130789\n", + "[INFO 23-08-22 17:19:35.1814 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", + "[INFO 23-08-22 17:19:35.3696 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", + "[INFO 23-08-22 17:19:35.9690 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.335985 train-rmse:29.335985 valid-loss:29.099661 valid-rmse:29.099661\n", + "[INFO 23-08-22 17:19:37.9991 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.214186 train-rmse:29.214186 valid-loss:29.899080 valid-rmse:29.899080\n", + "[INFO 23-08-22 17:19:38.5385 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.303213 train-rmse:29.303213 valid-loss:29.393198 valid-rmse:29.393198\n", + "[INFO 23-08-22 17:19:38.5435 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.399338 train-rmse:29.399338 valid-loss:29.028122 valid-rmse:29.028122\n", + "[INFO 23-08-22 17:19:38.6758 CEST gradient_boosted_trees.cc:1544] \tnum-trees:2 train-loss:27.390989 train-rmse:27.390989 valid-loss:27.127483 valid-rmse:27.127483\n", + "[INFO 23-08-22 17:19:38.9145 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.124109 train-rmse:29.124109 valid-loss:29.133936 valid-rmse:29.133936\n", + "[INFO 23-08-22 17:19:39.5908 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.295769 train-rmse:29.295769 valid-loss:28.769020 valid-rmse:28.769020\n", + "[INFO 23-08-22 17:19:39.7044 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.323404 train-rmse:29.323404 valid-loss:28.357626 valid-rmse:28.357626\n", + "[INFO 23-08-22 17:20:09.1838 CEST gradient_boosted_trees.cc:1544] \tnum-trees:15 train-loss:16.286356 train-rmse:16.286356 valid-loss:17.388578 valid-rmse:17.388578\n", + "[INFO 23-08-22 17:20:39.5899 CEST gradient_boosted_trees.cc:1544] \tnum-trees:26 train-loss:15.550566 train-rmse:15.550566 valid-loss:15.613172 valid-rmse:15.613172\n", + "[INFO 23-08-22 17:21:09.7584 CEST gradient_boosted_trees.cc:1544] \tnum-trees:36 train-loss:12.604913 train-rmse:12.604913 valid-loss:12.728271 valid-rmse:12.728271\n", + "[INFO 23-08-22 17:21:29.1166 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:13.866138 train-rmse:13.866138 valid-loss:15.359539 valid-rmse:15.359539\n", + "[INFO 23-08-22 17:21:29.1166 CEST gradient_boosted_trees.cc:247] Truncates the model to 48 tree(s) i.e. 48 iteration(s).\n", + "[INFO 23-08-22 17:21:29.1167 CEST gradient_boosted_trees.cc:310] Final model num-trees:48 valid-loss:15.355445 valid-rmse:15.355445\n", + "[WARNING 23-08-22 17:21:29.1246 CEST gradient_boosted_trees.cc:1818] \"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:21:29.1246 CEST gradient_boosted_trees.cc:1829] \"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:21:29.1246 CEST gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", + "[INFO 23-08-22 17:21:30.4713 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", + "[INFO 23-08-22 17:21:30.4714 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", + "[INFO 23-08-22 17:21:30.6207 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", + "[INFO 23-08-22 17:21:30.8606 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:13.575471 train-rmse:13.575471 valid-loss:15.029240 valid-rmse:15.029240\n", + "[INFO 23-08-22 17:21:30.8838 CEST gradient_boosted_trees.cc:247] Truncates the model to 50 tree(s) i.e. 50 iteration(s).\n", + "[INFO 23-08-22 17:21:30.8877 CEST gradient_boosted_trees.cc:310] Final model num-trees:50 valid-loss:15.029240 valid-rmse:15.029240\n", + "[WARNING 23-08-22 17:21:30.9208 CEST gradient_boosted_trees.cc:1818] \"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:21:30.9309 CEST gradient_boosted_trees.cc:1829] \"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:21:30.9312 CEST gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", + "[INFO 23-08-22 17:21:31.0555 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:13.423209 train-rmse:13.423209 valid-loss:13.453820 valid-rmse:13.453820\n", + "[INFO 23-08-22 17:21:31.0684 CEST gradient_boosted_trees.cc:247] Truncates the model to 50 tree(s) i.e. 50 iteration(s).\n", + "[INFO 23-08-22 17:21:31.0688 CEST gradient_boosted_trees.cc:310] Final model num-trees:50 valid-loss:13.453820 valid-rmse:13.453820\n", + "[WARNING 23-08-22 17:21:31.0988 CEST gradient_boosted_trees.cc:1818] \"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:21:31.0991 CEST gradient_boosted_trees.cc:1829] \"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:21:31.0993 CEST gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", + "[INFO 23-08-22 17:21:31.5115 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:14.071945 train-rmse:14.071945 valid-loss:14.601532 valid-rmse:14.601532\n", + "[INFO 23-08-22 17:21:31.5116 CEST gradient_boosted_trees.cc:247] Truncates the model to 47 tree(s) i.e. 47 iteration(s).\n", + "[INFO 23-08-22 17:21:31.5116 CEST gradient_boosted_trees.cc:310] Final model num-trees:47 valid-loss:14.593384 valid-rmse:14.593384\n", + "[WARNING 23-08-22 17:21:31.5663 CEST gradient_boosted_trees.cc:1818] \"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:21:31.5663 CEST gradient_boosted_trees.cc:1829] \"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:21:31.5663 CEST gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", + "[INFO 23-08-22 17:21:31.7412 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:13.070064 train-rmse:13.070064 valid-loss:13.403740 valid-rmse:13.403740\n", + "[INFO 23-08-22 17:21:31.7490 CEST gradient_boosted_trees.cc:247] Truncates the model to 50 tree(s) i.e. 50 iteration(s).\n", + "[INFO 23-08-22 17:21:31.7490 CEST gradient_boosted_trees.cc:310] Final model num-trees:50 valid-loss:13.403740 valid-rmse:13.403740\n", + "[WARNING 23-08-22 17:21:31.8163 CEST gradient_boosted_trees.cc:1818] \"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:21:31.8618 CEST gradient_boosted_trees.cc:1829] \"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:21:31.8654 CEST gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", + "[INFO 23-08-22 17:21:32.1903 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.487211 train-rmse:29.487211 valid-loss:28.607134 valid-rmse:28.607134\n", + "[INFO 23-08-22 17:21:33.1783 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", + "[INFO 23-08-22 17:21:33.1786 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", + "[INFO 23-08-22 17:21:33.3258 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", + "[INFO 23-08-22 17:21:33.4076 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:12.181689 train-rmse:12.181689 valid-loss:12.686768 valid-rmse:12.686768\n", + "[INFO 23-08-22 17:21:33.4866 CEST gradient_boosted_trees.cc:247] Truncates the model to 41 tree(s) i.e. 41 iteration(s).\n", + "[INFO 23-08-22 17:21:33.4872 CEST gradient_boosted_trees.cc:310] Final model num-trees:41 valid-loss:12.684937 valid-rmse:12.684937\n", + "[WARNING 23-08-22 17:21:33.5223 CEST gradient_boosted_trees.cc:1818] \"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:21:33.5226 CEST gradient_boosted_trees.cc:1829] \"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:21:33.5228 CEST gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", + "[INFO 23-08-22 17:21:34.5676 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", + "[INFO 23-08-22 17:21:34.5677 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", + "[INFO 23-08-22 17:21:34.7348 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", + "[INFO 23-08-22 17:21:34.8318 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.497025 train-rmse:29.497025 valid-loss:28.542063 valid-rmse:28.542063\n", + "[INFO 23-08-22 17:21:35.1272 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", + "[INFO 23-08-22 17:21:35.1273 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", + "[INFO 23-08-22 17:21:35.2377 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", + "[INFO 23-08-22 17:21:35.8849 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", + "[INFO 23-08-22 17:21:35.9068 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", + "[INFO 23-08-22 17:21:36.0690 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", + "[INFO 23-08-22 17:21:36.0691 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", + "[INFO 23-08-22 17:21:36.1502 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", + "[INFO 23-08-22 17:21:36.6343 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", + "[INFO 23-08-22 17:21:36.8570 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.453743 train-rmse:29.453743 valid-loss:29.037130 valid-rmse:29.037130\n", + "[INFO 23-08-22 17:21:37.0224 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:13.252879 train-rmse:13.252879 valid-loss:13.566219 valid-rmse:13.566219\n", + "[INFO 23-08-22 17:21:37.0225 CEST gradient_boosted_trees.cc:247] Truncates the model to 50 tree(s) i.e. 50 iteration(s).\n", + "[INFO 23-08-22 17:21:37.0225 CEST gradient_boosted_trees.cc:310] Final model num-trees:50 valid-loss:13.566219 valid-rmse:13.566219\n", + "[WARNING 23-08-22 17:21:37.0245 CEST gradient_boosted_trees.cc:1818] \"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:21:37.0245 CEST gradient_boosted_trees.cc:1829] \"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:21:37.0245 CEST gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", + "[INFO 23-08-22 17:21:37.1079 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.470354 train-rmse:29.470354 valid-loss:28.912182 valid-rmse:28.912182\n", + "[INFO 23-08-22 17:21:37.2099 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:14.514459 train-rmse:14.514459 valid-loss:14.994228 valid-rmse:14.994228\n", + "[INFO 23-08-22 17:21:37.2436 CEST gradient_boosted_trees.cc:247] Truncates the model to 50 tree(s) i.e. 50 iteration(s).\n", + "[INFO 23-08-22 17:21:37.2473 CEST gradient_boosted_trees.cc:310] Final model num-trees:50 valid-loss:14.994228 valid-rmse:14.994228\n", + "[WARNING 23-08-22 17:21:37.3063 CEST gradient_boosted_trees.cc:1818] \"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:21:37.3167 CEST gradient_boosted_trees.cc:1829] \"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:21:37.3167 CEST gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", + "[INFO 23-08-22 17:21:37.8581 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.474899 train-rmse:29.474899 valid-loss:28.634254 valid-rmse:28.634254\n", + "[INFO 23-08-22 17:21:38.7341 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.421598 train-rmse:29.421598 valid-loss:29.626032 valid-rmse:29.626032\n", + "[INFO 23-08-22 17:21:39.3875 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", + "[INFO 23-08-22 17:21:39.3893 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", + "[INFO 23-08-22 17:21:39.5529 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", + "[INFO 23-08-22 17:21:39.8978 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", + "[INFO 23-08-22 17:21:39.8978 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", + "[INFO 23-08-22 17:21:40.0404 CEST gradient_boosted_trees.cc:1544] \tnum-trees:6 train-loss:22.339996 train-rmse:22.339996 valid-loss:21.602955 valid-rmse:21.602955\n", + "[INFO 23-08-22 17:21:40.3224 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", + "[INFO 23-08-22 17:21:41.1769 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.545298 train-rmse:29.545298 valid-loss:29.298836 valid-rmse:29.298836\n", + "[INFO 23-08-22 17:21:43.4812 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.541935 train-rmse:29.541935 valid-loss:28.897583 valid-rmse:28.897583\n", + "[INFO 23-08-22 17:22:10.7016 CEST gradient_boosted_trees.cc:1544] \tnum-trees:13 train-loss:18.213362 train-rmse:18.213362 valid-loss:18.072496 valid-rmse:18.072496\n", + "[INFO 23-08-22 17:22:41.1286 CEST gradient_boosted_trees.cc:1544] \tnum-trees:23 train-loss:16.149427 train-rmse:16.149427 valid-loss:16.552889 valid-rmse:16.552889\n", + "[INFO 23-08-22 17:23:11.2261 CEST gradient_boosted_trees.cc:1544] \tnum-trees:41 train-loss:15.091777 train-rmse:15.091777 valid-loss:15.722352 valid-rmse:15.722352\n", + "[INFO 23-08-22 17:23:37.3905 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:14.713874 train-rmse:14.713874 valid-loss:15.431265 valid-rmse:15.431265\n", + "[INFO 23-08-22 17:23:37.4181 CEST gradient_boosted_trees.cc:247] Truncates the model to 50 tree(s) i.e. 50 iteration(s).\n", + "[INFO 23-08-22 17:23:37.4187 CEST gradient_boosted_trees.cc:310] Final model num-trees:50 valid-loss:15.431265 valid-rmse:15.431265\n", + "[WARNING 23-08-22 17:23:37.5522 CEST gradient_boosted_trees.cc:1818] \"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:23:37.5523 CEST gradient_boosted_trees.cc:1829] \"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:23:37.5524 CEST gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", + "[INFO 23-08-22 17:23:41.2318 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:14.750937 train-rmse:14.750937 valid-loss:15.528153 valid-rmse:15.528153\n", + "[INFO 23-08-22 17:23:41.2319 CEST gradient_boosted_trees.cc:247] Truncates the model to 50 tree(s) i.e. 50 iteration(s).\n", + "[INFO 23-08-22 17:23:41.2319 CEST gradient_boosted_trees.cc:310] Final model num-trees:50 valid-loss:15.528153 valid-rmse:15.528153\n", + "[WARNING 23-08-22 17:23:41.2358 CEST gradient_boosted_trees.cc:1818] \"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:23:41.2358 CEST gradient_boosted_trees.cc:1829] \"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:23:41.2358 CEST gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", + "[INFO 23-08-22 17:23:41.6881 CEST gradient_boosted_trees.cc:1544] \tnum-trees:46 train-loss:15.416377 train-rmse:15.416377 valid-loss:15.507325 valid-rmse:15.507325\n", + "[INFO 23-08-22 17:23:41.9143 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:15.019258 train-rmse:15.019258 valid-loss:15.995070 valid-rmse:15.995070\n", + "[INFO 23-08-22 17:23:41.9144 CEST gradient_boosted_trees.cc:247] Truncates the model to 50 tree(s) i.e. 50 iteration(s).\n", + "[INFO 23-08-22 17:23:41.9144 CEST gradient_boosted_trees.cc:310] Final model num-trees:50 valid-loss:15.995070 valid-rmse:15.995070\n", + "[WARNING 23-08-22 17:23:41.9572 CEST gradient_boosted_trees.cc:1818] \"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:23:41.9962 CEST gradient_boosted_trees.cc:1829] \"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:23:41.9966 CEST gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", + "[INFO 23-08-22 17:23:42.3678 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:14.769040 train-rmse:14.769040 valid-loss:15.246088 valid-rmse:15.246088\n", + "[INFO 23-08-22 17:23:42.3678 CEST gradient_boosted_trees.cc:247] Truncates the model to 50 tree(s) i.e. 50 iteration(s).\n", + "[INFO 23-08-22 17:23:42.3679 CEST gradient_boosted_trees.cc:310] Final model num-trees:50 valid-loss:15.246088 valid-rmse:15.246088\n", + "[WARNING 23-08-22 17:23:42.4095 CEST gradient_boosted_trees.cc:1818] \"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:23:42.4095 CEST gradient_boosted_trees.cc:1829] \"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:23:42.4095 CEST gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", + "[INFO 23-08-22 17:23:44.4584 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", + "[INFO 23-08-22 17:23:44.4585 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", + "[INFO 23-08-22 17:23:45.0821 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", + "[INFO 23-08-22 17:23:45.3081 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", + "[INFO 23-08-22 17:23:45.3082 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", + "[INFO 23-08-22 17:23:45.5279 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", + "[INFO 23-08-22 17:23:45.7003 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:15.393883 train-rmse:15.393883 valid-loss:15.816501 valid-rmse:15.816501\n", + "[INFO 23-08-22 17:23:45.7003 CEST gradient_boosted_trees.cc:247] Truncates the model to 50 tree(s) i.e. 50 iteration(s).\n", + "[INFO 23-08-22 17:23:45.7004 CEST gradient_boosted_trees.cc:310] Final model num-trees:50 valid-loss:15.816501 valid-rmse:15.816501\n", + "[WARNING 23-08-22 17:23:45.7447 CEST gradient_boosted_trees.cc:1818] \"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:23:45.7448 CEST gradient_boosted_trees.cc:1829] \"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:23:45.7448 CEST gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", + "[INFO 23-08-22 17:23:47.3018 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:14.870501 train-rmse:14.870501 valid-loss:15.861364 valid-rmse:15.861364\n", + "[INFO 23-08-22 17:23:47.3018 CEST gradient_boosted_trees.cc:247] Truncates the model to 50 tree(s) i.e. 50 iteration(s).\n", + "[INFO 23-08-22 17:23:47.3019 CEST gradient_boosted_trees.cc:310] Final model num-trees:50 valid-loss:15.861364 valid-rmse:15.861364\n", + "[WARNING 23-08-22 17:23:47.4206 CEST gradient_boosted_trees.cc:1818] \"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:23:47.4727 CEST gradient_boosted_trees.cc:1829] \"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:23:47.4728 CEST gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", + "[INFO 23-08-22 17:23:47.6738 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", + "[INFO 23-08-22 17:23:47.6739 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", + "[INFO 23-08-22 17:23:47.9977 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", + "[INFO 23-08-22 17:23:48.3165 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.561636 train-rmse:29.561636 valid-loss:28.802879 valid-rmse:28.802879\n", + "[INFO 23-08-22 17:23:48.4621 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.545330 train-rmse:29.545330 valid-loss:28.827353 valid-rmse:28.827353\n", + "[INFO 23-08-22 17:23:50.4980 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", + "[INFO 23-08-22 17:23:50.5432 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", + "[INFO 23-08-22 17:23:50.5479 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", + "[INFO 23-08-22 17:23:50.5480 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", + "[INFO 23-08-22 17:23:50.6301 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:14.620991 train-rmse:14.620991 valid-loss:15.723470 valid-rmse:15.723470\n", + "[INFO 23-08-22 17:23:50.6547 CEST gradient_boosted_trees.cc:247] Truncates the model to 50 tree(s) i.e. 50 iteration(s).\n", + "[INFO 23-08-22 17:23:50.6548 CEST gradient_boosted_trees.cc:310] Final model num-trees:50 valid-loss:15.723470 valid-rmse:15.723470\n", + "[WARNING 23-08-22 17:23:50.6926 CEST gradient_boosted_trees.cc:1818] \"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:23:50.6927 CEST gradient_boosted_trees.cc:1829] \"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:23:50.6927 CEST gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", + "[INFO 23-08-22 17:23:51.0291 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", + "[INFO 23-08-22 17:23:51.1478 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:15.277184 train-rmse:15.277184 valid-loss:15.470344 valid-rmse:15.470344\n", + "[INFO 23-08-22 17:23:51.1479 CEST gradient_boosted_trees.cc:247] Truncates the model to 50 tree(s) i.e. 50 iteration(s).\n", + "[INFO 23-08-22 17:23:51.1479 CEST gradient_boosted_trees.cc:310] Final model num-trees:50 valid-loss:15.470344 valid-rmse:15.470344\n", + "[WARNING 23-08-22 17:23:51.1515 CEST gradient_boosted_trees.cc:1818] \"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:23:51.1515 CEST gradient_boosted_trees.cc:1829] \"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:23:51.1516 CEST gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", + "[INFO 23-08-22 17:23:51.4062 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", + "[INFO 23-08-22 17:23:51.4393 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.519100 train-rmse:29.519100 valid-loss:28.915615 valid-rmse:28.915615\n", + "[INFO 23-08-22 17:23:51.4788 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", + "[INFO 23-08-22 17:23:51.4788 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", + "[INFO 23-08-22 17:23:51.6864 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", + "[INFO 23-08-22 17:23:53.6416 CEST [INFO 23-08-22 17:23:53.6617 CEST gradient_boosted_trees.cc:gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.555222 train-rmse:29.555222 valid-loss:29.081779 valid-rmse:29.081779\n", + "1542] \tnum-trees:1 train-loss:29.580265 train-rmse:29.580265 valid-loss:28.985928 valid-rmse:28.985928\n", + "[INFO 23-08-22 17:23:53.8442 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", + "[INFO 23-08-22 17:23:53.8442 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", + "[INFO 23-08-22 17:23:53.9488 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", + "[INFO 23-08-22 17:23:53.9779 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.485874 train-rmse:29.485874 valid-loss:29.859213 valid-rmse:29.859213\n", + "[INFO 23-08-22 17:23:54.5426 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", + "[INFO 23-08-22 17:23:54.5427 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", + "[INFO 23-08-22 17:23:54.6690 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", + "[INFO 23-08-22 17:23:55.6092 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.552290 train-rmse:29.552290 valid-loss:29.436209 valid-rmse:29.436209\n", + "[INFO 23-08-22 17:23:57.1400 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.592987 train-rmse:29.592987 valid-loss:29.220953 valid-rmse:29.220953\n", + "[INFO 23-08-22 17:24:11.9398 CEST gradient_boosted_trees.cc:1544] \tnum-trees:12 train-loss:19.211472 train-rmse:19.211472 valid-loss:19.920101 valid-rmse:19.920101\n", + "[INFO 23-08-22 17:24:42.3389 CEST gradient_boosted_trees.cc:1544] \tnum-trees:22 train-loss:16.972153 train-rmse:16.972153 valid-loss:17.439720 valid-rmse:17.439720\n", + "[INFO 23-08-22 17:25:12.4270 CEST gradient_boosted_trees.cc:1544] \tnum-trees:32 train-loss:16.314236 train-rmse:16.314236 valid-loss:16.762869 valid-rmse:16.762869\n", + "[INFO 23-08-22 17:25:42.6012 CEST gradient_boosted_trees.cc:1544] \tnum-trees:48 train-loss:15.584856 train-rmse:15.584856 valid-loss:16.379702 valid-rmse:16.379702\n", + "[INFO 23-08-22 17:25:43.3731 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:15.488959 train-rmse:15.488959 valid-loss:16.052216 valid-rmse:16.052216\n", + "[INFO 23-08-22 17:25:43.3966 CEST gradient_boosted_trees.cc:247] Truncates the model to 50 tree(s) i.e. 50 iteration(s).\n", + "[INFO 23-08-22 17:25:43.3967 CEST gradient_boosted_trees.cc:310] Final model num-trees:50 valid-loss:16.052216 valid-rmse:16.052216\n", + "[WARNING 23-08-22 17:25:43.4003 CEST gradient_boosted_trees.cc:1818] \"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:25:43.4004 CEST gradient_boosted_trees.cc:1829] \"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:25:43.4004 CEST gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", + "[INFO 23-08-22 17:25:47.8493 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:15.795024 train-rmse:15.795024 valid-loss:17.031176 valid-rmse:17.031176\n", + "[INFO 23-08-22 17:25:47.8494 CEST gradient_boosted_trees.cc:247] Truncates the model to 50 tree(s) i.e. 50 iteration(s).\n", + "[INFO 23-08-22 17:25:47.8495 CEST gradient_boosted_trees.cc:310] Final model num-trees:50 valid-loss:17.031176 valid-rmse:17.031176\n", + "[WARNING 23-08-22 17:25:47.9558 CEST gradient_boosted_trees.cc:1818] \"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:25:47.9558 CEST gradient_boosted_trees.cc:1829] \"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:25:47.9559 CEST gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", + "[INFO 23-08-22 17:25:48.0492 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:15.498506 train-rmse:15.498506 valid-loss:16.925005 valid-rmse:16.925005\n", + "[INFO 23-08-22 17:25:48.0493 CEST gradient_boosted_trees.cc:247] Truncates the model to 50 tree(s) i.e. 50 iteration(s).\n", + "[INFO 23-08-22 17:25:48.0493 CEST gradient_boosted_trees.cc:310] Final model num-trees:50 valid-loss:16.925005 valid-rmse:16.925005\n", + "[WARNING 23-08-22 17:25:48.1019 CEST gradient_boosted_trees.cc:1818] \"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:25:48.1020 CEST gradient_boosted_trees.cc:1829] \"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:25:48.1020 CEST gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", + "[INFO 23-08-22 17:25:49.0721 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:15.436479 train-rmse:15.436479 valid-loss:16.489502 valid-rmse:16.489502\n", + "[INFO 23-08-22 17:25:49.0858 CEST gradient_boosted_trees.cc:247] Truncates the model to 50 tree(s) i.e. 50 iteration(s).\n", + "[INFO 23-08-22 17:25:49.0859 CEST gradient_boosted_trees.cc:310] Final model num-trees:50 valid-loss:16.489502 valid-rmse:16.489502\n", + "[INFO 23-08-22 17:25:49.3451 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:15.940226 train-rmse:15.940226 valid-loss:16.628620 valid-rmse:16.628620\n", + "[INFO 23-08-22 17:25:49.3730 CEST gradient_boosted_trees.cc:247] Truncates the model to 50 tree(s) i.e. 50 iteration(s).\n", + "[INFO 23-08-22 17:25:49.3731 CEST gradient_boosted_trees.cc:310] Final model num-trees:50 valid-loss:16.628620 valid-rmse:16.628620\n", + "[INFO 23-08-22 17:25:49.5604 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:15.528732 train-rmse:15.528732 valid-loss:16.360813 valid-rmse:16.360813\n", + "[INFO 23-08-22 17:25:49.5826 CEST gradient_boosted_trees.cc:247] Truncates the model to 50 tree(s) i.e. 50 iteration(s).\n", + "[INFO 23-08-22 17:25:49.5827 CEST gradient_boosted_trees.cc:310] Final model num-trees:50 valid-loss:16.360813 valid-rmse:16.360813\n", + "[INFO 23-08-22 17:25:49.7487 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", + "[INFO 23-08-22 17:25:49.7488 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", + "[INFO 23-08-22 17:25:49.8992 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:15.585465 train-rmse:15.585465 valid-loss:16.446827 valid-rmse:16.446827\n", + "[INFO 23-08-22 17:25:49.9236 CEST gradient_boosted_trees.cc:247] Truncates the model to 50 tree(s) i.e. 50 iteration(s).\n", + "[INFO 23-08-22 17:25:49.9237 CEST gradient_boosted_trees.cc:310] Final model num-trees:50 valid-loss:16.446827 valid-rmse:16.446827\n", + "[INFO 23-08-22 17:25:50.0453 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", + "[INFO 23-08-22 17:25:51.9348 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", + "[INFO 23-08-22 17:25:51.9349 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", + "[INFO 23-08-22 17:25:52.1247 CEST gradient_boosted_trees.cc[INFO 23-08-22 17:25:52.1449 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", + "[INFO 23-08-22 17:25:52.1450 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", + ":1542] \tnum-trees:1 train-loss:29.473177 train-rmse:29.473177 valid-loss:30.028440 valid-rmse:30.028440\n", + "[INFO 23-08-22 17:25:52.1676 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", + "[INFO 23-08-22 17:25:52.8560 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", + "[INFO 23-08-22 17:25:53.4240 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:15.958979 train-rmse:15.958979 valid-loss:17.016514 valid-rmse:17.016514\n", + "[INFO 23-08-22 17:25:53.4504 CEST gradient_boosted_trees.cc:247] Truncates the model to 50 tree(s) i.e. 50 iteration(s).\n", + "[INFO 23-08-22 17:25:53.4529 CEST gradient_boosted_trees.cc:310] Final model num-trees:50 valid-loss:17.016514 valid-rmse:17.016514\n", + "[INFO 23-08-22 17:25:54.2969 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.512590 train-rmse:29.512590 valid-loss:29.749025 valid-rmse:29.749025\n", + "[INFO 23-08-22 17:25:55.0639 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.433599 train-rmse:29.433599 valid-loss:30.823900 valid-rmse:30.823900\n", + "[INFO 23-08-22 17:26:12.6080 CEST gradient_boosted_trees.cc:1544] \tnum-trees:14 train-loss:18.923498 train-rmse:18.923498 valid-loss:19.393936 valid-rmse:19.393936\n", + "[INFO 23-08-22 17:26:42.7529 CEST gradient_boosted_trees.cc:1544] \tnum-trees:46 train-loss:16.113173 train-rmse:16.113173 valid-loss:17.555851 valid-rmse:17.555851\n", + "[INFO 23-08-22 17:26:45.2334 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:15.972644 train-rmse:15.972644 valid-loss:17.476349 valid-rmse:17.476349\n", + "[INFO 23-08-22 17:26:45.2351 CEST gradient_boosted_trees.cc:247] Truncates the model to 50 tree(s) i.e. 50 iteration(s).\n", + "[INFO 23-08-22 17:26:45.2352 CEST gradient_boosted_trees.cc:310] Final model num-trees:50 valid-loss:17.476349 valid-rmse:17.476349\n", + "[INFO 23-08-22 17:26:46.1924 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:15.949216 train-rmse:15.949216 valid-loss:16.998175 valid-rmse:16.998175\n", + "[INFO 23-08-22 17:26:46.1925 CEST gradient_boosted_trees.cc:247] Truncates the model to 50 tree(s) i.e. 50 iteration(s).\n", + "[INFO 23-08-22 17:26:46.1925 CEST gradient_boosted_trees.cc:310] Final model num-trees:50 valid-loss:16.998175 valid-rmse:16.998175\n", + "[INFO 23-08-22 17:26:46.2895 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:15.933821 train-rmse:15.933821 valid-loss:18.116308 valid-rmse:18.116308\n", + "[INFO 23-08-22 17:26:46.2895 CEST gradient_boosted_trees.cc:247] Truncates the model to 50 tree(s) i.e. 50 iteration(s).\n", + "[INFO 23-08-22 17:26:46.2895 CEST gradient_boosted_trees.cc:310] Final model num-trees:50 valid-loss:18.116308 valid-rmse:18.116308\n", + "[INFO 23-08-22 17:26:46.2920 CEST kernel.cc:926] Export model in log directory: /tmp/tmpfn9n19rs with prefix 5b2c1467920142a0\n", + "[INFO 23-08-22 17:26:46.3781 CEST kernel.cc:944] Save model in resources\n", + "[WARNING 23-08-22 17:26:46.3801 CEST abstract_model.cc:1058] Validation evaluation not supported for MULTITASKER\n", + "[INFO 23-08-22 17:26:46.3803 CEST abstract_model.cc:849] Model self evaluation:\n", + "Number of predictions (with weights): 1\n", + "Task: REGRESSION\n", + "Loss (SQUARED_ERROR): 12.6849\n", + "\n", + "RMSE: 3.56159\n", + "Default RMSE: : 0\n", + "\n", + "[INFO 23-08-22 17:26:46.3808 CEST abstract_model.cc:849] Model self evaluation:\n", + "Number of predictions (with weights): 1\n", + "Task: REGRESSION\n", + "Loss (SQUARED_ERROR): 13.4037\n", + "\n", + "RMSE: 3.66111\n", + "Default RMSE: : 0\n", + "\n", + "[INFO 23-08-22 17:26:46.3813 CEST abstract_model.cc:849] Model self evaluation:\n", + "Number of predictions (with weights): 1\n", + "Task: REGRESSION\n", + "Loss (SQUARED_ERROR): 13.4538\n", + "\n", + "RMSE: 3.66794\n", + "Default RMSE: : 0\n", + "\n", + "[INFO 23-08-22 17:26:46.3818 CEST abstract_model.cc:849] Model self evaluation:\n", + "Number of predictions (with weights): 1\n", + "Task: REGRESSION\n", + "Loss (SQUARED_ERROR): 13.5662\n", + "\n", + "RMSE: 3.68323\n", + "Default RMSE: : 0\n", + "\n", + "[INFO 23-08-22 17:26:46.3823 CEST abstract_model.cc:849] Model self evaluation:\n", + "Number of predictions (with weights): 1\n", + "Task: REGRESSION\n", + "Loss (SQUARED_ERROR): 15.0292\n", + "\n", + "RMSE: 3.87676\n", + "Default RMSE: : 0\n", + "\n", + "[INFO 23-08-22 17:26:46.3828 CEST abstract_model.cc:849] Model self evaluation:\n", + "Number of predictions (with weights): 1\n", + "Task: REGRESSION\n", + "Loss (SQUARED_ERROR): 15.3554\n", + "\n", + "RMSE: 3.9186\n", + "Default RMSE: : 0\n", + "\n", + "[INFO 23-08-22 17:26:46.3833 CEST abstract_model.cc:849] Model self evaluation:\n", + "Number of predictions (with weights): 1\n", + "Task: REGRESSION\n", + "Loss (SQUARED_ERROR): 14.5934\n", + "\n", + "RMSE: 3.82013\n", + "Default RMSE: : 0\n", + "\n", + "[INFO 23-08-22 17:26:46.3836 CEST abstract_model.cc:849] Model self evaluation:\n", + "Number of predictions (with weights): 1\n", + "Task: REGRESSION\n", + "Loss (SQUARED_ERROR): 14.9942\n", + "\n", + "RMSE: 3.87224\n", + "Default RMSE: : 0\n", + "\n", + "[INFO 23-08-22 17:26:46.3841 CEST abstract_model.cc:849] Model self evaluation:\n", + "Number of predictions (with weights): 1\n", + "Task: REGRESSION\n", + "Loss (SQUARED_ERROR): 15.4313\n", + "\n", + "RMSE: 3.92826\n", + "Default RMSE: : 0\n", + "\n", + "[INFO 23-08-22 17:26:46.3846 CEST abstract_model.cc:849] Model self evaluation:\n", + "Number of predictions (with weights): 1\n", + "Task: REGRESSION\n", + "Loss (SQUARED_ERROR): 15.5282\n", + "\n", + "RMSE: 3.94058\n", + "Default RMSE: : 0\n", + "\n", + "[INFO 23-08-22 17:26:46.3850 CEST abstract_model.cc:849] Model self evaluation:\n", + "Number of predictions (with weights): 1\n", + "Task: REGRESSION\n", + "Loss (SQUARED_ERROR): 15.7235\n", + "\n", + "RMSE: 3.96528\n", + "Default RMSE: : 0\n", + "\n", + "[INFO 23-08-22 17:26:46.3854 CEST abstract_model.cc:849] Model self evaluation:\n", + "Number of predictions (with weights): 1\n", + "Task: REGRESSION\n", + "Loss (SQUARED_ERROR): 15.2461\n", + "\n", + "RMSE: 3.90462\n", + "Default RMSE: : 0\n", + "\n", + "[INFO 23-08-22 17:26:46.3858 CEST abstract_model.cc:849] Model self evaluation:\n", + "Number of predictions (with weights): 1\n", + "Task: REGRESSION\n", + "Loss (SQUARED_ERROR): 15.8614\n", + "\n", + "RMSE: 3.98263\n", + "Default RMSE: : 0\n", + "\n", + "[INFO 23-08-22 17:26:46.3862 CEST abstract_model.cc:849] Model self evaluation:\n", + "Number of predictions (with weights): 1\n", + "Task: REGRESSION\n", + "Loss (SQUARED_ERROR): 15.9951\n", + "\n", + "RMSE: 3.99938\n", + "Default RMSE: : 0\n", + "\n", + "[INFO 23-08-22 17:26:46.3865 CEST abstract_model.cc:849] Model self evaluation:\n", + "Number of predictions (with weights): 1\n", + "Task: REGRESSION\n", + "Loss (SQUARED_ERROR): 15.4703\n", + "\n", + "RMSE: 3.93324\n", + "Default RMSE: : 0\n", + "\n", + "[INFO 23-08-22 17:26:46.3869 CEST abstract_model.cc:849] Model self evaluation:\n", + "Number of predictions (with weights): 1\n", + "Task: REGRESSION\n", + "Loss (SQUARED_ERROR): 15.8165\n", + "\n", + "RMSE: 3.977\n", + "Default RMSE: : 0\n", + "\n", + "[INFO 23-08-22 17:26:46.3872 CEST abstract_model.cc:849] Model self evaluation:\n", + "Number of predictions (with weights): 1\n", + "Task: REGRESSION\n", + "Loss (SQUARED_ERROR): 16.0522\n", + "\n", + "RMSE: 4.00652\n", + "Default RMSE: : 0\n", + "\n", + "[INFO 23-08-22 17:26:46.3876 CEST abstract_model.cc:849] Model self evaluation:\n", + "Number of predictions (with weights): 1\n", + "Task: REGRESSION\n", + "Loss (SQUARED_ERROR): 16.4895\n", + "\n", + "RMSE: 4.06073\n", + "Default RMSE: : 0\n", + "\n", + "[INFO 23-08-22 17:26:46.3881 CEST abstract_model.cc:849] Model self evaluation:\n", + "Number of predictions (with weights): 1\n", + "Task: REGRESSION\n", + "Loss (SQUARED_ERROR): 16.3608\n", + "\n", + "RMSE: 4.04485\n", + "Default RMSE: : 0\n", + "\n", + "[INFO 23-08-22 17:26:46.3885 CEST abstract_model.cc:849] Model self evaluation:\n", + "Number of predictions (with weights): 1\n", + "Task: REGRESSION\n", + "Loss (SQUARED_ERROR): 16.925\n", + "\n", + "RMSE: 4.114\n", + "Default RMSE: : 0\n", + "\n", + "[INFO 23-08-22 17:26:46.3888 CEST abstract_model.cc:849] Model self evaluation:\n", + "Number of predictions (with weights): 1\n", + "Task: REGRESSION\n", + "Loss (SQUARED_ERROR): 16.4468\n", + "\n", + "RMSE: 4.05547\n", + "Default RMSE: : 0\n", + "\n", + "[INFO 23-08-22 17:26:46.3892 CEST abstract_model.cc:849] Model self evaluation:\n", + "Number of predictions (with weights): 1\n", + "Task: REGRESSION\n", + "Loss (SQUARED_ERROR): 17.0312\n", + "\n", + "RMSE: 4.12688\n", + "Default RMSE: : 0\n", + "\n", + "[INFO 23-08-22 17:26:46.3896 CEST abstract_model.cc:849] Model self evaluation:\n", + "Number of predictions (with weights): 1\n", + "Task: REGRESSION\n", + "Loss (SQUARED_ERROR): 16.6286\n", + "\n", + "RMSE: 4.07782\n", + "Default RMSE: : 0\n", + "\n", + "[INFO 23-08-22 17:26:46.3900 CEST abstract_model.cc:849] Model self evaluation:\n", + "Number of predictions (with weights): 1\n", + "Task: REGRESSION\n", + "Loss (SQUARED_ERROR): 17.0165\n", + "\n", + "RMSE: 4.12511\n", + "Default RMSE: : 0\n", + "\n", + "[INFO 23-08-22 17:26:46.3905 CEST abstract_model.cc:849] Model self evaluation:\n", + "Number of predictions (with weights): 1\n", + "Task: REGRESSION\n", + "Loss (SQUARED_ERROR): 17.4763\n", + "\n", + "RMSE: 4.18047\n", + "Default RMSE: : 0\n", + "\n", + "[INFO 23-08-22 17:26:46.3909 CEST abstract_model.cc:849] Model self evaluation:\n", + "Number of predictions (with weights): 1\n", + "Task: REGRESSION\n", + "Loss (SQUARED_ERROR): 16.9982\n", + "\n", + "RMSE: 4.12288\n", + "Default RMSE: : 0\n", + "\n", + "[INFO 23-08-22 17:26:46.3912 CEST abstract_model.cc:849] Model self evaluation:\n", + "Number of predictions (with weights): 1\n", + "Task: REGRESSION\n", + "Loss (SQUARED_ERROR): 18.1163\n", + "\n", + "RMSE: 4.25633\n", + "Default RMSE: : 0\n", + "\n", + "[INFO 23-08-22 17:26:46.4543 CEST kernel.cc:1243] Loading model from path /tmp/tmpfn9n19rs/model/ with prefix 5b2c1467920142a0_0\n", + "[INFO 23-08-22 17:26:46.4598 CEST abstract_model.cc:1311] Engine \"GradientBoostedTreesQuickScorerExtended\" built\n", + "[INFO 23-08-22 17:26:46.4599 CEST kernel.cc:1075] Use fast generic engine\n", + "[INFO 23-08-22 17:26:46.6703 CEST kernel.cc:1243] Loading model from path /tmp/tmpfn9n19rs/model/ with prefix 5b2c1467920142a0_1\n", + "[INFO 23-08-22 17:26:46.6773 CEST kernel.cc:1075] Use fast generic engine\n", + "[INFO 23-08-22 17:26:46.7141 CEST kernel.cc:1243] Loading model from path /tmp/tmpfn9n19rs/model/ with prefix 5b2c1467920142a0_2\n", + "[INFO 23-08-22 17:26:46.7188 CEST kernel.cc:1075] Use fast generic engine\n", + "[INFO 23-08-22 17:26:46.7456 CEST kernel.cc:1243] Loading model from path /tmp/tmpfn9n19rs/model/ with prefix 5b2c1467920142a0_3\n", + "[INFO 23-08-22 17:26:46.7522 CEST kernel.cc:1075] Use fast generic engine\n", + "[INFO 23-08-22 17:26:46.7786 CEST kernel.cc:1243] Loading model from path /tmp/tmpfn9n19rs/model/ with prefix 5b2c1467920142a0_4\n", + "[INFO 23-08-22 17:26:46.7844 CEST kernel.cc:1075] Use fast generic engine\n", + "[INFO 23-08-22 17:26:46.8141 CEST kernel.cc:1243] Loading model from path /tmp/tmpfn9n19rs/model/ with prefix 5b2c1467920142a0_5\n", + "[INFO 23-08-22 17:26:46.8192 CEST kernel.cc:1075] Use fast generic engine\n", + "[INFO 23-08-22 17:26:46.8565 CEST kernel.cc:1243] Loading model from path /tmp/tmpfn9n19rs/model/ with prefix 5b2c1467920142a0_6\n", + "[INFO 23-08-22 17:26:46.8639 CEST kernel.cc:1075] Use fast generic engine\n", + "[INFO 23-08-22 17:26:46.8908 CEST kernel.cc:1243] Loading model from path /tmp/tmpfn9n19rs/model/ with prefix 5b2c1467920142a0_7\n", + "[INFO 23-08-22 17:26:46.8960 CEST kernel.cc:1075] Use fast generic engine\n", + "[INFO 23-08-22 17:26:46.9250 CEST kernel.cc:1243] Loading model from path /tmp/tmpfn9n19rs/model/ with prefix 5b2c1467920142a0_8\n", + "[INFO 23-08-22 17:26:46.9325 CEST kernel.cc:1075] Use fast generic engine\n", + "[INFO 23-08-22 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Loading model from path /tmp/tmpfn9n19rs/model/ with prefix 5b2c1467920142a0_18\n", + "[INFO 23-08-22 17:26:47.2972 CEST kernel.cc:1075] Use fast generic engine\n", + "[INFO 23-08-22 17:26:47.3378 CEST kernel.cc:1243] Loading model from path /tmp/tmpfn9n19rs/model/ with prefix 5b2c1467920142a0_19\n", + "[INFO 23-08-22 17:26:47.3459 CEST kernel.cc:1075] Use fast generic engine\n", + "[INFO 23-08-22 17:26:47.3834 CEST kernel.cc:1243] Loading model from path /tmp/tmpfn9n19rs/model/ with prefix 5b2c1467920142a0_20\n", + "[INFO 23-08-22 17:26:47.3918 CEST kernel.cc:1075] Use fast generic engine\n", + "[INFO 23-08-22 17:26:47.4245 CEST kernel.cc:1243] Loading model from path /tmp/tmpfn9n19rs/model/ with prefix 5b2c1467920142a0_21\n", + "[INFO 23-08-22 17:26:47.4294 CEST kernel.cc:1075] Use fast generic engine\n", + "[INFO 23-08-22 17:26:47.4576 CEST kernel.cc:1243] Loading model from path /tmp/tmpfn9n19rs/model/ with prefix 5b2c1467920142a0_22\n", + "[INFO 23-08-22 17:26:47.4631 CEST kernel.cc:1075] Use fast generic engine\n", + "[INFO 23-08-22 17:26:47.4906 CEST kernel.cc:1243] Loading model from path /tmp/tmpfn9n19rs/model/ with prefix 5b2c1467920142a0_23\n", + "[INFO 23-08-22 17:26:47.4953 CEST kernel.cc:1075] Use fast generic engine\n", + "[INFO 23-08-22 17:26:47.5222 CEST kernel.cc:1243] Loading model from path /tmp/tmpfn9n19rs/model/ with prefix 5b2c1467920142a0_24\n", + "[INFO 23-08-22 17:26:47.5276 CEST kernel.cc:1075] Use fast generic engine\n", + "[INFO 23-08-22 17:26:47.5570 CEST kernel.cc:1243] Loading model from path /tmp/tmpfn9n19rs/model/ with prefix 5b2c1467920142a0_25\n", + "[INFO 23-08-22 17:26:47.5627 CEST kernel.cc:1075] Use fast generic engine\n", + "[INFO 23-08-22 17:26:47.5937 CEST kernel.cc:1243] Loading model from path /tmp/tmpfn9n19rs/model/ with prefix 5b2c1467920142a0_26\n", + "[INFO 23-08-22 17:26:47.6032 CEST kernel.cc:1075] Use fast generic engine\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model trained in 0:07:19.827763\n", + "Compiling model...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:AutoGraph could not transform and will run it as-is.\n", + "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n", + "Cause: could not get source code\n", + "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: AutoGraph could not transform and will run it as-is.\n", + "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n", + "Cause: could not get source code\n", + "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n", + "Model compiled.\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "model_path = os.path.join(work_directory, \"gbm.model\")\n", - "if os.path.exists(model_path):\n", - " model = tf.keras.models.load_model(model_path)\n", - "else:\n", - " model = tfdf.keras.GradientBoostedTreesModel(\n", - " multitask=[\n", - " tfdf.keras.MultiTaskItem(x, task=tfdf.keras.Task.REGRESSION)\n", - " for x in label_names\n", - " ],\n", - " num_trees=50, # Increase the number of trees (e.g., 100) for a better model.\n", - " verbose=2, # Remove to make the cell less verbose.\n", - " num_threads=8,\n", - " )\n", - " \n", - " model.fit(tf_train_dataset)\n", - " model.save(model_path)\n" + "model = tfdf.keras.GradientBoostedTreesModel(\n", + " multitask=[\n", + " tfdf.keras.MultiTaskItem(x, task=tfdf.keras.Task.REGRESSION)\n", + " for x in label_names\n", + " ],\n", + " num_trees=40, # Increase the number of trees (e.g., 100) for a better model.\n", + " verbose=2, # Remove to make the training less verbose.\n", + ")\n", + "model.fit(tf_train_dataset)" ] }, { @@ -4767,7 +5858,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 51, "id": "e985dba0-dc14-4341-b0c8-ad145fef7ca3", "metadata": {}, "outputs": [], @@ -4786,7 +5877,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 52, "id": "068ea997-52ae-40eb-b18e-a118a3d7b026", "metadata": {}, "outputs": [], @@ -4842,10 +5933,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 55, "id": "978fc1ff-7914-43df-af04-e5b054a9e9f2", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The number of plots (80) is larger than \"options.max_num_plots=6\". Only the first plots will be printed.\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "real_sales_evset = sales_evset[\"sales\"].filter(sales_evset[\"day\"] > 1914)\n", "\n", @@ -4859,55 +5968,11 @@ "tp.plot(\n", " [real_sales_evset, prediction_evset, past_sales_evset],\n", " max_num_plots=3*2,\n", + " width_px=600,\n", + " merge=True,\n", ")\n" ] }, - { - "cell_type": "markdown", - "id": "321bcd2c-624c-4909-a7be-3efe7ecd5f82", - "metadata": {}, - "source": [ - "#### Custom plots\n", - "\n", - "In order to show the predictions on top of real sales and compare them, we need to create our own custom plots." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "32bcf947-b3aa-4082-b453-3c4cacbb19da", - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "import matplotlib.pyplot as plt\n", - "\n", - "def convert_timestamps_to_datetimes(timestamps):\n", - " return [datetime.fromtimestamp(t, tz=timezone.utc) for t in timestamps]\n", - "\n", - "num_plot = 4\n", - "fig, axs = plt.subplots(num_plot, figsize=(10, 10))\n", - "for plot_idx, index in enumerate(real_sales_evset.data):\n", - " if plot_idx >= num_plot:\n", - " break\n", - "\n", - " ax = axs[plot_idx]\n", - " ax.set_title(index)\n", - "\n", - " # Convert timestamps to dates\n", - " dates_real = convert_timestamps_to_datetimes(real_sales_evset.data[index].timestamps)\n", - " dates_pred = convert_timestamps_to_datetimes(prediction_evset.data[index].timestamps)\n", - " dates_past = convert_timestamps_to_datetimes(past_sales_evset.data[index].timestamps)\n", - "\n", - " # Plot past sales, test ground-truth (real) and predicted sales\n", - " ax.plot(dates_past, past_sales_evset.data[index].features[0], label=\"past\")\n", - " ax.plot(dates_real, real_sales_evset.data[index].features[0], label=\"real\")\n", - " ax.plot(dates_pred, prediction_evset.data[index].features[0], label=\"pred\")\n", - " ax.legend()\n", - "\n", - "fig.tight_layout()" - ] - }, { "attachments": {}, "cell_type": "markdown", @@ -4936,7 +6001,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.10" + "version": "3.10.9" } }, "nbformat": 4,