From 76da49135871add575b9dbbd23bae59f2936eb1f Mon Sep 17 00:00:00 2001 From: Evan Ray Date: Fri, 18 Oct 2024 15:20:09 -0400 Subject: [PATCH 1/7] allow for observation masking --- src/postpredict/dependence.py | 30 ++++++++++++---- .../dependence/test_build_train_X_Y.py | 34 +++++++++++++++++++ .../postpredict/dependence/test_transform.py | 2 ++ 3 files changed, 60 insertions(+), 6 deletions(-) diff --git a/src/postpredict/dependence.py b/src/postpredict/dependence.py index c52fc71..ce597c7 100644 --- a/src/postpredict/dependence.py +++ b/src/postpredict/dependence.py @@ -44,7 +44,8 @@ def _build_templates(self, wide_model_out): def transform(self, model_out: pl.DataFrame, reference_time_col: str = "reference_date", horizon_col: str = "horizon", pred_col: str = "value", - idx_col: str = "output_type_id"): + idx_col: str = "output_type_id", + obs_mask: np.ndarray | None = None): """ Apply a postprocessing transformation to sample predictions to induce dependence across time in the predictive samples. @@ -63,6 +64,13 @@ def transform(self, model_out: pl.DataFrame, name of column in model_out with predicted values (samples) idx_col: str name of column in model_out with sample indices + obs_mask: np.ndarray | None + mask to use for observed data. The primary use case is to support + cross-validation. If None, all observed data are used to form + dependence templates. Otherwise, `obs_mask` should be a boolean + array of shape (self.df.shape[0], ). Rows of self.df where obs_mask + is True will be used, while rows of self.df where obs_mask is False + will not be used. Returns ------- @@ -76,7 +84,7 @@ def transform(self, model_out: pl.DataFrame, max_horizon = model_out[horizon_col].max() # extract train_X and train_Y from observed data (self.df) - self._build_train_X_Y(min_horizon, max_horizon) + self._build_train_X_Y(min_horizon, max_horizon, obs_mask) # perform the transformation, one group at a time transformed_wide_model_out = ( @@ -164,7 +172,8 @@ def _apply_shuffle(self, return shuffled_wmo - def _build_train_X_Y(self, min_horizon, max_horizon): + def _build_train_X_Y(self, min_horizon, max_horizon, + obs_mask: np.ndarray | None = None): """ Build training set data frames self.train_X with features and self.train_Y with observed values in windows from min_horizon to @@ -176,6 +185,13 @@ def _build_train_X_Y(self, min_horizon, max_horizon): minimum prediction horizon max_horizon: int maximum prediction horizon + obs_mask: np.ndarray | None + mask to use for observed data. The primary use case is to support + cross-validation. If None, all observed data are used to form + dependence templates. Otherwise, `obs_mask` should be a boolean + array of shape (self.df.shape[0], ). Rows of self.df where obs_mask + is True will be used, while rows of self.df where obs_mask is False + will not be used. Returns ------- @@ -205,9 +221,11 @@ def _build_train_X_Y(self, min_horizon, max_horizon): .alias(shift_varname) ) - df_dropnull = self.df.drop_nulls() - self.train_X = df_dropnull[self.feat_cols] - self.train_Y = df_dropnull[self.shift_varnames] + if obs_mask is None: + obs_mask = True + df_mask_and_dropnull = self.df.filter(obs_mask).drop_nulls() + self.train_X = df_mask_and_dropnull[self.feat_cols] + self.train_Y = df_mask_and_dropnull[self.shift_varnames] def _pivot_horizon(self, model_out, reference_time_col, horizon_col, diff --git a/tests/postpredict/dependence/test_build_train_X_Y.py b/tests/postpredict/dependence/test_build_train_X_Y.py index 101df3c..21e1878 100644 --- a/tests/postpredict/dependence/test_build_train_X_Y.py +++ b/tests/postpredict/dependence/test_build_train_X_Y.py @@ -104,3 +104,37 @@ def test_build_train_X_Y_negative_horizons(obs_data, monkeypatch): assert_frame_equal(tdp.train_X, expected_train_X) assert_frame_equal(tdp.train_Y, expected_train_Y) + + +def test_build_train_X_Y_mask(obs_data, monkeypatch): + # we use monkeypatch to remove abstract methods from the + # TimeDependencePostprocessor class, allowing us to create an object of + # that class so as to test the non-abstract _build_train_X_Y method it defines. + # See https://stackoverflow.com/a/77748100 + monkeypatch.setattr(TimeDependencePostprocessor, "__abstractmethods__", set()) + tdp = TimeDependencePostprocessor(rng = np.random.default_rng(42)) + tdp.df = obs_data + tdp.key_cols = ["location", "age_group"] + tdp.time_col = "date", + tdp.obs_col = "value" + tdp.feat_cols = ["location", "age_group", "date"] + + mask = (obs_data["date"] <= datetime.strptime("2020-01-02", "%Y-%m-%d")) \ + | (obs_data["date"] >= datetime.strptime("2020-01-06", "%Y-%m-%d")) + tdp._build_train_X_Y(1, 4, obs_mask = mask) + + expected_train_X = pl.DataFrame({ + "location": ["a"] * 6 + ["b"] * 6, + "age_group": (["young"] * 3 + ["old"] * 3) * 2, + "date": [datetime.strptime(d, "%Y-%m-%d") for d in ["2020-01-01", "2020-01-02", "2020-01-06"]] * 4 + }) + + expected_train_Y = pl.DataFrame({ + "value_shift_p1": [11, 12, 16] + [21, 22, 26] + [31, 32, 36] + [41, 42, 46], + "value_shift_p2": [12, 13, 17] + [22, 23, 27] + [32, 33, 37] + [42, 43, 47], + "value_shift_p3": [13, 14, 18] + [23, 24, 28] + [33, 34, 38] + [43, 44, 48], + "value_shift_p4": [14, 15, 19] + [24, 25, 29] + [34, 35, 39] + [44, 45, 49] + }) + + assert_frame_equal(tdp.train_X, expected_train_X) + assert_frame_equal(tdp.train_Y, expected_train_Y) diff --git a/tests/postpredict/dependence/test_transform.py b/tests/postpredict/dependence/test_transform.py index 520f8f4..9f09ee9 100644 --- a/tests/postpredict/dependence/test_transform.py +++ b/tests/postpredict/dependence/test_transform.py @@ -10,6 +10,8 @@ def test_transform(obs_data, long_model_out, templates, long_expected_final, mon # this tests the full transformation pipeline defined in # TimeDependencePostprocessor, *other than* the _build_templates method, # which is to be implemented by a subclass of the abstract base class. + # (Note, this means we also do not directly test _build_train_X_Y here, + # since that feeds into _build_templates.) # For this test, we use the fixed templates defined as a test fixture. # define a concrete subclass of TimeDependencePostprocessor whose From 018e545ede1a5d0839312d36679b7e1572d1e1ad Mon Sep 17 00:00:00 2001 From: Evan Ray Date: Fri, 18 Oct 2024 21:33:27 -0400 Subject: [PATCH 2/7] basic setup for parameter optimization --- docs/fit_schaake.ipynb | 1120 +++++++++++++++++ pyproject.toml | 3 + requirements/requirements-dev.txt | 66 +- requirements/requirements.txt | 31 +- src/postpredict/dependence.py | 16 +- src/postpredict/metrics.py | 13 +- src/postpredict/weighters.py | 10 +- .../postpredict/dependence/test_transform.py | 2 +- 8 files changed, 1246 insertions(+), 15 deletions(-) create mode 100644 docs/fit_schaake.ipynb diff --git a/docs/fit_schaake.ipynb b/docs/fit_schaake.ipynb new file mode 100644 index 0000000..38aa37f --- /dev/null +++ b/docs/fit_schaake.ipynb @@ -0,0 +1,1120 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import string\n", + "\n", + "import numpy as np\n", + "import optuna \n", + "import polars as pl\n", + "\n", + "from matplotlib import pyplot as plt\n", + "from postpredict.dependence import Schaake\n", + "from postpredict.weighters import UnivariateGaussianKernel\n", + "from postpredict.metrics import energy_score\n" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [], + "source": [ + "def sim_from_ar(n, n_timesteps, phi, tau, rng, y_0 = None):\n", + " \"\"\"\n", + " Simulate observations from a Gaussian AR(1) process with AR coefficient phi\n", + " and innovation standard deviation tau.\n", + " \"\"\"\n", + " if y_0 is None:\n", + " marginal_variance = tau**2 / (1 - phi**2)\n", + " y_0 = rng.normal(loc=0.0, scale=np.sqrt(marginal_variance), size=(n, 1))\n", + " \n", + " if type(y_0) == float:\n", + " y_0 = np.full((n, 1), y_0)\n", + " \n", + " innovations = rng.normal(loc=0.0, scale=tau, size=(n, n_timesteps))\n", + " result = [y_0]\n", + " for i in range(n_timesteps):\n", + " result.append(phi * result[-1] + innovations[:, i:(i+1)])\n", + " return np.concatenate(result[1:], axis = 1)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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t0t1t2t3
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5.1262723.9935242.3886573.071411
3.7105843.6347873.0124161.903678
1.779742.1114280.3958670.090831
2.2790212.2573623.0908642.356384
…………
0.647204-0.170586-1.081510.661459
2.8488982.5646513.8344183.81343
2.1658133.3277573.6228682.79157
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" + ], + "text/plain": [ + "shape: (15, 4)\n", + "┌──────────┬───────────┬──────────┬──────────┐\n", + "│ t0 ┆ t1 ┆ t2 ┆ t3 │\n", + "│ --- ┆ --- ┆ --- ┆ --- │\n", + "│ f64 ┆ f64 ┆ f64 ┆ f64 │\n", + "╞══════════╪═══════════╪══════════╪══════════╡\n", + "│ 2.473953 ┆ 3.253527 ┆ 3.536941 ┆ 3.505589 │\n", + "│ 5.126272 ┆ 3.993524 ┆ 2.388657 ┆ 3.071411 │\n", + "│ 3.710584 ┆ 3.634787 ┆ 3.012416 ┆ 1.903678 │\n", + "│ 1.77974 ┆ 2.111428 ┆ 0.395867 ┆ 0.090831 │\n", + "│ 2.279021 ┆ 2.257362 ┆ 3.090864 ┆ 2.356384 │\n", + "│ … ┆ … ┆ … ┆ … │\n", + "│ 0.647204 ┆ -0.170586 ┆ -1.08151 ┆ 0.661459 │\n", + "│ 2.848898 ┆ 2.564651 ┆ 3.834418 ┆ 3.81343 │\n", + "│ 2.165813 ┆ 3.327757 ┆ 3.622868 ┆ 2.79157 │\n", + "│ 0.993484 ┆ 1.134695 ┆ 3.71482 ┆ 3.217084 │\n", + "│ 3.270373 ┆ 3.859089 ┆ 4.041731 ┆ 3.676092 │\n", + "└──────────┴───────────┴──────────┴──────────┘" + ] + }, + "execution_count": 73, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.columns = [f\"hor{h}\" for h in range(horizon)]\n", + "data" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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locationpopulationref_toutput_typeoutput_type_idvaluehorizon
strf64i32stri64f64i64
"l"990000.010"sample"1501.5311980
"l"990000.010"sample"1513.6406620
"l"990000.010"sample"1522.1335110
"l"990000.010"sample"1530.9252670
"l"990000.010"sample"1543.6632430
…………………
"l"990000.010"sample"1601.9579073
"l"990000.010"sample"161-0.2680113
"l"990000.010"sample"1621.7535383
"l"990000.010"sample"1634.7905673
"l"990000.010"sample"1642.273543
" + ], + "text/plain": [ + "shape: (60, 7)\n", + "┌──────────┬────────────┬───────┬─────────────┬────────────────┬───────────┬─────────┐\n", + "│ location ┆ population ┆ ref_t ┆ output_type ┆ output_type_id ┆ value ┆ horizon │\n", + "│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │\n", + "│ str ┆ f64 ┆ i32 ┆ str ┆ i64 ┆ f64 ┆ i64 │\n", + "╞══════════╪════════════╪═══════╪═════════════╪════════════════╪═══════════╪═════════╡\n", + "│ l ┆ 990000.0 ┆ 10 ┆ sample ┆ 150 ┆ 1.531198 ┆ 0 │\n", + "│ l ┆ 990000.0 ┆ 10 ┆ sample ┆ 151 ┆ 3.640662 ┆ 0 │\n", + "│ l ┆ 990000.0 ┆ 10 ┆ sample ┆ 152 ┆ 2.133511 ┆ 0 │\n", + "│ l ┆ 990000.0 ┆ 10 ┆ sample ┆ 153 ┆ 0.925267 ┆ 0 │\n", + "│ l ┆ 990000.0 ┆ 10 ┆ sample ┆ 154 ┆ 3.663243 ┆ 0 │\n", + "│ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … │\n", + "│ l ┆ 990000.0 ┆ 10 ┆ sample ┆ 160 ┆ 1.957907 ┆ 3 │\n", + "│ l ┆ 990000.0 ┆ 10 ┆ sample ┆ 161 ┆ -0.268011 ┆ 3 │\n", + "│ l ┆ 990000.0 ┆ 10 ┆ sample ┆ 162 ┆ 1.753538 ┆ 3 │\n", + "│ l ┆ 990000.0 ┆ 10 ┆ sample ┆ 163 ┆ 4.790567 ┆ 3 │\n", + "│ l ┆ 990000.0 ┆ 10 ┆ sample ┆ 164 ┆ 2.27354 ┆ 3 │\n", + "└──────────┴────────────┴───────┴─────────────┴────────────────┴───────────┴─────────┘" + ] + }, + "execution_count": 81, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pl.concat([\n", + " pl.DataFrame({\n", + " \"location\": loc,\n", + " \"population\": df.filter(pl.col(\"location\") == loc)[\"population\"][0],\n", + " \"ref_t\": df[\"t\"].max(),\n", + " \"output_type\": \"sample\",\n", + " \"output_type_id\": range(l_ind * n_samples, (l_ind + 1) * n_samples)\n", + " }),\n", + " pl.DataFrame(\n", + " sim_from_ar(\n", + " n = n_samples,\n", + " n_timesteps=horizon,\n", + " phi=loc_phis[l_ind],\n", + " tau=1.0,\n", + " rng=np.random.default_rng(),\n", + " y_0 = df.filter(pl.col(\"location\") == loc)[\"y\"][-1]\n", + " )\n", + " )\n", + " ],\n", + " how='horizontal') \\\n", + " .unpivot(\n", + " on=[f\"column_{h}\" for h in range(horizon)],\n", + " index=[\"location\", \"population\", \"ref_t\", \"output_type\", \"output_type_id\"]\n", + " ) \\\n", + " .with_columns(\n", + " horizon=pl.col(\"variable\").str.slice(7).cast(int)\n", + " ) \\\n", + " .drop(\"variable\")" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/xs/t5qwsz_d0hlc6vkx6wzr98f5q78pzl/T/ipykernel_3946/4105992927.py:70: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n", + " fig.show()\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def generate_predictions(df, n_samples = 100, horizon = 4):\n", + " predictions = pl.concat([\n", + " pl.concat([\n", + " pl.concat(\n", + " [\n", + " pl.DataFrame({\n", + " \"location\": loc,\n", + " \"population\": df.filter(pl.col(\"location\") == loc)[\"population\"][0],\n", + " \"ref_t\": df[\"t\"].max(),\n", + " \"output_type\": \"sample\",\n", + " \"output_type_id\": range(l_ind * n_samples, (l_ind + 1) * n_samples)\n", + " }),\n", + " pl.DataFrame(\n", + " sim_from_ar(\n", + " n = n_samples,\n", + " n_timesteps=horizon,\n", + " phi=loc_phis[l_ind],\n", + " tau=1.0,\n", + " rng=np.random.default_rng(),\n", + " y_0 = df.filter(pl.col(\"location\") == loc)[\"y\"][-1]\n", + " )\n", + " )\n", + " ],\n", + " how='horizontal'\n", + " ) \\\n", + " .unpivot(\n", + " on=[f\"column_{h}\" for h in range(horizon)],\n", + " index=[\"location\", \"population\", \"ref_t\", \"output_type\", \"output_type_id\"]\n", + " ) \\\n", + " .with_columns(\n", + " horizon=pl.col(\"variable\").str.slice(7).cast(int) + 1\n", + " ) \\\n", + " .drop(\"variable\")\n", + " ]) \\\n", + " for l_ind, loc in enumerate(locations)\n", + " ])\n", + " return predictions\n", + "\n", + "n_samples = 1000\n", + "horizon = 12\n", + "predictions_time_10 = generate_predictions(obs_data.filter(pl.col(\"t\") <= 10), n_samples=n_samples, horizon=horizon)\n", + "\n", + "ncol = 3\n", + "nrow = 4\n", + "fig, ax = plt.subplots(nrow, ncol, figsize=(10, 6))\n", + "\n", + "for i, l, in enumerate(locations):\n", + " row_ind = i // 3\n", + " col_ind = i % 3\n", + " ax[row_ind, col_ind].plot(obs_data.filter(pl.col(\"location\") == l)[\"y\"], c=\"blue\")\n", + " ax[row_ind, col_ind].title.set_text(f\"{l}, {loc_phis[i]}\")\n", + " loc_preds = (\n", + " predictions_time_10\n", + " .filter(pl.col(\"location\") == l)\n", + " .with_columns(\n", + " target_t = pl.col(\"ref_t\") + pl.col(\"horizon\"),\n", + " idx_within_loc = pl.col(\"output_type_id\") - pl.col(\"output_type_id\").min()\n", + " )\n", + " )\n", + " for j in range(n_samples):\n", + " ax[row_ind, col_ind].plot(\n", + " loc_preds.filter(pl.col(\"idx_within_loc\") == j)[\"target_t\"],\n", + " loc_preds.filter(pl.col(\"idx_within_loc\") == j)[\"value\"],\n", + " c=\"gray\",\n", + " alpha=0.2\n", + " )\n", + " \n", + "\n", + "fig.tight_layout()\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": {}, + "outputs": [], + "source": [ + "horizon = 12\n", + "predictions_all_ref_times = pl.concat([\n", + " generate_predictions(obs_data.filter(pl.col(\"t\") <= t), n_samples=n_samples, horizon=horizon) \\\n", + " for t in range(n_obs_timesteps)\n", + "])\n", + "predictions_all_ref_times = predictions_all_ref_times.with_columns(pop_normalized = pl.col(\"population\") / 1000000)" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "shape: (14_400_000, 8)
locationpopulationref_toutput_typeoutput_type_idvaluehorizonpop_normalized
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"a"590000.00"sample"4-1.83894110.59
……………………
"l"990000.099"sample"11995-1.72871120.99
"l"990000.099"sample"119960.786952120.99
"l"990000.099"sample"11997-5.19547120.99
"l"990000.099"sample"11998-3.009168120.99
"l"990000.099"sample"11999-1.129592120.99
" + ], + "text/plain": [ + "shape: (14_400_000, 8)\n", + "┌──────────┬────────────┬───────┬─────────────┬───────────────┬───────────┬─────────┬──────────────┐\n", + "│ location ┆ population ┆ ref_t ┆ output_type ┆ output_type_i ┆ value ┆ horizon ┆ pop_normaliz │\n", + "│ --- ┆ --- ┆ --- ┆ --- ┆ d ┆ --- ┆ --- ┆ ed │\n", + "│ str ┆ f64 ┆ i32 ┆ str ┆ --- ┆ f64 ┆ i64 ┆ --- │\n", + "│ ┆ ┆ ┆ ┆ i64 ┆ ┆ ┆ f64 │\n", + "╞══════════╪════════════╪═══════╪═════════════╪═══════════════╪═══════════╪═════════╪══════════════╡\n", + "│ a ┆ 590000.0 ┆ 0 ┆ sample ┆ 0 ┆ -1.766497 ┆ 1 ┆ 0.59 │\n", + "│ a ┆ 590000.0 ┆ 0 ┆ sample ┆ 1 ┆ -0.292741 ┆ 1 ┆ 0.59 │\n", + "│ a ┆ 590000.0 ┆ 0 ┆ sample ┆ 2 ┆ -1.08503 ┆ 1 ┆ 0.59 │\n", + "│ a ┆ 590000.0 ┆ 0 ┆ sample ┆ 3 ┆ -3.1535 ┆ 1 ┆ 0.59 │\n", + "│ a ┆ 590000.0 ┆ 0 ┆ sample ┆ 4 ┆ -1.838941 ┆ 1 ┆ 0.59 │\n", + "│ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … │\n", + "│ l ┆ 990000.0 ┆ 99 ┆ sample ┆ 11995 ┆ -1.72871 ┆ 12 ┆ 0.99 │\n", + "│ l ┆ 990000.0 ┆ 99 ┆ sample ┆ 11996 ┆ 0.786952 ┆ 12 ┆ 0.99 │\n", + "│ l ┆ 990000.0 ┆ 99 ┆ sample ┆ 11997 ┆ -5.19547 ┆ 12 ┆ 0.99 │\n", + "│ l ┆ 990000.0 ┆ 99 ┆ sample ┆ 11998 ┆ -3.009168 ┆ 12 ┆ 0.99 │\n", + "│ l ┆ 990000.0 ┆ 99 ┆ sample ┆ 11999 ┆ -1.129592 ┆ 12 ┆ 0.99 │\n", + "└──────────┴────────────┴───────┴─────────────┴───────────────┴───────────┴─────────┴──────────────┘" + ] + }, + "execution_count": 91, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "predictions_all_ref_times" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "shape: (1_200_000, 18)
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………………………………………………
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"l"990000.099"sample"3199980.99-3.407289-4.098658-4.484941-4.549591-4.812409-1.925278-2.423417-1.969783-0.873422-0.8631730.343692-0.365871
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" + ], + "text/plain": [ + "shape: (1_200_000, 18)\n", + "┌──────────┬────────────┬───────┬────────────┬───┬────────────┬────────────┬───────────┬───────────┐\n", + "│ location ┆ population ┆ ref_t ┆ output_typ ┆ … ┆ postpredic ┆ postpredic ┆ postpredi ┆ postpredi │\n", + "│ --- ┆ --- ┆ --- ┆ e ┆ ┆ t_horizon8 ┆ t_horizon9 ┆ ct_horizo ┆ ct_horizo │\n", + "│ str ┆ f64 ┆ i32 ┆ --- ┆ ┆ --- ┆ --- ┆ n10 ┆ n11 │\n", + "│ ┆ ┆ ┆ str ┆ ┆ f64 ┆ f64 ┆ --- ┆ --- │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ f64 ┆ f64 │\n", + "╞══════════╪════════════╪═══════╪════════════╪═══╪════════════╪════════════╪═══════════╪═══════════╡\n", + "│ a ┆ 590000.0 ┆ 0 ┆ sample ┆ … ┆ 1.018184 ┆ 0.730651 ┆ 0.737746 ┆ 1.203316 │\n", + "│ a ┆ 590000.0 ┆ 0 ┆ sample ┆ … ┆ 0.609429 ┆ 0.806275 ┆ -0.922909 ┆ -1.874235 │\n", + "│ a ┆ 590000.0 ┆ 0 ┆ sample ┆ … ┆ 0.715243 ┆ -1.069448 ┆ -0.355619 ┆ -0.838845 │\n", + "│ a ┆ 590000.0 ┆ 0 ┆ sample ┆ … ┆ -1.61615 ┆ -0.446134 ┆ 0.066404 ┆ -1.209319 │\n", + "│ a ┆ 590000.0 ┆ 0 ┆ sample ┆ … ┆ -1.285212 ┆ -1.256208 ┆ -0.91518 ┆ -1.89079 │\n", + "│ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … │\n", + "│ l ┆ 990000.0 ┆ 99 ┆ sample ┆ … ┆ 2.85247 ┆ 2.289699 ┆ 2.70581 ┆ 3.444449 │\n", + "│ l ┆ 990000.0 ┆ 99 ┆ sample ┆ … ┆ -1.920177 ┆ -2.49334 ┆ -1.513407 ┆ -0.797648 │\n", + "│ l ┆ 990000.0 ┆ 99 ┆ sample ┆ … ┆ -2.888969 ┆ -2.496715 ┆ -4.347165 ┆ -3.97773 │\n", + "│ l ┆ 990000.0 ┆ 99 ┆ sample ┆ … ┆ -0.873422 ┆ -0.863173 ┆ 0.343692 ┆ -0.365871 │\n", + "│ l ┆ 990000.0 ┆ 99 ┆ sample ┆ … ┆ -6.452843 ┆ -5.579207 ┆ -3.6685 ┆ -3.678287 │\n", + "└──────────┴────────────┴───────┴────────────┴───┴────────────┴────────────┴───────────┴───────────┘" + ] + }, + "execution_count": 88, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wide_model_out" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3.913721093879478" + ] + }, + "execution_count": 92, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Is energy score sensitive to dependence structure, given fixed marginals?\n", + "ss = Schaake(weighter=UnivariateGaussianKernel(h = 1.0))\n", + "ss.fit(df=obs_data, key_cols=[\"location\"], time_col=\"t\", obs_col=\"y\", feat_cols=[\"pop_normalized\"])\n", + "\n", + "ss._build_train_X_Y(min_horizon=1, max_horizon=horizon,\n", + " obs_mask = None)\n", + "\n", + "wide_model_out = ss._pivot_horizon(\n", + " model_out=predictions_all_ref_times,\n", + " reference_time_col=\"ref_t\",\n", + " horizon_col=\"horizon\",\n", + " idx_col=\"output_type_id\",\n", + " pred_col=\"value\"\n", + ")\n", + "energy_score(\n", + " wide_model_out.with_columns(t = pl.col(\"ref_t\").cast(ss.df[\"t\"].dtype)),\n", + " ss.df,\n", + " key_cols = ss.key_cols + [\"t\"],\n", + " pred_cols = [f\"postpredict_horizon{h}\" for h in range(1, horizon + 1)],\n", + " obs_cols = [f\"y_shift_p{h}\" for h in range(1, horizon + 1)],\n", + " reduce_mean = True\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4.01469653248981" + ] + }, + "execution_count": 94, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wide_model_out_shuffled = (\n", + " wide_model_out\n", + " .with_columns(\n", + " postpredict_horizon1 = pl.col(\"postpredict_horizon1\").shuffle(seed=42).over([\"location\", \"population\", \"ref_t\", \"output_type\", \"pop_normalized\"]),\n", + " postpredict_horizon2 = pl.col(\"postpredict_horizon2\").shuffle(seed=420).over([\"location\", \"population\", \"ref_t\", \"output_type\", \"pop_normalized\"]),\n", + " postpredict_horizon3 = pl.col(\"postpredict_horizon3\").shuffle(seed=4200).over([\"location\", \"population\", \"ref_t\", \"output_type\", \"pop_normalized\"]),\n", + " postpredict_horizon4 = pl.col(\"postpredict_horizon4\").shuffle(seed=42000).over([\"location\", \"population\", \"ref_t\", \"output_type\", \"pop_normalized\"]),\n", + " postpredict_horizon5 = pl.col(\"postpredict_horizon5\").shuffle(seed=420000).over([\"location\", \"population\", \"ref_t\", \"output_type\", \"pop_normalized\"]),\n", + " postpredict_horizon6 = pl.col(\"postpredict_horizon6\").shuffle(seed=4200000).over([\"location\", \"population\", \"ref_t\", \"output_type\", \"pop_normalized\"]),\n", + " postpredict_horizon7 = pl.col(\"postpredict_horizon7\").shuffle(seed=42000000).over([\"location\", \"population\", \"ref_t\", \"output_type\", \"pop_normalized\"]),\n", + " postpredict_horizon8 = pl.col(\"postpredict_horizon8\").shuffle(seed=420000000).over([\"location\", \"population\", \"ref_t\", \"output_type\", \"pop_normalized\"]),\n", + " postpredict_horizon9 = pl.col(\"postpredict_horizon9\").shuffle(seed=4200000000).over([\"location\", \"population\", \"ref_t\", \"output_type\", \"pop_normalized\"]),\n", + " postpredict_horizon10 = pl.col(\"postpredict_horizon10\").shuffle(seed=42000000000).over([\"location\", \"population\", \"ref_t\", \"output_type\", \"pop_normalized\"]),\n", + " postpredict_horizon11 = pl.col(\"postpredict_horizon11\").shuffle(seed=420000000000).over([\"location\", \"population\", \"ref_t\", \"output_type\", \"pop_normalized\"]),\n", + " postpredict_horizon12 = pl.col(\"postpredict_horizon12\").shuffle(seed=4200000000000).over([\"location\", \"population\", \"ref_t\", \"output_type\", \"pop_normalized\"])\n", + " )\n", + ")\n", + "\n", + "energy_score(\n", + " wide_model_out_shuffled.with_columns(t = pl.col(\"ref_t\").cast(ss.df[\"t\"].dtype)),\n", + " ss.df,\n", + " key_cols = ss.key_cols + [\"t\"],\n", + " pred_cols = [f\"postpredict_horizon{h}\" for h in range(1, horizon + 1)],\n", + " obs_cols = [f\"y_shift_p{h}\" for h in range(1, horizon + 1)],\n", + " reduce_mean = True\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4.015100937484611" + ] + }, + "execution_count": 95, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wide_model_out_shuffled = (\n", + " wide_model_out\n", + " .with_columns(\n", + " postpredict_horizon1 = pl.col(\"postpredict_horizon1\").shuffle().over([\"location\", \"population\", \"ref_t\", \"output_type\", \"pop_normalized\"]),\n", + " postpredict_horizon2 = pl.col(\"postpredict_horizon2\").shuffle().over([\"location\", \"population\", \"ref_t\", \"output_type\", \"pop_normalized\"]),\n", + " postpredict_horizon3 = pl.col(\"postpredict_horizon3\").shuffle().over([\"location\", \"population\", \"ref_t\", \"output_type\", \"pop_normalized\"]),\n", + " postpredict_horizon4 = pl.col(\"postpredict_horizon4\").shuffle().over([\"location\", \"population\", \"ref_t\", \"output_type\", \"pop_normalized\"]),\n", + " postpredict_horizon5 = pl.col(\"postpredict_horizon5\").shuffle().over([\"location\", \"population\", \"ref_t\", \"output_type\", \"pop_normalized\"]),\n", + " postpredict_horizon6 = pl.col(\"postpredict_horizon6\").shuffle().over([\"location\", \"population\", \"ref_t\", \"output_type\", \"pop_normalized\"]),\n", + " postpredict_horizon7 = pl.col(\"postpredict_horizon7\").shuffle().over([\"location\", \"population\", \"ref_t\", \"output_type\", \"pop_normalized\"]),\n", + " postpredict_horizon8 = pl.col(\"postpredict_horizon8\").shuffle().over([\"location\", \"population\", \"ref_t\", \"output_type\", \"pop_normalized\"]),\n", + " postpredict_horizon9 = pl.col(\"postpredict_horizon9\").shuffle().over([\"location\", \"population\", \"ref_t\", \"output_type\", \"pop_normalized\"]),\n", + " postpredict_horizon10 = pl.col(\"postpredict_horizon10\").shuffle().over([\"location\", \"population\", \"ref_t\", \"output_type\", \"pop_normalized\"]),\n", + " postpredict_horizon11 = pl.col(\"postpredict_horizon11\").shuffle().over([\"location\", \"population\", \"ref_t\", \"output_type\", \"pop_normalized\"]),\n", + " postpredict_horizon12 = pl.col(\"postpredict_horizon12\").shuffle().over([\"location\", \"population\", \"ref_t\", \"output_type\", \"pop_normalized\"])\n", + " )\n", + ")\n", + "\n", + "energy_score(\n", + " wide_model_out_shuffled.with_columns(t = pl.col(\"ref_t\").cast(ss.df[\"t\"].dtype)),\n", + " ss.df,\n", + " key_cols = ss.key_cols + [\"t\"],\n", + " pred_cols = [f\"postpredict_horizon{h}\" for h in range(1, horizon + 1)],\n", + " obs_cols = [f\"y_shift_p{h}\" for h in range(1, horizon + 1)],\n", + " reduce_mean = True\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'t': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]},\n", + " {'t': [17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33]},\n", + " {'t': [34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50]},\n", + " {'t': [51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67]},\n", + " {'t': [68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84]}]" + ] + }, + "execution_count": 96, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "n_folds = 5\n", + "n_times_per_fold = (n_obs_timesteps - horizon) // n_folds\n", + "folds = [\n", + " {\"t\": list(range(i * n_times_per_fold, (i+1) * n_times_per_fold))} \\\n", + " for i in range(n_folds)\n", + "]\n", + "folds\n" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": {}, + "outputs": [], + "source": [ + "reference_time_col = \"ref_t\"\n", + "\n", + "def get_metric_one_val_fold(ss, model_out, folds, val_fold_ind, metric_fn):\n", + " \"\"\"\n", + " Obtain predictions for the validation set where the only observations\n", + " used for templates are from outside the validation set.\n", + " \"\"\"\n", + " val_model_out = model_out.filter(\n", + " pl.col(reference_time_col).is_in(folds[val_fold_ind][\"t\"])\n", + " )\n", + " \n", + " train_obs_mask = ~obs_data[\"t\"].is_in(folds[val_fold_ind][\"t\"])\n", + " \n", + " transformed_val_model_out = ss.transform(\n", + " model_out=val_model_out,\n", + " reference_time_col=reference_time_col,\n", + " horizon_col=\"horizon\",\n", + " pred_col=\"value\",\n", + " idx_col=\"output_type_id\",\n", + " obs_mask=train_obs_mask,\n", + " return_long_format=False\n", + " )\n", + " \n", + " metric = metric_fn(\n", + " transformed_val_model_out.with_columns(t = pl.col(\"ref_t\").cast(ss.df[\"t\"].dtype)),\n", + " ss.df,\n", + " key_cols = ss.key_cols + [\"t\"],\n", + " pred_cols = [f\"postpredict_horizon{h}\" for h in range(1, horizon + 1)],\n", + " obs_cols = [f\"y_shift_p{h}\" for h in range(1, horizon + 1)],\n", + " reduce_mean = True\n", + " )\n", + " \n", + " return metric\n", + "\n", + "\n", + "def get_metric_crossval(ss, model_out, folds, metric_fn):\n", + " metrics_by_fold = np.array([\n", + " get_metric_one_val_fold(ss, model_out, folds, val_fold_ind, metric_fn) \\\n", + " for val_fold_ind in range(len(folds))\n", + " ])\n", + " print(metrics_by_fold)\n", + " \n", + " return np.mean(metrics_by_fold)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[4.05213967 3.61819575 4.44042262 3.71457681 3.92153278]\n" + ] + }, + { + "data": { + "text/plain": [ + "np.float64(3.9493735277260074)" + ] + }, + "execution_count": 98, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ss = Schaake(weighter=UnivariateGaussianKernel(h = 1.0))\n", + "ss.fit(df=obs_data, key_cols=[\"location\"], time_col=\"t\", obs_col=\"y\", feat_cols=[\"pop_normalized\"])\n", + "\n", + "get_metric_crossval(ss=ss, model_out=predictions_all_ref_times, folds=folds, metric_fn=energy_score)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[I 2024-10-18 21:08:20,760] A new study created in memory with name: no-name-3556e132-0906-42f1-9406-6c592c456904\n", + "[I 2024-10-18 21:09:40,732] Trial 0 finished with value: 3.9374186567295957 and parameters: {'h': 0.003201565869803209}. Best is trial 0 with value: 3.9374186567295957.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[4.0417138 3.60511293 4.43241771 3.70587976 3.90196908]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[I 2024-10-18 21:11:07,850] Trial 1 finished with value: 3.9489273489441894 and parameters: {'h': 1.6221866070049595}. Best is trial 0 with value: 3.9374186567295957.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[4.05219123 3.61740804 4.43932081 3.71401008 3.92170658]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[I 2024-10-18 21:12:30,966] Trial 2 finished with value: 3.949997035622785 and parameters: {'h': 97.27895960642897}. Best is trial 0 with value: 3.9374186567295957.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[4.05375699 3.61818138 4.44037355 3.71560911 3.92206415]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[I 2024-10-18 21:13:52,529] Trial 3 finished with value: 3.958831279150824 and parameters: {'h': 7.060698463479763e-06}. Best is trial 0 with value: 3.9374186567295957.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[4.03056669 3.63887543 4.44020243 3.73512956 3.94938228]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[I 2024-10-18 21:15:30,509] Trial 4 finished with value: 3.9587373839855147 and parameters: {'h': 2.4543104043581227e-05}. Best is trial 0 with value: 3.9374186567295957.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[4.03067775 3.63932895 4.44042932 3.73544889 3.94780202]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[I 2024-10-18 21:17:00,589] Trial 5 finished with value: 3.937378689830181 and parameters: {'h': 0.0034229254124623887}. Best is trial 5 with value: 3.937378689830181.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[4.04245001 3.60514838 4.43243184 3.70602745 3.90083578]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[I 2024-10-18 21:18:30,981] Trial 6 finished with value: 3.9498839692828724 and parameters: {'h': 29.444808036645917}. Best is trial 5 with value: 3.937378689830181.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[4.05211403 3.6186859 4.44117941 3.71539775 3.92204275]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[I 2024-10-18 21:19:53,033] Trial 7 finished with value: 3.9372049013595527 and parameters: {'h': 0.003509043685204039}. Best is trial 7 with value: 3.9372049013595527.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[4.04145663 3.60569865 4.43261144 3.70438886 3.90186893]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[I 2024-10-18 21:21:14,946] Trial 8 finished with value: 3.9587945098851725 and parameters: {'h': 2.4231868614949738e-05}. Best is trial 7 with value: 3.9372049013595527.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[4.03180459 3.63924143 4.43902771 3.73576609 3.94813272]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[I 2024-10-18 21:22:38,867] Trial 9 finished with value: 3.950109006030201 and parameters: {'h': 58.04551117362054}. Best is trial 7 with value: 3.9372049013595527.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[4.05409494 3.61831079 4.440242 3.71611183 3.92178546]\n" + ] + } + ], + "source": [ + "def objective(trial):\n", + " h = trial.suggest_float(\"h\", low=1e-6, high=1e2, log=True)\n", + " ss = Schaake(weighter=UnivariateGaussianKernel(h = h))\n", + " ss.fit(df=obs_data, key_cols=[\"location\"], time_col=\"t\", obs_col=\"y\", feat_cols=[\"pop_normalized\"])\n", + " return get_metric_crossval(ss=ss, model_out=predictions_all_ref_times, folds=folds, metric_fn=energy_score)\n", + "\n", + "study = optuna.create_study()\n", + "study.optimize(objective, n_trials=10)" + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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numbervaluedatetime_startdatetime_completedurationparams_hstate
003.9374192024-10-18 21:08:20.7620552024-10-18 21:09:40.7326790 days 00:01:19.9706240.003202COMPLETE
113.9489272024-10-18 21:09:40.7337732024-10-18 21:11:07.8502180 days 00:01:27.1164451.622187COMPLETE
223.9499972024-10-18 21:11:07.8523452024-10-18 21:12:30.9660940 days 00:01:23.11374997.278960COMPLETE
333.9588312024-10-18 21:12:30.9667972024-10-18 21:13:52.5296120 days 00:01:21.5628150.000007COMPLETE
443.9587372024-10-18 21:13:52.5304262024-10-18 21:15:30.5092740 days 00:01:37.9788480.000025COMPLETE
553.9373792024-10-18 21:15:30.5100912024-10-18 21:17:00.5894790 days 00:01:30.0793880.003423COMPLETE
663.9498842024-10-18 21:17:00.5902962024-10-18 21:18:30.9812450 days 00:01:30.39094929.444808COMPLETE
773.9372052024-10-18 21:18:30.9822802024-10-18 21:19:53.0330570 days 00:01:22.0507770.003509COMPLETE
883.9587952024-10-18 21:19:53.0338462024-10-18 21:21:14.9464530 days 00:01:21.9126070.000024COMPLETE
993.9501092024-10-18 21:21:14.9472562024-10-18 21:22:38.8672250 days 00:01:23.91996958.045511COMPLETE
\n", + "
" + ], + "text/plain": [ + " number value datetime_start datetime_complete \\\n", + "0 0 3.937419 2024-10-18 21:08:20.762055 2024-10-18 21:09:40.732679 \n", + "1 1 3.948927 2024-10-18 21:09:40.733773 2024-10-18 21:11:07.850218 \n", + "2 2 3.949997 2024-10-18 21:11:07.852345 2024-10-18 21:12:30.966094 \n", + "3 3 3.958831 2024-10-18 21:12:30.966797 2024-10-18 21:13:52.529612 \n", + "4 4 3.958737 2024-10-18 21:13:52.530426 2024-10-18 21:15:30.509274 \n", + "5 5 3.937379 2024-10-18 21:15:30.510091 2024-10-18 21:17:00.589479 \n", + "6 6 3.949884 2024-10-18 21:17:00.590296 2024-10-18 21:18:30.981245 \n", + "7 7 3.937205 2024-10-18 21:18:30.982280 2024-10-18 21:19:53.033057 \n", + "8 8 3.958795 2024-10-18 21:19:53.033846 2024-10-18 21:21:14.946453 \n", + "9 9 3.950109 2024-10-18 21:21:14.947256 2024-10-18 21:22:38.867225 \n", + "\n", + " duration params_h state \n", + "0 0 days 00:01:19.970624 0.003202 COMPLETE \n", + "1 0 days 00:01:27.116445 1.622187 COMPLETE \n", + "2 0 days 00:01:23.113749 97.278960 COMPLETE \n", + "3 0 days 00:01:21.562815 0.000007 COMPLETE \n", + "4 0 days 00:01:37.978848 0.000025 COMPLETE \n", + "5 0 days 00:01:30.079388 0.003423 COMPLETE \n", + "6 0 days 00:01:30.390949 29.444808 COMPLETE \n", + "7 0 days 00:01:22.050777 0.003509 COMPLETE \n", + "8 0 days 00:01:21.912607 0.000024 COMPLETE \n", + "9 0 days 00:01:23.919969 58.045511 COMPLETE " + ] + }, + "execution_count": 100, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "study.trials_dataframe()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 104, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(\n", + " study.trials_dataframe()[\"params_h\"],\n", + " study.trials_dataframe()[\"value\"],\n", + " 'o'\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 103, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(\n", + " np.log(study.trials_dataframe()[\"params_h\"]),\n", + " study.trials_dataframe()[\"value\"],\n", + " 'o'\n", + ")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/pyproject.toml b/pyproject.toml index 431082f..71b81fe 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -12,6 +12,7 @@ dynamic = ["version"] dependencies = [ "numpy", + "optuna", "polars", "scikit-learn", "scipy" @@ -20,7 +21,9 @@ dependencies = [ [project.optional-dependencies] dev = [ "coverage", + "matplotlib", "mypy", + "pandas", "pre-commit", "pytest", "pytest-mock", diff --git a/requirements/requirements-dev.txt b/requirements/requirements-dev.txt index 751d4bb..b5da68d 100644 --- a/requirements/requirements-dev.txt +++ b/requirements/requirements-dev.txt @@ -1,19 +1,39 @@ # This file was autogenerated by uv via the following command: # uv pip compile pyproject.toml --extra dev -o requirements/requirements-dev.txt +alembic==1.13.3 + # via optuna cfgv==3.4.0 # via pre-commit +colorlog==6.8.2 + # via optuna +contourpy==1.3.0 + # via matplotlib coverage==7.5.1 # via postpredict (pyproject.toml) +cycler==0.12.1 + # via matplotlib distlib==0.3.8 # via virtualenv filelock==3.14.0 # via virtualenv +fonttools==4.54.1 + # via matplotlib +greenlet==3.1.1 + # via sqlalchemy identify==2.5.36 # via pre-commit iniconfig==2.0.0 # via pytest joblib==1.4.2 # via scikit-learn +kiwisolver==1.4.7 + # via matplotlib +mako==1.3.5 + # via alembic +markupsafe==3.0.2 + # via mako +matplotlib==3.9.2 + # via postpredict (pyproject.toml) mypy==1.10.0 # via postpredict (pyproject.toml) mypy-extensions==1.0.0 @@ -23,10 +43,23 @@ nodeenv==1.8.0 numpy==2.1.2 # via # postpredict (pyproject.toml) + # contourpy + # matplotlib + # optuna + # pandas # scikit-learn # scipy +optuna==4.0.0 + # via postpredict (pyproject.toml) packaging==24.0 - # via pytest + # via + # matplotlib + # optuna + # pytest +pandas==2.2.3 + # via postpredict (pyproject.toml) +pillow==11.0.0 + # via matplotlib platformdirs==4.2.1 # via virtualenv pluggy==1.5.0 @@ -35,29 +68,54 @@ polars==1.9.0 # via postpredict (pyproject.toml) pre-commit==3.7.0 # via postpredict (pyproject.toml) +pyparsing==3.2.0 + # via matplotlib pytest==8.2.0 # via # postpredict (pyproject.toml) # pytest-mock pytest-mock==3.14.0 # via postpredict (pyproject.toml) +python-dateutil==2.9.0.post0 + # via + # matplotlib + # pandas +pytz==2024.2 + # via pandas pyyaml==6.0.1 - # via pre-commit + # via + # optuna + # pre-commit ruff==0.4.3 # via postpredict (pyproject.toml) scikit-learn==1.5.2 # via postpredict (pyproject.toml) scipy==1.14.1 - # via scikit-learn + # via + # postpredict (pyproject.toml) + # scikit-learn setuptools==75.1.0 # via nodeenv +six==1.16.0 + # via python-dateutil +sqlalchemy==2.0.36 + # via + # alembic + # optuna threadpoolctl==3.5.0 # via scikit-learn toml==0.10.2 # via postpredict (pyproject.toml) +tqdm==4.66.5 + # via optuna types-toml==0.10.8.20240310 # via postpredict (pyproject.toml) typing-extensions==4.11.0 - # via mypy + # via + # alembic + # mypy + # sqlalchemy +tzdata==2024.2 + # via pandas virtualenv==20.26.1 # via pre-commit diff --git a/requirements/requirements.txt b/requirements/requirements.txt index f79de00..cb8b27c 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -1,17 +1,46 @@ # This file was autogenerated by uv via the following command: # uv pip compile pyproject.toml -o requirements/requirements.txt +alembic==1.13.3 + # via optuna +colorlog==6.8.2 + # via optuna +greenlet==3.1.1 + # via sqlalchemy joblib==1.4.2 # via scikit-learn +mako==1.3.5 + # via alembic +markupsafe==3.0.2 + # via mako numpy==2.1.2 # via # postpredict (pyproject.toml) + # optuna # scikit-learn # scipy +optuna==4.0.0 + # via postpredict (pyproject.toml) +packaging==24.1 + # via optuna polars==1.9.0 # via postpredict (pyproject.toml) +pyyaml==6.0.2 + # via optuna scikit-learn==1.5.2 # via postpredict (pyproject.toml) scipy==1.14.1 - # via scikit-learn + # via + # postpredict (pyproject.toml) + # scikit-learn +sqlalchemy==2.0.36 + # via + # alembic + # optuna threadpoolctl==3.5.0 # via scikit-learn +tqdm==4.66.5 + # via optuna +typing-extensions==4.12.2 + # via + # alembic + # sqlalchemy diff --git a/src/postpredict/dependence.py b/src/postpredict/dependence.py index ce597c7..7d30dff 100644 --- a/src/postpredict/dependence.py +++ b/src/postpredict/dependence.py @@ -36,7 +36,7 @@ def _build_templates(self, wide_model_out): Returns ------- - templates: np.ndarray + templates: pl.DataFrame Dependence templates of shape (wide_model_out.shape[0], self.train_Y.shape[1]) """ @@ -45,7 +45,8 @@ def transform(self, model_out: pl.DataFrame, reference_time_col: str = "reference_date", horizon_col: str = "horizon", pred_col: str = "value", idx_col: str = "output_type_id", - obs_mask: np.ndarray | None = None): + obs_mask: np.ndarray | None = None, + return_long_format: bool = True): """ Apply a postprocessing transformation to sample predictions to induce dependence across time in the predictive samples. @@ -71,6 +72,9 @@ def transform(self, model_out: pl.DataFrame, array of shape (self.df.shape[0], ). Rows of self.df where obs_mask is True will be used, while rows of self.df where obs_mask is False will not be used. + return_long_format: bool + If True, return long format. If False, return wide format with + horizon pivoted into columns. Returns ------- @@ -93,6 +97,9 @@ def transform(self, model_out: pl.DataFrame, .map_groups(self._transform_one_group) ) + if not return_long_format: + return transformed_wide_model_out + # unpivot back to long format pivot_index = [c for c in model_out.columns if c not in [horizon_col, pred_col]] transformed_model_out = ( @@ -111,12 +118,12 @@ def transform(self, model_out: pl.DataFrame, .cast(model_out[horizon_col].dtype) ) ) - + return transformed_model_out def _transform_one_group(self, wide_model_out): - templates = self._build_templates(wide_model_out) + templates = self._build_templates(wide_model_out).to_numpy() transformed_model_out = self._apply_shuffle( wide_model_out = wide_model_out, value_cols = self.wide_horizon_cols, @@ -362,4 +369,5 @@ def _build_templates(self, wide_model_out): # get the templates templates = self.train_Y[selected_inds, :] + return templates diff --git a/src/postpredict/metrics.py b/src/postpredict/metrics.py index bb5d879..fa16043 100644 --- a/src/postpredict/metrics.py +++ b/src/postpredict/metrics.py @@ -54,8 +54,14 @@ def energy_score_one_unit(df: pl.DataFrame): See """ - score = np.mean(pairwise_distances(df[pred_cols], df[0, obs_cols])) \ - - 0.5 * np.mean(pairwise_distances(df[pred_cols])) + if df[pred_cols + obs_cols].null_count().to_numpy().sum() > 0: + # Return np.nan rather than None here to avoid a rare schema + # error when the first processed group would yield None. + score = np.nan + else: + score = np.mean(pairwise_distances(df[pred_cols], df[0, obs_cols])) \ + - 0.5 * np.mean(pairwise_distances(df[pred_cols])) + return df[0, key_cols].with_columns(energy_score = pl.lit(score)) scores_by_unit = ( @@ -68,4 +74,5 @@ def energy_score_one_unit(df: pl.DataFrame): if not reduce_mean: return scores_by_unit - return scores_by_unit["energy_score"].mean() + # replace NaN with None to average only across non-missing values + return scores_by_unit["energy_score"].fill_nan(None).mean() diff --git a/src/postpredict/weighters.py b/src/postpredict/weighters.py index 830af74..1eab339 100644 --- a/src/postpredict/weighters.py +++ b/src/postpredict/weighters.py @@ -1,6 +1,7 @@ import collections import numpy as np +import polars as pl class Parameter(collections.UserDict): @@ -65,6 +66,11 @@ def get_weights(self, train_X, test_X): numpy array of shape (n_test, n_train) with weights for each training set instance, where weights sum to 1 within each row. """ - n_train = train_X.shape[0] - prop_weights = np.exp(-0.5 / self.parameters["h"].value * (test_X - train_X.reshape(1, n_train))**2) + if isinstance(train_X, pl.DataFrame): + train_X = train_X.to_numpy() + + if isinstance(test_X, pl.DataFrame): + test_X = test_X.to_numpy() + + prop_weights = np.exp(-0.5 / self.parameters["h"].value * (test_X - train_X.transpose())**2) return prop_weights / np.sum(prop_weights, axis = 1, keepdims = True) diff --git a/tests/postpredict/dependence/test_transform.py b/tests/postpredict/dependence/test_transform.py index 9f09ee9..29e5896 100644 --- a/tests/postpredict/dependence/test_transform.py +++ b/tests/postpredict/dependence/test_transform.py @@ -22,7 +22,7 @@ def fit(self, df, key_cols=None, time_col="date", obs_col="value", feat_cols=["d def _build_templates(self, wide_model_out): - return templates + return pl.DataFrame(templates) tdp = TestPostprocessor(rng = np.random.default_rng(42)) tdp.df = obs_data From 8115735e9ef4911bd0befb262459f82bca51caad Mon Sep 17 00:00:00 2001 From: Evan Ray Date: Mon, 21 Oct 2024 17:24:47 -0400 Subject: [PATCH 3/7] marginal_pit metric --- src/postpredict/metrics.py | 58 ++++++++++++++++- .../postpredict/metrics/test_marginal_pit.py | 65 +++++++++++++++++++ 2 files changed, 121 insertions(+), 2 deletions(-) create mode 100644 tests/postpredict/metrics/test_marginal_pit.py diff --git a/src/postpredict/metrics.py b/src/postpredict/metrics.py index fa16043..4f92f6d 100644 --- a/src/postpredict/metrics.py +++ b/src/postpredict/metrics.py @@ -51,8 +51,6 @@ def energy_score_one_unit(df: pl.DataFrame): Compute energy score for one observational unit based on a collection of samples. Note, we define this function here so that key_cols, pred_cols and obs_cols are in scope. - - See """ if df[pred_cols + obs_cols].null_count().to_numpy().sum() > 0: # Return np.nan rather than None here to avoid a rare schema @@ -76,3 +74,59 @@ def energy_score_one_unit(df: pl.DataFrame): # replace NaN with None to average only across non-missing values return scores_by_unit["energy_score"].fill_nan(None).mean() + + +def marginal_pit(model_out_wide: pl.DataFrame, obs_data_wide: pl.DataFrame, + key_cols: list[str] | None, pred_cols: list[str], obs_cols: list[str], + reduce_mean: bool = True) -> float | pl.DataFrame: + """ + Compute the probability integral transform (PIT) value for each of a + collection of marginal predictive distributions represented by a set of + samples. + + Parameters + ---------- + model_out_wide: pl.DataFrame + DataFrame of model outputs where each row corresponds to one + (multivariate) sample from a multivariate distribution for one + observational unit. + obs_data_wide: pl.DataFrame + DataFrame of observed values where each row corresponds to one + (multivariate) observed outcome for one observational unit. + key_cols: list[str] + Columns that appear in both `model_out_wide` and `obs_data_wide` that + identify observational units. + pred_cols: list[str] + Columns that appear in `model_out_wide` and identify predicted (sampled) + values. The order of these should match the order of `obs_cols`. + obs_cols: list[str] + Columns that appear in `obs_data_wide` and identify observed values. The + order of these should match the order of `pred_cols`. + reduce_mean: bool = True + Indicator of whether to return a numeric mean energy score (default) or + a pl.DataFrame with one row per observational unit. + + Returns + ------- + A pl.DataFrame with one row per observational unit and PIT values stored in + columns named according to `[f"pit_{c}" for c in pred_cols]`. + + Notes + ----- + Here, the PIT value is calculated as the proportion of samples that are less + than or equal to the observed value. + """ + scores_by_unit = ( + model_out_wide + .join(obs_data_wide, on = key_cols) + .group_by(key_cols) + .agg( + [ + (pl.col(pred_c) <= pl.col(obs_c)).mean().alias(f"pit_{pred_c}") \ + for pred_c, obs_c in zip(pred_cols, obs_cols) + ] + ) + .select(key_cols + [f"pit_{pred_c}" for pred_c in pred_cols]) + ) + + return scores_by_unit diff --git a/tests/postpredict/metrics/test_marginal_pit.py b/tests/postpredict/metrics/test_marginal_pit.py new file mode 100644 index 0000000..a7c22a6 --- /dev/null +++ b/tests/postpredict/metrics/test_marginal_pit.py @@ -0,0 +1,65 @@ +# Tests for postpredict.metrics.marginal_pit + +from datetime import datetime + +import numpy as np +import polars as pl +import pytest +from polars.testing import assert_frame_equal +from postpredict.metrics import marginal_pit + + +def test_marginal_pit(): + rng = np.random.default_rng(seed=123) + model_out_wide = pl.concat([ + pl.DataFrame({ + "location": "a", + "date": datetime.strptime("2024-10-01", "%Y-%m-%d"), + "output_type": "sample", + "output_type_id": np.linspace(0, 99, 100), + "horizon1": rng.permutation(np.linspace(0, 9, 100)), + "horizon2": rng.permutation(np.linspace(8, 17, 100)), + "horizon3": rng.permutation(np.linspace(5.1, 16.1, 100)) + }), + pl.DataFrame({ + "location": "b", + "date": datetime.strptime("2024-10-08", "%Y-%m-%d"), + "output_type": "sample", + "output_type_id": np.linspace(100, 199, 100), + "horizon1": rng.permutation(np.linspace(10.0, 19.0, 100)), + "horizon2": rng.permutation(np.linspace(-3.0, 6.0, 100)), + "horizon3": rng.permutation(np.linspace(10.99, 19.99, 100)) + }) + ]) + obs_data_wide = pl.DataFrame({ + "location": ["a", "a", "b", "b"], + "date": [datetime.strptime("2024-10-01", "%Y-%m-%d"), + datetime.strptime("2024-10-08", "%Y-%m-%d"), + datetime.strptime("2024-10-01", "%Y-%m-%d"), + datetime.strptime("2024-10-08", "%Y-%m-%d")], + "value": [3.0, 4.0, 0.0, 7.2], + "value_lead1": [4.0, 10.0, 7.2, 9.6], + "value_lead2": [10.0, 5.0, 9.6, 10.0], + "value_lead3": [5.0, 2.0, 10.0, 14.1] + }) + + # expected scores calculated in R using the scoringRules package: + expected_scores_df = pl.DataFrame({ + "location": ["a", "b"], + "date": [datetime.strptime("2024-10-01", "%Y-%m-%d"), + datetime.strptime("2024-10-08", "%Y-%m-%d")], + "pit_horizon1": [0.45, 0.0], + "pit_horizon2": [0.23, 1.0], + "pit_horizon3": [0.0, 0.35] + }) + + actual_scores_df = marginal_pit(model_out_wide=model_out_wide, + obs_data_wide=obs_data_wide, + key_cols=["location", "date"], + pred_cols=["horizon1", "horizon2", "horizon3"], + obs_cols=["value_lead1", "value_lead2", "value_lead3"], + reduce_mean=False) + + print(actual_scores_df) + + assert_frame_equal(actual_scores_df, expected_scores_df, check_row_order=False, atol=1e-19) From 0c7eedd665ed224f05bd9a7d3f9343d03b969036 Mon Sep 17 00:00:00 2001 From: Evan Ray Date: Mon, 21 Oct 2024 17:28:20 -0400 Subject: [PATCH 4/7] remove unused import, correct comments --- tests/postpredict/metrics/test_marginal_pit.py | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/tests/postpredict/metrics/test_marginal_pit.py b/tests/postpredict/metrics/test_marginal_pit.py index a7c22a6..b24dd98 100644 --- a/tests/postpredict/metrics/test_marginal_pit.py +++ b/tests/postpredict/metrics/test_marginal_pit.py @@ -4,7 +4,6 @@ import numpy as np import polars as pl -import pytest from polars.testing import assert_frame_equal from postpredict.metrics import marginal_pit @@ -43,7 +42,8 @@ def test_marginal_pit(): "value_lead3": [5.0, 2.0, 10.0, 14.1] }) - # expected scores calculated in R using the scoringRules package: + # expected PIT values: the number of samples less than or equal to + # corresponding observed values expected_scores_df = pl.DataFrame({ "location": ["a", "b"], "date": [datetime.strptime("2024-10-01", "%Y-%m-%d"), @@ -59,7 +59,5 @@ def test_marginal_pit(): pred_cols=["horizon1", "horizon2", "horizon3"], obs_cols=["value_lead1", "value_lead2", "value_lead3"], reduce_mean=False) - - print(actual_scores_df) assert_frame_equal(actual_scores_df, expected_scores_df, check_row_order=False, atol=1e-19) From 336ae901d50864cd0810e835e9976d05f3961220 Mon Sep 17 00:00:00 2001 From: Evan Ray Date: Tue, 22 Oct 2024 10:36:25 -0400 Subject: [PATCH 5/7] remove unused argument to marginal_pit --- src/postpredict/metrics.py | 7 ++----- 1 file changed, 2 insertions(+), 5 deletions(-) diff --git a/src/postpredict/metrics.py b/src/postpredict/metrics.py index 4f92f6d..4a612ee 100644 --- a/src/postpredict/metrics.py +++ b/src/postpredict/metrics.py @@ -77,8 +77,8 @@ def energy_score_one_unit(df: pl.DataFrame): def marginal_pit(model_out_wide: pl.DataFrame, obs_data_wide: pl.DataFrame, - key_cols: list[str] | None, pred_cols: list[str], obs_cols: list[str], - reduce_mean: bool = True) -> float | pl.DataFrame: + key_cols: list[str] | None, pred_cols: list[str], + obs_cols: list[str]) -> pl.DataFrame: """ Compute the probability integral transform (PIT) value for each of a collection of marginal predictive distributions represented by a set of @@ -102,9 +102,6 @@ def marginal_pit(model_out_wide: pl.DataFrame, obs_data_wide: pl.DataFrame, obs_cols: list[str] Columns that appear in `obs_data_wide` and identify observed values. The order of these should match the order of `pred_cols`. - reduce_mean: bool = True - Indicator of whether to return a numeric mean energy score (default) or - a pl.DataFrame with one row per observational unit. Returns ------- From 095a2b24682f039da4d82b839c41ad03d59a7485 Mon Sep 17 00:00:00 2001 From: Evan Ray Date: Tue, 22 Oct 2024 10:42:42 -0400 Subject: [PATCH 6/7] remove unused argument from marginal_pit test --- tests/postpredict/metrics/test_marginal_pit.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/tests/postpredict/metrics/test_marginal_pit.py b/tests/postpredict/metrics/test_marginal_pit.py index b24dd98..1045924 100644 --- a/tests/postpredict/metrics/test_marginal_pit.py +++ b/tests/postpredict/metrics/test_marginal_pit.py @@ -57,7 +57,6 @@ def test_marginal_pit(): obs_data_wide=obs_data_wide, key_cols=["location", "date"], pred_cols=["horizon1", "horizon2", "horizon3"], - obs_cols=["value_lead1", "value_lead2", "value_lead3"], - reduce_mean=False) + obs_cols=["value_lead1", "value_lead2", "value_lead3"]) assert_frame_equal(actual_scores_df, expected_scores_df, check_row_order=False, atol=1e-19) From 7d7914968c95c1cd51a0ebe8f2dd01163fd7b84d Mon Sep 17 00:00:00 2001 From: Evan Ray Date: Tue, 22 Oct 2024 11:20:26 -0400 Subject: [PATCH 7/7] in metrics functions, rename key_cols to index_cols --- src/postpredict/metrics.py | 28 +++++++++++-------- .../postpredict/metrics/test_energy_score.py | 4 +-- .../postpredict/metrics/test_marginal_pit.py | 2 +- 3 files changed, 19 insertions(+), 15 deletions(-) diff --git a/src/postpredict/metrics.py b/src/postpredict/metrics.py index 4a612ee..73117cb 100644 --- a/src/postpredict/metrics.py +++ b/src/postpredict/metrics.py @@ -4,7 +4,7 @@ def energy_score(model_out_wide: pl.DataFrame, obs_data_wide: pl.DataFrame, - key_cols: list[str] | None, pred_cols: list[str], obs_cols: list[str], + index_cols: list[str] | None, pred_cols: list[str], obs_cols: list[str], reduce_mean: bool = True) -> float | pl.DataFrame: """ Compute the energy score for a collection of predictive samples. @@ -18,9 +18,11 @@ def energy_score(model_out_wide: pl.DataFrame, obs_data_wide: pl.DataFrame, obs_data_wide: pl.DataFrame DataFrame of observed values where each row corresponds to one (multivariate) observed outcome for one observational unit. - key_cols: list[str] + index_cols: list[str] Columns that appear in both `model_out_wide` and `obs_data_wide` that - identify observational units. + identify the unit of a multivariate prediction (e.g., including the + location, age_group, and reference_time of a prediction). These columns + will be included in the returned dataframe. pred_cols: list[str] Columns that appear in `model_out_wide` and identify predicted (sampled) values. The order of these should match the order of `obs_cols`. @@ -60,12 +62,12 @@ def energy_score_one_unit(df: pl.DataFrame): score = np.mean(pairwise_distances(df[pred_cols], df[0, obs_cols])) \ - 0.5 * np.mean(pairwise_distances(df[pred_cols])) - return df[0, key_cols].with_columns(energy_score = pl.lit(score)) + return df[0, index_cols].with_columns(energy_score = pl.lit(score)) scores_by_unit = ( model_out_wide - .join(obs_data_wide, on = key_cols) - .group_by(*key_cols) + .join(obs_data_wide, on = index_cols) + .group_by(*index_cols) .map_groups(energy_score_one_unit) ) @@ -77,7 +79,7 @@ def energy_score_one_unit(df: pl.DataFrame): def marginal_pit(model_out_wide: pl.DataFrame, obs_data_wide: pl.DataFrame, - key_cols: list[str] | None, pred_cols: list[str], + index_cols: list[str] | None, pred_cols: list[str], obs_cols: list[str]) -> pl.DataFrame: """ Compute the probability integral transform (PIT) value for each of a @@ -93,9 +95,11 @@ def marginal_pit(model_out_wide: pl.DataFrame, obs_data_wide: pl.DataFrame, obs_data_wide: pl.DataFrame DataFrame of observed values where each row corresponds to one (multivariate) observed outcome for one observational unit. - key_cols: list[str] + index_cols: list[str] Columns that appear in both `model_out_wide` and `obs_data_wide` that - identify observational units. + identify the unit of a multivariate prediction (e.g., including the + location, age_group, and reference_time of a prediction). These columns + will be included in the returned dataframe. pred_cols: list[str] Columns that appear in `model_out_wide` and identify predicted (sampled) values. The order of these should match the order of `obs_cols`. @@ -115,15 +119,15 @@ def marginal_pit(model_out_wide: pl.DataFrame, obs_data_wide: pl.DataFrame, """ scores_by_unit = ( model_out_wide - .join(obs_data_wide, on = key_cols) - .group_by(key_cols) + .join(obs_data_wide, on = index_cols) + .group_by(index_cols) .agg( [ (pl.col(pred_c) <= pl.col(obs_c)).mean().alias(f"pit_{pred_c}") \ for pred_c, obs_c in zip(pred_cols, obs_cols) ] ) - .select(key_cols + [f"pit_{pred_c}" for pred_c in pred_cols]) + .select(index_cols + [f"pit_{pred_c}" for pred_c in pred_cols]) ) return scores_by_unit diff --git a/tests/postpredict/metrics/test_energy_score.py b/tests/postpredict/metrics/test_energy_score.py index 45dc63a..1d6467d 100644 --- a/tests/postpredict/metrics/test_energy_score.py +++ b/tests/postpredict/metrics/test_energy_score.py @@ -67,7 +67,7 @@ def test_energy_score(): actual_scores_df = energy_score(model_out_wide=model_out_wide, obs_data_wide=obs_data_wide, - key_cols=["location", "date"], + index_cols=["location", "date"], pred_cols=["horizon1", "horizon2", "horizon3"], obs_cols=["value_lead1", "value_lead2", "value_lead3"], reduce_mean=False) @@ -77,7 +77,7 @@ def test_energy_score(): expected_mean_score = np.mean([5.8560677725938221627, 5.9574451598773787708]) actual_mean_score = energy_score(model_out_wide=model_out_wide, obs_data_wide=obs_data_wide, - key_cols=["location", "date"], + index_cols=["location", "date"], pred_cols=["horizon1", "horizon2", "horizon3"], obs_cols=["value_lead1", "value_lead2", "value_lead3"], reduce_mean=True) diff --git a/tests/postpredict/metrics/test_marginal_pit.py b/tests/postpredict/metrics/test_marginal_pit.py index 1045924..94fccd0 100644 --- a/tests/postpredict/metrics/test_marginal_pit.py +++ b/tests/postpredict/metrics/test_marginal_pit.py @@ -55,7 +55,7 @@ def test_marginal_pit(): actual_scores_df = marginal_pit(model_out_wide=model_out_wide, obs_data_wide=obs_data_wide, - key_cols=["location", "date"], + index_cols=["location", "date"], pred_cols=["horizon1", "horizon2", "horizon3"], obs_cols=["value_lead1", "value_lead2", "value_lead3"])