From 01886e09f47659800a0c44acbf3f09c1b7440222 Mon Sep 17 00:00:00 2001 From: iguazio-cicd Date: Sun, 11 Feb 2024 12:52:09 +0000 Subject: [PATCH] Automatically generated by github-worflow[bot] for commit: 4620020 --- README.md | 64 + catalog.json | 2 +- .../auto_trainer/1.7.0/src/auto_trainer.ipynb | 464 ++++++ .../auto_trainer/1.7.0/src/auto_trainer.py | 401 ++++++ .../auto_trainer/1.7.0/src/function.yaml | 164 +++ .../master/auto_trainer/1.7.0/src/item.yaml | 26 + .../auto_trainer/1.7.0/src/requirements.txt | 4 + .../1.7.0/src/test_auto_trainer.py | 215 +++ .../1.7.0/static/auto_trainer.html | 541 +++++++ .../1.7.0/static/documentation.html | 339 +++++ .../auto_trainer/1.7.0/static/example.html | 541 +++++++ .../auto_trainer/1.7.0/static/function.html | 186 +++ .../auto_trainer/1.7.0/static/item.html | 48 + .../auto_trainer/1.7.0/static/source.html | 423 ++++++ .../auto_trainer/latest/src/auto_trainer.py | 14 +- .../auto_trainer/latest/src/function.yaml | 10 +- .../master/auto_trainer/latest/src/item.yaml | 2 +- .../latest/static/auto_trainer.html | 14 +- .../auto_trainer/latest/static/function.html | 10 +- .../auto_trainer/latest/static/item.html | 2 +- .../auto_trainer/latest/static/source.html | 14 +- functions/master/catalog.json | 2 +- .../1.4.0/src/data/metrics.pq | Bin 0 -> 170843 bytes .../1.4.0/src/feature_selection.ipynb | 1283 +++++++++++++++++ .../1.4.0/src/feature_selection.py | 347 +++++ .../feature_selection/1.4.0/src/function.yaml | 120 ++ .../feature_selection/1.4.0/src/item.yaml | 25 + .../1.4.0/src/requirements.txt | 5 + .../1.4.0/src/test_feature_selection.py | 48 + .../1.4.0/static/documentation.html | 262 ++++ .../1.4.0/static/example.html | 1185 +++++++++++++++ .../1.4.0/static/feature_selection.html | 487 +++++++ .../1.4.0/static/function.html | 142 ++ .../feature_selection/1.4.0/static/item.html | 47 + .../1.4.0/static/source.html | 369 +++++ .../latest/src/feature_selection.py | 2 +- .../latest/src/function.yaml | 29 +- .../feature_selection/latest/src/item.yaml | 2 +- .../latest/static/feature_selection.html | 2 +- .../latest/static/function.html | 29 +- .../feature_selection/latest/static/item.html | 2 +- .../latest/static/source.html | 2 +- functions/master/tags.json | 2 +- 43 files changed, 7809 insertions(+), 67 deletions(-) create mode 100644 functions/master/auto_trainer/1.7.0/src/auto_trainer.ipynb create mode 100755 functions/master/auto_trainer/1.7.0/src/auto_trainer.py create mode 100644 functions/master/auto_trainer/1.7.0/src/function.yaml create mode 100755 functions/master/auto_trainer/1.7.0/src/item.yaml create mode 100644 functions/master/auto_trainer/1.7.0/src/requirements.txt create mode 100644 functions/master/auto_trainer/1.7.0/src/test_auto_trainer.py create mode 100644 functions/master/auto_trainer/1.7.0/static/auto_trainer.html create mode 100644 functions/master/auto_trainer/1.7.0/static/documentation.html create mode 100644 functions/master/auto_trainer/1.7.0/static/example.html create mode 100644 functions/master/auto_trainer/1.7.0/static/function.html create mode 100644 functions/master/auto_trainer/1.7.0/static/item.html create mode 100644 functions/master/auto_trainer/1.7.0/static/source.html create mode 100644 functions/master/feature_selection/1.4.0/src/data/metrics.pq create mode 100644 functions/master/feature_selection/1.4.0/src/feature_selection.ipynb create mode 100644 functions/master/feature_selection/1.4.0/src/feature_selection.py create mode 100644 functions/master/feature_selection/1.4.0/src/function.yaml create mode 100644 functions/master/feature_selection/1.4.0/src/item.yaml create mode 100644 functions/master/feature_selection/1.4.0/src/requirements.txt create mode 100644 functions/master/feature_selection/1.4.0/src/test_feature_selection.py create mode 100644 functions/master/feature_selection/1.4.0/static/documentation.html create mode 100644 functions/master/feature_selection/1.4.0/static/example.html create mode 100644 functions/master/feature_selection/1.4.0/static/feature_selection.html create mode 100644 functions/master/feature_selection/1.4.0/static/function.html create mode 100644 functions/master/feature_selection/1.4.0/static/item.html create mode 100644 functions/master/feature_selection/1.4.0/static/source.html diff --git a/README.md b/README.md index 3868d478..1f751090 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,67 @@ +### Change log [2024-02-11 12:52:07] +1. Item Updated: `batch_inference` (from version: `1.7.0` to `1.7.0`) +2. Item Updated: `structured_data_generator` (from version: `1.3.0` to `1.3.0`) +3. Item Updated: `tf2_serving_v2` (from version: `1.1.0` to `1.1.0`) +4. Item Updated: `model_server` (from version: `1.1.0` to `1.1.0`) +5. Item Updated: `test_classifier` (from version: `1.1.0` to `1.1.0`) +6. Item Updated: `batch_inference_v2` (from version: `2.5.0` to `2.5.0`) +7. Item Updated: `pandas_profiling_report` (from version: `1.1.0` to `1.1.0`) +8. Item Updated: `xgb_serving` (from version: `1.1.2` to `1.1.2`) +9. Item Updated: `silero_vad` (from version: `1.1.0` to `1.1.0`) +10. Item Updated: `load_dataset` (from version: `1.1.0` to `1.1.0`) +11. Item Updated: `xgb_custom` (from version: `1.1.0` to `1.1.0`) +12. Item Updated: `v2_model_server` (from version: `1.1.0` to `1.1.0`) +13. Item Updated: `pii_recognizer` (from version: `0.2.0` to `0.2.0`) +14. Item Updated: `github_utils` (from version: `1.1.0` to `1.1.0`) +15. Item Updated: `send_email` (from version: `1.2.0` to `1.2.0`) +16. Item Updated: `snowflake_dask` (from version: `1.1.0` to `1.1.0`) +17. Item Updated: `ingest` (from version: `1.1.0` to `1.1.0`) +18. Item Updated: `hugging_face_serving` (from version: `1.0.0` to `1.0.0`) +19. Item Updated: `text_to_audio_generator` (from version: `1.1.0` to `1.1.0`) +20. Item Updated: `model_monitoring_batch` (from version: `1.1.0` to `1.1.0`) +21. Item Updated: `translate` (from version: `0.0.2` to `0.0.2`) +22. Item Updated: `xgb_trainer` (from version: `1.1.1` to `1.1.1`) +23. Item Updated: `model_server_tester` (from version: `1.1.0` to `1.1.0`) +24. Item Updated: `get_offline_features` (from version: `1.2.0` to `1.2.0`) +25. Item Updated: `load_dask` (from version: `1.1.0` to `1.1.0`) +26. Item Updated: `describe` (from version: `1.2.0` to `1.2.0`) +27. Item Updated: `bert_embeddings` (from version: `1.2.0` to `1.2.0`) +28. Item Updated: `huggingface_auto_trainer` (from version: `1.0.0` to `1.0.0`) +29. Item Updated: `tf1_serving` (from version: `1.1.0` to `1.1.0`) +30. Item Updated: `validate_great_expectations` (from version: `1.1.0` to `1.1.0`) +31. Item Updated: `concept_drift` (from version: `1.1.0` to `1.1.0`) +32. Item Updated: `onnx_utils` (from version: `1.2.0` to `1.2.0`) +33. Item Updated: `open_archive` (from version: `1.1.0` to `1.1.0`) +34. Item Updated: `coxph_trainer` (from version: `1.1.0` to `1.1.0`) +35. Item Updated: `stream_to_parquet` (from version: `1.1.0` to `1.1.0`) +36. Item Updated: `slack_notify` (from version: `1.1.0` to `1.1.0`) +37. Item Updated: `xgb_test` (from version: `1.1.1` to `1.1.1`) +38. Item Updated: `feature_selection` (from version: `1.4.0` to `1.4.0`) +39. Item Updated: `pyannote_audio` (from version: `1.0.0` to `1.0.0`) +40. Item Updated: `transcribe` (from version: `1.0.0` to `1.0.0`) +41. Item Updated: `azureml_serving` (from version: `1.1.0` to `1.1.0`) +42. Item Updated: `churn_server` (from version: `1.1.0` to `1.1.0`) +43. Item Updated: `describe_spark` (from version: `1.1.0` to `1.1.0`) +44. Item Updated: `tf2_serving` (from version: `1.1.0` to `1.1.0`) +45. Item Updated: `virtual_drift` (from version: `1.1.0` to `1.1.0`) +46. Item Updated: `auto_trainer` (from version: `1.7.0` to `1.7.0`) +47. Item Updated: `sklearn_classifier` (from version: `1.1.1` to `1.1.1`) +48. Item Updated: `gen_class_data` (from version: `1.2.0` to `1.2.0`) +49. Item Updated: `question_answering` (from version: `0.3.1` to `0.3.1`) +50. Item Updated: `aggregate` (from version: `1.3.0` to `1.3.0`) +51. Item Updated: `coxph_test` (from version: `1.1.0` to `1.1.0`) +52. Item Updated: `azureml_utils` (from version: `1.3.0` to `1.3.0`) +53. Item Updated: `v2_model_tester` (from version: `1.1.0` to `1.1.0`) +54. Item Updated: `sql_to_file` (from version: `1.1.0` to `1.1.0`) +55. Item Updated: `hugging_face_classifier_trainer` (from version: `0.1.0` to `0.1.0`) +56. Item Updated: `rnn_serving` (from version: `1.1.0` to `1.1.0`) +57. Item Updated: `concept_drift_streaming` (from version: `1.1.0` to `1.1.0`) +58. Item Updated: `model_monitoring_stream` (from version: `1.1.0` to `1.1.0`) +59. Item Updated: `feature_perms` (from version: `1.1.0` to `1.1.0`) +60. Item Updated: `arc_to_parquet` (from version: `1.4.1` to `1.4.1`) +61. Item Updated: `sklearn_classifier_dask` (from version: `1.1.1` to `1.1.1`) +62. Item Updated: `describe_dask` (from version: `1.1.0` to `1.1.0`) + ### Change log [2024-02-06 11:16:40] 1. Item Updated: `pyannote_audio` (from version: `1.0.0` to `1.0.0`) 2. 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"1.5.1", "name": "text_to_audio_generator", "platformVersion": "3.5.3", "spec": {"filename": "text_to_audio_generator.py", "handler": "generate_multi_speakers_audio", "image": "mlrun/mlrun", "kind": "job", "requirements": ["bark", "torchaudio"]}, "url": "", "version": "1.1.0", "test_valid": true}}, "silero_vad": {"latest": {"apiVersion": "v1", "categories": ["Deep Learning", "PyTorch", "Audio"], "description": "Silero VAD (Voice Activity Detection) functions.", "doc": "", "example": "silero_vad.ipynb", "generationDate": "2023-12-03:14-30", "hidden": false, "icon": "", "labels": {"author": "guyl"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.5.2", "name": "silero_vad", "platformVersion": "3.5.3", "spec": {"filename": "silero_vad.py", "handler": "detect_voice", "image": "mlrun/mlrun", "kind": "job", "requirements": ["torch", "torchaudio", "tqdm", "onnxruntime"]}, "url": "", "version": "1.1.0"}, "1.1.0": {"apiVersion": "v1", "categories": ["Deep Learning", "PyTorch", "Audio"], "description": "Silero VAD (Voice Activity Detection) functions.", "doc": "", "example": "silero_vad.ipynb", "generationDate": "2023-12-03:14-30", "hidden": false, "icon": "", "labels": {"author": "guyl"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.5.2", "name": "silero_vad", "platformVersion": "3.5.3", "spec": {"filename": "silero_vad.py", "handler": "detect_voice", "image": "mlrun/mlrun", "kind": "job", "requirements": ["torch", "torchaudio", "tqdm", "onnxruntime"]}, "url": "", "version": "1.1.0"}}, "pyannote_audio": {"latest": {"apiVersion": "v1", "categories": ["Deep Learning", "Huggingface", "Audio"], "description": "pyannote's speech diarization of audio files", "doc": "", "example": "pyannote_audio.ipynb", "generationDate": "2023-12-03:14-30", "hidden": false, "icon": "", "labels": {"author": "guyl"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.5.2", "name": "pyannote-audio", "platformVersion": "3.5.3", "spec": {"filename": "pyannote_audio.py", "handler": "diarize", "image": "mlrun/mlrun-gpu", "kind": "job", "requirements": ["pyannote.audio", "pyannote.core", "torchaudio", "tqdm"]}, "url": "", "version": "1.0.0"}, "1.0.0": {"apiVersion": "v1", "categories": ["Deep Learning", "Huggingface", "Audio"], "description": "pyannote's speech diarization of audio files", "doc": "", "example": "pyannote_audio.ipynb", "generationDate": "2023-12-03:14-30", "hidden": false, "icon": "", "labels": {"author": "guyl"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.5.2", "name": "pyannote-audio", "platformVersion": "3.5.3", "spec": {"filename": "pyannote_audio.py", "handler": "diarize", "image": "mlrun/mlrun-gpu", "kind": "job", "requirements": ["pyannote.audio", "pyannote.core", "torchaudio", "tqdm"]}, "url": "", "version": "1.0.0"}}}}} \ No newline at end of file diff --git a/functions/master/auto_trainer/1.7.0/src/auto_trainer.ipynb b/functions/master/auto_trainer/1.7.0/src/auto_trainer.ipynb new file mode 100644 index 00000000..9e0a3d48 --- /dev/null +++ b/functions/master/auto_trainer/1.7.0/src/auto_trainer.ipynb @@ -0,0 +1,464 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "# MLRun Auto-Trainer Tutorial" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "This notebook shows how to use the handlers of the MLRun's Auto-trainer.\n", + "the following handlers are:\n", + "- `train`\n", + "- `evaluate`\n", + "- `predict`\n", + "\n", + "All you need is simply **ML model type** and a **dataset**." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "import mlrun" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "mlrun.get_or_create_project('auto-trainer', context=\"./\", user_project=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "### **Fetching a Dataset**" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "To generate the dataset we used the \"gen_class_data\" function from the hub, \n", + "which wraps scikit-learn's [make_classification](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_classification.html#sklearn-datasets-make-classification).
\n", + "See the link for a description of all parameters." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "DATASET_URL = 'https://s3.wasabisys.com/iguazio/data/function-marketplace-data/xgb_trainer/classifier-data.csv'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "mlrun.get_dataitem(DATASET_URL).show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "### **Importing the MLhandlers functions from the Marketplace**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "auto_trainer = mlrun.import_function(\"hub://auto_trainer\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "### **Training a model**\n", + "\n", + "Choosing the `train` handler" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "#### Define task parameters¶\n", + "* Class parameters should contain the prefix `CLASS_`\n", + "* Fit parameters should contain the prefix `FIT_`\n", + "* Predict parameters should contain the prefix `PREDICT_`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "model_class = \"sklearn.ensemble.RandomForestClassifier\"\n", + "additional_parameters = {\n", + " \"CLASS_max_depth\": 8,\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "#### Running the Training job with the \"train\" handler" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "train_run = auto_trainer.run(\n", + " inputs={\"dataset\": DATASET_URL},\n", + " params = {\n", + " \"model_class\": model_class,\n", + " \"drop_columns\": [\"feat_0\", \"feat_2\"],\n", + " \"train_test_split_size\": 0.2,\n", + " \"random_state\": 42,\n", + " \"label_columns\": \"labels\",\n", + " \"model_name\": 'MyModel',\n", + " **additional_parameters\n", + " }, \n", + " handler='train',\n", + " local=True\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "#### The result of the train run" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "train_run.outputs" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "train_run.artifact('confusion-matrix').show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "#### Getting the model for evaluating and predicting" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "model_path = train_run.outputs['model']" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "### **Evaluating a model**\n", + "\n", + "Choosing the `evaluate` handler" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "evaluate_run = auto_trainer.run(\n", + " inputs={\"dataset\": train_run.outputs['test_set']},\n", + " params={\n", + " \"model\": model_path,\n", + " \"drop_columns\": [\"feat_0\", \"feat_2\"], # Not actually necessary on the test set (already done in the previous step)\n", + " \"label_columns\": \"labels\",\n", + " },\n", + " handler=\"evaluate\",\n", + " local=True,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "#### The result of the evaluate run" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "evaluate_run.outputs" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "### **Making a prediction**\n", + "\n", + "Choosing the `predict` handler. For predicting from a simple sample (a `list` of `lists`,`dict`) pass the dataset as a `param`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "sample = mlrun.get_dataitem(DATASET_URL).as_df().head().drop(\"labels\", axis=1)" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "sample = sample.values.tolist()" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "predict_run = auto_trainer.run(\n", + " params={\n", + " \"dataset\": sample,\n", + " \"model\": model_path,\n", + " \"drop_columns\": [0, 2],\n", + " \"label_columns\": \"labels\",\n", + " },\n", + " handler=\"predict\",\n", + " local=True,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "#### Showing the predeiction results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "predict_run.outputs" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "predict_run.artifact('prediction').show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "[Back to the top](#XGBoost-trainer)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda env:root] *", + "language": "python", + "name": "conda-root-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} \ No newline at end of file diff --git a/functions/master/auto_trainer/1.7.0/src/auto_trainer.py b/functions/master/auto_trainer/1.7.0/src/auto_trainer.py new file mode 100755 index 00000000..7b476470 --- /dev/null +++ b/functions/master/auto_trainer/1.7.0/src/auto_trainer.py @@ -0,0 +1,401 @@ +# Copyright 2019 Iguazio +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +from pathlib import Path +from typing import Any, Dict, List, Optional, Tuple, Union + +import mlrun +import mlrun.datastore +import mlrun.utils +import pandas as pd +from mlrun import feature_store as fs +from mlrun.datastore import DataItem +from mlrun.execution import MLClientCtx +from mlrun.frameworks.auto_mlrun import AutoMLRun +from mlrun.utils.helpers import create_class, create_function +from sklearn.model_selection import train_test_split + +PathType = Union[str, Path] + + +class KWArgsPrefixes: + MODEL_CLASS = "CLASS_" + FIT = "FIT_" + TRAIN = "TRAIN_" + + +def _get_sub_dict_by_prefix(src: Dict, prefix_key: str) -> Dict[str, Any]: + """ + Collect all the keys from the given dict that starts with the given prefix and creates a new dictionary with these + keys. + + :param src: The source dict to extract the values from. + :param prefix_key: Only keys with this prefix will be returned. The keys in the result dict will be without this + prefix. + """ + return { + key.replace(prefix_key, ""): val + for key, val in src.items() + if key.startswith(prefix_key) + } + + +def _get_dataframe( + context: MLClientCtx, + dataset: DataItem, + label_columns: Optional[Union[str, List[str]]] = None, + drop_columns: Union[str, List[str], int, List[int]] = None, +) -> Tuple[pd.DataFrame, Optional[Union[str, List[str]]]]: + """ + Getting the DataFrame of the dataset and drop the columns accordingly. + + :param context: MLRun context. + :param dataset: The dataset to train the model on. + Can be either a list of lists, dict, URI or a FeatureVector. + :param label_columns: The target label(s) of the column(s) in the dataset. for Regression or + Classification tasks. + :param drop_columns: str/int or a list of strings/ints that represent the column names/indices to drop. + """ + store_uri_prefix, _ = mlrun.datastore.parse_store_uri(dataset.artifact_url) + + # Getting the dataset: + if mlrun.utils.StorePrefix.FeatureVector == store_uri_prefix: + label_columns = label_columns or dataset.meta.status.label_column + context.logger.info(f"label columns: {label_columns}") + # FeatureVector case: + try: + fv = mlrun.datastore.get_store_resource(dataset.artifact_url) + dataset = fv.get_offline_features(drop_columns=drop_columns).to_dataframe() + except AttributeError: + # Leave here for backwards compatibility + dataset = fs.get_offline_features( + dataset.meta.uri, drop_columns=drop_columns + ).to_dataframe() + + elif not label_columns: + context.logger.info( + "label_columns not provided, mandatory when dataset is not a FeatureVector" + ) + raise ValueError + + elif isinstance(dataset, (list, dict)): + # list/dict case: + dataset = pd.DataFrame(dataset) + # Checking if drop_columns provided by integer type: + if drop_columns: + if isinstance(drop_columns, str) or ( + isinstance(drop_columns, list) + and any(isinstance(col, str) for col in drop_columns) + ): + context.logger.error( + "drop_columns must be an integer/list of integers if not provided with a URI/FeatureVector dataset" + ) + raise ValueError + dataset.drop(drop_columns, axis=1, inplace=True) + + else: + # simple URL case: + dataset = dataset.as_df() + if drop_columns: + if all(col in dataset for col in drop_columns): + dataset = dataset.drop(drop_columns, axis=1) + else: + context.logger.info( + "not all of the columns to drop in the dataset, drop columns process skipped" + ) + + return dataset, label_columns + + +def train( + context: MLClientCtx, + dataset: DataItem, + model_class: str, + label_columns: Optional[Union[str, List[str]]] = None, + drop_columns: List[str] = None, + model_name: str = "model", + tag: str = "", + sample_set: DataItem = None, + test_set: DataItem = None, + train_test_split_size: float = None, + random_state: int = None, + labels: dict = None, + **kwargs, +): + """ + Training a model with the given dataset. + + example:: + + import mlrun + project = mlrun.get_or_create_project("my-project") + project.set_function("hub://auto_trainer", "train") + trainer_run = project.run( + name="train", + handler="train", + inputs={"dataset": "./path/to/dataset.csv"}, + params={ + "model_class": "sklearn.linear_model.LogisticRegression", + "label_columns": "label", + "drop_columns": "id", + "model_name": "my-model", + "tag": "v1.0.0", + "sample_set": "./path/to/sample_set.csv", + "test_set": "./path/to/test_set.csv", + "CLASS_solver": "liblinear", + }, + ) + + :param context: MLRun context + :param dataset: The dataset to train the model on. Can be either a URI or a FeatureVector + :param model_class: The class of the model, e.g. `sklearn.linear_model.LogisticRegression` + :param label_columns: The target label(s) of the column(s) in the dataset. for Regression or + Classification tasks. Mandatory when dataset is not a FeatureVector. + :param drop_columns: str or a list of strings that represent the columns to drop + :param model_name: The model's name to use for storing the model artifact, default to 'model' + :param tag: The model's tag to log with + :param sample_set: A sample set of inputs for the model for logging its stats along the model in favour + of model monitoring. Can be either a URI or a FeatureVector + :param test_set: The test set to train the model with. + :param train_test_split_size: if test_set was provided then this argument is ignored. + Should be between 0.0 and 1.0 and represent the proportion of the dataset to include + in the test split. The size of the Training set is set to the complement of this + value. Default = 0.2 + :param random_state: Relevant only when using train_test_split_size. + A random state seed to shuffle the data. For more information, see: + https://scikit-learn.org/stable/glossary.html#term-random_state + Notice that here we only pass integer values. + :param labels: Labels to log with the model + :param kwargs: Here you can pass keyword arguments with prefixes, + that will be parsed and passed to the relevant function, by the following prefixes: + - `CLASS_` - for the model class arguments + - `FIT_` - for the `fit` function arguments + - `TRAIN_` - for the `train` function (in xgb or lgbm train function - future) + + """ + # Validate inputs: + # Check if exactly one of them is supplied: + if test_set is None: + if train_test_split_size is None: + context.logger.info( + "test_set or train_test_split_size are not provided, setting train_test_split_size to 0.2" + ) + train_test_split_size = 0.2 + + elif train_test_split_size: + context.logger.info( + "test_set provided, ignoring given train_test_split_size value" + ) + train_test_split_size = None + + # Get DataFrame by URL or by FeatureVector: + dataset, label_columns = _get_dataframe( + context=context, + dataset=dataset, + label_columns=label_columns, + drop_columns=drop_columns, + ) + + # Getting the sample set: + if sample_set is None: + context.logger.info( + f"Sample set not given, using the whole training set as the sample set" + ) + sample_set = dataset + else: + sample_set, _ = _get_dataframe( + context=context, + dataset=sample_set, + label_columns=label_columns, + drop_columns=drop_columns, + ) + + # Parsing kwargs: + # TODO: Use in xgb or lgbm train function. + train_kwargs = _get_sub_dict_by_prefix(src=kwargs, prefix_key=KWArgsPrefixes.TRAIN) + fit_kwargs = _get_sub_dict_by_prefix(src=kwargs, prefix_key=KWArgsPrefixes.FIT) + model_class_kwargs = _get_sub_dict_by_prefix( + src=kwargs, prefix_key=KWArgsPrefixes.MODEL_CLASS + ) + + # Check if model or function: + if hasattr(model_class, "train"): + # TODO: Need to call: model(), afterwards to start the train function. + # model = create_function(f"{model_class}.train") + raise NotImplementedError + else: + # Creating model instance: + model = create_class(model_class)(**model_class_kwargs) + + x = dataset.drop(label_columns, axis=1) + y = dataset[label_columns] + if train_test_split_size: + x_train, x_test, y_train, y_test = train_test_split( + x, y, test_size=train_test_split_size, random_state=random_state + ) + else: + x_train, y_train = x, y + + test_set = test_set.as_df() + if drop_columns: + test_set = dataset.drop(drop_columns, axis=1) + + x_test, y_test = test_set.drop(label_columns, axis=1), test_set[label_columns] + + AutoMLRun.apply_mlrun( + model=model, + model_name=model_name, + context=context, + tag=tag, + sample_set=sample_set, + y_columns=label_columns, + test_set=test_set, + x_test=x_test, + y_test=y_test, + artifacts=context.artifacts, + labels=labels, + ) + context.logger.info(f"training '{model_name}'") + model.fit(x_train, y_train, **fit_kwargs) + + +def evaluate( + context: MLClientCtx, + model: str, + dataset: mlrun.DataItem, + drop_columns: List[str] = None, + label_columns: Optional[Union[str, List[str]]] = None, + **kwargs, +): + """ + Evaluating a model. Artifacts generated by the MLHandler. + + :param context: MLRun context. + :param model: The model Store path. + :param dataset: The dataset to evaluate the model on. Can be either a URI or a FeatureVector. + :param drop_columns: str or a list of strings that represent the columns to drop. + :param label_columns: The target label(s) of the column(s) in the dataset. for Regression or + Classification tasks. Mandatory when dataset is not a FeatureVector. + :param kwargs: Here you can pass keyword arguments to the predict function + (PREDICT_ prefix is not required). + """ + # Get dataset by URL or by FeatureVector: + dataset, label_columns = _get_dataframe( + context=context, + dataset=dataset, + label_columns=label_columns, + drop_columns=drop_columns, + ) + + # Parsing label_columns: + parsed_label_columns = [] + if label_columns: + label_columns = ( + label_columns if isinstance(label_columns, list) else [label_columns] + ) + for lc in label_columns: + if fs.common.feature_separator in lc: + feature_set_name, label_name, alias = fs.common.parse_feature_string(lc) + parsed_label_columns.append(alias or label_name) + if parsed_label_columns: + label_columns = parsed_label_columns + + x = dataset.drop(label_columns, axis=1) + y = dataset[label_columns] + + # Loading the model and predicting: + model_handler = AutoMLRun.load_model( + model_path=model, context=context, model_name="model_LinearRegression" + ) + AutoMLRun.apply_mlrun(model_handler.model, y_test=y, model_path=model) + + context.logger.info(f"evaluating '{model_handler.model_name}'") + model_handler.model.predict(x, **kwargs) + + +def predict( + context: MLClientCtx, + model: str, + dataset: mlrun.DataItem, + drop_columns: Union[str, List[str], int, List[int]] = None, + label_columns: Optional[Union[str, List[str]]] = None, + result_set: Optional[str] = None, + **kwargs, +): + """ + Predicting dataset by a model. + + :param context: MLRun context. + :param model: The model Store path. + :param dataset: The dataset to predict the model on. Can be either a URI, a FeatureVector or a + sample in a shape of a list/dict. + When passing a sample, pass the dataset as a field in `params` instead of `inputs`. + :param drop_columns: str/int or a list of strings/ints that represent the column names/indices to drop. + When the dataset is a list/dict this parameter should be represented by integers. + :param label_columns: The target label(s) of the column(s) in the dataset. for Regression or + Classification tasks. Mandatory when dataset is not a FeatureVector. + :param result_set: The db key to set name of the prediction result and the filename. + Default to 'prediction'. + :param kwargs: Here you can pass keyword arguments to the predict function + (PREDICT_ prefix is not required). + """ + # Get dataset by URL or by FeatureVector: + dataset, label_columns = _get_dataframe( + context=context, + dataset=dataset, + label_columns=label_columns, + drop_columns=drop_columns, + ) + + # loading the model, and getting the model handler: + model_handler = AutoMLRun.load_model(model_path=model, context=context) + + # Dropping label columns if necessary: + if not label_columns: + label_columns = [] + elif isinstance(label_columns, str): + label_columns = [label_columns] + + # Predicting: + context.logger.info(f"making prediction by '{model_handler.model_name}'") + y_pred = model_handler.model.predict(dataset, **kwargs) + + # Preparing and validating label columns for the dataframe of the prediction result: + num_predicted = 1 if len(y_pred.shape) == 1 else y_pred.shape[1] + + if num_predicted > len(label_columns): + if num_predicted == 1: + label_columns = ["predicted labels"] + else: + label_columns.extend( + [ + f"predicted_label_{i + 1 + len(label_columns)}" + for i in range(num_predicted - len(label_columns)) + ] + ) + elif num_predicted < len(label_columns): + context.logger.error( + f"number of predicted labels: {num_predicted} is smaller than number of label columns: {len(label_columns)}" + ) + raise ValueError + + artifact_name = result_set or "prediction" + labels_inside_df = set(label_columns) & set(dataset.columns.tolist()) + if labels_inside_df: + context.logger.error( + f"The labels: {labels_inside_df} are already existed in the dataframe" + ) + raise ValueError + pred_df = pd.concat([dataset, pd.DataFrame(y_pred, columns=label_columns)], axis=1) + context.log_dataset(artifact_name, pred_df, db_key=result_set) diff --git a/functions/master/auto_trainer/1.7.0/src/function.yaml b/functions/master/auto_trainer/1.7.0/src/function.yaml new file mode 100644 index 00000000..0f86b7ea --- /dev/null +++ b/functions/master/auto_trainer/1.7.0/src/function.yaml @@ -0,0 +1,164 @@ +kind: job +metadata: + name: auto-trainer + tag: '' + hash: 1c415e6d3bd79c9ca0ee537e008643660c13fbc7 + project: '' + labels: + author: yonish + categories: + - machine-learning + - model-training +spec: + command: '' + args: [] + image: mlrun/mlrun + build: + functionSourceCode: # Copyright 2019 Iguazio
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
from pathlib import Path
from typing import Any, Dict, List, Optional, Tuple, Union

import mlrun
import mlrun.datastore
import mlrun.utils
import pandas as pd
from mlrun import feature_store as fs
from mlrun.datastore import DataItem
from mlrun.execution import MLClientCtx
from mlrun.frameworks.auto_mlrun import AutoMLRun
from mlrun.utils.helpers import create_class, create_function
from sklearn.model_selection import train_test_split

PathType = Union[str, Path]


class KWArgsPrefixes:
    MODEL_CLASS = "CLASS_"
    FIT = "FIT_"
    TRAIN = "TRAIN_"


def _get_sub_dict_by_prefix(src: Dict, prefix_key: str) -> Dict[str, Any]:
    """
    Collect all the keys from the given dict that starts with the given prefix and creates a new dictionary with these
    keys.

    :param src:         The source dict to extract the values from.
    :param prefix_key:  Only keys with this prefix will be returned. The keys in the result dict will be without this
                        prefix.
    """
    return {
        key.replace(prefix_key, ""): val
        for key, val in src.items()
        if key.startswith(prefix_key)
    }


def _get_dataframe(
    context: MLClientCtx,
    dataset: DataItem,
    label_columns: Optional[Union[str, List[str]]] = None,
    drop_columns: Union[str, List[str], int, List[int]] = None,
) -> Tuple[pd.DataFrame, Optional[Union[str, List[str]]]]:
    """
    Getting the DataFrame of the dataset and drop the columns accordingly.

    :param context:         MLRun context.
    :param dataset:         The dataset to train the model on.
                            Can be either a list of lists, dict, URI or a FeatureVector.
    :param label_columns:   The target label(s) of the column(s) in the dataset. for Regression or
                            Classification tasks.
    :param drop_columns:    str/int or a list of strings/ints that represent the column names/indices to drop.
    """
    store_uri_prefix, _ = mlrun.datastore.parse_store_uri(dataset.artifact_url)

    # Getting the dataset:
    if mlrun.utils.StorePrefix.FeatureVector == store_uri_prefix:
        label_columns = label_columns or dataset.meta.status.label_column
        context.logger.info(f"label columns: {label_columns}")
        # FeatureVector case:
        try:
            fv = mlrun.datastore.get_store_resource(dataset.artifact_url)
            dataset = fv.get_offline_features(drop_columns=drop_columns).to_dataframe()
        except AttributeError:
            # Leave here for backwards compatibility
            dataset = fs.get_offline_features(
                dataset.meta.uri, drop_columns=drop_columns
            ).to_dataframe()

    elif not label_columns:
        context.logger.info(
            "label_columns not provided, mandatory when dataset is not a FeatureVector"
        )
        raise ValueError

    elif isinstance(dataset, (list, dict)):
        # list/dict case:
        dataset = pd.DataFrame(dataset)
        # Checking if drop_columns provided by integer type:
        if drop_columns:
            if isinstance(drop_columns, str) or (
                isinstance(drop_columns, list)
                and any(isinstance(col, str) for col in drop_columns)
            ):
                context.logger.error(
                    "drop_columns must be an integer/list of integers if not provided with a URI/FeatureVector dataset"
                )
                raise ValueError
            dataset.drop(drop_columns, axis=1, inplace=True)

    else:
        # simple URL case:
        dataset = dataset.as_df()
        if drop_columns:
            if all(col in dataset for col in drop_columns):
                dataset = dataset.drop(drop_columns, axis=1)
            else:
                context.logger.info(
                    "not all of the columns to drop in the dataset, drop columns process skipped"
                )

    return dataset, label_columns


def train(
    context: MLClientCtx,
    dataset: DataItem,
    model_class: str,
    label_columns: Optional[Union[str, List[str]]] = None,
    drop_columns: List[str] = None,
    model_name: str = "model",
    tag: str = "",
    sample_set: DataItem = None,
    test_set: DataItem = None,
    train_test_split_size: float = None,
    random_state: int = None,
    labels: dict = None,
    **kwargs,
):
    """
    Training a model with the given dataset.

    example::

        import mlrun
        project = mlrun.get_or_create_project("my-project")
        project.set_function("hub://auto_trainer", "train")
        trainer_run = project.run(
            name="train",
            handler="train",
            inputs={"dataset": "./path/to/dataset.csv"},
            params={
                "model_class": "sklearn.linear_model.LogisticRegression",
                "label_columns": "label",
                "drop_columns": "id",
                "model_name": "my-model",
                "tag": "v1.0.0",
                "sample_set": "./path/to/sample_set.csv",
                "test_set": "./path/to/test_set.csv",
                "CLASS_solver": "liblinear",
            },
        )

    :param context:                 MLRun context
    :param dataset:                 The dataset to train the model on. Can be either a URI or a FeatureVector
    :param model_class:             The class of the model, e.g. `sklearn.linear_model.LogisticRegression`
    :param label_columns:           The target label(s) of the column(s) in the dataset. for Regression or
                                    Classification tasks. Mandatory when dataset is not a FeatureVector.
    :param drop_columns:            str or a list of strings that represent the columns to drop
    :param model_name:              The model's name to use for storing the model artifact, default to 'model'
    :param tag:                     The model's tag to log with
    :param sample_set:              A sample set of inputs for the model for logging its stats along the model in favour
                                    of model monitoring. Can be either a URI or a FeatureVector
    :param test_set:                The test set to train the model with.
    :param train_test_split_size:   if test_set was provided then this argument is ignored.
                                    Should be between 0.0 and 1.0 and represent the proportion of the dataset to include
                                    in the test split. The size of the Training set is set to the complement of this
                                    value. Default = 0.2
    :param random_state:            Relevant only when using train_test_split_size.
                                    A random state seed to shuffle the data. For more information, see:
                                    https://scikit-learn.org/stable/glossary.html#term-random_state
                                    Notice that here we only pass integer values.
    :param labels:                  Labels to log with the model
    :param kwargs:                  Here you can pass keyword arguments with prefixes,
                                    that will be parsed and passed to the relevant function, by the following prefixes:
                                    - `CLASS_` - for the model class arguments
                                    - `FIT_` - for the `fit` function arguments
                                    - `TRAIN_` - for the `train` function (in xgb or lgbm train function - future)

    """
    # Validate inputs:
    # Check if exactly one of them is supplied:
    if test_set is None:
        if train_test_split_size is None:
            context.logger.info(
                "test_set or train_test_split_size are not provided, setting train_test_split_size to 0.2"
            )
            train_test_split_size = 0.2

    elif train_test_split_size:
        context.logger.info(
            "test_set provided, ignoring given train_test_split_size value"
        )
        train_test_split_size = None

    # Get DataFrame by URL or by FeatureVector:
    dataset, label_columns = _get_dataframe(
        context=context,
        dataset=dataset,
        label_columns=label_columns,
        drop_columns=drop_columns,
    )

    # Getting the sample set:
    if sample_set is None:
        context.logger.info(
            f"Sample set not given, using the whole training set as the sample set"
        )
        sample_set = dataset
    else:
        sample_set, _ = _get_dataframe(
            context=context,
            dataset=sample_set,
            label_columns=label_columns,
            drop_columns=drop_columns,
        )

    # Parsing kwargs:
    # TODO: Use in xgb or lgbm train function.
    train_kwargs = _get_sub_dict_by_prefix(src=kwargs, prefix_key=KWArgsPrefixes.TRAIN)
    fit_kwargs = _get_sub_dict_by_prefix(src=kwargs, prefix_key=KWArgsPrefixes.FIT)
    model_class_kwargs = _get_sub_dict_by_prefix(
        src=kwargs, prefix_key=KWArgsPrefixes.MODEL_CLASS
    )

    # Check if model or function:
    if hasattr(model_class, "train"):
        # TODO: Need to call: model(), afterwards to start the train function.
        # model = create_function(f"{model_class}.train")
        raise NotImplementedError
    else:
        # Creating model instance:
        model = create_class(model_class)(**model_class_kwargs)

    x = dataset.drop(label_columns, axis=1)
    y = dataset[label_columns]
    if train_test_split_size:
        x_train, x_test, y_train, y_test = train_test_split(
            x, y, test_size=train_test_split_size, random_state=random_state
        )
    else:
        x_train, y_train = x, y

        test_set = test_set.as_df()
        if drop_columns:
            test_set = dataset.drop(drop_columns, axis=1)

        x_test, y_test = test_set.drop(label_columns, axis=1), test_set[label_columns]

    AutoMLRun.apply_mlrun(
        model=model,
        model_name=model_name,
        context=context,
        tag=tag,
        sample_set=sample_set,
        y_columns=label_columns,
        test_set=test_set,
        x_test=x_test,
        y_test=y_test,
        artifacts=context.artifacts,
        labels=labels,
    )
    context.logger.info(f"training '{model_name}'")
    model.fit(x_train, y_train, **fit_kwargs)


def evaluate(
    context: MLClientCtx,
    model: str,
    dataset: mlrun.DataItem,
    drop_columns: List[str] = None,
    label_columns: Optional[Union[str, List[str]]] = None,
    **kwargs,
):
    """
    Evaluating a model. Artifacts generated by the MLHandler.

    :param context:                 MLRun context.
    :param model:                   The model Store path.
    :param dataset:                 The dataset to evaluate the model on. Can be either a URI or a FeatureVector.
    :param drop_columns:            str or a list of strings that represent the columns to drop.
    :param label_columns:           The target label(s) of the column(s) in the dataset. for Regression or
                                    Classification tasks. Mandatory when dataset is not a FeatureVector.
    :param kwargs:                  Here you can pass keyword arguments to the predict function
                                    (PREDICT_ prefix is not required).
    """
    # Get dataset by URL or by FeatureVector:
    dataset, label_columns = _get_dataframe(
        context=context,
        dataset=dataset,
        label_columns=label_columns,
        drop_columns=drop_columns,
    )

    # Parsing label_columns:
    parsed_label_columns = []
    if label_columns:
        label_columns = (
            label_columns if isinstance(label_columns, list) else [label_columns]
        )
        for lc in label_columns:
            if fs.common.feature_separator in lc:
                feature_set_name, label_name, alias = fs.common.parse_feature_string(lc)
                parsed_label_columns.append(alias or label_name)
        if parsed_label_columns:
            label_columns = parsed_label_columns

    x = dataset.drop(label_columns, axis=1)
    y = dataset[label_columns]

    # Loading the model and predicting:
    model_handler = AutoMLRun.load_model(
        model_path=model, context=context, model_name="model_LinearRegression"
    )
    AutoMLRun.apply_mlrun(model_handler.model, y_test=y, model_path=model)

    context.logger.info(f"evaluating '{model_handler.model_name}'")
    model_handler.model.predict(x, **kwargs)


def predict(
    context: MLClientCtx,
    model: str,
    dataset: mlrun.DataItem,
    drop_columns: Union[str, List[str], int, List[int]] = None,
    label_columns: Optional[Union[str, List[str]]] = None,
    result_set: Optional[str] = None,
    **kwargs,
):
    """
    Predicting dataset by a model.

    :param context:                 MLRun context.
    :param model:                   The model Store path.
    :param dataset:                 The dataset to predict the model on. Can be either a URI, a FeatureVector or a
                                    sample in a shape of a list/dict.
                                    When passing a sample, pass the dataset as a field in `params` instead of `inputs`.
    :param drop_columns:            str/int or a list of strings/ints that represent the column names/indices to drop.
                                    When the dataset is a list/dict this parameter should be represented by integers.
    :param label_columns:           The target label(s) of the column(s) in the dataset. for Regression or
                                    Classification tasks. Mandatory when dataset is not a FeatureVector.
    :param result_set:              The db key to set name of the prediction result and the filename.
                                    Default to 'prediction'.
    :param kwargs:                  Here you can pass keyword arguments to the predict function
                                    (PREDICT_ prefix is not required).
    """
    # Get dataset by URL or by FeatureVector:
    dataset, label_columns = _get_dataframe(
        context=context,
        dataset=dataset,
        label_columns=label_columns,
        drop_columns=drop_columns,
    )

    # loading the model, and getting the model handler:
    model_handler = AutoMLRun.load_model(model_path=model, context=context)

    # Dropping label columns if necessary:
    if not label_columns:
        label_columns = []
    elif isinstance(label_columns, str):
        label_columns = [label_columns]

    # Predicting:
    context.logger.info(f"making prediction by '{model_handler.model_name}'")
    y_pred = model_handler.model.predict(dataset, **kwargs)

    # Preparing and validating label columns for the dataframe of the prediction result:
    num_predicted = 1 if len(y_pred.shape) == 1 else y_pred.shape[1]

    if num_predicted > len(label_columns):
        if num_predicted == 1:
            label_columns = ["predicted labels"]
        else:
            label_columns.extend(
                [
                    f"predicted_label_{i + 1 + len(label_columns)}"
                    for i in range(num_predicted - len(label_columns))
                ]
            )
    elif num_predicted < len(label_columns):
        context.logger.error(
            f"number of predicted labels: {num_predicted} is smaller than number of label columns: {len(label_columns)}"
        )
        raise ValueError

    artifact_name = result_set or "prediction"
    labels_inside_df = set(label_columns) & set(dataset.columns.tolist())
    if labels_inside_df:
        context.logger.error(
            f"The labels: {labels_inside_df} are already existed in the dataframe"
        )
        raise ValueError
    pred_df = pd.concat([dataset, pd.DataFrame(y_pred, columns=label_columns)], axis=1)
    context.log_dataset(artifact_name, pred_df, db_key=result_set)
 + commands: [] + code_origin: '' + origin_filename: '' + requirements: [] + entry_points: + train: + name: train + doc: "Training a model with the given dataset.\n\nexample::\n\n import mlrun\n\ + \ project = mlrun.get_or_create_project(\"my-project\")\n project.set_function(\"\ + hub://auto_trainer\", \"train\")\n trainer_run = project.run(\n \ + \ name=\"train\",\n handler=\"train\",\n inputs={\"dataset\"\ + : \"./path/to/dataset.csv\"},\n params={\n \"model_class\"\ + : \"sklearn.linear_model.LogisticRegression\",\n \"label_columns\"\ + : \"label\",\n \"drop_columns\": \"id\",\n \"model_name\"\ + : \"my-model\",\n \"tag\": \"v1.0.0\",\n \"sample_set\"\ + : \"./path/to/sample_set.csv\",\n \"test_set\": \"./path/to/test_set.csv\"\ + ,\n \"CLASS_solver\": \"liblinear\",\n },\n )" + parameters: + - name: context + type: MLClientCtx + doc: MLRun context + - name: dataset + type: DataItem + doc: The dataset to train the model on. Can be either a URI or a FeatureVector + - name: model_class + type: str + doc: The class of the model, e.g. `sklearn.linear_model.LogisticRegression` + - name: label_columns + type: Optional[Union[str, List[str]]] + doc: The target label(s) of the column(s) in the dataset. for Regression or + Classification tasks. Mandatory when dataset is not a FeatureVector. + default: null + - name: drop_columns + type: List[str] + doc: str or a list of strings that represent the columns to drop + default: null + - name: model_name + type: str + doc: The model's name to use for storing the model artifact, default to 'model' + default: model + - name: tag + type: str + doc: The model's tag to log with + default: '' + - name: sample_set + type: DataItem + doc: A sample set of inputs for the model for logging its stats along the + model in favour of model monitoring. Can be either a URI or a FeatureVector + default: null + - name: test_set + type: DataItem + doc: The test set to train the model with. + default: null + - name: train_test_split_size + type: float + doc: if test_set was provided then this argument is ignored. Should be between + 0.0 and 1.0 and represent the proportion of the dataset to include in the + test split. The size of the Training set is set to the complement of this + value. Default = 0.2 + default: null + - name: random_state + type: int + doc: 'Relevant only when using train_test_split_size. A random state seed + to shuffle the data. For more information, see: https://scikit-learn.org/stable/glossary.html#term-random_state + Notice that here we only pass integer values.' + default: null + - name: labels + type: dict + doc: Labels to log with the model + default: null + outputs: [] + lineno: 121 + has_varargs: false + has_kwargs: true + evaluate: + name: evaluate + doc: Evaluating a model. Artifacts generated by the MLHandler. + parameters: + - name: context + type: MLClientCtx + doc: MLRun context. + - name: model + type: str + doc: The model Store path. + - name: dataset + type: DataItem + doc: The dataset to evaluate the model on. Can be either a URI or a FeatureVector. + - name: drop_columns + type: List[str] + doc: str or a list of strings that represent the columns to drop. + default: null + - name: label_columns + type: Optional[Union[str, List[str]]] + doc: The target label(s) of the column(s) in the dataset. for Regression or + Classification tasks. Mandatory when dataset is not a FeatureVector. + default: null + outputs: [] + lineno: 273 + has_varargs: false + has_kwargs: true + predict: + name: predict + doc: Predicting dataset by a model. + parameters: + - name: context + type: MLClientCtx + doc: MLRun context. + - name: model + type: str + doc: The model Store path. + - name: dataset + type: DataItem + doc: The dataset to predict the model on. Can be either a URI, a FeatureVector + or a sample in a shape of a list/dict. When passing a sample, pass the dataset + as a field in `params` instead of `inputs`. + - name: drop_columns + type: Union[str, List[str], int, List[int]] + doc: str/int or a list of strings/ints that represent the column names/indices + to drop. When the dataset is a list/dict this parameter should be represented + by integers. + default: null + - name: label_columns + type: Optional[Union[str, List[str]]] + doc: The target label(s) of the column(s) in the dataset. for Regression or + Classification tasks. Mandatory when dataset is not a FeatureVector. + default: null + - name: result_set + type: Optional[str] + doc: The db key to set name of the prediction result and the filename. Default + to 'prediction'. + default: null + outputs: [] + lineno: 327 + has_varargs: false + has_kwargs: true + description: Automatic train, evaluate and predict functions for the ML frameworks + - Scikit-Learn, XGBoost and LightGBM. + default_handler: train + disable_auto_mount: false + clone_target_dir: '' + env: [] + priority_class_name: '' + preemption_mode: prevent + affinity: null + tolerations: null + security_context: {} +verbose: false diff --git a/functions/master/auto_trainer/1.7.0/src/item.yaml b/functions/master/auto_trainer/1.7.0/src/item.yaml new file mode 100755 index 00000000..ffa03bf0 --- /dev/null +++ b/functions/master/auto_trainer/1.7.0/src/item.yaml @@ -0,0 +1,26 @@ +apiVersion: v1 +categories: +- machine-learning +- model-training +description: Automatic train, evaluate and predict functions for the ML frameworks + - Scikit-Learn, XGBoost and LightGBM. +doc: '' +example: auto_trainer.ipynb +generationDate: 2022-08-28:17-25 +hidden: false +icon: '' +labels: + author: yonish +maintainers: [] +marketplaceType: '' +mlrunVersion: 1.5.2 +name: auto_trainer +platformVersion: 3.5.0 +spec: + filename: auto_trainer.py + handler: train + image: mlrun/mlrun + kind: job + requirements: [] +url: '' +version: 1.7.0 diff --git a/functions/master/auto_trainer/1.7.0/src/requirements.txt b/functions/master/auto_trainer/1.7.0/src/requirements.txt new file mode 100644 index 00000000..ad97214f --- /dev/null +++ b/functions/master/auto_trainer/1.7.0/src/requirements.txt @@ -0,0 +1,4 @@ +pandas +scikit-learn +xgboost +plotly \ No newline at end of file diff --git a/functions/master/auto_trainer/1.7.0/src/test_auto_trainer.py b/functions/master/auto_trainer/1.7.0/src/test_auto_trainer.py new file mode 100644 index 00000000..9a1ff554 --- /dev/null +++ b/functions/master/auto_trainer/1.7.0/src/test_auto_trainer.py @@ -0,0 +1,215 @@ +# Copyright 2019 Iguazio +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import os +import tempfile +from typing import Tuple + +import mlrun +import pandas as pd +import pytest +from sklearn.datasets import ( + make_classification, + make_multilabel_classification, + make_regression, +) + +MODELS = [ + ("sklearn.linear_model.LinearRegression", "regression"), + ("sklearn.ensemble.RandomForestClassifier", "classification"), + ("xgboost.XGBRegressor", "regression"), +] + +REQUIRED_ENV_VARS = [ + "MLRUN_DBPATH", + "MLRUN_ARTIFACT_PATH", + "V3IO_USERNAME", + "V3IO_API", + "V3IO_ACCESS_KEY", +] + + +def _validate_environment_variables() -> bool: + """ + Checks that all required Environment variables are set. + """ + environment_keys = os.environ.keys() + return all(key in environment_keys for key in REQUIRED_ENV_VARS) + + +def _get_dataset(problem_type: str, filepath: str = ".", n_classes: int = 2): + if problem_type == "classification": + x, y = make_classification(n_classes=n_classes) + elif problem_type == "regression": + x, y = make_regression(n_targets=1) + elif problem_type == "multilabel_classification": + x, y = make_multilabel_classification(n_classes=n_classes) + else: + raise ValueError(f"Not supporting problem type = {problem_type}") + + features = [f"f_{i}" for i in range(x.shape[1])] + if y.ndim == 1: + labels = ["labels"] + else: + labels = [f"label_{i}" for i in range(y.shape[1])] + dataset = pd.concat( + [pd.DataFrame(x, columns=features), pd.DataFrame(y, columns=labels)], axis=1 + ) + filename = f"{filepath}/{problem_type}_dataset.csv" + dataset.to_csv(filename, index=False) + return filename, labels + + +def _assert_train_handler(train_run): + assert train_run and all( + key in train_run.outputs for key in ["model", "test_set"] + ), "outputs should include more data" + + +@pytest.mark.parametrize("model", MODELS) +def test_train(model: Tuple[str, str]): + dataset, label_columns = _get_dataset(model[1]) + is_test_passed = True + + project = mlrun.new_project("auto-trainer-test", context="./") + fn = project.set_function("function.yaml", "train", kind="job", image="mlrun/mlrun") + + train_run = None + model_name = model[0].split(".")[-1] + labels = {"label1": "my-value"} + try: + train_run = fn.run( + inputs={"dataset": dataset}, + params={ + "drop_columns": ["f_0", "f_2"], + "model_class": model[0], + "model_name": f"model_{model_name}", + "label_columns": label_columns, + "train_test_split_size": 0.2, + "labels": labels, + }, + handler="train", + local=True, + ) + except Exception as exception: + print(f"- The test failed - raised the following error:\n- {exception}") + is_test_passed = False + + assert is_test_passed, "The test failed" + _assert_train_handler(train_run) + + +@pytest.mark.parametrize("model", MODELS) +@pytest.mark.skipif( + condition=not _validate_environment_variables(), + reason="Project's environment variables are not set", +) +def test_train_evaluate(model: Tuple[str, str]): + dataset, label_columns = _get_dataset(model[1]) + is_test_passed = True + # Importing function: + project = mlrun.new_project("auto-trainer-test", context="./") + fn = project.set_function("function.yaml", "train", kind="job", image="mlrun/mlrun") + temp_dir = tempfile.mkdtemp() + + evaluate_run = None + model_name = model[0].split(".")[-1] + try: + train_run = fn.run( + inputs={"dataset": dataset}, + params={ + "drop_columns": ["f_0", "f_2"], + "model_class": model[0], + "model_name": f"model_{model_name}", + "label_columns": label_columns, + "train_test_split_size": 0.2, + }, + handler="train", + local=True, + artifact_path=temp_dir, + ) + _assert_train_handler(train_run) + + evaluate_run = fn.run( + inputs={"dataset": train_run.outputs["test_set"]}, + params={ + "model": train_run.outputs["model"], + "label_columns": label_columns, + }, + handler="evaluate", + local=True, + artifact_path=temp_dir, + ) + except Exception as exception: + print(f"- The test failed - raised the following error:\n- {exception}") + is_test_passed = False + + assert is_test_passed, "The test failed" + assert ( + evaluate_run and "evaluation-test_set" in evaluate_run.outputs + ), "Missing fields in evaluate_run" + + +@pytest.mark.parametrize("model", MODELS) +@pytest.mark.skipif( + condition=not _validate_environment_variables(), + reason="Project's environment variables are not set", +) +def test_train_predict(model: Tuple[str, str]): + is_test_passed = True + dataset, label_columns = _get_dataset(model[1]) + df = pd.read_csv(dataset) + sample = df.head().drop("labels", axis=1).values.tolist() + # Importing function: + project = mlrun.new_project("auto-trainer-test", context="./") + fn = project.set_function("function.yaml", "train", kind="job", image="mlrun/mlrun") + temp_dir = tempfile.mkdtemp() + + predict_run = None + model_name = model[0].split(".")[-1] + try: + train_run = fn.run( + inputs={"dataset": dataset}, + params={ + "drop_columns": ["f_0", "f_2"], + "model_class": model[0], + "model_name": f"model_{model_name}", + "label_columns": label_columns, + "train_test_split_size": 0.2, + }, + handler="train", + local=True, + artifact_path=temp_dir, + ) + _assert_train_handler(train_run) + + predict_run = fn.run( + params={ + "dataset": sample, + "drop_columns": [0, 2], + "model": train_run.outputs["model"], + "label_columns": label_columns, + }, + handler="predict", + local=True, + artifact_path=temp_dir, + ) + except Exception as exception: + print(f"- The test failed - raised the following error:\n- {exception}") + is_test_passed = False + + assert is_test_passed, "The test failed" + assert ( + predict_run and "prediction" in predict_run.outputs + ), "Prediction field must be in the output" diff --git a/functions/master/auto_trainer/1.7.0/static/auto_trainer.html b/functions/master/auto_trainer/1.7.0/static/auto_trainer.html new file mode 100644 index 00000000..a3db2659 --- /dev/null +++ b/functions/master/auto_trainer/1.7.0/static/auto_trainer.html @@ -0,0 +1,541 @@ + + + + + + + +auto_trainer.auto_trainer + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Source code for auto_trainer.auto_trainer

+# Copyright 2019 Iguazio
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+from pathlib import Path
+from typing import Any, Dict, List, Optional, Tuple, Union
+
+import mlrun
+import mlrun.datastore
+import mlrun.utils
+import pandas as pd
+from mlrun import feature_store as fs
+from mlrun.datastore import DataItem
+from mlrun.execution import MLClientCtx
+from mlrun.frameworks.auto_mlrun import AutoMLRun
+from mlrun.utils.helpers import create_class, create_function
+from sklearn.model_selection import train_test_split
+
+PathType = Union[str, Path]
+
+
+
[docs]class KWArgsPrefixes:
+    MODEL_CLASS = "CLASS_"
+    FIT = "FIT_"
+    TRAIN = "TRAIN_"

+
+
+def _get_sub_dict_by_prefix(src: Dict, prefix_key: str) -> Dict[str, Any]:
+    """
+    Collect all the keys from the given dict that starts with the given prefix and creates a new dictionary with these
+    keys.
+
+    :param src:         The source dict to extract the values from.
+    :param prefix_key:  Only keys with this prefix will be returned. The keys in the result dict will be without this
+                        prefix.
+    """
+    return {
+        key.replace(prefix_key, ""): val
+        for key, val in src.items()
+        if key.startswith(prefix_key)
+    }
+
+
+def _get_dataframe(
+    context: MLClientCtx,
+    dataset: DataItem,
+    label_columns: Optional[Union[str, List[str]]] = None,
+    drop_columns: Union[str, List[str], int, List[int]] = None,
+) -> Tuple[pd.DataFrame, Optional[Union[str, List[str]]]]:
+    """
+    Getting the DataFrame of the dataset and drop the columns accordingly.
+
+    :param context:         MLRun context.
+    :param dataset:         The dataset to train the model on.
+                            Can be either a list of lists, dict, URI or a FeatureVector.
+    :param label_columns:   The target label(s) of the column(s) in the dataset. for Regression or
+                            Classification tasks.
+    :param drop_columns:    str/int or a list of strings/ints that represent the column names/indices to drop.
+    """
+    store_uri_prefix, _ = mlrun.datastore.parse_store_uri(dataset.artifact_url)
+
+    # Getting the dataset:
+    if mlrun.utils.StorePrefix.FeatureVector == store_uri_prefix:
+        label_columns = label_columns or dataset.meta.status.label_column
+        context.logger.info(f"label columns: {label_columns}")
+        # FeatureVector case:
+        try:
+            fv = mlrun.datastore.get_store_resource(dataset.artifact_url)
+            dataset = fv.get_offline_features(drop_columns=drop_columns).to_dataframe()
+        except AttributeError:
+            # Leave here for backwards compatibility
+            dataset = fs.get_offline_features(
+                dataset.meta.uri, drop_columns=drop_columns
+            ).to_dataframe()
+
+    elif not label_columns:
+        context.logger.info(
+            "label_columns not provided, mandatory when dataset is not a FeatureVector"
+        )
+        raise ValueError
+
+    elif isinstance(dataset, (list, dict)):
+        # list/dict case:
+        dataset = pd.DataFrame(dataset)
+        # Checking if drop_columns provided by integer type:
+        if drop_columns:
+            if isinstance(drop_columns, str) or (
+                isinstance(drop_columns, list)
+                and any(isinstance(col, str) for col in drop_columns)
+            ):
+                context.logger.error(
+                    "drop_columns must be an integer/list of integers if not provided with a URI/FeatureVector dataset"
+                )
+                raise ValueError
+            dataset.drop(drop_columns, axis=1, inplace=True)
+
+    else:
+        # simple URL case:
+        dataset = dataset.as_df()
+        if drop_columns:
+            if all(col in dataset for col in drop_columns):
+                dataset = dataset.drop(drop_columns, axis=1)
+            else:
+                context.logger.info(
+                    "not all of the columns to drop in the dataset, drop columns process skipped"
+                )
+
+    return dataset, label_columns
+
+
+
[docs]def train(
+    context: MLClientCtx,
+    dataset: DataItem,
+    model_class: str,
+    label_columns: Optional[Union[str, List[str]]] = None,
+    drop_columns: List[str] = None,
+    model_name: str = "model",
+    tag: str = "",
+    sample_set: DataItem = None,
+    test_set: DataItem = None,
+    train_test_split_size: float = None,
+    random_state: int = None,
+    labels: dict = None,
+    **kwargs,
+):
+    """
+    Training a model with the given dataset.
+
+    example::
+
+        import mlrun
+        project = mlrun.get_or_create_project("my-project")
+        project.set_function("hub://auto_trainer", "train")
+        trainer_run = project.run(
+            name="train",
+            handler="train",
+            inputs={"dataset": "./path/to/dataset.csv"},
+            params={
+                "model_class": "sklearn.linear_model.LogisticRegression",
+                "label_columns": "label",
+                "drop_columns": "id",
+                "model_name": "my-model",
+                "tag": "v1.0.0",
+                "sample_set": "./path/to/sample_set.csv",
+                "test_set": "./path/to/test_set.csv",
+                "CLASS_solver": "liblinear",
+            },
+        )
+
+    :param context:                 MLRun context
+    :param dataset:                 The dataset to train the model on. Can be either a URI or a FeatureVector
+    :param model_class:             The class of the model, e.g. `sklearn.linear_model.LogisticRegression`
+    :param label_columns:           The target label(s) of the column(s) in the dataset. for Regression or
+                                    Classification tasks. Mandatory when dataset is not a FeatureVector.
+    :param drop_columns:            str or a list of strings that represent the columns to drop
+    :param model_name:              The model's name to use for storing the model artifact, default to 'model'
+    :param tag:                     The model's tag to log with
+    :param sample_set:              A sample set of inputs for the model for logging its stats along the model in favour
+                                    of model monitoring. Can be either a URI or a FeatureVector
+    :param test_set:                The test set to train the model with.
+    :param train_test_split_size:   if test_set was provided then this argument is ignored.
+                                    Should be between 0.0 and 1.0 and represent the proportion of the dataset to include
+                                    in the test split. The size of the Training set is set to the complement of this
+                                    value. Default = 0.2
+    :param random_state:            Relevant only when using train_test_split_size.
+                                    A random state seed to shuffle the data. For more information, see:
+                                    https://scikit-learn.org/stable/glossary.html#term-random_state
+                                    Notice that here we only pass integer values.
+    :param labels:                  Labels to log with the model
+    :param kwargs:                  Here you can pass keyword arguments with prefixes,
+                                    that will be parsed and passed to the relevant function, by the following prefixes:
+                                    - `CLASS_` - for the model class arguments
+                                    - `FIT_` - for the `fit` function arguments
+                                    - `TRAIN_` - for the `train` function (in xgb or lgbm train function - future)
+
+    """
+    # Validate inputs:
+    # Check if exactly one of them is supplied:
+    if test_set is None:
+        if train_test_split_size is None:
+            context.logger.info(
+                "test_set or train_test_split_size are not provided, setting train_test_split_size to 0.2"
+            )
+            train_test_split_size = 0.2
+
+    elif train_test_split_size:
+        context.logger.info(
+            "test_set provided, ignoring given train_test_split_size value"
+        )
+        train_test_split_size = None
+
+    # Get DataFrame by URL or by FeatureVector:
+    dataset, label_columns = _get_dataframe(
+        context=context,
+        dataset=dataset,
+        label_columns=label_columns,
+        drop_columns=drop_columns,
+    )
+
+    # Getting the sample set:
+    if sample_set is None:
+        context.logger.info(
+            f"Sample set not given, using the whole training set as the sample set"
+        )
+        sample_set = dataset
+    else:
+        sample_set, _ = _get_dataframe(
+            context=context,
+            dataset=sample_set,
+            label_columns=label_columns,
+            drop_columns=drop_columns,
+        )
+
+    # Parsing kwargs:
+    # TODO: Use in xgb or lgbm train function.
+    train_kwargs = _get_sub_dict_by_prefix(src=kwargs, prefix_key=KWArgsPrefixes.TRAIN)
+    fit_kwargs = _get_sub_dict_by_prefix(src=kwargs, prefix_key=KWArgsPrefixes.FIT)
+    model_class_kwargs = _get_sub_dict_by_prefix(
+        src=kwargs, prefix_key=KWArgsPrefixes.MODEL_CLASS
+    )
+
+    # Check if model or function:
+    if hasattr(model_class, "train"):
+        # TODO: Need to call: model(), afterwards to start the train function.
+        # model = create_function(f"{model_class}.train")
+        raise NotImplementedError
+    else:
+        # Creating model instance:
+        model = create_class(model_class)(**model_class_kwargs)
+
+    x = dataset.drop(label_columns, axis=1)
+    y = dataset[label_columns]
+    if train_test_split_size:
+        x_train, x_test, y_train, y_test = train_test_split(
+            x, y, test_size=train_test_split_size, random_state=random_state
+        )
+    else:
+        x_train, y_train = x, y
+
+        test_set = test_set.as_df()
+        if drop_columns:
+            test_set = dataset.drop(drop_columns, axis=1)
+
+        x_test, y_test = test_set.drop(label_columns, axis=1), test_set[label_columns]
+
+    AutoMLRun.apply_mlrun(
+        model=model,
+        model_name=model_name,
+        context=context,
+        tag=tag,
+        sample_set=sample_set,
+        y_columns=label_columns,
+        test_set=test_set,
+        x_test=x_test,
+        y_test=y_test,
+        artifacts=context.artifacts,
+        labels=labels,
+    )
+    context.logger.info(f"training '{model_name}'")
+    model.fit(x_train, y_train, **fit_kwargs)

+
+
+
[docs]def evaluate(
+    context: MLClientCtx,
+    model: str,
+    dataset: mlrun.DataItem,
+    drop_columns: List[str] = None,
+    label_columns: Optional[Union[str, List[str]]] = None,
+    **kwargs,
+):
+    """
+    Evaluating a model. Artifacts generated by the MLHandler.
+
+    :param context:                 MLRun context.
+    :param model:                   The model Store path.
+    :param dataset:                 The dataset to evaluate the model on. Can be either a URI or a FeatureVector.
+    :param drop_columns:            str or a list of strings that represent the columns to drop.
+    :param label_columns:           The target label(s) of the column(s) in the dataset. for Regression or
+                                    Classification tasks. Mandatory when dataset is not a FeatureVector.
+    :param kwargs:                  Here you can pass keyword arguments to the predict function
+                                    (PREDICT_ prefix is not required).
+    """
+    # Get dataset by URL or by FeatureVector:
+    dataset, label_columns = _get_dataframe(
+        context=context,
+        dataset=dataset,
+        label_columns=label_columns,
+        drop_columns=drop_columns,
+    )
+
+    # Parsing label_columns:
+    parsed_label_columns = []
+    if label_columns:
+        label_columns = (
+            label_columns if isinstance(label_columns, list) else [label_columns]
+        )
+        for lc in label_columns:
+            if fs.common.feature_separator in lc:
+                feature_set_name, label_name, alias = fs.common.parse_feature_string(lc)
+                parsed_label_columns.append(alias or label_name)
+        if parsed_label_columns:
+            label_columns = parsed_label_columns
+
+    x = dataset.drop(label_columns, axis=1)
+    y = dataset[label_columns]
+
+    # Loading the model and predicting:
+    model_handler = AutoMLRun.load_model(
+        model_path=model, context=context, model_name="model_LinearRegression"
+    )
+    AutoMLRun.apply_mlrun(model_handler.model, y_test=y, model_path=model)
+
+    context.logger.info(f"evaluating '{model_handler.model_name}'")
+    model_handler.model.predict(x, **kwargs)

+
+
+
[docs]def predict(
+    context: MLClientCtx,
+    model: str,
+    dataset: mlrun.DataItem,
+    drop_columns: Union[str, List[str], int, List[int]] = None,
+    label_columns: Optional[Union[str, List[str]]] = None,
+    result_set: Optional[str] = None,
+    **kwargs,
+):
+    """
+    Predicting dataset by a model.
+
+    :param context:                 MLRun context.
+    :param model:                   The model Store path.
+    :param dataset:                 The dataset to predict the model on. Can be either a URI, a FeatureVector or a
+                                    sample in a shape of a list/dict.
+                                    When passing a sample, pass the dataset as a field in `params` instead of `inputs`.
+    :param drop_columns:            str/int or a list of strings/ints that represent the column names/indices to drop.
+                                    When the dataset is a list/dict this parameter should be represented by integers.
+    :param label_columns:           The target label(s) of the column(s) in the dataset. for Regression or
+                                    Classification tasks. Mandatory when dataset is not a FeatureVector.
+    :param result_set:              The db key to set name of the prediction result and the filename.
+                                    Default to 'prediction'.
+    :param kwargs:                  Here you can pass keyword arguments to the predict function
+                                    (PREDICT_ prefix is not required).
+    """
+    # Get dataset by URL or by FeatureVector:
+    dataset, label_columns = _get_dataframe(
+        context=context,
+        dataset=dataset,
+        label_columns=label_columns,
+        drop_columns=drop_columns,
+    )
+
+    # loading the model, and getting the model handler:
+    model_handler = AutoMLRun.load_model(model_path=model, context=context)
+
+    # Dropping label columns if necessary:
+    if not label_columns:
+        label_columns = []
+    elif isinstance(label_columns, str):
+        label_columns = [label_columns]
+
+    # Predicting:
+    context.logger.info(f"making prediction by '{model_handler.model_name}'")
+    y_pred = model_handler.model.predict(dataset, **kwargs)
+
+    # Preparing and validating label columns for the dataframe of the prediction result:
+    num_predicted = 1 if len(y_pred.shape) == 1 else y_pred.shape[1]
+
+    if num_predicted > len(label_columns):
+        if num_predicted == 1:
+            label_columns = ["predicted labels"]
+        else:
+            label_columns.extend(
+                [
+                    f"predicted_label_{i + 1 + len(label_columns)}"
+                    for i in range(num_predicted - len(label_columns))
+                ]
+            )
+    elif num_predicted < len(label_columns):
+        context.logger.error(
+            f"number of predicted labels: {num_predicted} is smaller than number of label columns: {len(label_columns)}"
+        )
+        raise ValueError
+
+    artifact_name = result_set or "prediction"
+    labels_inside_df = set(label_columns) & set(dataset.columns.tolist())
+    if labels_inside_df:
+        context.logger.error(
+            f"The labels: {labels_inside_df} are already existed in the dataframe"
+        )
+        raise ValueError
+    pred_df = pd.concat([dataset, pd.DataFrame(y_pred, columns=label_columns)], axis=1)
+    context.log_dataset(artifact_name, pred_df, db_key=result_set)

+
+
+
+
+ +
+
+
+
+
+
+

+ + © Copyright .
+

+
+
+
+
+
+ + + + \ No newline at end of file diff --git a/functions/master/auto_trainer/1.7.0/static/documentation.html b/functions/master/auto_trainer/1.7.0/static/documentation.html new file mode 100644 index 00000000..e545900f --- /dev/null +++ b/functions/master/auto_trainer/1.7.0/static/documentation.html @@ -0,0 +1,339 @@ + + + + + + + +auto_trainer package + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+
+ +
+
+ +
+ +
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+ +
+ +
+ +
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+ +
+
    +
  • + +
    • +
  • + +
    • +
+
+
+ +
+
+ +
+
+ Contents +
+ +
+
+
+
+ +
+

auto_trainer package

+ +
+ +
+
+
+
+
+

auto_trainer package#

+
+

Submodules#

+
+
+

auto_trainer.auto_trainer module#

+
+
+class auto_trainer.auto_trainer.KWArgsPrefixes[source]#
+

Bases: object

+
+
+FIT = 'FIT_'#
+
+
+
+MODEL_CLASS = 'CLASS_'#
+
+
+
+TRAIN = 'TRAIN_'#
+
+
+
+
+auto_trainer.auto_trainer.evaluate(context: mlrun.execution.MLClientCtx, model: str, dataset: mlrun.datastore.base.DataItem, drop_columns: Optional[List[str]] = None, label_columns: Optional[Union[str, List[str]]] = None, **kwargs)[source]#
+

Evaluating a model. Artifacts generated by the MLHandler.

+
+
Parameters
+
    +

  • context – MLRun context.

    • +

  • model – The model Store path.

    • +

  • dataset – The dataset to evaluate the model on. Can be either a URI or a FeatureVector.

    • +

  • drop_columns – str or a list of strings that represent the columns to drop.

    • +

  • label_columns – The target label(s) of the column(s) in the dataset. for Regression or +Classification tasks. Mandatory when dataset is not a FeatureVector.

    • +

  • kwargs – Here you can pass keyword arguments to the predict function +(PREDICT_ prefix is not required).

    • +
+
+
+
+
+
+auto_trainer.auto_trainer.predict(context: mlrun.execution.MLClientCtx, model: str, dataset: mlrun.datastore.base.DataItem, drop_columns: Optional[Union[str, List[str], int, List[int]]] = None, label_columns: Optional[Union[str, List[str]]] = None, result_set: Optional[str] = None, **kwargs)[source]#
+

Predicting dataset by a model.

+
+
Parameters
+
    +

  • context – MLRun context.

    • +

  • model – The model Store path.

    • +

  • dataset – The dataset to predict the model on. Can be either a URI, a FeatureVector or a +sample in a shape of a list/dict. +When passing a sample, pass the dataset as a field in params instead of inputs.

    • +

  • drop_columns – str/int or a list of strings/ints that represent the column names/indices to drop. +When the dataset is a list/dict this parameter should be represented by integers.

    • +

  • label_columns – The target label(s) of the column(s) in the dataset. for Regression or +Classification tasks. Mandatory when dataset is not a FeatureVector.

    • +

  • result_set – The db key to set name of the prediction result and the filename. +Default to ‘prediction’.

    • +

  • kwargs – Here you can pass keyword arguments to the predict function +(PREDICT_ prefix is not required).

    • +
+
+
+
+
+
+auto_trainer.auto_trainer.train(context: mlrun.execution.MLClientCtx, dataset: mlrun.datastore.base.DataItem, model_class: str, label_columns: Optional[Union[str, List[str]]] = None, drop_columns: Optional[List[str]] = None, model_name: str = 'model', tag: str = '', sample_set: Optional[mlrun.datastore.base.DataItem] = None, test_set: Optional[mlrun.datastore.base.DataItem] = None, train_test_split_size: Optional[float] = None, random_state: Optional[int] = None, labels: Optional[dict] = None, **kwargs)[source]#
+

Training a model with the given dataset.

+

example:

+
import mlrun
+project = mlrun.get_or_create_project("my-project")
+project.set_function("hub://auto_trainer", "train")
+trainer_run = project.run(
+    name="train",
+    handler="train",
+    inputs={"dataset": "./path/to/dataset.csv"},
+    params={
+        "model_class": "sklearn.linear_model.LogisticRegression",
+        "label_columns": "label",
+        "drop_columns": "id",
+        "model_name": "my-model",
+        "tag": "v1.0.0",
+        "sample_set": "./path/to/sample_set.csv",
+        "test_set": "./path/to/test_set.csv",
+        "CLASS_solver": "liblinear",
+    },
+)
+
+
+
+
Parameters
+
    +

  • context – MLRun context

    • +

  • dataset – The dataset to train the model on. Can be either a URI or a FeatureVector

    • +

  • model_class – The class of the model, e.g. sklearn.linear_model.LogisticRegression

    • +

  • label_columns – The target label(s) of the column(s) in the dataset. for Regression or +Classification tasks. Mandatory when dataset is not a FeatureVector.

    • +

  • drop_columns – str or a list of strings that represent the columns to drop

    • +

  • model_name – The model’s name to use for storing the model artifact, default to ‘model’

    • +

  • tag – The model’s tag to log with

    • +

  • sample_set – A sample set of inputs for the model for logging its stats along the model in favour +of model monitoring. Can be either a URI or a FeatureVector

    • +

  • test_set – The test set to train the model with.

    • +

  • train_test_split_size – if test_set was provided then this argument is ignored. +Should be between 0.0 and 1.0 and represent the proportion of the dataset to include +in the test split. The size of the Training set is set to the complement of this +value. Default = 0.2

    • +

  • random_state – Relevant only when using train_test_split_size. +A random state seed to shuffle the data. For more information, see: +https://scikit-learn.org/stable/glossary.html#term-random_state +Notice that here we only pass integer values.

    • +

  • labels – Labels to log with the model

    • +

  • kwargs – Here you can pass keyword arguments with prefixes, +that will be parsed and passed to the relevant function, by the following prefixes: +- CLASS_ - for the model class arguments +- FIT_ - for the fit function arguments +- TRAIN_ - for the train function (in xgb or lgbm train function - future)

    • +
+
+
+
+
+
+

Module contents#

+
+
+
+
+
+ +
+
+
+
+
+
+

+ + © Copyright .
+

+
+
+
+
+
+ + + + \ No newline at end of file diff --git a/functions/master/auto_trainer/1.7.0/static/example.html b/functions/master/auto_trainer/1.7.0/static/example.html new file mode 100644 index 00000000..35ef8da5 --- /dev/null +++ b/functions/master/auto_trainer/1.7.0/static/example.html @@ -0,0 +1,541 @@ + + + + + + + +MLRun Auto-Trainer Tutorial + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+
+ +
+
+ +
+ +
+
+
+
+
+
+ +
+ +
+ +
+
+
+ +
+ +
+
+ +
+
+ +
+
+ Contents +
+ +
+
+
+
+ +
+

MLRun Auto-Trainer Tutorial

+ +
+ +
+
+
+
+
+

MLRun Auto-Trainer Tutorial#

+

This notebook shows how to use the handlers of the MLRun’s Auto-trainer. +the following handlers are:

+
    +

  • train

    • +

  • evaluate

    • +

  • predict

    • +
+

All you need is simply ML model type and a dataset.

+
+
+
import mlrun
+
+
+
+
+
+
+
mlrun.get_or_create_project('auto-trainer', context="./", user_project=True)
+
+
+
+
+
+

Fetching a Dataset#

+

To generate the dataset we used the “gen_class_data” function from the hub, +which wraps scikit-learn’s make_classification.
+See the link for a description of all parameters.

+
+
+
DATASET_URL = 'https://s3.wasabisys.com/iguazio/data/function-marketplace-data/xgb_trainer/classifier-data.csv'
+
+
+
+
+
+
+
mlrun.get_dataitem(DATASET_URL).show()
+
+
+
+
+
+
+

Importing the MLhandlers functions from the Marketplace#

+
+
+
auto_trainer = mlrun.import_function("hub://auto_trainer")
+
+
+
+
+
+
+

Training a model#

+

Choosing the train handler

+
+

Define task parameters¶#

+
    +

  • Class parameters should contain the prefix CLASS_

    • +

  • Fit parameters should contain the prefix FIT_

    • +

  • Predict parameters should contain the prefix PREDICT_

    • +
+
+
+
model_class = "sklearn.ensemble.RandomForestClassifier"
+additional_parameters = {
+    "CLASS_max_depth": 8,
+}
+
+
+
+
+
+
+

Running the Training job with the “train” handler#

+
+
+
train_run = auto_trainer.run(
+    inputs={"dataset": DATASET_URL},
+    params = {
+        "model_class": model_class,
+        "drop_columns": ["feat_0", "feat_2"],
+        "train_test_split_size": 0.2,
+        "random_state": 42,
+        "label_columns": "labels",
+        "model_name": 'MyModel',
+        **additional_parameters
+    }, 
+    handler='train',
+    local=True
+)
+
+
+
+
+
+
+

The result of the train run#

+
+
+
train_run.outputs
+
+
+
+
+
+
+
train_run.artifact('confusion-matrix').show()
+
+
+
+
+
+
+

Getting the model for evaluating and predicting#

+
+
+
model_path = train_run.outputs['model']
+
+
+
+
+
+
+
+

Evaluating a model#

+

Choosing the evaluate handler

+
+
+
evaluate_run = auto_trainer.run(
+    inputs={"dataset": train_run.outputs['test_set']},
+    params={
+        "model": model_path,
+        "drop_columns": ["feat_0", "feat_2"], # Not actually necessary on the test set (already done in the previous step)
+        "label_columns": "labels",
+    },
+    handler="evaluate",
+    local=True,
+)
+
+
+
+
+
+

The result of the evaluate run#

+
+
+
evaluate_run.outputs
+
+
+
+
+
+
+
+

Making a prediction#

+

Choosing the predict handler. For predicting from a simple sample (a list of lists,dict) pass the dataset as a param.

+
+
+
sample = mlrun.get_dataitem(DATASET_URL).as_df().head().drop("labels", axis=1)
+
+
+
+
+
+
+
sample = sample.values.tolist()
+
+
+
+
+
+
+
predict_run = auto_trainer.run(
+    params={
+        "dataset": sample,
+        "model": model_path,
+        "drop_columns": [0, 2],
+        "label_columns": "labels",
+    },
+    handler="predict",
+    local=True,
+)
+
+
+
+
+
+

Showing the predeiction results#

+
+
+
predict_run.outputs
+
+
+
+
+
+
+
predict_run.artifact('prediction').show()
+
+
+
+
+

Back to the top

+
+
+
+
+
+
+ +
+
+
+
+
+
+

+ + © Copyright .
+

+
+
+
+
+
+ + + + \ No newline at end of file diff --git a/functions/master/auto_trainer/1.7.0/static/function.html b/functions/master/auto_trainer/1.7.0/static/function.html new file mode 100644 index 00000000..aed22593 --- /dev/null +++ b/functions/master/auto_trainer/1.7.0/static/function.html @@ -0,0 +1,186 @@ + + + + + + + + + + + Source + + + + + 
+        
+kind: job
+metadata:
+  name: auto-trainer
+  tag: ''
+  hash: 1c415e6d3bd79c9ca0ee537e008643660c13fbc7
+  project: ''
+  labels:
+    author: yonish
+  categories:
+  - machine-learning
+  - model-training
+spec:
+  command: ''
+  args: []
+  image: mlrun/mlrun
+  build:
+    functionSourceCode: # Copyright 2019 Iguazio
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
from pathlib import Path
from typing import Any, Dict, List, Optional, Tuple, Union

import mlrun
import mlrun.datastore
import mlrun.utils
import pandas as pd
from mlrun import feature_store as fs
from mlrun.datastore import DataItem
from mlrun.execution import MLClientCtx
from mlrun.frameworks.auto_mlrun import AutoMLRun
from mlrun.utils.helpers import create_class, create_function
from sklearn.model_selection import train_test_split

PathType = Union[str, Path]


class KWArgsPrefixes:
    MODEL_CLASS = "CLASS_"
    FIT = "FIT_"
    TRAIN = "TRAIN_"


def _get_sub_dict_by_prefix(src: Dict, prefix_key: str) -> Dict[str, Any]:
    """
    Collect all the keys from the given dict that starts with the given prefix and creates a new dictionary with these
    keys.

    :param src:         The source dict to extract the values from.
    :param prefix_key:  Only keys with this prefix will be returned. The keys in the result dict will be without this
                        prefix.
    """
    return {
        key.replace(prefix_key, ""): val
        for key, val in src.items()
        if key.startswith(prefix_key)
    }


def _get_dataframe(
    context: MLClientCtx,
    dataset: DataItem,
    label_columns: Optional[Union[str, List[str]]] = None,
    drop_columns: Union[str, List[str], int, List[int]] = None,
) -> Tuple[pd.DataFrame, Optional[Union[str, List[str]]]]:
    """
    Getting the DataFrame of the dataset and drop the columns accordingly.

    :param context:         MLRun context.
    :param dataset:         The dataset to train the model on.
                            Can be either a list of lists, dict, URI or a FeatureVector.
    :param label_columns:   The target label(s) of the column(s) in the dataset. for Regression or
                            Classification tasks.
    :param drop_columns:    str/int or a list of strings/ints that represent the column names/indices to drop.
    """
    store_uri_prefix, _ = mlrun.datastore.parse_store_uri(dataset.artifact_url)

    # Getting the dataset:
    if mlrun.utils.StorePrefix.FeatureVector == store_uri_prefix:
        label_columns = label_columns or dataset.meta.status.label_column
        context.logger.info(f"label columns: {label_columns}")
        # FeatureVector case:
        try:
            fv = mlrun.datastore.get_store_resource(dataset.artifact_url)
            dataset = fv.get_offline_features(drop_columns=drop_columns).to_dataframe()
        except AttributeError:
            # Leave here for backwards compatibility
            dataset = fs.get_offline_features(
                dataset.meta.uri, drop_columns=drop_columns
            ).to_dataframe()

    elif not label_columns:
        context.logger.info(
            "label_columns not provided, mandatory when dataset is not a FeatureVector"
        )
        raise ValueError

    elif isinstance(dataset, (list, dict)):
        # list/dict case:
        dataset = pd.DataFrame(dataset)
        # Checking if drop_columns provided by integer type:
        if drop_columns:
            if isinstance(drop_columns, str) or (
                isinstance(drop_columns, list)
                and any(isinstance(col, str) for col in drop_columns)
            ):
                context.logger.error(
                    "drop_columns must be an integer/list of integers if not provided with a URI/FeatureVector dataset"
                )
                raise ValueError
            dataset.drop(drop_columns, axis=1, inplace=True)

    else:
        # simple URL case:
        dataset = dataset.as_df()
        if drop_columns:
            if all(col in dataset for col in drop_columns):
                dataset = dataset.drop(drop_columns, axis=1)
            else:
                context.logger.info(
                    "not all of the columns to drop in the dataset, drop columns process skipped"
                )

    return dataset, label_columns


def train(
    context: MLClientCtx,
    dataset: DataItem,
    model_class: str,
    label_columns: Optional[Union[str, List[str]]] = None,
    drop_columns: List[str] = None,
    model_name: str = "model",
    tag: str = "",
    sample_set: DataItem = None,
    test_set: DataItem = None,
    train_test_split_size: float = None,
    random_state: int = None,
    labels: dict = None,
    **kwargs,
):
    """
    Training a model with the given dataset.

    example::

        import mlrun
        project = mlrun.get_or_create_project("my-project")
        project.set_function("hub://auto_trainer", "train")
        trainer_run = project.run(
            name="train",
            handler="train",
            inputs={"dataset": "./path/to/dataset.csv"},
            params={
                "model_class": "sklearn.linear_model.LogisticRegression",
                "label_columns": "label",
                "drop_columns": "id",
                "model_name": "my-model",
                "tag": "v1.0.0",
                "sample_set": "./path/to/sample_set.csv",
                "test_set": "./path/to/test_set.csv",
                "CLASS_solver": "liblinear",
            },
        )

    :param context:                 MLRun context
    :param dataset:                 The dataset to train the model on. Can be either a URI or a FeatureVector
    :param model_class:             The class of the model, e.g. `sklearn.linear_model.LogisticRegression`
    :param label_columns:           The target label(s) of the column(s) in the dataset. for Regression or
                                    Classification tasks. Mandatory when dataset is not a FeatureVector.
    :param drop_columns:            str or a list of strings that represent the columns to drop
    :param model_name:              The model's name to use for storing the model artifact, default to 'model'
    :param tag:                     The model's tag to log with
    :param sample_set:              A sample set of inputs for the model for logging its stats along the model in favour
                                    of model monitoring. Can be either a URI or a FeatureVector
    :param test_set:                The test set to train the model with.
    :param train_test_split_size:   if test_set was provided then this argument is ignored.
                                    Should be between 0.0 and 1.0 and represent the proportion of the dataset to include
                                    in the test split. The size of the Training set is set to the complement of this
                                    value. Default = 0.2
    :param random_state:            Relevant only when using train_test_split_size.
                                    A random state seed to shuffle the data. For more information, see:
                                    https://scikit-learn.org/stable/glossary.html#term-random_state
                                    Notice that here we only pass integer values.
    :param labels:                  Labels to log with the model
    :param kwargs:                  Here you can pass keyword arguments with prefixes,
                                    that will be parsed and passed to the relevant function, by the following prefixes:
                                    - `CLASS_` - for the model class arguments
                                    - `FIT_` - for the `fit` function arguments
                                    - `TRAIN_` - for the `train` function (in xgb or lgbm train function - future)

    """
    # Validate inputs:
    # Check if exactly one of them is supplied:
    if test_set is None:
        if train_test_split_size is None:
            context.logger.info(
                "test_set or train_test_split_size are not provided, setting train_test_split_size to 0.2"
            )
            train_test_split_size = 0.2

    elif train_test_split_size:
        context.logger.info(
            "test_set provided, ignoring given train_test_split_size value"
        )
        train_test_split_size = None

    # Get DataFrame by URL or by FeatureVector:
    dataset, label_columns = _get_dataframe(
        context=context,
        dataset=dataset,
        label_columns=label_columns,
        drop_columns=drop_columns,
    )

    # Getting the sample set:
    if sample_set is None:
        context.logger.info(
            f"Sample set not given, using the whole training set as the sample set"
        )
        sample_set = dataset
    else:
        sample_set, _ = _get_dataframe(
            context=context,
            dataset=sample_set,
            label_columns=label_columns,
            drop_columns=drop_columns,
        )

    # Parsing kwargs:
    # TODO: Use in xgb or lgbm train function.
    train_kwargs = _get_sub_dict_by_prefix(src=kwargs, prefix_key=KWArgsPrefixes.TRAIN)
    fit_kwargs = _get_sub_dict_by_prefix(src=kwargs, prefix_key=KWArgsPrefixes.FIT)
    model_class_kwargs = _get_sub_dict_by_prefix(
        src=kwargs, prefix_key=KWArgsPrefixes.MODEL_CLASS
    )

    # Check if model or function:
    if hasattr(model_class, "train"):
        # TODO: Need to call: model(), afterwards to start the train function.
        # model = create_function(f"{model_class}.train")
        raise NotImplementedError
    else:
        # Creating model instance:
        model = create_class(model_class)(**model_class_kwargs)

    x = dataset.drop(label_columns, axis=1)
    y = dataset[label_columns]
    if train_test_split_size:
        x_train, x_test, y_train, y_test = train_test_split(
            x, y, test_size=train_test_split_size, random_state=random_state
        )
    else:
        x_train, y_train = x, y

        test_set = test_set.as_df()
        if drop_columns:
            test_set = dataset.drop(drop_columns, axis=1)

        x_test, y_test = test_set.drop(label_columns, axis=1), test_set[label_columns]

    AutoMLRun.apply_mlrun(
        model=model,
        model_name=model_name,
        context=context,
        tag=tag,
        sample_set=sample_set,
        y_columns=label_columns,
        test_set=test_set,
        x_test=x_test,
        y_test=y_test,
        artifacts=context.artifacts,
        labels=labels,
    )
    context.logger.info(f"training '{model_name}'")
    model.fit(x_train, y_train, **fit_kwargs)


def evaluate(
    context: MLClientCtx,
    model: str,
    dataset: mlrun.DataItem,
    drop_columns: List[str] = None,
    label_columns: Optional[Union[str, List[str]]] = None,
    **kwargs,
):
    """
    Evaluating a model. Artifacts generated by the MLHandler.

    :param context:                 MLRun context.
    :param model:                   The model Store path.
    :param dataset:                 The dataset to evaluate the model on. Can be either a URI or a FeatureVector.
    :param drop_columns:            str or a list of strings that represent the columns to drop.
    :param label_columns:           The target label(s) of the column(s) in the dataset. for Regression or
                                    Classification tasks. Mandatory when dataset is not a FeatureVector.
    :param kwargs:                  Here you can pass keyword arguments to the predict function
                                    (PREDICT_ prefix is not required).
    """
    # Get dataset by URL or by FeatureVector:
    dataset, label_columns = _get_dataframe(
        context=context,
        dataset=dataset,
        label_columns=label_columns,
        drop_columns=drop_columns,
    )

    # Parsing label_columns:
    parsed_label_columns = []
    if label_columns:
        label_columns = (
            label_columns if isinstance(label_columns, list) else [label_columns]
        )
        for lc in label_columns:
            if fs.common.feature_separator in lc:
                feature_set_name, label_name, alias = fs.common.parse_feature_string(lc)
                parsed_label_columns.append(alias or label_name)
        if parsed_label_columns:
            label_columns = parsed_label_columns

    x = dataset.drop(label_columns, axis=1)
    y = dataset[label_columns]

    # Loading the model and predicting:
    model_handler = AutoMLRun.load_model(
        model_path=model, context=context, model_name="model_LinearRegression"
    )
    AutoMLRun.apply_mlrun(model_handler.model, y_test=y, model_path=model)

    context.logger.info(f"evaluating '{model_handler.model_name}'")
    model_handler.model.predict(x, **kwargs)


def predict(
    context: MLClientCtx,
    model: str,
    dataset: mlrun.DataItem,
    drop_columns: Union[str, List[str], int, List[int]] = None,
    label_columns: Optional[Union[str, List[str]]] = None,
    result_set: Optional[str] = None,
    **kwargs,
):
    """
    Predicting dataset by a model.

    :param context:                 MLRun context.
    :param model:                   The model Store path.
    :param dataset:                 The dataset to predict the model on. Can be either a URI, a FeatureVector or a
                                    sample in a shape of a list/dict.
                                    When passing a sample, pass the dataset as a field in `params` instead of `inputs`.
    :param drop_columns:            str/int or a list of strings/ints that represent the column names/indices to drop.
                                    When the dataset is a list/dict this parameter should be represented by integers.
    :param label_columns:           The target label(s) of the column(s) in the dataset. for Regression or
                                    Classification tasks. Mandatory when dataset is not a FeatureVector.
    :param result_set:              The db key to set name of the prediction result and the filename.
                                    Default to 'prediction'.
    :param kwargs:                  Here you can pass keyword arguments to the predict function
                                    (PREDICT_ prefix is not required).
    """
    # Get dataset by URL or by FeatureVector:
    dataset, label_columns = _get_dataframe(
        context=context,
        dataset=dataset,
        label_columns=label_columns,
        drop_columns=drop_columns,
    )

    # loading the model, and getting the model handler:
    model_handler = AutoMLRun.load_model(model_path=model, context=context)

    # Dropping label columns if necessary:
    if not label_columns:
        label_columns = []
    elif isinstance(label_columns, str):
        label_columns = [label_columns]

    # Predicting:
    context.logger.info(f"making prediction by '{model_handler.model_name}'")
    y_pred = model_handler.model.predict(dataset, **kwargs)

    # Preparing and validating label columns for the dataframe of the prediction result:
    num_predicted = 1 if len(y_pred.shape) == 1 else y_pred.shape[1]

    if num_predicted > len(label_columns):
        if num_predicted == 1:
            label_columns = ["predicted labels"]
        else:
            label_columns.extend(
                [
                    f"predicted_label_{i + 1 + len(label_columns)}"
                    for i in range(num_predicted - len(label_columns))
                ]
            )
    elif num_predicted < len(label_columns):
        context.logger.error(
            f"number of predicted labels: {num_predicted} is smaller than number of label columns: {len(label_columns)}"
        )
        raise ValueError

    artifact_name = result_set or "prediction"
    labels_inside_df = set(label_columns) & set(dataset.columns.tolist())
    if labels_inside_df:
        context.logger.error(
            f"The labels: {labels_inside_df} are already existed in the dataframe"
        )
        raise ValueError
    pred_df = pd.concat([dataset, pd.DataFrame(y_pred, columns=label_columns)], axis=1)
    context.log_dataset(artifact_name, pred_df, db_key=result_set)

+    commands: []
+    code_origin: ''
+    origin_filename: ''
+    requirements: []
+  entry_points:
+    train:
+      name: train
+      doc: "Training a model with the given dataset.\n\nexample::\n\n    import mlrun\n\
+        \    project = mlrun.get_or_create_project(\"my-project\")\n    project.set_function(\"\
+        hub://auto_trainer\", \"train\")\n    trainer_run = project.run(\n       \
+        \ name=\"train\",\n        handler=\"train\",\n        inputs={\"dataset\"\
+        : \"./path/to/dataset.csv\"},\n        params={\n            \"model_class\"\
+        : \"sklearn.linear_model.LogisticRegression\",\n            \"label_columns\"\
+        : \"label\",\n            \"drop_columns\": \"id\",\n            \"model_name\"\
+        : \"my-model\",\n            \"tag\": \"v1.0.0\",\n            \"sample_set\"\
+        : \"./path/to/sample_set.csv\",\n            \"test_set\": \"./path/to/test_set.csv\"\
+        ,\n            \"CLASS_solver\": \"liblinear\",\n        },\n    )"
+      parameters:
+      - name: context
+        type: MLClientCtx
+        doc: MLRun context
+      - name: dataset
+        type: DataItem
+        doc: The dataset to train the model on. Can be either a URI or a FeatureVector
+      - name: model_class
+        type: str
+        doc: The class of the model, e.g. `sklearn.linear_model.LogisticRegression`
+      - name: label_columns
+        type: Optional[Union[str, List[str]]]
+        doc: The target label(s) of the column(s) in the dataset. for Regression or
+          Classification tasks. Mandatory when dataset is not a FeatureVector.
+        default: null
+      - name: drop_columns
+        type: List[str]
+        doc: str or a list of strings that represent the columns to drop
+        default: null
+      - name: model_name
+        type: str
+        doc: The model's name to use for storing the model artifact, default to 'model'
+        default: model
+      - name: tag
+        type: str
+        doc: The model's tag to log with
+        default: ''
+      - name: sample_set
+        type: DataItem
+        doc: A sample set of inputs for the model for logging its stats along the
+          model in favour of model monitoring. Can be either a URI or a FeatureVector
+        default: null
+      - name: test_set
+        type: DataItem
+        doc: The test set to train the model with.
+        default: null
+      - name: train_test_split_size
+        type: float
+        doc: if test_set was provided then this argument is ignored. Should be between
+          0.0 and 1.0 and represent the proportion of the dataset to include in the
+          test split. The size of the Training set is set to the complement of this
+          value. Default = 0.2
+        default: null
+      - name: random_state
+        type: int
+        doc: 'Relevant only when using train_test_split_size. A random state seed
+          to shuffle the data. For more information, see: https://scikit-learn.org/stable/glossary.html#term-random_state
+          Notice that here we only pass integer values.'
+        default: null
+      - name: labels
+        type: dict
+        doc: Labels to log with the model
+        default: null
+      outputs: []
+      lineno: 121
+      has_varargs: false
+      has_kwargs: true
+    evaluate:
+      name: evaluate
+      doc: Evaluating a model. Artifacts generated by the MLHandler.
+      parameters:
+      - name: context
+        type: MLClientCtx
+        doc: MLRun context.
+      - name: model
+        type: str
+        doc: The model Store path.
+      - name: dataset
+        type: DataItem
+        doc: The dataset to evaluate the model on. Can be either a URI or a FeatureVector.
+      - name: drop_columns
+        type: List[str]
+        doc: str or a list of strings that represent the columns to drop.
+        default: null
+      - name: label_columns
+        type: Optional[Union[str, List[str]]]
+        doc: The target label(s) of the column(s) in the dataset. for Regression or
+          Classification tasks. Mandatory when dataset is not a FeatureVector.
+        default: null
+      outputs: []
+      lineno: 273
+      has_varargs: false
+      has_kwargs: true
+    predict:
+      name: predict
+      doc: Predicting dataset by a model.
+      parameters:
+      - name: context
+        type: MLClientCtx
+        doc: MLRun context.
+      - name: model
+        type: str
+        doc: The model Store path.
+      - name: dataset
+        type: DataItem
+        doc: The dataset to predict the model on. Can be either a URI, a FeatureVector
+          or a sample in a shape of a list/dict. When passing a sample, pass the dataset
+          as a field in `params` instead of `inputs`.
+      - name: drop_columns
+        type: Union[str, List[str], int, List[int]]
+        doc: str/int or a list of strings/ints that represent the column names/indices
+          to drop. When the dataset is a list/dict this parameter should be represented
+          by integers.
+        default: null
+      - name: label_columns
+        type: Optional[Union[str, List[str]]]
+        doc: The target label(s) of the column(s) in the dataset. for Regression or
+          Classification tasks. Mandatory when dataset is not a FeatureVector.
+        default: null
+      - name: result_set
+        type: Optional[str]
+        doc: The db key to set name of the prediction result and the filename. Default
+          to 'prediction'.
+        default: null
+      outputs: []
+      lineno: 327
+      has_varargs: false
+      has_kwargs: true
+  description: Automatic train, evaluate and predict functions for the ML frameworks
+    - Scikit-Learn, XGBoost and LightGBM.
+  default_handler: train
+  disable_auto_mount: false
+  clone_target_dir: ''
+  env: []
+  priority_class_name: ''
+  preemption_mode: prevent
+  affinity: null
+  tolerations: null
+  security_context: {}
+verbose: false
+
+        
+
+ + \ No newline at end of file diff --git a/functions/master/auto_trainer/1.7.0/static/item.html b/functions/master/auto_trainer/1.7.0/static/item.html new file mode 100644 index 00000000..c11f37e4 --- /dev/null +++ b/functions/master/auto_trainer/1.7.0/static/item.html @@ -0,0 +1,48 @@ + + + + + + + + + + + Source + + + + + 
+        
+apiVersion: v1
+categories:
+- machine-learning
+- model-training
+description: Automatic train, evaluate and predict functions for the ML frameworks
+  - Scikit-Learn, XGBoost and LightGBM.
+doc: ''
+example: auto_trainer.ipynb
+generationDate: 2022-08-28:17-25
+hidden: false
+icon: ''
+labels:
+  author: yonish
+maintainers: []
+marketplaceType: ''
+mlrunVersion: 1.5.2
+name: auto_trainer
+platformVersion: 3.5.0
+spec:
+  filename: auto_trainer.py
+  handler: train
+  image: mlrun/mlrun
+  kind: job
+  requirements: []
+url: ''
+version: 1.7.0
+
+        
+
+ + \ No newline at end of file diff --git a/functions/master/auto_trainer/1.7.0/static/source.html b/functions/master/auto_trainer/1.7.0/static/source.html new file mode 100644 index 00000000..1e132975 --- /dev/null +++ b/functions/master/auto_trainer/1.7.0/static/source.html @@ -0,0 +1,423 @@ + + + + + + + + + + + Source + + + + + 
+        
+# Copyright 2019 Iguazio
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+from pathlib import Path
+from typing import Any, Dict, List, Optional, Tuple, Union
+
+import mlrun
+import mlrun.datastore
+import mlrun.utils
+import pandas as pd
+from mlrun import feature_store as fs
+from mlrun.datastore import DataItem
+from mlrun.execution import MLClientCtx
+from mlrun.frameworks.auto_mlrun import AutoMLRun
+from mlrun.utils.helpers import create_class, create_function
+from sklearn.model_selection import train_test_split
+
+PathType = Union[str, Path]
+
+
+class KWArgsPrefixes:
+    MODEL_CLASS = "CLASS_"
+    FIT = "FIT_"
+    TRAIN = "TRAIN_"
+
+
+def _get_sub_dict_by_prefix(src: Dict, prefix_key: str) -> Dict[str, Any]:
+    """
+    Collect all the keys from the given dict that starts with the given prefix and creates a new dictionary with these
+    keys.
+
+    :param src:         The source dict to extract the values from.
+    :param prefix_key:  Only keys with this prefix will be returned. The keys in the result dict will be without this
+                        prefix.
+    """
+    return {
+        key.replace(prefix_key, ""): val
+        for key, val in src.items()
+        if key.startswith(prefix_key)
+    }
+
+
+def _get_dataframe(
+    context: MLClientCtx,
+    dataset: DataItem,
+    label_columns: Optional[Union[str, List[str]]] = None,
+    drop_columns: Union[str, List[str], int, List[int]] = None,
+) -> Tuple[pd.DataFrame, Optional[Union[str, List[str]]]]:
+    """
+    Getting the DataFrame of the dataset and drop the columns accordingly.
+
+    :param context:         MLRun context.
+    :param dataset:         The dataset to train the model on.
+                            Can be either a list of lists, dict, URI or a FeatureVector.
+    :param label_columns:   The target label(s) of the column(s) in the dataset. for Regression or
+                            Classification tasks.
+    :param drop_columns:    str/int or a list of strings/ints that represent the column names/indices to drop.
+    """
+    store_uri_prefix, _ = mlrun.datastore.parse_store_uri(dataset.artifact_url)
+
+    # Getting the dataset:
+    if mlrun.utils.StorePrefix.FeatureVector == store_uri_prefix:
+        label_columns = label_columns or dataset.meta.status.label_column
+        context.logger.info(f"label columns: {label_columns}")
+        # FeatureVector case:
+        try:
+            fv = mlrun.datastore.get_store_resource(dataset.artifact_url)
+            dataset = fv.get_offline_features(drop_columns=drop_columns).to_dataframe()
+        except AttributeError:
+            # Leave here for backwards compatibility
+            dataset = fs.get_offline_features(
+                dataset.meta.uri, drop_columns=drop_columns
+            ).to_dataframe()
+
+    elif not label_columns:
+        context.logger.info(
+            "label_columns not provided, mandatory when dataset is not a FeatureVector"
+        )
+        raise ValueError
+
+    elif isinstance(dataset, (list, dict)):
+        # list/dict case:
+        dataset = pd.DataFrame(dataset)
+        # Checking if drop_columns provided by integer type:
+        if drop_columns:
+            if isinstance(drop_columns, str) or (
+                isinstance(drop_columns, list)
+                and any(isinstance(col, str) for col in drop_columns)
+            ):
+                context.logger.error(
+                    "drop_columns must be an integer/list of integers if not provided with a URI/FeatureVector dataset"
+                )
+                raise ValueError
+            dataset.drop(drop_columns, axis=1, inplace=True)
+
+    else:
+        # simple URL case:
+        dataset = dataset.as_df()
+        if drop_columns:
+            if all(col in dataset for col in drop_columns):
+                dataset = dataset.drop(drop_columns, axis=1)
+            else:
+                context.logger.info(
+                    "not all of the columns to drop in the dataset, drop columns process skipped"
+                )
+
+    return dataset, label_columns
+
+
+def train(
+    context: MLClientCtx,
+    dataset: DataItem,
+    model_class: str,
+    label_columns: Optional[Union[str, List[str]]] = None,
+    drop_columns: List[str] = None,
+    model_name: str = "model",
+    tag: str = "",
+    sample_set: DataItem = None,
+    test_set: DataItem = None,
+    train_test_split_size: float = None,
+    random_state: int = None,
+    labels: dict = None,
+    **kwargs,
+):
+    """
+    Training a model with the given dataset.
+
+    example::
+
+        import mlrun
+        project = mlrun.get_or_create_project("my-project")
+        project.set_function("hub://auto_trainer", "train")
+        trainer_run = project.run(
+            name="train",
+            handler="train",
+            inputs={"dataset": "./path/to/dataset.csv"},
+            params={
+                "model_class": "sklearn.linear_model.LogisticRegression",
+                "label_columns": "label",
+                "drop_columns": "id",
+                "model_name": "my-model",
+                "tag": "v1.0.0",
+                "sample_set": "./path/to/sample_set.csv",
+                "test_set": "./path/to/test_set.csv",
+                "CLASS_solver": "liblinear",
+            },
+        )
+
+    :param context:                 MLRun context
+    :param dataset:                 The dataset to train the model on. Can be either a URI or a FeatureVector
+    :param model_class:             The class of the model, e.g. `sklearn.linear_model.LogisticRegression`
+    :param label_columns:           The target label(s) of the column(s) in the dataset. for Regression or
+                                    Classification tasks. Mandatory when dataset is not a FeatureVector.
+    :param drop_columns:            str or a list of strings that represent the columns to drop
+    :param model_name:              The model's name to use for storing the model artifact, default to 'model'
+    :param tag:                     The model's tag to log with
+    :param sample_set:              A sample set of inputs for the model for logging its stats along the model in favour
+                                    of model monitoring. Can be either a URI or a FeatureVector
+    :param test_set:                The test set to train the model with.
+    :param train_test_split_size:   if test_set was provided then this argument is ignored.
+                                    Should be between 0.0 and 1.0 and represent the proportion of the dataset to include
+                                    in the test split. The size of the Training set is set to the complement of this
+                                    value. Default = 0.2
+    :param random_state:            Relevant only when using train_test_split_size.
+                                    A random state seed to shuffle the data. For more information, see:
+                                    https://scikit-learn.org/stable/glossary.html#term-random_state
+                                    Notice that here we only pass integer values.
+    :param labels:                  Labels to log with the model
+    :param kwargs:                  Here you can pass keyword arguments with prefixes,
+                                    that will be parsed and passed to the relevant function, by the following prefixes:
+                                    - `CLASS_` - for the model class arguments
+                                    - `FIT_` - for the `fit` function arguments
+                                    - `TRAIN_` - for the `train` function (in xgb or lgbm train function - future)
+
+    """
+    # Validate inputs:
+    # Check if exactly one of them is supplied:
+    if test_set is None:
+        if train_test_split_size is None:
+            context.logger.info(
+                "test_set or train_test_split_size are not provided, setting train_test_split_size to 0.2"
+            )
+            train_test_split_size = 0.2
+
+    elif train_test_split_size:
+        context.logger.info(
+            "test_set provided, ignoring given train_test_split_size value"
+        )
+        train_test_split_size = None
+
+    # Get DataFrame by URL or by FeatureVector:
+    dataset, label_columns = _get_dataframe(
+        context=context,
+        dataset=dataset,
+        label_columns=label_columns,
+        drop_columns=drop_columns,
+    )
+
+    # Getting the sample set:
+    if sample_set is None:
+        context.logger.info(
+            f"Sample set not given, using the whole training set as the sample set"
+        )
+        sample_set = dataset
+    else:
+        sample_set, _ = _get_dataframe(
+            context=context,
+            dataset=sample_set,
+            label_columns=label_columns,
+            drop_columns=drop_columns,
+        )
+
+    # Parsing kwargs:
+    # TODO: Use in xgb or lgbm train function.
+    train_kwargs = _get_sub_dict_by_prefix(src=kwargs, prefix_key=KWArgsPrefixes.TRAIN)
+    fit_kwargs = _get_sub_dict_by_prefix(src=kwargs, prefix_key=KWArgsPrefixes.FIT)
+    model_class_kwargs = _get_sub_dict_by_prefix(
+        src=kwargs, prefix_key=KWArgsPrefixes.MODEL_CLASS
+    )
+
+    # Check if model or function:
+    if hasattr(model_class, "train"):
+        # TODO: Need to call: model(), afterwards to start the train function.
+        # model = create_function(f"{model_class}.train")
+        raise NotImplementedError
+    else:
+        # Creating model instance:
+        model = create_class(model_class)(**model_class_kwargs)
+
+    x = dataset.drop(label_columns, axis=1)
+    y = dataset[label_columns]
+    if train_test_split_size:
+        x_train, x_test, y_train, y_test = train_test_split(
+            x, y, test_size=train_test_split_size, random_state=random_state
+        )
+    else:
+        x_train, y_train = x, y
+
+        test_set = test_set.as_df()
+        if drop_columns:
+            test_set = dataset.drop(drop_columns, axis=1)
+
+        x_test, y_test = test_set.drop(label_columns, axis=1), test_set[label_columns]
+
+    AutoMLRun.apply_mlrun(
+        model=model,
+        model_name=model_name,
+        context=context,
+        tag=tag,
+        sample_set=sample_set,
+        y_columns=label_columns,
+        test_set=test_set,
+        x_test=x_test,
+        y_test=y_test,
+        artifacts=context.artifacts,
+        labels=labels,
+    )
+    context.logger.info(f"training '{model_name}'")
+    model.fit(x_train, y_train, **fit_kwargs)
+
+
+def evaluate(
+    context: MLClientCtx,
+    model: str,
+    dataset: mlrun.DataItem,
+    drop_columns: List[str] = None,
+    label_columns: Optional[Union[str, List[str]]] = None,
+    **kwargs,
+):
+    """
+    Evaluating a model. Artifacts generated by the MLHandler.
+
+    :param context:                 MLRun context.
+    :param model:                   The model Store path.
+    :param dataset:                 The dataset to evaluate the model on. Can be either a URI or a FeatureVector.
+    :param drop_columns:            str or a list of strings that represent the columns to drop.
+    :param label_columns:           The target label(s) of the column(s) in the dataset. for Regression or
+                                    Classification tasks. Mandatory when dataset is not a FeatureVector.
+    :param kwargs:                  Here you can pass keyword arguments to the predict function
+                                    (PREDICT_ prefix is not required).
+    """
+    # Get dataset by URL or by FeatureVector:
+    dataset, label_columns = _get_dataframe(
+        context=context,
+        dataset=dataset,
+        label_columns=label_columns,
+        drop_columns=drop_columns,
+    )
+
+    # Parsing label_columns:
+    parsed_label_columns = []
+    if label_columns:
+        label_columns = (
+            label_columns if isinstance(label_columns, list) else [label_columns]
+        )
+        for lc in label_columns:
+            if fs.common.feature_separator in lc:
+                feature_set_name, label_name, alias = fs.common.parse_feature_string(lc)
+                parsed_label_columns.append(alias or label_name)
+        if parsed_label_columns:
+            label_columns = parsed_label_columns
+
+    x = dataset.drop(label_columns, axis=1)
+    y = dataset[label_columns]
+
+    # Loading the model and predicting:
+    model_handler = AutoMLRun.load_model(
+        model_path=model, context=context, model_name="model_LinearRegression"
+    )
+    AutoMLRun.apply_mlrun(model_handler.model, y_test=y, model_path=model)
+
+    context.logger.info(f"evaluating '{model_handler.model_name}'")
+    model_handler.model.predict(x, **kwargs)
+
+
+def predict(
+    context: MLClientCtx,
+    model: str,
+    dataset: mlrun.DataItem,
+    drop_columns: Union[str, List[str], int, List[int]] = None,
+    label_columns: Optional[Union[str, List[str]]] = None,
+    result_set: Optional[str] = None,
+    **kwargs,
+):
+    """
+    Predicting dataset by a model.
+
+    :param context:                 MLRun context.
+    :param model:                   The model Store path.
+    :param dataset:                 The dataset to predict the model on. Can be either a URI, a FeatureVector or a
+                                    sample in a shape of a list/dict.
+                                    When passing a sample, pass the dataset as a field in `params` instead of `inputs`.
+    :param drop_columns:            str/int or a list of strings/ints that represent the column names/indices to drop.
+                                    When the dataset is a list/dict this parameter should be represented by integers.
+    :param label_columns:           The target label(s) of the column(s) in the dataset. for Regression or
+                                    Classification tasks. Mandatory when dataset is not a FeatureVector.
+    :param result_set:              The db key to set name of the prediction result and the filename.
+                                    Default to 'prediction'.
+    :param kwargs:                  Here you can pass keyword arguments to the predict function
+                                    (PREDICT_ prefix is not required).
+    """
+    # Get dataset by URL or by FeatureVector:
+    dataset, label_columns = _get_dataframe(
+        context=context,
+        dataset=dataset,
+        label_columns=label_columns,
+        drop_columns=drop_columns,
+    )
+
+    # loading the model, and getting the model handler:
+    model_handler = AutoMLRun.load_model(model_path=model, context=context)
+
+    # Dropping label columns if necessary:
+    if not label_columns:
+        label_columns = []
+    elif isinstance(label_columns, str):
+        label_columns = [label_columns]
+
+    # Predicting:
+    context.logger.info(f"making prediction by '{model_handler.model_name}'")
+    y_pred = model_handler.model.predict(dataset, **kwargs)
+
+    # Preparing and validating label columns for the dataframe of the prediction result:
+    num_predicted = 1 if len(y_pred.shape) == 1 else y_pred.shape[1]
+
+    if num_predicted > len(label_columns):
+        if num_predicted == 1:
+            label_columns = ["predicted labels"]
+        else:
+            label_columns.extend(
+                [
+                    f"predicted_label_{i + 1 + len(label_columns)}"
+                    for i in range(num_predicted - len(label_columns))
+                ]
+            )
+    elif num_predicted < len(label_columns):
+        context.logger.error(
+            f"number of predicted labels: {num_predicted} is smaller than number of label columns: {len(label_columns)}"
+        )
+        raise ValueError
+
+    artifact_name = result_set or "prediction"
+    labels_inside_df = set(label_columns) & set(dataset.columns.tolist())
+    if labels_inside_df:
+        context.logger.error(
+            f"The labels: {labels_inside_df} are already existed in the dataframe"
+        )
+        raise ValueError
+    pred_df = pd.concat([dataset, pd.DataFrame(y_pred, columns=label_columns)], axis=1)
+    context.log_dataset(artifact_name, pred_df, db_key=result_set)
+
+        
+
+ + \ No newline at end of file diff --git a/functions/master/auto_trainer/latest/src/auto_trainer.py b/functions/master/auto_trainer/latest/src/auto_trainer.py index cb72cc2d..7b476470 100755 --- a/functions/master/auto_trainer/latest/src/auto_trainer.py +++ b/functions/master/auto_trainer/latest/src/auto_trainer.py @@ -71,13 +71,17 @@ def _get_dataframe( # Getting the dataset: if mlrun.utils.StorePrefix.FeatureVector == store_uri_prefix: - # FeatureVector case: label_columns = label_columns or dataset.meta.status.label_column - dataset = fs.get_offline_features( - dataset.meta.uri, drop_columns=drop_columns - ).to_dataframe() - context.logger.info(f"label columns: {label_columns}") + # FeatureVector case: + try: + fv = mlrun.datastore.get_store_resource(dataset.artifact_url) + dataset = fv.get_offline_features(drop_columns=drop_columns).to_dataframe() + except AttributeError: + # Leave here for backwards compatibility + dataset = fs.get_offline_features( + dataset.meta.uri, drop_columns=drop_columns + ).to_dataframe() elif not label_columns: context.logger.info( diff --git a/functions/master/auto_trainer/latest/src/function.yaml b/functions/master/auto_trainer/latest/src/function.yaml index f4d090d3..0f86b7ea 100644 --- a/functions/master/auto_trainer/latest/src/function.yaml +++ b/functions/master/auto_trainer/latest/src/function.yaml @@ -2,7 +2,7 @@ kind: job metadata: name: auto-trainer tag: '' - hash: 95201dacf05c181e0257b5dd145cb79a503b6fbe + hash: 1c415e6d3bd79c9ca0ee537e008643660c13fbc7 project: '' labels: author: yonish @@ -14,7 +14,7 @@ spec: args: [] image: mlrun/mlrun build: - functionSourceCode: # Copyright 2019 Iguazio
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
from pathlib import Path
from typing import Any, Dict, List, Optional, Tuple, Union

import mlrun
import mlrun.datastore
import mlrun.utils
import pandas as pd
from mlrun import feature_store as fs
from mlrun.datastore import DataItem
from mlrun.execution import MLClientCtx
from mlrun.frameworks.auto_mlrun import AutoMLRun
from mlrun.utils.helpers import create_class, create_function
from sklearn.model_selection import train_test_split

PathType = Union[str, Path]


class KWArgsPrefixes:
    MODEL_CLASS = "CLASS_"
    FIT = "FIT_"
    TRAIN = "TRAIN_"


def _get_sub_dict_by_prefix(src: Dict, prefix_key: str) -> Dict[str, Any]:
    """
    Collect all the keys from the given dict that starts with the given prefix and creates a new dictionary with these
    keys.

    :param src:         The source dict to extract the values from.
    :param prefix_key:  Only keys with this prefix will be returned. The keys in the result dict will be without this
                        prefix.
    """
    return {
        key.replace(prefix_key, ""): val
        for key, val in src.items()
        if key.startswith(prefix_key)
    }


def _get_dataframe(
    context: MLClientCtx,
    dataset: DataItem,
    label_columns: Optional[Union[str, List[str]]] = None,
    drop_columns: Union[str, List[str], int, List[int]] = None,
) -> Tuple[pd.DataFrame, Optional[Union[str, List[str]]]]:
    """
    Getting the DataFrame of the dataset and drop the columns accordingly.

    :param context:         MLRun context.
    :param dataset:         The dataset to train the model on.
                            Can be either a list of lists, dict, URI or a FeatureVector.
    :param label_columns:   The target label(s) of the column(s) in the dataset. for Regression or
                            Classification tasks.
    :param drop_columns:    str/int or a list of strings/ints that represent the column names/indices to drop.
    """
    store_uri_prefix, _ = mlrun.datastore.parse_store_uri(dataset.artifact_url)

    # Getting the dataset:
    if mlrun.utils.StorePrefix.FeatureVector == store_uri_prefix:
        # FeatureVector case:
        label_columns = label_columns or dataset.meta.status.label_column
        dataset = fs.get_offline_features(
            dataset.meta.uri, drop_columns=drop_columns
        ).to_dataframe()

        context.logger.info(f"label columns: {label_columns}")

    elif not label_columns:
        context.logger.info(
            "label_columns not provided, mandatory when dataset is not a FeatureVector"
        )
        raise ValueError

    elif isinstance(dataset, (list, dict)):
        # list/dict case:
        dataset = pd.DataFrame(dataset)
        # Checking if drop_columns provided by integer type:
        if drop_columns:
            if isinstance(drop_columns, str) or (
                isinstance(drop_columns, list)
                and any(isinstance(col, str) for col in drop_columns)
            ):
                context.logger.error(
                    "drop_columns must be an integer/list of integers if not provided with a URI/FeatureVector dataset"
                )
                raise ValueError
            dataset.drop(drop_columns, axis=1, inplace=True)

    else:
        # simple URL case:
        dataset = dataset.as_df()
        if drop_columns:
            if all(col in dataset for col in drop_columns):
                dataset = dataset.drop(drop_columns, axis=1)
            else:
                context.logger.info(
                    "not all of the columns to drop in the dataset, drop columns process skipped"
                )

    return dataset, label_columns


def train(
    context: MLClientCtx,
    dataset: DataItem,
    model_class: str,
    label_columns: Optional[Union[str, List[str]]] = None,
    drop_columns: List[str] = None,
    model_name: str = "model",
    tag: str = "",
    sample_set: DataItem = None,
    test_set: DataItem = None,
    train_test_split_size: float = None,
    random_state: int = None,
    labels: dict = None,
    **kwargs,
):
    """
    Training a model with the given dataset.

    example::

        import mlrun
        project = mlrun.get_or_create_project("my-project")
        project.set_function("hub://auto_trainer", "train")
        trainer_run = project.run(
            name="train",
            handler="train",
            inputs={"dataset": "./path/to/dataset.csv"},
            params={
                "model_class": "sklearn.linear_model.LogisticRegression",
                "label_columns": "label",
                "drop_columns": "id",
                "model_name": "my-model",
                "tag": "v1.0.0",
                "sample_set": "./path/to/sample_set.csv",
                "test_set": "./path/to/test_set.csv",
                "CLASS_solver": "liblinear",
            },
        )

    :param context:                 MLRun context
    :param dataset:                 The dataset to train the model on. Can be either a URI or a FeatureVector
    :param model_class:             The class of the model, e.g. `sklearn.linear_model.LogisticRegression`
    :param label_columns:           The target label(s) of the column(s) in the dataset. for Regression or
                                    Classification tasks. Mandatory when dataset is not a FeatureVector.
    :param drop_columns:            str or a list of strings that represent the columns to drop
    :param model_name:              The model's name to use for storing the model artifact, default to 'model'
    :param tag:                     The model's tag to log with
    :param sample_set:              A sample set of inputs for the model for logging its stats along the model in favour
                                    of model monitoring. Can be either a URI or a FeatureVector
    :param test_set:                The test set to train the model with.
    :param train_test_split_size:   if test_set was provided then this argument is ignored.
                                    Should be between 0.0 and 1.0 and represent the proportion of the dataset to include
                                    in the test split. The size of the Training set is set to the complement of this
                                    value. Default = 0.2
    :param random_state:            Relevant only when using train_test_split_size.
                                    A random state seed to shuffle the data. For more information, see:
                                    https://scikit-learn.org/stable/glossary.html#term-random_state
                                    Notice that here we only pass integer values.
    :param labels:                  Labels to log with the model
    :param kwargs:                  Here you can pass keyword arguments with prefixes,
                                    that will be parsed and passed to the relevant function, by the following prefixes:
                                    - `CLASS_` - for the model class arguments
                                    - `FIT_` - for the `fit` function arguments
                                    - `TRAIN_` - for the `train` function (in xgb or lgbm train function - future)

    """
    # Validate inputs:
    # Check if exactly one of them is supplied:
    if test_set is None:
        if train_test_split_size is None:
            context.logger.info(
                "test_set or train_test_split_size are not provided, setting train_test_split_size to 0.2"
            )
            train_test_split_size = 0.2

    elif train_test_split_size:
        context.logger.info(
            "test_set provided, ignoring given train_test_split_size value"
        )
        train_test_split_size = None

    # Get DataFrame by URL or by FeatureVector:
    dataset, label_columns = _get_dataframe(
        context=context,
        dataset=dataset,
        label_columns=label_columns,
        drop_columns=drop_columns,
    )

    # Getting the sample set:
    if sample_set is None:
        context.logger.info(
            f"Sample set not given, using the whole training set as the sample set"
        )
        sample_set = dataset
    else:
        sample_set, _ = _get_dataframe(
            context=context,
            dataset=sample_set,
            label_columns=label_columns,
            drop_columns=drop_columns,
        )

    # Parsing kwargs:
    # TODO: Use in xgb or lgbm train function.
    train_kwargs = _get_sub_dict_by_prefix(src=kwargs, prefix_key=KWArgsPrefixes.TRAIN)
    fit_kwargs = _get_sub_dict_by_prefix(src=kwargs, prefix_key=KWArgsPrefixes.FIT)
    model_class_kwargs = _get_sub_dict_by_prefix(
        src=kwargs, prefix_key=KWArgsPrefixes.MODEL_CLASS
    )

    # Check if model or function:
    if hasattr(model_class, "train"):
        # TODO: Need to call: model(), afterwards to start the train function.
        # model = create_function(f"{model_class}.train")
        raise NotImplementedError
    else:
        # Creating model instance:
        model = create_class(model_class)(**model_class_kwargs)

    x = dataset.drop(label_columns, axis=1)
    y = dataset[label_columns]
    if train_test_split_size:
        x_train, x_test, y_train, y_test = train_test_split(
            x, y, test_size=train_test_split_size, random_state=random_state
        )
    else:
        x_train, y_train = x, y

        test_set = test_set.as_df()
        if drop_columns:
            test_set = dataset.drop(drop_columns, axis=1)

        x_test, y_test = test_set.drop(label_columns, axis=1), test_set[label_columns]

    AutoMLRun.apply_mlrun(
        model=model,
        model_name=model_name,
        context=context,
        tag=tag,
        sample_set=sample_set,
        y_columns=label_columns,
        test_set=test_set,
        x_test=x_test,
        y_test=y_test,
        artifacts=context.artifacts,
        labels=labels,
    )
    context.logger.info(f"training '{model_name}'")
    model.fit(x_train, y_train, **fit_kwargs)


def evaluate(
    context: MLClientCtx,
    model: str,
    dataset: mlrun.DataItem,
    drop_columns: List[str] = None,
    label_columns: Optional[Union[str, List[str]]] = None,
    **kwargs,
):
    """
    Evaluating a model. Artifacts generated by the MLHandler.

    :param context:                 MLRun context.
    :param model:                   The model Store path.
    :param dataset:                 The dataset to evaluate the model on. Can be either a URI or a FeatureVector.
    :param drop_columns:            str or a list of strings that represent the columns to drop.
    :param label_columns:           The target label(s) of the column(s) in the dataset. for Regression or
                                    Classification tasks. Mandatory when dataset is not a FeatureVector.
    :param kwargs:                  Here you can pass keyword arguments to the predict function
                                    (PREDICT_ prefix is not required).
    """
    # Get dataset by URL or by FeatureVector:
    dataset, label_columns = _get_dataframe(
        context=context,
        dataset=dataset,
        label_columns=label_columns,
        drop_columns=drop_columns,
    )

    # Parsing label_columns:
    parsed_label_columns = []
    if label_columns:
        label_columns = (
            label_columns if isinstance(label_columns, list) else [label_columns]
        )
        for lc in label_columns:
            if fs.common.feature_separator in lc:
                feature_set_name, label_name, alias = fs.common.parse_feature_string(lc)
                parsed_label_columns.append(alias or label_name)
        if parsed_label_columns:
            label_columns = parsed_label_columns

    x = dataset.drop(label_columns, axis=1)
    y = dataset[label_columns]

    # Loading the model and predicting:
    model_handler = AutoMLRun.load_model(
        model_path=model, context=context, model_name="model_LinearRegression"
    )
    AutoMLRun.apply_mlrun(model_handler.model, y_test=y, model_path=model)

    context.logger.info(f"evaluating '{model_handler.model_name}'")
    model_handler.model.predict(x, **kwargs)


def predict(
    context: MLClientCtx,
    model: str,
    dataset: mlrun.DataItem,
    drop_columns: Union[str, List[str], int, List[int]] = None,
    label_columns: Optional[Union[str, List[str]]] = None,
    result_set: Optional[str] = None,
    **kwargs,
):
    """
    Predicting dataset by a model.

    :param context:                 MLRun context.
    :param model:                   The model Store path.
    :param dataset:                 The dataset to predict the model on. Can be either a URI, a FeatureVector or a
                                    sample in a shape of a list/dict.
                                    When passing a sample, pass the dataset as a field in `params` instead of `inputs`.
    :param drop_columns:            str/int or a list of strings/ints that represent the column names/indices to drop.
                                    When the dataset is a list/dict this parameter should be represented by integers.
    :param label_columns:           The target label(s) of the column(s) in the dataset. for Regression or
                                    Classification tasks. Mandatory when dataset is not a FeatureVector.
    :param result_set:              The db key to set name of the prediction result and the filename.
                                    Default to 'prediction'.
    :param kwargs:                  Here you can pass keyword arguments to the predict function
                                    (PREDICT_ prefix is not required).
    """
    # Get dataset by URL or by FeatureVector:
    dataset, label_columns = _get_dataframe(
        context=context,
        dataset=dataset,
        label_columns=label_columns,
        drop_columns=drop_columns,
    )

    # loading the model, and getting the model handler:
    model_handler = AutoMLRun.load_model(model_path=model, context=context)

    # Dropping label columns if necessary:
    if not label_columns:
        label_columns = []
    elif isinstance(label_columns, str):
        label_columns = [label_columns]

    # Predicting:
    context.logger.info(f"making prediction by '{model_handler.model_name}'")
    y_pred = model_handler.model.predict(dataset, **kwargs)

    # Preparing and validating label columns for the dataframe of the prediction result:
    num_predicted = 1 if len(y_pred.shape) == 1 else y_pred.shape[1]

    if num_predicted > len(label_columns):
        if num_predicted == 1:
            label_columns = ["predicted labels"]
        else:
            label_columns.extend(
                [
                    f"predicted_label_{i + 1 + len(label_columns)}"
                    for i in range(num_predicted - len(label_columns))
                ]
            )
    elif num_predicted < len(label_columns):
        context.logger.error(
            f"number of predicted labels: {num_predicted} is smaller than number of label columns: {len(label_columns)}"
        )
        raise ValueError

    artifact_name = result_set or "prediction"
    labels_inside_df = set(label_columns) & set(dataset.columns.tolist())
    if labels_inside_df:
        context.logger.error(
            f"The labels: {labels_inside_df} are already existed in the dataframe"
        )
        raise ValueError
    pred_df = pd.concat([dataset, pd.DataFrame(y_pred, columns=label_columns)], axis=1)
    context.log_dataset(artifact_name, pred_df, db_key=result_set)
 + functionSourceCode: # Copyright 2019 Iguazio
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
from pathlib import Path
from typing import Any, Dict, List, Optional, Tuple, Union

import mlrun
import mlrun.datastore
import mlrun.utils
import pandas as pd
from mlrun import feature_store as fs
from mlrun.datastore import DataItem
from mlrun.execution import MLClientCtx
from mlrun.frameworks.auto_mlrun import AutoMLRun
from mlrun.utils.helpers import create_class, create_function
from sklearn.model_selection import train_test_split

PathType = Union[str, Path]


class KWArgsPrefixes:
    MODEL_CLASS = "CLASS_"
    FIT = "FIT_"
    TRAIN = "TRAIN_"


def _get_sub_dict_by_prefix(src: Dict, prefix_key: str) -> Dict[str, Any]:
    """
    Collect all the keys from the given dict that starts with the given prefix and creates a new dictionary with these
    keys.

    :param src:         The source dict to extract the values from.
    :param prefix_key:  Only keys with this prefix will be returned. The keys in the result dict will be without this
                        prefix.
    """
    return {
        key.replace(prefix_key, ""): val
        for key, val in src.items()
        if key.startswith(prefix_key)
    }


def _get_dataframe(
    context: MLClientCtx,
    dataset: DataItem,
    label_columns: Optional[Union[str, List[str]]] = None,
    drop_columns: Union[str, List[str], int, List[int]] = None,
) -> Tuple[pd.DataFrame, Optional[Union[str, List[str]]]]:
    """
    Getting the DataFrame of the dataset and drop the columns accordingly.

    :param context:         MLRun context.
    :param dataset:         The dataset to train the model on.
                            Can be either a list of lists, dict, URI or a FeatureVector.
    :param label_columns:   The target label(s) of the column(s) in the dataset. for Regression or
                            Classification tasks.
    :param drop_columns:    str/int or a list of strings/ints that represent the column names/indices to drop.
    """
    store_uri_prefix, _ = mlrun.datastore.parse_store_uri(dataset.artifact_url)

    # Getting the dataset:
    if mlrun.utils.StorePrefix.FeatureVector == store_uri_prefix:
        label_columns = label_columns or dataset.meta.status.label_column
        context.logger.info(f"label columns: {label_columns}")
        # FeatureVector case:
        try:
            fv = mlrun.datastore.get_store_resource(dataset.artifact_url)
            dataset = fv.get_offline_features(drop_columns=drop_columns).to_dataframe()
        except AttributeError:
            # Leave here for backwards compatibility
            dataset = fs.get_offline_features(
                dataset.meta.uri, drop_columns=drop_columns
            ).to_dataframe()

    elif not label_columns:
        context.logger.info(
            "label_columns not provided, mandatory when dataset is not a FeatureVector"
        )
        raise ValueError

    elif isinstance(dataset, (list, dict)):
        # list/dict case:
        dataset = pd.DataFrame(dataset)
        # Checking if drop_columns provided by integer type:
        if drop_columns:
            if isinstance(drop_columns, str) or (
                isinstance(drop_columns, list)
                and any(isinstance(col, str) for col in drop_columns)
            ):
                context.logger.error(
                    "drop_columns must be an integer/list of integers if not provided with a URI/FeatureVector dataset"
                )
                raise ValueError
            dataset.drop(drop_columns, axis=1, inplace=True)

    else:
        # simple URL case:
        dataset = dataset.as_df()
        if drop_columns:
            if all(col in dataset for col in drop_columns):
                dataset = dataset.drop(drop_columns, axis=1)
            else:
                context.logger.info(
                    "not all of the columns to drop in the dataset, drop columns process skipped"
                )

    return dataset, label_columns


def train(
    context: MLClientCtx,
    dataset: DataItem,
    model_class: str,
    label_columns: Optional[Union[str, List[str]]] = None,
    drop_columns: List[str] = None,
    model_name: str = "model",
    tag: str = "",
    sample_set: DataItem = None,
    test_set: DataItem = None,
    train_test_split_size: float = None,
    random_state: int = None,
    labels: dict = None,
    **kwargs,
):
    """
    Training a model with the given dataset.

    example::

        import mlrun
        project = mlrun.get_or_create_project("my-project")
        project.set_function("hub://auto_trainer", "train")
        trainer_run = project.run(
            name="train",
            handler="train",
            inputs={"dataset": "./path/to/dataset.csv"},
            params={
                "model_class": "sklearn.linear_model.LogisticRegression",
                "label_columns": "label",
                "drop_columns": "id",
                "model_name": "my-model",
                "tag": "v1.0.0",
                "sample_set": "./path/to/sample_set.csv",
                "test_set": "./path/to/test_set.csv",
                "CLASS_solver": "liblinear",
            },
        )

    :param context:                 MLRun context
    :param dataset:                 The dataset to train the model on. Can be either a URI or a FeatureVector
    :param model_class:             The class of the model, e.g. `sklearn.linear_model.LogisticRegression`
    :param label_columns:           The target label(s) of the column(s) in the dataset. for Regression or
                                    Classification tasks. Mandatory when dataset is not a FeatureVector.
    :param drop_columns:            str or a list of strings that represent the columns to drop
    :param model_name:              The model's name to use for storing the model artifact, default to 'model'
    :param tag:                     The model's tag to log with
    :param sample_set:              A sample set of inputs for the model for logging its stats along the model in favour
                                    of model monitoring. Can be either a URI or a FeatureVector
    :param test_set:                The test set to train the model with.
    :param train_test_split_size:   if test_set was provided then this argument is ignored.
                                    Should be between 0.0 and 1.0 and represent the proportion of the dataset to include
                                    in the test split. The size of the Training set is set to the complement of this
                                    value. Default = 0.2
    :param random_state:            Relevant only when using train_test_split_size.
                                    A random state seed to shuffle the data. For more information, see:
                                    https://scikit-learn.org/stable/glossary.html#term-random_state
                                    Notice that here we only pass integer values.
    :param labels:                  Labels to log with the model
    :param kwargs:                  Here you can pass keyword arguments with prefixes,
                                    that will be parsed and passed to the relevant function, by the following prefixes:
                                    - `CLASS_` - for the model class arguments
                                    - `FIT_` - for the `fit` function arguments
                                    - `TRAIN_` - for the `train` function (in xgb or lgbm train function - future)

    """
    # Validate inputs:
    # Check if exactly one of them is supplied:
    if test_set is None:
        if train_test_split_size is None:
            context.logger.info(
                "test_set or train_test_split_size are not provided, setting train_test_split_size to 0.2"
            )
            train_test_split_size = 0.2

    elif train_test_split_size:
        context.logger.info(
            "test_set provided, ignoring given train_test_split_size value"
        )
        train_test_split_size = None

    # Get DataFrame by URL or by FeatureVector:
    dataset, label_columns = _get_dataframe(
        context=context,
        dataset=dataset,
        label_columns=label_columns,
        drop_columns=drop_columns,
    )

    # Getting the sample set:
    if sample_set is None:
        context.logger.info(
            f"Sample set not given, using the whole training set as the sample set"
        )
        sample_set = dataset
    else:
        sample_set, _ = _get_dataframe(
            context=context,
            dataset=sample_set,
            label_columns=label_columns,
            drop_columns=drop_columns,
        )

    # Parsing kwargs:
    # TODO: Use in xgb or lgbm train function.
    train_kwargs = _get_sub_dict_by_prefix(src=kwargs, prefix_key=KWArgsPrefixes.TRAIN)
    fit_kwargs = _get_sub_dict_by_prefix(src=kwargs, prefix_key=KWArgsPrefixes.FIT)
    model_class_kwargs = _get_sub_dict_by_prefix(
        src=kwargs, prefix_key=KWArgsPrefixes.MODEL_CLASS
    )

    # Check if model or function:
    if hasattr(model_class, "train"):
        # TODO: Need to call: model(), afterwards to start the train function.
        # model = create_function(f"{model_class}.train")
        raise NotImplementedError
    else:
        # Creating model instance:
        model = create_class(model_class)(**model_class_kwargs)

    x = dataset.drop(label_columns, axis=1)
    y = dataset[label_columns]
    if train_test_split_size:
        x_train, x_test, y_train, y_test = train_test_split(
            x, y, test_size=train_test_split_size, random_state=random_state
        )
    else:
        x_train, y_train = x, y

        test_set = test_set.as_df()
        if drop_columns:
            test_set = dataset.drop(drop_columns, axis=1)

        x_test, y_test = test_set.drop(label_columns, axis=1), test_set[label_columns]

    AutoMLRun.apply_mlrun(
        model=model,
        model_name=model_name,
        context=context,
        tag=tag,
        sample_set=sample_set,
        y_columns=label_columns,
        test_set=test_set,
        x_test=x_test,
        y_test=y_test,
        artifacts=context.artifacts,
        labels=labels,
    )
    context.logger.info(f"training '{model_name}'")
    model.fit(x_train, y_train, **fit_kwargs)


def evaluate(
    context: MLClientCtx,
    model: str,
    dataset: mlrun.DataItem,
    drop_columns: List[str] = None,
    label_columns: Optional[Union[str, List[str]]] = None,
    **kwargs,
):
    """
    Evaluating a model. Artifacts generated by the MLHandler.

    :param context:                 MLRun context.
    :param model:                   The model Store path.
    :param dataset:                 The dataset to evaluate the model on. Can be either a URI or a FeatureVector.
    :param drop_columns:            str or a list of strings that represent the columns to drop.
    :param label_columns:           The target label(s) of the column(s) in the dataset. for Regression or
                                    Classification tasks. Mandatory when dataset is not a FeatureVector.
    :param kwargs:                  Here you can pass keyword arguments to the predict function
                                    (PREDICT_ prefix is not required).
    """
    # Get dataset by URL or by FeatureVector:
    dataset, label_columns = _get_dataframe(
        context=context,
        dataset=dataset,
        label_columns=label_columns,
        drop_columns=drop_columns,
    )

    # Parsing label_columns:
    parsed_label_columns = []
    if label_columns:
        label_columns = (
            label_columns if isinstance(label_columns, list) else [label_columns]
        )
        for lc in label_columns:
            if fs.common.feature_separator in lc:
                feature_set_name, label_name, alias = fs.common.parse_feature_string(lc)
                parsed_label_columns.append(alias or label_name)
        if parsed_label_columns:
            label_columns = parsed_label_columns

    x = dataset.drop(label_columns, axis=1)
    y = dataset[label_columns]

    # Loading the model and predicting:
    model_handler = AutoMLRun.load_model(
        model_path=model, context=context, model_name="model_LinearRegression"
    )
    AutoMLRun.apply_mlrun(model_handler.model, y_test=y, model_path=model)

    context.logger.info(f"evaluating '{model_handler.model_name}'")
    model_handler.model.predict(x, **kwargs)


def predict(
    context: MLClientCtx,
    model: str,
    dataset: mlrun.DataItem,
    drop_columns: Union[str, List[str], int, List[int]] = None,
    label_columns: Optional[Union[str, List[str]]] = None,
    result_set: Optional[str] = None,
    **kwargs,
):
    """
    Predicting dataset by a model.

    :param context:                 MLRun context.
    :param model:                   The model Store path.
    :param dataset:                 The dataset to predict the model on. Can be either a URI, a FeatureVector or a
                                    sample in a shape of a list/dict.
                                    When passing a sample, pass the dataset as a field in `params` instead of `inputs`.
    :param drop_columns:            str/int or a list of strings/ints that represent the column names/indices to drop.
                                    When the dataset is a list/dict this parameter should be represented by integers.
    :param label_columns:           The target label(s) of the column(s) in the dataset. for Regression or
                                    Classification tasks. Mandatory when dataset is not a FeatureVector.
    :param result_set:              The db key to set name of the prediction result and the filename.
                                    Default to 'prediction'.
    :param kwargs:                  Here you can pass keyword arguments to the predict function
                                    (PREDICT_ prefix is not required).
    """
    # Get dataset by URL or by FeatureVector:
    dataset, label_columns = _get_dataframe(
        context=context,
        dataset=dataset,
        label_columns=label_columns,
        drop_columns=drop_columns,
    )

    # loading the model, and getting the model handler:
    model_handler = AutoMLRun.load_model(model_path=model, context=context)

    # Dropping label columns if necessary:
    if not label_columns:
        label_columns = []
    elif isinstance(label_columns, str):
        label_columns = [label_columns]

    # Predicting:
    context.logger.info(f"making prediction by '{model_handler.model_name}'")
    y_pred = model_handler.model.predict(dataset, **kwargs)

    # Preparing and validating label columns for the dataframe of the prediction result:
    num_predicted = 1 if len(y_pred.shape) == 1 else y_pred.shape[1]

    if num_predicted > len(label_columns):
        if num_predicted == 1:
            label_columns = ["predicted labels"]
        else:
            label_columns.extend(
                [
                    f"predicted_label_{i + 1 + len(label_columns)}"
                    for i in range(num_predicted - len(label_columns))
                ]
            )
    elif num_predicted < len(label_columns):
        context.logger.error(
            f"number of predicted labels: {num_predicted} is smaller than number of label columns: {len(label_columns)}"
        )
        raise ValueError

    artifact_name = result_set or "prediction"
    labels_inside_df = set(label_columns) & set(dataset.columns.tolist())
    if labels_inside_df:
        context.logger.error(
            f"The labels: {labels_inside_df} are already existed in the dataframe"
        )
        raise ValueError
    pred_df = pd.concat([dataset, pd.DataFrame(y_pred, columns=label_columns)], axis=1)
    context.log_dataset(artifact_name, pred_df, db_key=result_set)
 commands: [] code_origin: '' origin_filename: '' @@ -86,7 +86,7 @@ spec: doc: Labels to log with the model default: null outputs: [] - lineno: 117 + lineno: 121 has_varargs: false has_kwargs: true evaluate: @@ -112,7 +112,7 @@ spec: Classification tasks. Mandatory when dataset is not a FeatureVector. default: null outputs: [] - lineno: 269 + lineno: 273 has_varargs: false has_kwargs: true predict: @@ -147,7 +147,7 @@ spec: to 'prediction'. default: null outputs: [] - lineno: 323 + lineno: 327 has_varargs: false has_kwargs: true description: Automatic train, evaluate and predict functions for the ML frameworks diff --git a/functions/master/auto_trainer/latest/src/item.yaml b/functions/master/auto_trainer/latest/src/item.yaml index 122c6804..ffa03bf0 100755 --- a/functions/master/auto_trainer/latest/src/item.yaml +++ b/functions/master/auto_trainer/latest/src/item.yaml @@ -23,4 +23,4 @@ spec: kind: job requirements: [] url: '' -version: 1.6.0 +version: 1.7.0 diff --git a/functions/master/auto_trainer/latest/static/auto_trainer.html b/functions/master/auto_trainer/latest/static/auto_trainer.html index ebee6e6d..a3db2659 100644 --- a/functions/master/auto_trainer/latest/static/auto_trainer.html +++ b/functions/master/auto_trainer/latest/static/auto_trainer.html @@ -187,13 +187,17 @@

Source code for auto_trainer.auto_trainer

 
     # Getting the dataset:
     if mlrun.utils.StorePrefix.FeatureVector == store_uri_prefix:
-        # FeatureVector case:
         label_columns = label_columns or dataset.meta.status.label_column
-        dataset = fs.get_offline_features(
-            dataset.meta.uri, drop_columns=drop_columns
-        ).to_dataframe()
-
         context.logger.info(f"label columns: {label_columns}")
+        # FeatureVector case:
+        try:
+            fv = mlrun.datastore.get_store_resource(dataset.artifact_url)
+            dataset = fv.get_offline_features(drop_columns=drop_columns).to_dataframe()
+        except AttributeError:
+            # Leave here for backwards compatibility
+            dataset = fs.get_offline_features(
+                dataset.meta.uri, drop_columns=drop_columns
+            ).to_dataframe()
 
     elif not label_columns:
         context.logger.info(
diff --git a/functions/master/auto_trainer/latest/static/function.html b/functions/master/auto_trainer/latest/static/function.html
index 6b318b69..aed22593 100644
--- a/functions/master/auto_trainer/latest/static/function.html
+++ b/functions/master/auto_trainer/latest/static/function.html
@@ -19,7 +19,7 @@
 metadata:
   name: auto-trainer
   tag: ''
-  hash: 95201dacf05c181e0257b5dd145cb79a503b6fbe
+  hash: 1c415e6d3bd79c9ca0ee537e008643660c13fbc7
   project: ''
   labels:
     author: yonish
@@ -31,7 +31,7 @@
   args: []
   image: mlrun/mlrun
   build:
-    functionSourceCode: # Copyright 2019 Iguazio
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
from pathlib import Path
from typing import Any, Dict, List, Optional, Tuple, Union

import mlrun
import mlrun.datastore
import mlrun.utils
import pandas as pd
from mlrun import feature_store as fs
from mlrun.datastore import DataItem
from mlrun.execution import MLClientCtx
from mlrun.frameworks.auto_mlrun import AutoMLRun
from mlrun.utils.helpers import create_class, create_function
from sklearn.model_selection import train_test_split

PathType = Union[str, Path]


class KWArgsPrefixes:
    MODEL_CLASS = "CLASS_"
    FIT = "FIT_"
    TRAIN = "TRAIN_"


def _get_sub_dict_by_prefix(src: Dict, prefix_key: str) -> Dict[str, Any]:
    """
    Collect all the keys from the given dict that starts with the given prefix and creates a new dictionary with these
    keys.

    :param src:         The source dict to extract the values from.
    :param prefix_key:  Only keys with this prefix will be returned. The keys in the result dict will be without this
                        prefix.
    """
    return {
        key.replace(prefix_key, ""): val
        for key, val in src.items()
        if key.startswith(prefix_key)
    }


def _get_dataframe(
    context: MLClientCtx,
    dataset: DataItem,
    label_columns: Optional[Union[str, List[str]]] = None,
    drop_columns: Union[str, List[str], int, List[int]] = None,
) -> Tuple[pd.DataFrame, Optional[Union[str, List[str]]]]:
    """
    Getting the DataFrame of the dataset and drop the columns accordingly.

    :param context:         MLRun context.
    :param dataset:         The dataset to train the model on.
                            Can be either a list of lists, dict, URI or a FeatureVector.
    :param label_columns:   The target label(s) of the column(s) in the dataset. for Regression or
                            Classification tasks.
    :param drop_columns:    str/int or a list of strings/ints that represent the column names/indices to drop.
    """
    store_uri_prefix, _ = mlrun.datastore.parse_store_uri(dataset.artifact_url)

    # Getting the dataset:
    if mlrun.utils.StorePrefix.FeatureVector == store_uri_prefix:
        # FeatureVector case:
        label_columns = label_columns or dataset.meta.status.label_column
        dataset = fs.get_offline_features(
            dataset.meta.uri, drop_columns=drop_columns
        ).to_dataframe()

        context.logger.info(f"label columns: {label_columns}")

    elif not label_columns:
        context.logger.info(
            "label_columns not provided, mandatory when dataset is not a FeatureVector"
        )
        raise ValueError

    elif isinstance(dataset, (list, dict)):
        # list/dict case:
        dataset = pd.DataFrame(dataset)
        # Checking if drop_columns provided by integer type:
        if drop_columns:
            if isinstance(drop_columns, str) or (
                isinstance(drop_columns, list)
                and any(isinstance(col, str) for col in drop_columns)
            ):
                context.logger.error(
                    "drop_columns must be an integer/list of integers if not provided with a URI/FeatureVector dataset"
                )
                raise ValueError
            dataset.drop(drop_columns, axis=1, inplace=True)

    else:
        # simple URL case:
        dataset = dataset.as_df()
        if drop_columns:
            if all(col in dataset for col in drop_columns):
                dataset = dataset.drop(drop_columns, axis=1)
            else:
                context.logger.info(
                    "not all of the columns to drop in the dataset, drop columns process skipped"
                )

    return dataset, label_columns


def train(
    context: MLClientCtx,
    dataset: DataItem,
    model_class: str,
    label_columns: Optional[Union[str, List[str]]] = None,
    drop_columns: List[str] = None,
    model_name: str = "model",
    tag: str = "",
    sample_set: DataItem = None,
    test_set: DataItem = None,
    train_test_split_size: float = None,
    random_state: int = None,
    labels: dict = None,
    **kwargs,
):
    """
    Training a model with the given dataset.

    example::

        import mlrun
        project = mlrun.get_or_create_project("my-project")
        project.set_function("hub://auto_trainer", "train")
        trainer_run = project.run(
            name="train",
            handler="train",
            inputs={"dataset": "./path/to/dataset.csv"},
            params={
                "model_class": "sklearn.linear_model.LogisticRegression",
                "label_columns": "label",
                "drop_columns": "id",
                "model_name": "my-model",
                "tag": "v1.0.0",
                "sample_set": "./path/to/sample_set.csv",
                "test_set": "./path/to/test_set.csv",
                "CLASS_solver": "liblinear",
            },
        )

    :param context:                 MLRun context
    :param dataset:                 The dataset to train the model on. Can be either a URI or a FeatureVector
    :param model_class:             The class of the model, e.g. `sklearn.linear_model.LogisticRegression`
    :param label_columns:           The target label(s) of the column(s) in the dataset. for Regression or
                                    Classification tasks. Mandatory when dataset is not a FeatureVector.
    :param drop_columns:            str or a list of strings that represent the columns to drop
    :param model_name:              The model's name to use for storing the model artifact, default to 'model'
    :param tag:                     The model's tag to log with
    :param sample_set:              A sample set of inputs for the model for logging its stats along the model in favour
                                    of model monitoring. Can be either a URI or a FeatureVector
    :param test_set:                The test set to train the model with.
    :param train_test_split_size:   if test_set was provided then this argument is ignored.
                                    Should be between 0.0 and 1.0 and represent the proportion of the dataset to include
                                    in the test split. The size of the Training set is set to the complement of this
                                    value. Default = 0.2
    :param random_state:            Relevant only when using train_test_split_size.
                                    A random state seed to shuffle the data. For more information, see:
                                    https://scikit-learn.org/stable/glossary.html#term-random_state
                                    Notice that here we only pass integer values.
    :param labels:                  Labels to log with the model
    :param kwargs:                  Here you can pass keyword arguments with prefixes,
                                    that will be parsed and passed to the relevant function, by the following prefixes:
                                    - `CLASS_` - for the model class arguments
                                    - `FIT_` - for the `fit` function arguments
                                    - `TRAIN_` - for the `train` function (in xgb or lgbm train function - future)

    """
    # Validate inputs:
    # Check if exactly one of them is supplied:
    if test_set is None:
        if train_test_split_size is None:
            context.logger.info(
                "test_set or train_test_split_size are not provided, setting train_test_split_size to 0.2"
            )
            train_test_split_size = 0.2

    elif train_test_split_size:
        context.logger.info(
            "test_set provided, ignoring given train_test_split_size value"
        )
        train_test_split_size = None

    # Get DataFrame by URL or by FeatureVector:
    dataset, label_columns = _get_dataframe(
        context=context,
        dataset=dataset,
        label_columns=label_columns,
        drop_columns=drop_columns,
    )

    # Getting the sample set:
    if sample_set is None:
        context.logger.info(
            f"Sample set not given, using the whole training set as the sample set"
        )
        sample_set = dataset
    else:
        sample_set, _ = _get_dataframe(
            context=context,
            dataset=sample_set,
            label_columns=label_columns,
            drop_columns=drop_columns,
        )

    # Parsing kwargs:
    # TODO: Use in xgb or lgbm train function.
    train_kwargs = _get_sub_dict_by_prefix(src=kwargs, prefix_key=KWArgsPrefixes.TRAIN)
    fit_kwargs = _get_sub_dict_by_prefix(src=kwargs, prefix_key=KWArgsPrefixes.FIT)
    model_class_kwargs = _get_sub_dict_by_prefix(
        src=kwargs, prefix_key=KWArgsPrefixes.MODEL_CLASS
    )

    # Check if model or function:
    if hasattr(model_class, "train"):
        # TODO: Need to call: model(), afterwards to start the train function.
        # model = create_function(f"{model_class}.train")
        raise NotImplementedError
    else:
        # Creating model instance:
        model = create_class(model_class)(**model_class_kwargs)

    x = dataset.drop(label_columns, axis=1)
    y = dataset[label_columns]
    if train_test_split_size:
        x_train, x_test, y_train, y_test = train_test_split(
            x, y, test_size=train_test_split_size, random_state=random_state
        )
    else:
        x_train, y_train = x, y

        test_set = test_set.as_df()
        if drop_columns:
            test_set = dataset.drop(drop_columns, axis=1)

        x_test, y_test = test_set.drop(label_columns, axis=1), test_set[label_columns]

    AutoMLRun.apply_mlrun(
        model=model,
        model_name=model_name,
        context=context,
        tag=tag,
        sample_set=sample_set,
        y_columns=label_columns,
        test_set=test_set,
        x_test=x_test,
        y_test=y_test,
        artifacts=context.artifacts,
        labels=labels,
    )
    context.logger.info(f"training '{model_name}'")
    model.fit(x_train, y_train, **fit_kwargs)


def evaluate(
    context: MLClientCtx,
    model: str,
    dataset: mlrun.DataItem,
    drop_columns: List[str] = None,
    label_columns: Optional[Union[str, List[str]]] = None,
    **kwargs,
):
    """
    Evaluating a model. Artifacts generated by the MLHandler.

    :param context:                 MLRun context.
    :param model:                   The model Store path.
    :param dataset:                 The dataset to evaluate the model on. Can be either a URI or a FeatureVector.
    :param drop_columns:            str or a list of strings that represent the columns to drop.
    :param label_columns:           The target label(s) of the column(s) in the dataset. for Regression or
                                    Classification tasks. Mandatory when dataset is not a FeatureVector.
    :param kwargs:                  Here you can pass keyword arguments to the predict function
                                    (PREDICT_ prefix is not required).
    """
    # Get dataset by URL or by FeatureVector:
    dataset, label_columns = _get_dataframe(
        context=context,
        dataset=dataset,
        label_columns=label_columns,
        drop_columns=drop_columns,
    )

    # Parsing label_columns:
    parsed_label_columns = []
    if label_columns:
        label_columns = (
            label_columns if isinstance(label_columns, list) else [label_columns]
        )
        for lc in label_columns:
            if fs.common.feature_separator in lc:
                feature_set_name, label_name, alias = fs.common.parse_feature_string(lc)
                parsed_label_columns.append(alias or label_name)
        if parsed_label_columns:
            label_columns = parsed_label_columns

    x = dataset.drop(label_columns, axis=1)
    y = dataset[label_columns]

    # Loading the model and predicting:
    model_handler = AutoMLRun.load_model(
        model_path=model, context=context, model_name="model_LinearRegression"
    )
    AutoMLRun.apply_mlrun(model_handler.model, y_test=y, model_path=model)

    context.logger.info(f"evaluating '{model_handler.model_name}'")
    model_handler.model.predict(x, **kwargs)


def predict(
    context: MLClientCtx,
    model: str,
    dataset: mlrun.DataItem,
    drop_columns: Union[str, List[str], int, List[int]] = None,
    label_columns: Optional[Union[str, List[str]]] = None,
    result_set: Optional[str] = None,
    **kwargs,
):
    """
    Predicting dataset by a model.

    :param context:                 MLRun context.
    :param model:                   The model Store path.
    :param dataset:                 The dataset to predict the model on. Can be either a URI, a FeatureVector or a
                                    sample in a shape of a list/dict.
                                    When passing a sample, pass the dataset as a field in `params` instead of `inputs`.
    :param drop_columns:            str/int or a list of strings/ints that represent the column names/indices to drop.
                                    When the dataset is a list/dict this parameter should be represented by integers.
    :param label_columns:           The target label(s) of the column(s) in the dataset. for Regression or
                                    Classification tasks. Mandatory when dataset is not a FeatureVector.
    :param result_set:              The db key to set name of the prediction result and the filename.
                                    Default to 'prediction'.
    :param kwargs:                  Here you can pass keyword arguments to the predict function
                                    (PREDICT_ prefix is not required).
    """
    # Get dataset by URL or by FeatureVector:
    dataset, label_columns = _get_dataframe(
        context=context,
        dataset=dataset,
        label_columns=label_columns,
        drop_columns=drop_columns,
    )

    # loading the model, and getting the model handler:
    model_handler = AutoMLRun.load_model(model_path=model, context=context)

    # Dropping label columns if necessary:
    if not label_columns:
        label_columns = []
    elif isinstance(label_columns, str):
        label_columns = [label_columns]

    # Predicting:
    context.logger.info(f"making prediction by '{model_handler.model_name}'")
    y_pred = model_handler.model.predict(dataset, **kwargs)

    # Preparing and validating label columns for the dataframe of the prediction result:
    num_predicted = 1 if len(y_pred.shape) == 1 else y_pred.shape[1]

    if num_predicted > len(label_columns):
        if num_predicted == 1:
            label_columns = ["predicted labels"]
        else:
            label_columns.extend(
                [
                    f"predicted_label_{i + 1 + len(label_columns)}"
                    for i in range(num_predicted - len(label_columns))
                ]
            )
    elif num_predicted < len(label_columns):
        context.logger.error(
            f"number of predicted labels: {num_predicted} is smaller than number of label columns: {len(label_columns)}"
        )
        raise ValueError

    artifact_name = result_set or "prediction"
    labels_inside_df = set(label_columns) & set(dataset.columns.tolist())
    if labels_inside_df:
        context.logger.error(
            f"The labels: {labels_inside_df} are already existed in the dataframe"
        )
        raise ValueError
    pred_df = pd.concat([dataset, pd.DataFrame(y_pred, columns=label_columns)], axis=1)
    context.log_dataset(artifact_name, pred_df, db_key=result_set)

+    functionSourceCode: # Copyright 2019 Iguazio
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
from pathlib import Path
from typing import Any, Dict, List, Optional, Tuple, Union

import mlrun
import mlrun.datastore
import mlrun.utils
import pandas as pd
from mlrun import feature_store as fs
from mlrun.datastore import DataItem
from mlrun.execution import MLClientCtx
from mlrun.frameworks.auto_mlrun import AutoMLRun
from mlrun.utils.helpers import create_class, create_function
from sklearn.model_selection import train_test_split

PathType = Union[str, Path]


class KWArgsPrefixes:
    MODEL_CLASS = "CLASS_"
    FIT = "FIT_"
    TRAIN = "TRAIN_"


def _get_sub_dict_by_prefix(src: Dict, prefix_key: str) -> Dict[str, Any]:
    """
    Collect all the keys from the given dict that starts with the given prefix and creates a new dictionary with these
    keys.

    :param src:         The source dict to extract the values from.
    :param prefix_key:  Only keys with this prefix will be returned. The keys in the result dict will be without this
                        prefix.
    """
    return {
        key.replace(prefix_key, ""): val
        for key, val in src.items()
        if key.startswith(prefix_key)
    }


def _get_dataframe(
    context: MLClientCtx,
    dataset: DataItem,
    label_columns: Optional[Union[str, List[str]]] = None,
    drop_columns: Union[str, List[str], int, List[int]] = None,
) -> Tuple[pd.DataFrame, Optional[Union[str, List[str]]]]:
    """
    Getting the DataFrame of the dataset and drop the columns accordingly.

    :param context:         MLRun context.
    :param dataset:         The dataset to train the model on.
                            Can be either a list of lists, dict, URI or a FeatureVector.
    :param label_columns:   The target label(s) of the column(s) in the dataset. for Regression or
                            Classification tasks.
    :param drop_columns:    str/int or a list of strings/ints that represent the column names/indices to drop.
    """
    store_uri_prefix, _ = mlrun.datastore.parse_store_uri(dataset.artifact_url)

    # Getting the dataset:
    if mlrun.utils.StorePrefix.FeatureVector == store_uri_prefix:
        label_columns = label_columns or dataset.meta.status.label_column
        context.logger.info(f"label columns: {label_columns}")
        # FeatureVector case:
        try:
            fv = mlrun.datastore.get_store_resource(dataset.artifact_url)
            dataset = fv.get_offline_features(drop_columns=drop_columns).to_dataframe()
        except AttributeError:
            # Leave here for backwards compatibility
            dataset = fs.get_offline_features(
                dataset.meta.uri, drop_columns=drop_columns
            ).to_dataframe()

    elif not label_columns:
        context.logger.info(
            "label_columns not provided, mandatory when dataset is not a FeatureVector"
        )
        raise ValueError

    elif isinstance(dataset, (list, dict)):
        # list/dict case:
        dataset = pd.DataFrame(dataset)
        # Checking if drop_columns provided by integer type:
        if drop_columns:
            if isinstance(drop_columns, str) or (
                isinstance(drop_columns, list)
                and any(isinstance(col, str) for col in drop_columns)
            ):
                context.logger.error(
                    "drop_columns must be an integer/list of integers if not provided with a URI/FeatureVector dataset"
                )
                raise ValueError
            dataset.drop(drop_columns, axis=1, inplace=True)

    else:
        # simple URL case:
        dataset = dataset.as_df()
        if drop_columns:
            if all(col in dataset for col in drop_columns):
                dataset = dataset.drop(drop_columns, axis=1)
            else:
                context.logger.info(
                    "not all of the columns to drop in the dataset, drop columns process skipped"
                )

    return dataset, label_columns


def train(
    context: MLClientCtx,
    dataset: DataItem,
    model_class: str,
    label_columns: Optional[Union[str, List[str]]] = None,
    drop_columns: List[str] = None,
    model_name: str = "model",
    tag: str = "",
    sample_set: DataItem = None,
    test_set: DataItem = None,
    train_test_split_size: float = None,
    random_state: int = None,
    labels: dict = None,
    **kwargs,
):
    """
    Training a model with the given dataset.

    example::

        import mlrun
        project = mlrun.get_or_create_project("my-project")
        project.set_function("hub://auto_trainer", "train")
        trainer_run = project.run(
            name="train",
            handler="train",
            inputs={"dataset": "./path/to/dataset.csv"},
            params={
                "model_class": "sklearn.linear_model.LogisticRegression",
                "label_columns": "label",
                "drop_columns": "id",
                "model_name": "my-model",
                "tag": "v1.0.0",
                "sample_set": "./path/to/sample_set.csv",
                "test_set": "./path/to/test_set.csv",
                "CLASS_solver": "liblinear",
            },
        )

    :param context:                 MLRun context
    :param dataset:                 The dataset to train the model on. Can be either a URI or a FeatureVector
    :param model_class:             The class of the model, e.g. `sklearn.linear_model.LogisticRegression`
    :param label_columns:           The target label(s) of the column(s) in the dataset. for Regression or
                                    Classification tasks. Mandatory when dataset is not a FeatureVector.
    :param drop_columns:            str or a list of strings that represent the columns to drop
    :param model_name:              The model's name to use for storing the model artifact, default to 'model'
    :param tag:                     The model's tag to log with
    :param sample_set:              A sample set of inputs for the model for logging its stats along the model in favour
                                    of model monitoring. Can be either a URI or a FeatureVector
    :param test_set:                The test set to train the model with.
    :param train_test_split_size:   if test_set was provided then this argument is ignored.
                                    Should be between 0.0 and 1.0 and represent the proportion of the dataset to include
                                    in the test split. The size of the Training set is set to the complement of this
                                    value. Default = 0.2
    :param random_state:            Relevant only when using train_test_split_size.
                                    A random state seed to shuffle the data. For more information, see:
                                    https://scikit-learn.org/stable/glossary.html#term-random_state
                                    Notice that here we only pass integer values.
    :param labels:                  Labels to log with the model
    :param kwargs:                  Here you can pass keyword arguments with prefixes,
                                    that will be parsed and passed to the relevant function, by the following prefixes:
                                    - `CLASS_` - for the model class arguments
                                    - `FIT_` - for the `fit` function arguments
                                    - `TRAIN_` - for the `train` function (in xgb or lgbm train function - future)

    """
    # Validate inputs:
    # Check if exactly one of them is supplied:
    if test_set is None:
        if train_test_split_size is None:
            context.logger.info(
                "test_set or train_test_split_size are not provided, setting train_test_split_size to 0.2"
            )
            train_test_split_size = 0.2

    elif train_test_split_size:
        context.logger.info(
            "test_set provided, ignoring given train_test_split_size value"
        )
        train_test_split_size = None

    # Get DataFrame by URL or by FeatureVector:
    dataset, label_columns = _get_dataframe(
        context=context,
        dataset=dataset,
        label_columns=label_columns,
        drop_columns=drop_columns,
    )

    # Getting the sample set:
    if sample_set is None:
        context.logger.info(
            f"Sample set not given, using the whole training set as the sample set"
        )
        sample_set = dataset
    else:
        sample_set, _ = _get_dataframe(
            context=context,
            dataset=sample_set,
            label_columns=label_columns,
            drop_columns=drop_columns,
        )

    # Parsing kwargs:
    # TODO: Use in xgb or lgbm train function.
    train_kwargs = _get_sub_dict_by_prefix(src=kwargs, prefix_key=KWArgsPrefixes.TRAIN)
    fit_kwargs = _get_sub_dict_by_prefix(src=kwargs, prefix_key=KWArgsPrefixes.FIT)
    model_class_kwargs = _get_sub_dict_by_prefix(
        src=kwargs, prefix_key=KWArgsPrefixes.MODEL_CLASS
    )

    # Check if model or function:
    if hasattr(model_class, "train"):
        # TODO: Need to call: model(), afterwards to start the train function.
        # model = create_function(f"{model_class}.train")
        raise NotImplementedError
    else:
        # Creating model instance:
        model = create_class(model_class)(**model_class_kwargs)

    x = dataset.drop(label_columns, axis=1)
    y = dataset[label_columns]
    if train_test_split_size:
        x_train, x_test, y_train, y_test = train_test_split(
            x, y, test_size=train_test_split_size, random_state=random_state
        )
    else:
        x_train, y_train = x, y

        test_set = test_set.as_df()
        if drop_columns:
            test_set = dataset.drop(drop_columns, axis=1)

        x_test, y_test = test_set.drop(label_columns, axis=1), test_set[label_columns]

    AutoMLRun.apply_mlrun(
        model=model,
        model_name=model_name,
        context=context,
        tag=tag,
        sample_set=sample_set,
        y_columns=label_columns,
        test_set=test_set,
        x_test=x_test,
        y_test=y_test,
        artifacts=context.artifacts,
        labels=labels,
    )
    context.logger.info(f"training '{model_name}'")
    model.fit(x_train, y_train, **fit_kwargs)


def evaluate(
    context: MLClientCtx,
    model: str,
    dataset: mlrun.DataItem,
    drop_columns: List[str] = None,
    label_columns: Optional[Union[str, List[str]]] = None,
    **kwargs,
):
    """
    Evaluating a model. Artifacts generated by the MLHandler.

    :param context:                 MLRun context.
    :param model:                   The model Store path.
    :param dataset:                 The dataset to evaluate the model on. Can be either a URI or a FeatureVector.
    :param drop_columns:            str or a list of strings that represent the columns to drop.
    :param label_columns:           The target label(s) of the column(s) in the dataset. for Regression or
                                    Classification tasks. Mandatory when dataset is not a FeatureVector.
    :param kwargs:                  Here you can pass keyword arguments to the predict function
                                    (PREDICT_ prefix is not required).
    """
    # Get dataset by URL or by FeatureVector:
    dataset, label_columns = _get_dataframe(
        context=context,
        dataset=dataset,
        label_columns=label_columns,
        drop_columns=drop_columns,
    )

    # Parsing label_columns:
    parsed_label_columns = []
    if label_columns:
        label_columns = (
            label_columns if isinstance(label_columns, list) else [label_columns]
        )
        for lc in label_columns:
            if fs.common.feature_separator in lc:
                feature_set_name, label_name, alias = fs.common.parse_feature_string(lc)
                parsed_label_columns.append(alias or label_name)
        if parsed_label_columns:
            label_columns = parsed_label_columns

    x = dataset.drop(label_columns, axis=1)
    y = dataset[label_columns]

    # Loading the model and predicting:
    model_handler = AutoMLRun.load_model(
        model_path=model, context=context, model_name="model_LinearRegression"
    )
    AutoMLRun.apply_mlrun(model_handler.model, y_test=y, model_path=model)

    context.logger.info(f"evaluating '{model_handler.model_name}'")
    model_handler.model.predict(x, **kwargs)


def predict(
    context: MLClientCtx,
    model: str,
    dataset: mlrun.DataItem,
    drop_columns: Union[str, List[str], int, List[int]] = None,
    label_columns: Optional[Union[str, List[str]]] = None,
    result_set: Optional[str] = None,
    **kwargs,
):
    """
    Predicting dataset by a model.

    :param context:                 MLRun context.
    :param model:                   The model Store path.
    :param dataset:                 The dataset to predict the model on. Can be either a URI, a FeatureVector or a
                                    sample in a shape of a list/dict.
                                    When passing a sample, pass the dataset as a field in `params` instead of `inputs`.
    :param drop_columns:            str/int or a list of strings/ints that represent the column names/indices to drop.
                                    When the dataset is a list/dict this parameter should be represented by integers.
    :param label_columns:           The target label(s) of the column(s) in the dataset. for Regression or
                                    Classification tasks. Mandatory when dataset is not a FeatureVector.
    :param result_set:              The db key to set name of the prediction result and the filename.
                                    Default to 'prediction'.
    :param kwargs:                  Here you can pass keyword arguments to the predict function
                                    (PREDICT_ prefix is not required).
    """
    # Get dataset by URL or by FeatureVector:
    dataset, label_columns = _get_dataframe(
        context=context,
        dataset=dataset,
        label_columns=label_columns,
        drop_columns=drop_columns,
    )

    # loading the model, and getting the model handler:
    model_handler = AutoMLRun.load_model(model_path=model, context=context)

    # Dropping label columns if necessary:
    if not label_columns:
        label_columns = []
    elif isinstance(label_columns, str):
        label_columns = [label_columns]

    # Predicting:
    context.logger.info(f"making prediction by '{model_handler.model_name}'")
    y_pred = model_handler.model.predict(dataset, **kwargs)

    # Preparing and validating label columns for the dataframe of the prediction result:
    num_predicted = 1 if len(y_pred.shape) == 1 else y_pred.shape[1]

    if num_predicted > len(label_columns):
        if num_predicted == 1:
            label_columns = ["predicted labels"]
        else:
            label_columns.extend(
                [
                    f"predicted_label_{i + 1 + len(label_columns)}"
                    for i in range(num_predicted - len(label_columns))
                ]
            )
    elif num_predicted < len(label_columns):
        context.logger.error(
            f"number of predicted labels: {num_predicted} is smaller than number of label columns: {len(label_columns)}"
        )
        raise ValueError

    artifact_name = result_set or "prediction"
    labels_inside_df = set(label_columns) & set(dataset.columns.tolist())
    if labels_inside_df:
        context.logger.error(
            f"The labels: {labels_inside_df} are already existed in the dataframe"
        )
        raise ValueError
    pred_df = pd.concat([dataset, pd.DataFrame(y_pred, columns=label_columns)], axis=1)
    context.log_dataset(artifact_name, pred_df, db_key=result_set)

     commands: []
     code_origin: ''
     origin_filename: ''
@@ -103,7 +103,7 @@
         doc: Labels to log with the model
         default: null
       outputs: []
-      lineno: 117
+      lineno: 121
       has_varargs: false
       has_kwargs: true
     evaluate:
@@ -129,7 +129,7 @@
           Classification tasks. Mandatory when dataset is not a FeatureVector.
         default: null
       outputs: []
-      lineno: 269
+      lineno: 273
       has_varargs: false
       has_kwargs: true
     predict:
@@ -164,7 +164,7 @@
           to 'prediction'.
         default: null
       outputs: []
-      lineno: 323
+      lineno: 327
       has_varargs: false
       has_kwargs: true
   description: Automatic train, evaluate and predict functions for the ML frameworks
diff --git a/functions/master/auto_trainer/latest/static/item.html b/functions/master/auto_trainer/latest/static/item.html
index 16e9b077..c11f37e4 100644
--- a/functions/master/auto_trainer/latest/static/item.html
+++ b/functions/master/auto_trainer/latest/static/item.html
@@ -40,7 +40,7 @@
   kind: job
   requirements: []
 url: ''
-version: 1.6.0
+version: 1.7.0
diff --git a/functions/master/auto_trainer/latest/static/source.html b/functions/master/auto_trainer/latest/static/source.html index df316659..1e132975 100644 --- a/functions/master/auto_trainer/latest/static/source.html +++ b/functions/master/auto_trainer/latest/static/source.html @@ -88,13 +88,17 @@ # Getting the dataset: if mlrun.utils.StorePrefix.FeatureVector == store_uri_prefix: - # FeatureVector case: label_columns = label_columns or dataset.meta.status.label_column - dataset = fs.get_offline_features( - dataset.meta.uri, drop_columns=drop_columns - ).to_dataframe() - context.logger.info(f"label columns: {label_columns}") + # FeatureVector case: + try: + fv = mlrun.datastore.get_store_resource(dataset.artifact_url) + dataset = fv.get_offline_features(drop_columns=drop_columns).to_dataframe() + except AttributeError: + # Leave here for backwards compatibility + dataset = fs.get_offline_features( + dataset.meta.uri, drop_columns=drop_columns + ).to_dataframe() elif not label_columns: context.logger.info( diff --git a/functions/master/catalog.json b/functions/master/catalog.json index ec30d1f2..6c453e19 100644 --- a/functions/master/catalog.json +++ b/functions/master/catalog.json @@ -1 +1 @@ -{"tf2_serving": {"latest": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "tf2 image classification server", "doc": "", "example": "tf2_serving.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "yaronh"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "tf2-serving", "platformVersion": "3.5.0", "spec": {"filename": "tf2_serving.py", "handler": "handler", "image": "mlrun/mlrun", "kind": "nuclio:serving", "requirements": ["requests", "pillow", "tensorflow>=2.1"]}, "url": "", "version": "1.1.0", "assets": {"example": "src/tf2_serving.ipynb", "source": "src/tf2_serving.py", "function": "src/function.yaml", "docs": "static/documentation.html"}}, "0.9.0": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "tf2 image classification server", "doc": "", "example": "tf2_serving.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "yaronh"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "tf2-serving", "platformVersion": "3.2.0", "spec": {"filename": "tf2_serving.py", "handler": "handler", "image": "mlrun/mlrun", "kind": "nuclio:serving", "requirements": ["requests", "pillow", "tensorflow>=2.1"]}, "url": "", "version": "0.9.0", "assets": {"example": "src/tf2_serving.ipynb", "source": "src/tf2_serving.py", "function": "src/function.yaml", "docs": "static/documentation.html"}}, "1.1.0": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "tf2 image classification server", "doc": "", "example": "tf2_serving.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "yaronh"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "tf2-serving", "platformVersion": "3.5.0", "spec": {"filename": "tf2_serving.py", "handler": "handler", "image": "mlrun/mlrun", "kind": "nuclio:serving", "requirements": ["requests", "pillow", "tensorflow>=2.1"]}, "url": "", "version": "1.1.0", "assets": {"example": "src/tf2_serving.ipynb", "source": "src/tf2_serving.py", "function": "src/function.yaml", "docs": "static/documentation.html"}}, "0.9.1": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "tf2 image classification server", "doc": "", "example": "tf2_serving.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "yaronh"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "tf2-serving", "platformVersion": "3.2.0", "spec": {"filename": "tf2_serving.py", "handler": "handler", "image": "mlrun/mlrun", "kind": "nuclio:serving", "requirements": ["requests", "pillow", 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'job'\n", + "%nuclio: setting spec.image to 'mlrun/ml-models'\n" + ] + } + ], + "source": [ + "%nuclio config kind = \"job\"\n", + "%nuclio config spec.image = \"mlrun/ml-models\"" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# nuclio: start-code" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import numpy as np\n", + "import os\n", + "import json\n", + "\n", + "# Feature selection strategies\n", + "from sklearn.feature_selection import SelectKBest\n", + "from sklearn.feature_selection import SelectFromModel\n", + "\n", + "# Model based feature selection\n", + "from sklearn.ensemble import ExtraTreesClassifier\n", + "from sklearn.svm import LinearSVC\n", + "from sklearn.linear_model import LogisticRegression\n", + "\n", + "# Scale feature scores\n", + "from sklearn.preprocessing import MinMaxScaler\n", + "\n", + "# SKLearn estimators list\n", + "from sklearn.utils import all_estimators\n", + "\n", + "# MLRun utils\n", + "from mlrun.mlutils.plots import gcf_clear\n", + "from mlrun.utils.helpers import create_class\n", + "from mlrun.artifacts import PlotArtifact\n", + "\n", + "# Feature Selection\n", + "from feature_selection import feature_selection, show_values_on_bars, plot_stat" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# nuclio: end-code" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Test" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "from mlrun import code_to_function, mount_v3io, mlconf, NewTask, run_local" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "mlconf.artifact_path = os.path.abspath('./artifacts')\n", + "mlconf.db_path = 'http://mlrun-api:8080'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Local Test" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "task = NewTask(params={'k': 2,\n", + " 'min_votes': 0.3,\n", + " 'label_column': 'is_error'},\n", + " inputs={'df_artifact': os.path.abspath('data/metrics.pq')})" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "> 2021-08-11 10:12:05,721 [info] starting run feature_selection uid=8765f9e7fde94efeb662fbe2c37a0e1a DB=http://mlrun-api:8080\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Pass k=2 as keyword args. From version 0.25 passing these as positional arguments will result in an error\n", + "Liblinear failed to converge, increase the number of iterations.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "> 2021-08-11 10:12:08,257 [info] votes needed to be selected: 2\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Converting input from bool to for compatibility.\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "
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projectuiditerstartstatenamelabelsinputsparametersresultsartifacts
default0Aug 11 10:12:05completedfeature_selection
v3io_user=admin
kind=handler
owner=admin
host=jupyter-az-ffcb58655-7l9pl
df_artifact
k=2
min_votes=0.3
label_column=is_error
f_classif
mutual_info_classif
chi2
f_regression
LinearSVC
LogisticRegression
ExtraTreesClassifier
feature_scores
max_scaled_scores_feature_scores
selected_features_count
selected_features
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\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "to track results use .show() or .logs() or in CLI: \n", + "!mlrun get run 8765f9e7fde94efeb662fbe2c37a0e1a --project default , !mlrun logs 8765f9e7fde94efeb662fbe2c37a0e1a --project default\n", + "> 2021-08-11 10:12:08,438 [info] run executed, status=completed\n" + ] + } + ], + "source": [ + "from feature_selection import feature_selection, show_values_on_bars, plot_stat\n", + "\n", + "runl = run_local(task=task,\n", + " name='feature_selection',\n", + " handler=feature_selection,\n", + " artifact_path=os.path.join(os.path.abspath('./'), 'artifacts'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Job Test" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "> 2021-08-11 10:12:22,071 [info] function spec saved to path: function.yaml\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fn = code_to_function(name='feature_selection',\n", + " handler='feature_selection')\n", + "fn.spec.default_handler = 'feature_selection'\n", + "fn.spec.description = \"Select features through multiple Statistical and Model filters\"\n", + "fn.metadata.categories = ['data-prep', 'ml']\n", + "fn.metadata.labels = {\"author\": \"alexz\"}\n", + "fn.export('function.yaml')\n", + "fn.apply(mount_v3io())" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "> 2021-08-11 10:12:22,083 [info] starting run feature-selection-feature_selection uid=a702d89990924e10b093ee1571b47dc2 DB=http://mlrun-api:8080\n", + "> 2021-08-11 10:12:22,347 [info] Job is running in the background, pod: feature-selection-feature-selection-8wkf8\n", + "> 2021-08-11 10:14:12,748 [info] votes needed to be selected: 2\n", + "> 2021-08-11 10:14:12,877 [info] run executed, status=completed\n", + "Pass k=2 as keyword args. From version 0.25 passing these as positional arguments will result in an error\n", + "Liblinear failed to converge, increase the number of iterations.\n", + "lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + "Converting input from bool to for compatibility.\n", + "final state: completed\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "
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projectuiditerstartstatenamelabelsinputsparametersresultsartifacts
default0Aug 11 10:14:09completedfeature-selection-feature_selection
v3io_user=admin
kind=job
owner=admin
host=feature-selection-feature-selection-8wkf8
df_artifact
k=2
min_votes=0.3
label_column=is_error
f_classif
mutual_info_classif
chi2
f_regression
LinearSVC
LogisticRegression
ExtraTreesClassifier
feature_scores
max_scaled_scores_feature_scores
selected_features_count
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Ferrell_LtdMurphy_Meadow151712976593172.6479640.5354630.000000212.944943False
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Ferrell_LtdMurphy_Meadow151712976593177.8314590.0000000.000000235.109321False
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Nicholas_Estate800289709816758.2654464.0902072.048268272.717982False
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" + ], + "text/plain": [ + " f_classif mutual_info_classif chi2 \\\n", + "cpu_utilization 2520.015809 0.185045 4457.429360 \n", + "latency 10152.151995 0.198947 272872.890194 \n", + "packet_loss 14120.490547 0.210517 157191.427524 \n", + "throughput 20421.721030 0.230557 109129.511665 \n", + "\n", + " f_regression LinearSVC LogisticRegression \\\n", + "cpu_utilization 2520.015809 -0.045040 0.232760 \n", + "latency 10152.151995 0.034251 0.069064 \n", + "packet_loss 14120.490547 0.048188 0.223673 \n", + "throughput 20421.721030 -0.009259 -0.064733 \n", + "\n", + " ExtraTreesClassifier \n", + "cpu_utilization 0.026625 \n", + "latency 0.026625 \n", + "packet_loss 0.026625 \n", + "throughput 0.026625 " + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mlrun.get_dataitem(fn_run.outputs['feature_scores']).as_df()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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289175586571250.0903733.2808490.000000229.889187False
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Ferrell_LtdMurphy_Meadow151712976593172.6479640.5354630.000000212.944943False
...........................
2021-04-27 15:46:46.780Smith_GroupDebra_Gateway963381369144177.8759543.2505840.000000245.150281False
Ferrell_LtdMurphy_Meadow151712976593177.8314590.0000000.000000235.109321False
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" + ], + "text/plain": [ + " cpu_utilization \\\n", + "timestamp company data_center device \n", + "2021-04-27 14:46:46.780 Smith_Group Denise_Crest 5124209057231 75.598891 \n", + " 2891755865712 50.090373 \n", + " Debra_Gateway 0388020295311 73.243063 \n", + " 9633813691441 60.830420 \n", + " Ferrell_Ltd Murphy_Meadow 1517129765931 72.647964 \n", + "... ... \n", + "2021-04-27 15:46:46.780 Smith_Group Debra_Gateway 9633813691441 77.875954 \n", + " Ferrell_Ltd Murphy_Meadow 1517129765931 77.831459 \n", + " 6964486699383 55.978514 \n", + " Nicholas_Estate 8002897098167 58.265446 \n", + " 8499880735104 71.245041 \n", + "\n", + " latency \\\n", + "timestamp company data_center device \n", + "2021-04-27 14:46:46.780 Smith_Group Denise_Crest 5124209057231 0.000000 \n", + " 2891755865712 3.280849 \n", + " Debra_Gateway 0388020295311 9.372341 \n", + " 9633813691441 12.241878 \n", + " Ferrell_Ltd Murphy_Meadow 1517129765931 0.535463 \n", + "... ... \n", + "2021-04-27 15:46:46.780 Smith_Group Debra_Gateway 9633813691441 3.250584 \n", + " Ferrell_Ltd Murphy_Meadow 1517129765931 0.000000 \n", + " 6964486699383 2.977447 \n", + " Nicholas_Estate 8002897098167 4.090207 \n", + " 8499880735104 0.000000 \n", + "\n", + " packet_loss \\\n", + "timestamp company data_center device \n", + "2021-04-27 14:46:46.780 Smith_Group Denise_Crest 5124209057231 0.000000 \n", + " 2891755865712 0.000000 \n", + " Debra_Gateway 0388020295311 2.170138 \n", + " 9633813691441 2.295717 \n", + " Ferrell_Ltd Murphy_Meadow 1517129765931 0.000000 \n", + "... ... \n", + "2021-04-27 15:46:46.780 Smith_Group Debra_Gateway 9633813691441 0.000000 \n", + " Ferrell_Ltd Murphy_Meadow 1517129765931 0.000000 \n", + " 6964486699383 0.533963 \n", + " Nicholas_Estate 8002897098167 2.048268 \n", + " 8499880735104 2.929407 \n", + "\n", + " throughput \\\n", + "timestamp company data_center device \n", + "2021-04-27 14:46:46.780 Smith_Group Denise_Crest 5124209057231 252.445971 \n", + " 2891755865712 229.889187 \n", + " Debra_Gateway 0388020295311 260.883807 \n", + " 9633813691441 244.238613 \n", + " Ferrell_Ltd Murphy_Meadow 1517129765931 212.944943 \n", + "... ... \n", + "2021-04-27 15:46:46.780 Smith_Group Debra_Gateway 9633813691441 245.150281 \n", + " Ferrell_Ltd Murphy_Meadow 1517129765931 235.109321 \n", + " 6964486699383 277.622402 \n", + " Nicholas_Estate 8002897098167 272.717982 \n", + " 8499880735104 235.659211 \n", + "\n", + " is_error \n", + "timestamp company data_center device \n", + "2021-04-27 14:46:46.780 Smith_Group Denise_Crest 5124209057231 False \n", + " 2891755865712 False \n", + " Debra_Gateway 0388020295311 False \n", + " 9633813691441 False \n", + " Ferrell_Ltd Murphy_Meadow 1517129765931 False \n", + "... ... \n", + "2021-04-27 15:46:46.780 Smith_Group Debra_Gateway 9633813691441 False \n", + " Ferrell_Ltd Murphy_Meadow 1517129765931 False \n", + " 6964486699383 False \n", + " Nicholas_Estate 8002897098167 False \n", + " 8499880735104 False \n", + "\n", + "[5768 rows x 5 columns]" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mlrun.get_dataitem(fn_run.outputs['selected_features']).as_df()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda env:root] *", + "language": "python", + "name": "conda-root-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/functions/master/feature_selection/1.4.0/src/feature_selection.py b/functions/master/feature_selection/1.4.0/src/feature_selection.py new file mode 100644 index 00000000..630a0969 --- /dev/null +++ b/functions/master/feature_selection/1.4.0/src/feature_selection.py @@ -0,0 +1,347 @@ +# Copyright 2019 Iguazio +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import json +import os + +import matplotlib.pyplot as plt +import mlrun +import mlrun.datastore +import mlrun.utils +import mlrun.feature_store as fs +import numpy as np +import pandas as pd +import seaborn as sns +from mlrun.artifacts import PlotArtifact +from mlrun.datastore.targets import ParquetTarget +# MLRun utils +from mlrun.utils.helpers import create_class +# Feature selection strategies +from sklearn.feature_selection import SelectFromModel, SelectKBest +# Scale feature scoresgit st +from sklearn.preprocessing import MinMaxScaler +# SKLearn estimators list +from sklearn.utils import all_estimators + +DEFAULT_STAT_FILTERS = ["f_classif", "mutual_info_classif", "chi2", "f_regression"] +DEFAULT_MODEL_FILTERS = { + "LinearSVC": "LinearSVC", + "LogisticRegression": "LogisticRegression", + "ExtraTreesClassifier": "ExtraTreesClassifier", +} + + +def _clear_current_figure(): + """ + Clear matplotlib current figure. + """ + plt.cla() + plt.clf() + plt.close() + + +def show_values_on_bars(axs, h_v="v", space=0.4): + def _show_on_single_plot(ax_): + if h_v == "v": + for p in ax_.patches: + _x = p.get_x() + p.get_width() / 2 + _y = p.get_y() + p.get_height() + value = int(p.get_height()) + ax_.text(_x, _y, value, ha="center") + elif h_v == "h": + for p in ax_.patches: + _x = p.get_x() + p.get_width() + float(space) + _y = p.get_y() + p.get_height() + value = int(p.get_width()) + ax_.text(_x, _y, value, ha="left") + + if isinstance(axs, np.ndarray): + for idx, ax in np.ndenumerate(axs): + _show_on_single_plot(ax) + else: + _show_on_single_plot(axs) + + +def plot_stat(context, stat_name, stat_df): + _clear_current_figure() + + # Add chart + ax = plt.axes() + stat_chart = sns.barplot( + x=stat_name, + y="index", + data=stat_df.sort_values(stat_name, ascending=False).reset_index(), + ax=ax, + ) + plt.tight_layout() + + for p in stat_chart.patches: + width = p.get_width() + plt.text( + 5 + p.get_width(), + p.get_y() + 0.55 * p.get_height(), + "{:1.2f}".format(width), + ha="center", + va="center", + ) + + context.log_artifact( + PlotArtifact(f"{stat_name}", body=plt.gcf()), + local_path=os.path.join("plots", "feature_selection", f"{stat_name}.html"), + ) + _clear_current_figure() + + +def feature_selection( + context, + df_artifact, + k: int = 5, + min_votes: float = 0.5, + label_column: str = None, + stat_filters: list = None, + model_filters: dict = None, + max_scaled_scores: bool = True, + sample_ratio: float = None, + output_vector_name: float = None, + ignore_type_errors: bool = False, + is_feature_vector: bool = False, +): + """ + Applies selected feature selection statistical functions or models on our 'df_artifact'. + + Each statistical function or model will vote for it's best K selected features. + If a feature has >= 'min_votes' votes, it will be selected. + + :param context: the function context. + :param df_artifact: dataframe to pass as input. + :param k: number of top features to select from each statistical + function or model. + :param min_votes: minimal number of votes (from a model or by statistical + function) needed for a feature to be selected. + Can be specified by percentage of votes or absolute + number of votes. + :param label_column: ground-truth (y) labels. + :param stat_filters: statistical functions to apply to the features + (from sklearn.feature_selection). + :param model_filters: models to use for feature evaluation, can be specified by + model name (ex. LinearSVC), formalized json (contains 'CLASS', + 'FIT', 'META') or a path to such json file. + :param max_scaled_scores: produce feature scores table scaled with max_scaler. + :param sample_ratio: percentage of the dataset the user whishes to compute the feature selection process on. + :param output_vector_name: creates a new feature vector containing only the identifies features. + :param ignore_type_errors: skips datatypes that are neither float nor int within the feature vector. + :param is_feature_vector: bool stating if the data is passed as a feature vector. + """ + stat_filters = stat_filters or DEFAULT_STAT_FILTERS + model_filters = model_filters or DEFAULT_MODEL_FILTERS + # Check if df.meta is valid, if it is, look for a feature vector + store_uri_prefix, _ = mlrun.datastore.parse_store_uri(df_artifact.artifact_url) + is_feature_vector = mlrun.utils.StorePrefix.FeatureVector == store_uri_prefix + + # Look inside meta.spec.label_feature to identify the label_column if the user did not specify it + if label_column is None: + if is_feature_vector: + label_column = df_artifact.meta.spec.label_feature.split(".")[1] + else: + raise ValueError("No label_column was given, please add a label_column.") + + # Use the feature vector as dataframe + df = df_artifact.as_df() + + # Ensure k is not bigger than the total number of features + if k > df.shape[1]: + raise ValueError( + f"K cannot be bigger than the total number of features ({df.shape[1]}). Please choose a smaller K." + ) + elif k < 1: + raise ValueError("K cannot be smaller than 1. Please choose a bigger K.") + + # Create a sample dataframe of the original feature vector + if sample_ratio: + df = ( + df.groupby(label_column) + .apply(lambda x: x.sample(frac=sample_ratio)) + .reset_index(drop=True) + ) + df = df.dropna() + + # Set feature vector and labels + y = df.pop(label_column) + X = df + + if np.object_ in list(X.dtypes) and ignore_type_errors is False: + raise ValueError( + f"{df.select_dtypes(include=['object']).columns.tolist()} are neither float or int." + ) + + # Create selected statistical estimators + stat_functions_list = { + stat_name: SelectKBest( + score_func=create_class(f"sklearn.feature_selection.{stat_name}"), k=k + ) + for stat_name in stat_filters + } + requires_abs = ["chi2"] + + # Run statistic filters + selected_features_agg = {} + stats_df = pd.DataFrame(index=X.columns).dropna() + + for stat_name, stat_func in stat_functions_list.items(): + try: + params = (X, y) if stat_name in requires_abs else (abs(X), y) + stat = stat_func.fit(*params) + + # Collect stat function results + stat_df = pd.DataFrame( + index=X.columns, columns=[stat_name], data=stat.scores_ + ) + plot_stat(context, stat_name, stat_df) + stats_df = stats_df.join(stat_df) + + # Select K Best features + selected_features = X.columns[stat_func.get_support()] + selected_features_agg[stat_name] = selected_features + + except Exception as e: + context.logger.info(f"Couldn't calculate {stat_name} because of: {e}") + + # Create models from class name / json file / json params + all_sklearn_estimators = dict(all_estimators()) if len(model_filters) > 0 else {} + selected_models = {} + for model_name, model in model_filters.items(): + if ".json" in model: + current_model = json.load(open(model, "r")) + classifier_class = create_class(current_model["META"]["class"]) + selected_models[model_name] = classifier_class(**current_model["CLASS"]) + elif model in all_sklearn_estimators: + selected_models[model_name] = all_sklearn_estimators[model_name]() + + else: + try: + current_model = json.loads(model) + classifier_class = create_class(current_model["META"]["class"]) + selected_models[model_name] = classifier_class(**current_model["CLASS"]) + except Exception as e: + context.logger.info(f"unable to load {model} because of: {e}") + + # Run model filters + models_df = pd.DataFrame(index=X.columns) + for model_name, model in selected_models.items(): + + if model_name == "LogisticRegression": + model.set_params(solver="liblinear") + + # Train model and get feature importance + select_from_model = SelectFromModel(model).fit(X, y) + feature_idx = select_from_model.get_support() + feature_names = X.columns[feature_idx] + selected_features_agg[model_name] = feature_names.tolist() + + # Collect model feature importance + if hasattr(select_from_model.estimator_, "coef_"): + stat_df = select_from_model.estimator_.coef_ + elif hasattr(select_from_model.estimator_, "feature_importances_"): + stat_df = select_from_model.estimator_.feature_importances_ + + stat_df = pd.DataFrame(index=X.columns, columns=[model_name], data=stat_df[0]) + models_df = models_df.join(stat_df) + + plot_stat(context, model_name, stat_df) + + # Create feature_scores DF with stat & model filters scores + result_matrix_df = pd.concat([stats_df, models_df], axis=1, sort=False) + context.log_dataset( + key="feature_scores", + df=result_matrix_df, + local_path="feature_scores.parquet", + format="parquet", + ) + if max_scaled_scores: + normalized_df = result_matrix_df.replace([np.inf, -np.inf], np.nan).values + min_max_scaler = MinMaxScaler() + normalized_df = min_max_scaler.fit_transform(normalized_df) + normalized_df = pd.DataFrame( + data=normalized_df, + columns=result_matrix_df.columns, + index=result_matrix_df.index, + ) + context.log_dataset( + key="max_scaled_scores_feature_scores", + df=normalized_df, + local_path="max_scaled_scores_feature_scores.parquet", + format="parquet", + ) + + # Create feature count DataFrame + for test_name in selected_features_agg: + result_matrix_df[test_name] = [ + 1 if x in selected_features_agg[test_name] else 0 for x in X.columns + ] + result_matrix_df.loc[:, "num_votes"] = result_matrix_df.sum(axis=1) + context.log_dataset( + key="selected_features_count", + df=result_matrix_df, + local_path="selected_features_count.parquet", + format="parquet", + ) + + # How many votes are needed for a feature to be selected? + if isinstance(min_votes, int): + votes_needed = min_votes + else: + num_filters = len(stat_filters) + len(model_filters) + votes_needed = int(np.floor(num_filters * max(min(min_votes, 1), 0))) + context.logger.info(f"votes needed to be selected: {votes_needed}") + + # Create final feature dataframe + selected_features = result_matrix_df[ + result_matrix_df.num_votes >= votes_needed + ].index.tolist() + good_feature_df = df.loc[:, selected_features] + final_df = pd.concat([good_feature_df, y], axis=1) + context.log_dataset( + key="selected_features", + df=final_df, + local_path="selected_features.parquet", + format="parquet", + ) + + # Creating a new feature vector containing only the identified top features + if is_feature_vector and df_artifact.meta.spec.features and output_vector_name: + # Selecting the top K features from our top feature dataframe + selected_features = result_matrix_df.head(k).index + + # Match the selected feature names to the FS Feature annotations + matched_selections = [ + feature + for feature in list(df_artifact.meta.spec.features) + for selected in list(selected_features) + if feature.endswith(selected) + ] + + # Defining our new feature vector + top_features_fv = fs.FeatureVector( + output_vector_name, + matched_selections, + label_feature="labels.label", + description="feature vector composed strictly of our top features", + ) + + # Saving + top_features_fv.save() + fs.get_offline_features(top_features_fv, target=ParquetTarget()) + + # Logging our new feature vector URI + context.log_result("top_features_vector", top_features_fv.uri) diff --git a/functions/master/feature_selection/1.4.0/src/function.yaml b/functions/master/feature_selection/1.4.0/src/function.yaml new file mode 100644 index 00000000..0851f54d --- /dev/null +++ b/functions/master/feature_selection/1.4.0/src/function.yaml @@ -0,0 +1,120 @@ +kind: job +metadata: + name: feature-selection + tag: '' + hash: 6dba16d062d81f78d3d210fee75edfe8b1def9b3 + project: '' + labels: + author: orz + categories: + - data-preparation + - machine-learning +spec: + command: '' + args: [] + image: mlrun/mlrun + build: + functionSourceCode: # Copyright 2019 Iguazio
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import json
import os

import matplotlib.pyplot as plt
import mlrun
import mlrun.datastore
import mlrun.utils
import mlrun.feature_store as fs
import numpy as np
import pandas as pd
import seaborn as sns
from mlrun.artifacts import PlotArtifact
from mlrun.datastore.targets import ParquetTarget
# MLRun utils
from mlrun.utils.helpers import create_class
# Feature selection strategies
from sklearn.feature_selection import SelectFromModel, SelectKBest
# Scale feature scoresgit st
from sklearn.preprocessing import MinMaxScaler
# SKLearn estimators list
from sklearn.utils import all_estimators

DEFAULT_STAT_FILTERS = ["f_classif", "mutual_info_classif", "chi2", "f_regression"]
DEFAULT_MODEL_FILTERS = {
    "LinearSVC": "LinearSVC",
    "LogisticRegression": "LogisticRegression",
    "ExtraTreesClassifier": "ExtraTreesClassifier",
}


def _clear_current_figure():
    """
    Clear matplotlib current figure.
    """
    plt.cla()
    plt.clf()
    plt.close()


def show_values_on_bars(axs, h_v="v", space=0.4):
    def _show_on_single_plot(ax_):
        if h_v == "v":
            for p in ax_.patches:
                _x = p.get_x() + p.get_width() / 2
                _y = p.get_y() + p.get_height()
                value = int(p.get_height())
                ax_.text(_x, _y, value, ha="center")
        elif h_v == "h":
            for p in ax_.patches:
                _x = p.get_x() + p.get_width() + float(space)
                _y = p.get_y() + p.get_height()
                value = int(p.get_width())
                ax_.text(_x, _y, value, ha="left")

    if isinstance(axs, np.ndarray):
        for idx, ax in np.ndenumerate(axs):
            _show_on_single_plot(ax)
    else:
        _show_on_single_plot(axs)


def plot_stat(context, stat_name, stat_df):
    _clear_current_figure()

    # Add chart
    ax = plt.axes()
    stat_chart = sns.barplot(
        x=stat_name,
        y="index",
        data=stat_df.sort_values(stat_name, ascending=False).reset_index(),
        ax=ax,
    )
    plt.tight_layout()

    for p in stat_chart.patches:
        width = p.get_width()
        plt.text(
            5 + p.get_width(),
            p.get_y() + 0.55 * p.get_height(),
            "{:1.2f}".format(width),
            ha="center",
            va="center",
        )

    context.log_artifact(
        PlotArtifact(f"{stat_name}", body=plt.gcf()),
        local_path=os.path.join("plots", "feature_selection", f"{stat_name}.html"),
    )
    _clear_current_figure()


def feature_selection(
    context,
    df_artifact,
    k: int = 5,
    min_votes: float = 0.5,
    label_column: str = None,
    stat_filters: list = None,
    model_filters: dict = None,
    max_scaled_scores: bool = True,
    sample_ratio: float = None,
    output_vector_name: float = None,
    ignore_type_errors: bool = False,
    is_feature_vector: bool = False,
):
    """
    Applies selected feature selection statistical functions or models on our 'df_artifact'.

    Each statistical function or model will vote for it's best K selected features.
    If a feature has >= 'min_votes' votes, it will be selected.

    :param context:             the function context.
    :param df_artifact:         dataframe to pass as input.
    :param k:                   number of top features to select from each statistical
                                function or model.
    :param min_votes:           minimal number of votes (from a model or by statistical
                                function) needed for a feature to be selected.
                                Can be specified by percentage of votes or absolute
                                number of votes.
    :param label_column:        ground-truth (y) labels.
    :param stat_filters:        statistical functions to apply to the features
                                (from sklearn.feature_selection).
    :param model_filters:       models to use for feature evaluation, can be specified by
                                model name (ex. LinearSVC), formalized json (contains 'CLASS',
                                'FIT', 'META') or a path to such json file.
    :param max_scaled_scores:   produce feature scores table scaled with max_scaler.
    :param sample_ratio:        percentage of the dataset the user whishes to compute the feature selection process on.
    :param output_vector_name:  creates a new feature vector containing only the identifies features.
    :param ignore_type_errors:  skips datatypes that are neither float nor int within the feature vector.
    :param is_feature_vector:   bool stating if the data is passed as a feature vector.
    """
    stat_filters = stat_filters or DEFAULT_STAT_FILTERS
    model_filters = model_filters or DEFAULT_MODEL_FILTERS
    # Check if df.meta is valid, if it is, look for a feature vector
    store_uri_prefix, _ = mlrun.datastore.parse_store_uri(df_artifact.artifact_url)
    is_feature_vector = mlrun.utils.StorePrefix.FeatureVector == store_uri_prefix

    # Look inside meta.spec.label_feature to identify the label_column if the user did not specify it
    if label_column is None:
        if is_feature_vector:
            label_column = df_artifact.meta.spec.label_feature.split(".")[1]
        else:
            raise ValueError("No label_column was given, please add a label_column.")

    # Use the feature vector as dataframe
    df = df_artifact.as_df()

    # Ensure k is not bigger than the total number of features
    if k > df.shape[1]:
        raise ValueError(
            f"K cannot be bigger than the total number of features ({df.shape[1]}). Please choose a smaller K."
        )
    elif k < 1:
        raise ValueError("K cannot be smaller than 1. Please choose a bigger K.")

    # Create a sample dataframe of the original feature vector
    if sample_ratio:
        df = (
            df.groupby(label_column)
            .apply(lambda x: x.sample(frac=sample_ratio))
            .reset_index(drop=True)
        )
        df = df.dropna()

    # Set feature vector and labels
    y = df.pop(label_column)
    X = df

    if np.object_ in list(X.dtypes) and ignore_type_errors is False:
        raise ValueError(
            f"{df.select_dtypes(include=['object']).columns.tolist()} are neither float or int."
        )

    # Create selected statistical estimators
    stat_functions_list = {
        stat_name: SelectKBest(
            score_func=create_class(f"sklearn.feature_selection.{stat_name}"), k=k
        )
        for stat_name in stat_filters
    }
    requires_abs = ["chi2"]

    # Run statistic filters
    selected_features_agg = {}
    stats_df = pd.DataFrame(index=X.columns).dropna()

    for stat_name, stat_func in stat_functions_list.items():
        try:
            params = (X, y) if stat_name in requires_abs else (abs(X), y)
            stat = stat_func.fit(*params)

            # Collect stat function results
            stat_df = pd.DataFrame(
                index=X.columns, columns=[stat_name], data=stat.scores_
            )
            plot_stat(context, stat_name, stat_df)
            stats_df = stats_df.join(stat_df)

            # Select K Best features
            selected_features = X.columns[stat_func.get_support()]
            selected_features_agg[stat_name] = selected_features

        except Exception as e:
            context.logger.info(f"Couldn't calculate {stat_name} because of: {e}")

    # Create models from class name / json file / json params
    all_sklearn_estimators = dict(all_estimators()) if len(model_filters) > 0 else {}
    selected_models = {}
    for model_name, model in model_filters.items():
        if ".json" in model:
            current_model = json.load(open(model, "r"))
            classifier_class = create_class(current_model["META"]["class"])
            selected_models[model_name] = classifier_class(**current_model["CLASS"])
        elif model in all_sklearn_estimators:
            selected_models[model_name] = all_sklearn_estimators[model_name]()

        else:
            try:
                current_model = json.loads(model)
                classifier_class = create_class(current_model["META"]["class"])
                selected_models[model_name] = classifier_class(**current_model["CLASS"])
            except Exception as e:
                context.logger.info(f"unable to load {model} because of: {e}")

    # Run model filters
    models_df = pd.DataFrame(index=X.columns)
    for model_name, model in selected_models.items():

        if model_name == "LogisticRegression":
            model.set_params(solver="liblinear")

        # Train model and get feature importance
        select_from_model = SelectFromModel(model).fit(X, y)
        feature_idx = select_from_model.get_support()
        feature_names = X.columns[feature_idx]
        selected_features_agg[model_name] = feature_names.tolist()

        # Collect model feature importance
        if hasattr(select_from_model.estimator_, "coef_"):
            stat_df = select_from_model.estimator_.coef_
        elif hasattr(select_from_model.estimator_, "feature_importances_"):
            stat_df = select_from_model.estimator_.feature_importances_

        stat_df = pd.DataFrame(index=X.columns, columns=[model_name], data=stat_df[0])
        models_df = models_df.join(stat_df)

        plot_stat(context, model_name, stat_df)

    # Create feature_scores DF with stat & model filters scores
    result_matrix_df = pd.concat([stats_df, models_df], axis=1, sort=False)
    context.log_dataset(
        key="feature_scores",
        df=result_matrix_df,
        local_path="feature_scores.parquet",
        format="parquet",
    )
    if max_scaled_scores:
        normalized_df = result_matrix_df.replace([np.inf, -np.inf], np.nan).values
        min_max_scaler = MinMaxScaler()
        normalized_df = min_max_scaler.fit_transform(normalized_df)
        normalized_df = pd.DataFrame(
            data=normalized_df,
            columns=result_matrix_df.columns,
            index=result_matrix_df.index,
        )
        context.log_dataset(
            key="max_scaled_scores_feature_scores",
            df=normalized_df,
            local_path="max_scaled_scores_feature_scores.parquet",
            format="parquet",
        )

    # Create feature count DataFrame
    for test_name in selected_features_agg:
        result_matrix_df[test_name] = [
            1 if x in selected_features_agg[test_name] else 0 for x in X.columns
        ]
    result_matrix_df.loc[:, "num_votes"] = result_matrix_df.sum(axis=1)
    context.log_dataset(
        key="selected_features_count",
        df=result_matrix_df,
        local_path="selected_features_count.parquet",
        format="parquet",
    )

    # How many votes are needed for a feature to be selected?
    if isinstance(min_votes, int):
        votes_needed = min_votes
    else:
        num_filters = len(stat_filters) + len(model_filters)
        votes_needed = int(np.floor(num_filters * max(min(min_votes, 1), 0)))
    context.logger.info(f"votes needed to be selected: {votes_needed}")

    # Create final feature dataframe
    selected_features = result_matrix_df[
        result_matrix_df.num_votes >= votes_needed
    ].index.tolist()
    good_feature_df = df.loc[:, selected_features]
    final_df = pd.concat([good_feature_df, y], axis=1)
    context.log_dataset(
        key="selected_features",
        df=final_df,
        local_path="selected_features.parquet",
        format="parquet",
    )

    # Creating a new feature vector containing only the identified top features
    if is_feature_vector and df_artifact.meta.spec.features and output_vector_name:
        # Selecting the top K features from our top feature dataframe
        selected_features = result_matrix_df.head(k).index

        # Match the selected feature names to the FS Feature annotations
        matched_selections = [
            feature
            for feature in list(df_artifact.meta.spec.features)
            for selected in list(selected_features)
            if feature.endswith(selected)
        ]

        # Defining our new feature vector
        top_features_fv = fs.FeatureVector(
            output_vector_name,
            matched_selections,
            label_feature="labels.label",
            description="feature vector composed strictly of our top features",
        )

        # Saving
        top_features_fv.save()
        fs.get_offline_features(top_features_fv, target=ParquetTarget())

        # Logging our new feature vector URI
        context.log_result("top_features_vector", top_features_fv.uri)
 + commands: [] + code_origin: '' + origin_filename: '' + requirements: [] + entry_points: + show_values_on_bars: + name: show_values_on_bars + doc: '' + parameters: + - name: axs + - name: h_v + default: v + - name: space + default: 0.4 + outputs: [] + lineno: 54 + has_varargs: false + has_kwargs: false + plot_stat: + name: plot_stat + doc: '' + parameters: + - name: context + - name: stat_name + - name: stat_df + outputs: [] + lineno: 76 + has_varargs: false + has_kwargs: false + feature_selection: + name: feature_selection + doc: 'Applies selected feature selection statistical functions or models on + our ''df_artifact''. + + + Each statistical function or model will vote for it''s best K selected features. + + If a feature has >= ''min_votes'' votes, it will be selected.' + parameters: + - name: context + doc: the function context. + - name: df_artifact + doc: dataframe to pass as input. + - name: k + type: int + doc: number of top features to select from each statistical function or model. + default: 5 + - name: min_votes + type: float + doc: minimal number of votes (from a model or by statistical function) needed + for a feature to be selected. Can be specified by percentage of votes or + absolute number of votes. + default: 0.5 + - name: label_column + type: str + doc: ground-truth (y) labels. + default: null + - name: stat_filters + type: list + doc: statistical functions to apply to the features (from sklearn.feature_selection). + default: null + - name: model_filters + type: dict + doc: models to use for feature evaluation, can be specified by model name + (ex. LinearSVC), formalized json (contains 'CLASS', 'FIT', 'META') or a + path to such json file. + default: null + - name: max_scaled_scores + type: bool + doc: produce feature scores table scaled with max_scaler. + default: true + - name: sample_ratio + type: float + doc: percentage of the dataset the user whishes to compute the feature selection + process on. + default: null + - name: output_vector_name + type: float + doc: creates a new feature vector containing only the identifies features. + default: null + - name: ignore_type_errors + type: bool + doc: skips datatypes that are neither float nor int within the feature vector. + default: false + - name: is_feature_vector + type: bool + doc: bool stating if the data is passed as a feature vector. + default: false + outputs: [] + lineno: 106 + has_varargs: false + has_kwargs: false + description: Select features through multiple Statistical and Model filters + default_handler: feature_selection + disable_auto_mount: false + clone_target_dir: '' + env: [] + priority_class_name: '' + preemption_mode: prevent + affinity: null + tolerations: null + security_context: {} +verbose: false diff --git a/functions/master/feature_selection/1.4.0/src/item.yaml b/functions/master/feature_selection/1.4.0/src/item.yaml new file mode 100644 index 00000000..7e80a417 --- /dev/null +++ b/functions/master/feature_selection/1.4.0/src/item.yaml @@ -0,0 +1,25 @@ +apiVersion: v1 +categories: +- data-preparation +- machine-learning +description: Select features through multiple Statistical and Model filters +doc: '' +example: feature_selection.ipynb +generationDate: 2022-08-28:17-25 +hidden: false +icon: '' +labels: + author: orz +maintainers: [] +marketplaceType: '' +mlrunVersion: 1.1.0 +name: feature-selection +platformVersion: 3.5.0 +spec: + filename: feature_selection.py + handler: feature_selection + image: mlrun/mlrun + kind: job + requirements: [] +url: '' +version: 1.4.0 diff --git a/functions/master/feature_selection/1.4.0/src/requirements.txt b/functions/master/feature_selection/1.4.0/src/requirements.txt new file mode 100644 index 00000000..961f64ea --- /dev/null +++ b/functions/master/feature_selection/1.4.0/src/requirements.txt @@ -0,0 +1,5 @@ +scikit-learn~=1.0.2 +matplotlib +seaborn +scikit-plot + diff --git a/functions/master/feature_selection/1.4.0/src/test_feature_selection.py b/functions/master/feature_selection/1.4.0/src/test_feature_selection.py new file mode 100644 index 00000000..6289648f --- /dev/null +++ b/functions/master/feature_selection/1.4.0/src/test_feature_selection.py @@ -0,0 +1,48 @@ +# Copyright 2019 Iguazio +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +from mlrun import code_to_function +from pathlib import Path +import shutil + +METRICS_PATH = 'data/metrics.pq' +ARTIFACTS_PATH = 'artifacts' +RUNS_PATH = 'runs' +SCHEDULES_PATH = 'schedules' + + +def _delete_outputs(paths): + for path in paths: + if Path(path).is_dir(): + shutil.rmtree(path) + + +def test_run_local_feature_selection(): + fn = code_to_function(name='test_run_local_feature_selection', + filename="feature_selection.py", + handler="feature_selection", + kind="local", + ) + fn.spec.command = "feature_selection.py" + run = fn.run( + params={ + 'k': 2, + 'min_votes': 0.3, + 'label_column': 'is_error', + }, + inputs={'df_artifact': 'data/metrics.pq'}, + artifact_path='artifacts/', + ) + assert run.artifact('feature_scores').get() and run.artifact('selected_features').get() + _delete_outputs({ARTIFACTS_PATH, RUNS_PATH, SCHEDULES_PATH}) diff --git a/functions/master/feature_selection/1.4.0/static/documentation.html b/functions/master/feature_selection/1.4.0/static/documentation.html new file mode 100644 index 00000000..101d2e4e --- /dev/null +++ b/functions/master/feature_selection/1.4.0/static/documentation.html @@ -0,0 +1,262 @@ + + + + + + + +feature_selection package + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
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+ +
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+ +
+ +
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+ +
+ +
+ +
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+ +
+
    +
  • + +
    • +
  • + +
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+ +
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+ +
+
+ Contents +
+ +
+
+
+
+ +
+

feature_selection package

+ +
+ +
+
+
+
+
+

feature_selection package#

+
+

Submodules#

+
+
+

feature_selection.feature_selection module#

+
+
+feature_selection.feature_selection.feature_selection(context, df_artifact, k: int = 5, min_votes: float = 0.5, label_column: Optional[str] = None, stat_filters: Optional[list] = None, model_filters: Optional[dict] = None, max_scaled_scores: bool = True, sample_ratio: Optional[float] = None, output_vector_name: Optional[float] = None, ignore_type_errors: bool = False, is_feature_vector: bool = False)[source]#
+

Applies selected feature selection statistical functions or models on our ‘df_artifact’.

+

Each statistical function or model will vote for it’s best K selected features. +If a feature has >= ‘min_votes’ votes, it will be selected.

+
+
Parameters
+
    +

  • context – the function context.

    • +

  • df_artifact – dataframe to pass as input.

    • +

  • k – number of top features to select from each statistical +function or model.

    • +

  • min_votes – minimal number of votes (from a model or by statistical +function) needed for a feature to be selected. +Can be specified by percentage of votes or absolute +number of votes.

    • +

  • label_column – ground-truth (y) labels.

    • +

  • stat_filters – statistical functions to apply to the features +(from sklearn.feature_selection).

    • +

  • model_filters – models to use for feature evaluation, can be specified by +model name (ex. LinearSVC), formalized json (contains ‘CLASS’, +‘FIT’, ‘META’) or a path to such json file.

    • +

  • max_scaled_scores – produce feature scores table scaled with max_scaler.

    • +

  • sample_ratio – percentage of the dataset the user whishes to compute the feature selection process on.

    • +

  • output_vector_name – creates a new feature vector containing only the identifies features.

    • +

  • ignore_type_errors – skips datatypes that are neither float nor int within the feature vector.

    • +

  • is_feature_vector – bool stating if the data is passed as a feature vector.

    • +
+
+
+
+
+
+feature_selection.feature_selection.plot_stat(context, stat_name, stat_df)[source]#
+
+
+
+feature_selection.feature_selection.show_values_on_bars(axs, h_v='v', space=0.4)[source]#
+
+
+
+

Module contents#

+
+
+
+
+
+ +
+
+
+
+
+
+

+ + © Copyright .
+

+
+
+
+
+
+ + + + \ No newline at end of file diff --git a/functions/master/feature_selection/1.4.0/static/example.html b/functions/master/feature_selection/1.4.0/static/example.html new file mode 100644 index 00000000..201e836c --- /dev/null +++ b/functions/master/feature_selection/1.4.0/static/example.html @@ -0,0 +1,1185 @@ + + + + + + + +Feature Selection + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
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+ +
+
+ +
+ +
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+ +
+ +
+ +
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+ +
+ +
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+ +
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+ +
+
+ Contents +
+ +
+
+
+
+ +
+

Feature Selection

+ +
+
+
+

Contents

+
+ +
+
+
+
+
+
+

Feature Selection#

+
+
+
import mlrun
+
+
+
+
+
+
+
%nuclio config kind = "job"
+%nuclio config spec.image = "mlrun/ml-models"
+
+
+
+
+
%nuclio: setting kind to 'job'
+%nuclio: setting spec.image to 'mlrun/ml-models'
+
+
+
+
+
+
+
# nuclio: start-code
+
+
+
+
+
+
+
import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+import numpy as np
+import os
+import json
+
+# Feature selection strategies
+from sklearn.feature_selection import SelectKBest
+from sklearn.feature_selection import SelectFromModel
+
+# Model based feature selection
+from sklearn.ensemble import ExtraTreesClassifier
+from sklearn.svm import LinearSVC
+from sklearn.linear_model import LogisticRegression
+
+# Scale feature scores
+from sklearn.preprocessing import MinMaxScaler
+
+# SKLearn estimators list
+from sklearn.utils import all_estimators
+
+# MLRun utils
+from mlrun.mlutils.plots import gcf_clear
+from mlrun.utils.helpers import create_class
+from mlrun.artifacts import PlotArtifact
+
+# Feature Selection
+from feature_selection import feature_selection, show_values_on_bars, plot_stat
+
+
+
+
+
+
+
# nuclio: end-code
+
+
+
+
+
+

Test#

+
+
+
from mlrun import code_to_function, mount_v3io, mlconf, NewTask, run_local
+
+
+
+
+
+
+
mlconf.artifact_path = os.path.abspath('./artifacts')
+mlconf.db_path = 'http://mlrun-api:8080'
+
+
+
+
+
+

Local Test#

+
+
+
task = NewTask(params={'k': 2,
+                       'min_votes': 0.3,
+                       'label_column': 'is_error'},
+               inputs={'df_artifact': os.path.abspath('data/metrics.pq')})
+
+
+
+
+
+
+
from feature_selection import feature_selection, show_values_on_bars, plot_stat
+
+runl = run_local(task=task,
+          name='feature_selection',
+          handler=feature_selection,
+          artifact_path=os.path.join(os.path.abspath('./'), 'artifacts'))
+
+
+
+
+
> 2021-08-11 10:12:05,721 [info] starting run feature_selection uid=8765f9e7fde94efeb662fbe2c37a0e1a DB=http://mlrun-api:8080
+
+
+
Pass k=2 as keyword args. From version 0.25 passing these as positional arguments will result in an error
+Liblinear failed to converge, increase the number of iterations.
+
+
+
> 2021-08-11 10:12:08,257 [info] votes needed to be selected: 2
+
+
+
Converting input from bool to <class 'numpy.uint8'> for compatibility.
+
+
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
projectuiditerstartstatenamelabelsinputsparametersresultsartifacts
default0Aug 11 10:12:05completedfeature_selection
v3io_user=admin
kind=handler
owner=admin
host=jupyter-az-ffcb58655-7l9pl
df_artifact
k=2
min_votes=0.3
label_column=is_error
f_classif
mutual_info_classif
chi2
f_regression
LinearSVC
LogisticRegression
ExtraTreesClassifier
feature_scores
max_scaled_scores_feature_scores
selected_features_count
selected_features
+
+ +
+
to track results use .show() or .logs() or in CLI: 
+!mlrun get run 8765f9e7fde94efeb662fbe2c37a0e1a --project default , !mlrun logs 8765f9e7fde94efeb662fbe2c37a0e1a --project default
+> 2021-08-11 10:12:08,438 [info] run executed, status=completed
+
+
+
+
+
+
+
+

Job Test#

+
+
+
fn = code_to_function(name='feature_selection',
+                      handler='feature_selection')
+fn.spec.default_handler = 'feature_selection'
+fn.spec.description = "Select features through multiple Statistical and Model filters"
+fn.metadata.categories = ['data-prep', 'ml']
+fn.metadata.labels = {"author": "alexz"}
+fn.export('function.yaml')
+fn.apply(mount_v3io())
+
+
+
+
+
> 2021-08-11 10:12:22,071 [info] function spec saved to path: function.yaml
+
+
+
<mlrun.runtimes.kubejob.KubejobRuntime at 0x7fa98e912890>
+
+
+
+
+
+
+
fn_run = fn.run(task)
+
+
+
+
+
> 2021-08-11 10:12:22,083 [info] starting run feature-selection-feature_selection uid=a702d89990924e10b093ee1571b47dc2 DB=http://mlrun-api:8080
+> 2021-08-11 10:12:22,347 [info] Job is running in the background, pod: feature-selection-feature-selection-8wkf8
+> 2021-08-11 10:14:12,748 [info] votes needed to be selected: 2
+> 2021-08-11 10:14:12,877 [info] run executed, status=completed
+Pass k=2 as keyword args. From version 0.25 passing these as positional arguments will result in an error
+Liblinear failed to converge, increase the number of iterations.
+lbfgs failed to converge (status=1):
+STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
+
+Increase the number of iterations (max_iter) or scale the data as shown in:
+    https://scikit-learn.org/stable/modules/preprocessing.html
+Please also refer to the documentation for alternative solver options:
+    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
+Converting input from bool to <class 'numpy.uint8'> for compatibility.
+final state: completed
+
+
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
projectuiditerstartstatenamelabelsinputsparametersresultsartifacts
default0Aug 11 10:14:09completedfeature-selection-feature_selection
v3io_user=admin
kind=job
owner=admin
host=feature-selection-feature-selection-8wkf8
df_artifact
k=2
min_votes=0.3
label_column=is_error
f_classif
mutual_info_classif
chi2
f_regression
LinearSVC
LogisticRegression
ExtraTreesClassifier
feature_scores
max_scaled_scores_feature_scores
selected_features_count
selected_features
+
+ +
+
to track results use .show() or .logs() or in CLI: 
+!mlrun get run a702d89990924e10b093ee1571b47dc2 --project default , !mlrun logs a702d89990924e10b093ee1571b47dc2 --project default
+> 2021-08-11 10:14:21,908 [info] run executed, status=completed
+
+
+
+
+
+
+
mlrun.get_dataitem(fn_run.spec.inputs['df_artifact']).as_df()
+
+
+
+
+
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cpu_utilizationlatencypacket_lossthroughputis_error
timestampcompanydata_centerdevice
2021-04-27 14:46:46.780Smith_GroupDenise_Crest512420905723175.5988910.0000000.000000252.445971False
289175586571250.0903733.2808490.000000229.889187False
Debra_Gateway038802029531173.2430639.3723412.170138260.883807False
963381369144160.83042012.2418782.295717244.238613False
Ferrell_LtdMurphy_Meadow151712976593172.6479640.5354630.000000212.944943False
...........................
2021-04-27 15:46:46.780Smith_GroupDebra_Gateway963381369144177.8759543.2505840.000000245.150281False
Ferrell_LtdMurphy_Meadow151712976593177.8314590.0000000.000000235.109321False
696448669938355.9785142.9774470.533963277.622402False
Nicholas_Estate800289709816758.2654464.0902072.048268272.717982False
849988073510471.2450410.0000002.929407235.659211False
+

5768 rows × 5 columns

+
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+
mlrun.get_dataitem(fn_run.outputs['feature_scores']).as_df()
+
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f_classifmutual_info_classifchi2f_regressionLinearSVCLogisticRegressionExtraTreesClassifier
cpu_utilization2520.0158090.1850454457.4293602520.015809-0.0450400.2327600.026625
latency10152.1519950.198947272872.89019410152.1519950.0342510.0690640.026625
packet_loss14120.4905470.210517157191.42752414120.4905470.0481880.2236730.026625
throughput20421.7210300.230557109129.51166520421.721030-0.009259-0.0647330.026625
+
+
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+
mlrun.get_dataitem(fn_run.outputs['selected_features']).as_df()
+
+
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
cpu_utilizationlatencypacket_lossthroughputis_error
timestampcompanydata_centerdevice
2021-04-27 14:46:46.780Smith_GroupDenise_Crest512420905723175.5988910.0000000.000000252.445971False
289175586571250.0903733.2808490.000000229.889187False
Debra_Gateway038802029531173.2430639.3723412.170138260.883807False
963381369144160.83042012.2418782.295717244.238613False
Ferrell_LtdMurphy_Meadow151712976593172.6479640.5354630.000000212.944943False
...........................
2021-04-27 15:46:46.780Smith_GroupDebra_Gateway963381369144177.8759543.2505840.000000245.150281False
Ferrell_LtdMurphy_Meadow151712976593177.8314590.0000000.000000235.109321False
696448669938355.9785142.9774470.533963277.622402False
Nicholas_Estate800289709816758.2654464.0902072.048268272.717982False
849988073510471.2450410.0000002.929407235.659211False
+

5768 rows × 5 columns

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+ + © Copyright .
+

+
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+
+ + + + \ No newline at end of file diff --git a/functions/master/feature_selection/1.4.0/static/feature_selection.html b/functions/master/feature_selection/1.4.0/static/feature_selection.html new file mode 100644 index 00000000..2c26b03b --- /dev/null +++ b/functions/master/feature_selection/1.4.0/static/feature_selection.html @@ -0,0 +1,487 @@ + + + + + + + +feature_selection.feature_selection + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+

Source code for feature_selection.feature_selection

+# Copyright 2019 Iguazio
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+import json
+import os
+
+import matplotlib.pyplot as plt
+import mlrun
+import mlrun.datastore
+import mlrun.utils
+import mlrun.feature_store as fs
+import numpy as np
+import pandas as pd
+import seaborn as sns
+from mlrun.artifacts import PlotArtifact
+from mlrun.datastore.targets import ParquetTarget
+# MLRun utils
+from mlrun.utils.helpers import create_class
+# Feature selection strategies
+from sklearn.feature_selection import SelectFromModel, SelectKBest
+# Scale feature scoresgit st
+from sklearn.preprocessing import MinMaxScaler
+# SKLearn estimators list
+from sklearn.utils import all_estimators
+
+DEFAULT_STAT_FILTERS = ["f_classif", "mutual_info_classif", "chi2", "f_regression"]
+DEFAULT_MODEL_FILTERS = {
+    "LinearSVC": "LinearSVC",
+    "LogisticRegression": "LogisticRegression",
+    "ExtraTreesClassifier": "ExtraTreesClassifier",
+}
+
+
+def _clear_current_figure():
+    """
+    Clear matplotlib current figure.
+    """
+    plt.cla()
+    plt.clf()
+    plt.close()
+
+
+
[docs]def show_values_on_bars(axs, h_v="v", space=0.4):
+    def _show_on_single_plot(ax_):
+        if h_v == "v":
+            for p in ax_.patches:
+                _x = p.get_x() + p.get_width() / 2
+                _y = p.get_y() + p.get_height()
+                value = int(p.get_height())
+                ax_.text(_x, _y, value, ha="center")
+        elif h_v == "h":
+            for p in ax_.patches:
+                _x = p.get_x() + p.get_width() + float(space)
+                _y = p.get_y() + p.get_height()
+                value = int(p.get_width())
+                ax_.text(_x, _y, value, ha="left")
+
+    if isinstance(axs, np.ndarray):
+        for idx, ax in np.ndenumerate(axs):
+            _show_on_single_plot(ax)
+    else:
+        _show_on_single_plot(axs)

+
+
+
[docs]def plot_stat(context, stat_name, stat_df):
+    _clear_current_figure()
+
+    # Add chart
+    ax = plt.axes()
+    stat_chart = sns.barplot(
+        x=stat_name,
+        y="index",
+        data=stat_df.sort_values(stat_name, ascending=False).reset_index(),
+        ax=ax,
+    )
+    plt.tight_layout()
+
+    for p in stat_chart.patches:
+        width = p.get_width()
+        plt.text(
+            5 + p.get_width(),
+            p.get_y() + 0.55 * p.get_height(),
+            "{:1.2f}".format(width),
+            ha="center",
+            va="center",
+        )
+
+    context.log_artifact(
+        PlotArtifact(f"{stat_name}", body=plt.gcf()),
+        local_path=os.path.join("plots", "feature_selection", f"{stat_name}.html"),
+    )
+    _clear_current_figure()

+
+
+
[docs]def feature_selection(
+    context,
+    df_artifact,
+    k: int = 5,
+    min_votes: float = 0.5,
+    label_column: str = None,
+    stat_filters: list = None,
+    model_filters: dict = None,
+    max_scaled_scores: bool = True,
+    sample_ratio: float = None,
+    output_vector_name: float = None,
+    ignore_type_errors: bool = False,
+    is_feature_vector: bool = False,
+):
+    """
+    Applies selected feature selection statistical functions or models on our 'df_artifact'.
+
+    Each statistical function or model will vote for it's best K selected features.
+    If a feature has >= 'min_votes' votes, it will be selected.
+
+    :param context:             the function context.
+    :param df_artifact:         dataframe to pass as input.
+    :param k:                   number of top features to select from each statistical
+                                function or model.
+    :param min_votes:           minimal number of votes (from a model or by statistical
+                                function) needed for a feature to be selected.
+                                Can be specified by percentage of votes or absolute
+                                number of votes.
+    :param label_column:        ground-truth (y) labels.
+    :param stat_filters:        statistical functions to apply to the features
+                                (from sklearn.feature_selection).
+    :param model_filters:       models to use for feature evaluation, can be specified by
+                                model name (ex. LinearSVC), formalized json (contains 'CLASS',
+                                'FIT', 'META') or a path to such json file.
+    :param max_scaled_scores:   produce feature scores table scaled with max_scaler.
+    :param sample_ratio:        percentage of the dataset the user whishes to compute the feature selection process on.
+    :param output_vector_name:  creates a new feature vector containing only the identifies features.
+    :param ignore_type_errors:  skips datatypes that are neither float nor int within the feature vector.
+    :param is_feature_vector:   bool stating if the data is passed as a feature vector.
+    """
+    stat_filters = stat_filters or DEFAULT_STAT_FILTERS
+    model_filters = model_filters or DEFAULT_MODEL_FILTERS
+    # Check if df.meta is valid, if it is, look for a feature vector
+    store_uri_prefix, _ = mlrun.datastore.parse_store_uri(df_artifact.artifact_url)
+    is_feature_vector = mlrun.utils.StorePrefix.FeatureVector == store_uri_prefix
+
+    # Look inside meta.spec.label_feature to identify the label_column if the user did not specify it
+    if label_column is None:
+        if is_feature_vector:
+            label_column = df_artifact.meta.spec.label_feature.split(".")[1]
+        else:
+            raise ValueError("No label_column was given, please add a label_column.")
+
+    # Use the feature vector as dataframe
+    df = df_artifact.as_df()
+
+    # Ensure k is not bigger than the total number of features
+    if k > df.shape[1]:
+        raise ValueError(
+            f"K cannot be bigger than the total number of features ({df.shape[1]}). Please choose a smaller K."
+        )
+    elif k < 1:
+        raise ValueError("K cannot be smaller than 1. Please choose a bigger K.")
+
+    # Create a sample dataframe of the original feature vector
+    if sample_ratio:
+        df = (
+            df.groupby(label_column)
+            .apply(lambda x: x.sample(frac=sample_ratio))
+            .reset_index(drop=True)
+        )
+        df = df.dropna()
+
+    # Set feature vector and labels
+    y = df.pop(label_column)
+    X = df
+
+    if np.object_ in list(X.dtypes) and ignore_type_errors is False:
+        raise ValueError(
+            f"{df.select_dtypes(include=['object']).columns.tolist()} are neither float or int."
+        )
+
+    # Create selected statistical estimators
+    stat_functions_list = {
+        stat_name: SelectKBest(
+            score_func=create_class(f"sklearn.feature_selection.{stat_name}"), k=k
+        )
+        for stat_name in stat_filters
+    }
+    requires_abs = ["chi2"]
+
+    # Run statistic filters
+    selected_features_agg = {}
+    stats_df = pd.DataFrame(index=X.columns).dropna()
+
+    for stat_name, stat_func in stat_functions_list.items():
+        try:
+            params = (X, y) if stat_name in requires_abs else (abs(X), y)
+            stat = stat_func.fit(*params)
+
+            # Collect stat function results
+            stat_df = pd.DataFrame(
+                index=X.columns, columns=[stat_name], data=stat.scores_
+            )
+            plot_stat(context, stat_name, stat_df)
+            stats_df = stats_df.join(stat_df)
+
+            # Select K Best features
+            selected_features = X.columns[stat_func.get_support()]
+            selected_features_agg[stat_name] = selected_features
+
+        except Exception as e:
+            context.logger.info(f"Couldn't calculate {stat_name} because of: {e}")
+
+    # Create models from class name / json file / json params
+    all_sklearn_estimators = dict(all_estimators()) if len(model_filters) > 0 else {}
+    selected_models = {}
+    for model_name, model in model_filters.items():
+        if ".json" in model:
+            current_model = json.load(open(model, "r"))
+            classifier_class = create_class(current_model["META"]["class"])
+            selected_models[model_name] = classifier_class(**current_model["CLASS"])
+        elif model in all_sklearn_estimators:
+            selected_models[model_name] = all_sklearn_estimators[model_name]()
+
+        else:
+            try:
+                current_model = json.loads(model)
+                classifier_class = create_class(current_model["META"]["class"])
+                selected_models[model_name] = classifier_class(**current_model["CLASS"])
+            except Exception as e:
+                context.logger.info(f"unable to load {model} because of: {e}")
+
+    # Run model filters
+    models_df = pd.DataFrame(index=X.columns)
+    for model_name, model in selected_models.items():
+
+        if model_name == "LogisticRegression":
+            model.set_params(solver="liblinear")
+
+        # Train model and get feature importance
+        select_from_model = SelectFromModel(model).fit(X, y)
+        feature_idx = select_from_model.get_support()
+        feature_names = X.columns[feature_idx]
+        selected_features_agg[model_name] = feature_names.tolist()
+
+        # Collect model feature importance
+        if hasattr(select_from_model.estimator_, "coef_"):
+            stat_df = select_from_model.estimator_.coef_
+        elif hasattr(select_from_model.estimator_, "feature_importances_"):
+            stat_df = select_from_model.estimator_.feature_importances_
+
+        stat_df = pd.DataFrame(index=X.columns, columns=[model_name], data=stat_df[0])
+        models_df = models_df.join(stat_df)
+
+        plot_stat(context, model_name, stat_df)
+
+    # Create feature_scores DF with stat & model filters scores
+    result_matrix_df = pd.concat([stats_df, models_df], axis=1, sort=False)
+    context.log_dataset(
+        key="feature_scores",
+        df=result_matrix_df,
+        local_path="feature_scores.parquet",
+        format="parquet",
+    )
+    if max_scaled_scores:
+        normalized_df = result_matrix_df.replace([np.inf, -np.inf], np.nan).values
+        min_max_scaler = MinMaxScaler()
+        normalized_df = min_max_scaler.fit_transform(normalized_df)
+        normalized_df = pd.DataFrame(
+            data=normalized_df,
+            columns=result_matrix_df.columns,
+            index=result_matrix_df.index,
+        )
+        context.log_dataset(
+            key="max_scaled_scores_feature_scores",
+            df=normalized_df,
+            local_path="max_scaled_scores_feature_scores.parquet",
+            format="parquet",
+        )
+
+    # Create feature count DataFrame
+    for test_name in selected_features_agg:
+        result_matrix_df[test_name] = [
+            1 if x in selected_features_agg[test_name] else 0 for x in X.columns
+        ]
+    result_matrix_df.loc[:, "num_votes"] = result_matrix_df.sum(axis=1)
+    context.log_dataset(
+        key="selected_features_count",
+        df=result_matrix_df,
+        local_path="selected_features_count.parquet",
+        format="parquet",
+    )
+
+    # How many votes are needed for a feature to be selected?
+    if isinstance(min_votes, int):
+        votes_needed = min_votes
+    else:
+        num_filters = len(stat_filters) + len(model_filters)
+        votes_needed = int(np.floor(num_filters * max(min(min_votes, 1), 0)))
+    context.logger.info(f"votes needed to be selected: {votes_needed}")
+
+    # Create final feature dataframe
+    selected_features = result_matrix_df[
+        result_matrix_df.num_votes >= votes_needed
+    ].index.tolist()
+    good_feature_df = df.loc[:, selected_features]
+    final_df = pd.concat([good_feature_df, y], axis=1)
+    context.log_dataset(
+        key="selected_features",
+        df=final_df,
+        local_path="selected_features.parquet",
+        format="parquet",
+    )
+
+    # Creating a new feature vector containing only the identified top features
+    if is_feature_vector and df_artifact.meta.spec.features and output_vector_name:
+        # Selecting the top K features from our top feature dataframe
+        selected_features = result_matrix_df.head(k).index
+
+        # Match the selected feature names to the FS Feature annotations
+        matched_selections = [
+            feature
+            for feature in list(df_artifact.meta.spec.features)
+            for selected in list(selected_features)
+            if feature.endswith(selected)
+        ]
+
+        # Defining our new feature vector
+        top_features_fv = fs.FeatureVector(
+            output_vector_name,
+            matched_selections,
+            label_feature="labels.label",
+            description="feature vector composed strictly of our top features",
+        )
+
+        # Saving
+        top_features_fv.save()
+        fs.get_offline_features(top_features_fv, target=ParquetTarget())
+
+        # Logging our new feature vector URI
+        context.log_result("top_features_vector", top_features_fv.uri)

+
+
+
+
+ +
+
+
+
+
+
+

+ + © Copyright .
+

+
+
+
+
+
+ + + + \ No newline at end of file diff --git a/functions/master/feature_selection/1.4.0/static/function.html b/functions/master/feature_selection/1.4.0/static/function.html new file mode 100644 index 00000000..b09632f5 --- /dev/null +++ b/functions/master/feature_selection/1.4.0/static/function.html @@ -0,0 +1,142 @@ + + + + + + + + + + + Source + + + + + 
+        
+kind: job
+metadata:
+  name: feature-selection
+  tag: ''
+  hash: 6dba16d062d81f78d3d210fee75edfe8b1def9b3
+  project: ''
+  labels:
+    author: orz
+  categories:
+  - data-preparation
+  - machine-learning
+spec:
+  command: ''
+  args: []
+  image: mlrun/mlrun
+  build:
+    functionSourceCode: # Copyright 2019 Iguazio
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import json
import os

import matplotlib.pyplot as plt
import mlrun
import mlrun.datastore
import mlrun.utils
import mlrun.feature_store as fs
import numpy as np
import pandas as pd
import seaborn as sns
from mlrun.artifacts import PlotArtifact
from mlrun.datastore.targets import ParquetTarget
# MLRun utils
from mlrun.utils.helpers import create_class
# Feature selection strategies
from sklearn.feature_selection import SelectFromModel, SelectKBest
# Scale feature scoresgit st
from sklearn.preprocessing import MinMaxScaler
# SKLearn estimators list
from sklearn.utils import all_estimators

DEFAULT_STAT_FILTERS = ["f_classif", "mutual_info_classif", "chi2", "f_regression"]
DEFAULT_MODEL_FILTERS = {
    "LinearSVC": "LinearSVC",
    "LogisticRegression": "LogisticRegression",
    "ExtraTreesClassifier": "ExtraTreesClassifier",
}


def _clear_current_figure():
    """
    Clear matplotlib current figure.
    """
    plt.cla()
    plt.clf()
    plt.close()


def show_values_on_bars(axs, h_v="v", space=0.4):
    def _show_on_single_plot(ax_):
        if h_v == "v":
            for p in ax_.patches:
                _x = p.get_x() + p.get_width() / 2
                _y = p.get_y() + p.get_height()
                value = int(p.get_height())
                ax_.text(_x, _y, value, ha="center")
        elif h_v == "h":
            for p in ax_.patches:
                _x = p.get_x() + p.get_width() + float(space)
                _y = p.get_y() + p.get_height()
                value = int(p.get_width())
                ax_.text(_x, _y, value, ha="left")

    if isinstance(axs, np.ndarray):
        for idx, ax in np.ndenumerate(axs):
            _show_on_single_plot(ax)
    else:
        _show_on_single_plot(axs)


def plot_stat(context, stat_name, stat_df):
    _clear_current_figure()

    # Add chart
    ax = plt.axes()
    stat_chart = sns.barplot(
        x=stat_name,
        y="index",
        data=stat_df.sort_values(stat_name, ascending=False).reset_index(),
        ax=ax,
    )
    plt.tight_layout()

    for p in stat_chart.patches:
        width = p.get_width()
        plt.text(
            5 + p.get_width(),
            p.get_y() + 0.55 * p.get_height(),
            "{:1.2f}".format(width),
            ha="center",
            va="center",
        )

    context.log_artifact(
        PlotArtifact(f"{stat_name}", body=plt.gcf()),
        local_path=os.path.join("plots", "feature_selection", f"{stat_name}.html"),
    )
    _clear_current_figure()


def feature_selection(
    context,
    df_artifact,
    k: int = 5,
    min_votes: float = 0.5,
    label_column: str = None,
    stat_filters: list = None,
    model_filters: dict = None,
    max_scaled_scores: bool = True,
    sample_ratio: float = None,
    output_vector_name: float = None,
    ignore_type_errors: bool = False,
    is_feature_vector: bool = False,
):
    """
    Applies selected feature selection statistical functions or models on our 'df_artifact'.

    Each statistical function or model will vote for it's best K selected features.
    If a feature has >= 'min_votes' votes, it will be selected.

    :param context:             the function context.
    :param df_artifact:         dataframe to pass as input.
    :param k:                   number of top features to select from each statistical
                                function or model.
    :param min_votes:           minimal number of votes (from a model or by statistical
                                function) needed for a feature to be selected.
                                Can be specified by percentage of votes or absolute
                                number of votes.
    :param label_column:        ground-truth (y) labels.
    :param stat_filters:        statistical functions to apply to the features
                                (from sklearn.feature_selection).
    :param model_filters:       models to use for feature evaluation, can be specified by
                                model name (ex. LinearSVC), formalized json (contains 'CLASS',
                                'FIT', 'META') or a path to such json file.
    :param max_scaled_scores:   produce feature scores table scaled with max_scaler.
    :param sample_ratio:        percentage of the dataset the user whishes to compute the feature selection process on.
    :param output_vector_name:  creates a new feature vector containing only the identifies features.
    :param ignore_type_errors:  skips datatypes that are neither float nor int within the feature vector.
    :param is_feature_vector:   bool stating if the data is passed as a feature vector.
    """
    stat_filters = stat_filters or DEFAULT_STAT_FILTERS
    model_filters = model_filters or DEFAULT_MODEL_FILTERS
    # Check if df.meta is valid, if it is, look for a feature vector
    store_uri_prefix, _ = mlrun.datastore.parse_store_uri(df_artifact.artifact_url)
    is_feature_vector = mlrun.utils.StorePrefix.FeatureVector == store_uri_prefix

    # Look inside meta.spec.label_feature to identify the label_column if the user did not specify it
    if label_column is None:
        if is_feature_vector:
            label_column = df_artifact.meta.spec.label_feature.split(".")[1]
        else:
            raise ValueError("No label_column was given, please add a label_column.")

    # Use the feature vector as dataframe
    df = df_artifact.as_df()

    # Ensure k is not bigger than the total number of features
    if k > df.shape[1]:
        raise ValueError(
            f"K cannot be bigger than the total number of features ({df.shape[1]}). Please choose a smaller K."
        )
    elif k < 1:
        raise ValueError("K cannot be smaller than 1. Please choose a bigger K.")

    # Create a sample dataframe of the original feature vector
    if sample_ratio:
        df = (
            df.groupby(label_column)
            .apply(lambda x: x.sample(frac=sample_ratio))
            .reset_index(drop=True)
        )
        df = df.dropna()

    # Set feature vector and labels
    y = df.pop(label_column)
    X = df

    if np.object_ in list(X.dtypes) and ignore_type_errors is False:
        raise ValueError(
            f"{df.select_dtypes(include=['object']).columns.tolist()} are neither float or int."
        )

    # Create selected statistical estimators
    stat_functions_list = {
        stat_name: SelectKBest(
            score_func=create_class(f"sklearn.feature_selection.{stat_name}"), k=k
        )
        for stat_name in stat_filters
    }
    requires_abs = ["chi2"]

    # Run statistic filters
    selected_features_agg = {}
    stats_df = pd.DataFrame(index=X.columns).dropna()

    for stat_name, stat_func in stat_functions_list.items():
        try:
            params = (X, y) if stat_name in requires_abs else (abs(X), y)
            stat = stat_func.fit(*params)

            # Collect stat function results
            stat_df = pd.DataFrame(
                index=X.columns, columns=[stat_name], data=stat.scores_
            )
            plot_stat(context, stat_name, stat_df)
            stats_df = stats_df.join(stat_df)

            # Select K Best features
            selected_features = X.columns[stat_func.get_support()]
            selected_features_agg[stat_name] = selected_features

        except Exception as e:
            context.logger.info(f"Couldn't calculate {stat_name} because of: {e}")

    # Create models from class name / json file / json params
    all_sklearn_estimators = dict(all_estimators()) if len(model_filters) > 0 else {}
    selected_models = {}
    for model_name, model in model_filters.items():
        if ".json" in model:
            current_model = json.load(open(model, "r"))
            classifier_class = create_class(current_model["META"]["class"])
            selected_models[model_name] = classifier_class(**current_model["CLASS"])
        elif model in all_sklearn_estimators:
            selected_models[model_name] = all_sklearn_estimators[model_name]()

        else:
            try:
                current_model = json.loads(model)
                classifier_class = create_class(current_model["META"]["class"])
                selected_models[model_name] = classifier_class(**current_model["CLASS"])
            except Exception as e:
                context.logger.info(f"unable to load {model} because of: {e}")

    # Run model filters
    models_df = pd.DataFrame(index=X.columns)
    for model_name, model in selected_models.items():

        if model_name == "LogisticRegression":
            model.set_params(solver="liblinear")

        # Train model and get feature importance
        select_from_model = SelectFromModel(model).fit(X, y)
        feature_idx = select_from_model.get_support()
        feature_names = X.columns[feature_idx]
        selected_features_agg[model_name] = feature_names.tolist()

        # Collect model feature importance
        if hasattr(select_from_model.estimator_, "coef_"):
            stat_df = select_from_model.estimator_.coef_
        elif hasattr(select_from_model.estimator_, "feature_importances_"):
            stat_df = select_from_model.estimator_.feature_importances_

        stat_df = pd.DataFrame(index=X.columns, columns=[model_name], data=stat_df[0])
        models_df = models_df.join(stat_df)

        plot_stat(context, model_name, stat_df)

    # Create feature_scores DF with stat & model filters scores
    result_matrix_df = pd.concat([stats_df, models_df], axis=1, sort=False)
    context.log_dataset(
        key="feature_scores",
        df=result_matrix_df,
        local_path="feature_scores.parquet",
        format="parquet",
    )
    if max_scaled_scores:
        normalized_df = result_matrix_df.replace([np.inf, -np.inf], np.nan).values
        min_max_scaler = MinMaxScaler()
        normalized_df = min_max_scaler.fit_transform(normalized_df)
        normalized_df = pd.DataFrame(
            data=normalized_df,
            columns=result_matrix_df.columns,
            index=result_matrix_df.index,
        )
        context.log_dataset(
            key="max_scaled_scores_feature_scores",
            df=normalized_df,
            local_path="max_scaled_scores_feature_scores.parquet",
            format="parquet",
        )

    # Create feature count DataFrame
    for test_name in selected_features_agg:
        result_matrix_df[test_name] = [
            1 if x in selected_features_agg[test_name] else 0 for x in X.columns
        ]
    result_matrix_df.loc[:, "num_votes"] = result_matrix_df.sum(axis=1)
    context.log_dataset(
        key="selected_features_count",
        df=result_matrix_df,
        local_path="selected_features_count.parquet",
        format="parquet",
    )

    # How many votes are needed for a feature to be selected?
    if isinstance(min_votes, int):
        votes_needed = min_votes
    else:
        num_filters = len(stat_filters) + len(model_filters)
        votes_needed = int(np.floor(num_filters * max(min(min_votes, 1), 0)))
    context.logger.info(f"votes needed to be selected: {votes_needed}")

    # Create final feature dataframe
    selected_features = result_matrix_df[
        result_matrix_df.num_votes >= votes_needed
    ].index.tolist()
    good_feature_df = df.loc[:, selected_features]
    final_df = pd.concat([good_feature_df, y], axis=1)
    context.log_dataset(
        key="selected_features",
        df=final_df,
        local_path="selected_features.parquet",
        format="parquet",
    )

    # Creating a new feature vector containing only the identified top features
    if is_feature_vector and df_artifact.meta.spec.features and output_vector_name:
        # Selecting the top K features from our top feature dataframe
        selected_features = result_matrix_df.head(k).index

        # Match the selected feature names to the FS Feature annotations
        matched_selections = [
            feature
            for feature in list(df_artifact.meta.spec.features)
            for selected in list(selected_features)
            if feature.endswith(selected)
        ]

        # Defining our new feature vector
        top_features_fv = fs.FeatureVector(
            output_vector_name,
            matched_selections,
            label_feature="labels.label",
            description="feature vector composed strictly of our top features",
        )

        # Saving
        top_features_fv.save()
        fs.get_offline_features(top_features_fv, target=ParquetTarget())

        # Logging our new feature vector URI
        context.log_result("top_features_vector", top_features_fv.uri)

+    commands: []
+    code_origin: ''
+    origin_filename: ''
+    requirements: []
+  entry_points:
+    show_values_on_bars:
+      name: show_values_on_bars
+      doc: ''
+      parameters:
+      - name: axs
+      - name: h_v
+        default: v
+      - name: space
+        default: 0.4
+      outputs: []
+      lineno: 54
+      has_varargs: false
+      has_kwargs: false
+    plot_stat:
+      name: plot_stat
+      doc: ''
+      parameters:
+      - name: context
+      - name: stat_name
+      - name: stat_df
+      outputs: []
+      lineno: 76
+      has_varargs: false
+      has_kwargs: false
+    feature_selection:
+      name: feature_selection
+      doc: 'Applies selected feature selection statistical functions or models on
+        our ''df_artifact''.
+
+
+        Each statistical function or model will vote for it''s best K selected features.
+
+        If a feature has >= ''min_votes'' votes, it will be selected.'
+      parameters:
+      - name: context
+        doc: the function context.
+      - name: df_artifact
+        doc: dataframe to pass as input.
+      - name: k
+        type: int
+        doc: number of top features to select from each statistical function or model.
+        default: 5
+      - name: min_votes
+        type: float
+        doc: minimal number of votes (from a model or by statistical function) needed
+          for a feature to be selected. Can be specified by percentage of votes or
+          absolute number of votes.
+        default: 0.5
+      - name: label_column
+        type: str
+        doc: ground-truth (y) labels.
+        default: null
+      - name: stat_filters
+        type: list
+        doc: statistical functions to apply to the features (from sklearn.feature_selection).
+        default: null
+      - name: model_filters
+        type: dict
+        doc: models to use for feature evaluation, can be specified by model name
+          (ex. LinearSVC), formalized json (contains 'CLASS', 'FIT', 'META') or a
+          path to such json file.
+        default: null
+      - name: max_scaled_scores
+        type: bool
+        doc: produce feature scores table scaled with max_scaler.
+        default: true
+      - name: sample_ratio
+        type: float
+        doc: percentage of the dataset the user whishes to compute the feature selection
+          process on.
+        default: null
+      - name: output_vector_name
+        type: float
+        doc: creates a new feature vector containing only the identifies features.
+        default: null
+      - name: ignore_type_errors
+        type: bool
+        doc: skips datatypes that are neither float nor int within the feature vector.
+        default: false
+      - name: is_feature_vector
+        type: bool
+        doc: bool stating if the data is passed as a feature vector.
+        default: false
+      outputs: []
+      lineno: 106
+      has_varargs: false
+      has_kwargs: false
+  description: Select features through multiple Statistical and Model filters
+  default_handler: feature_selection
+  disable_auto_mount: false
+  clone_target_dir: ''
+  env: []
+  priority_class_name: ''
+  preemption_mode: prevent
+  affinity: null
+  tolerations: null
+  security_context: {}
+verbose: false
+
+        
+
+ + \ No newline at end of file diff --git a/functions/master/feature_selection/1.4.0/static/item.html b/functions/master/feature_selection/1.4.0/static/item.html new file mode 100644 index 00000000..9f497252 --- /dev/null +++ b/functions/master/feature_selection/1.4.0/static/item.html @@ -0,0 +1,47 @@ + + + + + + + + + + + Source + + + + + 
+        
+apiVersion: v1
+categories:
+- data-preparation
+- machine-learning
+description: Select features through multiple Statistical and Model filters
+doc: ''
+example: feature_selection.ipynb
+generationDate: 2022-08-28:17-25
+hidden: false
+icon: ''
+labels:
+  author: orz
+maintainers: []
+marketplaceType: ''
+mlrunVersion: 1.1.0
+name: feature-selection
+platformVersion: 3.5.0
+spec:
+  filename: feature_selection.py
+  handler: feature_selection
+  image: mlrun/mlrun
+  kind: job
+  requirements: []
+url: ''
+version: 1.4.0
+
+        
+
+ + \ No newline at end of file diff --git a/functions/master/feature_selection/1.4.0/static/source.html b/functions/master/feature_selection/1.4.0/static/source.html new file mode 100644 index 00000000..40198374 --- /dev/null +++ b/functions/master/feature_selection/1.4.0/static/source.html @@ -0,0 +1,369 @@ + + + + + + + + + + + Source + + + + + 
+        
+# Copyright 2019 Iguazio
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+import json
+import os
+
+import matplotlib.pyplot as plt
+import mlrun
+import mlrun.datastore
+import mlrun.utils
+import mlrun.feature_store as fs
+import numpy as np
+import pandas as pd
+import seaborn as sns
+from mlrun.artifacts import PlotArtifact
+from mlrun.datastore.targets import ParquetTarget
+# MLRun utils
+from mlrun.utils.helpers import create_class
+# Feature selection strategies
+from sklearn.feature_selection import SelectFromModel, SelectKBest
+# Scale feature scoresgit st
+from sklearn.preprocessing import MinMaxScaler
+# SKLearn estimators list
+from sklearn.utils import all_estimators
+
+DEFAULT_STAT_FILTERS = ["f_classif", "mutual_info_classif", "chi2", "f_regression"]
+DEFAULT_MODEL_FILTERS = {
+    "LinearSVC": "LinearSVC",
+    "LogisticRegression": "LogisticRegression",
+    "ExtraTreesClassifier": "ExtraTreesClassifier",
+}
+
+
+def _clear_current_figure():
+    """
+    Clear matplotlib current figure.
+    """
+    plt.cla()
+    plt.clf()
+    plt.close()
+
+
+def show_values_on_bars(axs, h_v="v", space=0.4):
+    def _show_on_single_plot(ax_):
+        if h_v == "v":
+            for p in ax_.patches:
+                _x = p.get_x() + p.get_width() / 2
+                _y = p.get_y() + p.get_height()
+                value = int(p.get_height())
+                ax_.text(_x, _y, value, ha="center")
+        elif h_v == "h":
+            for p in ax_.patches:
+                _x = p.get_x() + p.get_width() + float(space)
+                _y = p.get_y() + p.get_height()
+                value = int(p.get_width())
+                ax_.text(_x, _y, value, ha="left")
+
+    if isinstance(axs, np.ndarray):
+        for idx, ax in np.ndenumerate(axs):
+            _show_on_single_plot(ax)
+    else:
+        _show_on_single_plot(axs)
+
+
+def plot_stat(context, stat_name, stat_df):
+    _clear_current_figure()
+
+    # Add chart
+    ax = plt.axes()
+    stat_chart = sns.barplot(
+        x=stat_name,
+        y="index",
+        data=stat_df.sort_values(stat_name, ascending=False).reset_index(),
+        ax=ax,
+    )
+    plt.tight_layout()
+
+    for p in stat_chart.patches:
+        width = p.get_width()
+        plt.text(
+            5 + p.get_width(),
+            p.get_y() + 0.55 * p.get_height(),
+            "{:1.2f}".format(width),
+            ha="center",
+            va="center",
+        )
+
+    context.log_artifact(
+        PlotArtifact(f"{stat_name}", body=plt.gcf()),
+        local_path=os.path.join("plots", "feature_selection", f"{stat_name}.html"),
+    )
+    _clear_current_figure()
+
+
+def feature_selection(
+    context,
+    df_artifact,
+    k: int = 5,
+    min_votes: float = 0.5,
+    label_column: str = None,
+    stat_filters: list = None,
+    model_filters: dict = None,
+    max_scaled_scores: bool = True,
+    sample_ratio: float = None,
+    output_vector_name: float = None,
+    ignore_type_errors: bool = False,
+    is_feature_vector: bool = False,
+):
+    """
+    Applies selected feature selection statistical functions or models on our 'df_artifact'.
+
+    Each statistical function or model will vote for it's best K selected features.
+    If a feature has >= 'min_votes' votes, it will be selected.
+
+    :param context:             the function context.
+    :param df_artifact:         dataframe to pass as input.
+    :param k:                   number of top features to select from each statistical
+                                function or model.
+    :param min_votes:           minimal number of votes (from a model or by statistical
+                                function) needed for a feature to be selected.
+                                Can be specified by percentage of votes or absolute
+                                number of votes.
+    :param label_column:        ground-truth (y) labels.
+    :param stat_filters:        statistical functions to apply to the features
+                                (from sklearn.feature_selection).
+    :param model_filters:       models to use for feature evaluation, can be specified by
+                                model name (ex. LinearSVC), formalized json (contains 'CLASS',
+                                'FIT', 'META') or a path to such json file.
+    :param max_scaled_scores:   produce feature scores table scaled with max_scaler.
+    :param sample_ratio:        percentage of the dataset the user whishes to compute the feature selection process on.
+    :param output_vector_name:  creates a new feature vector containing only the identifies features.
+    :param ignore_type_errors:  skips datatypes that are neither float nor int within the feature vector.
+    :param is_feature_vector:   bool stating if the data is passed as a feature vector.
+    """
+    stat_filters = stat_filters or DEFAULT_STAT_FILTERS
+    model_filters = model_filters or DEFAULT_MODEL_FILTERS
+    # Check if df.meta is valid, if it is, look for a feature vector
+    store_uri_prefix, _ = mlrun.datastore.parse_store_uri(df_artifact.artifact_url)
+    is_feature_vector = mlrun.utils.StorePrefix.FeatureVector == store_uri_prefix
+
+    # Look inside meta.spec.label_feature to identify the label_column if the user did not specify it
+    if label_column is None:
+        if is_feature_vector:
+            label_column = df_artifact.meta.spec.label_feature.split(".")[1]
+        else:
+            raise ValueError("No label_column was given, please add a label_column.")
+
+    # Use the feature vector as dataframe
+    df = df_artifact.as_df()
+
+    # Ensure k is not bigger than the total number of features
+    if k > df.shape[1]:
+        raise ValueError(
+            f"K cannot be bigger than the total number of features ({df.shape[1]}). Please choose a smaller K."
+        )
+    elif k < 1:
+        raise ValueError("K cannot be smaller than 1. Please choose a bigger K.")
+
+    # Create a sample dataframe of the original feature vector
+    if sample_ratio:
+        df = (
+            df.groupby(label_column)
+            .apply(lambda x: x.sample(frac=sample_ratio))
+            .reset_index(drop=True)
+        )
+        df = df.dropna()
+
+    # Set feature vector and labels
+    y = df.pop(label_column)
+    X = df
+
+    if np.object_ in list(X.dtypes) and ignore_type_errors is False:
+        raise ValueError(
+            f"{df.select_dtypes(include=['object']).columns.tolist()} are neither float or int."
+        )
+
+    # Create selected statistical estimators
+    stat_functions_list = {
+        stat_name: SelectKBest(
+            score_func=create_class(f"sklearn.feature_selection.{stat_name}"), k=k
+        )
+        for stat_name in stat_filters
+    }
+    requires_abs = ["chi2"]
+
+    # Run statistic filters
+    selected_features_agg = {}
+    stats_df = pd.DataFrame(index=X.columns).dropna()
+
+    for stat_name, stat_func in stat_functions_list.items():
+        try:
+            params = (X, y) if stat_name in requires_abs else (abs(X), y)
+            stat = stat_func.fit(*params)
+
+            # Collect stat function results
+            stat_df = pd.DataFrame(
+                index=X.columns, columns=[stat_name], data=stat.scores_
+            )
+            plot_stat(context, stat_name, stat_df)
+            stats_df = stats_df.join(stat_df)
+
+            # Select K Best features
+            selected_features = X.columns[stat_func.get_support()]
+            selected_features_agg[stat_name] = selected_features
+
+        except Exception as e:
+            context.logger.info(f"Couldn't calculate {stat_name} because of: {e}")
+
+    # Create models from class name / json file / json params
+    all_sklearn_estimators = dict(all_estimators()) if len(model_filters) > 0 else {}
+    selected_models = {}
+    for model_name, model in model_filters.items():
+        if ".json" in model:
+            current_model = json.load(open(model, "r"))
+            classifier_class = create_class(current_model["META"]["class"])
+            selected_models[model_name] = classifier_class(**current_model["CLASS"])
+        elif model in all_sklearn_estimators:
+            selected_models[model_name] = all_sklearn_estimators[model_name]()
+
+        else:
+            try:
+                current_model = json.loads(model)
+                classifier_class = create_class(current_model["META"]["class"])
+                selected_models[model_name] = classifier_class(**current_model["CLASS"])
+            except Exception as e:
+                context.logger.info(f"unable to load {model} because of: {e}")
+
+    # Run model filters
+    models_df = pd.DataFrame(index=X.columns)
+    for model_name, model in selected_models.items():
+
+        if model_name == "LogisticRegression":
+            model.set_params(solver="liblinear")
+
+        # Train model and get feature importance
+        select_from_model = SelectFromModel(model).fit(X, y)
+        feature_idx = select_from_model.get_support()
+        feature_names = X.columns[feature_idx]
+        selected_features_agg[model_name] = feature_names.tolist()
+
+        # Collect model feature importance
+        if hasattr(select_from_model.estimator_, "coef_"):
+            stat_df = select_from_model.estimator_.coef_
+        elif hasattr(select_from_model.estimator_, "feature_importances_"):
+            stat_df = select_from_model.estimator_.feature_importances_
+
+        stat_df = pd.DataFrame(index=X.columns, columns=[model_name], data=stat_df[0])
+        models_df = models_df.join(stat_df)
+
+        plot_stat(context, model_name, stat_df)
+
+    # Create feature_scores DF with stat & model filters scores
+    result_matrix_df = pd.concat([stats_df, models_df], axis=1, sort=False)
+    context.log_dataset(
+        key="feature_scores",
+        df=result_matrix_df,
+        local_path="feature_scores.parquet",
+        format="parquet",
+    )
+    if max_scaled_scores:
+        normalized_df = result_matrix_df.replace([np.inf, -np.inf], np.nan).values
+        min_max_scaler = MinMaxScaler()
+        normalized_df = min_max_scaler.fit_transform(normalized_df)
+        normalized_df = pd.DataFrame(
+            data=normalized_df,
+            columns=result_matrix_df.columns,
+            index=result_matrix_df.index,
+        )
+        context.log_dataset(
+            key="max_scaled_scores_feature_scores",
+            df=normalized_df,
+            local_path="max_scaled_scores_feature_scores.parquet",
+            format="parquet",
+        )
+
+    # Create feature count DataFrame
+    for test_name in selected_features_agg:
+        result_matrix_df[test_name] = [
+            1 if x in selected_features_agg[test_name] else 0 for x in X.columns
+        ]
+    result_matrix_df.loc[:, "num_votes"] = result_matrix_df.sum(axis=1)
+    context.log_dataset(
+        key="selected_features_count",
+        df=result_matrix_df,
+        local_path="selected_features_count.parquet",
+        format="parquet",
+    )
+
+    # How many votes are needed for a feature to be selected?
+    if isinstance(min_votes, int):
+        votes_needed = min_votes
+    else:
+        num_filters = len(stat_filters) + len(model_filters)
+        votes_needed = int(np.floor(num_filters * max(min(min_votes, 1), 0)))
+    context.logger.info(f"votes needed to be selected: {votes_needed}")
+
+    # Create final feature dataframe
+    selected_features = result_matrix_df[
+        result_matrix_df.num_votes >= votes_needed
+    ].index.tolist()
+    good_feature_df = df.loc[:, selected_features]
+    final_df = pd.concat([good_feature_df, y], axis=1)
+    context.log_dataset(
+        key="selected_features",
+        df=final_df,
+        local_path="selected_features.parquet",
+        format="parquet",
+    )
+
+    # Creating a new feature vector containing only the identified top features
+    if is_feature_vector and df_artifact.meta.spec.features and output_vector_name:
+        # Selecting the top K features from our top feature dataframe
+        selected_features = result_matrix_df.head(k).index
+
+        # Match the selected feature names to the FS Feature annotations
+        matched_selections = [
+            feature
+            for feature in list(df_artifact.meta.spec.features)
+            for selected in list(selected_features)
+            if feature.endswith(selected)
+        ]
+
+        # Defining our new feature vector
+        top_features_fv = fs.FeatureVector(
+            output_vector_name,
+            matched_selections,
+            label_feature="labels.label",
+            description="feature vector composed strictly of our top features",
+        )
+
+        # Saving
+        top_features_fv.save()
+        fs.get_offline_features(top_features_fv, target=ParquetTarget())
+
+        # Logging our new feature vector URI
+        context.log_result("top_features_vector", top_features_fv.uri)
+
+        
+
+ + \ No newline at end of file diff --git a/functions/master/feature_selection/latest/src/feature_selection.py b/functions/master/feature_selection/latest/src/feature_selection.py index fddf3ed1..630a0969 100644 --- a/functions/master/feature_selection/latest/src/feature_selection.py +++ b/functions/master/feature_selection/latest/src/feature_selection.py @@ -180,7 +180,7 @@ def feature_selection( y = df.pop(label_column) X = df - if np.object in list(X.dtypes) and ignore_type_errors is False: + if np.object_ in list(X.dtypes) and ignore_type_errors is False: raise ValueError( f"{df.select_dtypes(include=['object']).columns.tolist()} are neither float or int." ) diff --git a/functions/master/feature_selection/latest/src/function.yaml b/functions/master/feature_selection/latest/src/function.yaml index 37f5b2b7..0851f54d 100644 --- a/functions/master/feature_selection/latest/src/function.yaml +++ b/functions/master/feature_selection/latest/src/function.yaml @@ -2,7 +2,7 @@ kind: job metadata: name: feature-selection tag: '' - hash: 26a0b503f3248f852667d083b5a35b112254d067 + hash: 6dba16d062d81f78d3d210fee75edfe8b1def9b3 project: '' labels: author: orz @@ -14,10 +14,10 @@ spec: args: [] image: mlrun/mlrun build: - functionSourceCode: # Copyright 2019 Iguazio
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import json
import os

import matplotlib.pyplot as plt
import mlrun
import mlrun.datastore
import mlrun.utils
import mlrun.feature_store as fs
import numpy as np
import pandas as pd
import seaborn as sns
from mlrun.artifacts import PlotArtifact
from mlrun.datastore.targets import ParquetTarget
# MLRun utils
from mlrun.utils.helpers import create_class
# Feature selection strategies
from sklearn.feature_selection import SelectFromModel, SelectKBest
# Scale feature scoresgit st
from sklearn.preprocessing import MinMaxScaler
# SKLearn estimators list
from sklearn.utils import all_estimators

DEFAULT_STAT_FILTERS = ["f_classif", "mutual_info_classif", "chi2", "f_regression"]
DEFAULT_MODEL_FILTERS = {
    "LinearSVC": "LinearSVC",
    "LogisticRegression": "LogisticRegression",
    "ExtraTreesClassifier": "ExtraTreesClassifier",
}


def _clear_current_figure():
    """
    Clear matplotlib current figure.
    """
    plt.cla()
    plt.clf()
    plt.close()


def show_values_on_bars(axs, h_v="v", space=0.4):
    def _show_on_single_plot(ax_):
        if h_v == "v":
            for p in ax_.patches:
                _x = p.get_x() + p.get_width() / 2
                _y = p.get_y() + p.get_height()
                value = int(p.get_height())
                ax_.text(_x, _y, value, ha="center")
        elif h_v == "h":
            for p in ax_.patches:
                _x = p.get_x() + p.get_width() + float(space)
                _y = p.get_y() + p.get_height()
                value = int(p.get_width())
                ax_.text(_x, _y, value, ha="left")

    if isinstance(axs, np.ndarray):
        for idx, ax in np.ndenumerate(axs):
            _show_on_single_plot(ax)
    else:
        _show_on_single_plot(axs)


def plot_stat(context, stat_name, stat_df):
    _clear_current_figure()

    # Add chart
    ax = plt.axes()
    stat_chart = sns.barplot(
        x=stat_name,
        y="index",
        data=stat_df.sort_values(stat_name, ascending=False).reset_index(),
        ax=ax,
    )
    plt.tight_layout()

    for p in stat_chart.patches:
        width = p.get_width()
        plt.text(
            5 + p.get_width(),
            p.get_y() + 0.55 * p.get_height(),
            "{:1.2f}".format(width),
            ha="center",
            va="center",
        )

    context.log_artifact(
        PlotArtifact(f"{stat_name}", body=plt.gcf()),
        local_path=os.path.join("plots", "feature_selection", f"{stat_name}.html"),
    )
    _clear_current_figure()


def feature_selection(
    context,
    df_artifact,
    k: int = 5,
    min_votes: float = 0.5,
    label_column: str = None,
    stat_filters: list = None,
    model_filters: dict = None,
    max_scaled_scores: bool = True,
    sample_ratio: float = None,
    output_vector_name: float = None,
    ignore_type_errors: bool = False,
    is_feature_vector: bool = False,
):
    """
    Applies selected feature selection statistical functions or models on our 'df_artifact'.

    Each statistical function or model will vote for it's best K selected features.
    If a feature has >= 'min_votes' votes, it will be selected.

    :param context:             the function context.
    :param df_artifact:         dataframe to pass as input.
    :param k:                   number of top features to select from each statistical
                                function or model.
    :param min_votes:           minimal number of votes (from a model or by statistical
                                function) needed for a feature to be selected.
                                Can be specified by percentage of votes or absolute
                                number of votes.
    :param label_column:        ground-truth (y) labels.
    :param stat_filters:        statistical functions to apply to the features
                                (from sklearn.feature_selection).
    :param model_filters:       models to use for feature evaluation, can be specified by
                                model name (ex. LinearSVC), formalized json (contains 'CLASS',
                                'FIT', 'META') or a path to such json file.
    :param max_scaled_scores:   produce feature scores table scaled with max_scaler.
    :param sample_ratio:        percentage of the dataset the user whishes to compute the feature selection process on.
    :param output_vector_name:  creates a new feature vector containing only the identifies features.
    :param ignore_type_errors:  skips datatypes that are neither float nor int within the feature vector.
    :param is_feature_vector:   bool stating if the data is passed as a feature vector.
    """
    stat_filters = stat_filters or DEFAULT_STAT_FILTERS
    model_filters = model_filters or DEFAULT_MODEL_FILTERS
    # Check if df.meta is valid, if it is, look for a feature vector
    store_uri_prefix, _ = mlrun.datastore.parse_store_uri(df_artifact.artifact_url)
    is_feature_vector = mlrun.utils.StorePrefix.FeatureVector == store_uri_prefix

    # Look inside meta.spec.label_feature to identify the label_column if the user did not specify it
    if label_column is None:
        if is_feature_vector:
            label_column = df_artifact.meta.spec.label_feature.split(".")[1]
        else:
            raise ValueError("No label_column was given, please add a label_column.")

    # Use the feature vector as dataframe
    df = df_artifact.as_df()

    # Ensure k is not bigger than the total number of features
    if k > df.shape[1]:
        raise ValueError(
            f"K cannot be bigger than the total number of features ({df.shape[1]}). Please choose a smaller K."
        )
    elif k < 1:
        raise ValueError("K cannot be smaller than 1. Please choose a bigger K.")

    # Create a sample dataframe of the original feature vector
    if sample_ratio:
        df = (
            df.groupby(label_column)
            .apply(lambda x: x.sample(frac=sample_ratio))
            .reset_index(drop=True)
        )
        df = df.dropna()

    # Set feature vector and labels
    y = df.pop(label_column)
    X = df

    if np.object in list(X.dtypes) and ignore_type_errors is False:
        raise ValueError(
            f"{df.select_dtypes(include=['object']).columns.tolist()} are neither float or int."
        )

    # Create selected statistical estimators
    stat_functions_list = {
        stat_name: SelectKBest(
            score_func=create_class(f"sklearn.feature_selection.{stat_name}"), k=k
        )
        for stat_name in stat_filters
    }
    requires_abs = ["chi2"]

    # Run statistic filters
    selected_features_agg = {}
    stats_df = pd.DataFrame(index=X.columns).dropna()

    for stat_name, stat_func in stat_functions_list.items():
        try:
            params = (X, y) if stat_name in requires_abs else (abs(X), y)
            stat = stat_func.fit(*params)

            # Collect stat function results
            stat_df = pd.DataFrame(
                index=X.columns, columns=[stat_name], data=stat.scores_
            )
            plot_stat(context, stat_name, stat_df)
            stats_df = stats_df.join(stat_df)

            # Select K Best features
            selected_features = X.columns[stat_func.get_support()]
            selected_features_agg[stat_name] = selected_features

        except Exception as e:
            context.logger.info(f"Couldn't calculate {stat_name} because of: {e}")

    # Create models from class name / json file / json params
    all_sklearn_estimators = dict(all_estimators()) if len(model_filters) > 0 else {}
    selected_models = {}
    for model_name, model in model_filters.items():
        if ".json" in model:
            current_model = json.load(open(model, "r"))
            classifier_class = create_class(current_model["META"]["class"])
            selected_models[model_name] = classifier_class(**current_model["CLASS"])
        elif model in all_sklearn_estimators:
            selected_models[model_name] = all_sklearn_estimators[model_name]()

        else:
            try:
                current_model = json.loads(model)
                classifier_class = create_class(current_model["META"]["class"])
                selected_models[model_name] = classifier_class(**current_model["CLASS"])
            except Exception as e:
                context.logger.info(f"unable to load {model} because of: {e}")

    # Run model filters
    models_df = pd.DataFrame(index=X.columns)
    for model_name, model in selected_models.items():

        if model_name == "LogisticRegression":
            model.set_params(solver="liblinear")

        # Train model and get feature importance
        select_from_model = SelectFromModel(model).fit(X, y)
        feature_idx = select_from_model.get_support()
        feature_names = X.columns[feature_idx]
        selected_features_agg[model_name] = feature_names.tolist()

        # Collect model feature importance
        if hasattr(select_from_model.estimator_, "coef_"):
            stat_df = select_from_model.estimator_.coef_
        elif hasattr(select_from_model.estimator_, "feature_importances_"):
            stat_df = select_from_model.estimator_.feature_importances_

        stat_df = pd.DataFrame(index=X.columns, columns=[model_name], data=stat_df[0])
        models_df = models_df.join(stat_df)

        plot_stat(context, model_name, stat_df)

    # Create feature_scores DF with stat & model filters scores
    result_matrix_df = pd.concat([stats_df, models_df], axis=1, sort=False)
    context.log_dataset(
        key="feature_scores",
        df=result_matrix_df,
        local_path="feature_scores.parquet",
        format="parquet",
    )
    if max_scaled_scores:
        normalized_df = result_matrix_df.replace([np.inf, -np.inf], np.nan).values
        min_max_scaler = MinMaxScaler()
        normalized_df = min_max_scaler.fit_transform(normalized_df)
        normalized_df = pd.DataFrame(
            data=normalized_df,
            columns=result_matrix_df.columns,
            index=result_matrix_df.index,
        )
        context.log_dataset(
            key="max_scaled_scores_feature_scores",
            df=normalized_df,
            local_path="max_scaled_scores_feature_scores.parquet",
            format="parquet",
        )

    # Create feature count DataFrame
    for test_name in selected_features_agg:
        result_matrix_df[test_name] = [
            1 if x in selected_features_agg[test_name] else 0 for x in X.columns
        ]
    result_matrix_df.loc[:, "num_votes"] = result_matrix_df.sum(axis=1)
    context.log_dataset(
        key="selected_features_count",
        df=result_matrix_df,
        local_path="selected_features_count.parquet",
        format="parquet",
    )

    # How many votes are needed for a feature to be selected?
    if isinstance(min_votes, int):
        votes_needed = min_votes
    else:
        num_filters = len(stat_filters) + len(model_filters)
        votes_needed = int(np.floor(num_filters * max(min(min_votes, 1), 0)))
    context.logger.info(f"votes needed to be selected: {votes_needed}")

    # Create final feature dataframe
    selected_features = result_matrix_df[
        result_matrix_df.num_votes >= votes_needed
    ].index.tolist()
    good_feature_df = df.loc[:, selected_features]
    final_df = pd.concat([good_feature_df, y], axis=1)
    context.log_dataset(
        key="selected_features",
        df=final_df,
        local_path="selected_features.parquet",
        format="parquet",
    )

    # Creating a new feature vector containing only the identified top features
    if is_feature_vector and df_artifact.meta.spec.features and output_vector_name:
        # Selecting the top K features from our top feature dataframe
        selected_features = result_matrix_df.head(k).index

        # Match the selected feature names to the FS Feature annotations
        matched_selections = [
            feature
            for feature in list(df_artifact.meta.spec.features)
            for selected in list(selected_features)
            if feature.endswith(selected)
        ]

        # Defining our new feature vector
        top_features_fv = fs.FeatureVector(
            output_vector_name,
            matched_selections,
            label_feature="labels.label",
            description="feature vector composed strictly of our top features",
        )

        # Saving
        top_features_fv.save()
        fs.get_offline_features(top_features_fv, target=ParquetTarget())

        # Logging our new feature vector URI
        context.log_result("top_features_vector", top_features_fv.uri)
 + functionSourceCode: # Copyright 2019 Iguazio
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import json
import os

import matplotlib.pyplot as plt
import mlrun
import mlrun.datastore
import mlrun.utils
import mlrun.feature_store as fs
import numpy as np
import pandas as pd
import seaborn as sns
from mlrun.artifacts import PlotArtifact
from mlrun.datastore.targets import ParquetTarget
# MLRun utils
from mlrun.utils.helpers import create_class
# Feature selection strategies
from sklearn.feature_selection import SelectFromModel, SelectKBest
# Scale feature scoresgit st
from sklearn.preprocessing import MinMaxScaler
# SKLearn estimators list
from sklearn.utils import all_estimators

DEFAULT_STAT_FILTERS = ["f_classif", "mutual_info_classif", "chi2", "f_regression"]
DEFAULT_MODEL_FILTERS = {
    "LinearSVC": "LinearSVC",
    "LogisticRegression": "LogisticRegression",
    "ExtraTreesClassifier": "ExtraTreesClassifier",
}


def _clear_current_figure():
    """
    Clear matplotlib current figure.
    """
    plt.cla()
    plt.clf()
    plt.close()


def show_values_on_bars(axs, h_v="v", space=0.4):
    def _show_on_single_plot(ax_):
        if h_v == "v":
            for p in ax_.patches:
                _x = p.get_x() + p.get_width() / 2
                _y = p.get_y() + p.get_height()
                value = int(p.get_height())
                ax_.text(_x, _y, value, ha="center")
        elif h_v == "h":
            for p in ax_.patches:
                _x = p.get_x() + p.get_width() + float(space)
                _y = p.get_y() + p.get_height()
                value = int(p.get_width())
                ax_.text(_x, _y, value, ha="left")

    if isinstance(axs, np.ndarray):
        for idx, ax in np.ndenumerate(axs):
            _show_on_single_plot(ax)
    else:
        _show_on_single_plot(axs)


def plot_stat(context, stat_name, stat_df):
    _clear_current_figure()

    # Add chart
    ax = plt.axes()
    stat_chart = sns.barplot(
        x=stat_name,
        y="index",
        data=stat_df.sort_values(stat_name, ascending=False).reset_index(),
        ax=ax,
    )
    plt.tight_layout()

    for p in stat_chart.patches:
        width = p.get_width()
        plt.text(
            5 + p.get_width(),
            p.get_y() + 0.55 * p.get_height(),
            "{:1.2f}".format(width),
            ha="center",
            va="center",
        )

    context.log_artifact(
        PlotArtifact(f"{stat_name}", body=plt.gcf()),
        local_path=os.path.join("plots", "feature_selection", f"{stat_name}.html"),
    )
    _clear_current_figure()


def feature_selection(
    context,
    df_artifact,
    k: int = 5,
    min_votes: float = 0.5,
    label_column: str = None,
    stat_filters: list = None,
    model_filters: dict = None,
    max_scaled_scores: bool = True,
    sample_ratio: float = None,
    output_vector_name: float = None,
    ignore_type_errors: bool = False,
    is_feature_vector: bool = False,
):
    """
    Applies selected feature selection statistical functions or models on our 'df_artifact'.

    Each statistical function or model will vote for it's best K selected features.
    If a feature has >= 'min_votes' votes, it will be selected.

    :param context:             the function context.
    :param df_artifact:         dataframe to pass as input.
    :param k:                   number of top features to select from each statistical
                                function or model.
    :param min_votes:           minimal number of votes (from a model or by statistical
                                function) needed for a feature to be selected.
                                Can be specified by percentage of votes or absolute
                                number of votes.
    :param label_column:        ground-truth (y) labels.
    :param stat_filters:        statistical functions to apply to the features
                                (from sklearn.feature_selection).
    :param model_filters:       models to use for feature evaluation, can be specified by
                                model name (ex. LinearSVC), formalized json (contains 'CLASS',
                                'FIT', 'META') or a path to such json file.
    :param max_scaled_scores:   produce feature scores table scaled with max_scaler.
    :param sample_ratio:        percentage of the dataset the user whishes to compute the feature selection process on.
    :param output_vector_name:  creates a new feature vector containing only the identifies features.
    :param ignore_type_errors:  skips datatypes that are neither float nor int within the feature vector.
    :param is_feature_vector:   bool stating if the data is passed as a feature vector.
    """
    stat_filters = stat_filters or DEFAULT_STAT_FILTERS
    model_filters = model_filters or DEFAULT_MODEL_FILTERS
    # Check if df.meta is valid, if it is, look for a feature vector
    store_uri_prefix, _ = mlrun.datastore.parse_store_uri(df_artifact.artifact_url)
    is_feature_vector = mlrun.utils.StorePrefix.FeatureVector == store_uri_prefix

    # Look inside meta.spec.label_feature to identify the label_column if the user did not specify it
    if label_column is None:
        if is_feature_vector:
            label_column = df_artifact.meta.spec.label_feature.split(".")[1]
        else:
            raise ValueError("No label_column was given, please add a label_column.")

    # Use the feature vector as dataframe
    df = df_artifact.as_df()

    # Ensure k is not bigger than the total number of features
    if k > df.shape[1]:
        raise ValueError(
            f"K cannot be bigger than the total number of features ({df.shape[1]}). Please choose a smaller K."
        )
    elif k < 1:
        raise ValueError("K cannot be smaller than 1. Please choose a bigger K.")

    # Create a sample dataframe of the original feature vector
    if sample_ratio:
        df = (
            df.groupby(label_column)
            .apply(lambda x: x.sample(frac=sample_ratio))
            .reset_index(drop=True)
        )
        df = df.dropna()

    # Set feature vector and labels
    y = df.pop(label_column)
    X = df

    if np.object_ in list(X.dtypes) and ignore_type_errors is False:
        raise ValueError(
            f"{df.select_dtypes(include=['object']).columns.tolist()} are neither float or int."
        )

    # Create selected statistical estimators
    stat_functions_list = {
        stat_name: SelectKBest(
            score_func=create_class(f"sklearn.feature_selection.{stat_name}"), k=k
        )
        for stat_name in stat_filters
    }
    requires_abs = ["chi2"]

    # Run statistic filters
    selected_features_agg = {}
    stats_df = pd.DataFrame(index=X.columns).dropna()

    for stat_name, stat_func in stat_functions_list.items():
        try:
            params = (X, y) if stat_name in requires_abs else (abs(X), y)
            stat = stat_func.fit(*params)

            # Collect stat function results
            stat_df = pd.DataFrame(
                index=X.columns, columns=[stat_name], data=stat.scores_
            )
            plot_stat(context, stat_name, stat_df)
            stats_df = stats_df.join(stat_df)

            # Select K Best features
            selected_features = X.columns[stat_func.get_support()]
            selected_features_agg[stat_name] = selected_features

        except Exception as e:
            context.logger.info(f"Couldn't calculate {stat_name} because of: {e}")

    # Create models from class name / json file / json params
    all_sklearn_estimators = dict(all_estimators()) if len(model_filters) > 0 else {}
    selected_models = {}
    for model_name, model in model_filters.items():
        if ".json" in model:
            current_model = json.load(open(model, "r"))
            classifier_class = create_class(current_model["META"]["class"])
            selected_models[model_name] = classifier_class(**current_model["CLASS"])
        elif model in all_sklearn_estimators:
            selected_models[model_name] = all_sklearn_estimators[model_name]()

        else:
            try:
                current_model = json.loads(model)
                classifier_class = create_class(current_model["META"]["class"])
                selected_models[model_name] = classifier_class(**current_model["CLASS"])
            except Exception as e:
                context.logger.info(f"unable to load {model} because of: {e}")

    # Run model filters
    models_df = pd.DataFrame(index=X.columns)
    for model_name, model in selected_models.items():

        if model_name == "LogisticRegression":
            model.set_params(solver="liblinear")

        # Train model and get feature importance
        select_from_model = SelectFromModel(model).fit(X, y)
        feature_idx = select_from_model.get_support()
        feature_names = X.columns[feature_idx]
        selected_features_agg[model_name] = feature_names.tolist()

        # Collect model feature importance
        if hasattr(select_from_model.estimator_, "coef_"):
            stat_df = select_from_model.estimator_.coef_
        elif hasattr(select_from_model.estimator_, "feature_importances_"):
            stat_df = select_from_model.estimator_.feature_importances_

        stat_df = pd.DataFrame(index=X.columns, columns=[model_name], data=stat_df[0])
        models_df = models_df.join(stat_df)

        plot_stat(context, model_name, stat_df)

    # Create feature_scores DF with stat & model filters scores
    result_matrix_df = pd.concat([stats_df, models_df], axis=1, sort=False)
    context.log_dataset(
        key="feature_scores",
        df=result_matrix_df,
        local_path="feature_scores.parquet",
        format="parquet",
    )
    if max_scaled_scores:
        normalized_df = result_matrix_df.replace([np.inf, -np.inf], np.nan).values
        min_max_scaler = MinMaxScaler()
        normalized_df = min_max_scaler.fit_transform(normalized_df)
        normalized_df = pd.DataFrame(
            data=normalized_df,
            columns=result_matrix_df.columns,
            index=result_matrix_df.index,
        )
        context.log_dataset(
            key="max_scaled_scores_feature_scores",
            df=normalized_df,
            local_path="max_scaled_scores_feature_scores.parquet",
            format="parquet",
        )

    # Create feature count DataFrame
    for test_name in selected_features_agg:
        result_matrix_df[test_name] = [
            1 if x in selected_features_agg[test_name] else 0 for x in X.columns
        ]
    result_matrix_df.loc[:, "num_votes"] = result_matrix_df.sum(axis=1)
    context.log_dataset(
        key="selected_features_count",
        df=result_matrix_df,
        local_path="selected_features_count.parquet",
        format="parquet",
    )

    # How many votes are needed for a feature to be selected?
    if isinstance(min_votes, int):
        votes_needed = min_votes
    else:
        num_filters = len(stat_filters) + len(model_filters)
        votes_needed = int(np.floor(num_filters * max(min(min_votes, 1), 0)))
    context.logger.info(f"votes needed to be selected: {votes_needed}")

    # Create final feature dataframe
    selected_features = result_matrix_df[
        result_matrix_df.num_votes >= votes_needed
    ].index.tolist()
    good_feature_df = df.loc[:, selected_features]
    final_df = pd.concat([good_feature_df, y], axis=1)
    context.log_dataset(
        key="selected_features",
        df=final_df,
        local_path="selected_features.parquet",
        format="parquet",
    )

    # Creating a new feature vector containing only the identified top features
    if is_feature_vector and df_artifact.meta.spec.features and output_vector_name:
        # Selecting the top K features from our top feature dataframe
        selected_features = result_matrix_df.head(k).index

        # Match the selected feature names to the FS Feature annotations
        matched_selections = [
            feature
            for feature in list(df_artifact.meta.spec.features)
            for selected in list(selected_features)
            if feature.endswith(selected)
        ]

        # Defining our new feature vector
        top_features_fv = fs.FeatureVector(
            output_vector_name,
            matched_selections,
            label_feature="labels.label",
            description="feature vector composed strictly of our top features",
        )

        # Saving
        top_features_fv.save()
        fs.get_offline_features(top_features_fv, target=ParquetTarget())

        # Logging our new feature vector URI
        context.log_result("top_features_vector", top_features_fv.uri)
 commands: [] - code_origin: https://github.com/yonishelach/functions.git#ce312b8eb32691f56a57f53c0423c6c909b43d27:/Users/Yonatan_Shelach/projects/functions/feature_selection/feature_selection.py - origin_filename: /Users/Yonatan_Shelach/projects/functions/feature_selection/feature_selection.py + code_origin: '' + origin_filename: '' requirements: [] entry_points: show_values_on_bars: @@ -25,27 +25,25 @@ spec: doc: '' parameters: - name: axs - default: '' - name: h_v default: v - name: space default: 0.4 - outputs: - - default: '' + outputs: [] lineno: 54 + has_varargs: false + has_kwargs: false plot_stat: name: plot_stat doc: '' parameters: - name: context - default: '' - name: stat_name - default: '' - name: stat_df - default: '' - outputs: - - default: '' + outputs: [] lineno: 76 + has_varargs: false + has_kwargs: false feature_selection: name: feature_selection doc: 'Applies selected feature selection statistical functions or models on @@ -58,10 +56,8 @@ spec: parameters: - name: context doc: the function context. - default: '' - name: df_artifact doc: dataframe to pass as input. - default: '' - name: k type: int doc: number of top features to select from each statistical function or model. @@ -107,9 +103,10 @@ spec: type: bool doc: bool stating if the data is passed as a feature vector. default: false - outputs: - - default: '' + outputs: [] lineno: 106 + has_varargs: false + has_kwargs: false description: Select features through multiple Statistical and Model filters default_handler: feature_selection disable_auto_mount: false diff --git a/functions/master/feature_selection/latest/src/item.yaml b/functions/master/feature_selection/latest/src/item.yaml index eeea353b..7e80a417 100644 --- a/functions/master/feature_selection/latest/src/item.yaml +++ b/functions/master/feature_selection/latest/src/item.yaml @@ -22,4 +22,4 @@ spec: kind: job requirements: [] url: '' -version: 1.3.0 +version: 1.4.0 diff --git a/functions/master/feature_selection/latest/static/feature_selection.html b/functions/master/feature_selection/latest/static/feature_selection.html index c7f9aacf..2c26b03b 100644 --- a/functions/master/feature_selection/latest/static/feature_selection.html +++ b/functions/master/feature_selection/latest/static/feature_selection.html @@ -296,7 +296,7 @@

Source code for feature_selection.feature_selection

y = df.pop(label_column) X = df - if np.object in list(X.dtypes) and ignore_type_errors is False: + if np.object_ in list(X.dtypes) and ignore_type_errors is False: raise ValueError( f"{df.select_dtypes(include=['object']).columns.tolist()} are neither float or int." ) diff --git a/functions/master/feature_selection/latest/static/function.html b/functions/master/feature_selection/latest/static/function.html index 448b2170..b09632f5 100644 --- a/functions/master/feature_selection/latest/static/function.html +++ b/functions/master/feature_selection/latest/static/function.html @@ -19,7 +19,7 @@ metadata: name: feature-selection tag: '' - hash: 26a0b503f3248f852667d083b5a35b112254d067 + hash: 6dba16d062d81f78d3d210fee75edfe8b1def9b3 project: '' labels: author: orz @@ -31,10 +31,10 @@ args: [] image: mlrun/mlrun build: - functionSourceCode: # Copyright 2019 Iguazio
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import json
import os

import matplotlib.pyplot as plt
import mlrun
import mlrun.datastore
import mlrun.utils
import mlrun.feature_store as fs
import numpy as np
import pandas as pd
import seaborn as sns
from mlrun.artifacts import PlotArtifact
from mlrun.datastore.targets import ParquetTarget
# MLRun utils
from mlrun.utils.helpers import create_class
# Feature selection strategies
from sklearn.feature_selection import SelectFromModel, SelectKBest
# Scale feature scoresgit st
from sklearn.preprocessing import MinMaxScaler
# SKLearn estimators list
from sklearn.utils import all_estimators

DEFAULT_STAT_FILTERS = ["f_classif", "mutual_info_classif", "chi2", "f_regression"]
DEFAULT_MODEL_FILTERS = {
    "LinearSVC": "LinearSVC",
    "LogisticRegression": "LogisticRegression",
    "ExtraTreesClassifier": "ExtraTreesClassifier",
}


def _clear_current_figure():
    """
    Clear matplotlib current figure.
    """
    plt.cla()
    plt.clf()
    plt.close()


def show_values_on_bars(axs, h_v="v", space=0.4):
    def _show_on_single_plot(ax_):
        if h_v == "v":
            for p in ax_.patches:
                _x = p.get_x() + p.get_width() / 2
                _y = p.get_y() + p.get_height()
                value = int(p.get_height())
                ax_.text(_x, _y, value, ha="center")
        elif h_v == "h":
            for p in ax_.patches:
                _x = p.get_x() + p.get_width() + float(space)
                _y = p.get_y() + p.get_height()
                value = int(p.get_width())
                ax_.text(_x, _y, value, ha="left")

    if isinstance(axs, np.ndarray):
        for idx, ax in np.ndenumerate(axs):
            _show_on_single_plot(ax)
    else:
        _show_on_single_plot(axs)


def plot_stat(context, stat_name, stat_df):
    _clear_current_figure()

    # Add chart
    ax = plt.axes()
    stat_chart = sns.barplot(
        x=stat_name,
        y="index",
        data=stat_df.sort_values(stat_name, ascending=False).reset_index(),
        ax=ax,
    )
    plt.tight_layout()

    for p in stat_chart.patches:
        width = p.get_width()
        plt.text(
            5 + p.get_width(),
            p.get_y() + 0.55 * p.get_height(),
            "{:1.2f}".format(width),
            ha="center",
            va="center",
        )

    context.log_artifact(
        PlotArtifact(f"{stat_name}", body=plt.gcf()),
        local_path=os.path.join("plots", "feature_selection", f"{stat_name}.html"),
    )
    _clear_current_figure()


def feature_selection(
    context,
    df_artifact,
    k: int = 5,
    min_votes: float = 0.5,
    label_column: str = None,
    stat_filters: list = None,
    model_filters: dict = None,
    max_scaled_scores: bool = True,
    sample_ratio: float = None,
    output_vector_name: float = None,
    ignore_type_errors: bool = False,
    is_feature_vector: bool = False,
):
    """
    Applies selected feature selection statistical functions or models on our 'df_artifact'.

    Each statistical function or model will vote for it's best K selected features.
    If a feature has >= 'min_votes' votes, it will be selected.

    :param context:             the function context.
    :param df_artifact:         dataframe to pass as input.
    :param k:                   number of top features to select from each statistical
                                function or model.
    :param min_votes:           minimal number of votes (from a model or by statistical
                                function) needed for a feature to be selected.
                                Can be specified by percentage of votes or absolute
                                number of votes.
    :param label_column:        ground-truth (y) labels.
    :param stat_filters:        statistical functions to apply to the features
                                (from sklearn.feature_selection).
    :param model_filters:       models to use for feature evaluation, can be specified by
                                model name (ex. LinearSVC), formalized json (contains 'CLASS',
                                'FIT', 'META') or a path to such json file.
    :param max_scaled_scores:   produce feature scores table scaled with max_scaler.
    :param sample_ratio:        percentage of the dataset the user whishes to compute the feature selection process on.
    :param output_vector_name:  creates a new feature vector containing only the identifies features.
    :param ignore_type_errors:  skips datatypes that are neither float nor int within the feature vector.
    :param is_feature_vector:   bool stating if the data is passed as a feature vector.
    """
    stat_filters = stat_filters or DEFAULT_STAT_FILTERS
    model_filters = model_filters or DEFAULT_MODEL_FILTERS
    # Check if df.meta is valid, if it is, look for a feature vector
    store_uri_prefix, _ = mlrun.datastore.parse_store_uri(df_artifact.artifact_url)
    is_feature_vector = mlrun.utils.StorePrefix.FeatureVector == store_uri_prefix

    # Look inside meta.spec.label_feature to identify the label_column if the user did not specify it
    if label_column is None:
        if is_feature_vector:
            label_column = df_artifact.meta.spec.label_feature.split(".")[1]
        else:
            raise ValueError("No label_column was given, please add a label_column.")

    # Use the feature vector as dataframe
    df = df_artifact.as_df()

    # Ensure k is not bigger than the total number of features
    if k > df.shape[1]:
        raise ValueError(
            f"K cannot be bigger than the total number of features ({df.shape[1]}). Please choose a smaller K."
        )
    elif k < 1:
        raise ValueError("K cannot be smaller than 1. Please choose a bigger K.")

    # Create a sample dataframe of the original feature vector
    if sample_ratio:
        df = (
            df.groupby(label_column)
            .apply(lambda x: x.sample(frac=sample_ratio))
            .reset_index(drop=True)
        )
        df = df.dropna()

    # Set feature vector and labels
    y = df.pop(label_column)
    X = df

    if np.object in list(X.dtypes) and ignore_type_errors is False:
        raise ValueError(
            f"{df.select_dtypes(include=['object']).columns.tolist()} are neither float or int."
        )

    # Create selected statistical estimators
    stat_functions_list = {
        stat_name: SelectKBest(
            score_func=create_class(f"sklearn.feature_selection.{stat_name}"), k=k
        )
        for stat_name in stat_filters
    }
    requires_abs = ["chi2"]

    # Run statistic filters
    selected_features_agg = {}
    stats_df = pd.DataFrame(index=X.columns).dropna()

    for stat_name, stat_func in stat_functions_list.items():
        try:
            params = (X, y) if stat_name in requires_abs else (abs(X), y)
            stat = stat_func.fit(*params)

            # Collect stat function results
            stat_df = pd.DataFrame(
                index=X.columns, columns=[stat_name], data=stat.scores_
            )
            plot_stat(context, stat_name, stat_df)
            stats_df = stats_df.join(stat_df)

            # Select K Best features
            selected_features = X.columns[stat_func.get_support()]
            selected_features_agg[stat_name] = selected_features

        except Exception as e:
            context.logger.info(f"Couldn't calculate {stat_name} because of: {e}")

    # Create models from class name / json file / json params
    all_sklearn_estimators = dict(all_estimators()) if len(model_filters) > 0 else {}
    selected_models = {}
    for model_name, model in model_filters.items():
        if ".json" in model:
            current_model = json.load(open(model, "r"))
            classifier_class = create_class(current_model["META"]["class"])
            selected_models[model_name] = classifier_class(**current_model["CLASS"])
        elif model in all_sklearn_estimators:
            selected_models[model_name] = all_sklearn_estimators[model_name]()

        else:
            try:
                current_model = json.loads(model)
                classifier_class = create_class(current_model["META"]["class"])
                selected_models[model_name] = classifier_class(**current_model["CLASS"])
            except Exception as e:
                context.logger.info(f"unable to load {model} because of: {e}")

    # Run model filters
    models_df = pd.DataFrame(index=X.columns)
    for model_name, model in selected_models.items():

        if model_name == "LogisticRegression":
            model.set_params(solver="liblinear")

        # Train model and get feature importance
        select_from_model = SelectFromModel(model).fit(X, y)
        feature_idx = select_from_model.get_support()
        feature_names = X.columns[feature_idx]
        selected_features_agg[model_name] = feature_names.tolist()

        # Collect model feature importance
        if hasattr(select_from_model.estimator_, "coef_"):
            stat_df = select_from_model.estimator_.coef_
        elif hasattr(select_from_model.estimator_, "feature_importances_"):
            stat_df = select_from_model.estimator_.feature_importances_

        stat_df = pd.DataFrame(index=X.columns, columns=[model_name], data=stat_df[0])
        models_df = models_df.join(stat_df)

        plot_stat(context, model_name, stat_df)

    # Create feature_scores DF with stat & model filters scores
    result_matrix_df = pd.concat([stats_df, models_df], axis=1, sort=False)
    context.log_dataset(
        key="feature_scores",
        df=result_matrix_df,
        local_path="feature_scores.parquet",
        format="parquet",
    )
    if max_scaled_scores:
        normalized_df = result_matrix_df.replace([np.inf, -np.inf], np.nan).values
        min_max_scaler = MinMaxScaler()
        normalized_df = min_max_scaler.fit_transform(normalized_df)
        normalized_df = pd.DataFrame(
            data=normalized_df,
            columns=result_matrix_df.columns,
            index=result_matrix_df.index,
        )
        context.log_dataset(
            key="max_scaled_scores_feature_scores",
            df=normalized_df,
            local_path="max_scaled_scores_feature_scores.parquet",
            format="parquet",
        )

    # Create feature count DataFrame
    for test_name in selected_features_agg:
        result_matrix_df[test_name] = [
            1 if x in selected_features_agg[test_name] else 0 for x in X.columns
        ]
    result_matrix_df.loc[:, "num_votes"] = result_matrix_df.sum(axis=1)
    context.log_dataset(
        key="selected_features_count",
        df=result_matrix_df,
        local_path="selected_features_count.parquet",
        format="parquet",
    )

    # How many votes are needed for a feature to be selected?
    if isinstance(min_votes, int):
        votes_needed = min_votes
    else:
        num_filters = len(stat_filters) + len(model_filters)
        votes_needed = int(np.floor(num_filters * max(min(min_votes, 1), 0)))
    context.logger.info(f"votes needed to be selected: {votes_needed}")

    # Create final feature dataframe
    selected_features = result_matrix_df[
        result_matrix_df.num_votes >= votes_needed
    ].index.tolist()
    good_feature_df = df.loc[:, selected_features]
    final_df = pd.concat([good_feature_df, y], axis=1)
    context.log_dataset(
        key="selected_features",
        df=final_df,
        local_path="selected_features.parquet",
        format="parquet",
    )

    # Creating a new feature vector containing only the identified top features
    if is_feature_vector and df_artifact.meta.spec.features and output_vector_name:
        # Selecting the top K features from our top feature dataframe
        selected_features = result_matrix_df.head(k).index

        # Match the selected feature names to the FS Feature annotations
        matched_selections = [
            feature
            for feature in list(df_artifact.meta.spec.features)
            for selected in list(selected_features)
            if feature.endswith(selected)
        ]

        # Defining our new feature vector
        top_features_fv = fs.FeatureVector(
            output_vector_name,
            matched_selections,
            label_feature="labels.label",
            description="feature vector composed strictly of our top features",
        )

        # Saving
        top_features_fv.save()
        fs.get_offline_features(top_features_fv, target=ParquetTarget())

        # Logging our new feature vector URI
        context.log_result("top_features_vector", top_features_fv.uri)
 + functionSourceCode: # Copyright 2019 Iguazio
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import json
import os

import matplotlib.pyplot as plt
import mlrun
import mlrun.datastore
import mlrun.utils
import mlrun.feature_store as fs
import numpy as np
import pandas as pd
import seaborn as sns
from mlrun.artifacts import PlotArtifact
from mlrun.datastore.targets import ParquetTarget
# MLRun utils
from mlrun.utils.helpers import create_class
# Feature selection strategies
from sklearn.feature_selection import SelectFromModel, SelectKBest
# Scale feature scoresgit st
from sklearn.preprocessing import MinMaxScaler
# SKLearn estimators list
from sklearn.utils import all_estimators

DEFAULT_STAT_FILTERS = ["f_classif", "mutual_info_classif", "chi2", "f_regression"]
DEFAULT_MODEL_FILTERS = {
    "LinearSVC": "LinearSVC",
    "LogisticRegression": "LogisticRegression",
    "ExtraTreesClassifier": "ExtraTreesClassifier",
}


def _clear_current_figure():
    """
    Clear matplotlib current figure.
    """
    plt.cla()
    plt.clf()
    plt.close()


def show_values_on_bars(axs, h_v="v", space=0.4):
    def _show_on_single_plot(ax_):
        if h_v == "v":
            for p in ax_.patches:
                _x = p.get_x() + p.get_width() / 2
                _y = p.get_y() + p.get_height()
                value = int(p.get_height())
                ax_.text(_x, _y, value, ha="center")
        elif h_v == "h":
            for p in ax_.patches:
                _x = p.get_x() + p.get_width() + float(space)
                _y = p.get_y() + p.get_height()
                value = int(p.get_width())
                ax_.text(_x, _y, value, ha="left")

    if isinstance(axs, np.ndarray):
        for idx, ax in np.ndenumerate(axs):
            _show_on_single_plot(ax)
    else:
        _show_on_single_plot(axs)


def plot_stat(context, stat_name, stat_df):
    _clear_current_figure()

    # Add chart
    ax = plt.axes()
    stat_chart = sns.barplot(
        x=stat_name,
        y="index",
        data=stat_df.sort_values(stat_name, ascending=False).reset_index(),
        ax=ax,
    )
    plt.tight_layout()

    for p in stat_chart.patches:
        width = p.get_width()
        plt.text(
            5 + p.get_width(),
            p.get_y() + 0.55 * p.get_height(),
            "{:1.2f}".format(width),
            ha="center",
            va="center",
        )

    context.log_artifact(
        PlotArtifact(f"{stat_name}", body=plt.gcf()),
        local_path=os.path.join("plots", "feature_selection", f"{stat_name}.html"),
    )
    _clear_current_figure()


def feature_selection(
    context,
    df_artifact,
    k: int = 5,
    min_votes: float = 0.5,
    label_column: str = None,
    stat_filters: list = None,
    model_filters: dict = None,
    max_scaled_scores: bool = True,
    sample_ratio: float = None,
    output_vector_name: float = None,
    ignore_type_errors: bool = False,
    is_feature_vector: bool = False,
):
    """
    Applies selected feature selection statistical functions or models on our 'df_artifact'.

    Each statistical function or model will vote for it's best K selected features.
    If a feature has >= 'min_votes' votes, it will be selected.

    :param context:             the function context.
    :param df_artifact:         dataframe to pass as input.
    :param k:                   number of top features to select from each statistical
                                function or model.
    :param min_votes:           minimal number of votes (from a model or by statistical
                                function) needed for a feature to be selected.
                                Can be specified by percentage of votes or absolute
                                number of votes.
    :param label_column:        ground-truth (y) labels.
    :param stat_filters:        statistical functions to apply to the features
                                (from sklearn.feature_selection).
    :param model_filters:       models to use for feature evaluation, can be specified by
                                model name (ex. LinearSVC), formalized json (contains 'CLASS',
                                'FIT', 'META') or a path to such json file.
    :param max_scaled_scores:   produce feature scores table scaled with max_scaler.
    :param sample_ratio:        percentage of the dataset the user whishes to compute the feature selection process on.
    :param output_vector_name:  creates a new feature vector containing only the identifies features.
    :param ignore_type_errors:  skips datatypes that are neither float nor int within the feature vector.
    :param is_feature_vector:   bool stating if the data is passed as a feature vector.
    """
    stat_filters = stat_filters or DEFAULT_STAT_FILTERS
    model_filters = model_filters or DEFAULT_MODEL_FILTERS
    # Check if df.meta is valid, if it is, look for a feature vector
    store_uri_prefix, _ = mlrun.datastore.parse_store_uri(df_artifact.artifact_url)
    is_feature_vector = mlrun.utils.StorePrefix.FeatureVector == store_uri_prefix

    # Look inside meta.spec.label_feature to identify the label_column if the user did not specify it
    if label_column is None:
        if is_feature_vector:
            label_column = df_artifact.meta.spec.label_feature.split(".")[1]
        else:
            raise ValueError("No label_column was given, please add a label_column.")

    # Use the feature vector as dataframe
    df = df_artifact.as_df()

    # Ensure k is not bigger than the total number of features
    if k > df.shape[1]:
        raise ValueError(
            f"K cannot be bigger than the total number of features ({df.shape[1]}). Please choose a smaller K."
        )
    elif k < 1:
        raise ValueError("K cannot be smaller than 1. Please choose a bigger K.")

    # Create a sample dataframe of the original feature vector
    if sample_ratio:
        df = (
            df.groupby(label_column)
            .apply(lambda x: x.sample(frac=sample_ratio))
            .reset_index(drop=True)
        )
        df = df.dropna()

    # Set feature vector and labels
    y = df.pop(label_column)
    X = df

    if np.object_ in list(X.dtypes) and ignore_type_errors is False:
        raise ValueError(
            f"{df.select_dtypes(include=['object']).columns.tolist()} are neither float or int."
        )

    # Create selected statistical estimators
    stat_functions_list = {
        stat_name: SelectKBest(
            score_func=create_class(f"sklearn.feature_selection.{stat_name}"), k=k
        )
        for stat_name in stat_filters
    }
    requires_abs = ["chi2"]

    # Run statistic filters
    selected_features_agg = {}
    stats_df = pd.DataFrame(index=X.columns).dropna()

    for stat_name, stat_func in stat_functions_list.items():
        try:
            params = (X, y) if stat_name in requires_abs else (abs(X), y)
            stat = stat_func.fit(*params)

            # Collect stat function results
            stat_df = pd.DataFrame(
                index=X.columns, columns=[stat_name], data=stat.scores_
            )
            plot_stat(context, stat_name, stat_df)
            stats_df = stats_df.join(stat_df)

            # Select K Best features
            selected_features = X.columns[stat_func.get_support()]
            selected_features_agg[stat_name] = selected_features

        except Exception as e:
            context.logger.info(f"Couldn't calculate {stat_name} because of: {e}")

    # Create models from class name / json file / json params
    all_sklearn_estimators = dict(all_estimators()) if len(model_filters) > 0 else {}
    selected_models = {}
    for model_name, model in model_filters.items():
        if ".json" in model:
            current_model = json.load(open(model, "r"))
            classifier_class = create_class(current_model["META"]["class"])
            selected_models[model_name] = classifier_class(**current_model["CLASS"])
        elif model in all_sklearn_estimators:
            selected_models[model_name] = all_sklearn_estimators[model_name]()

        else:
            try:
                current_model = json.loads(model)
                classifier_class = create_class(current_model["META"]["class"])
                selected_models[model_name] = classifier_class(**current_model["CLASS"])
            except Exception as e:
                context.logger.info(f"unable to load {model} because of: {e}")

    # Run model filters
    models_df = pd.DataFrame(index=X.columns)
    for model_name, model in selected_models.items():

        if model_name == "LogisticRegression":
            model.set_params(solver="liblinear")

        # Train model and get feature importance
        select_from_model = SelectFromModel(model).fit(X, y)
        feature_idx = select_from_model.get_support()
        feature_names = X.columns[feature_idx]
        selected_features_agg[model_name] = feature_names.tolist()

        # Collect model feature importance
        if hasattr(select_from_model.estimator_, "coef_"):
            stat_df = select_from_model.estimator_.coef_
        elif hasattr(select_from_model.estimator_, "feature_importances_"):
            stat_df = select_from_model.estimator_.feature_importances_

        stat_df = pd.DataFrame(index=X.columns, columns=[model_name], data=stat_df[0])
        models_df = models_df.join(stat_df)

        plot_stat(context, model_name, stat_df)

    # Create feature_scores DF with stat & model filters scores
    result_matrix_df = pd.concat([stats_df, models_df], axis=1, sort=False)
    context.log_dataset(
        key="feature_scores",
        df=result_matrix_df,
        local_path="feature_scores.parquet",
        format="parquet",
    )
    if max_scaled_scores:
        normalized_df = result_matrix_df.replace([np.inf, -np.inf], np.nan).values
        min_max_scaler = MinMaxScaler()
        normalized_df = min_max_scaler.fit_transform(normalized_df)
        normalized_df = pd.DataFrame(
            data=normalized_df,
            columns=result_matrix_df.columns,
            index=result_matrix_df.index,
        )
        context.log_dataset(
            key="max_scaled_scores_feature_scores",
            df=normalized_df,
            local_path="max_scaled_scores_feature_scores.parquet",
            format="parquet",
        )

    # Create feature count DataFrame
    for test_name in selected_features_agg:
        result_matrix_df[test_name] = [
            1 if x in selected_features_agg[test_name] else 0 for x in X.columns
        ]
    result_matrix_df.loc[:, "num_votes"] = result_matrix_df.sum(axis=1)
    context.log_dataset(
        key="selected_features_count",
        df=result_matrix_df,
        local_path="selected_features_count.parquet",
        format="parquet",
    )

    # How many votes are needed for a feature to be selected?
    if isinstance(min_votes, int):
        votes_needed = min_votes
    else:
        num_filters = len(stat_filters) + len(model_filters)
        votes_needed = int(np.floor(num_filters * max(min(min_votes, 1), 0)))
    context.logger.info(f"votes needed to be selected: {votes_needed}")

    # Create final feature dataframe
    selected_features = result_matrix_df[
        result_matrix_df.num_votes >= votes_needed
    ].index.tolist()
    good_feature_df = df.loc[:, selected_features]
    final_df = pd.concat([good_feature_df, y], axis=1)
    context.log_dataset(
        key="selected_features",
        df=final_df,
        local_path="selected_features.parquet",
        format="parquet",
    )

    # Creating a new feature vector containing only the identified top features
    if is_feature_vector and df_artifact.meta.spec.features and output_vector_name:
        # Selecting the top K features from our top feature dataframe
        selected_features = result_matrix_df.head(k).index

        # Match the selected feature names to the FS Feature annotations
        matched_selections = [
            feature
            for feature in list(df_artifact.meta.spec.features)
            for selected in list(selected_features)
            if feature.endswith(selected)
        ]

        # Defining our new feature vector
        top_features_fv = fs.FeatureVector(
            output_vector_name,
            matched_selections,
            label_feature="labels.label",
            description="feature vector composed strictly of our top features",
        )

        # Saving
        top_features_fv.save()
        fs.get_offline_features(top_features_fv, target=ParquetTarget())

        # Logging our new feature vector URI
        context.log_result("top_features_vector", top_features_fv.uri)
 commands: [] - code_origin: https://github.com/yonishelach/functions.git#ce312b8eb32691f56a57f53c0423c6c909b43d27:/Users/Yonatan_Shelach/projects/functions/feature_selection/feature_selection.py - origin_filename: /Users/Yonatan_Shelach/projects/functions/feature_selection/feature_selection.py + code_origin: '' + origin_filename: '' requirements: [] entry_points: show_values_on_bars: @@ -42,27 +42,25 @@ doc: '' parameters: - name: axs - default: '' - name: h_v default: v - name: space default: 0.4 - outputs: - - default: '' + outputs: [] lineno: 54 + has_varargs: false + has_kwargs: false plot_stat: name: plot_stat doc: '' parameters: - name: context - default: '' - name: stat_name - default: '' - name: stat_df - default: '' - outputs: - - default: '' + outputs: [] lineno: 76 + has_varargs: false + has_kwargs: false feature_selection: name: feature_selection doc: 'Applies selected feature selection statistical functions or models on @@ -75,10 +73,8 @@ parameters: - name: context doc: the function context. - default: '' - name: df_artifact doc: dataframe to pass as input. - default: '' - name: k type: int doc: number of top features to select from each statistical function or model. @@ -124,9 +120,10 @@ type: bool doc: bool stating if the data is passed as a feature vector. default: false - outputs: - - default: '' + outputs: [] lineno: 106 + has_varargs: false + has_kwargs: false description: Select features through multiple Statistical and Model filters default_handler: feature_selection disable_auto_mount: false diff --git a/functions/master/feature_selection/latest/static/item.html b/functions/master/feature_selection/latest/static/item.html index 834427c5..9f497252 100644 --- a/functions/master/feature_selection/latest/static/item.html +++ b/functions/master/feature_selection/latest/static/item.html @@ -39,7 +39,7 @@ kind: job requirements: [] url: '' -version: 1.3.0 +version: 1.4.0 diff --git a/functions/master/feature_selection/latest/static/source.html b/functions/master/feature_selection/latest/static/source.html index ce04cd56..40198374 100644 --- a/functions/master/feature_selection/latest/static/source.html +++ b/functions/master/feature_selection/latest/static/source.html @@ -197,7 +197,7 @@ y = df.pop(label_column) X = df - if np.object in list(X.dtypes) and ignore_type_errors is False: + if np.object_ in list(X.dtypes) and ignore_type_errors is False: raise ValueError( f"{df.select_dtypes(include=['object']).columns.tolist()} are neither float or int." ) diff --git a/functions/master/tags.json b/functions/master/tags.json index 7e664987..104092f3 100644 --- a/functions/master/tags.json +++ b/functions/master/tags.json @@ -1 +1 @@ -{"kind": ["serving", "nuclio:serving", "dask", "nuclio", "job"], "categories": ["data-validation", "monitoring", "Audio", "NLP", "utils", "machine-learning", "Data Generation", "feature-store", "Deep Learning", "data-preparation", "PyTorch", "data-analysis", "deep-learning", "model-training", "model-testing", "Data Preparation", "Huggingface", "model-serving", "GenAI", "etl"]} \ No newline at end of file +{"kind": ["nuclio:serving", "job", "serving", "dask", "nuclio"], "categories": ["Data Preparation", "model-testing", "etl", "data-analysis", "machine-learning", "model-serving", "PyTorch", "feature-store", "NLP", "GenAI", "Data Generation", "data-preparation", "model-training", "data-validation", "Audio", "Deep Learning", "deep-learning", "utils", "monitoring", "Huggingface"]} \ No newline at end of file