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Kun-Lung WuKun-Lung Wu
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example of a PREDICT after FIT
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‎notebooks/plot_feature_selection_pipeline.ipynb‎

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"%matplotlib inline"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"\n",
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"# Pipeline ANOVA SVM\n",
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"\n",
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"This example shows how a feature selection can be easily integrated within\n",
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"a machine learning pipeline.\n",
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"\n",
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"We also show that you can easily introspect part of the pipeline.\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Automatically created module for IPython interactive environment\n",
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" precision recall f1-score support\n",
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"\n",
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" 0 0.92 0.80 0.86 15\n",
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" 1 0.75 0.90 0.82 10\n",
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"\n",
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" accuracy 0.84 25\n",
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" macro avg 0.84 0.85 0.84 25\n",
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"weighted avg 0.85 0.84 0.84 25\n",
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"\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"array([[0. , 0. , 0.75791043, 0. , 0. ,\n",
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" 0. , 0. , 0. , 0. , 0.27158921,\n",
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" 0. , 0. , 0. , 0. , 0. ,\n",
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" 0. , 0. , 0. , 0. , 0.26109702]])"
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]
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},
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"execution_count": 9,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"print(__doc__)\n",
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"\n",
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"from sklearn import set_config\n",
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"set_config(display='diagram')\n",
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"from sklearn.datasets import make_classification\n",
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"from sklearn.model_selection import train_test_split\n",
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"\n",
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"X, y = make_classification(\n",
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" n_features=20, n_informative=3, n_redundant=0, n_classes=2,\n",
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" n_clusters_per_class=2, random_state=42)\n",
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"X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)\n",
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"\n",
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"from sklearn.feature_selection import SelectKBest, f_classif\n",
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"from sklearn.pipeline import make_pipeline\n",
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"from sklearn.svm import LinearSVC\n",
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"\n",
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"anova_filter = SelectKBest(f_classif, k=3)\n",
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"clf = LinearSVC()\n",
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"anova_svm = make_pipeline(anova_filter, clf)\n",
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"anova_svm.fit(X_train, y_train)\n",
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"\n",
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"from sklearn.metrics import classification_report\n",
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"\n",
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"y_pred = anova_svm.predict(X_test)\n",
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"print(classification_report(y_test, y_pred))\n",
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"\n",
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"anova_svm[-1].coef_\n",
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"\n",
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"anova_svm[:-1].inverse_transform(anova_svm[-1].coef_)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"2021-06-02 09:20:27,751\tINFO services.py:1267 -- View the Ray dashboard at \u001b[1m\u001b[32mhttp://127.0.0.1:8266\u001b[39m\u001b[22m\n"
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]
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},
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{
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"ename": "RayTaskError(ValueError)",
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"evalue": "\u001b[36mray::execute_or_node_remote()\u001b[39m (pid=29747, ip=192.168.1.5)\n File \"python/ray/_raylet.pyx\", line 505, in ray._raylet.execute_task\n File \"/opt/anaconda3/lib/python3.8/site-packages/codeflare_pipelines-1.0.0-py3.8.egg/codeflare/pipelines/Runtime.py\", line 23, in execute_or_node_remote\n File \"/opt/anaconda3/lib/python3.8/site-packages/ray/_private/client_mode_hook.py\", line 47, in wrapper\n return func(*args, **kwargs)\nValueError: 'object_refs' must either be an object ref or a list of object refs.",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mRayTaskError(ValueError)\u001b[0m Traceback (most recent call last)",
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"\u001b[0;32m<ipython-input-11-5c12286d6a83>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 50\u001b[0m \u001b[0mpredict_output\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecute_pipeline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mselected_pipeline\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mExecutionType\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPREDICT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpipeline_input\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 51\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 52\u001b[0;31m \u001b[0mpredict_clf_output\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpredict_output\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_xyrefs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnode_clf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 53\u001b[0m \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mray\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpredict_clf_output\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_yref\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 54\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/codeflare_pipelines-1.0.0-py3.8.egg/codeflare/pipelines/Datamodel.py\u001b[0m in \u001b[0;36mget_xyrefs\u001b[0;34m(self, node)\u001b[0m\n\u001b[1;32m 397\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mpe\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPipelineNodeNotFoundException\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Node \"\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnode\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m\" not found\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 398\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 399\u001b[0;31m \u001b[0mxyrefs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mray\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxyrefs_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 400\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mxyrefs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 401\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/ray/_private/client_mode_hook.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 45\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mclient_mode_should_convert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 46\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 47\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 48\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 49\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/ray/worker.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(object_refs, timeout)\u001b[0m\n\u001b[1;32m 1479\u001b[0m \u001b[0mworker\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore_worker\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdump_object_store_memory_usage\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1480\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mRayTaskError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1481\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_instanceof_cause\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1482\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1483\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;31mRayTaskError(ValueError)\u001b[0m: \u001b[36mray::execute_or_node_remote()\u001b[39m (pid=29747, ip=192.168.1.5)\n File \"python/ray/_raylet.pyx\", line 505, in ray._raylet.execute_task\n File \"/opt/anaconda3/lib/python3.8/site-packages/codeflare_pipelines-1.0.0-py3.8.egg/codeflare/pipelines/Runtime.py\", line 23, in execute_or_node_remote\n File \"/opt/anaconda3/lib/python3.8/site-packages/ray/_private/client_mode_hook.py\", line 47, in wrapper\n return func(*args, **kwargs)\nValueError: 'object_refs' must either be an object ref or a list of object refs."
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]
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}
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],
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"source": [
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"import ray\n",
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"import codeflare.pipelines.Datamodel as dm\n",
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"import codeflare.pipelines.Runtime as rt\n",
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"from codeflare.pipelines.Datamodel import Xy\n",
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"from codeflare.pipelines.Datamodel import XYRef\n",
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"from codeflare.pipelines.Runtime import ExecutionType\n",
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"\n",
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"ray.shutdown()\n",
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"ray.init()\n",
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"\n",
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"from sklearn import set_config\n",
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"set_config(display='diagram')\n",
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"from sklearn.datasets import make_classification\n",
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"from sklearn.model_selection import train_test_split\n",
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"\n",
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"X, y = make_classification(\n",
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" n_features=20, n_informative=3, n_redundant=0, n_classes=2,\n",
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" n_clusters_per_class=2, random_state=42)\n",
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"X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)\n",
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"\n",
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"from sklearn.feature_selection import SelectKBest, f_classif\n",
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"from sklearn.pipeline import make_pipeline\n",
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"from sklearn.svm import LinearSVC\n",
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"\n",
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"anova_filter = SelectKBest(f_classif, k=3)\n",
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"clf = LinearSVC()\n",
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"\n",
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"pipeline = dm.Pipeline()\n",
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"node_anova_filter = dm.EstimatorNode('anova_filter', anova_filter)\n",
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"node_clf = dm.EstimatorNode('clf', clf)\n",
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"pipeline.add_edge(node_anova_filter, node_clf)\n",
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"\n",
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"pipeline_input = dm.PipelineInput()\n",
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"xy = dm.Xy(X_train, y_train)\n",
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"\n",
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"pipeline_input.add_xy_arg(node_anova_filter, xy)\n",
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"\n",
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"pipeline_output = rt.execute_pipeline(pipeline, ExecutionType.FIT, pipeline_input)\n",
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"\n",
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"node_clf_output = pipeline_output.get_xyrefs(node_clf)\n",
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"\n",
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"Xout = ray.get(node_clf_output[0].get_Xref())\n",
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"yout = ray.get(node_clf_output[0].get_yref())\n",
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"\n",
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"selected_pipeline = rt.select_pipeline(pipeline_output, node_clf_output[0])\n",
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"\n",
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"pipeline_input = dm.PipelineInput()\n",
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"pipeline_input.add_xy_arg(node_anova_filter, dm.Xy(X_test, y_test))\n",
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"\n",
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"predict_output = rt.execute_pipeline(selected_pipeline, ExecutionType.PREDICT, pipeline_input)\n",
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"\n",
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"predict_clf_output = predict_output.get_xyrefs(node_clf)\n",
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"y_pred = ray.get(predict_clf_output[0].get_yref())\n",
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"\n",
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"from sklearn.metrics import classification_report\n",
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"\n",
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"#y_pred = anova_svm.predict(X_test)\n",
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"print(classification_report(y_test, y_pred))\n",
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"\n",
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"#anova_svm[-1].coef_\n",
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"\n",
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"#anova_svm[:-1].inverse_transform(anova_svm[-1].coef_)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.8"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 1
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}

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