From f4b1899f4880f8a4c8206885f1230a55c84edac8 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=D0=9C=D0=B0=D0=BA=D1=81=D0=B8=D0=BC=20=D0=AF=D0=BA=D0=BE?= =?UTF-8?q?=D0=B2=D0=BB=D0=B5=D0=B2?= Date: Fri, 15 Nov 2024 22:30:29 +0400 Subject: [PATCH] =?UTF-8?q?=D0=B2=D1=81=D0=B5=20=D0=B5=D1=89=D0=B5=20?= =?UTF-8?q?=D0=BD=D0=B8=D1=87=D0=B5=20=D0=BD=D0=B5=20=D1=80=D0=B0=D0=B1?= =?UTF-8?q?=D0=BE=D1=82=D0=B0=D0=B5=D1=82?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- lab_4/lab_4.ipynb | 23 ++++++++++++----------- 1 file changed, 12 insertions(+), 11 deletions(-) diff --git a/lab_4/lab_4.ipynb b/lab_4/lab_4.ipynb index b1c86bc..d9069a0 100644 --- a/lab_4/lab_4.ipynb +++ b/lab_4/lab_4.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 313, + "execution_count": 337, "metadata": {}, "outputs": [ { @@ -375,7 +375,7 @@ "[10000 rows x 21 columns]" ] }, - "execution_count": 313, + "execution_count": 337, "metadata": {}, "output_type": "execute_result" } @@ -401,7 +401,7 @@ }, { "cell_type": "code", - "execution_count": 314, + "execution_count": 338, "metadata": {}, "outputs": [ { @@ -1026,7 +1026,7 @@ "[20 rows x 22 columns]" ] }, - "execution_count": 314, + "execution_count": 338, "metadata": {}, "output_type": "execute_result" } @@ -1062,7 +1062,7 @@ }, { "cell_type": "code", - "execution_count": 315, + "execution_count": 339, "metadata": {}, "outputs": [ { @@ -2110,7 +2110,7 @@ }, { "cell_type": "code", - "execution_count": 316, + "execution_count": 340, "metadata": {}, "outputs": [ { @@ -2713,7 +2713,7 @@ "[20 rows x 22 columns]" ] }, - "execution_count": 316, + "execution_count": 340, "metadata": {}, "output_type": "execute_result" } @@ -2799,6 +2799,7 @@ " ]\n", "\n", ")\n", + "# preprocessing_result = pipeline_end.fit_transform(X_train.values)\n", "cols = ['price_h', 'price_l', 'price_m', 'price_vh']\n", "preprocessing_result = features_preprocessing.fit_transform(X_train)\n", "preprocessing_result = pd.DataFrame(preprocessing_result, columns=num_columns + cat_columns + cols + columns_to_drop)\n", @@ -2834,7 +2835,7 @@ }, { "cell_type": "code", - "execution_count": 317, + "execution_count": 341, "metadata": {}, "outputs": [], "source": [ @@ -2877,7 +2878,7 @@ }, { "cell_type": "code", - "execution_count": 320, + "execution_count": 343, "metadata": {}, "outputs": [ { @@ -2898,7 +2899,7 @@ "\u001b[1;31mAttributeError\u001b[0m: 'numpy.ndarray' object has no attribute 'columns'", "\nDuring handling of the above exception, another exception occurred:\n", "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[320], line 9\u001b[0m\n\u001b[0;32m 6\u001b[0m model \u001b[38;5;241m=\u001b[39m class_models[model_name][\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodel\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m 8\u001b[0m model_pipeline \u001b[38;5;241m=\u001b[39m Pipeline([(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpipeline\u001b[39m\u001b[38;5;124m\"\u001b[39m, pipeline_end), (\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodel\u001b[39m\u001b[38;5;124m\"\u001b[39m, model)])\n\u001b[1;32m----> 9\u001b[0m model_pipeline \u001b[38;5;241m=\u001b[39m \u001b[43mmodel_pipeline\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_train\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalues\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mravel\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 11\u001b[0m y_train_predict \u001b[38;5;241m=\u001b[39m model_pipeline\u001b[38;5;241m.\u001b[39mpredict(X_train)\n\u001b[0;32m 12\u001b[0m y_test_probs \u001b[38;5;241m=\u001b[39m model_pipeline\u001b[38;5;241m.\u001b[39mpredict_proba(X_test)[:, \u001b[38;5;241m1\u001b[39m]\n", + "Cell \u001b[1;32mIn[343], line 9\u001b[0m\n\u001b[0;32m 6\u001b[0m model \u001b[38;5;241m=\u001b[39m class_models[model_name][\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodel\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m 8\u001b[0m model_pipeline \u001b[38;5;241m=\u001b[39m Pipeline([(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpipeline\u001b[39m\u001b[38;5;124m\"\u001b[39m, pipeline_end), (\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodel\u001b[39m\u001b[38;5;124m\"\u001b[39m, model)])\n\u001b[1;32m----> 9\u001b[0m model_pipeline \u001b[38;5;241m=\u001b[39m \u001b[43mmodel_pipeline\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_train\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_train\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalues\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mravel\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 11\u001b[0m y_train_predict \u001b[38;5;241m=\u001b[39m model_pipeline\u001b[38;5;241m.\u001b[39mpredict(X_train)\n\u001b[0;32m 12\u001b[0m y_test_probs \u001b[38;5;241m=\u001b[39m model_pipeline\u001b[38;5;241m.\u001b[39mpredict_proba(X_test)[:, \u001b[38;5;241m1\u001b[39m]\n", "File \u001b[1;32md:\\Study\\3 курс 5 семестр\\AIM\\AIM-PIbd-31-Yakovlev-M-G\\kernel\\Lib\\site-packages\\sklearn\\base.py:1473\u001b[0m, in \u001b[0;36m_fit_context..decorator..wrapper\u001b[1;34m(estimator, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1466\u001b[0m estimator\u001b[38;5;241m.\u001b[39m_validate_params()\n\u001b[0;32m 1468\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m config_context(\n\u001b[0;32m 1469\u001b[0m skip_parameter_validation\u001b[38;5;241m=\u001b[39m(\n\u001b[0;32m 1470\u001b[0m prefer_skip_nested_validation \u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[0;32m 1471\u001b[0m )\n\u001b[0;32m 1472\u001b[0m ):\n\u001b[1;32m-> 1473\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfit_method\u001b[49m\u001b[43m(\u001b[49m\u001b[43mestimator\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", "File \u001b[1;32md:\\Study\\3 курс 5 семестр\\AIM\\AIM-PIbd-31-Yakovlev-M-G\\kernel\\Lib\\site-packages\\sklearn\\pipeline.py:469\u001b[0m, in \u001b[0;36mPipeline.fit\u001b[1;34m(self, X, y, **params)\u001b[0m\n\u001b[0;32m 426\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Fit the model.\u001b[39;00m\n\u001b[0;32m 427\u001b[0m \n\u001b[0;32m 428\u001b[0m \u001b[38;5;124;03mFit all the transformers one after the other and sequentially transform the\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 466\u001b[0m \u001b[38;5;124;03m Pipeline with fitted steps.\u001b[39;00m\n\u001b[0;32m 467\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 468\u001b[0m routed_params \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_method_params(method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfit\u001b[39m\u001b[38;5;124m\"\u001b[39m, props\u001b[38;5;241m=\u001b[39mparams)\n\u001b[1;32m--> 469\u001b[0m Xt \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_fit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrouted_params\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 470\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m _print_elapsed_time(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPipeline\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_log_message(\u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msteps) \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m1\u001b[39m)):\n\u001b[0;32m 471\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_final_estimator \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpassthrough\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n", "File \u001b[1;32md:\\Study\\3 курс 5 семестр\\AIM\\AIM-PIbd-31-Yakovlev-M-G\\kernel\\Lib\\site-packages\\sklearn\\pipeline.py:406\u001b[0m, in \u001b[0;36mPipeline._fit\u001b[1;34m(self, X, y, routed_params)\u001b[0m\n\u001b[0;32m 404\u001b[0m cloned_transformer \u001b[38;5;241m=\u001b[39m clone(transformer)\n\u001b[0;32m 405\u001b[0m \u001b[38;5;66;03m# Fit or load from cache the current transformer\u001b[39;00m\n\u001b[1;32m--> 406\u001b[0m X, fitted_transformer \u001b[38;5;241m=\u001b[39m \u001b[43mfit_transform_one_cached\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 407\u001b[0m \u001b[43m \u001b[49m\u001b[43mcloned_transformer\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 408\u001b[0m \u001b[43m \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 409\u001b[0m \u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 410\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m 411\u001b[0m \u001b[43m \u001b[49m\u001b[43mmessage_clsname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mPipeline\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m 412\u001b[0m \u001b[43m \u001b[49m\u001b[43mmessage\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_log_message\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstep_idx\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 413\u001b[0m \u001b[43m \u001b[49m\u001b[43mparams\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrouted_params\u001b[49m\u001b[43m[\u001b[49m\u001b[43mname\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 414\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 415\u001b[0m \u001b[38;5;66;03m# Replace the transformer of the step with the fitted\u001b[39;00m\n\u001b[0;32m 416\u001b[0m \u001b[38;5;66;03m# transformer. This is necessary when loading the transformer\u001b[39;00m\n\u001b[0;32m 417\u001b[0m \u001b[38;5;66;03m# from the cache.\u001b[39;00m\n\u001b[0;32m 418\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msteps[step_idx] \u001b[38;5;241m=\u001b[39m (name, fitted_transformer)\n", @@ -2927,7 +2928,7 @@ " model = class_models[model_name][\"model\"]\n", "\n", " model_pipeline = Pipeline([(\"pipeline\", pipeline_end), (\"model\", model)])\n", - " model_pipeline = model_pipeline.fit(X_train, y_train.values.ravel())\n", + " model_pipeline = model_pipeline.fit(X_train.values, y_train.values.ravel())\n", "\n", " y_train_predict = model_pipeline.predict(X_train)\n", " y_test_probs = model_pipeline.predict_proba(X_test)[:, 1]\n",