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{
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"cell_type": "code",
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"execution_count": 7,
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"execution_count": 8,
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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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"Training LogisticRegression...\n"
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"Training LinearRegression...\n"
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]
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},
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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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"c:\\Users\\annal\\aim\\.venv\\Lib\\site-packages\\sklearn\\model_selection\\_search.py:320: UserWarning: The total space of parameters 3 is smaller than n_iter=10. Running 3 iterations. For exhaustive searches, use GridSearchCV.\n",
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"c:\\Users\\annal\\aim\\.venv\\Lib\\site-packages\\sklearn\\model_selection\\_search.py:320: UserWarning: The total space of parameters 1 is smaller than n_iter=10. Running 1 iterations. For exhaustive searches, use GridSearchCV.\n",
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" warnings.warn(\n",
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"c:\\Users\\annal\\aim\\.venv\\Lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:242: UserWarning: Found unknown categories in columns [0, 1] during transform. These unknown categories will be encoded as all zeros\n",
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" warnings.warn(\n",
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"c:\\Users\\annal\\aim\\.venv\\Lib\\site-packages\\sklearn\\model_selection\\_search.py:320: UserWarning: The total space of parameters 9 is smaller than n_iter=10. Running 9 iterations. For exhaustive searches, use GridSearchCV.\n",
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" warnings.warn(\n"
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]
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},
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{
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"ename": "ValueError",
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"evalue": "\nAll the 15 fits failed.\nIt is very likely that your model is misconfigured.\nYou can try to debug the error by setting error_score='raise'.\n\nBelow are more details about the failures:\n--------------------------------------------------------------------------------\n15 fits failed with the following error:\nTraceback (most recent call last):\n File \"c:\\Users\\annal\\aim\\.venv\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 888, in _fit_and_score\n estimator.fit(X_train, y_train, **fit_params)\n File \"c:\\Users\\annal\\aim\\.venv\\Lib\\site-packages\\sklearn\\base.py\", line 1473, in wrapper\n return fit_method(estimator, *args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"c:\\Users\\annal\\aim\\.venv\\Lib\\site-packages\\sklearn\\pipeline.py\", line 473, in fit\n self._final_estimator.fit(Xt, y, **last_step_params[\"fit\"])\n File \"c:\\Users\\annal\\aim\\.venv\\Lib\\site-packages\\sklearn\\base.py\", line 1473, in wrapper\n return fit_method(estimator, *args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"c:\\Users\\annal\\aim\\.venv\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py\", line 1231, in fit\n check_classification_targets(y)\n File \"c:\\Users\\annal\\aim\\.venv\\Lib\\site-packages\\sklearn\\utils\\multiclass.py\", line 219, in check_classification_targets\n raise ValueError(\nValueError: Unknown label type: continuous. Maybe you are trying to fit a classifier, which expects discrete classes on a regression target with continuous values.\n",
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"output_type": "error",
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"traceback": [
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"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
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"Cell \u001b[1;32mIn[7], line 44\u001b[0m\n\u001b[0;32m 42\u001b[0m param_grid \u001b[38;5;241m=\u001b[39m param_grids_classification[name]\n\u001b[0;32m 43\u001b[0m grid_search \u001b[38;5;241m=\u001b[39m RandomizedSearchCV(pipeline, param_grid, cv\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m5\u001b[39m, scoring\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mf1\u001b[39m\u001b[38;5;124m'\u001b[39m, n_jobs\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m---> 44\u001b[0m \u001b[43mgrid_search\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[43m)\u001b[49m\n\u001b[0;32m 46\u001b[0m \u001b[38;5;66;03m# Лучшая модель\u001b[39;00m\n\u001b[0;32m 47\u001b[0m best_model \u001b[38;5;241m=\u001b[39m grid_search\u001b[38;5;241m.\u001b[39mbest_estimator_\n",
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"File \u001b[1;32mc:\\Users\\annal\\aim\\.venv\\Lib\\site-packages\\sklearn\\base.py:1473\u001b[0m, in \u001b[0;36m_fit_context.<locals>.decorator.<locals>.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",
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"File \u001b[1;32mc:\\Users\\annal\\aim\\.venv\\Lib\\site-packages\\sklearn\\model_selection\\_search.py:1019\u001b[0m, in \u001b[0;36mBaseSearchCV.fit\u001b[1;34m(self, X, y, **params)\u001b[0m\n\u001b[0;32m 1013\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_format_results(\n\u001b[0;32m 1014\u001b[0m all_candidate_params, n_splits, all_out, all_more_results\n\u001b[0;32m 1015\u001b[0m )\n\u001b[0;32m 1017\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m results\n\u001b[1;32m-> 1019\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_run_search\u001b[49m\u001b[43m(\u001b[49m\u001b[43mevaluate_candidates\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1021\u001b[0m \u001b[38;5;66;03m# multimetric is determined here because in the case of a callable\u001b[39;00m\n\u001b[0;32m 1022\u001b[0m \u001b[38;5;66;03m# self.scoring the return type is only known after calling\u001b[39;00m\n\u001b[0;32m 1023\u001b[0m first_test_score \u001b[38;5;241m=\u001b[39m all_out[\u001b[38;5;241m0\u001b[39m][\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtest_scores\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n",
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"File \u001b[1;32mc:\\Users\\annal\\aim\\.venv\\Lib\\site-packages\\sklearn\\model_selection\\_search.py:1960\u001b[0m, in \u001b[0;36mRandomizedSearchCV._run_search\u001b[1;34m(self, evaluate_candidates)\u001b[0m\n\u001b[0;32m 1958\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_run_search\u001b[39m(\u001b[38;5;28mself\u001b[39m, evaluate_candidates):\n\u001b[0;32m 1959\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Search n_iter candidates from param_distributions\"\"\"\u001b[39;00m\n\u001b[1;32m-> 1960\u001b[0m \u001b[43mevaluate_candidates\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 1961\u001b[0m \u001b[43m \u001b[49m\u001b[43mParameterSampler\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 1962\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparam_distributions\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mn_iter\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrandom_state\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[43mrandom_state\u001b[49m\n\u001b[0;32m 1963\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1964\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n",
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"File \u001b[1;32mc:\\Users\\annal\\aim\\.venv\\Lib\\site-packages\\sklearn\\model_selection\\_search.py:996\u001b[0m, in \u001b[0;36mBaseSearchCV.fit.<locals>.evaluate_candidates\u001b[1;34m(candidate_params, cv, more_results)\u001b[0m\n\u001b[0;32m 989\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(out) \u001b[38;5;241m!=\u001b[39m n_candidates \u001b[38;5;241m*\u001b[39m n_splits:\n\u001b[0;32m 990\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m 991\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcv.split and cv.get_n_splits returned \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 992\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124minconsistent results. Expected \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 993\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msplits, got \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(n_splits, \u001b[38;5;28mlen\u001b[39m(out) \u001b[38;5;241m/\u001b[39m\u001b[38;5;241m/\u001b[39m n_candidates)\n\u001b[0;32m 994\u001b[0m )\n\u001b[1;32m--> 996\u001b[0m \u001b[43m_warn_or_raise_about_fit_failures\u001b[49m\u001b[43m(\u001b[49m\u001b[43mout\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43merror_score\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 998\u001b[0m \u001b[38;5;66;03m# For callable self.scoring, the return type is only know after\u001b[39;00m\n\u001b[0;32m 999\u001b[0m \u001b[38;5;66;03m# calling. If the return type is a dictionary, the error scores\u001b[39;00m\n\u001b[0;32m 1000\u001b[0m \u001b[38;5;66;03m# can now be inserted with the correct key. The type checking\u001b[39;00m\n\u001b[0;32m 1001\u001b[0m \u001b[38;5;66;03m# of out will be done in `_insert_error_scores`.\u001b[39;00m\n\u001b[0;32m 1002\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mcallable\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mscoring):\n",
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"File \u001b[1;32mc:\\Users\\annal\\aim\\.venv\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py:529\u001b[0m, in \u001b[0;36m_warn_or_raise_about_fit_failures\u001b[1;34m(results, error_score)\u001b[0m\n\u001b[0;32m 522\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m num_failed_fits \u001b[38;5;241m==\u001b[39m num_fits:\n\u001b[0;32m 523\u001b[0m all_fits_failed_message \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m 524\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mAll the \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnum_fits\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m fits failed.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 525\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mIt is very likely that your model is misconfigured.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 526\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mYou can try to debug the error by setting error_score=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mraise\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 527\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBelow are more details about the failures:\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mfit_errors_summary\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 528\u001b[0m )\n\u001b[1;32m--> 529\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(all_fits_failed_message)\n\u001b[0;32m 531\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 532\u001b[0m some_fits_failed_message \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m 533\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mnum_failed_fits\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m fits failed out of a total of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnum_fits\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 534\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe score on these train-test partitions for these parameters\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 538\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBelow are more details about the failures:\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mfit_errors_summary\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 539\u001b[0m )\n",
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"\u001b[1;31mValueError\u001b[0m: \nAll the 15 fits failed.\nIt is very likely that your model is misconfigured.\nYou can try to debug the error by setting error_score='raise'.\n\nBelow are more details about the failures:\n--------------------------------------------------------------------------------\n15 fits failed with the following error:\nTraceback (most recent call last):\n File \"c:\\Users\\annal\\aim\\.venv\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 888, in _fit_and_score\n estimator.fit(X_train, y_train, **fit_params)\n File \"c:\\Users\\annal\\aim\\.venv\\Lib\\site-packages\\sklearn\\base.py\", line 1473, in wrapper\n return fit_method(estimator, *args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"c:\\Users\\annal\\aim\\.venv\\Lib\\site-packages\\sklearn\\pipeline.py\", line 473, in fit\n self._final_estimator.fit(Xt, y, **last_step_params[\"fit\"])\n File \"c:\\Users\\annal\\aim\\.venv\\Lib\\site-packages\\sklearn\\base.py\", line 1473, in wrapper\n return fit_method(estimator, *args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"c:\\Users\\annal\\aim\\.venv\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py\", line 1231, in fit\n check_classification_targets(y)\n File \"c:\\Users\\annal\\aim\\.venv\\Lib\\site-packages\\sklearn\\utils\\multiclass.py\", line 219, in check_classification_targets\n raise ValueError(\nValueError: Unknown label type: continuous. Maybe you are trying to fit a classifier, which expects discrete classes on a regression target with continuous values.\n"
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Training RandomForestRegressor...\n"
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]
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},
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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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"c:\\Users\\annal\\aim\\.venv\\Lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:242: UserWarning: Found unknown categories in columns [0, 1] during transform. These unknown categories will be encoded as all zeros\n",
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" warnings.warn(\n"
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]
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},
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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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"Training GradientBoostingRegressor...\n",
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"\n",
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"Model: LinearRegression\n",
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"Best Params: {}\n",
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"MAE: 18059903.80176681\n",
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"RMSE: 411829080.6584508\n",
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"R2: -7135788186375614.0\n",
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"\n",
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"Model: RandomForestRegressor\n",
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"Best Params: {'model__n_estimators': 100, 'model__max_depth': None}\n",
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"MAE: 3.372747412240537\n",
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"RMSE: 8.304219801175332\n",
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"R2: -1.9013866015383956\n",
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"\n",
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"Model: GradientBoostingRegressor\n",
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"Best Params: {'model__n_estimators': 200, 'model__max_depth': 5, 'model__learning_rate': 0.2}\n",
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"MAE: 3.572597806187309\n",
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"RMSE: 10.306842221909957\n",
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"R2: -3.4695025074945356\n"
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]
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},
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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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"c:\\Users\\annal\\aim\\.venv\\Lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:242: UserWarning: Found unknown categories in columns [0, 1] during transform. These unknown categories will be encoded as all zeros\n",
|
||||
" warnings.warn(\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from sklearn.ensemble import RandomForestClassifier\n",
|
||||
"from sklearn.linear_model import LogisticRegression\n",
|
||||
"from sklearn.model_selection import RandomizedSearchCV\n",
|
||||
"from sklearn.neighbors import KNeighborsClassifier\n",
|
||||
"from sklearn.metrics import accuracy_score, confusion_matrix, f1_score\n",
|
||||
"import numpy as np\n",
|
||||
"from sklearn.linear_model import LinearRegression\n",
|
||||
"from sklearn.ensemble import RandomForestRegressor\n",
|
||||
"from sklearn.ensemble import GradientBoostingRegressor\n",
|
||||
"from sklearn.model_selection import GridSearchCV, RandomizedSearchCV\n",
|
||||
"from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"\n",
|
||||
"random_state = 42\n",
|
||||
"\n",
|
||||
"# Модели и параметры\n",
|
||||
"models_classification = {\n",
|
||||
" \"LogisticRegression\": LogisticRegression(max_iter=1000),\n",
|
||||
" \"RandomForestClassifier\": RandomForestClassifier(random_state=42),\n",
|
||||
" \"KNN\": KNeighborsClassifier()\n",
|
||||
"models_regression = {\n",
|
||||
" \"LinearRegression\": LinearRegression(),\n",
|
||||
" \"RandomForestRegressor\": RandomForestRegressor(random_state=random_state),\n",
|
||||
" \"GradientBoostingRegressor\": GradientBoostingRegressor(random_state=random_state)\n",
|
||||
"}\n",
|
||||
"\n",
|
||||
"param_grids_classification = {\n",
|
||||
" \"LogisticRegression\": {\n",
|
||||
" 'model__C': [0.1, 1, 10]\n",
|
||||
"param_grids_regression = {\n",
|
||||
" \"LinearRegression\": {},\n",
|
||||
" \"RandomForestRegressor\": {\n",
|
||||
" 'model__n_estimators': [50, 100, 200],\n",
|
||||
" 'model__max_depth': [None, 10, 20],\n",
|
||||
" },\n",
|
||||
" \"RandomForestClassifier\": {\n",
|
||||
" \"model__n_estimators\": [10, 20, 30, 40, 50, 100, 150, 200, 250, 500],\n",
|
||||
" \"model__max_features\": [\"sqrt\", \"log2\", 2],\n",
|
||||
" \"model__max_depth\": [2, 3, 4, 5, 6, 7, 8, 9 ,10, 20],\n",
|
||||
" \"model__criterion\": [\"gini\", \"entropy\", \"log_loss\"],\n",
|
||||
" },\n",
|
||||
" \"KNN\": {\n",
|
||||
" 'model__n_neighbors': [3, 5, 7, 9, 11],\n",
|
||||
" 'model__weights': ['uniform', 'distance']\n",
|
||||
" \"GradientBoostingRegressor\": {\n",
|
||||
" 'model__n_estimators': [50, 100, 200],\n",
|
||||
" 'model__learning_rate': [0.01, 0.1, 0.2],\n",
|
||||
" 'model__max_depth': [3, 5, 10]\n",
|
||||
" }\n",
|
||||
"}\n",
|
||||
"\n",
|
||||
"# Результаты\n",
|
||||
"results_classification = {}\n",
|
||||
"results_regression = {}\n",
|
||||
"\n",
|
||||
"# Перебор моделей\n",
|
||||
"for name, model in models_classification.items():\n",
|
||||
"for name, model in models_regression.items():\n",
|
||||
" print(f\"Training {name}...\")\n",
|
||||
" pipeline = Pipeline(steps=[\n",
|
||||
" ('features_preprocessing', features_preprocessing),\n",
|
||||
" ('model', model)\n",
|
||||
" ])\n",
|
||||
" \n",
|
||||
" param_grid = param_grids_classification[name]\n",
|
||||
" grid_search = RandomizedSearchCV(pipeline, param_grid, cv=5, scoring='f1', n_jobs=-1)\n",
|
||||
" param_grid = param_grids_regression[name]\n",
|
||||
" grid_search = RandomizedSearchCV(pipeline, param_grid, cv=5, scoring='neg_mean_absolute_error', n_jobs=-1)\n",
|
||||
" grid_search.fit(X_train, y_train)\n",
|
||||
"\n",
|
||||
" # Лучшая модель\n",
|
||||
@ -3849,26 +3884,109 @@
|
||||
" y_pred = best_model.predict(X_test)\n",
|
||||
"\n",
|
||||
" # Метрики\n",
|
||||
" acc = accuracy_score(y_test, y_pred)\n",
|
||||
" f1 = f1_score(y_test, y_pred)\n",
|
||||
"\n",
|
||||
" # Вычисление матрицы ошибок\n",
|
||||
" c_matrix = confusion_matrix(y_test, y_pred)\n",
|
||||
" mae = mean_absolute_error(y_test, y_pred)\n",
|
||||
" rmse = np.sqrt(mean_squared_error(y_test, y_pred))\n",
|
||||
" r2 = r2_score(y_test, y_pred)\n",
|
||||
"\n",
|
||||
" # Сохранение результатов\n",
|
||||
" results_classification[name] = {\n",
|
||||
" results_regression[name] = {\n",
|
||||
" \"Best Params\": grid_search.best_params_,\n",
|
||||
" \"Accuracy\": acc,\n",
|
||||
" \"F1 Score\": f1,\n",
|
||||
" \"Confusion_matrix\": c_matrix\n",
|
||||
" \"MAE\": mae,\n",
|
||||
" \"RMSE\": rmse,\n",
|
||||
" \"R2\": r2\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
"# Печать результатов\n",
|
||||
"for name, metrics in results_classification.items():\n",
|
||||
"for name, metrics in results_regression.items():\n",
|
||||
" print(f\"\\nModel: {name}\")\n",
|
||||
" for metric, value in metrics.items():\n",
|
||||
" print(f\"{metric}: {value}\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<style type=\"text/css\">\n",
|
||||
"#T_c5529_row0_col0, #T_c5529_row0_col1, #T_c5529_row1_col0, #T_c5529_row1_col1 {\n",
|
||||
" background-color: #26818e;\n",
|
||||
" color: #f1f1f1;\n",
|
||||
"}\n",
|
||||
"#T_c5529_row0_col2, #T_c5529_row1_col2 {\n",
|
||||
" background-color: #da5a6a;\n",
|
||||
" color: #f1f1f1;\n",
|
||||
"}\n",
|
||||
"#T_c5529_row2_col0, #T_c5529_row2_col1 {\n",
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" background-color: #a8db34;\n",
|
||||
" color: #000000;\n",
|
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"}\n",
|
||||
"#T_c5529_row2_col2 {\n",
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" background-color: #4e02a2;\n",
|
||||
" color: #f1f1f1;\n",
|
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"}\n",
|
||||
"</style>\n",
|
||||
"<table id=\"T_c5529\">\n",
|
||||
" <thead>\n",
|
||||
" <tr>\n",
|
||||
" <th class=\"blank level0\" > </th>\n",
|
||||
" <th id=\"T_c5529_level0_col0\" class=\"col_heading level0 col0\" >MAE</th>\n",
|
||||
" <th id=\"T_c5529_level0_col1\" class=\"col_heading level0 col1\" >RMSE</th>\n",
|
||||
" <th id=\"T_c5529_level0_col2\" class=\"col_heading level0 col2\" >R2</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th id=\"T_c5529_level0_row0\" class=\"row_heading level0 row0\" >RandomForestRegressor</th>\n",
|
||||
" <td id=\"T_c5529_row0_col0\" class=\"data row0 col0\" >3.372747</td>\n",
|
||||
" <td id=\"T_c5529_row0_col1\" class=\"data row0 col1\" >8.304220</td>\n",
|
||||
" <td id=\"T_c5529_row0_col2\" class=\"data row0 col2\" >-1.901387</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th id=\"T_c5529_level0_row1\" class=\"row_heading level0 row1\" >GradientBoostingRegressor</th>\n",
|
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" <td id=\"T_c5529_row1_col0\" class=\"data row1 col0\" >3.572598</td>\n",
|
||||
" <td id=\"T_c5529_row1_col1\" class=\"data row1 col1\" >10.306842</td>\n",
|
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" <td id=\"T_c5529_row1_col2\" class=\"data row1 col2\" >-3.469503</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th id=\"T_c5529_level0_row2\" class=\"row_heading level0 row2\" >LinearRegression</th>\n",
|
||||
" <td id=\"T_c5529_row2_col0\" class=\"data row2 col0\" >18059903.801767</td>\n",
|
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" <td id=\"T_c5529_row2_col1\" class=\"data row2 col1\" >411829080.658451</td>\n",
|
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" <td id=\"T_c5529_row2_col2\" class=\"data row2 col2\" >-7135788186375614.000000</td>\n",
|
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" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n"
|
||||
],
|
||||
"text/plain": [
|
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"<pandas.io.formats.style.Styler at 0x182a6929490>"
|
||||
]
|
||||
},
|
||||
"execution_count": 9,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Импортируем pandas для работы с таблицами\n",
|
||||
"import pandas as pd\n",
|
||||
"\n",
|
||||
"# Формируем таблицу метрик\n",
|
||||
"reg_metrics = pd.DataFrame.from_dict(results_regression, orient=\"index\")[\n",
|
||||
" [\"MAE\", \"RMSE\", \"R2\"]\n",
|
||||
"]\n",
|
||||
"\n",
|
||||
"# Визуализация результатов с помощью стилизации\n",
|
||||
"styled_metrics = (\n",
|
||||
" reg_metrics.sort_values(by=\"RMSE\")\n",
|
||||
" .style.background_gradient(cmap=\"viridis\", low=1, high=0.3, subset=[\"RMSE\", \"MAE\"])\n",
|
||||
" .background_gradient(cmap=\"plasma\", low=0.3, high=1, subset=[\"R2\"])\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"styled_metrics"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user