diff --git a/lab_4/lab4.ipynb b/lab_4/lab4.ipynb index 3d36cbf..c240afb 100644 --- a/lab_4/lab4.ipynb +++ b/lab_4/lab4.ipynb @@ -2834,7 +2834,7 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 69, "metadata": {}, "outputs": [ { @@ -2848,21 +2848,6 @@ "c:\\Users\\annal\\aim\\.venv\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n" ] - }, - { - "ename": "AxisError", - "evalue": "axis 1 is out of bounds for array of dimension 1", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mAxisError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[66], line 22\u001b[0m\n\u001b[0;32m 20\u001b[0m result[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAccuracy_train\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m metrics\u001b[38;5;241m.\u001b[39maccuracy_score(y_train, result[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtrain_preds\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[0;32m 21\u001b[0m result[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAccuracy_test\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m metrics\u001b[38;5;241m.\u001b[39maccuracy_score(y_test, result[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpreds\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[1;32m---> 22\u001b[0m result[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mROC_AUC_test\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[43mmetrics\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mroc_auc_score\u001b[49m\u001b[43m(\u001b[49m\u001b[43my_test\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mresult\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mprobs\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmulti_class\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43movr\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m 23\u001b[0m result[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mF1_train\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m metrics\u001b[38;5;241m.\u001b[39mf1_score(y_train, result[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtrain_preds\u001b[39m\u001b[38;5;124m\"\u001b[39m], average\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmacro\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 24\u001b[0m result[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mF1_test\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m metrics\u001b[38;5;241m.\u001b[39mf1_score(y_test, result[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpreds\u001b[39m\u001b[38;5;124m\"\u001b[39m], average\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmacro\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", - "File \u001b[1;32mc:\\Users\\annal\\aim\\.venv\\Lib\\site-packages\\sklearn\\utils\\_param_validation.py:213\u001b[0m, in \u001b[0;36mvalidate_params..decorator..wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 207\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m 208\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m config_context(\n\u001b[0;32m 209\u001b[0m skip_parameter_validation\u001b[38;5;241m=\u001b[39m(\n\u001b[0;32m 210\u001b[0m prefer_skip_nested_validation \u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[0;32m 211\u001b[0m )\n\u001b[0;32m 212\u001b[0m ):\n\u001b[1;32m--> 213\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\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\u001b[0;32m 214\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m InvalidParameterError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m 215\u001b[0m \u001b[38;5;66;03m# When the function is just a wrapper around an estimator, we allow\u001b[39;00m\n\u001b[0;32m 216\u001b[0m \u001b[38;5;66;03m# the function to delegate validation to the estimator, but we replace\u001b[39;00m\n\u001b[0;32m 217\u001b[0m \u001b[38;5;66;03m# the name of the estimator by the name of the function in the error\u001b[39;00m\n\u001b[0;32m 218\u001b[0m \u001b[38;5;66;03m# message to avoid confusion.\u001b[39;00m\n\u001b[0;32m 219\u001b[0m msg \u001b[38;5;241m=\u001b[39m re\u001b[38;5;241m.\u001b[39msub(\n\u001b[0;32m 220\u001b[0m \u001b[38;5;124mr\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mparameter of \u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mw+ must be\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m 221\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mparameter of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__qualname__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m must be\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m 222\u001b[0m \u001b[38;5;28mstr\u001b[39m(e),\n\u001b[0;32m 223\u001b[0m )\n", - "File \u001b[1;32mc:\\Users\\annal\\aim\\.venv\\Lib\\site-packages\\sklearn\\metrics\\_ranking.py:634\u001b[0m, in \u001b[0;36mroc_auc_score\u001b[1;34m(y_true, y_score, average, sample_weight, max_fpr, multi_class, labels)\u001b[0m\n\u001b[0;32m 632\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m multi_class \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mraise\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m 633\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmulti_class must be in (\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124movo\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m, \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124movr\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m)\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m--> 634\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_multiclass_roc_auc_score\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 635\u001b[0m \u001b[43m \u001b[49m\u001b[43my_true\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_score\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmulti_class\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maverage\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msample_weight\u001b[49m\n\u001b[0;32m 636\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 637\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m y_type \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbinary\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m 638\u001b[0m labels \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39munique(y_true)\n", - "File \u001b[1;32mc:\\Users\\annal\\aim\\.venv\\Lib\\site-packages\\sklearn\\metrics\\_ranking.py:706\u001b[0m, in \u001b[0;36m_multiclass_roc_auc_score\u001b[1;34m(y_true, y_score, labels, multi_class, average, sample_weight)\u001b[0m\n\u001b[0;32m 660\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Multiclass roc auc score.\u001b[39;00m\n\u001b[0;32m 661\u001b[0m \n\u001b[0;32m 662\u001b[0m \u001b[38;5;124;03mParameters\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 703\u001b[0m \n\u001b[0;32m 704\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 705\u001b[0m \u001b[38;5;66;03m# validation of the input y_score\u001b[39;00m\n\u001b[1;32m--> 706\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m np\u001b[38;5;241m.\u001b[39mallclose(\u001b[38;5;241m1\u001b[39m, \u001b[43my_score\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msum\u001b[49m\u001b[43m(\u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m):\n\u001b[0;32m 707\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m 708\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTarget scores need to be probabilities for multiclass \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 709\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mroc_auc, i.e. they should sum up to 1.0 over classes\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 710\u001b[0m )\n\u001b[0;32m 712\u001b[0m \u001b[38;5;66;03m# validation for multiclass parameter specifications\u001b[39;00m\n", - "File \u001b[1;32mc:\\Users\\annal\\aim\\.venv\\Lib\\site-packages\\numpy\\_core\\_methods.py:53\u001b[0m, in \u001b[0;36m_sum\u001b[1;34m(a, axis, dtype, out, keepdims, initial, where)\u001b[0m\n\u001b[0;32m 51\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_sum\u001b[39m(a, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, dtype\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, out\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, keepdims\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[0;32m 52\u001b[0m initial\u001b[38;5;241m=\u001b[39m_NoValue, where\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[1;32m---> 53\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mumr_sum\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkeepdims\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minitial\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwhere\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[1;31mAxisError\u001b[0m: axis 1 is out of bounds for array of dimension 1" - ] } ], "source": [ @@ -2878,8 +2863,8 @@ "\n", "result[\"pipeline\"] = Pipeline([(\"pipeline\", pipeline_end), (\"model\", optimized_model)]).fit(X_train, y_train.values.ravel())\n", "result[\"train_preds\"] = result[\"pipeline\"].predict(X_train)\n", - "result[\"probs\"] = result[\"pipeline\"].predict_proba(X_test)[:, 1]\n", - "result[\"preds\"] = np.where(result[\"probs\"] > 0.5, 1, 0)\n", + "result[\"probs\"] = result[\"pipeline\"].predict_proba(X_test)\n", + "result[\"preds\"] = np.argmax(y_test_probs, axis=1)\n", "\n", "result[\"Precision_train\"] = metrics.precision_score(y_train, result[\"train_preds\"],average=\"macro\")\n", "result[\"Precision_test\"] = metrics.precision_score(y_test, result[\"preds\"], average=\"macro\")\n", @@ -2894,6 +2879,286 @@ "result[\"Cohen_kappa_test\"] = metrics.cohen_kappa_score(y_test, result[\"preds\"])\n", "result[\"Confusion_matrix\"] = metrics.confusion_matrix(y_test, result[\"preds\"])" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Формирование данных для оценки старой и новой версии модели" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [], + "source": [ + "optimized_metrics = pd.DataFrame(columns=list(result.keys()))\n", + "optimized_metrics.loc[len(optimized_metrics)] = pd.Series(\n", + " data=class_models[optimized_model_type]\n", + ")\n", + "optimized_metrics.loc[len(optimized_metrics)] = pd.Series(\n", + " data=result\n", + ")\n", + "optimized_metrics.insert(loc=0, column=\"Name\", value=[\"Old\", \"New\"])\n", + "optimized_metrics = optimized_metrics.set_index(\"Name\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Оценка параметров старой и новой модели" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
 Precision_trainPrecision_testRecall_trainRecall_testAccuracy_trainAccuracy_testF1_trainF1_test
Name        
Old0.5815780.2365390.7354190.2465560.6274040.2884620.5997650.231541
New0.1813880.0357140.1576920.1428570.3062500.2500000.0907020.057143
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 71, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "optimized_metrics[\n", + " [\n", + " \"Precision_train\",\n", + " \"Precision_test\",\n", + " \"Recall_train\",\n", + " \"Recall_test\",\n", + " \"Accuracy_train\",\n", + " \"Accuracy_test\",\n", + " \"F1_train\",\n", + " \"F1_test\",\n", + " ]\n", + "].style.background_gradient(\n", + " cmap=\"plasma\",\n", + " low=0.3,\n", + " high=1,\n", + " subset=[\"Accuracy_train\", \"Accuracy_test\", \"F1_train\", \"F1_test\"],\n", + ").background_gradient(\n", + " cmap=\"viridis\",\n", + " low=1,\n", + " high=0.3,\n", + " subset=[\n", + " \"Precision_train\",\n", + " \"Precision_test\",\n", + " \"Recall_train\",\n", + " \"Recall_test\",\n", + " ],\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
 Accuracy_testF1_testROC_AUC_testCohen_kappa_testMCC_test
Name     
Old0.2884620.2315410.5995410.1268280.129917
New0.2500000.0571430.6054460.0000000.000000
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 72, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "optimized_metrics[\n", + " [\n", + " \"Accuracy_test\",\n", + " \"F1_test\",\n", + " \"ROC_AUC_test\",\n", + " \"Cohen_kappa_test\",\n", + " \"MCC_test\",\n", + " ]\n", + "].style.background_gradient(\n", + " cmap=\"plasma\",\n", + " low=0.3,\n", + " high=1,\n", + " subset=[\n", + " \"ROC_AUC_test\",\n", + " \"MCC_test\",\n", + " \"Cohen_kappa_test\",\n", + " ],\n", + ").background_gradient(\n", + " cmap=\"viridis\",\n", + " low=1,\n", + " high=0.3,\n", + " subset=[\n", + " \"Accuracy_test\",\n", + " \"F1_test\",\n", + " ],\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "_, ax = plt.subplots(1, 2, figsize=(10, 4), sharex=False, sharey=False\n", + ")\n", + "\n", + "for index in range(0, len(optimized_metrics)):\n", + " c_matrix = optimized_metrics.iloc[index][\"Confusion_matrix\"]\n", + " disp = ConfusionMatrixDisplay(\n", + " confusion_matrix=c_matrix, display_labels=[\"Under 30\", \"30-40\", \"40-50\", \"50-60\", \"60-70\", \"70-80\", \"80+\"]\n", + " ).plot(ax=ax.flat[index])\n", + "\n", + "plt.subplots_adjust(top=1, bottom=0, hspace=0.4, wspace=0.3)\n", + "plt.show()" + ] } ], "metadata": {