Imbalanced_Dataset.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Light</th>\n",
       "      <th>CO2</th>\n",
       "      <th>HumidityRatio</th>\n",
       "      <th>Occupancy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>23.7000</td>\n",
       "      <td>26.272</td>\n",
       "      <td>585.200000</td>\n",
       "      <td>749.200000</td>\n",
       "      <td>0.004764</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>23.7180</td>\n",
       "      <td>26.290</td>\n",
       "      <td>578.400000</td>\n",
       "      <td>760.400000</td>\n",
       "      <td>0.004773</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>23.7300</td>\n",
       "      <td>26.230</td>\n",
       "      <td>572.666667</td>\n",
       "      <td>769.666667</td>\n",
       "      <td>0.004765</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.7225</td>\n",
       "      <td>26.125</td>\n",
       "      <td>493.750000</td>\n",
       "      <td>774.750000</td>\n",
       "      <td>0.004744</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>23.7540</td>\n",
       "      <td>26.200</td>\n",
       "      <td>488.600000</td>\n",
       "      <td>779.000000</td>\n",
       "      <td>0.004767</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Temperature  Humidity       Light         CO2  HumidityRatio  Occupancy\n",
       "0      23.7000    26.272  585.200000  749.200000       0.004764          1\n",
       "1      23.7180    26.290  578.400000  760.400000       0.004773          1\n",
       "2      23.7300    26.230  572.666667  769.666667       0.004765          1\n",
       "3      23.7225    26.125  493.750000  774.750000       0.004744          1\n",
       "4      23.7540    26.200  488.600000  779.000000       0.004767          1"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from collections import Counter\n",
    "\n",
    "class_name = 'Occupancy'\n",
    "df_training = pd.read_csv('training.csv', skipinitialspace=True, na_values='?', \n",
    "                 keep_default_na=True)\n",
    "\n",
    "df_test = pd.read_csv('test.csv', skipinitialspace=True, na_values='?', \n",
    "                 keep_default_na=True)\n",
    "\n",
    "columns2remove = ['date', 'Unnamed: 0', 'cumulative_hour', 'cumulative_minute', 'day', 'hour', 'minute', 'day_minute', 'weekend']\n",
    "df_training.drop(columns2remove, inplace=True, axis=1)\n",
    "df_test.drop(columns2remove, inplace=True, axis=1)\n",
    "\n",
    "df_training.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    10739\n",
      "1     3653\n",
      "Name: Occupancy, dtype: int64\n",
      "\n",
      "Occupancy frequency:\n",
      "0: 74.62%\n",
      "1: 25.38%\n"
     ]
    }
   ],
   "source": [
    "print(df_training[class_name].value_counts())\n",
    "print(\"\\nOccupancy frequency:\\n0: {}%\\n1: {}%\".format(\n",
    "        round(df_training[class_name].value_counts()[0]/len(df_training)*100, 2),\n",
    "        round(df_training[class_name].value_counts()[1]/len(df_training)*100, 2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "rows2remove = np.random.choice(df_training[df_training[class_name] == 1].index, 3653 - 448, replace=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "448 saranno i 1 rimasti (che corrispondono al 4% del nuovo dataset)\n",
    "3223 sono gli 1 da togliere"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14392, 3205)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_training), len(rows2remove)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11187\n"
     ]
    }
   ],
   "source": [
    "# tolgiamo le righe\n",
    "df2_training = df_training.drop(index=rows2remove, axis=0)\n",
    "print(len(df2_training))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    10739\n",
      "1      448\n",
      "Name: Occupancy, dtype: int64\n",
      "\n",
      "Occupancy frequency:\n",
      "0: 96.0%\n",
      "1: 4.0%\n"
     ]
    }
   ],
   "source": [
    "# Ricalcoliamo la nuova frequenza di Occupancy\n",
    "print(df2_training[class_name].value_counts())\n",
    "print(\"\\nOccupancy frequency:\\n0: {}%\\n1: {}%\".format(\n",
    "        round(df2_training[class_name].value_counts()[0]/len(df2_training)*100, 2),\n",
    "        round(df2_training[class_name].value_counts()[1]/len(df2_training)*100, 2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "448 / ( 10739 + 448 ) * 100 = 4.00468252435863"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset shape Counter({0: 10739, 1: 448})\n"
     ]
    }
   ],
   "source": [
    "print('Dataset shape %s' % Counter(df2_training[class_name]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stessa trasformazione del dataset per il test set (test.csv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    5071\n",
      "1    1097\n",
      "Name: Occupancy, dtype: int64\n",
      "\n",
      "Occupancy frequency:\n",
      "0: 82.21%\n",
      "1: 17.79%\n"
     ]
    }
   ],
   "source": [
    "print(df_test[class_name].value_counts())\n",
    "\n",
    "print(\"\\nOccupancy frequency:\\n0: {}%\\n1: {}%\".format(\n",
    "        round(df_test[class_name].value_counts()[0]/len(df_test)*100, 2),\n",
    "        round(df_test[class_name].value_counts()[1]/len(df_test)*100, 2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6168, 885)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rows2remove = np.random.choice(df_test[df_test[class_name] == 1].index, 1097 - 212, replace=False)\n",
    "len(df_test), len(rows2remove)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5283\n"
     ]
    }
   ],
   "source": [
    "df2_test = df_test.drop(index=rows2remove, axis=0)\n",
    "print(len(df2_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    5071\n",
      "1     212\n",
      "Name: Occupancy, dtype: int64\n",
      "\n",
      "Occupancy frequency:\n",
      "0: 95.99%\n",
      "1: 4.01%\n"
     ]
    }
   ],
   "source": [
    "print(df2_test[class_name].value_counts())\n",
    "print(\"\\nOccupancy frequency:\\n0: {}%\\n1: {}%\".format(\n",
    "        round(df2_test[class_name].value_counts()[0]/len(df2_test)*100, 2),\n",
    "        round(df2_test[class_name].value_counts()[1]/len(df2_test)*100, 2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "212 / ( 5071 + 212 ) * 100 = 4.01287147454098"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Classification with imbalanced dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score, f1_score, classification_report\n",
    "from sklearn.metrics import roc_curve, auc, roc_auc_score\n",
    "\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "attributes = [col for col in df2_training.columns if col != class_name]\n",
    "scaler = StandardScaler()\n",
    "X_train = scaler.fit_transform(df2_training[attributes].values)\n",
    "y_train = df2_training[class_name]\n",
    "\n",
    "X_test = scaler.fit_transform(df2_test[attributes].values)\n",
    "y_test = df2_test[class_name]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Model training and testing with normal dataset (75%-25% balance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy 0.995136186770428\n",
      "F1-score [0.99703499 0.9864743 ]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.99      1.00      5071\n",
      "           1       0.98      1.00      0.99      1097\n",
      "\n",
      "    accuracy                           1.00      6168\n",
      "   macro avg       0.99      1.00      0.99      6168\n",
      "weighted avg       1.00      1.00      1.00      6168\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# I parametri sono stati recuperati dalla grid search fatta utilizzando tutte le variabili\n",
    "clf2 = DecisionTreeClassifier(min_samples_leaf=100, min_samples_split=10)\n",
    "clf2.fit(df_training[attributes].values, df_training[class_name])\n",
    "\n",
    "y_pred = clf2.predict(df_test[attributes].values)\n",
    "\n",
    "print('Accuracy %s' % accuracy_score(df_test[class_name], y_pred))\n",
    "print('F1-score %s' % f1_score(df_test[class_name], y_pred, average=None))\n",
    "print(classification_report(df_test[class_name], y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Model training and testing with Imbalanced dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy 0.979935642627295\n",
      "F1-score [0.98949247 0.77824268]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.98      0.99      5071\n",
      "           1       0.70      0.88      0.78       212\n",
      "\n",
      "    accuracy                           0.98      5283\n",
      "   macro avg       0.85      0.93      0.88      5283\n",
      "weighted avg       0.98      0.98      0.98      5283\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# clf2 e clf sono identici, tranne per il dataset su cui hanno fatto il training\n",
    "clf = DecisionTreeClassifier(min_samples_leaf=100, min_samples_split=10)\n",
    "clf.fit(X_train, y_train)\n",
    "\n",
    "y_pred = clf.predict(X_test)\n",
    "\n",
    "print('Accuracy %s' % accuracy_score(y_test, y_pred))\n",
    "print('F1-score %s' % f1_score(y_test, y_pred, average=None))\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Model parameters taken from DM2_Decision_Tree:"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Attributes to test in iteration 26: ('Temperature', 'Light', 'CO2', 'Humidity', 'HumidityRatio')\n",
    "Temperature 0.009854554871521813\n",
    "Light 0.9660021115243091\n",
    "CO2 0.008695598711003777\n",
    "Humidity 0.01037306391550738\n",
    "HumidityRatio 0.0050746709776578495\n",
    "Best parameter for this attributes: DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='entropy',\n",
    "                       max_depth=None, max_features=None, max_leaf_nodes=None,\n",
    "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
    "                       min_samples_leaf=100, min_samples_split=10,\n",
    "                       min_weight_fraction_leaf=0.0, presort='deprecated',\n",
    "                       random_state=None, splitter='best')\n",
    "Best performance with cv = 43, f1-score = 0.9768583077407306\n",
    "Best performance with cv = 43, accuracy = 0.9819384486840271\n",
    "\n",
    "Accuracy 0.9526588845654993\n",
    "F1-score [0.97143975 0.86174242]\n",
    "              precision    recall  f1-score   support\n",
    "\n",
    "           0       0.96      0.98      0.97      5071\n",
    "           1       0.90      0.83      0.86      1097\n",
    "\n",
    "    accuracy                           0.95      6168\n",
    "   macro avg       0.93      0.90      0.92      6168\n",
    "weighted avg       0.95      0.95      0.95      6168"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-------------------------------------------------------------------\n",
    "\n",
    "## ROC curve\n",
    "\n",
    "Comparing original and imbalaned datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_score_imb = clf.predict_proba(X_test)\n",
    "fpr0_imb, tpr0_imb, _ = roc_curve(y_test, y_score_imb[:, 1])\n",
    "roc_auc0_imb = auc(fpr0_imb, tpr0_imb)\n",
    "\n",
    "y_score_or = clf2.predict_proba(df2_test[attributes].values)\n",
    "fpr0_or, tpr0_or, _ = roc_curve(y_test, y_score_or[:, 1])\n",
    "roc_auc0_or = auc(fpr0_or, tpr0_or)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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       "      </defs>\r\n",
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      ],
      "text/plain": [
       "<Figure size 648x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f=22\n",
    "plt.figure(figsize=(9,7))\n",
    "sns.set(style='whitegrid')\n",
    "plt.plot(fpr0_imb, tpr0_imb, color='crimson', lw=3, label='$AUC_i$ = %.3f' % (roc_auc0_imb))\n",
    "plt.plot(fpr0_or, tpr0_or, color='g', lw=3, label='$AUC_o$ = %.3f' % (roc_auc0_or))\n",
    "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate', fontsize=f)\n",
    "plt.ylabel('True Positive Rate', fontsize=f)\n",
    "#plt.title('ROC curve', fontsize=f)\n",
    "plt.legend(loc=\"lower right\", fontsize=f)\n",
    "plt.tick_params(axis='both', which='major', labelsize=f)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy 0.9848570887753171\n",
      "F1-score [0.99204929 0.84126984]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.98      0.99      5071\n",
      "           1       0.73      1.00      0.84       212\n",
      "\n",
      "    accuracy                           0.98      5283\n",
      "   macro avg       0.86      0.99      0.92      5283\n",
      "weighted avg       0.99      0.98      0.99      5283\n",
      "\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
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     "metadata": {},
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   "source": [
    "clf = DecisionTreeClassifier(min_samples_leaf=100, min_samples_split=10)\n",
    "clf.fit(X_train, y_train)\n",
    "\n",
    "def adjusted_predict(X_test, thr=0.5):\n",
    "    y_score = clf.predict_proba(X_test)[:, 1]\n",
    "    return np.array([1 if y > thr else 0 for y in y_score])\n",
    "\n",
    "y_pred = adjusted_predict(X_test, thr=0.1)\n",
    "\n",
    "print('Accuracy %s' % accuracy_score(y_test, y_pred))\n",
    "print('F1-score %s' % f1_score(y_test, y_pred, average=None))\n",
    "print(classification_report(y_test, y_pred))\n",
    "\n",
    "#Tutti questi comandi non servono, il grafico che si costruisce è identico a prima\n",
    "y_score = clf.predict_proba(X_test)\n",
    "fpr, tpr, thr = roc_curve(y_test, y_pred)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "\n",
    "plt.plot(fpr0_imb, tpr0_imb, color='darkorange', lw=3, label='$AUC_0$ = %.3f' % (roc_auc0_imb))\n",
    "plt.plot(fpr0_or, tpr0_or, color='green', lw=3, label='$AUC_1$ = %.3f' % (roc_auc0_or))\n",
    "\n",
    "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate', fontsize=16)\n",
    "plt.ylabel('True Positive Rate', fontsize=16)\n",
    "plt.title('ROC curve', fontsize=16)\n",
    "plt.legend(loc=\"lower right\", fontsize=14, frameon=False)\n",
    "plt.tick_params(axis='both', which='major', labelsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "tags": [
     "outputPrepend"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       0.86      0.99      0.92      5283\n",
      "weighted avg       0.99      0.98      0.99      5283\n",
      "\n",
      "------------------------------\n",
      "0.06\n",
      "Accuracy 0.9848570887753171\n",
      "F1-score [0.99204929 0.84126984]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.98      0.99      5071\n",
      "           1       0.73      1.00      0.84       212\n",
      "\n",
      "    accuracy                           0.98      5283\n",
      "   macro avg       0.86      0.99      0.92      5283\n",
      "weighted avg       0.99      0.98      0.99      5283\n",
      "\n",
      "------------------------------\n",
      "0.08\n",
      "Accuracy 0.9848570887753171\n",
      "F1-score [0.99204929 0.84126984]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.98      0.99      5071\n",
      "           1       0.73      1.00      0.84       212\n",
      "\n",
      "    accuracy                           0.98      5283\n",
      "   macro avg       0.86      0.99      0.92      5283\n",
      "weighted avg       0.99      0.98      0.99      5283\n",
      "\n",
      "------------------------------\n",
      "0.1\n",
      "Accuracy 0.9848570887753171\n",
      "F1-score [0.99204929 0.84126984]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.98      0.99      5071\n",
      "           1       0.73      1.00      0.84       212\n",
      "\n",
      "    accuracy                           0.98      5283\n",
      "   macro avg       0.86      0.99      0.92      5283\n",
      "weighted avg       0.99      0.98      0.99      5283\n",
      "\n",
      "------------------------------\n",
      "0.12\n",
      "Accuracy 0.9848570887753171\n",
      "F1-score [0.99204929 0.84126984]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.98      0.99      5071\n",
      "           1       0.73      1.00      0.84       212\n",
      "\n",
      "    accuracy                           0.98      5283\n",
      "   macro avg       0.86      0.99      0.92      5283\n",
      "weighted avg       0.99      0.98      0.99      5283\n",
      "\n",
      "------------------------------\n",
      "0.14\n",
      "Accuracy 0.9848570887753171\n",
      "F1-score [0.99204929 0.84126984]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.98      0.99      5071\n",
      "           1       0.73      1.00      0.84       212\n",
      "\n",
      "    accuracy                           0.98      5283\n",
      "   macro avg       0.86      0.99      0.92      5283\n",
      "weighted avg       0.99      0.98      0.99      5283\n",
      "\n",
      "------------------------------\n",
      "0.16\n",
      "Accuracy 0.9848570887753171\n",
      "F1-score [0.99204929 0.84126984]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.98      0.99      5071\n",
      "           1       0.73      1.00      0.84       212\n",
      "\n",
      "    accuracy                           0.98      5283\n",
      "   macro avg       0.86      0.99      0.92      5283\n",
      "weighted avg       0.99      0.98      0.99      5283\n",
      "\n",
      "------------------------------\n",
      "0.18\n",
      "Accuracy 0.9848570887753171\n",
      "F1-score [0.99204929 0.84126984]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.98      0.99      5071\n",
      "           1       0.73      1.00      0.84       212\n",
      "\n",
      "    accuracy                           0.98      5283\n",
      "   macro avg       0.86      0.99      0.92      5283\n",
      "weighted avg       0.99      0.98      0.99      5283\n",
      "\n",
      "------------------------------\n",
      "0.2\n",
      "Accuracy 0.9848570887753171\n",
      "F1-score [0.99204929 0.84126984]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.98      0.99      5071\n",
      "           1       0.73      1.00      0.84       212\n",
      "\n",
      "    accuracy                           0.98      5283\n",
      "   macro avg       0.86      0.99      0.92      5283\n",
      "weighted avg       0.99      0.98      0.99      5283\n",
      "\n",
      "------------------------------\n",
      "0.22\n",
      "Accuracy 0.9848570887753171\n",
      "F1-score [0.99204929 0.84126984]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.98      0.99      5071\n",
      "           1       0.73      1.00      0.84       212\n",
      "\n",
      "    accuracy                           0.98      5283\n",
      "   macro avg       0.86      0.99      0.92      5283\n",
      "weighted avg       0.99      0.98      0.99      5283\n",
      "\n",
      "------------------------------\n",
      "0.24\n",
      "Accuracy 0.9848570887753171\n",
      "F1-score [0.99204929 0.84126984]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.98      0.99      5071\n",
      "           1       0.73      1.00      0.84       212\n",
      "\n",
      "    accuracy                           0.98      5283\n",
      "   macro avg       0.86      0.99      0.92      5283\n",
      "weighted avg       0.99      0.98      0.99      5283\n",
      "\n",
      "------------------------------\n",
      "0.26\n",
      "Accuracy 0.9848570887753171\n",
      "F1-score [0.99204929 0.84126984]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.98      0.99      5071\n",
      "           1       0.73      1.00      0.84       212\n",
      "\n",
      "    accuracy                           0.98      5283\n",
      "   macro avg       0.86      0.99      0.92      5283\n",
      "weighted avg       0.99      0.98      0.99      5283\n",
      "\n",
      "------------------------------\n",
      "0.28\n",
      "Accuracy 0.9848570887753171\n",
      "F1-score [0.99204929 0.84126984]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.98      0.99      5071\n",
      "           1       0.73      1.00      0.84       212\n",
      "\n",
      "    accuracy                           0.98      5283\n",
      "   macro avg       0.86      0.99      0.92      5283\n",
      "weighted avg       0.99      0.98      0.99      5283\n",
      "\n",
      "------------------------------\n",
      "0.3\n",
      "Accuracy 0.9848570887753171\n",
      "F1-score [0.99204929 0.84126984]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.98      0.99      5071\n",
      "           1       0.73      1.00      0.84       212\n",
      "\n",
      "    accuracy                           0.98      5283\n",
      "   macro avg       0.86      0.99      0.92      5283\n",
      "weighted avg       0.99      0.98      0.99      5283\n",
      "\n",
      "------------------------------\n",
      "0.32\n",
      "Accuracy 0.9848570887753171\n",
      "F1-score [0.99204929 0.84126984]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.98      0.99      5071\n",
      "           1       0.73      1.00      0.84       212\n",
      "\n",
      "    accuracy                           0.98      5283\n",
      "   macro avg       0.86      0.99      0.92      5283\n",
      "weighted avg       0.99      0.98      0.99      5283\n",
      "\n",
      "------------------------------\n",
      "0.34\n",
      "Accuracy 0.9848570887753171\n",
      "F1-score [0.99204929 0.84126984]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.98      0.99      5071\n",
      "           1       0.73      1.00      0.84       212\n",
      "\n",
      "    accuracy                           0.98      5283\n",
      "   macro avg       0.86      0.99      0.92      5283\n",
      "weighted avg       0.99      0.98      0.99      5283\n",
      "\n",
      "------------------------------\n",
      "0.36\n",
      "Accuracy 0.9848570887753171\n",
      "F1-score [0.99204929 0.84126984]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.98      0.99      5071\n",
      "           1       0.73      1.00      0.84       212\n",
      "\n",
      "    accuracy                           0.98      5283\n",
      "   macro avg       0.86      0.99      0.92      5283\n",
      "weighted avg       0.99      0.98      0.99      5283\n",
      "\n",
      "------------------------------\n",
      "0.38\n",
      "Accuracy 0.9848570887753171\n",
      "F1-score [0.99204929 0.84126984]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.98      0.99      5071\n",
      "           1       0.73      1.00      0.84       212\n",
      "\n",
      "    accuracy                           0.98      5283\n",
      "   macro avg       0.86      0.99      0.92      5283\n",
      "weighted avg       0.99      0.98      0.99      5283\n",
      "\n",
      "------------------------------\n",
      "0.4\n",
      "Accuracy 0.979935642627295\n",
      "F1-score [0.98949247 0.77824268]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.98      0.99      5071\n",
      "           1       0.70      0.88      0.78       212\n",
      "\n",
      "    accuracy                           0.98      5283\n",
      "   macro avg       0.85      0.93      0.88      5283\n",
      "weighted avg       0.98      0.98      0.98      5283\n",
      "\n",
      "------------------------------\n",
      "0.42\n",
      "Accuracy 0.979935642627295\n",
      "F1-score [0.98949247 0.77824268]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.98      0.99      5071\n",
      "           1       0.70      0.88      0.78       212\n",
      "\n",
      "    accuracy                           0.98      5283\n",
      "   macro avg       0.85      0.93      0.88      5283\n",
      "weighted avg       0.98      0.98      0.98      5283\n",
      "\n",
      "------------------------------\n",
      "0.44\n",
      "Accuracy 0.979935642627295\n",
      "F1-score [0.98949247 0.77824268]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.98      0.99      5071\n",
      "           1       0.70      0.88      0.78       212\n",
      "\n",
      "    accuracy                           0.98      5283\n",
      "   macro avg       0.85      0.93      0.88      5283\n",
      "weighted avg       0.98      0.98      0.98      5283\n",
      "\n",
      "------------------------------\n",
      "0.46\n",
      "Accuracy 0.979935642627295\n",
      "F1-score [0.98949247 0.77824268]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.98      0.99      5071\n",
      "           1       0.70      0.88      0.78       212\n",
      "\n",
      "    accuracy                           0.98      5283\n",
      "   macro avg       0.85      0.93      0.88      5283\n",
      "weighted avg       0.98      0.98      0.98      5283\n",
      "\n",
      "------------------------------\n",
      "0.48\n",
      "Accuracy 0.979935642627295\n",
      "F1-score [0.98949247 0.77824268]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.98      0.99      5071\n",
      "           1       0.70      0.88      0.78       212\n",
      "\n",
      "    accuracy                           0.98      5283\n",
      "   macro avg       0.85      0.93      0.88      5283\n",
      "weighted avg       0.98      0.98      0.98      5283\n",
      "\n",
      "------------------------------\n",
      "0.5\n",
      "Accuracy 0.979935642627295\n",
      "F1-score [0.98949247 0.77824268]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.98      0.99      5071\n",
      "           1       0.70      0.88      0.78       212\n",
      "\n",
      "    accuracy                           0.98      5283\n",
      "   macro avg       0.85      0.93      0.88      5283\n",
      "weighted avg       0.98      0.98      0.98      5283\n",
      "\n",
      "------------------------------\n",
      "0.52\n",
      "Accuracy 0.979935642627295\n",
      "F1-score [0.98949247 0.77824268]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.98      0.99      5071\n",
      "           1       0.70      0.88      0.78       212\n",
      "\n",
      "    accuracy                           0.98      5283\n",
      "   macro avg       0.85      0.93      0.88      5283\n",
      "weighted avg       0.98      0.98      0.98      5283\n",
      "\n",
      "------------------------------\n",
      "0.54\n",
      "Accuracy 0.979935642627295\n",
      "F1-score [0.98949247 0.77824268]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.98      0.99      5071\n",
      "           1       0.70      0.88      0.78       212\n",
      "\n",
      "    accuracy                           0.98      5283\n",
      "   macro avg       0.85      0.93      0.88      5283\n",
      "weighted avg       0.98      0.98      0.98      5283\n",
      "\n",
      "------------------------------\n",
      "0.56\n",
      "Accuracy 0.979935642627295\n",
      "F1-score [0.98949247 0.77824268]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.98      0.99      5071\n",
      "           1       0.70      0.88      0.78       212\n",
      "\n",
      "    accuracy                           0.98      5283\n",
      "   macro avg       0.85      0.93      0.88      5283\n",
      "weighted avg       0.98      0.98      0.98      5283\n",
      "\n",
      "------------------------------\n",
      "0.58\n",
      "Accuracy 0.979935642627295\n",
      "F1-score [0.98949247 0.77824268]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.98      0.99      5071\n",
      "           1       0.70      0.88      0.78       212\n",
      "\n",
      "    accuracy                           0.98      5283\n",
      "   macro avg       0.85      0.93      0.88      5283\n",
      "weighted avg       0.98      0.98      0.98      5283\n",
      "\n",
      "------------------------------\n",
      "0.6\n",
      "Accuracy 0.979935642627295\n",
      "F1-score [0.98949247 0.77824268]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.98      0.99      5071\n",
      "           1       0.70      0.88      0.78       212\n",
      "\n",
      "    accuracy                           0.98      5283\n",
      "   macro avg       0.85      0.93      0.88      5283\n",
      "weighted avg       0.98      0.98      0.98      5283\n",
      "\n",
      "------------------------------\n",
      "0.62\n",
      "Accuracy 0.979935642627295\n",
      "F1-score [0.98949247 0.77824268]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.98      0.99      5071\n",
      "           1       0.70      0.88      0.78       212\n",
      "\n",
      "    accuracy                           0.98      5283\n",
      "   macro avg       0.85      0.93      0.88      5283\n",
      "weighted avg       0.98      0.98      0.98      5283\n",
      "\n",
      "------------------------------\n",
      "0.64\n",
      "Accuracy 0.979935642627295\n",
      "F1-score [0.98949247 0.77824268]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.98      0.99      5071\n",
      "           1       0.70      0.88      0.78       212\n",
      "\n",
      "    accuracy                           0.98      5283\n",
      "   macro avg       0.85      0.93      0.88      5283\n",
      "weighted avg       0.98      0.98      0.98      5283\n",
      "\n",
      "------------------------------\n",
      "0.66\n",
      "Accuracy 0.979178497066061\n",
      "F1-score [0.98910028 0.76793249]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.98      0.99      5071\n",
      "           1       0.69      0.86      0.77       212\n",
      "\n",
      "    accuracy                           0.98      5283\n",
      "   macro avg       0.84      0.92      0.88      5283\n",
      "weighted avg       0.98      0.98      0.98      5283\n",
      "\n",
      "------------------------------\n",
      "0.68\n",
      "Accuracy 0.979178497066061\n",
      "F1-score [0.98910028 0.76793249]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.98      0.99      5071\n",
      "           1       0.69      0.86      0.77       212\n",
      "\n",
      "    accuracy                           0.98      5283\n",
      "   macro avg       0.84      0.92      0.88      5283\n",
      "weighted avg       0.98      0.98      0.98      5283\n",
      "\n",
      "------------------------------\n",
      "0.7\n",
      "Accuracy 0.979178497066061\n",
      "F1-score [0.98910028 0.76793249]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.98      0.99      5071\n",
      "           1       0.69      0.86      0.77       212\n",
      "\n",
      "    accuracy                           0.98      5283\n",
      "   macro avg       0.84      0.92      0.88      5283\n",
      "weighted avg       0.98      0.98      0.98      5283\n",
      "\n",
      "------------------------------\n",
      "0.72\n",
      "Accuracy 0.9721749006246451\n",
      "F1-score [0.98569621 0.49134948]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      1.00      0.99      5071\n",
      "           1       0.92      0.33      0.49       212\n",
      "\n",
      "    accuracy                           0.97      5283\n",
      "   macro avg       0.95      0.67      0.74      5283\n",
      "weighted avg       0.97      0.97      0.97      5283\n",
      "\n",
      "------------------------------\n",
      "0.74\n",
      "Accuracy 0.9721749006246451\n",
      "F1-score [0.98569621 0.49134948]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      1.00      0.99      5071\n",
      "           1       0.92      0.33      0.49       212\n",
      "\n",
      "    accuracy                           0.97      5283\n",
      "   macro avg       0.95      0.67      0.74      5283\n",
      "weighted avg       0.97      0.97      0.97      5283\n",
      "\n",
      "------------------------------\n",
      "0.76\n",
      "Accuracy 0.9721749006246451\n",
      "F1-score [0.98569621 0.49134948]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      1.00      0.99      5071\n",
      "           1       0.92      0.33      0.49       212\n",
      "\n",
      "    accuracy                           0.97      5283\n",
      "   macro avg       0.95      0.67      0.74      5283\n",
      "weighted avg       0.97      0.97      0.97      5283\n",
      "\n",
      "------------------------------\n",
      "0.78\n",
      "Accuracy 0.9721749006246451\n",
      "F1-score [0.98569621 0.49134948]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      1.00      0.99      5071\n",
      "           1       0.92      0.33      0.49       212\n",
      "\n",
      "    accuracy                           0.97      5283\n",
      "   macro avg       0.95      0.67      0.74      5283\n",
      "weighted avg       0.97      0.97      0.97      5283\n",
      "\n",
      "------------------------------\n",
      "0.8\n",
      "Accuracy 0.9721749006246451\n",
      "F1-score [0.98569621 0.49134948]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      1.00      0.99      5071\n",
      "           1       0.92      0.33      0.49       212\n",
      "\n",
      "    accuracy                           0.97      5283\n",
      "   macro avg       0.95      0.67      0.74      5283\n",
      "weighted avg       0.97      0.97      0.97      5283\n",
      "\n",
      "------------------------------\n",
      "0.82\n",
      "Accuracy 0.9721749006246451\n",
      "F1-score [0.98569621 0.49134948]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      1.00      0.99      5071\n",
      "           1       0.92      0.33      0.49       212\n",
      "\n",
      "    accuracy                           0.97      5283\n",
      "   macro avg       0.95      0.67      0.74      5283\n",
      "weighted avg       0.97      0.97      0.97      5283\n",
      "\n",
      "------------------------------\n",
      "0.84\n",
      "Accuracy 0.9721749006246451\n",
      "F1-score [0.98569621 0.49134948]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      1.00      0.99      5071\n",
      "           1       0.92      0.33      0.49       212\n",
      "\n",
      "    accuracy                           0.97      5283\n",
      "   macro avg       0.95      0.67      0.74      5283\n",
      "weighted avg       0.97      0.97      0.97      5283\n",
      "\n",
      "------------------------------\n",
      "0.86\n",
      "Accuracy 0.9721749006246451\n",
      "F1-score [0.98569621 0.49134948]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      1.00      0.99      5071\n",
      "           1       0.92      0.33      0.49       212\n",
      "\n",
      "    accuracy                           0.97      5283\n",
      "   macro avg       0.95      0.67      0.74      5283\n",
      "weighted avg       0.97      0.97      0.97      5283\n",
      "\n",
      "------------------------------\n",
      "0.88\n",
      "Accuracy 0.9721749006246451\n",
      "F1-score [0.98569621 0.49134948]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      1.00      0.99      5071\n",
      "           1       0.92      0.33      0.49       212\n",
      "\n",
      "    accuracy                           0.97      5283\n",
      "   macro avg       0.95      0.67      0.74      5283\n",
      "weighted avg       0.97      0.97      0.97      5283\n",
      "\n",
      "------------------------------\n",
      "0.9\n",
      "Accuracy 0.9598712852545902\n",
      "F1-score [0.97952482 0.        ]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      1.00      0.98      5071\n",
      "           1       0.00      0.00      0.00       212\n",
      "\n",
      "    accuracy                           0.96      5283\n",
      "   macro avg       0.48      0.50      0.49      5283\n",
      "weighted avg       0.92      0.96      0.94      5283\n",
      "\n",
      "------------------------------\n",
      "0.92\n",
      "Accuracy 0.9598712852545902\n",
      "F1-score [0.97952482 0.        ]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      1.00      0.98      5071\n",
      "           1       0.00      0.00      0.00       212\n",
      "\n",
      "    accuracy                           0.96      5283\n",
      "   macro avg       0.48      0.50      0.49      5283\n",
      "weighted avg       0.92      0.96      0.94      5283\n",
      "\n",
      "------------------------------\n",
      "0.94\n",
      "Accuracy 0.9598712852545902\n",
      "F1-score [0.97952482 0.        ]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      1.00      0.98      5071\n",
      "           1       0.00      0.00      0.00       212\n",
      "\n",
      "    accuracy                           0.96      5283\n",
      "   macro avg       0.48      0.50      0.49      5283\n",
      "weighted avg       0.92      0.96      0.94      5283\n",
      "\n",
      "------------------------------\n",
      "0.96\n",
      "Accuracy 0.9598712852545902\n",
      "F1-score [0.97952482 0.        ]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      1.00      0.98      5071\n",
      "           1       0.00      0.00      0.00       212\n",
      "\n",
      "    accuracy                           0.96      5283\n",
      "   macro avg       0.48      0.50      0.49      5283\n",
      "weighted avg       0.92      0.96      0.94      5283\n",
      "\n",
      "------------------------------\n",
      "0.98\n",
      "Accuracy 0.9598712852545902\n",
      "F1-score [0.97952482 0.        ]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      1.00      0.98      5071\n",
      "           1       0.00      0.00      0.00       212\n",
      "\n",
      "    accuracy                           0.96      5283\n",
      "   macro avg       0.48      0.50      0.49      5283\n",
      "weighted avg       0.92      0.96      0.94      5283\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#clf = DecisionTreeClassifier(min_samples_leaf=100, min_samples_split=10)\n",
    "#clf.fit(X_train, y_train)\n",
    "\n",
    "def adjusted_predict(X_test, thr=0.5):\n",
    "    y_score = clf.predict_proba(X_test)[:, 1]\n",
    "    return np.array([1 if y > thr else 0 for y in y_score])\n",
    "\n",
    "f1_score_l = []\n",
    "precision_l = []\n",
    "recall_l = []\n",
    "\n",
    "for thr_k in range(2, 100, 2):\n",
    "    y_pred = adjusted_predict(X_test, thr = thr_k/100)\n",
    "    print('------------------------------')\n",
    "    print(thr_k/100)\n",
    "    print('Accuracy %s' % accuracy_score(y_test, y_pred))\n",
    "    print('F1-score %s' % f1_score(y_test, y_pred, average=None))\n",
    "    print(classification_report(y_test, y_pred))\n",
    "    report = classification_report(y_test, y_pred, output_dict=True)\n",
    "    \n",
    "    f1_score_l.append(f1_score(y_test, y_pred, average=None)[1])\n",
    "    precision_l.append(report['1']['precision'])\n",
    "    recall_l.append(report['1']['recall'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "tags": [
     "outputPrepend"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       0.86      0.99      0.92      5283\n",
      "weighted avg       0.99      0.98      0.99      5283\n",
      "\n",
      "------------------------------\n",
      "0.06\n",
      "Accuracy 0.9848570887753171\n",
      "F1-score [0.99204929 0.84126984]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.98      0.99      5071\n",
      "           1       0.73      1.00      0.84       212\n",
      "\n",
      "    accuracy                           0.98      5283\n",
      "   macro avg       0.86      0.99      0.92      5283\n",
      "weighted avg       0.99      0.98      0.99      5283\n",
      "\n",
      "------------------------------\n",
      "0.08\n",
      "Accuracy 0.9848570887753171\n",
      "F1-score [0.99204929 0.84126984]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.98      0.99      5071\n",
      "           1       0.73      1.00      0.84       212\n",
      "\n",
      "    accuracy                           0.98      5283\n",
      "   macro avg       0.86      0.99      0.92      5283\n",
      "weighted avg       0.99      0.98      0.99      5283\n",
      "\n",
      "------------------------------\n",
      "0.1\n",
      "Accuracy 0.9848570887753171\n",
      "F1-score [0.99204929 0.84126984]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.98      0.99      5071\n",
      "           1       0.73      1.00      0.84       212\n",
      "\n",
      "    accuracy                           0.98      5283\n",
      "   macro avg       0.86      0.99      0.92      5283\n",
      "weighted avg       0.99      0.98      0.99      5283\n",
      "\n",
      "------------------------------\n",
      "0.12\n",
      "Accuracy 0.9848570887753171\n",
      "F1-score [0.99204929 0.84126984]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.98      0.99      5071\n",
      "           1       0.73      1.00      0.84       212\n",
      "\n",
      "    accuracy                           0.98      5283\n",
      "   macro avg       0.86      0.99      0.92      5283\n",
      "weighted avg       0.99      0.98      0.99      5283\n",
      "\n",
      "------------------------------\n",
      "0.14\n",
      "Accuracy 0.9848570887753171\n",
      "F1-score [0.99204929 0.84126984]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.98      0.99      5071\n",
      "           1       0.73      1.00      0.84       212\n",
      "\n",
      "    accuracy                           0.98      5283\n",
      "   macro avg       0.86      0.99      0.92      5283\n",
      "weighted avg       0.99      0.98      0.99      5283\n",
      "\n",
      "------------------------------\n",
      "0.16\n",
      "Accuracy 0.9848570887753171\n",
      "F1-score [0.99204929 0.84126984]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.98      0.99      5071\n",
      "           1       0.73      1.00      0.84       212\n",
      "\n",
      "    accuracy                           0.98      5283\n",
      "   macro avg       0.86      0.99      0.92      5283\n",
      "weighted avg       0.99      0.98      0.99      5283\n",
      "\n",
      "------------------------------\n",
      "0.18\n",
      "Accuracy 0.9848570887753171\n",
      "F1-score [0.99204929 0.84126984]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.98      0.99      5071\n",
      "           1       0.73      1.00      0.84       212\n",
      "\n",
      "    accuracy                           0.98      5283\n",
      "   macro avg       0.86      0.99      0.92      5283\n",
      "weighted avg       0.99      0.98      0.99      5283\n",
      "\n",
      "------------------------------\n",
      "0.2\n",
      "Accuracy 0.9848570887753171\n",
      "F1-score [0.99204929 0.84126984]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.98      0.99      5071\n",
      "           1       0.73      1.00      0.84       212\n",
      "\n",
      "    accuracy                           0.98      5283\n",
      "   macro avg       0.86      0.99      0.92      5283\n",
      "weighted avg       0.99      0.98      0.99      5283\n",
      "\n",
      "------------------------------\n",
      "0.22\n",
      "Accuracy 0.9848570887753171\n",
      "F1-score [0.99204929 0.84126984]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.98      0.99      5071\n",
      "           1       0.73      1.00      0.84       212\n",
      "\n",
      "    accuracy                           0.98      5283\n",
      "   macro avg       0.86      0.99      0.92      5283\n",
      "weighted avg       0.99      0.98      0.99      5283\n",
      "\n",
      "------------------------------\n",
      "0.24\n",
      "Accuracy 0.9848570887753171\n",
      "F1-score [0.99204929 0.84126984]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.98      0.99      5071\n",
      "           1       0.73      1.00      0.84       212\n",
      "\n",
      "    accuracy                           0.98      5283\n",
      "   macro avg       0.86      0.99      0.92      5283\n",
      "weighted avg       0.99      0.98      0.99      5283\n",
      "\n",
      "------------------------------\n",
      "0.26\n",
      "Accuracy 0.9848570887753171\n",
      "F1-score [0.99204929 0.84126984]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.98      0.99      5071\n",
      "           1       0.73      1.00      0.84       212\n",
      "\n",
      "    accuracy                           0.98      5283\n",
      "   macro avg       0.86      0.99      0.92      5283\n",
      "weighted avg       0.99      0.98      0.99      5283\n",
      "\n",
      "------------------------------\n",
      "0.28\n",
      "Accuracy 0.9848570887753171\n",
      "F1-score [0.99204929 0.84126984]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.98      0.99      5071\n",
      "           1       0.73      1.00      0.84       212\n",
      "\n",
      "    accuracy                           0.98      5283\n",
      "   macro avg       0.86      0.99      0.92      5283\n",
      "weighted avg       0.99      0.98      0.99      5283\n",
      "\n",
      "------------------------------\n",
      "0.3\n",
      "Accuracy 0.9848570887753171\n",
      "F1-score [0.99204929 0.84126984]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.98      0.99      5071\n",
      "           1       0.73      1.00      0.84       212\n",
      "\n",
      "    accuracy                           0.98      5283\n",
      "   macro avg       0.86      0.99      0.92      5283\n",
      "weighted avg       0.99      0.98      0.99      5283\n",
      "\n",
      "------------------------------\n",
      "0.32\n",
      "Accuracy 0.9848570887753171\n",
      "F1-score [0.99204929 0.84126984]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.98      0.99      5071\n",
      "           1       0.73      1.00      0.84       212\n",
      "\n",
      "    accuracy                           0.98      5283\n",
      "   macro avg       0.86      0.99      0.92      5283\n",
      "weighted avg       0.99      0.98      0.99      5283\n",
      "\n",
      "------------------------------\n",
      "0.34\n",
      "Accuracy 0.9848570887753171\n",
      "F1-score [0.99204929 0.84126984]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.98      0.99      5071\n",
      "           1       0.73      1.00      0.84       212\n",
      "\n",
      "    accuracy                           0.98      5283\n",
      "   macro avg       0.86      0.99      0.92      5283\n",
      "weighted avg       0.99      0.98      0.99      5283\n",
      "\n",
      "------------------------------\n",
      "0.36\n",
      "Accuracy 0.9848570887753171\n",
      "F1-score [0.99204929 0.84126984]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.98      0.99      5071\n",
      "           1       0.73      1.00      0.84       212\n",
      "\n",
      "    accuracy                           0.98      5283\n",
      "   macro avg       0.86      0.99      0.92      5283\n",
      "weighted avg       0.99      0.98      0.99      5283\n",
      "\n",
      "------------------------------\n",
      "0.38\n",
      "Accuracy 0.9848570887753171\n",
      "F1-score [0.99204929 0.84126984]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.98      0.99      5071\n",
      "           1       0.73      1.00      0.84       212\n",
      "\n",
      "    accuracy                           0.98      5283\n",
      "   macro avg       0.86      0.99      0.92      5283\n",
      "weighted avg       0.99      0.98      0.99      5283\n",
      "\n",
      "------------------------------\n",
      "0.4\n",
      "Accuracy 0.979935642627295\n",
      "F1-score [0.98949247 0.77824268]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.98      0.99      5071\n",
      "           1       0.70      0.88      0.78       212\n",
      "\n",
      "    accuracy                           0.98      5283\n",
      "   macro avg       0.85      0.93      0.88      5283\n",
      "weighted avg       0.98      0.98      0.98      5283\n",
      "\n",
      "------------------------------\n",
      "0.42\n",
      "Accuracy 0.979935642627295\n",
      "F1-score [0.98949247 0.77824268]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.98      0.99      5071\n",
      "           1       0.70      0.88      0.78       212\n",
      "\n",
      "    accuracy                           0.98      5283\n",
      "   macro avg       0.85      0.93      0.88      5283\n",
      "weighted avg       0.98      0.98      0.98      5283\n",
      "\n",
      "------------------------------\n",
      "0.44\n",
      "Accuracy 0.979935642627295\n",
      "F1-score [0.98949247 0.77824268]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.98      0.99      5071\n",
      "           1       0.70      0.88      0.78       212\n",
      "\n",
      "    accuracy                           0.98      5283\n",
      "   macro avg       0.85      0.93      0.88      5283\n",
      "weighted avg       0.98      0.98      0.98      5283\n",
      "\n",
      "------------------------------\n",
      "0.46\n",
      "Accuracy 0.979935642627295\n",
      "F1-score [0.98949247 0.77824268]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.98      0.99      5071\n",
      "           1       0.70      0.88      0.78       212\n",
      "\n",
      "    accuracy                           0.98      5283\n",
      "   macro avg       0.85      0.93      0.88      5283\n",
      "weighted avg       0.98      0.98      0.98      5283\n",
      "\n",
      "------------------------------\n",
      "0.48\n",
      "Accuracy 0.979935642627295\n",
      "F1-score [0.98949247 0.77824268]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.98      0.99      5071\n",
      "           1       0.70      0.88      0.78       212\n",
      "\n",
      "    accuracy                           0.98      5283\n",
      "   macro avg       0.85      0.93      0.88      5283\n",
      "weighted avg       0.98      0.98      0.98      5283\n",
      "\n",
      "------------------------------\n",
      "0.5\n",
      "Accuracy 0.979935642627295\n",
      "F1-score [0.98949247 0.77824268]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.98      0.99      5071\n",
      "           1       0.70      0.88      0.78       212\n",
      "\n",
      "    accuracy                           0.98      5283\n",
      "   macro avg       0.85      0.93      0.88      5283\n",
      "weighted avg       0.98      0.98      0.98      5283\n",
      "\n",
      "------------------------------\n",
      "0.52\n",
      "Accuracy 0.979935642627295\n",
      "F1-score [0.98949247 0.77824268]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.98      0.99      5071\n",
      "           1       0.70      0.88      0.78       212\n",
      "\n",
      "    accuracy                           0.98      5283\n",
      "   macro avg       0.85      0.93      0.88      5283\n",
      "weighted avg       0.98      0.98      0.98      5283\n",
      "\n",
      "------------------------------\n",
      "0.54\n",
      "Accuracy 0.979935642627295\n",
      "F1-score [0.98949247 0.77824268]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.98      0.99      5071\n",
      "           1       0.70      0.88      0.78       212\n",
      "\n",
      "    accuracy                           0.98      5283\n",
      "   macro avg       0.85      0.93      0.88      5283\n",
      "weighted avg       0.98      0.98      0.98      5283\n",
      "\n",
      "------------------------------\n",
      "0.56\n",
      "Accuracy 0.979935642627295\n",
      "F1-score [0.98949247 0.77824268]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.98      0.99      5071\n",
      "           1       0.70      0.88      0.78       212\n",
      "\n",
      "    accuracy                           0.98      5283\n",
      "   macro avg       0.85      0.93      0.88      5283\n",
      "weighted avg       0.98      0.98      0.98      5283\n",
      "\n",
      "------------------------------\n",
      "0.58\n",
      "Accuracy 0.979935642627295\n",
      "F1-score [0.98949247 0.77824268]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.98      0.99      5071\n",
      "           1       0.70      0.88      0.78       212\n",
      "\n",
      "    accuracy                           0.98      5283\n",
      "   macro avg       0.85      0.93      0.88      5283\n",
      "weighted avg       0.98      0.98      0.98      5283\n",
      "\n",
      "------------------------------\n",
      "0.6\n",
      "Accuracy 0.979935642627295\n",
      "F1-score [0.98949247 0.77824268]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.98      0.99      5071\n",
      "           1       0.70      0.88      0.78       212\n",
      "\n",
      "    accuracy                           0.98      5283\n",
      "   macro avg       0.85      0.93      0.88      5283\n",
      "weighted avg       0.98      0.98      0.98      5283\n",
      "\n",
      "------------------------------\n",
      "0.62\n",
      "Accuracy 0.979935642627295\n",
      "F1-score [0.98949247 0.77824268]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.98      0.99      5071\n",
      "           1       0.70      0.88      0.78       212\n",
      "\n",
      "    accuracy                           0.98      5283\n",
      "   macro avg       0.85      0.93      0.88      5283\n",
      "weighted avg       0.98      0.98      0.98      5283\n",
      "\n",
      "------------------------------\n",
      "0.64\n",
      "Accuracy 0.979935642627295\n",
      "F1-score [0.98949247 0.77824268]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.98      0.99      5071\n",
      "           1       0.70      0.88      0.78       212\n",
      "\n",
      "    accuracy                           0.98      5283\n",
      "   macro avg       0.85      0.93      0.88      5283\n",
      "weighted avg       0.98      0.98      0.98      5283\n",
      "\n",
      "------------------------------\n",
      "0.66\n",
      "Accuracy 0.979178497066061\n",
      "F1-score [0.98910028 0.76793249]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.98      0.99      5071\n",
      "           1       0.69      0.86      0.77       212\n",
      "\n",
      "    accuracy                           0.98      5283\n",
      "   macro avg       0.84      0.92      0.88      5283\n",
      "weighted avg       0.98      0.98      0.98      5283\n",
      "\n",
      "------------------------------\n",
      "0.68\n",
      "Accuracy 0.979178497066061\n",
      "F1-score [0.98910028 0.76793249]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.98      0.99      5071\n",
      "           1       0.69      0.86      0.77       212\n",
      "\n",
      "    accuracy                           0.98      5283\n",
      "   macro avg       0.84      0.92      0.88      5283\n",
      "weighted avg       0.98      0.98      0.98      5283\n",
      "\n",
      "------------------------------\n",
      "0.7\n",
      "Accuracy 0.979178497066061\n",
      "F1-score [0.98910028 0.76793249]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.98      0.99      5071\n",
      "           1       0.69      0.86      0.77       212\n",
      "\n",
      "    accuracy                           0.98      5283\n",
      "   macro avg       0.84      0.92      0.88      5283\n",
      "weighted avg       0.98      0.98      0.98      5283\n",
      "\n",
      "------------------------------\n",
      "0.72\n",
      "Accuracy 0.9721749006246451\n",
      "F1-score [0.98569621 0.49134948]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      1.00      0.99      5071\n",
      "           1       0.92      0.33      0.49       212\n",
      "\n",
      "    accuracy                           0.97      5283\n",
      "   macro avg       0.95      0.67      0.74      5283\n",
      "weighted avg       0.97      0.97      0.97      5283\n",
      "\n",
      "------------------------------\n",
      "0.74\n",
      "Accuracy 0.9721749006246451\n",
      "F1-score [0.98569621 0.49134948]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      1.00      0.99      5071\n",
      "           1       0.92      0.33      0.49       212\n",
      "\n",
      "    accuracy                           0.97      5283\n",
      "   macro avg       0.95      0.67      0.74      5283\n",
      "weighted avg       0.97      0.97      0.97      5283\n",
      "\n",
      "------------------------------\n",
      "0.76\n",
      "Accuracy 0.9721749006246451\n",
      "F1-score [0.98569621 0.49134948]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      1.00      0.99      5071\n",
      "           1       0.92      0.33      0.49       212\n",
      "\n",
      "    accuracy                           0.97      5283\n",
      "   macro avg       0.95      0.67      0.74      5283\n",
      "weighted avg       0.97      0.97      0.97      5283\n",
      "\n",
      "------------------------------\n",
      "0.78\n",
      "Accuracy 0.9721749006246451\n",
      "F1-score [0.98569621 0.49134948]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      1.00      0.99      5071\n",
      "           1       0.92      0.33      0.49       212\n",
      "\n",
      "    accuracy                           0.97      5283\n",
      "   macro avg       0.95      0.67      0.74      5283\n",
      "weighted avg       0.97      0.97      0.97      5283\n",
      "\n",
      "------------------------------\n",
      "0.8\n",
      "Accuracy 0.9721749006246451\n",
      "F1-score [0.98569621 0.49134948]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      1.00      0.99      5071\n",
      "           1       0.92      0.33      0.49       212\n",
      "\n",
      "    accuracy                           0.97      5283\n",
      "   macro avg       0.95      0.67      0.74      5283\n",
      "weighted avg       0.97      0.97      0.97      5283\n",
      "\n",
      "------------------------------\n",
      "0.82\n",
      "Accuracy 0.9721749006246451\n",
      "F1-score [0.98569621 0.49134948]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      1.00      0.99      5071\n",
      "           1       0.92      0.33      0.49       212\n",
      "\n",
      "    accuracy                           0.97      5283\n",
      "   macro avg       0.95      0.67      0.74      5283\n",
      "weighted avg       0.97      0.97      0.97      5283\n",
      "\n",
      "------------------------------\n",
      "0.84\n",
      "Accuracy 0.9721749006246451\n",
      "F1-score [0.98569621 0.49134948]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      1.00      0.99      5071\n",
      "           1       0.92      0.33      0.49       212\n",
      "\n",
      "    accuracy                           0.97      5283\n",
      "   macro avg       0.95      0.67      0.74      5283\n",
      "weighted avg       0.97      0.97      0.97      5283\n",
      "\n",
      "------------------------------\n",
      "0.86\n",
      "Accuracy 0.9721749006246451\n",
      "F1-score [0.98569621 0.49134948]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      1.00      0.99      5071\n",
      "           1       0.92      0.33      0.49       212\n",
      "\n",
      "    accuracy                           0.97      5283\n",
      "   macro avg       0.95      0.67      0.74      5283\n",
      "weighted avg       0.97      0.97      0.97      5283\n",
      "\n",
      "------------------------------\n",
      "0.88\n",
      "Accuracy 0.9721749006246451\n",
      "F1-score [0.98569621 0.49134948]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      1.00      0.99      5071\n",
      "           1       0.92      0.33      0.49       212\n",
      "\n",
      "    accuracy                           0.97      5283\n",
      "   macro avg       0.95      0.67      0.74      5283\n",
      "weighted avg       0.97      0.97      0.97      5283\n",
      "\n",
      "------------------------------\n",
      "0.9\n",
      "Accuracy 0.9598712852545902\n",
      "F1-score [0.97952482 0.        ]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      1.00      0.98      5071\n",
      "           1       0.00      0.00      0.00       212\n",
      "\n",
      "    accuracy                           0.96      5283\n",
      "   macro avg       0.48      0.50      0.49      5283\n",
      "weighted avg       0.92      0.96      0.94      5283\n",
      "\n",
      "------------------------------\n",
      "0.92\n",
      "Accuracy 0.9598712852545902\n",
      "F1-score [0.97952482 0.        ]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      1.00      0.98      5071\n",
      "           1       0.00      0.00      0.00       212\n",
      "\n",
      "    accuracy                           0.96      5283\n",
      "   macro avg       0.48      0.50      0.49      5283\n",
      "weighted avg       0.92      0.96      0.94      5283\n",
      "\n",
      "------------------------------\n",
      "0.94\n",
      "Accuracy 0.9598712852545902\n",
      "F1-score [0.97952482 0.        ]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      1.00      0.98      5071\n",
      "           1       0.00      0.00      0.00       212\n",
      "\n",
      "    accuracy                           0.96      5283\n",
      "   macro avg       0.48      0.50      0.49      5283\n",
      "weighted avg       0.92      0.96      0.94      5283\n",
      "\n",
      "------------------------------\n",
      "0.96\n",
      "Accuracy 0.9598712852545902\n",
      "F1-score [0.97952482 0.        ]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      1.00      0.98      5071\n",
      "           1       0.00      0.00      0.00       212\n",
      "\n",
      "    accuracy                           0.96      5283\n",
      "   macro avg       0.48      0.50      0.49      5283\n",
      "weighted avg       0.92      0.96      0.94      5283\n",
      "\n",
      "------------------------------\n",
      "0.98\n",
      "Accuracy 0.9598712852545902\n",
      "F1-score [0.97952482 0.        ]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      1.00      0.98      5071\n",
      "           1       0.00      0.00      0.00       212\n",
      "\n",
      "    accuracy                           0.96      5283\n",
      "   macro avg       0.48      0.50      0.49      5283\n",
      "weighted avg       0.92      0.96      0.94      5283\n",
      "\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "proviamo vari valori di decision threshold per determinare il valore predetto della class target.\n",
    "per il confronto, prendiamo i valori restituiti da clf.predict_proba()\n",
    "\"\"\"\n",
    "\n",
    "#clf = DecisionTreeClassifier(min_samples_leaf=100, min_samples_split=10)\n",
    "#clf.fit(X_train, y_train)\n",
    "\n",
    "def adjusted_predict(X_test, thr=0.5):\n",
    "    y_score = clf.predict_proba(X_test)[:, 1] # probabilità che sia 1\n",
    "    return np.array([1 if y > thr else 0 for y in y_score]) # 1 se prob > threshold 0 altrimenti\n",
    "\n",
    "f1_score_l = []\n",
    "precision_l = []\n",
    "recall_l = []\n",
    "accuracy_l = []\n",
    "\n",
    "for thr_k in range(2, 100, 2):\n",
    "    y_pred = adjusted_predict(X_test, thr = thr_k/100)\n",
    "    print('------------------------------')\n",
    "    print(thr_k/100)\n",
    "    print('Accuracy %s' % accuracy_score(y_test, y_pred))\n",
    "    print('F1-score %s' % f1_score(y_test, y_pred, average=None))\n",
    "    print(classification_report(y_test, y_pred))\n",
    "    report = classification_report(y_test, y_pred, output_dict=True)\n",
    "    \n",
    "    accuracy_l.append(accuracy_score(y_test, y_pred))\n",
    "    f1_score_l.append(f1_score(y_test, y_pred, average=None)[1])\n",
    "    precision_l.append(report['1']['precision'])\n",
    "    recall_l.append(report['1']['recall'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Da thr > 0.88, tutti i record vengono classificati 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
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     },
     "metadata": {},
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   ],
   "source": [
    "x = [i/100 for i in range(2, 100, 2)]\n",
    "\n",
    "plt.figure(figsize=(12,7))\n",
    "sns.set(style='whitegrid')\n",
    "plt.plot(x, accuracy_l, color = 'g', lw = 3)\n",
    "plt.plot(x, f1_score_l, color = 'darkorange', lw=3)\n",
    "plt.plot(x, precision_l, color = 'r', lw=3)\n",
    "plt.plot(x, recall_l, color = 'gold', lw=3)\n",
    "plt.plot(0.48, 0.74, marker='o', color='gold', markersize=7)\n",
    "plt.text(0.45, 0.67, '0.74', fontsize=15)\n",
    "plt.plot(0.56, 0.32, marker='o', color='gold', markersize=7)\n",
    "plt.text(0.53, 0.26, '0.32', fontsize=15)\n",
    "plt.xlabel('Thresholds', fontsize=22)\n",
    "plt.yticks(fontsize=f)\n",
    "#plt.yticks([x/100 for x in range(65, 105, 5)], fontsize=f)\n",
    "plt.xticks([x/100 for x in range(0, 110, 10)], fontsize=f)\n",
    "plt.legend(['Accuracy', 'F1 [1]', 'Precision [1]', 'Recall [1]'], fontsize=20, loc='lower left')\n",
    "plt.tick_params(axis='both', which='major', labelsize=f)\n",
    "plt.show()"
   ]
  }
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