3 - Regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": [
     "settings"
    ]
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "import pprint\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.style as style\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, AdaBoostClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import roc_auc_score, roc_curve, classification_report, confusion_matrix,  _score, \\\n",
    "    recall_score\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_predict, KFold\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.tree import DecisionTreeClassifier\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "style.use('seaborn-whitegrid')\n",
    "% matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Change format of charts to .svg\n",
    "%config InlineBackend.figure_format = 'svg'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Exception reporting mode: Plain\n"
     ]
    }
   ],
   "source": [
    "%xmode Plain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('data/clean_data_encoded_for_regression.csv')\n",
    "\n",
    "X = df[['gender', 'senior', 'partner', 'dependents', 'tenure', 'phone_service',\n",
    "       'multiple_lines', 'online_security', 'online_backup',\n",
    "       'device_protection', 'tech_support', 'streaming_tv', 'streaming_movies',\n",
    "       'paperless_billing', 'monthly_charges', 'total_charges',\n",
    "       'avg_monthly_charges', 'internet_service-fiber_optic',\n",
    "       'internet_service-no', 'contract-one_year', 'contract-two_year',\n",
    "       'payment_method-credit_card_auto', 'payment_method-electronic_check',\n",
    "       'payment_method-mailed_check']]\n",
    "\n",
    "y = df['churn']\n",
    "\n",
    "# Stratify our train-test-split so that we have a balanced split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=40, stratify=y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Upsample with SMOTE to get a balanced dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Currently, only 27% of our dataset is categorized as \"Churn\" (i.e., will leave the telecom company and no longer be a customer this month). In order to train our model better, we are upsampling with SMOTE so that 50% of our dataset is categorized as \"Churn\". This makes sense because we are more concerned about recall (making sure our model catches nearly everyone who is likely to churn) than it is about precision.\n",
    "\n",
    "The rationale? It's much cheaper to keep an existing customer than to acquire a new one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    3614\n",
      "1    1308\n",
      "Name: churn, dtype: int64 [3614 3614]\n"
     ]
    }
   ],
   "source": [
    "sm = SMOTE(random_state=42, ratio=1.0)\n",
    "X_train_res, y_train_res = sm.fit_sample(X_train, y_train)\n",
    "print(y_train.value_counts(), np.bincount(y_train_res)) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's do some quick and dirty analyses to determine which models we should tune further"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we'll do ANOTHER train test split on the training set. We want to make sure that we leave our actual test data (X_test and y_test) alone."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_2, X_test_2, y_train_2, y_test_2 = train_test_split(X_train_res, y_train_res, test_size=0.33, random_state=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Acc    0.770493\n",
      "Test Acc     0.766414\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "def quick_test(model, X, y):\n",
    "    xtrain, xtest, ytrain, ytest = train_test_split(X, y, test_size=0.3)\n",
    "    model.fit(xtrain, ytrain)\n",
    "    return model.score(xtrain, ytrain), model.score(xtest, ytest)\n",
    "\n",
    "\n",
    "def quick_test_a_few_times(model, X, y, n=10):\n",
    "    return (pd.DataFrame([quick_test(model, X, y) for j in range(n)],\n",
    "                         columns=['Train Acc', 'Test Acc'])).mean()\n",
    "\n",
    "new_model = make_pipeline(StandardScaler(), LinearSVC(max_iter=10000))\n",
    "# pred = new_model.predict(X_test)\n",
    "# Do the test 10 times with a LinearSVC and get the average score\n",
    "print(quick_test_a_few_times(new_model, X_train_2, y_train_2))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_scores(model):\n",
    "    mod = model\n",
    "    mod = mod.fit(X_train_2, y_train_2)\n",
    "    predictions = mod.predict(X_test_2)\n",
    "    # quick_test_a_few_times(decisiontree, X_train, y_train)\n",
    "\n",
    "    print(f'**Model: {mod}**')\n",
    "    print(f'Train accuracy: {mod.score(X_train_2, y_train_2)}')\n",
    "    print(f'Test accuracy: {mod.score(X_test_2, y_test_2)}')\n",
    "    print(f'Precision: {precision_score(y_test_2, predictions)}')\n",
    "    print(f'Recall: {recall_score(y_test_2, predictions)}')\n",
    "    print()\n",
    "    print(classification_report(y_test_2, predictions))\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train accuracy: 0.9993804213135068\n",
      "Test accuracy: 0.7925398155909472\n",
      "Precision: 0.7878535773710482\n",
      "Recall: 0.797809604043808\n",
      "\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.80      0.79      0.79      1199\n",
      "           1       0.79      0.80      0.79      1187\n",
      "\n",
      "   micro avg       0.79      0.79      0.79      2386\n",
      "   macro avg       0.79      0.79      0.79      2386\n",
      "weighted avg       0.79      0.79      0.79      2386\n",
      "\n"
     ]
    }
   ],
   "source": [
    "def evaluate_models(model_types: list):\n",
    "    for model in model_types:\n",
    "        m = model_types[model]\n",
    "        quick_test_a_few_times(X_train_2, y_train_2)\n",
    "        get_scores(m)\n",
    "\n",
    "\n",
    "models = [\n",
    "    {\n",
    "        'label': 'Logistic Regression',\n",
    "        'model': LogisticRegression(),\n",
    "    },\n",
    "    {\n",
    "        'label': 'Gradient Boosting',\n",
    "        'model': GradientBoostingClassifier(),\n",
    "    },\n",
    "    {\n",
    "        'label': 'Random Forest',\n",
    "        'model': RandomForestClassifier(),\n",
    "    },\n",
    "    {\n",
    "        'label': 'AdaBoost Classifier',\n",
    "        'model': AdaBoostClassifier(),\n",
    "    },\n",
    "    {\n",
    "        'label': 'Support Vector Classifier',\n",
    "        'model': SVC(probability=True),\n",
    "    }\n",
    "]\n",
    "\n",
    "# evaluate_models(models)\n",
    "\n",
    "decision_tree = DecisionTreeClassifier()\n",
    "quick_test_a_few_times(decision_tree, X_train_2, y_train_2)\n",
    "get_scores(decision_tree)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Random Forests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train accuracy: 0.9884345311854605\n",
      "Test accuracy: 0.8378038558256496\n",
      "Precision: 0.8690036900369004\n",
      "Recall: 0.793597304128054\n",
      "\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.81      0.88      0.85      1199\n",
      "           1       0.87      0.79      0.83      1187\n",
      "\n",
      "   micro avg       0.84      0.84      0.84      2386\n",
      "   macro avg       0.84      0.84      0.84      2386\n",
      "weighted avg       0.84      0.84      0.84      2386\n",
      "\n"
     ]
    }
   ],
   "source": [
    "random_forest = RandomForestClassifier()\n",
    "quick_test_a_few_times(random_forest, X_train_2, y_train_2)\n",
    "get_scores(random_forest)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Gradient Boosting Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train accuracy: 0.8764973151590252\n",
      "Test accuracy: 0.8562447611064543\n",
      "Precision: 0.8637931034482759\n",
      "Recall: 0.8441449031171019\n",
      "\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.85      0.87      0.86      1199\n",
      "           1       0.86      0.84      0.85      1187\n",
      "\n",
      "   micro avg       0.86      0.86      0.86      2386\n",
      "   macro avg       0.86      0.86      0.86      2386\n",
      "weighted avg       0.86      0.86      0.86      2386\n",
      "\n"
     ]
    }
   ],
   "source": [
    "grad_boost = GradientBoostingClassifier()\n",
    "quick_test_a_few_times(grad_boost, X_train_2, y_train_2)\n",
    "get_scores(grad_boost)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### AdaBoost Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train accuracy: 0.8597686906237092\n",
      "Test accuracy: 0.8365465213746857\n",
      "Precision: 0.8379983036471587\n",
      "Recall: 0.8323504633529908\n",
      "\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.84      0.84      0.84      1199\n",
      "           1       0.84      0.83      0.84      1187\n",
      "\n",
      "   micro avg       0.84      0.84      0.84      2386\n",
      "   macro avg       0.84      0.84      0.84      2386\n",
      "weighted avg       0.84      0.84      0.84      2386\n",
      "\n"
     ]
    }
   ],
   "source": [
    "ada_boost = AdaBoostClassifier()\n",
    "quick_test_a_few_times(ada_boost, X_train_2, y_train_2)\n",
    "get_scores(ada_boost)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train accuracy: 0.7719950433705081\n",
      "Test accuracy: 0.7669740150880134\n",
      "Precision: 0.7482297403619197\n",
      "Recall: 0.8011794439764112\n",
      "\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.79      0.73      0.76      1199\n",
      "           1       0.75      0.80      0.77      1187\n",
      "\n",
      "   micro avg       0.77      0.77      0.77      2386\n",
      "   macro avg       0.77      0.77      0.77      2386\n",
      "weighted avg       0.77      0.77      0.77      2386\n",
      "\n"
     ]
    }
   ],
   "source": [
    "logreg = LogisticRegression()\n",
    "quick_test_a_few_times(logreg, X_train_2, y_train_2)\n",
    "get_scores(logreg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Linear SVC (Support Vector Classifier)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Train Acc    0.625671\n",
       "Test Acc     0.624983\n",
       "dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linearsvc = LinearSVC()\n",
    "quick_test_a_few_times(linearsvc, X_train_2, y_train_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### SVC with rbf kernel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Train Acc    0.963588\n",
       "Test Acc     0.759601\n",
       "dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc_rbf = SVC(kernel='rbf')\n",
    "quick_test_a_few_times(svc_rbf, X_train_2, y_train_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### SVC with Polynomial kernel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "svc_poly = SVC(kernel='poly')\n",
    "quick_test_a_few_times(svc_poly, X_train_2, y_train_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Naive Bayes Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "gaussian_naive_bayes = GaussianNB()\n",
    "quick_test_a_few_times(gaussian_naive_bayes, X_train_2, y_train_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adding in pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = make_pipeline(StandardScaler(), SVC())\n",
    "model.fit(X_train_2, y_train_2)\n",
    "pred = model.predict(X_test_2)\n",
    "\n",
    "quick_test_a_few_times(model, X_train_2, y_train_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = make_pipeline(StandardScaler(), SVC(kernel='rbf'))\n",
    "model.fit(X_train_2, y_train_2)\n",
    "pred = model.predict(X_test_2)\n",
    "\n",
    "quick_test_a_few_times(model, X_train_2, y_train_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "decisiontree = DecisionTreeClassifier()\n",
    "decisiontree.fit(X_train_2, y_train_2)\n",
    "coeffs = decisiontree.feature_importances_ # it's a percentage\n",
    "\n",
    "list_of_coeffs = list(sorted(zip(coeffs, X.columns)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pprint\n",
    "pprint.pprint(list_of_coeffs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "list_of_coeffs = list(sorted(zip(coeffs, X.columns)))\n",
    "pprint.pprint(list_of_coeffs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "grad_boost = GradientBoostingClassifier()\n",
    "grad_boost.fit(X_train_2, y_train_2)\n",
    "grad_boost.feature_importances_ # values are between 0 and 1, not normalized\n",
    "grad_boost.train_score_.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "s2 = LinearSVC()\n",
    "s2.fit(X_train, y_train)\n",
    "s2.coef_ # the absolute value dictates importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "log_reg = LogisticRegression()\n",
    "log_reg.fit(X_train, y_train)\n",
    "log_reg.coef_ # the abosulute value dictates importance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ROC Curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
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    "plt.figure(figsize=(8, 8))\n",
    "\n",
    "# Iterate through the list of models\n",
    "for m in models:\n",
    "    model = m['model']  # select the model\n",
    "    model.fit(X_train_res, y_train_res)  # train the model\n",
    "    y_pred = model.predict(X_test)  # predict the test data\n",
    "    # Compute False postive rate, and True positive rate\n",
    "    fpr, tpr, thresholds = roc_curve(y_test, model.predict_proba(X_test)[:, 1])\n",
    "    # Calculate Area under the curve to display on the plot\n",
    "    auc = roc_auc_score(y_test, model.predict(X_test))\n",
    "    # Now, plot the computed values\n",
    "    plt.plot(fpr, tpr, label='%s ROC (area = %0.2f)' % (m['label'], auc))\n",
    "# Custom settings for the plot \n",
    "# plt.plot([0, 1], [0, 1],'r--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('Specificity(False Positive Rate)', fontsize=12, labelpad=10)\n",
    "plt.ylabel('Sensitivity(True Positive Rate)', fontsize=12, labelpad=10)\n",
    "plt.title('Receiver Operating Characteristic (ROC) Curve', fontsize=12)\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()\n",
    "plt.savefig('visualizations/ROC_curve.jpg', dpi=300)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
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      "text/plain": [
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     "metadata": {},
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   ],
   "source": [
    "plt.figure(figsize=(8,8))\n",
    "\n",
    "# Add the models to the list that you want to view on the ROC plot\n",
    "models = [\n",
    "{\n",
    "    'label': 'Logistic Regression',\n",
    "    'model': make_pipeline(StandardScaler(), LogisticRegression())\n",
    "},\n",
    "{\n",
    "    'label': 'Gradient Boosting',\n",
    "    'model': GradientBoostingClassifier(),\n",
    "},\n",
    "{\n",
    "    'label': 'Random Forest',\n",
    "    'model': RandomForestClassifier(),\n",
    "},\n",
    "{\n",
    "    'label': 'AdaBoost Classifier',\n",
    "    'model': AdaBoostClassifier(),\n",
    "},\n",
    "{\n",
    "    'label': 'Support Vector Classifier',\n",
    "    'model': SVC(probability=True),\n",
    "},\n",
    "{\n",
    "    'label': 'Naive Bayes',\n",
    "    'model': GaussianNB(),\n",
    "}\n",
    "]\n",
    "\n",
    "# Below for loop iterates through your models list\n",
    "for m in models:\n",
    "    model = m['model'] # select the model\n",
    "    model.fit(X_train_2, y_train_2) # train the model\n",
    "    y_pred=model.predict(X_test_2) # predict the test data\n",
    "# Compute False postive rate, and True positive rate\n",
    "    fpr, tpr, thresholds = roc_curve(y_test_2, model.predict_proba(X_test_2)[:,1])\n",
    "# Calculate Area under the curve to display on the plot\n",
    "    auc = roc_auc_score(y_test_2,model.predict(X_test_2))\n",
    "# Now, plot the computed values\n",
    "    plt.plot(fpr, tpr, label='%s ROC (area = %0.2f)' % (m['label'], auc))\n",
    "# Custom settings for the plot \n",
    "plt.plot([0, 1], [0, 1],'r--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('1-Specificity(False Positive Rate)')\n",
    "plt.ylabel('Sensitivity(True Positive Rate)')\n",
    "plt.title('Receiver Operating Characteristic (ROC) Curve')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()\n",
    "# plt.savefig('visualizations/ROC_curve.svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid = {'C': [0.1, 10.]}\n",
    "\n",
    "clf = make_pipeline(StandardScaler(), \n",
    "                    GridSearchCV(LogisticRegression(),\n",
    "                                 param_grid=param_grid,\n",
    "                                 cv=2,\n",
    "                                 refit=True))\n",
    "\n",
    "clf.fit(X_train, y_train)\n",
    "pred = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5405    1\n",
       "1247    1\n",
       "5414    1\n",
       "6666    0\n",
       "4073    0\n",
       "       ..\n",
       "842     1\n",
       "6797    0\n",
       "6831    0\n",
       "7012    0\n",
       "3974    0\n",
       "Name: churn, Length: 2321, dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['C', 'class_weight', 'dual', 'fit_intercept', 'intercept_scaling', 'max_iter', 'multi_class', 'n_jobs', 'penalty', 'random_state', 'solver', 'tol', 'verbose', 'warm_start'])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LogisticRegression().get_params().keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.84      0.88      0.86      1701\n",
      "           1       0.62      0.55      0.58       620\n",
      "\n",
      "   micro avg       0.79      0.79      0.79      2321\n",
      "   macro avg       0.73      0.71      0.72      2321\n",
      "weighted avg       0.78      0.79      0.79      2321\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    5163\n",
       "1    1869\n",
       "Name: churn, dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['churn'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.26578498293515357"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1869/(5163+1869)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = make_pipeline(StandardScaler(), \n",
    "                    GridSearchCV(LogisticRegression(),\n",
    "                                 param_grid={'C': [0.1, 1.0, 10.]},\n",
    "                                 cv=3,\n",
    "                                 refit=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "     steps=[('standardscaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('gridsearchcv', GridSearchCV(cv=3, error_score='raise-deprecating',\n",
       "       estimator=LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_c...}, pre_dispatch='2*n_jobs',\n",
       "       refit=True, return_train_score='warn', scoring=None, verbose=0))])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_x = pd.DataFrame(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4711, 24)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "     steps=[('standardscaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('gridsearchcv', GridSearchCV(cv=3, error_score='raise-deprecating',\n",
       "       estimator=LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_c...}, pre_dispatch='2*n_jobs',\n",
       "       refit=True, return_train_score='warn', scoring=None, verbose=0))])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "new = clf.predict(new_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Found input variables with inconsistent numbers of samples: [2321, 4711]",
     "output_type": "error",
     "traceback": [
      "Traceback \u001b[0;36m(most recent call last)\u001b[0m:\n",
      "  File \u001b[1;32m\"<ipython-input-56-ce58635cc473>\"\u001b[0m, line \u001b[1;32m1\u001b[0m, in \u001b[1;35m<module>\u001b[0m\n    print(classification_report(y_test, new))\n",
      "  File \u001b[1;32m\"/Users/brenner/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py\"\u001b[0m, line \u001b[1;32m1524\u001b[0m, in \u001b[1;35mclassification_report\u001b[0m\n    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
      "  File \u001b[1;32m\"/Users/brenner/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py\"\u001b[0m, line \u001b[1;32m71\u001b[0m, in \u001b[1;35m_check_targets\u001b[0m\n    check_consistent_length(y_true, y_pred)\n",
      "\u001b[0;36m  File \u001b[0;32m\"/Users/brenner/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py\"\u001b[0;36m, line \u001b[0;32m230\u001b[0;36m, in \u001b[0;35mcheck_consistent_length\u001b[0;36m\u001b[0m\n\u001b[0;31m    \" samples: %r\" % [int(l) for l in lengths])\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m\u001b[0;31m:\u001b[0m Found input variables with inconsistent numbers of samples: [2321, 4711]\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, new))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic Regression Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
       "       estimator=Pipeline(memory=None,\n",
       "     steps=[('scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('clf', LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='warn',\n",
       "          n_jobs=None, penalty='l2', random_state=None, solver='warn',\n",
       "          tol=0.0001, verbose=0, warm_start=False))]),\n",
       "       fit_params=None, iid='warn', n_jobs=None,\n",
       "       param_grid=[{'clf__C': [0.8, 1, 1.1, 1.2, 1.3, 1.5, 1.6], 'clf__class_weight': [None, 'balanced'], 'clf__solver': ['liblinear', 'sag', 'saga']}],\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring='recall', verbose=0)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe = Pipeline([('scaler', StandardScaler()), ('clf', LogisticRegression())])\n",
    "param_grid = [{'clf__C': [0.8, 1, 1.1, 1.2, 1.3, 1.5, 1.6], 'clf__class_weight': [None, 'balanced'],\n",
    "               'clf__solver': ['liblinear', 'sag', 'saga']}]\n",
    "gs = GridSearchCV(pipe, param_grid, cv=5, scoring='recall')\n",
    "gs.fit(X_train_res, y_train_res)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'clf__C': 1.5, 'clf__class_weight': None, 'clf__solver': 'liblinear'}"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = gs.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      0.72      0.81      1549\n",
      "           1       0.52      0.83      0.64       561\n",
      "\n",
      "   micro avg       0.75      0.75      0.75      2110\n",
      "   macro avg       0.72      0.78      0.72      2110\n",
      "weighted avg       0.82      0.75      0.76      2110\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1113  436]\n",
      " [  93  468]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test, pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.813503043718871"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.median(gs.cv_results_['mean_test_score'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = gs.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      0.73      0.81      3614\n",
      "           1       0.52      0.81      0.63      1308\n",
      "\n",
      "   micro avg       0.75      0.75      0.75      4922\n",
      "   macro avg       0.72      0.77      0.72      4922\n",
      "weighted avg       0.81      0.75      0.76      4922\n",
      "\n",
      "[[2621  993]\n",
      " [ 246 1062]]\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_train, pred))\n",
    "print(confusion_matrix(y_train, pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max net gain = 270400\n"
     ]
    }
   ],
   "source": [
    "logistic_gain_2 = []\n",
    "i_vals = []\n",
    "cost = []\n",
    "savings = []\n",
    "i = .01\n",
    "\n",
    "pred = gs.predict(X_train)\n",
    "\n",
    "for i in np.linspace(0, 1, 101):\n",
    "    folds = KFold(n_splits=5, shuffle=True)\n",
    "    probs = cross_val_predict(gs.best_estimator_, X_train, y_train, cv=folds, method='predict_proba', n_jobs=-1)\n",
    "    probs = pd.DataFrame(probs)\n",
    "    new_pred = probs[1].apply(lambda x: 1 if x > i else 0)\n",
    "    conf = confusion_matrix(y_train, new_pred)\n",
    "\n",
    "    total_cost = (conf[0][1] * 100) + (conf[1][1] * 100) + (conf[1][0] * 500)\n",
    "    total_savings = conf[1][1] * 500\n",
    "\n",
    "    net_gain = total_savings - total_cost\n",
    "    logistic_gain_2.append(net_gain)\n",
    "    i_vals.append(i)\n",
    "    cost.append(total_cost)\n",
    "    savings.append(total_savings)\n",
    "\n",
    "print(f'Max net gain = {max(logistic_gain_2)}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic Regression Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
       "       estimator=Pipeline(memory=None,\n",
       "     steps=[('scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('clf', LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='warn',\n",
       "          n_jobs=None, penalty='l2', random_state=11, solver='warn',\n",
       "          tol=0.0001, verbose=0, warm_start=False))]),\n",
       "       fit_params=None, iid='warn', n_jobs=-1,\n",
       "       param_grid=[{'clf__C': [0.001, 0.1, 1.0], 'clf__solver': ['newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga']}],\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring='recall', verbose=0)"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe = Pipeline([('scaler', StandardScaler()), ('clf', LogisticRegression(random_state=11))])\n",
    "param_grid = [{'clf__C': [0.001, 0.1, 1.0], 'clf__solver': ['newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga']}]\n",
    "gs = GridSearchCV(pipe, param_grid, cv=5, n_jobs=-1, scoring='recall')\n",
    "gs.fit(X_train_res, y_train_res)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'clf__C': 1.0, 'clf__solver': 'newton-cg'}"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8084532385286909"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.median(gs.cv_results_['mean_train_score'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = gs.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      0.72      0.81      3614\n",
      "           1       0.52      0.81      0.63      1308\n",
      "\n",
      "   micro avg       0.75      0.75      0.75      4922\n",
      "   macro avg       0.71      0.77      0.72      4922\n",
      "weighted avg       0.81      0.75      0.76      4922\n",
      "\n",
      "[[2619  995]\n",
      " [ 247 1061]]\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_train, pred))\n",
    "print(confusion_matrix(y_train, pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.  , 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1 ,\n",
       "       0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, 0.2 , 0.21,\n",
       "       0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3 , 0.31, 0.32,\n",
       "       0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4 , 0.41, 0.42, 0.43,\n",
       "       0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5 , 0.51, 0.52, 0.53, 0.54,\n",
       "       0.55, 0.56, 0.57, 0.58, 0.59, 0.6 , 0.61, 0.62, 0.63, 0.64, 0.65,\n",
       "       0.66, 0.67, 0.68, 0.69, 0.7 , 0.71, 0.72, 0.73, 0.74, 0.75, 0.76,\n",
       "       0.77, 0.78, 0.79, 0.8 , 0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87,\n",
       "       0.88, 0.89, 0.9 , 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98,\n",
       "       0.99, 1.  ])"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linspace(0, 1, 101)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**This is our winning model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max net gain = 272200\n"
     ]
    }
   ],
   "source": [
    "logistic_gain = []\n",
    "i_vals = []\n",
    "cost = []\n",
    "savings = []\n",
    "i = .01\n",
    "\n",
    "pred = gs.predict(X_train)\n",
    "\n",
    "for i in np.linspace(0, 1, 101):\n",
    "    folds = KFold(n_splits=5, shuffle=True)\n",
    "    probs = cross_val_predict(gs.best_estimator_, X_train, y_train, cv=folds, method='predict_proba', n_jobs=-1)\n",
    "    probs = pd.DataFrame(probs)\n",
    "    new_pred = probs[1].apply(lambda x: 1 if x > i else 0)\n",
    "    conf = confusion_matrix(y_train, new_pred)\n",
    "\n",
    "    total_cost = (conf[0][1] * 100) + (conf[1][1] * 100) + (conf[1][0] * 500)\n",
    "    total_savings = conf[1][1] * 500\n",
    "\n",
    "    net_gain = total_savings - total_cost\n",
    "    logistic_gain.append(net_gain)\n",
    "    i_vals.append(i)\n",
    "    cost.append(total_cost)\n",
    "    savings.append(total_savings)\n",
    "\n",
    "print(f'Max net gain = {max(logistic_gain)}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11\n"
     ]
    }
   ],
   "source": [
    "for i, v in enumerate(logistic_gain):\n",
    "    if v == 272200:\n",
    "        print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1718 1896]\n",
      " [  69 1239]]\n"
     ]
    }
   ],
   "source": [
    "probs = pd.DataFrame(gs.predict_proba(X_train))\n",
    "new_pred = probs[1].apply(lambda x: 1 if x > 0.22 else 0)\n",
    "conf = confusion_matrix(y_train, new_pred)\n",
    "print(conf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
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   "source": [
    "fig, ax = plt.subplots(figsize=(8,6))\n",
    "plt.plot(i_vals, logistic_gain, label='Net Gain')\n",
    "plt.plot(i_vals, cost, label='Cost')\n",
    "plt.plot(i_vals,savings, label='Savings')\n",
    "plt.legend(loc=0)"
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  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
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     "execution_count": 97,
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     "metadata": {},
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    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8,6))\n",
    "plt.plot(i_vals, logistic_gain, label='Net Gain')\n",
    "plt.plot(i_vals, cost, label='Cost')\n",
    "plt.plot(i_vals,savings, label='Savings')\n",
    "plt.legend(loc=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Calculating our total cost and total savings to get net gain:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "conf = confusion_matrix(y_train, pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_cost = (conf[0][1] * 100) + (conf[1][1] * 100)\n",
    "total_savings = conf[1][1] * 500"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "324900\n"
     ]
    }
   ],
   "source": [
    "net_gain = total_savings - total_cost\n",
    "print(net_gain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6796636085626911"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "coeffs = gs.best_estimator_.named_steps['clf'].coef_[0]\n",
    "list_of_coeffs = list(sorted(zip(coeffs * 10, X.columns)))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gradient Boosting Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
       "       estimator=Pipeline(memory=None,\n",
       "     steps=[('scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('clf', GradientBoostingClassifier(criterion='friedman_mse', init=None,\n",
       "              learning_rate=0.1, loss='deviance', max_depth=3,\n",
       "              max_features=None, max_leaf_nodes=None,\n",
       "              min_impurity_decreas...    subsample=1.0, tol=0.0001, validation_fraction=0.1,\n",
       "              verbose=0, warm_start=False))]),\n",
       "       fit_params=None, iid='warn', n_jobs=-1,\n",
       "       param_grid=[{'clf__learning_rate': [0.01, 0.1, 0.5, 1], 'clf__loss': ['deviance', 'exponential'], 'clf__n_estimators': [50, 100, 150], 'clf__max_depth': [2, 3, 4]}],\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring='recall', verbose=0)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe = Pipeline([('scaler', StandardScaler()), ('clf', GradientBoostingClassifier())])\n",
    "param_grid = [{'clf__learning_rate': [0.01, 0.1, 0.5, 1], 'clf__loss': ['deviance', 'exponential'],\n",
    "               'clf__n_estimators': [50, 100, 150], 'clf__max_depth': [2, 3, 4]}]\n",
    "gs = GridSearchCV(pipe, param_grid, cv=5, n_jobs=-1, scoring='recall')\n",
    "gs.fit(X_train_res, y_train_res)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'clf__learning_rate': 0.1,\n",
       " 'clf__loss': 'exponential',\n",
       " 'clf__max_depth': 2,\n",
       " 'clf__n_estimators': 50}"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8367459878251245"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "gs.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7963475373547316"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.median(gs.cv_results_['mean_test_score'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.82      0.85      3614\n",
      "           1       0.58      0.68      0.63      1308\n",
      "\n",
      "   micro avg       0.79      0.79      0.79      4922\n",
      "   macro avg       0.73      0.75      0.74      4922\n",
      "weighted avg       0.80      0.79      0.79      4922\n",
      "\n"
     ]
    }
   ],
   "source": [
    "pred2 = gs.predict(X_train)\n",
    "print(classification_report(y_train, pred2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max net gain = 265200\n"
     ]
    }
   ],
   "source": [
    "pred2 = gs.predict(X_train)\n",
    "print(classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
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      ],
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "plt.plot(i_vals, gradboost_gain, label='Net Gain')\n",
    "plt.plot(i_vals, cost, label='Cost')\n",
    "plt.plot(i_vals, savings, label='Savings')\n",
    "plt.legend(loc=0)\n",
    "# plt.savefig('gradientboost_triple.svg')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### AdaBoost Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
       "       estimator=Pipeline(memory=None,\n",
       "     steps=[('scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('clf', AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,\n",
       "          learning_rate=1.0, n_estimators=50, random_state=None))]),\n",
       "       fit_params=None, iid='warn', n_jobs=-1,\n",
       "       param_grid=[{'clf__learning_rate': [0.01, 0.1, 1], 'clf__algorithm': ['SAMME'], 'clf__n_estimators': [25, 50, 100]}],\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring='recall', verbose=0)"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe = Pipeline([('scaler', StandardScaler()), ('clf', AdaBoostClassifier())])\n",
    "param_grid = [{'clf__learning_rate': [0.01, 0.1, 1], 'clf__algorithm': ['SAMME'], 'clf__n_estimators': [25, 50, 100]}]\n",
    "gs = GridSearchCV(pipe, param_grid, cv=5, n_jobs=-1, scoring='recall')\n",
    "gs.fit(X_train_res, y_train_res)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'clf__algorithm': 'SAMME', 'clf__learning_rate': 0.1, 'clf__n_estimators': 25}"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8060320973990038"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.median(gs.cv_results_['mean_test_score'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      0.69      0.78      3614\n",
      "           1       0.47      0.78      0.59      1308\n",
      "\n",
      "   micro avg       0.71      0.71      0.71      4922\n",
      "   macro avg       0.69      0.73      0.68      4922\n",
      "weighted avg       0.78      0.71      0.73      4922\n",
      "\n"
     ]
    }
   ],
   "source": [
    "pred = gs.predict(X_train)\n",
    "print(classification_report(y_train, pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8004980630879911"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.median(gs.cv_results_['mean_test_score'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.77      0.83      3614\n",
      "           1       0.54      0.74      0.62      1308\n",
      "\n",
      "   micro avg       0.76      0.76      0.76      4922\n",
      "   macro avg       0.71      0.75      0.72      4922\n",
      "weighted avg       0.80      0.76      0.77      4922\n",
      "\n"
     ]
    }
   ],
   "source": [
    "pred = gs.predict(X_train)\n",
    "print(classification_report(y_train, pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
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   "source": [
    "np.median(gs.cv_results_['mean_test_score'])"
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  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max net gain = 246200\n"
     ]
    }
   ],
   "source": [
    "adaboost_gain = []\n",
    "i_vals = []\n",
    "cost = []\n",
    "savings = []\n",
    "\n",
    "pred = gs.predict(X_train)\n",
    "\n",
    "for i in np.linspace(0.0, 1.0, 101):\n",
    "    folds = KFold(n_splits=5, shuffle=True)\n",
    "    probs = cross_val_predict(gs.best_estimator_, X=X_train, y=y_train, cv=folds, method='predict_proba', n_jobs=-1)\n",
    "    probs = pd.DataFrame(probs)\n",
    "    #     probs = pd.DataFrame(gs.predict_proba(X_train))\n",
    "    new_pred = probs[1].apply(lambda x: 1 if x > i else 0)\n",
    "    conf = confusion_matrix(y_train, new_pred)\n",
    "\n",
    "    total_cost = (conf[0][1] * 100) + (conf[1][1] * 100) + conf[1][0] * 500\n",
    "    total_savings = conf[1][1] * 500\n",
    "\n",
    "    net_gain = total_savings - total_cost\n",
    "    adaboost_gain.append(net_gain)\n",
    "    i_vals.append(i)\n",
    "    cost.append(total_cost)\n",
    "    savings.append(total_savings)\n",
    "\n",
    "print(f'Max net gain = {max(adaboost_gain)}')\n"
   ]
  },
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   "execution_count": 88,
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      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
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     },
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     "output_type": "display_data"
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   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8,6))\n",
    "plt.plot(i_vals, adaboost_gain, label='Net Gain')\n",
    "plt.plot(i_vals, cost, label='Cost')\n",
    "plt.plot(i_vals,savings, label='Savings')\n",
    "plt.legend(loc=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
       "       estimator=Pipeline(memory=None,\n",
       "     steps=[('scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('clf', RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "     ...obs=None,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False))]),\n",
       "       fit_params=None, iid='warn', n_jobs=-1,\n",
       "       param_grid=[{'clf__max_depth': [4, 5, 6], 'clf__max_features': [5, 10, 15, 20], 'clf__n_estimators': [50, 100, 150]}],\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring='recall', verbose=0)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe = Pipeline([('scaler', StandardScaler()), ('clf', RandomForestClassifier())])\n",
    "param_grid = [{'clf__max_depth': [4, 5, 6], 'clf__max_features': [5, 10, 15, 20], 'clf__n_estimators': [50, 100, 150]}]\n",
    "gs = GridSearchCV(pipe, param_grid, cv=5, n_jobs=-1, scoring='recall')\n",
    "gs.fit(X_train_res, y_train_res)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'clf__max_depth': 6, 'clf__max_features': 5, 'clf__n_estimators': 100}"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8190370780298838"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.median(gs.cv_results_['mean_test_score'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.83      0.86      3614\n",
      "           1       0.60      0.72      0.66      1308\n",
      "\n",
      "   micro avg       0.80      0.80      0.80      4922\n",
      "   macro avg       0.75      0.77      0.76      4922\n",
      "weighted avg       0.81      0.80      0.80      4922\n",
      "\n"
     ]
    }
   ],
   "source": [
    "pred = gs.predict(X_train)\n",
    "print(classification_report(y_train, pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7379679144385026"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Test against our test set\n",
    "gs.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "np.median(gs.cv_results_['mean_test_score'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max net gain = 262800\n"
     ]
    }
   ],
   "source": [
    "randforest_gain = []\n",
    "i_vals = []\n",
    "cost = []\n",
    "savings = []\n",
    "\n",
    "pred = gs.predict(X_train)\n",
    "\n",
    "for i in np.linspace(0.0, 1.0, 101):\n",
    "    folds = KFold(n_splits=5, shuffle=True)\n",
    "    probs = cross_val_predict(gs.best_estimator_, X_train, y_train, cv=folds, method='predict_proba', n_jobs=-1)\n",
    "    probs = pd.DataFrame(probs)\n",
    "    #     probs = pd.DataFrame(gs.predict_proba(X_train))\n",
    "    new_pred = probs[1].apply(lambda x: 1 if x > i else 0)\n",
    "    conf = confusion_matrix(y_train, new_pred)\n",
    "\n",
    "    total_cost = (conf[0][1] * 100) + (conf[1][1] * 100) + conf[1][0] * 500\n",
    "    total_savings = conf[1][1] * 500\n",
    "\n",
    "    net_gain = total_savings - total_cost\n",
    "    randforest_gain.append(net_gain)\n",
    "    i_vals.append(i)\n",
    "    cost.append(total_cost)\n",
    "    savings.append(total_savings)\n",
    "\n",
    "print(f'Max net gain = {max(randforest_gain)}')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Pickle lists so we don't have to run everything again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_backups():\n",
    "    with open('backups/randforest.pkl', 'wb') as f:\n",
    "        pickle.dump(randforest_gain, f)\n",
    "        \n",
    "    with open('backups/logistic.pkl', 'wb') as f:\n",
    "        pickle.dump(logistic_gain, f)\n",
    "        \n",
    "    with open('backups/gradboost.pkl', 'wb') as f:\n",
    "        pickle.dump(gradboost_gain, f)\n",
    "        \n",
    "# make_backups()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Compute base cases where we predict all customers will churn, and none will churn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4922,)"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "161800\n"
     ]
    }
   ],
   "source": [
    "new_pred_ones = np.ones(shape=(4922,))\n",
    "\n",
    "conf = confusion_matrix(y_train, new_pred_ones)\n",
    "\n",
    "total_cost = (conf[0][1] * 100) + (conf[1][1] * 100) + conf[1][0] * 500\n",
    "total_savings = conf[1][1] * 500\n",
    "\n",
    "net_gain = total_savings - total_cost\n",
    "print(net_gain)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-654000\n"
     ]
    }
   ],
   "source": [
    "new_pred_zeros = np.zeros(shape=(4922,))\n",
    "\n",
    "conf = confusion_matrix(y_train, new_pred_zeros)\n",
    "\n",
    "total_cost = (conf[0][1] * 100) + (conf[1][1] * 100) + conf[1][0] * 500\n",
    "total_savings = conf[1][1] * 500\n",
    "\n",
    "net_gain = total_savings - total_cost\n",
    "print(net_gain)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[   0, 3614],\n",
       "       [   0, 1308]])"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "161800"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net_gain"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Compute base case where we do nothing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "654000"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1308 customers will churn, at an average cost to replace of $500 each\n",
    "# $654,000 loss, which is why our predictions bottom out at that number on the following chart (good to confirm that it does!)\n",
    "1308 * 500"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot all Net Gain scores against each other"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
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   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "plt.plot(i_vals, logistic_gain, label='Logistic Regression', color='green', lw=2)\n",
    "ax.axhline(y=0, color='black')\n",
    "ax.set_xlabel('Probability Threshold', fontsize=16, labelpad=10)\n",
    "ax.set_ylabel('$ Saved', rotation=90, fontsize=16, labelpad=10)\n",
    "ax.set_yticklabels(['-100k', '0', '100k', '200k', '300k'], fontsize=12)\n",
    "ax.set_xticklabels(['0', '0.1', '0.2', '0.3', '0.4', '0.5'], fontsize=12)\n",
    "ax.set_yticks([-100000, 0, 100000, 200000, 300000])\n",
    "ax.set_xbound(lower=0, upper=0.5)\n",
    "ax.set_ybound(lower=-100000, upper=300000)\n",
    "# plt.savefig('visualizations/net_cost_8.jpg', dpi=300)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'plt' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-33dabb3c62bd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi_vals\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogistic_gain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Logistic Regression'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlw\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi_vals\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrandforest_gain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Random Forests'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlw\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi_vals\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgradboost_gain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Gradient Boost'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlw\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;31m# ax.axhline(y=-139130, color='black')\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'plt' is not defined"
     ]
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "plt.plot(i_vals, logistic_gain, label='Logistic Regression', lw=2)\n",
    "plt.plot(i_vals, randforest_gain, label='Random Forests', lw=2)\n",
    "plt.plot(i_vals, gradboost_gain, label='Gradient Boost', lw=2)\n",
    "plt.legend(loc=0)\n",
    "ax.set_xlabel('Probability Threshold', fontsize=16, labelpad=10)\n",
    "ax.set_ylabel('$ Saved', rotation=90, fontsize=16, labelpad=10)\n",
    "ax.set_xbound(lower=0, upper=1)\n",
    "# plt.savefig('visualizations/net_cost_9.jpg', dpi=300)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Cost minimization: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "def makecost(obs,prob,falsepos_cost,falseneg_cost):\n",
    "    def cost(cutoff):\n",
    "        pred = np.array(prob > cutoff)\n",
    "        fpos = pred * (1 - obs)\n",
    "        fneg = (1 - pred) * obs\n",
    "        return np.sum(fpos * falsepos_cost + fneg * falseneg_cost)\n",
    "    return np.vectorize(cost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cut = np.linspace(0, 1, 100)\n",
    "cost = np.zeros_like(cut)\n",
    "from sklearn.model_selection import KFold, cross_val_predict\n",
    "\n",
    "obs = y_train\n",
    "\n",
    "K = 20\n",
    "for j in range(K):\n",
    "    folds = KFold(n_splits=5, shuffle=True)\n",
    "    prob = cross_val_predict(gs.best_estimator_, X_train, obs, cv=folds, method='predict_proba', n_jobs=5)[:, 1]\n",
    "    getcost = makecost(obs, prob, falsepos_cost=100, falseneg_cost=500)\n",
    "    currentcost = getcost(cut) / X.shape[0]\n",
    "    cost += currentcost\n",
    "    plt.plot(cut, currentcost, c='C0', alpha=0.05)\n",
    "cost /= K\n",
    "plt.plot(cut, cost, c='C0')\n",
    "plt.xlabel('cutoff')\n",
    "plt.ylabel('Expected cost per data point');\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.16161616161616163"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bestcut = cut[np.argmin(cost)]\n",
    "bestcut"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "categories = ['Logistic Regression', 'AdaBoost', 'Random Forests', 'Gradient Boosting']\n",
    "recall_pct = [0.81, 0.74, 0.72, 0.68]\n",
    "net_gain = [272000, 257200, 262800, 263600]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
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     "metadata": {},
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   ],
   "source": [
    "categories = ['Do nothing', 'Assume everyone will churn', 'Our model']\n",
    "gain = [-654000, 162000, 272000]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8,8))\n",
    "barlist = ax.bar(categories, gain, edgecolor='black')\n",
    "ax.axhline(y=0, color='black')\n",
    "ax.set_xticklabels(['Do\\nnothing', 'Retain\\neveryone', 'Our\\nmodel'], fontsize=20)\n",
    "ax.set_yticklabels(['-$600k', '-$400k', '-$200k', '$0', '200k','400k'], fontsize=20)\n",
    "ax.set_yticks([-600000, -400000, -200000, 0, 200000, 400000])\n",
    "ax.set_ybound(lower=-700000, upper=400000)\n",
    "barlist[2].set_edgecolor('black')\n",
    "plt.grid(axis='x')\n",
    "# plt.savefig('visualizations/our_model.jpg', dpi=300)"
   ]
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      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
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     "metadata": {},
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   ],
   "source": [
    "categories = ['Do nothing', 'Assume everyone will churn', 'Build a model']\n",
    "gain = [-654000, 162000, 0]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8,8))\n",
    "barlist = ax.bar(categories, gain, edgecolor='black')\n",
    "ax.axhline(y=0, color='black')\n",
    "ax.set_xticklabels(['Do\\nnothing', 'Retain\\neveryone', 'Build a\\nmodel'], fontsize=20)\n",
    "ax.set_yticklabels(['-$600k', '-$400k', '-$200k', '$0', '200k','400k'], fontsize=20)\n",
    "ax.set_yticks([-600000, -400000, -200000, 0, 200000, 400000])\n",
    "ax.set_ybound(lower=-700000, upper=400000)\n",
    "plt.grid(axis='x')\n",
    "# plt.savefig('visualizations/model_comparison.jpg', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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