2. Adult Census Income - Modelling and Hyperparameters tuning .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((32561, 14), (32561,))"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Split the data into X and y\n",
    "X = df1.drop('class', axis=1)\n",
    "y =df1['class']\n",
    "\n",
    "X.shape, y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((26048, 14), (6513, 14), (26048,), (6513,))"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Split the data into training and validation\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,\n",
    "                                                    y,\n",
    "                                                    test_size=0.2)\n",
    "\n",
    "X_train.shape, X_test.shape, y_train.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What's going to happen next?\n",
    "\n",
    "1. Find the right estimator\n",
    "    * Logistic Regression\n",
    "    * K Nearest Neighbors\n",
    "    * Randomm Forest Classifier\n",
    "    * XGBoost Classifier\n",
    "    * CatBoost Classifier\n",
    "    \n",
    "    \n",
    "2. Train and test them\n",
    "\n",
    "3. Compare results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Organize all models inside a dictionary\n",
    "\n",
    "models = {'Logistic Regression': LogisticRegression(max_iter=300),\n",
    "          'KNN': KNeighborsClassifier(),\n",
    "          'Random Forest': RandomForestClassifier(),\n",
    "          'Logistic Regression': LogisticRegression(),\n",
    "          'XGBoost': XGBClassifier(),\n",
    "          'CatBoost': CatBoostClassifier()}\n",
    "\n",
    "# Create a function which Fits and Scores all models\n",
    "def fit_and_score(models, X_train, X_test, y_train, y_test):\n",
    "    \"\"\"\n",
    "    Fits and evaluates different learning models\n",
    "    models: estimators dictionary\n",
    "    X_train: training data without labels\n",
    "    X_test: test data without labels\n",
    "    y_train: train set labels\n",
    "    y_test: test set labels\n",
    "    \"\"\"\n",
    "    # Set a random seed\n",
    "    np.random.seed(42)\n",
    "    \n",
    "    # Create an empty dictionary\n",
    "    model_scores = {}\n",
    "    for name, model in models.items():\n",
    "        # Fit the model to the data\n",
    "        model.fit(X_train, y_train)\n",
    "        # Evaluate model and append the results to the empty dictionary\n",
    "        model_scores[name] = model.score(X_test, y_test)\n",
    "        \n",
    "    return model_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "999:\tlearn: 0.2300221\ttotal: 49.1s\tremaining: 0us\n",
      "Wall time: 1min 6s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'Logistic Regression': 0.7941040994933211,\n",
       " 'KNN': 0.7762935667127284,\n",
       " 'Random Forest': 0.8610471364962383,\n",
       " 'XGBoost': 0.8696453247351451,\n",
       " 'CatBoost': 0.8724090280976509}"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "fit_and_score(models=models,\n",
    "             X_train = X_train,\n",
    "             X_test=X_test,\n",
    "             y_train=y_train,\n",
    "             y_test=y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model comparison\n",
    "\n",
    "Let's put the results into a DF and then plot them on a Barplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Learning rate set to 0.041445\n",
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      "Wall time: 1min 7s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "model_scores = fit_and_score(models=models,\n",
    "                                 X_train = X_train,\n",
    "                                 X_test=X_test,\n",
    "                                 y_train=y_train,\n",
    "                                 y_test=y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                     Accuracy\n",
      "Logistic Regression  0.794104\n",
      "KNN                  0.776294\n",
      "Random Forest        0.861047\n",
      "XGBoost              0.869645\n",
      "CatBoost             0.872409\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x1080 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_compare = pd.DataFrame(model_scores, index=['Accuracy'])\n",
    "print(model_compare.T)\n",
    "plt.figure(figsize=(6,15))\n",
    "model_compare.T.plot(kind='bar', \n",
    "                    title='Model comparison',\n",
    "                    legend=False, )\n",
    "plt.ylabel('Accuracy');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The results obtained from the `fit_and_score` function plus the visualization on the barplot confirmed that best performancing models are the RandomForestClassifier with the other two tree based models XGBoost and CatBoost. Therefore next steps in our analysis will be the following:\n",
    "\n",
    "* **Hyperparameter tuning**\n",
    "\n",
    "* **Metrics Evaluation:** Confusion Matrix, Cross-Validation, Precision, Recall, F1 Score, Classification Report, ROC Curve and Area under the curve (AUC)\n",
    "\n",
    "* **Feature importance**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hyperparameters tuning with RandomizedSearchCV\n",
    "\n",
    "* Random Forest Classifier\n",
    "\n",
    "* XGBoost Classifier\n",
    "\n",
    "* CatBoost Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the grids for all the estimators\n",
    "\n",
    "xgb_grid = {'min_child_weight': np.arange(1,10,2),\n",
    "           'max_depth': np.arange(3,13,3),\n",
    "            'learning_rate': [0.1, 0.5, 0.3],\n",
    "            'gamma': [0, 0.1, 0.2],\n",
    "            'n_estimators': [1000, 1200],\n",
    "            'subsample': [0.5, 1],\n",
    "            'colsample_bytree': [0.5, 1]}\n",
    "\n",
    "cat_grid = {'iterations': [200],\n",
    "           'learning_rate': [ 0.3],\n",
    "           'depth': np.arange(4, 8, 16),\n",
    "           'grow_policy': ['Depthwise', 'Lossguide'],\n",
    "            'max_leaves': [40, 60]\n",
    "           }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 10 candidates, totalling 30 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  30 out of  30 | elapsed: 12.1min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=3, error_score=nan,\n",
       "                   estimator=XGBClassifier(base_score=None, booster=None,\n",
       "                                           colsample_bylevel=None,\n",
       "                                           colsample_bynode=None,\n",
       "                                           colsample_bytree=None, gamma=None,\n",
       "                                           gpu_id=None, importance_type='gain',\n",
       "                                           interaction_constraints=None,\n",
       "                                           learning_rate=None,\n",
       "                                           max_delta_step=None, max_depth=None,\n",
       "                                           min_child_weight=None, missing=nan,\n",
       "                                           monotone_constraints=None,\n",
       "                                           n...\n",
       "                   iid='deprecated', n_iter=10, n_jobs=-1,\n",
       "                   param_distributions={'colsample_bytree': [0.5, 1],\n",
       "                                        'gamma': [0, 0.1, 0.2],\n",
       "                                        'learning_rate': [0.1, 0.5, 0.3],\n",
       "                                        'max_depth': array([ 3,  6,  9, 12]),\n",
       "                                        'min_child_weight': array([1, 3, 5, 7, 9]),\n",
       "                                        'n_estimators': [1000, 1200],\n",
       "                                        'subsample': [0.5, 1]},\n",
       "                   pre_dispatch='2*n_jobs', random_state=None, refit=True,\n",
       "                   return_train_score=False, scoring=None, verbose=True)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(42)\n",
    "\n",
    "rs_xgb = RandomizedSearchCV(XGBClassifier(),\n",
    "                           param_distributions=xgb_grid,\n",
    "                           cv=3,\n",
    "                           verbose=True,\n",
    "                           n_jobs=-1)\n",
    "\n",
    "rs_xgb.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'subsample': 0.5,\n",
       " 'n_estimators': 1200,\n",
       " 'min_child_weight': 9,\n",
       " 'max_depth': 6,\n",
       " 'learning_rate': 0.1,\n",
       " 'gamma': 0.2,\n",
       " 'colsample_bytree': 1}"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_xgb.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.860586519269154"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_xgb.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### CatBoost RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\david\\OneDrive\\Desktop\\Python\\Machine_learning_and_Data_Science\\env\\lib\\site-packages\\sklearn\\model_selection\\_search.py:277: UserWarning: The total space of parameters 4 is smaller than n_iter=10. Running 4 iterations. For exhaustive searches, use GridSearchCV.\n",
      "  warnings.warn(\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 4 candidates, totalling 20 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  20 out of  20 | elapsed:  1.5min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0:\tlearn: 0.4922778\ttotal: 50.4ms\tremaining: 10s\n",
      "1:\tlearn: 0.4184787\ttotal: 112ms\tremaining: 11.1s\n",
      "2:\tlearn: 0.3785452\ttotal: 168ms\tremaining: 11s\n",
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     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, error_score=nan,\n",
       "                   estimator=<catboost.core.CatBoostClassifier object at 0x0000021551219D90>,\n",
       "                   iid='deprecated', n_iter=10, n_jobs=-1,\n",
       "                   param_distributions={'depth': array([4]),\n",
       "                                        'grow_policy': ['Depthwise',\n",
       "                                                        'Lossguide'],\n",
       "                                        'iterations': [200],\n",
       "                                        'learning_rate': [0.3],\n",
       "                                        'max_leaves': [40, 60]},\n",
       "                   pre_dispatch='2*n_jobs', random_state=None, refit=True,\n",
       "                   return_train_score=False, scoring=None, verbose=True)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set a random seed\n",
    "np.random.seed(42)\n",
    "\n",
    "# Insatntiate the model\n",
    "rs_cat = RandomizedSearchCV(CatBoostClassifier(),\n",
    "                           param_distributions=cat_grid,\n",
    "                           cv=5,\n",
    "                           verbose=True,\n",
    "                           n_jobs=-1)\n",
    "# Fit the RandomizedSearchCV\n",
    "rs_cat.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_leaves': 40,\n",
       " 'learning_rate': 0.3,\n",
       " 'iterations': 200,\n",
       " 'grow_policy': 'Lossguide',\n",
       " 'depth': 4}"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_cat.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9042152948402948"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_cat.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                    XGBoost  CatBoost\n",
      "RandomizedSearchCV    86.06     90.42\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 360x864 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rs_scores = {'XGBoost': round(rs_xgb.score(X_test, y_test), 4)*100,\n",
    "            'CatBoost': round(rs_cat.score(X_train, y_train), 4)*100}\n",
    "compare_rs = pd.DataFrame(data=rs_scores, index=['RandomizedSearchCV'])\n",
    "print(compare_rs)\n",
    "\n",
    "plt.figure(figsize=(5,12))\n",
    "compare_rs.T.plot(kind='bar', legend=False, title='RandomizedSearchCV');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So far the best result is from the CabBoost Classifier; hence this will be the estimator we are going to tune next using a deeper GridSearchCV."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### CatBoost GridSearchCV "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 8 candidates, totalling 40 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  40 out of  40 | elapsed:  4.7min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0:\tlearn: 0.6118804\ttotal: 46.9ms\tremaining: 9.33s\n",
      "1:\tlearn: 0.5510195\ttotal: 97.2ms\tremaining: 9.62s\n",
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     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score=nan,\n",
       "             estimator=<catboost.core.CatBoostClassifier object at 0x000002154E7889D0>,\n",
       "             iid='deprecated', n_jobs=-1,\n",
       "             param_grid={'depth': [4], 'grow_policy': ['Lossguide'],\n",
       "                         'iterations': [100, 200], 'learning_rate': [0.3, 0.1],\n",
       "                         'max_leaves': [40, 60]},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring=None, verbose=True)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Setup random seed\n",
    "np.random.seed(42)\n",
    "\n",
    "# Create a GridSearchCV grid\n",
    "gs_grid = {'max_leaves': [40,60],\n",
    " 'learning_rate': [ 0.3, 0.1],\n",
    " 'iterations': [100,200],\n",
    " 'grow_policy': ['Lossguide'],\n",
    " 'depth': [4]}\n",
    "\n",
    "# Instantiate the model\n",
    "gs_cat = GridSearchCV(CatBoostClassifier(),\n",
    "                     param_grid=gs_grid,\n",
    "                     n_jobs=-1,\n",
    "                     cv=5,\n",
    "                     verbose=True)\n",
    "\n",
    "# Fit the GridSearchCV model\n",
    "gs_cat.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'depth': 4,\n",
       " 'grow_policy': 'Lossguide',\n",
       " 'iterations': 200,\n",
       " 'learning_rate': 0.1,\n",
       " 'max_leaves': 40}"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs_cat.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.873637340703209"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs_cat.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          GridSearchCV  RandomizedSearchCV\n",
      "CatBoost         87.36               90.42\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 360x864 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cat_scores = {'GridSearchCV': round(gs_cat.score(X_test, y_test), 4)*100,\n",
    "            'RandomizedSearchCV': round(rs_cat.score(X_train, y_train), 4)*100}\n",
    "compare_rs = pd.DataFrame(data=cat_scores, index=['CatBoost'])\n",
    "print(compare_rs)\n",
    "\n",
    "plt.figure(figsize=(5,12))\n",
    "compare_rs.T.plot(kind='bar', legend=False, title='CatBoost performances');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After comparing results, we understand that the highest Accuracy was given by the CatBoost Classifier tuned with RandomizedSearchCv, which will be used until the end of the project as `best_model`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluating tuned machine classifier, beyond accuracy\n",
    "\n",
    "Using Cross Validitation we will check the following evaluation metrics:\n",
    "\n",
    "\n",
    "* ROC curve and AUC\n",
    "* Confusion Matrix\n",
    "* Classification Report\n",
    "* Precision, Recall and F1 scores\n",
    "\n",
    "First of all to make comparison and evaluate our trained model, we need to make predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6513,)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make predictions with best tuned model\n",
    "y_preds = rs_cat.predict(X_test)\n",
    "y_preds.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ROC curve: comparing the true positive rates to false positive\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x2154dfc64c0>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot ROC curve and calculate AUC metric\n",
    "plot_roc_curve(rs_cat,\n",
    "              X_test,\n",
    "              y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Confusion matrix:\n",
    "\n",
    "Will give us a performance measurement for the machine learning classification problem we are analysing. Let's create now a function to plot this matrix using Seaborn Heatmap."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(font_scale=1.5)\n",
    "\n",
    "# Create a function to plot a nice looking confusion matrix\n",
    "def plot_conf_mat(y_tes, y_preds):\n",
    "    \"\"\"\n",
    "    Plots a nice looking confusion matrix\n",
    "    \n",
    "    y_test: true data labels\n",
    "    y_preds: predicted labels on X_test set\n",
    "    \n",
    "    \"\"\"\n",
    "    fig, ax = plt.subplots(figsize=(3,3))\n",
    "    ax = sns.heatmap(confusion_matrix(y_test, y_preds),\n",
    "                    annot=True,\n",
    "                    cbar=False)\n",
    "    plt.xlabel('True labels')\n",
    "    plt.ylabel('Predicted labels')\n",
    "    plt.title('Confusion Matrix')\n",
    "\n",
    "plot_conf_mat(y_test, y_preds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that Confusion Matrix, ROC curve and AUC analysis are completed, the evaluation process can move on to Classification report. Precision, recall and F1 will be one by one examined  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Classification Report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.89      0.94      0.92      4942\n",
      "           2       0.78      0.64      0.70      1571\n",
      "\n",
      "    accuracy                           0.87      6513\n",
      "   macro avg       0.84      0.79      0.81      6513\n",
      "weighted avg       0.86      0.87      0.87      6513\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_preds))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* This classificationreport has been done only on 1 data test split \n",
    "* Therefore next step will be to consider each parameter individually on a multiple set of data (Cross VAlidation on 5 folds) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate evaluation metrics using Cross Validation\n",
    "\n",
    "* Calculate Precision, Recall and F1 score on a cross validate data set (using scoring parameter with 5 folds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_leaves': 40,\n",
       " 'learning_rate': 0.3,\n",
       " 'iterations': 200,\n",
       " 'grow_policy': 'Lossguide',\n",
       " 'depth': 4}"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_cat.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_model = CatBoostClassifier(max_leaves=40,\n",
    "                                learning_rate=0.3,\n",
    "                               iterations=200,\n",
    "                                grow_policy='Lossguide',\n",
    "                                depth=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    },
    {
     "data": {
      "text/plain": [
       "0.8683702383552683"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cross Validated accuracy\n",
    "cv_acc = cross_val_score(best_model,\n",
    "                        X,\n",
    "                        y,\n",
    "                        cv=5,\n",
    "                        scoring='accuracy')\n",
    "cv_acc_mean = np.mean(cv_acc)\n",
    "cv_acc_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "data": {
      "text/plain": [
       "0.896072019379617"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cross Validated precision\n",
    "cv_prec = cross_val_score(best_model,\n",
    "                        X,\n",
    "                        y,\n",
    "                        cv=5,\n",
    "                        scoring='precision')\n",
    "cv_prec_mean = np.mean(cv_prec)\n",
    "cv_prec_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9350728155339805"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cross validated recall\n",
    "cv_recall = cross_val_score(best_model,\n",
    "                        X,\n",
    "                        y,\n",
    "                        cv=5,\n",
    "                        scoring='recall')\n",
    "cv_recall_mean = np.mean(cv_recall)\n",
    "cv_recall_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9151547436751372"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cross Validated f1\n",
    "cv_f1 = cross_val_score(best_model,\n",
    "                        X,\n",
    "                        y,\n",
    "                        cv=5,\n",
    "                        scoring='f1')\n",
    "cv_f1_mean = np.mean(cv_f1)\n",
    "cv_f1_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Accuracy  Precision    Recall        F1\n",
      "0   0.86837   0.896072  0.935073  0.915155\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Print and visualize cross-validated metrics\n",
    "cv_metrics = pd.DataFrame({'Accuracy': cv_acc_mean,\n",
    "                           'Precision': cv_prec_mean,\n",
    "                           'Recall': cv_recall_mean,\n",
    "                           'F1': cv_f1_mean},\n",
    "                     index=[0])\n",
    "print(cv_metrics)\n",
    "\n",
    "cv_metrics.T.plot(kind='bar',\n",
    "                  title='Cross-validated classification metrics',\n",
    "                 legend=False);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature importance\n",
    "\n",
    "Feature importance means asking 'which feature contributes the most to the outcomes of the model and how did they contribute?'. Let's have a look to the `best_model` feature importance:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0:\tlearn: 0.4908682\ttotal: 67.3ms\tremaining: 13.4s\n",
      "1:\tlearn: 0.4194458\ttotal: 124ms\tremaining: 12.3s\n",
      "2:\tlearn: 0.3751819\ttotal: 179ms\tremaining: 11.8s\n",
      "3:\tlearn: 0.3589593\ttotal: 232ms\tremaining: 11.4s\n",
      "4:\tlearn: 0.3434688\ttotal: 293ms\tremaining: 11.4s\n",
      "5:\tlearn: 0.3350108\ttotal: 367ms\tremaining: 11.9s\n",
      "6:\tlearn: 0.3269599\ttotal: 448ms\tremaining: 12.4s\n",
      "7:\tlearn: 0.3216840\ttotal: 531ms\tremaining: 12.7s\n",
      "8:\tlearn: 0.3173441\ttotal: 593ms\tremaining: 12.6s\n",
      "9:\tlearn: 0.3108165\ttotal: 632ms\tremaining: 12s\n",
      "10:\tlearn: 0.3077728\ttotal: 668ms\tremaining: 11.5s\n",
      "11:\tlearn: 0.3052361\ttotal: 733ms\tremaining: 11.5s\n",
      "12:\tlearn: 0.3031423\ttotal: 894ms\tremaining: 12.9s\n",
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      "14:\tlearn: 0.3006387\ttotal: 1.03s\tremaining: 12.7s\n",
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     ]
    },
    {
     "data": {
      "text/plain": [
       "<catboost.core.CatBoostClassifier at 0x17e5d391ac0>"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use the model that gave use best results in term of accuracy: Catboost RandomizedSearchCV\n",
    "best_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([12.77346334,  1.26775709,  3.03356217,  1.60932448,  9.1973369 ,\n",
       "        6.29882262,  4.28403262, 20.63580089,  0.51955146,  1.93768995,\n",
       "       24.05957824,  6.98890236,  6.23397512,  1.16020277,  0.        ,\n",
       "        0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
       "        0.        ,  0.        ,  0.        ])"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check best_model feature importance\n",
    "best_model_feat = best_model.get_feature_importance()\n",
    "best_model_feat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'age': 12.773463343340515,\n",
       " 'workclass': 1.2677570866024819,\n",
       " 'fnlwgt': 3.0335621686238783,\n",
       " 'education': 1.6093244759602983,\n",
       " 'education_num': 9.197336901375511,\n",
       " 'marital_status': 6.298822620267535,\n",
       " 'occupation': 4.284032615564992,\n",
       " 'relationship': 20.635800887267987,\n",
       " 'race': 0.5195514618713597,\n",
       " 'sex': 1.937689951593869,\n",
       " 'capital_gain': 24.059578238300766,\n",
       " 'capital_loss': 6.98890235877965,\n",
       " 'hours_per_week': 6.233975119875873,\n",
       " 'native_country': 1.1602027705752926,\n",
       " 'class': 0.0,\n",
       " 'workclass_is_missing': 0.0,\n",
       " 'education_is_missing': 0.0,\n",
       " 'marital_status_is_missing': 0.0,\n",
       " 'occupation_is_missing': 0.0,\n",
       " 'relationship_is_missing': 0.0,\n",
       " 'race_is_missing': 0.0,\n",
       " 'sex_is_missing': 0.0,\n",
       " 'native_country_is_missing': 0.0}"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_dict = dict(zip(df1.columns, list(best_model_feat)))\n",
    "feature_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 720x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the feature importance on a barplot\n",
    "plt.figure(figsize=(10,6))\n",
    "feat_df = pd.DataFrame(feature_dict, index=[0])\n",
    "feat_df.T.plot(kind='bar', title='Feature Importance', legend=False);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusions\n",
    "\n",
    "* We achieved the initial goal of 90% Accuracy with the CatBoost Classifier tuned with RandomizedSearchCV\n",
    "\n",
    "\n",
    "* From the classification report we got extremely good results in terms of Recall and F1 (both over 90%); good the Precision too with a score of almost 90%; slightly negative note comes from the Accuracy which decrased when calculated on 5 different CV folds.\n",
    "\n",
    "\n",
    "* From the Feature Importance analysis we can infer that most influencing features to predict whether a person might belong to the `>50K` or `<50K` classes are: `capital_gain`, `relationship`, `age` and `education_num`. \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}