Model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model\n",
    "\n",
    "In this notebook, we analyse feature importances and train a model to preform the desired prediction task.\n",
    "\n",
    "Due to the relatively small size of the dataset, we use Random Forests as our main tool in this notebook, with gradient boosted trees as our final prediction model.\n",
    "\n",
    "We run hyperparameter optimization with the two methods described in [Feature Engineering](feature-engineering.ipynb): \n",
    "- Diagnosis information only distinguishes between diabetes or no diabetes\n",
    "- Full diagnosis information\n",
    "\n",
    "The full results can be seen below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.preprocessing import MultiLabelBinarizer, StandardScaler\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.metrics import f1_score, accuracy_score, confusion_matrix\n",
    "from hypopt import GridSearch\n",
    "from sklearn.neural_network import MLPClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 6000 entries, 9011 to 1625\n",
      "Data columns (total 72 columns):\n",
      " #   Column                    Non-Null Count  Dtype \n",
      "---  ------                    --------------  ----- \n",
      " 0   race                      6000 non-null   object\n",
      " 1   gender                    6000 non-null   object\n",
      " 2   age                       6000 non-null   object\n",
      " 3   admission_type_id         6000 non-null   object\n",
      " 4   discharge_disposition_id  6000 non-null   object\n",
      " 5   admission_source_id       6000 non-null   object\n",
      " 6   time_in_hospital          6000 non-null   int64 \n",
      " 7   num_lab_procedures        6000 non-null   int64 \n",
      " 8   num_procedures            6000 non-null   int64 \n",
      " 9   num_medications           6000 non-null   int64 \n",
      " 10  number_outpatient         6000 non-null   int64 \n",
      " 11  number_emergency          6000 non-null   int64 \n",
      " 12  number_inpatient          6000 non-null   int64 \n",
      " 13  diag_1                    6000 non-null   object\n",
      " 14  number_diagnoses          6000 non-null   int64 \n",
      " 15  max_glu_serum             6000 non-null   object\n",
      " 16  A1Cresult                 6000 non-null   object\n",
      " 17  change                    6000 non-null   object\n",
      " 18  diabetesMed               6000 non-null   object\n",
      " 19  readmitted                6000 non-null   int64 \n",
      " 20  diag_1_desc               6000 non-null   object\n",
      " 21  acute                     6000 non-null   int64 \n",
      " 22  classified                6000 non-null   int64 \n",
      " 23  malignant                 6000 non-null   int64 \n",
      " 24  neoplasm                  6000 non-null   int64 \n",
      " 25  kidney                    6000 non-null   int64 \n",
      " 26  congestive                6000 non-null   int64 \n",
      " 27  heart                     6000 non-null   int64 \n",
      " 28  failure                   6000 non-null   int64 \n",
      " 29  unspecified               6000 non-null   int64 \n",
      " 30  essential                 6000 non-null   int64 \n",
      " 31  hypertension              6000 non-null   int64 \n",
      " 32  diabetes                  6000 non-null   int64 \n",
      " 33  mellitus                  6000 non-null   int64 \n",
      " 34  mention                   6000 non-null   int64 \n",
      " 35  complication              6000 non-null   int64 \n",
      " 36  type                      6000 non-null   int64 \n",
      " 37  ii                        6000 non-null   int64 \n",
      " 38  stated                    6000 non-null   int64 \n",
      " 39  uncontrolled              6000 non-null   int64 \n",
      " 40  urinary                   6000 non-null   int64 \n",
      " 41  tract                     6000 non-null   int64 \n",
      " 42  infection                 6000 non-null   int64 \n",
      " 43  site                      6000 non-null   int64 \n",
      " 44  specified                 6000 non-null   int64 \n",
      " 45  respiratory               6000 non-null   int64 \n",
      " 46  abnormality               6000 non-null   int64 \n",
      " 47  hypertensive              6000 non-null   int64 \n",
      " 48  chronic                   6000 non-null   int64 \n",
      " 49  disease                   6000 non-null   int64 \n",
      " 50  stage                     6000 non-null   int64 \n",
      " 51  iv                        6000 non-null   int64 \n",
      " 52  juvenile                  6000 non-null   int64 \n",
      " 53  coronary                  6000 non-null   int64 \n",
      " 54  atherosclerosis           6000 non-null   int64 \n",
      " 55  vessel                    6000 non-null   int64 \n",
      " 56  native                    6000 non-null   int64 \n",
      " 57  graft                     6000 non-null   int64 \n",
      " 58  airway                    6000 non-null   int64 \n",
      " 59  obstruction               6000 non-null   int64 \n",
      " 60  infarction                6000 non-null   int64 \n",
      " 61  paroxysmal                6000 non-null   int64 \n",
      " 62  supraventricular          6000 non-null   int64 \n",
      " 63  tachycardia               6000 non-null   int64 \n",
      " 64  syndrome                  6000 non-null   int64 \n",
      " 65  hyperosmolality           6000 non-null   int64 \n",
      " 66  hypernatremia             6000 non-null   int64 \n",
      " 67  episode                   6000 non-null   int64 \n",
      " 68  myocardial                6000 non-null   int64 \n",
      " 69  care                      6000 non-null   int64 \n",
      " 70  pneumonia                 6000 non-null   int64 \n",
      " 71  manifestations            6000 non-null   int64 \n",
      "dtypes: int64(60), object(12)\n",
      "memory usage: 3.3+ MB\n"
     ]
    }
   ],
   "source": [
    "def read_data(filename, data_dir='data'):\n",
    "    df = pd.read_csv(os.path.join(data_dir, filename), index_col=0)\n",
    "    df.fillna('?', inplace=True)\n",
    "    return df\n",
    "\n",
    "df_train = read_data('diab_train_filtered.csv')\n",
    "df_val = read_data('diab_validation_filtered.csv')\n",
    "df_test = read_data('diab_test_filtered.csv')\n",
    "df_train_alt = read_data('diab_train_alt.csv')\n",
    "df_val_alt = read_data('diab_validation_alt.csv')\n",
    "df_test_alt = read_data('diab_test_alt.csv')\n",
    "df_train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[70-80)      1550\n",
       "[60-70)      1330\n",
       "[80-100)]    1129\n",
       "[50-60)      1024\n",
       "[0-50)        967\n",
       "Name: age, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.age.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prepare data\n",
    "\n",
    "We replace categorical variables by one-hot encodings. \n",
    "\n",
    "Numerical data gets scaled by subtracting by the mean and dividing by the standard deviation calculated on the training set.\n",
    "\n",
    "Text and diagnosis data has already been vectorized previously."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def prepare_data(df, scaler, categorical_vars, numerical_vars):\n",
    "    categoricals = df.loc[:, categorical_vars]\n",
    "    ## rename values with >, [, ] for xgboost\n",
    "    categoricals.max_glu_serum = categoricals.max_glu_serum.replace({'>200': 'greater_200', '>300': 'greater_300'})\n",
    "    categoricals.A1Cresult = categoricals.A1Cresult.replace({'>8': 'greater_8', '>7': 'greater_7'})\n",
    "    categoricals.age = categoricals.age.str.replace('[', '(')\n",
    "    categoricals.age = categoricals.age.str.replace(']', ')')\n",
    "    categoricals = pd.get_dummies(categoricals)\n",
    "    y = df.readmitted\n",
    "    x = df.drop(columns=['readmitted', 'diag_1_desc'] + categorical_vars)\n",
    "    x[numerical_vars] = scaler.transform(x[numerical_vars])\n",
    "    x = x.join(categoricals)\n",
    "    \n",
    "    return x, y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical_vars = ['race', 'age', 'gender', 'diag_1', 'admission_type_id', 'discharge_disposition_id', 'admission_source_id', \\\n",
    "                    'max_glu_serum', 'A1Cresult', 'change', 'diabetesMed']\n",
    "numerical_vars = df_train.columns[6:13].tolist() + [df_train.columns[14]]\n",
    "\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(df_train[numerical_vars])\n",
    "\n",
    "X_train, Y_train = prepare_data(df_train, scaler, categorical_vars, numerical_vars)\n",
    "X_val, Y_val = prepare_data(df_val, scaler, categorical_vars, numerical_vars)\n",
    "X_test, Y_test = prepare_data(df_test, scaler, categorical_vars, numerical_vars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((6000, 93), (2000, 93), (2000, 93))"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, X_test.shape, X_val.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Importance\n",
    "We train a Random Forest Classifier and extract the feature importances. This is plotted in the graph below.\n",
    "\n",
    "We find that the number of lab procedures, number of medications, time in hospital, number of diagnostics and number of procedures and number inpatients seem to be the most relevant features. These features make intuitive sense. Patients that had to undertake more lab procedures, took more medication and spent more time in the hospital most likely had more aggravated prognostics. Simarly, patients that had to undertake many procedures and visited the hospital many times were likely in worse health.\n",
    "\n",
    "By training models that start by using only the most important feature, and successively add the next most important one, and plotting their validation and train accuracy, we see that using these less important features leads the model to aggressively overfitting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6525\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "clf = RandomForestClassifier(random_state=42)\n",
    "clf.fit(X_train, Y_train)\n",
    "print(clf.score(X_val, Y_val))\n",
    "importances = clf.feature_importances_\n",
    "\n",
    "relative = 100 * (importances / importances.max())\n",
    "indices = np.argsort(importances)\n",
    "features = X_train.columns.values.tolist()\n",
    "feature_names_sort = [features[i] for i in indices]\n",
    "\n",
    "plt.figure(figsize=(20, 15))\n",
    "plt.title('Relative Importance', fontsize=15)\n",
    "ax = sns.barplot(x=feature_names_sort, y=importances[indices])\n",
    "ax.set_xticklabels(ax.get_xticklabels(), rotation=40, ha=\"right\")\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f58b02bfca0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_acc = []\n",
    "val_acc = []\n",
    "cutoff = []\n",
    "for i in range(1, X_train.columns.shape[0] + 1):\n",
    "    clf = RandomForestClassifier(random_state=42)\n",
    "    filtered = X_train.columns[indices[:i]].tolist()\n",
    "    clf.fit(X_train[filtered], Y_train)\n",
    "    train_acc.append(clf.score(X_train[filtered], Y_train))\n",
    "    val_acc.append(clf.score(X_val[filtered], Y_val))\n",
    "    cutoff.append(i)\n",
    "\n",
    "data = pd.DataFrame({\n",
    "    'Feature Importance Cutoff': cutoff,\n",
    "    'Validation Accuracy': val_acc,\n",
    "    'Training Accuracy' : train_acc\n",
    "})\n",
    "plt.figure(figsize=(15, 10))\n",
    "sns.lineplot(x='Feature Importance Cutoff', y='Accuracy', hue='variable', \n",
    "             data=pd.melt(data, ['Feature Importance Cutoff'], value_name='Accuracy'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model selection:\n",
    "We choose gradient boosted decision trees as our classifiers. These are very powerful models, and tend to perform well especially in our case, where the dataset is not very large. We also train a Logistic Regression, mostly due to the possibility of obtaining probabilistic estimates. We made some attempts with Neural Networks but found their performance lacking. This can be explained, once again, by the limited sample size, as deeper Neural Networks are typically quite data hungry.\n",
    "\n",
    "We run a gridsearch to determine the best parameters for the gradient boosted trees.\n",
    "\n",
    "The results seem to be on par with what other works we found, when taking into account that those tend to work with much larger datasets.\n",
    "\n",
    "There are a few reasons that can explain this performance. \n",
    "- Firstly, the task itself is quite complex. Even trained medical professionals are not able to determine with high accuracy whether someone will have to be readmitted to the hospital in the near future. This event is dependent on the very intricate physiological processes going on inside the human body and, as such, it may be that the features in this dataset are simply insuficient.\n",
    "\n",
    "- Another possibility is that it is not reasonable to achieve a very accuracte prediction based on this data because the readmission may be very dependent on future data, that we do not have access to. In light of that, the results we achieve may be satisfactory, in the sense that they allow us to get a rough estimate of whether someone will or will not need to be readmitted.\n",
    "\n",
    "In practice, this may be especially useful for the case when we are able to get probabilistic values on the prediction, such as in Logistic Regression. In these cases, we could establish a threshold above which patients would be marked as most likely needing readmission in the near future.\n",
    "\n",
    "In general, XGBoost outpreforms Logistic Regression, which is to be expected from these are more powerful models, that leverage model ensemble techniques to achieve lower variance estimators."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---- Validation ----\n",
      "Accuracy : 0.6535\n",
      "F1-Score : 0.4151898734177215\n",
      "[[1061  146]\n",
      " [ 547  246]]\n",
      "---- Test ----\n",
      "Accuracy : 0.6365\n",
      "F1-Score : 0.3895885810243493\n",
      "[[1041  166]\n",
      " [ 561  232]]\n",
      "------ Log. Reg is able to preform probabilistic estimates -------\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[0.77726154, 0.22273846],\n",
       "       [0.74720552, 0.25279448],\n",
       "       [0.51439638, 0.48560362],\n",
       "       [0.46475011, 0.53524989],\n",
       "       [0.72081232, 0.27918768],\n",
       "       [0.66888096, 0.33111904],\n",
       "       [0.69546907, 0.30453093],\n",
       "       [0.68474962, 0.31525038],\n",
       "       [0.82684444, 0.17315556],\n",
       "       [0.72311575, 0.27688425]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = LogisticRegression(C=0.1, max_iter=100)\n",
    "clf.fit(X_train, Y_train)\n",
    "print(\"---- Validation ----\")\n",
    "predictions = clf.predict(X_val)\n",
    "print(\"Accuracy : {}\".format(accuracy_score(Y_val, predictions)))\n",
    "print(\"F1-Score : {}\".format(f1_score(Y_val, predictions)))\n",
    "print(confusion_matrix(Y_val, predictions))\n",
    "print(\"---- Test ----\")\n",
    "predictions = clf.predict(X_test)\n",
    "print(\"Accuracy : {}\".format(accuracy_score(Y_test, predictions)))\n",
    "print(\"F1-Score : {}\".format(f1_score(Y_test, predictions)))\n",
    "print(confusion_matrix(Y_test, predictions))\n",
    "print(\"------ Log. Reg is able to preform probabilistic estimates -------\")\n",
    "clf.predict_proba(X_train[0:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'learning_rate': 0.3, 'max_depth': 3, 'n_estimators': 50, 'reg_alpha': 0.4, 'reg_lambda': 1}\n",
      "---- Validation ----\n",
      "Accuracy : 0.6575\n",
      "F1-Score : 0.45331205107741424\n",
      "[[1031  176]\n",
      " [ 509  284]]\n",
      "---- Test ----\n",
      "Accuracy : 0.6475\n",
      "F1-Score : 0.41493775933609955\n",
      "[[1045  162]\n",
      " [ 543  250]]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "params = {'n_estimators': [20, 50, 100],\n",
    "          'max_depth' : [3, 6, 8],\n",
    "          'reg_alpha': [0, 0.2, 0.4],\n",
    "          'reg_lambda' : [0.7, 1],\n",
    "          'learning_rate': [0.3]}\n",
    "\n",
    "dummy = {'n_estimators': [100],\n",
    "          'max_depth' : [6],\n",
    "          'reg_alpha': [0],\n",
    "          'reg_lambda' : [1],\n",
    "          'learning_rate': [0.3]}\n",
    "opt = GridSearch(param_grid=params, model=XGBClassifier(random_state=42))\n",
    "opt.fit(X_train, Y_train, X_val, Y_val)\n",
    "print(opt.best_params)\n",
    "clf = opt.best_estimator_\n",
    "print(\"---- Validation ----\")\n",
    "predictions = clf.predict(X_val)\n",
    "print(\"Accuracy : {}\".format(accuracy_score(Y_val, predictions)))\n",
    "print(\"F1-Score : {}\".format(f1_score(Y_val, predictions)))\n",
    "print(confusion_matrix(Y_val, predictions))\n",
    "print(\"---- Test ----\")\n",
    "predictions = clf.predict(X_test)\n",
    "print(\"Accuracy : {}\".format(accuracy_score(Y_test, predictions)))\n",
    "print(\"F1-Score : {}\".format(f1_score(Y_test, predictions)))\n",
    "print(confusion_matrix(Y_test, predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Try including full diagnostic information\n",
    "This has many more features, due to the one hot encoding of diagnostics, so will be somewhat harder to train.\n",
    "\n",
    "We see an improvement in the test set but no improvement in validation set, so it is hard to take conclusions from this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical_vars = ['race', 'age', 'gender', 'admission_type_id', 'discharge_disposition_id', 'admission_source_id', \\\n",
    "                    'max_glu_serum', 'A1Cresult', 'change', 'diabetesMed']\n",
    "numerical_vars = df_train_alt.columns[6:14].tolist()\n",
    "\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(df_train_alt[numerical_vars])\n",
    "X_train_alt, Y_train_alt = prepare_data(df_train_alt, scaler, categorical_vars, numerical_vars)\n",
    "X_val_alt, Y_val_alt = prepare_data(df_val_alt, scaler, categorical_vars, numerical_vars)\n",
    "X_test_alt, Y_test_alt = prepare_data(df_test_alt, scaler, categorical_vars, numerical_vars)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---- Validation ----\n",
      "Accuracy : 0.6455\n",
      "F1-Score : 0.4221678891605542\n",
      "[[1032  175]\n",
      " [ 534  259]]\n",
      "---- Test ----\n",
      "Accuracy : 0.6395\n",
      "F1-Score : 0.41429731925264013\n",
      "[[1024  183]\n",
      " [ 538  255]]\n",
      "------ Log. Reg is able to preform probabilistic estimates -------\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[0.74259283, 0.25740717],\n",
       "       [0.76309577, 0.23690423],\n",
       "       [0.5214764 , 0.4785236 ],\n",
       "       [0.41621264, 0.58378736],\n",
       "       [0.70701268, 0.29298732],\n",
       "       [0.65146919, 0.34853081],\n",
       "       [0.63140315, 0.36859685],\n",
       "       [0.68518773, 0.31481227],\n",
       "       [0.83330953, 0.16669047],\n",
       "       [0.68385689, 0.31614311]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = LogisticRegression(C=0.1, max_iter=200)\n",
    "clf.fit(X_train_alt, Y_train_alt)\n",
    "print(\"---- Validation ----\")\n",
    "predictions = clf.predict(X_val_alt)\n",
    "print(\"Accuracy : {}\".format(accuracy_score(Y_val_alt, predictions)))\n",
    "print(\"F1-Score : {}\".format(f1_score(Y_val_alt, predictions)))\n",
    "print(confusion_matrix(Y_val_alt, predictions))\n",
    "print(\"---- Test ----\")\n",
    "predictions = clf.predict(X_test_alt)\n",
    "print(\"Accuracy : {}\".format(accuracy_score(Y_test_alt, predictions)))\n",
    "print(\"F1-Score : {}\".format(f1_score(Y_test_alt, predictions)))\n",
    "print(confusion_matrix(Y_test_alt, predictions))\n",
    "print(\"------ Log. Reg is able to preform probabilistic estimates -------\")\n",
    "clf.predict_proba(X_train_alt[0:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'learning_rate': 0.3, 'max_depth': 3, 'n_estimators': 20, 'reg_alpha': 0.4, 'reg_lambda': 0.7}\n",
      "---- Validation ----\n",
      "Accuracy : 0.655\n",
      "F1-Score : 0.42016806722689076\n",
      "[[1060  147]\n",
      " [ 543  250]]\n",
      "---- Test ----\n",
      "Accuracy : 0.648\n",
      "F1-Score : 0.42009884678747944\n",
      "[[1041  166]\n",
      " [ 538  255]]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "params = {'n_estimators': [20, 50, 100],\n",
    "          'max_depth' : [3, 6],\n",
    "          'reg_alpha': [0.2, 0.4],\n",
    "          'reg_lambda' : [0.7, 1],\n",
    "          'learning_rate': [0.3]}\n",
    "opt = GridSearch(param_grid=params, model=XGBClassifier(random_state=42, verbose=True))\n",
    "opt.fit(X_train_alt, Y_train_alt, X_val_alt, Y_val_alt)\n",
    "print(opt.best_params)\n",
    "print(\"---- Validation ----\")\n",
    "predictions = opt.predict(X_val_alt)\n",
    "print(\"Accuracy : {}\".format(accuracy_score(Y_val_alt, predictions)))\n",
    "print(\"F1-Score : {}\".format(f1_score(Y_val_alt, predictions)))\n",
    "print(confusion_matrix(Y_val_alt, predictions))\n",
    "print(\"---- Test ----\")\n",
    "predictions = opt.predict(X_test_alt)\n",
    "print(\"Accuracy : {}\".format(accuracy_score(Y_test_alt, predictions)))\n",
    "print(\"F1-Score : {}\".format(f1_score(Y_test_alt, predictions)))\n",
    "print(confusion_matrix(Y_test_alt, predictions))"
   ]
  }
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