BDC-team-1/Sport/Modelization/2_Modelization_sport.ipynb

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  {
   "cell_type": "markdown",
   "id": "3415114e-9577-4487-89eb-4931620ad9f0",
   "metadata": {},
   "source": [
    "# Predict Sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "f271eb45-1470-4764-8c2e-31374efa1fe5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import s3fs\n",
    "import re\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",
    "from sklearn.utils import class_weight\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.preprocessing import StandardScaler, MaxAbsScaler, MinMaxScaler\n",
    "from sklearn.metrics import make_scorer, f1_score, balanced_accuracy_score\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import roc_curve, auc, precision_recall_curve, average_precision_score\n",
    "from sklearn.exceptions import ConvergenceWarning, DataConversionWarning\n",
    "\n",
    "import pickle\n",
    "import warnings\n",
    "#import scikitplot as skplt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "3fecb606-22e5-4dee-8efa-f8dff0832299",
   "metadata": {},
   "outputs": [],
   "source": [
    "warnings.filterwarnings('ignore')\n",
    "warnings.filterwarnings(\"ignore\", category=ConvergenceWarning)\n",
    "warnings.filterwarnings(\"ignore\", category=DataConversionWarning)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae591854-3003-4c75-a0c7-5abf04246e81",
   "metadata": {},
   "source": [
    "### Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "59dd4694-a812-4923-b995-a2ee86c74f85",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create filesystem object\n",
    "S3_ENDPOINT_URL = \"https://\" + os.environ[\"AWS_S3_ENDPOINT\"]\n",
    "fs = s3fs.S3FileSystem(client_kwargs={'endpoint_url': S3_ENDPOINT_URL})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "017f7e9a-3ba0-40fa-bdc8-51b98cc1fdb3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_train_test():\n",
    "    BUCKET = \"projet-bdc2324-team1/Generalization/sport\"\n",
    "    File_path_train = BUCKET + \"/\" + \"Train_set.csv\"\n",
    "    File_path_test = BUCKET + \"/\" + \"Test_set.csv\"\n",
    "    \n",
    "    with fs.open( File_path_train, mode=\"rb\") as file_in:\n",
    "        dataset_train = pd.read_csv(file_in, sep=\",\")\n",
    "        dataset_train['y_has_purchased'] = dataset_train['y_has_purchased'].fillna(0)\n",
    "\n",
    "    with fs.open(File_path_test, mode=\"rb\") as file_in:\n",
    "        dataset_test = pd.read_csv(file_in, sep=\",\")\n",
    "        dataset_test['y_has_purchased'] = dataset_test['y_has_purchased'].fillna(0)\n",
    "    \n",
    "    return dataset_train, dataset_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "825d14a3-6967-4733-bfd4-64bf61c2bd43",
   "metadata": {},
   "outputs": [],
   "source": [
    "def features_target_split(dataset_train, dataset_test):\n",
    "    features_l = ['nb_tickets', 'nb_purchases', 'total_amount',\n",
    "       'nb_suppliers', 'nb_tickets_internet',\n",
    "       'opt_in',\n",
    "        'nb_campaigns', 'nb_campaigns_opened']\n",
    "    X_train = dataset_train[features_l]\n",
    "    y_train = dataset_train[['y_has_purchased']]\n",
    "\n",
    "    X_test = dataset_test[features_l]\n",
    "    y_test = dataset_test[['y_has_purchased']]\n",
    "    return X_train, X_test, y_train, y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c479b230-b4bd-4cfb-b76b-d9faf6d95772",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_train, dataset_test = load_train_test()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "69eaec12-b30f-4d30-a461-ea520d5cbf77",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = features_target_split(dataset_train, dataset_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1d6de94-4e11-481a-a0ce-412bf29f692c",
   "metadata": {},
   "source": [
    "### Prepare preprocessing and Hyperparameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b808da43-c444-4e94-995a-7ec6ccd01e2d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute Weights\n",
    "weights = class_weight.compute_class_weight(class_weight = 'balanced', classes = np.unique(y_train['y_has_purchased']),\n",
    "                                            y = y_train['y_has_purchased'])\n",
    "\n",
    "weight_dict = {np.unique(y_train['y_has_purchased'])[i]: weights[i] for i in range(len(np.unique(y_train['y_has_purchased'])))}\n",
    "weight_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b32a79ea-907f-4dfc-9832-6c74bef3200c",
   "metadata": {},
   "outputs": [],
   "source": [
    "numeric_features = ['nb_tickets', 'nb_purchases', 'total_amount', 'nb_suppliers',\n",
    "                    'nb_tickets_internet', 'nb_campaigns', 'nb_campaigns_opened']\n",
    "\n",
    "numeric_transformer = Pipeline(steps=[\n",
    "    #(\"imputer\", SimpleImputer(strategy=\"mean\")),  \n",
    "    (\"scaler\", StandardScaler()) \n",
    "])\n",
    "\n",
    "categorical_features = ['opt_in']  \n",
    "\n",
    "# Transformer for the categorical features\n",
    "categorical_transformer = Pipeline(steps=[\n",
    "    #(\"imputer\", SimpleImputer(strategy=\"most_frequent\")),  # Impute missing values with the most frequent\n",
    "    (\"onehot\", OneHotEncoder(handle_unknown='ignore', sparse_output=False))\n",
    "])\n",
    "\n",
    "preproc = ColumnTransformer(\n",
    "    transformers=[\n",
    "        (\"num\", numeric_transformer, numeric_features),\n",
    "        (\"cat\", categorical_transformer, categorical_features)\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9809a688-bfbc-4685-a77f-17a8b2b79ab3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set loss\n",
    "\n",
    "balanced_scorer = make_scorer(balanced_accuracy_score)\n",
    "f1_scorer = make_scorer(f1_score)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "206d9a95-7c37-4506-949b-e77d225e42c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hyperparameter\n",
    "\n",
    "param_grid = {'logreg__C': np.logspace(-10, 6, 17, base=2),\n",
    "              'logreg__penalty': ['l2', 'L1'],\n",
    "               'logreg__class_weight': ['balanced', weight_dict]} "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7ff2f7bd-efc1-4f7c-a3c9-caa916aa2f2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pipeline\n",
    "\n",
    "pipeline = Pipeline(steps=[\n",
    "    ('preprocessor', preproc),\n",
    "    ('logreg', LogisticRegression(solver='saga', max_iter=1000))  \n",
    "])\n",
    "\n",
    "pipeline.set_output(transform=\"pandas\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2b467511-2ae5-4a16-a502-397c3460471d",
   "metadata": {},
   "outputs": [],
   "source": [
    "pipeline.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6356e870-0dfc-4e60-9e48-e2de5e7f9f87",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = pipeline.predict(X_test)\n",
    "\n",
    "# Calculate the F1 score\n",
    "f1 = f1_score(y_test, y_pred)\n",
    "print(f\"F1 Score: {f1}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "09387a09-0d53-4c54-baac-f3c2a57a629a",
   "metadata": {},
   "outputs": [],
   "source": [
    "conf_matrix = confusion_matrix(y_test, y_pred)\n",
    "sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues', xticklabels=['Class 0', 'Class 1'], yticklabels=['Class 0', 'Class 1'])\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('Actual')\n",
    "plt.title('Confusion Matrix')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae8e9bd3-0f6a-4f82-bb4c-470cbdc8d6bb",
   "metadata": {},
   "source": [
    "## Cross Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f7fca463-d7d6-493b-8329-fdfa92457f78",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cross validation\n",
    "y_train = y_train['y_has_purchased']\n",
    "grid_search = GridSearchCV(pipeline, param_grid, cv=5, scoring=f1_scorer, error_score='raise',\n",
    "                          n_jobs=-1)\n",
    "\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "# Print the best parameters and the best score\n",
    "print(\"Best parameters found: \", grid_search.best_params_)\n",
    "print(\"Best cross-validation score: {:.2f}\".format(grid_search.best_score_))\n",
    "\n",
    "# Evaluate the best model on the test set\n",
    "test_score = grid_search.score(X_test, y_test)\n",
    "print(\"Test set score: {:.2f}\".format(test_score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "56bd7828-4de1-4166-bea0-5d5e152b9d38",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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