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": 2,
   "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.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",
    "import pickle\n",
    "#import scikitplot as skplt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae591854-3003-4c75-a0c7-5abf04246e81",
   "metadata": {},
   "source": [
    "### Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": 3,
   "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 + \"/\" + \"dataset_train.csv\"\n",
    "    File_path_test = BUCKET + \"/\" + \"dataset_train.csv\"\n",
    "    \n",
    "    with fs.open(FILE_PATH_S3, 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_S3, 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": null,
   "id": "825d14a3-6967-4733-bfd4-64bf61c2bd43",
   "metadata": {},
   "outputs": [],
   "source": [
    "def features_target_split(dataset_train, dataset_test):\n",
    "    X_train = dataset_train[]\n",
    "    y_train = dataset_train['y_has_purchased']\n",
    "\n",
    "    X_test = dataset_test[]\n",
    "    y_test = dataset_test['y_has_purchased']\n",
    "    return X_train, X_test, y_train, y_test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1d6de94-4e11-481a-a0ce-412bf29f692c",
   "metadata": {},
   "source": [
    "### Prepare preprocessing and Hyperparameters"
   ]
  },
  {
   "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', 'fidelity', 'nb_campaigns', 'nb_campaigns_opened']\n",
    "\n",
    "numeric_transformer = Pipeline(steps=[\n",
    "                                # (\"imputer\", SimpleImputer(strategy=\"mean\")), # NaN remplacés par la moyenne, mais peu importe car on a supprimé les valeurs manquantes\n",
    "                                (\"scaler\", StandardScaler())])\n",
    "\n",
    "preproc = ColumnTransformer(transformers=[(\"num\", numeric_transformer, numeric_features)])"
   ]
  },
  {
   "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",
    "parameters4 = {'logreg__C': np.logspace(-10, 6, 17, base=2),\n",
    "               'logreg__class_weight': ['balanced']} "
   ]
  }
 ],
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