gen_train_data.ipynb

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   "source": [
    "**Training Data Generation** \\\n",
    "_Author: Joaquín Torres Bravo_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set-up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from imblearn.combine import SMOTETomek\n",
    "from imblearn.under_sampling import TomekLinks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load clean datasets\n",
    "df_pre = pd.read_csv('../EDA/output/datasets/pre_dataset.csv')\n",
    "df_post = pd.read_csv('../EDA/output/datasets/post_dataset.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a numpy matrix (X) without the target variable and a list with the target variable (y) \n",
    "X_pre, y_pre = df_pre.loc[:, df_pre.columns != \"Treatment_Outcome\"].to_numpy(), df_pre.Treatment_Outcome\n",
    "X_post, y_post = df_post.loc[:, df_post.columns != \"Treatment_Outcome\"].to_numpy(), df_post.Treatment_Outcome\n",
    "feat = np.delete(df_pre.columns.to_numpy(),-1) # Get labels and remove target \n",
    "\n",
    "print(X_pre.shape)\n",
    "print(X_post.shape)\n",
    "print(y_pre.shape)\n",
    "print(y_post.shape)\n",
    "print(feat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training-Test Split & Sampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. ORIGINAL\n",
    "X_train_pre, X_test_pre, y_train_pre, y_test_pre = train_test_split(X_pre, y_pre, test_size=0.1, random_state=42) #90-10 split\n",
    "X_train_post, X_test_post, y_train_post, y_test_post = train_test_split(X_post, y_post, test_size=0.1, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save test data\n",
    "np.save('./output/pre/X_test_pre.npy', X_test_pre)\n",
    "np.save('./output/pre/y_test_pre.npy', y_test_pre)\n",
    "np.save('./output/post/X_test_post.npy', X_test_post)\n",
    "np.save('./output/post/y_test_post.npy', y_test_post)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save ORIGINAL training data\n",
    "np.save('./output/pre/X_train_pre.npy', X_train_pre)\n",
    "np.save('./output/pre/y_train_pre.npy', y_train_pre)\n",
    "np.save('./output/post/X_train_post.npy', X_train_post)\n",
    "np.save('./output/post/y_train_post.npy', y_train_post)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2. OVERSAMPLED training data\n",
    "smote_tomek = SMOTETomek()\n",
    "X_train_over_pre, y_train_over_pre = smote_tomek.fit_resample(X_train_pre, y_train_pre)\n",
    "X_train_over_post, y_train_over_post = smote_tomek.fit_resample(X_train_post, y_train_post)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save oversampled training data\n",
    "np.save('./output/pre/X_train_over_pre.npy', X_train_over_pre)\n",
    "np.save('./output/pre/y_train_over_pre.npy', y_train_over_pre)\n",
    "np.save('./output/post/X_train_over_post.npy', X_train_over_post)\n",
    "np.save('./output/post/y_train_over_post.npy', y_train_over_post)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 3. UNDERSAMPLING: TOMEK-LINKS \n",
    "tomek = TomekLinks()\n",
    "X_train_under_pre, y_train_under_pre = tomek.fit_resample(X_train_pre, y_train_pre)\n",
    "X_train_under_post, y_train_under_post = tomek.fit_resample(X_train_post, y_train_post)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save undersampled training data\n",
    "np.save('./output/pre/X_train_under_pre.npy', X_train_under_pre)\n",
    "np.save('./output/pre/y_train_under_pre.npy', y_train_under_pre)\n",
    "np.save('./output/post/X_train_under_post.npy', X_train_under_post)\n",
    "np.save('./output/post/y_train_under_post.npy', y_train_under_post)"
   ]
  }
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