mlessentials/Lab06/Exercise6.04/Exercise6_04.ipynb

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   "source": [
    "# Compute MAE of Second Model"
   ]
  },
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   "source": [
    "#Import Libraries\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "\n",
    "#preprocessing\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import PolynomialFeatures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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    "colab": {},
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   "source": [
    "# import the data\n",
    "# column headers\n",
    "_headers = ['CIC0', 'SM1', 'GATS1i', 'NdsCH', 'Ndssc', 'MLOGP', 'response']\n",
    "\n",
    "# read in data\n",
    "df = pd.read_csv('../Dataset/qsar_fish_toxicity.csv', names=_headers, sep=';')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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    "colab": {},
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   "source": [
    "# Let's split our data\n",
    "features = df.drop('response', axis=1).values\n",
    "labels = df[['response']].values\n",
    "\n",
    "X_train, X_eval, y_train, y_eval = train_test_split(features, labels, test_size=0.2, random_state=0)\n",
    "X_val, X_test, y_val, y_test = train_test_split(X_eval, y_eval, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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    "colab": {},
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   "source": [
    "#create a pipeline and engineer quadratic features\n",
    "steps = [\n",
    "    ('scaler', MinMaxScaler()),\n",
    "    ('poly', PolynomialFeatures(2)),\n",
    "    ('model', LinearRegression())\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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    "colab": {},
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   "outputs": [],
   "source": [
    "#create a Linear Regression model\n",
    "model = Pipeline(steps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
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    "outputId": "cc63bd38-323f-42ca-d23f-47f23eb22267"
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   "outputs": [],
   "source": [
    "#train the model\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
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   "outputs": [],
   "source": [
    "#predict on validation dataset\n",
    "y_pred = model.predict(X_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
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    "colab_type": "code",
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    "outputId": "8c48d04e-5842-4e8a-a96e-0d4e177f06f7"
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   "outputs": [],
   "source": [
    "#compute MAE\n",
    "mae = mean_absolute_error(y_val, y_pred)\n",
    "print('MAE: {}'.format(mae))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": [
    "# let's get the R2 score\n",
    "r2 = model.score(X_val, y_val)\n",
    "print('R^2 score: {}'.format(r2))"
   ]
  },
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