{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "eDVT1fgawPQi"
   },
   "source": [
    "# Compute Mean Absolute Error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "G_S8aKAuwPQk"
   },
   "outputs": [],
   "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.metrics import mean_absolute_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "7q-Lino2wPQu"
   },
   "outputs": [],
   "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,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "NppmnQCtwPQ3"
   },
   "outputs": [],
   "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,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ZFlnPasswPRC"
   },
   "outputs": [],
   "source": [
    "# create a simple Linear Regression model\n",
    "model = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "N0wGdeauwPRL",
    "outputId": "e8813bd4-fa92-49c4-88ea-dfbe6c91bece"
   },
   "outputs": [],
   "source": [
    "# train the model\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "60z-AiurwPRW"
   },
   "outputs": [],
   "source": [
    "# let's use our model to predict on our validation datast\n",
    "y_pred = model.predict(X_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "FwNWlwdlwPRc",
    "outputId": "0f691e8b-f35a-4c3e-d3a9-5cea904b1786"
   },
   "outputs": [],
   "source": [
    "# Let's compute our MEAN ABSOLUTE ERROR\n",
    "mae = mean_absolute_error(y_val, y_pred)\n",
    "print('MAE: {}'.format(mae))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "Y_N2YAtowPRl",
    "outputId": "b81871b9-c764-4d08-9399-05d944c52d2c"
   },
   "outputs": [],
   "source": [
    "#Let's get the R2 score\n",
    "r2 = model.score(X_val, y_val)\n",
    "print('R^2 score: {}'.format(r2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "uK2HIBvgwPRo"
   },
   "outputs": [],
   "source": []
  }
 ],
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   "name": "Exercise6_03.ipynb",
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  "file_extension": ".py",
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