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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 建立模型四步驟\n",
    "\n",
    "在 Scikit-learn 中，建立一個機器學習的模型其實非常簡單，流程大略是以下四個步驟\n",
    "\n",
    "1. 讀進資料，並檢查 shape (有多少 rows, features，label 是甚麼型態)\n",
    "    - pd.read_csv\n",
    "    - np.loadtxt \n",
    "    - sklearn.datasets.load_xxx\n",
    "    - data.shape (data need to be numpy array or pandas)\n",
    "2. 將資料切為訓練 (train) / 測試 (test)\n",
    "    - train_test_split(data)\n",
    "3. 建立模型，將資料 fit 進模型開始訓練\n",
    "    - clf = DecisionTreeClassifier()\n",
    "    - clf.fit(x_train, y_train)\n",
    "4. 將測試資料 (features) 放進訓練好的模型中，得到 prediction，與測試資料的 lable (y_test) 做評估\n",
    "    - clf.predict(x_test)\n",
    "    - accuracy_score(y_test, y_pred)\n",
    "    - f1_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read iris data\n",
    "iris = load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# check the data shape\n",
    "print(iris.data.shape, iris.target.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_seed = 5  # set seed for same train test data split\n",
    "x_train, x_test, y_train, y_test = train_test_split(iris.data,\n",
    "                                                    iris.target,\n",
    "                                                    random_state=random_seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"shape of X_train: \", x_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"shape of X_test: \", x_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = clf.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(iris.feature_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf.feature_importances_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### visualize our tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install graphviz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import graphviz\n",
    "from sklearn.tree import export_graphviz\n",
    "dot_data = export_graphviz(clf, out_file=None)\n",
    "graph = graphviz.Source(dot_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dot_data = export_graphviz(clf,\n",
    "                           out_file=None,\n",
    "                           feature_names=iris.feature_names,\n",
    "                           class_names=iris.target_names,\n",
    "                           filled=True,\n",
    "                           rounded=True,\n",
    "                           special_characters=True)\n",
    "graph = graphviz.Source(dot_data)\n",
    "graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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