DecisionTrees.ipynb

{
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      },
      "source": [
        "## Week 4 - Exercise 1"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tq3_meIyvNbA"
      },
      "source": [
        "Author: Khushee Kapoor\n",
        "\n",
        "Last Updated: 19/4/22"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2PXdDj-avNbB"
      },
      "source": [
        "### Setting Up"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NzMbslLOvNbC"
      },
      "source": [
        "To start, we have imported the following libraries:\n",
        "\n",
        "- NumPy: to work with the data\n",
        "- Pandas: to manipulate the dataframe\n",
        "- MatPlotLib: for data visualization\n",
        "- Seaborn: for data visulization"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "QeVwCYEkvNbC"
      },
      "outputs": [],
      "source": [
        "# importing the libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from matplotlib import pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0KKIZWqSvNbD"
      },
      "source": [
        "Next, we read the dataset and store it into a dataframe using the read_csv() function from the Pandas library."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "KviEMBKHvNbE"
      },
      "outputs": [],
      "source": [
        "# reading the dataset\n",
        "df = pd.read_csv('German Credit Data.csv')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NDQW6vdvvNbE"
      },
      "source": [
        "After that, we view the first few rows of the dataframe to get a glimpse of it. To do this, we use the head() function from the Pandas library."
      ]
    },
    {
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          "data": {
            "text/plain": [
              "  checkin_acc  duration credit_history  amount savings_acc present_emp_since  \\\n",
              "0         A11         6            A34    1169         A65               A75   \n",
              "1         A12        48            A32    5951         A61               A73   \n",
              "2         A14        12            A34    2096         A61               A74   \n",
              "3         A11        42            A32    7882         A61               A74   \n",
              "4         A11        24            A33    4870         A61               A73   \n",
              "\n",
              "   inst_rate personal_status  residing_since  age inst_plans  num_credits  \\\n",
              "0          4             A93               4   67       A143            2   \n",
              "1          2             A92               2   22       A143            1   \n",
              "2          2             A93               3   49       A143            1   \n",
              "3          2             A93               4   45       A143            1   \n",
              "4          3             A93               4   53       A143            2   \n",
              "\n",
              "    job  status  \n",
              "0  A173       0  \n",
              "1  A173       1  \n",
              "2  A172       0  \n",
              "3  A173       0  \n",
              "4  A173       1  "
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>checkin_acc</th>\n",
              "      <th>duration</th>\n",
              "      <th>credit_history</th>\n",
              "      <th>amount</th>\n",
              "      <th>savings_acc</th>\n",
              "      <th>present_emp_since</th>\n",
              "      <th>inst_rate</th>\n",
              "      <th>personal_status</th>\n",
              "      <th>residing_since</th>\n",
              "      <th>age</th>\n",
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              "      <th>num_credits</th>\n",
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              "      <th>status</th>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>A11</td>\n",
              "      <td>6</td>\n",
              "      <td>A34</td>\n",
              "      <td>1169</td>\n",
              "      <td>A65</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>A12</td>\n",
              "      <td>48</td>\n",
              "      <td>A32</td>\n",
              "      <td>5951</td>\n",
              "      <td>A61</td>\n",
              "      <td>A73</td>\n",
              "      <td>2</td>\n",
              "      <td>A92</td>\n",
              "      <td>2</td>\n",
              "      <td>22</td>\n",
              "      <td>A143</td>\n",
              "      <td>1</td>\n",
              "      <td>A173</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>A14</td>\n",
              "      <td>12</td>\n",
              "      <td>A34</td>\n",
              "      <td>2096</td>\n",
              "      <td>A61</td>\n",
              "      <td>A74</td>\n",
              "      <td>2</td>\n",
              "      <td>A93</td>\n",
              "      <td>3</td>\n",
              "      <td>49</td>\n",
              "      <td>A143</td>\n",
              "      <td>1</td>\n",
              "      <td>A172</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>A11</td>\n",
              "      <td>42</td>\n",
              "      <td>A32</td>\n",
              "      <td>7882</td>\n",
              "      <td>A61</td>\n",
              "      <td>A74</td>\n",
              "      <td>2</td>\n",
              "      <td>A93</td>\n",
              "      <td>4</td>\n",
              "      <td>45</td>\n",
              "      <td>A143</td>\n",
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              "      <td>A173</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>A11</td>\n",
              "      <td>24</td>\n",
              "      <td>A33</td>\n",
              "      <td>4870</td>\n",
              "      <td>A61</td>\n",
              "      <td>A73</td>\n",
              "      <td>3</td>\n",
              "      <td>A93</td>\n",
              "      <td>4</td>\n",
              "      <td>53</td>\n",
              "      <td>A143</td>\n",
              "      <td>2</td>\n",
              "      <td>A173</td>\n",
              "      <td>1</td>\n",
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          "metadata": {},
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        }
      ],
      "source": [
        "# viewing the first 5 rows\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sb70_4SrvNbH"
      },
      "source": [
        "### Q1. Use the German credit rating dataset “German Credit Data.csv”, Decision Tree Classifier to predict good or bad credit. Use “sklearn.model_selection” and GridSearchCV to search the hyperparameter values and report the most optimal one. Configure the grid search to search for optimal parameters:\n",
        "\n",
        "**- Splitting criteria: gini or entropy.**\n",
        "\n",
        "**- Maximum depth of decision tree ranging from 2 to 10.**\n",
        "\n",
        "**- The searching of optimal parameter will be validated using 10-fold cross validation and the most optimal parameter will be chosen based on ROC AUC score.**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i45mwyaHvNbI"
      },
      "source": [
        "To solve Question 1, we first encode the categorical columns (in theory the decision tree classifier can handle categorical values, but in sklearn implementation, we must encode them). To do this, we identify the categorical columns by picking only those which have an 'object' data type, then use the get_dummies() function from the Pandas library to encode the categorical values, merge them into the dataframe using the merge() function and then drop the original column using the drop() function from the Pandas library."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "dTZu75kdvNbI"
      },
      "outputs": [],
      "source": [
        "# identifying the categorical columns\n",
        "categorical_cols = [cname for cname in df.columns if df[cname].dtype == 'object']\n",
        "\n",
        "# encoding the categorical columns\n",
        "for column in categorical_cols:    \n",
        "    catdf = pd.get_dummies(df[column], prefix=column)    \n",
        "    df = pd.merge(left=df, right=catdf, left_index=True, right_index=True)    \n",
        "    df = df.drop(columns=column)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UViJiz9ovNbJ"
      },
      "source": [
        "Next we split the data into independent variables (x) and independent variable (y) and use the train_test_split() function from the sklearn library and divide the dataset into training and testing sets."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "0ngvH_civNbJ"
      },
      "outputs": [],
      "source": [
        "# splitting the data into independent and dependent variables\n",
        "x = df.drop(columns=['status'])\n",
        "y = df['status']\n",
        "\n",
        "# diving the dataset into training and testing sets\n",
        "from sklearn.model_selection import train_test_split\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, random_state=105)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zVT7QcXSvNbK"
      },
      "source": [
        "Next, we import the needed libraries, modules, and functions to solve the following questions."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "yzFsnxzUvNbK"
      },
      "outputs": [],
      "source": [
        "# importing the libraries\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "import graphviz\n",
        "from sklearn.tree import export_graphviz\n",
        "from sklearn import tree"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fq7QMJHMvNbL"
      },
      "source": [
        "After that, we create a dictionary of the parameters and a list of their values that we want to search within to optimize the model. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "COKF5QvqvNbL"
      },
      "outputs": [],
      "source": [
        "# creating a dictionary and list of their values to optimize the model\n",
        "params = {\n",
        "    'criterion' : ['gini', 'entropy'],\n",
        "    'max_depth' : list(range(2,11))\n",
        "}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h901zqrrvNbL"
      },
      "source": [
        "Following that, we configure a grid search model and fit the training data to analyze and test all the combinations of the parameters on a DecisionTreeClassifier model with fixed random state, and use the roc-auc scoring metric to evaluate each combination, with 10 cross validations."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bBv-aQvvvNbM",
        "outputId": "43fc80eb-0a7b-4eb5-f150-f7ee5e0bfaeb"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=10, estimator=DecisionTreeClassifier(random_state=105),\n",
              "             param_grid={'criterion': ['gini', 'entropy'],\n",
              "                         'max_depth': [2, 3, 4, 5, 6, 7, 8, 9, 10]},\n",
              "             scoring='roc_auc')"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "# intiating a grid search to find the most optimum parameters\n",
        "grid_search = GridSearchCV(DecisionTreeClassifier(random_state=105), params, scoring='roc_auc', cv=10)\n",
        "\n",
        "# fitting the training data\n",
        "grid_search.fit(x_train, y_train)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "--v44IvTvNbM"
      },
      "source": [
        "Next, we use the best_estimator_ attribute of the grid search model to obtain the best parameters for the DecisionTreeClassifier."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zDl1ckS6vNbN",
        "outputId": "05bd1b90-2c51-4106-b64d-296471fe6d5a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(criterion='entropy', max_depth=4, random_state=105)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "# obtaining the best combination of parameters\n",
        "grid_search.best_estimator_"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UWvBFguRvNbN"
      },
      "source": [
        "As we can see, the best combination of parameters is:\n",
        "\n",
        "- criterion for split: entropy\n",
        "- max_depth: 4"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k0IV0lGqvNbO"
      },
      "source": [
        "Now we use the above mentioned best combination to build a DecisionTreeClassifier model and fit the training set on it. We then check for the model's accuracy using the score() function."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fAPz810HvNbO",
        "outputId": "30023ff1-f9ee-4b91-c45d-e757942cea76"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 72.40%\n"
          ]
        }
      ],
      "source": [
        "# building a model with the best combination of parameters\n",
        "model = grid_search.best_estimator_\n",
        "\n",
        "# fitting the training data\n",
        "model.fit(x_train, y_train)\n",
        "\n",
        "# obtaining the accuracy\n",
        "print(str.format('Accuracy: {:.2f}%', model.score(x_test, y_test)*100))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vBFGIHHkvNbO"
      },
      "source": [
        "As we can see, the model gives us an accuracy of 72.40%, which is moderate."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QxdIipUkvNbP"
      },
      "source": [
        "### Q2. Visualize the tree using graphviz software.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4vau3eQ6vNbP"
      },
      "source": [
        "To solve Question 2, we first create a list of features using the columns attribute of a Pandas dataframe, and a list of class names of the status column - which are 1 and 0. Then we use the export_graphviz() function to visualize the tree."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
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        "id": "hzB-MYYsvNbP",
        "outputId": "a7613d2c-2c2e-4fa8-aa43-fbcd4f7f38d0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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points=\"1300,-306 1118,-306 1118,-223 1300,-223 1300,-306\"/>\n<text text-anchor=\"middle\" x=\"1209\" y=\"-290.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">credit_history_A34 &lt;= 0.5</text>\n<text text-anchor=\"middle\" x=\"1209\" y=\"-275.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">entropy = 0.449</text>\n<text text-anchor=\"middle\" x=\"1209\" y=\"-260.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 256</text>\n<text text-anchor=\"middle\" x=\"1209\" y=\"-245.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [232, 24]</text>\n<text text-anchor=\"middle\" x=\"1209\" y=\"-230.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = 0</text>\n</g>\n<!-- 14&#45;&gt;20 -->\n<g id=\"edge20\" class=\"edge\">\n<title>14&#45;&gt;20</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M1056.8418,-341.8796C1076.6141,-331.8244 1097.8632,-321.0183 1118.026,-310.7645\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"1119.8773,-313.7497 1127.2044,-306.0969 1116.7042,-307.5102 1119.8773,-313.7497\"/>\n</g>\n<!-- 16 -->\n<g id=\"node17\" class=\"node\">\n<title>16</title>\n<polygon fill=\"#f5d0b5\" stroke=\"#000000\" points=\"978.5,-187 823.5,-187 823.5,-104 978.5,-104 978.5,-187\"/>\n<text text-anchor=\"middle\" x=\"901\" y=\"-171.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">residing_since &lt;= 1.5</text>\n<text text-anchor=\"middle\" x=\"901\" y=\"-156.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">entropy = 0.961</text>\n<text text-anchor=\"middle\" x=\"901\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 39</text>\n<text text-anchor=\"middle\" x=\"901\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" 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          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "# creating a list of feature names\n",
        "fn = list(df.drop(columns=['status']).columns)\n",
        "\n",
        "# creating a list of class names\n",
        "cn = ['0', '1']\n",
        "\n",
        "# visualizing the tree\n",
        "dot_data = tree.export_graphviz(model, out_file=None, feature_names=fn, class_names=['0', '1'], filled=True)\n",
        "graph = graphviz.Source(dot_data, format=\"png\") \n",
        "graph"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
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)"
      ],
      "metadata": {
        "id": "IpgVuWDs6-u1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "As we can see, in each node, we can see the following specifications:\n",
        "\n",
        "- splitting condition (except leaves)\n",
        "- entropy: the splitting criteria\n",
        "- samples: total number of samples that are in that node\n",
        "- values: [number of samples with status = 0, number of samples with status = 1]\n",
        "- class: the class determined by that node by checking majority of values"
      ],
      "metadata": {
        "id": "fBFyoDU9yeDv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Q3. Display the text representation of the rules learnt."
      ],
      "metadata": {
        "id": "ZSePsFg36KsD"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "To solve Question 3, we use the export_tree() function from the sklearn library to obtain the text representation of the tree."
      ],
      "metadata": {
        "id": "Zgbji7SuyR3E"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mw3LxWZOvNbP",
        "outputId": "eda08652-2ee8-4f99-833f-b1414ec5ad86"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "|--- checkin_acc_A14 <= 0.50\n",
            "|   |--- duration <= 16.50\n",
            "|   |   |--- credit_history_A30 <= 0.50\n",
            "|   |   |   |--- credit_history_A31 <= 0.50\n",
            "|   |   |   |   |--- class: 0\n",
            "|   |   |   |--- credit_history_A31 >  0.50\n",
            "|   |   |   |   |--- class: 1\n",
            "|   |   |--- credit_history_A30 >  0.50\n",
            "|   |   |   |--- class: 1\n",
            "|   |--- duration >  16.50\n",
            "|   |   |--- savings_acc_A65 <= 0.50\n",
            "|   |   |   |--- duration <= 47.50\n",
            "|   |   |   |   |--- class: 0\n",
            "|   |   |   |--- duration >  47.50\n",
            "|   |   |   |   |--- class: 1\n",
            "|   |   |--- savings_acc_A65 >  0.50\n",
            "|   |   |   |--- amount <= 1681.00\n",
            "|   |   |   |   |--- class: 1\n",
            "|   |   |   |--- amount >  1681.00\n",
            "|   |   |   |   |--- class: 0\n",
            "|--- checkin_acc_A14 >  0.50\n",
            "|   |--- inst_plans_A143 <= 0.50\n",
            "|   |   |--- age <= 44.50\n",
            "|   |   |   |--- residing_since <= 1.50\n",
            "|   |   |   |   |--- class: 0\n",
            "|   |   |   |--- residing_since >  1.50\n",
            "|   |   |   |   |--- class: 0\n",
            "|   |   |--- age >  44.50\n",
            "|   |   |   |--- class: 0\n",
            "|   |--- inst_plans_A143 >  0.50\n",
            "|   |   |--- credit_history_A34 <= 0.50\n",
            "|   |   |   |--- num_credits <= 1.50\n",
            "|   |   |   |   |--- class: 0\n",
            "|   |   |   |--- num_credits >  1.50\n",
            "|   |   |   |   |--- class: 0\n",
            "|   |   |--- credit_history_A34 >  0.50\n",
            "|   |   |   |--- savings_acc_A62 <= 0.50\n",
            "|   |   |   |   |--- class: 0\n",
            "|   |   |   |--- savings_acc_A62 >  0.50\n",
            "|   |   |   |   |--- class: 0\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# obtaining the text representation of the tree\n",
        "text = tree.export_text(model, feature_names=fn)\n",
        "print(text)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "For each of the lines above, we can add an 'if' before the condition and a 'then' after the condition, to make it more english readable, except the class lines. \n",
        "\n",
        "Note: for categorical attributes - checkin_acc_A14, credit_history_A30, credit_history_A31, savings_acc_A65, inst_plans_A143, credit_history_A34, savings_acc_A62, >=0.5 signifies 1 or yes, and <0.5 signifies 0 or no."
      ],
      "metadata": {
        "id": "NfsId8m8zGA7"
      }
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
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