Decision_Tree_Final.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Decision Tree Final.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "TPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/aquarius31/Crime-Predictor/blob/master/Decision_Tree_Final.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "jnlGtZOjD7Hj",
        "colab_type": "code",
        "outputId": "42b1aa3e-a491-40af-9669-eec81e53f3fe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "%pylab inline"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Populating the interactive namespace from numpy and matplotlib\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "euiBUkO9FYp6",
        "colab_type": "code",
        "outputId": "d34a2d65-1c5d-4fb1-a7d0-b4e0daed4e9e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn import model_selection\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn import tree\n",
        "from sklearn.model_selection import cross_val_score\n",
        "import pydotplus\n",
        "from IPython.display import Image \n",
        "from google.colab import drive\n",
        "drive.mount(\"/content/gdrive\", force_remount=True)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "U1NSXOlvFlQa",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Read the dataset\n",
        "crimes_stacked = pd.read_csv('gdrive/My Drive/Crime Predictor/crimes_stacked.csv', index_col=None)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Rb503i16F_wj",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Get rid of useless columns\n",
        "X = crimes_stacked[['Arrest', 'Domestic', 'Beat', 'Community Area',\n",
        "                   'Latitude', 'Longitude', 'Year', 'Hour', 'Closest police station']]\n",
        "# Extract the target feature\n",
        "y = crimes_stacked['Primary Type']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "kL9oYQD7GaGi",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Converting into dummy features for scikit-learn\n",
        "# X = pd.get_dummies(X, prefix='is_') \n",
        "# Fill Nan with 0\n",
        "X = X.fillna(0)\n",
        "# Gather column names\n",
        "feature_names = list(X)\n",
        "target_names = list(y)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "cvzQZtlpGkvM",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Separate data into training and testing datasets\n",
        "seed = 42\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=seed)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "CBHKB7upGnED",
        "colab_type": "code",
        "outputId": "c8538921-d7f9-4736-8440-abe5880afb44",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "cell_type": "code",
      "source": [
        "# Parameters to be tested\n",
        "parameters = {'criterion':['gini', 'entropy'], 'max_depth':[None, 2, 3]}\n",
        "# Use grid search with 3 K-Fold to find the best parameters\n",
        "scoring = 'accuracy'\n",
        "kfold = model_selection.KFold(n_splits=3, random_state=seed)\n",
        "# Comparison\n",
        "dt = tree.DecisionTreeClassifier()\n",
        "clf = GridSearchCV(dt, parameters, cv=kfold, scoring=scoring)\n",
        "clf.fit(X_train, y_train)\n",
        "print(\"Best estimator: \", clf.best_estimator_)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Best estimator:  DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
            "            max_features=None, max_leaf_nodes=None,\n",
            "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
            "            min_samples_leaf=1, min_samples_split=2,\n",
            "            min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
            "            splitter='best')\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "EZ1pl1YVGuZf",
        "colab_type": "code",
        "outputId": "e5fc012a-650c-4b47-c4a5-966e8234e580",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "cell_type": "code",
      "source": [
        "# Train with the best parameters\n",
        "dt = clf.best_estimator_\n",
        "dt.fit(X_train, y_train)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
              "            max_features=None, max_leaf_nodes=None,\n",
              "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "            min_samples_leaf=1, min_samples_split=2,\n",
              "            min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
              "            splitter='best')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "metadata": {
        "id": "1OAnY7TzSn5A",
        "colab_type": "code",
        "outputId": "7325b4d7-e642-432d-8015-30841cedbf3a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        }
      },
      "cell_type": "code",
      "source": [
        "importances = dt.feature_importances_\n",
        "std = np.std([importances],\n",
        "             axis=0)\n",
        "indices = np.argsort(importances)[::-1]\n",
        "\n",
        "# Print the feature ranking\n",
        "print(\"Feature ranking:\")\n",
        "\n",
        "importance_to_plot = []\n",
        "for f in range(X.shape[1]):\n",
        "    if(importances[indices[f]]):\n",
        "        importance_to_plot.append(importances[indices[f]])\n",
        "        print(\"%d. feature %s (%f)\" % (f + 1, feature_names[f], importances[indices[f]]))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Feature ranking:\n",
            "1. feature Arrest (0.201825)\n",
            "2. feature Domestic (0.198337)\n",
            "3. feature Beat (0.187820)\n",
            "4. feature Community Area (0.153025)\n",
            "5. feature Latitude (0.109669)\n",
            "6. feature Longitude (0.051673)\n",
            "7. feature Year (0.046490)\n",
            "8. feature Hour (0.037946)\n",
            "9. feature Closest police station (0.013216)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "SjZXGi25TUKU",
        "colab_type": "code",
        "outputId": "59065712-74ff-493c-efd2-79c9a1f5a00d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        }
      },
      "cell_type": "code",
      "source": [
        "# Plot the feature importances of the forest\n",
        "plt.figure()\n",
        "plt.title(\"Feature importances\")\n",
        "plt.bar(range(X.shape[1]), importances[indices],\n",
        "       color=\"r\", yerr=std[indices], align=\"center\")\n",
        "plt.xticks(range(X.shape[1]), indices)\n",
        "plt.xlim([-1, len(importance_to_plot)])\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ICCCBEBERRQIhIiKABEJERBQJhIiIABIIERFRJBAiIgJIIERERJFAiIgIIIEQERFFkxvk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\nECIiokggREQEkECIiIji/wMHql4ODcKDJwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "2It9RCJtTWnI",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Plot and store the decision tree\n",
        "dot_data = tree.export_graphviz(dt, out_file=None, \n",
        "                         feature_names=feature_names,  \n",
        "                         class_names=target_names,  \n",
        "                         filled=True, rounded=True,  \n",
        "                         special_characters=True)  \n",
        "graph = pydotplus.graph_from_dot_data(dot_data) \n",
        "graph.write_pdf(\"DecisionTree.pdf\")\n",
        "Image(graph.create_png()) "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "RCSfzFzhTaVG",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}