A Classification using Decision Tree Algorithm ML.ipynb

{
    "cells": [
        {
            "metadata": {},
            "cell_type": "code",
            "source": "# Import libraries\nimport numpy as np \nimport pandas as pd\nfrom sklearn.tree import DecisionTreeClassifier\n\nprint(\"Imported\")",
            "execution_count": 2,
            "outputs": [
                {
                    "output_type": "stream",
                    "text": "Imported\n",
                    "name": "stdout"
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "# Download data for a study from IBM Storage\n!wget -O drug200.csv https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/drug200.csv",
            "execution_count": 3,
            "outputs": [
                {
                    "output_type": "stream",
                    "text": "--2020-10-08 15:41:38--  https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/drug200.csv\nResolving s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)... 67.228.254.196\nConnecting to s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)|67.228.254.196|:443... connected.\nHTTP request sent, awaiting response... 200 OK\nLength: 6027 (5.9K) [text/csv]\nSaving to: \u2018drug200.csv\u2019\n\n100%[======================================>] 6,027       --.-K/s   in 0s      \n\n2020-10-08 15:41:38 (593 MB/s) - \u2018drug200.csv\u2019 saved [6027/6027]\n\n",
                    "name": "stdout"
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "# read and view data\nmy_data = pd.read_csv(\"drug200.csv\", delimiter=\",\")\nmy_data[0:5]",
            "execution_count": 4,
            "outputs": [
                {
                    "output_type": "execute_result",
                    "execution_count": 4,
                    "data": {
                        "text/plain": "   Age Sex      BP Cholesterol  Na_to_K   Drug\n0   23   F    HIGH        HIGH   25.355  drugY\n1   47   M     LOW        HIGH   13.093  drugC\n2   47   M     LOW        HIGH   10.114  drugC\n3   28   F  NORMAL        HIGH    7.798  drugX\n4   61   F     LOW        HIGH   18.043  drugY",
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Age</th>\n      <th>Sex</th>\n      <th>BP</th>\n      <th>Cholesterol</th>\n      <th>Na_to_K</th>\n      <th>Drug</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>23</td>\n      <td>F</td>\n      <td>HIGH</td>\n      <td>HIGH</td>\n      <td>25.355</td>\n      <td>drugY</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>47</td>\n      <td>M</td>\n      <td>LOW</td>\n      <td>HIGH</td>\n      <td>13.093</td>\n      <td>drugC</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>47</td>\n      <td>M</td>\n      <td>LOW</td>\n      <td>HIGH</td>\n      <td>10.114</td>\n      <td>drugC</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>28</td>\n      <td>F</td>\n      <td>NORMAL</td>\n      <td>HIGH</td>\n      <td>7.798</td>\n      <td>drugX</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>61</td>\n      <td>F</td>\n      <td>LOW</td>\n      <td>HIGH</td>\n      <td>18.043</td>\n      <td>drugY</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
                    },
                    "metadata": {}
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "# get info about attributes\nmy_data.info()",
            "execution_count": 5,
            "outputs": [
                {
                    "output_type": "stream",
                    "text": "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 200 entries, 0 to 199\nData columns (total 6 columns):\nAge            200 non-null int64\nSex            200 non-null object\nBP             200 non-null object\nCholesterol    200 non-null object\nNa_to_K        200 non-null float64\nDrug           200 non-null object\ndtypes: float64(1), int64(1), object(4)\nmemory usage: 9.5+ KB\n",
                    "name": "stdout"
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "### Declare Independent Variable ( features of the data )  as ' X ' "
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "# Remove the target attribute from the data set and print values\nX = my_data[['Age', 'Sex', 'BP', 'Cholesterol', 'Na_to_K']].values\nprint(X[0:5])",
            "execution_count": 6,
            "outputs": [
                {
                    "output_type": "stream",
                    "text": "[[23 'F' 'HIGH' 'HIGH' 25.355]\n [47 'M' 'LOW' 'HIGH' 13.093]\n [47 'M' 'LOW' 'HIGH' 10.113999999999999]\n [28 'F' 'NORMAL' 'HIGH' 7.797999999999999]\n [61 'F' 'LOW' 'HIGH' 18.043]]\n",
                    "name": "stdout"
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "# Convert Object values (Categorical Variables) into numerical values \n# sklearn.tree do not hanle categorical values\n\nfrom sklearn import preprocessing\nle_sex = preprocessing.LabelEncoder()\nle_sex.fit(['F','M'])\nX[:,1] = le_sex.transform(X[:,1]) \n\n\nle_BP = preprocessing.LabelEncoder()\nle_BP.fit([ 'LOW', 'NORMAL', 'HIGH'])\nX[:,2] = le_BP.transform(X[:,2])\n\n\nle_Chol = preprocessing.LabelEncoder()\nle_Chol.fit([ 'NORMAL', 'HIGH'])\nX[:,3] = le_Chol.transform(X[:,3]) \n\nX[0:5]",
            "execution_count": 7,
            "outputs": [
                {
                    "output_type": "execute_result",
                    "execution_count": 7,
                    "data": {
                        "text/plain": "array([[23, 0, 0, 0, 25.355],\n       [47, 1, 1, 0, 13.093],\n       [47, 1, 1, 0, 10.113999999999999],\n       [28, 0, 2, 0, 7.797999999999999],\n       [61, 0, 1, 0, 18.043]], dtype=object)"
                    },
                    "metadata": {}
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "### Declare Dependent Variables ( target ) as ' y'"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "# target variable \ny = my_data[\"Drug\"]\ny[0:5]",
            "execution_count": 8,
            "outputs": [
                {
                    "output_type": "execute_result",
                    "execution_count": 8,
                    "data": {
                        "text/plain": "0    drugY\n1    drugC\n2    drugC\n3    drugX\n4    drugY\nName: Drug, dtype: object"
                    },
                    "metadata": {}
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "## Setting up the Decission Tree"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "# Import 'train_test_split' from sklearn.cross_validation\nfrom sklearn.model_selection import train_test_split\n\n# split 70/30 (train/test) \nX_trainset, X_testset, y_trainset, y_testset = train_test_split(X, y, test_size=0.3, random_state=3)          \n## (X,y) arrays, (test_size = ration of the dataset), (random_state = ensure we get the same split) ",
            "execution_count": 9,
            "outputs": []
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "# check the shape of train/test shape variables\nprint('X-trainset shape :', X_trainset.shape)\nprint('y-trainset shape :', y_trainset.shape)\n\nprint('X-testset shape :', X_testset.shape)\nprint('y-testset shape :', y_testset.shape)\n",
            "execution_count": 11,
            "outputs": [
                {
                    "output_type": "stream",
                    "text": "X-trainset shape : (140, 5)\ny-trainset shape : (140,)\nX-testset shape : (60, 5)\ny-testset shape : (60,)\n",
                    "name": "stdout"
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "## Modeling the Decision Tree"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "# create an instance of DecisionTreeClassifier. (criterion shows the information of each node) \ndrugTree = DecisionTreeClassifier(criterion=\"entropy\", max_depth = 4)\n\n# print the constructor \nprint(drugTree)",
            "execution_count": 14,
            "outputs": [
                {
                    "output_type": "stream",
                    "text": "DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=4,\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": {},
            "cell_type": "code",
            "source": "# fit the data w/ training parameters\ndrugTree.fit(X_trainset,y_trainset)",
            "execution_count": 16,
            "outputs": [
                {
                    "output_type": "execute_result",
                    "execution_count": 16,
                    "data": {
                        "text/plain": "DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=4,\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": {}
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "## Prediction"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "# create testing prediciton dataset variable\nprediction_tree = drugTree.predict(X_testset)",
            "execution_count": 18,
            "outputs": []
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "# check the prediction to the actual values of the data set",
            "execution_count": 19,
            "outputs": []
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "print ('Actual Drug\\n',y_testset [0:5])\nprint ('Predicted drug\\n'  ,prediction_tree [0:5])\n",
            "execution_count": 28,
            "outputs": [
                {
                    "output_type": "stream",
                    "text": "Actual Drug\n 40     drugY\n51     drugX\n139    drugX\n197    drugX\n170    drugX\nName: Drug, dtype: object\nPredicted drug\n ['drugY' 'drugX' 'drugX' 'drugX' 'drugX']\n",
                    "name": "stdout"
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "#### Prediction was successful!"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "## Evaluation "
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "# check the accuracy of the model using metrics from sklearn \nfrom sklearn import metrics\nimport matplotlib.pyplot as plt\nprint(\"DecisionTrees's Accuracy: \", metrics.accuracy_score(y_testset, prediction_tree))",
            "execution_count": 29,
            "outputs": [
                {
                    "output_type": "stream",
                    "text": "DecisionTrees's Accuracy:  0.9833333333333333\n",
                    "name": "stdout"
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "#### In multilabel classification, the function returns the subset accuracy. \n#### If the entire set of predicted labels for a sample strictly match with the true set of labels, then the subset accuracy is 1.0; otherwise it is 0.0."
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "# calculate the accuracy score without sklearn\nyhat = np.array(y_testset)\ny = np.array(prediction_tree)\nacc = np.mean(y==yhat)\nprint('Accuracy score',acc)",
            "execution_count": 32,
            "outputs": [
                {
                    "output_type": "stream",
                    "text": "Accuracy score 0.9833333333333333\n",
                    "name": "stdout"
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "## Visualization"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "# Install the pydotplus and graphviz Libraries\n!pip install pydotplus\nfrom sklearn.externals.six import StringIO\nimport pydotplus\nimport matplotlib.image as mpimg\nfrom sklearn import tree\n%matplotlib inline ",
            "execution_count": 34,
            "outputs": [
                {
                    "output_type": "stream",
                    "text": "Collecting pydotplus\n\u001b[?25l  Downloading https://files.pythonhosted.org/packages/60/bf/62567830b700d9f6930e9ab6831d6ba256f7b0b730acb37278b0ccdffacf/pydotplus-2.0.2.tar.gz (278kB)\n\u001b[K     |\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 286kB 9.8MB/s eta 0:00:01\n\u001b[?25hRequirement already satisfied: pyparsing>=2.0.1 in /opt/conda/envs/Python36/lib/python3.6/site-packages (from pydotplus) (2.3.1)\nBuilding wheels for collected packages: pydotplus\n  Building wheel for pydotplus (setup.py) ... \u001b[?25ldone\n\u001b[?25h  Stored in directory: /home/dsxuser/.cache/pip/wheels/35/7b/ab/66fb7b2ac1f6df87475b09dc48e707b6e0de80a6d8444e3628\nSuccessfully built pydotplus\nInstalling collected packages: pydotplus\nSuccessfully installed pydotplus-2.0.2\n",
                    "name": "stdout"
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "dot_data = StringIO()\nfilename = \"drugtree.png\"\nfeatureNames = my_data.columns[0:5]\ntargetNames = my_data[\"Drug\"].unique().tolist()\nout=tree.export_graphviz(drugTree,feature_names=featureNames, out_file=dot_data, class_names= np.unique(y_trainset), filled=True,  special_characters=True,rotate=False)  \ngraph = pydotplus.graph_from_dot_data(dot_data.getvalue())  \ngraph.write_png(filename)\nimg = mpimg.imread(filename)\nplt.figure(figsize=(100, 200))\nplt.imshow(img,interpolation='nearest')",
            "execution_count": 35,
            "outputs": [
                {
                    "output_type": "execute_result",
                    "execution_count": 35,
                    "data": {
                        "text/plain": "<matplotlib.image.AxesImage at 0x7fa399306a90>"
                    },
                    "metadata": {}
                },
                {
                    "output_type": "display_data",
                    "data": {
                        "text/plain": "<Figure size 7200x14400 with 1 Axes>",
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                    },
                    "metadata": {
                        "needs_background": "light"
                    }
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "",
            "execution_count": null,
            "outputs": []
        }
    ],
    "metadata": {
        "kernelspec": {
            "name": "python3",
            "display_name": "Python 3.6",
            "language": "python"
        }
    },
    "nbformat": 4,
    "nbformat_minor": 1
}