DecisionTree algorithm.py

from tkinter import Image
from matplotlib import pyplot as plt
import numpy as np
import pandas as pd
from sklearn.tree import DecisionTreeClassifier
from sklearn import preprocessing
from sklearn import metrics
from io import StringIO
import pydotplus
import matplotlib.image as mpimg
from sklearn import tree
from sklearn import tree
from sklearn.model_selection import train_test_split

#Using my_data as the Drug.csv data read by pandas
my_data = pd.read_csv("drug200.csv", delimiter=",")
print(my_data[0:5])

X = my_data[['Age', 'Sex', 'BP', 'Cholesterol', 'Na_to_K']].values
print(X[0:5])

#convert categorical variables like sex BP ... to numerical values.
le_sex = preprocessing.LabelEncoder()
le_sex.fit(['F','M'])
X[:,1] = le_sex.transform(X[:,1])

le_BP = preprocessing.LabelEncoder()
le_BP.fit([ 'LOW', 'NORMAL', 'HIGH'])
X[:,2] = le_BP.transform(X[:,2])

le_Chol = preprocessing.LabelEncoder()
le_Chol.fit([ 'NORMAL', 'HIGH'])
X[:,3] = le_Chol.transform(X[:,3])

print( "Converted : ", X[0:5])

#Filling the target variable
y = my_data["Drug"]
print("Y :",y[0:5])

#using train/test split on the decision tree from sklearn.cross_validation
X_trainset, X_testset, y_trainset, y_testset = train_test_split(X, y, test_size=0.3, random_state=3)

# Create a DecisionTreeClassifier.
drugTree = DecisionTreeClassifier(criterion="entropy", max_depth = 4)

#Training the data
drugTree.fit(X_trainset,y_trainset)

#Prediction
predTree = drugTree.predict(X_testset)
print (predTree [0:5])
print (y_testset [0:5])

#Evaluation: check the accuracy of our model
print("DecisionTrees's Accuracy using  sklearn: ", metrics.accuracy_score(y_testset, predTree))
print("DecisionTrees's Accuracy using numpy: ",np.mean(y_testset==predTree))

dot_data = StringIO()
filename = "drugtree.png"
featureNames = my_data.columns[0:5]
targetNames = my_data["Drug"].unique().tolist()
out=tree.export_graphviz(drugTree,feature_names=featureNames, out_file=dot_data, class_names= np.unique(y_trainset), filled=True,  special_characters=True,rotate=False)
graph = pydotplus.graph_from_dot_data(dot_data.getvalue())
graph.write_png(filename)
img = mpimg.imread(filename)
plt.figure(figsize=(50, 100))
plt.imshow(img,interpolation='nearest')
plt.show()