from sklearn import metrics
import pandas as pd
from sklearn import tree
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
import pydotplus
from IPython.display import Image
from sklearn.externals.six import StringIO
from sklearn.tree import export_graphviz

mushData=pd.read_csv('mushrooms.csv')
X=pd.get_dummies(mushData.iloc[:,1:22]) #need to get dumies as
y=mushData['class']
#need confusion matrix, training accurady, test accuracy, class tree,
#top 3 features, classify mushy
X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.30,random_state=1)
dt=DecisionTreeClassifier(max_depth=6,random_state=12)


dt.fit(X_train,y_train)
y_pred=dt.predict(X_test)

confusionmatrix=metrics.confusion_matrix(y_test,y_pred)
print(confusionmatrix)

print(y_pred)
print(y_test)
print(y_train)
acctest=accuracy_score(y_pred,y_test)
print("Test classification accuracy: ",acctest)

y_predtrain=dt.predict(X_train)
acctrain=accuracy_score(y_predtrain,y_train)
print('Train classification accuracy: ',acctrain)


dectree=tree.export_graphviz(dt)
dot_data=export_graphviz(dt,out_file='treeq3.pdf',filled=True,rounded=True)
pydot_graph=pydotplus.graph_from_dot_data(dectree)
pydot_graph.write_pdf("treeq3.pdf")