-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathAppleStore_DecissionTreeClassifier.py
103 lines (88 loc) · 3.55 KB
/
AppleStore_DecissionTreeClassifier.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
from AppleStore_Milestone2 import *
from sklearn import tree
import numpy as np
from sklearn import model_selection
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import Ridge
from sklearn.linear_model import Lasso
from sklearn.linear_model import ElasticNet
from sklearn.neighbors import KNeighborsRegressor
from sklearn.tree import DecisionTreeRegressor
from sklearn.svm import SVR
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import r2_score
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
from math import sqrt
import matplotlib.pyplot as plt
import joblib
from sklearn.tree import export_graphviz
from io import StringIO
from IPython.display import Image
import pydotplus
import time
#clf = tree.DecisionTreeClassifier(max_depth=4,)
print('\t\t\t\t Decission Tree Classifier Model \t\t\t\t\n','*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*')
start_t=time.time()
DecisionTreeClassifierModel =tree.DecisionTreeClassifier(max_depth=4)
DecisionTreeClassifierModel = DecisionTreeClassifierModel.fit(X_train, Y_train)
end_t=time.time()
"""tree.DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=,
# max_features=None, max_leaf_nodes=10, min_samples_leaf=5,
# min_samples_split=2, min_weight_fraction_leaf=0.0,
# presort=False, random_state=None, splitter='random')
# #criterion = "gini", max_leaf_nodes = 7, min_samples_leaf = 4,
# """
Y_trainPred=DecisionTreeClassifierModel.predict(X_train)
print('MSE of train = ',np.sqrt(mean_squared_error(Y_train,Y_trainPred)))
accuracy = DecisionTreeClassifierModel.score(X_train, Y_train)
print('Decission tree accuracy train: ' + str(accuracy),'\n training time = ',end_t-start_t,'\n')
start_t=time.time()
Y_testPred=DecisionTreeClassifierModel.predict(X_test)
accuracy = DecisionTreeClassifierModel.score(X_test, Y_test)
end_t=time.time()
print('MSE of test = ',np.sqrt(mean_squared_error(Y_test,Y_testPred)))
print('Decission tree accuracy test : ' + str(accuracy),'\n testing time = ',end_t-start_t,'\n')
joblib.dump(DecisionTreeClassifierModel,'joblib_DecisionTreeClassifierModel.pkl')
# loaded_model = joblib.load('joblib_DecisionTreeClassifierModel.pkl')
# predict = loaded_model.predict(X_test)
# print('MSE of test = ',np.sqrt(mean_squared_error(Y_test,predict)))
# accuracy = loaded_model.score(X_test, Y_test)
# print('Decission tree accuracy test : ' + str(accuracy),'\n')
max_depth = []
acc_gini = []
acc_entropy = []
for i in range(1,30):
dtree = tree.DecisionTreeClassifier(criterion='gini', max_depth=i)
dtree.fit(X_train, Y_train)
pred = dtree.predict(X_test)
acc_gini.append(dtree.score(X_test, Y_test))
dtree = tree.DecisionTreeClassifier(criterion='entropy', max_depth=i)
dtree.fit(X_train, Y_train)
pred = dtree.predict(X_test)
acc_entropy.append(dtree.score(X_test, Y_test))
####
max_depth.append(i)
# visualizing changes in parameters
print('changing of max depth \n',max_depth)
print('changing of information gain \n',acc_gini)
print('changing of entropy \n',acc_entropy)
plt.plot(max_depth,acc_gini, label='gini')
plt.plot(max_depth,acc_entropy, label='entropy')
plt.xlabel('max_depth')
plt.ylabel('accuracy')
plt.legend()
plt.show()
"""
# Fit the model on training set
model = LogisticRegression()
model.fit(X_train, Y_train)
# save the model to disk
filename = 'finalized_model.sav'
joblib.dump(model, filename)
# some time later...
# load the model from disk
loaded_model = joblib.load(filename)
result = loaded_model.score(X_test, Y_test)
print(result)
"""