import pandas as pd

from sklearn.tree import DecisionTreeClassifier

from sklearn.cross_validation import train_test_split

from sklearn.metrics import classification_report

from sklearn.pipeline import Pipeline

from sklearn.grid_search import GridSearchCV

import zipfile

#压缩节省空间

z=zipfile.ZipFile('ad-dataset.zip')

# df=pd.read_csv(z.open(z.namelist()[0]),header=None,low_memory=False)

# df = pd.read_csv(z.open(z.namelist()[0]), header=None, low_memory=False)

df=pd.read_csv('.\\tree_data\\ad.data',header=None)

explanatory_variable_columns=set(df.columns.values)

response_variable_column=df[len(df.columns.values)-1]

#最后一列是代表的标签类型

explanatory_variable_columns.remove(len(df.columns)-1)

y=[1 if e =='ad.' else 0 for e in response_variable_column]

X=df.loc[:,list(explanatory_variable_columns)]

#匹配？字符，并把值转化为-1

X.replace(to_replace=' *\?', value=-1, regex=True, inplace=True)

X_train,X_test,y_train,y_test=train_test_split(X,y)

#用信息增益启发式算法建立决策树

pipeline=Pipeline([('clf',DecisionTreeClassifier(criterion='entropy'))])

parameters = {

'clf__max_depth': (150, 155, 160),

'clf__min_samples_split': (1, 2, 3),

'clf__min_samples_leaf': (1, 2, 3)

}

#f1查全率和查准率的调和平均

grid_search=GridSearchCV(pipeline,parameters,n_jobs=-1,

                         verbose=1,scoring='f1')

grid_search.fit(X_train,y_train)

print '最佳效果：%0.3f'%grid_search.best_score_

print '最优参数'

best_parameters=grid_search.best_estimator_.get_params()

best_parameters

for param_name in sorted(parameters.keys()):

    print ('\t%s:%r'%(param_name,best_parameters[param_name]))

predictions=grid_search.predict(X_test)

print classification_report(y_test,predictions)

df.head()

#coding:utf-8

import pandas as pd

from sklearn.ensemble import RandomForestClassifier

from sklearn.cross_validation import train_test_split

from sklearn.metrics import classification_report

from sklearn.pipeline import Pipeline

from sklearn.grid_search import GridSearchCV

df=pd.read_csv('.\\tree_data\\ad.data',header=None,low_memory=False)

explanatory_variable_columns=set(df.columns.values)

response_variable_column=df[len(df.columns.values)-1]

df.head()

#The last column describes the targets(去掉最后一列)

explanatory_variable_columns.remove(len(df.columns.values)-1)

y=[1 if e=='ad.' else 0 for e in response_variable_column]

X=df.loc[:,list(explanatory_variable_columns)]

#置换有？的为-1

X.replace(to_replace=' *\?', value=-1, regex=True, inplace=True)

X_train,X_test,y_train,y_test=train_test_split(X,y)

pipeline=Pipeline([('clf',RandomForestClassifier(criterion='entropy'))])

parameters = {

'clf__n_estimators': (5, 10, 20, 50),

'clf__max_depth': (50, 150, 250),

'clf__min_samples_split': (1, 2, 3),

'clf__min_samples_leaf': (1, 2, 3)

}

grid_search = GridSearchCV(pipeline,parameters,n_jobs=-1,verbose=1,scoring='f1')

grid_search.fit(X_train,y_train)

print(u'最佳效果：%0.3f'%grid_search.best_score_)

print u'最优的参数：'

best_parameters=grid_search.best_estimator_.get_params()

for param_name in sorted(parameters.keys()):

    print('\t%s:%r'%(param_name,best_parameters[param_name]))

predictions=grid_search.predict(X_test)

print classification_report(y_test,predictions)