sklearnライブラリ--MLアルゴリズムすべてのコード整理
このコードには、初心者向けのマシン学習の基本アルゴリズムが含まれています.質問があれば、このコードは更新されます.
このコードは自分のためにまとめて、そして検証して、もし何か問題があれば、質問を歓迎して、そしてみんなが一緒にこのテンプレートを改善することを歓迎して、便利な後の使用.
#
import numpy as np
import pandas as pd
import os
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import GaussianNB
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.svm import SVC
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.ensemble import AdaBoostClassifier
from sklearn import metrics
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import cross_val_score
from sklearn import preprocessing
#
os.environ["TF_CPP_MIN_LOG_LEVEL"] = '3'
os.chdir("/home/ftt/DL/transfer_learning/classification_2/data_9_feature")
#
def data_preprocess(filename):
data = pd.read_csv(filename)
'''
data[data<0]=0
data=data.fillna(0)
print(data.max().max())
column=data.columns
for i in column[:-1]:
data[i] = data[i].map(lambda x: np.log(x+2)*10000)
data=data.astype('int')
'''
'''
min_max_scaler = preprocessing.MaxAbsScaler()
data=min_max_scaler.fit_transform(data)*10000
data=data.drop_duplicates(keep='first')
'''
return data
# ( )
def load_data_np():
Adware = np.loadtxt('adware.csv',skiprows=1,delimiter=',')
Begin = np.loadtxt('begin.csv',skiprows=1,delimiter=',')
GM = np.loadtxt('GM.csv',skiprows=1,delimiter=',')
np.random.shuffle(Adware)
np.random.shuffle(Begin)
np.random.shuffle(GM)
train=np.row_stack((Adware[:125000],Begin[:380000]))
# train=np.row_stack((Adware[:125000],train))
test=np.row_stack((Adware[125000:],Begin[380000:]))
np.random.shuffle(train)
np.random.shuffle(test)
x_train,y_train=train[:,:9],train[:,9].ravel()
x_test,y_test=test[:,:9], test[:,9]
print(train.shape,test.shape)
return x_train,y_train,x_test,y_test
# ( )
def load_data_pd():
Adware = data_preprocess('adware.csv')
GM = data_preprocess('GM.csv')
Begin = data_preprocess('begin.csv')
Adware = Adware.sample(frac=1.0)
GM = GM.sample(frac=1.0)
Begin = Begin.sample(frac=1.0)
print(len(Adware),len(GM),len(Begin))
train = pd.merge(GM[:int(0.8*len(GM))],Adware[:int(0.8*len(Adware))], how='outer')
test = pd.merge(GM[int(0.8*len(GM)):],Adware[int(0.8*len(Adware)):], how='outer')
train =pd.merge(train,Begin[:int(0.8*len(Begin))], how='outer')
test =pd.merge(test,Begin[int(0.8*len(Begin)):], how='outer')
train=train.values
test=test.values
x_train,y_train=train[:,:9],train[:,9].ravel()
x_test,y_test=test[:,:9], test[:,9]
# min_max_scaler = preprocessing.MaxAbsScaler()
# x_train=min_max_scaler.fit_transform(x_train)*10000
# x_test=min_max_scaler.transform(x_test)*10000
return x_train,y_train,x_test,y_test
# ( )
def load_data_split():
Adware = pd.read_csv('adware.csv')
GM =pd.read_csv('GM.csv')
Begin = pd.read_csv('begin.csv')
train_data = pd.merge(Adware,GM, how='outer')
train_data=train_data.drop_duplicates(keep='first')
train_data = train_data.sample(frac=1.0)
train_data = train_data.values
print(sum(train_data[:,9].ravel()),len(train_data)-sum(train_data[:,9].ravel()))
num_features = train_data.shape[0]
print("Number of all features: \t", num_features)
split = int(num_features * 0.8)
train = train_data[:split]
test = train_data[split:]
x_train,y_train=train[:,:9],train[:,9].ravel()
x_test,y_test=test[:,:9], test[:,9]
print(train.shape,test.shape)
return x_train,y_train,x_test,y_test
#
def model():
# clf = RandomForestClassifier(n_estimators=200)
# clf = DecisionTreeClassifier()
# clf = KNeighborsClassifier()
# clf = GaussianNB()
# clf = SVC()
# clf = LogisticRegression()
# clf = GradientBoostingClassifier(init=None,n_estimators=1000,learning_rate=0.1, subsample=0.8,loss='deviance',max_features='sqrt',criterion='friedman_mse',min_samples_split =1200, min_impurity_split=None,min_impurity_decrease=0.0,max_depth=7,max_leaf_nodes=None,min_samples_leaf =60, warm_start=False,random_state=10)
clf = GradientBoostingRegressor(init=None,n_estimators=1000,learning_rate=0.1, subsample=0.8,loss='ls',max_features='sqrt',criterion='friedman_mse',min_samples_split =1200, min_impurity_split=None,min_impurity_decrease=0.0,max_depth=7,max_leaf_nodes=None,min_samples_leaf =60, warm_start=False,random_state=10)
# clf = ExtraTreesClassifier(n_estimators=200, max_depth=None,min_samples_split=2, random_state=0)
# clf = AdaBoostClassifier(n_estimators=1000)
return clf
#
def train():
print('RandomForestClassifier')
x_train,y_train,x_test,y_test=load_data_pd()
clf=model()
clf.fit(x_train,y_train)
y_pred = clf.predict(x_test)
y_predprob = clf.predict_proba(x_test)[:,1]
print("Accuracy : %.4g" % clf.score(x_test,y_test))
print("precision_recall_f1-score_accuracy:
",metrics.classification_report(y_test,y_pred))
print("confusion_matrix:
",metrics.confusion_matrix(y_test,y_pred))
print("Feature importances:",clf.feature_importances_)
print("Accuracy : %.4g" % metrics.accuracy_score(y_test, y_pred))
# ,
# print("AUC Score (Train): %f" % metrics.roc_auc_score(test[:,9].ravel(), y_predprob))
# print(metrics.mean_squared_error(test[:,9].ravel(), y_pred))
#
'''
m,n=0.0,0.0
for i in range(len(test)):
if predict_proba[i][1]>0.5:
m+=1.0
if predict_proba[i][1]>0.5 and test[:,9].ravel()[i]==1:
n+=1.0
k=sum(test[:,9].ravel())
recall=n/k
precision=n/m
print("recall:",recall,"precision:",precision)
print(k,m,n)
'''
#
def Gridsearch():
x_train,y_train,x_test,y_test=load_data_pd()
clf=model()
gsearch = GridSearchCV(estimator = clf, param_grid = {'n_estimators':[100,200,300,500,800,1000]}, scoring='accuracy',iid=False,cv=5)
gsearch.fit(x_train,y_train)
print(gsearch.grid_scores_, gsearch.best_params_, gsearch.best_score_)
#
def CV():
train,test=load_data_pd()
clf=model()
scores = cross_val_score(clf, train[:,:9],train[:,9].ravel())
print(scores)
print(scores.mean())
#
for i in range(10):
print('train()-----gr')
train()
このコードは自分のためにまとめて、そして検証して、もし何か問題があれば、質問を歓迎して、そしてみんなが一緒にこのテンプレートを改善することを歓迎して、便利な後の使用.