Code:
from sklearn.datasets import load_breast_cancer
from sklearn.cross_validation import train_test_split as tsplit
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression,SGDClassifier
from sklearn.metrics import classification_report as crt
import numpy as np
import pandas as pd
import time
breast_cancer = load_breast_cancer()
X_train, X_test, Y_train, Y_test = tsplit(breast_cancer.data,breast_cancer.target,test_size=0.2,random_state=1)
sts = StandardScaler()
X_train_sts = sts.fit_transform(X_train)
X_test_sts = sts.transform(X_test)
print(X_train_sts.shape,X_test_sts.shape)
lr = LogisticRegression()
sgdc = SGDClassifier()
ts1 = time.time()
lr.fit(X_train_sts,Y_train)
te1 = time.time()
print(te1-ts1)
ts2 = time.time()
sgdc.fit(X_train_sts,Y_train)
te2 = time.time()
print(te2-ts2)
score1 = lr.score(X_test_sts,Y_test)
score2 = sgdc.score(X_test_sts,Y_test)
print(score1,score2)
lr_pre1 = lr.predict(X_test_sts)
socres1 = crt(Y_test,lr_pre1,target_names=["0"."1"])
print(socres1)
lr_pre2 = sgdc.predict(X_test_sts)
socres2 = crt(Y_test,lr_pre2,target_names=["0"."1"])
print(socres2)
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Out:
(455, 30) (114, 30) 0.004028797149658203 0.0019457340240478516 0.9824561403508771 0.9736842105263158 Precision Recall F1-score support 0 1.00 0.95 0.98 42 1 0.97 1.00 0.99 72 AVG/total 0.98 0.98 0.98 114 Precision Recall F1-score support 0 1.00 0.93 0.96 42 1 0.96 1.00 0.98 72 AVg/total 0.97 0.97 0.97 114Copy the code