%matplotlib inline
import matplotlib
.pyplot
as plt
import numpy
as np
from sklearn
.datasets
import load_breast_cancer
cancer
= load_breast_cancer
()
X
= cancer
.data
y
= cancer
.target
print('data shape: {0}; no. positive: {1}; no. negative: {2}'.format(
X
.shape
, y
[y
==1].shape
[0], y
[y
==0].shape
[0]))
print(cancer
.data
[0])
data shape: (569, 30); no. positive: 357; no. negative: 212
[1.799e+01 1.038e+01 1.228e+02 1.001e+03 1.184e-01 2.776e-01 3.001e-01
1.471e-01 2.419e-01 7.871e-02 1.095e+00 9.053e-01 8.589e+00 1.534e+02
6.399e-03 4.904e-02 5.373e-02 1.587e-02 3.003e-02 6.193e-03 2.538e+01
1.733e+01 1.846e+02 2.019e+03 1.622e-01 6.656e-01 7.119e-01 2.654e-01
4.601e-01 1.189e-01]
cancer
.feature_names
array(['mean radius', 'mean texture', 'mean perimeter', 'mean area',
'mean smoothness', 'mean compactness', 'mean concavity',
'mean concave points', 'mean symmetry', 'mean fractal dimension',
'radius error', 'texture error', 'perimeter error', 'area error',
'smoothness error', 'compactness error', 'concavity error',
'concave points error', 'symmetry error',
'fractal dimension error', 'worst radius', 'worst texture',
'worst perimeter', 'worst area', 'worst smoothness',
'worst compactness', 'worst concavity', 'worst concave points',
'worst symmetry', 'worst fractal dimension'], dtype='<U23')
from sklearn
.model_selection
import train_test_split
X_train
, X_test
, y_train
, y_test
= train_test_split
(X
, y
, test_size
=0.2)
from sklearn
.linear_model
import LogisticRegression
model
= LogisticRegression
(solver
='liblinear')
model
.fit
(X_train
, y_train
)
train_score
= model
.score
(X_train
, y_train
)
test_score
= model
.score
(X_test
, y_test
)
print('train score: {train_score:.6f}; test score: {test_score:.6f}'.format(
train_score
=train_score
, test_score
=test_score
))
train score: 0.956044; test score: 0.956140
y_pred
= model
.predict
(X_test
)
print('matchs: {0}/{1}'.format(np
.equal
(y_pred
, y_test
).sum(), y_test
.shape
[0]))
matchs: 109/114
y_pred_proba
= model
.predict_proba
(X_test
)
print('sample of predict probability: {0}'.format(y_pred_proba
[0]))
y_pred_proba_0
= y_pred_proba
[:, 0] > 0.1
result
= y_pred_proba
[y_pred_proba_0
]
y_pred_proba_1
= result
[:, 1] > 0.1
print(result
[y_pred_proba_1
])
sample of predict probability: [0.1495694 0.8504306]
[[0.1495694 0.8504306 ]
[0.11096724 0.88903276]
[0.19499883 0.80500117]
[0.73727435 0.26272565]
[0.1612553 0.8387447 ]
[0.79987232 0.20012768]
[0.89256972 0.10743028]
[0.88427977 0.11572023]
[0.28478309 0.71521691]
[0.26459028 0.73540972]
[0.18916204 0.81083796]
[0.508977 0.491023 ]
[0.85503032 0.14496968]
[0.69726084 0.30273916]
[0.40779235 0.59220765]
[0.77126139 0.22873861]
[0.71761711 0.28238289]]
import time
from sklearn
.linear_model
import LogisticRegression
from sklearn
.preprocessing
import PolynomialFeatures
from sklearn
.pipeline
import Pipeline
def polynomial_model(degree
=1, **kwarg
):
polynomial_features
= PolynomialFeatures
(degree
=degree
,
include_bias
=False)
logistic_regression
= LogisticRegression
(**kwarg
)
pipeline
= Pipeline
([("polynomial_features", polynomial_features
),
("logistic_regression", logistic_regression
)])
return pipeline
model
= polynomial_model
(degree
=2, penalty
='l1', solver
='liblinear')
start
= time
.clock
()
model
.fit
(X_train
, y_train
)
train_score
= model
.score
(X_train
, y_train
)
cv_score
= model
.score
(X_test
, y_test
)
print('elaspe: {0:.6f}; train_score: {1:0.6f}; cv_score: {2:.6f}'.format(
time
.clock
()-start
, train_score
, cv_score
))
elaspe: 1.709350; train_score: 1.000000; cv_score: 0.991228
import time
from sklearn
.linear_model
import LogisticRegression
from sklearn
.preprocessing
import PolynomialFeatures
from sklearn
.pipeline
import Pipeline
def polynomial_model(degree
=1, **kwarg
):
polynomial_features
= PolynomialFeatures
(degree
=degree
,
include_bias
=False)
logistic_regression
= LogisticRegression
(**kwarg
)
pipeline
= Pipeline
([("polynomial_features", polynomial_features
),
("logistic_regression", logistic_regression
)])
return pipeline
model
= polynomial_model
(degree
=2, penalty
='l2', solver
='liblinear')
start
= time
.clock
()
model
.fit
(X_train
, y_train
)
train_score
= model
.score
(X_train
, y_train
)
cv_score
= model
.score
(X_test
, y_test
)
print('elaspe: {0:.6f}; train_score: {1:0.6f}; cv_score: {2:.6f}'.format(
time
.clock
()-start
, train_score
, cv_score
))
elaspe: 0.590021; train_score: 0.971429; cv_score: 0.964912
