python MLP 神经网络使用 MinMaxScaler 没有 StandardScaler效果好
MLP 64,2 preprocessing.MinMaxScaler().fit(X)
test confusion_matrix:
[[129293 2734]
[ 958 23375]]
precision recall f1-score support
0 0.99 0.98 0.99 132027
1 0.90 0.96 0.93 24333
avg / total 0.98 0.98 0.98 156360
all confusion_matrix:
[[646945 13384]
[ 4455 117015]]
precision recall f1-score support
0 0.99 0.98 0.99 660329
1 0.90 0.96 0.93 121470
avg / total 0.98 0.98 0.98 781799
black verify confusion_matrix:
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0
0 0 0 0 0]
/root/anaconda2/lib/python2.7/site-packages/sklearn/metrics/classification.py:1137: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples.
'recall', 'true', average, warn_for)
precision recall f1-score support
0 0.00 0.00 0.00 0
1 1.00 0.07 0.13 42
avg / total 1.00 0.07 0.13 42
white verify confusion_matrix:
[1 1 1 1 1 1 0]
precision recall f1-score support
0 1.00 0.14 0.25 7
1 0.00 0.00 0.00 0
avg / total 1.00 0.14 0.25 7
unknown_verify:
[1 0 0 1 1 0 0 0 1 1 0 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 0 0 1 1 1 1
0 1 1 1 1 0 1 0 0 1 0 1 0 1 0 0 1 0 0 1 1 0 0 1 0 0 0 1 0 1 1 0 0 1 0 0 0]
MLP 64，2 使用preprocessing.StandardScaler().fit(X)
[[131850 180]
[ 230 24100]]
precision recall f1-score support
0 1.00 1.00 1.00 132030
1 0.99 0.99 0.99 24330
avg / total 1.00 1.00 1.00 156360
all confusion_matrix:
[[659500 829]
[ 1195 120275]]
precision recall f1-score support
0 1.00 1.00 1.00 660329
1 0.99 0.99 0.99 121470
avg / total 1.00 1.00 1.00 781799
black verify confusion_matrix:
[0 1 1 0 0 0 0 1 1 1 0 1 1 1 1 1 1 0 1 1 1 0 0 0 1 1 1 0 0 0 1 1 1 1 1 1 1
0 0 0 1 1]
/root/anaconda2/lib/python2.7/site-packages/sklearn/metrics/classification.py:1137: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples.
'recall', 'true', average, warn_for)
precision recall f1-score support
0 0.00 0.00 0.00 0
1 1.00 0.62 0.76 42
avg / total 1.00 0.62 0.76 42
white verify confusion_matrix:
[0 0 1 0 1 1 0]
precision recall f1-score support
0 1.00 0.57 0.73 7
1 0.00 0.00 0.00 0
avg / total 1.00 0.57 0.73 7
unknown_verify:
[1 0 0 0 1 0 1 1 0 0 1 0 1 1 0 1 0 1 0 0 0 0 1 0 1 0 0 0 0 0 0 1 0 0 1 0 0
0 1 1 1 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0]
代码：
from sklearn import preprocessing
scaler = preprocessing.StandardScaler().fit(X)
#scaler = preprocessing.MinMaxScaler().fit(X)
X = scaler.transform(X)
print("standard X sample:", X[:3])
black_verify = scaler.transform(black_verify)
print(black_verify)
white_verify = scaler.transform(white_verify)
print(white_verify)
unknown_verify = scaler.transform(unknown_verify)
print(unknown_verify)
# ValueError: operands could not be broadcast together with shapes (756140,75) (42,75) (756140,75)
for i in range(20):
X = np.concatenate((X, black_verify))
y += black_verify_labels
labels = ['white', 'CC']
if True:
# pdb.set_trace()
ratio_of_train = 0.8
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=(1 - ratio_of_train))
# X_train=preprocessing.normalize(X_train)
# X_test=preprocessing.normalize(X_test)
clf = MLPClassifier(solver='sgd', batch_size=128, learning_rate='adaptive', max_iter=256,
hidden_layer_sizes=(64, 2), random_state=1)
"""
clf = sklearn.ensemble.RandomForestClassifier(n_estimators=n_estimators, verbose=verbose, n_jobs=n_jobs,
random_state=random_state, oob_score=True)
"""
clf.fit(X_train, y_train)
print "test confusion_matrix:"
# print clf.feature_importances_
y_pred = clf.predict(X_test)
print(sklearn.metrics.confusion_matrix(y_test, y_pred))
print(classification_report(y_test, y_pred))
else:
#clf = pickle.loads(open("mpl-acc97-recall98.pkl", 'rb').read())
clf = pickle.loads(open("mlp-add-topx10.model", 'rb').read())
y_pred = clf.predict(X)
print(sklearn.metrics.confusion_matrix(y, y_pred))
print(classification_report(y, y_pred))
import sys
#sys.exit(0)
print "all confusion_matrix:"
y_pred = clf.predict(X)
print(sklearn.metrics.confusion_matrix(y, y_pred))
print(classification_report(y, y_pred))
