def cross_validation(self, content_file):
"""
进行交叉验证
:param content_file:
:return:
"""
dataset = self.load_data(content_file)
row, col = dataset.shape
X = dataset[:, :col - 1]
Y = dataset[:, -1]
clf = SVC(kernel='rbf', C=1000)
clf.fit(X, Y)
scores = cs(clf, X, Y, cv=5)
print("Accuracy: %0.2f (+- %0.2f)" % (scores.mean(), scores.std()))
return clf
def train_creale_plk(self, content_file, plk_file):
"""
训练数据并且生成训练结果文件
:param content_file:
:param plk_file: 训练结果文件
:return:
"""
dataset = self.load_data(content_file)
if not dataset.any():
raise Exception('特征值文件为空')
row, col = dataset.shape
X = dataset[:, :col - 1]
Y = dataset[:, -1]
clf = SVC(kernel='rbf', C=1000)
clf.fit(X, Y)
scores = cs(clf, X, Y, cv=5)
print("Accuracy: %0.2f (+- %0.2f)" % (scores.mean(), scores.std()))
joblib.dump(clf, plk_file)
print(len(train_X))
print(len(train_y))
print(len(test_X))
print(len(test_y))
# In[ ]:
#Classifying the splited data and check accuracy
model = dtr()
model.fit(train_X, train_y)
a = model.score(test_X, test_y)
print('Score with model', a)
z = cs(model, test_X, test_y)
print('This is error in list', z)
# In[ ]:
#Predict your data
prediction = model.predict(test_X)
ans = aus(test_y, prediction)
Final_score1 = round(model.score(train_X, train_y) * 100, 6)
print('Error', ans)
classifier.add(
Dense(input_dim=nh, output_dim=50, init='uniform', activation='relu'))
#internal layers
classifier.add(Dense(output_dim=50, init='uniform', activation='relu'))
classifier.add(Dense(output_dim=50, init='uniform', activation='relu'))
#output layer
classifier.add(Dense(output_dim=1, init='uniform', activation='sigmoid'))
#compiling ANN
classifier.compile(optimizer='rmsprop',
metrics=['accuracy'],
loss='binary_crossentropy')
return classifier
#kfoldcrossvalidaton
from sklearn.model_selection import cross_val_score as cs
classifier = KerasClassifier(build_fn=build_classifier,
batch_size=5,
nb_epoch=100)
accuracies = cs(classifier, X=X, y=y, cv=10, n_jobs=-1)
accuracy = accuracies.mean()
#fitting the model
classifier.fit(X, y)
#predicting results
y_pred = classifier.predict(X_test1)
classifier.add(Dense(output_dim=50, init='uniform', activation='relu'))
classifier.add(Dense(output_dim=50, init='uniform', activation='relu'))
#output layer
classifier.add(Dense(output_dim=1, init='uniform', activation='sigmoid'))
#compiling ANN
classifier.compile(optimizer='rmsprop',
metrics=['accuracy'],
loss='binary_crossentropy')
return classifier
#kfoldcrossvalidaton
from sklearn.model_selection import cross_val_score as cs
classifier = KerasClassifier(build_fn=build_classifier,
batch_size=5,
nb_epoch=100)
accuracies = cs(classifier, X=X_train, y=y_train, cv=10, n_jobs=-1)
accuracy = accuracies.mean()
#fitting the model
classifier.fit(X_train, y_train)
#predicting
y_pred = classifier.predict(X_test)
#confusionmatrix
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_pred, y_test)