def training_model(self, code, data, features):
X = data[features]
y = data['next_direction']
# normalization
X = preprocessing.scale(X)
# pca缩放
pca = PCAModel(self.module_name).load(code)
X = pca.transform(X)
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=.3,
shuffle=False)
parameters_grid = [{
'learning_rate': [0.05, 0.1, 0.3],
'max_depth': range(2, 8, 2),
'subsample': [
0.7,
],
'min_child_weight': range(1, 6, 2)
}]
gs_search = GridSearchCV(estimator=xgb.XGBClassifier(n_estimators=100,
random_state=10),
param_grid=parameters_grid,
n_jobs=-1)
gs_result = gs_search.fit(X_train, y_train)
logger.debug(gs_search.best_params_)
logger.debug("XGBoost Classier's best score: %.4f" %
gs_result.best_score_) # 训练的评分结果
xgb_classifier = gs_search.best_estimator_
# 使用训练数据, 重新训练
xgb_classifier.fit(X_train, y_train)
# 使用测试数据对模型进评平分
y_test_pred = xgb_classifier.predict(X_test)
# 在测试集中的评分
test_score = accuracy_score(y_test, y_test_pred)
logger.debug('test score: %.4f' % test_score)
# 使用所有数据, 重新训练
xgb_classifier.fit(X, y)
# 记录日志
k_data_model_log_dao.insert(code=code,
name=self.model_name,
best_estimator=gs_search.best_estimator_,
train_score=gs_search.best_score_,
test_score=test_score)
# 输出模型
joblib.dump(
xgb_classifier,
self.get_model_path(code, self.module_name, self.model_name))
def training_model(self, code, data, features):
X = data[features]
y = data['next_direction']
# normalization
X = preprocessing.scale(X)
# pca缩放
pca = PCAModel(self.module_name).load(code)
X = pca.transform(X)
X_train, x_test, y_train, y_test = train_test_split(X, y, test_size=.3)
# normalization
X_train = preprocessing.scale(X_train)
x_test = preprocessing.scale(x_test)
input_dim_len = len(features)
sequantial_model = Sequential()
sequantial_model.add(
Dense(512, input_dim=input_dim_len, activation='relu'))
sequantial_model.add(Dropout(0.5))
sequantial_model.add(Dense(128, activation='relu'))
sequantial_model.add(Dropout(0.5))
sequantial_model.add(Dense(1, activation='tanh'))
sequantial_model.compile(optimizer='sgd',
loss='binary_crossentropy',
metrics=['accuracy'])
# traning performance
sequantial_model.fit(X_train, y_train, epochs=10, batch_size=128)
train_model_score = sequantial_model.evaluate(X_train,
y_train,
batch_size=128)
# test performance
test_model_score = sequantial_model.evaluate(x_test,
y_test,
batch_size=128)
logger.debug('test model score: %s' % test_model_score)
full_model_score = sequantial_model.evaluate(data[features],
data['next_direction'])
logger.debug('full model score: %s' % full_model_score)
# 记录日志
k_data_60m_model_log_dao.insert(code=code,
name=self.model_name,
best_estimator=None,
train_score=train_model_score[1],
test_score=test_model_score[1],
desc="full_model_score:%s" %
full_model_score[1])
# 输出模型, 使用h5的格式保存起来
sequantial_model.save(
self.get_model_path(code, self.module_name, self.model_name, 'h5'))
def training_model(self, code, data, features):
X = data[features]
y = data['next_direction']
# normalization
X = preprocessing.scale(X)
# pca缩放
pca = PCAModel(self.module_name).load(code)
X = pca.transform(X)
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=.3,
shuffle=False)
tuned_parameters = [{
'kernel': ['rbf'],
'gamma': [1e-3, 1e-4],
'C': [1, 10, 100, 1000]
}]
# # tsne缩放
# X_train = TSNE(n_components=2, learning_rate=100).fit_transform(X_train)
# X_test = TSNE(n_components=2, learning_rate=100).fit_transform(X_test)
# 网格搜寻最优参数
grid = GridSearchCV(svm.SVC(), tuned_parameters, cv=None, n_jobs=-1)
grid.fit(X_train, y_train)
logger.debug(grid.best_estimator_) # 训练的结果
logger.debug("Support Vector Classifier's best score: %.4f" %
grid.best_score_) # 训练的评分结果
support_vector_classifier = grid.best_estimator_
# 使用训练数据, 重新训练
support_vector_classifier.fit(X_train, y_train)
# 使用测试数据对模型进评平分
y_test_pred = support_vector_classifier.predict(X_test)
# 在测试集中的评分
test_score = accuracy_score(y_test, y_test_pred)
logger.debug('test score: %.4f' % test_score)
# 使用所有数据, 重新训练
support_vector_classifier.fit(X, y)
# 记录日志
k_data_model_log_dao.insert(code=code,
name=self.model_name,
best_estimator=grid.best_estimator_,
train_score=grid.best_score_,
test_score=test_score)
# 输出模型
joblib.dump(
support_vector_classifier,
self.get_model_path(code, self.module_name, self.model_name))
def training_model(self, code, data, features):
X = data[features]
y = data['next_direction']
X = preprocessing.scale(X)
pca = PCAModel(self.module_name).load(code)
X = pca.transform(X)
# 数据按30%测试数据, 70%训练数据进行拆分
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=.3,
shuffle=False)
# 交叉验证查找合适的超参数: penalty, C
# penalty
tuned_parameters = {
'penalty': ['l1', 'l2'],
'C': [0.001, 0.01, 0.1, 1, 10, 100]
}
# 网格搜索训练
grid = GridSearchCV(LogisticRegression(), tuned_parameters, cv=None)
grid.fit(X_train, y_train)
logger.debug(grid.best_estimator_) # 训练的结果
logger.debug("logistic regression's best score: %.4f" %
grid.best_score_) # 训练的评分结果
logistic_regression = grid.best_estimator_
# 使用训练数据, 重新训练
logistic_regression.fit(X_train, y_train)
# 使用测试数据对模型进评平分
y_test_pred = logistic_regression.predict(X_test)
# test_score = logistic_regression.score(X_test, y_test)
# 在测试集中的评分
test_score = accuracy_score(y_test, y_test_pred)
logger.debug('test score: %.4f' % test_score)
# 使用所有数据, 重新训练
logistic_regression.fit(X, y)
# 记录日志
k_data_model_log_dao.insert(code=code,
name=self.model_name,
best_estimator=grid.best_estimator_,
train_score=grid.best_score_,
test_score=test_score)
# 输出模型
joblib.dump(
logistic_regression,
self.get_model_path(code, self.module_name, self.model_name))
def predict(self, code, data):
model_path = self.get_model_path(code, self.module_name, self.model_name)
if not os.path.exists(model_path):
logger.error('model not found, code is %s:' % code)
return
X = preprocessing.scale(data)
pac = PCAModel(self.module_name).load(code)
X = pac.transform(X)
ridge_regression_model = joblib.load(model_path)
y_pred = ridge_regression_model.predict(X)
return int(y_pred[0])
def training_model(self, code, data, features, *args):
X = data[features]
# if not args:
y = data['close']
# normalization
X = preprocessing.scale(X)
# pca缩放
pca = PCAModel(self.module_name).load(code)
X = pca.transform(X)
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=.3,
shuffle=False,
random_state=10)
LR_model = linear_model.LinearRegression()
LR_model.fit(X_train, y_train)
test_score = LR_model.score(X_test, y_test)
y_pred = LR_model.predict(X_test)
mse = metrics.mean_squared_error(y_test, y_pred)
mse = '%.4e' % mse
logger.debug('mse: %s' % metrics.mean_squared_error(y_test, y_pred))
# full data training
LR_model.fit(X, y)
# 记录日志
k_data_model_log_dao.insert(code=code,
name=self.model_name,
best_estimator=LR_model,
train_score=test_score,
test_score=mse)
# 输出模型
joblib.dump(
LR_model,
self.get_model_path(code, self.module_name, self.model_name))
def predict(self, code, data):
model_path = self.get_model_path(code, self.module_name,
self.model_name, 'h5')
if not os.path.exists(model_path):
logger.error('model not found, code is %s:' % code)
return
X = preprocessing.scale(data)
pac = PCAModel(self.module_name).load(code)
X = pac.transform(X)
sequantial_model = load_model(model_path)
y_pred = sequantial_model.predict(X)
return int(y_pred[0][0])
def training_model(self, code, data, features, *args):
X = data[features]
y = data['close']
# normalization
X = preprocessing.scale(X)
# pca缩放
pca = PCAModel(self.module_name).load(code)
X = pca.transform(X)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.3, shuffle=False, random_state=10)
tuned_parameters = [
{'kernel': ['rbf'], 'gamma': [1e-3, 1e-4], 'C': [1, 10, 100, 1000]}
]
svr_model = GridSearchCV(svm.SVR(), tuned_parameters, n_jobs=-1)
svr_model.fit(X_train, y_train)
test_score = svr_model.score(X_test, y_test)
y_pred = svr_model.predict(X_test)
mse = metrics.mean_squared_error(y_test, y_pred)
mse = '%.4e' % mse
# full data set training
svr_model.fit(X, y)
# 记录日志
k_data_model_log_dao.insert(code=code, name=self.model_name
, best_estimator=svr_model,
train_score=test_score, test_score=mse)
# 输出模型
joblib.dump(svr_model, self.get_model_path(code, self.module_name, self.model_name))
def training_model(self, code, data, features, *args):
X = data[features]
y = data['close']
# normalization
X = preprocessing.scale(X)
# pca缩放
pca = PCAModel(self.module_name).load(code)
X = pca.transform(X)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.3, shuffle=False)
ridge_model = linear_model.RidgeCV(alphas=[10, 1, 0.5, 0.25, 0.1, 0.005, 0.0025, 0.001])
ridge_model.fit(X_train, y_train)
test_score = ridge_model.score(X_test, y_test)
y_pred = ridge_model.predict(X_test)
mse = metrics.mean_squared_error(y_test, y_pred)
mse = '%.4e' % mse
# full data set training
ridge_model.fit(X, y)
# 记录日志
k_data_model_log_dao.insert(code=code, name=self.model_name
, best_estimator=ridge_model,
train_score=test_score, test_score=mse)
# 输出模型
joblib.dump(ridge_model, self.get_model_path(code, self.module_name, self.model_name))
def training_model(self, code, data, features):
X = data[features]
y = data['next_direction']
# normalization
X = preprocessing.scale(X)
# pca缩放
pca = PCAModel(self.module_name).load(code)
X = pca.transform(X)
X_train, X_test, y_train, y_test = train_test_split(
data[features],
data['next_direction'],
test_size=.3,
shuffle=False)
rfc_model = RandomForestClassifier(max_features='sqrt',
max_depth=14,
oob_score=True)
tuned_parameter = {
'n_estimators': [
50,
],
'min_samples_leaf': range(10, 60, 10),
'min_samples_split': range(20, 100, 20)
}
gs_result = GridSearchCV(estimator=rfc_model,
param_grid=tuned_parameter,
scoring='roc_auc',
cv=None,
n_jobs=-1)
gs_result.fit(X_train, y_train)
logger.debug('auc: %s' % gs_result.best_score_)
min_samples_leaf = gs_result.best_params_['min_samples_leaf']
min_samples_split = gs_result.best_params_['min_samples_split']
rf1 = RandomForestClassifier(n_estimators=50,
min_samples_leaf=min_samples_leaf,
min_samples_split=min_samples_split,
max_features='sqrt',
max_depth=3,
oob_score=True,
n_jobs=-1,
random_state=10)
rf1.fit(X_train, y_train)
logger.debug('oob: %s' % rf1.oob_score_)
# 在测试集中的评分
test_score = rf1.score(X_test, y_test)
logger.debug('test score: %.4f' % test_score)
# 使用所有数据, 重新训练
rf1.fit(X, y)
rf1_str = "RandomForestClassifier(n_estimators=50, min_samples_leaf=%s" \
",min_samples_split=%s, max_features='sqrt',max_depth=3, " \
"oob_score=True, n_jobs=-1, random_state=10)" % (min_samples_leaf, min_samples_split)
# 记录日志
k_data_model_log_dao.insert(code=code,
name=self.model_name,
best_estimator=rf1_str,
train_score=gs_result.best_score_,
test_score=test_score,
desc="oob_score_:%s" % rf1.oob_score_)
# 输出模型
joblib.dump(
rf1, self.get_model_path(code, self.module_name, self.model_name))
