class Train(Process):
def __init__(self,
model_name=None,
feature_name=None,
gender=False,
feature_tables=None):
"""Train model Controller, dispatch the training tasks;
Input:
model_name: certain model depend on papers
feature_name: support for a group of absolute features
feature_tables: support for different feature table, which make it
is possible for us to combine different modality
features freely. But note that the train controller
is not responsible for processing the feature table,
it should be completed by a certain model.
gender: if the model should consider the gender difference
Output:
Result and realted information will be printed by each estimator in logs'
"""
super().__init__()
self.model_name = model_name
self.feature_name = feature_name
self.feature_tables = feature_tables
self.gender = gender
self.sql_handler = SqlHandler()
self._set_feature()
def _set_feature(self):
if self.feature_name is not None:
# you r using feature from a ceratin way!
if self.feature_name == FEATURE_EXP_2:
# if choose exp2 the data will be in pandas's dataframe by defaut
self.data = get_data_by_id(config.tbl_exp2_audio_fea,
self.gender)
self.feature_list = self.sql_handler.get_cloumns_from_table(
config.tbl_exp2_audio_fea)
self.feature_list.remove('ID')
else:
print('not finished yet')
elif self.feature_tables is not None:
# Now you r using a multi-modality model!
self.data = get_data_multi_modality(self.feature_tables,
self.gender)
self.audio_fea, self.video_fea, self.text_fea = \
self.sql_handler.get_cloumns_from_table(self.feature_tables)
else:
print('You must choose a set of features to train!!!')
def _train_eval(self, train, dev, model):
model = model(train, dev, features=self.feature_list)
model.train()
return model.eval()
def run(self):
if self.model_name == MODEL_RF:
from core.predictor.RF.rf_predict import RfPredictor
if self.feature_name is not None:
if not self.gender:
train, dev = self.data
score = self._train_eval(train, dev, RfPredictor)
logger.info(
f'Evalutaion Scores {self.model_name} with {self.feature_name}: {score}'
)
else:
train_m, dev_m, train_f, dev_f = self.data
score = self._train_eval(train_m, dev_m, RfPredictor)
logger.info(
f'Evalutaion Scores Male {self.model_name} with {self.feature_name}: {score}'
)
score = self._train_eval(train_f, dev_f, RfPredictor)
logger.info(
f'Evalutaion Scores Female {self.model_name} with {self.feature_name}: {score}'
)
else:
if not self.gender:
# multi_modality
from core.predictor.RF.rf_predict import MultiModalRandomForest
mmrf = MultiModalRandomForest(self.data)
else:
print('not finish yet!')
class Train(Process):
def __init__(self,
model_name=None,
feature_name=None,
gender=False,
feature_tables=None):
"""Train model Controller, dispatch the training tasks;
Input:
model_name: certain model depend on papers
feature_name: support for a group of absolute features
feature_tables: support for different feature table, which make it
is possible for us to combine different modality
features freely. But note that the train controller
is not responsible for processing the feature table,
it should be completed by a certain model.
gender: if the model should consider the gender difference
Output:
Result and realted information will be printed by each estimator in logs'
"""
super().__init__() #调用父类PROCESS
self.model_name = model_name
self.feature_name = feature_name
self.feature_tables = feature_tables
self.gender = gender
self.sql_handler = SqlHandler()
self._set_feature() #?
def _set_feature(self):
if self.feature_name is not None:
# you r using feature from a ceratin way!
if self.feature_name == FEATURE_EXP_2:
# if choose exp2 the data will be in pandas's dataframe by defaut
self.data = get_data_by_id(config.tbl_exp2_audio_fea,
self.gender)
self.feature_list = self.sql_handler.get_cloumns_from_table(
config.tbl_exp2_audio_fea)
self.feature_list.remove('ID')
elif self.feature_name == FEATURE_EXP_1:
self.data = get_data_by_id(config.tbl_exp1_fea, self.gender)
self.feature_list = self.sql_handler.get_cloumns_from_table(
config.tbl_exp1_fea)
self.feature_list.remove('ID')
#---------------------baseline----------------------------------
elif self.feature_name == FEATURE_EXP_3_VEDIO: #
self.data = get_data_by_id(config.tbl_exp3_vedio_fea,
self.gender)
self.feature_list = self.sql_handler.get_cloumns_from_table(
config.tbl_exp3_vedio_fea)
self.feature_list.remove('ID')
elif self.feature_name == FEATURE_EXP_3_TEXT: #
self.data = get_data_by_id(config.tbl_exp3_text_fea,
self.gender)
self.feature_list = self.sql_handler.get_cloumns_from_table(
config.tbl_exp3_text_fea)
self.feature_list.remove('ID')
elif self.feature_name == FEATURE_EXP_3_AUDIO: #
self.data = get_data_by_id(config.tbl_exp3_audio_fea,
self.gender)
self.feature_list = self.sql_handler.get_cloumns_from_table(
config.tbl_exp3_audio_fea)
self.feature_list.remove('ID')
elif self.feature_name == FEATURE_EXP_3_HOGPCA: #
self.data = get_data_by_id(config.tbl_exp3_hog_fea,
self.gender)
self.feature_list = self.sql_handler.get_cloumns_from_table(
config.tbl_exp3_hog_fea)
self.feature_list.remove('ID')
#-----------------baseline---------------------------------------------
#-----------------finalmodel------------------------------------
elif self.feature_name == FEATURE_FINAL_COVAREP:
path = 'E:/rnn_models/data/covarep/'
self.data = get_npdata_by_id(path, self.gender)
elif self.feature_name == FEATURE_FINAL_FORMANT:
path = 'E:/rnn_models/data/formant/'
self.data = get_npdata_by_id(path, self.gender)
elif self.feature_name == FEATURE_FINAL_FAUs:
path = 'E:/rnn_models/data/faus/'
self.data = get_npdata_by_id(path, self.gender)
elif self.feature_name == FEATURE_FINAL_GAZE_POSE:
path = 'E:/rnn_models/data/gaze_pose/'
self.data = get_npdata_by_id(path, self.gender)
elif self.feature_name == FEATURE_FINAL_TEXT:
self.data = get_data_by_id(config.tbl_exp3_text_fea,
self.gender)
self.feature_list = self.sql_handler.get_cloumns_from_table(
config.tbl_exp3_text_fea)
self.feature_list.remove('ID')
elif self.feature_name == FEATURE_FINAL_VGG:
path = 'E:/rnn_models/data/ds_vgg/'
self.data = get_npdata_by_id(path, self.gender)
elif self.feature_name == FEATURE_FINAL_ALEXNET:
path = 'E:/rnn_models/data/ds_alexnet/'
self.data = get_npdata_by_id(path, self.gender)
elif self.feature_name == FEATURE_FINAL_GAZE_POSE_BOW:
path = 'E:/rnn_models/data/gaze_pose_bow/'
self.data = get_npdata_by_id(path, self.gender)
elif self.feature_name == FEATURE_FINAL_FAUs_BOW:
path = 'E:/rnn_models/data/faus_bow/'
self.data = get_npdata_by_id(path, self.gender)
elif self.feature_name == FEATURE_FINAL_COVAREP_BOW:
path = 'E:/rnn_models/data/covarep_bow/'
self.data = get_npdata_by_id(path, self.gender)
elif self.feature_name == FEATURE_FINAL_FORMANT_BOW:
path = 'E:/rnn_models/data/formant_bow/'
self.data = get_npdata_by_id(path, self.gender)
elif self.feature_name == FEATURE_FINAL_FUSION:
path = 'E:/data/'
if self.gender:
m_dev = pd.read_csv(path + 'm_pre_dev_scores.csv')
m_dev = m_dev.values
m_dev_label = m_dev[:, 0].reshape(m_dev.shape[0], 1)
m_dev_features = m_dev[:, 1:]
m_test = pd.read_csv(path + 'm_pre_test_scores.csv')
m_test = m_test.values
m_test_label = m_test[:, 0].reshape(m_test.shape[0], 1)
m_test_features = m_test[:, 1:]
f_dev = pd.read_csv(path + 'f_pre_dev_scores.csv')
f_dev = f_dev.values
f_dev_label = f_dev[:, 0].reshape(f_dev.shape[0], 1)
f_dev_features = f_dev[:, 1:]
f_test = pd.read_csv(path + 'f_pre_test_scores.csv')
f_test = f_test.values
f_test_label = f_test[:, 0].reshape(f_test.shape[0], 1)
f_test_features = f_test[:, 1:]
self.data = m_dev_features,m_dev_label,m_test_features,m_test_label,\
f_dev_features,f_dev_label,f_test_features,f_test_label
else:
dev = pd.read_csv(path + 'pre_dev_scores.csv')
dev = dev.values
dev_label = dev[:, 0].reshape(dev.shape[0], 1)
dev_features = dev[:, 1:]
test = pd.read_csv(path + 'pre_test_scores.csv')
test = test.values
test_label = test[:, 0].reshape(test.shape[0], 1)
test_features = test[:, 1:]
self.data = dev_features, dev_label, test_features, test_label
#------------------finalmodel-----------------------------------------------
else:
print('not finished yet')
elif self.feature_tables is not None:
# Now you r using a multi-modality model!
#audio特征暂时只计算一个
#feature_tables 需要与数据库中的表名吻合 不然会报错
self.data = get_data_multi_modality(self.feature_tables,
self.gender)
self.audio_fea, self.vedio_fea, self.text_fea = \
self.sql_handler.get_cloumns_from_table(self.feature_tables)
self.audio_fea.remove('ID')
self.vedio_fea.remove('ID')
self.text_fea.remove('ID')
self.feature_list = {
'audio': self.audio_fea,
'vedio': self.vedio_fea,
'text': self.text_fea
}
else:
print('You must choose a set of features to train!!!')
def _train_eval(self, train, dev, test, model, feature):
model = model(train, dev, test,
features=feature) # rf 和rnn不一样 差了一个test参数 记得改
model.train()
return model.eval()
def run(self):
if self.model_name == MODEL_RF:
from core.predictor.randomForest.rf_predict import RfPredictor
if self.feature_name is not None:
if not self.gender:
train, dev, test = self.data
#运行的时候 需要改一下_train_eval函数 加入一个test参数
score = self._train_eval(train, dev, test, RfPredictor,
self.feature_list)
logger.info(
f'Evalutaion Scores {self.model_name} with {self.feature_name}: {score}'
)
else:
train_m, dev_m, test_m, train_f, dev_f, test_f = self.data
score = self._train_eval(train_m, dev_m, test_m,
RfPredictor, self.feature_list)
logger.info(
f'Evalutaion Scores Male {self.model_name} with {self.feature_name}: {score}'
)
score = self._train_eval(train_f, dev_f, test_f,
RfPredictor, self.feature_list)
logger.info(
f'Evalutaion Scores Female {self.model_name} with {self.feature_name}: {score}'
)
else:
from core.predictor.randomForest.rf_predict import MultiModalRandomForest
if not self.gender:
# multi_modality
mmrf = MultiModalRandomForest(self.data, self.feature_list)
score = mmrf.eval()
logger.info(
f'Evalutaion Scores {self.model_name} with {self.feature_tables}: {score}'
)
else:
data_male = self.data['male']
mmrf = MultiModalRandomForest(data_male, self.feature_list)
score = mmrf.eval()
logger.info(
f'Evalutaion Scores Male {self.model_name} with {self.feature_tables}: {score}'
)
data_female = self.data['female']
mmrf = MultiModalRandomForest(data_female,
self.feature_list)
score = mmrf.eval()
logger.info(
f'Evalutaion Scores Female {self.model_name} with {self.feature_tables}: {score}'
)
elif self.model_name == MODEL_RNN:
if self.feature_name is not None:
from core.predictor.rnn.RNN import RnnPredictor
if self.gender:
m_train_X, m_train_Y, m_dev_X, m_dev_Y, m_test_X, m_test_Y, f_train_X, f_train_Y, f_dev_X, f_dev_Y, f_test_X, f_test_Y = self.data
#m_train,m_dev,m_test = (m_train_X,m_train_Y),(m_dev_X,m_dev_Y),(m_test_X,m_test_Y)
f_train, f_dev, f_test = (f_train_X,
f_train_Y), (f_dev_X,
f_dev_Y), (f_test_X,
f_test_Y)
#score = self._train_eval(m_train,m_dev,m_test,RnnPredictor,'m_'+self.feature_name)
#logger.info(f'Evalutaion Scores male {self.model_name} with {self.feature_name}: {score}')
score = self._train_eval(f_train, f_dev, f_test,
RnnPredictor,
'f_' + self.feature_name)
logger.info(
f'Evalutaion Scores female {self.model_name} with {self.feature_name}: {score}'
)
else:
train_X, train_Y, dev_X, dev_Y, test_X, test_Y = self.data
train, dev, test = (train_X, train_Y), (dev_X,
dev_Y), (test_X,
test_Y)
score = self._train_eval(train, dev, test, RnnPredictor,
self.feature_name)
logger.info(
f'Evalutaion Scores {self.model_name} with {self.feature_name}: {score}'
)
elif self.model_name == MODEL_LINEAR:
from sklearn import linear_model
from sklearn.metrics import mean_squared_error
reg = linear_model.Ridge(alpha=10)
if self.gender:
pass
else:
dev_features, dev_label, test_features, test_label = self.data
reg.fit(dev_features, dev_label)
dev_pre = reg.predict(dev_features)
test_pre = reg.predict(test_features)
dev_rmse = np.sqrt(mean_squared_error(dev_label, dev_pre))
test_rmse = np.sqrt(mean_squared_error(test_label, test_pre))
logger.info(f"dev_rmse: {dev_rmse}; test_rmse: {test_rmse}")
else:
print('not finish yet!')