def lstm_fit(self, X, y, full=False):
if full:
cluster_dir = os.path.join(self.model_dir, 'LSTM_best')
else:
cluster_dir = os.path.join(self.model_dir, 'LSTM_combine')
lstm_model = lstm_3d_model(self.static_data, self.rated, cluster_dir)
if lstm_model.istrained == False:
model = lstm_model.train_lstm(X, y)
else:
model = lstm_model.to_dict()
return model
def fit_model(self, cvs, method, static_data, cluster_dir, models, gpu, X_cnn=np.array([]), X_lstm=np.array([]), y=np.array([]), rated=1):
if method == 'ML_RBF_ALL':
model_rbf = rbf_model(static_data['RBF'], rated, cluster_dir)
model_rbf_ols = rbf_ols_module(cluster_dir, rated, static_data['sklearn']['njobs'], GA=False)
model_rbf_ga = rbf_ols_module(cluster_dir, rated, static_data['sklearn']['njobs'], GA=True)
if model_rbf_ols.istrained==False or static_data['train_online']==True:
self.models['RBF_OLS'] = model_rbf_ols.optimize_rbf_TL(cvs, models['RBF_OLS'])
else:
self.models['RBF_OLS'] = model_rbf_ols.to_dict()
if model_rbf_ga.istrained==False or static_data['train_online']==True:
self.models['GA_RBF_OLS'] = model_rbf_ga.optimize_rbf_TL(cvs, models['GA_RBF_OLS'])
else:
self.models['GA_RBF_OLS'] = model_rbf_ga.to_dict()
if model_rbf.istrained==False or static_data['train_online']==True:
self.models['RBFNN'] = model_rbf.rbf_train_TL(cvs, models['RBFNN'], gpu)
else:
self.models['RBFNN'] = model_rbf.to_dict()
elif method == 'ML_RBF_ALL_CNN':
model_rbf = rbf_model(static_data['RBF'], rated, cluster_dir)
model_rbf_ols = rbf_ols_module(cluster_dir, rated, static_data['sklearn']['njobs'], GA=False)
model_rbf_ga = rbf_ols_module(cluster_dir, rated, static_data['sklearn']['njobs'], GA=True)
if model_rbf_ols.istrained == False or static_data['train_online'] == True:
self.models['RBF_OLS'] = model_rbf_ols.optimize_rbf_TL(cvs, models['RBF_OLS'])
else:
self.models['RBF_OLS'] = model_rbf_ols.to_dict()
if model_rbf_ga.istrained == False or static_data['train_online'] == True:
self.models['GA_RBF_OLS'] = model_rbf_ga.optimize_rbf_TL(cvs, models['GA_RBF_OLS'])
else:
self.models['GA_RBF_OLS'] = model_rbf_ga.to_dict()
if model_rbf.istrained == False or static_data['train_online'] == True:
self.models['RBFNN'] = model_rbf.rbf_train_TL(cvs, models['RBFNN'], gpu)
else:
self.models['RBFNN'] = model_rbf.to_dict()
rbf_dir = [model_rbf_ols.cluster_dir, model_rbf_ga.cluster_dir, model_rbf.cluster_dir]
model_cnn = cnn_model(static_data, rated, cluster_dir, rbf_dir)
if model_cnn.istrained==False or static_data['train_online']==True:
self.models['RBF-CNN'] = model_cnn.train_cnn_TL(cvs, models['RBF-CNN'], gpu)
else:
self.models['RBF-CNN'] = model_cnn.to_dict()
elif method == 'ML_NUSVM':
method =method.replace('ML_','')
model_sklearn = sklearn_model_tl(cluster_dir, rated, method, static_data['sklearn']['njobs'])
if model_sklearn.istrained==False or static_data['train_online']==True:
self.models['NUSVM'] = model_sklearn.train(cvs,models['NUSVM']['best_params'])
else:
self.models['NUSVM'] = model_sklearn.to_dict()
elif method == 'ML_MLP':
method = method.replace('ML_','')
model_sklearn = sklearn_model_tl(cluster_dir, rated, method, static_data['sklearn']['njobs'])
if model_sklearn.istrained==False or static_data['train_online']==True:
self.models['MLP'] = model_sklearn.train(cvs, models['MLP']['best_params'])
else:
self.models['MLP'] = model_sklearn.to_dict()
elif method == 'ML_SVM':
method = method.replace('ML_','')
model_sklearn = sklearn_model_tl(cluster_dir, rated, method, static_data['sklearn']['njobs'])
if model_sklearn.istrained==False or static_data['train_online']==True:
self.models['SVM'] = model_sklearn.train(cvs, models['SVM']['best_params'])
else:
self.models['SVM'] = model_sklearn.to_dict()
elif method == 'ML_RF':
method = method.replace('ML_','')
model_sklearn = sklearn_model_tl(cluster_dir, rated, method, static_data['sklearn']['njobs'])
if model_sklearn.istrained==False or static_data['train_online']==True:
self.models['RF'] = model_sklearn.train(cvs, models['RF']['best_params'])
else:
self.models['RF'] = model_sklearn.to_dict()
elif method == 'ML_XGB':
method = method.replace('ML_','')
model_sklearn = sklearn_model_tl(cluster_dir, rated, method, static_data['sklearn']['njobs'])
if model_sklearn.istrained==False or static_data['train_online']==True:
self.models['XGB'] = model_sklearn.train(cvs, models['XGB']['best_params'])
else:
self.models['XGB'] = model_sklearn.to_dict()
elif method == 'ML_CNN_3d':
cnn_model_3d = cnn_3d_model(static_data, rated, cluster_dir)
if cnn_model_3d.istrained == False or static_data['train_online'] == True:
self.models['CNN_3d'] = cnn_model_3d.train_cnn_TL(X_cnn, y, models['CNN_3d'], gpu)
else:
self.models['CNN_3d'] = cnn_model_3d.to_dict()
elif method == 'ML_LSTM_3d':
lstm_model_3d = lstm_3d_model(static_data, rated, cluster_dir)
if lstm_model_3d.istrained == False or static_data['train_online'] == True:
self.models['LSTM_3d'] = lstm_model_3d.train_lstm_TL(X_lstm, y, models['LSTM_3d'], gpu)
else:
self.models['LSTM_3d'] = lstm_model_3d.to_dict()
self.save(self.cluster_dir)
def fit_model(self,
cvs,
method,
static_data,
cluster_dir,
optimize_method,
X_cnn=np.array([]),
X_lstm=np.array([]),
y=np.array([]),
rated=1):
# deap, optuna, skopt, grid_search
if optimize_method == 'deap':
from Fuzzy_clustering.ver_tf2.Sklearn_models_deap import sklearn_model
elif optimize_method == 'optuna':
from Fuzzy_clustering.ver_tf2.Sklearn_models_optuna import sklearn_model
elif optimize_method == 'skopt':
from Fuzzy_clustering.ver_tf2.Sklearn_models_skopt import sklearn_model
else:
from Fuzzy_clustering.ver_tf2.SKlearn_models import sklearn_model
# if (datetime.now().hour>=8 and datetime.now().hour<10):
# time.sleep(2*60*60)
if method == 'ML_RBF_ALL':
model_rbf = rbf_model(static_data['RBF'], rated, cluster_dir)
model_rbf_ols = rbf_ols_module(cluster_dir,
rated,
static_data['sklearn']['njobs'],
GA=False)
model_rbf_ga = rbf_ols_module(cluster_dir,
rated,
static_data['sklearn']['njobs'],
GA=True)
if model_rbf_ols.istrained == False or static_data[
'train_online'] == True:
self.logger.info('Start of training of model_rbf_ols')
self.models['RBF_OLS'] = model_rbf_ols.optimize_rbf(cvs)
else:
self.models['RBF_OLS'] = model_rbf_ols.to_dict()
if model_rbf_ga.istrained == False or static_data[
'train_online'] == True:
self.logger.info('Start of training of model_rbf_ga')
self.models['GA_RBF_OLS'] = model_rbf_ga.optimize_rbf(cvs)
else:
self.models['GA_RBF_OLS'] = model_rbf_ga.to_dict()
if model_rbf.istrained == False or static_data[
'train_online'] == True:
self.logger.info('Start of training of model_rbf_adam')
self.models['RBFNN'] = model_rbf.rbf_train(cvs)
else:
self.models['RBFNN'] = model_rbf.to_dict()
elif method == 'ML_RBF_ALL_CNN':
model_rbf = rbf_model(static_data['RBF'], rated, cluster_dir)
model_rbf_ols = rbf_ols_module(cluster_dir,
rated,
static_data['sklearn']['njobs'],
GA=False)
model_rbf_ga = rbf_ols_module(cluster_dir,
rated,
static_data['sklearn']['njobs'],
GA=True)
if model_rbf_ols.istrained == False or static_data[
'train_online'] == True:
self.logger.info('Start of training of model_rbf_ols')
self.models['RBF_OLS'] = model_rbf_ols.optimize_rbf(cvs)
else:
self.models['RBF_OLS'] = model_rbf_ols.to_dict()
if model_rbf_ga.istrained == False or static_data[
'train_online'] == True:
self.logger.info('Start of training of model_rbf_ga')
self.models['GA_RBF_OLS'] = model_rbf_ga.optimize_rbf(cvs)
else:
self.models['GA_RBF_OLS'] = model_rbf_ga.to_dict()
if model_rbf.istrained == False or static_data[
'train_online'] == True:
self.logger.info('Start of training of model_rbf_adam')
self.models['RBFNN'] = model_rbf.rbf_train(cvs)
else:
self.models['RBFNN'] = model_rbf.to_dict()
rbf_dir = [
model_rbf_ols.cluster_dir, model_rbf_ga.cluster_dir,
model_rbf.cluster_dir
]
model_cnn = cnn_model(static_data, rated, cluster_dir, rbf_dir)
if model_cnn.istrained == False or static_data[
'train_online'] == True:
self.logger.info('Start of training of model_cnn')
self.models['RBF-CNN'] = model_cnn.train_cnn(cvs)
else:
self.models['RBF-CNN'] = model_cnn.to_dict()
elif method == 'ML_NUSVM':
method = method.replace('ML_', '')
model_sklearn = sklearn_model(cluster_dir, rated, method,
static_data['sklearn']['njobs'])
if model_sklearn.istrained == False or static_data[
'train_online'] == True:
self.logger.info('Start of training of NUSVM')
self.models['NUSVM'] = model_sklearn.train(cvs)
else:
self.models['NUSVM'] = model_sklearn.to_dict()
elif method == 'ML_MLP':
method = method.replace('ML_', '')
model_sklearn = sklearn_model(cluster_dir, rated, method,
static_data['sklearn']['njobs'])
if model_sklearn.istrained == False or static_data[
'train_online'] == True:
self.logger.info('Start of training of MLP')
self.models['MLP'] = model_sklearn.train(cvs)
else:
self.models['MLP'] = model_sklearn.to_dict()
elif method == 'ML_SVM':
method = method.replace('ML_', '')
model_sklearn = sklearn_model(cluster_dir, rated, method,
static_data['sklearn']['njobs'])
if model_sklearn.istrained == False or static_data[
'train_online'] == True:
self.logger.info('Start of training of SVM')
self.models['SVM'] = model_sklearn.train(cvs)
else:
self.models['SVM'] = model_sklearn.to_dict()
elif method == 'ML_RF':
method = method.replace('ML_', '')
model_sklearn = sklearn_model(cluster_dir, rated, method,
static_data['sklearn']['njobs'])
if model_sklearn.istrained == False or static_data[
'train_online'] == True:
self.logger.info('Start of training of RF')
self.models['RF'] = model_sklearn.train(cvs)
else:
self.models['RF'] = model_sklearn.to_dict()
elif method == 'ML_XGB':
method = method.replace('ML_', '')
model_sklearn = sklearn_model(cluster_dir, rated, method,
static_data['sklearn']['njobs'])
if model_sklearn.istrained == False or static_data[
'train_online'] == True:
self.logger.info('Start of training of XGB')
self.models['XGB'] = model_sklearn.train(cvs)
else:
self.models['XGB'] = model_sklearn.to_dict()
elif method == 'ML_CNN_3d':
cnn_model_3d = cnn_3d_model(static_data, rated, cluster_dir)
if cnn_model_3d.istrained == False or static_data[
'train_online'] == True:
self.logger.info('Start of training of CNN_3d')
self.models['CNN_3d'] = cnn_model_3d.train_cnn(X_cnn, y)
else:
self.models['CNN_3d'] = cnn_model_3d.to_dict()
elif method == 'ML_LSTM_3d':
lstm_model_3d = lstm_3d_model(static_data, rated, cluster_dir)
if lstm_model_3d.istrained == False or static_data[
'train_online'] == True:
self.logger.info('Start of training of LSTM_3d')
self.models['LSTM_3d'] = lstm_model_3d.train_lstm(X_lstm, y)
else:
self.models['LSTM_3d'] = lstm_model_3d.to_dict()
self.save(self.cluster_dir)