def main(X, Y, Params, print_info=False, is_regression=True, Y_other=None):
parameters = Params['Algorithm'][1]
is_cv_run = False
starttime = time.time()
if print_info:
print('Fitting model \'%s\' for %s' %
(Params['Algorithm'][0],
'regression' if is_regression else 'classification'))
if Params['Algorithm'][0] == 'BayesianRidge':
if not is_regression:
model = BayesianRidge(n_iter=300,
tol=0.001,
compute_score=False,
fit_intercept=True,
normalize=False,
copy_X=True,
verbose=False,
**parameters)
#parameters = {'alpha_1': [1e-6,1e-5,1e-4],'alpha_2': [1e-6,1e-5,1e-4], 'lambda_1': [1e-6,1e-5,1e-4], 'lambda_2': [1e-6,1e-5,1e-4]}
else:
model = BayesianRidge(n_iter=300,
tol=0.001,
compute_score=False,
fit_intercept=True,
normalize=False,
copy_X=True,
verbose=False,
**parameters)
elif Params['Algorithm'][0] == 'StringKernel':
if not is_regression:
raise (Exception('not implemented'))
else:
# we create an instance of SVM and fit out data.
#
# model = KernelRidge(alpha=parameters['alpha'], kernel='precomputed')
model = SVR(kernel='precomputed',
gamma='auto',
coef0=0.0,
shrinking=True,
tol=0.001,
cache_size=400,
verbose=False,
max_iter=-1)
param_grid = {
'C': np.logspace(np.log10(0.0001), np.log10(500), 25)
}
model = NuSVR(
kernel='precomputed'
) #cache_size=400, coef0=0.0, gamma='auto', max_iter=-1, shrinking=True, tol=0.001, verbose=False,**parameters)
param_grid = {'nu': (0.50, )}
model = GridSearchCV(model,
param_grid,
n_jobs=1,
iid=True,
refit=True,
cv=7,
verbose=0,
scoring=neg_mean_squared_error_scorer)
is_cv_run = True
elif Params['Algorithm'][0] == 'XGBoost':
# max_depth = 3, learning_rate = 0.1, n_estimators = 100, silent = True, objective = 'reg:linear',
# booster = 'gbtree', n_jobs = 1, nthread = None, gamma = 0, min_child_weight = 1,
# max_delta_step = 0, subsample = 1, colsample_bytree = 1, colsample_bylevel = 1, reg_alpha = 0,
# reg_lambda = 1, scale_pos_weight = 1, base_score = 0.5, random_state = 0, seed = None,
# missing = None
if not is_regression:
model = xgboost.XGBClassifier(
missing=None,
silent=True,
learning_rate=0.10,
objective='rank:pairwise',
booster='gbtree',
n_jobs=1,
max_delta_step=0,
colsample_bylevel=1,
scale_pos_weight=1,
base_score=0.5,
random_state=666,
colsample_bytree=0.75, # default 1
subsample=0.75,
gamma=0,
reg_alpha=0.01, # default 0
min_child_weight=6,
**parameters)
else:
# model=xgboost.XGBRegressor(missing=None, silent=True,
# learning_rate=0.10,
# objective='reg:linear',#'rank:pairwise' booster='gbtree'
# n_jobs=1,
# booster='gbtree',
# max_delta_step=0,
# colsample_bylevel=1,
# scale_pos_weight=1,
# base_score=0.5,
# random_state=666,
# colsample_bytree=0.75, # default 1
# subsample=0.75,
# gamma=0,
# reg_alpha=0.01, # default 0
# reg_lambda=1.0,
# min_child_weight=6,
# **parameters)
model = xgboost.XGBRegressor(
missing=None,
silent=True,
learning_rate=0.10,
objective='reg:linear', #'rank:pairwise' booster='gbtree'
n_jobs=1,
booster='gbtree',
random_state=666,
**parameters)
param_grid = {
'colsample_bytree': (0.75, 1.0),
'subsample': (0.75, 1.0),
'min_child_weight': (3, 6, 9),
'reg_lambda': (0.80, 1.0, 1.20),
'reg_alpha': (0.001, 0.01)
}
model = GridSearchCV(model,
param_grid,
n_jobs=1,
iid=True,
refit=True,
cv=7,
verbose=0,
scoring=neg_mean_squared_error_scorer)
is_cv_run = True
elif Params['Algorithm'][0] == "Keras_ElasticNet":
#use_keras_CPU()
if not is_regression:
raise (Exception('ElasticNet is only for regression!'))
else:
param_grid = {
'l1_ratio': (Params['Algorithm'][1]['l1_ratio'], ),
'alpha': np.logspace(-3, 1, 15)
}
model = GridSearchCV(KerasENet(),
param_grid,
n_jobs=1,
iid=True,
refit=True,
cv=5,
verbose=0,
scoring=neg_mean_squared_error_scorer)
# first_output = Dense(1,activation='sigmoid')(first_output)
is_cv_run = True
elif Params['Algorithm'][0] == "Ridge":
if not is_regression:
raise (Exception('Ridge is only for regression!'))
else:
model = RidgeCV(alphas=np.logspace(-1, np.log10(700),
parameters['n_alphas']),
fit_intercept=True,
normalize=False,
scoring=None,
cv=8,
gcv_mode=None,
store_cv_values=False)
elif Params['Algorithm'][0] == "ElasticNet":
tol = 0.0001
selection = 'cyclic'
n_alphas = 90
max_iter = 1300
if X.shape[1] > 4000:
tol = 0.001
selection = 'random'
n_alphas = 60
max_iter = 1000
if not is_regression:
raise (Exception('ElasticNet is only for regression!'))
else:
if Params['is_multitarget']:
model = MultiTaskElasticNetCV(eps=0.001,
alphas=None,
fit_intercept=True,
normalize=False,
max_iter=max_iter,
tol=tol,
cv=7,
copy_X=True,
verbose=0,
n_alphas=n_alphas,
n_jobs=1,
random_state=666,
selection=selection,
**parameters)
else:
model = ElasticNetCV(eps=0.001,
alphas=None,
fit_intercept=True,
normalize=False,
max_iter=max_iter,
tol=tol,
cv=7,
copy_X=True,
verbose=0,
n_alphas=n_alphas,
n_jobs=1,
random_state=666,
selection=selection,
**parameters)
elif Params['Algorithm'][0] == "RandomForest":
if not is_regression:
raise (Exception('not set up (lazy)'))
else:
model = RandomForestRegressor(criterion='mse',
min_samples_leaf=1,
min_weight_fraction_leaf=0.0,
max_leaf_nodes=None,
min_impurity_decrease=0.0,
min_impurity_split=None,
bootstrap=True,
oob_score=False,
n_jobs=1,
random_state=None,
verbose=0,
warm_start=False,
**parameters)
param_grid = {
'max_features': ('auto', 'sqrt'),
'min_samples_split': (
2,
4,
),
}
model = GridSearchCV(model,
param_grid,
n_jobs=1,
iid=True,
refit=True,
cv=7,
verbose=0,
scoring=neg_mean_squared_error_scorer)
is_cv_run = True
elif Params['Algorithm'][0] == 'SVM':
# 0.001, 0.005, 0.01, 0.05, 0.1, 0.5,1.0,1.5,2.0,3.0,4.0,5.0,10.0
if not is_regression:
model = SVC(cache_size=400,
coef0=0.0,
gamma='auto',
max_iter=-1,
shrinking=True,
tol=0.001,
verbose=False,
**parameters)
#parameters = {'reg__C':[0.5],'reg__epsilon':[0.1]}
else:
model = SVR(cache_size=400,
coef0=0.0,
gamma='auto',
max_iter=-1,
shrinking=True,
tol=0.001,
verbose=False,
**parameters)
param_grid = {'C': np.logspace(np.log10(0.0005), np.log10(10), 30)}
#param_grid = {'nu':(0.1,0.3,0.5,0.7,0.9)}
model = GridSearchCV(model,
param_grid,
n_jobs=1,
iid=True,
refit=True,
cv=8,
verbose=0,
scoring=neg_mean_squared_error_scorer)
is_cv_run = True
elif Params['Algorithm'][0] == 'GradientBoosting':
if not is_regression:
model = GradientBoostingClassifier(random_state=1, **parameters)
#parameters = {'reg__n_estimators': [140], 'reg__max_depth': [6],'learning_rate':[0.01,0.03,0.1],'min_samples_leaf':[2,3,4]}
else:
model = GradientBoostingRegressor(random_state=1, **parameters)
#parameters = {'reg__n_estimators': [140], 'reg__max_depth': [6]}
elif Params['Algorithm'][0] == 'MLP':
#parameters['hidden_layer_sizes']=[parameters['hidden_layer_sizes']]
#model = MLPRegressorCV(hidden_layer_sizes=parameters['hidden_layer_sizes'])
model = MLPRegressor(
activation="relu",
solver="lbfgs",
learning_rate="constant",
learning_rate_init=0.0011,
max_iter=450,
random_state=None,
tol=0.00013,
epsilon=1e-08,
hidden_layer_sizes=parameters['hidden_layer_sizes'])
param_grid = {'alpha': np.logspace(0, np.log10(350), 20)}
model = GridSearchCV(model,
param_grid,
n_jobs=1,
iid=True,
refit=True,
cv=7,
verbose=0,
scoring=neg_mean_squared_error_scorer)
is_cv_run = True
#model = MLPRegressor(activation="relu", solver ="lbfgs",learning_rate ="constant",
# learning_rate_init = 0.001, power_t = 0.5, max_iter = 500, shuffle = True, random_state = None,
# tol = 0.0001, verbose = False, warm_start = False, momentum = 0.9, epsilon = 1e-08,**parameters)
elif Params['Algorithm'][0] == 'MLP_KERAS':
from keras.models import Sequential
from keras import regularizers
from keras.layers import Dense, Dropout
from keras.callbacks import EarlyStopping
from sklearn.preprocessing import LabelEncoder
from keras.utils import np_utils
import tensorflow as tf
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
session = tf.Session(config=config)
early_stopping = EarlyStopping(monitor='val_loss', patience=5)
model = Sequential()
model.add(
Dense(
parameters['layers_and_nodes'][0],
activation='tanh',
input_shape=(X.shape[1], ),
kernel_initializer='glorot_uniform',
kernel_regularizer=regularizers.l2(
parameters['l2_regularization']),
))
model.add(Dropout(parameters['dropout'], noise_shape=None, seed=1))
for layer in range(1, len(parameters['layers_and_nodes'])):
model.add(
Dense(parameters['layers_and_nodes'][layer],
activation='relu',
input_shape=(parameters['layers_and_nodes'][layer -
1], ),
kernel_initializer='glorot_normal',
kernel_regularizer=regularizers.l2(
parameters['l2_regularization'])))
model.add(Dropout(parameters['dropout'], noise_shape=None, seed=1))
if not is_regression:
model.add(
Dense(1,
activation='softmax',
input_shape=(parameters['nodes'][-1], )))
model.compile(loss='categorical_crossentropy',
optimizer='rmsprop',
metrics=['f1'])
encoder = LabelEncoder()
encoder.fit(Y)
encoded_Y = encoder.transform(Y)
# convert integers to dummy variables (i.e. one hot encoded)
Y = np_utils.to_categorical(encoded_Y)
else:
model.add(
Dense(1,
activation='linear',
input_shape=(parameters['layers_and_nodes'][-1], )))
model.compile(loss='mean_squared_error',
optimizer='adam',
metrics=['mse'])
model.fit(X,
Y,
batch_size=X.shape[0],
epochs=100,
validation_split=0,
verbose=0) #,callbacks=[early_stopping])
return model
else:
raise (Exception('unknown model'))
#decomposer = LatentDirichletAllocation(n_topics=10, max_iter=10,learning_method='online',learning_offset=50.,random_state=1)
#decomposer = TruncatedSVD(n_components=100,random_state=666)
"""
X = data.iloc[:]['text'].values
y = data.iloc[:]['mylabel'].values.astype(str)
dat = vect.fit_transform(X)
dat = tfidf.fit_transform(dat)
dat = decomposer.fit_transform(dat)
for a in numpy.unique(y):
plt.scatter(dat[y==a,0],dat[y==a,1])
"""
"""
START LOOP
"""
#t0 = time()
# if get_set_count(parameters)>1:
# grid_search = GridSearchCV(model, parameters, n_jobs=6,verbose=1,cv=10,refit=True)
# grid_search.fit(X=X,y=Y)
# best_parameters = grid_search.best_estimator_.get_params()
# print('--> best parameters: %s' % best_parameters)
# return grid_search
# else:
if 1:
start_time = time.time()
print('... training model (X.shape=%s)' % str(X.shape), end='')
warnings.filterwarnings("ignore")
if Y_other is not None and Params['is_multitarget']:
Y = np.expand_dims(Y, axis=1)
model.fit(X=X, y=np.concatenate((Y, Y_other), axis=1))
else:
Y = Y.flatten()
model.fit(X=X, y=Y)
if is_cv_run:
print(' [best gridsearch params: %s] ' % model.best_params_, end='')
if 1:
end_time = time.time()
print(' ... done (%1.1f min)' % ((end_time - start_time) / 60.0))
#elapsedtime = (time.time() - starttime) / 60.0
#print('fit done (took %f minutes)' % elapsedtime)
return model
