import numpy as np

from pyemits.core.ml.regression.trainer import RegTrainer, RegressionDataModel

X = np.random.randint(1, 100, size=(1000, 10))
y = np.random.randint(1, 100, size=(1000, 1))

raw_data_model = RegressionDataModel(X, y)
trainer = RegTrainer(['XGBoost'], [None], raw_data_model)
trainer.fit()

import numpy as np

from pyemits.core.ml.regression.trainer import RegTrainer, RegressionDataModel
from pyemits.core.ml.regression.nn import KerasWrapper

X = np.random.randint(1, 100, size=(1000, 10, 10))
y = np.random.randint(1, 100, size=(1000, 4))

keras_lstm_model = KerasWrapper.from_simple_lstm_model((10, 10), 4)
raw_data_model = RegressionDataModel(X, y)
trainer = RegTrainer([keras_lstm_model], [None], raw_data_model)
trainer.fit()

import numpy as np

from pyemits.core.ml.regression.trainer import RegTrainer, RegressionDataModel
from pyemits.core.ml.regression.nn import KerasWrapper

X = np.random.randint(1, 100, size=(1000, 10, 10))
y = np.random.randint(1, 100, size=(1000, 4))

from keras.layers import Dense, Dropout, LSTM
from keras import Sequential

model = Sequential()
model.add(LSTM(128,
               activation='softmax',
               input_shape=(10, 10),
               ))
model.add(Dropout(0.1))
model.add(Dense(4))
model.compile(loss='mse', optimizer='adam', metrics=['mse'])

keras_lstm_model = KerasWrapper(model, nickname='LSTM')
raw_data_model = RegressionDataModel(X, y)
trainer = RegTrainer([keras_lstm_model], [None], raw_data_model)
trainer.fit()

import numpy as np

from pyemits.core.ml.regression.trainer import RegTrainer, RegressionDataModel
from pyemits.core.ml.regression.nn import KerasWrapper
from pyemits.common.config_model import KerasSequentialConfig

X = np.random.randint(1, 100, size=(1000, 10, 10))
y = np.random.randint(1, 100, size=(1000, 4))

from keras.layers import Dense, Dropout, LSTM
from keras import Sequential

keras_lstm_model = KerasWrapper(nickname='LSTM')
config = KerasSequentialConfig(layer=[LSTM(128,
                                           activation='softmax',
                                           input_shape=(10, 10),
                                           ),
                                      Dropout(0.1),
                                      Dense(4)],
                               compile=dict(loss='mse', optimizer='adam', metrics=['mse']))

raw_data_model = RegressionDataModel(X, y)
trainer = RegTrainer([keras_lstm_model],
                     [config],
                     raw_data_model, 
                     {'fit_config' : [dict(epochs=10, batch_size=32)]})
trainer.fit()

import numpy as np 

from pyemits.core.ml.regression.trainer import RegressionDataModel, MultiOutputRegTrainer
from pyemits.core.preprocessing.splitting import SlidingWindowSplitter

X = np.random.randint(1, 100, size=(10000, 1))
y = np.random.randint(1, 100, size=(10000, 1))

# when use auto-regressive like MultiOutput, pls set ravel = True
# ravel = False, when you are using LSTM which support multiple dimension
splitter = SlidingWindowSplitter(24,24,ravel=True)
X, y = splitter.split(X, y)

raw_data_model = RegressionDataModel(X,y)
trainer = MultiOutputRegTrainer(['XGBoost'], [None], raw_data_model)
trainer.fit()

import numpy as np 

from pyemits.core.ml.regression.trainer import RegressionDataModel, ParallelRegTrainer

X = np.random.randint(1, 100, size=(10000, 1))
y = np.random.randint(1, 100, size=(10000, 1))

raw_data_model = RegressionDataModel(X,y)
trainer = ParallelRegTrainer(['XGBoost', 'LightGBM'], [None, None], raw_data_model)
trainer.fit()

import numpy as np 

from pyemits.core.ml.regression.trainer import RegressionDataModel,  RegTrainer

X = np.random.randint(1, 100, size=(10000, 1))
y = np.random.randint(1, 100, size=(10000, 1))

raw_data_model = RegressionDataModel(X,y)
trainer = RegTrainer(['XGBoost', 'LightGBM'], [None, None], raw_data_model)
trainer.fit()

import numpy as np 

from pyemits.core.ml.regression.trainer import RegressionDataModel,  KFoldCVTrainer
from pyemits.common.config_model import KFoldConfig

X = np.random.randint(1, 100, size=(10000, 1))
y = np.random.randint(1, 100, size=(10000, 1))

raw_data_model = RegressionDataModel(X,y)
trainer = KFoldCVTrainer(['XGBoost', 'LightGBM'], [None, None], raw_data_model, {'kfold_config':KFoldConfig(n_splits=10)})
trainer.fit()

import numpy as np 
from pyemits.core.ml.regression.trainer import RegressionDataModel,  RegTrainer
from pyemits.core.ml.regression.predictor import RegPredictor

X = np.random.randint(1, 100, size=(10000, 1))
y = np.random.randint(1, 100, size=(10000, 1))

raw_data_model = RegressionDataModel(X,y)
trainer = RegTrainer(['XGBoost', 'LightGBM'], [None, None], raw_data_model)
trainer.fit()

predictor = RegPredictor(trainer.clf_models, 'RegTrainer')
predictor.predict(RegressionDataModel(X))

from pyemits.common.data_model import RegressionDataModel
import numpy as np
X = np.random.randint(1, 100, size=(1000,10,10))
y = np.random.randint(1, 100, size=(1000, 1))

data_model = RegressionDataModel(X, y)

data_model._update_variable('X_shape', (1000,10,10))
data_model.X_shape

data_model.add_meta_data('X_shape', (1000,10,10))
data_model.meta_data