def test_whole(self):
# Create datatype
datatype = TimeSeries()
# Load observations
observations = lib.load_instanbul_stocks(as_ts=True)
# Transform observations
input_variable = 'ise_lagged'
mapper = DataFrameMapper(
[([input_variable], datatype.default_transformation_pipeline)],
df_out=True)
transformed_df = mapper.fit_transform(observations)
# Create network
input_layer, input_nub = datatype.input_nub_generator(
input_variable, transformed_df)
output_nub = Dense(1)
x = input_nub
x = output_nub(x)
model = Model(input_layer, x)
model.compile(optimizer='adam', loss='mse')
pass
def main():
# Load data
observations = lib.load_instanbul_stocks(as_ts=True)
print('Observation columns: {}'.format(list(observations.columns)))
# Heuristic data transformations
# Train /test split
train_observations, test_observations = train_test_split(observations)
train_observations = train_observations.copy()
test_observations = test_observations.copy()
# List out variable types
timeseries_vars = ['ise_lagged', 'ise.1_lagged', 'sp_lagged', 'dax_lagged']
numerical_vars = ['ise']
# Create and fit Automater
auto = Automater(numerical_vars=numerical_vars, timeseries_vars=timeseries_vars,
response_var='ise')
auto.fit(train_observations)
# Create and fit keras (deep learning) model.
# The auto.transform, auto.input_nub, auto.input_layers, auto.output_nub, and auto.loss are provided by
# keras-pandas, and everything else is core Keras
x = auto.input_nub
x = Dense(16)(x)
x = Dense(16, activation='relu')(x)
x = Dense(16)(x)
x = auto.output_nub(x)
model = Model(inputs=auto.input_layers, outputs=x)
model.compile(optimizer='adam', loss=auto.loss)
train_X, train_y = auto.transform(train_observations)
model.fit(train_X, train_y)
# Inverse transform model output, to get usable results
test_X, test_y = auto.transform(test_observations)
test_y_pred = model.predict(test_X)
test_observations[auto.response_var + '_pred'] = auto.inverse_transform_output(test_y_pred)
print('Predictions: {}'.format(test_observations[auto.response_var + '_pred']))
# TODO Save all results
pass
def test_timeseries_whole(self):
observations = lib.load_instanbul_stocks(as_ts=True)
# Train test split
train_observations, test_observations = train_test_split(observations)
train_observations = train_observations.copy()
test_observations = test_observations.copy()
# Create data type lists
timeseries_vars = ['ise_lagged', 'sp_lagged']
numerical_vars = ['ise']
# Create automater
auto = Automater(numerical_vars=numerical_vars,
timeseries_vars=timeseries_vars,
response_var='ise')
# Fit automater
auto.fit(train_observations)
# Create model
x = auto.input_nub
x = Dense(32)(x)
x = auto.output_nub(x)
model = Model(inputs=auto.input_layers, outputs=x)
model.compile(optimizer='adam', loss=auto.loss)
# Train model
train_X, train_y = auto.transform(train_observations)
print(len(train_X))
print(train_X[0].shape)
model.fit(train_X, train_y)
# TODO Use model to predict
pass
def main():
# List out which components are supplied by Automater
# In this example, we're utilizing X and y generated by the Automater, auto.input_nub, auto.input_layers,
# auto.output_nub, and auto.suggest_loss
save_results = True
# Load data
observations = lib.load_instanbul_stocks(as_ts=True)
print('Observation columns: {}'.format(list(observations.columns)))
# Notice that the lagged variables are an array of values
print('One of the lagged variables: \n{}'.format(
observations['ise_lagged']))
# Train /test split
train_observations, test_observations = train_test_split(observations)
train_observations = train_observations.copy()
test_observations = test_observations.copy()
# List out variable types
data_type_dict = {
'numerical':
['ise', 'ise.1', 'sp', 'dax', 'ftse', 'nikkei', 'bovespa', 'eu', 'em'],
'categorical': [],
'text': [],
'timeseries':
['ise_lagged', 'ise.1_lagged', 'sp_lagged', 'dax_lagged']
}
output_var = 'ise'
# Create and fit Automater
auto = Automater(data_type_dict=data_type_dict, output_var=output_var)
auto.fit(train_observations)
# Transform data
train_X, train_y = auto.fit_transform(train_observations)
test_X, test_y = auto.transform(test_observations)
# Create and fit keras (deep learning) model.
x = auto.input_nub
x = Dense(32)(x)
x = Dense(32)(x)
x = auto.output_nub(x)
model = Model(inputs=auto.input_layers, outputs=x)
model.compile(optimizer='adam', loss=auto.suggest_loss())
model.fit(train_X, train_y)
# Make model predictions and inverse transform model predictions, to get usable results
pred_test_y = model.predict(test_X)
auto.inverse_transform_output(pred_test_y)
# Save all results
if save_results:
temp_dir = lib.get_temp_dir()
model.save(os.path.join(temp_dir, 'model.h5py'))
pickle.dump(train_X, open(os.path.join(temp_dir, 'train_X.pkl'), 'wb'))
pickle.dump(train_y, open(os.path.join(temp_dir, 'train_y.pkl'), 'wb'))
pickle.dump(test_X, open(os.path.join(temp_dir, 'test_X.pkl'), 'wb'))
pickle.dump(test_y, open(os.path.join(temp_dir, 'test_y.pkl'), 'wb'))
pickle.dump(pred_test_y,
open(os.path.join(temp_dir, 'pred_test_y.pkl'), 'wb'))