def train():
'''
Train the model, need train.h5 -> run prepare_data.py
before training module execution
'''
pd.main()
srcnn_model = model()
data, label = pd.read_training_data("./model/train.h5")
# srcnn_model.load_weights("m_model_adam.h5")
srcnn_model.fit(data, label, batch_size=128, epochs=30)
srcnn_model.save_weights("./model/srcnn_model.h5")
def main(input_filepath, output_filepath):
""" Runs data processing scripts to turn raw data from (../raw) into
cleaned data ready to be analyzed (saved in ../processed).
"""
machine_name = "PerschmannHermleC32USpindle"
raw_dir_path = utils.get_raw_dir_path(project_dir)
interim_dir_path = utils.get_interim_dir_path(project_dir)
processed_dir_path = utils.get_processed_dir_path(project_dir)
# load relevant data collectors after selection
collector_load, collector_speed, metadata = prepare_data.main(
project_dir, machine_name
)
X_load = utils.to_same_length_time_series_dataset(collector_load)
X_speed = utils.to_same_length_time_series_dataset(collector_speed)
# get labels
labels = prepare_labels.create_labels_from_metadata(metadata)
"""TO DO: transform labels into categories, then one hot encode"""
# compute autocorrelation
X_load_autocorr = process_dataset.to_autocorrelation_dataset(X_load)
X_speed_autocorr = process_dataset.to_autocorrelation_dataset(X_speed)
#
print("LENGTH LABEL VECTOR {}".format(len(labels)))
print("project_dir: {}".format(project_dir))
print("FINISHED")
logger = logging.getLogger(__name__)
logger.info("making final data set from raw data")
def main():
train_X,train_y,test_X,test_y=prepare_data.main()
train,train_X,train_y,test_X,test_y=prepare_logistic.main(train_X,train_y,test_X,test_y)
aucs=x_validation.main(train)
def main():
prepare_data.main()
nn_classifier.main()
def write_records(records, table_name):
insert_header = f'''
insert into {table_name}
values
'''
for batch in chunker(records, 1000):
insert_values = ','.join(batch)
insert_statement = insert_header + insert_values
mssql_cursor.execute(insert_statement)
mssql_conn.commit()
def main():
print('DATA LOADING')
empty_import_tables()
read_import_interviews()
read_import_funnel()
read_import_fc()
read_import_open()
print('Data loading complete', end='\n\n')
if __name__ == '__main__':
import initialize
import prepare_data
initialize.main()
prepare_data.main()
from timeit import timeit
print(timeit(main,number=1))
def main():
prepare_data.main()
nn_classifier.main()