len(data.train_x),
len(data.test_x),
len(data.train_x[0])
],
columns=[
'X_params', 'model_params', 'num_train',
'num_test', 'num_features'
])
hash_X = model_db.find_hash(fname_X)
model_db.store_cur_data([hash_X], columns=['X_hash'])
serial_num = model_db.find_serial_number()
# Store the model with the appended serial_number
fname_model = "pickled_files/models/mlp_regression_" + str(
serial_num) + ".pkl"
mlpr.dump(fname_model)
hash_model = model_db.find_hash(fname_model)
model_db.store_cur_data([hash_model], columns=['model_hash'])
# Store the data that trained the model
data.dump_X(serial_num=serial_num)
data.dump_Y(serial_num=serial_num)
# Store all the data in the data db and dump the db
model_db.store_data(serial_num)
model_db.dump()
# Analyze the model
mlpr_acc = mlpr.evaluate()
print(mlpr_acc)
