def setUp(self):
self.input_data, self.config_map = read_input(
'Input/historical_data.csv', 'Input/data_config.json')
self.nan_data = self.input_data.head(100)
self.input_data = self.input_data.dropna()
self.input_data = self.input_data.reset_index(drop=True)
self.data = self.input_data.head(5)
def estimator_selection(
estimators=None,
data_path=None,
config_path=None,
Kfolds=10,
n_jobs=-1):
'''
Cross Validator Best Estimator Analysis
'''
if data_path is None or config_path is None or estimators is None:
raise ValueError('Need Estimators, Data path and Config Path as arguments !')
data, config_map = read_input(data_path, config_path)
data = data_preprocessor(data, config_map, 5, 'string')
y = data[config_map['label']]
data.drop(y[config_map['label']], axis=1, inplace=True)
data[config_map["scale_columns"]] = scaling_type(
'min-max').fit_transform(data[config_map["scale_columns"]])
y[config_map["label"]] = scaling_type(
'min-max').fit_transform(y[config_map["label"]])
rmse_errors = []
for estimator in estimators:
mse = np.sum(-cross_val_score(estimator, data, y,
scoring='neg_mean_squared_error', cv=Kfolds, n_jobs=n_jobs))
rmse = np.sqrt(mse)
rmse_errors.append(rmse)
index_of_lowest_error = rmse_errors.index(min(rmse_errors))
return estimators[index_of_lowest_error]
def setUp(self):
self.data, self.config_map = read_input('Input/test_data.csv',
'Input/data_config.json')
self.estimator = linear_model.LinearRegression()
self.data = data_preprocessor(self.data, self.config_map, 5, 'string')
self.X_train, self.y_train, self.X_validation, self.y_validation, self.X_test, self.y_test = split_train_test_validation_data(
self.data, self.config_map, 0.25, 0.2, 5)
self.X_scaler, self.y_scaler, self.model, self.training_rmse = training(
self.estimator, self.X_train, self.y_train, self.config_map)
def run_fractional_stratification_model(
estimator=None,
data_path=None,
config_path=None,
num_iter=1,
seed=None):
'''
Fractional Stratification Model Analysis
'''
if estimator is None or data_path is None or config_path is None:
raise ValueError('Need Estimator, Data path and Config Path as arguments !')
data, config_map = read_input(data_path, config_path)
data = data_preprocessor(data, config_map, 5, 'string')
training_map = {}
for _ in range(0, num_iter):
training_data, validation_data, testing_data = fractional_stratification(
data, data.columns, 4, [0.6, 0.2, 0.2], config_map, seed)
X_train, y_train = split_data(training_data, config_map)
X_validation, y_validation = split_data(validation_data, config_map)
X_test, y_test = split_data(testing_data, config_map)
X_scaler, y_scaler, model, training_rmse = training(
estimator, X_train, y_train, config_map)
validation_rmse = calculate_rmse(
X_validation,
y_validation,
X_scaler,
y_scaler,
model,
config_map)
testing_rmse = calculate_rmse(
X_test, y_test, X_scaler, y_scaler, model, config_map)
if training_rmse < validation_rmse:
model_properties = {}
model_properties['estimator'] = estimator
model_properties['config_map'] = config_map
model_properties['X_train'] = X_train
model_properties['y_train'] = y_train
model_properties['X_validation'] = X_validation
model_properties['y_validation'] = y_validation
model_properties['X_test'] = X_test
model_properties['y_test'] = y_test
model_properties['X_scaler'] = X_scaler
model_properties['y_scaler'] = y_scaler
model_properties['model'] = model
model_properties['training_rmse'] = training_rmse
model_properties['validation_rmse'] = validation_rmse
model_properties['testing_rmse'] = testing_rmse
training_map[validation_rmse] = model_properties
if(len(training_map) > 0):
best_model_properties = training_map[min(training_map)]
print('Best Model train error: {} | Best Model validation error: {} | Best Model test error: {}'.format(
round(best_model_properties['training_rmse'], 7),
round(best_model_properties['validation_rmse'], 7),
round(best_model_properties['testing_rmse'], 7)))
return best_model_properties
return None
def run_train_test_model(
estimator=None,
data_path=None,
config_path=None,
num_iter=1,
seed=None):
'''
Train/test Model Analysis
'''
if estimator is None or data_path is None or config_path is None:
raise ValueError('Need Estimator, Data path and Config Path as arguments !')
data, config_map = read_input(data_path, config_path)
data = data_preprocessor(data, config_map, 5, 'string')
training_map = {}
for _ in range(0, num_iter):
X_train, y_train, X_test, y_test = split_train_test_data(
data, config_map, 0.3, seed)
X_scaler, y_scaler, model, training_rmse = training(
estimator, X_train, y_train, config_map)
testing_rmse = calculate_rmse(
X_test, y_test, X_scaler, y_scaler, model, config_map)
if training_rmse < testing_rmse:
model_properties = {}
model_properties['estimator'] = estimator
model_properties['config_map'] = config_map
model_properties['X_train'] = X_train
model_properties['y_train'] = y_train
model_properties['X_test'] = X_test
model_properties['y_test'] = y_test
model_properties['X_scaler'] = X_scaler
model_properties['y_scaler'] = y_scaler
model_properties['model'] = model
model_properties['training_rmse'] = training_rmse
model_properties['testing_rmse'] = testing_rmse
training_map[testing_rmse] = model_properties
if(len(training_map) > 0):
best_model_properties = training_map[min(training_map)]
print('Best Model train error: {} | Best Model test error: {}'.format(
round(best_model_properties['training_rmse'], 7),
round(best_model_properties['testing_rmse'], 7)))
return best_model_properties
return None