def run_training():
"""Train the model"""
#Read Data
data = load_dataset()
#Training pipeline
pipeline.fake_news_pipeline.fit(data["traducción"].values,
data["fake"].values)
save_pipeline(pipeline_to_save=pipeline.fake_news_pipeline)
def test_make_single_prediction():
# Given
test_data = load_dataset(file_name='test.csv')
single_test_input = test_data[0:1]
# When
subject = make_prediction(input_data=single_test_input)
# Then
assert subject is not None
assert isinstance(subject.get('predictions')[0], float)
assert math.ceil(subject.get('predictions')[0]) == 112476
def run_training():
"""Train the model."""
# read in data
data = load_dataset(file_name=config.TRAINING_DATA_FILE)
# clean the target variable
data = clean_target_variable(data)
# split training & validation data
X_train, X_val, y_train, y_val = split_dataset(data)
print('...fitting model')
pipeline.interest_rate_pipe.fit(X_train[config.FEATURES], y_train)
_logger.info(f"...saving model version: {_version}")
save_pipeline(pipeline.interest_rate_pipe)
def test_make_multiple_predictions():
# Given
test_data = load_dataset(file_name='test.csv')
original_data_length = len(test_data)
multiple_test_input = test_data
# When
subject = make_prediction(input_data=multiple_test_input)
# Then
assert subject is not None
assert len(subject.get('predictions')) == 1451
# We expect some rows to be filtered out
assert len(subject.get('predictions')) != original_data_length
def run_training() -> None:
"""Train the model."""
# read training data
data = load_dataset(file_name=config.TRAINING_DATA_FILE)
# transform the target
data[config.TARGET] = data[config.PRE_TARGET].map({
'Fully Paid': 1,
'Charged Off': 0
})
data = data.drop(config.PRE_TARGET, axis=1)
# divide train and test, set random_state
X_train, X_test, y_train, y_test = train_test_split(data[config.FEATURES],
data[config.TARGET],
test_size=0.1,
random_state=0)
# Set MLFlow experiment
mlflow.set_experiment(config.EXPERIMENT_NAME)
with mlflow.start_run():
loan_pipe.fit(X_train[config.FEATURES], y_train)
# Log dataset
dataset_full_path_name = dataset_location()
mlflow.set_tag("dataset", dataset_full_path_name)
# Log parameters
mlflow.log_param("penalty", model_config.PENALTY)
mlflow.log_param("dual", model_config.DUAL)
mlflow.log_param("C", model_config.C)
mlflow.log_param("fit_intercept", model_config.FIT_INTERCEPT)
mlflow.log_param("random_state", model_config.RANDOM_STATE)
mlflow.log_param("class_weight", model_config.CLASS_WEIGHT)
mlflow.log_param("max_iter", model_config.MAX_ITER)
mlflow.log_param("multi_class", model_config.MULTI_CLASS)
# Log the metrics
score = loan_pipe.score(X_train, y_train)
mlflow.log_metric("score", score)
# Save the sklearn pipeline as mlflow model
conda_env = mlflow.sklearn.get_default_conda_env()
mlflow.sklearn.log_model(loan_pipe,
"sklearn_pipeline",
conda_env=conda_env)
def run_training() -> None:
"""Train the model."""
# read training data
data = load_dataset(file_name=config.TRAINING_DATA_FILE)
# divide train and test
X_train, X_test, y_train, y_test = train_test_split(
data[config.FEATURES],
data[config.TARGET],
test_size=0.1,
random_state=0) # we are setting the seed here
# transform the target
y_train = np.log(y_train)
y_test = np.log(y_test)
pipeline.price_pipe.fit(X_train[config.FEATURES], y_train)
save_pipeline(pipeline_to_persist=pipeline.price_pipe)