def run_training():
"""Train the model."""
# read training data
data = pd.read_csv(config.TRAINING_DATA_FILE)
# divide train and test
X_train, X_test, y_train, y_test = train_test_split(data.drop(config.TARGET, axis = 1), data[config.TARGET], test_size = 0.2,
random_state = 0)
# fit pipeline
titanic_pipe.fit(X_train, y_train)
# save pipeline
joblib.dump(titanic_pipe, config.PIPELINE_NAME)
def run_training():
"""Train the model."""
data = pd.read_csv(config.TRAINING_DATA_FILE)
training_data, testing_data, training_target, testing_target = train_test_split(
data.drop(config.TARGET, axis=1),
data[config.TARGET],
test_size=0.2,
random_state=0) # we are setting the seed here
titanic_pipe.fit(training_data, training_target)
joblib.dump(titanic_pipe, config.PIPELINE_NAME)
def run_training():
"""Train the model."""
# read training data
data = pd.read_csv(config.TRAINING_DATA_FILE)
# divide train and test
X_train, X_test, y_train, y_test = train_test_split(
data.drop('survived', axis=1), # predictors
data['survived'], # target
test_size=0.2, # percentage of obs in test set
random_state=0) # seed to ensure reproducibility
# fit pipeline
titanic_pipe.fit(X_train, y_train)
# save pipeline
joblib.dump(titanic_pipe, config.PIPELINE_NAME)
def run_training():
"""Train the model."""
# read training data
X = pd.read_csv(config.TRAINING_DATA_FILE)
# divide train and test
features = config.NUMERICAL_VARS + config.CATEGORICAL_VARS
X_train, X_test, y_train, y_test = train_test_split(X[features],
X[config.TARGET],
test_size=0.2,
random_state=0)
# fit pipeline
titanic_pipe.fit(X_train[features], y_train)
# save pipeline
joblib.dump(titanic_pipe, config.PIPELINE_NAME)
def run_training():
"""Train the model."""
# read training data
data = pd.read_csv(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
# fit pipeline
titanic_pipe.fit(X_train[config.FEATURES], y_train)
# save pipeline
joblib.dump(titanic_pipe, config.PIPELINE_NAME)
def run_training() -> None:
"""Train the model."""
# read training data
data = pd.read_csv(config.CLEANED_DATA)
# divide train and test
X_train, X_test, y_train, y_test = train_test_split(
data.drop(config.TARGET, axis=1),
data[config.TARGET],
test_size=0.2,
random_state=config.SEED) # we are setting the seed here
# Fit ML pipeline
titanic_pipe.fit(X_train, y_train)
logger.info("**Training ML Pipeline started")
# save pipeline
save_pipeline(pipeline_to_persist=titanic_pipe)
logger.info("**Saved trained ML pipeline")
def run_training():
"""Train the model."""
# read training data
config = ut.read_config_file('config.yaml')
path = config[0]['Paths'].get('directory')
filename = config[0]['Paths'].get('data_filename')
extension = config[0]['Paths'].get('data_extension')
cols = config[2]['Feature_Groups'].get('data_columns')
target = config[2]['Feature_Groups'].get('target')
data = ut.load_data(path=path,
filename=filename,
extension=extension,
cols=cols)
# divide train and test
data[target] = data[target].astype(int)
X_train, X_test, y_train, y_test = train_test_split(data.drop(target,
axis=1),
data[target],
test_size=0.2,
random_state=0)
# fit pipeline
titanic_pipe.fit(X_train, y_train)
# save pipeline
joblib.dump(titanic_pipe, config[0]['Paths'].get('output_model_path'))
