def run_training() -> None:
"""Train the model."""
_logger.info(f"training the pipeline with version: {_version}")
# read training data
data = load_dataset(file_name=config.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
pipeline.marathon_pipeline.fit(X_train[config.FEATURES], y_train)
_logger.info(f"saving model version: {_version}")
save_pipeline(pipeline_to_persist=pipeline.marathon_pipeline)
print("########################################")
print("Test prediction: ")
test_data = load_dataset(file_name='test.csv')
single_test_json = test_data[0:1].to_json(orient='records')
# When
subject = make_prediction(input_data=single_test_json)
print(subject)
def run_training() -> None:
"""Train the model."""
# read training data
data = load_dataset(file_name=config.TRAINING_DATA_FILE)
# print(data.head())
# 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
pipeline.end_to_end_pipeline.fit(X_train[config.FEATURES], y_train)
pred = pipeline.end_to_end_pipeline.predict(X_test)
# determine mse and rmse
print("test mse: {}".format(int(mean_squared_error(y_test, (pred)))))
print("test rmse: {}".format(
int(np.sqrt(mean_squared_error(y_test, (pred))))))
print("test r2: {}".format(r2_score(y_test, (pred))))
print(pipeline.end_to_end_pipeline.named_steps["Linear_model"].coef_)
_version = "0.0.1"
_logger.info(f"saving model version: {_version}")
save_pipeline(pipeline_to_persist=pipeline.end_to_end_pipeline)
def run_training() :
data = load_dataset(file_name= TRAINING_DATA_FILE)
X_train, X_test , Y_train, Y_test = train_test_split(data[FEATURES],data[TARGET],
test_size = 0.2, random_state=0 )
Y_train = np.log(Y_train)
Y_test = np.log(Y_test)
price_pipe.fit(X_train[FEATURES], Y_train)
_logger.info(f"saving model version:{_version}")
save_pipeline(pipeline_to_save=price_pipe)
def run_training() -> None:
"""Train the model"""
data = load_dataset(file_name=config.TRAINING_DATA_FILE)
# divide train and test
X, y = data[config.FEATURES], data[config.TARGET]
pipeline.fit(X, y)
_logger.info(f"saving model version: {_version}")
save_pipeline(pipeline_to_persist=pipeline)
def run_training() -> None:
"""Train the model"""
#read training data
data = load_dataset(filename=config.TRAINING_DATA_FILE)
#train test split
X_train, X_test, y_train, y_test = train_test_split(
data[config.FEATURES],
data[config.TARGET],
test_size=config.TEST_SIZE,
random_state=config.RANDOM_STATE)
pipeline.energy_pipe.fit(X_train[config.FEATURES], y_train)
_logger.info(f"saving model version: {_version}")
save_pipeline(pipeline_to_save=pipeline.energy_pipe)
def run_training() -> None:
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)
#transforming the target
y_train = np.log(y_train)
y_test = np.log(y_test)
pipeline.price_pipe.fit(X_train[config.FEATURES], y_train)
_logger.info(f"saving model version: {_version}")
save_pipeline(pipeline_to_persist=pipeline.price_pipe)
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
pipeline.price_pipe.fit(X_train[config.FEATURES], y_train)
_logger.info(f'saving model version: {_version}')
save_pipeline(pipeline_to_persist=pipeline.price_pipe)
def run_training() -> None:
"""Train the model."""
# read training data
data = load_dataset(file_name=config.DATASET_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) # we are setting the seed here
pipeline.titanic_pipe.fit(X_train[config.FEATURES], y_train)
_logger.info(f"saving model version: {_version}")
save_pipeline(pipeline_to_persist=pipeline.titanic_pipe)
def run_training() -> None:
"""Train the model."""
# read training data
data = load_dataset(file_name=config.TRAINING_DATA_FILE)
# split data into train and test data
X_train, X_test, y_train, y_test = train_test_split(data[config.FEATURES],
data[config.TARGET],
test_size=0.1,
random_state=0)
# transform the target
y_train = np.log(y_train)
pipeline.price_pipe.fit(X_train[config.FEATURES], y_train)
save_pipeline(pipeline_to_persist=pipeline.price_pipe)
def run_training() -> None:
""" Train the model """
"""Read training data"""
data = load_dataset(file_name = config.TRAINING_DATA_FILE)
"""Divide the dataset into training and testing"""
X_train, X_test, y_train, y_test = train_test_split(data[config.FEATURES], data[config.TARGET], test_size = 0.1, random_state = 0)
"""Transform the target """
y_train = np.log(y_train)
pipeline.price_pipe.fit(X_train[config.FEATURES], y_train)
_logger.info(f"saving model version: {_version}")
save_pipeline(pipeline_to_persist = pipeline.price_pipe)
def run_training() -> None:
"""Train the model."""
# read training data
data = load_dataset(file_name=config.TRAINING_DATA_FILE)
# _data = pd.read_csv(f"{config.DATASET_DIR}/{config.TRAINING_DATA_FILE}")
# print(_data.head())
# divide train and test
X_train, X_test, y_train, y_test = train_test_split(
data[config.FEATURES], data[config.TARGET],
random_state=0) # we are setting the seed here
y_train = y_train.apply(lambda x: 1 if x == "M" else 0)
pipeline.price_pipe.fit(X_train[config.FEATURES], y_train)
_logger.info(f"saving model version: {_version}")
save_pipeline(pipeline_to_persist=pipeline.price_pipe)
def run_training() -> None:
"""Train the model."""
# read training data
data = load_dataset(file_path_name=config.TRAINING_DATA_FILE)
# divide train and test
X_train, X_test, y_train, y_test = train_test_split(
data.iloc[:, :-1], 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)
#print('Data divided into training and test')
pipeline.price_pipe.fit(X_train, y_train)
_logger.info(f"saving model version: {_version}")
save_pipeline(pipeline_to_persist=pipeline.price_pipe)
def run_training() -> None:
"""Entenar el modelo."""
# Lee los datos de entrenamiento
data = load_dataset(file_name=config.TRAINING_DATA_FILE)
# Divide entre set de entrenamiento y prueba
X_train, X_test, y_train, y_test = train_test_split(
data[config.FEATURES],
data[config.TARGET],
test_size=0.1,
random_state=0) # indicamos la semilla!!!
# Transformamos la variable objetivo
y_train = np.log(y_train)
pipeline.price_pipe.fit(X_train[config.FEATURES], y_train)
_logger.info(f"saving model version: {_version}")
save_pipeline(pipeline_to_persist=pipeline.price_pipe)
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=pipeline.price_pipe)
def run_training() -> None:
"""モデルを学習する。"""
# 学習データの読み込み
data = load_dataset(file_name=config.TRAINING_DATA_FILE)
# 学習データとテストデータを分割
X_train, X_test, y_train, y_test = train_test_split(
data[config.FEATURES],
data[config.TARGET],
test_size=0.1,
random_state=0) # ここにシードを設定しています
# ターゲットを変換する
y_train = np.log(y_train)
y_test = np.log(y_test)
pipeline.price_pipe.fit(X_train[config.FEATURES], y_train)
_logger.info(f'saving model version: {_version}')
save_pipeline(pipeline_to_persist=pipeline.price_pipe)
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, use log format because it is expected by the sklearn pipeline
y_train = np.log(y_train)
y_test = np.log(y_test)
pipeline.price_pipe.fit(X_train[config.FEATURES],
y_train)
#after applying fit we save to pipeline
_logger.info(f'saving model version: {_version}')
save_pipeline(pipeline_to_persist=pipeline.price_pipe)
def run_training() -> None:
"""Train the model."""
# read training data
print(f'Current directory: {os.getcwd()}')
print(f"Train path: {config.DATASET_DIR}")
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)
pipeline.price_pipe.fit(X_train[config.FEATURES], y_train)
#joblib.dump(pipeline.price_pipe, config.PIPELINE_NAME)
save_pipeline(pipeline_to_persist=pipeline.price_pipe)
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)
# call fit for all class of pipeline.py
# each class contains __init__, fit with return = self and preprocessing with return X
# this is the right way to make a class for pipeline
pipeline.price_pipe.fit(X_train[config.FEATURES], y_train)
_logger.info(f'saving model version: {_version}')
save_pipeline(pipeline_to_persist=pipeline.price_pipe)
