from regression_model.config import config
from regression_model.processing.validation import validate_inputs
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from regression_model.config.logging_config import get_logger
>>>>>>> 6162c318b58b225e0061fccd6c64cd67fe205c1b
from regression_model import __version__ as _version
import logging
import typing as t
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_logger = logging.getLogger(__name__)
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_logger = get_logger(logger_name=__name__)
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pipeline_file_name = f'{config.PIPELINE_SAVE_FILE}{_version}.pkl'
_price_pipe = load_pipeline(file_name=pipeline_file_name)
def make_prediction(*, input_data: t.Union[pd.DataFrame, dict],
) -> dict:
"""Make a prediction using a saved model pipeline.
Args:
input_data: Array of model prediction inputs.
Returns:
Predictions for each input row, as well as the model version.
import numpy as np
from sklearn.model_selection import train_test_split
from regression_model import pipeline
from regression_model.processing.data_management import load_dataset, save_pipeline
from regression_model.config import config
from regression_model.config import logging_config
from regression_model import __version__ as _version
_logger = logging_config.get_logger(__name__)
def run_training() -> None:
"""Train the model."""
# read the 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)
pipeline.price_pipe.fit(X_train[config.FEATURES], y_train)
_logger.info(f"saving model version: {_version}")
from regression_model import pipeline
from regression_model.processing.data_management import (load_dataset,
save_pipeline)
from regression_model.config import config
import numpy as np
from sklearn.model_selection import train_test_split
from regression_model import __version__ as _version
from regression_model.config import logging_config
_logger = logging_config.get_logger()
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)