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# {% include 'template/license_header' %}
from typing import List, Optional
from steps import (
data_loader,
inference_preprocessor,
inference_predict,
)
from zenml import pipeline, ExternalArtifact
from zenml.logger import get_logger
logger = get_logger(__name__)
@pipeline
def inference(
test_size: float = 0.2,
drop_na: Optional[bool] = None,
normalize: Optional[bool] = None,
drop_columns: Optional[List[str]] = None,
):
"""
Model training pipeline.
This is a pipeline that loads the data, processes it and splits
it into train and test sets, then search for best hyperparameters,
trains and evaluates a model.
Args:
test_size: Size of holdout set for training 0.0..1.0
drop_na: If `True` NA values will be removed from dataset
normalize: If `True` dataset will be normalized with MinMaxScaler
drop_columns: List of columns to drop from dataset
"""
### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ###
# Link all the steps together by calling them and passing the output
# of one step as the input of the next step.
random_state = 60
target = "target"
df_inference = data_loader(random_state=random_state, is_inference=True)
df_inference = inference_preprocessor(
dataset_inf=df_inference,
preprocess_pipeline=ExternalArtifact(name="preprocess_pipeline"),
target=target,
)
inference_predict(
dataset_inf=df_inference,
)
### END CODE HERE ###
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