predict_pipeline.py

import sys
import logging
import click
import pandas as pd
import numpy as np
from sklearn.pipeline import Pipeline
from enities.predict_pipeline_params import read_predict_pipeline_params
from data.make_dataset import read_data, save_data
from model_gen.model_fit import open_model
from model_gen.model_predict import predict_model

from custom_logs.log_decorator import log

logger = logging.getLogger(__name__)
handler = logging.StreamHandler(sys.stdout)
logger.setLevel(logging.INFO)
logger.addHandler(handler)

@log
def predict_pipeline(config_path: str):
    predict_params = read_predict_pipeline_params(config_path)
    logger.info(f"start {__name__} with params{predict_params}")
    features = read_data(predict_params.test_set_path)
    logger.info(f"data.shape is {features.shape}")
    model  = open_model(predict_params.model_path)
    predicts = predict_model(model, features)
    logger.info(f"predicted shape is {predicts.shape}")
    save_data(predicts, predict_params.predict_path)
    return 

@click.command(name="predict_pipeline")
@click.argument("config_path")
def predict_pipeline_command(config_path: str):
    predict_pipeline(config_path)

if __name__ == "__main__":
    predict_pipeline_command()