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demo_main.py
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demo_main.py
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from backorder.utils import read_yaml, write_yaml
from backorder.ml.model.esitmator import TargetValueMapping
import importlib
from backorder.pipeline.training_pipeline import TrainingPipeline
from backorder.pipeline.prediciton_pipeline import PredictionPipeline
import pandas as pd
from backorder.logger import logging
# training_pipeline = TrainingPipeline()
# prediction_pipeline = PredictionPipeline()
# # model_config = read_yaml("config\model.yaml")
# # print(type(model_config["model_selection"]))
# # data_validation = DataValidation()
# # dic1 = {"numerical_features":{'sales_6_month': 'float64',
# # 'sales_9_month': 'float64',
# # 'min_bank': 'float64',
# # 'potential_issue': 'object',
# # 'pieces_past_due': 'float64',
# # 'perf_6_month_avg': 'float64',
# # 'perf_12_month_avg': 'float64'}}
# # write_yaml(content=dic1, file_path="demo/sample.yaml")
# # print(TargetValueMapping().to_dict())
# df = pd.read_csv("sample_dataset\\backorders_data_sample.csv")
# df =df.sample(n=1)
# # df.drop(columns=["sku","went_on_backorder"],inplace=True)
# # pred_df = prediction_pipeline.single_instance_predict(df)
# # logging.info(f"{pred_df.value_counts()}")
# prediction_pipeline.start_single_instance_prediction(dataframe=df)
# # prediction_pipeline.start_batch_prediction()
from backorder.pipeline.training_pipeline import TrainingPipeline
training = TrainingPipeline()
training.start()