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from com.highradius.Products.Deductions.valid_invalid.feature_functions.UtilityMethods import *
from com.highradius.ml_automation.preprocessing.Deductions.valid_invalid.generic_preprocessors import CategoricalColumnPreprocessor
import pandas as pd
import numpy as np
from sklearn.preprocessing import LabelEncoder
class PrimaryPreprocessor(Preprocessing):
def performPreprocessing(self, merged_df, preprocessing_arguments, preProcessorContext):
generic_preprocess = CategoricalColumnPreprocessor()
#check
# Getting the Data
df = merged_df.copy()
# index created
df['ID'] = df.index
# target columns
f1 = df['fk_action_code_id'] == 95
f2 = df['fk_lu_invalid_deduction_identifier_id'].notnull()
f3 = df['correspondence_flag'] == 1
df['isValid'] = np.where((f1 | f2 | f3), 0, 1) # 1 for valid, 0 for invalid
# fk_customer
df = df[df['fk_customer_map_id'].notnull()]
# original_dispute_amount
df = df[df['original_dispute_amount'] >= 0] # LATER
if ( np.isnan(df['original_dispute_amount'].mean()) ):
df['original_dispute_amount'] = df['original_dispute_amount'].fillna(0)
else:
df['original_dispute_amount'] = df['original_dispute_amount'].fillna(
value=df['original_dispute_amount'].mean())
# dates
df['deduction_created_date'] = pd.to_datetime(df['deduction_created_date'], errors='coerce')
df.dropna(axis=0, subset=['deduction_created_date'], inplace=True)
# ar_reason_code
le = LabelEncoder()
df['ar_reason_code'] = le.fit_transform(df['ar_reason_code'].astype(str))
# Resetting the Index
df.reset_index(drop=True, inplace=True)
return df
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