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CreateHistoryTwoLevelNew.py
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CreateHistoryTwoLevelNew.py
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# -*- coding: utf-8 -*-
"""
Created on Wed Oct 17 17:49:15 2018
#this is final
@author: rajneesh.jha
"""
import pandas as pd
import numpy as np
import warnings
warnings.filterwarnings("ignore")
data = pd.read_pickle(r"C:\Users\rajneesh.jha\Downloads\deduction\three_tables_4_lakhs.pkl")
data = data.drop_duplicates('pk_deduction_id')
data.reset_index(inplace=True, drop=True)
data['fk_action_code_id'] = data['fk_action_code_id'].fillna(-1)
data.fk_action_code_id.value_counts(dropna=False)
data['fk_action_code_id'] = data['fk_action_code_id'].astype(int)
listid = [341, 419, 422, 425, 240, 243, 246, 428, 300]
data['output'] = np.nan
data.loc[(data.fk_action_code_id.isin(listid)), 'output'] = 0
data.output.fillna(value=1, inplace=True)
data['output'] = data.output.astype(int)
import datetime
train = data.loc[(data.deduction_created_date >= datetime.datetime(2015, 1, 2)) & (
data.deduction_created_date <= datetime.datetime(2018, 5, 1))]
# print(train.shape)
# print(train)
test = data.loc[data.deduction_created_date > datetime.datetime(2018, 1, 2)]
# print(test.shape)
def getOthers(column1, train_Data_frame, test_Data_frame,primary_id_column,output_column,value):
train_Data_frame[column1].replace('\\N', np.nan, inplace=True)
train_Data_frame[column1].replace(np.nan, 'null', inplace=True)
train_Data_frame[column1] = train_Data_frame[column1].apply(
lambda x: int(x) if (pd.Series(x).dtype == 'float64') else x)
train_Data_frame[column1] = train_Data_frame[column1].astype(str)
print(train_Data_frame[column1].value_counts(dropna=False))
print(test_Data_frame[column1].value_counts(dropna=False))
# test Data processing
test_Data_frame[column1].replace('\\N', np.nan, inplace=True)
test_Data_frame[column1].replace(np.nan, 'null', inplace=True)
test_Data_frame[column1] = test_Data_frame[column1].apply(
lambda x: int(x) if (pd.Series(x).dtype == 'float64') else x)
test_Data_frame[column1] = test_Data_frame[column1].astype(str)
train_Data_frame[column1] = train_Data_frame[column1].astype(str)
grp_hstry_map_id = train_Data_frame.groupby(column1).agg({output_column: 'sum', primary_id_column: 'count'})
grp_hstry_map_id.reset_index(inplace=True)
grp_hstry_map_id[output_column] = grp_hstry_map_id[primary_id_column] - grp_hstry_map_id[output_column]
grp_hstry_map_id.columns = [column1, 'invalid', 'total']
grp_hstry_map_id.sort_values(by='total', ascending=False, inplace=True)
grp_hstry_map_id.loc[grp_hstry_map_id.total <= int(value), column1] = 'others'
grp_hstry_map_id[column1] = grp_hstry_map_id[column1].astype(str)
grp_cust_list = grp_hstry_map_id[column1].unique().tolist()
len(grp_cust_list)
train_Data_frame[column1] = train[column1].apply(lambda x: x if (x in grp_cust_list) else 'others')
train_Data_frame.reset_index(inplace=True)
train_Data_frame.drop('index', axis=1, inplace=True)
test_Data_frame[column1] = test_Data_frame[column1].apply(lambda x: x if (x in grp_cust_list) else 'others')
return train_Data_frame, test_Data_frame
def handlePreprocessing(train_Data_frame, test_Data_frame, column1, column2):
train_Data_frame[column1].replace('\\N', np.nan, inplace=True)
train_Data_frame[column1].replace(np.nan, 'null', inplace=True)
#train_Data_frame[column1] = train_Data_frame[column1].apply(
# lambda x: int(x) if (pd.Series(x).dtype == 'float64') else x)
train_Data_frame[column1] = train_Data_frame[column1].astype(str)
train_Data_frame[column2].replace('\\N', np.nan, inplace=True)
train_Data_frame[column2].replace(np.nan, 'null', inplace=True)
#train_Data_frame[column2] = train_Data_frame[column2].apply(
# lambda x: int(x) if (pd.Series(x).dtype == 'float64') else x)
train_Data_frame[column2] = train_Data_frame[column2].astype(str)
# test Data processing
test_Data_frame[column1].replace('\\N', np.nan, inplace=True)
test_Data_frame[column1].replace(np.nan, 'null', inplace=True)
test_Data_frame[column1] = test_Data_frame[column1].apply(
lambda x: int(x) if (pd.Series(x).dtype == 'float64') else x)
test_Data_frame[column1] = test_Data_frame[column1].astype(str)
test_Data_frame[column2].replace('\\N', np.nan, inplace=True)
test_Data_frame[column2].replace(np.nan, 'null', inplace=True)
test_Data_frame[column2] = test_Data_frame[column2].apply(
lambda x: int(x) if (pd.Series(x).dtype == 'float64') else x)
test_Data_frame[column2] = test_Data_frame[column2].astype(str)
return train_Data_frame, test_Data_frame
def twoLevelHistory(column1, column2, train_Data_frame, test_Data_frame,primary_key_column,output_column,value):
# reason code and category level history
train_Data_frame["temp1"]=train_Data_frame[column2]
test_Data_frame["temp1"]=test_Data_frame[column2]
train_Data_frame, test_Data_frame = getOthers(column1, train_Data_frame, test_Data_frame,primary_key_column,output_column,value)
train_Data_frame, test_Data_frame = handlePreprocessing(train_Data_frame, test_Data_frame, column1, column2)
train_Data_frame[column1] = train_Data_frame[column1].astype(str)
train_Data_frame[column2] = train_Data_frame[column2].astype(str)
reason_category = train_Data_frame.groupby([column1, column2]).agg(
{output_column: 'sum', primary_key_column: 'count'}).sort_values(primary_key_column, ascending=False)
reason_category.reset_index(inplace=True)
reason_category.groupby([column1, column2]).agg({output_column: 'first', primary_key_column: 'first'})
reason_category.reset_index(inplace=True)
del reason_category['index']
reason_category[output_column] = reason_category[primary_key_column] - reason_category[output_column]
reason_category.columns = [column1, column2, 'invalid', 'new_total']
rt_category = train_Data_frame.groupby([column1]).agg({primary_key_column: 'count'})
rt_category.reset_index(inplace=True)
rt_category.columns = [column1, 'grand_total_sum']
final_reason_category = pd.merge(reason_category, rt_category, how='left', on=column1)
x = final_reason_category.groupby([column1])
u = list(x.groups.keys())
final = pd.DataFrame()
for i in u:
df = x.get_group(i)
df['cumsum'] = df['new_total'].cumsum()
df['grand_total_sum'] = (0.90 * df['grand_total_sum']) - df['cumsum']
df['label'] = df[column2].astype(str)
if (df.ix[df['grand_total_sum'] < 0, :].shape[0]) != 1:
df.ix[df['grand_total_sum'] < 0, :] = df.ix[df['grand_total_sum'] < 0, :].set_value(
df.loc[df['grand_total_sum'] < 0, 'grand_total_sum'].index[0], 'grand_total_sum', 0.1)
df.ix[df['new_total'] < int(value), :] = df.ix[df['new_total'] < int(value), :].set_value(
df.loc[df['new_total'] < int(value), 'new_total'].index, 'grand_total_sum', -0.1)
df['invalid_ratio'] = df['invalid'] / df['new_total']
df.loc[df['grand_total_sum'] < 0, 'label'] = 'others'
if len(df.loc[df['grand_total_sum'] < 0, 'invalid_ratio']) != 0:
df.loc[df['grand_total_sum'] < 0, 'invalid_ratio'] = ((df[df['grand_total_sum'] < 0]['invalid'].sum()) / (
df[df['grand_total_sum'] < 0]['new_total'].sum()))
df.sort_values('invalid_ratio', ascending=False, inplace=True)
df['rank'] = df['invalid_ratio']
df = df[[column1, column2, 'label', 'rank']]
final = pd.concat([final, df])
#final.to_csv('table3.csv')
final.columns = [column1, column2, column2+"_xref_label", column1 + "_" + column2 + "_history"]
final[column1] = final[column1].astype(str)
final[column2] = final[column2].astype(str)
train_Data_frame[column1] = train_Data_frame[column1].astype(str)
train_Data_frame[column2] = train_Data_frame[column2].astype(str)
train_Data_frame = pd.merge(train_Data_frame, final, how='left', on=[column1, column2])
test_Data_frame[column2] = test_Data_frame[column2].astype(str)
test_Data_frame[column1] = test_Data_frame[column1].astype(str)
test_Data_frame = pd.merge(test_Data_frame, final, how='left', on=[column1, column2])
del train_Data_frame[column1 + "_" + column2 + "_history"]
del test_Data_frame[column1 + "_" + column2 + "_history"]
test_Data_frame.loc[test_Data_frame[column2+"_xref_label"].isnull(), column2+"_xref_label"] = 'others'
train_Data_frame.loc[train_Data_frame[column2+"_xref_label"].isnull(), column2+"_xref_label"] = 'others'
train_Data_frame = pd.merge(train_Data_frame, final.drop_duplicates([column1, column2+"_xref_label"]),
on=[column1, column2+"_xref_label"], how='left')
test_Data_frame = pd.merge(test_Data_frame, final.drop_duplicates([column1, column2+"_xref_label"]),
on=[column1, column2+"_xref_label"], how='left')
train_Data_frame.rename(columns={column2+'_x': column2}, inplace=True)
test_Data_frame.rename(columns={column2+'_x': column2}, inplace=True)
#train_Data_frame.rename(columns={'temp1':column2},inplace=True)
#test_Data_frame.rename(columns={'temp1': column2}, inplace=True)
del train_Data_frame[column2+"_xref_label"]
del test_Data_frame[column2+"_xref_label"]
del train_Data_frame[column2+'_y']
del test_Data_frame[column2+'_y']
return train_Data_frame, test_Data_frame
print(train.columns.tolist())
print(test.columns.tolist())
train, test = twoLevelHistory('business_area', 'ar_reason_code', train, test,'pk_deduction_id','output',90)
print(train.columns.tolist())
print(test.columns.tolist())
train,test=twoLevelHistory('payer','ar_reason_code',train,test,'pk_deduction_id','output',90)
print(train.columns.tolist())
print(test.columns.tolist())
train,test=twoLevelHistory('fk_customer_map_id','ar_reason_code',train,test,'pk_deduction_id','output',90)
print(train['business_area_ar_reason_code_history'])
print(test['business_area_ar_reason_code_history'])
print(train['fk_customer_map_id_ar_reason_code_history'])
print(test['fk_customer_map_id_ar_reason_code_history'])
print(train['business_area_ar_reason_code_history'].isnull().sum())
print(test['business_area_ar_reason_code_history'].isnull().sum())
print(train['fk_customer_map_id_ar_reason_code_history'].isnull().sum())
print(test['fk_customer_map_id_ar_reason_code_history'].isnull().sum())
print(train['payer_ar_reason_code_history'].isnull().sum())
print(test['payer_ar_reason_code_history'].isnull().sum())