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extract_multivalue_feature.py
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extract_multivalue_feature.py
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import pandas as pd
import numpy as np
import globalparameter
# def dummy_filter(dummy_array, threshold, name):
# ratio could be added later
def extractskill(datapath, non_datapath, pos_start_index, pos_end_index, neg_start_index, neg_end_index):
name = 'skills'
user_data = pd.read_csv(datapath, header=None)
non_user_data = pd.read_csv(non_datapath, header=None)
# create df data same as train set
df = pd.concat([user_data.iloc[0:globalparameter.extract_number, [65]],
non_user_data.iloc[0:globalparameter.total_number - globalparameter.extract_number,
[65]]]).values.tolist()
user_skill_data = []
user_skill_data1 = []
user_skill_data_for_dummy = pd.DataFrame()
# print(df)
for i in range(len(df)):
user_skill_data.append(' '.join(str(k) for k in df[i]))
# print(user_skill_data)
for i in range(len(user_skill_data)):
user_skill_data1.append(user_skill_data[i].split())
# print(user_skill_data1)
user_skill_data1 = pd.DataFrame({'skills': user_skill_data1})
# print(user_skill_data1)
length1 = len(user_skill_data1)
i = 0
for row in user_skill_data1.iterrows():
user_skill_data_for_dummy['user_skill' + str(i)] = row[1]
i = i + 1
# filtered_skill_variable_array = dummy_filter(pd.DataFrame(user_skill_data1.skills.values.tolist()),0.8,name)
# dummy_col_list = []
# test1 = pd.DataFrame(user_skill_data1.skills.values.tolist())
# dummy_col = pd.DataFrame(user_skill_data1.skills.values.tolist())
#
# count_test = dummy_col.count()
# count = pd.value_counts(dummy_col)/len(dummy_col)
# mask = dummy_col.isin(count[count>0.8].index)
# dummy_col[~mask] = "others"
# filtered_skill_variable_array = pd.get_dummies(dummy_col,prefix=name)
# test_skill_array = filtered_skill_variable_array
# print(user_skill_data_for_dummy)
skill_variable_array = pd.get_dummies(pd.DataFrame(user_skill_data1.skills.values.tolist()), drop_first=True)
new_skill_variable_array = pd.concat([skill_variable_array.iloc[pos_start_index:pos_end_index],
skill_variable_array.iloc[neg_start_index:neg_end_index]])
new_length = len(new_skill_variable_array)
print(skill_variable_array.shape)
return new_skill_variable_array
def extractschool(datapath, non_datapath, pos_start_index, pos_end_index, neg_start_index, neg_end_index):
user_data = pd.read_csv(datapath, header=None)
non_user_data = pd.read_csv(non_datapath, header=None)
df = pd.concat([user_data.iloc[:, [45]],
non_user_data.iloc[:, [45]]]).values.tolist()
user_school_data = []
user_school_data1 = []
user_school_data_for_dummy = pd.DataFrame()
# print(df)
for i in range(len(df)):
user_school_data.append(' '.join(str(k) for k in df[i]))
# print(user_skill_data)
for i in range(len(user_school_data)):
user_school_data1.append(user_school_data[i].split())
# print(user_skill_data1)
user_school_data1 = pd.DataFrame({'schools': user_school_data1})
# print(user_skill_data1)
length1 = len(user_school_data1)
i = 0
for row in user_school_data1.iterrows():
user_school_data_for_dummy['user_school' + str(i)] = row[1]
i = i + 1
# print(user_school_data_for_dummy)
school_variable_array = pd.get_dummies(pd.DataFrame(user_school_data1.schools.values.tolist()), drop_first=True)
new_school_variable_array = pd.concat([school_variable_array.iloc[pos_start_index:pos_end_index],
school_variable_array.iloc[neg_start_index:neg_end_index]])
new_length = len(new_school_variable_array)
print(school_variable_array.shape)
return new_school_variable_array
def extractmajor(datapath, non_datapath, pos_start_index, pos_end_index, neg_start_index, neg_end_index):
user_data = pd.read_csv(datapath, header=None)
non_user_data = pd.read_csv(non_datapath, header=None)
df = pd.concat([user_data.iloc[:, [47]],
non_user_data.iloc[:, [47]]]).values.tolist()
user_major_data = []
user_major_data1 = []
user_major_data_for_dummy = pd.DataFrame()
# print(df)
for i in range(len(df)):
user_major_data.append(' '.join(str(k) for k in df[i]))
# print(user_skill_data)
for i in range(len(user_major_data)):
user_major_data1.append(user_major_data[i].split())
# print(user_skill_data1)
user_major_data1 = pd.DataFrame({'schools': user_major_data1})
# print(user_skill_data1)
length1 = len(user_major_data1)
i = 0
for row in user_major_data1.iterrows():
user_major_data_for_dummy['user_school' + str(i)] = row[1]
i = i + 1
# print(user_major_data_for_dummy)
major_variable_array = pd.get_dummies(pd.DataFrame(user_major_data1.schools.values.tolist()), drop_first=True)
new_major_variable_array = pd.concat([major_variable_array.iloc[pos_start_index:pos_end_index],
major_variable_array.iloc[neg_start_index:neg_end_index]])
new_length = len(new_major_variable_array)
print(major_variable_array.shape)
return new_major_variable_array
def extractworkcompany(datapath, non_datapath, pos_start_index, pos_end_index, neg_start_index, neg_end_index,
column_index):
# now work company index = [4], past work company index = [10,16,22,28,34,40]
user_data = pd.read_csv(datapath, header=None)
non_user_data = pd.read_csv(non_datapath, header=None)
df = pd.concat([user_data.iloc[:, [column_index]],
non_user_data.iloc[:, [column_index]]]).values.tolist()
user_company_data = []
user_company_data1 = []
user_company_data_for_dummy = pd.DataFrame()
# print(df)
for i in range(len(df)):
user_company_data.append(' '.join(str(k) for k in df[i]))
# print(user_skill_data)
for i in range(len(user_company_data)):
user_company_data1.append(user_company_data[i].split())
# print(user_skill_data1)
user_company_data1 = pd.DataFrame({'companies': user_company_data1})
# print(user_skill_data1)
length1 = len(user_company_data1)
i = 0
for row in user_company_data1.iterrows():
user_company_data_for_dummy['user_company' + str(i)] = row[1]
i = i + 1
# print(user_company_data_for_dummy)
company_variable_array = pd.get_dummies(pd.DataFrame(user_company_data1.companies.values.tolist()), drop_first=True)
new_company_variable_array = pd.concat([company_variable_array.iloc[pos_start_index:pos_end_index],
company_variable_array.iloc[neg_start_index:neg_end_index]])
new_length = len(new_company_variable_array)
print(company_variable_array.shape)
return new_company_variable_array