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multiprocess_extraction.py
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import pandas as pd
import numpy as np
import pickle
import preprocess_tools as tools
import itertools
from multiprocessing import Process
from sklearn.metrics import precision_score,recall_score,accuracy_score,confusion_matrix, roc_auc_score
# load data to be predicted
df_measurement = pd.read_csv('/path/to/measurements.csv')
df_label = pd.read_csv('/path/to/labels.csv')
# change data type of bookingID to str
df_measurement['bookingID'] = df_measurement['bookingID'].astype(str)
df_label['bookingID'] = df_label['bookingID'].astype(str)
# combine the label and the measurement
df_merge = df_measurement.merge(df_label, on='bookingID')
# group dataframe by bookingId
grouped = df_merge.groupby('bookingID')
groups = dict(list(grouped))
if __name__ == "__main__":
# extract g
print('extracting g')
s1 = int(len(groups) * 0.25)
s2 = int(len(groups) * 0.5)
s3 = int(len(groups) * 0.75)
groups_slice1 = dict(itertools.islice(groups.items(), 0, s1))
groups_slice2 = dict(itertools.islice(groups.items(), s1, s2))
groups_slice3 = dict(itertools.islice(groups.items(), s2, s3))
groups_slice4 = dict(itertools.islice(groups.items(), s3, len(groups)))
procs = []
p = Process(target=tools.create_gravity_adjustment_df_multi, args=(groups_slice1, 1))
procs.append(p)
p2 = Process(target=tools.create_gravity_adjustment_df_multi, args=(groups_slice2, 2))
procs.append(p2)
p3 = Process(target=tools.create_gravity_adjustment_df_multi, args=(groups_slice3, 3))
procs.append(p3)
p4 = Process(target=tools.create_gravity_adjustment_df_multi, args=(groups_slice4, 4))
procs.append(p4)
for p in procs: p.start()
for p in procs: p.join()
df_g1 = pd.read_csv('multi_extract_g_1.csv')
df_g2 = pd.read_csv('multi_extract_g_2.csv')
df_g3 = pd.read_csv('multi_extract_g_3.csv')
df_g4 = pd.read_csv('multi_extract_g_4.csv')
df_g = pd.concat([df_g1,df_g2,df_g3,df_g4], ignore_index=True)
df_g.drop(['Unnamed: 0'], axis=1, inplace=True)
df_g['bookingID'] = df_g['bookingID'].astype(str)
# clean and reorient
print('reorienting')
s1 = int(len(df_merge) * 0.25)
s2 = int(len(df_merge) * 0.5)
s3 = int(len(df_merge) * 0.75)
df_merge_a = df_merge.iloc[:s1, :]
df_merge_b = df_merge.iloc[s1:s2, :]
df_merge_c = df_merge.iloc[s2:s3, :]
df_merge_d = df_merge.iloc[s3:, :]
procs = []
p = Process(target=tools.process_clean_and_reorient_multi, args=(df_merge_a, df_g, 1))
procs.append(p)
p2 = Process(target=tools.process_clean_and_reorient_multi, args=(df_merge_b, df_g, 2))
procs.append(p2)
p3 = Process(target=tools.process_clean_and_reorient_multi, args=(df_merge_c, df_g, 3))
procs.append(p3)
p4 = Process(target=tools.process_clean_and_reorient_multi, args=(df_merge_d, df_g, 4))
procs.append(p4)
for p in procs: p.start()
for p in procs: p.join()
# extract features
print('extracting features')
df1 = pd.read_csv('multi_reorient_1.csv')
df2 = pd.read_csv('multi_reorient_2.csv')
df3 = pd.read_csv('multi_reorient_3.csv')
df4 = pd.read_csv('multi_reorient_4.csv')
df_merge = pd.concat([df1,df2,df3,df4], ignore_index=True)
df_merge['bookingID'] = df_merge['bookingID'].astype(str)
grouped_feature = df_merge.groupby('bookingID')
groups_feature = dict(list(grouped_feature))
s1 = int(len(groups_feature) * 0.25)
s2 = int(len(groups_feature) * 0.5)
s3 = int(len(groups_feature) * 0.75)
s4 = int(len(groups_feature) * 1)
groups_slice1 = dict(itertools.islice(groups_feature.items(), 0, s1))
groups_slice2 = dict(itertools.islice(groups_feature.items(), s1, s2))
groups_slice3 = dict(itertools.islice(groups_feature.items(), s2, s3))
groups_slice4 = dict(itertools.islice(groups_feature.items(), s3, s4))
procs = []
p = Process(target=tools.extract_features_multi, args=(groups_slice1, 1))
procs.append(p)
p2 = Process(target=tools.extract_features_multi, args=(groups_slice2, 2))
procs.append(p2)
p3 = Process(target=tools.extract_features_multi, args=(groups_slice3, 3))
procs.append(p3)
p4 = Process(target=tools.extract_features_multi, args=(groups_slice4, 4))
procs.append(p4)
for p in procs: p.start()
for p in procs: p.join()
print('Extraction Finished')