Enigma on GridSearchCV & requests

not_finish.py

# -*- coding: utf-8 -*-
from sklearn.grid_search import GridSearchCV
from sklearn.linear_model import LogisticRegression
import requests
import numpy as np
import random

sample_num = 1400
dimension = 100

#Add request
requests.get('http://google.com')

X = []
y = []

for i in range(0, sample_num):
	x = []
	for j in range(0, dimension):
		x.append(random.random())
	X.append(x)
	y.append(i%2)

print('start gscv')

tuned_parameters = [{'C': [10]}]
gscv = GridSearchCV(LogisticRegression(class_weight='auto'), tuned_parameters, cv=2, n_jobs=-1)
gscv.fit(X, y)

print('finished')

work.py

# -*- coding: utf-8 -*-
from sklearn.grid_search import GridSearchCV
from sklearn.linear_model import LogisticRegression
import requests
import numpy as np
import random

sample_num = 1400
dimension = 100

# requests.get('http://google.com')

X = []
y = []

for i in range(0, sample_num):
	x = []
	for j in range(0, dimension):
		x.append(random.random())
	X.append(x)
	y.append(i%2)

print('start gscv')

tuned_parameters = [{'C': [10]}]
gscv = GridSearchCV(LogisticRegression(class_weight='auto'), tuned_parameters, cv=2, n_jobs=-1)
gscv.fit(X, y)

print('finished')

work_on_less_sample_num.py

# -*- coding: utf-8 -*-
from sklearn.grid_search import GridSearchCV
from sklearn.linear_model import LogisticRegression
import requests
import numpy as np
import random

sample_num = 1300 # Decrease from 1400
dimension = 100

requests.get('http://google.com')

X = []
y = []

for i in range(0, sample_num):
	x = []
	for j in range(0, dimension):
		x.append(random.random())
	X.append(x)
	y.append(i%2)

print('start gscv')

tuned_parameters = [{'C': [10]}]
gscv = GridSearchCV(LogisticRegression(class_weight='auto'), tuned_parameters, cv=2, n_jobs=-1)
gscv.fit(X, y)

print('finished')

work_on_not_parallel.py

# -*- coding: utf-8 -*-
from sklearn.grid_search import GridSearchCV
from sklearn.linear_model import LogisticRegression
import requests
import numpy as np
import random

sample_num = 1400
dimension = 100

requests.get('http://google.com')

X = []
y = []

for i in range(0, sample_num):
	x = []
	for j in range(0, dimension):
		x.append(random.random())
	X.append(x)
	y.append(i%2)

print('start gscv')

tuned_parameters = [{'C': [10]}]
gscv = GridSearchCV(LogisticRegression(class_weight='auto'), tuned_parameters, cv=2) # Delete n_jobs
gscv.fit(X, y)

print('finished')