guess-reddit-date/linear_regression.py
2020-05-02 22:45:56 +02:00

231 lines
6.2 KiB
Python

#using NLTK library, we can do lot of text preprocesing
import nltk
from nltk.tokenize import word_tokenize
#nltk.download('stopwords')
from nltk.corpus import stopwords
import random
import pickle
#function to split text into word
def my_tokenize(text):
tokens = word_tokenize(text)
stop_words = set(stopwords.words('english'))
tokens = [w for w in tokens if not w in stop_words]
return tokens
def post_list(in_file):
post_list = []
with open(in_file) as f:
for line in f:
tokens = my_tokenize(line)
post_list.append(tokens)
return post_list
def exp_list(in_file):
exp_list = []
with open(in_file) as f:
for line in f:
exp_list.append(float(line))
return exp_list
def make_dictionary(posts):
my_dict = dict()
for post in posts:
for t in post:
if not t in my_dict:
my_dict[t] = random.randint(-1,1)*0.1
with open('dict.pickle', 'wb') as handle:
pickle.dump(my_dict, handle)
def train(in_file, exp_file):
pl = post_list(in_file)
print("pl created")
el = exp_list(exp_file)
print("el created")
#make_dictionary(pl)
with open('dict.pickle', 'rb') as f:
dictionary = pickle.load(f)
print("dict created")
lr = 0.001
w0 = 0.1
loss_sum = 0
loss_sum_counter = 1
while True:
for post, y in zip(pl,el):
y_hat = w0
for token in post:
y_hat += dictionary[token]
loss = (y_hat - y)**2
loss_sum += loss
if loss_sum_counter % 10000 == 0:
print(str(loss_sum_counter) + " " + str(loss_sum / 10000))
loss_sum = 0.0
loss_sum_counter += 1
#updating weights
delta = (y_hat - y) * lr
w0 -= delta
for token in post:
dictionary[token] -= delta
if loss_sum_counter > 7000000:
break
#We save only things we need for prediction
model = (dictionary)
pickle.dump(model, open("model.pkl", "wb"))
train("train/in.tsv", "train/expected.tsv")
# import csv
# import re
# import random
# import json
# from math import sqrt
# def make_dict(path):
# dict = {}
# with open(path) as in_file:
# for line in in_file:
# for word in re.findall(r"[\w']+", line):
# if not word in dict:
# weight = round(random.random()%0.2-0.1,2)
# dict[word] = weight
# print("dict maked")
# with open('dict.txt', 'w') as file:
# json.dump(dict, file)
# return dict
# def make_posts_list(in_file):
# posts = []
# counter = 0
# with open(in_file) as f:
# for line in f:
# if counter < 1000:
# posts.append(line)
# else:
# counter +=1
# return posts
# def make_exp_list(exp_file):
# exp_list = []
# with open(exp_file) as f:
# for exp_line in f:
# y = exp_line
# exp_list.append(float(y.split('\n')[0]))
# return exp_list
# def train_model(in_path, exp_path):
# with open('dict.txt', 'r') as file:
# dict = json.load(file)
# posts = make_posts_list(in_path)
# exp = make_exp_list(exp_path)
# w0 = 2013
# lr = 0.0000001
# epchos = 0
# loss_sum = 0
# last_sum = 10
# loss_counter = 0
# print("Zaczynam")
# while epchos < 10000:
# loss_cost = 0
# for in_line, exp_line in zip(posts, exp):
# loss_counter+=1
# #losowy przykład ze zbioru uczącego
# #print("new post" + str(random.randint(0,10)))
# post = in_line
# error_rate = 1
# y = int(exp_line)
# #loop_counter = 0
# #while (error_rate > 0.2 and loop_counter < 10000):
# #loop_counter +=1
# y_hat = w0
# for word in re.findall(r"[\w']+", post):
# #dict[word] -= (y_hat - y)*lr
# y_hat += dict[word]
# loss = (y_hat - y)**2
# loss_sum += loss
# #error_rate = (y_hat - y)**2
# # if loop_counter%1000 == 0:
# # print(error_rate)
# # loss_cost += error_rate
# # if loss_counter%1000==0:
# # print(loss_sum/1000)
# # loss_sum = 0
# #uczenie
# delta = (y_hat - y) * lr
# w0 = w0 - delta
# for word in re.findall(r"[\w']+", post):
# dict[word] -= delta
# real_loss = loss_sum/loss_counter
# print(real_loss)
# # if real_loss > last_sum:
# # break
# # else:
# # last_sum = real_loss
# last_sum = real_loss
# loss_sum = 0
# loss_counter = 0
# epchos +=1
# with open('dict2.txt', 'w') as file:
# json.dump(dict, file)
# def predict(path):
# results = []
# with open('dict2.txt', 'r') as file:
# dict = json.load(file)
# with open(path+"/in.tsv") as in_file:
# for in_line in in_file:
# print("new post" + str(random.randint(0,10)))
# post = in_line
# y=0
# for word in re.findall(r"[\w']+", post):
# if word in dict:
# y += dict[word]
# if y > 0.5:
# results.append("1")
# else:
# results.append("0")
# with open(path+"/out.tsv", 'wt') as tsvfile:
# tsv_writer = csv.writer(tsvfile, delimiter='\t')
# for i in results:
# tsv_writer.writerow(i)
# #make_dict("train/in.tsv")
# #train_model("train/in.tsv", "train/expected.tsv")
# def check_dev():
# with open("dev-0/out.tsv") as out_file, open("dev-0/expected.tsv") as exp_file:
# counter = 0
# positive = 0
# for out_line, exp_line in zip(out_file, exp_file):
# counter+=1
# if out_line == exp_line:
# positive += 1
# print(positive/counter)
# #predict("dev-0")
# #predict("test-A")