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import html
import os
import pickle
import re
import warnings
import customtkinter
import nltk
from nltk.corpus import stopwords
from nltk.stem import SnowballStemmer
from scipy.sparse import hstack
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.preprocessing import LabelBinarizer
warnings.filterwarnings('ignore')
nltk.download('stopwords')
nltk.download('wordnet')
from string import punctuation
import numpy as np
from sklearn.dummy import DummyRegressor
from sklearn.ensemble import GradientBoostingRegressor, RandomForestRegressor
from sklearn.linear_model import LinearRegression, RidgeCV
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsRegressor
from sklearn.tree import DecisionTreeRegressor
def preprocess(message):
stemmer = SnowballStemmer('english')
stuff_to_be_removed = list(stopwords.words('english'))+list(punctuation)
# Convert message to lower case
message = str(message)
message = message.lower()
# Remove all the links from the messages
message = re.sub('http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+#]|[!*\(\),]|'\
'(?:%[0-9a-fA-F][0-9a-fA-F]))+','', message)
# Remove all the mentions
message =re.sub("(@[A-Za-z0-9_]+)","", message)
# Remove all the emojis
message = re.sub(re.compile("["
u"\U0001F600-\U0001F64F" # emoticons
u"\U0001F300-\U0001F5FF" # symbols & pictographs
u"\U0001F680-\U0001F6FF" # transport & map symbols
u"\U0001F1E0-\U0001F1FF" # flags (iOS)
u"\U00002500-\U00002BEF" # chinese char
u"\U00002702-\U000027B0"
u"\U00002702-\U000027B0"
u"\U000024C2-\U0001F251"
u"\U0001f926-\U0001f937"
u"\U00010000-\U0010ffff"
u"\u2640-\u2642"
u"\u2600-\u2B55"
u"\u200d"
u"\u23cf"
u"\u23e9"
u"\u231a"
u"\ufe0f" # dingbats
u"\u3030"
"]+", flags=re.UNICODE), '', message)
# Remove HTML entities
message = html.unescape(message)
# strip blank spaces
message = message.strip()
# Remove all the punctuations
message = message.translate(str.maketrans('', '', punctuation))
# Remove stopwords and perform stemming
message = ' '.join([stemmer.stem(word) for word in message.split() if word not in stuff_to_be_removed])
# Return the message
return message
class ModelTrainer(customtkinter.CTkToplevel):
model_dir = 'models/'
def __init__(self, parent, posts):
super().__init__(parent)
self.parent = parent
self.posts = posts # posts is already a dataframe
self.features = ['title', 'selftext', 'subreddit', 'distinguished', 'hour', 'day']
self.targets = ['ups', 'num_comments']
for col in self.posts.columns:
if col not in self.features + self.targets:
self.posts.drop(col, axis=1, inplace=True)
self.categorical_features = ['subreddit', 'distinguished', 'hour', 'day']
self.posts['text'] = self.posts['title'] + ' ' + self.posts['selftext']
self.posts['text'] = self.posts['text'].apply(lambda x: preprocess(x))
self.text_features = ['text']
self.title('Reddit Data Analysis - Building Models')
posx = int(self.winfo_screenwidth()/2 - 300)
posy = int(self.winfo_screenheight()/2 - 150)
self.geometry('600x300+{}+{}'.format(posx, posy))
self.resizable(False, False)
self.protocol('WM_DELETE_WINDOW', self.disable_event)
self.updates = customtkinter.CTkTextbox(self, height=300, width=600, state = 'disabled')
self.updates.pack(fill='both', expand=True)
# Create a hash table to store the model objects
self.model_hashmap = {
"DummyRegressor": DummyRegressor(),
"LinearRegression": LinearRegression(),
"RidgeCV": RidgeCV(cv=10),
"KNeighborsRegressor": KNeighborsRegressor(),
"DecisionTreeRegressor": DecisionTreeRegressor(min_samples_split=45, min_samples_leaf=45, random_state = 10),
"RandomForestRegressor": RandomForestRegressor(n_jobs=-1, n_estimators=70, min_samples_leaf=10, random_state = 10),
"GradientBoostingRegressor": GradientBoostingRegressor(n_estimators=70, max_depth=5)
}
self.start()
def disable_event(self):
pass
def edit_textbox(self, text, line, type='wait'):
emoji = '🕐' if type == 'wait' else '✅'
line_next = line + 1
line = str(line) + '.0'
line_next = str(line_next) + '.0'
self.updates.configure(state='normal')
if type == 'wait':
self.updates.insert(line, emoji + ' ' + text + '...' + '\n\n')
else:
self.updates.delete(line, line_next)
self.updates.insert(line, emoji + ' ' + text + '\n\n')
self.updates.configure(state='disabled')
# scroll to line
self.updates.see(line)
# update the window
self.update()
def start(self):
self.ups = self.posts['ups']
self.num_comments = self.posts['num_comments']
# select only text, subreddit, link_flair_text, distinguished, hour, day, ups, num_comments
self.posts_ups = self.posts[self.categorical_features + self.text_features + ['ups']]
self.posts_num_comments = self.posts[self.categorical_features + self.text_features + ['num_comments']]
self.tfidf = TfidfVectorizer()
self.label_binarizer = LabelBinarizer()
self.edit_textbox('Preparing Data (Upvotes)', 1, 'wait')
# generate tfidf - label_binarizer for ups
self.tfidf_ups = self.tfidf.fit_transform(self.posts_ups['text'])
self.category_ups = [self.label_binarizer.fit_transform(self.posts_ups[col]) for col in self.categorical_features]
self.category_ups = np.concatenate(self.category_ups, axis=1)
self.X_ups = hstack([self.tfidf_ups, self.category_ups])
self.y_ups = self.posts_ups['ups']
# split data into train and test sets
self.X_train_ups, self.X_test_ups, self.y_train_ups, self.y_test_ups = train_test_split(self.X_ups, self.y_ups, test_size=0.2, random_state=42)
self.edit_textbox('Preparing Data (Upvotes)', 1, 'done')
self.edit_textbox('Preparing Data (Number of Comments)', 3, 'wait')
# generate tfidf - label_binarizer for num_comments
self.tfidf_num_comments = self.tfidf.fit_transform(self.posts_num_comments['text'])
self.category_num_comments = [self.label_binarizer.fit_transform(self.posts_num_comments[col]) for col in self.categorical_features]
self.category_num_comments = np.concatenate(self.category_num_comments, axis=1)
self.X_num_comments = hstack([self.tfidf_num_comments, self.category_num_comments])
self.y_num_comments = self.posts_num_comments['num_comments']
# split data into train and test sets
self.X_train_num_comments, self.X_test_num_comments, self.y_train_num_comments, self.y_test_num_comments = train_test_split(self.X_num_comments, self.y_num_comments, test_size=0.2, random_state=42)
self.edit_textbox('Preparing Data (Number of Comments)', 2, 'done')
# train models
self.train_models()
# Create a function to save the models
def save_model(self, model, model_name):
"""
Saves the model to the models/ directory
"""
if not os.path.exists(self.model_dir):
os.mkdir(self.model_dir)
with open(self.model_dir + model_name + '.pkl', 'wb') as f:
pickle.dump(model, f)
def train_models(self):
line_count = 3
for model_name, model in self.model_hashmap.items():
self.edit_textbox('Training {} for Upvotes'.format(model_name), line_count, 'wait')
model.fit(self.X_train_ups, self.y_train_ups)
self.save_model(model, model_name + '_ups')
self.edit_textbox('Training {} for Upvotes'.format(model_name), line_count, 'done')
line_count += 1
self.edit_textbox('Training {} for Number of Comments'.format(model_name), line_count, 'wait')
model.fit(self.X_train_num_comments, self.y_train_num_comments)
self.save_model(model, model_name + '_num_comments')
self.edit_textbox('Training {} for Number of Comments'.format(model_name), line_count, 'done')
line_count += 1
self.edit_textbox('Training Complete. Models saved to models/ directory. You may now close this window.', line_count, 'done')
# allow user to close window
self.protocol("WM_DELETE_WINDOW", self.enable_close)
def enable_close(self):
self.destroy()
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