Stack Overflow Tag Predictor.py

# -*- coding: utf-8 -*-
"""StackOverflowTagPredictor.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1etFlkQhQzq-77rOhEn_KJwawsf5rBt0f

## Installing missing packages.
"""

!pip install scikit-multilearn

from google.colab import drive
drive.mount('/content/drive')

"""## Importing Packages"""

import warnings
warnings.filterwarnings('ignore')

import pandas as pd
import numpy as np
import sqlite3
import csv
import matplotlib.pyplot as plt
import seaborn as sns
import re
import os
from wordcloud import WordCloud
from sqlalchemy import create_engine
import datetime as dt
from datetime import datetime
import nltk
nltk.download('stopwords')
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from nltk.stem.snowball import SnowballStemmer

from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.multiclass import OneVsRestClassifier
from sklearn.linear_model import SGDClassifier
from sklearn import metrics
from sklearn.metrics import f1_score,precision_score,recall_score
from sklearn import svm
from sklearn.linear_model import LogisticRegression
from skmultilearn.adapt import mlknn
from skmultilearn.problem_transform import ClassifierChain
from skmultilearn.problem_transform import BinaryRelevance
from skmultilearn.problem_transform import LabelPowerset
from sklearn.naive_bayes import GaussianNB

"""## Importing Data

### Creating a db file from raw data
"""

#Creating db file from csv
if not os.path.isfile('/content/drive/MyDrive/AAIC/Case Studies/Stackoverflow Tag Predictor/AAIC Data/train.db'):
    start = datetime.now()
    disk_engine = create_engine('sqlite:///train.db')
    start = dt.datetime.now()
    chunksize = 180000
    j = 0
    index_start = 1
    for df in pd.read_csv('Train.csv', names=['Id', 'Title', 'Body', 'Tags'], chunksize=chunksize, iterator=True, encoding='utf-8', ):
        df.index += index_start
        j+=1
        print('{} rows'.format(j*chunksize))
        df.to_sql('data', disk_engine, if_exists='append')
        index_start = df.index[-1] + 1
    print("Time taken to run this cell :", datetime.now() - start)

if os.path.isfile('/content/drive/MyDrive/AAIC/Case Studies/Stackoverflow Tag Predictor/AAIC Data/train.db'):
    start = datetime.now()
    con = sqlite3.connect('/content/drive/MyDrive/AAIC/Case Studies/Stackoverflow Tag Predictor/AAIC Data/train.db')
    pd.read_sql_query('show tables;', con)
    # num_rows = pd.read_sql_query("""SELECT count(*) FROM data;""", con)
    #Always remember to close the database
    # print("Number of rows in the database :","\n",num_rows['count(*)'].values[0])
    # con.close()
    # print("Time taken to count the number of rows :", datetime.now() - start)
else:
    print("train.db does not exist.")

"""### Checking for duplicates"""

if os.path.isfile('/content/drive/MyDrive/AAIC/Case Studies/Stackoverflow Tag Predictor/AAIC Data/train.db'):
    start = datetime.now()
    con = sqlite3.connect('train.db')
    df_no_dup = pd.read_sql_query('SELECT Title, Body, Tags, COUNT(*) as cnt_dup FROM data GROUP BY Title, Body, Tags', con)
    con.close()
    print("Time taken to run this cell :", datetime.now() - start)
else:
    print("train.db does not exist.")

df_no_dup.head()

print("number of duplicate questions :", num_rows['count(*)'].values[0]- df_no_dup.shape[0], "(",(1-((df_no_dup.shape[0])/(num_rows['count(*)'].values[0])))*100,"% )")

df_no_dup.cnt_dup.value_counts()

start = datetime.now()
df_no_dup["tag_count"] = df_no_dup["Tags"].apply(lambda text: len(text.split(" ")))
# adding a new feature number of tags per question
print("Time taken to run this cell :", datetime.now() - start)
df_no_dup.head()

df_no_dup.tag_count.value_counts()

"""### Storing data without duplicates as a CSV"""

if not os.path.isfile('/content/drive/MyDrive/AAIC/Case Studies/Stackoverflow Tag Predictor/AAIC Data/train_no_dup.db'):
    disk_dup = create_engine("sqlite:///train_no_dup.db")
    no_dup = pd.DataFrame(df_no_dup, columns=['Title', 'Body', 'Tags'])
    no_dup.to_sql('no_dup_train',disk_dup)

if os.path.isfile('/content/drive/MyDrive/AAIC/Case Studies/Stackoverflow Tag Predictor/AAIC Data/train_no_dup.db'):
    start = datetime.now()
    con = sqlite3.connect('/content/drive/MyDrive/AAIC/Case Studies/Stackoverflow Tag Predictor/AAIC Data/train_no_dup.db')
    tag_data = pd.read_sql_query("""SELECT Tags FROM no_dup_train""", con)
    #Always remember to close the database
    con.close()

    # Let's now drop unwanted column.
    tag_data.drop(tag_data.index[0], inplace=True)
    #Printing first 5 columns from our data frame
    tag_data.head()
    print("Time taken to run this cell :", datetime.now() - start)
else:
    print("train_no_dup.db does not exist.")

"""## Analysis of tags

### Total Number of unique tags
"""

vectorizer = CountVectorizer(tokenizer = lambda x: x.split())
tag_dtm = vectorizer.fit_transform(tag_data['Tags'])

print("Number of data points :", tag_dtm.shape[0])
print("Number of unique tags :", tag_dtm.shape[1])

tags = vectorizer.get_feature_names()
print(f"Sample tags: {tags[:10]}")

"""### Number of times a tag appears"""

freqs = tag_dtm .sum(axis=0).A1 #A1 flattens the sparse array
result = dict(zip(tags, freqs))

"""#### Storing the result dict as a csv"""

if not os.path.isfile('/content/drive/MyDrive/AAIC/Case Studies/Stackoverflow Tag Predictor/AAIC Data/tag_counts_dict_dtm.csv'):
  with open('/content/drive/MyDrive/AAIC/Case Studies/Stackoverflow Tag Predictor/AAIC Data/tag_counts_dict_dtm.csv', 'w') as csv_file:
    writer = csv.writer(csv_file)
    for key, value in result.items():
      writer.writerow([key, value])
tag_df = pd.read_csv('/content/drive/MyDrive/AAIC/Case Studies/Stackoverflow Tag Predictor/AAIC Data/tag_counts_dict_dtm.csv', names=['Tags', 'Counts'])
tag_df.head()

tag_df_sorted = tag_df.sort_values(['Counts'], ascending=False)
tag_counts = tag_df_sorted['Counts'].values

plt.plot(tag_counts)
plt.title('Distribution of number of times a particular tag appears in the dataset')
plt.grid()
plt.xlabel('Tag Number')
plt.ylabel('Frequency')
plt.show()

plt.plot(tag_counts[0:10000])
plt.title('Analysing only the first 10000 tags')
plt.grid()
plt.xlabel("Tag number")
plt.ylabel("Frequency")
plt.show()
# print(len(tag_counts[0:10000:25]), tag_counts[0:10000:25])

plt.plot(tag_counts[0:1000])
plt.title('Analysing only the first 1000 tags')
plt.grid()
plt.xlabel("Tag number")
plt.ylabel("Frequency")
plt.show()
# print(len(tag_counts[0:1000:25]), tag_counts[0:1000:25])

plt.plot(tag_counts[:100], c='g')
plt.scatter(x=list(range(0,100,5)), y=tag_counts[0:100:5], c='orange', label="quantiles with 0.05 intervals")
plt.scatter(x=list(range(0,100,25)), y=tag_counts[0:100:25], c='orange', label='quantiles with intervals of 0.25')
for x, y in zip(list(range(0,100,25)), tag_counts[0:100:25]):
  plt.annotate(s="({} , {})".format(x,y), xy=(x,y), xytext=(x-0.05, y+500))
plt.title("First 100 tags; Distribution and Frequency")
plt.grid()
plt.xlabel('Tag Number')
plt.ylabel("Frequency")
plt.legend()
plt.show()

"""### Number of Tags per Questions"""

tag_quest_count = tag_dtm.sum(axis=1).tolist()
tag_quest_count = [int(j) for i in tag_quest_count for j in i]
print(f"Number of datapoints: {len(tag_quest_count)}")

print(f"Maximum Number of tags in a question: {max(tag_quest_count)}")
print(f"Minimum Number of tags in a question: {min(tag_quest_count)}")
print(f"Average Number of tags in the questions: {(sum(tag_quest_count)*1.0)/len(tag_quest_count)}")

sns.countplot(tag_quest_count, palette='gist_rainbow')
plt.title("Number of tags in the questions ")
plt.xlabel("Number of Tags")
plt.ylabel("Number of questions")
plt.show()

"""### Most Frequent Tags

#### Wordcloud
"""

start = datetime.now()

tup = dict(result.items())
wordcloud = WordCloud(
    background_color = 'black',
    width = 1600,
    height = 800
).generate_from_frequencies(tup)

fig = plt.figure(figsize=(20,8.5))
plt.imshow(wordcloud)
plt.axis('off')
plt.tight_layout(pad = 0)
fig.savefig('/content/drive/MyDrive/AAIC/Case Studies/Stackoverflow Tag Predictor/tags-wordcloud.jpg')
plt.show()

print(f"Time taken to generate wordcloud: {datetime.now() - start}")

"""#### Top 30 Tags"""

i = np.arange(0,30)
tag_df_sorted.head(30).plot(kind='bar')
plt.title('Frequency of top 30 tags')
plt.xticks(i, tag_df_sorted['Tags'])
plt.xlabel('Tags')
plt.ylabel('Counts')
plt.show()

"""## Cleaning and Pre Processing of Questions"""

def striphtml(data):
  cleanre = re.compile('<.*?>')
  cleantext = re.sub(cleanre, ' ', str(data))
  return cleantext

stop_words = set(stopwords.words('english'))
stemmer = SnowballStemmer('english')

def create_connection(db_file):
    """ create a database connection to the SQLite database
        specified by db_file
    :param db_file: database file
    :return: Connection object or None
    """
    try:
        conn = sqlite3.connect(db_file)
        return conn
    except Error as e:
        print(e)
 
    return None

def create_table(conn, create_table_sql):
    """ create a table from the create_table_sql statement
    :param conn: Connection object
    :param create_table_sql: a CREATE TABLE statement
    :return:
    """
    try:
        c = conn.cursor()
        c.execute(create_table_sql)
    except Error as e:
        print(e)

def checkTableExists(dbcon):
    cursr = dbcon.cursor()
    str = "select name from sqlite_master where type='table'"
    table_names = cursr.execute(str)
    print("Tables in the database:")
    tables =table_names.fetchall() 
    print(tables[0][0])
    return(len(tables))

def create_database_table(database, query):
    conn = create_connection(database)
    if conn is not None:
        create_table(conn, query)
        checkTableExists(conn)
    else:
        print("Error! cannot create the database connection.")
    conn.close()

sql_create_table = """CREATE TABLE IF NOT EXISTS QuestionsProcessed (question text NOT NULL, code text, tags text, words_pre integer, words_post integer, is_code integer);"""
create_database_table('/content/drive/MyDrive/AAIC/Case Studies/Stackoverflow Tag Predictor/AAIC Data/Processed.db', sql_create_table)

start = datetime.now()
read_db = '/content/drive/MyDrive/AAIC/Case Studies/Stackoverflow Tag Predictor/AAIC Data/train_no_dup.db'
write_db = '/content/drive/MyDrive/AAIC/Case Studies/Stackoverflow Tag Predictor/Processed.db'

if os.path.isfile(read_db):
    conn_r = create_connection(read_db)
    if conn_r is not None:
        reader =conn_r.cursor()
        reader.execute("SELECT Title, Body, Tags From no_dup_train ORDER BY RANDOM() LIMIT 500000;")

if os.path.isfile(write_db):
    conn_w = create_connection(write_db)
    if conn_w is not None:
        tables = checkTableExists(conn_w)
        writer =conn_w.cursor()
        print(tables)
        if tables != 0:
            writer.execute("DELETE FROM QuestionsProcessed WHERE 1")
            print("Cleared All the rows")

print("Time taken to run this cell :", datetime.now() - start)

from tqdm import tqdm

nltk.download('punkt')

start = datetime.now()
preprocessed_data_list=[]
reader.fetchone()
questions_with_code=0
len_pre=0
len_post=0
questions_proccesed = 0
for row in tqdm(reader):
    is_code = 0
    title, question, tags = row[0], row[1], row[2]
    if '<code>' in question:
        questions_with_code+=1
        is_code = 1
    x = len(question)+len(title)
    len_pre+=x
    code = str(re.findall(r'<code>(.*?)</code>', question, flags=re.DOTALL))
    question=re.sub('<code>(.*?)</code>', '', question, flags=re.MULTILINE|re.DOTALL)
    question=striphtml(question.encode('utf-8'))
    title=title.encode('utf-8')
    question=str(title)+" "+str(question)
    question=re.sub(r'[^A-Za-z]+',' ',question)
    words=word_tokenize(str(question.lower()))
    #Removing all single letter and and stopwords from question except for the letter 'c'
    question=' '.join(str(stemmer.stem(j)) for j in words if j not in stop_words and (len(j)!=1 or j=='c'))
    len_post+=len(question)
    tup = (question,code,tags,x,len(question),is_code)
    questions_proccesed += 1
    writer.execute("insert into QuestionsProcessed(question,code,tags,words_pre,words_post,is_code) values (?,?,?,?,?,?)",tup)
    if (questions_proccesed%100000==0):
        print("number of questions completed=",questions_proccesed)

no_dup_avg_len_pre=(len_pre*1.0)/questions_proccesed
no_dup_avg_len_post=(len_post*1.0)/questions_proccesed

print( "Avg. length of questions(Title+Body) before processing: %d"%no_dup_avg_len_pre)
print( "Avg. length of questions(Title+Body) after processing: %d"%no_dup_avg_len_post)
print ("Percent of questions containing code: %d"%((questions_with_code*100.0)/questions_proccesed))

print("Time taken to run this cell :", datetime.now() - start)

conn_r.commit()
conn_w.commit()
conn_r.close()
conn_w.close()

if os.path.isfile(write_db):
    conn_r = create_connection(write_db)
    if conn_r is not None:
        reader =conn_r.cursor()
        reader.execute("SELECT question From QuestionsProcessed LIMIT 10")
        print("Questions after preprocessed")
        print('='*100)
        reader.fetchone()
        for row in reader:
            print(row)
            print('-'*100)
conn_r.commit()
conn_r.close()

write_db = '/content/drive/MyDrive/AAIC/Case Studies/Stackoverflow Tag Predictor/Processed.db'
if os.path.isfile(write_db):
    conn_r = create_connection(write_db)
    if conn_r is not None:
        preprocessed_data = pd.read_sql_query("""SELECT question, Tags FROM QuestionsProcessed""", conn_r)
conn_r.commit()
conn_r.close()

preprocessed_data.head()

print(f"number of data points in sample : {preprocessed_data.shape[0]}")
print(f"number of dimensions : {preprocessed_data.shape[1]}")

"""## Machine Learning Models

### Converting Tags to pose the problem as a Multi Label Problem
"""

vectorizer = CountVectorizer(tokenizer=lambda x: x.split(), binary='true') # binary = True gives a binary vectorizer
multilabel_y = vectorizer.fit_transform(preprocessed_data['tags'])

multilabel_y.shape

"""Taking all tags into consideration with Binary Vectoriser, will require lots of computational power. So sampling only some number of tags based on their frequency would be a better choice."""

def tags_to_choose(n):
  t = multilabel_y.sum(axis=0).tolist()[0]
  sorted_tags_i = sorted(range(len(t)), key=lambda i: t[i], reverse=True)
  multilabel_yn = multilabel_y[:, sorted_tags_i[:n]]
  return multilabel_yn

def questions_explained_fn(n):
  """
  This function returns the percent of questions covered by choosing top n tags to create the binary vectorizer.
  """
  multilabel_yn = tags_to_choose(n)
  x = multilabel_yn.sum(axis = 1)
  return (np.count_nonzero(x==0))

questions_explained = []
total_tags = multilabel_y.shape[1]
total_qs = preprocessed_data.shape[0]
for i in range(500, total_tags, 100):
    questions_explained.append(np.round(((total_qs-questions_explained_fn(i))/total_qs)*100,3))

fig, ax = plt.subplots()
ax.plot(questions_explained)
xlabel = list(500+np.array(range(-50,450,50))*50)
ax.set_xticklabels(xlabel)
plt.xlabel("Number of tags")
plt.ylabel("Number Questions coverd partially")
plt.grid()
plt.show()

print(f"By choosing 5500 tags, we can cover {questions_explained[50]}% of the questions")

multilabel_yx = tags_to_choose(5500)
print(f"Number of questions that are not covered: {questions_explained_fn(5500)} out of {total_qs}")

print(f"Number of tags in sample : {multilabel_y.shape[1]}")
print(f"Number of tags taken :  {multilabel_yx.shape[1]} ({(multilabel_yx.shape[1]/multilabel_y.shape[1])*100} %)")

"""### Splitting Data into Train and Test (80:20)"""

total_size=preprocessed_data.shape[0]
train_size=int(0.80*total_size)

x_train=preprocessed_data.head(train_size)
x_test=preprocessed_data.tail(total_size - train_size)

y_train = multilabel_yx[0:train_size,:]
y_test = multilabel_yx[train_size:total_size,:]

print(f"Number of data points in train data : {y_train.shape}")
print(f"Number of data points in test data : {y_test.shape}")

"""### Featurizing Data"""

start = datetime.now()
vectorizer = TfidfVectorizer(min_df=0.00009, max_features=200000, smooth_idf=True, norm="l2", \
                             tokenizer = lambda x: x.split(), sublinear_tf=False, ngram_range=(1,3))
x_train_multilabel = vectorizer.fit_transform(x_train['question'])
x_test_multilabel = vectorizer.transform(x_test['question'])
print(f"Time taken to run this cell : {datetime.now() - start}")

print(f"Dimensions of train data X: {x_train_multilabel.shape} Y : {y_train.shape}")
print(f"Dimensions of test data X: {x_test_multilabel.shape} Y : {y_test.shape}")

"""### Multi Label K Nearest Neighbours"""

# https://www.analyticsvidhya.com/blog/2017/08/introduction-to-multi-label-classification/
#https://stats.stackexchange.com/questions/117796/scikit-multi-label-classification
# classifier = LabelPowerset(GaussianNB())
"""
from skmultilearn.adapt import MLkNN
classifier = MLkNN(k=21)

# train
classifier.fit(x_train_multilabel, y_train)

# predict
predictions = classifier.predict(x_test_multilabel)
print(accuracy_score(y_test,predictions))
print(metrics.f1_score(y_test, predictions, average = 'macro'))
print(metrics.f1_score(y_test, predictions, average = 'micro'))
print(metrics.hamming_loss(y_test,predictions))
"""
# we are getting memory error because the multilearn package 
# is trying to convert the data into dense matrix
# ---------------------------------------------------------------------------
#MemoryError                               Traceback (most recent call last)
#<ipython-input-170-f0e7c7f3e0be> in <module>()
#----> classifier.fit(x_train_multilabel, y_train)

"""### Logistic Regression with OneVsRest Classifier

classifier = OneVsRestClassifier(SGDClassifier(loss='log', alpha=0.00001, penalty='l1'), n_jobs=-1)
classifier.fit(x_train_multilabel, y_train)
predictions = classifier.predict(x_test_multilabel)

print("accuracy :",metrics.accuracy_score(y_test,predictions))
print("macro f1 score :",metrics.f1_score(y_test, predictions, average = 'macro'))
print("micro f1 scoore :",metrics.f1_score(y_test, predictions, average = 'micro'))
print("hamming loss :",metrics.hamming_loss(y_test,predictions))
print("Precision recall report :\n",metrics.classification_report(y_test, predictions))
"""

from sklearn.externals import joblib

pkl_file = '/content/drive/MyDrive/AAIC/Case Studies/Stackoverflow Tag Predictor/AAIC Data/lr_with_equal_weight.pkl'
classifier = joblib.load(pkl_file)
preditions = classifier.predict(x_test_multilabel)

print("accuracy :",metrics.accuracy_score(y_test,predictions))
print("macro f1 score :",metrics.f1_score(y_test, predictions, average = 'macro'))
print("micro f1 scoore :",metrics.f1_score(y_test, predictions, average = 'micro'))
print("hamming loss :",metrics.hamming_loss(y_test,predictions))
print("Precision recall report :\n",metrics.classification_report(y_test, predictions))

"""### Adding more wieght to title but using only 500 tags


"""

sql_create_table = """CREATE TABLE IF NOT EXISTS QuestionsProcessed (question text NOT NULL, code text, tags text, words_pre integer, words_post integer, is_code integer);"""
create_database_table("Titlemoreweight.db", sql_create_table)

read_db = '/content/drive/MyDrive/AAIC/Case Studies/Stackoverflow Tag Predictor/AAIC Data/train_no_dup.db'
write_db = '/content/drive/MyDrive/AAIC/Case Studies/Stackoverflow Tag Predictor/AAIC Data/Titlemoreweight.db'
train_datasize = 400000
if os.path.isfile(read_db):
    conn_r = create_connection(read_db)
    if conn_r is not None:
        reader =conn_r.cursor()
        # for selecting first 0.5M rows
        reader.execute("SELECT Title, Body, Tags From no_dup_train LIMIT 500001;")
        # for selecting random points
        #reader.execute("SELECT Title, Body, Tags From no_dup_train ORDER BY RANDOM() LIMIT 500001;")

if os.path.isfile(write_db):
    conn_w = create_connection(write_db)
    if conn_w is not None:
        tables = checkTableExists(conn_w)
        writer =conn_w.cursor()
        if tables != 0:
            writer.execute("DELETE FROM QuestionsProcessed WHERE 1")
            print("Cleared All the rows")

"""#### Preprocessing Questions

1. Seperate Code from Body
2. Remove special characters from Title and Description (exclusing code)

    Remove stop words (Except 'C')

    Remove HTML Tags

3. Give more weightage to title. One way to achieve this is repeating title mutliple times. 

4. Convert all characters to small characters

5. SnowballStemmer to stem the words.
"""

#http://www.bernzilla.com/2008/05/13/selecting-a-random-row-from-an-sqlite-table/
start = datetime.now()
preprocessed_data_list=[]
reader.fetchone()
questions_with_code=0
len_pre=0
len_post=0
questions_proccesed = 0
for row in reader:
    
    is_code = 0
    
    title, question, tags = row[0], row[1], str(row[2])
    
    if '<code>' in question:
        questions_with_code+=1
        is_code = 1
    x = len(question)+len(title)
    len_pre+=x
    
    code = str(re.findall(r'<code>(.*?)</code>', question, flags=re.DOTALL))
    
    question=re.sub('<code>(.*?)</code>', '', question, flags=re.MULTILINE|re.DOTALL)
    question=striphtml(question.encode('utf-8'))
    
    title=title.encode('utf-8')
    
    # adding title three time to the data to increase its weight
    # add tags string to the training data
    
    question=str(title)+" "+str(title)+" "+str(title)+" "+question
    
#     if questions_proccesed<=train_datasize:
#         question=str(title)+" "+str(title)+" "+str(title)+" "+question+" "+str(tags)
#     else:
#         question=str(title)+" "+str(title)+" "+str(title)+" "+question

    question=re.sub(r'[^A-Za-z0-9#+.\-]+',' ',question)
    words=word_tokenize(str(question.lower()))
    
    #Removing all single letter and and stopwords from question exceptt for the letter 'c'
    question=' '.join(str(stemmer.stem(j)) for j in words if j not in stop_words and (len(j)!=1 or j=='c'))
    
    len_post+=len(question)
    tup = (question,code,tags,x,len(question),is_code)
    questions_proccesed += 1
    writer.execute("insert into QuestionsProcessed(question,code,tags,words_pre,words_post,is_code) values (?,?,?,?,?,?)",tup)
    if (questions_proccesed%100000==0):
        print("number of questions completed=",questions_proccesed)

no_dup_avg_len_pre=(len_pre*1.0)/questions_proccesed
no_dup_avg_len_post=(len_post*1.0)/questions_proccesed

print( "Avg. length of questions(Title+Body) before processing: %d"%no_dup_avg_len_pre)
print( "Avg. length of questions(Title+Body) after processing: %d"%no_dup_avg_len_post)
print ("Percent of questions containing code: %d"%((questions_with_code*100.0)/questions_proccesed))

print("Time taken to run this cell :", datetime.now() - start)

conn_r.commit()
conn_w.commit()
conn_r.close()
conn_w.close()

if os.path.isfile(write_db):
    conn_r = create_connection(write_db)
    if conn_r is not None:
        reader =conn_r.cursor()
        reader.execute("SELECT question From QuestionsProcessed LIMIT 10")
        print("Questions after preprocessed")
        print('='*100)
        reader.fetchone()
        for row in reader:
            print(row)
            print('-'*100)
conn_r.commit()
conn_r.close()

write_db = '/content/drive/MyDrive/AAIC/Case Studies/Stackoverflow Tag Predictor/AAIC Data/Titlemoreweight.db'
if os.path.isfile(write_db):
    conn_r = create_connection(write_db)
    if conn_r is not None:
        preprocessed_data = pd.read_sql_query("""SELECT question, Tags FROM QuestionsProcessed""", conn_r)
conn_r.commit()
conn_r.close()

preprocessed_data.head()

preprocessed_data.shape

print(f"Number of data points in sample: {preprocessed_data.shape[0]}")
print(f"Number of dimensions: {preprocessed_data.shape[1]}")

"""#### Preparing tags to work with MultiLabel Problems"""

vectorizer = CountVectorizer(tokenizer = lambda x: x.split(), binary='true')
multilabel_y = vectorizer.fit_transform(preprocessed_data['tags'])

"""#### Selecting only 500 tags"""

questions_explained = []
total_tags=multilabel_y.shape[1]
total_qs=preprocessed_data.shape[0]
for i in range(500, total_tags, 100):
    questions_explained.append(np.round(((total_qs-questions_explained_fn(i))/total_qs)*100,3))

fig, ax = plt.subplots()
ax.plot(questions_explained)
xlabel = list(500+np.array(range(-50,450,50))*50)
ax.set_xticklabels(xlabel)
plt.xlabel("Number of tags")
plt.ylabel("Number Questions coverd partially")
plt.grid()
plt.show()
print(f"With 5500 tags, {questions_explained[50]}% of questions can be covered")
print(f"With 500 tags, {questions_explained[0]}% of questions can be covered")

multilabel_yx = tags_to_choose(500)
print(f"With 500 tags, {questions_explained_fn(500)} questions are not covered out of {total_qs}")

train_datasize = 400000

x_train=preprocessed_data.head(train_datasize)
x_test=preprocessed_data.tail(preprocessed_data.shape[0] - 400000)

y_train = multilabel_yx[0:train_datasize,:]
y_test = multilabel_yx[train_datasize:preprocessed_data.shape[0],:]

print(f"Number of data points in train data : {y_train.shape}")
print(f"Number of data points in test data : {y_test.shape}")

"""#### Featurizing Data with TFIDF Vectorizer"""

start = datetime.now()
vectorizer = TfidfVectorizer(min_df=0.00009, max_features=200000, smooth_idf=True, norm="l2", \
                             tokenizer = lambda x: x.split(), sublinear_tf=False, ngram_range=(1,3))
x_train_multilabel = vectorizer.fit_transform(x_train['question'])
x_test_multilabel = vectorizer.transform(x_test['question'])
print(f"Time taken to run this cell : {datetime.now() - start}")

print(f"Dimensions of train data X: {x_train_multilabel.shape} Y : {y_train.shape}")
print(f"Dimensions of test data X: {x_test_multilabel.shape} Y: {y_test.shape}")

from sklearn.externals import joblib
classifier = joblib.load('/content/drive/MyDrive/AAIC/Case Studies/Stackoverflow Tag Predictor/AAIC Data/lr_with_more_title_weight.pkl')

start = datetime.now()
classifier = OneVsRestClassifier(SGDClassifier(loss='log', alpha=0.00001, penalty='l1'), n_jobs=-1)
classifier.fit(x_train_multilabel, y_train)
predictions = classifier.predict (x_test_multilabel)

print("Accuracy :",metrics.accuracy_score(y_test, predictions))
print("Hamming loss ",metrics.hamming_loss(y_test,predictions))

precision = precision_score(y_test, predictions, average='micro')
recall = recall_score(y_test, predictions, average='micro')
f1 = f1_score(y_test, predictions, average='micro')
 
print("Micro-average quality numbers")
print("Precision: {:.4f}, Recall: {:.4f}, F1-measure: {:.4f}".format(precision, recall, f1))

precision = precision_score(y_test, predictions, average='macro')
recall = recall_score(y_test, predictions, average='macro')
f1 = f1_score(y_test, predictions, average='macro')
 
print("Macro-average quality numbers")
print("Precision: {:.4f}, Recall: {:.4f}, F1-measure: {:.4f}".format(precision, recall, f1))

print (metrics.classification_report(y_test, predictions))
print("Time taken to run this cell :", datetime.now() - start)