Source Code/notebooks/testing.py

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

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1MCstbEJ_U20yRJDGRmZTjIpGTCzTFL_o
"""

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

!pip install -qqq ftfy

!pip install -qqq json_file

!python -m spacy download en_core_web_lg

!pip install -U SpaCy==2.2.0

## Import required libraries

## warnings
import warnings
warnings.filterwarnings("ignore")

## for data
import numpy as np
import pandas as pd

## for plotting
import matplotlib.pyplot as plt
import seaborn as sns

## Bag of Words
from sklearn.feature_extraction.text import CountVectorizer

## TF-IDF 
from sklearn.feature_extraction.text import TfidfVectorizer

## Train-Test Split
from sklearn.model_selection import train_test_split

## for processing
import nltk
import re
import ftfy
from nltk.stem import WordNetLemmatizer 
from nltk.corpus import stopwords
nltk.download('stopwords')
nltk.download('punkt')
nltk.download('wordnet')
nltk.download('averaged_perceptron_tagger')

## Feature selection
from sklearn import feature_selection

## Support vector machine
from sklearn.pipeline import Pipeline
import sklearn.metrics as skm
from sklearn.metrics import confusion_matrix, accuracy_score
from sklearn.svm import SVC

## for saving and loading model
import pickle

## for word embedding with Spacy
import spacy
import en_core_web_lg

# ## for word embedding
# import gensim
# import gensim.downloader as gensim_api
# from gensim.models import Word2Vec
# from gensim.models import KeyedVectors
# from keras.preprocessing.text import Tokenizer
# from keras.preprocessing.sequence import pad_sequences

# ## for deep learning
# from keras.models import load_model
# from keras.models import Model, Sequential
# from keras.callbacks import EarlyStopping, ModelCheckpoint
# from keras.layers import Conv1D, Dense, Input, LSTM, Embedding, Dropout, Activation, MaxPooling1D
# from tensorflow.keras import models, layers, preprocessing as kprocessing
# from tensorflow.keras import backend as K
# from keras.models import model_from_json
# from keras.layers import Lambda
# import tensorflow as tf
# import json
# import json_file

# Expand Contraction
cList = {
  "ain't": "am not",
  "aren't": "are not",
  "can't": "cannot",
  "can't've": "cannot have",
  "'cause": "because",
  "could've": "could have",
  "couldn't": "could not",
  "couldn't've": "could not have",
  "didn't": "did not",
  "doesn't": "does not",
  "don't": "do not",
  "hadn't": "had not",
  "hadn't've": "had not have",
  "hasn't": "has not",
  "haven't": "have not",
  "he'd": "he would",
  "he'd've": "he would have",
  "he'll": "he will",
  "he'll've": "he will have",
  "he's": "he is",
  "how'd": "how did",
  "how'd'y": "how do you",
  "how'll": "how will",
  "how's": "how is",
  "I'd": "I would",
  "I'd've": "I would have",
  "I'll": "I will",
  "I'll've": "I will have",
  "I'm": "I am",
  "I've": "I have",
  "isn't": "is not",
  "it'd": "it had",
  "it'd've": "it would have",
  "it'll": "it will",
  "it'll've": "it will have",
  "it's": "it is",
  "let's": "let us",
  "ma'am": "madam",
  "mayn't": "may not",
  "might've": "might have",
  "mightn't": "might not",
  "mightn't've": "might not have",
  "must've": "must have",
  "mustn't": "must not",
  "mustn't've": "must not have",
  "needn't": "need not",
  "needn't've": "need not have",
  "o'clock": "of the clock",
  "oughtn't": "ought not",
  "oughtn't've": "ought not have",
  "shan't": "shall not",
  "sha'n't": "shall not",
  "shan't've": "shall not have",
  "she'd": "she would",
  "she'd've": "she would have",
  "she'll": "she will",
  "she'll've": "she will have",
  "she's": "she is",
  "should've": "should have",
  "shouldn't": "should not",
  "shouldn't've": "should not have",
  "so've": "so have",
  "so's": "so is",
  "that'd": "that would",
  "that'd've": "that would have",
  "that's": "that is",
  "there'd": "there had",
  "there'd've": "there would have",
  "there's": "there is",
  "they'd": "they would",
  "they'd've": "they would have",
  "they'll": "they will",
  "they'll've": "they will have",
  "they're": "they are",
  "they've": "they have",
  "to've": "to have",
  "wasn't": "was not",
  "we'd": "we had",
  "we'd've": "we would have",
  "we'll": "we will",
  "we'll've": "we will have",
  "we're": "we are",
  "we've": "we have",
  "weren't": "were not",
  "what'll": "what will",
  "what'll've": "what will have",
  "what're": "what are",
  "what's": "what is",
  "what've": "what have",
  "when's": "when is",
  "when've": "when have",
  "where'd": "where did",
  "where's": "where is",
  "where've": "where have",
  "who'll": "who will",
  "who'll've": "who will have",
  "who's": "who is",
  "who've": "who have",
  "why's": "why is",
  "why've": "why have",
  "will've": "will have",
  "won't": "will not",
  "won't've": "will not have",
  "would've": "would have",
  "wouldn't": "would not",
  "wouldn't've": "would not have",
  "y'all": "you all",
  "y'alls": "you alls",
  "y'all'd": "you all would",
  "y'all'd've": "you all would have",
  "y'all're": "you all are",
  "y'all've": "you all have",
  "you'd": "you had",
  "you'd've": "you would have",
  "you'll": "you you will",
  "you'll've": "you you will have",
  "you're": "you are",
  "you've": "you have"
}

c_re = re.compile('(%s)' % '|'.join(cList.keys()))

def expandContractions(text, c_re=c_re):
    def replace(match):
        return cList[match.group(0)]
    return c_re.sub(replace, text)

## Function to perform stepwise cleaning process
def tweets_cleaner(tweet):
  cleaned_tweets = []
  tweet = tweet.lower() #lowercase
    
  # if url links then don't append to avoid news articles
  # also check tweet length, save those > 5 
  if re.match("(\w+:\/\/\S+)", tweet) == None and len(tweet) > 5:
    
    #remove hashtag, @mention, emoji and image URLs
    tweet = ' '.join(re.sub("(@[A-Za-z0-9]+)|(\#[A-Za-z0-9]+)|(<Emoji:.*>)|(pic\.twitter\.com\/.*)", " ", tweet).split())

    #fix weirdly encoded texts
    tweet = ftfy.fix_text(tweet)

    #expand contraction
    tweet = expandContractions(tweet)


    #remove punctuation
    tweet = ' '.join(re.sub("([^0-9A-Za-z \t])", " ", tweet).split())

    #stop words and lemmatization
    stop_words = set(stopwords.words('english'))
    word_tokens = nltk.word_tokenize(tweet)

    lemmatizer=WordNetLemmatizer()
    filtered_sentence = [lemmatizer.lemmatize(word) for word in word_tokens if not word in stop_words]
    # back to string from list
    tweet = ' '.join(filtered_sentence) # join words with a space in between them

    cleaned_tweets.append(tweet)

  return cleaned_tweets

nlp = en_core_web_lg.load()

## Load the model
SVM = "/content/drive/MyDrive/NLP/Depression_Detection/modeling/model_svm.pkl" 
with open(SVM, 'rb') as file:  
    clf = pickle.load(file)

clf

test_tweet = "I hate my life"

corpus = tweets_cleaner(test_tweet)

corpus

## word-embedding
test = pd.np.array([pd.np.array([token.vector for token in nlp(s)]).mean(axis=0) * pd.np.ones((300)) \
                           for s in corpus])

labels_pred = clf.predict(test)

labels_pred[0]

# loaded_model = model_from_json(open("/content/drive/MyDrive/NLP/Depression_Detection/modeling/model.json", "r").read(),
#                               custom_objects={'tf': tf}) 
# # load weights into new model
# loaded_model.load_weights("/content/drive/MyDrive/NLP/Depression_Detection/modeling/model.h5")
# print("Loaded model from disk")