Untitled

import re
import string
import nltk
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
from collections import Counter
import matplotlib.pyplot as plt
from wordcloud import WordCloud
import gensim
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.decomposition import TruncatedSVD

# 1. Tokenization, Lemmatization, Stop words removal
def preprocess_text(text):
    # Tokenization
    tokens = nltk.word_tokenize(text.lower())
    
    # Remove punctuation
    tokens = [token for token in tokens if token not in string.punctuation]
    
    # Lemmatization
    lemmatizer = WordNetLemmatizer()
    tokens = [lemmatizer.lemmatize(token) for token in tokens]
    
    # Stop words removal
    stop_words = set(stopwords.words('english'))
    tokens = [token for token in tokens if token not in stop_words]
    
    return tokens

# 2. TF-IDF, Bar chart, Word cloud
def visualize_top_words(texts, n=10):
    vectorizer = TfidfVectorizer()
    X = vectorizer.fit_transform(texts)
    
    # Get feature names and TF-IDF scores
    feature_names = vectorizer.get_feature_names_out()
    tfidf_scores = X.toarray().sum(axis=0)
    
    # Sort by TF-IDF scores
    top_words = sorted(zip(feature_names, tfidf_scores), key=lambda x: x[1], reverse=True)[:n]
    
    # Bar chart
    word_scores = [score for word, score in top_words]
    word_names = [word for word, score in top_words]
    plt.figure(figsize=(10, 6))
    plt.bar(range(n), word_scores, align='center')
    plt.xticks(range(n), word_names, rotation=90)
    plt.xlabel('Words')
    plt.ylabel('TF-IDF Score')
    plt.title('Top Words by TF-IDF')
    plt.show()
    
    # Word cloud
    word_cloud = WordCloud(width=800, height=400).generate_from_frequencies(dict(top_words))
    plt.figure(figsize=(10, 6))
    plt.imshow(word_cloud, interpolation='bilinear')
    plt.axis('off')
    plt.show()

# 3. Word2Vec
def train_word2vec(texts):
    sentences = [preprocess_text(text) for text in texts]
    model = gensim.models.Word2Vec(sentences, vector_size=100, window=5, min_count=1, workers=4)
    return model

# 4. Autoencoder
def train_autoencoder(texts):
    vectorizer = TfidfVectorizer()
    X = vectorizer.fit_transform(texts)
    svd = TruncatedSVD(n_components=100)
    X_reduced = svd.fit_transform(X)
    return svd, vectorizer

# 5. Named Entity Recognition (NER)
def perform_ner(texts):
    # You can use a library like spaCy or NLTK for NER
    pass

# 6. Exploratory Data Analysis (EDA)
def perform_eda(texts):
    # Implement your EDA analysis here
    pass

# 7. Summarization
def summarize_texts(texts):
    # You can use a library like NLTK or Gensim for text summarization
    pass

# 8. GPT model
def use_gpt_model(texts, task):
    # You can use a pre-trained GPT model like GPT-2 or GPT-3 for various tasks
    pass

# 9. Compare results
def compare_results(result1, result2):
    # Implement your comparison logic here
    pass

# 10. Propose algorithm/metric change
def propose_change():
    # Propose your algorithm or metric change and explain the rationale
    pass

# 11. Compare algorithm performance with different parameters
def compare_algorithm_performance(algorithm, texts, params):
    # Implement your algorithm comparison logic here
    pass

# 12. Document methodology and results
def document_results(methodology, results):
    # Write your methodology and results to a Word file
    pass

# 13. Present outputs on GitHub
def present_on_github(python_files, documentation):
    # Upload your Python files and documentation to GitHub
    pass

# Example usage
texts = ["This is a sample text.", "Another example text."]

# Preprocess texts
processed_texts = [" ".join(preprocess_text(text)) for text in texts]

# Visualize top words
visualize_top_words(processed_texts)

# Train Word2Vec model
word2vec_model = train_word2vec(processed_texts)

# Train Autoencoder
autoencoder, vectorizer = train_autoencoder(processed_texts)

# Perform NER
perform_ner(processed_texts)

# Perform EDA
perform_eda(processed_texts)

# Summarize texts
summaries = summarize_texts(processed_texts)

# Use GPT model
gpt_results = use_gpt_model(processed_texts, "question_answering")

# Compare results
compare_results(word2vec_model.wv.most_similar("sample"), autoencoder.components_)

# Propose algorithm/metric change
propose_change()

# Compare algorithm performance
compare_algorithm_performance(train_word2vec, processed_texts, {"vector_size": [100, 200]})

# Document results
document_results("This is the methodology.", "These are the results.")

# Present on GitHub
present_on_github(["script.py"], ["documentation.txt"])
```