# Import modules import numpy as np import pandas as pd import nltk # Set seed for reproducibility np.random.seed(5) # Read in IMDb and Wikipedia movie data (both in same file) movies_df = pd.read_csv("datasets/movies.csv") print("Number of movies loaded: %s " % (len(movies_df))) # Display the data movies_df

# Combine wiki_plot and imdb_plot into a single column movies_df["plot"] = movies_df["wiki_plot"].astype(str) + "\n" + \ movies_df["imdb_plot"].astype(str) # Inspect the new DataFrame movies_df.head()

# Tokenize a paragraph into sentences and store in sent_tokenized sent_tokenized = [sent for sent in nltk.sent_tokenize(""" Today (May 19, 2016) is his only daughter's wedding. Vito Corleone is the Godfather. """)] # Word Tokenize first sentence from sent_tokenized, save as words_tokenized words_tokenized = [word for word in nltk.word_tokenize(sent_tokenized[0])] # Remove tokens that do not contain any letters from words_tokenized import re filtered = [word for word in words_tokenized if re.search('[a-zA-Z]', word)] # Display filtered words to observe words after tokenization filtered

# Import the SnowballStemmer to perform stemming from nltk.stem.snowball import SnowballStemmer # Create an English language SnowballStemmer object stemmer = SnowballStemmer("english") # Print filtered to observe words without stemming print("Without stemming: ", filtered) # Stem the words from filtered and store in stemmed_words stemmed_words = [stemmer.stem(t) for t in filtered] # Print the stemmed_words to observe words after stemming print("After stemming: ", stemmed_words)

# Define a function to perform both stemming and tokenization def tokenize_and_stem(text): # Tokenize by sentence, then by word tokens = [word for sent in nltk.sent_tokenize(text) for word in nltk.word_tokenize(sent)] # Filter out raw tokens to remove noise filtered_tokens = [token for token in tokens if re.search('[a-zA-Z]', token)] # Stem the filtered_tokens stems = [stemmer.stem(t) for t in filtered_tokens] return stems words_stemmed = tokenize_and_stem("Today (May 19, 2016) is his only daughter's wedding.") print(words_stemmed)

# Import TfidfVectorizer to create TF-IDF vectors from sklearn.feature_extraction.text import TfidfVectorizer # Instantiate TfidfVectorizer object with stopwords and tokenizer # parameters for efficient processing of text tfidf_vectorizer = TfidfVectorizer(max_df=0.8, max_features=200000, min_df=0.2, stop_words='english', use_idf=True, tokenizer=tokenize_and_stem, ngram_range=(1,3))

# Fit and transform the tfidf_vectorizer with the "plot" of each movie # to create a vector representation of the plot summaries tfidf_matrix = tfidf_vectorizer.fit_transform([x for x in movies_df["plot"]]) print(tfidf_matrix.shape)

# Import k-means to perform clustering from sklearn.cluster import KMeans # Create a KMeans object with 5 clusters and save as km km = KMeans(n_clusters=5) # Fit the k-means object with tfidf_matrix km.fit(tfidf_matrix) clusters = km.labels_.tolist() # Create a column cluster to denote the generated cluster for each movie movies_df["cluster"] = clusters # Display number of films per cluster (clusters from 0 to 4) movies_df['cluster'].value_counts()

# Import cosine_similarity to calculate similarity of movie plots from sklearn.metrics.pairwise import cosine_similarity # Calculate the similarity distance similarity_distance = 1 - cosine_similarity(tfidf_matrix)

# Import matplotlib.pyplot for plotting graphs import matplotlib.pyplot as plt # Configure matplotlib to display the output inline %matplotlib inline # Import modules necessary to plot dendrogram from scipy.cluster.hierarchy import linkage, dendrogram

# Create mergings matrix mergings = linkage(similarity_distance, method='complete') # Plot the dendrogram, using title as label column dendrogram_ = dendrogram(mergings, labels=[x for x in movies_df["title"]], leaf_rotation=90, leaf_font_size=16, ) # Adjust the plot fig = plt.gcf() _ = [lbl.set_color('r') for lbl in plt.gca().get_xmajorticklabels()] fig.set_size_inches(108, 21) # Show the plotted dendrogram plt.show()

# Answer the question ans = "Gladiator" print(ans)