import numpy as np
import time
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.feature_extraction import text
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.cluster import KMeans
from nltk.tokenize import RegexpTokenizer
from nltk.stem.snowball import SnowballStemmer
import psutil
import os
import multiprocessing as mp


num_cores = mp.cpu_count()
print("The kernal has",num_cores, "cores and you can find information regarding memory usage in",
      psutil.virtual_memory())

items = pd.read_csv("product.csv")
print(items.head())
print(items.info())
items[items['Items'].duplicated(keep=False)].sort_values('Items').head(8)
items = items.drop_duplicates('Items')
start_time = time.time()

punc = ['.', ',', '"', "'", '?', '!', ':', ';', '(', ')', '[', ']', '{', '}',"%"]
stop_words = text.ENGLISH_STOP_WORDS #commonly used words to ignore (such as and,or,is,etc)
desc = items['Items'].values
vectorizer = TfidfVectorizer(stop_words=stop_words)
X = vectorizer.fit_transform(desc)

word_features = vectorizer.get_feature_names()
print(len(word_features))
print(word_features[10000:10002])

stemmer = SnowballStemmer('english')
tokenizer = RegexpTokenizer(r'[a-zA-Z\']+')

def tokenize(text):
    return [stemmer.stem(word) for word in tokenizer.tokenize(text.lower())]

vectorizer2 = TfidfVectorizer(stop_words=stop_words, tokenizer=tokenize)
X2 = vectorizer2.fit_transform(desc)
word_features2 = vectorizer2.get_feature_names()
print(len(word_features2))
print(word_features2[:10])

vectorizer3 = TfidfVectorizer(stop_words=stop_words, tokenizer=tokenize, max_features=1000)
X3 = vectorizer3.fit_transform(desc)
words = vectorizer3.get_feature_names()

size = []
for i in range(1,11):
    kmeans = KMeans(n_clusters=i,init='k-means++', max_iter=300, n_init=10, random_state=0)
    kmeans.fit(X3)
    size.append(kmeans.inertia_)
plt.plot(range(1,11), size)
plt.title('The Elbow Method')
plt.xlabel('Number of clusters')
plt.ylabel('WCSS')
plt.savefig('elbow.png')
plt.show()

print(words[250:300])

# n_init(number of iterations for clustering) n_jobs(number of cpu cores to use)
kmeans = KMeans(n_clusters=3, n_init=20, n_jobs=2)
kmeans.fit(X3)
# We look at 3 the clusters generated by k-means.
common_words = kmeans.cluster_centers_.argsort()[:, -1:-26:-1]
print(common_words)
for num, centroid in enumerate(common_words):
    print("Interest group", str(num) + ' : ' + ', '.join(words[word] for word in centroid))

print("--- %s seconds ---" % (time.time() - start_time))