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KNN_IMDB.py
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KNN_IMDB.py
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#!/usr/bin/env python
# coding: utf-8
# In[1]:
import pandas as pd
import os
import numpy as np
from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer, TfidfVectorizer
#from sklearn.feature_extraction.text import TfidfTransformer
import nltk
from nltk.stem.porter import PorterStemmer
# In[2]:
count = CountVectorizer() #from sklearn.feature_extraction.text import CountVectorizer
docs = np.array([
'The sun is shining',
'The weather is sweet',
'The sun is shining, the weather is sweet, and one and one is two'])
bag = count.fit_transform(docs)
# In[3]:
print(count.vocabulary_) # vocabulary_ attribute of CountVectorizer() shows a mapping of terms to feature indices.
# In[4]:
print(bag.toarray())
# In[5]:
count_2 = CountVectorizer(ngram_range=(1,2))
bag_2 = count_2.fit_transform(docs)
print(count_2.vocabulary_)
print(bag_2.toarray())
# In[6]:
np.set_printoptions(precision=2) # These options determine the way floating point numbers are displayed.
# In[7]:
tfidf = TfidfTransformer(use_idf=True,
norm='l2',
smooth_idf=True)
print(tfidf.fit_transform(count.fit_transform(docs))
.toarray())
# In[8]:
tf_is = 3 # suppose term "is" has a frequency of 3
n_docs = 3
idf_is = np.log((n_docs+1) / (3+1))
tfidf_is = tf_is * (idf_is + 1)
print('tf-idf of term "is" = %.2f' % tfidf_is)
# In[9]:
tfidf = TfidfTransformer(use_idf=True, norm=None, smooth_idf=True)
raw_tfidf = tfidf.fit_transform(count.fit_transform(docs)).toarray()[-1]
raw_tfidf
# In[10]:
l2_tfidf = raw_tfidf / np.sqrt(np.sum(raw_tfidf**2))
l2_tfidf
# In[11]:
corpus = [
'This is the first document.',
'This document is the second document.',
'And this is the third one.',
'Is this the first document?',
]
vectorizer = TfidfVectorizer()
X = vectorizer.fit_transform(corpus)
print(vectorizer.get_feature_names())
print(X.shape)
# In[12]:
vectorizer_123 = TfidfVectorizer(ngram_range=(1,3))
X_123 = vectorizer_123.fit_transform(corpus)
print(vectorizer_123.get_feature_names())
print(X_123.shape)
# In[13]:
vectorizer_mm = TfidfVectorizer(max_df=1.0,min_df=0.5)
X_mm = vectorizer_mm.fit_transform(corpus)
print(vectorizer_mm.get_feature_names())
print(X_mm.shape)
# In[14]:
df = pd.read_csv('movie_data_cat.csv', encoding='utf-8')
df.head(10)
# In[15]:
df.shape
df.columns
# In[16]:
class_mapping = {label:idx for idx,label in enumerate(np.unique(df['sentiment']))}
print(class_mapping)
#use the mapping dictionary to transform the class labels into integers
df['sentiment'] = df['sentiment'].map(class_mapping)
df.head(10)
# In[17]:
df.loc[5635, 'review']#[-50:]
# In[18]:
#import regular expressions to clean up the text
import re
def preprocessor(text):
text = re.sub('<[^>]*>', '', text) # remove all html markup
emoticons = re.findall('(?::|;|=)(?:-)?(?:\)|\(|D|P)', text) # findall the emoticons
# remove the non-word chars '[\W]+'
# append the emoticons to end
#convert all to lowercase
# remove nose char for consistency
text = (re.sub('[\W]+', ' ', text.lower()) +
' '.join(emoticons).replace('-', ''))
return text
# In[19]:
preprocessor(df.loc[3635, 'review'])#[-50:]
# ## Apply the clean data preprocessor to the text
# In[20]:
preprocessor("</a>This :) is :( a test :-)!")
# In[21]:
# apply the preprocessor to the entire dataframe (i.e. column review)
df['review'] = df['review'].apply(preprocessor)
# ## Tokenise - break text into tokens
# In[22]:
def tokenizer(text):
return text.split()
# In[23]:
print(tokenizer("Tokenise this sentence into its individual words"))
# In[24]:
from nltk.corpus import stopwords
nltk.download('stopwords')
# create a method to accept a piece of tokenised text and return text back without the stopped words
# In[25]:
stop = set(stopwords.words('english'))
def stop_removal(text):
return [w for w in text if not w in stop]
# In[26]:
text = "This is a sample sentence, demonstrating the removal of stop words."
stopped_text = stop_removal(text.split())
print(stopped_text)
# ## Stemming - Processing tokens into their root form
# In[27]:
from nltk.stem.snowball import SnowballStemmer
from nltk.corpus import stopwords
#See which languages are supported.
print(" ".join(SnowballStemmer.languages))
# In[28]:
#get the english stemmer
stemmer = SnowballStemmer("english")
#stem a word
print(stemmer.stem("running"))
# In[29]:
#Decide not to stem stopwords with ignore_stopwords
stemmer2 = SnowballStemmer("english", ignore_stopwords=True)
#compare the two versions of the stemmer
print(stemmer.stem("having"))
print(stemmer2.stem("having"))
# In[30]:
#The 'english' stemmer is better than the original 'porter' stemmer.
print(SnowballStemmer("english").stem("generously"))
print(SnowballStemmer("porter").stem("generously"))
# # Tokenise + Stemming
# In[31]:
def tokenizer_stemmer(text):
return [stemmer.stem(word) for word in tokenizer(text)]#text.split()]
# In[32]:
tokenizer('runners like running and thus they run')
# In[33]:
tokenizer_stemmer('runners like running and thus they run')
# You can clearly see from the code above the effect of the stemmer on the tokens
# In[34]:
from nltk.corpus import stopwords
stop = stopwords.words('english')
[w for w in tokenizer_stemmer('A runner likes running and runs a lot')[-8:]
if w.lower() not in stop]
# # Training a model for sentiment classification
# In[35]:
X_train = df.loc[:25000, 'review'].values
y_train = df.loc[:25000, 'sentiment'].values
X_test = df.loc[25000:, 'review'].values
y_test = df.loc[25000:, 'sentiment'].values
### smaller sample
X_train = df.loc[:2500, 'review'].values
y_train = df.loc[:2500, 'sentiment'].values
# In[36]:
param_grid = [{'vect__ngram_range': [(1, 1)], #can also extract 2-grams of words in addition to the 1-grams (individual words)
'vect__stop_words': [stop, None], # use the stop dictionary of stopwords or not
'vect__tokenizer': [tokenizer_stemmer]}, # use a tokeniser and the stemmer
]
# In[38]:
from sklearn.neighbors import KNeighborsClassifier
from sklearn.pipeline import Pipeline
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import MultinomialNB
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.model_selection import GridSearchCV
param_grid = [{'vect__ngram_range': [(1, 5)], #can also extract 2-grams of words in addition to the 1-grams (individual words)
'vect__stop_words': [stop, None], # use the stop dictionary of stopwords or not
'vect__tokenizer': [tokenizer]}, # use a tokeniser and the stemmer
]
tfidf = TfidfVectorizer(strip_accents=None,
lowercase=False,
preprocessor=None)
mnb_tfidf = Pipeline([('vect', tfidf),
('clf', KNeighborsClassifier(n_neighbors=5))])
gs_mnb_tfidf = GridSearchCV(mnb_tfidf, param_grid,
scoring='accuracy',
cv=5,
verbose=1,
n_jobs=1)
gs_mnb_tfidf.fit(X_train, y_train)
print('Best parameter set: %s ' % gs_mnb_tfidf.best_params_)
print('CV Accuracy: %.3f' % gs_mnb_tfidf.best_score_)
clf = gs_mnb_tfidf.best_estimator_
print('Test Accuracy: %.3f' % clf.score(X_test, y_test))
# In[ ]: