issue #57 : Added NLP algorithm in Python and R

Author: Deepak Honakeri

Machine_Learning/src/NLP/NLP.py

@@ -0,0 +1,84 @@
+"""
+Created on Fri Apr  7 12:43:06 2017
+
+@author: Robert
+"""
+
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+plt.style.use('seaborn-deep')
+
+# Reading in data
+ds = pd.read_csv('Restaurant_Reviews.tsv', sep = '\t', quoting = 3)
+
+# Cleaning the text 
+import re
+from nltk.stem.porter import PorterStemmer
+from nltk.corpus import stopwords
+
+corpus = []
+
+# Remove non-alphabetic characters, make everything lower case, 
+# remove stopwords, append to corpus. 
+for i in range(len(ds)):
+   
+    ps = PorterStemmer()
+    review = re.sub('[^a-zA-Z]', ' ', ds['Review'][i])
+    review = review.split()
+    review = [ps.stem(w) for w in review if not w in set(stopwords.words('english'))]
+    review = ' '.join(review)
+    corpus.append(review)
+
+# Bag of words
+from sklearn.feature_extraction.text import CountVectorizer
+
+cv = CountVectorizer(lowercase = True, 
+                     max_features = 1500 )
+
+X = cv.fit_transform(corpus).toarray()
+y = ds['Liked'].values
+
+# Classifying
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import confusion_matrix, accuracy_score
+from sklearn.ensemble import RandomForestClassifier
+
+X_train, X_test, y_train, y_test = train_test_split(X,y, test_size = 0.2)
+
+clf = RandomForestClassifier(n_estimators = 100, criterion = "entropy")
+clf.fit(X_train, y_train)
+y_pred = clf.predict(X_test)
+cm = confusion_matrix(y_test, y_pred)
+acc = accuracy_score(y_test, y_pred)
+
+
+# Hadelin's challenge
+
+from sklearn.naive_bayes import GaussianNB
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.svm import SVC
+from sklearn.linear_model import LogisticRegression
+
+models = []
+models.append(('Logistic regression', LogisticRegression()))
+models.append(('KNN', KNeighborsClassifier()))
+models.append(('Random forest', RandomForestClassifier(n_estimators = 100, 
+                                                       criterion = 'entropy')))
+models.append(('NB', GaussianNB()))
+models.append(('KernelSVM', SVC(kernel='rbf') ))
+
+for name, model in models:
+    model.fit(X_train,y_train)
+    y_pred = model.predict(X_test)
+    
+    cm = confusion_matrix(y_test, y_pred)
+    print ("---------- model: " + name + '-------------------')
+    print("Acurracy: " + str(accuracy_score(y_test, y_pred)))
+    TP, TN, FP, FN = cm[1][1], cm[0][0], cm[0][1], cm[1][0]
+    precision = TP / (TP + FP)
+    recall = TP / (TP + FN)
+    F1 = 2* precision * recall/(precision+recall)
+    print("Precision: " + str( round(precision,2)) )
+    print("Recall: " + str(round(recall,2)) )
+    print("F1: " + str(round(F1,2)))