functions for nlp and cnn model

1 files changed, 82 insertions, 0 deletions

diff --git a/nlp.py b/nlp.py
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+import string
+import re
+import os
+from os import listdir
+from nltk.corpus import stopwords
+from nltk.stem.porter import PorterStemmer
+from collections import Counter
+import numpy as np
+from numpy import array
+import keras
+from keras.models import Model
+from keras.preprocessing.text import Tokenizer
+from keras.preprocessing.sequence import pad_sequences
+from keras.models import Sequential
+from keras.layers import Dense
+from keras.layers import Flatten
+from keras.layers import Embedding
+from keras.layers.convolutional import Conv1D
+from keras.layers.convolutional import MaxPooling1D
+from keras import metrics
+from keras import optimizers
+import pickle
+
+def clean_doc(doc, vocab):
+    doc = doc.lower()
+    # split into tokens by white space
+    tokens = doc.split()
+    # remove punctuation from each word
+    re_punc = re.compile('[%s]' % re.escape(string.punctuation))    
+    tokens = [re_punc.sub('' , w) for w in tokens]    
+    # filter out short tokens
+    tokens = [word for word in tokens if len(word) > 1]
+    # filter out stop words
+    stop_words = set(stopwords.words('english'))
+    tokens = [w for w in tokens if not w in stop_words]
+    # stemming of words
+    porter = PorterStemmer()
+    stemmed = [porter.stem(word) for word in tokens]
+    #print(stemmed[:100])
+    return tokens
+
+# load tokenizer
+with open('./nlp/tokenizer.pickle', 'rb') as handle:
+    tokenizer = pickle.load(handle)
+
+# load vocabulary
+with open('./nlp/vocabulary.txt', 'r') as vocab:
+    vocab = vocab.read()
+
+# create the CNN model
+def create_model(vocab_size, max_length):
+    model = Sequential()
+    model.add(Embedding(vocab_size, 32, input_length=max_length))
+    model.add(Conv1D(filters=16, kernel_size=4, activation='relu'))
+    model.add(MaxPooling1D(pool_size=2))
+    model.add(Flatten())
+    model.add(Dense(10, activation='relu'))
+    model.add(Dense(1, activation='sigmoid'))
+    opt = keras.optimizers.Adamax(learning_rate=0.002, beta_1=0.9, beta_2=0.999)
+
+    model.compile(loss='binary_crossentropy', optimizer=opt, metrics=[keras.metrics.AUC()])
+    return model
+
+def predict_sent(sent_for_pred):
+    max_length = 64
+    tokens = clean_doc(sent_for_pred, vocab)
+    tokens = [w for w in tokens if w in vocab]
+    # convert to line
+    line = ' '.join(tokens)
+    line = [line]
+    tokenized_sent = tokenizer.texts_to_sequences(line)
+    tokenized_sent = pad_sequences(tokenized_sent, maxlen=max_length, padding='post')
+    model = create_model(23154, 64)
+    # load the weights
+    checkpoint_path = "./nlp/weights.ckpt"
+    model.load_weights(checkpoint_path)
+    predict_sent = model.predict(tokenized_sent, verbose=0)
+    percent_sent = predict_sent[0,0]
+    if round(percent_sent) == 0:
+        return 'neg'
+    else:
+        return 'pos'
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