<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
 Sentiment Classification & How To "Frame Problems" for a Neural Network
by Andrew Trask
Twitter
Step1:  Lesson
Step2:  Project 1
Step6:  Transforming Text into Numbers
<ASSISTANT_TASK:>
Python Code:
def pretty_print_review_and_label(i):
    print(labels[i] + "\t:\t" + reviews[i][:80] + "...")
g = open('reviews.txt','r') # What we know!
reviews = list(map(lambda x:x[:-1],g.readlines()))
g.close()
g = open('labels.txt','r') # What we WANT to know!
labels = list(map(lambda x:x[:-1].upper(),g.readlines()))
g.close()
len(reviews)
reviews[0]
labels[0]
Explanation: Sentiment Classification & How To "Frame Problems" for a Neural Network
by Andrew Trask
Twitter: @iamtrask
Blog: http://iamtrask.github.io
What You Should Already Know
neural networks, forward and back-propagation
stochastic gradient descent
mean squared error
and train/test splits
Where to Get Help if You Need it
Re-watch previous Udacity Lectures
Leverage the recommended Course Reading Material - Grokking Deep Learning (40% Off: traskud17)
Shoot me a tweet @iamtrask
Tutorial Outline:
Intro: The Importance of "Framing a Problem"
Curate a Dataset
Developing a "Predictive Theory"
PROJECT 1: Quick Theory Validation
Transforming Text to Numbers
PROJECT 2: Creating the Input/Output Data
Putting it all together in a Neural Network
PROJECT 3: Building our Neural Network
Understanding Neural Noise
PROJECT 4: Making Learning Faster by Reducing Noise
Analyzing Inefficiencies in our Network
PROJECT 5: Making our Network Train and Run Faster
Further Noise Reduction
PROJECT 6: Reducing Noise by Strategically Reducing the Vocabulary
Analysis: What's going on in the weights?
Lesson: Curate a Dataset
End of explanation
print("labels.txt \t : \t reviews.txt\n")
pretty_print_review_and_label(2137)
pretty_print_review_and_label(12816)
pretty_print_review_and_label(6267)
pretty_print_review_and_label(21934)
pretty_print_review_and_label(5297)
pretty_print_review_and_label(4998)
Explanation: Lesson: Develop a Predictive Theory
End of explanation
from collections import Counter
import numpy as np
positive_counts = Counter()
negative_counts = Counter()
total_counts = Counter()
for i in range(len(reviews)):
    if(labels[i] == 'POSITIVE'):
        for word in reviews[i].split(" "):
            positive_counts[word] += 1
            total_counts[word] += 1
    else:
        for word in reviews[i].split(" "):
            negative_counts[word] += 1
            total_counts[word] += 1
positive_counts.most_common()
pos_neg_ratios = Counter()
for term,cnt in list(total_counts.most_common()):
    if(cnt > 100):
        pos_neg_ratio = positive_counts[term] / float(negative_counts[term]+1)
        pos_neg_ratios[term] = pos_neg_ratio
for word,ratio in pos_neg_ratios.most_common():
    if(ratio > 1):
        pos_neg_ratios[word] = np.log(ratio)
    else:
        pos_neg_ratios[word] = -np.log((1 / (ratio+0.01)))
# words most frequently seen in a review with a "POSITIVE" label
pos_neg_ratios.most_common()
# words most frequently seen in a review with a "NEGATIVE" label
list(reversed(pos_neg_ratios.most_common()))[0:30]
Explanation: Project 1: Quick Theory Validation
End of explanation
from IPython.display import Image
review = "This was a horrible, terrible movie."
Image(filename='sentiment_network.png')
review = "The movie was excellent"
Image(filename='sentiment_network_pos.png')
vocab = set(total_counts.keys())
# print(vocab)
vocab_size = len(vocab)
print(vocab_size)
import numpy as np
layer_0 = np.zeros((1, vocab_size))
layer_0
word2index = {}
for i, word in enumerate(vocab):
    word2index[word] = i
word2index
def update_input_layer(review):
     Modify the global layer_0 to represent the vector form of review.
    The element at a given index of layer_0 should represent \
    how many times the given word occurs in the review.
    Args:
        review(string) - the string of the review
    Returns:
        None
    
    global layer_0
    # clear out previous state, reset the layer to be all 0s
    layer_0 *= 0
    
    review_split = review.split(" ")
    # print(review_split)
    
    word2index = An index of our vocabulary
    For each word in our review,
    append layer_0 with the index of that word?
    
    for word in review_split:
        #print(layer_0)
        layer_0[0][word2index[word]] += 1
        #print(layer_0)
    
update_input_layer(reviews[0])
reviews[0]
layer_0
def get_target_for_label(label):
    Convert a label to `0` or `1`.
    Args:
        label(string) - Either "POSITIVE" or "NEGATIVE".
    Returns:
        `0` or `1`.
    
    if label == "POSITIVE": return 1
    elif label == "NEGATIVE": return 0
    else: return "Error, not positive or negative"
get_target_for_label("NEGATIVE")
print(get_target_for_label(labels[0]))
labels[0]
Explanation: Transforming Text into Numbers
End of explanation
<END_TASK>