from __future__ import division
import graphlab
import math
import string

"""
Explanation: Predicting sentiment from product reviews
The goal of this first notebook is to explore logistic regression and feature engineering with existing GraphLab functions.
In this notebook you will use product review data from Amazon.com to predict whether the sentiments about a product (from its reviews) are positive or negative.

Use SFrames to do some feature engineering
Train a logistic regression model to predict the sentiment of product reviews.
Inspect the weights (coefficients) of a trained logistic regression model.
Make a prediction (both class and probability) of sentiment for a new product review.
Given the logistic regression weights, predictors and ground truth labels, write a function to compute the accuracy of the model.
Inspect the coefficients of the logistic regression model and interpret their meanings.
Compare multiple logistic regression models.

Let's get started!
Fire up GraphLab Create
Make sure you have the latest version of GraphLab Create.
End of explanation
"""


products = graphlab.SFrame('amazon_baby.gl/')

"""
Explanation: Data preparation
We will use a dataset consisting of baby product reviews on Amazon.com.
End of explanation
"""


products

"""
Explanation: Now, let us see a preview of what the dataset looks like.
End of explanation
"""


products[269]

"""
Explanation: Build the word count vector for each review
Let us explore a specific example of a baby product.
End of explanation
"""


def remove_punctuation(text):
    import string
    return text.translate(None, string.punctuation) 

review_without_punctuation = products['review'].apply(remove_punctuation)
products['word_count'] = graphlab.text_analytics.count_words(review_without_punctuation)

"""
Explanation: Now, we will perform 2 simple data transformations:

Remove punctuation using Python's built-in string functionality.
Transform the reviews into word-counts.

Aside. In this notebook, we remove all punctuations for the sake of simplicity. A smarter approach to punctuations would preserve phrases such as "I'd", "would've", "hadn't" and so forth. See this page for an example of smart handling of punctuations.
End of explanation
"""


products[269]['word_count']

"""
Explanation: Now, let us explore what the sample example above looks like after these 2 transformations. Here, each entry in the word_count column is a dictionary where the key is the word and the value is a count of the number of times the word occurs.
End of explanation
"""


products = products[products['rating'] != 3]
len(products)

"""
Explanation: Extract sentiments
We will ignore all reviews with rating = 3, since they tend to have a neutral sentiment.
End of explanation
"""


products['sentiment'] = products['rating'].apply(lambda rating : +1 if rating > 3 else -1)
products

"""
Explanation: Now, we will assign reviews with a rating of 4 or higher to be positive reviews, while the ones with rating of 2 or lower are negative. For the sentiment column, we use +1 for the positive class label and -1 for the negative class label.
End of explanation
"""


train_data, test_data = products.random_split(.8, seed=1)
print len(train_data)
print len(test_data)

"""
Explanation: Now, we can see that the dataset contains an extra column called sentiment which is either positive (+1) or negative (-1).
Split data into training and test sets
Let's perform a train/test split with 80% of the data in the training set and 20% of the data in the test set. We use seed=1 so that everyone gets the same result.
End of explanation
"""


sentiment_model = graphlab.logistic_classifier.create(train_data,
                                                      target = 'sentiment',
                                                      features=['word_count'],
                                                      validation_set=None)

sentiment_model

"""
Explanation: Train a sentiment classifier with logistic regression
We will now use logistic regression to create a sentiment classifier on the training data. This model will use the column word_count as a feature and the column sentiment as the target. We will use validation_set=None to obtain same results as everyone else.
Note: This line may take 1-2 minutes.
End of explanation
"""


weights = sentiment_model.coefficients
weights.column_names()

"""
Explanation: Aside. You may get a warning to the effect of "Terminated due to numerical difficulties --- this model may not be ideal". It means that the quality metric (to be covered in Module 3) failed to improve in the last iteration of the run. The difficulty arises as the sentiment model puts too much weight on extremely rare words. A way to rectify this is to apply regularization, to be covered in Module 4. Regularization lessens the effect of extremely rare words. For the purpose of this assignment, however, please proceed with the model above.
Now that we have fitted the model, we can extract the weights (coefficients) as an SFrame as follows:
End of explanation
"""


num_positive_weights = ...
num_negative_weights = ...

print "Number of positive weights: %s " % num_positive_weights
print "Number of negative weights: %s " % num_negative_weights

"""
Explanation: There are a total of 121713 coefficients in the model. Recall from the lecture that positive weights $w_j$ correspond to weights that cause positive sentiment, while negative weights correspond to negative sentiment. 
Fill in the following block of code to calculate how many weights are positive ( >= 0). (Hint: The 'value' column in SFrame weights must be positive ( >= 0)).
End of explanation
"""


sample_test_data = test_data[10:13]
print sample_test_data['rating']
sample_test_data

"""
Explanation: Quiz Question: How many weights are >= 0?
Making predictions with logistic regression
Now that a model is trained, we can make predictions on the test data. In this section, we will explore this in the context of 3 examples in the test dataset.  We refer to this set of 3 examples as the sample_test_data.
End of explanation
"""


sample_test_data[0]['review']

"""
Explanation: Let's dig deeper into the first row of the sample_test_data. Here's the full review:
End of explanation
"""


sample_test_data[1]['review']

"""
Explanation: That review seems pretty positive.
Now, let's see what the next row of the sample_test_data looks like. As we could guess from the sentiment (-1), the review is quite negative.
End of explanation
"""


scores = sentiment_model.predict(sample_test_data, output_type='margin')
print scores

"""
Explanation: We will now make a class prediction for the sample_test_data. The sentiment_model should predict +1 if the sentiment is positive and -1 if the sentiment is negative. Recall from the lecture that the score (sometimes called margin) for the logistic regression model  is defined as:
$$
\mbox{score}_i = \mathbf{w}^T h(\mathbf{x}_i)
$$ 
where $h(\mathbf{x}_i)$ represents the features for example $i$.  We will write some code to obtain the scores using GraphLab Create. For each row, the score (or margin) is a number in the range [-inf, inf].
End of explanation
"""


print "Class predictions according to GraphLab Create:" 
print sentiment_model.predict(sample_test_data)

"""
Explanation: Predicting sentiment
These scores can be used to make class predictions as follows:
$$
\hat{y} = 
\left{
\begin{array}{ll}
      +1 & \mathbf{w}^T h(\mathbf{x}_i) > 0 \
      -1 & \mathbf{w}^T h(\mathbf{x}_i) \leq 0 \
\end{array} 
\right.
$$
Using scores, write code to calculate $\hat{y}$, the class predictions:
Run the following code to verify that the class predictions obtained by your calculations are the same as that obtained from GraphLab Create.
End of explanation
"""


print "Class predictions according to GraphLab Create:" 
print sentiment_model.predict(sample_test_data, output_type='probability')

"""
Explanation: Checkpoint: Make sure your class predictions match with the one obtained from GraphLab Create.
Probability predictions
Recall from the lectures that we can also calculate the probability predictions from the scores using:
$$
P(y_i = +1 | \mathbf{x}_i,\mathbf{w}) = \frac{1}{1 + \exp(-\mathbf{w}^T h(\mathbf{x}_i))}.
$$
Using the variable scores calculated previously, write code to calculate the probability that a sentiment is positive using the above formula. For each row, the probabilities should be a number in the range [0, 1].
Checkpoint: Make sure your probability predictions match the ones obtained from GraphLab Create.
End of explanation
"""


def get_classification_accuracy(model, data, true_labels):
    # First get the predictions
    ## YOUR CODE HERE
    ...
    
    # Compute the number of correctly classified examples
    ## YOUR CODE HERE
    ...

    # Then compute accuracy by dividing num_correct by total number of examples
    ## YOUR CODE HERE
    ...
    
    return accuracy

"""
Explanation: Quiz Question: Of the three data points in sample_test_data, which one (first, second, or third) has the lowest probability of being classified as a positive review?
Find the most positive (and negative) review
We now turn to examining the full test dataset, test_data, and use GraphLab Create to form predictions on all of the test data points for faster performance.
Using the sentiment_model, find the 20 reviews in the entire test_data with the highest probability of being classified as a positive review. We refer to these as the "most positive reviews."
To calculate these top-20 reviews, use the following steps:
1.  Make probability predictions on test_data using the sentiment_model. (Hint: When you call .predict to make predictions on the test data, use option output_type='probability' to output the probability rather than just the most likely class.)
2.  Sort the data according to those predictions and pick the top 20. (Hint: You can use the .topk method on an SFrame to find the top k rows sorted according to the value of a specified column.)
Quiz Question: Which of the following products are represented in the 20 most positive reviews? [multiple choice]
Now, let us repeat this exercise to find the "most negative reviews." Use the prediction probabilities to find the  20 reviews in the test_data with the lowest probability of being classified as a positive review. Repeat the same steps above but make sure you sort in the opposite order.
Quiz Question: Which of the following products are represented in the 20 most negative reviews?  [multiple choice]
Compute accuracy of the classifier
We will now evaluate the accuracy of the trained classifier. Recall that the accuracy is given by
$$
\mbox{accuracy} = \frac{\mbox{# correctly classified examples}}{\mbox{# total examples}}
$$
This can be computed as follows:

Step 1: Use the trained model to compute class predictions (Hint: Use the predict method)
Step 2: Count the number of data points when the predicted class labels match the ground truth labels (called true_labels below).
Step 3: Divide the total number of correct predictions by the total number of data points in the dataset.

Complete the function below to compute the classification accuracy:
End of explanation
"""


get_classification_accuracy(sentiment_model, test_data, test_data['sentiment'])

"""
Explanation: Now, let's compute the classification accuracy of the sentiment_model on the test_data.
End of explanation
"""


significant_words = ['love', 'great', 'easy', 'old', 'little', 'perfect', 'loves', 
      'well', 'able', 'car', 'broke', 'less', 'even', 'waste', 'disappointed', 
      'work', 'product', 'money', 'would', 'return']

len(significant_words)

"""
Explanation: Quiz Question: What is the accuracy of the sentiment_model on the test_data? Round your answer to 2 decimal places (e.g. 0.76).
Quiz Question: Does a higher accuracy value on the training_data always imply that the classifier is better?
Learn another classifier with fewer words
There were a lot of words in the model we trained above. We will now train a simpler logistic regression model using only a subset of words that occur in the reviews. For this assignment, we selected a 20 words to work with. These are:
End of explanation
"""


train_data['word_count_subset'] = train_data['word_count'].dict_trim_by_keys(significant_words, exclude=False)
test_data['word_count_subset'] = test_data['word_count'].dict_trim_by_keys(significant_words, exclude=False)

"""
Explanation: For each review, we will use the word_count column and trim out all words that are not in the significant_words list above. We will use the SArray dictionary trim by keys functionality. Note that we are performing this on both the training and test set.
End of explanation
"""


train_data[0]['review']

"""
Explanation: Let's see what the first example of the dataset looks like:
End of explanation
"""


print train_data[0]['word_count']

"""
Explanation: The word_count column had been working with before looks like the following:
End of explanation
"""


print train_data[0]['word_count_subset']

"""
Explanation: Since we are only working with a subset of these words, the column word_count_subset is a subset of the above dictionary. In this example, only 2 significant words are present in this review.
End of explanation
"""


simple_model = graphlab.logistic_classifier.create(train_data,
                                                   target = 'sentiment',
                                                   features=['word_count_subset'],
                                                   validation_set=None)
simple_model

"""
Explanation: Train a logistic regression model on a subset of data
We will now build a classifier with word_count_subset as the feature and sentiment as the target.
End of explanation
"""


get_classification_accuracy(simple_model, test_data, test_data['sentiment'])

"""
Explanation: We can compute the classification accuracy using the get_classification_accuracy function you implemented earlier.
End of explanation
"""


simple_model.coefficients

"""
Explanation: Now, we will inspect the weights (coefficients) of the simple_model:
End of explanation
"""


simple_model.coefficients.sort('value', ascending=False).print_rows(num_rows=21)

"""
Explanation: Let's sort the coefficients (in descending order) by the value to obtain the coefficients with the most positive effect on the sentiment.
End of explanation
"""


num_positive  = (train_data['sentiment'] == +1).sum()
num_negative = (train_data['sentiment'] == -1).sum()
print num_positive
print num_negative

"""
Explanation: Quiz Question: Consider the coefficients of simple_model. There should be 21 of them, an intercept term + one for each word in significant_words. How many of the 20 coefficients (corresponding to the 20 significant_words and excluding the intercept term) are positive for the simple_model?
Quiz Question: Are the positive words in the simple_model (let us call them positive_significant_words) also positive words in the sentiment_model?
Comparing models
We will now compare the accuracy of the sentiment_model and the simple_model using the get_classification_accuracy method you implemented above.
First, compute the classification accuracy of the sentiment_model on the train_data:
Now, compute the classification accuracy of the simple_model on the train_data:
Quiz Question: Which model (sentiment_model or simple_model) has higher accuracy on the TRAINING set?
Now, we will repeat this exercise on the test_data. Start by computing the classification accuracy of the sentiment_model on the test_data:
Next, we will compute the classification accuracy of the simple_model on the test_data:
Quiz Question: Which model (sentiment_model or simple_model) has higher accuracy on the TEST set?
Baseline: Majority class prediction
It is quite common to use the majority class classifier as the a baseline (or reference) model for comparison with your classifier model. The majority classifier model predicts the majority class for all data points. At the very least, you should healthily beat the majority class classifier, otherwise, the model is (usually) pointless.
What is the majority class in the train_data?
End of explanation
"""


