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CSE6240-Homework 1 Analyzing a Movie Review Dataset Solved
The aim of this homework is to give hands-on experience with various text preprocessing, classification, and visualization modules. In this assignment, you will clean a text dataset, create term matrices, perform a classification experiment with cross-validation to tune the hyper-parameters, and visualize and interpret the results.
The Problem Statement:
Read through this tutorial on Kaggle, https://www.kaggle.com/c/word2vec-nlp-tutorial/details/part-1-forbeginners-bag-of-words, to familiarize yourself with its python tools and workflow. Write your own annotated IPython notebook(s) to reproduce the steps in the blog and complete the exercises below. You can start with the solution template and the sample code provided in the tutorial, but you should clean it up, document and refactor as necessary.
1: Processing text to create design matrices. Prepare four design matrices, X_counts, X_binary,
X_tfidf, X_binary_imbalance following the blog’s procedure for removing markup, punctuation and stop words. X_counts entries contain the raw word counts (the blog does this). X_binary modifies X_counts so that all elements are either 0 or 1. X_tfidf modifies X_counts by applying the sklearn tfidf vectorizer (http://scikit-learn.org/stable/modules/feature_extraction.html) with smooth_idf=false. For the X_binary_imbalance, start with X_counts, set your rng seed to 0 and then delete 75% of the rows corresponding to sentiment=1. This creates an imbalanced data set.
2: Feature space similarity experiment. Write a function dist(X, i, j,
distance_function=’Euclidean’) which returns the (Euclidean) distance between rows i and j of a design matrix. Also write a function topk(X, k) which returns ((i1,j1,d1),...(ik,jk,dk)) where (ix,jx) are the indices of the xth closest pair, and dx is the corresponding distance. You can break ties randomly. Then use topk() to find the closest review pairs for each design matrix and print the following: the indices of the reviews, the distance, the first 20 characters of each review, the labels for each review. Are the pairs always the same?
3. Classification Experiment. Now you’re going to tune an SVM classifier using each design matrix and measure the resultant performance. Read the sklearn docs (http://scikitlearn.org/stable/modules/cross_validation.html) on cross-validation to see the methods to use.
● Set your rng seed to 0 and create an initial learning_set / test_set split of 80-20.
● Now we want to use a linear SVM (svm.SVC with kernel=linear) and pick the best
C value for our classifier
○ Repeat for each of the four design matrices:
■
Repeat 30 times:
● Pick a random value of C uniformly in the interval (1e-4, 1e4)
● Use 5-fold cross-validation to train the SVM
● Estimate and record the F1-score
■
Select the value of C which produced the best F1-score and estimate the f1_score on the test dataset.
■
Retrain the classifier using the entire learning set with this C value.
.
Which design matrix performed best (e.g., which encoding method worked best)? What was the lift (improvement in F1-Score) between the worst and best cases for each experiment?
4. Learning Curve Experiment. Using a logistic regression classifier and the design matrix X_counts generate a learning curve:
● Set your rng seed to 0 and create an initial learning_set / test_set split of 80-20
● Generate a learning curve (xval vs training error) for n=(100, 500, 1000, 2000, 3000,
4000, 5000, 7500, 10000, 15000, 20000) training instances.
● Interpret the learning curve.
The Problem Statement:
Read through this tutorial on Kaggle, https://www.kaggle.com/c/word2vec-nlp-tutorial/details/part-1-forbeginners-bag-of-words, to familiarize yourself with its python tools and workflow. Write your own annotated IPython notebook(s) to reproduce the steps in the blog and complete the exercises below. You can start with the solution template and the sample code provided in the tutorial, but you should clean it up, document and refactor as necessary.
1: Processing text to create design matrices. Prepare four design matrices, X_counts, X_binary,
X_tfidf, X_binary_imbalance following the blog’s procedure for removing markup, punctuation and stop words. X_counts entries contain the raw word counts (the blog does this). X_binary modifies X_counts so that all elements are either 0 or 1. X_tfidf modifies X_counts by applying the sklearn tfidf vectorizer (http://scikit-learn.org/stable/modules/feature_extraction.html) with smooth_idf=false. For the X_binary_imbalance, start with X_counts, set your rng seed to 0 and then delete 75% of the rows corresponding to sentiment=1. This creates an imbalanced data set.
2: Feature space similarity experiment. Write a function dist(X, i, j,
distance_function=’Euclidean’) which returns the (Euclidean) distance between rows i and j of a design matrix. Also write a function topk(X, k) which returns ((i1,j1,d1),...(ik,jk,dk)) where (ix,jx) are the indices of the xth closest pair, and dx is the corresponding distance. You can break ties randomly. Then use topk() to find the closest review pairs for each design matrix and print the following: the indices of the reviews, the distance, the first 20 characters of each review, the labels for each review. Are the pairs always the same?
3. Classification Experiment. Now you’re going to tune an SVM classifier using each design matrix and measure the resultant performance. Read the sklearn docs (http://scikitlearn.org/stable/modules/cross_validation.html) on cross-validation to see the methods to use.
● Set your rng seed to 0 and create an initial learning_set / test_set split of 80-20.
● Now we want to use a linear SVM (svm.SVC with kernel=linear) and pick the best
C value for our classifier
○ Repeat for each of the four design matrices:
■
Repeat 30 times:
● Pick a random value of C uniformly in the interval (1e-4, 1e4)
● Use 5-fold cross-validation to train the SVM
● Estimate and record the F1-score
■
Select the value of C which produced the best F1-score and estimate the f1_score on the test dataset.
■
Retrain the classifier using the entire learning set with this C value.
.
Which design matrix performed best (e.g., which encoding method worked best)? What was the lift (improvement in F1-Score) between the worst and best cases for each experiment?
4. Learning Curve Experiment. Using a logistic regression classifier and the design matrix X_counts generate a learning curve:
● Set your rng seed to 0 and create an initial learning_set / test_set split of 80-20
● Generate a learning curve (xval vs training error) for n=(100, 500, 1000, 2000, 3000,
4000, 5000, 7500, 10000, 15000, 20000) training instances.
● Interpret the learning curve.
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