[SOLVED] CSC411 Machine Learning & Data Mining ASSIGNMENT#1

Description

In this assignment we will be working with the Boston Houses dataset. This dataset contains 506 entries. Each entry consists of a house price and 13 features for houses within the Boston area. We suggest working in python and using the scikit-learn package to load the data.

Starter code written in python is provided for each question.

1          Learning basics of regression in Python (3%)

This question will take you step-by-step through performing basic linear regression on the Boston Houses dataset. You need to submit modular code for this question. Your code needs to be functional once downloaded. Non-functional code will result in losing all marks for this question. If your code is non-modular or otherwise difficult to understand, you risk losing a significant portion of the marks, even if the code is functional.

Environment setup: For this question you are strongly encouraged to use the following python packages:

• sklearn
• matplotlib
• numpy

It is strongly recommended that you download and install Anaconda 3.4 to manage the above installations. This is a Data Science package that can be downloaded from https://www.anaconda. com/download/.

You will submit a complete regression analysis for the Boston Housing data. To do that, here are the necessary steps:

• Load the Boston housing data from the sklearn datasets module
• Describe and summarize the data in terms of number of data points, dimensions, target, etc
• Visualization: present a single grid containing plots for each feature against the target. Choose the appropriate axis for dependent vs. independent variables. Hint: use pyplot.tight layout function to make your grid readable
• Divide your data into training and test sets, where the training set consists of 80% of the data points (chosen at random). Hint: You may find numpy.random.choice useful
• Write code to perform linear regression to predict the targets using the training data. Remember to add a bias term to your model.
• Tabulate each feature along with its associated weight and present them in a table. Explain what the sign of the weight means in the third column (’INDUS’) of this table. Does the sign match what you expected? Why?
• Test the fitted model on your test set and calculate the Mean Square Error of the result.
• Suggest and calculate two more error measurement metrics; justify your choice.
• Feature Selection: Based on your results, what are the most significant features that best predict the price? Justify your answer.

2          Locally reweighted regression (6%)

1. Given {(x⁽¹⁾,y⁽¹⁾),..,(x^(N),y^(N))} and positive weights a⁽¹⁾,…,a^(N) show that the solution to the weighted least square problem

w^∗ = argmin                                            (1)

is given by the formula

w^∗ = X^TAXAy                                                                    (2)

where X is the design matrix (defined in class) and A is a diagonal matrix where A_ii = a⁽ⁱ⁾

2. Locally reweighted least squares combines ideas from k-NN and linear regression. For each new test example x we compute distance-based weights for each training example a⁽ⁱ⁾ =

, computes w^∗ = argmin  and predicts

yˆ = x^Tw^∗. Complete the implementation of locally reweighted least squares by providing the missing parts for q2.py.

Important things to notice while implementing: First, do not invert any matrix, use a linear solver (numpy.linalg.solve is one example). Second, notice that  but if we use B = max_j A_j it is much more numerically stable as  overflows/underflows easily. This is handled automatically in the scipy package with the scipy.misc.logsumexp function.

3. Use k-fold cross-validation to compute the average loss for different values of τ in the range [10,1000] when performing regression on the Boston Houses dataset. Plot these loss values for each choice of τ.
4. How does this algorithm behave when τ → ∞? When τ → 0?

3          Mini-batch SGD Gradient Estimator (6%)

Consider a dataset D of size n consisting of (x,y) pairs. Consider also a model M with parameters θ to be optimized with respect to a loss function.

We will aim to optimize L using mini-batches drawn randomly from D of size m. The indices of these points are contained in the set I = {i₁,…,i_m}, where each index is distinct and drawn uniformly without replacement from {1,…,n}. We define the loss function for a single mini-batch as,

(3)

1. Given a set {a₁,…,a_n} and random mini-batches I of size m, show that
2. Show that EI [∇L_I(x,y,θ)] = ∇L(x,y,θ)
3. Write, in a sentence, the importance of this result.
4. (a) Write down the gradient, ∇L above, for a linear regression model with cost function `(x,y,θ) = (y − w^Tx)².

(b) Write code to compute this gradient.

5. Using your code from the previous section, for m = 50 and K = 500 compute

, where I_k is the mini-batch sampled for the kth time.

Randomly initialize the weight parameters for your model from a N(0,I) distribution. Compare the value you have computed to the true gradient, ∇L, using both the squared distance metric and cosine similarity. Which is a more meaningful measure in this case and why?

a · b

[Note: Cosine similarity between two vectors a and b is given by cos(θ) =        .]

||a||₂||b||₂

6. For a single parameter, w_j, compare the sample variance, σ˜_j, of the mini-batch gradient estimate for values of m in the range [0,400] (using K = 500 again). Plot log ˜σ_j against logm.