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Principles of Machine Learning EPFL - Team #96

Team members

  • Victor Faramond
  • Dario Anongba Varela
  • Mathieu Schopfer

This file explains the organisation and functions of the python scripts. For more information about the implementation, see the PDF report and the commented code.

costs

Contain 3 different cost functions like:

  • calculate_mse: Mean square error
  • calculate_mae: Mean absolute error
  • compute_loss_neg_log_likelihood: Negative log likelihood

cross_validation

Contain helper methods for cross validation.

  • build_k_indices: Builds k indices for k-fold cross validation
  • cross_validation_visualization: Creates a plot showing the accuracy given a lambda value

helpers

Contain multiple methods for data processing and utilitary methods necessary to achieve the regression methods:

  • standardize, buid_poly, add_constant_column, na, impute_data and process_data: All the processing functions. See the report for explications about those functions.
  • compute_gradient: Computes the gradient for gradient descent and stochastic gradient descent
  • batch_iter: Generate a minibatch iterator for a dataset

proj1_helpers

Contain functions used to load the data and generate a CSV submission file.

implementations

Contain the 6 regression methods needed for this project

  • least_squares_gd: Linear regression using gradient descent
  • least_squares_sgd: Linear regression using stochastic gradient descent
  • least_squares: Least squares regression using normal equations
  • ridge_regression: Ridge regression using normal equations
  • logistic_regression: using stochastic gradient descent
  • reg_logistic_regression: Regularized logistic regression

run

Script that generates the exact CSV file submitted on kaggle.

project1.ipynb

Python notebook used for tests during this project.