From 11f25ecb73f0fbdfd94ed77edf6217e9fd495332 Mon Sep 17 00:00:00 2001 From: Adrien <adrien.payen@student.uclouvain.be> Date: Fri, 24 May 2024 21:24:51 +0200 Subject: [PATCH] update readme --- README.md | 22 +++++++++++++++++++--- 1 file changed, 19 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 3899728e..76390b13 100644 --- a/README.md +++ b/README.md @@ -24,9 +24,6 @@ pip install lightgbm ``` - -## Project Structure - ## Project Structure The project is organized into the following key components: @@ -90,6 +87,25 @@ The system supports the following regression models for predicting user ratings: - `lightgbm` +### evaluator.ipynb + +`generate_split_predictions`: This function splits the dataset into training and testing sets, trains the model on the training set, and generates predictions on the test set. + +`generate_loo_top_n`: This function performs a leave-one-out (LOO) split of the dataset, then generates top-n recommendations for each user. + +`generate_full_top_n`: This function generates top-n recommendations for each user using the full dataset. +precomputed_information: This function precomputes relevant information for evaluation in full mode. + +`create_evaluation_report`: This function creates a DataFrame evaluating various models on metrics specified in an evaluation configuration. + +`get_hit_rate`: Computes the average hit rate of top-n recommendations. + +`get_novelty`: Computes the average novelty of top-n recommendations. + +Definition of a dictionary containing available metrics for different evaluation steps (splits, LOO, full). + +Loading data, generating evaluation reports for different models, and saving experimental outcomes. + ### Dataset Organized under the data/test/ directory, which contains three subdirectories: - content: Contains movies.csv & tags.csv files. -- GitLab