From a6152aa8e2c9a26c27a842e02dd9940e09a6beec Mon Sep 17 00:00:00 2001
From: Teo Baldi <teo.baldi@student.uclouvain.be>
Date: Mon, 29 May 2023 15:51:42 +0000
Subject: [PATCH] complete assignement coding 2 - notebook
---
evaluator.ipynb | 9384 +++++++++++++++++++++++++++++++++++++++++++++++
1 file changed, 9384 insertions(+)
create mode 100644 evaluator.ipynb
diff --git a/evaluator.ipynb b/evaluator.ipynb
new file mode 100644
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+++ b/evaluator.ipynb
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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Evaluator Module\n",
+ "The Evaluator module creates evaluation reports.\n",
+ "\n",
+ "Reports contain evaluation metrics depending on models specified in the evaluation config."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# reloads modules automatically before entering the execution of code\n",
+ "%load_ext autoreload\n",
+ "%autoreload 2\n",
+ "\n",
+ "# third parties imports\n",
+ "import numpy as np \n",
+ "import pandas as pd\n",
+ "# -- add new imports here --\n",
+ "import surprise.model_selection\n",
+ "import surprise.accuracy\n",
+ "import random as rd\n",
+ "\n",
+ "# local imports\n",
+ "from configs import EvalConfig\n",
+ "from constants import Constant as C\n",
+ "from loaders import export_evaluation_report\n",
+ "from loaders import load_ratings\n",
+ "from models import ModelBaseline1\n",
+ "# -- add new imports here --\n",
+ "from surprise import Reader, Dataset\n",
+ "from surprise.model_selection import train_test_split\n",
+ "from surprise.model_selection import LeaveOneOut\n",
+ "from models import get_top_n\n",
+ "from collections import defaultdict"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<surprise.dataset.DatasetAutoFolds at 0x7f9727ab5f90>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "dataset = load_ratings(surprise_format=True)\n",
+ "display(dataset)\n",
+ "\n",
+ "#ratings = dataset.raw_ratings[:5]\n",
+ "#print(ratings)\n",
+ "#for uid, iid, r, _ in ratings :\n",
+ " #print(f\"user {uid} rated item {iid} with rating {r}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 1. Model validation functions\n",
+ "Validation functions are a way to perform crossvalidation on recommender system models. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def generate_split_predictions(algo, ratings_dataset, eval_config):\n",
+ " \"\"\"Generate predictions on a random test set specified in eval_config\"\"\"\n",
+ " # -- implement the function generate_split_predictions --\n",
+ " #Split dataset into trainset and testset\n",
+ " trainset, testset = train_test_split(ratings_dataset, test_size = eval_config.test_size)\n",
+ " #Train the algorithm on the trainset\n",
+ " algo.fit(trainset)\n",
+ " #Make predictions on the testset\n",
+ " predictions = algo.test(testset)\n",
+ " return predictions\n",
+ "\n",
+ "def get_top_n(predictions, n):\n",
+ " \"\"\"Return the top-N recommendation for each user from a set of predictions.\n",
+ " Source: inspired by https://github.com/NicolasHug/Surprise/blob/master/examples/top_n_recommendations.py\n",
+ " and modified by cvandekerckh for random tie breaking\n",
+ "\n",
+ " Args:\n",
+ " predictions(list of Prediction objects): The list of predictions, as\n",
+ " returned by the test method of an algorithm.\n",
+ " n(int): The number of recommendation to output for each user. Default\n",
+ " is 10.\n",
+ " Returns:\n",
+ " A dict where keys are user (raw) ids and values are lists of tuples:\n",
+ " [(raw item id, rating estimation), ...] of size n.\n",
+ " \"\"\"\n",
+ "\n",
+ " rd.seed(0)\n",
+ "\n",
+ " # First map the predictions to each user.\n",
+ " top_n = defaultdict(list)\n",
+ " for uid, iid, true_r, est, _ in predictions:\n",
+ " top_n[uid].append((iid, est))\n",
+ "\n",
+ " # Then sort the predictions for each user and retrieve the k highest ones.\n",
+ " for uid, user_ratings in top_n.items():\n",
+ " rd.shuffle(user_ratings)\n",
+ " user_ratings.sort(key=lambda x: x[1], reverse=True)\n",
+ " top_n[uid] = user_ratings[:n]\n",
+ "\n",
+ " return top_n\n",
+ "\n",
+ "def generate_loo_top_n(algo, ratings_dataset, eval_config):\n",
+ " \"\"\"Generate top-n recommendations for each user on a random Leave-one-out split (LOO)\"\"\"\n",
+ " \n",
+ " # Perform leave-one-out cross-validation\n",
+ " loo = LeaveOneOut(n_splits=1)\n",
+ " \n",
+ " #Build trainset and testset \n",
+ " trainset, testset = next(loo.split(ratings_dataset))\n",
+ " \n",
+ " # Train the algorithm on the trainset\n",
+ " algo.fit(trainset)\n",
+ " \n",
+ " # Build the anti-testset (the items that the user has not rated)\n",
+ " anti_testset = trainset.build_anti_testset()\n",
+ " \n",
+ " # Make predictions\n",
+ " predictions = algo.test(anti_testset)\n",
+ " \n",
+ " # Get the top-N recommendations for each user\n",
+ " anti_testset_top_n = get_top_n(predictions, eval_config.top_n_value)\n",
+ " \n",
+ " return anti_testset_top_n, testset\n",
+ "\n",
+ "def generate_full_top_n(algo, ratings_dataset, eval_config):\n",
+ " \"\"\"Generate top-n recommendations for each user with full training set (LOO)\"\"\"\n",
+ " # -- implement the function generate_full_top_n --\n",
+ " # Build full trainset with all ratings\n",
+ " full_trainset = ratings_dataset.build_full_trainset()\n",
+ "\n",
+ " # Train the algorithm on the full trainset\n",
+ " algo.fit(full_trainset)\n",
+ "\n",
+ " # Build anti testset for all users (the items that the user has not rated)\n",
+ " anti_testset = full_trainset.build_anti_testset()\n",
+ "\n",
+ " # Get the top-n recommendations for all users on the anti testset\n",
+ " anti_testset_top_n = algo.test(anti_testset)\n",
+ " anti_testset_top_n = get_top_n(anti_testset_top_n, eval_config.top_n_value)\n",
+ " return anti_testset_top_n\n",
+ "\n",
+ "def precompute_information():\n",
+ " \"\"\" Returns a dictionary that precomputes relevant information for evaluating in full mode\n",
+ "\n",
+ " Dictionary keys:\n",
+ " - precomputed_dict[\"item_to_rank\"] : contains a dictionary mapping movie ids to rankings\n",
+ " \"\"\"\n",
+ " \n",
+ " df = load_ratings()\n",
+ " most = df.groupby(\"movieId\")[\"movieId\"].count().to_frame(\"nb_ratings\")\n",
+ " sorting = most.sort_values([\"nb_ratings\"], ascending=False)\n",
+ " \n",
+ " movie_ids = []\n",
+ " for movie_id, count in sorting.iterrows():\n",
+ " movie_ids.append(movie_id)\n",
+ "\n",
+ " l = [i for i in range(1,len(movie_ids)+1)]\n",
+ "\n",
+ " item_to_rank={}\n",
+ " i=1\n",
+ " for j in movie_ids :\n",
+ " item_to_rank[i] = j\n",
+ " i+=1\n",
+ " precomputed_dict={}\n",
+ " precomputed_dict[\"item_to_rank\"]=item_to_rank\n",
+ "\n",
+ " return precomputed_dict\n",
+ "\n",
+ "def create_evaluation_report(eval_config, sp_ratings, precomputed_dict, available_metrics):\n",
+ " \"\"\" Create a DataFrame evaluating various models on metrics specified in an evaluation config. \n",
+ " \"\"\"\n",
+ " evaluation_dict = {}\n",
+ " for model_name, model, arguments in eval_config.models:\n",
+ " print(f'Handling model {model_name}')\n",
+ " algo = model(**arguments)\n",
+ " evaluation_dict[model_name] = {}\n",
+ " \n",
+ " # Type 1 : split evaluations\n",
+ " if len(eval_config.split_metrics) > 0:\n",
+ " print('Training split predictions')\n",
+ " predictions = generate_split_predictions(algo, sp_ratings, eval_config)\n",
+ " for metric in eval_config.split_metrics:\n",
+ " print(f'- computing metric {metric}')\n",
+ " assert metric in available_metrics['split']\n",
+ " evaluation_function, parameters = available_metrics[\"split\"][metric]\n",
+ " evaluation_dict[model_name][metric] = evaluation_function(predictions, **parameters) \n",
+ "\n",
+ " # Type 2 : loo evaluations\n",
+ " if len(eval_config.loo_metrics) > 0:\n",
+ " print('Training loo predictions')\n",
+ " anti_testset_top_n, testset = generate_loo_top_n(algo, sp_ratings, eval_config)\n",
+ " for metric in eval_config.loo_metrics:\n",
+ " assert metric in available_metrics['loo']\n",
+ " evaluation_function, parameters = available_metrics[\"loo\"][metric]\n",
+ " evaluation_dict[model_name][metric] = evaluation_function(anti_testset_top_n, testset, **parameters)\n",
+ " \n",
+ " # Type 3 : full evaluations\n",
+ " if len(eval_config.full_metrics) > 0:\n",
+ " print('Training full predictions')\n",
+ " anti_testset_top_n = generate_full_top_n(algo, sp_ratings, eval_config)\n",
+ " for metric in eval_config.full_metrics:\n",
+ " assert metric in available_metrics['full']\n",
+ " evaluation_function, parameters = available_metrics[\"full\"][metric]\n",
+ " evaluation_dict[model_name][metric] = evaluation_function(\n",
+ " anti_testset_top_n,\n",
+ " **precomputed_dict,\n",
+ " **parameters\n",
+ " )\n",
+ " \n",
+ " return pd.DataFrame.from_dict(evaluation_dict).T"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Tests"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "user: 468 item: 3429 r_ui = 4.00 est = 3.15 {'was_impossible': False}\n",
+ "user: 212 item: 858 r_ui = 5.00 est = 3.77 {'was_impossible': False}\n",
+ "user: 607 item: 4557 r_ui = 3.50 est = 3.38 {'was_impossible': False}\n",
+ "user: 547 item: 125 r_ui = 5.00 est = 3.30 {'was_impossible': False}\n",
+ "user: 30 item: 3334 r_ui = 4.00 est = 3.99 {'was_impossible': False}\n",
+ "user: 134 item: 500 r_ui = 4.00 est = 3.59 {'was_impossible': False}\n",
+ "user: 294 item: 1453 r_ui = 4.00 est = 3.22 {'was_impossible': False}\n",
+ "user: 105 item: 4643 r_ui = 2.50 est = 3.09 {'was_impossible': False}\n",
+ "user: 431 item: 1225 r_ui = 4.50 est = 4.30 {'was_impossible': False}\n",
+ "user: 427 item: 5945 r_ui = 3.00 est = 3.40 {'was_impossible': False}\n",
+ "user: 463 item: 2717 r_ui = 2.00 est = 2.70 {'was_impossible': False}\n",
+ "user: 394 item: 77658 r_ui = 3.00 est = 2.47 {'was_impossible': False}\n",
+ "user: 624 item: 5752 r_ui = 3.00 est = 2.62 {'was_impossible': False}\n",
+ "user: 461 item: 2473 r_ui = 1.00 est = 2.41 {'was_impossible': False}\n",
+ "user: 387 item: 3011 r_ui = 5.00 est = 3.91 {'was_impossible': False}\n",
+ "user: 585 item: 1299 r_ui = 5.00 est = 4.55 {'was_impossible': False}\n",
+ "user: 105 item: 5669 r_ui = 3.00 est = 3.61 {'was_impossible': False}\n",
+ "user: 243 item: 4682 r_ui = 4.00 est = 3.50 {'was_impossible': False}\n",
+ "user: 294 item: 2006 r_ui = 4.50 est = 3.30 {'was_impossible': False}\n",
+ "user: 564 item: 532 r_ui = 5.00 est = 3.13 {'was_impossible': False}\n",
+ "user: 73 item: 69306 r_ui = 3.00 est = 3.22 {'was_impossible': False}\n",
+ "user: 30 item: 162 r_ui = 1.00 est = 4.21 {'was_impossible': False}\n",
+ "user: 285 item: 1974 r_ui = 2.00 est = 3.48 {'was_impossible': False}\n",
+ "user: 547 item: 1212 r_ui = 5.00 est = 3.82 {'was_impossible': False}\n",
+ "user: 15 item: 139644 r_ui = 3.00 est = 2.68 {'was_impossible': False}\n",
+ "user: 457 item: 27611 r_ui = 0.50 est = 2.42 {'was_impossible': False}\n",
+ "user: 547 item: 4810 r_ui = 4.00 est = 3.09 {'was_impossible': False}\n",
+ "user: 346 item: 1073 r_ui = 4.00 est = 3.91 {'was_impossible': False}\n",
+ "user: 457 item: 3168 r_ui = 4.00 est = 2.77 {'was_impossible': False}\n",
+ "user: 48 item: 106002 r_ui = 2.50 est = 3.43 {'was_impossible': False}\n"
+ ]
+ }
+ ],
+ "source": [
+ "algo = surprise.SVD()\n",
+ "#algo = ModelBaseline1()\n",
+ "sp_ratings = load_ratings(surprise_format=True)\n",
+ "eval_config = EvalConfig()\n",
+ "predi = generate_split_predictions(algo, sp_ratings, eval_config)\n",
+ "for prediction in predi[:30]:\n",
+ " print(prediction)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "collapsed": true,
+ "jupyter": {
+ "outputs_hidden": true
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
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+ " (654, 708, 3.0),\n",
+ " (664, 96737, 4.5),\n",
+ " (665, 2953, 2.0)])"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "generate_loo_top_n(algo, dataset, eval_config)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "collapsed": true,
+ "jupyter": {
+ "outputs_hidden": true
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
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+ " (2739, 4.554641778184588),\n",
+ " (79132, 4.552828436547091),\n",
+ " (109374, 4.5310837012476455),\n",
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+ " (4979, 4.507922982636135),\n",
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+ " (3198, 4.4862414996210696),\n",
+ " (3499, 4.482045210385841),\n",
+ " (49272, 4.481276554231418),\n",
+ " (3168, 4.479681514135123)],\n",
+ " 654: [(899, 4.768029384588899),\n",
+ " (923, 4.718684296206397),\n",
+ " (79132, 4.692055667419752),\n",
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+ " (55820, 4.489758581188043),\n",
+ " (1952, 4.453527462877919),\n",
+ " (49272, 4.41543852822601),\n",
+ " (1208, 4.4106846708629845),\n",
+ " (1250, 4.406700576511367),\n",
+ " (56782, 4.406407091538918),\n",
+ " (1212, 4.406319062517817),\n",
+ " (1305, 4.403023922420751),\n",
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+ " (1219, 4.396010548216619),\n",
+ " (1945, 4.382702484944558),\n",
+ " (3310, 4.341317479370158),\n",
+ " (5669, 4.341212786689849),\n",
+ " (1217, 4.318635253661788),\n",
+ " (30749, 4.306870773206487),\n",
+ " (3030, 4.293103665620288),\n",
+ " (3724, 4.2761399598222525),\n",
+ " (1953, 4.264921484715103),\n",
+ " (3504, 4.255168036284226),\n",
+ " (106920, 4.251065676124625),\n",
+ " (2739, 4.250847179963429),\n",
+ " (1032, 4.2338134544363255),\n",
+ " (26152, 4.230623566951648),\n",
+ " (52885, 4.228828970397859),\n",
+ " (599, 4.227204572226608),\n",
+ " (26271, 4.21844438388067),\n",
+ " (233, 4.204636665332777),\n",
+ " (139385, 4.204609307456717),\n",
+ " (3007, 4.193057716163484),\n",
+ " (2352, 4.183441439546268),\n",
+ " (1299, 4.182092023541333),\n",
+ " (3198, 4.16873973172778),\n",
+ " (942, 4.167995311320456),\n",
+ " (96829, 4.165811606839665),\n",
+ " (31658, 4.162907059727463),\n",
+ " (2183, 4.161374270583775)],\n",
+ " 664: [(899, 4.677617146461869),\n",
+ " (3362, 4.493847204666676),\n",
+ " (923, 4.478468291640172),\n",
+ " (1217, 4.437056353831616),\n",
+ " (1952, 4.415346417144655),\n",
+ " (527, 4.414949810469189),\n",
+ " (106920, 4.378945609455041),\n",
+ " (2501, 4.327963868831103),\n",
+ " (1968, 4.3163687931710655),\n",
+ " (1250, 4.29394501045516),\n",
+ " (1208, 4.2937633708424805),\n",
+ " (1945, 4.285314769721282),\n",
+ " (1212, 4.264523177266486),\n",
+ " (2987, 4.257963426201903),\n",
+ " (3504, 4.223590128586588),\n",
+ " (56782, 4.220895793162825),\n",
+ " (3310, 4.220032184176226),\n",
+ " (1172, 4.19976168517977),\n",
+ " (515, 4.196614912211225),\n",
+ " (1305, 4.186077409601684),\n",
+ " (1073, 4.1854736354312445),\n",
+ " (599, 4.185180041145096),\n",
+ " (3728, 4.179313859372379),\n",
+ " (1219, 4.167795350950652),\n",
+ " (3070, 4.1549384783658905),\n",
+ " (1942, 4.140390160160262),\n",
+ " (233, 4.139785601263654),\n",
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+ " (2804, 4.119552113954555),\n",
+ " (3198, 4.1050423046368865),\n",
+ " (6377, 4.0924039834665535),\n",
+ " (1225, 4.0823276621020135),\n",
+ " (3007, 4.080581440753721),\n",
+ " (31658, 4.0779244938700066),\n",
+ " (3334, 4.073924896719281),\n",
+ " (3724, 4.069078701257818),\n",
+ " (1264, 4.062592078730625),\n",
+ " (90357, 4.060121612131501),\n",
+ " (1274, 4.056638708403854),\n",
+ " (1299, 4.056232565978193)],\n",
+ " 665: [(858, 4.443186009240601),\n",
+ " (899, 4.280737506591541),\n",
+ " (106920, 4.259436832705159),\n",
+ " (1219, 4.149557464197055),\n",
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+ " (1945, 4.102245848481996),\n",
+ " (2804, 4.071382904439875),\n",
+ " (1225, 4.057014446835347),\n",
+ " (3362, 4.0463293162852585),\n",
+ " (1250, 4.038651616381265),\n",
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+ " (3504, 3.9361873829434986),\n",
+ " (5669, 3.9310600581706634),\n",
+ " (5064, 3.925445291197594),\n",
+ " (1172, 3.913715545261684),\n",
+ " (1968, 3.902925296436303),\n",
+ " (3030, 3.8796018494919964),\n",
+ " (30749, 3.8769333090282787),\n",
+ " (2501, 3.8642632376736197),\n",
+ " (2739, 3.8631391382236306),\n",
+ " (1264, 3.8630949405226067),\n",
+ " (37741, 3.855330542115119),\n",
+ " (26614, 3.854774800473502),\n",
+ " (6333, 3.8444778061025593),\n",
+ " (233, 3.843751875453148),\n",
+ " (942, 3.8427666964833644),\n",
+ " (3091, 3.8394066378899914),\n",
+ " (109374, 3.8315170755583803),\n",
+ " (139385, 3.8279570488348287),\n",
+ " (48783, 3.8092707263573646)]})"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "generate_full_top_n(algo, dataset, eval_config)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 2. Evaluation metrics\n",
+ "Implement evaluation metrics for either rating predictions (split metrics) or for top-n recommendations (loo metric, full metric)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def get_hit_rate(anti_testset_top_n, testset):\n",
+ " \"\"\"Compute the average hit over the users (loo metric)\n",
+ "\n",
+ " A hit (1) happens when the movie in the testset has been picked by the top-n recommender\n",
+ " A fail (0) happens when the movie in the testset has not been picked by the top-n recommender\n",
+ " \"\"\"\n",
+ " hits = 0\n",
+ " total_users = len(set([uid for uid, _, _ in testset]))\n",
+ "\n",
+ " for uid, iid, _ in testset:\n",
+ " recommended_iids = [iid for (iid, _) in anti_testset_top_n[int(uid)]] # recommended movies for user\n",
+ " if iid in recommended_iids:\n",
+ " hits += 1\n",
+ " #print(hits)\n",
+ " #print(total_users)\n",
+ " hit_rate = hits / total_users\n",
+ "\n",
+ " return hit_rate\n",
+ "\n",
+ "def get_novelty(anti_testset_top_n, item_to_rank):\n",
+ " \"\"\"Compute the average novelty of the top-n recommendation over the users (full metric)\n",
+ " \n",
+ " The novelty is defined as the average ranking of the movies recommended\n",
+ " \"\"\"\n",
+ " # -- implement the function get_novelty --\n",
+ " list_rank_sum=[]\n",
+ " for i in anti_testset_top_n.items() :\n",
+ " #print(i)\n",
+ " l=[]\n",
+ " rank_sum=0\n",
+ " for j in i[1]:\n",
+ " #print(j)\n",
+ " if j[0] not in l :\n",
+ " l.append(j[0])\n",
+ " #print(l)\n",
+ " \n",
+ " for key,value in item_to_rank.items() :\n",
+ " if value in l :\n",
+ " rank_sum+=key\n",
+ " list_rank_sum.append(rank_sum)\n",
+ " #print(rank_sum_liste)\n",
+ " \n",
+ " min_rank_sum = min(list_rank_sum)\n",
+ " max_rank_sum = max(list_rank_sum)\n",
+ " normalized_ranks = [(r - min_rank_sum) / (max_rank_sum - min_rank_sum) for r in list_rank_sum]\n",
+ " \n",
+ " average_rank_sum = sum(normalized_ranks) / len(normalized_ranks)\n",
+ " \n",
+ " return average_rank_sum"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "##### What are the pitfalls of using a sum of ranks as a novelty metric ?\n",
+ "\n",
+ "When averaging the ranks, it is obvious that the average is influenced by the extreme values, namely the most popular films (with a low rank) and the least popular films (with a high rank). "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 3. Evaluation workflow\n",
+ "Load data, evaluate models and save the experimental outcomes"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<surprise.dataset.DatasetAutoFolds at 0x7f97274c1390>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Handling model baseline_1\n",
+ "Training split predictions\n",
+ "- computing metric mae\n",
+ "- computing metric rmse\n",
+ "Training loo predictions\n",
+ "Training full predictions\n",
+ "Handling model baseline_2\n",
+ "Training split predictions\n",
+ "- computing metric mae\n",
+ "- computing metric rmse\n",
+ "Training loo predictions\n",
+ "Training full predictions\n",
+ "Handling model baseline_3\n",
+ "Training split predictions\n",
+ "- computing metric mae\n",
+ "- computing metric rmse\n",
+ "Training loo predictions\n",
+ "Training full predictions\n",
+ "Handling model baseline_4\n",
+ "Training split predictions\n",
+ "- computing metric mae\n",
+ "- computing metric rmse\n",
+ "Training loo predictions\n",
+ "Training full predictions\n",
+ "Handling model user_based_msd\n",
+ "Training split predictions\n",
+ "- computing metric mae\n",
+ "- computing metric rmse\n",
+ "Training loo predictions\n",
+ "Training full predictions\n",
+ "Handling model user_based_jacard\n",
+ "Training split predictions\n",
+ "- computing metric mae\n",
+ "- computing metric rmse\n",
+ "Training loo predictions\n",
+ "Training full predictions\n",
+ "Handling model random_score\n",
+ "Training split predictions\n",
+ "- computing metric mae\n",
+ "- computing metric rmse\n",
+ "Training loo predictions\n",
+ "Training full predictions\n",
+ "Handling model random_sample\n",
+ "Training split predictions\n",
+ "- computing metric mae\n",
+ "- computing metric rmse\n",
+ "Training loo predictions\n",
+ "Training full predictions\n",
+ "Handling model linear and date\n",
+ "Training split predictions\n",
+ "- computing metric mae\n",
+ "- computing metric rmse\n",
+ "Training loo predictions\n",
+ "Training full predictions\n",
+ "Handling model linear and title\n",
+ "Training split predictions\n",
+ "- computing metric mae\n",
+ "- computing metric rmse\n",
+ "Training loo predictions\n",
+ "Training full predictions\n",
+ "Handling model linear and genres\n",
+ "Training split predictions\n",
+ "- computing metric mae\n",
+ "- computing metric rmse\n",
+ "Training loo predictions\n",
+ "Training full predictions\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>mae</th>\n",
+ " <th>rmse</th>\n",
+ " <th>hit_rate</th>\n",
+ " <th>novelty</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>baseline_1</th>\n",
+ " <td>1.578550</td>\n",
+ " <td>1.805392</td>\n",
+ " <td>0.028037</td>\n",
+ " <td>0.511618</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>baseline_2</th>\n",
+ " <td>1.490753</td>\n",
+ " <td>1.822888</td>\n",
+ " <td>0.065421</td>\n",
+ " <td>0.447978</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>baseline_3</th>\n",
+ " <td>0.880705</td>\n",
+ " <td>1.088199</td>\n",
+ " <td>0.037383</td>\n",
+ " <td>0.511618</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>baseline_4</th>\n",
+ " <td>0.732431</td>\n",
+ " <td>0.941478</td>\n",
+ " <td>0.317757</td>\n",
+ " <td>0.209231</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>user_based_msd</th>\n",
+ " <td>0.789168</td>\n",
+ " <td>1.022609</td>\n",
+ " <td>0.093458</td>\n",
+ " <td>0.605386</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>user_based_jacard</th>\n",
+ " <td>0.750236</td>\n",
+ " <td>0.965420</td>\n",
+ " <td>0.102804</td>\n",
+ " <td>0.569613</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>random_score</th>\n",
+ " <td>1.456447</td>\n",
+ " <td>1.784380</td>\n",
+ " <td>0.074766</td>\n",
+ " <td>0.530963</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>random_sample</th>\n",
+ " <td>1.075586</td>\n",
+ " <td>1.420208</td>\n",
+ " <td>0.065421</td>\n",
+ " <td>0.482651</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>linear and date</th>\n",
+ " <td>1.024617</td>\n",
+ " <td>1.329360</td>\n",
+ " <td>0.009346</td>\n",
+ " <td>0.744561</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>linear and title</th>\n",
+ " <td>1.651916</td>\n",
+ " <td>1.988417</td>\n",
+ " <td>0.084112</td>\n",
+ " <td>0.587664</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>linear and genres</th>\n",
+ " <td>1.224664</td>\n",
+ " <td>1.557368</td>\n",
+ " <td>0.084112</td>\n",
+ " <td>0.420021</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " mae rmse hit_rate novelty\n",
+ "baseline_1 1.578550 1.805392 0.028037 0.511618\n",
+ "baseline_2 1.490753 1.822888 0.065421 0.447978\n",
+ "baseline_3 0.880705 1.088199 0.037383 0.511618\n",
+ "baseline_4 0.732431 0.941478 0.317757 0.209231\n",
+ "user_based_msd 0.789168 1.022609 0.093458 0.605386\n",
+ "user_based_jacard 0.750236 0.965420 0.102804 0.569613\n",
+ "random_score 1.456447 1.784380 0.074766 0.530963\n",
+ "random_sample 1.075586 1.420208 0.065421 0.482651\n",
+ "linear and date 1.024617 1.329360 0.009346 0.744561\n",
+ "linear and title 1.651916 1.988417 0.084112 0.587664\n",
+ "linear and genres 1.224664 1.557368 0.084112 0.420021"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "AVAILABLE_METRICS = {\n",
+ " \"split\": {\n",
+ " \"mae\": (surprise.accuracy.mae, {'verbose': False}),\n",
+ " # -- add new split metrics here --\n",
+ " \"rmse\": (surprise.accuracy.rmse, {'verbose' : False})\n",
+ " },\n",
+ " # -- add new types of metrics here --\n",
+ " \"loo\": {\n",
+ " \"hit_rate\": (get_hit_rate, {}),\n",
+ " },\n",
+ " \"full\": {\n",
+ " \"novelty\": (get_novelty, {}),\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "sp_ratings = load_ratings(surprise_format=True)\n",
+ "display(sp_ratings)\n",
+ "precomputed_dict = precompute_information()\n",
+ "evaluation_report = create_evaluation_report(EvalConfig, sp_ratings, precomputed_dict, AVAILABLE_METRICS)\n",
+ "export_evaluation_report(evaluation_report)\n",
+ "\n",
+ "display(evaluation_report)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Observations"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Lorsque nous utilisons le dataset tiny, nous remarquons que:\n",
+ "\n",
+ "Les 3 meilleurs modèles en termes de précision des prédictions sont : 1) baseline4, 2) user_based avec similarité de jacard et 3) user_based avec similarité msd.\n",
+ "\n",
+ "En termes de taux de réussite (hit_rate), le baseline4 est de loin de meilleur, c'est à dire que la proportion de fois où un item recommandé a été effectivement apprécié par l'utilisateur est plus élevé dans ce modèle.\n",
+ "\n",
+ "En termes de nouveauté (novelty), c'est le modèle Content_Based \"linear and date\" qui offre le plus de films qui sont moins populaires et plus originaux.\n",
+ "\n",
+ "S'il fallait choisir un modèle, nous aurions choisi le modèle baseline4. Néanmoins, ce dernier a une mesure de nouveauté faible. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
--
GitLab