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Valider d24ee4ed rédigé par Corentin Vande Kerckhove's avatar Corentin Vande Kerckhove
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# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
*$py.class
# C extensions
*.so
# Distribution / packaging
.Python
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
wheels/
pip-wheel-metadata/
share/python-wheels/
*.egg-info/
.installed.cfg
*.egg
MANIFEST
# PyInstaller
# Usually these files are written by a python script from a template
# before PyInstaller builds the exe, so as to inject date/other infos into it.
*.manifest
*.spec
# Installer logs
pip-log.txt
pip-delete-this-directory.txt
# Unit test / coverage reports
htmlcov/
.tox/
.nox/
.coverage
.coverage.*
.cache
nosetests.xml
coverage.xml
*.cover
*.py,cover
.hypothesis/
.pytest_cache/
# Translations
*.mo
*.pot
# Django stuff:
*.log
local_settings.py
db.sqlite3
db.sqlite3-journal
# Flask stuff:
instance/
.webassets-cache
# Scrapy stuff:
.scrapy
# Sphinx documentation
docs/_build/
# PyBuilder
target/
# Jupyter Notebook
.ipynb_checkpoints
# IPython
profile_default/
ipython_config.py
# pyenv
.python-version
# pipenv
# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
# However, in case of collaboration, if having platform-specific dependencies or dependencies
# having no cross-platform support, pipenv may install dependencies that don't work, or not
# install all needed dependencies.
#Pipfile.lock
# PEP 582; used by e.g. github.com/David-OConnor/pyflow
__pypackages__/
# Celery stuff
celerybeat-schedule
celerybeat.pid
# SageMath parsed files
*.sage.py
# Environments
.env
.venv
env/
venv/
ENV/
env.bak/
venv.bak/
# Spyder project settings
.spyderproject
.spyproject
# Rope project settings
.ropeproject
# mkdocs documentation
/site
# mypy
.mypy_cache/
.dmypy.json
dmypy.json
# Pyre type checker
.pyre/
# MacOS
.DS_Store
# Images and data
*.pdf
images
data
*.zip
posters
Pipfile 0 → 100644
+ 21
− 0
Voir le fichier @ d24ee4ed
[[source]]
url = "https://pypi.org/simple"
verify_ssl = true
name = "pypi"
[packages]
pandas = "*"
ipykernel = "*"
jupyter = "*"
matplotlib = "*"
scipy = "*"
seaborn = "*"
scikit-surprise = "*"
python-dotenv = "*"
scikit-learn = "*"
streamlit = "*"
[dev-packages]
[requires]
python_version = "3.9"
Pipfile.lock 0 → 100644
+ 1368
− 0
Voir le fichier @ d24ee4ed
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README.md 0 → 100644
+ 12
− 0
Voir le fichier @ d24ee4ed
# Group XX - Movie Recommender System
Welcome in the README file :)
Write here a few introduction words for your project.
If you want inspiration on how to write an awesome README, check this github repo : https://github.com/navendu-pottekkat/awesome-readme . But don't spend to much time on it. This is not the topic of this course.
If you need help with the Markdown syntax, you might find some help here : https://www.markdownguide.org/basic-syntax/ .
Good luck with your project.
May the force be with you.
\ No newline at end of file
analytics.ipynb 0 → 100644
+ 106
− 0
Voir le fichier @ d24ee4ed
%% Cell type:markdown id: tags:
# Analytics Module
The Analytics module provides descriptive statistics on content data, evidence data and model evaluations
%% Cell type:code id: tags:
``` python
# reloads modules automatically before entering the execution of code
%load_ext autoreload
%autoreload 2
# third parties imports
import numpy as np
import pandas as pd
# -- add new imports here --
# local imports
from constants import Constant as C
from loaders import load_ratings
from loaders import load_items
```
%% Cell type:markdown id: tags:
# 1 - Content analytics
Explore and perform descriptive statistics on content data
%% Cell type:code id: tags:
``` python
# -- load the items and display the Dataframe
```
%% Cell type:code id: tags:
``` python
# -- display relevant informations that can be extracted from the dataset
```
%% Cell type:markdown id: tags:
# 2 - Evidence analytics
Explore and perform descriptive statistics on evidence data
%% Cell type:code id: tags:
``` python
# -- load the items and display the Dataframe
```
%% Cell type:code id: tags:
``` python
# -- display relevant informations that can be extracted from the dataset
```
configs.py 0 → 100644
+ 18
− 0
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# local imports
from models import *
class EvalConfig:
models = [
("baseline_1", ModelBaseline1, {}), # model_name, model class, model parameters (dict)
]
split_metrics = ["mae"]
loo_metrics = []
full_metrics = []
# Split parameters
test_size = None # -- configure the test_size (from 0 to 1) --
# Loo parameters
top_n_value = None # -- configure the numer of recommendations (> 1) --
constants.py 0 → 100644
+ 27
− 0
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# third parties imports
from pathlib import Path
class Constant:
DATA_PATH = Path('data/test') # -- fill here the dataset size to use
# Content
CONTENT_PATH = DATA_PATH / 'content'
# - item
ITEMS_FILENAME = 'movies.csv'
ITEM_ID_COL = 'movieId'
LABEL_COL = 'title'
GENRES_COL = 'genres'
# Evidence
EVIDENCE_PATH = DATA_PATH / 'evidence'
# - ratings
RATINGS_FILENAME = 'ratings.csv'
USER_ID_COL = 'userId'
RATING_COL = 'rating'
TIMESTAMP_COL = 'timestamp'
USER_ITEM_RATINGS = [USER_ID_COL, ITEM_ID_COL, RATING_COL]
# Rating scale
RATINGS_SCALE = None # -- fill in here the ratings scale as a tuple (min_value, max_value)
loaders.py 0 → 100644
+ 28
− 0
Voir le fichier @ d24ee4ed
# third parties imports
import pandas as pd
# local imports
from constants import Constant as C
def load_ratings(surprise_format=False):
df_ratings = pd.read_csv(C.EVIDENCE_PATH / C.RATINGS_FILENAME)
if surprise_format:
pass
else:
return df_ratings
def load_items():
df_items = pd.read_csv(C.CONTENT_PATH / C.ITEMS_FILENAME)
df_items = df_items.set_index(C.ITEM_ID_COL)
return df_items
def export_evaluation_report(df):
""" Export the report to the evaluation folder.
The name of the report is versioned using today's date
"""
pass
\ No newline at end of file
models.py 0 → 100644
+ 83
− 0
Voir le fichier @ d24ee4ed
# standard library imports
from collections import defaultdict
# third parties imports
import numpy as np
import random as rd
from surprise import AlgoBase
from surprise import KNNWithMeans
from surprise import SVD
def get_top_n(predictions, n):
"""Return the top-N recommendation for each user from a set of predictions.
Source: inspired by https://github.com/NicolasHug/Surprise/blob/master/examples/top_n_recommendations.py
and modified by cvandekerckh for random tie breaking
Args:
predictions(list of Prediction objects): The list of predictions, as
returned by the test method of an algorithm.
n(int): The number of recommendation to output for each user. Default
is 10.
Returns:
A dict where keys are user (raw) ids and values are lists of tuples:
[(raw item id, rating estimation), ...] of size n.
"""
rd.seed(0)
# First map the predictions to each user.
top_n = defaultdict(list)
for uid, iid, true_r, est, _ in predictions:
top_n[uid].append((iid, est))
# Then sort the predictions for each user and retrieve the k highest ones.
for uid, user_ratings in top_n.items():
rd.shuffle(user_ratings)
user_ratings.sort(key=lambda x: x[1], reverse=True)
top_n[uid] = user_ratings[:n]
return top_n
# First algorithm
class ModelBaseline1(AlgoBase):
def __init__(self):
AlgoBase.__init__(self)
def estimate(self, u, i):
return 2
# Second algorithm
class ModelBaseline2(AlgoBase):
def __init__(self):
AlgoBase.__init__(self)
def fit(self, trainset):
AlgoBase.fit(self, trainset)
rd.seed(0)
def estimate(self, u, i):
return rd.uniform(self.trainset.rating_scale[0], self.trainset.rating_scale[1])
# Third algorithm
class ModelBaseline3(AlgoBase):
def __init__(self):
AlgoBase.__init__(self)
def fit(self, trainset):
AlgoBase.fit(self, trainset)
self.the_mean = np.mean([r for (_, _, r) in self.trainset.all_ratings()])
return self
def estimate(self, u, i):
return self.the_mean
# Fourth Model
class ModelBaseline4(SVD):
def __init__(self):
SVD.__init__(self, n_factors=100)
unzip_data.py 0 → 100644
+ 24
− 0
Voir le fichier @ d24ee4ed
import os
import shutil
import zipfile as zf
import errno
def unzip_and_remove(zip_filename):
if not os.path.exists(zip_filename):
raise FileNotFoundError(
errno.ENOENT,
os.strerror(errno.ENOENT),
zip_filename,
)
files = zf.ZipFile(zip_filename, 'r')
files.extractall('.')
files.close()
os.remove(zip_filename)
shutil.rmtree('__MACOSX') # remove macosx data for Mac users
if __name__ == "__main__":
unzip_and_remove("data.zip")
\ No newline at end of file
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