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add recommandation
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sdpython committed Feb 11, 2018
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229 changes: 229 additions & 0 deletions _doc/notebooks/lectures/movielens_fm.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Factorisation de matrice et recommandations\n",
"\n",
"Le notebook utilise la factorisation de matrice pour calculer des recommandations sur la base [movielens](https://grouplens.org/datasets/movielens/). On utilise le jeu de donn\u00e9es [ml-latest-small.zip](http://files.grouplens.org/datasets/movielens/ml-latest-small.zip)."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['links', 'movies', 'ratings', 'tags']"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from papierstat.datasets import load_movielens_dataset\n",
"data = load_movielens_dataset()\n",
"list(sorted(data))"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"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>movieId</th>\n",
" <th>title</th>\n",
" <th>genres</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>9123</th>\n",
" <td>164977</td>\n",
" <td>The Gay Desperado (1936)</td>\n",
" <td>Comedy</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9124</th>\n",
" <td>164979</td>\n",
" <td>Women of '69, Unboxed</td>\n",
" <td>Documentary</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" movieId title genres\n",
"9123 164977 The Gay Desperado (1936) Comedy\n",
"9124 164979 Women of '69, Unboxed Documentary"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data['movies'].tail(n=2)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"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>userId</th>\n",
" <th>movieId</th>\n",
" <th>rating</th>\n",
" <th>timestamp</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>100002</th>\n",
" <td>671</td>\n",
" <td>6385</td>\n",
" <td>2.5</td>\n",
" <td>1070979663</td>\n",
" </tr>\n",
" <tr>\n",
" <th>100003</th>\n",
" <td>671</td>\n",
" <td>6565</td>\n",
" <td>3.5</td>\n",
" <td>1074784724</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" userId movieId rating timestamp\n",
"100002 671 6385 2.5 1070979663\n",
"100003 671 6565 3.5 1074784724"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rate = data[\"ratings\"]\n",
"rate.tail(n=2)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(671, 9066)"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(set(rate['userId'])), len(set(rate['movieId']))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"671 utilisateurs et 9066 films. C'est petit mais assez pour voir la factorisation et le temps que cela prend."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
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"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
11 changes: 0 additions & 11 deletions _doc/sphinxdoc/source/blog/2018/2018-02-08_sessions2.rst
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`Ridge <http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Ridge.html>`_,
`Lasso <http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Lasso.html#sklearn.linear_model.Lasso>`_,
`ElasticNet <http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.ElasticNet.html#sklearn.linear_model.ElasticNet>`_
* `clustering <http://scikit-learn.org/stable/modules/clustering.html#clustering>`_,
`k-means <http://www.xavierdupre.fr/app/mlstatpy/helpsphinx/c_clus/kmeans.html>`_
un exemple avec
les `vélos à Chicago <http://www.xavierdupre.fr/app/ensae_projects/helpsphinx/notebooks/city_bike_challenge.html>`_
et l'utilisation du clustering pour trouver les
`profils de cyclistes à Chicago <http://www.xavierdupre.fr/app/ensae_projects/helpsphinx/notebooks/city_bike_solution_cluster_start.html>`_
* `ranking <https://github.com/dmlc/xgboost/tree/master/demo/rank>`_
* recommandations,
`Factorisation de matrices non-négatives <http://scikit-learn.org/stable/modules/generated/sklearn.decomposition.NMF.html>`_,
`Liens entre factorisation de matrices, ACP, k-means <http://www.xavierdupre.fr/app/mlstatpy/helpsphinx/c_ml/missing_values_mf.html>`_
* :ref:`l-cheatsheet-ml`
* résumé de l'`interface scikit-learn <http://www.xavierdupre.fr/app/ensae_teaching_cs/helpsphinx3/notebooks/02_basic_of_machine_learning_with_scikit-learn.html#a-recap-on-scikit-learn-s-estimator-interface>`_

Exercices :

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32 changes: 32 additions & 0 deletions _doc/sphinxdoc/source/blog/2018/2018-02-11_sessions3.rst
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.. blogpost::
:title: Session 3
:keywords: session 3
:date: 2018-02-11
:categories: session

Suite et fin :

* :ref:`l-regclass`
* formalisation de la :ref:`régression <l-regression-f>`,
de la :ref:`classification <l-classification-f>`
* :ref:`classification multi-classe <l-multiclass>`

Prétraitements :

* :ref:`l-preprocessing`

* `clustering <http://scikit-learn.org/stable/modules/clustering.html#clustering>`_,
`k-means <http://www.xavierdupre.fr/app/mlstatpy/helpsphinx/c_clus/kmeans.html>`_
un exemple avec
les `vélos à Chicago <http://www.xavierdupre.fr/app/ensae_projects/helpsphinx/notebooks/city_bike_challenge.html>`_
et l'utilisation du clustering pour trouver les
`profils de cyclistes à Chicago <http://www.xavierdupre.fr/app/ensae_projects/helpsphinx/notebooks/city_bike_solution_cluster_start.html>`_
* `ranking <https://github.com/dmlc/xgboost/tree/master/demo/rank>`_
* :ref:`recommandations <l-recsys-section>`
* :ref:`l-cheatsheet-ml`
* résumé de l'`interface scikit-learn <http://www.xavierdupre.fr/app/ensae_teaching_cs/helpsphinx3/notebooks/02_basic_of_machine_learning_with_scikit-learn.html#a-recap-on-scikit-learn-s-estimator-interface>`_

Exercices :

* :ref:`Utiliser deux learners dans un pipeline scikit-learn <ex-pipe2learner>`
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14 changes: 7 additions & 7 deletions _doc/sphinxdoc/source/lectures/index.rst
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========
Lectures
========
================================
Lectures sur le machine learning
================================

Le :epkg:`machine learning` répond à de plus en plus de
problématiques, beaucoup d'entre elles sont citées
Expand All @@ -15,13 +15,13 @@ pour explorer quelques points récurrents ou techniques.
:maxdepth: 1

regclass
pipeline
gradienttree
seriestemp
nonsupervise
otherml
preprocessing
gradienttree
textembed
seriestemp
imagedeep
reinforce

Le machine learning cache bien des choses mais
au final il s'agit de calculer une prédiction
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2 changes: 2 additions & 0 deletions _doc/sphinxdoc/source/lectures/nonsupervise.rst
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Réduction de dimension
++++++++++++++++++++++

* `Analyse en Composantes Principales <https://fr.wikipedia.org/wiki/Analyse_en_composantes_principales>`_

Clustering
++++++++++

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