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e0e47965e..d667eda6c 100644 --- a/_quarto.yml +++ b/_quarto.yml @@ -4,6 +4,7 @@ project: - index.qmd - 404.qmd - content/getting-started/03_revisions.qmd + - content/modelisation/5_clustering.qmd profile: default: fr diff --git a/content/modelisation/5_clustering.qmd b/content/modelisation/5_clustering.qmd index 163d234f9..6d0974747 100644 --- a/content/modelisation/5_clustering.qmd +++ b/content/modelisation/5_clustering.qmd @@ -219,8 +219,32 @@ import matplotlib.pyplot as plt import numpy as np import pandas as pd import geopandas as gpd -from sklearn.cluster import KMeans #pour kmeans -import seaborn as sns #pour scatterplots +from sklearn.cluster import KMeans +import seaborn as sns +``` + +:::: {.content-visible when-profile="en"} +Dans le prochain exercice, nous allons utiliser les variables suivantes: +:::: + +:::: {.content-visible when-profile="fr"} +You will need the following variables in the next exercise: +:::: + +```{python} +#| output: false +#| echo: true + +# 1. Chargement de la base restreinte. +xvars = [ + 'Unemployment_rate_2019', 'Median_Household_Income_2021', + 'Percent of adults with less than a high school diploma, 2018-22', + "Percent of adults with a bachelor's degree or higher, 2018-22" +] + +votes = votes.dropna(subset = xvars + ["per_gop"]) + +df2 = votes.loc[:, xvars + ["per_gop"]] ``` @@ -228,13 +252,12 @@ import seaborn as sns #pour scatterplots ::: {.callout-tip} ## Exercice 1 : Principe des k-means -1. Importer les données et se restreindre aux variables `'Unemployment_rate_2019', 'Median_Household_Income_2021', 'Percent of adults with less than a high school diploma, 2018-22', "Percent of adults with a bachelor's degree or higher, 2018-22"` et bien sûr `'per_gop'`. Appelez cette base restreinte `df2` et enlevez les valeurs manquantes. -2. Faire un *k-means* avec $k=4$. -3. Créer une variable `label` dans `votes` stockant le résultat de la typologie. -4. Afficher cette typologie sur une carte. -5. Choisir les variables `Median_Household_Income_2021` et `Unemployment_rate_2019` et représenter le nuage de points en colorant différemment en fonction du label obtenu. Quel est le problème ? -6. Refaire les questions 2 à 5 en standardisant les variables en amont. -7. Représenter la distribution du vote pour chaque *cluster*. +1. Faire un *k-means* avec $k=4$. +2. Créer une variable `label` dans `votes` stockant le résultat de la typologie. +3. Afficher cette typologie sur une carte. +4. Choisir les variables `Median_Household_Income_2021` et `Unemployment_rate_2019` et représenter le nuage de points en colorant différemment en fonction du label obtenu. Quel est le problème ? +5. Refaire les questions 2 à 5 en standardisant les variables en amont. +6. Représenter la distribution du vote pour chaque *cluster*. ::: :::: @@ -243,36 +266,22 @@ import seaborn as sns #pour scatterplots ::: {.callout-tip} ## Exercise 1: Principle of k-means -1. Import the data and limit it to the variables `'Unemployment_rate_2019', 'Median_Household_Income_2021', 'Percent of adults with less than a high school diploma, 2018-22', "Percent of adults with a bachelor's degree or higher, 2018-22"`, and of course `'per_gop'`. Call this restricted dataset `df2` and remove missing values. -2. Perform a *k-means* with $k=4$. -3. Create a `label` variable in `votes` to store the typology results. -4. Display this typology on a map. -5. Choose the variables `Median_Household_Income_2021` and `Unemployment_rate_2019` and represent the scatter plot, coloring it differently based on the obtained label. What is the problem? -6. Repeat questions 2 to 5, standardizing the variables beforehand. -7. Represent the distribution of the vote for each *cluster*. +1. Perform a *k-means* with $k=4$. +2. Create a `label` variable in `votes` to store the typology results. +3. Display this typology on a map. +4. Choose the variables `Median_Household_Income_2021` and `Unemployment_rate_2019` and represent the scatter plot, coloring it differently based on the obtained label. What is the problem? +5. Repeat questions 2 to 5, standardizing the variables beforehand. +6. Represent the distribution of the vote for each *cluster*. ::: :::: -```{python} -#| output: false - -# 1. Chargement de la base restreinte. -xvars = ['Unemployment_rate_2019', 'Median_Household_Income_2021', -'Percent of adults with less than a high school diploma, 2018-22', -"Percent of adults with a bachelor's degree or higher, 2018-22"] - -votes = votes.dropna(subset = xvars + ["per_gop"]) - -df2 = votes[xvars + ["per_gop"]] -``` - ```{python} #| output: false -#2. kmeans avec k=4 +# Question 1 model = KMeans(n_clusters=4) model.fit(df2[xvars]) ``` @@ -282,9 +291,8 @@ model.fit(df2[xvars]) ```{python} #| output: false -#3. Création de la variable label dans votes +# Question 3 votes['label'] = model.labels_ -#votes['label'].head() ``` :::: {.content-visible when-profile="fr"} @@ -304,7 +312,7 @@ as follows: ```{python} #| output: false -#4. Carte de la typologie +# Question 4 p = votes.plot(column = "label", cmap = "inferno") p.set_axis_off() ``` @@ -328,11 +336,11 @@ and `Unemployment_rate_2019`, will have the following appearance: ```{python} #| output: false - from plotnine import * -#5. Nuage de points de 2 variables et coloration selon le label +# Question 5 votes['label'] = pd.Categorical(votes['label']) + p = ( ggplot(votes) + geom_point( @@ -351,11 +359,6 @@ p = ( p ``` -```{python} -#| output: false -ggsave(p, "featured_clustering.png") -``` - :::: {.content-visible when-profile="fr"} La classification apparaît un peu trop nettement dans cette figure. Cela suggère que la variable de revenu (`Median_Household_Income_2021`) @@ -427,8 +430,8 @@ votes['label'] = pd.Categorical(votes['label']) x = "Median_Household_Income_2021/1000", y = "Unemployment_rate_2019/100", color = "label" - ), - alpha = 0.4 + ), + alpha = 0.4 ) + theme_bw() + scale_x_log10() + scale_y_continuous(labels=percent_format()) + @@ -452,7 +455,7 @@ Finally, regarding question 6, the following histogram of votes for each cluster ```{python} #| output: false -# 6. Distribution du vote selon chaque cluster +# Question 6 plt.figure() p2 = ( ggplot(votes) + @@ -476,7 +479,7 @@ p2 Il faut noter plusieurs points sur l'algorithme implémenté par défaut par `scikit-learn`, que l'on peut lire dans la documentation : -- l'algorithme implémenté par défaut est kmeans++ (cf. paramètre `init`). Cela signifie que +- l'algorithme implémenté par défaut est _kmeans++_ (cf. paramètre `init`). Cela signifie que l'initialisation des centroïdes est faite de manière intelligente pour que les centroïdes initiaux soient choisis afin de ne pas être trop proches. - l'algorithme va être démarré avec `n_init` centroïdes différents et le modèle va choisir la meilleure initialisation @@ -495,7 +498,7 @@ Le modèle renvoie les `cluster_centers_`, les labels `labels_`, l'inertie `iner Several points about the algorithm implemented by default in `scikit-learn` should be noted, as outlined in the documentation: -- The default algorithm is kmeans++ (see the `init` parameter). This means that +- The default algorithm is _kmeans++_ (see the `init` parameter). This means that the initialization of centroids is done intelligently so that the initial centroids are chosen to avoid being too close to each other. - The algorithm will start with `n_init` different centroids, and the model will select the best initialization @@ -511,92 +514,128 @@ The model outputs the `cluster_centers_`, the labels `labels_`, the inertia `ine :::: {.content-visible when-profile="fr"} ## Choisir le nombre de clusters -Le nombre de clusters est fixé par le modélisateur. -Il existe plusieurs façons de fixer ce nombre : +Jusqu'à présent nous avons pris comme donné le nombre de cluster, comme s'il y avait une raison légitime de penser qu'il fallait 4 plutôt que 7 profils de votes. -* connaissance a priori du problème ; -* analyse d'une métrique spécifique pour définir le nombre de clusters à choisir ; -* etc. +Comme tout (hyper)paramètre dans une approche de _machine learning_ on peut vouloir jouer sur la valeur et prendre, en l'absence de théorie permettant de trancher, la moins mauvaise valeur empirique. -Il y a un arbitrage à faire -entre biais et variance : -un trop grand nombre de clusters implique une variance -intra-cluster très faible (sur-apprentissage, même s'il n'est jamais possible de déterminer -le vrai type d'une observation puisqu'on est en apprentissage non supervisé). +Il y a un arbitrage à faire entre biais et variance : un trop grand nombre de clusters implique une variance intra-cluster très faible ce qui est typique du sur-apprentissage, même s'il n'est jamais possible de déterminer le vrai type d'une observation puisqu'on est en apprentissage non supervisé. Sans connaissance a priori du nombre de clusters, on peut recourir à deux familles de méthodes : -* **La méthode du coude** (*elbow method*) : On prend le point d'inflexion de la courbe -de performance du modèle. Cela représente le moment où ajouter un cluster -(complexité croissante du modèle) n'apporte que des gains modérés dans la -modélisation des données. - -* **Le score de silhouette** : On mesure la similarité entre un point et les autres points -du cluster par rapport aux autres clusters. Plus spécifiquement : - -> Silhouette value is a measure of how similar an object is to its own cluster -> (cohesion) compared to other clusters (separation). The silhouette ranges -> from −1 to +1, where a high value indicates that the object is -> well matched to its own cluster and poorly matched to neighboring -> clusters. If most objects have a high value, then the clustering -> configuration is appropriate. If many points have a low or negative -> value, then the clustering configuration may have too many or too few clusters +* **La méthode du coude** (*elbow method*) : On prend le point d'inflexion de la courbe de performance du modèle. Cela représente le moment où ajouter un cluster supplémentaire, qui se traduit par une complexité croissante du modèle, n'apporte que des gains modérés dans la modélisation des données. + +* **Le score de silhouette** : On mesure la similarité entre un point et les autres points du cluster par rapport aux autres clusters et choisit le modèle qui permet de mieux distinguer les modèles (voir @tip-silhouette). +:::: + +:::: {.content-visible when-profile="en"} +## Choosing the number of clusters + +Up to now, we have taken the number of clusters as given, as if there were a legitimate reason to think that we need 4 rather than 7 voting profiles. + +Like any (hyper)parameter in a machine learning approach, we may want to vary its value and, in the absence of a theory to decide, pick the least bad empirical choice. + +There is a trade-off between bias and variance: too large a number of clusters implies very low within-cluster variance, which is typical of overfitting, even though it is never possible to determine the true type of an observation since we are in unsupervised learning. + +Without prior knowledge of the number of clusters, we can rely on two families of methods: + +* **Elbow method**: We take the inflection point of the model performance curve. This corresponds to the moment when adding an additional cluster, which increases model complexity, brings only modest gains in modeling the data. + +* **Silhouette score**: We measure the similarity between a point and the other points in its cluster relative to other clusters, and choose the model that best separates clusters (see @tip-silhouette-en). +:::: + +:::: {.content-visible when-profile="fr"} + +::: {#tip-silhouette .callout-tip collapse="true"} +## Le score de silhouette + +> Silhouette value is a measure of how similar an object is to its own cluster (cohesion) compared to other clusters (separation). The silhouette ranges from −1 to +1, where a high value indicates that the object iswell matched to its own cluster and poorly matched to neighboring clusters. If most objects have a high value, then the clustering configuration is appropriate. If many points have a low or negative value, then the clustering configuration may have too many or too few clusters > > Source: [Wikipedia](https://en.wikipedia.org/wiki/Silhouette_(clustering)) -Le score de silhouette d'une observation est donc égal à -`(m_nearest_cluster - m_intra_cluster)/max( m_nearest_cluster,m_intra_cluster)` -où `m_intra_cluster` est la moyenne des distances de l'observation aux observations du même cluster -et `m_nearest_cluster` est la moyenne des distances de l'observation aux observations du cluster le plus proche. +Le score de silhouette est donc une mesure de l'arbitrage entre cohésion des clusters (dans quelle mesure les observations au sein du clusters sont homogènes) et séparation de ceux-ci (dans quelle mesure les clusters se dissocient-ils les uns des autres). +Pour chaque observation $i$, la silhouette du point est -Le package `yellowbrick` fournit une extension utile à `scikit` pour représenter -visuellement la performance de nos algorithmes de *clustering*. +$$ +s(i) = \frac{b(i)-a(i)}{\max(a(i),b(i))} +$$ -Repartons d'un modèle simple -:::: -:::: {.content-visible when-profile="en"} -## Choosing the Number of Clusters +avec $a(i)$ la distance moyenne entre $i$ et les autres points de son propre cluster (mesure de la cohésion) et $b(i)$ la plus petite distance moyenne entre $i$ et les points d'un autre cluster (mesure de la séparation). -The number of clusters is determined by the modeler. -There are several ways to determine this number: +La valeur \(s(i)\) est comprise entre **-1** et **1** : -* Prior knowledge of the problem; -* Analysis of a specific metric to define the number of clusters to choose; -* Etc. +- **$s(i) \approx 1$** : $a(i) \ll b(i)$ + Le point est bien assigné à son cluster : il est proche des points de son cluster et loin des autres. -There is a trade-off between bias and variance: -too many clusters result in very low intra-cluster variance (overfitting, although it is never possible to determine the true type of an observation since this is unsupervised learning). +- **$s(i) \approx 0$** : $a(i) \approx b(i)$ + Le point est à la frontière entre deux clusters : la séparation est faible localement. -Without prior knowledge of the number of clusters, two families of methods can be used: +- **$s(i) < 0$** : $a(i) > b(i)$ + Le point est probablement mal assigné : en moyenne, il est plus proche d’un autre cluster que du sien. -* **The elbow method**: Take the inflection point of the model's performance curve. This represents the moment when adding another cluster (increasing model complexity) provides only moderate gains in data modeling. +Le score de silhouette est la moyenne des scores de silhouette des points. +::: + +:::: + +:::: {.content-visible when-profile="en"} -* **The silhouette score**: This measures the similarity between a point and other points in the cluster relative to other clusters. Specifically: +::: {#tip-silhouette-en .callout-tip collapse="true"} -> Silhouette value is a measure of how similar an object is to its own cluster -> (cohesion) compared to other clusters (separation). The silhouette ranges -> from −1 to +1, where a high value indicates that the object is -> well matched to its own cluster and poorly matched to neighboring -> clusters. If most objects have a high value, then the clustering -> configuration is appropriate. If many points have a low or negative -> value, then the clustering configuration may have too many or too few clusters +> Silhouette value is a measure of how similar an object is to its own cluster (cohesion) compared to other clusters (separation). The silhouette ranges from −1 to +1, where a high value indicates that the object iswell matched to its own cluster and poorly matched to neighboring clusters. If most objects have a high value, then the clustering configuration is appropriate. If many points have a low or negative value, then the clustering configuration may have too many or too few clusters > > Source: [Wikipedia](https://en.wikipedia.org/wiki/Silhouette_(clustering)) -The silhouette score for an observation is therefore equal to -`(m_nearest_cluster - m_intra_cluster)/max( m_nearest_cluster,m_intra_cluster)` -where `m_intra_cluster` is the mean distance of the observation to observations in the same cluster, -and `m_nearest_cluster` is the mean distance of the observation to observations in the nearest cluster. +The silhouette score is therefore a measure of the trade-off between **cluster cohesion** (to what extent observations within a cluster are homogeneous) and **cluster separation** (to what extent clusters are distinct from one another). + +For each observation $i$, the silhouette of the point is: +$$ +s(i) = \frac{b(i)-a(i)}{\max(a(i),b(i))} +$$ -The `yellowbrick` package provides a useful extension to `scikit` for visually representing -the performance of our clustering algorithms. +where $a(i)$ is the average distance between $i$ and the other points in its own cluster (a measure of cohesion) and $b(i)$ is the smallest average distance between $i$ and the points of another cluster (a measure of separation). -Let us start again with a simple model. +The value \(s(i)\) lies between **-1** and **1**: + +- **$s(i) \approx 1$** : $a(i) \ll b(i)$ + The point is well assigned to its cluster: it is close to the points in its cluster and far from the others. + +- **$s(i) \approx 0$** : $a(i) \approx b(i)$ + The point lies on the boundary between two clusters: separation is weak locally. + +- **$s(i) < 0$** : $a(i) > b(i)$ + The point is probably misassigned: on average, it is closer to another cluster than to its own. + +The silhouette score is the average of the points’ silhouette values. + +::: +:::: + + +:::: {.content-visible when-profile="fr"} +::: {.callout-tip} +## Exercice: déterminer le nombre optimal de partition par la méthode du coude + +1. Evaluer l'inertie et la distorsion en jouant sur le nombre de _clusters_ (de 1 à 9). +2. Représenter graphiquement et interpréter + +::: +:::: + +:::: {.content-visible when-profile="en"} +::: {.callout-tip} +## Exercise: determine the optimal number of clusters using the elbow method + +1. Evaluate inertia and distortion by varying the number of _clusters_ (from 1 to 9). +2. Plot the results and interpret them. + +::: :::: + + ```{python} #| echo: true xvars = [ @@ -605,91 +644,85 @@ xvars = [ "Percent of adults with a bachelor's degree or higher, 2018-22" ] -df2 = votes[xvars + ["per_gop"]].dropna() -model = KMeans(n_clusters=4) +df2 = votes.loc[:, xvars + ["per_gop"]].dropna() +X = df2.copy() ``` ```{python} -#| echo: true -#| output: false -from sklearn.cluster import KMeans -from yellowbrick.cluster import KElbowVisualizer +# Question 1 +from scipy.spatial.distance import cdist -visualizer = KElbowVisualizer(model, k=(2,12)) -visualizer.fit(df2.loc[:,xvars]) # Fit the data to the visualizer -visualizer.show() -``` +distortions = [] +inertias = [] +mapping1 = {} +mapping2 = {} +K = range(1, 10) +X = df2.loc[:, xvars] -:::: {.content-visible when-profile="fr"} -Pour la méthode du coude, la courbe -de performance du modèle marque un coude léger à $k=4$. Le modèle initial -semblait donc approprié. +for k in K: + kmeanModel = KMeans(n_clusters=k, random_state=42).fit(X) + distortions.append(sum(np.min(cdist(X, kmeanModel.cluster_centers_, 'euclidean'), axis=1)**2) / X.shape[0]) + inertias.append(kmeanModel.inertia_) -`yellowbrick` permet également de représenter des silhouettes mais -l'interprétation est moins aisée et le coût computationnel plus élevé : -:::: -:::: {.content-visible when-profile="en"} -For the elbow method, the model's performance curve shows a slight elbow at $k=4$. The initial model -therefore seemed appropriate. +performance_analysis = pd.DataFrame( + { + "K": range(1, 10), + "distorsions": distortions, + "inertias": inertias + } +) +``` + +```{python} +def represent_performance( + df_performance: pd.DataFrame, metric: str +): + + kmin = int(df_performance["K"].min()) + kmax = int(df_performance["K"].max()) + + p = ( + ggplot(df_performance, aes(x="K", y=metric)) + + geom_line() + + geom_point() + + theme_minimal() + + theme( + axis_text_y=element_blank(), + axis_ticks_major_y=element_blank(), + axis_title_y=element_blank() + ) + + scale_x_continuous(breaks=list(range(kmin, kmax + 1))) + ) + + return p +``` -`yellowbrick` also allows the representation of silhouettes, but the interpretation is less straightforward, and the computational cost is higher: +:::: {.content-visible when-profile="fr"} +L'inertie de notre modèle est la suivante: :::: ```{python} -#| echo: true -#| output: false - -from yellowbrick.cluster import SilhouetteVisualizer - -fig, ax = plt.subplots(2, 2, figsize=(15,8)) -j=0 -for i in [3, 4, 6, 10]: - j += 1 - ''' - Create KMeans instance for different number of clusters - ''' - km = KMeans(n_clusters=i, init='k-means++', n_init=10, max_iter=100, random_state=42) - q, mod = divmod(j, 2) - ''' - Create SilhouetteVisualizer instance with KMeans instance - Fit the visualizer - ''' - visualizer = SilhouetteVisualizer(km, colors='yellowbrick', ax=ax[q-1][mod]) - ax[q-1][mod].set_title("k = " + str(i)) - visualizer.fit(df2[xvars]) +represent_performance(performance_analysis, "inertias") ``` +:::: {.content-visible when-profile="fr"} +Là où la distorsion suit la courbe +:::: + ```{python} -#| output: false -fig.savefig("silhouette-yellowbrick.png") +represent_performance(performance_analysis, "distorsions") ``` -![](silhouette-yellowbrick.png) :::: {.content-visible when-profile="fr"} -Le score de silhouette offre une représentation plus riche que la courbe coudée. - -Sur ce graphique, les barres verticales en rouge et en pointillé représentent le score de silhouette -global pour chaque `k` choisi. On voit par exemple que pour tous les `k` représentés ici, le -score de silhouette se situe entre 0.5 et 0.6 et varie peu. -Ensuite, pour un `k` donné, on va avoir la représentation des scores de silhouette de chaque -observation, regroupées par cluster. -Par exemple, pour `k = 4`, ici, on voit bien quatre couleurs différentes qui sont les 4 clusters modélisés. -Les ordonnées sont toutes les observations clusterisées et en abscisses on a le score de silhouette de -chaque observation. Si au sein d'un cluster, les observations ont un score de silhouette plus faible que le -score de silhouette global (ligne verticale en rouge), cela signifie que les observations du cluster sont -trop proches des autres clusters. - -Grâce à cette représentation, on peut aussi se rendre compte de la taille relative des clusters. Par exemple, -avec `k = 3`, on voit qu'on a deux clusters conséquents et un plus "petit" cluster relativement aux deux autres. -Cela peut nous permettre de choisir des clusters de tailles homogènes ou non. - -Enfin, quand le score de silhouette est négatif, cela signifie que la moyenne des distances de l'observation -aux observations du cluster le plus proche est inférieure à la moyenne des distances de l'observation aux -observations de son cluster. Cela signifie que l'observation est mal classée. +XXXX +:::: + + +:::: {.content-visible when-profile="fr"} ## Autres méthodes de clustering Il existe de nombreuses autres méthodes de clustering. Parmi les plus connues, on peut citer trois exemples en particulier : @@ -744,28 +777,8 @@ Il existe de nombreuses autres méthodes de clustering : - Etc. ::: :::: -:::: {.content-visible when-profile="en"} -The silhouette score provides a richer representation than the elbow curve. - -In this graph, the red dashed vertical lines represent the global silhouette score -for each chosen `k`. For example, we can see that for all the `k` values represented here, the -silhouette score is between 0.5 and 0.6 and varies little. -Then, for a given `k`, the silhouette scores for each -observation are displayed, grouped by cluster. -For instance, for `k = 4`, here, we clearly see four different colors representing the 4 modeled clusters. -The y-axis represents all clustered observations, and the x-axis shows the silhouette score of -each observation. If, within a cluster, observations have a silhouette score lower than the -global silhouette score (red vertical line), this indicates that the cluster's observations are -too close to other clusters. - -This representation also allows us to assess the relative size of clusters. For example, -with `k = 3`, we see two large clusters and one "smaller" cluster relative to the others. -This can help us decide whether to aim for clusters of homogeneous sizes or not. - -Finally, when the silhouette score is negative, it indicates that the mean distance of an observation -to observations in the nearest cluster is smaller than the mean distance of the observation to -observations in its own cluster. This means the observation is misclassified. +:::: {.content-visible when-profile="en"} ## Other Clustering Methods There are many other clustering methods. 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"https://files.pythonhosted.org/packages/80/b2/6032bf79d0acdf57fbd4885ab33789fd21c6003487e91994626a1b106fba/skrub-0.7.0-py3-none-any.whl", hash = "sha256:0a0cdb0f533c702bfefb23550220df70f9bc2626f11eaecf33181c1b9e64c11c", size = 498291, upload-time = "2025-12-11T22:08:35.281Z" }, +] + [[package]] name = "smart-open" version = "7.5.0" From e7967f80c6bb0e58a9534ed64cea8c2d27af7b12 Mon Sep 17 00:00:00 2001 From: lgaliana Date: Sun, 21 Dec 2025 20:45:49 +0000 Subject: [PATCH 5/6] clean chapitre --- content/manipulation/02_pandas_suite.qmd | 1 - content/manipulation/03_geopandas_intro.qmd | 2 +- content/modelisation/0_preprocessing.qmd | 1 - content/modelisation/5_clustering.qmd | 8 +------- pyproject.toml | 4 +--- 5 files changed, 3 insertions(+), 13 deletions(-) diff --git a/content/manipulation/02_pandas_suite.qmd b/content/manipulation/02_pandas_suite.qmd index 89d9ecf57..36fbca37e 100644 --- a/content/manipulation/02_pandas_suite.qmd +++ b/content/manipulation/02_pandas_suite.qmd @@ -145,7 +145,6 @@ As explained below, we will use a library called `pynsee` to retrieve Insee data ```{python} #| eval: false #| echo: true -!pip install xlrd --quiet !pip install pynsee --quiet !pip install great_tables --quiet ``` diff --git a/content/manipulation/03_geopandas_intro.qmd b/content/manipulation/03_geopandas_intro.qmd index d8ad46b14..4a0cc0b6c 100644 --- a/content/manipulation/03_geopandas_intro.qmd +++ b/content/manipulation/03_geopandas_intro.qmd @@ -348,7 +348,7 @@ you can remove the `!` at the beginning of the line): #| echo: true #| label: install-cartiflette !pip install py7zr geopandas openpyxl tqdm s3fs --quiet -!pip install PyYAML xlrd --quiet +!pip install PyYAML --quiet !pip install cartiflette --quiet ``` diff --git a/content/modelisation/0_preprocessing.qmd b/content/modelisation/0_preprocessing.qmd index 9118c636a..b659a58cf 100644 --- a/content/modelisation/0_preprocessing.qmd +++ b/content/modelisation/0_preprocessing.qmd @@ -195,7 +195,6 @@ The following packages are needed to import and visualize the election data: ```{python} #| eval: false #| echo: true -!pip install --upgrade xlrd !pip install geopandas ``` diff --git a/content/modelisation/5_clustering.qmd b/content/modelisation/5_clustering.qmd index 6d0974747..d4f0a7458 100644 --- a/content/modelisation/5_clustering.qmd +++ b/content/modelisation/5_clustering.qmd @@ -644,7 +644,7 @@ xvars = [ "Percent of adults with a bachelor's degree or higher, 2018-22" ] -df2 = votes.loc[:, xvars + ["per_gop"]].dropna() +df2 = votes.loc[:, xvars].dropna() X = df2.copy() ``` @@ -716,12 +716,6 @@ represent_performance(performance_analysis, "distorsions") ``` -:::: {.content-visible when-profile="fr"} -XXXX -:::: - - - :::: {.content-visible when-profile="fr"} ## Autres méthodes de clustering diff --git a/pyproject.toml b/pyproject.toml index f8f90f36f..b3f2cd24f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -39,9 +39,7 @@ dependencies = [ "skore>=0.11.4", "spacy==3.8.4", "webdriver-manager==4.0.2", - "wordcloud==1.9.3", - "xlrd==2.0.1", - "yellowbrick==1.5", + "wordcloud==1.9.3" ] [tool.uv.sources] From 63a24bc23efde6cd0da79fc17b22b71fcd37192f Mon Sep 17 00:00:00 2001 From: lgaliana Date: Sun, 21 Dec 2025 21:11:59 +0000 Subject: [PATCH 6/6] retire yellowbricks --- content/modelisation/5_clustering.qmd | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/content/modelisation/5_clustering.qmd b/content/modelisation/5_clustering.qmd index d4f0a7458..17e55e81e 100644 --- a/content/modelisation/5_clustering.qmd +++ b/content/modelisation/5_clustering.qmd @@ -644,8 +644,7 @@ xvars = [ "Percent of adults with a bachelor's degree or higher, 2018-22" ] -df2 = votes.loc[:, xvars].dropna() -X = df2.copy() +df2 = votes.loc[:, xvars + ["per_gop"]].dropna() ``` ```{python}