Evaluando resultados de hierarchical clustering

Lo mismo pero con plotly, me guie de este codigo https://chart-studio.plotly.com/~Diksha_Gabha/2853.embed, pero le solucione errores que tenia que no permitian visualizar algunas funciones y sustitui librerias antiguas por sus equivalentes actuales.

import plotly.graph_objects as go
from plotly.subplots import make_subplots


from __future__ import print_function

from sklearn.datasets import make_blobs
from sklearn.cluster import KMeans
from sklearn.metrics import silhouette_samples, silhouette_score

import matplotlib
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import numpy as np

range_n_clusters = [2, 3, 4, 5, 6]

figures = []

for n_clusters in range_n_clusters:
    # Create a subplot with 1 row and 2 columns
    fig = make_subplots(rows=1, cols=2,
                        print_grid=False,
                        subplot_titles=('The silhouette plot for the various clusters.',
                                              'The visualization of the clustered data.'))

    # The 1st subplot is the silhouette plot
    # The silhouette coefficient can range from -1, 1 but in this example all
    # lie within [-0.1, 1]
    fig['layout']['xaxis1'].update(title='The silhouette coefficient values',
                                   range=[-0.1, 1])

    # The (n_clusters+1)*10 is for inserting blank space between silhouette
    # plots of individual clusters, to demarcate them clearly.
    fig['layout']['yaxis1'].update(title='Cluster label',
                                   showticklabels=False,
                                   range=[0, len(X) + (n_clusters + 1) * 10])



    # Initialize the clusterer with n_clusters value and a random generator
    # seed of 10 for reproducibility.
    clusterer = AgglomerativeClustering(n_clusters=n_clusters, metric='euclidean', linkage='ward')
    cluster_labels = clusterer.fit_predict(X)

    # The silhouette_score gives the average value for all the samples.
    # This gives a perspective into the density and separation of the formed
    # clusters
    silhouette_avg = silhouette_score(X, cluster_labels)
    print(
        "For n_clusters =",
        n_clusters,
        "The average silhouette_score is :",
        silhouette_avg,
    )

    # Compute the silhouette scores for each sample
    sample_silhouette_values = silhouette_samples(X, cluster_labels)

    y_lower = 10

    color = []

    for i in range(n_clusters):
        # Aggregate the silhouette scores for samples belonging to
        # cluster i, and sort them
        ith_cluster_silhouette_values = \
            sample_silhouette_values[cluster_labels == i]

        ith_cluster_silhouette_values.sort()

        size_cluster_i = ith_cluster_silhouette_values.shape[0]
        y_upper = y_lower + size_cluster_i


        colors = matplotlib.colors.colorConverter.to_rgb(cm.nipy_spectral(float(i) / n_clusters))
        colors = 'rgb'+str(colors)
        color.append(colors)
        filled_area = go.Scatter(y=np.arange(y_lower, y_upper),
                                 x=ith_cluster_silhouette_values,
                                 mode='lines',
                                 showlegend=False,
                                 line=dict(width=0.5,
                                          color=colors),
                                 fill='tozerox',
                                 name='Silhouette')
        fig.add_traces(filled_area, 1, 1)

        # Compute the new y_lower for next plot
        y_lower = y_upper + 10  # 10 for the 0 samples


    # The vertical line for average silhouette score of all the values
    axis_line = go.Scatter(x=[silhouette_avg, silhouette_avg],
                           y=[0, y_upper],
                           showlegend=False,
                           mode='lines',
                           line=dict(color="red", dash='dash',
                                     width =2) )


    fig.append_trace(axis_line, 1, 1)


    # 2nd Plot showing the actual clusters formed
    clusters = go.Scatter(x=X[:, 0], 
                          y=X[:, 1], 
                          showlegend=False,
                          mode='markers',
                          marker=dict(color=cluster_labels,
                                     size=4, colorscale=color),
                          name='Data'
                         )
    fig.append_trace(clusters, 1, 2)

#         # Labeling the clusters
#         centers_ = clusterer.cluster_centers_
#         # Draw white circles at cluster centers
#         centers = go.Scatter(x=centers_[:, 0], 
#                              y=centers_[:, 1],
#                              showlegend=False,
#                              mode='markers',
#                              marker=dict(color='green', size=10,
#                                          line=dict(color='black',
#                                                                  width=1))
#                             )

#     fig.append_trace(centers, 1, 2)

    fig['layout']['xaxis2'].update(title='Feature space for the 1st feature',
                                   zeroline=False)
    fig['layout']['yaxis2'].update(title='Feature space for the 2nd feature',
                                  zeroline=False)


    fig['layout'].update(title="Silhouette analysis for KMeans clustering on sample data "
                         "with n_clusters = %d" % n_clusters)

    fig.update_layout(showlegend=True)
    figures.append(fig)
    fig.show()

Quizas requieras:

# !pip install chart_studio

Esta función tal vez pueda ser de ayuda para visualizar el silhouette_score(avg) y el silhouette_samples.

def plot_silhouette(df,X,y, clust_var_name=""):
    """
    Args:
        - clust_var_name:string  
            Nombre columna con el valor del cluster asignado a cada observacion(y_pred)
        - df: DataFrame con los features y la columna clust_var_name
        - X: np.Array
            Array con los features
        - y: np.Array
            Array con los valores de la prediccion del cluster
    """
    silhouette = round(silhouette_score(X,y),2)
    samples = silhouette_samples(X,y) 
    df["silhouette_samples"] = samples
    clusters = df[clust_var_name].unique()
    n_samples = len(df)
    y_pos = n_samples*0.05
    for i,cluster in enumerate(clusters):
        df_aux = df[df[clust_var_name] == cluster].sort_values("silhouette_samples")
        plt.figure(i)
        df_aux["silhouette_samples"].plot(kind="barh")
        plt.vlines(x=silhouette,ymin=0,ymax=n_samples,linestyles="--",color ='red')
        plt.text(x=0.8,y=y_pos,s=f"avg_silhouette_score: {silhouette}")
        plt.xlabel("silhouette_score")
        plt.ylabel("n_sample")
        plt.yticks([])
        plt.title(f"Sample silhouettes for custer {cluster}")

Excelentes explicaciones, hacia falta curso¡

Uff me encanto esta clase, mas que todo la forma tan clara de ver los K , con los graficos de velas y su respectiva clasificacion al lado.💪💪💪💪

¡Buena clase! Sigo este curso con un dataset de transacciones con y sin fraude, comparto en la imagen la gráfica de mi índice de silhouette para 2 clusters, estoy aún dandole una explicación porque de los 400 puntos o trasacciones solo 2 corresponden al cluster 1 y 398 al cluster 2, y por ello el ancho tan delgado y grueso respectivamente. Entendería que aún debo seguir trabajando en la variables de mi dataset para tener mejores resultados. ¡Todo feedback es bienbenido! Gracias de antemano.

from sklearn.metrics import silhouette_samples, silhouette_score
import matplotlib.cm as cm


range_n_clusters = [2,3,4,5]

for n_clusters in range_n_clusters:
    # Create a subplot with 1 row and 2 columns
    fig, (ax1, ax2) = plt.subplots(1, 2)
    fig.set_size_inches(18, 7)

    # The 1st subplot is the silhouette plot
    # The silhouette coefficient can range from -1, 1 but in this example all
    # lie within [-0.1, 1]
    ax1.set_xlim([-0.1, 1])
    # The (n_clusters+1)*10 is for inserting blank space between silhouette
    # plots of individual clusters, to demarcate them clearly.
    ax1.set_ylim([0, len(X) + (n_clusters + 1) * 10])

    # Initialize the clusterer with n_clusters value and a random generator
    # seed of 10 for reproducibility.
    clusterer = AgglomerativeClustering(n_clusters=n_clusters, affinity='euclidean', linkage='ward')
    cluster_labels = clusterer.fit_predict(X)

    # The silhouette_score gives the average value for all the samples.
    # This gives a perspective into the density and separation of the formed
    # clusters
    silhouette_avg = silhouette_score(X, cluster_labels)
    print(
        "For n_clusters =",
        n_clusters,
        "The average silhouette_score is :",
        silhouette_avg,
    )

    # Compute the silhouette scores for each sample
    sample_silhouette_values = silhouette_samples(X, cluster_labels)

    y_lower = 10
    for i in range(n_clusters):
        # Aggregate the silhouette scores for samples belonging to
        # cluster i, and sort them
        ith_cluster_silhouette_values = sample_silhouette_values[cluster_labels == i]

        ith_cluster_silhouette_values.sort()

        size_cluster_i = ith_cluster_silhouette_values.shape[0]
        y_upper = y_lower + size_cluster_i

        color = cm.nipy_spectral(float(i) / n_clusters)
        ax1.fill_betweenx(
            np.arange(y_lower, y_upper),
            0,
            ith_cluster_silhouette_values,
            facecolor=color,
            edgecolor=color,
            alpha=0.7,
        )

        # Label the silhouette plots with their cluster numbers at the middle
        ax1.text(-0.05, y_lower + 0.5 * size_cluster_i, str(i))

        # Compute the new y_lower for next plot
        y_lower = y_upper + 10  # 10 for the 0 samples

    ax1.set_title("The silhouette plot for the various clusters.")
    ax1.set_xlabel("The silhouette coefficient values")
    ax1.set_ylabel("Cluster label")

    # The vertical line for average silhouette score of all the values
    ax1.axvline(x=silhouette_avg, color="red", linestyle="--")

    ax1.set_yticks([])  # Clear the yaxis labels / ticks
    ax1.set_xticks([-0.1, 0, 0.2, 0.4, 0.6, 0.8, 1])

    # 2nd Plot showing the actual clusters formed
    colors = cm.nipy_spectral(cluster_labels.astype(float) / n_clusters)
    ax2.scatter(
        X[:, 0], X[:, 1], marker=".", s=30, lw=0, alpha=0.7, c=colors, edgecolor="k"
    )


plt.show()