Excelentes explicaciones, hacia falta curso¡
Fundamentos de clustering
¿Qué es el clustering en machine learning?
Tu primer clustering con scikit-learn
¿Cuándo usar clustering?
¿Cómo evaluar modelos de clustering?
K-means
¿Qué es el algoritmo de K-means y cómo funciona?
¿Cuándo usar K-means?
Implementando K-means
Encontrando K
Evaluando resultados de K-means
Hierarchical clustering
¿Qué es hierarchical clustering y cómo funciona?
¿Cuándo usar hierarchical clustering?
Implementando hierarchical clustering
Evaluando resultados de hierarchical clustering
DBSCAN
¿Qué es DBSCAN y cómo funciona?
¿Cuándo usar DBSCAN?
Implementando DBSCAN
Encontrar híper-parámetros
Evaluando resultados de DBSCAN
Proyecto: resolviendo un problema con clustering
Preparar datos para clusterizar
Aplicando PCA para clustering
Resolviendo con K-means
Resolviendo con hierarchical clustering
Resolviendo con DBSCAN
Resolviendo con DBSCAN (sin PCA)
Evaluación resultados de distintos modelos de clustering
Conclusiones
Proyecto final y cierre
Comparte tu proyecto de segmentación con clustering y certifícate
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Aportes 4
Preguntas 1
Excelentes explicaciones, hacia falta curso¡
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}")
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
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()
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