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import streamlit as st
from sklearn.cluster import KMeans, DBSCAN, AgglomerativeClustering, SpectralClustering
from sklearn.mixture import GaussianMixture
@st.cache_data
def train_select_cluster_model(X_train, n, model_type, model_params=None):
"""
Trains a clustering model based on the specified model type and parameters.
Parameters:
- X_train (array-like): The training data set.
- n (int): The number of clusters to form or the number of components for the Gaussian Mixture model.
- model_type (int): An integer representing the type of model to train.
1 for KMeans, 2 for DBSCAN, 3 for GaussianMixture, 4 for Hierarchical clustering, and 5 for Spectral clustering.
- model_params (dict, optional): A dictionary of model-specific parameters. Default is None.
Returns:
- The trained clustering model object based on the specified model type.
"""
if model_type == 1:
return KMeans_train(X_train, n, model_params)
elif model_type == 2:
return DBSCAN_train(X_train, model_params)
elif model_type == 3:
return GaussianMixture_train(X_train, n, model_params)
elif model_type == 4:
return Hierarchical_train(X_train, n, model_params)
elif model_type == 5:
return Spectral_train(X_train, n, model_params)
def KMeans_train(X_train, n_clusters=3, model_params=None):
if model_params is None: model_params = {}
kmeans = KMeans(n_clusters=n_clusters, **model_params)
kmeans.fit(X_train)
return kmeans
def DBSCAN_train(X_train, model_params=None):
if model_params is None: model_params = {}
dbscan = DBSCAN(**model_params)
dbscan.fit(X_train)
return dbscan
def GaussianMixture_train(X_train, n_components=1, model_params=None):
if model_params is None: model_params = {}
gmm = GaussianMixture(n_components=n_components, **model_params)
gmm.fit(X_train)
return gmm
def Hierarchical_train(X_train, n_clusters=3, model_params=None):
if model_params is None: model_params = {}
hierarchical = AgglomerativeClustering(n_clusters=n_clusters, **model_params)
hierarchical.fit(X_train)
return hierarchical
def Spectral_train(X_train, n_clusters=3, model_params=None):
if model_params is None: model_params = {}
spectral = SpectralClustering(n_clusters=n_clusters, **model_params)
spectral.fit(X_train)
return spectral |