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如何将多个不同参数的sklearn算法应用于多个数据帧?

subplot scikit-learn python

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santoku · 技术社区 · 6 年前

我正在寻找一个有效的方法,以应用多个sklearn聚类算法的多个数据帧没有太多重复。

import pandas as pd
import numpy as np
from sklearn.datasets import make_moons,make_blobs
from sklearn.cluster import KMeans, DBSCAN
from matplotlib import pyplot

X1, y1 = make_moons(n_samples=100, noise=0.1)
X2, y2 = make_blobs(n_samples=100, centers=3, n_features=2)

我想在这些数据集上同时应用kmeans和dbscan,但是每个数据集需要不同的参数,我如何使用一个循环将多个模型应用于多个数据并最终在网格中绘制出来?谢谢。

0 回复 | 直到 6 年前

Venkatachalam 6 年前

您已经创建了一些dict来定义每个数据集的超参数| clustering_algo组合。

下面的方法可能对你有用![从 sklearn clustering's documentation]

import pandas as pd
import numpy as np
from sklearn.datasets import make_moons,make_blobs
from sklearn.cluster import KMeans, DBSCAN
from matplotlib import pyplot as plt

noisy_moons = make_moons(n_samples=100, noise=0.1)
blobs = make_blobs(n_samples=100, centers=3 , center_box = (-1,1),cluster_std=0.1)

colors = np.array(['#377eb8', '#ff7f00', '#4daf4a',
                   '#f781bf', '#a65628', '#984ea3',
                   '#999999', '#e41a1c', '#dede00'])

#defining the clustering algo which we want to try
clustering_models = [KMeans,DBSCAN]

from collections import namedtuple
Model = namedtuple('Model', ['name', 'model'])
models = [Model(model.__module__.split('.')[-1][:-1], model) 
          for model in clustering_models]

#defn of params for each dataset|clustering_algo
datasets_w_hyperparams = [(noisy_moons[0], 
                           {models[0][0]: {'n_clusters': 2}, models[1][0]: {'eps': .3, }}),
                          (blobs[0], 
                           {models[0][0]: {'n_clusters': 2}, models[1][0]: {'eps': .1, }})]

f,axes=plt.subplots(len(datasets_w_hyperparams),len(models),figsize = (15,10))
for data_id,(dataset,params) in enumerate(datasets_w_hyperparams):
    for model_id,model in enumerate(models):
        ax = axes[data_id][model_id]
        name, clus_model = model
        pred = clus_model(**params[name]).fit_predict(dataset)
        ax.scatter(dataset[:,0],dataset[:,1], s=20, color= colors[pred])
        ax.set_title(name)
plt.show()