两组数据点之间的聚类

部分列表A. 使用KMeans方法识别数据中的簇B. 导入库C. 虚拟数据D. 自定义函数E. 计算True聚类中心KMeansF. 使用模型定义、拟合和预测F.1。预测y_train使用X_trainF.2。预测y_test使用X_testG. 用train,test和prediction数据制作图形参考A. 使用KMeans方法识别数据中的簇我们将用它sklearn.cluster.KMeans来识别集群。该属性model.cluster_centers_将为我们提供预测的聚类中心。比如说，我们想找出5训练数据中X_train形状为：的簇(n_samples, n_features)和y_train形状为标签的簇：(n_samples,)。以下代码块将模型拟合到数据 ( X_train)，然后进行预测y并将预测结果保存在y_pred_train变量中。# Define modelmodel = KMeans(n_clusters = 5)# Fit model to training datamodel.fit(X_train)# Make prediction on training datay_pred_train = model.predict(X_train)# Get predicted cluster centersmodel.cluster_centers_ # shape: (n_cluster, n_features)## Displaying cluster centers on a plot # if you just want to add cluster centers # to your existing scatter-plot, # just do this --->>cluster_centers = model.cluster_centers_plt.scatter(cluster_centers[:, 0], cluster_centers[:, 1], marker='s', color='orange', s = 100, alpha=0.5, label='pred')这就是结果⭐⭐⭐ 跳转到部分G查看用于制作绘图的代码。B. 导入库import numpy as npimport pandas as pdimport matplotlib.pyplot as pltfrom sklearn.model_selection import train_test_splitfrom sklearn.datasets import make_blobsfrom sklearn.cluster import KMeansimport pprint%matplotlib inline %config InlineBackend.figure_format = 'svg' # 'svg', 'retina' plt.style.use('seaborn-white')C. 虚拟数据我们将使用以下代码块中生成的数据。根据设计，我们创建一个包含5集群和以下规范的数据集。然后使用将数据分为train和块。testsklearn.model_selection.train_test_split## Creating data with # n_samples = 2500# n_features = 4# Expected clusters = 5# centers = 5# cluster_std = [1.0, 2.5, 0.5, 1.5, 2.0]NUM_SAMPLES = 2500RANDOM_STATE = 42NUM_FEATURES = 4NUM_CLUSTERS = 5CLUSTER_STD = [1.0, 2.5, 0.5, 1.5, 2.0]TEST_SIZE = 0.20def dummy_data(): ## Creating data with # n_samples = 2500 # n_features = 4 # Expected clusters = 5 # centers = 5 # cluster_std = [1.0, 2.5, 0.5, 1.5, 2.0] X, y = make_blobs( n_samples = NUM_SAMPLES, random_state = RANDOM_STATE, n_features = NUM_FEATURES, centers = NUM_CLUSTERS, cluster_std = CLUSTER_STD ) return X, ydef test_dummy_data(X, y): assert X.shape == (NUM_SAMPLES, NUM_FEATURES), "Shape mismatch for X" assert set(y) == set(np.arange(NUM_CLUSTERS)), "NUM_CLUSTER mismatch for y"## D. Create Dummy DataX, y = dummy_data()test_dummy_data(X, y)## Create train-test-splitX_train, X_test, y_train, y_test = train_test_split( X, y, test_size=TEST_SIZE, random_state=RANDOM_STATE)D. 自定义函数我们将使用以下3自定义函数：get_cluster_centers()scatterplot()add_cluster_centers()def get_cluster_centers(X, y, num_clusters=None): """Returns the cluster-centers as numpy.array of shape: (num_cluster, num_features). """ num_clusters = NUM_CLUSTERS if (num_clusters is None) else num_clusters return np.stack([X[y==i].mean(axis=0) for i in range(NUM_CLUSTERS)])def scatterplot(X, y, cluster_centers=None, alpha=0.5, cmap='viridis', legend_title="Classes", legend_loc="upper left", ax=None): if ax is not None: plt.sca(ax) scatter = plt.scatter(X[:, 0], X[:, 1], s=None, c=y, alpha=alpha, cmap=cmap) legend = ax.legend(*scatter.legend_elements(), loc=legend_loc, title=legend_title) ax.add_artist(legend) if cluster_centers is not None: plt.scatter(cluster_centers[:, 0], cluster_centers[:, 1], marker='o', color='red', alpha=1.0) ax = plt.gca() return axdef add_cluster_centers(true_cluster_centers=None, pred_cluster_centers=None, markers=('o', 's'), colors=('red, ''orange'), s = (None, 200), alphas = (1.0, 0.5), center_labels = ('true', 'pred'), legend_title = "Cluster Centers", legend_loc = "upper right", ax = None): if ax is not None: plt.sca(ax) for idx, cluster_centers in enumerate([true_cluster_centers, pred_cluster_centers]): if cluster_centers is not None: scatter = plt.scatter( cluster_centers[:, 0], cluster_centers[:, 1], marker = markers[idx], color = colors[idx], s = s[idx], alpha = alphas[idx], label = center_labels[idx] ) legend = ax.legend(loc=legend_loc, title=legend_title) ax.add_artist(legend) return axE. 计算True聚类中心我们将计算和数据集true的聚类中心并将结果保存到: 。traintestdicttrue_cluster_centerstrue_cluster_centers = { 'train': get_cluster_centers(X = X_train, y = y_train, num_clusters = NUM_CLUSTERS), 'test': get_cluster_centers(X = X_test, y = y_test, num_clusters = NUM_CLUSTERS)}# Show resultpprint.pprint(true_cluster_centers, indent=2)输出：{ 'test': array([[-2.44425795, 9.06004013, 4.7765817 , 2.02559904], [-6.68967507, -7.09292101, -8.90860337, 7.16545582], [ 1.99527271, 4.11374524, -9.62610383, 9.32625443], [ 6.46362854, -5.90122349, -6.2972843 , -6.04963714], [-4.07799392, 0.61599582, -1.82653858, -4.34758032]]), 'train': array([[-2.49685525, 9.08826 , 4.64928719, 2.01326914], [-6.82913109, -6.86790673, -8.99780554, 7.39449295], [ 2.04443863, 4.12623661, -9.64146529, 9.39444917], [ 6.74707792, -5.83405806, -6.3480674 , -6.37184345], [-3.98420601, 0.45335025, -1.23919526, -3.98642807]])}KMeansF. 使用模型定义、拟合和预测model = KMeans(n_clusters = NUM_CLUSTERS, random_state = RANDOM_STATE)model.fit(X_train)## Output# KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,# n_clusters=5, n_init=10, n_jobs=None, precompute_distances='auto',# random_state=42, tol=0.0001, verbose=0)F.1。预测y_train使用X_train## Process Prediction: train datay_pred_train = model.predict(X_train)# get model predicted cluster-centerspred_train_cluster_centers = model.cluster_centers_ # shape: (n_cluster, n_features)# sanity checkassert all([ y_pred_train.shape == (NUM_SAMPLES * (1 - TEST_SIZE),), set(y_pred_train) == set(y_train)])F.2。预测y_test使用X_test## Process Prediction: test datay_pred_test = model.predict(X_test)# get model predicted cluster-centerspred_test_cluster_centers = model.cluster_centers_ # shape: (n_cluster, n_features)# sanity checkassert all([ y_pred_test.shape == (NUM_SAMPLES * TEST_SIZE,), set(y_pred_test) == set(y_test)])G. 用train,test和prediction数据制作图形fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(15, 6))FONTSIZE = {'title': 16, 'suptitle': 20}TITLE = { 'train': 'Train Data Clusters', 'test': 'Test Data Clusters', 'suptitle': 'Cluster Identification using KMeans Method', }CENTER_LEGEND_LABELS = ('true', 'pred')LAGEND_PARAMS = { 'data': {'title': "Classes", 'loc': "upper left"}, 'cluster_centers': {'title': "Cluster Centers", 'loc': "upper right"}}SCATTER_ALPHA = 0.4 CMAP = 'viridis'CLUSTER_CENTER_PLOT_PARAMS = dict( markers = ('o', 's'), colors = ('red', 'orange'), s = (None, 200), alphas = (1.0, 0.5), center_labels = CENTER_LEGEND_LABELS, legend_title = LAGEND_PARAMS['cluster_centers']['title'], legend_loc = LAGEND_PARAMS['cluster_centers']['loc'])SCATTER_PLOT_PARAMS = dict( alpha = SCATTER_ALPHA, cmap = CMAP, legend_title = LAGEND_PARAMS['data']['title'], legend_loc = LAGEND_PARAMS['data']['loc'],)## plot train datadata_label = 'train'ax = axs[0]plt.sca(ax)ax = scatterplot(X = X_train, y = y_train, cluster_centers = None, ax = ax, **SCATTER_PLOT_PARAMS)ax = add_cluster_centers( true_cluster_centers = true_cluster_centers[data_label], pred_cluster_centers = pred_train_cluster_centers, ax = ax, **CLUSTER_CENTER_PLOT_PARAMS)plt.title(TITLE[data_label], fontsize = FONTSIZE['title'])## plot test datadata_label = 'test'ax = axs[1]plt.sca(ax)ax = scatterplot(X = X_test, y = y_test, cluster_centers = None, ax = ax, **SCATTER_PLOT_PARAMS)ax = add_cluster_centers( true_cluster_centers = true_cluster_centers[data_label], pred_cluster_centers = pred_test_cluster_centers, ax = ax, **CLUSTER_CENTER_PLOT_PARAMS)plt.title(TITLE[data_label], fontsize = FONTSIZE['title'])plt.suptitle(TITLE['suptitle'], fontsize = FONTSIZE['suptitle'])plt.show()# save figurefig.savefig("kmeans_fit_result.png", dpi=300)结果：

两组数据点之间的聚类 - Python

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