tsne pca 自编码器 绘图（CC2）——一定记得做无量纲化处理使用standardscaler，数据聚类更明显

tsne

数据不做预处理：

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# coding: utf-8 import collections import numpy as np import os import pickle from sklearn.neighbors import NearestNeighbors import numpy as np from sklearn.manifold import TSNE

 

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# ....... X = X+black_verify+white_verify+unknown_verify+bd_verify print black_verify_labels+white_verify_labels+unknown_verify_labels+bd_verify_labels y = y+black_verify_labels+white_verify_labels+unknown_verify_labels+bd_verify_labels print("ALL data check:") print("len of X:", len(X)) print("len of y:", len(y)) # print(unknown_verify)   X_embedded = TSNE(n_components=2).fit_transform(X)   with open("tsne_data_X.pkl", "wb") as f:     pickle.dump([X_embedded, y], f)

 

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import pickle from collections import Counter import numpy as np import matplotlib.pyplot as Plot   def main():     with open("tsne_data_X.pkl", "rb") as f:         [X_embedded, y] = pickle.load(f, encoding='iso-8859-1')       print(len(X_embedded))     print(len(y))     print(X_embedded[:3])     print(y[:3])     i = 0     for l in y:         if type(l) == type([]):             raise Exception(str([i,y]))         i+=1     print(Counter(y))     Y, labels = np.array(X_embedded), np.array(y)     titles = ("white","black","black_verify_labels","white_verify_labels","unknown_verify_labels","bd_verify_labels")     colors=['b', 'c', 'y', 'm', 'r', 'g', 'peru']     for i in range(0, 6):        idx_1 = [i1 for i1 in range(len(labels)) if labels[i1]==i]        flg1=Plot.scatter(Y[idx_1,0], Y[idx_1,1], 20,color=colors[i],label=titles[i]);     Plot.legend()     Plot.savefig('tsne.pdf')     Plot.show() main()

 

 

数据做standard标准化处理

使用pca，不进行预处理：

使用standard scaler预处理，再做pca：

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from sklearn import preprocessing scaler = preprocessing.StandardScaler().fit(X) #scaler = preprocessing.MinMaxScaler().fit(X) X = scaler.transform(X) print("standard X sample:", X[:3])   black_verify = scaler.transform(black_verify) print(black_verify)   white_verify = scaler.transform(white_verify) print(white_verify)   unknown_verify = scaler.transform(unknown_verify) print(unknown_verify)   bd_verify = scaler.transform(bd_verify) print(bd_verify)   #print black_verify_labels+white_verify_labels+unknown_verify_labels+bd_verify_labels   X = np.concatenate((X,black_verify,white_verify,unknown_verify,bd_verify)) #X = X+black_verify+white_verify+unknown_verify+bd_verify y = y+black_verify_labels+white_verify_labels+unknown_verify_labels+bd_verify_labels print("ALL data check:") print("len of X:", len(X)) print("len of y:", len(y)) # print(unknown_verify)   X_embedded = PCA(n_components=2).fit_transform(X)   with open("pca_data_X_scaled.pkl", "wb") as f:     pickle.dump([X_embedded, y], f)

 

最后效果：

最后使用自编码器来来降维：

代码：

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X = np.concatenate((X,black_verify,white_verify,unknown_verify,bd_verify)) y = y+black_verify_labels+white_verify_labels+unknown_verify_labels+bd_verify_labels print("ALL data check:") print("len of X:", len(X)) print("len of y:", len(y)) # print(unknown_verify)   ratio_of_train = 0.8 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=(1 - ratio_of_train)) # Building the encoder encoder = tflearn.input_data(shape=[None, 75]) encoder = tflearn.fully_connected(encoder, 64) encoder = tflearn.fully_connected(encoder, 2)   # Building the decoder decoder = tflearn.fully_connected(encoder, 64) decoder = tflearn.fully_connected(decoder, 75, activation='sigmoid')   # Regression, with mean square error net = tflearn.regression(decoder, optimizer='adam', learning_rate=0.0001,                          loss='mean_square', metric=None)   # Training the auto encoder model = tflearn.DNN(net, tensorboard_verbose=0) model.fit(X_train, X_train, n_epoch=200, validation_set=(X_test, X_test),           run_id="auto_encoder", batch_size=1024)   # Encoding X[0] for test print("\nTest encoding of X[0]:") # New model, re-using the same session, for weights sharing encoding_model = tflearn.DNN(encoder, session=model.session) print(encoding_model.predict([X[0]]))     X_embedded = encoding_model.predict(X) #TSNE(n_components=2).fit_transform(X)   with open("tflearn_auto_enc_data_X_scaled.pkl", "wb") as f:     pickle.dump([X_embedded, y], f)

如果是迭代次数不一样，则可能有一些差别，见下图，和上面的可能有些差别：

 

修改64为128: