• 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:

    # 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
    
        # .......
        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)
    
    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:

        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:

        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("
    Test 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:

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  • 原文地址:https://www.cnblogs.com/bonelee/p/9116202.html
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