Python pca.PCA类代码示例

def main():
    print('Reading in data file...')
    data = pd.read_csv(path + 'Sentiment Analysis Dataset.csv',
                       usecols=['Sentiment', 'SentimentText'], error_bad_lines=False)

    print('Pre-processing tweet text...')
    corpus = data['SentimentText']
    vectorizer = TfidfVectorizer(decode_error='replace', strip_accents='unicode',
                                 stop_words='english', tokenizer=tokenize)
    X = vectorizer.fit_transform(corpus.values)
    y = data['Sentiment'].values

    print('Training sentiment classification model...')
    classifier = MultinomialNB()
    classifier.fit(X, y)

    print('Training word2vec model...')
    corpus = corpus.map(lambda x: tokenize(x))
    word2vec = Word2Vec(corpus.tolist(), size=100, window=4, min_count=10, workers=4)
    word2vec.init_sims(replace=True)

    print('Fitting PCA transform...')
    word_vectors = [word2vec[word] for word in word2vec.vocab]
    pca = PCA(n_components=2)
    pca.fit(word_vectors)

    print('Saving artifacts to disk...')
    joblib.dump(vectorizer, path + 'vectorizer.pkl')
    joblib.dump(classifier, path + 'classifier.pkl')
    joblib.dump(pca, path + 'pca.pkl')
    word2vec.save(path + 'word2vec.pkl')

    print('Process complete.')

class LogisticClassifier(object):
    def __init__(self, learning_rate=0.01, reg=0., momentum=0.5):
        self.classifier = LogisticRegression(learning_rate, reg, momentum)
        self.pca = None
        self.scaler = None

    def sgd_optimize(self, data, n_epochs, mini_batch_size):
        data = self._preprocess_data(data)
        sgd_optimization(data, self.classifier, n_epochs, mini_batch_size)

    def _preprocess_data(self, data):
        # center data and scale to unit std
        if self.scaler is None:
             self.scaler = StandardScaler()
             data = self.scaler.fit_transform(data)
        else:
            data = self.scaler.transform(data)

        if self.pca is None:
            # use minika's mle to guess appropriate dimension
            self.pca = PCA(n_components='mle')
            data = self.pca.fit_transform(data)
        else:
            data = self.pca.transform(data)

        return data

def pca_plot(fp_list, clusters):
    np_fps = []
    for fp in fp_list:
        arr = numpy.zeros((1,))
        DataStructs.ConvertToNumpyArray(fp, arr)
        np_fps.append(arr)
    pca = PCA(n_components=3)
    pca.fit(np_fps)
    np_fps_r = pca.transform(np_fps)
    p1 = figure(x_axis_label="PC1",
                y_axis_label="PC2",
                title="PCA clustering of PAINS")
    p2 = figure(x_axis_label="PC2",
                y_axis_label="PC3",
                title="PCA clustering of PAINS")
    color_vector = ["blue", "red", "green", "orange", "pink", "cyan", "magenta",
                    "brown", "purple"]
    print len(set(clusters))
    for clust_num in set(clusters):
        print clust_num
        local_cluster = []
        for i in xrange(len(clusters)):
            if clusters[i] == clust_num:
                local_cluster.append(np_fps_r[i])
        print len(local_cluster)
        p1.scatter(np_fps_r[:,0], np_fps_r[:,1],
                   color=color_vector[clust_num])
        p2.scatter(np_fps_r[:,1], np_fps_r[:,2],
                   color=color_vector[clust_num])
    return HBox(p1, p2)

def calc_pca(bnd, npc=None, preaverage=False, use_unbiased=False, \
    method='mdp'):
    '''
    Parameters
    ----------
    bnd : BinnedData
      binned data
    npc : int or None, optional
      number of PCs to calculate, defaults to None
    preaverage : bool
      average across repeats?
      
    Returns
    -------
    score : ndarray
      (npc, nobs)
    weight : ndarray
      (npc, nvar)
    '''
    assert method in ['mdp', 'skl']
    data = format_for_fa(bnd, preaverage=preaverage,
                         use_unbiased=use_unbiased)
    if method == 'mdp':    
        pca_node = mdp.nodes.PCANode(output_dim=npc)
        score = pca_node.execute(data)
        weight = pca_node.get_projmatrix()
    elif method == 'skl':
        pca_obj = PCA(n_components=npc)
        score = pca_obj.fit(data).transform(data)
        weight = pca_obj.components_.T
    return score.T, weight.T

def pca(target, control, title, name_one, name_two):
    np_fps = []
    for fp in target + control:
        arr = numpy.zeros((1,))
        DataStructs.ConvertToNumpyArray(fp, arr)
        np_fps.append(arr)
    ys_fit = [1] * len(target) + [0] * len(control)
    names = ["PAINS", "Control"]
    pca = PCA(n_components=3)
    pca.fit(np_fps)
    np_fps_r = pca.transform(np_fps)
    p1 = figure(x_axis_label="PC1",
                y_axis_label="PC2",
                title=title)
    p1.scatter(np_fps_r[:len(target), 0], np_fps_r[:len(target), 1],
               color="blue", legend=name_one)
    p1.scatter(np_fps_r[len(target):, 0], np_fps_r[len(target):, 1],
               color="red", legend=name_two)
    p2 = figure(x_axis_label="PC2",
                y_axis_label="PC3",
                title=title)
    p2.scatter(np_fps_r[:len(target), 1], np_fps_r[:len(target), 2],
               color="blue", legend=name_one)
    p2.scatter(np_fps_r[len(target):, 1], np_fps_r[len(target):, 2],
               color="red", legend=name_two)
    return HBox(p1, p2)

def pca(tx, ty, rx, ry):
    compressor = PCA(n_components = tx[1].size/2)
    compressor.fit(tx, y=ty)
    newtx = compressor.transform(tx)
    newrx = compressor.transform(rx)
    em(newtx, ty, newrx, ry, add="wPCAtr", times=10)
    km(newtx, ty, newrx, ry, add="wPCAtr", times=10)
    nn(newtx, ty, newrx, ry, add="wPCAr")

def pca(tx, ty, rx, ry):
    print "pca"
    compressor = PCA(n_components = tx[1].size/2)
    compressor.fit(tx, y=ty)
    newtx = compressor.transform(tx)
    newrx = compressor.transform(rx)
    em(newtx, ty, newrx, ry, add="wPCAtr")  
    km(newtx, ty, newrx, ry, add="wPCAtr")
    nn(newtx, ty, newrx, ry, add="wPCAtr")
    print "pca done"

	def PCA佮SVM模型(self, 問題, 答案):
		sample_weight_constant = np.ones(len(問題))
		clf = svm.SVC(C=1)
		pca = PCA(n_components=100)
# 		clf = svm.NuSVC()
		print('訓練PCA')
		pca.fit(問題)
		print('訓練SVM')
		clf.fit(pca.transform(問題), 答案, sample_weight=sample_weight_constant)
		print('訓練了')
		return lambda 問:clf.predict(pca.transform(問))

def train_pca(pains_fps, num_components=3):
    '''
    Dimensional reduction of fps bit vectors to principal components
    :param pains_fps:
    :return: pca reduced fingerprints bit vectors
    '''
    np_fps = []
    for fp in pains_fps:
        arr = numpy.zeros((1,))
        DataStructs.ConvertToNumpyArray(fp, arr)
        np_fps.append(arr)
    pca = PCA(n_components=num_components)
    pca.fit(np_fps)
    fps_reduced = pca.transform(np_fps)
    return fps_reduced

def classify_for_benchmark(data_set_df, user_info_df, features, label='gender', classifier=None, num=None):
    instance_num = len(data_set_df.columns)
    x = data_set_df.loc[features]
    x = x.dropna(how='all', axis=0)
    x = x.dropna(how='all', axis=1)

    imp = Imputer(missing_values='NaN', strategy='most_frequent', axis=1)
    x_replaced = x.replace([np.inf, -np.inf], np.nan)
    x_imp = imp.transform(x_replaced)

    y = user_info_df.get(label)
    y_filtered = y[(map(int, x.columns.values))]

    clf = nb.BernoulliNB() if classifier is None else classifier
    cv_num = min(len(y_filtered), 10)
    if cv_num <= 1 or len(y_filtered.unique()) <= 1:
        return 0.0, 100.0
    else:
        final_score = 0.0
        for i in range(100):
            score = 0.0
            cnt = 0
            skf = StratifiedKFold(y_filtered, n_folds=cv_num, shuffle=True)
            for tr_index, te_index in skf:
                x_train, x_test = x_imp.T[tr_index], x_imp.T[te_index]
                y_train, y_test = y_filtered.iloc[tr_index], y_filtered.iloc[te_index]
                min_num = min(len(x_train), len(x_train.T), len(x_test), len(x_test.T), num)
                pca = PCA(min_num)
                x_train = pca.fit_transform(x_train)
                x_test = pca.fit_transform(x_test)

                try:
                    clf.fit(x_train, y_train)
                    score += clf.score(x_test, y_test)
                    cnt += 1
                    # cv_score = cross_validation.cross_val_score(clf, x_imp.T, y_filtered, cv=cv_num)
                except ValueError:
                    traceback.print_exc()
                    print i, "why error? skip!"
            if cnt > 0:
                score /= cnt
                print i, score
            else:
                return 0.0, (float(instance_num - len(y_filtered)) / instance_num)
            final_score += score
        final_score /= 100
        miss_clf_rate = (float(instance_num - len(y_filtered)) / instance_num)
        return final_score, miss_clf_rate

def reduction(data, params):

    # parse parameters

    for item in params:
        if isinstance(params[item], str):
            exec(item+'='+'"'+params[item]+'"')
        else:
            exec(item+'='+str(params[item]))

    # apply PCA

    pca = PCA(n_components=n_components)
    pca.fit(data)
    X = pca.transform(data)

    return X

def pca_no_labels(target, title="PCA clustering of PAINS", color="blue"):
    np_fps = []
    for fp in target:
        arr = numpy.zeros((1,))
        DataStructs.ConvertToNumpyArray(fp, arr)
        np_fps.append(arr)
    pca = PCA(n_components=3)
    pca.fit(np_fps)
    np_fps_r = pca.transform(np_fps)
    p3 = figure(x_axis_label="PC1",
                y_axis_label="PC2",
                title=title)
    p3.scatter(np_fps_r[:, 0], np_fps_r[:, 1], color=color)
    p4 = figure(x_axis_label="PC2",
                y_axis_label="PC3",
                title=title)
    p4.scatter(np_fps_r[:, 1], np_fps_r[:, 2], color=color)
    return HBox(p3, p4)

def airline_pca():
    X = np.array(pca_data)
    pca = PCA(n_components=3)
    pca.fit(X)
    Y=pca.transform(normalize(X))
    
    fig = plt.figure(1, figsize=(8, 6))
    ax = Axes3D(fig, elev=-150, azim=110)
    colordict = {carrier:i for i,carrier in enumerate(major_carriers)}
    pointcolors  = [colordict[carrier] for carrier in target_carrier]
    ax.scatter(Y[:, 0], Y[:, 1], Y[:, 2], c=pointcolors)
    ax.set_title("First three PCA directions")
    ax.set_xlabel("1st eigenvector")
    ax.w_xaxis.set_ticklabels([])
    ax.set_ylabel("2nd eigenvector")
    ax.w_yaxis.set_ticklabels([])
    ax.set_zlabel("3rd eigenvector")
    ax.w_zaxis.set_ticklabels([])

def test_pipeline_transform():
    # Test whether pipeline works with a transformer at the end.
    # Also test pipline.transform and pipeline.inverse_transform
    iris = load_iris()
    X = iris.data
    pca = PCA(n_components=2)
    pipeline = Pipeline([('pca', pca)])

    # test transform and fit_transform:
    X_trans = pipeline.fit(X).transform(X)
    X_trans2 = pipeline.fit_transform(X)
    X_trans3 = pca.fit_transform(X)
    assert_array_almost_equal(X_trans, X_trans2)
    assert_array_almost_equal(X_trans, X_trans3)

    X_back = pipeline.inverse_transform(X_trans)
    X_back2 = pca.inverse_transform(X_trans)
    assert_array_almost_equal(X_back, X_back2)

def do_train_with_freq():
    tf_mix = TrainFiles(train_path = train_path_mix, labels_file = labels_file, test_size = 0.)
    tf_freq = TrainFiles(train_path = train_path_freq, labels_file = labels_file, test_size = 0.)

    X_m, Y_m, _, _ = tf_mix.prepare_inputs()
    X_f, Y_f, _, _ = tf_freq.prepare_inputs()

    X = np.c_[X_m, X_f]
    Y = Y_f

    X, Xt, Y, Yt = train_test_split(X, Y, test_size = 0.1)
    sl = SKSupervisedLearning(SVC, X, Y, Xt, Yt)
    sl.fit_standard_scaler()

    pca = PCA(250)
    pca.fit(np.r_[sl.X_train_scaled, sl.X_test_scaled])
    X_pca = pca.transform(sl.X_train_scaled)
    X_pca_test = pca.transform(sl.X_test_scaled)

    #sl.train_params = {'C': 100, 'gamma': 0.0001, 'probability' : True}
    #print "Start SVM: ", time_now_str()
    #sl_ll_trn, sl_ll_tst = sl.fit_and_validate()
    #print "Finish Svm: ", time_now_str()

    ##construct a dataset for RBM
    #X_rbm = X[:, 257:]
    #Xt_rbm = X[:, 257:]

    #rng = np.random.RandomState(123)
    #rbm = RBM(X_rbm, n_visible=X_rbm.shape[1], n_hidden=X_rbm.shape[1]/4, numpy_rng=rng)

    #pretrain_lr = 0.1
    #k = 2
    #pretraining_epochs = 200
    #for epoch in xrange(pretraining_epochs):
    #    rbm.contrastive_divergence(lr=pretrain_lr, k=k)
    #    cost = rbm.get_reconstruction_cross_entropy()
    #    print >> sys.stderr, 'Training epoch %d, cost is ' % epoch, cost


    trndata, tstdata = createDataSets(X_pca, Y, X_pca_test, Yt)
    fnn = train(trndata, tstdata, epochs = 1000, test_error = 0.025, momentum = 0.2, weight_decay = 0.0001)