Python decomposition.TruncatedSVD方法代碼示例

# 需要導入模塊: from sklearn import decomposition [as 別名]
# 或者: from sklearn.decomposition import TruncatedSVD [as 別名]
def optimize(self):
        """
        Learning an embedding.
        """
        print("\nOptimization started.\n")
        self.embeddings = []
        for step in tqdm(range(self.args.order)):
            target_matrix = self._create_target_matrix()

            svd = TruncatedSVD(n_components=self.args.dimensions,
                               n_iter=self.args.iterations,
                               random_state=self.args.seed)

            svd.fit(target_matrix)
            embedding = svd.transform(target_matrix)
            self.embeddings.append(embedding) 

# 需要導入模塊: from sklearn import decomposition [as 別名]
# 或者: from sklearn.decomposition import TruncatedSVD [as 別名]
def test_resolve_embeddings(self):
        tdm = self.corpus.get_unigram_corpus().select(ClassPercentageCompactor(term_count=1))
        embeddings_resolver = EmbeddingsResolver(tdm)
        # embeddings = TruncatedSVD(n_components=20).fit_transform(tdm.get_term_doc_mat().T).T
        # embeddings_resolver.set_embeddings(embeddings)
        embeddings_resolver = embeddings_resolver.set_embeddings(tdm.get_term_doc_mat())
        if self.assertRaisesRegex:
            with self.assertRaisesRegex(Exception,
                                        "You have already set embeddings by running set_embeddings or set_embeddings_model."):
                embeddings_resolver.set_embeddings_model(None)
        embeddings_resolver = EmbeddingsResolver(tdm)

        embeddings_resolver = embeddings_resolver.set_embeddings_model(MockWord2Vec(tdm.get_terms()))
        if self.assertRaisesRegex:
            with self.assertRaisesRegex(Exception,
                                        "You have already set embeddings by running set_embeddings or set_embeddings_model."):
                embeddings_resolver.set_embeddings(tdm.get_term_doc_mat())
        c, axes = embeddings_resolver.project_embeddings(projection_model=TruncatedSVD(3))
        self.assertIsInstance(c, ParsedCorpus)
        self.assertEqual(axes.to_dict(), pd.DataFrame(index=['speak'], data={'x': [0.,], 'y':[0.,]}).to_dict()) 

# 需要導入模塊: from sklearn import decomposition [as 別名]
# 或者: from sklearn.decomposition import TruncatedSVD [as 別名]
def test_selective_tsvd():
    original = X
    cols = [original.columns[0], original.columns[1]]  # Only perform on first two columns...
    compare_cols = np.array(
        original[['petal length (cm)', 'petal width (cm)']].as_matrix())  # should be the same as the trans cols

    transformer = SelectiveTruncatedSVD(cols=cols, n_components=1).fit(original)
    transformed = transformer.transform(original)

    untouched_cols = np.array(transformed[['petal length (cm)', 'petal width (cm)']].as_matrix())
    assert_array_almost_equal(compare_cols, untouched_cols)
    assert 'Concept1' in transformed.columns
    assert transformed.shape[1] == 3
    assert isinstance(transformer.get_decomposition(), TruncatedSVD)
    assert SelectiveTruncatedSVD().get_decomposition() is None  # default None

    # test the selective mixin
    assert isinstance(transformer.cols, list) 

# 需要導入模塊: from sklearn import decomposition [as 別名]
# 或者: from sklearn.decomposition import TruncatedSVD [as 別名]
def transform(self):
        # ngrams
        obs_ngrams = list(map(lambda x: ngram_utils._ngrams(x.split(" "), self.obs_ngram, "_"), self.obs_corpus))
        target_ngrams = list(map(lambda x: ngram_utils._ngrams(x.split(" "), self.target_ngram, "_"), self.target_corpus))
        # cooccurrence ngrams
        cooc_terms = list(map(lambda lst1,lst2: self._get_cooc_terms(lst1, lst2, "X"), obs_ngrams, target_ngrams))
        ## tfidf
        tfidf = self._init_word_ngram_tfidf(ngram=1)
        X = tfidf.fit_transform(cooc_terms)
        ## svd
        svd = TruncatedSVD(n_components=self.svd_dim, 
                n_iter=self.svd_n_iter, random_state=config.RANDOM_SEED)
        return svd.fit_transform(X)


# 2nd in CrowdFlower (preprocessing_mikhail.py) 

# 需要導入模塊: from sklearn import decomposition [as 別名]
# 或者: from sklearn.decomposition import TruncatedSVD [as 別名]
def _reduce_dimensions(self, X):
        """
        Using Truncated SVD.

        Arg types:
            * **X** *(Scipy COO or Numpy array)* - The wide feature matrix.

        Return types:
            * **X** *(Numpy array)* - The reduced feature matrix of nodes.
        """
        svd = TruncatedSVD(n_components=self.reduction_dimensions,
                           n_iter=self.svd_iterations,
                           random_state=self.seed)
        svd.fit(X)
        X = svd.transform(X)
        return X 

# 需要導入模塊: from sklearn import decomposition [as 別名]
# 或者: from sklearn.decomposition import TruncatedSVD [as 別名]
def _create_reduced_features(self, X):
        """
        Creating a dense reduced node feature matrix.

        Arg types:
            * **X** *(Scipy COO or Numpy array)* - The wide feature matrix.

        Return types:
            * **T** *(Numpy array)* - The reduced feature matrix of nodes.
        """
        svd = TruncatedSVD(n_components=self.reduction_dimensions,
                           n_iter=self.svd_iterations,
                           random_state=self.seed)
        svd.fit(X)
        T = svd.transform(X)
        return T.T 

# 需要導入模塊: from sklearn import decomposition [as 別名]
# 或者: from sklearn.decomposition import TruncatedSVD [as 別名]
def fit_truncatedSVD(data):
    '''
        Fit the model with truncated SVD principal components
    '''
    # keyword parameters for the PCA
    kwrd_params = {
        'algorithm': 'randomized', 
        'n_components': 5, 
        'n_iter': 5,
        'random_state': 42, 
        'tol': 0.0
    }

    # reduce the data
    reduced = reduceDimensions(cd.TruncatedSVD, 
        data, **kwrd_params)

    # prepare the data for the classifier
    data_l = prepare_data(data, reduced, 
        kwrd_params['n_components'])

    # fit the model
    class_fit_predict_print(data_l)

# the file name of the dataset 

# 需要導入模塊: from sklearn import decomposition [as 別名]
# 或者: from sklearn.decomposition import TruncatedSVD [as 別名]
def test_random_hasher():
    # test random forest hashing on circles dataset
    # make sure that it is linearly separable.
    # even after projected to two SVD dimensions
    # Note: Not all random_states produce perfect results.
    hasher = RandomTreesEmbedding(n_estimators=30, random_state=1)
    X, y = datasets.make_circles(factor=0.5)
    X_transformed = hasher.fit_transform(X)

    # test fit and transform:
    hasher = RandomTreesEmbedding(n_estimators=30, random_state=1)
    assert_array_equal(hasher.fit(X).transform(X).toarray(),
                       X_transformed.toarray())

    # one leaf active per data point per forest
    assert_equal(X_transformed.shape[0], X.shape[0])
    assert_array_equal(X_transformed.sum(axis=1), hasher.n_estimators)
    svd = TruncatedSVD(n_components=2)
    X_reduced = svd.fit_transform(X_transformed)
    linear_clf = LinearSVC()
    linear_clf.fit(X_reduced, y)
    assert_equal(linear_clf.score(X_reduced, y), 1.) 

# 需要導入模塊: from sklearn import decomposition [as 別名]
# 或者: from sklearn.decomposition import TruncatedSVD [as 別名]
def test_truncated_svd_eq_pca():
    # TruncatedSVD should be equal to PCA on centered data

    X_c = X - X.mean(axis=0)

    params = dict(n_components=10, random_state=42)

    svd = TruncatedSVD(algorithm='arpack', **params)
    pca = PCA(svd_solver='arpack', **params)

    Xt_svd = svd.fit_transform(X_c)
    Xt_pca = pca.fit_transform(X_c)

    assert_allclose(Xt_svd, Xt_pca, rtol=1e-9)
    assert_allclose(pca.mean_, 0, atol=1e-9)
    assert_allclose(svd.components_, pca.components_) 

# 需要導入模塊: from sklearn import decomposition [as 別名]
# 或者: from sklearn.decomposition import TruncatedSVD [as 別名]
def dim_reduction_method(self):
        """
        select dimensionality reduction method
        """
        if self.dim_reduction=='pca':
            return PCA()
        elif self.dim_reduction=='factor-analysis':
            return FactorAnalysis()
        elif self.dim_reduction=='fast-ica':
            return FastICA()
        elif self.dim_reduction=='kernel-pca':
            return KernelPCA()
        elif self.dim_reduction=='sparse-pca':
            return SparsePCA()
        elif self.dim_reduction=='truncated-svd':
            return TruncatedSVD()
        elif self.dim_reduction!=None:
            raise ValueError('%s is not a supported dimensionality reduction method. Valid inputs are: \
                             "pca","factor-analysis","fast-ica,"kernel-pca","sparse-pca","truncated-svd".' 
                             %(self.dim_reduction)) 

# 需要導入模塊: from sklearn import decomposition [as 別名]
# 或者: from sklearn.decomposition import TruncatedSVD [as 別名]
def plot_z_run(z_run, label, ):
    f1, ax1 = plt.subplots(2, 1)

    # First fit a PCA
    PCA_model = TruncatedSVD(n_components=3).fit(z_run)
    z_run_reduced = PCA_model.transform(z_run)
    ax1[0].scatter(z_run_reduced[:, 0], z_run_reduced[:, 1], c=label, marker='*', linewidths=0)
    ax1[0].set_title('PCA on z_run')

    # THen fit a tSNE
    tSNE_model = TSNE(verbose=2, perplexity=80, min_grad_norm=1E-12, n_iter=3000)
    z_run_tsne = tSNE_model.fit_transform(z_run)
    ax1[1].scatter(z_run_tsne[:, 0], z_run_tsne[:, 1], c=label, marker='*', linewidths=0)
    ax1[1].set_title('tSNE on z_run')

    plt.show()
    return 

# 需要導入模塊: from sklearn import decomposition [as 別名]
# 或者: from sklearn.decomposition import TruncatedSVD [as 別名]
def __init__(self, k=3, **kwargs):
        self.k = k
        self.pipeline = Pipeline([
            ('norm', TextNormalizer(minimum=10, maximum=100)),
            ('tfidf', TfidfVectorizer()),
            ('knn', Pipeline([
                ('svd', TruncatedSVD(n_components=100)),
                ('model', KNNTransformer(k=self.k, algorithm='ball_tree'))
            ]))
        ])

        self.lex_path = "lexicon.pkl"
        self.vect_path = "vect.pkl"
        self.vectorizer = False
        self.lexicon = None
        self.load() 

# 需要導入模塊: from sklearn import decomposition [as 別名]
# 或者: from sklearn.decomposition import TruncatedSVD [as 別名]
def fit_transform(self, documents):
        # Vectorizer will be False if pipeline hasn't been fit yet,
        # Trigger fit_transform and save the vectorizer and lexicon.
        if self.vectorizer == False:
            self.lexicon = self.pipeline.fit_transform(documents)
            self.vect = self.pipeline.named_steps['tfidf']
            self.knn = self.pipeline.named_steps['knn']
            self.save()
        # If there's a stored vectorizer and prefitted lexicon,
        # use them instead.
        else:
            self.vect = self.vectorizer
            self.knn = Pipeline([
                ('svd', TruncatedSVD(n_components=100)),
                ('knn', KNNTransformer(k=self.k, algorithm='ball_tree'))
            ])
            self.knn.fit_transform(self.lexicon) 

# 需要導入模塊: from sklearn import decomposition [as 別名]
# 或者: from sklearn.decomposition import TruncatedSVD [as 別名]
def __init__(self, n_topics=50, estimator='LDA'):
        """
        n_topics is the desired number of topics
        To use Latent Semantic Analysis, set estimator to 'LSA',
        To use Non-Negative Matrix Factorization, set estimator to 'NMF',
        otherwise, defaults to Latent Dirichlet Allocation ('LDA').
        """
        self.n_topics = n_topics

        if estimator == 'LSA':
            self.estimator = TruncatedSVD(n_components=self.n_topics)
        elif estimator == 'NMF':
            self.estimator = NMF(n_components=self.n_topics)
        else:
            self.estimator = LatentDirichletAllocation(n_topics=self.n_topics)

        self.model = Pipeline([
            ('norm', TextNormalizer()),
            ('tfidf', CountVectorizer(tokenizer=identity,
                                      preprocessor=None, lowercase=False)),
            ('model', self.estimator)
        ]) 

# 需要導入模塊: from sklearn import decomposition [as 別名]
# 或者: from sklearn.decomposition import TruncatedSVD [as 別名]
def create_pipeline(estimator, reduction=False):

    steps = [
        ('normalize', TextNormalizer()),
        ('vectorize', TfidfVectorizer(
            tokenizer=identity, preprocessor=None, lowercase=False
        ))
    ]

    if reduction:
        steps.append((
            'reduction', TruncatedSVD(n_components=10000)
        ))

    # Add the estimator
    steps.append(('classifier', estimator))
    return Pipeline(steps)