Merge pull request #3552 from pavlin-policar/update-tsne

Update openTSNE version to >=0.3

Orange/projection/manifold.py

@@ -8,9 +8,9 @@
from scipy.sparse.linalg import eigsh as arpack_eigh
import sklearn.manifold as skl_manifold

import fastTSNE
import fastTSNE.affinity
import fastTSNE.initialization
import openTSNE
import openTSNE.affinity
import openTSNE.initialization

import Orange
from Orange.data import Table, Domain, ContinuousVariable
@@ -22,8 +22,8 @@
           "TSNE"]

# Disable t-SNE user warnings
fastTSNE.tsne.log.setLevel(logging.ERROR)
fastTSNE.affinity.log.setLevel(logging.ERROR)
openTSNE.tsne.log.setLevel(logging.ERROR)
openTSNE.affinity.log.setLevel(logging.ERROR)


def torgerson(distances, n_components=2, eigen_solver="auto"):
@@ -204,7 +204,7 @@ class TSNEModel(Projection):
    pre_domain : Domain
        Original data domain
    """
    def __init__(self, embedding: fastTSNE.TSNEEmbedding, table: Table,
    def __init__(self, embedding: openTSNE.TSNEEmbedding, table: Table,
                 pre_domain: Domain):
        transformer = TransformDomain(self)

@@ -221,13 +221,13 @@ def proj_variable(i):
            class_vars=table.domain.class_vars,
            metas=table.domain.metas)

    def transform(self, X: np.ndarray, **kwargs) -> fastTSNE.PartialTSNEEmbedding:
    def transform(self, X: np.ndarray, learning_rate=1, **kwargs) -> openTSNE.PartialTSNEEmbedding:
        if sp.issparse(X):
            raise TypeError(
                "A sparse matrix was passed, but dense data is required. Use "
                "X.toarray() to convert to a dense numpy array."
            )
        if isinstance(self.embedding_.affinities, fastTSNE.affinity.Multiscale):
        if isinstance(self.embedding_.affinities, openTSNE.affinity.Multiscale):
            perplexity = kwargs.pop("perplexity", False)
            if perplexity:
                if not isinstance(self.perplexity, Iterable):
@@ -242,7 +242,7 @@ def transform(self, X: np.ndarray, **kwargs) -> fastTSNE.PartialTSNEEmbedding:

        embedding = self.embedding_.prepare_partial(X, **perplexity_params,
                                                    **kwargs)
        embedding.optimize(100, inplace=True, momentum=0.4)
        embedding.optimize(100, inplace=True, momentum=0.4, learning_rate=learning_rate)
        return embedding

    def __call__(self, data: Table, **kwargs) -> Table:
@@ -400,7 +400,7 @@ def __init__(self, n_components=2, perplexity=30, learning_rate=200,
        self.callbacks_every_iters = callbacks_every_iters
        self.random_state = random_state

    def fit(self, X: np.ndarray, Y: np.ndarray = None) -> fastTSNE.TSNEEmbedding:
    def fit(self, X: np.ndarray, Y: np.ndarray = None) -> openTSNE.TSNEEmbedding:
        # Sparse data are not supported
        if sp.issparse(X):
            raise TypeError(
@@ -416,33 +416,33 @@ def fit(self, X: np.ndarray, Y: np.ndarray = None) -> fastTSNE.TSNEEmbedding:
                raise ValueError(
                    "Perplexity should be an instance of `Iterable`, `%s` "
                    "given." % type(self.perplexity).__name__)
            affinities = fastTSNE.affinity.Multiscale(
            affinities = openTSNE.affinity.Multiscale(
                X, perplexities=self.perplexity, metric=self.metric,
                method=self.neighbors, random_state=self.random_state, n_jobs=self.n_jobs)
        else:
            if isinstance(self.perplexity, Iterable):
                raise ValueError(
                    "Perplexity should be an instance of `float`, `%s` "
                    "given." % type(self.perplexity).__name__)
            affinities = fastTSNE.affinity.PerplexityBasedNN(
            affinities = openTSNE.affinity.PerplexityBasedNN(
                X, perplexity=self.perplexity, metric=self.metric,
                method=self.neighbors, random_state=self.random_state, n_jobs=self.n_jobs)

        # Create an initial embedding
        if isinstance(self.initialization, np.ndarray):
            initialization = self.initialization
        elif self.initialization == "pca":
            initialization = fastTSNE.initialization.pca(
            initialization = openTSNE.initialization.pca(
                X, self.n_components, random_state=self.random_state)
        elif self.initialization == "random":
            initialization = fastTSNE.initialization.random(
                X.shape[0], self.n_components, random_state=self.random_state)
            initialization = openTSNE.initialization.random(
                X, self.n_components, random_state=self.random_state)
        else:
            raise ValueError(
                "Invalid initialization `%s`. Please use either `pca` or "
                "`random` or provide a numpy array." % self.initialization)

        embedding = fastTSNE.TSNEEmbedding(
        embedding = openTSNE.TSNEEmbedding(
            initialization, affinities, learning_rate=self.learning_rate,
            theta=self.theta, min_num_intervals=self.min_num_intervals,
            ints_in_interval=self.ints_in_interval, n_jobs=self.n_jobs,
@@ -486,5 +486,5 @@ def __call__(self, data: Table) -> TSNEModel:

    @staticmethod
    def default_initialization(data, n_components=2, random_state=None):
        return fastTSNE.initialization.pca(
        return openTSNE.initialization.pca(
            data, n_components, random_state=random_state)

conda-recipe/meta.yaml

@@ -51,7 +51,7 @@ requirements:
    - commonmark
    - serverfiles
    - matplotlib    >=2.0.0
    - fasttsne      ==0.2.13
    - opentsne      >=0.3.0

test:
  # Python imports

requirements-core.txt

@@ -18,4 +18,4 @@ serverfiles		# for Data Sets synchronization
networkx
python-louvain
requests
fastTSNE==0.2.13
openTSNE>=0.3.0