sklearn.manifold
.TSNE¶

class
sklearn.manifold.
TSNE
(n_components=2, perplexity=30.0, early_exaggeration=4.0, learning_rate=1000.0, n_iter=1000, n_iter_without_progress=30, min_grad_norm=1e07, metric='euclidean', init='random', verbose=0, random_state=None, method='barnes_hut', angle=0.5)[source]¶ tdistributed Stochastic Neighbor Embedding.
tSNE [1] is a tool to visualize highdimensional data. It converts similarities between data points to joint probabilities and tries to minimize the KullbackLeibler divergence between the joint probabilities of the lowdimensional embedding and the highdimensional data. tSNE has a cost function that is not convex, i.e. with different initializations we can get different results.
It is highly recommended to use another dimensionality reduction method (e.g. PCA for dense data or TruncatedSVD for sparse data) to reduce the number of dimensions to a reasonable amount (e.g. 50) if the number of features is very high. This will suppress some noise and speed up the computation of pairwise distances between samples. For more tips see Laurens van der Maaten’s FAQ [2].
Read more in the User Guide.
Parameters: n_components : int, optional (default: 2)
Dimension of the embedded space.
perplexity : float, optional (default: 30)
The perplexity is related to the number of nearest neighbors that is used in other manifold learning algorithms. Larger datasets usually require a larger perplexity. Consider selecting a value between 5 and 50. The choice is not extremely critical since tSNE is quite insensitive to this parameter.
early_exaggeration : float, optional (default: 4.0)
Controls how tight natural clusters in the original space are in the embedded space and how much space will be between them. For larger values, the space between natural clusters will be larger in the embedded space. Again, the choice of this parameter is not very critical. If the cost function increases during initial optimization, the early exaggeration factor or the learning rate might be too high.
learning_rate : float, optional (default: 1000)
The learning rate can be a critical parameter. It should be between 100 and 1000. If the cost function increases during initial optimization, the early exaggeration factor or the learning rate might be too high. If the cost function gets stuck in a bad local minimum increasing the learning rate helps sometimes.
n_iter : int, optional (default: 1000)
Maximum number of iterations for the optimization. Should be at least 200.
n_iter_without_progress : int, optional (default: 30)
Only used if method=’exact’ Maximum number of iterations without progress before we abort the optimization. If method=’barnes_hut’ this parameter is fixed to a value of 30 and cannot be changed.
New in version 0.17: parameter n_iter_without_progress to control stopping criteria.
min_grad_norm : float, optional (default: 1e7)
Only used if method=’exact’ If the gradient norm is below this threshold, the optimization will be aborted. If method=’barnes_hut’ this parameter is fixed to a value of 1e3 and cannot be changed.
metric : string or callable, optional
The metric to use when calculating distance between instances in a feature array. If metric is a string, it must be one of the options allowed by scipy.spatial.distance.pdist for its metric parameter, or a metric listed in pairwise.PAIRWISE_DISTANCE_FUNCTIONS. If metric is “precomputed”, X is assumed to be a distance matrix. Alternatively, if metric is a callable function, it is called on each pair of instances (rows) and the resulting value recorded. The callable should take two arrays from X as input and return a value indicating the distance between them. The default is “euclidean” which is interpreted as squared euclidean distance.
init : string or numpy array, optional (default: “random”)
Initialization of embedding. Possible options are ‘random’, ‘pca’, and a numpy array of shape (n_samples, n_components). PCA initialization cannot be used with precomputed distances and is usually more globally stable than random initialization.
verbose : int, optional (default: 0)
Verbosity level.
random_state : int or RandomState instance or None (default)
Pseudo Random Number generator seed control. If None, use the numpy.random singleton. Note that different initializations might result in different local minima of the cost function.
method : string (default: ‘barnes_hut’)
By default the gradient calculation algorithm uses BarnesHut approximation running in O(NlogN) time. method=’exact’ will run on the slower, but exact, algorithm in O(N^2) time. The exact algorithm should be used when nearestneighbor errors need to be better than 3%. However, the exact method cannot scale to millions of examples.
New in version 0.17: Approximate optimization method via the BarnesHut.
angle : float (default: 0.5)
Only used if method=’barnes_hut’ This is the tradeoff between speed and accuracy for BarnesHut TSNE. ‘angle’ is the angular size (referred to as theta in [3]) of a distant node as measured from a point. If this size is below ‘angle’ then it is used as a summary node of all points contained within it. This method is not very sensitive to changes in this parameter in the range of 0.2  0.8. Angle less than 0.2 has quickly increasing computation time and angle greater 0.8 has quickly increasing error.
Attributes: embedding_ : arraylike, shape (n_samples, n_components)
Stores the embedding vectors.
kl_divergence_ : float
KullbackLeibler divergence after optimization.
References
 [1] van der Maaten, L.J.P.; Hinton, G.E. Visualizing HighDimensional Data
 Using tSNE. Journal of Machine Learning Research 9:25792605, 2008.
 [2] van der Maaten, L.J.P. tDistributed Stochastic Neighbor Embedding
 http://homepage.tudelft.nl/19j49/tSNE.html
 [3] L.J.P. van der Maaten. Accelerating tSNE using TreeBased Algorithms.
 Journal of Machine Learning Research 15(Oct):32213245, 2014. http://lvdmaaten.github.io/publications/papers/JMLR_2014.pdf
Examples
>>> import numpy as np >>> from sklearn.manifold import TSNE >>> X = np.array([[0, 0, 0], [0, 1, 1], [1, 0, 1], [1, 1, 1]]) >>> model = TSNE(n_components=2, random_state=0) >>> np.set_printoptions(suppress=True) >>> model.fit_transform(X) array([[ 0.00017599, 0.00003993], [ 0.00009891, 0.00021913], [ 0.00018554, 0.00009357], [ 0.00009528, 0.00001407]])
Methods
fit
(X[, y])Fit X into an embedded space. fit_transform
(X[, y])Fit X into an embedded space and return that transformed output. get_params
([deep])Get parameters for this estimator. set_params
(\*\*params)Set the parameters of this estimator. 
__init__
(n_components=2, perplexity=30.0, early_exaggeration=4.0, learning_rate=1000.0, n_iter=1000, n_iter_without_progress=30, min_grad_norm=1e07, metric='euclidean', init='random', verbose=0, random_state=None, method='barnes_hut', angle=0.5)[source]¶

fit
(X, y=None)[source]¶ Fit X into an embedded space.
Parameters: X : array, shape (n_samples, n_features) or (n_samples, n_samples)
If the metric is ‘precomputed’ X must be a square distance matrix. Otherwise it contains a sample per row. If the method is ‘exact’, X may be a sparse matrix of type ‘csr’, ‘csc’ or ‘coo’.

fit_transform
(X, y=None)[source]¶ Fit X into an embedded space and return that transformed output.
Parameters: X : array, shape (n_samples, n_features) or (n_samples, n_samples)
If the metric is ‘precomputed’ X must be a square distance matrix. Otherwise it contains a sample per row.
Returns: X_new : array, shape (n_samples, n_components)
Embedding of the training data in lowdimensional space.

get_params
(deep=True)[source]¶ Get parameters for this estimator.
Parameters: deep : boolean, optional
If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns: params : mapping of string to any
Parameter names mapped to their values.

set_params
(**params)[source]¶ Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form
<component>__<parameter>
so that it’s possible to update each component of a nested object.Returns: self :