extorch.utils.visual

extorch.utils.visual.tsne_fit(feature: numpy.ndarray, n_components: int = 2, init: str = 'pca', **kwargs)[source]

Fit input features into an embedded space and return that transformed output.

Parameters

  • feature (np.ndarray) – The features to be embedded.

  • n_components (int) – Dimension of the embedded space. Default: 2.

  • init (str) – 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. Default: “pca”.

  • kwargs – Other configurations for TSNE model construction.

Returns

The representation in the embedding space.

Return type

node_pos (np.ndarray)

Examples::
>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> features = np.random.randn(50, 10)
>>> labels = np.random.randint(0, 2, (50, 1))
>>> node_pos = tsne_fit(features, 2, "pca")
>>> plt.figure()
>>> plt.scatter(node_pos[:, 0], node_pos[:, 1], c = labels)
>>> plt.show()