8000 [MRG+1] ENH exposing extra parameters in t-sne by sdvillal · Pull Request #5186 · scikit-learn/scikit-learn · GitHub
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[MRG+1] ENH exposing extra parameters in t-sne #5186

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Merged
merged 1 commit into from
Aug 30, 2015
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[MRG+1] ENH exposing extra parameters in t-sne #5186

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13 changes: 13 additions & 0 deletions sklearn/manifold/t_sne.py
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Original file line number Diff line number Diff line change
Expand Up @@ -338,6 +338,14 @@ class TSNE(BaseEstimator):
Maximum number of iterations for the optimization. Should be at
least 200.

n_iter_without_progress : int, optional (default: 30)
Maximum number of iterations without progress before we abort the
optimization.

min_grad_norm : float, optional (default: 1E-7)
If the gradient norm is below this threshold, the optimization will
be aborted.

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
Expand Down Expand Up @@ -395,6 +403,7 @@ class TSNE(BaseEstimator):
"""
def __init__(self, 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=1e-7,
metric="euclidean", init="random", verbose=0,
random_state=None):
if init not in ["pca", "random"]:
Expand All @@ -404,6 +413,8 @@ def __init__(self, n_components=2, perplexity=30.0,
self.early_exaggeration = early_exaggeration
self.learning_rate = learning_rate
self.n_iter = n_iter
self.n_iter_without_progress = n_iter_without_progress
self.min_grad_norm = min_grad_norm
self.metric = metric
self.init = init
self.verbose = verbose
Expand Down Expand Up @@ -504,6 +515,8 @@ def _tsne(self, P, alpha, n_samples, random_state, X_embedded=None):
P /= self.early_exaggeration
params, error, it = _gradient_descent(
_kl_divergence, params, it=it + 1, n_iter=self.n_iter,
min_grad_norm=self.min_grad_norm,
n_iter_without_progress=self.n_iter_without_progress,
momentum=0.8, learning_rate=self.learning_rate,
verbose=self.verbose, args=[P, alpha, n_samples,
self.n_components])
Expand Down
0