Alexander already reported it and it seemes that the bleeding-edge version of sklearn from github resolves the issue. However, I have trouble now to install it on my Windows machine (using Python 2.7 and Anaconda 4.0.5) via pip command. Somehow it fails to find the required DLLs during programme execution, even though sklearn appears under the list of installed packages.
Did anyone experience this problem before and can possibly help me with it? Best, Sarah Gesendet: Donnerstag, 21. April 2016 um 17:19 Uhr Von: "Andreas Mueller" <t3k...@gmail.com> An: scikit-learn-general@lists.sourceforge.net Betreff: Re: [Scikit-learn-general] tSNE assertion errors Can you please report this on the issue tracker? Thanks! On 04/18/2016 09:28 AM, leg...@web.de wrote: Thanks for your response Alexander! Here is a simplified version of my script applied to the MNIST data set. It wasn't clear from my first mail but I don't want to train it incrementally but instead apply tsne to each batch within the data set (for several epochs). This works for an unspecified number of epochs/batches until the program crashes. for epoch in range(20) for batch in batch_iterator(mnist_data): # reshape from (500, 1, 28, 28) to (500, 784) data = batch.reshape(batch.shape[0], -1) tsne = TSNE(n_components=2, random_state=0, init='pca', verbose=0).fit_transform(data) # continue with scatter plot visualization... plt.scatter(tsne[:,0], tsne[:,1], c=labels) plt.show() Gesendet: Montag, 18. April 2016 um 14:44 Uhr Von: "Alexander Fabisch" <afabi...@informatik.uni-bremen.de>[afabi...@informatik.uni-bremen.de] An: scikit-learn-general@lists.sourceforge.net[scikit-learn-general@lists.sourceforge.net] Betreff: Re: [Scikit-learn-general] tSNE assertion errors Hi Sarah, t-SNE does not support incremental training. Your model will be retrained every time you fit a new batch of data (see https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/manifold/t_sne.py#L664[https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/manifold/t_sne.py#L664]). That means you might have found a dataset that reveals an error in implementation. Could you provide a small script that reproduces the error? Best regards, Alexander Am 18.04.2016 um 14:28 schrieb leg...@web.de: Hey everyone! I am new to Python and the scikit learn package so I hope someone can help me with the two issues I encountered during use of the sklearn.manifold implementation of the t-SNE algorithm. First a little bit of context: I am repeatedly feeding batches of dimensionality 500x784 to the algorithm for visualization. However, before my script finishes, one of the two following error messages occurs: AssertionError: [t-SNE] Insertion failed or AssertionError: Tree consistency failed: unexpected number of points=499 at root node=500 Furthermore, these messages do not occur at a fixed time but their behaviour seems rather non-deterministic. Hopefully someone came across this problem before and can help me to fix it. Best, Sarah ------------------------------------------------------------------------------ Find and fix application performance issues faster with Applications Manager Applications Manager provides deep performance insights into multiple tiers of your business applications. It resolves application problems quickly and reduces your MTTR. 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