{"action":"closed","issue":{"url":"https://api.github.com/repos/kudkudak/word-embeddings-benchmarks/issues/43","repository_url":"https://api.github.com/repos/kudkudak/word-embeddings-benchmarks","labels_url":"https://api.github.com/repos/kudkudak/word-embeddings-benchmarks/issues/43/labels{/name}","comments_url":"https://api.github.com/repos/kudkudak/word-embeddings-benchmarks/issues/43/comments","events_url":"https://api.github.com/repos/kudkudak/word-embeddings-benchmarks/issues/43/events","html_url":"https://github.com/kudkudak/word-embeddings-benchmarks/issues/43","id":258990868,"number":43,"title":"ValueError: Input contains NaN, infinity or a value too large for dtype('float32').","user":{"login":"hate5six","id":3986906,"avatar_url":"https://avatars0.githubusercontent.com/u/3986906?v=4","gravatar_id":"","url":"https://api.github.com/users/hate5six","html_url":"https://github.com/hate5six","followers_url":"https://api.github.com/users/hate5six/followers","following_url":"https://api.github.com/users/hate5six/following{/other_user}","gists_url":"https://api.github.com/users/hate5six/gists{/gist_id}","starred_url":"https://api.github.com/users/hate5six/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/hate5six/subscriptions","organizations_url":"https://api.github.com/users/hate5six/orgs","repos_url":"https://api.github.com/users/hate5six/repos","events_url":"https://api.github.com/users/hate5six/events{/privacy}","received_events_url":"https://api.github.com/users/hate5six/received_events","type":"User","site_admin":false},"labels":[],"state":"closed","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":1,"created_at":"2017-09-19T23:04:33Z","updated_at":"2018-04-20T11:24:55Z","closed_at":"2018-04-20T11:24:55Z","author_association":"NONE","body":"I've generated embeddings (GloVe and word2vec) on a corpus I have and am attempting to compare their performance using evaluate_on_all.py. I'm able to run the script on the GloVe vectors but when running on the word2vec embeddings I get the following. I've checked the matrix and do not see any nans or infs. Has anyone else had this problem?\r\n\r\n> Traceback (most recent call last):\r\n>   File \"./evaluate_on_all.py\", line 83, in <module>\r\n>     results = evaluate_on_all(w)\r\n>   File \"/home/ubuntu/miniconda3/envs/attextract/lib/python3.5/site-packages/web-0.0.1-py3.5.egg/web/evaluate.py\", line 409, in evaluate_on_all\r\n>     categorization_results[name] = evaluate_categorization(w, data.X, data.y)\r\n>   File \"/home/ubuntu/miniconda3/envs/attextract/lib/python3.5/site-packages/web-0.0.1-py3.5.egg/web/evaluate.py\", line 98, in evaluate_categorization\r\n>     linkage=\"ward\").fit_predict(words[ids]))\r\n>   File \"/home/ubuntu/miniconda3/envs/attextract/lib/python3.5/site-packages/scikit_learn-0.18.1-py3.5-linux-x86_64.egg/sklearn/base.py\", line 410, in fit_predict\r\n>     self.fit(X)\r\n>   File \"/home/ubuntu/miniconda3/envs/attextract/lib/python3.5/site-packages/scikit_learn-0.18.1-py3.5-linux-x86_64.egg/sklearn/cluster/hierarchical.py\", line 686, in fit\r\n>     X = check_array(X, ensure_min_samples=2, estimator=self)\r\n>   File \"/home/ubuntu/miniconda3/envs/attextract/lib/python3.5/site-packages/scikit_learn-0.18.1-py3.5-linux-x86_64.egg/sklearn/utils/validation.py\", line 407, in check_array\r\n>     _assert_all_finite(array)\r\n>   File \"/home/ubuntu/miniconda3/envs/attextract/lib/python3.5/site-packages/scikit_learn-0.18.1-py3.5-linux-x86_64.egg/sklearn/utils/validation.py\", line 58, in _assert_all_finite\r\n>     \" or a value too large for %r.\" % X.dtype)\r\n> ValueError: Input contains NaN, infinity or a value too large for dtype('float32').\r\n> "}}
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