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I am building scikit learn ```python setup.py build_ext --inplace``` then adding the directory to my ```PYTHONPATH```. 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PullRequestReviewCommentEvent | true | {"action":"created","comment":{"url":"https://api.github.com/repos/scikit-learn/scikit-learn/pulls/comments/16216121","id":16216121,"diff_hunk":"@@ -0,0 +1,505 @@\n+\"\"\"\n+Locality Sensitive Hashing Forest for Approximate Nearest Neighbor Search\n+-------------------------------------------------------------------------\n+\"\"\"\n+# Author: Maheshakya Wijewardena <[email protected]>\n+\n+import numpy as np\n+import itertools\n+from bisect import bisect_left, bisect_right\n+from ..base import BaseEstimator\n+from ..utils.validation import check_array\n+from ..utils import check_random_state\n+\n+from ..random_projection import GaussianRandomProjection\n+\n+__all__ = [\"LSHForest\"]\n+\n+\n+def _find_matching_indices(sorted_array, item, left_mask, right_mask):\n+ \"\"\"Finds indices in sorted array of integers.\n+\n+ Most significant h bits in the binary representations of the\n+ integers are matched with the items' most significant h bits.\n+ \"\"\"\n+ left_index = bisect_left(sorted_array, item & left_mask)\n+ right_index = bisect_right(sorted_array, item | right_mask)\n+ return np.arange(left_index, right_index)\n+\n+\n+def _find_longest_prefix_match(bit_string_array, query, hash_size,\n+ left_masks, right_masks):\n+ \"\"\"Private function to find the longest prefix match for query.\n+\n+ Most significant bits are considered as the prefix.\n+ \"\"\"\n+ hi = hash_size\n+ lo = 0\n+\n+ if _find_matching_indices(bit_string_array, query, left_masks[hi],\n+ right_masks[hi]).shape[0] > 0:\n+ return hi\n+\n+ while lo < hi:\n+ mid = (lo+hi)//2\n+\n+ k = _find_matching_indices(bit_string_array, query,\n+ left_masks[mid],\n+ right_masks[mid]).shape[0]\n+ if k > 0:\n+ lo = mid + 1\n+ res = mid\n+ else:\n+ hi = mid\n+\n+ return res\n+\n+\n+def _simple_euclidean_distance(query, candidates):","path":"sklearn/neighbors/lsh_forest.py","position":null,"original_position":58,"commit_id":"bd3b3cf7240673d5b379d51b1763ce5a6392b5c1","original_commit_id":"f78dcf5b111f01a75fd624e801c73616fd275002","user":{"login":"jnothman","id":78827,"avatar_url":"https://avatars.githubusercontent.com/u/78827?v=2","gravatar_id":"7677b43f6049d41c64cc332c381028ac","url":"https://api.github.com/users/jnothman","html_url":"https://github.com/jnothman","followers_url":"https://api.github.com/users/jnothman/followers","following_url":"https://api.github.com/users/jnothman/following{/other_user}","gists_url":"https://api.github.com/users/jnothman/gists{/gist_id}","starred_url":"https://api.github.com/users/jnothman/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/jnothman/subscriptions","organizations_url":"https://api.github.com/users/jnothman/orgs","repos_url":"https://api.github.com/users/jnothman/repos","events_url":"https://api.github.com/users/jnothman/events{/privacy}","received_events_url":"https://api.github.com/users/jnothman/received_events","type":"User","site_admin":false},"body":"If the vectorized version of _simple_euclidean_distance is slower when\r\nn_features\r\nis large, this is possibly because a large array is being allocated in the\r\nsubtraction. But I don't think 10000 is a reasonable number of candidates\r\nat this point, is it? Surely if LSH is doing its job, we should have a much\r\nsmaller sample by this point. In any case, the computation could be batched\r\nin groups larger than 1 row at a time.\r\n\r\n\r\nOn 14 August 2014 08:34, Maheshakya Wijewardena <[email protected]>\r\nwrote:\r\n\r\n> In sklearn/neighbors/lsh_forest.py:\r\n>\r\n> > + while lo < hi:\r\n> > + mid = (lo+hi)//2\r\n> > +\r\n> > + k = _find_matching_indices(bit_\r\n> string_array, query,\r\n> > + left_masks[mid],\r\n> > + right_masks[mid]).shape[0]\r\n> > + if k > 0:\r\n> > + lo = mid + 1\r\n> > + res = mid\r\n> > + else:\r\n> > + hi = mid\r\n> > +\r\n> > + return res\r\n> > +\r\n> > +\r\n> > +def _simple_euclidean_distance(query, candidates):\r\n>\r\n> @robertlayton <https://github.com/robertlayton> , I found that, with\r\n> loop, it is faster when the n_features is large. I can obtain the same\r\n> results as the loop with np.linalg.norm(candidates - query, axis=1). This\r\n> is super fast when the dimension is low. But with high dimensions, this\r\n> gets slower than the loop. Here are some profiling results:\r\n>\r\n> n_samples = 10000 (fixed)\r\n>\r\n> n_features = 100 :\r\n> without loop: 57.1 ms\r\n> with loop: 3.86 ms\r\n>\r\n> n_features = 500 :\r\n> without loop: 21 ms\r\n> with loop: 70.6 ms\r\n>\r\n> n_features = 1000 :\r\n> without loop: 40.5 ms\r\n> with loop: 82.3 ms\r\n>\r\n> n_features = 2000 :\r\n> without loop: 79.6 ms\r\n> with loop: 104 ms\r\n>\r\n> n_features = 5000 :\r\n> without loop: 200 ms\r\n> with loop: 177 ms\r\n>\r\n> n_features = 10000 :\r\n> without loop: 377 ms\r\n> with loop: 302 ms\r\n>\r\n> n_features = 20000 :\r\n> without loop: 752 ms\r\n> with loop: 551 ms\r\n>\r\n> —\r\n> Reply to this email directly or view it on GitHub\r\n> 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