# HG changeset patch
# User James Bergstra <bergstrj@iro.umontreal.ca>
# Date 1269035723 14400
# Node ID 5cb947647432c49ccb8f306d53ff0ff90e286cc2
# Parent  519e82748a553b35de3078d3defe4e1b9e765de2
adding majorminer dataset

diff -r 519e82748a55 -r 5cb947647432 pylearn/dataset_ops/majorminer.py
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/pylearn/dataset_ops/majorminer.py	Fri Mar 19 17:55:23 2010 -0400
@@ -0,0 +1,65 @@
+from __future__ import absolute_import
+
+import os
+import numpy
+
+import theano
+import theano.sparse
+import scipy.sparse
+
+from ..datasets.majorminer import Meta
+
+_meta = None
+
+class MajorMiner(theano.Op):
+    """Meta-information of major-miner dataset"""
+
+    def __init__(self, meta=None):
+        global _meta
+        # on construction we make sure a *global* configuration is set
+        # this is done because self.* might get pickled and we don't want to pickle
+        # the whole dataset
+        if _meta is None:
+            if meta is None: _meta = Meta()
+            else: _meta = meta
+        else:
+            if meta is None: pass # no problem, we use global _meta
+            else: raise NotImplementedError('global MajorMiner meta-information already set')
+
+    def __eq__(self, other):
+        return type(self) == type(other)
+    def __hash__(self):
+        return hash(type(self))
+
+    def make_node(self, idx):
+        _idx = theano.tensor.as_tensor_variable(idx, ndim=1)
+        return theano.Apply(self, 
+                [_idx], 
+                [theano.sparse.csr_matrix('MajorMiner.tag_counts'),
+                 theano.generic('MajorMiner.:track_path')])
+    def perform(self, node, (idx,), out_storage):
+        global _meta
+        lil = scipy.sparse.lil_matrix((len(idx), len(_meta.tags)), dtype='int8')
+        tracks = []
+        for j,i in enumerate(idx):
+            for tag_id, count in _meta.track_tags[i]:
+                lil[j,tag_id] = count
+            tracks.append(_meta.tracks[i])
+
+        out_storage[0][0] = lil.tocsr()
+        out_storage[1][0] = tracks
+
+    def grad(self, inputs, output):
+        return [None for i in inputs]
+
+
+def test_basic():
+    a = theano.tensor.lvector()
+    f = theano.function([a], MajorMiner()(a))
+    print 'f([0]):', f([0])
+    rval_0_1 = f([0,1])
+    rval_0_8 = f([0,8])
+
+    assert rval_0_1[1][0] == rval_0_8[1][0] #compare strings
+    assert rval_0_1[1][1] != rval_0_8[1][1] #track 1 != track 8
+
diff -r 519e82748a55 -r 5cb947647432 pylearn/datasets/majorminer.py
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/pylearn/datasets/majorminer.py	Fri Mar 19 17:55:23 2010 -0400
@@ -0,0 +1,78 @@
+"""
+Load the MajorMiner dataset
+"""
+
+import logging, os
+from .config import data_root
+_logger = logging.getLogger('pylearn.datasets.majorminer')
+
+def three_column(tagfile=None, trackroot=None, expected_tagfile_len=51556):
+    """Load meta-information of major-miner dataset
+
+    Data is stored as a three-column file:
+
+        <tag> <count> <mp3 path>
+
+    This function returns the parsed file as a list of 3-tuples.
+    
+    """
+    if tagfile is None:
+        tagfile = os.path.join(data_root(), 'majorminer', 'three_column.txt')
+        _logger.info('Majorminer loading %s'%tagfile)
+
+    if trackroot is None:
+        trackroot = os.path.join(data_root(), 'majorminer')
+        _logger.info('Majorminer using trackroot %s'%tagfile)
+
+    tag_count_track = []
+
+    for line in open(tagfile):
+        if line:
+            tag, count, track = line.split('\t')
+            tag_count_track.append((tag, int(count), os.path.join(trackroot, track)))
+
+    if expected_tagfile_len:
+        if len(tag_count_track) != expected_tagfile_len:
+            raise Exception('Wrong number of files listed')
+
+    return tag_count_track
+
+def list_tracks(three_col):
+    tracks = list(set(tup[2] for tup in three_col))
+    tracks.sort()
+    return tracks
+
+def list_tags(three_col):
+    tags = list(set(tup[0] for tup in three_col))
+    tags.sort()
+    return tags
+
+def track_tags(three_col, tracks, tags):
+    """Return the count of each tag for each track
+    [ [(tag_id, count), (tag_id, count), ...],   <---- for tracks[0]
+      [(tag_id, count), (tag_id, count), ...],   <---- for tracks[1]
+      ...
+    ]
+    """
+    tag_id = dict(((t,i) for i,t in enumerate(tags)))
+    track_id = dict(((t,i) for i,t in enumerate(tracks)))
+    rval = [[] for t in tracks]
+    for tag, count, track in three_col:
+        rval[track_id[track]].append((tag_id[tag], count))
+    return rval
+
+
+
+class Meta(object):
+    def __init__(self, tagfile=None, trackroot=None, expected_tagfile_len=51556):
+        self.three_column = three_column(tagfile, trackroot, expected_tagfile_len)
+        self.tracks = list_tracks(self.three_column)
+        self.tags = list_tags(self.three_column)
+        self.track_tags = track_tags(self.three_column, self.tracks, self.tags)
+
+        _logger.info('MajorMiner meta-information: %i tracks, %i tags' %(
+            len(self.tracks), len(self.tags)))
+
+        #for tt in self.track_tags:
+        #    print tt
+