From 5d2a7fd193ce0606ca8b6e5358864eda42359ac3 Mon Sep 17 00:00:00 2001
From: Olivier Grisel <olivier.grisel@ensta.org>
Date: Tue, 6 Jul 2010 02:18:45 +0200
Subject: [PATCH] small fixes
---
examples/mlcomp_document_classification.py | 4 ++--
scikits/learn/datasets/mlcomp.py | 6 ++----
2 files changed, 4 insertions(+), 6 deletions(-)
diff --git a/examples/mlcomp_document_classification.py b/examples/mlcomp_document_classification.py
index 2782a393b8..2c126f488b 100644
--- a/examples/mlcomp_document_classification.py
+++ b/examples/mlcomp_document_classification.py
@@ -58,9 +58,9 @@ print "done in %fs" % (time() - t0)
# The documents have been hashed into TF-IDF (Term Frequencies times Inverse
# Document Frequencies) vectors of a fixed dimension.
# Currently most scikits.learn wrappers or algorithm implementations are unable
-# to leverage efficiently a sparse datastracture; hence we use of a this dense
+# to leverage efficiently a sparse datastracture; hence we use a dense
# representation of a text dataset. Efficient handling of sparse data
-# structures should be expected for in an upcoming version of scikits.learn
+# structures should be expected in an upcoming version of scikits.learn
print "n_samples: %d, n_features: %d" % news_train.data.shape
print "Training a linear classification model with L1 penalty... "
diff --git a/scikits/learn/datasets/mlcomp.py b/scikits/learn/datasets/mlcomp.py
index f6678dbe32..d47ed5be45 100644
--- a/scikits/learn/datasets/mlcomp.py
+++ b/scikits/learn/datasets/mlcomp.py
@@ -8,7 +8,7 @@ from scikits.learn.datasets.base import Bunch
from scikits.learn.features.text import HashingVectorizer
-def load_document_classification(dataset_path, metadata, set_, **kw):
+def _load_document_classification(dataset_path, metadata, set_, **kw):
"""Loader implementation for the DocumentClassification format"""
target = []
target_names = {}
@@ -39,7 +39,7 @@ def load_document_classification(dataset_path, metadata, set_, **kw):
LOADERS = {
- 'DocumentClassification': load_document_classification,
+ 'DocumentClassification': _load_document_classification,
# TODO: implement the remaining domain formats
}
@@ -127,5 +127,3 @@ def load_mlcomp(name_or_id, set_="raw", mlcomp_root=None, **kwargs):
return loader(dataset_path, metadata, set_=set_, **kwargs)
-if __name__ == "__main__":
- twentynews = load_mlcomp('20news-18828')
--
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