def test_dense_sparse_idf_sanity():
hv = HashingVectorizer(dim=100, probes=3)
shv = SparseHashingVectorizer(dim=100, probes=3)
hv.vectorize(JUNK_FOOD_DOCS)
shv.vectorize(JUNK_FOOD_DOCS)
# check that running TF IDF estimates are the same
dense_tf_idf = hv.get_tfidf()
sparse_tfidf = shv.get_tfidf().todense()
assert_array_almost_equal(dense_tf_idf, sparse_tfidf)
# check that incremental behaviour stays the same
hv.vectorize(NOTJUNK_FOOD_DOCS)
shv.vectorize(NOTJUNK_FOOD_DOCS)
dense_tf_idf = hv.get_tfidf()
sparse_tfidf = shv.get_tfidf().todense()
assert_array_almost_equal(dense_tf_idf, sparse_tfidf)
def test_dense_tf_idf():
hv = HashingVectorizer(dim=1000, probes=3)
hv.vectorize(JUNK_FOOD_DOCS)
hv.vectorize(NOTJUNK_FOOD_DOCS)
# extract the TF-IDF data
X = hv.get_tfidf()
assert_equal(X.shape, (11, 1000))
# label junk food as -1, the others as +1
y = np.ones(X.shape[0])
y[:6] = -1
# train and test a classifier
clf = DenseLinearSVC().fit(X[1:-1], y[1:-1])
assert_equal(clf.predict([X[0]]), [-1])
assert_equal(clf.predict([X[-1]]), [1])
def _load_document_classification(dataset_path, metadata, set_, sparse, **kw):
"""Loader implementation for the DocumentClassification format"""
target = []
target_names = {}
filenames = []
vectorizer = kw.get('vectorizer')
if vectorizer is None:
if sparse:
vectorizer = SparseHashingVectorizer()
else:
vectorizer = HashingVectorizer()
# TODO: make it possible to plug a several pass system to filter-out tokens
# that occur in more than 30% of the documents for instance.
# TODO: use joblib.Parallel or multiprocessing to parallelize the following
# (provided this is not IO bound)
dataset_path = os.path.join(dataset_path, set_)
folders = [
f for f in sorted(os.listdir(dataset_path))
if os.path.isdir(os.path.join(dataset_path, f))
]
for label, folder in enumerate(folders):
target_names[label] = folder
folder_path = os.path.join(dataset_path, folder)
documents = [
os.path.join(folder_path, d)
for d in sorted(os.listdir(folder_path))
]
vectorizer.vectorize_files(documents)
target.extend(len(documents) * [label])
filenames.extend(documents)
return Bunch(data=vectorizer.get_vectors(),
target=np.array(target),
target_names=target_names,
filenames=filenames,
DESCR=metadata.get('description'))
def test_dense_tf_idf():
hv = HashingVectorizer(dim=1000, probes=3)
hv.vectorize(JUNK_FOOD_DOCS)
hv.vectorize(NOTJUNK_FOOD_DOCS)
# extract the TF-IDF data
X = hv.get_tfidf()
assert_equal(X.shape, (11, 1000))
# label junk food as -1, the others as +1
y = np.ones(X.shape[0])
y[:6] = -1
# train and test a classifier
clf = LogisticRegression().fit(X[1:-1], y[1:-1])
assert_equal(clf.predict([X[0]]), [-1])
assert_equal(clf.predict([X[-1]]), [1])
def test_dense_sparse_idf_sanity():
hv = HashingVectorizer(dim=100, probes=3)
shv = SparseHashingVectorizer(dim=100, probes=3)
hv.vectorize(JUNK_FOOD_DOCS)
shv.vectorize(JUNK_FOOD_DOCS)
# check that running TF IDF estimates are the same
dense_tf_idf = hv.get_tfidf()
sparse_tfidf = shv.get_tfidf().todense()
assert_array_almost_equal(dense_tf_idf, sparse_tfidf)
# check that incremental behaviour stays the same
hv.vectorize(NOTJUNK_FOOD_DOCS)
shv.vectorize(NOTJUNK_FOOD_DOCS)
dense_tf_idf = hv.get_tfidf()
sparse_tfidf = shv.get_tfidf().todense()
assert_array_almost_equal(dense_tf_idf, sparse_tfidf)