def test_label_binarizer_multilabel_unlabeled():
"""Check that LabelBinarizer can handle an unlabeled sample"""
lb = LabelBinarizer()
y = [[1, 2], [1], []]
Y = np.array([[1, 1],
[1, 0],
[0, 0]])
assert_array_equal(lb.fit_transform(y), Y)
def test_label_binarizer_unseen_labels():
lb = LabelBinarizer()
expected = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
got = lb.fit_transform(["b", "d", "e"])
assert_array_equal(expected, got)
expected = np.array([[0, 0, 0], [1, 0, 0], [0, 0, 0], [0, 1, 0], [0, 0, 1], [0, 0, 0]])
got = lb.transform(["a", "b", "c", "d", "e", "f"])
assert_array_equal(expected, got)
def test_label_binarizer_multilabel():
lb = LabelBinarizer()
# test input as lists of tuples
inp = [(2, 3), (1,), (1, 2)]
indicator_mat = np.array([[0, 1, 1],
[1, 0, 0],
[1, 1, 0]])
got = lb.fit_transform(inp)
assert_true(lb.multilabel_)
assert_array_equal(indicator_mat, got)
assert_equal(lb.inverse_transform(got), inp)
# test input as label indicator matrix
lb.fit(indicator_mat)
assert_array_equal(indicator_mat,
lb.inverse_transform(indicator_mat))
# regression test for the two-class multilabel case
lb = LabelBinarizer()
inp = [[1, 0], [0], [1], [0, 1]]
expected = np.array([[1, 1],
[1, 0],
[0, 1],
[1, 1]])
got = lb.fit_transform(inp)
assert_true(lb.multilabel_)
assert_array_equal(expected, got)
assert_equal([set(x) for x in lb.inverse_transform(got)],
[set(x) for x in inp])
def test_label_binarizer_iris():
lb = LabelBinarizer()
Y = lb.fit_transform(iris.target)
clfs = [SGDClassifier().fit(iris.data, Y[:, k])
for k in range(len(lb.classes_))]
Y_pred = np.array([clf.decision_function(iris.data) for clf in clfs]).T
y_pred = lb.inverse_transform(Y_pred)
accuracy = np.mean(iris.target == y_pred)
y_pred2 = SGDClassifier().fit(iris.data, iris.target).predict(iris.data)
accuracy2 = np.mean(iris.target == y_pred2)
assert_almost_equal(accuracy, accuracy2)
def dbpedia_convgemb(sample=None, n_procs=None):
if not n_procs:
n_procs = cpu_count()
df = get_dbpedia_data(size=sample)
if sample:
test_size = int(round(np.sum(5000 * df.category.value_counts().values / 45000)))
else:
test_size = 5000 * 14
split = StratifiedShuffleSplit(df.category, test_size=test_size)
train_split, test_split = next(iter(split))
train_df = df.iloc[train_split]
test_df = df.iloc[test_split]
train_docs = DataframeSentences(train_df, cols=['title', 'abstract'], flatten=True)
vocab = Dictionary(train_docs)
vocab.filter_extremes(keep_n=5000)
bin = LabelBinarizer()
x_train = np.array(pad_sentences([[vocab.token2id[tok] + 1 for tok in s if tok in vocab.token2id]
for s in train_docs],
max_length=100, padding_word=0))
y_train = bin.fit_transform(train_df.category.values)
test_docs = DataframeSentences(test_df, cols=['title', 'abstract'], flatten=True)
x_test = np.array(pad_sentences([[vocab.token2id[tok] + 1 for tok in s if tok in vocab.token2id]
for s in test_docs],
max_length=100, padding_word=0))
y_test = bin.transform(test_df.category.values)
emb_weights = load_w2v_weights(vocab)
model = Sequential()
model.add(Embedding(5001, 300, input_length=100, dropout=.2, weights=[emb_weights], trainable=False))
model.add(Convolution1D(nb_filter=50, filter_length=3, border_mode='valid',
activation='relu', subsample_length=1))
model.add(MaxPooling1D(pool_length=model.output_shape[1]))
model.add(Flatten())
model.add(Dense(100, activation='relu'))
model.add(Dropout(.2))
model.add(Dense(14, activation='sigmoid'))
model.compile(loss='binary_crossentropy',
optimizer='adam',
metrics=['accuracy'])
model.fit(x_train, y_train)
print(accuracy_score(np.argwhere(y_test)[:,1], model.predict_classes(x_test)))
def test_label_binarizer():
lb = LabelBinarizer()
# one-class case defaults to negative label
inp = ["pos", "pos", "pos", "pos"]
expected = np.array([[0, 0, 0, 0]]).T
got = lb.fit_transform(inp)
assert_false(assert_warns(DeprecationWarning, getattr, lb, "multilabel_"))
assert_array_equal(lb.classes_, ["pos"])
assert_array_equal(expected, got)
assert_array_equal(lb.inverse_transform(got), inp)
# two-class case
inp = ["neg", "pos", "pos", "neg"]
expected = np.array([[0, 1, 1, 0]]).T
got = lb.fit_transform(inp)
assert_false(assert_warns(DeprecationWarning, getattr, lb, "multilabel_"))
assert_array_equal(lb.classes_, ["neg", "pos"])
assert_array_equal(expected, got)
to_invert = np.array([[1, 0], [0, 1], [0, 1], [1, 0]])
assert_array_equal(lb.inverse_transform(to_invert), inp)
# multi-class case
inp = ["spam", "ham", "eggs", "ham", "0"]
expected = np.array([[0, 0, 0, 1], [0, 0, 1, 0], [0, 1, 0, 0],
[0, 0, 1, 0], [1, 0, 0, 0]])
got = lb.fit_transform(inp)
assert_array_equal(lb.classes_, ['0', 'eggs', 'ham', 'spam'])
assert_false(assert_warns(DeprecationWarning, getattr, lb, "multilabel_"))
assert_array_equal(expected, got)
assert_array_equal(lb.inverse_transform(got), inp)
def test_label_binarizer_column_y():
# first for binary classification vs multi-label with 1 possible class
# lists are multi-label, array is multi-class :-/
inp_list = [[1], [2], [1]]
inp_array = np.array(inp_list)
multilabel_indicator = np.array([[1, 0], [0, 1], [1, 0]])
binaryclass_array = np.array([[0], [1], [0]])
lb_1 = LabelBinarizer()
out_1 = lb_1.fit_transform(inp_list)
lb_2 = LabelBinarizer()
out_2 = lb_2.fit_transform(inp_array)
assert_array_equal(out_1, multilabel_indicator)
assert_array_equal(out_2, binaryclass_array)
# second for multiclass classification vs multi-label with multiple
# classes
inp_list = [[1], [2], [1], [3]]
inp_array = np.array(inp_list)
# the indicator matrix output is the same in this case
indicator = np.array([[1, 0, 0], [0, 1, 0], [1, 0, 0], [0, 0, 1]])
lb_1 = LabelBinarizer()
out_1 = lb_1.fit_transform(inp_list)
lb_2 = LabelBinarizer()
out_2 = lb_2.fit_transform(inp_array)
assert_array_equal(out_1, out_2)
assert_array_equal(out_2, indicator)
def test_label_binarizer_errors():
"""Check that invalid arguments yield ValueError"""
one_class = np.array([0, 0, 0, 0])
lb = LabelBinarizer().fit(one_class)
multi_label = [(2, 3), (0, ), (0, 2)]
assert_raises(ValueError, lb.transform, multi_label)
lb = LabelBinarizer()
assert_raises(ValueError, lb.transform, [])
assert_raises(ValueError, lb.inverse_transform, [])
assert_raises(ValueError, LabelBinarizer, neg_label=2, pos_label=1)
assert_raises(ValueError, LabelBinarizer, neg_label=2, pos_label=2)
assert_raises(ValueError,
LabelBinarizer,
neg_label=1,
pos_label=2,
sparse_output=True)
# Fail on y_type
assert_raises(ValueError,
_inverse_binarize_thresholding,
y=csr_matrix([[1, 2], [2, 1]]),
output_type="foo",
classes=[1, 2],
threshold=0)
# Fail on the number of classes
assert_raises(ValueError,
_inverse_binarize_thresholding,
y=csr_matrix([[1, 2], [2, 1]]),
output_type="foo",
classes=[1, 2, 3],
threshold=0)
# Fail on the dimension of 'binary'
assert_raises(ValueError,
_inverse_binarize_thresholding,
y=np.array([[1, 2, 3], [2, 1, 3]]),
output_type="binary",
classes=[1, 2, 3],
threshold=0)
# Fail on multioutput data
assert_raises(ValueError, LabelBinarizer().fit, np.array([[1, 3], [2, 1]]))
assert_raises(ValueError, label_binarize, np.array([[1, 3], [2, 1]]),
[1, 2, 3])
def __init__(self, trainFile, devFile, testFile, d=100, yita=0.1):
self.d = d
self.yita = yita
self.languages = {"ENGLISH": 1, "FRENCH": 3, "ITALIAN": 2}
self.punctuations = [".", "'", ":", ",", "-", "...", "!", "_", "(", ")", "?", '"', ";", "/", "\\", "{", "}", \
"[", "]", "|", "<", ">", "+", "=", "@", "#", "$", "%", "^","&", "*"]
self.noPunctuation = False
self.answerLables = LabelBinarizer()
self.answerLables.fit([1, 2, 3])
self.c = set()
self.Initialize(trainFile, devFile, testFile)
self.input = len(self.c) * 5 + 1
self.setParameters(d, yita)
def test_label_binarizer_errors():
"""Check that invalid arguments yield ValueError"""
one_class = np.array([0, 0, 0, 0])
lb = LabelBinarizer().fit(one_class)
assert_false(lb.multilabel_)
multi_label = [(2, 3), (0, ), (0, 2)]
assert_raises(ValueError, lb.transform, multi_label)
lb = LabelBinarizer()
assert_raises(ValueError, lb.transform, [])
assert_raises(ValueError, lb.inverse_transform, [])
assert_raises(ValueError, LabelBinarizer, neg_label=2, pos_label=1)
assert_raises(ValueError, LabelBinarizer, neg_label=2, pos_label=2)
def test_label_binarizer_multilabel_unlabeled():
"""Check that LabelBinarizer can handle an unlabeled sample"""
lb = LabelBinarizer()
y = [[1, 2], [1], []]
Y = np.array([[1, 1], [1, 0], [0, 0]])
assert_array_equal(assert_warns(DeprecationWarning, lb.fit_transform, y),
Y)
def fit_binarizers(all_values):
binarizers = {}
for f in range(len(all_values[0])):
cur_features = [context[f] for context in all_values]
# only categorical values need to be binarized, ints/floats are left as they are
if type(cur_features[0]) == str or type(cur_features[0]) == unicode:
lb = LabelBinarizer()
lb.fit(cur_features)
binarizers[f] = lb
elif type(cur_features[0]) == list:
mlb = MultiLabelBinarizer()
# default feature for unknown values
cur_features.append(tuple(("__unk__",)))
mlb.fit([tuple(x) for x in cur_features])
binarizers[f] = mlb
return binarizers
def __init__(self, neg_label=0, pos_label=1, sparse_output=False):
self._hyperparams = {
'neg_label': neg_label,
'pos_label': pos_label,
'sparse_output': sparse_output
}
self._wrapped_model = SKLModel(**self._hyperparams)
def fit(self, X, y=None):
self._sklearn_model = SKLModel(**self._hyperparams)
if (y is not None):
self._sklearn_model.fit(X, y)
else:
self._sklearn_model.fit(X)
return self
def fit_binarizers(all_values):
binarizers = {}
for f in range(len(all_values[0])):
cur_features = [context[f] for context in all_values]
# only categorical values need to be binarized, ints/floats are left as they are
if type(cur_features[0]) == str or type(cur_features[0]) == unicode:
lb = LabelBinarizer()
lb.fit(cur_features)
binarizers[f] = lb
elif type(cur_features[0]) == list:
mlb = MultiLabelBinarizer()
# default feature for unknown values
cur_features.append(tuple(("__unk__",)))
mlb.fit([tuple(x) for x in cur_features])
binarizers[f] = mlb
return binarizers
def test_label_binarizer_unseen_labels():
lb = LabelBinarizer()
expected = np.array([[1, 0, 0],
[0, 1, 0],
[0, 0, 1]])
got = lb.fit_transform(['b', 'd', 'e'])
assert_array_equal(expected, got)
expected = np.array([[0, 0, 0],
[1, 0, 0],
[0, 0, 0],
[0, 1, 0],
[0, 0, 1],
[0, 0, 0]])
got = lb.transform(['a', 'b', 'c', 'd', 'e', 'f'])
assert_array_equal(expected, got)
def test_label_binarizer_unseen_labels():
lb = LabelBinarizer()
expected = np.array([[1, 0, 0],
[0, 1, 0],
[0, 0, 1]])
got = lb.fit_transform(['b', 'd', 'e'])
assert_array_equal(expected, got)
expected = np.array([[0, 0, 0],
[1, 0, 0],
[0, 0, 0],
[0, 1, 0],
[0, 0, 1],
[0, 0, 0]])
got = lb.transform(['a', 'b', 'c', 'd', 'e', 'f'])
assert_array_equal(expected, got)
class CategoryBinarizer(TransformerMixin):
def __init__(self):
self.__encoder = LabelBinarizer(sparse_output=False)
def fit(self, X, y=None):
# X = X.astype(str)
X = X.values
self.__encoder.fit(X)
return self
def transform(self, X):
X = X.values
result = self.__encoder.transform(X)
result = pd.DataFrame(result)
result.columns = self.__encoder.classes_
return result
def test_label_binarizer():
lb = LabelBinarizer()
# one-class case defaults to negative label
inp = ["pos", "pos", "pos", "pos"]
expected = np.array([[0, 0, 0, 0]]).T
got = lb.fit_transform(inp)
assert_false(assert_warns(DeprecationWarning, getattr, lb, "multilabel_"))
assert_array_equal(lb.classes_, ["pos"])
assert_array_equal(expected, got)
assert_array_equal(lb.inverse_transform(got), inp)
# two-class case
inp = ["neg", "pos", "pos", "neg"]
expected = np.array([[0, 1, 1, 0]]).T
got = lb.fit_transform(inp)
assert_false(assert_warns(DeprecationWarning, getattr, lb, "multilabel_"))
assert_array_equal(lb.classes_, ["neg", "pos"])
assert_array_equal(expected, got)
to_invert = np.array([[1, 0], [0, 1], [0, 1], [1, 0]])
assert_array_equal(lb.inverse_transform(to_invert), inp)
# multi-class case
inp = ["spam", "ham", "eggs", "ham", "0"]
expected = np.array([[0, 0, 0, 1], [0, 0, 1, 0], [0, 1, 0, 0], [0, 0, 1, 0], [1, 0, 0, 0]])
got = lb.fit_transform(inp)
assert_array_equal(lb.classes_, ["0", "eggs", "ham", "spam"])
assert_false(assert_warns(DeprecationWarning, getattr, lb, "multilabel_"))
assert_array_equal(expected, got)
assert_array_equal(lb.inverse_transform(got), inp)
def test_label_binarizer():
lb = LabelBinarizer()
# one-class case defaults to negative label
inp = ["pos", "pos", "pos", "pos"]
expected = np.array([[0, 0, 0, 0]]).T
got = lb.fit_transform(inp)
assert_array_equal(lb.classes_, ["pos"])
assert_array_equal(expected, got)
assert_array_equal(lb.inverse_transform(got), inp)
# two-class case
inp = ["neg", "pos", "pos", "neg"]
expected = np.array([[0, 1, 1, 0]]).T
got = lb.fit_transform(inp)
assert_array_equal(lb.classes_, ["neg", "pos"])
assert_array_equal(expected, got)
to_invert = np.array([[1, 0],
[0, 1],
[0, 1],
[1, 0]])
assert_array_equal(lb.inverse_transform(to_invert), inp)
# multi-class case
inp = ["spam", "ham", "eggs", "ham", "0"]
expected = np.array([[0, 0, 0, 1],
[0, 0, 1, 0],
[0, 1, 0, 0],
[0, 0, 1, 0],
[1, 0, 0, 0]])
got = lb.fit_transform(inp)
assert_array_equal(lb.classes_, ['0', 'eggs', 'ham', 'spam'])
assert_array_equal(expected, got)
assert_array_equal(lb.inverse_transform(got), inp)
def check_binarized_results(y, classes, pos_label, neg_label, expected):
for sparse_output in [True, False]:
if ((pos_label == 0 or neg_label != 0) and sparse_output):
assert_raises(ValueError,
label_binarize,
y,
classes,
neg_label=neg_label,
pos_label=pos_label,
sparse_output=sparse_output)
continue
# check label_binarize
binarized = label_binarize(y,
classes,
neg_label=neg_label,
pos_label=pos_label,
sparse_output=sparse_output)
assert_array_equal(toarray(binarized), expected)
assert_equal(issparse(binarized), sparse_output)
# check inverse
y_type = type_of_target(y)
if y_type == "multiclass":
inversed = _inverse_binarize_multiclass(binarized, classes=classes)
else:
inversed = _inverse_binarize_thresholding(
binarized,
output_type=y_type,
classes=classes,
threshold=((neg_label + pos_label) / 2.))
assert_array_equal(toarray(inversed), toarray(y))
# Check label binarizer
lb = LabelBinarizer(neg_label=neg_label,
pos_label=pos_label,
sparse_output=sparse_output)
binarized = lb.fit_transform(y)
assert_array_equal(toarray(binarized), expected)
assert_equal(issparse(binarized), sparse_output)
inverse_output = lb.inverse_transform(binarized)
assert_array_equal(toarray(inverse_output), toarray(y))
assert_equal(issparse(inverse_output), issparse(y))
class LabelBinarizerImpl():
def __init__(self, neg_label=0, pos_label=1, sparse_output=False):
self._hyperparams = {
'neg_label': neg_label,
'pos_label': pos_label,
'sparse_output': sparse_output
}
self._wrapped_model = SKLModel(**self._hyperparams)
def fit(self, X, y=None):
if (y is not None):
self._wrapped_model.fit(X, y)
else:
self._wrapped_model.fit(X)
return self
def transform(self, X):
return self._wrapped_model.transform(X)
def getLoss(w, x, y, lam):
m = x.shape[0] # First we get the number of training examples
#y_mat = oneHotIt(y) #Next we convert the integer class coding into a one-hot representation
lb = LabelBinarizer()
y_mat = lb.fit_transform(y)
b = np.random.rand(len(x), 10)
#scores = np.sum(np.dot(x, w), b) # Then we compute raw class scores given our input and current weights
scores = np.dot(
x, w
) # Then we compute raw class scores given our input and current weights
prob = softmax(
scores
) # Next we perform a softmax on these scores to get their probabilities
loss = (-1 / m) * np.sum(y_mat * np.log(prob)) + (lam / 2) * np.sum(
w * w) # We then find the loss of the probabilities
grad = (-1 / m) * np.dot(
x.T,
(y_mat - prob)) + lam * w # And compute the gradient for that loss
return loss, grad
def _log_loss(y_true, y_pred, eps=1e-10, sample_weight=None):
""" This is shorter ans simpler version og log_loss, which supports sample_weight """
sample_weight = check_sample_weight(y_true, sample_weight=sample_weight)
y_true, y_pred, sample_weight = check_arrays(y_true, y_pred, sample_weight)
y_true = column_or_1d(y_true)
lb = LabelBinarizer()
T = lb.fit_transform(y_true)
if T.shape[1] == 1:
T = numpy.append(1 - T, T, axis=1)
# Clipping
Y = numpy.clip(y_pred, eps, 1 - eps)
# Check if dimensions are consistent.
T, Y = check_arrays(T, Y)
# Renormalize
Y /= Y.sum(axis=1)[:, numpy.newaxis]
loss = -(T * numpy.log(Y) * sample_weight[:, numpy.newaxis]).sum() / numpy.sum(sample_weight)
return loss
def _log_loss(y_true, y_pred, eps=1e-10, sample_weight=None):
""" This is shorter ans simpler version og log_loss, which supports sample_weight """
sample_weight = check_sample_weight(y_true, sample_weight=sample_weight)
y_true, y_pred, sample_weight = check_arrays(y_true, y_pred, sample_weight)
y_true = column_or_1d(y_true)
lb = LabelBinarizer()
T = lb.fit_transform(y_true)
if T.shape[1] == 1:
T = numpy.append(1 - T, T, axis=1)
# Clipping
Y = numpy.clip(y_pred, eps, 1 - eps)
# Check if dimensions are consistent.
T, Y = check_arrays(T, Y)
# Renormalize
Y /= Y.sum(axis=1)[:, numpy.newaxis]
loss = -(T * numpy.log(Y) *
sample_weight[:, numpy.newaxis]).sum() / numpy.sum(sample_weight)
return loss
def check_binarized_results(y, classes, pos_label, neg_label, expected):
for sparse_output in [True, False]:
if ((pos_label == 0 or neg_label != 0) and sparse_output):
assert_raises(ValueError, label_binarize, y, classes,
neg_label=neg_label, pos_label=pos_label,
sparse_output=sparse_output)
continue
# check label_binarize
binarized = label_binarize(y, classes, neg_label=neg_label,
pos_label=pos_label,
sparse_output=sparse_output)
assert_array_equal(toarray(binarized), expected)
assert_equal(issparse(binarized), sparse_output)
# check inverse
y_type = type_of_target(y)
if y_type == "multiclass":
inversed = _inverse_binarize_multiclass(binarized, classes=classes)
else:
inversed = _inverse_binarize_thresholding(binarized,
output_type=y_type,
classes=classes,
threshold=((neg_label +
pos_label) /
2.))
assert_array_equal(toarray(inversed), toarray(y))
# Check label binarizer
lb = LabelBinarizer(neg_label=neg_label, pos_label=pos_label,
sparse_output=sparse_output)
binarized = lb.fit_transform(y)
assert_array_equal(toarray(binarized), expected)
assert_equal(issparse(binarized), sparse_output)
inverse_output = lb.inverse_transform(binarized)
assert_array_equal(toarray(inverse_output), toarray(y))
assert_equal(issparse(inverse_output), issparse(y))
def test_label_binarize_with_multilabel_indicator():
"""Check that passing a binary indicator matrix is not noop"""
classes = np.arange(3)
neg_label = -1
pos_label = 2
y = np.array([[0, 1, 0], [1, 1, 1]])
expected = np.array([[-1, 2, -1], [2, 2, 2]])
# With label binarize
output = label_binarize(y, classes, multilabel=True, neg_label=neg_label,
pos_label=pos_label)
assert_array_equal(output, expected)
# With the transformer
lb = LabelBinarizer(pos_label=pos_label, neg_label=neg_label)
output = lb.fit_transform(y)
assert_array_equal(output, expected)
output = lb.fit(y).transform(y)
assert_array_equal(output, expected)
def test_label_binarizer_set_label_encoding():
lb = LabelBinarizer(neg_label=-2, pos_label=2)
# two-class case
inp = np.array([0, 1, 1, 0])
expected = np.array([[-2, 2, 2, -2]]).T
got = lb.fit_transform(inp)
assert_false(lb.multilabel_)
assert_array_equal(expected, got)
assert_array_equal(lb.inverse_transform(got), inp)
# multi-class case
inp = np.array([3, 2, 1, 2, 0])
expected = np.array([[-2, -2, -2, +2],
[-2, -2, +2, -2],
[-2, +2, -2, -2],
[-2, -2, +2, -2],
[+2, -2, -2, -2]])
got = lb.fit_transform(inp)
assert_false(lb.multilabel_)
assert_array_equal(expected, got)
assert_array_equal(lb.inverse_transform(got), inp)
def test_label_binarizer_set_label_encoding():
lb = LabelBinarizer(neg_label=-2, pos_label=0)
# two-class case with pos_label=0
inp = np.array([0, 1, 1, 0])
expected = np.array([[-2, 0, 0, -2]]).T
got = lb.fit_transform(inp)
assert_array_equal(expected, got)
assert_array_equal(lb.inverse_transform(got), inp)
lb = LabelBinarizer(neg_label=-2, pos_label=2)
# multi-class case
inp = np.array([3, 2, 1, 2, 0])
expected = np.array([[-2, -2, -2, +2], [-2, -2, +2, -2], [-2, +2, -2, -2],
[-2, -2, +2, -2], [+2, -2, -2, -2]])
got = lb.fit_transform(inp)
assert_array_equal(expected, got)
assert_array_equal(lb.inverse_transform(got), inp)
def test_label_binarizer_multilabel():
lb = LabelBinarizer()
# test input as lists of tuples
inp = [(2, 3), (1, ), (1, 2)]
indicator_mat = np.array([[0, 1, 1], [1, 0, 0], [1, 1, 0]])
got = assert_warns(DeprecationWarning, lb.fit_transform, inp)
assert_true(lb.multilabel_)
assert_array_equal(indicator_mat, got)
assert_equal(lb.inverse_transform(got), inp)
# test input as label indicator matrix
lb.fit(indicator_mat)
assert_array_equal(indicator_mat, lb.inverse_transform(indicator_mat))
# regression test for the two-class multilabel case
lb = LabelBinarizer()
inp = [[1, 0], [0], [1], [0, 1]]
expected = np.array([[1, 1], [1, 0], [0, 1], [1, 1]])
got = assert_warns(DeprecationWarning, lb.fit_transform, inp)
assert_true(lb.multilabel_)
assert_array_equal(expected, got)
assert_equal([set(x) for x in lb.inverse_transform(got)],
[set(x) for x in inp])
def test_label_binarizer_set_label_encoding():
lb = LabelBinarizer(neg_label=-2, pos_label=0)
# two-class case with pos_label=0
inp = np.array([0, 1, 1, 0])
expected = np.array([[-2, 0, 0, -2]]).T
got = lb.fit_transform(inp)
assert_false(assert_warns(DeprecationWarning, getattr, lb, "multilabel_"))
assert_array_equal(expected, got)
assert_array_equal(lb.inverse_transform(got), inp)
lb = LabelBinarizer(neg_label=-2, pos_label=2)
# multi-class case
inp = np.array([3, 2, 1, 2, 0])
expected = np.array([[-2, -2, -2, +2], [-2, -2, +2, -2], [-2, +2, -2, -2], [-2, -2, +2, -2], [+2, -2, -2, -2]])
got = lb.fit_transform(inp)
assert_false(assert_warns(DeprecationWarning, getattr, lb, "multilabel_"))
assert_array_equal(expected, got)
assert_array_equal(lb.inverse_transform(got), inp)
def test_label_binarizer():
lb = LabelBinarizer()
# two-class case
inp = ["neg", "pos", "pos", "neg"]
expected = np.array([[0, 1, 1, 0]]).T
got = lb.fit_transform(inp)
assert_false(lb.multilabel_)
assert_array_equal(lb.classes_, ["neg", "pos"])
assert_array_equal(expected, got)
assert_array_equal(lb.inverse_transform(got), inp)
# multi-class case
inp = ["spam", "ham", "eggs", "ham", "0"]
expected = np.array([[0, 0, 0, 1], [0, 0, 1, 0], [0, 1, 0, 0],
[0, 0, 1, 0], [1, 0, 0, 0]])
got = lb.fit_transform(inp)
assert_array_equal(lb.classes_, ['0', 'eggs', 'ham', 'spam'])
assert_false(lb.multilabel_)
assert_array_equal(expected, got)
assert_array_equal(lb.inverse_transform(got), inp)
def __init__(self):
self.__encoder = LabelBinarizer(sparse_output=False)
class GOAMultilayerPerceptron:
def __init__(self, N, hidden_layer_sizes, max_iter, random_state, x_val, y_val, activation="relu"):
self.N = N
self.hidden_layer_sizes = hidden_layer_sizes
self.activation = activation
self.max_iter = max_iter
self.random_state = check_random_state(random_state)
self.xval = x_val
self.yval = y_val
def _forward_pass(self, activations, coefs, intercepts):
hidden_activation = ACTIVATIONS[self.activation]
# Iterate over the hidden layers
for i in range(self.n_layers_ - 1):
activations[i + 1] = safe_sparse_dot(activations[i], coefs[i])
activations[i + 1] += intercepts[i]
# For the hidden layers
if (i + 1) != (self.n_layers_ - 1):
activations[i + 1] = hidden_activation(activations[i + 1])
# For the last layer
activations[self.n_layers_-1] = logistic(activations[self.n_layers_-1])
return activations
def initialize(self, y, layer_units, coefs_, intercepts_):
self.n_outputs_ = y.shape[1]
self.n_layers_ = len(layer_units)
self.out_activation_ = 'logistic'
self.n_coefs = []
self.n_intercepts = []
self.bound = 0
bound = 0
self.coefs_ = coefs_
self.intercepts_ = intercepts_
grasshopper_vector = self.encode(coefs_, intercepts_)
for x in grasshopper_vector:
if abs(x) > bound:
bound = abs(x)
bound = math.ceil(bound)
self.grasshopper_vector = grasshopper_vector
self.dim = len(grasshopper_vector)
self.ub = bound
self.lb = -bound
def fit(self, X, y):
inicial_mlp = MLPClassifier(solver='sgd', alpha=1e-5, hidden_layer_sizes=self.hidden_layer_sizes, random_state=8997)
inicial_mlp.fit(X, y)
N = self.N
max_iter = self.max_iter
hidden_layer_sizes = self.hidden_layer_sizes
hidden_layer_sizes = list(hidden_layer_sizes)
X, y = self.validate_input(X, y)
n_samples, n_features = X.shape
if y.ndim == 1:
y = y.reshape((-1, 1))
self.n_outputs_ = y.shape[1]
layer_units = ([n_features] + hidden_layer_sizes +
[self.n_outputs_])
self.initialize(y, layer_units, inicial_mlp.coefs_, inicial_mlp.intercepts_)
y = self.label_binarizer.inverse_transform(y)
bestauc = 0
flag = 0
dim = self.dim
print("dim:", dim)
lb = self.lb
ub = self.ub
ub = np.ones((dim, 1)) * ub
lb = np.ones((dim, 1)) * lb
if dim % 2 != 0:
dim = dim + 1
ub = np.append(ub, self.ub)
lb = np.append(lb, self.lb)
flag = 1
if flag == 1:
self.grasshopper_vector.append(0)
grasshopper_positions = []
for i in range(N):
grasshopper_positions.append(self.grasshopper_vector)
# grasshopper_positions = initialization(N, dim, self.lb, self.ub)
grasshopper_positions = np.array(grasshopper_positions)
grasshopper_fitness = []
cmax = 1
cmin = 0.00004
for i in range(np.size(grasshopper_positions, 0)):
if flag == 1:
grasshopper_position = grasshopper_positions[i][0:-1]
coefs, intercepts = self.decode(grasshopper_position)
y_pred = self._predict(X, coefs, intercepts)
y_pred = y_pred.ravel()
self.label_binarizer.inverse_transform(y_pred)
fpr, tpr, thresholds = roc_curve(y, y_pred)
auc1 = auc(fpr, tpr)
grasshopper_fitness.append(auc1)
# grasshopper_fitness.append(binary_log_loss(y, y_pred))
else:
grasshopper_position = grasshopper_positions[i]
coefs, intercepts = self.decode(grasshopper_position)
y_pred = self._predict(X, coefs, intercepts)
y_pred = y_pred.ravel()
self.label_binarizer.inverse_transform(y_pred)
fpr, tpr, thresholds = roc_curve(y, y_pred)
auc1 = auc(fpr, tpr)
grasshopper_fitness.append(auc1)
# grasshopper_fitness.append(binary_log_loss(y, y_pred))
sorted_indexes = list(np.array(grasshopper_fitness).argsort())
grasshopper_fitness.sort(reverse=True)
sorted_grasshopper = []
for new_index in range(N):
sorted_grasshopper.append(grasshopper_positions[sorted_indexes[new_index]])
target_position = sorted_grasshopper[0]
target_fitness = grasshopper_fitness[0]
print("target_position:", target_position)
print("target_fitness:", target_fitness)
l = 2
grasshopper_positions = np.array(grasshopper_positions)
print(np.shape(grasshopper_positions))
while l < max_iter + 1:
print("iteration ", l)
tp = np.array(target_position)
cc = cmax - l * ((cmax - cmin) / max_iter)
for i in range(np.size(grasshopper_positions, 0)):
temp = np.transpose(grasshopper_positions)
s_i = np.zeros((dim, 1))
for j in range(N):
if i != j:
dist = distance(temp[:, j], temp[:, i])
r_ij_vec = (temp[:, j] - temp[:, i]) / (dist + eps(1))
xj_xi = 2 + dist % 2
s_ij = np.multiply((ub - lb)*cc/2*s_func(xj_xi), r_ij_vec)
s_i = s_i + np.transpose(s_ij)
X_new = cc * np.transpose(s_i) + tp
grasshopper_positions[i, :] = np.squeeze(np.transpose(X_new))
for i in range(N):
# Relocate grasshoppers that go outside the search space
tp = np.greater(grasshopper_positions[i, :], np.transpose(ub))
tm = np.less(grasshopper_positions[i, :], np.transpose(lb))
grasshopper_positions[i, :] = grasshopper_positions[i, :] * np.logical_not(tp + tm) + np.transpose(
ub) * tp + np.transpose(lb) * tm
if flag == 1:
grasshopper_position = grasshopper_positions[i][0:-1]
coefs, intercepts = self.decode(grasshopper_position)
y_pred = self._predict(X, coefs, intercepts)
y_pred = y_pred.ravel()
self.label_binarizer.inverse_transform(y_pred)
fpr, tpr, thresholds = roc_curve(y, y_pred)
auc1 = auc(fpr, tpr)
grasshopper_fitness = auc1
# grasshopper_fitness = binary_log_loss(y, y_pred)
else:
grasshopper_position = grasshopper_positions[i]
coefs, intercepts = self.decode(grasshopper_position)
y_pred = self._predict(X, coefs, intercepts)
y_pred = y_pred.ravel()
self.label_binarizer.inverse_transform(y_pred)
fpr, tpr, thresholds = roc_curve(y, y_pred)
auc1 = auc(fpr, tpr)
grasshopper_fitness = auc1
#grasshopper_fitness = binary_log_loss(y, y_pred)
if grasshopper_fitness > target_fitness:
target_position = grasshopper_positions[i]
target_fitness = grasshopper_fitness
print("new_fitness:", target_fitness)
y_pred = self._predict(X, coefs, intercepts)
y_pred = y_pred.ravel()
self.label_binarizer.inverse_transform(y_pred)
fpr, tpr, thresholds = roc_curve(y, y_pred)
auc1 = auc(fpr, tpr)
print("training auc:", auc1)
y_pred = self._predict(self.xval, coefs, intercepts)
y_pred = y_pred.ravel()
self.label_binarizer.inverse_transform(y_pred)
fpr, tpr, thresholds = roc_curve(self.yval, y_pred)
auc1 = auc(fpr, tpr)
if auc1>bestauc:
bestauc = auc1
print("best auc on validation set:", bestauc)
l=l+1
if flag == 1:
target_position = target_position[0:-1]
coefss, interceptss = self.decode(target_position)
self.coefs_ = coefss
self.intercepts_ = interceptss
def init_coef(self, fan_in, fan_out):
# Use the initialization method recommended by
# Glorot et al.
factor = 6.
if self.activation == 'logistic':
factor = 2.
init_bound = np.sqrt(factor / (fan_in + fan_out))
# Generate weights and bias:
coef_init = self.random_state.uniform(-init_bound, init_bound, (fan_in, fan_out))
intercept_init = self.random_state.uniform(-init_bound, init_bound, fan_out)
return coef_init, intercept_init, init_bound
def encode(self, coefs, intercepts):
self.n_coefs = []
self.n_intercepts = []
grasshopper_position = []
for array in coefs:
self.n_coefs.append(np.shape(array))
for line in array:
grasshopper_position += list(line)
for array in intercepts:
self.n_intercepts.append(np.shape(array))
grasshopper_position += list(array)
return grasshopper_position
def decode(self, grasshopper_position:list):
coefs = []
intercepts = []
pos = 0
for shape in self.n_coefs:
coef = []
for j in range(shape[0]):
coe = []
for k in range(shape[1]):
coe.append(grasshopper_position[pos])
pos = pos+1
coef.append(coe)
coefs.append(np.array(coef))
for shape in self.n_intercepts:
intercept = []
for j in range(shape[0]):
intercept.append(grasshopper_position[pos])
pos = pos+1
intercepts.append(np.array(intercept))
return coefs, intercepts
def _predict(self, X, coefs, intercepts):
X = check_array(X, accept_sparse=['csr', 'csc', 'coo'])
# Make sure self.hidden_layer_sizes is a list
hidden_layer_sizes = self.hidden_layer_sizes
if not hasattr(hidden_layer_sizes, "__iter__"):
hidden_layer_sizes = [hidden_layer_sizes]
hidden_layer_sizes = list(hidden_layer_sizes)
layer_units = [X.shape[1]] + hidden_layer_sizes + [self.n_outputs_]
# Initialize layers
activations = [X]
for i in range(self.n_layers_ - 1):
activations.append(np.empty((X.shape[0], layer_units[i + 1])))
# forward propagate
self._forward_pass(activations, coefs, intercepts)
y_pred = activations[-1]
return y_pred
def predict(self, X):
X = check_array(X, accept_sparse=['csr', 'csc', 'coo'])
# Make sure self.hidden_layer_sizes is a list
hidden_layer_sizes = self.hidden_layer_sizes
if not hasattr(hidden_layer_sizes, "__iter__"):
hidden_layer_sizes = [hidden_layer_sizes]
hidden_layer_sizes = list(hidden_layer_sizes)
layer_units = [X.shape[1]] + hidden_layer_sizes + [self.n_outputs_]
# Initialize layers
activations = [X]
for i in range(self.n_layers_ - 1):
activations.append(np.empty((X.shape[0], layer_units[i + 1])))
# forward propagate
self._forward_pass(activations, self.coefs_, self.intercepts_)
y_pred = activations[-1]
if self.n_outputs_ == 1:
y_pred = y_pred.ravel()
return self.label_binarizer.inverse_transform(y_pred)
def validate_input(self, X, y):
X, y = check_X_y(X, y, accept_sparse=['csr', 'csc', 'coo'],
multi_output=True)
if y.ndim == 2 and y.shape[1] == 1:
y = column_or_1d(y, warn=True)
classes = unique_labels(y)
self.label_binarizer = LabelBinarizer()
self.label_binarizer.fit(classes)
y = self.label_binarizer.transform(y)
return X, y
def main():
print("Loading samples and labels")
samples, labels, _ = load_files("data")
print("Loaded {} samples".format(samples.shape[0]))
sequence_dim = 100
print("Converting to sequences of length {}".format(sequence_dim))
samples, labels = make_sequences(samples, labels, sequence_dim)
print("Number of samples from sequences: {}".format(samples.shape[0]))
lb = LabelBinarizer()
labels = lb.fit_transform(labels)
# flattened samples for Decision Tree
flatSamples = samples.reshape(samples.shape[0], -1) #tree!
(trainSamples, testSamples, trainLabels,
testLabels) = train_test_split(flatSamples,
labels,
test_size=0.25,
random_state=42)
print("=" * 20)
print("Building DecisionTree model")
model = DecisionTreeClassifier()
model.fit(trainSamples, trainLabels)
treeResults = model.predict(testSamples)
print(
confusion_matrix(testLabels.argmax(axis=1),
treeResults.argmax(axis=1)))
print(
classification_report(testLabels.argmax(axis=1),
treeResults.argmax(axis=1)))
treeAcc = accuracy_score(testLabels.argmax(axis=1),
treeResults.argmax(axis=1))
print("Accuracy Tree: {:.2f}".format(treeAcc))
print("Cohen's Kappa {:.2f}".format(
cohen_kappa_score(testLabels.argmax(axis=1),
treeResults.argmax(axis=1))))
print("=" * 20)
print("Building CNN model")
(trainSamples, testSamples, trainLabels,
testLabels) = train_test_split(samples,
labels,
test_size=0.25,
random_state=42)
inputShape = (samples.shape[1], samples.shape[2])
model = Sequential()
model.add(Conv1D(32, 10, padding="same", input_shape=inputShape))
model.add(Activation("relu"))
model.add(BatchNormalization())
model.add(Dropout(0.2))
model.add(Conv1D(64, 10, padding="same"))
model.add(Activation("relu"))
model.add(BatchNormalization())
model.add(Dropout(0.2))
model.add(Conv1D(128, 10, padding="same"))
model.add(Activation("relu"))
model.add(Dropout(0.2))
model.add(Flatten(input_shape=inputShape))
model.add(Dense(128, activation='sigmoid'))
model.add(Dense(64, activation='sigmoid'))
model.add(Dense(labels.shape[1], activation='softmax'))
model.compile(loss='categorical_crossentropy',
optimizer="adam",
metrics=['accuracy'])
EPOCHS = 10
BATCH = 128
model.fit(trainSamples,
trainLabels,
batch_size=BATCH,
epochs=EPOCHS,
validation_data=(testSamples, testLabels))
cnnResults = model.predict(testSamples)
print(
confusion_matrix(testLabels.argmax(axis=1), cnnResults.argmax(axis=1)))
print(
classification_report(testLabels.argmax(axis=1),
cnnResults.argmax(axis=1),
target_names=lb.classes_))
print("CNN Accuracy: {:.2f}".format(
accuracy_score(testLabels.argmax(axis=1), cnnResults.argmax(axis=1))))
print("Cohen's Kappa {:.2f}".format(
cohen_kappa_score(testLabels.argmax(axis=1),
cnnResults.argmax(axis=1))))
input("")
# find pairs for samplesIMG in samplesCSV
samples_paired = []
for i in range(samplesIMG.shape[0]):
for j in range(samplesCSV.shape[0]):
if namesCSV[j] == namesIMG[i]:
samples_paired.append(samplesCSV[j])
samplesCSV = np.array(samples_paired)
samplesIMG = np.expand_dims(samplesIMG, axis=3)
print("Paired")
print("Samples IMG: {}".format(len(samplesIMG)))
print("Samples CSV: {}".format(len(samplesCSV)))
# one-hot encoding
lb = LabelBinarizer()
labels = lb.fit_transform(labels)
numClasses = labels.shape[1]
inputShape = (108, 192, 1) #samplesIMG.shape
#model for images
cnnmodel = Sequential()
cnnmodel.add(Conv2D(16, (3, 3), padding="same", input_shape=inputShape))
cnnmodel.add(Activation("relu"))
cnnmodel.add(MaxPooling2D(pool_size=(2, 2)))
cnnmodel.add(Conv2D(32, (3, 3), padding="same"))
cnnmodel.add(Activation("relu"))
cnnmodel.add(MaxPooling2D(pool_size=(2, 2)))
cnnmodel.add(Dropout(0.25))
cnnmodel.add(Flatten())
def dbpedia_smallcharconv(sample=None, n_procs=None):
if not n_procs:
n_procs = cpu_count()
df = get_dbpedia_data(size=sample)
if sample:
test_size = int(
round(np.sum(5000 * df.category.value_counts().values / 45000)))
else:
test_size = 5000 * 14
logging.info('creating train test split ...')
split = StratifiedShuffleSplit(df.category, test_size=test_size)
train_split, test_split = next(iter(split))
train_df = df.iloc[train_split]
test_df = df.iloc[test_split]
logging.info('preprocessing, padding and binarizing data ...')
train_docs = [[
CHAR_MAP.index(c) if c in CHAR_MAP else len(CHAR_MAP) for c in text
] for text in train_df[['title', 'abstract']].apply(
lambda cols: u'\n'.join(cols), axis=1).values]
bin = LabelBinarizer()
x_train = np.array(
pad_sentences(train_docs,
max_length=1014,
padding_word=CHAR_MAP.index(' ')))
y_train = bin.fit_transform(train_df.category.values)
test_docs = [[
CHAR_MAP.index(c) if c in CHAR_MAP else len(CHAR_MAP) for c in text
] for text in test_df[['title', 'abstract']].apply(
lambda cols: u'\n'.join(cols), axis=1).values]
x_test = np.array(pad_sentences(test_docs, max_length=1014,
padding_word=0))
y_test = bin.transform(test_df.category.values)
logging.info('building model ...')
model = Sequential()
model.add(
Embedding(len(CHAR_MAP) + 1,
len(CHAR_MAP) + 1,
input_length=1014,
weights=[char_embedding()],
trainable=False))
model.add(
Convolution1D(nb_filter=256,
filter_length=7,
border_mode='valid',
activation='relu'))
model.add(MaxPooling1D(pool_length=3))
model.add(
Convolution1D(nb_filter=256,
filter_length=7,
border_mode='valid',
activation='relu',
subsample_length=1))
model.add(MaxPooling1D(pool_length=3))
model.add(
Convolution1D(nb_filter=256,
filter_length=3,
border_mode='valid',
activation='relu',
subsample_length=1))
model.add(
Convolution1D(nb_filter=256,
filter_length=3,
border_mode='valid',
activation='relu',
subsample_length=1))
model.add(
Convolution1D(nb_filter=256,
filter_length=3,
border_mode='valid',
activation='relu',
subsample_length=1))
model.add(
Convolution1D(nb_filter=256,
filter_length=3,
border_mode='valid',
activation='relu',
subsample_length=1))
model.add(MaxPooling1D(pool_length=3))
model.add(Flatten())
model.add(Dense(1024, activation='relu'))
model.add(Dropout(.5))
model.add(Dense(1024, activation='relu'))
model.add(Dropout(.5))
model.add(Dense(14, activation='sigmoid'))
model.compile(loss='binary_crossentropy',
optimizer='adam',
metrics=['categorical_accuracy'])
print(model.summary())
model.fit(x_train,
y_train,
batch_size=64,
nb_epoch=5,
validation_data=[x_test, y_test])
print(
accuracy_score(
np.argwhere(y_test)[:, 1], model.predict_classes(x_test)))
def dbpedia_smallwordconv(sample=None, n_procs=None):
if not n_procs:
n_procs = cpu_count()
df = get_dbpedia_data(size=sample)
if sample:
test_size = int(
round(np.sum(5000 * df.category.value_counts().values / 45000)))
else:
test_size = 5000 * 14
logging.info('creating train test split ...')
split = StratifiedShuffleSplit(df.category, test_size=test_size)
train_split, test_split = next(iter(split))
train_df = df.iloc[train_split]
test_df = df.iloc[test_split]
logging.info('preprocessing, padding and binarizing data ...')
train_docs = DataframeSentences(train_df,
cols=['title', 'abstract'],
flatten=True)
vocab = Dictionary(train_docs)
vocab.filter_extremes(keep_n=5000)
bin = LabelBinarizer()
x_train = np.array(
pad_sentences(
[[vocab.token2id[tok] + 1 for tok in s if tok in vocab.token2id]
for s in train_docs],
max_length=100,
padding_word=0))
y_train = bin.fit_transform(train_df.category.values)
test_docs = DataframeSentences(test_df,
cols=['title', 'abstract'],
flatten=True)
x_test = np.array(
pad_sentences(
[[vocab.token2id[tok] + 1 for tok in s if tok in vocab.token2id]
for s in test_docs],
max_length=100,
padding_word=0))
y_test = bin.transform(test_df.category.values)
logging.info('building model ...')
model = Sequential()
model.add(Embedding(5001, 300, input_length=100))
model.add(
Convolution1D(nb_filter=300,
filter_length=7,
border_mode='valid',
activation='relu',
subsample_length=1))
model.add(MaxPooling1D(pool_length=3, stride=1))
model.add(
Convolution1D(nb_filter=300,
filter_length=7,
border_mode='valid',
activation='relu',
subsample_length=1))
model.add(MaxPooling1D(pool_length=3, stride=1))
model.add(
Convolution1D(nb_filter=300,
filter_length=3,
border_mode='valid',
activation='relu',
subsample_length=1))
model.add(
Convolution1D(nb_filter=300,
filter_length=3,
border_mode='valid',
activation='relu',
subsample_length=1))
model.add(
Convolution1D(nb_filter=300,
filter_length=3,
border_mode='valid',
activation='relu',
subsample_length=1))
model.add(
Convolution1D(nb_filter=300,
filter_length=3,
border_mode='valid',
activation='relu',
subsample_length=1))
model.add(MaxPooling1D(pool_length=3, stride=1))
model.add(Flatten())
model.add(Dense(1024, activation='relu'))
model.add(Dropout(.5))
model.add(Dense(1024, activation='relu'))
model.add(Dropout(.5))
model.add(Dense(14, activation='sigmoid'))
model.compile(loss='binary_crossentropy',
optimizer='adam',
metrics=['categorical_accuracy'])
model.fit(x_train,
y_train,
batch_size=32,
nb_epoch=5,
validation_data=[x_test, y_test])
print(
accuracy_score(
np.argwhere(y_test)[:, 1], model.predict_classes(x_test)))
class languageIdentification(object):
"""
Using characters as features, each encoded by sklearn OneHotEncoder
Languages are encoded into vectors using sklearn LabelBinarizer
"""
def __init__(self, trainFile, devFile, testFile, d=100, yita=0.1):
self.d = d
self.yita = yita
self.languages = {"ENGLISH": 1, "FRENCH": 3, "ITALIAN": 2}
self.punctuations = [".", "'", ":", ",", "-", "...", "!", "_", "(", ")", "?", '"', ";", "/", "\\", "{", "}", \
"[", "]", "|", "<", ">", "+", "=", "@", "#", "$", "%", "^","&", "*"]
self.noPunctuation = False
self.answerLables = LabelBinarizer()
self.answerLables.fit([1, 2, 3])
self.c = set()
self.Initialize(trainFile, devFile, testFile)
self.input = len(self.c) * 5 + 1
self.setParameters(d, yita)
def Initialize(self, trainFileName, devFileName, testFileName):
trainList = []
trainResult = []
self.testFeatures = []
self.devFeatures = []
self.trainFeatures = []
self.train = []
#self.dev = []
#self.test = []
self.devResult = []
self.rawResult = []
print "train feature processing..."
with open(trainFileName) as trainFile:
for line in trainFile:
line = line.decode('utf-8').strip()
if not line:
continue
space = line.find(" ")
if space < 5:
continue
answer, train = line[:space].upper(), line[space + 1:]
li, ans = self.lineProc(train, answer, True)
trainList += li
trainResult += ans
self.trainFeatures.append(li)
self.rawResult.append(self.languages[answer])
with open(devFileName) as devFile:
for line in devFile:
line = line.decode('utf-8').strip()
if not line:
continue
space = line.find(" ")
if space < 5:
continue
answer, train = line[:space].upper(), line[space + 1:]
li = self.lineProc(train, answer, False)
self.devFeatures.append(li)
self.devResult.append(self.languages[answer])
with open(testFileName) as testFile:
for line in testFile:
if not line:
continue
line = line.decode('latin-1').strip()
test = self.lineProc(line, "", False)
self.testFeatures.append(test)
trainList, trainResult = self.FisherYatesShuffle(
trainList, trainResult)
trainResult = np.array(trainResult)
self.trainResult = self.answerLables.fit_transform(trainResult)
self.trainLabels = preprocessing.LabelEncoder()
featureList = list(self.c)
self.trainLabels.fit(featureList)
#print self.trainLabels.classes_
length = len(self.c)
print "feature length:", length
self.v = preprocessing.OneHotEncoder(n_values=length)
trainList = np.array(trainList)
self.train = self.trainLabels.transform(
trainList.ravel()).reshape(*trainList.shape)
self.train = self.v.fit_transform(self.train).toarray()
print "train shape", self.train.shape
def directPredict(self, featureList, type):
types = {
"train": "self.rawResult",
"dev": "self.devResult",
"test": "self.testResult"
}
prediction = self.predictAll(featureList)
accuracy = self.evaluate(prediction, eval(types[type]))
return prediction, accuracy
def devProcess(self, epoch, initial=True):
trainAccuracy = []
devAccuracy = []
if initial:
print "initial predictions..."
initial_train = self.directPredict(self.trainFeatures, "train")[1]
trainAccuracy.append(initial_train)
print "initial train accuracy: ", initial_train
initial_dev = self.directPredict(self.devFeatures, "dev")[1]
print "initial dev accuracy: ", initial_dev
devAccuracy.append(initial_dev)
for i in xrange(epoch):
print "************************************epoch:", i + 1, "************************************"
self.trainNN(1)
trainac = self.directPredict(self.trainFeatures, "train")[1]
print "train accuracy:", trainac
trainAccuracy.append(trainac)
devac = self.directPredict(self.devFeatures, "dev")[1]
print "dev accuracy:", devac
devAccuracy.append(devac)
if initial:
x = [i for i in xrange(epoch + 1)]
pl.plot(x, trainAccuracy, 'r--', x, devAccuracy, 'bs')
pl.show()
def getTestResult(self):
test_results = open('languageIdentification.data/test_solutions', 'r')
self.testResult = []
for line in test_results.readlines():
self.testResult.append(
solution.languages[line.strip().split(" ")[1].upper()])
def setParameters(self, d, yita):
self.d = d
self.yita = yita
self.hidden = d
self.output = 3
self.ai = np.array([1.0] * self.input)
self.ah = np.array([1.0] * (self.hidden + 1))
self.ao = [1.0] * self.output
self.wi = np.random.uniform(size=(self.input, self.hidden))
self.wo = np.random.randn(self.hidden + 1, self.output)
self.ci = np.zeros((self.input, self.hidden))
self.co = np.zeros((self.hidden + 1, self.output))
def resetParameters(self):
self.ai = np.array([1.0] * self.input)
self.ah = np.array([1.0] * (self.hidden + 1))
self.ao = [1.0] * self.output
self.ci = np.zeros((self.input, self.hidden))
self.co = np.zeros((self.hidden + 1, self.output))
def lineProc(self, line, answer, isTraining=True):
text = []
result = []
for ch in line:
self.c.add(ch)
if len(line) < 5:
line += " " * (5 - len(line))
for i in xrange(len(line) - 4):
text.append(list(line[i:i + 5]))
if isTraining:
result.append(self.languages[answer])
if isTraining:
return (text, result)
else:
return text
def FisherYatesShuffle(self, train, result):
l = len(train)
for i in xrange(l - 1, 0, -1):
j = randint(0, i)
train[i], train[j] = train[j], train[i]
result[i], result[j] = result[j], result[i]
#print result
return train[:], result[:]
def feedForward(self, inputs):
self.resetParameters()
for i in range(self.input - 1):
self.ai[i] = inputs[i]
self.ah[:self.hidden] = np.dot(self.ai, self.wi)
self.ah[-1] = 1
self.ah = self.sigmoid(self.ah)
self.ao = np.dot(self.ah, self.wo)
self.ao = self.softMax(self.ao)
return self.ao[:]
def softMax(self, out):
total = sum(np.exp(out))
#for i in xrange(self.output):
out = np.exp(out) * 1.0 / total
return out
def backPropagate(self, result):
# p(L, y) = y - y_hat
d4 = self.ao - np.array(result)
# kronecker delta: P(L, y_hat) = P(L, y) * P(y, y_hat)
#print "before tune:", self.ao, result
d3 = np.array([0.0] * self.output)
for j in xrange(self.output):
for i in xrange(self.output):
if i == j:
d3[j] += d4[i] * self.ao[i] * (1 - self.ao[j])
else:
d3[j] += d4[i] * self.ao[i] * -self.ao[j]
# p(L, ah) = P(L, y) * P(y, y_hat) * p(y_hat, ah)
d2 = np.dot(self.wo, d3)
# p(L, ah_hat) = p(L, y) * P(y, y_hat) * p(y_hat, ah) * P(ah, ah_hat)
d1 = d2 * self.partialDerivativeSigmoid(self.ah)
# p(L, W2) = p(L, y) * p(y, y_hat) * p(y_hat, W2)
D2 = self.yita * np.outer(self.ah, d3)
self.wo -= D2 + self.co
self.co = D2
# p(L, w1) = p(L, y) * P(y, y_hat) * p(y_hat, ah) * P(ah, ah_hat) * P(ah_hat, w1)
D1 = self.yita * np.outer(self.ai, d1[1:])
self.wi -= D1 + self.ci
self.ci = D1
error = 1.0 / 2 * np.dot(d4, d4)
return error
def trainNN(self, epoch=3):
for i in xrange(epoch):
error = 0.0
for j in xrange(len(self.train)):
entry = self.train[j]
res = self.trainResult[j]
self.feedForward(entry)
error += self.backPropagate(res)
print "error:", error
self.resetParameters()
def predict(self, test):
result = Counter()
for entry in test:
r = self.feedForward(entry)
#print r
idx = np.argmax(r) + 1
result[idx] += 1
return result.most_common(1)[0][0]
def partialDerivativeSigmoid(self, out):
return out * 1.0 * (1.0 - out)
def sigmoid(self, x):
#x = np.clip(x, -500, 500)
return 1.0 / (1 + np.exp(-x))
def evaluate(self, predictions, golden):
return accuracy_score(golden, predictions)
def predictAll(self, features):
predict_result = []
for f in features:
f = np.array(f)
feature = self.trainLabels.transform(f.ravel()).reshape(*f.shape)
feature = self.v.transform(feature).toarray()
res = self.predict(feature)
predict_result.append(res)
return predict_result
def testResultOutput(self, testFile, testPrediction):
inverse = {1: "ENGLISH", 3: "FRENCH", 2: "ITALIAN"}
testFile = open(testFileName, 'r')
with open('./languageIdentification.output', 'w') as output:
i = 0
for line in testFile.readlines():
output.write(line.strip() + " " + inverse[testPrediction[i]] +
'\n')
i += 1
def test_label_binarizer():
# one-class case defaults to negative label
# For dense case:
inp = ["pos", "pos", "pos", "pos"]
lb = LabelBinarizer(sparse_output=False)
expected = np.array([[0, 0, 0, 0]]).T
got = lb.fit_transform(inp)
assert_array_equal(lb.classes_, ["pos"])
assert_array_equal(expected, got)
assert_array_equal(lb.inverse_transform(got), inp)
# For sparse case:
lb = LabelBinarizer(sparse_output=True)
got = lb.fit_transform(inp)
assert_true(issparse(got))
assert_array_equal(lb.classes_, ["pos"])
assert_array_equal(expected, got.toarray())
assert_array_equal(lb.inverse_transform(got.toarray()), inp)
lb = LabelBinarizer(sparse_output=False)
# two-class case
inp = ["neg", "pos", "pos", "neg"]
expected = np.array([[0, 1, 1, 0]]).T
got = lb.fit_transform(inp)
assert_array_equal(lb.classes_, ["neg", "pos"])
assert_array_equal(expected, got)
to_invert = np.array([[1, 0],
[0, 1],
[0, 1],
[1, 0]])
assert_array_equal(lb.inverse_transform(to_invert), inp)
# multi-class case
inp = ["spam", "ham", "eggs", "ham", "0"]
expected = np.array([[0, 0, 0, 1],
[0, 0, 1, 0],
[0, 1, 0, 0],
[0, 0, 1, 0],
[1, 0, 0, 0]])
got = lb.fit_transform(inp)
assert_array_equal(lb.classes_, ['0', 'eggs', 'ham', 'spam'])
assert_array_equal(expected, got)
assert_array_equal(lb.inverse_transform(got), inp)
def main():
samples, labels, _ = loader.load_img("radio_img")
#add the fourth dimension (color)
samples = np.expand_dims(samples, axis=4)
print("shape = {}".format(samples.shape))
inputShape = (samples.shape[1], samples.shape[2], samples.shape[3])
print("inputShape = {}".format(inputShape))
#weights
class_weights = class_weight.compute_class_weight('balanced',
np.unique(labels),
labels)
d_class_weights = dict(enumerate(class_weights))
print("weights {}".format(d_class_weights))
#one-hot encoding
lb = LabelBinarizer()
labels = lb.fit_transform(labels)
classesNum = labels.shape[1]
print("Classes: {}".format(classesNum))
#split to training and test
(trainSamples, testSamples, trainLabels,
testLabels) = train_test_split(samples,
labels,
test_size=0.25,
random_state=42)
model = cnn_model(inputShape, classesNum)
## checkpoints
# checkpt1 = ModelCheckpoint(filepath='model.{epoch:02d}-{val_loss:.2f}.h5', save_best_only=True)
# checkpt2 = EarlyStopping(monitor='val_loss', patience=3)
EPOCHS = 20
BATCH = 50
model.fit(
trainSamples,
trainLabels,
batch_size=BATCH,
epochs=EPOCHS,
class_weight=d_class_weights,
verbose=1,
#callbacks = [checkpt1,checkpt2],
validation_data=(testSamples, testLabels))
cnnResults = model.predict(testSamples)
print(
confusion_matrix(testLabels.argmax(axis=1), cnnResults.argmax(axis=1)))
print(
classification_report(testLabels.argmax(axis=1),
cnnResults.argmax(axis=1)))
cnnAcc = accuracy_score(testLabels.argmax(axis=1),
cnnResults.argmax(axis=1))
print("Accuracy CNN: {:.2f}".format(cnnAcc))
print("Cohen's Kappa {:.2f}".format(
cohen_kappa_score(testLabels.argmax(axis=1),
cnnResults.argmax(axis=1))))
input("")
def dbpedia_convgemb(sample=None, n_procs=None):
if not n_procs:
n_procs = cpu_count()
df = get_dbpedia_data(size=sample)
if sample:
test_size = int(
round(np.sum(5000 * df.category.value_counts().values / 45000)))
else:
test_size = 5000 * 14
split = StratifiedShuffleSplit(df.category, test_size=test_size)
train_split, test_split = next(iter(split))
train_df = df.iloc[train_split]
test_df = df.iloc[test_split]
train_docs = DataframeSentences(train_df,
cols=['title', 'abstract'],
flatten=True)
vocab = Dictionary(train_docs)
vocab.filter_extremes(keep_n=5000)
bin = LabelBinarizer()
x_train = np.array(
pad_sentences(
[[vocab.token2id[tok] + 1 for tok in s if tok in vocab.token2id]
for s in train_docs],
max_length=100,
padding_word=0))
y_train = bin.fit_transform(train_df.category.values)
test_docs = DataframeSentences(test_df,
cols=['title', 'abstract'],
flatten=True)
x_test = np.array(
pad_sentences(
[[vocab.token2id[tok] + 1 for tok in s if tok in vocab.token2id]
for s in test_docs],
max_length=100,
padding_word=0))
y_test = bin.transform(test_df.category.values)
emb_weights = load_w2v_weights(vocab)
model = Sequential()
model.add(
Embedding(5001,
300,
input_length=100,
dropout=.2,
weights=[emb_weights],
trainable=False))
model.add(
Convolution1D(nb_filter=50,
filter_length=3,
border_mode='valid',
activation='relu',
subsample_length=1))
model.add(MaxPooling1D(pool_length=model.output_shape[1]))
model.add(Flatten())
model.add(Dense(100, activation='relu'))
model.add(Dropout(.2))
model.add(Dense(14, activation='sigmoid'))
model.compile(loss='binary_crossentropy',
optimizer='adam',
metrics=['accuracy'])
model.fit(x_train, y_train)
print(
accuracy_score(
np.argwhere(y_test)[:, 1], model.predict_classes(x_test)))
def main():
#load data
file = "datasetA_3c.csv"
dataframe = pandas.read_csv(file)
dataset = dataframe.values
samples = dataset[:,1:]
labels = dataset[:,0]
samples = np.array(samples)
labels = np.array(labels)
labels = labels.astype(str)
print("Class distribution:")
print(Counter(labels))
### choose k best attributes
# from sklearn.feature_selection.univariate_selection import SelectKBest
# newSamples = SelectKBest(k=100).fit_transform(samples, labels)
# print(newSamples.shape)
# samples = newSamples
### Calculate weights for unbalanced classes
# from sklearn.utils import class_weight
# d_class_weights = None
# class_weights = class_weight.compute_class_weight('balanced',np.unique(labels),labels)
# print("Class weights:")
# print(class_weights)
# d_class_weights = dict(enumerate(class_weights))
### Normalize samples
# from sklearn.preprocessing.data import normalize
# normalize(samples)
## convert to one-hot encoding
lb = LabelBinarizer()
labels = lb.fit_transform(labels)
classesNum = labels.shape[1]
print ("Classes: {}".format(classesNum))
trainSamples = samples
trainLabels = labels
testSamples = samples
testLabels = labels
### Division into training and test samples
# from sklearn.model_selection._split import train_test_split
# (trainSamples, testSamples, trainLabels, testLabels) = train_test_split(samples, labels, test_size=0.25, random_state=42)
model = Sequential()
model.add(Dense(250, activation='sigmoid'))
model.add(Dense(250, activation='sigmoid'))
model.add(Dense(250, activation='sigmoid'))
model.add(Dense(classesNum, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer="adam",metrics=['accuracy'])
EPOCHS=50
BATCH=50
H = model.fit(trainSamples, trainLabels, batch_size=BATCH, epochs=EPOCHS
#,class_weight=d_class_weights
#,validation_data=(testSamples,testLabels)
#,validation_split=0.1
)
mlpResults = model.predict(testSamples)
print(confusion_matrix(testLabels.argmax(axis=1), mlpResults.argmax(axis=1)))
print(classification_report(testLabels.argmax(axis=1), mlpResults.argmax(axis=1),target_names=lb.classes_))
print("MLP Accuracy: {:.2f}".format(accuracy_score(testLabels.argmax(axis=1), mlpResults.argmax(axis=1))))
print("Cohen's Kappa {:.2f}".format(cohen_kappa_score(testLabels.argmax(axis=1), mlpResults.argmax(axis=1))))
N = np.arange(0, EPOCHS)
plt.style.use("ggplot")
plt.figure()
plt.plot(N, H.history["loss"], label="train_loss")
plt.plot(N, H.history["acc"], label="train_acc")
#plt.plot(N, H.history["val_loss"], label="val_loss")
#plt.plot(N, H.history["val_acc"], label="val_acc")
plt.title("Training Loss and Accuracy")
plt.xlabel("Epoch #")
plt.ylabel("Loss/Accuracy")
plt.legend()
plt.show()
def dbpedia_smallcharconv(sample=None, n_procs=None):
if not n_procs:
n_procs = cpu_count()
df = get_dbpedia_data(size=sample)
if sample:
test_size = int(round(np.sum(5000 * df.category.value_counts().values / 45000)))
else:
test_size = 5000 * 14
logging.info('creating train test split ...')
split = StratifiedShuffleSplit(df.category, test_size=test_size)
train_split, test_split = next(iter(split))
train_df = df.iloc[train_split]
test_df = df.iloc[test_split]
logging.info('preprocessing, padding and binarizing data ...')
train_docs = [[CHAR_MAP.index(c) if c in CHAR_MAP else len(CHAR_MAP) for c in text] for text
in train_df[['title', 'abstract']].apply(lambda cols: u'\n'.join(cols), axis=1).values]
bin = LabelBinarizer()
x_train = np.array(pad_sentences(train_docs, max_length=1014, padding_word=CHAR_MAP.index(' ')))
y_train = bin.fit_transform(train_df.category.values)
test_docs = [[CHAR_MAP.index(c) if c in CHAR_MAP else len(CHAR_MAP) for c in text] for text
in test_df[['title', 'abstract']].apply(lambda cols: u'\n'.join(cols), axis=1).values]
x_test = np.array(pad_sentences(test_docs, max_length=1014, padding_word=0))
y_test = bin.transform(test_df.category.values)
logging.info('building model ...')
model = Sequential()
model.add(Embedding(len(CHAR_MAP) + 1, len(CHAR_MAP) + 1, input_length=1014,
weights=[char_embedding()], trainable=False))
model.add(Convolution1D(nb_filter=256, filter_length=7, border_mode='valid',
activation='relu'))
model.add(MaxPooling1D(pool_length=3))
model.add(Convolution1D(nb_filter=256, filter_length=7, border_mode='valid',
activation='relu', subsample_length=1))
model.add(MaxPooling1D(pool_length=3))
model.add(Convolution1D(nb_filter=256, filter_length=3, border_mode='valid',
activation='relu', subsample_length=1))
model.add(Convolution1D(nb_filter=256, filter_length=3, border_mode='valid',
activation='relu', subsample_length=1))
model.add(Convolution1D(nb_filter=256, filter_length=3, border_mode='valid',
activation='relu', subsample_length=1))
model.add(Convolution1D(nb_filter=256, filter_length=3, border_mode='valid',
activation='relu', subsample_length=1))
model.add(MaxPooling1D(pool_length=3))
model.add(Flatten())
model.add(Dense(1024, activation='relu'))
model.add(Dropout(.5))
model.add(Dense(1024, activation='relu'))
model.add(Dropout(.5))
model.add(Dense(14, activation='sigmoid'))
model.compile(loss='binary_crossentropy',
optimizer='adam',
metrics=['categorical_accuracy'])
print(model.summary())
model.fit(x_train, y_train, batch_size=64, nb_epoch=5, validation_data=[x_test, y_test])
print(accuracy_score(np.argwhere(y_test)[:,1], model.predict_classes(x_test)))
def test_label_binarizer():
# one-class case defaults to negative label
# For dense case:
inp = ["pos", "pos", "pos", "pos"]
lb = LabelBinarizer(sparse_output=False)
expected = np.array([[0, 0, 0, 0]]).T
got = lb.fit_transform(inp)
assert_array_equal(lb.classes_, ["pos"])
assert_array_equal(expected, got)
assert_array_equal(lb.inverse_transform(got), inp)
# For sparse case:
lb = LabelBinarizer(sparse_output=True)
got = lb.fit_transform(inp)
assert issparse(got)
assert_array_equal(lb.classes_, ["pos"])
assert_array_equal(expected, got.toarray())
assert_array_equal(lb.inverse_transform(got.toarray()), inp)
lb = LabelBinarizer(sparse_output=False)
# two-class case
inp = ["neg", "pos", "pos", "neg"]
expected = np.array([[0, 1, 1, 0]]).T
got = lb.fit_transform(inp)
assert_array_equal(lb.classes_, ["neg", "pos"])
assert_array_equal(expected, got)
to_invert = np.array([[1, 0],
[0, 1],
[0, 1],
[1, 0]])
assert_array_equal(lb.inverse_transform(to_invert), inp)
# multi-class case
inp = ["spam", "ham", "eggs", "ham", "0"]
expected = np.array([[0, 0, 0, 1],
[0, 0, 1, 0],
[0, 1, 0, 0],
[0, 0, 1, 0],
[1, 0, 0, 0]])
got = lb.fit_transform(inp)
assert_array_equal(lb.classes_, ['0', 'eggs', 'ham', 'spam'])
assert_array_equal(expected, got)
assert_array_equal(lb.inverse_transform(got), inp)
def prepocess_label(y):
return LabelBinarizer().fit_transform(y)
def dbpedia_smallwordconv(sample=None, n_procs=None):
if not n_procs:
n_procs = cpu_count()
df = get_dbpedia_data(size=sample)
if sample:
test_size = int(round(np.sum(5000 * df.category.value_counts().values / 45000)))
else:
test_size = 5000 * 14
logging.info('creating train test split ...')
split = StratifiedShuffleSplit(df.category, test_size=test_size)
train_split, test_split = next(iter(split))
train_df = df.iloc[train_split]
test_df = df.iloc[test_split]
logging.info('preprocessing, padding and binarizing data ...')
train_docs = DataframeSentences(train_df, cols=['title', 'abstract'], flatten=True)
vocab = Dictionary(train_docs)
vocab.filter_extremes(keep_n=5000)
bin = LabelBinarizer()
x_train = np.array(pad_sentences([[vocab.token2id[tok] + 1 for tok in s if tok in vocab.token2id]
for s in train_docs],
max_length=100, padding_word=0))
y_train = bin.fit_transform(train_df.category.values)
test_docs = DataframeSentences(test_df, cols=['title', 'abstract'], flatten=True)
x_test = np.array(pad_sentences([[vocab.token2id[tok] + 1 for tok in s if tok in vocab.token2id]
for s in test_docs],
max_length=100, padding_word=0))
y_test = bin.transform(test_df.category.values)
logging.info('building model ...')
model = Sequential()
model.add(Embedding(5001, 300, input_length=100))
model.add(Convolution1D(nb_filter=300, filter_length=7, border_mode='valid',
activation='relu', subsample_length=1))
model.add(MaxPooling1D(pool_length=3, stride=1))
model.add(Convolution1D(nb_filter=300, filter_length=7, border_mode='valid',
activation='relu', subsample_length=1))
model.add(MaxPooling1D(pool_length=3, stride=1))
model.add(Convolution1D(nb_filter=300, filter_length=3, border_mode='valid',
activation='relu', subsample_length=1))
model.add(Convolution1D(nb_filter=300, filter_length=3, border_mode='valid',
activation='relu', subsample_length=1))
model.add(Convolution1D(nb_filter=300, filter_length=3, border_mode='valid',
activation='relu', subsample_length=1))
model.add(Convolution1D(nb_filter=300, filter_length=3, border_mode='valid',
activation='relu', subsample_length=1))
model.add(MaxPooling1D(pool_length=3, stride=1))
model.add(Flatten())
model.add(Dense(1024, activation='relu'))
model.add(Dropout(.5))
model.add(Dense(1024, activation='relu'))
model.add(Dropout(.5))
model.add(Dense(14, activation='sigmoid'))
model.compile(loss='binary_crossentropy',
optimizer='adam',
metrics=['categorical_accuracy'])
model.fit(x_train, y_train, batch_size=32, nb_epoch=5, validation_data=[x_test, y_test])
print(accuracy_score(np.argwhere(y_test)[:,1], model.predict_classes(x_test)))
def test_label_binarizer_column_y():
# first for binary classification vs multi-label with 1 possible class
# lists are multi-label, array is multi-class :-/
inp_list = [[1], [2], [1]]
inp_array = np.array(inp_list)
multilabel_indicator = np.array([[1, 0], [0, 1], [1, 0]])
binaryclass_array = np.array([[0], [1], [0]])
lb_1 = LabelBinarizer()
out_1 = lb_1.fit_transform(inp_list)
lb_2 = LabelBinarizer()
out_2 = lb_2.fit_transform(inp_array)
assert_array_equal(out_1, multilabel_indicator)
assert_true(assert_warns(DeprecationWarning, getattr, lb_1, "multilabel_"))
assert_false(assert_warns(DeprecationWarning, getattr, lb_1,
"indicator_matrix_"))
assert_array_equal(out_2, binaryclass_array)
assert_false(assert_warns(DeprecationWarning, getattr, lb_2,
"multilabel_"))
# second for multiclass classification vs multi-label with multiple
# classes
inp_list = [[1], [2], [1], [3]]
inp_array = np.array(inp_list)
# the indicator matrix output is the same in this case
indicator = np.array([[1, 0, 0], [0, 1, 0], [1, 0, 0], [0, 0, 1]])
lb_1 = LabelBinarizer()
out_1 = lb_1.fit_transform(inp_list)
lb_2 = LabelBinarizer()
out_2 = lb_2.fit_transform(inp_array)
assert_array_equal(out_1, out_2)
assert_true(assert_warns(DeprecationWarning, getattr, lb_1, "multilabel_"))
assert_array_equal(out_2, indicator)
assert_false(assert_warns(DeprecationWarning, getattr, lb_2,
"multilabel_"))