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_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_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)
# Sequence of seq type should raise ValueError
y_seq_of_seqs = [[], [1, 2], [3], [0, 1, 3], [2]]
assert_raises(ValueError, LabelBinarizer().fit_transform, y_seq_of_seqs)
# 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 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(lb_1.multilabel_)
assert_array_equal(out_2, binaryclass_array)
assert_false(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(lb_1.multilabel_)
assert_array_equal(out_2, indicator)
assert_false(lb_2.multilabel_)
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(assert_warns(DeprecationWarning, getattr, 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, [])
y = np.array([[0, 1, 0], [1, 1, 1]])
classes = np.arange(3)
assert_raises(ValueError, label_binarize, y, classes, multilabel=True,
neg_label=2, pos_label=1)
assert_raises(ValueError, label_binarize, y, classes, multilabel=True,
neg_label=2, pos_label=2)
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)
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 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)]
with pytest.raises(ValueError):
lb.transform(multi_label)
lb = LabelBinarizer()
with pytest.raises(ValueError):
lb.transform([])
with pytest.raises(ValueError):
lb.inverse_transform([])
with pytest.raises(ValueError):
LabelBinarizer(neg_label=2, pos_label=1)
with pytest.raises(ValueError):
LabelBinarizer(neg_label=2, pos_label=2)
with pytest.raises(ValueError):
LabelBinarizer(neg_label=1, pos_label=2, sparse_output=True)
# Fail on y_type
with pytest.raises(ValueError):
_inverse_binarize_thresholding(y=csr_matrix([[1, 2], [2, 1]]),
output_type="foo",
classes=[1, 2],
threshold=0)
# Sequence of seq type should raise ValueError
y_seq_of_seqs = [[], [1, 2], [3], [0, 1, 3], [2]]
with pytest.raises(ValueError):
LabelBinarizer().fit_transform(y_seq_of_seqs)
# Fail on the number of classes
with pytest.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'
with pytest.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
with pytest.raises(ValueError):
LabelBinarizer().fit(np.array([[1, 3], [2, 1]]))
with pytest.raises(ValueError):
label_binarize(np.array([[1, 3], [2, 1]]), [1, 2, 3])
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 __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_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 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 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 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 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 _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 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 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)))
# 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 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 prepocess_label(y):
return LabelBinarizer().fit_transform(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("")
def __init__(self):
self.__encoder = LabelBinarizer(sparse_output=False)
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 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_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 __init__(self, *args, **kwargs):
self.encoder = LabelBinarizer(*args, **kwargs)
