def test_balanced_batch_generator_class_sparse(keep_sparse):
training_generator = BalancedBatchGenerator(sparse.csr_matrix(X), y,
batch_size=10,
keep_sparse=keep_sparse,
random_state=42)
for idx in range(len(training_generator)):
X_batch, y_batch = training_generator.__getitem__(idx)
if keep_sparse:
assert sparse.issparse(X_batch)
else:
assert not sparse.issparse(X_batch)
def test_balanced_batch_generator_class_sparse(is_sparse):
training_generator = BalancedBatchGenerator(sparse.csr_matrix(X), y,
batch_size=10,
sparse=is_sparse,
random_state=42)
for idx in range(len(training_generator)):
X_batch, y_batch = training_generator.__getitem__(idx)
if is_sparse:
assert sparse.issparse(X_batch)
else:
assert not sparse.issparse(X_batch)
def __init__(self, x, y, datagen, batch_size=32):
self.datagen = datagen
self.batch_size = batch_size
self._shape = x.shape
datagen.fit(x)
self.gen, self.steps_per_epoch, *rest = BalancedBatchGenerator(
x.reshape(x.shape[0], -1),
y,
sampler=RandomOverSampler(),
batch_size=self.batch_size,
keep_sparse=True)
def test_balanced_batch_generator_class_sparse(data, keep_sparse):
X, y = data
training_generator = BalancedBatchGenerator(
sparse.csr_matrix(X),
y,
batch_size=10,
keep_sparse=keep_sparse,
random_state=42,
)
for idx in range(len(training_generator)):
X_batch, _ = training_generator.__getitem__(idx)
if keep_sparse:
assert sparse.issparse(X_batch)
else:
assert not sparse.issparse(X_batch)
def fit_with_undersampling(self, train_data, train_labels, valid_data,
valid_labels):
train_data = self.encode_sequences(train_data)
valid_data = self.encode_sequences(valid_data)
print('encode:', train_data.shape)
training_generator = BalancedBatchGenerator(train_data,
train_labels,
batch_size=self.batch_size,
random_state=42)
early_stopping = EarlyStopping(monitor='val_acc',
patience=50,
verbose=1,
mode='max')
check_point = ModelCheckpoint(self.best_model_path,
monitor='val_acc',
mode='max',
verbose=1,
save_best_only=True)
callbacks_list = [check_point, early_stopping]
self.model.fit_generator(generator=training_generator,
epochs=self.max_epoches,
verbose=2,
callbacks=callbacks_list,
validation_data=(valid_data, valid_labels))
def fit_predict_balanced_model(X_train, y_train, X_test, y_test):
model = make_model(X_train.shape[1])
training_generator = BalancedBatchGenerator(X_train, y_train,
batch_size=1000,
random_state=42)
model.fit_generator(generator=training_generator, epochs=5, verbose=1)
y_pred = model.predict_proba(X_test, batch_size=1000)
return roc_auc_score(y_test, y_pred)
def train(model, X, y, X_val, y_val, checkpoint, num_epochs=20):
sgd = SGD(lr=0.01)
model.compile(optimizer=sgd,
loss='mean_squared_error',
metrics=[accuracy])
training_generator = BalancedBatchGenerator(X, y, sampler=RandomOverSampler(), batch_size=64, random_state=42)
model.fit_generator(generator=training_generator, epochs=num_epochs, validation_data=(X_val,y_val))
model.save_weights(checkpoint)
def test_balanced_batch_generator_class(sampler, sample_weight):
model = _build_keras_model(y.shape[1], X.shape[1])
training_generator = BalancedBatchGenerator(X, y,
sample_weight=sample_weight,
sampler=sampler,
batch_size=10,
random_state=42)
model.fit_generator(generator=training_generator,
epochs=10)
def fit_predict_balanced_model(X_train, y_train, X_test, y_test):
model = make_model(X_train.shape[1])
training_generator = BalancedBatchGenerator(X_train,
y_train,
batch_size=1000,
random_state=42)
model.fit_generator(generator=training_generator, epochs=200, verbose=0)
y_pred = model.predict(X_test, batch_size=1000)
y_pred = tf.argmax(y_pred, axis=-1).numpy()
y_test = tf.argmax(y_test, axis=-1).numpy()
return balanced_accuracy_score(y_test, y_pred)
def create_model(x_train_sequence, y_train, x_test_sequence, y_test):
verbose = 1
max_sequence_length = 110
vocab_size = 3000
embedding_dim = {{choice([32, 64, 128, 256, 512])}}
lstm = {{choice([32, 64, 128, 256, 512])}}
num_epochs = {{choice([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])}}
dropout = {{uniform(0, 1)}}
recurrent_dropout = {{uniform(0, 1)}}
alpha = {{uniform(0, 3)}}
batch_size = {{choice([32, 64, 128, 256])}}
model = Sequential()
model.add(
Embedding(vocab_size,
embedding_dim,
input_length=max_sequence_length,
mask_zero=True))
model.add(
LSTM(lstm, recurrent_dropout=recurrent_dropout,
return_sequences=False))
model.add(ELU(alpha=alpha))
model.add(Dropout(dropout))
model.add(Dense(2, activation='softmax'))
model.compile(loss='binary_crossentropy',
optimizer='adam',
metrics=["binary_accuracy"])
model.summary()
# Fit the model and evaluate
#result = model.fit(x_train_sequence, y_train, batch_size=batch_size,
# validation_data=(x_test_sequence, y_test), verbose=verbose, shuffle=True, epochs=num_epochs)
generator = BalancedBatchGenerator(x_train_sequence,
y_train,
sampler=NearMiss(),
batch_size=batch_size,
random_state=1)
result = model.fit_generator(generator=generator,
epochs=num_epochs,
verbose=verbose)
#get the highest validation accuracy of the training epochs
validation_acc = np.amax(result.history["binary_accuracy"])
print('Best validation acc of epoch:', validation_acc)
print('Embedding_dim: ', embedding_dim)
print('Number of neurons: ', lstm)
print('Epochs: ', num_epochs)
print('Dropout: ', dropout)
print('Recurrent Dropout: ', recurrent_dropout)
print('Batch Size: ', batch_size)
#print('Alpha: ', alpha)
return {'loss': -validation_acc, 'status': STATUS_OK, 'model': model}
class BalancedDataGenerator(Sequence):
"""ImageDataGenerator + RandomOversampling"""
def __init__(self, x, y, datagen, batch_size=32):
self.datagen = datagen
self.batch_size = batch_size
self._shape = x.shape
datagen.fit(x)
self.gen, self.steps_per_epoch, *rest = BalancedBatchGenerator(
x.reshape(x.shape[0], -1),
y,
sampler=RandomOverSampler(),
batch_size=self.batch_size,
keep_sparse=True)
def __len__(self):
return self._shape[0] // self.batch_size
def __getitem__(self, idx):
x_batch, y_batch = self.gen.__next__()
x_batch = x_batch.reshape(-1, *self._shape[1:])
return self.datagen.flow(x_batch, y_batch,
batch_size=self.batch_size).next()
#dropout?
model.add(Dense(128, activation='relu'))
model.add(Dense(y_count, activation='softmax'))
# compile the keras model
sgd = optimizers.adam(lr=0.02)
model.compile(loss='binary_crossentropy',
optimizer='adam',
metrics=['accuracy'])
# fit the keras model on the dataset
x_train, x_test = X[train_index], X[test_index]
y_train, y_test = y[train_index], y[test_index]
training_generator = BalancedBatchGenerator(X,
y,
sampler=NearMiss(),
batch_size=8,
random_state=42)
model.fit_generator(generator=training_generator, epochs=32, verbose=1)
cvscores.append(model.evaluate(x_test, y_test))
print('Model evaluation ', cvscores[-1])
print('\n')
cfm = confusion_matrix(np.argmax(y_test, axis=1),
model.predict_classes(x_test),
labels=[i for i in range(y_count)])
cfm = pd.DataFrame(cfm, col, col)
print(cfm)
print('\n')
print('mean accuracy is at: %s' % np.mean(list(zip(*cvscores))[1]))
def test_balanced_batch_generator_class_no_return_indices(data):
with pytest.raises(ValueError, match="needs to have an attribute"):
BalancedBatchGenerator(*data,
sampler=ClusterCentroids(),
batch_size=10)
def test_balanced_batch_generator_class_no_return_indices():
with pytest.raises(ValueError, match='needs to return the indices'):
BalancedBatchGenerator(X, y, sampler=ClusterCentroids(), batch_size=10)
create_dir('log')
log_fname = 'log/balanced_log.csv'
if not os.path.exists(log_fname):
with open(log_fname, 'w+') as f:
writer = csv.writer(f)
writer.writerow(['model_name', 'loss'])
# upsample minority classes ######################################
model_name = 'upsample_random'
create_dir(f'log/{model_name}')
sampler = RandomOverSampler(random_state=42)
training_generator = BalancedBatchGenerator(X_train,
y_train_onehot,
sampler=sampler,
batch_size=batchsize,
random_state=42)
train(training_generator, X_test, y_test, X_val, y_val, model_name,
batchsize, log_fname)
# downsample majority classes #####################################
model_name = 'downsample-random'
create_dir(f'log/{model_name}')
sampler = RandomUnderSampler(random_state=42)
training_generator = BalancedBatchGenerator(X_train,
y_train_onehot,
sampler=sampler,
batch_size=batchsize,
random_state=42)
train(training_generator, X_test, y_test, X_val, y_val, model_name,
# input layer
model = Sequential()
model.add(Dense(output_dim=256, input_dim=83, activation="relu"))
# model.add(Dense(output_dim = 32, activation = "relu",kernel_regularizer=regularizers.l2(0.01)))
model.add(Dense(output_dim=256, activation="relu"))
model.add(Dense(output_dim=256, activation="relu"))
model.add(Dense(output_dim=1, activation="sigmoid"))
model.compile(loss='binary_crossentropy', optimizer='sgd', metrics=['acc'])
print(model.summary())
# # fit the keras model on the dataset
from imblearn.keras import BalancedBatchGenerator
training_generator = BalancedBatchGenerator(
X_train, y_train, sampler=RandomUnderSampler(), batch_size=16, random_state=0)
# model.fit(X_train, y_train, epochs=10, batch_size=512, verbose=2)
callback_history = model.fit_generator(generator=training_generator,
epochs=15, verbose=2, validation_data=(X_test, y_test))
# # evaluate the keras model
# _, accuracy = model.evaluate(X, y)
# print('Accuracy: %.2f' % (accuracy * 100))
# # DT
# DT = tree.DecisionTreeClassifier()
# dataDT = DT.fit(X_train, y_train)
# print('finish modeling')
# predict
# test_y_predicted = dataDT.predict(X_test)
