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MNT remove deprecation warning in keras example #775

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Nov 3, 2020
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MNT remove deprecation warning in keras example #775

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MNT update keras examples
glemaitre Nov 3, 2020
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glemaitre Nov 3, 2020
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14 changes: 9 additions & 5 deletions examples/applications/porto_seguro_keras_under_sampling.py
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Original file line number Diff line number Diff line change
Expand Up @@ -89,9 +89,13 @@ def convert_float64(X):
###############################################################################
# Create a neural-network
###############################################################################

from keras.models import Sequential
from keras.layers import Activation, Dense, Dropout, BatchNormalization
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import (
Activation,
Dense,
Dropout,
BatchNormalization,
)


def make_model(n_features):
Expand Down Expand Up @@ -169,8 +173,8 @@ def fit_predict_balanced_model(X_train, y_train, X_test, y_test):
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)
model.fit(training_generator, epochs=5, verbose=1)
y_pred = model.predict(X_test, batch_size=1000)
return roc_auc_score(y_test, y_pred)


Expand Down
36 changes: 20 additions & 16 deletions imblearn/keras/_generator.py
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Original file line number Diff line number Diff line change
Expand Up @@ -112,19 +112,21 @@ class BalancedBatchGenerator(*ParentClass):
>>> class_dict = dict()
>>> class_dict[0] = 30; class_dict[1] = 50; class_dict[2] = 40
>>> X, y = make_imbalance(iris.data, iris.target, class_dict)
>>> import keras
>>> y = keras.utils.to_categorical(y, 3)
>>> model = keras.models.Sequential()
>>> model.add(keras.layers.Dense(y.shape[1], input_dim=X.shape[1],
... activation='softmax'))
>>> import tensorflow
>>> y = tensorflow.keras.utils.to_categorical(y, 3)
>>> model = tensorflow.keras.models.Sequential()
>>> model.add(
... tensorflow.keras.layers.Dense(
... y.shape[1], input_dim=X.shape[1], activation='softmax'
... )
... )
>>> model.compile(optimizer='sgd', loss='categorical_crossentropy',
... metrics=['accuracy'])
>>> from imblearn.keras import BalancedBatchGenerator
>>> from imblearn.under_sampling import NearMiss
>>> training_generator = BalancedBatchGenerator(
... X, y, sampler=NearMiss(), batch_size=10, random_state=42)
>>> callback_history = model.fit_generator(generator=training_generator,
... epochs=10, verbose=0)
>>> callback_history = model.fit(training_generator, epochs=10, verbose=0)
"""

# flag for keras sequence duck-typing
Expand Down Expand Up @@ -264,21 +266,23 @@ def balanced_batch_generator(
>>> class_dict[0] = 30; class_dict[1] = 50; class_dict[2] = 40
>>> from imblearn.datasets import make_imbalance
>>> X, y = make_imbalance(X, y, class_dict)
>>> import keras
>>> y = keras.utils.to_categorical(y, 3)
>>> model = keras.models.Sequential()
>>> model.add(keras.layers.Dense(y.shape[1], input_dim=X.shape[1],
... activation='softmax'))
>>> import tensorflow
>>> y = tensorflow.keras.utils.to_categorical(y, 3)
>>> model = tensorflow.keras.models.Sequential()
>>> model.add(
... tensorflow.keras.layers.Dense(
... y.shape[1], input_dim=X.shape[1], activation='softmax'
... )
... )
>>> model.compile(optimizer='sgd', loss='categorical_crossentropy',
... metrics=['accuracy'])
>>> from imblearn.keras import balanced_batch_generator
>>> from imblearn.under_sampling import NearMiss
>>> training_generator, steps_per_epoch = balanced_batch_generator(
... X, y, sampler=NearMiss(), batch_size=10, random_state=42)
>>> callback_history = model.fit_generator(generator=training_generator,
... steps_per_epoch=steps_per_epoch,
... epochs=10, verbose=0)

>>> callback_history = model.fit(training_generator,
... steps_per_epoch=steps_per_epoch,
... epochs=10, verbose=0)
"""

return tf_bbg(
Expand Down