DiCE exaple
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# seeding random numbers for reproducability | |
from numpy.random import seed | |
seed(1) | |
tf.random.set_seed(2) | |
sess = tf.compat.v1.InteractiveSession() | |
d = dice_ml.Data(dataframe=df, continuous_features=['age', 'fare'], outcome_name='survived') | |
train, _ = d.split_data(d.normalize_data(d.one_hot_encoded_data)) | |
X_train = train.loc[:, train.columns != 'survived'] | |
y_train = train.loc[:, train.columns == 'survived'] | |
X_train.head() | |
ann_model = keras.Sequential() | |
ann_model.add(keras.layers.Dense(20, input_shape=(X_train.shape[1],), | |
kernel_regularizer=keras.regularizers.l1(0.001), | |
activation=tf.nn.relu)) | |
ann_model.add(keras.layers.Dense(1, activation=tf.nn.sigmoid)) | |
ann_model.compile(loss='binary_crossentropy', | |
optimizer=tf.keras.optimizers.Adam(0.01), | |
metrics=['accuracy']) | |
ann_model.fit(X_train, y_train, validation_split=0.20, epochs=100, verbose=0, class_weight={0:1,1:2}) | |
# provide the trained ML model to DiCE's model object | |
backend = 'TF'+tf.__version__[0] | |
m = dice_ml.Model(model=ann_model, backend=backend) |
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