def run(i):
msg = "CF_FAKE.run(%s): OK." % i
print("python: " + msg)
#sys.stdout.flush()
model_nt3 = tf.keras.models.load_model('/gpfs/alpine/med106/scratch/jain/Scratch/explainers/nt3.autosave.model')
with open('/gpfs/alpine/med106/scratch/jain/Scratch/explainers/nt3.autosave.data.pkl', 'rb') as pickle_file:
X_train,Y_train,X_test,Y_test = pickle.load(pickle_file)
print ("opened files")
shape_cf = (1,) + X_train.shape[1:]
print(shape_cf)
target_proba = 0.9
tol = 0.1 # want counterfactuals with p(class)>0.90
target_class = 'other' # any class other than will do
max_iter = 1000
lam_init = 1e-1
max_lam_steps = 20
learning_rate_init = 0.1
feature_range = (0,1)
cf = CounterFactual(model_nt3, shape=shape_cf, target_proba=target_proba, tol=tol,
target_class=target_class, max_iter=max_iter, lam_init=lam_init,
max_lam_steps=max_lam_steps, learning_rate_init=learning_rate_init,
feature_range=feature_range)
bunch = 10
results=[]
for j in range(bunch*(i), bunch*(i+1)):
shape = X_train[0].shape[0]
X = np.concatenate([X_train,X_test])
x_sample=X[j+1:j+2]
print(x_sample.shape)
start = time()
explanation = cf.explain(x_sample)
#print('Counterfactual prediction: {}, {}'.format(explanation.cf['class'], explanation.cf['proba']))
#print("Actual prediction: {}".format(model_nt3.predict(x_sample)))
results.append([explanation.cf['X'],explanation.cf['class'], explanation.cf['proba']])
print("DONE sample=", j)
filename = "save.p" + str(i)
pickle.dump(results, open(filename, "wb"))
results=[]
return msg
def setup_cf(args):
(x_train, y_train), (x_test, y_test) = mnist_data()
model = load_model(args.model)
X = x_test[0].reshape((1, ) + x_test[0].shape)
shape = X.shape
target_proba = 1.0
tol = 0.01 # want counterfactuals with p(class)>0.99
target_class = 'other' # any class other than 7 will do
max_iter = 1000
lam_init = 1e-1
max_lam_steps = 10
learning_rate_init = 0.1
feature_range = (x_train.min(), x_train.max())
cf = CounterFactual(model,
shape=shape,
target_proba=target_proba,
tol=tol,
target_class=target_class,
max_iter=max_iter,
lam_init=lam_init,
max_lam_steps=max_lam_steps,
learning_rate_init=learning_rate_init,
feature_range=feature_range)
return cf, X
def keras_mnist_cf_explainer(request, models):
cf_explainer = CounterFactual(predict_fn=models[0], shape=(1, 28, 28, 1),
target_class=request.param, lam_init=1e-1, max_iter=1000,
max_lam_steps=10)
yield models[0], cf_explainer
keras.backend.clear_session()
tf.keras.backend.clear_session()
def keras_mnist_cf_explainer(request, keras_logistic_mnist):
X, y, model = keras_logistic_mnist
cf_explainer = CounterFactual(predict_fn=model,
shape=(1, 784),
target_class=request.param,
lam_init=1e-1,
max_iter=1000,
max_lam_steps=10)
return X, y, model, cf_explainer
def cf_iris_explainer(request, logistic_iris):
X, y, lr = logistic_iris
predict_fn = lr.predict_proba
cf_explainer = CounterFactual(predict_fn=predict_fn, shape=(1, 4),
target_class=request.param, lam_init=1e-1, max_iter=1000,
max_lam_steps=10)
yield X, y, lr, cf_explainer
keras.backend.clear_session()
tf.keras.backend.clear_session()
def cf_iris_explainer(request, logistic_iris):
X, y, lr = logistic_iris
predict_fn = lr.predict_proba
cf_explainer = CounterFactual(predict_fn=predict_fn,
shape=(1, 4),
target_class=request.param,
lam_init=1e-1,
max_iter=1000,
max_lam_steps=10)
return X, y, lr, cf_explainer
def keras_mnist_cf_explainer(request, keras_logistic_mnist):
X, y, model = keras_logistic_mnist
cf_explainer = CounterFactual(predict_fn=model,
shape=(1, 784),
target_class=request.param,
lam_init=1e-1,
max_iter=1000,
max_lam_steps=10)
yield X, y, model, cf_explainer
keras.backend.clear_session()
tf.keras.backend.clear_session()
def tf_keras_mnist_explainer(request, tf_keras_logistic_mnist):
X, y, model = tf_keras_logistic_mnist
sess = K.get_session()
cf_explainer = CounterFactual(sess=sess,
predict_fn=model,
shape=(1, 784),
target_class=request.param,
lam_init=1e-1,
max_iter=1000,
max_lam_steps=10)
yield X, y, model, cf_explainer
def iris_explainer(request, logistic_iris):
X, y, lr = logistic_iris
predict_fn = lr.predict_proba
sess = tf.Session()
cf_explainer = CounterFactual(sess=sess,
predict_fn=predict_fn,
shape=(1, 4),
target_class=request.param,
lam_init=1e-1,
max_iter=1000,
max_lam_steps=10)
yield X, y, lr, cf_explainer
tf.reset_default_graph()
sess.close()
def __init__(
self,
model,
shape,
distance_fn='l1',
target_proba=1.0,
target_class='other',
max_iter=1000,
early_stop=50,
lam_init=1e-1,
max_lam_steps=10,
tol=0.05,
learning_rate_init=0.1,
feature_range=(-1e10, 1e10),
eps=0.01, # feature-wise epsilons
init='identity',
decay=True,
write_dir=None,
debug=False,
sess=None
):
"""Constructor.
References:
Counterfactual explanation implementation, parameter docstrings and defaults adapted from:
https://github.com/SeldonIO/alibi/blob/14804f07457da881a5f70ccff2dcbfed2378b860/alibi/explainers/counterfactual.py#L85 # noqa
The major difference in the constructor signature is that model and
ae_model shoud be instances of depictions BaseModel
instead of type Union[Callable, tf.keras.Model, 'keras.Model']
Args:
model (BaseModel): Instance implementing predict method returning
class probabilities that is passed to the explainer
implementation.
shape (tuple): Shape of input data starting with batch size.
distance_fn (str, optional): Distance function to use in the loss term. Defaults to 'l1'.
target_proba (float, optional): Target probability for the counterfactual to reach. Defaults to 1.0.
target_class (Union[str, int], optional): Target class for the counterfactual to reach, one of 'other',
'same' or an integer denoting desired class membership for the counterfactual instance. Defaults to 'other'.
max_iter (int, optional): Maximum number of interations to run the gradient descent for (inner loop).
Defaults to 1000.
early_stop (int, optional): Number of steps after which to terminate gradient descent if all or none of found
instances are solutions. Defaults to 50.
lam_init (float, optional): Initial regularization constant for the prediction part of the Wachter loss.
Defaults to 1e-1.
max_lam_steps (int, optional): Maximum number of times to adjust the regularization constant (outer loop)
before terminating the search. Defaults to 10.
tol (float, optional): Tolerance for the counterfactual target probability. Defaults to 0.05.
learning_rate_init (float, optional): Initial learning rate for each outer loop of lambda. Defaults to 0.1.
feature_range (Union[Tuple, str], optional): Tuple with min and max ranges to allow for perturbed instances.
Min and max ranges can be floats or numpy arrays with dimension (1 x nb of features)
for feature-wise ranges. Defaults to (-1e10, 1e10).
eps (Union[float, np.ndarray], optional): Gradient step sizes used in calculating numerical gradients,
defaults to a single value for all features, but can be passed an array for
feature-wise step sizes. Defaults to 0.01.
init (str): Initialization method for the search of counterfactuals, currently must be 'identity'.
decay (bool, optional): Flag to decay learning rate to zero for each outer loop over lambda.
Defaults to True.
write_dir (str, optional): Directory to write Tensorboard files to. Defaults to None.
debug (bool, optional): Flag to write Tensorboard summaries for debugging. Defaults to False.
sess (tf.compat.v1.Session, optional): Optional Tensorflow session that will be used if passed
instead of creating or inferring one internally. Defaults to None.
"""
super().__init__(model)
self.explainer = CounterFactual(
model.predict, shape, distance_fn, target_proba, target_class,
max_iter, early_stop, lam_init, max_lam_steps, tol,
learning_rate_init, feature_range, eps, init, decay, write_dir,
debug, sess
)
shape_cf = (1, ) + X_train.shape[1:]
print(shape_cf)
target_proba = 0.9
tol = 0.1 # want counterfactuals with p(class)>0.90
target_class = 'other' # any class other than will do
max_iter = 1000
lam_init = 1e-1
max_lam_steps = 20
learning_rate_init = 0.1
feature_range = (0, 1)
cf = CounterFactual(model_nt3,
shape=shape_cf,
target_proba=target_proba,
tol=tol,
target_class=target_class,
max_iter=max_iter,
lam_init=lam_init,
max_lam_steps=max_lam_steps,
learning_rate_init=learning_rate_init,
feature_range=feature_range)
shape = X_train[0].shape[0]
results = []
X = np.concatenate([X_train, X_test])
print(X_train.shape[0], X_test.shape[0], X.shape[0], "-x shape 0")
for i in np.arange(0, X.shape[0]):
x_sample = X[i:i + 1]
print(x_sample.shape)
start = time()
explanation = cf.explain(x_sample)
