def predict(d):
if len(d) == 1:
# This is to make sure the progress bar of SHAP display properly:
# 1. The newline makes the progress bar string captured in pipe
# 2. The ASCII control code moves cursor up twice for alignment
print("\033[A" * 2)
def input_fn():
return tf.data.Dataset.from_tensor_slices(
dict(pd.DataFrame(d,
columns=shap_dataset.columns))).batch(1000)
if isinstance(estimator, tf.keras.Model):
return np.array(estimator.predict(input_fn()))
if plot_type == 'bar':
predictions = [
p['logits'] if 'logits' in p else p['predictions']
for p in estimator.predict(input_fn)
]
else:
predictions = [
p['logits'][-1] if 'logits' in p else p['predictions'][-1]
for p in estimator.predict(input_fn)
]
return np.array(predictions)
def _input_fn():
dataset = input_fn("",
datasource,
feature_column_names,
feature_metas,
label_meta,
is_pai=True,
pai_table=pai_table)
return dataset.batch(1).cache()
def _input_fn():
dataset = input_fn(select, datasource, feature_column_names,
feature_metas, label_meta)
return dataset.batch(1).cache()
