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Fix runtime validation bypass by removing asserts in metrics #13

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Fix runtime validation bypass by removing asserts in metrics #13

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12 changes: 8 additions & 4 deletions twml/twml/contrib/metrics/metrics.py
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Original file line number Diff line number Diff line change
Expand Up @@ -31,7 +31,8 @@ def get_eval_metric_ops(graph_output, labels, weights):
else:
predcol_list=list(predcols)
for col in predcol_list:
assert 0 <= col < graph_output['output'].shape[class_dim], 'Invalid Prediction Column Index !'
if not (0 <= col < graph_output['output'].shape[class_dim]):
raise ValueError('Invalid Prediction Column Index !')
preds = tf.gather(graph_output['output'], indices=predcol_list, axis=class_dim) # [batchSz, num_col]
labels = tf.gather(labels, indices=predcol_list, axis=class_dim) # [batchSz, num_col]

Expand All @@ -55,7 +56,8 @@ def mean_numeric_label_topK(labels, predictions, weights, name, topK_id):
return tf.metrics.mean(values=top_k_labels, name=name)

def mean_gated_numeric_label_topK(labels, predictions, weights, name, topK_id, bar=2.0):
assert isinstance(bar, int) or isinstance(bar, float), "bar must be int or float"
if not (isinstance(bar, int) or isinstance(bar, float)):
raise TypeError("bar must be int or float")
top_k_labels = tf.gather(params=labels, indices=topK_id, axis=0) # [topK, 1]
gated_top_k_labels = tf.cast(top_k_labels > bar*1.0, tf.int32)
return tf.metrics.mean(values=gated_top_k_labels, name=name)
Expand Down Expand Up @@ -108,8 +110,10 @@ def get_eval_metric_ops(graph_output, labels, weights):
if predcol is None:
pred = graph_output['output']
else:
assert 0 <= predcol < graph_output['output'].shape[1], 'Invalid Prediction Column Index !'
assert labelcol is not None
if not (0 <= predcol < graph_output['output'].shape[1]):
raise ValueError('Invalid Prediction Column Index !')
if labelcol is None:
raise ValueError('labelcol must be provided when predcol is set')
pred = tf.reshape(graph_output['output'][:, predcol], shape=[-1, 1])
labels = tf.reshape(labels[:, labelcol], shape=[-1, 1])
numOut = graph_output['output'].shape[1]
Expand Down
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