fix ddpg metrics
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a9f1556184
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@ -152,28 +152,22 @@ def clean_metric_data(metric_matrix, metric_labels, session):
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metric_cat = [session.target_objective]
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for metric_obj in metric_objs:
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metric_cat.append(metric_obj.name)
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matrix = np.array(metric_matrix)
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missing_columns = sorted(set(metric_cat) - set(metric_labels))
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unused_columns = set(metric_labels) - set(metric_cat)
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LOG.debug("clean_metric_data added %d metrics and removed %d metric.", len(missing_columns),
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len(unused_columns))
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# If columns are missing from the matrix
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if missing_columns:
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for metric in missing_columns:
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# append a missing column after the last column
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index = matrix.shape[1] # pylint: disable=unsubscriptable-object
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default_val = 0
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matrix = np.insert(matrix, index, default_val, axis=1)
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metric_labels.append(metric)
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metric_cat_size = len(metric_cat)
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matrix = np.ones((len(metric_matrix), metric_cat_size)) * default_val
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metric_labels_dict = {n: i for i, n in enumerate(metric_labels)}
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# column labels in matrix has the same order as ones in metric catalog
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# missing values are filled with default_val
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for i, metric_name in enumerate(metric_cat):
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if metric_name in metric_labels_dict:
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index = metric_labels_dict[metric_name]
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matrix[:, i] = metric_matrix[:, index]
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LOG.debug(matrix.shape)
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# If they are useless columns in the matrix
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if unused_columns:
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indexes = [i for i, n in enumerate(metric_labels) if n in unused_columns]
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# Delete unused columns
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matrix = np.delete(matrix, indexes, 1)
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for i in sorted(indexes, reverse=True):
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del metric_labels[i]
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return matrix, metric_labels
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return matrix, metric_cat
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def save_execution_time(start_ts, fn, result):
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