65 lines
2.5 KiB
Python
65 lines
2.5 KiB
Python
# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Utility methods for working with the Experiment Data Access API."""
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import numpy as np
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def pivot_dataframe(dataframe):
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"""Gets a pivoted wide-form pandas dataframe.
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The wide-form DataFrame has all its tags included as columns of the
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DataFrame, which is more convenient to work. If the condition of having
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uniform sets of step values across all tags in all runs is not met,
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this will error.
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Args:
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dataframe: pandas dataframe to pivot.
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Returns:
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Pivoted wide-form pandas dataframe.
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Raises:
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ValueError if step values across all tags are not uniform.
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"""
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num_missing_0 = np.count_nonzero(dataframe.isnull().values)
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dataframe = dataframe.pivot_table(
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values=(
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["value", "wall_time"]
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if "wall_time" in dataframe.columns
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else "value"
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),
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index=["run", "step"],
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columns="tag",
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dropna=False,
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)
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num_missing_1 = np.count_nonzero(dataframe.isnull().values)
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if num_missing_1 > num_missing_0:
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raise ValueError(
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"pivoted DataFrame contains missing value(s). "
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"This is likely due to two timeseries having different "
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"sets of steps in your experiment. "
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"You can avoid this error by calling `get_scalars()` with "
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"`pivot=False` to disable the DataFrame pivoting."
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)
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# `reset_index()` removes the MultiIndex structure of the pivoted
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# DataFrame. Before the call, the DataFrame consits of two levels
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# of index: "run" and "step". After the call, the index become a
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# single range index (e.g,. `dataframe[:2]` works).
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dataframe = dataframe.reset_index()
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# Remove the columns name "tag".
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dataframe.columns.name = None
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dataframe.columns.names = [None for name in dataframe.columns.names]
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return dataframe
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