mirror of
https://github.com/microsoft/autogen.git
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98 lines
3.5 KiB
Python
98 lines
3.5 KiB
Python
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import numpy as np
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import pandas as pd
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from sklearn.preprocessing import RobustScaler
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from sklearn.metrics import pairwise_distances
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def _augment(row):
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max, avg, id = row.max(), row.mean(), row.index[0]
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return row.apply(lambda x: (x, max, avg, id))
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def construct_portfolio(regret_matrix, meta_features, regret_bound):
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"""The portfolio construction algorithm.
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(Reference)[https://arxiv.org/abs/2202.09927].
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Args:
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regret_matrix: A dataframe of regret matrix.
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meta_features: None or a dataframe of metafeatures matrix.
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When set to None, the algorithm uses greedy strategy.
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Otherwise, the algorithm uses greedy strategy with feedback
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from the nearest neighbor predictor.
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regret_bound: A float of the regret bound.
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Returns:
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A list of configuration names.
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"""
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configs = []
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all_configs = set(regret_matrix.index.tolist())
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tasks = regret_matrix.columns
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# pre-processing
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if meta_features is not None:
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scaler = RobustScaler()
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meta_features = meta_features.loc[tasks]
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meta_features.loc[:, :] = scaler.fit_transform(meta_features)
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nearest_task = {}
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for t in tasks:
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other_meta_features = meta_features.drop(t)
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dist = pd.DataFrame(
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pairwise_distances(
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meta_features.loc[t].to_numpy().reshape(1, -1),
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other_meta_features,
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metric="l2",
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),
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columns=other_meta_features.index,
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)
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nearest_task[t] = dist.idxmin(axis=1)
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regret_matrix = regret_matrix.apply(_augment, axis=1)
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print(regret_matrix)
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def loss(configs):
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"""Loss of config set `configs`, according to nearest neighbor config predictor."""
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if meta_features is not None:
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r = []
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best_config_per_task = regret_matrix.loc[configs, :].min()
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for t in tasks:
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config = best_config_per_task[nearest_task[t]].iloc[0][-1]
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r.append(regret_matrix[t][config][0])
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else:
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r = regret_matrix.loc[configs].min()
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excessive_regret = (np.array(r) - regret_bound).clip(min=0).sum()
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avg_regret = np.array(r).mean()
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return excessive_regret, avg_regret
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prev = np.inf
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i = 0
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eps = 1e-5
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while True:
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candidates = [configs + [d] for d in all_configs.difference(configs)]
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losses, avg_regret = tuple(zip(*(loss(x) for x in candidates)))
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sorted_losses = np.sort(losses)
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if sorted_losses[1] - sorted_losses[0] < eps:
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minloss = np.nanmin(losses)
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print(
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f"tie detected at loss = {sorted_losses[0]}, using alternative metric."
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)
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tied = np.flatnonzero(losses - minloss < eps)
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losses = [(avg_regret[i], i) for i in tied]
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minloss, ind = min(losses)
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if minloss > prev - eps:
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print(
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f"May be overfitting at k = {i + 1}, current = {minloss:.5f}, "
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f"prev = {prev:.5f}. Stopping."
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)
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break
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configs = candidates[ind]
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prev = minloss
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else:
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configs = candidates[np.nanargmin(losses)]
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i += 1
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if sorted_losses[0] <= eps:
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print(
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f"Reached target regret bound of {regret_bound}! k = {i}. Declining to pick further!"
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)
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break
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return configs
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