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# Copyright (c) Microsoft Corporation.
# SPDX-License-Identifier: Apache-2.0
# DeepSpeed Team
import numpy as np
import itertools
from ..utils import *
import collections.abc
def index_to_feature(p, dims):
"""convert index form (single integer) to feature form (vector)"""
feature = []
for dim in dims:
feature.append(p % dim)
p //= dim
return feature
def feature_to_index(feature, dims):
"""convert feature form (vector) to index form (single integer)"""
p = 0
for j, k in enumerate(feature):
print("j:", "k:", k, "dims", dims[:j])
p += int(np.prod(dims[:j])) * k
return p
def dict_to_dims(tuning_space):
dims = []
for key, val in tuning_space.items():
if isinstance(val, dict):
dims.extend(dict_to_dims(val))
elif isinstance(val, list):
dims.append(len(val))
else:
dims.append(1)
return dims
def gen_combinations(d: dict):
keys, values = d.keys(), d.values()
for v in values:
if not isinstance(v, list):
v = [v]
values_choices = (gen_combinations(v) if isinstance(v, dict) else get_list(v) for v in values)
for comb in itertools.product(*values_choices):
yield dict(zip(keys, comb))
def flatten(d, parent_key='', sep='_'):
items = []
for k, v in d.items():
new_key = parent_key + sep + k if parent_key else k
if isinstance(v, collections.abc.MutableMapping):
items.extend(flatten(v, new_key, sep=sep).items())
else:
items.append((new_key, v))
return dict(items)
def dict_to_feature(feature_dict, keys, max_value=None):
"""Extract values from dict"""
feature = []
for key, val in feature_dict.items(): # First level
if key not in keys:
continue
if val is None or val == "auto" or key == "autotuning" or val == "":
continue
if isinstance(val, dict):
feature.append(dict_to_feature(val, max_value))
else:
feature.append(float(val))
# normalization, should not matter in tree models
if max_value is not None:
norm_feature = []
for f, mv in zip(feature, max_value):
norm_feature.append(f / mv)
feature = norm_feature
return feature
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