mirror of
https://github.com/microsoft/autogen.git
synced 2025-11-29 16:39:52 +00:00
Jirka Borovec
GitHub
parent
c18dd456c7
commit
6aa1d16ebc
@ -1,19 +1,28 @@
|
||||
default_language_version:
|
||||
python: python3
|
||||
|
||||
ci:
|
||||
autofix_prs: true
|
||||
autoupdate_commit_msg: '[pre-commit.ci] pre-commit suggestions'
|
||||
autoupdate_schedule: 'quarterly'
|
||||
|
||||
repos:
|
||||
- repo: https://github.com/psf/black
|
||||
rev: 22.3.0
|
||||
rev: 23.1.0
|
||||
hooks:
|
||||
- id: black
|
||||
language_version: python3
|
||||
- repo: https://github.com/pycqa/flake8
|
||||
rev: 4.0.1
|
||||
hooks:
|
||||
- id: flake8
|
||||
- repo: https://github.com/pycqa/flake8
|
||||
rev: 6.0.0
|
||||
hooks:
|
||||
- id: flake8
|
||||
- repo: https://github.com/pre-commit/pre-commit-hooks
|
||||
rev: v4.3.0
|
||||
rev: v4.4.0
|
||||
hooks:
|
||||
- id: check-added-large-files
|
||||
- id: check-ast
|
||||
- id: check-yaml
|
||||
- id: check-toml
|
||||
- id: check-json
|
||||
- id: check-byte-order-marker
|
||||
- id: check-merge-conflict
|
||||
- id: detect-private-key
|
||||
|
||||
@ -1104,7 +1104,6 @@ class AutoML(BaseEstimator):
|
||||
groups_val=None,
|
||||
groups=None,
|
||||
):
|
||||
|
||||
if X_train_all is not None and y_train_all is not None:
|
||||
assert (
|
||||
isinstance(X_train_all, np.ndarray)
|
||||
@ -1266,7 +1265,6 @@ class AutoML(BaseEstimator):
|
||||
self._state.groups = groups
|
||||
|
||||
def _prepare_data(self, eval_method, split_ratio, n_splits):
|
||||
|
||||
X_val, y_val = self._state.X_val, self._state.y_val
|
||||
if issparse(X_val):
|
||||
X_val = X_val.tocsr()
|
||||
|
||||
@ -439,7 +439,6 @@ def get_val_loss(
|
||||
fit_kwargs={},
|
||||
free_mem_ratio=0,
|
||||
):
|
||||
|
||||
start = time.time()
|
||||
# if groups_val is not None:
|
||||
# fit_kwargs['groups_val'] = groups_val
|
||||
|
||||
@ -175,7 +175,6 @@ class BaseEstimator:
|
||||
return X
|
||||
|
||||
def _fit(self, X_train, y_train, **kwargs):
|
||||
|
||||
current_time = time.time()
|
||||
if "groups" in kwargs:
|
||||
kwargs = kwargs.copy()
|
||||
@ -447,7 +446,7 @@ class TransformersEstimator(BaseEstimator):
|
||||
def _set_training_args(self, **kwargs):
|
||||
from .nlp.utils import date_str, Counter
|
||||
|
||||
for (key, val) in kwargs.items():
|
||||
for key, val in kwargs.items():
|
||||
assert key not in self.params, (
|
||||
"Since {} is in the search space, it cannot exist in 'custom_fit_kwargs' at the same time."
|
||||
"If you need to fix the value of {} to {}, the only way is to add a single-value domain in the search "
|
||||
|
||||
@ -112,7 +112,6 @@ class TrainingArgumentsForAuto(TrainingArguments):
|
||||
|
||||
@dataclass
|
||||
class Seq2SeqTrainingArgumentsForAuto(TrainingArgumentsForAuto):
|
||||
|
||||
model_path: str = field(
|
||||
default="t5-small",
|
||||
metadata={
|
||||
|
||||
@ -135,7 +135,6 @@ def tokenize_and_align_labels(
|
||||
|
||||
|
||||
def tokenize_text_tokclassification(X, Y, tokenizer, hf_args=None):
|
||||
|
||||
# If the label_all_tokens flag is True, prepare two dicts label_to_id and b_to_i_label to convert the B- labels to I- labels
|
||||
label_to_id = {i: i for i in range(len(hf_args.label_list))}
|
||||
b_to_i_label = []
|
||||
@ -275,7 +274,6 @@ def tokenize_row(
|
||||
|
||||
|
||||
def tokenize_text_multiplechoice(X, tokenizer, hf_args=None):
|
||||
|
||||
t = X[["sent1", "sent2", "ending0", "ending1", "ending2", "ending3"]]
|
||||
_, tokenized_column_names = tokenize_swag(
|
||||
t.iloc[0],
|
||||
|
||||
@ -11,7 +11,6 @@ from flaml.automl.data import (
|
||||
|
||||
|
||||
def load_default_huggingface_metric_for_task(task):
|
||||
|
||||
if task == SEQCLASSIFICATION:
|
||||
return "accuracy"
|
||||
elif task == SEQREGRESSION:
|
||||
|
||||
@ -647,5 +647,4 @@ def qrandn(mean: float, sd: float, q: float):
|
||||
def polynomial_expansion_set(
|
||||
init_monomials: set, highest_poly_order: int = None, allow_self_inter: bool = False
|
||||
):
|
||||
|
||||
return PolynomialExpansionSet(init_monomials, highest_poly_order, allow_self_inter)
|
||||
|
||||
@ -282,9 +282,9 @@ def _split_resolved_unresolved_values(
|
||||
_resolved_children,
|
||||
_unresolved_children,
|
||||
) = _split_resolved_unresolved_values(v)
|
||||
for (path, value) in _resolved_children.items():
|
||||
for path, value in _resolved_children.items():
|
||||
resolved_vars[(k,) + path] = value
|
||||
for (path, value) in _unresolved_children.items():
|
||||
for path, value in _unresolved_children.items():
|
||||
unresolved_vars[(k,) + path] = value
|
||||
elif isinstance(v, list):
|
||||
# Recurse into a list
|
||||
@ -293,9 +293,9 @@ def _split_resolved_unresolved_values(
|
||||
_resolved_children,
|
||||
_unresolved_children,
|
||||
) = _split_resolved_unresolved_values({i: elem})
|
||||
for (path, value) in _resolved_children.items():
|
||||
for path, value in _resolved_children.items():
|
||||
resolved_vars[(k,) + path] = value
|
||||
for (path, value) in _unresolved_children.items():
|
||||
for path, value in _unresolved_children.items():
|
||||
unresolved_vars[(k,) + path] = value
|
||||
else:
|
||||
resolved_vars[(k,)] = v
|
||||
|
||||
@ -142,7 +142,6 @@ class ExperimentAnalysis(EA):
|
||||
|
||||
|
||||
def report(_metric=None, **kwargs):
|
||||
|
||||
"""A function called by the HPO application to report final or intermediate
|
||||
results.
|
||||
|
||||
|
||||
@ -11,7 +11,6 @@ from flaml.automl.training_log import training_log_reader
|
||||
|
||||
class MyRegularizedGreedyForest(SKLearnEstimator):
|
||||
def __init__(self, task="binary", **config):
|
||||
|
||||
super().__init__(task, **config)
|
||||
|
||||
if task in CLASSIFICATION:
|
||||
|
||||
@ -10,11 +10,9 @@ import io
|
||||
|
||||
class TestLogging(unittest.TestCase):
|
||||
def test_logging_level(self):
|
||||
|
||||
from flaml import logger, logger_formatter
|
||||
|
||||
with tempfile.TemporaryDirectory() as d:
|
||||
|
||||
training_log = os.path.join(d, "training.log")
|
||||
|
||||
# Configure logging for the FLAML logger
|
||||
|
||||
@ -12,7 +12,6 @@ class TestTrainingLog(unittest.TestCase):
|
||||
def test_training_log(
|
||||
self, path="test_training_log.log", estimator_list="auto", use_ray=False
|
||||
):
|
||||
|
||||
with TemporaryDirectory() as d:
|
||||
filename = os.path.join(d, path)
|
||||
|
||||
|
||||
@ -1574,7 +1574,6 @@ def get_toy_data_tokenclassification_tokenlabel():
|
||||
|
||||
|
||||
def get_automl_settings(estimator_name="transformer"):
|
||||
|
||||
automl_settings = {
|
||||
"gpu_per_trial": 0,
|
||||
"max_iter": 3,
|
||||
|
||||
@ -101,7 +101,7 @@ def get_oml_to_vw(did, max_ns_num, ds_dir=VW_DS_DIR):
|
||||
target_attribute = ds.default_target_attribute
|
||||
# if target_attribute is None and did in OML_target_attribute_dict:
|
||||
# target_attribute = OML_target_attribute_dict[did]
|
||||
except (SSLError) as e:
|
||||
except SSLError as e:
|
||||
print(e)
|
||||
return
|
||||
|
||||
|
||||
@ -17,7 +17,6 @@ def rosenbrock_function(config: dict):
|
||||
|
||||
|
||||
def test_record_incumbent(method="BlendSearch"):
|
||||
|
||||
if method != "CFOCat":
|
||||
search_space = {
|
||||
"x1": tune.randint(1, 9),
|
||||
|
||||
@ -35,7 +35,6 @@ def test_tune(externally_setup_searcher=False, use_ray=False, use_raytune=False)
|
||||
"height": tune.uniform(-100, 100),
|
||||
}
|
||||
if externally_setup_searcher is True:
|
||||
|
||||
searcher = BlendSearch(
|
||||
space=search_space,
|
||||
time_budget_s=5,
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user