github/autogen
1
1
Fork 0
mirror of https://github.com/microsoft/autogen.git synced 2026-04-20 03:02:16 -04:00
Code Issues Packages Projects Releases Wiki Activity

fix bug in hierarchical search space (#248); optional dependency on lgbm and xgb (#250)

Browse Source 
* close #249

* admissible region

* best_config can be None

* optional dependency on lgbm and xgb
resolve #252
This commit is contained in:
Chi Wang
2021-10-15 21:36:42 -07:00
committed by GitHub
parent fe65fa143d
commit 524f22bcc5
10 changed files with 365 additions and 262 deletions
Download Patch File Download Diff File Expand all files Collapse all files

11
test/test_automl.py
View File

@@ -13,10 +13,9 @@ import pandas as pd
from datetime import datetime
from flaml import AutoML
from flaml.data import get_output_from_log
from flaml.data import CLASSIFICATION, get_output_from_log
from flaml.model import LGBMEstimator, SKLearnEstimator, XGBoostEstimator
from rgf.sklearn import RGFClassifier, RGFRegressor
from flaml import tune
from flaml.training_log import training_log_reader
@@ -26,9 +25,13 @@ class MyRegularizedGreedyForest(SKLearnEstimator):
super().__init__(task, **config)
if task in ("binary", "multi"):
if task in CLASSIFICATION:
from rgf.sklearn import RGFClassifier
self.estimator_class = RGFClassifier
else:
from rgf.sklearn import RGFRegressor
self.estimator_class = RGFRegressor
@classmethod
@@ -628,7 +631,7 @@ class TestAutoML(unittest.TestCase):
"log_file_name": "test/california.log",
"log_type": "all",
"n_jobs": 1,
"n_concurrent_trials": 2,
"n_concurrent_trials": 10,
"hpo_method": hpo_method,
}
X_train, y_train = fetch_california_housing(return_X_y=True)

2
test/test_notebook_example.py
View File

@@ -109,4 +109,4 @@ def test_mlflow():
if __name__ == "__main__":
test_automl(300)
test_automl(120)

18
test/test_python_log.py
View File

@@ -64,18 +64,20 @@ class TestLogging(unittest.TestCase):
automl.search_space, automl.low_cost_partial_config, automl.cat_hp_cost
)
logger.info(automl.search_space["ml"].categories)
config = automl.best_config.copy()
config["learner"] = automl.best_estimator
automl.trainable({"ml": config})
if automl.best_config:
config = automl.best_config.copy()
config["learner"] = automl.best_estimator
automl.trainable({"ml": config})
from flaml import tune, BlendSearch
from flaml.automl import size
from functools import partial
low_cost_partial_config = automl.low_cost_partial_config
search_alg = BlendSearch(
metric="val_loss",
mode="min",
space=automl.search_space,
low_cost_partial_config=automl.low_cost_partial_config,
low_cost_partial_config=low_cost_partial_config,
points_to_evaluate=automl.points_to_evaluate,
cat_hp_cost=automl.cat_hp_cost,
prune_attr=automl.prune_attr,
@@ -95,6 +97,14 @@ class TestLogging(unittest.TestCase):
print(min(trial.last_result["val_loss"] for trial in analysis.trials))
config = analysis.trials[-1].last_result["config"]["ml"]
automl._state._train_with_config(config["learner"], config)
for _ in range(3):
print(
search_alg._ls.complete_config(
low_cost_partial_config,
search_alg._ls_bound_min,
search_alg._ls_bound_max,
)
)
# Check if the log buffer is populated.
self.assertTrue(len(buf.getvalue()) > 0)