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warning -> info for low cost partial config (#231)

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* warning -> info for low cost partial config
#195, #110

* when n_estimators < 0, use trained_estimator's

* log debug info

* test random seed

* remove "objective"; avoid ZeroDivisionError

* hp config to estimator params

* check type of searcher

* default n_jobs

* try import

* Update searchalgo_auto.py

* CLASSIFICATION

* auto_augment flag

* min_sample_size

* make catboost optional
This commit is contained in:
Chi Wang
2021-10-08 16:09:43 -07:00
committed by GitHub
parent a99e939404
commit f48ca2618f
22 changed files with 1938 additions and 1859 deletions
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README.md
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@@ -103,7 +103,7 @@ print(automl.model)
```python
from flaml import AutoML
from sklearn.datasets import load_boston
from sklearn.datasets import fetch_california_housing
# Initialize an AutoML instance
automl = AutoML()
# Specify automl goal and constraint
@@ -113,7 +113,7 @@ automl_settings = {
"task": 'regression',
"log_file_name": "test/boston.log",
}
X_train, y_train = load_boston(return_X_y=True)
X_train, y_train = fetch_california_housing(return_X_y=True)
# Train with labeled input data
automl.fit(X_train=X_train, y_train=y_train,
**automl_settings)