mirror of
https://github.com/microsoft/autogen.git
synced 2025-08-06 15:51:58 +00:00
a0b318b12e
* version update post release v1.2.2 * automl option * import pandas * remove automl.utils * default * test * type hint and version update * dependency update * link to open in colab * use packging.version to close #725 --------- Co-authored-by: Li Jiang <lijiang1@microsoft.com> Co-authored-by: Li Jiang <bnujli@gmail.com>
110 lines
3.9 KiB
Markdown
110 lines
3.9 KiB
Markdown
# Default - Flamlized Estimator
|
|
|
|
Flamlized estimators automatically use data-dependent default hyperparameter configurations for each estimator, offering a unique zero-shot AutoML capability, or "no tuning" AutoML.
|
|
|
|
## Flamlized LGBMRegressor
|
|
|
|
### Prerequisites
|
|
|
|
This example requires the [autozero] option.
|
|
|
|
```bash
|
|
pip install flaml[autozero] lightgbm openml
|
|
```
|
|
|
|
### Zero-shot AutoML
|
|
|
|
```python
|
|
from flaml.automl.data import load_openml_dataset
|
|
from flaml.default import LGBMRegressor
|
|
from flaml.automl.ml import sklearn_metric_loss_score
|
|
|
|
X_train, X_test, y_train, y_test = load_openml_dataset(dataset_id=537, data_dir="./")
|
|
lgbm = LGBMRegressor()
|
|
lgbm.fit(X_train, y_train)
|
|
y_pred = lgbm.predict(X_test)
|
|
print("flamlized lgbm r2", "=", 1 - sklearn_metric_loss_score("r2", y_pred, y_test))
|
|
print(lgbm)
|
|
```
|
|
|
|
#### Sample output
|
|
|
|
```
|
|
load dataset from ./openml_ds537.pkl
|
|
Dataset name: houses
|
|
X_train.shape: (15480, 8), y_train.shape: (15480,);
|
|
X_test.shape: (5160, 8), y_test.shape: (5160,)
|
|
flamlized lgbm r2 = 0.8537444671194614
|
|
LGBMRegressor(colsample_bytree=0.7019911744574896,
|
|
learning_rate=0.022635758411078528, max_bin=511,
|
|
min_child_samples=2, n_estimators=4797, num_leaves=122,
|
|
reg_alpha=0.004252223402511765, reg_lambda=0.11288241427227624,
|
|
verbose=-1)
|
|
```
|
|
|
|
### Suggest hyperparameters without training
|
|
|
|
```
|
|
from flaml.data import load_openml_dataset
|
|
from flaml.default import LGBMRegressor
|
|
from flaml.ml import sklearn_metric_loss_score
|
|
|
|
X_train, X_test, y_train, y_test = load_openml_dataset(dataset_id=537, data_dir="./")
|
|
lgbm = LGBMRegressor()
|
|
hyperparams, estimator_name, X_transformed, y_transformed = lgbm.suggest_hyperparams(X_train, y_train)
|
|
print(hyperparams)
|
|
```
|
|
|
|
#### Sample output
|
|
```
|
|
load dataset from ./openml_ds537.pkl
|
|
Dataset name: houses
|
|
X_train.shape: (15480, 8), y_train.shape: (15480,);
|
|
X_test.shape: (5160, 8), y_test.shape: (5160,)
|
|
{'n_estimators': 4797, 'num_leaves': 122, 'min_child_samples': 2, 'learning_rate': 0.022635758411078528, 'colsample_bytree': 0.7019911744574896, 'reg_alpha': 0.004252223402511765, 'reg_lambda': 0.11288241427227624, 'max_bin': 511, 'verbose': -1}
|
|
```
|
|
|
|
[Link to notebook](https://github.com/microsoft/FLAML/blob/main/notebook/zeroshot_lightgbm.ipynb) | [Open in colab](https://colab.research.google.com/github/microsoft/FLAML/blob/main/notebook/zeroshot_lightgbm.ipynb)
|
|
|
|
## Flamlized XGBClassifier
|
|
|
|
### Prerequisites
|
|
|
|
This example requires xgboost, sklearn, openml==0.10.2.
|
|
|
|
### Zero-shot AutoML
|
|
|
|
```python
|
|
from flaml.automl.data import load_openml_dataset
|
|
from flaml.default import XGBClassifier
|
|
from flaml.automl.ml import sklearn_metric_loss_score
|
|
|
|
X_train, X_test, y_train, y_test = load_openml_dataset(dataset_id=1169, data_dir="./")
|
|
xgb = XGBClassifier()
|
|
xgb.fit(X_train, y_train)
|
|
y_pred = xgb.predict(X_test)
|
|
print("flamlized xgb accuracy", "=", 1 - sklearn_metric_loss_score("accuracy", y_pred, y_test))
|
|
print(xgb)
|
|
```
|
|
|
|
#### Sample output
|
|
|
|
```
|
|
load dataset from ./openml_ds1169.pkl
|
|
Dataset name: airlines
|
|
X_train.shape: (404537, 7), y_train.shape: (404537,);
|
|
X_test.shape: (134846, 7), y_test.shape: (134846,)
|
|
flamlized xgb accuracy = 0.6729009388487608
|
|
XGBClassifier(base_score=0.5, booster='gbtree',
|
|
colsample_bylevel=0.4601573737792679, colsample_bynode=1,
|
|
colsample_bytree=1.0, gamma=0, gpu_id=-1, grow_policy='lossguide',
|
|
importance_type='gain', interaction_constraints='',
|
|
learning_rate=0.04039771837785377, max_delta_step=0, max_depth=0,
|
|
max_leaves=159, min_child_weight=0.3396294979905001, missing=nan,
|
|
monotone_constraints='()', n_estimators=540, n_jobs=4,
|
|
num_parallel_tree=1, random_state=0,
|
|
reg_alpha=0.0012362430984376035, reg_lambda=3.093428791531145,
|
|
scale_pos_weight=1, subsample=1.0, tree_method='hist',
|
|
use_label_encoder=False, validate_parameters=1, verbosity=0)
|
|
```
|