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* Refactor into automl subpackage Moved some of the packages into an automl subpackage to tidy before the task-based refactor. This is in response to discussions with the group and a comment on the first task-based PR. Only changes here are moving subpackages and modules into the new automl, fixing imports to work with this structure and fixing some dependencies in setup.py. * Fix doc building post automl subpackage refactor * Fix broken links in website post automl subpackage refactor * Fix broken links in website post automl subpackage refactor * Remove vw from test deps as this is breaking the build * Move default back to the top-level I'd moved this to automl as that's where it's used internally, but had missed that this is actually part of the public interface so makes sense to live where it was. * Re-add top level modules with deprecation warnings flaml.data, flaml.ml and flaml.model are re-added to the top level, being re-exported from flaml.automl for backwards compatability. Adding a deprecation warning so that we can have a planned removal later. * Fix model.py line-endings * Pin pytorch-lightning to less than 1.8.0 We're seeing strange lightning related bugs from pytorch-forecasting since the release of lightning 1.8.0. Going to try constraining this to see if we have a fix. * Fix the lightning version pin Was optimistic with setting it in the 1.7.x range, but that isn't compatible with python 3.6 * Remove lightning version pin * Revert dependency version changes * Minor change to retrigger the build * Fix line endings in ml.py and model.py Co-authored-by: Qingyun Wu <qingyun.wu@psu.edu> Co-authored-by: EgorKraevTransferwise <egor.kraev@transferwise.com>
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13 KiB
AutoML for XGBoost
Prerequisites for this example
Install the [notebook] option.
pip install "flaml[notebook]"
This option is not necessary in general.
Use built-in XGBoostSklearnEstimator
from flaml import AutoML
from flaml.automl.data import load_openml_dataset
# Download [houses dataset](https://www.openml.org/d/537) from OpenML. The task is to predict median price of the house in the region based on demographic composition and a state of housing market in the region.
X_train, X_test, y_train, y_test = load_openml_dataset(dataset_id=537, data_dir='./')
automl = AutoML()
settings = {
"time_budget": 60, # total running time in seconds
"metric": 'r2', # primary metrics for regression can be chosen from: ['mae','mse','r2']
"estimator_list": ['xgboost'], # list of ML learners; we tune XGBoost in this example
"task": 'regression', # task type
"log_file_name": 'houses_experiment.log', # flaml log file
"seed": 7654321, # random seed
}
automl.fit(X_train=X_train, y_train=y_train, **settings)
Sample output
[flaml.automl: 09-29 23:06:46] {1446} INFO - Data split method: uniform
[flaml.automl: 09-29 23:06:46] {1450} INFO - Evaluation method: cv
[flaml.automl: 09-29 23:06:46] {1496} INFO - Minimizing error metric: 1-r2
[flaml.automl: 09-29 23:06:46] {1533} INFO - List of ML learners in AutoML Run: ['xgboost']
[flaml.automl: 09-29 23:06:46] {1763} INFO - iteration 0, current learner xgboost
[flaml.automl: 09-29 23:06:47] {1880} INFO - Estimated sufficient time budget=2621s. Estimated necessary time budget=3s.
[flaml.automl: 09-29 23:06:47] {1952} INFO - at 0.3s, estimator xgboost's best error=2.1267, best estimator xgboost's best error=2.1267
[flaml.automl: 09-29 23:06:47] {1763} INFO - iteration 1, current learner xgboost
[flaml.automl: 09-29 23:06:47] {1952} INFO - at 0.5s, estimator xgboost's best error=2.1267, best estimator xgboost's best error=2.1267
[flaml.automl: 09-29 23:06:47] {1763} INFO - iteration 2, current learner xgboost
[flaml.automl: 09-29 23:06:47] {1952} INFO - at 0.6s, estimator xgboost's best error=0.8485, best estimator xgboost's best error=0.8485
[flaml.automl: 09-29 23:06:47] {1763} INFO - iteration 3, current learner xgboost
[flaml.automl: 09-29 23:06:47] {1952} INFO - at 0.8s, estimator xgboost's best error=0.3799, best estimator xgboost's best error=0.3799
[flaml.automl: 09-29 23:06:47] {1763} INFO - iteration 4, current learner xgboost
[flaml.automl: 09-29 23:06:47] {1952} INFO - at 1.0s, estimator xgboost's best error=0.3799, best estimator xgboost's best error=0.3799
[flaml.automl: 09-29 23:06:47] {1763} INFO - iteration 5, current learner xgboost
[flaml.automl: 09-29 23:06:47] {1952} INFO - at 1.2s, estimator xgboost's best error=0.3799, best estimator xgboost's best error=0.3799
[flaml.automl: 09-29 23:06:47] {1763} INFO - iteration 6, current learner xgboost
[flaml.automl: 09-29 23:06:48] {1952} INFO - at 1.5s, estimator xgboost's best error=0.2992, best estimator xgboost's best error=0.2992
[flaml.automl: 09-29 23:06:48] {1763} INFO - iteration 7, current learner xgboost
[flaml.automl: 09-29 23:06:48] {1952} INFO - at 1.9s, estimator xgboost's best error=0.2992, best estimator xgboost's best error=0.2992
[flaml.automl: 09-29 23:06:48] {1763} INFO - iteration 8, current learner xgboost
[flaml.automl: 09-29 23:06:49] {1952} INFO - at 2.2s, estimator xgboost's best error=0.2992, best estimator xgboost's best error=0.2992
[flaml.automl: 09-29 23:06:49] {1763} INFO - iteration 9, current learner xgboost
[flaml.automl: 09-29 23:06:49] {1952} INFO - at 2.5s, estimator xgboost's best error=0.2513, best estimator xgboost's best error=0.2513
[flaml.automl: 09-29 23:06:49] {1763} INFO - iteration 10, current learner xgboost
[flaml.automl: 09-29 23:06:49] {1952} INFO - at 2.8s, estimator xgboost's best error=0.2513, best estimator xgboost's best error=0.2513
[flaml.automl: 09-29 23:06:49] {1763} INFO - iteration 11, current learner xgboost
[flaml.automl: 09-29 23:06:49] {1952} INFO - at 3.0s, estimator xgboost's best error=0.2513, best estimator xgboost's best error=0.2513
[flaml.automl: 09-29 23:06:49] {1763} INFO - iteration 12, current learner xgboost
[flaml.automl: 09-29 23:06:50] {1952} INFO - at 3.3s, estimator xgboost's best error=0.2113, best estimator xgboost's best error=0.2113
[flaml.automl: 09-29 23:06:50] {1763} INFO - iteration 13, current learner xgboost
[flaml.automl: 09-29 23:06:50] {1952} INFO - at 3.5s, estimator xgboost's best error=0.2113, best estimator xgboost's best error=0.2113
[flaml.automl: 09-29 23:06:50] {1763} INFO - iteration 14, current learner xgboost
[flaml.automl: 09-29 23:06:50] {1952} INFO - at 4.0s, estimator xgboost's best error=0.2090, best estimator xgboost's best error=0.2090
[flaml.automl: 09-29 23:06:50] {1763} INFO - iteration 15, current learner xgboost
[flaml.automl: 09-29 23:06:51] {1952} INFO - at 4.5s, estimator xgboost's best error=0.2090, best estimator xgboost's best error=0.2090
[flaml.automl: 09-29 23:06:51] {1763} INFO - iteration 16, current learner xgboost
[flaml.automl: 09-29 23:06:51] {1952} INFO - at 5.2s, estimator xgboost's best error=0.1919, best estimator xgboost's best error=0.1919
[flaml.automl: 09-29 23:06:51] {1763} INFO - iteration 17, current learner xgboost
[flaml.automl: 09-29 23:06:52] {1952} INFO - at 5.5s, estimator xgboost's best error=0.1919, best estimator xgboost's best error=0.1919
[flaml.automl: 09-29 23:06:52] {1763} INFO - iteration 18, current learner xgboost
[flaml.automl: 09-29 23:06:54] {1952} INFO - at 8.0s, estimator xgboost's best error=0.1797, best estimator xgboost's best error=0.1797
[flaml.automl: 09-29 23:06:54] {1763} INFO - iteration 19, current learner xgboost
[flaml.automl: 09-29 23:06:55] {1952} INFO - at 9.0s, estimator xgboost's best error=0.1797, best estimator xgboost's best error=0.1797
[flaml.automl: 09-29 23:06:55] {1763} INFO - iteration 20, current learner xgboost
[flaml.automl: 09-29 23:07:08] {1952} INFO - at 21.8s, estimator xgboost's best error=0.1797, best estimator xgboost's best error=0.1797
[flaml.automl: 09-29 23:07:08] {1763} INFO - iteration 21, current learner xgboost
[flaml.automl: 09-29 23:07:11] {1952} INFO - at 24.4s, estimator xgboost's best error=0.1797, best estimator xgboost's best error=0.1797
[flaml.automl: 09-29 23:07:11] {1763} INFO - iteration 22, current learner xgboost
[flaml.automl: 09-29 23:07:16] {1952} INFO - at 30.0s, estimator xgboost's best error=0.1782, best estimator xgboost's best error=0.1782
[flaml.automl: 09-29 23:07:16] {1763} INFO - iteration 23, current learner xgboost
[flaml.automl: 09-29 23:07:20] {1952} INFO - at 33.5s, estimator xgboost's best error=0.1782, best estimator xgboost's best error=0.1782
[flaml.automl: 09-29 23:07:20] {1763} INFO - iteration 24, current learner xgboost
[flaml.automl: 09-29 23:07:29] {1952} INFO - at 42.3s, estimator xgboost's best error=0.1782, best estimator xgboost's best error=0.1782
[flaml.automl: 09-29 23:07:29] {1763} INFO - iteration 25, current learner xgboost
[flaml.automl: 09-29 23:07:30] {1952} INFO - at 43.2s, estimator xgboost's best error=0.1782, best estimator xgboost's best error=0.1782
[flaml.automl: 09-29 23:07:30] {1763} INFO - iteration 26, current learner xgboost
[flaml.automl: 09-29 23:07:50] {1952} INFO - at 63.4s, estimator xgboost's best error=0.1663, best estimator xgboost's best error=0.1663
[flaml.automl: 09-29 23:07:50] {2059} INFO - selected model: <xgboost.core.Booster object at 0x7f6399005910>
[flaml.automl: 09-29 23:07:55] {2122} INFO - retrain xgboost for 5.4s
[flaml.automl: 09-29 23:07:55] {2128} INFO - retrained model: <xgboost.core.Booster object at 0x7f6398fc0eb0>
[flaml.automl: 09-29 23:07:55] {1557} INFO - fit succeeded
[flaml.automl: 09-29 23:07:55] {1558} INFO - Time taken to find the best model: 63.427649974823
[flaml.automl: 09-29 23:07:55] {1569} WARNING - Time taken to find the best model is 106% of the provided time budget and not all estimators' hyperparameter search converged. Consider increasing the time budget.
Retrieve best config
print('Best hyperparmeter config:', automl.best_config)
print('Best r2 on validation data: {0:.4g}'.format(1-automl.best_loss))
print('Training duration of best run: {0:.4g} s'.format(automl.best_config_train_time))
print(automl.model.estimator)
# Best hyperparmeter config: {'n_estimators': 473, 'max_leaves': 35, 'max_depth': 0, 'min_child_weight': 0.001, 'learning_rate': 0.26865031351923346, 'subsample': 0.9718245679598786, 'colsample_bylevel': 0.7421362469066445, 'colsample_bytree': 1.0, 'reg_alpha': 0.06824336834995245, 'reg_lambda': 250.9654222583276}
# Best r2 on validation data: 0.8384
# Training duration of best run: 2.194 s
# XGBRegressor(base_score=0.5, booster='gbtree',
# colsample_bylevel=0.7421362469066445, colsample_bynode=1,
# colsample_bytree=1.0, gamma=0, gpu_id=-1, grow_policy='lossguide',
# importance_type='gain', interaction_constraints='',
# learning_rate=0.26865031351923346, max_delta_step=0, max_depth=0,
# max_leaves=35, min_child_weight=0.001, missing=nan,
# monotone_constraints='()', n_estimators=473, n_jobs=-1,
# num_parallel_tree=1, random_state=0, reg_alpha=0.06824336834995245,
# reg_lambda=250.9654222583276, scale_pos_weight=1,
# subsample=0.9718245679598786, tree_method='hist',
# use_label_encoder=False, validate_parameters=1, verbosity=0)
Plot feature importance
import matplotlib.pyplot as plt
plt.barh(automl.feature_names_in_, automl.feature_importances_)
Compute predictions of testing dataset
y_pred = automl.predict(X_test)
print('Predicted labels', y_pred)
# Predicted labels [139062.95 237622. 140522.03 ... 182125.5 252156.36 264884.5 ]
Compute different metric values on testing dataset
from flaml.automl.ml import sklearn_metric_loss_score
print('r2', '=', 1 - sklearn_metric_loss_score('r2', y_pred, y_test))
print('mse', '=', sklearn_metric_loss_score('mse', y_pred, y_test))
print('mae', '=', sklearn_metric_loss_score('mae', y_pred, y_test))
# r2 = 0.8456494234135888
# mse = 2040284106.2781258
# mae = 30212.830996680445
Compare with untuned XGBoost
from xgboost import XGBRegressor
xgb = XGBRegressor()
xgb.fit(X_train, y_train)
y_pred = xgb.predict(X_test)
from flaml.automl.ml import sklearn_metric_loss_score
print('default xgboost r2', '=', 1 - sklearn_metric_loss_score('r2', y_pred, y_test))
# default xgboost r2 = 0.8265451174596482
Plot learning curve
How does the model accuracy improve as we search for different hyperparameter configurations?
from flaml.automl.data import get_output_from_log
import numpy as np
time_history, best_valid_loss_history, valid_loss_history, config_history, metric_history =
get_output_from_log(filename=settings['log_file_name'], time_budget=60)
plt.title('Learning Curve')
plt.xlabel('Wall Clock Time (s)')
plt.ylabel('Validation r2')
plt.step(time_history, 1 - np.array(best_valid_loss_history), where='post')
plt.show()
Use a customized XGBoost learner
You can easily enable a custom objective function by adding a customized XGBoost learner (inherit XGBoostEstimator or XGBoostSklearnEstimator) in FLAML. In the following example, we show how to add such a customized XGBoost learner with a custom objective function.
import numpy as np
# define your customized objective function
def logregobj(preds, dtrain):
labels = dtrain.get_label()
preds = 1.0 / (1.0 + np.exp(-preds)) # transform raw leaf weight
grad = preds - labels
hess = preds * (1.0 - preds)
return grad, hess
from flaml.automl.model import XGBoostEstimator
class MyXGB1(XGBoostEstimator):
'''XGBoostEstimator with the logregobj function as the objective function
'''
def __init__(self, **config):
super().__init__(objective=logregobj, **config)
class MyXGB2(XGBoostEstimator):
'''XGBoostEstimator with 'reg:squarederror' as the objective function
'''
def __init__(self, **config):
super().__init__(objective='reg:gamma', **config)
Add the customized learners and tune them
automl = AutoML()
automl.add_learner(learner_name='my_xgb1', learner_class=MyXGB1)
automl.add_learner(learner_name='my_xgb2', learner_class=MyXGB2)
settings["estimator_list"] = ['my_xgb1', 'my_xgb2'] # change the estimator list
automl.fit(X_train=X_train, y_train=y_train, **settings)