XGBRegressor does not work with HyperbandSearchCV

[vecorro]

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What happened: Got an error from XGBoost: "XGBoostError: need to call fit or load_model beforehand"

What you expected to happen: I want to use dask_ml.xgboost.XGBRegressor with dask_ml.model_selection.HyperbandSearchCV.

Minimal Complete Verifiable Example:

import dask
import dask.dataframe as dd
from distributed import Client
from dask_ml.model_selection import train_test_split
from dask_ml.xgboost import XGBRegressor
from dask_ml.model_selection import HyperbandSearchCV
from dask_ml import datasets

client = Client('10.118.232.173:8786')

X, y = datasets.make_classification(chunks=50)
param_space = {
    'n_estimators': range(100, 200, 50),
    'max_depth': range(3, 6, 2),
    'booster': ('gbtree', 'dart'),
}

model = XGBRegressor()
search = HyperbandSearchCV(model, param_space, random_state=0, patience=True, verbose=True, test_size=0.05)
search.fit(X, y)

Anything else we need to know?: Here the full error message:

_opt/conda/lib/python3.8/site-packages/sklearn/model_selection/_search.py:285: UserWarning: The total space of parameters 8 is smaller than n_iter=81. Running 8 iterations. For exhaustive searches, use GridSearchCV. warnings.warn( /opt/conda/lib/python3.8/site-packages/sklearn/model_selection/_search.py:285: UserWarning: The total space of parameters 8 is smaller than n_iter=34. Running 8 iterations. For exhaustive searches, use GridSearchCV. warnings.warn( /opt/conda/lib/python3.8/site-packages/sklearn/model_selection/_search.py:285: UserWarning: The total space of parameters 8 is smaller than n_iter=15. Running 8 iterations. For exhaustive searches, use GridSearchCV. warnings.warn(

---

XGBoostError Traceback (most recent call last)

in 11 search = HyperbandSearchCV(model, param_space, random_state=0, patience=True, verbose=True, test_size=0.05) 12 ---> 13 search.fit(X, y) /opt/conda/lib/python3.8/site-packages/dask_ml/model_selection/_incremental.py in fit(self, X, y, **fit_params) 715 client = default_client() 716 if not client.asynchronous: --> 717 return client.sync(self._fit, X, y, **fit_params) 718 return self._fit(X, y, **fit_params) 719 /opt/conda/lib/python3.8/site-packages/distributed/client.py in sync(self, func, asynchronous, callback_timeout, *args, **kwargs) 849 return future 850 else: --> 851 return sync( 852 self.loop, func, *args, callback_timeout=callback_timeout, **kwargs 853 ) /opt/conda/lib/python3.8/site-packages/distributed/utils.py in sync(loop, func, callback_timeout, *args, **kwargs) 352 if error[0]: 353 typ, exc, tb = error[0] --> 354 raise exc.with_traceback(tb) 355 else: 356 return result[0] /opt/conda/lib/python3.8/site-packages/distributed/utils.py in f() 335 if callback_timeout is not None: 336 future = asyncio.wait_for(future, callback_timeout) --> 337 result[0] = yield future 338 except Exception as exc: 339 error[0] = sys.exc_info() /opt/conda/lib/python3.8/site-packages/tornado/gen.py in run(self) 760 761 try: --> 762 value = future.result() 763 except Exception: 764 exc_info = sys.exc_info() /opt/conda/lib/python3.8/site-packages/dask_ml/model_selection/_hyperband.py in _fit(self, X, y, **fit_params) 399 _brackets_ids = list(reversed(sorted(SHAs))) 400 --> 401 _SHAs = await asyncio.gather( 402 *[SHAs[b]._fit(X, y, **fit_params) for b in _brackets_ids] 403 ) /opt/conda/lib/python3.8/site-packages/dask_ml/model_selection/_incremental.py in _fit(self, X, y, **fit_params) 661 662 with context: --> 663 results = await fit( 664 self.estimator, 665 self._get_params(), /opt/conda/lib/python3.8/site-packages/dask_ml/model_selection/_incremental.py in fit(model, params, X_train, y_train, X_test, y_test, additional_calls, fit_params, scorer, random_state, verbose, prefix) 475 A history of all models scores over time 476 """ --> 477 return await _fit( 478 model, 479 params, /opt/conda/lib/python3.8/site-packages/dask_ml/model_selection/_incremental.py in _fit(model, params, X_train, y_train, X_test, y_test, additional_calls, fit_params, scorer, random_state, verbose, prefix) 169 logger.info("[CV%s] creating %d models", prefix, len(params)) 170 for ident, param in enumerate(params): --> 171 model = client.submit(_create_model, original_model, ident, **param) 172 info[ident] = [] 173 models[ident] = model /opt/conda/lib/python3.8/site-packages/distributed/client.py in submit(self, func, key, workers, resources, retries, priority, fifo_timeout, allow_other_workers, actor, actors, pure, *args, **kwargs) 1571 if key is None: 1572 if pure: -> 1573 key = funcname(func) + "-" + tokenize(func, kwargs, *args) 1574 else: 1575 key = funcname(func) + "-" + str(uuid.uuid4()) /opt/conda/lib/python3.8/site-packages/dask/base.py in tokenize(*args, **kwargs) 793 if kwargs: 794 args = args + (kwargs,) --> 795 return md5(str(tuple(map(normalize_token, args))).encode()).hexdigest() 796 797 /opt/conda/lib/python3.8/site-packages/dask/utils.py in __call__(self, arg, *args, **kwargs) 510 """ 511 meth = self.dispatch(type(arg)) --> 512 return meth(arg, *args, **kwargs) 513 514 @property /opt/conda/lib/python3.8/site-packages/dask_ml/model_selection/_normalize.py in normalize_estimator(est) 36 continue 37 try: ---> 38 val = getattr(est, attr) 39 except (sklearn.exceptions.NotFittedError, AttributeError): 40 continue /opt/conda/lib/python3.8/site-packages/xgboost/sklearn.py in feature_importances_(self) 540 raise AttributeError('Feature importance is not defined for Booster type {}' 541 .format(self.booster)) --> 542 b = self.get_booster() 543 score = b.get_score(importance_type=self.importance_type) 544 all_features = [score.get(f, 0.) for f in b.feature_names] /opt/conda/lib/python3.8/site-packages/xgboost/sklearn.py in get_booster(self) 191 """ 192 if self._Booster is None: --> 193 raise XGBoostError('need to call fit or load_model beforehand') 194 return self._Booster 195 XGBoostError: need to call fit or load_model beforehand_ **Environment**: - Dask version: daskdev/dask:2021.5.0 Docker image - Python version: 3.8 - Operating System: Linux (Ubuntu) - Install method (conda, pip, source): Kubernetes Helm Chart. I'm running the script from the Jupyter pod in the K8 cluster.

[stsievert]

Thanks for the minimal example; I can reproduce.

I'm not sure Dask-ML's XGBRegressor and XGBoost's XGBRegressor should work with HyperbandSearchCV; they don't have the partial_fit function, a requirement for using incremental hyperparameter optimization.

It might be possible to jerry-rig XGBoost into supporting partial_fit; good references are https://github.com/dmlc/xgboost/issues/3055 and this SO question.

[vecorro]

Thanks,

I think that Dask and XGBoost make a great couple. At the moment it seems to be that more suited for situations where data fits in memory. I'm starting to use HyperbandSearchCV and it's very useful (fast and performant), I think that getting this integration would draw more people to adopt Dask for tabular data problems that where the dataset is bigger than the system memory.

Should I close the issue based on the fact that the functionality I was expecting to use actually is not implemented?

[stsievert]

I don't think so; I've filed #840 to make the error more clear.