---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-13-23f3b9353ae8> in <module>
3
4 est=XGBClassifier()
----> 5 est.fit(X_train, y_train)
~/anaconda3/lib/python3.7/site-packages/dask_xgboost/core.py in fit(self, X, y, classes, eval_set, sample_weight_eval_set, eval_metric, early_stopping_rounds)
515 missing=self.missing,
516 n_jobs=self.n_jobs,
--> 517 early_stopping_rounds=early_stopping_rounds,
518 )
519
~/anaconda3/lib/python3.7/site-packages/dask_xgboost/core.py in train(client, params, data, labels, dmatrix_kwargs, **kwargs)
240 """
241 return client.sync(
--> 242 _train, client, params, data, labels, dmatrix_kwargs, **kwargs
243 )
244
~/anaconda3/lib/python3.7/site-packages/distributed/client.py in sync(self, func, asynchronous, callback_timeout, *args, **kwargs)
754 else:
755 return sync(
--> 756 self.loop, func, *args, callback_timeout=callback_timeout, **kwargs
757 )
758
~/anaconda3/lib/python3.7/site-packages/distributed/utils.py in sync(loop, func, callback_timeout, *args, **kwargs)
331 if error[0]:
332 typ, exc, tb = error[0]
--> 333 raise exc.with_traceback(tb)
334 else:
335 return result[0]
~/anaconda3/lib/python3.7/site-packages/distributed/utils.py in f()
315 if callback_timeout is not None:
316 future = gen.with_timeout(timedelta(seconds=callback_timeout), future)
--> 317 result[0] = yield future
318 except Exception as exc:
319 error[0] = sys.exc_info()
~/anaconda3/lib/python3.7/site-packages/tornado/gen.py in run(self)
733
734 try:
--> 735 value = future.result()
736 except Exception:
737 exc_info = sys.exc_info()
~/anaconda3/lib/python3.7/site-packages/tornado/gen.py in run(self)
746 exc_info = None
747 else:
--> 748 yielded = self.gen.send(value)
749
750 except (StopIteration, Return) as e:
~/anaconda3/lib/python3.7/site-packages/dask_xgboost/core.py in _train(client, params, data, labels, dmatrix_kwargs, **kwargs)
182
183 # Start the XGBoost tracker on the Dask scheduler
--> 184 host, port = parse_host_port(client.scheduler.address)
185 env = yield client._run_on_scheduler(
186 start_tracker, host.strip("/:"), len(worker_map)
~/anaconda3/lib/python3.7/site-packages/dask_xgboost/core.py in parse_host_port(address)
29 if "://" in address:
30 address = address.rsplit("://", 1)[1]
---> 31 host, port = address.split(":")
32 port = int(port)
33 return host, port
ValueError: not enough values to unpack (expected 2, got 1)
import pandas as pd
from dask.distributed import Client, progress
#from sklearn.ensemble import RandomForestClassifier
#from sklearn.model_selection import train_test_split
import joblib
from dask import dataframe as ddf
import numpy as np
from dask_ml.model_selection import train_test_split
from dask_ml.xgboost import XGBClassifier, train, predict
client=Client(processes=False,threads_per_worker=8,n_workers=1, memory_limit="16GB")
#split dataset into train and test
X_train, X_test, y_train, y_test = train_test_split(df["Store Area"], df[Y_column])
est=XGBClassifier()
est.fit(X_train, y_train)
I'm getting the following error:
While trying to fit the model:
Any idea why this might be happening?