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Using the regression metrics like dask_ml.metrics.regression.mean_squared_error, I expected that passing y_true and y_pred with shape (n_samples, n_outputs) and multoutput="raw_values" would return:
a Dask Array with shape (n_outputs,), if compute=False
a numpy array with shape (n_outputs,), if compute=True
What you expected to happen:
Using dask_ml.metrics.regression.mean_squared_error() or dask_ml.merics.regression.mean_absolute_error() with multioutput="raw_values" always returns a Dask Array.
What happened:
Using the regression metrics like
dask_ml.metrics.regression.mean_squared_error
, I expected that passingy_true
andy_pred
with shape(n_samples, n_outputs)
andmultoutput="raw_values"
would return:(n_outputs,)
, ifcompute=False
numpy
array with shape(n_outputs,)
, ifcompute=True
What you expected to happen:
Using
dask_ml.metrics.regression.mean_squared_error()
ordask_ml.merics.regression.mean_absolute_error()
withmultioutput="raw_values"
always returns a Dask Array.Minimal Complete Verifiable Example:
Anything else we need to know?:
I started looking into this while working on #756
Environment:
output of 'conda list | grep -E "dask|distributed"'
output of 'conda info'
python setup.py install
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