[BUG] Confusion matrix should convert dtype as necessary

[beckernick]

confusion_matrix should automatically convert dtypes as appropriate in order to avoid failing, like other metric functions.

from sklearn.metrics import confusion_matrix
import numpy as np
import cuml
​
y = np.array([0.0, 1.0, 0.0])
y_pred = np.array([0.0, 1.0, 1.0])
print(confusion_matrix(y, y_pred))
cuml.metrics.confusion_matrix(y, y_pred)
[[1 1]
 [0 1]]
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-12-7e819a19dcd9> in <module>
      6 y_pred = np.array([0.0, 1.0, 1.0])
      7 print(confusion_matrix(y, y_pred))
----> 8 cuml.metrics.confusion_matrix(y, y_pred)

/raid/nicholasb/miniconda3/envs/rapids-gpu-bdb-automated-tests/lib/python3.7/site-packages/cuml/internals/api_decorators.py in inner_with_getters(*args, **kwargs)
    464 
    465                 # Call the function
--> 466                 ret_val = func(*args, **kwargs)
    467 
    468             return cm.process_return(ret_val)

/raid/nicholasb/miniconda3/envs/rapids-gpu-bdb-automated-tests/lib/python3.7/site-packages/cuml/metrics/confusion_matrix.py in confusion_matrix(y_true, y_pred, labels, sample_weight, normalize)
     60     """
     61     y_true, n_rows, n_cols, dtype = \
---> 62         input_to_cuml_array(y_true, check_dtype=[cp.int32, cp.int64])
     63 
     64     y_pred, _, _, _ = \

/raid/nicholasb/miniconda3/envs/rapids-gpu-bdb-automated-tests/lib/python3.7/site-packages/cuml/internals/api_decorators.py in inner(*args, **kwargs)
    359         def inner(*args, **kwargs):
    360             with self._recreate_cm(func, args):
--> 361                 return func(*args, **kwargs)
    362 
    363         return inner

/raid/nicholasb/miniconda3/envs/rapids-gpu-bdb-automated-tests/lib/python3.7/site-packages/cuml/common/input_utils.py in input_to_cuml_array(X, order, deepcopy, check_dtype, convert_to_dtype, check_cols, check_rows, fail_on_order, force_contiguous)
    360             del X_m
    361             raise TypeError("Expected input to be of type in " +
--> 362                             str(check_dtype) + " but got " + str(type_str))
    363 
    364     # Checks based on parameters

TypeError: Expected input to be of type in [dtype('int32'), dtype('int64')] but got float64

conda list | grep "rapids\|scikit-learn"
# packages in environment at /raid/nicholasb/miniconda3/envs/rapids-gpubdb-20210301:
cudf                      0.19.0a210301   cuda_10.2_py37_gf79a841f92_147    rapidsai-nightly
cuml                      0.19.0a210301   cuda10.2_py37_g8fa2b9067_88    rapidsai-nightly
dask-cuda                 0.19.0a210301           py37_34    rapidsai-nightly
dask-cudf                 0.19.0a210301   py37_gf79a841f92_147    rapidsai-nightly
libcudf                   0.19.0a210301   cuda10.2_gf79a841f92_147    rapidsai-nightly
libcuml                   0.19.0a210301   cuda10.2_g8fa2b9067_88    rapidsai-nightly
libcumlprims              0.19.0a210210   cuda10.2_g269fe04_0    rapidsai-nightly
librmm                    0.19.0a210301   cuda10.2_g38a350f_32    rapidsai-nightly
rmm                       0.19.0a210301   cuda_10.2_py37_g38a350f_32    rapidsai-nightly
scikit-learn              0.24.1           py37h69acf81_0    conda-forge
ucx                       1.9.0+gcd9efd3       cuda10.2_0    rapidsai-nightly
ucx-proc                  1.0.0                       gpu    rapidsai-nightly
ucx-py                    0.19.0a210301   py37_gcd9efd3_16    rapidsai-nightly

[esnvidia]

Adding to this bug:

Code:

from cuml.metrics import confusion_matrix
print(confusion_matrix(y_true=labels.type(dtype=torch.int32).to(device),
                                   y_pred=pred.argmax(1).type(dtype=torch.int32).to(device)
                                   ))

---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
<ipython-input-23-b32704b34c7b> in <module>
     20 
     21     print(confusion_matrix(y_true=labels.type(dtype=torch.int32).to(device),
---> 22                            y_pred=pred.argmax(1).type(dtype=torch.int32).to(device)))


/opt/conda/envs/rapids/lib/python3.7/site-packages/cuml/internals/api_decorators.py in inner_with_getters(*args, **kwargs)
    464 
    465                 # Call the function
--> 466                 ret_val = func(*args, **kwargs)
    467 
    468             return cm.process_return(ret_val)

/opt/conda/envs/rapids/lib/python3.7/site-packages/cuml/metrics/confusion_matrix.py in confusion_matrix(y_true, y_pred, labels, sample_weight, normalize)
     67 
     68     if labels is None:
---> 69         labels = sorted_unique_labels(y_true, y_pred)
     70         n_labels = len(labels)
     71     else:

/opt/conda/envs/rapids/lib/python3.7/site-packages/cuml/metrics/utils.py in sorted_unique_labels(*ys)
     22     labels."""
     23     ys = (cp.unique(y) for y in ys)
---> 24     labels = cp.unique(cp.concatenate(ys))
     25     return labels

/opt/conda/envs/rapids/lib/python3.7/site-packages/cupy/manipulation/join.py in concatenate(tup, axis, out)
     55         tup = [m.ravel() for m in tup]
     56         axis = 0
---> 57     return core.concatenate_method(tup, axis, out)
     58 
     59 

cupy/core/_routines_manipulation.pyx in cupy.core._routines_manipulation.concatenate_method()

cupy/core/_routines_manipulation.pyx in cupy.core._routines_manipulation.concatenate_method()

/opt/conda/envs/rapids/lib/python3.7/site-packages/cuml/metrics/utils.py in <genexpr>(.0)
     21     """Extract an ordered array of unique labels from one or more arrays of
     22     labels."""
---> 23     ys = (cp.unique(y) for y in ys)
     24     labels = cp.unique(cp.concatenate(ys))
     25     return labels

/opt/conda/envs/rapids/lib/python3.7/site-packages/cupy/manipulation/add_remove.py in unique(ar, return_index, return_inverse, return_counts, axis)
    110         aux = ar[perm]
    111     else:
--> 112         ar.sort()
    113         aux = ar
    114     mask = cupy.empty(aux.shape, dtype=cupy.bool_)

cupy/core/core.pyx in cupy.core.core.ndarray.sort()

cupy/core/core.pyx in cupy.core.core.ndarray.sort()

cupy/core/_routines_sorting.pyx in cupy.core._routines_sorting._ndarray_sort()

cupy/cuda/thrust.pyx in cupy.cuda.thrust.sort()

RuntimeError: radix_sort: failed on 1st step: cudaErrorInvalidDevice: invalid device ordinal

Code that does work by converting torch.tensor with .cpu():

from sklearn.metrics import confusion_matrix
 
 print(confusion_matrix(y_true=labels.type(dtype=torch.int32).cpu(),
                                    y_pred=pred.argmax(1).type(dtype=torch.int32).cpu()
                                    ))
                                    
[[1      2]
 [  3      4]]

[divyegala]

@JohnZed @dantegd should we divide this issue into two parts, or should I look to solve this in one PR? The second bug added here seems like it's going something to do with torch so that would involve more testing

[JohnZed]

Yes, should split it into two separate issues, agreed