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Apply new validation to metrics.pairwise_distances #8061

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120 changes: 68 additions & 52 deletions python/cuml/cuml/metrics/pairwise_distances.pyx
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Original file line number Diff line number Diff line change
Expand Up @@ -10,11 +10,12 @@ import numpy as np
import pandas as pd
import scipy.sparse

from cuml.common import CumlArray, input_to_cuml_array
from cuml.common import CumlArray
from cuml.common.sparse_utils import is_sparse
from cuml.internals import get_handle, reflect
from cuml.internals.array_sparse import SparseCumlArray
from cuml.internals.input_utils import sparse_scipy_to_cp
from cuml.internals.validation import check_array
from cuml.thirdparty_adapters import _get_mask

from libc.stdint cimport uintptr_t
Expand Down Expand Up @@ -176,23 +177,27 @@ def nan_euclidean_distances(
if (Y.isnull().any()).any():
Y.fillna(0, inplace=True)

X_m, _n_samples_x, _n_features_x, dtype_x = \
input_to_cuml_array(X,
order="K",
convert_to_dtype=(np.float32 if convert_dtype
else None),
check_dtype=[np.float32, np.float64])
X_m = check_array(
X,
order="A",
dtype=[np.float32, np.float64],
convert_dtype=convert_dtype,
ensure_all_finite=False,
input_name="X",
)
dtype_x = X_m.dtype

if Y is None:
Y = X_m

Y_m, _n_samples_y, _n_features_y, _dtype_y = \
input_to_cuml_array(
Y, order=X_m.order, convert_to_dtype=dtype_x,
check_dtype=[dtype_x])

X_m = cp.asarray(X_m)
Y_m = cp.asarray(Y_m)
Y_m = X_m
else:
Y_m = check_array(
Y,
order="F" if X_m.flags.f_contiguous else "C",
dtype=[dtype_x],
convert_dtype=convert_dtype,
ensure_all_finite=False,
input_name="Y",
)

# Get missing mask for X
missing_X = _get_mask(X_m, missing_values)
Expand Down Expand Up @@ -347,49 +352,60 @@ def pairwise_distances(
X = np.where(X != 0., 1.0, 0.0)

# Get the input arrays, preserve order and type where possible
X_m, n_samples_x, n_features_x, dtype_x = \
input_to_cuml_array(X,
order="K",
convert_to_dtype=(np.float32 if convert_dtype
else None),
check_dtype=[np.float32, np.float64])

# Get the order from the CumlArray
input_order = X_m.order
X_m = check_array(
X,
order="A",
dtype=[np.float32, np.float64],
convert_dtype=convert_dtype,
input_name="X",
)
cdef int n_samples_x = X_m.shape[0]
cdef int n_features_x = X_m.shape[1]
dtype_x = X_m.dtype

# Derive the row/column-major flag from X. For degenerate shapes
# (1 sample or 1 feature) X is both C- and F-contiguous, so we'll
# re-derive from Y below if Y is provided.
cdef bint is_row_major = X_m.flags.c_contiguous

cdef uintptr_t d_X_ptr
cdef uintptr_t d_Y_ptr
cdef uintptr_t d_dest_ptr

if (Y is not None):

# Check for the odd case where one dimension of X is 1. In this case,
# CumlArray always returns order=="C" so instead get the order from Y
if (n_samples_x == 1 or n_features_x == 1):
input_order = "K"
cdef int n_samples_y, n_features_y

if (Y is not None):
if metric in ['russellrao'] and not np.all(Y.data == 1.):
warnings.warn("Y was converted to boolean for metric {}"
.format(metric))
Y = np.where(Y != 0., 1.0, 0.0)

Y_m, n_samples_y, n_features_y, dtype_y = \
input_to_cuml_array(Y, order=input_order,
convert_to_dtype=(dtype_x if convert_dtype
else None),
check_dtype=[dtype_x])
# Get the order from Y if necessary (It's possible to set order="F" in
# input_to_cuml_array and have Y_m.order=="C")
if (input_order == "K"):
input_order = Y_m.order
# When X is degenerate (1 sample or 1 feature) we cannot tell the
# intended layout from X alone, so let Y choose by allowing 'A'.
# Otherwise force Y's layout to match X's.
if n_samples_x == 1 or n_features_x == 1:
y_order = "A"
else:
y_order = "C" if is_row_major else "F"

Y_m = check_array(
Y,
order=y_order,
dtype=[dtype_x],
convert_dtype=convert_dtype,
input_name="Y",
)
n_samples_y = Y_m.shape[0]
n_features_y = Y_m.shape[1]

# If we let Y choose the layout, propagate it back.
if y_order == "A":
is_row_major = Y_m.flags.c_contiguous
else:
# Shallow copy X variables
Y_m = X_m
n_samples_y = n_samples_x
n_features_y = n_features_x
dtype_y = dtype_x

is_row_major = input_order == "C"

# Check feature sizes are equal
if (n_features_x != n_features_y):
Expand All @@ -402,10 +418,10 @@ def pairwise_distances(

# Create the output array
dest_m = CumlArray.zeros((n_samples_x, n_samples_y), dtype=dtype_x,
order=input_order)
order="C" if is_row_major else "F")

d_X_ptr = X_m.ptr
d_Y_ptr = Y_m.ptr
d_X_ptr = X_m.data.ptr
d_Y_ptr = Y_m.data.ptr
d_dest_ptr = dest_m.ptr

# Now execute the functions
Expand All @@ -414,9 +430,9 @@ def pairwise_distances(
<float*> d_X_ptr,
<float*> d_Y_ptr,
<float*> d_dest_ptr,
<int> n_samples_x,
<int> n_samples_y,
<int> n_features_x,
n_samples_x,
n_samples_y,
n_features_x,
<DistanceType> metric_val,
<bool> is_row_major,
<float> metric_arg)
Expand All @@ -425,9 +441,9 @@ def pairwise_distances(
<double*> d_X_ptr,
<double*> d_Y_ptr,
<double*> d_dest_ptr,
<int> n_samples_x,
<int> n_samples_y,
<int> n_features_x,
n_samples_x,
n_samples_y,
n_features_x,
<DistanceType> metric_val,
<bool> is_row_major,
<double> metric_arg)
Expand Down
2 changes: 1 addition & 1 deletion python/cuml/tests/test_metrics.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1317,7 +1317,7 @@ def test_pairwise_distances_exceptions():
pairwise_distances(X_double, X_int, metric="euclidean")

# Test sending different types with convert_dtype=False
with pytest.raises(TypeError):
with pytest.raises(ValueError, match="dtype"):
pairwise_distances(
X_double, X_float, metric="euclidean", convert_dtype=False
)
Expand Down
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