rapidsai#4783 Added nan_euclidean distance metric to pairwise_distanc…

…es (rapidsai#4797)

Added nan_euclidean distance metric to pairwise_distances to calculate euclidean distance on data with missing values.

- Added Test cases for nan_euclidean_distance functions

Time taken to calculate:
#Data Points | Sklearn | Cuml
      10000       402 us     2.54 ms
      100k         23 ms      3.8 ms
      1M            760 ms    16 ms

GPU specifications:
- Tesla T4 15109MiB

CPU specifications:
- 11th gen intel i7, 8 cores, 16 Logical processors, 32 GB Memory
- Sklearn njobs as default

Authors:
  - https://github.com/Sreekiran096

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: rapidsai#4797

python/cuml/metrics/__init__.py

@@ -34,6 +34,7 @@
 from cuml.metrics.cluster.entropy import cython_entropy as entropy
 from cuml.metrics.pairwise_distances import pairwise_distances
 from cuml.metrics.pairwise_distances import sparse_pairwise_distances
+from cuml.metrics.pairwise_distances import nan_euclidean_distances
 from cuml.metrics.pairwise_distances import PAIRWISE_DISTANCE_METRICS
 from cuml.metrics.pairwise_distances import PAIRWISE_DISTANCE_SPARSE_METRICS
 from cuml.metrics.pairwise_kernels import pairwise_kernels
@@ -59,6 +60,7 @@
     "mutual_info_score",
     "confusion_matrix",
     "entropy",
+    "nan_euclidean_distances"
     "pairwise_distances",
     "sparse_pairwise_distances",
     "pairwise_kernels",

python/cuml/metrics/pairwise_distances.pyx

@@ -24,6 +24,8 @@ from raft.common.handle cimport handle_t
 from raft.common.handle import Handle
 import cupy as cp
 import numpy as np
+import pandas as pd
+import cudf
 import scipy
 import cupyx
 import cuml.internals
@@ -34,6 +36,7 @@ from cuml.common.sparse_utils import is_sparse
 from cuml.common.array_sparse import SparseCumlArray
 from cuml.metrics.cluster.utils import prepare_cluster_metric_inputs
 from cuml.metrics.distance_type cimport DistanceType
+from cuml.thirdparty_adapters import _get_mask
 
 cdef extern from "cuml/metrics/metrics.hpp" namespace "ML::Metrics":
     void pairwise_distance(const handle_t &handle, const double *x,
@@ -78,7 +81,8 @@ PAIRWISE_DISTANCE_METRICS = {
     "jensenshannon": DistanceType.JensenShannon,
     "hamming": DistanceType.HammingUnexpanded,
     "kldivergence": DistanceType.KLDivergence,
-    "russellrao": DistanceType.RusselRaoExpanded
+    "russellrao": DistanceType.RusselRaoExpanded,
+    "nan_euclidean": DistanceType.L2Expanded
 }
 
 PAIRWISE_DISTANCE_SPARSE_METRICS = {
@@ -117,9 +121,6 @@ def _determine_metric(metric_str, is_sparse=False):
     if metric_str == 'haversine':
         raise ValueError(" The metric: '{}', is not supported at this time."
                          .format(metric_str))
-    elif metric_str == 'nan_euclidean':
-        raise ValueError(" The metric: '{}', is not supported at this time."
-                         .format(metric_str))
 
     if not(is_sparse) and (metric_str not in PAIRWISE_DISTANCE_METRICS):
         if metric_str in PAIRWISE_DISTANCE_SPARSE_METRICS:
@@ -136,6 +137,126 @@ def _determine_metric(metric_str, is_sparse=False):
         return PAIRWISE_DISTANCE_METRICS[metric_str]
 
 
+def nan_euclidean_distances(
+    X, Y=None, *, squared=False, missing_values=cp.nan
+):
+    """Calculate the euclidean distances in the presence of missing values.
+
+    Compute the euclidean distance between each pair of samples in X and Y,
+    where Y=X is assumed if Y=None. When calculating the distance between a
+    pair of samples, this formulation ignores feature coordinates with a
+    missing value in either sample and scales up the weight of the remaining
+    coordinates:
+
+        dist(x,y) = sqrt(weight * sq. distance from present coordinates)
+        where,
+        weight = Total # of coordinates / # of present coordinates
+
+    For example, the distance between ``[3, na, na, 6]`` and ``[1, na, 4, 5]``
+    is:
+
+        .. math::
+            \\sqrt{\\frac{4}{2}((3-1)^2 + (6-5)^2)}
+
+    If all the coordinates are missing or if there are no common present
+    coordinates then NaN is returned for that pair.
+
+    Parameters
+    ----------
+    X : Dense matrix of shape (n_samples_X, n_features)
+        Acceptable formats: cuDF DataFrame, Pandas DataFrame, NumPy ndarray,
+        cuda array interface compliant array like CuPy.
+
+    Y : Dense matrix of shape (n_samples_Y, n_features), default=None
+        Acceptable formats: cuDF DataFrame, Pandas DataFrame, NumPy ndarray,
+        cuda array interface compliant array like CuPy.
+
+    squared : bool, default=False
+        Return squared Euclidean distances.
+
+    missing_values : np.nan or int, default=np.nan
+        Representation of missing value.
+
+    Returns
+    -------
+    distances : ndarray of shape (n_samples_X, n_samples_Y)
+        Returns the distances between the row vectors of `X`
+        and the row vectors of `Y`.
+    """
+
+    if isinstance(X, cudf.DataFrame) or isinstance(X, pd.DataFrame):
+        if (X.isnull().any()).any():
+            X.fillna(0, inplace=True)
+
+    if isinstance(Y, cudf.DataFrame) or isinstance(Y, pd.DataFrame):
+        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", check_dtype=[np.float32, np.float64])
+
+    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)
+
+    # Get missing mask for X
+    missing_X = _get_mask(X_m, missing_values)
+
+    # Get missing mask for Y
+    missing_Y = missing_X if Y is X else _get_mask(Y_m, missing_values)
+
+    # set missing values to zero
+    X_m[missing_X] = 0
+    Y_m[missing_Y] = 0
+
+    # Adjust distances for sqaured
+    if X_m.shape == Y_m.shape:
+        if (X_m == Y_m).all():
+            distances = cp.asarray(pairwise_distances(
+                X_m, metric="sqeuclidean"))
+        else:
+            distances = cp.asarray(pairwise_distances(
+                X_m, Y_m, metric="sqeuclidean"))
+    else:
+        distances = cp.asarray(pairwise_distances(
+            X_m, Y_m, metric="sqeuclidean"))
+
+    # Adjust distances for missing values
+    XX = X_m * X_m
+    YY = Y_m * Y_m
+    distances -= cp.dot(XX, missing_Y.T)
+    distances -= cp.dot(missing_X, YY.T)
+
+    cp.clip(distances, 0, None, out=distances)
+
+    if X_m is Y_m:
+        # Ensure that distances between vectors and themselves are set to 0.0.
+        # This may not be the case due to floating point rounding errors.
+        cp.fill_diagonal(distances, 0.0)
+
+    present_X = 1 - missing_X
+    present_Y = present_X if Y_m is X_m else ~missing_Y
+    present_count = cp.dot(present_X, present_Y.T)
+    distances[present_count == 0] = cp.nan
+
+    # avoid divide by zero
+    cp.maximum(1, present_count, out=present_count)
+    distances /= present_count
+    distances *= X_m.shape[1]
+
+    if not squared:
+        cp.sqrt(distances, out=distances)
+
+    return distances
+
+
 @cuml.internals.api_return_array(get_output_type=True)
 def pairwise_distances(X, Y=None, metric="euclidean", handle=None,
                        convert_dtype=True, metric_arg=2, **kwds):
@@ -225,6 +346,9 @@ def pairwise_distances(X, Y=None, metric="euclidean", handle=None,
     handle = Handle() if handle is None else handle
     cdef handle_t *handle_ = <handle_t*> <size_t> handle.getHandle()
 
+    if metric in ['nan_euclidean']:
+        return nan_euclidean_distances(X, Y, **kwds)
+
     if metric in ['russellrao'] and not np.all(X.data == 1.):
         warnings.warn("X was converted to boolean for metric {}"
                       .format(metric))

python/cuml/tests/test_metrics.py

@@ -177,7 +177,7 @@ def test_sklearn_search():
 def test_accuracy(nrows, ncols, n_info, datatype):
 
     use_handle = True
-    train_rows = np.int32(nrows*0.8)
+    train_rows = np.int32(nrows * 0.8)
     X, y = make_classification(n_samples=nrows, n_features=ncols,
                                n_clusters_per_class=1, n_informative=n_info,
                                random_state=123, n_classes=5)
@@ -251,7 +251,7 @@ def test_rand_index_score(name, nrows):
 def test_silhouette_score_batched(metric, chunk_divider, labeled_clusters):
     X, labels = labeled_clusters
     cuml_score = cu_silhouette_score(X, labels, metric=metric,
-                                     chunksize=int(X.shape[0]/chunk_divider))
+                                     chunksize=int(X.shape[0] / chunk_divider))
     sk_score = sk_silhouette_score(X, labels, metric=metric)
     assert_almost_equal(cuml_score, sk_score, decimal=2)
 
@@ -988,6 +988,7 @@ def test_pairwise_distances_sklearn_comparison(metric: str, matrix_size):
     # For fp64, compare at 10 decimals, (5 places less than the ~15 max)
     compare_precision = 10
 
+    print(X.shape, Y.shape, metric)
     # Compare to sklearn, fp64
     S = pairwise_distances(X, Y, metric=metric)
 
@@ -1076,7 +1077,7 @@ def test_pairwise_distances_one_dimension_order(metric: str):
     cp.testing.assert_array_almost_equal(S, S2, decimal=compare_precision)
 
 
-@pytest.mark.parametrize("metric", ["haversine", "nan_euclidean"])
+@pytest.mark.parametrize("metric", ["haversine"])
 def test_pairwise_distances_unsuppored_metrics(metric):
     rng = np.random.RandomState(3)
 
@@ -1397,7 +1398,7 @@ def test_sparse_pairwise_distances_output_types(input_type, output_type):
 @pytest.mark.parametrize("input_type", ["cudf", "cupy"])
 @pytest.mark.parametrize("n_classes", [2, 5])
 def test_hinge_loss(nrows, ncols, n_info, input_type, n_classes):
-    train_rows = np.int32(nrows*0.8)
+    train_rows = np.int32(nrows * 0.8)
     X, y = make_classification(n_samples=nrows, n_features=ncols,
                                n_clusters_per_class=1, n_informative=n_info,
                                random_state=123, n_classes=n_classes)