Add AD kNN

docs/modules/applicability_domain.rst

@@ -18,5 +18,6 @@ Classes for checking applicability domain.
     ConvexHullADChecker
     DistanceToCentroidADChecker
     HotellingT2TestADChecker
+    KNNADChecker
     LeverageADChecker
     PCABoundingBoxADChecker

examples/09_molecular_filters.ipynb

@@ -711,7 +711,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -725,7 +725,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.18"
+   "version": "3.11.4"
   }
  },
  "nbformat": 4,

skfp/applicability_domain/__init__.py

@@ -2,5 +2,6 @@
 from .convex_hull import ConvexHullADChecker
 from .distance_to_centroid import DistanceToCentroidADChecker
 from .hotelling_t2_test import HotellingT2TestADChecker
+from .knn import KNNADChecker
 from .leverage import LeverageADChecker
 from .pca_bounding_box import PCABoundingBoxADChecker

skfp/applicability_domain/knn.py

@@ -0,0 +1,210 @@
+from collections.abc import Callable
+from numbers import Integral, Real
+
+import numpy as np
+from sklearn.neighbors import NearestNeighbors
+from sklearn.utils._param_validation import Interval, InvalidParameterError, StrOptions
+from sklearn.utils.validation import check_is_fitted, validate_data
+
+from skfp.bases.base_ad_checker import BaseADChecker
+from skfp.distances import (
+    _BULK_METRIC_NAMES as SKFP_BULK_METRIC_NAMES,
+)
+from skfp.distances import (
+    _BULK_METRICS as SKFP_BULK_METRICS,
+)
+from skfp.distances import (
+    _METRIC_NAMES as SKFP_METRIC_NAMES,
+)
+from skfp.distances import (
+    _METRICS as SKFP_METRICS,
+)
+
+METRIC_FUNCTIONS = {**SKFP_METRICS, **SKFP_BULK_METRICS}
+METRIC_NAMES = set(SKFP_METRIC_NAMES) | set(SKFP_BULK_METRIC_NAMES)
+
+
+class KNNADChecker(BaseADChecker):
+    r"""
+    k-nearest neighbors applicability domain checker.
+
+    This method determines whether a query molecule falls within the applicability
+    domain by comparing its distance to k nearest neighbors [1]_ [2]_ [3]_ in the training set,
+    using a threshold derived from the training data.
+
+    The applicability domain is defined as one of:
+
+    - the mean distance to k nearest neighbors,
+    - the distance to k-th nearest neighbor (max distance),
+    - the distance to the closest neighbor from the training set (min distance)
+
+    A threshold is then set at the 95th percentile of these aggregated distances.
+    Query molecules with an aggregated distance to their k nearest neighbors below
+    this threshold are considered within the applicability domain.
+
+    This implementation supports binary and count Tanimoto similarity metrics.
+
+    Parameters
+    ----------
+    k : int, default=1
+        Number of nearest neighbors to consider for distance calculations.
+        Must be smaller than the number of training samples.
+
+    metric: Callable or string, default="tanimoto_binary_distance"
+        Distance metric to use.
+
+    agg: "mean" or "max" or "min", default="mean"
+        Aggregation method for distances to k nearest neigbors:
+            - "mean": use the mean distance to k neighbors,
+            - "max": use the maximum distance among k neighbors,
+            - "min": use the distance to the closest neigbor.
+
+    n_jobs : int, default=None
+        The number of jobs to run in parallel. :meth:`transform_x_y` and
+        :meth:`transform` are parallelized over the input molecules. ``None`` means 1
+        unless in a :obj:`joblib.parallel_backend` context. ``-1`` means using all
+        processors. See scikit-learn documentation on ``n_jobs`` for more details.
+
+    verbose : int or dict, default=0
+        Controls the verbosity when filtering molecules.
+        If a dictionary is passed, it is treated as kwargs for ``tqdm()``,
+        and can be used to control the progress bar.
+
+    References
+    ----------
+    .. [1] `Klingspohn, W., Mathea, M., ter Laak, A. et al.
+        "Efficiency of different measures for defining the applicability domain of
+        classification models."
+        J Cheminform 9, 44 (2017)
+        <https://doi.org/10.1186/s13321-017-0230-2>`_
+
+    .. [2] `Harmeling, S., Dornhege G., Tax D., Meinecke F., Müller KR.
+        "From outliers to prototypes: Ordering data."
+        Neurocomputing, 69, 13, pages 1608-1618, (2006)
+        <https://doi.org/10.1016/j.neucom.2005.05.015>`_
+
+    .. [3] `Kar S., Roy K., Leszczynski J.
+        "Applicability Domain: A Step Toward Confident Predictions and Decidability for QSAR Modeling"
+        Methods Mol Biol, 1800, pages 141-169, (2018)
+        <https://doi.org/10.1007/978-1-4939-7899-1_6>`_
+
+    Examples
+    --------
+    >>> from skfp.applicability_domain import KNNADChecker
+    >>> import numpy as np
+    >>> X_train_binary = np.array([
+    ...     [1, 1, 1],
+    ...     [0, 1, 1],
+    ...     [0, 0, 1]
+    ... ])
+    >>> X_test_binary = 1 - X_train_binary
+    >>> knn_ad_checker_binary = KNNADChecker(k=2, metric="tanimoto_binary_distance", agg="mean")
+    >>> knn_ad_checker_binary
+    KNNADChecker()
+
+    >>> knn_ad_checker_binary.fit(X_train_binary)
+    KNNADChecker()
+
+    >>> knn_ad_checker_binary.predict(X_test_binary)
+    array([False, False, False])
+
+    """
+
+    _parameter_constraints: dict = {
+        **BaseADChecker._parameter_constraints,
+        "k": [Interval(Integral, 1, None, closed="left")],
+        "metric": [callable, StrOptions(METRIC_NAMES)],
+        "agg": [StrOptions({"mean", "max", "min"})],
+        "threshold": [None, Interval(Real, 0, 1, closed="both")],
+    }
+
+    def __init__(
+        self,
+        k: int = 1,
+        metric: str | Callable = "tanimoto_binary_distance",
+        agg: str = "mean",
+        threshold: float = 0.95,
+        n_jobs: int | None = None,
+        verbose: int | dict = 0,
+    ):
+        super().__init__(
+            n_jobs=n_jobs,
+            verbose=verbose,
+        )
+        self.k = k
+        self.metric = metric
+        self.agg = agg
+        self.threshold = threshold
+
+    def _validate_params(self) -> None:
+        super()._validate_params()
+        if isinstance(self.metric, str) and self.metric not in METRIC_FUNCTIONS:
+            raise InvalidParameterError(
+                f"Allowed metrics: {METRIC_NAMES}. Got: {self.metric}"
+            )
+
+    def fit(  # noqa: D102
+        self,
+        X: np.ndarray,
+        y: np.ndarray | None = None,  # noqa: ARG002
+    ):
+        X = validate_data(self, X=X)
+        if self.k > X.shape[0]:
+            raise ValueError(
+                f"k ({self.k}) must be smaller than or equal to the number of training samples ({X.shape[0]})"
+            )
+
+        self.X_train_ = X
+        self.k_used = 1 if self.agg == "min" else self.k
+
+        if callable(self.metric):
+            metric_func = self.metric
+        elif isinstance(self.metric, str) and self.metric in METRIC_FUNCTIONS:
+            metric_func = METRIC_FUNCTIONS[self.metric]
+        else:
+            raise KeyError(
+                f"Unknown metric: {self.metric}. Must be a callable or one of {list(METRIC_FUNCTIONS.keys())}"
+            )
+
+        self.knn_ = NearestNeighbors(
+            n_neighbors=self.k_used, metric=metric_func, n_jobs=self.n_jobs
+        )
+        self.knn_.fit(X)
+        k_nearest, _ = self.knn_.kneighbors(X)
+
+        agg_dists = self._get_agg_dists(k_nearest)
+        self.threshold_ = np.percentile(agg_dists, self.threshold)
+
+    def predict(self, X: np.ndarray) -> np.ndarray:  # noqa: D102
+        return self.score_samples(X) <= self.threshold_
+
+    def score_samples(self, X: np.ndarray) -> np.ndarray:
+        """
+        Calculate the applicability domain score of samples. It is simply a 0/1
+        decision equal to ``.predict()``.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            The data matrix.
+
+        Returns
+        -------
+        scores : ndarray of shape (n_samples,)
+            Applicability domain scores of samples.
+        """
+        check_is_fitted(self)
+        X = validate_data(self, X=X, reset=False)
+        k_nearest, _ = self.knn_.kneighbors(X, n_neighbors=self.k_used)
+
+        return self._get_agg_dists(k_nearest)
+
+    def _get_agg_dists(self, k_nearest) -> np.ndarray[float]:
+        if self.agg == "mean":
+            agg_dists = np.mean(k_nearest, axis=1)
+        elif self.agg == "max":
+            agg_dists = np.max(k_nearest, axis=1)
+        elif self.agg == "min":
+            agg_dists = np.min(k_nearest, axis=1)
+
+        return agg_dists

tests/applicability_domain/knn.py

@@ -0,0 +1,115 @@
+import numpy as np
+import pytest
+
+from skfp.applicability_domain import KNNADChecker
+from tests.applicability_domain.utils import get_data_inside_ad, get_data_outside_ad
+
+ALLOWED_METRICS = [
+    "tanimoto_binary_distance",
+    "tanimoto_count_distance",
+]
+
+ALLOWED_AGGS = ["mean", "max", "min"]
+
+
+@pytest.mark.parametrize("metric", ALLOWED_METRICS)
+@pytest.mark.parametrize("agg", ALLOWED_AGGS)
+def test_inside_knn_ad(metric, agg):
+    if "binary" in metric:
+        X_train, X_test = get_data_inside_ad(binarize=True)
+    else:
+        X_train, X_test = get_data_inside_ad()
+
+    ad_checker = KNNADChecker(k=3, agg=agg)
+    ad_checker.fit(X_train)
+
+    scores = ad_checker.score_samples(X_test)
+    assert scores.shape == (len(X_test),)
+
+    preds = ad_checker.predict(X_test)
+    assert isinstance(preds, np.ndarray)
+    assert np.issubdtype(preds.dtype, np.bool_)
+    assert preds.shape == (len(X_test),)
+    assert np.all(preds == 1)
+
+
+@pytest.mark.parametrize("metric", ALLOWED_METRICS)
+@pytest.mark.parametrize("agg", ALLOWED_AGGS)
+def test_outside_knn_ad(metric, agg):
+    if "binary" in metric:
+        X_train, X_test = get_data_outside_ad(binarize=True)
+    else:
+        X_train, X_test = get_data_outside_ad()
+
+    ad_checker = KNNADChecker(k=3, metric=metric, agg=agg)
+    ad_checker.fit(X_train)
+
+    scores = ad_checker.score_samples(X_test)
+    assert np.all(scores >= 0)
+
+    preds = ad_checker.predict(X_test)
+    print(preds)
+    assert isinstance(preds, np.ndarray)
+    assert np.issubdtype(preds.dtype, np.bool_)
+    assert preds.shape == (len(X_test),)
+    assert np.all(preds == 0)
+
+
+@pytest.mark.parametrize("metric", ALLOWED_METRICS)
+@pytest.mark.parametrize("agg", ALLOWED_AGGS)
+def test_knn_different_k_values(metric, agg):
+    if "binary" in metric:
+        X_train, X_test = get_data_inside_ad(binarize=True)
+    else:
+        X_train, X_test = get_data_inside_ad()
+
+    # smaller k, stricter check
+    ad_checker_k1 = KNNADChecker(k=1, metric=metric, agg=agg)
+    ad_checker_k1.fit(X_train)
+    passed_k1 = ad_checker_k1.predict(X_test).sum()
+
+    # larger k, potentially less strict
+    ad_checker_k5 = KNNADChecker(k=5, metric=metric, agg=agg)
+    ad_checker_k5.fit(X_train)
+    passed_k5 = ad_checker_k5.predict(X_test).sum()
+
+    # both should be valid results
+    assert isinstance(passed_k1, (int, np.integer))
+    assert isinstance(passed_k5, (int, np.integer))
+
+
+@pytest.mark.parametrize("metric", ALLOWED_METRICS)
+@pytest.mark.parametrize("agg", ALLOWED_AGGS)
+def test_knn_pass_y_train(metric, agg):
+    # smoke test, should not throw errors
+    if "binary" in metric:
+        X_train, _ = get_data_inside_ad(binarize=True)
+    else:
+        X_train, _ = get_data_inside_ad()
+
+    y_train = np.zeros(len(X_train))
+    ad_checker = KNNADChecker(k=3, metric=metric, agg=agg)
+    ad_checker.fit(X_train, y_train)
+
+
+@pytest.mark.parametrize("metric", ALLOWED_METRICS)
+@pytest.mark.parametrize("agg", ALLOWED_AGGS)
+def test_knn_invalid_k(metric, agg):
+    if "binary" in metric:
+        X_train, _ = get_data_inside_ad(binarize=True)
+    else:
+        X_train, _ = get_data_inside_ad()
+
+    with pytest.raises(
+        ValueError,
+        match=r"k \(\d+\) must be smaller than or equal to the number of training samples \(\d+\)",
+    ):
+        ad_checker = KNNADChecker(k=len(X_train) + 1, metric=metric, agg=agg)
+        ad_checker.fit(X_train)
+
+
+def test_knn_invalid_metric():
+    X_train, _ = get_data_inside_ad()
+    ad_checker = KNNADChecker(k=3, metric="euclidean")
+    with pytest.raises(KeyError):
+        ad_checker.fit(X_train)