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---------

Co-authored-by: Matt Gregory <matt.gregory@oregonstate.edu>
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@aazuspan @grovduck
aazuspan and grovduck committed Jul 24, 2024
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43 changes: 24 additions & 19 deletions src/sknnr/_gnn.py
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Original file line number Diff line number Diff line change
Expand Up @@ -14,8 +14,8 @@ class GNNRegressor(YFitMixin, TransformedKNeighborsRegressor):
Regression using Gradient Nearest Neighbor (GNN) imputation.
The target is predicted by local interpolation of the targets associated with
the nearest neighbors in the training set, calculated in transformed Canonical
Correspondence Analysis (CCA) space.
the nearest neighbors in the training set, with distances calculated in transformed
Canonical Correspondence Analysis (CCA) space.
See `sklearn.neighbors.KNeighborsRegressor` for more information on parameters
and implementation.
Expand All @@ -24,46 +24,51 @@ class GNNRegressor(YFitMixin, TransformedKNeighborsRegressor):
----------
n_neighbors : int, default=5
Number of neighbors to use by default for `kneighbors` queries.
n_components : int, default=None
Number of components to keep during CCA transformation. If None, all
components are kept. If n_components is less than the number of available
n_components : int, optional
Number of components to keep during CCA transformation. If `None`, all
components are kept. If `n_components` is greater than the number of available
components, an error will be raised.
weights : {'uniform', 'distance'} or callable, default='uniform'
Weight function used in prediction.
algorithm : {'auto', 'ball_tree', 'kd_tree', 'brute'}, default='auto'
Algorithm used to compute the nearest neighbors.
leaf_size : int, default=30
Leaf size passed to BallTree or KDTree.
Leaf size passed to `BallTree` or `KDTree`.
p : int, default=2
Power parameter for the Minkowski metric.
metric : str or callable, default='minkowski'
The distance metric to use for the tree, calculated in CCA space.
metric_params : dict, default=None
Additional keyword arguments for the metric function.
n_jobs : int, default=None
The number of parallel jobs to run for neighbors search. None means 1 unless
in a joblib.parallel_backend context. -1 means using all processors.
n_jobs : int, optional
The number of parallel jobs to run for neighbors search. `None` means 1 unless
in a `joblib.parallel_backend` context. `-1` means using all processors.
Attributes
----------
effective_metric_ : str or callable
The distance metric to use. It will be same as the `metric` parameter
or a synonym of it, e.g. 'euclidean' if the `metric` parameter set to
'minkowski' and `p` parameter set to 2.
The distance metric to use. It will be same as the `metric` parameter or a
synonym of it, e.g. 'euclidean' if the `metric` parameter set to 'minkowski' and
`p` parameter set to 2.
effective_metric_params_ : dict
Additional keyword arguments for the metric function. For most metrics
will be same with `metric_params` parameter, but may also contain the
`p` parameter value if the `effective_metric_` attribute is set to
'minkowski'.
Additional keyword arguments for the metric function. For most metrics will be
same with `metric_params` parameter, but may also contain the `p` parameter
value if the `effective_metric_` attribute is set to 'minkowski'.
n_features_in_ : int
Number of features seen during :term:`fit`.
Number of features seen during `fit`.
feature_names_in_ : ndarray of shape (`n_features_in_`,)
Names of features seen during :term:`fit`. Defined only when `X`
has feature names that are all strings.
Names of features seen during `fit`. Defined only when `X` has feature names
that are all strings.
n_samples_fit_ : int
Number of samples in the fitted data.
transformer_ : CCATransformer
Fitted transformer.
References
----------
Ohmann JL, Gregory MJ. 2002. Predictive Mapping of Forest Composition and Structure
with Direct Gradient Analysis and Nearest Neighbor Imputation in Coastal Oregon,
USA. Canadian Journal of Forest Research, 32, 725–741.
"""

def __init__(
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44 changes: 24 additions & 20 deletions src/sknnr/_msn.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -14,8 +14,8 @@ class MSNRegressor(YFitMixin, TransformedKNeighborsRegressor):
Regression using Most Similar Neighbor (MSN) imputation.
The target is predicted by local interpolation of the targets associated with
the nearest neighbors in the training set, calculated in transformed Canonical
Correlation Analysis (CCorA) space.
the nearest neighbors in the training set, with distances calculated in transformed
Canonical Correlation Analysis (CCorA) space.
See `sklearn.neighbors.KNeighborsRegressor` for more information on parameters
and implementation.
Expand All @@ -24,46 +24,50 @@ class MSNRegressor(YFitMixin, TransformedKNeighborsRegressor):
----------
n_neighbors : int, default=5
Number of neighbors to use by default for `kneighbors` queries.
n_components : int, default=None
Number of components to keep during CCorA transformation. If None, all
components are kept. If n_components is less than the number of available
n_components : int, optional
Number of components to keep during CCorA transformation. If `None`, all
components are kept. If `n_components` is greater than the number of available
components, an error will be raised.
weights : {'uniform', 'distance'} or callable, default='uniform'
Weight function used in prediction.
algorithm : {'auto', 'ball_tree', 'kd_tree', 'brute'}, default='auto'
Algorithm used to compute the nearest neighbors.
leaf_size : int, default=30
Leaf size passed to BallTree or KDTree.
Leaf size passed to `BallTree` or `KDTree`.
p : int, default=2
Power parameter for the Minkowski metric.
metric : str or callable, default='minkowski'
The distance metric to use for the tree, calculated in CCorA space.
The distance metric to use for the tree, calculated in CCA space.
metric_params : dict, default=None
Additional keyword arguments for the metric function.
n_jobs : int, default=None
The number of parallel jobs to run for neighbors search. None means 1 unless
in a joblib.parallel_backend context. -1 means using all processors.
n_jobs : int, optional
The number of parallel jobs to run for neighbors search. `None` means 1 unless
in a `joblib.parallel_backend` context. `-1` means using all processors.
Attributes
----------
effective_metric_ : str or callable
The distance metric to use. It will be same as the `metric` parameter
or a synonym of it, e.g. 'euclidean' if the `metric` parameter set to
'minkowski' and `p` parameter set to 2.
The distance metric to use. It will be same as the `metric` parameter or a
synonym of it, e.g. 'euclidean' if the `metric` parameter set to 'minkowski' and
`p` parameter set to 2.
effective_metric_params_ : dict
Additional keyword arguments for the metric function. For most metrics
will be same with `metric_params` parameter, but may also contain the
`p` parameter value if the `effective_metric_` attribute is set to
'minkowski'.
Additional keyword arguments for the metric function. For most metrics will be
same with `metric_params` parameter, but may also contain the `p` parameter
value if the `effective_metric_` attribute is set to 'minkowski'.
n_features_in_ : int
Number of features seen during :term:`fit`.
Number of features seen during `fit`.
feature_names_in_ : ndarray of shape (`n_features_in_`,)
Names of features seen during :term:`fit`. Defined only when `X`
has feature names that are all strings.
Names of features seen during `fit`. Defined only when `X` has feature names
that are all strings.
n_samples_fit_ : int
Number of samples in the fitted data.
transformer_ : CCorATransformer
Fitted transformer.
References
----------
Moeur M, Stage AR. 1995. Most Similar Neighbor: An Improved Sampling Inference
Procedure for Natural Resources Planning. Forest Science, 41(2), 337–359.
"""

def __init__(
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