torchml.neighbors¶
Classes¶
torchml.neighbors.NearestNeighbors
¶
Description¶
Unsupervised learner for implementing neighbor searches.
Implementation of scikit-learn's nearest neighbors APIs using PyTorch.
References¶
- Fix, E. and Hodges, J.L. (1951) Discriminatory Analysis, Nonparametric Discrimination: Consistency Properties. Technical Report 4, USAF School of Aviation Medicine, Randolph Field.
- MIT 6.034 Artificial Intelligence, Fall 2010, 10. Introduction to Learning, Nearest Neighbors
- The scikit-learn documentation page for nearest neighbors.
- Referenced Implementation
Arguments¶
-
n_neighbors(int, default=5): Number of neighbors to use by default for kneighbors queries. -
radius(float, default=1.0): Range of parameter space to use by default for radius_neighbors queries. -
algorithm(string, default=’auto’): Dummy variable to mimic the sklearn API -
leaf_size(int, default=30): Dummy variable to mimic the sklearn API -
metric(str or callable, default=’minkowski’): No metric supprt right now, dummy variable and always minkowski -
p(int, default=2): No metric supprt right now, dummy variable and always 2 -
metric_paramsdict(default=None): Dummy variable to mimic the sklearn API -
n_jobs(int, default=None): Dummy variable to mimic the sklearn API
Example¶
import numpy as np
import torchml as ml
samples = np.array([[1, 0, 0], [0, 1, 0], [0, 5, 0], [20, 50, 30]])
point = np.array([[20, 50, 1]])
neigh = ml.neighbors.NearestNeighbors(n_neighbors=3)
neigh.fit(torch.from_numpy(samples))
neigh.kneighbors(torch.from_numpy(point))
fit(self, X: Tensor, y = None)
¶
kneighbors(self, X: Tensor, n_neighbors = None, return_distance = True) -> <built-in function any>
¶
Description¶
Computes the knearest neighbors and returns those k neighbors
Arguments¶
X(torch.Tensor): the target pointn_neighbors(int, default=None): optional argument to respecify the parameter k in k nearest neighborsreturn_distance(bool, default=True): returns the distances to the neighbors if true
torchml.neighbors.NearestCentroid
¶
Description¶
Implementation of scikit-learn's Nearest centroid APIs using pytorch. Euclidean metric by default.
References¶
- The scikit-learn [documentation page] (https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.NearestCentroid.html) for nearest centroids.
- M. Thulasidas, "Nearest Centroid: A Bridge between Statistics and Machine Learning," 2020 IEEE International Conference on Teaching, Assessment, and Learning for Engineering (TALE), 2020, pp. 9-16, doi: 10.1109/TALE48869.2020.9368396.
Arguments¶
-
metric(str or callable, default="euclidean"): Metric to use for distance computation. Only Euclidiean metric is supported for now. -
shrink_threshold(float, default=None): Threshold for shrinking centroids to remove features. Not supported for now.
Example¶
import numpy as np
import torchml as ml
X = np.array([[3,2],[1,8],[3,5],[9,7],[7,7]])
y = np.array([9,1,9,8,3])
samp = np.array([[6,1],[8,9],[5,3],[8,2],[9,8]])
torchX = torch.from_numpy(X)
torchy = torch.from_numpy(y)
centroid = ml.neighbors.NearestCentroid()
centroid.fit(torchX,torchy)
output = centroid.predict(torch.from_numpy(samp)).numpy()
fit(self, X: Tensor, y: Tensor)
¶
Description¶
Fit the NearestCentroid model according to the given training data.
Arguments¶
X(torch.Tensor): array-like, sparse matrix of shape (n_samples, n_features) Training vector, wheren_samplesis the number of samples andn_featuresis the number of featuresy(torch.Tensor): array-like of shape (n_samples,) Target values
torchml.neighbors.KNeighborsClassifier
¶
Description¶
Unsupervised learner for implementing KNN Classifier.
References¶
- Fix, E. and Hodges, J.L. (1951) Discriminatory Analysis, Nonparametric Discrimination: Consistency Properties. Technical Report 4, USAF School of Aviation Medicine, Randolph Field.
- MIT 6.034 Artificial Intelligence, Fall 2010, 10. Introduction to Learning, Nearest Neighbors
- The scikit-learn documentation page for KNeighborsClassifier.
Arguments¶
-
n_neighbors(int, default=5): Number of neighbors to use by default for kneighbors queries. -
weights(str {'uniform', 'distance'} or callable, default='uniform'):- 'uniform' : uniform weights. All points in each neighborhood are weighted equally.
- 'distance' : weight points by the inverse of their distance. in this case, closer neighbors of a query point will have a greater influence than neighbors which are further away.
- [callable] : not implemented yet
-
algorithm(str, default='auto'): Dummy variable to keep consistency with SKlearn's API, always 'brute' for now. -
leaf_size(int, default=30) Dummy variable to keep consistency with SKlearn's API. -
metric(str or callable, default=’minkowski’): No metric supprt right now, dummy variable and always minkowski -
p(int, default=2): No metric supprt right now, dummy variable and always 2 -
metric_paramsdict(default=None): Dummy variable to mimic the sklearn API -
n_jobs(int, default=None): Dummy variable to mimic the sklearn API
Example¶
import numpy as np
import torchml as ml
samples = np.array([[0], [1], [2], [3]])
y = np.array([0, 0, 1, 1])
point = np.array([1.1])
neigh = ml.neighbors.KNeighborsClassifier(n_neighbors=3)
neigh.fit(torch.from_numpy(samples), torch.from_numpy(y))
neigh.predict(torch.from_numpy(point))
neigh.predict_proba(torch.from_numpy(point))
fit(self, X: Tensor, y: Tensor)
¶
kneighbors(self, X: Tensor = None, n_neighbors: int = None, return_distance: bool = True) -> Any
¶
Description¶
Computes the knearest neighbors and returns those k neighbors
Arguments¶
X(torch.Tensor): the target pointn_neighbors(int, default=None): optional argument to respecify the parameter k in k nearest neighborsreturn_distance(bool, default=True): returns the distances to the neighbors if true