Bases: Metalearner_Base
A k-Nearest Neighbors based Metalearner.
This class should be passed to an ensemble function/class like Stacking for combining predictions.
Info
Can be utilized for binary, multi-class and multi-label tasks.
Reference - Implementation
https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html
Scikit-learn: Machine Learning in Python, Pedregosa et al., JMLR 12, pp. 2825-2830, 2011.
https://jmlr.csail.mit.edu/papers/v12/pedregosa11a.html
Source code in aucmedi/ensemble/metalearner/k_neighbors.py
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85 | class KNearestNeighbors(Metalearner_Base):
""" A k-Nearest Neighbors based Metalearner.
This class should be passed to an ensemble function/class like Stacking for combining predictions.
!!! info
Can be utilized for binary, multi-class and multi-label tasks.
???+ abstract "Reference - Implementation"
https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html
Scikit-learn: Machine Learning in Python, Pedregosa et al., JMLR 12, pp. 2825-2830, 2011.
https://jmlr.csail.mit.edu/papers/v12/pedregosa11a.html
"""
#---------------------------------------------#
# Initialization #
#---------------------------------------------#
def __init__(self):
self.model = KNeighborsClassifier()
#---------------------------------------------#
# Training #
#---------------------------------------------#
def train(self, x, y):
# Train model
self.model = self.model.fit(x, y)
#---------------------------------------------#
# Prediction #
#---------------------------------------------#
def predict(self, data):
# Compute prediction probabilities via fitted model
pred = self.model.predict_proba(data)
# Postprocess decision tree predictions
pred = np.asarray(pred)
pred = np.swapaxes(pred[:,:,1], 0, 1)
# Return results as NumPy array
return pred
#---------------------------------------------#
# Dump Model to Disk #
#---------------------------------------------#
def dump(self, path):
# Dump model to disk via pickle
with open(path, "wb") as pickle_writer:
pickle.dump(self.model, pickle_writer)
#---------------------------------------------#
# Load Model from Disk #
#---------------------------------------------#
def load(self, path):
# Load model from disk via pickle
with open(path, "rb") as pickle_reader:
self.model = pickle.load(pickle_reader)
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