class KNeighborsClassifierImpl():
def __init__(self, n_neighbors=5, weights='uniform', algorithm='auto', leaf_size=30, p=2, metric='minkowski', metric_params=None, n_jobs=None):
self._hyperparams = {
'n_neighbors': n_neighbors,
'weights': weights,
'algorithm': algorithm,
'leaf_size': leaf_size,
'p': p,
'metric': metric,
'metric_params': metric_params,
'n_jobs': n_jobs}
def fit(self, X, y=None):
self._sklearn_model = SKLModel(**self._hyperparams)
if (y is not None):
self._sklearn_model.fit(X, y)
else:
self._sklearn_model.fit(X)
return self
def predict(self, X):
return self._sklearn_model.predict(X)
def predict_proba(self, X):
return self._sklearn_model.predict_proba(X)
class InstanceReductionMixin(InstanceReductionBase, ClassifierMixin):
"""Mixin class for all instance reduction techniques"""
def set_classifier(self):
"""Sets the classified to be used in the instance reduction process
and classification.
Parameters
----------
classifier : classifier, following the KNeighborsClassifier style
(default = KNN)
y : array-like, shape = [n_samples]
Labels for X.
Returns
-------
P : array-like, shape = [indeterminated, n_features]
Resulting training set.
q : array-like, shape = [indertaminated]
Labels for P
"""
self.classifier = classifier
def reduce_data(self, X, y):
"""Perform the instance reduction procedure on the given training data.
Parameters
----------
X : array-like, shape = [n_samples, n_features]
Training set.0
y : array-like, shape = [n_samples]
Labels for X.
Returns
-------
X_ : array-like, shape = [indeterminated, n_features]
Resulting training set.
y_ : array-like, shape = [indertaminated]
Labels for X_
"""
pass
def get_prototypes(self):
return self.X_, self.y_
def fit(self, X, y, reduce_data=True):
"""
Fit the InstanceReduction model according to the given training data.
Parameters
----------
X : {array-like, sparse matrix}, shape = [n_samples, n_features]
Training vector, where n_samples in the number of samples and
n_features is the number of features.
Note that centroid shrinking cannot be used with sparse matrices.
y : array, shape = [n_samples]
Target values (integers)
reduce_data : bool, flag indicating if the reduction would be performed
"""
self.X = X
self.y = y
self.labels = set(y)
self.prototypes = None
self.prototypes_labels = None
self.reduction_ratio = 0.0
if reduce_data:
self.reduce_data(X, y)
return self
def predict(self, X, n_neighbors=1):
"""Perform classification on an array of test vectors X.
The predicted class C for each sample in X is returned.
Parameters
----------
X : array-like, shape = [n_samples, n_features]
Returns
-------
C : array, shape = [n_samples]
Notes
-----
The default prediction is using KNeighborsClassifier, if the
instance reducition algorithm is to be performed with another
classifier, it should be explicited overwritten and explained
in the documentation.
"""
X = atleast2d_or_csr(X)
if not hasattr(self, "X_") or self.X_ is None:
raise AttributeError("Model has not been trained yet.")
if not hasattr(self, "y_") or self.y_ is None:
raise AttributeError("Model has not been trained yet.")
if self.classifier == None:
self.classifier = KNeighborsClassifier(n_neighbors=n_neighbors)
self.classifier.fit(self.X_, self.y_)
return self.classifier.predict(X)
def predict_proba(self, X):
"""Return probability estimates for the test data X.
after a given prototype selection algorithm.
Parameters
----------
X : array, shape = (n_samples, n_features)
A 2-D array representing the test points.
Returns
-------
p : array of shape = [n_samples, n_classes], or a list of n_outputs
of such arrays if n_outputs > 1.
The class probabilities of the input samples. Classes are ordered
by lexicographic order.
"""
self.classifier.fit(self.X_, self.y_)
return self.classifier.predict_proba(X)
class InstanceReductionMixin(InstanceReductionBase, ClassifierMixin):
"""Mixin class for all instance reduction techniques"""
def set_classifier(self):
"""Sets the classified to be used in the instance reduction process
and classification.
Parameters
----------
classifier : classifier, following the KNeighborsClassifier style
(default = KNN)
y : array-like, shape = [n_samples]
Labels for X.
Returns
-------
P : array-like, shape = [indeterminated, n_features]
Resulting training set.
q : array-like, shape = [indertaminated]
Labels for P
"""
self.classifier = classifier
def reduce_data(self, X, y):
"""Perform the instance reduction procedure on the given training data.
Parameters
----------
X : array-like, shape = [n_samples, n_features]
Training set.0
y : array-like, shape = [n_samples]
Labels for X.
Returns
-------
X_ : array-like, shape = [indeterminated, n_features]
Resulting training set.
y_ : array-like, shape = [indertaminated]
Labels for X_
"""
pass
def fit(self, X, y, reduce_data=True):
"""
Fit the InstanceReduction model according to the given training data.
Parameters
----------
X : {array-like, sparse matrix}, shape = [n_samples, n_features]
Training vector, where n_samples in the number of samples and
n_features is the number of features.
Note that centroid shrinking cannot be used with sparse matrices.
y : array, shape = [n_samples]
Target values (integers)
reduce_data : bool, flag indicating if the reduction would be performed
"""
self.X = X
self.y = y
if reduce_data:
self.reduce_data(X, y)
return self
def predict(self, X, n_neighbors=1):
"""Perform classification on an array of test vectors X.
The predicted class C for each sample in X is returned.
Parameters
----------
X : array-like, shape = [n_samples, n_features]
Returns
-------
C : array, shape = [n_samples]
Notes
-----
The default prediction is using KNeighborsClassifier, if the
instance reducition algorithm is to be performed with another
classifier, it should be explicited overwritten and explained
in the documentation.
"""
X = check_array(X)
if not hasattr(self, "X_") or self.X_ is None:
raise AttributeError("Model has not been trained yet.")
if not hasattr(self, "y_") or self.y_ is None:
raise AttributeError("Model has not been trained yet.")
if self.classifier == None:
self.classifier = KNeighborsClassifier(n_neighbors=n_neighbors)
self.classifier.fit(self.X_, self.y_)
return self.classifier.predict(X)
def predict_proba(self, X):
"""Return probability estimates for the test data X.
after a given prototype selection algorithm.
Parameters
----------
X : array, shape = (n_samples, n_features)
A 2-D array representing the test points.
Returns
-------
p : array of shape = [n_samples, n_classes], or a list of n_outputs
of such arrays if n_outputs > 1.
The class probabilities of the input samples. Classes are ordered
by lexicographic order.
"""
self.classifier.fit(self.X_, self.y_)
return self.classifier.predict_proba(X)
def run_knn_probabilistic_classifier(train, train_labels, validate,
validate_labels):
knn = KNeighborsClassifier(n_neighbors=3)
knn.fit(train, train_labels)
predicted_labels = knn.predict_proba(validate)
return metrics.log_loss(validate_labels, predicted_labels)
