Ordinal_Classifier.py

from sklearn.neighbors import KNeighborsClassifier
from sklearn.base import clone, BaseEstimator, ClassifierMixin
from sklearn.utils.validation import check_X_y, check_is_fitted, check_array
from sklearn.utils.multiclass import check_classification_targets
from sklearn.neighbors import KNeighborsClassifier
import numpy as np

class OrdinalClassifier(BaseEstimator, ClassifierMixin):
 #https://towardsdatascience.com/simple-trick-to-train-an-ordinal-regression-with-any-classifier-6911183d2a3c
 """
 A classifier that can be trained on a range of classes.
 @param classifier: A scikit-learn classifier.
 """
 def __init__(self,clf):
 self.clf = clf
 self.clfs = {}
 self.uniques_class = None

 def fit(self,X,y):
 self.uniques_class = np.sort(np.unique(y))
 assert self.uniques_class.shape[0] >= 3, f'OrdinalClassifier needs at least 3 classes, only {self.uniques_class.shape[0]} found'

 for i in range(self.uniques_class.shape[0]-1):
 binary_y = (y > self.uniques_class[i]).astype(np.uint8)
 
 clf = clone(self.clf)
 clf.fit(X,binary_y)
 self.clfs[i] = clf

 def predict(self,X):
 return np.argmax( self.predict_proba(X), axis=1 )

 def predict_proba(self,X):
 predicted = [self.clfs[k].predict_proba(X)[:,1].reshape(-1,1) for k in self.clfs]

 p_x_first = 1-predicted[0]
 p_x_last = predicted[-1]
 p_x_middle= [predicted[i] - predicted[i+1] for i in range(len(predicted) - 1)]
 
 probs = np.hstack([p_x_first, *p_x_middle, p_x_last])

 return probs

 def set_params(self, **params):
 self.clf.set_params(**params)
 for _,clf in self.clfs.items():
 clf.set_params(**params)