Merge pull request #13 from pglez82/dys_implementation

Dys implementation

quapy/functional.py

@@ -78,6 +78,12 @@ def HellingerDistance(P, Q):
     """
     return np.sqrt(np.sum((np.sqrt(P) - np.sqrt(Q))**2))
 
+def TopsoeDistance(P, Q, epsilon=1e-20):
+    """ Topsoe
+    """
+    return np.sum(P*np.log((2*P+epsilon)/(P+Q+epsilon)) +
+                  Q*np.log((2*Q+epsilon)/(P+Q+epsilon)))
+                  
 
 def uniform_prevalence_sampling(n_classes, size=1):
     """

quapy/method/__init__.py

@@ -19,6 +19,8 @@ AGGREGATIVE_METHODS = {
     aggregative.PACC,
     aggregative.EMQ,
     aggregative.HDy,
+    aggregative.DyS,
+    aggregative.SMM,
     aggregative.X,
     aggregative.T50,
     aggregative.MAX,

quapy/method/aggregative.py

@@ -1,6 +1,6 @@
 from abc import abstractmethod
 from copy import deepcopy
-from typing import Union
+from typing import Callable, Union
 import numpy as np
 from joblib import Parallel, delayed
 from sklearn.base import BaseEstimator
@@ -638,6 +638,119 @@ class HDy(AggregativeProbabilisticQuantifier, BinaryQuantifier):
         return np.asarray([1 - class1_prev, class1_prev])
 
 
+class DyS(AggregativeProbabilisticQuantifier, BinaryQuantifier):
+    """
+    `DyS framework <https://ojs.aaai.org/index.php/AAAI/article/view/4376>`_ (DyS).
+    DyS is a generalization of HDy method, using a Ternary Search in order to find the prevalence that
+    minimizes the distance between distributions.
+    Details for the ternary search have been got from <https://dl.acm.org/doi/pdf/10.1145/3219819.3220059>
+
+    :param learner: a sklearn's Estimator that generates a binary classifier
+    :param val_split: a float in range (0,1) indicating the proportion of data to be used as a stratified held-out
+        validation distribution, or a :class:`quapy.data.base.LabelledCollection` (the split itself).
+    :param n_bins: an int with the number of bins to use to compute the histograms.
+    :param distance: an str with a distance already included in the librar (HD or topsoe), of a function
+        that computes the distance between two distributions.
+    :param tol: a float with the tolerance for the ternary search algorithm.
+    """
+
+    def __init__(self, learner: BaseEstimator, val_split=0.4, n_bins=8, distance: Union[str, Callable]='HD', tol=1e-05):
+        self.learner = learner
+        self.val_split = val_split
+        self.tol = tol
+        self.distance = distance
+        self.n_bins = n_bins
+
+    def _ternary_search(self, f, left, right, tol):
+        """
+        Find maximum of unimodal function f() within [left, right]
+        """
+        while abs(right - left) >= tol:
+            left_third = left + (right - left) / 3
+            right_third = right - (right - left) / 3
+
+            if f(left_third) > f(right_third):
+                left = left_third
+            else:
+                right = right_third
+
+        # Left and right are the current bounds; the maximum is between them
+        return (left + right) / 2
+
+    def _compute_distance(self, Px_train, Px_test, distance: Union[str, Callable]='HD'):
+        if distance=='HD':
+            return F.HellingerDistance(Px_train, Px_test)
+        elif distance=='topsoe':
+            return F.TopsoeDistance(Px_train, Px_test)
+        else:
+            return distance(Px_train, Px_test)
+
+    def fit(self, data: LabelledCollection, fit_learner=True, val_split: Union[float, LabelledCollection] = None):
+        if val_split is None:
+            val_split = self.val_split
+
+        self._check_binary(data, self.__class__.__name__)
+        self.learner, validation = _training_helper(
+            self.learner, data, fit_learner, ensure_probabilistic=True, val_split=val_split)
+        Px = self.classify(validation.instances)[:, 1]  # takes only the P(y=+1|x)
+        self.Pxy1 = Px[validation.labels == self.learner.classes_[1]]
+        self.Pxy0 = Px[validation.labels == self.learner.classes_[0]]
+        self.Pxy1_density = np.histogram(self.Pxy1, bins=self.n_bins, range=(0, 1), density=True)[0]
+        self.Pxy0_density = np.histogram(self.Pxy0, bins=self.n_bins, range=(0, 1), density=True)[0]
+        return self
+
+    def aggregate(self, classif_posteriors):
+        Px = classif_posteriors[:, 1]  # takes only the P(y=+1|x)
+
+        Px_test = np.histogram(Px, bins=self.n_bins, range=(0, 1), density=True)[0]
+
+        def distribution_distance(prev):
+            Px_train = prev * self.Pxy1_density + (1 - prev) * self.Pxy0_density
+            return self._compute_distance(Px_train,Px_test,self.distance)
+            
+        class1_prev = self._ternary_search(f=distribution_distance, left=0, right=1, tol=self.tol)
+        return np.asarray([1 - class1_prev, class1_prev])
+
+
+class SMM(AggregativeProbabilisticQuantifier, BinaryQuantifier):
+    """
+    `SMM method <https://ieeexplore.ieee.org/document/9260028>`_ (SMM).
+    SMM is a simplification of matching distribution methods where the representation of the examples
+    is created using the mean instead of a histogram.
+
+    :param learner: a sklearn's Estimator that generates a binary classifier.
+    :param val_split: a float in range (0,1) indicating the proportion of data to be used as a stratified held-out
+        validation distribution, or a :class:`quapy.data.base.LabelledCollection` (the split itself).
+    """
+
+    def __init__(self, learner: BaseEstimator, val_split=0.4):
+        self.learner = learner
+        self.val_split = val_split
+      
+    def fit(self, data: LabelledCollection, fit_learner=True, val_split: Union[float, LabelledCollection] = None):
+        if val_split is None:
+            val_split = self.val_split
+
+        self._check_binary(data, self.__class__.__name__)
+        self.learner, validation = _training_helper(
+            self.learner, data, fit_learner, ensure_probabilistic=True, val_split=val_split)
+        Px = self.classify(validation.instances)[:, 1]  # takes only the P(y=+1|x)
+        self.Pxy1 = Px[validation.labels == self.learner.classes_[1]]
+        self.Pxy0 = Px[validation.labels == self.learner.classes_[0]]
+        self.Pxy1_mean = np.mean(self.Pxy1)
+        self.Pxy0_mean = np.mean(self.Pxy0)
+        return self
+
+    def aggregate(self, classif_posteriors):
+        Px = classif_posteriors[:, 1]  # takes only the P(y=+1|x)
+        Px_mean = np.mean(Px)
+     
+        class1_prev = (Px_mean - self.Pxy0_mean)/(self.Pxy1_mean - self.Pxy0_mean)
+        class1_prev = np.clip(class1_prev, 0, 1)
+
+        return np.asarray([1 - class1_prev, class1_prev])
+
+
 class ELM(AggregativeQuantifier, BinaryQuantifier):
     """
     Class of Explicit Loss Minimization (ELM) quantifiers.

quapy/model_selection.py

@@ -83,7 +83,8 @@ class GridSearchQ(BaseQuantifier):
         tinit = time()
 
         hyper = [dict({k: values[i] for i, k in enumerate(params_keys)}) for values in itertools.product(*params_values)]
-        scores = qp.util.parallel(self._delayed_eval, ((params, training) for params in hyper), n_jobs=self.n_jobs)
+        #pass a seed to parallel so it is set in clild processes
+        scores = qp.util.parallel(self._delayed_eval, ((params, training) for params in hyper), seed=qp.environ.get('_R_SEED', None), n_jobs=self.n_jobs)
 
         for params, score, model in scores:
             if score is not None:

quapy/util.py

@@ -5,6 +5,7 @@ import os
 import pickle
 import urllib
 from pathlib import Path
+from contextlib import ExitStack
 import quapy as qp
 
 import numpy as np
@@ -36,7 +37,7 @@ def map_parallel(func, args, n_jobs):
     return list(itertools.chain.from_iterable(results))
 
 
-def parallel(func, args, n_jobs):
+def parallel(func, args, n_jobs, seed = None):
     """
     A wrapper of multiprocessing:
 
@@ -44,14 +45,20 @@ def parallel(func, args, n_jobs):
     >>>      delayed(func)(args_i) for args_i in args
     >>> )
 
-    that takes the `quapy.environ` variable as input silently
+    that takes the `quapy.environ` variable as input silently.
+    Seeds the child processes to ensure reproducibility when n_jobs>1
     """
-    def func_dec(environ, *args):
+    def func_dec(environ, seed, *args):
         qp.environ = environ.copy()
         qp.environ['N_JOBS'] = 1
-        return func(*args)
+        #set a context with a temporal seed to ensure results are reproducibles in parallel
+        with ExitStack() as stack:
+            if seed is not None:
+                stack.enter_context(qp.util.temp_seed(seed))
+            return func(*args)
+    
     return Parallel(n_jobs=n_jobs)(
-        delayed(func_dec)(qp.environ, args_i) for args_i in args
+        delayed(func_dec)(qp.environ, None if seed is None else seed+i, args_i) for i, args_i in enumerate(args)
     )
 
 
@@ -66,6 +73,8 @@ def temp_seed(random_state):
     :param random_state: the seed to set within the "with" context
     """
     state = np.random.get_state()
+    #save the seed just in case is needed (for instance for setting the seed to child processes)
+    qp.environ['_R_SEED'] = random_state
     np.random.seed(random_state)
     try:
         yield