From 7febaa269356033d448c0938df4d472a901231bc Mon Sep 17 00:00:00 2001
From: Alejandro Moreo <alejandro.moreo@isti.cnr.it>
Date: Wed, 29 May 2024 11:23:17 +0200
Subject: [PATCH] documenting the regressor
---
LeQua2024/regressor.py | 38 ++++++++++++++++++++++++++++++++------
1 file changed, 32 insertions(+), 6 deletions(-)
diff --git a/LeQua2024/regressor.py b/LeQua2024/regressor.py
index 064a925..03293f9 100644
--- a/LeQua2024/regressor.py
+++ b/LeQua2024/regressor.py
@@ -18,6 +18,7 @@ from scripts.evaluate import normalized_match_distance, match_distance
def projection_simplex_sort(unnormalized_arr) -> np.ndarray:
"""Projects a point onto the probability simplex.
+ [This code is taken from the devel branch, that will correspond to the future QuaPy 0.1.9]
The code is adapted from Mathieu Blondel's BSD-licensed
`implementation <https://gist.github.com/mblondel/6f3b7aaad90606b98f71>`_
@@ -42,22 +43,51 @@ def projection_simplex_sort(unnormalized_arr) -> np.ndarray:
class RegressionToSimplex(BaseEstimator):
+ """
+ A very simple regressor of probability distributions.
+ Internally, this class works by invoking an SVR regressor multioutput
+ followed by a mapping onto the probability simplex.
+
+ :param C: regularziation parameter for SVR
+ """
+
def __init__(self, C=1):
self.C = C
def fit(self, X, y):
+ """
+ Learns the correction
+
+ :param X: array-like of shape `(n_instances, n_classes)` with uncorrected prevalence vectors
+ :param y: array-like of shape `(n_instances, n_classes)` with true prevalence vectors
+ :return: self
+ """
self.reg = MultiOutputRegressor(SVR(C=self.C), n_jobs=-1)
self.reg.fit(X, y)
return self
def predict(self, X):
+ """
+ Corrects the a vector of prevalence values
+
+ :param X: array-like of shape `(n_classes,)` with one vector of uncorrected prevalence values
+ :return: array-like of shape `(n_classes,)` with one vector of corrected prevalence values
+ """
y_ = self.reg.predict(X)
- # y_ = F.normalize_prevalence(y_)
y_ = np.asarray([projection_simplex_sort(y_i) for y_i in y_])
return y_
class KDEyRegressor(BaseQuantifier):
+ """
+ This class implements a regressor-based correction on top of a quantifier.
+ The quantifier is taken to be KDEy-ML, which is considered to be already trained (this
+ method simply loads a pickled object).
+ The method then optimizes a regressor that corrects prevalence vectors using the
+ validation samples as training data.
+ The regressor is based on a multioutput SVR and relies on a post-processing to guarantee
+ that the output lies on the probability simplex (see also RegressionToSimplex)
+ """
def __init__(self, kde_path, Cs=np.logspace(-3,3,7)):
self.kde_path = kde_path
@@ -96,12 +126,8 @@ class KDEyRegressor(BaseQuantifier):
if __name__ == '__main__':
- train, gen_val, gen_test = fetch_lequa2024(task='T3', data_home='./data', merge_T3=True)
+ train, gen_val, _ = fetch_lequa2024(task='T3', data_home='./data', merge_T3=True)
kdey_r = KDEyRegressor('./models/T3/KDEy-ML.pkl')
kdey_r.fit(gen_val)
- prev_hat_tr = kdey_r.quantify(train.X)
- print(prev_hat_tr)
- print(train.prevalence())
-
pickle.dump(kdey_r, open('./models/T3/KDEyRegressor.pkl', 'wb'), protocol=pickle.HIGHEST_PROTOCOL)