From f643a2c28b0f8b0b44cb83680f21b95f3f31d726 Mon Sep 17 00:00:00 2001
From: Amir Mohammadi <183.amir@gmail.com>
Date: Tue, 31 Jan 2017 17:43:46 +0100
Subject: [PATCH] allow for preprocessors to use label for training
---
bob/fusion/base/algorithm/Algorithm.py | 242 ++++++++++++-------------
1 file changed, 121 insertions(+), 121 deletions(-)
diff --git a/bob/fusion/base/algorithm/Algorithm.py b/bob/fusion/base/algorithm/Algorithm.py
index ac78197..a56cafb 100644
--- a/bob/fusion/base/algorithm/Algorithm.py
+++ b/bob/fusion/base/algorithm/Algorithm.py
@@ -11,126 +11,126 @@ logger = bob.core.log.setup("bob.fusion.base")
class Algorithm(object):
- """A class to be used in score fusion"""
-
- def __init__(self,
- preprocessors=None,
- classifier=None,
- *args,
- **kwargs
- ):
- """
- preprocessors: A list of preprocessors that follow the API of
- :py:meth:`sklearn.preprocessing.StandardScaler`. Especially `fit_transform`
- and `transform` must be implemented.
-
- classifier: An instance of a class that implements `fit(X[, y])` and
- `decision_function(X)` like:
- :py:meth:`sklearn.linear_model.LogisticRegression`
-
- kwargs : ``key=value`` pairs
- A list of keyword arguments to be written in the
- :py:meth:`__str__` function.
+ """A class to be used in score fusion"""
+
+ def __init__(self,
+ preprocessors=None,
+ classifier=None,
+ *args,
+ **kwargs
+ ):
+ """
+ preprocessors: A list of preprocessors that follow the API of
+ :py:meth:`sklearn.preprocessing.StandardScaler`. Especially `fit_transform`
+ and `transform` must be implemented.
+
+ classifier: An instance of a class that implements `fit(X[, y])` and
+ `decision_function(X)` like:
+ :py:meth:`sklearn.linear_model.LogisticRegression`
+
+ kwargs : ``key=value`` pairs
+ A list of keyword arguments to be written in the
+ :py:meth:`__str__` function.
"""
- super(Algorithm, self).__init__()
- self.classifier = classifier
- self.preprocessors = preprocessors
- self._kwargs = kwargs
- self._kwargs['preprocessors'] = preprocessors
- if classifier is not self:
- self._kwargs['classifier'] = classifier
-
- def train_preprocessors(self, X):
- """Train preprocessors in order.
- X: numpy.ndarray with the shape of (n_samples, n_systems)."""
- if self.preprocessors is not None:
- for preprocessor in self.preprocessors:
- X = preprocessor.fit_transform(X)
-
- def preprocess(self, scores):
- """
- scores: numpy.ndarray with the shape of (n_samples, n_systems).
- returns the transformed scores."""
- if self.preprocessors is not None:
- for preprocessor in self.preprocessors:
- scores = preprocessor.transform(scores)
- return scores
-
- def train(self, train_neg, train_pos, devel_neg=None, devel_pos=None):
- """If you use development data for training you need to override this
- method.
-
- train_neg: numpy.ndarray
- Negatives training data should be numpy.ndarray with the shape of
- (n_samples, n_systems).
- train_pos: numpy.ndarray
- Positives training data should be numpy.ndarray with the shape of
- (n_samples, n_systems).
- devel_neg, devel_pos: numpy.ndarray
- Same as ``train`` but used for development (validation).
- """
- train_scores = np.vstack((train_neg, train_pos))
- neg_len = train_neg.shape[0]
- y = np.zeros((train_scores.shape[0],), dtype='bool')
- y[neg_len:] = True
- self.classifier.fit(train_scores, y)
-
- def fuse(self, scores):
- """
- scores: numpy.ndarray
- A numpy.ndarray with the shape of (n_samples, n_systems).
-
- **Returns:**
-
- fused_score: numpy.ndarray
- The fused scores in shape of (n_samples,).
- """
- return self.classifier.decision_function(scores)
-
- def __str__(self):
- """Return all parameters of this class (and its derived class) in string.
-
-
- **Returns:**
-
- info: str
- A string containing the full information of all parameters of this
- (and the derived) class.
- """
- return "%s(%s)" % (str(self.__class__), ", ".join(
- ["%s=%s" % (key, value) for key, value in
- self._kwargs.items() if value is not None]))
-
- def save(self, model_file):
- """Save the instance of the algorithm.
-
- model_file: str
- A path to save the file. Please note that file objects
- are not accepted. The filename MUST end with ".pkl".
- Also, an algorithm may save itself in multiple files with different
- extensions such as model_file and model_file[:-3]+'hdf5'.
- """
- # support for bob machines
- if hasattr(self, "custom_save"):
- self.custom_save(model_file)
- else:
- with open(model_file, "wb") as f:
- pickle.dump(type(self), f)
- pickle.dump(self, f)
-
- def load(self, model_file):
- """Load the algorithm the same way it was saved.
- A new instance will be returned.
-
- **Returns:**
-
- loaded_algorithm: Algorithm
- A new instance of the loaded algorithm.
- """
- with open(model_file, "rb") as f:
- algo_class = pickle.load(f)
- algo = algo_class()
- if not hasattr(algo, 'custom_save'):
- return pickle.load(f)
- return algo.load(model_file)
+ super(Algorithm, self).__init__()
+ self.classifier = classifier
+ self.preprocessors = preprocessors
+ self._kwargs = kwargs
+ self._kwargs['preprocessors'] = preprocessors
+ if classifier is not self:
+ self._kwargs['classifier'] = classifier
+
+ def train_preprocessors(self, X, y=None):
+ """Train preprocessors in order.
+ X: numpy.ndarray with the shape of (n_samples, n_systems)."""
+ if self.preprocessors is not None:
+ for preprocessor in self.preprocessors:
+ X = preprocessor.fit_transform(X, y)
+
+ def preprocess(self, scores):
+ """
+ scores: numpy.ndarray with the shape of (n_samples, n_systems).
+ returns the transformed scores."""
+ if self.preprocessors is not None:
+ for preprocessor in self.preprocessors:
+ scores = preprocessor.transform(scores)
+ return scores
+
+ def train(self, train_neg, train_pos, devel_neg=None, devel_pos=None):
+ """If you use development data for training you need to override this
+ method.
+
+ train_neg: numpy.ndarray
+ Negatives training data should be numpy.ndarray with the shape of
+ (n_samples, n_systems).
+ train_pos: numpy.ndarray
+ Positives training data should be numpy.ndarray with the shape of
+ (n_samples, n_systems).
+ devel_neg, devel_pos: numpy.ndarray
+ Same as ``train`` but used for development (validation).
+ """
+ train_scores = np.vstack((train_neg, train_pos))
+ neg_len = train_neg.shape[0]
+ y = np.zeros((train_scores.shape[0],), dtype='bool')
+ y[neg_len:] = True
+ self.classifier.fit(train_scores, y)
+
+ def fuse(self, scores):
+ """
+ scores: numpy.ndarray
+ A numpy.ndarray with the shape of (n_samples, n_systems).
+
+ **Returns:**
+
+ fused_score: numpy.ndarray
+ The fused scores in shape of (n_samples,).
+ """
+ return self.classifier.decision_function(scores)
+
+ def __str__(self):
+ """Return all parameters of this class (and its derived class) in string.
+
+
+ **Returns:**
+
+ info: str
+ A string containing the full information of all parameters of this
+ (and the derived) class.
+ """
+ return "%s(%s)" % (str(self.__class__), ", ".join(
+ ["%s=%s" % (key, value) for key, value in
+ self._kwargs.items() if value is not None]))
+
+ def save(self, model_file):
+ """Save the instance of the algorithm.
+
+ model_file: str
+ A path to save the file. Please note that file objects
+ are not accepted. The filename MUST end with ".pkl".
+ Also, an algorithm may save itself in multiple files with different
+ extensions such as model_file and model_file[:-3]+'hdf5'.
+ """
+ # support for bob machines
+ if hasattr(self, "custom_save"):
+ self.custom_save(model_file)
+ else:
+ with open(model_file, "wb") as f:
+ pickle.dump(type(self), f)
+ pickle.dump(self, f)
+
+ def load(self, model_file):
+ """Load the algorithm the same way it was saved.
+ A new instance will be returned.
+
+ **Returns:**
+
+ loaded_algorithm: Algorithm
+ A new instance of the loaded algorithm.
+ """
+ with open(model_file, "rb") as f:
+ algo_class = pickle.load(f)
+ algo = algo_class()
+ if not hasattr(algo, 'custom_save'):
+ return pickle.load(f)
+ return algo.load(model_file)
--
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