[algorithm] added LDA and unit tests, fixed the doc index to include algorithms

bob/pad/base/algorithm/MLP.py

@@ -21,8 +21,12 @@ class MLP(Algorithm):
     ----------
     hidden_units : :py:obj:`tuple` of :any:`int`
       The number of hidden units in each hidden layer
-    max_iter : int
+    max_iter : :any:`int`
       The maximum number of training iterations
+    precision : :any:`float`
+      criterion to stop the training: if the difference
+      between current and last loss is smaller than
+      this number, then stop training.
     """
 
     def __init__(self, hidden_units=(10, 10), max_iter=1000, precision=0.001, **kwargs):

bob/pad/base/algorithm/PadLDA.py

+#!/usr/bin/env python
+# vim: set fileencoding=utf-8 :
+
+import numpy
+from bob.bio.base.algorithm import LDA
+
+class PadLDA(LDA):
+    """Wrapper for bob.bio.base.algorithm.LDA,
+    
+    Here, LDA is used in a PAD context. This means that the feature
+    will be projected on a two-dimensional subspace, where the two
+    dimensions represents the real and attack classes.
+
+    For more details, you may want to have a look at 
+    `bob.learn.linear Documentation`_
+
+    .. _bob.learn.linear Documentation:
+      https://www.idiap.ch/software/bob/docs/bob/bob.learn.linear/stable/index.html
+
+    Attributes
+    ----------
+    lda_subspace_dimension : int
+      the dimension of the LDA subspace. In the PAD case, the default
+      value is *always* used, and corresponds to the number of classes
+      in the training set (i.e. 2).
+    pca_subspace_dimension : int
+      The dimension of the PCA subspace to be applied
+      before on the data, before applying LDA.
+    use_pinv : bool
+      Use the pseudo-inverse in LDA computation.
+
+    """
+    def __init__(self,
+                 lda_subspace_dimension = None, # if set, the LDA subspace will be truncated to the given number of dimensions; by default it is limited to the number of classes in the training set
+                 pca_subspace_dimension = None, # if set, a PCA subspace truncation is performed before applying LDA; might be integral or float
+                 use_pinv = False,
+                 **kwargs
+                 ):
+        """Init function
+
+        Parameters
+        ----------
+        lda_subspace_dimension : int
+          the dimension of the LDA subspace. In the PAD case, the default
+          value is *always* used, and corresponds to the number of classes
+          in the training set (i.e. 2).
+        pca_subspace_dimension : int
+          The dimension of the PCA subspace to be applied
+          before on the data, before applying LDA.
+        use_pinv : bool
+          Use the pseudo-inverse in LDA computation.
+
+        """
+        super(PadLDA, self).__init__(
+            lda_subspace_dimension = lda_subspace_dimension,
+            pca_subspace_dimension = pca_subspace_dimension,
+            use_pinv = use_pinv,
+            **kwargs
+          )
+
+    def read_toscore_object(self, toscore_object_file):
+        """Reads the toscore_object feature from a file.
+      
+        By default, the toscore_object feature is identical to the projected feature.
+        Hence, this base class implementation simply calls :py:meth:`bob.pad.base.algorithm.Algorithm.read_feature`.
+
+        If your algorithm requires different behavior, please overwrite this function.
+
+        Parameters
+        ----------
+        toscore_object_file : str or :py:class:`bob.io.base.HDF5File`
+          The file open for reading, or the file name to read from.
+
+        Returns
+        -------
+        object:
+          The toscore_object that was read from file.
+        
+        """
+        return self.read_feature(toscore_object_file)
+  
+    def score(self, toscore):
+        return [toscore[0]]

bob/pad/base/algorithm/__init__.py

@@ -6,6 +6,7 @@ from .SVMCascadePCA import SVMCascadePCA
 from .Predictions import Predictions
 
 from .MLP import MLP
+from .PadLDA import PadLDA
 
 # to fix sphinx warnings of not able to find classes, when path is shortened
 def __appropriate__(*args):
@@ -33,6 +34,8 @@ __appropriate__(
     LogRegr,
     SVMCascadePCA,
     Predictions,
+    MLP,
+    PadLDA
 )
 
 # gets sphinx autodoc done right - don't remove it

bob/pad/base/test/test_algorithms.py

@@ -203,9 +203,36 @@ def test_MLP():
     mlp.train_projector(training_features, '/tmp/mlp.hdf5')
 
     real_sample = real_array[0]
-
     prob = mlp.project(real_sample)
-
     assert prob[0] > prob[1]
 
 
+def test_LDA():
+    """
+    Test the LDA PAD algorithm.
+    """
+
+    random.seed(7)
+
+    N = 20000
+    mu = 1
+    sigma = 1
+    real_array = np.transpose(
+        np.vstack([[random.gauss(mu, sigma) for _ in range(N)],
+                   [random.gauss(mu, sigma) for _ in range(N)]]))
+
+    mu = 5
+    sigma = 1
+    attack_array = np.transpose(
+        np.vstack([[random.gauss(mu, sigma) for _ in range(N)],
+                   [random.gauss(mu, sigma) for _ in range(N)]]))
+
+    training_features = [real_array, attack_array]
+
+    lda = PadLDA()
+    lda.train_projector(training_features, '/tmp/lda.hdf5')
+
+    real_sample = real_array[0]
+    prob = lda.project(real_sample)
+    assert prob[0] > prob[1]
+

doc/py_api.rst

@@ -28,6 +28,7 @@ Algorithm
 .. autosummary::
    bob.pad.base.tools.train_projector
    bob.pad.base.tools.project
+   bob.pad.base.algorithm
 
 Scoring
 ~~~~~~~