Merge branch 'one-class-gmm' into 'master'

Add a new one class GMM based on bob's GMMs

See merge request !62

bob/pad/base/algorithm/OneClassGMM.py

-#!/usr/bin/env python2
 # -*- coding: utf-8 -*-
 """
 Created on Mon Aug 28 16:47:47 2017
-
 @author: Olegs Nikisins
 """
 
 # ==============================================================================
 # Import what is needed here:
 
-from bob.pad.base.algorithm import Algorithm
 from bob.bio.video.utils import FrameContainer
-
-import numpy as np
-
-import bob.io.base
-
+from bob.pad.base.algorithm import Algorithm
+from bob.pad.base.utils import convert_frame_cont_to_array, mean_std_normalize, convert_and_prepare_features
 from sklearn import mixture
+import bob.io.base
+import logging
+import numpy as np
 
-from bob.pad.base.utils import convert_frame_cont_to_array, mean_std_normalize, convert_and_prepare_features
+logger = logging.getLogger(__name__)
 
 # ==============================================================================
 # Main body :
@@ -44,7 +41,7 @@ class OneClassGMM(Algorithm):
 
     ``random_state`` : :py:class:`int`
         A seed for the random number generator used in the initialization of
-        the OneClassGMM. Default: 7 .
+        the OneClassGMM. Default: 3 .
 
     ``frame_level_scores_flag`` : :py:class:`bool`
         Return scores for each frame individually if True. Otherwise, return a
@@ -54,7 +51,10 @@ class OneClassGMM(Algorithm):
     def __init__(self,
                  n_components=1,
                  random_state=3,
-                 frame_level_scores_flag=False):
+                 frame_level_scores_flag=False,
+                 covariance_type='full',
+                 reg_covar=1e-06,
+                 ):
 
         Algorithm.__init__(
             self,
@@ -65,15 +65,13 @@ class OneClassGMM(Algorithm):
             requires_projector_training=True)
 
         self.n_components = n_components
-
         self.random_state = random_state
-
         self.frame_level_scores_flag = frame_level_scores_flag
+        self.covariance_type = covariance_type
+        self.reg_covar = reg_covar
 
         self.machine = None  # this argument will be updated with pretrained OneClassGMM machine
-
         self.features_mean = None  # this argument will be updated with features mean
-
         self.features_std = None  # this argument will be updated with features std
 
         # names of the arguments of the pretrained OneClassGMM machine to be saved/loaded to/from HDF5 file:
@@ -84,7 +82,7 @@ class OneClassGMM(Algorithm):
         ]
 
     # ==========================================================================
-    def train_gmm(self, real, n_components, random_state):
+    def train_gmm(self, real):
         """
         Train OneClassGMM classifier given real class. Prior to the training the data is
         mean-std normalized.
@@ -94,13 +92,6 @@ class OneClassGMM(Algorithm):
         ``real`` : 2D :py:class:`numpy.ndarray`
             Training features for the real class.
 
-        ``n_components`` : :py:class:`int`
-            Number of Gaussians in the OneClassGMM. Default: 1 .
-
-        ``random_state`` : :py:class:`int`
-            A seed for the random number generator used in the initialization of
-            the OneClassGMM. Default: 7 .
-
         **Returns:**
 
         ``machine`` : object
@@ -113,16 +104,41 @@ class OneClassGMM(Algorithm):
             Standart deviation of the features.
         """
 
-        features_norm, features_mean, features_std = mean_std_normalize(
-            real)
         # real is now mean-std normalized
+        features_norm, features_mean, features_std = mean_std_normalize(real, copy=False)
+
+        if isinstance(self.n_components, (tuple, list)) or isinstance(self.covariance_type, (tuple, list)):
+            # perform grid search on covariance_type and n_components
+            n_components = self.n_components if isinstance(self.n_components, (tuple, list)) else [self.n_components]
+            covariance_type = self.covariance_type if isinstance(self.covariance_type, (tuple, list)) else [self.covariance_type]
+            logger.info("Performing grid search for GMM on covariance_type: %s and n_components: %s", self.covariance_type, self.n_components)
+            bic = []
+            lowest_bic = np.infty
+            for cv_type in covariance_type:
+                for nc in n_components:
+                    logger.info("Testing for n_components: %s, covariance_type: %s", nc, cv_type)
+                    gmm = mixture.GaussianMixture(
+                        n_components=nc, covariance_type=cv_type,
+                        reg_covar=self.reg_covar)
+                    try:
+                        gmm.fit(features_norm)
+                    except Exception:
+                        logger.warn("Failed to train current GMM", exc_info=True)
+                        continue
+                    bic.append(gmm.bic(features_norm))
+                    if bic[-1] < lowest_bic:
+                        lowest_bic = bic[-1]
+                        logger.info("Best parameters so far: nc %s, cv_type: %s", nc, cv_type)
+                        machine = gmm
 
-        machine = mixture.GaussianMixture(
-            n_components=n_components,
-            random_state=random_state,
-            covariance_type='full')
+        else:
+            machine = mixture.GaussianMixture(
+                n_components=self.n_components,
+                random_state=self.random_state,
+                covariance_type=self.covariance_type,
+                reg_covar=self.reg_covar)
 
-        machine.fit(features_norm)
+            machine.fit(features_norm)
 
         return machine, features_mean, features_std
 
@@ -150,19 +166,17 @@ class OneClassGMM(Algorithm):
             Standart deviation of the features.
         """
 
-        f = bob.io.base.HDF5File(projector_file,
-                                 'w')  # open hdf5 file to save to
+        # open hdf5 file to save to
+        with bob.io.base.HDF5File(projector_file, 'w') as f:
 
-        for key in self.gmm_param_keys:
-            data = getattr(machine, key)
+            for key in self.gmm_param_keys:
+                data = getattr(machine, key)
 
-            f.set(key, data)
+                f.set(key, data)
 
-        f.set("features_mean", features_mean)
+            f.set("features_mean", features_mean)
 
-        f.set("features_std", features_std)
-
-        del f
+            f.set("features_std", features_std)
 
     # ==========================================================================
     def train_projector(self, training_features, projector_file):
@@ -183,18 +197,16 @@ class OneClassGMM(Algorithm):
             ``bob.pad.base`` framework.
         """
 
+        del training_features[1]
         # training_features[0] - training features for the REAL class.
-        real = convert_and_prepare_features(
-            training_features[0])  # output is array
+        real = convert_and_prepare_features(training_features[0], dtype=None)
+        del training_features[0]
 
         # training_features[1] - training features for the ATTACK class.
         #        attack = self.convert_and_prepare_features(training_features[1]) # output is array
 
         # Train the OneClassGMM machine and get normalizers:
-        machine, features_mean, features_std = self.train_gmm(
-            real=real,
-            n_components=self.n_components,
-            random_state=self.random_state)
+        machine, features_mean, features_std = self.train_gmm(real=real)
 
         # Save the GNN machine and normalizers:
         self.save_gmm_machine_and_mean_std(projector_file, machine,
@@ -224,23 +236,19 @@ class OneClassGMM(Algorithm):
             Standart deviation of the features.
         """
 
-        f = bob.io.base.HDF5File(projector_file,
-                                 'r')  # file to read the machine from
-
-        # initialize the machine:
-        machine = mixture.GaussianMixture()
-
-        # set the params of the machine:
-        for key in self.gmm_param_keys:
-            data = f.read(key)
+        # file to read the machine from
+        with bob.io.base.HDF5File(projector_file, 'r') as f:
 
-            setattr(machine, key, data)
+            # initialize the machine:
+            machine = mixture.GaussianMixture()
 
-        features_mean = f.read("features_mean")
+            # set the params of the machine:
+            for key in self.gmm_param_keys:
+                data = f.read(key)
+                setattr(machine, key, data)
 
-        features_std = f.read("features_std")
-
-        del f
+            features_mean = f.read("features_mean")
+            features_std = f.read("features_std")
 
         return machine, features_mean, features_std
 
@@ -272,9 +280,7 @@ class OneClassGMM(Algorithm):
             projector_file)
 
         self.machine = machine
-
         self.features_mean = features_mean
-
         self.features_std = features_std
 
     # ==========================================================================
@@ -320,7 +326,7 @@ class OneClassGMM(Algorithm):
             features_array = feature
 
         features_array_norm, _, _ = mean_std_normalize(
-            features_array, self.features_mean, self.features_std)
+            features_array, self.features_mean, self.features_std, copy=False)
 
         scores = self.machine.score_samples(features_array_norm)
 

bob/pad/base/algorithm/OneClassGMM2.py

+# -*- coding: utf-8 -*-
+# @author: Amir Mohammadi
+
+from bob.pad.base.algorithm import Algorithm
+from bob.pad.base.utils import convert_and_prepare_features
+from bob.bio.gmm.algorithm import GMM
+import logging
+import numpy as np
+from collections.abc import Iterable
+from multiprocessing import cpu_count
+
+logger = logging.getLogger(__name__)
+
+
+def bic(trainer, machine, X):
+    """Bayesian information criterion for the current model on the input X.
+
+    Parameters
+    ----------
+    X : array of shape (n_samples, n_dimensions)
+
+    Returns
+    -------
+    bic : float
+        The lower the better.
+    """
+    log_likelihood = trainer.compute_likelihood(machine)
+    n_parameters = (
+        machine.means.size + machine.variances.size + len(machine.weights) - 1
+    )
+    return -2 * log_likelihood * X.shape[0] + n_parameters * np.log(X.shape[0])
+
+
+class OneClassGMM2(Algorithm):
+    """A one class GMM implementation based on Bob's GMM implementation which is more
+    stable than scikit-learn's one."""
+
+    def __init__(
+        self,
+        # parameters for the GMM
+        number_of_gaussians,
+        # parameters of UBM training
+        kmeans_training_iterations=25,  # Maximum number of iterations for K-Means
+        gmm_training_iterations=25,  # Maximum number of iterations for ML GMM Training
+        training_threshold=5e-4,  # Threshold to end the ML training
+        variance_threshold=5e-4,  # Minimum value that a variance can reach
+        update_weights=True,
+        update_means=True,
+        update_variances=True,
+        n_threads=cpu_count(),
+        **kwargs
+    ):
+        kwargs.setdefault("performs_projection", True)
+        kwargs.setdefault("requires_projector_training", True)
+        super().__init__(**kwargs)
+        self.gmm_alg = GMM(
+            number_of_gaussians=number_of_gaussians,
+            kmeans_training_iterations=kmeans_training_iterations,
+            gmm_training_iterations=gmm_training_iterations,
+            training_threshold=training_threshold,
+            variance_threshold=variance_threshold,
+            update_weights=update_weights,
+            update_means=update_means,
+            update_variances=update_variances,
+            n_threads=n_threads,
+        )
+        self.number_of_gaussians = number_of_gaussians
+
+    def train_projector(self, training_features, projector_file):
+        del training_features[1]
+        real = convert_and_prepare_features(training_features[0], dtype="float64")
+        del training_features[0]
+
+        if isinstance(self.number_of_gaussians, Iterable):
+            logger.info(
+                "Performing grid search for GMM on number_of_gaussians: %s",
+                self.number_of_gaussians,
+            )
+            lowest_bic = np.infty
+            best_n_gaussians = None
+            for nc in self.number_of_gaussians:
+                logger.info("Testing for number_of_gaussians: %s", nc)
+                self.gmm_alg.gaussians = nc
+                self.gmm_alg.train_ubm(real)
+                bic_ = bic(self.gmm_alg.ubm_trainer, self.gmm_alg.ubm, real)
+                logger.info("BIC for number_of_gaussians: %s is %s", nc, bic_)
+                if bic_ < lowest_bic:
+                    gmm = self.gmm_alg.ubm
+                    lowest_bic = bic_
+                    best_n_gaussians = nc
+                    logger.info("Best parameters so far: number_of_gaussians %s", nc)
+
+            assert best_n_gaussians is not None
+            self.gmm_alg.gaussians = best_n_gaussians
+        else:
+            self.gmm_alg.train_ubm(real)
+            gmm = self.gmm_alg.ubm
+
+        self.gmm_alg.ubm = gmm
+        self.gmm_alg.save_ubm(projector_file)
+
+    def load_projector(self, projector_file):
+        self.gmm_alg.load_ubm(projector_file)
+
+    def project(self, feature):
+        feature = convert_and_prepare_features([feature], dtype="float64")[0]
+
+        return self.gmm_alg.ubm(feature)
+
+    def score(self, toscore):
+        return [toscore]

bob/pad/base/algorithm/Predictions.py

 from bob.pad.base.algorithm import Algorithm
+import numpy
 
 
 class Predictions(Algorithm):
@@ -6,9 +7,31 @@ class Predictions(Algorithm):
     scoring."""
 
     def __init__(self, **kwargs):
-        super(Predictions, self).__init__(
-            **kwargs)
+        super(Predictions, self).__init__(**kwargs)
 
     def score(self, predictions):
+        predictions = numpy.asarray(predictions)
+        if predictions.size == 1:
+            # output of a sigmoid binary layer
+            return predictions
         # Assuming the predictions are the output of a softmax layer
         return [predictions[1]]
+
+
+class VideoPredictions(Algorithm):
+    """An algorithm that takes the precomputed predictions and uses them for
+    scoring."""
+
+    def __init__(self, axis=1, frame_level_scoring=False, **kwargs):
+        super(VideoPredictions, self).__init__(**kwargs)
+        self.frame_level_scoring = frame_level_scoring
+        self.axis = axis
+
+    def score(self, predictions):
+        # Assuming the predictions are the output of a softmax layer
+        predictions = predictions.as_array()[:, self.axis]
+
+        if self.frame_level_scoring:
+            return predictions
+        else:
+            return [numpy.mean(predictions)]

bob/pad/base/algorithm/__init__.py

 from .Algorithm import Algorithm
 from .SVM import SVM
 from .OneClassGMM import OneClassGMM
+from .OneClassGMM2 import OneClassGMM2
 from .LogRegr import LogRegr
 from .SVMCascadePCA import SVMCascadePCA
-from .Predictions import Predictions
+from .Predictions import Predictions, VideoPredictions
 
 from .MLP import MLP
 from .PadLDA import PadLDA
@@ -31,9 +32,11 @@ __appropriate__(
     Algorithm,
     SVM,
     OneClassGMM,
+    OneClassGMM2,
     LogRegr,
     SVMCascadePCA,
     Predictions,
+    VideoPredictions,
     MLP,
     PadLDA
 )

bob/pad/base/script/cross.py

@@ -7,9 +7,12 @@ import logging
 import math
 import os
 import yaml
-from bob.bio.base.score.load import split
+from bob.bio.base.score.load import load_score, get_negatives_positives
 from bob.extension.scripts.click_helper import (
-    verbosity_option, bool_option, log_parameters)
+    verbosity_option,
+    bool_option,
+    log_parameters,
+)
 from bob.measure import eer_threshold, farfrr
 from bob.measure.script import common_options
 from bob.measure.utils import get_fta
@@ -19,40 +22,96 @@ from tabulate import tabulate
 logger = logging.getLogger(__name__)
 
 
-@click.command(epilog='''\b
+@click.command(
+    epilog="""\b
 Examples:
   $ bin/bob pad cross 'results/{{ evaluation.database }}/{{ algorithm }}/{{ evaluation.protocol }}/scores/scores-{{ group }}' \
-    -td replaymobile -d replaymobile -p grandtest -d oulunpu -p Protocol_1 \
-    -a replaymobile_frame-diff-svm \
-    -a replaymobile_qm-svm-64 \
-    -a replaymobile_lbp-svm-64 \
+    -td replaymobile \
+    -d replaymobile -p grandtest \
+    -d oulunpu -p Protocol_1 \
+    -a replaymobile_grandtest_frame-diff-svm \
+    -a replaymobile_grandtest_qm-svm-64 \
+    -a replaymobile_grandtest_lbp-svm-64 \
     > replaymobile.rst &
-''')
-@click.argument('score_jinja_template')
-@click.option('-d', '--database', 'databases', multiple=True, required=True,
-              show_default=True,
-              help='Names of the evaluation databases')
-@click.option('-p', '--protocol', 'protocols', multiple=True, required=True,
-              show_default=True,
-              help='Names of the protocols of the evaluation databases')
-@click.option('-a', '--algorithm', 'algorithms', multiple=True, required=True,
-              show_default=True,
-              help='Names of the algorithms')
-@click.option('-n', '--names', type=click.File('r'),
-              help='Name of algorithms to show in the table. Provide a path '
-              'to a json file maps algorithm names to names that you want to '
-              'see in the table.')
-@click.option('-td', '--train-database', required=True,
-              help='The database that was used to train the algorithms.')
-@click.option('-g', '--group', 'groups', multiple=True, show_default=True,
-              default=['train', 'dev', 'eval'])
-@bool_option('sort', 's', 'whether the table should be sorted.', True)
+"""
+)
+@click.argument("score_jinja_template")
+@click.option(
+    "-d",
+    "--database",
+    "databases",
+    multiple=True,
+    required=True,
+    show_default=True,
+    help="Names of the evaluation databases",
+)
+@click.option(
+    "-p",
+    "--protocol",
+    "protocols",
+    multiple=True,
+    required=True,
+    show_default=True,
+    help="Names of the protocols of the evaluation databases",
+)
+@click.option(
+    "-a",
+    "--algorithm",
+    "algorithms",
+    multiple=True,
+    required=True,
+    show_default=True,
+    help="Names of the algorithms",
+)
+@click.option(
+    "-n",
+    "--names",
+    type=click.File("r"),
+    help="Name of algorithms to show in the table. Provide a path "
+    "to a json file maps algorithm names to names that you want to "
+    "see in the table.",
+)
+@click.option(
+    "-td",
+    "--train-database",
+    required=True,
+    help="The database that was used to train the algorithms.",
+)
+@click.option(
+    "-pn",
+    "--pai-names",
+    type=click.File("r"),
+    help="Name of PAIs to compute the errors per PAI. Provide a path "
+    "to a json file maps attack_type in scores to PAIs that you want to "
+    "see in the table.",
+)
+@click.option(
+    "-g",
+    "--group",
+    "groups",
+    multiple=True,
+    show_default=True,
+    default=["train", "dev", "eval"],
+)
+@bool_option("sort", "s", "whether the table should be sorted.", True)
 @common_options.table_option()
 @common_options.output_log_metric_option()
 @verbosity_option()
 @click.pass_context
-def cross(ctx, score_jinja_template, databases, protocols, algorithms,
-          names, train_database, groups, sort, **kwargs):
+def cross(
+    ctx,
+    score_jinja_template,
+    databases,
+    protocols,
+    algorithms,
+    names,
+    train_database,
+    pai_names,
+    groups,
+    sort,
+    verbose,
+    **kwargs
+):
     """Cross-db analysis metrics
     """
     log_parameters(logger)
@@ -62,10 +121,12 @@ def cross(ctx, score_jinja_template, databases, protocols, algorithms,
     env = jinja2.Environment(undefined=jinja2.StrictUndefined)
 
     data = {
-        'evaluation': [{'database': db, 'protocol': proto}
-                       for db, proto in zip(databases, protocols)],
-        'algorithm': algorithms,
-        'group': groups,
+        "evaluation": [
+            {"database": db, "protocol": proto}
+            for db, proto in zip(databases, protocols)
+        ],
+        "algorithm": algorithms,
+        "group": groups,
     }
 
     metrics = {}
@@ -74,27 +135,30 @@ def cross(ctx, score_jinja_template, databases, protocols, algorithms,
         logger.debug(variables)
 
         score_path = env.from_string(score_jinja_template).render(variables)
-        logger.debug(score_path)
+        logger.info(score_path)
 
-        database, protocol, algorithm, group = \
-            variables['evaluation']['database'], \
-            variables['evaluation']['protocol'], \
-            variables['algorithm'], variables['group']
+        database, protocol, algorithm, group = (
+            variables["evaluation"]["database"],
+            variables["evaluation"]["protocol"],
+            variables["algorithm"],
+            variables["group"],
+        )
 
         # if algorithm name does not have train_database name in it.
         if train_database not in algorithm and database != train_database:
-            score_path = score_path.replace(
-                algorithm, database + '_' + algorithm)
+            score_path = score_path.replace(algorithm, database + "_" + algorithm)
+            logger.info("Score path changed to: %s", score_path)
 
         if not os.path.exists(score_path):
-            metrics[(database, protocol, algorithm, group)] = \
-                (float('nan'), ) * 5
+            metrics[(database, protocol, algorithm, group)] = (float("nan"),) * 5
             continue
 
-        (neg, pos), fta = get_fta(split(score_path))
+        scores = load_score(score_path)
+        neg, pos = get_negatives_positives(scores)
+        (neg, pos), fta = get_fta((neg, pos))
 
-        if group == 'eval':
-            threshold = metrics[(database, protocol, algorithm, 'dev')][1]
+        if group