Matija Polajnar avatar Matija Polajnar committed 55e4bdc

Initial version as moved from main Orange. Without documentation.

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+                    GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
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+ Copyright (C) 2007 Free Software Foundation, Inc. <http://fsf.org/>
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+WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
+THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
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+PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
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+SUCH DAMAGES.
+
+  17. Interpretation of Sections 15 and 16.
+
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+above cannot be given local legal effect according to their terms,
+reviewing courts shall apply local law that most closely approximates
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+                     END OF TERMS AND CONDITIONS
+
+            How to Apply These Terms to Your New Programs
+
+  If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+  To do so, attach the following notices to the program.  It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+    <one line to give the program's name and a brief idea of what it does.>
+    Copyright (C) <year>  <name of author>
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
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+
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+    along with this program.  If not, see <http://www.gnu.org/licenses/>.
+
+Also add information on how to contact you by electronic and paper mail.
+
+  If the program does terminal interaction, make it output a short
+notice like this when it starts in an interactive mode:
+
+    <program>  Copyright (C) <year>  <name of author>
+    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
+    This is free software, and you are welcome to redistribute it
+    under certain conditions; type `show c' for details.
+
+The hypothetical commands `show w' and `show c' should show the appropriate
+parts of the General Public License.  Of course, your program's commands
+might be different; for a GUI interface, you would use an "about box".
+
+  You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU GPL, see
+<http://www.gnu.org/licenses/>.
+
+  The GNU General Public License does not permit incorporating your program
+into proprietary programs.  If your program is a subroutine library, you
+may consider it more useful to permit linking proprietary applications with
+the library.  If this is what you want to do, use the GNU Lesser General
+Public License instead of this License.  But first, please read
+<http://www.gnu.org/philosophy/why-not-lgpl.html>.
+Copyright (C) 2003-2012 Bioinformatics Laboratory, FRI UL
+
+This program is free software: you can redistribute it and/or modify it under
+the terms of the GNU General Public License as published by the Free Software
+Foundation, either version 3 of the License, or (at your option) any later
+version.
+
+This program is distributed in the hope that it will be useful, but WITHOUT ANY
+WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A
+PARTICULAR PURPOSE.  See the GNU General Public License for more details.
+
+You should have received a copy of the GNU General Public License along with
+this program.  If not, see <http://www.gnu.org/licenses/>.
+
+Together with the source code this program may contain also additional content.
+Unless specified otherwise, this content is available under Creative Commons
+Attribution-ShareAlike license, either version 3.0 of the license, or (at your
+option) any later version.  You are free to copy, distribute, transmit, adapt
+and/or commercially use this content or part(s) of it, provided you publicly,
+clearly and visibly attribute Bioinformatics Laboratory, FRI UL, and provide a
+link to its website <http://www.biolab.si/>, if applicable.  If you alter,
+transform, or build upon this content or part(s) of it, you may distribute the
+results only under the same or similar license to this one.
+
+For more information about Creative Commons Attribution-ShareAlike license see
+<http://creativecommons.org/>.
+
+This program may contain, use, link to and/or distribute also parts under third
+party copyright with possibly different licensing conditions. Make sure you
+check and respect also those conditions.
+Orange Reliability
+==================
+
+Orange Reliability is an add-on for Orange_ data mining software package. It
+extends Orange by providing functionality to estimate reliability of individual
+regression and classification predictions.
+
+.. _Orange: http://orange.biolab.si/
+
+Documentation is found at:
+
+http://orange-reliability.readthedocs.org/

_reliability/__init__.py

+import Orange
+
+import random
+from Orange import statc
+import math
+import warnings
+import numpy
+
+from collections import defaultdict
+from itertools import izip
+
+# Labels and final variables
+labels = ["SAvar", "SAbias", "BAGV", "CNK", "LCV", "BVCK", "Mahalanobis", "ICV"]
+
+"""
+# All the estimators calculation constants
+DO_SA = 0
+DO_BAGV = 1
+DO_CNK = 2
+DO_LCV = 3
+DO_BVCK = 4
+DO_MAHAL = 5
+"""
+
+# All the estimator method constants
+SAVAR_ABSOLUTE = 0
+SABIAS_SIGNED = 1
+SABIAS_ABSOLUTE = 2
+BAGV_ABSOLUTE = 3
+CNK_SIGNED = 4
+CNK_ABSOLUTE = 5
+LCV_ABSOLUTE = 6
+BVCK_ABSOLUTE = 7
+MAHAL_ABSOLUTE = 8
+BLENDING_ABSOLUTE = 9
+ICV_METHOD = 10
+MAHAL_TO_CENTER_ABSOLUTE = 13
+
+# Type of estimator constant
+SIGNED = 0
+ABSOLUTE = 1
+
+# Names of all the estimator methods
+METHOD_NAME = {0: "SAvar absolute", 1: "SAbias signed", 2: "SAbias absolute",
+               3: "BAGV absolute", 4: "CNK signed", 5: "CNK absolute",
+               6: "LCV absolute", 7: "BVCK_absolute", 8: "Mahalanobis absolute",
+               9: "BLENDING absolute", 10: "ICV", 11: "RF Variance", 12: "RF Std",
+               13: "Mahalanobis to center"}
+
+select_with_repeat = Orange.core.MakeRandomIndicesMultiple()
+select_with_repeat.random_generator = Orange.misc.Random()
+
+def get_reliability_estimation_list(res, i):
+    return [result.probabilities[0].reliability_estimate[i].estimate for result in res.results], res.results[0].probabilities[0].reliability_estimate[i].signed_or_absolute, res.results[0].probabilities[0].reliability_estimate[i].method
+
+def get_prediction_error_list(res):
+    return [result.actual_class - result.classes[0] for result in res.results]
+
+def get_description_list(res, i):
+    return [result.probabilities[0].reliability_estimate[i].text_description for result in res.results]
+
+def get_pearson_r(res):
+    """
+    :param res: results of evaluation, done using learners,
+        wrapped into :class:`Orange.evaluation.reliability.Classifier`.
+    :type res: :class:`Orange.evaluation.testing.ExperimentResults`
+
+    Return Pearson's coefficient between the prediction error and each of the
+    used reliability estimates. Also, return the p-value of each of
+    the coefficients.
+    """
+    prediction_error = get_prediction_error_list(res)
+    results = []
+    for i in xrange(len(res.results[0].probabilities[0].reliability_estimate)):
+        reliability_estimate, signed_or_absolute, method = get_reliability_estimation_list(res, i)
+        try:
+            if signed_or_absolute == SIGNED:
+                r, p = statc.pearsonr(prediction_error, reliability_estimate)
+            else:
+                r, p = statc.pearsonr([abs(pe) for pe in prediction_error], reliability_estimate)
+        except Exception:
+            r = p = float("NaN")
+        results.append((r, p, signed_or_absolute, method))
+    return results
+
+def get_spearman_r(res):
+    """
+    :param res: results of evaluation, done using learners,
+        wrapped into :class:`Orange.evaluation.reliability.Classifier`.
+    :type res: :class:`Orange.evaluation.testing.ExperimentResults`
+
+    Return Spearman's coefficient between the prediction error and each of the
+    used reliability estimates. Also, return the p-value of each of
+    the coefficients.
+    """
+    prediction_error = get_prediction_error_list(res)
+    results = []
+    for i in xrange(len(res.results[0].probabilities[0].reliability_estimate)):
+        reliability_estimate, signed_or_absolute, method = get_reliability_estimation_list(res, i)
+        try:
+            if signed_or_absolute == SIGNED:
+                r, p = statc.spearmanr(prediction_error, reliability_estimate)
+            else:
+                r, p = statc.spearmanr([abs(pe) for pe in prediction_error], reliability_estimate)
+        except Exception:
+            r = p = float("NaN")
+        results.append((r, p, signed_or_absolute, method))
+    return results
+
+def get_pearson_r_by_iterations(res):
+    """
+    :param res: results of evaluation, done using learners,
+        wrapped into :class:`Orange.evaluation.reliability.Classifier`.
+    :type res: :class:`Orange.evaluation.testing.ExperimentResults`
+
+    Return average Pearson's coefficient over all folds between prediction error
+    and each of the used estimates.
+    """
+    results_by_fold = Orange.evaluation.scoring.split_by_iterations(res)
+    number_of_estimates = len(res.results[0].probabilities[0].reliability_estimate)
+    number_of_instances = len(res.results)
+    number_of_folds = len(results_by_fold)
+    results = [0 for _ in xrange(number_of_estimates)]
+    sig = [0 for _ in xrange(number_of_estimates)]
+    method_list = [0 for _ in xrange(number_of_estimates)]
+
+    for res in results_by_fold:
+        prediction_error = get_prediction_error_list(res)
+        for i in xrange(number_of_estimates):
+            reliability_estimate, signed_or_absolute, method = get_reliability_estimation_list(res, i)
+            try:
+                if signed_or_absolute == SIGNED:
+                    r, _ = statc.pearsonr(prediction_error, reliability_estimate)
+                else:
+                    r, _ = statc.pearsonr([abs(pe) for pe in prediction_error], reliability_estimate)
+            except Exception:
+                r = float("NaN")
+            results[i] += r
+            sig[i] = signed_or_absolute
+            method_list[i] = method
+
+    # Calculate p-values
+    results = [float(res) / number_of_folds for res in results]
+    ps = [p_value_from_r(r, number_of_instances) for r in results]
+
+    return zip(results, ps, sig, method_list)
+
+def p_value_from_r(r, n):
+    """
+    Calculate p-value from the paerson coefficient and the sample size.
+    """
+    df = n - 2
+    t = r * (df / ((-r + 1.0 + 1e-30) * (r + 1.0 + 1e-30))) ** 0.5
+    return statc.betai (df * 0.5, 0.5, df / (df + t * t))
+
+class Estimate:
+    """
+    Reliability estimate. Contains attributes that describe the results of
+    reliability estimation.
+
+    .. attribute:: estimate
+
+        A numerical reliability estimate.
+
+    .. attribute:: signed_or_absolute
+
+        Determines whether the method used gives a signed or absolute result.
+        Has a value of either :obj:`SIGNED` or :obj:`ABSOLUTE`.
+
+    .. attribute:: method
+
+        An integer ID of reliability estimation method used.
+
+    .. attribute:: method_name
+
+        Name (string) of reliability estimation method used.
+
+    .. attribute:: icv_method
+
+        An integer ID of reliability estimation method that performed best,
+        as determined by ICV, and of which estimate is stored in the
+        :obj:`estimate` field. (:obj:`None` when ICV was not used.)
+
+    .. attribute:: icv_method_name
+
+        Name (string) of reliability estimation method that performed best,
+        as determined by ICV. (:obj:`None` when ICV was not used.)
+
+    """
+    def __init__(self, estimate, signed_or_absolute, method, icv_method= -1):
+        self.estimate = estimate
+        self.signed_or_absolute = signed_or_absolute
+        self.method = method
+        self.method_name = METHOD_NAME[method]
+        self.icv_method = icv_method
+        self.icv_method_name = METHOD_NAME[icv_method] if icv_method != -1 else ""
+        self.text_description = None
+
+class DescriptiveAnalysis:
+    def __init__(self, estimator, desc=["high", "medium", "low"], procentage=[0.00, 0.33, 0.66]):
+        self.desc = desc
+        self.procentage = procentage
+        self.estimator = estimator
+
+    def __call__(self, instances, weight=None, **kwds):
+
+        # Calculate borders using cross validation
+        res = Orange.evaluation.testing.cross_validation([self.estimator], instances)
+        all_borders = []
+        for i in xrange(len(res.results[0].probabilities[0].reliability_estimate)):
+            estimates, signed_or_absolute, method = get_reliability_estimation_list(res, i)
+            sorted_estimates = sorted(abs(x) for x in estimates)
+            borders = [sorted_estimates[int(len(estimates) * p) - 1]  for p in self.procentage]
+            all_borders.append(borders)
+
+        # Learn on whole train data
+        estimator_classifier = self.estimator(instances)
+
+        return DescriptiveAnalysisClassifier(estimator_classifier, all_borders, self.desc)
+
+class DescriptiveAnalysisClassifier:
+    def __init__(self, estimator_classifier, all_borders, desc):
+        self.estimator_classifier = estimator_classifier
+        self.all_borders = all_borders
+        self.desc = desc
+
+    def __call__(self, instance, result_type=Orange.core.GetValue):
+        predicted, probabilities = self.estimator_classifier(instance, Orange.core.GetBoth)
+
+        for borders, estimate in zip(self.all_borders, probabilities.reliability_estimate):
+            estimate.text_description = self.desc[0]
+            for lower_border, text_desc in zip(borders, self.desc):
+                if estimate.estimate >= lower_border:
+                    estimate.text_description = text_desc
+
+        # Return the appropriate type of result
+        if result_type == Orange.core.GetValue:
+            return predicted
+        elif result_type == Orange.core.GetProbabilities:
+            return probabilities
+        else:
+            return predicted, probabilities
+
+class SensitivityAnalysis:
+    """
+    
+    :param e: List of possible :math:`\epsilon` values for SAvar and SAbias
+        reliability estimates.
+    :type e: list of floats
+    
+    :rtype: :class:`Orange.evaluation.reliability.SensitivityAnalysisClassifier`
+    
+    To estimate the reliability of prediction for given instance,
+    the learning set is extended with this instance, labeled with
+    :math:`K + \epsilon (l_{max} - l_{min})`,
+    where :math:`K` denotes the initial prediction,
+    :math:`\epsilon` is sensitivity parameter and :math:`l_{min}` and
+    :math:`l_{max}` denote lower and the upper bound of the learning
+    instances' labels. After computing different sensitivity predictions
+    using different values of :math:`\epsilon`, the prediction are combined
+    into SAvar and SAbias. SAbias can be used in a signed or absolute form.
+
+    :math:`SAvar = \\frac{\sum_{\epsilon \in E}(K_{\epsilon} - K_{-\epsilon})}{|E|}`
+
+    :math:`SAbias = \\frac{\sum_{\epsilon \in E} (K_{\epsilon} - K ) + (K_{-\epsilon} - K)}{2 |E|}`
+    
+    
+    """
+    def __init__(self, e=[0.01, 0.1, 0.5, 1.0, 2.0]):
+        self.e = e
+
+    def __call__(self, instances, learner):
+        min_value = max_value = instances[0].getclass().value
+        for ex in instances:
+            if ex.getclass().value > max_value:
+                max_value = ex.getclass().value
+            if ex.getclass().value < min_value:
+                min_value = ex.getclass().value
+        return SensitivityAnalysisClassifier(self.e, instances, min_value, max_value, learner)
+
+class SensitivityAnalysisClassifier:
+    def __init__(self, e, instances, min_value, max_value, learner):
+        self.e = e
+        self.instances = instances
+        self.max_value = max_value
+        self.min_value = min_value
+        self.learner = learner
+
+    def __call__(self, instance, predicted, probabilities):
+        # Create new dataset
+        r_data = Orange.data.Table(self.instances)
+
+        # Create new instance
+        modified_instance = Orange.data.Instance(instance)
+
+        # Append it to the data
+        r_data.append(modified_instance)
+
+        # Calculate SAvar & SAbias
+        SAvar = SAbias = 0
+
+        for eps in self.e:
+            # +epsilon
+            r_data[-1].setclass(predicted.value + eps * (self.max_value - self.min_value))
+            c = self.learner(r_data)
+            k_plus = c(instance, Orange.core.GetValue)
+
+            # -epsilon
+            r_data[-1].setclass(predicted.value - eps * (self.max_value - self.min_value))
+            c = self.learner(r_data)
+            k_minus = c(instance, Orange.core.GetValue)
+            #print len(r_data)
+            #print eps*(self.max_value - self.min_value)
+            #print k_plus
+            #print k_minus
+            # calculate part SAvar and SAbias
+            SAvar += k_plus.value - k_minus.value
+            SAbias += k_plus.value + k_minus.value - 2 * predicted.value
+
+        SAvar /= len(self.e)
+        SAbias /= 2 * len(self.e)
+
+        return [Estimate(SAvar, ABSOLUTE, SAVAR_ABSOLUTE),
+                Estimate(SAbias, SIGNED, SABIAS_SIGNED),
+                Estimate(abs(SAbias), ABSOLUTE, SABIAS_ABSOLUTE)]
+
+class BaggingVariance:
+    """
+    
+    :param m: Number of bagging models to be used with BAGV estimate
+    :type m: int
+    
+    :rtype: :class:`Orange.evaluation.reliability.BaggingVarianceClassifier`
+    
+    :math:`m` different bagging models are constructed and used to estimate
+    the value of dependent variable for a given instance. The variance of
+    those predictions is used as a prediction reliability estimate.
+
+    :math:`BAGV = \\frac{1}{m} \sum_{i=1}^{m} (K_i - K)^2`
+
+    where :math:`K = \\frac{\sum_{i=1}^{m} K_i}{m}` and :math:`K_i` are
+    predictions of individual constructed models.
+    
+    """
+    def __init__(self, m=50):
+        self.m = m
+
+    def __call__(self, instances, learner):
+        classifiers = []
+
+        # Create bagged classifiers using sampling with replacement
+        for _ in xrange(self.m):
+            selection = select_with_repeat(len(instances))
+            data = instances.select(selection)
+            classifiers.append(learner(data))
+        return BaggingVarianceClassifier(classifiers)
+
+class BaggingVarianceClassifier:
+    def __init__(self, classifiers):
+        self.classifiers = classifiers
+
+    def __call__(self, instance, *args):
+        BAGV = 0
+
+        # Calculate the bagging variance
+        bagged_values = [c(instance, Orange.core.GetValue).value for c in self.classifiers if c is not None]
+
+        k = sum(bagged_values) / len(bagged_values)
+
+        BAGV = sum((bagged_value - k) ** 2 for bagged_value in bagged_values) / len(bagged_values)
+
+        return [Estimate(BAGV, ABSOLUTE, BAGV_ABSOLUTE)]
+
+class LocalCrossValidation:
+    """
+    
+    :param k: Number of nearest neighbours used in LCV estimate
+    :type k: int
+    
+    :rtype: :class:`Orange.evaluation.reliability.LocalCrossValidationClassifier`
+    
+    :math:`k` nearest neighbours to the given instance are found and put in
+    a separate data set. On this data set, a leave-one-out validation is
+    performed. Reliability estimate is then the distance weighted absolute
+    prediction error.
+
+    If a special value 0 is passed as :math:`k` (as is by default),
+    it is set as 1/20 of data set size (or 5, whichever is greater).
+    
+    1. Determine the set of k nearest neighours :math:`N = { (x_1, c_1),...,
+       (x_k, c_k)}`.
+    2. On this set, compute leave-one-out predictions :math:`K_i` and
+       prediction errors :math:`E_i = | C_i - K_i |`.
+    3. :math:`LCV(x) = \\frac{ \sum_{(x_i, c_i) \in N} d(x_i, x) * E_i }{ \sum_{(x_i, c_i) \in N} d(x_i, x) }`
+    
+    """
+    def __init__(self, k=0):
+        self.k = k
+
+    def __call__(self, instances, learner):
+        nearest_neighbours_constructor = Orange.classification.knn.FindNearestConstructor()
+        nearest_neighbours_constructor.distanceConstructor = Orange.distance.Euclidean()
+
+        distance_id = Orange.feature.Descriptor.new_meta_id()
+        nearest_neighbours = nearest_neighbours_constructor(instances, 0, distance_id)
+
+        if self.k == 0:
+            self.k = max(5, len(instances) / 20)
+
+        return LocalCrossValidationClassifier(distance_id, nearest_neighbours, self.k, learner)
+
+class LocalCrossValidationClassifier:
+    def __init__(self, distance_id, nearest_neighbours, k, learner):
+        self.distance_id = distance_id
+        self.nearest_neighbours = nearest_neighbours
+        self.k = k
+        self.learner = learner
+
+    def __call__(self, instance, *args):
+        LCVer = 0
+        LCVdi = 0
+
+        # Find k nearest neighbors
+
+        knn = [ex for ex in self.nearest_neighbours(instance, self.k)]
+
+        # leave one out of prediction error
+        for i in xrange(len(knn)):
+            train = knn[:]
+            del train[i]
+
+            classifier = self.learner(Orange.data.Table(train))
+
+            returned_value = classifier(knn[i], Orange.core.GetValue)
+
+            e = abs(knn[i].getclass().value - returned_value.value)
+
+            LCVer += e * math.exp(-knn[i][self.distance_id])
+            LCVdi += math.exp(-knn[i][self.distance_id])
+
+        LCV = LCVer / LCVdi if LCVdi != 0 else 0
+        if math.isnan(LCV):
+            LCV = 0.0
+        return [ Estimate(LCV, ABSOLUTE, LCV_ABSOLUTE) ]
+
+class CNeighbours:
+    """
+    
+    :param k: Number of nearest neighbours used in CNK estimate
+    :type k: int
+    
+    :rtype: :class:`Orange.evaluation.reliability.CNeighboursClassifier`
+    
+    CNK is defined for an unlabeled instance as a difference between average
+    label of its nearest neighbours and its prediction. CNK can be used as a
+    signed or absolute estimate.
+    
+    :math:`CNK = \\frac{\sum_{i=1}^{k}C_i}{k} - K`
+    
+    where :math:`k` denotes number of neighbors, C :sub:`i` denotes neighbours'
+    labels and :math:`K` denotes the instance's prediction.
+    
+    """
+    def __init__(self, k=5):
+        self.k = k
+
+    def __call__(self, instances, learner):
+        nearest_neighbours_constructor = Orange.classification.knn.FindNearestConstructor()
+        nearest_neighbours_constructor.distanceConstructor = Orange.distance.Euclidean()
+
+        distance_id = Orange.feature.Descriptor.new_meta_id()
+        nearest_neighbours = nearest_neighbours_constructor(instances, 0, distance_id)
+        return CNeighboursClassifier(nearest_neighbours, self.k)
+
+class CNeighboursClassifier:
+    def __init__(self, nearest_neighbours, k):
+        self.nearest_neighbours = nearest_neighbours
+        self.k = k
+
+    def __call__(self, instance, predicted, probabilities):
+        CNK = 0
+
+        # Find k nearest neighbors
+
+        knn = [ex for ex in self.nearest_neighbours(instance, self.k)]
+
+        # average label of neighbors
+        for ex in knn:
+            CNK += ex.getclass().value
+
+        CNK /= self.k
+        CNK -= predicted.value
+
+        return [Estimate(CNK, SIGNED, CNK_SIGNED),
+                Estimate(abs(CNK), ABSOLUTE, CNK_ABSOLUTE)]
+
+class Mahalanobis:
+    """
+    
+    :param k: Number of nearest neighbours used in Mahalanobis estimate.
+    :type k: int
+    
+    :rtype: :class:`Orange.evaluation.reliability.MahalanobisClassifier`
+    
+    Mahalanobis distance reliability estimate is defined as
+    `mahalanobis distance <http://en.wikipedia.org/wiki/Mahalanobis_distance>`_
+    to the evaluated instance's :math:`k` nearest neighbours.
+
+    
+    """
+    def __init__(self, k=3):
+        self.k = k
+
+    def __call__(self, instances, *args):
+        nnm = Orange.classification.knn.FindNearestConstructor()
+        nnm.distanceConstructor = Orange.distance.Mahalanobis()
+
+        mid = Orange.feature.Descriptor.new_meta_id()
+        nnm = nnm(instances, 0, mid)
+        return MahalanobisClassifier(self.k, nnm, mid)
+
+class MahalanobisClassifier:
+    def __init__(self, k, nnm, mid):
+        self.k = k
+        self.nnm = nnm
+        self.mid = mid
+
+    def __call__(self, instance, *args):
+        mahalanobis_distance = 0
+
+        mahalanobis_distance = sum(ex[self.mid].value for ex in self.nnm(instance, self.k))
+
+        return [ Estimate(mahalanobis_distance, ABSOLUTE, MAHAL_ABSOLUTE) ]
+
+class MahalanobisToCenter:
+    """
+    :rtype: :class:`Orange.evaluation.reliability.MahalanobisToCenterClassifier`
+    
+    Mahalanobis distance to center reliability estimate is defined as a
+    `mahalanobis distance <http://en.wikipedia.org/wiki/Mahalanobis_distance>`_
+    between the predicted instance and the centroid of the data.
+
+    
+    """
+    def __init__(self):
+        pass
+
+    def __call__(self, instances, *args):
+        dc = Orange.core.DomainContinuizer()
+        dc.classTreatment = Orange.core.DomainContinuizer.Ignore
+        dc.continuousTreatment = Orange.core.DomainContinuizer.NormalizeBySpan
+        dc.multinomialTreatment = Orange.core.DomainContinuizer.NValues
+
+        new_domain = dc(instances)
+        new_instances = instances.translate(new_domain)
+
+        X, _, _ = new_instances.to_numpy()
+        instance_avg = numpy.average(X, 0)
+
+        distance_constructor = Orange.distance.Mahalanobis()
+        distance = distance_constructor(new_instances)
+
+        average_instance = Orange.data.Instance(new_instances.domain, list(instance_avg) + ["?"])
+
+        return MahalanobisToCenterClassifier(distance, average_instance, new_domain)
+
+class MahalanobisToCenterClassifier:
+    def __init__(self, distance, average_instance, new_domain):
+        self.distance = distance
+        self.average_instance = average_instance
+        self.new_domain = new_domain
+
+    def __call__(self, instance, *args):
+
+        inst = Orange.data.Instance(self.new_domain, instance)
+
+        mahalanobis_to_center = self.distance(inst, self.average_instance)
+
+        return [ Estimate(mahalanobis_to_center, ABSOLUTE, MAHAL_TO_CENTER_ABSOLUTE) ]
+
+
+class BaggingVarianceCNeighbours:
+    """
+    
+    :param bagv: Instance of Bagging Variance estimator.
+    :type bagv: :class:`BaggingVariance`
+    
+    :param cnk: Instance of CNK estimator.
+    :type cnk: :class:`CNeighbours`
+    
+    :rtype: :class:`Orange.evaluation.reliability.BaggingVarianceCNeighboursClassifier`
+    
+    BVCK is a combination (average) of Bagging variance and local modeling of
+    prediction error.
+    
+    """
+    def __init__(self, bagv=BaggingVariance(), cnk=CNeighbours()):
+        self.bagv = bagv
+        self.cnk = cnk
+
+    def __call__(self, instances, learner):
+        bagv_classifier = self.bagv(instances, learner)
+        cnk_classifier = self.cnk(instances, learner)
+        return BaggingVarianceCNeighboursClassifier(bagv_classifier, cnk_classifier)
+
+class BaggingVarianceCNeighboursClassifier:
+    def __init__(self, bagv_classifier, cnk_classifier):
+        self.bagv_classifier = bagv_classifier
+        self.cnk_classifier = cnk_classifier
+
+    def __call__(self, instance, predicted, probabilities):
+        bagv_estimates = self.bagv_classifier(instance, predicted, probabilities)
+        cnk_estimates = self.cnk_classifier(instance, predicted, probabilities)
+
+        bvck_value = (bagv_estimates[0].estimate + cnk_estimates[1].estimate) / 2
+        bvck_estimates = [ Estimate(bvck_value, ABSOLUTE, BVCK_ABSOLUTE) ]
+        bvck_estimates.extend(bagv_estimates)
+        bvck_estimates.extend(cnk_estimates)
+        return bvck_estimates
+
+class ErrorPredicting:
+    def __init__(self):
+        pass
+
+    def __call__(self, instances, learner):
+        res = Orange.evaluation.testing.cross_validation([learner], instances)
+        prediction_errors = get_prediction_error_list(res)
+
+        new_domain = Orange.data.Domain(instances.domain.attributes, Orange.core.FloatVariable("pe"))
+        new_dataset = Orange.data.Table(new_domain, instances)
+
+        for instance, prediction_error in izip(new_dataset, prediction_errors):
+            instance.set_class(prediction_error)
+
+        rf = Orange.ensemble.forest.RandomForestLearner()
+        rf_classifier = rf(new_dataset)
+
+        return ErrorPredictingClassification(rf_classifier, new_domain)
+
+class ErrorPredictingClassification:
+    def __init__(self, rf_classifier, new_domain):
+        self.rf_classifier = rf_classifier
+        self.new_domain = new_domain
+
+    def __call__(self, instance, predicted, probabilities):
+        new_instance = Orange.data.Instance(self.new_domain, instance)
+        value = self.rf_classifier(new_instance, Orange.core.GetValue)
+
+        return [Estimate(value.value, SIGNED, SABIAS_SIGNED)]
+
+class Learner:
+    """
+    Reliability estimation wrapper around a learner we want to test.
+    Different reliability estimation algorithms can be used on the
+    chosen learner. This learner works as any other and can be used as one,
+    but it returns the classifier, wrapped into an instance of
+    :class:`Orange.evaluation.reliability.Classifier`.
+    
+    :param box_learner: Learner we want to wrap into a reliability estimation
+        classifier.
+    :type box_learner: :obj:`~Orange.classification.Learner`
+    
+    :param estimators: List of different reliability estimation methods we
+                       want to use on the chosen learner.
+    :type estimators: :obj:`list` of reliability estimators
+    
+    :param name: Name of this reliability learner
+    :type name: string
+    
+    :rtype: :class:`Orange.evaluation.reliability.Learner`
+    """
+    def __init__(self, box_learner, name="Reliability estimation",
+                 estimators=[SensitivityAnalysis(),
+                             LocalCrossValidation(),
+                             BaggingVarianceCNeighbours(),
+                             Mahalanobis(),
+                             MahalanobisToCenter()],
+                 **kwds):
+        self.__dict__.update(kwds)
+        self.name = name
+        self.estimators = estimators
+        self.box_learner = box_learner
+        self.blending = False
+
+
+    def __call__(self, instances, weight=None, **kwds):
+        """Learn from the given table of data instances.
+        
+        :param instances: Data instances to learn from.
+        :type instances: Orange.data.Table
+        :param weight: Id of meta attribute with weights of instances
+        :type weight: int
+        :rtype: :class:`Orange.evaluation.reliability.Classifier`
+        """
+
+        blending_classifier = None
+        new_domain = None
+
+#        if instances.domain.class_var.var_type != Orange.feature.Continuous.Continuous:
+#            raise Exception("This method only works on data with continuous class.")
+
+        return Classifier(instances, self.box_learner, self.estimators, self.blending, new_domain, blending_classifier)
+
+    def internal_cross_validation(self, instances, folds=10):
+        """ Perform the internal cross validation for getting the best
+        reliability estimate. It uses the reliability estimators defined in
+        estimators attribute.
+
+        Returns the id of the method that scored the best.
+
+        :param instances: Data instances to use for ICV.
+        :type instances: :class:`Orange.data.Table`
+        :param folds: number of folds for ICV.
+        :type folds: int
+        :rtype: int
+
+        """
+        res = Orange.evaluation.testing.cross_validation([self], instances, folds=folds)
+        results = get_pearson_r(res)
+        sorted_results = sorted(results)
+        return sorted_results[-1][3]
+
+    def internal_cross_validation_testing(self, instances, folds=10):
+        """ Perform internal cross validation (as in Automatic selection of
+        reliability estimates for individual regression predictions,
+        Zoran Bosnic, 2010) and return id of the method
+        that scored best on this data.
+
+        :param instances: Data instances to use for ICV.
+        :type instances: :class:`Orange.data.Table`
+        :param folds: number of folds for ICV.
+        :type folds: int
+        :rtype: int
+
+        """
+        cv_indices = Orange.core.MakeRandomIndicesCV(instances, folds)
+
+        list_of_rs = []
+
+        sum_of_rs = defaultdict(float)
+
+        for fold in xrange(folds):
+            data = instances.select(cv_indices, fold)
+            if len(data) < 10:
+                res = Orange.evaluation.testing.leave_one_out([self], data)
+            else:
+                res = Orange.evaluation.testing.cross_validation([self], data)
+            results = get_pearson_r(res)
+            for r, _, _, method in results:
+                sum_of_rs[method] += r
+        sorted_sum_of_rs = sorted(sum_of_rs.items(), key=lambda estimate: estimate[1], reverse=True)
+        return sorted_sum_of_rs[0][0]
+
+    labels = ["SAvar", "SAbias", "BAGV", "CNK", "LCV", "BVCK", "Mahalanobis", "ICV"]
+
+class Classifier:
+    """
+    A reliability estimation wrapper for classifiers.
+
+    What distinguishes this classifier is that the returned probabilities (if
+    :obj:`Orange.classification.Classifier.GetProbabilities` or
+    :obj:`Orange.classification.Classifier.GetBoth` is passed) contain an
+    additional attribute :obj:`reliability_estimate`, which is an instance of
+    :class:`~Orange.evaluation.reliability.Estimate`.
+
+    """
+
+    def __init__(self, instances, box_learner, estimators, blending, blending_domain, rf_classifier, **kwds):
+        self.__dict__.update(kwds)
+        self.instances = instances
+        self.box_learner = box_learner
+        self.estimators = estimators
+        self.blending = blending
+        self.blending_domain = blending_domain
+        self.rf_classifier = rf_classifier
+
+        # Train the learner with original data
+        self.classifier = box_learner(instances)
+
+        # Train all the estimators and create their classifiers
+        self.estimation_classifiers = [estimator(instances, box_learner) for estimator in estimators]
+
+    def __call__(self, instance, result_type=Orange.core.GetValue):
+        """
+        Classify and estimate reliability of estimation for a new instance.
+        When :obj:`result_type` is set to
+        :obj:`Orange.classification.Classifier.GetBoth` or
+        :obj:`Orange.classification.Classifier.GetProbabilities`,
+        an additional attribute :obj:`reliability_estimate`,
+        which is an instance of
+        :class:`~Orange.evaluation.reliability.Estimate`,
+        is added to the distribution object.
+        
+        :param instance: instance to be classified.
+        :type instance: :class:`Orange.data.Instance`
+        :param result_type: :class:`Orange.classification.Classifier.GetValue` or \
+              :class:`Orange.classification.Classifier.GetProbabilities` or
+              :class:`Orange.classification.Classifier.GetBoth`
+        
+        :rtype: :class:`Orange.data.Value`, 
+              :class:`Orange.statistics.Distribution` or a tuple with both
+        """
+        predicted, probabilities = self.classifier(instance, Orange.core.GetBoth)
+
+        # Create a place holder for estimates
+        if probabilities is None:
+            probabilities = Orange.statistics.distribution.Continuous()
+        #with warnings.catch_warnings():
+        #    warnings.simplefilter("ignore")
+        probabilities.setattr('reliability_estimate', [])
+
+        # Calculate all the estimates and add them to the results
+        for estimate in self.estimation_classifiers:
+            probabilities.reliability_estimate.extend(estimate(instance, predicted, probabilities))
+
+        # Return the appropriate type of result
+        if result_type == Orange.core.GetValue:
+            return predicted
+        elif result_type == Orange.core.GetProbabilities:
+            return probabilities
+        else:
+            return predicted, probabilities

_reliability/widgets/OWReliability.py

+"""
+<name>Reliability</name>
+<contact>Ales Erjavec (ales.erjavec(@at@)fri.uni-lj.si)</contact>
+<priority>310</priority>
+<icon>icons/Reliability.png</icon>
+"""
+
+from __future__ import absolute_import
+
+import Orange
+import _reliability as reliability
+from Orange.evaluation import testing
+#from Orange.utils import progress_bar_milestones
+from functools import partial
+ 
+from OWWidget import *
+import OWGUI
+
+class OWReliability(OWWidget):
+    settingsList = ["variance_checked", "bias_checked", "bagged_variance",
+        "local_cv", "local_model_pred_error", "bagging_variance_cn", 
+        "mahalanobis_distance", "var_e", "bias_e", "bagged_m", "local_cv_k",
+        "local_pe_k", "bagged_cn_m", "bagged_cn_k", "mahalanobis_k",
+        "include_error", "include_class", "include_input_features",
+        "auto_commit"]
+    
+    def __init__(self, parent=None, signalManager=None, title="Reliability"):
+        OWWidget.__init__(self, parent, signalManager, title, wantMainArea=False)
+        
+        self.inputs = [("Learner", Orange.core.Learner, self.set_learner),
+                       ("Training Data", Orange.data.Table, self.set_train_data),
+                       ("Test Data", Orange.data.Table, self.set_test_data)]
+        
+        self.outputs = [("Reliability Scores", Orange.data.Table)]
+        
+        self.variance_checked = False
+        self.bias_checked = False
+        self.bagged_variance = False
+        self.local_cv = False
+        self.local_model_pred_error = False
+        self.bagging_variance_cn = False
+        self.mahalanobis_distance = True
+        
+        self.var_e = "0.01, 0.1, 0.5, 1.0, 2.0"
+        self.bias_e =  "0.01, 0.1, 0.5, 1.0, 2.0"
+        self.bagged_m = 10
+        self.local_cv_k = 2
+        self.local_pe_k = 5
+        self.bagged_cn_m = 5
+        self.bagged_cn_k = 1
+        self.mahalanobis_k = 3
+        
+        self.include_error = True
+        self.include_class = True
+        self.include_input_features = False
+        self.auto_commit = False
+        
+        # (selected attr name, getter function, count of returned estimators, indices of estimator results to use)
+        self.estimators = \
+            [("variance_checked", self.get_SAVar, 3, [0]),
+             ("bias_checked", self.get_SABias, 3, [1, 2]),
+             ("bagged_variance", self.get_BAGV, 1, [0]),
+             ("local_cv", self.get_LCV, 1, [0]),
+             ("local_model_pred_error", self.get_CNK, 2, [0, 1]),
+             ("bagging_variance_cn", self.get_BVCK, 4, [0]),
+             ("mahalanobis_distance", self.get_Mahalanobis, 1, [0])]
+        
+        #####
+        # GUI
+        #####
+        self.loadSettings()
+        
+        box = OWGUI.widgetBox(self.controlArea, "Info", addSpace=True)
+        self.info_box = OWGUI.widgetLabel(box, "\n\n")
+        
+        rbox = OWGUI.widgetBox(self.controlArea, "Methods", addSpace=True)
+        def method_box(parent, name, value):
+            box = OWGUI.widgetBox(rbox, name, flat=False)
+            box.setCheckable(True)
+            box.setChecked(bool(getattr(self, value)))
+            self.connect(box, SIGNAL("toggled(bool)"),
+                         lambda on: (setattr(self, value, on),
+                                     self.method_selection_changed(value)))
+            return box
+            
+        e_validator = QRegExpValidator(QRegExp(r"\s*(-?[0-9]+(\.[0-9]*)\s*,\s*)+"), self)
+        variance_box = method_box(rbox, "Sensitivity analysis (variance)",
+                                  "variance_checked")
+        OWGUI.lineEdit(variance_box, self, "var_e", "Sensitivities:", 
+                       tooltip="List of possible e values (comma separated) for SAvar reliability estimates.", 
+                       callback=partial(self.method_param_changed, 0),
+                       validator=e_validator)
+        
+        bias_box = method_box(rbox, "Sensitivity analysis (bias)",
+                                    "bias_checked")
+        OWGUI.lineEdit(bias_box, self, "bias_e", "Sensitivities:", 
+                       tooltip="List of possible e values (comma separated) for SAbias reliability estimates.", 
+                       callback=partial(self.method_param_changed, 1),
+                       validator=e_validator)
+        
+        bagged_box = method_box(rbox, "Variance of bagged models",
+                                "bagged_variance")
+        
+        OWGUI.spin(bagged_box, self, "bagged_m", 2, 100, step=1,
+                   label="Models:",
+                   tooltip="Number of bagged models to be used with BAGV estimate.",
+                   callback=partial(self.method_param_changed, 2),
+                   keyboardTracking=False)
+        
+        local_cv_box = method_box(rbox, "Local cross validation",
+                                  "local_cv")
+        
+        OWGUI.spin(local_cv_box, self, "local_cv_k", 2, 20, step=1,
+                   label="Nearest neighbors:",
+                   tooltip="Number of nearest neighbors used in LCV estimate.",
+                   callback=partial(self.method_param_changed, 3),
+                   keyboardTracking=False)
+        
+        local_pe = method_box(rbox, "Local modeling of prediction error",
+                              "local_model_pred_error")
+        
+        OWGUI.spin(local_pe, self, "local_pe_k", 1, 20, step=1,
+                   label="Nearest neighbors:",
+                   tooltip="Number of nearest neighbors used in CNK estimate.",
+                   callback=partial(self.method_param_changed, 4),
+                   keyboardTracking=False)
+        
+        bagging_cnn = method_box(rbox, "Bagging variance c-neighbors",
+                                 "bagging_variance_cn")
+        
+        OWGUI.spin(bagging_cnn, self, "bagged_cn_m", 2, 100, step=1,
+                   label="Models:",
+                   tooltip="Number of bagged models to be used with BVCK estimate.",
+                   callback=partial(self.method_param_changed, 5),
+                   keyboardTracking=False)
+        
+        OWGUI.spin(bagging_cnn, self, "bagged_cn_k", 1, 20, step=1,
+                   label="Nearest neighbors:",
+                   tooltip="Number of nearest neighbors used in BVCK estimate.",
+                   callback=partial(self.method_param_changed, 5),
+                   keyboardTracking=False)
+        
+        mahalanobis_box = method_box(rbox, "Mahalanobis distance",
+                                     "mahalanobis_distance")
+        OWGUI.spin(mahalanobis_box, self, "mahalanobis_k", 1, 20, step=1,
+                   label="Nearest neighbors:",
+                   tooltip="Number of nearest neighbors used in BVCK estimate.",
+                   callback=partial(self.method_param_changed, 6),
+                   keyboardTracking=False)
+        
+        box = OWGUI.widgetBox(self.controlArea, "Output")
+        
+        OWGUI.checkBox(box, self, "include_error", "Include prediction error",
+                       tooltip="Include prediction error in the output",
+                       callback=self.commit_if)
+        
+        OWGUI.checkBox(box, self, "include_class", "Include original class and prediction",
+                       tooltip="Include original class and prediction in the output.",
+                       callback=self.commit_if)
+        
+        OWGUI.checkBox(box, self, "include_input_features", "Include input features",
+                       tooltip="Include features from the input data set.",
+                       callback=self.commit_if)
+        
+        cb = OWGUI.checkBox(box, self, "auto_commit", "Commit on any change",
+                            callback=self.commit_if)
+        
+        self.commit_button = b = OWGUI.button(box, self, "Commit",
+                                              callback=self.commit,
+                                              autoDefault=True)
+        
+        OWGUI.setStopper(self, b, cb, "output_changed", callback=self.commit)
+        
+        self.commit_button.setEnabled(any([getattr(self, selected) \
+                                for selected, _, _, _ in  self.estimators]))
+        
+        self.learner = None
+        self.train_data = None
+        self.test_data = None
+        self.output_changed = False
+        self.train_data_has_no_class = False
+        self.train_data_has_discrete_class = False
+        self.invalidate_results()
+        
+    def set_train_data(self, data=None):
+        self.error()
+        self.train_data_has_no_class = False
+        self.train_data_has_discrete_class = False
+        
+        if data is not None:
+            if not self.isDataWithClass(data, Orange.core.VarTypes.Continuous):
+                if not data.domain.class_var:
+                    self.train_data_has_no_class = True
+                elif not isinstance(data.domain.class_var,
+                                    Orange.feature.Continuous):
+                    self.train_data_has_discrete_class = True
+                    
+                data = None
+        
+        self.train_data = data
+        self.invalidate_results() 
+        
+    def set_test_data(self, data=None):
+        self.test_data = data
+        self.invalidate_results()
+        
+    def set_learner(self, learner=None):
+        self.learner = learner
+        self.invalidate_results()
+        
+    def handleNewSignals(self):
+        name = "No learner on input"
+        train = "No train data on input"
+        test = "No test data on input"
+        
+        if self.learner:
+            name = "Learner: " + (getattr(self.learner, "name") or type(self.learner).__name__)
+            
+        if self.train_data is not None:
+            train = "Train Data: %i features, %i instances" % \
+                (len(self.train_data.domain), len(self.train_data))
+        elif self.train_data_has_no_class:
+            train = "Train Data has no class variable"
+        elif self.train_data_has_discrete_class:
+            train = "Train Data doesn't have a continuous class"
+            
+        if self.test_data is not None:
+            test = "Test Data: %i features, %i instances" % \
+                (len(self.test_data.domain), len(self.test_data))
+        elif self.train_data:
+            test = "Test data: using training data"
+        
+        self.info_box.setText("\n".join([name, train, test]))
+        
+        if self.learner and self._test_data() is not None:
+            self.commit_if()
+        
+    def invalidate_results(self, which=None):
+        if which is None:
+            self.results = [None for f in self.estimators]
+#            print "Invalidating all"
+        else:
+            for i in which:
+                self.results[i] = None
+#            print "Invalidating", which
+        
+    def run(self):
+        plan = []
+        estimate_index = 0
+        for i, (selected, method, count, offsets) in enumerate(self.estimators):
+            if self.results[i] is None and getattr(self, selected):
+                plan.append((i, method, [estimate_index + offset for offset in offsets]))
+                estimate_index += count
+                
+        estimators = [method() for _, method, _ in plan]
+        
+        if not estimators:
+            return
+            
+        pb = OWGUI.ProgressBar(self, len(self._test_data()))
+        estimates = self.run_estimation(estimators, pb.advance)
+        pb.finish()
+        
+        self.predictions = [v for v, _ in estimates]
+        estimates = [prob.reliability_estimate for _, prob in estimates]
+        
+        for i, (index, method, estimate_indices) in enumerate(plan):
+            self.results[index] = [[e[est_index] for e in estimates] \
+                                   for est_index in estimate_indices]
+        
+    def _test_data(self):
+        if self.test_data is not None:
+            return self.test_data
+        else:
+            return self.train_data
+    
+    def get_estimates(self, estimator, advance=None):
+        test = self._test_data()
+        res = []
+        for i, inst in enumerate(test):
+            value, prob = estimator(inst, result_type=Orange.core.GetBoth)
+            res.append((value, prob))
+            if advance:
+                advance()
+        return res
+                
+    def run_estimation(self, estimators, advance=None):
+        rel = reliability.Learner(self.learner, estimators=estimators)
+        estimator = rel(self.train_data)
+        return self.get_estimates(estimator, advance) 
+    
+    def get_SAVar(self):
+        return reliability.SensitivityAnalysis(e=eval(self.var_e))
+    
+    def get_SABias(self):
+        return reliability.SensitivityAnalysis(e=eval(self.bias_e))
+    
+    def get_BAGV(self):
+        return reliability.BaggingVariance(m=self.bagged_m)
+    
+    def get_LCV(self):
+        return reliability.LocalCrossValidation(k=self.local_cv_k)
+    
+    def get_CNK(self):
+        return reliability.CNeighbours(k=self.local_pe_k)
+    
+    def get_BVCK(self):
+        bagv = reliability.BaggingVariance(m=self.bagged_cn_m)
+        cnk = reliability.CNeighbours(k=self.bagged_cn_k)
+        return reliability.BaggingVarianceCNeighbours(bagv, cnk)
+    
+    def get_Mahalanobis(self):
+        return reliability.Mahalanobis(k=self.mahalanobis_k)
+    
+    def method_selection_changed(self, method=None):
+        self.commit_button.setEnabled(any([getattr(self, selected) \
+                                for selected, _, _, _ in  self.estimators]))
+        self.commit_if()
+    
+    def method_param_changed(self, method=None):
+        if method is not None:
+            self.invalidate_results([method])
+        self.commit_if()
+        
+    def commit_if(self):
+        if self.auto_commit:
+            self.commit()
+        else:
+            self.output_changed = True
+            
+    def commit(self):
+        from Orange import feature as variable
+        name_mapper = {"Mahalanobis absolute": "Mahalanobis"}
+        all_predictions = []
+        all_estimates = []
+        score_vars = []
+        features = []
+        table = None
+        if self.learner and self.train_data is not None \
+                and self._test_data() is not None:
+            self.run()
+            
+            scores = []
+            if self.include_class and not self.include_input_features:
+                original_class = self._test_data().domain.class_var
+                features.append(original_class)
+                
+            if self.include_class:
+                prediction_var = variable.Continuous("Prediction")
+                features.append(prediction_var)
+                
+            if self.include_error:
+                error_var = variable.Continuous("Error")
+                abs_error_var = variable.Continuous("Abs. Error")
+                features.append(error_var)
+                features.append(abs_error_var)
+                
+            for estimates, (selected, method, _, _) in zip(self.results, self.estimators):
+                if estimates is not None and getattr(self, selected):
+                    for estimate in estimates:
+                        name = estimate[0].method_name
+                        name = name_mapper.get(name, name)
+                        var = variable.Continuous(name)
+                        features.append(var)
+                        score_vars.append(var)
+                        all_estimates.append(estimate)
+                    
+            if self.include_input_features:
+                dom = self._test_data().domain
+                attributes = list(dom.attributes) + features
+                domain = Orange.data.Domain(attributes, dom.class_var)
+                domain.add_metas(dom.get_metas())
+                
+                data = Orange.data.Table(domain, self._test_data())
+            else:
+                domain = Orange.data.Domain(features, None)
+                data = Orange.data.Table(domain, [[None] * len(features) for _ in self._test_data()])
+            
+            if self.include_class:
+                for d, inst, pred in zip(data, self._test_data(), self.predictions):
+                    if not self.include_input_features:
+                        d[features[0]] = float(inst.get_class())
+                    d[prediction_var] = float(pred)
+            
+            if self.include_error:
+                for d, inst, pred in zip(data, self._test_data(), self.predictions):
+                    error = float(pred) - float(inst.get_class())
+                    d[error_var] = error
+                    d[abs_error_var] = abs(error)
+                    
+            for estimations, var in zip(all_estimates, score_vars):
+                for d, e in zip(data, estimations):
+                    d[var] = e.estimate
+            
+            table = data
+            
+        self.send("Reliability Scores", table)
+        self.output_changed = False
+        
+        
+if __name__ == "__main__":
+    import sys
+    app = QApplication(sys.argv)
+    w = OWReliability()
+    data = Orange.data.Table("housing")
+    indices = Orange.core.MakeRandomIndices2(p0=20)(data)
+    data = data.select(indices, 0)
+    
+    learner = Orange.regression.tree.TreeLearner()
+    w.set_learner(learner)
+    w.set_train_data(data)
+    w.handleNewSignals()
+    w.show()
+    app.exec_()
+    
+        
Add a comment to this file

_reliability/widgets/__init__.py

Empty file added.

Add a comment to this file

_reliability/widgets/icons/Reliability_60.png

Added
New image

distribute_setup.py

+#!python
+"""Bootstrap distribute installation
+
+If you want to use setuptools in your package's setup.py, just include this
+file in the same directory with it, and add this to the top of your setup.py::
+
+    from distribute_setup import use_setuptools
+    use_setuptools()
+
+If you want to require a specific version of setuptools, set a download
+mirror, or use an alternate download directory, you can do so by supplying
+the appropriate options to ``use_setuptools()``.
+
+This file can also be run as a script to install or upgrade setuptools.
+"""
+import os
+import sys
+import time
+import fnmatch
+import tempfile
+import tarfile
+from distutils import log
+
+try:
+    from site import USER_SITE
+except ImportError:
+    USER_SITE = None
+
+try:
+    import subprocess
+
+    def _python_cmd(*args):
+        args = (sys.executable,) + args
+        return subprocess.call(args) == 0
+
+except ImportError:
+    # will be used for python 2.3
+    def _python_cmd(*args):
+        args = (sys.executable,) + args
+        # quoting arguments if windows
+        if sys.platform == 'win32':
+            def quote(arg):
+                if ' ' in arg:
+                    return '"%s"' % arg
+                return arg
+            args = [quote(arg) for arg in args]
+        return os.spawnl(os.P_WAIT, sys.executable, *args) == 0
+
+DEFAULT_VERSION = "0.6.26"
+DEFAULT_URL = "http://pypi.python.org/packages/source/d/distribute/"
+SETUPTOOLS_FAKED_VERSION = "0.6c11"
+
+SETUPTOOLS_PKG_INFO = """\
+Metadata-Version: 1.0
+Name: setuptools
+Version: %s
+Summary: xxxx
+Home-page: xxx
+Author: xxx
+Author-email: xxx
+License: xxx
+Description: xxx
+""" % SETUPTOOLS_FAKED_VERSION
+
+
+def _install(tarball, install_args=()):
+    # extracting the tarball
+    tmpdir = tempfile.mkdtemp()
+    log.warn('Extracting in %s', tmpdir)
+    old_wd = os.getcwd()
+    try:
+        os.chdir(tmpdir)
+        tar = tarfile.open(tarball)
+        _extractall(tar)
+        tar.close()
+
+        # going in the directory
+        subdir = os.path.join(tmpdir, os.listdir(tmpdir)[0])
+        os.chdir(subdir)
+        log.warn('Now working in %s', subdir)
+
+        # installing
+        log.warn('Installing Distribute')
+        if not _python_cmd('setup.py', 'install', *install_args):
+            log.warn('Something went wrong during the installation.')
+            log.warn('See the error message above.')
+    finally:
+        os.chdir(old_wd)
+
+
+def _build_egg(egg, tarball, to_dir):
+    # extracting the tarball
+    tmpdir = tempfile.mkdtemp()
+    log.warn('Extracting in %s', tmpdir)
+    old_wd = os.getcwd()
+    try:
+        os.chdir(tmpdir)
+        tar = tarfile.open(tarball)
+        _extractall(tar)
+        tar.close()
+
+        # going in the directory
+        subdir = os.path.join(tmpdir, os.listdir(tmpdir)[0])
+        os.chdir(subdir)
+        log.warn('Now working in %s', subdir)
+
+        # building an egg
+        log.warn('Building a Distribute egg in %s', to_dir)
+        _python_cmd('setup.py', '-q', 'bdist_egg', '--dist-dir', to_dir)
+
+    finally:
+        os.chdir(old_wd)
+    # returning the result
+    log.warn(egg)
+    if not os.path.exists(egg):
+        raise IOError('Could not build the egg.')
+
+
+def _do_download(version, download_base, to_dir, download_delay):
+    egg = os.path.join(to_dir, 'distribute-%s-py%d.%d.egg'
+                       % (version, sys.version_info[0], sys.version_info[1]))
+    if not os.path.exists(egg):
+        tarball = download_setuptools(version, download_base,
+                                      to_dir, download_delay)
+        _build_egg(egg, tarball, to_dir)
+    sys.path.insert(0, egg)
+    import setuptools
+    setuptools.bootstrap_install_from = egg
+
+
+def use_setuptools(version=DEFAULT_VERSION, download_base=DEFAULT_URL,
+                   to_dir=os.curdir, download_delay=15, no_fake=True):
+    # making sure we use the absolute path
+    to_dir = os.path.abspath(to_dir)
+    was_imported = 'pkg_resources' in sys.modules or \
+        'setuptools' in sys.modules
+    try:
+        try:
+            import pkg_resources
+            if not hasattr(pkg_resources, '_distribute'):
+                if not no_fake:
+                    _fake_setuptools()
+                raise ImportError
+        except ImportError:
+            return _do_download(version, download_base, to_dir, download_delay)
+        try:
+            pkg_resources.require("distribute>="+version)
+            return
+        except pkg_resources.VersionConflict:
+            e = sys.exc_info()[1]
+            if was_imported:
+                sys.stderr.write(
+                "The required version of distribute (>=%s) is not available,\n"
+                "and can't be installed while this script is running. Please\n"
+                "install a more recent version first, using\n"
+                "'easy_install -U distribute'."
+                "\n\n(Currently using %r)\n" % (version, e.args[0]))
+                sys.exit(2)
+            else:
+                del pkg_resources, sys.modules['pkg_resources']    # reload ok
+                return _do_download(version, download_base, to_dir,
+                                    download_delay)
+        except pkg_resources.DistributionNotFound:
+            return _do_download(version, download_base, to_dir,
+                                download_delay)
+    finally:
+        if not no_fake:
+            _create_fake_setuptools_pkg_info(to_dir)
+
+def download_setuptools(version=DEFAULT_VERSION, download_base=DEFAULT_URL,
+                        to_dir=os.curdir, delay=15):
+    """Download distribute from a specified location and return its filename
+
+    `version` should be a valid distribute version number that is available
+    as an egg for download under the `download_base` URL (which should end
+    with a '/'). `to_dir` is the directory where the egg will be downloaded.
+    `delay` is the number of seconds to pause before an actual download
+    attempt.
+    """
+    # making sure we use the absolute path
+    to_dir = os.path.abspath(to_dir)
+    try:
+        from urllib.request import urlopen
+    except ImportError:
+        from urllib2 import urlopen
+    tgz_name = "distribute-%s.tar.gz" % version
+    url = download_base + tgz_name
+    saveto = os.path.join(to_dir, tgz_name)
+    src = dst = None
+    if not os.path.exists(saveto):  # Avoid repeated downloads
+        try:
+            log.warn("Downloading %s", url)
+            src = urlopen(url)
+            # Read/write all in one block, so we don't create a corrupt file
+            # if the download is interrupted.
+            data = src.read()
+            dst = open(saveto, "wb")
+            dst.write(data)
+        finally:
+            if src:
+                src.close()
+            if dst:
+                dst.close()
+    return os.path.realpath(saveto)
+
+def _no_sandbox(function):
+    def __no_sandbox(*args, **kw):
+        try:
+            from setuptools.sandbox import DirectorySandbox
+            if not hasattr(DirectorySandbox, '_old'):
+                def violation(*args):
+                    pass
+                DirectorySandbox._old = DirectorySandbox._violation
+                DirectorySandbox._violation = violation
+                patched = True
+            else:
+                patched = False
+        except ImportError:
+            patched = False
+
+        try:
+            return function(*args, **kw)
+        finally:
+            if patched:
+                DirectorySandbox._violation = DirectorySandbox._old
+                del DirectorySandbox._old
+
+    return __no_sandbox
+
+def _patch_file(path, content):
+    """Will backup the file then patch it"""
+    existing_content = open(path).read()
+    if existing_content == content:
+        # already patched
+        log.warn('Already patched.')
+        return False
+    log.warn('Patching...')
+    _rename_path(path)
+    f = open(path, 'w')
+    try:
+        f.write(content)
+    finally:
+        f.close()
+    return True
+
+_patch_file = _no_sandbox(_patch_file)
+
+def _same_content(path, content):
+    return open(path).read() == content
+
+def _rename_path(path):
+    new_name = path + '.OLD.%s' % time.time()
+    log.warn('Renaming %s into %s', path, new_name)
+    os.rename(path, new_name)
+    return new_name
+
+def _remove_flat_installation(placeholder):
+    if not os.path.isdir(placeholder):
+        log.warn('Unkown installation at %s', placeholder)
+        return False
+    found = False
+    for file in os.listdir(placeholder):
+        if fnmatch.fnmatch(file, 'setuptools*.egg-info'):
+            found = True
+            break
+    if not found:
+        log.warn('Could not locate setuptools*.egg-info')
+        return
+