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How to use criterion method in hypothesis

Best Python code snippet using hypothesis

test_criterion.py

Source:test_criterion.py

1# Copyright (C) 2018-2021 CS GROUP - France. All Rights Reserved.2#3# This file is part of the Prewikka program.4#5# SPDX-License-Identifier: BSD-2-Clause6#7# Redistribution and use in source and binary forms, with or without8# modification, are permitted provided that the following conditions are met:9#10# 1. Redistributions of source code must retain the above copyright notice, this11#    list of conditions and the following disclaimer.12#13# 2. Redistributions in binary form must reproduce the above copyright notice,14#    this list of conditions and the following disclaimer in the documentation15#    and/or other materials provided with the distribution.16#17# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND18# ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIEDi19# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE20# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR21# ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES22# (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;23# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND24# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT25# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS26# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.27"""28Tests for `prewikka.dataprovider.Criterion()`.29"""30import copy31from prewikka.dataprovider import Criterion, CriterionOperator32def test_criterion_to_string():33    """34    Test `prewikka.dataprovider.Criterion.to_string()` method.35    """36    # empty criterion37    criterion = Criterion()38    assert criterion.to_string() == ''39    # simple criterion40    criterion = Criterion('alert.messageid', '=', 'fakemessageid')41    assert criterion.to_string() == "alert.messageid = 'fakemessageid'"42    criterion = Criterion('alert.messageid', '==', 'fakemessageid')43    assert criterion.to_string() == "alert.messageid = 'fakemessageid'"44    criterion = Criterion('alert.messageid', '!=', 'fakemessageid')45    assert criterion.to_string() == "alert.messageid != 'fakemessageid'"46    criterion = Criterion('alert.messageid', '>', 'fakemessageid')47    assert criterion.to_string() == "alert.messageid > 'fakemessageid'"48    criterion = Criterion('alert.messageid', '>=', 'fakemessageid')49    assert criterion.to_string() == "alert.messageid >= 'fakemessageid'"50    criterion = Criterion('alert.messageid', '<', 'fakemessageid')51    assert criterion.to_string() == "alert.messageid < 'fakemessageid'"52    criterion = Criterion('alert.messageid', '<=', 'fakemessageid')53    assert criterion.to_string() == "alert.messageid <= 'fakemessageid'"54    criterion = Criterion('alert.messageid', '<>*', 'fakemessageid')55    assert criterion.to_string() == "alert.messageid <>* 'fakemessageid'"56    # combined criterion57    criterion_1 = Criterion('alert.messageid', '=', 'fakemessageid1')58    criterion_2 = Criterion('alert.messageid', '=', 'fakemessageid2')59    criterion = Criterion(criterion_1, '||', criterion_2)60    assert criterion.to_string() == "alert.messageid = 'fakemessageid1' || alert.messageid = 'fakemessageid2'"61    criterion = Criterion(criterion_1, '&&', criterion_2)62    assert criterion.to_string() == "alert.messageid = 'fakemessageid1' && alert.messageid = 'fakemessageid2'"63def test_criterion_get_path():64    """65    Test `prewikka.dataprovider.Criterion.get_path()` method.66    """67    # empty criterion68    criterion = Criterion()69    results = set()70    assert criterion.get_paths() == results71    # simple criterion72    criterion = Criterion('alert.messageid', '=', 'fakemessageid')73    results = set()74    results.add('alert.messageid')75    assert criterion.get_paths() == results76    # combined criterion (with || operator)77    criterion_1 = Criterion('alert.messageid', '=', 'fakemessageid1')78    criterion_2 = Criterion('heartbeat.messageid', '=', 'fakemessageid2')79    criterion = Criterion(criterion_1, '||', criterion_2)80    results = set()81    results.add('alert.messageid')82    results.add('heartbeat.messageid')83    assert criterion.get_paths() == results84    # combined criterion (with && operator)85    criterion_1 = Criterion('alert.messageid', '=', 'fakemessageid1')86    criterion_2 = Criterion('heartbeat.messageid', '=', 'fakemessageid2')87    criterion = Criterion(criterion_1, '&&', criterion_2)88    results = set()89    results.add('alert.messageid')90    results.add('heartbeat.messageid')91    assert criterion.get_paths() == results92def test_criterion_flatten():93    """94    Test `prewikka.dataprovider.Criterion.flatten()` method.95    """96    criterion_1 = Criterion('alert.messageid', '=', 'fakemessageid1')97    criterion_2 = Criterion('alert.messageid', '=', 'fakemessageid2')98    criterion_3 = Criterion('alert.messageid', '=', 'fakemessageid3')99    criterion_4 = Criterion('alert.messageid', '=', 'fakemessageid4')100    criterion = ((criterion_1 & criterion_2) & criterion_3) | criterion_4101    flattened = criterion.flatten()102    assert flattened.operator == CriterionOperator.OR103    assert len(flattened.operands) == 2104    assert flattened.operands[0].operator == CriterionOperator.AND105    assert flattened.operands[0].operands == [criterion_1, criterion_2, criterion_3]106    assert flattened.operands[1] == criterion_4107    criterion = Criterion(None, '!', Criterion(None, '!', Criterion(None, '!', criterion_1)))108    flattened = criterion.flatten()109    assert flattened.operator == CriterionOperator.NOT110    assert len(flattened.operands) == 1111    assert flattened.operands[0] == criterion_1112def test_criterion_operations():113    """114    Test `prewikka.dataprovider.Criterion` operations.115    """116    criterion_0 = Criterion()117    criterion_1 = Criterion('alert.messageid', '=', 'fakemessageid1')118    criterion_2 = Criterion('heartbeat.messageid', '=', 'fakemessageid2')119    # __str__()120    assert str(criterion_1) == criterion_1.to_string()121    # __bool__() / __nonzero__()122    assert not criterion_0123    assert criterion_1124    # __copy__()125    criterion_copy = copy.copy(criterion_1)126    assert criterion_copy != criterion_1127    assert criterion_copy.to_string() == criterion_1.to_string()128    # __json__()129    assert criterion_1.__json__() == {'left': 'alert.messageid', 'operator': CriterionOperator.EQUAL, 'right': 'fakemessageid1'}130    # __iadd__()131    criterion_iadd = copy.copy(criterion_1)132    criterion_iadd += criterion_2133    assert criterion_iadd.to_string() == Criterion(criterion_1, '&&', criterion_2).to_string()134    # __ior__()135    criterion_ior = copy.copy(criterion_1)136    criterion_ior |= criterion_2137    assert criterion_ior.to_string() == Criterion(criterion_1, '||', criterion_2).to_string()138    # __iand__()139    criterion_iand = copy.copy(criterion_1)140    criterion_iand &= criterion_2141    assert criterion_iand.to_string() == Criterion(criterion_1, '&&', criterion_2).to_string()142    # __add__()143    criterion_add = criterion_1 + criterion_2144    assert criterion_add.to_string() == Criterion(criterion_1, '&&', criterion_2).to_string()145    # __or__()146    criterion_or = criterion_1 | criterion_2147    assert criterion_or.to_string() == Criterion(criterion_1, '||', criterion_2).to_string()148    # __and__()149    criterion_and = criterion_1 & criterion_2...

composite_loss.py

Source:composite_loss.py

...4# LICENSE file in the root directory of this source tree.5from fairseq import utils6from fairseq.criterions import LegacyFairseqCriterion, register_criterion7from torch import nn8@register_criterion("composite_loss")9class CompositeLoss(LegacyFairseqCriterion):10    """This is a composite loss that, given a list of model outputs and a list of targets,11    computes an average of losses for each output-target pair"""12    def __init__(self, args, task):13        super().__init__(args, task)14        self.underlying_criterion = args.underlying_criterion15    @staticmethod16    def add_args(parser):17        """Add criterion-specific arguments to the parser."""18        # fmt: off19        parser.add_argument('--underlying-criterion', type=str, metavar='VAL', required=True,20                            help='underlying criterion to use for the composite loss')21        # fmt: on22    @staticmethod23    def build_underlying_criterion(args, task):24        saved_criterion = args.criterion25        args.criterion = args.underlying_criterion26        assert saved_criterion != args.underlying_criterion27        underlying_criterion = task.build_criterion(args)28        args.criterion = saved_criterion29        return underlying_criterion30    @classmethod31    def build_criterion(cls, args, task):32        underlying_criterion = CompositeLoss.build_underlying_criterion(args, task)33        class FakeModel(nn.Module):34            def __init__(self, model, net_out, target):35                super().__init__()36                self.model = model37                self.net_out = net_out38                self.target = target39            def forward(self, **unused):40                return self.net_out41            def get_normalized_probs(self, net_output, log_probs, sample=None):42                return self.model.get_normalized_probs(43                    net_output, log_probs, sample=sample44                )45            def get_targets(self, *unused):46                return self.target47            @property48            def decoder(self):49                return self.model.decoder50        class _CompositeLoss(LegacyFairseqCriterion):51            def __init__(self, args, task, underlying_criterion):52                super().__init__(args, task)53                self.underlying_criterion = underlying_criterion54            def forward(self, model, sample, reduce=True):55                net_outputs = model(**sample["net_input"])56                targets = sample["target"]57                bsz = targets[0].size(0)58                loss = net_outputs[0][0].new(1 if reduce else bsz).float().zero_()59                sample_size = 060                logging_output = {}61                for o, t in zip(net_outputs[0], targets):62                    m = FakeModel(model, (o, net_outputs[1]), t)63                    sample["target"] = t64                    l, ss, logging_output = self.underlying_criterion(m, sample, reduce)65                    loss += l66                    sample_size += ss67                loss.div_(len(targets))68                sample_size /= len(targets)69                logging_output["loss"] = utils.item(loss.data) if reduce else loss.data70                return loss, sample_size, logging_output71            @staticmethod72            def aggregate_logging_outputs(logging_outputs):73                return underlying_criterion.__class__.aggregate_logging_outputs(74                    logging_outputs75                )76            @staticmethod77            def reduce_metrics(logging_outputs) -> None:78                underlying_criterion.__class__.reduce_metrics(logging_outputs)...

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