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How to use _get_args method in Molotov

Best Python code snippet using molotov_python

test_tra.py

Source:test_tra.py

...17    def setUpClass(cls):18        if cls is _AbstractTRATest:19            raise unittest.SkipTest('Skip abstract parent class')20        super(_AbstractTRATest, cls).setUpClass()21    def _get_args(self):22        # Add arguments for the Torch Ranker Agent to test23        # Override in child classes24        return dict(25            task='integration_tests:candidate',26            optimizer='adamax',27            learningrate=7e-3,28            batchsize=16,29            embedding_size=32,30            num_epochs=4,31        )32    def _get_threshold(self):33        # Accuracy threshold34        return 0.835    # test train inline cands36    @testing_utils.retry(ntries=3)37    def test_train_inline(self):38        args = self._get_args()39        args['candidates'] = 'inline'40        valid, test = testing_utils.train_model(args)41        threshold = self._get_threshold()42        self.assertGreaterEqual(valid['hits@1'], threshold)43    # test train batch cands44    @testing_utils.retry(ntries=3)45    def test_train_batch(self):46        args = self._get_args()47        args['candidates'] = 'batch'48        valid, test = testing_utils.train_model(args)49        threshold = self._get_threshold()50        self.assertGreaterEqual(valid['hits@1'], threshold)51    # test train fixed52    @testing_utils.retry(ntries=3)53    def test_train_fixed(self):54        args = self._get_args()55        args['candidates'] = 'fixed'56        args['encode_candidate_vecs'] = False57        valid, test = testing_utils.train_model(args)58        threshold = self._get_threshold()59        self.assertGreaterEqual(valid['hits@1'], threshold)60    # test train batch all cands61    @testing_utils.retry(ntries=3)62    def test_train_batch_all(self):63        args = self._get_args()64        args['candidates'] = 'batch-all-cands'65        valid, test = testing_utils.train_model(args)66        threshold = self._get_threshold()67        self.assertGreaterEqual(valid['hits@1'], threshold)68    # test eval inline ecands69    @testing_utils.retry(ntries=3)70    def test_eval_inline(self):71        args = self._get_args()72        args['eval_candidates'] = 'inline'73        valid, test = testing_utils.train_model(args)74        threshold = self._get_threshold()75        self.assertGreaterEqual(valid['hits@1'], threshold)76    # test eval batch ecands77    @testing_utils.retry(ntries=3)78    def test_eval_batch(self):79        args = self._get_args()80        args['eval_candidates'] = 'batch'81        valid, test = testing_utils.train_model(args)82        threshold = self._get_threshold()83        self.assertGreaterEqual(valid['hits@1'], threshold)84    # test eval fixed ecands85    @testing_utils.retry(ntries=3)86    def test_eval_fixed(self):87        args = self._get_args()88        args['eval_candidates'] = 'fixed'89        args['encode_candidate_vecs'] = True90        args['ignore_bad_candidates'] = True91        valid, test = testing_utils.train_model(args)92        # none of the train candidates appear in evaluation, so should have93        # zero accuracy: this tests whether the fixed candidates were built94        # properly (i.e., only using candidates from the train set)95        self.assertEqual(valid['hits@1'], 0)96        # now try again with a fixed candidate file that includes all possible97        # candidates98        teacher = CandidateTeacher({'datatype': 'train'})99        all_cands = teacher.train + teacher.val + teacher.test100        all_cands_str = '\n'.join([' '.join(x) for x in all_cands])101        with testing_utils.tempdir() as tmpdir:102            tmp_cands_file = os.path.join(tmpdir, 'all_cands.text')103            with open(tmp_cands_file, 'w') as f:104                f.write(all_cands_str)105            args['fixed_candidates_path'] = tmp_cands_file106            args['encode_candidate_vecs'] = False  # don't encode before training107            args['ignore_bad_candidates'] = False108            args['num_epochs'] = 4109            valid, test = testing_utils.train_model(args)110            self.assertGreaterEqual(valid['hits@100'], 0.1)111    # test eval vocab ecands112    @testing_utils.retry(ntries=3)113    def test_eval_vocab(self):114        args = self._get_args()115        args['eval_candidates'] = 'vocab'116        args['encode_candidate_vecs'] = True117        valid, test = testing_utils.train_model(args)118        # accuracy should be zero, none of the vocab candidates should be the119        # correct label120        self.assertEqual(valid['hits@100'], 0)121class TestTransformerRanker(_AbstractTRATest):122    def _get_args(self):123        args = super()._get_args()124        new_args = dict(125            model='transformer/ranker',126            n_layers=1,127            n_heads=4,128            ffn_size=64,129            gradient_clip=0.5,130        )131        for k, v in new_args.items():132            args[k] = v133        return args134class TestMemNN(_AbstractTRATest):135    def _get_args(self):136        args = super()._get_args()137        args['model'] = 'memnn'138        return args139    def _get_threshold(self):140        # this is a slightly worse model, so we expect it to perform worse141        return 0.5142class TestPolyRanker(_AbstractTRATest):143    def _get_args(self):144        args = super()._get_args()145        new_args = dict(146            model='transformer/polyencoder',147            n_layers=1,148            n_heads=4,149            ffn_size=64,150            gradient_clip=0.5,151        )152        for k, v in new_args.items():153            args[k] = v154        return args155    def _get_threshold(self):156        return 0.6157    def test_eval_fixed_label_not_in_cands(self):158        # test where cands during eval do not contain test label159        args = self._get_args()160        args[161            'model'162        ] = 'parlai.agents.transformer.polyencoder:IRFriendlyPolyencoderAgent'163        args['eval_candidates'] = 'fixed'164        teacher = CandidateTeacher({'datatype': 'train'})165        all_cands = teacher.train + teacher.val + teacher.test166        train_val_cands = teacher.train + teacher.val167        all_cands_str = '\n'.join([' '.join(x) for x in all_cands])168        train_val_cands_str = '\n'.join([' '.join(x) for x in train_val_cands])169        with testing_utils.tempdir() as tmpdir:170            tmp_cands_file = os.path.join(tmpdir, 'all_cands.text')171            with open(tmp_cands_file, 'w') as f:172                f.write(all_cands_str)173            tmp_train_val_cands_file = os.path.join(tmpdir, 'train_val_cands.text')...

test_run.py

Source:test_run.py

...4import numpy as np5import os6import pandas as pd7import pytest8def _get_args(infile: str, k: list, outdir: str):9    args = RUN_DEFAULTS.copy()10    args['outdir'] = outdir11    args['k'] = k12    args["infile"] = infile13    return args14def test_init(tmpdir):15    # incorrect parameters16    with pytest.raises(AttributeError):17        SumoRun()18    fname = os.path.join(tmpdir, "data.npz")19    outdir = os.path.join(tmpdir, "outdir")20    samples = 1021    sample_labels = ['sample_{}'.format(i) for i in range(samples)]22    args = _get_args(fname, [2], outdir)23    # no input file24    with pytest.raises(FileNotFoundError):25        SumoRun(**args)26    save_arrays_to_npz({'0': np.random.random((samples, samples)),27                        '1': np.random.random((samples, samples)),28                        'samples': np.array(sample_labels)}, fname)29    # supervised sumo no labels file30    labels_fname = os.path.join(tmpdir, "labels.tsv")31    labels = np.array([sample_labels[0:2] + sample_labels[7:9], [1, 1, 2, 2]]).T32    labels_df = pd.DataFrame(data=labels, columns=['sample', 'label'])33    args = _get_args(fname, [2], outdir)34    args['labels'] = labels_fname35    with pytest.raises(FileNotFoundError):36        SumoRun(**args)37    labels_df.to_csv(labels_fname, sep="\t")38    args['sparsity'] = [10]39    args['n'] = 10  # makes test run quicker40    SumoRun(**args)41    # incorrect number of repetitions42    args['n'] = -143    with pytest.raises(ValueError):44        SumoRun(**args)45    # incorrect number of threads46    args = _get_args(fname, [2], outdir)47    args['t'] = -148    with pytest.raises(ValueError):49        SumoRun(**args)50    # incorrect outdir51    args = _get_args(fname, [2], fname)52    with pytest.raises(NotADirectoryError):53        SumoRun(**args)54    # incorrect k55    args = _get_args(fname, [2, 3, 4], outdir)56    with pytest.raises(ValueError):57        SumoRun(**args)58    args = _get_args(fname, [2], outdir)59    SumoRun(**args)60    args = _get_args(fname, [2, 5], outdir)61    SumoRun(**args)62    # incorrect k range63    args = _get_args(fname, [5, 2], outdir)64    with pytest.raises(ValueError):65        SumoRun(**args)66    # incorrect subsample argument67    args = _get_args(fname, [2], outdir)68    args['subsample'] = -0.169    with pytest.raises(ValueError):70        SumoRun(**args)71    args['subsample'] = 0.972    with pytest.raises(ValueError):73        SumoRun(**args)74def test_run(tmpdir):75    fname = os.path.join(tmpdir, "data.npz")76    outdir = os.path.join(tmpdir, "outdir")77    samples = 1078    a0 = np.random.random((samples, samples))79    a0 = (a0 * a0.T) / 280    a1 = np.random.random((samples, samples))81    a1 = (a1 * a1.T) / 282    sample_labels = ['sample_{}'.format(i) for i in range(samples)]83    args = _get_args(fname, [2], outdir)84    # no sample names85    save_arrays_to_npz({'0': a0, '1': a1}, fname)86    with pytest.raises(ValueError):87        sr = SumoRun(**args)88        sr.run()89    # incorrect sample names90    save_arrays_to_npz({'0': a0, '1': a1, 'samples': np.array(sample_labels[1:])}, fname)91    with pytest.raises(ValueError):92        sr = SumoRun(**args)93        sr.run()94    # incorrect adjacency matrices95    save_arrays_to_npz({'samples': np.array(sample_labels)}, fname)96    with pytest.raises(ValueError):97        sr = SumoRun(**args)98        sr.run()99    # incorrect value of h_init100    save_arrays_to_npz({'0': a0, '1': a1, 'samples': np.array(sample_labels)}, fname)101    args = _get_args(fname, [2], outdir)102    args['h_init'] = -1103    with pytest.raises(ValueError):104        sr = SumoRun(**args)105        sr.run()106    args['h_init'] = 3107    with pytest.raises(ValueError):108        sr = SumoRun(**args)109        sr.run()110    # supervised sumo111    labels_fname = os.path.join(tmpdir, "labels.tsv")112    labels = np.array([sample_labels[0:2] + sample_labels[7:9], [1, 1, 2, 2]]).T113    labels_df = pd.DataFrame(data=labels, columns=['sample', 'label'])114    labels_df.to_csv(labels_fname, sep="\t")115    args = _get_args(fname, [2], outdir)116    args['labels'] = labels_fname117    args['sparsity'] = [10]118    args['n'] = 10  # makes test run quicker119    sr = SumoRun(**args)120    sr.run()121    # unsupervised sumo122    args = _get_args(fname, [2], outdir)123    args['sparsity'] = [10]124    args['n'] = 10  # makes test run quicker125    sr = SumoRun(**args)126    sr.run()127    assert all([os.path.exists(os.path.join(outdir, x)) for x in ['k2', 'plots',128                                                                  os.path.join('plots', 'consensus_k2.png'),...

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