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How to use _sanity_check method in autotest

Best Python code snippet using autotest_python

graph.py

Source:graph.py

...20    def __init__(self, numVertices, directed=False):21        super(AdjacencyMatrixGraph, self).__init__(numVertices, directed)22        self.matrix = np.zeros((numVertices, numVertices))23    def add_edge(self, v1, v2, weight=1):24        self._sanity_check(v1)25        self._sanity_check(v2)26        if v1 == v2:27            raise ValueError("both vertices are the same")28        if weight < 1:29            raise ValueError("weight number is incorrect")30        self.matrix[v1][v2] = weight31        if self.directed is False:32            self.matrix[v2][v1] = weight33    def get_adjacent_vertices(self, v):34        self._sanity_check(v)35        adjacent_vertices = []36        for i in range(self.numVertices):37            if self.matrix[v][i] != 0:38                adjacent_vertices.append(i)39        return adjacent_vertices40    def get_indegree(self, v):41        self._sanity_check(v)42        indegree = 043        for i in range(self.numVertices):44            if self.matrix[i][v] != 0:45                indegree += 146        return indegree47    def get_edge_weight(self, v1, v2):48        return self.matrix[v1][v2]49    def display_matrix(self):50        print(self.matrix)51    def display(self):52        for v in range(self.numVertices):53            for v_a in range(self.numVertices):54                if self.matrix[v][v_a] != 0:55                    print(v, "-->", v_a)56    def _sanity_check(self, v):57        if v >= self.numVertices or v < 0:58            raise ValueError("vertex number is incorrect")59# ################################################# AdjacencySetGraph ##################################################60# unweighted (all weights are 1)61class Vertex:62    """graph vertex for AdjacencySetGraph"""63    def __init__(self, vertex_id):64        self.vertex_id = vertex_id65        self.adjacent = set()66    def add_edge(self, v):67        if v == self.vertex_id:68            raise ValueError(f"the vertex {v} cant be adjacent to itself")69        self.adjacent.add(v)70    def get_adjacent_vertices(self):71        return sorted(self.adjacent)72class AdjacencySetGraph(Graph):73    def __init__(self, num_vertices, directed=False):74        super(AdjacencySetGraph, self).__init__(num_vertices, directed)75        self.vertices = []76        for i in range(self.numVertices):77            self.vertices.append(Vertex(i))78    def add_edge(self, v1, v2, weight=1):79        self._sanity_check(v1)80        self._sanity_check(v2)81        if v1 == v2:82            raise ValueError("both vertices are the same")83        if weight != 1:84            raise ValueError("only weight=1 is currently supported")85        self.vertices[v1].add_edge(v2)86        if self.directed is False:87            self.vertices[v2].add_edge(v1)88    def get_adjacent_vertices(self, v):89        return self.vertices[v].get_adjacent_vertices()90    def get_indegree(self, v):91        count = 092        for vertex in self.vertices:93            if vertex.vertex_id != v:94                if v in vertex.adjacent:95                    count += 196        return count97    def get_edge_weight(self, v1, v2):98        return 1  # only weight=1 is currently supported99    def display(self):100        for v in self.vertices:101            print(v.vertex_id, "-->", sorted(v.adjacent))102    def _sanity_check(self, v):103        if v >= self.numVertices or v < 0:...

test_datasets.py

Source:test_datasets.py

...19    assert val.n_ood != 020    assert val.n_indomain < len(val)21    assert test.n_ood != 022    assert test.n_indomain < len(test)23def _sanity_check(train_dataset, val_dataset, test_dataset):24    def test_single(dataset):25        assert len(dataset.raw_texts) > 126        assert len(dataset.raw_texts) == len(dataset.raw_labels)27        assert dataset.vectorized_texts28        for idx in range(0, len(dataset.raw_texts), 1000):29            assert " ".join(dataset.tokenized_texts[idx]) == " ".join(dataset.tokenized_texts[idx]).lower()30    test_single(train_dataset)31    test_single(val_dataset)32    test_single(test_dataset)33    label_vocab_test(train_dataset, val_dataset)34    label_vocab_test(train_dataset, test_dataset)35    ood_test(train_dataset, val_dataset, test_dataset)36def _vectorize_simple(train_dataset, val_dataset, test_dataset):37    vocab = Vocab()38    vocab.build(train_dataset.tokenized_texts + val_dataset.tokenized_texts)39    train_dataset.vectorize_texts(vocab)40    val_dataset.vectorize_texts(vocab)41    test_dataset.vectorize_texts(vocab)42@pytest.mark.parametrize("K,version", itertools.product([25, 75], [0, 1, 2, 3, 4]))43def test_snips_dataset_simple(K, version):44    datasets, vocab = get_dataset_simple(f'snips_{K}', version=version)45    train_dataset, val_dataset, test_dataset = datasets46    _vectorize_simple(train_dataset, val_dataset, test_dataset)47    _sanity_check(train_dataset, val_dataset, test_dataset)48@pytest.mark.parametrize("coarse", [True, False])49def test_rostd_dataset_simple(coarse):50    dataset_name = "rostd_coarse" if coarse else "rostd"51    datasets, vocab = get_dataset_simple(dataset_name=dataset_name)52    train_dataset, val_dataset, test_dataset = datasets53    _vectorize_simple(train_dataset, val_dataset, test_dataset)54    _sanity_check(train_dataset, val_dataset, test_dataset)55    if coarse:56        assert len(train_dataset.label_vocab) == 357    else:58        assert len(train_dataset.label_vocab) == 1259def test_clinc_dataset_simple():60    datasets, vocab = get_dataset_simple('clinc')61    train_dataset, val_dataset, test_dataset = datasets62    _vectorize_simple(train_dataset, val_dataset, test_dataset)63    _sanity_check(train_dataset, val_dataset, test_dataset)64@pytest.fixture(scope="session")65def bert_tok():66    return BertTokenizer.from_pretrained("bert-base-uncased")67@pytest.mark.transformer68def test_snips_dataset_bert(bert_tok):69    train_dataset, val_dataset, test_dataset = get_dataset_transformers(bert_tok, 'snips_75', version=0)70    _sanity_check(train_dataset, val_dataset, test_dataset)71@pytest.mark.transformer72def test_rostd_dataset_bert(bert_tok):73    train_dataset, val_dataset, test_dataset = get_dataset_transformers(bert_tok, 'rostd')74    _sanity_check(train_dataset, val_dataset, test_dataset)75@pytest.mark.transformer76def test_clinc_dataset_bert(bert_tok):77    train_dataset, val_dataset, test_dataset = get_dataset_transformers(bert_tok, 'clinc')...

simple_pkpd.py

Source:simple_pkpd.py

1import numpy as np2import pandas as pd3from ...data import Dataset, EventSamples, TimeSeriesSamples4from . import _simple_pkpd5_SANITY_CHECK_ON = False6_sanity_check = dict()7def simple_pkpd_dataset(8    n_timesteps: int = 30,9    time_index_treatment_event: int = 25,10    n_control_samples: int = 200,11    n_treated_samples: int = 200,12    seed: int = 100,13):14    print(f"Generating simple PKPD dataset with random seed {seed}...")15    hidden_confounder: int = 016    (x_full, t_full, mask_full, batch_ind_full, y_full, y_control, y_mask_full, y_full_all,) = _simple_pkpd.generate(17        seed=seed,18        train_step=time_index_treatment_event,19        step=n_timesteps,20        control_sample=n_control_samples,21        treatment_sample=n_treated_samples,22        hidden_confounder=hidden_confounder,23    )24    if _SANITY_CHECK_ON:25        _sanity_check["x_full"] = x_full26        _sanity_check["t_full"] = t_full27        _sanity_check["mask_full"] = mask_full28        _sanity_check["batch_ind_full"] = batch_ind_full29        _sanity_check["y_full"] = y_full30        _sanity_check["y_control"] = y_control31        _sanity_check["y_mask_full"] = y_mask_full32    x_everything = np.concatenate([x_full, y_full_all], axis=0)33    assert (x_everything[:time_index_treatment_event, :, :] == x_full).all()34    assert (x_everything[time_index_treatment_event:, :, [2]] == y_full).all()35    assert (x_everything[time_index_treatment_event:, :n_control_samples, [2]] == y_control).all()36    sample_index = batch_ind_full.astype(int)37    tss = TimeSeriesSamples(38        data=[39            pd.DataFrame(data=x_everything[:, idx, :], columns=["k_in", "p", "y"], index=range(n_timesteps))40            for idx in sample_index41        ]42    )43    treat_event_feature = np.zeros(shape=(n_control_samples + n_treated_samples,), dtype=float)44    treat_event_feature[n_control_samples:] = 1.045    df = pd.DataFrame(46        data={47            "si": sample_index,48            "ti": [time_index_treatment_event] * (n_control_samples + n_treated_samples),49            "a": treat_event_feature,50        }51    )52    es = EventSamples.from_df(data=df, column_sample_index="si", column_time_index="ti")...

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