repo_id stringclasses 409
values | prefix large_stringlengths 34 36.3k | target large_stringlengths 1 498 | assertion_type stringclasses 31
values | difficulty stringclasses 8
values | test_file stringlengths 10 121 | test_function stringlengths 1 104 | test_class stringlengths 0 51 | lineno int32 2 11.3k | commit_idx int32 |
|---|---|---|---|---|---|---|---|---|---|
microsoft/causica | import pytest
import torch
from causica.distributions.noise import UnivariateLaplaceNoise, UnivariateLaplaceNoiseModule
@pytest.mark.parametrize(("batch", "dimension"), [(1, 10), (2, 5)])
def test_init(batch, dimension):
mean = torch.randn((batch, dimension))
scale = torch.ones((batch, dimension))
noise_m... | (batch, dimension) | assert | collection | test/distributions/noise/test_univariate_laplace.py | test_init | 12 | null | |
microsoft/causica | import os
from typing import Optional
import fsspec
import numpy as np
import pytorch_lightning as pl
import torch
import torch.utils.data as data_utils
from torch.utils.data import Dataset
class DatasetCounterFactual(Dataset):
def __init__(
self,
data_dir: str,
num_interventions: int,
... | 0 | assert | numeric_literal | research_experiments/fip/src/fip/data_modules/numpy_tensor_data_module.py | __init__ | DatasetCounterFactual | 35 | null |
microsoft/causica | import pytest
import torch
from tensordict import TensorDict
from causica.distributions import (
BernoulliNoise,
CategoricalNoise,
IndependentNoise,
JointNoise,
Noise,
UnivariateNormalNoise,
)
torch.manual_seed(0)
NOISE_DISTRIBUTIONS = [
IndependentNoise(BernoulliNoise(torch.randn(3), tor... | noise.mean) | assert_* | complex_expr | test/distributions/noise/test_joint.py | test_joint_noise_passthrough | 34 | null | |
microsoft/causica | import pytest
import torch
from causica.distributions.adjacency.gibbs_dag_prior import ExpertGraphContainer, GibbsDAGPrior
def test_expert_graph_dataclass():
mask = torch.Tensor([[0, 1, 1], [0, 0, 0], [0, 0, 0]])
dag = torch.Tensor([[0, 0, 1], [0, 0, 0], [0, 0, 0]])
confidence = 0.8
scale = 10
e... | 0.8 | assert | numeric_literal | test/distributions/adjacency/test_gibbs_dag_prior.py | test_expert_graph_dataclass | 22 | null | |
microsoft/causica | import networkx as nx
import torch
from causica.distributions import EdgesPerNodeErdosRenyiDAGDistribution
def test_samples_dags():
"""Test that all samples are DAGs"""
edges_per_node = [1, 2]
n = 5
sample_shape = torch.Size([3, 4])
dist = EdgesPerNodeErdosRenyiDAGDistribution(num_nodes=n, edges_p... | torch.Size(sample_shape + (n, n)) | assert | func_call | test/distributions/adjacency/test_edges_per_node_erdos_renyi.py | test_samples_dags | 14 | null | |
microsoft/causica | from pathlib import Path
import mlflow
from causica.lightning.loggers import BufferingMlFlowLogger
def test_buffering_mlflow_logger(tmp_path: Path):
mlflow.set_tracking_uri(tmp_path)
client = mlflow.tracking.MlflowClient(tracking_uri=str(tmp_path))
experiment_id = client.create_experiment("test")
run... | 2 | assert | numeric_literal | test/lightning/test_loggers.py | test_buffering_mlflow_logger | 16 | null | |
microsoft/causica | from dataclasses import replace
from pathlib import Path
import numpy as np
import pytest
import torch
from tensordict import TensorDict
from torch.utils.data import DataLoader
from causica.datasets.causica_dataset_format import Variable, VariablesMetadata
from causica.datasets.timeseries_dataset import (
Indexed... | num_timeseries | assert | variable | test/datasets/test_timeseries_dataset.py | test_indexed_timeseries_dataset | 115 | null | |
microsoft/causica | import pytest
import torch
from causica.distributions.adjacency.gibbs_dag_prior import ExpertGraphContainer, GibbsDAGPrior
def test_log_prob():
gibbs_dag_prior = GibbsDAGPrior(num_nodes=123, sparsity_lambda=torch.tensor(1))
A = torch.Tensor([[0, 0, 1], [0, 0, 1], [0, 0, 0]])
with pytest.raises( | AssertionError) | pytest.raises | variable | test/distributions/adjacency/test_gibbs_dag_prior.py | test_log_prob | 121 | null | |
microsoft/causica | import math
import pytest
import torch
from tensordict import TensorDict
from causica.functional_relationships import (
DECIEmbedFunctionalRelationships,
LinearFunctionalRelationships,
RFFFunctionalRelationships,
)
def fixture_two_variable_dict():
return {"x1": torch.Size([1]), "x2": torch.Size([2])}... | (3, 2, 1) | assert | collection | test/functional_relationships/test_functional_relationships.py | test_func_rel_forward_multigraph | 57 | null | |
microsoft/causica | import pytest
import torch
from causica.distributions.noise import UnivariateCauchyNoise, UnivariateCauchyNoiseModule
@pytest.mark.parametrize(("batch", "dimension"), [(1, 10), (2, 5)])
def test_init(batch, dimension):
mean = torch.randn((batch, dimension))
scale = torch.ones((batch, dimension))
noise_mod... | (batch, dimension) | assert | collection | test/distributions/noise/test_univariate_cauchy.py | test_init | 12 | null | |
microsoft/causica | import os
import tempfile
import numpy as np
import pytest
import torch
import torch.distributions as td
from tensordict import TensorDict
from causica.data_generation.generate_data import (
generate_sem_sampler,
get_variable_definitions,
plot_dataset,
sample_counterfactual,
sample_dataset,
sa... | 10 | assert | numeric_literal | test/data_generation/test_generate_data.py | test_generate_sem_variable_groups | 170 | null | |
microsoft/causica | import os
import tempfile
import pytest
import torch
from causica.datasets.causica_dataset_format import CAUSICA_DATASETS_PATH, DataEnum, load_data, save_data, save_dataset
from causica.datasets.tensordict_utils import tensordict_shapes
def _load_and_test_dataset(root_path: str):
variables_metadata = load_data(r... | tensordict_shapes(test_data) | assert | func_call | test/integration/test_save_load_csuite.py | _load_and_test_dataset | 16 | null | |
microsoft/causica | from dataclasses import replace
from pathlib import Path
import numpy as np
import pytest
import torch
from tensordict import TensorDict
from torch.utils.data import DataLoader
from causica.datasets.causica_dataset_format import Variable, VariablesMetadata
from causica.datasets.timeseries_dataset import (
Indexed... | int(key.lstrip("x")) | assert | func_call | test/datasets/test_timeseries_dataset.py | test_preprocess_data_with_tensor | 27 | null | |
microsoft/causica | import math
import pytest
import torch
import torch.distributions as td
from causica.distributions.noise import BernoulliNoise
def sample_logistic_noise(n_samples, input_dim, base_logits):
"""
Samples a Logistic random variable that can be used to sample this variable.
This method does **not** return har... | eight_sigma | assert | variable | test/distributions/noise/test_bernoulli.py | test_sample_to_noise | 67 | null | |
microsoft/causica | from dataclasses import replace
from pathlib import Path
import numpy as np
import pytest
import torch
from tensordict import TensorDict
from torch.utils.data import DataLoader
from causica.datasets.causica_dataset_format import Variable, VariablesMetadata
from causica.datasets.timeseries_dataset import (
Indexed... | tensor_data[..., i]) | assert_* | complex_expr | test/datasets/test_timeseries_dataset.py | test_preprocess_data_with_tensor | 28 | null | |
microsoft/causica | from functools import partial
import torch
from causica.distributions.noise.spline import SplineNoiseModule
INPUT_DIM = 4
torch.manual_seed(123)
assert_close = partial(torch.testing.assert_close, rtol=2e-6, atol=2e-5)
def test_sample_to_noise():
"""Test sample to noise and noise to sample work as expected."""... | dist.sample_to_noise(recon_samples)) | assert_* | func_call | test/distributions/noise/spline/test_splines.py | test_sample_to_noise | 27 | null | |
microsoft/causica | import pytest
import torch
from tensordict import TensorDict
from causica.functional_relationships import LinearFunctionalRelationships, create_do_functional_relationship
def fixture_two_variable_dict():
return {"x1": torch.Size([1]), "x2": torch.Size([2])}
def fixture_three_variable_dict():
return {"x1": to... | input_noise.keys() | assert | func_call | test/functional_relationships/test_do_functional_relationships.py | test_linear_3d_graph_do_1_node | 44 | null | |
microsoft/causica | import networkx as nx
import torch
from causica.distributions import ErdosRenyiDAGDistribution
def test_num_deges():
num_edges = 16
samples = ErdosRenyiDAGDistribution(num_nodes=8, num_edges=torch.tensor(num_edges)).sample(torch.Size([100]))
assert samples.shape == | torch.Size([100, 8, 8]) | assert | func_call | test/distributions/adjacency/test_erdos_renyi.py | test_num_deges | 47 | null | |
microsoft/causica | import pytest
import torch
import torch.distributions as td
from tensordict import TensorDict
from causica.datasets.variable_types import VariableTypeEnum
from causica.distributions import JointNoiseModule, create_noise_modules
from causica.distributions.noise.joint import ContinuousNoiseDist
from causica.functional_r... | inferred_noise["x3"]) | assert_* | complex_expr | test/sem/test_sem.py | test_batched_intervention_batched_3d_graph | 221 | null | |
microsoft/causica | import pytest
import torch
from tensordict import TensorDict, TensorDictBase
from causica.datasets.tensordict_utils import expand_tensordict_groups, tensordict_shapes, unbind_values, unbound_items
def _assert_tensordict_allclose(a: TensorDictBase, b: TensorDictBase) -> None:
paired_items = zip(
a.items(in... | torch.Size([1]) | assert | func_call | test/datasets/test_tensordict_utils.py | test_unbind_values_different_dims | 91 | null | |
microsoft/causica | from typing import Optional, Type, Union
import numpy as np
import pytest
import torch
from causica.distributions.adjacency import (
AdjacencyDistribution,
ConstrainedAdjacencyDistribution,
ENCOAdjacencyDistribution,
TemporalConstrainedAdjacencyDistribution,
ThreeWayAdjacencyDistribution,
)
from c... | batch_shape + (num_nodes, num_nodes) | assert | func_call | test/distributions/adjacency/test_adjacency_distributions.py | test_mean | 308 | null | |
microsoft/causica | import pytest
import torch
from causica.distributions.adjacency.gibbs_dag_prior import ExpertGraphContainer, GibbsDAGPrior
def test_get_expert_graph_term():
mask = torch.Tensor([[0, 0, 0], [0, 0, 0], [0, 0, 0]])
dag = torch.Tensor([[0, 0, 1], [0, 0, 0], [0, 0, 0]])
confidence = 0.8
scale = 10
ex... | torch.tensor(0.2)) | assert_* | func_call | test/distributions/adjacency/test_gibbs_dag_prior.py | test_get_expert_graph_term | 79 | null | |
microsoft/causica | import os
import tempfile
import numpy as np
import pytest
import torch
import torch.distributions as td
from tensordict import TensorDict
from causica.data_generation.generate_data import (
generate_sem_sampler,
get_variable_definitions,
plot_dataset,
sample_counterfactual,
sample_dataset,
sa... | 5 | assert | numeric_literal | test/data_generation/test_generate_data.py | test_generate_sem_er | 146 | null | |
microsoft/causica | import math
import pytest
import torch
from tensordict import TensorDict
from causica.functional_relationships import (
DECIEmbedFunctionalRelationships,
LinearFunctionalRelationships,
RFFFunctionalRelationships,
)
def fixture_two_variable_dict():
return {"x1": torch.Size([1]), "x2": torch.Size([2])}... | (3, 2) | assert | collection | test/functional_relationships/test_functional_relationships.py | test_func_rel_forward | 46 | null | |
microsoft/causica | from dataclasses import replace
from pathlib import Path
import numpy as np
import pytest
import torch
from tensordict import TensorDict
from torch.utils.data import DataLoader
from causica.datasets.causica_dataset_format import Variable, VariablesMetadata
from causica.datasets.timeseries_dataset import (
Indexed... | data | assert | variable | test/datasets/test_timeseries_dataset.py | test_preprocess_data_with_tensordict | 45 | null | |
microsoft/causica | import io
import itertools
from typing import Any, TypeVar
import pytest
import torch
from tensordict import TensorDictBase, make_tensordict
from causica.distributions.transforms import SequentialTransformModule
from causica.distributions.transforms.base import TransformModule
from causica.distributions.transforms.jo... | state_dict[name]) | assert_* | complex_expr | test/distributions/transforms/test_transform_modules.py | test_registration | 81 | null | |
microsoft/causica | import numpy as np
import pandas as pd
import torch
from tensordict import TensorDict
from causica.datasets.causica_dataset_format import Variable, tensordict_from_variables_metadata
from causica.datasets.tensordict_utils import convert_one_hot, tensordict_from_pandas
def test_dataset_with_groups():
"""Test a dat... | data[:, start:end]) | assert_* | complex_expr | test/datasets/test_datasets.py | test_dataset_with_groups | 71 | null | |
microsoft/causica | import pytest
import torch
from causica.triangular_transformations import fill_triangular, unfill_triangular
DIM = 5
@pytest.mark.parametrize("batch_size", [tuple(), (3,), (4, 2)])
def test_fill_unfill(batch_size):
"""Test that filling and unfilling results in the same tensor"""
matrix = torch.randn(batch_si... | fill_triangular(upper_vec, upper=True)) | assert_* | func_call | test/test_triangular_transformations.py | test_fill_unfill | 17 | null | |
microsoft/causica | import itertools
from causica.datasets.samplers import SubsetBatchSampler
def test_subset_batch_sampler_ordered_drop_last():
sampler = SubsetBatchSampler([2, 3, 5], 2, shuffle=False, drop_last=True)
samples = list(sampler)
assert samples == | [[0, 1], [2, 3], [5, 6], [7, 8]] | assert | collection | test/datasets/test_samplers.py | test_subset_batch_sampler_ordered_drop_last | 16 | null | |
microsoft/causica | import pytest
import torch
from tensordict import TensorDict
from causica.functional_relationships import LinearFunctionalRelationships, create_do_functional_relationship
def fixture_two_variable_dict():
return {"x1": torch.Size([1]), "x2": torch.Size([2])}
def fixture_three_variable_dict():
return {"x1": to... | (100, 2) | assert | collection | test/functional_relationships/test_do_functional_relationships.py | test_linear_3d_graph_do_1_node | 45 | null | |
microsoft/causica | import json
import numpy as np
import pandas as pd
import pytest
import torch
from pytorch_lightning.trainer import Trainer
from tensordict import TensorDict
from causica.datasets.causica_dataset_format import Variable, VariablesMetadata
from causica.lightning.data_modules.variable_spec_data import VariableSpecDataMo... | torch.zeros(2)) | assert_* | func_call | test/lightning/test_variable_spec_data.py | test_variable_spec_data | 185 | null | |
microsoft/causica | import mlflow
import numpy as np
import pytest
import pytorch_lightning as pl
import torch
from causica.distributions import ExpertGraphContainer
from causica.distributions.noise.joint import ContinuousNoiseDist
from causica.lightning.data_modules.variable_spec_data import CSuiteDataModule
from causica.lightning.modul... | _module_to_parameter(module2)) | assert_* | func_call | test/integration/test_pytorch_lightning.py | test_pytorch_lightning_deterministic | 44 | null | |
microsoft/causica | import pytest
import torch
from causica.distributions.adjacency.gibbs_dag_prior import ExpertGraphContainer, GibbsDAGPrior
def test_log_prob():
gibbs_dag_prior = GibbsDAGPrior(num_nodes=123, sparsity_lambda=torch.tensor(1))
A = torch.Tensor([[0, 0, 1], [0, 0, 1], [0, 0, 0]])
with pytest.raises(Assertion... | torch.tensor(-1.0)) | assert_* | func_call | test/distributions/adjacency/test_gibbs_dag_prior.py | test_log_prob | 135 | null | |
microsoft/causica | import numpy as np
import pandas as pd
import torch
from tensordict import TensorDict
from causica.datasets.causica_dataset_format import Variable, tensordict_from_variables_metadata
from causica.datasets.tensordict_utils import convert_one_hot, tensordict_from_pandas
def test_dataset_categorical():
"""Test the p... | (batch_size, 1) | assert | collection | test/datasets/test_datasets.py | test_dataset_categorical | 87 | null | |
microsoft/causica | import numpy as np
import pandas as pd
import torch
from tensordict import TensorDict
from causica.datasets.causica_dataset_format import Variable, tensordict_from_variables_metadata
from causica.datasets.tensordict_utils import convert_one_hot, tensordict_from_pandas
def test_dataset_without_groups():
"""Test da... | dataset_from_np[key].shape | assert | complex_expr | test/datasets/test_datasets.py | test_dataset_without_groups | 22 | null | |
microsoft/causica | import math
import pytest
import torch
from tensordict import TensorDict
from causica.sem.structural_equation_model import ate, counterfactual, ite
from . import create_lingauss_sem
def fixture_two_variable_dict():
return {"x1": torch.Size([1]), "x2": torch.Size([2])}
def fixture_three_variable_dict():
ret... | expected_mean_a.expand((sample_size, 1))) | assert_* | func_call | test/sem/test_treatment_effects.py | test_ate_ite_cf_two_node | 43 | null | |
microsoft/causica | import pytest
import torch
from causica.distributions.noise import UnivariateNormalNoise, UnivariateNormalNoiseModule
@pytest.mark.parametrize(("batch", "dimension"), [(1, 10), (2, 5)])
def test_init(batch, dimension):
mean = torch.randn((batch, dimension))
scale = torch.ones((batch, dimension))
noise_mod... | (batch, dimension) | assert | collection | test/distributions/noise/test_univariate_normal.py | test_init | 12 | null | |
microsoft/causica | import numpy as np
import torch
from torch.distributions.utils import probs_to_logits
from causica.distributions import ThreeWayAdjacencyDistribution
def test_threeway():
"""Test ThreeWay methods are correct for a known distribution."""
probs = torch.Tensor([[0.4, 0.25, 0.35]])
dist = ThreeWayAdjacencyDis... | np.array([[0.0, 0.0], [1.0, 0.0]])) | assert_* | func_call | test/distributions/adjacency/test_three_way.py | test_threeway | 27 | null | |
microsoft/causica | from dataclasses import replace
from pathlib import Path
import numpy as np
import pytest
import torch
from tensordict import TensorDict
from torch.utils.data import DataLoader
from causica.datasets.causica_dataset_format import Variable, VariablesMetadata
from causica.datasets.timeseries_dataset import (
Indexed... | ValueError) | pytest.raises | variable | test/datasets/test_timeseries_dataset.py | test_preprocess_data_with_tensor | 38 | null | |
microsoft/causica | import numpy as np
import torch
from causica.graph.dag_constraint import calculate_dagness
def test_calculate_dagness():
dag = torch.Tensor([[0, 0, 1], [0, 0, 1], [0, 0, 0]])
assert calculate_dagness(dag) == | 0 | assert | numeric_literal | test/test_dag_constraint.py | test_calculate_dagness | 11 | null | |
microsoft/causica | import pytest
import torch
from causica.distributions.adjacency.gibbs_dag_prior import ExpertGraphContainer, GibbsDAGPrior
def test_get_expert_graph_term_temporal():
mask = torch.zeros(2, 3, 3)
dag = torch.stack([torch.Tensor([[0, 0, 1], [0, 0, 0], [0, 0, 0]]), torch.ones(3, 3)])
confidence = 0.8
sca... | torch.tensor(0.4)) | assert_* | func_call | test/distributions/adjacency/test_gibbs_dag_prior.py | test_get_expert_graph_term_temporal | 113 | null | |
microsoft/causica | import pytest
import torch
from tensordict import TensorDict
from causica.distributions import (
BernoulliNoise,
CategoricalNoise,
IndependentNoise,
JointNoise,
Noise,
UnivariateNormalNoise,
)
torch.manual_seed(0)
NOISE_DISTRIBUTIONS = [
IndependentNoise(BernoulliNoise(torch.randn(3), tor... | noise_a.entropy() + noise_b.entropy()) | assert_* | func_call | test/distributions/noise/test_joint.py | test_joint_noise_properties | 44 | null | |
microsoft/causica | import os
import tempfile
import numpy as np
import pytest
import torch
import torch.distributions as td
from tensordict import TensorDict
from causica.data_generation.generate_data import (
generate_sem_sampler,
get_variable_definitions,
plot_dataset,
sample_counterfactual,
sample_dataset,
sa... | ImportError) | pytest.raises | variable | test/data_generation/test_generate_data.py | test_plot_data | 272 | null | |
microsoft/causica | from typing import Optional, Type, Union
import numpy as np
import pytest
import torch
from causica.distributions.adjacency import (
AdjacencyDistribution,
ConstrainedAdjacencyDistribution,
ENCOAdjacencyDistribution,
TemporalConstrainedAdjacencyDistribution,
ThreeWayAdjacencyDistribution,
)
from c... | ValueError) | pytest.raises | variable | test/distributions/adjacency/test_adjacency_distributions.py | test_support | 115 | null | |
microsoft/causica | import json
import numpy as np
import pandas as pd
import pytest
import torch
from pytorch_lightning.trainer import Trainer
from tensordict import TensorDict
from causica.datasets.causica_dataset_format import Variable, VariablesMetadata
from causica.lightning.data_modules.variable_spec_data import VariableSpecDataMo... | torch.Size([10]) | assert | func_call | test/lightning/test_variable_spec_data.py | test_variable_spec_data | 181 | null | |
microsoft/causica | import os
import tempfile
import pytest
import torch
from causica.datasets.causica_dataset_format import CAUSICA_DATASETS_PATH, DataEnum, load_data, save_data, save_dataset
from causica.datasets.tensordict_utils import tensordict_shapes
def _load_and_test_dataset(root_path: str):
variables_metadata = load_data(r... | adj_mat_2) | assert_* | variable | test/integration/test_save_load_csuite.py | test_load_save_csuite | 72 | null | |
microsoft/causica | import pandas as pd
import torch
from causica.datasets.causica_dataset_format import Variable
from causica.lightning.data_modules.basic_data_module import BasicDECIDataModule
def test_basic_data_module():
"""Test Basic Data Module functionality"""
df = pd.DataFrame(
{
"x0_0": {
... | torch.Size([2]) | assert | func_call | test/lightning/test_basic_data_module.py | test_basic_data_module | 90 | null | |
microsoft/causica | import os
import torch
from torch.utils.data import DataLoader
from causica.datasets.causica_dataset_format import CAUSICA_DATASETS_PATH
from causica.datasets.timeseries_dataset import IndexedTimeseriesDataset
def test_ecoli_100():
dataset_root = os.path.join(CAUSICA_DATASETS_PATH, "Ecoli1_100")
data_path = ... | (46, 21) | assert | collection | test/integration/test_timeseries_dataset.py | test_ecoli_100 | 18 | null | |
microsoft/causica | import pytest
import torch
from causica.distributions.adjacency.adjacency_distributions import AdjacencyDistribution
from causica.distributions.adjacency.enco import ENCOAdjacencyDistribution
from causica.distributions.noise.joint import JointNoiseModule
from causica.distributions.noise.noise import Noise, NoiseModule... | sem_dist.entropy()) | assert_* | func_call | test/distributions/test_sem_distribution.py | test_sem_distribution_passthrough | 44 | null | |
microsoft/causica | import pytest
import torch
from tensordict import TensorDict, TensorDictBase
from causica.datasets.tensordict_utils import expand_tensordict_groups, tensordict_shapes, unbind_values, unbound_items
def _assert_tensordict_allclose(a: TensorDictBase, b: TensorDictBase) -> None:
paired_items = zip(
a.items(in... | torch.Size([]) | assert | func_call | test/datasets/test_tensordict_utils.py | test_unbind_values_different_dims | 83 | null | |
microsoft/causica | import pytest
import torch
from tensordict import TensorDict, TensorDictBase
from causica.datasets.tensordict_utils import expand_tensordict_groups, tensordict_shapes, unbind_values, unbound_items
def _assert_tensordict_allclose(a: TensorDictBase, b: TensorDictBase) -> None:
paired_items = zip(
a.items(in... | {"var1", "var2", "var3", "categorical"} | assert | collection | test/datasets/test_tensordict_utils.py | test_expand_tensordict_groups | 29 | null | |
microsoft/causica | import pytest
import torch
import torch.distributions as td
from tensordict import TensorDict
from causica.datasets.variable_types import VariableTypeEnum
from causica.distributions import JointNoiseModule, create_noise_modules
from causica.distributions.noise.joint import ContinuousNoiseDist
from causica.functional_r... | sample["x1"]) | assert_* | complex_expr | test/sem/test_sem.py | test_do_linear_sem | 45 | null | |
microsoft/causica | import json
import numpy as np
import pandas as pd
import pytest
import torch
from pytorch_lightning.trainer import Trainer
from tensordict import TensorDict
from causica.datasets.causica_dataset_format import Variable, VariablesMetadata
from causica.lightning.data_modules.variable_spec_data import VariableSpecDataMo... | load_offsets) | assert_* | variable | test/lightning/test_variable_spec_data.py | test_save_load_variable_spec_data | 250 | null | |
microsoft/causica | import pytest
import torch
from causica.distributions.adjacency.adjacency_distributions import AdjacencyDistribution
from causica.distributions.adjacency.enco import ENCOAdjacencyDistribution
from causica.distributions.noise.joint import JointNoiseModule
from causica.distributions.noise.noise import Noise, NoiseModule... | sem_dist.mean.graph) | assert_* | complex_expr | test/distributions/test_sem_distribution.py | test_sem_distribution_passthrough | 45 | null | |
microsoft/causica | import pytest
import torch
from tensordict import TensorDict, TensorDictBase
from causica.datasets.tensordict_utils import expand_tensordict_groups, tensordict_shapes, unbind_values, unbound_items
def _assert_tensordict_allclose(a: TensorDictBase, b: TensorDictBase) -> None:
paired_items = zip(
a.items(in... | expected_shapes | assert | variable | test/datasets/test_tensordict_utils.py | test_unbind_values | 61 | null | |
microsoft/causica | import io
import itertools
from typing import Any, TypeVar
import pytest
import torch
from tensordict import TensorDictBase, make_tensordict
from causica.distributions.transforms import SequentialTransformModule
from causica.distributions.transforms.base import TransformModule
from causica.distributions.transforms.jo... | offset) | assert_* | variable | test/distributions/transforms/test_transform_modules.py | test_transform_module_registration_buffers | 106 | null | |
microsoft/causica | from dataclasses import replace
from pathlib import Path
import numpy as np
import pytest
import torch
from tensordict import TensorDict
from torch.utils.data import DataLoader
from causica.datasets.causica_dataset_format import Variable, VariablesMetadata
from causica.datasets.timeseries_dataset import (
Indexed... | expected_value) | assert_* | variable | test/datasets/test_timeseries_dataset.py | test_index_contiguous_chunks | 67 | null | |
microsoft/causica | import math
import pytest
import torch
from tensordict import TensorDict
from causica.functional_relationships import (
DECIEmbedFunctionalRelationships,
LinearFunctionalRelationships,
RFFFunctionalRelationships,
)
def fixture_two_variable_dict():
return {"x1": torch.Size([1]), "x2": torch.Size([2])}... | (3, 1) | assert | collection | test/functional_relationships/test_functional_relationships.py | test_func_rel_forward | 45 | null | |
microsoft/causica | import math
import pytest
import torch
import torch.distributions as td
from causica.distributions.noise import BernoulliNoise
def sample_logistic_noise(n_samples, input_dim, base_logits):
"""
Samples a Logistic random variable that can be used to sample this variable.
This method does **not** return har... | torch.Size([3]) | assert | func_call | test/distributions/noise/test_bernoulli.py | test_init | 34 | null | |
microsoft/causica | import pytest
import torch
from tensordict import TensorDict
from causica.distributions import (
BernoulliNoise,
CategoricalNoise,
IndependentNoise,
JointNoise,
Noise,
UnivariateNormalNoise,
)
torch.manual_seed(0)
NOISE_DISTRIBUTIONS = [
IndependentNoise(BernoulliNoise(torch.randn(3), tor... | joint_samples.get("b").shape | assert | func_call | test/distributions/noise/test_joint.py | test_joint_noise_empirical | 87 | null | |
microsoft/causica | import networkx as nx
import torch
from causica.distributions import ErdosRenyiDAGDistribution
def test_samples_dags():
"""Test that all samples are DAGs"""
p = torch.tensor([[0.7, 0.4]])
n = 5
sample_shape = torch.Size([3, 4])
dist = ErdosRenyiDAGDistribution(num_nodes=n, probs=p)
samples = d... | torch.Size(sample_shape + p.shape + (n, n)) | assert | func_call | test/distributions/adjacency/test_erdos_renyi.py | test_samples_dags | 14 | null | |
microsoft/causica | import numpy as np
import pytest
import torch
from torch.distributions.utils import probs_to_logits
from causica.distributions.adjacency.enco import ENCOAdjacencyDistribution
from causica.distributions.adjacency.temporal_adjacency_distributions import (
RhinoLaggedAdjacencyDistribution,
TemporalAdjacencyDistri... | dist.mode) | assert_* | complex_expr | test/distributions/adjacency/test_enco.py | test_enco | 175 | null | |
microsoft/causica | import numpy as np
import pandas as pd
import torch
from tensordict import TensorDict
from causica.datasets.causica_dataset_format import Variable, tensordict_from_variables_metadata
from causica.datasets.tensordict_utils import convert_one_hot, tensordict_from_pandas
def test_dataset_without_groups():
"""Test da... | dataset_from_np[key]) | assert_* | complex_expr | test/datasets/test_datasets.py | test_dataset_without_groups | 33 | null | |
microsoft/causica | import networkx as nx
import torch
from causica.distributions import EdgesPerNodeErdosRenyiDAGDistribution
def test_num_edges():
n = 8
edges_per_node = [2]
num_edges_expected = edges_per_node[0] * n
samples = EdgesPerNodeErdosRenyiDAGDistribution(num_nodes=n, edges_per_node=edges_per_node).sample(
... | torch.Size([100, 8, 8]) | assert | func_call | test/distributions/adjacency/test_edges_per_node_erdos_renyi.py | test_num_edges | 28 | null | |
microsoft/causica | import io
import itertools
from typing import Any, TypeVar
import pytest
import torch
from tensordict import TensorDictBase, make_tensordict
from causica.distributions.transforms import SequentialTransformModule
from causica.distributions.transforms.base import TransformModule
from causica.distributions.transforms.jo... | tensors_modified[name]) | assert_* | complex_expr | test/distributions/transforms/test_transform_modules.py | test_registration | 72 | null | |
microsoft/causica | import pytest
import torch
from tensordict import TensorDict, TensorDictBase
from causica.datasets.tensordict_utils import expand_tensordict_groups, tensordict_shapes, unbind_values, unbound_items
def _assert_tensordict_allclose(a: TensorDictBase, b: TensorDictBase) -> None:
paired_items = zip(
a.items(in... | IndexError) | pytest.raises | variable | test/datasets/test_tensordict_utils.py | test_unbind_values_different_dims | 117 | null | |
microsoft/causica | import pytest
import torch
from tensordict import TensorDict
from causica.functional_relationships import LinearFunctionalRelationships, create_do_functional_relationship
def fixture_two_variable_dict():
return {"x1": torch.Size([1]), "x2": torch.Size([2])}
def fixture_three_variable_dict():
return {"x1": to... | (100, 1) | assert | collection | test/functional_relationships/test_do_functional_relationships.py | test_linear_3d_graph_do_1_node | 46 | null | |
microsoft/causica | import pytest
import torch
from causica.triangular_transformations import fill_triangular, unfill_triangular
DIM = 5
@pytest.mark.parametrize("batch_size", [tuple(), (3,), (4, 2)])
def test_fill_unfill(batch_size):
"""Test that filling and unfilling results in the same tensor"""
matrix = torch.randn(batch_si... | fill_triangular(lower_vec, upper=False)) | assert_* | func_call | test/test_triangular_transformations.py | test_fill_unfill | 16 | null | |
microsoft/causica | import numpy as np
import pytest
import torch
from torch.distributions.utils import probs_to_logits
from causica.distributions.adjacency.enco import ENCOAdjacencyDistribution
from causica.distributions.adjacency.temporal_adjacency_distributions import (
RhinoLaggedAdjacencyDistribution,
TemporalAdjacencyDistri... | dist.mean) | assert_* | complex_expr | test/distributions/adjacency/test_enco.py | test_enco | 174 | null | |
microsoft/causica | import numpy as np
import torch
from torch.distributions.utils import probs_to_logits
from causica.distributions import ThreeWayAdjacencyDistribution
def test_threeway_entropy():
"""Test entropy is correct for a known distribution"""
num_nodes = 3
logits = torch.nn.Parameter(
torch.zeros(((num_nod... | logits.shape[0] * np.log(3)) | assert_* | func_call | test/distributions/adjacency/test_three_way.py | test_threeway_entropy | 16 | null | |
microsoft/causica | import random
from dataclasses import dataclass, field
from typing import Callable, Iterable, Optional, Union
import pytorch_lightning as pl
import torch
from tensordict import TensorDict, TensorDictBase
from torch.utils.data import ConcatDataset, DataLoader, Dataset
from causica.data_generation.samplers.sem_sampler ... | list(self.factual_data.keys()) | assert | func_call | research_experiments/fip/src/fip/data_modules/synthetic_data_module.py | __post_init__ | CounterfactualDataNoise | 41 | null |
microsoft/causica | import os
import tempfile
import pytest
import torch
from causica.datasets.causica_dataset_format import CAUSICA_DATASETS_PATH, DataEnum, load_data, save_data, save_dataset
from causica.datasets.tensordict_utils import tensordict_shapes
def _load_and_test_dataset(root_path: str):
variables_metadata = load_data(r... | len(interventions_2) | assert | func_call | test/integration/test_save_load_csuite.py | test_load_save_csuite | 76 | null | |
microsoft/causica | import numpy as np
import pytest
import torch
from torch.distributions.utils import probs_to_logits
from causica.distributions.adjacency.enco import ENCOAdjacencyDistribution
from causica.distributions.adjacency.temporal_adjacency_distributions import (
RhinoLaggedAdjacencyDistribution,
TemporalAdjacencyDistri... | np.zeros_like(logits_exist.detach().numpy())) | assert_* | func_call | test/distributions/adjacency/test_enco.py | test_enco_entropy | 31 | null | |
microsoft/causica | import pytest
import torch
from tensordict import TensorDict
from causica.distributions import (
BernoulliNoise,
CategoricalNoise,
IndependentNoise,
JointNoise,
Noise,
UnivariateNormalNoise,
)
torch.manual_seed(0)
NOISE_DISTRIBUTIONS = [
IndependentNoise(BernoulliNoise(torch.randn(3), tor... | noise_a.mean) | assert_* | complex_expr | test/distributions/noise/test_joint.py | test_joint_noise_properties | 47 | null | |
microsoft/causica | import torch
from tensordict import TensorDict
from causica.datasets.tensordict_utils import tensordict_shapes
from causica.sem.temporal_distribution_parameters_sem import split_lagged_and_instanteneous_values
def test_split_lagged_and_instanteneous_values():
td = TensorDict(
{
"a": torch.rand... | instantaneous.batch_size | assert | complex_expr | test/sem/test_temporal_distribution_parameters_sem.py | test_split_lagged_and_instanteneous_values | 21 | null | |
microsoft/causica | import itertools
from causica.datasets.samplers import SubsetBatchSampler
def test_subset_batch_sampler_shuffled():
sampler = SubsetBatchSampler([2, 3, 5], 2, shuffle=True)
samples = list(sampler)
sorted_samples = list(sorted(itertools.chain.from_iterable(samples)))
assert sorted_samples == | list(range(10)) | assert | func_call | test/datasets/test_samplers.py | test_subset_batch_sampler_shuffled | 24 | null | |
microsoft/causica | import pytest
import torch
from causica.distributions.adjacency.gibbs_dag_prior import ExpertGraphContainer, GibbsDAGPrior
def test_log_prob_temporal():
gibbs_dag_prior = GibbsDAGPrior(num_nodes=3, sparsity_lambda=torch.tensor(1), context_length=3)
A = torch.Tensor([[0, 0, 1], [0, 0, 1], [0, 0, 0]])
wit... | torch.tensor(-6.0)) | assert_* | func_call | test/distributions/adjacency/test_gibbs_dag_prior.py | test_log_prob_temporal | 150 | null | |
microsoft/causica | import mlflow
import numpy as np
import pytest
import pytorch_lightning as pl
import torch
from causica.distributions import ExpertGraphContainer
from causica.distributions.noise.joint import ContinuousNoiseDist
from causica.lightning.data_modules.variable_spec_data import CSuiteDataModule
from causica.lightning.modul... | module1_params.keys() | assert | func_call | test/integration/test_pytorch_lightning.py | test_pytorch_lightning_save_checkpoint | 90 | null | |
microsoft/causica | import pytest
import torch
from tensordict import TensorDict, TensorDictBase
from causica.datasets.tensordict_utils import expand_tensordict_groups, tensordict_shapes, unbind_values, unbound_items
def _assert_tensordict_allclose(a: TensorDictBase, b: TensorDictBase) -> None:
paired_items = zip(
a.items(in... | (2, 1) | assert | collection | test/datasets/test_tensordict_utils.py | test_expand_tensordict_groups | 32 | null | |
microsoft/causica | import os
import tempfile
import pytest
import torch
from causica.datasets.causica_dataset_format import CAUSICA_DATASETS_PATH, DataEnum, load_data, save_data, save_dataset
from causica.datasets.tensordict_utils import tensordict_shapes
def _load_and_test_dataset(root_path: str):
variables_metadata = load_data(r... | tensordict_shapes(intervention_a.factual_data) | assert | func_call | test/integration/test_save_load_csuite.py | load_and_test_counterfactuals | 48 | null | |
microsoft/causica | import math
import pytest
import torch
from tensordict import TensorDict
from causica.sem.structural_equation_model import ate, counterfactual, ite
from . import create_lingauss_sem
def fixture_two_variable_dict():
return {"x1": torch.Size([1]), "x2": torch.Size([2])}
def fixture_three_variable_dict():
ret... | factual_data["x1"]) | assert_* | complex_expr | test/sem/test_treatment_effects.py | test_ate_ite_cf_three_node | 67 | null | |
microsoft/causica | import os
import tempfile
import pytest
import torch
from causica.datasets.causica_dataset_format import CAUSICA_DATASETS_PATH, DataEnum, load_data, save_data, save_dataset
from causica.datasets.tensordict_utils import tensordict_shapes
def _load_and_test_dataset(root_path: str):
variables_metadata = load_data(r... | getattr(cf_2[1], field)) | assert_* | func_call | test/integration/test_save_load_csuite.py | test_load_save_counterfactuals | 100 | null | |
microsoft/causica | from functools import partial
import torch
from causica.distributions.noise.spline import SplineNoiseModule
INPUT_DIM = 4
torch.manual_seed(123)
assert_close = partial(torch.testing.assert_close, rtol=2e-6, atol=2e-5)
def test_sample_to_noise():
"""Test sample to noise and noise to sample work as expected."""... | recon_samples) | assert_* | variable | test/distributions/noise/spline/test_splines.py | test_sample_to_noise | 26 | null | |
microsoft/causica | from dataclasses import replace
from pathlib import Path
import numpy as np
import pytest
import torch
from tensordict import TensorDict
from torch.utils.data import DataLoader
from causica.datasets.causica_dataset_format import Variable, VariablesMetadata
from causica.datasets.timeseries_dataset import (
Indexed... | data.select(*selected_keys)) | assert_* | func_call | test/datasets/test_timeseries_dataset.py | test_preprocess_data_with_tensordict | 50 | null | |
microsoft/causica | import pytest
import torch
from tensordict import TensorDict
from causica.distributions import (
BernoulliNoise,
CategoricalNoise,
IndependentNoise,
JointNoise,
Noise,
UnivariateNormalNoise,
)
torch.manual_seed(0)
NOISE_DISTRIBUTIONS = [
IndependentNoise(BernoulliNoise(torch.randn(3), tor... | joint_samples.get("a").shape | assert | func_call | test/distributions/noise/test_joint.py | test_joint_noise_empirical | 86 | null | |
microsoft/causica | from pathlib import Path
import mlflow
from causica.lightning.loggers import BufferingMlFlowLogger
def test_buffering_mlflow_logger(tmp_path: Path):
mlflow.set_tracking_uri(tmp_path)
client = mlflow.tracking.MlflowClient(tracking_uri=str(tmp_path))
experiment_id = client.create_experiment("test")
run... | 0 | assert | numeric_literal | test/lightning/test_loggers.py | test_buffering_mlflow_logger | 18 | null | |
microsoft/causica | import pytest
import torch
from causica.triangular_transformations import fill_triangular, unfill_triangular
DIM = 5
@pytest.mark.parametrize("batch_size", [tuple(), (3,), (4, 2)])
def test_fill_unfill(batch_size):
"""Test that filling and unfilling results in the same tensor"""
matrix = torch.randn(batch_si... | fill_triangular(upper_vec, upper=False)) | assert_* | func_call | test/test_triangular_transformations.py | test_fill_unfill | 21 | null | |
microsoft/causica | import pytest
import torch
from causica.distributions.adjacency.gibbs_dag_prior import ExpertGraphContainer, GibbsDAGPrior
def test_get_sparsity_term():
gibbs_dag_prior = GibbsDAGPrior(num_nodes=2, sparsity_lambda=torch.tensor(1))
dag = torch.Tensor([[0, 0], [0, 0]])
assert gibbs_dag_prior.get_sparsity_... | gibbs_dag_prior.get_sparsity_term(sparse_dag) | assert | func_call | test/distributions/adjacency/test_gibbs_dag_prior.py | test_get_sparsity_term | 36 | null | |
microsoft/causica | import os
import tempfile
import pytest
import torch
from causica.datasets.causica_dataset_format import CAUSICA_DATASETS_PATH, DataEnum, load_data, save_data, save_dataset
from causica.datasets.tensordict_utils import tensordict_shapes
def _load_and_test_dataset(root_path: str):
variables_metadata = load_data(r... | int_groups_b | assert | variable | test/integration/test_save_load_csuite.py | _load_and_test_dataset | 23 | null | |
microsoft/causica | import os
import tempfile
import pytest
import torch
from causica.datasets.causica_dataset_format import CAUSICA_DATASETS_PATH, DataEnum, load_data, save_data, save_dataset
from causica.datasets.tensordict_utils import tensordict_shapes
def _load_and_test_dataset(root_path: str):
variables_metadata = load_data(r... | (num_nodes, num_nodes) | assert | collection | test/integration/test_save_load_csuite.py | _load_and_test_dataset | 30 | null | |
microsoft/causica | import os
from typing import Optional
import fsspec
import numpy as np
import pytorch_lightning as pl
import torch
import torch.utils.data as data_utils
from torch.utils.data import Dataset
def sample_base_datasets(
data_dir: str = "",
standardize: bool = True,
with_true_graph: bool = True,
split_data... | self.test_batch_size_init | assert | complex_expr | research_experiments/fip/src/fip/data_modules/numpy_tensor_data_module.py | __init__ | NumpyTensorDataModule | 325 | null |
microsoft/causica | import pytest
import torch
import torch.distributions as td
from tensordict import TensorDict
from causica.datasets.variable_types import VariableTypeEnum
from causica.distributions import JointNoiseModule, create_noise_modules
from causica.distributions.noise.joint import ContinuousNoiseDist
from causica.functional_r... | 1 | assert | numeric_literal | test/sem/test_sem.py | test_batched_intervention_2d_graph | 76 | null | |
microsoft/causica | import itertools
from causica.datasets.samplers import SubsetBatchSampler
def test_subset_batch_sampler_ordered():
sampler = SubsetBatchSampler([2, 3, 5], 2, shuffle=False)
samples = list(sampler)
assert samples == | [[0, 1], [2, 3], [4], [5, 6], [7, 8], [9]] | assert | collection | test/datasets/test_samplers.py | test_subset_batch_sampler_ordered | 9 | null | |
microsoft/causica | from dataclasses import replace
from pathlib import Path
import numpy as np
import pytest
import torch
from tensordict import TensorDict
from torch.utils.data import DataLoader
from causica.datasets.causica_dataset_format import Variable, VariablesMetadata
from causica.datasets.timeseries_dataset import (
Indexed... | (num_timeseries % batch_size, max_length) | assert | collection | test/datasets/test_timeseries_dataset.py | test_batching_indexed_timeseries_dataset_fixed_length | 143 | null | |
microsoft/causica | import mlflow
import numpy as np
import pytest
import pytorch_lightning as pl
import torch
from causica.distributions import ExpertGraphContainer
from causica.distributions.noise.joint import ContinuousNoiseDist
from causica.lightning.data_modules.variable_spec_data import CSuiteDataModule
from causica.lightning.modul... | module2_params.keys() | assert | func_call | test/integration/test_pytorch_lightning.py | test_pytorch_lightning_save_checkpoint | 84 | null | |
microsoft/causica | import pytest
import torch
from causica.distributions.adjacency.gibbs_dag_prior import ExpertGraphContainer, GibbsDAGPrior
def test_expert_graph_dataclass():
mask = torch.Tensor([[0, 1, 1], [0, 0, 0], [0, 0, 0]])
dag = torch.Tensor([[0, 0, 1], [0, 0, 0], [0, 0, 0]])
confidence = 0.8
scale = 10
e... | 3 | assert | numeric_literal | test/distributions/adjacency/test_gibbs_dag_prior.py | test_expert_graph_dataclass | 16 | null | |
microsoft/causica | import pytest
import torch
import torch.distributions as td
from tensordict import TensorDict
from causica.datasets.variable_types import VariableTypeEnum
from causica.distributions import JointNoiseModule, create_noise_modules
from causica.distributions.noise.joint import ContinuousNoiseDist
from causica.functional_r... | inferred_noise["x1"]) | assert_* | complex_expr | test/sem/test_sem.py | test_batched_intervention_batched_3d_graph | 220 | null | |
microsoft/causica | import pytest
import torch
from causica.triangular_transformations import fill_triangular, unfill_triangular
DIM = 5
@pytest.mark.parametrize("batch_size", [tuple(), (3,), (4, 2)])
def test_unfill_fill(batch_size):
"""Test that unfilling and filling results in the same tensor"""
vec = torch.randn(batch_size ... | torch.zeros_like(vec)) | assert_* | func_call | test/test_triangular_transformations.py | test_unfill_fill | 35 | null | |
microsoft/causica | import os
import tempfile
import pytest
import torch
from causica.datasets.causica_dataset_format import CAUSICA_DATASETS_PATH, DataEnum, load_data, save_data, save_dataset
from causica.datasets.tensordict_utils import tensordict_shapes
def _load_and_test_dataset(root_path: str):
variables_metadata = load_data(r... | cf_2[2] | assert | complex_expr | test/integration/test_save_load_csuite.py | test_load_save_counterfactuals | 101 | null | |
microsoft/causica | import math
import pytest
import torch
from causica.distributions.noise import CategoricalNoise
@pytest.mark.parametrize(
"base_logits,x_hat",
[
(0.0, torch.tensor([0.0, 0.0, 0.0])),
(1.0, torch.tensor([0.0, 0.0, 0.0])),
(0.0, torch.tensor([0.1, 0.5, 1.0])),
(1.0, torch.tensor... | eight_sigma | assert | variable | test/distributions/noise/test_categorical.py | test_noise_reconstruction | 56 | null |
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