id list | project string | origin_file list | test_list list | prob_info list | type list | node list | language string | toolfunc_count int64 | func_count int64 | pytest_info dict |
|---|---|---|---|---|---|---|---|---|---|---|
[
"finam.src.finam.sdk.output.Output::push_info",
"finam.src.finam.sdk.component.IOList::add",
"finam.src.finam.data.tools.mask.mask_specified",
"finam.src.finam.data.tools.mask.masks_compatible",
"finam.src.finam.data.tools.info.Info::accepts"
] | finam | [
"finam/sdk/output.py",
"finam/sdk/output.py",
"finam/sdk/component.py",
"finam/data/tools/mask.py",
"finam/data/tools/info.py",
"finam/data/tools/mask.py",
"finam/data/tools/info.py"
] | [
"tests/components/test_control.py",
"tests/components/test_parametric.py",
"tests/core/test_units.py"
] | [
{
"class_start_lineno": 25,
"class_end_lineno": 461,
"func_start_lineno": 204,
"func_end_lineno": 216,
"func_code": " def push_info(self, info):\n \"\"\"Push data info into the output.\n\n Parameters\n ----------\n info : :class:`.Info`\n Delivered data ... | [
"function_empty"
] | [
"finam.sdk.output.Output.push_info",
"finam.sdk.output.Output.__init__",
"finam.sdk.component.IOList.add",
"finam.data.tools.mask.mask_specified",
"finam.data.tools.info.Info.mask",
"finam.data.tools.mask.masks_compatible",
"finam.data.tools.info.Info.accepts"
] | Python | 5 | 5 | {
"total_num": 16,
"base_passed_num": 1
} |
[
"finam.src.finam.data.grid_tools.gen_axes",
"finam.src.finam.data.grid_spec.EsriGrid::to_uniform",
"finam.src.finam.data.grid_tools.prepare_vtk_data",
"finam.src.finam.data.grid_tools.prepare_vtk_kwargs",
"finam.src.finam.data.grid_spec.UniformGrid::export_vtk"
] | finam | [
"finam/data/grid_tools.py",
"finam/data/grid_spec.py",
"finam/data/grid_spec.py",
"finam/data/grid_tools.py",
"finam/data/grid_tools.py",
"finam/data/grid_spec.py"
] | [
"tests/data/test_grid_spec.py"
] | [
{
"class_start_lineno": 1,
"class_end_lineno": 526,
"func_start_lineno": 78,
"func_end_lineno": 107,
"func_code": "def gen_axes(dims, spacing, origin, axes_increase=None):\n \"\"\"\n Generate uniform axes.\n\n Parameters\n ----------\n dims : iterable\n Dimensions of the un... | [
"function_empty",
"TDD"
] | [
"finam.data.grid_tools.gen_axes",
"finam.data.grid_spec.UniformGrid.__init__",
"finam.data.grid_spec.EsriGrid.to_uniform",
"finam.data.grid_tools.prepare_vtk_data",
"finam.data.grid_tools.prepare_vtk_kwargs",
"finam.data.grid_spec.UniformGrid.export_vtk"
] | Python | 2 | 5 | {
"total_num": 10,
"base_passed_num": 2
} |
[
"skfolio.src.skfolio.datasets._base.load_gzip_compressed_csv_data",
"skfolio.src.skfolio.datasets._base.load_sp500_dataset",
"skfolio.src.skfolio.utils.stats.assert_is_square",
"skfolio.src.skfolio.utils.stats.assert_is_symmetric",
"skfolio.src.skfolio.utils.stats.assert_is_distance"
] | skfolio | [
"skfolio/datasets/_base.py",
"skfolio/datasets/_base.py",
"skfolio/utils/stats.py",
"skfolio/utils/stats.py",
"skfolio/utils/stats.py"
] | [
"tests/test_cluster/test_hierarchical.py"
] | [
{
"class_start_lineno": 1,
"class_end_lineno": 448,
"func_start_lineno": 71,
"func_end_lineno": 113,
"func_code": "def load_gzip_compressed_csv_data(\n data_filename: str,\n data_module: str = DATA_MODULE,\n encoding=\"utf-8\",\n datetime_index: bool = True,\n) -> pd.DataFrame:\n ... | [
"function_empty"
] | [
"skfolio.datasets._base.load_gzip_compressed_csv_data",
"skfolio.datasets._base.load_sp500_dataset",
"skfolio.utils.stats.assert_is_square",
"skfolio.utils.stats.assert_is_symmetric",
"skfolio.utils.stats.assert_is_distance"
] | Python | 5 | 5 | {
"total_num": 65,
"base_passed_num": 0
} |
[
"skfolio.src.skfolio.datasets._base.load_gzip_compressed_csv_data",
"skfolio.src.skfolio.datasets._base.load_sp500_dataset",
"skfolio.src.skfolio.utils.stats.assert_is_square",
"skfolio.src.skfolio.utils.stats.assert_is_symmetric",
"skfolio.src.skfolio.utils.stats.cov_nearest"
] | skfolio | [
"skfolio/datasets/_base.py",
"skfolio/datasets/_base.py",
"skfolio/utils/stats.py",
"skfolio/utils/stats.py",
"skfolio/utils/stats.py"
] | [
"tests/test_distance/test_distance.py",
"tests/test_metrics/test_scorer.py",
"tests/test_moment/test_expected_returns/test_expected_returns.py"
] | [
{
"class_start_lineno": 1,
"class_end_lineno": 448,
"func_start_lineno": 71,
"func_end_lineno": 113,
"func_code": "def load_gzip_compressed_csv_data(\n data_filename: str,\n data_module: str = DATA_MODULE,\n encoding=\"utf-8\",\n datetime_index: bool = True,\n) -> pd.DataFrame:\n ... | [
"function_empty"
] | [
"skfolio.datasets._base.load_gzip_compressed_csv_data",
"skfolio.datasets._base.load_sp500_dataset",
"skfolio.utils.stats.assert_is_square",
"skfolio.utils.stats.assert_is_symmetric",
"skfolio.utils.stats.cov_nearest"
] | Python | 5 | 5 | {
"total_num": 26,
"base_passed_num": 6
} |
[
"skfolio.src.skfolio.distribution.copula._clayton._base_sample_scores",
"skfolio.src.skfolio.distribution.copula._clayton._neg_log_likelihood",
"skfolio.src.skfolio.distribution.copula._utils._apply_copula_rotation",
"skfolio.src.skfolio.distribution.copula._utils._apply_margin_swap",
"skfolio.src.skfolio.d... | skfolio | [
"skfolio/distribution/copula/_clayton.py",
"skfolio/distribution/copula/_clayton.py",
"skfolio/distribution/copula/_utils.py",
"skfolio/distribution/copula/_utils.py",
"skfolio/distribution/copula/_clayton.py",
"skfolio/distribution/copula/_utils.py"
] | [
"tests/test_distribution/test_copula/test_clayton.py"
] | [
{
"class_start_lineno": 1,
"class_end_lineno": 539,
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"func_end_lineno": 448,
"func_code": "def _base_sample_scores(X: np.ndarray, theta: float) -> np.ndarray:\n r\"\"\"Compute the log-likelihood of each sample (log-pdf) under the bivariate Clayton\n copula.\n\n ... | [
"function_empty",
"TDD"
] | [
"skfolio.distribution.copula._clayton._base_sample_scores",
"skfolio.distribution.copula._clayton._neg_log_likelihood",
"skfolio.distribution.copula._utils._apply_copula_rotation",
"skfolio.distribution.copula._utils._apply_margin_swap",
"skfolio.distribution.copula._clayton._base_partial_derivative",
"sk... | Python | 2 | 6 | {
"total_num": 69,
"base_passed_num": 5
} |
[
"skfolio.src.skfolio.distribution.copula._gaussian._base_sample_scores",
"skfolio.src.skfolio.distribution.copula._gaussian._neg_log_likelihood"
] | skfolio | [
"skfolio/distribution/copula/_gaussian.py",
"skfolio/distribution/copula/_gaussian.py"
] | [
"tests/test_distribution/test_copula/test_gaussian.py"
] | [
{
"class_start_lineno": 1,
"class_end_lineno": 407,
"func_start_lineno": 373,
"func_end_lineno": 407,
"func_code": "def _base_sample_scores(X: np.ndarray, rho: float) -> np.ndarray:\n \"\"\"Compute the log-likelihood of each sample (log-pdf) under the bivariate\n Gaussian copula model.\n\n... | [
"function_empty",
"TDD"
] | [
"skfolio.distribution.copula._gaussian._base_sample_scores",
"skfolio.distribution.copula._gaussian._neg_log_likelihood"
] | Python | 1 | 2 | {
"total_num": 38,
"base_passed_num": 26
} |
[
"skfolio.src.skfolio.distribution.copula._gumbel._base_sample_scores",
"skfolio.src.skfolio.distribution.copula._gumbel._neg_log_likelihood",
"skfolio.src.skfolio.distribution.copula._utils._apply_copula_rotation",
"skfolio.src.skfolio.distribution.copula._utils._apply_margin_swap",
"skfolio.src.skfolio.dis... | skfolio | [
"skfolio/distribution/copula/_gumbel.py",
"skfolio/distribution/copula/_gumbel.py",
"skfolio/distribution/copula/_utils.py",
"skfolio/distribution/copula/_utils.py",
"skfolio/distribution/copula/_gumbel.py",
"skfolio/distribution/copula/_utils.py"
] | [
"tests/test_distribution/test_copula/test_gumbel.py"
] | [
{
"class_start_lineno": 1,
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"func_code": "def _base_sample_scores(X: np.ndarray, theta: float) -> np.ndarray:\n r\"\"\"Compute the log-likelihood of each sample (log-pdf) under the bivariate Gumbel\n copula.\n\n P... | [
"function_empty",
"TDD"
] | [
"skfolio.distribution.copula._gumbel._base_sample_scores",
"skfolio.distribution.copula._gumbel._neg_log_likelihood",
"skfolio.distribution.copula._utils._apply_copula_rotation",
"skfolio.distribution.copula._utils._apply_margin_swap",
"skfolio.distribution.copula._gumbel._base_partial_derivative",
"skfol... | Python | 2 | 6 | {
"total_num": 69,
"base_passed_num": 5
} |
[
"skfolio.src.skfolio.distribution.copula._joe._base_sample_scores",
"skfolio.src.skfolio.distribution.copula._joe._neg_log_likelihood",
"skfolio.src.skfolio.distribution.copula._utils._apply_copula_rotation",
"skfolio.src.skfolio.distribution.copula._utils._apply_margin_swap",
"skfolio.src.skfolio.distribut... | skfolio | [
"skfolio/distribution/copula/_joe.py",
"skfolio/distribution/copula/_joe.py",
"skfolio/distribution/copula/_utils.py",
"skfolio/distribution/copula/_utils.py",
"skfolio/distribution/copula/_joe.py",
"skfolio/distribution/copula/_utils.py"
] | [
"tests/test_distribution/test_copula/test_joe.py"
] | [
{
"class_start_lineno": 1,
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"func_start_lineno": 439,
"func_end_lineno": 473,
"func_code": "def _base_sample_scores(X: np.ndarray, theta: float) -> np.ndarray:\n \"\"\"Compute the log-likelihood of each sample (log-pdf) under the bivariate\n Joe copula model.\n\n ... | [
"function_empty",
"TDD"
] | [
"skfolio.distribution.copula._joe._base_sample_scores",
"skfolio.distribution.copula._joe._neg_log_likelihood",
"skfolio.distribution.copula._utils._apply_copula_rotation",
"skfolio.distribution.copula._utils._apply_margin_swap",
"skfolio.distribution.copula._joe._base_partial_derivative",
"skfolio.distri... | Python | 3 | 6 | {
"total_num": 69,
"base_passed_num": 5
} |
[
"skfolio.src.skfolio.distribution.copula._clayton._base_sample_scores",
"skfolio.src.skfolio.distribution.copula._clayton._neg_log_likelihood"
] | skfolio | [
"skfolio/distribution/copula/_clayton.py",
"skfolio/distribution/copula/_clayton.py"
] | [
"tests/test_distribution/test_copula/test_selection.py"
] | [
{
"class_start_lineno": 1,
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"func_code": "def _base_sample_scores(X: np.ndarray, theta: float) -> np.ndarray:\n r\"\"\"Compute the log-likelihood of each sample (log-pdf) under the bivariate Clayton\n copula.\n\n ... | [
"function_empty",
"TDD"
] | [
"skfolio.distribution.copula._clayton._base_sample_scores",
"skfolio.distribution.copula._clayton._neg_log_likelihood"
] | Python | 1 | 2 | {
"total_num": 4,
"base_passed_num": 3
} |
[
"skfolio.src.skfolio.distribution.copula._student_t._sample_scores",
"skfolio.src.skfolio.distribution.copula._student_t._neg_log_likelihood"
] | skfolio | [
"skfolio/distribution/copula/_student_t.py",
"skfolio/distribution/copula/_student_t.py"
] | [
"tests/test_distribution/test_copula/test_student_t.py"
] | [
{
"class_start_lineno": 1,
"class_end_lineno": 486,
"func_start_lineno": 445,
"func_end_lineno": 486,
"func_code": "def _sample_scores(X: np.ndarray, rho: float, dof: float) -> np.ndarray:\n \"\"\"Compute the log-likelihood of each sample (log-pdf) under the bivariate\n Gaussian copula mod... | [
"function_empty",
"TDD"
] | [
"skfolio.distribution.copula._student_t._sample_scores",
"skfolio.distribution.copula._student_t._neg_log_likelihood"
] | Python | 1 | 2 | {
"total_num": 40,
"base_passed_num": 17
} |
[
"skfolio.src.skfolio.distribution.copula._utils._apply_copula_rotation",
"skfolio.src.skfolio.distribution.copula._utils._apply_rotation_cdf",
"skfolio.src.skfolio.distribution.copula._utils._apply_rotation_partial_derivatives"
] | skfolio | [
"skfolio/distribution/copula/_utils.py",
"skfolio/distribution/copula/_utils.py",
"skfolio/distribution/copula/_utils.py"
] | [
"tests/test_distribution/test_copula/test_utils.py"
] | [
{
"class_start_lineno": 1,
"class_end_lineno": 509,
"func_start_lineno": 341,
"func_end_lineno": 380,
"func_code": "def _apply_copula_rotation(X: npt.ArrayLike, rotation: CopulaRotation) -> np.ndarray:\n r\"\"\"Apply a bivariate copula rotation using the standard (clockwise) convention.\n\n ... | [
"function_empty",
"TDD"
] | [
"skfolio.distribution.copula._utils._apply_copula_rotation",
"skfolio.distribution.copula._utils._apply_rotation_cdf",
"skfolio.distribution.copula._utils._apply_rotation_partial_derivatives"
] | Python | 2 | 3 | {
"total_num": 10,
"base_passed_num": 6
} |
[
"skfolio.src.skfolio.datasets._base.load_gzip_compressed_csv_data",
"skfolio.src.skfolio.datasets._base.load_sp500_dataset"
] | skfolio | [
"skfolio/datasets/_base.py",
"skfolio/datasets/_base.py"
] | [
"tests/test_distribution/test_multivariate/test_utils.py",
"tests/test_model_selection/test_walk_forward.py",
"tests/test_utils/test_bootstrap.py",
"tests/test_utils/test_validation.py"
] | [
{
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"func_code": "def load_gzip_compressed_csv_data(\n data_filename: str,\n data_module: str = DATA_MODULE,\n encoding=\"utf-8\",\n datetime_index: bool = True,\n) -> pd.DataFrame:\n ... | [
"function_empty"
] | [
"skfolio.datasets._base.load_gzip_compressed_csv_data",
"skfolio.datasets._base.load_sp500_dataset"
] | Python | 2 | 2 | {
"total_num": 24,
"base_passed_num": 5
} |
[
"skfolio.src.skfolio.datasets._base.load_gzip_compressed_csv_data",
"skfolio.src.skfolio.datasets._base.load_sp500_dataset",
"skfolio.src.skfolio.measures._measures.get_cumulative_returns",
"skfolio.src.skfolio.measures._measures.get_drawdowns"
] | skfolio | [
"skfolio/datasets/_base.py",
"skfolio/datasets/_base.py",
"skfolio/measures/_measures.py",
"skfolio/measures/_measures.py"
] | [
"tests/test_measures/test_measures.py"
] | [
{
"class_start_lineno": 1,
"class_end_lineno": 448,
"func_start_lineno": 71,
"func_end_lineno": 113,
"func_code": "def load_gzip_compressed_csv_data(\n data_filename: str,\n data_module: str = DATA_MODULE,\n encoding=\"utf-8\",\n datetime_index: bool = True,\n) -> pd.DataFrame:\n ... | [
"function_empty",
"TDD"
] | [
"skfolio.datasets._base.load_gzip_compressed_csv_data",
"skfolio.datasets._base.load_sp500_dataset",
"skfolio.measures._measures.get_cumulative_returns",
"skfolio.measures._measures.get_drawdowns"
] | Python | 2 | 4 | {
"total_num": 17,
"base_passed_num": 0
} |
[
"skfolio.src.skfolio.model_selection._combinatorial._n_splits",
"skfolio.src.skfolio.model_selection._combinatorial._n_test_paths"
] | skfolio | [
"skfolio/model_selection/_combinatorial.py",
"skfolio/model_selection/_combinatorial.py",
"skfolio/model_selection/_combinatorial.py"
] | [
"tests/test_model_selection/test_combinatorial.py"
] | [
{
"class_start_lineno": 1,
"class_end_lineno": 564,
"func_start_lineno": 415,
"func_end_lineno": 431,
"func_code": "def _n_splits(n_folds: int, n_test_folds: int) -> int:\n \"\"\"Number of splits.\n\n Parameters\n ----------\n n_folds : int\n Number of folds.\n\n n_test_fol... | [
"function_empty"
] | [
"skfolio.model_selection._combinatorial._n_splits",
"skfolio.model_selection._combinatorial._n_test_paths",
"skfolio.model_selection._combinatorial.CombinatorialPurgedCV.n_test_paths"
] | Python | 2 | 2 | {
"total_num": 8,
"base_passed_num": 0
} |
[
"skfolio.src.skfolio.datasets._base.load_gzip_compressed_csv_data",
"skfolio.src.skfolio.datasets._base.load_sp500_dataset",
"skfolio.src.skfolio.utils.tools.safe_indexing",
"skfolio.src.skfolio.utils.tools.safe_split",
"skfolio.src.skfolio.model_selection._validation.cross_val_predict"
] | skfolio | [
"skfolio/datasets/_base.py",
"skfolio/datasets/_base.py",
"skfolio/utils/tools.py",
"skfolio/utils/tools.py",
"skfolio/model_selection/_validation.py"
] | [
"tests/test_model_selection/test_validation.py"
] | [
{
"class_start_lineno": 1,
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"func_start_lineno": 71,
"func_end_lineno": 113,
"func_code": "def load_gzip_compressed_csv_data(\n data_filename: str,\n data_module: str = DATA_MODULE,\n encoding=\"utf-8\",\n datetime_index: bool = True,\n) -> pd.DataFrame:\n ... | [
"function_empty",
"TDD"
] | [
"skfolio.datasets._base.load_gzip_compressed_csv_data",
"skfolio.datasets._base.load_sp500_dataset",
"skfolio.utils.tools.safe_indexing",
"skfolio.utils.tools.safe_split",
"skfolio.model_selection._validation.cross_val_predict"
] | Python | 3 | 5 | {
"total_num": 3,
"base_passed_num": 0
} |
[
"skfolio.src.skfolio.datasets._base.load_gzip_compressed_csv_data",
"skfolio.src.skfolio.datasets._base.load_sp500_dataset",
"skfolio.src.skfolio.datasets._base.get_data_home",
"skfolio.src.skfolio.datasets._base.download_dataset",
"skfolio.src.skfolio.datasets._base.load_sp500_implied_vol_dataset"
] | skfolio | [
"skfolio/datasets/_base.py",
"skfolio/datasets/_base.py",
"skfolio/datasets/_base.py",
"skfolio/datasets/_base.py",
"skfolio/datasets/_base.py"
] | [
"tests/test_moment/test_covariance/test_implied_covariance.py"
] | [
{
"class_start_lineno": 1,
"class_end_lineno": 448,
"func_start_lineno": 71,
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"func_code": "def load_gzip_compressed_csv_data(\n data_filename: str,\n data_module: str = DATA_MODULE,\n encoding=\"utf-8\",\n datetime_index: bool = True,\n) -> pd.DataFrame:\n ... | [
"function_empty"
] | [
"skfolio.datasets._base.load_gzip_compressed_csv_data",
"skfolio.datasets._base.load_sp500_dataset",
"skfolio.datasets._base.get_data_home",
"skfolio.datasets._base.download_dataset",
"skfolio.datasets._base.load_sp500_implied_vol_dataset"
] | Python | 5 | 5 | {
"total_num": 25,
"base_passed_num": 0
} |
[
"skfolio.src.skfolio.datasets._base.load_gzip_compressed_csv_data",
"skfolio.src.skfolio.datasets._base.load_sp500_dataset",
"skfolio.src.skfolio.optimization.cluster._nco.NestedClustersOptimization::get_metadata_routing",
"skfolio.src.skfolio.optimization.cluster._nco.NestedClustersOptimization::fit"
] | skfolio | [
"skfolio/datasets/_base.py",
"skfolio/datasets/_base.py",
"skfolio/optimization/cluster/_nco.py",
"skfolio/optimization/cluster/_nco.py"
] | [
"tests/test_optimization/test_cluster/test_nco.py"
] | [
{
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"func_end_lineno": 113,
"func_code": "def load_gzip_compressed_csv_data(\n data_filename: str,\n data_module: str = DATA_MODULE,\n encoding=\"utf-8\",\n datetime_index: bool = True,\n) -> pd.DataFrame:\n ... | [
"function_empty",
"TDD"
] | [
"skfolio.datasets._base.load_gzip_compressed_csv_data",
"skfolio.datasets._base.load_sp500_dataset",
"skfolio.optimization.cluster._nco.NestedClustersOptimization.get_metadata_routing",
"skfolio.optimization.cluster._nco.NestedClustersOptimization.fit"
] | Python | 3 | 4 | {
"total_num": 15,
"base_passed_num": 0
} |
[
"skfolio.src.skfolio.datasets._base.load_gzip_compressed_csv_data",
"skfolio.src.skfolio.datasets._base.load_sp500_dataset",
"skfolio.src.skfolio.prior._empirical.EmpiricalPrior::get_metadata_routing",
"skfolio.src.skfolio.prior._empirical.EmpiricalPrior::fit"
] | skfolio | [
"skfolio/datasets/_base.py",
"skfolio/datasets/_base.py",
"skfolio/prior/_empirical.py",
"skfolio/prior/_empirical.py"
] | [
"tests/test_optimization/test_cluster/test_hierarchical/test_herc.py",
"tests/test_optimization/test_cluster/test_hierarchical/test_hrp.py",
"tests/test_optimization/test_convex/test_maximum_diversification.py",
"tests/test_optimization/test_convex/test_risk_budgeting.py",
"tests/test_prior/test_empirical.p... | [
{
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"func_code": "def load_gzip_compressed_csv_data(\n data_filename: str,\n data_module: str = DATA_MODULE,\n encoding=\"utf-8\",\n datetime_index: bool = True,\n) -> pd.DataFrame:\n ... | [
"function_empty",
"TDD"
] | [
"skfolio.datasets._base.load_gzip_compressed_csv_data",
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"skfolio.prior._empirical.EmpiricalPrior.get_metadata_routing",
"skfolio.prior._empirical.EmpiricalPrior.fit"
] | Python | 3 | 4 | {
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] | Python | 3 | 4 | {
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