Commit 77e87b60 authored by Robert Brekelmans's avatar Robert Brekelmans

Merge remote-tracking branch 'upstream/master' into old_primitive_names

master updat
parents c737fc88 3ae093a6
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"package_uri": "git+https://github.com/NewKnowledge/TimeSeries-D3M-Wrappers.git@b88cb8dd36d39cceebfdf18c930fb316dd12a6f9#egg=TimeSeriesD3MWrappers"
}
],
"python_path": "d3m.primitives.clustering.hdbscan.Hdbscan",
......@@ -241,6 +241,6 @@
}
},
"structural_type": "TimeSeriesD3MWrappers.Hdbscan.Hdbscan",
"description": "Produce primitive's best guess for the cluster number of each series using Hierarchical Density-Based\nClustering or Density-Based Clustering.\n\nAttributes\n----------\nmetadata : PrimitiveMetadata\n Primitive's metadata. Available as a class attribute.\nlogger : Logger\n Primitive's logger. Available as a class attribute.\nhyperparams : Hyperparams\n Hyperparams passed to the constructor.\nrandom_seed : int\n Random seed passed to the constructor.\ndocker_containers : Dict[str, DockerContainer]\n A dict mapping Docker image keys from primitive's metadata to (named) tuples containing\n container's address under which the container is accessible by the primitive, and a\n dict mapping exposed ports to ports on that address.\nvolumes : Dict[str, str]\n A dict mapping volume keys from primitive's metadata to file and directory paths\n where downloaded and extracted files are available to the primitive.\ntemporary_directory : str\n An absolute path to a temporary directory a primitive can use to store any files\n for the duration of the current pipeline run phase. Directory is automatically\n cleaned up after the current pipeline run phase finishes.",
"digest": "932b64fbb3cc079f76ea4eb2965aeedda0898f1a1eb207336bbd47d2ac704d9a"
}
\ No newline at end of file
"description": "Primitive that applies Hierarchical Density-Based Clustering or Density-Based Clustering\nalgorithms to time series data. This is an unsupervised, clustering primitive, but has been\nrepresentend as a supervised classification problem to produce a compliant primitive.\n\nTraining inputs: D3M dataset with features and labels, and D3M indices\nOutputs: D3M dataset with predicted labels and D3M indices\n\nAttributes\n----------\nmetadata : PrimitiveMetadata\n Primitive's metadata. Available as a class attribute.\nlogger : Logger\n Primitive's logger. Available as a class attribute.\nhyperparams : Hyperparams\n Hyperparams passed to the constructor.\nrandom_seed : int\n Random seed passed to the constructor.\ndocker_containers : Dict[str, DockerContainer]\n A dict mapping Docker image keys from primitive's metadata to (named) tuples containing\n container's address under which the container is accessible by the primitive, and a\n dict mapping exposed ports to ports on that address.\nvolumes : Dict[str, str]\n A dict mapping volume keys from primitive's metadata to file and directory paths\n where downloaded and extracted files are available to the primitive.\ntemporary_directory : str\n An absolute path to a temporary directory a primitive can use to store any files\n for the duration of the current pipeline run phase. Directory is automatically\n cleaned up after the current pipeline run phase finishes.",
"digest": "164c049d686e4a191505c6fd451f0a74dd1e6319793b31df8bdba9d6adc7c333"
}
......@@ -21,7 +21,7 @@
},
{
"type": "PIP",
"package_uri": "git+https://github.com/NewKnowledge/TimeSeries-D3M-Wrappers.git@a4e60264eab737d4d04e8e3f4792fa9067a3d142#egg=TimeSeriesD3MWrappers"
"package_uri": "git+https://github.com/NewKnowledge/TimeSeries-D3M-Wrappers.git@b88cb8dd36d39cceebfdf18c930fb316dd12a6f9#egg=TimeSeriesD3MWrappers"
}
],
"python_path": "d3m.primitives.clustering.k_means.Sloth",
......@@ -224,6 +224,6 @@
"params": {}
},
"structural_type": "TimeSeriesD3MWrappers.Storc.Storc",
"description": "Produce primitive's best guess for the cluster number of each series.\n\nAttributes\n----------\nmetadata : PrimitiveMetadata\n Primitive's metadata. Available as a class attribute.\nlogger : Logger\n Primitive's logger. Available as a class attribute.\nhyperparams : Hyperparams\n Hyperparams passed to the constructor.\nrandom_seed : int\n Random seed passed to the constructor.\ndocker_containers : Dict[str, DockerContainer]\n A dict mapping Docker image keys from primitive's metadata to (named) tuples containing\n container's address under which the container is accessible by the primitive, and a\n dict mapping exposed ports to ports on that address.\nvolumes : Dict[str, str]\n A dict mapping volume keys from primitive's metadata to file and directory paths\n where downloaded and extracted files are available to the primitive.\ntemporary_directory : str\n An absolute path to a temporary directory a primitive can use to store any files\n for the duration of the current pipeline run phase. Directory is automatically\n cleaned up after the current pipeline run phase finishes.",
"digest": "07126398909ef24cdb634526f7932a1e12f38d228fa35d68baf30ea8e2427de6"
}
\ No newline at end of file
"description": "Primitive that applies kmeans clustering to time series data. Algorithm options are 'GlobalAlignmentKernelKMeans'\nor 'TimeSeriesKMeans,' both of which are bootstrapped from the base library tslearn.clustering. This is an unsupervised,\nclustering primitive, but has been represented as a supervised classification problem to produce a compliant primitive.\n\nTraining inputs: D3M dataset with features and labels, and D3M indices\nOutputs: D3M dataset with predicted labels and D3M indices\n\nAttributes\n----------\nmetadata : PrimitiveMetadata\n Primitive's metadata. Available as a class attribute.\nlogger : Logger\n Primitive's logger. Available as a class attribute.\nhyperparams : Hyperparams\n Hyperparams passed to the constructor.\nrandom_seed : int\n Random seed passed to the constructor.\ndocker_containers : Dict[str, DockerContainer]\n A dict mapping Docker image keys from primitive's metadata to (named) tuples containing\n container's address under which the container is accessible by the primitive, and a\n dict mapping exposed ports to ports on that address.\nvolumes : Dict[str, str]\n A dict mapping volume keys from primitive's metadata to file and directory paths\n where downloaded and extracted files are available to the primitive.\ntemporary_directory : str\n An absolute path to a temporary directory a primitive can use to store any files\n for the duration of the current pipeline run phase. Directory is automatically\n cleaned up after the current pipeline run phase finishes.",
"digest": "f003dff2c915b6977f7c93f26a70e8498320f5253e7e236bd619a9886939c5ac"
}
{"id": "508e5cd3-9ba7-4cde-b58b-0bc49c0235a4", "schema": "https://metadata.datadrivendiscovery.org/schemas/v0/pipeline.json", "created": "2019-06-07T21:17:57.816220Z", "inputs": [{"name": "inputs"}], "outputs": [{"data": "steps.5.produce", "name": "output predictions"}], "steps": [{"type": "PRIMITIVE", "primitive": {"id": "f31f8c1f-d1c5-43e5-a4b2-2ae4a761ef2e", "version": "0.2.0", "python_path": "d3m.primitives.data_transformation.denormalize.Common", "name": "Denormalize datasets", "digest": "27d4ad4f968d5d8dc144d255c3ed7d2fc4f7931da6a13e2bb8e66f38eec24c72"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "inputs.0"}}, "outputs": [{"id": "produce"}]}, {"type": "PRIMITIVE", "primitive": {"id": "4b42ce1e-9b98-4a25-b68e-fad13311eb65", "version": "0.3.0", "python_path": "d3m.primitives.data_transformation.dataset_to_dataframe.Common", "name": "Extract a DataFrame from a Dataset", "digest": "79913c8489de1e8fa662b31cda560f8f8e3bcf55d1b8a25d053b1caa1c2b9599"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "steps.0.produce"}}, "outputs": [{"id": "produce"}], "hyperparams": {"dataframe_resource": {"type": "VALUE", "data": "learningData"}}}, {"type": "PRIMITIVE", "primitive": {"id": "fc6bf33a-f3e0-3496-aa47-9a40289661bc", "version": "3.0.1", "python_path": "d3m.primitives.data_cleaning.data_cleaning.Datacleaning", "name": "Data cleaning", "digest": "4b2ad84f9b1d4c906da37cd2c9ae5fc23c054a3e974be17ce6e75147d257cc3d"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "steps.1.produce"}}, "outputs": [{"id": "produce"}]}, {"type": "PRIMITIVE", "primitive": {"id": "d510cb7a-1782-4f51-b44c-58f0236e47c7", "version": "0.5.0", "python_path": "d3m.primitives.data_transformation.column_parser.DataFrameCommon", "name": "Parses strings into their types", "digest": "59fcf4780a9e65d34f5c358bb8242f105b5bc1d3bb65049c2a92c5a2db4a1900"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "steps.2.produce"}}, "outputs": [{"id": "produce"}]}, {"type": "PRIMITIVE", "primitive": {"id": "d016df89-de62-3c53-87ed-c06bb6a23cde", "version": "2019.4.4", "python_path": "d3m.primitives.data_cleaning.imputer.SKlearn", "name": "sklearn.impute.SimpleImputer", "digest": "8c36c71cc0018d0566008dd256748672a4d686bf00174f2c17ae063238be7b29"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "steps.3.produce"}}, "outputs": [{"id": "produce"}], "hyperparams": {"return_result": {"type": "VALUE", "data": "replace"}, "use_semantic_types": {"type": "VALUE", "data": true}}}, {"type": "PRIMITIVE", "primitive": {"id": "f9822847-d19f-40f9-8e23-3fdcd5dcb847", "version": "1.0.0", "python_path": "d3m.primitives.data_augmentation.data_conversion.FairnessInProcessing", "name": "In-processing Fairness Techniques", "digest": "b0ed11e750d35139359c91e038255b6ce6a9a451cd31f6a475c57725f21e97b1"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "steps.4.produce"}, "outputs": {"type": "CONTAINER", "data": "steps.4.produce"}}, "outputs": [{"id": "produce"}], "hyperparams": {"protected_attribute_cols": {"type": "VALUE", "data": [3]}, "favorable_label": {"type": "VALUE", "data": 0.0}}}], "digest": "5640204b2b46461ba4328f8e4aca22f26df35d7a998b11a2c3d79e6d884bb6b0"}
\ No newline at end of file
{
"problem": "uu5_heartstatlog_problem",
"full_inputs": [
"uu5_heartstatlog_dataset"
],
"train_inputs": [
"uu5_heartstatlog_dataset_TRAIN"
],
"test_inputs": [
"uu5_heartstatlog_dataset_TEST"
],
"score_inputs": [
"uu5_heartstatlog_dataset_SCORE"
]
}
{"id": "5757db0a-bbfd-4dea-8fd3-a87818213e7b", "schema": "https://metadata.datadrivendiscovery.org/schemas/v0/pipeline.json", "created": "2019-06-07T21:20:57.319530Z", "inputs": [{"name": "inputs"}], "outputs": [{"data": "steps.6.produce", "name": "output predictions"}], "steps": [{"type": "PRIMITIVE", "primitive": {"id": "f31f8c1f-d1c5-43e5-a4b2-2ae4a761ef2e", "version": "0.2.0", "python_path": "d3m.primitives.data_transformation.denormalize.Common", "name": "Denormalize datasets", "digest": "27d4ad4f968d5d8dc144d255c3ed7d2fc4f7931da6a13e2bb8e66f38eec24c72"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "inputs.0"}}, "outputs": [{"id": "produce"}]}, {"type": "PRIMITIVE", "primitive": {"id": "4b42ce1e-9b98-4a25-b68e-fad13311eb65", "version": "0.3.0", "python_path": "d3m.primitives.data_transformation.dataset_to_dataframe.Common", "name": "Extract a DataFrame from a Dataset", "digest": "79913c8489de1e8fa662b31cda560f8f8e3bcf55d1b8a25d053b1caa1c2b9599"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "steps.0.produce"}}, "outputs": [{"id": "produce"}], "hyperparams": {"dataframe_resource": {"type": "VALUE", "data": "learningData"}}}, {"type": "PRIMITIVE", "primitive": {"id": "fc6bf33a-f3e0-3496-aa47-9a40289661bc", "version": "3.0.1", "python_path": "d3m.primitives.data_cleaning.data_cleaning.Datacleaning", "name": "Data cleaning", "digest": "4b2ad84f9b1d4c906da37cd2c9ae5fc23c054a3e974be17ce6e75147d257cc3d"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "steps.1.produce"}}, "outputs": [{"id": "produce"}]}, {"type": "PRIMITIVE", "primitive": {"id": "d510cb7a-1782-4f51-b44c-58f0236e47c7", "version": "0.5.0", "python_path": "d3m.primitives.data_transformation.column_parser.DataFrameCommon", "name": "Parses strings into their types", "digest": "59fcf4780a9e65d34f5c358bb8242f105b5bc1d3bb65049c2a92c5a2db4a1900"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "steps.2.produce"}}, "outputs": [{"id": "produce"}]}, {"type": "PRIMITIVE", "primitive": {"id": "d016df89-de62-3c53-87ed-c06bb6a23cde", "version": "2019.4.4", "python_path": "d3m.primitives.data_cleaning.imputer.SKlearn", "name": "sklearn.impute.SimpleImputer", "digest": "8c36c71cc0018d0566008dd256748672a4d686bf00174f2c17ae063238be7b29"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "steps.3.produce"}}, "outputs": [{"id": "produce"}], "hyperparams": {"return_result": {"type": "VALUE", "data": "replace"}, "use_semantic_types": {"type": "VALUE", "data": true}}}, {"type": "PRIMITIVE", "primitive": {"id": "37c2b19d-bdab-4a30-ba08-6be49edcc6af", "version": "0.4.0", "python_path": "d3m.primitives.classification.random_forest.DataFrameCommon", "name": "Random forest classifier", "digest": "ba99c20e29777f150c0a5e50b3c8a7819e22f547f35612ce0a317206be06ed9d"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "steps.3.produce"}, "outputs": {"type": "CONTAINER", "data": "steps.3.produce"}}, "outputs": [{"id": "produce"}], "hyperparams": {"use_inputs_columns": {"type": "VALUE", "data": [2, 3, 4, 5, 6, 7]}, "use_outputs_columns": {"type": "VALUE", "data": [1]}}}, {"type": "PRIMITIVE", "primitive": {"id": "f29f6404-fbe5-4017-bb72-5c47efc5a415", "version": "1.0.0", "python_path": "d3m.primitives.data_augmentation.data_conversion.FairnessPostProcessing", "name": "Post-processing Fairness Techniques", "digest": "35be77ce8278aec972db2b2efff698d3355c1ff7870c5e0bbd0ceb3b5eceaea3"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "steps.5.produce"}, "outputs": {"type": "CONTAINER", "data": "steps.5.produce"}}, "outputs": [{"id": "produce"}], "hyperparams": {"algorithm": {"type": "VALUE", "data": "Reject_Option_Classification"}, "metric_name": {"type": "VALUE", "data": "Statistical parity difference"}, "low_class_threshold": {"type": "VALUE", "data": 0.4}, "high_class_threshold": {"type": "VALUE", "data": 0.6}, "protected_attribute_cols": {"type": "VALUE", "data": [3]}, "favorable_label": {"type": "VALUE", "data": 0.0}}}], "digest": "413d85011f133884e6734c1b41e48a6b1b871fe3d47ecedee47644afa074894c"}
\ No newline at end of file
{
"problem": "uu5_heartstatlog_problem",
"full_inputs": [
"uu5_heartstatlog_dataset"
],
"train_inputs": [
"uu5_heartstatlog_dataset_TRAIN"
],
"test_inputs": [
"uu5_heartstatlog_dataset_TEST"
],
"score_inputs": [
"uu5_heartstatlog_dataset_SCORE"
]
}
{"id": "559dee3f-cdd2-4b43-af11-22d8fa363f08", "schema": "https://metadata.datadrivendiscovery.org/schemas/v0/pipeline.json", "created": "2019-06-07T21:19:42.472292Z", "inputs": [{"name": "inputs"}], "outputs": [{"data": "steps.7.produce", "name": "output predictions"}], "steps": [{"type": "PRIMITIVE", "primitive": {"id": "f31f8c1f-d1c5-43e5-a4b2-2ae4a761ef2e", "version": "0.2.0", "python_path": "d3m.primitives.data_transformation.denormalize.Common", "name": "Denormalize datasets", "digest": "27d4ad4f968d5d8dc144d255c3ed7d2fc4f7931da6a13e2bb8e66f38eec24c72"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "inputs.0"}}, "outputs": [{"id": "produce"}]}, {"type": "PRIMITIVE", "primitive": {"id": "4b42ce1e-9b98-4a25-b68e-fad13311eb65", "version": "0.3.0", "python_path": "d3m.primitives.data_transformation.dataset_to_dataframe.Common", "name": "Extract a DataFrame from a Dataset", "digest": "79913c8489de1e8fa662b31cda560f8f8e3bcf55d1b8a25d053b1caa1c2b9599"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "steps.0.produce"}}, "outputs": [{"id": "produce"}], "hyperparams": {"dataframe_resource": {"type": "VALUE", "data": "learningData"}}}, {"type": "PRIMITIVE", "primitive": {"id": "fc6bf33a-f3e0-3496-aa47-9a40289661bc", "version": "3.0.1", "python_path": "d3m.primitives.data_cleaning.data_cleaning.Datacleaning", "name": "Data cleaning", "digest": "4b2ad84f9b1d4c906da37cd2c9ae5fc23c054a3e974be17ce6e75147d257cc3d"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "steps.1.produce"}}, "outputs": [{"id": "produce"}]}, {"type": "PRIMITIVE", "primitive": {"id": "d510cb7a-1782-4f51-b44c-58f0236e47c7", "version": "0.5.0", "python_path": "d3m.primitives.data_transformation.column_parser.DataFrameCommon", "name": "Parses strings into their types", "digest": "59fcf4780a9e65d34f5c358bb8242f105b5bc1d3bb65049c2a92c5a2db4a1900"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "steps.2.produce"}}, "outputs": [{"id": "produce"}]}, {"type": "PRIMITIVE", "primitive": {"id": "d016df89-de62-3c53-87ed-c06bb6a23cde", "version": "2019.4.4", "python_path": "d3m.primitives.data_cleaning.imputer.SKlearn", "name": "sklearn.impute.SimpleImputer", "digest": "8c36c71cc0018d0566008dd256748672a4d686bf00174f2c17ae063238be7b29"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "steps.3.produce"}}, "outputs": [{"id": "produce"}], "hyperparams": {"return_result": {"type": "VALUE", "data": "replace"}, "use_semantic_types": {"type": "VALUE", "data": true}}}, {"type": "PRIMITIVE", "primitive": {"id": "20736e8c-4f8c-484d-b128-33aa6fb20549", "version": "1.0.0", "python_path": "d3m.primitives.data_preprocessing.data_conversion.FairnessPreProcessing", "name": "Pre-processing Fairness Techniques", "digest": "c4e68c8784d9441b53aa442cdbfdf70a6dbb69a69c34f4ec833f8db9d44a6994"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "steps.4.produce"}}, "outputs": [{"id": "produce"}], "hyperparams": {"algorithm": {"type": "VALUE", "data": "Learning_Fair_Representations"}, "protected_attribute_cols": {"type": "VALUE", "data": [3]}, "favorable_label": {"type": "VALUE", "data": 0.0}}}, {"type": "PRIMITIVE", "primitive": {"id": "37c2b19d-bdab-4a30-ba08-6be49edcc6af", "version": "0.4.0", "python_path": "d3m.primitives.classification.random_forest.DataFrameCommon", "name": "Random forest classifier", "digest": "ba99c20e29777f150c0a5e50b3c8a7819e22f547f35612ce0a317206be06ed9d"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "steps.5.produce"}, "outputs": {"type": "CONTAINER", "data": "steps.5.produce"}}, "outputs": [{"id": "produce"}], "hyperparams": {"use_inputs_columns": {"type": "VALUE", "data": [2, 3, 4, 5, 6, 7]}, "use_outputs_columns": {"type": "VALUE", "data": [1]}}}, {"type": "PRIMITIVE", "primitive": {"id": "8d38b340-f83f-4877-baaa-162f8e551736", "version": "0.3.0", "python_path": "d3m.primitives.data_transformation.construct_predictions.DataFrameCommon", "name": "Construct pipeline predictions output", "digest": "0283172dd7988d1a1f2b71137c0aa08353b8b12b0aaa4c66a10831bc7f6fb95b"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "steps.6.produce"}, "reference": {"type": "CONTAINER", "data": "steps.1.produce"}}, "outputs": [{"id": "produce"}]}], "digest": "db716d41c19ad6ac3bdcfc869092ad95592d94bba12325a6993d524df61ee1ee"}
\ No newline at end of file
{
"problem": "uu5_heartstatlog_problem",
"full_inputs": [
"uu5_heartstatlog_dataset"
],
"train_inputs": [
"uu5_heartstatlog_dataset_TRAIN"
],
"test_inputs": [
"uu5_heartstatlog_dataset_TEST"
],
"score_inputs": [
"uu5_heartstatlog_dataset_SCORE"
]
}
......@@ -20,7 +20,7 @@
},
{
"type": "PIP",
"package_uri": "git+https://github.com/NewKnowledge/TimeSeries-D3M-Wrappers.git@a4e60264eab737d4d04e8e3f4792fa9067a3d142#egg=TimeSeriesD3MWrappers"
"package_uri": "git+https://github.com/NewKnowledge/TimeSeries-D3M-Wrappers.git@b88cb8dd36d39cceebfdf18c930fb316dd12a6f9#egg=TimeSeriesD3MWrappers"
}
],
"python_path": "d3m.primitives.time_series_classification.k_neighbors.Kanine",
......@@ -210,6 +210,6 @@
"params": {}
},
"structural_type": "TimeSeriesD3MWrappers.Kanine.Kanine",
"description": "Produce primitive's classifications for new time series data. The input is a numpy ndarray of\nsize (number_of_time_series, time_series_length) containing new time series.\nThe output is a numpy ndarray containing a predicted class for each of the input time series.\n\nAttributes\n----------\nmetadata : PrimitiveMetadata\n Primitive's metadata. Available as a class attribute.\nlogger : Logger\n Primitive's logger. Available as a class attribute.\nhyperparams : Hyperparams\n Hyperparams passed to the constructor.\nrandom_seed : int\n Random seed passed to the constructor.\ndocker_containers : Dict[str, DockerContainer]\n A dict mapping Docker image keys from primitive's metadata to (named) tuples containing\n container's address under which the container is accessible by the primitive, and a\n dict mapping exposed ports to ports on that address.\nvolumes : Dict[str, str]\n A dict mapping volume keys from primitive's metadata to file and directory paths\n where downloaded and extracted files are available to the primitive.\ntemporary_directory : str\n An absolute path to a temporary directory a primitive can use to store any files\n for the duration of the current pipeline run phase. Directory is automatically\n cleaned up after the current pipeline run phase finishes.",
"digest": "26e52bbfa6547bc4b608134a87b59f9889f981cfe77d29d2f7611a03517c8e22"
}
\ No newline at end of file
"description": "Primitive that applies the k nearest neighbor classification algorithm to time series data.\nThe tslearn KNeighborsTimeSeriesClassifier implementation is wrapped.\n\nTraining inputs: D3M dataset with features and labels, and D3M indices\nOutputs: D3M dataset with predicted labels and D3M indices\n\nAttributes\n----------\nmetadata : PrimitiveMetadata\n Primitive's metadata. Available as a class attribute.\nlogger : Logger\n Primitive's logger. Available as a class attribute.\nhyperparams : Hyperparams\n Hyperparams passed to the constructor.\nrandom_seed : int\n Random seed passed to the constructor.\ndocker_containers : Dict[str, DockerContainer]\n A dict mapping Docker image keys from primitive's metadata to (named) tuples containing\n container's address under which the container is accessible by the primitive, and a\n dict mapping exposed ports to ports on that address.\nvolumes : Dict[str, str]\n A dict mapping volume keys from primitive's metadata to file and directory paths\n where downloaded and extracted files are available to the primitive.\ntemporary_directory : str\n An absolute path to a temporary directory a primitive can use to store any files\n for the duration of the current pipeline run phase. Directory is automatically\n cleaned up after the current pipeline run phase finishes.",
"digest": "652c9e412f8413b934d2101f2f8b2fc750a1d090c7defec5bab6b8b5e52ca6d8"