From d0e2ecfa20d6193389bbc36844ec540f431087a3 Mon Sep 17 00:00:00 2001 From: David Sullivan <davesullivan41@gmail.com> Date: Mon, 25 Nov 2019 09:03:21 -0600 Subject: [PATCH 1/3] Add files for d3m.primitives.data_preprocessing.data_conversion.FairnessPreProcessing --- .../pipeline_runs/uu5_heartstatlog.yml.gz | Bin 0 -> 6981 bytes .../1b6f69a5-022a-4ca0-b11d-93dc388329f4.json | 1 + .../1.0.0/primitive.json | 208 ++++++++++++++++++ 3 files changed, 209 insertions(+) create mode 100644 v2019.11.10/Distil/d3m.primitives.data_preprocessing.data_conversion.FairnessPreProcessing/1.0.0/pipeline_runs/uu5_heartstatlog.yml.gz create mode 100644 v2019.11.10/Distil/d3m.primitives.data_preprocessing.data_conversion.FairnessPreProcessing/1.0.0/pipelines/1b6f69a5-022a-4ca0-b11d-93dc388329f4.json create mode 100644 v2019.11.10/Distil/d3m.primitives.data_preprocessing.data_conversion.FairnessPreProcessing/1.0.0/primitive.json diff --git 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"Denormalize datasets", "digest": "fa376cea4c03e06d896d6cce68f0a18661bbbb9ebe30653ea739785eedc02198"}, "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": "bddea02d001c6633722c14643ec2a065fb4a977354ddbdf74282d076da77e530"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "steps.0.produce"}}, "outputs": [{"id": "produce"}], "hyperparams": {"dataframe_resource": {"type": "VALUE", "data": "learningData"}}}, {"type": "PRIMITIVE", "primitive": {"id": "3b09ba74-cc90-4f22-9e0a-0cf4f29a7e28", "version": "0.1.0", "python_path": "d3m.primitives.data_transformation.remove_columns.Common", "name": "Removes columns", "digest": "48221645501106d194c96172f28d209cfd95ecc7cbb1fc520d26bd5e0eacbeee"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "steps.1.produce"}}, "outputs": [{"id": "produce"}], "hyperparams": {"columns": {"type": "VALUE", "data": [8, 9, 10, 11, 12, 13, 14]}}}, {"type": "PRIMITIVE", "primitive": {"id": "fc6bf33a-f3e0-3496-aa47-9a40289661bc", "version": "3.0.2", "python_path": "d3m.primitives.data_cleaning.data_cleaning.Datacleaning", "name": "Data cleaning", "digest": "86f391bddc57bcab2d5af1728f442e7469298425a6678dc004312ed4a470f4d8"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "steps.2.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.Common", "name": "Parses strings into their types", "digest": "c162d57bc73b6f30a0d600af31136b5028fe0efcd852efc15b9ad2826a2f391f"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "steps.3.produce"}}, "outputs": [{"id": "produce"}]}, {"type": "PRIMITIVE", "primitive": {"id": "d016df89-de62-3c53-87ed-c06bb6a23cde", "version": "2019.6.7", "python_path": "d3m.primitives.data_cleaning.imputer.SKlearn", "name": "sklearn.impute.SimpleImputer", "digest": "adc79e644eec35eb9d616be755a5de83b27f66e42b04f6508a9ceb82d99cc739"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "steps.4.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": "2b99d1c79d46d01edc306175344ede2770190f5f70cfc8d52c7732c6e46bcac8"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "steps.5.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.Common", "name": "Random forest classifier", "digest": "8f296d60a3d31b77f3a6b34cd0cdb698fb3894ef1605db56d85a561d19201f26"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "steps.6.produce"}, "outputs": {"type": "CONTAINER", "data": "steps.6.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.Common", "name": "Construct pipeline predictions output", "digest": "5144bad4fea16168f6667c991e137067721dd0573c68ab1bf172ead9e2c82869"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "steps.7.produce"}, "reference": {"type": "CONTAINER", "data": "steps.1.produce"}}, "outputs": [{"id": "produce"}]}], "digest": "c0ba0874f2f36eaa8660da7606f09e2c9bb7e99f36a578f406fb3bcb22f10208"} \ No newline at end of file diff --git a/v2019.11.10/Distil/d3m.primitives.data_preprocessing.data_conversion.FairnessPreProcessing/1.0.0/primitive.json b/v2019.11.10/Distil/d3m.primitives.data_preprocessing.data_conversion.FairnessPreProcessing/1.0.0/primitive.json new file mode 100644 index 0000000000..47367befaf --- /dev/null +++ b/v2019.11.10/Distil/d3m.primitives.data_preprocessing.data_conversion.FairnessPreProcessing/1.0.0/primitive.json @@ -0,0 +1,208 @@ +{ + "id": "20736e8c-4f8c-484d-b128-33aa6fb20549", + "version": "1.0.0", + "name": "Pre-processing Fairness Techniques", + "keywords": [ + "fairness, bias, debias, data preprocessing, data augmentation" + ], + "source": { + "name": "Distil", + "contact": "mailto:nklabs@newknowledge.com", + "uris": [ + "https://github.com/NewKnowledge/D3M-Fairness-Primitives" + ] + }, + "installation": [ + { + "type": "PIP", + "package_uri": "git+https://github.com/NewKnowledge/D3M-Fairness-Primitives.git@7a48c3d0b1750a5942c4ba21d89c4e55557a3625#egg=FairnessPrimitives" + } + ], + "python_path": "d3m.primitives.data_preprocessing.data_conversion.FairnessPreProcessing", + "algorithm_types": [ + "DATA_CONVERSION" + ], + "primitive_family": "DATA_PREPROCESSING", + "schema": "https://metadata.datadrivendiscovery.org/schemas/v0/primitive.json", + "original_python_path": "FairnessPrimitives.pre_processing.FairnessPreProcessing", + "primitive_code": { + "class_type_arguments": { + "Inputs": "d3m.container.pandas.DataFrame", + "Outputs": "d3m.container.pandas.DataFrame", + "Hyperparams": "FairnessPrimitives.pre_processing.Hyperparams", + "Params": "NoneType" + }, + "interfaces_version": "2019.11.10", + "interfaces": [ + "transformer.TransformerPrimitiveBase", + "base.PrimitiveBase" + ], + "hyperparams": { + "algorithm": { + "type": "d3m.metadata.hyperparams.Enumeration", + "default": "Disparate_Impact_Remover", + "structural_type": "str", + "semantic_types": [ + "https://metadata.datadrivendiscovery.org/types/ControlParameter" + ], + "description": "type of fairness pre-processing algorithm to use", + "values": [ + "Disparate_Impact_Remover", + "Learning_Fair_Representations", + "Reweighing" + ] + }, + "protected_attribute_cols": { + "type": "d3m.metadata.hyperparams.List", + "default": [], + "structural_type": "typing.Sequence[int]", + "semantic_types": [ + "https://metadata.datadrivendiscovery.org/types/ControlParameter" + ], + "description": "A set of column indices to use as protected attributes.", + "elements": { + "type": "d3m.metadata.hyperparams.Hyperparameter", + "default": -1, + "structural_type": "int", + "semantic_types": [] + }, + "is_configuration": false, + "min_size": 0 + }, + "favorable_label": { + "type": "d3m.metadata.hyperparams.Bounded", + "default": 1.0, + "structural_type": "float", + "semantic_types": [ + "https://metadata.datadrivendiscovery.org/types/ControlParameter" + ], + "description": "label value which is considered favorable (i.e. positive) in the binary label case", + "lower": 0.0, + "upper": 1.0, + "lower_inclusive": true, + "upper_inclusive": true + } + }, + "arguments": { + "hyperparams": { + "type": "FairnessPrimitives.pre_processing.Hyperparams", + "kind": "RUNTIME" + }, + "random_seed": { + "type": "int", + "kind": "RUNTIME", + "default": 0 + }, + "timeout": { + "type": "typing.Union[NoneType, float]", + "kind": "RUNTIME", + "default": null + }, + "iterations": { + "type": "typing.Union[NoneType, int]", + "kind": "RUNTIME", + "default": null + }, + "produce_methods": { + "type": "typing.Sequence[str]", + "kind": "RUNTIME" + }, + "inputs": { + "type": "d3m.container.pandas.DataFrame", + "kind": "PIPELINE" + }, + "params": { + "type": "NoneType", + "kind": "RUNTIME" + } + }, + "class_methods": {}, + "instance_methods": { + "__init__": { + "kind": "OTHER", + "arguments": [ + "hyperparams", + "random_seed" + ], + "returns": "NoneType" + }, + "fit": { + "kind": "OTHER", + "arguments": [ + "timeout", + "iterations" + ], + "returns": "d3m.primitive_interfaces.base.CallResult[NoneType]", + "description": "A noop.\n\nParameters\n----------\ntimeout : float\n A maximum time this primitive should be fitting during this method call, in seconds.\niterations : int\n How many of internal iterations should the primitive do.\n\nReturns\n-------\nCallResult[None]\n A ``CallResult`` with ``None`` value." + }, + "fit_multi_produce": { + "kind": "OTHER", + "arguments": [ + "produce_methods", + "inputs", + "timeout", + "iterations" + ], + "returns": "d3m.primitive_interfaces.base.MultiCallResult", + "description": "A method calling ``fit`` and after that multiple produce methods at once.\n\nParameters\n----------\nproduce_methods : Sequence[str]\n A list of names of produce methods to call.\ninputs : Inputs\n The inputs given to all produce methods.\ntimeout : float\n A maximum time this primitive should take to both fit the primitive and produce outputs\n for all produce methods listed in ``produce_methods`` argument, in seconds.\niterations : int\n How many of internal iterations should the primitive do for both fitting and producing\n outputs of all produce methods.\n\nReturns\n-------\nMultiCallResult\n A dict of values for each produce method wrapped inside ``MultiCallResult``." + }, + "get_params": { + "kind": "OTHER", + "arguments": [], + "returns": "NoneType", + "description": "A noop.\n\nReturns\n-------\nParams\n An instance of parameters." + }, + "multi_produce": { + "kind": "OTHER", + "arguments": [ + "produce_methods", + "inputs", + "timeout", + "iterations" + ], + "returns": "d3m.primitive_interfaces.base.MultiCallResult", + "description": "A method calling multiple produce methods at once.\n\nWhen a primitive has multiple produce methods it is common that they might compute the\nsame internal results for same inputs but return different representations of those results.\nIf caller is interested in multiple of those representations, calling multiple produce\nmethods might lead to recomputing same internal results multiple times. To address this,\nthis method allows primitive author to implement an optimized version which computes\ninternal results only once for multiple calls of produce methods, but return those different\nrepresentations.\n\nIf any additional method arguments are added to primitive's produce method(s), they have\nto be added to this method as well. This method should accept an union of all arguments\naccepted by primitive's produce method(s) and then use them accordingly when computing\nresults.\n\nThe default implementation of this method just calls all produce methods listed in\n``produce_methods`` in order and is potentially inefficient.\n\nIf primitive should have been fitted before calling this method, but it has not been,\nprimitive should raise a ``PrimitiveNotFittedError`` exception.\n\nParameters\n----------\nproduce_methods : Sequence[str]\n A list of names of produce methods to call.\ninputs : Inputs\n The inputs given to all produce methods.\ntimeout : float\n A maximum time this primitive should take to produce outputs for all produce methods\n listed in ``produce_methods`` argument, in seconds.\niterations : int\n How many of internal iterations should the primitive do.\n\nReturns\n-------\nMultiCallResult\n A dict of values for each produce method wrapped inside ``MultiCallResult``." + }, + "produce": { + "kind": "PRODUCE", + "arguments": [ + "inputs", + "timeout", + "iterations" + ], + "returns": "d3m.primitive_interfaces.base.CallResult[d3m.container.pandas.DataFrame]", + "singleton": false, + "inputs_across_samples": [], + "description": "Produce pre-processed D3M Dataframe according to some distance / fairness / representation / distribution\nconstraint defined by the algorithm. This pre-processing is only applied to training data and\nnot to testing data.\n\nParameters\n----------\ninputs : D3M dataframe\n\nReturns\n-------\nOutputs : D3M dataframe after pre-processing algorithm has been applied" + }, + "set_params": { + "kind": "OTHER", + "arguments": [ + "params" + ], + "returns": "NoneType", + "description": "A noop.\n\nParameters\n----------\nparams : Params\n An instance of parameters." + }, + "set_training_data": { + "kind": "OTHER", + "arguments": [], + "returns": "NoneType", + "description": "A noop.\n\nParameters\n----------" + } + }, + "class_attributes": { + "logger": "logging.Logger", + "metadata": "d3m.metadata.base.PrimitiveMetadata" + }, + "instance_attributes": { + "hyperparams": "d3m.metadata.hyperparams.Hyperparams", + "random_seed": "int", + "docker_containers": "typing.Dict[str, d3m.primitive_interfaces.base.DockerContainer]", + "volumes": "typing.Dict[str, str]", + "temporary_directory": "typing.Union[NoneType, str]" + } + }, + "structural_type": "FairnessPrimitives.pre_processing.FairnessPreProcessing", + "description": "Primitive that applies one of three pre-processing algorithm to training data before fitting a learning algorithm. Algorithm\noptions are 'Disparate_Impact_Remover', 'Learning_Fair_Representations', and 'Reweighing'.\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": "2b99d1c79d46d01edc306175344ede2770190f5f70cfc8d52c7732c6e46bcac8" +} -- GitLab From 8961bc16c3d7a79ae24740f1b28d35aadb996809 Mon Sep 17 00:00:00 2001 From: David Sullivan <davesullivan41@gmail.com> Date: Mon, 25 Nov 2019 10:19:25 -0600 Subject: [PATCH 2/3] Update package versions to match d3m --- .../pipeline_runs/uu5_heartstatlog.yml.gz | Bin 6981 -> 7020 bytes .../1.0.0/pipelines/uu5_heartstatlog.json | 1 + 2 files changed, 1 insertion(+) create mode 100644 v2019.11.10/Distil/d3m.primitives.data_preprocessing.data_conversion.FairnessPreProcessing/1.0.0/pipelines/uu5_heartstatlog.json diff --git a/v2019.11.10/Distil/d3m.primitives.data_preprocessing.data_conversion.FairnessPreProcessing/1.0.0/pipeline_runs/uu5_heartstatlog.yml.gz b/v2019.11.10/Distil/d3m.primitives.data_preprocessing.data_conversion.FairnessPreProcessing/1.0.0/pipeline_runs/uu5_heartstatlog.yml.gz index 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at end of file -- GitLab From 6e71eb4c2ef15694caa50e670cc295524cef2a92 Mon Sep 17 00:00:00 2001 From: David Sullivan <davesullivan41@gmail.com> Date: Mon, 25 Nov 2019 11:12:18 -0600 Subject: [PATCH 3/3] Update primitive submission to point to appropriate commit for git repo. Should have proper libraries now --- .../pipeline_runs/uu5_heartstatlog.yml.gz | Bin 7020 -> 7011 bytes ...6ad556c1-94bd-4f6f-ab09-c2e8af0bf080.json} | 2 +- .../1.0.0/pipelines/uu5_heartstatlog.json | 1 - .../1.0.0/primitive.json | 4 ++-- 4 files changed, 3 insertions(+), 4 deletions(-) rename v2019.11.10/Distil/d3m.primitives.data_preprocessing.data_conversion.FairnessPreProcessing/1.0.0/pipelines/{1b6f69a5-022a-4ca0-b11d-93dc388329f4.json => 6ad556c1-94bd-4f6f-ab09-c2e8af0bf080.json} (93%) delete mode 100644 v2019.11.10/Distil/d3m.primitives.data_preprocessing.data_conversion.FairnessPreProcessing/1.0.0/pipelines/uu5_heartstatlog.json diff --git a/v2019.11.10/Distil/d3m.primitives.data_preprocessing.data_conversion.FairnessPreProcessing/1.0.0/pipeline_runs/uu5_heartstatlog.yml.gz b/v2019.11.10/Distil/d3m.primitives.data_preprocessing.data_conversion.FairnessPreProcessing/1.0.0/pipeline_runs/uu5_heartstatlog.yml.gz index 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at end of file diff --git a/v2019.11.10/Distil/d3m.primitives.data_preprocessing.data_conversion.FairnessPreProcessing/1.0.0/pipelines/uu5_heartstatlog.json b/v2019.11.10/Distil/d3m.primitives.data_preprocessing.data_conversion.FairnessPreProcessing/1.0.0/pipelines/uu5_heartstatlog.json deleted file mode 100644 index 824413e1d3..0000000000 --- a/v2019.11.10/Distil/d3m.primitives.data_preprocessing.data_conversion.FairnessPreProcessing/1.0.0/pipelines/uu5_heartstatlog.json +++ /dev/null @@ -1 +0,0 @@ -{"id": "8a5adbcd-6dca-4e61-9397-20538d2481ec", "schema": "https://metadata.datadrivendiscovery.org/schemas/v0/pipeline.json", "created": "2019-11-25T15:26:20.881157Z", "inputs": [{"name": "inputs"}], "outputs": [{"data": "steps.8.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", 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"5144bad4fea16168f6667c991e137067721dd0573c68ab1bf172ead9e2c82869"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "steps.7.produce"}, "reference": {"type": "CONTAINER", "data": "steps.1.produce"}}, "outputs": [{"id": "produce"}]}], "digest": "87dde190cb4dac45a252fb521f161f2bda3370b66c07bffba9cb9026f9f144a9"} \ No newline at end of file diff --git a/v2019.11.10/Distil/d3m.primitives.data_preprocessing.data_conversion.FairnessPreProcessing/1.0.0/primitive.json b/v2019.11.10/Distil/d3m.primitives.data_preprocessing.data_conversion.FairnessPreProcessing/1.0.0/primitive.json index 47367befaf..65870d7fb2 100644 --- a/v2019.11.10/Distil/d3m.primitives.data_preprocessing.data_conversion.FairnessPreProcessing/1.0.0/primitive.json +++ b/v2019.11.10/Distil/d3m.primitives.data_preprocessing.data_conversion.FairnessPreProcessing/1.0.0/primitive.json @@ -15,7 +15,7 @@ "installation": [ { "type": "PIP", - "package_uri": "git+https://github.com/NewKnowledge/D3M-Fairness-Primitives.git@7a48c3d0b1750a5942c4ba21d89c4e55557a3625#egg=FairnessPrimitives" + "package_uri": "git+https://github.com/NewKnowledge/D3M-Fairness-Primitives.git@56db675b3f851d608f2a4e52fedfe39cc79f0b84#egg=FairnessPrimitives" } ], "python_path": "d3m.primitives.data_preprocessing.data_conversion.FairnessPreProcessing", @@ -204,5 +204,5 @@ }, "structural_type": "FairnessPrimitives.pre_processing.FairnessPreProcessing", "description": "Primitive that applies one of three pre-processing algorithm to training data before fitting a learning algorithm. Algorithm\noptions are 'Disparate_Impact_Remover', 'Learning_Fair_Representations', and 'Reweighing'.\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": "2b99d1c79d46d01edc306175344ede2770190f5f70cfc8d52c7732c6e46bcac8" + "digest": "92aa6c244fd6c2644ab19206848722ce0bb4ea431a0609b3025b972d39f3d868" } -- GitLab