Commit 6a5bbfb0 authored by Jeffrey Gleason's avatar Jeffrey Gleason Committed by Sujen

Pca prim base

parent 709ef57f
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{"id": "d83e8a6e-3aaf-47e9-ae40-828d49c60f57", "schema": "https://metadata.datadrivendiscovery.org/schemas/v0/pipeline.json", "created": "2019-06-26T22:07:44.862338Z", "inputs": [{"name": "inputs"}], "outputs": [{"data": "steps.5.produce", "name": "output predictions"}], "steps": [{"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": "a141e6821de7ae586968b0986237745a5510850e6940cf946db9d50d3828b030"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "inputs.0"}}, "outputs": [{"id": "produce"}]}, {"type": "PRIMITIVE", "primitive": {"id": "d2fa8df2-6517-3c26-bafc-87b701c4043a", "version": "1.2.1", "python_path": "d3m.primitives.data_cleaning.column_type_profiler.Simon", "name": "simon", "digest": "6fa0e87f8044df78a99507a60648d3b7336161862aee8994ff2ed57bea5b0f05"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "steps.0.produce"}, "outputs": {"type": "CONTAINER", "data": "steps.0.produce"}}, "outputs": [{"id": "produce"}], "hyperparams": {"overwrite": {"type": "VALUE", "data": true}, "multi_label_classification": {"type": "VALUE", "data": true}, "statistical_classification": {"type": "VALUE", "data": true}}}, {"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": "d95eb0ea8a5e6f9abc0965a97e9c4f5d8f74a3df591c11c4145faea3e581cd06"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "steps.1.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": "d6902b0ef72b4cd6fc5f79054f7a534404c708e1244e94a2713a9dd525c78eed"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "steps.2.produce"}}, "outputs": [{"id": "produce"}], "hyperparams": {"return_result": {"type": "VALUE", "data": "replace"}, "use_semantic_types": {"type": "VALUE", "data": true}}}, {"type": "PRIMITIVE", "primitive": {"id": "fe0841b7-6e70-4bc3-a56c-0670a95ebc6a", "version": "0.1.0", "python_path": "d3m.primitives.classification.xgboost_gbtree.DataFrameCommon", "name": "XGBoost GBTree classifier", "digest": "92c2ba639ead09cba5626c5e81f4e57a55e4bccb25cfc5d16d0c945a936a104d"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "steps.3.produce"}, "outputs": {"type": "CONTAINER", "data": "steps.3.produce"}}, "outputs": [{"id": "produce"}], "hyperparams": {"add_index_columns": {"type": "VALUE", "data": true}}}, {"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": "96382129c2d9e87a2c0ab0b477b410947e5644d4dfae24e905d16a72d32dc41b"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "steps.4.produce"}, "reference": {"type": "CONTAINER", "data": "steps.0.produce"}}, "outputs": [{"id": "produce"}]}], "digest": "eb20b610d8171a2f814a182d684a819da5280d55b3f45ba9df375e9280bf66f9"}
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......@@ -19,7 +19,7 @@
"installation": [
{
"type": "PIP",
"package_uri": "git+https://github.com/NewKnowledge/[email protected]2c95c371f4462ffae1cf8ea1ae6902ca23539adc#egg=SimonD3MWrapper"
"package_uri": "git+https://github.com/NewKnowledge/[email protected]60088ab82217767cd77f8a540ba56a24cd85401b#egg=SimonD3MWrapper"
},
{
"type": "TGZ",
......@@ -61,7 +61,7 @@
"default": false,
"structural_type": "bool",
"semantic_types": [
"https://metadata.datadrivendiscovery.org/types/ControlParameter"
"https://metadata.datadrivendiscovery.org/types/TuningParameter"
],
"description": "whether to append categorical / ordinal annotations using rule-based classification"
},
......@@ -70,7 +70,7 @@
"default": true,
"structural_type": "bool",
"semantic_types": [
"https://metadata.datadrivendiscovery.org/types/ControlParameter"
"https://metadata.datadrivendiscovery.org/types/TuningParameter"
],
"description": "whether to perfrom multi-label classification and append multiple annotations to metadata"
}
......@@ -239,5 +239,5 @@
},
"structural_type": "SimonD3MWrapper.wrapper.simon",
"description": "The primitive infers the semantic type of each column from a LSTM-FCN neural network trained on 18\ndifferent semantic types. The primitive's annotations will overwrite the default annotations if 'overwrite'\nis set to True and will annotate columns with multiple annotations if multi_label_classification is set to 'True'.\nFinally, a different mode of categorical and ordinal classification using rule-based heuristics can be activated if\n'statistical_classification' is set to True.\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": "de2b17670fc83f720917dbd7076f178fe6ef4f2bf458a2faa7ef0c156a271e2c"
"digest": "6fa0e87f8044df78a99507a60648d3b7336161862aee8994ff2ed57bea5b0f05"
}
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{"id": "1279504a-62ec-4ae2-8bfa-de8856cde102", "schema": "https://metadata.datadrivendiscovery.org/schemas/v0/pipeline.json", "created": "2019-06-26T16:55:36.436512Z", "inputs": [{"name": "inputs"}], "outputs": [{"data": "steps.5.produce", "name": "output predictions"}], "steps": [{"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": "4e971e251e548f1188c280c60f4e2f9a460865f21bcfd341114270bdafa4c4dc"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "inputs.0"}}, "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": "b14b7bcd1a54ee658a7093658eb4b6c9a1b029698f981fec7ad26408a0b5cf64"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "steps.0.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": "1933705a04d8da0c4a34f4d4fc2ec4c9d826abaea53bc073b654588daaa5988d"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "steps.1.produce"}}, "outputs": [{"id": "produce"}], "hyperparams": {"return_result": {"type": "VALUE", "data": "replace"}, "use_semantic_types": {"type": "VALUE", "data": true}}}, {"type": "PRIMITIVE", "primitive": {"id": "04573880-d64f-4791-8932-52b7c3877639", "version": "3.0.2", "python_path": "d3m.primitives.feature_selection.pca_features.Pcafeatures", "name": "PCA Features", "digest": "31d6448e0a6d134ddeb6f8a6e802541fcfda48dc7c23332252727ccdf44310c5"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "steps.2.produce"}, "outputs": {"type": "CONTAINER", "data": "steps.2.produce"}}, "outputs": [{"id": "produce"}], "hyperparams": {"only_numeric_cols": {"type": "VALUE", "data": false}, "threshold": {"type": "VALUE", "data": 0.0}}}, {"type": "PRIMITIVE", "primitive": {"id": "fe0841b7-6e70-4bc3-a56c-0670a95ebc6a", "version": "0.1.0", "python_path": "d3m.primitives.classification.xgboost_gbtree.DataFrameCommon", "name": "XGBoost GBTree classifier", "digest": "a79f1fbf541dae2a6b716b1f87fc8858eb35f29eda13836daf129f3487fe6c37"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "steps.3.produce"}, "outputs": {"type": "CONTAINER", "data": "steps.3.produce"}}, "outputs": [{"id": "produce"}], "hyperparams": {"add_index_columns": {"type": "VALUE", "data": true}}}, {"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": "0c744dd6ea7eafb9c143f70b40452c6238bc6cab06a070d50868822f904388d3"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "steps.4.produce"}, "reference": {"type": "CONTAINER", "data": "steps.0.produce"}}, "outputs": [{"id": "produce"}]}], "digest": "28da83ecc9c976ef86fa15fb73c10e8a280d4b8c112056ac73907e16514d1179"}
\ No newline at end of file
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......@@ -15,7 +15,7 @@
"installation": [
{
"type": "PIP",
"package_uri": "git+https://github.com/NewKnowledge/[email protected]ff15ca192c5386e2a003cca59f440df285b02f73#egg=PcafeaturesD3MWrapper"
"package_uri": "git+https://github.com/NewKnowledge/[email protected]132dc9fa87351e3e79633ebb97e1c15bc1d39f3b#egg=PcafeaturesD3MWrapper"
}
],
"python_path": "d3m.primitives.feature_selection.pca_features.Pcafeatures",
......@@ -42,7 +42,7 @@
"default": 0.0,
"structural_type": "float",
"semantic_types": [
"https://metadata.datadrivendiscovery.org/types/ControlParameter"
"https://metadata.datadrivendiscovery.org/types/TuningParameter"
],
"description": "pca score threshold for feature selection",
"lower": 0.0,
......@@ -55,7 +55,7 @@
"default": true,
"structural_type": "bool",
"semantic_types": [
"https://metadata.datadrivendiscovery.org/types/ControlParameter"
"https://metadata.datadrivendiscovery.org/types/TuningParameter"
],
"description": "consider only numeric columns for feature selection"
}
......@@ -218,5 +218,5 @@
},
"structural_type": "PcafeaturesD3MWrapper.wrapper.pcafeatures",
"description": "Perform principal component analysis on all numeric data in the dataset\nand then use each original features contribution to the first principal\ncomponent as a proxy for the 'score' of that feature. Returns a dataframe\nthat only contains features whose score is above a threshold (HP)\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": "30be9386ba49ccedd630e83fdbeed2fb22261b225b6e54abfd7ded434ed67d3f"
"digest": "48b7c56260320b32b800f628fbdb103741aa923750a4926a3ae9067f117ae119"
}
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{"id": "5e7bdd2c-3710-4d8a-84b4-775ad23dde75", "schema": "https://metadata.datadrivendiscovery.org/schemas/v0/pipeline.json", "created": "2019-05-23T16:11:26.607460Z", "inputs": [{"name": "inputs"}], "outputs": [{"data": "steps.5.produce", "name": "output predictions"}], "steps": [{"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": "b8251591d8f5f03e1c4d3ec740016c386ad6c20216cfd7d7dd9b8b2e3ab0595f"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "inputs.0"}}, "outputs": [{"id": "produce"}]}, {"type": "PRIMITIVE", "primitive": {"id": "ef6f3887-b253-4bfd-8b35-ada449efad0c", "version": "3.1.1", "python_path": "d3m.primitives.feature_selection.rffeatures.Rffeatures", "name": "RF Features", "digest": "a3e6a75534534f52f6564c6d9c0ee8d76e8ceb7b3a9fddb90f0413c2a98faba6"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "steps.0.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": "d268f582f5bf30206f1e43ef46f435576a30413132bc73279f3fca36937184eb"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "steps.1.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.2.produce"}}, "outputs": [{"id": "produce"}], "hyperparams": {"return_result": {"type": "VALUE", "data": "replace"}, "use_semantic_types": {"type": "VALUE", "data": true}}}, {"type": "PRIMITIVE", "primitive": {"id": "1dd82833-5692-39cb-84fb-2455683075f3", "version": "2019.4.4", "python_path": "d3m.primitives.classification.random_forest.SKlearn", "name": "sklearn.ensemble.forest.RandomForestClassifier", "digest": "b1c373d38eeffe7ca7e2fe12b7cae22b2518b9bef3cbf4c7b9167af3a972bee3"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "steps.3.produce"}, "outputs": {"type": "CONTAINER", "data": "steps.3.produce"}}, "outputs": [{"id": "produce"}], "hyperparams": {"add_index_columns": {"type": "VALUE", "data": true}, "use_semantic_types": {"type": "VALUE", "data": true}}}, {"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": "1fb337b1987ecedebfa35bbd80ac6125f4a7f71435b02d3b349ef5547588bb71"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "steps.4.produce"}, "reference": {"type": "CONTAINER", "data": "steps.0.produce"}}, "outputs": [{"id": "produce"}]}], "digest": "1a4d31b170db982a554b431cd47a19a7e6b40d13b7716d0684031c4932eae0b0"}
\ No newline at end of file
{
"problem": "185_baseball_problem",
"full_inputs": [
"185_baseball_dataset"
],
"train_inputs": [
"185_baseball_dataset_TRAIN"
],
"test_inputs": [
"185_baseball_dataset_TEST"
],
"score_inputs": [
"185_baseball_dataset_SCORE"
]
}
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