Commit b8258ec7 authored by Brandon Schoenfeld's avatar Brandon Schoenfeld

fixed pipelines

parent 2875157c
{
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"created": "2019-04-26T16:54:17.367925Z",
"context": "TESTING",
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{
......@@ -21,8 +21,7 @@
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"version": "0.3.0",
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"arguments": {
"inputs": {
......@@ -42,8 +41,7 @@
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"version": "0.5.0",
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"name": "Parses strings into their types"
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"inputs": {
......@@ -63,8 +61,7 @@
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"version": "0.2.0",
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"name": "Extracts columns by semantic type",
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"name": "Extracts columns by semantic type"
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"arguments": {
"inputs": {
......@@ -92,8 +89,7 @@
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"version": "0.2.0",
"python_path": "d3m.primitives.data_transformation.extract_columns_by_semantic_types.DataFrameCommon",
"name": "Extracts columns by semantic type",
"digest": "8d2b6d7e2ba7d08920a7f0d9e143bab6f6c8f4950979134e647d0f252c2f4836"
"name": "Extracts columns by semantic type"
},
"arguments": {
"inputs": {
......@@ -122,7 +118,7 @@
"version": "0.1.4",
"python_path": "d3m.primitives.data_preprocessing.random_sampling_imputer.BYU",
"name": "Random Sampling Imputer",
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"arguments": {
"inputs": {
......@@ -142,8 +138,7 @@
"id": "f0fd7a62-09b5-3abc-93bb-f5f999f7cc80",
"version": "2019.4.4",
"python_path": "d3m.primitives.regression.random_forest.SKlearn",
"name": "sklearn.ensemble.forest.RandomForestRegressor",
"digest": "796f24a7ad29a3a2dfb5e7b668860bd50b4486afce29e4f55e4e75df3c586e6a"
"name": "sklearn.ensemble.forest.RandomForestRegressor"
},
"arguments": {
"inputs": {
......@@ -173,8 +168,7 @@
"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": "d62af6fae9ea50a5bf034d13ac67f117b62ff9c06152dcbbfdd5312136a4d80a"
"name": "Construct pipeline predictions output"
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......@@ -192,6 +186,5 @@
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{
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"created": "2019-04-22T17:58:32.528260Z",
"created": "2019-04-26T16:54:16.903635Z",
"context": "TESTING",
"inputs": [
{
......@@ -21,8 +21,7 @@
"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": "1e0fc07f5306223300e3966c50daf7a5f2095d201d316b31cdc86e48a5f98b4a"
"name": "Extract a DataFrame from a Dataset"
},
"arguments": {
"inputs": {
......@@ -42,8 +41,7 @@
"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",
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"name": "Parses strings into their types"
},
"arguments": {
"inputs": {
......@@ -63,8 +61,7 @@
"id": "4503a4c6-42f7-45a1-a1d4-ed69699cf5e1",
"version": "0.2.0",
"python_path": "d3m.primitives.data_transformation.extract_columns_by_semantic_types.DataFrameCommon",
"name": "Extracts columns by semantic type",
"digest": "8d2b6d7e2ba7d08920a7f0d9e143bab6f6c8f4950979134e647d0f252c2f4836"
"name": "Extracts columns by semantic type"
},
"arguments": {
"inputs": {
......@@ -92,8 +89,7 @@
"id": "4503a4c6-42f7-45a1-a1d4-ed69699cf5e1",
"version": "0.2.0",
"python_path": "d3m.primitives.data_transformation.extract_columns_by_semantic_types.DataFrameCommon",
"name": "Extracts columns by semantic type",
"digest": "8d2b6d7e2ba7d08920a7f0d9e143bab6f6c8f4950979134e647d0f252c2f4836"
"name": "Extracts columns by semantic type"
},
"arguments": {
"inputs": {
......@@ -122,7 +118,7 @@
"version": "0.1.4",
"python_path": "d3m.primitives.data_preprocessing.random_sampling_imputer.BYU",
"name": "Random Sampling Imputer",
"digest": "5f808d46ed8e5dace09749455f4e05adc542d3218632919c3307351d59d0da41"
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"arguments": {
"inputs": {
......@@ -142,8 +138,7 @@
"id": "1dd82833-5692-39cb-84fb-2455683075f3",
"version": "2019.4.4",
"python_path": "d3m.primitives.classification.random_forest.SKlearn",
"name": "sklearn.ensemble.forest.RandomForestClassifier",
"digest": "0f35b19bc025b235419b728aa19758f2e599b8d8be1f26790448af40a3e0ab2a"
"name": "sklearn.ensemble.forest.RandomForestClassifier"
},
"arguments": {
"inputs": {
......@@ -173,8 +168,7 @@
"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": "d62af6fae9ea50a5bf034d13ac67f117b62ff9c06152dcbbfdd5312136a4d80a"
"name": "Construct pipeline predictions output"
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"inputs": {
......@@ -192,6 +186,5 @@
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......@@ -3,7 +3,7 @@
"IMPUTATION"
],
"description": "This imputes missing values in a DataFrame by sampling known values from\neach column independently. If the training data has no known values in a\nparticular column, no values are imputed.\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.",
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"effects": [
"NO_MISSING_VALUES"
],
......@@ -12,7 +12,7 @@
{
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"type": "PIP",
"version": "0.6.2"
"version": "0.6.3"
}
],
"location_uris": [
......
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"created": "2019-04-26T16:54:17.507488Z",
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{
......@@ -21,8 +21,7 @@
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"name": "Extract a DataFrame from a Dataset"
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"arguments": {
"inputs": {
......@@ -42,8 +41,7 @@
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"version": "0.5.0",
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"name": "Parses strings into their types",
"digest": "3ef2a21d912b7f572df8ccd5edd75f640d09454bbfe2a39bab85c840f8b23f43"
"name": "Parses strings into their types"
},
"arguments": {
"inputs": {
......@@ -64,7 +62,7 @@
"version": "0.4.3",
"python_path": "d3m.primitives.metafeature_extraction.meta_feature_extractor.BYU",
"name": "Dataset Metafeature Extraction",
"digest": "aa5e61cbb77cdf73eb24ceeb0dce0892a8580bc8513c0f33dc1a6b230abd427d"
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"inputs": {
......@@ -84,8 +82,7 @@
"id": "4503a4c6-42f7-45a1-a1d4-ed69699cf5e1",
"version": "0.2.0",
"python_path": "d3m.primitives.data_transformation.extract_columns_by_semantic_types.DataFrameCommon",
"name": "Extracts columns by semantic type",
"digest": "8d2b6d7e2ba7d08920a7f0d9e143bab6f6c8f4950979134e647d0f252c2f4836"
"name": "Extracts columns by semantic type"
},
"arguments": {
"inputs": {
......@@ -113,8 +110,7 @@
"id": "4503a4c6-42f7-45a1-a1d4-ed69699cf5e1",
"version": "0.2.0",
"python_path": "d3m.primitives.data_transformation.extract_columns_by_semantic_types.DataFrameCommon",
"name": "Extracts columns by semantic type",
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"name": "Extracts columns by semantic type"
},
"arguments": {
"inputs": {
......@@ -142,8 +138,7 @@
"id": "d016df89-de62-3c53-87ed-c06bb6a23cde",
"version": "2019.4.4",
"python_path": "d3m.primitives.data_cleaning.imputer.SKlearn",
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"name": "sklearn.impute.SimpleImputer"
},
"arguments": {
"inputs": {
......@@ -169,8 +164,7 @@
"id": "f0fd7a62-09b5-3abc-93bb-f5f999f7cc80",
"version": "2019.4.4",
"python_path": "d3m.primitives.regression.random_forest.SKlearn",
"name": "sklearn.ensemble.forest.RandomForestRegressor",
"digest": "796f24a7ad29a3a2dfb5e7b668860bd50b4486afce29e4f55e4e75df3c586e6a"
"name": "sklearn.ensemble.forest.RandomForestRegressor"
},
"arguments": {
"inputs": {
......@@ -200,8 +194,7 @@
"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": "d62af6fae9ea50a5bf034d13ac67f117b62ff9c06152dcbbfdd5312136a4d80a"
"name": "Construct pipeline predictions output"
},
"arguments": {
"inputs": {
......@@ -219,6 +212,5 @@
}
]
}
],
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{
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"schema": "https://metadata.datadrivendiscovery.org/schemas/v0/pipeline.json",
"created": "2019-04-22T17:58:32.888909Z",
"created": "2019-04-26T16:54:17.281854Z",
"context": "TESTING",
"inputs": [
{
......@@ -21,8 +21,7 @@
"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": "1e0fc07f5306223300e3966c50daf7a5f2095d201d316b31cdc86e48a5f98b4a"
"name": "Extract a DataFrame from a Dataset"
},
"arguments": {
"inputs": {
......@@ -42,8 +41,7 @@
"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": "3ef2a21d912b7f572df8ccd5edd75f640d09454bbfe2a39bab85c840f8b23f43"
"name": "Parses strings into their types"
},
"arguments": {
"inputs": {
......@@ -64,7 +62,7 @@
"version": "0.4.3",
"python_path": "d3m.primitives.metafeature_extraction.meta_feature_extractor.BYU",
"name": "Dataset Metafeature Extraction",
"digest": "aa5e61cbb77cdf73eb24ceeb0dce0892a8580bc8513c0f33dc1a6b230abd427d"
"digest": "c3ada53d06a883fcbb24098f2c37b1be9ac1ad2aa92a2f968eb7222b61669f2e"
},
"arguments": {
"inputs": {
......@@ -84,8 +82,7 @@
"id": "4503a4c6-42f7-45a1-a1d4-ed69699cf5e1",
"version": "0.2.0",
"python_path": "d3m.primitives.data_transformation.extract_columns_by_semantic_types.DataFrameCommon",
"name": "Extracts columns by semantic type",
"digest": "8d2b6d7e2ba7d08920a7f0d9e143bab6f6c8f4950979134e647d0f252c2f4836"
"name": "Extracts columns by semantic type"
},
"arguments": {
"inputs": {
......@@ -113,8 +110,7 @@
"id": "4503a4c6-42f7-45a1-a1d4-ed69699cf5e1",
"version": "0.2.0",
"python_path": "d3m.primitives.data_transformation.extract_columns_by_semantic_types.DataFrameCommon",
"name": "Extracts columns by semantic type",
"digest": "8d2b6d7e2ba7d08920a7f0d9e143bab6f6c8f4950979134e647d0f252c2f4836"
"name": "Extracts columns by semantic type"
},
"arguments": {
"inputs": {
......@@ -142,8 +138,7 @@
"id": "d016df89-de62-3c53-87ed-c06bb6a23cde",
"version": "2019.4.4",
"python_path": "d3m.primitives.data_cleaning.imputer.SKlearn",
"name": "sklearn.impute.SimpleImputer",
"digest": "a442529ad564892290c76819d033429e7b9d7c2912e80ef5a0b0526a2dfd4ebd"
"name": "sklearn.impute.SimpleImputer"
},
"arguments": {
"inputs": {
......@@ -169,8 +164,7 @@
"id": "1dd82833-5692-39cb-84fb-2455683075f3",
"version": "2019.4.4",
"python_path": "d3m.primitives.classification.random_forest.SKlearn",
"name": "sklearn.ensemble.forest.RandomForestClassifier",
"digest": "0f35b19bc025b235419b728aa19758f2e599b8d8be1f26790448af40a3e0ab2a"
"name": "sklearn.ensemble.forest.RandomForestClassifier"
},
"arguments": {
"inputs": {
......@@ -200,8 +194,7 @@
"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": "d62af6fae9ea50a5bf034d13ac67f117b62ff9c06152dcbbfdd5312136a4d80a"
"name": "Construct pipeline predictions output"
},
"arguments": {
"inputs": {
......@@ -219,6 +212,5 @@
}
]
}
],
"digest": "b94ab3ed97d8e96e096071fa07fee163b2af2afaf757d5356d5326f172d0a482"
]
}
\ No newline at end of file
......@@ -8,13 +8,13 @@
"STATISTICAL_MOMENT_ANALYSIS"
],
"description": "A primitive which takes a DataFrame and computes metafeatures on the data.\nTarget column is identified by being labeled with 'https://metadata.datadrivendiscovery.org/types/TrueTarget' in 'semantic_types' metadata.\nOtherwise primitive assumes there is no target column and only metafeatures that do not involve targets are returned.\nIf DataFrame metadata does not include semantic type labels for each column, columns will be classified as CATEGORICAL or NUMERIC according\nto their dtype: int and float are NUMERIC, all others are CATEGORICAL.\nMetafeatures are stored in the metadata object of the DataFrame, and the DataFrame itself is returned unchanged\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": "aa5e61cbb77cdf73eb24ceeb0dce0892a8580bc8513c0f33dc1a6b230abd427d",
"digest": "c3ada53d06a883fcbb24098f2c37b1be9ac1ad2aa92a2f968eb7222b61669f2e",
"id": "28d12214-8cb0-4ac0-8946-d31fcbcd4142",
"installation": [
{
"package": "byudml",
"type": "PIP",
"version": "0.6.2"
"version": "0.6.3"
}
],
"location_uris": [
......
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