Commit 93f44c2d authored by Sujen's avatar Sujen

Merge branch 'brekelma/primitives-old_primitive_names'

parents 3ae093a6 77e87b60
......@@ -13,7 +13,7 @@
"installation": [
{
"type": "PIP",
"package_uri": "git+https://github.com/brekelma/dsbox_graphs@873a9827c96b32dbda45b4a319df8c9f04a4446c#egg=dsbox_graphs"
"package_uri": "git+https://github.com/brekelma/dsbox_graphs@524f058d83f925c49ac6257722d8f3cc0bcc2e2f#egg=dsbox_graphs"
}
],
"algorithm_types": [
......@@ -201,5 +201,5 @@
},
"structural_type": "graph_dataset_to_list.GraphDatasetToList",
"description": "A primitive which extracts a DataFrame out of a Dataset.\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": "833a0c82cbf7eba638df59de0d992d75bf0d6b484cb66d3034dc79dfc3339300"
}
\ No newline at end of file
"digest": "3445676c930e4e309d0090048d605a994f460b3d760aba6706cac90cb39f9fda"
}
{
"id":"05e40229-23a5-4ec1-b7fa-297de9f18668",
"schema":"https://metadata.datadrivendiscovery.org/schemas/v0/pipeline.json",
"created":"2019-05-05T05:35:39.048366Z",
"context":"PRETRAINING",
"inputs":[
{
"name":"input dataset"
}
],
"outputs":[
{
"data":"steps.5.produce",
"name":"predictions of input dataset"
}
],
"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":"81f07c01fddaff5d5d931730d73f691bd35645c4dc4c7bcb00f137d197dad42f"
},
"arguments":{
"inputs":{
"type":"CONTAINER",
"data":"inputs.0"
}
},
"outputs":[
{
"id":"produce"
}
],
"hyperparams":{
"starting_resource":{
"type":"VALUE",
"data":null
},
"recursive":{
"type":"VALUE",
"data":true
},
"many_to_many":{
"type":"VALUE",
"data":false
},
"discard_not_joined_tabular_resources":{
"type":"VALUE",
"data":false
}
}
},
{
"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"
},
"arguments":{
"inputs":{
"type":"CONTAINER",
"data":"steps.0.produce"
}
},
"outputs":[
{
"id":"produce"
}
]
},
{
"type":"PRIMITIVE",
"primitive":{
"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":"67977ed362c96178cd3e61cafd94f84e17a288cf5ed9c5f2f5a0cb81a99f77bc"
},
"arguments":{
"inputs":{
"type":"CONTAINER",
"data":"steps.1.produce"
}
},
"outputs":[
{
"id":"produce"
}
],
"hyperparams":{
"semantic_types":{
"type":"VALUE",
"data":[
"https://metadata.datadrivendiscovery.org/types/PrimaryKey",
"https://metadata.datadrivendiscovery.org/types/Attribute"
]
}
}
},
{
"type":"PRIMITIVE",
"primitive":{
"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":"67977ed362c96178cd3e61cafd94f84e17a288cf5ed9c5f2f5a0cb81a99f77bc"
},
"arguments":{
"inputs":{
"type":"CONTAINER",
"data":"steps.1.produce"
}
},
"outputs":[
{
"id":"produce"
}
],
"hyperparams":{
"semantic_types":{
"type":"VALUE",
"data":[
"https://metadata.datadrivendiscovery.org/types/TrueTarget"
]
}
}
},
{
"type":"PRIMITIVE",
"primitive":{
"id":"18f0bb42-6350-3753-8f2d-d1c3da70f279",
"version":"1.5.1",
"python_path":"d3m.primitives.data_preprocessing.encoder.DSBOX",
"name":"ISI DSBox Data Encoder",
"digest":"09795e9b7c78a5463c10749a29ce40bf6a2a56cc46541bd00b0d7308483ec0be"
},
"arguments":{
"inputs":{
"type":"CONTAINER",
"data":"steps.2.produce"
}
},
"outputs":[
{
"id":"produce"
}
]
},
{
"type":"PRIMITIVE",
"primitive":{
"id":"7ddf2fd8-2f7f-4e53-96a7-0d9f5aeecf93",
"version":"1.5.1",
"python_path":"d3m.primitives.data_transformation.to_numeric.DSBOX",
"name":"ISI DSBox To Numeric DataFrame",
"digest":"18d17321f9da8a928559bbdae94ecdd570697119ac4bf90dc63af9da1c326693"
},
"arguments":{
"inputs":{
"type":"CONTAINER",
"data":"steps.4.produce"
}
},
"outputs":[
{
"id":"produce"
}
]
},
{
"type":"PRIMITIVE",
"primitive":{
"id":"7894b699-61e9-3a50-ac9f-9bc510466667",
"version":"1.5.1",
"python_path":"d3m.primitives.data_preprocessing.mean_imputation.DSBOX",
"name":"DSBox Mean Imputer",
"digest":"071ff34161c624ec1732627e096ed32eb6e4a5901b28c12d16ab57289767af0e"
},
"arguments":{
"inputs":{
"type":"CONTAINER",
"data":"steps.5.produce"
}
},
"outputs":[
{
"id":"produce"
}
]
},
{
"type":"PRIMITIVE",
"primitive":{
"id":"393f9de8-a5b9-4d92-aaff-8808d563b6c4",
"version":"1.0.0",
"python_path":"d3m.primitives.feature_construction.corex_supervised.EchoIB",
"name":"Echo"
},
"arguments":{
"inputs":{
"type":"CONTAINER",
"data":"steps.6.produce"
},
"outputs":
{
"type":"CONTAINER",
"data":"steps.3.produce"
}
},
"outputs":[
{
"id":"produce"
}
],
"hyperparams":{
"beta":{
"type":"VALUE",
"data": 0.1
},
"task":{
"type":"VALUE",
"data":"CLASSIFICATION"
}
}
},
{
"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"
},
"arguments":{
"inputs":{
"type":"CONTAINER",
"data":"steps.7.produce"
},
"outputs":
{
"type":"CONTAINER",
"data":"steps.3.produce"
}
},
"outputs":[
{
"id":"produce"
}],
"hyperparams":{
"add_index_columns":{
"type":"VALUE",
"data":true
},
"return_result":{
"type":"VALUE",
"data":"new"
},
"use_semantic_types":{
"type":"VALUE",
"data":true
}
}
}
],
"name":"ISI_echo_clf:139758531711192",
"description":"",
"metric":"accuracy"
}
{
"problem":"38_sick_problem",
"full_inputs":[
"38_sick_dataset"
],
"train_inputs":[
"38_sick_dataset_TRAIN"
],
"test_inputs":[
"38_sick_dataset_TEST"
],
"score_inputs":[
"38_sick_dataset_SCORE"
]
}
{
"id":"6cd0cbed-a9fe-4cb2-acfa-3b8d2ae58846",
"schema":"https://metadata.datadrivendiscovery.org/schemas/v0/pipeline.json",
"created":"2019-05-05T05:35:39.048366Z",
"context":"PRETRAINING",
"inputs":[
{
"name":"input dataset"
}
],
"outputs":[
{
"data":"steps.5.produce",
"name":"predictions of input dataset"
}
],
"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":"81f07c01fddaff5d5d931730d73f691bd35645c4dc4c7bcb00f137d197dad42f"
},
"arguments":{
"inputs":{
"type":"CONTAINER",
"data":"inputs.0"
}
},
"outputs":[
{
"id":"produce"
}
],
"hyperparams":{
"starting_resource":{
"type":"VALUE",
"data":null
},
"recursive":{
"type":"VALUE",
"data":true
},
"many_to_many":{
"type":"VALUE",
"data":false
},
"discard_not_joined_tabular_resources":{
"type":"VALUE",
"data":false
}
}
},
{
"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"
},
"arguments":{
"inputs":{
"type":"CONTAINER",
"data":"steps.0.produce"
}
},
"outputs":[
{
"id":"produce"
}
]
},
{
"type":"PRIMITIVE",
"primitive":{
"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":"67977ed362c96178cd3e61cafd94f84e17a288cf5ed9c5f2f5a0cb81a99f77bc"
},
"arguments":{
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"type":"CONTAINER",
"data":"steps.1.produce"
}
},
"outputs":[
{
"id":"produce"
}
],
"hyperparams":{
"semantic_types":{
"type":"VALUE",
"data":[
"https://metadata.datadrivendiscovery.org/types/PrimaryKey",
"https://metadata.datadrivendiscovery.org/types/Attribute"
]
}
}
},
{
"type":"PRIMITIVE",
"primitive":{
"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":"67977ed362c96178cd3e61cafd94f84e17a288cf5ed9c5f2f5a0cb81a99f77bc"
},
"arguments":{
"inputs":{
"type":"CONTAINER",
"data":"steps.1.produce"
}
},
"outputs":[
{
"id":"produce"
}
],
"hyperparams":{
"semantic_types":{
"type":"VALUE",
"data":[
"https://metadata.datadrivendiscovery.org/types/TrueTarget"
]
}
}
},
{
"type":"PRIMITIVE",
"primitive":{
"id":"18f0bb42-6350-3753-8f2d-d1c3da70f279",
"version":"1.5.1",
"python_path":"d3m.primitives.data_preprocessing.encoder.DSBOX",
"name":"ISI DSBox Data Encoder",
"digest":"09795e9b7c78a5463c10749a29ce40bf6a2a56cc46541bd00b0d7308483ec0be"
},
"arguments":{
"inputs":{
"type":"CONTAINER",
"data":"steps.2.produce"
}
},
"outputs":[
{
"id":"produce"
}
]
},
{
"type":"PRIMITIVE",
"primitive":{
"id":"7ddf2fd8-2f7f-4e53-96a7-0d9f5aeecf93",
"version":"1.5.1",
"python_path":"d3m.primitives.data_transformation.to_numeric.DSBOX",
"name":"ISI DSBox To Numeric DataFrame",
"digest":"18d17321f9da8a928559bbdae94ecdd570697119ac4bf90dc63af9da1c326693"
},
"arguments":{
"inputs":{
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"data":"steps.4.produce"
}
},
"outputs":[
{
"id":"produce"
}
]
},
{
"type":"PRIMITIVE",
"primitive":{
"id":"7894b699-61e9-3a50-ac9f-9bc510466667",
"version":"1.5.1",
"python_path":"d3m.primitives.data_preprocessing.mean_imputation.DSBOX",
"name":"DSBox Mean Imputer",
"digest":"071ff34161c624ec1732627e096ed32eb6e4a5901b28c12d16ab57289767af0e"
},
"arguments":{
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"data":"steps.5.produce"
}
},
"outputs":[
{
"id":"produce"
}
]
},
{
"type":"PRIMITIVE",
"primitive":{
"id":"393f9de8-a5b9-4d92-aaff-8808d563b6c4",
"version":"1.0.0",
"python_path":"d3m.primitives.feature_construction.corex_supervised.EchoIB",
"name":"Echo"
},
"arguments":{
"inputs":{
"type":"CONTAINER",
"data":"steps.6.produce"
},
"outputs":
{
"type":"CONTAINER",
"data":"steps.3.produce"
}
},
"outputs":[
{
"id":"produce"
}
],
"hyperparams":{
"beta":{
"type":"VALUE",
"data": 0.1
},
"task":{
"type":"VALUE",
"data":"REGRESSION"
}
}
},
{
"type":"PRIMITIVE",
"primitive":{
"id":"f0fd7a62-09b5-3abc-93bb-f5f999f7cc80",
"version":"2019.4.4",
"python_path":"d3m.primitives.regression.random_forest.SKlearn",
"name":"sklearn.ensemble.forest.RandomForestRegressor"
},
"arguments":{
"inputs":{
"type":"CONTAINER",
"data":"steps.7.produce"
},
"outputs":
{
"type":"CONTAINER",
"data":"steps.3.produce"
}
},
"outputs":[
{
"id":"produce"
}],
"hyperparams":{
"add_index_columns":{
"type":"VALUE",
"data":true
},
"return_result":{
"type":"VALUE",
"data":"new"
},
"use_semantic_types":{
"type":"VALUE",
"data":true
}
}
}
],
"name":"ISI_echo_reg:139758531711192",
"description":"",
"metric":"accuracy"
}
{
"problem":"196_autompg_problem",
"full_inputs":[
"196_autompg_dataset"
],
"train_inputs":[
"196_autompg_dataset_TRAIN"
],
"test_inputs":[
"196_autompg_dataset_TEST"
],
"score_inputs":[
"196_autompg_dataset_SCORE"
]
}
\ No newline at end of file
......@@ -16,7 +16,7 @@
"installation": [
{
"type": "PIP",
"package_uri": "git+https://github.com/brekelma/dsbox_corex@37a754bbd020169e5811e0c535c16b3ef060aba1#egg=dsbox-corex"
"package_uri": "git+https://github.com/brekelma/dsbox_corex@f506dc67815f897d32541d6ceee75d802c52b950#egg=dsbox-corex"
}
],
"algorithm_types": [
......@@ -282,5 +282,5 @@
}
},
"structural_type": "echo_ib.EchoIB",
"digest": "04b959a35427554753b22a831aa1b8e043e181a7dcfc9a271d1ecb10cf0d3c15"
}
\ No newline at end of file
"digest": "e3ba88812c84b8321129f32418ea15af2fa91ef08584f8d2bc0fc11e2f2def44"
}
......@@ -59,8 +59,7 @@
"id":"dbb3792d-a44b-4941-a88e-5520c0a23488",
"version":"0.1.0",
"python_path":"d3m.primitives.data_transformation.normalize_graphs.Common",
"name":"Normalize graphs",
"digest":"19b36c34cf5b5bdbd9e9c9af319e409893b1dc6538da0e2b1b1ab660abcf7979"
"name":"Normalize graphs"
},
"arguments":{
"inputs":{
......@@ -80,8 +79,7 @@
"id":"dfb8c278-5382-47cd-bd39-f9429890a239",