Commit f18cc449 authored by Sujen's avatar Sujen

Merge branch 'final_pipelines' into 'master'

ISI: disregard if other passes in time

See merge request datadrivendiscovery/primitives!320
parents ba13eb8e e82efff6
......@@ -13,7 +13,7 @@
"installation": [
{
"type": "PIP",
"package_uri": "git+https://github.com/brekelma/[email protected]9d26a8a0c0ae9394462768e90cd795685123da60#egg=dsbox_graphs"
"package_uri": "git+https://github.com/brekelma/[email protected]5d2a773cba8e672814dba120b30c3f2d48582c5d#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": "7574f62c1a6f1c29f0528f72edef98f74304cce517544334951613bf53d450f7"
"digest": "29e685691852c0ec4dc49408e9ff5481776949a6588ac19d257ce6982768062f"
}
{
"id":"2d02fdf3-9681-46ff-9470-62fd2222893c",
"schema":"https://metadata.datadrivendiscovery.org/schemas/v0/pipeline.json",
"created":"2019-06-06T05:29:22.640712Z",
"inputs":[
{
"name":"input dataset"
}
],
"outputs":[
{
"data":"steps.9.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"
},
"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"
},
"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"
},
"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":"1.5.3",
"python_path":"d3m.primitives.data_preprocessing.encoder.DSBOX",
"name":"ISI DSBox Data Encoder"
},
"arguments":{
"inputs":{
"type":"CONTAINER",
"data":"steps.2.produce"
}
},
"outputs":[
{
"id":"produce"
}
]
},
{
"type":"PRIMITIVE",
"primitive":{
"id":"7ddf2fd8-2f7f-4e53-96a7-0d9f5aeecf93",
"version":"1.5.3",
"python_path":"d3m.primitives.data_transformation.to_numeric.DSBOX",
"name":"ISI DSBox To Numeric DataFrame"
},
"arguments":{
"inputs":{
"type":"CONTAINER",
"data":"steps.4.produce"
}
},
"outputs":[
{
"id":"produce"
}
]
},
{
"type":"PRIMITIVE",
"primitive":{
"id":"7894b699-61e9-3a50-ac9f-9bc510466667",
"version":"1.5.3",
"python_path":"d3m.primitives.data_preprocessing.MeanImputation.DSBOX",
"name":"DSBox Mean Imputer"
},
"arguments":{
"inputs":{
"type":"CONTAINER",
"data":"steps.5.produce"
}
},
"outputs":[
{
"id":"produce"
}
]
},
{
"type":"PRIMITIVE",
"primitive":{
"id":"dsbox-multi-table-feature-scaler",
"version":"1.4.4",
"python_path":"d3m.primitives.normalization.IQRScaler.DSBOX",
"name":"DSBox feature scaler"
},
"arguments":{
"inputs":{
"type":"CONTAINER",
"data":"steps.6.produce"
}
},
"outputs":[
{
"id":"produce"
}
]
},
{
"type":"PRIMITIVE",
"primitive":{
"id":"d2d4fefc-0859-3522-91df-7e445f61a69b",
"version":"1.0.0",
"python_path":"d3m.primitives.feature_construction.corex_continuous.DSBOX",
"name":"CorexContinuous"
},
"arguments":{
"inputs":{
"type":"CONTAINER",
"data":"steps.7.produce"
}
},
"outputs":[
{
"id":"produce"
}
]
},
{
"type":"PRIMITIVE",
"primitive":{
"id":"1dd82833-5692-39cb-84fb-2455683075f3",
"version":"2019.4.4",
"python_path":"d3m.primitives.regression.random_forest.SKlearn",
"name":"sklearn.ensemble.forest.RandomForestRegressor"
},
"arguments":{
"inputs":{
"type":"CONTAINER",
"data":"steps.8.produce"
},
"outputs":
{
"type":"CONTAINER",
"data":"steps.4.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":"TA1_regression_template_1:139972216851912"
}
{
"problem":"26_radon_seed_problem",
"full_inputs":[
"26_radon_seed_dataset"
],
"train_inputs":[
"26_radon_seed_dataset_TRAIN"
],
"test_inputs":[
"26_radon_seed_dataset_TEST"
],
"score_inputs":[
"26_radon_seed_dataset_SCORE"
]
}
\ No newline at end of file
......@@ -9,7 +9,7 @@
],
"outputs":[
{
"data":"steps.8.produce",
"data":"steps.9.produce",
"name":"predictions of input dataset"
}
],
......@@ -189,6 +189,26 @@
}
]
},
{
"type":"PRIMITIVE",
"primitive":{
"id":"dsbox-multi-table-feature-scaler",
"version":"1.4.4",
"python_path":"d3m.primitives.normalization.IQRScaler.DSBOX",
"name":"DSBox feature scaler"
},
"arguments":{
"inputs":{
"type":"CONTAINER",
"data":"steps.6.produce"
}
},
"outputs":[
{
"id":"produce"
}
]
},
{
"type":"PRIMITIVE",
"primitive":{
......@@ -200,7 +220,7 @@
"arguments":{
"inputs":{
"type":"CONTAINER",
"data":"steps.6.produce"
"data":"steps.7.produce"
}
},
"outputs":[
......@@ -214,13 +234,13 @@
"primitive":{
"id":"18e63b10-c5b7-34bc-a670-f2c831d6b4bf",
"version":"1.0.0",
"python_path":"d3m.primitives.regression.corex_supervised.echo_linear",
"python_path":"d3m.primitives.regression.corex_supervised.EchoLinear",
"name":"EchoLinearRegression"
},
"arguments":{
"inputs":{
"type":"CONTAINER",
"data":"steps.7.produce"
"data":"steps.8.produce"
},
"outputs":{
"type":"CONTAINER",
......
{
"id":"c3da2f69-8273-4a24-a85a-944dbbb18f78",
"schema":"https://metadata.datadrivendiscovery.org/schemas/v0/pipeline.json",
"created":"2019-06-06T05:29:22.640712Z",
"inputs":[
{
"name":"input dataset"
}
],
"outputs":[
{
"data":"steps.9.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"
},
"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"
},
"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"
},
"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":"1.5.2",
"python_path":"d3m.primitives.data_preprocessing.encoder.DSBOX",
"name":"ISI DSBox Data Encoder"
},
"arguments":{
"inputs":{
"type":"CONTAINER",
"data":"steps.2.produce"
}
},
"outputs":[
{
"id":"produce"
}
]
},
{
"type":"PRIMITIVE",
"primitive":{
"id":"7ddf2fd8-2f7f-4e53-96a7-0d9f5aeecf93",
"version":"1.5.2",
"python_path":"d3m.primitives.data_transformation.to_numeric.DSBOX",
"name":"ISI DSBox To Numeric DataFrame"
},
"arguments":{
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"data":"steps.4.produce"
}
},
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}
]
},
{
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"python_path":"d3m.primitives.data_preprocessing.MeanImputation.DSBOX",
"name":"DSBox Mean Imputer"
},
"arguments":{
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"type":"CONTAINER",
"data":"steps.5.produce"
}
},
"outputs":[
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"id":"produce"
}
]
},
{
"type":"PRIMITIVE",
"primitive":{
"id":"dsbox-multi-table-feature-scaler",
"version":"1.4.4",
"python_path":"d3m.primitives.normalization.IQRScaler.DSBOX",
"name":"DSBox feature scaler"
},
"arguments":{
"inputs":{
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"data":"steps.6.produce"
}
},
"outputs":[
{
"id":"produce"
}
]
},
{