Commit d792b1a1 authored by Jeffrey Gleason's avatar Jeffrey Gleason

update ts annotations and VAR pipelines (automatic HP calculation)

parent bac8787d
......@@ -23,7 +23,7 @@
},
{
"type": "PIP",
"package_uri": "git+https://github.com/NewKnowledge/TimeSeries-D3M-Wrappers.git@6d93ce700c0751c48d57c9cd41cd7280acda103a#egg=TimeSeriesD3MWrappers"
"package_uri": "git+https://github.com/NewKnowledge/TimeSeries-D3M-Wrappers.git@61e3ce5f48b7b63aa25ecdd8b796a3188dcb3437#egg=TimeSeriesD3MWrappers"
}
],
"python_path": "d3m.primitives.time_series_classification.convolutional_neural_net.LSTM_FCN",
......@@ -262,5 +262,5 @@
},
"structural_type": "TimeSeriesD3MWrappers.LSTM_FCN.LSTM_FCN",
"description": "Primitive that applies a LSTM FCN (LSTM fully convolutional network) for time\nseries classification. The implementation is based off this paper:\nhttps://ieeexplore.ieee.org/document/8141873 and this base library:\nhttps://github.com/NewKnowledge/LSTM-FCN.\n\nTraining inputs: D3M dataset with features and labels, and D3M indices\nOutputs: D3M dataset with predicted labels and D3M indices\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": "b8fd624f171ea538ee2f8af1123af94abb671d7ac80dce6f8d04ee326d06c4bb"
"digest": "d6d85baba6879e1926ecbe47336c92c68c4a7689018fff4f0caa915bb127084b"
}
......@@ -20,7 +20,7 @@
},
{
"type": "PIP",
"package_uri": "git+https://github.com/NewKnowledge/TimeSeries-D3M-Wrappers.git@6d93ce700c0751c48d57c9cd41cd7280acda103a#egg=TimeSeriesD3MWrappers"
"package_uri": "git+https://github.com/NewKnowledge/TimeSeries-D3M-Wrappers.git@61e3ce5f48b7b63aa25ecdd8b796a3188dcb3437#egg=TimeSeriesD3MWrappers"
}
],
"python_path": "d3m.primitives.time_series_classification.k_neighbors.Kanine",
......@@ -211,5 +211,5 @@
},
"structural_type": "TimeSeriesD3MWrappers.Kanine.Kanine",
"description": "Primitive that applies the k nearest neighbor classification algorithm to time series data.\nThe tslearn KNeighborsTimeSeriesClassifier implementation is wrapped.\n\nTraining inputs: D3M dataset with features and labels, and D3M indices\nOutputs: D3M dataset with predicted labels and D3M indices\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": "e27a451832a5514f97336e345d329241abe0638c78405a3e02f6051d8c8d75a6"
"digest": "82a2914cda9a8fc0a3e3b4267ad8e84b50d107fcdc8cc1982a163a3119432604"
}
......@@ -21,7 +21,7 @@
},
{
"type": "PIP",
"package_uri": "git+https://github.com/NewKnowledge/TimeSeries-D3M-Wrappers.git@6d93ce700c0751c48d57c9cd41cd7280acda103a#egg=TimeSeriesD3MWrappers"
"package_uri": "git+https://github.com/NewKnowledge/TimeSeries-D3M-Wrappers.git@61e3ce5f48b7b63aa25ecdd8b796a3188dcb3437#egg=TimeSeriesD3MWrappers"
}
],
"python_path": "d3m.primitives.time_series_classification.shapelet_learning.Shallot",
......@@ -290,5 +290,5 @@
},
"structural_type": "TimeSeriesD3MWrappers.Shallot.Shallot",
"description": "Primitive that applies the shapelet classification algorithm to time series data. The shapelet\nclassification algorithm was introduced by Grabocka et al. in\nhttps://www.ismll.uni-hildesheim.de/pub/pdfs/grabocka2014e-kdd.pdf and learns discriminative subsequences\n(\"shapes\") that can be used to classify series.\n\nTraining inputs: D3M dataset with features and labels, and D3M indices\nOutputs: D3M dataset with predicted labels and D3M indices\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": "e8b61e58c13ade7024cf65fdcab1ec65dc6dffaaa183cd40f8c1533910534939"
"digest": "e75dbd4a2c9915b6a99dbb0dc5fefb0ca4247846fdd51920cb556a9c87762ef5"
}
......@@ -20,7 +20,7 @@
},
{
"type": "PIP",
"package_uri": "git+https://github.com/NewKnowledge/TimeSeries-D3M-Wrappers.git@6d93ce700c0751c48d57c9cd41cd7280acda103a#egg=TimeSeriesD3MWrappers"
"package_uri": "git+https://github.com/NewKnowledge/TimeSeries-D3M-Wrappers.git@61e3ce5f48b7b63aa25ecdd8b796a3188dcb3437#egg=TimeSeriesD3MWrappers"
}
],
"python_path": "d3m.primitives.time_series_forecasting.arima.Parrot",
......@@ -295,5 +295,5 @@
},
"structural_type": "TimeSeriesD3MWrappers.Parrot.Parrot",
"description": "Primitive that applies an ARIMA forecasting model to time series data. The AR and MA terms\nof the ARIMA model are automatically selected and stationarity is induced before fitting\nthe model.\n\nTraining inputs: D3M dataset with training time series observations and a time series index\n column\nOutputs: D3M dataset with predicted observations for a length of 'n_periods' in the future\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": "d658d346055df337cd30d1e50d88de9de0f01da9950603369e508bd2cfce6070"
"digest": "8ebcf4a39279ed29cc9d9561851083dc4393c985296067dd5498ef82a216641a"
}
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{"problem": "56_sunspots_monthly_problem","full_inputs": ["56_sunspots_monthly_dataset"],"train_inputs": ["56_sunspots_monthly_dataset_TRAIN"],"test_inputs": ["56_sunspots_monthly_dataset_TEST"],"score_inputs": ["56_sunspots_monthly_dataset_SCORE"]}
\ No newline at end of file
{"id": "b3c78817-0722-4480-a617-ab7657a51a11", "schema": "https://metadata.datadrivendiscovery.org/schemas/v0/pipeline.json", "created": "2019-09-16T19:57:04.604883Z", "inputs": [{"name": "inputs"}], "outputs": [{"data": "steps.3.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", "digest": "6a80776d244347f0d29f4358df1cd0286c25f67e03a7e2ee517c6e853e6a9d1f"}, "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": "a141e6821de7ae586968b0986237745a5510850e6940cf946db9d50d3828b030"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "steps.0.produce"}}, "outputs": [{"id": "produce"}], "hyperparams": {"dataframe_resource": {"type": "VALUE", "data": "learningData"}}}, {"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": "76b5a479-c209-4d94-92b5-7eba7a4d4499", "version": "1.0.1", "python_path": "d3m.primitives.time_series_forecasting.vector_autoregression.VAR", "name": "VAR", "digest": "6225643608aef2838ec0731c52336985ecd6a31edb30cd62bd24cc988509222e"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "steps.2.produce"}, "outputs": {"type": "CONTAINER", "data": "steps.2.produce"}}, "outputs": [{"id": "produce"}]}], "digest": "ff9f325b9323266cc9a7bf99ca10652918138a46e5a23ac32de5b1b2ebeb4a25"}
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{"id": "c8c38951-dc30-401a-80a5-585d94ad9740", "schema": "https://metadata.datadrivendiscovery.org/schemas/v0/pipeline.json", "created": "2019-06-16T21:44:55.374879Z", "inputs": [{"name": "inputs"}], "outputs": [{"data": "steps.2.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", "digest": "00ae7955cc0abce2a3ddee96247209f3395009ae6553c7ce8caa577e402754db"}, "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": "0d46a2c5bc374e305682dc4f1c322518c07638153a8365034a513ea46960802b"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "steps.0.produce"}}, "outputs": [{"id": "produce"}], "hyperparams": {"dataframe_resource": {"type": "VALUE", "data": "learningData"}}}, {"type": "PRIMITIVE", "primitive": {"id": "76b5a479-c209-4d94-92b5-7eba7a4d4499", "version": "1.0.1", "python_path": "d3m.primitives.time_series_forecasting.vector_autoregression.VAR", "name": "VAR", "digest": "0854014c0399e5b09aefb7cefeaeaf58956f7632b73c54d8111af7069540c409"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "steps.1.produce"}, "outputs": {"type": "CONTAINER", "data": "steps.1.produce"}}, "outputs": [{"id": "produce"}], "hyperparams": {"filter_index_one": {"type": "VALUE", "data": 2}, "filter_index_two": {"type": "VALUE", "data": 1}, "n_periods": {"type": "VALUE", "data": 58}, "specific_intervals": {"type": "VALUE", "data": {"encoding": "pickle", "value": "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"}}, "datetime_index_unit": {"type": "VALUE", "data": "D"}}}], "digest": "009d9acdf6052862aa78a669142c9854e2bb49683ca2efb44f0949c1b59b17d6"}
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{"id": "e43edcae-00ca-452d-9d49-97ceb060c73b", "schema": "https://metadata.datadrivendiscovery.org/schemas/v0/pipeline.json", "created": "2019-09-16T19:57:35.800622Z", "inputs": [{"name": "inputs"}], "outputs": [{"data": "steps.3.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", "digest": "6a80776d244347f0d29f4358df1cd0286c25f67e03a7e2ee517c6e853e6a9d1f"}, "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": "a141e6821de7ae586968b0986237745a5510850e6940cf946db9d50d3828b030"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "steps.0.produce"}}, "outputs": [{"id": "produce"}], "hyperparams": {"dataframe_resource": {"type": "VALUE", "data": "learningData"}}}, {"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": "76b5a479-c209-4d94-92b5-7eba7a4d4499", "version": "1.0.1", "python_path": "d3m.primitives.time_series_forecasting.vector_autoregression.VAR", "name": "VAR", "digest": "6225643608aef2838ec0731c52336985ecd6a31edb30cd62bd24cc988509222e"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "steps.2.produce"}, "outputs": {"type": "CONTAINER", "data": "steps.2.produce"}}, "outputs": [{"id": "produce"}]}], "digest": "e6bbc6da47965fae710516654df1be1ecdd06e2e1e2b8aa856302bbb53eb0785"}
\ No newline at end of file
......@@ -20,7 +20,7 @@
},
{
"type": "PIP",
"package_uri": "git+https://github.com/NewKnowledge/TimeSeries-D3M-Wrappers.git@6d93ce700c0751c48d57c9cd41cd7280acda103a#egg=TimeSeriesD3MWrappers"
"package_uri": "git+https://github.com/NewKnowledge/TimeSeries-D3M-Wrappers.git@61e3ce5f48b7b63aa25ecdd8b796a3188dcb3437#egg=TimeSeriesD3MWrappers"
}
],
"python_path": "d3m.primitives.time_series_forecasting.vector_autoregression.VAR",
......@@ -59,73 +59,6 @@
"is_configuration": false,
"min_size": 0
},
"datetime_index_unit": {
"type": "d3m.metadata.hyperparams.Hyperparameter",
"default": null,
"structural_type": "typing.Union[NoneType, str]",
"semantic_types": [
"https://metadata.datadrivendiscovery.org/types/ControlParameter"
],
"description": "unit of the datetime column if datetime column is integer or float"
},
"filter_index_one": {
"type": "d3m.metadata.hyperparams.Hyperparameter",
"default": null,
"structural_type": "typing.Union[NoneType, int]",
"semantic_types": [
"https://metadata.datadrivendiscovery.org/types/ControlParameter"
],
"description": "top-level index of column in input dataset that contain unique identifiers of different time series"
},
"filter_index_two": {
"type": "d3m.metadata.hyperparams.Hyperparameter",
"default": null,
"structural_type": "typing.Union[NoneType, int]",
"semantic_types": [
"https://metadata.datadrivendiscovery.org/types/ControlParameter"
],
"description": "second-level index of column in input dataset that contain unique identifiers of different time series"
},
"n_periods": {
"type": "d3m.metadata.hyperparams.UniformInt",
"default": 61,
"structural_type": "int",
"semantic_types": [
"https://metadata.datadrivendiscovery.org/types/ControlParameter"
],
"description": "number of periods to predict",
"lower": 1,
"upper": 9223372036854775807,
"lower_inclusive": true,
"upper_inclusive": false
},
"interval": {
"type": "d3m.metadata.hyperparams.Hyperparameter",
"default": null,
"structural_type": "typing.Union[NoneType, int]",
"semantic_types": [
"https://metadata.datadrivendiscovery.org/types/ControlParameter"
],
"description": "interval with which to sample future predictions"
},
"specific_intervals": {
"type": "d3m.metadata.hyperparams.Hyperparameter",
"default": null,
"structural_type": "typing.Union[NoneType, typing.List[typing.List[int]]]",
"semantic_types": [
"https://metadata.datadrivendiscovery.org/types/ControlParameter"
],
"description": "defines specific prediction intervals if different time series require different intervals for output predictions"
},
"datetime_interval_exception": {
"type": "d3m.metadata.hyperparams.Hyperparameter",
"default": null,
"structural_type": "typing.Union[NoneType, str]",
"semantic_types": [
"https://metadata.datadrivendiscovery.org/types/ControlParameter"
],
"description": "to handle different prediction intervals (stock market dataset). If this HP is set, primitive will just make next forecast for this datetime value (not multiple forecasts at multiple intervals"
},
"max_lags": {
"type": "d3m.metadata.hyperparams.UniformInt",
"default": 10,
......@@ -160,15 +93,6 @@
"upper": 365,
"lower_inclusive": true,
"upper_inclusive": false
},
"weights_filter_value": {
"type": "d3m.metadata.hyperparams.Hyperparameter",
"default": null,
"structural_type": "typing.Union[NoneType, str]",
"semantic_types": [
"https://metadata.datadrivendiscovery.org/types/ControlParameter"
],
"description": "value to select a filter from column filter index for which to return correlation coefficient matrix."
}
},
"arguments": {
......@@ -329,5 +253,5 @@
},
"structural_type": "TimeSeriesD3MWrappers.VAR.VAR",
"description": "Primitive that applies a VAR multivariate forecasting model to time series data. The VAR\nimplementation comes from the statsmodels library. The primitive is implemented with a number\nof hyperparameters to handle hierarchical indices and forecasting various timelines and\nintervals into the future.\n\nTraining inputs: D3M dataset with multivariate time series (potentially structured according to\n hierarchical indices) and a time series index column.\nOutputs: D3M dataset with predicted observations for a length of 'n_periods' at a certain 'interval'\n into the future\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": "5cb398ceb5b4a8706bcdebc2f10e8976b2cfab560d9b3a1c04bb81e81893da44"
"digest": "6225643608aef2838ec0731c52336985ecd6a31edb30cd62bd24cc988509222e"
}
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