Commit 3943af8f authored by Mitar's avatar Mitar

Merge branch 'master' into 'master'

Master

See merge request !61
parents c616c01c 8d96e145
{"id": "73e6aead-ec0f-4ea6-a886-92a91e51c539", "schema": "https://metadata.datadrivendiscovery.org/schemas/v0/pipeline.json", "created": "2019-04-24T19:09:17.669597Z", "context": "TESTING", "inputs": [{"name": "inputs"}], "outputs": [{"data": "steps.0.produce", "name": "output predictions"}], "steps": [{"type": "PRIMITIVE", "primitive": {"id": "ca014488-6004-4b54-9403-5920fbe5a834", "version": "1.0.2", "python_path": "d3m.primitives.clustering.hdbscan.Hdbscan", "name": "hdbscan"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "inputs.0"}}, "outputs": [{"id": "produce"}]}]}
{
"problem": "66_chlorineConcentration_problem",
"full_inputs": [
"66_chlorineConcentration_dataset"
],
"train_inputs": [
"66_chlorineConcentration_dataset_TRAIN"
],
"test_inputs": [
"66_chlorineConcentration_dataset_TEST"
],
"score_inputs": [
"66_chlorineConcentration_dataset_SCORE"
]
}
......@@ -21,7 +21,7 @@
},
{
"type": "PIP",
"package_uri": "git+https://github.com/NewKnowledge/TimeSeries-D3M-Wrappers.git@7344aab8ef611492c4a72e3f015cae093dceee95#egg=TimeSeriesD3MWrappers"
"package_uri": "git+https://github.com/NewKnowledge/TimeSeries-D3M-Wrappers.git@fbb5e81f6d20620cee1cd3dbbd902f616fcae65f#egg=TimeSeriesD3MWrappers"
}
],
"python_path": "d3m.primitives.clustering.k_means.Sloth",
......@@ -224,5 +224,5 @@
},
"structural_type": "TimeSeriesD3MWrappers.Storc.Storc",
"description": "Produce primitive's best guess for the cluster number of each series.\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": "5c62a19922a60b7dede6459e764c6a59bf8efa929180172bfe94650c9592526b"
"digest": "c60dfcda8ea542a82a12df90d1d23a25df957e03eeb39ebc94b4f6c273818edb"
}
......@@ -105,9 +105,9 @@
],
"primitive": {
"id": "d016df89-de62-3c53-87ed-c06bb6a23cde",
"name": "sklearn.preprocessing.imputation.Imputer",
"name": "sklearn.impute.SimpleImputer",
"python_path": "d3m.primitives.data_cleaning.imputer.SKlearn",
"version": "v2019.2.12"
"version": "2019.4.4"
},
"type": "PRIMITIVE"
},
......@@ -141,7 +141,7 @@
"id": "0ae7d42d-f765-3348-a28c-57d94880aa6a",
"name": "sklearn.svm.classes.SVC",
"python_path": "d3m.primitives.classification.svc.SKlearn",
"version": "v2019.2.12"
"version": "2019.4.4"
},
"type": "PRIMITIVE"
},
......
{"id": "da46a492-d5e0-4e87-a5be-2d7627d1b7f5", "schema": "https://metadata.datadrivendiscovery.org/schemas/v0/pipeline.json", "created": "2019-04-24T19:10:59.206234Z", "context": "TESTING", "inputs": [{"name": "inputs"}], "outputs": [{"data": "steps.0.produce", "name": "output predictions"}], "steps": [{"type": "PRIMITIVE", "primitive": {"id": "2d6d3223-1b3c-49cc-9ddd-50f571818268", "version": "1.0.2", "python_path": "d3m.primitives.time_series_classification.k_neighbors.Kanine", "name": "kanine"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "inputs.0"}, "outputs": {"type": "CONTAINER", "data": "inputs.0"}}, "outputs": [{"id": "produce"}]}]}
{
"problem": "66_chlorineConcentration_problem",
"full_inputs": [
"66_chlorineConcentration_dataset"
],
"train_inputs": [
"66_chlorineConcentration_dataset_TRAIN"
],
"test_inputs": [
"66_chlorineConcentration_dataset_TEST"
],
"score_inputs": [
"66_chlorineConcentration_dataset_SCORE"
]
}
......@@ -21,7 +21,7 @@
},
{
"type": "PIP",
"package_uri": "git+https://github.com/NewKnowledge/TimeSeries-D3M-Wrappers.git@7344aab8ef611492c4a72e3f015cae093dceee95#egg=TimeSeriesD3MWrappers"
"package_uri": "git+https://github.com/NewKnowledge/TimeSeries-D3M-Wrappers.git@fbb5e81f6d20620cee1cd3dbbd902f616fcae65f#egg=TimeSeriesD3MWrappers"
}
],
"python_path": "d3m.primitives.time_series_classification.shapelet_learning.Shallot",
......@@ -259,5 +259,5 @@
},
"structural_type": "TimeSeriesD3MWrappers.Shallot.Shallot",
"description": "Produce primitive's classifications for new time series data The input is a numpy ndarray of\nsize (number_of_time_series, time_series_length, dimension) containing new time series.\nThe output is a numpy ndarray containing a predicted class for each of the input time series.\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": "ba75b10ed6ef2ae1e8d4733a4b39a37e49f0ec286c5d92670cef6ac056def470"
"digest": "5c308cb4642dcec37105fa792bb9a30b63179a75db52dd3913a8711f8cad4acc"
}
{"id": "40a22d7d-0247-4475-896f-e5f8aacac378", "schema": "https://metadata.datadrivendiscovery.org/schemas/v0/pipeline.json", "created": "2019-04-24T19:13:24.427146Z", "context": "TESTING", "inputs": [{"name": "inputs"}], "outputs": [{"data": "steps.1.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"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "inputs.0"}}, "outputs": [{"id": "produce"}], "hyperparams": {"dataframe_resource": {"type": "VALUE", "data": "learningData"}}}, {"type": "PRIMITIVE", "primitive": {"id": "d473d487-2c32-49b2-98b5-a2b48571e07c", "version": "1.0.3", "python_path": "d3m.primitives.time_series_forecasting.arima.Parrot", "name": "parrot"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "steps.0.produce"}, "outputs": {"type": "CONTAINER", "data": "steps.0.produce"}}, "outputs": [{"id": "produce"}], "hyperparams": {"seasonal_differencing": {"type": "VALUE", "data": 11}, "n_periods": {"type": "VALUE", "data": 29}}}]}
{"id": "d60c0d34-919e-4b8e-925d-ac6da0671eed", "schema": "https://metadata.datadrivendiscovery.org/schemas/v0/pipeline.json", "created": "2019-05-01T16:53:49.172945Z", "context": "TESTING", "inputs": [{"name": "inputs"}], "outputs": [{"data": "steps.1.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": "6ee6b94c42491892321d83c7a88a6a93b173c532d9783eafd520c7fd4bd03c55"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "inputs.0"}}, "outputs": [{"id": "produce"}], "hyperparams": {"dataframe_resource": {"type": "VALUE", "data": "learningData"}}}, {"type": "PRIMITIVE", "primitive": {"id": "d473d487-2c32-49b2-98b5-a2b48571e07c", "version": "1.0.3", "python_path": "d3m.primitives.time_series_forecasting.arima.Parrot", "name": "parrot", "digest": "487941aa502738ad68c21496e08d8dfa4ef507c5a424342ff4094942f96848c4"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "steps.0.produce"}, "outputs": {"type": "CONTAINER", "data": "steps.0.produce"}}, "outputs": [{"id": "produce"}], "hyperparams": {"seasonal_differencing": {"type": "VALUE", "data": 11}, "n_periods": {"type": "VALUE", "data": 21}}}], "digest": "5bff4f7f6afbf81113fd434ae2302bb93af22e1dbfe4f1722da913a8940c11f3"}
......@@ -20,7 +20,7 @@
},
{
"type": "PIP",
"package_uri": "git+https://github.com/NewKnowledge/TimeSeries-D3M-Wrappers.git@7344aab8ef611492c4a72e3f015cae093dceee95#egg=TimeSeriesD3MWrappers"
"package_uri": "git+https://github.com/NewKnowledge/TimeSeries-D3M-Wrappers.git@fbb5e81f6d20620cee1cd3dbbd902f616fcae65f#egg=TimeSeriesD3MWrappers"
}
],
"python_path": "d3m.primitives.time_series_forecasting.arima.Parrot",
......@@ -231,5 +231,5 @@
},
"structural_type": "TimeSeriesD3MWrappers.Parrot.Parrot",
"description": "Produce the primitive's prediction for future time series data. The output\nis a list of length 'n_periods' that contains a prediction for each of 'n_periods'\nfuture time periods. 'n_periods' is a hyperparameter that must be set before making the prediction.\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": "8dd10aafa71f683ffdcf7de79d5811680d94d985e490b75782c1a874753c265b"
"digest": "487941aa502738ad68c21496e08d8dfa4ef507c5a424342ff4094942f96848c4"
}
{"id": "566f1bf2-3309-4c7e-b3b1-bd3752786a28", "schema": "https://metadata.datadrivendiscovery.org/schemas/v0/pipeline.json", "created": "2019-05-01T17:06:35.395271Z", "context": "TESTING", "inputs": [{"name": "inputs"}], "outputs": [{"data": "steps.1.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": "6ee6b94c42491892321d83c7a88a6a93b173c532d9783eafd520c7fd4bd03c55"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "inputs.0"}}, "outputs": [{"id": "produce"}], "hyperparams": {"dataframe_resource": {"type": "VALUE", "data": "learningData"}}}, {"type": "PRIMITIVE", "primitive": {"id": "76b5a479-c209-4d94-92b5-7eba7a4d4499", "version": "1.0.0", "python_path": "d3m.primitives.time_series_forecasting.vector_autoregression.VAR", "name": "VAR", "digest": "a72aafdedd4fed2e0d54507080fffc1cc2d282a1779cff0dfedb3993aa4a2479"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "steps.0.produce"}, "outputs": {"type": "CONTAINER", "data": "steps.0.produce"}}, "outputs": [{"id": "produce"}], "hyperparams": {"filter_index": {"type": "VALUE", "data": 1}, "datetime_filter": {"type": "VALUE", "data": 2}, "n_periods": {"type": "VALUE", "data": 52}, "interval": {"type": "VALUE", "data": 26}, "datetime_interval_exception": {"type": "VALUE", "data": "2017"}}}], "digest": "d8214d4b2dff24c2f4e76eb6665da2d364b10aaf79e0d05fc11f191fd8281997"}
{"id": "f12e6903-d558-4365-87ab-8e0db3fe0c3a", "schema": "https://metadata.datadrivendiscovery.org/schemas/v0/pipeline.json", "created": "2019-04-24T19:17:34.501834Z", "context": "TESTING", "inputs": [{"name": "inputs"}], "outputs": [{"data": "steps.1.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"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "inputs.0"}}, "outputs": [{"id": "produce"}], "hyperparams": {"dataframe_resource": {"type": "VALUE", "data": "learningData"}}}, {"type": "PRIMITIVE", "primitive": {"id": "76b5a479-c209-4d94-92b5-7eba7a4d4499", "version": "1.0.0", "python_path": "d3m.primitives.time_series_forecasting.vector_autoregression.VAR", "name": "VAR"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "steps.0.produce"}, "outputs": {"type": "CONTAINER", "data": "steps.0.produce"}}, "outputs": [{"id": "produce"}], "hyperparams": {"filter_index": {"type": "VALUE", "data": 1}, "datetime_filter": {"type": "VALUE", "data": 2}, "n_periods": {"type": "VALUE", "data": 52}, "interval": {"type": "VALUE", "data": 26}, "datetime_interval_exception": {"type": "VALUE", "data": "2017"}}}]}
......@@ -20,7 +20,7 @@
},
{
"type": "PIP",
"package_uri": "git+https://github.com/NewKnowledge/TimeSeries-D3M-Wrappers.git@7344aab8ef611492c4a72e3f015cae093dceee95#egg=TimeSeriesD3MWrappers"
"package_uri": "git+https://github.com/NewKnowledge/TimeSeries-D3M-Wrappers.git@fbb5e81f6d20620cee1cd3dbbd902f616fcae65f#egg=TimeSeriesD3MWrappers"
}
],
"python_path": "d3m.primitives.time_series_forecasting.vector_autoregression.VAR",
......@@ -103,13 +103,21 @@
"description": "index of column in input dataset that contain unique identifiers of different time series"
},
"datetime_index": {
"type": "d3m.metadata.hyperparams.Hyperparameter",
"default": 0,
"structural_type": "typing.Union[NoneType, int]",
"type": "d3m.metadata.hyperparams.Set",
"default": [],
"structural_type": "typing.Sequence[int]",
"semantic_types": [
"https://metadata.datadrivendiscovery.org/types/ControlParameter"
],
"description": "if multiple datetime indices exist, this HP specifies which to apply to training data"
"description": "if multiple datetime indices exist, this HP specifies which to apply to training data. If None, the primitive assumes there is only one datetime index. This HP can also specify multiple indices which should be concatenated to form datetime_index",
"elements": {
"type": "d3m.metadata.hyperparams.Hyperparameter",
"default": -1,
"structural_type": "int",
"semantic_types": []
},
"is_configuration": false,
"min_size": 0
},
"arma_p": {
"type": "d3m.metadata.hyperparams.Hyperparameter",
......@@ -297,5 +305,5 @@
},
"structural_type": "TimeSeriesD3MWrappers.VAR.VAR",
"description": "Produce primitive's prediction for future time series data. The output is a data frame containing the d3m index and a\nforecast for each of the 'n_periods' future time periods, modified if desired by the 'interval' HP. The default is a\nfuture forecast for each of the selected input variables. This can be modified to just one output variable with\nthe associated 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": "246192f51513e4c8ddfd71399f824018527c053e21ef622ec5113fb6ece66839"
"digest": "a72aafdedd4fed2e0d54507080fffc1cc2d282a1779cff0dfedb3993aa4a2479"
}
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