Commit b84b77f8 authored by Sujen's avatar Sujen

Merge 'Jg/v5.8'

parent 3873ee9e
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\ No newline at end of file
{
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"uu5_heartstatlog_dataset"
],
"train_inputs": [
"uu5_heartstatlog_dataset_TRAIN"
],
"test_inputs": [
"uu5_heartstatlog_dataset_TEST"
],
"score_inputs": [
"uu5_heartstatlog_dataset_SCORE"
]
}
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\ No newline at end of file
{
"problem": "uu5_heartstatlog_problem",
"full_inputs": [
"uu5_heartstatlog_dataset"
],
"train_inputs": [
"uu5_heartstatlog_dataset_TRAIN"
],
"test_inputs": [
"uu5_heartstatlog_dataset_TEST"
],
"score_inputs": [
"uu5_heartstatlog_dataset_SCORE"
]
}
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\ No newline at end of file
{
"problem": "uu5_heartstatlog_problem",
"full_inputs": [
"uu5_heartstatlog_dataset"
],
"train_inputs": [
"uu5_heartstatlog_dataset_TRAIN"
],
"test_inputs": [
"uu5_heartstatlog_dataset_TEST"
],
"score_inputs": [
"uu5_heartstatlog_dataset_SCORE"
]
}
......@@ -20,7 +20,7 @@
},
{
"type": "PIP",
"package_uri": "git+https://github.com/NewKnowledge/TimeSeries-D3M-Wrappers.git@a4e60264eab737d4d04e8e3f4792fa9067a3d142#egg=TimeSeriesD3MWrappers"
"package_uri": "git+https://github.com/NewKnowledge/TimeSeries-D3M-Wrappers.git@b88cb8dd36d39cceebfdf18c930fb316dd12a6f9#egg=TimeSeriesD3MWrappers"
}
],
"python_path": "d3m.primitives.clustering.hdbscan.Hdbscan",
......@@ -241,6 +241,6 @@
}
},
"structural_type": "TimeSeriesD3MWrappers.Hdbscan.Hdbscan",
"description": "Produce primitive's best guess for the cluster number of each series using Hierarchical Density-Based\nClustering or Density-Based Clustering.\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": "932b64fbb3cc079f76ea4eb2965aeedda0898f1a1eb207336bbd47d2ac704d9a"
}
\ No newline at end of file
"description": "Primitive that applies Hierarchical Density-Based Clustering or Density-Based Clustering\nalgorithms to time series data. This is an unsupervised, clustering primitive, but has been\nrepresentend as a supervised classification problem to produce a compliant primitive.\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": "164c049d686e4a191505c6fd451f0a74dd1e6319793b31df8bdba9d6adc7c333"
}
......@@ -21,7 +21,7 @@
},
{
"type": "PIP",
"package_uri": "git+https://github.com/NewKnowledge/TimeSeries-D3M-Wrappers.git@a4e60264eab737d4d04e8e3f4792fa9067a3d142#egg=TimeSeriesD3MWrappers"
"package_uri": "git+https://github.com/NewKnowledge/TimeSeries-D3M-Wrappers.git@b88cb8dd36d39cceebfdf18c930fb316dd12a6f9#egg=TimeSeriesD3MWrappers"
}
],
"python_path": "d3m.primitives.clustering.k_means.Sloth",
......@@ -224,6 +224,6 @@
"params": {}
},
"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": "07126398909ef24cdb634526f7932a1e12f38d228fa35d68baf30ea8e2427de6"
}
\ No newline at end of file
"description": "Primitive that applies kmeans clustering to time series data. Algorithm options are 'GlobalAlignmentKernelKMeans'\nor 'TimeSeriesKMeans,' both of which are bootstrapped from the base library tslearn.clustering. This is an unsupervised,\nclustering primitive, but has been represented as a supervised classification problem to produce a compliant primitive.\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": "f003dff2c915b6977f7c93f26a70e8498320f5253e7e236bd619a9886939c5ac"
}
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\ No newline at end of file
{
"problem": "uu5_heartstatlog_problem",
"full_inputs": [
"uu5_heartstatlog_dataset"
],
"train_inputs": [
"uu5_heartstatlog_dataset_TRAIN"
],
"test_inputs": [
"uu5_heartstatlog_dataset_TEST"
],
"score_inputs": [
"uu5_heartstatlog_dataset_SCORE"
]
}
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\ No newline at end of file
{
"problem": "uu5_heartstatlog_problem",
"full_inputs": [
"uu5_heartstatlog_dataset"
],
"train_inputs": [
"uu5_heartstatlog_dataset_TRAIN"
],
"test_inputs": [
"uu5_heartstatlog_dataset_TEST"
],
"score_inputs": [
"uu5_heartstatlog_dataset_SCORE"
]
}
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\ No newline at end of file
{
"problem": "uu5_heartstatlog_problem",
"full_inputs": [
"uu5_heartstatlog_dataset"
],
"train_inputs": [
"uu5_heartstatlog_dataset_TRAIN"
],
"test_inputs": [
"uu5_heartstatlog_dataset_TEST"
],
"score_inputs": [
"uu5_heartstatlog_dataset_SCORE"
]
}
......@@ -20,7 +20,7 @@
},
{
"type": "PIP",
"package_uri": "git+https://github.com/NewKnowledge/TimeSeries-D3M-Wrappers.git@a4e60264eab737d4d04e8e3f4792fa9067a3d142#egg=TimeSeriesD3MWrappers"
"package_uri": "git+https://github.com/NewKnowledge/TimeSeries-D3M-Wrappers.git@b88cb8dd36d39cceebfdf18c930fb316dd12a6f9#egg=TimeSeriesD3MWrappers"
}
],
"python_path": "d3m.primitives.time_series_classification.k_neighbors.Kanine",
......@@ -210,6 +210,6 @@
"params": {}
},
"structural_type": "TimeSeriesD3MWrappers.Kanine.Kanine",
"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) 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": "26e52bbfa6547bc4b608134a87b59f9889f981cfe77d29d2f7611a03517c8e22"
}
\ No newline at end of file
"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": "652c9e412f8413b934d2101f2f8b2fc750a1d090c7defec5bab6b8b5e52ca6d8"
}
......@@ -21,7 +21,7 @@
},
{
"type": "PIP",
"package_uri": "git+https://github.com/NewKnowledge/TimeSeries-D3M-Wrappers.git@a4e60264eab737d4d04e8e3f4792fa9067a3d142#egg=TimeSeriesD3MWrappers"
"package_uri": "git+https://github.com/NewKnowledge/TimeSeries-D3M-Wrappers.git@b88cb8dd36d39cceebfdf18c930fb316dd12a6f9#egg=TimeSeriesD3MWrappers"
}
],
"python_path": "d3m.primitives.time_series_classification.shapelet_learning.Shallot",
......@@ -263,6 +263,6 @@
"params": {}
},
"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": "aeee3cb0997b057bcc282caf1a5d9a2e3c45e80e4fd5bc15c557d35b625c484e"
}
\ No newline at end of file
"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": "6c961848ed7b7e4603537dc44886d94b671120d627afcbf098aa8c0a5a7e298e"
}
......@@ -20,7 +20,7 @@
},
{
"type": "PIP",
"package_uri": "git+https://github.com/NewKnowledge/TimeSeries-D3M-Wrappers.git@a4e60264eab737d4d04e8e3f4792fa9067a3d142#egg=TimeSeriesD3MWrappers"
"package_uri": "git+https://github.com/NewKnowledge/TimeSeries-D3M-Wrappers.git@b88cb8dd36d39cceebfdf18c930fb316dd12a6f9#egg=TimeSeriesD3MWrappers"
}
],
"python_path": "d3m.primitives.time_series_forecasting.arima.Parrot",
......@@ -232,6 +232,6 @@
"params": {}
},
"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": "56369c29680736d2239b8d32cc07647d2bea9f338841ca42f29113f49f7b9cc0"
}
\ No newline at end of file
"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": "a895f56c10d886de1a70f8452c518f1a6b51b82555ffe65bdcd884ba49c1ecb6"
}
......@@ -20,7 +20,7 @@
},
{
"type": "PIP",
"package_uri": "git+https://github.com/NewKnowledge/TimeSeries-D3M-Wrappers.git@a4e60264eab737d4d04e8e3f4792fa9067a3d142#egg=TimeSeriesD3MWrappers"
"package_uri": "git+https://github.com/NewKnowledge/TimeSeries-D3M-Wrappers.git@b88cb8dd36d39cceebfdf18c930fb316dd12a6f9#egg=TimeSeriesD3MWrappers"
}
],
"python_path": "d3m.primitives.time_series_forecasting.vector_autoregression.VAR",
......@@ -315,6 +315,6 @@
"params": {}
},
"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": "f7298c6a8e0d515f64d7cfd976278a789f1a8916c1c865a70251901d5733150b"
}
\ No newline at end of file
"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": "dc901e51dbaed4b4ef78439df0787db43605be4327eb81eaca828efd7f96708b"
}
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