Commit 8a1e2508 authored by Sujen's avatar Sujen

Merge branch 'clustering_pipelines' into 'master'

Clustering pipelines

See merge request datadrivendiscovery/primitives!295
parents 1bf0c631 48fc49c7
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{"problem": "LL1_CinC_ECG_torso_problem","full_inputs": ["LL1_CinC_ECG_torso_dataset"],"train_inputs": ["LL1_CinC_ECG_torso_dataset_TRAIN"],"test_inputs": ["LL1_CinC_ECG_torso_dataset_TEST"],"score_inputs": ["LL1_CinC_ECG_torso_dataset_SCORE"]}
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......@@ -21,7 +21,7 @@
},
{
"type": "PIP",
"package_uri": "git+https://github.com/NewKnowledge/[email protected]ca557e5250c383bbb80a71d25543f686eed8fab9#egg=TimeSeriesD3MWrappers"
"package_uri": "git+https://github.com/NewKnowledge/[email protected]86b8566d3767f79562ecec3316e77247b1a39061#egg=TimeSeriesD3MWrappers"
}
],
"python_path": "d3m.primitives.clustering.k_means.Sloth",
......@@ -187,7 +187,7 @@
"timeout",
"iterations"
],
"returns": "d3m.primitive_interfaces.base.CallResult[d3m.container.dataset.Dataset]",
"returns": "d3m.primitive_interfaces.base.CallResult[d3m.container.pandas.DataFrame]",
"singleton": false,
"inputs_across_samples": [],
"description": "Produce primitive's best choice of the output for each of the inputs.\n\nThe output value should be wrapped inside ``CallResult`` object before returning.\n\nIn many cases producing an output is a quick operation in comparison with ``fit``, but not\nall cases are like that. For example, a primitive can start a potentially long optimization\nprocess to compute outputs. ``timeout`` and ``iterations`` can serve as a way for a caller\nto guide the length of this process.\n\nIdeally, a primitive should adapt its call to try to produce the best outputs possible\ninside the time allocated. If this is not possible and the primitive reaches the timeout\nbefore producing outputs, it should raise a ``TimeoutError`` exception to signal that the\ncall was unsuccessful in the given time. The state of the primitive after the exception\nshould be as the method call has never happened and primitive should continue to operate\nnormally. The purpose of ``timeout`` is to give opportunity to a primitive to cleanly\nmanage its state instead of interrupting execution from outside. Maintaining stable internal\nstate should have precedence over respecting the ``timeout`` (caller can terminate the\nmisbehaving primitive from outside anyway). If a longer ``timeout`` would produce\ndifferent outputs, then ``CallResult``'s ``has_finished`` should be set to ``False``.\n\nSome primitives have internal iterations (for example, optimization iterations).\nFor those, caller can provide how many of primitive's internal iterations\nshould a primitive do before returning outputs. Primitives should make iterations as\nsmall as reasonable. If ``iterations`` is ``None``, then there is no limit on\nhow many iterations the primitive should do and primitive should choose the best amount\nof iterations on its own (potentially controlled through hyper-parameters).\nIf ``iterations`` is a number, a primitive has to do those number of iterations,\nif possible. ``timeout`` should still be respected and potentially less iterations\ncan be done because of that. Primitives with internal iterations should make\n``CallResult`` contain correct values.\n\nFor primitives which do not have internal iterations, any value of ``iterations``\nmeans that they should run fully, respecting only ``timeout``.\n\nIf primitive should have been fitted before calling this method, but it has not been,\nprimitive should raise a ``PrimitiveNotFittedError`` exception.\n\nParameters\n----------\ninputs : Input pandas frame where each row is a series. Series timestamps are store in the column names.\n\nReturns\n-------\nOutputs\n The output is a dataframe containing a single column where each entry is the associated series' cluster number."
......@@ -225,5 +225,5 @@
},
"structural_type": "TimeSeriesD3MWrappers.Storc.Storc",
"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": "f995120d3023e014a9e56c242f840f5909e389dfb6209ce69c3172a0c2973ece"
"digest": "cd237a13cbcc980d3c80ebdbc148c6a29dbb588c2897792e4b12aa65a39ce1f6"
}
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......@@ -23,7 +23,7 @@
},
{
"type": "PIP",
"package_uri": "git+https://github.com/NewKnowledge/[email protected]ca557e5250c383bbb80a71d25543f686eed8fab9#egg=TimeSeriesD3MWrappers"
"package_uri": "git+https://github.com/NewKnowledge/[email protected]86b8566d3767f79562ecec3316e77247b1a39061#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": "d8c522d319ed859438e3ea942f1877bb63ba23afb5b625feed97ae28e83b7c11"
"digest": "95faac47ab0b0c401ca774e23cd35250905dd7c7433723bb5c47f7ba13143427"
}
......@@ -20,7 +20,7 @@
},
{
"type": "PIP",
"package_uri": "git+https://github.com/NewKnowledge/[email protected]ca557e5250c383bbb80a71d25543f686eed8fab9#egg=TimeSeriesD3MWrappers"
"package_uri": "git+https://github.com/NewKnowledge/[email protected]86b8566d3767f79562ecec3316e77247b1a39061#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": "c929b67d5543c35602829c6059ad6c7626edfef0f067a6377845abcae7708ad1"
"digest": "bd406bdf73a720898fb3f27451eca996356809abb3181a25ed3c85caea13ddc6"
}
......@@ -21,7 +21,7 @@
},
{
"type": "PIP",
"package_uri": "git+https://github.com/NewKnowledge/[email protected]ca557e5250c383bbb80a71d25543f686eed8fab9#egg=TimeSeriesD3MWrappers"
"package_uri": "git+https://github.com/NewKnowledge/[email protected]86b8566d3767f79562ecec3316e77247b1a39061#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": "e30ef5206ef5aad94c534b32a3072368c8363616d75d95ba82bbd0328be79f51"
"digest": "6f90e1245d76eebc27b1d37a7c766fcd6c9221a982a477837787297df2831f3f"
}
......@@ -20,7 +20,7 @@
},
{
"type": "PIP",
"package_uri": "git+https://github.com/NewKnowledge/[email protected]ca557e5250c383bbb80a71d25543f686eed8fab9#egg=TimeSeriesD3MWrappers"
"package_uri": "git+https://github.com/NewKnowledge/[email protected]86b8566d3767f79562ecec3316e77247b1a39061#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": "7acf3d60814df47ec3640cd6b788a3b22b2626408d55a5b2ae5a09615f5fff12"
"digest": "dc8f72c5002ab542d801723e77ce16ddacfb6bb67efa315934eee6afb69f9e20"