Commit fafa6a19 authored by Mitar's avatar Mitar
Browse files

Merge branch 'test_ts_pipelines' into 'master'

add initial pipelines for ts forecasting

See merge request !104
parents 4ca70e8f 944695c2
Pipeline #109986137 failed with stages
in 34 minutes and 20 seconds
......@@ -65,4 +65,12 @@ v2020.1.9/ISI/d3m.primitives.feature_construction.corex_text.DSBOX/1.0.0/pipelin
v2020.1.9/common-primitives/d3m.primitives.data_augmentation.datamart_augmentation.Common/0.1/pipelines/3afd2bd2-7ba1-4ac1-928f-fad0c39a05e5.json filter=lfs diff=lfs merge=lfs -text
v2020.1.9/CMU/d3m.primitives.semisupervised_classification.iterative_labeling.AutonBox/0.2.1/pipeline_runs/pipeline_run_SEMI_1217_click_prediction_small.yaml.gz filter=lfs diff=lfs merge=lfs -text
v2020.1.9/CMU/d3m.primitives.semisupervised_classification.iterative_labeling.AutonBox/0.2.1/pipeline_runs/pipeline_run_SEMI_155_pokerhand.yaml.gz filter=lfs diff=lfs merge=lfs -text
v2020.1.9/Distil/d3m.primitives.time_series_forecasting.vector_autoregression.VAR/1.2.0/pipeline_runs/LL1_736_population_spawn_simpler_MIN_METADATA.yml.gz filter=lfs diff=lfs merge=lfs -text
v2020.1.9/Distil/d3m.primitives.time_series_forecasting.vector_autoregression.VAR/1.2.0/pipeline_runs/LL1_736_stock_market_MIN_METADATA.yml.gz filter=lfs diff=lfs merge=lfs -text
v2020.1.9/Distil/d3m.primitives.time_series_forecasting.vector_autoregression.VAR/1.2.0/pipeline_runs/LL1_736_population_spawn_MIN_METADATA.yml.gz filter=lfs diff=lfs merge=lfs -text
v2020.1.9/Distil/d3m.primitives.time_series_forecasting.lstm.DeepAR/1.2.0/pipeline_runs/LL1_terra_canopy_height_long_form_s4_80_MIN_METADATA_validation.yml.gz filter=lfs diff=lfs merge=lfs -text
v2020.1.9/Distil/d3m.primitives.time_series_forecasting.lstm.DeepAR/1.2.0/pipeline_runs/LL1_736_stock_market_MIN_METADATA_validation.yml.gz filter=lfs diff=lfs merge=lfs -text
v2020.1.9/Distil/d3m.primitives.time_series_forecasting.lstm.DeepAR/1.2.0/pipeline_runs/LL1_736_population_spawn_simpler_MIN_METADATA_validation.yml.gz filter=lfs diff=lfs merge=lfs -text
v2020.1.9/Distil/d3m.primitives.time_series_forecasting.lstm.DeepAR/1.2.0/pipeline_runs/LL1_736_population_spawn_MIN_METADATA_validation.yml.gz filter=lfs diff=lfs merge=lfs -text
v2020.1.9/Distil/d3m.primitives.time_series_forecasting.lstm.DeepAR/1.2.0/pipeline_runs/LL1_terra_canopy_height_long_form_s4_100_MIN_METADATA_validation.yml.gz filter=lfs diff=lfs merge=lfs -text
v2020.1.9/ISI/d3m.primitives.feature_construction.corex_text.DSBOX/1.0.0/pipeline_runs/LL1_TXT_CLS_airline_opinion_MIN_METADATA_pipeline_run.yml filter=lfs diff=lfs merge=lfs -text
{
"id": "a55cef3a-a7a9-411e-9dde-5c935ff3504b",
"version": "1.2.0",
"name": "lstm_fcn",
"keywords": [
"Time Series",
"convolutional neural network",
"cnn",
"lstm",
"time series classification"
],
"source": {
"name": "Distil",
"contact": "mailto:jeffrey.gleason@yonder.co",
"uris": [
"https://github.com/NewKnowledge/TimeSeries-D3M-Wrappers"
]
},
"installation": [
{
"type": "PIP",
"package": "cython",
"version": "0.29.14"
},
{
"type": "PIP",
"package_uri": "git+https://github.com/NewKnowledge/TimeSeries-D3M-Wrappers.git@58851c5be12b13a2e414bafc6e2c59b27f86c403#egg=TimeSeriesD3MWrappers"
}
],
"python_path": "d3m.primitives.time_series_classification.convolutional_neural_net.LSTM_FCN",
"algorithm_types": [
"CONVOLUTIONAL_NEURAL_NETWORK"
],
"primitive_family": "TIME_SERIES_CLASSIFICATION",
"schema": "https://metadata.datadrivendiscovery.org/schemas/v0/primitive.json",
"original_python_path": "TimeSeriesD3MWrappers.primitives.classification_lstm.LSTM_FCN",
"primitive_code": {
"class_type_arguments": {
"Inputs": "d3m.container.pandas.DataFrame",
"Outputs": "d3m.container.pandas.DataFrame",
"Params": "TimeSeriesD3MWrappers.primitives.classification_lstm.Params",
"Hyperparams": "TimeSeriesD3MWrappers.primitives.classification_lstm.Hyperparams"
},
"interfaces_version": "2020.1.9",
"interfaces": [
"supervised_learning.SupervisedLearnerPrimitiveBase",
"base.PrimitiveBase"
],
"hyperparams": {
"attention_lstm": {
"type": "d3m.metadata.hyperparams.UniformBool",
"default": false,
"structural_type": "bool",
"semantic_types": [
"https://metadata.datadrivendiscovery.org/types/TuningParameter"
],
"description": "whether to use attention in the lstm component of the model"
},
"lstm_dim": {
"type": "d3m.metadata.hyperparams.UniformInt",
"default": 128,
"structural_type": "int",
"semantic_types": [
"https://metadata.datadrivendiscovery.org/types/TuningParameter"
],
"description": "number of cells to use in the lstm component of the model",
"lower": 8,
"upper": 256,
"lower_inclusive": true,
"upper_inclusive": true
},
"epochs": {
"type": "d3m.metadata.hyperparams.UniformInt",
"default": 5000,
"structural_type": "int",
"semantic_types": [
"https://metadata.datadrivendiscovery.org/types/TuningParameter"
],
"description": "number of training epochs",
"lower": 1,
"upper": 9223372036854775807,
"lower_inclusive": true,
"upper_inclusive": false
},
"learning_rate": {
"type": "d3m.metadata.hyperparams.Uniform",
"default": 0.001,
"structural_type": "float",
"semantic_types": [
"https://metadata.datadrivendiscovery.org/types/TuningParameter"
],
"description": "learning rate",
"lower": 0.0,
"upper": 1.0,
"lower_inclusive": true,
"upper_inclusive": false
},
"batch_size": {
"type": "d3m.metadata.hyperparams.UniformInt",
"default": 32,
"structural_type": "int",
"semantic_types": [
"https://metadata.datadrivendiscovery.org/types/TuningParameter"
],
"description": "batch size",
"lower": 1,
"upper": 256,
"lower_inclusive": true,
"upper_inclusive": true
},
"dropout_rate": {
"type": "d3m.metadata.hyperparams.Uniform",
"default": 0.2,
"structural_type": "float",
"semantic_types": [
"https://metadata.datadrivendiscovery.org/types/TuningParameter"
],
"description": "dropout rate (before lstm layer in model)",
"lower": 0.0,
"upper": 1.0,
"lower_inclusive": true,
"upper_inclusive": false
},
"val_split": {
"type": "d3m.metadata.hyperparams.Uniform",
"default": 0.2,
"structural_type": "float",
"semantic_types": [
"https://metadata.datadrivendiscovery.org/types/TuningParameter"
],
"description": "proportion of training records to set aside for validation. Ignored if iterations flag in `fit` method is not None",
"lower": 0.0,
"upper": 1.0,
"lower_inclusive": true,
"upper_inclusive": false
},
"early_stopping_patience": {
"type": "d3m.metadata.hyperparams.UniformInt",
"default": 100,
"structural_type": "int",
"semantic_types": [
"https://metadata.datadrivendiscovery.org/types/TuningParameter"
],
"description": "number of epochs to wait before invoking early stopping criterion",
"lower": 0,
"upper": 9223372036854775807,
"lower_inclusive": true,
"upper_inclusive": false
},
"use_multiprocessing": {
"type": "d3m.metadata.hyperparams.UniformBool",
"default": true,
"structural_type": "bool",
"semantic_types": [
"https://metadata.datadrivendiscovery.org/types/TuningParameter"
],
"description": "whether to use multiprocessing in training"
},
"num_workers": {
"type": "d3m.metadata.hyperparams.UniformInt",
"default": 8,
"structural_type": "int",
"semantic_types": [
"https://metadata.datadrivendiscovery.org/types/TuningParameter"
],
"description": "number of workers to do if using multiprocessing threading",
"lower": 1,
"upper": 16,
"lower_inclusive": true,
"upper_inclusive": true
}
},
"arguments": {
"hyperparams": {
"type": "TimeSeriesD3MWrappers.primitives.classification_lstm.Hyperparams",
"kind": "RUNTIME"
},
"random_seed": {
"type": "int",
"kind": "RUNTIME",
"default": 0
},
"timeout": {
"type": "typing.Union[NoneType, float]",
"kind": "RUNTIME",
"default": null
},
"iterations": {
"type": "typing.Union[NoneType, int]",
"kind": "RUNTIME",
"default": null
},
"produce_methods": {
"type": "typing.Sequence[str]",
"kind": "RUNTIME"
},
"inputs": {
"type": "d3m.container.pandas.DataFrame",
"kind": "PIPELINE"
},
"outputs": {
"type": "d3m.container.pandas.DataFrame",
"kind": "PIPELINE"
},
"params": {
"type": "TimeSeriesD3MWrappers.primitives.classification_lstm.Params",
"kind": "RUNTIME"
}
},
"class_methods": {},
"instance_methods": {
"__init__": {
"kind": "OTHER",
"arguments": [
"hyperparams",
"random_seed"
],
"returns": "NoneType"
},
"fit": {
"kind": "OTHER",
"arguments": [
"timeout",
"iterations"
],
"returns": "d3m.primitive_interfaces.base.CallResult[NoneType]",
"description": "Fits LSTM_FCN classifier using training data from set_training_data and hyperparameters\n\nKeyword Arguments:\n timeout {float} -- timeout, considered (default: {None})\n iterations {int} -- iterations, considered (default: {None})\n\nReturns:\n CallResult[None]\n\nParameters\n----------\ntimeout : float\n A maximum time this primitive should be fitting during this method call, in seconds.\niterations : int\n How many of internal iterations should the primitive do.\n\nReturns\n-------\nCallResult[None]\n A ``CallResult`` with ``None`` value."
},
"fit_multi_produce": {
"kind": "OTHER",
"arguments": [
"produce_methods",
"inputs",
"outputs",
"timeout",
"iterations"
],
"returns": "d3m.primitive_interfaces.base.MultiCallResult",
"description": "A method calling ``fit`` and after that multiple produce methods at once.\n\nThis method allows primitive author to implement an optimized version of both fitting\nand producing a primitive on same data.\n\nIf any additional method arguments are added to primitive's ``set_training_data`` method\nor produce method(s), or removed from them, they have to be added to or removed from this\nmethod as well. This method should accept an union of all arguments accepted by primitive's\n``set_training_data`` method and produce method(s) and then use them accordingly when\ncomputing results.\n\nThe default implementation of this method just calls first ``set_training_data`` method,\n``fit`` method, and all produce methods listed in ``produce_methods`` in order and is\npotentially inefficient.\n\nParameters\n----------\nproduce_methods : Sequence[str]\n A list of names of produce methods to call.\ninputs : Inputs\n The inputs given to ``set_training_data`` and all produce methods.\noutputs : Outputs\n The outputs given to ``set_training_data``.\ntimeout : float\n A maximum time this primitive should take to both fit the primitive and produce outputs\n for all produce methods listed in ``produce_methods`` argument, in seconds.\niterations : int\n How many of internal iterations should the primitive do for both fitting and producing\n outputs of all produce methods.\n\nReturns\n-------\nMultiCallResult\n A dict of values for each produce method wrapped inside ``MultiCallResult``."
},
"get_params": {
"kind": "OTHER",
"arguments": [],
"returns": "TimeSeriesD3MWrappers.primitives.classification_lstm.Params",
"description": "Returns parameters of this primitive.\n\nParameters are all parameters of the primitive which can potentially change during a life-time of\na primitive. Parameters which cannot are passed through constructor.\n\nParameters should include all data which is necessary to create a new instance of this primitive\nbehaving exactly the same as this instance, when the new instance is created by passing the same\nparameters to the class constructor and calling ``set_params``.\n\nNo other arguments to the method are allowed (except for private arguments).\n\nReturns\n-------\nParams\n An instance of parameters."
},
"multi_produce": {
"kind": "OTHER",
"arguments": [
"produce_methods",
"inputs",
"timeout",
"iterations"
],
"returns": "d3m.primitive_interfaces.base.MultiCallResult",
"description": "A method calling multiple produce methods at once.\n\nWhen a primitive has multiple produce methods it is common that they might compute the\nsame internal results for same inputs but return different representations of those results.\nIf caller is interested in multiple of those representations, calling multiple produce\nmethods might lead to recomputing same internal results multiple times. To address this,\nthis method allows primitive author to implement an optimized version which computes\ninternal results only once for multiple calls of produce methods, but return those different\nrepresentations.\n\nIf any additional method arguments are added to primitive's produce method(s), they have\nto be added to this method as well. This method should accept an union of all arguments\naccepted by primitive's produce method(s) and then use them accordingly when computing\nresults.\n\nThe default implementation of this method just calls all produce methods listed in\n``produce_methods`` in order and is potentially inefficient.\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----------\nproduce_methods : Sequence[str]\n A list of names of produce methods to call.\ninputs : Inputs\n The inputs given to all produce methods.\ntimeout : float\n A maximum time this primitive should take to produce outputs for all produce methods\n listed in ``produce_methods`` argument, in seconds.\niterations : int\n How many of internal iterations should the primitive do.\n\nReturns\n-------\nMultiCallResult\n A dict of values for each produce method wrapped inside ``MultiCallResult``."
},
"produce": {
"kind": "PRODUCE",
"arguments": [
"inputs",
"timeout",
"iterations"
],
"returns": "d3m.primitive_interfaces.base.CallResult[d3m.container.pandas.DataFrame]",
"singleton": false,
"inputs_across_samples": [],
"description": "Produce primitive's classifications for new time series data\n\nArguments:\n inputs {Inputs} -- full D3M dataframe, containing attributes, key, and target\n\nKeyword Arguments:\n timeout {float} -- timeout, not considered (default: {None})\n iterations {int} -- iterations, not considered (default: {None})\n\nRaises:\n PrimitiveNotFittedError: if primitive not fit\n\nReturns:\n CallResult[Outputs] -- dataframe with a column containing a predicted class\n for each input time series\n\nParameters\n----------\ninputs : Inputs\n The inputs of shape [num_inputs, ...].\ntimeout : float\n A maximum time this primitive should take to produce outputs during this method call, in seconds.\niterations : int\n How many of internal iterations should the primitive do.\n\nReturns\n-------\nCallResult[Outputs]\n The outputs of shape [num_inputs, ...] wrapped inside ``CallResult``."
},
"set_params": {
"kind": "OTHER",
"arguments": [
"params"
],
"returns": "NoneType",
"description": "Sets parameters of this primitive.\n\nParameters are all parameters of the primitive which can potentially change during a life-time of\na primitive. Parameters which cannot are passed through constructor.\n\nNo other arguments to the method are allowed (except for private arguments).\n\nParameters\n----------\nparams : Params\n An instance of parameters."
},
"set_training_data": {
"kind": "OTHER",
"arguments": [
"inputs",
"outputs"
],
"returns": "NoneType",
"description": "Sets primitive's training data\n\nArguments:\n inputs {Inputs} -- D3M dataframe containing attributes\n outputs {Outputs} -- D3M dataframe containing targets\n\nParameters\n----------\ninputs : Inputs\n The inputs.\noutputs : Outputs\n The outputs."
}
},
"class_attributes": {
"logger": "logging.Logger",
"metadata": "d3m.metadata.base.PrimitiveMetadata"
},
"instance_attributes": {
"hyperparams": "d3m.metadata.hyperparams.Hyperparams",
"random_seed": "int",
"docker_containers": "typing.Dict[str, d3m.primitive_interfaces.base.DockerContainer]",
"volumes": "typing.Dict[str, str]",
"temporary_directory": "typing.Union[NoneType, str]"
},
"params": {}
},
"structural_type": "TimeSeriesD3MWrappers.primitives.classification_lstm.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\nArguments:\n hyperparams {Hyperparams} -- D3M Hyperparameter object\n\nKeyword Arguments:\n random_seed {int} -- random seed (default: {0})\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": "ba4beb39f7e29f6656b6f4af8484461da42e07585847dcfbe6eb7008293bbc37"
}
{
"id": "2d6d3223-1b3c-49cc-9ddd-50f571818268",
"version": "1.2.0",
"name": "kanine",
"keywords": [
"time series",
"knn",
"k nearest neighbor",
"time series classification"
],
"source": {
"name": "Distil",
"contact": "mailto:jeffrey.gleason@yonder.co",
"uris": [
"https://github.com/NewKnowledge/TimeSeries-D3M-Wrappers"
]
},
"installation": [
{
"type": "PIP",
"package": "cython",
"version": "0.29.14"
},
{
"type": "PIP",
"package_uri": "git+https://github.com/NewKnowledge/TimeSeries-D3M-Wrappers.git@58851c5be12b13a2e414bafc6e2c59b27f86c403#egg=TimeSeriesD3MWrappers"
}
],
"python_path": "d3m.primitives.time_series_classification.k_neighbors.Kanine",
"algorithm_types": [
"K_NEAREST_NEIGHBORS"
],
"primitive_family": "TIME_SERIES_CLASSIFICATION",
"schema": "https://metadata.datadrivendiscovery.org/schemas/v0/primitive.json",
"original_python_path": "TimeSeriesD3MWrappers.primitives.classification_knn.Kanine",
"primitive_code": {
"class_type_arguments": {
"Inputs": "d3m.container.pandas.DataFrame",
"Outputs": "d3m.container.pandas.DataFrame",
"Params": "TimeSeriesD3MWrappers.primitives.classification_knn.Params",
"Hyperparams": "TimeSeriesD3MWrappers.primitives.classification_knn.Hyperparams"
},
"interfaces_version": "2020.1.9",
"interfaces": [
"supervised_learning.SupervisedLearnerPrimitiveBase",
"base.PrimitiveBase"
],
"hyperparams": {
"n_neighbors": {
"type": "d3m.metadata.hyperparams.UniformInt",
"default": 5,
"structural_type": "int",
"semantic_types": [
"https://metadata.datadrivendiscovery.org/types/TuningParameter"
],
"description": "number of neighbors on which to make classification decision",
"lower": 0,
"upper": 9223372036854775807,
"lower_inclusive": true,
"upper_inclusive": false
},
"distance_metric": {
"type": "d3m.metadata.hyperparams.Enumeration",
"default": "euclidean",
"structural_type": "str",
"semantic_types": [
"https://metadata.datadrivendiscovery.org/types/TuningParameter"
],
"description": "whether to use euclidean or dynamic time warping distance metric in KNN computation",
"values": [
"euclidean",
"dtw"
]
},
"sample_weighting": {
"type": "d3m.metadata.hyperparams.Enumeration",
"default": "uniform",
"structural_type": "str",
"semantic_types": [
"https://metadata.datadrivendiscovery.org/types/TuningParameter"
],
"description": "whether to weight points uniformly or by the inverse of their distance",
"values": [
"uniform",
"inverse_distance"
]
}
},
"arguments": {
"hyperparams": {
"type": "TimeSeriesD3MWrappers.primitives.classification_knn.Hyperparams",
"kind": "RUNTIME"
},
"random_seed": {
"type": "int",
"kind": "RUNTIME",
"default": 0
},
"timeout": {
"type": "typing.Union[NoneType, float]",
"kind": "RUNTIME",
"default": null
},
"iterations": {
"type": "typing.Union[NoneType, int]",
"kind": "RUNTIME",
"default": null
},
"produce_methods": {
"type": "typing.Sequence[str]",
"kind": "RUNTIME"
},
"inputs": {
"type": "d3m.container.pandas.DataFrame",
"kind": "PIPELINE"
},
"outputs": {
"type": "d3m.container.pandas.DataFrame",
"kind": "PIPELINE"
},
"params": {
"type": "TimeSeriesD3MWrappers.primitives.classification_knn.Params",
"kind": "RUNTIME"
}
},
"class_methods": {},
"instance_methods": {
"__init__": {
"kind": "OTHER",
"arguments": [
"hyperparams",
"random_seed"
],
"returns": "NoneType"
},
"fit": {
"kind": "OTHER",
"arguments": [
"timeout",
"iterations"
],
"returns": "d3m.primitive_interfaces.base.CallResult[NoneType]",
"description": "Fits KNN model using training data from set_training_data and hyperparameters\n\nKeyword Arguments:\n timeout {float} -- timeout, not considered (default: {None})\n iterations {int} -- iterations, not considered (default: {None})\n\nReturns:\n CallResult[None]\n\nParameters\n----------\ntimeout : float\n A maximum time this primitive should be fitting during this method call, in seconds.\niterations : int\n How many of internal iterations should the primitive do.\n\nReturns\n-------\nCallResult[None]\n A ``CallResult`` with ``None`` value."
},
"fit_multi_produce": {
"kind": "OTHER",
"arguments": [
"produce_methods",
"inputs",
"outputs",
"timeout",
"iterations"
],
"returns": "d3m.primitive_interfaces.base.MultiCallResult",
"description": "A method calling ``fit`` and after that multiple produce methods at once.\n\nThis method allows primitive author to implement an optimized version of both fitting\nand producing a primitive on same data.\n\nIf any additional method arguments are added to primitive's ``set_training_data`` method\nor produce method(s), or removed from them, they have to be added to or removed from this\nmethod as well. This method should accept an union of all arguments accepted by primitive's\n``set_training_data`` method and produce method(s) and then use them accordingly when\ncomputing results.\n\nThe default implementation of this method just calls first ``set_training_data`` method,\n``fit`` method, and all produce methods listed in ``produce_methods`` in order and is\npotentially inefficient.\n\nParameters\n----------\nproduce_methods : Sequence[str]\n A list of names of produce methods to call.\ninputs : Inputs\n The inputs given to ``set_training_data`` and all produce methods.\noutputs : Outputs\n The outputs given to ``set_training_data``.\ntimeout : float\n A maximum time this primitive should take to both fit the primitive and produce outputs\n for all produce methods listed in ``produce_methods`` argument, in seconds.\niterations : int\n How many of internal iterations should the primitive do for both fitting and producing\n outputs of all produce methods.\n\nReturns\n-------\nMultiCallResult\n A dict of values for each produce method wrapped inside ``MultiCallResult``."
},
"get_params": {
"kind": "OTHER",
"arguments": [],
"returns": "TimeSeriesD3MWrappers.primitives.classification_knn.Params",
"description": "Returns parameters of this primitive.\n\nParameters are all parameters of the primitive which can potentially change during a life-time of\na primitive. Parameters which cannot are passed through constructor.\n\nParameters should include all data which is necessary to create a new instance of this primitive\nbehaving exactly the same as this instance, when the new instance is created by passing the same\nparameters to the class constructor and calling ``set_params``.\n\nNo other arguments to the method are allowed (except for private arguments).\n\nReturns\n-------\nParams\n An instance of parameters."
},
"multi_produce": {
"kind": "OTHER",
"arguments": [
"produce_methods",
"inputs",
"timeout",
"iterations"
],
"returns": "d3m.primitive_interfaces.base.MultiCallResult",
"description": "A method calling multiple produce methods at once.\n\nWhen a primitive has multiple produce methods it is common that they might compute the\nsame internal results for same inputs but return different representations of those results.\nIf caller is interested in multiple of those representations, calling multiple produce\nmethods might lead to recomputing same internal results multiple times. To address this,\nthis method allows primitive author to implement an optimized version which computes\ninternal results only once for multiple calls of produce methods, but return those different\nrepresentations.\n\nIf any additional method arguments are added to primitive's produce method(s), they have\nto be added to this method as well. This method should accept an union of all arguments\naccepted by primitive's produce method(s) and then use them accordingly when computing\nresults.\n\nThe default implementation of this method just calls all produce methods listed in\n``produce_methods`` in order and is potentially inefficient.\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----------\nproduce_methods : Sequence[str]\n A list of names of produce methods to call.\ninputs : Inputs\n The inputs given to all produce methods.\ntimeout : float\n A maximum time this primitive should take to produce outputs for all produce methods\n listed in ``produce_methods`` argument, in seconds.\niterations : int\n How many of internal iterations should the primitive do.\n\nReturns\n-------\nMultiCallResult\n A dict of values for each produce method wrapped inside ``MultiCallResult``."
},
"produce": {
"kind": "PRODUCE",
"arguments": [
"inputs",
"timeout",
"iterations"
],
"returns": "d3m.primitive_interfaces.base.CallResult[d3m.container.pandas.DataFrame]",
"singleton": false,
"inputs_across_samples": [],
"description": "Produce primitive's classifications for new time series data\n\nArguments:\n inputs {Inputs} -- full D3M dataframe, containing attributes, key, and target\n\nKeyword Arguments:\n timeout {float} -- timeout, not considered (default: {None})\n iterations {int} -- iterations, not considered (default: {None})\n\nRaises:\n PrimitiveNotFittedError: if primitive not fit\n\nReturns:\n CallResult[Outputs] -- dataframe with a column containing a predicted class\n for each input time series\n\nParameters\n----------\ninputs : Inputs\n The inputs of shape [num_inputs, ...].\ntimeout : float\n A maximum time this primitive should take to produce outputs during this method call, in seconds.\niterations : int\n How many of internal iterations should the primitive do.\n\nReturns\n-------\nCallResult[Outputs]\n The outputs of shape [num_inputs, ...] wrapped inside ``CallResult``."
},
"set_params": {
"kind": "OTHER",
"arguments": [
"params"
],
"returns": "NoneType",
"description": "Sets parameters of this primitive.\n\nParameters are all parameters of the primitive which can potentially change during a life-time of\na primitive. Parameters which cannot are passed through constructor.\n\nNo other arguments to the method are allowed (except for private arguments).\n\nParameters\n----------\nparams : Params\n An instance of parameters."
},
"set_training_data": {
"kind": "OTHER",
"arguments": [
"inputs",
"outputs"
],
"returns": "NoneType",
"description": "Sets primitive's training data\n\nArguments:\n inputs {Inputs} -- D3M dataframe containing attributes\n outputs {Outputs} -- D3M dataframe containing targets\n\nParameters\n----------\ninputs : Inputs\n The inputs.\noutputs : Outputs\n The outputs."
}