Commit 2b0661a8 authored by Sujen's avatar Sujen
Browse files

Merge branch 'sri-d3m-1.8.1' into 'master'

Sri d3m 1.8.1

See merge request !192
parents dd6d7df5 3a37afc8
Pipeline #117104819 passed with stages
in 87 minutes and 9 seconds
{
"id": "91640d62-ae22-46cb-a356-6010331678f2",
"id": "47e41aff-8d1e-426a-bfd3-ec888835b896",
"schema": "https://metadata.datadrivendiscovery.org/schemas/v0/pipeline.json",
"created": "2020-01-15T22:11:43.056779Z",
"created": "2020-02-11T23:42:20.212809Z",
"inputs": [
{
"name": "inputs"
......@@ -18,7 +18,7 @@
"type": "PRIMITIVE",
"primitive": {
"id": "4d989759-affd-4a51-a4e6-1a05a5c1d1c8",
"version": "1.7.8",
"version": "1.8.1",
"python_path": "d3m.primitives.classification.general_relational_dataset.GeneralRelationalDataset",
"name": "General Relational Dataset"
},
......
{
"id": "4d989759-affd-4a51-a4e6-1a05a5c1d1c8",
"version": "1.7.8",
"version": "1.8.1",
"name": "General Relational Dataset",
"description": "An extension to the GeneralRelational primitive that deals with a dataset as input.\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.",
"python_path": "d3m.primitives.classification.general_relational_dataset.GeneralRelationalDataset",
......@@ -27,7 +27,7 @@
{
"type": "PIP",
"package": "sri-d3m",
"version": "1.7.8"
"version": "1.8.1"
},
{
"type": "UBUNTU",
......@@ -315,5 +315,5 @@
}
},
"structural_type": "sri.psl.general_relational_dataset.GeneralRelationalDataset",
"digest": "9a6da59b754ba31e6abaeb53e707453f571a988b91e9c2784432eecea91b3c28"
"digest": "987eac2d5b6a05f11b80adbff7a9632cb83bfd495761b56c7e5a601056b354af"
}
{
"id": "4a2fb696-bf29-410d-934d-c4b17b273938",
"id": "3a087a0c-aaa5-4bf2-a14b-f78408570b91",
"schema": "https://metadata.datadrivendiscovery.org/schemas/v0/pipeline.json",
"created": "2020-01-15T22:06:24.463213Z",
"created": "2020-02-11T23:34:25.237235Z",
"inputs": [
{
"name": "inputs"
......@@ -18,7 +18,7 @@
"type": "PRIMITIVE",
"primitive": {
"id": "a22f9bd3-818e-44e9-84a3-9592c5a85408",
"version": "1.7.8",
"version": "1.8.1",
"python_path": "d3m.primitives.data_transformation.vertex_classification_parser.VertexClassificationParser",
"name": "Vertex Classification Parser"
},
......@@ -38,7 +38,7 @@
"type": "PRIMITIVE",
"primitive": {
"id": "dca25a46-7a5f-48d9-ac9b-d14d4d671b0b",
"version": "1.7.8",
"version": "1.8.1",
"python_path": "d3m.primitives.classification.vertex_nomination.VertexClassification",
"name": "Vertex Classification"
},
......
{
"id": "dca25a46-7a5f-48d9-ac9b-d14d4d671b0b",
"version": "1.7.8",
"version": "1.8.1",
"name": "Vertex Classification",
"description": "Solve vertex classification with PSL.\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.",
"python_path": "d3m.primitives.classification.vertex_nomination.VertexClassification",
......@@ -27,7 +27,7 @@
{
"type": "PIP",
"package": "sri-d3m",
"version": "1.7.8"
"version": "1.8.1"
},
{
"type": "UBUNTU",
......@@ -243,5 +243,5 @@
}
},
"structural_type": "sri.psl.vertex_classification.VertexClassification",
"digest": "a95759023f73df2de85c24e3cd55823b0280a44d51faae67e23218ef4df658d3"
"digest": "84d8f3b658ec9cabb3eded621578c1265e4082f5d29e488d31a81f76ad5f9460"
}
{
"id": "c52432ba-d925-4c3d-add7-c95241e33714",
"id": "27f32a02-4f40-47f6-8419-d44b22acc3ba",
"schema": "https://metadata.datadrivendiscovery.org/schemas/v0/pipeline.json",
"created": "2020-01-15T22:01:05.791736Z",
"created": "2020-02-11T23:25:43.955880Z",
"inputs": [
{
"name": "inputs"
......@@ -18,7 +18,7 @@
"type": "PRIMITIVE",
"primitive": {
"id": "005941a3-e3ca-49d9-9e99-4f5566831acd",
"version": "1.7.8",
"version": "1.8.1",
"python_path": "d3m.primitives.data_preprocessing.dataset_text_reader.DatasetTextReader",
"name": "Columns text reader"
},
......@@ -160,7 +160,7 @@
"type": "PRIMITIVE",
"primitive": {
"id": "6fdcf530-2cfe-4e87-9d9e-b8770753e19c",
"version": "1.7.8",
"version": "1.8.1",
"python_path": "d3m.primitives.data_transformation.conditioner.Conditioner",
"name": "Autoflow Data Conditioner"
},
......
{
"id": "005941a3-e3ca-49d9-9e99-4f5566831acd",
"version": "1.7.8",
"version": "1.8.1",
"name": "Columns text reader",
"python_path": "d3m.primitives.data_preprocessing.dataset_text_reader.DatasetTextReader",
"keywords": [
......@@ -19,7 +19,7 @@
{
"type": "PIP",
"package": "sri-d3m",
"version": "1.7.8"
"version": "1.8.1"
}
],
"algorithm_types": [
......@@ -228,5 +228,5 @@
},
"structural_type": "sri.autoflow.dataset_text_reader.DatasetTextReader",
"description": "This primitive accepts a dataset with columns of file names containing textual data, and replaces each such\ncolumn with one containing the string contents of the corresponding files, returning a dataset.\n\nIt offers the following hyperparameters:\n\n* **dataframe_resource**: The key of the dataset resource on which to operate. If None is provided (default),\n the primitive operates on the resource with the semantic type DatasetEntryPoint.\n* **use_columns**: A set of column indexes on which to operate. Default is an empty set, in which case all\n suitable columns will be selected. A column is suitable if it has semantic type FileName with media type\n text/plain. The primitive will not process any columns deemed unsuitable, whatever is specified by this\n hyperparameter.\n* **exclude_columns**: A set of column indexes on which not to operate. Tested only if use_columns is empty.\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": "b064eb07125f763a8312fbae4420923be9f067cc1b1cd3b2cfd4fe6daa44ec86"
"digest": "d9ef58b668ecc6bc5e260d362aac4661c67942ea6ebbed4bca81668adfb1c418"
}
{
"id": "16192178-bd38-4024-b4ec-ccfc5a781329",
"id": "4b8afa2b-5de5-45ef-8265-0f1dc8324dfa",
"schema": "https://metadata.datadrivendiscovery.org/schemas/v0/pipeline.json",
"created": "2020-01-15T22:00:55.958456Z",
"created": "2020-02-11T23:25:32.739668Z",
"inputs": [
{
"name": "inputs"
......@@ -18,7 +18,7 @@
"type": "PRIMITIVE",
"primitive": {
"id": "005941a3-e3ca-49d9-9e99-4f5566831acd",
"version": "1.7.8",
"version": "1.8.1",
"python_path": "d3m.primitives.data_preprocessing.dataset_text_reader.DatasetTextReader",
"name": "Columns text reader"
},
......@@ -160,7 +160,7 @@
"type": "PRIMITIVE",
"primitive": {
"id": "6fdcf530-2cfe-4e87-9d9e-b8770753e19c",
"version": "1.7.8",
"version": "1.8.1",
"python_path": "d3m.primitives.data_transformation.conditioner.Conditioner",
"name": "Autoflow Data Conditioner"
},
......
{
"id": "6fdcf530-2cfe-4e87-9d9e-b8770753e19c",
"version": "1.7.8",
"version": "1.8.1",
"name": "Autoflow Data Conditioner",
"description": "Perform robust type inference and imputation. The intention is to deliver a\nbuck-stops-here primitive to put at the end of a data processing pipeline\nand just before a pipeline that involves primitives that expect purely\nnumeric data with no missing values. This primitive guarantees that the data\nis safe for processing by such primitives.\n\nIt achieves this by performing type inference on each column in the input (ignoring\nthe assigned structural type), then assigning a type-specific conditioner to the column.\nThis column conditioner then transforms the data in the column to make it safe for\ndownstream learners. The following types are recognized:\n\n* **Integer**: replaces missing or ill-typed values with the column median.\n* **Float**: replaces missing or ill-typed values with the column median.\n* **String categorical**: Tabulates column string values. If the number of distinct values is\n less than 20 (hard-coded currently), each distinct value is replaced with a\n distinct integer.\n* **Text**: If a string column is determined not to be categorical, this\n conditioner tokenizes column contents and replaces it with one or more additional\n columns, each representing a different term, with weights set by SKLearn's TFIDFVectorizer.\n This conditioner is not selected unless all column elements are strings.\n* **NoOp**: Fall-through conditioner, which is selected if none of the above conditioners\n is selected for a column\n\nHyperparameters:\n\n* **ensure_numeric**: If True, NoOp columns (typically columns of mixed type) are dropped.\n* **maximum_expansion**: An integer controlling the number of additional columns generated for\n each textual column. A positive value serves as an upper bound on the number of vocabulary\n terms to be represented, using column-wide term frequency as the ordering principle. 0 means\n full expansion (all terms). A negative value suppresses this kind of expansion; all columns\n that would have been handled by this conditioner are instead assigned to the NoOp conditioner\n (and may be dropped, depending on how ensure_numeric is set).\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.",
"python_path": "d3m.primitives.data_transformation.conditioner.Conditioner",
......@@ -26,7 +26,7 @@
{
"type": "PIP",
"package": "sri-d3m",
"version": "1.7.8"
"version": "1.8.1"
}
],
"schema": "https://metadata.datadrivendiscovery.org/schemas/v0/primitive.json",
......@@ -188,5 +188,5 @@
}
},
"structural_type": "sri.autoflow.conditioner.Conditioner",
"digest": "ed655c198878021c0373574cb2618fc53b9b89db112c4351f00a4e83dd5cb667"
"digest": "7ed445d3fcdfa53d7124b79b59a968bbe5503e43720444086fdb9028ea5bf245"
}
{
"id": "ec24b04c-dbcc-11e8-9f8b-f2801f1b9fd1",
"version": "1.7.8",
"version": "1.8.1",
"name": "Autoflow Static Ensembler",
"description": "Performs a linear transformation of input model predictions, based on the weights hyperparameter, with optional\nmaxarg selection in the case of classification. This primitive serves to persist the determinations of some\nensembling operation that combines a number of atomic pipelines. It does not perform ensembling itself and is\ntherefore not fittable. It accepts a dataframe containing the predictions of its constituent pipelines, and produces\na dataframe with ensembled predictions. The interpretation of both dataframes is controlled by hyperparameters\n(see below).\n\nIt takes the following hyperparameters:\n\n* **weights**: a list of real-valued weights over the constituent pipelines, one per pipeline, which record the\n relative importance of each.\n* **scalar_input**: If true, the input contains one column per constituent pipeline, each element representing a\n categorical prediction. If false, each constituent generates a dataframe as wide as the number of classes\n representing a probability distribution over the set of classes. This hyperparameter is ignored for regression\n problems.\n* **scalar_output**: Has an interpretation similar to scalar_input, but governs the structure of the dataframe\n produced by the primitive. Again, it is ignored for regression problem, in which case there is always a single\n column of predictions.\n* **class_count**: Stores the number of classes for categorical classification problems. A value less than or\n equal to 1 signals a regression problem.\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.",
"python_path": "d3m.primitives.data_transformation.conditioner.StaticEnsembler",
......@@ -25,7 +25,7 @@
{
"type": "PIP",
"package": "sri-d3m",
"version": "1.7.8"
"version": "1.8.1"
}
],
"schema": "https://metadata.datadrivendiscovery.org/schemas/v0/primitive.json",
......@@ -193,5 +193,5 @@
}
},
"structural_type": "sri.autoflow.static_ensembler.StaticEnsembler",
"digest": "8d833f1ee040070083a852d5188534b3ff91b4b7b1521ae27d6cabc85462a33c"
"digest": "317d41856d93ab953dcc3adb7ca005e21d0bdb3e766485a3cbc05d39ef2000ce"
}
{
"id": "d519cb7a-1782-4f51-b44c-58f0236e47c7",
"version": "1.7.8",
"version": "1.8.1",
"name": "Parses strings into their types",
"description": "A primitive which parses strings into their parsed values. This primitive was adapted\nfrom Mitars ColumnParserPrimitive in the Common Primitives repo.\n\nIt goes over all columns (by default, controlled by ``use_columns``, ``exclude_columns``)\nand checks those with structural type ``str`` if they have a semantic type suggesting\nthat they are a boolean value, categorical, integer, float, or time (by default,\ncontrolled by ``parse_semantic_types``). Categorical values are converted to integer\nencodings.\n\nWhat is returned is controlled by ``return_result`` and ``add_index_columns``.\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.",
"python_path": "d3m.primitives.data_transformation.simple_column_parser.DataFrameCommon",
......@@ -16,7 +16,7 @@
{
"type": "PIP",
"package": "sri-d3m",
"version": "1.7.8"
"version": "1.8.1"
}
],
"algorithm_types": [
......@@ -46,9 +46,11 @@
"http://schema.org/Integer",
"http://schema.org/Float",
"https://metadata.datadrivendiscovery.org/types/FloatVector",
"http://schema.org/DateTime"
[
"http://schema.org/DateTime"
]
],
"structural_type": "typing.Sequence[str]",
"structural_type": "typing.Sequence[typing.Union[str, typing.Tuple[str]]]",
"semantic_types": [
"https://metadata.datadrivendiscovery.org/types/ControlParameter"
],
......@@ -56,7 +58,7 @@
"elements": {
"type": "d3m.metadata.hyperparams.Enumeration",
"default": "http://schema.org/Boolean",
"structural_type": "str",
"structural_type": "typing.Union[str, typing.Tuple[str]]",
"semantic_types": [],
"values": [
"http://schema.org/Boolean",
......@@ -64,7 +66,9 @@
"http://schema.org/Integer",
"http://schema.org/Float",
"https://metadata.datadrivendiscovery.org/types/FloatVector",
"http://schema.org/DateTime"
[
"http://schema.org/DateTime"
]
]
},
"is_configuration": false,
......@@ -151,26 +155,6 @@
"type": "sri.autoflow.simple_column_parser.Hyperparams",
"kind": "RUNTIME"
},
"random_seed": {
"type": "int",
"kind": "RUNTIME",
"default": 0
},
"docker_containers": {
"type": "typing.Union[NoneType, typing.Dict[str, d3m.primitive_interfaces.base.DockerContainer]]",
"kind": "RUNTIME",
"default": null
},
"volumes": {
"type": "typing.Union[NoneType, typing.Dict[str, str]]",
"kind": "RUNTIME",
"default": null
},
"temporary_directory": {
"type": "typing.Union[NoneType, str]",
"kind": "RUNTIME",
"default": null
},
"timeout": {
"type": "typing.Union[NoneType, float]",
"kind": "RUNTIME",
......@@ -214,14 +198,9 @@
"__init__": {
"kind": "OTHER",
"arguments": [
"hyperparams",
"random_seed",
"docker_containers",
"volumes",
"temporary_directory"
"hyperparams"
],
"returns": "NoneType",
"description": "All primitives should accept all their hyper-parameters in a constructor as one value,\nan instance of type ``Hyperparams``.\n\nProvided random seed should control all randomness used by this primitive.\nPrimitive should behave exactly the same for the same random seed across multiple\ninvocations. You can call `numpy.random.RandomState(random_seed)` to obtain an\ninstance of a random generator using provided seed. If your primitive does not\nuse randomness, consider not exposing this argument in your primitive's constructor\nto signal that.\n\nPrimitives can be wrappers around or use one or more Docker images which they can\nspecify as part of ``installation`` field in their metadata. Each Docker image listed\nthere has a ``key`` field identifying that image. When primitive is created,\n``docker_containers`` contains a mapping between those keys and connection information\nwhich primitive can use to connect to a running Docker container for a particular Docker\nimage and its exposed ports. Docker containers might be long running and shared between\nmultiple instances of a primitive. If your primitive does not use Docker images,\nconsider not exposing this argument in your primitive's constructor.\n\n**Note**: Support for primitives using Docker containers has been put on hold.\nCurrently it is not expected that any runtime running primitives will run\nDocker containers for a primitive.\n\nPrimitives can also use additional static files which can be added as a dependency\nto ``installation`` metadata. When done so, given volumes are provided to the\nprimitive through ``volumes`` argument to the primitive's constructor as a\ndict mapping volume keys to file and directory paths where downloaded and\nextracted files are available to the primitive. All provided files and directories\nare read-only. If your primitive does not use static files, consider not exposing\nthis argument in your primitive's constructor.\n\nPrimitives can also use the provided temporary directory to store any files for\nthe duration of the current pipeline run phase. Directory is automatically\ncleaned up after the current pipeline run phase finishes. Do not store in this\ndirectory any primitive's state you would like to preserve between \"fit\" and\n\"produce\" phases of pipeline execution. Use ``Params`` for that. The main intent\nof this temporary directory is to store files referenced by any ``Dataset`` object\nyour primitive might create and followup primitives in the pipeline should have\naccess to. When storing files into this directory consider using capabilities\nof Python's `tempfile` module to generate filenames which will not conflict with\nany other files stored there. Use provided temporary directory as ``dir`` argument\nto set it as base directory to generate additional temporary files and directories\nas needed. If your primitive does not use temporary directory, consider not exposing\nthis argument in your primitive's constructor.\n\nNo other arguments to the constructor are allowed (except for private arguments)\nbecause we want instances of primitives to be created without a need for any other\nprior computation.\n\nModule in which a primitive is defined should be kept lightweight and on import not do\nany (pre)computation, data loading, or resource allocation/reservation. Any loading\nand resource allocation/reservation should be done in the constructor. Any (pre)computation\nshould be done lazily when needed once requested through other methods and not in the constructor."
"returns": "NoneType"
},
"fit": {
"kind": "OTHER",
......@@ -229,7 +208,7 @@
"timeout",
"iterations"
],
"returns": "NoneType",
"returns": "d3m.primitive_interfaces.base.CallResult[NoneType]",
"description": "Fits primitive using inputs and outputs (if any) using currently set training data.\n\nThe returned value should be a ``CallResult`` object with ``value`` set to ``None``.\n\nIf ``fit`` has already been called in the past on different training data,\nthis method fits it **again from scratch** using currently set training data.\n\nOn the other hand, caller can call ``fit`` multiple times on the same training data\nto continue fitting.\n\nIf ``fit`` fully fits using provided training data, there is no point in making further\ncalls to this method with same training data, and in fact further calls can be noops,\nor a primitive can decide to fully refit from scratch.\n\nIn the case fitting can continue with same training data (even if it is maybe not reasonable,\nbecause the internal metric primitive is using looks like fitting will be degrading), if ``fit``\nis called again (without setting training data), the primitive has to continue fitting.\n\nCaller can provide ``timeout`` information to guide the length of the fitting process.\nIdeally, a primitive should adapt its fitting process to try to do the best fitting possible\ninside the time allocated. If this is not possible and the primitive reaches the timeout\nbefore fitting, it should raise a ``TimeoutError`` exception to signal that fitting was\nunsuccessful in the given time. The state of the primitive after the exception should be\nas the method call has never happened and primitive should continue to operate normally.\nThe purpose of ``timeout`` is to give opportunity to a primitive to cleanly manage\nits state instead of interrupting execution from outside. Maintaining stable internal state\nshould have precedence over respecting the ``timeout`` (caller can terminate the misbehaving\nprimitive from outside anyway). If a longer ``timeout`` would produce different fitting,\nthen ``CallResult``'s ``has_finished`` should be set to ``False``.\n\nSome primitives have internal fitting iterations (for example, epochs). For those, caller\ncan provide how many of primitive's internal iterations should a primitive do before returning.\nPrimitives should make iterations as small as reasonable. If ``iterations`` is ``None``,\nthen there is no limit on how many iterations the primitive should do and primitive should\nchoose the best amount of iterations on its own (potentially controlled through\nhyper-parameters). If ``iterations`` is a number, a primitive has to do those number of\niterations (even if not reasonable), if possible. ``timeout`` should still be respected\nand potentially less iterations can be done because of that. Primitives with internal\niterations should make ``CallResult`` contain correct values.\n\nFor primitives which do not have internal iterations, any value of ``iterations``\nmeans that they should fit fully, respecting only ``timeout``.\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": {
......@@ -301,11 +280,9 @@
"temporary_directory": "typing.Union[NoneType, str]"
},
"params": {
"column_conditioners": "typing.Sequence[typing.Any]",
"width": "int",
"tossers": "typing.Sequence[int]"
"column_map": "typing.Dict[str, int]"
}
},
"structural_type": "sri.autoflow.simple_column_parser.SimpleColumnParser",
"digest": "30672767ee1fb4d3814c58dce4721e8238ef16578f8aa0e2a207c5f6d54fe0e1"
"digest": "c1a4fd5f7b41056e20544ba2d805816b83e9da2f406b91068d99ece02fdd7ed9"
}
{
"id": "069caf23-c074-42f0-9965-1b52d921444e",
"id": "41aa53ab-a6fc-4552-a705-7380ce3bdcc7",
"schema": "https://metadata.datadrivendiscovery.org/schemas/v0/pipeline.json",
"created": "2020-01-15T22:01:24.116810Z",
"created": "2020-02-11T23:26:03.861241Z",
"inputs": [
{
"name": "inputs"
......@@ -18,7 +18,7 @@
"type": "PRIMITIVE",
"primitive": {
"id": "a22f9bd3-818e-44e9-84a3-9592c5a85408",
"version": "1.7.8",
"version": "1.8.1",
"python_path": "d3m.primitives.data_transformation.vertex_classification_parser.VertexClassificationParser",
"name": "Vertex Classification Parser"
},
......@@ -38,7 +38,7 @@
"type": "PRIMITIVE",
"primitive": {
"id": "dca25a46-7a5f-48d9-ac9b-d14d4d671b0b",
"version": "1.7.8",
"version": "1.8.1",
"python_path": "d3m.primitives.classification.vertex_nomination.VertexClassification",
"name": "Vertex Classification"
},
......
{
"id": "a22f9bd3-818e-44e9-84a3-9592c5a85408",
"version": "1.7.8",
"version": "1.8.1",
"name": "Vertex Classification Parser",
"description": "Pull all the graph data out of a 'vertex classification' style problem.\nIn the output edgelist, there may be nodes that are not in the nodelist.\nThis is because not all nodes have features associated with them.\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.",
"python_path": "d3m.primitives.data_transformation.vertex_classification_parser.VertexClassificationParser",
......@@ -28,7 +28,7 @@
{
"type": "PIP",
"package": "sri-d3m",
"version": "1.7.8"
"version": "1.8.1"
}
],
"location_uris": [],
......@@ -174,5 +174,5 @@
}
},
"structural_type": "sri.graph.vertex_classification.VertexClassificationParser",
"digest": "4b9b080840b6732bc8c4a2e9dd5d4cccecdd1e9a87a86d77ce51be9575a70cc1"
"digest": "fcf3b4b0f4e069ac33dce3b3aaa392d482766c12d539d22ff46b55dd0dd68208"
}
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