Commit 075146b9 authored by Peter Lin's avatar Peter Lin

update for v2019.5.8

parent b3bf01fe
...@@ -335,7 +335,7 @@ ...@@ -335,7 +335,7 @@
"iterations" "iterations"
], ],
"returns": "d3m.primitive_interfaces.base.MultiCallResult", "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\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``." "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": { "produce": {
"kind": "PRODUCE", "kind": "PRODUCE",
...@@ -397,5 +397,5 @@ ...@@ -397,5 +397,5 @@
}, },
"structural_type": "lupi_svm.lupisvm.LupiSvmClassifier", "structural_type": "lupi_svm.lupisvm.LupiSvmClassifier",
"description": "This is an implementation of the LUPISVM primitive. This is the sklearn implementation of the LUPI primitive,\nwhich can solve the *same* binary and multiclass classification tasks as standard sklearn.svm, except that privileged\ninformation (i.e., additional features available for training dataset, but absent for test dataset) is present.\nThe training dataset should provide a list of the indices indicating the features are privileged.\nThe test data (or the unlabeled data to predict) should not have the privileged features.\nThe values of the features in dataset need to be numerical, the label classes to be predicted should be categorical,\nand the missing values need to be imputed. The current implementation solves binary classification tasks.\nFuture versions will also solve multiclass classification tasks.\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.", "description": "This is an implementation of the LUPISVM primitive. This is the sklearn implementation of the LUPI primitive,\nwhich can solve the *same* binary and multiclass classification tasks as standard sklearn.svm, except that privileged\ninformation (i.e., additional features available for training dataset, but absent for test dataset) is present.\nThe training dataset should provide a list of the indices indicating the features are privileged.\nThe test data (or the unlabeled data to predict) should not have the privileged features.\nThe values of the features in dataset need to be numerical, the label classes to be predicted should be categorical,\nand the missing values need to be imputed. The current implementation solves binary classification tasks.\nFuture versions will also solve multiclass classification tasks.\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": "aa4556deb53e2367a3d5838d92711387e2b1cca615ddaf0686f4fc0891767215" "digest": "6a41de1945bce797fc96de646e5f759465e048e84f11fcb3067f9af86ca87592"
} }
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