Commit e449d4ff authored by Peter Lin's avatar Peter Lin Committed by Mitar

update hyperparameters for v2019.5.8

parent b25457fe
{
"context": "TESTING",
"created": "2019-05-06T21:51:53.205524Z",
"created": "2019-05-18T12:31:44.632331Z",
"id": "ta1-perspecta-pipeline-2019-04",
"inputs": [
{
......@@ -62,6 +62,81 @@
"type": "CONTAINER"
}
},
"hyperparams": {
"C": {
"data": 1,
"type": "VALUE"
},
"C_gridsearch": {
"data": {
"encoding": "pickle",
"value": "gANK/P///0dAOgAAAAAAAEc/0zMzMzMzM4dxAC4="
},
"type": "VALUE"
},
"add_index_columns": {
"data": false,
"type": "VALUE"
},
"class_weight": {
"data": {
"encoding": "pickle",
"value": "gANYCAAAAGJhbGFuY2VkcQAu"
},
"type": "VALUE"
},
"coef0": {
"data": 0,
"type": "VALUE"
},
"degree": {
"data": 3,
"type": "VALUE"
},
"gamma": {
"data": 0.1,
"type": "VALUE"
},
"gamma_gridsearch": {
"data": {
"encoding": "pickle",
"value": "gANHwBAAAAAAAABHQDoAAAAAAABHP9MzMzMzMzOHcQAu"
},
"type": "VALUE"
},
"kernel": {
"data": "rbf",
"type": "VALUE"
},
"max_iter": {
"data": -1,
"type": "VALUE"
},
"n_jobs": {
"data": 4,
"type": "VALUE"
},
"probability": {
"data": false,
"type": "VALUE"
},
"return_result": {
"data": "new",
"type": "VALUE"
},
"shrinking": {
"data": true,
"type": "VALUE"
},
"tol": {
"data": 0.001,
"type": "VALUE"
},
"use_semantic_types": {
"data": false,
"type": "VALUE"
}
},
"outputs": [
{
"id": "produce"
......
{
"context": "TESTING",
"created": "2019-04-28T21:49:53.215624Z",
"id": "ta1-perspecta-pipeline-2019-01",
"created": "2019-05-18T06:54:40.178792Z",
"id": "ta1-perspecta-pipeline-2019-05",
"inputs": [
{
"name": "inputs"
......@@ -62,6 +62,81 @@
"type": "CONTAINER"
}
},
"hyperparams": {
"C": {
"data": 1,
"type": "VALUE"
},
"C_gridsearch": {
"data": {
"encoding": "pickle",
"value": "gANK/P///0dANIAAAAAAAEc/7MzMzMzMzYdxAC4="
},
"type": "VALUE"
},
"add_index_columns": {
"data": false,
"type": "VALUE"
},
"class_weight": {
"data": {
"encoding": "pickle",
"value": "gANYCAAAAGJhbGFuY2VkcQAu"
},
"type": "VALUE"
},
"coef0": {
"data": 0,
"type": "VALUE"
},
"degree": {
"data": 3,
"type": "VALUE"
},
"gamma": {
"data": 0.1,
"type": "VALUE"
},
"gamma_gridsearch": {
"data": {
"encoding": "pickle",
"value": "gANHwBAAAAAAAABHQDSAAAAAAABHP+zMzMzMzM2HcQAu"
},
"type": "VALUE"
},
"kernel": {
"data": "rbf",
"type": "VALUE"
},
"max_iter": {
"data": -1,
"type": "VALUE"
},
"n_jobs": {
"data": 4,
"type": "VALUE"
},
"probability": {
"data": false,
"type": "VALUE"
},
"return_result": {
"data": "new",
"type": "VALUE"
},
"shrinking": {
"data": true,
"type": "VALUE"
},
"tol": {
"data": 0.001,
"type": "VALUE"
},
"use_semantic_types": {
"data": false,
"type": "VALUE"
}
},
"outputs": [
{
"id": "produce"
......
{
"context": "TESTING",
"created": "2019-05-08T21:50:53.205624Z",
"id": "ta1-perspecta-pipeline-2019-02",
"created": "2019-05-18T14:24:17.111095Z",
"id": "ta1-perspecta-pipeline-2019-06",
"inputs": [
{
"name": "inputs"
......@@ -62,6 +62,81 @@
"type": "CONTAINER"
}
},
"hyperparams": {
"C": {
"data": 1,
"type": "VALUE"
},
"C_gridsearch": {
"data": {
"encoding": "pickle",
"value": "gANHQCwAAAAAAABHQERAAAAAAABHP9MzMzMzMzOHcQAu"
},
"type": "VALUE"
},
"add_index_columns": {
"data": false,
"type": "VALUE"
},
"class_weight": {
"data": {
"encoding": "pickle",
"value": "gANYCAAAAGJhbGFuY2VkcQAu"
},
"type": "VALUE"
},
"coef0": {
"data": 0,
"type": "VALUE"
},
"degree": {
"data": 3,
"type": "VALUE"
},
"gamma": {
"data": 0.1,
"type": "VALUE"
},
"gamma_gridsearch": {
"data": {
"encoding": "pickle",
"value": "gANHQCwAAAAAAABHQERAAAAAAABHP9MzMzMzMzOHcQAu"
},
"type": "VALUE"
},
"kernel": {
"data": "rbf",
"type": "VALUE"
},
"max_iter": {
"data": -1,
"type": "VALUE"
},
"n_jobs": {
"data": 4,
"type": "VALUE"
},
"probability": {
"data": false,
"type": "VALUE"
},
"return_result": {
"data": "new",
"type": "VALUE"
},
"shrinking": {
"data": true,
"type": "VALUE"
},
"tol": {
"data": 0.001,
"type": "VALUE"
},
"use_semantic_types": {
"data": false,
"type": "VALUE"
}
},
"outputs": [
{
"id": "produce"
......
{
"context": "TESTING",
"created": "2019-05-07T21:51:53.225624Z",
"id": "ta1-perspecta-pipeline-2019-03",
"created": "2019-05-18T12:29:54.777580Z",
"id": "ta1-perspecta-pipeline-2019-07",
"inputs": [
{
"name": "inputs"
......@@ -62,6 +62,81 @@
"type": "CONTAINER"
}
},
"hyperparams": {
"C": {
"data": 1,
"type": "VALUE"
},
"C_gridsearch": {
"data": {
"encoding": "pickle",
"value": "gANK/P///0dANIAAAAAAAEc/4zMzMzMzM4dxAC4="
},
"type": "VALUE"
},
"add_index_columns": {
"data": false,
"type": "VALUE"
},
"class_weight": {
"data": {
"encoding": "pickle",
"value": "gANYCAAAAGJhbGFuY2VkcQAu"
},
"type": "VALUE"
},
"coef0": {
"data": 0,
"type": "VALUE"
},
"degree": {
"data": 3,
"type": "VALUE"
},
"gamma": {
"data": 0.1,
"type": "VALUE"
},
"gamma_gridsearch": {
"data": {
"encoding": "pickle",
"value": "gANHwBAAAAAAAABHQDSAAAAAAABHP+MzMzMzMzOHcQAu"
},
"type": "VALUE"
},
"kernel": {
"data": "rbf",
"type": "VALUE"
},
"max_iter": {
"data": -1,
"type": "VALUE"
},
"n_jobs": {
"data": 4,
"type": "VALUE"
},
"probability": {
"data": false,
"type": "VALUE"
},
"return_result": {
"data": "new",
"type": "VALUE"
},
"shrinking": {
"data": true,
"type": "VALUE"
},
"tol": {
"data": 0.001,
"type": "VALUE"
},
"use_semantic_types": {
"data": false,
"type": "VALUE"
}
},
"outputs": [
{
"id": "produce"
......
......@@ -18,7 +18,7 @@
"installation": [
{
"type": "PIP",
"package_uri": "git+https://gitlab.com/d3m-perspectalabs-primitives/[email protected]932dc1ad68765b31af58e5a66f2d356d05a25d60#egg=lupi_svm"
"package_uri": "git+https://gitlab.com/d3m-perspectalabs-primitives/[email protected]d0c05fe12976f0e1f42b2ec2a6ebf151b5594c8d#egg=lupi_svm"
}
],
"schema": "https://metadata.datadrivendiscovery.org/schemas/v0/primitive.json",
......@@ -397,5 +397,5 @@
},
"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.",
"digest": "9b1f6b435f82e22e63cd8a20e2795cb9acb56641ca218065dd7d7652b03c8ce1"
"digest": "aaed12fff9485da2f3793d036c6f551e32deaefcd064dc9507fa30ac50b35c5f"
}
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