Commit 300bcf83 authored by Ben Erichson's avatar Ben Erichson Committed by Mitar

updating ICSI primitives and pipelines

parent 6acfa0dc
{
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"id": "00364e85-04d5-4668-b110-3892a4a1105f",
"schema": "https://metadata.datadrivendiscovery.org/schemas/v0/pipeline.json",
"created": "2019-11-24T21:24:43.197293Z",
"created": "2019-11-25T22:22:22.333349Z",
"inputs": [
{
"name": "inputs"
......@@ -181,10 +181,10 @@
"type": "PRIMITIVE",
"primitive": {
"id": "f8de43e0-7f81-4edd-9ef6-51bcd2953784",
"version": "2.8.3",
"version": "2.8.4",
"python_path": "d3m.primitives.classification.tensor_machines_binary_classification.TensorMachinesBinaryClassification",
"name": "Tensor Machine Binary Classifier",
"digest": "0a9ddb9697dceb63a751a3e9ee15c95068b854c4eb9a725a146e159479b31f19"
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......@@ -249,5 +249,5 @@
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{
"id": "f8de43e0-7f81-4edd-9ef6-51bcd2953784",
"version": "2.8.3",
"version": "2.8.4",
"name": "Tensor Machine Binary Classifier",
"description": "Learns a polynomial function using logistic regression for binary classification by modeling the polynomial's coefficients as low-rank tensors.\nMeant as a faster, more scalable alternative to polynomial random feature map approaches like CRAFTMaps.\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.tensor_machines_binary_classification.TensorMachinesBinaryClassification",
......@@ -28,7 +28,7 @@
"installation": [
{
"type": "PIP",
"package_uri": "git+https://github.com/ICSI-RealML/realML.git@e298e75e2274ca6e51ce871405e8f3a6edbcbe18#egg=realML"
"package_uri": "git+https://github.com/ICSI-RealML/realML.git@8d8cf78580ec74e23ea754cf83d2a4026475cc9c#egg=realML"
}
],
"location_uris": [
......@@ -283,5 +283,5 @@
}
},
"structural_type": "realML.kernel.TensorMachinesBinaryClassification.TensorMachinesBinaryClassification",
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"id": "733b72f8-2194-4520-aa04-c5519c460f84",
"schema": "https://metadata.datadrivendiscovery.org/schemas/v0/pipeline.json",
"created": "2019-11-24T21:24:46.917847Z",
"created": "2019-11-25T22:22:26.103296Z",
"inputs": [
{
"name": "inputs"
......@@ -139,10 +139,10 @@
"type": "PRIMITIVE",
"primitive": {
"id": "ea3b78a6-dc8c-4772-a329-b653583817b4",
"version": "2.8.3",
"version": "2.8.4",
"python_path": "d3m.primitives.feature_extraction.l1_low_rank.L1LowRank",
"name": "Fast Approximate Entrywise L1-Norm Low Rank Factorization",
"digest": "a530bddad26b855ef77373530090089e4c28b8ffce3a90c68d320d003fb45a15"
"digest": "8f4d45e9def9734db55647147f09373fafe88a53a49f04d423b21665374ae155"
},
"arguments": {
"inputs": {
......@@ -241,5 +241,5 @@
]
}
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\ No newline at end of file
{
"id": "ea3b78a6-dc8c-4772-a329-b653583817b4",
"version": "2.8.3",
"version": "2.8.4",
"name": "Fast Approximate Entrywise L1-Norm Low Rank Factorization",
"description": "Performs fast approximate solution to the NP-hard problem of computing a low-rank approximation that minimizes\nthe entrywise l1-norm approximation error\n (A,B) = argmin ||X - A B||_1\nsubject to A and B.T have a fixed number of columns k\nThe algorithm is principled, and has an approximation factor that grows like poly(k*log(n)), when X is n-by-n; it\nmeets this guarantee with constant probability.\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.feature_extraction.l1_low_rank.L1LowRank",
......@@ -22,7 +22,7 @@
"installation": [
{
"type": "PIP",
"package_uri": "git+https://github.com/ICSI-RealML/realML.git@e298e75e2274ca6e51ce871405e8f3a6edbcbe18#egg=realML"
"package_uri": "git+https://github.com/ICSI-RealML/realML.git@8d8cf78580ec74e23ea754cf83d2a4026475cc9c#egg=realML"
}
],
"location_uris": [
......@@ -220,5 +220,5 @@
}
},
"structural_type": "realML.matrix.L1LowRank.L1LowRank",
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"schema": "https://metadata.datadrivendiscovery.org/schemas/v0/pipeline.json",
"created": "2019-11-24T21:24:50.680359Z",
"created": "2019-11-25T22:22:29.946596Z",
"inputs": [
{
"name": "inputs"
......@@ -170,10 +170,10 @@
"type": "PRIMITIVE",
"primitive": {
"id": "2b39791f-03aa-41ea-b370-abdd043a8887",
"version": "2.8.3",
"version": "2.8.4",
"python_path": "d3m.primitives.feature_extraction.pca_features.RandomizedPolyPCA",
"name": "Randomized Principal Component Analysis using Polynomial Features",
"digest": "5d5c48769a371685a9c8b3426ac98e4f54afaa18db2a2196a40d6e687c59103f"
"digest": "6e64c670b7ac395fd272fac996cd4ddf67b89f14a0bfd826b35a790611d49562"
},
"arguments": {
"inputs": {
......@@ -283,5 +283,5 @@
]
}
],
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"digest": "c758b398ade3085d26ebfb589aaa8371d129492eb127c5c02adedec18a2d10de"
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\ No newline at end of file
{
"id": "2b39791f-03aa-41ea-b370-abdd043a8887",
"version": "2.8.3",
"version": "2.8.4",
"name": "Randomized Principal Component Analysis using Polynomial Features",
"description": "Given a mean rectangular matrix `A` with shape `(m, n)`, a set of polynomial features of degree n\nis constructed. Then the randomized PCA is used to extract a new set of components\nthat captures most of the variation in the data.\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.feature_extraction.pca_features.RandomizedPolyPCA",
......@@ -22,7 +22,7 @@
"installation": [
{
"type": "PIP",
"package_uri": "git+https://github.com/ICSI-RealML/realML.git@e298e75e2274ca6e51ce871405e8f3a6edbcbe18#egg=realML"
"package_uri": "git+https://github.com/ICSI-RealML/realML.git@8d8cf78580ec74e23ea754cf83d2a4026475cc9c#egg=realML"
}
],
"location_uris": [
......@@ -186,5 +186,5 @@
}
},
"structural_type": "realML.matrix.randomizedpolypca.RandomizedPolyPCA",
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"id": "b388b8a1-91f5-41c5-bf2e-b5cfc6c8c572",
"schema": "https://metadata.datadrivendiscovery.org/schemas/v0/pipeline.json",
"created": "2019-11-24T21:24:48.750878Z",
"created": "2019-11-25T22:22:28.003783Z",
"inputs": [
{
"name": "inputs"
......@@ -139,10 +139,10 @@
"type": "PRIMITIVE",
"primitive": {
"id": "3ed8e16e-1d5f-45c8-90f7-fe3c4ce2e758",
"version": "2.8.3",
"version": "2.8.4",
"python_path": "d3m.primitives.feature_extraction.sparse_pca.RobustSparsePCA",
"name": "Robust Sparse Principal Component Analysis",
"digest": "00934ec5692bda7693dac68f96667e6c095580c5e3e1b648c5dd0a5bcc4b05bc"
"digest": "013cfceb7f66e9896b6bccbf20488345041f72c2cba573358cf70eea2ffa4576"
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"arguments": {
"inputs": {
......@@ -246,5 +246,5 @@
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"digest": "d352c263f4a26b0f4b687528057ef649aeccab5a9eabe0cb99c518514e32f21a"
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\ No newline at end of file
{
"id": "3ed8e16e-1d5f-45c8-90f7-fe3c4ce2e758",
"version": "2.8.3",
"version": "2.8.4",
"name": "Robust Sparse Principal Component Analysis",
"description": "Given a mean centered rectangular matrix `A` with shape `(m, n)`, SPCA\ncomputes a set of sparse components that can optimally reconstruct the\ninput data. The amount of sparseness is controllable by the coefficient\nof the L1 penalty, given by the parameter alpha. In addition, some ridge\nshrinkage can be applied in order to improve conditioning.\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.feature_extraction.sparse_pca.RobustSparsePCA",
......@@ -22,7 +22,7 @@
"installation": [
{
"type": "PIP",
"package_uri": "git+https://github.com/ICSI-RealML/realML.git@e298e75e2274ca6e51ce871405e8f3a6edbcbe18#egg=realML"
"package_uri": "git+https://github.com/ICSI-RealML/realML.git@8d8cf78580ec74e23ea754cf83d2a4026475cc9c#egg=realML"
}
],
"location_uris": [
......@@ -236,5 +236,5 @@
}
},
"structural_type": "realML.matrix.robustsparsepca.RobustSparsePCA",
"digest": "00934ec5692bda7693dac68f96667e6c095580c5e3e1b648c5dd0a5bcc4b05bc"
"digest": "013cfceb7f66e9896b6bccbf20488345041f72c2cba573358cf70eea2ffa4576"
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{
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"id": "0eef11cd-bf70-4308-9447-a83b800881db",
"schema": "https://metadata.datadrivendiscovery.org/schemas/v0/pipeline.json",
"created": "2019-11-24T21:24:45.064119Z",
"created": "2019-11-25T22:22:24.205853Z",
"inputs": [
{
"name": "inputs"
......@@ -160,10 +160,10 @@
"type": "PRIMITIVE",
"primitive": {
"id": "b158a49d-5deb-462e-b7e3-e321624dad89",
"version": "2.8.3",
"version": "2.8.4",
"python_path": "d3m.primitives.regression.fast_lad.FastLAD",
"name": "Coreset-based Fast Least Absolute Deviations Solver",
"digest": "7b4aa1019c668d4e0e1e1382ef775bd751f4613d84bd3fec1532640ceb31bd26"
"digest": "3ad0db0cc0044d435a94d9647fbf0b2a10a810957a8b6a7e8b3479ce1e919996"
},
"arguments": {
"inputs": {
......@@ -228,5 +228,5 @@
]
}
],
"digest": "5781cd5aea15eebe48c82c6e6e5df480ed47ac23ff93cdc78d175682e4761431"
"digest": "b74b94b2277c5e98a136db72ae20ada6d47c6159c96653ec679c9b6661b02b42"
}
\ No newline at end of file
{
"id": "b158a49d-5deb-462e-b7e3-e321624dad89",
"version": "2.8.3",
"version": "2.8.4",
"name": "Coreset-based Fast Least Absolute Deviations Solver",
"description": "Performs fast least absolute deviations regression by forming a coreset and solving LAD on that coreset using IRLS\nto return an approximate solution alphahat to\n alpha = argmin ||A alpha - y||_1\npredictions are then formed by\n ypred = trainingData * alphahat\nFor details see Magdon-Ismail, Gittens 2018\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.regression.fast_lad.FastLAD",
......@@ -25,7 +25,7 @@
"installation": [
{
"type": "PIP",
"package_uri": "git+https://github.com/ICSI-RealML/realML.git@e298e75e2274ca6e51ce871405e8f3a6edbcbe18#egg=realML"
"package_uri": "git+https://github.com/ICSI-RealML/realML.git@8d8cf78580ec74e23ea754cf83d2a4026475cc9c#egg=realML"
}
],
"location_uris": [
......@@ -229,5 +229,5 @@
}
},
"structural_type": "realML.matrix.FastLADSolver.FastLAD",
"digest": "7b4aa1019c668d4e0e1e1382ef775bd751f4613d84bd3fec1532640ceb31bd26"
"digest": "3ad0db0cc0044d435a94d9647fbf0b2a10a810957a8b6a7e8b3479ce1e919996"
}
{
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"id": "6e59970c-5ed7-49ee-ac7a-75d815e54649",
"schema": "https://metadata.datadrivendiscovery.org/schemas/v0/pipeline.json",
"created": "2019-11-24T21:24:37.570283Z",
"created": "2019-11-25T22:22:16.597911Z",
"inputs": [
{
"name": "inputs"
......@@ -214,10 +214,10 @@
"type": "PRIMITIVE",
"primitive": {
"id": "90d9eefc-2db3-4738-a0e7-72eedab2d93a",
"version": "2.8.3",
"version": "2.8.4",
"python_path": "d3m.primitives.regression.rfm_precondition_ed_gaussian_krr.RFMPreconditionedGaussianKRR",
"name": "RFM Preconditioned Gaussian Kernel Ridge Regression",
"digest": "031585496ba64f9bf8cfd20f1fa113ebceca2ea4e4e68f79e5cd1dbc8fe1dac7"
"digest": "593b6eb37451070e06e4cd257417fa757409eff57d65198ed74593f8e925c931"
},
"arguments": {
"inputs": {
......@@ -292,5 +292,5 @@
]
}
],
"digest": "f198afb3cadebdd2919f28a057c0055bde6a9f3f4753a3ce77242d7ee8cc94d9"
"digest": "c7e7a7a048431908768d7067e26dab2c805713bcef4e850ff01c1ff02201d5d7"
}
\ No newline at end of file
{
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"id": "89292503-dfb6-412d-9c0a-9f8ebbc2b09a",
"schema": "https://metadata.datadrivendiscovery.org/schemas/v0/pipeline.json",
"created": "2019-11-24T21:24:37.540921Z",
"created": "2019-11-25T22:22:16.567783Z",
"inputs": [
{
"name": "inputs"
......@@ -214,10 +214,10 @@
"type": "PRIMITIVE",
"primitive": {
"id": "90d9eefc-2db3-4738-a0e7-72eedab2d93a",
"version": "2.8.3",
"version": "2.8.4",
"python_path": "d3m.primitives.regression.rfm_precondition_ed_gaussian_krr.RFMPreconditionedGaussianKRR",
"name": "RFM Preconditioned Gaussian Kernel Ridge Regression",
"digest": "031585496ba64f9bf8cfd20f1fa113ebceca2ea4e4e68f79e5cd1dbc8fe1dac7"
"digest": "593b6eb37451070e06e4cd257417fa757409eff57d65198ed74593f8e925c931"
},
"arguments": {
"inputs": {
......@@ -292,5 +292,5 @@
]
}
],
"digest": "da16a176a5a4f444b08c8e5fbce15159001e4d0f5298514064e15094b4e8d9b8"
"digest": "969374b94a2367577dea4730237bb3295a41e3174e2488f4ab05085357a3b416"
}
\ No newline at end of file
{
"id": "39779941-60d1-4a17-83a2-e71240daea4f",
"id": "bad5a3b9-bce9-4177-8e92-d7109e025dc2",
"schema": "https://metadata.datadrivendiscovery.org/schemas/v0/pipeline.json",
"created": "2019-11-24T21:24:37.510616Z",
"created": "2019-11-25T22:22:16.537226Z",
"inputs": [
{
"name": "inputs"
......@@ -160,10 +160,10 @@
"type": "PRIMITIVE",
"primitive": {
"id": "90d9eefc-2db3-4738-a0e7-72eedab2d93a",
"version": "2.8.3",
"version": "2.8.4",
"python_path": "d3m.primitives.regression.rfm_precondition_ed_gaussian_krr.RFMPreconditionedGaussianKRR",
"name": "RFM Preconditioned Gaussian Kernel Ridge Regression",
"digest": "031585496ba64f9bf8cfd20f1fa113ebceca2ea4e4e68f79e5cd1dbc8fe1dac7"
"digest": "593b6eb37451070e06e4cd257417fa757409eff57d65198ed74593f8e925c931"
},
"arguments": {
"inputs": {
......@@ -228,5 +228,5 @@
]
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"digest": "f5d77172a10c1e86e15cb1db59c0e42e849e7888300dd0ee103b3822f355771c"
"digest": "c80d0a8d2f10fd8de6e50a0e56e2c61bfbca658e23294708d64728a15b200d1f"
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\ No newline at end of file
{
"id": "90d9eefc-2db3-4738-a0e7-72eedab2d93a",
"version": "2.8.3",
"version": "2.8.4",
"name": "RFM Preconditioned Gaussian Kernel Ridge Regression",
"description": "Performs gaussian kernel regression using a random feature map to precondition the\nproblem for faster convergence:\nforms the kernel\n K_{ij} = exp(-||x_i - x_j||^2/(2sigma^2))\nand solves\n alphahat = argmin ||K alpha - y||_F^2 + lambda ||alpha||_F^2\npredictions are then formed by\n ypred = K(trainingData, x) alphahat\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.regression.rfm_precondition_ed_gaussian_krr.RFMPreconditionedGaussianKRR",
......@@ -27,7 +27,7 @@
"installation": [
{
"type": "PIP",
"package_uri": "git+https://github.com/ICSI-RealML/realML.git@e298e75e2274ca6e51ce871405e8f3a6edbcbe18#egg=realML"
"package_uri": "git+https://github.com/ICSI-RealML/realML.git@8d8cf78580ec74e23ea754cf83d2a4026475cc9c#egg=realML"
}
],
"location_uris": [
......@@ -244,5 +244,5 @@
}
},
"structural_type": "realML.kernel.RFMPreconditionedGaussianKRRSolver.RFMPreconditionedGaussianKRR",
"digest": "031585496ba64f9bf8cfd20f1fa113ebceca2ea4e4e68f79e5cd1dbc8fe1dac7"
"digest": "593b6eb37451070e06e4cd257417fa757409eff57d65198ed74593f8e925c931"
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"id": "0e17cea9-45d2-4a5c-b35f-05a2e5033a08",
"schema": "https://metadata.datadrivendiscovery.org/schemas/v0/pipeline.json",
"created": "2019-11-24T21:24:39.399740Z",
"created": "2019-11-25T22:22:18.451101Z",
"inputs": [
{
"name": "inputs"
......@@ -187,10 +187,10 @@
"type": "PRIMITIVE",
"primitive": {
"id": "c7a35a32-444c-4530-aeb4-e7a95cbe2cbf",
"version": "2.8.3",
"version": "2.8.4",
"python_path": "d3m.primitives.regression.rfm_precondition_ed_polynomial_krr.RFMPreconditionedPolynomialKRR",
"name": "RFM Preconditioned Polynomial Kernel Ridge Regression",
"digest": "680cc1832eecb19d5455d147f5c14d3dc5fd23fdbfb968ce1035c638bfbcd054"
"digest": "63a23aa5e5355f09472043a2227829e887a68d3fa4603a48929b188afc6883b7"
},
"arguments": {
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......@@ -255,5 +255,5 @@
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\ No newline at end of file
{
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"id": "bf0223d2-e044-44e6-9d7f-a0a699b81a4e",
"schema": "https://metadata.datadrivendiscovery.org/schemas/v0/pipeline.json",
"created": "2019-11-24T21:24:39.429881Z",
"created": "2019-11-25T22:22:18.481328Z",
"inputs": [
{
"name": "inputs"
......@@ -214,10 +214,10 @@
"type": "PRIMITIVE",
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"id": "c7a35a32-444c-4530-aeb4-e7a95cbe2cbf",
"version": "2.8.3",
"version": "2.8.4",
"python_path": "d3m.primitives.regression.rfm_precondition_ed_polynomial_krr.RFMPreconditionedPolynomialKRR",
"name": "RFM Preconditioned Polynomial Kernel Ridge Regression",
"digest": "680cc1832eecb19d5455d147f5c14d3dc5fd23fdbfb968ce1035c638bfbcd054"
"digest": "63a23aa5e5355f09472043a2227829e887a68d3fa4603a48929b188afc6883b7"
},
"arguments": {
"inputs": {
......@@ -296,5 +296,5 @@
]
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\ No newline at end of file
{
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"id": "f944022c-b377-4041-9581-22f39ed4adf5",
"schema": "https://metadata.datadrivendiscovery.org/schemas/v0/pipeline.json",
"created": "2019-11-24T21:24:39.460237Z",
"created": "2019-11-25T22:22:18.511973Z",
"inputs": [
{
"name": "inputs"
......@@ -214,10 +214,10 @@
"type": "PRIMITIVE",
"primitive": {
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"version": "2.8.3",
"version": "2.8.4",
"python_path": "d3m.primitives.regression.rfm_precondition_ed_polynomial_krr.RFMPreconditionedPolynomialKRR",
"name": "RFM Preconditioned Polynomial Kernel Ridge Regression",
"digest": "680cc1832eecb19d5455d147f5c14d3dc5fd23fdbfb968ce1035c638bfbcd054"
"digest": "63a23aa5e5355f09472043a2227829e887a68d3fa4603a48929b188afc6883b7"
},
"arguments": {
"inputs": {
......@@ -296,5 +296,5 @@
]
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],
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"digest": "ebdaabcd93a6132e4c8c1cc10941620317c5b1b3a8c9e08fbdb24fc3d0751c7b"
}
\ No newline at end of file
{
"id": "c7a35a32-444c-4530-aeb4-e7a95cbe2cbf",
"version": "2.8.3",
"version": "2.8.4",
"name": "RFM Preconditioned Polynomial Kernel Ridge Regression",
"description": "Performs polynomial kernel regression using a TensorSketch polynomial random feature map to precondition the\nproblem for faster convergence:\nforms the kernel\n K_{ij} = (sf<x,y>+offset)^degree\nand solves\n alphahat = argmin ||K alpha - y||_F^2 + lambda ||alpha||_F^2\npredictions are then formed by\n ypred = K(trainingData, x) alphahat\n\nWarning: the data should be normalized (e.g. have every row of X be very low l2 norm), or numerical issues will arise when the degree is greater than 2\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.regression.rfm_precondition_ed_polynomial_krr.RFMPreconditionedPolynomialKRR",
......@@ -26,7 +26,7 @@
"installation": [
{
"type": "PIP",
"package_uri": "git+https://github.com/ICSI-RealML/realML.git@e298e75e2274ca6e51ce871405e8f3a6edbcbe18#egg=realML"
"package_uri": "git+https://github.com/ICSI-RealML/realML.git@8d8cf78580ec74e23ea754cf83d2a4026475cc9c#egg=realML"
}
],
"location_uris": [
......@@ -269,5 +269,5 @@
}
},
"structural_type": "realML.kernel.RFMPreconditionedPolynomialKRRSolver.RFMPreconditionedPolynomialKRR",
"digest": "680cc1832eecb19d5455d147f5c14d3dc5fd23fdbfb968ce1035c638bfbcd054"
"digest": "63a23aa5e5355f09472043a2227829e887a68d3fa4603a48929b188afc6883b7"
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{
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"id": "19469f63-add0-4f3b-b5f9-1897c756d22a",
"schema": "https://metadata.datadrivendiscovery.org/schemas/v0/pipeline.json",
"created": "2019-11-24T21:24:41.299475Z",
"created": "2019-11-25T22:22:20.441455Z",
"inputs": [
{
"name": "inputs"
......@@ -214,10 +214,10 @@
"type": "PRIMITIVE",
"primitive": {
"id": "79a494ee-22a2-4768-8dcb-aa282486c5ef",
"version": "2.8.3",
"version": "2.8.4",
"python_path": "d3m.primitives.regression.tensor_machines_regularized_least_squares.TensorMachinesRegularizedLeastSquares",
"name": "Tensor Machine Regularized Least Squares",
"digest": "91df85025e99376995c5cf98e64c4e850bf7bd4a9b0a3900bbfb73525511ba6f"
"digest": "e3ebabec10b6cc6f00777cc0d0117e6f830b139d3390cc070bf70b8cd3a77ee9"
},
"arguments": {
"inputs": {
......@@ -300,5 +300,5 @@
]
}
],
"digest": "7117f566db17df6615996a29202f430a6e89d2dc3c9239ada81106be7ae194cd"
"digest": "adf181e3009c6723bf119ad498d78afba5988c8c02b6ad0ed9f069f90e208948"
}
\ No newline at end of file
{
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"id": "58c5179d-fb08-4ea1-9f74-7352801575be",
"schema": "https://metadata.datadrivendiscovery.org/schemas/v0/pipeline.json",
"created": "2019-11-24T21:24:41.330864Z",
"created": "2019-11-25T22:22:20.473144Z",
"inputs": [
{
"name": "inputs"
......@@ -214,10 +214,10 @@
"type": "PRIMITIVE",
"primitive": {
"id": "79a494ee-22a2-4768-8dcb-aa282486c5ef",
"version": "2.8.3",
"version": "2.8.4",
"python_path": "d3m.primitives.regression.tensor_machines_regularized_least_squares.TensorMachinesRegularizedLeastSquares",
"name": "Tensor Machine Regularized Least Squares",
"digest": "91df85025e99376995c5cf98e64c4e850bf7bd4a9b0a3900bbfb73525511ba6f"
"digest": "e3ebabec10b6cc6f00777cc0d0117e6f830b139d3390cc070bf70b8cd3a77ee9"
},
"arguments": {
"inputs": {
......@@ -300,5 +300,5 @@
]
}
],
"digest": "fadd8106427b84e3c488653a863ee9ca53c54d753b1bfd8d0475a2642b19d492"
"digest": "3a29755b382015c51fc0a7c17fdef2ea97d48578361b3728835fbda9b7ae5a7a"
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\ No newline at end of file
{
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"id": "f9f4dabd-84b6-4456-ae8b-e96a67a4cef1",
"schema": "https://metadata.datadrivendiscovery.org/schemas/v0/pipeline.json",
"created": "2019-11-24T21:24:41.272470Z",
"created": "2019-11-25T22:22:20.414048Z",
"inputs": [
{
"name": "inputs"
......@@ -160,10 +160,10 @@
"type": "PRIMITIVE",
"primitive": {
"id": "79a494ee-22a2-4768-8dcb-aa282486c5ef",
"version": "2.8.3",
"version": "2.8.4",
"python_path": "d3m.primitives.regression.tensor_machines_regularized_least_squares.TensorMachinesRegularizedLeastSquares",
"name": "Tensor Machine Regularized Least Squares",
"digest": "91df85025e99376995c5cf98e64c4e850bf7bd4a9b0a3900bbfb73525511ba6f"
"digest": "e3ebabec10b6cc6f00777cc0d0117e6f830b139d3390cc070bf70b8cd3a77ee9"
},
"arguments": {
"inputs": {
......@@ -228,5 +228,5 @@
]
}
],
"digest": "57e5d4a2b3d1262c71e80131903b3b0bef1fd4cae4dbc4e119dc87fd0a6b90e6"
"digest": "eda7eb2104978e47abebed1c04a3ce37952c3d1c9ba41595080c4c0153e3c138"
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\ No newline at end of file
{
"id": "79a494ee-22a2-4768-8dcb-aa282486c5ef",
"version": "2.8.3",
"version": "2.8.4",
"name": "Tensor Machine Regularized Least Squares",
"description": "Fits an l2-regularized least squares polynomial regression model by modeling the coefficients of the polynomial with a low-rank tensor. Intended\nas a scalable alternative to polynomial random feature maps like CRAFTMaps.\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.regression.tensor_machines_regularized_least_squares.TensorMachinesRegularizedLeastSquares",
......@@ -28,7 +28,7 @@
"installation": [
{
"type": "PIP",
"package_uri": "git+https://github.com/ICSI-RealML/realML.git@e298e75e2274ca6e51ce871405e8f3a6edbcbe18#egg=realML"
"package_uri": "git+https://github.com/ICSI-RealML/realML.git@8d8cf78580ec74e23ea754cf83d2a4026475cc9c#egg=realML"
}
],
"location_uris": [
......@@ -283,5 +283,5 @@
}
},
"structural_type": "realML.kernel.TensorMachinesRegularizedLeastSquares.TensorMachinesRegularizedLeastSquares",