Commit 6c2e204a authored by Sujen's avatar Sujen

Merge branch 'icsiupdate' into 'master'

Icsiupdate

See merge request datadrivendiscovery/primitives!223
parents c8e1c8a2 946df32c
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"name": "Sparse Principal Component Analysis",
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"full_inputs": [
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"train_inputs": [
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],
"test_inputs": [
"196_autoMpg_problem"
],
"score_inputs": [
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]
}
\ No newline at end of file
{
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"id": "8911da03-f932-4e2c-839e-863cd1ff6738",
"schema": "https://metadata.datadrivendiscovery.org/schemas/v0/pipeline.json",
"created": "2019-06-11T19:17:12.275087Z",
"created": "2019-06-13T19:38:55.846954Z",
"inputs": [
{
"name": "inputs"
......@@ -160,10 +160,10 @@
"type": "PRIMITIVE",
"primitive": {
"id": "b158a49d-5deb-462e-b7e3-e321624dad89",
"version": "2.7.3",
"version": "2.7.4",
"python_path": "d3m.primitives.regression.fast_lad.FastLAD",
"name": "Coreset-based Fast Least Absolute Deviations Solver",
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......@@ -228,5 +228,5 @@
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"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 @@
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......@@ -245,5 +245,5 @@
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"inputs": [
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......@@ -160,10 +160,10 @@
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"version": "2.7.3",
"version": "2.7.4",
"python_path": "d3m.primitives.regression.rfm_precondition_ed_gaussian_krr.RFMPreconditionedGaussianKRR",
"name": "RFM Preconditioned Gaussian Kernel Ridge Regression",
"digest": "1d87d281e62ee2a259bb9d8b2fa2425efea8802fc550aa16476eac628e44e157"
"digest": "1d0a5ecdc2ad2681f15d64deab384c1f399f65ef97825c8eb5774e7fd1a58aba"
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"arguments": {
"inputs": {
......@@ -228,5 +228,5 @@
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"version": "2.7.4",
"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": [
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"package_uri": "git+https://github.com/ICSI-RealML/realML.git@7332c0b061c105c575e2242e8355094e34930039#egg=realML"
}
],
"location_uris": [
......@@ -260,5 +260,5 @@
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"created": "2019-06-11T19:17:07.971608Z",
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"inputs": [
{
"name": "inputs"
......@@ -160,10 +160,10 @@
"type": "PRIMITIVE",
"primitive": {
"id": "c7a35a32-444c-4530-aeb4-e7a95cbe2cbf",
"version": "2.7.3",
"version": "2.7.4",
"python_path": "d3m.primitives.regression.rfm_precondition_ed_polynomial_krr.RFMPreconditionedPolynomialKRR",
"name": "RFM Preconditioned Polynomial Kernel Ridge Regression",
"digest": "6f1afe1eab687c0607af8b74eef505e7f210beee3de36330114c1023f061ea74"
"digest": "4600b3330919e2ab8c0c702a749f08a330e9cc187982fd8b15749c3a1fad4179"
},
"arguments": {
"inputs": {
......@@ -228,5 +228,5 @@
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{
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"version": "2.7.3",
"version": "2.7.4",
"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@faa1a6c554609aaeff91ba9e09f26aaedbd7317b#egg=realML"
"package_uri": "git+https://github.com/ICSI-RealML/realML.git@7332c0b061c105c575e2242e8355094e34930039#egg=realML"
}
],
"location_uris": [
......@@ -285,5 +285,5 @@
}
},
"structural_type": "realML.kernel.RFMPreconditionedPolynomialKRRSolver.RFMPreconditionedPolynomialKRR",
"digest": "6f1afe1eab687c0607af8b74eef505e7f210beee3de36330114c1023f061ea74"
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{
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"id": "19db359a-4c31-4037-8580-724e6c8a5ae6",
"schema": "https://metadata.datadrivendiscovery.org/schemas/v0/pipeline.json",
"created": "2019-06-11T19:17:10.107925Z",
"created": "2019-06-13T19:38:53.547979Z",
"inputs": [
{
"name": "inputs"
......@@ -160,10 +160,10 @@
"type": "PRIMITIVE",
"primitive": {
"id": "79a494ee-22a2-4768-8dcb-aa282486c5ef",
"version": "2.7.3",
"version": "2.7.4",
"python_path": "d3m.primitives.regression.tensor_machines_regularized_least_squares.TensorMachinesRegularizedLeastSquares",
"name": "Tensor Machine Regularized Least Squares",
"digest": "386859da07439dd3a9cd427616255a0d048fba5cb1977bdbf0f3ab61d61c50c4"
"digest": "ad60c2936827b440a76090d67bf4b3976caba6ac89a32e5b3a43aaad693a24cb"
},
"arguments": {
"inputs": {
......@@ -228,5 +228,5 @@
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"digest": "af558ec98cf57d795fe262c5cd29a93db16ca40afd3dedc3e3f701ebf51b54df"
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\ No newline at end of file
{
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"version": "2.7.3",
"version": "2.7.4",
"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@faa1a6c554609aaeff91ba9e09f26aaedbd7317b#egg=realML"
"package_uri": "git+https://github.com/ICSI-RealML/realML.git@7332c0b061c105c575e2242e8355094e34930039#egg=realML"
}
],
"location_uris": [
......@@ -299,5 +299,5 @@
}
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"structural_type": "realML.kernel.TensorMachinesRegularizedLeastSquares.TensorMachinesRegularizedLeastSquares",
"digest": "386859da07439dd3a9cd427616255a0d048fba5cb1977bdbf0f3ab61d61c50c4"
"digest": "ad60c2936827b440a76090d67bf4b3976caba6ac89a32e5b3a43aaad693a24cb"
}
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