Commit 796dab69 authored by Robert Brekelmans's avatar Robert Brekelmans Committed by Sujen

updates

parent 06042f4e
{
"problem":"30_personae_problem",
"problem":"LL1_net_nomination_seed_problem",
"full_inputs":[
"30_personae_dataset"
"LL1_net_nomination_seed_dataset"
],
"train_inputs":[
"30_personae_dataset_TRAIN"
"LL1_net_nomination_seed_dataset_TRAIN"
],
"test_inputs":[
"30_personae_dataset_TEST"
"LL1_net_nomination_seed_dataset_TEST"
],
"score_inputs":[
"30_personae_dataset_SCORE"
"LL1_net_nomination_seed_dataset_SCORE"
]
}
......@@ -13,7 +13,7 @@
"installation": [
{
"type": "PIP",
"package_uri": "git+https://github.com/brekelma/[email protected]a2f5253e7f7c0f7bc096b34cd30d4f9b786456e6#egg=dsbox_graphs"
"package_uri": "git+https://github.com/brekelma/[email protected]828d2fad21c257a9c4c04e7725c116d185339294#egg=dsbox_graphs"
}
],
"algorithm_types": [
......@@ -201,5 +201,5 @@
},
"structural_type": "graph_dataset_to_list.GraphDatasetToList",
"description": "A primitive which extracts a DataFrame out of a Dataset.\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": "82f436fc0758fa4b372d2824dda29002a35ff1df1751c4f132e9a47f8f8576f9"
"digest": "0a5d2844f8997cba038235151ba13874038d9ee70cd9d2483ae2dfab19ef7f07"
}
......@@ -16,7 +16,7 @@
"installation": [
{
"type": "PIP",
"package_uri": "git+https://github.com/brekelma/[email protected]414c0e77583575ffa9d869455d714d8650839b5d#egg=dsbox_corex"
"package_uri": "git+https://github.com/brekelma/[email protected]f7b6319bd593017345b03e27408b097e9af821e9#egg=dsbox-corex"
}
],
"algorithm_types": [
......@@ -45,7 +45,7 @@
"hyperparams": {
"n_hidden": {
"type": "d3m.metadata.hyperparams.Union",
"default": 0.2,
"default": 0.5,
"structural_type": "float",
"semantic_types": [
"https://metadata.datadrivendiscovery.org/types/TuningParameter"
......@@ -65,12 +65,12 @@
},
"n_hidden pct": {
"type": "d3m.metadata.hyperparams.Uniform",
"default": 0.2,
"default": 0.5,
"structural_type": "float",
"semantic_types": [],
"description": "number of hidden factors as percentage of # input columns",
"lower": 0,
"upper": 0.5,
"upper": 1.0,
"lower_inclusive": true,
"upper_inclusive": false,
"q": 0.05
......@@ -214,5 +214,5 @@
}
},
"structural_type": "corex_continuous.CorexContinuous",
"digest": "c82bd23e6077c23dfbdc0f9ffd952d4510610d347bfe6c84f14142ae2f1fa42f"
"digest": "f64e6571e41701029c7cda222200df4258e25c6f2c7a3ccba53a60dee9834fb1"
}
......@@ -16,7 +16,7 @@
"installation": [
{
"type": "PIP",
"package_uri": "git+https://github.com/brekelma/[email protected]414c0e77583575ffa9d869455d714d8650839b5d#egg=dsbox_corex"
"package_uri": "git+https://github.com/brekelma/[email protected]f7b6319bd593017345b03e27408b097e9af821e9#egg=dsbox-corex"
}
],
"algorithm_types": [
......@@ -43,7 +43,7 @@
"hyperparams": {
"n_hidden": {
"type": "d3m.metadata.hyperparams.Uniform",
"default": 200,
"default": 20,
"structural_type": "float",
"semantic_types": [
"https://metadata.datadrivendiscovery.org/types/TuningParameter"
......@@ -57,7 +57,7 @@
},
"beta": {
"type": "d3m.metadata.hyperparams.Uniform",
"default": 0.1,
"default": 0.2,
"structural_type": "float",
"semantic_types": [
"https://metadata.datadrivendiscovery.org/types/TuningParameter"
......@@ -70,11 +70,11 @@
"q": 0.01
},
"epochs": {
"type": "d3m.metadata.hyperparams.Uniform",
"type": "d3m.metadata.hyperparams.UniformInt",
"default": 100,
"structural_type": "float",
"structural_type": "int",
"semantic_types": [
"https://metadata.datadrivendiscovery.org/types/TuningParameter"
"https://metadata.datadrivendiscovery.org/types/ControlParameter"
],
"description": "number of epochs to train",
"lower": 1,
......@@ -82,6 +82,93 @@
"lower_inclusive": true,
"upper_inclusive": false
},
"batch": {
"type": "d3m.metadata.hyperparams.UniformInt",
"default": 50,
"structural_type": "int",
"semantic_types": [
"https://metadata.datadrivendiscovery.org/types/ControlParameter"
],
"description": "batch size",
"lower": 10,
"upper": 501,
"lower_inclusive": true,
"upper_inclusive": false
},
"units": {
"type": "d3m.metadata.hyperparams.UniformInt",
"default": 200,
"structural_type": "int",
"semantic_types": [
"https://metadata.datadrivendiscovery.org/types/ControlParameter"
],
"description": "number of fc units",
"lower": 1,
"upper": 500,
"lower_inclusive": true,
"upper_inclusive": false
},
"layers": {
"type": "d3m.metadata.hyperparams.UniformInt",
"default": 3,
"structural_type": "int",
"semantic_types": [
"https://metadata.datadrivendiscovery.org/types/ControlParameter"
],
"description": "number of layers",
"lower": 1,
"upper": 20,
"lower_inclusive": true,
"upper_inclusive": false
},
"lr": {
"type": "d3m.metadata.hyperparams.Uniform",
"default": 0.0005,
"structural_type": "float",
"semantic_types": [
"https://metadata.datadrivendiscovery.org/types/ControlParameter"
],
"description": "learning rate",
"lower": 0.0001,
"upper": 1.0001,
"lower_inclusive": true,
"upper_inclusive": false
},
"warm_up": {
"type": "d3m.metadata.hyperparams.UniformInt",
"default": 0,
"structural_type": "int",
"semantic_types": [
"https://metadata.datadrivendiscovery.org/types/ControlParameter"
],
"description": "epochs of reduced MI regularization",
"lower": 0,
"upper": 20,
"lower_inclusive": true,
"upper_inclusive": false
},
"sgd": {
"type": "d3m.metadata.hyperparams.UniformBool",
"default": false,
"structural_type": "bool",
"semantic_types": [
"https://metadata.datadrivendiscovery.org/types/ControlParameter"
],
"description": "whether to use sgd (alternatively, Adam)"
},
"clipnorm": {
"type": "d3m.metadata.hyperparams.Uniform",
"default": 1.0,
"structural_type": "float",
"semantic_types": [
"https://metadata.datadrivendiscovery.org/types/ControlParameter"
],
"description": "gradient norm clipping ",
"lower": 0.5,
"upper": 100,
"lower_inclusive": true,
"upper_inclusive": false
},
"convolutional": {
"type": "d3m.metadata.hyperparams.UniformBool",
"default": false,
......@@ -91,6 +178,58 @@
],
"description": "whether to use a convolutional architecture"
},
"final_kernel": {
"type": "d3m.metadata.hyperparams.UniformInt",
"default": 5,
"structural_type": "int",
"semantic_types": [
"https://metadata.datadrivendiscovery.org/types/ControlParameter"
],
"description": "epochs of reduced MI regularization",
"lower": 1,
"upper": 20,
"lower_inclusive": true,
"upper_inclusive": false
},
"img_dim_1": {
"type": "d3m.metadata.hyperparams.UniformInt",
"default": 28,
"structural_type": "int",
"semantic_types": [
"https://metadata.datadrivendiscovery.org/types/ControlParameter"
],
"description": "img dim 1 ",
"lower": 2,
"upper": 256,
"lower_inclusive": true,
"upper_inclusive": false
},
"img_dim_2": {
"type": "d3m.metadata.hyperparams.UniformInt",
"default": 28,
"structural_type": "int",
"semantic_types": [
"https://metadata.datadrivendiscovery.org/types/ControlParameter"
],
"description": "img dim 2 ",
"lower": 2,
"upper": 256,
"lower_inclusive": true,
"upper_inclusive": false
},
"strides": {
"type": "d3m.metadata.hyperparams.UniformInt",
"default": 2,
"structural_type": "int",
"semantic_types": [
"https://metadata.datadrivendiscovery.org/types/ControlParameter"
],
"description": "last argument of convolutional strides for fitting img size",
"lower": 1,
"upper": 5,
"lower_inclusive": true,
"upper_inclusive": false
},
"task": {
"type": "d3m.metadata.hyperparams.Enumeration",
"default": "REGRESSION",
......@@ -112,29 +251,6 @@
"https://metadata.datadrivendiscovery.org/types/ControlParameter"
],
"description": "whether to return constructed features AND predictions (else, used for modeling i.e. only predictions"
},
"error_on_no_input": {
"type": "d3m.metadata.hyperparams.UniformBool",
"default": true,
"structural_type": "bool",
"semantic_types": [
"https://metadata.datadrivendiscovery.org/types/ControlParameter"
],
"description": "Throw an exception if no input column is selected/provided. Defaults to true to behave like sklearn. To prevent pipelines from breaking set this to False."
},
"gpus": {
"type": "d3m.metadata.hyperparams.Uniform",
"default": 0,
"structural_type": "float",
"semantic_types": [
"https://metadata.datadrivendiscovery.org/types/ResourcesUseParameter"
],
"description": "GPUs to Use",
"lower": 0,
"upper": 5,
"lower_inclusive": true,
"upper_inclusive": false,
"q": 1
}
},
"arguments": {
......@@ -278,9 +394,10 @@
"model_weights": "typing.Union[NoneType, typing.Any]",
"fitted": "typing.Union[NoneType, bool]",
"label_encode": "typing.Union[NoneType, sklearn.preprocessing.label.LabelEncoder]",
"output_columns": "typing.Union[NoneType, list, pandas.core.indexes.base.Index]"
"output_columns": "typing.Union[NoneType, list, pandas.core.indexes.base.Index]",
"embed": "typing.Union[NoneType, keras.engine.training.Model]"
}
},
"structural_type": "echo_ib.EchoIB",
"digest": "025d638d9708d3d8ccb047c600ddaf4432bbb3183c77abf4d297dc057d82994b"
"digest": "038b459324f4e5de273e7de4c26e02ff9bfa766486d3083d22a8c7d6df8f5d31"
}
......@@ -184,7 +184,7 @@
"hyperparams":{
"n_hidden":{
"type":"VALUE",
"data":5
"data":25
},
"threshold":{
"type":"VALUE",
......
......@@ -16,7 +16,7 @@
"installation": [
{
"type": "PIP",
"package_uri": "git+https://github.com/brekelma/[email protected]414c0e77583575ffa9d869455d714d8650839b5d#egg=dsbox_corex"
"package_uri": "git+https://github.com/brekelma/[email protected]f7b6319bd593017345b03e27408b097e9af821e9#egg=dsbox-corex"
}
],
"algorithm_types": [
......@@ -261,5 +261,5 @@
}
},
"structural_type": "corex_text.CorexText",
"digest": "102801b2291dc98866e12822f1aee45c19099e230601c1f0cbe9519ec4a48f83"
"digest": "7210babace7d873323d9aa99ee67089f7272b78c25224a01da3cbf0afc6597c0"
}
{
"id":"42e376c5-6e93-44f8-b099-04bc8b091ad7",
"schema":"https://metadata.datadrivendiscovery.org/schemas/v0/pipeline.json",
"created":"2019-06-05T03:44:33.899641Z",
"inputs":[
{
"name":"input dataset"
}
"problem":"LL1_net_nomination_seed_problem",
"full_inputs":[
"LL1_net_nomination_seed_dataset"
],
"outputs":[
{
"data":"steps.6.produce",
"name":"predictions of input dataset"
}
"train_inputs":[
"LL1_net_nomination_seed_dataset_TRAIN"
],
"steps":[
{
"type":"PRIMITIVE",
"primitive":{
"id":"f31f8c1f-d1c5-43e5-a4b2-2ae4a761ef2e",
"version":"0.2.0",
"python_path":"d3m.primitives.data_transformation.denormalize.Common",
"name":"Denormalize datasets",
"digest":"406f9beb9ad211f4fde228be374f07a427d6b2b45aba4f1f4ac1b5510ae84e3e"
},
"arguments":{
"inputs":{
"type":"CONTAINER",
"data":"inputs.0"
}
},
"outputs":[
{
"id":"produce"
}
],
"hyperparams":{
"starting_resource":{
"type":"VALUE",
"data":null
},
"recursive":{
"type":"VALUE",
"data":true
},
"many_to_many":{
"type":"VALUE",
"data":false
},
"discard_not_joined_tabular_resources":{
"type":"VALUE",
"data":false
}
}
},
{
"type":"PRIMITIVE",
"primitive":{
"id":"dbb3792d-a44b-4941-a88e-5520c0a23488",
"version":"0.1.0",
"python_path":"d3m.primitives.data_transformation.normalize_graphs.Common",
"name":"Normalize graphs",
"digest":"19b36c34cf5b5bdbd9e9c9af319e409893b1dc6538da0e2b1b1ab660abcf7979"
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"arguments":{
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"data":"steps.0.produce"
}
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"outputs":[
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"id":"produce"
}
]
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"primitive":{
"id":"dfb8c278-5382-47cd-bd39-f9429890a239",
"version":"1.0.0",
"python_path":"d3m.primitives.data_transformation.graph_to_edge_list.DSBOX",
"name":"Extract graph tables from Dataset into list of DataFrame",
"digest":"7d4ac2ca05dc06953b2df24b68a3d36ce2f9e8d7c5b7ec07862eb1e943a50c4a"
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"primitive":{
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"version":"0.3.0",
"python_path":"d3m.primitives.data_transformation.dataset_to_dataframe.Common",
"name":"Extract a DataFrame from a Dataset",
"digest":"262d651c0711efd819511cb67355d9c247c7c44470c2281fa495e8701d5d3ecb"
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"python_path":"d3m.primitives.data_transformation.extract_columns_by_semantic_types.DataFrameCommon",
"name":"Extracts columns by semantic type",
"digest":"8d9e45e2eca7ac7a5510df443b041449cb558e751dd3876223b25f9f590246e3"
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"outputs":[
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"id":"produce"
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],
"hyperparams":{
"semantic_types":{
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"data":[
"https://metadata.datadrivendiscovery.org/types/TrueTarget",
"https://metadata.datadrivendiscovery.org/types/SuggestedTarget"
]
}
}
},
{
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"primitive":{
"id":"48572851-b86b-4fda-961d-f3f466adb58e",
"version":"1.0.0",
"python_path":"d3m.primitives.feature_construction.gcn_mixhop.DSBOX",
"name":"GCN",
"digest":"0fac4a956488c6285c6b5e1ee853bf78297e67693764f0492301f91bf04000ef"
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"arguments":{
"inputs":{
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"data":"steps.2.produce"
},
"outputs":{
"type":"CONTAINER",
"data":"steps.4.produce"
}
},
"outputs":[
{
"id":"produce"
}
]
},
{
"type":"PRIMITIVE",
"primitive":{
"id":"1dd82833-5692-39cb-84fb-2455683075f3",
"version":"v2019.4.4",
"python_path":"d3m.primitives.classification.random_forest.SKlearn",
"name":"sklearn.ensemble.forest.RandomForestClassifier",
"digest":"7ea6cf52c696329ea9270dfc9d1cea219de388b5714ad850c6a055bd9b699ed9"
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"arguments":{
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"data":"steps.5.produce"
},
"outputs":{
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"data":"steps.4.produce"
}
},
"outputs":[
{
"id":"produce"
}
],
"hyperparams":{
"max_depth":{
"type":"VALUE",
"data":{
"case":"int",
"value":30
}
},
"min_samples_leaf":{
"type":"VALUE",
"data":{
"case":"absolute",
"value":2
}
},
"min_samples_split":{
"type":"VALUE",
"data":{
"case":"absolute",
"value":2
}
},
"max_features":{
"type":"VALUE",
"data":{
"case":"calculated",
"value":"sqrt"
}
},
"n_estimators":{
"type":"VALUE",
"data":100
},
"add_index_columns":{
"type":"VALUE",
"data":true
},
"use_semantic_types":{
"type":"VALUE",
"data":false
},
"error_on_no_input":{
"type":"VALUE",
"data":true
}
}
}
"test_inputs":[
"LL1_net_nomination_seed_dataset_TEST"
],
"name":"ISI_gcn:140244824113296",
"description":"",
"digest":"e55fbfb13e9d0e851477fa7f83175178289e03d4178f71e4487d3daa72d76464",
"pipeline_rank":1.0,
"metric":"accuracy",
"metric_value":1.0,
"template_name":"ISI_gcn",
"template_task":"{'LINK_PREDICTION', 'COMMUNITY_DETECTION', 'VERTEX_NOMINATION', 'COLLABORATIVE_FILTERING'}",
"template_subtask":"{'OVERLAPPING', 'NONE', 'NONOVERLAPPING'}",
"problem_taskType":"vertexNomination",
"problem_taskSubType":"NONE"
"score_inputs":[
"LL1_net_nomination_seed_dataset_SCORE"
]
}
......@@ -16,7 +16,7 @@
"installation": [
{
"type": "PIP",
"package_uri": "git+https://github.com/brekelma/[email protected]a2f5253e7f7c0f7bc096b34cd30d4f9b786456e6#egg=dsbox_graphs"
"package_uri": "git+https://github.com/brekelma/[email protected]4032efe97fe12c2743e5ecf4d187dbe51830b64b#egg=dsbox_graphs"
}
],
"algorithm_types": [