Commit 2867dcab authored by Mitar's avatar Mitar

Merge branch 'master' into 'master'

fixed two problems of Cornell pipelines

See merge request datadrivendiscovery/primitives!321
parents f18cc449 fff3de42
{
"problem": "LL1_VTXC_1343_cora_problem",
"full_inputs": [
"LLL1_VTXC_1343_cora_dataset"
"LL1_VTXC_1343_cora_dataset"
],
"train_inputs": [
"LL1_VTXC_1343_cora_dataset_TRAIN"
......
{
"created": "2019-06-18T02:33:33.891619Z",
"digest": "364116706159d55a172def71b231a637450d54c35a86ad53cad6ecef37327ff7",
"id": "9f453e97-77b1-44c7-bc87-e3802997039f",
"created": "2019-06-21T19:37:20.305486Z",
"digest": "3f347699b19390aff8f77ce9abce428deef3f20556da423bb8702d0f4258ca02",
"id": "b2e3d1ef-9772-407a-8d81-046b112d8df9",
"inputs": [
{
"name": "inputs"
......@@ -9,7 +9,7 @@
],
"outputs": [
{
"data": "steps.9.produce",
"data": "steps.10.produce",
"name": "output"
}
],
......@@ -207,29 +207,21 @@
{
"arguments": {
"inputs": {
"data": "steps.0.produce",
"data": "steps.3.produce",
"type": "CONTAINER"
}
},
"hyperparams": {
"semantic_types": {
"data": [
"https://metadata.datadrivendiscovery.org/types/TrueTarget"
],
"type": "VALUE"
}
},
"outputs": [
{
"id": "produce"
}
],
"primitive": {
"digest": "297a4943484bcd532650d5727e23b3d11ca702688d7b64dfb5df8bf1282eaa47",
"id": "4503a4c6-42f7-45a1-a1d4-ed69699cf5e1",
"name": "Extracts columns by semantic type",
"python_path": "d3m.primitives.data_transformation.extract_columns_by_semantic_types.DataFrameCommon",
"version": "0.2.0"
"digest": "4183552e52f20262bfbb3711300a39216d15749d204cc7b475cad55f8dda1cfd",
"id": "d639947e-ece0-3a39-a666-e974acf4521d",
"name": "sklearn.preprocessing.data.StandardScaler",
"python_path": "d3m.primitives.data_preprocessing.standard_scaler.SKlearn",
"version": "2019.4.4"
},
"type": "PRIMITIVE"
},
......@@ -240,40 +232,23 @@
"type": "CONTAINER"
}
},
"outputs": [
{
"id": "produce"
}
],
"primitive": {
"digest": "98c24b0b4a8ed1f98d01dfd575144a13a019ea5c9996d17554db38b434a91876",
"id": "26fc8fd3-f6b2-4c65-8afb-edb54ed2a3e4",
"name": "Label encoder with an unseen category",
"python_path": "d3m.primitives.data_preprocessing.label_encoder.DataFrameCommon",
"version": "0.2.0"
},
"type": "PRIMITIVE"
},
{
"arguments": {
"inputs": {
"data": "steps.3.produce",
"type": "CONTAINER"
}
},
"hyperparams": {
"alpha": {
"data": 0.01,
"type": "VALUE"
},
"d": {
"data": 20,
"data": 10,
"type": "VALUE"
},
"epsilon": {
"data": 0.1,
"type": "VALUE"
},
"maxiter": {
"data": 2000,
"type": "VALUE"
},
"t": {
"data": 0.001,
"type": "VALUE"
......@@ -293,20 +268,74 @@
},
"type": "PRIMITIVE"
},
{
"arguments": {
"inputs": {
"data": "steps.0.produce",
"type": "CONTAINER"
}
},
"hyperparams": {
"semantic_types": {
"data": [
"https://metadata.datadrivendiscovery.org/types/TrueTarget"
],
"type": "VALUE"
}
},
"outputs": [
{
"id": "produce"
}
],
"primitive": {
"digest": "297a4943484bcd532650d5727e23b3d11ca702688d7b64dfb5df8bf1282eaa47",
"id": "4503a4c6-42f7-45a1-a1d4-ed69699cf5e1",
"name": "Extracts columns by semantic type",
"python_path": "d3m.primitives.data_transformation.extract_columns_by_semantic_types.DataFrameCommon",
"version": "0.2.0"
},
"type": "PRIMITIVE"
},
{
"arguments": {
"inputs": {
"data": "steps.6.produce",
"type": "CONTAINER"
}
},
"outputs": [
{
"id": "produce"
}
],
"primitive": {
"digest": "98c24b0b4a8ed1f98d01dfd575144a13a019ea5c9996d17554db38b434a91876",
"id": "26fc8fd3-f6b2-4c65-8afb-edb54ed2a3e4",
"name": "Label encoder with an unseen category",
"python_path": "d3m.primitives.data_preprocessing.label_encoder.DataFrameCommon",
"version": "0.2.0"
},
"type": "PRIMITIVE"
},
{
"arguments": {
"inputs": {
"data": "steps.5.produce",
"type": "CONTAINER"
},
"outputs": {
"data": "steps.5.produce",
"data": "steps.7.produce",
"type": "CONTAINER"
}
},
"hyperparams": {
"C": {
"data": 100,
"data": 200,
"type": "VALUE"
},
"kernel": {
"data": "rbf",
"type": "VALUE"
}
},
......@@ -327,13 +356,13 @@
{
"arguments": {
"inputs": {
"data": "steps.7.produce",
"data": "steps.8.produce",
"type": "CONTAINER"
}
},
"hyperparams": {
"encoder": {
"data": 5,
"data": 7,
"type": "PRIMITIVE"
}
},
......@@ -354,7 +383,7 @@
{
"arguments": {
"inputs": {
"data": "steps.8.produce",
"data": "steps.9.produce",
"type": "CONTAINER"
},
"reference": {
......
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