Commit 72253bf5 authored by Ke-Thia Yao's avatar Ke-Thia Yao Committed by Sujen

V2019.6.7

parent f638f6fa
{
"id": "dsbox-ensemble-voting",
"version": "1.5.1",
"version": "1.5.2",
"name": "DSBox ensemble voting",
"description": "A ensemble voting primitive. The input dataframe should be the output of multiple learners concatenated together\nusing the data_preprocessing.horizontal_concat.DSBOX primitve.\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.ensemble_voting.DSBOX",
......@@ -22,7 +22,7 @@
"installation": [
{
"type": "PIP",
"package_uri": "git+https://github.com/usc-isi-i2/[email protected]9ffd48258d3f3d7d8d116162b9ed1a01222a014e#egg=dsbox-primitives"
"package_uri": "git+https://github.com/usc-isi-i2/[email protected]ca2ed654966f411288c78954c2e143be84559138#egg=dsbox-primitives"
}
],
"schema": "https://metadata.datadrivendiscovery.org/schemas/v0/primitive.json",
......@@ -185,5 +185,5 @@
}
},
"structural_type": "dsbox.datapostprocessing.ensemble_voting.EnsembleVoting",
"digest": "1fc222892430d52533ed4a47aacd3cc439b9e8bf692fc929bca3cc2de125aa7f"
}
\ No newline at end of file
"digest": "6f4e68c271c6c8a5963c9eef717c3dca6ffa1309c8be3a57f67989b07e86f7fb"
}
{
"id": "b7b40c43-3879-4f78-980c-12b824034cc4",
"schema": "https://metadata.datadrivendiscovery.org/schemas/v0/pipeline.json",
"created": "2019-05-17T20:20:42.321786Z",
"inputs": [
{
"name": "input dataset"
}
],
"outputs": [
{
"data": "steps.5.produce",
"name": "predictions of input dataset"
}
],
"steps": [
{
"type": "PRIMITIVE",
"primitive": {
"id": "4b42ce1e-9b98-4a25-b68e-fad13311eb65",
"version": "0.3.0",
"python_path": "d3m.primitives.data_transformation.dataset_to_dataframe.Common",
"name": "Extract a DataFrame from a Dataset",
"digest": "789969c6a4a81b25b04c95963721259599f8c43c90a6091e49f44c7fe08f51de"
},
"arguments": {
"inputs": {
"type": "CONTAINER",
"data": "inputs.0"
}
},
"outputs": [
{
"id": "produce"
}
]
},
{
"type": "PRIMITIVE",
"primitive": {
"id": "4503a4c6-42f7-45a1-a1d4-ed69699cf5e1",
"version": "0.2.0",
"python_path": "d3m.primitives.data_transformation.extract_columns_by_semantic_types.DataFrameCommon",
"name": "Extracts columns by semantic type",
"digest": "57db870295ef8a63e62429950d7e19c417f07bb9e52124e1f9065b2b0cd55a11"
},
"arguments": {
"inputs": {
"type": "CONTAINER",
"data": "steps.0.produce"
}
},
"outputs": [
{
"id": "produce"
}
],
"hyperparams": {
"semantic_types": {
"type": "VALUE",
"data": [
"https://metadata.datadrivendiscovery.org/types/PrimaryKey",
"https://metadata.datadrivendiscovery.org/types/FileName"
]
}
}
},
{
"type": "PRIMITIVE",
"primitive": {
"id": "4503a4c6-42f7-45a1-a1d4-ed69699cf5e1",
"version": "0.2.0",
"python_path": "d3m.primitives.data_transformation.extract_columns_by_semantic_types.DataFrameCommon",
"name": "Extracts columns by semantic type",
"digest": "57db870295ef8a63e62429950d7e19c417f07bb9e52124e1f9065b2b0cd55a11"
},
"arguments": {
"inputs": {
"type": "CONTAINER",
"data": "steps.0.produce"
}
},
"outputs": [
{
"id": "produce"
}
],
"hyperparams": {
"semantic_types": {
"type": "VALUE",
"data": [
"https://metadata.datadrivendiscovery.org/types/TrueTarget"
]
}
}
},
{
"type": "PRIMITIVE",
"primitive": {
"id": "a29b0080-aeff-407d-9edb-0aa3eefbde01",
"version": "0.2.0",
"python_path": "d3m.primitives.data_preprocessing.video_reader.DataFrameCommon",
"name": "Columns video reader",
"digest": "a60b5a6093cbbc6c836d51b244b5b07aef1e3055da5e0fafb649cbb68b86c073"
},
"arguments": {
"inputs": {
"type": "CONTAINER",
"data": "steps.1.produce"
}
},
"outputs": [
{
"id": "produce"
}
]
},
{
"type": "PRIMITIVE",
"primitive": {
"id": "dsbox-featurizer-image-inceptionV3",
"version": "1.5.2",
"python_path": "d3m.primitives.feature_extraction.inceptionV3_image_feature.DSBOX",
"name": "DSBox Image Featurizer inceptionV3",
"digest": "51445943e1f1610bf277e615d0fb9464a782aea6ba1d02232ec1f0ffbb028a9f"
},
"arguments": {
"inputs": {
"type": "CONTAINER",
"data": "steps.3.produce"
}
},
"outputs": [
{
"id": "produce"
}
],
"hyperparams": {
"use_limitation": {
"type": "VALUE",
"data": false
}
}
},
{
"type": "PRIMITIVE",
"primitive": {
"id": "dsbox-featurizer-video-classification-lstm",
"version": "1.5.2",
"python_path": "d3m.primitives.classification.lstm.DSBOX",
"name": "DSBox Video Classification LSTM",
"digest": "c097fda5631c64829931685f2f99df149188c5c5187c6623e47828a2a49dd8b3"
},
"arguments": {
"inputs": {
"type": "CONTAINER",
"data": "steps.4.produce"
},
"outputs": {
"type": "CONTAINER",
"data": "steps.2.produce"
}
},
"outputs": [
{
"id": "produce"
}
],
"hyperparams": {
"LSTM_units": {
"type": "VALUE",
"data": 1024
},
"epochs": {
"type": "VALUE",
"data": 100
}
}
}
],
"name": "DefaultVideoClassificationTemplate:5019768584",
"description": "",
"digest": "65a6654a0574bf291dc9f522c2a6983f46829ff45cf8b7c84bde54443301a267"
}
{
"problem": "LL1_3476_HMDB_actio_recognition_problem",
"full_inputs":[
"LL1_3476_HMDB_actio_recognition_dataset"
],
"train_inputs":[
"LL1_3476_HMDB_actio_recognition_dataset_TRAIN"
],
"test_inputs":[
"LL1_3476_HMDB_actio_recognition_dataset_TEST"
],
"score_inputs":[
"LL1_3476_HMDB_actio_recognition_dataset_SCORE"
]
}
\ No newline at end of file
{
"id": "dsbox-featurizer-video-classification-lstm",
"version": "1.5.1",
"version": "1.5.2",
"name": "DSBox Video Classification LSTM",
"description": "video classification primitive that use lstm RNN network\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.lstm.DSBOX",
......@@ -23,7 +23,7 @@
"installation": [
{
"type": "PIP",
"package_uri": "git+https://github.com/usc-isi-i2/[email protected]9ffd48258d3f3d7d8d116162b9ed1a01222a014e#egg=dsbox-primitives"
"package_uri": "git+https://github.com/usc-isi-i2/[email protected]ca2ed654966f411288c78954c2e143be84559138#egg=dsbox-primitives"
}
],
"precondition": [],
......@@ -324,5 +324,5 @@
}
},
"structural_type": "dsbox.datapreprocessing.featurizer.image.video_classification.LSTM",
"digest": "9271fbaea6dde07a98899944e1125165f5af58661feb8f29d4d349b90849b994"
}
\ No newline at end of file
"digest": "c097fda5631c64829931685f2f99df149188c5c5187c6623e47828a2a49dd8b3"
}
{
"id": "wikidata-wikifier",
"version": "1.5.1",
"version": "1.5.2",
"name": "wikidata wikifier",
"python_path": "d3m.primitives.data_augmentation.wikifier.DSBOX",
"description": "A primitive that takes a list of datamart dataset and choose 1 or a few best dataframe and perform join, return an accessible d3m.dataframe for further processing\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.",
......@@ -23,7 +23,7 @@
"installation": [
{
"type": "PIP",
"package_uri": "git+https://github.com/usc-isi-i2/[email protected]9ffd48258d3f3d7d8d116162b9ed1a01222a014e#egg=dsbox-primitives"
"package_uri": "git+https://github.com/usc-isi-i2/[email protected]ca2ed654966f411288c78954c2e143be84559138#egg=dsbox-primitives"
}
],
"schema": "https://metadata.datadrivendiscovery.org/schemas/v0/primitive.json",
......@@ -215,5 +215,5 @@
}
},
"structural_type": "dsbox.datapreprocessing.cleaner.wikifier.Wikifier",
"digest": "8fe38b41c155a1e638411cd50f1d17427c2adc6216230dc06f591957b372490e"
}
\ No newline at end of file
"digest": "b2ca83827d4001fd5f7ad95004775c703da172b4f87b9fc7e72c9860ada892dd"
}
{
"id": "dsbox-cleaning-featurizer",
"version": "1.5.1",
"version": "1.5.2",
"name": "DSBox Cleaning Featurizer",
"python_path": "d3m.primitives.data_cleaning.cleaning_featurizer.DSBOX",
"primitive_family": "DATA_CLEANING",
......@@ -24,7 +24,7 @@
"installation": [
{
"type": "PIP",
"package_uri": "git+https://github.com/usc-isi-i2/[email protected]9ffd48258d3f3d7d8d116162b9ed1a01222a014e#egg=dsbox-primitives"
"package_uri": "git+https://github.com/usc-isi-i2/[email protected]ca2ed654966f411288c78954c2e143be84559138#egg=dsbox-primitives"
}
],
"location_uris": [],
......@@ -298,5 +298,5 @@
},
"structural_type": "dsbox.datapreprocessing.cleaner.cleaning_featurizer.CleaningFeaturizer",
"description": "A cleaning featurizer for imperfect data. Capabilities of this featurizer include:\n\n+ Split a column with compound string values (e.g. \"118,32\") to multiple columns.\n+ Split a date column into year, month, day, day-of-week.\n+ Split American phone number column into area code, prefix, and number.\n+ Split alphanumeric columns into multiple columns.\n\nThis primitive requires d3m.primitives.schema_discovery.profiler.DSBOX profiler.\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": "85940ee7b9aea24297dbbd5b1cd5f9466e21f5641d6281307424a09d300f818e"
}
\ No newline at end of file
"digest": "6076b109002bb9b638d6a5fddcd68134d5b18068e52ee8ce2149ce9144f3ca83"
}
{
"id": "dsbox-fold-columns",
"version": "1.5.1",
"version": "1.5.2",
"name": "DSBox Fold Columns",
"python_path": "d3m.primitives.data_cleaning.column_fold.DSBOX",
"primitive_family": "DATA_CLEANING",
......@@ -20,7 +20,7 @@
"installation": [
{
"type": "PIP",
"package_uri": "git+https://github.com/usc-isi-i2/[email protected]9ffd48258d3f3d7d8d116162b9ed1a01222a014e#egg=dsbox-primitives"
"package_uri": "git+https://github.com/usc-isi-i2/[email protected]ca2ed654966f411288c78954c2e143be84559138#egg=dsbox-primitives"
}
],
"location_uris": [],
......@@ -176,5 +176,5 @@
},
"structural_type": "dsbox.datapreprocessing.cleaner.column_fold.FoldColumns",
"description": "A column folding primitive for imperfect data. Fold multiple columns into one column based on common column name prefix.\nFor example, columns with names 'month-jan', 'month-feb', 'month-mar' and so on are folded into one column named 'month'.\"\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": "562575fdd9482d0dd6c50430b1fdaa4cdae1c0cf4a976540277a7e03aad87de5"
}
\ No newline at end of file
"digest": "5759ca2d668288e87f92b46efe1c7d1e1219028e0292ca580e2a608a33b60157"
}
{
"id": "dsbox-multi-table-feature-labler",
"version": "1.5.1",
"version": "1.5.2",
"name": "DSBox feature labeler",
"description": "A primitive which encode all categorical values into integers. This primitive can\nhandle values not seen during training.\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.data_cleaning.label_encoder.DSBOX",
......@@ -22,7 +22,7 @@
"installation": [
{
"type": "PIP",
"package_uri": "git+https://github.com/usc-isi-i2/[email protected]9ffd48258d3f3d7d8d116162b9ed1a01222a014e#egg=dsbox-primitives"
"package_uri": "git+https://github.com/usc-isi-i2/[email protected]ca2ed654966f411288c78954c2e143be84559138#egg=dsbox-primitives"
}
],
"precondition": [
......@@ -248,5 +248,5 @@
}
},
"structural_type": "dsbox.datapreprocessing.cleaner.labler.Labler",
"digest": "b710e6a43591a39113a6eeae5f5b1323c54e331b69f19123022839ccebde8c98"
}
\ No newline at end of file
"digest": "9680f265a1b349a4fcdc9317d874a156e345177a020f655aa9d35320b47fcc8f"
}
{
"id": "ab725912-a17d-4477-8624-eb0799109511",
"schema": "https://metadata.datadrivendiscovery.org/schemas/v0/pipeline.json",
"created": "2019-05-15T00:55:08.020918Z",
"inputs": [
{
"name": "input dataset"
}
],
"outputs": [
{
"data": "steps.7.produce",
"name": "predictions of input dataset"
}
],
"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": "33efea32abc3e9f8496ca4f266711622227e629cf84c896637e3e40279547949"
},
"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": "4b42ce1e-9b98-4a25-b68e-fad13311eb65",
"version": "0.3.0",
"python_path": "d3m.primitives.data_transformation.dataset_to_dataframe.Common",
"name": "Extract a DataFrame from a Dataset",
"digest": "789969c6a4a81b25b04c95963721259599f8c43c90a6091e49f44c7fe08f51de"
},
"arguments": {
"inputs": {
"type": "CONTAINER",
"data": "steps.0.produce"
}
},
"outputs": [
{
"id": "produce"
}
]
},
{
"type": "PRIMITIVE",
"primitive": {
"id": "4503a4c6-42f7-45a1-a1d4-ed69699cf5e1",
"version": "0.2.0",
"python_path": "d3m.primitives.data_transformation.extract_columns_by_semantic_types.DataFrameCommon",
"name": "Extracts columns by semantic type",
"digest": "57db870295ef8a63e62429950d7e19c417f07bb9e52124e1f9065b2b0cd55a11"
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"arguments": {
"inputs": {
"type": "CONTAINER",
"data": "steps.1.produce"
}
},
"outputs": [
{
"id": "produce"
}
],
"hyperparams": {
"semantic_types": {
"type": "VALUE",
"data": [
"https://metadata.datadrivendiscovery.org/types/TrueTarget"
]
}
}
},
{
"type": "PRIMITIVE",
"primitive": {
"id": "7ddf2fd8-2f7f-4e53-96a7-0d9f5aeecf93",
"version": "1.5.2",
"python_path": "d3m.primitives.data_transformation.to_numeric.DSBOX",
"name": "ISI DSBox To Numeric DataFrame",
"digest": "571beb63f5a194d54fe181fe938cecfde2e5da70d4c990693c50a83982cab8fc"
},
"arguments": {
"inputs": {
"type": "CONTAINER",
"data": "steps.2.produce"
}
},
"outputs": [
{
"id": "produce"
}
],
"hyperparams": {
"drop_non_numeric_columns": {
"type": "VALUE",
"data": false
}
}
},
{
"type": "PRIMITIVE",
"primitive": {
"id": "dsbox-featurizer-image-dataframe-to-tensor",
"version": "1.5.2",
"python_path": "d3m.primitives.data_preprocessing.dataframe_to_tensor.DSBOX",
"name": "DSBox Image Featurizer dataframe to tensor transformer",
"digest": "d456be9f07492a5538dd158a3c6010c2b303d37df944400fe239ec6efa669a5b"
},