Commit 7f7cca38 authored by Mitar's avatar Mitar

Updating common primitives.

Adding sampling primitive.
parent 1fb30b80
......@@ -18,7 +18,7 @@
"installation": [
{
"type": "PIP",
"package_uri": "git+https://gitlab.com/datadrivendiscovery/[email protected]85e9d77376fa731f82188011237f1c8f258323f4#egg=common-primitives"
"package_uri": "git+https://gitlab.com/datadrivendiscovery/[email protected]5ed432cb095d6b06776c1bf129ff91b46827c825#egg=common-primitives"
}
],
"algorithm_types": [
......@@ -622,5 +622,5 @@
},
"structural_type": "common_primitives.lgbm_classifier.LightGBMClassifierPrimitive",
"description": "A lightGBM classifier using ``lgbm.LGBMClassifier``.\n\nIt uses semantic types to determine which columns to operate on.\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": "0e2ce6c9b56ade3d45bf24371ffddad3fcc81b601d0424e7ce80cdd57dc0bf1d"
"digest": "64160b8409835d222a794e62e10b7bc9b20c91f6b92783129adaa05971355b19"
}
......@@ -18,7 +18,7 @@
"installation": [
{
"type": "PIP",
"package_uri": "git+https://gitlab.com/datadrivendiscovery/[email protected]85e9d77376fa731f82188011237f1c8f258323f4#egg=common-primitives"
"package_uri": "git+https://gitlab.com/datadrivendiscovery/[email protected]5ed432cb095d6b06776c1bf129ff91b46827c825#egg=common-primitives"
}
],
"algorithm_types": [
......@@ -648,5 +648,5 @@
},
"structural_type": "common_primitives.random_forest.RandomForestClassifierPrimitive",
"description": "A random forest classifier using ``sklearn.ensemble.forest.RandomForestClassifier``.\n\nIt uses semantic types to determine which columns to operate on.\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": "8441523cdc8e7420791fb185fbdf35a29193e05f5bd9ab332722c61158391b35"
"digest": "53720eaafd225e940636d0fee8a9648228fa3c139cd1357b81bb442ac66c67a0"
}
......@@ -18,7 +18,7 @@
"installation": [
{
"type": "PIP",
"package_uri": "git+https://gitlab.com/datadrivendiscovery/[email protected]85e9d77376fa731f82188011237f1c8f258323f4#egg=common-primitives"
"package_uri": "git+https://gitlab.com/datadrivendiscovery/[email protected]5ed432cb095d6b06776c1bf129ff91b46827c825#egg=common-primitives"
}
],
"algorithm_types": [
......@@ -664,5 +664,5 @@
},
"structural_type": "common_primitives.xgboost_dart.XGBoostDartClassifierPrimitive",
"description": "A XGBoost classifier using ``xgb.XGBoostClassifier`` with Dart Boosting type.\n\nIt uses semantic types to determine which columns to operate on.\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": "2c224c3611b1ffe483474e644d6aaac88dfd1346b8c4c4f01a025ef5c80402b3"
"digest": "a5174f2442e8f21cc33f305b6d0a4ec112fd307e0e402c777326e6630703b439"
}
......@@ -18,7 +18,7 @@
"installation": [
{
"type": "PIP",
"package_uri": "git+https://gitlab.com/datadrivendiscovery/[email protected]85e9d77376fa731f82188011237f1c8f258323f4#egg=common-primitives"
"package_uri": "git+https://gitlab.com/datadrivendiscovery/[email protected]5ed432cb095d6b06776c1bf129ff91b46827c825#egg=common-primitives"
}
],
"algorithm_types": [
......@@ -625,5 +625,5 @@
},
"structural_type": "common_primitives.xgboost_gbtree.XGBoostGBTreeClassifierPrimitive",
"description": "A XGBoost classifier using ``xgb.XGBoostClassifier`` with GBTree Boosting type.\n\nIt uses semantic types to determine which columns to operate on.\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": "648ce9e39da59fa2c5d9629c2f441f9545fcaaf91d626635abd0966f79364ef3"
"digest": "f09eb630a8117ce85d54de6e6a392a5dcb72ec0cc00be5dbd381c1ebd4e62787"
}
......@@ -14,7 +14,7 @@
"installation": [
{
"type": "PIP",
"package_uri": "git+https://gitlab.com/datadrivendiscovery/[email protected]85e9d77376fa731f82188011237f1c8f258323f4#egg=common_primitives"
"package_uri": "git+https://gitlab.com/datadrivendiscovery/[email protected]5ed432cb095d6b06776c1bf129ff91b46827c825#egg=common_primitives"
}
],
"algorithm_types": [
......@@ -237,5 +237,5 @@
},
"structural_type": "common_primitives.datamart_augment.DataMartAugmentPrimitive",
"description": "Augment supplied dataset with additional columns.\n\nUse ``DATAMART_NYU_URL`` and ``DATAMART_ISI_URL`` environment variables to control where\ncan the primitive connect to respective DataMarts.\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": "2a3d08784413b1c8e490321cadca74564cf3d9c65408fa3c46a3682541adc461"
"digest": "46db094054a141ac5a14f34f3310462ce74454abf6125a80dc01b36cf4ee4a3e"
}
......@@ -14,7 +14,7 @@
"installation": [
{
"type": "PIP",
"package_uri": "git+https://gitlab.com/datadrivendiscovery/[email protected]85e9d77376fa731f82188011237f1c8f258323f4#egg=common_primitives"
"package_uri": "git+https://gitlab.com/datadrivendiscovery/[email protected]5ed432cb095d6b06776c1bf129ff91b46827c825#egg=common_primitives"
}
],
"algorithm_types": [
......@@ -212,5 +212,5 @@
},
"structural_type": "common_primitives.datamart_download.DataMartDownloadPrimitive",
"description": "Download a dataset from DataMart.\n\nUse ``DATAMART_NYU_URL`` and ``DATAMART_ISI_URL`` environment variables to control where\ncan the primitive connect to respective DataMarts.\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": "ce5af6c527d1e4b1d291c23634602aeb167031e78b7ca154be8d5ed264b54354"
"digest": "a4fd5e4faec222c676ade4b7e88708343f242028ae3461879cd1b7162348a7fe"
}
......@@ -14,7 +14,7 @@
"installation": [
{
"type": "PIP",
"package_uri": "git+https://gitlab.com/datadrivendiscovery/[email protected]85e9d77376fa731f82188011237f1c8f258323f4#egg=common-primitives"
"package_uri": "git+https://gitlab.com/datadrivendiscovery/[email protected]5ed432cb095d6b06776c1bf129ff91b46827c825#egg=common-primitives"
}
],
"algorithm_types": [
......@@ -211,5 +211,5 @@
},
"structural_type": "common_primitives.tabular_extractor.AnnotatedTabularExtractorPrimitive",
"description": "A primitive wrapping for MIT-LL slacker's ``AnnotatedTabularExtractor``.\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": "cf194c61770b97a9db877b02679a3ae9d829c080cb4e7e9158b0f064cb55ad91"
"digest": "1ed99ed4753f58607c82dc9e353ec9462c8a426967653f729833dbf6363476ed"
}
......@@ -43,7 +43,7 @@
},
{
"type": "PIP",
"package_uri": "git+https://gitlab.com/datadrivendiscovery/[email protected]85e9d77376fa731f82188011237f1c8f258323f4#egg=common_primitives"
"package_uri": "git+https://gitlab.com/datadrivendiscovery/[email protected]5ed432cb095d6b06776c1bf129ff91b46827c825#egg=common_primitives"
}
],
"algorithm_types": [
......@@ -261,5 +261,5 @@
},
"structural_type": "common_primitives.audio_reader.AudioReaderPrimitive",
"description": "A primitive which reads columns referencing audio files.\n\nEach column which has ``https://metadata.datadrivendiscovery.org/types/FileName`` semantic type\nand a valid media type (``audio/aiff``, ``audio/flac``, ``audio/ogg``, ``audio/wav``, ``audio/mpeg``)\nhas every filename read into an audio represented as a numpy array. By default the resulting column\nwith read arrays is appended to existing columns.\n\nThe shape of numpy arrays is S x C. S is the number of samples, C is the number of\nchannels in an audio (e.g., C = 1 for mono, C = 2 for stereo). dtype is float32.\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": "61133a76688b8e7696ca295c6450bfb65c3239e38cf97cc16111684f00b28c0d"
"digest": "47249834eddb0a642ee6e51bf3268c3a6cac6fb35677e884cc29d606580e3963"
}
......@@ -18,7 +18,7 @@
"installation": [
{
"type": "PIP",
"package_uri": "git+https://gitlab.com/datadrivendiscovery/[email protected]85e9d77376fa731f82188011237f1c8f258323f4#egg=common_primitives"
"package_uri": "git+https://gitlab.com/datadrivendiscovery/[email protected]5ed432cb095d6b06776c1bf129ff91b46827c825#egg=common_primitives"
}
],
"algorithm_types": [
......@@ -232,5 +232,5 @@
},
"structural_type": "common_primitives.csv_reader.CSVReaderPrimitive",
"description": "A primitive which reads columns referencing CSV files.\n\nEach column which has ``https://metadata.datadrivendiscovery.org/types/FileName`` semantic type\nand a valid media type (``text/csv``) has every filename read as a pandas DataFrame. By default\nthe resulting column with read pandas DataFrames is appended to existing columns.\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": "dfbddaef4587bbf23cc628201332d40d2bac71c4f7a0641d625da8c14e99e4d1"
"digest": "8ee694114ee4a856cf1ea3b72da42e4e6c24064366a4042b6198aadb68146c25"
}
{
"problem": "185_baseball_problem",
"full_inputs": ["185_baseball_dataset"],
"train_inputs": ["185_baseball_dataset_TRAIN"],
"test_inputs": ["185_baseball_dataset_TEST"],
"score_inputs": ["185_baseball_dataset_SCORE"]
}
id: 387d432a-9893-4558-b190-1c5e9e399dbf
schema: https://metadata.datadrivendiscovery.org/schemas/v0/pipeline.json
source:
name: Jeffrey Gleason
created: "2019-06-05T2:48:52.806069Z"
context: TESTING
name: Dataset sample test pipeline
description: |
A simple pipeline which runs Random Forest classifier on tabular data after sampling the dataset (50% of rows)
inputs:
- name: input dataset
outputs:
- name: predictions
data: steps.6.produce
steps:
# Step 0.
- type: PRIMITIVE
primitive:
id: 268315c1-7549-4aee-a4cc-28921cba74c0
version: 0.1.0
python_path: d3m.primitives.data_preprocessing.dataset_sample.Common
name: Dataset sampling primitive
arguments:
inputs:
type: CONTAINER
data: inputs.0
outputs:
- id: produce
# Step 1.
- type: PRIMITIVE
primitive:
id: f31f8c1f-d1c5-43e5-a4b2-2ae4a761ef2e
version: 0.2.0
python_path: d3m.primitives.data_transformation.denormalize.Common
name: Denormalize datasets
arguments:
inputs:
type: CONTAINER
data: steps.0.produce
outputs:
- id: produce
# Step 2.
- 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
arguments:
inputs:
type: CONTAINER
data: steps.1.produce
outputs:
- id: produce
# Step 3.
- type: PRIMITIVE
primitive:
id: d510cb7a-1782-4f51-b44c-58f0236e47c7
version: 0.5.0
python_path: d3m.primitives.data_transformation.column_parser.DataFrameCommon
name: Parses strings into their types
arguments:
inputs:
type: CONTAINER
data: steps.2.produce
outputs:
- id: produce
# Step 4.
- type: PRIMITIVE
primitive:
id: d016df89-de62-3c53-87ed-c06bb6a23cde
version: 2019.6.7
python_path: d3m.primitives.data_cleaning.imputer.SKlearn
name: sklearn.impute.SimpleImputer
arguments:
inputs:
type: CONTAINER
data: steps.3.produce
outputs:
- id: produce
hyperparams:
use_semantic_types:
type: VALUE
data: true
return_result:
type: VALUE
data: replace
# Step 5.
- type: PRIMITIVE
primitive:
id: 37c2b19d-bdab-4a30-ba08-6be49edcc6af
version: 0.4.0
python_path: d3m.primitives.classification.random_forest.DataFrameCommon
name: Random forest classifier
arguments:
inputs:
type: CONTAINER
data: steps.4.produce
outputs:
type: CONTAINER
data: steps.4.produce
outputs:
- id: produce
hyperparams:
return_result:
type: VALUE
data: replace
# Step 6.
- type: PRIMITIVE
primitive:
id: 8d38b340-f83f-4877-baaa-162f8e551736
version: 0.3.0
python_path: d3m.primitives.data_transformation.construct_predictions.DataFrameCommon
name: Construct pipeline predictions output
arguments:
inputs:
type: CONTAINER
data: steps.5.produce
reference:
type: CONTAINER
data: steps.3.produce
outputs:
- id: produce
......@@ -14,7 +14,7 @@
"installation": [
{
"type": "PIP",
"package_uri": "git+https://gitlab.com/datadrivendiscovery/[email protected]85e9d77376fa731f82188011237f1c8f258323f4#egg=common_primitives"
"package_uri": "git+https://gitlab.com/datadrivendiscovery/[email protected]5ed432cb095d6b06776c1bf129ff91b46827c825#egg=common_primitives"
}
],
"algorithm_types": [
......@@ -266,5 +266,5 @@
},
"structural_type": "common_primitives.datetime_range_filter.DatetimeRangeFilterPrimitive",
"description": "A primitive which filters rows from a DataFrame based on a datetime range applied to a given column.\nColumns are identified by their index, and the filter itself can be inclusive (values within range are retained)\nor exclusive (values within range are removed). Boundaries values can be included in the filter (ie. <=) or excluded\n(ie. <).\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": "666f16a0666e59b665eaa47b54d74f87797849cf0cd165ea7618ffb8ac2b04d9"
"digest": "b8f65d3cba428be5d0ce68863fe3e9671854ac37e4cf2272df5aa64740dfe12b"
}
......@@ -18,7 +18,7 @@
"installation": [
{
"type": "PIP",
"package_uri": "git+https://gitlab.com/datadrivendiscovery/[email protected]85e9d77376fa731f82188011237f1c8f258323f4#egg=common_primitives"
"package_uri": "git+https://gitlab.com/datadrivendiscovery/[email protected]5ed432cb095d6b06776c1bf129ff91b46827c825#egg=common_primitives"
}
],
"algorithm_types": [
......@@ -262,5 +262,5 @@
},
"structural_type": "common_primitives.dataframe_flatten.DataFrameFlattenPrimitive",
"description": "Cycles through the input dataframe and flattens the encountered nested structures (series & dataframes).\nFlattening involves creating a new row for each nested data row, and replicating the unnested row features.\n[\n a, b, [w, x],\n c, d, [y, z],\n]\n\nyields:\n\n[\n a, b, w,\n a, b, x,\n c, d, y,\n c, d, z\n]\n\nIf the d3m index field is present and set as index, it will be updated to be multi index\nas needed. The primitive should be called after the referenced files have\nalready been nested in the dataframe (using the CSVReader primitive for example). The primitive can\nflatten mutiple nested columns, but is currently limited to supporting a nesting depth of 1.\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": "164b063b3570aec89ca4e35a22c96e1e2a582971b073580241f2e5b92b4699be"
"digest": "7277f8dc8e4934d8e7342b1bee4656b444e7addc14cc42a301e7093f39a2a0d0"
}
......@@ -20,7 +20,7 @@
"installation": [
{
"type": "PIP",
"package_uri": "git+https://gitlab.com/datadrivendiscovery/[email protected]85e9d77376fa731f82188011237f1c8f258323f4#egg=common_primitives"
"package_uri": "git+https://gitlab.com/datadrivendiscovery/[email protected]5ed432cb095d6b06776c1bf129ff91b46827c825#egg=common_primitives"
}
],
"algorithm_types": [
......@@ -235,5 +235,5 @@
},
"structural_type": "common_primitives.dataframe_image_reader.DataFrameImageReaderPrimitive",
"description": "A primitive which reads columns referencing image files.\n\nEach column which has ``https://metadata.datadrivendiscovery.org/types/FileName`` semantic type\nand a valid media type (``image/jpeg``, ``image/png``) has every filename read into an image\nrepresented as a numpy array. By default the resulting column with read arrays is appended\nto existing columns.\n\nThe shape of numpy arrays is H x W x C. C is the number of channels in an image\n(e.g., C = 1 for greyscale, C = 3 for RGB), H is the height, and W is the width.\ndtype is uint8.\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": "3f4a1f8f204d684ae699bc1f911c954fc4ad40886acfe6c41f42b3db1cc0de1d"
"digest": "92858da3945af010517714120368955ef0edb1d534bf24d6584a7ed97884805d"
}
......@@ -14,7 +14,7 @@
"installation": [
{
"type": "PIP",
"package_uri": "git+https://gitlab.com/datadrivendiscovery/[email protected]85e9d77376fa731f82188011237f1c8f258323f4#egg=common-primitives"
"package_uri": "git+https://gitlab.com/datadrivendiscovery/[email protected]5ed432cb095d6b06776c1bf129ff91b46827c825#egg=common-primitives"
}
],
"algorithm_types": [
......@@ -262,5 +262,5 @@
},
"structural_type": "common_primitives.unseen_label_decoder.UnseenLabelDecoderPrimitive",
"description": "A primitive which inverses the label encoding by ``UnseenLabelEncoderPrimitive``.\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": "fd7b1242ed96f1bb852e68debc5284d6c86768076127729ae9c10b8df3f05ea8"
"digest": "95ba092a387953e657f473e26c64f2d002a35752cebd4a60ee0af26989f79577"
}
......@@ -14,7 +14,7 @@
"installation": [
{
"type": "PIP",
"package_uri": "git+https://gitlab.com/datadrivendiscovery/[email protected]85e9d77376fa731f82188011237f1c8f258323f4#egg=common-primitives"
"package_uri": "git+https://gitlab.com/datadrivendiscovery/[email protected]5ed432cb095d6b06776c1bf129ff91b46827c825#egg=common-primitives"
}
],
"algorithm_types": [
......@@ -240,5 +240,5 @@
},
"structural_type": "common_primitives.unseen_label_encoder.UnseenLabelEncoderPrimitive",
"description": "Label encoder that can puts any unseen categories into a single category.\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": "a96eb331eba72f9b6d1d5a4a944beb61c198f16851c5ea7e77cdb541cde2558d"
"digest": "f7189226de1de8182236de32459901ff5986cfd940102e8590cd363aec2ec67d"
}
......@@ -14,7 +14,7 @@
"installation": [
{
"type": "PIP",
"package_uri": "git+https://gitlab.com/datadrivendiscovery/[email protected]85e9d77376fa731f82188011237f1c8f258323f4#egg=common_primitives"
"package_uri": "git+https://gitlab.com/datadrivendiscovery/[email protected]5ed432cb095d6b06776c1bf129ff91b46827c825#egg=common_primitives"
}
],
"algorithm_types": [
......@@ -266,5 +266,5 @@
},
"structural_type": "common_primitives.numeric_range_filter.NumericRangeFilterPrimitive",
"description": "A primitive which filters rows from a DataFrame based on a numeric range applied to a given column.\nColumns are identified by their index, and the filter itself can be inclusive (values within range are retained)\nor exclusive (values within range are removed). Boundaries values can be included in the filter (ie. <=) or excluded\n(ie. <).\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": "de26d7c6c7be53c3466becb70338d7c83af6716350a92b91c21cfe21af02bdb7"
"digest": "b373c5ac56b40a0eb80d3e72a63d3f3804e5243024f1a4c535cd9caaa342179d"
}
......@@ -17,7 +17,7 @@
"installation": [
{
"type": "PIP",
"package_uri": "git+https://gitlab.com/datadrivendiscovery/[email protected]85e9d77376fa731f82188011237f1c8f258323f4#egg=common-primitives"
"package_uri": "git+https://gitlab.com/datadrivendiscovery/[email protected]5ed432cb095d6b06776c1bf129ff91b46827c825#egg=common-primitives"
}
],
"python_path": "d3m.primitives.data_preprocessing.one_hot_encoder.MakerCommon",
......@@ -295,5 +295,5 @@
},
"structural_type": "common_primitives.one_hot_maker.OneHotMakerPrimitive",
"description": "Attempts to detect discrete values in data and convert these to a\none-hot embedding.\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": "bf5d97ae3b55237a9e61af3451ab08c3387b2582c1432fe8af56de6f29e15cdd"
"digest": "ca1a79168604c253e0d375fb21f08718076a4adedc9d9481f46f212894d271e9"
}
......@@ -14,7 +14,7 @@
"installation": [
{
"type": "PIP",
"package_uri": "git+https://gitlab.com/datadrivendiscovery/[email protected]85e9d77376fa731f82188011237f1c8f258323f4#egg=common-primitives"
"package_uri": "git+https://gitlab.com/datadrivendiscovery/[email protected]5ed432cb095d6b06776c1bf129ff91b46827c825#egg=common-primitives"
}
],
"algorithm_types": [
......@@ -257,5 +257,5 @@
},
"structural_type": "common_primitives.pandas_onehot_encoder.PandasOneHotEncoderPrimitive",
"description": "One-hot encoder using Pandas implementation.\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": "1d5a852664534f56fe21a0623bd5bdfee61ad1cf91fe35f5a9aa43ffcd0356a4"
"digest": "9becaa9ca9026bd0a5d8e0c5b02b5932fbbac85db53c2f97f837c2580135b6ed"
}
......@@ -14,7 +14,7 @@
"installation": [
{
"type": "PIP",
"package_uri": "git+https://gitlab.com/datadrivendiscovery/[email protected]85e9d77376fa731f82188011237f1c8f258323f4#egg=common_primitives"
"package_uri": "git+https://gitlab.com/datadrivendiscovery/[email protected]5ed432cb095d6b06776c1bf129ff91b46827c825#egg=common_primitives"
}
],
"algorithm_types": [
......@@ -220,5 +220,5 @@
},
"structural_type": "common_primitives.regex_filter.RegexFilterPrimitive",
"description": "A primitive which filters rows from a DataFrame based on a regex applied to a given column.\nColumns are identified by index.\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": "0fb3a0bd496b23443c3b4b9c412874357f600840ef6c219d877b29b588044ab0"
"digest": "5b5e3acbaa4d5e343bdb60fdf57021e701946b9ee08f2afc8f5f006a565386d1"
}
......@@ -14,7 +14,7 @@
"installation": [
{
"type": "PIP",
"package_uri": "git+https://gitlab.com/datadrivendiscovery/[email protected]85e9d77376fa731f82188011237f1c8f258323f4#egg=common_primitives"
"package_uri": "git+https://gitlab.com/datadrivendiscovery/[email protected]5ed432cb095d6b06776c1bf129ff91b46827c825#egg=common_primitives"
}
],
"algorithm_types": [
......@@ -237,5 +237,5 @@
},
"structural_type": "common_primitives.term_filter.TermFilterPrimitive",
"description": "A primitive which filters rows from a DataFrame based on a column value containing a match\nagainst a caller supplied term list. Supports search-style matching where the target need only\ncontain a term, as well as whole word matching where the target is tokenized using regex word boundaries.\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": "9332347160109a0970d4142064b5ce170c31a84b3c3c47a6905b09c2ad124127"
"digest": "c54b9f96e27dd12f2e2f5dd1cf75a35b4c0c7bc1e115a8e4958d1ab15d3755da"
}
......@@ -19,7 +19,7 @@
"installation": [
{
"type": "PIP",
"package_uri": "git+https://gitlab.com/datadrivendiscovery/[email protected]85e9d77376fa731f82188011237f1c8f258323f4#egg=common_primitives"
"package_uri": "git+https://gitlab.com/datadrivendiscovery/[email protected]5ed432cb095d6b06776c1bf129ff91b46827c825#egg=common_primitives"
}
],
"algorithm_types": [
......@@ -233,5 +233,5 @@
},
"structural_type": "common_primitives.text_reader.TextReaderPrimitive",
"description": "A primitive which reads columns referencing plain text files.\n\nEach column which has ``https://metadata.datadrivendiscovery.org/types/FileName`` semantic type\nand a valid media type (``text/plain``) has every filename read as a Python string. By default\nthe resulting column with read strings is appended to existing columns.\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": "6fb4aa17c37c60379e5e773e1c6423acfe50d05d0dcf0e85594520efaec5992e"
"digest": "5cbfde2dc031e9b4a143de423f04bef7a5970b4b62382a2263e81bdcc92279ee"
}
......@@ -20,7 +20,7 @@
"installation": [
{
"type": "PIP",
"package_uri": "git+https://gitlab.com/datadrivendiscovery/[email protected]85e9d77376fa731f82188011237f1c8f258323f4#egg=common_primitives"
"package_uri": "git+https://gitlab.com/datadrivendiscovery/[email protected]5ed432cb095d6b06776c1bf129ff91b46827c825#egg=common_primitives"
}
],
"algorithm_types": [
......@@ -235,5 +235,5 @@
},
"structural_type": "common_primitives.video_reader.VideoReaderPrimitive",
"description": "A primitive which reads columns referencing video files.\n\nEach column which has ``https://metadata.datadrivendiscovery.org/types/FileName`` semantic type\nand a valid media type (``video/mp4``, ``video/avi``) has every filename read into a video\nrepresented as a numpy array. By default the resulting column with read arrays is appended\nto existing columns.\n\nThe shape of numpy arrays is F x H x W x C. F is the number of frames, C is the number of\nchannels in a video (e.g., C = 1 for greyscale, C = 3 for RGB), H is the height, and W\nis the width. dtype is uint8.\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": "ac50bea91b9a1a2d028ddc394d5062c9f660e2ae180c1a2aee496b1acc9a6e92"
"digest": "a6a2aa47d1d3e30f572fb51363f6ec82c2537f97e1f1543629571a2d4780dbed"
}
......@@ -14,7 +14,7 @@
"installation": [
{
"type": "PIP",
"package_uri": "git+https://gitlab.com/datadrivendiscovery/[email protected]85e9d77376fa731f82188011237f1c8f258323f4#egg=common_primitives"
"package_uri": "git+https://gitlab.com/datadrivendiscovery/[email protected]5ed432cb095d6b06776c1bf129ff91b46827c825#egg=common_primitives"
}
],
"algorithm_types": [
......@@ -227,5 +227,5 @@
},
"structural_type": "common_primitives.add_semantic_types.AddSemanticTypesPrimitive",
"description": "A primitive which adds semantic types for columns in a DataFrame.\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": "283c836f91de1494ae181a6ffc2f739b2d60dd9cdc5c05d7114136109764e24f"
"digest": "f96f9d114f7031a6486683a5efe8eaa4a3b5f761b1cab727b94a63b5a3608b72"
}
......@@ -14,7 +14,7 @@
"installation": [
{
"type": "PIP",
"package_uri": "git+https://gitlab.com/datadrivendiscovery/[email protected]85e9d77376fa731f82188011237f1c8f258323f4#egg=common_primitives"
"package_uri": "git+https://gitlab.com/datadrivendiscovery/[email protected]5ed432cb095d6b06776c1bf129ff91b46827c825#egg=common_primitives"
}
],
"algorithm_types": [
......@@ -239,5 +239,5 @@
},
"structural_type": "common_primitives.cast_to_type.CastToTypePrimitive",
"description": "A primitive which casts all columns it can cast (by default, controlled by ``use_columns``,\n``exclude_columns``) of an input DataFrame to a given structural type (dtype).\nIt removes columns which are not cast.\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": "6a88e3f1282131e3cb1767e1cc0aad685e7d30cebea611d2f7a38b510a640906"
"digest": "5346efad38a0566db4d899afea19398d8abdb7d27ef2ea1983cecb9ef182fba6"
}
......@@ -14,7 +14,7 @@
"installation": [
{
"type": "PIP",
"package_uri": "git+https://gitlab.com/datadrivendiscovery/[email protected]85e9d77376fa731f82188011237f1c8f258323f4#egg=common_primitives"
"package_uri": "git+https://gitlab.com/datadrivendiscovery/[email protected]5ed432cb095d6b06776c1bf129ff91b46827c825#egg=common_primitives"
}
],
"algorithm_types": [
......@@ -300,5 +300,5 @@
},
"structural_type": "common_primitives.column_parser.ColumnParserPrimitive",
"description": "A primitive which parses strings into their parsed values.\n\nIt goes over all columns (by default, controlled by ``use_columns``, ``exclude_columns``)\nand checks those with structural type ``str`` if they have a semantic type suggesting\nthat they are a boolean value, categorical, integer, float, or time (by default,\ncontrolled by ``parse_semantic_types``). Categorical values are converted with\nhash encoding.\n\nWhat is returned is controlled by ``return_result`` and ``add_index_columns``.\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": "d41ad0c56ef55a233b21f4a4d8df1ac782aca7a78ef98dbfb72215690b3e9850"
"digest": "d77b7c3696c65ed763bda6dec038755d462674ee23737a561c76b7f487163b22"
}
......@@ -14,7 +14,7 @@
"installation": [
{
"type": "PIP",
"package_uri": "git+https://gitlab.com/datadrivendiscovery/[email protected]85e9d77376fa731f82188011237f1c8f258323f4#egg=common_primitives"
"package_uri": "git+https://gitlab.com/datadrivendiscovery/[email protected]5ed432cb095d6b06776c1bf129ff91b46827c825#egg=common_primitives"
}
],
"algorithm_types": [
......@@ -234,5 +234,5 @@
},
"structural_type": "common_primitives.construct_predictions.ConstructPredictionsPrimitive",
"description": "A primitive which takes as input a DataFrame and outputs a DataFrame in Lincoln Labs predictions\nformat: first column is a d3mIndex column (and other primary index columns, e.g., for object detection\nproblem), and then predicted targets, each in its column, followed by optional confidence column(s).\n\nIt supports both input columns annotated with semantic types (``https://metadata.datadrivendiscovery.org/types/PrimaryKey``,\n``https://metadata.datadrivendiscovery.org/types/PrimaryMultiKey``, ``https://metadata.datadrivendiscovery.org/types/PredictedTarget``,\n``https://metadata.datadrivendiscovery.org/types/Confidence``), or trying to reconstruct metadata.\nThis is why the primitive takes also additional input of a reference DataFrame which should\nhave metadata to help reconstruct missing metadata. If metadata is missing, the primitive\nassumes that all ``inputs`` columns are predicted targets, without confidence column(s).\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": "53087aaa6baf0ccc96b6525ca5b79fd4e51b4cce996ab39773a9b0b3e746bf05"
"digest": "894c863294aa91449b50fed1570836e723b80a81fb58a2da38cc0454b05fa2f1"
}