Merge branch 'var_separate_targets' into 'master'

update VAR to take inputs + outputs as separate arguments, update corresponding pipelines

See merge request !183

v2020.1.9/Distil/d3m.primitives.time_series_classification.convolutional_neural_net.LSTM_FCN/1.2.0/primitive.json

@@ -24,7 +24,7 @@
         },
         {
             "type": "PIP",
-            "package_uri": "git+https://github.com/NewKnowledge/TimeSeries-D3M-Wrappers.git@69eb689a7679361fb4e150f3d406db2580bbc381#egg=TimeSeriesD3MWrappers"
+            "package_uri": "git+https://github.com/NewKnowledge/TimeSeries-D3M-Wrappers.git@97d94579ee5c4e585a9fd044083972d197bb9d57#egg=TimeSeriesD3MWrappers"
         }
     ],
     "python_path": "d3m.primitives.time_series_classification.convolutional_neural_net.LSTM_FCN",
@@ -300,5 +300,5 @@
     },
     "structural_type": "TimeSeriesD3MWrappers.primitives.classification_lstm.LSTM_FCN",
     "description": "Primitive that applies a LSTM FCN (LSTM fully convolutional network) for time\nseries classification. The implementation is based off this paper:\nhttps://ieeexplore.ieee.org/document/8141873 and this base library:\nhttps://github.com/NewKnowledge/LSTM-FCN.\n\nArguments:\n    hyperparams {Hyperparams} -- D3M Hyperparameter object\n\nKeyword Arguments:\n    random_seed {int} -- random seed (default: {0})\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": "ea0b13b8732e469e3c210b68fa4d5c089b06fe667e3f8c0a097537c4caea3892"
+    "digest": "9fcbd794186e369a504b86d6f9153af6798aee793adcc26319f2abdf09799ac7"
 }

v2020.1.9/Distil/d3m.primitives.time_series_classification.k_neighbors.Kanine/1.2.0/primitive.json

@@ -23,7 +23,7 @@
         },
         {
             "type": "PIP",
-            "package_uri": "git+https://github.com/NewKnowledge/TimeSeries-D3M-Wrappers.git@69eb689a7679361fb4e150f3d406db2580bbc381#egg=TimeSeriesD3MWrappers"
+            "package_uri": "git+https://github.com/NewKnowledge/TimeSeries-D3M-Wrappers.git@97d94579ee5c4e585a9fd044083972d197bb9d57#egg=TimeSeriesD3MWrappers"
         }
     ],
     "python_path": "d3m.primitives.time_series_classification.k_neighbors.Kanine",
@@ -216,5 +216,5 @@
     },
     "structural_type": "TimeSeriesD3MWrappers.primitives.classification_knn.Kanine",
     "description": "Primitive that applies the k nearest neighbor classification algorithm to time series data.\nThe tslearn KNeighborsTimeSeriesClassifier implementation is wrapped.\n\nTraining inputs: 1) Feature dataframe, 2) Target dataframe\nOutputs: Dataframe with predictions for specific time series at specific future time instances\n\nArguments:\n    hyperparams {Hyperparams} -- D3M Hyperparameter object\n\nKeyword Arguments:\n    random_seed {int} -- random seed (default: {0})\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": "4d2dc0b0ecf10cd655dd0983933115e4ebf96c20485fa662acae776ac93dc861"
+    "digest": "9c3309a7af1781902333bd929aca97732fa65f06561ad93f3e27b38d1b5444b9"
 }

v2020.1.9/Distil/d3m.primitives.time_series_forecasting.lstm.DeepAR/1.2.0/primitive.json

@@ -23,7 +23,7 @@
         },
         {
             "type": "PIP",
-            "package_uri": "git+https://github.com/NewKnowledge/TimeSeries-D3M-Wrappers.git@69eb689a7679361fb4e150f3d406db2580bbc381#egg=TimeSeriesD3MWrappers"
+            "package_uri": "git+https://github.com/NewKnowledge/TimeSeries-D3M-Wrappers.git@97d94579ee5c4e585a9fd044083972d197bb9d57#egg=TimeSeriesD3MWrappers"
         }
     ],
     "python_path": "d3m.primitives.time_series_forecasting.lstm.DeepAR",
@@ -416,5 +416,5 @@
     },
     "structural_type": "TimeSeriesD3MWrappers.primitives.forecasting_deepar.DeepAR",
     "description": "Primitive that applies a deep autoregressive forecasting algorithm for time series\nprediction. The implementation is based off of this paper: https://arxiv.org/pdf/1704.04110.pdf\nand is implemented in AWS's Sagemaker interface.\n\nTraining inputs: 1) Feature dataframe, 2) Target dataframe\nOutputs: Dataframe with predictions for specific time series at specific future time instances\n\nArguments:\n    hyperparams {Hyperparams} -- D3M Hyperparameter object\n\nKeyword Arguments:\n    random_seed {int} -- random seed (default: {0})\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": "6b75beda6d1b93bbb688536a185cc866a8b5b0e12c53fdba915d572b331c4b89"
+    "digest": "7beff404ef3efe8991030d84f521076224ca497a853c53dd85e0edf107ab074e"
 }

v2020.1.9/Distil/d3m.primitives.time_series_forecasting.vector_autoregression.VAR/1.2.0/pipelines/f576f02c-8afd-455a-b465-957a3d797ece.json → v2020.1.9/Distil/d3m.primitives.time_series_forecasting.vector_autoregression.VAR/1.2.0/pipelines/a272b79c-5698-4b9e-83e9-736498948fc1.json

-{"id": "f576f02c-8afd-455a-b465-957a3d797ece", "schema": "https://metadata.datadrivendiscovery.org/schemas/v0/pipeline.json", "created": "2020-01-16T21:03:16.474957Z", "inputs": [{"name": "inputs"}], "outputs": [{"data": "steps.3.produce", "name": "output predictions"}], "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": "422744651afd5995d029a227a1dd7b1696038816b7eb9601f37d661757812aee"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "inputs.0"}}, "outputs": [{"id": "produce"}]}, {"type": "PRIMITIVE", "primitive": {"id": "e193afa1-b45e-4d29-918f-5bb1fa3b88a7", "version": "0.2.0", "python_path": "d3m.primitives.schema_discovery.profiler.Common", "name": "Determine missing semantic types for columns automatically", "digest": "8b12a9aececdc5b7a4d5ef47cd04cda75592fd24f49922776b614d4bbeeb97f1"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "steps.0.produce"}}, "outputs": [{"id": "produce"}]}, {"type": "PRIMITIVE", "primitive": {"id": "d510cb7a-1782-4f51-b44c-58f0236e47c7", "version": "0.6.0", "python_path": "d3m.primitives.data_transformation.column_parser.Common", "name": "Parses strings into their types", "digest": "f1215fe3351a2e8bf495f9ed5ed50e88b30d78d4d7ebd9e13bb544e63a10994b"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "steps.1.produce"}}, "outputs": [{"id": "produce"}], "hyperparams": {"parse_semantic_types": {"type": "VALUE", "data": ["http://schema.org/Boolean", "http://schema.org/Integer", "http://schema.org/Float", "https://metadata.datadrivendiscovery.org/types/FloatVector", "http://schema.org/DateTime"]}}}, {"type": "PRIMITIVE", "primitive": {"id": "76b5a479-c209-4d94-92b5-7eba7a4d4499", "version": "1.2.0", "python_path": "d3m.primitives.time_series_forecasting.vector_autoregression.VAR", "name": "VAR", "digest": "92ac8f78c70df695cc048089ecec666d6e3626bf4998623354841fefcb42ab6c"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "steps.2.produce"}, "outputs": {"type": "CONTAINER", "data": "steps.2.produce"}}, "outputs": [{"id": "produce"}]}], "digest": "10b29cec562b607204a493855d4535acd5e1782492e92dd2722f0f508781a43c"}
\ No newline at end of file
+{"id": "a272b79c-5698-4b9e-83e9-736498948fc1", "schema": "https://metadata.datadrivendiscovery.org/schemas/v0/pipeline.json", "created": "2020-02-05T21:14:12.556323Z", "inputs": [{"name": "inputs"}], "outputs": [{"data": "steps.5.produce", "name": "output predictions"}], "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": "990784f527a78250fcab70af9714314490e91f9a5916eb38834d74e8c38f435b"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "inputs.0"}}, "outputs": [{"id": "produce"}]}, {"type": "PRIMITIVE", "primitive": {"id": "e193afa1-b45e-4d29-918f-5bb1fa3b88a7", "version": "0.2.0", "python_path": "d3m.primitives.schema_discovery.profiler.Common", "name": "Determine missing semantic types for columns automatically", "digest": "812f8751faff8fa1fd614e29448206db50074486bae0fc127badaf8b04e127de"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "steps.0.produce"}}, "outputs": [{"id": "produce"}]}, {"type": "PRIMITIVE", "primitive": {"id": "d510cb7a-1782-4f51-b44c-58f0236e47c7", "version": "0.6.0", "python_path": "d3m.primitives.data_transformation.column_parser.Common", "name": "Parses strings into their types", "digest": "96e020725140c0a67033e5e340c555514b0a5432179254c38813a4e85687528d"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "steps.1.produce"}}, "outputs": [{"id": "produce"}], "hyperparams": {"parse_semantic_types": {"type": "VALUE", "data": ["http://schema.org/Boolean", "http://schema.org/Integer", "http://schema.org/Float", "https://metadata.datadrivendiscovery.org/types/FloatVector", "http://schema.org/DateTime"]}}}, {"type": "PRIMITIVE", "primitive": {"id": "4503a4c6-42f7-45a1-a1d4-ed69699cf5e1", "version": "0.4.0", "python_path": "d3m.primitives.data_transformation.extract_columns_by_semantic_types.Common", "name": "Extracts columns by semantic type", "digest": "591d3ee40b266bda04fa345aeea7b56d81db182fb144cde164e02be019a05f10"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "steps.2.produce"}}, "outputs": [{"id": "produce"}], "hyperparams": {"semantic_types": {"type": "VALUE", "data": ["https://metadata.datadrivendiscovery.org/types/Attribute", "https://metadata.datadrivendiscovery.org/types/PrimaryKey"]}}}, {"type": "PRIMITIVE", "primitive": {"id": "4503a4c6-42f7-45a1-a1d4-ed69699cf5e1", "version": "0.4.0", "python_path": "d3m.primitives.data_transformation.extract_columns_by_semantic_types.Common", "name": "Extracts columns by semantic type", "digest": "591d3ee40b266bda04fa345aeea7b56d81db182fb144cde164e02be019a05f10"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "steps.2.produce"}}, "outputs": [{"id": "produce"}], "hyperparams": {"semantic_types": {"type": "VALUE", "data": ["https://metadata.datadrivendiscovery.org/types/Target"]}}}, {"type": "PRIMITIVE", "primitive": {"id": "76b5a479-c209-4d94-92b5-7eba7a4d4499", "version": "1.2.0", "python_path": "d3m.primitives.time_series_forecasting.vector_autoregression.VAR", "name": "VAR", "digest": "6bd897d2780bbd35f2a9c937bb7cf47abcff2ddeef8021a88605bc720a5d945c"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "steps.3.produce"}, "outputs": {"type": "CONTAINER", "data": "steps.4.produce"}}, "outputs": [{"id": "produce"}]}], "digest": "a156c4b8693dfbc5a5d3f0333423f5bbc0f8f2a886e7504175791fc1edd69fbb"}
\ No newline at end of file

v2020.1.9/Distil/d3m.primitives.time_series_forecasting.vector_autoregression.VAR/1.2.0/primitive.json

@@ -20,7 +20,7 @@
         },
         {
             "type": "PIP",
-            "package_uri": "git+https://github.com/NewKnowledge/TimeSeries-D3M-Wrappers.git@69eb689a7679361fb4e150f3d406db2580bbc381#egg=TimeSeriesD3MWrappers"
+            "package_uri": "git+https://github.com/NewKnowledge/TimeSeries-D3M-Wrappers.git@97d94579ee5c4e585a9fd044083972d197bb9d57#egg=TimeSeriesD3MWrappers"
         }
     ],
     "python_path": "d3m.primitives.time_series_forecasting.vector_autoregression.VAR",
@@ -304,7 +304,7 @@
                     "outputs"
                 ],
                 "returns": "NoneType",
-                "description": "Sets primitive's training data\n\nArguments:\n    inputs {Inputs} -- full D3M dataframe, containing attributes, key, and target\n    outputs {Outputs} -- full D3M dataframe, containing attributes, key, and target\n\nRaises:\n    ValueError: If multiple columns are annotated with 'Time' or 'DateTime' metadata\n\nParameters\n----------\ninputs : Inputs\n    The inputs.\noutputs : Outputs\n    The outputs."
+                "description": "Sets primitive's training data\n\nArguments:\n    inputs {Inputs} -- D3M dataframe containing attributes\n    outputs {Outputs} -- D3M dataframe containing targets\n\nRaises:\n    ValueError: If multiple columns are annotated with 'Time' or 'DateTime' metadata\n\nParameters\n----------\ninputs : Inputs\n    The inputs.\noutputs : Outputs\n    The outputs."
             }
         },
         "class_attributes": {
@@ -322,5 +322,5 @@
     },
     "structural_type": "TimeSeriesD3MWrappers.primitives.forecasting_var.VAR",
     "description": "Primitive that applies a VAR multivariate forecasting model to time series data. The VAR\nimplementation comes from the statsmodels library. It will default to an ARIMA model if\ntimeseries is univariate. The lag order and AR, MA, and differencing terms for the VAR\nand ARIMA models respectively are selected automatically and independently for each regression.\nUser can override automatic selection with 'max_lag_order' HP.\n\nArguments:\n    hyperparams {Hyperparams} -- D3M Hyperparameter object\n\nKeyword Arguments:\n    random_seed {int} -- random seed (default: {0})\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": "92ac8f78c70df695cc048089ecec666d6e3626bf4998623354841fefcb42ab6c"
+    "digest": "6bd897d2780bbd35f2a9c937bb7cf47abcff2ddeef8021a88605bc720a5d945c"
 }