Commit 844da4db authored by Mark Hoffmann's avatar Mark Hoffmann

Merge branch 'master' of gitlab.com:datadrivendiscovery/primitives into jpl-primitives

parents f0c4c03a 472cc273
......@@ -124,7 +124,7 @@ $ ./run_validation.py 'v2017.12.27/Test team/d3m.primitives.test.IncrementPrimit
To validate pipeline description do:
```bash
$ python3 -m d3m.metadata.pipeline -c <path_to_JSON>
$ python3 -m d3m.metadata.pipeline --strict-resolving -c <path_to_JSON>
```
It will print out the pipeline JSON if it succeeds, or an error otherwise. You should probably run it inside
......
{"id": "e773da63-d20f-4490-a604-62c8470b74ed", "schema": "https://metadata.datadrivendiscovery.org/schemas/v0/pipeline.json", "created": "2019-05-03T15:38:57.577477Z", "context": "TESTING", "inputs": [{"name": "inputs"}], "outputs": [{"data": "steps.0.produce", "name": "output predictions"}], "steps": [{"type": "PRIMITIVE", "primitive": {"id": "ca014488-6004-4b54-9403-5920fbe5a834", "version": "1.0.2", "python_path": "d3m.primitives.clustering.hdbscan.Hdbscan", "name": "hdbscan", "digest": "418c095df03c20a671d53168555e35469b9bcf0c827f73044f6650985ce20d0c"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "inputs.0"}}, "outputs": [{"id": "produce"}]}], "digest": "e4f77e91869ba1f623e9634e9bc0f69196dc7373020257a0d598a775061eec92"}
\ No newline at end of file
{
"problem": "66_chlorineConcentration_problem",
"full_inputs": [
"66_chlorineConcentration_dataset"
],
"train_inputs": [
"66_chlorineConcentration_dataset_TRAIN"
],
"test_inputs": [
"66_chlorineConcentration_dataset_TEST"
],
"score_inputs": [
"66_chlorineConcentration_dataset_SCORE"
]
}
{"id": "e37ac229-2d62-4564-950b-488948c0b1ea", "schema": "https://metadata.datadrivendiscovery.org/schemas/v0/pipeline.json", "created": "2019-05-03T15:51:19.186977Z", "context": "TESTING", "inputs": [{"name": "inputs"}], "outputs": [{"data": "steps.0.produce", "name": "output predictions"}], "steps": [{"type": "PRIMITIVE", "primitive": {"id": "2d6d3223-1b3c-49cc-9ddd-50f571818268", "version": "1.0.2", "python_path": "d3m.primitives.time_series_classification.k_neighbors.Kanine", "name": "kanine", "digest": "9d111d297bf566893fb00d154bb50a4bd677fe4d7e0297e3603408ca748dad37"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "inputs.0"}, "outputs": {"type": "CONTAINER", "data": "inputs.0"}}, "outputs": [{"id": "produce"}]}], "digest": "2381aeff97b395ac8f8056df6fbd30b9fe5bcb0035a5a1a25c63a5d1a6c25b28"}
\ No newline at end of file
{
"problem": "66_chlorineConcentration_problem",
"full_inputs": [
"66_chlorineConcentration_dataset"
],
"train_inputs": [
"66_chlorineConcentration_dataset_TRAIN"
],
"test_inputs": [
"66_chlorineConcentration_dataset_TEST"
],
"score_inputs": [
"66_chlorineConcentration_dataset_SCORE"
]
}
{"id": "566f1bf2-3309-4c7e-b3b1-bd3752786a28", "schema": "https://metadata.datadrivendiscovery.org/schemas/v0/pipeline.json", "created": "2019-05-01T17:06:35.395271Z", "context": "TESTING", "inputs": [{"name": "inputs"}], "outputs": [{"data": "steps.1.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": "6ee6b94c42491892321d83c7a88a6a93b173c532d9783eafd520c7fd4bd03c55"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "inputs.0"}}, "outputs": [{"id": "produce"}], "hyperparams": {"dataframe_resource": {"type": "VALUE", "data": "learningData"}}}, {"type": "PRIMITIVE", "primitive": {"id": "76b5a479-c209-4d94-92b5-7eba7a4d4499", "version": "1.0.0", "python_path": "d3m.primitives.time_series_forecasting.vector_autoregression.VAR", "name": "VAR", "digest": "a72aafdedd4fed2e0d54507080fffc1cc2d282a1779cff0dfedb3993aa4a2479"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "steps.0.produce"}, "outputs": {"type": "CONTAINER", "data": "steps.0.produce"}}, "outputs": [{"id": "produce"}], "hyperparams": {"filter_index": {"type": "VALUE", "data": 1}, "datetime_filter": {"type": "VALUE", "data": 2}, "n_periods": {"type": "VALUE", "data": 52}, "interval": {"type": "VALUE", "data": 26}, "datetime_interval_exception": {"type": "VALUE", "data": "2017"}}}], "digest": "d8214d4b2dff24c2f4e76eb6665da2d364b10aaf79e0d05fc11f191fd8281997"}
{"id": "f89d77ba-4694-41a1-a27b-203761633ce0", "schema": "https://metadata.datadrivendiscovery.org/schemas/v0/pipeline.json", "created": "2019-05-02T17:53:23.878057Z", "context": "TESTING", "inputs": [{"name": "inputs"}], "outputs": [{"data": "steps.1.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"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "inputs.0"}}, "outputs": [{"id": "produce"}], "hyperparams": {"dataframe_resource": {"type": "VALUE", "data": "learningData"}}}, {"type": "PRIMITIVE", "primitive": {"id": "76b5a479-c209-4d94-92b5-7eba7a4d4499", "version": "1.0.0", "python_path": "d3m.primitives.time_series_forecasting.vector_autoregression.VAR", "name": "VAR"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "steps.0.produce"}, "outputs": {"type": "CONTAINER", "data": "steps.0.produce"}}, "outputs": [{"id": "produce"}], "hyperparams": {"datetime_index": {"type": "VALUE", "data": [3, 2]}, "filter_index": {"type": "VALUE", "data": 1}, "datetime_filter": {"type": "VALUE", "data": 2}, "n_periods": {"type": "VALUE", "data": 52}, "interval": {"type": "VALUE", "data": 26}, "datetime_interval_exception": {"type": "VALUE", "data": "2017"}}}]}
......@@ -20,7 +20,7 @@
},
{
"type": "PIP",
"package_uri": "git+https://github.com/NewKnowledge/[email protected]fbb5e81f6d20620cee1cd3dbbd902f616fcae65f#egg=TimeSeriesD3MWrappers"
"package_uri": "git+https://github.com/NewKnowledge/[email protected]aa776910e7c170c7ce3830982f6d6b4c5cbc4cac#egg=TimeSeriesD3MWrappers"
}
],
"python_path": "d3m.primitives.time_series_forecasting.vector_autoregression.VAR",
......@@ -305,5 +305,5 @@
},
"structural_type": "TimeSeriesD3MWrappers.VAR.VAR",
"description": "Produce primitive's prediction for future time series data. The output is a data frame containing the d3m index and a\nforecast for each of the 'n_periods' future time periods, modified if desired by the 'interval' HP. The default is a\nfuture forecast for each of the selected input variables. This can be modified to just one output variable with\nthe associated HP\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": "a72aafdedd4fed2e0d54507080fffc1cc2d282a1779cff0dfedb3993aa4a2479"
"digest": "88e40dcf530adea199583dd97dfe3d2b60acf773ffa35cc04531bcc9109512e3"
}
{
"problem": "66_chlorineConcentration_problem",
"train_inputs": ["66_chlorineConcentration_dataset_TRAIN"],
"test_inputs": ["66_chlorineConcentration_dataset_TEST"]
}
{
"problem": "66_chlorineConcentration_problem",
"train_inputs": ["66_chlorineConcentration_dataset_TRAIN"],
"test_inputs": ["66_chlorineConcentration_dataset_TEST"]
}
{
"problem": "66_chlorineConcentration_problem",
"train_inputs": ["66_chlorineConcentration_dataset_TRAIN"],
"test_inputs": ["66_chlorineConcentration_dataset_TEST"]
}
{
"problem": "66_chlorineConcentration_problem",
"train_inputs": ["66_chlorineConcentration_dataset_TRAIN"],
"test_inputs": ["66_chlorineConcentration_dataset_TEST"]
}
{
"problem": "66_chlorineConcentration_problem",
"train_inputs": ["66_chlorineConcentration_dataset_TRAIN"],
"test_inputs": ["66_chlorineConcentration_dataset_TEST"]
}
{
"problem": "66_chlorineConcentration_problem",
"train_inputs": ["66_chlorineConcentration_dataset_TRAIN"],
"test_inputs": ["66_chlorineConcentration_dataset_TEST"]
}
{
"problem": "66_chlorineConcentration_problem",
"train_inputs": ["66_chlorineConcentration_dataset_TRAIN"],
"test_inputs": ["66_chlorineConcentration_dataset_TEST"]
}
{
"problem": "66_chlorineConcentration_problem",
"train_inputs": ["66_chlorineConcentration_dataset_TRAIN"],
"test_inputs": ["66_chlorineConcentration_dataset_TEST"]
}
{
"problem": "66_chlorineConcentration_problem",
"train_inputs": ["66_chlorineConcentration_dataset_TRAIN"],
"test_inputs": ["66_chlorineConcentration_dataset_TEST"]
}
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