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Neeratyoy Mallik: Making some unit tests work (#1000)
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‎develop/.buildinfo‎

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# Sphinx build info version 1
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# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
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config: c67f9e82b87e6d5502ebba386244da75
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config: fbe988eb3582ca246bb81ab0b775fcd7
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tags: 645f666f9bcd5a90fca523b33c5a78b7

‎develop/_downloads/9015033ecdd320a033e2856e20fc25f4/custom_flow_tutorial.ipynb‎

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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"It is possible to build a flow which uses other flows.\nFor example, the Random Forest Classifier is a flow, but you could also construct a flow\nwhich uses a Random Forest Classifier in a ML pipeline. When constructing the pipeline flow,\nyou can use the Random Forest Classifier flow as a *subflow*. It allows for\nall hyperparameters of the Random Classifier Flow to also be specified in your pipeline flow.\n\nIn this example, the auto-sklearn flow is a subflow: the auto-sklearn flow is entirely executed as part of this flow.\nThis allows people to specify auto-sklearn hyperparameters used in this flow.\nIn general, using a subflow is not required.\n\nNote: flow 15275 is not actually the right flow on the test server,\nbut that does not matter for this demonstration.\n\n"
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"It is possible to build a flow which uses other flows.\nFor example, the Random Forest Classifier is a flow, but you could also construct a flow\nwhich uses a Random Forest Classifier in a ML pipeline. When constructing the pipeline flow,\nyou can use the Random Forest Classifier flow as a *subflow*. It allows for\nall hyperparameters of the Random Classifier Flow to also be specified in your pipeline flow.\n\nIn this example, the auto-sklearn flow is a subflow: the auto-sklearn flow is entirely executed as part of this flow.\nThis allows people to specify auto-sklearn hyperparameters used in this flow.\nIn general, using a subflow is not required.\n\nNote: flow 9313 is not actually the right flow on the test server,\nbut that does not matter for this demonstration.\n\n"
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]
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},
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{
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},
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"outputs": [],
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"source": [
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"autosklearn_flow = openml.flows.get_flow(15275) # auto-sklearn 0.5.1\nsubflow = dict(components=OrderedDict(automl_tool=autosklearn_flow),)"
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"autosklearn_flow = openml.flows.get_flow(9313) # auto-sklearn 0.5.1\nsubflow = dict(components=OrderedDict(automl_tool=autosklearn_flow),)"
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]
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},
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{
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},
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"outputs": [],
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"source": [
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"flow_id = autosklearn_amlb_flow.flow_id\n\nparameters = [\n OrderedDict([(\"oml:name\", \"cores\"), (\"oml:value\", 4), (\"oml:component\", flow_id)]),\n OrderedDict([(\"oml:name\", \"memory\"), (\"oml:value\", 16), (\"oml:component\", flow_id)]),\n OrderedDict([(\"oml:name\", \"time\"), (\"oml:value\", 120), (\"oml:component\", flow_id)]),\n]\n\ntask_id = 1408 # Iris Task\ntask = openml.tasks.get_task(task_id)\ndataset_id = task.get_dataset().dataset_id"
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"flow_id = autosklearn_amlb_flow.flow_id\n\nparameters = [\n OrderedDict([(\"oml:name\", \"cores\"), (\"oml:value\", 4), (\"oml:component\", flow_id)]),\n OrderedDict([(\"oml:name\", \"memory\"), (\"oml:value\", 16), (\"oml:component\", flow_id)]),\n OrderedDict([(\"oml:name\", \"time\"), (\"oml:value\", 120), (\"oml:component\", flow_id)]),\n]\n\ntask_id = 1965 # Iris Task\ntask = openml.tasks.get_task(task_id)\ndataset_id = task.get_dataset().dataset_id"
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]
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},
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{

‎develop/_downloads/a8c9cf480c327557392da8a19a0b9378/custom_flow_tutorial.py‎

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# This allows people to specify auto-sklearn hyperparameters used in this flow.
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# In general, using a subflow is not required.
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#
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# Note: flow 15275 is not actually the right flow on the test server,
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# Note: flow 9313 is not actually the right flow on the test server,
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# but that does not matter for this demonstration.
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autosklearn_flow = openml.flows.get_flow(15275) # auto-sklearn 0.5.1
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autosklearn_flow = openml.flows.get_flow(9313) # auto-sklearn 0.5.1
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subflow = dict(components=OrderedDict(automl_tool=autosklearn_flow),)
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####################################################################################################
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OrderedDict([("oml:name", "time"), ("oml:value", 120), ("oml:component", flow_id)]),
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]
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task_id = 1408 # Iris Task
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task_id = 1965 # Iris Task
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task = openml.tasks.get_task(task_id)
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dataset_id = task.get_dataset().dataset_id
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‎develop/_downloads/b95c071188526f5ef5d991e382df9fa5/datasets_tutorial.py‎

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############################################################################
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# Edit a created dataset
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# =================================================
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# ======================
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# This example uses the test server, to avoid editing a dataset on the main server.
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openml.config.start_using_configuration_for_example()
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############################################################################
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# tasks associated with it. To edit critical fields of a dataset (without tasks) owned by you,
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# configure the API key:
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# openml.config.apikey = 'FILL_IN_OPENML_API_KEY'
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data_id = edit_dataset(564, default_target_attribute="y")
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print(f"Edited dataset ID: {data_id}")
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# This example here only shows a failure when trying to work on a dataset not owned by you:
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try:
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data_id = edit_dataset(1, default_target_attribute="shape")
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except openml.exceptions.OpenMLServerException as e:
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print(e)
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############################################################################
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# Fork dataset
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# ============
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# Used to create a copy of the dataset with you as the owner.
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# Use this API only if you are unable to edit the critical fields (default_target_attribute,
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# ignore_attribute, row_id_attribute) of a dataset through the edit_dataset API.
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# After the dataset is forked, you can edit the new version of the dataset using edit_dataset.
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data_id = fork_dataset(564)
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data_id = fork_dataset(1)
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print(data_id)
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data_id = edit_dataset(data_id, default_target_attribute="shape")
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print(f"Forked dataset ID: {data_id}")
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openml.config.stop_using_configuration_for_example()

‎develop/_downloads/bc82bea3a5dd7bdba60b65220891d9e5/examples_python.zip‎

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‎develop/_downloads/ea59c0f4a0e0075b24ecbb3b84c5744d/datasets_tutorial.ipynb‎

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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Editing critical fields (default_target_attribute, row_id_attribute, ignore_attribute) is allowed\nonly for the dataset owner. Further, critical fields cannot be edited if the dataset has any\ntasks associated with it. To edit critical fields of a dataset (without tasks) owned by you,\nconfigure the API key:\nopenml.config.apikey = 'FILL_IN_OPENML_API_KEY'\n\n"
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"Editing critical fields (default_target_attribute, row_id_attribute, ignore_attribute) is allowed\nonly for the dataset owner. Further, critical fields cannot be edited if the dataset has any\ntasks associated with it. To edit critical fields of a dataset (without tasks) owned by you,\nconfigure the API key:\nopenml.config.apikey = 'FILL_IN_OPENML_API_KEY'\nThis example here only shows a failure when trying to work on a dataset not owned by you:\n\n"
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]
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},
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{
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},
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"outputs": [],
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"source": [
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"data_id = edit_dataset(564, default_target_attribute=\"y\")\nprint(f\"Edited dataset ID: {data_id}\")"
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"try:\n data_id = edit_dataset(1, default_target_attribute=\"shape\")\nexcept openml.exceptions.OpenMLServerException as e:\n print(e)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Fork dataset\nUsed to create a copy of the dataset with you as the owner.\nUse this API only if you are unable to edit the critical fields (default_target_attribute,\nignore_attribute, row_id_attribute) of a dataset through the edit_dataset API.\nAfter the dataset is forked, you can edit the new version of the dataset using edit_dataset.\n\n"
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"### Fork dataset\nUsed to create a copy of the dataset with you as the owner.\nUse this API only if you are unable to edit the critical fields (default_target_attribute,\nignore_attribute, row_id_attribute) of a dataset through the edit_dataset API.\nAfter the dataset is forked, you can edit the new version of the dataset using edit_dataset.\n\n"
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]
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},
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{
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},
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"outputs": [],
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"source": [
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"data_id = fork_dataset(564)\nprint(f\"Forked dataset ID: {data_id}\")\n\nopenml.config.stop_using_configuration_for_example()"
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"data_id = fork_dataset(1)\nprint(data_id)\ndata_id = edit_dataset(data_id, default_target_attribute=\"shape\")\nprint(f\"Forked dataset ID: {data_id}\")\n\nopenml.config.stop_using_configuration_for_example()"
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]
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}
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],

‎develop/_downloads/fb625db3c50d423b1b7881136ffdeec8/examples_jupyter.zip‎

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‎develop/_images/sphx_glr_2018_ida_strang_example_001.png‎

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‎develop/_images/sphx_glr_2018_ida_strang_example_002.png‎

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‎develop/_images/sphx_glr_2018_ida_strang_example_003.png‎

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