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    "info": {
        "author": "Jeroen van Hoof",
        "author_email": "jeroen@jeroenvanhoof.nl",
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        "description": "Arbok\n=====\n\nArbok (**A**\\ utoml w\\ **r**\\ apper tool\\ **b**\\ ox for **o**\\ penml\n**c**\\ ompatibility) provides wrappers for TPOT and Auto-Sklearn, as a\ncompatibility layer between these tools and OpenML.\n\nThe wrapper extends Sklearn\u2019s ``BaseSearchCV`` and provides all the\ninternal parameters that OpenML needs, such as ``cv_results_``,\n``best_index_``, ``best_params_``, ``best_score_`` and ``classes_``.\n\nInstallation\n------------\n\n::\n\n    pip install arbok\n\nSimple example\n--------------\n\n.. code:: python\n\n    import openml\n    from arbok import AutoSklearnWrapper, TPOTWrapper\n\n\n    task = openml.tasks.get_task(31)\n    dataset = task.get_dataset()\n\n    # Get the AutoSklearn wrapper and pass parameters like you would to AutoSklearn\n    clf = AutoSklearnWrapper(\n        time_left_for_this_task=3600, per_run_time_limit=360\n    )\n\n    # Or get the TPOT wrapper and pass parameters like you would to TPOT\n    clf = TPOTWrapper(\n        generations=100, population_size=100, verbosity=2\n    )\n\n    # Execute the task\n    run = openml.runs.run_model_on_task(task, clf)\n    run.publish()\n\n    print('URL for run: %s/run/%d' % (openml.config.server, run.run_id))\n\nPreprocessing data\n------------------\n\nTo make the wrapper more robust, we need to preprocess the data. We can\nfill the missing values, and one-hot encode categorical data.\n\nFirst, we get a mask that tells us whether a feature is a categorical\nfeature or not.\n\n.. code:: python\n\n    dataset = task.get_dataset()\n    _, categorical = dataset.get_data(return_categorical_indicator=True)\n    categorical = categorical[:-1]  # Remove last index (which is the class)\n\nNext, we setup a pipeline for the preprocessing. We are using a\n``ConditionalImputer``, which is an imputer which is able to use\ndifferent strategies for categorical (nominal) and numerical data.\n\n.. code:: python\n\n    from sklearn.pipeline import make_pipeline\n    from sklearn.preprocessing import OneHotEncoder\n    from arbok import ConditionalImputer\n\n    preprocessor = make_pipeline(\n\n        ConditionalImputer(\n            categorical_features=categorical,\n            strategy=\"mean\",\n            strategy_nominal=\"most_frequent\"\n        ),\n\n        OneHotEncoder(\n            categorical_features=categorical, handle_unknown=\"ignore\", sparse=False\n        )\n    )\n\nAnd finally, we put everything together in one of the wrappers.\n\n.. code:: python\n\n    clf = AutoSklearnWrapper(\n        preprocessor=preprocessor, time_left_for_this_task=3600, per_run_time_limit=360\n    )\n\nLimitations\n~~~~~~~~~~~\n\n-  Currently only the classifiers are implemented. Regression is\n   therefore not possible.\n-  For TPOT, the ``config_dict`` variable can not be set, because this\n   causes problems with the API.\n\nBenchmarking\n------------\n\nInstalling the ``arbok`` package includes the ``arbench`` cli tool. We\ncan generate a json file like this:\n\n.. code:: python\n\n    from arbok.bench import Benchmark\n    bench = Benchmark()\n    config_file = bench.create_config_file(\n\n        # Wrapper parameters\n        wrapper={\"refit\": True, \"verbose\": False, \"retry_on_error\": True},\n\n        # TPOT parameters\n        tpot={\n            \"max_time_mins\": 6,              # Max total time in minutes\n            \"max_eval_time_mins\": 1          # Max time per candidate in minutes\n        },\n\n        # Autosklearn parameters\n        autosklearn={\n            \"time_left_for_this_task\": 360,  # Max total time in seconds\n            \"per_run_time_limit\": 60         # Max time per candidate in seconds\n        }\n    )\n\nAnd then, we can call arbench like this:\n\n.. code:: bash\n\n    arbench --classifier autosklearn --task-id 31 --config config.json\n\nOr calling arbok as a python module:\n\n.. code:: bash\n\n    python -m arbok --classifier autosklearn --task-id 31 --config config.json\n\nRunning a benchmark on batch systems\n------------------------------------\n\nTo run a large scale benchmark, we can create a configuration file like\nabove, and generate and submit jobs to a batch system as follows.\n\n.. code:: python\n\n    # We create a benchmark setup where we specify the headers, the interpreter we\n    # want to use, the directory to where we store the jobs (.sh-files), and we give\n    # it the config-file we created earlier.\n    bench = Benchmark(\n        headers=\"#PBS -lnodes=1:cpu3\\n#PBS -lwalltime=1:30:00\",\n        python_interpreter=\"python3\",  # Path to interpreter\n        root=\"/path/to/project/\",\n        jobs_dir=\"jobs\",\n        config_file=\"config.json\",\n        log_file=\"log.json\"\n    )\n\n    # Create the config file like we did in the section above\n    config_file = bench.create_config_file(\n\n        # Wrapper parameters\n        wrapper={\"refit\": True, \"verbose\": False, \"retry_on_error\": True},\n\n        # TPOT parameters\n        tpot={\n            \"max_time_mins\": 6,              # Max total time in minutes\n            \"max_eval_time_mins\": 1          # Max time per candidate in minutes\n        },\n\n        # Autosklearn parameters\n        autosklearn={\n            \"time_left_for_this_task\": 360,  # Max total time in seconds\n            \"per_run_time_limit\": 60         # Max time per candidate in seconds\n        }\n    )\n\n    # Next, we load the tasks we want to benchmark on from OpenML.\n    # In this case, we load a list of task id's from study 99.\n    tasks = openml.study.get_study(99).tasks\n\n    # Next, we create jobs for both tpot and autosklearn.\n    bench.create_jobs(tasks, classifiers=[\"tpot\", \"autosklearn\"])\n\n    # And finally, we submit the jobs using qsub\n    bench.submit_jobs()\n\nPreprocessing parameters\n------------------------\n\n.. code:: python\n\n    from arbok import ParamPreprocessor\n    import numpy as np\n    from sklearn.feature_selection import VarianceThreshold\n    from sklearn.pipeline import make_pipeline\n\n    X = np.array([\n        [1, 2, True, \"foo\", \"one\"],\n        [1, 3, False, \"bar\", \"two\"],\n        [np.nan, \"bar\", None, None, \"three\"],\n        [1, 7, 0, \"zip\", \"four\"],\n        [1, 9, 1, \"foo\", \"five\"],\n        [1, 10, 0.1, \"zip\", \"six\"]\n    ], dtype=object)\n\n    # Manually specify types, or use types=\"detect\" to automatically detect types\n    types = [\"numeric\", \"mixed\", \"bool\", \"nominal\", \"nominal\"]\n\n    pipeline = make_pipeline(ParamPreprocessor(types=\"detect\"), VarianceThreshold())\n\n    pipeline.fit_transform(X)\n\nOutput:\n\n::\n\n    [[-0.4472136  -0.4472136   1.41421356 -0.70710678 -0.4472136  -0.4472136\n       2.23606798 -0.4472136  -0.4472136  -0.4472136   0.4472136  -0.4472136\n      -0.85226648  1.        ]\n     [-0.4472136   2.23606798 -0.70710678 -0.70710678 -0.4472136  -0.4472136\n      -0.4472136  -0.4472136  -0.4472136   2.23606798  0.4472136  -0.4472136\n      -0.5831297  -1.        ]\n     [ 2.23606798 -0.4472136  -0.70710678 -0.70710678 -0.4472136  -0.4472136\n      -0.4472136  -0.4472136   2.23606798 -0.4472136  -2.23606798  2.23606798\n      -1.39054004 -1.        ]\n     [-0.4472136  -0.4472136  -0.70710678  1.41421356 -0.4472136   2.23606798\n      -0.4472136  -0.4472136  -0.4472136  -0.4472136   0.4472136  -0.4472136\n       0.49341743 -1.        ]\n     [-0.4472136  -0.4472136   1.41421356 -0.70710678  2.23606798 -0.4472136\n      -0.4472136  -0.4472136  -0.4472136  -0.4472136   0.4472136  -0.4472136\n       1.031691    1.        ]\n     [-0.4472136  -0.4472136  -0.70710678  1.41421356 -0.4472136  -0.4472136\n      -0.4472136   2.23606798 -0.4472136  -0.4472136   0.4472136  -0.4472136\n       1.30082778  1.        ]]\n\n\n",
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        "maintainer": "",
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        "name": "arbok",
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        "project_url": "https://pypi.org/project/arbok/",
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