if __name__ == '__main__':
    import context

import autem
import autem.runners as runners
import autem.scorers as scorers
import autem.workflows as workflows
import autem.learners.classification as learners
import autem.preprocessors as preprocessors
import autem.hyper_learners as hyper_learners
import autem.loaders as loaders
import autem.reporters as reporters
import autem.validators as validators

import hyper.configuration as configuration
from hyper import HyperAnalysis, Study
import config
import openml
import os

def prepare_OpenML():
    openml.config.apikey = config.OPENML_APIKEY
    openml.config.cache_directory = os.path.expanduser('~/.openml/cache')

learner_builders = {
    'baseline': hyper_learners.ClassificationBaseline,
    'linear': hyper_learners.ClassificationLinear,
    'trees': hyper_learners.ClassificationTrees,
    'svm': hyper_learners.ClassificationSVM,
    'bayes': hyper_learners.ClassificationBayes,
}

def make_snapshot_simulation(study, baseline_name, hyperlearner):
    prepare_OpenML()

    hyper_configuration = configuration.get_hyper_configuration(baseline_name)
    task_id = hyper_configuration["task_id"]
    task = openml.tasks.get_task(task_id)
    data_id = task.dataset_id
    name = "%s %s" % (baseline_name,study)
    path = configuration.get_hyper_simulations_path().joinpath(study).joinpath(baseline_name)
    n_jobs = 4
    seed = 1
    memory = str(path.joinpath("cache"))

    identity = {
        'study': study,
        'dataset': baseline_name,
        'scorer': 'League1x10',
        'workflow': 'snapshot',
        'learner': hyperlearner,
    }    

    simulation = autem.Simulation(
        name,
        [
            loaders.OpenMLLoader(data_id),
            scorers.LeagueScorer(scorers.accuracy_score, [ [ 1, 4, 5 ] ]),
            workflows.SnapshotWorkflow(),
            learner_builders[hyperlearner](),
            reporters.Csv(path),
        ])

    settings = autem.SimulationSettings(simulation)
    settings.set_identity(identity)
    settings.set_n_jobs(4)
    settings.set_seed(seed)
    settings.set_memory(memory)

    return simulation


def make_standard_simulation(study, baseline_name, hyperlearner):
    prepare_OpenML()

    hyper_configuration = configuration.get_hyper_configuration(baseline_name)
    task_id = hyper_configuration["task_id"]
    task = openml.tasks.get_task(task_id)
    data_id = task.dataset_id
    name = "%s %s" % (baseline_name, study)
    path = configuration.get_hyper_simulations_path().joinpath(study).joinpath(baseline_name)
    n_jobs = 4
    seed = 1
    memory = str(path.joinpath("cache"))

    identity = {
        'study': study,
        'dataset': baseline_name,
        'scorer': 'League1x10',
        'workflow': 'standard',
        'learner': hyperlearner,
    }    

    simulation = autem.Simulation(
        name,
        [
            loaders.OpenMLLoader(data_id),
            scorers.LeagueScorer(scorers.accuracy_score, [ [ 1, 4, 5 ] ]),
            workflows.StandardWorkflow(),
            learner_builders[hyperlearner](),
            reporters.Csv(path),
        ])

    settings = autem.SimulationSettings(simulation)
    settings.set_identity(identity)
    settings.set_n_jobs(4)
    settings.set_seed(seed)
    settings.set_memory(memory)

    return simulation

def make_hyper_analysis():
    hyper_analysis = HyperAnalysis(studies = [
        Study("snapshot"),
        Study('linear'),
        Study('trees'),
        Study('svm'),
        Study('bayes'),
    ])
    return hyper_analysis

def get_outstanding_datasets(study):
    hyper_analysis = make_hyper_analysis()
    df = hyper_analysis.get_study(study).get_datasets_status() 
    dataset_names = df[df.active & ~df.attempted].name.tolist()
    return dataset_names

def run_snapshot_analysis(hyperlearners, debug):
    dataset_names = get_outstanding_datasets(hyperlearners[-1])
    max_time = 4 * 60 * 60
    for baseline_name in dataset_names:
        for hyperlearner in hyperlearners:
            simulation = make_snapshot_simulation(hyperlearner, baseline_name, hyperlearner)
            runner = runners.LocalRunner(simulation, max_time) if not debug else runners.DebugRunner(simulation)
            runner.run()
            if runners.RunQuery(simulation).was_escaped():
                return False
    return True

def run_standard_analysis(study, hyperlearner, debug):
    dataset_names = get_outstanding_datasets(study)
    max_time = 4 * 60 * 60
    for baseline_name in dataset_names:
        simulation = make_standard_simulation(study, baseline_name, hyperlearner)
        runner = runners.LocalRunner(simulation, max_time) if not debug else runners.DebugRunner(simulation)
        runner.run()
        if runners.RunQuery(simulation).was_escaped():
            return False
    return True

if __name__ == '__main__':
    run_snapshot_analysis(["linear", "svm", "trees"], False)

463dfb0b3f47c86f01b04a3ad45c353530be1ae6
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def prepare_OpenML():
    openml.config.apikey = config.OPENML_APIKEY
    openml.config.cache_directory = os.path.expanduser('~/.openml/cache')


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def make_standard_simulation(study, baseline_name, hyperlearner):
    prepare_OpenML()
    hyper_configuration = configuration.get_hyper_configuration(baseline_name)
    task_id = hyper_configuration['task_id']
    task = openml.tasks.get_task(task_id)
    data_id = task.dataset_id
    name = '%s %s' % (baseline_name, study)
    path = configuration.get_hyper_simulations_path().joinpath(study).joinpath(
        baseline_name)
    n_jobs = 4
    seed = 1
    memory = str(path.joinpath('cache'))
    identity = {'study': study, 'dataset': baseline_name, 'scorer':
        'League1x10', 'workflow': 'standard', 'learner': hyperlearner}
    simulation = autem.Simulation(name, [loaders.OpenMLLoader(data_id),
        scorers.LeagueScorer(scorers.accuracy_score, [[1, 4, 5]]),
        workflows.StandardWorkflow(), learner_builders[hyperlearner](),
        reporters.Csv(path)])
    settings = autem.SimulationSettings(simulation)
    settings.set_identity(identity)
    settings.set_n_jobs(4)
    settings.set_seed(seed)
    settings.set_memory(memory)
    return simulation


def make_hyper_analysis():
    hyper_analysis = HyperAnalysis(studies=[Study('snapshot'), Study(
        'linear'), Study('trees'), Study('svm'), Study('bayes')])
    return hyper_analysis


def get_outstanding_datasets(study):
    hyper_analysis = make_hyper_analysis()
    df = hyper_analysis.get_study(study).get_datasets_status()
    dataset_names = df[df.active & ~df.attempted].name.tolist()
    return dataset_names


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def run_standard_analysis(study, hyperlearner, debug):
    dataset_names = get_outstanding_datasets(study)
    max_time = 4 * 60 * 60
    for baseline_name in dataset_names:
        simulation = make_standard_simulation(study, baseline_name,
            hyperlearner)
        runner = runners.LocalRunner(simulation, max_time
            ) if not debug else runners.DebugRunner(simulation)
        runner.run()
        if runners.RunQuery(simulation).was_escaped():
            return False
    return True


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def prepare_OpenML():
    openml.config.apikey = config.OPENML_APIKEY
    openml.config.cache_directory = os.path.expanduser('~/.openml/cache')


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def make_standard_simulation(study, baseline_name, hyperlearner):
    prepare_OpenML()
    hyper_configuration = configuration.get_hyper_configuration(baseline_name)
    task_id = hyper_configuration['task_id']
    task = openml.tasks.get_task(task_id)
    data_id = task.dataset_id
    name = '%s %s' % (baseline_name, study)
    path = configuration.get_hyper_simulations_path().joinpath(study).joinpath(
        baseline_name)
    n_jobs = 4
    seed = 1
    memory = str(path.joinpath('cache'))
    identity = {'study': study, 'dataset': baseline_name, 'scorer':
        'League1x10', 'workflow': 'standard', 'learner': hyperlearner}
    simulation = autem.Simulation(name, [loaders.OpenMLLoader(data_id),
        scorers.LeagueScorer(scorers.accuracy_score, [[1, 4, 5]]),
        workflows.StandardWorkflow(), learner_builders[hyperlearner](),
        reporters.Csv(path)])
    settings = autem.SimulationSettings(simulation)
    settings.set_identity(identity)
    settings.set_n_jobs(4)
    settings.set_seed(seed)
    settings.set_memory(memory)
    return simulation


def make_hyper_analysis():
    hyper_analysis = HyperAnalysis(studies=[Study('snapshot'), Study(
        'linear'), Study('trees'), Study('svm'), Study('bayes')])
    return hyper_analysis


def get_outstanding_datasets(study):
    hyper_analysis = make_hyper_analysis()
    df = hyper_analysis.get_study(study).get_datasets_status()
    dataset_names = df[df.active & ~df.attempted].name.tolist()
    return dataset_names


def run_snapshot_analysis(hyperlearners, debug):
    dataset_names = get_outstanding_datasets(hyperlearners[-1])
    max_time = 4 * 60 * 60
    for baseline_name in dataset_names:
        for hyperlearner in hyperlearners:
            simulation = make_snapshot_simulation(hyperlearner,
                baseline_name, hyperlearner)
            runner = runners.LocalRunner(simulation, max_time
                ) if not debug else runners.DebugRunner(simulation)
            runner.run()
            if runners.RunQuery(simulation).was_escaped():
                return False
    return True


def run_standard_analysis(study, hyperlearner, debug):
    dataset_names = get_outstanding_datasets(study)
    max_time = 4 * 60 * 60
    for baseline_name in dataset_names:
        simulation = make_standard_simulation(study, baseline_name,
            hyperlearner)
        runner = runners.LocalRunner(simulation, max_time
            ) if not debug else runners.DebugRunner(simulation)
        runner.run()
        if runners.RunQuery(simulation).was_escaped():
            return False
    return True


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<mask token>


def prepare_OpenML():
    openml.config.apikey = config.OPENML_APIKEY
    openml.config.cache_directory = os.path.expanduser('~/.openml/cache')


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def make_snapshot_simulation(study, baseline_name, hyperlearner):
    prepare_OpenML()
    hyper_configuration = configuration.get_hyper_configuration(baseline_name)
    task_id = hyper_configuration['task_id']
    task = openml.tasks.get_task(task_id)
    data_id = task.dataset_id
    name = '%s %s' % (baseline_name, study)
    path = configuration.get_hyper_simulations_path().joinpath(study).joinpath(
        baseline_name)
    n_jobs = 4
    seed = 1
    memory = str(path.joinpath('cache'))
    identity = {'study': study, 'dataset': baseline_name, 'scorer':
        'League1x10', 'workflow': 'snapshot', 'learner': hyperlearner}
    simulation = autem.Simulation(name, [loaders.OpenMLLoader(data_id),
        scorers.LeagueScorer(scorers.accuracy_score, [[1, 4, 5]]),
        workflows.SnapshotWorkflow(), learner_builders[hyperlearner](),
        reporters.Csv(path)])
    settings = autem.SimulationSettings(simulation)
    settings.set_identity(identity)
    settings.set_n_jobs(4)
    settings.set_seed(seed)
    settings.set_memory(memory)
    return simulation


def make_standard_simulation(study, baseline_name, hyperlearner):
    prepare_OpenML()
    hyper_configuration = configuration.get_hyper_configuration(baseline_name)
    task_id = hyper_configuration['task_id']
    task = openml.tasks.get_task(task_id)
    data_id = task.dataset_id
    name = '%s %s' % (baseline_name, study)
    path = configuration.get_hyper_simulations_path().joinpath(study).joinpath(
        baseline_name)
    n_jobs = 4
    seed = 1
    memory = str(path.joinpath('cache'))
    identity = {'study': study, 'dataset': baseline_name, 'scorer':
        'League1x10', 'workflow': 'standard', 'learner': hyperlearner}
    simulation = autem.Simulation(name, [loaders.OpenMLLoader(data_id),
        scorers.LeagueScorer(scorers.accuracy_score, [[1, 4, 5]]),
        workflows.StandardWorkflow(), learner_builders[hyperlearner](),
        reporters.Csv(path)])
    settings = autem.SimulationSettings(simulation)
    settings.set_identity(identity)
    settings.set_n_jobs(4)
    settings.set_seed(seed)
    settings.set_memory(memory)
    return simulation


def make_hyper_analysis():
    hyper_analysis = HyperAnalysis(studies=[Study('snapshot'), Study(
        'linear'), Study('trees'), Study('svm'), Study('bayes')])
    return hyper_analysis


def get_outstanding_datasets(study):
    hyper_analysis = make_hyper_analysis()
    df = hyper_analysis.get_study(study).get_datasets_status()
    dataset_names = df[df.active & ~df.attempted].name.tolist()
    return dataset_names


def run_snapshot_analysis(hyperlearners, debug):
    dataset_names = get_outstanding_datasets(hyperlearners[-1])
    max_time = 4 * 60 * 60
    for baseline_name in dataset_names:
        for hyperlearner in hyperlearners:
            simulation = make_snapshot_simulation(hyperlearner,
                baseline_name, hyperlearner)
            runner = runners.LocalRunner(simulation, max_time
                ) if not debug else runners.DebugRunner(simulation)
            runner.run()
            if runners.RunQuery(simulation).was_escaped():
                return False
    return True


def run_standard_analysis(study, hyperlearner, debug):
    dataset_names = get_outstanding_datasets(study)
    max_time = 4 * 60 * 60
    for baseline_name in dataset_names:
        simulation = make_standard_simulation(study, baseline_name,
            hyperlearner)
        runner = runners.LocalRunner(simulation, max_time
            ) if not debug else runners.DebugRunner(simulation)
        runner.run()
        if runners.RunQuery(simulation).was_escaped():
            return False
    return True


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if __name__ == '__main__':
    import context
import autem
import autem.runners as runners
import autem.scorers as scorers
import autem.workflows as workflows
import autem.learners.classification as learners
import autem.preprocessors as preprocessors
import autem.hyper_learners as hyper_learners
import autem.loaders as loaders
import autem.reporters as reporters
import autem.validators as validators
import hyper.configuration as configuration
from hyper import HyperAnalysis, Study
import config
import openml
import os


def prepare_OpenML():
    openml.config.apikey = config.OPENML_APIKEY
    openml.config.cache_directory = os.path.expanduser('~/.openml/cache')


learner_builders = {'baseline': hyper_learners.ClassificationBaseline,
    'linear': hyper_learners.ClassificationLinear, 'trees': hyper_learners.
    ClassificationTrees, 'svm': hyper_learners.ClassificationSVM, 'bayes':
    hyper_learners.ClassificationBayes}


def make_snapshot_simulation(study, baseline_name, hyperlearner):
    prepare_OpenML()
    hyper_configuration = configuration.get_hyper_configuration(baseline_name)
    task_id = hyper_configuration['task_id']
    task = openml.tasks.get_task(task_id)
    data_id = task.dataset_id
    name = '%s %s' % (baseline_name, study)
    path = configuration.get_hyper_simulations_path().joinpath(study).joinpath(
        baseline_name)
    n_jobs = 4
    seed = 1
    memory = str(path.joinpath('cache'))
    identity = {'study': study, 'dataset': baseline_name, 'scorer':
        'League1x10', 'workflow': 'snapshot', 'learner': hyperlearner}
    simulation = autem.Simulation(name, [loaders.OpenMLLoader(data_id),
        scorers.LeagueScorer(scorers.accuracy_score, [[1, 4, 5]]),
        workflows.SnapshotWorkflow(), learner_builders[hyperlearner](),
        reporters.Csv(path)])
    settings = autem.SimulationSettings(simulation)
    settings.set_identity(identity)
    settings.set_n_jobs(4)
    settings.set_seed(seed)
    settings.set_memory(memory)
    return simulation


def make_standard_simulation(study, baseline_name, hyperlearner):
    prepare_OpenML()
    hyper_configuration = configuration.get_hyper_configuration(baseline_name)
    task_id = hyper_configuration['task_id']
    task = openml.tasks.get_task(task_id)
    data_id = task.dataset_id
    name = '%s %s' % (baseline_name, study)
    path = configuration.get_hyper_simulations_path().joinpath(study).joinpath(
        baseline_name)
    n_jobs = 4
    seed = 1
    memory = str(path.joinpath('cache'))
    identity = {'study': study, 'dataset': baseline_name, 'scorer':
        'League1x10', 'workflow': 'standard', 'learner': hyperlearner}
    simulation = autem.Simulation(name, [loaders.OpenMLLoader(data_id),
        scorers.LeagueScorer(scorers.accuracy_score, [[1, 4, 5]]),
        workflows.StandardWorkflow(), learner_builders[hyperlearner](),
        reporters.Csv(path)])
    settings = autem.SimulationSettings(simulation)
    settings.set_identity(identity)
    settings.set_n_jobs(4)
    settings.set_seed(seed)
    settings.set_memory(memory)
    return simulation


def make_hyper_analysis():
    hyper_analysis = HyperAnalysis(studies=[Study('snapshot'), Study(
        'linear'), Study('trees'), Study('svm'), Study('bayes')])
    return hyper_analysis


def get_outstanding_datasets(study):
    hyper_analysis = make_hyper_analysis()
    df = hyper_analysis.get_study(study).get_datasets_status()
    dataset_names = df[df.active & ~df.attempted].name.tolist()
    return dataset_names


def run_snapshot_analysis(hyperlearners, debug):
    dataset_names = get_outstanding_datasets(hyperlearners[-1])
    max_time = 4 * 60 * 60
    for baseline_name in dataset_names:
        for hyperlearner in hyperlearners:
            simulation = make_snapshot_simulation(hyperlearner,
                baseline_name, hyperlearner)
            runner = runners.LocalRunner(simulation, max_time
                ) if not debug else runners.DebugRunner(simulation)
            runner.run()
            if runners.RunQuery(simulation).was_escaped():
                return False
    return True


def run_standard_analysis(study, hyperlearner, debug):
    dataset_names = get_outstanding_datasets(study)
    max_time = 4 * 60 * 60
    for baseline_name in dataset_names:
        simulation = make_standard_simulation(study, baseline_name,
            hyperlearner)
        runner = runners.LocalRunner(simulation, max_time
            ) if not debug else runners.DebugRunner(simulation)
        runner.run()
        if runners.RunQuery(simulation).was_escaped():
            return False
    return True


if __name__ == '__main__':
    run_snapshot_analysis(['linear', 'svm', 'trees'], False)

if __name__ == '__main__':
    import context

import autem
import autem.runners as runners
import autem.scorers as scorers
import autem.workflows as workflows
import autem.learners.classification as learners
import autem.preprocessors as preprocessors
import autem.hyper_learners as hyper_learners
import autem.loaders as loaders
import autem.reporters as reporters
import autem.validators as validators

import hyper.configuration as configuration
from hyper import HyperAnalysis, Study
import config
import openml
import os

def prepare_OpenML():
    openml.config.apikey = config.OPENML_APIKEY
    openml.config.cache_directory = os.path.expanduser('~/.openml/cache')

learner_builders = {
    'baseline': hyper_learners.ClassificationBaseline,
    'linear': hyper_learners.ClassificationLinear,
    'trees': hyper_learners.ClassificationTrees,
    'svm': hyper_learners.ClassificationSVM,
    'bayes': hyper_learners.ClassificationBayes,
}

def make_snapshot_simulation(study, baseline_name, hyperlearner):
    prepare_OpenML()

    hyper_configuration = configuration.get_hyper_configuration(baseline_name)
    task_id = hyper_configuration["task_id"]
    task = openml.tasks.get_task(task_id)
    data_id = task.dataset_id
    name = "%s %s" % (baseline_name,study)
    path = configuration.get_hyper_simulations_path().joinpath(study).joinpath(baseline_name)
    n_jobs = 4
    seed = 1
    memory = str(path.joinpath("cache"))

    identity = {
        'study': study,
        'dataset': baseline_name,
        'scorer': 'League1x10',
        'workflow': 'snapshot',
        'learner': hyperlearner,
    }    

    simulation = autem.Simulation(
        name,
        [
            loaders.OpenMLLoader(data_id),
            scorers.LeagueScorer(scorers.accuracy_score, [ [ 1, 4, 5 ] ]),
            workflows.SnapshotWorkflow(),
            learner_builders[hyperlearner](),
            reporters.Csv(path),
        ])

    settings = autem.SimulationSettings(simulation)
    settings.set_identity(identity)
    settings.set_n_jobs(4)
    settings.set_seed(seed)
    settings.set_memory(memory)

    return simulation


def make_standard_simulation(study, baseline_name, hyperlearner):
    prepare_OpenML()

    hyper_configuration = configuration.get_hyper_configuration(baseline_name)
    task_id = hyper_configuration["task_id"]
    task = openml.tasks.get_task(task_id)
    data_id = task.dataset_id
    name = "%s %s" % (baseline_name, study)
    path = configuration.get_hyper_simulations_path().joinpath(study).joinpath(baseline_name)
    n_jobs = 4
    seed = 1
    memory = str(path.joinpath("cache"))

    identity = {
        'study': study,
        'dataset': baseline_name,
        'scorer': 'League1x10',
        'workflow': 'standard',
        'learner': hyperlearner,
    }    

    simulation = autem.Simulation(
        name,
        [
            loaders.OpenMLLoader(data_id),
            scorers.LeagueScorer(scorers.accuracy_score, [ [ 1, 4, 5 ] ]),
            workflows.StandardWorkflow(),
            learner_builders[hyperlearner](),
            reporters.Csv(path),
        ])

    settings = autem.SimulationSettings(simulation)
    settings.set_identity(identity)
    settings.set_n_jobs(4)
    settings.set_seed(seed)
    settings.set_memory(memory)

    return simulation

def make_hyper_analysis():
    hyper_analysis = HyperAnalysis(studies = [
        Study("snapshot"),
        Study('linear'),
        Study('trees'),
        Study('svm'),
        Study('bayes'),
    ])
    return hyper_analysis

def get_outstanding_datasets(study):
    hyper_analysis = make_hyper_analysis()
    df = hyper_analysis.get_study(study).get_datasets_status() 
    dataset_names = df[df.active & ~df.attempted].name.tolist()
    return dataset_names

def run_snapshot_analysis(hyperlearners, debug):
    dataset_names = get_outstanding_datasets(hyperlearners[-1])
    max_time = 4 * 60 * 60
    for baseline_name in dataset_names:
        for hyperlearner in hyperlearners:
            simulation = make_snapshot_simulation(hyperlearner, baseline_name, hyperlearner)
            runner = runners.LocalRunner(simulation, max_time) if not debug else runners.DebugRunner(simulation)
            runner.run()
            if runners.RunQuery(simulation).was_escaped():
                return False
    return True

def run_standard_analysis(study, hyperlearner, debug):
    dataset_names = get_outstanding_datasets(study)
    max_time = 4 * 60 * 60
    for baseline_name in dataset_names:
        simulation = make_standard_simulation(study, baseline_name, hyperlearner)
        runner = runners.LocalRunner(simulation, max_time) if not debug else runners.DebugRunner(simulation)
        runner.run()
        if runners.RunQuery(simulation).was_escaped():
            return False
    return True

if __name__ == '__main__':
    run_snapshot_analysis(["linear", "svm", "trees"], False)
