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带 Ax 的多目标 NAS

译者：片刻小哥哥

项目地址：https://pytorch.apachecn.org/2.0/tutorials/intermediate/ax_multiobjective_nas_tutorial

原始地址：https://pytorch.org/tutorials/intermediate/ax_multiobjective_nas_tutorial.html

作者： David Eriksson , Max Balandat , and Meta 的自适应实验团队。

在本教程中，我们将展示如何使用 Ax 在流行的网络上运行 多目标神经架构搜索 (NAS)，以获取简单的神经网络模型 MNIST 数据集。虽然底层方法通常用于更复杂的模型和更大的数据集，但我们选择的教程可以在 20 分钟内轻松地在笔记本电脑上端到端运行。

在许多 NAS 应用程序中，多个感兴趣的目标之间存在自然的权衡。例如，在设备上部署模型时 我们可能希望最大化模型性能(例如准确性)，同时 最小化功耗、推理延迟或模型大小等竞争指标，以满足部署 约束。通常，我们可以通过接受最低限度的较低模型性能来大幅减少计算要求或预测延迟。高效地探索此类权衡的原则方法是可扩展和可持续人工智能的关键推动者，并且在 Meta 上有许多成功的应用 - 例如，请参阅我们的 案例研究 在自然语言理解模型上。

在我们的示例中，我们将调整两个隐藏层的宽度、学习率、丢失概率、批量大小和训练周期数。目标是权衡性能 (验证集的准确性)和模型大小( 模型参数的数量)。

本教程使用以下 PyTorch 库：

  • PyTorch Lightning (指定模型和训练循环)
  • TorchX \ n(用于远程/异步运行训练作业)
  • BoTorch (支持 Ax’s 算法的贝叶斯优化库)

定义 TorchX 应用程序

我们的目标是优化 mnist_train_nas.py 中定义的 PyTorch Lightning 训练作业 。 为了使用 TorchX 执行此操作，我们编写了一个辅助函数，该函数接受 训练作业的架构和超参数的值，并创建一个 TorchX AppDef 使用适当的设置。

from pathlib import Path

import torchx

from torchx import specs
from torchx.components import utils


def trainer(
    log_path: str,
    hidden_size_1: int,
    hidden_size_2: int,
    learning_rate: float,
    epochs: int,
    dropout: float,
    batch_size: int,
    trial_idx: int = -1,
) -> specs.AppDef:

    # define the log path so we can pass it to the TorchX ``AppDef``
    if trial_idx >= 0:
        log_path = Path(log_path).joinpath(str(trial_idx)).absolute().as_posix()

    return utils.python(
        # command line arguments to the training script
        "--log_path",
        log_path,
        "--hidden_size_1",
        str(hidden_size_1),
        "--hidden_size_2",
        str(hidden_size_2),
        "--learning_rate",
        str(learning_rate),
        "--epochs",
        str(epochs),
        "--dropout",
        str(dropout),
        "--batch_size",
        str(batch_size),
        # other config options
        name="trainer",
        script="mnist_train_nas.py",
        image=torchx.version.TORCHX_IMAGE,
    )

设置运行器

Ax’s Runner 抽象允许将接口写入各种后端。 Ax 已经TorchX 附带了 Runner，因此我们只需配置它。出于本教程的目的，我们以完全异步的方式在本地 运行作业。

为了在集群上启动它们，您可以指定不同的 TorchX 调度程序并适当调整配置。 例如，如果您有 Kubernetes 集群，则只需将 调度程序从 local _cwd 到 kubernetes )。

import tempfile
from ax.runners.torchx import TorchXRunner

# Make a temporary dir to log our results into
log_dir = tempfile.mkdtemp()

ax_runner = TorchXRunner(
    tracker_base="/tmp/",
    component=trainer,
    # NOTE: To launch this job on a cluster instead of locally you can
    # specify a different scheduler and adjust arguments appropriately.
    scheduler="local_cwd",
    component_const_params={"log_path": log_dir},
    cfg={},
)

设置

SearchSpace

首先，我们定义搜索空间。 Ax 支持整数和浮点类型的范围参数，以及可以具有非数字类型(例如字符串)的选择参数。 我们将调整隐藏大小、学习率、dropout 和 epoch 数量作为范围参数，将批量大小调整为有序选择参数 以强制其为 2 的幂。

from ax.core import (
    ChoiceParameter,
    ParameterType,
    RangeParameter,
    SearchSpace,
)

parameters = [
    # NOTE: In a real-world setting, hidden_size_1 and hidden_size_2
    # should probably be powers of 2, but in our simple example this
    # would mean that ``num_params`` can't take on that many values, which
    # in turn makes the Pareto frontier look pretty weird.
    RangeParameter(
        name="hidden_size_1",
        lower=16,
        upper=128,
        parameter_type=ParameterType.INT,
        log_scale=True,
    ),
    RangeParameter(
        name="hidden_size_2",
        lower=16,
        upper=128,
        parameter_type=ParameterType.INT,
        log_scale=True,
    ),
    RangeParameter(
        name="learning_rate",
        lower=1e-4,
        upper=1e-2,
        parameter_type=ParameterType.FLOAT,
        log_scale=True,
    ),
    RangeParameter(
        name="epochs",
        lower=1,
        upper=4,
        parameter_type=ParameterType.INT,
    ),
    RangeParameter(
        name="dropout",
        lower=0.0,
        upper=0.5,
        parameter_type=ParameterType.FLOAT,
    ),
    ChoiceParameter(  # NOTE: ``ChoiceParameters`` don't require log-scale
        name="batch_size",
        values=[32, 64, 128, 256],
        parameter_type=ParameterType.INT,
        is_ordered=True,
        sort_values=True,
    ),
]

search_space = SearchSpace(
    parameters=parameters,
    # NOTE: In practice, it may make sense to add a constraint
    # hidden_size_2 <= hidden_size_1
    parameter_constraints=[],
)

设置指标

Ax 具有 指标 的概念，它定义了结果的属性以及如何获取这些结果的观察 。这允许例如编码如何从 某些分布式执行后端获取数据并在 作为输入传递到 Ax 之前对其进行后处理。

在本教程中，我们将使用 多目标优化 ，其目标是最大化验证精度并最小化 模型参数的数量。后者代表模型延迟的简单代理， 对于小型 ML 模型来说很难准确估计 (在实际应用中， 我们会在设备上运行模型时 对延迟进行基准测试)。

在我们的示例中，TorchX 将以完全异步的方式在本地运行训练作业，并根据试验索引将结果写入 log_dir (请参阅 trainer() 函数如上)。我们将定义一个了解该日志目录的度量类。通过子类化 TensorboardCurveMetric ，我们获得了免费读取和解析 TensorBoard 日志的逻辑。 

from ax.metrics.tensorboard import TensorboardCurveMetric


class MyTensorboardMetric(TensorboardCurveMetric):

    # NOTE: We need to tell the new TensorBoard metric how to get the id /
    # file handle for the TensorBoard logs from a trial. In this case
    # our convention is to just save a separate file per trial in
    # the prespecified log dir.
    @classmethod
    def get_ids_from_trials(cls, trials):
        return {
            trial.index: Path(log_dir).joinpath(str(trial.index)).as_posix()
            for trial in trials
        }

    # This indicates whether the metric is queryable while the trial is
    # still running. We don't use this in the current tutorial, but Ax
    # utilizes this to implement trial-level early-stopping functionality.
    @classmethod
    def is_available_while_running(cls):
        return False

现在我们可以实例化准确性和模型参数数量的指标。这里

curve_name

是TensorBoard 日志中的指标名称，而

name

是Ax 内部使用的指标名称。我们还指定

lower_is _better

来指示两个指标的 有利方向。

val_acc = MyTensorboardMetric(
    name="val_acc",
    curve_name="val_acc",
    lower_is_better=False,
)
model_num_params = MyTensorboardMetric(
    name="num_params",
    curve_name="num_params",
    lower_is_better=True,
)

设置

OptimizationConfig

告诉 Ax 应该优化什么的方法是通过 OptimizationConfig 。 这里我们使用 MultiObjectiveOptimizationConfig 因为我们将 执行多目标优化。

此外，Axe 支持通过指定目标阈值来对不同的指标施加约束，这些阈值限制了我们想要探索的结果空间中感兴趣的区域。对于此 示例，我们将验证精度限制为至少 0.94 (94%)，并将模型参数的数量限制为最多 80,000。

from ax.core import MultiObjective, Objective, ObjectiveThreshold
from ax.core.optimization_config import MultiObjectiveOptimizationConfig


opt_config = MultiObjectiveOptimizationConfig(
    objective=MultiObjective(
        objectives=[
            Objective(metric=val_acc, minimize=False),
            Objective(metric=model_num_params, minimize=True),
        ],
    ),
    objective_thresholds=[
        ObjectiveThreshold(metric=val_acc, bound=0.94, relative=False),
        ObjectiveThreshold(metric=model_num_params, bound=80_000, relative=False),
    ],
)

创建 Axe 实验

在 Ax 中， 实验 对象是存储有关问题的所有信息的对象 设置.

from ax.core import Experiment

experiment = Experiment(
    name="torchx_mnist",
    search_space=search_space,
    optimization_config=opt_config,
    runner=ax_runner,
)

选择生成策略

A GenerationStrategy 是我们希望如何执行优化的抽象表示。虽然这可以自定义(如果您’d喜欢这样做，请参阅 本教程 )，\在大多数情况下Axe可以根据搜索空间、优化配置和我们想要运行的试验总数 自动确定适当的策略。

通常，Axe 在启动基于模型的贝叶斯优化策略之前 选择评估一些随机配置。

total_trials = 48  # total evaluation budget

from ax.modelbridge.dispatch_utils import choose_generation_strategy

gs = choose_generation_strategy(
    search_space=experiment.search_space,
    optimization_config=experiment.optimization_config,
    num_trials=total_trials,
  )

[INFO 11-15 00:37:56] ax.modelbridge.dispatch_utils: Using Models.BOTORCH_MODULAR since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 11-15 00:37:56] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=6 num_trials=48 use_batch_trials=False
[INFO 11-15 00:37:56] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=9
[INFO 11-15 00:37:56] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=9
[INFO 11-15 00:37:56] ax.modelbridge.dispatch_utils: `verbose`, `disable_progbar`, and `jit_compile` are not yet supported when using `choose_generation_strategy` with ModularBoTorchModel, dropping these arguments.
[INFO 11-15 00:37:56] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+BoTorch', steps=[Sobol for 9 trials, BoTorch for subsequent trials]). Iterations after 9 will take longer to generate due to model-fitting.

配置调度程序

Scheduler 充当优化的循环控制。 它与后端通信以启动试验，检查其状态， 并检索结果。在本教程中，它只是读取 并解析本地保存的日志。在远程执行设置中， it 将调用 API。以下来自 Ax 调度程序教程 的插图总结了调度程序如何与用于运行试验评估的外部系统进行交互：

https://pytorch.org/tutorials/_static/img/ax_scheduler_illustration.png

Scheduler 需要 实验 和 GenerationStrategy 。 可以通过 SchedulerOptions 传入一组选项。在这里，我们 配置总评估数以及 max_pending_Trials ， 应同时运行的最大试验数。在我们的本地设置中，这是作为单独进程运行的训练作业的数量，而在远程执行设置中，这将是您要并行使用的计算机的数量。

from ax.service.scheduler import Scheduler, SchedulerOptions

scheduler = Scheduler(
    experiment=experiment,
    generation_strategy=gs,
    options=SchedulerOptions(
        total_trials=total_trials, max_pending_trials=4
    ),
)

[INFO 11-15 00:37:56] Scheduler: `Scheduler` requires experiment to have immutable search space and optimization config. Setting property immutable_search_space_and_opt_config to `True` on experiment.

运行优化

现在一切都已配置完毕，我们可以让 Ax 以全自动方式 运行优化。计划程序将定期检查 日志中所有当前正在运行的试验的状态，如果试验完成 ，计划程序将更新其 试验状态并获取贝叶斯 优化算法所需的观察结果。

scheduler.run_all_trials()

[INFO 11-15 00:37:56] Scheduler: Running trials [0]...
[INFO 11-15 00:37:57] Scheduler: Running trials [1]...
[INFO 11-15 00:37:58] Scheduler: Running trials [2]...
[INFO 11-15 00:38:00] Scheduler: Running trials [3]...
[INFO 11-15 00:38:01] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 4).
[INFO 11-15 00:38:02] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 4).
[INFO 11-15 00:38:03] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 4).
[INFO 11-15 00:38:05] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 4).
[INFO 11-15 00:38:09] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 4).
[INFO 11-15 00:38:14] Scheduler: Waiting for completed trials (for 7 sec, currently running trials: 4).
[INFO 11-15 00:38:21] Scheduler: Waiting for completed trials (for 11 sec, currently running trials: 4).
[INFO 11-15 00:38:33] Scheduler: Waiting for completed trials (for 17 sec, currently running trials: 4).
[INFO 11-15 00:38:50] Scheduler: Retrieved COMPLETED trials: [1, 3].
[INFO 11-15 00:38:50] Scheduler: Fetching data for trials: [1, 3].
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 11-15 00:38:50] Scheduler: Running trials [4]...
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 11-15 00:38:51] Scheduler: Running trials [5]...
[INFO 11-15 00:38:52] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 4).
[INFO 11-15 00:38:53] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 4).
[INFO 11-15 00:38:54] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 4).
[INFO 11-15 00:38:57] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 4).
[INFO 11-15 00:39:00] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 4).
[INFO 11-15 00:39:05] Scheduler: Retrieved COMPLETED trials: [2].
[INFO 11-15 00:39:05] Scheduler: Fetching data for trials: [2].
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 11-15 00:39:05] Scheduler: Running trials [6]...
[INFO 11-15 00:39:06] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 4).
[INFO 11-15 00:39:07] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 4).
[INFO 11-15 00:39:09] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 4).
[INFO 11-15 00:39:11] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 4).
[INFO 11-15 00:39:14] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 4).
[INFO 11-15 00:39:19] Scheduler: Retrieved COMPLETED trials: [0].
[INFO 11-15 00:39:19] Scheduler: Fetching data for trials: [0].
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 11-15 00:39:20] Scheduler: Running trials [7]...
[INFO 11-15 00:39:21] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 4).
[INFO 11-15 00:39:22] Scheduler: Retrieved COMPLETED trials: [4].
[INFO 11-15 00:39:22] Scheduler: Fetching data for trials: [4].
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 11-15 00:39:22] Scheduler: Running trials [8]...
[INFO 11-15 00:39:23] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 4).
[INFO 11-15 00:39:24] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 4).
[INFO 11-15 00:39:25] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 4).
[INFO 11-15 00:39:27] Scheduler: Retrieved COMPLETED trials: [5].
[INFO 11-15 00:39:27] Scheduler: Fetching data for trials: [5].
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 11-15 00:39:30] Scheduler: Running trials [9]...
[INFO 11-15 00:39:31] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 4).
[INFO 11-15 00:39:32] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 4).
[INFO 11-15 00:39:33] Scheduler: Retrieved COMPLETED trials: [6].
[INFO 11-15 00:39:33] Scheduler: Fetching data for trials: [6].
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 11-15 00:39:36] Scheduler: Running trials [10]...
[INFO 11-15 00:39:37] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 4).
[INFO 11-15 00:39:38] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 4).
[INFO 11-15 00:39:40] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 4).
[INFO 11-15 00:39:42] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 4).
[INFO 11-15 00:39:46] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 4).
[INFO 11-15 00:39:51] Scheduler: Retrieved COMPLETED trials: [8].
[INFO 11-15 00:39:51] Scheduler: Fetching data for trials: [8].
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 11-15 00:39:52] Scheduler: Running trials [11]...
[INFO 11-15 00:39:53] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 4).
[INFO 11-15 00:39:54] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 4).
[INFO 11-15 00:39:56] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 4).
[INFO 11-15 00:39:58] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 4).
[INFO 11-15 00:40:01] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 4).
[INFO 11-15 00:40:06] Scheduler: Waiting for completed trials (for 7 sec, currently running trials: 4).
[INFO 11-15 00:40:14] Scheduler: Retrieved COMPLETED trials: [7].
[INFO 11-15 00:40:14] Scheduler: Fetching data for trials: [7].
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 11-15 00:40:14] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
[INFO 11-15 00:40:15] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
[INFO 11-15 00:40:17] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
[INFO 11-15 00:40:19] Scheduler: Retrieved COMPLETED trials: [9].
[INFO 11-15 00:40:19] Scheduler: Fetching data for trials: [9].
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 11-15 00:40:22] Scheduler: Running trials [12]...
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 11-15 00:40:23] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
[INFO 11-15 00:40:23] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
[INFO 11-15 00:40:23] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
[INFO 11-15 00:40:24] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
[INFO 11-15 00:40:25] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
[INFO 11-15 00:40:28] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
[INFO 11-15 00:40:31] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 3).
[INFO 11-15 00:40:36] Scheduler: Waiting for completed trials (for 7 sec, currently running trials: 3).
[INFO 11-15 00:40:44] Scheduler: Retrieved COMPLETED trials: 10 - 11.
[INFO 11-15 00:40:44] Scheduler: Fetching data for trials: 10 - 11.
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 11-15 00:40:46] Scheduler: Running trials [13]...
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 11-15 00:40:47] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
[INFO 11-15 00:40:49] Scheduler: Running trials [14]...
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 11-15 00:40:50] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
[INFO 11-15 00:40:50] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
[INFO 11-15 00:40:50] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
[INFO 11-15 00:40:51] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
[INFO 11-15 00:40:52] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
[INFO 11-15 00:40:54] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
[INFO 11-15 00:40:58] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 3).
[INFO 11-15 00:41:03] Scheduler: Waiting for completed trials (for 7 sec, currently running trials: 3).
[INFO 11-15 00:41:10] Scheduler: Retrieved COMPLETED trials: [12].
[INFO 11-15 00:41:10] Scheduler: Fetching data for trials: [12].
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 11-15 00:41:13] Scheduler: Running trials [15]...
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 11-15 00:41:14] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
[INFO 11-15 00:41:14] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
[INFO 11-15 00:41:14] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
[INFO 11-15 00:41:15] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
[INFO 11-15 00:41:16] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
[INFO 11-15 00:41:19] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
[INFO 11-15 00:41:22] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 3).
[INFO 11-15 00:41:27] Scheduler: Waiting for completed trials (for 7 sec, currently running trials: 3).
[INFO 11-15 00:41:35] Scheduler: Waiting for completed trials (for 11 sec, currently running trials: 3).
[INFO 11-15 00:41:46] Scheduler: Retrieved COMPLETED trials: 13 - 14.
[INFO 11-15 00:41:46] Scheduler: Fetching data for trials: 13 - 14.
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 11-15 00:41:49] Scheduler: Running trials [16]...
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 11-15 00:41:50] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
[INFO 11-15 00:41:52] Scheduler: Running trials [17]...
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 11-15 00:41:53] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
[INFO 11-15 00:41:53] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
[INFO 11-15 00:41:53] Scheduler: Retrieved COMPLETED trials: [15].
[INFO 11-15 00:41:53] Scheduler: Fetching data for trials: [15].
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 11-15 00:41:56] Scheduler: Running trials [18]...
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 11-15 00:41:57] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
[INFO 11-15 00:41:57] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
[INFO 11-15 00:41:57] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
[INFO 11-15 00:41:58] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
[INFO 11-15 00:42:00] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
[INFO 11-15 00:42:02] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
[INFO 11-15 00:42:05] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 3).
[INFO 11-15 00:42:11] Scheduler: Waiting for completed trials (for 7 sec, currently running trials: 3).
[INFO 11-15 00:42:18] Scheduler: Waiting for completed trials (for 11 sec, currently running trials: 3).
[INFO 11-15 00:42:30] Scheduler: Retrieved COMPLETED trials: [18].
[INFO 11-15 00:42:30] Scheduler: Fetching data for trials: [18].
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 11-15 00:42:34] Scheduler: Running trials [19]...
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 11-15 00:42:35] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
[INFO 11-15 00:42:35] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
[INFO 11-15 00:42:35] Scheduler: Retrieved COMPLETED trials: [16].
[INFO 11-15 00:42:35] Scheduler: Fetching data for trials: [16].
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 11-15 00:42:39] Scheduler: Running trials [20]...
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 11-15 00:42:40] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
[INFO 11-15 00:42:40] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
[INFO 11-15 00:42:40] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
[INFO 11-15 00:42:41] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
[INFO 11-15 00:42:42] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
[INFO 11-15 00:42:44] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
[INFO 11-15 00:42:48] Scheduler: Retrieved COMPLETED trials: [17].
[INFO 11-15 00:42:48] Scheduler: Fetching data for trials: [17].
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 11-15 00:42:51] Scheduler: Running trials [21]...
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 11-15 00:42:52] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
[INFO 11-15 00:42:52] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
[INFO 11-15 00:42:52] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
[INFO 11-15 00:42:53] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
[INFO 11-15 00:42:55] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
[INFO 11-15 00:42:57] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
[INFO 11-15 00:43:00] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 3).
[INFO 11-15 00:43:05] Scheduler: Waiting for completed trials (for 7 sec, currently running trials: 3).
[INFO 11-15 00:43:13] Scheduler: Waiting for completed trials (for 11 sec, currently running trials: 3).
[INFO 11-15 00:43:24] Scheduler: Retrieved COMPLETED trials: [19].
[INFO 11-15 00:43:24] Scheduler: Fetching data for trials: [19].
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 11-15 00:43:28] Scheduler: Running trials [22]...
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 11-15 00:43:29] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
[INFO 11-15 00:43:29] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
[INFO 11-15 00:43:29] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
[INFO 11-15 00:43:30] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
[INFO 11-15 00:43:32] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
[INFO 11-15 00:43:34] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
[INFO 11-15 00:43:37] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 3).
[INFO 11-15 00:43:43] Scheduler: Waiting for completed trials (for 7 sec, currently running trials: 3).
[INFO 11-15 00:43:50] Scheduler: Retrieved COMPLETED trials: [20].
[INFO 11-15 00:43:50] Scheduler: Fetching data for trials: [20].
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 11-15 00:43:53] Scheduler: Running trials [23]...
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 11-15 00:43:54] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
[INFO 11-15 00:43:54] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
[INFO 11-15 00:43:54] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
[INFO 11-15 00:43:55] Scheduler: Retrieved COMPLETED trials: [21].
[INFO 11-15 00:43:55] Scheduler: Fetching data for trials: [21].
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 11-15 00:44:00] Scheduler: Running trials [24]...
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 11-15 00:44:01] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
[INFO 11-15 00:44:01] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
[INFO 11-15 00:44:01] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
[INFO 11-15 00:44:02] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
[INFO 11-15 00:44:04] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
[INFO 11-15 00:44:06] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
[INFO 11-15 00:44:10] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 3).
[INFO 11-15 00:44:15] Scheduler: Retrieved COMPLETED trials: [22].
[INFO 11-15 00:44:15] Scheduler: Fetching data for trials: [22].
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 11-15 00:44:18] Scheduler: Running trials [25]...
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 11-15 00:44:19] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
[INFO 11-15 00:44:19] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
[INFO 11-15 00:44:19] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
[INFO 11-15 00:44:20] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
[INFO 11-15 00:44:22] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
[INFO 11-15 00:44:24] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
[INFO 11-15 00:44:28] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 3).
[INFO 11-15 00:44:33] Scheduler: Waiting for completed trials (for 7 sec, currently running trials: 3).
[INFO 11-15 00:44:40] Scheduler: Waiting for completed trials (for 11 sec, currently running trials: 3).
[INFO 11-15 00:44:52] Scheduler: Waiting for completed trials (for 17 sec, currently running trials: 3).
[INFO 11-15 00:45:09] Scheduler: Retrieved COMPLETED trials: 23 - 24.
[INFO 11-15 00:45:09] Scheduler: Fetching data for trials: 23 - 24.
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 11-15 00:45:12] Scheduler: Running trials [26]...
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 11-15 00:45:13] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
[INFO 11-15 00:45:17] Scheduler: Running trials [27]...
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 11-15 00:45:18] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
[INFO 11-15 00:45:18] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
[INFO 11-15 00:45:18] Scheduler: Retrieved COMPLETED trials: [25].
[INFO 11-15 00:45:18] Scheduler: Fetching data for trials: [25].
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 11-15 00:45:22] Scheduler: Running trials [28]...
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 11-15 00:45:23] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
[INFO 11-15 00:45:23] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
[INFO 11-15 00:45:23] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
[INFO 11-15 00:45:24] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
[INFO 11-15 00:45:25] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
[INFO 11-15 00:45:27] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
[INFO 11-15 00:45:31] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 3).
[INFO 11-15 00:45:36] Scheduler: Waiting for completed trials (for 7 sec, currently running trials: 3).
[INFO 11-15 00:45:43] Scheduler: Waiting for completed trials (for 11 sec, currently running trials: 3).
[INFO 11-15 00:45:55] Scheduler: Waiting for completed trials (for 17 sec, currently running trials: 3).
[INFO 11-15 00:46:12] Scheduler: Retrieved COMPLETED trials: 26 - 27.
[INFO 11-15 00:46:12] Scheduler: Fetching data for trials: 26 - 27.
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 11-15 00:46:15] Scheduler: Running trials [29]...
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 11-15 00:46:15] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
[INFO 11-15 00:46:18] Scheduler: Running trials [30]...
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 11-15 00:46:19] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
[INFO 11-15 00:46:19] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
[INFO 11-15 00:46:19] Scheduler: Retrieved COMPLETED trials: [28].
[INFO 11-15 00:46:19] Scheduler: Fetching data for trials: [28].
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 11-15 00:46:24] Scheduler: Running trials [31]...
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 11-15 00:46:25] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
[INFO 11-15 00:46:25] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
[INFO 11-15 00:46:25] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
[INFO 11-15 00:46:26] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
[INFO 11-15 00:46:27] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
[INFO 11-15 00:46:30] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
[INFO 11-15 00:46:33] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 3).
[INFO 11-15 00:46:38] Scheduler: Waiting for completed trials (for 7 sec, currently running trials: 3).
[INFO 11-15 00:46:46] Scheduler: Waiting for completed trials (for 11 sec, currently running trials: 3).
[INFO 11-15 00:46:57] Scheduler: Waiting for completed trials (for 17 sec, currently running trials: 3).
[INFO 11-15 00:47:14] Scheduler: Retrieved COMPLETED trials: 29 - 30.
[INFO 11-15 00:47:14] Scheduler: Fetching data for trials: 29 - 30.
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 11-15 00:47:18] Scheduler: Running trials [32]...
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 11-15 00:47:19] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
[INFO 11-15 00:47:21] Scheduler: Running trials [33]...
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 11-15 00:47:22] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
[INFO 11-15 00:47:22] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
[INFO 11-15 00:47:22] Scheduler: Retrieved COMPLETED trials: [31].
[INFO 11-15 00:47:22] Scheduler: Fetching data for trials: [31].
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 11-15 00:47:26] Scheduler: Running trials [34]...
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 11-15 00:47:27] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
[INFO 11-15 00:47:27] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
[INFO 11-15 00:47:27] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
[INFO 11-15 00:47:28] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
[INFO 11-15 00:47:30] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
[INFO 11-15 00:47:32] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
[INFO 11-15 00:47:35] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 3).
[INFO 11-15 00:47:41] Scheduler: Waiting for completed trials (for 7 sec, currently running trials: 3).
[INFO 11-15 00:47:48] Scheduler: Waiting for completed trials (for 11 sec, currently running trials: 3).
[INFO 11-15 00:48:00] Scheduler: Waiting for completed trials (for 17 sec, currently running trials: 3).
[INFO 11-15 00:48:17] Scheduler: Retrieved COMPLETED trials: 32 - 33.
[INFO 11-15 00:48:17] Scheduler: Fetching data for trials: 32 - 33.
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 11-15 00:48:20] Scheduler: Running trials [35]...
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 11-15 00:48:20] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
[INFO 11-15 00:48:24] Scheduler: Running trials [36]...
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 11-15 00:48:25] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
[INFO 11-15 00:48:25] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
[INFO 11-15 00:48:25] Scheduler: Retrieved COMPLETED trials: [34].
[INFO 11-15 00:48:25] Scheduler: Fetching data for trials: [34].
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 11-15 00:48:28] Scheduler: Running trials [37]...
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 11-15 00:48:28] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
[INFO 11-15 00:48:28] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
[INFO 11-15 00:48:28] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
[INFO 11-15 00:48:29] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
[INFO 11-15 00:48:31] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
[INFO 11-15 00:48:33] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
[INFO 11-15 00:48:36] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 3).
[INFO 11-15 00:48:41] Scheduler: Waiting for completed trials (for 7 sec, currently running trials: 3).
[INFO 11-15 00:48:49] Scheduler: Waiting for completed trials (for 11 sec, currently running trials: 3).
[INFO 11-15 00:49:00] Scheduler: Waiting for completed trials (for 17 sec, currently running trials: 3).
[INFO 11-15 00:49:18] Scheduler: Retrieved COMPLETED trials: 35 - 37.
[INFO 11-15 00:49:18] Scheduler: Fetching data for trials: 35 - 37.
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 11-15 00:49:23] Scheduler: Running trials [38]...
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 11-15 00:49:24] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
[INFO 11-15 00:49:27] Scheduler: Running trials [39]...
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 11-15 00:49:28] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
[INFO 11-15 00:49:31] Scheduler: Running trials [40]...
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 11-15 00:49:32] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
[INFO 11-15 00:49:32] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
[INFO 11-15 00:49:32] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
[INFO 11-15 00:49:33] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
[INFO 11-15 00:49:35] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
[INFO 11-15 00:49:37] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
[INFO 11-15 00:49:41] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 3).
[INFO 11-15 00:49:46] Scheduler: Waiting for completed trials (for 7 sec, currently running trials: 3).
[INFO 11-15 00:49:53] Scheduler: Waiting for completed trials (for 11 sec, currently running trials: 3).
[INFO 11-15 00:50:05] Scheduler: Waiting for completed trials (for 17 sec, currently running trials: 3).
[INFO 11-15 00:50:22] Scheduler: Retrieved COMPLETED trials: 38 - 39.
[INFO 11-15 00:50:22] Scheduler: Fetching data for trials: 38 - 39.
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 11-15 00:50:28] Scheduler: Running trials [41]...
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 11-15 00:50:29] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
[INFO 11-15 00:50:32] Scheduler: Running trials [42]...
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 11-15 00:50:33] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
[INFO 11-15 00:50:33] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
[INFO 11-15 00:50:34] Scheduler: Retrieved COMPLETED trials: [40].
[INFO 11-15 00:50:34] Scheduler: Fetching data for trials: [40].
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 11-15 00:50:39] Scheduler: Running trials [43]...
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 11-15 00:50:40] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
[INFO 11-15 00:50:40] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
[INFO 11-15 00:50:40] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
[INFO 11-15 00:50:41] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
[INFO 11-15 00:50:42] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
[INFO 11-15 00:50:44] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
[INFO 11-15 00:50:48] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 3).
[INFO 11-15 00:50:53] Scheduler: Waiting for completed trials (for 7 sec, currently running trials: 3).
[INFO 11-15 00:51:00] Scheduler: Waiting for completed trials (for 11 sec, currently running trials: 3).
[INFO 11-15 00:51:12] Scheduler: Waiting for completed trials (for 17 sec, currently running trials: 3).
[INFO 11-15 00:51:29] Scheduler: Retrieved COMPLETED trials: 41 - 42.
[INFO 11-15 00:51:29] Scheduler: Fetching data for trials: 41 - 42.
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 11-15 00:51:34] Scheduler: Running trials [44]...
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 11-15 00:51:34] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
[INFO 11-15 00:51:39] Scheduler: Running trials [45]...
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 11-15 00:51:40] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
[INFO 11-15 00:51:40] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
[INFO 11-15 00:51:40] Scheduler: Retrieved COMPLETED trials: [43].
[INFO 11-15 00:51:40] Scheduler: Fetching data for trials: [43].
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 11-15 00:51:45] Scheduler: Running trials [46]...
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 11-15 00:51:46] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
[INFO 11-15 00:51:46] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
[INFO 11-15 00:51:46] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
[INFO 11-15 00:51:47] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
[INFO 11-15 00:51:48] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
[INFO 11-15 00:51:51] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
[INFO 11-15 00:51:54] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 3).
[INFO 11-15 00:51:59] Scheduler: Waiting for completed trials (for 7 sec, currently running trials: 3).
[INFO 11-15 00:52:07] Scheduler: Waiting for completed trials (for 11 sec, currently running trials: 3).
[INFO 11-15 00:52:18] Scheduler: Waiting for completed trials (for 17 sec, currently running trials: 3).
[INFO 11-15 00:52:35] Scheduler: Retrieved COMPLETED trials: 44 - 45.
[INFO 11-15 00:52:35] Scheduler: Fetching data for trials: 44 - 45.
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 11-15 00:52:41] Scheduler: Running trials [47]...
[INFO 11-15 00:52:42] Scheduler: Retrieved COMPLETED trials: [46].
[INFO 11-15 00:52:42] Scheduler: Fetching data for trials: [46].
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 11-15 00:52:42] Scheduler: Done submitting trials, waiting for remaining 1 running trials...
[INFO 11-15 00:52:42] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 1).
[INFO 11-15 00:52:43] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 1).
[INFO 11-15 00:52:44] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 1).
[INFO 11-15 00:52:46] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 1).
[INFO 11-15 00:52:50] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 1).
[INFO 11-15 00:52:55] Scheduler: Waiting for completed trials (for 7 sec, currently running trials: 1).
[INFO 11-15 00:53:02] Scheduler: Waiting for completed trials (for 11 sec, currently running trials: 1).
[INFO 11-15 00:53:14] Scheduler: Waiting for completed trials (for 17 sec, currently running trials: 1).
[INFO 11-15 00:53:31] Scheduler: Retrieved COMPLETED trials: [47].
[INFO 11-15 00:53:31] Scheduler: Fetching data for trials: [47].
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.


OptimizationResult()

评估结果

我们现在可以使用 Axe 中包含的辅助函数和可视化来检查优化结果。

首先，我们生成一个数据框，其中包含实验结果的摘要。此数据框中的每一行对应于一个试验(即运行的训练作业)，并包含试验状态、评估的参数配置以及观察到的指标值的信息。这提供 简单的方法来检查优化的健全性。

from ax.service.utils.report_utils import exp_to_df

df = exp_to_df(experiment)
df.head(10)

/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

.dataframe tbody tr th:only-of-type { 垂直对齐: 中间; }

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| | 试用_index | 手臂_name | 试用_status | 生成_method | num_params | val_acc | 是可行的 | 隐藏_size_1 | 隐藏_size_2 | 学习率 | 纪元 | 辍学 | 批次_size | | ---| ---| ---| ---| ---| ---| ---| ---| ---| ---| ---| ---| ---| --- | | 0 | 0 | 0_0 | 已完成 | 索博尔 | 16810.0 | 0.911510 | 错误 | 19 | 19 66 | 66 0.003182 | 4 | 0.190970 | 32 | | 1 | 1 | 1_0 | 已完成 | 索博尔 | 21926.0 | 0.877746 | 错误 | 23 | 23 118 | 118 0.000145 | 3 | 0.465754 | 256 | | 2 | 2 | 2_0 | 已完成 | 索博尔 | 37560.0 | 0.944843 | 正确 | 40 | 40 124 | 124 0.002745 | 4 | 0.196600 | 64 | | 3 | 3 | 3_0 | 已完成 | 索博尔 | 14756.0 | 0.887423 | 错误 | 18 | 18 23 | 23 0.000166 | 4 | 0.169496 | 256 | | 4 | 4 | 4_0 | 已完成 | 索博尔 | 71630.0 | 0.950876 | 正确 | 80 | 99 | 99 0.000642 | 2 | 0.291277 | 128 | | 5 | 5 | 5_0 | 已完成 | 索博尔 | 13948.0 | 0.920357 | 错误 | 16 | 16 54 | 54 0.000444 | 2 | 0.057552 | 64 | | 6 | 6 | 6_0 | 已完成 | 索博尔 | 24686.0 | 0.850797 | 错误 | 29 | 29 50 | 50 0.000177 | 2 | 0.435030 | 256 | | 7 | 7 | 7_0 | 已完成 | 索博尔 | 18290.0 | 0.870250 | 错误 | 20 | 87 | 87 0.000119 | 4 | 0.462744 | 256 | | 8 | 8 | 8_0 | 已完成 | 索博尔 | 20996.0 | 0.833255 | 错误 | 26 | 26 17 | 17 0.005245 | 1 | 0.455813 | 32 | | 9 | 9 | 9_0 | 已完成 | BoTorch | 45860.0 | 0.953712 | 正确 | 50 | 50 111 | 111 0.001712 | 3 | 0.162625 | 64 | 64

我们还可以可视化验证精度和模型参数数量之间 权衡的帕累托前沿。

提示

Axe 使用 Plotly 生成交互式绘图， 您可以执行缩放、裁剪或悬停等操作， 以查看绘图组件的详细信息。 尝试一下，并查看 可视化教程 (如果您’d 想要了解更多信息)。

最终的优化结果如下图所示，其中 颜色对应于每次试验的迭代次数。 我们看到我们的方法能够成功地探索 权衡，并发现两个大型模型都具有较高的性能验证 准确度以及验证准确度相对较低 的小模型。

from ax.service.utils.report_utils import _pareto_frontier_scatter_2d_plotly

_pareto_frontier_scatter_2d_plotly(experiment)

/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

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为了更好地了解我们的代理模型对黑盒目标的了解，我们可以查看留一交叉验证结果。由于我们的模型是高斯过程， 它们不仅提供点预测，而且还 提供关于这些预测的不确定性估计。一个好的模型意味着预测均值 (图中的点)接近 45 度线，并且置信区间以预期频率覆盖 45 度线 (这里我们使用 95% 置信区间，因此我们希望它们在 95% 的情况下 包含真实的观察结果。

如下图所示，模型大小 ( num_params ) 指标比验证准确性 ( val_acc ) 指标 更容易建模。

from ax.modelbridge.cross_validation import compute_diagnostics, cross_validate
from ax.plot.diagnostic import interact_cross_validation_plotly
from ax.utils.notebook.plotting import init_notebook_plotting, render

cv = cross_validate(model=gs.model)  # The surrogate model is stored on the ``GenerationStrategy``
compute_diagnostics(cv)

interact_cross_validation_plotly(cv)

if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: "STIX-Web"}});}

\ n window.PlotlyConfig = {MathJaxConfig: 'local'};

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0_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 16810.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 17849.998 [11413.179, 24286.818]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 19\u003cbr\u003ehidden_size_2: 66\u003cbr\u003elearning_rate: 0.0031816019941386738\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.1909700185060501\u003cbr\u003ebatch_size: 32","\u003cb\u003eArm 1_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 21926.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 22495.944 [17573.088, 27418.801]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 118\u003cbr\u003elearning_rate: 0.00014466425599951336\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.46575378254055977\u003cbr\u003ebatch_size: 256","\u003cb\u003eArm 2_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 37560.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 37503.644 [34719.773, 40287.515]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 40\u003cbr\u003ehidden_size_2: 124\u003cbr\u003elearning_rate: 0.002744711802661285\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.19660000363364816\u003cbr\u003ebatch_size: 64","\u003cb\u003eArm 3_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 14756.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 16409.936 [6478.429, 26341.443]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 18\u003cbr\u003ehidden_size_2: 23\u003cbr\u003elearning_rate: 0.00016592709310927874\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.16949627175927162\u003cbr\u003ebatch_size: 256","\u003cb\u003eArm 4_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 71630.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 77019.996 [69817.952, 84222.041]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 80\u003cbr\u003ehidden_size_2: 99\u003cbr\u003elearning_rate: 0.0006421366253755414\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.2912765285000205\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 5_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 13948.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 15485.837 [6913.757, 24057.917]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 54\u003cbr\u003elearning_rate: 0.00044353892687835314\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.05755229713395238\u003cbr\u003ebatch_size: 64","\u003cb\u003eArm 6_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 24686.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 23875.229 [15704.277, 32046.180]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 29\u003cbr\u003ehidden_size_2: 50\u003cbr\u003elearning_rate: 0.00017699389919183936\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.4350298522040248\u003cbr\u003ebatch_size: 256","\u003cb\u003eArm 7_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 18290.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 18738.366 [13180.018, 24296.715]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 20\u003cbr\u003ehidden_size_2: 87\u003cbr\u003elearning_rate: 0.00011908174276707618\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.46274396777153015\u003cbr\u003ebatch_size: 256","\u003cb\u003eArm 8_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 20996.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 25563.187 [8605.792, 42520.582]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 26\u003cbr\u003ehidden_size_2: 17\u003cbr\u003elearning_rate: 0.005244787716785168\u003cbr\u003eepochs: 1\u003cbr\u003edropout: 0.45581294130533934\u003cbr\u003ebatch_size: 32","\u003cb\u003eArm 9_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 45860.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 46016.867 [44046.927, 47986.808]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 50\u003cbr\u003ehidden_size_2: 111\u003cbr\u003elearning_rate: 0.0017119872193851328\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.1626253363035843\u003cbr\u003ebatch_size: 64","\u003cb\u003eArm 10_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 64208.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 64670.708 [61629.768, 67711.648]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 69\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0019763578166809106\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.259316335098853\u003cbr\u003ebatch_size: 64","\u003cb\u003eArm 11_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 21596.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 21493.324 [18565.869, 24420.779]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 108\u003cbr\u003elearning_rate: 0.0014176948306373726\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.13191533085443444\u003cbr\u003ebatch_size: 64","\u003cb\u003eArm 12_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 31804.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 31991.623 [30248.510, 33734.735]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 34\u003cbr\u003ehidden_size_2: 117\u003cbr\u003elearning_rate: 0.0017682849341538593\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.21527757384029156\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 13_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 31333.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 31391.474 [28918.656, 33864.292]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 33\u003cbr\u003ehidden_size_2: 127\u003cbr\u003elearning_rate: 0.0008034895309647404\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.2522180161102007\u003cbr\u003ebatch_size: 64","\u003cb\u003eArm 14_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 118016.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 87633.905 [75087.221, 100180.589]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 128\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0012495345329416427\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.21701457003271024\u003cbr\u003ebatch_size: 64","\u003cb\u003eArm 15_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 31376.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 31370.151 [27533.708, 35206.593]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 33\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0025339615460667807\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.2558536566731061\u003cbr\u003ebatch_size: 64","\u003cb\u003eArm 16_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 45968.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 45659.231 [43904.470, 47413.992]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 49\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0010121670998437997\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.22460406083035264\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 17_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 37244.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 37214.606 [36005.681, 38423.532]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 41\u003cbr\u003ehidden_size_2: 100\u003cbr\u003elearning_rate: 0.0006241196076673286\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.18155149028594822\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 18_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 29552.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 30250.173 [25486.469, 35013.877]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 31\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0004596529946405398\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.12320000102423312\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 19_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 54176.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 54047.497 [52264.414, 55830.580]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 58\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0009143431623853883\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.22543377039810672\u003cbr\u003ebatch_size: 64","\u003cb\u003eArm 20_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 23912.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 24336.642 [21087.926, 27585.358]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 26\u003cbr\u003ehidden_size_2: 98\u003cbr\u003elearning_rate: 0.00031507737987175274\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.141046539994748\u003cbr\u003ebatch_size: 64","\u003cb\u003eArm 21_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 46968.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 46650.762 [45284.530, 48016.995]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 52\u003cbr\u003ehidden_size_2: 100\u003cbr\u003elearning_rate: 0.0008538624689831454\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.22353669250564473\u003cbr\u003ebatch_size: 64","\u003cb\u003eArm 22_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 24461.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 23868.121 [21716.856, 26019.386]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 27\u003cbr\u003ehidden_size_2: 89\u003cbr\u003elearning_rate: 0.0008098473118942149\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.18173527455954647\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 23_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 58094.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 58494.756 [57140.514, 59848.999]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 64\u003cbr\u003ehidden_size_2: 107\u003cbr\u003elearning_rate: 0.0008822667864361012\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.22249019748543886\u003cbr\u003ebatch_size: 64","\u003cb\u003eArm 24_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 21860.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 21479.059 [18923.241, 24034.878]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 116\u003cbr\u003elearning_rate: 0.0007132888431535224\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.17699132603343667\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 25_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 27920.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 27943.924 [26618.848, 29269.001]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 30\u003cbr\u003ehidden_size_2: 110\u003cbr\u003elearning_rate: 0.0009959498157920334\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.06883225640513538\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 26_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 54460.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 54179.409 [53262.960, 55095.857]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 60\u003cbr\u003ehidden_size_2: 106\u003cbr\u003elearning_rate: 0.0009751366637403521\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.19573037953398628\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 27_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 43838.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 43949.683 [43321.122, 44578.243]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 48\u003cbr\u003ehidden_size_2: 107\u003cbr\u003elearning_rate: 0.0007539691702221078\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.1680041669682534\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 28_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 43432.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 43502.681 [42719.114, 44286.248]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 48\u003cbr\u003ehidden_size_2: 100\u003cbr\u003elearning_rate: 0.0009374847373449053\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.06051509457999761\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 29_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 20540.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 20553.225 [19670.946, 21435.505]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 76\u003cbr\u003elearning_rate: 0.0013265829625614177\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 30_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 32042.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 32033.312 [30707.259, 33359.366]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 36\u003cbr\u003ehidden_size_2: 83\u003cbr\u003elearning_rate: 0.0023959168306528413\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0159492549978008\u003cbr\u003ebatch_size: 256","\u003cb\u003eArm 31_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 58316.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 58637.763 [57553.744, 59721.783]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 64\u003cbr\u003ehidden_size_2: 110\u003cbr\u003elearning_rate: 0.001256911127763119\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.13512921091553712\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 32_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 32533.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 32507.91 [31710.149, 33305.672]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 37\u003cbr\u003ehidden_size_2: 75\u003cbr\u003elearning_rate: 0.0018033707293106925\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 33_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 15083.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 15051.791 [13289.929, 16813.652]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 17\u003cbr\u003ehidden_size_2: 65\u003cbr\u003elearning_rate: 0.002225614279072248\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 34_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 35110.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 35059.148 [34268.582, 35849.714]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 40\u003cbr\u003ehidden_size_2: 75\u003cbr\u003elearning_rate: 0.0012023350782572205\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 35_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 18080.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 17842.247 [16115.125, 19569.370]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 20\u003cbr\u003ehidden_size_2: 80\u003cbr\u003elearning_rate: 0.001377011144263105\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0\u003cbr\u003ebatch_size: 256","\u003cb\u003eArm 36_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 29699.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 29816.128 [28281.898, 31350.358]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 33\u003cbr\u003ehidden_size_2: 89\u003cbr\u003elearning_rate: 0.0009741195869283281\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0\u003cbr\u003ebatch_size: 256","\u003cb\u003eArm 37_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 14858.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 15727.098 [12842.745, 18611.451]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 89\u003cbr\u003elearning_rate: 0.001715170409297942\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0\u003cbr\u003ebatch_size: 256","\u003cb\u003eArm 38_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 14130.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 13932.24 [11933.579, 15930.901]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 61\u003cbr\u003elearning_rate: 0.0011865583682861731\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 39_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 64208.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 64207.985 [62658.235, 65757.735]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 69\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0009800254663132498\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.1734206948580121\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 40_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 50528.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 50627.144 [48711.963, 52542.326]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 54\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0007136807990233074\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.06398222330299602\u003cbr\u003ebatch_size: 64","\u003cb\u003eArm 41_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 37610.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 37631.175 [35793.212, 39469.139]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 40\u003cbr\u003ehidden_size_2: 125\u003cbr\u003elearning_rate: 0.000853052339102696\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 42_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 23048.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 23068.091 [22244.420, 23891.761]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 26\u003cbr\u003ehidden_size_2: 74\u003cbr\u003elearning_rate: 0.0018191156152693412\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 43_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 51440.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 51195.387 [50196.330, 52194.443]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 55\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0008853072275992673\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.08456151600050249\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 44_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 24194.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 24292.77 [22327.893, 26257.647]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 28\u003cbr\u003ehidden_size_2: 59\u003cbr\u003elearning_rate: 0.0031385809266299093\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0\u003cbr\u003ebatch_size: 256","\u003cb\u003eArm 45_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 30320.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 30329.907 [27484.551, 33175.263]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 35\u003cbr\u003ehidden_size_2: 64\u003cbr\u003elearning_rate: 0.006875192082605954\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.09899770831383276\u003cbr\u003ebatch_size: 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0_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.912 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.923 [0.901, 0.945]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 19\u003cbr\u003ehidden_size_2: 66\u003cbr\u003elearning_rate: 0.0031816019941386738\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.1909700185060501\u003cbr\u003ebatch_size: 32","\u003cb\u003eArm 1_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.878 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.909 [0.895, 0.922]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 118\u003cbr\u003elearning_rate: 0.00014466425599951336\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.46575378254055977\u003cbr\u003ebatch_size: 256","\u003cb\u003eArm 2_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.945 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.948 [0.938, 0.958]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 40\u003cbr\u003ehidden_size_2: 124\u003cbr\u003elearning_rate: 0.002744711802661285\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.19660000363364816\u003cbr\u003ebatch_size: 64","\u003cb\u003eArm 3_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.887 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.92 [0.894, 0.946]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 18\u003cbr\u003ehidden_size_2: 23\u003cbr\u003elearning_rate: 0.00016592709310927874\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.16949627175927162\u003cbr\u003ebatch_size: 256","\u003cb\u003eArm 4_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.951 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.952 [0.936, 0.968]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 80\u003cbr\u003ehidden_size_2: 99\u003cbr\u003elearning_rate: 0.0006421366253755414\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.2912765285000205\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 5_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.92 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.929 [0.909, 0.949]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 54\u003cbr\u003elearning_rate: 0.00044353892687835314\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.05755229713395238\u003cbr\u003ebatch_size: 64","\u003cb\u003eArm 6_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.851 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.909 [0.888, 0.929]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 29\u003cbr\u003ehidden_size_2: 50\u003cbr\u003elearning_rate: 0.00017699389919183936\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.4350298522040248\u003cbr\u003ebatch_size: 256","\u003cb\u003eArm 7_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.87 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.905 [0.890, 0.919]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 20\u003cbr\u003ehidden_size_2: 87\u003cbr\u003elearning_rate: 0.00011908174276707618\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.46274396777153015\u003cbr\u003ebatch_size: 256","\u003cb\u003eArm 8_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.833 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.916 [0.885, 0.947]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 26\u003cbr\u003ehidden_size_2: 17\u003cbr\u003elearning_rate: 0.005244787716785168\u003cbr\u003eepochs: 1\u003cbr\u003edropout: 0.45581294130533934\u003cbr\u003ebatch_size: 32","\u003cb\u003eArm 9_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.954 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.954 [0.946, 0.961]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 50\u003cbr\u003ehidden_size_2: 111\u003cbr\u003elearning_rate: 0.0017119872193851328\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.1626253363035843\u003cbr\u003ebatch_size: 64","\u003cb\u003eArm 10_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.964 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.959 [0.950, 0.968]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 69\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0019763578166809106\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.259316335098853\u003cbr\u003ebatch_size: 64","\u003cb\u003eArm 11_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.938 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.938 [0.928, 0.948]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 108\u003cbr\u003elearning_rate: 0.0014176948306373726\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.13191533085443444\u003cbr\u003ebatch_size: 64","\u003cb\u003eArm 12_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.95 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.946 [0.938, 0.954]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 34\u003cbr\u003ehidden_size_2: 117\u003cbr\u003elearning_rate: 0.0017682849341538593\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.21527757384029156\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 13_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.949 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.944 [0.935, 0.953]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 33\u003cbr\u003ehidden_size_2: 127\u003cbr\u003elearning_rate: 0.0008034895309647404\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.2522180161102007\u003cbr\u003ebatch_size: 64","\u003cb\u003eArm 14_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.972 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.961 [0.945, 0.977]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 128\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0012495345329416427\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.21701457003271024\u003cbr\u003ebatch_size: 64","\u003cb\u003eArm 15_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.93 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.938 [0.924, 0.951]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 33\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0025339615460667807\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.2558536566731061\u003cbr\u003ebatch_size: 64","\u003cb\u003eArm 16_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.957 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.96 [0.954, 0.966]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 49\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0010121670998437997\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.22460406083035264\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 17_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.958 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.955 [0.951, 0.960]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 41\u003cbr\u003ehidden_size_2: 100\u003cbr\u003elearning_rate: 0.0006241196076673286\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.18155149028594822\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 18_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.941 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.94 [0.925, 0.954]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 31\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0004596529946405398\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.12320000102423312\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 19_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.963 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.963 [0.956, 0.969]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 58\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0009143431623853883\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.22543377039810672\u003cbr\u003ebatch_size: 64","\u003cb\u003eArm 20_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.944 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.942 [0.927, 0.956]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 26\u003cbr\u003ehidden_size_2: 98\u003cbr\u003elearning_rate: 0.00031507737987175274\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.141046539994748\u003cbr\u003ebatch_size: 64","\u003cb\u003eArm 21_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.961 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.96 [0.955, 0.964]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 52\u003cbr\u003ehidden_size_2: 100\u003cbr\u003elearning_rate: 0.0008538624689831454\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.22353669250564473\u003cbr\u003ebatch_size: 64","\u003cb\u003eArm 22_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.943 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.944 [0.936, 0.951]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 27\u003cbr\u003ehidden_size_2: 89\u003cbr\u003elearning_rate: 0.0008098473118942149\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.18173527455954647\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 23_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.964 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.966 [0.962, 0.970]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 64\u003cbr\u003ehidden_size_2: 107\u003cbr\u003elearning_rate: 0.0008822667864361012\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.22249019748543886\u003cbr\u003ebatch_size: 64","\u003cb\u003eArm 24_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.941 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.947 [0.938, 0.955]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 116\u003cbr\u003elearning_rate: 0.0007132888431535224\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.17699132603343667\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 25_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.959 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.959 [0.954, 0.964]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 30\u003cbr\u003ehidden_size_2: 110\u003cbr\u003elearning_rate: 0.0009959498157920334\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.06883225640513538\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 26_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.968 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.964 [0.961, 0.968]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 60\u003cbr\u003ehidden_size_2: 106\u003cbr\u003elearning_rate: 0.0009751366637403521\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.19573037953398628\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 27_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.961 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.964 [0.961, 0.967]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 48\u003cbr\u003ehidden_size_2: 107\u003cbr\u003elearning_rate: 0.0007539691702221078\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.1680041669682534\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 28_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.966 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.965 [0.962, 0.969]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 48\u003cbr\u003ehidden_size_2: 100\u003cbr\u003elearning_rate: 0.0009374847373449053\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.06051509457999761\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 29_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.962 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.957 [0.953, 0.960]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 76\u003cbr\u003elearning_rate: 0.0013265829625614177\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 30_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.963 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.959 [0.952, 0.967]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 36\u003cbr\u003ehidden_size_2: 83\u003cbr\u003elearning_rate: 0.0023959168306528413\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0159492549978008\u003cbr\u003ebatch_size: 256","\u003cb\u003eArm 31_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.967 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.97 [0.966, 0.974]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 64\u003cbr\u003ehidden_size_2: 110\u003cbr\u003elearning_rate: 0.001256911127763119\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.13512921091553712\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 32_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.957 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.959 [0.954, 0.963]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 37\u003cbr\u003ehidden_size_2: 75\u003cbr\u003elearning_rate: 0.0018033707293106925\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 33_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.955 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.952 [0.945, 0.959]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 17\u003cbr\u003ehidden_size_2: 65\u003cbr\u003elearning_rate: 0.002225614279072248\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 34_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.959 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.96 [0.956, 0.965]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 40\u003cbr\u003ehidden_size_2: 75\u003cbr\u003elearning_rate: 0.0012023350782572205\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 35_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.954 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.95 [0.946, 0.955]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 20\u003cbr\u003ehidden_size_2: 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我们还可以绘制等值线图，以更好地理解不同的目标如何依赖于两个输入参数。在下图中， 我们将模型预测的验证准确度显示为 两个隐藏大小的函数。随着隐藏大小的增加，验证准确度明显提高。

from ax.plot.contour import interact_contour_plotly

interact_contour_plotly(model=gs.model, metric_name="val_acc")

if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: "STIX-Web"}});}

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0_0\u003cbr\u003eval_acc: 0.91151 (SEM: None)\u003cbr\u003ehidden_size_1: 19\u003cbr\u003ehidden_size_2: 66\u003cbr\u003elearning_rate: 0.0031816\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.19097\u003cbr\u003ebatch_size: 32","Arm 10_0\u003cbr\u003eval_acc: 0.96377 (SEM: None)\u003cbr\u003ehidden_size_1: 69\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00197636\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.259316\u003cbr\u003ebatch_size: 64","Arm 11_0\u003cbr\u003eval_acc: 0.937728 (SEM: None)\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 108\u003cbr\u003elearning_rate: 0.00141769\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.131915\u003cbr\u003ebatch_size: 64","Arm 12_0\u003cbr\u003eval_acc: 0.950317 (SEM: None)\u003cbr\u003ehidden_size_1: 34\u003cbr\u003ehidden_size_2: 117\u003cbr\u003elearning_rate: 0.00176828\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.215278\u003cbr\u003ebatch_size: 128","Arm 13_0\u003cbr\u003eval_acc: 0.949074 (SEM: None)\u003cbr\u003ehidden_size_1: 33\u003cbr\u003ehidden_size_2: 127\u003cbr\u003elearning_rate: 0.00080349\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.252218\u003cbr\u003ebatch_size: 64","Arm 14_0\u003cbr\u003eval_acc: 0.97215 (SEM: None)\u003cbr\u003ehidden_size_1: 128\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00124953\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.217015\u003cbr\u003ebatch_size: 64","Arm 15_0\u003cbr\u003eval_acc: 0.9298 (SEM: None)\u003cbr\u003ehidden_size_1: 33\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00253396\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.255854\u003cbr\u003ebatch_size: 64","Arm 16_0\u003cbr\u003eval_acc: 0.957152 (SEM: None)\u003cbr\u003ehidden_size_1: 49\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00101217\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.224604\u003cbr\u003ebatch_size: 128","Arm 17_0\u003cbr\u003eval_acc: 0.958022 (SEM: None)\u003cbr\u003ehidden_size_1: 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类似地，我们在下图中将模型参数的数量显示为隐藏大小的函数，并且看到它也随着隐藏大小的函数而增加(依赖于 hidden_size_1 要大得多)。

interact_contour_plotly(model=gs.model, metric_name="num_params")

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0_0\u003cbr\u003enum_params: 16810 (SEM: None)\u003cbr\u003ehidden_size_1: 19\u003cbr\u003ehidden_size_2: 66\u003cbr\u003elearning_rate: 0.0031816\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.19097\u003cbr\u003ebatch_size: 32","Arm 10_0\u003cbr\u003enum_params: 64208 (SEM: None)\u003cbr\u003ehidden_size_1: 69\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00197636\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.259316\u003cbr\u003ebatch_size: 64","Arm 11_0\u003cbr\u003enum_params: 21596 (SEM: None)\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 108\u003cbr\u003elearning_rate: 0.00141769\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.131915\u003cbr\u003ebatch_size: 64","Arm 12_0\u003cbr\u003enum_params: 31804 (SEM: None)\u003cbr\u003ehidden_size_1: 34\u003cbr\u003ehidden_size_2: 117\u003cbr\u003elearning_rate: 0.00176828\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.215278\u003cbr\u003ebatch_size: 128","Arm 13_0\u003cbr\u003enum_params: 31333 (SEM: None)\u003cbr\u003ehidden_size_1: 33\u003cbr\u003ehidden_size_2: 127\u003cbr\u003elearning_rate: 0.00080349\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.252218\u003cbr\u003ebatch_size: 64","Arm 14_0\u003cbr\u003enum_params: 118016 (SEM: None)\u003cbr\u003ehidden_size_1: 128\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00124953\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.217015\u003cbr\u003ebatch_size: 64","Arm 15_0\u003cbr\u003enum_params: 31376 (SEM: None)\u003cbr\u003ehidden_size_1: 33\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00253396\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.255854\u003cbr\u003ebatch_size: 64","Arm 16_0\u003cbr\u003enum_params: 45968 (SEM: None)\u003cbr\u003ehidden_size_1: 49\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00101217\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.224604\u003cbr\u003ebatch_size: 128","Arm 17_0\u003cbr\u003enum_params: 37244 (SEM: None)\u003cbr\u003ehidden_size_1: 41\u003cbr\u003ehidden_size_2: 100\u003cbr\u003elearning_rate: 0.00062412\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.181551\u003cbr\u003ebatch_size: 128","Arm 18_0\u003cbr\u003enum_params: 29552 (SEM: None)\u003cbr\u003ehidden_size_1: 31\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.000459653\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.1232\u003cbr\u003ebatch_size: 128","Arm 19_0\u003cbr\u003enum_params: 54176 (SEM: None)\u003cbr\u003ehidden_size_1: 58\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.000914343\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.225434\u003cbr\u003ebatch_size: 64","Arm 1_0\u003cbr\u003enum_params: 21926 (SEM: None)\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 118\u003cbr\u003elearning_rate: 0.000144664\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.465754\u003cbr\u003ebatch_size: 256","Arm 20_0\u003cbr\u003enum_params: 23912 (SEM: None)\u003cbr\u003ehidden_size_1: 26\u003cbr\u003ehidden_size_2: 98\u003cbr\u003elearning_rate: 0.000315077\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.141047\u003cbr\u003ebatch_size: 64","Arm 21_0\u003cbr\u003enum_params: 46968 (SEM: None)\u003cbr\u003ehidden_size_1: 52\u003cbr\u003ehidden_size_2: 100\u003cbr\u003elearning_rate: 0.000853862\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.223537\u003cbr\u003ebatch_size: 64","Arm 22_0\u003cbr\u003enum_params: 24461 (SEM: None)\u003cbr\u003ehidden_size_1: 27\u003cbr\u003ehidden_size_2: 89\u003cbr\u003elearning_rate: 0.000809847\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.181735\u003cbr\u003ebatch_size: 128","Arm 23_0\u003cbr\u003enum_params: 58094 (SEM: None)\u003cbr\u003ehidden_size_1: 64\u003cbr\u003ehidden_size_2: 107\u003cbr\u003elearning_rate: 0.000882267\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.22249\u003cbr\u003ebatch_size: 64","Arm 24_0\u003cbr\u003enum_params: 21860 (SEM: None)\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 116\u003cbr\u003elearning_rate: 0.000713289\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.176991\u003cbr\u003ebatch_size: 128","Arm 25_0\u003cbr\u003enum_params: 27920 (SEM: None)\u003cbr\u003ehidden_size_1: 30\u003cbr\u003ehidden_size_2: 110\u003cbr\u003elearning_rate: 0.00099595\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0688323\u003cbr\u003ebatch_size: 128","Arm 26_0\u003cbr\u003enum_params: 54460 (SEM: None)\u003cbr\u003ehidden_size_1: 60\u003cbr\u003ehidden_size_2: 106\u003cbr\u003elearning_rate: 0.000975137\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.19573\u003cbr\u003ebatch_size: 128","Arm 27_0\u003cbr\u003enum_params: 43838 (SEM: None)\u003cbr\u003ehidden_size_1: 48\u003cbr\u003ehidden_size_2: 107\u003cbr\u003elearning_rate: 0.000753969\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.168004\u003cbr\u003ebatch_size: 128","Arm 28_0\u003cbr\u003enum_params: 43432 (SEM: None)\u003cbr\u003ehidden_size_1: 48\u003cbr\u003ehidden_size_2: 100\u003cbr\u003elearning_rate: 0.000937485\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0605151\u003cbr\u003ebatch_size: 128","Arm 29_0\u003cbr\u003enum_params: 20540 (SEM: None)\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 76\u003cbr\u003elearning_rate: 0.00132658\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 128","Arm 2_0\u003cbr\u003enum_params: 37560 (SEM: None)\u003cbr\u003ehidden_size_1: 40\u003cbr\u003ehidden_size_2: 124\u003cbr\u003elearning_rate: 0.00274471\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.1966\u003cbr\u003ebatch_size: 64","Arm 30_0\u003cbr\u003enum_params: 32042 (SEM: None)\u003cbr\u003ehidden_size_1: 36\u003cbr\u003ehidden_size_2: 83\u003cbr\u003elearning_rate: 0.00239592\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0159493\u003cbr\u003ebatch_size: 256","Arm 31_0\u003cbr\u003enum_params: 58316 (SEM: None)\u003cbr\u003ehidden_size_1: 64\u003cbr\u003ehidden_size_2: 110\u003cbr\u003elearning_rate: 0.00125691\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.135129\u003cbr\u003ebatch_size: 128","Arm 32_0\u003cbr\u003enum_params: 32533 (SEM: None)\u003cbr\u003ehidden_size_1: 37\u003cbr\u003ehidden_size_2: 75\u003cbr\u003elearning_rate: 0.00180337\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 128","Arm 33_0\u003cbr\u003enum_params: 15083 (SEM: None)\u003cbr\u003ehidden_size_1: 17\u003cbr\u003ehidden_size_2: 65\u003cbr\u003elearning_rate: 0.00222561\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 128","Arm 34_0\u003cbr\u003enum_params: 35110 (SEM: None)\u003cbr\u003ehidden_size_1: 40\u003cbr\u003ehidden_size_2: 75\u003cbr\u003elearning_rate: 0.00120234\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 128","Arm 35_0\u003cbr\u003enum_params: 18080 (SEM: None)\u003cbr\u003ehidden_size_1: 20\u003cbr\u003ehidden_size_2: 80\u003cbr\u003elearning_rate: 0.00137701\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 256","Arm 36_0\u003cbr\u003enum_params: 29699 (SEM: None)\u003cbr\u003ehidden_size_1: 33\u003cbr\u003ehidden_size_2: 89\u003cbr\u003elearning_rate: 0.00097412\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 256","Arm 37_0\u003cbr\u003enum_params: 14858 (SEM: None)\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 89\u003cbr\u003elearning_rate: 0.00171517\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 256","Arm 38_0\u003cbr\u003enum_params: 14130 (SEM: None)\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 61\u003cbr\u003elearning_rate: 0.00118656\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 128","Arm 39_0\u003cbr\u003enum_params: 64208 (SEM: None)\u003cbr\u003ehidden_size_1: 69\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.000980025\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.173421\u003cbr\u003ebatch_size: 128","Arm 3_0\u003cbr\u003enum_params: 14756 (SEM: None)\u003cbr\u003ehidden_size_1: 18\u003cbr\u003ehidden_size_2: 23\u003cbr\u003elearning_rate: 0.000165927\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.169496\u003cbr\u003ebatch_size: 256","Arm 40_0\u003cbr\u003enum_params: 50528 (SEM: None)\u003cbr\u003ehidden_size_1: 54\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.000713681\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0639822\u003cbr\u003ebatch_size: 64","Arm 41_0\u003cbr\u003enum_params: 37610 (SEM: None)\u003cbr\u003ehidden_size_1: 40\u003cbr\u003ehidden_size_2: 125\u003cbr\u003elearning_rate: 0.000853052\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 128","Arm 42_0\u003cbr\u003enum_params: 23048 (SEM: None)\u003cbr\u003ehidden_size_1: 26\u003cbr\u003ehidden_size_2: 74\u003cbr\u003elearning_rate: 0.00181912\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 128","Arm 43_0\u003cbr\u003enum_params: 51440 (SEM: None)\u003cbr\u003ehidden_size_1: 55\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.000885307\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0845615\u003cbr\u003ebatch_size: 128","Arm 44_0\u003cbr\u003enum_params: 24194 (SEM: None)\u003cbr\u003ehidden_size_1: 28\u003cbr\u003ehidden_size_2: 59\u003cbr\u003elearning_rate: 0.00313858\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 256","Arm 45_0\u003cbr\u003enum_params: 30320 (SEM: None)\u003cbr\u003ehidden_size_1: 35\u003cbr\u003ehidden_size_2: 64\u003cbr\u003elearning_rate: 0.00687519\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0989977\u003cbr\u003ebatch_size: 256","Arm 4_0\u003cbr\u003enum_params: 71630 (SEM: None)\u003cbr\u003ehidden_size_1: 80\u003cbr\u003ehidden_size_2: 99\u003cbr\u003elearning_rate: 0.000642137\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.291277\u003cbr\u003ebatch_size: 128","Arm 5_0\u003cbr\u003enum_params: 13948 (SEM: None)\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 54\u003cbr\u003elearning_rate: 0.000443539\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.0575523\u003cbr\u003ebatch_size: 64","Arm 6_0\u003cbr\u003enum_params: 24686 (SEM: None)\u003cbr\u003ehidden_size_1: 29\u003cbr\u003ehidden_size_2: 50\u003cbr\u003elearning_rate: 0.000176994\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.43503\u003cbr\u003ebatch_size: 256","Arm 7_0\u003cbr\u003enum_params: 18290 (SEM: None)\u003cbr\u003ehidden_size_1: 20\u003cbr\u003ehidden_size_2: 87\u003cbr\u003elearning_rate: 0.000119082\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.462744\u003cbr\u003ebatch_size: 256","Arm 8_0\u003cbr\u003enum_params: 20996 (SEM: None)\u003cbr\u003ehidden_size_1: 26\u003cbr\u003ehidden_size_2: 17\u003cbr\u003elearning_rate: 0.00524479\u003cbr\u003eepochs: 1\u003cbr\u003edropout: 0.455813\u003cbr\u003ebatch_size: 32","Arm 9_0\u003cbr\u003enum_params: 45860 (SEM: None)\u003cbr\u003ehidden_size_1: 50\u003cbr\u003ehidden_size_2: 111\u003cbr\u003elearning_rate: 0.00171199\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.162625\u003cbr\u003ebatch_size: 64"],"visible":false,"x":[19,69,23,34,33,128,33,49,41,31,58,23,26,52,27,64,23,30,60,48,48,23,40,36,64,37,17,40,20,33,16,16,69,18,54,40,26,55,28,35,80,16,29,20,26,50],"xaxis":"x","y":[19,69,23,34,33,128,33,49,41,31,58,23,26,52,27,64,23,30,60,48,48,23,40,36,64,37,17,40,20,33,16,16,69,18,54,40,26,55,28,35,80,16,29,20,26,50],"yaxis":"y","type":"scatter"},{"hoverinfo":"text","legendgroup":"In-sample","marker":{"color":"black","opacity":0.5,"symbol":1},"mode":"markers","name":"In-sample","showlegend":false,"text":["Arm 0_0\u003cbr\u003enum_params: 16810 (SEM: None)\u003cbr\u003ehidden_size_1: 19\u003cbr\u003ehidden_size_2: 66\u003cbr\u003elearning_rate: 0.0031816\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.19097\u003cbr\u003ebatch_size: 32","Arm 10_0\u003cbr\u003enum_params: 64208 (SEM: None)\u003cbr\u003ehidden_size_1: 69\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00197636\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.259316\u003cbr\u003ebatch_size: 64","Arm 11_0\u003cbr\u003enum_params: 21596 (SEM: None)\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 108\u003cbr\u003elearning_rate: 0.00141769\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.131915\u003cbr\u003ebatch_size: 64","Arm 12_0\u003cbr\u003enum_params: 31804 (SEM: None)\u003cbr\u003ehidden_size_1: 34\u003cbr\u003ehidden_size_2: 117\u003cbr\u003elearning_rate: 0.00176828\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.215278\u003cbr\u003ebatch_size: 128","Arm 13_0\u003cbr\u003enum_params: 31333 (SEM: None)\u003cbr\u003ehidden_size_1: 33\u003cbr\u003ehidden_size_2: 127\u003cbr\u003elearning_rate: 0.00080349\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.252218\u003cbr\u003ebatch_size: 64","Arm 14_0\u003cbr\u003enum_params: 118016 (SEM: None)\u003cbr\u003ehidden_size_1: 128\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00124953\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.217015\u003cbr\u003ebatch_size: 64","Arm 15_0\u003cbr\u003enum_params: 31376 (SEM: None)\u003cbr\u003ehidden_size_1: 33\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00253396\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.255854\u003cbr\u003ebatch_size: 64","Arm 16_0\u003cbr\u003enum_params: 45968 (SEM: None)\u003cbr\u003ehidden_size_1: 49\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00101217\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.224604\u003cbr\u003ebatch_size: 128","Arm 17_0\u003cbr\u003enum_params: 37244 (SEM: None)\u003cbr\u003ehidden_size_1: 41\u003cbr\u003ehidden_size_2: 100\u003cbr\u003elearning_rate: 0.00062412\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.181551\u003cbr\u003ebatch_size: 128","Arm 18_0\u003cbr\u003enum_params: 29552 (SEM: None)\u003cbr\u003ehidden_size_1: 31\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.000459653\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.1232\u003cbr\u003ebatch_size: 128","Arm 19_0\u003cbr\u003enum_params: 54176 (SEM: None)\u003cbr\u003ehidden_size_1: 58\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.000914343\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.225434\u003cbr\u003ebatch_size: 64","Arm 1_0\u003cbr\u003enum_params: 21926 (SEM: None)\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 118\u003cbr\u003elearning_rate: 0.000144664\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.465754\u003cbr\u003ebatch_size: 256","Arm 20_0\u003cbr\u003enum_params: 23912 (SEM: None)\u003cbr\u003ehidden_size_1: 26\u003cbr\u003ehidden_size_2: 98\u003cbr\u003elearning_rate: 0.000315077\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.141047\u003cbr\u003ebatch_size: 64","Arm 21_0\u003cbr\u003enum_params: 46968 (SEM: None)\u003cbr\u003ehidden_size_1: 52\u003cbr\u003ehidden_size_2: 100\u003cbr\u003elearning_rate: 0.000853862\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.223537\u003cbr\u003ebatch_size: 64","Arm 22_0\u003cbr\u003enum_params: 24461 (SEM: None)\u003cbr\u003ehidden_size_1: 27\u003cbr\u003ehidden_size_2: 89\u003cbr\u003elearning_rate: 0.000809847\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.181735\u003cbr\u003ebatch_size: 128","Arm 23_0\u003cbr\u003enum_params: 58094 (SEM: None)\u003cbr\u003ehidden_size_1: 64\u003cbr\u003ehidden_size_2: 107\u003cbr\u003elearning_rate: 0.000882267\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.22249\u003cbr\u003ebatch_size: 64","Arm 24_0\u003cbr\u003enum_params: 21860 (SEM: None)\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 116\u003cbr\u003elearning_rate: 0.000713289\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.176991\u003cbr\u003ebatch_size: 128","Arm 25_0\u003cbr\u003enum_params: 27920 (SEM: None)\u003cbr\u003ehidden_size_1: 30\u003cbr\u003ehidden_size_2: 110\u003cbr\u003elearning_rate: 0.00099595\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0688323\u003cbr\u003ebatch_size: 128","Arm 26_0\u003cbr\u003enum_params: 54460 (SEM: None)\u003cbr\u003ehidden_size_1: 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0_0\u003cbr\u003enum_params: 16810 (SEM: None)\u003cbr\u003ehidden_size_1: 19\u003cbr\u003ehidden_size_2: 66\u003cbr\u003elearning_rate: 0.0031816\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.19097\u003cbr\u003ebatch_size: 32","Arm 10_0\u003cbr\u003enum_params: 64208 (SEM: None)\u003cbr\u003ehidden_size_1: 69\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00197636\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.259316\u003cbr\u003ebatch_size: 64","Arm 11_0\u003cbr\u003enum_params: 21596 (SEM: None)\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 108\u003cbr\u003elearning_rate: 0.00141769\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.131915\u003cbr\u003ebatch_size: 64","Arm 12_0\u003cbr\u003enum_params: 31804 (SEM: None)\u003cbr\u003ehidden_size_1: 34\u003cbr\u003ehidden_size_2: 117\u003cbr\u003elearning_rate: 0.00176828\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.215278\u003cbr\u003ebatch_size: 128","Arm 13_0\u003cbr\u003enum_params: 31333 (SEM: None)\u003cbr\u003ehidden_size_1: 33\u003cbr\u003ehidden_size_2: 127\u003cbr\u003elearning_rate: 0.00080349\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.252218\u003cbr\u003ebatch_size: 64","Arm 14_0\u003cbr\u003enum_params: 118016 (SEM: None)\u003cbr\u003ehidden_size_1: 128\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00124953\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.217015\u003cbr\u003ebatch_size: 64","Arm 15_0\u003cbr\u003enum_params: 31376 (SEM: None)\u003cbr\u003ehidden_size_1: 33\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00253396\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.255854\u003cbr\u003ebatch_size: 64","Arm 16_0\u003cbr\u003enum_params: 45968 (SEM: None)\u003cbr\u003ehidden_size_1: 49\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00101217\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.224604\u003cbr\u003ebatch_size: 128","Arm 17_0\u003cbr\u003enum_params: 37244 (SEM: None)\u003cbr\u003ehidden_size_1: 41\u003cbr\u003ehidden_size_2: 100\u003cbr\u003elearning_rate: 0.00062412\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.181551\u003cbr\u003ebatch_size: 128","Arm 18_0\u003cbr\u003enum_params: 29552 (SEM: None)\u003cbr\u003ehidden_size_1: 31\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.000459653\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.1232\u003cbr\u003ebatch_size: 128","Arm 19_0\u003cbr\u003enum_params: 54176 (SEM: None)\u003cbr\u003ehidden_size_1: 58\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.000914343\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.225434\u003cbr\u003ebatch_size: 64","Arm 1_0\u003cbr\u003enum_params: 21926 (SEM: None)\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 118\u003cbr\u003elearning_rate: 0.000144664\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.465754\u003cbr\u003ebatch_size: 256","Arm 20_0\u003cbr\u003enum_params: 23912 (SEM: None)\u003cbr\u003ehidden_size_1: 26\u003cbr\u003ehidden_size_2: 98\u003cbr\u003elearning_rate: 0.000315077\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.141047\u003cbr\u003ebatch_size: 64","Arm 21_0\u003cbr\u003enum_params: 46968 (SEM: None)\u003cbr\u003ehidden_size_1: 52\u003cbr\u003ehidden_size_2: 100\u003cbr\u003elearning_rate: 0.000853862\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.223537\u003cbr\u003ebatch_size: 64","Arm 22_0\u003cbr\u003enum_params: 24461 (SEM: None)\u003cbr\u003ehidden_size_1: 27\u003cbr\u003ehidden_size_2: 89\u003cbr\u003elearning_rate: 0.000809847\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.181735\u003cbr\u003ebatch_size: 128","Arm 23_0\u003cbr\u003enum_params: 58094 (SEM: None)\u003cbr\u003ehidden_size_1: 64\u003cbr\u003ehidden_size_2: 107\u003cbr\u003elearning_rate: 0.000882267\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.22249\u003cbr\u003ebatch_size: 64","Arm 24_0\u003cbr\u003enum_params: 21860 (SEM: None)\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 116\u003cbr\u003elearning_rate: 0.000713289\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.176991\u003cbr\u003ebatch_size: 128","Arm 25_0\u003cbr\u003enum_params: 27920 (SEM: None)\u003cbr\u003ehidden_size_1: 30\u003cbr\u003ehidden_size_2: 110\u003cbr\u003elearning_rate: 0.00099595\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0688323\u003cbr\u003ebatch_size: 128","Arm 26_0\u003cbr\u003enum_params: 54460 (SEM: None)\u003cbr\u003ehidden_size_1: 60\u003cbr\u003ehidden_size_2: 106\u003cbr\u003elearning_rate: 0.000975137\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.19573\u003cbr\u003ebatch_size: 128","Arm 27_0\u003cbr\u003enum_params: 43838 (SEM: None)\u003cbr\u003ehidden_size_1: 48\u003cbr\u003ehidden_size_2: 107\u003cbr\u003elearning_rate: 0.000753969\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.168004\u003cbr\u003ebatch_size: 128","Arm 28_0\u003cbr\u003enum_params: 43432 (SEM: None)\u003cbr\u003ehidden_size_1: 48\u003cbr\u003ehidden_size_2: 100\u003cbr\u003elearning_rate: 0.000937485\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0605151\u003cbr\u003ebatch_size: 128","Arm 29_0\u003cbr\u003enum_params: 20540 (SEM: None)\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 76\u003cbr\u003elearning_rate: 0.00132658\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 128","Arm 2_0\u003cbr\u003enum_params: 37560 (SEM: None)\u003cbr\u003ehidden_size_1: 40\u003cbr\u003ehidden_size_2: 124\u003cbr\u003elearning_rate: 0.00274471\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.1966\u003cbr\u003ebatch_size: 64","Arm 30_0\u003cbr\u003enum_params: 32042 (SEM: None)\u003cbr\u003ehidden_size_1: 36\u003cbr\u003ehidden_size_2: 83\u003cbr\u003elearning_rate: 0.00239592\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0159493\u003cbr\u003ebatch_size: 256","Arm 31_0\u003cbr\u003enum_params: 58316 (SEM: None)\u003cbr\u003ehidden_size_1: 64\u003cbr\u003ehidden_size_2: 110\u003cbr\u003elearning_rate: 0.00125691\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.135129\u003cbr\u003ebatch_size: 128","Arm 32_0\u003cbr\u003enum_params: 32533 (SEM: None)\u003cbr\u003ehidden_size_1: 37\u003cbr\u003ehidden_size_2: 75\u003cbr\u003elearning_rate: 0.00180337\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 128","Arm 33_0\u003cbr\u003enum_params: 15083 (SEM: None)\u003cbr\u003ehidden_size_1: 17\u003cbr\u003ehidden_size_2: 65\u003cbr\u003elearning_rate: 0.00222561\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 128","Arm 34_0\u003cbr\u003enum_params: 35110 (SEM: None)\u003cbr\u003ehidden_size_1: 40\u003cbr\u003ehidden_size_2: 75\u003cbr\u003elearning_rate: 0.00120234\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 128","Arm 35_0\u003cbr\u003enum_params: 18080 (SEM: None)\u003cbr\u003ehidden_size_1: 20\u003cbr\u003ehidden_size_2: 80\u003cbr\u003elearning_rate: 0.00137701\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 256","Arm 36_0\u003cbr\u003enum_params: 29699 (SEM: None)\u003cbr\u003ehidden_size_1: 33\u003cbr\u003ehidden_size_2: 89\u003cbr\u003elearning_rate: 0.00097412\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 256","Arm 37_0\u003cbr\u003enum_params: 14858 (SEM: None)\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 89\u003cbr\u003elearning_rate: 0.00171517\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 256","Arm 38_0\u003cbr\u003enum_params: 14130 (SEM: None)\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 61\u003cbr\u003elearning_rate: 0.00118656\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 128","Arm 39_0\u003cbr\u003enum_params: 64208 (SEM: None)\u003cbr\u003ehidden_size_1: 69\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.000980025\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.173421\u003cbr\u003ebatch_size: 128","Arm 3_0\u003cbr\u003enum_params: 14756 (SEM: None)\u003cbr\u003ehidden_size_1: 18\u003cbr\u003ehidden_size_2: 23\u003cbr\u003elearning_rate: 0.000165927\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.169496\u003cbr\u003ebatch_size: 256","Arm 40_0\u003cbr\u003enum_params: 50528 (SEM: None)\u003cbr\u003ehidden_size_1: 54\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.000713681\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0639822\u003cbr\u003ebatch_size: 64","Arm 41_0\u003cbr\u003enum_params: 37610 (SEM: None)\u003cbr\u003ehidden_size_1: 40\u003cbr\u003ehidden_size_2: 125\u003cbr\u003elearning_rate: 0.000853052\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 128","Arm 42_0\u003cbr\u003enum_params: 23048 (SEM: None)\u003cbr\u003ehidden_size_1: 26\u003cbr\u003ehidden_size_2: 74\u003cbr\u003elearning_rate: 0.00181912\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 128","Arm 43_0\u003cbr\u003enum_params: 51440 (SEM: None)\u003cbr\u003ehidden_size_1: 55\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.000885307\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0845615\u003cbr\u003ebatch_size: 128","Arm 44_0\u003cbr\u003enum_params: 24194 (SEM: None)\u003cbr\u003ehidden_size_1: 28\u003cbr\u003ehidden_size_2: 59\u003cbr\u003elearning_rate: 0.00313858\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 256","Arm 45_0\u003cbr\u003enum_params: 30320 (SEM: None)\u003cbr\u003ehidden_size_1: 35\u003cbr\u003ehidden_size_2: 64\u003cbr\u003elearning_rate: 0.00687519\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0989977\u003cbr\u003ebatch_size: 256","Arm 4_0\u003cbr\u003enum_params: 71630 (SEM: None)\u003cbr\u003ehidden_size_1: 80\u003cbr\u003ehidden_size_2: 99\u003cbr\u003elearning_rate: 0.000642137\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.291277\u003cbr\u003ebatch_size: 128","Arm 5_0\u003cbr\u003enum_params: 13948 (SEM: None)\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 54\u003cbr\u003elearning_rate: 0.000443539\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.0575523\u003cbr\u003ebatch_size: 64","Arm 6_0\u003cbr\u003enum_params: 24686 (SEM: None)\u003cbr\u003ehidden_size_1: 29\u003cbr\u003ehidden_size_2: 50\u003cbr\u003elearning_rate: 0.000176994\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.43503\u003cbr\u003ebatch_size: 256","Arm 7_0\u003cbr\u003enum_params: 18290 (SEM: None)\u003cbr\u003ehidden_size_1: 20\u003cbr\u003ehidden_size_2: 87\u003cbr\u003elearning_rate: 0.000119082\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.462744\u003cbr\u003ebatch_size: 256","Arm 8_0\u003cbr\u003enum_params: 20996 (SEM: None)\u003cbr\u003ehidden_size_1: 26\u003cbr\u003ehidden_size_2: 17\u003cbr\u003elearning_rate: 0.00524479\u003cbr\u003eepochs: 1\u003cbr\u003edropout: 0.455813\u003cbr\u003ebatch_size: 32","Arm 9_0\u003cbr\u003enum_params: 45860 (SEM: None)\u003cbr\u003ehidden_size_1: 50\u003cbr\u003ehidden_size_2: 111\u003cbr\u003elearning_rate: 0.00171199\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.162625\u003cbr\u003ebatch_size: 64"],"visible":true,"x":[19,69,23,34,33,128,33,49,41,31,58,23,26,52,27,64,23,30,60,48,48,23,40,36,64,37,17,40,20,33,16,16,69,18,54,40,26,55,28,35,80,16,29,20,26,50],"xaxis":"x","y":[66,128,108,117,127,128,128,128,100,128,128,118,98,100,89,107,116,110,106,107,100,76,124,83,110,75,65,75,80,89,89,61,128,23,128,125,74,128,59,64,99,54,50,87,17,111],"yaxis":"y","type":"scatter"},{"hoverinfo":"text","legendgroup":"In-sample","marker":{"color":"black","opacity":0.5,"symbol":1},"mode":"markers","name":"In-sample","showlegend":false,"text":["Arm 0_0\u003cbr\u003enum_params: 16810 (SEM: None)\u003cbr\u003ehidden_size_1: 19\u003cbr\u003ehidden_size_2: 66\u003cbr\u003elearning_rate: 0.0031816\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.19097\u003cbr\u003ebatch_size: 32","Arm 10_0\u003cbr\u003enum_params: 64208 (SEM: None)\u003cbr\u003ehidden_size_1: 69\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00197636\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.259316\u003cbr\u003ebatch_size: 64","Arm 11_0\u003cbr\u003enum_params: 21596 (SEM: None)\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 108\u003cbr\u003elearning_rate: 0.00141769\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.131915\u003cbr\u003ebatch_size: 64","Arm 12_0\u003cbr\u003enum_params: 31804 (SEM: None)\u003cbr\u003ehidden_size_1: 34\u003cbr\u003ehidden_size_2: 117\u003cbr\u003elearning_rate: 0.00176828\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.215278\u003cbr\u003ebatch_size: 128","Arm 13_0\u003cbr\u003enum_params: 31333 (SEM: None)\u003cbr\u003ehidden_size_1: 33\u003cbr\u003ehidden_size_2: 127\u003cbr\u003elearning_rate: 0.00080349\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.252218\u003cbr\u003ebatch_size: 64","Arm 14_0\u003cbr\u003enum_params: 118016 (SEM: None)\u003cbr\u003ehidden_size_1: 128\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00124953\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.217015\u003cbr\u003ebatch_size: 64","Arm 15_0\u003cbr\u003enum_params: 31376 (SEM: None)\u003cbr\u003ehidden_size_1: 33\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00253396\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.255854\u003cbr\u003ebatch_size: 64","Arm 16_0\u003cbr\u003enum_params: 45968 (SEM: None)\u003cbr\u003ehidden_size_1: 49\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00101217\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.224604\u003cbr\u003ebatch_size: 128","Arm 17_0\u003cbr\u003enum_params: 37244 (SEM: None)\u003cbr\u003ehidden_size_1: 41\u003cbr\u003ehidden_size_2: 100\u003cbr\u003elearning_rate: 0.00062412\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.181551\u003cbr\u003ebatch_size: 128","Arm 18_0\u003cbr\u003enum_params: 29552 (SEM: None)\u003cbr\u003ehidden_size_1: 31\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.000459653\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.1232\u003cbr\u003ebatch_size: 128","Arm 19_0\u003cbr\u003enum_params: 54176 (SEM: None)\u003cbr\u003ehidden_size_1: 58\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.000914343\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.225434\u003cbr\u003ebatch_size: 64","Arm 1_0\u003cbr\u003enum_params: 21926 (SEM: None)\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 118\u003cbr\u003elearning_rate: 0.000144664\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.465754\u003cbr\u003ebatch_size: 256","Arm 20_0\u003cbr\u003enum_params: 23912 (SEM: None)\u003cbr\u003ehidden_size_1: 26\u003cbr\u003ehidden_size_2: 98\u003cbr\u003elearning_rate: 0.000315077\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.141047\u003cbr\u003ebatch_size: 64","Arm 21_0\u003cbr\u003enum_params: 46968 (SEM: None)\u003cbr\u003ehidden_size_1: 52\u003cbr\u003ehidden_size_2: 100\u003cbr\u003elearning_rate: 0.000853862\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.223537\u003cbr\u003ebatch_size: 64","Arm 22_0\u003cbr\u003enum_params: 24461 (SEM: None)\u003cbr\u003ehidden_size_1: 27\u003cbr\u003ehidden_size_2: 89\u003cbr\u003elearning_rate: 0.000809847\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.181735\u003cbr\u003ebatch_size: 128","Arm 23_0\u003cbr\u003enum_params: 58094 (SEM: None)\u003cbr\u003ehidden_size_1: 64\u003cbr\u003ehidden_size_2: 107\u003cbr\u003elearning_rate: 0.000882267\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.22249\u003cbr\u003ebatch_size: 64","Arm 24_0\u003cbr\u003enum_params: 21860 (SEM: None)\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 116\u003cbr\u003elearning_rate: 0.000713289\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.176991\u003cbr\u003ebatch_size: 128","Arm 25_0\u003cbr\u003enum_params: 27920 (SEM: None)\u003cbr\u003ehidden_size_1: 30\u003cbr\u003ehidden_size_2: 110\u003cbr\u003elearning_rate: 0.00099595\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0688323\u003cbr\u003ebatch_size: 128","Arm 26_0\u003cbr\u003enum_params: 54460 (SEM: 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64","Arm 41_0\u003cbr\u003enum_params: 37610 (SEM: None)\u003cbr\u003ehidden_size_1: 40\u003cbr\u003ehidden_size_2: 125\u003cbr\u003elearning_rate: 0.000853052\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 128","Arm 42_0\u003cbr\u003enum_params: 23048 (SEM: None)\u003cbr\u003ehidden_size_1: 26\u003cbr\u003ehidden_size_2: 74\u003cbr\u003elearning_rate: 0.00181912\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 128","Arm 43_0\u003cbr\u003enum_params: 51440 (SEM: None)\u003cbr\u003ehidden_size_1: 55\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.000885307\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0845615\u003cbr\u003ebatch_size: 128","Arm 44_0\u003cbr\u003enum_params: 24194 (SEM: None)\u003cbr\u003ehidden_size_1: 28\u003cbr\u003ehidden_size_2: 59\u003cbr\u003elearning_rate: 0.00313858\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 256","Arm 45_0\u003cbr\u003enum_params: 30320 (SEM: None)\u003cbr\u003ehidden_size_1: 35\u003cbr\u003ehidden_size_2: 64\u003cbr\u003elearning_rate: 0.00687519\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0989977\u003cbr\u003ebatch_size: 256","Arm 4_0\u003cbr\u003enum_params: 71630 (SEM: None)\u003cbr\u003ehidden_size_1: 80\u003cbr\u003ehidden_size_2: 99\u003cbr\u003elearning_rate: 0.000642137\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.291277\u003cbr\u003ebatch_size: 128","Arm 5_0\u003cbr\u003enum_params: 13948 (SEM: None)\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 54\u003cbr\u003elearning_rate: 0.000443539\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.0575523\u003cbr\u003ebatch_size: 64","Arm 6_0\u003cbr\u003enum_params: 24686 (SEM: None)\u003cbr\u003ehidden_size_1: 29\u003cbr\u003ehidden_size_2: 50\u003cbr\u003elearning_rate: 0.000176994\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.43503\u003cbr\u003ebatch_size: 256","Arm 7_0\u003cbr\u003enum_params: 18290 (SEM: None)\u003cbr\u003ehidden_size_1: 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0_0\u003cbr\u003enum_params: 16810 (SEM: None)\u003cbr\u003ehidden_size_1: 19\u003cbr\u003ehidden_size_2: 66\u003cbr\u003elearning_rate: 0.0031816\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.19097\u003cbr\u003ebatch_size: 32","Arm 10_0\u003cbr\u003enum_params: 64208 (SEM: None)\u003cbr\u003ehidden_size_1: 69\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00197636\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.259316\u003cbr\u003ebatch_size: 64","Arm 11_0\u003cbr\u003enum_params: 21596 (SEM: None)\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 108\u003cbr\u003elearning_rate: 0.00141769\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.131915\u003cbr\u003ebatch_size: 64","Arm 12_0\u003cbr\u003enum_params: 31804 (SEM: None)\u003cbr\u003ehidden_size_1: 34\u003cbr\u003ehidden_size_2: 117\u003cbr\u003elearning_rate: 0.00176828\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.215278\u003cbr\u003ebatch_size: 128","Arm 13_0\u003cbr\u003enum_params: 31333 (SEM: None)\u003cbr\u003ehidden_size_1: 33\u003cbr\u003ehidden_size_2: 127\u003cbr\u003elearning_rate: 0.00080349\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.252218\u003cbr\u003ebatch_size: 64","Arm 14_0\u003cbr\u003enum_params: 118016 (SEM: None)\u003cbr\u003ehidden_size_1: 128\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00124953\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.217015\u003cbr\u003ebatch_size: 64","Arm 15_0\u003cbr\u003enum_params: 31376 (SEM: None)\u003cbr\u003ehidden_size_1: 33\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00253396\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.255854\u003cbr\u003ebatch_size: 64","Arm 16_0\u003cbr\u003enum_params: 45968 (SEM: None)\u003cbr\u003ehidden_size_1: 49\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00101217\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.224604\u003cbr\u003ebatch_size: 128","Arm 17_0\u003cbr\u003enum_params: 37244 (SEM: None)\u003cbr\u003ehidden_size_1: 41\u003cbr\u003ehidden_size_2: 100\u003cbr\u003elearning_rate: 0.00062412\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.181551\u003cbr\u003ebatch_size: 128","Arm 18_0\u003cbr\u003enum_params: 29552 (SEM: None)\u003cbr\u003ehidden_size_1: 31\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.000459653\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.1232\u003cbr\u003ebatch_size: 128","Arm 19_0\u003cbr\u003enum_params: 54176 (SEM: None)\u003cbr\u003ehidden_size_1: 58\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.000914343\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.225434\u003cbr\u003ebatch_size: 64","Arm 1_0\u003cbr\u003enum_params: 21926 (SEM: None)\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 118\u003cbr\u003elearning_rate: 0.000144664\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.465754\u003cbr\u003ebatch_size: 256","Arm 20_0\u003cbr\u003enum_params: 23912 (SEM: None)\u003cbr\u003ehidden_size_1: 26\u003cbr\u003ehidden_size_2: 98\u003cbr\u003elearning_rate: 0.000315077\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.141047\u003cbr\u003ebatch_size: 64","Arm 21_0\u003cbr\u003enum_params: 46968 (SEM: None)\u003cbr\u003ehidden_size_1: 52\u003cbr\u003ehidden_size_2: 100\u003cbr\u003elearning_rate: 0.000853862\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.223537\u003cbr\u003ebatch_size: 64","Arm 22_0\u003cbr\u003enum_params: 24461 (SEM: None)\u003cbr\u003ehidden_size_1: 27\u003cbr\u003ehidden_size_2: 89\u003cbr\u003elearning_rate: 0.000809847\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.181735\u003cbr\u003ebatch_size: 128","Arm 23_0\u003cbr\u003enum_params: 58094 (SEM: None)\u003cbr\u003ehidden_size_1: 64\u003cbr\u003ehidden_size_2: 107\u003cbr\u003elearning_rate: 0.000882267\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.22249\u003cbr\u003ebatch_size: 64","Arm 24_0\u003cbr\u003enum_params: 21860 (SEM: None)\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 116\u003cbr\u003elearning_rate: 0.000713289\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.176991\u003cbr\u003ebatch_size: 128","Arm 25_0\u003cbr\u003enum_params: 27920 (SEM: None)\u003cbr\u003ehidden_size_1: 30\u003cbr\u003ehidden_size_2: 110\u003cbr\u003elearning_rate: 0.00099595\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0688323\u003cbr\u003ebatch_size: 128","Arm 26_0\u003cbr\u003enum_params: 54460 (SEM: None)\u003cbr\u003ehidden_size_1: 60\u003cbr\u003ehidden_size_2: 106\u003cbr\u003elearning_rate: 0.000975137\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.19573\u003cbr\u003ebatch_size: 128","Arm 27_0\u003cbr\u003enum_params: 43838 (SEM: None)\u003cbr\u003ehidden_size_1: 48\u003cbr\u003ehidden_size_2: 107\u003cbr\u003elearning_rate: 0.000753969\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.168004\u003cbr\u003ebatch_size: 128","Arm 28_0\u003cbr\u003enum_params: 43432 (SEM: None)\u003cbr\u003ehidden_size_1: 48\u003cbr\u003ehidden_size_2: 100\u003cbr\u003elearning_rate: 0.000937485\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0605151\u003cbr\u003ebatch_size: 128","Arm 29_0\u003cbr\u003enum_params: 20540 (SEM: None)\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 76\u003cbr\u003elearning_rate: 0.00132658\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 128","Arm 2_0\u003cbr\u003enum_params: 37560 (SEM: None)\u003cbr\u003ehidden_size_1: 40\u003cbr\u003ehidden_size_2: 124\u003cbr\u003elearning_rate: 0.00274471\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.1966\u003cbr\u003ebatch_size: 64","Arm 30_0\u003cbr\u003enum_params: 32042 (SEM: None)\u003cbr\u003ehidden_size_1: 36\u003cbr\u003ehidden_size_2: 83\u003cbr\u003elearning_rate: 0.00239592\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0159493\u003cbr\u003ebatch_size: 256","Arm 31_0\u003cbr\u003enum_params: 58316 (SEM: None)\u003cbr\u003ehidden_size_1: 64\u003cbr\u003ehidden_size_2: 110\u003cbr\u003elearning_rate: 0.00125691\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.135129\u003cbr\u003ebatch_size: 128","Arm 32_0\u003cbr\u003enum_params: 32533 (SEM: None)\u003cbr\u003ehidden_size_1: 37\u003cbr\u003ehidden_size_2: 75\u003cbr\u003elearning_rate: 0.00180337\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 128","Arm 33_0\u003cbr\u003enum_params: 15083 (SEM: None)\u003cbr\u003ehidden_size_1: 17\u003cbr\u003ehidden_size_2: 65\u003cbr\u003elearning_rate: 0.00222561\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 128","Arm 34_0\u003cbr\u003enum_params: 35110 (SEM: None)\u003cbr\u003ehidden_size_1: 40\u003cbr\u003ehidden_size_2: 75\u003cbr\u003elearning_rate: 0.00120234\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 128","Arm 35_0\u003cbr\u003enum_params: 18080 (SEM: None)\u003cbr\u003ehidden_size_1: 20\u003cbr\u003ehidden_size_2: 80\u003cbr\u003elearning_rate: 0.00137701\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 256","Arm 36_0\u003cbr\u003enum_params: 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18\u003cbr\u003ehidden_size_2: 23\u003cbr\u003elearning_rate: 0.000165927\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.169496\u003cbr\u003ebatch_size: 256","Arm 40_0\u003cbr\u003enum_params: 50528 (SEM: None)\u003cbr\u003ehidden_size_1: 54\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.000713681\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0639822\u003cbr\u003ebatch_size: 64","Arm 41_0\u003cbr\u003enum_params: 37610 (SEM: None)\u003cbr\u003ehidden_size_1: 40\u003cbr\u003ehidden_size_2: 125\u003cbr\u003elearning_rate: 0.000853052\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 128","Arm 42_0\u003cbr\u003enum_params: 23048 (SEM: None)\u003cbr\u003ehidden_size_1: 26\u003cbr\u003ehidden_size_2: 74\u003cbr\u003elearning_rate: 0.00181912\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 128","Arm 43_0\u003cbr\u003enum_params: 51440 (SEM: None)\u003cbr\u003ehidden_size_1: 55\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.000885307\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0845615\u003cbr\u003ebatch_size: 128","Arm 44_0\u003cbr\u003enum_params: 24194 (SEM: None)\u003cbr\u003ehidden_size_1: 28\u003cbr\u003ehidden_size_2: 59\u003cbr\u003elearning_rate: 0.00313858\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 256","Arm 45_0\u003cbr\u003enum_params: 30320 (SEM: None)\u003cbr\u003ehidden_size_1: 35\u003cbr\u003ehidden_size_2: 64\u003cbr\u003elearning_rate: 0.00687519\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0989977\u003cbr\u003ebatch_size: 256","Arm 4_0\u003cbr\u003enum_params: 71630 (SEM: None)\u003cbr\u003ehidden_size_1: 80\u003cbr\u003ehidden_size_2: 99\u003cbr\u003elearning_rate: 0.000642137\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.291277\u003cbr\u003ebatch_size: 128","Arm 5_0\u003cbr\u003enum_params: 13948 (SEM: None)\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 54\u003cbr\u003elearning_rate: 0.000443539\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.0575523\u003cbr\u003ebatch_size: 64","Arm 6_0\u003cbr\u003enum_params: 24686 (SEM: None)\u003cbr\u003ehidden_size_1: 29\u003cbr\u003ehidden_size_2: 50\u003cbr\u003elearning_rate: 0.000176994\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.43503\u003cbr\u003ebatch_size: 256","Arm 7_0\u003cbr\u003enum_params: 18290 (SEM: None)\u003cbr\u003ehidden_size_1: 20\u003cbr\u003ehidden_size_2: 87\u003cbr\u003elearning_rate: 0.000119082\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.462744\u003cbr\u003ebatch_size: 256","Arm 8_0\u003cbr\u003enum_params: 20996 (SEM: None)\u003cbr\u003ehidden_size_1: 26\u003cbr\u003ehidden_size_2: 17\u003cbr\u003elearning_rate: 0.00524479\u003cbr\u003eepochs: 1\u003cbr\u003edropout: 0.455813\u003cbr\u003ebatch_size: 32","Arm 9_0\u003cbr\u003enum_params: 45860 (SEM: None)\u003cbr\u003ehidden_size_1: 50\u003cbr\u003ehidden_size_2: 111\u003cbr\u003elearning_rate: 0.00171199\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.162625\u003cbr\u003ebatch_size: 64"],"visible":false,"x":[19,69,23,34,33,128,33,49,41,31,58,23,26,52,27,64,23,30,60,48,48,23,40,36,64,37,17,40,20,33,16,16,69,18,54,40,26,55,28,35,80,16,29,20,26,50],"xaxis":"x","y":[0.0031816019941386738,0.0019763578166809106,0.0014176948306373726,0.0017682849341538593,0.0008034895309647404,0.0012495345329416427,0.0025339615460667807,0.0010121670998437997,0.0006241196076673286,0.0004596529946405398,0.0009143431623853883,0.00014466425599951336,0.00031507737987175274,0.0008538624689831454,0.0008098473118942149,0.0008822667864361012,0.0007132888431535224,0.0009959498157920334,0.0009751366637403521,0.0007539691702221078,0.0009374847373449053,0.0013265829625614177,0.002744711802661285,0.0023959168306528413,0.001256911127763119,0.0018033707293106925,0.002225614279072248,0.0012023350782572205,0.001377011144263105,0.0009741195869283281,0.001715170409297942,0.0011865583682861731,0.0009800254663132498,0.00016592709310927874,0.0007136807990233074,0.000853052339102696,0.0018191156152693412,0.0008853072275992673,0.0031385809266299093,0.006875192082605954,0.0006421366253755414,0.00044353892687835314,0.00017699389919183936,0.00011908174276707618,0.005244787716785168,0.0017119872193851328],"yaxis":"y","type":"scatter"},{"hoverinfo":"text","legendgroup":"In-sample","marker":{"color":"black","opacity":0.5,"symbol":1},"mode":"markers","name":"In-sample","showlegend":false,"text":["Arm 0_0\u003cbr\u003enum_params: 16810 (SEM: None)\u003cbr\u003ehidden_size_1: 19\u003cbr\u003ehidden_size_2: 66\u003cbr\u003elearning_rate: 0.0031816\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.19097\u003cbr\u003ebatch_size: 32","Arm 10_0\u003cbr\u003enum_params: 64208 (SEM: None)\u003cbr\u003ehidden_size_1: 69\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00197636\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.259316\u003cbr\u003ebatch_size: 64","Arm 11_0\u003cbr\u003enum_params: 21596 (SEM: None)\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 108\u003cbr\u003elearning_rate: 0.00141769\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.131915\u003cbr\u003ebatch_size: 64","Arm 12_0\u003cbr\u003enum_params: 31804 (SEM: None)\u003cbr\u003ehidden_size_1: 34\u003cbr\u003ehidden_size_2: 117\u003cbr\u003elearning_rate: 0.00176828\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.215278\u003cbr\u003ebatch_size: 128","Arm 13_0\u003cbr\u003enum_params: 31333 (SEM: None)\u003cbr\u003ehidden_size_1: 33\u003cbr\u003ehidden_size_2: 127\u003cbr\u003elearning_rate: 0.00080349\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.252218\u003cbr\u003ebatch_size: 64","Arm 14_0\u003cbr\u003enum_params: 118016 (SEM: None)\u003cbr\u003ehidden_size_1: 128\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00124953\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.217015\u003cbr\u003ebatch_size: 64","Arm 15_0\u003cbr\u003enum_params: 31376 (SEM: None)\u003cbr\u003ehidden_size_1: 33\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00253396\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.255854\u003cbr\u003ebatch_size: 64","Arm 16_0\u003cbr\u003enum_params: 45968 (SEM: None)\u003cbr\u003ehidden_size_1: 49\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00101217\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.224604\u003cbr\u003ebatch_size: 128","Arm 17_0\u003cbr\u003enum_params: 37244 (SEM: None)\u003cbr\u003ehidden_size_1: 41\u003cbr\u003ehidden_size_2: 100\u003cbr\u003elearning_rate: 0.00062412\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.181551\u003cbr\u003ebatch_size: 128","Arm 18_0\u003cbr\u003enum_params: 29552 (SEM: None)\u003cbr\u003ehidden_size_1: 31\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.000459653\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.1232\u003cbr\u003ebatch_size: 128","Arm 19_0\u003cbr\u003enum_params: 54176 (SEM: None)\u003cbr\u003ehidden_size_1: 58\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.000914343\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.225434\u003cbr\u003ebatch_size: 64","Arm 1_0\u003cbr\u003enum_params: 21926 (SEM: None)\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 118\u003cbr\u003elearning_rate: 0.000144664\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.465754\u003cbr\u003ebatch_size: 256","Arm 20_0\u003cbr\u003enum_params: 23912 (SEM: None)\u003cbr\u003ehidden_size_1: 26\u003cbr\u003ehidden_size_2: 98\u003cbr\u003elearning_rate: 0.000315077\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.141047\u003cbr\u003ebatch_size: 64","Arm 21_0\u003cbr\u003enum_params: 46968 (SEM: None)\u003cbr\u003ehidden_size_1: 52\u003cbr\u003ehidden_size_2: 100\u003cbr\u003elearning_rate: 0.000853862\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.223537\u003cbr\u003ebatch_size: 64","Arm 22_0\u003cbr\u003enum_params: 24461 (SEM: None)\u003cbr\u003ehidden_size_1: 27\u003cbr\u003ehidden_size_2: 89\u003cbr\u003elearning_rate: 0.000809847\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.181735\u003cbr\u003ebatch_size: 128","Arm 23_0\u003cbr\u003enum_params: 58094 (SEM: None)\u003cbr\u003ehidden_size_1: 64\u003cbr\u003ehidden_size_2: 107\u003cbr\u003elearning_rate: 0.000882267\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.22249\u003cbr\u003ebatch_size: 64","Arm 24_0\u003cbr\u003enum_params: 21860 (SEM: None)\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 116\u003cbr\u003elearning_rate: 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0_0\u003cbr\u003enum_params: 16810 (SEM: None)\u003cbr\u003ehidden_size_1: 19\u003cbr\u003ehidden_size_2: 66\u003cbr\u003elearning_rate: 0.0031816\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.19097\u003cbr\u003ebatch_size: 32","Arm 10_0\u003cbr\u003enum_params: 64208 (SEM: None)\u003cbr\u003ehidden_size_1: 69\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00197636\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.259316\u003cbr\u003ebatch_size: 64","Arm 11_0\u003cbr\u003enum_params: 21596 (SEM: None)\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 108\u003cbr\u003elearning_rate: 0.00141769\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.131915\u003cbr\u003ebatch_size: 64","Arm 12_0\u003cbr\u003enum_params: 31804 (SEM: None)\u003cbr\u003ehidden_size_1: 34\u003cbr\u003ehidden_size_2: 117\u003cbr\u003elearning_rate: 0.00176828\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.215278\u003cbr\u003ebatch_size: 128","Arm 13_0\u003cbr\u003enum_params: 31333 (SEM: None)\u003cbr\u003ehidden_size_1: 33\u003cbr\u003ehidden_size_2: 127\u003cbr\u003elearning_rate: 0.00080349\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.252218\u003cbr\u003ebatch_size: 64","Arm 14_0\u003cbr\u003enum_params: 118016 (SEM: None)\u003cbr\u003ehidden_size_1: 128\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00124953\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.217015\u003cbr\u003ebatch_size: 64","Arm 15_0\u003cbr\u003enum_params: 31376 (SEM: None)\u003cbr\u003ehidden_size_1: 33\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00253396\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.255854\u003cbr\u003ebatch_size: 64","Arm 16_0\u003cbr\u003enum_params: 45968 (SEM: None)\u003cbr\u003ehidden_size_1: 49\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00101217\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.224604\u003cbr\u003ebatch_size: 128","Arm 17_0\u003cbr\u003enum_params: 37244 (SEM: None)\u003cbr\u003ehidden_size_1: 41\u003cbr\u003ehidden_size_2: 100\u003cbr\u003elearning_rate: 0.00062412\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.181551\u003cbr\u003ebatch_size: 128","Arm 18_0\u003cbr\u003enum_params: 29552 (SEM: None)\u003cbr\u003ehidden_size_1: 31\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.000459653\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.1232\u003cbr\u003ebatch_size: 128","Arm 19_0\u003cbr\u003enum_params: 54176 (SEM: None)\u003cbr\u003ehidden_size_1: 58\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.000914343\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.225434\u003cbr\u003ebatch_size: 64","Arm 1_0\u003cbr\u003enum_params: 21926 (SEM: None)\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 118\u003cbr\u003elearning_rate: 0.000144664\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.465754\u003cbr\u003ebatch_size: 256","Arm 20_0\u003cbr\u003enum_params: 23912 (SEM: None)\u003cbr\u003ehidden_size_1: 26\u003cbr\u003ehidden_size_2: 98\u003cbr\u003elearning_rate: 0.000315077\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.141047\u003cbr\u003ebatch_size: 64","Arm 21_0\u003cbr\u003enum_params: 46968 (SEM: None)\u003cbr\u003ehidden_size_1: 52\u003cbr\u003ehidden_size_2: 100\u003cbr\u003elearning_rate: 0.000853862\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.223537\u003cbr\u003ebatch_size: 64","Arm 22_0\u003cbr\u003enum_params: 24461 (SEM: None)\u003cbr\u003ehidden_size_1: 27\u003cbr\u003ehidden_size_2: 89\u003cbr\u003elearning_rate: 0.000809847\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.181735\u003cbr\u003ebatch_size: 128","Arm 23_0\u003cbr\u003enum_params: 58094 (SEM: None)\u003cbr\u003ehidden_size_1: 64\u003cbr\u003ehidden_size_2: 107\u003cbr\u003elearning_rate: 0.000882267\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.22249\u003cbr\u003ebatch_size: 64","Arm 24_0\u003cbr\u003enum_params: 21860 (SEM: None)\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 116\u003cbr\u003elearning_rate: 0.000713289\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.176991\u003cbr\u003ebatch_size: 128","Arm 25_0\u003cbr\u003enum_params: 27920 (SEM: None)\u003cbr\u003ehidden_size_1: 30\u003cbr\u003ehidden_size_2: 110\u003cbr\u003elearning_rate: 0.00099595\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0688323\u003cbr\u003ebatch_size: 128","Arm 26_0\u003cbr\u003enum_params: 54460 (SEM: None)\u003cbr\u003ehidden_size_1: 60\u003cbr\u003ehidden_size_2: 106\u003cbr\u003elearning_rate: 0.000975137\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.19573\u003cbr\u003ebatch_size: 128","Arm 27_0\u003cbr\u003enum_params: 43838 (SEM: None)\u003cbr\u003ehidden_size_1: 48\u003cbr\u003ehidden_size_2: 107\u003cbr\u003elearning_rate: 0.000753969\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.168004\u003cbr\u003ebatch_size: 128","Arm 28_0\u003cbr\u003enum_params: 43432 (SEM: None)\u003cbr\u003ehidden_size_1: 48\u003cbr\u003ehidden_size_2: 100\u003cbr\u003elearning_rate: 0.000937485\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0605151\u003cbr\u003ebatch_size: 128","Arm 29_0\u003cbr\u003enum_params: 20540 (SEM: None)\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 76\u003cbr\u003elearning_rate: 0.00132658\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 128","Arm 2_0\u003cbr\u003enum_params: 37560 (SEM: None)\u003cbr\u003ehidden_size_1: 40\u003cbr\u003ehidden_size_2: 124\u003cbr\u003elearning_rate: 0.00274471\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.1966\u003cbr\u003ebatch_size: 64","Arm 30_0\u003cbr\u003enum_params: 32042 (SEM: None)\u003cbr\u003ehidden_size_1: 36\u003cbr\u003ehidden_size_2: 83\u003cbr\u003elearning_rate: 0.00239592\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0159493\u003cbr\u003ebatch_size: 256","Arm 31_0\u003cbr\u003enum_params: 58316 (SEM: None)\u003cbr\u003ehidden_size_1: 64\u003cbr\u003ehidden_size_2: 110\u003cbr\u003elearning_rate: 0.00125691\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.135129\u003cbr\u003ebatch_size: 128","Arm 32_0\u003cbr\u003enum_params: 32533 (SEM: None)\u003cbr\u003ehidden_size_1: 37\u003cbr\u003ehidden_size_2: 75\u003cbr\u003elearning_rate: 0.00180337\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 128","Arm 33_0\u003cbr\u003enum_params: 15083 (SEM: None)\u003cbr\u003ehidden_size_1: 17\u003cbr\u003ehidden_size_2: 65\u003cbr\u003elearning_rate: 0.00222561\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 128","Arm 34_0\u003cbr\u003enum_params: 35110 (SEM: None)\u003cbr\u003ehidden_size_1: 40\u003cbr\u003ehidden_size_2: 75\u003cbr\u003elearning_rate: 0.00120234\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 128","Arm 35_0\u003cbr\u003enum_params: 18080 (SEM: None)\u003cbr\u003ehidden_size_1: 20\u003cbr\u003ehidden_size_2: 80\u003cbr\u003elearning_rate: 0.00137701\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 256","Arm 36_0\u003cbr\u003enum_params: 29699 (SEM: None)\u003cbr\u003ehidden_size_1: 33\u003cbr\u003ehidden_size_2: 89\u003cbr\u003elearning_rate: 0.00097412\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 256","Arm 37_0\u003cbr\u003enum_params: 14858 (SEM: None)\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 89\u003cbr\u003elearning_rate: 0.00171517\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 256","Arm 38_0\u003cbr\u003enum_params: 14130 (SEM: None)\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 61\u003cbr\u003elearning_rate: 0.00118656\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 128","Arm 39_0\u003cbr\u003enum_params: 64208 (SEM: None)\u003cbr\u003ehidden_size_1: 69\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.000980025\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.173421\u003cbr\u003ebatch_size: 128","Arm 3_0\u003cbr\u003enum_params: 14756 (SEM: None)\u003cbr\u003ehidden_size_1: 18\u003cbr\u003ehidden_size_2: 23\u003cbr\u003elearning_rate: 0.000165927\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.169496\u003cbr\u003ebatch_size: 256","Arm 40_0\u003cbr\u003enum_params: 50528 (SEM: None)\u003cbr\u003ehidden_size_1: 54\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.000713681\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0639822\u003cbr\u003ebatch_size: 64","Arm 41_0\u003cbr\u003enum_params: 37610 (SEM: None)\u003cbr\u003ehidden_size_1: 40\u003cbr\u003ehidden_size_2: 125\u003cbr\u003elearning_rate: 0.000853052\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 128","Arm 42_0\u003cbr\u003enum_params: 23048 (SEM: None)\u003cbr\u003ehidden_size_1: 26\u003cbr\u003ehidden_size_2: 74\u003cbr\u003elearning_rate: 0.00181912\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 128","Arm 43_0\u003cbr\u003enum_params: 51440 (SEM: None)\u003cbr\u003ehidden_size_1: 55\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.000885307\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0845615\u003cbr\u003ebatch_size: 128","Arm 44_0\u003cbr\u003enum_params: 24194 (SEM: None)\u003cbr\u003ehidden_size_1: 28\u003cbr\u003ehidden_size_2: 59\u003cbr\u003elearning_rate: 0.00313858\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 256","Arm 45_0\u003cbr\u003enum_params: 30320 (SEM: None)\u003cbr\u003ehidden_size_1: 35\u003cbr\u003ehidden_size_2: 64\u003cbr\u003elearning_rate: 0.00687519\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0989977\u003cbr\u003ebatch_size: 256","Arm 4_0\u003cbr\u003enum_params: 71630 (SEM: None)\u003cbr\u003ehidden_size_1: 80\u003cbr\u003ehidden_size_2: 99\u003cbr\u003elearning_rate: 0.000642137\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.291277\u003cbr\u003ebatch_size: 128","Arm 5_0\u003cbr\u003enum_params: 13948 (SEM: None)\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 54\u003cbr\u003elearning_rate: 0.000443539\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.0575523\u003cbr\u003ebatch_size: 64","Arm 6_0\u003cbr\u003enum_params: 24686 (SEM: None)\u003cbr\u003ehidden_size_1: 29\u003cbr\u003ehidden_size_2: 50\u003cbr\u003elearning_rate: 0.000176994\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.43503\u003cbr\u003ebatch_size: 256","Arm 7_0\u003cbr\u003enum_params: 18290 (SEM: None)\u003cbr\u003ehidden_size_1: 20\u003cbr\u003ehidden_size_2: 87\u003cbr\u003elearning_rate: 0.000119082\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.462744\u003cbr\u003ebatch_size: 256","Arm 8_0\u003cbr\u003enum_params: 20996 (SEM: None)\u003cbr\u003ehidden_size_1: 26\u003cbr\u003ehidden_size_2: 17\u003cbr\u003elearning_rate: 0.00524479\u003cbr\u003eepochs: 1\u003cbr\u003edropout: 0.455813\u003cbr\u003ebatch_size: 32","Arm 9_0\u003cbr\u003enum_params: 45860 (SEM: None)\u003cbr\u003ehidden_size_1: 50\u003cbr\u003ehidden_size_2: 111\u003cbr\u003elearning_rate: 0.00171199\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.162625\u003cbr\u003ebatch_size: 64"],"visible":false,"x":[19,69,23,34,33,128,33,49,41,31,58,23,26,52,27,64,23,30,60,48,48,23,40,36,64,37,17,40,20,33,16,16,69,18,54,40,26,55,28,35,80,16,29,20,26,50],"xaxis":"x","y":[0.1909700185060501,0.259316335098853,0.13191533085443444,0.21527757384029156,0.2522180161102007,0.21701457003271024,0.2558536566731061,0.22460406083035264,0.18155149028594822,0.12320000102423312,0.22543377039810672,0.46575378254055977,0.141046539994748,0.22353669250564473,0.18173527455954647,0.22249019748543886,0.17699132603343667,0.06883225640513538,0.19573037953398628,0.1680041669682534,0.06051509457999761,0.0,0.19660000363364816,0.0159492549978008,0.13512921091553712,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1734206948580121,0.16949627175927162,0.06398222330299602,0.0,0.0,0.08456151600050249,0.0,0.09899770831383276,0.2912765285000205,0.05755229713395238,0.4350298522040248,0.46274396777153015,0.45581294130533934,0.1626253363035843],"yaxis":"y","type":"scatter"},{"hoverinfo":"text","legendgroup":"In-sample","marker":{"color":"black","opacity":0.5,"symbol":1},"mode":"markers","name":"In-sample","showlegend":false,"text":["Arm 0_0\u003cbr\u003enum_params: 16810 (SEM: None)\u003cbr\u003ehidden_size_1: 19\u003cbr\u003ehidden_size_2: 66\u003cbr\u003elearning_rate: 0.0031816\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.19097\u003cbr\u003ebatch_size: 32","Arm 10_0\u003cbr\u003enum_params: 64208 (SEM: None)\u003cbr\u003ehidden_size_1: 69\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00197636\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.259316\u003cbr\u003ebatch_size: 64","Arm 11_0\u003cbr\u003enum_params: 21596 (SEM: None)\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 108\u003cbr\u003elearning_rate: 0.00141769\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.131915\u003cbr\u003ebatch_size: 64","Arm 12_0\u003cbr\u003enum_params: 31804 (SEM: None)\u003cbr\u003ehidden_size_1: 34\u003cbr\u003ehidden_size_2: 117\u003cbr\u003elearning_rate: 0.00176828\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.215278\u003cbr\u003ebatch_size: 128","Arm 13_0\u003cbr\u003enum_params: 31333 (SEM: None)\u003cbr\u003ehidden_size_1: 33\u003cbr\u003ehidden_size_2: 127\u003cbr\u003elearning_rate: 0.00080349\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.252218\u003cbr\u003ebatch_size: 64","Arm 14_0\u003cbr\u003enum_params: 118016 (SEM: None)\u003cbr\u003ehidden_size_1: 128\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00124953\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.217015\u003cbr\u003ebatch_size: 64","Arm 15_0\u003cbr\u003enum_params: 31376 (SEM: None)\u003cbr\u003ehidden_size_1: 33\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00253396\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.255854\u003cbr\u003ebatch_size: 64","Arm 16_0\u003cbr\u003enum_params: 45968 (SEM: None)\u003cbr\u003ehidden_size_1: 49\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00101217\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.224604\u003cbr\u003ebatch_size: 128","Arm 17_0\u003cbr\u003enum_params: 37244 (SEM: None)\u003cbr\u003ehidden_size_1: 41\u003cbr\u003ehidden_size_2: 100\u003cbr\u003elearning_rate: 0.00062412\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.181551\u003cbr\u003ebatch_size: 128","Arm 18_0\u003cbr\u003enum_params: 29552 (SEM: None)\u003cbr\u003ehidden_size_1: 31\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.000459653\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.1232\u003cbr\u003ebatch_size: 128","Arm 19_0\u003cbr\u003enum_params: 54176 (SEM: None)\u003cbr\u003ehidden_size_1: 58\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.000914343\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.225434\u003cbr\u003ebatch_size: 64","Arm 1_0\u003cbr\u003enum_params: 21926 (SEM: None)\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 118\u003cbr\u003elearning_rate: 0.000144664\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.465754\u003cbr\u003ebatch_size: 256","Arm 20_0\u003cbr\u003enum_params: 23912 (SEM: None)\u003cbr\u003ehidden_size_1: 26\u003cbr\u003ehidden_size_2: 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致谢

我们感谢 TorchX 团队(特别是 Kiuk Chung 和 Tristan Rice) 在将 TorchX 与 Axe 集成方面提供的帮助。

脚本的总运行时间: ( 15 分钟 54.230 秒)

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