[
  {
    "path": ".gitignore",
    "content": "# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\n# C extensions\n*.so\n\n# Distribution / packaging\n.Python\nbuild/\ndevelop-eggs/\ndist/\ndownloads/\neggs/\n.eggs/\nlib/\nlib64/\nparts/\nsdist/\nvar/\nwheels/\nshare/python-wheels/\n*.egg-info/\n.installed.cfg\n*.egg\nMANIFEST\n\n# PyInstaller\n#  Usually these files are written by a python script from a template\n#  before PyInstaller builds the exe, so as to inject date/other infos into it.\n*.manifest\n*.spec\n\n# Installer logs\npip-log.txt\npip-delete-this-directory.txt\n\n# Unit test / coverage reports\nhtmlcov/\n.tox/\n.nox/\n.coverage\n.coverage.*\n.cache\nnosetests.xml\ncoverage.xml\n*.cover\n*.py,cover\n.hypothesis/\n.pytest_cache/\ncover/\n\n# Translations\n*.mo\n*.pot\n\n# Django stuff:\n*.log\nlocal_settings.py\ndb.sqlite3\ndb.sqlite3-journal\n\n# Flask stuff:\ninstance/\n.webassets-cache\n\n# Scrapy stuff:\n.scrapy\n\n# Sphinx documentation\ndocs/_build/\n\n# PyBuilder\n.pybuilder/\ntarget/\n\n# Jupyter Notebook\n.ipynb_checkpoints\n\n# IPython\nprofile_default/\nipython_config.py\n\n# pyenv\n#   For a library or package, you might want to ignore these files since the code is\n#   intended to run in multiple environments; otherwise, check them in:\n.python-version\n\n# pipenv\n#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.\n#   However, in case of collaboration, if having platform-specific dependencies or dependencies\n#   having no cross-platform support, pipenv may install dependencies that don't work, or not\n#   install all needed dependencies.\nPipfile.lock\n\n# poetry\n#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.\n#   This is especially recommended for binary packages to ensure reproducibility, and is more\n#   commonly ignored for libraries.\n#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control\n# poetry.lock\n\n# pdm\n#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.\n#pdm.lock\n#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it\n#   in version control.\n#   https://pdm.fming.dev/latest/usage/project/#working-with-version-control\n.pdm.toml\n.pdm-python\n.pdm-build/\n\n# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm\n__pypackages__/\n\n# Celery stuff\ncelerybeat-schedule\ncelerybeat.pid\n\n# SageMath parsed files\n*.sage.py\n\n# Environments\n.env\n.venv\nenv/\nvenv/\nENV/\nenv.bak/\nvenv.bak/\n\n# Spyder project settings\n.spyderproject\n.spyproject\n\n# Rope project settings\n.ropeproject\n\n# mkdocs documentation\n/site\n\n# mypy\n.mypy_cache/\n.dmypy.json\ndmypy.json\n\n# Pyre type checker\n.pyre/\n\n# pytype static type analyzer\n.pytype/\n\n# Cython debug symbols\ncython_debug/\n\n# PyCharm\n#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can\n#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore\n#  and can be added to the global gitignore or merged into this file.  For a more nuclear\n#  option (not recommended) you can uncomment the following to ignore the entire idea folder.\n#.idea/\n"
  },
  {
    "path": "LICENSE.txt",
    "content": "MIT License\n\nCopyright (c) 2024 Imbue\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n"
  },
  {
    "path": "README.md",
    "content": "# Cost Aware pareto-Region Bayesian Search\n\nCARBS is a hyperparameter optimizer that can optimize both regular hyperparameters (like learning rate) and cost-related hyperparameters (like the number of epochs over data). It is a local search algorithm, so it benefits significantly from a good starting point. It searches around the pareto frontier of cost and performance, making it effective in finding compute efficient solutions to problems. See more in [our paper](https://arxiv.org/abs/2306.08055) or the [related blog post](https://imbue.com/research/carbs/). We have [used CARBS extensively](https://imbue.com/research/70b-carbs/) in training and scaling up large language models.\n\n## Installing\n\nCARBS depends primarily on pytorch and pyro for the Gaussian Process model. To get started, clone this directory and run,\n\n```bash\npip install -e /path/to/carbs\n```\n\n## Using CARBS\n\nThe primary CARBS interface is through `suggest` (which will return a new point to test) and `observe` (to report the result).\n\nHere is the core part of calling CARBS, (for the full example see `notebooks/carbs_demo.ipynb`):\n\n```python\nparam_spaces = [\n    Param(name=\"learning_rate\", space=LogSpace(scale=0.5), search_center=1e-4),\n    Param(name=\"momentum\", space=LogitSpace(), search_center=0.9),\n    Param(name=\"epochs\", space=LogSpace(is_integer=True, min=2, max=512), search_center=10),\n]\ncarbs_params = CARBSParams(\n    better_direction_sign=-1,\n    is_wandb_logging_enabled=False,\n    resample_frequency=0,\n)\ncarbs = CARBS(carbs_params, param_spaces)\nfor i in range(10):\n    suggestion = carbs.suggest().suggestion\n    observed_value = run_test_fn(suggestion)\n    obs_out = carbs.observe(ObservationInParam(input=suggestion, output=observed_value, cost=suggestion[\"epochs\"]))\n```\n\nBy default, suggestions will be remembered and included in the GP model using Thompson sampling, to avoid suggesting the same point repeatedly if experiments are being done in parallel. Use `suggest(is_suggestion_remembered=False)` to disable this behavior.\n\n### Configuration\n\nOptions for CARBS are described on the `CARBSParams` class in `carbs/utils.py`.\n\nOn the configuration class `CARBSParams`, be sure to set:\n* `better_direction_sign` to 1 to find a maximum or -1 to find a minimum. \n* `wandb_params` there to configure wandb logging. \n\nOptionally also set:\n* `max_suggestion_cost` (defaults to None) which is a soft restriction on the maximum cost of a suggestion made by the algorithm. This uses the GP model of the cost, so it may not be completely accurate early on in an experiment.\n* `num_random_samples` (defaults to 4) will be the number of observations seen before CARBS starts make its own suggestions\n* `is_saved_on_every_observation` (defaults to True) will pickle and save the whole class to the wandb run on each observation\n* `resample_frequency` (defaults to 5) will be the frequency at which CARBS resamples points on the pareto front (starting from the lowest cost point). Set to 0 to disable resampling\n* `min_pareto_cost_fraction` (defaults to 0.2) has the effect of bucketing together the lowest cost observations -- in the default case 20% -- As these observations are typically much noisier and less interesting than the high cost observations. Set to 0.0 to disable.\n* `initial_search_radius` (defaults to 0.3) will change the scale over all search variables. We've found 0.3 to be fairly good across different types of problems.\n\n\n### Search space\n\nCARBS only supports continuous and integer search spaces. The spaces do not need to have bounds, but `min` and `max` values may be specified. The three main types are:\n* `LinearSpace`: Be sure to set a `scale` parameter to describe a relevant scale length for the problem. If you are using an integer space and the default radius of 0.3, you will need to choose a scale >3 to ensure that neighboring integers can be reached.\n* `LogSpace`: Good for cost or scale related variables, as well as other typically log distributed continuous variables. If using an integer space, you will need to set a minimum value.\n* `LogitSpace`: This will only output values between 0 to 1, so cannot be used with integer values.\n\n\n## Concepts\n\nHere are some concepts to be familiar with when using CARBS:\n\n### Cost\n\nWe usually use number of seconds of runtime as cost. \n\nIt is recommended to start out the search in a low cost region, so the algorithm can get many iterations in quickly. If increasing the cost will increase the performance (as it usually does), CARBS will explore the higher cost area later. \n\nThe `max_suggestion_cost` argument to `CARBSParams` is roughly used to cap the cost of suggestions. CARBS will not make any suggestions that it thinks will cost more than `max_suggestion_cost`. Because its cost model is not completely accurate, some suggestions will take longer than this time. They will not be truncated at the `max_suggestion_cost` amount of runtime.\n\n### Success / Failure\n\nCARBS keeps a separate model for whether a run will succeed or fail. Usually, we report a success if we are able to measure the target metric during eval at the end of training. A run should be reported as a failure if the hyperparameters suggested by CARBS caused the failure, for example a batch size that is too large that caused an OOM failure. If a failure occurs that is not related to the hyperparameters, it is better to forget the suggestion or retry it. Report a failure by making an `ObservationInParam` with `is_failure=True`\n\n### Basic / Param Space\n\nWe map parameter spaces into a more natural search space internally to CARBS for modeling purposes. We call the raw parameter space, used for input and output, **Parameter Space**. We map that to a **Basic Space** using the parameter type, so a `LogSpace` will be transformed by the `log`/`exp` functions. We also use the `scale` factor in this transformation.\n\n### Integer spaces and rounding\n\nLog and Linear spaces can take the flag `is_integer=True` and a `rounding_factor` to round to a nearest value (eg, to round to the nearest multiple of 8). One potential gotcha here is that if the `search_radius` (which defaults to 0.3) does not reach the next integer value, CARBS will not be able to vary this parameter. Adding a `scale` factor here that is at least `1/search_radius` is necessary for `LinearSpace` to work properly. `LogSpace` is a little more complicated, but if search space starts too small (<4) or at a small multiple of the `rounding_factor`, the same issues can occur and a higher `scale` may be required.\n\n### Observations, Suggestions, and Candidates\n\n* **Observations** are the result of a full experiment.\n* **Suggestions** are points that have an outstanding request to get results from a full experiment, but which have not yet been observed.\n* **Candidates** are points that are under consideration to become suggestions.\n\n### Surrogate model fitting\n\nThe `SurrogateModel` builds a surrogate model for the function you are testing. It in turn has four fit functions, for different inputs:\n\n* **fit_observations**: Used to fit `success_observations` to produce the initial models of the target function's outputs and costs.\n* **fit_suggestions**: Modifies the model of the target function output to include predictions for outstanding suggestions, using either Thompson sampling or a kriging believer.\n* **fit_failures**: Pass both `success_observations` and `failure_observations` to create a model of the failure probability\n* **fit_pareto_set**: Pass observations in the pareto set, to create a model of the pareto output value versus cost.\n"
  },
  {
    "path": "carbs/__init__.py",
    "content": "from carbs.carbs import CARBS\nfrom carbs.utils import *\n"
  },
  {
    "path": "carbs/carbs.py",
    "content": "# %%\nimport base64\nimport io\nimport math\nimport os\nimport random\nimport threading\nimport traceback\nimport uuid\nfrom collections import OrderedDict\nfrom pathlib import Path\nfrom typing import Any\nfrom typing import Dict\nfrom typing import List\nfrom typing import Optional\nfrom typing import Sequence\nfrom typing import Tuple\nfrom typing import Union\nfrom typing import cast\n\nimport numpy as np\nimport torch\nimport wandb\nfrom attr import evolve\nfrom loguru import logger\nfrom torch import Tensor\nfrom torch.distributions import Categorical\nfrom torch.distributions import Distribution\nfrom torch.distributions import Normal\nfrom wandb.sdk.lib import RunDisabled\nfrom wandb.sdk.wandb_run import Run\n\nfrom carbs.model import SurrogateModel\nfrom carbs.utils import CARBSParams, load_latest_checkpoint_from_wandb_run\nfrom carbs.utils import CARBS_CHECKPOINT_PREFIX\nfrom carbs.utils import CARBS_CHECKPOINT_SUFFIX\nfrom carbs.utils import ObservationGroup\nfrom carbs.utils import ObservationInBasic\nfrom carbs.utils import ObservationInParam\nfrom carbs.utils import ObserveOutput\nfrom carbs.utils import Param\nfrom carbs.utils import ParamDictType\nfrom carbs.utils import RealNumberSpace\nfrom carbs.utils import SUGGESTION_ID_DICT_KEY\nfrom carbs.utils import SuggestOutput\nfrom carbs.utils import SuggestionInBasic\nfrom carbs.utils import SurrogateModelParams\nfrom carbs.utils import add_dict_key_prefix\nfrom carbs.utils import aggregate_logical_and_across_dim\nfrom carbs.utils import assert_empty\nfrom carbs.utils import expected_improvement\nfrom carbs.utils import get_pareto_curve_plot\nfrom carbs.utils import get_pareto_groups\nfrom carbs.utils import get_pareto_groups_conservative\nfrom carbs.utils import group_observations\nfrom carbs.utils import observation_group_cost\nfrom carbs.utils import observation_group_output\nfrom carbs.utils import ordered_dict_index\nfrom carbs.utils import pareto_area_from_groups\n\n\nclass CARBS:\n    \"\"\"\n    C.A.R.B.S = Cost Aware (pareto-) Regional Bayesian Search\n\n    Definitions\n        Target function: the real function we're trying to find optimal hyperparameters for\n        Surrogate (fitness): the estimated output of the target function from the Gaussian process model\n        Param space: original space of all the hyperparameters before transforms to the given target function\n        Basic space: intermediate space that contains the actual input space of our target function\n            e.g. Learning rate in param space is any real positive number and in basic space we reduce the search range\n              to a log space `lr = LogSpace(max=1)`\n    \"\"\"\n\n    def __init__(self, config: CARBSParams, params: List[Param]) -> None:\n        logger.info(f\"Running CARBS with config {config}\")\n        self.config = config\n        self.params = params\n        experiment_name = os.environ.get(\n            \"EXPERIMENT_NAME\", config.wandb_params.run_name\n        )\n        self.experiment_name = (\n            experiment_name if experiment_name is not None else \"carbs_experiment\"\n        )\n        self.param_space_by_name = {param.name: param.space for param in params}\n        self._real_number_space_by_name = OrderedDict(\n            (k, dim)\n            for k, dim in self.param_space_by_name.items()\n            if isinstance(dim, RealNumberSpace)\n        )\n        assert len(self._real_number_space_by_name) == len(\n            self.param_space_by_name\n        ), \"Real numbers only supported now\"\n        self.real_dim = len(self._real_number_space_by_name)\n        self.search_center_in_basic = torch.zeros(\n            (self.real_dim,)\n        )  # Immediately overwritten in set_search_center\n        self.search_radius_in_basic = torch.tensor(\n            float(self.config.initial_search_radius)\n        )\n        self.set_search_center({param.name: param.search_center for param in params})\n\n        self.success_observations: List[ObservationInBasic] = []\n        self.failure_observations: List[ObservationInBasic] = []\n\n        self.min_bounds_in_basic = torch.tensor(\n            [\n                dim.basic_from_param(dim.min_bound)\n                for dim in self._real_number_space_by_name.values()\n            ]\n        )\n        self.max_bounds_in_basic = torch.tensor(\n            [\n                dim.basic_from_param(dim.max_bound)\n                for dim in self._real_number_space_by_name.values()\n            ]\n        )\n        self._suggest_or_observe_lock = threading.Lock()\n        self.outstanding_suggestions: Dict[str, SuggestionInBasic] = (\n            {}\n        )  # Keys are UUIDs\n\n        self._set_seed(self.config.seed)\n\n        # Only used so far to keep track of how many resamples we have suggested\n        self.resample_count: int = 0\n\n        num_dims_with_bounds = sum(\n            (not math.isinf(dim.min_bound)) or (not math.isinf(dim.max_bound))\n            for dim in self._real_number_space_by_name.values()\n        )\n        if num_dims_with_bounds > 10:\n            logger.info(\n                f\"Many dimensions with bounds ({num_dims_with_bounds}), sampling may be slow\"\n            )\n        self.overgenerate_candidate_factor = 2 ** (num_dims_with_bounds // 2)\n\n        self.wandb_run: Union[Run, RunDisabled, None] = None\n        self._init_wandb()\n\n        self.device = \"cpu\"\n        if torch.cuda.is_available():\n            self.device = f\"cuda:{torch.cuda.current_device()}\"\n\n    def set_search_center(self, input_in_param: ParamDictType) -> None:\n        self.search_center_in_basic = self._param_space_real_to_basic_space_real(\n            input_in_param\n        )\n\n    def suggest(\n        self, suggestion_id: Optional[str] = None, is_suggestion_remembered: bool = True\n    ) -> SuggestOutput:\n        \"\"\"\n        Return a new suggestion\n\n        Keyword arguments:\n        suggestion_id: Optional[str] -- will be used to keep track of the suggestion\n        is_suggestion_remembered: bool -- whether to store the suggestion, which be fit with a surrogate value until it is observed\n\n        Returns:\n        SuggestOutput, which contains:\n        suggestion: ParamDictType -- Dictionary of parameters suggested\n        log: Dict[str, Any] -- Additional details about the suggestion, such as predicted cost and output\n        \"\"\"\n        with self._suggest_or_observe_lock:\n            if self._is_random_sampling():\n                return self._get_random_suggestion(\n                    suggestion_id, is_suggestion_remembered\n                )\n\n            suggestion_in_basic: Optional[SuggestionInBasic]\n            if (\n                self.config.resample_frequency > 0\n                and len(self.success_observations)\n                > (self.resample_count + 1) * self.config.resample_frequency\n            ):\n                suggestion_in_basic = self._get_resample_suggestion()\n                suggestion_in_param = self._basic_space_to_param_space(\n                    suggestion_in_basic.real_number_input\n                )\n                if is_suggestion_remembered:\n                    self._remember_suggestion(\n                        suggestion_in_param,\n                        suggestion_in_basic,\n                        suggestion_id=suggestion_id,\n                    )\n                return SuggestOutput(suggestion=suggestion_in_param)\n\n            try:\n                suggestion_in_basic = self._generate_candidate()\n            except Exception as e:\n                logger.warning(\n                    f\"Got error generating candidate {e}: {traceback.format_exc()}\"\n                )\n                suggestion_in_basic = None\n\n            if suggestion_in_basic is None:\n                logger.warning(\"No candidates found, choosing at random\")\n                return self._get_random_suggestion(\n                    suggestion_id, is_suggestion_remembered\n                )\n\n            suggestion_in_param = self._basic_space_to_param_space(\n                suggestion_in_basic.real_number_input\n            )\n            if is_suggestion_remembered:\n                self._remember_suggestion(\n                    suggestion_in_param,\n                    suggestion_in_basic,\n                    suggestion_id=suggestion_id,\n                )\n            log_dict = self._get_suggestion_log(\n                suggestion_in_param, suggestion_in_basic\n            )\n\n            return SuggestOutput(suggestion=suggestion_in_param, log=log_dict)\n\n    def observe(self, new_observation_in_param: ObservationInParam) -> ObserveOutput:\n        \"\"\"\n        Observe a new data point\n\n        Argument:\n        new_observation_in_param: ObservationInParam(\n            input: ParamDictType -- Dictionary mapping search vars to the values used for input to the function\n            output: float -- Target function output\n            cost: float = 1.0 -- Usually the time in seconds that the target function took to run\n            is_failure: bool = False -- Should be True if training did not complete properly (eg OOM error)\n        )\n        \"\"\"\n        with self._suggest_or_observe_lock:\n            self.forget_suggestion(new_observation_in_param.input)\n            self._add_observation(new_observation_in_param)\n            logs = self._get_observation_log(new_observation_in_param)\n\n            if self.config.is_saved_on_every_observation:\n                self._autosave()\n\n            return ObserveOutput(logs=logs)\n\n    def forget_suggestion(self, suggestion_to_forget: ParamDictType) -> None:\n        \"\"\"\n        Removes suggestion from outstanding_suggestions\n        \"\"\"\n        if SUGGESTION_ID_DICT_KEY in suggestion_to_forget:\n            suggestion_index = suggestion_to_forget[SUGGESTION_ID_DICT_KEY]\n            assert isinstance(suggestion_index, str)\n            if suggestion_index in self.outstanding_suggestions:\n                del self.outstanding_suggestions[suggestion_index]\n            else:\n                logger.info(f\"Got unrecognized suggestion uuid `{suggestion_index}`\")\n        else:\n            pass  # It's fine to forget a suggestion without a UUID, but it doesn't do anything, we weren't remembering it anyway.\n\n    def initialize_from_observations(\n        self, observations: List[ObservationInParam]\n    ) -> ObserveOutput:\n        logs: Dict[str, Any] = {}\n        for observation in observations:\n            self._add_observation(observation)\n            # logs.update(self._get_observation_log(observation))\n\n        if len(observations) > 0:\n            best_obs = max(\n                observations, key=lambda x: x.output * self.config.better_direction_sign\n            )\n            self.set_search_center(best_obs.input)\n        else:\n            logger.warning(\"No way to set the search center!\")\n\n        return ObserveOutput(logs=logs)\n\n    def __getstate__(self) -> Dict[str, object]:\n        state = self.__dict__.copy()\n        state[\"wandb_run\"] = None\n        state.pop(\"_suggest_or_observe_lock\")\n        return state\n\n    def __setstate__(self, state: Dict[str, object]) -> None:\n        self.__dict__.update(state)\n        self._suggest_or_observe_lock = threading.Lock()\n\n    def _set_seed(self, seed: int) -> None:\n        torch.manual_seed(seed)\n        np.random.seed(seed)\n        random.seed(seed)\n\n    def _get_mask_for_invalid_points_in_basic(self, input_in_basic: Tensor) -> Tensor:\n        # mypy understand these types but pycharm does not :'(\n        is_above_min_bounds = aggregate_logical_and_across_dim(\n            input_in_basic > self.min_bounds_in_basic.unsqueeze(0)\n        )\n        is_below_max_bounds = aggregate_logical_and_across_dim(\n            input_in_basic < self.max_bounds_in_basic.unsqueeze(0)\n        )\n        mask = torch.logical_and(is_above_min_bounds, is_below_max_bounds)\n        return mask\n\n    def _round_integer_values_in_basic(self, input_in_basic: Tensor) -> Tensor:\n        for idx, space in enumerate(self._real_number_space_by_name.values()):\n            input_in_basic[..., idx] = space.round_tensor_in_basic(\n                input_in_basic[..., idx]\n            )\n        return input_in_basic\n\n    def _param_space_real_to_basic_space_real(\n        self, input_in_param: ParamDictType\n    ) -> Tensor:\n        return torch.tensor(\n            [\n                dim.basic_from_param(input_in_param[k])\n                for k, dim in self._real_number_space_by_name.items()\n            ]\n        )\n\n    def _param_space_obs_to_basic_space_obs(\n        self, observation_in_params: ObservationInParam\n    ) -> ObservationInBasic:\n        input_in_param = observation_in_params.input\n        suggestion_id = input_in_param.pop(SUGGESTION_ID_DICT_KEY, None)\n        real_number_input = self._param_space_real_to_basic_space_real(input_in_param)\n        return ObservationInBasic(\n            real_number_input=real_number_input,\n            output=float(observation_in_params.output),\n            cost=float(observation_in_params.cost),\n            suggestion_id=str(suggestion_id) if suggestion_id else None,\n        )\n\n    def _basic_space_to_param_space(self, real_number_input: Tensor) -> ParamDictType:\n        return {\n            k: dim.param_from_basic(float(v))\n            for (k, dim), v in zip(\n                self._real_number_space_by_name.items(), real_number_input\n            )\n        }\n\n    def _basic_space_to_unrounded_param_space(\n        self, real_number_input: Tensor\n    ) -> ParamDictType:\n        return {\n            k: dim.param_from_basic(float(v), is_rounded=False)\n            for (k, dim), v in zip(\n                self._real_number_space_by_name.items(), real_number_input\n            )\n        }\n\n    def _remember_suggestion(\n        self,\n        suggestion_in_param: ParamDictType,\n        suggestion_in_basic: SuggestionInBasic,\n        suggestion_id: Optional[str],\n    ) -> ParamDictType:\n        if suggestion_id is None:\n            suggestion_id = str(uuid.uuid4())\n        suggestion_in_param[SUGGESTION_ID_DICT_KEY] = suggestion_id\n        self.outstanding_suggestions[suggestion_id] = suggestion_in_basic\n        return suggestion_in_param\n\n    def _add_observation(\n        self, observation_in_param: ObservationInParam\n    ) -> Optional[ObservationInBasic]:\n        observation_in_basic = self._param_space_obs_to_basic_space_obs(\n            observation_in_param\n        )\n        if (\n            observation_in_param.is_failure\n            or not np.isfinite(observation_in_basic.output)\n            or np.isnan(observation_in_basic.output)\n        ):\n            self.failure_observations.append(observation_in_basic)\n            return None\n\n        self.success_observations.append(observation_in_basic)\n        return observation_in_basic\n\n    @property\n    def _search_distribution_in_basic(self) -> Distribution:\n        return Normal(0, self.search_radius_in_basic)\n\n    def _sample_around_origins_in_basic(\n        self, num_samples: int, origins_in_basic: Tensor\n    ) -> Tuple[Tensor, Tensor]:\n        assert (\n            len(origins_in_basic.shape) == 2\n            and origins_in_basic.shape[1] == self.real_dim\n        ), f\"Bad shape for origins_in_natural: {origins_in_basic.shape}\"\n        origin_index = Categorical(\n            logits=torch.zeros((origins_in_basic.shape[0],))\n        ).sample(torch.Size((num_samples,)))\n        origin_samples = origins_in_basic[origin_index]\n        real_samples: Tensor = (\n            origin_samples\n            + self._search_distribution_in_basic.sample(\n                torch.Size((num_samples, self.real_dim))\n            )\n        )\n        probabilities = self._get_probability_in_search_space(\n            input_in_basic=real_samples, origins_in_basic=origins_in_basic\n        )\n        return real_samples, probabilities\n\n    def _get_probability_in_search_space(\n        self,\n        input_in_basic: Tensor,\n        origins_in_basic: Tensor,\n    ) -> Tensor:\n        input_distance = torch.norm(\n            input_in_basic.unsqueeze(1) - origins_in_basic.unsqueeze(0), dim=-1\n        ).to(self.device)\n        real_relative_probability_per_origin = torch.exp(\n            self._search_distribution_in_basic.log_prob(input_distance)\n        )\n        # we take the max probability origin\n        real_relative_probability: Tensor = real_relative_probability_per_origin.max(\n            dim=-1\n        ).values\n        return real_relative_probability\n\n    def _is_random_sampling(self) -> bool:\n        # Random sampling as opposed to Bayesian optimization.\n        return len(self.success_observations) < self.config.num_random_samples\n\n    def sample_search_space(self, num_samples: int) -> List[SuggestionInBasic]:\n        # The length of the list returned will be less than or equal to num_samples. Filtering is done by _get_mask_for_invalid_points_in_basic, which checks min and max values.\n        if self._is_random_sampling():\n            # Main case\n            origins_in_basic = self.search_center_in_basic.unsqueeze(0)\n        else:\n            # Edge case that only occurs as a fallback when we can't generate a candidate\n            pareto_groups = self._get_pareto_groups(\n                is_conservative=self.config.is_pareto_group_selection_conservative\n            )\n            origins_in_basic = torch.stack(\n                [x[0].real_number_input for x in pareto_groups], dim=0\n            )\n\n        samples_in_basic, _ = self._sample_around_origins_in_basic(\n            num_samples * self.overgenerate_candidate_factor, origins_in_basic\n        )\n\n        valid_sample_mask = self._get_mask_for_invalid_points_in_basic(samples_in_basic)\n        samples_in_basic = samples_in_basic[valid_sample_mask][:num_samples]\n\n        if samples_in_basic.shape[0] < num_samples:\n            logger.warning(\n                f\"Undergenerated valid samples, requested {num_samples} generated {samples_in_basic.shape[0]}\"\n            )\n            self._crank_oversampling_up()\n\n        suggestions_in_basic = [\n            SuggestionInBasic(real_number_input=x) for x in samples_in_basic\n        ]\n\n        return suggestions_in_basic\n\n    @torch.no_grad()\n    def _generate_candidate(self) -> Optional[SuggestionInBasic]:\n        surrogate_model = self.get_surrogate_model()\n        surrogate_model.fit_observations(self.success_observations)\n        surrogate_model.fit_suggestions(list(self.outstanding_suggestions.values()))\n        surrogate_model.fit_failures(\n            self.success_observations, self.failure_observations\n        )\n        pareto_groups = self._get_pareto_groups(\n            is_conservative=self.config.is_pareto_group_selection_conservative\n        )\n        all_pareto_observations = [obs for group in pareto_groups for obs in group]\n        surrogate_model.fit_pareto_set(all_pareto_observations)\n\n        num_samples_to_generate = (\n            self.config.num_candidates_for_suggestion_per_dim * self.real_dim\n        )\n\n        origins_in_basic = torch.stack(\n            [x[0].real_number_input for x in pareto_groups], dim=0\n        )\n        samples_in_basic, probabilities = self._sample_around_origins_in_basic(\n            num_samples_to_generate * self.overgenerate_candidate_factor,\n            origins_in_basic,\n        )\n\n        # do rounding first\n        samples_in_basic = self._round_integer_values_in_basic(samples_in_basic)\n\n        assert samples_in_basic.shape[0] > 0\n\n        # This is why we overgenerated\n        valid_sample_mask = self._get_mask_for_invalid_points_in_basic(samples_in_basic)\n        samples_in_basic = samples_in_basic[valid_sample_mask][:num_samples_to_generate]\n        probabilities = probabilities[valid_sample_mask][:num_samples_to_generate]\n\n        if samples_in_basic.shape[0] < num_samples_to_generate:\n            logger.info(\n                f\"Undergenerated valid samples, requested {num_samples_to_generate} generated {samples_in_basic.shape[0]}\"\n            )\n            self._crank_oversampling_up()\n        if samples_in_basic.shape[0] == 0:\n            return None\n\n        surrogate_model_outputs = surrogate_model.observe_surrogate(samples_in_basic)\n        assert surrogate_model_outputs.pareto_surrogate is not None\n        assert surrogate_model_outputs.pareto_estimate is not None\n\n        pareto_surrogate = surrogate_model_outputs.pareto_surrogate\n        if self.config.is_expected_improvement_pareto_value_clamped:\n            # Biasing technique to encourage higher cost exploration: Choose a random cost along pareto front, and make the pareto value there the minimum for all observations\n\n            # The pareto_groups are sorted, so the 0th and -1th elements represent the low and high values\n            log_min_cost = math.log(observation_group_cost(pareto_groups[0]))\n            if len(pareto_groups) > 1:\n                log_max_cost = math.log(observation_group_cost(pareto_groups[-1]))\n            else:\n                log_max_cost = log_min_cost\n            if self.config.max_suggestion_cost is not None:\n                log_max_cost = min(\n                    log_max_cost, math.log(self.config.max_suggestion_cost)\n                )\n\n            # Choose a random cost along the curve\n            cost_threshold = math.exp(random.uniform(log_min_cost, log_max_cost))\n\n            # Choose the surrogate value at that cost\n            pareto_surrogate_at_threshold = (\n                surrogate_model.get_pareto_surrogate_for_cost(cost_threshold)\n            )\n\n            # Modify the pareto surrogate to be at least that value. This effectively cuts off exploration of low expected value points, which biases the search toward the right side of the pareto curve.\n            pareto_surrogate = torch.clamp(\n                pareto_surrogate, min=pareto_surrogate_at_threshold\n            )\n\n        if self.config.is_expected_improvement_value_always_max:\n            # NB: Only used for ablations\n            best_pareto_group_output = observation_group_output(pareto_groups[-1])\n            best_output_in_surrogate = surrogate_model._target_to_surrogate(\n                torch.tensor([best_pareto_group_output])\n            ).item()\n            pareto_surrogate = (\n                torch.ones_like(pareto_surrogate) * best_output_in_surrogate\n            )\n\n        max_cost_masking = torch.ones_like(surrogate_model_outputs.cost_estimate)\n        if self.config.max_suggestion_cost is not None:\n            max_cost_masking = cast(\n                torch.BoolTensor,\n                surrogate_model_outputs.cost_estimate < self.config.max_suggestion_cost,\n            ).to(torch.float)\n            assert (\n                max_cost_masking.max().item() > 0.5\n            ), f\"No candidates below max cost bound {self.config.max_suggestion_cost}\"\n\n        ei_value = expected_improvement(\n            surrogate_model_outputs.surrogate_output,\n            surrogate_model_outputs.surrogate_var,\n            best_mu=pareto_surrogate,\n            exploration_bias=self.config.exploration_bias,\n        )\n\n        # Central equation: bias points by expected improvement, prior probability, and success probability, excluding points that are too expensive\n        acquisition_function_value = (\n            ei_value\n            * probabilities\n            * surrogate_model_outputs.success_probability\n            * max_cost_masking\n        )\n        best_idx = int(torch.argmax(acquisition_function_value).item())\n\n        # A single point is chosen by that argmax, so log the info for that point\n        log_info = dict(\n            surrogate_output=surrogate_model_outputs.surrogate_output[best_idx].item(),\n            pareto_surrogate=surrogate_model_outputs.pareto_surrogate[best_idx].item(),\n            surrogate_var=surrogate_model_outputs.surrogate_var[best_idx].item(),\n            pareto_estimate=surrogate_model_outputs.pareto_estimate[best_idx].item(),\n            cost_estimate=surrogate_model_outputs.cost_estimate[best_idx].item(),\n            target_estimate=surrogate_model_outputs.target_estimate[best_idx].item(),\n            target_var=surrogate_model_outputs.target_var[best_idx].item(),\n            probabilities=probabilities[best_idx].item(),\n            expected_improvement=ei_value[best_idx].item(),\n            success_probability=surrogate_model_outputs.success_probability[\n                best_idx\n            ].item(),\n        )\n        return SuggestionInBasic(\n            real_number_input=samples_in_basic[best_idx], log_info=log_info\n        )\n\n    def get_surrogate_model(self) -> SurrogateModel:\n        params = SurrogateModelParams(\n            real_dims=self.real_dim,\n            better_direction_sign=self.config.better_direction_sign,\n            outstanding_suggestion_estimator=self.config.outstanding_suggestion_estimator,\n            device=self.device,\n            scale_length=self.search_radius_in_basic.item(),\n        )\n        return SurrogateModel(params)\n\n    def _get_random_suggestion(\n        self,\n        suggestion_id: Optional[str] = None,\n        is_suggestion_remembered: bool = True,\n        num_sampling_attempts: int = 8,\n    ) -> SuggestOutput:\n        for i in range(num_sampling_attempts):\n            # Might as well try to grab 32 points since we get some parallelism for free. sample_search_space will crank up oversampling if needed.\n            suggestions = self.sample_search_space(32)\n            if len(suggestions) > 0:\n                suggestion_in_param = self._basic_space_to_param_space(\n                    suggestions[0].real_number_input\n                )\n                if is_suggestion_remembered:\n                    self._remember_suggestion(\n                        suggestion_in_param, suggestions[0], suggestion_id=suggestion_id\n                    )\n                return SuggestOutput(suggestion=suggestion_in_param)\n        raise Exception(\"Cannot get a random sample :(\")\n\n    def _observation_group_output_pos_better(self, group: Sequence[ObservationInBasic]):\n        return observation_group_output(group) * self.config.better_direction_sign\n\n    def _crank_oversampling_up(self) -> None:\n        self.overgenerate_candidate_factor = min(\n            self.overgenerate_candidate_factor * 2, 2**self.real_dim\n        )\n\n    def _get_pareto_groups(\n        self, is_conservative: bool = False\n    ) -> Tuple[ObservationGroup, ...]:\n        \"\"\"\n        Get pareto groups from success observations.\n\n        :param is_conservative: see get_pareto_groups_conservative for description\n        :return:\n        \"\"\"\n        grouped_observations = group_observations(self.success_observations)\n        if is_conservative:\n            pareto_groups = get_pareto_groups_conservative(\n                grouped_observations,\n                self.config.min_pareto_cost_fraction,\n                self.config.better_direction_sign,\n            )\n        else:\n            pareto_groups = get_pareto_groups(\n                grouped_observations,\n                self.config.min_pareto_cost_fraction,\n                self.config.better_direction_sign,\n            )\n\n        return pareto_groups\n\n    def _get_pareto_set(\n        self, is_conservative: bool = False\n    ) -> Tuple[ObservationInBasic, ...]:\n        pareto_set: List[ObservationInBasic] = []\n        for group in self._get_pareto_groups(is_conservative):\n            pareto_set.extend(group)\n        return tuple(pareto_set)\n\n    def _get_resample_suggestion(self) -> SuggestionInBasic:\n        # we specifically want the non-conservative version so we can resample!\n        pareto_groups = list(self._get_pareto_groups(is_conservative=False))\n\n        # This ordering will determine which obs we select:\n        # we select the first one which has the minimum number of observations/suggestions already\n        pareto_groups.sort(key=observation_group_cost)\n\n        pareto_obs_with_counts: List[Tuple[ObservationInBasic, int]] = []\n        for group in pareto_groups:\n            # Take the first obs because they all have the same sample point\n            first_obs = group[0]\n            count = len(group)\n            # Add to the count of the group the number of outstanding suggestions that are already out to resample this group.\n            for suggestion in self.outstanding_suggestions.values():\n                if torch.all(\n                    torch.isclose(\n                        suggestion.real_number_input, first_obs.real_number_input\n                    )\n                ):\n                    count += 1\n            pareto_obs_with_counts.append((first_obs, count))\n\n        # Resample one of the observation groups with the minimum count along the pareto front\n        min_count = min(x[1] for x in pareto_obs_with_counts)\n        selected_obs = next(x[0] for x in pareto_obs_with_counts if x[1] == min_count)\n\n        resample_suggestion = SuggestionInBasic(selected_obs.real_number_input)\n        self.resample_count += 1\n        return resample_suggestion\n\n    def _init_wandb(self) -> None:\n        if self.config.is_wandb_logging_enabled:\n            wandb_params = self.config.wandb_params\n            if wandb_params.run_id is None:\n                self.wandb_run = wandb.init(\n                    config=self.config.to_dict(),\n                    project=wandb_params.project_name,\n                    group=wandb_params.group_name,\n                    name=wandb_params.run_name,\n                    dir=wandb_params.root_dir,\n                )\n            else:\n                self.wandb_run = wandb.init(\n                    config=self.config.to_dict(),\n                    project=wandb_params.project_name,\n                    group=wandb_params.group_name,\n                    name=wandb_params.run_name,\n                    dir=wandb_params.root_dir,\n                    reinit=True,\n                    resume=\"allow\",\n                    id=wandb_params.run_id,\n                )\n\n    @property\n    def cumulative_cost(self) -> float:\n        # We can't add cost of failed observations, they may not be valid\n        return sum(x.cost for x in self.success_observations)\n\n    @property\n    def observation_count(self) -> int:\n        return len(self.success_observations) + len(self.failure_observations)\n\n    def _get_observation_log(self, observation: ObservationInParam) -> Dict[str, Any]:\n        log_dict: Dict[str, Any] = {\n            \"observation_count\": self.observation_count,\n            \"cumulative_cost\": self.cumulative_cost,\n            \"failed_observation_count\": len(self.failure_observations),\n        }\n        log_dict.update(add_dict_key_prefix(observation.input, \"observation/\"))\n        log_dict[\"observation/is_failure\"] = 1 if observation.is_failure else 0\n        if not observation.is_failure:\n            log_dict[\"observation/output\"] = observation.output\n            log_dict[\"observation/cost\"] = observation.cost\n\n        if len(self.success_observations) > 0:\n            best_observation_in_basic = max(\n                self.success_observations,\n                key=lambda x: x.output * self.config.better_direction_sign,\n            )\n            best_observation_in_param = self._basic_space_to_param_space(\n                best_observation_in_basic.real_number_input\n            )\n            log_dict.update(\n                add_dict_key_prefix(best_observation_in_param, \"best_observation/\")\n            )\n\n            log_dict[\"best_observation/output\"] = best_observation_in_basic.output\n            log_dict[\"best_observation/cost\"] = best_observation_in_basic.cost\n\n            grouped_observations = group_observations(self.success_observations)\n            pareto_groups = self._get_pareto_groups(\n                self.config.is_pareto_group_selection_conservative\n            )\n            log_dict[\"pareto_group_count\"] = len(pareto_groups)\n            log_dict[\"pareto_area\"] = pareto_area_from_groups(pareto_groups)\n\n            resampled_groups = [x for x in grouped_observations if len(x) > 1]\n            if len(resampled_groups) > 0:\n                best_resampled_observations = max(\n                    resampled_groups,\n                    key=lambda x: observation_group_output(x)\n                    * self.config.better_direction_sign,\n                )\n                resampled_observation_in_param = self._basic_space_to_param_space(\n                    best_resampled_observations[0].real_number_input\n                )\n                best_resampled_observation_outputs = [\n                    x.output for x in best_resampled_observations\n                ]\n                log_dict.update(\n                    add_dict_key_prefix(\n                        resampled_observation_in_param, \"best_resampled_observation/\"\n                    )\n                )\n\n                log_dict[\"best_resampled_observation/output_mean\"] = np.mean(\n                    best_resampled_observation_outputs\n                )\n                log_dict[\"best_resampled_observation/output_std_dev\"] = np.std(\n                    best_resampled_observation_outputs\n                )\n                log_dict[\"best_resampled_observation/cost_mean\"] = (\n                    observation_group_cost(best_resampled_observations)\n                )\n                log_dict[\"best_resampled_observation/sample_count\"] = len(\n                    best_resampled_observations\n                )\n\n            if self.config.is_wandb_logging_enabled and len(pareto_groups) > 0:\n                plot_path = get_pareto_curve_plot(\n                    self.success_observations,\n                    pareto_groups,\n                    self.config.wandb_params.root_dir,\n                    obs_count=self.observation_count,\n                )\n                if plot_path is not None:\n                    log_dict[\"pareto_curve\"] = wandb.Image(plot_path)\n\n        if (\n            self.config.is_wandb_logging_enabled\n            and self.config.wandb_params.is_observation_logged\n            and self.wandb_run\n        ):\n            wandb.log(log_dict)\n\n        return log_dict\n\n    def _get_suggestion_log(\n        self, suggestion: ParamDictType, observation_in_surrogate: SuggestionInBasic\n    ) -> Dict[str, Any]:\n        log_dict: Dict[str, Any] = {\n            \"observation_count\": self.observation_count,\n            \"cumulative_cost\": self.cumulative_cost,\n            \"failed_observation_count\": len(self.failure_observations),\n        }\n        log_dict.update(add_dict_key_prefix(suggestion, \"suggestion/\"))\n        log_dict.update(\n            add_dict_key_prefix(observation_in_surrogate.log_info, \"suggestion_meta/\")\n        )\n        if (\n            self.config.is_wandb_logging_enabled\n            and self.config.wandb_params.is_suggestion_logged\n            and self.wandb_run\n        ):\n            wandb.log(log_dict)\n\n        return log_dict\n\n    def _autosave(self) -> None:\n        filename = f\"{CARBS_CHECKPOINT_PREFIX}{self.observation_count}{CARBS_CHECKPOINT_SUFFIX}\"\n        self.save_to_file(\n            filename, upload_to_wandb=self.config.is_wandb_logging_enabled\n        )\n\n    @staticmethod\n    def load_from_file(\n        f: Union[str, io.BytesIO],\n        is_wandb_logging_enabled: bool = False,\n        override_params: Optional[CARBSParams] = None,\n    ) -> \"CARBS\":\n        state = torch.load(f)\n        if override_params is not None:\n            state[\"config\"] = override_params\n        if not is_wandb_logging_enabled:\n            state[\"config\"] = evolve(state[\"config\"], is_wandb_logging_enabled=False)\n        optimizer = CARBS.load_state_dict(state)\n        return optimizer\n\n    @classmethod\n    def load_from_string(\n        cls, state: str, is_wandb_logging_enabled: bool = False\n    ) -> \"CARBS\":\n        return cls.load_from_file(\n            io.BytesIO(base64.b64decode(state)), is_wandb_logging_enabled\n        )\n\n    def get_state_dict(self) -> Dict[str, Any]:\n        outstanding_suggestions: Dict[str, SuggestionInBasic] = {}\n        for key, suggestion in self.outstanding_suggestions.items():\n            # TODO: don't carry around these enormous real_number_inputs in the first place\n            outstanding_suggestions[key] = evolve(\n                suggestion, real_number_input=suggestion.real_number_input.clone()\n            )\n        return {\n            \"config\": self.config,\n            \"params\": self.params,\n            \"success_observations\": self.success_observations,\n            \"failure_observations\": self.failure_observations,\n            \"outstanding_suggestions\": outstanding_suggestions,\n            \"resample_count\": self.resample_count,\n        }\n\n    @classmethod\n    def load_state_dict(cls, state: Dict[str, Any]) -> \"CARBS\":\n        optimizer = CARBS(state[\"config\"], state[\"params\"])\n        optimizer.success_observations = state[\"success_observations\"]\n        optimizer.failure_observations = state[\"failure_observations\"]\n        optimizer.outstanding_suggestions = state[\"outstanding_suggestions\"]\n        optimizer.resample_count = state[\"resample_count\"]\n        return optimizer\n\n    def save_to_file(self, filename: str, upload_to_wandb: bool = False) -> None:\n        checkpoint_path = (\n            Path(self.config.checkpoint_dir) / self.experiment_name / filename\n        )\n        checkpoint_path.parent.mkdir(exist_ok=True, parents=True)\n        torch.save(self.get_state_dict(), checkpoint_path)\n        if upload_to_wandb:\n            wandb.save(checkpoint_path)\n\n    def serialize(self) -> str:\n        buf = io.BytesIO()\n        torch.save(self.get_state_dict(), buf)\n        buf.seek(0)\n        return base64.b64encode(buf.read()).decode(\"utf-8\")\n\n    def warm_start_from_wandb(\n        self,\n        run_name: str,\n        is_prior_observation_valid: bool = False,\n        added_parameters: Optional[ParamDictType] = None,\n    ) -> None:\n        filename = load_latest_checkpoint_from_wandb_run(run_name)\n        self.warm_start(filename, is_prior_observation_valid, added_parameters)\n\n    def warm_start(\n        self,\n        filename: str,\n        is_prior_observation_valid: bool = False,\n        added_parameters: Optional[ParamDictType] = None,\n    ) -> None:\n        prior_carbs = CARBS.load_from_file(filename)\n\n        # Copying over observations\n        if is_prior_observation_valid:\n            prior_keys = set(prior_carbs.param_space_by_name.keys())\n            current_keys = set(self.param_space_by_name.keys())\n            added_keys = (\n                set() if added_parameters is None else set(added_parameters.keys())\n            )\n            assert_empty(\n                prior_keys - current_keys,\n                \"Prior observations used params that are not included in search\",\n            )\n            assert_empty(\n                current_keys - (prior_keys | added_keys), \"Pass in added parameters\"\n            )\n            assert_empty(added_keys & prior_keys, \"Cannot add param already in prior\")\n\n            for prior_observation_in_basic in prior_carbs.success_observations:\n                prior_observation_in_params = prior_carbs._basic_space_to_param_space(\n                    prior_observation_in_basic.real_number_input\n                )\n                if added_parameters is not None:\n                    prior_observation_in_params.update(added_parameters)\n\n                try:\n                    self._add_observation(\n                        ObservationInParam(\n                            input=prior_observation_in_params,\n                            output=prior_observation_in_basic.output,\n                            cost=prior_observation_in_basic.cost,\n                        )\n                    )\n                except ValueError as e:\n                    logger.warning(\n                        f\"Observation {prior_observation_in_params} not valid in current space: {e}; skipping\"\n                    )\n\n            logger.info(\n                f\"Loaded {len(self.success_observations)} observations from prior run\"\n            )\n\n            for prior_observation_in_basic in prior_carbs.failure_observations:\n                prior_observation_in_params = prior_carbs._basic_space_to_param_space(\n                    prior_observation_in_basic.real_number_input\n                )\n                if added_parameters is not None:\n                    prior_observation_in_params.update(added_parameters)\n\n                try:\n                    self._add_observation(\n                        ObservationInParam(\n                            input=prior_observation_in_params,\n                            output=prior_observation_in_basic.output,\n                            cost=prior_observation_in_basic.cost,\n                            is_failure=True,\n                        )\n                    )\n                except ValueError as e:\n                    logger.warning(\n                        f\"Observation {prior_observation_in_params} not valid in current space: {e}; skipping\"\n                    )\n\n            logger.info(\n                f\"Loaded {len(self.failure_observations)} failed observations from prior run\"\n            )\n\n            self.resample_count = prior_carbs.resample_count\n\n        if added_parameters is not None:\n            for param_name, param_value in added_parameters.items():\n                if param_name in self._real_number_space_by_name:\n                    param_idx = ordered_dict_index(\n                        self._real_number_space_by_name, param_name\n                    )\n                    param_value_in_basic = self._real_number_space_by_name[\n                        param_name\n                    ].basic_from_param(param_value)\n                    self.search_center_in_basic[param_idx] = param_value_in_basic\n"
  },
  {
    "path": "carbs/model.py",
    "content": "import math\nfrom typing import List\nfrom typing import Optional\n\nimport attr\nimport numpy as np\nimport pyro\nimport torch\nfrom pyro.contrib import gp as gp\nfrom pyro.contrib.gp.kernels import Kernel\nfrom pyro.contrib.gp.models import GPRegression\nfrom sklearn.preprocessing import MinMaxScaler\nfrom sklearn.preprocessing import QuantileTransformer\nfrom torch import Tensor\nfrom torch.distributions import Normal\n\nfrom carbs.utils import ObservationInBasic\nfrom carbs.utils import OutstandingSuggestionEstimatorEnum\nfrom carbs.utils import SuggestionInBasic\nfrom carbs.utils import SurrogateModelParams\n\n\n@attr.s(auto_attribs=True, collect_by_mro=True)\nclass SurrogateObservationOutputs:\n    surrogate_output: Tensor\n    surrogate_var: Tensor\n    cost_estimate: Tensor\n    target_estimate: Tensor\n    target_var: Tensor\n    success_probability: Tensor\n    pareto_surrogate: Optional[Tensor]\n    pareto_estimate: Optional[Tensor]\n\n\nclass SurrogateModel:\n    def __init__(\n        self,\n        params: SurrogateModelParams,\n    ) -> None:\n        self.params = params\n        self.output_transformer: Optional[QuantileTransformer] = None\n        self.cost_transformer: Optional[MinMaxScaler] = None\n        self.output_model: Optional[GPRegression] = None\n        self.cost_model: Optional[GPRegression] = None\n        self.pareto_model: Optional[GPRegression] = None\n        self.min_logcost: float = float(\"-inf\")\n        self.max_logcost: float = float(\"inf\")\n        self.min_pareto_logcost: float = float(\"-inf\")\n        self.max_pareto_logcost: float = float(\"inf\")\n        self.success_model: Optional[GPRegression] = None\n\n    def _get_kernel(self) -> Kernel:\n        matern_kernel = gp.kernels.Matern32(\n            input_dim=self.params.real_dims,\n            lengthscale=self.params.scale_length * torch.ones((self.params.real_dims,)),\n        )\n        linear_kernel = gp.kernels.Linear(input_dim=self.params.real_dims)\n        return gp.kernels.Sum(linear_kernel, matern_kernel)\n\n    def _get_model(self, inputs: Tensor, outputs: Tensor, kernel: Optional[Kernel] = None) -> GPRegression:\n        # Heavily inspired by the HEBO paper. Some choices that appear arbitrary, such as the model noise, are copied from that paper.\n        # https://arxiv.org/abs/2012.03826\n        if kernel is None:\n            assert inputs.shape[-1] == self.params.real_dims, f\"Must provide kernel for input shape {inputs.shape}\"\n            kernel = self._get_kernel()\n        # Gaussian Process Regression\n        model = gp.models.GPRegression(\n            inputs.to(self.params.device),\n            outputs.to(self.params.device),\n            kernel=kernel,\n            jitter=1.0e-4,\n        ).to(self.params.device)\n        model.noise = pyro.nn.PyroSample(pyro.distributions.LogNormal(math.log(1e-2), 0.5))\n\n        optimizer = torch.optim.Adam(model.parameters(), lr=0.0001)\n        gp.util.train(model, optimizer)\n        model.eval()\n        return model\n\n    def fit_observations(self, success_observations: List[ObservationInBasic]) -> None:\n        self._fit_target_transformers(success_observations)\n\n        inputs_in_basic = torch.stack([x.real_number_input for x in success_observations]).detach()\n        outputs_in_surrogate = self._target_to_surrogate(torch.tensor([x.output for x in success_observations]))\n        self.output_model = self._get_model(inputs_in_basic, outputs_in_surrogate)\n\n        costs_after_transform = self._cost_to_logcost(torch.tensor([x.cost for x in success_observations]))\n        self.cost_model = self._get_model(inputs_in_basic, costs_after_transform)\n\n    def _fit_target_transformers(self, success_observations: List[ObservationInBasic]) -> None:\n        raw_outputs = np.array([x.output for x in success_observations])\n        # Using n_quantiles < len(observations_in_basic) preserves more distance information within the quantiles\n        n_quantiles = int(np.sqrt(len(success_observations)))\n        self.output_transformer = QuantileTransformer(output_distribution=\"normal\", n_quantiles=n_quantiles)\n        self.output_transformer.fit(raw_outputs.reshape(-1, 1))\n\n        log_costs = np.log(np.array([x.cost for x in success_observations]))\n        self.cost_transformer = MinMaxScaler(feature_range=(-1, 1))\n        self.cost_transformer.fit(log_costs.reshape(-1, 1))\n        transformed_costs = self.cost_transformer.transform(log_costs.reshape(-1, 1))\n        self.min_logcost = transformed_costs.min()\n        self.max_logcost = transformed_costs.max()\n\n    # Target space is the space of the score function that we're optimizing.\n    # Surrogate space is a well-behaved Gaussian space. It's the output analog of basic space.s\n    def _target_to_surrogate(self, x: Tensor) -> Tensor:\n        # Goes from target function space to the surrogate function space we model with GP\n        assert self.output_transformer is not None, \"Fit output_transformer before calling target_to_surrogate!\"\n        x_shape = x.shape\n        x = x.view(-1, 1).cpu()\n        transformed_x = torch.from_numpy(\n            self.output_transformer.transform(x.numpy()) * self.params.better_direction_sign\n        ).to(self.params.device)\n        return transformed_x.view(*x_shape)\n\n    def _surrogate_to_target(self, x: Tensor) -> Tensor:\n        # TODO: a good unit test would be that this inverts the above\n        assert self.output_transformer is not None, \"Fit output_transformer before calling surrogate_to_target!\"\n        x_shape = x.shape\n        x = x.view(-1, 1).cpu()\n        transformed_x = torch.from_numpy(\n            self.output_transformer.inverse_transform(x.numpy() * self.params.better_direction_sign)\n        ).to(self.params.device)\n        return transformed_x.view(*x_shape)\n\n    # Cost space is in seconds or dollars or something like that.\n    # Logcost space is the log of that, scaled a min-max range of -1 to 1.\n    def _cost_to_logcost(self, x: Tensor) -> Tensor:\n        # Goes from cost space to the logcost function space we model with GP\n        assert self.cost_transformer is not None, \"Fit cost_transformer before calling cost_to_logcost!\"\n        x_shape = x.shape\n        x = torch.log(x.view(-1, 1)).cpu()\n        transformed_x = torch.from_numpy(self.cost_transformer.transform(x.numpy())).to(self.params.device)\n        return transformed_x.view(*x_shape)\n\n    def _logcost_to_cost(self, x: Tensor) -> Tensor:\n        # TODO: a good unit test would be that this inverts the above\n        assert self.cost_transformer is not None, \"Fit cost_transformer before calling logcost_to_cost!\"\n        x_shape = x.shape\n        transformed_x = torch.from_numpy(self.cost_transformer.inverse_transform(x.view(-1, 1).cpu().numpy())).to(\n            self.params.device\n        )\n        return torch.exp(transformed_x).view(*x_shape)\n\n    def fit_suggestions(self, outstanding_suggestions: List[SuggestionInBasic]) -> None:\n        # Note that although we are fitting, no target outputs are passed in, and they have to be estimated using the surrogate model.\n        if len(outstanding_suggestions) == 0:\n            return\n\n        assert self.output_model is not None, \"Fit observations before suggestions!\"\n        assert self.cost_model is not None, \"Fit observations before suggestions!\"\n\n        inputs_in_basic = (\n            torch.stack([x.real_number_input for x in outstanding_suggestions]).detach().to(self.params.device)\n        )\n\n        # In both mean and Thompson sampling, the output model, which was trained on all real observations, is used to estimate the outputs of the suggestions. This is necessary to do as we await the real outputs.\n        if self.params.outstanding_suggestion_estimator == OutstandingSuggestionEstimatorEnum.MEAN:\n            output_predictions, _unused_output_vars = self.output_model(\n                inputs_in_basic, full_cov=False, noiseless=True\n            )\n        elif self.params.outstanding_suggestion_estimator == OutstandingSuggestionEstimatorEnum.THOMPSON:\n            sampler = self.output_model.iter_sample(noiseless=True)\n            outputs = []\n            for input_in_basic in inputs_in_basic:\n                outputs.append(sampler(input_in_basic.unsqueeze(0)))\n            output_predictions = torch.cat(outputs, dim=0)\n        else:\n            raise Exception(f\"Invalid estimator {self.params.outstanding_suggestion_estimator}\")\n        # With these new guesses at the outputs, we retrain the output model, which tells the Bayesian optimizer not to look for uncertainty near these points.\n        combined_inputs = torch.cat([self.output_model.X, inputs_in_basic])\n        combined_outputs = torch.cat([self.output_model.y, output_predictions])\n        self.output_model = self._get_model(combined_inputs, combined_outputs)\n\n    def fit_pareto_set(self, pareto_observations: List[ObservationInBasic]) -> None:\n        costs_after_transform = self._cost_to_logcost(torch.tensor([x.cost for x in pareto_observations]))\n        outputs_in_surrogate = self._target_to_surrogate(torch.tensor([x.output for x in pareto_observations]))\n        # TODO(research): This model is not guaranteed to be monotonic, which would be nice\n        self.pareto_model = self._get_model(\n            costs_after_transform, outputs_in_surrogate, kernel=gp.kernels.RBF(input_dim=1)\n        )\n        self.min_pareto_logcost = costs_after_transform.min().item()\n        self.max_pareto_logcost = costs_after_transform.max().item()\n\n    def get_pareto_surrogate_for_cost(self, cost: float) -> float:\n        assert self.pareto_model is not None\n        costs_after_transform = self._cost_to_logcost(torch.tensor([cost]))\n        costs_after_transform = torch.clamp(\n            costs_after_transform, min=self.min_pareto_logcost, max=self.max_pareto_logcost\n        )\n        pareto_output, pareto_var = self.pareto_model(costs_after_transform)\n        return float(pareto_output.item())\n\n    def fit_failures(\n        self, success_observations: List[ObservationInBasic], failure_observations: List[ObservationInBasic]\n    ) -> None:\n        num_success, num_failure = len(success_observations), len(failure_observations)\n        if num_failure == 0 or num_success == 0:\n            self.success_predictor = None\n            return\n\n        all_observations = success_observations + failure_observations\n        inputs_in_basic = torch.stack(([x.real_number_input for x in all_observations])).detach()\n        # Use this labeling so we can just get the CDF at zero as probability of success\n        labels = torch.tensor([-1] * num_success + [1] * num_failure)\n\n        self.success_model = self._get_model(inputs_in_basic, labels)\n\n    def _get_success_prob(self, samples_in_natural: Tensor):\n        if self.success_model is None:\n            return torch.ones((samples_in_natural.shape[0],), device=self.params.device)\n\n        success_pred, success_var = self.success_model(samples_in_natural)\n        prior = Normal(success_pred, success_var)\n        success_pred = prior.cdf(torch.zeros((samples_in_natural.shape[0],), device=self.params.device))\n        return success_pred\n\n    @torch.no_grad()\n    def observe_surrogate(self, samples_in_basic: Tensor) -> SurrogateObservationOutputs:\n        assert self.output_model is not None\n        surrogate_output, surrogate_var = self.output_model(samples_in_basic.to(self.params.device))\n        assert self.cost_model is not None\n        logcost, logcost_var = self.cost_model(samples_in_basic.to(self.params.device))\n        if self.pareto_model is not None:\n            pareto_logcost = torch.clamp(logcost, min=self.min_pareto_logcost, max=self.max_pareto_logcost)\n            pareto_surrogate, pareto_var = self.pareto_model(pareto_logcost)\n            pareto_estimate = self._surrogate_to_target(pareto_surrogate)\n        else:\n            pareto_surrogate = None\n            pareto_estimate = None\n\n        success_probability = self._get_success_prob(samples_in_basic.to(self.params.device))\n\n        cost_estimate = self._logcost_to_cost(logcost)\n        target_estimate = self._surrogate_to_target(surrogate_output)\n        # target_var is only used for logging, but we want it to be correct-ish\n        # self._surrogate_to_target will get clamped at the top or bottom, so we take half the distance between\n        # +1 and -1 std dev as the new std dev. Return variance which is std dev ** 2\n        target_var = torch.square(\n            (\n                self._surrogate_to_target(surrogate_output + torch.sqrt(surrogate_var))\n                - self._surrogate_to_target(surrogate_output - torch.sqrt(surrogate_var))\n            )\n            / 2.0\n        )\n        return SurrogateObservationOutputs(\n            surrogate_output=surrogate_output,\n            surrogate_var=surrogate_var,\n            cost_estimate=cost_estimate,\n            pareto_surrogate=pareto_surrogate,\n            pareto_estimate=pareto_estimate,\n            target_estimate=target_estimate,\n            target_var=target_var,\n            success_probability=success_probability,\n        )\n"
  },
  {
    "path": "carbs/serialization.py",
    "content": "from __future__ import (\n    annotations,  # using this to get Postponed Evaluation of Annotations -- https://www.python.org/dev/peps/pep-0563/\n)\n\nimport multiprocessing\nfrom contextlib import contextmanager\nfrom copy import deepcopy\nfrom datetime import datetime\nfrom enum import Enum\nfrom typing import Any\nfrom typing import ContextManager\nfrom typing import Dict\nfrom typing import List\nfrom typing import Set\nfrom typing import Type\nfrom typing import TypeVar\nfrom typing import Union\nfrom uuid import UUID\n\nimport attr\n\nFREEZE_KEY = \"$_frozen\"\n\nTC = TypeVar(\"TC\", bound=\"Serializable\")\n\n\n_CLASS_KEY = \"$type\"\n\nlock = multiprocessing.Lock()\n\n\n@attr.s(hash=True, collect_by_mro=True)\nclass Serializable:\n    # noinspection PyDataclass\n    def __attrs_post_init__(self) -> None:\n        self.__dict__[FREEZE_KEY] = True\n\n    def __setattr__(self, item: str, value: Any):\n        if self.__dict__.get(FREEZE_KEY):\n            raise ValueError(\"instance is frozen; see: .mutable_clone()\")\n        else:\n            self.__dict__[item] = value\n\n    def mutable_clone(self: TC) -> ContextManager[TC]:\n        freeze_list: List[Serializable] = []\n\n        def thaw(attr_object: Serializable) -> Serializable:\n            kwargs: Dict[str, Any] = {}\n            for attribute in attr.fields(attr_object.__class__):\n                k = attribute.name\n                v = getattr(attr_object, k)\n                if isinstance(v, Serializable):\n                    kwargs[k] = thaw(v)\n                elif isinstance(v, tuple) and len(v) > 0 and isinstance(v[0], Serializable):\n                    kwargs[k] = tuple([thaw(x) for x in v])\n                else:\n                    kwargs[k] = deepcopy(v)\n\n            # noinspection PyArgumentList\n            attr_cloned = attr_object.__class__(**kwargs)\n            attr_cloned.__dict__[FREEZE_KEY] = False\n            freeze_list.append(attr_cloned)\n\n            return attr_cloned\n\n        @contextmanager\n        def context():\n            try:\n                yield thaw(self)\n            finally:\n                for attr_cloned in freeze_list:\n                    attr_cloned.__dict__[FREEZE_KEY] = True\n\n        return context()\n\n    def to_dict(self) -> dict:\n        dump: Dict[str, Any] = {_CLASS_KEY: get_qualname_from_serializable_type(type(self))}\n        for attribute in attr.fields(self.__class__):\n            k = attribute.name\n            v = getattr(self, k)\n            dump[k] = _to_dict(v)\n        return dump\n\n    @classmethod\n    def from_dict(cls: Type[TC], dump: dict, is_upgrade_allowed: bool = False) -> TC:\n        \"\"\"\n        Using is_upgrade_allowed is dangerous:\n        - Silently ignores keys that exist in the serialization but no longer in the object\n          Could definitely lose data.\n        - Silently ignores missing attributes, assuming they will have default values.\n          This will likely blow up if there are no default values.\n        The most likely case that this will be problematic is if you RENAMED an attribute\n        There's no way for us to really detect that.\n        I'd be ok with someone adding explicit \"rename\" support when they actually need it\n        \"\"\"\n        kwargs = {}\n        remaining_dict_keys = set(dump.keys())\n        for attribute in attr.fields(cls):\n            k = attribute.name\n            if is_upgrade_allowed and k not in dump:\n                # this enables us to load older ubjects when new default attributes have been added\n                # if there is no default value, the later construction will fail, there is\n                # no easy way around that without getting into more complex migration schemes\n                continue\n            if k not in remaining_dict_keys:\n                raise Exception(f\"Serialized object {cls} is missing key={k}\")\n            remaining_dict_keys.remove(k)\n            kwargs[k] = _from_value(dump[k], is_upgrade_allowed)\n        if _CLASS_KEY in remaining_dict_keys:\n            remaining_dict_keys.remove(_CLASS_KEY)\n        if len(remaining_dict_keys) > 0:\n            # silently drops keys here!\n            if not is_upgrade_allowed:\n                bad_keys = sorted(remaining_dict_keys)\n                raise ParamTypeError(f\"Tried to load data for {cls} but got unexpected keys: {bad_keys}\")\n        # noinspection PyArgumentList\n        return cls(**kwargs)\n\n\ndef _to_dict(v: Any) -> Any:\n    # better to check for the existance of this attribute than to check isinstance\n    # even works in jupyter notebooks when doing code reloading then..\n    if hasattr(v, \"to_dict\"):\n        result = v.to_dict()\n    elif isinstance(v, tuple):\n        if len(v) > 0:\n            result = tuple([_to_dict(x) for x in v])\n        else:\n            result = v\n    elif isinstance(v, Enum):\n        result = {\"value\": v.value, _CLASS_KEY: get_qualname_from_serializable_type(type(v))}\n    elif isinstance(v, UUID):\n        result = dict(value=str(v))\n        result[_CLASS_KEY] = UUID.__name__\n    elif isinstance(v, datetime):\n        result = dict(value=v.isoformat())\n        result[_CLASS_KEY] = datetime.__name__\n    else:\n        assert isinstance(v, (float, int, bool, str, type(None))), \"Unexpected type: \" + str(v)\n        result = v\n    return result\n\n\ndef _from_value(v: Dict[str, Any], is_upgrade_allowed: bool) -> Any:\n    result: Any\n    if isinstance(v, Dict) and _CLASS_KEY in v:\n        class_name = v[_CLASS_KEY]\n        if class_name == UUID.__name__:\n            result = UUID(v[\"value\"])\n        elif class_name == datetime.__name__:\n            result = datetime.fromisoformat(v[\"value\"])\n        else:\n            t = get_serializable_type_from_qualname(class_name)\n            if issubclass(t, Enum):\n                # noinspection PyArgumentList\n                result = t(value=v[\"value\"])  # type: ignore\n            else:\n                result = t.from_dict(v, is_upgrade_allowed=is_upgrade_allowed)\n    # TAKS c00f9a38-bd11-4e14-97d8-02b9bf843ca6: delete this after we get rid of OldSerializable\n    elif isinstance(v, Dict) and \"_serializable_type\" in v:\n        t = get_serializable_type_from_qualname(v[\"_serializable_type\"])\n        result = t.from_dict(v, is_upgrade_allowed=is_upgrade_allowed)\n    elif isinstance(v, (list, tuple)):\n        if len(v) == 0:\n            result = tuple()\n        else:\n            inner_objects = []\n            for inner_value in v:\n                inner_objects.append(_from_value(inner_value, is_upgrade_allowed))\n            result = tuple(inner_objects)\n    else:\n        assert isinstance(v, (float, int, bool, str, type(None))), \"Unexpected type: \" + str(v)\n        result = v\n    return result\n\n\nqualname_to_serializable_type: Dict[str, Type[Serializable]] = {}\nserializable_type_to_qualname: Dict[Type[Serializable], str] = {}\n# this is here because some libraries make enums with the same names, but we dont use those, so, fine\nunserializable_types: Set[str] = set()\n\n\nclass ParamTypeError(TypeError):\n    pass\n\n\ndef _get_all_subclasses(cls):\n    all_subclasses = []\n    for subclass in cls.__subclasses__():\n        all_subclasses.append(subclass)\n        all_subclasses.extend(_get_all_subclasses(subclass))\n    return all_subclasses\n\n\ndef _compute_unserializable(qualnames: List[str]) -> None:\n    global unserializable_types\n\n    if not unserializable_types:\n        already_seen_names = set()\n        for qualname in qualnames:\n            if qualname in already_seen_names:\n                unserializable_types.add(qualname)\n            already_seen_names.add(qualname)\n\n\ndef _dedupe_subclasses_by_qualname(subclasses: List[type]) -> List[type]:\n    qualname_to_serializable_type = {x.__qualname__: x for x in subclasses}\n    return list(qualname_to_serializable_type.values())\n\n\ndef _dedupe_subclasses_by_id(subclasses: List[type]) -> List[type]:\n    id_to_serializable_type = {id(x): x for x in subclasses}\n    return list(id_to_serializable_type.values())\n\n\ndef get_all_serializable_classes() -> List[type]:\n    # we don't reload the Enum class (since it's standard library) so we end up having duplicate subclasses for Enums\n    enum_subclasses = _dedupe_subclasses_by_qualname(_get_all_subclasses(Enum))\n    # we want to make sure we have classes with different names which is why we need to dedupe with the ids\n    serializable_subclasses = _dedupe_subclasses_by_id(_get_all_subclasses(Serializable))\n    return serializable_subclasses + enum_subclasses\n\n\ndef get_serializable_type_from_qualname(qualname: str) -> Type[Serializable]:\n    global qualname_to_serializable_type\n    global unserializable_types\n    if not qualname_to_serializable_type:\n        # makes our globals thread safe and hopefully free from weird race conditions\n        with lock:\n            if not qualname_to_serializable_type:\n                all_serializable_classes = get_all_serializable_classes()\n                qualname_to_serializable_type = {x.__qualname__: x for x in all_serializable_classes}\n                _compute_unserializable([x.__qualname__ for x in all_serializable_classes])\n    if qualname in unserializable_types:\n        raise Exception(f\"{qualname} cannot be deserialized because there are multiple definitions\")\n    return qualname_to_serializable_type[qualname]\n\n\ndef get_qualname_from_serializable_type(serializable_type: type) -> str:\n    global serializable_type_to_qualname\n    global unserializable_types\n    if not serializable_type_to_qualname:\n        # makes our globals thread safe and hopefully free from weird race conditions\n        with lock:\n            if not serializable_type_to_qualname:\n                all_serializable_classes = get_all_serializable_classes()\n                serializable_type_to_qualname = {x: x.__qualname__ for x in all_serializable_classes}\n                _compute_unserializable(list(serializable_type_to_qualname.values()))\n    result = serializable_type_to_qualname.get(serializable_type, serializable_type.__qualname__)\n    if result in unserializable_types:\n        raise Exception(f\"{result} cannot be serialized because there are multiple definitions\")\n    return result\n\n\nT = TypeVar(\"T\", bool, int, float, str, tuple)\nDictNest = Dict[str, Union[T, Dict[str, Any]]]\nDictFlat = Dict[str, T]\n\n\ndef flatten_dict(d: DictNest, prefix: str = \"\") -> DictFlat:\n    flattened_dict = {}\n    for k, v in d.items():\n        if isinstance(v, dict):\n            flattened_dict.update(flatten_dict(v, f\"{prefix}{k}.\"))\n        else:\n            flattened_dict[f\"{prefix}{k}\"] = v\n    return flattened_dict\n\n\ndef inflate_dict(d: DictFlat) -> DictNest:\n    inflated_dict: DictNest = {}\n    for key, value in d.items():\n        parts = key.split(\".\")\n        d = inflated_dict\n        for part in parts[:-1]:\n            d = d.setdefault(part, {})\n        d[parts[-1]] = value\n    return inflated_dict\n"
  },
  {
    "path": "carbs/test_carbs.py",
    "content": "import os\nfrom typing import List\n\nimport pytest\nimport wandb\n\nfrom carbs import LogitSpace\nfrom carbs import ObservationInParam\nfrom carbs.carbs import CARBS\nfrom carbs.utils import CARBSParams\nfrom carbs.utils import LinearSpace\nfrom carbs.utils import LogSpace\nfrom carbs.utils import Param\n\n# Set wandb to dryrun mode for testing\nos.environ[\"WANDB_MODE\"] = \"dryrun\"\n\n# Initialize wandb\nwandb.init(project=\"my_project\", job_type=\"train\")\n\n\n@pytest.fixture\ndef carbs_config() -> CARBSParams:\n    return CARBSParams(is_wandb_logging_enabled=False, is_saved_on_every_observation=False)\n\n\n@pytest.fixture\ndef params() -> List[Param]:\n    return [\n        Param(\"p1\", LogSpace(scale=1), 1e-2),\n        Param(\"p2\", LinearSpace(scale=2), 0),\n        Param(\"p3\", LogitSpace(scale=0.5), 0.5),\n    ]\n\n\n@pytest.fixture\ndef carbs_instance(carbs_config: CARBSParams, params: List[Param]) -> CARBS:\n    return CARBS(carbs_config, params)\n\n\ndef test_suggest_one(carbs_instance: CARBS) -> None:\n    start_suggestions = len(carbs_instance.outstanding_suggestions)\n    suggestion = carbs_instance.suggest()\n    assert len(carbs_instance.outstanding_suggestions) == start_suggestions + 1\n    assert suggestion is not None\n    assert \"suggestion_uuid\" in suggestion.suggestion\n    for param in carbs_instance.params:\n        assert param.name in suggestion.suggestion\n\n\ndef test_suggest_observe_ten(carbs_instance: CARBS) -> None:\n    num_to_suggest = 10\n    start_suggestions = len(carbs_instance.outstanding_suggestions)\n    start_success_observations = len(carbs_instance.success_observations)\n    for i in range(num_to_suggest):\n        suggestion = carbs_instance.suggest()\n        observation = ObservationInParam(input=suggestion.suggestion, output=i, cost=i + 1)\n        carbs_instance.observe(observation)\n    assert len(carbs_instance.outstanding_suggestions) == start_suggestions\n    assert len(carbs_instance.success_observations) == start_success_observations + num_to_suggest\n\n\ndef test_observe(carbs_instance: CARBS) -> None:\n    start_success_obs = len(carbs_instance.success_observations)\n    start_failure_obs = len(carbs_instance.failure_observations)\n    obs_success = ObservationInParam(input={x.name: x.search_center for x in carbs_instance.params}, output=1, cost=1)\n    obs_failure = ObservationInParam(\n        input={x.name: x.search_center for x in carbs_instance.params}, output=1, cost=1, is_failure=True\n    )\n    obs_success_output = carbs_instance.observe(obs_success)\n    obs_failure_output = carbs_instance.observe(obs_failure)\n    assert len(carbs_instance.success_observations) == start_success_obs + 1\n    assert len(carbs_instance.failure_observations) == start_failure_obs + 1\n\n\ndef test_forget(carbs_instance: CARBS) -> None:\n    start_suggestions = len(carbs_instance.outstanding_suggestions)\n    suggestion = carbs_instance.suggest()\n    carbs_instance.forget_suggestion(suggestion.suggestion)\n    assert len(carbs_instance.outstanding_suggestions) == start_suggestions\n"
  },
  {
    "path": "carbs/utils.py",
    "content": "import math\nimport os\nfrom collections import OrderedDict\nfrom enum import Enum\nfrom pathlib import Path\nfrom typing import Any\nfrom typing import Dict\nfrom typing import List\nfrom typing import Optional\nfrom typing import Sequence\nfrom typing import Sized\nfrom typing import Tuple\nfrom typing import Union\nfrom pathlib import Path\nfrom typing import Optional\n\nimport attr\nimport numpy as np\nimport seaborn as sns\nimport torch\nimport wandb\nfrom loguru import logger\nfrom matplotlib import pyplot as plt\nfrom scipy.special import wofz\nfrom torch import Tensor\nfrom torch.distributions import Normal\n\nfrom carbs.serialization import Serializable\n\nParamType = Union[int, float, str, bool, Enum]\nParamDictType = Dict[str, ParamType]\n\n\n@attr.s(auto_attribs=True, hash=True)\nclass ParamSpace(Serializable):\n    def basic_from_param(self, value: ParamType) -> Any:\n        raise NotImplementedError()\n\n    def param_from_basic(self, value: Any) -> ParamType:\n        raise NotImplementedError()\n\n    def drop_type(self) -> Any:\n        return self\n\n\n@attr.s(auto_attribs=True, frozen=True)\nclass Param:\n    name: str\n    space: ParamSpace\n    search_center: Union[float, int]\n\n\n@attr.s(auto_attribs=True, hash=True)\nclass RealNumberSpace(ParamSpace):\n    min: float = float(\"-inf\")\n    max: float = float(\"+inf\")\n    scale: float = 1.0\n    is_integer: bool = False\n    rounding_factor: int = 1\n\n    def basic_from_param(self, value: ParamType) -> float:\n        raise NotImplementedError()\n\n    def param_from_basic(self, value: float, is_rounded: bool = True) -> ParamType:\n        raise NotImplementedError()\n\n    def round_tensor_in_basic(self, value: Tensor) -> Tensor:\n        if not self.is_integer:\n            return value\n        raise NotImplementedError()\n\n    @property\n    def min_bound(self):\n        # compensate for floats being bad bounds for integers\n        if self.is_integer:\n            return self.min - 0.1\n        return self.min\n\n    @property\n    def max_bound(self):\n        # compensate for floats being bad bounds for integers\n        if self.is_integer:\n            return self.max + 0.1\n        return self.max\n\n    @property\n    def plot_scale(self) -> str:\n        raise NotImplementedError()\n\n\n@attr.s(auto_attribs=True, hash=True)\nclass LinearSpace(RealNumberSpace):\n    min: float = float(\"-inf\")\n    max: float = float(\"+inf\")\n\n    def __attrs_post_init__(self) -> None:\n        if self.is_integer and self.scale < 3:\n            logger.info(\n                \"scale<3 on integer LinearSpace, so may not be able to search neighboring integers!\"\n            )\n\n    def basic_from_param(self, value: ParamType) -> float:\n        assert isinstance(value, (int, float))\n        return value / self.scale\n\n    def param_from_basic(self, value: float, is_rounded: bool = True) -> float:\n        value = value * self.scale\n        if self.is_integer and is_rounded:\n            value = round(value / self.rounding_factor) * self.rounding_factor\n        return value\n\n    def round_tensor_in_basic(self, value: Tensor) -> Tensor:\n        if self.is_integer:\n            return (\n                torch.round(value * self.scale / self.rounding_factor)\n                * self.rounding_factor\n                / self.scale\n            )\n        else:\n            return value\n\n    @property\n    def plot_scale(self) -> str:\n        return \"linear\"\n\n\n@attr.s(auto_attribs=True, hash=True)\nclass LogSpace(RealNumberSpace):\n    min: float = 0.0\n    max: float = float(\"+inf\")\n    base: int = 10\n\n    def basic_from_param(self, value: ParamType) -> float:\n        assert isinstance(value, (int, float))\n        if value == 0.0:\n            return float(\"-inf\")\n        return math.log(value, self.base) / self.scale\n\n    def param_from_basic(self, value: float, is_rounded: bool = True) -> float:\n        value = self.base ** (value * self.scale)\n        if self.is_integer and is_rounded:\n            value = round(value / self.rounding_factor) * self.rounding_factor\n        return value\n\n    def round_tensor_in_basic(self, value: Tensor) -> Tensor:\n        if self.is_integer:\n            rounded_value = (\n                torch.round(self.base ** (value * self.scale) / self.rounding_factor)\n                * self.rounding_factor\n            )\n            if self.base == 10:\n                return torch.log10(rounded_value) / self.scale\n            else:\n                return torch.log(rounded_value) / self.scale / math.log(self.base)\n        else:\n            return value\n\n    @property\n    def plot_scale(self) -> str:\n        return \"log\"\n\n\n@attr.s(auto_attribs=True, hash=True)\nclass LogitSpace(RealNumberSpace):\n    min: float = 0.0\n    max: float = 1.0\n\n    def basic_from_param(self, value: ParamType) -> float:\n        assert isinstance(value, (int, float))\n        if value == 0.0:\n            return float(\"-inf\")\n        if value == 1.0:\n            return float(\"+inf\")\n        return math.log10(value / (1 - value)) / self.scale\n\n    def param_from_basic(self, value: float, is_rounded: bool = True) -> float:\n        value = 1 / (10 ** (-value * self.scale) + 1)\n        return value\n\n    @property\n    def plot_scale(self) -> str:\n        return \"logit\"\n\n\nCategoricalTuple = Tuple[int, ...]\nSUGGESTION_ID_DICT_KEY = \"suggestion_uuid\"\n\n\ndef log_norm_cdf(z: Tensor):\n    \"\"\"\n    @MISC {256009,\n        TITLE = {Approximation of logarithm of standard normal CDF for x&lt;0},\n        AUTHOR = {Isaac Asher (https://stats.stackexchange.com/users/145180/isaac-asher)},\n        HOWPUBLISHED = {Cross Validated},\n        URL = {https://stats.stackexchange.com/q/256009}\n    }\n    This contains -z**2/2 which exactly cancels the same term in prior.log_prob for EI... We can then take the log\n    out of the exp for the simplified form below\n    \"\"\"\n    return torch.log(wofz(-z * 1j / math.sqrt(2)).real) - z**2 / 2 + math.log(0.5)\n\n\n# See explanation for expected improvement in https://distill.pub/2020/bayesian-optimization/\ndef expected_improvement(\n    mu: Tensor, variance: Tensor, best_mu: Tensor, exploration_bias: float = 0.5\n) -> Tensor:\n    prior = Normal(0, 1)\n    sigma = variance.sqrt()\n    z = (mu - best_mu - exploration_bias) / sigma\n    # original form:\n    # ei: Tensor = sigma * torch.exp(prior.log_prob(z)) * (1 + z * torch.exp(log_norm_cdf(z) - prior.log_prob(z)))\n    # simplified form:\n    wofz_output = wofz(-z.cpu() * 1j / math.sqrt(2)).real.to(z.device)\n    ei: Tensor = (\n        sigma\n        * torch.exp(prior.log_prob(z))\n        * (1 + z * wofz_output * math.sqrt(math.pi / 2))\n    )\n    return ei\n\n\ndef probability_of_improvement(\n    mu: Tensor,\n    variance: Tensor,\n    best_mu: Tensor,\n    better_direction_sign: int,\n    exploration_bias: float = 0.0,\n) -> Tensor:\n    prior = Normal(0, 1)\n    mu_improvement = (mu - best_mu) * better_direction_sign - exploration_bias\n    sigma = variance.sqrt()\n    poi: Tensor = prior.cdf(mu_improvement / sigma)\n    return poi\n\n\ndef aggregate_logical_and_across_dim(x: Tensor, dim: int = -1) -> Tensor:\n    \"\"\"\n    Takes in BoolTensor x, aggregates across dimension dim.\n    \"\"\"\n    return torch.min(torch.where(x, 1, 0), dim=dim).values > 0\n\n\ndef add_dict_key_prefix(input_dict: Dict[str, Any], prefix: str):\n    return {f\"{prefix}{k}\": v for k, v in input_dict.items()}\n\n\n@attr.s(auto_attribs=True, collect_by_mro=True)\nclass ObservationInParam(Serializable):\n    input: ParamDictType\n    output: float\n    cost: float = 1.0\n    is_failure: bool = False\n\n\n@attr.s(auto_attribs=True, collect_by_mro=True)\nclass ObservationInBasic(Serializable):\n    real_number_input: Tensor\n    output: float\n    cost: float = 1.0\n    suggestion_id: Optional[str] = None\n\n\n@attr.s(auto_attribs=True, collect_by_mro=True)\nclass SuggestionInBasic(Serializable):\n    real_number_input: Tensor\n    log_info: Dict[str, float] = attr.Factory(dict)\n\n\nclass OutstandingSuggestionEstimatorEnum(Enum):\n    MEAN = \"MEAN\"\n    CONSTANT = \"CONSTANT\"\n    THOMPSON = \"THOMPSON\"\n\n\nclass SuggestionRedistributionMethodEnum(Enum):\n    NONE = \"NONE\"\n    LOG_COST_CLAMPING = \"LOG_COST_CLAMPING\"\n\n\n@attr.s(auto_attribs=True, collect_by_mro=True)\nclass WandbLoggingParams(Serializable):\n    project_name: Optional[str] = None\n    group_name: Optional[str] = None\n    run_name: Optional[str] = None\n    run_id: Optional[str] = None\n    is_suggestion_logged: bool = True\n    is_observation_logged: bool = True\n    is_search_space_logged: bool = True\n    root_dir: str = \"/mnt/private\"\n\n\n@attr.s(auto_attribs=True, collect_by_mro=True)\nclass CARBSParams(Serializable):\n    \"\"\"\n    Set `better_direction_sign`, `max_suggestion_cost` and `wandb_params` for your run.\n    I'm not aware of any situation in which we should change the other parameters -- they are mostly for testing.\n    \"\"\"\n\n    better_direction_sign: int = attr.field(\n        validator=attr.validators.in_([-1, 1]), default=1\n    )  # 1 for maximizing, -1 for minimizing\n    seed: int = 0\n\n    # will do random suggestions until this many observations are made\n    num_random_samples: int = attr.field(validator=attr.validators.ge(1), default=4)\n    is_wandb_logging_enabled: bool = True\n    wandb_params: WandbLoggingParams = WandbLoggingParams()\n    checkpoint_dir: str = \"checkpoints/\"\n    s3_checkpoint_path: str = \"s3://int8/checkpoints\"\n    is_saved_on_every_observation: bool = True\n\n    initial_search_radius: float = attr.field(\n        validator=attr.validators.gt(0), default=0.3\n    )\n\n    exploration_bias: float = (\n        1.0  # hyperparameter biasing BO acquisition function toward exploration\n    )\n\n    num_candidates_for_suggestion_per_dim: int = 100\n\n    resample_frequency: int = (\n        5  # resample a pareto point every n observations, set 0 to disable\n    )\n\n    max_suggestion_cost: Optional[float] = (\n        None  # Will not make suggestions with predicted cost above this value\n    )\n\n    # takes minimum cost for pareto set to be this percentile of cost data\n    min_pareto_cost_fraction: float = attr.field(\n        validator=attr.validators.ge(0), default=0.2\n    )\n    is_pareto_group_selection_conservative: bool = True\n    is_expected_improvement_pareto_value_clamped: bool = True\n    is_expected_improvement_value_always_max: bool = False\n\n    outstanding_suggestion_estimator: OutstandingSuggestionEstimatorEnum = (\n        OutstandingSuggestionEstimatorEnum.THOMPSON\n    )\n\n\n@attr.s(auto_attribs=True, collect_by_mro=True)\nclass SurrogateModelParams(Serializable):\n    real_dims: int\n    better_direction_sign: int  # 1 for maximizing, -1 for minimizing\n    device: str = \"cpu\"\n    min_category_observations: int = 3\n    scale_length: float = 1\n    outstanding_suggestion_estimator: OutstandingSuggestionEstimatorEnum = (\n        OutstandingSuggestionEstimatorEnum.MEAN\n    )\n\n\n@attr.s(auto_attribs=True, collect_by_mro=True)\nclass SuggestOutput:\n    suggestion: ParamDictType\n    log: Dict[str, Any] = attr.Factory(dict)\n\n\n@attr.s(auto_attribs=True, collect_by_mro=True)\nclass ObserveOutput:\n    logs: Dict[str, Any] = attr.Factory(dict)\n\n\ndef load_observations_from_wandb_run(\n    run_name: str,\n    prefix: str = \"observation/\",\n    add_params: Optional[ParamDictType] = None,\n) -> List[ObservationInParam]:\n    api = wandb.Api()\n    run = api.run(run_name)\n    history_df = run.history()\n    observations: List[ObservationInParam] = []\n    for idx, row in (\n        history_df[[x for x in history_df.keys() if x.startswith(prefix)]]\n        .dropna()\n        .iterrows()\n    ):\n        input: Dict[str, ParamType] = {}\n        if add_params is not None:\n            input.update(add_params)\n        output = float(\"inf\")\n        for k, v in row.to_dict().items():\n            if k == f\"{prefix}output\":\n                output = v\n            else:\n                input[k[len(prefix) :]] = v\n        observations.append(ObservationInParam(input=input, output=output))\n\n    return observations\n\n\nCARBS_CHECKPOINT_PREFIX = \"carbs_\"\nCARBS_CHECKPOINT_SUFFIX = \"obs.pt\"\n\n\ndef get_checkpoint_obs_count(checkpoint_name: str) -> int:\n    return int(\n        checkpoint_name.removeprefix(CARBS_CHECKPOINT_PREFIX).removesuffix(\n            CARBS_CHECKPOINT_SUFFIX\n        )\n    )\n\n\ndef load_latest_checkpoint_from_wandb_run(\n    run_path: str, temp_dir: Optional[str] = None\n) -> str:\n    api = wandb.Api()\n    run = api.run(run_path)\n    checkpoint_filenames = [\n        file.name\n        for file in run.files()\n        if file.name.startswith(CARBS_CHECKPOINT_PREFIX)\n        and file.name.endswith(CARBS_CHECKPOINT_SUFFIX)\n    ]\n    checkpoint_filenames = sorted(checkpoint_filenames, key=get_checkpoint_obs_count)\n    latest_checkpoint_filename = checkpoint_filenames[-1]\n    return load_checkpoint_from_wandb_run(\n        run_path, latest_checkpoint_filename, temp_dir\n    )\n\n\ndef load_checkpoint_from_wandb_run(\n    run_path: str, checkpoint_filename: str, temp_dir: Optional[str] = None\n) -> str:\n    if temp_dir is None:\n        temp_dir = f\"/tmp/carbs/{run_path}\"\n        os.makedirs(temp_dir, exist_ok=True)\n\n    checkpoint_path = wandb.restore(\n        checkpoint_filename, run_path=run_path, replace=True, root=temp_dir\n    )\n    assert checkpoint_path is not None, \"Could not load checkpoint\"\n    return checkpoint_path.name\n\n\ndef assert_empty(x: Sized, message: str = \"unexpected elements\") -> None:\n    assert len(x) == 0, f\"{message}: {x}\"\n\n\ndef ordered_dict_index(od: OrderedDict, value: Any) -> int:\n    for i, k in enumerate(od.keys()):\n        if k == value:\n            return i\n    raise KeyError(f\"{value} not found in {od}\")\n\n\n# All members of an ObservationGroup have the same parameters\nObservationGroup = Tuple[ObservationInBasic, ...]\n\n\ndef group_observations(\n    observations_in_basic: List[ObservationInBasic],\n) -> Tuple[ObservationGroup, ...]:\n    \"\"\"\n    Gets observations grouped by matching input params\n    \"\"\"\n    outputs: List[ObservationGroup] = []\n    observations = observations_in_basic.copy()\n    observations.sort(key=lambda x: tuple(v.item() for v in x.real_number_input))\n    while len(observations) > 0:\n        obs = observations.pop()\n        nearby_obs = [obs]\n        for i in range(len(observations) - 1, -1, -1):\n            if torch.all(\n                torch.isclose(observations[i].real_number_input, obs.real_number_input)\n            ):\n                nearby_obs.append(observations.pop(i))\n        outputs.append(tuple(nearby_obs))\n\n    return tuple(outputs)\n\n\ndef observation_group_cost(group: Sequence[ObservationInBasic]) -> float:\n    return sum(obs.cost for obs in group) / len(group)\n\n\ndef observation_group_output(group: Sequence[ObservationInBasic]) -> float:\n    return sum(obs.output for obs in group) / len(group)\n\n\ndef pareto_area_from_groups(obs_groups: Tuple[ObservationGroup, ...]) -> float:\n    if len(obs_groups) < 2:\n        return 0\n    last_cost = observation_group_cost(obs_groups[0])\n    last_output = observation_group_output(obs_groups[0])\n    total_area = 0.0\n    for group in obs_groups[1:]:\n        new_cost = observation_group_cost(group)\n        new_output = observation_group_output(group)\n        total_area += (new_cost - last_cost) * last_output\n        last_cost = new_cost\n        last_output = new_output\n    return total_area\n\n\ndef get_pareto_groups(\n    grouped_observations: Tuple[ObservationGroup, ...],\n    min_pareto_cost_fraction: float,\n    better_direction_sign: int,\n) -> Tuple[ObservationGroup, ...]:\n    min_pareto_cost = np.quantile(\n        np.array([x.cost for group in grouped_observations for x in group]),\n        min_pareto_cost_fraction,\n    )\n    observations_below_min_threshold = [\n        group\n        for group in grouped_observations\n        if observation_group_cost(group) <= min_pareto_cost\n    ]\n\n    group_output_pos_better = (\n        lambda x: observation_group_output(x) * better_direction_sign\n    )\n    first_pareto_group = max(\n        observations_below_min_threshold, key=group_output_pos_better\n    )\n\n    remaining_observations = [\n        group\n        for group in grouped_observations\n        if observation_group_cost(group) > min_pareto_cost\n    ]\n    remaining_observations.sort(key=observation_group_cost)\n\n    pareto_groups: List[ObservationGroup] = [first_pareto_group]\n    best_output = group_output_pos_better(first_pareto_group)\n    for obs_group in remaining_observations:\n        mean_output = group_output_pos_better(obs_group)\n        if mean_output > best_output:\n            pareto_groups.append(obs_group)\n            best_output = mean_output\n\n    return tuple(pareto_groups)\n\n\ndef get_pareto_groups_conservative(\n    grouped_observations: Tuple[ObservationGroup, ...],\n    min_pareto_cost_fraction: float,\n    better_direction_sign: int,\n) -> Tuple[ObservationGroup, ...]:\n    \"\"\"\n    Just like get_pareto_groups but prefers groups with multiple samples. A single sample can only be \"better\" if\n    it is better than the max of the previous group. However, a multiple sample group is better if the mean is higher.\n    For example, with better_direction_sign=1:\n\n    Group A: ((output=0,cost=1),(output=1,cost=1))       # mean=0.5\n    Group B: ((output=0.6,cost=2))                       # mean=0.6\n    Group C: ((output=0.55,cost=3),(output=0.75,cost=3)) # mean=0.65\n    Group D: ((output=0.8,cost=4))                       # mean=0.8\n\n    With the normal pareto algorithm, A, B, C and D would all be in the pareto front. With this algorithm, groups\n    A, C and D will be in the pareto front. B will not be included because it is not greater than the max of group A.\n\n    The purpose of this is to reduce thrash in which groups are used as search centers in noisy areas of search space.\n\n    \"\"\"\n\n    min_pareto_cost = np.quantile(\n        np.array([x.cost for group in grouped_observations for x in group]),\n        min_pareto_cost_fraction,\n    )\n    observations_below_min_threshold = [\n        group\n        for group in grouped_observations\n        if observation_group_cost(group) <= min_pareto_cost\n    ]\n    resampled_observations_below_min_threshold = [\n        group for group in observations_below_min_threshold if len(group) > 1\n    ]\n\n    # Only use resampled observations if there are any\n    if len(resampled_observations_below_min_threshold) > 0:\n        observations_below_min_threshold = resampled_observations_below_min_threshold\n\n    group_output_pos_better = (\n        lambda x: observation_group_output(x) * better_direction_sign\n    )\n    max_group_output_pos_better = lambda x: max(\n        [obs.output * better_direction_sign for obs in x]\n    )\n    first_pareto_group = max(\n        observations_below_min_threshold, key=group_output_pos_better\n    )\n\n    remaining_observations = [\n        group\n        for group in grouped_observations\n        if observation_group_cost(group) > min_pareto_cost\n    ]\n    remaining_observations.sort(key=observation_group_cost)\n\n    pareto_groups: List[ObservationGroup] = [first_pareto_group]\n    best_output = group_output_pos_better(first_pareto_group)\n    best_output_max = max_group_output_pos_better(first_pareto_group)\n    for obs_group in remaining_observations:\n        mean_output = group_output_pos_better(obs_group)\n        # If only one sample, it must be better than the max of the last group\n        # Otherwise, only must have better mean\n        if (len(obs_group) > 1 and mean_output > best_output) or (\n            len(obs_group) == 1 and mean_output > best_output_max\n        ):\n            pareto_groups.append(obs_group)\n            best_output = mean_output\n            best_output_max = max_group_output_pos_better(obs_group)\n\n    return tuple(pareto_groups)\n\n\ndef get_pareto_curve_plot(\n    observations: List[ObservationInBasic],\n    pareto_groups: Tuple[ObservationGroup, ...],\n    save_dir: Optional[str] = None,\n    obs_count: Optional[int] = None,\n) -> Optional[str]:\n    sns.set_theme(style=\"whitegrid\")\n    plt.clf()\n    pareto_set = [obs for group in pareto_groups for obs in group]\n    plt.scatter(\n        [x.cost for x in pareto_set],\n        [x.output for x in pareto_set],\n        c=\"black\",\n        s=100,\n        # marker=\"o\",\n    )\n    plt.plot(\n        [observation_group_cost(g) for g in pareto_groups],\n        [observation_group_output(g) for g in pareto_groups],\n        linewidth=4,\n        color=\"black\",\n    )\n    plt.scatter(\n        [x.cost for x in observations],\n        [x.output for x in observations],\n        c=list(range(len(observations))),\n        cmap=\"plasma\",\n    )\n    plt.xscale(\"log\")\n    if obs_count is None:\n        obs_count = len(observations)\n    plt.title(f\"Pareto curve at {obs_count} obs\")\n    plt.ylabel(\"performance\")\n    plt.xlabel(\"cost\")\n\n    if save_dir is not None:\n        img_path = Path(save_dir) / f\"pareto_curve_{obs_count}.png\"\n        plt.savefig(img_path)\n        return str(img_path)\n    return None\n"
  },
  {
    "path": "excluded.txt",
    "content": "setup.py\n"
  },
  {
    "path": "notebooks/analyze_carbs.sync.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import math\\n\",\n    \"import warnings\\n\",\n    \"from functools import partial\\n\",\n    \"\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import numpy as np\\n\",\n    \"import seaborn as sns\\n\",\n    \"import torch\\n\",\n    \"import wandb\\n\",\n    \"\\n\",\n    \"%matplotlib inline\\n\",\n    \"from carbs import CARBS\\n\",\n    \"from carbs import ObservationInBasic\\n\",\n    \"from carbs import get_pareto_curve_plot\\n\",\n    \"from carbs import load_latest_checkpoint_from_wandb_run\\n\",\n    \"from carbs import observation_group_cost\\n\",\n    \"from carbs import observation_group_output\\n\",\n    \"from matplotlib import MatplotlibDeprecationWarning\\n\",\n    \"from matplotlib.ticker import LogFormatter\\n\",\n    \"from scipy.interpolate import interp1d\\n\",\n    \"from sklearn.linear_model import LinearRegression\\n\",\n    \"\\n\",\n    \"from research.quarantine.abe.plot_helpers import set_axes_style\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"\\n\",\n    \"api = wandb.Api()\\n\",\n    \"run_path = \\\"sourceress/abe__bones/2i8gnlf9\\\"\\n\",\n    \"run = api.run(run_path)\\n\",\n    \"history_df = run.history()\\n\",\n    \"history_df = history_df.replace(\\\"Infinity\\\", float(\\\"-inf\\\"))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"\\n\",\n    \"carbs_checkpoint_path = load_latest_checkpoint_from_wandb_run(run_path)\\n\",\n    \"carbs = CARBS.load_from_file(carbs_checkpoint_path)\\n\",\n    \"search_vars = list(carbs._real_number_space_by_name.keys())\\n\",\n    \"search_space_scale = {k: v.plot_scale for k, v in carbs._real_number_space_by_name.items()}\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"\\n\",\n    \"# Performance\\n\",\n    \"\\n\",\n    \"is_best_shown = True\\n\",\n    \"is_resampled_shown = True\\n\",\n    \"is_search_space_shown = False\\n\",\n    \"performance_min, performance_max = (3, 6)\\n\",\n    \"\\n\",\n    \"# sns.set(rc={\\\"figure.figsize\\\": (12, 4)})\\n\",\n    \"# sns.set_theme(style=\\\"whitegrid\\\")\\n\",\n    \"cmap = sns.color_palette(\\\"viridis\\\" if carbs.params.better_direction_sign > 0 else \\\"viridis_r\\\", as_cmap=True)\\n\",\n    \"\\n\",\n    \"output_observation_df = history_df[[\\\"observation_count\\\", f\\\"observation/output\\\"]].dropna()\\n\",\n    \"observation_x, observation_y = output_observation_df.to_numpy().T\\n\",\n    \"\\n\",\n    \"if is_resampled_shown:\\n\",\n    \"    resampled_df = history_df[\\n\",\n    \"        [\\\"observation_count\\\", \\\"best_resampled_observation/output_mean\\\", \\\"best_resampled_observation/output_std_dev\\\"]\\n\",\n    \"    ].dropna()\\n\",\n    \"    resampled_x, resampled_mean, resampled_std = resampled_df.to_numpy().T\\n\",\n    \"    plt.plot(resampled_x, resampled_mean, color=\\\"green\\\", linewidth=2, label=\\\"best parameters mean\\\", linestyle=\\\"dotted\\\")\\n\",\n    \"    plt.fill_between(\\n\",\n    \"        resampled_x,\\n\",\n    \"        resampled_mean - resampled_std,\\n\",\n    \"        resampled_mean + resampled_std,\\n\",\n    \"        color=\\\"green\\\",\\n\",\n    \"        alpha=0.1,\\n\",\n    \"        label=\\\"best parameters variance\\\",\\n\",\n    \"    )\\n\",\n    \"if is_best_shown:\\n\",\n    \"    output_best_observation_df = history_df[[\\\"observation_count\\\", f\\\"best_observation/output\\\"]].dropna()\\n\",\n    \"    best_observation_x, best_observation_y = output_best_observation_df.to_numpy().T\\n\",\n    \"    plt.plot(best_observation_x, best_observation_y, linestyle=\\\"dashed\\\", linewidth=2, label=\\\"best single observation\\\")\\n\",\n    \"plt.scatter(\\n\",\n    \"    observation_x,\\n\",\n    \"    observation_y,\\n\",\n    \"    c=observation_y,\\n\",\n    \"    s=20,\\n\",\n    \"    label=\\\"observation\\\",\\n\",\n    \"    cmap=cmap,\\n\",\n    \"    vmin=performance_min,\\n\",\n    \"    vmax=performance_max,\\n\",\n    \")\\n\",\n    \"plt.title(\\\"Combined performance metric\\\")\\n\",\n    \"plt.xlabel(\\\"Observation count\\\")\\n\",\n    \"plt.ylabel(\\\"Performance\\\")\\n\",\n    \"plt.ylim(performance_min, performance_max)\\n\",\n    \"plt.legend()\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"scrolled\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"\\n\",\n    \"\\\"\\\"\\\"\\n\",\n    \"Convergence by parameter\\n\",\n    \"\\n\",\n    \"Color matches above plot: lighter yellow = better performance.\\n\",\n    \"\\\"Best\\\" observation shows input parameter for the best output performance so far\\n\",\n    \"\\\"\\\"\\\"\\n\",\n    \"for search_var in search_vars[:]:\\n\",\n    \"    search_var_observation_df = history_df[\\n\",\n    \"        [\\\"observation_count\\\", f\\\"observation/{search_var}\\\", \\\"observation/output\\\"]\\n\",\n    \"    ].dropna()\\n\",\n    \"    observation_x, observation_y, observation_z = search_var_observation_df.to_numpy().T\\n\",\n    \"\\n\",\n    \"    if is_best_shown:\\n\",\n    \"        search_var_best_observation_df = history_df[[\\\"observation_count\\\", f\\\"best_observation/{search_var}\\\"]].dropna()\\n\",\n    \"        best_observation_x, best_observation_y = search_var_best_observation_df.to_numpy().T\\n\",\n    \"        plt.plot(\\n\",\n    \"            best_observation_x, best_observation_y, linestyle=\\\"dashed\\\", linewidth=2, label=\\\"best single observation\\\"\\n\",\n    \"        )\\n\",\n    \"    if is_resampled_shown:\\n\",\n    \"        search_var_resampled_observation_df = history_df[\\n\",\n    \"            [\\\"observation_count\\\", f\\\"best_resampled_observation/{search_var}\\\"]\\n\",\n    \"        ].dropna()\\n\",\n    \"        resampled_observation_x, resampled_observation_y = search_var_resampled_observation_df.to_numpy().T\\n\",\n    \"        plt.plot(\\n\",\n    \"            resampled_observation_x,\\n\",\n    \"            resampled_observation_y,\\n\",\n    \"            linestyle=\\\"dotted\\\",\\n\",\n    \"            color=\\\"green\\\",\\n\",\n    \"            linewidth=2,\\n\",\n    \"            label=\\\"best parameter value\\\",\\n\",\n    \"        )\\n\",\n    \"    search_var_name = search_var.replace(\\\"_\\\", \\\" \\\").replace(\\\"pdrop\\\", \\\"dropout\\\")\\n\",\n    \"\\n\",\n    \"    plt.scatter(\\n\",\n    \"        observation_x,\\n\",\n    \"        observation_y,\\n\",\n    \"        c=observation_z,\\n\",\n    \"        s=20,\\n\",\n    \"        label=\\\"observation\\\",\\n\",\n    \"        cmap=cmap,\\n\",\n    \"        vmin=performance_min,\\n\",\n    \"        vmax=performance_max,\\n\",\n    \"    )\\n\",\n    \"    plt.title(f\\\"{search_var_name} convergence\\\")\\n\",\n    \"    plt.xlabel(\\\"Observation count\\\")\\n\",\n    \"    plt.ylabel(search_var)\\n\",\n    \"    plt.yscale(search_space_scale[search_var])\\n\",\n    \"    plt.legend()\\n\",\n    \"    plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"\\n\",\n    \"# Pareto curve plot\\n\",\n    \"\\n\",\n    \"pareto_groups = carbs._get_pareto_groups(True)\\n\",\n    \"\\n\",\n    \"get_pareto_curve_plot(carbs.observations_in_basic, pareto_groups, obs_count=carbs.observation_count)\\n\",\n    \"plt.ylim(performance_min, performance_max)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"\\n\",\n    \"\\n\",\n    \"surrogate_model = carbs.get_surrogate_model()\\n\",\n    \"surrogate_model.fit_observations(carbs.observations_in_basic)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"\\n\",\n    \"# Get loguniform inputs by interpolating the pareto points from the random sampling\\n\",\n    \"num_uniform_inputs = 30\\n\",\n    \"num_contour_levels = 10\\n\",\n    \"\\n\",\n    \"pareto_costs = [observation_group_cost(x) for x in pareto_groups]\\n\",\n    \"pareto_logcosts = [math.log(x) for x in pareto_costs]\\n\",\n    \"pareto_outputs = [observation_group_output(x) for x in pareto_groups]\\n\",\n    \"uniform_logcosts = np.linspace(min(pareto_logcosts), max(pareto_logcosts), num=num_uniform_inputs)\\n\",\n    \"pareto_inputs = torch.stack([x[0].real_number_input for x in pareto_groups], dim=0)\\n\",\n    \"\\n\",\n    \"reg = LinearRegression()\\n\",\n    \"reg.fit(np.array(pareto_logcosts)[:, None], pareto_inputs)\\n\",\n    \"\\n\",\n    \"uniform_pareto_inputs = torch.from_numpy(reg.predict(np.array(uniform_logcosts)[:, None])).float()\\n\",\n    \"\\n\",\n    \"# Then evaluate those on the surrogate\\n\",\n    \"uniform_surrogate_outputs = surrogate_model.observe_surrogate(uniform_pareto_inputs)\\n\",\n    \"# print(f\\\"Outputs: {uniform_surrogate_outputs.target_estimate}\\\")\\n\",\n    \"# print(f\\\"Cost: {uniform_surrogate_outputs.cost_estimate}\\\")\\n\",\n    \"\\n\",\n    \"# Filter observations to those in the range of the pareto front\\n\",\n    \"observations_in_basic = [\\n\",\n    \"    x for x in carbs.observations_in_basic if x.cost >= min(pareto_costs) and x.cost <= max(pareto_costs)\\n\",\n    \"]\\n\",\n    \"obs_cost = [x.cost for x in observations_in_basic]\\n\",\n    \"obs_output = [x.output for x in observations_in_basic]\\n\",\n    \"contour_levels = np.linspace(min(pareto_outputs), max(pareto_outputs), num_contour_levels)\\n\",\n    \"vmin, vmax = min(pareto_outputs), max(pareto_outputs)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"\\n\",\n    \"\\n\",\n    \"import matplotlib as mpl\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"from matplotlib import cm\\n\",\n    \"\\n\",\n    \"scalar_map = cm.ScalarMappable(norm=mpl.colors.Normalize(vmin=vmin, vmax=vmax), cmap=cmap)\\n\",\n    \"\\n\",\n    \"interp_pareto_value = interp1d(uniform_logcosts, uniform_pareto_inputs, axis=0, fill_value=\\\"extrapolate\\\")\\n\",\n    \"observation_pareto_distance = [\\n\",\n    \"    torch.norm(torch.from_numpy(interp_pareto_value(np.log(x.cost))).float() - x.real_number_input).item()\\n\",\n    \"    for x in observations_in_basic\\n\",\n    \"]\\n\",\n    \"search_radius = carbs.params.initial_search_radius\\n\",\n    \"rescaled_observation_pareto_distance = [search_radius / (search_radius + x) for x in observation_pareto_distance]\\n\",\n    \"observation_marker_size = [200 * x for x in rescaled_observation_pareto_distance]\\n\",\n    \"obs_color = [\\n\",\n    \"    scalar_map.to_rgba(output)[:3] + (min(2 * alpha, 1),)\\n\",\n    \"    for output, alpha in zip(obs_output, rescaled_observation_pareto_distance)\\n\",\n    \"]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"\\n\",\n    \"pareto_set = set()\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def obs_to_key(obs: ObservationInBasic):\\n\",\n    \"    return tuple(obs.real_number_input.tolist())\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"for group in pareto_groups:\\n\",\n    \"    for obs in group:\\n\",\n    \"        pareto_set.add(obs_to_key(obs))\\n\",\n    \"\\n\",\n    \"obs_is_in_pareto_set = [obs_to_key(obs) in pareto_set for obs in observations_in_basic]\\n\",\n    \"edgecolors = [\\\"black\\\" if x else \\\"none\\\" for x in obs_is_in_pareto_set]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"\\n\",\n    \"\\n\",\n    \"class CustomLogFormatter(LogFormatter):\\n\",\n    \"    def _num_to_string(self, x, vmin, vmax) -> str:\\n\",\n    \"        return f\\\"{int(x)}\\\"\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"scrolled\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"\\n\",\n    \"base_two_search_vars = {\\n\",\n    \"    \\\"model.n_layers\\\",\\n\",\n    \"    \\\"model.n_heads\\\",\\n\",\n    \"    \\\"model.kv_size\\\",\\n\",\n    \"    \\\"model.ffw_size\\\",\\n\",\n    \"}\\n\",\n    \"\\n\",\n    \"# Parameter variation along pareto front\\n\",\n    \"# TODO: add failed observations, predict cost from GP model, make red x\\n\",\n    \"sel_search_vars = search_vars\\n\",\n    \"fig, axs = plt.subplots(\\n\",\n    \"    nrows=np.ceil((1 + len(sel_search_vars)) / 2).astype(int),\\n\",\n    \"    ncols=2,\\n\",\n    \"    figsize=(14, 4 * (1 + len(sel_search_vars)) // 2),\\n\",\n    \"    sharex=True,\\n\",\n    \")\\n\",\n    \"fig.tight_layout()\\n\",\n    \"fig.subplots_adjust(hspace=0.2, wspace=0.2)\\n\",\n    \"axs = axs.flatten()\\n\",\n    \"warnings.simplefilter(\\\"ignore\\\", MatplotlibDeprecationWarning)\\n\",\n    \"for search_var_idx, search_var in enumerate(sel_search_vars):\\n\",\n    \"    # search_var_idx += 10\\n\",\n    \"    param_from_basic = carbs._real_number_space_by_name[search_var].param_from_basic\\n\",\n    \"    obs_search_var = [param_from_basic(x.real_number_input[search_var_idx].item()) for x in observations_in_basic]\\n\",\n    \"\\n\",\n    \"    num_search_var_grid_points = 50\\n\",\n    \"    search_var_linspace_in_basic = torch.linspace(\\n\",\n    \"        min([x.real_number_input[search_var_idx].item() for x in observations_in_basic]),\\n\",\n    \"        max([x.real_number_input[search_var_idx].item() for x in observations_in_basic]),\\n\",\n    \"        steps=num_search_var_grid_points,\\n\",\n    \"    )\\n\",\n    \"    # search_var_linspace_in_basic\\n\",\n    \"    input_grid = uniform_pareto_inputs.repeat(num_search_var_grid_points, 1, 1)\\n\",\n    \"    for i in range(num_uniform_inputs):\\n\",\n    \"        input_grid[:, i, search_var_idx] = search_var_linspace_in_basic\\n\",\n    \"    input_grid_flat = input_grid.view(-1, carbs.real_dim)\\n\",\n    \"    surrogate_output_on_flat_grid = surrogate_model.observe_surrogate(input_grid_flat)\\n\",\n    \"    cost_grid = surrogate_output_on_flat_grid.cost_estimate.view(num_search_var_grid_points, num_uniform_inputs).cpu()\\n\",\n    \"    output_grid = surrogate_output_on_flat_grid.target_estimate.view(\\n\",\n    \"        num_search_var_grid_points, num_uniform_inputs\\n\",\n    \"    ).cpu()\\n\",\n    \"    search_var_grid = input_grid[:, :, search_var_idx].cpu()\\n\",\n    \"    search_var_grid.apply_(partial(param_from_basic, is_rounded=False))\\n\",\n    \"\\n\",\n    \"    ax = axs[search_var_idx]\\n\",\n    \"    contour_plot = ax.contour(\\n\",\n    \"        cost_grid,\\n\",\n    \"        search_var_grid,\\n\",\n    \"        output_grid,\\n\",\n    \"        cmap=cmap,\\n\",\n    \"        vmin=vmin,\\n\",\n    \"        vmax=vmax,\\n\",\n    \"        levels=contour_levels,\\n\",\n    \"    )\\n\",\n    \"    pareto_search_var = [\\n\",\n    \"        param_from_basic(x.item(), is_rounded=False) for x in uniform_pareto_inputs[:, search_var_idx]\\n\",\n    \"    ]\\n\",\n    \"    (pareto_line,) = ax.plot(\\n\",\n    \"        np.exp(uniform_logcosts), pareto_search_var, color=\\\"black\\\", linewidth=2, linestyle=\\\"dashed\\\"\\n\",\n    \"    )\\n\",\n    \"\\n\",\n    \"    scatter_plot = ax.scatter(\\n\",\n    \"        obs_cost,\\n\",\n    \"        obs_search_var,\\n\",\n    \"        c=obs_color,\\n\",\n    \"        s=observation_marker_size,\\n\",\n    \"        edgecolors=edgecolors,\\n\",\n    \"    )\\n\",\n    \"    if \\\".\\\" in search_var:\\n\",\n    \"        ax.set_ylabel(search_var.split(\\\".\\\")[-1])\\n\",\n    \"    else:\\n\",\n    \"        ax.set_ylabel(search_var)\\n\",\n    \"    if search_var in base_two_search_vars:\\n\",\n    \"        ax.set_yscale(\\\"log\\\", base=2)\\n\",\n    \"        ax.yaxis.set_major_formatter(CustomLogFormatter(base=2.0, labelOnlyBase=True))\\n\",\n    \"        ax.yaxis.set_minor_formatter(CustomLogFormatter(base=2.0, labelOnlyBase=False, minor_thresholds=(10.0, 0.1)))\\n\",\n    \"        ax.xaxis.set_major_formatter(LogFormatter(labelOnlyBase=True))\\n\",\n    \"        ax.xaxis.set_minor_formatter(LogFormatter(labelOnlyBase=False, minor_thresholds=(10.0, 0.1)))\\n\",\n    \"    else:\\n\",\n    \"        ax.set_yscale(search_space_scale[search_var])\\n\",\n    \"    ax.set_xscale(\\\"log\\\")\\n\",\n    \"    ax.set_xlabel(\\\"Cost\\\")\\n\",\n    \"    ax.set_xlim(min(pareto_costs), max(pareto_costs))\\n\",\n    \"    set_axes_style(ax, grid=\\\"both\\\")\\n\",\n    \"\\n\",\n    \"fig.legend([\\\"Pareto front (fit)\\\", \\\"Observations\\\"])\\n\",\n    \"# cbar_ax = fig.add_axes([0.96, 0.2, 0.02, 0.6])\\n\",\n    \"cbar = fig.colorbar(mappable=scalar_map, location=\\\"bottom\\\")  # , cax=cbar_ax\\n\",\n    \"for ax in axs[len(sel_search_vars) : len(axs)]:\\n\",\n    \"    fig.delaxes(ax)\\n\",\n    \"# fig.colorbar()\\n\",\n    \"# cbar = fig.colorbar(contour_plot)\\n\",\n    \"cbar.set_label(\\\"Validation Cross Entropy\\\")\\n\",\n    \"plt.savefig(\\\"/home/user/hyperspace_appendix_plot_2.pdf\\\", bbox_inches=\\\"tight\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (ipykernel)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.9.5\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "notebooks/carbs_demo.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"pycharm\": {\n     \"is_executing\": true\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Running CARBS with params CARBSParams(better_direction_sign=-1, seed=0, num_random_samples=4, is_wandb_logging_enabled=False, wandb_params=WandbLoggingParams(project_name=None, group_name=None, run_name=None, run_id=None, is_suggestion_logged=True, is_observation_logged=True, is_search_space_logged=True, root_dir='/mnt/private'), is_saved_on_every_observation=True, initial_search_radius=0.3, exploration_bias=1.0, num_candidates_for_suggestion_per_dim=100, resample_frequency=-1, max_cost=None, min_pareto_cost_fraction=0.2, is_pareto_group_selection_conservative=True, is_expected_improvement_pareto_value_clamped=True, is_expected_improvement_value_always_max=False, outstanding_suggestion_estimator=<OutstandingSuggestionEstimatorEnum.THOMPSON: 'THOMPSON'>)\\n\",\n      \"Observation 1\\n\",\n      \"Observed lr=3.93e-05, epochs=15, output 67.511\\n\",\n      \"Best lr=3.93e-05, epochs=15, output 67.511\\n\",\n      \"Observation 2\\n\",\n      \"Observed lr=2.84e-04, epochs=33, output 4.718\\n\",\n      \"Best lr=2.84e-04, epochs=33, output 4.718\\n\",\n      \"Observation 3\\n\",\n      \"Observed lr=5.43e-05, epochs=6, output 136.616\\n\",\n      \"Best lr=2.84e-04, epochs=33, output 4.718\\n\",\n      \"Observation 4\\n\",\n      \"Observed lr=1.98e-04, epochs=15, output 16.960\\n\",\n      \"Best lr=2.84e-04, epochs=33, output 4.718\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/opt/venv/lib/python3.9/site-packages/pyro/contrib/gp/models/gpr.py:81: UserWarning: torch.cholesky is deprecated in favor of torch.linalg.cholesky and will be removed in a future PyTorch release.\\n\",\n      \"L = torch.cholesky(A)\\n\",\n      \"should be replaced with\\n\",\n      \"L = torch.linalg.cholesky(A)\\n\",\n      \"and\\n\",\n      \"U = torch.cholesky(A, upper=True)\\n\",\n      \"should be replaced with\\n\",\n      \"U = torch.linalg.cholesky(A).mH().\\n\",\n      \"This transform will produce equivalent results for all valid (symmetric positive definite) inputs. (Triggered internally at  ../aten/src/ATen/native/BatchLinearAlgebra.cpp:1744.)\\n\",\n      \"  Lff = Kff.cholesky()\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Observation 5\\n\",\n      \"Observed lr=2.76e-04, epochs=82, output 1.993\\n\",\n      \"Best lr=2.76e-04, epochs=82, output 1.993\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/opt/venv/lib/python3.9/site-packages/pyro/contrib/gp/util.py:109: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\\n\",\n      \"torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\\n\",\n      \"X = torch.triangular_solve(B, A).solution\\n\",\n      \"should be replaced with\\n\",\n      \"X = torch.linalg.solve_triangular(A, B). (Triggered internally at  ../aten/src/ATen/native/BatchLinearAlgebra.cpp:2183.)\\n\",\n      \"  Lffinv_pack = pack.triangular_solve(Lff, upper=False)[0]\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Observation 6\\n\",\n      \"Observed lr=2.64e-04, epochs=163, output 1.118\\n\",\n      \"Best lr=2.64e-04, epochs=163, output 1.118\\n\",\n      \"Observation 7\\n\",\n      \"Observed lr=3.01e-04, epochs=207, output 0.718\\n\",\n      \"Best lr=3.01e-04, epochs=207, output 0.718\\n\",\n      \"Observation 8\\n\",\n      \"Observed lr=2.45e-04, epochs=207, output 1.012\\n\",\n      \"Best lr=3.01e-04, epochs=207, output 0.718\\n\",\n      \"Observation 9\\n\",\n      \"Observed lr=4.58e-04, epochs=173, output 0.437\\n\",\n      \"Best lr=4.58e-04, epochs=173, output 0.437\\n\",\n      \"Observation 10\\n\",\n      \"Observed lr=7.14e-04, epochs=111, output 0.137\\n\",\n      \"Best lr=7.14e-04, epochs=111, output 0.137\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# type: ignore\\n\",\n    \"import math\\n\",\n    \"import sys\\n\",\n    \"from collections import OrderedDict\\n\",\n    \"\\n\",\n    \"import numpy as np\\n\",\n    \"from loguru import logger\\n\",\n    \"\\n\",\n    \"from carbs import CARBS\\n\",\n    \"from carbs import CARBSParams\\n\",\n    \"from carbs import LogSpace\\n\",\n    \"from carbs import LogitSpace\\n\",\n    \"from carbs import ObservationInParam\\n\",\n    \"from carbs import ParamDictType\\n\",\n    \"from carbs import Param\\n\",\n    \"\\n\",\n    \"logger.remove()\\n\",\n    \"logger.add(sys.stdout, level=\\\"DEBUG\\\", format=\\\"{message}\\\")\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def run_test_fn(input_in_param: ParamDictType):\\n\",\n    \"    # A noisy function minimized at lr=1e-3, max hidden_dim\\n\",\n    \"    result = (math.log10(input_in_param[\\\"learning_rate\\\"]) + 3) ** 2 * 512 / input_in_param[\\n\",\n    \"        \\\"epochs\\\"\\n\",\n    \"    ] + np.random.uniform() * 0.1\\n\",\n    \"    return result\\n\",\n    \"\\n\",\n    \"param_spaces = [\\n\",\n    \"    Param(name=\\\"learning_rate\\\", space=LogSpace(scale=0.5), search_center=1e-4),\\n\",\n    \"    Param(name=\\\"momentum\\\", space=LogitSpace(), search_center=0.9),\\n\",\n    \"    Param(name=\\\"epochs\\\", space=LogSpace(is_integer=True, min=2, max=512), search_center=10),\\n\",\n    \"]\\n\",\n    \"\\n\",\n    \"carbs_params = CARBSParams(\\n\",\n    \"    better_direction_sign=-1,\\n\",\n    \"    is_wandb_logging_enabled=False,\\n\",\n    \"    resample_frequency=0,\\n\",\n    \")\\n\",\n    \"carbs = CARBS(carbs_params, param_spaces)\\n\",\n    \"for i in range(10):\\n\",\n    \"    suggestion = carbs.suggest().suggestion\\n\",\n    \"    observed_value = run_test_fn(suggestion)\\n\",\n    \"    obs_out = carbs.observe(ObservationInParam(input=suggestion, output=observed_value, cost=suggestion[\\\"epochs\\\"]))\\n\",\n    \"    logger.info(f\\\"Observation {obs_out.logs['observation_count']}\\\")\\n\",\n    \"    logger.info(\\n\",\n    \"        f\\\"Observed lr={obs_out.logs['observation/learning_rate']:.2e}, \\\"\\n\",\n    \"        f\\\"epochs={obs_out.logs['observation/epochs']}, \\\"\\n\",\n    \"        f\\\"output {obs_out.logs['observation/output']:.3f}\\\"\\n\",\n    \"    )\\n\",\n    \"    logger.info(\\n\",\n    \"        f\\\"Best lr={obs_out.logs['best_observation/learning_rate']:.2e}, \\\"\\n\",\n    \"        f\\\"epochs={obs_out.logs['best_observation/epochs']}, \\\"\\n\",\n    \"        f\\\"output {obs_out.logs['best_observation/output']:.3f}\\\"\\n\",\n    \"    )\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (ipykernel)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.9.5\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n"
  },
  {
    "path": "notebooks/carbs_simple_2d.sync.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Running CARBS with params CARBSParams(better_direction_sign=-1, seed=0, num_random_samples=4, is_wandb_logging_enabled=False, wandb_params=WandbLoggingParams(project_name=None, group_name=None, run_name=None, run_id=None, is_suggestion_logged=True, is_observation_logged=True, is_search_space_logged=True, root_dir='/mnt/private'), is_saved_on_every_observation=True, initial_search_radius=0.5, exploration_bias=1.0, num_candidates_for_suggestion_per_dim=100, resample_frequency=5, max_cost=None, search_distribution_function=<SearchDistributionFunctionEnum.NORMAL: 'NORMAL'>, min_pareto_cost_fraction=0.2, is_pareto_group_selection_conservative=True, is_expected_improvement_pareto_value_clamped=True, is_expected_improvement_value_always_max=False, outstanding_suggestion_estimator=<OutstandingSuggestionEstimatorEnum.THOMPSON: 'THOMPSON'>)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# type: ignore\\n\",\n    \"import math\\n\",\n    \"from collections import OrderedDict\\n\",\n    \"\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import numpy as np\\n\",\n    \"import torch\\n\",\n    \"from carbs import CARBS\\n\",\n    \"from carbs import CARBSParams\\n\",\n    \"from carbs import LogSpace\\n\",\n    \"from carbs import ObservationInParam\\n\",\n    \"from carbs import ParamDictType\\n\",\n    \"\\n\",\n    \"from avalon.common.log_utils import configure_local_logger\\n\",\n    \"\\n\",\n    \"# logger.remove()\\n\",\n    \"# logger.add(sys.stdout, level=\\\"DEBUG\\\", format=\\\"{message}\\\")\\n\",\n    \"configure_local_logger()\\n\",\n    \"\\n\",\n    \"param_space_by_name = OrderedDict(\\n\",\n    \"    [\\n\",\n    \"        (\\\"learning_rate\\\", LogSpace()),\\n\",\n    \"        # (\\\"momentum\\\", LogitSpace()),\\n\",\n    \"        (\\\"hidden_dim\\\", LogSpace(is_integer=True, min=2, max=512)),\\n\",\n    \"    ]\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def run_test_fn(input_in_param: ParamDictType):\\n\",\n    \"    # A noisy function minimized at lr=1e-3, max hidden_dim\\n\",\n    \"    # if input_in_param[\\\"learning_rate\\\"] > 1e-3:\\n\",\n    \"    #     return float(\\\"inf\\\")\\n\",\n    \"    result = ((math.log10(input_in_param[\\\"learning_rate\\\"]) + 3) ** 2 + 0.2) * 512 / input_in_param[\\n\",\n    \"        \\\"hidden_dim\\\"\\n\",\n    \"    ] + np.random.uniform() * 0.1\\n\",\n    \"    return result\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"initial_params = {\\\"learning_rate\\\": 1e-5, \\\"hidden_dim\\\": 32}  # , \\\"momentum\\\": 0.9}\\n\",\n    \"\\n\",\n    \"carbs_params = CARBSParams(\\n\",\n    \"    better_direction_sign=-1,\\n\",\n    \"    is_wandb_logging_enabled=False,\\n\",\n    \"    initial_search_radius=0.5,\\n\",\n    \"    # resample_frequency=-1,\\n\",\n    \")\\n\",\n    \"carbs = CARBS(carbs_params, param_space_by_name)\\n\",\n    \"carbs.set_search_center(initial_params)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"\\n\",\n    \"\\n\",\n    \"def plot_and_suggest():\\n\",\n    \"    if len(carbs.observations_in_basic) < carbs.params.num_random_samples:\\n\",\n    \"        return carbs.suggest()\\n\",\n    \"    pareto_observations = np.stack([x.real_number_input for x in carbs._get_pareto_set()])\\n\",\n    \"    observations = np.stack([x.real_number_input for x in carbs.observations_in_basic])\\n\",\n    \"    min_lr, min_hid = observations.min(axis=0)\\n\",\n    \"    max_lr, max_hid = observations.max(axis=0)\\n\",\n    \"    dlr, dhid = (max_lr - min_lr) / 20, (max_hid - min_hid) / 20\\n\",\n    \"    lr_range = np.arange(min_lr - dlr * 4, max_lr + dlr * 10.5, dlr)\\n\",\n    \"    hid_range = np.arange(min_hid - dhid * 4, max_hid + dhid * 10.5, dhid)\\n\",\n    \"    nlr, nhid = lr_range.shape[0], hid_range.shape[0]\\n\",\n    \"    assert nlr == nhid, \\\"Aaargh this is broken for some reason\\\"\\n\",\n    \"    grid_inputs = np.stack(np.meshgrid(lr_range, hid_range), axis=-1).reshape((-1, 2))\\n\",\n    \"    (\\n\",\n    \"        surrogate_model_outputs,\\n\",\n    \"        probabilities,\\n\",\n    \"        ei_value,\\n\",\n    \"        acquisition_function_value,\\n\",\n    \"    ) = carbs.evaluate_candidates(torch.from_numpy(grid_inputs).to(torch.float), (0,), is_rounding_candidates=True)\\n\",\n    \"\\n\",\n    \"    fig, axs = plt.subplots(1, 2, constrained_layout=True)\\n\",\n    \"    fig.set_size_inches(12, 6)\\n\",\n    \"    cs = axs[0].contourf(\\n\",\n    \"        grid_inputs[:, 0].reshape((nlr, nhid)),\\n\",\n    \"        grid_inputs[:, 1].reshape((nlr, nhid)),\\n\",\n    \"        surrogate_model_outputs.surrogate_output.cpu().numpy().reshape((nlr, nhid)),\\n\",\n    \"    )\\n\",\n    \"    axs[0].scatter(observations[:, 0], observations[:, 1], color=\\\"orange\\\")\\n\",\n    \"    axs[0].scatter(pareto_observations[:, 0], pareto_observations[:, 1], color=\\\"white\\\")\\n\",\n    \"    fig.colorbar(cs, ax=axs[0])\\n\",\n    \"    axs[0].set_xlim((lr_range[0], lr_range[-1]))\\n\",\n    \"    axs[0].set_ylim((hid_range[0], hid_range[-1]))\\n\",\n    \"    axs[0].set_title(\\\"Surrogate function value\\\")\\n\",\n    \"\\n\",\n    \"    cs = axs[1].contourf(\\n\",\n    \"        grid_inputs[:, 0].reshape((nlr, nhid)),\\n\",\n    \"        grid_inputs[:, 1].reshape((nlr, nhid)),\\n\",\n    \"        torch.log(torch.clamp(ei_value / surrogate_model_outputs.cost_estimate, min=1e-10))\\n\",\n    \"        .cpu()\\n\",\n    \"        .numpy()\\n\",\n    \"        .reshape((nlr, nhid)),\\n\",\n    \"    )\\n\",\n    \"    prob_max = probabilities.max().item()\\n\",\n    \"    axs[1].contour(\\n\",\n    \"        grid_inputs[:, 0].reshape((nlr, nhid)),\\n\",\n    \"        grid_inputs[:, 1].reshape((nlr, nhid)),\\n\",\n    \"        probabilities.cpu().numpy().reshape((nlr, nhid)),\\n\",\n    \"        levels=[prob_max / 9, prob_max / 1.5],\\n\",\n    \"        colors=\\\"w\\\",\\n\",\n    \"        linestyles=(\\\"dotted\\\", \\\"dashed\\\"),\\n\",\n    \"    )\\n\",\n    \"    # if surrogate_model_outputs.success_probability.min()[0] < 0.5:\\n\",\n    \"    #     axs[1].contour(\\n\",\n    \"    #         grid_inputs[:, 0].reshape((nlr, nhid)),\\n\",\n    \"    #         grid_inputs[:, 1].reshape((nlr, nhid)),\\n\",\n    \"    #         surrogate_model_outputs.success_probability.numpy().reshape((nlr, nhid)),\\n\",\n    \"    #         levels=[0.5],\\n\",\n    \"    #         colors=\\\"r\\\",\\n\",\n    \"    #         linestyles=\\\"dashed\\\",\\n\",\n    \"    #     )\\n\",\n    \"    axs[1].scatter(observations[:, 0], observations[:, 1], color=\\\"orange\\\")\\n\",\n    \"    axs[1].scatter(pareto_observations[:, 0], pareto_observations[:, 1], color=\\\"white\\\")\\n\",\n    \"    fig.colorbar(cs, ax=axs[1])\\n\",\n    \"    axs[1].set_xlim((lr_range[0], lr_range[-1]))\\n\",\n    \"    axs[1].set_ylim((hid_range[0], hid_range[-1]))\\n\",\n    \"    axs[1].set_title(\\\"Acquisiton function value\\\")\\n\",\n    \"\\n\",\n    \"    suggest_out = carbs.suggest()\\n\",\n    \"\\n\",\n    \"    assert len(carbs.outstanding_suggestions) == 1\\n\",\n    \"\\n\",\n    \"    suggestion_in_basic = list(carbs.outstanding_suggestions.values())[0].real_number_input\\n\",\n    \"\\n\",\n    \"    axs[0].scatter(\\n\",\n    \"        [suggestion_in_basic[0]], [suggestion_in_basic[1]], color=\\\"pink\\\", marker=\\\"*\\\", s=500, edgecolors=\\\"black\\\"\\n\",\n    \"    )\\n\",\n    \"    axs[1].scatter(\\n\",\n    \"        [suggestion_in_basic[0]], [suggestion_in_basic[1]], color=\\\"pink\\\", marker=\\\"*\\\", s=500, edgecolors=\\\"black\\\"\\n\",\n    \"    )\\n\",\n    \"    if len(carbs.failed_observations_in_basic) > 0:\\n\",\n    \"        failed_observations = np.stack([x.real_number_input for x in carbs.failed_observations_in_basic])\\n\",\n    \"        axs[0].scatter(failed_observations[:, 0], failed_observations[:, 1], color=\\\"red\\\", marker=\\\"x\\\")\\n\",\n    \"        axs[1].scatter(failed_observations[:, 0], failed_observations[:, 1], color=\\\"red\\\", marker=\\\"x\\\")\\n\",\n    \"    plt.show()\\n\",\n    \"    return suggest_out\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/opt/venv/lib/python3.9/site-packages/pyro/contrib/gp/models/gpr.py:81: UserWarning: torch.cholesky is deprecated in favor of torch.linalg.cholesky and will be removed in a future PyTorch release.\\n\",\n      \"L = torch.cholesky(A)\\n\",\n      \"should be replaced with\\n\",\n      \"L = torch.linalg.cholesky(A)\\n\",\n      \"and\\n\",\n      \"U = torch.cholesky(A, upper=True)\\n\",\n      \"should be replaced with\\n\",\n      \"U = torch.linalg.cholesky(A).mH().\\n\",\n      \"This transform will produce equivalent results for all valid (symmetric positive definite) inputs. (Triggered internally at  ../aten/src/ATen/native/BatchLinearAlgebra.cpp:1744.)\\n\",\n      \"  Lff = Kff.cholesky()\\n\",\n      \"/opt/venv/lib/python3.9/site-packages/pyro/contrib/gp/util.py:109: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\\n\",\n      \"torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\\n\",\n      \"X = torch.triangular_solve(B, A).solution\\n\",\n      \"should be replaced with\\n\",\n      \"X = torch.linalg.solve_triangular(A, B). (Triggered internally at  ../aten/src/ATen/native/BatchLinearAlgebra.cpp:2183.)\\n\",\n      \"  Lffinv_pack = pack.triangular_solve(Lff, upper=False)[0]\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 864x432 with 4 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 864x432 with 4 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 864x432 with 4 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 864x432 with 4 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 864x432 with 4 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 864x432 with 4 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 864x432 with 4 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 864x432 with 4 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 864x432 with 4 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 864x432 with 4 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 864x432 with 4 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 864x432 with 4 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 864x432 with 4 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 864x432 with 4 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 864x432 with 4 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 864x432 with 4 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"\\n\",\n    \"for i in range(20):\\n\",\n    \"    suggest_out = plot_and_suggest()\\n\",\n    \"    # logger.info(f\\\"Suggestion details {suggest_out.log}\\\")\\n\",\n    \"    suggestion = suggest_out.suggestion\\n\",\n    \"    observed_value = run_test_fn(suggestion)\\n\",\n    \"    obs_out = carbs.observe(ObservationInParam(input=suggestion, output=observed_value, cost=suggestion[\\\"hidden_dim\\\"]))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (ipykernel)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.9.5\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "pyproject.toml",
    "content": "[build-system]\nrequires = [\"setuptools\", \"wheel\"]\nbuild-backend = \"setuptools.build_meta\"\n\n[project]\nname = \"carbs\"\nversion = \"0.1.0\"\nreadme = \"README.md\"\nlicense = {file = \"LICENSE.txt\"}\nauthors = [\n  {name = \"Imbue\", email = \"abe@generallyintelligent.com\"},\n]\ndependencies = [\n    \"attrs\",\n    \"cattrs\",\n    \"loguru\",\n    \"numpy\",\n    \"pyro-ppl\",\n    \"scikit-learn\",\n    \"seaborn\",\n    \"torch\",\n    \"wandb\",\n]\nrequires-python = \">=3.11\"\n\n[tool.setuptools]\npackages = [\"carbs\"]\n\n[tool.pytest.ini_options]\naddopts = [\"--ignore-glob=**/quarantine/**\", \"--ignore-glob=**/notebooks/**\"]\n"
  },
  {
    "path": "setup.py",
    "content": "from distutils.core import setup\n\nsetup(\n    name=\"carbs\",\n    version=\"0.1.0\",\n    author=\"Untitled AI\",\n    author_email=\"abe@generallyintelligent.com\",\n    packages=[\"carbs\"],\n    license=\"LICENSE.txt\",\n    description=\"CARBS hyperparameter tuning algorithm\",\n    long_description=open(\"README.md\").read(),\n    install_requires=[\n        \"pyro-ppl>=1.6.0\",\n        \"torch>=1.8.1\",\n        \"loguru>=0.5.3\",\n        \"cattrs>=1.3.0\",\n        \"numpy\",\n        \"wandb\",\n        \"seaborn\",\n    ],\n)\n"
  }
]