MyoGestic Model Configurations

class myogestic.models.config.BoolParameter

Bases: TypedDict

TypedDict for boolean parameters.

Parameters:

default_value (bool) – The default value for the parameter.

class myogestic.models.config.CategoricalParameter

Bases: TypedDict

TypedDict for categorical parameters.

Parameters:

• values (list[str]) – The values for the parameter.

• default_value (str) – The default value for the parameter.

class myogestic.models.config.FloatParameter

Bases: TypedDict

TypedDict for float parameters.

Parameters:

• start_value (float) – The start value for the parameter.

• end_value (float) – The end value for the parameter.

• step (float) – The step for the parameter.

• default_value (float) – The default value for the parameter.

class myogestic.models.config.IntParameter

Bases: TypedDict

TypedDict for integer parameters.

Parameters:

• start_value (int) – The start value for the parameter.

• end_value (int) – The end value for the parameter.

• step (int) – The step for the parameter.

• default_value (int) – The default value for the parameter.

class myogestic.models.config.StringParameter

Bases: TypedDict

TypedDict for string parameters.

Parameters:

default_value (str) – The default value for the parameter.

myogestic.models.config.ChangeableParameter

Union of the TypedDicts for the changeable parameters.

myogestic.models.config.UnchangeableParameter

Union of the types for the unchangeable parameters.

myogestic.models.config.MODELS_MAP

Dictionary to get the models class and whether it is a classifier or regressor.

The keys are the models names, the values are tuples with the models class and a boolean indicating whether the models is a classifier.

The model class must be a callable that receives the parameters as keyword arguments.

MODELS_MAP

MODELS_MAP: dict[str, tuple[object, bool]] = {
    "CatBoost Classifier": (CatBoostClassifier, True),
    "CatBoost Regressor": (CatBoostRegressor, False),
    "Linear Regressor": (LinearRegression, False),
    "Logistic Classifier": (LogisticRegression, True),
    "Gaussian Process Classifier": (GaussianProcessClassifier, True),
    "AdaBoost Classifier": (AdaBoostClassifier, True),
    "MLP Classifier": (MLPClassifier, True),
    "Support Vector Classifier": (SVC, True),
}

myogestic.models.config.FUNCTIONS_MAP

Dictionary to get the functions to save and load the models.

The keys are the models names, the values are dictionaries with the keys “save_function”, “load_function” and “train_function” and the values are the functions to save, load and train the models, respectively.

FUNCTIONS_MAP

FUNCTIONS_MAP: dict[
    str, dict[Literal["save_function", "load_function", "train_function"], callable]
] = {
    "CatBoost Classifier": {
        "save_function": catboost_models.save,
        "load_function": catboost_models.load,
        "train_function": catboost_models.train,
    },
    "CatBoost Regressor": {
        "save_function": catboost_models.save,
        "load_function": catboost_models.load,
        "train_function": catboost_models.train,
    },
    "Linear Regressor": {
        "save_function": sklearn_models.save,
        "load_function": sklearn_models.load,
        "train_function": sklearn_models.train,
    },
    "Linear Regressor Per Finger": {
        "save_function": sklearn_models.save,
        "load_function": sklearn_models.load,
        "train_function": sklearn_models.train,
    },
    "Gaussian Process Classifier": {
        "save_function": sklearn_models.save,
        "load_function": sklearn_models.load,
        "train_function": sklearn_models.train,
    },
    "AdaBoost Classifier": {
        "save_function": sklearn_models.save,
        "load_function": sklearn_models.load,
        "train_function": sklearn_models.train,
    },
    "MLP Classifier": {
        "save_function": sklearn_models.save,
        "load_function": sklearn_models.load,
        "train_function": sklearn_models.train,
    },
    "Support Vector Classifier": {
        "save_function": sklearn_models.save,
        "load_function": sklearn_models.load,
        "train_function": sklearn_models.train,
    },
}

myogestic.models.config.PARAMETERS_MAP

Dictionary to get the parameters for the models.

The keys are the models names, the values are dictionaries with two keys: “changeable” and “unchangeable”. The values are dictionaries with the parameter names as keys and the parameter values as values.

The changeable parameters must be of type ChangeableParameter and the unchangeable parameters must be of type UnchangeableParameter.

PARAMETERS_MAP

PARAMETERS_MAP: dict[
    str,
    dict[
        Literal["changeable", "unchangeable"],
        dict[str, ChangeableParameter | UnchangeableParameter],
    ],
] = {
    "CatBoost Classifier": {
        "changeable": {
            "iterations": {
                "start_value": 10,
                "end_value": 10000,
                "step": 100,
                "default_value": 1000,
            },
            "l2_leaf_reg": {
                "start_value": 1,
                "end_value": 10,
                "step": 1,
                "default_value": 5,
            },
            "border_count": {
                "start_value": 1,
                "end_value": 255,
                "step": 1,
                "default_value": 254,
            },
        },
        "unchangeable": {
            "task_type": "GPU" if get_gpu_device_count() > 0 else "CPU",
            "train_dir": None,
        },
    },
    "CatBoost Regressor": {
        "changeable": {
            "iterations": {
                "start_value": 10,
                "end_value": 1000,
                "step": 10,
                "default_value": 100,
            },
            "l2_leaf_reg": {
                "start_value": 1,
                "end_value": 10,
                "step": 1,
                "default_value": 5,
            },
            "border_count": {
                "start_value": 1,
                "end_value": 255,
                "step": 1,
                "default_value": 254,
            },
        },
        "unchangeable": {
            "task_type": "GPU" if get_gpu_device_count() > 0 else "CPU",
            "train_dir": None,
            "loss_function": "MultiRMSE",
            "boosting_type": "Plain",
        },
    },
    "Linear Regressor": {
        "changeable": {},
        "unchangeable": {},
    },
    "Logistic Classifier": {
        "changeable": {},
        "unchangeable": {},
    },
    "Gaussian Process Classifier": {
        "changeable": {},
        "unchangeable": {},
    },
    "AdaBoost Classifier": {
        "changeable": {},
        "unchangeable": {},
    },
    "MLP Classifier": {
        "changeable": {},
        "unchangeable": {},
    },
    "Support Vector Classifier": {
        "changeable": {},
        "unchangeable": {},
    },
}

myogestic.models.config.FEATURES_MAP

Dictionary to get the EMG features class.

The keys are the feature names, the values are the features class. The features must subclass the FilterBaseClass.

FEATURES_MAP

FEATURES_MAP: dict[str, FilterBaseClass] = { # noqa
    "Root Mean Square": RMSFilter,
    "Mean Absolute Value": MAVFilter,
    "Integrated Absolute Value": IAVFilter,
    "Variance": VARFilter,
    "Waveform Length": WFLFilter,
    "Zero Crossings": ZCFilter,
    "Slope Sign Change": SSCFilter,
    # TODO: Add these back
    # "Difference Absolute Standard Deviation": DASDVFilter,
    # "V-Order": VOrderFilter,
    # "Average Amplitude Change": AACFilter,
    # "Maximum Fractal Length": MFLFilter,
}