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Metrics

siapy.models.metrics

ClassificationMetrics

Bases: NamedTuple

accuracy instance-attribute 

accuracy: float

precision instance-attribute 

precision: float

recall instance-attribute 

recall: float

f1 instance-attribute 

f1: float

to_dict 

to_dict() -> dict
Source code in siapy/models/metrics.py 
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def to_dict(self) -> dict:
    return self._asdict()

RegressionMetrics

Bases: NamedTuple

mae instance-attribute 

mae: float

mse instance-attribute 

mse: float

rmse instance-attribute 

rmse: float

r2 instance-attribute 

r2: float

pe instance-attribute 

pe: float

maxe instance-attribute 

maxe: float

nrmse_mean instance-attribute 

nrmse_mean: float

nrmse_range instance-attribute 

nrmse_range: float

to_dict 

to_dict() -> dict
Source code in siapy/models/metrics.py 
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def to_dict(self) -> dict:
    return self._asdict()

normalized_RMSE 

normalized_RMSE(y_true: ndarray, y_pred: ndarray, normalize_by: Literal['range', 'mean'] = 'range')
PARAMETER DESCRIPTION
y_true

TYPE: ndarray

y_pred

TYPE: ndarray

normalize_by

TYPE: Literal['range', 'mean'] DEFAULT: 'range'

Source code in siapy/models/metrics.py 
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def normalized_RMSE(
    y_true: np.ndarray,
    y_pred: np.ndarray,
    normalize_by: Literal["range", "mean"] = "range",
):
    rmse = mean_squared_error(y_true, y_pred, squared=False)
    if normalize_by == "range":
        normalizer = np.max(y_true) - np.min(y_true)
    elif normalize_by == "mean":
        normalizer = np.mean(y_true)
    else:
        raise InvalidInputError(
            input_value=normalize_by,
            message="Unknown normalizer. Possible values are: 'range' or 'mean'.",
        )
    return rmse / normalizer

calculate_classification_metrics 

calculate_classification_metrics(y_true, y_pred, average: Literal['micro', 'macro', 'samples', 'weighted', 'binary'] | None = 'weighted')
PARAMETER DESCRIPTION
y_true

y_pred

average

TYPE: Literal['micro', 'macro', 'samples', 'weighted', 'binary'] | None DEFAULT: 'weighted'

Source code in siapy/models/metrics.py 
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def calculate_classification_metrics(
    y_true,
    y_pred,
    average: Literal["micro", "macro", "samples", "weighted", "binary"]
    | None = "weighted",
):
    accuracy = accuracy_score(y_true, y_pred)
    precision = precision_score(y_true, y_pred, average=average)
    recall = recall_score(y_true, y_pred, average=average)
    f1 = f1_score(y_true, y_pred, average=average)
    return ClassificationMetrics(
        accuracy=accuracy,
        precision=precision,
        recall=recall,
        f1=f1,
    )

calculate_regression_metrics 

calculate_regression_metrics(y_true, y_pred)
PARAMETER DESCRIPTION
y_true

y_pred

Source code in siapy/models/metrics.py 
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def calculate_regression_metrics(y_true, y_pred):
    mae = mean_absolute_error(y_true, y_pred)
    mse = mean_squared_error(y_true, y_pred, squared=True)
    rmse = mean_squared_error(y_true, y_pred, squared=False)
    r2 = r2_score(y_true, y_pred)
    pe = mean_absolute_percentage_error(y_true, y_pred)
    maxe = max_error(y_true, y_pred)
    nrmse_mean = normalized_RMSE(y_true, y_pred, normalize_by="mean")
    nrmse_range = normalized_RMSE(y_true, y_pred, normalize_by="range")
    return RegressionMetrics(
        mae=mae,
        mse=mse,
        rmse=rmse,
        r2=r2,
        pe=pe,
        maxe=maxe,
        nrmse_mean=nrmse_mean,
        nrmse_range=nrmse_range,
    )