nannyml.sampling_error.regression module

nannyml.sampling_error.regression.mae_sampling_error(sampling_error_components, data) → float[source]

Calculate Mean Absolute Error (MAE) sampling error for a chunk of data.

Parameters:

  • sampling_error_components (a set of parameters that were derived from reference data.) –

  • data (the (analysis) data you want to calculate or estimate a metric for.) –

Returns:

sampling_error

Return type:

float

nannyml.sampling_error.regression.mae_sampling_error_components(y_true_reference: Series, y_pred_reference: Series) → Tuple[source]

Calculate sampling error components for Mean Absolute Error (MAE) using reference data.

Parameters:

  • y_true_reference (pd.Series) – Target values for the reference dataset.

  • y_pred_reference (pd.Series) – Predictions for the reference dataset.

Returns:

(std,)

Return type:

Tuple[np.ndarray]

nannyml.sampling_error.regression.mape_sampling_error(sampling_error_components, data) → float[source]

Calculate Mean Absolute Percentage Error (MAPE) sampling error for a chunk of data.

Parameters:

  • sampling_error_components (a set of parameters that were derived from reference data.) –

  • data (the (analysis) data you want to calculate or estimate a metric for.) –

Returns:

sampling_error

Return type:

float

nannyml.sampling_error.regression.mape_sampling_error_components(y_true_reference: Series, y_pred_reference: Series) → Tuple[source]

Calculate sampling error components for Mean Absolute Percentage Error (MAPE) using reference data.

Parameters:

  • y_true_reference (pd.Series) – Target values for the reference dataset.

  • y_pred_reference (pd.Series) – Predictions for the reference dataset.

Returns:

(std,)

Return type:

Tuple[np.ndarray]

nannyml.sampling_error.regression.mse_sampling_error(sampling_error_components, data) → float[source]

Calculate Mean Squared Error (MSE) sampling error for a chunk of data.

Parameters:

  • sampling_error_components (a set of parameters that were derived from reference data.) –

  • data (the (analysis) data you want to calculate or estimate a metric for.) –

Returns:

sampling_error

Return type:

float

nannyml.sampling_error.regression.mse_sampling_error_components(y_true_reference: Series, y_pred_reference: Series) → Tuple[source]

Calculate sampling error components for Mean Squared Error (MSE) using reference data.

Parameters:

  • y_true_reference (pd.Series) – Target values for the reference dataset.

  • y_pred_reference (pd.Series) – Predictions for the reference dataset.

Returns:

(std,)

Return type:

Tuple[np.ndarray]

nannyml.sampling_error.regression.msle_sampling_error(sampling_error_components, data) → float[source]

Calculate Mean Squared Logarithmic Error (MSLE) sampling error for a chunk of data.

Parameters:

  • sampling_error_components (a set of parameters that were derived from reference data.) –

  • data (the (analysis) data you want to calculate or estimate a metric for.) –

Returns:

sampling_error

Return type:

float

nannyml.sampling_error.regression.msle_sampling_error_components(y_true_reference: Series, y_pred_reference: Series) → Tuple[source]

Calculate sampling error components for Mean Squared Logarithmic Error (MSLE) using reference data.

Parameters:

  • y_true_reference (pd.Series) – Target values for the reference dataset.

  • y_pred_reference (pd.Series) – Predictions for the reference dataset.

Returns:

(std,)

Return type:

Tuple[np.ndarray]

nannyml.sampling_error.regression.rmse_sampling_error(sampling_error_components, data) → float[source]

Calculate Root Mean Squared Error (RMSE) sampling error for a chunk of data.

Parameters:

  • sampling_error_components (a set of parameters that were derived from reference data.) –

  • data (the (analysis) data you want to calculate or estimate a metric for.) –

Returns:

sampling_error

Return type:

float

nannyml.sampling_error.regression.rmse_sampling_error_components(y_true_reference: Series, y_pred_reference: Series) → Tuple[source]

Calculate sampling error components for Root Mean Squared Error (RMSE) using reference data.

Parameters:

  • y_true_reference (pd.Series) – Target values for the reference dataset.

  • y_pred_reference (pd.Series) – Predictions for the reference dataset.

Returns:

(std,)

Return type:

Tuple[np.ndarray]

nannyml.sampling_error.regression.rmsle_sampling_error(sampling_error_components, data) → float[source]

Calculate Root Mean Squared Logarithmic Error (RMSLE) sampling error for a chunk of data.

Parameters:

  • sampling_error_components (a set of parameters that were derived from reference data.) –

  • data (the (analysis) data you want to calculate or estimate a metric for.) –

Returns:

sampling_error

Return type:

float

nannyml.sampling_error.regression.rmsle_sampling_error_components(y_true_reference: Series, y_pred_reference: Series) → Tuple[source]

Calculate sampling error components for Root Mean Squared Logarithmic Error (RMSLE) using reference data.

Parameters:

  • y_true_reference (pd.Series) – Target values for the reference dataset.

  • y_pred_reference (pd.Series) – Predictions for the reference dataset.

Returns:

(std,)

Return type:

Tuple[np.ndarray]