vastorbit.machine_learning.metrics.regression_report¶

vastorbit.machine_learning.metrics.regression_report(y_true: str, y_score: str, input_relation: Annotated[str | VastFrame, ''], metrics: None | str | list[str] = None, k: int = 1, genSQL: bool = False) → float | TableSample¶

Computes a regression report using multiple metrics to evaluate the model (r2, mse, max error…).

Parameters:

y_true (str) – Response column.
y_score (str) – Prediction.
input_relation (SQLRelation) – Relation to use for scoring. This relation can be a view, table, or a customized relation (if an alias is used at the end of the relation). For example: (SELECT … FROM …) x
metrics (list, optional) –
List of the metrics used to compute the final report.
- aic:
  Akaike’s Information Criterion
  
  \[AIC = 2k - 2\ln(\hat{L})\]
- bic:
  Bayesian Information Criterion
  
  \[BIC = -2\ln(\hat{L}) + k \ln(n)\]
- max:
  Max Error.
  
  \[ME = \max_{i=1}^{n} \left| y_i - \hat{y}_i \right|\]
- mae:
  Mean Absolute Error.
  
  \[MAE = \frac{1}{n} \sum_{i=1}^{n} \left| y_i - \hat{y}_i \right|\]
- median:
  Median Absolute Error.
  
  \[MedAE = \text{median}_{i=1}^{n} \left| y_i - \hat{y}_i \right|\]
- mse:
  Mean Squared Error.
  
  \[MsE = \frac{1}{n} \sum_{i=1}^{n} \left( y_i - \hat{y}_i \right)^2\]
- msle:
  Mean Squared Log Error.
  
  \[MSLE = \frac{1}{n} \sum_{i=1}^{n} (\log(1 + y_i) - \log(1 + \hat{y}_i))^2\]
- r2:
  R squared coefficient.
  
  \[R^2 = 1 - \frac{\sum_{i=1}^{n} (y_i - \hat{y}_i)^2}{\sum_{i=1}^{n} (y_i - \bar{y})^2}\]
- r2a:
  R2 adjusted
  
  \[\text{Adjusted } R^2 = 1 - \frac{(1 - R^2)(n - 1)}{n - k - 1}\]
- qe:
  quantile error, the quantile must be included in the name. Example: qe50.1% will return the quantile error using q=0.501.
- rmse:
  Root-mean-squared error
  
  \[RMSE = \sqrt{\frac{1}{n} \sum_{i=1}^{n} (y_i - \hat{y}_i)^2}\]
- var:
  Explained Variance
  
  \[\text{Explained Variance} = 1 - \frac{Var(y - \hat{y})}{Var(y)}\]
k (int, optional) – Number of predictors. Used to compute the adjusted R2
genSQL (bool, optional) – If set to True, returns the sql that is used to generate the metrics.

Returns:

report.

Return type:

TableSample

Examples

We should first import vastorbit.

import vastorbit as vo

Let’s create a small dataset that has:

true value
predicted value

data = vo.VastFrame(
    {
        "y_true": [1, 1.5, 3, 2, 5],
        "y_pred": [1.1, 1.55, 2.9, 2.01, 4.5],
    }
)

Next, we import the metric:

from vastorbit.machine_learning.metrics import regression_report

Now we can conveniently compute the report:

regression_report(
    y_true  = "y_true",
    y_score = "y_pred",
    input_relation = data,
)

Note

vastorbit uses simple SQL queries to compute various metrics. You can use the set_option() function with the sql_on parameter to enable SQL generation and examine the generated queries.