# dbnl.eval

### create\_run\_schema\_from\_results

```python
dbnl.eval.create_run_schema_from_results(*, column_data: DataFrame, scalar_data: dict[str, Any] | DataFrame | None = None, index: list[str] | None = None, metrics: Sequence[[Metric](dbnl.eval.metrics.md#dbnl.eval.metrics.Metric)] | None = None) → RunSchema
```

Create a new RunSchema from column results, scalar results, and metrics.

This function assumes that the metrics have already been evaluated on the original, un-augmented data.\
In other words, the column data for the metrics should also be present in the column\_data.

* **Parameters:**
  * **column\_data** – DataFrame with the results for the columns
  * **scalar\_data** – Dictionary or DataFrame with the results for the scalars, defaults to None
  * **index** – List of column names that are the unique identifier, defaults to None
  * **metrics** – List of metrics to report with the run, defaults to None
* **Raises:DBNLInputValidationError** – Input does not conform to expected format
* **Returns:**\
  RunSchema with the desired schema for columns and scalars, if provided

### evaluate

```python
dbnl.eval.evaluate(*, df: DataFrame, metrics: Sequence[[Metric](dbnl.eval.metrics.md#dbnl.eval.metrics.Metric)], inplace: bool = False) → DataFrame
```

Evaluates a set of metrics on a dataframe, returning an augmented dataframe.

* **Parameters:**
  * **df** – input dataframe
  * **metrics** – metrics to compute
  * **inplace** – whether to modify the input dataframe in place
* **Returns:**\
  input dataframe augmented with metrics

### get\_column\_schemas\_from\_dataframe\_and\_metrics

```python
dbnl.eval.get_column_schemas_from_dataframe_and_metrics(*, df: DataFrame, metrics: list[[Metric](dbnl.eval.metrics.md#dbnl.eval.metrics.Metric)]) → list[RunSchemaColumnSchemaDict]
```

Gets the run schema column schemas for a dataframe that was augmented with a list of metrics.

* **Parameters:**
  * **df** – Dataframe to get column schemas from
  * **metrics** – list of metrics added to the dataframe
* **Returns:**\
  list of columns schemas for dataframe and metrics

### get\_column\_schemas\_from\_metrics

```python
dbnl.eval.get_column_schemas_from_metrics(metrics: list[[Metric](dbnl.eval.metrics.md#dbnl.eval.metrics.Metric)]) → list[RunSchemaColumnSchemaDict]
```

Gets the run schema column schemas from a list of metrics.

* **Parameters:metrics** – list of metrics to get column schemas from
* **Returns:**\
  list of column schemas for metrics

### get\_run\_schema\_columns\_from\_metrics

```python
dbnl.eval.get_run_schema_columns_from_metrics(metrics: list[[Metric](dbnl.eval.metrics.md#dbnl.eval.metrics.Metric)]) → list[RunSchemaColumnSchema]
```

Gets the run schema column schemas from a list of metrics.

* **Parameters:metrics** – list of metrics to get column schemas from
* **Returns:**\
  list of column schemas for metrics

### report\_run\_with\_results

```python
dbnl.eval.report_run_with_results(*, project: Project, column_data: DataFrame, scalar_data: dict[str, Any] | DataFrame | None = None, display_name: str | None = None, index: list[str] | None = None, run_schema: RunSchema | None = None, metadata: dict[str, str] | None = None, metrics: Sequence[[Metric](dbnl.eval.metrics.md#dbnl.eval.metrics.Metric)] | None = None, wait_for_close: bool = True) → Run
```

Create a new Run, report results to it, and close it.

If run\_schema is not provided, a RunSchema will be created from the data.\
If a run\_schema is provided, the results are validated against it.

If metrics are provided, they are evaluated on the column data before reporting.

* **Parameters:**
  * **project** – DBNL Project to create the Run for
  * **column\_data** – DataFrame with the results for the columns
  * **scalar\_data** – Dictionary or DataFrame with the results for the scalars, if any. Defaults to None
  * **display\_name** – Display name for the Run, defaults to None.
  * **index** – List of column names that are the unique identifier, defaults to None. Only used when creating a new schema.
  * **run\_schema** – RunSchema to use for the Run, defaults to None.
  * **metadata** – Additional key:value pairs user wants to track, defaults to None
  * **metrics** – List of metrics to report with the run, defaults to None
  * **wait\_for\_close** – If True, the function will block for up to 3 minutes until the Run is closed, defaults to True
* **Raises:**
  * **DBNLNotLoggedInError** – DBNL SDK is not logged in
  * **DBNLInputValidationError** – Input does not conform to expected format
* **Returns:**\
  Run, after reporting results and closing it

### report\_run\_with\_results\_and\_start\_test\_session

```python
dbnl.eval.report_run_with_results_and_start_test_session(*, project: Project, column_data: DataFrame, scalar_data: dict[str, Any] | DataFrame | None = None, display_name: str | None = None, index: list[str] | None = None, run_schema: RunSchema | None = None, metadata: dict[str, str] | None = None, baseline: Run | RunQuery | None = None, include_tags: list[str] | None = None, exclude_tags: list[str] | None = None, require_tags: list[str] | None = None, metrics: Sequence[[Metric](dbnl.eval.metrics.md#dbnl.eval.metrics.Metric)] | None = None) → Run
```

Create a new Run, report results to it, and close it. Start a TestSession with the given inputs.\
If metrics are provided, they are evaluated on the column data before reporting.

* **Parameters:**
  * **project** – DBNL Project to create the Run for
  * **column\_data** – DataFrame with the results for the columns
  * **scalar\_data** – Dictionary or DataFrame with the scalar results to report to DBNL, defaults to None.
  * **display\_name** – Display name for the Run, defaults to None.
  * **index** – List of column names that are the unique identifier, defaults to None. Only used when creating a new schema.
  * **run\_schema** – RunSchema to use for the Run, defaults to None.
  * **metadata** – Additional key:value pairs user wants to track, defaults to None
  * **baseline** – DBNL Run or RunQuery to use as the baseline run, defaults to None. If None, the baseline defined in the TestConfig is used.
  * **include\_tags** – List of Test Tag names to include in the Test Session
  * **exclude\_tags** – List of Test Tag names to exclude in the Test Session
  * **require\_tags** – List of Test Tag names to require in the Test Session
  * **metrics** – List of metrics to report with the run, defaults to None
* **Raises:**
  * **DBNLNotLoggedInError** – DBNL SDK is not logged in
  * **DBNLInputValidationError** – Input does not conform to expected format
* **Returns:**\
  Run, after reporting results and closing it