Customize_Assessor.md

# Customize Assessor

## Customize Assessor

NNI also support building an assessor by yourself to adjust your tuning demand.

If you want to implement a customized Assessor, there are three things for you to do:

1) Inherit an assessor of a base Assessor class
2) Implement assess_trial function
3) Configure your customized Assessor in experiment yaml config file

**1. Inherit an assessor of a base Assessor class**

```python
from nni.assessor import Assessor

class CustomizedAssessor(Assessor):
    def __init__(self, ...):
        ...
```

**2. Implement assess trial function**
```python
from nni.assessor import Assessor, AssessResult

class CustomizedAssessor(Assessor):
    def __init__(self, ...):
        ...

    def assess_trial(self, trial_history):
        """
        Determines whether a trial should be killed. Must override.
        trial_history: a list of intermediate result objects.
        Returns AssessResult.Good or AssessResult.Bad.
        """
        # you code implement here.
        ...
```

**3. Configure your customized Assessor in experiment yaml config file**

NNI needs to locate your customized Assessor class and instantiate the class, so you need to specify the location of the customized Assessor class and pass literal values as parameters to the \_\_init__ constructor.

```yaml

assessor:
  codeDir: /home/abc/myassessor
  classFileName: my_customized_assessor.py
  className: CustomizedAssessor
  # Any parameter need to pass to your Assessor class __init__ constructor
  # can be specified in this optional classArgs field, for example 
  classArgs:
    arg1: value1

```

Please noted in **2**. The object `trial_history` are exact the object that Trial send to Assessor by using SDK `report_intermediate_result` function.

More detail example you could see:
> * [medianstop-assessor](../src/sdk/pynni/nni/medianstop_assessor)
> * [curvefitting-assessor](../src/sdk/pynni/nni/curvefitting_assessor)