Adds an example about FunctionTransformer

[xadupre]

Answer to issue #609.

[paranjapeved15]

@xadupre what do these 2 function arguments mean? And is there any doc that explains the structure/attributes that these have?

[xadupre]

I should have added annotations: scope: Scope, operator: Operator, container: ModelComponentContainer. scope is a class storing names and giving unique names. operator is a container holding the scikit-learn operator, container stores the created onnx nodes. I usually look. The documentation contains examples and a documentation for some methods: http://onnx.ai/sklearn-onnx/api_summary.html#.

[paranjapeved15]

Thanks @xadupre . I have the following transformer:
class Clipper(BaseEstimator, TransformerMixin):
def init(self,lower_limit,upper_limit):
self.lower_limit = lower_limit
self.upper_limit = upper_limit
pass

def clip(self,data):
    return np.clip(data,self.lower_limit,self.upper_limit)

def fit(self, X, y=None):
    return self

def transform(self,X,y=None):
    x = X.apply(lambda x: self.clip(x), axis=1)
    return x.values.reshape((-1, 1))

def get_feature_names_out(self):
        pass

Questions:

1. Is there a way to write a converter which can accept the lower limit and upper limit arguments (similar to my init function)?
2. In your example below, you use operator.inputs[0]. What other contents does the inputs list contain?

[xadupre]

The converter gets access to the model it converts, so it has access to any of its attributes. If the information you need is not stored in the transformer, you can use the option mechanism: https://onnx.ai/sklearn-onnx/parameterized.html.