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"""This module provides the Transformer class. Its constructor can be used to create data transformation methods.""" |
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import abc |
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import inspect |
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import types |
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__all__ = ['Transformer'] |
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class TransformerInterface(abc.ABC): |
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"""The interface with a method to transform structured data. Anyone, implementing this has the ability to receive |
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some kind of data and return some kind of transformed version of them. |
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""" |
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@abc.abstractmethod |
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def transform(self, data, **kwargs): |
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"""Takes data and optional keyword arguments and transforms them. |
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Input data can represent either a single variable of an observation (scalar) |
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or a vector of observations of the same variable (if N observations then returns a [N x 1] array-like). |
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Example 1: |
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obs1 = [x1, y1, z1] |
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fa = f_a(x) |
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fb = f_b(x) |
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fc = f_c(x) |
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feature_vector1 = [fa(x1), fb(y1), fc(z1)] |
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So, each of fa, fb and fc can implement the Transformer interface. |
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Example 2: |
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obs1 = [x1, y1, z1] |
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obs2 = [x2, y2, z2] |
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obs3 = [x3, y3, z3] |
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obs4 = [x4, y4, z4] |
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data = [obs1; |
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obs2; |
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obs3; |
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obs4] shape = (4,3) |
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fa = f_a(x) |
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fb = f_b(x) |
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fc = f_c(x) |
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feature_vectors = [fa(data[:0], fb(data[:1], fc(data[:2])] - shape = (4,3) |
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Again each of fa, fb and fc can implement the Transformer interface. |
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Args: |
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data (object): the input data to transform; the x in an f(x) invocation |
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Raises: |
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NotImplementedError: [description] |
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""" |
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raise NotImplementedError |
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class RuntimeTransformer(TransformerInterface, abc.ABC): |
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"""Examines whether the input object is callable, if it can receive at least one input argument and also |
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whether it can accept kwargs. Depending on the kwargs check, "configures" the '_transform' method to process |
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any kwargs at runtime or to ignore them. |
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Delegates all the transformation operation to its '_transform' method provided by its '_callable' field. |
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Args: |
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a_callable (callable): a callable object used to delegate the transformation operation |
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""" |
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def __new__(cls, *args, **kwargs): |
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instance_object = super().__new__(cls) |
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a_callable = args[0] |
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if not callable(a_callable): |
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raise ValueError(f"Expected a callable as argument; instead got '{type(a_callable)}'") |
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nb_mandatory_arguments = a_callable.__code__.co_argcount # this counts sums both *args and **kwargs |
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# use syntax like 'def a(b, *, c=1, d=2): .. to separate pos args from kwargs & to inform 'inspect' lib about it |
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if nb_mandatory_arguments < 1: |
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raise ValueError("Expected a callable that receives at least one positional argument; " |
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f"instead got a callable that receives '{nb_mandatory_arguments}' arguments.") |
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signature = inspect.signature(a_callable) |
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parameters = list(signature.parameters.values()) |
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if nb_mandatory_arguments > 1: |
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def _transform(_self, data, **keyword_args): |
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return a_callable(data, **keyword_args) |
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elif nb_mandatory_arguments == len(parameters): |
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def _transform(_self, data, **_keyword_args_): |
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return a_callable(data, **_keyword_args_) |
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else: |
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raise Exception(f"Something went really bad above! Parameters: [{', '.join(str(_) for _ in parameters)}]") |
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instance_object._transform = types.MethodType(_transform, instance_object) |
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instance_object._callable = a_callable |
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return instance_object |
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def transform(self, data, **kwargs): |
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return self._transform(data, **kwargs) |
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class Transformer(RuntimeTransformer): pass |
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boromir674 /
so-magic
