Python toolz.curry方法代码示例

# 需要导入模块: import toolz [as 别名]
# 或者: from toolz import curry [as 别名]
def ensure_doctest(f, name=None):
    """Ensure that an object gets doctested. This is useful for instances
    of objects like curry or partial which are not discovered by default.

    Parameters
    ----------
    f : any
        The thing to doctest.
    name : str, optional
        The name to use in the doctest function mapping. If this is None,
        Then ``f.__name__`` will be used.

    Returns
    -------
    f : any
       ``f`` unchanged.
    """
    _getframe(2).f_globals.setdefault('__test__', {})[
        f.__name__ if name is None else name
    ] = f
    return f 

# 需要导入模块: import toolz [as 别名]
# 或者: from toolz import curry [as 别名]
def get(self) -> Optional[PluginType]:
        """Return the currently active plugin."""
        if self._options:
            return curry(self._active, **self._options)
        else:
            return self._active 

# 需要导入模块: import toolz [as 别名]
# 或者: from toolz import curry [as 别名]
def test_build_pipeline_learner_assertion(has_repeated_learners):
    @fp.curry
    def learner(df, a, b, c=3):
        return lambda dataset: dataset + a + b + c, df, {}

    learner_fn = learner(b=2)

    with pytest.raises(ValueError):
        build_pipeline(learner_fn, has_repeated_learners=has_repeated_learners)

    learner_fn = learner(a=1, b=2)

    build_pipeline(learner_fn) 

# 需要导入模块: import toolz [as 别名]
# 或者: from toolz import curry [as 别名]
def test_build_pipeline_predict_arguments_assertion(has_repeated_learners):
    test_df = pd.DataFrame({"x": [1, 2, 3, 4, 5], "y": [2, 4, 6, 8, 10]})

    @fp.curry
    def invalid_learner(df):
        def p(dataset, *a, **b):
            return dataset + len(a) + len(b)

        return p, df, {}

    with pytest.raises(ValueError):
        build_pipeline(invalid_learner, has_repeated_learners=has_repeated_learners)(test_df) 

# 需要导入模块: import toolz [as 别名]
# 或者: from toolz import curry [as 别名]
def test_build_pipeline_serialisation():
    df_train = pd.DataFrame({
        'id': ["id1"],
        'x1': [10.0],
        'y': [2.3]
    })

    fn = lambda x: x

    @fp.curry
    def dummy_learner(df, fn, call):
        return fn, df, {f"dummy_learner_{call}": {}}

    @fp.curry
    def dummy_learner_2(df, fn, call):
        return dummy_learner(df, fn, call)

    @fp.curry
    def dummy_learner_3(df, fn, call):
        return fn, df, {f"dummy_learner_{call}": {}, "obj": "a"}

    train_fn = build_pipeline(
        dummy_learner(fn=fn, call=1),
        dummy_learner_2(fn=fn, call=2),
        dummy_learner_3(fn=fn, call=3))

    predict_fn, pred_train, log = train_fn(df_train)

    fkml = {"pipeline": ["dummy_learner", "dummy_learner_2", "dummy_learner_3"],
            "output_columns": ['id', 'x1', 'y'],
            "features": ['id', 'x1', 'y'],
            "learners": {"dummy_learner": {"fn": fn, "log": {"dummy_learner_1": {}}},
                         "dummy_learner_2": {"fn": fn, "log": {"dummy_learner_2": {}}},
                         "dummy_learner_3": {"fn": fn, "log": {"dummy_learner_3": {}}, "obj": "a"}}}

    assert log["__fkml__"] == fkml
    assert "obj" not in log.keys() 

# 需要导入模块: import toolz [as 别名]
# 或者: from toolz import curry [as 别名]
def test_build_pipeline_repeated_learners_serialisation():
    df_train = pd.DataFrame({
        'id': ["id1"],
        'x1': [10.0],
        'y': [2.3]
    })

    fn = lambda x: x

    @fp.curry
    def dummy_learner(df, fn, call):
        return fn, df, {f"dummy_learner_{call}": {}}

    @fp.curry
    def dummy_learner_2(df, fn, call):
        return dummy_learner(df, fn, call)

    train_fn = build_pipeline(
        dummy_learner(fn=fn, call=1),
        dummy_learner_2(fn=fn, call=2),
        dummy_learner(fn=fn, call=3),
        has_repeated_learners=True)

    predict_fn, pred_train, log = train_fn(df_train)

    fkml = {"pipeline": ["dummy_learner", "dummy_learner_2", "dummy_learner"],
            "output_columns": ['id', 'x1', 'y'],
            "features": ['id', 'x1', 'y'],
            "learners": {
                "dummy_learner": [
                    {"fn": fn, "log": {"dummy_learner_1": {}}},
                    {"fn": fn, "log": {"dummy_learner_3": {}}}],
                "dummy_learner_2": [{"fn": fn, "log": {"dummy_learner_2": {}}}]}}

    assert log["__fkml__"] == fkml
    assert "obj" not in log.keys() 

# 需要导入模块: import toolz [as 别名]
# 或者: from toolz import curry [as 别名]
def test_curried_namespace():
    def should_curry(value):
        if not callable(value) or isinstance(value, curry) or isinstance(value, type):
            return False
        if isinstance(value, type) and issubclass(value, Exception):
            return False
        nargs = enhanced_num_required_args(value)
        if nargs is None or nargs > 1:
            return True
        else:
            return nargs == 1 and enhanced_has_keywords(value)

    def curry_namespace(ns):
        return dict(
            (name, curry(f) if should_curry(f) else f)
            for name, f in ns.items()
            if "__" not in name
        )

    all_auto_curried = curry_namespace(vars(eth_utils))

    inferred_namespace = valfilter(callable, all_auto_curried)
    curried_namespace = valfilter(callable, eth_utils.curried.__dict__)

    if inferred_namespace != curried_namespace:
        missing = set(inferred_namespace) - set(curried_namespace)
        if missing:
            to_insert = sorted("%s," % f for f in missing)
            raise AssertionError(
                "There are missing functions in eth_utils.curried:\n"
                + "\n".join(to_insert)
            )
        extra = set(curried_namespace) - set(inferred_namespace)
        if extra:
            raise AssertionError(
                "There are extra functions in eth_utils.curried:\n"
                + "\n".join(sorted(extra))
            )
        unequal = merge_with(list, inferred_namespace, curried_namespace)
        unequal = valfilter(lambda x: x[0] != x[1], unequal)
        to_curry = keyfilter(lambda x: should_curry(getattr(eth_utils, x)), unequal)
        if to_curry:
            to_curry_formatted = sorted("{0} = curry({0})".format(f) for f in to_curry)
            raise AssertionError(
                "There are missing functions to curry in eth_utils.curried:\n"
                + "\n".join(to_curry_formatted)
            )
        elif unequal:
            not_to_curry_formatted = sorted(unequal)
            raise AssertionError(
                "Missing functions NOT to curry in eth_utils.curried:\n"
                + "\n".join(not_to_curry_formatted)
            )
        else:
            raise AssertionError(
                "unexplained difference between %r and %r"
                % (inferred_namespace, curried_namespace)
            )