コード例 #1
0
def test_emwmv_unbiased():

    # bias: True
    v = [np.nan, 2, np.nan, 6, 8, 5]
    s = Stream(v, "s")

    specs = [{
        "alpha": 0.68,
        "adjust": True,
        "ignore_na": True,
        "min_periods": 3
    }, {
        "alpha": 0.68,
        "adjust": True,
        "ignore_na": False,
        "min_periods": 3
    }, {
        "alpha": 0.68,
        "adjust": False,
        "ignore_na": True,
        "min_periods": 3
    }, {
        "alpha": 0.68,
        "adjust": False,
        "ignore_na": False,
        "min_periods": 3
    }]

    for spec in specs:
        d = spec.copy()
        d["warmup"] = d["min_periods"]
        del d["min_periods"]
        var = s.ewm(**d).var(bias=False).rename("var")

        feed = DataFeed([var])
        feed.compile()

        expected = list(pd.Series(v).ewm(**spec).var(bias=False))

        actual = []
        while feed.has_next():
            actual += [feed.next()["var"]]

        assert all(np.isclose(actual, expected))
コード例 #2
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def test_ewa_beginning_na():
    # adjust: True, ignore_na: False
    v = [np.nan, 2, np.nan, 6, 8, 5]
    s = Stream(v).rename("s")

    specs = [{
        "alpha": 0.68,
        "adjust": True,
        "ignore_na": True,
        "min_periods": 3
    }, {
        "alpha": 0.68,
        "adjust": True,
        "ignore_na": False,
        "min_periods": 3
    }, {
        "alpha": 0.68,
        "adjust": False,
        "ignore_na": True,
        "min_periods": 3
    }, {
        "alpha": 0.68,
        "adjust": False,
        "ignore_na": False,
        "min_periods": 3
    }]

    for spec in specs:
        d = spec.copy()
        d["warmup"] = d["min_periods"]
        del d["min_periods"]
        mean = s.ewm(**d).mean().rename("mean")

        feed = DataFeed([mean])
        feed.compile()

        expected = list(pd.Series(v).ewm(**spec).mean())

        actual = []
        while feed.has_next():
            actual += [feed.next()["mean"]]

        assert all(np.isclose(actual, expected))
コード例 #3
0
def test_ewma():

    # adjust: True, ignore_na: True
    v = [5, 2, 4, 6]
    s = Stream(v, "s")

    mean = s.ewm(alpha=0.68, adjust=True, ignore_na=True).mean().rename("mean")

    feed = DataFeed([mean])
    feed.compile()

    expected = list(
        pd.Series(v).ewm(alpha=0.68, adjust=True, ignore_na=True).mean())

    actual = []
    while feed.has_next():
        actual += [feed.next()["mean"]]

    assert all(np.isclose(actual, expected))

    # adjust: True, ignore_na: False
    v = [5, 2, np.nan, 6]
    s = Stream(v, "s")

    mean = s.ewm(alpha=0.68, adjust=True,
                 ignore_na=False).mean().rename("mean")

    feed = DataFeed([mean])
    feed.compile()

    expected = list(
        pd.Series(v).ewm(alpha=0.68, adjust=True, ignore_na=False).mean())

    actual = []
    while feed.has_next():
        actual += [feed.next()["mean"]]

    assert all(np.isclose(actual, expected))

    # adjust: True, ignore_na: False
    v = [5, 2, np.nan, 6]
    s = Stream(v, "s")

    mean = s.ewm(alpha=0.68, adjust=False,
                 ignore_na=True).mean().rename("mean")

    feed = DataFeed([mean])
    feed.compile()

    expected = list(
        pd.Series(v).ewm(alpha=0.68, adjust=False, ignore_na=True).mean())

    actual = []
    while feed.has_next():
        actual += [feed.next()["mean"]]

    assert all(np.isclose(actual, expected))

    # adjust: True, ignore_na: False
    v = [5, 2, np.nan, 6]
    s = Stream(v, "s")

    mean = s.ewm(alpha=0.68, adjust=False,
                 ignore_na=False).mean().rename("mean")

    feed = DataFeed([mean])
    feed.compile()

    expected = list(
        pd.Series(v).ewm(alpha=0.68, adjust=False, ignore_na=False).mean())

    actual = []
    while feed.has_next():
        actual += [feed.next()["mean"]]

    assert all(np.isclose(actual, expected))