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Populate make_classification_df date functionality with random dates #851

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Populate make_classification_df date functionality with random dates #851

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4b55f9a
fix populates make_classification_df with random dates
BhuvanashreeM Aug 29, 2021
424fca4
added-seed-to-random_date-and-modified-test_datasets
BhuvanashreeM Aug 31, 2021
b740b27
check-for-unique-values
BhuvanashreeM Sep 3, 2021
24461fb
checks-for-random_state-type
BhuvanashreeM Sep 3, 2021
3a7c9d1
removed-redundant-compute-calls-in-random_date
BhuvanashreeM Sep 4, 2021
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21 changes: 17 additions & 4 deletions dask_ml/datasets.py
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Original file line number Diff line number Diff line change
Expand Up @@ -381,10 +381,11 @@ def make_classification(
return X, y


def random_date(start, end):
def random_date(start, end, random_state=None):
rng_random_date = sklearn.utils.check_random_state(random_state)
delta = end - start
int_delta = (delta.days * 24 * 60 * 60) + delta.seconds
random_second = np.random.randint(int_delta)
random_second = rng_random_date.randint(int_delta)
return start + timedelta(seconds=random_second)


Expand Down Expand Up @@ -430,6 +431,13 @@ def make_classification_df(
The output values.

"""
if (
random_state is not None
or not isinstance(random_state, np.random.RandomState)
or not isinstance(random_state, int)
):
random_state = 42

X_array, y_array = make_classification(
n_samples=n_samples,
flip_y=(1 - predictability),
Expand All @@ -451,8 +459,13 @@ def make_classification_df(
[
X_df,
dd.from_array(
np.array([random_date(*dates)] * len(X_df)),
chunksize=chunks,
np.array(
[
random_date(*dates, random_state + i)
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What happens when random_state isn't an integer? Scikit-learn allows for random_state to be an integer, None or an instance of np.random.RandomState (source).

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Okay, I will have to raise a ValueError exception there, on it.

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Why not use this code?

rng = check_random_state(random_state)
dates = [random_date(*dates, rng) for i in range(len(X_df))]
...

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The code above will produce the same random number since the seed(rng) remains the same in subsequent calls.

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My main point: I think np.random.RandomState and None should be acceptable types for random_state. I'm fine expanding the for-loop, though I don't think that needs to happen:

[ins] In [193]: def random_dates(random_state):
           ...:     return random_state.randint(100)
           ...:

[ins] In [194]: rng = np.random.RandomState(42)

[ins] In [196]: [random_dates(rng) for _ in range(20)]
Out[196]: [51, 92, 14, 71, 60, 20, 82, 86, 74, 74, 87, 99, 23, 2, 21, 52, 1, 87, 29, 37]

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I can maybe just check if random_state is one of the accepted values like this and accordingly proceed -
if random_state is not None or not isinstance(random_state, np.random.RandomState) or not isinstance(random_state,int): print("random_state is not to be accepted")

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That runs counter to the use of random_state in Scikit-learn. random_date is public, so it should accept all types of random_state that Scikit-learn accepts.

If random_date were a private function, I wouldn't really care

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@BhuvanashreeM BhuvanashreeM Sep 3, 2021 •
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random_state : int, RandomState instance or None, optional (default=None), these are values accepted by Scikit-Learn's random_state. I think I can check if the random_state is in neither of the accepted values(Scitkit and Numpy) and set is as the default None.

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Scikit-learn's check_random_state function will likely be useful: https://scikit-learn.org/stable/modules/generated/sklearn.utils.check_random_state.html

It takes those values and produces the correct random seed generator.

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@stsievert the accepted types of random_state in Scikit and Numpy appear to be the same.
Refer this: Scikit's version also accepts Numpy's accepted values. Refer this: https://scikit-learn.org/dev/glossary.html#term-random_state

for i in range(len(X_df))
]
),
chunksize=n_samples,
columns=["date"],
),
],
Expand Down
12 changes: 12 additions & 0 deletions tests/test_datasets.py
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Original file line number Diff line number Diff line change
Expand Up @@ -73,10 +73,22 @@ def test_make_classification_df():
dates=(date(2014, 1, 1), date(2015, 1, 1)),
)

X_df1, y_series1 = dask_ml.datasets.make_classification_df(
n_samples=100,
n_features=5,
random_state=123,
chunks=100,
dates=(date(2014, 1, 1), date(2015, 1, 1)),
)
check_randomness = np.unique((X_df["date"] == X_df1["date"]).compute())
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Good, this checks the random state.

Shouldn't this also check that there's more than one unique value? That's what #845 is focused on.

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Yes, the code I've written checks for repeatability, on account of the seed. Since the the numpy's randint function is a deterministic pseudo random number generator, we can be sure that it will produce a random set of numbers. I've ensured in the code that the function random_date is called multiple times, each with a different seed. Hence I feel the line np.unique(X["date"]).size >= threshold would be redundant. Open to any thoughts you might have here @stsievert

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The same random_seed=123 is passed to both calls to make_classification_df; I would expect every value in X_df["date"] and X_df1["date"] to be the same.

I think X["date"].nunique() >= threshold would be a lot simpler.

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Okay, that sounds good. Now I've to figure out, what would be a good threshold. Will n_samples/2 be good?

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Oh yeah, threshold=n_samples/2 is more than good. I think threshold=2 would suffice; that'd make sure #845 is resolved.


assert X_df is not None
assert y_series is not None
assert "date" in X_df.columns
assert len(X_df.columns) == 6
assert len(X_df) == 100
assert len(y_series) == 100
assert isinstance(y_series, dask.dataframe.core.Series)
assert check_randomness.size == 1
assert check_randomness[0] is True
assert np.unique(X_df["date"]).size >= 2