Evalml: AutoMLSearch uses slightly different splits for each pipeline

[freddyaboulton]

Repro (need to checkout the random-split-seeds branch because we don't store the state of the random seed of the data split):

from evalml.demos import load_breast_cancer
from evalml.automl import AutoMLSearch
from evalml.utils.gen_utils import check_random_state_equality
import numpy as np
import itertools

def make_seed_from_state(state):
    rs = np.random.RandomState()
    rs.set_state(state)
    return rs

def check_random_state(state_1, state_2):
    rs_1 = make_seed_from_state(state_1)
    rs_2 = make_seed_from_state(state_2)
    return check_random_state_equality(rs_1, rs_2)

X, y = load_breast_cancer()

automl = AutoMLSearch(max_batches=2, problem_type="binary")
automl.search(X, y)

seeds_equal = []
for i, j in itertools.combinations(range(14), 2): 
    are_equal = check_random_state(automl.data_split_seeds[i], automl.data_split_seeds[j])
    seeds_equal.append(are_equal)

assert not all(seeds_equal)

The issue with having a different random state everytime data_split.split is called is that the split will be slightly different each time:

from sklearn.model_selection import StratifiedKFold
seed_1 = make_seed_from_state(automl.data_split_seeds[0])
seed_2 = make_seed_from_state(automl.data_split_seeds[1])
split_1 = StratifiedKFold(n_splits=3, random_state=seed_1, shuffle=True)
split_2 = StratifiedKFold(n_splits=3, random_state=seed_2, shuffle=True)

for (train_index_1, test_index_1), (train_index_2, test_index_2) in zip(split_1.split(X, y), split_2.split(X, y)):
    assert not set(train_index_1) == set(train_index_2)
    assert not set(test_index_1) == set(test_index_2)

I think we should change this because it is introducing more variability into the results of automl than is necessary and prevents a true apples-to-apples comparison between pipelines. That being said, I don't think fixing this would substantially impact the results of automl search (the pipeline ranking would probably be the same).

One possible solution is to create the split class with an integer random seed as opposed to the np.random.RandomState that is stored in the automl state. I believe the indices will be the same in repeated calls:

from sklearn.model_selection import StratifiedKFold
split_1 = StratifiedKFold(n_splits=3, random_state=10, shuffle=True)

first_train_set = []
first_test_set = []
for (train_index_1, test_index_1) in split_1.split(X, y):
    first_train_set.append(set(train_index_1))
    first_test_set.append(set(test_index_1))

second_train_set = []
second_test_set = []
for (train_index_2, test_index_2) in split_1.split(X, y):
    second_train_set.append(set(train_index_2))
    second_test_set.append(set(test_index_2))

assert first_train_set == second_train_set
assert first_test_set == second_test_set

[dsherry]

@freddyaboulton great point!! I think I follow, and yes I agree. Every pipeline should be operating on the same exact splits during CV, no difference, so that the CV scores are comparable.

Do you have thoughts on what we should do to fix this?

[freddyaboulton]

@dsherry I think we may have to pass in an int as the random_state in the default splits. Using np.random.RandomState can lead to different splits in subsequent calls. For example, this will fail:

from sklearn.model_selection import StratifiedKFold
from evalml.demos import load_breast_cancer
split_1 = StratifiedKFold(n_splits=3, random_state=np.random.RandomState(10), shuffle=True)

X, y = load_breast_cancer()
X = X.to_dataframe()
y = y.to_series()

first_train_set = []
first_test_set = []
for (train_index_1, test_index_1) in split_1.split(X, y):
    first_train_set.append(set(train_index_1))
    first_test_set.append(set(test_index_1))

second_train_set = []
second_test_set = []
for (train_index_2, test_index_2) in split_1.split(X, y):
    second_train_set.append(set(train_index_2))
    second_test_set.append(set(test_index_2))

assert first_train_set == second_train_set
assert first_test_set == second_test_set

However, passing in random_state=10 will pass.

from sklearn.model_selection import StratifiedKFold
from evalml.demos import load_breast_cancer
split_1 = StratifiedKFold(n_splits=3, random_state=10, shuffle=True)

X, y = load_breast_cancer()
X = X.to_dataframe()
y = y.to_series()

first_train_set = []
first_test_set = []
for (train_index_1, test_index_1) in split_1.split(X, y):
    first_train_set.append(set(train_index_1))
    first_test_set.append(set(test_index_1))

second_train_set = []
second_test_set = []
for (train_index_2, test_index_2) in split_1.split(X, y):
    second_train_set.append(set(train_index_2))
    second_test_set.append(set(test_index_2))

assert first_train_set == second_train_set
assert first_test_set == second_test_set

I think an alternative will be to create a new split instance for each pipeline but I don't like that better.

[dsherry]

@freddyaboulton definitely, agreed!

Where I'd start with this would be to use the random_state RNG to get a random seed. We already do this for catboost and some other estimators. I suggest we follow the same pattern.

[bchen1116]

@dsherry @freddyaboulton I'm not sure if I'm misunderstanding this, but don't we already set the random state in AutoMLSearch to be an int and pass this int value into our data splitter? This issue is tracking the initialization of the data splitter, which seems to be fine? Am I misunderstanding the issue?

[freddyaboulton]

@bchen1116 get_random_state returns a np.random.RandomState:

I think since we create the splitter with a reference to this mutable random state, calling split for one pipeline will change the state of the random state for the next pipeline when we call split again. For that reason, the splits are slightly different for each pipeline.

[bchen1116]

@freddyaboulton oh, thanks for the clarification. I got that mixed up with get_random_seed. That makes sense then! Is there a reason why we use np.random.RandomState for initializing AutoMLSearch params/pipelines, versus using a random int generated through get_random_seed?