def test_search_offset_mix_7(transactions, total_spent_fn):
"""
Test offset mix with window_size (relative), minimum_data (relative), and gap (relative).
"""
lm = LabelMaker(
target_entity='customer_id',
time_index='time',
labeling_function=total_spent_fn,
window_size=10,
)
given_labels = lm.search(
transactions,
num_examples_per_instance=float('inf'),
verbose=False,
)
given_labels = to_csv(given_labels, index=False)
labels = [
'customer_id,cutoff_time,total_spent',
'0,2019-01-01 08:00:00,2',
'1,2019-01-01 09:00:00,3',
'2,2019-01-01 10:30:00,4',
'3,2019-01-01 12:30:00,1',
]
assert given_labels == labels
def test_search_offset_mix_2(transactions, total_spent_fn):
"""
Test offset mix with window_size (absolute), minimum_data (relative), and gap (absolute).
"""
lm = LabelMaker(
target_entity='customer_id',
time_index='time',
labeling_function=total_spent_fn,
window_size='30min',
)
given_labels = lm.search(
transactions,
num_examples_per_instance=2,
minimum_data=2,
verbose=False,
)
given_labels = to_csv(given_labels, index=False)
labels = [
'customer_id,cutoff_time,total_spent',
'1,2019-01-01 10:00:00,1',
'2,2019-01-01 11:30:00,1',
'2,2019-01-01 12:00:00,1',
]
assert given_labels == labels
def test_search_offset_mix_3(transactions, total_spent_fn):
"""
Test offset mix with window_size (absolute), minimum_data (absolute), and gap (relative).
"""
lm = LabelMaker(
target_entity='customer_id',
time_index='time',
labeling_function=total_spent_fn,
window_size='8h',
)
given_labels = lm.search(
transactions,
num_examples_per_instance=-1,
minimum_data='2019-01-01 08:00:00',
gap=1,
)
given_labels = to_csv(given_labels, index=False)
labels = [
'customer_id,time,total_spent',
'0,2019-01-01 08:00:00,2',
'0,2019-01-01 08:30:00,1',
'1,2019-01-01 09:00:00,3',
'1,2019-01-01 09:30:00,2',
'1,2019-01-01 10:00:00,1',
'2,2019-01-01 10:30:00,4',
'2,2019-01-01 11:00:00,3',
'2,2019-01-01 11:30:00,2',
'2,2019-01-01 12:00:00,1',
'3,2019-01-01 12:30:00,1',
]
assert given_labels == labels
def test_search_with_undefined_labels(transactions, total_spent_fn):
def total_spent(ds):
return total_spent_fn(ds) % 3
lm = LabelMaker(target_entity='customer_id',
time_index='time',
labeling_function=total_spent)
n_examples = {1: 1, 2: 1}
given_labels = lm.search(transactions,
num_examples_per_instance=n_examples,
gap=1)
given_labels = to_csv(given_labels, index=False)
labels = [
'customer_id,time,total_spent',
'0,2019-01-01 08:00:00,2',
'0,2019-01-01 08:30:00,1',
'1,2019-01-01 09:30:00,2',
'1,2019-01-01 10:00:00,1',
'2,2019-01-01 10:30:00,1',
'2,2019-01-01 11:30:00,2',
'3,2019-01-01 12:30:00,1',
]
assert given_labels == labels
def test_sample_frac_int(labels):
given_answer = labels.sample(frac=.25, random_state=0)
given_answer = given_answer.sort_index()
given_answer = to_csv(given_answer, index=True)
answer = [
'label_id,customer_id,cutoff_time,my_labeling_function',
'2,2,2014-01-01 00:01:00,True',
]
assert given_answer == answer
def test_distribution_categorical(total_spent):
labels = range(2)
given_answer = total_spent.bin(2, labels=labels).distribution
given_answer = to_csv(given_answer)
answer = [
'total_spent,count',
'0,5',
'1,5',
]
assert given_answer == answer
def test_count(total_spent):
given_answer = total_spent.count
given_answer = to_csv(given_answer, index=True)
answer = [
'customer_id,count',
'0,2',
'1,3',
'2,4',
'3,1',
]
assert given_answer == answer
def test_sample_frac_per_label(labels):
frac = {True: 1., False: .5}
given_answer = labels.sample(frac=frac, random_state=0)
given_answer = given_answer.sort_index()
given_answer = to_csv(given_answer, index=True)
answer = [
'label_id,customer_id,cutoff_time,my_labeling_function',
'0,1,2014-01-01 00:45:00,True',
'2,2,2014-01-01 00:01:00,True',
'3,2,2014-01-01 00:04:00,False',
]
assert given_answer == answer
def test_sample_n_per_label(labels):
n = {True: 1, False: 2}
given_answer = labels.sample(n=n, random_state=0)
given_answer = given_answer.sort_index()
given_answer = to_csv(given_answer, index=True)
answer = [
'label_id,customer_id,cutoff_time,my_labeling_function',
'1,1,2014-01-01 00:48:00,False',
'2,2,2014-01-01 00:01:00,True',
'3,2,2014-01-01 00:04:00,False',
]
assert given_answer == answer
def test_search_default(transactions, total_spent_fn):
lm = LabelMaker(target_entity='customer_id', time_index='time', labeling_function=total_spent_fn)
given_labels = lm.search(transactions, num_examples_per_instance=1)
given_labels = to_csv(given_labels, index=False)
labels = [
'customer_id,time,total_spent',
'0,2019-01-01 08:00:00,2',
'1,2019-01-01 09:00:00,3',
'2,2019-01-01 10:30:00,4',
'3,2019-01-01 12:30:00,1',
]
assert given_labels == labels
def test_count_by_time_categorical(total_spent):
given_answer = total_spent.bin(2, labels=range(2))
given_answer = to_csv(given_answer.count_by_time)
answer = [
'time,0,1',
'2019-01-01 08:00:00,0,1',
'2019-01-01 08:30:00,0,2',
'2019-01-01 09:00:00,0,3',
'2019-01-01 09:30:00,0,4',
'2019-01-01 10:00:00,0,5',
'2019-01-01 10:30:00,1,5',
'2019-01-01 11:00:00,2,5',
'2019-01-01 11:30:00,3,5',
'2019-01-01 12:00:00,4,5',
'2019-01-01 12:30:00,5,5',
]
assert given_answer == answer
def test_count_by_time_continuous(total_spent):
given_answer = total_spent.count_by_time
given_answer = to_csv(given_answer, header=True, index=True)
answer = [
'time,total_spent',
'2019-01-01 08:00:00,1',
'2019-01-01 08:30:00,2',
'2019-01-01 09:00:00,3',
'2019-01-01 09:30:00,4',
'2019-01-01 10:00:00,5',
'2019-01-01 10:30:00,6',
'2019-01-01 11:00:00,7',
'2019-01-01 11:30:00,8',
'2019-01-01 12:00:00,9',
'2019-01-01 12:30:00,10',
]
assert given_answer == answer
def test_count_by_time_categorical(total_spent):
labels = range(2)
given_answer = total_spent.bin(2, labels=labels).count_by_time
given_answer = to_csv(given_answer)
answer = [
'cutoff_time,0,1',
'2019-01-01 08:00:00,0.0,1.0',
'2019-01-01 08:30:00,0.0,2.0',
'2019-01-01 09:00:00,0.0,3.0',
'2019-01-01 09:30:00,0.0,4.0',
'2019-01-01 10:00:00,0.0,5.0',
'2019-01-01 10:30:00,1.0,5.0',
'2019-01-01 11:00:00,2.0,5.0',
'2019-01-01 11:30:00,3.0,5.0',
'2019-01-01 12:00:00,4.0,5.0',
'2019-01-01 12:30:00,5.0,5.0',
]
assert given_answer == answer
