def test_cum_mean(es):
log_value_feat = es['log']['value']
cum_mean = CumMean(log_value_feat, es['log']['session_id'])
features = [cum_mean]
pandas_backend = PandasBackend(es, features)
df = pandas_backend.calculate_all_features(instance_ids=range(15),
time_last=None)
cvalues = df[cum_mean.get_name()].values
assert len(cvalues) == 15
cum_mean_values = [0, 2.5, 5, 7.5, 10, 0, .5, 1, 1.5, 0, 0, 2.5, 0, 3.5, 7]
for i, v in enumerate(cum_mean_values):
assert v == cvalues[i]
def test_cum_mean(es):
log_value_feat = es['log']['value']
cum_mean = CumMean(log_value_feat, es['log']['session_id'])
features = [cum_mean]
pandas_backend = PandasBackend(es, features)
df = pandas_backend.calculate_all_features(instance_ids=range(15),
time_last=None)
cvalues = df[cum_mean.get_name()].values
assert len(cvalues) == 15
cum_mean_values = [0, 2.5, 5, 7.5, 10, 0, .5, 1, 1.5, 0, 0, 2.5, 0, 3.5, 7]
for i, v in enumerate(cum_mean_values):
assert v == cvalues[i]
def test_cum_mean_where(es):
log_value_feat = es['log']['value']
compare_feat = GreaterThan(log_value_feat, 3)
dfeat = Feature(es['sessions']['customer_id'], es['log'])
cum_mean = CumMean(log_value_feat, dfeat, where=compare_feat)
features = [cum_mean]
pandas_backend = PandasBackend(es, features)
df = pandas_backend.calculate_all_features(instance_ids=range(15),
time_last=None)
cvalues = df[cum_mean.get_name()].values
assert len(cvalues) == 15
cum_mean_values = [
0, 5, 7.5, 10, 12.5, 12.5, 12.5, 12.5, 12.5, 12.5, 0, 5, 5, 6, 26. / 3
]
for i, v in enumerate(cum_mean_values):
if not np.isnan(v):
assert v == cvalues[i]
else:
assert (np.isnan(cvalues[i]))
def test_cum_mean_use_previous_and_where(es):
log_value_feat = es['log']['value']
compare_feat = GreaterThan(log_value_feat, 3)
# todo should this be cummean?
dfeat = Feature(es['sessions']['customer_id'], es['log'])
cum_mean = CumMean(log_value_feat, dfeat,
where=compare_feat,
use_previous=Timedelta(2, 'observations',
entity=es['log']))
features = [cum_mean]
pandas_backend = PandasBackend(es, features)
df = pandas_backend.calculate_all_features(instance_ids=range(15),
time_last=None)
cum_mean_values = [0, 5, 7.5, 12.5, 17.5, 17.5, 17.5, 17.5, 17.5, 17.5,
0, 5, 5, 6, 10.5]
cvalues = df[cum_mean.get_name()].values
assert len(cvalues) == 15
for i, v in enumerate(cum_mean_values):
assert v == cvalues[i]
def test_cum_mean_use_previous_and_where(es):
log_value_feat = es['log']['value']
compare_feat = GreaterThan(log_value_feat, 3)
# todo should this be cummean?
dfeat = Feature(es['sessions']['customer_id'], es['log'])
cum_mean = CumMean(log_value_feat, dfeat,
where=compare_feat,
use_previous=Timedelta(2, 'observations',
entity=es['log']))
features = [cum_mean]
pandas_backend = PandasBackend(es, features)
df = pandas_backend.calculate_all_features(instance_ids=range(15),
time_last=None)
cum_mean_values = [0, 5, 7.5, 12.5, 17.5, 17.5, 17.5, 17.5, 17.5, 17.5,
0, 5, 5, 6, 10.5]
cvalues = df[cum_mean.get_name()].values
assert len(cvalues) == 15
for i, v in enumerate(cum_mean_values):
assert v == cvalues[i]
def test_cum_mean_where(es):
log_value_feat = es['log']['value']
compare_feat = GreaterThan(log_value_feat, 3)
dfeat = Feature(es['sessions']['customer_id'], es['log'])
cum_mean = CumMean(log_value_feat, dfeat,
where=compare_feat)
features = [cum_mean]
pandas_backend = PandasBackend(es, features)
df = pandas_backend.calculate_all_features(instance_ids=range(15),
time_last=None)
cvalues = df[cum_mean.get_name()].values
assert len(cvalues) == 15
cum_mean_values = [0, 5, 7.5, 10, 12.5, 12.5, 12.5, 12.5, 12.5, 12.5,
0, 5, 5, 6, 26. / 3]
for i, v in enumerate(cum_mean_values):
if not np.isnan(v):
assert v == cvalues[i]
else:
assert (np.isnan(cvalues[i]))