def test_init_and_name(es):
log = es['log']
features = [Feature(v) for v in log.variables] +\
[GreaterThan(Feature(es["products"]["rating"], es["log"]), 2.5)]
# Add Timedelta feature
features.append(pd.Timestamp.now() - Feature(log['datetime']))
for transform_prim in get_transform_primitives():
if issubclass(transform_prim, Compare):
continue
# use the input_types matching function from DFS
input_types = transform_prim.input_types
if type(input_types[0]) == list:
matching_inputs = [
g for s in input_types for g in match(s, features)
]
else:
matching_inputs = match(input_types, features)
if len(matching_inputs) == 0:
raise Exception("Transform Primitive %s not tested" %
transform_prim.name)
for s in matching_inputs:
instance = transform_prim(*s)
# try to get name and calculate
instance.get_name()
instance.head()
def test_make_transform_sets_kwargs_correctly(es):
def pd_is_in(array, list_of_outputs=None):
if list_of_outputs is None:
list_of_outputs = []
return pd.Series(array).isin(list_of_outputs)
def isin_generate_name(self):
return u"%s.isin(%s)" % (self.base_features[0].get_name(),
str(self.kwargs['list_of_outputs']))
IsIn = make_trans_primitive(
pd_is_in, [Variable],
Boolean,
name="is_in",
description="For each value of the base feature, checks whether it is "
"in a list that is provided.",
cls_attributes={"generate_name": isin_generate_name})
isin_1_list = ["toothpaste", "coke_zero"]
isin_1_base_f = Feature(es['log']['product_id'])
isin_1 = IsIn(isin_1_base_f, list_of_outputs=isin_1_list)
isin_2_list = ["coke_zero"]
isin_2_base_f = Feature(es['log']['session_id'])
isin_2 = IsIn(isin_2_base_f, list_of_outputs=isin_2_list)
assert isin_1_base_f == isin_1.base_features[0]
assert isin_1_list == isin_1.kwargs['list_of_outputs']
assert isin_2_base_f == isin_2.base_features[0]
assert isin_2_list == isin_2.kwargs['list_of_outputs']
def test_max_hlevel(es):
kwargs = dict(
target_entity_id='log',
entityset=es,
agg_primitives=[Count, Last],
trans_primitives=[Hour],
max_depth=-1,
)
dfs_h_n1 = DeepFeatureSynthesis(max_hlevel=-1, **kwargs)
dfs_h_0 = DeepFeatureSynthesis(max_hlevel=0, **kwargs)
dfs_h_1 = DeepFeatureSynthesis(max_hlevel=1, **kwargs)
feats_n1 = dfs_h_n1.build_features()
feats_n1 = [f.get_name() for f in feats_n1]
feats_0 = dfs_h_0.build_features()
feats_0 = [f.get_name() for f in feats_0]
feats_1 = dfs_h_1.build_features()
feats_1 = [f.get_name() for f in feats_1]
customer_log = Last(es['log']['value'], es['customers'])
session_log = Last(es['log']['value'], es['sessions'])
log_customer_log = Feature(customer_log, es['log'])
log_session_log = Feature(session_log, es['log'])
assert log_customer_log.get_name() in feats_n1
assert log_session_log.get_name() in feats_n1
assert log_customer_log.get_name() not in feats_1
assert log_session_log.get_name() in feats_1
assert log_customer_log.get_name() not in feats_0
assert log_session_log.get_name() not in feats_0
def test_isin_feat_other_syntax(es):
isin = Feature(es['log']['product_id']).isin(["toothpaste", "coke zero"])
features = [isin]
pandas_backend = PandasBackend(es, features)
df = pandas_backend.calculate_all_features(range(8), None)
true = [True, True, True, False, False, True, True, True]
v = df[isin.get_name()].values.tolist()
assert true == v
def test_isin_feat_other_syntax_int(es):
isin = Feature(es['log']['value']).isin([5, 10])
features = [isin]
pandas_backend = PandasBackend(es, features)
df = pandas_backend.calculate_all_features(range(8), None)
true = [False, True, True, False, False, False, False, False]
v = df[isin.get_name()].values.tolist()
assert true == v
def test_percentile(es):
v = Feature(es['log']['value'])
p = Percentile(v)
pandas_backend = PandasBackend(es, [v, p])
df = pandas_backend.calculate_all_features(range(17), None)
true = df[v.get_name()].rank(pct=True)
for t, a in zip(true.values, df[p.get_name()].values):
assert (pd.isnull(t) and pd.isnull(a)) or t == a
def test_isin_feat_other_syntax_int(es):
isin = Feature(es['log']['value']).isin([5, 10])
features = [isin]
pandas_backend = PandasBackend(es, features)
df = pandas_backend.calculate_all_features(range(8), None)
true = [False, True, True, False, False, False, False, False]
v = df[isin.get_name()].values.tolist()
assert true == v
def test_isin_feat_other_syntax(es):
isin = Feature(es['log']['product_id']).isin(["toothpaste", "coke zero"])
features = [isin]
pandas_backend = PandasBackend(es, features)
df = pandas_backend.calculate_all_features(range(8), None)
true = [True, True, True, False, False, True, True, True]
v = df[isin.get_name()].values.tolist()
assert true == v
def test_percentile(es):
v = Feature(es['log']['value'])
p = Percentile(v)
pandas_backend = PandasBackend(es, [p])
df = pandas_backend.calculate_all_features(range(10, 17), None)
true = es['log'].df[v.get_name()].rank(pct=True)
true = true.loc[range(10, 17)]
for t, a in zip(true.values, df[p.get_name()].values):
assert (pd.isnull(t) and pd.isnull(a)) or t == a
def test_dependent_percentile(es):
v = Feature(es['log']['value'])
p = Percentile(v)
p2 = Percentile(p - 1)
pandas_backend = PandasBackend(es, [p, p2])
df = pandas_backend.calculate_all_features(range(10, 17), None)
true = es['log'].df[v.get_name()].rank(pct=True)
true = true.loc[range(10, 17)]
for t, a in zip(true.values, df[p.get_name()].values):
assert (pd.isnull(t) and pd.isnull(a)) or t == a
def test_return_type_inference_id(es):
# direct features should keep Id variable type
direct_id_feature = Feature(es["sessions"]["customer_id"], es["log"])
assert direct_id_feature.variable_type == Id
# aggregations of Id variable types should get converted
mode = Mode(es["log"]["session_id"], es["customers"])
assert mode.variable_type == Categorical
# also test direct feature of aggregation
mode_direct = Feature(mode, es["sessions"])
assert mode_direct.variable_type == Categorical
def test_agg_percentile(es):
v = Feature(es['log']['value'])
p = Percentile(v)
agg = Sum(p, es['sessions'])
pandas_backend = PandasBackend(es, [agg])
df = pandas_backend.calculate_all_features([0, 1], None)
log_vals = es['log'].df[[v.get_name(), 'session_id']]
log_vals['percentile'] = log_vals[v.get_name()].rank(pct=True)
true_p = log_vals.groupby('session_id')['percentile'].sum()[[0, 1]]
for t, a in zip(true_p.values, df[agg.get_name()].values):
assert (pd.isnull(t) and pd.isnull(a)) or t == a
def test_agg_percentile(es):
v = Feature(es['log']['value'])
p = Percentile(v)
agg = Sum(p, es['sessions'])
pandas_backend = PandasBackend(es, [agg])
df = pandas_backend.calculate_all_features([0, 1], None)
log_vals = es['log'].df[[v.get_name(), 'session_id']]
log_vals['percentile'] = log_vals[v.get_name()].rank(pct=True)
true_p = log_vals.groupby('session_id')['percentile'].sum()[[0, 1]]
for t, a in zip(true_p.values, df[agg.get_name()].values):
assert (pd.isnull(t) and pd.isnull(a)) or t == a
def test_direct_percentile(es):
v = Feature(es['customers']['age'])
p = Percentile(v)
d = Feature(p, es['sessions'])
pandas_backend = PandasBackend(es, [d])
df = pandas_backend.calculate_all_features([0, 1], None)
cust_vals = es['customers'].df[[v.get_name()]]
cust_vals['percentile'] = cust_vals[v.get_name()].rank(pct=True)
true_p = cust_vals['percentile'].loc[[0, 0]]
for t, a in zip(true_p.values, df[d.get_name()].values):
assert (pd.isnull(t) and pd.isnull(a)) or t == a
def test_get_depth(es):
log_id_feat = es['log']['id']
customer_id_feat = es['customers']['id']
count_logs = Count(log_id_feat, parent_entity=es['sessions'])
sum_count_logs = Sum(count_logs, parent_entity=es['customers'])
num_logs_greater_than_5 = sum_count_logs > 5
count_customers = Count(customer_id_feat,
parent_entity=es[u'régions'],
where=num_logs_greater_than_5)
num_customers_region = Feature(count_customers, es["customers"])
depth = num_customers_region.get_depth()
assert depth == 5
def test_max_hlevel(es):
kwargs = dict(
target_entity_id='log',
entityset=es,
agg_primitives=[Count, Last],
trans_primitives=[Hour],
max_depth=-1,
)
dfs_h_n1 = DeepFeatureSynthesis(max_hlevel=-1, **kwargs)
dfs_h_0 = DeepFeatureSynthesis(max_hlevel=0, **kwargs)
dfs_h_1 = DeepFeatureSynthesis(max_hlevel=1, **kwargs)
feats_n1 = dfs_h_n1.build_features()
feats_n1 = [f.get_name() for f in feats_n1]
feats_0 = dfs_h_0.build_features()
feats_0 = [f.get_name() for f in feats_0]
feats_1 = dfs_h_1.build_features()
feats_1 = [f.get_name() for f in feats_1]
customer_log = Last(es['log']['value'], es['customers'])
session_log = Last(es['log']['value'], es['sessions'])
log_customer_log = Feature(customer_log, es['log'])
log_session_log = Feature(session_log, es['log'])
assert log_customer_log.get_name() in feats_n1
assert log_session_log.get_name() in feats_n1
assert log_customer_log.get_name() not in feats_1
assert log_session_log.get_name() in feats_1
assert log_customer_log.get_name() not in feats_0
assert log_session_log.get_name() not in feats_0
def test_direct_agg_percentile(es):
v = Feature(es['log']['value'])
p = Percentile(v)
agg = Sum(p, es['customers'])
d = Feature(agg, es['sessions'])
pandas_backend = PandasBackend(es, [d])
df = pandas_backend.calculate_all_features([0, 1], None)
log_vals = es['log'].df[[v.get_name(), 'session_id']]
log_vals['percentile'] = log_vals[v.get_name()].rank(pct=True)
log_vals['customer_id'] = [0] * 10 + [1] * 5 + [2] * 2
true_p = log_vals.groupby('customer_id')['percentile'].sum().fillna(0)
true_p = true_p[[0, 0]]
for t, a in zip(true_p.values, df[d.get_name()].values):
assert (pd.isnull(t) and pd.isnull(a)) or round(t, 3) == round(a, 3)
def test_get_depth(es):
log_id_feat = es['log']['id']
customer_id_feat = es['customers']['id']
count_logs = Count(log_id_feat,
parent_entity=es['sessions'])
sum_count_logs = Sum(count_logs,
parent_entity=es['customers'])
num_logs_greater_than_5 = sum_count_logs > 5
count_customers = Count(customer_id_feat,
parent_entity=es['regions'],
where=num_logs_greater_than_5)
num_customers_region = Feature(count_customers, es["customers"])
depth = num_customers_region.get_depth()
assert depth == 5
def test_percentile_with_cutoff(es):
v = Feature(es['log']['value'])
p = Percentile(v)
pandas_backend = PandasBackend(es, [p])
df = pandas_backend.calculate_all_features(
[2], pd.Timestamp('2011/04/09 10:30:13'))
assert df[p.get_name()].tolist()[0] == 1.0
def test_custom_primitive_default_kwargs(es):
def sum_n_times(numeric, n=1):
return np.nan_to_num(numeric).sum(dtype=np.float) * n
SumNTimes = make_agg_primitive(function=sum_n_times,
input_types=[Numeric],
return_type=Numeric)
sum_n_1_n = 1
sum_n_1_base_f = Feature(es['log']['value'])
sum_n_1 = SumNTimes([sum_n_1_base_f], es['sessions'], n=sum_n_1_n)
sum_n_2_n = 2
sum_n_2_base_f = Feature(es['log']['value_2'])
sum_n_2 = SumNTimes([sum_n_2_base_f], es['sessions'], n=sum_n_2_n)
assert sum_n_1_base_f == sum_n_1.base_features[0]
assert sum_n_1_n == sum_n_1.kwargs['n']
assert sum_n_2_base_f == sum_n_2.base_features[0]
assert sum_n_2_n == sum_n_2.kwargs['n']
def test_get_depth(es):
es = make_ecommerce_entityset()
f = Feature(es['log']['value'])
g = Feature(es['log']['value'])
agg1 = Last(f, es['sessions'])
agg2 = Last(agg1, es['customers'])
d1 = Feature(agg2, es['sessions'])
d2 = Feature(d1, es['log'])
assert d2.get_depth() == 4
# Make sure this works if we pass in two of the same
# feature. This came up when user supplied duplicates
# in the seed_features of DFS.
assert d2.get_depth(stop_at=[f, g]) == 4
assert d2.get_depth(stop_at=[f, g, agg1]) == 3
assert d2.get_depth(stop_at=[f, g, agg1]) == 3
assert d2.get_depth(stop_at=[f, g, agg2]) == 2
assert d2.get_depth(stop_at=[f, g, d1]) == 1
assert d2.get_depth(stop_at=[f, g, d2]) == 0
def test_two_kinds_of_dependents(es):
v = Feature(es['log']['value'])
product = Feature(es['log']['product_id'])
agg = Sum(v, es['customers'], where=product == 'coke zero')
p = Percentile(agg)
g = Absolute(agg)
agg2 = Sum(v, es['sessions'], where=product == 'coke zero')
# Adding this feature in tests line 218 in pandas_backend
# where we remove columns in result_frame that already exist
# in the output entity_frames in preparation for pd.concat
# In a prior version, this failed because we changed the result_frame
# variable itself, rather than making a new variable _result_frame.
# When len(output_frames) > 1, the second iteration won't have
# all the necessary columns because they were removed in the first
agg3 = Sum(agg2, es['customers'])
pandas_backend = PandasBackend(es, [p, g, agg3])
df = pandas_backend.calculate_all_features([0, 1], None)
assert df[p.get_name()].tolist() == [0.5, 1.0]
assert df[g.get_name()].tolist() == [15, 26]
def test_get_dependencies(es):
f = Feature(es['log']['value'])
agg1 = Sum(f, es['sessions'])
agg2 = Sum(agg1, es['customers'])
d1 = Feature(agg2, es['sessions'])
shallow = d1.get_dependencies(deep=False, ignored=None)
deep = d1.get_dependencies(deep=True, ignored=None)
ignored = set([agg1.hash()])
deep_ignored = d1.get_dependencies(deep=True, ignored=ignored)
assert [s.hash() for s in shallow] == [agg2.hash()]
assert [d.hash() for d in deep] == [agg2.hash(), agg1.hash(), f.hash()]
assert [d.hash() for d in deep_ignored] == [agg2.hash()]
def test_isin_feat_custom(es):
def pd_is_in(array, list_of_outputs=None):
if list_of_outputs is None:
list_of_outputs = []
return pd.Series(array).isin(list_of_outputs)
def isin_generate_name(self):
return u"%s.isin(%s)" % (self.base_features[0].get_name(),
str(self.kwargs['list_of_outputs']))
IsIn = make_trans_primitive(
pd_is_in,
[Variable],
Boolean,
name="is_in",
description="For each value of the base feature, checks whether it is "
"in a list that is provided.",
cls_attributes={"generate_name": isin_generate_name})
isin = IsIn(es['log']['product_id'],
list_of_outputs=["toothpaste", "coke zero"])
features = [isin]
pandas_backend = PandasBackend(es, features)
df = pandas_backend.calculate_all_features(range(8), None)
true = [True, True, True, False, False, True, True, True]
v = df[isin.get_name()].values.tolist()
assert true == v
isin = Feature(es['log']['product_id']).isin(["toothpaste", "coke zero"])
features = [isin]
pandas_backend = PandasBackend(es, features)
df = pandas_backend.calculate_all_features(range(8), None)
true = [True, True, True, False, False, True, True, True]
v = df[isin.get_name()].values.tolist()
assert true == v
isin = Feature(es['log']['value']).isin([5, 10])
features = [isin]
pandas_backend = PandasBackend(es, features)
df = pandas_backend.calculate_all_features(range(8), None)
true = [False, True, True, False, False, False, False, False]
v = df[isin.get_name()].values.tolist()
assert true == v
def test_direct_percentile(es):
v = Feature(es['customers']['age'])
p = Percentile(v)
d = Feature(p, es['sessions'])
pandas_backend = PandasBackend(es, [d])
df = pandas_backend.calculate_all_features([0, 1], None)
cust_vals = es['customers'].df[[v.get_name()]]
cust_vals['percentile'] = cust_vals[v.get_name()].rank(pct=True)
true_p = cust_vals['percentile'].loc[[0, 0]]
for t, a in zip(true_p.values, df[d.get_name()].values):
assert (pd.isnull(t) and pd.isnull(a)) or t == a
def test_init_and_name(es):
from featuretools import calculate_feature_matrix
log = es['log']
features = [Feature(v) for v in log.variables] +\
[GreaterThan(Feature(es["products"]["rating"], es["log"]), 2.5)]
# Add Timedelta feature
features.append(pd.Timestamp.now() - Feature(log['datetime']))
for transform_prim in get_transform_primitives().values():
# use the input_types matching function from DFS
input_types = transform_prim.input_types
if type(input_types[0]) == list:
matching_inputs = match(input_types[0], features)
else:
matching_inputs = match(input_types, features)
if len(matching_inputs) == 0:
raise Exception("Transform Primitive %s not tested" %
transform_prim.name)
for s in matching_inputs:
instance = transform_prim(*s)
# try to get name and calculate
instance.get_name()
calculate_feature_matrix([instance], entityset=es).head(5)
def test_direct_agg_percentile(es):
v = Feature(es['log']['value'])
p = Percentile(v)
agg = Sum(p, es['customers'])
d = Feature(agg, es['sessions'])
pandas_backend = PandasBackend(es, [d])
df = pandas_backend.calculate_all_features([0, 1], None)
log_vals = es['log'].df[[v.get_name(), 'session_id']]
log_vals['percentile'] = log_vals[v.get_name()].rank(pct=True)
log_vals['customer_id'] = [0] * 10 + [1] * 5 + [2] * 2
true_p = log_vals.groupby('customer_id')['percentile'].sum().fillna(0)
true_p = true_p[[0, 0]]
for t, a in zip(true_p.values, df[d.get_name()].values):
assert (pd.isnull(t) and pd.isnull(a)) or round(t, 3) == round(a, 3)
def test_cum_sum_where(es):
log_value_feat = es['log']['value']
compare_feat = GreaterThan(log_value_feat, 3)
dfeat = Feature(es['sessions']['customer_id'], es['log'])
cum_sum = CumSum(log_value_feat, dfeat, where=compare_feat)
features = [cum_sum]
pandas_backend = PandasBackend(es, features)
df = pandas_backend.calculate_all_features(instance_ids=range(15),
time_last=None)
cvalues = df[cum_sum.get_name()].values
assert len(cvalues) == 15
cum_sum_values = [0, 5, 15, 30, 50, 50, 50, 50, 50, 50, 0, 5, 5, 12, 26]
for i, v in enumerate(cum_sum_values):
if not np.isnan(v):
assert v == cvalues[i]
else:
assert (np.isnan(cvalues[i]))
def test_get_dependencies(es):
f = Feature(es['log']['value'])
agg1 = Sum(f, es['sessions'])
agg2 = Sum(agg1, es['customers'])
d1 = Feature(agg2, es['sessions'])
shallow = d1.get_dependencies(deep=False, ignored=None)
deep = d1.get_dependencies(deep=True, ignored=None)
ignored = set([agg1.hash()])
deep_ignored = d1.get_dependencies(deep=True, ignored=ignored)
assert [s.hash() for s in shallow] == [agg2.hash()]
assert [d.hash() for d in deep] == [agg2.hash(), agg1.hash(), f.hash()]
assert [d.hash() for d in deep_ignored] == [agg2.hash()]
def test_cum_sum_use_previous_and_where_absolute(es):
log_value_feat = es['log']['value']
compare_feat = GreaterThan(log_value_feat, 3)
dfeat = Feature(es['sessions']['customer_id'], es['log'])
cum_sum = CumSum(log_value_feat, dfeat, es["log"]["datetime"],
where=compare_feat,
use_previous=Timedelta(40, 'seconds'))
features = [cum_sum]
pandas_backend = PandasBackend(es, features)
df = pandas_backend.calculate_all_features(instance_ids=range(15),
time_last=None)
cum_sum_values = [0, 5, 15, 30, 50, 0, 0, 0, 0, 0,
0, 5, 0, 7, 21]
cvalues = df[cum_sum.get_name()].values
assert len(cvalues) == 15
for i, v in enumerate(cum_sum_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_init_and_name(es):
session = es['sessions']
log = es['log']
features = [Feature(v) for v in log.variables]
for agg_prim in get_aggregation_primitives().values():
input_types = agg_prim.input_types
if type(input_types[0]) != list:
input_types = [input_types]
# test each allowed input_types for this primitive
for it in input_types:
# use the input_types matching function from DFS
matching_types = match(it, features)
if len(matching_types) == 0:
raise Exception("Agg Primitive %s not tested" % agg_prim.name)
for t in matching_types:
instance = agg_prim(t, parent_entity=session)
# try to get name and calculate
instance.get_name()
ft.calculate_feature_matrix([instance], entityset=es).head(5)
def test_get_depth(es):
es = make_ecommerce_entityset()
f = Feature(es['log']['value'])
g = Feature(es['log']['value'])
agg1 = Last(f, es['sessions'])
agg2 = Last(agg1, es['customers'])
d1 = Feature(agg2, es['sessions'])
d2 = Feature(d1, es['log'])
assert d2.get_depth() == 4
# Make sure this works if we pass in two of the same
# feature. This came up when user supplied duplicates
# in the seed_features of DFS.
assert d2.get_depth(stop_at=[f, g]) == 4
assert d2.get_depth(stop_at=[f, g, agg1]) == 3
assert d2.get_depth(stop_at=[f, g, agg1]) == 3
assert d2.get_depth(stop_at=[f, g, agg2]) == 2
assert d2.get_depth(stop_at=[f, g, d1]) == 1
assert d2.get_depth(stop_at=[f, g, d2]) == 0
def test_return_type_inference_direct_feature(es):
mode = Mode(es["log"]["priority_level"], es["customers"])
mode_session = Feature(mode, es["sessions"])
assert mode_session.variable_type == es["log"]["priority_level"].__class__
def test_squared(es):
feature = Feature(es['log']['value'])
squared = feature * feature
assert len(squared.base_features) == 1
assert squared.base_features[0].hash() == feature.hash()
def session_id_feat(es):
return Feature(es['sessions']['id'])
def product_id_feat(es):
return Feature(es['log']['product_id'])
def datetime_feat(es):
return Feature(es['log']['datetime'])
def test_squared(es):
feature = Feature(es['log']['value'])
squared = feature * feature
assert len(squared.base_features) == 1
assert squared.base_features[0].hash() == feature.hash()
def test_overrides(es):
value = Feature(es['log']['value'])
value2 = Feature(es['log']['value_2'])
feats = [Add, Subtract, Multiply, Divide]
compare_ops = [
GreaterThan, LessThan, Equals, NotEquals, GreaterThanEqualTo,
LessThanEqualTo
]
assert Negate(value).hash() == (-value).hash()
compares = [(value, value), (value, value2), (value2, 2)]
overrides = [
value + value,
value - value,
value * value,
value / value,
value > value,
value < value,
value == value,
value != value,
value >= value,
value <= value,
value + value2,
value - value2,
value * value2,
value / value2,
value > value2,
value < value2,
value == value2,
value != value2,
value >= value2,
value <= value2,
value2 + 2,
value2 - 2,
value2 * 2,
value2 / 2,
value2 > 2,
value2 < 2,
value2 == 2,
value2 != 2,
value2 >= 2,
value2 <= 2,
]
i = 0
for left, right in compares:
for feat in feats:
f = feat(left, right)
o = overrides[i]
assert o.hash() == f.hash()
i += 1
for compare_op in compare_ops:
f = compare_op(left, right)
o = overrides[i]
assert o.hash() == f.hash()
i += 1
our_reverse_overrides = [2 + value2, 2 - value2, 2 * value2, 2 / value2]
i = 0
for feat in feats:
if feat != Mod:
f = feat(2, value2)
o = our_reverse_overrides[i]
assert o.hash() == f.hash()
i += 1
python_reverse_overrides = [
2 < value2, 2 > value2, 2 == value2, 2 != value2, 2 <= value2,
2 >= value2
]
i = 0
for compare_op in compare_ops:
f = compare_op(value2, 2)
o = python_reverse_overrides[i]
assert o.hash() == f.hash()
i += 1
