def test_direct_description(es):
feature = DirectFeature(
IdentityFeature(es["customers"].ww["loves_ice_cream"]), "sessions"
)
description = (
'The "loves_ice_cream" for the instance of "customers" associated '
'with this instance of "sessions".'
)
assert describe_feature(feature) == description
deep_direct = DirectFeature(feature, "log")
deep_description = (
'The "loves_ice_cream" for the instance of "customers" '
'associated with the instance of "sessions" associated with '
'this instance of "log".'
)
assert describe_feature(deep_direct) == deep_description
agg = AggregationFeature(
IdentityFeature(es["log"].ww["purchased"]), "sessions", PercentTrue
)
complicated_direct = DirectFeature(agg, "log")
agg_on_direct = AggregationFeature(complicated_direct, "products", Mean)
complicated_description = (
"The average of the percentage of true values in "
'the "purchased" of all instances of "log" for each "id" in "sessions" for '
'the instance of "sessions" associated with this instance of "log" of all '
'instances of "log" for each "id" in "products".'
)
assert describe_feature(agg_on_direct) == complicated_description
def test_generic_description(es):
class NoName(TransformPrimitive):
input_types = [ColumnSchema(semantic_tags={'category'})]
output_type = ColumnSchema(semantic_tags={'category'})
def generate_name(self, base_feature_names):
return u"%s(%s%s)" % (
'NO_NAME',
u", ".join(base_feature_names),
self.get_args_string(),
)
class CustomAgg(AggregationPrimitive):
name = 'custom_aggregation'
input_types = [ColumnSchema(semantic_tags={'category'})]
output_type = ColumnSchema(semantic_tags={'category'})
class CustomTrans(TransformPrimitive):
name = 'custom_transform'
input_types = [ColumnSchema(semantic_tags={'category'})]
output_type = ColumnSchema(semantic_tags={'category'})
no_name = TransformFeature(IdentityFeature(es['log'].ww['zipcode']), NoName)
no_name_description = 'The result of applying NoName to the "zipcode".'
assert describe_feature(no_name) == no_name_description
custom_agg = AggregationFeature(IdentityFeature(es['log'].ww['zipcode']), 'customers', CustomAgg)
custom_agg_description = 'The result of applying CUSTOM_AGGREGATION to the "zipcode" of all instances of "log" for each "id" in "customers".'
assert describe_feature(custom_agg) == custom_agg_description
custom_trans = TransformFeature(IdentityFeature(es['log'].ww['zipcode']), CustomTrans)
custom_trans_description = 'The result of applying CUSTOM_TRANSFORM to the "zipcode".'
assert describe_feature(custom_trans) == custom_trans_description
def test_aggregation_description(es):
feature = AggregationFeature(IdentityFeature(es['log'].ww['value']), 'sessions', Mean)
description = 'The average of the "value" of all instances of "log" for each "id" in "sessions".'
assert describe_feature(feature) == description
stacked_agg = AggregationFeature(feature, 'customers', Sum)
stacked_description = 'The sum of t{} of all instances of "sessions" for each "id" ' \
'in "customers".'.format(description[1:-1])
assert describe_feature(stacked_agg) == stacked_description
def test_aggregation_description_use_previous(es):
feature = AggregationFeature(
IdentityFeature(es["log"].ww["value"]), "sessions", Mean, use_previous="5d"
)
description = 'The average of the "value" of the previous 5 days of "log" for each "id" in "sessions".'
assert describe_feature(feature) == description
def test_column_description(es):
column_description = "the name of the device used for each session"
es["sessions"].ww.columns["device_name"].description = column_description
identity_feat = IdentityFeature(es["sessions"].ww["device_name"])
assert (
describe_feature(identity_feat)
== column_description[0].upper() + column_description[1:] + "."
)
def test_aggregation_description_use_previous(es):
feature = AggregationFeature(es['log']['value'],
es['sessions'],
Mean,
use_previous='5d')
description = 'The average of the "value" of the previous 5 days of "log" for each "id" in "sessions".'
assert describe_feature(feature) == description
def test_aggregation_description_where(es):
where_feature = TransformFeature(IdentityFeature(es['log'].ww['countrycode']), EqualScalar('US'))
feature = AggregationFeature(IdentityFeature(es['log'].ww['value']), 'sessions',
Mean, where=where_feature)
description = 'The average of the "value" of all instances of "log" where the ' \
'"countrycode" is US for each "id" in "sessions".'
assert describe_feature(feature) == description
def test_metadata(es, tmpdir):
identity_feature_descriptions = {'sessions: device_name': 'the name of the device used for each session',
'customers: id': "the customer's id"}
agg_feat = AggregationFeature(IdentityFeature(es['sessions'].ww['device_name']), 'customers', NumUnique)
agg_description = 'The number of unique elements in the name of the device used for each '\
'session of all instances of "sessions" for each customer\'s id.'
assert describe_feature(agg_feat, feature_descriptions=identity_feature_descriptions) == agg_description
transform_feat = GroupByTransformFeature(IdentityFeature(es['log'].ww['value']), CumMean, IdentityFeature(es['log'].ww['session_id']))
transform_description = 'The running average of the "value" for each "session_id".'
primitive_templates = {"cum_mean": "the running average of {}"}
assert describe_feature(transform_feat, primitive_templates=primitive_templates) == transform_description
custom_agg = AggregationFeature(IdentityFeature(es['log'].ww['zipcode']), 'sessions', Mode)
auto_description = 'The most frequently occurring value of the "zipcode" of all instances of "log" for each "id" in "sessions".'
custom_agg_description = "the most frequently used zipcode"
custom_feature_description = custom_agg_description[0].upper() + custom_agg_description[1:] + '.'
feature_description_dict = {'sessions: MODE(log.zipcode)': custom_agg_description}
assert describe_feature(custom_agg) == auto_description
assert describe_feature(custom_agg, feature_descriptions=feature_description_dict) == custom_feature_description
metadata = {
'feature_descriptions': {**identity_feature_descriptions, **feature_description_dict},
'primitive_templates': primitive_templates
}
metadata_path = os.path.join(tmpdir, 'description_metadata.json')
with open(metadata_path, 'w') as f:
json.dump(metadata, f)
assert describe_feature(agg_feat, metadata_file=metadata_path) == agg_description
assert describe_feature(transform_feat, metadata_file=metadata_path) == transform_description
assert describe_feature(custom_agg, metadata_file=metadata_path) == custom_feature_description
def test_groupby_transform_description(es):
feature = GroupByTransformFeature(
IdentityFeature(es["log"].ww["value"]),
CumMean,
IdentityFeature(es["log"].ww["session_id"]),
)
description = 'The cumulative mean of the "value" for each "session_id".'
assert describe_feature(feature) == description
def test_direct_description(es):
feature = DirectFeature(IdentityFeature(es['customers'].ww['loves_ice_cream']), 'sessions')
description = 'The "loves_ice_cream" for the instance of "customers" associated ' \
'with this instance of "sessions".'
assert describe_feature(feature) == description
deep_direct = DirectFeature(feature, 'log')
deep_description = 'The "loves_ice_cream" for the instance of "customers" ' \
'associated with the instance of "sessions" associated with ' \
'this instance of "log".'
assert describe_feature(deep_direct) == deep_description
agg = AggregationFeature(IdentityFeature(es['log'].ww['purchased']), 'sessions', PercentTrue)
complicated_direct = DirectFeature(agg, 'log')
agg_on_direct = AggregationFeature(complicated_direct, 'products', Mean)
complicated_description = 'The average of the percentage of true values in ' \
'the "purchased" of all instances of "log" for each "id" in "sessions" for ' \
'the instance of "sessions" associated with this instance of "log" of all ' \
'instances of "log" for each "id" in "products".'
assert describe_feature(agg_on_direct) == complicated_description
def test_aggregation_description_where(es):
where_feature = TransformFeature(
IdentityFeature(es["log"].ww["countrycode"]), EqualScalar("US")
)
feature = AggregationFeature(
IdentityFeature(es["log"].ww["value"]), "sessions", Mean, where=where_feature
)
description = (
'The average of the "value" of all instances of "log" where the '
'"countrycode" is US for each "id" in "sessions".'
)
assert describe_feature(feature) == description
# %%
feature_matrix
# %%
# Feature lineage graphs
# Understanding Feature Output¶
# In general, Featuretools references generated features through the feature name. In order to make features easier to understand, Featuretools offers two additional tools, featuretools.graph_feature() and featuretools.describe_feature(), to help explain what a feature is and the steps Featuretools took to generate it.
feature = feature_names[54]
ft.graph_feature(feature)
# %%
ft.describe_feature(feature)
# %%
feature_matrix,feature_names =ft.dfs(entityset = es, target_entity = 'WIP',
trans_primitives=['weekday', 'day'],
#agg_primitives=default_agg_primitives,
verbose = 1,
features_only = False)
# %%
#累績總計 與總數
def test_metadata(es, tmpdir):
identity_feature_descriptions = {
"sessions: device_name": "the name of the device used for each session",
"customers: id": "the customer's id",
}
agg_feat = AggregationFeature(
IdentityFeature(es["sessions"].ww["device_name"]), "customers", NumUnique
)
agg_description = (
"The number of unique elements in the name of the device used for each "
'session of all instances of "sessions" for each customer\'s id.'
)
assert (
describe_feature(agg_feat, feature_descriptions=identity_feature_descriptions)
== agg_description
)
transform_feat = GroupByTransformFeature(
IdentityFeature(es["log"].ww["value"]),
CumMean,
IdentityFeature(es["log"].ww["session_id"]),
)
transform_description = 'The running average of the "value" for each "session_id".'
primitive_templates = {"cum_mean": "the running average of {}"}
assert (
describe_feature(transform_feat, primitive_templates=primitive_templates)
== transform_description
)
custom_agg = AggregationFeature(
IdentityFeature(es["log"].ww["zipcode"]), "sessions", Mode
)
auto_description = 'The most frequently occurring value of the "zipcode" of all instances of "log" for each "id" in "sessions".'
custom_agg_description = "the most frequently used zipcode"
custom_feature_description = (
custom_agg_description[0].upper() + custom_agg_description[1:] + "."
)
feature_description_dict = {"sessions: MODE(log.zipcode)": custom_agg_description}
assert describe_feature(custom_agg) == auto_description
assert (
describe_feature(custom_agg, feature_descriptions=feature_description_dict)
== custom_feature_description
)
metadata = {
"feature_descriptions": {
**identity_feature_descriptions,
**feature_description_dict,
},
"primitive_templates": primitive_templates,
}
metadata_path = os.path.join(tmpdir, "description_metadata.json")
with open(metadata_path, "w") as f:
json.dump(metadata, f)
assert describe_feature(agg_feat, metadata_file=metadata_path) == agg_description
assert (
describe_feature(transform_feat, metadata_file=metadata_path)
== transform_description
)
assert (
describe_feature(custom_agg, metadata_file=metadata_path)
== custom_feature_description
)
def test_transform_description(es):
feature = TransformFeature(IdentityFeature(es['log'].ww['value']), Absolute)
description = 'The absolute value of the "value".'
assert describe_feature(feature) == description
def test_identity_description(es):
feature = IdentityFeature(es['log'].ww['session_id'])
description = 'The "session_id".'
assert describe_feature(feature) == description
def test_column_description(es):
column_description = 'the name of the device used for each session'
es['sessions'].ww.columns['device_name'].description = column_description
identity_feat = IdentityFeature(es['sessions'].ww['device_name'])
assert describe_feature(identity_feat) == column_description[0].upper() + column_description[1:] + '.'
def test_multioutput_description(es):
n_most_common = NMostCommon(2)
n_most_common_feature = AggregationFeature(IdentityFeature(es['log'].ww['zipcode']), 'sessions', n_most_common)
first_most_common_slice = n_most_common_feature[0]
second_most_common_slice = n_most_common_feature[1]
n_most_common_base = 'The 2 most common values of the "zipcode" of all instances of "log" for each "id" in "sessions".'
n_most_common_first = 'The most common value of the "zipcode" of all instances of "log" ' \
'for each "id" in "sessions".'
n_most_common_second = 'The 2nd most common value of the "zipcode" of all instances of ' \
'"log" for each "id" in "sessions".'
assert describe_feature(n_most_common_feature) == n_most_common_base
assert describe_feature(first_most_common_slice) == n_most_common_first
assert describe_feature(second_most_common_slice) == n_most_common_second
class CustomMultiOutput(TransformPrimitive):
name = "custom_multioutput"
input_types = [ColumnSchema(semantic_tags={'category'})]
return_type = ColumnSchema(semantic_tags={'category'})
number_output_features = 4
custom_feat = TransformFeature(IdentityFeature(es['log'].ww['zipcode']), CustomMultiOutput)
generic_base = 'The result of applying CUSTOM_MULTIOUTPUT to the "zipcode".'
generic_first = 'The 1st output from applying CUSTOM_MULTIOUTPUT to the "zipcode".'
generic_second = 'The 2nd output from applying CUSTOM_MULTIOUTPUT to the "zipcode".'
assert describe_feature(custom_feat) == generic_base
assert describe_feature(custom_feat[0]) == generic_first
assert describe_feature(custom_feat[1]) == generic_second
CustomMultiOutput.description_template = ['the multioutput of {}',
'the {nth_slice} multioutput part of {}']
template_base = 'The multioutput of the "zipcode".'
template_first_slice = 'The 1st multioutput part of the "zipcode".'
template_second_slice = 'The 2nd multioutput part of the "zipcode".'
template_third_slice = 'The 3rd multioutput part of the "zipcode".'
template_fourth_slice = 'The 4th multioutput part of the "zipcode".'
assert describe_feature(custom_feat) == template_base
assert describe_feature(custom_feat[0]) == template_first_slice
assert describe_feature(custom_feat[1]) == template_second_slice
assert describe_feature(custom_feat[2]) == template_third_slice
assert describe_feature(custom_feat[3]) == template_fourth_slice
CustomMultiOutput.description_template = ['the multioutput of {}',
'the primary multioutput part of {}',
'the secondary multioutput part of {}']
custom_base = 'The multioutput of the "zipcode".'
custom_first_slice = 'The primary multioutput part of the "zipcode".'
custom_second_slice = 'The secondary multioutput part of the "zipcode".'
bad_slice_error = 'Slice out of range of template'
assert describe_feature(custom_feat) == custom_base
assert describe_feature(custom_feat[0]) == custom_first_slice
assert describe_feature(custom_feat[1]) == custom_second_slice
with pytest.raises(IndexError, match=bad_slice_error):
describe_feature(custom_feat[2])
def test_groupby_transform_description(es):
feature = GroupByTransformFeature(es['log']['value'], CumMean,
es['log']['session_id'])
description = 'The cumulative mean of the "value" for each "session_id".'
assert describe_feature(feature) == description
def test_variable_description(es):
variable_description = 'the name of the device used for each session'
es['sessions']['device_name'].description = variable_description
identity_feat = IdentityFeature(es['sessions']['device_name'])
assert describe_feature(identity_feat) == variable_description[0].upper(
) + variable_description[1:] + '.'
