def test_get_default_transform_primitives():
primitives = get_default_transform_primitives()
expected_primitives = [
Age,
Day,
Year,
Month,
Weekday,
Haversine,
NumWords,
NumCharacters,
]
assert set(primitives) == set(expected_primitives)
def __init__(self,
target_entity_id,
entityset,
agg_primitives=None,
trans_primitives=None,
where_primitives=None,
groupby_trans_primitives=None,
max_depth=2,
max_features=-1,
allowed_paths=None,
ignore_entities=None,
ignore_variables=None,
primitive_options=None,
seed_features=None,
drop_contains=None,
drop_exact=None,
where_stacking_limit=1):
if target_entity_id not in entityset.entity_dict:
es_name = entityset.id or 'entity set'
msg = 'Provided target entity %s does not exist in %s' % (
target_entity_id, es_name)
raise KeyError(msg)
# need to change max_depth to None because DFs terminates when <0
if max_depth == -1:
max_depth = None
self.max_depth = max_depth
self.max_features = max_features
self.allowed_paths = allowed_paths
if self.allowed_paths:
self.allowed_paths = set()
for path in allowed_paths:
self.allowed_paths.add(tuple(path))
if ignore_entities is None:
self.ignore_entities = set()
else:
if not isinstance(ignore_entities, list):
raise TypeError('ignore_entities must be a list')
assert target_entity_id not in ignore_entities,\
"Can't ignore target_entity!"
self.ignore_entities = set(ignore_entities)
self.ignore_variables = defaultdict(set)
if ignore_variables is not None:
# check if ignore_variables is not {str: list}
if not all(isinstance(i, str)
for i in ignore_variables.keys()) or not all(
isinstance(i, list)
for i in ignore_variables.values()):
raise TypeError('ignore_variables should be dict[str -> list]')
# check if list values are all of type str
elif not all(
all(isinstance(v, str) for v in value)
for value in ignore_variables.values()):
raise TypeError('list values should be of type str')
for eid, vars in ignore_variables.items():
self.ignore_variables[eid] = set(vars)
self.target_entity_id = target_entity_id
self.es = entityset
if agg_primitives is None:
agg_primitives = primitives.get_default_aggregation_primitives()
self.agg_primitives = []
agg_prim_dict = primitives.get_aggregation_primitives()
for a in agg_primitives:
if isinstance(a, str):
if a.lower() not in agg_prim_dict:
raise ValueError(
"Unknown aggregation primitive {}. ".format(a),
"Call ft.primitives.list_primitives() to get",
" a list of available primitives")
a = agg_prim_dict[a.lower()]
a = handle_primitive(a)
if not isinstance(a, AggregationPrimitive):
raise ValueError("Primitive {} in agg_primitives is not an "
"aggregation primitive".format(type(a)))
self.agg_primitives.append(a)
if trans_primitives is None:
trans_primitives = primitives.get_default_transform_primitives()
self.trans_primitives = []
for t in trans_primitives:
t = check_trans_primitive(t)
self.trans_primitives.append(t)
if where_primitives is None:
where_primitives = [primitives.Count]
self.where_primitives = []
for p in where_primitives:
if isinstance(p, str):
prim_obj = agg_prim_dict.get(p.lower(), None)
if prim_obj is None:
raise ValueError(
"Unknown where primitive {}. ".format(p),
"Call ft.primitives.list_primitives() to get",
" a list of available primitives")
p = prim_obj
p = handle_primitive(p)
self.where_primitives.append(p)
if groupby_trans_primitives is None:
groupby_trans_primitives = []
self.groupby_trans_primitives = []
for p in groupby_trans_primitives:
p = check_trans_primitive(p)
self.groupby_trans_primitives.append(p)
if primitive_options is None:
primitive_options = {}
all_primitives = self.trans_primitives + self.agg_primitives + \
self.where_primitives + self.groupby_trans_primitives
self.primitive_options, self.ignore_entities, self.ignore_variables =\
generate_all_primitive_options(all_primitives,
primitive_options,
self.ignore_entities,
self.ignore_variables,
self.es)
self.seed_features = seed_features or []
self.drop_exact = drop_exact or []
self.drop_contains = drop_contains or []
self.where_stacking_limit = where_stacking_limit
def __init__(
self,
target_dataframe_name,
entityset,
agg_primitives=None,
trans_primitives=None,
where_primitives=None,
groupby_trans_primitives=None,
max_depth=2,
max_features=-1,
allowed_paths=None,
ignore_dataframes=None,
ignore_columns=None,
primitive_options=None,
seed_features=None,
drop_contains=None,
drop_exact=None,
where_stacking_limit=1,
):
if target_dataframe_name not in entityset.dataframe_dict:
es_name = entityset.id or "entity set"
msg = "Provided target dataframe %s does not exist in %s" % (
target_dataframe_name,
es_name,
)
raise KeyError(msg)
# need to change max_depth to None because DFs terminates when <0
if max_depth == -1:
max_depth = None
# if just one dataframe, set max depth to 1 (transform stacking rule)
if len(entityset.dataframe_dict) == 1 and (max_depth is None
or max_depth > 1):
warnings.warn(
"Only one dataframe in entityset, changing max_depth to "
"1 since deeper features cannot be created")
max_depth = 1
self.max_depth = max_depth
self.max_features = max_features
self.allowed_paths = allowed_paths
if self.allowed_paths:
self.allowed_paths = set()
for path in allowed_paths:
self.allowed_paths.add(tuple(path))
if ignore_dataframes is None:
self.ignore_dataframes = set()
else:
if not isinstance(ignore_dataframes, list):
raise TypeError("ignore_dataframes must be a list")
assert (target_dataframe_name
not in ignore_dataframes), "Can't ignore target_dataframe!"
self.ignore_dataframes = set(ignore_dataframes)
self.ignore_columns = defaultdict(set)
if ignore_columns is not None:
# check if ignore_columns is not {str: list}
if not all(isinstance(i, str)
for i in ignore_columns.keys()) or not all(
isinstance(i, list)
for i in ignore_columns.values()):
raise TypeError("ignore_columns should be dict[str -> list]")
# check if list values are all of type str
elif not all(
all(isinstance(v, str) for v in value)
for value in ignore_columns.values()):
raise TypeError("list values should be of type str")
for df_name, cols in ignore_columns.items():
self.ignore_columns[df_name] = set(cols)
self.target_dataframe_name = target_dataframe_name
self.es = entityset
for library in Library:
if library.value == self.es.dataframe_type:
df_library = library
break
aggregation_primitive_dict = primitives.get_aggregation_primitives()
transform_primitive_dict = primitives.get_transform_primitives()
if agg_primitives is None:
agg_primitives = [
p for p in primitives.get_default_aggregation_primitives()
if df_library in p.compatibility
]
self.agg_primitives = []
self.agg_primitives = sorted([
check_primitive(
p,
"aggregation",
aggregation_primitive_dict,
transform_primitive_dict,
) for p in agg_primitives
])
if trans_primitives is None:
trans_primitives = [
p for p in primitives.get_default_transform_primitives()
if df_library in p.compatibility
]
self.trans_primitives = sorted([
check_primitive(p, "transform", aggregation_primitive_dict,
transform_primitive_dict) for p in trans_primitives
])
if where_primitives is None:
where_primitives = [primitives.Count]
self.where_primitives = sorted([
check_primitive(p, "where", aggregation_primitive_dict,
transform_primitive_dict) for p in where_primitives
])
if groupby_trans_primitives is None:
groupby_trans_primitives = []
self.groupby_trans_primitives = sorted([
check_primitive(
p,
"groupby transform",
aggregation_primitive_dict,
transform_primitive_dict,
) for p in groupby_trans_primitives
])
if primitive_options is None:
primitive_options = {}
all_primitives = (self.trans_primitives + self.agg_primitives +
self.where_primitives +
self.groupby_trans_primitives)
bad_primitives = [
prim.name for prim in all_primitives
if df_library not in prim.compatibility
]
if bad_primitives:
msg = "Selected primitives are incompatible with {} EntitySets: {}"
raise ValueError(
msg.format(df_library.value, ", ".join(bad_primitives)))
(
self.primitive_options,
self.ignore_dataframes,
self.ignore_columns,
) = generate_all_primitive_options(
all_primitives,
primitive_options,
self.ignore_dataframes,
self.ignore_columns,
self.es,
)
self.seed_features = sorted(seed_features or [],
key=lambda f: f.unique_name())
self.drop_exact = drop_exact or []
self.drop_contains = drop_contains or []
self.where_stacking_limit = where_stacking_limit
def __init__(self,
target_dataframe_name,
entityset,
agg_primitives=None,
trans_primitives=None,
where_primitives=None,
groupby_trans_primitives=None,
max_depth=2,
max_features=-1,
allowed_paths=None,
ignore_dataframes=None,
ignore_columns=None,
primitive_options=None,
seed_features=None,
drop_contains=None,
drop_exact=None,
where_stacking_limit=1):
if target_dataframe_name not in entityset.dataframe_dict:
es_name = entityset.id or 'entity set'
msg = 'Provided target dataframe %s does not exist in %s' % (
target_dataframe_name, es_name)
raise KeyError(msg)
# need to change max_depth to None because DFs terminates when <0
if max_depth == -1:
max_depth = None
# if just one dataframe, set max depth to 1 (transform stacking rule)
if len(entityset.dataframe_dict) == 1 and (max_depth is None
or max_depth > 1):
warnings.warn(
"Only one dataframe in entityset, changing max_depth to "
"1 since deeper features cannot be created")
max_depth = 1
self.max_depth = max_depth
self.max_features = max_features
self.allowed_paths = allowed_paths
if self.allowed_paths:
self.allowed_paths = set()
for path in allowed_paths:
self.allowed_paths.add(tuple(path))
if ignore_dataframes is None:
self.ignore_dataframes = set()
else:
if not isinstance(ignore_dataframes, list):
raise TypeError('ignore_dataframes must be a list')
assert target_dataframe_name not in ignore_dataframes,\
"Can't ignore target_dataframe!"
self.ignore_dataframes = set(ignore_dataframes)
self.ignore_columns = defaultdict(set)
if ignore_columns is not None:
# check if ignore_columns is not {str: list}
if not all(isinstance(i, str)
for i in ignore_columns.keys()) or not all(
isinstance(i, list)
for i in ignore_columns.values()):
raise TypeError('ignore_columns should be dict[str -> list]')
# check if list values are all of type str
elif not all(
all(isinstance(v, str) for v in value)
for value in ignore_columns.values()):
raise TypeError('list values should be of type str')
for df_name, cols in ignore_columns.items():
self.ignore_columns[df_name] = set(cols)
self.target_dataframe_name = target_dataframe_name
self.es = entityset
for library in Library:
if library.value == self.es.dataframe_type:
df_library = library
break
if agg_primitives is None:
agg_primitives = [
p for p in primitives.get_default_aggregation_primitives()
if df_library in p.compatibility
]
self.agg_primitives = []
agg_prim_dict = primitives.get_aggregation_primitives()
for a in agg_primitives:
if isinstance(a, str):
if a.lower() not in agg_prim_dict:
raise ValueError(
"Unknown aggregation primitive {}. ".format(a),
"Call ft.primitives.list_primitives() to get",
" a list of available primitives")
a = agg_prim_dict[a.lower()]
a = handle_primitive(a)
if not isinstance(a, AggregationPrimitive):
raise ValueError("Primitive {} in agg_primitives is not an "
"aggregation primitive".format(type(a)))
self.agg_primitives.append(a)
self.agg_primitives.sort()
if trans_primitives is None:
trans_primitives = [
p for p in primitives.get_default_transform_primitives()
if df_library in p.compatibility
]
self.trans_primitives = []
for t in trans_primitives:
t = check_trans_primitive(t)
self.trans_primitives.append(t)
self.trans_primitives.sort()
if where_primitives is None:
where_primitives = [primitives.Count]
self.where_primitives = []
for p in where_primitives:
if isinstance(p, str):
prim_obj = agg_prim_dict.get(p.lower(), None)
if prim_obj is None:
raise ValueError(
"Unknown where primitive {}. ".format(p),
"Call ft.primitives.list_primitives() to get",
" a list of available primitives")
p = prim_obj
p = handle_primitive(p)
self.where_primitives.append(p)
self.where_primitives.sort()
if groupby_trans_primitives is None:
groupby_trans_primitives = []
self.groupby_trans_primitives = []
for p in groupby_trans_primitives:
p = check_trans_primitive(p)
self.groupby_trans_primitives.append(p)
self.groupby_trans_primitives.sort()
if primitive_options is None:
primitive_options = {}
all_primitives = self.trans_primitives + self.agg_primitives + \
self.where_primitives + self.groupby_trans_primitives
bad_primitives = [
prim.name for prim in all_primitives
if df_library not in prim.compatibility
]
if bad_primitives:
msg = 'Selected primitives are incompatible with {} EntitySets: {}'
raise ValueError(
msg.format(df_library.value, ', '.join(bad_primitives)))
self.primitive_options, self.ignore_dataframes, self.ignore_columns =\
generate_all_primitive_options(all_primitives,
primitive_options,
self.ignore_dataframes,
self.ignore_columns,
self.es)
self.seed_features = sorted(seed_features or [],
key=lambda f: f.unique_name())
self.drop_exact = drop_exact or []
self.drop_contains = drop_contains or []
self.where_stacking_limit = where_stacking_limit