def test_makes_numtrue(es):
dfs = DeepFeatureSynthesis(target_entity_id='sessions',
entityset=es,
agg_primitives=[NumTrue],
trans_primitives=[])
features = dfs.build_features()
assert feature_with_name(features, 'customers.NUM_TRUE(log.purchased)')
assert feature_with_name(features, 'NUM_TRUE(log.purchased)')
def test_makes_numtrue(es):
dfs = DeepFeatureSynthesis(target_entity_id='sessions',
entityset=es,
agg_primitives=[NumTrue],
trans_primitives=[])
features = dfs.build_features()
assert feature_with_name(features, 'customers.NUM_TRUE(log.purchased)')
assert feature_with_name(features, 'NUM_TRUE(log.purchased)')
def test_makes_numtrue(es):
if ks and any(isinstance(e.df, ks.DataFrame) for e in es.entities):
pytest.xfail('Koalas EntitySets do not support NumTrue primitive')
dfs = DeepFeatureSynthesis(target_entity_id='sessions',
entityset=es,
agg_primitives=[NumTrue],
trans_primitives=[])
features = dfs.build_features()
assert feature_with_name(features, 'customers.NUM_TRUE(log.purchased)')
assert feature_with_name(features, 'NUM_TRUE(log.purchased)')
def test_makes_numtrue(es):
if es.dataframe_type == Library.KOALAS.value:
pytest.xfail('Koalas EntitySets do not support NumTrue primitive')
dfs = DeepFeatureSynthesis(target_dataframe_name='sessions',
entityset=es,
agg_primitives=[NumTrue],
trans_primitives=[])
features = dfs.build_features()
assert feature_with_name(features, 'customers.NUM_TRUE(log.purchased)')
assert feature_with_name(features, 'NUM_TRUE(log.purchased)')
def test_makes_numtrue(es):
if es.dataframe_type == Library.SPARK.value:
pytest.xfail("Spark EntitySets do not support NumTrue primitive")
dfs = DeepFeatureSynthesis(
target_dataframe_name="sessions",
entityset=es,
agg_primitives=[NumTrue],
trans_primitives=[],
)
features = dfs.build_features()
assert feature_with_name(features, "customers.NUM_TRUE(log.purchased)")
assert feature_with_name(features, "NUM_TRUE(log.purchased)")
def test_makes_count(es):
dfs = DeepFeatureSynthesis(target_entity_id='sessions',
entityset=es,
agg_primitives=[Count],
trans_primitives=[])
features = dfs.build_features()
assert feature_with_name(features, 'device_type')
assert feature_with_name(features, 'customer_id')
assert feature_with_name(features, u'customers.région_id')
assert feature_with_name(features, 'customers.age')
assert feature_with_name(features, 'COUNT(log)')
assert feature_with_name(features, 'customers.COUNT(sessions)')
assert feature_with_name(features, u'customers.régions.language')
assert feature_with_name(features, 'customers.COUNT(log)')
def test_makes_count(es):
dfs = DeepFeatureSynthesis(target_entity_id='sessions',
entityset=es,
agg_primitives=[Count],
trans_primitives=[])
features = dfs.build_features()
assert feature_with_name(features, 'device_type')
assert feature_with_name(features, 'customer_id')
assert feature_with_name(features, u'customers.région_id')
assert feature_with_name(features, 'customers.age')
assert feature_with_name(features, 'COUNT(log)')
assert feature_with_name(features, 'customers.COUNT(sessions)')
assert feature_with_name(features, u'customers.régions.language')
assert feature_with_name(features, 'customers.COUNT(log)')
def test_makes_count(es):
dfs = DeepFeatureSynthesis(
target_dataframe_name="sessions",
entityset=es,
agg_primitives=[Count],
trans_primitives=[],
)
features = dfs.build_features()
assert feature_with_name(features, "device_type")
assert feature_with_name(features, "customer_id")
assert feature_with_name(features, "customers.région_id")
assert feature_with_name(features, "customers.age")
assert feature_with_name(features, "COUNT(log)")
assert feature_with_name(features, "customers.COUNT(sessions)")
assert feature_with_name(features, "customers.régions.language")
assert feature_with_name(features, "customers.COUNT(log)")
def dfs(entities=None,
relationships=None,
entityset=None,
target_entity=None,
cutoff_time=None,
instance_ids=None,
agg_primitives=None,
trans_primitives=None,
groupby_trans_primitives=None,
allowed_paths=None,
max_depth=2,
ignore_entities=None,
ignore_variables=None,
seed_features=None,
drop_contains=None,
drop_exact=None,
where_primitives=None,
max_features=-1,
cutoff_time_in_index=False,
save_progress=None,
features_only=False,
training_window=None,
approximate=None,
chunk_size=None,
n_jobs=1,
dask_kwargs=None,
verbose=False,
return_variable_types=None,
progress_callback=None):
'''Calculates a feature matrix and features given a dictionary of entities
and a list of relationships.
Args:
entities (dict[str -> tuple(pd.DataFrame, str, str)]): Dictionary of
entities. Entries take the format
{entity id -> (dataframe, id column, (time_column))}.
relationships (list[(str, str, str, str)]): List of relationships
between entities. List items are a tuple with the format
(parent entity id, parent variable, child entity id, child variable).
entityset (EntitySet): An already initialized entityset. Required if
entities and relationships are not defined.
target_entity (str): Entity id of entity on which to make predictions.
cutoff_time (pd.DataFrame or Datetime): Specifies times at which to
calculate each instance. The resulting feature matrix will use data
up to and including the cutoff_time. Can either be a DataFrame with
'instance_id' and 'time' columns, a DataFrame with the name of the
index variable in the target entity and a time column, a
list of values, or a single
value to calculate for all instances. If the dataframe has more than
two columns, any additional columns will be added to the resulting
feature matrix.
instance_ids (list): List of instances on which to calculate features. Only
used if cutoff_time is a single datetime.
agg_primitives (list[str or AggregationPrimitive], optional): List of Aggregation
Feature types to apply.
Default: ["sum", "std", "max", "skew", "min", "mean", "count", "percent_true", "num_unique", "mode"]
trans_primitives (list[str or TransformPrimitive], optional):
List of Transform Feature functions to apply.
Default: ["day", "year", "month", "weekday", "haversine", "num_words", "num_characters"]
groupby_trans_primitives (list[str or :class:`.primitives.TransformPrimitive`], optional):
list of Transform primitives to make GroupByTransformFeatures with
allowed_paths (list[list[str]]): Allowed entity paths on which to make
features.
max_depth (int) : Maximum allowed depth of features.
ignore_entities (list[str], optional): List of entities to
blacklist when creating features.
ignore_variables (dict[str -> list[str]], optional): List of specific
variables within each entity to blacklist when creating features.
seed_features (list[:class:`.FeatureBase`]): List of manually defined
features to use.
drop_contains (list[str], optional): Drop features
that contains these strings in name.
drop_exact (list[str], optional): Drop features that
exactly match these strings in name.
where_primitives (list[str or PrimitiveBase], optional):
List of Primitives names (or types) to apply with where clauses.
Default:
["count"]
max_features (int, optional) : Cap the number of generated features to
this number. If -1, no limit.
features_only (bool, optional): If True, returns the list of
features without calculating the feature matrix.
cutoff_time_in_index (bool): If True, return a DataFrame with a MultiIndex
where the second index is the cutoff time (first is instance id).
DataFrame will be sorted by (time, instance_id).
training_window (Timedelta or str, optional):
Window defining how much time before the cutoff time data
can be used when calculating features. If ``None`` , all data
before cutoff time is used. Defaults to ``None``. Month and year
units are not relative when Pandas Timedeltas are used. Relative
units should be passed as a Featuretools Timedelta or a string.
approximate (Timedelta): Bucket size to group instances with similar
cutoff times by for features with costly calculations. For example,
if bucket is 24 hours, all instances with cutoff times on the same
day will use the same calculation for expensive features.
save_progress (str, optional): Path to save intermediate computational results.
n_jobs (int, optional): number of parallel processes to use when
calculating feature matrix
chunk_size (int or float or None or "cutoff time", optional): Number
of rows of output feature matrix to calculate at time. If passed an
integer greater than 0, will try to use that many rows per chunk.
If passed a float value between 0 and 1 sets the chunk size to that
percentage of all instances. If passed the string "cutoff time",
rows are split per cutoff time.
dask_kwargs (dict, optional): Dictionary of keyword arguments to be
passed when creating the dask client and scheduler. Even if n_jobs
is not set, using `dask_kwargs` will enable multiprocessing.
Main parameters:
cluster (str or dask.distributed.LocalCluster):
cluster or address of cluster to send tasks to. If unspecified,
a cluster will be created.
diagnostics port (int):
port number to use for web dashboard. If left unspecified, web
interface will not be enabled.
Valid keyword arguments for LocalCluster will also be accepted.
return_variable_types (list[Variable] or str, optional): Types of
variables to return. If None, default to
Numeric, Discrete, and Boolean. If given as
the string 'all', use all available variable types.
progress_callback (callable): function to be called with incremental progress updates.
Has the following parameters:
update: percentage change (float between 0 and 100) in progress since last call
progress_percent: percentage (float between 0 and 100) of total computation completed
time_elapsed: total time in seconds that has elapsed since start of call
Examples:
.. code-block:: python
from featuretools.primitives import Mean
# cutoff times per instance
entities = {
"sessions" : (session_df, "id"),
"transactions" : (transactions_df, "id", "transaction_time")
}
relationships = [("sessions", "id", "transactions", "session_id")]
feature_matrix, features = dfs(entities=entities,
relationships=relationships,
target_entity="transactions",
cutoff_time=cutoff_times)
feature_matrix
features = dfs(entities=entities,
relationships=relationships,
target_entity="transactions",
features_only=True)
'''
if not isinstance(entityset, EntitySet):
entityset = EntitySet("dfs", entities, relationships)
dfs_object = DeepFeatureSynthesis(
target_entity,
entityset,
agg_primitives=agg_primitives,
trans_primitives=trans_primitives,
groupby_trans_primitives=groupby_trans_primitives,
max_depth=max_depth,
where_primitives=where_primitives,
allowed_paths=allowed_paths,
drop_exact=drop_exact,
drop_contains=drop_contains,
ignore_entities=ignore_entities,
ignore_variables=ignore_variables,
max_features=max_features,
seed_features=seed_features)
features = dfs_object.build_features(
verbose=verbose, return_variable_types=return_variable_types)
if features_only:
return features
if isinstance(cutoff_time, pd.DataFrame):
feature_matrix = calculate_feature_matrix(
features,
entityset=entityset,
cutoff_time=cutoff_time,
training_window=training_window,
approximate=approximate,
cutoff_time_in_index=cutoff_time_in_index,
save_progress=save_progress,
chunk_size=chunk_size,
n_jobs=n_jobs,
dask_kwargs=dask_kwargs,
verbose=verbose,
progress_callback=progress_callback)
else:
feature_matrix = calculate_feature_matrix(
features,
entityset=entityset,
cutoff_time=cutoff_time,
instance_ids=instance_ids,
training_window=training_window,
approximate=approximate,
cutoff_time_in_index=cutoff_time_in_index,
save_progress=save_progress,
chunk_size=chunk_size,
n_jobs=n_jobs,
dask_kwargs=dask_kwargs,
verbose=verbose,
progress_callback=progress_callback)
return feature_matrix, features
def get_valid_primitives(entityset,
target_dataframe_name,
max_depth=2,
selected_primitives=None):
"""
Returns two lists of primitives (transform and aggregation) containing
primitives that can be applied to the specific target dataframe to create
features. If the optional 'selected_primitives' parameter is not used,
all discoverable primitives will be considered.
Note:
When using a ``max_depth`` greater than 1, some primitives returned by
this function may not create any features if passed to DFS alone. These
primitives relied on features created by other primitives as input
(primitive stacking).
Args:
entityset (EntitySet): An already initialized entityset
target_dataframe_name (str): Name of dataframe to create features for.
max_depth (int, optional): Maximum allowed depth of features.
selected_primitives(list[str or AggregationPrimitive/TransformPrimitive], optional):
list of primitives to consider when looking for valid primitives.
If None, all primitives will be considered
Returns:
list[AggregationPrimitive], list[TransformPrimitive]:
The list of valid aggregation primitives and the list of valid
transform primitives.
"""
agg_primitives = []
trans_primitives = []
available_aggs = get_aggregation_primitives()
available_trans = get_transform_primitives()
for library in Library:
if library.value == entityset.dataframe_type:
df_library = library
break
if selected_primitives:
for prim in selected_primitives:
if not isinstance(prim, str):
if issubclass(prim, AggregationPrimitive):
prim_list = agg_primitives
elif issubclass(prim, TransformPrimitive):
prim_list = trans_primitives
else:
raise ValueError(
f"Selected primitive {prim} is not an "
"AggregationPrimitive, TransformPrimitive, or str")
elif prim in available_aggs:
prim = available_aggs[prim]
prim_list = agg_primitives
elif prim in available_trans:
prim = available_trans[prim]
prim_list = trans_primitives
else:
raise ValueError(
f"'{prim}' is not a recognized primitive name")
if df_library in prim.compatibility:
prim_list.append(prim)
else:
agg_primitives = [
agg for agg in available_aggs.values()
if df_library in agg.compatibility
]
trans_primitives = [
trans for trans in available_trans.values()
if df_library in trans.compatibility
]
dfs_object = DeepFeatureSynthesis(
target_dataframe_name,
entityset,
agg_primitives=agg_primitives,
trans_primitives=trans_primitives,
max_depth=max_depth,
)
features = dfs_object.build_features()
trans, agg, _, _ = _categorize_features(features)
trans_unused = get_unused_primitives(trans_primitives, trans)
agg_unused = get_unused_primitives(agg_primitives, agg)
# switch from str to class
agg_unused = [available_aggs[name] for name in agg_unused]
trans_unused = [available_trans[name] for name in trans_unused]
used_agg_prims = set(agg_primitives).difference(set(agg_unused))
used_trans_prims = set(trans_primitives).difference(set(trans_unused))
return list(used_agg_prims), list(used_trans_prims)
def dfs(dataframes=None,
relationships=None,
entityset=None,
target_dataframe_name=None,
cutoff_time=None,
instance_ids=None,
agg_primitives=None,
trans_primitives=None,
groupby_trans_primitives=None,
allowed_paths=None,
max_depth=2,
ignore_dataframes=None,
ignore_columns=None,
primitive_options=None,
seed_features=None,
drop_contains=None,
drop_exact=None,
where_primitives=None,
max_features=-1,
cutoff_time_in_index=False,
save_progress=None,
features_only=False,
training_window=None,
approximate=None,
chunk_size=None,
n_jobs=1,
dask_kwargs=None,
verbose=False,
return_types=None,
progress_callback=None,
include_cutoff_time=True):
'''Calculates a feature matrix and features given a dictionary of dataframes
and a list of relationships.
Args:
dataframes (dict[str -> tuple(DataFrame, str, str, dict[str -> str/Woodwork.LogicalType], dict[str->str/set], boolean)]):
Dictionary of DataFrames. Entries take the format
{dataframe name -> (dataframe, index column, time_index, logical_types, semantic_tags, make_index)}.
Note that only the dataframe is required. If a Woodwork DataFrame is supplied, any other parameters
will be ignored.
relationships (list[(str, str, str, str)]): List of relationships
between dataframes. List items are a tuple with the format
(parent dataframe name, parent column, child dataframe name, child column).
entityset (EntitySet): An already initialized entityset. Required if
dataframes and relationships are not defined.
target_dataframe_name (str): Name of dataframe on which to make predictions.
cutoff_time (pd.DataFrame or Datetime): Specifies times at which to calculate
the features for each instance. The resulting feature matrix will use data
up to and including the cutoff_time. Can either be a DataFrame or a single
value. If a DataFrame is passed the instance ids for which to calculate features
must be in a column with the same name as the target dataframe index or a column
named `instance_id`. The cutoff time values in the DataFrame must be in a column with
the same name as the target dataframe time index or a column named `time`. If the
DataFrame has more than two columns, any additional columns will be added to the
resulting feature matrix. If a single value is passed, this value will be used for
all instances.
instance_ids (list): List of instances on which to calculate features. Only
used if cutoff_time is a single datetime.
agg_primitives (list[str or AggregationPrimitive], optional): List of Aggregation
Feature types to apply.
Default: ["sum", "std", "max", "skew", "min", "mean", "count", "percent_true", "num_unique", "mode"]
trans_primitives (list[str or TransformPrimitive], optional):
List of Transform Feature functions to apply.
Default: ["day", "year", "month", "weekday", "haversine", "num_words", "num_characters"]
groupby_trans_primitives (list[str or TransformPrimitive], optional):
list of Transform primitives to make GroupByTransformFeatures with
allowed_paths (list[list[str]]): Allowed dataframe paths on which to make
features.
max_depth (int) : Maximum allowed depth of features.
ignore_dataframes (list[str], optional): List of dataframes to
blacklist when creating features.
ignore_columns (dict[str -> list[str]], optional): List of specific
columns within each dataframe to blacklist when creating features.
primitive_options (list[dict[str or tuple[str] -> dict] or dict[str or tuple[str] -> dict, optional]):
Specify options for a single primitive or a group of primitives.
Lists of option dicts are used to specify options per input for primitives
with multiple inputs. Each option ``dict`` can have the following keys:
``"include_dataframes"``
List of dataframes to be included when creating features for
the primitive(s). All other dataframes will be ignored
(list[str]).
``"ignore_dataframes"``
List of dataframes to be blacklisted when creating features
for the primitive(s) (list[str]).
``"include_columns"``
List of specific columns within each dataframe to include when
creating features for the primitive(s). All other columns
in a given dataframe will be ignored (dict[str -> list[str]]).
``"ignore_columns"``
List of specific columns within each dataframe to blacklist
when creating features for the primitive(s) (dict[str ->
list[str]]).
``"include_groupby_dataframes"``
List of dataframes to be included when finding groupbys. All
other dataframes will be ignored (list[str]).
``"ignore_groupby_dataframes"``
List of dataframes to blacklist when finding groupbys
(list[str]).
``"include_groupby_columns"``
List of specific columns within each dataframe to include as
groupbys, if applicable. All other columns in each
dataframe will be ignored (dict[str -> list[str]]).
``"ignore_groupby_columns"``
List of specific columns within each dataframe to blacklist
as groupbys (dict[str -> list[str]]).
seed_features (list[:class:`.FeatureBase`]): List of manually defined
features to use.
drop_contains (list[str], optional): Drop features
that contains these strings in name.
drop_exact (list[str], optional): Drop features that
exactly match these strings in name.
where_primitives (list[str or PrimitiveBase], optional):
List of Primitives names (or types) to apply with where clauses.
Default:
["count"]
max_features (int, optional) : Cap the number of generated features to
this number. If -1, no limit.
features_only (bool, optional): If True, returns the list of
features without calculating the feature matrix.
cutoff_time_in_index (bool): If True, return a DataFrame with a MultiIndex
where the second index is the cutoff time (first is instance id).
DataFrame will be sorted by (time, instance_id).
training_window (Timedelta or str, optional):
Window defining how much time before the cutoff time data
can be used when calculating features. If ``None`` , all data
before cutoff time is used. Defaults to ``None``. Month and year
units are not relative when Pandas Timedeltas are used. Relative
units should be passed as a Featuretools Timedelta or a string.
approximate (Timedelta): Bucket size to group instances with similar
cutoff times by for features with costly calculations. For example,
if bucket is 24 hours, all instances with cutoff times on the same
day will use the same calculation for expensive features.
save_progress (str, optional): Path to save intermediate computational results.
n_jobs (int, optional): number of parallel processes to use when
calculating feature matrix
chunk_size (int or float or None or "cutoff time", optional): Number
of rows of output feature matrix to calculate at time. If passed an
integer greater than 0, will try to use that many rows per chunk.
If passed a float value between 0 and 1 sets the chunk size to that
percentage of all instances. If passed the string "cutoff time",
rows are split per cutoff time.
dask_kwargs (dict, optional): Dictionary of keyword arguments to be
passed when creating the dask client and scheduler. Even if n_jobs
is not set, using `dask_kwargs` will enable multiprocessing.
Main parameters:
cluster (str or dask.distributed.LocalCluster):
cluster or address of cluster to send tasks to. If unspecified,
a cluster will be created.
diagnostics port (int):
port number to use for web dashboard. If left unspecified, web
interface will not be enabled.
Valid keyword arguments for LocalCluster will also be accepted.
return_types (list[woodwork.ColumnSchema] or str, optional):
List of ColumnSchemas defining the types of
columns to return. If None, defaults to returning all
numeric, categorical and boolean types. If given as
the string 'all', returns all available types.
progress_callback (callable): function to be called with incremental progress updates.
Has the following parameters:
update: percentage change (float between 0 and 100) in progress since last call
progress_percent: percentage (float between 0 and 100) of total computation completed
time_elapsed: total time in seconds that has elapsed since start of call
include_cutoff_time (bool): Include data at cutoff times in feature calculations. Defaults to ``True``.
Returns:
list[:class:`.FeatureBase`], pd.DataFrame:
The list of generated feature defintions, and the feature matrix.
If ``features_only`` is ``True``, the feature matrix will not be generated.
Examples:
.. code-block:: python
from featuretools.primitives import Mean
# cutoff times per instance
dataframes = {
"sessions" : (session_df, "id"),
"transactions" : (transactions_df, "id", "transaction_time")
}
relationships = [("sessions", "id", "transactions", "session_id")]
feature_matrix, features = dfs(dataframes=dataframes,
relationships=relationships,
target_dataframe_name="transactions",
cutoff_time=cutoff_times)
feature_matrix
features = dfs(dataframes=dataframes,
relationships=relationships,
target_dataframe_name="transactions",
features_only=True)
'''
if not isinstance(entityset, EntitySet):
entityset = EntitySet("dfs", dataframes, relationships)
dfs_object = DeepFeatureSynthesis(
target_dataframe_name,
entityset,
agg_primitives=agg_primitives,
trans_primitives=trans_primitives,
groupby_trans_primitives=groupby_trans_primitives,
max_depth=max_depth,
where_primitives=where_primitives,
allowed_paths=allowed_paths,
drop_exact=drop_exact,
drop_contains=drop_contains,
ignore_dataframes=ignore_dataframes,
ignore_columns=ignore_columns,
primitive_options=primitive_options,
max_features=max_features,
seed_features=seed_features)
features = dfs_object.build_features(verbose=verbose,
return_types=return_types)
trans, agg, groupby, where = _categorize_features(features)
trans_unused = get_unused_primitives(trans_primitives, trans)
agg_unused = get_unused_primitives(agg_primitives, agg)
groupby_unused = get_unused_primitives(groupby_trans_primitives, groupby)
where_unused = get_unused_primitives(where_primitives, where)
unused_primitives = [
trans_unused, agg_unused, groupby_unused, where_unused
]
if any(unused_primitives):
warn_unused_primitives(unused_primitives)
if features_only:
return features
feature_matrix = calculate_feature_matrix(
features,
entityset=entityset,
cutoff_time=cutoff_time,
instance_ids=instance_ids,
training_window=training_window,
approximate=approximate,
cutoff_time_in_index=cutoff_time_in_index,
save_progress=save_progress,
chunk_size=chunk_size,
n_jobs=n_jobs,
dask_kwargs=dask_kwargs,
verbose=verbose,
progress_callback=progress_callback,
include_cutoff_time=include_cutoff_time)
return feature_matrix, features