def test_to_disk_with_whitespace(whitespace_df, tmpdir, format):
df = whitespace_df.copy()
df.ww.init(index="id", logical_types={"comments": "NaturalLanguage"})
if format == "pickle" and not isinstance(df, pd.DataFrame):
msg = "DataFrame type not compatible with pickle serialization. Please serialize to another format."
with pytest.raises(ValueError, match=msg):
df.ww.to_disk(str(tmpdir), format="pickle")
else:
df.ww.to_disk(str(tmpdir), format=format)
if format == "parquet":
filename = "data.parquet"
format = None
if _is_dask_dataframe(whitespace_df) or _is_spark_dataframe(
whitespace_df):
filename = None
format = "parquet"
deserialized_df = read_woodwork_table(
path=str(tmpdir),
filename=filename,
format=format,
)
else:
deserialized_df = read_woodwork_table(str(tmpdir))
assert deserialized_df.ww.schema == df.ww.schema
pd.testing.assert_frame_equal(
to_pandas(deserialized_df,
index=deserialized_df.ww.index,
sort_index=True),
to_pandas(df, index=df.ww.index, sort_index=True),
)
def test_to_csv_use_standard_tags(sample_df, tmpdir):
no_standard_tags_df = sample_df.copy()
no_standard_tags_df.ww.init(use_standard_tags=False)
no_standard_tags_df.ww.to_disk(str(tmpdir),
format="csv",
encoding="utf-8",
engine="python")
deserialized_no_tags_df = read_woodwork_table(str(tmpdir))
shutil.rmtree(str(tmpdir))
standard_tags_df = sample_df.copy()
standard_tags_df.ww.init(use_standard_tags=True)
standard_tags_df.ww.to_disk(str(tmpdir),
format="csv",
encoding="utf-8",
engine="python")
deserialized_tags_df = read_woodwork_table(str(tmpdir))
shutil.rmtree(str(tmpdir))
assert no_standard_tags_df.ww.schema != standard_tags_df.ww.schema
assert deserialized_no_tags_df.ww.schema == no_standard_tags_df.ww.schema
assert deserialized_tags_df.ww.schema == standard_tags_df.ww.schema
def test_to_disk_parquet_no_file_extension(sample_df, tmpdir):
if _is_dask_dataframe(sample_df) or _is_spark_dataframe(sample_df):
pytest.skip(
"Specifying filename for writing Dask or Spark DataFrames to parquet is not supported."
)
sample_df.ww.init(index="id")
sample_df.ww.to_disk(str(tmpdir),
filename="parquet_data",
format="parquet")
error_msg = "Could not determine format. Please specify filename and/or format."
# Without specifying format, WW doens't know what type of file this is
with pytest.raises(ValueError, match=error_msg):
deserialized_df = read_woodwork_table(
str(tmpdir),
filename="parquet_data",
)
deserialized_df = read_woodwork_table(
str(tmpdir),
filename="parquet_data",
format="parquet",
)
pd.testing.assert_frame_equal(
to_pandas(sample_df, index=sample_df.ww.index, sort_index=True),
to_pandas(deserialized_df,
index=deserialized_df.ww.index,
sort_index=True),
)
def test_categorical_dtype_serialization(serialize_df, tmpdir):
ltypes = {
"cat_int": Categorical,
"ord_int": Ordinal(order=[1, 2]),
"cat_float": Categorical,
"ord_float": Ordinal(order=[1.0, 2.0]),
"cat_bool": Categorical,
"ord_bool": Ordinal(order=[True, False]),
}
if isinstance(serialize_df, pd.DataFrame):
formats = ["csv", "pickle", "parquet"]
else:
formats = ["csv"]
for format in formats:
df = serialize_df.copy()
df.ww.init(index="id", logical_types=ltypes)
df.ww.to_disk(str(tmpdir), format=format)
deserialized_df = read_woodwork_table(str(tmpdir),
filename=f"data.{format}")
pd.testing.assert_frame_equal(
to_pandas(deserialized_df,
index=deserialized_df.ww.index,
sort_index=True),
to_pandas(df, index=df.ww.index, sort_index=True),
)
assert deserialized_df.ww.schema == df.ww.schema
shutil.rmtree(str(tmpdir))
def test_to_csv_S3(sample_df, s3_client, s3_bucket, profile_name):
xfail_tmp_disappears(sample_df)
sample_df.ww.init(
name="test_data",
index="id",
semantic_tags={"id": "tag1"},
logical_types={"age": Ordinal(order=[25, 33, 57])},
)
sample_df.ww.to_disk(
TEST_S3_URL,
format="csv",
encoding="utf-8",
engine="python",
profile_name=profile_name,
)
make_public(s3_client, s3_bucket)
deserialized_df = read_woodwork_table(TEST_S3_URL,
profile_name=profile_name)
pd.testing.assert_frame_equal(
to_pandas(sample_df, index=sample_df.ww.index, sort_index=True),
to_pandas(deserialized_df,
index=deserialized_df.ww.index,
sort_index=True),
)
assert sample_df.ww.schema == deserialized_df.ww.schema
def test_to_csv(sample_df, tmpdir):
sample_df.ww.init(name='test_data',
index='id',
semantic_tags={'id': 'tag1'},
logical_types={'age': Ordinal(order=[25, 33, 57])},
column_descriptions={
'signup_date': 'original signup date',
'age': 'age of the user'
},
column_metadata={
'id': {
'is_sorted': True
},
'age': {
'interesting_values': [33, 57]
}
})
sample_df.ww.to_csv(str(tmpdir), encoding='utf-8', engine='python')
deserialized_df = deserialize.read_woodwork_table(str(tmpdir))
pd.testing.assert_frame_equal(
to_pandas(deserialized_df,
index=deserialized_df.ww.index,
sort_index=True),
to_pandas(sample_df, index=sample_df.ww.index, sort_index=True))
assert deserialized_df.ww.schema == sample_df.ww.schema
def description_to_entityset(description, **kwargs):
'''Deserialize entityset from data description.
Args:
description (dict) : Description of an :class:`.EntitySet`. Likely generated using :meth:`.serialize.entityset_to_description`
kwargs (keywords): Additional keyword arguments to pass as keywords arguments to the underlying deserialization method.
Returns:
entityset (EntitySet) : Instance of :class:`.EntitySet`.
'''
check_schema_version(description, 'entityset')
from featuretools.entityset import EntitySet
# If data description was not read from disk, path is None.
path = description.get('path')
entityset = EntitySet(description['id'])
for df in description['dataframes'].values():
if path is not None:
data_path = os.path.join(path, 'data', df['name'])
dataframe = read_woodwork_table(data_path,
validate=False,
**kwargs)
else:
dataframe = empty_dataframe(df)
entityset.add_dataframe(dataframe)
for relationship in description['relationships']:
rel = Relationship.from_dictionary(relationship, entityset)
entityset.add_relationship(relationship=rel)
return entityset
def test_deserialize_s3_csv(sample_df_pandas):
sample_df_pandas.ww.init(index='id')
deserialized_df = deserialize.read_woodwork_table(S3_URL)
pd.testing.assert_frame_equal(
to_pandas(sample_df_pandas, index=sample_df_pandas.ww.index),
to_pandas(deserialized_df, index=deserialized_df.ww.index))
assert sample_df_pandas.ww.schema == deserialized_df.ww.schema
def test_deserialize_url_csv(sample_df_pandas, profile_name):
sample_df_pandas.ww.init(index="id")
deserialized_df = read_woodwork_table(URL, profile_name=profile_name)
pd.testing.assert_frame_equal(
to_pandas(sample_df_pandas, index=sample_df_pandas.ww.index),
to_pandas(deserialized_df, index=deserialized_df.ww.index),
)
assert sample_df_pandas.ww.schema == deserialized_df.ww.schema
def test_deserialize_url_csv_anon(sample_df_pandas):
sample_df_pandas.ww.init(index='id')
deserialized_df = deserialize.read_woodwork_table(URL, profile_name=False)
pd.testing.assert_frame_equal(
to_pandas(sample_df_pandas, index=sample_df_pandas.ww.index),
to_pandas(deserialized_df, index=deserialized_df.ww.index))
assert sample_df_pandas.ww.schema == deserialized_df.ww.schema
def test_to_parquet(sample_df, tmpdir):
sample_df.ww.init(index='id')
sample_df.ww.to_parquet(str(tmpdir))
deserialized_df = deserialize.read_woodwork_table(str(tmpdir))
pd.testing.assert_frame_equal(
to_pandas(sample_df, index=sample_df.ww.index, sort_index=True),
to_pandas(deserialized_df,
index=deserialized_df.ww.index,
sort_index=True))
assert sample_df.ww.schema == deserialized_df.ww.schema
def test_deserialize_handles_indexes(sample_df, tmpdir):
sample_df.ww.init(
name="test_data",
index="id",
time_index="signup_date",
)
sample_df.ww.to_disk(str(tmpdir), format="csv")
deserialized_df = read_woodwork_table(str(tmpdir))
assert deserialized_df.ww.index == "id"
assert deserialized_df.ww.time_index == "signup_date"
def test_to_csv_use_standard_tags(sample_df, tmpdir):
no_standard_tags_df = sample_df.copy()
no_standard_tags_df.ww.init(use_standard_tags=False)
no_standard_tags_df.ww.to_csv(str(tmpdir),
encoding='utf-8',
engine='python')
deserialized_no_tags_df = deserialize.read_woodwork_table(str(tmpdir))
standard_tags_df = sample_df.copy()
standard_tags_df.ww.init(use_standard_tags=True)
standard_tags_df.ww.to_csv(str(tmpdir), encoding='utf-8', engine='python')
deserialized_tags_df = deserialize.read_woodwork_table(str(tmpdir))
assert no_standard_tags_df.ww.schema != standard_tags_df.ww.schema
assert deserialized_no_tags_df.ww.schema == no_standard_tags_df.ww.schema
assert deserialized_tags_df.ww.schema == standard_tags_df.ww.schema
def test_serialize_s3_pickle_anon(sample_df_pandas, s3_client, s3_bucket):
sample_df_pandas.ww.init()
sample_df_pandas.ww.to_pickle(TEST_S3_URL, profile_name=False)
make_public(s3_client, s3_bucket)
deserialized_df = deserialize.read_woodwork_table(TEST_S3_URL,
profile_name=False)
pd.testing.assert_frame_equal(
to_pandas(sample_df_pandas, index=sample_df_pandas.ww.index),
to_pandas(deserialized_df, index=deserialized_df.ww.index))
assert sample_df_pandas.ww.schema == deserialized_df.ww.schema
def test_to_disk(sample_df, tmpdir, file_format):
if file_format in ("arrow",
"feather") and not isinstance(sample_df, pd.DataFrame):
pytest.xfail(
"Arrow IPC format (Feather) not supported on Dask or Spark")
sample_df.ww.init(index="id")
error_msg = None
if file_format == "orc" and _is_dask_dataframe(sample_df):
error_msg = "DataFrame type not compatible with orc serialization. Please serialize to another format."
error_type = ValueError
elif file_format == "pickle" and not isinstance(sample_df, pd.DataFrame):
error_msg = "DataFrame type not compatible with pickle serialization. Please serialize to another format."
error_type = ValueError
if error_msg:
with pytest.raises(error_type, match=error_msg):
sample_df.ww.to_disk(str(tmpdir), format=file_format)
else:
sample_df.ww.to_disk(str(tmpdir), format=file_format)
if file_format == "parquet":
filename = "data.parquet"
format = None
if _is_dask_dataframe(sample_df) or _is_spark_dataframe(sample_df):
filename = None
format = "parquet"
deserialized_df = read_woodwork_table(
path=str(tmpdir),
filename=filename,
format=format,
)
else:
deserialized_df = read_woodwork_table(str(tmpdir))
pd.testing.assert_frame_equal(
to_pandas(sample_df, index=sample_df.ww.index, sort_index=True),
to_pandas(deserialized_df,
index=deserialized_df.ww.index,
sort_index=True),
)
assert sample_df.ww.schema == deserialized_df.ww.schema
def test_to_parquet_with_latlong(latlong_df, tmpdir):
latlong_df.ww.init(
logical_types={col: 'LatLong'
for col in latlong_df.columns})
latlong_df.ww.to_parquet(str(tmpdir))
deserialized_df = deserialize.read_woodwork_table(str(tmpdir))
pd.testing.assert_frame_equal(
to_pandas(latlong_df, index=latlong_df.ww.index, sort_index=True),
to_pandas(deserialized_df,
index=deserialized_df.ww.index,
sort_index=True))
assert latlong_df.ww.schema == deserialized_df.ww.schema
def test_to_disk_custom_data_filename(sample_df, tmpdir, file_format):
if file_format in ("arrow",
"feather") and not isinstance(sample_df, pd.DataFrame):
pytest.xfail(
"Arrow IPC format (Feather) not supported on Dask or Spark")
sample_df.ww.init(index="id")
error_msg = None
if file_format == "orc" and _is_dask_dataframe(sample_df):
error_msg = "DataFrame type not compatible with orc serialization. Please serialize to another format."
error_type = ValueError
elif file_format == "pickle" and not isinstance(sample_df, pd.DataFrame):
error_msg = "DataFrame type not compatible with pickle serialization. Please serialize to another format."
error_type = ValueError
elif file_format == "parquet" and _is_dask_dataframe(sample_df):
error_msg = "Writing a Dask dataframe to parquet with a filename specified is not supported"
error_type = ValueError
elif file_format == "csv" and _is_spark_dataframe(sample_df):
error_msg = "Writing a Spark dataframe to csv with a filename specified is not supported"
error_type = ValueError
elif file_format == "parquet" and _is_spark_dataframe(sample_df):
error_msg = "Writing a Spark dataframe to parquet with a filename specified is not supported"
error_type = ValueError
data_filename = f"custom_data.{file_format}"
filename_to_check = data_filename
if _is_dask_dataframe(sample_df):
data_filename = f"custom_data-*.{file_format}"
filename_to_check = f"custom_data-0.{file_format}"
if error_msg:
with pytest.raises(error_type, match=error_msg):
sample_df.ww.to_disk(path=str(tmpdir),
format=file_format,
filename=data_filename)
else:
sample_df.ww.to_disk(path=str(tmpdir),
format=file_format,
filename=data_filename)
assert os.path.isfile(os.path.join(tmpdir, "data", filename_to_check))
deserialized_df = read_woodwork_table(
path=str(tmpdir),
filename=data_filename,
)
pd.testing.assert_frame_equal(
to_pandas(sample_df, index=sample_df.ww.index, sort_index=True),
to_pandas(deserialized_df,
index=deserialized_df.ww.index,
sort_index=True),
)
assert sample_df.ww.schema == deserialized_df.ww.schema
def test_serialize_s3_parquet(sample_df, s3_client, s3_bucket):
xfail_tmp_disappears(sample_df)
sample_df.ww.init()
sample_df.ww.to_parquet(TEST_S3_URL)
make_public(s3_client, s3_bucket)
deserialized_df = deserialize.read_woodwork_table(TEST_S3_URL)
pd.testing.assert_frame_equal(
to_pandas(sample_df, index=sample_df.ww.index, sort_index=True),
to_pandas(deserialized_df,
index=deserialized_df.ww.index,
sort_index=True))
assert sample_df.ww.schema == deserialized_df.ww.schema
def test_s3_test_profile(sample_df, s3_client, s3_bucket, setup_test_profile):
xfail_tmp_disappears(sample_df)
sample_df.ww.init()
sample_df.ww.to_csv(TEST_S3_URL,
encoding='utf-8',
engine='python',
profile_name='test')
make_public(s3_client, s3_bucket)
deserialized_df = deserialize.read_woodwork_table(TEST_S3_URL,
profile_name='test')
pd.testing.assert_frame_equal(
to_pandas(sample_df, index=sample_df.ww.index),
to_pandas(deserialized_df, index=deserialized_df.ww.index))
assert sample_df.ww.schema == deserialized_df.ww.schema
def test_to_pickle(sample_df, tmpdir):
sample_df.ww.init()
if not isinstance(sample_df, pd.DataFrame):
msg = 'DataFrame type not compatible with pickle serialization. Please serialize to another format.'
with pytest.raises(ValueError, match=msg):
sample_df.ww.to_pickle(str(tmpdir))
else:
sample_df.ww.to_pickle(str(tmpdir))
deserialized_df = deserialize.read_woodwork_table(str(tmpdir))
pd.testing.assert_frame_equal(
to_pandas(deserialized_df,
index=deserialized_df.ww.index,
sort_index=True),
to_pandas(sample_df, index=sample_df.ww.index, sort_index=True))
assert deserialized_df.ww.schema == sample_df.ww.schema
def test_to_csv_S3(sample_df, s3_client, s3_bucket):
xfail_tmp_disappears(sample_df)
sample_df.ww.init(name='test_data',
index='id',
semantic_tags={'id': 'tag1'},
logical_types={'age': Ordinal(order=[25, 33, 57])})
sample_df.ww.to_csv(TEST_S3_URL, encoding='utf-8', engine='python')
make_public(s3_client, s3_bucket)
deserialized_df = deserialize.read_woodwork_table(TEST_S3_URL)
pd.testing.assert_frame_equal(
to_pandas(sample_df, index=sample_df.ww.index, sort_index=True),
to_pandas(deserialized_df,
index=deserialized_df.ww.index,
sort_index=True))
assert sample_df.ww.schema == deserialized_df.ww.schema
def test_to_pickle_with_latlong(latlong_df, tmpdir):
latlong_df.ww.init(
logical_types={col: 'LatLong'
for col in latlong_df.columns})
if not isinstance(latlong_df, pd.DataFrame):
msg = 'DataFrame type not compatible with pickle serialization. Please serialize to another format.'
with pytest.raises(ValueError, match=msg):
latlong_df.ww.to_pickle(str(tmpdir))
else:
latlong_df.ww.to_pickle(str(tmpdir))
deserialized_df = deserialize.read_woodwork_table(str(tmpdir))
pd.testing.assert_frame_equal(
to_pandas(latlong_df, index=latlong_df.ww.index, sort_index=True),
to_pandas(deserialized_df,
index=deserialized_df.ww.index,
sort_index=True))
assert latlong_df.ww.schema == deserialized_df.ww.schema
def test_s3_test_profile(sample_df, s3_client, s3_bucket, setup_test_profile):
xfail_tmp_disappears(sample_df)
sample_df.ww.init()
sample_df.ww.to_disk(
TEST_S3_URL,
format="csv",
encoding="utf-8",
engine="python",
profile_name="test",
)
make_public(s3_client, s3_bucket)
deserialized_df = read_woodwork_table(TEST_S3_URL, profile_name="test")
pd.testing.assert_frame_equal(
to_pandas(sample_df, index=sample_df.ww.index),
to_pandas(deserialized_df, index=deserialized_df.ww.index),
)
assert sample_df.ww.schema == deserialized_df.ww.schema
def test_serialize_s3_parquet(sample_df, s3_client, s3_bucket, profile_name):
xfail_tmp_disappears(sample_df)
sample_df.ww.init()
sample_df.ww.to_disk(TEST_S3_URL,
format="parquet",
profile_name=profile_name)
make_public(s3_client, s3_bucket)
deserialized_df = read_woodwork_table(TEST_S3_URL,
filename="data.parquet",
profile_name=profile_name)
pd.testing.assert_frame_equal(
to_pandas(sample_df, index=sample_df.ww.index, sort_index=True),
to_pandas(deserialized_df,
index=deserialized_df.ww.index,
sort_index=True),
)
assert sample_df.ww.schema == deserialized_df.ww.schema
def test_to_disk_with_latlong(latlong_df, tmpdir, file_format):
if file_format in ("arrow",
"feather") and not isinstance(latlong_df, pd.DataFrame):
pytest.xfail(
"Arrow IPC format (Feather) not supported on Dask or Spark")
latlong_df.ww.init(
logical_types={col: "LatLong"
for col in latlong_df.columns})
error_msg = None
if file_format == "orc" and _is_dask_dataframe(latlong_df):
error_msg = "DataFrame type not compatible with orc serialization. Please serialize to another format."
error_type = ValueError
elif file_format == "pickle" and not isinstance(latlong_df, pd.DataFrame):
error_msg = "DataFrame type not compatible with pickle serialization. Please serialize to another format."
error_type = ValueError
if error_msg:
with pytest.raises(error_type, match=error_msg):
latlong_df.ww.to_disk(str(tmpdir), format=file_format)
else:
latlong_df.ww.to_disk(str(tmpdir), format=file_format)
filename = None
format = None
if file_format == "parquet":
if _is_dask_dataframe(latlong_df) or _is_spark_dataframe(
latlong_df):
format = "parquet"
else:
filename = "data.parquet"
deserialized_df = read_woodwork_table(str(tmpdir),
filename=filename,
format=format)
pd.testing.assert_frame_equal(
to_pandas(latlong_df, index=latlong_df.ww.index, sort_index=True),
to_pandas(deserialized_df,
index=deserialized_df.ww.index,
sort_index=True),
)
assert latlong_df.ww.schema == deserialized_df.ww.schema
def test_serialize_s3_pickle(sample_df_pandas, s3_client, s3_bucket,
profile_name):
sample_df_pandas.ww.init()
sample_df_pandas.ww.to_disk(TEST_S3_URL,
format="pickle",
profile_name=profile_name)
make_public(s3_client, s3_bucket)
deserialized_df = read_woodwork_table(TEST_S3_URL,
profile_name=profile_name)
pd.testing.assert_frame_equal(
to_pandas(sample_df_pandas,
index=sample_df_pandas.ww.index,
sort_index=True),
to_pandas(deserialized_df,
index=deserialized_df.ww.index,
sort_index=True),
)
assert sample_df_pandas.ww.schema == deserialized_df.ww.schema
def test_to_disk_parquet_typing_info_file_is_none(sample_df, tmpdir):
sample_df.ww.init(index="id")
sample_df.ww.to_disk(str(tmpdir), format="parquet")
filename = "data.parquet"
format = None
if _is_dask_dataframe(sample_df) or _is_spark_dataframe(sample_df):
filename = None
format = "parquet"
deserialized_df = read_woodwork_table(
str(tmpdir),
filename=filename,
typing_info_filename=None,
format=format,
)
pd.testing.assert_frame_equal(
to_pandas(sample_df, index=sample_df.ww.index, sort_index=True),
to_pandas(deserialized_df,
index=deserialized_df.ww.index,
sort_index=True),
)
def test_to_csv(sample_df, tmpdir):
if _is_dask_dataframe(sample_df):
# Dask errors with pd.NA in some partitions, but not others
sample_df["age"] = sample_df["age"].fillna(25)
sample_df.ww.init(
name="test_data",
index="id",
semantic_tags={"id": "tag1"},
logical_types={"age": Ordinal(order=[25, 33, 57])},
column_descriptions={
"signup_date": "original signup date",
"age": "age of the user",
},
column_origins={
"phone_number": "base",
"age": "base",
"signup_date": "engineered",
},
column_metadata={
"id": {
"is_sorted": True
},
"age": {
"interesting_values": [33, 57]
},
},
)
sample_df.ww.to_disk(str(tmpdir),
format="csv",
encoding="utf-8",
engine="python")
deserialized_df = read_woodwork_table(str(tmpdir))
pd.testing.assert_frame_equal(
to_pandas(deserialized_df,
index=deserialized_df.ww.index,
sort_index=True),
to_pandas(sample_df, index=sample_df.ww.index, sort_index=True),
)
assert deserialized_df.ww.schema == sample_df.ww.schema
def description_to_entityset(description, **kwargs):
"""Deserialize entityset from data description.
Args:
description (dict) : Description of an :class:`.EntitySet`. Likely generated using :meth:`.serialize.entityset_to_description`
kwargs (keywords): Additional keyword arguments to pass as keywords arguments to the underlying deserialization method.
Returns:
entityset (EntitySet) : Instance of :class:`.EntitySet`.
"""
check_schema_version(description, "entityset")
from featuretools.entityset import EntitySet
# If data description was not read from disk, path is None.
path = description.get("path")
entityset = EntitySet(description["id"])
for df in description["dataframes"].values():
if path is not None:
data_path = os.path.join(path, "data", df["name"])
format = description.get("format")
if format is not None:
kwargs["format"] = format
if format == "parquet" and df["loading_info"][
"table_type"] == "pandas":
kwargs["filename"] = df["name"] + ".parquet"
dataframe = read_woodwork_table(data_path,
validate=False,
**kwargs)
else:
dataframe = empty_dataframe(df)
entityset.add_dataframe(dataframe)
for relationship in description["relationships"]:
rel = Relationship.from_dictionary(relationship, entityset)
entityset.add_relationship(relationship=rel)
return entityset
def test_deserialize_validation_control(mock_validate_accessor_params):
assert not mock_validate_accessor_params.called
deserialize.read_woodwork_table(URL)
assert not mock_validate_accessor_params.called
deserialize.read_woodwork_table(URL, validate=True)
assert mock_validate_accessor_params.called
