def testCustomData(self):
CustomData = custom_data_class(
column_data_types={
"metadata": str,
"created_time": pd.Timestamp
},
required_columns={"metadata"},
)
data_entry = {
"arm_name": "0_1",
"mean": 3.7,
"sem": 0.5,
"metric_name": "b",
"metadata": "42",
"created_time": "2018-09-20",
}
data = CustomData(df=pd.DataFrame([data_entry]))
self.assertTrue(
isinstance(data.df.iloc[0]["created_time"], pd.Timestamp))
data_entry2 = {
"arm_name": "0_1",
"mean": 3.7,
"sem": 0.5,
"metric_name": "b",
"created_time": "2018-09-20",
}
# Test without required column
with self.assertRaises(ValueError):
CustomData(df=pd.DataFrame([data_entry2]))
# Try making regular data with extra column
with self.assertRaises(ValueError):
Data(df=pd.DataFrame([data_entry2]))
def testFromMultipleDataReturnSubclass(self):
CustomData = custom_data_class(column_data_types={"metadata": str},
required_columns={"metadata"})
data = [
CustomData(df=pd.DataFrame([
{
"arm_name": "0_1",
"mean": 3.7,
"sem": 0.5,
"metric_name": "b",
"metadata": "42",
},
{
"arm_name": "0_2",
"mean": 3.7,
"sem": 1.5,
"metric_name": "x",
"metadata": "43",
},
])),
CustomData(df=pd.DataFrame([{
"arm_name": "0_3",
"mean": 2.4,
"sem": 0.1,
"metric_name": "a",
"metadata": "42",
}])),
]
returned_data_object = Data.from_multiple_data(data)
self.assertIsInstance(returned_data_object, CustomData)
def testFromMultipleDataMismatchedTypes(self):
# create two custom data types
CustomDataA = custom_data_class(
column_data_types={"metadata": str, "created_time": pd.Timestamp},
required_columns={"metadata"},
)
CustomDataB = custom_data_class(column_data_types={"year": pd.Timestamp})
# Test data of multiple empty custom types raises a value error
with self.assertRaises(ValueError):
Data.from_multiple_data([CustomDataA(), CustomDataB()])
# Test data of multiple non-empty types raises a value error
with self.assertRaises(ValueError):
data_elt_A = CustomDataA(
df=pd.DataFrame(
[
{
"arm_name": "0_1",
"mean": 3.7,
"sem": 0.5,
"metric_name": "b",
"metadata": "42",
"created_time": "2018-09-20",
}
]
)
)
data_elt_B = CustomDataB(
df=pd.DataFrame(
[
{
"arm_name": "0_1",
"mean": 3.7,
"sem": 0.5,
"metric_name": "b",
"year": "2018-09-20",
}
]
)
)
Data.from_multiple_data([data_elt_A, data_elt_B])
