def test_estimate_segments():
df = pd.DataFrame({"target": ["hat", "jug", "hat"], "confidence": [1.2, 3.4, 4.5], "sentiment": ["happy", "sad", "sad"]})
res = _estimate_segments(df, target_field="confidence", max_segments=4)
assert res == ["target"]
res = _estimate_segments(df, target_field="confidence", max_segments=3)
assert res == ["target"]
res = _estimate_segments(df, target_field="confidence", max_segments=1)
assert res == []
def estimate_segments(
self,
df: pd.DataFrame,
name: str,
target_field: str = None,
max_segments: int = 30,
dry_run: bool = False,
) -> Optional[Union[List[Dict], List[str]]]:
"""
Estimates the most important features and values on which to segment
data profiling using entropy-based methods.
:param df: the dataframe of data to profile
:param name: name for discovery in the logger, automatically applied
to loggers with same dataset_name
:param target_field: target field (optional)
:param max_segments: upper threshold for total combinations of segments,
default 30
:param dry_run: run calculation but do not write results to metadata
:return: a list of segmentation feature names
"""
segments = _estimate_segments(df=df, target_field=target_field, max_segments=max_segments)
if not dry_run:
self.metadata_writer.autosegmentation_write(name, segments)
return segments
def test_estimate_segments_empty():
df = pd.DataFrame({"target": [], "confidence": [], "sentiment": []})
res = _estimate_segments(df, target_field="confidence", max_segments=4)
assert res == []