mobgap.initial_contacts.evaluation.get_matching_ics#

mobgap.initial_contacts.evaluation.get_matching_ics( *, metrics_detected: DataFrame, metrics_reference: DataFrame, matches: DataFrame, ) → DataFrame[source]#

Extract the detected and reference initial contacts that are considered as matches sequence-by-sequence (tps).

The metrics of the detected and reference initial contacts are extracted and returned in a DataFrame for further comparison.

Parameters:

metrics_detected: Each row corresponds to a detected initial contact interval as output from the ICD algorithms. The columns contain the metrics estimated for each respective initial contact based on these detected intervals. The columns present in both metrics_detected and metrics_reference are regarded for the matching, while the other columns are discarded.
metrics_reference: Each row corresponds to a reference initial contact interval as retrieved from the reference system. The columns contain the metrics estimated for each respective initial contact based on these reference intervals. The columns present in both metrics_detected and metrics_reference are regarded for the matching, while the other columns are discarded.
matches: A DataFrame containing the matched initial contacts as output by calculate_matched_icd_performance_metrics. Must have been calculated based on the same interval data as metrics_detected and metrics_reference. Expected to have the columns ic_id_detected, ic_id_reference, and match_type.

Returns:

matches: pd.DataFrame: The detected initial contaccts that are considered as matches assigned to the reference sequences they are matching with. As index, the unique identifier for each matched initial contact assigned in the matches DataFrame is used. The columns are two-level MultiIndex columns, consisting of a metrics and an origin level. As first column level, all columns present in both metrics_detected and metrics_reference are included. The second column level indicates the origin of the respective value, either detected or reference for metrics that were estimated based on the detected or reference initial contacts, respectively.

Examples

>>> from mobgap.initial_contacts.evaluation import (
...     categorize_ic_list,
...     get_matching_ics,
... )
>>> ic_detected = pd.DataFrame([11, 23, 30, 50], columns=["ic"]).rename_axis(
...     "ic_id"
... )
>>> ic_reference = pd.DataFrame([10, 20, 32, 40], columns=["ic"]).rename_axis(
...     "ic_id"
... )
>>> matches = categorize_ic_list(
...     ic_list_detected=ic_detected,
...     ic_list_reference=ic_reference,
...     tolerance_samples=2,
... )
>>> match_ics = get_matching_ics(
...     metrics_detected=ic_detected,
...     metrics_reference=ic_reference,
...     matches=matches,
... )
>>> match_ics
        ic
        detected reference
id
0       11        10
1       30        32

mobgap.initial_contacts.evaluation.get_matching_ics#

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