:py:mod:`gnn_tracking.metrics.cluster_metrics` ============================================== .. py:module:: gnn_tracking.metrics.cluster_metrics .. autoapi-nested-parse:: Metrics evaluating the quality of clustering/i.e., the usefulness of the algorithm for tracking. Module Contents --------------- Classes ~~~~~~~ .. autoapisummary:: gnn_tracking.metrics.cluster_metrics.ClusterMetricType gnn_tracking.metrics.cluster_metrics.TrackingMetrics Functions ~~~~~~~~~ .. autoapisummary:: gnn_tracking.metrics.cluster_metrics.tracking_metric_df gnn_tracking.metrics.cluster_metrics.count_tracking_metrics gnn_tracking.metrics.cluster_metrics.tracking_metrics gnn_tracking.metrics.cluster_metrics.tracking_metrics_data gnn_tracking.metrics.cluster_metrics.tracking_metrics_vs_pt gnn_tracking.metrics.cluster_metrics.tracking_metrics_vs_eta gnn_tracking.metrics.cluster_metrics.flatten_track_metrics gnn_tracking.metrics.cluster_metrics.count_hits_per_cluster gnn_tracking.metrics.cluster_metrics.hits_per_cluster_count_to_flat_dict gnn_tracking.metrics.cluster_metrics._sklearn_signature_wrap Attributes ~~~~~~~~~~ .. autoapisummary:: gnn_tracking.metrics.cluster_metrics._tracking_metrics_nan_results gnn_tracking.metrics.cluster_metrics.common_metrics .. py:class:: ClusterMetricType Bases: :py:obj:`Protocol` Function type that calculates a clustering metric. .. py:method:: __call__(*, truth: numpy.ndarray, predicted: numpy.ndarray, pts: numpy.ndarray, reconstructable: numpy.ndarray, pt_thlds: list[float]) -> float | dict[str, float] .. py:class:: TrackingMetrics Bases: :py:obj:`TypedDict` Initialize self. See help(type(self)) for accurate signature. .. py:attribute:: n_particles :type: int .. py:attribute:: n_cleaned_clusters :type: int .. py:attribute:: perfect :type: float .. py:attribute:: double_majority :type: float .. py:attribute:: lhc :type: float .. py:attribute:: fake_perfect :type: float .. py:attribute:: fake_double_majority :type: float .. py:attribute:: fake_lhc :type: float .. py:data:: _tracking_metrics_nan_results :type: TrackingMetrics .. py:function:: tracking_metric_df(h_df: pandas.DataFrame, predicted_count_thld=3) -> pandas.DataFrame Label clusters as double majority/perfect/LHC. :param h_df: Hit information dataframe :param predicted_count_thld: Number of hits a cluster must have to be considered a valid cluster :returns: cluster dataframe with columns such as "double_majority" etc. .. py:function:: count_tracking_metrics(c_df: pandas.DataFrame, h_df: pandas.DataFrame, c_mask: numpy.ndarray, h_mask: numpy.ndarray) -> TrackingMetrics Calculate TrackingMetrics from cluster and hit information. :param c_df: Output dataframe from `tracking_metric_dfs` :param h_df: Hit information dataframe :param c_mask: Cluster mask :param h_mask: Hit mask :returns: TrackingMetrics namedtuple. .. py:function:: tracking_metrics(*, truth: numpy.ndarray, predicted: numpy.ndarray, pts: numpy.ndarray, reconstructable: numpy.ndarray, eta: numpy.ndarray, pt_thlds: Iterable[float], predicted_count_thld=3, max_eta=4) -> dict[float, TrackingMetrics] Calculate 'custom' metrics for matching tracks and hits. :param truth: Truth labels/PIDs for each hit :param predicted: Predicted labels/cluster index for each hit. Negative labels are interpreted as noise (because this is how DBSCAN outputs it) and are ignored :param pts: true pt value of particle belonging to each hit :param reconstructable: Whether the hit belongs to a "reconstructable tracks" (this usually implies a cut on the number of layers that are being hit etc.) :param eta: true pseudorapidity of particle belong to each hit :param pt_thlds: pt thresholds to calculate the metrics for :param predicted_count_thld: Minimal number of hits in a cluster for it to not be rejected. :param max_eta: Maximum eta value to count :returns: See `TrackingMetrics` .. py:function:: tracking_metrics_data(data: torch_geometric.data.Data, labels, pt_thlds: Iterable[float], predicted_count_thld=3, max_eta=4) -> dict[float, TrackingMetrics] Convenience function to apply `tracking_metrics` to a Data object. :param data: Data object :param labels: Predicted labels/cluster index for each hit. Negative labels are treated as noise :param pt_thlds: pt thresholds to calculate the metrics for :param predicted_count_thld: Minimal number of hits in a cluster for it to not be rejected. :param max_eta: Maximum eta value to count .. py:function:: tracking_metrics_vs_pt(h_dfs: list[pandas.DataFrame], c_dfs: list[pandas.DataFrame], pts: list[float], *, max_eta: float = 4.0) -> pandas.DataFrame Calculate tracking metrics for pt slices. :param h_dfs: List of hit dataframes for different batches (see `tracking_metrics_df`) :param c_dfs: List of cluster dataframes for different batches (see `tracking_metrics_df`) :param pts: List of pt points to calculate the metrics for :param max_eta: Maximum eta value to count :returns: Dataframe with tracking metrics for each pt slice .. py:function:: tracking_metrics_vs_eta(h_dfs: list[pandas.DataFrame], c_dfs: list[pandas.DataFrame], etas: list[float], pt_thld: float = 0.9) -> pandas.DataFrame :param h_dfs: List of hit dataframes for different batches (see `tracking_metrics_df`) :param c_dfs: List of cluster dataframes for different batches (see `tracking_metrics_df`) :param etas: Eta points to calculate metrics for :param pt_thld: :returns: Dataframe with tracking metrics for each pt slice .. py:function:: flatten_track_metrics(custom_metrics_result: dict[float, dict[str, float]]) -> dict[str, float] Flatten the result of `custom_metrics` by using pt suffixes to arrive at a flat dictionary, rather than a nested one. .. py:function:: count_hits_per_cluster(predicted: numpy.ndarray) -> numpy.ndarray Count number of hits per cluster .. py:function:: hits_per_cluster_count_to_flat_dict(counts: numpy.ndarray, min_max=10) -> dict[str, float] Turn result array from `count_hits_per_cluster` into a dictionary with cumulative counts. :param counts: Result from `count_hits_per_cluster` :param min_max: Pad the counts with zeros to at least this length .. py:function:: _sklearn_signature_wrap(func: Callable) -> ClusterMetricType A decorator to make an sklearn cluster metric function accept/take the arguments from ``ClusterMetricType``. .. py:data:: common_metrics :type: dict[str, ClusterMetricType]