feat: improve scoring workflows for large OSW datasets

pyprophet/_config.py

@@ -114,6 +114,8 @@ class RunnerConfig:
         ipf_max_peakgroup_pep (float): Max PEP for peak group consideration in IPF.
         ipf_max_transition_isotope_overlap (float): Max isotope overlap for transition selection in IPF.
         ipf_min_transition_sn (float): Min log S/N for transition selection in IPF.
+        transition_training_require_unique_mapping (bool): Whether to restrict transition semi-supervised target training peaks to uniquely mapped transitions.
+        transition_training_require_phospho_loss (bool): Whether to restrict transition semi-supervised target training peaks to phospho-loss transitions.
 
         glyco (bool): Whether glycopeptide-specific scoring is enabled.
         density_estimator (str): Score density estimation method ('kde' or 'gmm').
@@ -124,6 +126,8 @@ class RunnerConfig:
         threads (int): Number of CPU threads to use; -1 means all CPUs.
         test (bool): Whether to enable test mode with deterministic behavior.
         color_palette (str): Color palette used in PDF report rendering.
+        report_mode (str): PDF report scope: 'full', 'main', or 'none'.
+        apply_weights_run_batch_size (int): Number of runs to score together per streamed OSW apply batch. `0` means auto.
     """
 
     # Scoring / classifier options
@@ -160,6 +164,8 @@ class RunnerConfig:
     ipf_max_peakgroup_pep: float = 0.7
     ipf_max_transition_isotope_overlap: float = 0.5
     ipf_min_transition_sn: float = 0.0
+    transition_training_require_unique_mapping: bool = False
+    transition_training_require_phospho_loss: bool = False
 
     # Glyco options
     glyco: bool = False
@@ -172,6 +178,8 @@ class RunnerConfig:
     threads: int = 1
     test: bool = False
     color_palette: str = "normal"
+    report_mode: Literal["full", "main", "none"] = "full"
+    apply_weights_run_batch_size: int = 0
 
     def __post_init__(self):
         # Check for auto main score selection
@@ -215,6 +223,8 @@ def __str__(self):
                 f"  ipf_max_peakgroup_pep={self.ipf_max_peakgroup_pep}",
                 f"  ipf_max_transition_isotope_overlap={self.ipf_max_transition_isotope_overlap}",
                 f"  ipf_min_transition_sn={self.ipf_min_transition_sn}",
+                f"  transition_training_require_unique_mapping={self.transition_training_require_unique_mapping}",
+                f"  transition_training_require_phospho_loss={self.transition_training_require_phospho_loss}",
             ]
         )
 
@@ -235,6 +245,8 @@ def __str__(self):
                 f"  threads={self.threads}",
                 f"  test={self.test}",
                 f"  color_palette='{self.color_palette}'",
+                f"  report_mode='{self.report_mode}'",
+                f"  apply_weights_run_batch_size={self.apply_weights_run_batch_size}",
                 ")",
             ]
         )
@@ -247,7 +259,11 @@ def __repr__(self):
             f"ss_main_score='{self.ss_main_score}', xeval_fraction={self.xeval_fraction}, "
             f"xeval_num_iter={self.xeval_num_iter}, ss_initial_fdr={self.ss_initial_fdr}, "
             f"ss_iteration_fdr={self.ss_iteration_fdr}, ss_num_iter={self.ss_num_iter}, "
-            f"group_id='{self.group_id}', glyco={self.glyco}, threads={self.threads})"
+            f"group_id='{self.group_id}', glyco={self.glyco}, threads={self.threads}, "
+            f"transition_training_require_unique_mapping={self.transition_training_require_unique_mapping}, "
+            f"transition_training_require_phospho_loss={self.transition_training_require_phospho_loss}, "
+            f"report_mode='{self.report_mode}', "
+            f"apply_weights_run_batch_size={self.apply_weights_run_batch_size})"
         )
 
 
@@ -267,6 +283,7 @@ class RunnerIOConfig(BaseIOConfig):
     """
 
     runner: RunnerConfig
+    run_id_filter: Optional[Union[int, List[int], tuple]] = None
     extra_writes: dict = field(init=False)
 
     def __post_init__(self):
@@ -294,6 +311,7 @@ def to_kwargs(self) -> Dict[str, Any]:
             "subsample_ratio": self.subsample_ratio,
             "level": self.level,
             "prefix": self.prefix,
+            "run_id_filter": self.run_id_filter,
             **vars(self.runner),
         }
 
@@ -331,6 +349,8 @@ def from_cli_args(
         ipf_max_peakgroup_pep,
         ipf_max_transition_isotope_overlap,
         ipf_min_transition_sn,
+        transition_training_require_unique_mapping,
+        transition_training_require_phospho_loss,
         add_alignment_features,
         glyco,
         density_estimator,
@@ -340,6 +360,8 @@ def from_cli_args(
         test,
         color_palette,
         main_score_selection_report,
+        report_mode,
+        apply_weights_run_batch_size,
     ):
         """
         Creates a configuration object from command-line arguments.
@@ -399,6 +421,8 @@ def from_cli_args(
             ipf_max_peakgroup_pep=ipf_max_peakgroup_pep,
             ipf_max_transition_isotope_overlap=ipf_max_transition_isotope_overlap,
             ipf_min_transition_sn=ipf_min_transition_sn,
+            transition_training_require_unique_mapping=transition_training_require_unique_mapping,
+            transition_training_require_phospho_loss=transition_training_require_phospho_loss,
             add_alignment_features=add_alignment_features,
             glyco=glyco,
             density_estimator=density_estimator,
@@ -407,6 +431,8 @@ def from_cli_args(
             threads=threads,
             test=test,
             color_palette=color_palette,
+            report_mode=report_mode,
+            apply_weights_run_batch_size=apply_weights_run_batch_size,
         )
 
         return cls(

pyprophet/cli/score.py

@@ -16,6 +16,8 @@
 # Defer import of runner to avoid premature sklearn import before OMP_NUM_THREADS is set
 # from ..scoring.runner import PyProphetLearner, PyProphetWeightApplier
 
+LARGE_RUN_MAIN_REPORT_THRESHOLD = 50
+
 
 # PyProphet semi-supervised learning and scoring
 @click.command(name="score", cls=AdvancedHelpCommand)
@@ -176,6 +178,18 @@
     help="Minimum log signal-to-noise level to consider transitions in IPF. Set -1 to disable this filter.",
     hidden=True,
 )
+@click.option(
+    "--transition_training_require_unique_mapping/--no-transition_training_require_unique_mapping",
+    default=False,
+    show_default=True,
+    help="Experimental: when learning transition scores, restrict target training peaks to uniquely mapped transitions.",
+)
+@click.option(
+    "--transition_training_require_phospho_loss/--no-transition_training_require_phospho_loss",
+    default=False,
+    show_default=True,
+    help="Experimental: when learning transition scores, restrict target training peaks to phospho-loss transitions.",
+)
 # Glyco/GproDIA Options
 @click.option(
     "--glyco/--no-glyco",
@@ -224,6 +238,20 @@
     help="Generate a report for main score selection process.",
     hidden=True,
 )
+@click.option(
+    "--report_mode",
+    default="auto",
+    show_default=True,
+    type=click.Choice(["auto", "full", "main", "none"]),
+    help="PDF report scope: 'full' writes all report pages, 'main' writes only the core score diagnostics, and 'none' disables report generation.",
+)
+@click.option(
+    "--apply_weights_run_batch_size",
+    default=0,
+    show_default=True,
+    type=int,
+    help="When streamed OSW weight application is used, score this many runs per batch. Use 0 for automatic batching and 1 to force one run at a time.",
+)
 # Processing
 @click.option(
     "--threads",
@@ -283,12 +311,16 @@ def score(
     ipf_max_peakgroup_pep,
     ipf_max_transition_isotope_overlap,
     ipf_min_transition_sn,
+    transition_training_require_unique_mapping,
+    transition_training_require_phospho_loss,
     glyco,
     density_estimator,
     grid_size,
     tric_chromprob,
     color_palette,
     main_score_selection_report,
+    report_mode,
+    apply_weights_run_batch_size,
     threads,
     test,
     profile,  # NOQA: F841 unused variable, but used in decorator
@@ -357,6 +389,8 @@ def score(
         ipf_max_peakgroup_pep,
         ipf_max_transition_isotope_overlap,
         ipf_min_transition_sn,
+        transition_training_require_unique_mapping,
+        transition_training_require_phospho_loss,
         add_alignment_features,
         glyco,
         density_estimator,
@@ -366,6 +400,8 @@ def score(
         test,
         color_palette,
         main_score_selection_report,
+        report_mode,
+        apply_weights_run_batch_size,
     )
 
     write_logfile(
@@ -374,10 +410,13 @@ def score(
         ctx.obj["LOG_HEADER"],
     )
 
+    num_runs = None
+    if subsample_ratio == 1.0 or report_mode == "auto":
+        num_runs = get_num_runs(infile, config.file_type)
+
     # Auto-subsample based on number of runs if applicable
     if subsample_ratio == 1.0:
         # Check if we should auto-subsample
-        num_runs = get_num_runs(infile, config.file_type)
         if num_runs > 20:
             config.subsample_ratio = 1.0 / num_runs
             logger.info(
@@ -393,6 +432,19 @@ def score(
             "Using full dataset for semi-supervised learning."
         )
 
+    if report_mode == "auto":
+        if num_runs and num_runs > LARGE_RUN_MAIN_REPORT_THRESHOLD:
+            config.runner.report_mode = "main"
+            logger.info(
+                f"Large experiment detected ({num_runs} runs). "
+                "Switching report_mode to 'main' to skip expensive identification/quantification report pages. "
+                "Use --report_mode full to force the complete report."
+            )
+        else:
+            config.runner.report_mode = "full"
+    else:
+        config.runner.report_mode = report_mode
+
     # Validate file type and subsample ratio. OSW, parquet, parquet_split, and parquet_split_multi all support subsampling
     if (
         config.file_type not in ["osw", "parquet", "parquet_split", "parquet_split_multi"]

pyprophet/io/_base.py

@@ -351,6 +351,14 @@ def save_weights(self, weights):
                 f"Classifier {self.classifier} not supported for saving weights."
             )
 
+    def save_scorer(self, scorer):
+        """
+        Persist a scorer object when the backend supports it.
+
+        The default implementation is a no-op.
+        """
+        return None
+
     def _prepare_score_dataframe(
         self, df: pd.DataFrame, level: str, prefix: str
     ) -> pd.DataFrame:
@@ -446,6 +454,11 @@ def _write_pdf_report(self, result, pi0):
         Write a PDF report if the scoring results contain final statistics.
         """
 
+        report_mode = getattr(self.config.runner, "report_mode", "full")
+        if report_mode == "none":
+            logger.info("Skipping PDF report generation (report_mode=none).")
+            return
+
         if result.final_statistics is None:
             return
 
@@ -503,6 +516,7 @@ def _write_pdf_report(self, result, pi0):
             self.config.runner.color_palette,
             self.level,
             df=df,
+            report_mode=report_mode,
         )
         logger.success(f"{pdf_path} written.")
 

pyprophet/io/export/osw.py

@@ -781,9 +781,9 @@ def _add_transition_data(self, data, con, cfg):
         if check_sqlite_table(con, "SCORE_TRANSITION"):
             transition_query = f"""
                 SELECT FEATURE_TRANSITION.FEATURE_ID AS id,
-                      GROUP_CONCAT(AREA_INTENSITY,';') AS aggr_Peak_Area,
-                      GROUP_CONCAT(APEX_INTENSITY,';') AS aggr_Peak_Apex,
-                      GROUP_CONCAT(TRANSITION.ID || "_" || TRANSITION.TYPE || TRANSITION.ORDINAL || "_" || TRANSITION.CHARGE,';') AS aggr_Fragment_Annotation
+                      GROUP_CONCAT(AREA_INTENSITY,';' ORDER BY TRANSITION.ID) AS aggr_Peak_Area,
+                      GROUP_CONCAT(APEX_INTENSITY,';' ORDER BY TRANSITION.ID) AS aggr_Peak_Apex,
+                      GROUP_CONCAT(TRANSITION.ID || "_" || TRANSITION.TYPE || TRANSITION.ORDINAL || "_" || TRANSITION.CHARGE,';' ORDER BY TRANSITION.ID) AS aggr_Fragment_Annotation
                 FROM FEATURE_TRANSITION
                 INNER JOIN TRANSITION ON FEATURE_TRANSITION.TRANSITION_ID = TRANSITION.ID
                 INNER JOIN SCORE_TRANSITION ON FEATURE_TRANSITION.TRANSITION_ID = SCORE_TRANSITION.TRANSITION_ID AND FEATURE_TRANSITION.FEATURE_ID = SCORE_TRANSITION.FEATURE_ID
@@ -793,9 +793,9 @@ def _add_transition_data(self, data, con, cfg):
         else:
             transition_query = """
                 SELECT FEATURE_ID AS id,
-                      GROUP_CONCAT(AREA_INTENSITY,';') AS aggr_Peak_Area,
-                      GROUP_CONCAT(APEX_INTENSITY,';') AS aggr_Peak_Apex,
-                      GROUP_CONCAT(TRANSITION.ID || "_" || TRANSITION.TYPE || TRANSITION.ORDINAL || "_" || TRANSITION.CHARGE,';') AS aggr_Fragment_Annotation
+                      GROUP_CONCAT(AREA_INTENSITY,';' ORDER BY TRANSITION.ID) AS aggr_Peak_Area,
+                      GROUP_CONCAT(APEX_INTENSITY,';' ORDER BY TRANSITION.ID) AS aggr_Peak_Apex,
+                      GROUP_CONCAT(TRANSITION.ID || "_" || TRANSITION.TYPE || TRANSITION.ORDINAL || "_" || TRANSITION.CHARGE,';' ORDER BY TRANSITION.ID) AS aggr_Fragment_Annotation
                 FROM FEATURE_TRANSITION
                 INNER JOIN TRANSITION ON FEATURE_TRANSITION.TRANSITION_ID = TRANSITION.ID
                 GROUP BY FEATURE_ID