Cleanup and preliminary refactoring of feature extractor

deeplc/__init__.py

@@ -1,14 +1,8 @@
-import sys
+__all__ = ["DeepLC"]
 
+from importlib.metadata import version
 
-if sys.version_info >= (3,8):
-    from importlib.metadata import version
-    __version__ = version('deeplc')
-else:
-    import pkg_resources
-    __version__ = pkg_resources.require("deeplc")[0].version
+__version__ = version("deeplc")
 
 
 from deeplc.deeplc import DeepLC
-from deeplc.feat_extractor import FeatExtractor
-

deeplc/__main__.py

@@ -1,7 +1,12 @@
 """Main command line interface to DeepLC."""
 
 __author__ = ["Robbin Bouwmeester", "Ralf Gabriels"]
-__credits__ = ["Robbin Bouwmeester", "Ralf Gabriels", "Prof. Lennart Martens", "Sven Degroeve"]
+__credits__ = [
+    "Robbin Bouwmeester",
+    "Ralf Gabriels",
+    "Prof. Lennart Martens",
+    "Sven Degroeve",
+]
 __license__ = "Apache License, Version 2.0"
 __maintainer__ = ["Robbin Bouwmeester", "Ralf Gabriels"]
 __email__ = ["Robbin.Bouwmeester@ugent.be", "Ralf.Gabriels@ugent.be"]
@@ -12,12 +17,12 @@
 import warnings
 
 import pandas as pd
+from psm_utils.io import read_file
 from psm_utils.io.peptide_record import peprec_to_proforma
 from psm_utils.psm import PSM
 from psm_utils.psm_list import PSMList
-from psm_utils.io import read_file
 
-from deeplc import __version__, DeepLC, FeatExtractor
+from deeplc import DeepLC, __version__
 from deeplc._argument_parser import parse_arguments
 from deeplc._exceptions import DeepLCError
 
@@ -26,27 +31,28 @@
 
 def setup_logging(passed_level):
     log_mapping = {
-        'critical': logging.CRITICAL,
-        'error': logging.ERROR,
-        'warning': logging.WARNING,
-        'info': logging.INFO,
-        'debug': logging.DEBUG,
+        "critical": logging.CRITICAL,
+        "error": logging.ERROR,
+        "warning": logging.WARNING,
+        "info": logging.INFO,
+        "debug": logging.DEBUG,
     }
 
     if passed_level.lower() not in log_mapping:
         print(
             "Invalid log level. Should be one of the following: ",
-            ', '.join(log_mapping.keys())
+            ", ".join(log_mapping.keys()),
         )
         exit(1)
 
     logging.basicConfig(
         stream=sys.stdout,
-        format='%(asctime)s - %(levelname)s - %(message)s',
-        datefmt='%Y-%m-%d %H:%M:%S',
-        level=log_mapping[passed_level.lower()]
+        format="%(asctime)s - %(levelname)s - %(message)s",
+        datefmt="%Y-%m-%d %H:%M:%S",
+        level=log_mapping[passed_level.lower()],
     )
 
+
 def main(gui=False):
     """Main function for the CLI."""
     argu = parse_arguments(gui=gui)
@@ -55,13 +61,13 @@ def main(gui=False):
 
     # Reset logging levels if DEBUG (see deeplc.py)
     if argu.log_level.lower() == "debug":
-        os.environ['TF_CPP_MIN_LOG_LEVEL'] = '0'
-        logging.getLogger('tensorflow').setLevel(logging.DEBUG)
-        warnings.filterwarnings('default', category=DeprecationWarning)
-        warnings.filterwarnings('default', category=FutureWarning)
-        warnings.filterwarnings('default', category=UserWarning)
+        os.environ["TF_CPP_MIN_LOG_LEVEL"] = "0"
+        logging.getLogger("tensorflow").setLevel(logging.DEBUG)
+        warnings.filterwarnings("default", category=DeprecationWarning)
+        warnings.filterwarnings("default", category=FutureWarning)
+        warnings.filterwarnings("default", category=UserWarning)
     else:
-        os.environ['KMP_WARNINGS'] = '0'
+        os.environ["KMP_WARNINGS"] = "0"
 
     try:
         run(**vars(argu))
@@ -101,13 +107,13 @@ def run(
         for fm in file_model:
             if len(sel_group) == 0:
                 sel_group = "_".join(fm.split("_")[:-1])
-                fm_dict[sel_group]= fm
+                fm_dict[sel_group] = fm
                 continue
             m_group = "_".join(fm.split("_")[:-1])
             if m_group == sel_group:
                 fm_dict[m_group] = fm
         file_model = fm_dict
-    
+
     with open(file_pred) as f:
         first_line_pred = f.readline().strip()
     if file_cal:
@@ -118,53 +124,68 @@ def run(
         # Read input files
         df_pred = pd.read_csv(file_pred)
         if len(df_pred.columns) < 2:
-            df_pred = pd.read_csv(file_pred,sep=" ")
+            df_pred = pd.read_csv(file_pred, sep=" ")
         df_pred = df_pred.fillna("")
         file_pred = ""
 
         list_of_psms = []
-        for seq,mod,ident in zip(df_pred["seq"],df_pred["modifications"],df_pred.index):
-            list_of_psms.append(PSM(peptidoform=peprec_to_proforma(seq,mod),spectrum_id=ident))
+        for seq, mod, ident in zip(df_pred["seq"], df_pred["modifications"], df_pred.index):
+            list_of_psms.append(PSM(peptidoform=peprec_to_proforma(seq, mod), spectrum_id=ident))
         psm_list_pred = PSMList(psm_list=list_of_psms)
         df_pred = None
     else:
         psm_list_pred = read_file(file_pred)
         if "msms" in file_pred and ".txt" in file_pred:
-            mapper = pd.read_csv(os.path.join(os.path.dirname(os.path.realpath(__file__)), "unimod/map_mq_file.csv"),index_col=0)["value"].to_dict()
+            mapper = pd.read_csv(
+                os.path.join(
+                    os.path.dirname(os.path.realpath(__file__)),
+                    "unimod/map_mq_file.csv",
+                ),
+                index_col=0,
+            )["value"].to_dict()
             psm_list_pred.rename_modifications(mapper)
 
     # Allow for calibration file to be empty (undefined), fill in if/elif if present
     psm_list_cal = []
-    if "modifications" in first_line_cal.split(",") and "seq" in first_line_cal.split(",") and file_cal:
+    if (
+        "modifications" in first_line_cal.split(",")
+        and "seq" in first_line_cal.split(",")
+        and file_cal
+    ):
         df_cal = pd.read_csv(file_cal)
         if len(df_cal.columns) < 2:
-            df_cal = pd.read_csv(df_cal,sep=" ")
+            df_cal = pd.read_csv(df_cal, sep=" ")
         df_cal = df_cal.fillna("")
         file_cal = ""
 
         list_of_psms = []
-        for seq,mod,ident,tr in zip(df_cal["seq"],df_cal["modifications"],df_cal.index,df_cal["tr"]):
-            list_of_psms.append(PSM(peptidoform=peprec_to_proforma(seq,mod),spectrum_id=ident,retention_time=tr))
+        for seq, mod, ident, tr in zip(
+            df_cal["seq"], df_cal["modifications"], df_cal.index, df_cal["tr"]
+        ):
+            list_of_psms.append(
+                PSM(
+                    peptidoform=peprec_to_proforma(seq, mod),
+                    spectrum_id=ident,
+                    retention_time=tr,
+                )
+            )
         psm_list_cal = PSMList(psm_list=list_of_psms)
         df_cal = None
     elif file_cal:
         psm_list_cal = read_file(file_cal)
         if "msms" in file_cal and ".txt" in file_cal:
-            mapper = pd.read_csv(os.path.join(os.path.dirname(os.path.realpath(__file__)), "unimod/map_mq_file.csv"),index_col=0)["value"].to_dict()
+            mapper = pd.read_csv(
+                os.path.join(
+                    os.path.dirname(os.path.realpath(__file__)),
+                    "unimod/map_mq_file.csv",
+                ),
+                index_col=0,
+            )["value"].to_dict()
             psm_list_cal.rename_modifications(mapper)
-    # Make a feature extraction object; you can skip this if you do not want to
-    # use the default settings for DeepLC. Here we want to use a model that does
-    # not use RDKit features so we skip the chemical descriptor making
-    # procedure.
-    f_extractor = FeatExtractor(
-        cnn_feats=True,
-        verbose=verbose
-    )
-
+
     # Make the DeepLC object that will handle making predictions and calibration
     dlc = DeepLC(
         path_model=file_model,
-        f_extractor=f_extractor,
         cnn_model=True,
         split_cal=split_cal,
         dict_cal_divider=dict_divider,
@@ -173,9 +194,9 @@ def run(
         batch_num=batch_num,
         n_jobs=n_threads,
         verbose=verbose,
-        deeplc_retrain=transfer_learning
+        deeplc_retrain=transfer_learning,
     )
-    
+
     # Calibrate the original model based on the new retention times
     if len(psm_list_cal) > 0:
         logger.info("Selecting best model and calibrating predictions...")
@@ -185,16 +206,21 @@ def run(
     # Make predictions; calibrated or uncalibrated
     logger.info("Making predictions using model: %s", dlc.model)
     if len(psm_list_cal) > 0:
-        preds = dlc.make_preds(seq_df=df_pred, infile=file_pred, psm_list=psm_list_pred)
+        preds = dlc._make_preds(seq_df=df_pred, infile=file_pred, psm_list=psm_list_pred)
     else:
-        preds = dlc.make_preds(seq_df=df_pred, infile=file_pred, psm_list=psm_list_pred, calibrate=False)
-
-    #df_pred["predicted_tr"] = preds
+        preds = dlc._make_preds(
+            seq_df=df_pred,
+            infile=file_pred,
+            psm_list=psm_list_pred,
+            calibrate=False,
+        )
+
+    # df_pred["predicted_tr"] = preds
     logger.info("Writing predictions to file: %s", file_pred_out)
-    
-    file_pred_out = open(file_pred_out,"w")
+
+    file_pred_out = open(file_pred_out, "w")
     file_pred_out.write("Sequence proforma,predicted retention time\n")
-    for psm,tr in zip(psm_list_pred,preds):
+    for psm, tr in zip(psm_list_pred, preds):
         file_pred_out.write(f"{psm.peptidoform.proforma},{tr}\n")
     file_pred_out.close()
 

deeplc/_argument_parser.py

@@ -74,17 +74,13 @@ def parse_arguments(gui=False):
 
     parser = ArgumentParser(
         prog="DeepLC",
-        description=(
-            "Retention time prediction for (modified) peptides using deep " "learning."
-        ),
+        description=("Retention time prediction for (modified) peptides using deep learning."),
         usage="deeplc [OPTIONS] --file_pred <peptide_file>",
         formatter_class=lambda prog: HelpFormatter(prog, max_help_position=42),
         add_help=False,
     )
 
-    io_args = parser.add_argument_group(
-        "Input and output files", **gooey_args["io_args"]
-    )
+    io_args = parser.add_argument_group("Input and output files", **gooey_args["io_args"])
     io_args.add_argument(
         "--file_pred",
         required=True,
@@ -97,9 +93,7 @@ def parse_arguments(gui=False):
         type=str,
         default=None,
         metavar="Input peptides for calibration" if gui else "",
-        help=(
-            "path to peptide CSV file with retention times to use for " "calibration"
-        ),
+        help=("path to peptide CSV file with retention times to use for calibration"),
         **gooey_args["file_cal"],
     )
     io_args.add_argument(
@@ -166,10 +160,7 @@ def parse_arguments(gui=False):
         dest="split_cal",
         default=50,
         metavar="split cal" if gui else "",
-        help=(
-            "number of splits in the chromatogram for piecewise linear "
-            "calibration fit"
-        ),
+        help=("number of splits in the chromatogram for piecewise linear calibration fit"),
         **gooey_args["split_cal"],
     )
     model_cal_args.add_argument(
@@ -265,8 +256,6 @@ def parse_arguments(gui=False):
     results = parser.parse_args()
 
     if not results.file_pred_out:
-        results.file_pred_out = (
-            os.path.splitext(results.file_pred)[0] + "_deeplc_predictions.csv"
-        )
+        results.file_pred_out = os.path.splitext(results.file_pred)[0] + "_deeplc_predictions.csv"
 
     return results

deeplc/_exceptions.py

@@ -1,5 +1,6 @@
 """DeepLC exceptions."""
 
+
 class DeepLCError(Exception):
     pass