Thibault Lavigne · Thibault Lavigne · Thibault Lavigne
--- a/nerval/evaluate.py
+++ b/nerval/evaluate.py
    return scores


+def run_multiple(file_csv, annot_folder, predict_folder, threshold):
+    """Run the program for multiple file (correlation indicated in the csv file)"""
--- a/nerval/evaluate.py
+++ b/nerval/evaluate.py
        default=THRESHOLD,
        type=threshold_float_type,
    )
+    parser.add_argument(
+        "-c",
+        "--csv",
+        help="csv with the correlation between the annotation bio file and the predict bio file",
--- a/nerval/evaluate.py
+++ b/nerval/evaluate.py
        try:
            word, label = line.split()
        except ValueError:
+            print(index)
+            print(line)
            raise (Exception(f"The file {path} given in input is not in BIO format."))
--- a/nerval/evaluate.py
+++ b/nerval/evaluate.py

+def run_multiple(file_csv, annot_folder, predict_folder, threshold):
+    """Run the program for multiple file (correlation indicated in the csv file)"""
+    # Read the csv in a dataframe
+    df_cor = pd.read_csv(file_csv)
+
+    # Check if the variable given are folder
+    if os.path.isdir(annot_folder) and os.path.isdir(predict_folder):
+        list_bio_annot = glob.glob(annot_folder + "/**/*.bio", recursive=True)
+        list_bio_predict = glob.glob(predict_folder + "/**/*.bio", recursive=True)
+
+        for index, row in df_cor.iterrows():
+            annot = None
+            predict = None
+
+            # Check if the file exist
--- a/nerval/evaluate.py
+++ b/nerval/evaluate.py
        type=threshold_float_type,
    )
+    parser.add_argument(
+        "-c",
+        "--csv",
+        help="csv with the correlation between the annotation bio file and the predict bio file",
+        required=False,
+        type=Path,
+    )
    args = parser.parse_args()

-    run(args.annot, args.predict, args.threshold)
+    if args.csv:
+        run_multiple(args.csv, args.annot, args.predict, args.threshold)
+    else:
+        run(args.annot, args.predict, args.threshold)
--- a/nerval/evaluate.py
+++ b/nerval/evaluate.py
+            for file_annot in list_bio_annot:
+                if row["annot"] == os.path.basename(file_annot):
+                    annot = file_annot
+            for file_predict in list_bio_predict:
+                if row["predict"] == os.path.basename(file_predict):
+                    predict = file_predict
+
+            # Apply the evaluation
+            if annot and predict:
+                run(annot, predict, threshold)
+                print("")
+            else:
+                print(f"No file found for row {index}")
+
+    else:
+        print("Error this is no folder")
--- a/nerval/evaluate.py
+++ b/nerval/evaluate.py
+        for index, row in df_cor.iterrows():
+            annot = None
+            predict = None
+
+            # Check if the file exist
+            for file_annot in list_bio_annot:
+                if row["annot"] == os.path.basename(file_annot):
+                    annot = file_annot
+            for file_predict in list_bio_predict:
+                if row["predict"] == os.path.basename(file_predict):
+                    predict = file_predict
+
+            # Apply the evaluation
+            if annot and predict:
+                run(annot, predict, threshold)
+                print("")
--- a/nerval/evaluate.py
+++ b/nerval/evaluate.py
    return scores


+def run_multiple(file_csv, folder, threshold):
+    """Run the program for multiple files (correlation indicated in the csv file)"""
+    # Read the csv in a dataframe
+    df_cor = pd.read_csv(file_csv)
--- a/demo/cor.csv 0 → 100644
+++ b/demo/cor.csv 0 → 100644
+annot,predict
--- a/nerval/evaluate.py
+++ b/nerval/evaluate.py
                    predict = file

            if annot and predict:
+                print(os.path.basename(predict))
                run(annot, predict, threshold)
                print()
            else:
-                raise f"No file found for row {index}"
-
+                raise f"No file found for files {annot}, {predict}"
    else:
        raise "This is no folder"
--- a/nerval/evaluate.py
+++ b/nerval/evaluate.py
                    predict = file

            if annot and predict:
+                print(os.path.basename(predict))
                run(annot, predict, threshold)
                print()
            else:
-                raise f"No file found for row {index}"
--- a/nerval/evaluate.py
+++ b/nerval/evaluate.py

 def run_multiple(file_csv, folder, threshold):
    """Run the program for multiple files (correlation indicated in the csv file)"""
-    # Read the csv in a dataframe
-    df_cor = pd.read_csv(file_csv)
+    # Read the csv in a list
+    with open(file_csv, "r") as read_obj:
+        csv_reader = reader(read_obj)
--- a/nerval/evaluate.py
+++ b/nerval/evaluate.py
        "-f",
        "--folder",
        help="Folder containing the bio files referred to in the csv file",
        required=False,
--- a/nerval/evaluate.py
+++ b/nerval/evaluate.py

    logging.basicConfig(level=logging.INFO)

    parser = argparse.ArgumentParser(description="Compute score of NER on predict.")