Add single language mode statistics to scripts

scripts/accuracy_plot_drawer.py

@@ -18,6 +18,7 @@
 import numpy as np
 import pandas as pd
 import seaborn as sns
+from lingua import Language
 from math import floor
 from matplotlib.patches import Patch
 from pathlib import Path
@@ -43,11 +44,12 @@ class AccuracyPlotDrawer:
         "fasttext": "FastText 0.9.2",
         "langdetect": "Langdetect 1.0.9",
         "langid": "Langid 1.1.6",
-        "lingua-low-accuracy": "Lingua 1.3.5\nlow accuracy mode",
-        "lingua-high-accuracy": "Lingua 1.3.5\nhigh accuracy mode",
+        "lingua-low-accuracy": "Lingua 1.4.0\nlow accuracy mode",
+        "lingua-high-accuracy": "Lingua 1.4.0\nhigh accuracy mode",
+        "lingua-single-language-detector": "Lingua 1.4.0\nsingle language mode",
         "simplemma": "Simplemma 0.9.1",
     }
-    _hatches = ("|", "-", "/", "x", "+", "\\", "o", ".", "*", "O")
+    _hatches = ("|", "-", "/", "x", "+", "\\", "o", "oo", ".", "*", "O")
     _palette = (
         "#39d7e6",
         "#6bbcff",
@@ -57,6 +59,7 @@ class AccuracyPlotDrawer:
         "#ff8800",
         "#ffb866",
         "#ffc400",
+        "#fff480",
         "#8edca7",
         "#41c46b",
     )
@@ -68,8 +71,22 @@ def __init__(self, plot_title: str, report_file_path: Path):
 
     def _read_into_dataframe(self, report_file_path: Path) -> pd.DataFrame:
         df = pd.read_csv(report_file_path, index_col="language")
+
+        single_language_mode_columns = [
+            f"lingua-{language.name.lower()}-detector" for language in Language
+        ]
+        merged_single_language_mode_column = {
+            "lingua-single-language-detector": df[single_language_mode_columns].sum(
+                axis="columns"
+            )
+        }
+        df = df.assign(**merged_single_language_mode_column).drop(
+            single_language_mode_columns, axis="columns"
+        )
+
         # Sort classifier columns by their mean value
-        df = df.reindex(df.mean().sort_values().index, axis=1)
+        df = df.reindex(df.mean().sort_values().index, axis="columns")
+
         return pd.melt(
             frame=df.reset_index(),
             id_vars="language",
@@ -78,10 +95,12 @@ def _read_into_dataframe(self, report_file_path: Path) -> pd.DataFrame:
         )
 
     def draw_barplot(self, file_path: Path):
-        row_filter = self._dataframe[self._hue].isin(self._column_labels.keys())
+        column_labels = self._column_labels.copy()
+        del column_labels["lingua-single-language-detector"]
+        row_filter = self._dataframe[self._hue].isin(column_labels.keys())
         data = self._dataframe[row_filter]
         classifiers = data[self._hue].unique()
-        labels = [self._column_labels[classifier] for classifier in classifiers]
+        labels = [column_labels[classifier] for classifier in classifiers]
         handles = [
             Patch(facecolor=color, edgecolor="black", label=label, hatch=hatch)
             for color, label, hatch in zip(self._palette, labels, self._hatches)

scripts/accuracy_table_writer.py

@@ -16,6 +16,7 @@
 import math
 import pandas as pd
 
+from lingua import Language
 from pathlib import Path
 
 
@@ -30,6 +31,7 @@ class AccuracyTableWriter:
         "langid": "Langid",
         "lingua-low-accuracy": "Lingua<br>(low accuracy mode)",
         "lingua-high-accuracy": "Lingua<br>(high accuracy mode)",
+        "lingua-single-language-detector": "Lingua<br>(single language mode)",
         "simplemma": "Simplemma",
     }
 
@@ -104,7 +106,21 @@ def write_accuracy_table(self, file_path: Path):
             accuracy_table_file.write(table)
 
     def _read_into_dataframe(self, report_file_path: Path) -> pd.DataFrame:
-        return pd.read_csv(report_file_path, index_col="language")
+        df = pd.read_csv(report_file_path, index_col="language")
+
+        single_language_mode_columns = [
+            f"lingua-{language.name.lower()}-detector" for language in Language
+        ]
+        merged_single_language_mode_column = {
+            "lingua-single-language-detector": df[single_language_mode_columns].sum(
+                axis="columns"
+            )
+        }
+        df = df.assign(**merged_single_language_mode_column).drop(
+            single_language_mode_columns, axis="columns"
+        )
+
+        return df
 
     def _get_square_color(self, accuracy_value: float) -> str:
         if math.isnan(accuracy_value):