From 335a8bac777273ab2048f5b993005c0b43ec5e2b Mon Sep 17 00:00:00 2001
From: flux9665 <lux.florian@gmail.com>
Date: Mon, 7 Oct 2024 14:51:05 +0200
Subject: [PATCH] overhaul boxplots once more

---
 .../eval_lang_emb_approximation.py            | 49 +++++++++----------
 1 file changed, 24 insertions(+), 25 deletions(-)

diff --git a/Preprocessing/multilinguality/eval_lang_emb_approximation.py b/Preprocessing/multilinguality/eval_lang_emb_approximation.py
index ca372add..9e3108ea 100644
--- a/Preprocessing/multilinguality/eval_lang_emb_approximation.py
+++ b/Preprocessing/multilinguality/eval_lang_emb_approximation.py
@@ -7,12 +7,13 @@
 import torch
 from huggingface_hub import hf_hub_download
 
-# matplotlib.rcParams['mathtext.fontset'] = 'stix'
-# matplotlib.rcParams['font.family'] = 'STIXGeneral'
+matplotlib.rcParams['mathtext.fontset'] = 'stix'
+matplotlib.rcParams['font.family'] = 'STIXGeneral'
 matplotlib.rcParams['font.size'] = 7
 import matplotlib.pyplot as plt
 from Utility.utils import load_json_from_path
 
+
 def compute_loss_for_approximated_embeddings(csv_path, iso_lookup, language_embeddings, weighted_avg=False, min_n_langs=5, max_n_langs=30, threshold_percentile=95, loss_fn="MSE"):
     df = pd.read_csv(csv_path, sep="|")
 
@@ -23,7 +24,7 @@ def compute_loss_for_approximated_embeddings(csv_path, iso_lookup, language_embe
 
     features_per_closest_lang = 2
     # for combined, df has up to 5 features (if containing individual distances) per closest lang + 1 target lang column
-    if "combined_dist_0" in df.columns: 
+    if "combined_dist_0" in df.columns:
         if "map_dist_0" in df.columns:
             features_per_closest_lang += 1
         if "asp_dist_0" in df.columns:
@@ -77,7 +78,7 @@ def compute_loss_for_approximated_embeddings(csv_path, iso_lookup, language_embe
                 lang_emb = language_embeddings[iso_lookup[-1][lang]]
                 avg_emb += lang_emb
             normalization_factor = len(langs)
-        avg_emb /= normalization_factor # normalize
+        avg_emb /= normalization_factor  # normalize
         current_loss = loss_fn(avg_emb, y).item()
         all_losses.append(current_loss)
 
@@ -111,36 +112,34 @@ def compute_loss_for_approximated_embeddings(csv_path, iso_lookup, language_embe
     os.makedirs(OUT_DIR, exist_ok=True)
 
     fig, ax = plt.subplots(figsize=(6, 4))
-    plt.ylabel(f"{args.loss_fn} between Approximated and Real")
+    plt.ylabel(args.loss_fn)
     for i, csv_path in enumerate(csv_paths):
         print(f"csv_path: {os.path.basename(csv_path)}")
         for condition in weighted:
-            losses = compute_loss_for_approximated_embeddings(csv_path, 
-                                                         iso_lookup, 
-                                                         lang_embs, 
-                                                         condition, 
-                                                         min_n_langs=args.min_n_langs, 
-                                                         max_n_langs=args.max_n_langs,
-                                                         threshold_percentile=args.threshold_percentile,
-                                                         loss_fn=args.loss_fn)
+            losses = compute_loss_for_approximated_embeddings(csv_path,
+                                                              iso_lookup,
+                                                              lang_embs,
+                                                              condition,
+                                                              min_n_langs=args.min_n_langs,
+                                                              max_n_langs=args.max_n_langs,
+                                                              threshold_percentile=args.threshold_percentile,
+                                                              loss_fn=args.loss_fn)
             print(f"weighted average: {condition} | mean loss: {np.mean(losses)}")
             losses_of_multiple_datasets.append(losses)
 
     bp_dict = ax.boxplot(losses_of_multiple_datasets,
-                         labels =[
-                             "Random Neighbors",
-                             "Nearest according \nto inverse ASPF",
-                             "Nearest according \nto Map Distance",
-                             "Nearest according \nto Tree Distance",
-                             "Nearest according \nto Learned Distance",
-                             "Actual Nearest\n(Oracle)",
-                             ], 
+                         labels=["Random",
+                                 "Inverse ASP",
+                                 "Map Distance",
+                                 "Tree Distance",
+                                 "Learned Distance",
+                                 "Oracle"],
                          patch_artist=True,
-                         boxprops=dict(facecolor = "lightblue", 
+                         boxprops=dict(facecolor="lightblue",
                                        ),
-                        showfliers=False,
+                         showfliers=False,
                          widths=0.55
-                        )
+                         )
     # major ticks every 0.1, minor ticks every 0.05, between 0.0 and 0.6
     major_ticks = np.arange(0, 1.0, 0.1)
     minor_ticks = np.arange(0, 1.0, 0.05)
@@ -148,7 +147,7 @@ def compute_loss_for_approximated_embeddings(csv_path, iso_lookup, language_embe
     ax.set_yticks(minor_ticks, minor=True)
     # horizontal grid lines for minor and major ticks
     ax.grid(which='both', linestyle='-', color='lightgray', linewidth=0.3, axis='y')
-    plt.title(f"Using between {args.min_n_langs} and {args.max_n_langs} Nearest Neighbors to approximate an unseen Embedding")
+    # plt.title(f"Using between {args.min_n_langs} and {args.max_n_langs} Nearest Neighbors to approximate an unseen Embedding")
     plt.xticks(rotation=45)
     plt.tight_layout()
     plt.show()