setting default baseline to 0.5

code_bert_score/score.py

@@ -164,10 +164,10 @@ def score(
  baselines = torch.from_numpy(pd.read_csv(baseline_path).iloc[num_layers].to_numpy())[1:].float()
  else:
  baselines = torch.from_numpy(pd.read_csv(baseline_path).to_numpy())[:, 1:].unsqueeze(1).float()
-
- all_preds = (all_preds - baselines) / (1 - baselines)
  else:
- print(f"Warning: Baseline not Found for {model_type} on {lang} at {baseline_path}", file=sys.stderr)
+ # print(f"Warning: Baseline not Found for {model_type} on {lang} at {baseline_path}", file=sys.stderr)
+ baselines = 0.5
+ all_preds = (all_preds - baselines) / (1 - baselines)
 
  out = all_preds[..., 0], all_preds[..., 1], all_preds[..., 2], all_preds[..., 3] # P, R, F, F3
 
@@ -252,9 +252,11 @@ def plot_example(
  baseline_path = os.path.join(os.path.dirname(__file__), f"rescale_baseline/{lang}/{model_type}.tsv")
  if os.path.isfile(baseline_path):
  baselines = torch.from_numpy(pd.read_csv(baseline_path).iloc[num_layers].to_numpy())[1:].float()
- sim = (sim - baselines[2].item()) / (1 - baselines[2].item())
+ baseline = baselines[2].item()
  else:
- print(f"Warning: Baseline not Found for {model_type} on {lang} at {baseline_path}", file=sys.stderr)
+ # print(f"Warning: Baseline not Found for {model_type} on {lang} at {baseline_path}", file=sys.stderr)
+ baseline = 0.5
+ sim = (sim - baseline) / (1 - baseline)
 
  import matplotlib.pyplot as plt
  from mpl_toolkits.axes_grid1 import make_axes_locatable

example.py

@@ -56,16 +56,19 @@ def print_results(predictions, refs, pred_results):
  with open('idf_dicts/java_idf.pkl', 'rb') as f:
  java_idf = pickle.load(f)
 
- # pred_results = code_bert_score.score([''],['a'], sources=["a"], lang="python")
- # pred_results = code_bert_score.score(cands=predictions, refs=refs, no_punc=True, lang='java', idf=java_idf)
- # print_results(predictions, refs, pred_results)
+ pred_results = code_bert_score.score([''],['a'], sources=["a"], lang="python")
+ pred_results = code_bert_score.score(cands=predictions, refs=refs, no_punc=True, lang='java', idf=java_idf)
+ print_results(predictions, refs, pred_results)
 
- # print('When providing the context: "find the index of target in this.elements"')
- # pred_results = code_bert_score.score(cands=predictions, refs=refs, no_punc=True, lang='java', idf=java_idf, sources=['find the index of target in this.elements'] * 2)
- # print_results(predictions, refs, pred_results)
+ print('When providing the context: "find the index of target in this.elements"')
+ pred_results = code_bert_score.score(cands=predictions, refs=refs, no_punc=True, lang='java', idf=java_idf, sources=['find the index of target in this.elements'] * 2)
+ print_results(predictions, refs, pred_results)
 
 
  with open('idf_dicts/python_idf.pkl', 'rb') as f:
  python_idf = pickle.load(f)
  pred_results = code_bert_score.score(cands=['math.sqrt(x)'], refs=[['x ** 0.5']], no_punc=True, lang='python', idf=python_idf)
+ print(pred_results)
+
+ pred_results = code_bert_score.score(cands=['math.sqrt(x)'], refs=[['x ** 0.5']], rescale_with_baseline=True, lang='en')
  print(pred_results)