rzanoli commited on
Commit
9e1a6ae
·
1 Parent(s): b89d424

Small changes

Browse files
Files changed (1) hide show
  1. app.py +7 -9
app.py CHANGED
@@ -48,7 +48,7 @@ def mean_of_max_per_field(df):
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  def boxplot_per_task(dataframe=None, baselines=None):
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- print(dataframe.columns)
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  tasks = ["TE", "SA", "HS", "AT", "WIC", "FAQ", "LS", "SU", "NER", "REL"]
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@@ -76,7 +76,6 @@ def boxplot_per_task(dataframe=None, baselines=None):
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  y=y_data,
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  name=task,
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  marker=dict(color=colors[i]),
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- # Modifica: Impostiamo il colore della linea della scatola su un colore diverso dal riempimento
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  line=dict(color="black", width=2),
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  fillcolor=colors[i],
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  opacity=0.7,
@@ -105,8 +104,8 @@ def boxplot_per_task(dataframe=None, baselines=None):
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  fig.update_layout(
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  title="Distribution of Model Accuracy by Task",
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- xaxis_title="Task",
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- yaxis_title="Accuracy (%)",
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  template="plotly_white",
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  boxmode="group",
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  dragmode=False,
@@ -119,7 +118,7 @@ def boxplot_per_task(dataframe=None, baselines=None):
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  "indicate best-performing supervised models evaluated on EVALITA."
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  ),
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  xref="paper", yref="paper",
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- x=0.5, y=-0.33,
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  showarrow=False,
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  font=dict(size=12, color="gray")
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  )
@@ -130,7 +129,6 @@ def boxplot_per_task(dataframe=None, baselines=None):
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  return fig
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- # 🔹 Esempio d’uso
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  BASELINES = {
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  "TE":71.00, "SA": 66.38, "HS": 80.88, "AT": 82.40, "WIC": 85.00,
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  "LS": 38.82, "SU": 38.91, "NER":88.00, "REL": 62.99
@@ -187,16 +185,16 @@ def boxplot_prompts_per_task(dataframe, tasks=None):
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  for x, y, text in zip(best_x, best_y, best_text):
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  fig.add_annotation(
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  x=x,
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- y=y + 1, # leggermente sopra la barra
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  text=text,
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  showarrow=False,
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  font=dict(size=12, color="black")
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  )
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  fig.update_layout(
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- title="Comparison of Average Prompt Accuracy vs Best Prompt Accuracy per Task",
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  xaxis_title="Task",
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- yaxis_title="Accuracy (%)",
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  barmode='group',
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  template="plotly_white",
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  font=dict(family="Arial", size=13),
 
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  def boxplot_per_task(dataframe=None, baselines=None):
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+ #print(dataframe.columns)
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  tasks = ["TE", "SA", "HS", "AT", "WIC", "FAQ", "LS", "SU", "NER", "REL"]
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  y=y_data,
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  name=task,
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  marker=dict(color=colors[i]),
 
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  line=dict(color="black", width=2),
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  fillcolor=colors[i],
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  opacity=0.7,
 
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  fig.update_layout(
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  title="Distribution of Model Accuracy by Task",
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+ #xaxis_title="Task",
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+ yaxis_title="Avg. Combined Performance ⬆️",
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  template="plotly_white",
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  boxmode="group",
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  dragmode=False,
 
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  "indicate best-performing supervised models evaluated on EVALITA."
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  ),
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  xref="paper", yref="paper",
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+ x=0.5, y=-0.30,
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  showarrow=False,
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  font=dict(size=12, color="gray")
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  )
 
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  return fig
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  BASELINES = {
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  "TE":71.00, "SA": 66.38, "HS": 80.88, "AT": 82.40, "WIC": 85.00,
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  "LS": 38.82, "SU": 38.91, "NER":88.00, "REL": 62.99
 
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  for x, y, text in zip(best_x, best_y, best_text):
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  fig.add_annotation(
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  x=x,
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+ y=y + 3, # leggermente sopra la barra
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  text=text,
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  showarrow=False,
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  font=dict(size=12, color="black")
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  )
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194
  fig.update_layout(
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+ title="Average Prompt Accuracy vs Best Prompt Accuracy per Task",
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  xaxis_title="Task",
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+ yaxis_title="Avg. Combined Performance ⬆️",
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  barmode='group',
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  template="plotly_white",
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  font=dict(family="Arial", size=13),