Spaces:
Sleeping
Sleeping
Blair Yang
commited on
Commit
•
db9289d
1
Parent(s):
a53cf00
debug
Browse files
app.py
CHANGED
@@ -24,25 +24,26 @@ def generate_plot(meta_index, topic_index):
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data = pd.read_csv(f"data/{meta_topic}/response_rec.csv", sep=",")
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topic_data = data[data['sub_topic'] == topic]
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# Compute human and llm accuracy
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topic_data['human_acc'] = topic_data['no_correct_human'] / topic_data['no_responses_human'].replace(0, np.nan)
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topic_data['llm_acc'] = topic_data['no_correct_llm'] / topic_data['no_responses_llm'].replace(0, np.nan)
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#
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# Prepare the plot data
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plot_data = []
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# Define a consistent color scheme
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colors = ['#FFA07A', '#20B2AA', '#778899'] # Light Salmon, Light Sea Green, Light Slate Gray
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# Add bars with error bars for the averages
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for acc_type, color
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plot_data.append(go.Bar(
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x=mean_data['model_name'],
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y=mean_data[acc_type],
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@@ -52,7 +53,7 @@ def generate_plot(meta_index, topic_index):
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visible=True
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),
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name=acc_type.split('_')[0].capitalize(),
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marker=dict(color=color
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))
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# Layout
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data = pd.read_csv(f"data/{meta_topic}/response_rec.csv", sep=",")
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topic_data = data.loc[data['sub_topic'] == topic].copy()
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# Compute human and llm accuracy
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topic_data['human_acc'] = topic_data['no_correct_human'] / topic_data['no_responses_human'].replace(0, np.nan)
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topic_data['llm_acc'] = topic_data['no_correct_llm'] / topic_data['no_responses_llm'].replace(0, np.nan)
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# Selecting only numeric columns for aggregation
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numeric_cols = ['no_responses_human', 'no_correct_human', 'no_responses_llm', 'no_correct_llm', 'oracle_acc', 'human_acc', 'llm_acc']
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mean_data = topic_data.groupby('model_name')[numeric_cols].mean().reset_index()
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std_deviation = topic_data.groupby('model_name')[numeric_cols].std().reset_index()
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# Prepare the plot data
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plot_data = []
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# Define a consistent color scheme with different opacities
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colors = ['#FFA07A', '#20B2AA', '#778899'] # Light Salmon, Light Sea Green, Light Slate Gray
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acc_types = ['oracle_acc', 'human_acc', 'llm_acc']
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# Add bars with error bars for the averages
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for acc_type, color in zip(acc_types, colors):
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plot_data.append(go.Bar(
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x=mean_data['model_name'],
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y=mean_data[acc_type],
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visible=True
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),
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name=acc_type.split('_')[0].capitalize(),
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marker=dict(color=color)
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))
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# Layout
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