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import plotly.graph_objects as go |
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import plotly.express as px |
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import numpy as np |
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def plot_elo_mle(df): |
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fig = px.scatter(df, x="model", y="rating", error_y="error_y", |
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error_y_minus="error_y_minus", |
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) |
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fig.update_layout(xaxis_title="Model", |
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yaxis_title="Rating", |
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autosize=True, |
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) |
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return fig |
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def plot_solve_rate(df, task, rows=30, cols=38): |
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keys = df["task_id"] |
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values = df["solve_rate"] |
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values = np.array(values) |
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n = len(values) |
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values = np.pad(values, (0, rows * cols - n), 'constant', constant_values=np.nan).reshape((rows, cols)) |
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keys = np.pad(keys, (0, rows * cols - n), 'constant', constant_values='').reshape((rows, cols)) |
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hover_text = np.empty_like(values, dtype=object) |
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for i in range(rows): |
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for j in range(cols): |
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if not np.isnan(values[i, j]): |
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hover_text[i, j] = f"{keys[i, j]}<br>Solve Rate: {values[i, j]:.2f}" |
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else: |
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hover_text[i, j] = "NaN" |
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upper_solve_rate = round(np.count_nonzero(values)/n*100, 2) |
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fig = go.Figure(data=go.Heatmap( |
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z=values, |
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text=hover_text, |
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hoverinfo='text', |
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colorscale='teal', |
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zmin=0, |
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zmax=100 |
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)) |
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fig.update_layout( |
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title=f'BigCodeBench-{task}<br><i>Lowest Upper Limit: {upper_solve_rate}%</i>', |
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xaxis_nticks=cols, |
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yaxis_nticks=rows, |
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xaxis=dict(showticklabels=False), |
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yaxis=dict(showticklabels=False), |
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autosize=True, |
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) |
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return fig |
