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import db |
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import numpy as np |
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import plotly.graph_objects as go |
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def plot_estimates_distribution(): |
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"""Affiche une gaussienne par prompt (Plotly) + lignes verticales pointillées sur les moyennes.""" |
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estimates = db.load("estimates") |
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prompts = db.load("prompts") |
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if estimates.empty or prompts.empty: |
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fig = go.Figure() |
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fig.add_annotation( |
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text="Aucune estimation disponible", x=0.5, y=0.5, showarrow=False |
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) |
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return fig |
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x = np.linspace( |
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estimates["mu"].min() - 3 * estimates["sigma"].max(), |
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estimates["mu"].max() + 3 * estimates["sigma"].max(), |
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500, |
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) |
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fig = go.Figure() |
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shapes = [] |
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for _, row in estimates.iterrows(): |
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mu = row["mu"] |
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sigma = row["sigma"] |
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prompt_id = row["prompt_id"] if "prompt_id" in row else row["id"] |
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name = str(prompt_id) |
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if "name" in prompts.columns: |
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match = prompts[prompts["id"] == prompt_id] |
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if not match.empty: |
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name = match.iloc[0]["name"] |
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y = 1 / (sigma * np.sqrt(2 * np.pi)) * np.exp(-0.5 * ((x - mu) / sigma) ** 2) |
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fig.add_trace( |
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go.Scatter( |
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x=x, |
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y=y, |
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mode="lines", |
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name=f"{name}", |
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hovertemplate=f"<b>{name}</b><br>Score (mu): {mu:.2f}<br>Sigma: {sigma:.2f}<extra></extra>", |
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) |
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) |
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shapes.append( |
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dict( |
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type="line", |
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x0=mu, |
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x1=mu, |
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y0=0, |
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y1=max(y), |
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line=dict( |
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color="gray", |
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width=2, |
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dash="dot", |
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), |
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xref="x", |
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yref="y", |
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) |
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) |
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fig.update_layout( |
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title="Distribution gaussienne de chaque prompt", |
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xaxis_title="Score (mu)", |
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yaxis_title="Densité", |
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template="plotly_white", |
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shapes=shapes, |
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) |
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return fig |
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