File size: 2,938 Bytes
94d2494 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 | """
src.visualisations.classification – Classification-specific charts.
"""
from __future__ import annotations
from typing import Dict, Optional
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import plotly.graph_objects as go
import seaborn as sns
from sklearn.metrics import confusion_matrix, roc_curve, auc
from sklearn.pipeline import Pipeline
from src import config
from src.visualisations.common import _base_layout
def roc_curves_chart(
fitted_models: Dict[str, Pipeline],
X_test: pd.DataFrame,
y_test: pd.Series,
) -> Optional[go.Figure]:
"""Return a Plotly ROC-curve figure (binary classification only)."""
if len(np.unique(y_test)) != 2:
return None
fig = go.Figure()
fig.add_trace(go.Scatter(
x=[0, 1], y=[0, 1], mode="lines",
line=dict(dash="dash", color="gray"),
name="Random Classifier", showlegend=True,
))
for i, (name, pipeline) in enumerate(fitted_models.items()):
if not hasattr(pipeline, "predict_proba"):
continue
try:
y_prob = pipeline.predict_proba(X_test)[:, 1]
fpr, tpr, _ = roc_curve(y_test, y_prob)
roc_auc = auc(fpr, tpr)
fig.add_trace(go.Scatter(
x=fpr, y=tpr, mode="lines",
name=f"{name} (AUC = {roc_auc:.3f})",
line=dict(color=config.COLOR_PALETTE[i % len(config.COLOR_PALETTE)], width=2),
))
except Exception:
pass
fig.update_layout(
**_base_layout(title="🎯 ROC Curves"),
xaxis=dict(title="False Positive Rate", range=[0, 1], gridcolor="#2A2E3F"),
yaxis=dict(title="True Positive Rate", range=[0, 1.05], gridcolor="#2A2E3F"),
legend=dict(x=0.6, y=0.1),
)
return fig
def confusion_matrices_chart(
fitted_models: Dict[str, Pipeline],
X_test: pd.DataFrame,
y_test: pd.Series,
) -> plt.Figure:
"""Matplotlib figure with one confusion-matrix heatmap per model."""
n = len(fitted_models)
n_cols = min(2, n)
n_rows = (n + n_cols - 1) // n_cols
fig, axes = plt.subplots(n_rows, n_cols, figsize=(7 * n_cols, 5 * n_rows))
fig.patch.set_facecolor("#0E1117")
axes_flat = np.array(axes).ravel() if n > 1 else [axes]
for ax, (name, pipeline) in zip(axes_flat, fitted_models.items()):
y_pred = pipeline.predict(X_test)
cm = confusion_matrix(y_test, y_pred)
sns.heatmap(
cm, annot=True, fmt="d", cmap="Blues", ax=ax,
linewidths=0.5, linecolor="#1E2130", annot_kws={"color": "white"},
)
ax.set_facecolor("#0E1117")
ax.set_title(name, color="white", fontsize=13)
ax.set_xlabel("Predicted", color="white")
ax.set_ylabel("Actual", color="white")
ax.tick_params(colors="white")
for ax in axes_flat[n:]:
ax.set_visible(False)
plt.tight_layout()
return fig
|