Matrice de confusion avec indicateurs

import matplotlib.pyplot as plt
import numpy as np
import statsmodels.api as sm
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import (accuracy_score, confusion_matrix, f1_score, matthews_corrcoef,
                             precision_score, recall_score)
from sklearn.model_selection import StratifiedKFold, cross_val_predict
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler

# Data: 944 voters in the 1996 US presidential election; 1 = voted Dole, 0 = voted Clinton
d = sm.datasets.anes96.load_pandas().data
X = d[["PID", "selfLR", "age", "educ", "income", "TVnews", "logpopul"]]
y = d["vote"].to_numpy().astype(int)
labels = ["Clinton", "Dole"]

# Out-of-fold predictions from a 10-fold cross-validation
model = make_pipeline(StandardScaler(), LogisticRegression(max_iter=1000))
cv = StratifiedKFold(n_splits=10, shuffle=True, random_state=0)
pred = cross_val_predict(model, X, y, cv=cv)
cm = confusion_matrix(y, pred)

# Metrics with 95% bootstrap intervals
metrics = {"Accuracy": accuracy_score, "Precision": precision_score, "Recall": recall_score,
           "F1": f1_score, "MCC": matthews_corrcoef}
rng = np.random.default_rng(1)
boot = {k: [] for k in metrics}
for _ in range(2000):
    i = rng.integers(0, len(y), len(y))
    for k, f in metrics.items():
        boot[k].append(f(y[i], pred[i]))
point = {k: f(y, pred) for k, f in metrics.items()}
ci = {k: np.percentile(v, [2.5, 97.5]) for k, v in boot.items()}

plt.rcParams["figure.constrained_layout.use"] = False   # manual margins leave room for the subtitle
fig, (ax, axm) = plt.subplots(1, 2, figsize=(9.5, 5), gridspec_kw=dict(width_ratios=(1, 1.1)))
fig.subplots_adjust(left=0.12, right=0.97, top=0.80, bottom=0.13, wspace=0.45)

# Confusion matrix: colour = share of the true class
share = cm / cm.sum(axis=1, keepdims=True)
ax.imshow(share, cmap="Blues", vmin=0, vmax=1)
for r in range(2):
    for c in range(2):
        ax.text(c, r, f"{cm[r, c]}\n{share[r, c]:.1%}", ha="center", va="center", fontsize=13,
                fontweight="semibold", color="white" if share[r, c] > 0.5 else "#1d1d1f")
ax.set_xticks([0, 1], labels)
ax.set_yticks([0, 1], labels)
ax.set_xlabel("Predicted vote")
ax.set_ylabel("Reported vote")
ax.grid(False)
for s in ax.spines.values():
    s.set_visible(False)

# Metrics with intervals
names = list(metrics)[::-1]
for j, k in enumerate(names):
    axm.plot(ci[k], [j, j], color="#007AFF", lw=3, solid_capstyle="round", alpha=0.35)
    axm.scatter(point[k], j, s=70, color="#007AFF", zorder=3, edgecolor="white", lw=1.5)
    axm.text(ci[k][1] + 0.006, j, f"{point[k]:.3f}  [{ci[k][0]:.3f}, {ci[k][1]:.3f}]",
             va="center", fontsize=9.5, color="#3A3A3C")
axm.set_yticks(range(len(names)), names)
axm.set_xlim(0.75, 1.0)
axm.set_xlabel("Score (out-of-fold, 95% bootstrap CI)")
axm.grid(axis="x"); axm.grid(axis="y", visible=False)

fn, pos = cm[1, 0], cm[1].sum()
fig.suptitle(f"The model calls {point['Accuracy']:.0%} of votes right but misses {fn} of {pos} Dole voters",
             x=0.02, y=0.97, ha="left")
fig.text(0.02, 0.885, f"Logistic regression · 10-fold out-of-fold predictions · n = {len(y)} voters "
         f"({y.sum()} for Dole) · ANES 1996 · 2,000 bootstrap resamples", fontsize=9.5, color="#6E6E73")
plt.show()