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import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from numpy import ndarray
from sklearn import metrics as M
from src.utils.decorators import TryExcept
@TryExcept("plot_curve")
def plot_curve(
xs: list[ndarray],
ys: list[ndarray],
auc_threshold: float = 0.01,
class_names: None | dict[int, str] = None,
):
# Create figure with larger size and better aspect ratio
plt.figure(figsize=(10, 8), tight_layout=True)
# Create two subplots - one for the plot, one for the legend
gs = plt.GridSpec(1, 2, width_ratios=[4, 1])
ax_plot = plt.subplot(gs[0])
ax_legend = plt.subplot(gs[1])
palette = sns.husl_palette(len(xs))
linestyles = ["-", "--", "-.", ":"]
# Plot curves on the main axis
active_classes = []
for c, (x, y) in enumerate(zip(xs, ys)):
auc = M.auc(x, y)
if auc >= auc_threshold: # Only plot and include in legend if AUC > threshold
class_name = f"{c}: {class_names[c]}" if class_names else c
label = f"{class_name} (AUC: {auc:.2f})"
linestyle = linestyles[c % len(linestyles)]
line = ax_plot.plot(x, y, label=label, linewidth=1.5, color=palette[c], linestyle=linestyle)
active_classes.append((line[0], label))
# Set square aspect ratio
ax_plot.set_aspect("equal")
# Set limits explicitly to ensure square plot
ax_plot.set_xlim(-0.02, 1.02) # Slight padding for better visibility
ax_plot.set_ylim(-0.02, 1.02)
# Customize the main plot
ax_plot.grid(True, linestyle="--", alpha=0.3)
# Create legend in the second subplot
ax_legend.axis("off") # Hide the axis
if active_classes:
lines, labels = zip(*active_classes)
ax_legend.legend(lines, labels, loc="center left", fontsize=10, borderaxespad=0)
return ax_plot
@TryExcept("plot_roc_curve")
def plot_roc_curve(
fprs: list[ndarray],
tprs: list[ndarray],
ths: list[ndarray],
title: str = "ROC",
path: str = "roc_curve.png",
auc_threshold: float = 0.01,
class_names: None | dict[int, str] = None,
):
"""
Plot ROC curve for multiple classes.
"""
ax_plot = plot_curve(fprs, tprs, auc_threshold, class_names)
# Add the diagonal line
ax_plot.plot([0, 1], [0, 1], color="black", linestyle="--", alpha=0.5)
ax_plot.set_xlabel("False Positive Rate (FPR)", fontsize=12)
ax_plot.set_ylabel("True Positive Rate (TPR)", fontsize=12)
ax_plot.set_title(title, fontsize=14, pad=20)
# Save with high quality
os.makedirs(os.path.dirname(path), exist_ok=True)
plt.savefig(path, dpi=300, bbox_inches="tight")
plt.close()
@TryExcept("plot_prc_curve")
def plot_prc_curve(
prcs: list[ndarray],
recs: list[ndarray],
ths: list[ndarray],
title: str = "PRC",
path: str = "pr_curve.png",
auc_threshold: float = 0.01,
class_names: None | dict[int, str] = None,
show_f1_lines: bool = True,
):
"""
Plot Precision-Recall curve for multiple classes.
"""
ax_plot = plot_curve(recs, prcs, auc_threshold, class_names)
if show_f1_lines:
f_scores = np.linspace(0.1, 0.9, num=9) # F1 scores to plot
for f_score in f_scores:
r = np.linspace(0.001, 1, 100) # Recall
p = f_score * r / (2 * r - f_score) # Precision for given F1 score
mask = p > 0
ax_plot.plot(r[mask], p[mask], color="gray", alpha=0.2, linestyle="--")
ax_plot.annotate("F1={0:0.1f}".format(f_score), xy=(0.95, p[-1] - 0.02), alpha=0.2)
# Customize the main plot
ax_plot.set_xlabel("Recall", fontsize=12)
ax_plot.set_ylabel("Precision", fontsize=12)
ax_plot.set_title(title, fontsize=14, pad=20)
# Save with high quality
os.makedirs(os.path.dirname(path), exist_ok=True)
plt.savefig(path, dpi=300, bbox_inches="tight")
plt.close()
@TryExcept("plot_f1_curve")
def plot_f1_curve(
prcs: list[ndarray],
recs: list[ndarray],
ths: list[ndarray],
title: str = "F1",
path: str = "f1_curve.png",
auc_threshold: float = 0.01,
class_names: None | dict[int, str] = None,
):
"""
Plot F1 curve for multiple classes
"""
f1s = []
for prc, rec in zip(prcs, recs):
with np.errstate(divide="ignore", invalid="ignore"):
f1 = np.where((prc + rec) == 0, 0, 2 * prc * rec / (prc + rec))
f1 = f1[:-1]
f1s.append(f1)
ax_plot = plot_curve(ths, f1s, auc_threshold, class_names)
# Customize the main plot
ax_plot.set_xlabel("Threshold", fontsize=12)
ax_plot.set_ylabel("F1 Score", fontsize=12)
# Save with high quality
os.makedirs(os.path.dirname(path), exist_ok=True)
plt.savefig(path, dpi=300, bbox_inches="tight")
plt.close()
@TryExcept("plot_confusion_matrix")
def plot_confusion_matrix(
confusion_matrix: ndarray,
class_names: None | dict[int, str] = None,
title: str = "Confusion Matrix",
path: str = "confusion_matrix.png",
normalize: bool = False,
):
"""
Plot confusion matrix
"""
N = len(confusion_matrix)
size = max(10, N / 2)
plt.figure(figsize=(size, size), tight_layout=True)
fmt = "d"
if normalize:
confusion_matrix = confusion_matrix / confusion_matrix.sum(axis=1, keepdims=True) * 100
confusion_matrix[np.isnan(confusion_matrix)] = 0
fmt = ".2f"
labels = [f"{k}: {v}" for k, v in class_names.items()] if class_names else None
sns.heatmap(
confusion_matrix,
annot=True,
fmt=fmt,
cmap="Blues",
xticklabels=labels,
yticklabels=labels,
annot_kws={"fontsize": 8},
)
plt.xlabel("Predicted", fontsize=12)
plt.ylabel("Actual", fontsize=12)
plt.title(title, fontsize=14, pad=20)
dirname = os.path.dirname(path)
if dirname:
os.makedirs(dirname, exist_ok=True)
plt.savefig(path, dpi=100, bbox_inches="tight")
plt.close()
@TryExcept("plot_tsne")
def plot_tsne(
tsne_features: np.ndarray, # (N, 2)
set_ids: np.ndarray, # (N,)
id2label: dict[int, str], # dict {id: label}
output_path: str,
):
assert isinstance(tsne_features, np.ndarray)
assert isinstance(set_ids, np.ndarray)
assert isinstance(id2label, dict)
plt.figure(figsize=(25, 25))
palette = sns.husl_palette(len(id2label))
id2color = {id: palette[i] for i, id in enumerate(id2label)}
for id, label in id2label.items():
mask = set_ids == id
if not np.any(mask):
continue
xs = tsne_features[mask, 0]
ys = tsne_features[mask, 1]
if "real" in label:
marker = "."
else:
marker = "x"
plt.scatter(xs, ys, c=[id2color[id]] * len(xs), marker=marker, label=label)
for x, y, label in zip(xs, ys, set_ids[mask]):
plt.text(x, y, label, c=id2color[id], fontsize=9)
plt.legend(loc="best", title="Models")
plt.tight_layout()
os.makedirs(os.path.dirname(output_path), exist_ok=True)
plt.savefig(output_path)
plt.savefig(output_path.replace(".png", ".svg"))
@TryExcept("plot_probs_distribution")
def plot_probs_distribution(
probs: np.ndarray, # (N, C)
labels: np.ndarray, # (N,)
class_names: dict[int, str], # dict {id: label}
output_path: str,
):
n_classes = len(class_names)
fig, axes = plt.subplots(n_classes, 1, figsize=(10, 4 * n_classes))
palette = sns.husl_palette(n_classes)
# Find global min and max for x-axis limits
x_min = probs.min()
x_max = probs.max()
for idx, (class_idx, class_name) in enumerate(class_names.items()):
ax = axes[idx]
# Get probabilities for current class
class_mask = labels == class_idx
class_probs = probs[class_mask]
# Plot probability distribution for each possible class prediction
for pred_idx, pred_name in class_names.items():
pred_probs = class_probs[:, pred_idx]
sns.histplot(
data=pred_probs,
label=pred_name,
color=palette[pred_idx],
alpha=0.2,
# bins=30,
stat="probability",
kde=True,
element="step",
ax=ax,
)
ax.set_xlabel("Scores")
ax.set_ylabel("Probability")
ax.set_title(f"True Class: {class_name}", color=palette[class_idx])
ax.set_xlim(x_min, x_max)
ax.legend()
plt.tight_layout()
os.makedirs(os.path.dirname(output_path), exist_ok=True)
plt.savefig(output_path, dpi=300, bbox_inches="tight")
plt.close()
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