File size: 3,125 Bytes
714cf46 | 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 95 96 | import os
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
from scipy.stats import spearmanr, pearsonr
from sklearn.metrics import r2_score
from visualization.pauc_plot import plot_roc_with_ci
def regression_ci_plot(y_true, y_pred, save_path, title=None):
"""
Calculate the spearman rho and p-value of the regression model.
Plot the line of best fit with 95% confidence intervals for spearman rho.
Display the R-squared value, spearman rho, pearson rho, and p-values.
"""
# Compute R‑squared, Spearman and Pearson correlations
y_true, y_pred = y_true.flatten(), y_pred.flatten()
mask = np.isfinite(y_true) & np.isfinite(y_pred)
y_true, y_pred = y_true[mask], y_pred[mask]
r2 = r2_score(y_true, y_pred)
r_s, p_s = spearmanr(y_true, y_pred)
r_p, p_p = pearsonr(y_true, y_pred)
# Create scatter plot and regression line with 95% CI
fig, ax = plt.subplots(figsize=(8, 6))
sns.scatterplot(x=y_true, y=y_pred, ax=ax)
sns.regplot(
x=y_true, y=y_pred,
ci=95, ax=ax, scatter=False,
line_kws={'color': 'red'}
)
ax.set_xlabel('True Values')
ax.set_ylabel('Predicted Values')
if title:
ax.set_title(title)
else:
ax.set_title('Regression Plot with 95% Confidence Interval')
# Annotate statistics on the plot
stats_text = (
f"$R^2$ = {r2:.2f}\n"
f"Spearman $\\rho$ = {r_s:.2f} (p = {p_s:.2e})\n"
f"Pearson $\\rho$ = {r_p:.2f} (p = {p_p:.2e})"
)
ax.text(
0.05, 0.95, stats_text,
transform=ax.transAxes,
fontsize=12, verticalalignment='top'
)
# Save the figure
fig.savefig(save_path, dpi=300, bbox_inches='tight')
plt.close(fig)
def classification_ci_plot(y_true, y_pred, save_path, title=None):
"""
Use pauc to display classification plot
"""
if len(y_pred.shape) == 3 and len(y_true.shape) == 2:
y_pred = y_pred.reshape(-1, y_pred.shape[-1])
y_true = y_true.reshape(-1)
### Note: removing this gives you one plot per multilabel class
if len(y_pred.shape) == 2 and len(y_true.shape) == 2:
y_pred = y_pred.flatten()
y_true = y_true.flatten()
# if more than 10,000 data points, only pass 10,000
# else, pAUC can be very slow
if y_true.shape[0] > 10000:
y_pred = y_pred[:10000]
y_true = y_true[:10000]
print(y_true.shape, y_pred.shape)
try:
plot_roc_with_ci(y_true, y_pred, save_path, fig_title=title)
except Exception as e:
print(f"Error plotting pAUC curve, likely the wrong version: {e}")
if __name__ == "__main__":
# py -m visualization.ci_plots
import os
os.makedirs("plots/test_plots", exist_ok=True)
y_true = np.random.rand(100)
y_pred = np.random.rand(100)
regression_ci_plot(y_true, y_pred, "plots/test_plots/regression.png", title="Regression Plot")
y_true = np.random.randint(0, 2, (50, 514))
y_pred = np.random.rand(50, 514, 4)
classification_ci_plot(y_true, y_pred, "plots/test_plots/classification.png", title="Classification Plot")
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