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import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from scipy import stats
from scipy.spatial.distance import jensenshannon
from sklearn.metrics import r2_score
from sklearn.decomposition import PCA
from sklearn.neighbors import NearestNeighbors
import warnings
warnings.filterwarnings('ignore')
# TODO: To be implemented into flow generation
class FlowMatchingMetrics:
def __init__(self, device='cuda'):
self.device = device
def reconstruction_metrics(self, generated, target):
if isinstance(generated, torch.Tensor):
generated = generated.detach().cpu().numpy()
if isinstance(target, torch.Tensor):
target = target.detach().cpu().numpy()
mae = np.mean(np.abs(generated - target))
mse = np.mean((generated - target) ** 2)
rmse = np.sqrt(mse)
pos_mask = target > 0
zero_mask = target == 0
pos_mae = np.mean(np.abs(generated[pos_mask] - target[pos_mask])) if pos_mask.any() else 0.0
zero_mae = np.mean(np.abs(generated[zero_mask] - target[zero_mask])) if zero_mask.any() else 0.0
r2 = r2_score(target.flatten(), generated.flatten())
rel_error = np.mean(np.abs((generated - target) / (target + 1e-8)))
return {
'mae': mae,
'mse': mse,
'rmse': rmse,
'pos_mae': pos_mae,
'zero_mae': zero_mae,
'r2_score': r2,
'relative_error': rel_error
}
def distribution_metrics(self, generated, target):
if isinstance(generated, torch.Tensor):
generated = generated.detach().cpu().numpy()
if isinstance(target, torch.Tensor):
target = target.detach().cpu().numpy()
metrics = {}
mean_diff = np.mean(np.abs(np.mean(generated, axis=0) - np.mean(target, axis=0)))
var_diff = np.mean(np.abs(np.var(generated, axis=0) - np.var(target, axis=0)))
metrics['mean_difference'] = mean_diff
metrics['variance_difference'] = var_diff
correlations = []
for i in range(generated.shape[1]):
if np.var(generated[:, i]) > 1e-8 and np.var(target[:, i]) > 1e-8:
corr, _ = stats.pearsonr(generated[:, i], target[:, i])
if not np.isnan(corr):
correlations.append(corr)
metrics['mean_correlation'] = np.mean(correlations) if correlations else 0.0
kl_divs = []
for i in range(min(generated.shape[1], 100)):
try:
bins = np.linspace(
min(generated[:, i].min(), target[:, i].min()),
max(generated[:, i].max(), target[:, i].max()),
50
)
hist_gen, _ = np.histogram(generated[:, i], bins=bins, density=True)
hist_target, _ = np.histogram(target[:, i], bins=bins, density=True)
hist_gen = hist_gen + 1e-8
hist_target = hist_target + 1e-8
hist_gen = hist_gen / np.sum(hist_gen)
hist_target = hist_target / np.sum(hist_target)
kl_div = stats.entropy(hist_gen, hist_target)
if not np.isnan(kl_div) and not np.isinf(kl_div):
kl_divs.append(kl_div)
except:
continue
metrics['mean_kl_divergence'] = np.mean(kl_divs) if kl_divs else float('inf')
js_divs = []
for i in range(min(generated.shape[1], 100)):
try:
bins = np.linspace(
min(generated[:, i].min(), target[:, i].min()),
max(generated[:, i].max(), target[:, i].max()),
50
)
hist_gen, _ = np.histogram(generated[:, i], bins=bins, density=True)
hist_target, _ = np.histogram(target[:, i], bins=bins, density=True)
hist_gen = hist_gen + 1e-8
hist_target = hist_target + 1e-8
hist_gen = hist_gen / np.sum(hist_gen)
hist_target = hist_target / np.sum(hist_target)
js_div = jensenshannon(hist_gen, hist_target)
if not np.isnan(js_div):
js_divs.append(js_div)
except:
continue
metrics['mean_js_divergence'] = np.mean(js_divs) if js_divs else 1.0
return metrics
def embedding_metrics(self, generated, target):
if isinstance(generated, torch.Tensor):
generated = generated.detach().cpu().numpy()
if isinstance(target, torch.Tensor):
target = target.detach().cpu().numpy()
metrics = {}
cosine_sims = []
for i in range(generated.shape[0]):
sim = np.dot(generated[i], target[i]) / (
np.linalg.norm(generated[i]) * np.linalg.norm(target[i]) + 1e-8
)
cosine_sims.append(sim)
metrics['mean_cosine_similarity'] = np.mean(cosine_sims)
euclidean_dists = np.linalg.norm(generated - target, axis=1)
metrics['mean_euclidean_distance'] = np.mean(euclidean_dists)
if generated.shape[0] > 10:
k = min(5, generated.shape[0] // 2)
nn_target = NearestNeighbors(n_neighbors=k)
nn_target.fit(target)
_, target_indices = nn_target.kneighbors(target)
nn_generated = NearestNeighbors(n_neighbors=k)
nn_generated.fit(generated)
_, generated_indices = nn_generated.kneighbors(generated)
preservation = 0
for i in range(generated.shape[0]):
intersection = len(set(target_indices[i]) & set(generated_indices[i]))
preservation += intersection / k
metrics['neighbor_preservation'] = preservation / generated.shape[0]
else:
metrics['neighbor_preservation'] = 0.0
return metrics
def flow_specific_metrics(self, generated, target, flow_model=None):
metrics = {}
if isinstance(generated, torch.Tensor):
generated = generated.detach().cpu().numpy()
if isinstance(target, torch.Tensor):
target = target.detach().cpu().numpy()
pairwise_dists = []
for i in range(min(generated.shape[0], 100)):
for j in range(i + 1, min(generated.shape[0], 100)):
dist = np.linalg.norm(generated[i] - generated[j])
pairwise_dists.append(dist)
metrics['generation_diversity'] = np.mean(pairwise_dists) if pairwise_dists else 0.0
if generated.shape[0] > 10 and target.shape[0] > 10:
combined = np.vstack([generated, target])
pca = PCA(n_components=min(10, combined.shape[1]))
combined_pca = pca.fit_transform(combined)
generated_pca = combined_pca[:generated.shape[0]]
target_pca = combined_pca[generated.shape[0]:]
coverage = 0
for dim in range(generated_pca.shape[1]):
gen_min, gen_max = generated_pca[:, dim].min(), generated_pca[:, dim].max()
target_min, target_max = target_pca[:, dim].min(), target_pca[:, dim].max()
if target_max > target_min:
overlap = max(0, min(gen_max, target_max) - max(gen_min, target_min))
coverage += overlap / (target_max - target_min)
metrics['mode_coverage'] = coverage / generated_pca.shape[1]
else:
metrics['mode_coverage'] = 0.0
return metrics
def comprehensive_evaluation(self, generated, target, embeddings_generated=None, embeddings_target=None):
all_metrics = {}
recon_metrics = self.reconstruction_metrics(generated, target)
all_metrics.update({f'recon_{k}': v for k, v in recon_metrics.items()})
dist_metrics = self.distribution_metrics(generated, target)
all_metrics.update({f'dist_{k}': v for k, v in dist_metrics.items()})
flow_metrics = self.flow_specific_metrics(generated, target)
all_metrics.update({f'flow_{k}': v for k, v in flow_metrics.items()})
if embeddings_generated is not None and embeddings_target is not None:
emb_metrics = self.embedding_metrics(embeddings_generated, embeddings_target)
all_metrics.update({f'emb_{k}': v for k, v in emb_metrics.items()})
return all_metrics
def evaluate_flow_generation(generated_data, target_data, generated_embeddings=None, target_embeddings=None, device='cuda'):
metrics = FlowMatchingMetrics(device=device)
return metrics.comprehensive_evaluation(
generated_data, target_data,
generated_embeddings, target_embeddings
)
def print_metrics(metrics_dict, title="Metrics"):
print(f"\n{'='*50}")
print(f"{title:^50}")
print(f"{'='*50}")
categories = {
'Reconstruction': [k for k in metrics_dict.keys() if k.startswith('recon_')],
'Distribution': [k for k in metrics_dict.keys() if k.startswith('dist_')],
'Flow-specific': [k for k in metrics_dict.keys() if k.startswith('flow_')],
'Embedding': [k for k in metrics_dict.keys() if k.startswith('emb_')]
}
for category, keys in categories.items():
if keys:
print(f"\n{category}:")
print("-" * 20)
for key in keys:
clean_key = key.replace(f"{category.lower().replace('-', '_')}_", "")
value = metrics_dict[key]
if isinstance(value, float):
print(f" {clean_key:<20}: {value:.6f}")
else:
print(f" {clean_key:<20}: {value}")
print(f"{'='*50}\n")
def test_metrics():
batch_size, n_features = 100, 2000
generated = torch.randn(batch_size, n_features)
target = torch.randn(batch_size, n_features)
metrics = evaluate_flow_generation(generated, target)
print_metrics(metrics, "Flow Matching Evaluation")
return metrics
def pairwise_sq_dists(X, Y):
# X:[m,d], Y:[n,d] -> [m,n]
return torch.cdist(X, Y, p=2)**2
@torch.no_grad()
def median_sigmas(X, scales=(0.5, 1.0, 2.0, 4.0)):
Z = X
D2 = pairwise_sq_dists(Z, Z)
tri = D2[~torch.eye(D2.size(0), dtype=bool, device=D2.device)]
m = torch.median(tri).clamp_min(1e-12)
s2 = torch.tensor(scales, device=Z.device) * m
sigmas = torch.sqrt(s2)
return [float(s.item()) for s in sigmas]
def mmd2_unbiased_multi_sigma(X, Y, sigmas):
"""
"""
m, n = X.size(0), Y.size(0)
Dxx = pairwise_sq_dists(X, X) # [m,m]
Dyy = pairwise_sq_dists(Y, Y) # [n,n]
Dxy = pairwise_sq_dists(X, Y) # [m,n]
vals = []
for sigma in sigmas:
beta = 1.0 / (2.0 * (sigma ** 2) + 1e-12)
Kxx = torch.exp(-beta * Dxx)
Kyy = torch.exp(-beta * Dyy)
Kxy = torch.exp(-beta * Dxy)
term_xx = (Kxx.sum() - Kxx.diag().sum()) / (m * (m - 1) + 1e-12)
term_yy = (Kyy.sum() - Kyy.diag().sum()) / (n * (n - 1) + 1e-12)
term_xy = Kxy.mean() # / (m*n)
vals.append(term_xx + term_yy - 2.0 * term_xy)
return torch.stack(vals).mean()
if __name__ == "__main__":
test_metrics()
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