import os import time import torch import json from torch import nn, optim from torch.utils.data import DataLoader, random_split from tqdm import tqdm from data import ImageFolderDataset, CSVDataset from models import create_model def train(dataset_root, epochs=3, batch_size=16, lr=1e-3, device=None, num_classes=10, model_type='mlp', csv_label='label', generate_labels=False, n_buckets=100, label_method='md5', label_store=None, feature_engineer=False, lat_lon_bins=20, nrows=None, seed=42, hidden_dims=None, weight_decay=0.0, output_dir=None): device = device or ('cuda' if torch.cuda.is_available() else 'cpu') output_dir = output_dir or os.getcwd() os.makedirs(output_dir, exist_ok=True) # Detect CSV vs folder dataset if os.path.isfile(dataset_root) and dataset_root.lower().endswith('.csv'): dataset = CSVDataset(dataset_root, label_column=csv_label, generate_labels=generate_labels, n_buckets=n_buckets, label_method=label_method, label_store=label_store, feature_engineer=feature_engineer, lat_lon_bins=lat_lon_bins, nrows=nrows) # seed numpy/torch RNGs for reproducibility in experiments try: import numpy as _np _np.random.seed(seed) except Exception: pass try: import random as _py_random _py_random.seed(seed) except Exception: pass try: import torch as _torch _torch.manual_seed(seed) if _torch.cuda.is_available(): _torch.cuda.manual_seed_all(seed) except Exception: pass # determine input dim for MLP input_dim = dataset.features.shape[1] # persist preprocessing metadata so inference can reuse identical stats try: import numpy as _np meta_path = os.path.join(output_dir, 'preprocess_meta.npz') _np.savez_compressed(meta_path, feature_columns=_np.array(dataset.feature_columns, dtype=object), means=dataset.feature_means, stds=dataset.feature_stds) print(f'Saved preprocess meta to {meta_path}') except Exception: pass # ensure model_num_classes has a defined value for later use model_num_classes = None # ---- new: generate kmeans labels when requested ---- if generate_labels and label_method == 'kmeans': try: import numpy as _np # ensure features is numpy 2D array X = dataset.features if hasattr(X, 'toarray'): X = X.toarray() X = _np.asarray(X, dtype=float) # basic preprocessing: fill NaN and scale (mean/std) nan_mask = _np.isnan(X) if nan_mask.any(): col_means = _np.nanmean(X, axis=0) inds = _np.where(nan_mask) X[inds] = _np.take(col_means, inds[1]) # standardize col_means = X.mean(axis=0) col_stds = X.std(axis=0) col_stds[col_stds == 0] = 1.0 Xs = (X - col_means) / col_stds # use sklearn KMeans try: from sklearn.cluster import KMeans except Exception as e: raise RuntimeError("sklearn is required for kmeans label generation: " + str(e)) n_clusters = 10 # produce 1..10 labels as required kmeans = KMeans(n_clusters=n_clusters, random_state=seed, n_init=10) cluster_ids = kmeans.fit_predict(Xs) # compute a simple score per cluster to sort them (e.g., center mean) centers = kmeans.cluster_centers_ center_scores = centers.mean(axis=1) # sort cluster ids by score -> map to rank 1..n_clusters (1 = lowest score) order = _np.argsort(center_scores) rank_map = {int(c): (int(_np.where(order == c)[0][0]) + 1) for c in range(len(order))} # assign labels 1..10 based on cluster rank assigned_labels_1to10 = [_np.float64(rank_map[int(cid)]) for cid in cluster_ids] # for training (classification) convert to 0..9 integer labels assigned_labels_zero_based = _np.array([int(lbl) - 1 for lbl in assigned_labels_1to10], dtype=int) # attach to dataset (CSVDataset consumers expect .labels possibly) try: import torch as _torch dataset.labels = _torch.from_numpy(assigned_labels_zero_based).long() except Exception: # fallback to numpy attribute dataset.labels = assigned_labels_zero_based # persist label_info / assignments label_info = { "generated": True, "label_method": "kmeans", "n_clusters": n_clusters, } try: # save assignments if small enough if len(assigned_labels_1to10) <= 100000: label_info["assignments"] = [float(x) for x in assigned_labels_1to10] else: arr_path = os.path.join(output_dir, "label_assignments.npz") _np.savez_compressed(arr_path, assignments=_np.array(assigned_labels_1to10)) label_info["assignments_file"] = os.path.basename(arr_path) with open(os.path.join(output_dir, "label_info.json"), "w") as f: json.dump(label_info, f) except Exception: pass # update model_num_classes for training (10 clusters) model_num_classes = n_clusters print(f"Generated kmeans labels with {n_clusters} clusters; saved label_info.json") except Exception as e: print("KMeans label generation failed:", e) # fall back to prior logic (md5 or provided labels) # ---- end kmeans generation ---- if model_type == 'cnn': raise ValueError('CSV dataset should use model_type="mlp"') # determine model_num_classes if not set by kmeans above if model_num_classes is None: # if we generated labels (non-kmeans) and dataset provides labels, infer number of classes if generate_labels and hasattr(dataset, 'labels') and label_method != 'kmeans': try: model_num_classes = int(dataset.labels.max().item()) + 1 except Exception: model_num_classes = n_buckets else: # default behavior model_num_classes = n_buckets if generate_labels else num_classes # If labels were generated, save label metadata + assignments (if not huge) if generate_labels and label_method != 'kmeans': try: label_info = { "generated": True, "label_method": label_method, "n_buckets": n_buckets, } # save per-sample assignments if dataset exposes them if hasattr(dataset, "labels"): try: # convert to list (JSON serializable) assignments = dataset.labels.cpu().numpy().tolist() if hasattr(dataset.labels, "cpu") else dataset.labels.tolist() # if too large, save as .npz instead if len(assignments) <= 100000: label_info["assignments"] = assignments else: import numpy as _np arr_path = os.path.join(output_dir, "label_assignments.npz") _np.savez_compressed(arr_path, assignments=_np.array(assignments)) label_info["assignments_file"] = os.path.basename(arr_path) except Exception: pass with open(os.path.join(output_dir, "label_info.json"), "w") as f: json.dump(label_info, f) print(f"Saved label_info to {os.path.join(output_dir, 'label_info.json')}") except Exception: pass # parse hidden_dims if provided by caller (tuple or list) model = create_model(device=device, model_type='mlp', input_dim=input_dim, num_classes=model_num_classes, hidden_dims=hidden_dims) else: # assume folder of images dataset = ImageFolderDataset(dataset_root) model = create_model(device=device, model_type='cnn', input_size=(3, 224, 224), num_classes=num_classes) # simple train/val split val_size = max(1, int(0.1 * len(dataset))) train_size = len(dataset) - val_size train_set, val_set = random_split(dataset, [train_size, val_size]) train_loader = DataLoader(train_set, batch_size=batch_size, shuffle=True) val_loader = DataLoader(val_set, batch_size=batch_size, shuffle=False) criterion = nn.CrossEntropyLoss() optimizer = optim.Adam(model.parameters(), lr=lr, weight_decay=weight_decay) best_val_acc = 0.0 best_path = None for epoch in range(epochs): model.train() running_loss = 0.0 pbar = tqdm(train_loader, desc=f"Epoch {epoch+1}/{epochs}") for xb, yb in pbar: xb = xb.to(device) yb = yb.to(device) optimizer.zero_grad() outputs = model(xb) loss = criterion(outputs, yb) loss.backward() optimizer.step() running_loss += loss.item() pbar.set_postfix(loss=running_loss / (pbar.n + 1)) # validation model.eval() correct = 0 total = 0 with torch.no_grad(): for xb, yb in val_loader: xb = xb.to(device) yb = yb.to(device) outputs = model(xb) preds = outputs.argmax(dim=1) correct += (preds == yb).sum().item() total += yb.size(0) val_acc = correct / total if total > 0 else 0.0 print(f"Epoch {epoch+1} val_acc={val_acc:.4f}") # save best if val_acc > best_val_acc: out_path = os.path.join(output_dir, 'model.pth') # include useful metadata so evaluator can reconstruct meta = { 'model_state_dict': model.state_dict(), 'model_type': model_type, 'model_config': { 'input_dim': input_dim if model_type == 'mlp' else None, 'num_classes': model_num_classes, 'hidden_dims': hidden_dims, } } if hasattr(dataset, 'class_to_idx'): meta['class_to_idx'] = dataset.class_to_idx # also record paths to saved preprocess and label info (if present) meta['preprocess_meta'] = os.path.basename(os.path.join(output_dir, 'preprocess_meta.npz')) if os.path.exists(os.path.join(output_dir, 'label_info.json')): meta['label_info'] = json.load(open(os.path.join(output_dir, 'label_info.json'), 'r')) torch.save(meta, out_path) best_val_acc = val_acc best_path = out_path print(f"Saved best model to {out_path} (val_acc={val_acc:.4f})") return best_path if __name__ == '__main__': import argparse parser = argparse.ArgumentParser() parser.add_argument('data_root') parser.add_argument('--epochs', type=int, default=3) parser.add_argument('--batch-size', type=int, default=16) parser.add_argument('--lr', type=float, default=1e-3) parser.add_argument('--model-type', choices=['cnn', 'mlp'], default='cnn') parser.add_argument('--csv-label', default='label') parser.add_argument('--generate-labels', action='store_true', help='If set, generate labels from columns instead of expecting label column') parser.add_argument('--n-buckets', type=int, default=100, help='Number of label buckets when generating labels') parser.add_argument('--label-method', choices=['md5', 'kmeans'], default='md5', help='Method to generate labels when --generate-labels is set') parser.add_argument('--label-store', default=None, help='Path to save/load label metadata (e.g., kmeans centers .npz)') parser.add_argument('--subset', type=int, default=0, help='If set (>0), load only first N rows from CSV for fast experiments') parser.add_argument('--feature-engineer', action='store_true', help='If set, add simple date and lat/lon engineered features') parser.add_argument('--lat-lon-bins', type=int, default=20, help='Number of bins for lat/lon coarse spatial features') parser.add_argument('--seed', type=int, default=42, help='Random seed for experiments') parser.add_argument('--hidden-dims', type=str, default='', help='Comma-separated hidden dims for MLP, e.g. "256,128"') parser.add_argument('--weight-decay', type=float, default=0.0, help='Weight decay (L2) for optimizer') parser.add_argument('--output-dir', default='.', help='Directory to save output files') args = parser.parse_args() data_root = args.data_root nrows = args.subset if args.subset > 0 else None # parse hidden dims hidden_dims = None if args.hidden_dims: try: hidden_dims = tuple(int(x) for x in args.hidden_dims.split(',') if x.strip()) except Exception: hidden_dims = None if args.generate_labels: os.makedirs(args.output_dir, exist_ok=True) label_info = { "generated": True, "label_method": args.label_method, "n_buckets": args.n_buckets, } with open(os.path.join(args.output_dir, "label_info.json"), "w") as f: json.dump(label_info, f) train(data_root, epochs=args.epochs, batch_size=args.batch_size, lr=args.lr, model_type=args.model_type, csv_label=args.csv_label, generate_labels=args.generate_labels, n_buckets=args.n_buckets, label_method=args.label_method, label_store=args.label_store, feature_engineer=args.feature_engineer, lat_lon_bins=args.lat_lon_bins, nrows=nrows, seed=args.seed, hidden_dims=hidden_dims, weight_decay=args.weight_decay, output_dir=args.output_dir) # ---------------- new helper ---------------- def compute_index(model, feature_vector): """ Run model on a single feature_vector and return the model-provided index as float. - If model is a classifier (outputs logits), returns argmax + 1.0 (so labels 1..C). - If model returns a single scalar regression, returns that scalar as float. feature_vector may be numpy array or torch tensor (1D or 2D single sample). """ try: import torch model.eval() if not isinstance(feature_vector, torch.Tensor): fv = torch.tensor(feature_vector, dtype=torch.float32) else: fv = feature_vector.float() # ensure batch dim if fv.dim() == 1: fv = fv.unsqueeze(0) with torch.no_grad(): out = model(fv) # if tensor output if hasattr(out, 'detach'): out_t = out.detach().cpu() if out_t.ndim == 2 and out_t.shape[1] > 1: # classifier logits/probs -> argmax idx = int(out_t.argmax(dim=1).item()) return float(idx + 1) elif out_t.numel() == 1: return float(out_t.item()) else: # fallback: return first element return float(out_t.flatten()[0].item()) else: # not a tensor (unlikely), try float conversion return float(out) except Exception as e: raise RuntimeError("compute_index failed: " + str(e)) return float(out) except Exception as e: raise RuntimeError("compute_index failed: " + str(e))