Update README.md

README.md

@@ -68,180 +68,3 @@ ds = load_dataset('mpg-ranch/leafy_spurge', 'labelled', split='train')
 ds['image'][405]
 ```
 <img src="https://huggingface.co/datasets/mpg-ranch/leafy_spurge/resolve/main/doc_figures/full_size_tile.png" width="1024px" height="1024px">
-
-We will now center crop the image to the size of the ground truth:
-
-```python
-from torchvision.transforms import CenterCrop, Compose
-
-ground_truth_sz = 39
-
-ccrop = Compose([CenterCrop(ground_truth_sz)])
-
-def preproc_transforms(examples):
-    examples["pixel_values"] = [ccrop(image.convert("RGB")) for image in examples["image"]]
-    return examples
-
-ds = ds.map(preproc_transforms, batched=True)
-ds['pixel_values'][405]
-```
-<img src="https://huggingface.co/datasets/mpg-ranch/leafy_spurge/resolve/main/doc_figures/ground_truth_tile.png" width="39px" height="39px">
-
-# Geographic splits within the training set
-<img src="https://huggingface.co/datasets/mpg-ranch/leafy_spurge/resolve/main/doc_figures/train_clusters.png" width="75%" height="75%">
-
-We gathered ground truth at multiple sites and observations within a site were geographically clustered. We suggest using the cluster feature to establish holdout sets for cross-validated hyperparameter tuning. This will simluate model performance when classifying leafy spurge at new sites (such as those of the test set). You can filter by cluster metadata as follows:
-
-```python
- #define holdout sets with ground truth clusters; 6 and 7 overlap geographically
-holdout_sets = [[0], [1], [2], [4], [5], [6,7], [8]] 
-
-set_0 = ds.filter(lambda example: example['cluster'] in holdout_sets[0])
-unq_vals = list(set(set_0['cluster']))
-print(f'Unique cluster values in set 0: {unq_vals}')
-```
-
-# Example cross-validation loop
-We will use the the geographic cluster feature to cross-validate performance. First let's reformat the dataset for torch and define some functions for our training loop:
-
-```python
-import torch.nn as nn
-import torch.optim as optim
-from torchvision.models import resnet50
-from tqdm import tqdm
-import pandas as pd
-
-ds = ds.with_format("torch")
-
-def train_one_epoch(model, train_loader, criterion, optimizer, device):
-    model.train()
-    running_loss = 0.0
-    correct_predictions = 0
-    total_predictions = 0
-    for i, batch in enumerate(train_loader):
-        inputs = batch['pixel_values'].permute(0,3,1,2).float().to(device)
-        labels = batch['label'].to(device)
-
-        optimizer.zero_grad()
-
-        outputs = model(inputs)
-        _, predicted = torch.max(outputs.data, 1)
-        total_predictions += labels.size(0)
-        correct_predictions += (predicted == labels).sum().item()
-
-        loss = criterion(outputs, labels)
-        loss.backward()
-        optimizer.step()
-
-        running_loss += loss.item()
-
-    train_accuracy = correct_predictions / total_predictions
-    train_loss = running_loss / len(train_loader)
-
-    return train_loss, train_accuracy
-
-def evaluate_one_epoch(model, test_loader, criterion, device):
-    model.eval()
-    running_loss = 0.0
-    correct_predictions = 0
-    total_predictions = 0
-    with torch.no_grad():
-        for batch in test_loader:
-            inputs = batch['pixel_values'].permute(0,3,1,2).float().to(device)
-            labels = batch['label'].to(device)
-
-            outputs = model(inputs)
-            _, predicted = torch.max(outputs.data, 1)
-            total_predictions += labels.size(0)
-            correct_predictions += (predicted == labels).sum().item()
-
-            loss = criterion(outputs, labels)
-            running_loss += loss.item()
-
-    test_accuracy = correct_predictions / total_predictions
-    test_loss = running_loss / len(test_loader)
-
-    return test_loss, test_accuracy
-
-def cross_val(ds, holdout_set):
-    train = ds.filter(lambda example: example['cluster'] not in holdout_set)
-    test = ds.filter(lambda example: example['cluster'] in holdout_set)
-
-    model = resnet50(pretrained=True)
-    num_classes = len(ds['label'].unique())
-    model.fc = nn.Linear(2048, num_classes)
-
-    # Define the loss function and optimizer
-    criterion = nn.CrossEntropyLoss()
-    optimizer = optim.Adam(model.parameters(), lr=0.001)
-
-    # Define the data loaders
-    train_loader = torch.utils.data.DataLoader(train, batch_size=32, shuffle=True)
-    test_loader = torch.utils.data.DataLoader(test, batch_size=32, shuffle=False)
-
-    # Train the model
-    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    model.to(device)
-
-    results = []
-
-    for epoch in range(5):
-        train_loss, train_accuracy = train_one_epoch(model, train_loader, criterion, optimizer, device)
-        test_loss, test_accuracy = evaluate_one_epoch(model, test_loader, criterion, device)
-
-        results.append({
-            'epoch': epoch + 1,
-            'train_loss': train_loss,
-            'train_accuracy': train_accuracy,
-            'test_loss': test_loss,
-            'test_accuracy': test_accuracy,
-            'holdout_set': holdout_set
-        })
-    
-    results_df = pd.DataFrame(results)
-
-    return results_df
-
-```
-
-Next we'll sequentially holdout geographic clusters and store performance:
-```python
-results = []
-pbar_holdout = tqdm(holdout_sets, desc="Holdout Sets")
-for holdout_set in pbar_holdout:
-    results.append(cross_val(ds, holdout_set))
-    pbar_holdout.set_postfix_str(f"Completed holdout set {holdout_set}")
-
-results_df = pd.concat(results)
-```
-
-Finally, we plot the results of geographic cross-validation:
-```python
-import numpy as np
-import matplotlib.pyplot as plt
-
-# Group the results by epoch
-grouped_results = results_df.groupby('epoch')
-
-# Compute the mean and standard deviation of the test accuracy at each epoch
-mean_test_accuracy = grouped_results['test_accuracy'].mean()
-std_test_accuracy = grouped_results['test_accuracy'].std()
-
-# Compute the 68% confidence interval
-lower_bound = mean_test_accuracy - std_test_accuracy
-upper_bound = mean_test_accuracy + std_test_accuracy
-
-# Plot the mean test accuracy
-plt.plot(mean_test_accuracy.index, mean_test_accuracy)
-
-# Plot the error ribbon
-plt.fill_between(lower_bound.index, lower_bound, upper_bound, color='b', alpha=.1)
-
-# Set the labels and title
-plt.xlabel('Epoch')
-plt.ylabel('Cross-validated Accuracy')
-
-# Show the plot
-plt.show()
-```
-<img src="https://huggingface.co/datasets/mpg-ranch/leafy_spurge/resolve/main/doc_figures/cluster_cv_fig.png" width="75%" height="75%">