Local Testing in FLAME: Pancreatic Cancer Analysis

INFO

For an explanation of this example's main code, please refer to the federated logistic regression coding example.

1. Initializing Local Testing

Other than the coding example, here we simulate node-local data by loading CSV files from local directories instead of S3 buckets. The data structure mimics S3 objects by encoding the CSV content as bytes.

def main():
    # Load node-local CSV files and encode as bytes
    data_1 = [{
        'pancreasData.csv':
            open('node1/pancreasData.csv').read().encode('utf-8')
    }]

    data_2 = [{
        'pancreasData.csv':
            open('node2/pancreasData.csv').read().encode('utf-8')
    }]

    # Combine node data into federated data splits
    data_splits = [data_1, data_2]

    # Run federated training
    StarModelTester(
        data_splits,                    # List of node-local datasets
        PancreasAnalyzer,               # Analyzer class
        FederatedLogisticRegression,    # Aggregator class
        's3',                           # Data source type
        simple_analysis=False,          # Multi-round analysis
        output_type='pickle',           # Output format
        filename="./pancreas.pkl"       # Save final model
    )

1.1. Data Preparation:

data_1 = [{
    'pancreasData.csv': open('node1/pancreasData.csv').read().encode('utf-8')
}]

This creates a specific data structure:

• List: [...] - Each node gets one list entry
• Dictionary: {'pancreasData.csv': <bytes>} - Maps filenames to content
• Bytes: .encode('utf-8') - Converts string to bytes (simulating S3 objects)

Why this structure?

• Mimics real federated deployments where data comes from S3 buckets
• Each node might have multiple files (hence the dictionary)
• Bytes representation prepares for network transmission

1.2. StarModelTester Configuration:

Parameter	Value	Purpose
data_splits	[data_1, data_2]	Two nodes (hospitals)
PancreasAnalyzer	Class	Local training logic
FederatedLogisticRegression	Class	Aggregation logic
's3'	Data type	Treats data as S3-like objects
simple_analysis=False	Iterative	Enables multi-round training
output_type='pickle'	Format	Serializes the final model
filename	Path	Saves model to disk

2. Running the Example

2.1. Prerequisites (not mandatory, just for clarity in this example)

1. Project structure:

   test/
   ├── test_flame_pancreas_analysis.py
   ├── node1/
   │   └── pancreasData.csv
   └── node2/
       └── pancreasData.csv

2. Data format (pancreasData.csv):

   feature1,feature2,feature3,...,label
   0.5,1.2,3.4,...,0
   0.8,1.5,2.9,...,1
   ...

   • Multiple feature columns (patient measurements)
   • Last column is binary label (0 = healthy, 1 = disease)

2.2. Running the Test

cd test/
python test_flame_pancreas_analysis.py

2.3. Expected Output

--- Starting Iteration 0 ---
Analyzer node_0 started
	Data extracted: [{'pancreasData.csv': b'5S_rRNA,7SK,A1BG,A1BG-AS1,A1CF,A2M,A2M-AS1,A2ML1,A4GALT,A4GNT,AA06,AAAS,AACS
Analyzer node_1 started
	Data extracted: [{'pancreasData.csv': b'5S_rRNA,7SK,A1BG,A1BG-AS1,A1CF,A2M,A2M-AS1,A2ML1,A4GALT,A4GNT,AA06,AAAS,AACS
Aggregator started
--- Ending Iteration 0 ---

--- Starting Iteration 1 ---
Analyzer node_0 started
	Data extracted: [{'pancreasData.csv': b'5S_rRNA,7SK,A1BG,A1BG-AS1,A1CF,A2M,A2M-AS1,A2ML1,A4GALT,A4GNT,AA06,AAAS,AACS
Analyzer node_1 started
	Data extracted: [{'pancreasData.csv': b'5S_rRNA,7SK,A1BG,A1BG-AS1,A1CF,A2M,A2M-AS1,A2ML1,A4GALT,A4GNT,AA06,AAAS,AACS
Aggregator started
--- Ending Iteration 1 ---

*** ... (similar output for iterations 2 through 9) ... ***

--- Starting Iteration 10 ---
Analyzer node_0 started
	Data extracted: [{'pancreasData.csv': b'5S_rRNA,7SK,A1BG,A1BG-AS1,A1CF,A2M,A2M-AS1,A2ML1,A4GALT,A4GNT,AA06,AAAS,AACS
Analyzer node_1 started
	Data extracted: [{'pancreasData.csv': b'5S_rRNA,7SK,A1BG,A1BG-AS1,A1CF,A2M,A2M-AS1,A2ML1,A4GALT,A4GNT,AA06,AAAS,AACS
Aggregator started
Maximum number of 10 iterations reached. Returning current results.
Maximum number of 10 iterations reached. Returning current results.
Final result written to ./pancreas.pkl
--- Ending Iteration 10 ---

3. Loading the Trained Model

If you want to load and use the trained model later, you can do so with the following code:

import pickle

# Load the trained model coefficients
with open('pancreas.pkl', 'rb') as f:
    global_coefficients = pickle.load(f)

print(f"Model coefficients: {global_coefficients}")
print(f"Shape: {global_coefficients.shape}")

# Use the model for predictions
# (You'd need to reconstruct a LogisticRegression object)
clf = LogisticRegression(fit_intercept=False)
clf.coef_ = global_coefficients
# clf.classes_ = np.array([0, 1])  # Set class labels

# predictions = clf.predict(new_patient_data)