ML (Serialization)

save_model() (Helper)¶

Demonstrates the save_model function. This is a client-side (cleartext) helper function. It is not an FHE circuit and cannot be compiled with fhe.Compiler.

save_model serializes a model's public parameters (weights, trees, centroids, etc.) to a portable JSON file. Compiled circuits and keys are not serialized — recompile after loading.

In [ ]:

from concrete_fhe_toolkit.ml.serialization import save_model
from concrete_fhe_toolkit.ml.classes import FHELinearRegression
import os

# Create a model with known parameters
model = FHELinearRegression(weights=[3, 2], bias=-7)

# Save the model to a JSON file
save_model(model, 'test_save_model.json')

# Verify the file was created and contains valid JSON
assert os.path.exists('test_save_model.json'), 'File was not created'

import json
with open('test_save_model.json') as f:
    data = json.load(f)
assert data['model'] == 'FHELinearRegression'
assert data['params']['weights'] == [3, 2]
assert data['params']['bias'] == -7
print(f'Model saved successfully. Contents: {json.dumps(data, indent=2)}')

# Cleanup
os.remove('test_save_model.json')
print('save_model tests passed!')

from concrete_fhe_toolkit.ml.serialization import save_model from concrete_fhe_toolkit.ml.classes import FHELinearRegression import os # Create a model with known parameters model = FHELinearRegression(weights=[3, 2], bias=-7) # Save the model to a JSON file save_model(model, 'test_save_model.json') # Verify the file was created and contains valid JSON assert os.path.exists('test_save_model.json'), 'File was not created' import json with open('test_save_model.json') as f: data = json.load(f) assert data['model'] == 'FHELinearRegression' assert data['params']['weights'] == [3, 2] assert data['params']['bias'] == -7 print(f'Model saved successfully. Contents: {json.dumps(data, indent=2)}') # Cleanup os.remove('test_save_model.json') print('save_model tests passed!')

load_model() (Helper)¶

Demonstrates the load_model function. This is a client-side (cleartext) helper function. It is not an FHE circuit and cannot be compiled with fhe.Compiler.

load_model reads a JSON file produced by save_model and reconstructs a fresh, uncompiled model instance. Call compile(inputset) on the loaded model before predicting.

In [ ]:

from concrete_fhe_toolkit.ml.serialization import save_model, load_model
from concrete_fhe_toolkit.ml.classes import FHELinearRegression, FHEDecisionTree
import os

# Test 1: Save and load a linear regression model
original = FHELinearRegression(weights=[3, 2], bias=-7)
save_model(original, 'test_load_model.json')
loaded = load_model('test_load_model.json')

assert isinstance(loaded, FHELinearRegression), f'Expected FHELinearRegression, got {type(loaded).__name__}'
assert loaded.weights == [3, 2], f'Weights mismatch: {loaded.weights}'
assert loaded.bias == -7, f'Bias mismatch: {loaded.bias}'
print(f'Linear model loaded successfully: weights={loaded.weights}, bias={loaded.bias}')
os.remove('test_load_model.json')

# Test 2: Save and load a decision tree model
tree_dict = {'feature': 0, 'threshold': 5, 'left': 1, 'right': 0}
original_tree = FHEDecisionTree(tree=tree_dict)
save_model(original_tree, 'test_load_tree.json')
loaded_tree = load_model('test_load_tree.json')

assert isinstance(loaded_tree, FHEDecisionTree), f'Expected FHEDecisionTree, got {type(loaded_tree).__name__}'
assert loaded_tree.tree == tree_dict, f'Tree mismatch'
print(f'Tree model loaded successfully: tree={loaded_tree.tree}')
os.remove('test_load_tree.json')

print('load_model tests passed!')

from concrete_fhe_toolkit.ml.serialization import save_model, load_model from concrete_fhe_toolkit.ml.classes import FHELinearRegression, FHEDecisionTree import os # Test 1: Save and load a linear regression model original = FHELinearRegression(weights=[3, 2], bias=-7) save_model(original, 'test_load_model.json') loaded = load_model('test_load_model.json') assert isinstance(loaded, FHELinearRegression), f'Expected FHELinearRegression, got {type(loaded).__name__}' assert loaded.weights == [3, 2], f'Weights mismatch: {loaded.weights}' assert loaded.bias == -7, f'Bias mismatch: {loaded.bias}' print(f'Linear model loaded successfully: weights={loaded.weights}, bias={loaded.bias}') os.remove('test_load_model.json') # Test 2: Save and load a decision tree model tree_dict = {'feature': 0, 'threshold': 5, 'left': 1, 'right': 0} original_tree = FHEDecisionTree(tree=tree_dict) save_model(original_tree, 'test_load_tree.json') loaded_tree = load_model('test_load_tree.json') assert isinstance(loaded_tree, FHEDecisionTree), f'Expected FHEDecisionTree, got {type(loaded_tree).__name__}' assert loaded_tree.tree == tree_dict, f'Tree mismatch' print(f'Tree model loaded successfully: tree={loaded_tree.tree}') os.remove('test_load_tree.json') print('load_model tests passed!')