pickle.py

Here’s how you can serialize a Betacal model using each of the alternative methods:

Using hickle

import hickle as hkl
from betacal import BetaCalibrator

# Example: Create and train a BetaCalibrator model
model = BetaCalibrator()
# Assume you have `predictions` and `labels` for calibration
# model.fit(predictions, labels)

# Save the model
hkl.dump(model, 'betacal_model.h5')

# Load the model
loaded_model = hkl.load('betacal_model.h5')

Using quickle

import quickle
from betacal import BetaCalibrator

# Example: Create and train a BetaCalibrator model
model = BetaCalibrator()
# Assume you have `predictions` and `labels` for calibration
# model.fit(predictions, labels)

# Save the model
with open('betacal_model.qkl', 'wb') as f:
    quickle.dump(model, f)

# Load the model
with open('betacal_model.qkl', 'rb') as f:
    loaded_model = quickle.load(f)

Using JSON

For JSON, you need to convert the model’s parameters into a serializable format since JSON doesn’t support arbitrary Python objects.

import json
from betacal import BetaCalibrator

# Example: Create and train a BetaCalibrator model
model = BetaCalibrator()
# Assume you have `predictions` and `labels` for calibration
# model.fit(predictions, labels)

# Serialize model parameters to JSON
model_data = {'alpha': model.alpha, 'beta': model.beta}
with open('betacal_model.json', 'w') as f:
    json.dump(model_data, f)

# Load the model parameters from JSON
with open('betacal_model.json', 'r') as f:
    model_data = json.load(f)

# Recreate the model
loaded_model = BetaCalibrator()
loaded_model.alpha = model_data['alpha']
loaded_model.beta = model_data['beta']

Using dill

import dill
from betacal import BetaCalibrator

# Example: Create and train a BetaCalibrator model
model = BetaCalibrator()
# Assume you have `predictions` and `labels` for calibration
# model.fit(predictions, labels)

# Save the model
with open('betacal_model.dill', 'wb') as f:
    dill.dump(model, f)

# Load the model
with open('betacal_model.dill', 'rb') as f:
    loaded_model = dill.load(f)

Using joblib

import joblib
from betacal import BetaCalibrator

# Example: Create and train a BetaCalibrator model
model = BetaCalibrator()
# Assume you have `predictions` and `labels` for calibration
# model.fit(predictions, labels)

# Save the model
joblib.dump(model, 'betacal_model.joblib')

# Load the model
loaded_model = joblib.load('betacal_model.joblib')

Recommendations

	•	Use joblib for machine learning models as it is optimized for numerical data and arrays.
	•	Use hickle for faster serialization and built-in compression.
	•	Use quickle for better performance and security than pickle.
	•	Use dill if you need to serialize functions or more complex objects.
	•	Use JSON if interoperability and human-readability are important, though it requires custom handling for complex objects like the Betacal model.

Let me know if you want a specific one explained further!