Dyploma project: building an ensemble of classifiers and regressors with TPOT and auto-sklearn using genetic alorithm
This project was carried out under the supervision of Andriy Konovalov, Philosophy Doctor of Physics and Mathematics
This project implements functions for building an ensemble of classifiers and regressors using the TPOT and auto-sklearn libraries with the help of a genetic algorithm evolved using the deap library. The functions allow using these libraries to optimise machine learning models and build an ensemble of them.
Below is a flowchart demonstrating the stages of the developed methodology:
Usage. Import the functions from this file into your project.
- Use the gen_ensemble_builder_clf function to build a classifier ensemble.
- Use the gen_ensemble_builder_regr function to build a regressor ensemble.
# load data
X_train, X_test, y_train, y_test = load_data()
# run classification func
result = gen_ensemble_builder_clf(X_train, X_test, y_train, y_test)
# print result
print(result)Using own TPOT object for regression
pipeline_optimizer = TPOTClassifier(max_time_mins=5, scoring='r2')
result = gen_ensemble_builder_regr(X_train, X_test, y_train, y_test, TPOT_object=pipeline_optimizer, use_aurosklearn=False)Using own crossover and mutation genetic operators
def cxSet(ind1, ind2):
temp = set(ind1) # Used in order to keep type
ind1 &= ind2 # Intersection (inplace)
ind2 ^= temp # Symmetric Difference (inplace)
return ind1, ind2
def mutSet(individual):
"""Mutation that pops or add an element."""
if random.random() < 0.5:
if len(individual) > 0: # We cannot pop from an empty set
individual.remove(random.choice(sorted(tuple(individual))))
else:
individual.add(random.randrange(NBR_ITEMS))
return individual,
result = gen_ensemble_builder_clf(X_train, X_test, y_train, y_test, cx_operator=cxSet, mut_operator=mutSet)
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