def dynamical_systems_model_nsga_2(self, train_X, train_y, test_X, test_y, columns, equations, targets, parameters, pop_size=10, max_generations=100, per_time_step=True):
prng = random.Random()
evaluator = Evaluator()
model = Model()
# Create the model.
model.set_model(columns, equations, parameters)
# Set the desired/known values in our evaluator.
evaluator.set_values(model, train_X, train_y, test_X, test_y, targets)
# Initialize the NSGA2 algorithm.
ea = inspyred.ec.emo.NSGA2(prng)
ea.variator = [inspyred.ec.variators.blend_crossover,
inspyred.ec.variators.gaussian_mutation]
ea.terminator = inspyred.ec.terminators.generation_termination
# Let it run.
final_pop = ea.evolve(generator=evaluator.generator, evaluator=evaluator.evaluator_multi_objective, pop_size=pop_size, maximize=False, bounder=None,max_generations=max_generations)
final_arc = ea.archive
# For all solutions (they reside on the pareto front)
return_values = []
for f in final_arc:
# Predict the results.
train_fitness, y_train_pred = evaluator.predict(f.candidate, training=True, per_time_step=per_time_step)
test_fitness, y_test_pred = evaluator.predict(f.candidate, training=False, per_time_step=per_time_step)
# And collect the predictions and fitness values.
row = [y_train_pred, train_fitness, y_test_pred, test_fitness]
return_values.append(row)
return return_values
def dynamical_systems_model_nsga_2(train_X, train_y, test_X, test_y, columns, equations, targets, parameters,
pop_size=10, max_generations=100, per_time_step=True):
"""
Apply a known dynamical systems model for a regression problem by tuning its parameters towards the data.
Hereto, it can use multiple objectives as it uses the nsga 2 algorithm. To be provided are: training set (
both input and target) test set (both input and target) a list of columns the model addresses (i.e. the
states), the string should be preceded by 'self.' in order for the approach to work. a list of equations to
derive the specified states, again using 'self.' preceding all parameters and columns names. a list of
targets (a subset of the columns) (again with 'self.') a list of parameters in the equations (again with
'self.') the population size of nsga 2 the maximum number of generations for nsga 2 whether we want to
predict per time point (i.e. we reset the state values of the previous time point to their observed values.
It returns a series of predictions for the training and test sets that are the results of parameter setting
that are positioned on the Pareto front.
"""
prng = random.Random()
evaluator = Evaluator()
model = Model()
# Create the model
model.set_model(columns, equations, parameters)
# Set the desired/known values in our evaluator
evaluator.set_values(model, train_X, train_y, test_X, test_y, targets)
# Initialize the NSGA2 algorithm
ea = inspyred.ec.emo.NSGA2(prng)
ea.variator = [inspyred.ec.variators.blend_crossover,
inspyred.ec.variators.gaussian_mutation]
ea.terminator = inspyred.ec.terminators.generation_termination
# Let it run
ea.evolve(generator=evaluator.generator, evaluator=evaluator.evaluator_multi_objective,
pop_size=pop_size, maximize=False, bounder=None, max_generations=max_generations)
final_arc = ea.archive
# For all solutions (they reside on the pareto front)
return_values = []
for f in final_arc:
# Predict the results
train_fitness, y_train_pred = evaluator.predict(f.candidate, training=True, per_time_step=per_time_step)
test_fitness, y_test_pred = evaluator.predict(f.candidate, training=False, per_time_step=per_time_step)
# And collect the predictions and fitness values
row = [y_train_pred, train_fitness, y_test_pred, test_fitness]
return_values.append(row)
return return_values
def dynamical_systems_model_sa(self, train_X, train_y, test_X, test_y, columns, equations, targets, parameters, pop_size=10, max_generations=100, per_time_step=True):
prng = random.Random()
evaluator = Evaluator()
model = Model()
# Create the model.
model.set_model(columns, equations, parameters)
# Set the desired/known values in our evaluator.
evaluator.set_values(model, train_X, train_y, test_X, test_y, targets)
ea = inspyred.ec.SA(prng)
ea.terminator = inspyred.ec.terminators.generation_termination
# Let it run.
final_pop = ea.evolve(generator=evaluator.generator, evaluator=evaluator.evaluator_single_objective, maximize=False, bounder=None, max_generations=max_generations)
# Select the best one and use it to predict.
best = min(final_pop)
train_fitness, y_train_pred = evaluator.predict(best.candidate, training=True, per_time_step=per_time_step)
test_fitness, y_test_pred = evaluator.predict(best.candidate, training=False, per_time_step=per_time_step)
return y_train_pred, y_test_pred
def dynamical_systems_model_sa(train_X, train_y, test_X, test_y, columns, equations, targets, parameters,
max_generations=100, per_time_step=True):
"""
Apply a known dynamical systems model for a regression problem by tuning its parameters towards the data
using SA. Hereto, it can use multiple objectives but will just average the values of each one. In the end,
one solution will be provided. To be provided are: training set (both input and target) test set (both input
and target) a list of columns the model addresses (i.e. the states), the string should be preceded by 'self.'
in order for the approach to work. a list of equations to derive the specified states, again using 'self.'
preceding all parameters and columns names. a list of targets (a subset of the columns) (again with 'self.')
a list of parameters in the equations (again with 'self.') the population size for SA the maximum number of
generations for the SA. whether we want to predict per time point (i.e. we reset the state values of the
previous time point to their observed values. It returns a prediction for the training and test sets that are
the result of the best parameter setting.
"""
prng = random.Random()
evaluator = Evaluator()
model = Model()
# Create the model
model.set_model(columns, equations, parameters)
# Set the desired/known values in our evaluator
evaluator.set_values(model, train_X, train_y, test_X, test_y, targets)
ea = inspyred.ec.SA(prng)
ea.terminator = inspyred.ec.terminators.generation_termination
# Let it run
final_pop = ea.evolve(generator=evaluator.generator, evaluator=evaluator.evaluator_single_objective,
maximize=False, bounder=None, max_generations=max_generations)
# Select the best one and use it to predict
best = min(final_pop)
train_fitness, y_train_pred = evaluator.predict(best.candidate, training=True, per_time_step=per_time_step)
test_fitness, y_test_pred = evaluator.predict(best.candidate, training=False, per_time_step=per_time_step)
return y_train_pred, y_test_pred