'self.c * self.acc_phone_y + self.d * self.acc_phone_z',
'self.e * self.acc_phone_x + self.f * self.acc_phone_z'
], ['self.acc_phone_x', 'self.acc_phone_y'],
['self.a', 'self.b', 'self.c', 'self.d', 'self.e', 'self.f'],
pop_size=5,
max_generations=10,
per_time_step=True)
DataViz.plot_numerical_prediction_versus_real(
train_X.index, train_y['acc_phone_x'], regr_train_y['acc_phone_x'],
test_X.index, test_y['acc_phone_x'], regr_test_y['acc_phone_x'],
'acc_phone_x')
regr_train_y, regr_test_y = learner.dynamical_systems_model_sa(
train_X,
train_y,
test_X,
test_y, ['self.acc_phone_x', 'self.acc_phone_y', 'self.acc_phone_z'], [
'self.a * self.acc_phone_x + self.b * self.acc_phone_y',
'self.c * self.acc_phone_y + self.d * self.acc_phone_z',
'self.e * self.acc_phone_x + self.f * self.acc_phone_z'
], ['self.acc_phone_x', 'self.acc_phone_y'],
['self.a', 'self.b', 'self.c', 'self.d', 'self.e', 'self.f'],
max_generations=10,
per_time_step=True)
DataViz.plot_numerical_prediction_versus_real(
train_X.index, train_y['acc_phone_x'], regr_train_y['acc_phone_x'],
test_X.index, test_y['acc_phone_x'], regr_test_y['acc_phone_x'],
'acc_phone_x')
# 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.')
performance_tr_dyn = 0
performance_tr_dyn_std = 0
performance_te_dyn = 0
performance_te_dyn_std = 0
for repeat in range(0, repeats):
print '----', repeat
regr_train_y, regr_test_y = learner.dynamical_systems_model_sa(
selected_train_X,
train_y.to_frame(name='hr_watch_rate'), selected_test_X,
test_y.to_frame(name='hr_watch_rate'), columns, equations, targets,
parameters)
mean_tr, std_tr = eval.mean_squared_error_with_std(
train_y.ix[washout_time:, ], regr_train_y.ix[washout_time:, ])
mean_te, std_te = eval.mean_squared_error_with_std(
test_y.ix[washout_time:, ], regr_test_y.ix[washout_time:, ])
performance_tr_dyn += mean_tr
performance_tr_dyn_std += std_tr
performance_te_dyn += mean_te
performance_te_dyn_std += std_te
overall_performance_tr_dyn = performance_tr_dyn / repeats
overall_performance_tr_dyn_std = performance_tr_dyn_std / repeats
overall_performance_te_dyn = performance_te_dyn / repeats