Saving and Loading models using .syspy extension¶
Example created by Samir Angelo Milani Martins
Obtaining the model using FROLS.¶
In [1]:
Copied!
import pandas as pd
from sysidentpy.model_structure_selection import FROLS
from sysidentpy.basis_function._basis_function import Polynomial
from sysidentpy.utils.generate_data import get_siso_data
from sysidentpy.utils.display_results import results
from sysidentpy.utils.plotting import plot_results
from sysidentpy.utils.save_load import save_model, load_model
# Generating 1 input 1 output sample data from a benchmark system
x_train, x_valid, y_train, y_valid = get_siso_data(
n=1000,
colored_noise=False,
sigma=0.0001,
train_percentage=90
)
basis_function = Polynomial(degree=2)
model = FROLS(
order_selection=True,
n_info_values=3,
extended_least_squares=False,
ylag=2, xlag=2,
info_criteria='aic',
estimator='least_squares',
basis_function=basis_function
)
model.fit(X=x_train, y=y_train)
yhat = model.predict(X=x_valid, y=y_valid)
# Gathering results
r = pd.DataFrame(
results(
model.final_model, model.theta, model.err,
model.n_terms, err_precision=8, dtype='sci'
),
columns=['Regressors', 'Parameters', 'ERR'])
import pandas as pd from sysidentpy.model_structure_selection import FROLS from sysidentpy.basis_function._basis_function import Polynomial from sysidentpy.utils.generate_data import get_siso_data from sysidentpy.utils.display_results import results from sysidentpy.utils.plotting import plot_results from sysidentpy.utils.save_load import save_model, load_model # Generating 1 input 1 output sample data from a benchmark system x_train, x_valid, y_train, y_valid = get_siso_data( n=1000, colored_noise=False, sigma=0.0001, train_percentage=90 ) basis_function = Polynomial(degree=2) model = FROLS( order_selection=True, n_info_values=3, extended_least_squares=False, ylag=2, xlag=2, info_criteria='aic', estimator='least_squares', basis_function=basis_function ) model.fit(X=x_train, y=y_train) yhat = model.predict(X=x_valid, y=y_valid) # Gathering results r = pd.DataFrame( results( model.final_model, model.theta, model.err, model.n_terms, err_precision=8, dtype='sci' ), columns=['Regressors', 'Parameters', 'ERR'])
Saving obtained model in file "model_name.syspy"¶
In [2]:
Copied!
# save_model(model_variable, file_name.syspy, path (optional))
save_model(model=model, file_name='model_name.syspy')
# save_model(model_variable, file_name.syspy, path (optional)) save_model(model=model, file_name='model_name.syspy')
Loading model and checking if everything went smoothly¶
In [3]:
Copied!
# load_model(file_name.syspy, path (optional))
loaded_model = load_model(file_name='model_name.syspy')
# Predicting output with loaded_model
yhat_loaded = loaded_model.predict(X=x_valid, y=y_valid)
r_loaded = pd.DataFrame(
results(
loaded_model.final_model, loaded_model.theta, loaded_model.err,
loaded_model.n_terms, err_precision=8, dtype='sci'
),
columns=['Regressors', 'Parameters', 'ERR'])
# Printing both: original model and model loaded from file
print('\n Original model \n', r)
print('\n Model Loaded from file \n',r_loaded)
# Checking predictions from both: original model and model loaded from file
if (yhat==yhat_loaded).all():
print('\n Predictions are the same!')
# Ploting results
plot_results(y=y_valid, yhat=yhat_loaded, n=1000)
# load_model(file_name.syspy, path (optional)) loaded_model = load_model(file_name='model_name.syspy') # Predicting output with loaded_model yhat_loaded = loaded_model.predict(X=x_valid, y=y_valid) r_loaded = pd.DataFrame( results( loaded_model.final_model, loaded_model.theta, loaded_model.err, loaded_model.n_terms, err_precision=8, dtype='sci' ), columns=['Regressors', 'Parameters', 'ERR']) # Printing both: original model and model loaded from file print('\n Original model \n', r) print('\n Model Loaded from file \n',r_loaded) # Checking predictions from both: original model and model loaded from file if (yhat==yhat_loaded).all(): print('\n Predictions are the same!') # Ploting results plot_results(y=y_valid, yhat=yhat_loaded, n=1000)
Original model Regressors Parameters ERR 0 x1(k-2) 9.0000E-01 9.57022506E-01 1 y(k-1) 1.9999E-01 3.96079210E-02 2 x1(k-1)y(k-1) 1.0001E-01 3.36953837E-03 Model Loaded from file Regressors Parameters ERR 0 x1(k-2) 9.0000E-01 9.57022506E-01 1 y(k-1) 1.9999E-01 3.96079210E-02 2 x1(k-1)y(k-1) 1.0001E-01 3.36953837E-03 Predictions are the same!