Saving and Loading models using .syspy extension¶
Example created by Samir Angelo Milani Martins
Obtaining the model using FROLS.¶
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import pandas as pd
from sysidentpy.model_structure_selection import FROLS
from sysidentpy.basis_function._basis_function import Polynomial
from sysidentpy.parameter_estimation import LeastSquares
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)
estimator = LeastSquares()
model = FROLS(
order_selection=True,
n_info_values=3,
ylag=2,
xlag=2,
info_criteria="aic",
estimator=estimator,
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.parameter_estimation import LeastSquares 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) estimator = LeastSquares() model = FROLS( order_selection=True, n_info_values=3, ylag=2, xlag=2, info_criteria="aic", estimator=estimator, 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"¶
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# 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¶
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# 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 9.56631676E-01
1 y(k-1) 1.9999E-01 3.99688899E-02
2 x1(k-1)y(k-1) 1.0000E-01 3.39940092E-03
Model Loaded from file
Regressors Parameters ERR
0 x1(k-2) 9.0000E-01 9.56631676E-01
1 y(k-1) 1.9999E-01 3.99688899E-02
2 x1(k-1)y(k-1) 1.0000E-01 3.39940092E-03
Predictions are the same!
