Simulate a Predefined Model¶
Example created by Wilson Rocha Lacerda Junior
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pip install sysidentpy
pip install sysidentpy
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import numpy as np
import pandas as pd
from sysidentpy.simulation import SimulateNARMAX
from sysidentpy.metrics import root_relative_squared_error
from sysidentpy.utils.generate_data import get_siso_data
from sysidentpy.basis_function import Polynomial
from sysidentpy.parameter_estimation import LeastSquares
from sysidentpy.utils.display_results import results
from sysidentpy.utils.plotting import plot_residues_correlation, plot_results
from sysidentpy.residues.residues_correlation import (
compute_residues_autocorrelation,
compute_cross_correlation,
)
import numpy as np import pandas as pd from sysidentpy.simulation import SimulateNARMAX from sysidentpy.metrics import root_relative_squared_error from sysidentpy.utils.generate_data import get_siso_data from sysidentpy.basis_function import Polynomial from sysidentpy.parameter_estimation import LeastSquares from sysidentpy.utils.display_results import results from sysidentpy.utils.plotting import plot_residues_correlation, plot_results from sysidentpy.residues.residues_correlation import ( compute_residues_autocorrelation, compute_cross_correlation, )
Generating 1 input 1 output sample data¶
The data is generated by simulating the following model:¶
$y_k = 0.2y_{k-1} + 0.1y_{k-1}x_{k-1} + 0.9x_{k-2} + e_{k}$
If colored_noise is set to True:
$e_{k} = 0.8\nu_{k-1} + \nu_{k}$
where $x$ is a uniformly distributed random variable and $\nu$ is a gaussian distributed variable with $\mu=0$ and $\sigma=0.1$
In the next example we will generate a data with 1000 samples with white noise and selecting 90% of the data to train the model.
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x_train, x_test, y_train, y_test = get_siso_data(
n=1000, colored_noise=False, sigma=0.001, train_percentage=90
)
x_train, x_test, y_train, y_test = get_siso_data( n=1000, colored_noise=False, sigma=0.001, train_percentage=90 )
Defining the model¶
We already know that the generated data is a result of the model $𝑦_𝑘=0.2𝑦_{𝑘−1}+0.1𝑦_{𝑘−1}𝑥_{𝑘−1}+0.9𝑥_{𝑘−2}+𝑒_𝑘$ . Thus, we can create a model with those regressors follwing a codification pattern:
- $0$ is the constant term,
- $[1001] = y_{k-1}$
- $[100n] = y_{k-n}$
- $[200n] = x1_{k-n}$
- $[300n] = x2_{k-n}$
- $[1011, 1001] = y_{k-11} \times y_{k-1}$
- $[100n, 100m] = y_{k-n} \times y_{k-m}$
- $[12001, 1003, 1001] = x11_{k-1} \times y_{k-3} \times y_{k-1}$
- and so on
Important Note¶
The order of the arrays matter.
If you use [2001, 1001], it will work, but [1001, 2001] will not (the regressor will be ignored). Always put the highest value first:
- $[2003, 2001]$ works
- $[2001, 2003]$ do not work
We will handle this limitation in upcoming update.
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s = SimulateNARMAX(
basis_function=Polynomial(), calculate_err=True, estimate_parameter=False
)
# the model must be a numpy array
model = np.array(
[
[1001, 0], # y(k-1)
[2001, 1001], # x1(k-1)y(k-1)
[2002, 0], # x1(k-2)
]
)
# theta must be a numpy array of shape (n, 1) where n is the number of regressors
theta = np.array([[0.2, 0.9, 0.1]]).T
s = SimulateNARMAX( basis_function=Polynomial(), calculate_err=True, estimate_parameter=False ) # the model must be a numpy array model = np.array( [ [1001, 0], # y(k-1) [2001, 1001], # x1(k-1)y(k-1) [2002, 0], # x1(k-2) ] ) # theta must be a numpy array of shape (n, 1) where n is the number of regressors theta = np.array([[0.2, 0.9, 0.1]]).T
Simulating the model¶
After defining the model and theta we just need to use the simulate method.
The simulate method returns the predicted values and the results where we can look at regressors, parameters and ERR values.
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yhat = s.simulate(
X_test=x_test,
y_test=y_test,
model_code=model,
theta=theta,
)
r = pd.DataFrame(
results(s.final_model, s.theta, s.err, s.n_terms, err_precision=8, dtype="sci"),
columns=["Regressors", "Parameters", "ERR"],
)
print(r)
plot_results(y=y_test, yhat=yhat, n=1000)
ee = compute_residues_autocorrelation(y_test, yhat)
plot_residues_correlation(data=ee, title="Residues", ylabel="$e^2$")
x1e = compute_cross_correlation(y_test, yhat, x_test)
plot_residues_correlation(data=x1e, title="Residues", ylabel="$x_1e$")
yhat = s.simulate( X_test=x_test, y_test=y_test, model_code=model, theta=theta, ) r = pd.DataFrame( results(s.final_model, s.theta, s.err, s.n_terms, err_precision=8, dtype="sci"), columns=["Regressors", "Parameters", "ERR"], ) print(r) plot_results(y=y_test, yhat=yhat, n=1000) ee = compute_residues_autocorrelation(y_test, yhat) plot_residues_correlation(data=ee, title="Residues", ylabel="$e^2$") x1e = compute_cross_correlation(y_test, yhat, x_test) plot_residues_correlation(data=x1e, title="Residues", ylabel="$x_1e$")
Regressors Parameters ERR 0 y(k-1) 2.0000E-01 0.00000000E+00 1 x1(k-2) 9.0000E-01 0.00000000E+00 2 x1(k-1)y(k-1) 1.0000E-01 0.00000000E+00
Options¶
You can set the steps_ahead to run the prediction/simulation:
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yhat = s.simulate(
X_test=x_test,
y_test=y_test,
model_code=model,
theta=theta,
steps_ahead=1,
)
rrse = root_relative_squared_error(y_test, yhat)
print(rrse)
yhat = s.simulate( X_test=x_test, y_test=y_test, model_code=model, theta=theta, steps_ahead=1, ) rrse = root_relative_squared_error(y_test, yhat) print(rrse)
0.001980394341423956
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yhat = s.simulate(
X_test=x_test,
y_test=y_test,
model_code=model,
theta=theta,
steps_ahead=21,
)
rrse = root_relative_squared_error(y_test, yhat)
print(rrse)
yhat = s.simulate( X_test=x_test, y_test=y_test, model_code=model, theta=theta, steps_ahead=21, ) rrse = root_relative_squared_error(y_test, yhat) print(rrse)
0.0019394741034286557
Estimating the parameters¶
If you have only the model strucuture, you can create an object with estimate_parameter=True and choose the methed for estimation using estimator. In this case, you have to pass the training data for parameters estimation.
When estimate_parameter=True, we also computate the ERR considering only the regressors defined by the user.
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s = SimulateNARMAX(
basis_function=Polynomial(),
estimate_parameter=True,
estimator=LeastSquares(),
calculate_err=True,
)
yhat = s.simulate(
X_train=x_train,
y_train=y_train,
X_test=x_test,
y_test=y_test,
model_code=model,
# theta will be estimated using the defined estimator
)
r = pd.DataFrame(
results(s.final_model, s.theta, s.err, s.n_terms, err_precision=8, dtype="sci"),
columns=["Regressors", "Parameters", "ERR"],
)
print(r)
plot_results(y=y_test, yhat=yhat, n=1000)
ee = compute_residues_autocorrelation(y_test, yhat)
plot_residues_correlation(data=ee, title="Residues", ylabel="$e^2$")
x1e = compute_cross_correlation(y_test, yhat, x_test)
plot_residues_correlation(data=x1e, title="Residues", ylabel="$x_1e$")
s = SimulateNARMAX( basis_function=Polynomial(), estimate_parameter=True, estimator=LeastSquares(), calculate_err=True, ) yhat = s.simulate( X_train=x_train, y_train=y_train, X_test=x_test, y_test=y_test, model_code=model, # theta will be estimated using the defined estimator ) r = pd.DataFrame( results(s.final_model, s.theta, s.err, s.n_terms, err_precision=8, dtype="sci"), columns=["Regressors", "Parameters", "ERR"], ) print(r) plot_results(y=y_test, yhat=yhat, n=1000) ee = compute_residues_autocorrelation(y_test, yhat) plot_residues_correlation(data=ee, title="Residues", ylabel="$e^2$") x1e = compute_cross_correlation(y_test, yhat, x_test) plot_residues_correlation(data=x1e, title="Residues", ylabel="$x_1e$")
Regressors Parameters ERR 0 y(k-1) 1.9999E-01 9.57682046E-01 1 x1(k-2) 9.0003E-01 3.87716434E-02 2 x1(k-1)y(k-1) 1.0009E-01 3.54306118E-03
