def produce_simulation(self):
if not (path.exists(self.errorCSV)):
print(
"No simulation for these parameters exists in Uncertainty data. Proceeding with simulation"
)
# Initialize the models that are saved using the parameters declared above
predictor = Model(self.nstep)
predictor.load_FOPTD()
deviations = np.arange(0, self.maxError)
stdev = np.array([0])
error = np.array([0])
kp_pred = np.array([0])
theta_pred = np.array([0])
tau_pred = np.array([0])
kp_true = np.array([0])
theta_true = np.array([0])
tau_true = np.array([0])
for deviation in deviations:
numTrials = self.numTrials
nstep = self.nstep
timelength = self.timelength
trainFrac = self.trainFrac
# then simulates using the initialized model
sig = Signal(numTrials, nstep, timelength, trainFrac)
sig.training_simulation(KpRange=[1, 10],
tauRange=[1, 10],
thetaRange=[1, 10])
# In this case, since we are only loading the model, not trying to train it,
# we can use function simulate and preprocess
xData, yData = sig.simulate_and_preprocess(stdev=deviation)
# Function to make predictions based off the simulation
predictor.predict(sig, savePredict=False, plotPredict=False)
error = np.concatenate((predictor.errors, error))
kp_pred = np.concatenate((predictor.kpPredictions[:,
0], kp_pred))
theta_pred = np.concatenate(
(predictor.thetaPredictions[:, 0], theta_pred))
tau_pred = np.concatenate(
(predictor.tauPredictions[:, 0], tau_pred))
kp_true = np.concatenate((sig.kps, kp_true))
theta_true = np.concatenate((sig.thetas, theta_true))
tau_true = np.concatenate((sig.taus, tau_true))
stdev = np.concatenate((np.full_like(predictor.errors,
deviation), stdev))
sd = pd.DataFrame()
sd['stdev'] = stdev
sd['mse'] = error
sd['kpPred'] = kp_pred
sd['tauPred'] = tau_pred
sd['thetaPred'] = theta_pred
sd['kpTrue'] = kp_true
sd['tauTrue'] = tau_true
sd['thetaTrue'] = theta_true
sd.to_csv(self.errorCSV, index=False)
else:
print(
"Data exists for the parameters, proceeding to producing uncertainty estimate"
)
try:
sd = pd.read_csv(self.errorCSV).drop(['Unnamed: 0'], axis=1)
sd.drop(sd.tail(1).index, inplace=True)
except:
sd = pd.read_csv(self.errorCSV)
sd.drop(sd.tail(1).index, inplace=True)
self.errorDict = {}
prefixes = ['kp', 'tau', 'theta']
for prefix in prefixes:
sd[prefix + 'Error'] = (sd[prefix + 'Pred'] - sd[prefix + 'True'])
h = np.std(sd[prefix + 'Error'])
self.errorDict[prefix] = h
if self.plotUncertainty:
plt.figure(dpi=200)
plt.hist(sd[prefix + 'Error'], bins=100)
plt.xlabel('Standard Error in ' + prefix)
plt.ylabel("Frequency Distribution")
plt.figure(dpi=200)
plt.plot(sd[prefix + 'True'], sd[prefix + 'Pred'], '.')
plt.plot(np.linspace(1, 10), np.linspace(1, 10), 'r--')
plt.plot(np.linspace(1, 10), np.linspace(1, 10) + h, 'g--')
plt.plot(np.linspace(1, 10), np.linspace(1, 10) - h, 'g--')
def get_errors(self):
return self.errorDict.values
tau_pred = np.array([0])
kp_true = np.array([0])
theta_true = np.array([0])
tau_true = np.array([0])
for deviation in deviations:
# then simulates using the initialized model
sig = Signal(numTrials, nstep, timelength, trainFrac)
sig.training_simulation(KpRange=[0.5, 10],
tauRange=[0.5, 10],
thetaRange=[0.5, 10])
# In this case, since we are only loading the model, not trying to train it,
# we can use function simulate and preprocess
xData, yData = sig.simulate_and_preprocess(stdev=deviation)
# Function to make predictions based off the simulation
predictor.predict(sig, savePredict=False, plotPredict=False)
error = np.concatenate((predictor.errors, error))
kp_pred = np.concatenate((predictor.kpPredictions[:, 0], kp_pred))
theta_pred = np.concatenate(
(predictor.thetaPredictions[:, 0], theta_pred))
tau_pred = np.concatenate((predictor.tauPredictions[:, 0], tau_pred))
kp_true = np.concatenate((sig.kps, kp_true))
theta_true = np.concatenate((sig.thetas, theta_true))
tau_true = np.concatenate((sig.taus, tau_true))
stdev = np.concatenate((np.full_like(predictor.errors,
deviation), stdev))
