def transform_1d_w_der(self, eval_point):
# the first element of the transform is the original eval_point
# hence +1 on the next line
y_first = np.zeros((eval_point.shape[0], self.n_w_knots + 2))
for i in range(self.n_w_knots + 2):
y_first[:, i] = BSpline.construct_fast(
self.w_np_knots,
(np.arange(len(self.w_knots) + 2) == i).astype(float),
3,
extrapolate=False).derivative()(eval_point)
return y_first
def transform_1d_l_der(self, eval_point):
y_first = np.zeros((eval_point.shape[0], self.n_l_knots + 2))
for i in range(self.n_l_knots + 2):
y_first[:, i] = BSpline.construct_fast(
self.l_np_knots,
(np.arange(len(self.l_knots) + 2) == i).astype(float),
3,
extrapolate=False).derivative()(eval_point)
return y_first
def transform_1d_l(self, eval_point):
# the first element of the transform is the original eval_point
# hence +1 on the next line
y_py = np.zeros((eval_point.shape[0], self.n_l_knots + 2 + 1))
y_py[:, 0] = eval_point
for i in range(self.n_l_knots + 2):
y_py[:, i + 1] = BSpline.construct_fast(
self.l_np_knots,
(np.arange(len(self.l_knots) + 2) == i).astype(float),
3,
extrapolate=True)(eval_point)
return y_py
def colloc_matrix(x, knots, order, deriv=None):
"""
Create collocation matrix of a univariate function on `x` in terms of a
B-spline representation of order `k`.
The computation of the splines is based on the scipy-package.
Parameters
----------
x : ndarray, shape (N,)
`N` points to evaluate the B-spline at.
knots : ndarray, shape >= (k+1,)
Vector of knots derived from breaks (with appropriate endpoint
multiplicity).
order : int, positive
Order `k` of the B-spline (4 = cubic).
deriv: int, positive, optional
Derivative of the B-spline partition (defaults to 0).
Returns
-------
collmat : ndarray, shape (N, n-k)
Collocation matrix, `n` is the size of `knots`.
See Also
--------
augment_breaks
"""
if deriv is None:
deriv = 0
if deriv >= order:
return np.zeros((max(x.size, 1), knots.size - order))
else:
# create spline using scipy.interpolate
coll = np.empty((max(x.size, 1), knots.size - order))
for n in range(knots.size - order):
c = np.zeros(knots.size - order)
c[n] = 1.
b = BSpline.construct_fast(knots, c, order-1, extrapolate=False)
coll[:, n] = b(x, nu=deriv)
return coll
def splev(
x_new: np.ndarray,
t: np.ndarray,
c: np.ndarray,
k: int = 3,
extrapolate: bool | str = True,
) -> np.ndarray:
"""Generate a BSpline object on the fly from knots and coefficients and
evaluate it on x_new.
See :class:`scipy.interpolate.BSpline` for all parameters.
"""
t = _memoryview_safe(t)
c = _memoryview_safe(c)
x_new = _memoryview_safe(x_new)
spline = BSpline.construct_fast(t, c, k, axis=0, extrapolate=extrapolate)
return spline(x_new)
def fit(self, X, y=None):
"""Compute knot positions of splines.
Parameters
----------
X : array-like of shape (n_samples, n_features)
The data.
y : None
Ignored.
Returns
-------
self : object
Fitted transformer.
"""
X = self._validate_data(
X,
reset=True,
accept_sparse=False,
ensure_min_samples=2,
ensure_2d=True,
)
n_samples, n_features = X.shape
if not (
isinstance(self.degree, numbers.Integral) and self.degree >= 0
):
raise ValueError("degree must be a non-negative integer.")
if not (
isinstance(self.n_knots, numbers.Integral) and self.n_knots >= 2
):
raise ValueError("n_knots must be a positive integer >= 2.")
if isinstance(self.knots, str) and self.knots in [
"uniform",
"quantile",
]:
base_knots = self._get_base_knot_positions(
X, n_knots=self.n_knots, knots=self.knots
)
else:
base_knots = check_array(self.knots, dtype=np.float64)
if base_knots.shape[0] < 2:
raise ValueError(
"Number of knots, knots.shape[0], must be >= " "2."
)
elif base_knots.shape[1] != n_features:
raise ValueError("knots.shape[1] == n_features is violated.")
elif not np.all(np.diff(base_knots, axis=0) > 0):
raise ValueError("knots must be sorted without duplicates.")
if self.extrapolation not in (
"error",
"constant",
"linear",
"continue",
"periodic",
):
raise ValueError(
"extrapolation must be one of 'error', "
"'constant', 'linear', 'continue' or 'periodic'."
)
if not isinstance(self.include_bias, (bool, np.bool_)):
raise ValueError("include_bias must be bool.")
# number of knots for base interval
n_knots = base_knots.shape[0]
if self.extrapolation == "periodic" and n_knots <= self.degree:
raise ValueError(
"Periodic splines require degree < n_knots. Got n_knots="
f"{n_knots} and degree={self.degree}."
)
# number of splines basis functions
if self.extrapolation != "periodic":
n_splines = n_knots + self.degree - 1
else:
# periodic splines have self.degree less degrees of freedom
n_splines = n_knots - 1
degree = self.degree
n_out = n_features * n_splines
# We have to add degree number of knots below, and degree number knots
# above the base knots in order to make the spline basis complete.
if self.extrapolation == "periodic":
# For periodic splines the spacing of the first / last degree knots
# needs to be a continuation of the spacing of the last / first
# base knots.
period = base_knots[-1] - base_knots[0]
knots = np.r_[
base_knots[-(degree + 1): -1] - period,
base_knots,
base_knots[1: (degree + 1)] + period
]
else:
# Eilers & Marx in "Flexible smoothing with B-splines and
# penalties" https://doi.org/10.1214/ss/1038425655 advice
# against repeating first and last knot several times, which
# would have inferior behaviour at boundaries if combined with
# a penalty (hence P-Spline). We follow this advice even if our
# splines are unpenalized. Meaning we do not:
# knots = np.r_[
# np.tile(base_knots.min(axis=0), reps=[degree, 1]),
# base_knots,
# np.tile(base_knots.max(axis=0), reps=[degree, 1])
# ]
# Instead, we reuse the distance of the 2 fist/last knots.
dist_min = base_knots[1] - base_knots[0]
dist_max = base_knots[-1] - base_knots[-2]
knots = np.r_[
linspace(
base_knots[0] - degree * dist_min,
base_knots[0] - dist_min,
num=degree,
),
base_knots,
linspace(
base_knots[-1] + dist_max,
base_knots[-1] + degree * dist_max,
num=degree,
),
]
# With a diagonal coefficient matrix, we get back the spline basis
# elements, i.e. the design matrix of the spline.
# Note, BSpline appreciates C-contiguous float64 arrays as c=coef.
coef = np.eye(n_splines, dtype=np.float64)
if self.extrapolation == "periodic":
coef = np.concatenate((coef, coef[:degree, :]))
extrapolate = self.extrapolation in ["periodic", "continue"]
bsplines = [
BSpline.construct_fast(
knots[:, i], coef, self.degree, extrapolate=extrapolate
)
for i in range(n_features)
]
self.bsplines_ = bsplines
self.n_features_out_ = n_out - n_features * (1 - self.include_bias)
return self
def _make_random_spline(n=35, k=3):
np.random.seed(123)
t = np.sort(np.random.random(n+k+1))
c = np.random.random(n)
return BSpline.construct_fast(t, c, k)
def _make_random_spline(n=35, k=3):
np.random.seed(123)
t = np.sort(np.random.random(n + k + 1))
c = np.random.random(n)
return BSpline.construct_fast(t, c, k)
class Fitting:
"""
Fitting of the curve in the initial days to obtain the estimated values
for day day march, 16th, 2020, of the pandemic.
p: (tau, sigma, rho, delta, gamma1, gamma2).
tyme_varying: definitions about beta and mu bspline (knots, number of parameters and order)
hmax: max value Runge Kutta integration method.
"""
def __init__(
self,
p,
time_varying,
initial_day='2020-03-16',
final_day='2020-07-15',
hmax=0.15,
init_cond={
'x0': [0.8, 0.3, 0.00001, 0.00001, 0.00001],
'bounds': [(0, 1), (0, 1), (0, 0.0001), (0, 0.0001), (0, 0.0001)]
}):
# parameters pre-determined
self.p = np.array(p, dtype=np.float64)
self.hmax = hmax
self.init_cond = init_cond
self.initial_day = initial_day
self.final_day = final_day
# Reading data
ROOT_DIR = os.path.dirname(os.path.abspath(__file__))
filename = os.path.join(ROOT_DIR, "../data/covid_data_organized.csv")
df = pd.read_csv(filename, index_col=0)
self.T = df['confirmed'].loc[initial_day:final_day].to_numpy()
self.D = df['deaths'].loc[initial_day:final_day].to_numpy()
self.tf = len(self.T)
# time-varying hiperparameters
self.sbeta = time_varying['beta']['coefficients']
self.order_beta = time_varying['beta']['bspline_order']
self.smu = time_varying['mu']['coefficients']
self.order_mu = time_varying['mu']['bspline_order']
self.knots_beta = np.linspace(0, self.tf,
self.sbeta + self.order_beta + 1)
self.knots_mu = np.linspace(0, self.tf, self.smu + self.order_mu + 1)
# define the time-varying parameters
self.beta = BSpline(self.knots_beta, np.zeros(self.sbeta),
self.order_beta)
self.mu = BSpline(self.knots_mu, np.zeros(self.smu), self.order_mu)
# Calculate initial conditions
print('Model SEIAQR for Covid-19')
print('-------------------------')
print('Estimating initial Conditions...')
self.initial_conditions()
print('Initiation done!')
def derivative(self, x, t, alpha, beta_, mu_, tau, sigma, rho, delta,
gamma1, gamma2):
"""
System of derivatives simplified.
"""
beta = max(beta_(t), 0)
mu = max(mu_(t), 0)
dx = np.zeros(shape=(len(x), ))
dx[4] = -beta * x[4] * (x[1] + x[2])
dx[0] = -dx[4] - (rho * delta + tau) * x[0]
dx[1] = tau * x[0] - (sigma + rho) * x[1]
dx[2] = sigma * alpha * x[1] - (gamma1 + rho) * x[2]
dx[5] = gamma1 * x[2] + gamma2 * x[3]
dx[6] = mu * x[3]
dx[7] = sigma * (1 - alpha) * x[1] + rho * (delta * x[0] + x[1] + x[2])
dx[3] = dx[7] - gamma2 * x[3] - dx[6]
return dx
def integrate(self, theta, p, time=[]):
"""
Integrate the system given a tuple of parameters.
p is the parameters estimated by the literature. time is a list always
with 0 that indicates where to integrate.
"""
if len(time) == 0:
time = range(self.tf)
self.beta = self.beta.construct_fast(self.knots_beta,
theta[1:1 + self.sbeta],
self.order_beta)
self.mu = self.mu.construct_fast(self.knots_mu, theta[-self.smu:],
self.order_mu)
self.states = odeint(func=self.derivative,
y0=self.y0,
t=time,
args=(theta[0], self.beta, self.mu, *p),
hmax=self.hmax)
return self.states
def rt_calculation(self, theta):
"""
Calculate the reproduction number based on the model.
"""
S = self.states[:, 4]
self.repro_number = np.zeros(shape=(2, self.tf))
beta_ = self.beta.construct_fast(self.knots_beta,
theta[1:1 + self.sbeta],
self.order_beta)
mu_ = self.mu.construct_fast(self.knots_mu, theta[-self.smu:],
self.order_mu)
alpha = theta[0]
tau, sigma, rho, delta, gamma1, _ = self.p
for t in range(self.tf):
beta = max(beta_(t), 0)
mu = max(mu_(t), 0)
varphi = np.array([beta * tau, beta * tau * S[t]
]) # difference between R0 and Rt
varphi /= ((rho * delta + tau) * (sigma + rho))
r0_rt = 1 / 2 * (varphi + np.sqrt(varphi**2 + varphi *
(4 * sigma * alpha) /
(rho + gamma1)))
self.repro_number[:, t] = r0_rt
def initial_conditions(self):
"""
Estimate Initial Conditions
"""
parameters = self.p[[0, 1, 4, 5]]
model = FittingInitial(parameters, self.initial_day, self.hmax)
E0, I0, A0, _, Q0, R0 = model.get_initial_values(
self.init_cond['x0'], self.init_cond['bounds'])
self.initial_phase = model.y
T0 = self.T[0]
D0 = self.D[0]
S0 = 1 - E0 - I0 - A0 - Q0 - R0
self.y0 = [E0, I0, A0, Q0 - D0, S0, R0, D0, T0]
def objective(self, theta, psi):
# theta = (alpha, beta_1, ..., beta_s, mu_1, ..., mu_r)
integrate = self.integrate(theta, self.p)
obj1 = (self.T - integrate[:, 7]) @ self.weights @ (self.T -
integrate[:, 7])
obj2 = (self.D - integrate[:, 6]) @ self.weights @ (self.D -
integrate[:, 6])
obj = 100 * (obj1 + psi * obj2)
return obj
def fit(self, psi, x0, bounds, algorithm='L-BFGS-B'):
"""
Fits the model to the data and recover the estimated parameters.
"""
self.weights = np.array([[min(i, j) + 15 for i in range(self.tf)]
for j in range(self.tf)])
self.weights = np.linalg.inv(self.weights)
print('Starting estimation!')
t0 = time.time()
res = minimize(fun=self.objective,
x0=x0,
method=algorithm,
bounds=bounds,
args=(psi, ))
self.counter = time.time() - t0
print('Estimation finished. It took {} seconds'.format(self.counter))
curve = self.integrate(res.x, self.p)
# Rt calculation
self.rt_calculation(res.x)
# Store important values
self.obj = res.fun
self.res = res
self.theta = res.x
self.psi = psi
self.x0 = x0
self.bounds = bounds
self.algorithm = algorithm
n = self.tf
K = len(self.theta)
# Estimate variances
self.sigma2_1 = (self.T - curve[:, 7]) @ self.weights @ (
self.T - curve[:, 7]) / (n - K)
self.sigma2_2 = (self.D - curve[:, 6]) @ self.weights @ (
self.D - curve[:, 6]) / (n - K)
# Information Criterion
common = n * np.log(self.obj / n)
self.aic = common + 2 * K
self.bic = common + np.log(n) * K
self.aicc = common + 2 * K * n / (n - K - 1)
return res.x
def check_residuals(self):
"""
Simple residual analysis for the fitting. It must be called after the
function fit.
"""
diary_curves = self.integrate(self.theta, self.p)
T = diary_curves[:, 7]
D = diary_curves[:, 6]
errorT = np.diff(self.T - T)
errorD = np.diff(self.D - D)
return errorT, errorD
def correlation_matrix(self):
"""
Calculate the correlation matrix with an estimated parameter. It must be called after the
function fit.
"""
def f(parameters, time, curve):
theta = parameters[0:len(self.theta)]
#p = parameters[len(self.theta):]
return self.integrate(theta, self.p, [0, time])[1, curve]
K = len(self.theta) #+ len(self.p)
J1 = np.zeros((self.tf, K))
J2 = np.zeros((self.tf, K))
parameters = self.theta #np.concatenate([self.theta, self.p])
for i in range(self.tf):
J1[i, :] = approx_fprime(parameters, f,
np.ones_like(parameters) * 1e-5, i, 7)
J2[i, :] = approx_fprime(parameters, f,
np.ones_like(parameters) * 1e-5, i, 6)
# Fisher Information matrix
FIM = J1.transpose() @ self.weights @ J1 / self.sigma2_1 + J2.transpose(
) @ self.weights @ J2 / self.sigma2_2
# Covariance matrix
C = np.linalg.inv(FIM)
# Correlation matrix
R = [[C[i, j] / np.sqrt(C[i, i] * C[j, j]) for i in range(K)]
for j in range(K)]
return np.array(R)
def _get_exp(self, pathname):
with open(pathname, 'r') as f:
line = f.readline()
while line != '':
lineold = line
line = f.readline()
exp = lineold[:lineold.find(';')]
exp = 1 if exp == 'exp' else int(exp) + 1
return exp
def save_experiment(self, objective_function):
"""
Save information about the experiment.
objective_function: name given to compare, like quadratic and divided.
"""
pathname = '../experiments/' + objective_function + '.csv'
if not os.path.exists(pathname):
with open(pathname, 'w') as f:
f.write(
'exp;tau;sigma;rho;delta;gamma1;gamma2;sbeta;order_beta;smu;order_mu;'
)
f.write('initial_day;final_day;hmax;psi;x0;bounds;algorithm;')
f.write('E0;I0;A0;Q0;R0;D0;T0;alpha;beta;mu;obj;time')
f.write('\n')
else:
with open(pathname, 'a') as f:
exp = self._get_exp(pathname)
tau, sigma, rho, delta, gamma1, gamma2 = self.p
info = [
exp, tau, sigma, rho, delta, gamma1, gamma2, self.sbeta,
self.order_beta, self.smu, self.order_mu
]
info2 = [self.initial_day, self.final_day, self.hmax, self.psi]
f.write(';'.join(map(str, info)))
f.write(';')
f.write(';'.join(map(str, info2)))
f.write(';')
f.write(str(self.x0))
f.write(';')
f.write(str(self.bounds))
f.write(';')
f.write(self.algorithm + ';')
f.write(';'.join(map(str, self.y0)))
f.write(str(self.theta[0]) + ';')
f.write(str(self.theta[1:1 + self.sbeta]))
f.write(';')
f.write(str(self.theta[-self.smu:]))
f.write(';')
f.write(str(self.obj) + ';')
f.write(str(self.counter))
f.write('\n')
# if __name__ == '__main__':
# p = [0.3125, 0.5, 2e-5, 1, 1/9.5, 1/18]
# beta = {'sbeta': 4, 'bspline_order': 3}
# model = Fitting(p, beta)
# psi = 0
# bounds = [(0.7,0.95), (0.05,0.3), (0.05,0.3), (0.05,0.3), (0.05,0.3), (0, 0.2)] # bound the parameters
# x0 = [0.9, 0.1, 0.1, 0.1, 0.1, 0.12/14] # initial guess
# theta = model.fit(psi, x0, bounds)
