def gen_data_frame(links_coeffs, time, seed_val):
# Set the random seed
np.random.seed(seed_val)
# Generate the data
data, _ = pp.var_process(links_coeffs, T=time)
# Get the true parents
true_parents = _get_parent_graph(links_coeffs)
return pp.DataFrame(data), true_parents
def gen_data_frame(links_coeffs, time, seed_val):
# Set the random seed
np.random.seed(seed_val)
# Generate the data
data, _ = pp.var_process(links_coeffs, T=time)
# Get the true parents
true_parents = _get_parent_graph(links_coeffs)
return pp.DataFrame(data), true_parents
def a_sample(request):
# Set the parameters
links_coeffs, time, seed_val = request.param
# Set the random seed
np.random.seed(seed_val)
# Generate the data
data, _ = pp.var_process(links_coeffs, T=time)
# Get the true parents
true_parents = _get_parent_graph(links_coeffs)
return pp.DataFrame(data), true_parents
def a_sample(request):
# Set the parameters
links_coeffs, time, seed_val = request.param
# Set the random seed
np.random.seed(seed_val)
# Generate the data
data, _ = pp.var_process(links_coeffs, T=time)
# Get the true parents
true_parents = _get_parent_graph(links_coeffs)
return pp.DataFrame(data), true_parents
def gen_process(a_process):
"""
Calls var_process for the process fixtures
"""
# Get the initial values and setup for the decay process
_, init_vals, coefs, expect = a_process
# Deducte the max time from the expected answer shape
max_time = expect.shape[0]
# Generate the data
data, true_parents_neighbors = pp.var_process(coefs,
T=max_time,
initial_values=init_vals,
use="no_noise")
return data, true_parents_neighbors
def gen_process(a_process):
"""
Calls var_process for the process fixtures
"""
# Get the initial values and setup for the decay process
_, init_vals, coefs, expect = a_process
# Deducte the max time from the expected answer shape
max_time = expect.shape[0]
# Generate the data
data, true_parents_neighbors = pp.var_process(coefs,
T=max_time,
initial_values=init_vals,
use="no_noise")
return data, true_parents_neighbors
def test_noise_generation(covariance_parameters):
"""
Ensure the covariance parameters are respected when the noise is generated
"""
# Unpack the parameters and covariance matrix
good_params, covar_matrix = covariance_parameters
# Generate noise-only from this parameter set
data, _ = pp.var_process(good_params, T=10000, use='inno_cov',
verbosity=0, initial_values=None)
# Get the covariance of the data set
covar_result = np.cov(data.T)
err_message = "Covariance of data does not match covariance implied by "+\
" parameter set"
np.testing.assert_allclose(covar_matrix, covar_result,
rtol=1e-1, atol=0.025,
verbose=True, err_msg=err_message)
def setUp(self):
auto = .5
coeff = 0.6
T = 1000
numpy.random.seed(42)
# True graph
links_coeffs = {
0: [((0, -1), auto)],
1: [((1, -1), auto), ((0, -1), coeff)],
2: [((2, -1), auto), ((1, -1), coeff)]
}
self.data, self.true_parents_coeffs = pp.var_process(links_coeffs, T=T)
T, N = self.data.shape
self.true_parents = _get_parent_graph(self.true_parents_coeffs)
def setUp(self):
auto = 0.6
coeff = 0.6
T = 1000
numpy.random.seed(42)
# True graph
links_coeffs = {
0: [((0, -1), auto)],
1: [((1, -1), auto), ((0, -1), coeff)],
2: [((2, -1), auto), ((1, -1), coeff)]
}
self.data, self.true_parents_coeffs = pp.var_process(links_coeffs, T=T)
T, N = self.data.shape
self.ci_par_corr = ParCorr(use_mask=False,
mask_type=None,
significance='analytic',
fixed_thres=None,
sig_samples=10000,
sig_blocklength=3,
confidence='analytic',
conf_lev=0.9,
conf_samples=10000,
conf_blocklength=1,
recycle_residuals=False,
verbosity=0)
self.ci_gpdc = GPDC(significance='analytic',
sig_samples=1000,
sig_blocklength=1,
confidence='bootstrap',
conf_lev=0.9,
conf_samples=100,
conf_blocklength=None,
use_mask=False,
mask_type='y',
recycle_residuals=False,
verbosity=0)
def test_noise_generation(covariance_parameters):
"""
Ensure the covariance parameters are respected when the noise is generated
"""
# Unpack the parameters and covariance matrix
good_params, covar_matrix = covariance_parameters
# Generate noise-only from this parameter set
data, _ = pp.var_process(good_params,
T=10000,
use='inno_cov',
verbosity=0,
initial_values=None)
# Get the covariance of the data set
covar_result = np.cov(data.T)
err_message = "Covariance of data does not match covariance implied by "+\
" parameter set"
np.testing.assert_allclose(covar_matrix,
covar_result,
rtol=1e-1,
atol=0.025,
verbose=True,
err_msg=err_message)
max_conds_px=max_conds_px,
)
return j, results_in_j
# Example data, here the real dataset can be loaded as a numpy array of shape
# (T, N)
numpy.random.seed(42) # Fix random seed
links_coeffs = {0: [((0, -1), 0.7)],
1: [((1, -1), 0.8), ((0, -1), 0.8)],
2: [((2, -1), 0.5), ((1, -2), 0.5)],
}
T = 500 # time series length
data, true_parents_neighbors = pp.var_process(links_coeffs, T=T)
T, N = data.shape
# Initialize dataframe object
dataframe = pp.DataFrame(data)
# Optionally specify variable names
var_names = [r'$X^0$', r'$X^1$', r'$X^2$', r'$X^3$']
# Significance level in condition-selection step. If a list of levels is is
# provided or pc_alpha=None, the optimal pc_alpha is automatically chosen via
# model-selection.
pc_alpha = 0.2 # [0.01, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5]
selected_variables = range(N) #[2] # [2] # [2]
# Maximum time lag
def run(self):
data, _ = pp.var_process(self.links_coeffs, T=1000)
dataframe = pp.DataFrame(data)
cond_ind_test = ParCorr()
self.pcmciobj = PCMCI(dataframe=dataframe, cond_ind_test=cond_ind_test)
self.results = self.pcmciobj.run_pcmci(tau_max=2, pc_alpha=None)
def plot__gpdc_get_single_residuals(self):
#######
ci_test = self.ci_gpdc
# ci_test = self.ci_par_corr
a = 0.
c = .3
T = 500
# Each key refers to a variable and the incoming links are supplied as a
# list of format [((driver, lag), coeff), ...]
links_coeffs = {
0: [((0, -1), a)],
1: [((1, -1), a), ((0, -1), c)],
}
numpy.random.seed(42)
data, true_parents_neighbors = pp.var_process(links_coeffs,
use='inv_inno_cov',
T=T)
dataframe = pp.DataFrame(data)
ci_test.set_dataframe(dataframe)
# ci_test.set_tau_max(1)
# X=[(1, -1)]
# Y=[(1, 0)]
# Z=[(0, -1)] + [(1, -tau) for tau in range(1, 2)]
# array, xyz, XYZ = ci_test.get_array(X, Y, Z,
# verbosity=0)]
# ci_test.run_test(X, Y, Z,)
def func(x):
return x * (1. - 4. * x**0 * numpy.exp(-x**2 / 2.))
true_residual = numpy.random.randn(3, T)
array = numpy.copy(true_residual)
array[1] += c * func(array[2]) #.sum(axis=0)
xyz = numpy.array([0, 1] + [2 for i in range(array.shape[0] - 2)])
print 'xyz ', xyz, numpy.where(xyz == 1)
target_var = 1
dim, T = array.shape
# array -= array.mean(axis=1).reshape(dim, 1)
c_std = c #/array[1].std()
# array /= array.std(axis=1).reshape(dim, 1)
array_orig = numpy.copy(array)
import matplotlib
from matplotlib import pyplot
(est_residual, pred) = ci_test._get_single_residuals(array,
target_var,
standardize=False,
return_means=True)
(resid_, pred_parcorr) = self.ci_par_corr._get_single_residuals(
array, target_var, standardize=False, return_means=True)
fig = pyplot.figure()
ax = fig.add_subplot(111)
ax.scatter(array_orig[2], array_orig[1])
ax.scatter(array_orig[2], pred, color='red')
ax.scatter(array_orig[2], pred_parcorr, color='green')
ax.plot(numpy.sort(array_orig[2]),
c_std * func(numpy.sort(array_orig[2])),
color='black')
pyplot.savefig('/home/jakobrunge/test/gpdctest.pdf')
max_conds_px=max_conds_px,
)
return j, results_in_j
# Example data, here the real dataset can be loaded as a numpy array of shape
# (T, N)
numpy.random.seed(42) # Fix random seed
links_coeffs = {0: [((0, -1), 0.7)],
1: [((1, -1), 0.8), ((0, -1), 0.8)],
2: [((2, -1), 0.5), ((1, -2), 0.5)],
}
T = 500 # time series length
data, true_parents_neighbors = pp.var_process(links_coeffs, T=T)
T, N = data.shape
# Optionally specify variable names
var_names = [r'$X^0$', r'$X^1$', r'$X^2$', r'$X^3$']
# Initialize dataframe object
dataframe = pp.DataFrame(data, var_names=var_names)
# Significance level in condition-selection step. If a list of levels is is
# provided or pc_alpha=None, the optimal pc_alpha is automatically chosen via
# model-selection.
pc_alpha = 0.2 # [0.01, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5]
selected_variables = range(N) #[2] # [2] # [2]
# Maximum time lag