def test_10_bins_2D(self): """ Test that 2D marginals sum to 1 and have right shape. """ (bins, marginals) = plotP.calculate_2D_marginal_probs(self.P_samples, self.samples, self.lam_domain, nbins=10) nptest.assert_almost_equal(np.sum(marginals[(0, 1)]), 1.0) nptest.assert_equal(marginals[(0, 1)].shape, (10, 10))
def test_2D_smoothing(self): """ Test :meth:`bet.postProcess.plotP.smooth_marginals_2D`. """ (bins, marginals) = plotP.calculate_2D_marginal_probs(self.P_samples, self.samples, self.lam_domain, nbins=10) marginals_smooth = plotP.smooth_marginals_2D(marginals, bins, sigma=10.0) nptest.assert_equal(marginals_smooth[(0, 1)].shape, marginals[(0, 1)].shape) nptest.assert_almost_equal(np.sum(marginals_smooth[(0, 1)]), 1.0)
def test_5_10_bins_2D(self): """ Test that 1D marginals sum to 1 and have right shape. """ (bins, marginals) = plotP.calculate_2D_marginal_probs(self.samples, nbins = [5,10]) nptest.assert_almost_equal(np.sum(marginals[(0,1)]), 1.0) nptest.assert_equal(marginals[(0,1)].shape, (5,10))
def test_1_bin_2D(self): """ Test that 2D marginals sum to 1 and have right shape. """ (bins, marginals) = plotP.calculate_2D_marginal_probs(self.samples, nbins = 1) nptest.assert_almost_equal(marginals[(0,1)][0], 1.0) nptest.assert_equal(marginals[(0,1)].shape, (1,1))
def test_2D_smoothing(self): """ Test :meth:`bet.postProcess.plotP.smooth_marginals_2D`. """ (bins, marginals) = plotP.calculate_2D_marginal_probs(self.samples, nbins = 10) marginals_smooth = plotP.smooth_marginals_2D(marginals, bins, sigma = 10.0) nptest.assert_equal(marginals_smooth[(0,1)].shape, marginals[(0,1)].shape) nptest.assert_almost_equal(np.sum(marginals_smooth[(0,1)]), 1.0)
def test_plot_marginals_2D(self): """ Test :meth:`bet.postProcess.plotP.plot_2D_marginal_probs`. """ (bins, marginals) = plotP.calculate_2D_marginal_probs(self.P_samples, self.samples, self.lam_domain, nbins=10) marginals[(0, 1)][0][0] = 0.0 marginals[(0, 1)][0][1] *= 2.0 try: plotP.plot_2D_marginal_probs(marginals, bins, self.lam_domain, filename="file", interactive=False) go = True if os.path.exists("file_2D_0_1.eps"): os.remove("file_2D_0_1.eps") except (RuntimeError, TypeError, NameError): go = False nptest.assert_equal(go, True)
def test_plot_marginals_2D(self): """ Test :meth:`bet.postProcess.plotP.plot_2D_marginal_probs`. """ (bins, marginals) = plotP.calculate_2D_marginal_probs(self.samples, nbins = 10) marginals[(0,1)][0][0]=0.0 marginals[(0,1)][0][1]*=2.0 try: plotP.plot_2D_marginal_probs(marginals, bins, self.samples, filename = "file", interactive=False) go = True if os.path.exists("file_2D_0_1.png") and comm.rank == 0: os.remove("file_2D_0_1.png") except (RuntimeError, TypeError, NameError): go = False nptest.assert_equal(go, True)
''' Suggested changes for user: At this point, the only thing that should change in the plotP.* inputs should be either the nbins values or sigma (which influences the kernel density estimation with smaller values implying a density estimate that looks more like a histogram and larger values smoothing out the values more). There are ways to determine "optimal" smoothing parameters (e.g., see CV, GCV, and other similar methods), but we have not incorporated these into the code as lower-dimensional marginal plots generally have limited value in understanding the structure of a high dimensional non-parametric probability measure. ''' # calculate 2d marginal probs (bins, marginals2D) = plotP.calculate_2D_marginal_probs(input_samples, nbins = [30, 30]) # plot 2d marginals probs plotP.plot_2D_marginal_probs(marginals2D, bins, input_samples, filename = "validation_raw", file_extension = ".eps", plot_surface=False) # smooth 2d marginals probs (optional) marginals2D = plotP.smooth_marginals_2D(marginals2D, bins, sigma=0.1) # plot 2d marginals probs plotP.plot_2D_marginal_probs(marginals2D, bins, input_samples, filename = "validation_smooth", file_extension = ".eps", plot_surface=False) # calculate 1d marginal probs
Q_ref=Q_ref, rect_scale=0.25, cells_per_dimension=1) else: simpleFunP.uniform_partition_uniform_distribution_rectangle_scaled( data_set=my_discretization, Q_ref=Q_ref, rect_scale=0.25, M=50, num_d_emulate=1E5) # calculate probabilities making Monte Carlo assumption calculateP.prob(my_discretization) # calculate 2D marginal probabilities (bins, marginals2D) = plotP.calculate_2D_marginal_probs(my_discretization, nbins=10) # smooth 2D marginal probabilites for plotting (optional) marginals2D = plotP.smooth_marginals_2D(marginals2D, bins, sigma=1.0) # plot 2D marginal probabilities plotP.plot_2D_marginal_probs(marginals2D, bins, my_discretization, filename="contaminant_map", plot_surface=False, lam_ref=param_ref, lambda_label=labels, interactive=False) # calculate 1d marginal probs
''' Suggested changes for user: At this point, the only thing that should change in the plotP.* inputs should be either the nbins values or sigma (which influences the kernel density estimation with smaller values implying a density estimate that looks more like a histogram and larger values smoothing out the values more). There are ways to determine "optimal" smoothing parameters (e.g., see CV, GCV, and other similar methods), but we have not incorporated these into the code as lower-dimensional marginal plots have limited value in understanding the structure of a high dimensional non-parametric probability measure. ''' (bins, marginals2D) = plotP.calculate_2D_marginal_probs(P_samples=P, samples=lambda_emulate, lam_domain=lam_domain, nbins=[10, 10]) # smooth 2d marginals probs (optional) #marginals2D = plotP.smooth_marginals_2D(marginals2D,bins, sigma=0.01) # plot 2d marginals probs plotP.plot_2D_marginal_probs(marginals2D, bins, lam_domain, filename="linearMapValidation", plot_surface=False) # calculate 1d marginal probs (bins, marginals1D) = plotP.calculate_1D_marginal_probs(P_samples=P, samples=lambda_emulate, lam_domain=lam_domain,
center_pts_per_edge = 1) else: (d_distr_prob, d_distr_samples, d_Tree) = simpleFunP.unif_unif(data=data, Q_ref=Q_ref, M=50, bin_ratio=bin_ratio, num_d_emulate=1E5) # calculate probablities making Monte Carlo assumption (P, lam_vol, io_ptr) = calculateP.prob(samples=samples, data=data, rho_D_M=d_distr_prob, d_distr_samples=d_distr_samples) # calculate 2D marginal probabilities (bins, marginals2D) = plotP.calculate_2D_marginal_probs(P_samples = P, samples = samples, lam_domain = lam_domain, nbins = 10) # smooth 2D marginal probabilites for plotting (optional) marginals2D = plotP.smooth_marginals_2D(marginals2D,bins, sigma=1.0) # plot 2D marginal probabilities plotP.plot_2D_marginal_probs(marginals2D, bins, lam_domain, filename = "contaminant_map", plot_surface=False, lam_ref = ref_lam, lambda_label=labels, interactive=False) # calculate 1d marginal probs (bins, marginals1D) = plotP.calculate_1D_marginal_probs(P_samples = P, samples = samples, lam_domain = lam_domain, nbins = 20) # smooth 1d marginal probs (optional)
# calculate 2d marginal probs ''' Suggested changes for user: At this point, the only thing that should change in the plotP.* inputs should be either the nbins values or sigma (which influences the kernel density estimation with smaller values implying a density estimate that looks more like a histogram and larger values smoothing out the values more). There are ways to determine "optimal" smoothing parameters (e.g., see CV, GCV, and other similar methods), but we have not incorporated these into the code as lower-dimensional marginal plots have limited value in understanding the structure of a high dimensional non-parametric probability measure. ''' (bins, marginals2D) = plotP.calculate_2D_marginal_probs(P_samples = P, samples = lambda_emulate, lam_domain = lam_domain, nbins = [10, 10]) # smooth 2d marginals probs (optional) #marginals2D = plotP.smooth_marginals_2D(marginals2D,bins, sigma=0.01) # plot 2d marginals probs plotP.plot_2D_marginal_probs(marginals2D, bins, lam_domain, filename = "linearMapValidation", plot_surface=False) # calculate 1d marginal probs (bins, marginals1D) = plotP.calculate_1D_marginal_probs(P_samples = P, samples = lambda_emulate, lam_domain = lam_domain, nbins = [10, 10]) # smooth 1d marginal probs (optional) #marginals1D = plotP.smooth_marginals_1D(marginals1D, bins, sigma=0.01) # plot 1d marginal probs plotP.plot_1D_marginal_probs(marginals1D, bins, lam_domain, filename = "linearMapValidation")
if deterministic_discretize_D == True: (d_distr_prob, d_distr_samples, d_Tree) = simpleFunP.uniform_hyperrectangle( data=data, Q_ref=Q_ref, bin_ratio=bin_ratio, center_pts_per_edge=1 ) else: (d_distr_prob, d_distr_samples, d_Tree) = simpleFunP.unif_unif( data=data, Q_ref=Q_ref, M=50, bin_ratio=bin_ratio, num_d_emulate=1e5 ) # calculate probablities making Monte Carlo assumption (P, lam_vol, io_ptr) = calculateP.prob( samples=samples, data=data, rho_D_M=d_distr_prob, d_distr_samples=d_distr_samples ) # calculate 2D marginal probabilities (bins, marginals2D) = plotP.calculate_2D_marginal_probs(P_samples=P, samples=samples, lam_domain=lam_domain, nbins=10) # smooth 2D marginal probabilites for plotting (optional) marginals2D = plotP.smooth_marginals_2D(marginals2D, bins, sigma=1.0) # plot 2D marginal probabilities plotP.plot_2D_marginal_probs( marginals2D, bins, lam_domain, filename="contaminant_map", plot_surface=False, lam_ref=ref_lam, lambda_label=labels, interactive=False, )
simpleFunP.regular_partition_uniform_distribution_rectangle_scaled(data_set=my_discretization, Q_ref=Q_ref, rect_scale=0.25, cells_per_dimension = 1) else: simpleFunP.uniform_partition_uniform_distribution_rectangle_scaled(data_set=my_discretization, Q_ref=Q_ref, rect_scale=0.25, M=50, num_d_emulate=1E5) # calculate probablities making Monte Carlo assumption calculateP.prob(my_discretization) # calculate 2D marginal probabilities (bins, marginals2D) = plotP.calculate_2D_marginal_probs(my_discretization, nbins = 10) # smooth 2D marginal probabilites for plotting (optional) marginals2D = plotP.smooth_marginals_2D(marginals2D, bins, sigma=1.0) # plot 2D marginal probabilities plotP.plot_2D_marginal_probs(marginals2D, bins, my_discretization, filename = "contaminant_map", plot_surface=False, lam_ref = param_ref, lambda_label=labels, interactive=False) # calculate 1d marginal probs (bins, marginals1D) = plotP.calculate_1D_marginal_probs(my_discretization, nbins = 20) # smooth 1d marginal probs (optional)
Suggested changes for user: At this point, the only thing that should change in the plotP.* inputs should be either the nbins values or sigma (which influences the kernel density estimation with smaller values implying a density estimate that looks more like a histogram and larger values smoothing out the values more). There are ways to determine "optimal" smoothing parameters (e.g., see CV, GCV, and other similar methods), but we have not incorporated these into the code as lower-dimensional marginal plots have limited value in understanding the structure of a high dimensional non-parametric probability measure. ''' (bins, marginals2D) = \ plotP.calculate_2D_marginal_probs(P_samples=P, samples=lambda_emulate, lam_domain=lam_domain, nbins=10) # smooth 2d marginals probs (optional) # marginals2D = plotP.smooth_marginals_2D(marginals2D,bins, sigma=0.1) # plot 2d marginals probs plotP.plot_2D_marginal_probs(marginals2D, bins, lam_domain, filename="linearMap", plot_surface=False) # calculate 1d marginal probs (bins, marginals1D) = \ plotP.calculate_1D_marginal_probs(P_samples=P, samples=lambda_emulate, lam_domain=lam_domain, nbins=10)