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plotting_utils.py
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plotting_utils.py
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# Some plotting utility functions used for generating diagnostic plots and visualisations in the paper
import os
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import rc
from matplotlib.ticker import AutoMinorLocator
from matplotlib.patches import Patch
def plot_causal_structure(a: np.ndarray, log_transform=False, diag=True, plot_legend=True, plot_ticks=True):
if not diag:
np.fill_diagonal(a, np.Inf)
plotting_setup()
fig = plt.figure(dpi=200)
ax = fig.add_subplot(111, aspect='equal')
plt.pcolormesh(a, edgecolor="white", linewidth=1, facecolor=['#440154', 'silver'])
ax.set_xticks([], [])
ax.set_yticks([], [])
ax.set_ylabel("Effect")
ax.set_xlabel("Cause")
if plot_legend:
legend_elements = [Patch(facecolor='#fde725', label='Granger-causal'),
Patch(facecolor='#440154', label='Not Granger-causal')]
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.85, box.height])
plt.legend(legend_elements, ['Granger-causal', 'Not Granger-causal'], loc='center left',
bbox_to_anchor=(1, 0.5))
plt.show()
if log_transform:
plt.pcolormesh(np.log(np.abs(a + 1e-6)), edgecolor="white", linewidth=1)
else:
plt.pcolormesh(a, edgecolor="white", linewidth=1)
if plot_ticks:
plt.xticks(ticks=np.arange(0.5, a.shape[0] + 0.5), labels=[str(j) for j in np.arange(1, a.shape[0] + 1)])
plt.yticks(ticks=np.arange(0.5, a.shape[0] + 0.5), labels=[str(j) for j in np.arange(1, a.shape[0] + 1)])
plt.ylabel("Effect")
plt.xlabel("Cause")
plt.show()
def plot_causal_structure_signed(a: np.ndarray, diag=True):
if not diag:
np.fill_diagonal(a, np.Inf)
plotting_setup()
fig = plt.figure(dpi=200)
ax = fig.add_subplot(111, aspect='equal')
plt.pcolormesh(a, edgecolor="white", linewidth=1)
ax.set_xticks([], [])
ax.set_yticks([], [])
ax.set_ylabel("Effect")
ax.set_xlabel("Cause")
legend_elements = [Patch(facecolor='#440154', label='Negative'),
Patch(facecolor='#21918c', label='No GC relatiosnhip'),
Patch(facecolor='#fde725', label='Positive')]
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.85, box.height])
plt.legend(legend_elements, ['Negative', 'No GC relatiosnhip', 'Positive'], loc='center left', bbox_to_anchor=(1, 0.5))
plt.show()
def set_axis_style(ax, labels):
ax.get_xaxis().set_tick_params(direction='out')
ax.xaxis.set_ticks_position('bottom')
ax.set_xticks(np.arange(1, len(labels) + 1))
ax.set_xticklabels(labels, rotation='vertical')
ax.set_xlim(0.25, len(labels) + 0.75)
def plotting_setup(font_size=20):
# plot settings
plt.style.use("seaborn-colorblind")
plt.rcParams['font.size'] = font_size
rc('text', usetex=False)
plt.rcParams["font.family"] = "Times New Roman"
rc('font', **{'family': 'serif', 'serif': ['Computer Modern']})
def plot_lambda_gamma_grid(logdir, dec=3, cmap='viridis', title=None, savedir=None, display=False, **kwargs):
plotting_setup(20)
lambdas = np.loadtxt(fname=os.path.join(logdir, "lambdas.csv"))
gammas = np.loadtxt(fname=os.path.join(logdir, "gammas.csv"))
aurocs = np.loadtxt(fname=os.path.join(logdir, "mean_aurocs.csv"))
auprcs = np.loadtxt(fname=os.path.join(logdir, "mean_auprcs.csv"))
# AUROC
fig, ax = plt.subplots(**kwargs)
plt.imshow(aurocs, cmap=cmap, interpolation='nearest')
plt.yticks(ticks=np.arange(0, len(lambdas)), labels=np.round(lambdas, decimals=dec))
plt.ylabel('λ')
plt.xticks(ticks=np.arange(0, len(gammas)), labels=np.round(gammas, decimals=dec))
plt.xlabel('γ')
plt.colorbar(label="AUROC")
plt.minorticks_on()
minor_locator = AutoMinorLocator(2)
ax.xaxis.set_minor_locator(minor_locator)
ax.yaxis.set_minor_locator(minor_locator)
plt.grid(which='minor', color="white", linewidth=4)
if title is not None:
plt.title(title)
if savedir is not None:
plt.savefig(os.path.join(savedir, "aurocs.png"), dpi=300)
if display:
plt.show()
# AUPRC
fig, ax = plt.subplots(**kwargs)
plt.imshow(auprcs, cmap=cmap, interpolation='nearest')
plt.yticks(ticks=np.arange(0, len(lambdas)), labels=np.round(lambdas, decimals=dec))
plt.ylabel('λ')
plt.xticks(ticks=np.arange(0, len(gammas)), labels=np.round(gammas, decimals=dec))
plt.xlabel('γ')
plt.colorbar(label="AUPRC")
plt.minorticks_on()
minor_locator = AutoMinorLocator(2)
ax.xaxis.set_minor_locator(minor_locator)
ax.yaxis.set_minor_locator(minor_locator)
plt.grid(which='minor', color="white", linewidth=4)
if title is not None:
plt.title(title)
if savedir is not None:
plt.savefig(os.path.join(savedir, "auprcs.png"), dpi=300)
if display:
plt.show()
return
def plot_lambda_gamma_grid_binary(logdir, dec=3, cmap='viridis', title=None, savedir=None, display=False, **kwargs):
plotting_setup(20)
lambdas = np.loadtxt(fname=os.path.join(logdir, "lambdas.csv"))
gammas = np.loadtxt(fname=os.path.join(logdir, "gammas.csv"))
accs = np.loadtxt(fname=os.path.join(logdir, "mean_accs.csv"))
bal_accs = np.loadtxt(fname=os.path.join(logdir, "mean_bal_accs.csv"))
precs = np.loadtxt(fname=os.path.join(logdir, "mean_precs.csv"))
recs = np.loadtxt(fname=os.path.join(logdir, "mean_recs.csv"))
# Accuracies
fig, ax = plt.subplots(**kwargs)
plt.imshow(accs, cmap=cmap, interpolation='nearest')
plt.yticks(ticks=np.arange(0, len(lambdas)), labels=np.round(lambdas, decimals=dec))
plt.ylabel('λ')
plt.xticks(ticks=np.arange(0, len(gammas)), labels=np.round(gammas, decimals=dec))
plt.xlabel('γ')
plt.colorbar(label="Accuracy")
plt.minorticks_on()
minor_locator = AutoMinorLocator(2)
ax.xaxis.set_minor_locator(minor_locator)
ax.yaxis.set_minor_locator(minor_locator)
plt.grid(which='minor', color="white", linewidth=4)
if title is not None:
plt.title(title)
if savedir is not None:
plt.savefig(os.path.join(savedir, "accs.png"), dpi=300)
if display:
plt.show()
# Balanced accuracies
fig, ax = plt.subplots(**kwargs)
plt.imshow(bal_accs, cmap=cmap, interpolation='nearest')
plt.yticks(ticks=np.arange(0, len(lambdas)), labels=np.round(lambdas, decimals=dec))
plt.ylabel('λ')
plt.xticks(ticks=np.arange(0, len(gammas)), labels=np.round(gammas, decimals=dec))
plt.xlabel('γ')
plt.colorbar(label="Bal. Accuracy")
plt.minorticks_on()
minor_locator = AutoMinorLocator(2)
ax.xaxis.set_minor_locator(minor_locator)
ax.yaxis.set_minor_locator(minor_locator)
plt.grid(which='minor', color="white", linewidth=4)
if title is not None:
plt.title(title)
if savedir is not None:
plt.savefig(os.path.join(savedir, "bal_accs.png"), dpi=300)
if display:
plt.show()
# Precision
fig, ax = plt.subplots(**kwargs)
plt.imshow(precs, cmap=cmap, interpolation='nearest')
plt.yticks(ticks=np.arange(0, len(lambdas)), labels=np.round(lambdas, decimals=dec))
plt.ylabel('λ')
plt.xticks(ticks=np.arange(0, len(gammas)), labels=np.round(gammas, decimals=dec))
plt.xlabel('γ')
plt.colorbar(label="Precision")
plt.minorticks_on()
minor_locator = AutoMinorLocator(2)
ax.xaxis.set_minor_locator(minor_locator)
ax.yaxis.set_minor_locator(minor_locator)
plt.grid(which='minor', color="white", linewidth=4)
if title is not None:
plt.title(title)
if savedir is not None:
plt.savefig(os.path.join(savedir, "precs.png"), dpi=300)
if display:
plt.show()
# Recall
fig, ax = plt.subplots(**kwargs)
plt.imshow(recs, cmap=cmap, interpolation='nearest')
plt.yticks(ticks=np.arange(0, len(lambdas)), labels=np.round(lambdas, decimals=dec))
plt.ylabel('λ')
plt.xticks(ticks=np.arange(0, len(gammas)), labels=np.round(gammas, decimals=dec))
plt.xlabel('γ')
plt.colorbar(label="Recall")
plt.minorticks_on()
minor_locator = AutoMinorLocator(2)
ax.xaxis.set_minor_locator(minor_locator)
ax.yaxis.set_minor_locator(minor_locator)
plt.grid(which='minor', color="white", linewidth=4)
if title is not None:
plt.title(title)
if savedir is not None:
plt.savefig(os.path.join(savedir, "recs.png"), dpi=300)
if display:
plt.show()
return
def plot_lambda_gamma_grid_signed(logdir, dec=3, cmap='viridis', title=None, savedir=None, display=False, **kwargs):
plotting_setup(20)
lambdas = np.loadtxt(fname=os.path.join(logdir, "lambdas.csv"))
gammas = np.loadtxt(fname=os.path.join(logdir, "gammas.csv"))
bal_accs_pos = np.loadtxt(fname=os.path.join(logdir, "mean_bal_accs_pos.csv"))
bal_accs_neg = np.loadtxt(fname=os.path.join(logdir, "mean_bal_accs_neg.csv"))
fig, ax = plt.subplots(**kwargs)
plt.imshow(bal_accs_pos, cmap=cmap, interpolation='nearest')
plt.yticks(ticks=np.arange(0, len(lambdas)), labels=np.round(lambdas, decimals=dec))
plt.ylabel('λ')
plt.xticks(ticks=np.arange(0, len(gammas)), labels=np.round(gammas, decimals=dec))
plt.xlabel('γ')
plt.colorbar(label="BA pos.")
plt.minorticks_on()
minor_locator = AutoMinorLocator(2)
ax.xaxis.set_minor_locator(minor_locator)
ax.yaxis.set_minor_locator(minor_locator)
plt.grid(which='minor', color="white", linewidth=4)
if title is not None:
plt.title(title)
if savedir is not None:
plt.savefig(os.path.join(savedir, "bal_accs_pos.png"), dpi=300)
if display:
plt.show()
fig, ax = plt.subplots(**kwargs)
plt.imshow(bal_accs_neg, cmap=cmap, interpolation='nearest')
plt.yticks(ticks=np.arange(0, len(lambdas)), labels=np.round(lambdas, decimals=dec))
plt.ylabel('λ')
plt.xticks(ticks=np.arange(0, len(gammas)), labels=np.round(gammas, decimals=dec))
plt.xlabel('γ')
plt.colorbar(label="BA neg.")
plt.minorticks_on()
minor_locator = AutoMinorLocator(2)
ax.xaxis.set_minor_locator(minor_locator)
ax.yaxis.set_minor_locator(minor_locator)
plt.grid(which='minor', color="white", linewidth=4)
if title is not None:
plt.title(title)
if savedir is not None:
plt.savefig(os.path.join(savedir, "bal_accs_neg.png"), dpi=300)
if display:
plt.show()
return
def plot_stability(alphas, agreements, agreements_ground=None):
plotting_setup()
fig = plt.figure(figsize=(7.5, 6.5))
plt.plot(alphas, agreements, linewidth=4, label="Agreement (ς)", marker="^", markersize=10)
if agreements_ground is not None:
plt.plot(alphas, agreements_ground, linewidth=4, label="BA ground truth", marker="x", markersize=10)
plt.legend()
plt.xlabel("Quantile (α)")
plt.ylabel("Bal. Accuracy")
plt.show()
def visualise_gen_coeffs_lotka_volterra(gen_coeffs, struct):
# gen_coeffs.shape: [T x K x p x p]
T = gen_coeffs.shape[0]
p = gen_coeffs.shape[2]
plotting_setup()
cnt0 = 0
cnt1 = 0
cnt2 = 0
for i in range(p):
for j in range(p):
if struct[i, j] == 0:
if cnt2 < 1:
plt.plot(np.arange(200, T), gen_coeffs[200:, 0, i, j], label="Non-causal", color="grey",
linewidth=1, alpha=0.25)
else:
plt.plot(np.arange(200, T), gen_coeffs[200:, 0, i, j], color="grey", linewidth=0.5, alpha=0.25)
cnt2 += 1
elif i != j and struct[i, j] == 1:
if i < p / 2 <= j:
clr = "#d66308"
lst = "-."
if cnt0 < 1:
lab = "Predator → Prey"
else:
lab = None
cnt0 += 1
else:
clr = "#cc79a7"
lst = ":"
if cnt1 < 1:
lab = "Prey → Predator"
else:
lab = None
cnt1 += 1
if lab is not None:
plt.plot(np.arange(200, T), gen_coeffs[200:, 0, i, j], label=lab, color=clr, linestyle=lst,
linewidth=1, alpha=0.75)
else:
plt.plot(np.arange(200, T), gen_coeffs[200:, 0, i, j], color=clr, linestyle=lst, linewidth=1,
alpha=0.75)
plt.axhline(y=0, color="red", linestyle="--")
leg = plt.legend()
plt.xlabel("Time")
plt.ylabel("Generalised Coefficient")
for line in leg.get_lines():
line.set_linewidth(3.0)
plt.show()
def visualise_gen_coeffs_linear_var(gen_coeffs, struct):
# gen_coeffs.shape: [T x K x p x p]
T = gen_coeffs.shape[0]
p = gen_coeffs.shape[2]
plotting_setup()
cnt0 = 0
cnt1 = 0
cnt2 = 0
for i in range(p):
for j in range(p):
if struct[i, j] == 0:
if cnt2 < 1:
plt.plot(gen_coeffs[:, 0, i, j], label="Non-causal", color="grey", linewidth=2, alpha=0.5)
else:
plt.plot(gen_coeffs[:, 0, i, j], color="grey", linewidth=2, alpha=0.5)
cnt2 += 1
elif i != j and struct[i, j] == -1:
if cnt0 < 1:
plt.plot(gen_coeffs[:, 0, i, j], label=r'$a_i<0$', color="#d66308", linewidth=2, linestyle="-.")
else:
plt.plot(gen_coeffs[:, 0, i, j], color="#d66308", linewidth=2, linestyle="-.")
cnt0 += 1
elif i != j and struct[i, j] == 1:
if cnt1 < 1:
plt.plot(gen_coeffs[:, 0, i, j], label=r'$a_i>0$', color="#cc79a7", linewidth=2, linestyle=":")
else:
plt.plot(gen_coeffs[:, 0, i, j], color="#cc79a7", linewidth=2, linestyle=":")
cnt1 += 1
plt.axhline(y=0, color="red", linestyle="--")
leg = plt.legend()
plt.xlabel("Time")
plt.ylabel("Generalised Coefficient")
for line in leg.get_lines():
line.set_linewidth(3.0)
plt.show()