Source code for dowhy.plotter

import matplotlib.pyplot as plt
from datetime import datetime

plt.rc('font', size=SMALL_SIZE)          # controls default text sizes
plt.rc('axes', titlesize=BIGGER_SIZE)    # fontsize of the axes title
plt.rc('axes', labelsize=MEDIUM_SIZE)    # fontsize of the x and y labels
plt.rc('xtick', labelsize=MEDIUM_SIZE)   # fontsize of the tick labels
plt.rc('ytick', labelsize=MEDIUM_SIZE)   # fontsize of the tick labels
plt.rc('legend', fontsize=MEDIUM_SIZE)   # legend fontsize
plt.rc('figure', titlesize=BIGGER_SIZE)  # fontsize of the figure title

[docs]def plot_treatment_outcome(treatment, outcome, time_var): fig, ax = plt.subplots() tline = ax.plot(time_var, treatment, 'o', label="Treatment") oline = ax.plot(time_var, outcome, 'r^', label="Outcome") ax.legend(loc="upper left", bbox_to_anchor=(1.04, 1)) plt.xlabel("Time") fig.set_size_inches(8, 6) fig.savefig("obs_data" +"%H-%M-%S") + ".png", bbox_inches="tight")
[docs]def plot_causal_effect(estimate, treatment, outcome): fig, ax = plt.subplots() x_min = 0 x_max = max(treatment) y_min = estimate.params["intercept"] y_max = y_min + estimate.value * (x_max - x_min) ax.scatter(treatment, outcome, c="gray", marker="o", label="Observed data") ax.plot([x_min, x_max], [y_min, y_max], c="black", ls="solid", lw=4, label="Causal variation") ax.set_ylim(0, max(outcome)) ax.set_xlim(0, x_max) bbox_props = dict(boxstyle="round", fc="w", ec="0.5", alpha=0.9) ax.text(10.8, 1, r"DoWhy estimate $\rho$ (slope) = " + str(round(estimate.value, 2)), ha="right", va="bottom", size=20, bbox=bbox_props) ax.legend(loc="upper left") plt.xlabel("Treatment") plt.ylabel("Outcome") fig.set_size_inches(8, 6) fig.savefig("effect" +"%H-%M-%S") + ".png", bbox_inches='tight')