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plot.py
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plot.py
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"""
This file contains function for visualizing experiment
results.
"""
from __future__ import print_function
import functools
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import pickle
import eval as evaluate
import solvers as solvers
import exp_grad as fairlearn
import run_exp as run_exp
from scipy.stats import norm
import data_parser as parser
print = functools.partial(print, flush=True)
import run_exp as run_exp
from load_logged_exp import *
_PARETO = True
DATA_SPLIT_SEED = 4
uni_fontsize = 12
def disp_curve_list(result_list, base_list):
"""
Read in a result list and trace out the disp_curve_list
for different methods
"""
# initialize
constraint = "DP"
err_bar = False
dataset = ''
for result in result_list:
train_eval = result['train_eval']
test_eval = result['test_eval']
loss = result['loss']
constraint = result['constraint']
learner = result['learner']
dataset = result['dataset']
eps_vals = train_eval.keys()
train_disp_dic = {}
test_disp_dic = {}
train_err_dic = {}
test_err_dic = {}
test_loss_std_dic = {}
test_disp_dev_dic = {}
for eps in eps_vals:
if constraint == "DP":
train_disp = train_eval[eps]["DP_disp"]
test_disp = test_eval[eps]["DP_disp"]
elif constraint == "QEO":
train_disp = train_eval[eps]["QEO_disp"]
test_disp = test_eval[eps]["QEO_disp"]
else:
raise Exception('Constraint not supported: ', str(constraint))
train_disp_dic[eps] = train_disp
test_disp_dic[eps] = test_disp
test_loss_std_dic[eps] = test_eval[eps]['loss_std']
test_disp_dev_dic[eps] = test_eval[eps]['disp_std']
if loss == "square":
# taking the RMSE
train_err_dic[eps] = np.sqrt(train_eval[eps]['weighted_loss'])
test_err_dic[eps] = np.sqrt(test_eval[eps]['weighted_loss'])
else:
train_err_dic[eps] = (train_eval[eps]['weighted_loss'])
test_err_dic[eps] = (test_eval[eps]['weighted_loss'])
if _PARETO:
pareto_epsilons_train = convex_env_train(train_disp_dic,
train_err_dic)
pareto_epsilons_test = convex_env_test(pareto_epsilons_train,
test_disp_dic,
test_err_dic)
else:
pareto_epsilons_train = eps_vals
pareto_epsilons_test = eps_vals
# taking the pareto frontier
train_disp_list = [train_disp_dic[k] for k in pareto_epsilons_train]
test_disp_list = [test_disp_dic[k] for k in pareto_epsilons_test]
train_err_list = [train_err_dic[k] for k in pareto_epsilons_train]
test_err_list = [test_err_dic[k] for k in pareto_epsilons_test]
# Getting error bars
if loss == "square":
err_upperconf = [np.sqrt(test_eval[k]['weighted_loss'] + 2
* test_loss_std_dic[k]) - test_err_dic[k]
for k in pareto_epsilons_test]
err_lowerconf = [test_err_dic[k] -
np.sqrt(test_eval[k]['weighted_loss'] - 2 *
test_loss_std_dic[k]) for k in pareto_epsilons_test]
else:
err_upperconf = [2 * test_loss_std_dic[k] for k in
pareto_epsilons_test]
err_lowerconf = [2 * test_loss_std_dic[k] for k in
pareto_epsilons_test]
disp_conf = [test_disp_dev_dic[k] for k in
pareto_epsilons_test]
if learner == 'SVM_LP':
color = 'orange'
err_bar = True
elif learner == 'OLS':
color = 'red'
err_bar = True
elif learner[:2] == "RF":
color = 'brown'
err_bar = True
elif learner[:3] == "XGB":
color = 'green'
err_bar = True
elif learner == "LR":
color = "blue"
err_bar = True
else:
color = 'tan'
err_bar = True
# Plotting fair model curves
plt.subplot(1, 2, 1)
plt.plot(train_disp_list, train_err_list, # marker="s",
color=color, linewidth =2, label=learner+' Fair Train')
# plt.xlabel(constraint+' Disparity')
if loss == "square":
plt.ylabel('RMSE')
elif loss == "logistic":
plt.ylabel('Log loss')
plt.title(dataset + ' / train')
plt.subplot(1, 2, 2)
if err_bar:
plt.fill_between(np.array(test_disp_list),
np.array(test_err_list) -
np.array(err_lowerconf),
np.array(test_err_list) +
np.array(err_upperconf), alpha=0.2,
facecolor=color,
antialiased=True)
plt.errorbar(test_disp_list, test_err_list,
# xerr=disp_conf, yerr=[err_lowerconf,
# err_upperconf],
linestyle='-', color=color,
label=learner+' Fair Test', # fmt='s',
ecolor=color, capthick=1, markersize=5, capsize=2, linewidth=2)
else:
plt.plot(test_disp_list, test_err_list, # marker="s",
color=color, label=learner+' Fair Test', linewidth=2)
# plt.xlabel(constraint+' Disparity')
if loss == "square":
plt.ylabel('RMSE')
elif loss == "logistic":
plt.ylabel('Log loss')
plt.title(dataset + ' / test')
# Plotting benchmark
for base_res in base_list:
base_train_eval = base_res['base_train_eval']
base_test_eval = base_res['base_test_eval']
loss = base_res['loss']
learner = base_res['learner']
base_test_disp_conf = base_test_eval['disp_std']
base_test_loss_std = base_test_eval['loss_std']
dataset = base_res['dataset']
print(learner)
# Getting error bars
if loss == "square":
err_upperconf = np.sqrt(base_test_eval['average_loss'] + 2 *
base_test_loss_std) - np.sqrt(base_test_eval['average_loss'])
err_lowerconf = np.sqrt(base_test_eval['average_loss']) - np.sqrt(base_test_eval['average_loss'] - 2 * base_test_loss_std)
print('train loss', np.sqrt(base_train_eval['average_loss']))
print('test loss', np.sqrt(base_test_eval['average_loss']))
print('2 loss std', err_lowerconf, err_upperconf)
else:
err_upperconf = 2 * base_test_loss_std
err_lowerconf = 2 * base_test_loss_std
print('train loss', (base_train_eval['average_loss']))
print('test loss', (base_test_eval['average_loss']))
print('2 loss std', err_lowerconf, err_upperconf)
marker = 'o'
if learner == 'OLS':
color = 'red'
err_bar = True
elif learner == "SEO":
color = 'deepskyblue'
err_bar = False
marker = '^'
elif learner[:2] == "RF":
color = 'brown'
err_bar = True
elif learner[:3] == "XGB":
color = "green"
err_bar = True
elif learner == "LR":
color = "blue"
err_bar = True
else:
color = 'tan'
err_bar = False
plt.subplot(1, 2, 1)
if loss == "square":
plt.scatter([base_train_eval[constraint+'_disp']],
[np.sqrt(base_train_eval['average_loss'])],
marker=marker, color=color, label=
'Unconstrained '+learner+' ')
plt.ylabel('RMSE')
else:
plt.scatter([base_train_eval[constraint+'_disp']],
[(base_train_eval['average_loss'])],
marker=marker, color=color, label='Unconstrained '+learner+' ')
plt.ylabel('log loss')
plt.title(dataset + ' / train')
plt.subplot(1, 2, 2)
if loss == "square":
if err_bar:
plt.errorbar([base_test_eval[constraint+'_disp']],
[np.sqrt(base_test_eval['average_loss'])],
xerr=base_test_disp_conf,# yerr=[[err_lowerconf], [err_upperconf]],
marker="o",
color=color, label= 'Unconstrained '+learner+' ', ecolor='black', capthick=1, markersize=6, capsize=2)
else:
plt.scatter([base_test_eval[constraint+'_disp']],
[np.sqrt(base_test_eval['average_loss'])],
marker=marker, color=color, label= 'Unconstrained '+learner+' ')
plt.ylabel('RMSE')
else:
if err_bar:
plt.errorbar([base_test_eval[constraint+'_disp']],
[base_test_eval['average_loss']],
xerr=base_test_disp_conf,# yerr=[[err_lowerconf], [err_upperconf]],
marker="o",
color=color, label= 'Unconstrained '+learner+' ', ecolor='black', capthick=1, markersize=6, capsize=2)
else:
plt.scatter([base_test_eval[constraint+'_disp']],
[(base_test_eval['average_loss'])],
marker=marker, color=color, label='Unconstrained '+learner+' ')
plt.ylabel('log loss')
# plt.ylim(0.20, 0.4)
# plt.legend()
plt.title(dataset + ' / test')
plt.show()
def disp_test_res(result_list, base_list, full=True, paired_test=True):
"""
Read in a result list and trace out the disp_curve_list
for different methods
"""
# initialize
constraint = "DP"
err_bar = False
# First calcuate the baseline loss vector
base_res = base_list[0]
dataset = base_res['dataset']
if dataset == 'law_school':
x, a, y = parser.clean_lawschool_full()
elif dataset == 'communities':
x, a, y = parser.clean_communities_full()
elif dataset == 'adult':
x, a, y = parser.clean_adult_full()
if not full:
x, a, y = run_exp.subsample(x, a, y, 2000)
_, _, _, x_test, a_test, y_test = run_exp.train_test_split_groups(x, a, y, random_seed=DATA_SPLIT_SEED)
n = len(y_test)
loss = base_res['loss']
if paired_test:
base_pred = base_res['base_test_eval']['pred']
base_loss_vec = evaluate.loss_vec2(base_pred, y_test, loss)
base_mean_loss = np.mean(base_loss_vec)
print(base_mean_loss)
for result in result_list:
train_eval = result['train_eval']
test_eval = result['test_eval']
constraint = result['constraint']
learner = result['learner']
dataset = result['dataset']
eps_vals = train_eval.keys()
train_disp_dic = {}
test_disp_dic = {}
train_err_dic = {}
test_err_dic = {}
test_loss_std_dic = {}
test_disp_dev_dic = {}
linestyle = '-'
if learner == 'SVM_LP':
color = 'orange'
err_bar = True
elif learner == 'OLS':
color = 'red'
err_bar = True
elif learner[:2] == "RF":
color = 'brown'
err_bar = True
elif learner == "XGB Classifier":
color = 'blue'
err_bar = True
linestyle = '--'
elif learner == "XGB Regression":
color = 'red'
err_bar = True
linestyle = '--'
elif learner == "LR":
color = "blue"
err_bar = True
else:
color = 'tan'
err_bar = True
for eps in eps_vals:
if constraint == "DP":
train_disp = train_eval[eps]["DP_disp"]
test_disp = test_eval[eps]["DP_disp"]
elif constraint == "QEO":
train_disp = train_eval[eps]["QEO_disp"]
test_disp = test_eval[eps]["QEO_disp"]
else:
raise Exception('Constraint not supported: ', str(constraint))
train_disp_dic[eps] = train_disp
test_disp_dic[eps] = test_disp
if paired_test:
test_total_pred = test_eval[eps]['pred']
test_res_weights = test_eval[eps]['classifier_weights']
weighted_loss_vec = evaluate.loss_vec(test_total_pred, y_test,
test_res_weights, loss)
diff_vec = weighted_loss_vec - base_loss_vec
loss_mean, loss_std = norm.fit(diff_vec)
test_loss_std_dic[eps] = loss_std / np.sqrt(n)
else:
test_loss_std_dic[eps] = test_eval[eps]['loss_std']
test_disp_dev_dic[eps] = test_eval[eps]['disp_std']
if loss == "square":
# taking the RMSE
train_err_dic[eps] = np.sqrt(train_eval[eps]['weighted_loss'])
test_err_dic[eps] = np.sqrt(test_eval[eps]['weighted_loss']) - np.sqrt(base_mean_loss)
else:
train_err_dic[eps] = (train_eval[eps]['weighted_loss'])
test_err_dic[eps] = (test_eval[eps]['weighted_loss'] - base_mean_loss)
if _PARETO:
pareto_epsilons_train = convex_env_train(train_disp_dic,
train_err_dic)
pareto_epsilons_test = convex_env_test(pareto_epsilons_train,
test_disp_dic,
test_err_dic)
else:
pareto_epsilons_train = eps_vals
pareto_epsilons_test = eps_vals
# taking the pareto frontier
train_disp_list = [train_disp_dic[k] for k in pareto_epsilons_train]
test_disp_list = [test_disp_dic[k] for k in pareto_epsilons_test]
train_err_list = [train_err_dic[k] for k in pareto_epsilons_train]
test_err_list = [test_err_dic[k] for k in pareto_epsilons_test]
# Getting error bars
if loss == "square":
err_upperconf = [np.sqrt(test_eval[k]['weighted_loss'] + 2
* test_loss_std_dic[k]) - np.sqrt(test_eval[k]['weighted_loss']) for k in
pareto_epsilons_test]
err_lowerconf = [np.sqrt(test_eval[k]['weighted_loss']) - np.sqrt(test_eval[k]['weighted_loss'] - 2 * test_loss_std_dic[k]) for k in
pareto_epsilons_test]
else:
err_upperconf = [2 * test_loss_std_dic[k] for k in
pareto_epsilons_test]
err_lowerconf = [2 * test_loss_std_dic[k] for k in
pareto_epsilons_test]
disp_conf = [test_disp_dev_dic[k] for k in
pareto_epsilons_test]
plt.fill_between(np.array(test_disp_list),
np.array(test_err_list) -
np.array(err_lowerconf),
np.array(test_err_list) +
np.array(err_upperconf), alpha=0.2,
facecolor=color, antialiased=True)
plt.errorbar(test_disp_list, test_err_list, color=color,
capthick=1, markersize=5, capsize=2,
linewidth=2, linestyle=linestyle)
# Plotting benchmark
for base_res in base_list:
base_train_eval = base_res['base_train_eval']
base_test_eval = base_res['base_test_eval']
loss = base_res['loss']
learner = base_res['learner']
base_test_disp_conf = base_test_eval['disp_std']
base_test_loss_std = base_test_eval['loss_std']
dataset = base_res['dataset']
marker = '^'
label = 'unconstrained'
if learner == 'OLS':
# color = 'red'
color = 'darksalmon'
err_bar = True
elif learner == "SEO":
marker = 'v'
color = 'deepskyblue'
err_bar = True
label='SEO'
elif learner[:2] == "RF":
color = 'brown'
err_bar = True
elif learner[:3] == "XGB":
color = "green"
err_bar = True
elif learner == "LR":
color = "darksalmon"
err_bar = True
else:
color = 'tan'
err_bar = False
# Getting error bars
if loss == "square":
err_upperconf = np.sqrt(base_test_eval['average_loss'] + 2 *
base_test_loss_std) - np.sqrt(base_test_eval['average_loss'])
err_lowerconf = np.sqrt(base_test_eval['average_loss']) - np.sqrt(base_test_eval['average_loss'] - 2 * base_test_loss_std)
else:
err_upperconf = 2 * base_test_loss_std
err_lowerconf = 2 * base_test_loss_std
if loss == "square":
if err_bar:
plt.errorbar([base_test_eval[constraint+'_disp']],
[np.sqrt(base_test_eval['average_loss']) - np.sqrt(base_mean_loss)],
xerr=base_test_disp_conf,
marker=marker, markeredgecolor = 'black',
color='black', markerfacecolor=color, ecolor='black', capthick=1, markersize=11, capsize=2)
else:
plt.scatter([base_test_eval[constraint+'_disp']],
[np.sqrt(base_test_eval['average_loss']) - np.sqrt(base_mean_loss)],
marker=marker, edgecolors = 'black', s=95,
label= label)
else:
if err_bar:
plt.errorbar([base_test_eval[constraint+'_disp']],
[base_test_eval['average_loss']] - base_mean_loss,
xerr=base_test_disp_conf,
marker=marker, markeredgecolor = 'black', markerfacecolor=color,
color='black', ecolor='black', capthick=1, markersize=11, capsize=2)
else:
plt.scatter([base_test_eval[constraint+'_disp']],
[(base_test_eval['average_loss'])] - base_mean_loss,
marker=marker, label=label)
def disp_train_res(result_list, base_list):
"""
Read in a result list and trace out the disp_curve_list
for different methods
"""
# initialize
constraint = "DP"
err_bar = False
for result in result_list:
train_eval = result['train_eval']
test_eval = result['test_eval']
constraint = result['constraint']
learner = result['learner']
dataset = result['dataset']
eps_vals = train_eval.keys()
train_disp_dic = {}
test_disp_dic = {}
train_err_dic = {}
test_err_dic = {}
test_loss_std_dic = {}
test_disp_dev_dic = {}
linestyle = '-'
if learner == 'SVM_LP':
color = 'orange'
elif learner == 'OLS':
color = 'red'
elif learner[:2] == "RF":
color = 'brown'
elif learner == "XGB Classifier":
color = 'blue'
linestyle = '--'
elif learner == "XGB Regression":
color = 'red'
linestyle = '--'
elif learner == "LR":
color = "blue"
else:
color = 'tan'
for eps in eps_vals:
train_disp = train_eval[eps]["DP_disp"]
test_disp = test_eval[eps]["DP_disp"]
train_disp_dic[eps] = train_disp
test_disp_dic[eps] = test_disp
loss = result['loss']
test_loss_std_dic[eps] = test_eval[eps]['loss_std']
test_disp_dev_dic[eps] = test_eval[eps]['disp_std']
if loss == "square":
# taking the RMSE
train_err_dic[eps] = np.sqrt(train_eval[eps]['weighted_loss'])
else:
train_err_dic[eps] = (train_eval[eps]['weighted_loss'])
pareto_epsilons_train = convex_env_train(train_disp_dic,
train_err_dic)
# taking the pareto frontier
train_disp_list = [train_disp_dic[k] for k in pareto_epsilons_train]
train_err_list = [train_err_dic[k] for k in pareto_epsilons_train]
plt.plot(train_disp_list, train_err_list, color=color, linewidth=2, linestyle=linestyle)
# Plotting benchmark
for base_res in base_list:
base_train_eval = base_res['base_train_eval']
loss = base_res['loss']
learner = base_res['learner']
dataset = base_res['dataset']
marker = '^'
label = 'unconstrained'
if learner == 'OLS':
# color = 'red'
color = 'darksalmon'
elif learner == "SEO":
marker = 'v'
color = 'deepskyblue'
label='SEO'
elif learner[:2] == "RF":
color = 'brown'
elif learner[:3] == "XGB":
color = "green"
elif learner == "LR":
color = "darksalmon"
else:
color = 'tan'
if loss == "square":
plt.scatter([base_train_eval[constraint+'_disp']],
[np.sqrt(base_train_eval['average_loss'])],
marker=marker, color=color, edgecolors = 'black', s=95)
else:
plt.scatter([base_train_eval[constraint+'_disp']],
[(base_train_eval['average_loss'])],
marker=marker, color=color, edgecolors =
'black', s=95)
def disp_grid_search_train(result):
"""
This function is specific to plotting the grid search method.
"""
# initialize
constraint = "DP"
err_bar = False
train_eval = result['train_eval']
test_eval = result['test_eval']
constraint = result['constraint']
learner = result['learner']
eps_vals = train_eval.keys()
train_disp_dic = {}
test_disp_dic = {}
train_err_dic = {}
test_err_dic = {}
test_loss_std_dic = {}
test_disp_dev_dic = {}
if learner[:2] == "LR":
linestyle='-'
else:
linestyle = '--'
for eps in eps_vals:
train_disp = train_eval[eps]["DP_disp"]
test_disp = test_eval[eps]["DP_disp"]
train_disp_dic[eps] = train_disp
test_disp_dic[eps] = test_disp
loss = result['loss']
test_loss_std_dic[eps] = test_eval[eps]['loss_std']
test_disp_dev_dic[eps] = test_eval[eps]['disp_std']
train_err_dic[eps] = (train_eval[eps]['average_loss'])
pareto_epsilons_train = convex_env_train(train_disp_dic,
train_err_dic)
# taking the pareto frontier
train_disp_list = [train_disp_dic[k] for k in pareto_epsilons_train]
train_err_list = [train_err_dic[k] for k in pareto_epsilons_train]
plt.plot(train_disp_list, train_err_list, color='black', linewidth=2, linestyle=linestyle)
def disp_grid_search_test(result, base_list, full=True, paired_test=True):
"""
This function is specific to plotting grid search method
"""
# initialize
constraint = "DP"
err_bar = False
# First calcuate the baseline loss vector
base_res = base_list[0]
x, a, y = parser.clean_adult_full()
if not full:
x, a, y = run_exp.subsample(x, a, y, 2000)
_, _, _, x_test, a_test, y_test = run_exp.train_test_split_groups(x, a, y, random_seed=DATA_SPLIT_SEED)
n = len(y_test)
loss = base_res['loss']
if paired_test:
base_pred = base_res['base_test_eval']['pred']
base_loss_vec = evaluate.loss_vec2(base_pred, y_test, loss)
base_mean_loss = np.mean(base_loss_vec)
train_eval = result['train_eval']
test_eval = result['test_eval']
constraint = result['constraint']
learner = result['learner']
eps_vals = train_eval.keys()
train_disp_dic = {}
test_disp_dic = {}
train_err_dic = {}
test_err_dic = {}
test_loss_std_dic = {}
test_disp_dev_dic = {}
if learner[:2] == "LR":
linestyle='-'
else:
linestyle = '--'
for eps in eps_vals:
if constraint == "DP":
train_disp = train_eval[eps]["DP_disp"]
test_disp = test_eval[eps]["DP_disp"]
else:
raise Exception('Constraint not supported: ', str(constraint))
train_disp_dic[eps] = train_disp
test_disp_dic[eps] = test_disp
if paired_test:
test_total_pred = test_eval[eps]['pred']
loss_vec = evaluate.loss_vec2(test_total_pred, y_test, loss)
diff_vec = loss_vec - base_loss_vec
loss_mean, loss_std = norm.fit(diff_vec)
test_loss_std_dic[eps] = loss_std / np.sqrt(n)
else:
test_loss_std_dic[eps] = test_eval[eps]['loss_std']
test_disp_dev_dic[eps] = test_eval[eps]['disp_std']
train_err_dic[eps] = (train_eval[eps]['average_loss'])
test_err_dic[eps] = (test_eval[eps]['average_loss'] - base_mean_loss)
if _PARETO:
pareto_epsilons_train = convex_env_train(train_disp_dic,
train_err_dic)
pareto_epsilons_test = convex_env_test(pareto_epsilons_train,
test_disp_dic,
test_err_dic)
else:
pareto_epsilons_train = eps_vals
pareto_epsilons_test = eps_vals
# taking the pareto frontier
train_disp_list = [train_disp_dic[k] for k in pareto_epsilons_train]
test_disp_list = [test_disp_dic[k] for k in pareto_epsilons_test]
train_err_list = [train_err_dic[k] for k in pareto_epsilons_train]
test_err_list = [test_err_dic[k] for k in pareto_epsilons_test]
err_upperconf = [2 * test_loss_std_dic[k] for k in
pareto_epsilons_test]
err_lowerconf = [2 * test_loss_std_dic[k] for k in
pareto_epsilons_test]
disp_conf = [test_disp_dev_dic[k] for k in pareto_epsilons_test]
print(pareto_epsilons_test)
plt.fill_between(np.array(test_disp_list),
np.array(test_err_list) -
np.array(err_lowerconf),
np.array(test_err_list) +
np.array(err_upperconf), alpha=0.2,
facecolor='black', antialiased=True)
plt.errorbar(test_disp_list, test_err_list, color='black',
capthick=1, markersize=5, capsize=2,
linewidth=2, linestyle=linestyle)
def find_pareto_frontier_dict(Xs, Ys):
"""
Find the pareto curve using the dict data structures Xs and Ys: are
dictionaries that share the same set of keys return a subset of
keys
"""
# Sort the list in either ascending or descending order of the
# items values in Xs
key_X_pairs = sorted(Xs.items(), key=lambda x: x[1],
reverse=False) # this is a list of (key, val) pairs
# Start the Pareto frontier with the first key value in the sorted list
p_front = [key_X_pairs[0][0]]
# Loop through the sorted list
for (key, X) in key_X_pairs:
if Ys[key] <= Ys[p_front[-1]]: # Look for lower values of Y
p_front.append(key)
return p_front
def convex_env_train(Xs, Ys):
"""
Identify the convex envelope on the set of models
from the train set.
"""
# Sort the list in either ascending or descending order of the
# items values in Xs
key_X_pairs = sorted(Xs.items(), key=lambda x: x[1],
reverse=False) # this is a list of (key, val) pairs
# Start the Pareto frontier with the first key value in the sorted list
p_front = [key_X_pairs[0][0]]
# Loop through the sorted list
count = 0
for (key, X) in key_X_pairs:
if Ys[key] <= Ys[p_front[-1]]: # Look for lower values of Y
if count > 0:
p_front.append(key)
count = count + 1
return remove_interior(p_front, Xs, Ys)
def convex_env_test(pareto_epsilons_train, test_disp_dic, test_err_dic):
Xs = {k : test_disp_dic[k] for k in pareto_epsilons_train}
Ys = {k : test_err_dic[k] for k in pareto_epsilons_train}
return convex_env_train(Xs, Ys)
def remove_interior(p_front, Xs, Ys):
if len(p_front) < 3:
return p_front
[k1, k2, k3] = p_front[:3]
x1 = Xs[k1]
y1 = Ys[k1]
x2 = Xs[k2]
y2 = Ys[k2]
x3 = Xs[k3]
y3 = Ys[k3]
# compute the linear interpolation between 1 and 3 when x = x2
if x1 == x3: # equal values
return remove_interior([k1] + p_front[3:], Xs, Ys)
else:
alpha = (x2 - x1) / (x3 - x1)
y_hat = y1 - (y1 - y3) * alpha
if y_hat >= y2: # keep the triplet
return [k1] + remove_interior(p_front[1:], Xs, Ys)
else: # remove 2
return remove_interior([k1, k3]+p_front[3:], Xs, Ys)
def plot_Ncalls(result_list):
# initialize
constraint = "DP"
err_bar = False
# print(result_list[0]['learner'], base_list[0]['learner'])
for result in result_list:
train_eval = result['train_eval']
test_eval = result['test_eval']
constraint = result['constraint']
learner = result['learner']
dataset = result['dataset']
eps_vals = train_eval.keys()
train_disp_dic = {}
test_disp_dic = {}
train_err_dic = {}
test_err_dic = {}
test_loss_std_dic = {}
test_disp_dev_dic = {}
linestyle = '-'
if learner == 'SVM_LP':
color = 'orange'
label = 'Fair reg. (oracle=CS, class=linear)'
elif learner == 'OLS':
color = 'red'
label = 'Fair reg. (oracle=LS, class=linear)'
elif learner[:2] == "RF":
color = 'brown'
elif learner == "XGB Classifier":
color = 'blue'
linestyle = '--'
elif learner == "XGB Regression":
color = 'red'
linestyle = '--'
elif learner == "LR":
color = "blue"
label = 'Fair reg. (oracle=LR, class=linear)'
else:
color = 'tan'
n_calls = {}
for eps in eps_vals:
train_disp = train_eval[eps]["DP_disp"]
test_disp = test_eval[eps]["DP_disp"]
n_calls[eps] = train_eval[eps]['n_oracle_calls']
train_disp_dic[eps] = train_disp
test_disp_dic[eps] = test_disp
test_loss_std_dic[eps] = test_eval[eps]['loss_std']
test_disp_dev_dic[eps] = test_eval[eps]['disp_std']
train_err_dic[eps] = (train_eval[eps]['weighted_loss'])
test_err_dic[eps] = (test_eval[eps]['weighted_loss'])
pareto_epsilons_train = eps_vals
pareto_epsilons_test = eps_vals
# taking the pareto frontier
train_disp_list = [train_disp_dic[k] for k in pareto_epsilons_train]
test_disp_list = [test_disp_dic[k] for k in pareto_epsilons_test]
train_err_list = [train_err_dic[k] for k in pareto_epsilons_train]
test_err_list = [test_err_dic[k] for k in pareto_epsilons_test]
plt.plot(eps_vals, list(n_calls.values()), color = color, linestyle=linestyle, marker='o', label=label)
print(n_calls)
plt.legend()
def plot_orcale_calls():
fig, ax = plt.subplots(nrows=1, ncols=3, sharex=True, sharey=True, figsize=(20, 18))
fig.text(0.5, 0.01, 'specified disparity', ha='center', fontsize=uni_fontsize)
plt.subplot(131)
plt.ylabel('# oracle calls', fontsize=uni_fontsize)
plot_Ncalls([lawschool_Ncalls_OLS, lawschool_Ncalls_SVM])
plt.title('law school subsampled', fontsize=uni_fontsize)
plt.subplot(132)
plot_Ncalls([adult_Ncalls_OLS, adult_Ncalls_SVM, adult_Ncalls_Logistic])
plt.title('adult subsampled', fontsize=uni_fontsize)
plt.subplot(133)
plot_Ncalls([comm_Ncalls_OLS, comm_Ncalls_SVM])
plt.title('communities & crime', fontsize=uni_fontsize)
plt.show()
def plot_singles():
fig, ax = plt.subplots(nrows=4, ncols=3, sharex=True, sharey=True, figsize=(20, 18))
fig.text(0.5, 0.08, 'DP disparity', ha='center', fontsize=12)
plt.subplot(531)
plt.ylim(0.29, 0.45)
disp_test_res([adult_OLS], [adult_bl[0]])
plt.ylabel("Log Loss", fontsize=12)
plt.title('adult / least square')
plt.subplot(532)
plt.ylim(0.29, 0.45)
disp_test_res([adult_Logistic], [adult_bl[0]])
plt.title('adult / log. reg.')
plt.subplot(533)
plt.ylim(0.29, 0.45)
disp_test_res([adult_XGB], [adult_bl[1]])
plt.title('adult / grad. boosting')
plt.subplot(534)
plt.ylim(0.092, 0.116)
disp_test_res([lawschool_OLS], [lawschool_bl[0], lawschool_bl[3]])
plt.ylabel("RMSE", horizontalalignment='left' , fontsize=12)
plt.title('law school / least square')
plt.subplot(535)
plt.ylim(0.092, 0.116)
disp_test_res([lawschool_RF], [lawschool_bl[1]])
plt.title('law school / random forest')
plt.subplot(536)
plt.ylim(0.092, 0.116)
disp_test_res([lawschool_XGB], [lawschool_bl[2]])
plt.title('law school / grad. boosting')
plt.subplot(537)
plt.ylim(0.13, 0.22)
disp_test_res([comm_OLS], [comm_bl[0], comm_bl[2]])
plt.ylabel("relative RMSE", horizontalalignment='left', fontsize=12)
plt.title('communities & crime / least square')
plt.subplot(538)
plt.ylim(0.13, 0.22)
disp_test_res([comm_SVM], [comm_bl[0], comm_bl[2]])
plt.title('communities & crime / SVM')
plt.subplot(539)
plt.ylim(0.13, 0.22)
disp_test_res([comm_RF], [comm_bl[1]])
plt.title('communities & crime / random forest')
plt.subplot(5, 3, 10)
plt.ylim(0.35, 0.52)
disp_test_res([adult_short_OLS], [adult_short_bl[0]], full=False)
plt.ylabel("Log Loss", horizontalalignment='left', fontsize=12)
plt.title('adult small / least square')
plt.subplot(5, 3, 11)
plt.ylim(0.35, 0.52)
disp_test_res([adult_short_SVM], [adult_short_bl[0]], full=False)
plt.title('adult small / SVM')
plt.subplot(5, 3, 12)
plt.ylim(0.35, 0.52)
disp_test_res([adult_short_Logistic], [adult_short_bl[0]], full=False)
plt.title('adult small / log. reg.')
plt.subplot(5, 3, 13)
plt.ylim(0.098, 0.112)
disp_test_res([lawschool_short_OLS], [lawschool_short_bl[0], lawschool_short_bl[1]], full=False)
plt.ylabel("RMSE", horizontalalignment='left', fontsize=12)
plt.title('law school small / least square')
plt.subplot(5, 3, 14)
plt.ylim(0.098, 0.112)
disp_test_res([lawschool_short_SVM], [lawschool_short_bl[0], lawschool_short_bl[1]], full=False)
plt.legend(loc=10, bbox_to_anchor=(1.6, 0.4), fontsize=12)
plt.title('law school small / SVM')
plt.savefig('test.pdf', bbox_inches='tight')
plt.show()
def plot_multiples_test():
fig, _ = plt.subplots(nrows=2, sharex=True, sharey=True, figsize=(8, 18))
# fig.text(0.5, 0.03, 'SP disparity', ha='center', fontsize=uni_fontsize)
# fig.text(0.51, 0.06, 'SP disparity', ha='center', fontsize=uni_fontsize*0.9)
# fig.text(0.8, 0.06, 'SP disparity', ha='center', fontsize=uni_fontsize*0.9)
plt.subplot(231)
plt.ylim(-0.005, 0.07)