def run_eval(dataset, iterations):
suffixes = [ 'Adaboost', 'AdaFair', 'SMOTEBoost', 'Fish et al.' ]
if dataset == "compass-gender":
X, y, sa_index, p_Group, x_control = load_compas("sex")
elif dataset == "compass-race":
X, y, sa_index, p_Group, x_control = load_compas("race")
elif dataset == "adult-gender":
X, y, sa_index, p_Group, x_control = load_adult("sex")
elif dataset == "adult-race":
X, y, sa_index, p_Group, x_control = load_adult("race")
elif dataset == "dutch":
X, y, sa_index, p_Group, x_control = load_dutch_data()
elif dataset == "bank":
X, y, sa_index, p_Group, x_control = load_bank()
elif dataset == "credit":
X, y, sa_index, p_Group, x_control = load_credit()
elif dataset == "diabetes":
X, y, sa_index, p_Group, x_control = load_diabetes()
elif dataset == "kdd":
X, y, sa_index, p_Group, x_control = load_kdd()
else:
exit(1)
create_temp_files(dataset, suffixes)
threads = []
mutex = []
for lock in range(0, 8):
mutex.append(Lock())
print (dataset)
random.seed(int(time.time()))
for iter in range(0, iterations):
sss = StratifiedShuffleSplit(n_splits=1, test_size=.5, random_state=iter)
for train_index, test_index in sss.split(X, y):
X_train, X_test = X[train_index], X[test_index]
y_train, y_test = y[train_index], y[test_index]
# for proc in range(0, 4):
# threads.append(Process(target=train_classifier, args=( X_train, X_test, y_train, y_test, sa_index, p_Group, dataset + suffixes[proc], mutex[proc],proc, 200, 1, dataset)))
threads.append(Process(target=train_classifier, args=( X_train, X_test, y_train, y_test, sa_index, p_Group, dataset + suffixes[1], mutex[1],1, 500, 1, dataset)))
break
for process in threads:
process.start()
for process in threads:
process.join()
results = []
for suffix in suffixes:
infile = open(dataset + suffix, 'rb')
temp_buffer = pickle.load(infile)
results.append(temp_buffer.performance)
infile.close()
plot_my_results_sp(results, suffixes, "Images/StatisticalParity/" + dataset, dataset)
delete_temp_files(dataset, suffixes)
def load_datasets(dataset, names):
if dataset == "wilt":
X, y, cl_names = load_wilt()
elif dataset == "adult":
X, y, cl_names = load_adult()
elif dataset == "diabetes":
X, y, cl_names = load_diabetes()
elif dataset == "phoneme":
X, y, cl_names = load_phoneme()
elif dataset == "mushroom":
X, y, cl_names = load_mushroom()
elif dataset == "electricity":
X, y, cl_names = load_electricity()
elif dataset == "speeddating":
X, y, cl_names = load_speed_dating()
elif dataset == "credit":
X, y, cl_names = load_credit()
elif dataset == "eeg_eye":
X, y, cl_names = load_eeg_eye()
elif dataset == "spam":
X, y, cl_names = load_spam()
elif dataset == "skin":
X, y, cl_names = load_skin()
elif dataset == "bank":
X, y, cl_names = load_bank()
elif dataset == "kdd":
X, y, cl_names = load_kdd()
elif dataset == "landsatM":
X, y, cl_names = load_mat_data(dataset)
elif dataset == "musk2":
X, y, cl_names = load_mat_data(dataset)
elif dataset == "spliceM":
X, y, cl_names = load_mat_data(dataset)
elif dataset == "semeion_orig":
X, y, cl_names = load_mat_data(dataset)
elif dataset == "waveformM":
X, y, cl_names = load_mat_data(dataset)
else:
from imblearn import datasets
data = datasets.fetch_datasets()[dataset]
cl_names = [
"feature_" + str(i) for i in range(0, data['data'].shape[1])
]
X = data['data']
y = data['target']
y[y != 1] = 0
names.add(dataset)
output = []
output.append(X.shape[0])
output.append(X.shape[1])
output.append(float(format(len(abs(y[y != 1])) / sum(y[y == 1]), '.2f')))
return output
def get_dataset(dataset):
if dataset == "wilt":
X, y, cl_names = load_wilt()
elif dataset == "adult":
X, y, cl_names = load_adult()
elif dataset == "diabetes":
X, y, cl_names = load_diabetes()
elif dataset == "phoneme":
X, y, cl_names = load_phoneme()
elif dataset == "mushroom":
X, y, cl_names = load_mushroom()
elif dataset == "electricity":
X, y, cl_names = load_electricity()
elif dataset == "speeddating":
X, y, cl_names = load_speed_dating()
elif dataset == "credit":
X, y, cl_names = load_credit()
elif dataset == "eeg_eye":
X, y, cl_names = load_eeg_eye()
elif dataset == "spam":
X, y, cl_names = load_spam()
elif dataset == "skin":
X, y, cl_names = load_skin()
elif dataset == "bank":
X, y, cl_names = load_bank()
elif dataset == "kdd":
X, y, cl_names = load_kdd()
elif dataset == "landsatM":
X, y, cl_names = load_mat_data(dataset)
elif dataset == "musk2":
X, y, cl_names = load_mat_data(dataset)
elif dataset == "spliceM":
X, y, cl_names = load_mat_data(dataset)
elif dataset == "semeion_orig":
X, y, cl_names = load_mat_data(dataset)
elif dataset == "waveformM":
X, y, cl_names = load_mat_data(dataset)
elif dataset not in ['bloob', 'circle', 'moon']:
from imblearn import datasets
data = datasets.fetch_datasets()[dataset]
cl_names = [
"feature_" + str(i) for i in range(0, data['data'].shape[1])
]
X = data['data']
y = data['target']
y[y != 1] = 0
return X, y, cl_names
def run_eval(dataset, base_learners, methods):
if dataset == "wilt":
X, y, cl_names = load_wilt()
elif dataset == "adult":
X, y, cl_names = load_adult()
elif dataset == "diabetes":
X, y, cl_names = load_diabetes()
elif dataset == "phoneme":
X, y, cl_names = load_phoneme()
elif dataset == "mushroom":
X, y, cl_names = load_mushroom()
elif dataset == "electricity":
X, y, cl_names = load_electricity()
elif dataset == "speeddating":
X, y, cl_names = load_speed_dating()
elif dataset == "credit":
X, y, cl_names = load_credit()
elif dataset == "eeg_eye":
X, y, cl_names = load_eeg_eye()
elif dataset == "spam":
X, y, cl_names = load_spam()
elif dataset == "skin":
X, y, cl_names = load_skin()
elif dataset == "bank":
X, y, cl_names = load_bank()
elif dataset == "kdd":
X, y, cl_names = load_kdd()
elif dataset == "landsatM":
X, y, cl_names = load_mat_data(dataset)
elif dataset == "musk2":
X, y, cl_names = load_mat_data(dataset)
elif dataset == "spliceM":
X, y, cl_names = load_mat_data(dataset)
elif dataset == "semeion_orig":
X, y, cl_names = load_mat_data(dataset)
elif dataset == "waveformM":
X, y, cl_names = load_mat_data(dataset)
else:
from imblearn import datasets
data = datasets.fetch_datasets()[dataset]
cl_names = ["feature_" + str(i) for i in range(0, data['data'].shape[1])]
X = data['data']
y = data['target']
y[y != 1] = 0
list_of_scores = []
processes = []
for method in methods:
p = Process(target=train_classifier, args=(X, y, base_learners, method, cl_names)) # Passing the list
p.start()
processes.append(p)
for p in processes:
p.join()
for method in methods:
with open('temp/' + method, 'rb') as filehandle:
# read the data as binary data stream
list_of_scores.append(pickle.load(filehandle))
y[y != 1] = -1
for idx in range(0, len(list_of_scores)):
list_of_scores[idx] = numpy.array(list_of_scores[idx]) * y
overall_confs = []
positive_confs = []
negative_confs = []
for conf in list_of_scores:
overall_confs.append(conf)
positive_confs.append(conf[y == 1])
negative_confs.append(conf[y == -1])
num_bins = 40
fig, (ax1, ax2, ax3) = plt.subplots(nrows=1, ncols=3, figsize=(20, 4))
plt.rcParams.update({'font.size': 12})
colors = ['b', 'g', 'r', 'c', 'm', 'y', 'k', 'dimgray', 'peru', 'hotpink', 'tomato']
default_cycler = (cycler(color=colors) +
cycler(linestyle=['-', (0, (1, 1)), '--', '-.',
(0, (5, 10)),
(0, (5, 1)),
'-', (0, (1, 1)), '--', '-.',
(0, (5, 10))]))
ax1.set_prop_cycle(default_cycler)
ax2.set_prop_cycle(default_cycler)
ax3.set_prop_cycle(default_cycler)
ax1.set_title("Positive CDF")
ax1.grid(True)
ax1.set_xlim(-1, 1)
ax2.set_xlim(-1, 1)
ax3.set_xlim(-1, 1)
colors = ['b', 'g', 'r', 'c', 'm', 'y', 'k', 'dimgray', 'peru', 'hotpink', 'tomato', 'indigo', 'lightskyblue']
output = defaultdict(list)
for idx in range(0, len(positive_confs)):
pos_conf = positive_confs[idx]
counts_positives, bin_edges_positives = numpy.histogram(pos_conf, bins=num_bins, normed=True)
cdf_positives = numpy.cumsum(counts_positives)
# ax1.plot(bin_edges_positives[:-1], cdf_positives / cdf_positives[-1], label=methods[idx],color=colors[idx])
ax1.plot(bin_edges_positives[:-1], cdf_positives / cdf_positives[-1], label=methods[idx])
output[methods[idx]].append(bin_edges_positives[:-1])
output[methods[idx]].append(cdf_positives)
# ax1.legend(loc='best')
ax1.set_xlabel("Margin")
ax1.set_ylabel("Cumulative Distribution")
ax1.axhline(0, color='black')
ax1.axvline(0, color='black')
ax2.grid(True)
ax2.axhline(0, color='black')
ax2.axvline(0, color='black')
ax2.set_title("Negative CDF")
for idx in range(0, len(negative_confs)):
if idx == 0:
ax2.set_ylabel("Cumulative Distribution")
ax2.set_xlabel("Margin")
neg_conf = negative_confs[idx]
counts_negatives, bin_edges_negatives = numpy.histogram(neg_conf, bins=num_bins, normed=True)
cdf_negatives = numpy.cumsum(counts_negatives)
# ax2.plot(bin_edges_negatives[:-1], cdf_negatives / cdf_negatives[-1], label=methods[idx],color=colors[idx])
ax2.plot(bin_edges_negatives[:-1], cdf_negatives / cdf_negatives[-1], label=methods[idx])
output[methods[idx]].append(bin_edges_negatives[:-1])
output[methods[idx]].append(cdf_negatives)
ax3.grid(True)
ax3.axhline(0, color='black')
ax3.axvline(0, color='black')
ax3.set_title("Overall CDF")
for idx in range(0, len(negative_confs)):
if idx == 0:
ax3.set_ylabel("Cumulative Distribution")
ax3.set_xlabel("Margin")
over_conf = overall_confs[idx]
counts_overall, bin_edges_overall = numpy.histogram(over_conf, bins=num_bins, normed=True)
cdf_overall = numpy.cumsum(counts_overall)
# ax3.plot(bin_edges_overall[:-1], cdf_overall / cdf_overall[-1], label=methods[idx], color=colors[idx])
ax3.plot(bin_edges_overall[:-1], cdf_overall / cdf_overall[-1], label=methods[idx])
output[methods[idx]].append(bin_edges_overall[:-1])
output[methods[idx]].append(cdf_overall)
plt.legend(loc='upper center', bbox_to_anchor=(-0.7, 1.305), ncol=5)
if not os.path.exists("Images/cdf_plots/" + dataset):
os.makedirs("Images/cdf_plots/" + dataset)
plt.savefig("Images/cdf_plots/" + dataset + "/cdf_" + str(base_learners) + ".png", bbox_inches='tight',
dpi=200)
return output
def run_eval(dataset, baseL, methods):
if dataset == "wilt":
X, y, cl_names = load_wilt()
elif dataset == "adult":
X, y, cl_names = load_adult()
elif dataset == "diabetes":
X, y, cl_names = load_diabetes()
elif dataset == "phoneme":
X, y, cl_names = load_phoneme()
elif dataset == "mushroom":
X, y, cl_names = load_mushroom()
elif dataset == "electricity":
X, y, cl_names = load_electricity()
elif dataset == "speeddating":
X, y, cl_names = load_speed_dating()
elif dataset == "credit":
X, y, cl_names = load_credit()
elif dataset == "eeg_eye":
X, y, cl_names = load_eeg_eye()
elif dataset == "spam":
X, y, cl_names = load_spam()
elif dataset == "skin":
X, y, cl_names = load_skin()
elif dataset == "bank":
X, y, cl_names = load_bank()
elif dataset == "kdd":
X, y, cl_names = load_kdd()
elif dataset == "landsatM":
X, y, cl_names = load_mat_data(dataset)
elif dataset == "musk2":
X, y, cl_names = load_mat_data(dataset)
elif dataset == "spliceM":
X, y, cl_names = load_mat_data(dataset)
elif dataset == "semeion_orig":
X, y, cl_names = load_mat_data(dataset)
elif dataset == "waveformM":
X, y, cl_names = load_mat_data(dataset)
else:
from imblearn import datasets
data = datasets.fetch_datasets()[dataset]
cl_names = ["feature_" + str(i) for i in range(0, data['data'].shape[1])]
X = data['data']
y = data['target']
y[y != 1] = 0
print("===============-- " + dataset + " --===============")
processes = []
for method in methods:
p = Process(target=train_and_predict, args=(X, y, baseL, method))
p.start()
processes.append(p)
for p in processes:
p.join()
list_of_dicts = []
for method in methods:
with open('temp_preds_adaac/' + method, 'rb') as filehandle:
list_of_dicts.append(update_stats(pickle.load(filehandle)))
plot_amort_vs_non_amort(methods, list_of_dicts, baseL, "Images/Amort_vs_non_amort/" + dataset + "/")
return list_of_dicts
def run_eval(dataset, folds, iterations, baseL, methods):
if dataset == "wilt":
X, y, cl_names = load_wilt()
elif dataset == "adult":
X, y, cl_names = load_adult()
elif dataset == "diabetes":
X, y, cl_names = load_diabetes()
elif dataset == "phoneme":
X, y, cl_names = load_phoneme()
elif dataset == "mushroom":
X, y, cl_names = load_mushroom()
elif dataset == "electricity":
X, y, cl_names = load_electricity()
elif dataset == "speeddating":
X, y, cl_names = load_speed_dating()
elif dataset == "credit":
X, y, cl_names = load_credit()
elif dataset == "eeg_eye":
X, y, cl_names = load_eeg_eye()
elif dataset == "spam":
X, y, cl_names = load_spam()
elif dataset == "skin":
X, y, cl_names = load_skin()
elif dataset == "bank":
X, y, cl_names = load_bank()
elif dataset == "kdd":
X, y, cl_names = load_kdd()
elif dataset == "landsatM":
X, y, cl_names = load_mat_data(dataset)
elif dataset == "musk2":
X, y, cl_names = load_mat_data(dataset)
elif dataset == "spliceM":
X, y, cl_names = load_mat_data(dataset)
elif dataset == "semeion_orig":
X, y, cl_names = load_mat_data(dataset)
elif dataset == "rain_aus":
X, y, cl_names = load_rain_aus()
elif dataset == "waveformM":
X, y, cl_names = load_mat_data(dataset)
else:
from imblearn import datasets
data = datasets.fetch_datasets()[dataset]
cl_names = ["feature_" + str(i) for i in range(0, data['data'].shape[1])]
X = data['data']
y = data['target']
y[y != 1] = 0
unique_attr = set([i.split("?")[0] for i in cl_names])
print(dataset + "\t" + str(len(unique_attr)) + "\t" + str(f'{sum(abs(y[y == 1])):,}') + "\t" + str(
f'{len(abs(y[y != 1])):,}') + "\t1:" + str(format(len(abs(y[y != 1])) / sum(y[y == 1]), '.2f')))
list_of_dicts = []
list_of_dicts_stats = []
for t_dict in range(0, len(methods)):
list_of_dicts.append(defaultdict(dict))
list_of_dicts_stats.append(defaultdict(dict))
for weak_learners in baseL:
for item in list_of_dicts:
item[weak_learners] = defaultdict(list)
for weak_learners in baseL:
for item in list_of_dicts_stats:
item[weak_learners] = defaultdict(list)
for samples in range(0, iterations):
sss = StratifiedKFold(n_splits=folds, shuffle=True, random_state=int(time.time()))
for weak_learners in baseL:
print("iteration=", samples, " weak learners=", weak_learners)
# for weak_learners in baseL:
for train_index, test_index in sss.split(X, y):
X_train, X_test = X[train_index], X[test_index]
y_train, y_test = y[train_index], y[test_index]
processes = []
for method in methods:
p = Process(target=train_and_predict,
args=(X_train, y_train, X_test, weak_learners, method, cl_names))
p.start()
processes.append(p)
for p in processes:
p.join()
for index, method in enumerate(methods):
with open('temp_preds/' + method, 'rb') as filehandle:
list_of_dicts[index] = update_performance_stats(
calculate_performance(y_test, pickle.load(filehandle)),
list_of_dicts[index],
weak_learners
)
with open('temp_preds/stats_' + method, 'rb') as filehandle:
list_of_dicts_stats[index] = update_resource_stats(pickle.load(filehandle),
list_of_dicts_stats[index],
weak_learners,
method
)
plot_single_dataset(methods, list_of_dicts, "Images/Performance/" + dataset + "/", baseL)
plot_resource_stats_time(methods, list_of_dicts_stats, "Images/Performance/" + dataset + "/Resource/", baseL)
plot_resource_stats_scores(methods, list_of_dicts_stats, "Images/Performance/" + dataset + "/Resource/", baseL)
return list_of_dicts, list_of_dicts_stats
def run_eval(dataset, base_learners, methods):
if dataset == "wilt":
X, y, cl_names = load_wilt()
elif dataset == "adult":
X, y, cl_names = load_adult()
elif dataset == "diabetes":
X, y, cl_names = load_diabetes()
elif dataset == "phoneme":
X, y, cl_names = load_phoneme()
elif dataset == "mushroom":
X, y, cl_names = load_mushroom()
elif dataset == "electricity":
X, y, cl_names = load_electricity()
elif dataset == "speeddating":
X, y, cl_names = load_speed_dating()
elif dataset == "credit":
X, y, cl_names = load_credit()
elif dataset == "eeg_eye":
X, y, cl_names = load_eeg_eye()
elif dataset == "spam":
X, y, cl_names = load_spam()
elif dataset == "skin":
X, y, cl_names = load_skin()
elif dataset == "bank":
X, y, cl_names = load_bank()
elif dataset == "kdd":
X, y, cl_names = load_kdd()
elif dataset == "landsatM":
X, y, cl_names = load_mat_data(dataset)
elif dataset == "musk2":
X, y, cl_names = load_mat_data(dataset)
elif dataset == "spliceM":
X, y, cl_names = load_mat_data(dataset)
elif dataset == "semeion_orig":
X, y, cl_names = load_mat_data(dataset)
elif dataset == "waveformM":
X, y, cl_names = load_mat_data(dataset)
else:
from imblearn import datasets
data = datasets.fetch_datasets()[dataset]
cl_names = [
"feature_" + str(i) for i in range(0, data['data'].shape[1])
]
X = data['data']
y = data['target']
y[y != 1] = 0
processes = []
for method in methods:
p = Process(target=train_classifier,
args=(X, y, base_learners, method,
cl_names)) # Passing the list
p.start()
processes.append(p)
for p in processes:
p.join()
N = len(methods)
ind = numpy.arange(N) # the x locations for the groups
width = 0.35 # the width of the bars: can also be len(x) sequence
raw_data = dict()
for method in methods:
with open('temp_features/' + method, 'rb') as filehandle:
# read the data as binary data stream
model = pickle.load(filehandle)
# print (method, model.feature_importances_)
raw_data[method] = model.feature_importances_
f_num = len(model.feature_importances_)
index = ["Feature " + str(k) for k in range(1, f_num + 1)]
# index = ["Atrribute 1","Atrribute 2","Atrribute 3","Atrribute 4","Atrribute 5","Atrribute 6"]
df = pd.DataFrame(raw_data, index=index)
df = df.transpose()
ax = df.plot.bar(stacked=True, alpha=0.75, rot=25)
ax.set_ylabel("Feature importance")
ax.set_xlabel("Methods")
ax.legend(loc='center left', bbox_to_anchor=(0.1, 01.07),
ncol=3) # here is the magic
ax.figure.savefig('Images/features/' + dataset + '.png',
bbox_inches='tight',
dpi=200)
