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figure1.py
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figure1.py
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# Script for generating Figure 1, showing the cost - accuracy trade off
import sklearn, sklearn.svm, sklearn.kernel_ridge, sklearn.datasets
import hierarchyrl.powerset, hierarchyrl.utils, hierarchyrl.policy2
import numpy as np, os, functools, itertools, argparse
import matplotlib.pyplot as plt
import tqdm, multiprocessing
from experiments import dataReading, tools
# Parameters
classifier = lambda: tools.addPredictProba(hierarchyrl.utils.PredictorStandardise(
sklearn.model_selection.GridSearchCV(
sklearn.svm.SVC(probability = False, C = 1., class_weight = 'balanced'),
param_grid = {'C' : np.logspace(-2, 2, num = 5)},
cv = sklearn.model_selection.StratifiedKFold(n_splits=5, shuffle= True, random_state = 0)
)
))
classifier = lambda: tools.addPredictProba(hierarchyrl.utils.PredictorStandardise(
sklearn.svm.SVC(probability = False, C = 1., class_weight = 'balanced')
))
regressor = lambda: hierarchyrl.utils.PredictorStandardise(
sklearn.model_selection.GridSearchCV(
sklearn.kernel_ridge.KernelRidge(alpha = 1, kernel = 'rbf'),
param_grid = {'alpha' : np.logspace(-2, 2, num = 5)},
cv = sklearn.model_selection.KFold(n_splits=5, shuffle= True, random_state = 0)
),
scaleY = True)
def doComputation(X_p_train, X_p_test, costs, t):
res = {}
X_p_train_1, _, X_p_val, _ = X_p_train.split(.8)
policy = hierarchyrl.policy2.PolicyPowerset(X_p_train,
modelQAction = regressor,
modelClassification = classifier,
loss='accuracy',
acquisitionCost = {k:v *t for k,v in costs.items()}
)
policy.train(debug = False, nIts = 1, offPolicyEpsilon = 0.5)
yPred_dqn, ks, _ = policy.simulateEvaluateInPolicy( X_powerset= X_p_test)
res['Reinforcement learning'] = (X_p_test.y, yPred_dqn,ks)
model = classifier()
best_val_acc = -10000
nVars = len(X_p_train.variablesNames)
for v in itertools.chain(*[itertools.combinations(range(nVars), i) for i in range(0,nVars + 1)]):
k = functools.reduce(lambda x, y: x | y, [1 << vv for vv in v], 0)
if k != 0:
model.fit(X_p_train_1.getData(k),X_p_train_1.y)
val_acc = np.mean(policy.loss(X_p_val.y, model.predict_proba(X_p_val.getData(k)))) - policy.stateCost[k]
else:
yPred = np.repeat([1 - np.mean(X_p_train_1.y), np.mean(X_p_train_1.y)] ,X_p_val.nSamples).reshape((2, -1)).T
val_acc = np.mean(policy.loss(X_p_val.y, yPred))
if val_acc > best_val_acc:
best_val_acc = val_acc
best_k = k
if best_k != 0:
model.fit(X_p_train.getData(best_k),X_p_train.y)
yPred = model.predict_proba(X_p_test.getData(best_k))
else:
yPred = np.repeat([1 - np.mean(X_p_train.y), np.mean(X_p_train.y)] ,X_p_test.nSamples).reshape((2, -1)).T
res['Populationwise feature selection'] = (X_p_test.y, yPred, [best_k])
return res
if __name__ == '__main__':
parser = argparse.ArgumentParser(description= 'Figure 1 of the paper, experiment in which the cost-accuracy trade-off is evaluated.')
parser.add_argument('-output', default = './FiguresMICCAI')
parser.add_argument('-nIts', default = 40, type =int)
parser.add_argument('-numTsamples', default = 20, type = int)
parser.add_argument('--ignore-cleveland', action = 'store_true', default = False)
parser.add_argument('--parallel', action = 'store_true', default = False)
parser.add_argument('--readOnly', action = 'store_true', default = False)
args = parser.parse_args()
num_t_samples = args.numTsamples
nIts = args.nIts
resultPath = args.output
# Data reading
all_costs = {}
all_data = {}
if not args.ignore_cleveland:
all_data['Heart Disease'], all_costs['Heart Disease'], _ = dataReading.readDataCleveland()
all_data['Hypertension'], all_costs['Hypertension'], _ = dataReading.readDataHypertensionCensored()
#
metrics_fcts = {}
metrics_fcts['acc'] = lambda y, y1: sklearn.metrics.accuracy_score(y, np.argmax(y1, axis = 1))
results = {}
if args.readOnly:
for dataset in all_costs:
costs = all_costs[dataset]
X_p = all_data[dataset]
ts = np.logspace(-3, -1, num = num_t_samples)
policyTest = hierarchyrl.policy2.PolicyPowerset(X_p,
modelQAction = regressor,
modelClassification = classifier,
loss='accuracy',
acquisitionCost = {k:v for k,v in costs.items()}
)
results[dataset] = tools.DataResultList(ts, metrics_fcts, X_p.DATA_MASK, policyTest.stateCost)
for i in tqdm.tqdm(range(nIts)):
X_p_train, _, X_p_test, _ = X_p.split(.90, random_rng = np.random.RandomState(i))
#X_p_test = X_p_train
# If not parallel
if not args.parallel:
for t in ts:
res = doComputation(X_p_train, X_p_test, costs, t)
for k, r in res.items():
results[dataset].results[t].addResult(k, r[0], r[1], r[2])
# If parallel
else:
with multiprocessing.Pool() as pool:
rs = pool.starmap(doComputation, zip(itertools.repeat(X_p_train), itertools.repeat(X_p_test), itertools.repeat(costs), ts))
for t, res in zip(ts, rs):
for k, r in res.items():
results[dataset].results[t].addResult(k, r[0], r[1], r[2])
# Save data and figures
for dataset, r in results.items():
r.dump(os.path.join(resultPath, 'exp1_' + dataset + '.pkl'))
else:
for dataset,in all_data:
results[dataset] = tools.DataResultList.read(os.path.join(resultPath, 'exp1_' + dataset + '.pkl'))
score = 'acc'
_, fs = plt.subplots(ncols = max(2, len(results)), figsize = (12, 3))
fs = fs.flatten()
for i, (k, r) in enumerate(results.items()):
plt.sca(fs[i])
maxCosts = []
minCosts = []
for p in r.results[r.ts[0]].names:
if p == 'NIPS - 2015':
continue
mean_cost_cv = np.array([np.mean(r.results[t].costs_usage[p]) for t in r.ts])
mean_score_cv = np.array([np.mean(r.results[t].metrics[p][score]) for t in r.ts])
std_score_cv = np.array([np.std(r.results[t].metrics[p][score])/np.sqrt(nIts) for t in r.ts])
std_score_cv_orig = np.array([np.std(r.results[t].metrics[p][score]) for t in r.ts])
maxCosts.append(np.max(mean_cost_cv))
minCosts.append(np.min(mean_cost_cv))
line = plt.plot(mean_cost_cv, mean_score_cv, '-o', label = p)
plt.fill_between(mean_cost_cv, mean_score_cv - 1.96*std_score_cv, mean_score_cv + 1.96*std_score_cv, alpha = .45, color = line[0].get_color())
plt.fill_between(mean_cost_cv, mean_score_cv - std_score_cv_orig, mean_score_cv + std_score_cv_orig, alpha = .10, color = line[0].get_color())
plt.legend(loc = 4, fontsize = 12)
plt.xlim(max(minCosts), min(maxCosts))
plt.xlabel('Average cost [Arbitrary unit]', fontsize = 12)
plt.ylabel('Accuracy', fontsize =12)
plt.title(k, y = 1, pad = -14, fontsize = 14, bbox = dict(boxstyle='round', facecolor='lightgray', alpha=0.5))
plt.tight_layout()
plt.savefig(os.path.join(resultPath, 'exp1.pdf'))