def test_desp():
pool_classifiers, X_dsel, y_dsel, X_test, y_test = setup_classifiers()
desp = DESP(pool_classifiers, DFP=True)
desp.fit(X_dsel, y_dsel)
assert np.isclose(desp.score(X_test, y_test), 0.896969696969697)
def test_desp():
pool_classifiers, X_dsel, y_dsel, X_test, y_test = setup_classifiers()
desp = DESP(pool_classifiers, DFP=True, with_IH=True, IH_rate=0.1)
desp.fit(X_dsel, y_dsel)
assert np.isclose(desp.score(X_test, y_test), 0.906060606060606)
def test_desp(knn_methods):
pool_classifiers, X_dsel, y_dsel, X_test, y_test = setup_classifiers()
desp = DESP(pool_classifiers, knn_classifier=knn_methods)
desp.fit(X_dsel, y_dsel)
assert np.isclose(desp.score(X_test, y_test), 0.97340425531914898)
# can estimate probabilities
pool_classifiers = RandomForestClassifier(n_estimators=10, max_depth=5)
pool_classifiers.fit(X_train, y_train)
# Initialize a DS technique. Here we specify the size of the region of competence (5 neighbors)
knorau = KNORAU(pool_classifiers)
kne = KNORAE(pool_classifiers, k=5)
desp = DESP(pool_classifiers, k=5)
ola = OLA(pool_classifiers, k=5)
mcb = MCB(pool_classifiers, k=5)
meta = METADES(pool_classifiers, k=5)
# Fit the DS techniques
knorau.fit(X_dsel, y_dsel)
kne.fit(X_dsel, y_dsel)
desp.fit(X_dsel, y_dsel)
meta.fit(X_dsel, y_dsel)
ola.fit(X_dsel, y_dsel)
mcb.fit(X_dsel, y_dsel)
# Calculate classification accuracy of each technique
print('Classification accuracy RF: ', RF.score(X_test, y_test))
print('Evaluating DS techniques:')
print('Classification accuracy KNORAU: ', knorau.score(X_test, y_test))
print('Classification accuracy KNORA-Eliminate: ',
kne.score(X_test, y_test))
print('Classification accuracy DESP: ', desp.score(X_test, y_test))
print('Classification accuracy OLA: ', ola.score(X_test, y_test))
print('Classification accuracy MCB: ', mcb.score(X_test, y_test))
print('Classification accuracy META-DES: ', meta.score(X_test, y_test))
def test_desp():
pool_classifiers, X_dsel, y_dsel, X_test, y_test = setup_classifiers()
desp = DESP(pool_classifiers)
desp.fit(X_dsel, y_dsel)
assert np.isclose(desp.score(X_test, y_test), 0.6954545454545454)
'Stacked LR', 'Stacked Decision Tree'
]
classifiers = [svm, mlp, forest, boosting, stacked_lr, stacked_dt]
for clf, ax, title in zip(classifiers, sub.flatten(), titles):
plot_classifier_decision(ax, clf, X_test, mode='filled', alpha=0.4)
plot_dataset(X_test, y_test, ax=ax)
ax.set_xlim(np.min(X[:, 0]), np.max(X[:, 0]))
ax.set_ylim(np.min(X[:, 1]), np.max(X[:, 1]))
ax.set_title(title, fontsize=15)
plt.show()
plt.tight_layout()
###############################################################################
# Evaluation on the test set
# --------------------------
#
# Finally, let's evaluate the baselines and the Dynamic Selection methods on
# the test set:
print('KNORAE score = {}'.format(knora_e.score(X_test, y_test)))
print('DESP score = {}'.format(desp.score(X_test, y_test)))
print('OLA score = {}'.format(ola.score(X_test, y_test)))
print('Rank score = {}'.format(rank.score(X_test, y_test)))
print('SVM score = {}'.format(svm.score(X_test, y_test)))
print('MLP score = {}'.format(mlp.score(X_test, y_test)))
print('RF score = {}'.format(forest.score(X_test, y_test)))
print('Boosting score = {}'.format(boosting.score(X_test, y_test)))
print('Stacking LR score = {}'.format(stacked_lr.score(X_test, y_test)))
print('Staking Decision Tree = {}'.format(stacked_dt.score(X_test, y_test)))
kne.fit(X_dsel, y_dsel)
desp.fit(X_dsel, y_dsel)
meta.fit(X_dsel, y_dsel)
ola.fit(X_dsel, y_dsel)
mcb.fit(X_dsel, y_dsel)
###############################################################################
# Plotting the results
# -----------------------
# Let's now evaluate the methods on the test set.
rf_score = RF.score(X_test, y_test)
stacked_score = stacked.score(X_test, y_test)
knorau_score = knorau.score(X_test, y_test)
kne_score = kne.score(X_test, y_test)
desp_score = desp.score(X_test, y_test)
ola_score = ola.score(X_test, y_test)
mcb_score = mcb.score(X_test, y_test)
meta_score = meta.score(X_test, y_test)
print('Classification accuracy RF: ', rf_score)
print('Classification accuracy Stacked: ', stacked_score)
print('Evaluating DS techniques:')
print('Classification accuracy KNORA-U: ', knorau_score)
print('Classification accuracy KNORA-E: ', kne_score)
print('Classification accuracy DESP: ', desp_score)
print('Classification accuracy OLA: ', ola_score)
print('Classification accuracy MCB: ', mcb_score)
print('Classification accuracy META-DES: ', meta_score)
cmap = get_cmap('Dark2')
colors = [cmap(i) for i in np.linspace(0, 1, 7)]
def test_desp(knne, expected):
pool_classifiers, X_dsel, y_dsel, X_test, y_test = setup_classifiers()
desp = DESP(pool_classifiers, DFP=True, knne=knne)
desp.fit(X_dsel, y_dsel)
assert np.isclose(desp.score(X_test, y_test), expected)
meta = METADES(pool_classifiers)
apriori.fit(X_dsel, y_dsel)
aposteriori.fit(X_dsel, y_dsel)
ola.fit(X_dsel, y_dsel)
lca.fit(X_dsel, y_dsel)
desp.fit(X_dsel, y_dsel)
meta.fit(X_dsel, y_dsel)
print('Evaluating DS techniques:')
print('Classification accuracy of OLA: ', ola.score(X_test, y_test))
print('Classification accuracy of LCA: ', lca.score(X_test, y_test))
print('Classification accuracy of A priori: ', apriori.score(X_test, y_test))
print('Classification accuracy of A posteriori: ',
aposteriori.score(X_test, y_test))
print('Classification accuracy of DES-P: ', desp.score(X_test, y_test))
print('Classification accuracy of META-DES: ', meta.score(X_test, y_test))
# Testing fire:
fire_apriori = APriori(pool_classifiers, DFP=True)
fire_aposteriori = APosteriori(pool_classifiers, DFP=True)
fire_ola = OLA(pool_classifiers, DFP=True)
fire_lca = LCA(pool_classifiers, DFP=True)
fire_desp = DESP(pool_classifiers, DFP=True)
fire_meta = METADES(pool_classifiers, DFP=True)
fire_apriori.fit(X_dsel, y_dsel)
fire_aposteriori.fit(X_dsel, y_dsel)
fire_ola.fit(X_dsel, y_dsel)
fire_lca.fit(X_dsel, y_dsel)
fire_desp.fit(X_dsel, y_dsel)
def main():
###############################################################################
# Preparing the dataset
# ---------------------
# In this part we load the breast cancer dataset from scikit-learn and
# preprocess it in order to pass to the DS models. An important point here is
# to normalize the data so that it has zero mean and unit variance, which is
# a common requirement for many machine learning algorithms.
# This step can be easily done using the StandardScaler class.
rng = np.random.RandomState(123)
data = load_breast_cancer()
X = data.data
y = data.target
# split the data into training and test data
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.33,
random_state=rng)
# Scale the variables to have 0 mean and unit variance
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)
# Split the data into training and DSEL for DS techniques
X_train, X_dsel, y_train, y_dsel = train_test_split(X_train,
y_train,
test_size=0.5,
random_state=rng)
# Train a pool of 100 base classifiers
pool_classifiers = BaggingClassifier(Perceptron(max_iter=10),
n_estimators=100,
random_state=rng)
pool_classifiers.fit(X_train, y_train)
# Initialize the DS techniques
knorau = KNORAU(pool_classifiers)
kne = KNORAE(pool_classifiers)
desp = DESP(pool_classifiers)
ola = OLA(pool_classifiers)
mcb = MCB(pool_classifiers)
###############################################################################
# Calibrating base classifiers
# -----------------------------
# Some dynamic selection techniques requires that the base classifiers estimate
# probabilities in order to estimate its competence level. Since the Perceptron
# model is not a probabilistic classifier (does not implements the
# predict_proba method, it needs to be calibrated for
# probability estimation before being used by such DS techniques. This step can
# be conducted using the CalibrateClassifierCV class from scikit-learn. Note
# that in this example we pass a prefited pool of classifiers to the
# calibration method in order to use exactly the same pool used in the other
# DS methods.
calibrated_pool = []
for clf in pool_classifiers:
calibrated = CalibratedClassifierCV(base_estimator=clf, cv='prefit')
calibrated.fit(X_dsel, y_dsel)
calibrated_pool.append(calibrated)
apriori = APriori(calibrated_pool)
meta = METADES(calibrated_pool)
knorau.fit(X_dsel, y_dsel)
kne.fit(X_dsel, y_dsel)
desp.fit(X_dsel, y_dsel)
ola.fit(X_dsel, y_dsel)
mcb.fit(X_dsel, y_dsel)
apriori.fit(X_dsel, y_dsel)
meta.fit(X_dsel, y_dsel)
###############################################################################
# Evaluating the methods
# -----------------------
# Let's now evaluate the methods on the test set. We also use the performance
# of Bagging (pool of classifiers without any selection) as a baseline
# comparison. We can see that the majority of DS methods achieve higher
# classification accuracy.
print('Evaluating DS techniques:')
print('Classification accuracy KNORA-Union: ',
knorau.score(X_test, y_test))
print('Classification accuracy KNORA-Eliminate: ',
kne.score(X_test, y_test))
print('Classification accuracy DESP: ', desp.score(X_test, y_test))
print('Classification accuracy OLA: ', ola.score(X_test, y_test))
print('Classification accuracy A priori: ', apriori.score(X_test, y_test))
print('Classification accuracy MCB: ', mcb.score(X_test, y_test))
print('Classification accuracy META-DES: ', meta.score(X_test, y_test))
print('Classification accuracy Bagging: ',
pool_classifiers.score(X_test, y_test))
