def _create_bmr_model(model, X_val, y_val, calibration=True):
y_hat_val_proba = model.predict_proba(X_val)
bmr = BayesMinimumRiskClassifier(calibration=calibration)
bmr.fit(y_val, y_hat_val_proba)
return model, bmr
def cost_sensitive_classification(model, X_train, X_test, y_train, y_test, cost_mat_test): c_model = BayesMinimumRiskClassifier() y_prob_test = model.predict_proba(X_test) y_pred_test_model = model.predict(X_test) c_model.fit(y_test, y_prob_test) y_pred_test_c_model = c_model.predict(y_prob_test, cost_mat_test) c_accuracy = accuracy_score(y_test, y_pred_test_c_model) return c_accuracy, y_pred_test_c_model
clf = RandomForestClassifier(random_state=0, n_estimators=100)
model = clf.fit(X_train, y_train)
prob_test = model.predict_proba(X_test)
bmr = BayesMinimumRiskClassifier(calibration=False)
pred_test = bmr.predict(prob_test, cost_matrix)
print(classification_report(y_test, pred_test, target_names=data.target_names))
loss = cost_loss(y_test, pred_test, cost_matrix)
print("%d\n" % loss)
print(confusion_matrix(y_test, pred_test).T) # transpose to align with slides
print("costcla calibration on training set")
clf = RandomForestClassifier(random_state=0, n_estimators=100)
model = clf.fit(X_train, y_train)
prob_train = model.predict_proba(X_train)
bmr = BayesMinimumRiskClassifier(calibration=True)
bmr.fit(y_train, prob_train)
prob_test = model.predict_proba(X_test)
pred_test = bmr.predict(prob_test, cost_matrix)
print(classification_report(y_test, pred_test, target_names=data.target_names))
loss = cost_loss(y_test, pred_test, cost_matrix)
print("%d\n" % loss)
print(confusion_matrix(y_test, pred_test).T) # transpose to align with slides
print("\nsigmoid calibration")
clf = RandomForestClassifier(random_state=0, n_estimators=100)
cc = CalibratedClassifierCV(clf, method="sigmoid", cv=3)
model = cc.fit(X_train, y_train)
prob_test = model.predict_proba(X_test)
bmr = BayesMinimumRiskClassifier(calibration=False)
pred_test = bmr.predict(prob_test, cost_matrix)
print(classification_report(y_test, pred_test, target_names=data.target_names))
class BaseBagging(with_metaclass(ABCMeta, BaseEnsemble)):
"""Base class for Bagging meta-estimator.
Warning: This class should not be used directly. Use derived classes
instead.
"""
@abstractmethod
def __init__(self,
base_estimator=None,
n_estimators=10,
max_samples=1.0,
max_features=1.0,
bootstrap=True,
bootstrap_features=False,
combination='majority_voting',
n_jobs=1,
random_state=None,
verbose=0):
super(BaseBagging, self).__init__(base_estimator=base_estimator,
n_estimators=n_estimators)
self.max_samples = max_samples
self.max_features = max_features
self.bootstrap = bootstrap
self.bootstrap_features = bootstrap_features
self.combination = combination
self.n_jobs = n_jobs
self.random_state = random_state
self.verbose = verbose
def fit(self, X, y, cost_mat, sample_weight=None):
"""Build a Bagging ensemble of estimators from the training set (X, y).
Parameters
----------
X : {array-like, sparse matrix} of shape = [n_samples, n_features]
The training input samples. Sparse matrices are accepted only if
they are supported by the base estimator.
y : array-like, shape = [n_samples]
The target values (class labels in classification, real numbers in
regression).
cost_mat : array-like of shape = [n_samples, 4]
Cost matrix of the classification problem
Where the columns represents the costs of: false positives, false negatives,
true positives and true negatives, for each example.
sample_weight : array-like, shape = [n_samples] or None
Sample weights. If None, then samples are equally weighted.
Note that this is supported only if the base estimator supports
sample weighting.
Returns
-------
self : object
Returns self.
"""
random_state = check_random_state(self.random_state)
# Convert data
# X, y = check_X_y(X, y, ['csr', 'csc', 'coo']) # Not in sklearn verion 0.15
# Remap output
n_samples, self.n_features_ = X.shape
y = self._validate_y(y)
# Check parameters
self._validate_estimator()
if isinstance(self.max_samples, (numbers.Integral, np.integer)):
max_samples = self.max_samples
else: # float
max_samples = int(self.max_samples * X.shape[0])
if not (0 < max_samples <= X.shape[0]):
raise ValueError("max_samples must be in (0, n_samples]")
if isinstance(self.max_features, (numbers.Integral, np.integer)):
max_features = self.max_features
else: # float
max_features = int(self.max_features * self.n_features_)
if not (0 < max_features <= self.n_features_):
raise ValueError("max_features must be in (0, n_features]")
# Free allocated memory, if any
self.estimators_ = None
# Parallel loop
n_jobs, n_estimators, starts = _partition_estimators(
self.n_estimators, self.n_jobs)
seeds = random_state.randint(MAX_INT, size=self.n_estimators)
all_results = Parallel(n_jobs=n_jobs, verbose=self.verbose)(
delayed(_parallel_build_estimators)(n_estimators[i],
self,
X,
y,
cost_mat,
seeds[starts[i]:starts[i + 1]],
verbose=self.verbose)
for i in range(n_jobs))
# Reduce
self.estimators_ = list(
itertools.chain.from_iterable(t[0] for t in all_results))
self.estimators_samples_ = list(
itertools.chain.from_iterable(t[1] for t in all_results))
self.estimators_features_ = list(
itertools.chain.from_iterable(t[2] for t in all_results))
self._evaluate_oob_savings(X, y, cost_mat)
if self.combination in [
'stacking', 'stacking_proba', 'stacking_bmr',
'stacking_proba_bmr'
]:
self._fit_stacking_model(X, y, cost_mat)
if self.combination in [
'majority_bmr', 'weighted_bmr', 'stacking_bmr',
'stacking_proba_bmr'
]:
self._fit_bmr_model(X, y)
return self
def _fit_bmr_model(self, X, y):
"""Private function used to fit the BayesMinimumRisk model."""
self.f_bmr = BayesMinimumRiskClassifier()
X_bmr = self.predict_proba(X)
self.f_bmr.fit(y, X_bmr)
return self
def _fit_stacking_model(self, X, y, cost_mat, max_iter=100):
"""Private function used to fit the stacking model."""
self.f_staking = CostSensitiveLogisticRegression(verbose=self.verbose,
max_iter=max_iter)
X_stacking = _create_stacking_set(self.estimators_,
self.estimators_features_,
self.estimators_weight_, X,
self.combination)
self.f_staking.fit(X_stacking, y, cost_mat)
return self
#TODO: _evaluate_oob_savings in parallel
def _evaluate_oob_savings(self, X, y, cost_mat):
"""Private function used to calculate the OOB Savings of each estimator."""
estimators_weight = []
for estimator, samples, features in zip(self.estimators_,
self.estimators_samples_,
self.estimators_features_):
# Test if all examples where used for training
if not np.any(~samples):
# Then use training
oob_pred = estimator.predict(X[:, features])
oob_savings = max(0, savings_score(y, oob_pred, cost_mat))
else:
# Then use OOB
oob_pred = estimator.predict((X[~samples])[:, features])
oob_savings = max(
0, savings_score(y[~samples], oob_pred,
cost_mat[~samples]))
estimators_weight.append(oob_savings)
# Control in case were all weights are 0
if sum(estimators_weight) == 0:
self.estimators_weight_ = np.ones(
len(estimators_weight)) / len(estimators_weight)
else:
self.estimators_weight_ = (np.array(estimators_weight) /
sum(estimators_weight)).tolist()
return self
def _validate_y(self, y):
# Default implementation
return column_or_1d(y, warn=True)
class BaseBagging(with_metaclass(ABCMeta, BaseEnsemble)):
"""Base class for Bagging meta-estimator.
Warning: This class should not be used directly. Use derived classes
instead.
"""
@abstractmethod
def __init__(self,
base_estimator=None,
n_estimators=10,
max_samples=1.0,
max_features=1.0,
bootstrap=True,
bootstrap_features=False,
combination='majority_voting',
n_jobs=1,
random_state=None,
verbose=0):
super(BaseBagging, self).__init__(
base_estimator=base_estimator,
n_estimators=n_estimators)
self.max_samples = max_samples
self.max_features = max_features
self.bootstrap = bootstrap
self.bootstrap_features = bootstrap_features
self.combination = combination
self.n_jobs = n_jobs
self.random_state = random_state
self.verbose = verbose
def fit(self, X, y, cost_mat, sample_weight=None):
"""Build a Bagging ensemble of estimators from the training set (X, y).
Parameters
----------
X : {array-like, sparse matrix} of shape = [n_samples, n_features]
The training input samples. Sparse matrices are accepted only if
they are supported by the base estimator.
y : array-like, shape = [n_samples]
The target values (class labels in classification, real numbers in
regression).
cost_mat : array-like of shape = [n_samples, 4]
Cost matrix of the classification problem
Where the columns represents the costs of: false positives, false negatives,
true positives and true negatives, for each example.
sample_weight : array-like, shape = [n_samples] or None
Sample weights. If None, then samples are equally weighted.
Note that this is supported only if the base estimator supports
sample weighting.
Returns
-------
self : object
Returns self.
"""
random_state = check_random_state(self.random_state)
# Convert data
# X, y = check_X_y(X, y, ['csr', 'csc', 'coo']) # Not in sklearn verion 0.15
# Remap output
n_samples, self.n_features_ = X.shape
y = self._validate_y(y)
# Check parameters
self._validate_estimator()
if isinstance(self.max_samples, (numbers.Integral, np.integer)):
max_samples = self.max_samples
else: # float
max_samples = int(self.max_samples * X.shape[0])
if not (0 < max_samples <= X.shape[0]):
raise ValueError("max_samples must be in (0, n_samples]")
if isinstance(self.max_features, (numbers.Integral, np.integer)):
max_features = self.max_features
else: # float
max_features = int(self.max_features * self.n_features_)
if not (0 < max_features <= self.n_features_):
raise ValueError("max_features must be in (0, n_features]")
# Free allocated memory, if any
self.estimators_ = None
# Parallel loop
n_jobs, n_estimators, starts = _partition_estimators(self.n_estimators,
self.n_jobs)
seeds = random_state.randint(MAX_INT, size=self.n_estimators)
all_results = Parallel(n_jobs=n_jobs, verbose=self.verbose)(
delayed(_parallel_build_estimators)(
n_estimators[i],
self,
X,
y,
cost_mat,
seeds[starts[i]:starts[i + 1]],
verbose=self.verbose)
for i in range(n_jobs))
# Reduce
self.estimators_ = list(itertools.chain.from_iterable(
t[0] for t in all_results))
self.estimators_samples_ = list(itertools.chain.from_iterable(
t[1] for t in all_results))
self.estimators_features_ = list(itertools.chain.from_iterable(
t[2] for t in all_results))
self._evaluate_oob_savings(X, y, cost_mat)
if self.combination in ['stacking', 'stacking_proba', 'stacking_bmr', 'stacking_proba_bmr']:
self._fit_stacking_model(X, y, cost_mat)
if self.combination in ['majority_bmr', 'weighted_bmr', 'stacking_bmr', 'stacking_proba_bmr']:
self._fit_bmr_model(X, y)
return self
def _fit_bmr_model(self, X, y):
"""Private function used to fit the BayesMinimumRisk model."""
self.f_bmr = BayesMinimumRiskClassifier()
X_bmr = self.predict_proba(X)
self.f_bmr.fit(y, X_bmr)
return self
def _fit_stacking_model(self,X, y, cost_mat, max_iter=100):
"""Private function used to fit the stacking model."""
self.f_staking = CostSensitiveLogisticRegression(verbose=self.verbose, max_iter=max_iter)
X_stacking = _create_stacking_set(self.estimators_, self.estimators_features_,
self.estimators_weight_, X, self.combination)
self.f_staking.fit(X_stacking, y, cost_mat)
return self
#TODO: _evaluate_oob_savings in parallel
def _evaluate_oob_savings(self, X, y, cost_mat):
"""Private function used to calculate the OOB Savings of each estimator."""
estimators_weight = []
for estimator, samples, features in zip(self.estimators_, self.estimators_samples_,
self.estimators_features_):
# Test if all examples where used for training
if not np.any(~samples):
# Then use training
oob_pred = estimator.predict(X[:, features])
oob_savings = max(0, savings_score(y, oob_pred, cost_mat))
else:
# Then use OOB
oob_pred = estimator.predict((X[~samples])[:, features])
oob_savings = max(0, savings_score(y[~samples], oob_pred, cost_mat[~samples]))
estimators_weight.append(oob_savings)
# Control in case were all weights are 0
if sum(estimators_weight) == 0:
self.estimators_weight_ = np.ones(len(estimators_weight)) / len(estimators_weight)
else:
self.estimators_weight_ = (np.array(estimators_weight) / sum(estimators_weight)).tolist()
return self
def _validate_y(self, y):
# Default implementation
return column_or_1d(y, warn=True)
data.target,
data.cost_mat,
test_size=0.33,
random_state=10)
X_train, X_test, y_train, y_test, cost_mat_train, cost_mat_test = sets
y_pred_test_rf = RandomForestClassifier(random_state=0).fit(
X_train, y_train).predict(X_test)
fpr, tpr, threshold = metrics.roc_curve(y_test, y_pred_test_rf)
print('The auc_score of RandomForest is {:.2f}'.format(metrics.auc(fpr, tpr)))
print('*' * 90)
y_prob_test = RandomForestClassifier(random_state=0).fit(
X_train, y_train).predict_proba(X_test)
f_bmr = BayesMinimumRiskClassifier(calibration=True)
f_bmr.fit(y_test, y_prob_test)
y_pred_test_bmr = f_bmr.predict(y_prob_test, cost_mat_test)
fpr, tpr, threshold = metrics.roc_curve(y_test, y_pred_test_bmr)
print(
'The auc_score of using RandomForest and BayesMinimumRiskClassifieris{:.2f}'
.format(metrics.auc(fpr, tpr)))
print('*' * 90)
f = CostSensitiveLogisticRegression(solver='ga')
f.fit(X_train, y_train, cost_mat_train)
y_pred_test_cslr = f.predict(X_test)
fpr, tpr, threshold = metrics.roc_curve(y_test, y_pred_test_lr)
print('The auc_score of CostSensitiveLogisticRegression is {:.2f}'.format(
metrics.auc(fpr, tpr)))
print('*' * 90)
# Probability calibration using Isotonic Method
cc = CalibratedClassifierCV(clf, method="isotonic", cv=3)
model = cc.fit(data, target)
prob_test = model.predict_proba(X_test)
bmr = BayesMinimumRiskClassifier(calibration=False)
prediction = bmr.predict(prob_test, cost_matrix)
loss = cost_loss(y_test[:, e], prediction, cost_matrix)
pred_BR.append(prediction)
cost_BR.append(loss)
elif cm == 2:
# Probability calibration using CostCla calibration
model = clf.fit(data, target)
prob_train = model.predict_proba(data)
bmr = BayesMinimumRiskClassifier(calibration=True)
bmr.fit(target, prob_train)
prob_test = model.predict_proba(X_test)
prediction = bmr.predict(prob_test, cost_matrix)
loss = cost_loss(y_test[:, e], prediction, cost_matrix)
pred_BR.append(prediction)
cost_BR.append(loss)
elif cm == 3:
# Cost minimization using class weighting
clf = LogisticRegression(C=12.0, class_weight={0: 1, 1: cost})
model = clf.fit(data, target)
prediction = model.predict(X_test)
loss = cost_loss(y_test[:, e], prediction, cost_matrix)
pred_BR.append(prediction)
cost_BR.append(loss)
