def train(classifier, df,y, user_id):
''' The main training function that runs on a seperate process'''
X_train, X_test, y_train, y_test = train_test_split(df, y, test_size=0.33, random_state=0)
base_estimator = AdaBoostClassifier(n_estimators=10)
rusboost = RUSBoostClassifier(n_estimators=10, base_estimator=base_estimator)
rusboost.fit(X_train, y_train)
y_pred_rusboost = rusboost.predict(X_test)
print('Balanced accuracy: {:.2f} - Geometric mean {:.2f}'.format(balanced_accuracy_score(y_test, y_pred_rusboost), geometric_mean_score(y_test, y_pred_rusboost)))
cm_rusboost = confusion_matrix(y_test, y_pred_rusboost)
joblib.dump(rusboost, user_id+'.pkl')
classifier.classifierStatus = "trained"
print("Done training")
return classifier
def get_best_parameters(self,
features,
labels,
base_estimator=None,
n_iter=300,
cv=3,
verbose=1,
random_state=1,
n_jobs=-1):
clf_random =\
GridSearchCV(
estimator=RUSBoostClassifier(),
param_grid=self.random_grid,
cv=cv,
verbose=verbose,
n_jobs=n_jobs,
iid=False,
error_score=0
)
_features = features
if 1 == len(features.values.shape):
# imbalanced learn RUSBoostClassifier
# doesn't like shapes of (N=1,) ?
_features = features.values.reshape(-1, 1)
clf_random.fit(_features, labels)
return clf_random.best_params_
def train(X_train, y_train, method_name, base_classifier, T):
if method_name == 'adaboost':
clf = AdaBoostClassifier(base_estimator=base_classifier,
n_estimators=T)
elif method_name == 'RUSBoost':
clf = RUSBoostClassifier(base_estimator=base_classifier,
n_estimators=T,
sampling_strategy='majority')
elif method_name == 'SMOTEBoost':
clf = OversampleBoost(oversampling_algorithm='SMOTE',
base_estimator=base_classifier,
n_estimators=T)
elif method_name == 'SMOTETomekBoost':
clf = OversampleBoost(oversampling_algorithm='SMOTE-TOMEK',
base_estimator=base_classifier,
n_estimators=T)
elif method_name == 'SMOTEENNBoost':
clf = OversampleBoost(oversampling_algorithm='SMOTE-ENN',
base_estimator=base_classifier,
n_estimators=T)
elif method_name == 'DERSBoost':
clf = DERSBoost(base_estimator=base_classifier,
n_estimators=T,
NGEN=50)
start_time = time()
clf.fit(X_train, y_train)
elapsed_time = time() - start_time
return clf, elapsed_time
def test_rusboost_sample_weight(imbalanced_dataset, algorithm):
X, y = imbalanced_dataset
sample_weight = np.ones_like(y)
rusboost = RUSBoostClassifier(algorithm=algorithm, random_state=0)
# Predictions should be the same when sample_weight are all ones
y_pred_sample_weight = rusboost.fit(X, y, sample_weight).predict(X)
y_pred_no_sample_weight = rusboost.fit(X, y).predict(X)
assert_array_equal(y_pred_sample_weight, y_pred_no_sample_weight)
rng = np.random.RandomState(42)
sample_weight = rng.rand(y.shape[0])
y_pred_sample_weight = rusboost.fit(X, y, sample_weight).predict(X)
with pytest.raises(AssertionError):
assert_array_equal(y_pred_no_sample_weight, y_pred_sample_weight)
def test_rusboost_sample_weight(imbalanced_dataset, algorithm):
X, y = imbalanced_dataset
sample_weight = np.ones_like(y)
rusboost = RUSBoostClassifier(algorithm=algorithm,
random_state=0)
# Predictions should be the same when sample_weight are all ones
y_pred_sample_weight = rusboost.fit(X, y, sample_weight).predict(X)
y_pred_no_sample_weight = rusboost.fit(X, y).predict(X)
assert_array_equal(y_pred_sample_weight, y_pred_no_sample_weight)
rng = np.random.RandomState(42)
sample_weight = rng.rand(y.shape[0])
y_pred_sample_weight = rusboost.fit(X, y, sample_weight).predict(X)
with pytest.raises(AssertionError):
assert_array_equal(y_pred_no_sample_weight, y_pred_sample_weight)
def fit(self, X, Y, sample_weight=None):
import sklearn.tree
self.n_estimators = int(self.n_estimators)
self.learning_rate = float(self.learning_rate)
self.max_depth = int(self.max_depth)
base_estimator = sklearn.tree.DecisionTreeClassifier(
max_depth=self.max_depth)
from imblearn.ensemble import RUSBoostClassifier
estimator = RUSBoostClassifier(base_estimator=base_estimator,
n_estimators=self.n_estimators,
learning_rate=self.learning_rate,
algorithm=self.algorithm,
random_state=self.random_state)
estimator.fit(X, Y, sample_weight=sample_weight)
self.estimator = estimator
return self
def test_rusboost(imbalanced_dataset, algorithm):
X, y = imbalanced_dataset
X_train, X_test, y_train, y_test = train_test_split(X,
y,
stratify=y,
random_state=1)
classes = np.unique(y)
n_estimators = 500
rusboost = RUSBoostClassifier(n_estimators=n_estimators,
algorithm=algorithm,
random_state=0)
rusboost.fit(X_train, y_train)
assert_array_equal(classes, rusboost.classes_)
# check that we have an ensemble of samplers and estimators with a
# consistent size
assert len(rusboost.estimators_) > 1
assert len(rusboost.estimators_) == len(rusboost.samplers_)
assert len(rusboost.pipelines_) == len(rusboost.samplers_)
# each sampler in the ensemble should have different random state
assert (len({sampler.random_state
for sampler in rusboost.samplers_
}) == len(rusboost.samplers_))
# each estimator in the ensemble should have different random state
assert (len({est.random_state
for est in rusboost.estimators_
}) == len(rusboost.estimators_))
# check the consistency of the feature importances
assert len(rusboost.feature_importances_) == imbalanced_dataset[0].shape[1]
# check the consistency of the prediction outpus
y_pred = rusboost.predict_proba(X_test)
assert y_pred.shape[1] == len(classes)
assert rusboost.decision_function(X_test).shape[1] == len(classes)
score = rusboost.score(X_test, y_test)
assert score > 0.7, "Failed with algorithm {} and score {}".format(
algorithm, score)
y_pred = rusboost.predict(X_test)
assert y_pred.shape == y_test.shape
def get_models(self):
base_lr = LogisticRegression(class_weight='balanced')
ovr_lr = OneVsRestClassifier(base_lr)
base_eec = EasyEnsembleClassifier(n_estimators=10)
ovr_eec = OneVsRestClassifier(base_eec)
base_rus = RUSBoostClassifier(n_estimators=50)
ovr_rus = OneVsRestClassifier(base_rus)
base_bbc = BalancedBaggingClassifier(n_estimators=10)
ovr_bbc = OneVsRestClassifier(base_bbc)
base_brf = BalancedRandomForestClassifier(n_estimators=100)
ovr_brf = OneVsRestClassifier(base_brf)
estimators = [('lr', ovr_lr), ('eec', ovr_eec), ('rus', ovr_rus),
('bbc', ovr_bbc), ('brf', ovr_brf)]
return estimators
def __init__(self):
from imblearn.over_sampling import SMOTE, ADASYN, SVMSMOTE, BorderlineSMOTE, RandomOverSampler
from imblearn.under_sampling import ClusterCentroids, RandomUnderSampler, InstanceHardnessThreshold, NearMiss, \
TomekLinks, EditedNearestNeighbours, RepeatedEditedNearestNeighbours, AllKNN, OneSidedSelection, \
CondensedNearestNeighbour, NeighbourhoodCleaningRule
from imblearn.ensemble import EasyEnsemble, EasyEnsembleClassifier, BalancedBaggingClassifier, \
BalancedRandomForestClassifier, BalanceCascade, RUSBoostClassifier
self.oversamplers = {
'ADASYN': ADASYN(),
'RandomOverSampler': RandomOverSampler(),
'SMOTE': SMOTE(),
'BorderlineSMOTE': BorderlineSMOTE(),
'SVMSMOTE': SVMSMOTE()
}
self.undersamplers = {
'ClusterCentroids': ClusterCentroids(),
'RandomUnderSampler': RandomUnderSampler(),
'InstanceHardnessThreshold': InstanceHardnessThreshold(),
'NearMiss': NearMiss(),
'TomekLinks': TomekLinks(),
'EditedNearestNeighbours': EditedNearestNeighbours(),
'RepeatedEditedNearestNeighbours':
RepeatedEditedNearestNeighbours(),
'AllKNN': AllKNN(),
'OneSidedSelection': OneSidedSelection(),
'CondensedNearestNeighbour': CondensedNearestNeighbour(),
'NeighbourhoodCleaningRule': NeighbourhoodCleaningRule()
}
self.ensemblesamplers = {
'EasyEnsemble': EasyEnsemble(),
'EasyEnsembleClassifier': EasyEnsembleClassifier(),
'BalancedBaggingClassifier': BalancedBaggingClassifier(),
'BalanceCascade': BalanceCascade(),
'BalancedRandomForestClassifier': BalancedRandomForestClassifier,
'RUSBoostClassifier': RUSBoostClassifier()
}
def test_rusboost(imbalanced_dataset, algorithm):
X, y = imbalanced_dataset
X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y)
classes = np.unique(y)
n_estimators = 500
rusboost = RUSBoostClassifier(n_estimators=n_estimators,
algorithm=algorithm,
random_state=0)
rusboost.fit(X_train, y_train)
assert_array_equal(classes, rusboost.classes_)
# check that we have an ensemble of samplers and estimators with a
# consistent size
assert len(rusboost.estimators_) > 1
assert len(rusboost.estimators_) == len(rusboost.samplers_)
assert len(rusboost.pipelines_) == len(rusboost.samplers_)
# each sampler in the ensemble should have different random state
assert (len(set(sampler.random_state for sampler in rusboost.samplers_)) ==
len(rusboost.samplers_))
# each estimator in the ensemble should have different random state
assert (len(set(est.random_state for est in rusboost.estimators_)) ==
len(rusboost.estimators_))
# check the consistency of the feature importances
assert len(rusboost.feature_importances_) == imbalanced_dataset[0].shape[1]
# check the consistency of the prediction outpus
y_pred = rusboost.predict_proba(X_test)
assert y_pred.shape[1] == len(classes)
assert rusboost.decision_function(X_test).shape[1] == len(classes)
score = rusboost.score(X_test, y_test)
assert score > 0.7, "Failed with algorithm {} and score {}".format(
algorithm, score)
y_pred = rusboost.predict(X_test)
assert y_pred.shape == y_test.shape
def get_models():
models, names = list(), list()
# LR
models.append(
LogisticRegression(solver='liblinear',
class_weight='balanced',
penalty='l2'))
names.append('Logistic Regression')
# Ada Boost
names.append('Ada Boost')
models.append(AdaBoostClassifier())
# Gradient Boosting
names.append('Gradient Boosting')
models.append(GradientBoostingClassifier())
# RUSBoostClassifier
names.append('RUSBoost Classifier')
models.append(RUSBoostClassifier())
# BalancedRandomForestClassifier
names.append('RandomForestClassifier')
models.append(RandomForestClassifier(class_weight='balanced'))
# BalancedRandomForestClassifier
names.append('EasyEnsembleClassifier')
models.append(EasyEnsembleClassifier())
return models, names
def test_balanced_random_forest_error(imbalanced_dataset, boosting_params,
err_msg):
rusboost = RUSBoostClassifier(**boosting_params)
with pytest.raises(ValueError, message=err_msg):
rusboost.fit(*imbalanced_dataset)
def _init_classifier(self, opt):
if "classifier_opt" in opt:
opt = opt['classifier_opt']
if "base_estimator" in opt:
b_est = self._init_classifier(opt["base_estimator"])
else:
b_est = None
if "n_estimators" in opt:
n_estimators = opt["n_estimators"]
else:
n_estimators = 200
if "max_iter" in opt:
max_iter = opt["max_iter"]
else:
max_iter = 100000
if "num_parallel_tree" in opt:
num_parallel_tree = opt["num_parallel_tree"]
else:
num_parallel_tree = 5
if "layer_structure" in opt:
layer_structure = opt["layer_structure"]
else:
layer_structure = (100,)
if opt["type"] in ["random_forrest", "rf"]:
return RandomForestClassifier(n_estimators=n_estimators, class_weight="balanced", n_jobs=-1)
elif opt["type"] == "ada_boost":
return AdaBoostClassifier(base_estimator=b_est, n_estimators=n_estimators)
elif opt["type"] in ["logistic_regression", "lr"]:
return LogisticRegression(class_weight='balanced', max_iter=max_iter)
elif opt["type"] == "sgd":
return SGDClassifier(class_weight='balanced', max_iter=max_iter)
elif opt["type"] in ["gaussian_bayes", "bayes", "gaussian_nb"]:
return GaussianNB()
elif opt["type"] in ["support_vector_machine", "svm"]:
return SVC(kernel='rbf', class_weight='balanced', gamma="scale")
elif opt["type"] in ["multilayer_perceptron", "mlp"]:
return MLPClassifier(hidden_layer_sizes=layer_structure, max_iter=max_iter)
elif opt["type"] in ["decision_tree", "dt", "tree"]:
return DecisionTreeClassifier()
elif opt["type"] in ["b_decision_tree", "b_dt", "b_tree"]:
return DecisionTreeClassifier(class_weight="balanced")
elif opt["type"] in ["neighbours", "knn"]:
return KNeighborsClassifier(n_neighbors=opt["n_neighbours"])
elif opt["type"] == "extra_tree":
return ExtraTreesClassifier(n_estimators=n_estimators, class_weight="balanced", n_jobs=-1)
elif opt["type"] == "xgboost":
return XGBClassifier(objective='binary:logistic',
n_estimators=n_estimators,
num_parallel_tree=num_parallel_tree,
tree_method="hist",
booster="gbtree",
n_jobs=-1)
elif opt["type"] in ["b_random_forrest", "b_rf"]:
return BalancedRandomForestClassifier(n_estimators=n_estimators, n_jobs=-1)
elif opt["type"] == "b_bagging":
return BalancedBaggingClassifier(base_estimator=b_est, n_estimators=n_estimators)
elif opt["type"] == "b_boosting":
return RUSBoostClassifier(base_estimator=b_est, n_estimators=n_estimators)
else:
raise ValueError("type: {} not recognised".format(opt["type"]))
# achieve worse performance.
base_estimator = AdaBoostClassifier(n_estimators=10)
eec = EasyEnsembleClassifier(n_estimators=10,
base_estimator=base_estimator,
n_jobs=-1)
eec.fit(X_train, y_train)
y_pred_eec = eec.predict(X_test)
print('Easy ensemble classifier performance:')
print('Balanced accuracy: {:.2f} - Geometric mean {:.2f}'
.format(balanced_accuracy_score(y_test, y_pred_eec),
geometric_mean_score(y_test, y_pred_eec)))
cm_eec = confusion_matrix(y_test, y_pred_eec)
fig, ax = plt.subplots(ncols=2)
plot_confusion_matrix(cm_eec, classes=np.unique(satimage.target), ax=ax[0],
title='Easy ensemble classifier')
rusboost = RUSBoostClassifier(n_estimators=10,
base_estimator=base_estimator)
rusboost.fit(X_train, y_train)
y_pred_rusboost = rusboost.predict(X_test)
print('RUSBoost classifier performance:')
print('Balanced accuracy: {:.2f} - Geometric mean {:.2f}'
.format(balanced_accuracy_score(y_test, y_pred_rusboost),
geometric_mean_score(y_test, y_pred_rusboost)))
cm_rusboost = confusion_matrix(y_test, y_pred_rusboost)
plot_confusion_matrix(cm_rusboost, classes=np.unique(satimage.target),
ax=ax[1], title='RUSBoost classifier')
plt.show()
def nosampling_pipeline(data=[], verbose=False, clean=False, plot=False):
results_table = []
results = []
rand_state = 42
if clean:
X = data.drop('Class', axis=1)
y = data['Class']
X_vals = X.values
y_vals = y.values
X_inliners, y_inliners = reject_sampler.fit_resample(X_vals, y_vals)
X = X_inliners
y = y_inliners
else:
X = data.drop('Class', axis=1)
y = data['Class']
X = X.values
y = y.values
pass
sss = StratifiedKFold(n_splits=10, random_state=rand_state, shuffle=False)
print("StratKFold:", sss)
#List of models to be used
models = [
DecisionTreeClassifier(random_state=rand_state),
RUSBoostClassifier(random_state=rand_state),
LogisticRegression(random_state=rand_state),
BalancedBaggingClassifier(random_state=rand_state),
RandomForestClassifier(random_state=rand_state),
EasyEnsembleClassifier(
base_estimator=RandomForestClassifier(random_state=rand_state),
random_state=rand_state),
BalancedRandomForestClassifier(random_state=rand_state)
]
results_table = pd.DataFrame(columns=['models', 'fpr', 'tpr', 'auc'])
#Create training and testing data sets depending on wheather or not they have been generated previously.
#Instantiate lists to store each of the models results
strategy = []
classifier = []
strategy = []
samp_technique = []
accuracy = []
f1 = []
auc = []
recall = []
precision = []
g_mean = []
start = time.time()
#Run thorugh each of the models to get their performance metrics
sampling_strat = 'no_sampling'
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]
# X_train=X_train.values
# X_test=X_test.values
# y_train=y_train.values
# y_test=y_test.values
for model in models:
print(
"Using lentgh of X for training: {}; Using Length of Y for training: {}"
.format(len(X_train), len(y_train)))
print(
"Using lentgh of X for testing: {}; Using Length of Y for test: {}"
.format(len(X_test), len(y_test)))
print("Currently training model - {} using sampling strategy - {}".
format(model.__class__.__name__, sampling_strat))
print("--" * 20)
clf = model
pipe = make_pipeline(clf) # LOG_REG_MODEL WITH BOTHER
pipe.fit(X_train, y_train)
test_preds = pipe.predict(X_test)
#yproba = pipe.predict_proba(X_test)[::,1]
classifier.append(model.__class__.__name__)
samp_technique.append(sampling_strat)
strategy.append(" %s+%s " %
(str(model.__class__.__name__), sampling_strat))
f1.append(f1_score(y_test, test_preds))
accuracy.append(accuracy_score(y_test, test_preds))
auc.append(roc_auc_score(y_test, test_preds))
recall.append(recall_score(y_test, test_preds))
precision.append(precision_score(y_test, test_preds))
g_mean.append(
geometric_mean_score(y_test, test_preds, average='binary'))
fpr, tpr, _ = roc_curve(y_test, test_preds)
auc_score = roc_auc_score(y_test, test_preds)
results_table = results_table.append(
{
'classifiers': model.__class__.__name__,
'fpr': fpr,
'tpr': tpr,
'auc_score': auc_score
},
ignore_index=True)
#Print the model and its report
if verbose:
print('Classification Model: ', model.__class__.__name__, '\n')
print('Sampling Strategy Model: ', sampling_strat, '\n')
print(confusion_matrix(y_test, test_preds), '\n')
print(classification_report_imbalanced(y_test, test_preds), '\n')
#round the results for convenience
f1 = [float(round(n, 4)) for n in f1]
auc = [float(round(n, 4)) for n in auc]
g_mean = [float(round(n, 4)) for n in g_mean]
accuracy = [float(round(n, 4)) for n in accuracy]
precision = [float(round(n, 4)) for n in precision]
recall = [float(round(n, 4)) for n in recall]
#store results in dataframe
results = pd.DataFrame(
[
classifier, strategy, samp_technique, f1, auc, g_mean, accuracy,
precision, recall
],
index=[
'classifier', 'strategy', 'samp_technique', 'f1', 'roc_auc',
'g_mean', 'accuracy', 'precision', 'recall'
],
columns=[
'DecisionTreeClassifier', 'RUSBoostClaassifier',
'LogisiticRegression', 'BalancedBaggingClassifier',
'RandomForestClassifier', 'EasyEnsembleClassifier',
'BalancedRandomForestClassifier'
])
if plot:
results_table.set_index('classifiers', inplace=True)
fig = plt.figure(figsize=(8, 6))
results_table.sort_values(by=['auc_score'], ascending=False)
for i in results_table.index:
plt.plot(results_table.loc[i]['fpr'],
results_table.loc[i]['tpr'],
label="{}, AUC={:.4f}".format(
i, results_table.loc[i]['auc_score']))
plt.plot([0, 1], [0, 1], color='orange', linestyle='--')
plt.xticks(np.arange(0.0, 1.1, step=0.1))
plt.xlabel("Flase Positive Rate", fontsize=15)
plt.yticks(np.arange(0.0, 1.1, step=0.1))
plt.ylabel("True Positive Rate", fontsize=15)
plt.title(
'ROC Curve for classifiers using Full data split using sampling technique: {}'
.format(sampling_strat),
fontweight='bold',
fontsize=15)
plt.legend(prop={'size': 13}, loc='lower right')
plt.show()
#Change orientation of the dataframe
end = time.time()
print("Time elapsed:", start - end)
return results.transpose()
df = pd.read_csv('data/poker-8-9_vs_5.csv')
X, y, z = prepare_data(df)
X_train, X_test, y_train, y_test = train_test_split(X,
y,
random_state=0,
test_size=0.7)
kf = StratifiedKFold(n_splits=10)
kf.get_n_splits(X, y)
bbc = BalancedBaggingClassifier(
base_estimator=DecisionTreeClassifier(random_state=0), random_state=42)
brfc = BalancedRandomForestClassifier(max_depth=2, random_state=0)
eec = EasyEnsembleClassifier(
base_estimator=DecisionTreeClassifier(random_state=0), random_state=42)
rbc = RUSBoostClassifier(base_estimator=DecisionTreeClassifier(random_state=0),
random_state=0)
bbc_score = []
brfc_score = []
eec_score = []
rbc_score = []
for train_index, test_index in kf.split(X, y):
X_train, X_test = X[train_index], X[test_index]
y_train, y_test = y[train_index], y[test_index]
bbc.fit(X_train, y_train)
brfc.fit(X_train, y_train)
eec.fit(X_train, y_train)
rbc.fit(X_train, y_train)
y_pred_bbc = bbc.predict(X_test)
y_pred_brfc = brfc.predict(X_test)
def test_balanced_random_forest_error(imbalanced_dataset, boosting_params,
err_msg):
rusboost = RUSBoostClassifier(**boosting_params)
with pytest.raises(ValueError, message=err_msg):
rusboost.fit(*imbalanced_dataset)
DecisionTreeClassifier(random_state=r),
KNeighborsClassifier(),
GaussianNB(),
MultinomialNB(),
LogisticRegression(random_state=r),
SVC(random_state=r, kernel='sigmoid'),
MLPClassifier(random_state=r),
BaggingClassifier(random_state=r),
RandomForestClassifier(random_state=r),
GradientBoostingClassifier(random_state=r),
LGBMClassifier(),
XGBClassifier(random_state=r),
CatBoostClassifier(random_state=r, verbose=False),
BalancedBaggingClassifier(random_state=r),
BalancedRandomForestClassifier(random_state=r),
RUSBoostClassifier(random_state=r)
]
names = [
"DecisionTree", "KNeighbors", "GaussianNB", "MultinomialNB",
"LogisticRegression", "SVC", "MLPClassifier", "Ensemble-Bagging",
"Ensemble-RandomForest", "Ensemble-GradientBoosting",
"LightGradientBoosting", "XGBoost", "CatBoost", "BalancedBagging",
"BalancedRandomForest", "RUSBoost"
]
outputs = {}
for name, model in zip(names, models):
model.fit(x_train, y_train)
output = model.predict(test_dataframe)
outputs[name] = output
# initialize cv5
skf = StratifiedKFold(n_splits=5)
cv5_ids = list(skf.split(full_data, labels))
# print(cv5_ids)
# initialize model
# lin_clf = svm.SVC(decision_function_shape='ovo', probability=True)
# lin_clf = svm.LinearSVC()
# lin_clf = LogisticRegression()
# lin_clf = svm.SVC(kernel='sigmoid')
# lin_clf = MLPClassifier((256,256), activation='relu', max_iter=1000)
# lin_clf = RandomForestClassifier(n_estimators=5000, max_depth=2, random_state=0)
single_clf = tree.DecisionTreeClassifier(max_depth=1)
# single_clf = LogisticRegression()
lin_clf = RUSBoostClassifier(base_estimator=single_clf, n_estimators=5000)
# initialize booster
sm = SMOTE(random_state=42)
# perform cv5
precision_avg = []
recall_avg = []
fscore_avg = []
acc_avg = 0.
for sp in cv5_ids:
train_data, train_labels = full_data[sp[0]], labels[sp[0]]
# train_data, train_labels = sm.fit_sample(train_data, train_labels)
test_data, test_labels = full_data[sp[1]], labels[sp[1]]
lin_clf.fit(train_data, train_labels)
def learning_model(year, class_weight):
iters = 300
gap = 2
year_test = year
data_test = reader.ordinary_data_reader('uscecchini28.csv', year_test, year_test)
x_test = data_test.features
y_test = data_test.labels
test = np.c_[data_test.years, data_test.firms]
'''
an if-else is used to judge whether the class_weight is None to prevent Exception from string concatenation
a try-except for RusBoost with DecisionTreeClassifier using custom class_weight
if we can find the right model trained last time on disk, we can directly use that model to predict
the result without training twice
otherwise, we have to train that model and save it on disk
'''
# if class_weight is not None:
# we use current_model_name to find/save the trained model with custom class_weight
# current_model_name = class_weight + "_" + str(year_test) + ".m"
# else:
# current_model_name = str(year_test) + ".m"
current_model_name = class_weight + "_" + str(year_test) + ".m"
try:
rusboost_model = joblib.load(current_model_name)
except Exception as e:
print('Running RUSBoost (training period: 1991-' + str(year_test - gap) + ', testing period: ' + str(
year_test) + ', with ' + str(gap) + '-year gap)...')
data_train = reader.ordinary_data_reader('uscecchini28.csv', 1991, year_test - gap)
x_train = data_train.features
y_train = data_train.labels
newpaaer_train = data_train.newpaaers
# formatter labels and newpaaers for the step: data_test.newpaaers(data_test.labels~=0)
data_test.newpaaers = np.array(data_test.newpaaers)
data_test.labels = np.array(data_test.labels)
# replace the nan that should be remained in the array with 0
for i in range(len(data_test.newpaaers)):
if np.isnan(data_test.newpaaers[i]):
if data_test.labels[i] != 0:
data_test.newpaaers[i] = 0
# replace all the nans remain in the array
data_test.newpaaers = np.array([x for x in data_test.newpaaers if str(x) != 'nan'])
# replace all the 0 back to nan
for i in range(len(data_test.newpaaers)):
if int(data_test.newpaaers[i]) == 0.0:
data_test.newpaaers[i] = np.NaN
# do the unique to get final result for newpaaer_test
newpaaer_test = np.unique(data_test.newpaaers)
'''
Caution:
here we change the type of variable called y_train for matching the array index of
formatted array newpaaer_train in the following loop
'''
y_train = np.array(y_train)
num_frauds = sum(y_train == 1)
print(num_frauds)
'''
here we use the function in1d to replace the function ismember used in matlab
and a temp array for the other operation to handle serial frauds finish the step:
y_train[ismember(newpaaer_train, newpaaer_test)] = 0
'''
temp_array = np.array(np.in1d(newpaaer_train, newpaaer_test)).astype(int)
for i in range(len(temp_array)):
if temp_array[i] == 1:
y_train[i] = 0
# delete the temp array
del temp_array
num_frauds = num_frauds - sum(y_train == 1)
print('Recode', num_frauds, 'overlapped frauds (i.e., change fraud label from 1 to 0).')
start_time = time.perf_counter()
rusboost_model = RUSBoostClassifier(DecisionTreeClassifier(min_samples_leaf=5, class_weight=class_weight),
learning_rate=0.1, n_estimators=iters)
rusboost_model.fit(x_train, y_train)
end_time = time.perf_counter()
t_train = end_time - start_time
joblib.dump(rusboost_model, current_model_name)
print(end_time - start_time)
print('Training time: %.3f seconds' % t_train)
start_time = time.perf_counter()
predit = rusboost_model.predict(x_test)
prob = rusboost_model.predict_proba(x_test)
end_time = time.perf_counter()
t_test = end_time - start_time
print('Testing time %.3f seconds' % t_test)
# test figures
print("AUC: %.4f" % metrics.roc_auc_score(y_test, predit))
# np.set_printoptions(precision=4, threshold=8, edgeitems=4, linewidth=75, suppress=True, nanstr='nan', infstr='inf')
print("precision: %.2f%%" % np.multiply(metrics.precision_score(y_test, predit, zero_division=0), 100))
print("recall: %.2f%%" % np.multiply(metrics.recall_score(y_test, predit), 100))
# dump part of the results(fraud probability)
prob = np.around(np.delete(prob, 0, axis=1) * 100, decimals=5)
data = np.c_[predit, prob]
data = np.c_[test, data]
file_data = pd.DataFrame(data)
csv_file_name = 'data.csv'
file_data.to_csv(csv_file_name, header=False, index=False)
def Gridsearchcv(X_train, X_test, y_train, y_test):
############
# Scale numeric values
num_transformer = Pipeline(steps=[
('scaler', MinMaxScaler())])
preprocessor = ColumnTransformer(
remainder='passthrough',
transformers=[
('num', num_transformer, make_column_selector(pattern='EDAD'))
])
############
pipe = Pipeline([
('preprocessor', preprocessor),
('clf', PipelineHelper([
('svc', SVC()),
('gb', GradientBoostingClassifier()),
('xgb', XGBClassifier(use_label_encoder=False)),
('eec', EasyEnsembleClassifier()),
('rbc', RUSBoostClassifier()),
('bbc', BalancedBaggingClassifier()),
('brf', BalancedRandomForestClassifier()),
])),
])
params = {
'clf__selected_model': pipe.named_steps['clf'].generate({
# # #EasyEnsembleClassifier
'eec__n_estimators' : [10, 25, 50, 100],
'eec__warm_start' : [False, True],
'eec__replacement' : [False, True],
# # #RUSBoostClassifier
'rbc__algorithm' : ['SAMME','SAMME.R'],
'rbc__n_estimators' : [10, 50, 100, 200, 500],
'rbc__learning_rate' : [1e-3, 1e-2, 1e-1, 0.5, 1.],
# # #BalancedBaggingClassifier
'bbc__base_estimator': [HistGradientBoostingClassifier(), None],
'bbc__n_estimators' : [10, 50, 100, 200, 500,750,1000],
'bbc__max_samples':[0.5,0.6,0.7,0.8,0.9,1.0],
'bbc__max_features':[0.5,0.6,0.7,0.8,0.9,1.0],
# #BalancedRandomForestClassifier
'brf__criterion': ['gini', 'entropy'],
'brf__n_estimators' : [int(x) for x in np.linspace(start = 20, stop = 200, num = 5)],
'brf__max_depth' : [int(x) for x in np.linspace(1, 45, num = 3)],
'brf__min_samples_split' : range(2,10),
'brf__min_samples_leaf': [1,3,5,10],
'brf__max_features' : ['auto', 'sqrt', 'log2'],
# # #svm
'svc__C': [0.1, 0.5, 1, 10, 30, 40, 50, 75, 100, 500, 1000],
'svc__gamma' : [0.0001, 0.001, 0.005, 0.01, 0.05, 0.07, 0.1, 0.5, 1, 5, 10, 50],
'svc__kernel': ['rbf'],
# # #gb 3780
"gb__learning_rate": [0.0001, 0.001, 0.01, 0.025, 0.05, 0.075, 0.1, 0.15, 0.2],
"gb__max_depth":[3,7,8,9,10,50],
"gb__max_features":["log2","sqrt"],
"gb__subsample":[0.5, 0.618, 0.8, 0.85, 0.9, 0.95, 1.0],
"gb__n_estimators":[10, 50, 100, 200, 300],
# #xgboost
'xgb__learning_rate' : [1e-3, 1e-2, 1e-1, 0.5, 1.],
'xgb__min_child_weight': np.arange(1, 21, 5),
'xgb__subsample': np.arange(0.05, 1.01, 0.05),
'xgb__verbosity': [0],
# 'xgb__booster': ['gbtree', 'gblinear' ,'dart'],
# 'xgb__learning_rate' : [1e-3, 1e-2, 1e-1, 0.5, 1.],
# 'xgb__min_child_weight': range(1, 21, 5),
# 'xgb__subsample': np.arange(0.05, 1.01, 0.05),
# 'xgb__max_depth': [15,20,25],
# 'xgb__verbosity': [0],
# 'xgb__n_estimators': [100],
# 'xgb__max_depth': range(1, 11),
# 'xgb__learning_rate': [1e-3, 1e-2, 1e-1, 0.5, 1.],
# 'xgb__subsample': np.arange(0.05, 1.01, 0.05),
# 'xgb__min_child_weight': range(1, 21),
# 'xgb__verbosity': [0], # add this line to slient warning
# 'xgb__n_estimators': [400, 700, 1000],
# 'xgb__colsample_bytree': [0.7, 0.8],
# 'xgb__max_depth': [15,20,25],
# 'xgb__reg_alpha': [1.1, 1.2, 1.3],
# 'xgb__reg_lambda': [1.1, 1.2, 1.3],
# 'xgb__subsample': [0.7, 0.8, 0.9],
# 'xgb__eval_metric' : ['mlogloss']
}),
}
scoring = {'ba': 'balanced_accuracy','ap': 'average_precision', 'F1' : 'f1', 'ra': 'roc_auc', 'rc': 'recall'}
cv = RepeatedStratifiedKFold(n_splits=5, n_repeats=3)
#cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=5)
#https://towardsdatascience.com/hyper-parameter-tuning-with-randomised-grid-search-54f865d27926
#n_iter: 30,60, 100
grid = RandomizedSearchCV(
pipe,
params,
refit = 'ba',
cv = cv,
verbose = 3,
n_jobs=-1,
n_iter = 60,
scoring= scoring,
return_train_score = True
)
grid.fit(X_train, y_train)
df_grid=pd.DataFrame(grid.cv_results_)
df_grid = df_grid.sort_values(by=['mean_test_ba'],ascending=False)
df_grid = df_grid[[
'param_clf__selected_model',
'params',
'mean_fit_time',
'std_fit_time',
'mean_test_ba',
'std_test_ba',
'rank_test_ba',
'mean_test_ap',
'std_test_ap',
'rank_test_ap',
'mean_test_ra',
'std_test_ra',
'rank_test_ra',
'mean_test_F1',
'std_test_F1',
'rank_test_F1'
]]
print("Best-Fit Parameters From Training Data:\n",grid.best_params_)
grid_predictions = grid.best_estimator_.predict(X_test)
report = classification_report(y_test, grid_predictions, output_dict=True)
report = pd.DataFrame(report).transpose()
print(report)
print(confusion_matrix(y_test, grid_predictions))
return grid, df_grid, report
y_test = y[test_index]
#classifier = CUSBoostClassifier(**a)
#classifier = AdaboostClassifier(**a)
#classifier = RusBoost(depth=depth, n_estimators=estimators)
#classifier = AdaboostNC_Classifier(**a)
#classifier = CUSBoostNC_Classifier(**a)
#classifier = RusBoost(**a)
classifier = RUSBoostClassifier(DecisionTreeClassifier(max_depth=8), n_estimators=64)
#classifier.fit(X_train, y_train, number_of_clusters, 0.5) #CUSBoost classifier
#classifier.fit(X_train, y_train) #Adaboost classifier
#classifier.fit(X_train, y_train, 0.5) #AdaboostNC classifier
#classifier.fit(X_train, y_train, 6, 0.5)
#classifier.fit(X_train, y_train, 6, fraction/100, 8)
classifier.fit(X_train, y_train)
predictions = classifier.predict_proba(X_test)
prediction_ = classifier.predict(X_test)
auc = roc_auc_score(y_test, predictions[:, 1])
f1 = f1_score(y_test, prediction_)
def test_rusboost_error(imbalanced_dataset, boosting_params, err_msg):
rusboost = RUSBoostClassifier(**boosting_params)
with pytest.raises(ValueError, match=err_msg):
rusboost.fit(*imbalanced_dataset)
clf_results = pd.DataFrame()
# define models
models = {
'ExtraTrees': ExtraTreesClassifier(),
'RandomForest': RandomForestClassifier(),
'AdaBoost': AdaBoostClassifier(),
'GradientBoosting': GradientBoostingClassifier(),
'SVC': SVC(),
'LogitBoost': LogitBoost(),
'XGBClassifier': XGBClassifier(),
'ComplementNB': ComplementNB(),
'BalancedBagging': BalancedBaggingClassifier(),
'BalancedRandomForest': BalancedRandomForestClassifier(),
'RUSBoost': RUSBoostClassifier(),
'EasyEnsemble': EasyEnsembleClassifier()
}
# define model parameters for parameter search
param_extra_trees = {
'n_estimators': [5, 10, 50, 100, 200],
'min_samples_split': [2, 4],
'max_depth': [2, 3, None],
'max_features': ['sqrt', None],
'class_weight': ['balanced']
}
param_random_forest = {
'n_estimators': [5, 10, 50, 100, 200],
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.3,
shuffle=True)
model = GradientBoostingClassifier(n_estimators=100, random_state=42)
imbl_methods = {
'eec':
EasyEnsembleClassifier(random_state=42,
sampling_strategy=1.,
n_jobs=-1,
base_estimator=model),
'rub':
RUSBoostClassifier(random_state=42,
sampling_strategy=1.,
base_estimator=model)
}
for method in imbl_methods.keys():
imbl = imbl_methods[method]
imbl.fit(X_train, y_train)
y_hat_test = imbl.predict(X_test)
y_hat_train = imbl.predict(X_train)
print(f"Reults of {method}")
print(imbl.score(X_test, y_test))
print("Train data")
print(classification_report(y_train, y_hat_train))
print("Test data")
print(classification_report(y_test, y_hat_test))
def classiferSet(pre_cost_weight=20):
#xgt = xgb.XGBClassifier(learning_rate=0.1, scale_pos_weight=10, n_estimators=100, random_state=1) #80.77%
xgt = xgb.XGBClassifier(
learning_rate=0.1,
#subsample=0.99,
max_depth=3,
scale_pos_weight=pre_cost_weight,
n_estimators=80,
#cv=5,
#subsample=.99,
random_state=27,
nthread=2 #use more threads only for large dataset
) #84.62%
ada = AdaBoostClassifier(n_estimators=100,
learning_rate=.1,
random_state=1234) #(0,130): .815
#gbt = GradientBoostingClassifier(n_estimators=100, subsample=1.0, learning_rate=1, random_state=1234) #(0,130): .830
gbt = GradientBoostingClassifier(
n_estimators=100, subsample=0.99, learning_rate=.1,
random_state=1234) #(0,130): .861 #
rf = RandomForestClassifier(
n_estimators=100,
#max_depth=10,
oob_score=True,
class_weight={
0: 1,
1: pre_cost_weight
},
#class_weight='balanced',
random_state=1234) #.846
brf = BalancedRandomForestClassifier(n_estimators=100,
oob_score=True,
class_weight={
0: 1,
1: pre_cost_weight
},
random_state=1234)
rus = RUSBoostClassifier(n_estimators=100, random_state=1234)
#https://www.kaggle.com/c/home-credit-default-risk/discussion/60921
#https://sites.google.com/view/lauraepp/parameters
lgbm = lightgbm.LGBMClassifier(
boosting_type='dart', #'gbdt', 'goss', 'dart'
num_leaves=31,
max_depth=-1,
learning_rate=0.1,
class_weight=
None, #{0:1,1:pre_cost_weight}, using this is inferior to default
random_state=1234)
ourmodels = dict({
'AdaBoost': ada,
'GradientBoost': gbt,
'RandomForest': rf,
'BalancedRandomForest': brf,
'RUSBoost': rus,
'XGBoost': xgt,
'LightGBM': lgbm
})
return ourmodels
def pipe_main(pipe=None):
'''pipeline construction using sklearn estimators, final step support only
classifiers currently
.. note::
data flows through a pipeline consisting of steps as below:
raw data --> clean --> encoding --> scaling --> feature construction
--> feature selection --> resampling --> final estimator
see scikit-learn preprocess & estimators
parameter
----
pipe - str
- in the format of 'xx_xx' of which 'xx' means steps in pipeline,
default None
return
----
1) pipeline instance of chosen steps
2) if pipe is None, a dict indicating possible choice of 'steps'
'''
clean = {
'clean':
Split_cls(dtype_filter='not_datetime', na1='null', na2=-999),
'cleanNA':
Split_cls(dtype_filter='not_datetime', na1=None, na2=None),
'cleanMean':
Split_cls(dtype_filter='not_datetime', na1='most_frequent',
na2='mean'),
}
#
encode = {
'woe': Woe_encoder(max_leaf_nodes=5),
'oht': Oht_encoder(),
'ordi': Ordi_encoder(),
}
resample = {
# over_sampling
'rover':
RandomOverSampler(),
'smote':
SMOTE(),
'bsmote':
BorderlineSMOTE(),
'adasyn':
ADASYN(),
# under sampling controlled methods
'runder':
RandomUnderSampler(),
'nearmiss':
NearMiss(version=3),
'pcart':
InstanceHardnessThreshold(),
# under sampling cleaning methods
'tlinks':
TomekLinks(n_jobs=-1),
'oside':
OneSidedSelection(n_jobs=-1),
'cleanNN':
NeighbourhoodCleaningRule(n_jobs=-1),
'enn':
EditedNearestNeighbours(n_jobs=-1),
'ann':
AllKNN(n_jobs=-1),
'cnn':
CondensedNearestNeighbour(n_jobs=-1),
# clean outliers
'inlierForest':
FunctionSampler(outlier_rejection,
kw_args={'method': 'IsolationForest'}),
'inlierLocal':
FunctionSampler(outlier_rejection,
kw_args={'method': 'LocalOutlierFactor'}),
'inlierEllip':
FunctionSampler(outlier_rejection,
kw_args={'method': 'EllipticEnvelope'}),
'inlierOsvm':
FunctionSampler(outlier_rejection, kw_args={'method': 'OneClassSVM'}),
# combine
'smoteenn':
SMOTEENN(),
'smotelink':
SMOTETomek(),
}
scale = {
'stdscale': StandardScaler(),
'maxscale': MinMaxScaler(),
'rscale': RobustScaler(quantile_range=(10, 90)),
'qauntile': QuantileTransformer(), # uniform distribution
'power': PowerTransformer(), # Gaussian distribution
'norm': Normalizer(), # default L2 norm
# scale sparse data
'maxabs': MaxAbsScaler(),
'stdscalesp': StandardScaler(with_mean=False),
}
# feature construction
feature_c = {
'pca': PCA(whiten=True),
'spca': SparsePCA(normalize_components=True, n_jobs=-1),
'ipca': IncrementalPCA(whiten=True),
'kpca': KernelPCA(kernel='rbf', n_jobs=-1),
'poly': PolynomialFeatures(degree=2),
'rtembedding': RandomTreesEmbedding(n_estimators=10),
'LDA': LinearDiscriminantAnalysis(),
'QDA': QuadraticDiscriminantAnalysis(),
}
# select from model
feature_m = {
'fwoe':
SelectFromModel(Woe_encoder(max_leaf_nodes=5)),
'flog':
SelectFromModel(
LogisticRegressionCV(penalty='l1',
solver='saga',
scoring='roc_auc')),
'fsgd':
SelectFromModel(SGDClassifier(penalty="l1")),
'fsvm':
SelectFromModel(LinearSVC('l1', dual=False, C=1e-2)),
'fxgb':
SelectFromModel(XGBClassifier(n_jobs=-1)),
'frf':
SelectFromModel(ExtraTreesClassifier(n_estimators=100, max_depth=5)),
'fRFExgb':
RFE(XGBClassifier(n_jobs=-1), step=0.1, n_features_to_select=20),
'fRFErf':
RFE(ExtraTreesClassifier(n_estimators=100, max_depth=5),
step=0.3,
n_features_to_select=20),
'fRFElog':
RFE(LogisticRegressionCV(penalty='l1',
solver='saga',
scoring='roc_auc'),
step=0.3,
n_features_to_select=20)
}
# Univariate feature selection
feature_u = {
'fchi2':
GenericUnivariateSelect(chi2, 'percentile', 25),
'fMutualclf':
GenericUnivariateSelect(mutual_info_classif, 'percentile', 25),
'fFclf':
GenericUnivariateSelect(f_classif, 'percentile', 25),
}
# sklearn estimator
t = all_estimators(type_filter=['classifier'])
estimator = {}
for i in t:
try:
estimator.update({i[0]: i[1]()})
except Exception:
continue
estimator.update(
dummy=DummyClassifier(),
XGBClassifier=XGBClassifier(n_jobs=-1),
LogisticRegressionCV=LogisticRegressionCV(scoring='roc_auc'),
EasyEnsembleClassifier=EasyEnsembleClassifier(),
BalancedRandomForestClassifier=BalancedRandomForestClassifier(),
RUSBoostClassifier=RUSBoostClassifier(),
SVC=SVC(C=0.01, gamma='auto'))
if pipe is None:
feature_s = {}
feature_s.update(**feature_m, **feature_u)
return {
'clean': clean.keys(),
'encoding': encode.keys(),
'resample': resample.keys(),
'scale': scale.keys(),
'feature_c': feature_c.keys(),
'feature_s': feature_s.keys(),
'classifier': estimator.keys()
}
elif isinstance(pipe, str):
l = pipe.split('_')
all_keys_dict = {}
all_keys_dict.update(**clean, **encode, **scale, **feature_c,
**feature_m, **feature_u, **estimator, **resample)
steps = []
for i in l:
if all_keys_dict.get(i) is not None:
steps.append((i, all_keys_dict.get(i)))
else:
raise KeyError(
"'{}' invalid key for sklearn estimators".format(i))
return Pipeline(steps)
else:
raise ValueError("input pipe must be a string in format 'xx[_xx]'")
# eec = EasyEnsembleClassifier(n_estimators=10,
# base_estimator=base_estimator,
# n_jobs=-1)
# eec.fit(X_train_seek, y_train_seek)
# y_pred_eec = eec.predict(X_test_seek)
# print('Easy ensemble classifier performance:')
# print('Balanced accuracy: {:.2f} - Geometric mean {:.2f}'
# .format(balanced_accuracy_score(y_test_seek, y_pred_eec),
# geometric_mean_score(y_test_seek, y_pred_eec)))
# cm_eec = confusion_matrix(y_test_seek, y_pred_eec)
# fig, ax = plt.subplots(ncols=2)
# plot_confusion_matrix(cm_eec, classes=np.unique(dataset.target), ax=ax[0],
# title='Easy ensemble classifier')
base_estimator = AdaBoostClassifier(n_estimators=10)
rusboost = RUSBoostClassifier(n_estimators=10, base_estimator=base_estimator)
rusboost.fit(X_train, y_train)
y_pred_rusboost = rusboost.predict(X_test)
print('RUSBoost classifier performance:')
print('Balanced accuracy: {:.2f} - Geometric mean {:.2f}'.format(
balanced_accuracy_score(y_test, y_pred_rusboost),
geometric_mean_score(y_test, y_pred_rusboost)))
cm_rusboost = confusion_matrix(y_test, y_pred_rusboost)
fig, ax = plt.subplots(ncols=2)
plot_confusion_matrix(cm_rusboost,
classes=np.unique(dataset.target),
ax=ax[1],
title='RUSBoost classifier')
rusboost.fit(X_train_seek, y_train_seek)
exLpred.append(float(lineE[j]))
#cellTypesTrue.append(lineE[int(len(lineE))-1])
exMpred.append(exLpred)
#s.append("\n")
exLpred = []
cellID.append(lineE[0])
#cellTypesTrue = np.array(cellTypesTrue)
exMpred = np.array(exMpred)
cellID = np.array(cellID)
###################################
##### Everything is ready for cell type prediction #####
rusboost = RUSBoostClassifier(random_state=0)
rusboost.fit(exMtrain, cellTypesTrain)
##### Cell types prediction #####
cellTypesPred = rusboost.predict(exMpred)
#accuracy_score = balanced_accuracy_score(cellTypesTrue, cellTypesPred)
#print accuracy_score
#classification_report(cellTypesTrue, cellTypesPred)
##### Checking performance #####
#confusionMatrix = confusion_matrix(cellTypesTrue, cellTypesPred)
cellTypesProbs = rusboost.predict_proba(exMpred)
#print confusionMatrix
##### Merging the cell types and probability score #####