def extreme_randomize_applied(Train,
New,
treshold,
bootstrap=False,
n_estimators: int = 200,
max_depth: int = 50,
oob_score: bool = False,
class_weight='balanced_subsample',
sampling=None,
label='FRAUDE'):
yTrain = Train[[label]]
xTrain = Train
del xTrain[label]
if sampling == None:
pass
elif sampling == 'ALLKNN':
xTrain, yTrain = under_sampling(xTrain, yTrain)
else:
xTrain, yTrain = over_sampling(xTrain, yTrain, model=sampling)
min_sample_leaf = round((len(xTrain.index)) * 0.005)
min_sample_split = min_sample_leaf * 10
max_features = round(len(xTrain.columns) / 3)
fileModel = ensemble.ExtraTreesClassifier(
criterion='entropy',
bootstrap=bootstrap,
min_samples_leaf=min_sample_leaf,
min_samples_split=min_sample_split,
n_estimators=n_estimators,
max_depth=max_depth,
max_features=max_features,
oob_score=oob_score,
random_state=531,
verbose=1,
class_weight=class_weight,
n_jobs=1)
fileModel.fit(
xTrain.drop(['id_siniestro'], axis=1).values, yTrain.values)
y_hat_New = fileModel.predict_proba(
New.drop('id_siniestro', axis=1).values)
y_hat_New = np.delete(y_hat_New, 0, axis=1)
df_proba = pd.DataFrame(y_hat_New,
columns=['probabilidad'],
index=New.index)
df_proba = pd.concat([New['id_siniestro'], df_proba], axis=1)
# y_random = (y_hat_New <= treshold).astype(int)
y_hat_New = (y_hat_New > treshold).astype(int)
print(y_hat_New)
df_proba_threshold = pd.DataFrame(y_hat_New,
columns=['Treshold'],
index=New.index)
print(df_proba_threshold)
df_proba_conc = pd.concat([df_proba, df_proba_threshold], axis=1)
print(df_proba_conc)
return df_proba_conc
def gb_tunning(Train, Test, sampling=None, scores='f1', label='FRAUDE'):
Train = pd.concat([Train, Test], axis=0, ignore_index=True)
yTrain = Train[[label]]
xTrain = Train
del xTrain[label]
if sampling == None:
pass
elif sampling == 'ALLKNN':
xTrain, yTrain = under_sampling(xTrain, yTrain)
else:
xTrain, yTrain = over_sampling(xTrain, yTrain, model=sampling)
min_sample_leaf = round((len(xTrain.index)) * 0.005)
min_sample_split = min_sample_leaf * 10
tuned_parameters = [{
'min_samples_leaf': [min_sample_leaf],
'min_samples_split': [min_sample_split],
'n_estimators': list(numpy.arange(200, 500, 100)),
'max_depth': list(numpy.arange(100, 200, 100)),
'random_state': [531],
'verbose': [1],
'learning_rate': [0.005]
}]
fileModel = GridSearchCV(ensemble.GradientBoostingClassifier(),
param_grid=tuned_parameters,
cv=10,
scoring='%s_macro' % scores)
fileModel.fit(
xTrain.drop(['id_siniestro'], axis=1).values, yTrain[label].values)
print("Best parameters set found on development set:")
print()
dict_values = fileModel.best_params_
print(dict_values)
print()
print("Grid scores on development set:")
print()
means = fileModel.cv_results_['mean_test_score']
stds = fileModel.cv_results_['std_test_score']
for mean, std, params in zip(means, stds,
fileModel.cv_results_['params']):
print("%0.3f (+/-%0.03f) for %r" % (mean, std * 2, params))
max_depth = int(dict_values['max_depth'])
n_estimators = int(dict_values['n_estimators'])
df = pd.DataFrame.from_dict(dict_values, orient="index")
df.to_csv('final_files\\sup_gb.csv',
sep=';',
encoding='latin1',
index=False)
return max_depth, n_estimators, sampling, min_sample_leaf, min_sample_split
def rnc_tunning(Train, Test, sampling=None, scores='f1', label='FRAUDE'):
Train = pd.concat([Train, Test], axis=0, ignore_index=True)
yTrain = Train[[label]]
xTrain = Train
del xTrain[label]
if sampling == None:
pass
elif sampling == 'ALLKNN':
xTrain, yTrain = under_sampling(xTrain, yTrain)
else:
xTrain, yTrain = over_sampling(xTrain, yTrain, model=sampling)
tuned_parameters = [{'radius': list(numpy.arange(1, 101, 1)),
'weights': ['distance'], 'algorithm': ['auto'],
'leaf_size': list(numpy.arange(30, 301, 30)),
'p': [2],
'outlier_label': [-1]
}]
fileModel = GridSearchCV(neighbors.RadiusNeighborsClassifier(), param_grid=tuned_parameters, cv=10,
scoring='%s_macro' % scores)
fileModel.fit(xTrain.drop(['id_siniestro'], axis=1).values, yTrain[label].values)
print("Best parameters set found on development set:")
print()
dict_values = fileModel.best_params_
print(dict_values)
print()
print("Grid scores on development set:")
print()
means = fileModel.cv_results_['mean_test_score']
stds = fileModel.cv_results_['std_test_score']
for mean, std, params in zip(means, stds, fileModel.cv_results_['params']):
print("%0.3f (+/-%0.03f) for %r"
% (mean, std * 2, params))
radius = int(dict_values['radius'])
leaf_size = int(dict_values['leaf_size'])
df = pd.DataFrame.from_dict(dict_values, orient="index")
df.to_csv('final_files\\sup_rnc.csv', sep=';', encoding='latin1', index=False)
return radius, leaf_size, sampling
def gb_treshold(Train,
Valid,
Test,
comparative,
loss='deviance',
n_estimators: int = 200,
max_depth: int = 50,
learning_rate=0.005,
sampling=None,
label='FRAUDE',
beta=2):
# With beta = 2, we give the same importance to Recall and Precision
yTrain = Train[[label]]
xTrain = Train
del xTrain[label]
names = Train.columns.values.tolist()
fileNames = numpy.array(names)
if sampling == None:
pass
elif sampling == 'ALLKNN':
xTrain, yTrain = under_sampling(xTrain, yTrain)
else:
xTrain, yTrain = over_sampling(xTrain, yTrain, model=sampling)
min_sample_leaf = round((len(xTrain.index)) * 0.005)
min_sample_split = min_sample_leaf * 10
fileModel = ensemble.GradientBoostingClassifier(
loss=loss,
learning_rate=learning_rate,
n_estimators=n_estimators,
subsample=1.0,
criterion='friedman_mse',
min_samples_split=min_sample_split,
min_samples_leaf=min_sample_leaf,
min_weight_fraction_leaf=0.,
max_depth=max_depth,
min_impurity_decrease=0.,
min_impurity_split=None,
init=None,
random_state=531,
max_features=None,
verbose=1,
max_leaf_nodes=None,
warm_start=False,
presort='auto')
fileModel.fit(
xTrain.drop(['id_siniestro'], axis=1).values, yTrain.values)
print(
np.median(
fileModel.predict_proba(Valid[Valid[label] == 0].drop(
[label] + ['id_siniestro'], axis=1).values)))
print(
np.median(
fileModel.predict_proba(Valid[Valid[label] == 1].drop(
[label] + ['id_siniestro'], axis=1).values)))
tresholds = np.linspace(0.1, 1.0, 200)
scores = []
y_pred_score = fileModel.predict_proba(
Valid.drop([label] + ['id_siniestro'], axis=1).values)
y_pred_score = np.delete(y_pred_score, 0, axis=1)
print('min', y_pred_score.min())
print('max', y_pred_score.max())
for treshold in tresholds:
y_hat = (y_pred_score > treshold).astype(int)
y_hat = y_hat.tolist()
y_hat = [item for sublist in y_hat for item in sublist]
scores.append([
recall_score(y_pred=y_hat, y_true=Valid[label].values),
precision_score(y_pred=y_hat, y_true=Valid[label].values),
fbeta_score(y_pred=y_hat, y_true=Valid[label].values, beta=2)
])
scores = np.array(scores)
print('max_scores', scores[:, 2].max(), scores[:, 2].argmax())
plot.plot(tresholds, scores[:, 0], label='$Recall$')
plot.plot(tresholds, scores[:, 1], label='$Precision$')
plot.plot(tresholds, scores[:, 2], label='$F_2$')
plot.ylabel('Score')
plot.xlabel('Threshold')
plot.legend(loc='best')
plot.show()
final_tresh = tresholds[scores[:, 2].argmax()]
y_hat_test = fileModel.predict_proba(
Test.drop([label] + ['id_siniestro'], axis=1).values)
y_hat_test = np.delete(y_hat_test, 0, axis=1)
y_hat_test = (y_hat_test > final_tresh).astype(int)
y_hat_test = y_hat_test.tolist()
y_hat_test = [item for sublist in y_hat_test for item in sublist]
print('Final threshold: %.3f' % final_tresh)
print('Test Recall Score: %.3f' %
recall_score(y_pred=y_hat_test, y_true=Test[label].values))
print('Test Precision Score: %.3f' %
precision_score(y_pred=y_hat_test, y_true=Test[label].values))
print('Test F2 Score: %.3f' % fbeta_score(
y_pred=y_hat_test, y_true=Test[label].values, beta=beta))
del comparative['FRAUDE_Clusters']
Test = pd.merge(Test, comparative, how='left', on='id_siniestro')
cnf_matrix = confusion_matrix(Test['FRAUDE_Clusters'].values,
y_hat_test)
plot_confusion_matrix(cnf_matrix,
classes=['No Fraude', 'Fraude'],
title='Confusion matrix')
cnf_matrix = confusion_matrix(Test['FRAUDE'].values, y_hat_test)
plot_confusion_matrix(cnf_matrix,
classes=['Normal', 'Anormal'],
title='Confusion matrix')
featureImportance = fileModel.feature_importances_
featureImportance = featureImportance / featureImportance.max()
sorted_idx = numpy.argsort(featureImportance)
barPos = numpy.arange(sorted_idx.shape[0]) + 0.5
plot.barh(barPos, featureImportance[sorted_idx], align='center')
plot.yticks(barPos, fileNames[sorted_idx])
plot.xlabel('Variable Importance')
plot.show()
return final_tresh
def rnc_treshold(Train, Valid, Test, radius, leaf_size,
sampling=None, label='FRAUDE', beta=2):
# With beta = 2, we give the same importance to Recall and Precision
yTrain = Train[label]
xTrain = Train
del xTrain[label]
names = Train.columns.values.tolist()
fileNames = numpy.array(names)
if sampling == None:
pass
elif sampling == 'ALLKNN':
xTrain, yTrain = under_sampling(xTrain, yTrain)
else:
xTrain, yTrain = over_sampling(xTrain, yTrain, model=sampling)
min_sample_leaf = round((len(xTrain.index)) * 0.005)
fileModel = neighbors.RadiusNeighborsClassifier(radius= radius,
weights= 'distance', algorithm= 'auto',
leaf_size= leaf_size,
p= 2,
outlier_label= -1)
fileModel.fit(xTrain.values, yTrain.values)
print(np.median(fileModel.predict_proba(Valid[Valid[label] == 0].drop(label, axis=1).values)))
print(np.median(fileModel.predict_proba(Valid[Valid[label] == 1].drop(label, axis=1).values)))
tresholds = np.linspace(0.1, 1.0, 200)
scores = []
y_pred_score = fileModel.predict_proba(Valid.drop(label, axis=1).values)
y_pred_score = np.delete(y_pred_score, 0, axis=1)
print('min', y_pred_score.min())
print('max', y_pred_score.max())
for treshold in tresholds:
y_hat = (y_pred_score > treshold).astype(int)
y_hat = y_hat.tolist()
y_hat = [item for sublist in y_hat for item in sublist]
scores.append([
recall_score(y_pred=y_hat, y_true=Valid[label].values),
precision_score(y_pred=y_hat, y_true=Valid[label].values),
fbeta_score(y_pred=y_hat, y_true=Valid[label].values,
beta=2)])
scores = np.array(scores)
print('max_scores', scores[:, 2].max(), scores[:, 2].argmax())
plot.plot(tresholds, scores[:, 0], label='$Recall$')
plot.plot(tresholds, scores[:, 1], label='$Precision$')
plot.plot(tresholds, scores[:, 2], label='$F_2$')
plot.ylabel('Score')
plot.xlabel('Threshold')
plot.legend(loc='best')
plot.show()
final_tresh = tresholds[scores[:, 2].argmax()]
y_hat_test = fileModel.predict_proba(Test.drop(label, axis=1).values)
y_hat_test = np.delete(y_hat_test, 0, axis=1)
y_hat_test = (y_hat_test > final_tresh).astype(int)
y_hat_test = y_hat_test.tolist()
y_hat_test = [item for sublist in y_hat_test for item in sublist]
print('Final threshold: %.3f' % final_tresh)
print('Test Recall Score: %.3f' % recall_score(y_pred=y_hat_test, y_true=Test[label].values))
print('Test Precision Score: %.3f' % precision_score(y_pred=y_hat_test, y_true=Test[label].values))
print('Test F2 Score: %.3f' % fbeta_score(y_pred=y_hat_test, y_true=Test[label].values, beta=beta))
cnf_matrix = confusion_matrix(Test[label].values, y_hat_test)
plot_confusion_matrix(cnf_matrix, classes=['Normal', 'Anormal'], title='Confusion matrix')
featureImportance = fileModel.feature_importances_
featureImportance = featureImportance / featureImportance.max()
sorted_idx = numpy.argsort(featureImportance)
barPos = numpy.arange(sorted_idx.shape[0]) + 0.5
plot.barh(barPos, featureImportance[sorted_idx], align='center')
plot.yticks(barPos, fileNames[sorted_idx])
plot.xlabel('Variable Importance')
plot.show()
return final_tresh
def extreme_randomize_evaluation(Train,
Test,
comparative,
treshold,
bootstrap=False,
n_estimators: int = 200,
max_depth: int = 50,
oob_score: bool = False,
class_weight='balanced_subsample',
sampling=None,
label='FRAUDE',
beta=2):
yTrain = Train[[label]]
xTrain = Train
del xTrain[label]
if sampling == None:
pass
elif sampling == 'ALLKNN':
class_weight = None
xTrain, yTrain = under_sampling(xTrain, yTrain)
else:
xTrain, yTrain = over_sampling(xTrain, yTrain, model=sampling)
class_weight = None
model_name = str(sampling)
min_sample_leaf = round((len(xTrain.index)) * 0.005)
min_sample_split = min_sample_leaf * 10
max_features = round(len(xTrain.columns) / 3)
fileModel = ensemble.ExtraTreesClassifier(
criterion='entropy',
bootstrap=bootstrap,
min_samples_leaf=min_sample_leaf,
min_samples_split=min_sample_split,
n_estimators=n_estimators,
max_depth=max_depth,
max_features=max_features,
oob_score=oob_score,
random_state=531,
verbose=1,
class_weight=class_weight,
n_jobs=1)
fileModel.fit(
xTrain.drop(['id_siniestro'], axis=1).values, yTrain.values)
y_hat_test = fileModel.predict_proba(
Test.drop([label] + ['id_siniestro'], axis=1).values)
y_hat_test = np.delete(y_hat_test, 0, axis=1)
y_hat_test = (y_hat_test > treshold).astype(int)
y_hat_test = y_hat_test.tolist()
y_hat_test = [item for sublist in y_hat_test for item in sublist]
print('Final threshold: %.3f' % treshold)
print('Test Recall Score: %.3f' %
recall_score(y_pred=y_hat_test, y_true=Test[label].values))
print('Test Precision Score: %.3f' %
precision_score(y_pred=y_hat_test, y_true=Test[label].values))
print('Test F2 Score: %.3f' % fbeta_score(
y_pred=y_hat_test, y_true=Test[label].values, beta=beta))
del comparative['FRAUDE_Clusters']
Test = pd.merge(Test, comparative, how='left', on='id_siniestro')
cnf_matrix = confusion_matrix(Test['FRAUDE_Clusters'].values,
y_hat_test)
plot_confusion_matrix(cnf_matrix,
classes=['No Fraude', 'Fraude'],
title='Confusion matrix')
cnf_matrix = confusion_matrix(Test['FRAUDE'].values, y_hat_test)
plot_confusion_matrix(cnf_matrix,
classes=['Normal', 'Anormal'],
title='Confusion matrix')
def extreme_randomize_treshold(Train,
Valid,
Test,
comparative,
bootstrap=False,
n_estimators: int = 200,
max_depth: int = 50,
oob_score: bool = False,
class_weight='balanced_subsample',
sampling=None,
label='FRAUDE',
beta=2):
# With beta = 2, we give the same importance to Recall and Precision
yTrain = Train[[label]]
xTrain = Train
del xTrain[label]
if sampling == None:
pass
elif sampling == 'ALLKNN':
xTrain, yTrain = under_sampling(xTrain, yTrain)
class_weight = None
else:
xTrain, yTrain = over_sampling(xTrain, yTrain, model=sampling)
class_weight = None
model_name = str(sampling)
min_sample_leaf = round((len(xTrain.index)) * 0.005)
min_sample_split = min_sample_leaf * 10
max_features = round(len(xTrain.columns) / 3)
fileModel = ensemble.ExtraTreesClassifier(
criterion='entropy',
bootstrap=bootstrap,
min_samples_leaf=min_sample_leaf,
min_samples_split=min_sample_split,
n_estimators=n_estimators,
max_depth=max_depth,
max_features=max_features,
oob_score=oob_score,
random_state=531,
verbose=1,
class_weight=class_weight,
n_jobs=1)
fileModel.fit(
xTrain.drop(['id_siniestro'], axis=1).values, yTrain.values)
print(
np.median(
fileModel.predict_proba(Valid[Valid[label] == 0].drop(
[label] + ['id_siniestro'], axis=1).values)))
print(
np.median(
fileModel.predict_proba(Valid[Valid[label] == 1].drop(
[label] + ['id_siniestro'], axis=1).values)))
tresholds = np.linspace(0.1, 1.0, 200)
scores = []
y_pred_score = fileModel.predict_proba(
Valid.drop([label] + ['id_siniestro'], axis=1).values)
print(fileModel.classes_)
print(y_pred_score)
y_pred_score = np.delete(y_pred_score, 0, axis=1)
print('min', y_pred_score.min())
print('max', y_pred_score.max())
for treshold in tresholds:
y_hat = (y_pred_score > treshold).astype(int)
y_hat = y_hat.tolist()
y_hat = [item for sublist in y_hat for item in sublist]
scores.append([
recall_score(y_pred=y_hat, y_true=Valid[label].values),
precision_score(y_pred=y_hat, y_true=Valid[label].values),
fbeta_score(y_pred=y_hat, y_true=Valid[label].values, beta=2)
])
scores = np.array(scores)
print('max_scores', scores[:, 2].max(), scores[:, 2].argmax())
plot.plot(tresholds, scores[:, 0], label='$Recall$')
plot.plot(tresholds, scores[:, 1], label='$Precision$')
plot.plot(tresholds, scores[:, 2], label='$F_2$')
plot.ylabel('Score')
plot.xlabel('Threshold')
plot.legend(loc='best')
file_name = 'ERT_treshold_' + str(sampling) + '.png'
plot.close()
final_tresh = tresholds[scores[:, 2].argmax()]
y_hat_test = fileModel.predict_proba(
Test.drop([label] + ['id_siniestro'], axis=1).values)
y_hat_test = np.delete(y_hat_test, 0, axis=1)
y_hat_test = (y_hat_test > final_tresh).astype(int)
y_hat_test = y_hat_test.tolist()
y_hat_test = [item for sublist in y_hat_test for item in sublist]
print('Final threshold: %.3f' % final_tresh)
print('Test Recall Score: %.3f' %
recall_score(y_pred=y_hat_test, y_true=Test[label].values))
print('Test Precision Score: %.3f' %
precision_score(y_pred=y_hat_test, y_true=Test[label].values))
print('Test F2 Score: %.3f' % fbeta_score(
y_pred=y_hat_test, y_true=Test[label].values, beta=beta))
del comparative['FRAUDE_Clusters']
Test = pd.merge(Test, comparative, how='left', on='id_siniestro')
cnf_matrix = confusion_matrix(Test['FRAUDE_Clusters'].values,
y_hat_test)
plot_confusion_matrix(cnf_matrix,
classes=['No Fraude', 'Fraude'],
title='Confusion matrix')
cnf_matrix = confusion_matrix(Test['FRAUDE'].values, y_hat_test)
plot_confusion_matrix(cnf_matrix,
classes=['Normal', 'Anormal'],
title='Confusion matrix')
return final_tresh
def extreme_randomize_tunning(Train,
Test,
sampling=None,
scores='f1',
label='FRAUDE'):
Train = pd.concat([Train, Test], axis=0, ignore_index=True)
yTrain = Train[[label]]
xTrain = Train
del xTrain[label]
if sampling == None:
pass
elif sampling == 'ALLKNN':
xTrain, yTrain = under_sampling(xTrain, yTrain)
class_weight = None
else:
xTrain, yTrain = over_sampling(xTrain, yTrain, model=sampling)
class_weight = None
min_sample_leaf = round((len(xTrain.index)) * 0.005)
min_sample_split = min_sample_leaf * 10
features = round(len(xTrain.columns) / 3)
if sampling != None:
tuned_parameters = [{
'bootstrap': [True, False],
'min_samples_leaf': [min_sample_leaf],
'min_samples_split': [min_sample_split],
'n_estimators': [200, 300],
'max_depth': [50, 100],
'max_features': [features],
'oob_score': [True, False],
'random_state': [531],
'verbose': [1]
}]
if sampling == None:
tuned_parameters = [{
'bootstrap': [True],
'min_samples_leaf': [min_sample_leaf],
'min_samples_split': [min_sample_split],
'n_estimators': [200, 300],
'max_depth': [50, 100],
'max_features': [features],
'oob_score': [True, False],
'random_state': [531],
'verbose': [1],
'class_weight': [
'balanced', 'balanced_subsample', {
0: 1,
1: 2
}, {
0: 1,
1: 3
}, {
0: 1,
1: 5
}, {
0: 1,
1: 10
}, {
0: 1,
1: 20
}, {
0: 1,
1: 30
}, {
0: 1,
1: 40
}, {
0: 1,
1: 50
}, {
0: 1,
1: 60
}, {
0: 1,
1: 70
}, {
0: 1,
1: 80
}, {
0: 1,
1: 90
}, {
0: 1,
1: 100
}, {
0: 1,
1: 110
}, {
0: 1,
1: 120
}, {
0: 1,
1: 130
}, {
0: 1,
1: 140
}, {
0: 1,
1: 142
}
],
'n_jobs': [-1]
}]
fileModel = GridSearchCV(ensemble.ExtraTreesClassifier(),
param_grid=tuned_parameters,
cv=10,
scoring='%s_macro' % scores)
fileModel.fit(
xTrain.drop(['id_siniestro'], axis=1).values, yTrain[label].values)
print("Best parameters set found on development set:")
print()
dict_values = fileModel.best_params_
print(dict_values)
print()
print("Grid scores on development set:")
print()
means = fileModel.cv_results_['mean_test_score']
stds = fileModel.cv_results_['std_test_score']
for mean, std, params in zip(means, stds,
fileModel.cv_results_['params']):
print("%0.3f (+/-%0.03f) for %r" % (mean, std * 2, params))
max_depth = int(dict_values['max_depth'])
n_estimators = int(dict_values['n_estimators'])
bootstrap = bool(dict_values['bootstrap'])
oob_score = bool(dict_values['oob_score'])
class_weight = dict_values['class_weight']
max_features = int(dict_values['max_features'])
df = pd.DataFrame.from_dict(dict_values, orient="index")
df.to_csv('final_files\\sup_ert.csv',
sep=';',
encoding='latin1',
index=False)
return max_depth, n_estimators, bootstrap, oob_score, class_weight, max_features, sampling, min_sample_leaf, min_sample_split, max_features
def model_evaluation(Train,
Valid,
Test,
comparative,
bootstrap=False,
n_estimators: int = 200,
max_depth: int = 50,
oob_score: bool = False,
class_weight='balanced_subsample',
sampling=None,
label='FRAUDE',
model='ert'):
# With beta = 2, we give the same importance to Recall and Precision
if sampling is not None:
class_weight = None
model_name = str(sampling)
# fileModel.fit(xTrain.drop(['id_siniestro'], axis=1).values, yTrain.values)
# print(np.median(fileModel.predict_proba(Valid[Valid[label] == 0].drop([label] + ['id_siniestro'], axis=1).values)))
# print(np.median(fileModel.predict_proba(Valid[Valid[label] == 1].drop([label] + ['id_siniestro'], axis=1).values)))
tresholds = np.linspace(0.1, 1.0, 200)
scores = []
y_pred_score = np.empty(shape=[0, 2])
predicted_index = np.empty(shape=[
0,
])
# y_pred_score = fileModel.predict_proba(Valid.drop([label] + ['id_siniestro'], axis=1).values)
skf = StratifiedKFold(n_splits=5, random_state=42, shuffle=False)
Test = pd.concat([Train, Valid, Test], axis=0).reset_index()
print(Test.shape)
X = Test.drop([label] + ['id_siniestro'], axis=1)
y = Test[[label]]
for train_index, test_index in skf.split(X.values, y[label].values):
X_train, X_test = X.loc[train_index].values, X.loc[
test_index].values
y_train, y_test = y.loc[train_index].values, y.loc[
test_index].values
if sampling == None:
pass
elif sampling == 'ALLKNN':
X_train, y_train = under_sampling(X_train, y_train)
class_weight = None
else:
X_train, y_train = over_sampling(X_train,
y_train,
model=sampling)
class_weight = None
min_sample_leaf = round(y_train.shape[0] * 0.005)
min_sample_split = min_sample_leaf * 10
max_features = round(X_train.shape[1] / 3)
if model == 'ert':
fileModel = ensemble.ExtraTreesClassifier(
criterion='entropy',
bootstrap=bootstrap,
min_samples_leaf=min_sample_leaf,
min_samples_split=min_sample_split,
n_estimators=n_estimators,
max_depth=max_depth,
max_features=max_features,
oob_score=oob_score,
random_state=531,
verbose=1,
class_weight=class_weight,
n_jobs=-1)
elif model == 'gb':
fileModel = ensemble.GradientBoostingClassifier(
loss='deviance',
learning_rate=0.01,
n_estimators=200,
subsample=1.0,
criterion='friedman_mse',
min_samples_split=min_sample_split,
min_samples_leaf=min_sample_leaf,
min_weight_fraction_leaf=0.,
max_depth=max_depth,
min_impurity_decrease=0.,
min_impurity_split=None,
init=None,
random_state=531,
max_features=None,
verbose=1,
max_leaf_nodes=None,
warm_start=False,
presort='auto')
elif model == 'lxgb':
fileModel = lgbx.LGBMClassifier(
boosting_type="gbdt",
num_leaves=2000,
max_depth=200,
learning_rate=0.005,
n_estimators=300,
max_bin=500,
objective='binary',
min_split_gain=0.,
min_child_weight=5,
min_child_samples=min_sample_leaf,
subsample=1.,
subsample_freq=1,
colsample_bytree=1.,
reg_alpha=0.,
reg_lambda=0.,
random_state=531,
n_jobs=-1,
silent=True)
elif model.startswith('stacked'):
ERT = ensemble.ExtraTreesClassifier(
bootstrap=bootstrap,
n_estimators=n_estimators,
max_depth=max_depth,
oob_score=oob_score,
class_weight=class_weight,
min_samples_leaf=min_sample_leaf,
min_samples_split=min_sample_split,
max_features='auto',
n_jobs=-1)
Gboost = ensemble.GradientBoostingClassifier(
n_estimators=n_estimators,
learning_rate=0.005,
max_depth=max_depth,
loss='deviance',
random_state=531,
min_samples_split=min_sample_split,
min_samples_leaf=min_sample_leaf)
Light_Gboost = lgbx.LGBMClassifier(
boosting_type="gbdt",
num_leaves=2000,
max_depth=-1,
learning_rate=0.005,
n_estimators=300,
max_bin=500,
objective='binary',
min_split_gain=0.,
min_child_weight=5,
min_child_samples=min_sample_leaf,
subsample=1.,
subsample_freq=1,
colsample_bytree=1.,
reg_alpha=0.,
reg_lambda=0.,
random_state=531,
n_jobs=-1,
silent=False)
if model.endswith('_ERT'):
fileModel = StackingClassifier(
classifiers=[Gboost, Light_Gboost],
meta_classifier=ERT,
average_probas=True,
use_probas=True)
elif model.endswith('_GB'):
fileModel = StackingClassifier(
classifiers=[ERT, Light_Gboost],
meta_classifier=Gboost,
average_probas=True,
use_probas=True)
elif model.endswith('_LXGB'):
fileModel = StackingClassifier(
classifiers=[ERT, Gboost],
meta_classifier=Light_Gboost,
average_probas=True,
use_probas=True)
fileModel.fit(X_train, y_train)
y_pred_score_i = fileModel.predict_proba(X_test)
y_pred_score = np.append(y_pred_score, y_pred_score_i, axis=0)
print(y_pred_score.shape)
print(test_index.shape)
print(predicted_index.shape)
predicted_index = np.append(predicted_index, test_index, axis=0)
print(predicted_index)
del X_train, X_test, y_train, y_test
y_pred_score = np.delete(y_pred_score, 0, axis=1)
print('min', y_pred_score.min())
print('max', y_pred_score.max())
for treshold in tresholds:
y_hat = (y_pred_score > treshold).astype(int)
y_hat = y_hat.tolist()
y_hat = [item for sublist in y_hat for item in sublist]
scores.append([
recall_score(y_pred=y_hat, y_true=Test[label].values),
precision_score(y_pred=y_hat, y_true=Test[label].values),
fbeta_score(y_pred=y_hat, y_true=Test[label].values, beta=2)
])
scores = np.array(scores)
print('F-Score', scores[:, 2].max(), scores[:, 2].argmax())
print('scores', scores[scores[:2].argmax()])
print(scores)
plot.plot(tresholds, scores[:, 0], label='$Recall$')
plot.plot(tresholds, scores[:, 1], label='$Precision$')
plot.plot(tresholds, scores[:, 2], label='$F_2$')
plot.ylabel('Score')
plot.xlabel('Threshold')
plot.legend(loc='best')
plot.show()
plot.close()
final_tresh = tresholds[scores[:, 2].argmax()]
print(final_tresh)
y_hat_test = (y_pred_score > final_tresh).astype(int)
y_hat_test = y_hat_test.tolist()
y_hat_test = [item for sublist in y_hat_test for item in sublist]
Test['id_siniestro'] = Test['id_siniestro'].map(int)
comparative['id_siniestro'] = comparative['id_siniestro'].map(int)
Test = pd.merge(Test,
comparative[['id_siniestro', 'FRAUDE']],
how='left',
on='id_siniestro')
cnf_matrix = confusion_matrix(Test['FRAUDE_Clusters'].values,
y_hat_test)
plot_confusion_matrix(cnf_matrix,
classes=['Normal', 'Abnormal'],
title='Confusion matrix')
cnf_matrix = confusion_matrix(Test['FRAUDE'].values, y_hat_test)
plot_confusion_matrix(cnf_matrix,
classes=['Unknown', 'Fraud'],
title='Confusion matrix')
return None