def get_forest_model(data=data, balanced=False, model_name='model_forest_unbalanced_ori.pkl'):
if balanced == True:
X_train, X_test, y_train, y_test = get_balanced_data(data)
else:
X = data.drop('class', axis=1)
y = data['class']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=1)
# Number of trees in random forest
n_estimators = [10, 100] # [int(x) for x in np.linspace(start = 200, stop = 2000, num = 10)]
# Number of features to consider at every split
#max_features = ['auto'] # ['auto', 'sqrt']
# Maximum number of levels in tree
max_depth = [3, 5, 10] # [int(x) for x in np.linspace(10, 110, num = 11)]
# Minimum number of samples required to split a node
min_samples_split = [2, 10, 20]
#criterion = ['gini', 'entropy']
# Minimum number of samples required at each leaf node
# min_samples_leaf = [1, 2]
# Method of selecting samples for training each tree
bootstrap = [True, False]
# Create the random grid
random_grid = {'n_estimators': n_estimators,
# 'max_features': max_features,
'max_depth': max_depth,
'min_samples_split': min_samples_split,
# 'min_samples_leaf': min_samples_leaf,
#'criterion': criterion,
'bootstrap': bootstrap}
# Use the random grid to search for best hyperparameters
# First create the base model to tune
rf = RandomForestRegressor()
# Random search of parameters, using 3 fold cross validation,
# search across 100 different combinations, and use all available cores
rf_random = GridSearchCV(estimator=rf, param_grid=random_grid, cv=3, verbose=2, n_jobs=7)
rf_random.fit(X_train, y_train)
clf = rf_random.best_estimator_
print(rf_random.best_params_)
model = clf.fit(X_train, y_train)
path = '1) classification algorithms/random forest/credit card fraud/' + model_name
with open(path, 'wb') as file:
pickle.dump(model, file)
return
def get_SVM_model(data=data, kernel='rbf', gamma=0.0001, C=1000, probability=True, model_name='model_SVM_balanced.pkl'):
X_train, X_test, y_train, y_test = get_balanced_data(data)
# Create SVM classifer object
clf = svm.SVC(kernel=kernel, gamma=gamma, C=C, probability=probability)
# Train SVM classifer
model = clf.fit(X_train, y_train)
path = '1) classification algorithms/SVM/credit card fraud/'+model_name
with open(path, 'wb') as file:
pickle.dump(model, file)
return
from global_functions import plot_confusion_matrix, cm_analysis
np.random.seed(7)
# load the data
file_name = 'data/credit card fraud/data_creditcard.pkl' # set working directory to MSc Project
data = pd.read_pickle(file_name)
# unbalanced data
X = data.drop('class', axis=1)
y = data['class']
X_train_unbalanced, X_test_unbalanced, y_train_unbalanced, y_test_unbalanced = train_test_split(
X, y, test_size=0.25, random_state=1)
# balanced data
# even out the data set -> 1:1 ratio of fraud and non fraud
X_train_balanced, X_test_balanced, y_train_balanced, y_test_balanced = get_balanced_data(
data)
# unpack unbalanced model
path = '1) classification algorithms/random forest/credit card fraud/model_forest_unbalanced_ori.pkl'
with open(path, 'rb') as file:
unbalanced_model = pickle.load(file)
# unpack balanced model
path = '1) classification algorithms/random forest/credit card fraud/model_forest_balanced_ori.pkl'
with open(path, 'rb') as file:
balanced_model = pickle.load(file)
# predict labels
unbalanced_predictions = unbalanced_model.predict(X_test_unbalanced)
unbalanced_predictions = [int(round(x)) for x in unbalanced_predictions]
balanced_predictions = balanced_model.predict(X_test_balanced)
import pickle
from sklearn.preprocessing import StandardScaler, RobustScaler
from sklearn.model_selection import StratifiedKFold
from sklearn.model_selection import GridSearchCV
from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import Adam
from global_functions import get_balanced_data
import numpy
numpy.random.seed(7)
# load the data
file_name = 'data/customer churn/customer churn modified.pkl' # set working directory to MSc Project
data = pd.read_pickle(file_name)
X_train, X_test, y_train, y_test = get_balanced_data(data)
def get_NN_model(data=data,
lr=0.001,
loss='sparse_categorical_crossentropy',
metrics=['accuracy'],
validation_split=0.2,
batch_size=25,
epochs=20,
shuffle=True,
verbose=2,
model_name='model_NN_balanced_churn.pkl'):
# create the neural net
n_inputs = X_train.shape[1]
def get_accuracies(data):
X_train, X_test, y_train, y_test = get_balanced_data(data)
seed = 1
rfc = RandomForestClassifier(bootstrap=True,
max_depth=10,
max_features='auto',
min_samples_leaf=2,
min_samples_split=10,
n_estimators=500)
rfc2 = RandomForestClassifier(bootstrap=False,
max_depth=2,
max_features='auto',
min_samples_leaf=5,
min_samples_split=20,
n_estimators=100)
gbm = GradientBoostingClassifier(min_samples_split=25,
min_samples_leaf=25,
loss='deviance',
learning_rate=0.1,
max_depth=5,
max_features='auto',
criterion='friedman_mse',
n_estimators=100)
def baseline_model(optimizer='adam', learn_rate=0.01):
model = Sequential()
model.add(Dense(100, input_dim=X_train.shape[1], activation='relu'))
model.add(
Dense(50, activation='relu')
) # 8 is the dim/ the number of hidden units (units are the kernel)
model.add(Dense(2, activation='softmax'))
model.compile(loss='categorical_crossentropy',
optimizer=optimizer,
metrics=['accuracy'])
return model
keras = KerasClassifier(build_fn=baseline_model,
batch_size=32,
epochs=100,
verbose=0,
optimizer='Adam')
outer_cv = KFold(n_splits=5, shuffle=True, random_state=seed)
svm = SVC(gamma="scale", probability=True, kernel='rbf', C=0.5)
models = [('GBM', gbm), ('RFC', rfc), ('RFC2', rfc2), ('Keras', keras),
('SVM', svm)]
results = []
names = []
scoring = 'accuracy'
accuracy = []
for name, model in models:
cv_results = cross_val_score(model,
X_train,
y_train,
cv=outer_cv,
scoring=scoring)
results.append(cv_results)
names.append(name)
# msg = "Cross-validation Accuracy %s: %f (+/- %f )" % (name, cv_results.mean() * 100, cv_results.std() * 100)
# print(msg)
model.fit(X_train, y_train)
# print('Test set accuracy: {:.2f}'.format(model.score(X_test, y_test) * 100), '%')
# accuracy.append(name)
accuracy.append(model.score(X_test, y_test))
return accuracy
