def run_mlp_neural_network(train, test, ss_split, labels):
# prepare training and test data
X_train, X_test, y_train, y_test = hpr.prepData(train, test, ss_split,
labels)
scaler = StandardScaler().fit(X_train)
X_train_scaled = scaler.transform(X_train)
scaler = StandardScaler().fit(X_test)
X_test_scaled = scaler.transform(X_test)
print('ML Model: MLP Neural Network')
model = MLPClassifier(hidden_layer_sizes=(150, ),
activation='logistic',
solver='lbfgs',
alpha=0.001,
max_iter=200,
early_stopping=True,
validation_fraction=0.2,
learning_rate='adaptive',
tol=1e-8,
random_state=1).fit(X_train_scaled, y_train)
# Accuracy
train_predictions = model.predict(X_test_scaled)
acc = accuracy_score(y_test, train_predictions)
# Logloss
train_predictions_p = model.predict_proba(X_test_scaled)
ll = log_loss(y_test, train_predictions_p)
scaler = StandardScaler().fit(test)
test_scaled = scaler.transform(test)
test_predictions = model.predict_proba(test_scaled)
return test_predictions, acc, ll
def run_k_nearest_neighbours(train, test, ss_split, labels):
# prepare training and test data
X_train, X_test, y_train, y_test = hpr.prepData(train, test, ss_split,
labels)
clf = KNeighborsClassifier(3) # Instantiate a classifier
clf.fit(X_train, y_train) # Fit this classifier to the data
print('ML Model: K-Nearest Neighbours')
# Cross-validation
scores = cross_val_score(KNeighborsClassifier(3),
train.values,
labels,
cv=ss_split)
#print 'Mean Cross-validation scores: {}'.format(np.mean(scores))
# Accuracy
train_predictions = clf.predict(X_test)
acc = accuracy_score(y_test, train_predictions)
# Logloss
train_predictions_p = clf.predict_proba(X_test)
ll = log_loss(y_test, train_predictions_p)
test_predictions = clf.predict_proba(test)
return test_predictions, acc, ll
def run_linear_discriminant_analysis(train, test, ss_split, labels):
# prepare training and test data
X_train, X_test, y_train, y_test = hpr.prepData(train, test, ss_split,
labels)
clf = LinearDiscriminantAnalysis().fit(X_train, y_train)
print('ML Model: Linear Discriminant Analysis')
train_predictions = clf.predict(X_test)
acc = accuracy_score(y_test, train_predictions)
train_predictions_p = clf.predict_proba(X_test)
ll = log_loss(y_test, train_predictions_p)
test_predictions = clf.predict_proba(test)
return test_predictions, acc, ll
def run_random_forest(train, test, ss_split, labels):
# prepare training and test data
X_train, X_test, y_train, y_test = hpr.prepData(train, test, ss_split,
labels)
clf = RandomForestClassifier().fit(X_train, y_train)
print('ML Model: Random Forest')
# Accuracy
train_predictions = clf.predict(X_test)
acc = accuracy_score(y_test, train_predictions)
# Logloss
train_predictions_p = clf.predict_proba(X_test)
ll = log_loss(y_test, train_predictions_p)
test_predictions = clf.predict_proba(test)
return test_predictions, acc, ll
def run_support_vector_machine(train, test, ss_split, labels):
# prepare training and test data
X_train, X_test, y_train, y_test = hpr.prepData(train, test, ss_split,
labels)
clf = SVC(probability=True)
# Gird search
#param_grid = {'C': [1, 10, 100, 1000, 10000, 100000],
# 'gamma': [1, 10, 100, 1000, 10000, 100000]}
param_grid = {
'C': [0.001, 0.01, 0.1, 1, 10, 100],
'gamma': [0.001, 0.01, 0.1, 1, 10, 100]
}
grid_search = GridSearchCV(SVC(probability=True),
param_grid=param_grid,
cv=ss_split)
grid_search.fit(X_train, y_train)
print('Best parameter: {}'.format(grid_search.best_params_))
print('Best cross-validation accuracy score: {}'.format(
grid_search.best_score_))
print('\nBest estimator:\n{}'.format(grid_search.best_estimator_))
# results = pd.DataFrame(grid_search.cv_results_)
# Show the first 5 rows of the result
#print results.head()
# scores = np.array(results.mean_test_score).reshape(6, 6)
#
# ax = sns.heatmap(scores, annot=True, fmt=".2f",linewidths=.5);
# ax.invert_yaxis()
# ax.set(xticklabels=param_grid['gamma']); ax.set(yticklabels=param_grid['C'])
# plt.yticks(rotation=0)
# plt.xlabel('gamma'); plt.ylabel('C'); plt.show()
print('ML Model: Suppoort Vector Machine')
# Accuracy
train_predictions = grid_search.predict(X_test)
acc = accuracy_score(y_test, train_predictions)
# Logloss
train_predictions_p = grid_search.predict_proba(X_test)
ll = log_loss(y_test, train_predictions_p)
test_predictions = grid_search.predict_proba(test)
return test_predictions, acc, ll
def run_logistic_regression(train, test, ss_split, labels):
# prepare training and test data
X_train, X_test, y_train, y_test = hpr.prepData(train, test, ss_split,
labels)
#param_grid = {'C':[1, 10],
# 'tol': [0.001, 0.0001]}
# Standardize the training data.
scaler = StandardScaler().fit(X_train)
X_train_scaled = scaler.transform(X_train)
scaler = StandardScaler().fit(X_test)
X_test_scaled = scaler.transform(X_test)
param_grid = {'C': [1000, 10000], 'tol': [0.000001, 0.00001]}
log_reg = LogisticRegression(solver='newton-cg', multi_class='multinomial')
grid_search = GridSearchCV(log_reg,
param_grid,
scoring='neg_log_loss',
refit='True',
n_jobs=1,
cv=ss_split)
grid_search.fit(X_train_scaled, y_train)
print('Best parameter: {}'.format(grid_search.best_params_))
print('Best cross-validation neg_log_loss score: {}'.format(
grid_search.best_score_))
print('\nBest estimator:\n{}'.format(grid_search.best_estimator_))
print('ML Model: Logistic Regression')
# Accuracy
train_predictions = grid_search.predict(X_test_scaled)
acc = accuracy_score(y_test, train_predictions)
# Logloss
train_predictions_p = grid_search.predict_proba(X_test_scaled)
ll = log_loss(y_test, train_predictions_p)
scaler = StandardScaler().fit(test)
test_scaled = scaler.transform(test)
test_predictions = grid_search.predict_proba(test_scaled)
# visualize error
# hpr.visualize_error(train_predictions, y_test)
return test_predictions, acc, ll
def run_naive_bayes(train, test, ss_split, labels):
# prepare training and test data
X_train, X_test, y_train, y_test = hpr.prepData(train, test, ss_split,
labels)
clf = GaussianNB().fit(
X_train, y_train
) # Instantiate a classifier and fit this classifier to the data
print('ML Model: Naive Bayes')
# Cross-validation
scores = cross_val_score(GaussianNB(), train.values, labels, cv=ss_split)
print('Mean Cross-validation scores: {}'.format(np.mean(scores)))
# Accuracy
train_predictions = clf.predict(X_test)
acc = accuracy_score(y_test, train_predictions)
# Logloss
train_predictions_p = clf.predict_proba(X_test)
ll = log_loss(y_test, train_predictions_p)
test_predictions = clf.predict_proba(test)
return test_predictions, acc, ll