def MLP(M, labels, seed, split=0.8, hidden_layer_size_nb=50):
"""
linear ridge algorithm for input M and output labels
Inputs:
M : matrix m*n where each row is a different example and the columns are composed of the features
labels : vector m*1 where each row is the correponding class of the row of M
seed : random seed to do the split between test/validation/training
split: number between 0 and 1. Split between training and testing set. Default : 0.8
Ouputs:
roc_auc_rf_train: AUC score on the train set
roc_auc_rf_val: AUC score on the validation set
roc_auc_rf: AUC score on the test set
"""
M_float = preprocessing_dataset.preprocessing_nan_normalization(M_str)
M_train_val, M_val, M_test, labels_train_val, labels_val, labels_test = preprocessing_dataset.split_train_val_test(
M_float, seed, labels, nb_val=3, split=0.8)
X_train = M_train_val
Y_train = labels_train_val
X_test = M_test
Y_train = np.reshape(Y_train, (Y_train.shape[0], ))
# Create our imputer to replace missing values with the mean e.g.
imp = Imputer(missing_values='NaN', strategy='mean', axis=0)
imp = imp.fit(X_train)
# Impute our data, then train
X_train_imp = imp.transform(X_train)
# Impute each test item, then predict
X_test_imp = imp.transform(X_test)
X_val_imp = imp.transform(M_val)
clf = MLPClassifier(solver='adam',
alpha=1e-5,
hidden_layer_sizes=(hidden_layer_size_nb),
random_state=1)
clf = clf.fit(X_train_imp, Y_train)
y_pred_proba_train = clf.predict_proba(X_train_imp)[:, 1]
y_pred_proba_train = np.reshape(y_pred_proba_train,
(y_pred_proba_train.shape[0], 1))
fpr_svm, tpr_svm, thresholds_train = roc_curve(Y_train, y_pred_proba_train)
roc_auc_train = auc(fpr_svm, tpr_svm)
y_pred_proba_val = clf.predict_proba(X_val_imp)[:, 1]
y_pred_proba_val = np.reshape(y_pred_proba_val,
(y_pred_proba_val.shape[0], 1))
fpr_svm, tpr_svm, thresholds_train = roc_curve(labels_val,
y_pred_proba_val)
roc_auc_val = auc(fpr_svm, tpr_svm)
y_pred_proba = clf.predict_proba(X_test_imp)[:, 1]
y_pred_proba = np.reshape(y_pred_proba, (y_pred_proba.shape[0], 1))
fpr_svm, tpr_svm, thresholds_train = roc_curve(labels_test, y_pred_proba)
roc_auc_test = auc(fpr_svm, tpr_svm)
print('MLP: train set: %0.5f, validation: %0.5f, test set: %0.5f' %
(roc_auc_train, roc_auc_val, roc_auc_test))
return roc_auc_train, roc_auc_val, roc_auc_test
def svm(M, labels, seed, split=0.8):
"""
linear SVM algorithm for input M and output labels
Inputs:
M : matrix m*n where each row is a different example and the columns are composed of the features
labels : vector m*1 where each row is the correponding class of the row of M
seed : random seed to do the split between test/validation/training
split: number between 0 and 1. Split between training and testing set. Default : 0.8
Ouputs:
roc_auc_svm_train: AUC score on the train set
roc_auc_svm_val: AUC score on the validation set
roc_auc_svm: AUC score on the test set
"""
M_float = preprocessing_dataset.preprocessing_nan_normalization(M_str)
M_train_val, M_val, M_test, labels_train_val, labels_val, labels_test = preprocessing_dataset.split_train_val_test(
M_float, seed, labels, nb_val=3, split=0.8)
X_train = M_train_val
Y_train = labels_train_val
X_test = M_test
Y_train = np.reshape(Y_train, (Y_train.shape[0], ))
# Create our imputer to replace missing values with the mean e.g.
imp = Imputer(missing_values='NaN', strategy='mean', axis=0)
imp = imp.fit(X_train)
# Impute our data, then train
X_train_imp = imp.transform(X_train)
clf = SVC(kernel='linear', probability=True)
clf = clf.fit(X_train_imp, Y_train)
# Impute each test item, then predict
X_test_imp = imp.transform(X_test)
X_val_imp = imp.transform(M_val)
y_pred = clf.predict_proba(X_train_imp)[:, 1]
y_pred = np.reshape(y_pred, (y_pred.shape[0], 1))
fpr_svm, tpr_svm, thresholds_train = roc_curve(Y_train, y_pred)
roc_auc_svm_train = auc(fpr_svm, tpr_svm)
y_pred_val = clf.predict_proba(X_val_imp)[:, 1]
y_pred_val = np.reshape(y_pred_val, (y_pred_val.shape[0], 1))
fpr_svm, tpr_svm, thresholds_train = roc_curve(labels_val, y_pred_val)
roc_auc_svm_val = auc(fpr_svm, tpr_svm)
y_pred_proba = clf.predict_proba(X_test_imp)[:, 1]
y_pred_proba = np.reshape(y_pred_proba, (y_pred_proba.shape[0], 1))
fpr_svm, tpr_svm, thresholds_train = roc_curve(labels_test, y_pred_proba)
roc_auc_svm = auc(fpr_svm, tpr_svm)
print('linear SVM: train set: %0.5f, val set: %0.5f, test set: %0.5f' %
(roc_auc_svm_train, roc_auc_svm_val, roc_auc_svm))
return roc_auc_svm_train, roc_auc_svm_val, roc_auc_svm
def linear_ridge(M, labels, seed, split=0.8):
"""
linear ridge algorithm for input M and output labels
Inputs:
M : matrix m*n where each row is a different example and the columns are composed of the features
labels : vector m*1 where each row is the correponding class of the row of M
seed : random seed to do the split between test/validation/training
split: number between 0 and 1. Split between training and testing set. Default : 0.8
Ouputs:
roc_auc_train: AUC score on the train set
roc_auc_val: AUC score on the validation set
roc_auc_test: AUC score on the test set
"""
M_float = preprocessing_dataset.preprocessing_nan_normalization(M_str)
M_train_val, M_val, M_test, labels_train_val, labels_val, labels_test = preprocessing_dataset.split_train_val_test(
M_float, seed, labels, nb_val=3, split=0.8)
X_train = M_train_val
Y_train = labels_train_val
X_test = M_test
Y_train = np.reshape(Y_train, (Y_train.shape[0], ))
# Create our imputer to replace missing values with the mean e.g.
imp = Imputer(missing_values='NaN', strategy='mean', axis=0)
imp = imp.fit(X_train)
# Impute our data, then train
X_train_imp = imp.transform(X_train)
clf = RidgeClassifier()
clf = clf.fit(X_train_imp, Y_train)
# Impute each test item, then predict
X_test_imp = imp.transform(X_test)
X_val_imp = imp.transform(M_val)
# Compute the accuracy
lin_acc = clf.score(X_test_imp, labels_test)
# Compute the AUC
pred_train = clf.decision_function(X_train_imp)
pred = clf.decision_function(X_test_imp)
pred_val = clf.decision_function(X_val_imp)
fpr_svm, tpr_svm, thresholds_train = roc_curve(Y_train, pred_train)
roc_auc_train = auc(fpr_svm, tpr_svm)
fpr_svm, tpr_svm, thresholds_train = roc_curve(labels_val, pred_val)
roc_auc_val = auc(fpr_svm, tpr_svm)
fpr_svm, tpr_svm, thresholds_train = roc_curve(labels_test, pred)
roc_auc_test = auc(fpr_svm, tpr_svm)
print(
'linear ridge: train set: %0.5f, validation: %0.5f, test set: %0.5f' %
(roc_auc_train, roc_auc_val, roc_auc_test))
return roc_auc_train, roc_auc_val, roc_auc_test
mask_age, _, _ = read_tadpole.load_csv_no_header(path_mask_X1) mask_sex, _, _ = read_tadpole.load_csv_no_header(path_mask_X2) mask_agesex, _, _ = read_tadpole.load_csv_no_header(path_mask_X3) mask_nosignificance, _, _ = read_tadpole.load_csv_no_header(path_mask_X4) A1, _, _ = read_tadpole.load_csv_no_header(path_dataset_affinity_matrix_age) A2, _, _ = read_tadpole.load_csv_no_header(path_dataset_affinity_matrix_sex) A3, _, _ = read_tadpole.load_csv_no_header(path_dataset_affinity_matrix_agesex) A_age = preprocessing_dataset.str_to_float(A1) A_sex = preprocessing_dataset.str_to_float(A2) A_sexage = preprocessing_dataset.str_to_float(A3) labels, _, _ = read_tadpole.load_csv_no_header(labels_path) labels = preprocessing_dataset.str_to_float(labels) M_float = preprocessing_dataset.preprocessing_nan_normalization(M_str) imp = Imputer(missing_values='NaN', strategy='mean', axis=0) imp = imp.fit(M_float) # Impute our data, then train M_float_imp = imp.transform(M_float) M = M_float_imp A_age = preprocessing_dataset.normalize_adj(A_age) A_sex = preprocessing_dataset.normalize_adj(A_sex) A_sexage = preprocessing_dataset.normalize_adj(A_sexage) mask_age = preprocessing_dataset.str_to_float(mask_age) mask_sex = preprocessing_dataset.str_to_float(mask_sex)
