def rf_model(outfile, X_train, Y_train, labels_train, XName_train, X_test, Y_test, labels_test, XName_test):
"""
"""
regr = RandomForestRegressor(max_depth = 9)
regr_model = regr.fit(X_train, Y_train)
ytrain_regr = regr_model.predict(X_train)
ytrain_regr_label = create_binary_labels(ytrain_regr, 4)
ytest_regr = regr_model.predict(X_test)
ytest_regr_label = create_binary_labels(ytest_regr, 4)
ytrain_regr_label = ytrain_regr_label.tolist()
labels_train = labels_train.tolist()
train_rmse = sqrt(mean_squared_error(Y_train, ytrain_regr))
train_accuracy = accuracy_score(labels_train, ytrain_regr_label)
train_precision = precision_score(labels_train, ytrain_regr_label)
train_recall = recall_score(labels_train, ytrain_regr_label)
train_f1 = f1_score(labels_train, ytrain_regr_label)
test_rmse = sqrt(mean_squared_error(Y_test, ytest_regr))
test_accuracy = accuracy_score(labels_test, ytest_regr_label)
test_precision = precision_score(labels_test, ytest_regr_label)
test_recall = recall_score(labels_test, ytest_regr_label)
test_f1 = f1_score(labels_test, ytest_regr_label)
feature_importance = regr.feature_importances_
result = {}
result["rf_model"] = regr_model
result["feature_importance"] = feature_importance
result["train_rmse"] = train_rmse
result["test_rmse"] = test_rmse
result["train_accu"] = train_accuracy
result["test_accu"] = test_accuracy
result["train_prec"] = train_precision
result["test_prec"] = test_precision
result["train_rec"] = train_recall
result["test_rec"] = test_recall
result["train_f1"] = train_f1
result["test_f1"] = test_f1
with open(outfile, "w") as f:
pickle.dump(result, f)
return result
def transfer_learning(test_data):
# Train on any three datasets and test on the rest
# e.g. train on H1N1+H3N2+H5N1; test on H9N2
print test_data
dh1n1, dh3n2, dh5n1, dh9n2, dmixed = mix_subtypes_dist()
h1n1_mutMatrix_file = mutMatrix_path + "h1n1.mut"
h3n2_mutMatrix_file = mutMatrix_path + "h3n2.mut"
h5n1_mutMatrix_file = mutMatrix_path + "h5n1.mut"
h9n2_mutMatrix_file = mutMatrix_path + "h9n2.mut"
X_h1n1, Y_h1n1, XName_h1n1 = load_mut_dataset(h1n1_mutMatrix_file, dh1n1)
X_h3n2, Y_h3n2, XName_h3n2 = load_mut_dataset(h3n2_mutMatrix_file, dh3n2)
X_h5n1, Y_h5n1, XName_h5n1 = load_mut_dataset(h5n1_mutMatrix_file, dh5n1)
X_h9n2, Y_h9n2, XName_h9n2 = load_mut_dataset(h9n2_mutMatrix_file, dh9n2)
dataset = {}
dataset["h1n1"] = (X_h1n1, Y_h1n1, XName_h1n1)
dataset["h3n2"] = (X_h3n2, Y_h3n2, XName_h3n2)
dataset["h5n1"] = (X_h5n1, Y_h5n1, XName_h5n1)
dataset["h9n2"] = (X_h9n2, Y_h9n2, XName_h9n2)
tmp = ["h1n1", "h3n2", "h5n1", "h9n2"]
tmp.remove(test_data)
(X_test, Y_test, XName_test) = dataset[test_data]
X_test = np.array(X_test)
Y_test = np.array(Y_test)
XName_test = np.array(XName_test)
labels_test = create_binary_labels(Y_test, 4)
(X1, Y1, XName1) = dataset[tmp[0]]
(X2, Y2, XName2) = dataset[tmp[1]]
(X3, Y3, XName3) = dataset[tmp[2]]
X_train = np.concatenate((X1, X2, X3), axis=0)
Y_train = np.concatenate((Y1, Y2, Y3), axis=0)
XName_train = np.concatenate((XName1, XName2, XName3), axis=0)
labels_train = create_binary_labels(Y_train, 4)
for i in range(10):
outfile = "../result/bioinfo2008Liao/tl_" + test_data + "_" + str(
i) + ".gm5"
X_train, Y_train, labels_train, XName_train = shuffle_dataset_with_labels(
X_train, Y_train, labels_train, XName_train)
X_test, Y_test, labels_test, XName_test = shuffle_dataset_with_labels(
X_test, Y_test, labels_test, XName_test)
result = multiReg_GM5(outfile, X_train, Y_train, labels_train,
XName_train, X_test, Y_test, labels_test,
XName_test)
def rf_cross_validation(indexID, prefix): dataset = load_dataset(indexID) (X, Y, XName) = dataset[prefix] X, Y = avoid_none(X, Y, 15) labels = create_binary_labels(Y, 4) kfold_trainRF(10, indexID, prefix, X, Y, labels, XName)
def transfer_learning(test_data):
# print "transfer learning test on: " + test_data
dh1n1, dh3n2, dh5n1, dh9n2, dmixed = mix_subtypes_dist()
h1n1_fm_file = featureMatrix_path + "h1n1.feats"
h3n2_fm_file = featureMatrix_path + "h3n2.feats"
h5n1_fm_file = featureMatrix_path + "h5n1.feats"
h9n2_fm_file = featureMatrix_path + "h9n2.feats"
X_h1n1, Y_h1n1, XName_h1n1 = load_features_dataset(h1n1_fm_file, dh1n1)
X_h3n2, Y_h3n2, XName_h3n2 = load_features_dataset(h3n2_fm_file, dh3n2)
X_h5n1, Y_h5n1, XName_h5n1 = load_features_dataset(h5n1_fm_file, dh5n1)
X_h9n2, Y_h9n2, XName_h9n2 = load_features_dataset(h9n2_fm_file, dh9n2)
dataset = {}
dataset["h1n1"] = (X_h1n1, Y_h1n1, XName_h1n1)
dataset["h3n2"] = (X_h3n2, Y_h3n2, XName_h3n2)
dataset["h5n1"] = (X_h5n1, Y_h5n1, XName_h5n1)
dataset["h9n2"] = (X_h9n2, Y_h9n2, XName_h9n2)
tmp = ["h1n1", "h3n2", "h5n1", "h9n2"]
tmp.remove(test_data)
(X_test, Y_test, XName_test) = dataset[test_data]
X_test = np.array(X_test)
Y_test = np.array(Y_test)
XName_test = np.array(XName_test)
labels_test = create_binary_labels(Y_test, 4)
(X1, Y1, XName1) = dataset[tmp[0]]
(X2, Y2, XName2) = dataset[tmp[1]]
(X3, Y3, XName3) = dataset[tmp[2]]
X_train = np.concatenate((X1, X2, X3), axis=0)
Y_train = np.concatenate((Y1, Y2, Y3), axis=0)
XName_train = np.concatenate((XName1, XName2, XName3), axis=0)
labels_train = create_binary_labels(Y_train, 4)
for i in range(10):
outfile = "../result/srep2017Adapted/tl_" + test_data + "_" + str(
i) + ".rf_srep"
X_train, Y_train, labels_train, XName_train = shuffle_dataset_with_labels(
X_train, Y_train, labels_train, XName_train)
X_test, Y_test, labels_test, XName_test = shuffle_dataset_with_labels(
X_test, Y_test, labels_test, XName_test)
result = rf_model(outfile, X_train, Y_train, labels_train, XName_train,
X_test, Y_test, labels_test, XName_test)
def multiReg_GM5(outfile, Xtrain, Ytrain, labelsTrain, XNameTrain, Xtest,
Ytest, labelsTest, XNameTest):
"""
"""
model = sm.OLS(Ytrain, Xtrain).fit()
Ytrain_est = model.predict(Xtrain)
labels_train_est = create_binary_labels(Ytrain_est, 4)
Ytest_est = model.predict(Xtest)
labels_test_est = create_binary_labels(Ytest_est, 4)
train_rmse = sqrt(mean_squared_error(Ytrain, Ytrain_est))
train_accu = accuracy_score(labelsTrain, labels_train_est)
train_prec = precision_score(labelsTrain, labels_train_est)
train_rec = recall_score(labelsTrain, labels_train_est)
train_f1 = f1_score(labelsTrain, labels_train_est)
test_rmse = sqrt(mean_squared_error(Ytest, Ytest_est))
test_accu = accuracy_score(labelsTest, labels_test_est)
test_prec = precision_score(labelsTest, labels_test_est)
test_rec = recall_score(labelsTest, labels_test_est)
test_f1 = f1_score(labelsTest, labels_test_est)
result = {}
result["model"] = model
result["train_rmse"] = train_rmse
result["train_accu"] = train_accu
result["train_prec"] = train_prec
result["train_rec"] = train_rec
result["train_f1"] = train_f1
result["test_rmse"] = test_rmse
result["test_accu"] = test_accu
result["test_prec"] = test_prec
result["test_rec"] = test_rec
result["test_f1"] = test_f1
with open(outfile, "w") as f:
pickle.dump(result, f)
return result
def transfer_learning_2():
# Train on H1N1+H3N2; Test on H5N1+H9N2
dh1n1, dh3n2, dh5n1, dh9n2, dmixed = mix_subtypes_dist()
h1n1_fm_file = featureMatrix_path + "h1n1.feats"
h3n2_fm_file = featureMatrix_path + "h3n2.feats"
h5n1_fm_file = featureMatrix_path + "h5n1.feats"
h9n2_fm_file = featureMatrix_path + "h9n2.feats"
X_h1n1, Y_h1n1, XName_h1n1 = load_features_dataset(h1n1_fm_file, dh1n1)
X_h3n2, Y_h3n2, XName_h3n2 = load_features_dataset(h3n2_fm_file, dh3n2)
X_h5n1, Y_h5n1, XName_h5n1 = load_features_dataset(h5n1_fm_file, dh5n1)
X_h9n2, Y_h9n2, XName_h9n2 = load_features_dataset(h9n2_fm_file, dh9n2)
dataset = {}
dataset["h1n1"] = (X_h1n1, Y_h1n1, XName_h1n1)
dataset["h3n2"] = (X_h3n2, Y_h3n2, XName_h3n2)
dataset["h5n1"] = (X_h5n1, Y_h5n1, XName_h5n1)
dataset["h9n2"] = (X_h9n2, Y_h9n2, XName_h9n2)
Xtrain2 = np.concatenate((X_h1n1, X_h3n2), axis=0)
Ytrain2 = np.concatenate((Y_h1n1, Y_h3n2), axis=0)
XNameTrain2 = np.concatenate((XName_h1n1, XName_h3n2), axis=0)
Xtest2 = np.concatenate((X_h5n1, X_h9n2), axis=0)
Ytest2 = np.concatenate((Y_h5n1, Y_h9n2), axis=0)
XNameTest2 = np.concatenate((XName_h5n1, XName_h9n2), axis=0)
labels_train2 = create_binary_labels(Ytrain2, 4)
labels_test2 = create_binary_labels(Ytest2, 4)
for i in range(10):
outfile = "../result/srep2017Adapted/tl2_" + str(i) + ".rf_srep"
X_train, Y_train, labels_train, XName_train = shuffle_dataset_with_labels(
Xtrain2, Ytrain2, labels_train2, XNameTrain2)
X_test, Y_test, labels_test, XName_test = shuffle_dataset_with_labels(
Xtest2, Ytest2, labels_test2, XNameTest2)
result = rf_model(outfile, X_train, Y_train, labels_train, XName_train,
X_test, Y_test, labels_test, XName_test)
def transfer_learning_2():
dh1n1, dh3n2, dh5n1, dh9n2, dmixed = mix_subtypes_dist()
h1n1_mutMatrix_file = mutMatrix_path + "h1n1.mut"
h3n2_mutMatrix_file = mutMatrix_path + "h3n2.mut"
h5n1_mutMatrix_file = mutMatrix_path + "h5n1.mut"
h9n2_mutMatrix_file = mutMatrix_path + "h9n2.mut"
X_h1n1, Y_h1n1, XName_h1n1 = load_mut_dataset(h1n1_mutMatrix_file, dh1n1)
X_h3n2, Y_h3n2, XName_h3n2 = load_mut_dataset(h3n2_mutMatrix_file, dh3n2)
X_h5n1, Y_h5n1, XName_h5n1 = load_mut_dataset(h5n1_mutMatrix_file, dh5n1)
X_h9n2, Y_h9n2, XName_h9n2 = load_mut_dataset(h9n2_mutMatrix_file, dh9n2)
dataset = {}
dataset["h1n1"] = (X_h1n1, Y_h1n1, XName_h1n1)
dataset["h3n2"] = (X_h3n2, Y_h3n2, XName_h3n2)
dataset["h5n1"] = (X_h5n1, Y_h5n1, XName_h5n1)
dataset["h9n2"] = (X_h9n2, Y_h9n2, XName_h9n2)
# Train on H1N1+H3N2; Test on H5N1+H9N2
Xtrain2 = np.concatenate((X_h1n1, X_h3n2), axis=0)
Ytrain2 = np.concatenate((Y_h1n1, Y_h3n2), axis=0)
XNameTrain2 = np.concatenate((XName_h1n1, XName_h3n2), axis=0)
Xtest2 = np.concatenate((X_h5n1, X_h9n2), axis=0)
Ytest2 = np.concatenate((Y_h5n1, Y_h9n2), axis=0)
XNameTest2 = np.concatenate((XName_h5n1, XName_h9n2), axis=0)
labels_train2 = create_binary_labels(Ytrain2, 4)
labels_test2 = create_binary_labels(Ytest2, 4)
for i in range(10):
outfile = "../result/bioinfo2008Liao/tl2_" + str(i) + ".gm5"
X_train, Y_train, labels_train, XName_train = shuffle_dataset_with_labels(
Xtrain2, Ytrain2, labels_train2, XNameTrain2)
X_test, Y_test, labels_test, XName_test = shuffle_dataset_with_labels(
Xtest2, Ytest2, labels_test2, XNameTest2)
result = multiReg_GM5(outfile, X_train, Y_train, labels_train,
XName_train, X_test, Y_test, labels_test,
XName_test)
def kfoldNB(k, prefix, X, Y, XName): for i in range(k): outfile = "../result/srep2017Peng/" + prefix + "_" + str(i) + ".nb" labels = create_binary_labels(Y, 4) X = np.array(X) Y = np.array(Y).reshape(len(Y),1) XName = np.array(XName).reshape(len(XName),1) labels = np.array(labels) X, Y, labels, XName = shuffle_dataset_with_labels(X, Y, labels, XName) Xtrain, Ytrain, labelsTrain, XNameTrain, Xtest, Ytest, labelsTest, XNameTest = split_train_test_with_labels(X, Y, labels, XName, 0.8) result = naiveBayes(outfile, Xtrain, Ytrain, labelsTrain, XNameTrain, Xtest, Ytest, labelsTest, XNameTest)
def kfold_trainGM5(k, prefix, X, Y, XName):
for i in range(k):
outfile = "../result/bioinfo2008Liao/" + prefix + "_" + str(i) + ".gm5"
labels = create_binary_labels(Y, 4)
X = np.array(X)
Y = np.array(Y).reshape(len(Y), 1)
XName = np.array(XName).reshape(len(XName), 1)
labels = np.array(labels)
X, Y, labels, XName = shuffle_dataset_with_labels(X, Y, labels, XName)
Xtrain, Ytrain, labelsTrain, XNameTrain, Xtest, Ytest, labelsTest, XNameTest = split_train_test_with_labels(
X, Y, labels, XName, 0.8)
result = multiReg_GM5(outfile, Xtrain, Ytrain, labelsTrain, XNameTrain,
Xtest, Ytest, labelsTest, XNameTest)
def rf_transfer_learning(indexID): dataset = load_dataset(indexID) (X_h1n1, Y_h1n1, XName_h1n1) = dataset["h1n1"] (X_h3n2, Y_h3n2, XName_h3n2) = dataset["h3n2"] (X_h5n1, Y_h5n1, XName_h5n1) = dataset["h5n1"] (X_h9n2, Y_h9n2, XName_h9n2) = dataset["h9n2"] X_h1n1, Y_h1n1 = avoid_none(X_h1n1, Y_h1n1, 15) X_h3n2, Y_h3n2 = avoid_none(X_h3n2, Y_h3n2, 15) X_h5n1, Y_h5n1 = avoid_none(X_h5n1, Y_h5n1, 15) X_h9n2, Y_h9n2 = avoid_none(X_h9n2, Y_h9n2, 15) X_h1n1 = X_h1n1.reshape(X_h1n1.shape[0], -1) X_h3n2 = X_h3n2.reshape(X_h3n2.shape[0], -1) X_h5n1 = X_h5n1.reshape(X_h5n1.shape[0], -1) X_h9n2 = X_h9n2.reshape(X_h9n2.shape[0], -1) h1n1_labels = create_binary_labels(Y_h1n1, 4) h3n2_labels = create_binary_labels(Y_h3n2, 4) h5n1_labels = create_binary_labels(Y_h5n1, 4) h9n2_labels = create_binary_labels(Y_h9n2, 4) # Train on H1N1+H3N2;l Test on H5N1+H9N2 Xtrain2 = np.concatenate((X_h1n1, X_h3n2), axis = 0) Ytrain2 = np.concatenate((Y_h1n1, Y_h3n2), axis = 0) XName_train2 = np.concatenate((XName_h1n1, XName_h3n2), axis = 0) Xtest2 = np.concatenate((X_h5n1, X_h9n2), axis = 0) Ytest2 = np.concatenate((Y_h5n1, Y_h9n2), axis = 0) XName_test2 = np.concatenate((XName_h5n1, XName_h9n2), axis = 0) Xtrain2, Ytrain2 = avoid_none(Xtrain2, Ytrain2, 15) Xtest2, Ytest2 = avoid_none(Xtest2, Ytest2, 15) labels_train2 = create_binary_labels(Ytrain2, 4) labels_test2 = create_binary_labels(Ytest2, 4) train_rmse_list = [] train_accu_list = [] train_prec_list = [] train_rec_list = [] train_f1_list = [] test_rmse_list = [] test_accu_list = [] test_prec_list = [] test_rec_list = [] test_f1_list = [] for i in range(10): outfile = "../result/randomForest/tl2_" + indexID + "_" + str(i) + ".rf" X_train, Y_train, labels_train, XName_train = shuffle_dataset_with_labels(Xtrain2, Ytrain2, labels_train2, XName_train2) X_test, Y_test, labels_test, XName_test = shuffle_dataset_with_labels(Xtest2, Ytest2, labels_test2, XName_test2) result = rf_model(outfile, X_train, Y_train, labels_train, XName_train, X_test, Y_test, labels_test, XName_test) train_rmse_list.append(result["train_rmse"]) train_accu_list.append(result["train_accu"]) train_prec_list.append(result["train_prec"]) train_rec_list.append(result["train_rec"]) train_f1_list.append(result["train_f1"]) test_rmse_list.append(result["test_rmse"]) test_accu_list.append(result["test_accu"]) test_prec_list.append(result["test_prec"]) test_rec_list.append(result["test_rec"]) test_f1_list.append(result["test_f1"]) avg_rmse1 = np.array(train_rmse_list).mean() avg_rmse2 = np.array(test_rmse_list).mean() avg_accu1 = np.array(train_accu_list).mean() avg_accu2 = np.array(test_accu_list).mean() avg_prec1 = np.array(train_prec_list).mean() avg_prec2 = np.array(test_prec_list).mean() avg_rec1 = np.array(train_rec_list).mean() avg_rec2 = np.array(test_rec_list).mean() avg_f1_1 = np.array(train_f1_list).mean() avg_f1_2 = np.array(test_f1_list).mean() print "*** Transfer learning ** Train on H1N1+H3N2; Test on H5N1+H9N2 **** " + indexID + "****" print "avg_rmse1: " + str(avg_rmse1) + "\t avg_rmse2: " + str(avg_rmse2) print "avg_accu1: " + str(avg_accu1) + "\t avg_accu2: " + str(avg_accu2) print "avg_prec1: " + str(avg_prec1) + "\t avg_prec2: " + str(avg_prec2) print "avg_rec1: " + str(avg_rec1) + "\t avg_rec2: " + str(avg_rec2) print "avg_f1_1: " + str(avg_f1_1) + "\t avg_f1_2: " + str(avg_f1_2) print " " # Train on any three datasets and test on the rest names = ["h1n1", "h3n2", "h5n1", "h9n2"] for name in names: (X_test, Y_test, XName_test) = dataset[name] X_test = X_test.reshape(X_test.shape[0], -1) tmp = ["h1n1", "h3n2", "h5n1", "h9n2"] tmp.remove(name) (X1, Y1, XName1) = dataset[tmp[0]] (X2, Y2, XName2) = dataset[tmp[1]] (X3, Y3, XName3) = dataset[tmp[2]] X1 = X1.reshape(X1.shape[0], -1) X2 = X2.reshape(X2.shape[0], -1) X3 = X3.reshape(X3.shape[0], -1) X_train = np.concatenate((X1, X2, X3), axis = 0) Y_train = np.concatenate((Y1, Y2, Y3), axis = 0) XName_train = np.concatenate((XName1, XName2, XName3), axis = 0) X_train, Y_train = avoid_none(X_train, Y_train, 15) X_test, Y_test = avoid_none(X_test, Y_test, 15) labels_train = create_binary_labels(Y_train, 4) labels_test = create_binary_labels(Y_test, 4) train_rmse_list = [] train_accu_list = [] train_prec_list = [] train_rec_list = [] train_f1_list = [] test_rmse_list = [] test_accu_list = [] test_prec_list = [] test_rec_list = [] test_f1_list = [] for i in range(10): outfile = "../result/randomForest/tl_" + name +"_" + indexID + "_" + str(i) + ".rf" X_train, Y_train, labels_train, XName_train = shuffle_dataset_with_labels(X_train, Y_train, labels_train, XName_train) X_test, Y_test, labels_test, XName_test = shuffle_dataset_with_labels(X_test, Y_test, labels_test, XName_test) result = rf_model(outfile, X_train, Y_train, labels_train, XName_train, X_test, Y_test, labels_test, XName_test) train_rmse_list.append(result["train_rmse"]) train_accu_list.append(result["train_accu"]) train_prec_list.append(result["train_prec"]) train_rec_list.append(result["train_rec"]) train_f1_list.append(result["train_f1"]) test_rmse_list.append(result["test_rmse"]) test_accu_list.append(result["test_accu"]) test_prec_list.append(result["test_prec"]) test_rec_list.append(result["test_rec"]) test_f1_list.append(result["test_f1"]) avg_rmse1 = np.array(train_rmse_list).mean() avg_rmse2 = np.array(test_rmse_list).mean() avg_accu1 = np.array(train_accu_list).mean() avg_accu2 = np.array(test_accu_list).mean() avg_prec1 = np.array(train_prec_list).mean() avg_prec2 = np.array(test_prec_list).mean() avg_rec1 = np.array(train_rec_list).mean() avg_rec2 = np.array(test_rec_list).mean() avg_f1_1 = np.array(train_f1_list).mean() avg_f1_2 = np.array(test_f1_list).mean() print "*** Transfer learning *** Test @: " + name + " ****** " + indexID + " **" print "avg_rmse1: " + str(avg_rmse1) + "\t avg_rmse2: " + str(avg_rmse2) print "avg_accu1: " + str(avg_accu1) + "\t avg_accu2: " + str(avg_accu2) print "avg_prec1: " + str(avg_prec1) + "\t avg_prec2: " + str(avg_prec2) print "avg_rec1: " + str(avg_rec1) + "\t avg_rec2: " + str(avg_rec2) print "avg_f1_1: " + str(avg_f1_1) + "\t avg_f1_2: " + str(avg_f1_2) print " "