"../../models/dlmodel10_5.h5"
]
model_types = ["pca_split", "pca_split", "pca_split", "pca_split", "pca_split"]
# load model and transform inputs
members = load_keras_models(model_files)
[ensembleX_train, ensembleX_test] = transform_inputs(x_train, x_test,
model_types)
# result dictionary to keep track of model results
results = {}
# SVM ensemble
print("\nSVM ENSEMBLE:")
from sklearn.svm import SVC
model = SVC(verbose=True, max_iter=1e5)
params = {"kernel": ["linear", "rbf", "poly", "sigmoid"], "C": [0.1, 1, 10]}
ensemble_model = fit_ensemble(members, ensembleX_train, y_train, train_size,
model, params)
# grid search results
ensemble_results = pd.DataFrame.from_dict(ensemble_model.cv_results_)
print(ensemble_results)
ensemble_results.to_csv("../../results/ensembles_svm.csv")
print("Best model score:", ensemble_model.best_score_)
print(ensemble_model.best_params_)
# predict output using ensemble model
yhat_train = ensemble_predict(members, ensemble_model, ensembleX_train,
train_size)
train_acc = accuracy_score(y_train, yhat_train)
print("Train accuracy:", train_acc)
yhat_test = ensemble_predict(members, ensemble_model, ensembleX_test,
test_size)
test_acc = accuracy_score(y_test, yhat_test)
print("Test accuracy:", test_acc)
model_files = ["../models/dlmodel10_1.h5",
"../models/dlmodel10_2.h5",
"../models/dlmodel10_3.h5",
"../models/dlmodel10_4.h5",
"../models/dlmodel10_5.h5"]
model_types = ["pca_split", "pca_split", "pca_split", "pca_split", "pca_split"]
# load model and transform inputs
members = load_keras_models(model_files)
[ensembleX_train, ensembleX_test] = transform_inputs(x_train, x_test, model_types)
# result dictionary to keep track of model results
results = {}
# Naive Bayes ensemble
print("\nNAIVE BAYES ENSEMBLE:")
from sklearn.naive_bayes import GaussianNB
ensemble_model = fit_ensemble(members, ensembleX_train, y_train, train_size, GaussianNB(), {})
# predict output using ensemble model
yhat_train = ensemble_predict(members, ensemble_model, ensembleX_train, train_size)
train_acc = accuracy_score(y_train, yhat_train)
print("Train accuracy:", train_acc)
yhat_test = ensemble_predict(members, ensemble_model, ensembleX_test, test_size)
test_acc = accuracy_score(y_test, yhat_test)
print("Test accuracy:", test_acc)
results["GaussianNB(E)"] = {"Train": train_acc, "Test": test_acc}
# results to data frame
df = pd.DataFrame.from_dict(results, orient = "index")
df = df.reset_index()
df = df.rename(columns = {"index": "Model", "Train": "Train Accuracy", "Test": "Test Accuracy"})
# save results
df.to_csv("../results/ensembleresults.csv", mode = "a", header = False)
"../models/dlmodel10_1.h5", "../models/dlmodel10_2.h5",
"../models/dlmodel10_3.h5", "../models/dlmodel10_4.h5",
"../models/dlmodel10_5.h5"
]
model_types = ["pca_split", "pca_split", "pca_split", "pca_split", "pca_split"]
# load model and transform inputs
members = load_keras_models(model_files)
[ensembleX_train, ensembleX_test] = transform_inputs(x_train, x_test,
model_types)
# result dictionary to keep track of model results
results = {}
# LDA ensemble
print("\nLDA ENSEMBLE:")
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
ensemble_model = fit_ensemble(members, ensembleX_train, y_train, train_size,
LinearDiscriminantAnalysis(), {})
# predict output using ensemble model
yhat_train = ensemble_predict(members, ensemble_model, ensembleX_train,
train_size)
train_acc = accuracy_score(y_train, yhat_train)
print("Train accuracy:", train_acc)
yhat_test = ensemble_predict(members, ensemble_model, ensembleX_test,
test_size)
test_acc = accuracy_score(y_test, yhat_test)
print("Test accuracy:", test_acc)
results["LDA(E)"] = {"Train": train_acc, "Test": test_acc}
# results to data frame
df = pd.DataFrame.from_dict(results, orient="index")
df = df.reset_index()
df = df.rename(columns={
"../../models/dlmodel10_1.h5", "../../models/dlmodel10_2.h5",
"../../models/dlmodel10_3.h5", "../../models/dlmodel10_4.h5",
"../../models/dlmodel10_5.h5"
]
model_types = ["pca_split", "pca_split", "pca_split", "pca_split", "pca_split"]
# load model and transform inputs
members = load_keras_models(model_files)
[ensembleX_train, ensembleX_test] = transform_inputs(x_train, x_test,
model_types)
# result dictionary to keep track of model results
results = {}
# QDA ensemble
print("\nQDA ENSEMBLE:")
from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis
ensemble_model = fit_ensemble(members, ensembleX_train, y_train, train_size,
QuadraticDiscriminantAnalysis(), {})
# predict output using ensemble model
yhat_train = ensemble_predict(members, ensemble_model, ensembleX_train,
train_size)
train_acc = accuracy_score(y_train, yhat_train)
print("Train accuracy:", train_acc)
yhat_test = ensemble_predict(members, ensemble_model, ensembleX_test,
test_size)
test_acc = accuracy_score(y_test, yhat_test)
print("Test accuracy:", test_acc)
results["QDA(E)"] = {"Train": train_acc, "Test": test_acc}
# results to data frame
df = pd.DataFrame.from_dict(results, orient="index")
df = df.reset_index()
df = df.rename(columns={