for x, y in enumerate(base):
oDataSet.add_sample_of_attribute(
np.array(list(np.float32(y)) + [classes[x]]))
oDataSet.attributes = oDataSet.attributes.astype(float)
oDataSet.normalize_data_set()
for j in range(20):
print(j)
oData = Data(len(oDataSet.labelsNames), 31, samples=50)
oData.random_training_test_by_percent(
np.unique(classes, return_counts=True)[1], 0.8)
perc = Layered_perceptron_Logistic(learning_rate,
len(oDataSet.labelsNames))
perc.train(oDataSet.attributes[oData.Training_indexes],
oDataSet.labels[oData.Training_indexes], epochs)
oData.model = perc
oData.confusion_matrix = np.zeros(
(len(oDataSet.labelsNames), len(oDataSet.labelsNames)))
for i in oData.Testing_indexes:
data = np.matrix(np.hstack(([-1], oDataSet.attributes[i]))).T
predicted = perc.predict(data)
oData.confusion_matrix[int(oDataSet.labels[i]), predicted] += 1
print(oData)
oDataSet.append(oData)
oExp.add_data_set(
oDataSet,
description=" Experimento Dermatologia LP 20 realizaçoes.".format())
oExp.save("Objects/EXP01_4_LP_20.gzip".format())
oExp = Experiment.load("Objects/EXP01_4_LP_20.gzip".format())
print(oExp)
model.fit(oDataSet.attributes[oData.Training_indexes],
binarizer(oDataSet.labels[oData.Training_indexes]),
batch_size=50,
epochs=epochs,
verbose=1)
y_pred = model.predict(
oDataSet.attributes[oData.Testing_indexes]).argmax(axis=1)
y_true = oDataSet.labels[oData.Testing_indexes]
print(accuracy_score(y_true, y_pred))
print(confusion_matrix(y_true, y_pred))
oData.confusion_matrix = confusion_matrix(y_true, y_pred)
model.save('model.h5')
myArr = None
with open("model.h5", "rb") as binaryfile:
myArr = bytearray(binaryfile.read())
oData.model = myArr, model.history.history['loss']
oData.params = {
"k_fold": K_FOLD,
"GRID_RESULT": grid_result,
"GRID_VALUES_NEURON": GRID_NEURON,
"GRID_VALUES_BETA": GRID_B,
"LEARNING RATE": LEARNING_RATE,
"EPOCHS": epochs
}
oDataSet.append(oData)
print(oData)
oExp.add_data_set(
oDataSet, description=" Experimento cancer LP 20 realizaçoes.".format())
oExp.save("Objects/EXP01_5_LP_20.gzip".format())
oDataSet.labels[oData.Training_indexes[train]], epochs)
y_pred = []
y_true = []
for i in test:
y_pred.append(mpl.predict(oDataSet.attributes[oData.Training_indexes[i]])[0, 0])
y_true.append(oDataSet.labels[oData.Training_indexes[i]])
grid_result[g1, k_slice] = mean_squared_error(y_true, y_pred)
k_slice += 1
print(grid_result)
best_p = GRID[np.argmin(np.mean(grid_result, axis=1))]
mpl = multi_Layered_perceptron_linear(LEARNING_RATE, (oDataSet.attributes.shape[1], best_p, 1))
mpl.train_regression(oDataSet.attributes[oData.Training_indexes],
oDataSet.labels[oData.Training_indexes], epochs)
y_pred = []
y_true = []
for i in oData.Testing_indexes:
y_pred.append(mpl.predict(oDataSet.attributes[i])[0, 0])
y_true.append(oDataSet.labels[i])
plt.scatter(oDataSet.attributes[i], y_pred[-1], color='red')
plt.scatter(oDataSet.attributes[i], y_true[-1], color='green')
plt.show()
oData.model = mpl
oData.params = {"k_fold": K_FOLD, "GRID_RESULT": grid_result, "GRID_VALUES": GRID, "LEARNING RATE": LEARNING_RATE,
"EPOCHS": epochs, "MSE": mean_squared_error(y_true, y_pred),
"RMSE": np.sqrt(mean_squared_error(y_true, y_pred))}
oDataSet.append(oData)
oExp.add_data_set(oDataSet,
description=" Experimento Artificial MLP 20 realizaçoes.".format())
oExp.save("Objects/EXP02_1_LP_20.gzip".format())
experiment.log_metric("test_accuracy", accuracy_score(y_true, y_pred))
experiment.log_metric("beta", best_b)
experiment.log_metric("neurons", best_p)
experiment.log_confusion_matrix(matrix=confusion_matrix(y_true,
y_pred).tolist(),
labels=oDataSet.labelsNames)
# model.save('model.h5')
# experiment.log_asset("model.h5")
model.save_weights('model.weights')
experiment.log_asset("model.weights")
print(accuracy_score(y_true, y_pred))
print(confusion_matrix(y_true, y_pred))
oData.confusion_matrix = confusion_matrix(y_true, y_pred)
oData.model = model
oData.params = {
"k_fold": K_FOLD,
"GRID_RESULT": grid_result,
"GRID_VALUES_NEURON": GRID_NEURON,
"GRID_VALUES_BETA": GRID_B,
"LEARNING RATE": LEARNING_RATE,
"EPOCHS": epochs
}
experiment.log_other("params", oData.params)
y_pred = model.predict(
oDataSet.attributes[oData.Training_indexes]).argmax(axis=1)
y_true = oDataSet.labels[oData.Training_indexes]
experiment.log_metric("train_accuracy", accuracy_score(y_true, y_pred))
experiment.end()
oDataSet.append(oData)
