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())
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)
print(oExp.experimentResults[0].sum_confusion_matrix)
ax1 = fig.add_subplot(111)
# ax2 = ax1.twiny()
p = [oDataSet.attributes[0], oDataSet.attributes[-1]]
res = []
for i in p:
data = np.matrix(np.hstack(([-1], i))).T
predict = perc.predict(data)[0, 0]
res.append([i, predict])
res = np.array(res)
ax1.plot(base[[0, -1]], res[:, 1])
p = [base[0], base[-1]]
res = []
for i in p:
predict = 2 * i + 3
res.append([i, predict])
res = np.array(res)
ax1.plot(res[:, 0], res[:, 1])
plt.show()
oData.params = {
"MSE": ert / oData.Testing_indexes.shape[0],
"RMSE": np.sqrt(ert / oData.Testing_indexes.shape[0])
}
print(oData.params)
oDataSet.append(oData)
oExp.add_data_set(oDataSet)
oExp.save("Objects/EXP01_DT1_20.gzip")
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())
COLOR = cm.rainbow(np.linspace(0, 1, 5))
learning_rate = 0.10
epochs = 30000
oExp = Experiment()
oDataSet = DataSet()
base = np.loadtxt("Datasets/column_3C.dat", usecols=range(6), delimiter=" ")
classes = np.loadtxt("Datasets/column_3C.dat", dtype=object, usecols=-1, delimiter=" ")
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()
oExp.add_data_set(oDataSet,
description=" Experimento COLUNA 3C LP 20 realizaçoes. com 30000 epocas".format())
for j in range(20):
print(j)
oData = Data(len(oDataSet.labelsNames), 31, samples=50)
oData.random_training_test_by_percent([60, 150, 100], 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
oDataSet.append(oData)
oExp.experimentResults[0] = oDataSet
oExp.save("Objects/EXP02_3_LP_20.gzip".format())
oDataSet = DataSet()
base = np.loadtxt("Datasets/dataset2.txt", usecols=range(2), delimiter=",")
classes = np.loadtxt("Datasets/dataset2.txt", usecols=-1, delimiter=",")
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(2, 31, samples=50)
oData.random_training_test_by_percent([600, 600], 0.8)
perc = Perceptron(learning_rate)
perc.train(oDataSet.attributes[oData.Training_indexes],
oDataSet.labels[oData.Training_indexes], epochs)
oData.model = perc
oData.confusion_matrix = np.zeros((2, 2))
for i in oData.Testing_indexes:
data = np.matrix(np.hstack(([-1], oDataSet.attributes[i]))).T
oData.confusion_matrix[int(oDataSet.labels[i]),
perc.predict(data)] += 1
oDataSet.append(oData)
oExp.add_data_set(
oDataSet, description=" Experimento dataset2 PS 20 realizaçoes.".format())
oExp.save("Objects/EXP02_PS_20.gzip".format())
oExp = Experiment.load("Objects/EXP02_PS_20.gzip".format())
print(oExp)
print(oExp.experimentResults[0].sum_confusion_matrix)
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([50, 50, 50], 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 Iris LP 20 realizaçoes. 2 atributos".format())
oExp.save("Objects/EXP02_2_LP_20.gzip".format())
oExp = Experiment.load("Objects/EXP02_2_LP_20.gzip".format())
print(oExp)
print(oExp.experimentResults[0].sum_confusion_matrix)
grid_result[g1, k_slice] = mean_squared_error(y_true, y_pred)
k_slice += 1
print(k_slice)
print(grid_result, j)
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])
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 Temperatura rotor MLP 20 realizaçoes.".format())
oExp.save("Objects/EXP02_4_LP_20.gzip".format())
model.compile(loss='mse', optimizer=_OPTIMIZER)
model.fit(oDataSet.attributes[oData.Training_indexes],
oDataSet.labels[oData.Training_indexes],
batch_size=50,
epochs=epochs,
verbose=1)
y_pred = model.predict(oDataSet.attributes[oData.Testing_indexes])
y_true = oDataSet.labels[oData.Testing_indexes]
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": (best_b, best_p),
"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 Consumo Gasolina MLP 20 realizaçoes.".format())
oExp.save("Objects/EXP02_3_LP_20.gzip".format())