def doStuffDecently():
model = MLPRegressor()
initial_X = get_random_state_representation()
model.partial_fit(initial_X, np.array([2, 2]).reshape(1, -1))
max_replays = 50
exp = ExperienceReplayStore(model=model,
hash_func=lambda x: tuple(x[0].tolist()),
max_replays=max_replays)
num_iter = 50
for i in range(num_iter):
if i % (0.1 * num_iter) == 0:
print('Done with iter %d' % i)
new_point = get_random_state_representation()
reward = get_reward_for_state(new_point)
exp.add_state(new_point, reward)
print('Starting to get points')
data = []
num_iter = 1000
for i in range(num_iter):
if i % (0.1 * num_iter) == 0:
print('Done with iter %d' % i)
new_point = get_random_state_representation()
reward = get_reward_for_state(new_point)
exp.add_state(new_point, reward)
# exp.iterate(1)
new_rows = []
for dat in exp.experiences_states:
mapping = exp.model.predict(dat)[0]
new_rows.append([
mapping[0], mapping[1],
get_reward_for_state([[dat[0][0], dat[0][1]]])
])
data.extend(new_rows)
animate_scatter_3d(data, len(exp.experiences_states))
def test_partial_fit_regression():
# Test partial_fit on regression.
# `partial_fit` should yield the same results as 'fit' for regression.
X = X_reg
y = y_reg
for momentum in [0, 0.9]:
mlp = MLPRegressor(
solver="sgd",
max_iter=100,
activation="relu",
random_state=1,
learning_rate_init=0.01,
batch_size=X.shape[0],
momentum=momentum,
)
with warnings.catch_warnings(record=True):
# catch convergence warning
mlp.fit(X, y)
pred1 = mlp.predict(X)
mlp = MLPRegressor(
solver="sgd",
activation="relu",
learning_rate_init=0.01,
random_state=1,
batch_size=X.shape[0],
momentum=momentum,
)
for i in range(100):
mlp.partial_fit(X, y)
pred2 = mlp.predict(X)
assert_allclose(pred1, pred2)
score = mlp.score(X, y)
assert score > 0.65
class SimpleNNPredictor(Predictor):
def __init__(self, predictionDelta):
Predictor.__init__(self, predictionDelta)
self.LookBack = [89, 55, 34, 21, 13, 8, 5, 3, 2, 1]
self.regressor = MLPRegressor(hidden_layer_sizes=(3, 2),
activation='tanh',
solver='sgd',
learning_rate='invscaling')
self.name = "SimpleNNPredictor"
def extractFeatures(self, dateIndex):
history = []
currentPrice = self.getPrice(dateIndex)
for xi in self.LookBack:
history += [
getPriceChange(self.getPrice(dateIndex - xi), currentPrice)
]
return history
def train(self, phiX, y):
X = np.array(phiX).reshape(1, -1)
Y = [y]
self.regressor.partial_fit(X, Y)
def predict(self, phiX):
X = np.array(phiX).reshape(1, -1)
result = self.regressor.predict(X)[0]
return result
def train_machine_interactive(board_size):
X = [[0,0,0,0,0,0,0,0,0]]
y = [[0,0,0,0,0,0,0,0,0]]
#MACHINE_ALL = MLPRegressor(solver='sgd', alpha=1.0, hidden_layer_sizes=(1500, 29), random_state=1000, activation="relu", max_iter=4000, batch_size=5, learning_rate="constant", learning_rate_init=0.001)
#MACHINE_ALL = MLPRegressor(solver='sgd', tol=0.0000001, alpha=0.0001, hidden_layer_sizes=(350,185), random_state=1000, activation="relu", max_iter=4000, learning_rate="invscaling", learning_rate_init=0.0000001, warm_start=True) # 3 loss # home 19
MACHINE_ALL = MLPRegressor(solver='sgd', tol=0.0000001, alpha=0.0001, hidden_layer_sizes=(350,185), random_state=1000, activation="relu", max_iter=4000, learning_rate="invscaling", learning_rate_init=0.01, warm_start=True) # 3 loss # home 19
MACHINE_ALL.fit(X, y)
while True:
myprint("loss : {}, n_iter : {}".format(MACHINE_ALL.loss_, MACHINE_ALL.n_iter_),5)
a_game = TicTacToe(board_size)
winner_moves, loser_moves, is_null = play_interactive(MACHINE_ALL, a_game)
X, new_y = MLP_training(MACHINE_ALL, winner_moves, board_size, NULL if is_null else REWARD)
X2, new_y2 = MLP_training(MACHINE_ALL, loser_moves, board_size, NULL if is_null else LOSS)
myprint("X2 " + str(X2))
myprint("new_y2 " + str(new_y2))
myprint("loser_moves " + str(loser_moves))
X.extend(X2)
new_y.extend(new_y2)
myprint("partial_fit X : " + str(X))
myprint("partial_fit y : " + str(new_y))
MACHINE_ALL.partial_fit(X, new_y)
class SimpleNNLearner(Learner):
def __init__(self):
self.regressor = MLPRegressor()
self.hasFitted = False
def extractFeatures(self, state, action):
# currentPrice: Float
# history: [0, 1, 0, ...]
# ownedStocks: Int
currentPrice, history, ownedStocks, cash = state
features = []
for priorPrice in history:
features += [float(priorPrice - currentPrice) / currentPrice]
features += [ownedStocks]
features += [cash]
features += [ action > 0 ]
return features
def train(self, phiX, target):
self.hasFitted = True
X = np.array(phiX).reshape(1, -1)
self.regressor.partial_fit(X, [target])
def predict(self, phiX):
result = 0
if self.hasFitted:
X = np.array(phiX).reshape(1, -1)
result = self.regressor.predict(X)[0]
return result
def train_machine():
X = [[0, 0, 0, 0, 0, 0, 0, 0, 0]]
y = [[0, 0, 0, 0, 0, 0, 0, 0, 0]]
MACHINE_ALL = MLPRegressor(solver='sgd',
alpha=1.0,
hidden_layer_sizes=(1500, 29),
random_state=1000,
activation="relu",
max_iter=4000,
batch_size=5,
learning_rate="constant",
learning_rate_init=0.001)
MACHINE_ALL.partial_fit(X, y)
max_game = 75000
actual_epsilon = Epsilon
dec_every = int(max_game / EpsilonParts)
for i in range(max_game):
if i % 10 == 0:
myprint("Game {} of {}".format(i, max_game), 5)
run_MLP_game(MACHINE_ALL, board_size, actual_epsilon)
if i % dec_every == 0:
actual_epsilon -= EpsilonStep
if actual_epsilon < 0.0:
actual_epsilon = 0.0
myprint("Epsilon now : " + str(actual_epsilon), 2)
save_machine(MACHINE_ALL)
return MACHINE_ALL
class MLPValueFunction:
def __init__(self, file_name, hidden_layers=(100, )):
self.estimator = MLPRegressor(hidden_layers)
self.init_X = np.load('init_mlp.npy')
self.init_y = np.ones(
self.init_X.shape[0]) * 30 # initialize to encourage exploration
self.estimator.fit(self.init_X, self.init_y)
self.filename = file_name
def update(self, X, y):
self.estimator.partial_fit(X, y)
def get_value(self, X):
return self.estimator.predict(X)
def save_model(self):
dump(self.estimator, self.filename)
def load_model(self):
self.estimator = load(self.filename)
def set_coefs(self, coefs, interceps):
self.estimator.coefs_ = coefs
self.estimator.intercepts_ = interceps
def test_partial_fit_regression():
# Test partial_fit on regression.
# `partial_fit` should yield the same results as 'fit' for regression.
X = Xboston
y = yboston
for momentum in [0, .9]:
mlp = MLPRegressor(algorithm='sgd',
max_iter=100,
activation='relu',
random_state=1,
learning_rate_init=0.01,
batch_size=X.shape[0],
momentum=momentum)
with warnings.catch_warnings(record=True):
# catch convergence warning
mlp.fit(X, y)
pred1 = mlp.predict(X)
mlp = MLPRegressor(algorithm='sgd',
activation='relu',
learning_rate_init=0.01,
random_state=1,
batch_size=X.shape[0],
momentum=momentum)
for i in range(100):
mlp.partial_fit(X, y)
pred2 = mlp.predict(X)
assert_almost_equal(pred1, pred2, decimal=2)
score = mlp.score(X, y)
assert_greater(score, 0.75)
def SK_MLPR_training(X, y, minMSE, momentumAll, solver, hidden_layer,
activation, learning_rate, qtd_batch):
print("####CRIANDO MLPR####")
for momentum in momentumAll:
mlp = MLPRegressor(solver=solver,
hidden_layer_sizes=hidden_layer,
activation=activation,
learning_rate_init=learning_rate,
random_state=1,
batch_size=qtd_batch,
momentum=momentum)
#TREINO
mse = []
bmse = 99999
i = 0
#for i in range(Epochs):
while bmse > minMSE:
i += 1
mlp.partial_fit(X, y)
bmse = mean_squared_error(y, mlp.predict(X))
mse.append(bmse)
print(i, " M.S.E:", bmse)
return mlp, mse
class NeuralAgent(Agent):
def __init__(self, grader=ScaledGrader, net=None):
super(NeuralAgent, self).__init__()
if net is None:
self.net = MLPRegressor(hidden_layer_sizes=(30, 15),
max_iter=50,
alpha=1e-4,
random_state=1,
solver='sgd',
tol=1e-2,
learning_rate_init=0.1,
warm_start=True)
else:
self.net = net
self.fitted = False
self.training = True
self.grader = grader(self.player)
def load_net(self, net, fitted=True, training=False):
self.net = net
self.fitted = fitted
self.training = training
def play(self, board):
best_action = self._best_action(board)
self._game.play(self.player, best_action)
if self.training:
board = self._game.get_board(self.player)
score = self.grader.grade_board(board)
state = np.array(board.flatten(), dtype='int64')
self.net.partial_fit([state], [score])
self.fitted = True
def _value_state(self, state):
flat = state.flatten().reshape(1, -1)
value = self.net.predict(flat)[0]
return value
def _best_action(self, board):
states, indexes = next_states(board, self.player, index=True)
if not self.fitted:
r = np.random.randint(0, len(indexes))
return indexes[r]
max_value = None
max_indexes = []
for state, index in zip(states, indexes):
value = self._value_state(state)
# print 'predict score:', value
if max_value is None:
max_value = value
max_indexes = [index]
elif value > max_value:
max_value = value
max_indexes = [index]
elif value == max_value:
max_indexes.append(index)
size = len(max_indexes)
r = np.random.randint(0, size)
return max_indexes[r]
class OnlineBoardEvaluator:
def __init__(self, netFilename):
try:
print("Trying to load neural net:", netFilename + ".p")
self.regressor = pickle.load(open(netFilename + ".p", "rb"))
print("Loading was successful.")
except:
print("Creating new neural net.")
self.regressor = MLPRegressor(
solver='adam',
alpha=1e-5,
activation="logistic",
random_state=1,
max_iter=1000000,
)
self.filenameBase = netFilename
self.updates = 0
self.saveEveryXth = 1
def getBoardRating(self, tokens):
return self.regressor.predict([tokens])
def learnBatch(self, boards, whiteWins, redWins):
X = boards
pWhiteWins = [
whiteWins[i] / (whiteWins[i] + redWins[i]) for i in range(len(X))
]
pRedWins = [
redWins[i] / (whiteWins[i] + redWins[i]) for i in range(len(X))
]
y = [pWhiteWins[i] - pRedWins[i] for i in range(len(X))]
#X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)
#self.regressor.fit(X_train, y_train)
#print("net score:", self.regressor.score(X_test, y_test))
self.regressor.partial_fit(X, y)
self.updates += 1
if (self.updates >= self.saveEveryXth):
print("Saving net.")
dateString = str(datetime.datetime.now())
pickle.dump(
self.regressor,
open(self.filenameBase + "_" + dateString + ".p", "xb"))
pickle.dump(self.regressor, open(self.filenameBase + ".p", "wb"))
self.updates = 0
print("saving a sample batch.")
batchFile = open(
"training_batch_" + str(self.saveEveryXth) + "_" + dateString +
"_" + str(len(boards)) + ".p", "xb")
pickle.dump(X, batchFile)
pickle.dump(y, batchFile)
pickle.dump(whiteWins, batchFile)
pickle.dump(redWins, batchFile)
batchFile.close()
"""
class Player:
def __init__(self, smart=False, seed=None):
if seed:
self.nn = MLPRegressor(hidden_layer_sizes=(9, 9),
random_state=seed)
self.nn.partial_fit([[0] * 9], [[0] * 9]) # Dummy fit
self.get_moves = lambda x: np.flip(
np.argsort(self.nn.predict([x]))[0], 0)
elif smart:
self.get_moves = lambda x: [4] + random.sample(
[0, 1, 2, 3, 5, 6, 7, 8], k=8)
else:
self.get_moves = lambda x: random.sample(range(9), k=9)
def __init__(self,
env,
task,
n_actions,
alpha=None,
gamma=0.99,
epsilon=1,
epsilon_decay=0.9,
K_discountFactor=None,
epsilon_min=0.05,
lambd=0.9,
metrique_reward=0):
assert (alpha != None
and K_discountFactor == None) or (alpha == None
and K_discountFactor != None)
# self.env = env
self.task = task
self.n_in = task.outdim
self.n_actions = n_actions
self.alpha = alpha
self.gamma = gamma
self.epsilon = epsilon
self.epsilon_decay = epsilon_decay
self.epsilon_min = epsilon_min
self.epsilon_init = 1000
self.K_discountFactor = K_discountFactor
self.lambd = lambd
self.metrique_reward = metrique_reward
self.state_nonvisited = set(range(self.n_in))
self.lastaction = None
self.laststate = None
self.history = []
# We create a model for each action
self.models = []
env.reset()
for _ in range(n_actions):
# model = SGDRegressor()
model = MLPRegressor(hidden_layer_sizes=())
model.partial_fit([self.task.getObservation()], [0])
self.models.append(model)
self.num_episode = 0
self.traces = np.zeros((self.task.outdim, n_actions))
self.Q = np.zeros((self.task.outdim, n_actions))
class NFQ(QLearning):
gamma = 0.9
beta = 0.8
def __init__(self, **kwargs):
self.mlp = MLPRegressor(hidden_layer_sizes=(5, 5),
activation='logistic',
batch_size=400)
set_all_args(self, kwargs)
def fit(self, data, max_iter=300, intra_step=50):
"""
data is the triple (ss, as, rs)
"""
for _ in range(max_iter):
inputs, targets = self.compute_inputs_targets(data)
for _ in range(intra_step):
self.mlp.partial_fit(inputs, targets)
def compute_inputs_targets(self, data):
inputs, targets = [], []
for i in range(len(data[0]) - 1):
s, a, r = list(data[0][i]), data[1][i], data[2][i]
s_next = list(data[0][i + 1])
inputs.append(s + [self.actions.index(a)])
to_prs = [s_next + [act] for act in range(len(self.actions))]
try:
q_values = self.mlp.predict(to_prs)
targets.append(r + self.gamma * np.max(q_values))
except NotFittedError:
targets.append(r)
return np.array(inputs), np.array(targets)
def score(self, data):
inputs, targes = self.compute_inputs_targets(data)
return self.mlp.score(inputs, targes)
def decision(self, state):
state = list(state)
to_prs = [state + [act] for act in range(len(self.actions))]
q_values = self.mlp.predict(to_prs)
ps = np.exp(self.beta * q_values)
a_num = np.random.choice(len(self.actions), p=ps / np.sum(ps))
return self.actions[a_num]
class MLPAlg(IterativeAlgorithm):
def __init__(self,
neurons_hidden,
learning_rate: float,
batch_size: int = 32):
super(MLPAlg, self).__init__()
self.neurons_hidden = neurons_hidden
self.total_iterations: int = 0
self.batch_size = batch_size
self.learning_rate = learning_rate
self.alg = MLPRegressor(hidden_layer_sizes=neurons_hidden,
activation='tanh',
solver='sgd',
batch_size=batch_size,
learning_rate="constant",
learning_rate_init=learning_rate)
self.validation_data = None
self.train_data = None
def partial_fit(self, X, num_iterations: int):
self.total_iterations += num_iterations
# data_indices = np.random.choice([i for i in range(len(X))],
# size=self.batch_size*num_iterations)
start = process_time()
for _ in range(num_iterations):
self.alg.partial_fit(X, X)
# data = X[data_indices]
# assert len(data_indices) == len(data)
# start = process_time()
# for i in range(num_iterations):
# d = data[i*32:(i+1)*32]
# self.alg.partial_fit(d, d)
duration = process_time() - start
return duration
def warm_up(self, X) -> float:
return self.validate(X)
def validate(self, X):
prediction = self.alg.predict(X)
diff = X - prediction
norm = np.mean([np.linalg.norm(d) for d in diff])
return -norm
def should_terminate(self, *args, **kwargs) -> bool:
return self.total_iterations >= 100
def _build_model(self):
model = MLPRegressor(tol=1e-3,
hidden_layer_sizes=(
24,
24,
),
activation='tanh',
learning_rate='adaptive',
solver='sgd',
warm_start=True
#, batch_size=1
)
#initialize agent to the correct dimensions
model.partial_fit([
self._concat(self.initial_state, a) for a in range(self.n_actions)
], range(self.n_actions))
return model
class RedNeuronal(object):
def __init__(self,
neuronas,
activation_function='tanh',
factor_descuento=0.9,
num_iters=200,
batch_size=32,
learning_rate=0.0001,
regularization=0.5,
momentum=0.9):
# Inicializar capas y neuronas de la red
if not isinstance(neuronas, str):
print(
'[-] Inicializando neuronas de la red con valores aleatorios')
self.mlp = MLPRegressor(hidden_layer_sizes=neuronas,
activation=activation_function,
solver='adam',
alpha=regularization,
batch_size=batch_size,
learning_rate='adaptive',
learning_rate_init=learning_rate,
max_iter=num_iters,
momentum=momentum)
else:
print(
'[-] Inicializando neuronas de la red con valores del archivo {file}'
.format(file=neuronas))
self.mlp = cargar_red(neuronas)
self.factor_descuento = factor_descuento
print('[-] Red neuronal inicializada')
# Recibe una instancia y devuelve una valoración del tablero.
def forwardpropagation(self, input):
return self.mlp.predict(np.array(input).reshape(1, -1))
# Cargar la partida del archivo seleccionado.
# Retorna las instancias en un arreglo numpy (a su vez, cada instancia es otro arreglo numpy).
def cargar_partida(self, archivo_instancias):
return np.load(archivo_instancias)['arr_0']
def cargar_evaluaciones(self, archivo_evals):
with open(archivo_evals, 'r') as archivo:
evals = archivo.readlines()
for i in range(len(evals)):
evals[i] = float(evals[i])
return evals
# Aplicar el algoritmo de backpropagation sobre el archivo de instancias seleccionado.
# Devuelve el error total y promedio que se cometió en la evaluación, junto con el error total y promedio
# de las capas de la red.
def backpropagation(self, archivo_instancias, archivo_evals):
# print('[-] Comenzando backpropagation')
instancias = self.cargar_partida(archivo_instancias)
evaluaciones = self.cargar_evaluaciones(archivo_evals)
self.mlp = self.mlp.partial_fit(instancias, evaluaciones)
def generateUntrainedStarterNet():
knownBoards = [[
-2, 0, 0, 0, 0, 5, 0, 3, 0, 0, 0, -5, 5, 0, 0, 0, -3, 0, -5, 0, 0, 0,
0, 2, 0, 0, 0, 0
],
[
-2, 0, 0, 0, 0, 5, 0, 3, 0, 0, 0, -5, 5, 0, 0, 0, -3, 0,
-5, 0, 0, 0, 0, 2, 0, 0, 0, 0
],
[
-2, 0, 0, 0, 0, 5, 0, 3, 0, 0, 0, -5, 5, 0, 0, 0, -3, 0,
-5, 0, 0, 0, 0, 2, 0, 0, 0, 0
],
[
-2, 0, 0, 0, 0, 5, 0, 3, 0, 0, 0, -5, 5, 0, 0, 0, -3, 0,
-5, 0, 0, 0, 0, 2, 0, 0, 0, 0
]]
X = knownBoards
pWhiteWins = [1, 1, 1, 1]
pRedWins = [1, 1, 1, 1]
y = [pWhiteWins[i] - pRedWins[i] for i in range(len(X))]
#regressor = MLPRegressor(solver='adam', alpha=1e-5, hidden_layer_sizes=(10, 4),activation="logistic", random_state=1, max_iter=1000000,)
# super simple net. Inputs directly to single output.
#regressor = MLPRegressor(solver='adam', alpha=1e-5, hidden_layer_sizes=(), activation="logistic", random_state=1, max_iter=1000000,)
# Trying to "mask" with the "rectified linear unit" (relu) activation function,
# in order to only count "red" tokens.
regressor = MLPRegressor(
solver='adam',
alpha=1e-5,
hidden_layer_sizes=(56, 2),
activation="relu",
random_state=1,
max_iter=1000000,
)
regressor.partial_fit(X, y)
pickle.dump(regressor, open("trained_net_white.p", "xb"))
pickle.dump(regressor, open("trained_net_red.p", "xb"))
class Ann:
def __init__(self):
self._nn = MLPRegressor(hidden_layer_sizes=(10,), verbose=False, warm_start=True)
self._entradas_entrenamiento = []
self._salidas_esperadas_entrenamiento = []
self.lambdaCoefficient = 0.9
def evaluar(self, entrada):
return self._nn.predict(entrada)
def agregar_a_entrenamiento(self, tableros, resultado):
tableros.reverse()
for i in xrange(len(tableros)):
tablero, valorEstimado = tableros[i][0], tableros[i][1]
self._entradas_entrenamiento.append(tablero)
if i == 0 or True:
self._salidas_esperadas_entrenamiento.append(resultado.value)
else:
valorAAprender = valorEstimado + self.lambdaCoefficient * (self._salidas_esperadas_entrenamiento[i-1] -
valorEstimado)
self._salidas_esperadas_entrenamiento.append(valorAAprender)
def entrenar(self):
self._nn.partial_fit(self._entradas_entrenamiento, self._salidas_esperadas_entrenamiento)
self._entradas_entrenamiento = []
self._salidas_esperadas_entrenamiento = []
def almacenar(self):
pickle.dump(self._nn, open(self.path,'wb'))
def cargar(self, path, red):
self.path = path
if os.path.isfile(path):
self._nn = pickle.load(open(path, 'rb'))
else:
self._nn = red
tableroVacio = ([EnumCasilla.EMPTY.value for _ in xrange(64)],0)
self.agregar_a_entrenamiento([tableroVacio], EnumResultado.EMPATE)
self.entrenar()
def main(layers):
"""The main function of the program"""
# num_batches = int(TRAIN_SIZE / BATCH_SIZE)
num_batches = int(DELTA_TRAIN_SIZE / BATCH_SIZE)
print("The program will run in", num_batches)
past_err = long(999999)
new_err = 0
regr = MLPRegressor(hidden_layer_sizes=layers)
for i in range(MAX_TRIES):
for batch in range(num_batches):
print("\nCurrent loop =", i + 1)
print("Current layers:", layers)
print("Starting batch", batch + 1, "of", num_batches)
print("Loading data...")
# train_data, train_labels = load_train(PATH_TRAIN, batch * BATCH_SIZE, BATCH_SIZE)
train_data, train_labels = load_simple_train(
PATH_DELTA_TRAIN, batch * BATCH_SIZE, BATCH_SIZE)
print("Training neural network...")
regr.partial_fit(train_data, train_labels)
print("\nTesting the network...")
# val_data, val_labels = load_val(PATH_VALIDATION, BATCH_SIZE, VAL_SIZE)
val_data, val_labels = load_simple_val(PATH_DELTA_VAL, BATCH_SIZE,
DELTA_VAL_SIZE)
new_err = test_regressor(regr, val_data, val_labels)
if abs(past_err - new_err) < CONVERGENCE:
print("Convergence has reached:", past_err - new_err)
break
print("Current error:", np.sqrt(new_err), "improvement",
np.sqrt(past_err) - np.sqrt(new_err), "\n")
past_err = new_err
# store(new_err, PATH_OUTPUT)
store(new_err, PATH_DELTA_OUTPUT)
print("Done!\nThe regression has an average error of:", np.sqrt(new_err))
class NeuralModelLearner(ModelLearner):
def __init__(self, rate):
self.learning_rate = rate
self.network = MLPRegressor(random_state=1,
learning_rate='constant',
solver='sgd',
learning_rate_init=rate,
max_iter=500)
self.to_adapt = False
def observe(self, X, y, drifted):
if drifted != False:
self.to_adapt = True
self.network.partial_fit(X, y)
def predict(self, X):
xnew_predicted = self.network.predict(X)[0]
return xnew_predicted
def status(self):
return self.network
def main(layers,
startfile=None,
act='relu',
solv='adam',
mini_batch_size='auto'):
"""The main function of the program"""
num_batches = int(TRAIN_SIZE / BATCH_SIZE)
print("The program will run in", num_batches)
i = 0
if startfile: #pickle file
regr = joblib.load(SAVED_NN)
else:
regr = MLPRegressor(hidden_layer_sizes=layers,
activation=act,
solver=solv,
alpha=0.0001,
batch_size=mini_batch_size)
x_val = 0
for i in range(MAX_TRIES):
shuffled_batches = range(num_batches)
shuffle(shuffled_batches)
batches_passed = 0
for batch in shuffled_batches:
train, labels, _ = load_train(PATH_TRAIN, batch * BATCH_SIZE,
BATCH_SIZE)
# train, labels, _ = load_data(PATH_TRAIN, batch * BATCH_SIZE, BATCH_SIZE)
regr.partial_fit(train, labels)
batches_passed += 1
if batches_passed % ERROR_INTERVAL == 0:
print("\nProgress:")
print("Loop", i + 1, ", batch", batches_passed + 1, "of",
num_batches)
print("...Saving and testing, don't interrupt the program...")
x_val += ERROR_INTERVAL
save_stuff(regr, x_val)
print("...Done saving.")
def doStuff():
model = MLPRegressor()
initial_X = get_random_state_representation()
model.partial_fit(initial_X, np.array([0, 0]).reshape(1, -1))
points = []
MAX_SIZE = 10
while len(points) < MAX_SIZE:
points.append(get_random_state_representation())
# train here?
num_iter = 2000
learning_rate = 0.1
for i in range(num_iter):
if i % (0.01 * num_iter) == 0:
print('Done with iter %d' % i)
new_point = get_random_state_representation()
old_vec = model.predict(new_point)
target_for_new_x = model.predict(new_point)
max_val = float("-inf")
d_v = None
for point in points:
smaller = min(get_reward_for_state(point),
get_reward_for_state(new_point))
bigger = max(get_reward_for_state(point),
get_reward_for_state(new_point))
similarity = smaller / bigger
if abs(similarity) > 1:
similarity = 1.0 / similarity
direction_vector = model.predict(new_point)[0] - model.predict(
point)[0]
scale = 1 / (euclidean_distance(point[0], new_point[0]))
similarity * learning_rate * direction_vector * scale
if max_val <= similarity * scale:
max_val = similarity * scale
d_v = direction_vector
target_for_new_x += max_val * learning_rate * d_v
model.partial_fit(new_point, target_for_new_x)
add_point_to_set(new_point, points)
threeD_plot(points, model)
def test_partial_fit_regression():
# Test partial_fit on regression.
# `partial_fit` should yield the same results as 'fit' for regression.
boston = load_boston()
Xboston = StandardScaler().fit_transform(boston.data)[: 200]
yboston = boston.target[:200]
X = Xboston
y = yboston
for momentum in [0, .9]:
mlp = MLPRegressor(solver='sgd', max_iter=100, activation='relu',
random_state=1, learning_rate_init=0.01,
batch_size=X.shape[0], momentum=momentum)
with warnings.catch_warnings(record=True):
# catch convergence warning
mlp.fit(X, y)
pred1 = mlp.predict(X)
mlp = MLPRegressor(solver='sgd', activation='relu',
learning_rate_init=0.01, random_state=1,
batch_size=X.shape[0], momentum=momentum)
for i in range(100):
mlp.partial_fit(X, y)
pred2 = mlp.predict(X)
assert_almost_equal(pred1, pred2, decimal=2)
score = mlp.score(X, y)
assert_greater(score, 0.75)
#avalia a saida da mlp
print(yboston)
print(X)
plt.plot(yboston)
plt.plot(pred2)
plt.legend("RP")
plt.title("Right vs Predicted values")
#plt.savefig('./outPut/{0}-{1}-{2}-{3}.eps'.format(len(X),hidden_layer, taxa, Epochs), format='eps', dpi=1000)
plt.show()
def criarMLPR():
print("####CRIANDO MLPR####")
for momentum in momentumAll:
mlp = MLPRegressor(solver=solver,
hidden_layer_sizes=hidden_layer,
activation=activation,
learning_rate_init=learning_rate,
random_state=1,
batch_size=qtd_batch,
momentum=momentum)
#TREINO
for i in range(Epochs):
mlp.partial_fit(X, y)
pred = mlp.predict(X)
mse = mean_squared_error(y, pred)
print((100 * i / Epochs), "% M.S.E:", mse)
#TESTE
pred = mlp.predict(Xt)
score = mlp.score(Xt, yt)
print('score:', score)
#assert_greater(score, 0.70)
#assert_almost_equal(pred, yt, decimal=2)
#######SAVE
if (saveDNN == True):
with open(fileDNN, 'wb') as fid:
cPickle.dump(mlp, fid)
#avalia a saida da mlp
####VER DISTRIBUICAO DOS DADOS
if (debugDeep == True):
debugMLPR(pred)
def test_partial_fit_regression():
# Test partial_fit on regression.
# `partial_fit` should yield the same results as 'fit' for regression.
X = Xboston
y = yboston
for momentum in [0, .9]:
mlp = MLPRegressor(solver='sgd', max_iter=100, activation='relu',
random_state=1, learning_rate_init=0.01,
batch_size=X.shape[0], momentum=momentum)
with warnings.catch_warnings(record=True):
# catch convergence warning
mlp.fit(X, y)
pred1 = mlp.predict(X)
mlp = MLPRegressor(solver='sgd', activation='relu',
learning_rate_init=0.01, random_state=1,
batch_size=X.shape[0], momentum=momentum)
for i in range(100):
mlp.partial_fit(X, y)
pred2 = mlp.predict(X)
assert_almost_equal(pred1, pred2, decimal=2)
score = mlp.score(X, y)
assert_greater(score, 0.75)
bcnt += 1
# Get training data
_tet1b = tm.time()
X, y = read_data_indexed(t_file, N, M, Ntd, i, t_Ainx, t_cmats)
_tet1e = tm.time()
# Transform data
_tet2b = tm.time()
X = X_scaler.transform(X)
y = y / y_min
_tet2e = tm.time()
# Fit model
_tet3b = tm.time()
clf.partial_fit(X, y)
_tet3e = tm.time()
# Compute training delta E
_tet4b = tm.time()
dE = y_min * clf.predict(X) - y_min * y
_tet4e = tm.time()
# Compute sum of squared diff
btcherr = (dE * dE).sum()
sqd += btcherr
progress(bcnt, Ntb, 'Training progress')
if verbose:
print('\n Batch: ' + str(bcnt) + ' of ' + str(Ntb))
class AutoEncoder():
def __init__(self,
encoder_layer_sizes,
latent_layer_size,
decoder_layer_sizes,
activation,
solver,
eta=0.0001,
max_iterations=30000,
tol=0.0000001,
verbose=False):
self.encoder_layer_sizes = encoder_layer_sizes
self.latent_layer_size = latent_layer_size
self.decoder_layer_sizes = decoder_layer_sizes
self.encoder_layer = len(encoder_layer_sizes)
self.latent_layer = self.encoder_layer + 1
self.decoder_layer = self.latent_layer + len(decoder_layer_sizes) + 1
self.activation = activation
self.solver = solver
self.eta = eta
self.max_iterations = max_iterations
aux = encoder_layer_sizes
aux.append(latent_layer_size)
self.hidden_layer_sizes = aux + decoder_layer_sizes
self.mlp = MLPRegressor(hidden_layer_sizes=self.hidden_layer_sizes,
activation=activation,
solver=solver,
learning_rate_init=eta,
max_iter=max_iterations,
tol=0.0000001,
verbose=verbose)
def fit(self, training_input, training_output):
return self.mlp.fit(training_input, training_output)
def fit_and_plot(self, training_input, training_output):
scores_train = []
# scores_test = []
epoch = 0
while epoch < self.max_iterations:
self.mlp.partial_fit(training_input, training_output)
# SCORE TRAIN
scores_train.append(self.mlp.score(training_input,
training_output))
# SCORE TEST
# scores_test.append(self.mlp.score(X_test, y_test))
epoch += 1
plt.plot(scores_train)
plt.ylabel('Accuracy', fontsize=16)
plt.xlabel('Iteration', fontsize=16)
plt.tight_layout()
plt.show()
plt.plot(self.mlp.loss_curve_)
plt.ylabel('Loss', fontsize=16)
plt.xlabel('Iteration', fontsize=16)
plt.tight_layout()
plt.show()
def predict(self, data):
return self.mlp.predict(data.reshape(-1, len(self.mlp.coefs_[0])))
def encode(self, data):
return self.get_data_from_layer(data, self.latent_layer, 0)
def decode(self, data):
return self.get_data_from_layer(data, self.decoder_layer,
self.latent_layer)
def get_data_from_layer(self, data, to_layer, from_layer=0):
L = ACTIVATIONS[
self.activation](np.matmul(data, self.mlp.coefs_[from_layer]) +
self.mlp.intercepts_[from_layer])
from_layer += 1
if from_layer >= to_layer or from_layer >= len(self.mlp.coefs_):
return L
else:
return self.get_data_from_layer(L, to_layer, from_layer=from_layer)
def get_loss_curve(self):
return self.mlp.loss_curve_
# print("y-test")
# print(np.shape(y_test))
# In[46]:
from sklearn.neural_network import MLPRegressor
# model = MLPRegressor(hidden_layer_sizes=(6,1),activation ='identity',solver = 'lbfgs', learning_rate = 'constant',
# learning_rate_init =0.2,max_iter = 2000, random_state =13)
model = MLPRegressor(random_state=13)
# In[47]:
#To get pre-training Weights and Output Values
model.partial_fit(x_train, y_train)
# In[48]:
#Get model parameters
Params = model.get_params(deep=True)
type(Params)
print(Params)
# In[49]:
#Get Inital Parameters to compare pre-training and post-training
InitialWeightsI_H = model.coefs_[0]
# print(InitialWeightsI_H)
print(np.shape(InitialWeightsI_H))
def getBestValidationScore(hidden_shape,
regularisation,
learn_rate=0.01,
noise_sigma=0,
stop_tolerance=EARLY_STOP_TOLERANCE):
print(f"""\
Hidden Shape: {hidden_shape}
Alpha: {regularisation}\
""")
nn = MLPRegressor(
# random_state=5,
hidden_layer_sizes=hidden_shape,
activation="logistic",
solver="sgd",
batch_size=len(target),
learning_rate_init=learn_rate,
alpha=regularisation, # L2 Penality
)
early_stop_count = 0
mse_validation_list = []
num_failed = -1
failed = True
while failed and num_failed < 5:
num_failed += 1
for eps in range(MAX_EPOCHS):
noise = noise_sigma * np.random.randn(pattern.shape[0],
pattern.shape[1])
nn.partial_fit(pattern + noise, target)
prediction_validation = nn.predict(pattern_validation)
mse_validation_list.append(
mean_squared_error(target_validation, prediction_validation))
if eps > 2: # Do Early Stopping
# improvement from last validation less than threshold
if mse_validation_list[-2] - mse_validation_list[
-1] < stop_tolerance:
if early_stop_count > EARLY_STOP_THRESHOLD:
break
else:
early_stop_count += 1
else:
early_stop_count = 0
failed = nn.score(pattern_test, target_test) < .5
print("Num Epochs:", len(nn.loss_curve_))
print("Validation MSE:", mse_validation_list[-1])
# ## Plot weights histogram
# w_ = []
# for layerWeights in nn.coefs_:
# w_ += list(layerWeights.flatten())
# plt.hist(w_, bins=50, range=(-1,1))
# plt.ylabel("Count")
# plt.xlabel("Weights in Range")
# plt.title(f"Histogram of Weights (Alpha={regularisation}, 50 bins)")
# # plt.savefig(f"pictures/4_3_histogram_alpha{regularisation}.png")
# # plt.show()
# plt.clf()
# ## Plot learning curve
# plt.plot(1 + np.arange(len(nn.loss_curve_)), nn.loss_curve_, label="Training Loss")
# plt.plot(1 + np.arange(len(mse_validation_list)), mse_validation_list, label="Validation Loss")
# plt.yscale("log")
# plt.xlabel("Epochs")
# plt.ylabel("Loss (MSE)")
# plt.legend()
# plt.title(f"Loss VS Epochs ({hidden_shape} Hidden)")
# # plt.savefig(f"pictures/4_3_loss_curve_{hidden_shape[0]}_hidden.png")
# # plt.show()
# plt.clf()
prediction_test = nn.predict(pattern_test)
print("Test MSE:", mean_squared_error(target_test, prediction_test))
print("Test R2:", nn.score(pattern_test, target_test))
# Test Prediction
# plt.plot(np.arange(len(prediction_test)), prediction_test, label="prediction")
# plt.plot(np.arange(len(prediction_test)), target_test, label="actual")
# plt.xlabel("Sample IDX")
# plt.ylabel("Output Value")
# plt.title(f"Prediction vs Actual (Network Shape {hidden_shape})")
# plt.legend()
# plt.savefig(f"pictures/4_3_prediction_vs_actual_{hidden_shape}.png")
# # plt.show()
# plt.clf()
return mean_squared_error(target_test, prediction_test)
return mse_validation_list[-1]
]
counter = 0
print(datetime.datetime.now())
for i in range(0, 2):
for train_df in pd.read_csv('data/train_preprocessed.csv',
chunksize=chunk_size):
train_X = train_df[feature_classes]
train_y = train_df["fare_amount"]
X_train, X_test, y_train, y_test = train_test_split(train_X,
train_y,
test_size=0.1,
random_state=42)
clf.partial_fit(X_train, y_train)
val_preds = clf.predict(X_test)
counter += 1
print(i, counter, datetime.datetime.now(),
np.sqrt(((val_preds - y_test)**2).mean()))
test_df = pd.read_csv('data/test.csv', parse_dates=['pickup_datetime'])
test_df['distance_miles'] = distance(test_df.pickup_latitude,
test_df.pickup_longitude,
test_df.dropoff_latitude,
test_df.dropoff_longitude)
test_df['distance_to_center'] = distance(nyc[1], nyc[0],
test_df.pickup_latitude,
test_df.pickup_longitude)
test_df['distance_to_center_drop_off'] = distance(
Truth1[i,0] = Samples1[i,:].mean()
Truth2[i,0] = Samples2[i,:].mean()
Truth1[i,1] = Samples1[i,:].std()
Truth2[i,1] = Samples2[i,:].std()
trainSamples1,testSamples1,trainTruth1,testTruth1 = train_test_split(Samples1,Truth1,test_size=0.1)
trainSamples2,testSamples2,trainTruth2,testTruth2 = train_test_split(Samples2,Truth2,test_size=0.1)
# =============================================================================
# Train and predict
# =============================================================================
trials = 30
for i in np.arange(maxIt):
mlp.partial_fit(trainSamples1,trainTruth1)
for i in np.arange(maxIt):
mlp.partial_fit(trainSamples2,trainTruth2)
for j in np.arange(trials):
print '\nTrial ' + str(j) + ':'
for i in np.arange(maxIt):
mlp.partial_fit(trainSamples1,trainTruth1)
testPred1 = mlp.predict(testSamples1)
testPred2 = mlp.predict(testSamples2)
r2Score1 = r2_score(testTruth1,testPred1)
r2Score2 = r2_score(testTruth2,testPred2)
print 'Trained on set 1'
class Ann:
'''
Implementación e interfaz de la funcionalidad presentada de la ANN
'''
def __init__(self):
self._nn = MLPRegressor(hidden_layer_sizes=(10,), verbose=False, warm_start=True)
self._entradas_entrenamiento = []
self._salidas_esperadas_entrenamiento = []
# Parámetro de TD-lambda
self.lambdaCoefficient = 0.9
def evaluar(self, entrada):
'''
Devuelve la evaluación de la red para la entrada
'''
return self._nn.predict(entrada)
def agregar_a_entrenamiento(self, tableros, resultado):
'''
Incorpora los datos de la partida a los ejemplos de entrenamiento
'''
# Presento la partida de adelante para atrás
tableros.reverse()
for i in xrange(len(tableros)):
# Representación del tablero, Valor estimado
tablero, valorEstimado = tableros[i][0], tableros[i][1]
self._entradas_entrenamiento.append(tablero)
if i == 0 or True:
# Si es el resultado final, utilizo como salida esperada el resultado de la partida
self._salidas_esperadas_entrenamiento.append(resultado.value)
else:
# El valor a aprender dado por según TD-lambda
valorAAprender = valorEstimado + self.lambdaCoefficient * (
self._salidas_esperadas_entrenamiento[i - 1] - valorEstimado)
self._salidas_esperadas_entrenamiento.append(valorAAprender)
def entrenar(self):
'''
Aplico el entrenamiento a partir de los datos almacenados
'''
self._nn.partial_fit(self._entradas_entrenamiento, self._salidas_esperadas_entrenamiento)
self._entradas_entrenamiento = []
self._salidas_esperadas_entrenamiento = []
def almacenar(self):
'''
Serializo y persisto la red
'''
pickle.dump(self._nn, open(self.path, 'wb'))
def cargar(self, path, red):
'''
Deserealizo o creo una nueva red
'''
self.path = path
if os.path.isfile(path):
# Si el archivo especificado existe, deserealizo la red
self._nn = pickle.load(open(path, 'rb'))
else:
# Si no, inicializo la red especificada
self._nn = red
tableroVacio = ([EnumCasilla.EMPTY.value for _ in xrange(64)], 0)
self.agregar_a_entrenamiento([tableroVacio], EnumResultado.EMPATE)
self.entrenar()