def refit(self, samples):
df = Unpickler(
open(
pkg_resources.resource_filename('hate_cl', 'hate_cl/data.sav',
'rb'))).load()
aux_df = DataFrame(samples, columns=['hate', 'sentence'])
df = df.append(aux_df, ignore_index=True)
print(df)
X = df['sentence'].tolist()
y = df['hate'].tolist()
cl = Pipeline([('tfidf', TfidfVectorizer(ngram_range=(1, 4))),
('clf',
RandomForestClassifier(n_estimators=100,
max_depth=None,
min_samples_leaf=1,
min_samples_split=2,
min_weight_fraction_leaf=0))])
cl.fit(X, y)
self.classifier = cl
cl_filename = pkg_resources.resource_filename(
'hate_cl', 'hate_cl/randomforest.sav')
df_filename = pkg_resources.resource_filename('hate_cl',
'hate_cl/data.sav')
f = open(cl_filename, 'wb')
Pickler(f).dump(cl)
f.close()
f = open(df_filename, 'wb')
Pickler(f).dump(df)
f.close()
class AlphaZero(Approach):
def __init__(self, argdict, approach_name="alphazero"):
"""
Initializes an approach before
it applies itself to a task
Parameters
----------
argdict : rlait.util.misc.dotdict
A dictionary containing all the extra arguments that AlphaZero uses.
Fully explained in the Notes section
approach_name : str
Name of the approach, used for printing
Notes
-----
Full list of possible arguments that can be provided in argdict,
as well as their default values:
* lr : float (0.001)
Backpropagation learning rate
* dropout : float (0.3)
Dropout factor to use in the densely connected layers
* epochs : int (10)
Number of epochs to train network on past examples each iteration
* batch_size : int (64)
Input batch size to use with neural network
* cuda : bool (True)
Use CUDA to speed training?
* num_channels : int (512)
Number of features to detect in the convolutional layers
(The number of layers, activation functions are fixed)
* startFromEp : int (0)
The episode number to start from. Useful if a previous run got interrupted
* numEps : int (30)
The number of playout episodes to run per training iteration
* tempThreshold : int (15)
The number of moves to make using weighted plays instead of maximum
plays when training
* updateThreshold : float (0.51)
Fraction of games a challenger network needs to win in order to
become the new base.
* maxlenOfQueue : int (200000)
The maximum number of training examples to train on.
* numMCTSSims : int (30)
The number of times to run MCTS per move during self-play and actual play
* arenaCompare : int (11)
The number of games to play against the previous best AI at the end
of a training iteration
* cpuct : float (1.0)
A factor that determines how likely the MCTS is to explore.
* maxDepth : int (5000)
The maximum search depth to explore for one move
* startWithRandomPlay : bool (false)
If starting at the first iteration, do we use random play to generate
some training examples to look at first?
* numRandomPlayExamples : int (100)
Number of random game examples to generate
* load_checkpoint : bool (False)
Do we load a checkpoint?
* prevHistory : str (None)
Previous history to load. Can be set if `load_checkpoint` is set. If
set, AlphaZero skips the first self-play iteration and jumps straight
to training a new network on the provided history.
* checkpoint : str (None)
Checkpoint to load. Must be set if `load_checkpoint` is set, should
be a file path relative to the below directory.
* checkpoint_dir : str ("./checkpoints")
Folder to store the checkpoints in. Must be an absolute path or a
path relative to the location of the script running.
* numItersForTrainExamplesHistory : int(30)
The number of past iterations to store in a single history file.
"""
super().__init__(approach_name)
self.args = dotdict(argdict)
self.args.lr = self.args.get("lr", 0.001)
self.args.dropout = self.args.get("dropout", 0.3)
self.args.epochs = self.args.get("epochs", 10)
self.args.batch_size = self.args.get("batch_size", 64)
self.args.cuda = self.args.get("cuda", True)
self.args.num_channels = self.args.get("num_channels", 512)
self.args.startFromEp = self.args.get("startFromEp", 0)
self.args.numEps = self.args.get("numEps", 30)
self.args.tempThreshold = self.args.get("tempThreshold", 15)
self.args.updateThreshold = self.args.get("updateThreshold", 0.51)
self.args.maxlenOfQueue = self.args.get("maxlenOfQueue", 200000)
self.args.numMCTSSims = self.args.get("numMCTSSims", 30)
self.args.arenaCompare = self.args.get("arenaCompare", 11)
self.args.cpuct = self.args.get("cpuct", 1.0)
self.args.maxDepth = self.args.get("maxDepth", 500)
self.args.startWithRandomPlay = self.args.get("startWithRandomPlay", False)
self.args.numRandomPlayExamples = self.args.get("numRandomPlayExamples", 100)
self.args.load_checkpoint = self.args.get("load_checkpoint", False)
self.args.checkpoint = self.args.get("checkpoint", None)
self.args.prevHistory = self.args.get("prevHistory", None)
self.args.checkpoint_dir = self.args.get("checkpoint_dir", "./checkpoints")
self.args.numItersForTrainExamplesHistory = self.args.get("numItersForTrainExamplesHistory", 30)
self.trainExamplesHistory = []
self.skipFirstSelfPlay = False
def _reset_mcts(self):
# Reset MCTS variables (clears cache)
self.Qsa = {} # stores Q values for s,a (as defined in the paper)
self.Nsa = {} # stores #times edge s,a was visited
self.Ns = {} # stores #times board s was visited
self.Ps = {} # stores initial policy (returned by neural net)
self.Es = {} # stores game.getGameEnded ended for board s
self.Vs = {} # stores game.getValidMoves for board s
def init_model(self):
"""
Initializes AlphaZero with a list of models (one for each task phase)
in self.models
"""
# Assumes that the input board shape will remain constant between phases
# Unfortunately, there's no good way to do this without that assumption.
###########################################
# Define network based on Task parameters #
###########################################
empty_state = self.task.empty_state(0)
for phase in range(1, self.task.num_phases):
try:
assert self.task.empty_state(phase).shape == empty_state.shape
except AssertionError:
raise TypeError("{} cannot be applied to tasks with variant board representations!".format(self.approach_name))
if 'flat' in STATE_TYPE_OPTION[empty_state.state_type]:
raise TypeError("{} currently does not support tasks with board type \"flat\"".format(self.approeach_name))
self.input = Input(shape=empty_state.shape)
if STATE_TYPE_OPTION[empty_state.state_type] == 'deeprect':
extra_dim = 1
for i in range(2, len(empty_state.shape)):
extra_dim *= empty_state.shape[i]
x_image = Reshape((empty_state.shape[0], empty_state.shape[1], extra_dim))(self.input)
elif STATE_TYPE_OPTION[empty_state.state_type] == 'rect':
x_image = Reshape((empty_state.shape[0], empty_state.shape[1], 1))(self.input)
else:
raise TypeError("Unknown state type \"{}\"".format(empty_state.state_type))
x_image = None
conv_model = SimpleConvNet(self.args, x_image)
self.v = Dense(self.task.num_players, activation='tanh', name='v')(conv_model.output) # batch_size x 1
self.outputs = [self.v]
self.output_sizes = [self.task.num_players]
self.models = []
for phase in range(self.task.num_phases):
empty_move = self.task.empty_move(phase)
output_size = 1
for i in range(len(empty_move.shape)):
output_size *= empty_move.shape[i]
output = Dense(output_size, activation='softmax', name='pi{}'.format(phase))(conv_model.output) # batch_size x self.output_size
self.outputs.append(output)
self.output_sizes.append(output_size)
model = Model(inputs=self.input, outputs=[self.v, output])
model.compile(loss=['mean_squared_error', 'categorical_crossentropy'], optimizer=Adam(self.args.lr))
self.models.append(model)
# Doing the above instead of this because we need to have multiple models
# in order to train correctly. Unfortunately, that means we cannot share
# self.model = Model(inputs=self.input, outputs=self.outputs)
def init_to_task(self, task, make_competetor=True):
"""
Customizes an approach to work on
a specific task
Parameters
----------
task : Task
The Task object to customize to. Should provide
all the necessary methods for this approach to customize,
like length of move vectors for different phases.
competetor : bool (True)
Optional, controls whether we initialize a competetor AI for selfplay.
When creating a competetor, this is turned to False so we don't
infinitely recur.
Returns
-------
self
For daisy-chaining purposes. Other methods return
self for the same reason
Notes
-----
Although the parameters for the neural network size are passed through
the __init__ method, the neural networks are actually created in this
method. This is to handle different sizes of input and output layers
required for different Tasks.
"""
self.task = task
self.init_model()
self._reset_mcts()
#######################
# Load previous model #
#######################
self.iteration = self.args.startFromEp
if self.args.load_checkpoint:
if not (self.args.checkpoint is None):
self.load_weights(self.args.checkpoint)
else:
try:
self.load_weights(self._get_checkpoint_filename(self.iteration))
except:
log.warn("Tried to load checkpoint from starting iteration, could not find it.")
if not (self.args.prevHistory is None) and make_competetor:
self.load_history(self.args.prevHistory)
self.skipFirstSelfPlay = True
if make_competetor:
self.pnet = self.__class__(self.args).init_to_task(self.task, make_competetor=False)
else:
self.pnet = None
# Returning self so that constructs like
# `ai = CustomApproach(args).init_to_task(Task(more_args))`
# become possible
return self
def _nn_predict(self, state):
v, pi = self.models[state.phase].predict(np.reshape(state, (1,)+state.shape))
return v[0], pi[0]
def _search(self, board, depth, maxdepth=float('inf')):
"""
This function performs one iteration of MCTS. It is recursively called
till a leaf node is found. The action chosen at each node is one that
has the maximum upper confidence bound as in the paper.
Once a leaf node is found, the neural network is called to return an
initial policy P and a value v for the state. This value is propogated
up the search path. In case the leaf node is a terminal state, the
outcome is propogated up the search path. The values of Ns, Nsa, Qsa are
updated.
Parameters
----------
board : State
The board to search
Returns
-------
v: the player value vector corresponding to the searched board
Notes
-----
Because of the internal use of canonical boards, every v returned is
relative to the perspective of the first player.
"""
if depth > maxdepth: return np.zeros((self.task.num_players,))
canonicalBoard = self.task.get_canonical_form(board)
phase = board.phase
s = self.task.state_string_representation(board)
if s not in self.Es:
if self.task.is_terminal_state(board):
winners = self.task.get_winners(board)
self.Es[s] = np.array([1 if (i in winners) else -1 for i in range(self.task.num_players)])
else:
self.Es[s] = np.zeros((self.task.num_players,))
if not (self.Es[s] == 0).all():
# terminal node
return self.Es[s]
if s not in self.Ps:
# leaf node
# NOTE: this section is the only place canonical boards are introduced
# the rest of the searching takes place on non-canonical boards
# for ease of passing hidden information.
# NOTE: but I shouldn't even be passing around hidden information because it should be hidden even when searching???
v, self.Ps[s] = self._nn_predict(canonicalBoard)
# correcting ordering of v
v = np.concatenate((v[(self.task.num_players-board.next_player):], v[:(self.task.num_players-board.next_player)]))
self.Ps[s] = np.reshape(self.Ps[s], self.task.empty_move(board.phase).shape)
# unfortunately, this requires that canonical forms do nothing
# like flipping the board, which is usually normal...
valids = self.task.get_legal_mask(board)
self.Ps[s] = self.Ps[s]*valids # masking invalid moves
sum_Ps_s = np.sum(self.Ps[s])
if sum_Ps_s > 0.0:
self.Ps[s] /= sum_Ps_s # renormalize
else:
# if all valid moves were masked make all valid moves equally probable
# NB! All valid moves may be masked if either your NNet architecture is insufficient or you've get overfitting or something else.
# If you have got dozens or hundreds of these messages you should pay attention to your NNet and/or training process.
log.warn("All valid moves were masked, do workaround.")
self.Ps[s] = self.Ps[s] + valids
self.Ps[s] /= np.sum(self.Ps[s])
self.Vs[s] = list(self.task.iterate_legal_moves(board))
self.Ns[s] = 1
#print("For state {}".format(self.task.state_string_representation(canonicalBoard)))
#print("Predicted move: {} and value: {}".format(self.Ps[s], v))
return v
valid_moves = self.Vs[s]
cur_best = -float('inf')
best_act = -1
# pick the action with the highest upper confidence bound
for move in valid_moves:
a = self.task.move_string_representation(move, board)
if (s,a) in self.Qsa:
u = self.Qsa[(s,a)][board.next_player] + self.args.cpuct*(move*self.Ps[s]).sum()*math.sqrt(self.Ns[s])/(1+self.Nsa[(s,a)])
else:
u = self.args.cpuct*(move*self.Ps[s]).sum()*math.sqrt(self.Ns[s] + EPS) # Q = 0 ?
if u > cur_best:
cur_best = u
best_act = a
a = best_act
#print("For state {}".format(self.task.state_string_representation(board)))
#print("Best move is {}".format(a))
next_s = self.task.apply_move(self.task.string_to_move(a, board), board)
try:
v = self._search(next_s, depth+1, maxdepth=maxdepth)
except RecursionError:
# NOTE: this might cause some issues, but it hits the
# recursion limit pretty infrequently anyways (most likely due
# to inadequate tie handling anyways, which should be fixed)
return 0
if (s,a) in self.Qsa:
self.Qsa[(s,a)] = (self.Nsa[(s,a)]*self.Qsa[(s,a)] + v)/(self.Nsa[(s,a)]+1)
self.Nsa[(s,a)] += 1
else:
self.Qsa[(s,a)] = v
self.Nsa[(s,a)] = 1
self.Ns[s] += 1
return v
def _get_action_prob(self, state, temp=1):
"""
Returns the probabilities for each legal move given a state.
Also returns the legal moves themselves
Parameters
----------
state : State
Returns
-------
probs : list(float)
A policy vector where the probability of the ith action is
proportional to Nsa[(s,a)]**(1./temp)
avail_moves : list(Move)
A list of available moves where each entry corresponds to the
same index in probs
"""
for i in range(self.args.numMCTSSims):
self._search(state, 0, maxdepth=self.args.maxDepth)
s = self.task.state_string_representation(state)
avail_moves = list(map(lambda x: self.task.move_string_representation(x, state),
self.task.iterate_legal_moves(state)))
counts = [self.Nsa[(s,a)] if (s,a) in self.Nsa else 0 \
for a in avail_moves]
probs = []
if temp == 0:
bestA = np.argmax(counts)
probs = [0]*len(counts)
probs[bestA] = 1
else:
probs = [x**(1./temp) for x in counts]
return probs, avail_moves
def _get_random_action_prob(self, state, temp=1):
"""
Just like _get_action_prob, only completely random
"""
s = self.task.state_string_representation(state)
avail_moves = list(map(lambda x: self.task.move_string_representation(x, state),
self.task.iterate_legal_moves(state)))
probs = [1.0/len(avail_moves)] * len(avail_moves)
return probs, avail_moves
def get_move(self, state, temp=0):
"""
Gets a move the AI wants to play for the passed in state.
Parameters
----------
state : State
The state object containing all the state information and
next player information. Size and shape varies per Task.
Ideally should be canonicalized.
Returns
-------
move : Move
The move object containing the move information. Size and
shape varies per Task.
"""
probs, avail_moves = self._get_action_prob(state, temp)
chosen_move = random.choices(
population=avail_moves,
weights=probs,
k=1
)[0]
return self.task.string_to_move(chosen_move, state)
def load_weights(self, filename):
"""
Loads a previous Approach state from a file. Just the
weights, history is loaded separately.
Parameters
----------
filename : str
File to load the weights from. Not having this be a
list allows for other data encoding schemes.
Returns
-------
self
"""
filepath = filename
if not os.path.exists(filepath):
filepath = os.path.join(self.args.checkpoint_dir, filename)
if not os.path.exists(filepath):
raise("No model in local file {} or path {}!".format(filename, filepath))
all_model_files = []
with open(filepath, "rb") as f:
all_model_files = Unpickler(f).load()
for i in range(len(all_model_files)):
buf = all_model_files[i]
self.models[i].load_weights(buf)
return self
def save_weights(self, filename):
"""
Saves the weights of the current state to a file.
Parameters
----------
filename : str
File to save the weights to.
Returns
-------
self
"""
filepath = os.path.join(self.args.checkpoint_dir, filename)
if not os.path.exists(self.args.checkpoint_dir):
print("Checkpoint Directory does not exist! Making directory {}".format(self.args.checkpoint_dir))
os.mkdir(self.args.checkpoint_dir)
# This was the best thing I could think of to fit multiple models in one file:
# Keras's h5py backend supports writing data directly to a BytesIO object
# So, we just tell it to do that for all the models and write the resulting
# list to a pickle file.
# Will most certainly fail for large enough models
all_model_files = []
for i in range(len(self.models)):
buf = io.BytesIO()
self.models[i].save_weights(buf, overwrite=True)
all_model_files.append(buf)
with open(filepath, "wb") as f:
Pickler(f).dump(all_model_files)
return self
def load_history(self, filename):
"""
Loads a game history from a file. A file can optionally
contain one or many History classes, and this method
can be extended with optional arguments to specify how
many histories to load.
Parameters
----------
filename : str
File to load history from.
Returns
-------
self
"""
filepath = filename
if not os.path.exists(filepath):
filepath = os.path.join(self.args.checkpoint_dir, filename)
if not os.path.exists(filepath):
raise("No checkpoint in local file {} or path {}!".format(filename, filepath))
with open(filepath, "rb") as f:
log.info(f"Loading History from {filepath}")
self.trainExamplesHistory = Unpickler(f).load()
return self
def save_history(self, filename):
"""
Saves the current game history to a file. Should generally
append to the history in the file if it exists.
Parameters
----------
filename : str
File to save/append history to.
Returns
-------
self
"""
folder = self.args.checkpoint_dir
if not os.path.exists(folder):
os.makedirs(folder)
filepath = os.path.join(folder, filename)
with open(filepath, "wb") as f:
Pickler(f).dump(self.trainExamplesHistory)
return self
def _get_checkpoint_filename(self, iteration):
return "checkpoint_{}.pth.tar".format(iteration)
def _run_one_selfplay(self, action_prob_func):
trainExamples = []
board = self.task.empty_state(phase=0)
episodeStep = 0
while not self.task.is_terminal_state(board):
episodeStep += 1
temp = int(episodeStep < self.args.tempThreshold)
probs, avail_moves = action_prob_func(board, temp=temp) #self._get_action_prob(board, temp=temp)
# not applicable to all games, but might include later
#sym = self.task.getSymmetries(canonicalBoard, pi)
#for b,p in sym:
pi = np.asarray([probs[i]*self.task.string_to_move(avail_moves[i],board) for i in range(len(probs))])
pi = np.sum(pi, axis=0).flatten()
trainExamples.append((board, board.next_player, pi))
action = random.choices(
population=avail_moves,
weights=probs,
k=1
)[0]
board = self.task.apply_move(self.task.string_to_move(action, board), board)
# Game is over
winners = self.task.get_winners(board)
#print("SELFPLAY GAME OVER. Board: {}".format(self.task.state_string_representation(board)))
#print("Winners: {}. Final player: {}.".format(winners, board.next_player))
# This might be biased towards or against ties depending on the last player
# to move, but in sufficiently complex games this should result in a 50/50
# split anyways
fmtTrainExamples = []
for old_board, player, pi in trainExamples:
r = np.array([1 if (i in winners) else -1 for i in range(self.task.num_players)])
fmtTrainExamples.append((old_board, pi, r))
return fmtTrainExamples
def _run_selfplay(self):
# bookkeeping
log.info('------ITER ' + str(self.iteration) + '------')
# examples of the iteration
if self.iteration == 0 and self.args.startWithRandomPlay:
iterationTrainExamples = deque([], maxlen=self.args.maxlenOfQueue)
log.info("Running Random Play to start")
bar = Progbar(self.args.numRandomPlayExamples)
bar.update(0)
for eps in range(self.args.numRandomPlayExamples):
iterationTrainExamples += self._run_one_selfplay(self._get_random_action_prob)
bar.add(1)
self.trainExamplesHistory.append(iterationTrainExamples)
elif self.iteration > self.args.startFromEp or not self.skipFirstSelfPlay:
iterationTrainExamples = deque([], maxlen=self.args.maxlenOfQueue)
bar = Progbar(self.args.numEps)
bar.update(0)
for eps in range(self.args.numEps):
self._reset_mcts()
iterationTrainExamples += self._run_one_selfplay(self._get_action_prob)
# bookkeeping + plot progress
bar.add(1)
# save the iteration examples to the history
self.trainExamplesHistory.append(iterationTrainExamples)
if len(self.trainExamplesHistory) > self.args.numItersForTrainExamplesHistory:
log.debug("len(trainExamplesHistory) =", len(self.trainExamplesHistory), " => remove the oldest trainExamples")
self.trainExamplesHistory.pop(0)
# backup history to a file
# NB! the examples were collected using the model from the previous iteration
self.save_history(self._get_checkpoint_filename(self.iteration)+".examples")
def _match_phase(self, phase):
def _internal_match_phase(a):
return a.phase == phase
return _internal_match_phase
def _train_nnet(self, examples):
"""
examples: list of examples, each example is of form (board, pi, v)
"""
#input_boards, target_pis, target_vs = list(zip(*examples))
# for each phase, filter out all the examples from that phase
# and train the corresponding model on them
for phase in range(self.task.num_phases):
match_phase = self._match_phase(phase)
f_input_boards, f_target_pis, f_target_vs = list(zip(*
list(filter(
lambda x: match_phase(x[0]),
examples
))
))
f_input_boards = np.asarray(f_input_boards)
f_target_pis = np.asarray(f_target_pis)
f_target_vs = np.asarray(f_target_vs)
#f_target_vs = f_target_vs.reshape(f_target_vs.shape + (1,))
self.models[phase].fit(
x=f_input_boards,
y=[f_target_vs, f_target_pis],
batch_size=self.args.batch_size,
epochs=self.args.epochs
)
def _arena_play_once(self, first_player, second_player, verbose=False):
# TODO: Currently only supports 2-player games, should probably fix that
if verbose:
assert(self.task.state_string_representation)
board = self.task.empty_state(phase=0)
first_player._reset_mcts()
second_player._reset_mcts()
it = 0
first_player_number = board.next_player
while not self.task.is_terminal_state(board):
it += 1
if verbose:
print(f"Turn {it} Player {board.next_player}")
print(self.task.state_string_representation(board))
temp = it < self.args.tempThreshold
# can't count on consistent player numbers
# or even consisten player counts
if board.next_player == first_player_number:
#print("FIRST PLAYER")
move = first_player.get_move(board, temp)
board = self.task.apply_move(move, board)
else:
#print("SECOND PLAYER")
move = second_player.get_move(board, temp)
board = self.task.apply_move(move, board)
winners = self.task.get_winners(board)
if verbose:
print(f"Game Over: Turn {it} Winners {winners}")
print(self.task.state_string_representation(board))
r = 0
if first_player_number in winners:
r = 1
elif first_player_number not in winners and len(winners)>0:
r = -1
return r
def _arena_play(self, num, verbose=True):
"""
Plays num games in which player 1 and 2 both start num/2 times each
Parameters
----------
num : int
Returns
-------
oneWon : int
games won by player1
twoWon : int
games won by player2
draws:
games won by nobody
"""
num = int(num/2)
eps_time = Progbar(2*num, stateful_metrics=["wins", "draws", "losses"])
eps_time.update(0, values=[("wins",0),("draws",0),("losses",0)])
oneWon = 0
twoWon = 0
draws = 0
for _ in range(num):
#import pdb; pdb.set_trace()
result = self._arena_play_once(self, self.pnet, verbose=verbose)
if result == 1:
oneWon += 1
elif result == -1:
twoWon += 1
else:
draws += 1
eps_time.add(1, [("wins", oneWon), ("losses", twoWon), ("draws", draws)])
result = self._arena_play_once(self.pnet, self, verbose=verbose)
if result == 1:
twoWon += 1
elif result == -1:
oneWon += 1
else:
draws += 1
eps_time.add(1, [("wins", oneWon), ("losses", twoWon), ("draws", draws)])
return oneWon, twoWon, draws
def train_once(self):
"""
Runs a single training iteration to fine-tune the weights. Possible
side effects include:
* Changing the internals weights (duh)
* Adding to the history (optional)
* Printing to console
* Automatically calling `save_history` and `save_weights`
In implementations, can take custom arguments here in a single dict
or have arguments passed in an earlier initialization phase.
Any settings passed in here are expected to override default settings.
"""
self._run_selfplay()
# shuffle examlpes before training
trainExamples = []
for e in self.trainExamplesHistory:
trainExamples.extend(e)
random.shuffle(trainExamples)
# training new network, keeping a copy of the old one
self.save_weights('temp.pth.tar')
self.pnet.load_weights('temp.pth.tar')
self.pnet._reset_mcts()
self._train_nnet(trainExamples)
#print(self.trainExamplesHistory)
self._reset_mcts()
log.info('PITTING AGAINST PREVIOUS VERSION')
nwins, pwins, draws = self._arena_play(self.args.arenaCompare)
log.info('NEW/PREV WINS : %d / %d ; DRAWS : %d' % (nwins, pwins, draws))
if pwins+nwins > 0 and float(nwins)/(pwins+nwins) < self.args.updateThreshold:
log.info('REJECTING NEW MODEL')
self.load_weights('temp.pth.tar')
else:
log.info('ACCEPTING NEW MODEL')
self.save_weights(self._get_checkpoint_filename(self.iteration))
self.save_weights('best.pth.tar')
self.iteration += 1
def test_once(self):
"""
Runs a single testing interation. Does not change weights, and usually
does not change the history either.
Returns
-------
score : float
ELO, win percentage, or another number where higher is better
"""
return None
br.follow_link(text_regex='Oversikt')
br.follow_link(text_regex='Resultater')
data = br.response().get_data()
soup = BeautifulSoup(data)
main = soup.find('td', {'class':'main'})
rows = main.findAll('tr', {'class': re.compile('pysj(0|1)')})
for row in rows:
tds = row.findAll('td')
course_id = str(tds[1].string)
course_name = unicode(tds[2].string)
grade = str(tds[7].string)
if course_id not in known_grades:
# found new!
update = True
known_grades.append(course_id)
notify("New grade: %s - %s: %s" % (course_id, course_name, grade))
if update:
fp = open(STORAGE_FILE, 'w')
Pickler(fp).dump(known_grades)
fp.close()
br.follow_link(text_regex='Logg ut')