def as_board(moves):
states = np.empty((len(moves), 7*6))
labels = np.empty((len(moves), 2))
game = Game()
for _,i in moves.iterrows():
idx, player, move, winner = i['idx'], i['player'], i['move'], i['winner']
game.play_move(player, move)
states[idx,:] = game.board.reshape((-1))
labels[idx,0] = winner
labels[idx,1] = player
return (states, labels)
def _predict_move_probability(self, player, check_early_win=True):
# Predict chance of winning for each move
# and return column with highest chance.
max_probability = (0, 0.0)
print(self.print_player_string(player) + ": Testing cols: |", end="")
for move in range(self.game.width):
if not self.game.is_legal_move(move):
print(style(f" {0.0:.3f} |", Colours.FG.BRIGHT_RED), end="")
continue
test_game = Game(board=self.game.board.copy())
test_game.play_move(player, move)
if check_early_win and test_game.status == player:
# Win reached
print(" win", end="")
max_probability = (move, 1.0)
break
# Get prediction for move (make board positive by adding 1)
test_input = np.concatenate(
(test_game.board.flatten(), [player])).reshape(
(1, *self.input_shape)) + 1
prediction = self.model.predict(test_input)[0][
player + 1] # [[player_0_prob, draw (?), player_1_prob]]
if np.isnan(prediction):
raise Exception("Error: prediction is NaN?")
print(f" {prediction:.3f} |", end="")
if prediction > max_probability[1]:
max_probability = (move, prediction)
print(
f" => Predicted move at col {max_probability[0]} with {max_probability[1] * 100:.2f}%"
)
return max_probability
class MLC4:
def __init__(self, board=None, print_friendly=False):
super().__init__()
self.game = Game(
board=board) # Cannot do inheritance due to deepcopy in Game...
self.states = []
self.model = None
self.board_nodes = self.game.width * self.game.height
self.input_shape = (self.board_nodes + 1, )
# If True, use another character for the other player so they are distinct
# when printing without colours, e.g. ■ ▀ • ⦿
self.print_friendly = print_friendly
self.__players = {
-1: style("■", Colours.FG.YELLOW),
0: " ",
1: style("•" if self.print_friendly else "■", Colours.FG.RED)
}
def __str__(self):
top_row = "┌" + ("─" * 3 + "┬") * (self.game.width - 1) + "─" * 3 + "┐"
sep_row = "╞" + ("═" * 3 + "╪") * (self.game.width - 1) + "═" * 3 + "╡"
bot_row = "└" + ("─" * 3 + "┴") * (self.game.width - 1) + "─" * 3 + "┘"
return top_row \
+ "\n" + "\n".join(f"│ {f' │ '.join(map(self.print_player, li))} │" for li in reversed(self.game.board)) \
+ "\n" + sep_row \
+ "\n│ " + f' │ '.join(map(str, range(self.game.width))) + " │" \
+ "\n" + bot_row
def _add_state(self, game, player, move):
if player is not None and move is not None:
self.states.append((player, move))
print(self)
def print_states(self, game_id=0, fp=None):
for idx, move in enumerate(self.states):
print(game_id,
idx,
move[0],
move[1],
self.game.status,
sep=",",
file=fp or sys.stdout)
def print_player(self, p):
return self.__players.get(p, self.__players[0])
def print_player_string(self, p):
if p == 0:
return "Player DRAW"
return f"Player {max(0, p) + 1} " + self.print_player(p)
def reset(self):
self.game = Game()
self.states = []
def play_original_vs_random(self, starting=None, legal_only=True, n=100):
# Dummy call original play function
return self.game.random_play(starting, legal_only, self._add_state)
def play_original_vs_smart(self, starting=None, legal_only=True, n=100):
# Dummy call original play function
return self.game.smart_play(starting, legal_only, n, self._add_state)
def _start_game(self, player, other_strat_descr="plays randomly"):
print("-" * 80 + f"\n\nStarting game with {self.print_player_string(player)}. " \
+ f"AI player is {self.print_player_string(-1)}." \
+ f" {self.print_player(1)} {other_strat_descr}.")
self._add_state(self.game, None, None)
def play_vs_random(self,
starting=None,
legal_only=True,
check_early_win=True,
prevent_other_win=True):
# Against random player
player = starting if starting is not None else starting_player()
self._start_game(player, other_strat_descr="plays randomly")
while self.game.status is None:
if player < 0 and self.model:
move = self.predict(check_early_win=check_early_win,
prevent_other_win=prevent_other_win)
else:
move = self.game.random_action(legal_only=legal_only)
print(
f"{self.print_player_string(player)} adds to column {move}...")
self.game.play_move(player, move)
self._add_state(self.game, player, move)
player = player * -1
print(f"{self.print_player_string(self.game.status)} wins!")
return self.game.status
def play_vs_smart(self,
starting=None,
legal_only=True,
n=100,
check_early_win=True,
prevent_other_win=True):
# Against smart player
player = starting if starting is not None else starting_player()
self._start_game(player, other_strat_descr="plays smart")
while self.game.status is None:
if player < 0 and self.model:
move = self.predict(check_early_win=check_early_win,
prevent_other_win=prevent_other_win)
else:
move, p = self.game.smart_action(player,
legal_only=legal_only,
n=n)
if not self.game.is_legal_move(move):
print(
style("Illegal move smart player! ",
Colours.FG.BRIGHT_RED), player, move)
print(
f"{self.print_player_string(player)} adds to column {move}...")
self.game.play_move(player, move)
self._add_state(self.game, player, move)
player = player * -1
print(f"{self.print_player_string(self.game.status)} wins!")
return self.game.status
def play_vs_ai(self,
starting=None,
legal_only=True,
check_early_win=True,
prevent_other_win=True,
random_move_chance=0.0):
# Against own model
player = starting if starting is not None else starting_player()
self._start_game(player, other_strat_descr="plays also as AI")
while self.game.status is None:
if player > 0 and random_move_chance > 0.0 and random.random(
) <= random_move_chance:
move = self.game.random_action(legal_only=legal_only)
else:
move = self.predict(ai_player=player,
check_early_win=check_early_win,
prevent_other_win=prevent_other_win)
if not self.game.is_legal_move(move):
print(
style("Illegal move from player! ", Colours.FG.BRIGHT_RED),
player, move)
print(
f"{self.print_player_string(player)} adds to column {move}...")
self.game.play_move(player, move)
self._add_state(self.game, player, move)
player = player * -1
print(f"{self.print_player_string(self.game.status)} wins!")
return self.game.status
###########################################################################
def has_model(self):
return self.model is not None
def load_existing_model(self, name, basepath="../data/models/"):
try:
self.model = load_model(f"{basepath}{name}", compile=True)
self.model.predict(np.zeros(
(1, *self.input_shape))) # Init predictor
except Exception as e:
self.model = None
print(style(f"Could not load model!\n{e}", Colours.FG.RED))
else:
self.model.summary()
def build_network(self, name="", learning_rate=0.001):
"""
Input : self.width * self.height board (42 squares) + player
https://keras.io/api/layers/activations/#relu-function
https://keras.io/api/layers/activations/#softmax-function
https://keras.io/api/losses/probabilistic_losses/#sparsecategoricalcrossentropy-class
https://keras.io/api/optimizers/Nadam/
https://keras.io/api/metrics/accuracy_metrics/#accuracy-class
Output: 2 => [[player_0_prob, player_1_prob]]
"""
print(f"Building model{(' ' + name) if name else ''}...")
self.model = keras.Sequential(name=name or None)
# Input layer
self.model.add(
Dense(self.input_shape[0], input_dim=self.input_shape[0]))
# One or more large layers
self.model.add(Dense(64, activation='relu'))
self.model.add(Dense(256, activation='relu'))
self.model.add(Dense(256, activation='relu'))
# self.model.add(Dense(64, activation='relu'))
# Smaller ending layer
self.model.add(Dense(self.board_nodes, activation='relu'))
# self.model.add(Dense(self.game.width, activation='relu'))
# Output end layer
self.model.add(Dense(3, activation='softmax'))
self.model.compile(loss=SparseCategoricalCrossentropy(),
optimizer=Nadam(learning_rate=learning_rate),
metrics=["accuracy"])
self.model.summary()
def prepare_data(self, input_file, train_ratio=0.8):
print("Preparing data...")
# Read data, should contain [ (board:42, winner:1, player:1), ... ]
data = np.load(input_file)
# Board and player values are made positive by adding one
data += 1
Y = data[:, -2:-1] # (winner), result is 0 or 2
X = np.delete(
data, -2,
1) # Drop second to last column, result = (board state, player)
size = int(train_ratio * X.shape[0])
X_train, X_test, Y_train, Y_test = X[:size], X[size:], Y[:size], Y[
size:]
print("Data loaded.")
return (X_train, Y_train), (X_test, Y_test)
def train(self,
train_data,
test_data,
epochs=10,
batch_size=200,
show_plot=False,
save_plot_path=""):
print("Training model...")
train_x, train_y = train_data
hist = self.model.fit(train_x,
train_y,
validation_data=test_data,
shuffle=True,
epochs=epochs,
batch_size=batch_size)
test_score, test_acc = self.model.evaluate(test_data[0],
test_data[1],
verbose=0)
print(style("Final accuracy on training set : ", Colours.FG.MAGENTA) \
+ style(f"{hist.history['accuracy'][-1] * 100:.2f}%", Colours.FG.BRIGHT_MAGENTA))
print(style("Average accuracy while training: ", Colours.FG.MAGENTA) \
+ style(f"{np.average(np.array(hist.history['val_accuracy'])) * 100:.2f}%", Colours.FG.BRIGHT_MAGENTA))
print(style("Average accuracy on test set : ", Colours.FG.MAGENTA) \
+ style(f"{test_acc * 100:.2f}% (score={test_score:.4f})", Colours.FG.BRIGHT_MAGENTA))
if show_plot or save_plot_path:
plt.style.use("ggplot")
plt.figure()
plt.plot(
np.arange(0, epochs),
[1.0] * epochs,
"r--",
label="Accuracy target",
)
plt.plot(np.arange(0, epochs),
hist.history["loss"],
"cyan",
label="train_loss")
plt.plot(np.arange(0, epochs),
hist.history["val_loss"],
"blue",
label="val_loss")
plt.plot(np.arange(0, epochs),
hist.history["accuracy"],
"yellow",
label="train_acc")
plt.plot(np.arange(0, epochs),
hist.history["val_accuracy"],
"orange",
label="val_acc")
# Optionally plot other metrics?
for k, v in hist.history.items():
if k not in ("loss", "val_loss", "accuracy", "val_accuracy"):
plt.plot(np.arange(0, epochs), v, label=k)
plt.title("Training Loss and Accuracy")
plt.xlabel("Epoch #")
plt.ylabel("Loss/Accuracy")
plt.legend(loc="lower left")
plt.xlim(0, epochs - 1)
plt.ylim(bottom=0)
if save_plot_path: plt.savefig(save_plot_path)
if show_plot: plt.show()
self.model.predict(np.zeros((1, *self.input_shape))) # Init predictor
def save_model(self,
name="trained_1",
basepath="../data/models/",
save_structure=False):
# if not name.endswith(".h5"):
# name += ".h5"
print(f"Saving model to: '{basepath}{name}'...")
if not os.path.exists(basepath):
os.makedirs(basepath)
if save_structure:
model_json = self.model.to_json()
with open(f"{basepath}{name}.json", "w") as fp:
fp.write(model_json)
save_model(self.model, f"{basepath}{name}")
print("Saving complete.")
def _predict_move_probability(self, player, check_early_win=True):
# Predict chance of winning for each move
# and return column with highest chance.
max_probability = (0, 0.0)
print(self.print_player_string(player) + ": Testing cols: |", end="")
for move in range(self.game.width):
if not self.game.is_legal_move(move):
print(style(f" {0.0:.3f} |", Colours.FG.BRIGHT_RED), end="")
continue
test_game = Game(board=self.game.board.copy())
test_game.play_move(player, move)
if check_early_win and test_game.status == player:
# Win reached
print(" win", end="")
max_probability = (move, 1.0)
break
# Get prediction for move (make board positive by adding 1)
test_input = np.concatenate(
(test_game.board.flatten(), [player])).reshape(
(1, *self.input_shape)) + 1
prediction = self.model.predict(test_input)[0][
player + 1] # [[player_0_prob, draw (?), player_1_prob]]
if np.isnan(prediction):
raise Exception("Error: prediction is NaN?")
print(f" {prediction:.3f} |", end="")
if prediction > max_probability[1]:
max_probability = (move, prediction)
print(
f" => Predicted move at col {max_probability[0]} with {max_probability[1] * 100:.2f}%"
)
return max_probability
def predict(self,
ai_player=-1,
check_early_win=True,
prevent_other_win=True):
# Get (move, chance) that AI wins
ai_move = self._predict_move_probability(ai_player, check_early_win)
if prevent_other_win:
other_player_move = self._predict_move_probability(
ai_player * -1, check_early_win)
if other_player_move[1] > ai_move[1]:
print(style("Trying to prevent", Colours.FG.BRIGHT_RED) \
+ f" {self.print_player_string(ai_player * -1)} from winning...")
return other_player_move[0]
return ai_move[0]