~piotr-machura/sweep-ai

8aed484b1f1f902e130cccc7ab77ab7343cda6ce — Piotr Machura 10 months ago 5119fba
Not very good Binary Classifier
7 files changed, 381 insertions(+), 48 deletions(-)

M poetry.lock
M pyproject.toml
M sweep_ai/ai.py
M sweep_ai/logic.py
M sweep_ai/window.py
A tests/test_ai.py
M tests/test_logic.py
M poetry.lock => poetry.lock +260 -1
@@ 91,6 91,14 @@ optional = false
python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"

[[package]]
name = "cycler"
version = "0.11.0"
description = "Composable style cycles"
category = "dev"
optional = false
python-versions = ">=3.6"

[[package]]
name = "flatbuffers"
version = "2.0"
description = "The FlatBuffers serialization format for Python"


@@ 99,6 107,27 @@ optional = false
python-versions = "*"

[[package]]
name = "fonttools"
version = "4.29.0"
description = "Tools to manipulate font files"
category = "dev"
optional = false
python-versions = ">=3.7"

[package.extras]
all = ["fs (>=2.2.0,<3)", "lxml (>=4.0,<5)", "zopfli (>=0.1.4)", "lz4 (>=1.7.4.2)", "matplotlib", "sympy", "skia-pathops (>=0.5.0)", "brotlicffi (>=0.8.0)", "scipy", "brotli (>=1.0.1)", "munkres", "unicodedata2 (>=14.0.0)", "xattr"]
graphite = ["lz4 (>=1.7.4.2)"]
interpolatable = ["scipy", "munkres"]
lxml = ["lxml (>=4.0,<5)"]
pathops = ["skia-pathops (>=0.5.0)"]
plot = ["matplotlib"]
symfont = ["sympy"]
type1 = ["xattr"]
ufo = ["fs (>=2.2.0,<3)"]
unicode = ["unicodedata2 (>=14.0.0)"]
woff = ["zopfli (>=0.1.4)", "brotlicffi (>=0.8.0)", "brotli (>=1.0.1)"]

[[package]]
name = "gast"
version = "0.4.0"
description = "Python AST that abstracts the underlying Python version"


@@ 248,6 277,14 @@ pep8 = ["flake8"]
tests = ["pandas", "pillow", "tensorflow", "keras", "pytest", "pytest-xdist", "pytest-cov"]

[[package]]
name = "kiwisolver"
version = "1.3.2"
description = "A fast implementation of the Cassowary constraint solver"
category = "dev"
optional = false
python-versions = ">=3.7"

[[package]]
name = "lazy-object-proxy"
version = "1.7.1"
description = "A fast and thorough lazy object proxy."


@@ 278,6 315,25 @@ importlib-metadata = {version = ">=4.4", markers = "python_version < \"3.10\""}
testing = ["coverage", "pyyaml"]

[[package]]
name = "matplotlib"
version = "3.5.1"
description = "Python plotting package"
category = "dev"
optional = false
python-versions = ">=3.7"

[package.dependencies]
cycler = ">=0.10"
fonttools = ">=4.22.0"
kiwisolver = ">=1.0.1"
numpy = ">=1.17"
packaging = ">=20.0"
pillow = ">=6.2.0"
pyparsing = ">=2.2.1"
python-dateutil = ">=2.7"
setuptools_scm = ">=4"

[[package]]
name = "mccabe"
version = "0.6.1"
description = "McCabe checker, plugin for flake8"


@@ 358,6 414,14 @@ python-versions = ">=3.6"
pyparsing = ">=2.0.2,<3.0.5 || >3.0.5"

[[package]]
name = "pillow"
version = "9.0.0"
description = "Python Imaging Library (Fork)"
category = "dev"
optional = false
python-versions = ">=3.7"

[[package]]
name = "platformdirs"
version = "2.4.1"
description = "A small Python module for determining appropriate platform-specific dirs, e.g. a \"user data dir\"."


@@ 417,6 481,14 @@ python-versions = "*"
pyasn1 = ">=0.4.6,<0.5.0"

[[package]]
name = "pycairo"
version = "1.20.1"
description = "Python interface for cairo"
category = "dev"
optional = false
python-versions = ">=3.6, <4"

[[package]]
name = "pydocstyle"
version = "6.1.1"
description = "Python docstring style checker"


@@ 457,6 529,17 @@ docs = ["pygame (>=1.9.3)", "pyperclip", "typing-extensions", "sphinx", "sphinx-
test = ["pygame (>=1.9.3)", "pyperclip", "typing-extensions", "codecov", "nose"]

[[package]]
name = "pygobject"
version = "3.42.0"
description = "Python bindings for GObject Introspection"
category = "dev"
optional = false
python-versions = ">=3.6, <4"

[package.dependencies]
pycairo = ">=1.16,<2.0"

[[package]]
name = "pylint"
version = "2.12.2"
description = "python code static checker"


@@ 514,6 597,17 @@ toml = "*"
testing = ["argcomplete", "hypothesis (>=3.56)", "mock", "nose", "requests", "xmlschema"]

[[package]]
name = "python-dateutil"
version = "2.8.2"
description = "Extensions to the standard Python datetime module"
category = "dev"
optional = false
python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7"

[package.dependencies]
six = ">=1.5"

[[package]]
name = "requests"
version = "2.27.1"
description = "Python HTTP for Humans."


@@ 558,6 652,22 @@ python-versions = ">=3.6,<4"
pyasn1 = ">=0.1.3"

[[package]]
name = "setuptools-scm"
version = "6.4.2"
description = "the blessed package to manage your versions by scm tags"
category = "dev"
optional = false
python-versions = ">=3.6"

[package.dependencies]
packaging = ">=20.0"
tomli = ">=1.0.0"

[package.extras]
test = ["pytest (>=6.2)", "virtualenv (>20)"]
toml = ["setuptools (>=42)"]

[[package]]
name = "six"
version = "1.16.0"
description = "Python 2 and 3 compatibility utilities"


@@ 749,7 859,7 @@ testing = ["pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-flake8", "pytest-
[metadata]
lock-version = "1.1"
python-versions = ">=3.8,<3.11"
content-hash = "d8664e7f8764575a449928803edc42ac041bb8a7890c6c6e08b47c39ff2ce804"
content-hash = "71d09bed82225317a6fb4531770e645a39d566d8ce8f53ee5967ee01c6d32f89"

[metadata.files]
absl-py = [


@@ 788,10 898,18 @@ colorama = [
    {file = "colorama-0.4.4-py2.py3-none-any.whl", hash = "sha256:9f47eda37229f68eee03b24b9748937c7dc3868f906e8ba69fbcbdd3bc5dc3e2"},
    {file = "colorama-0.4.4.tar.gz", hash = "sha256:5941b2b48a20143d2267e95b1c2a7603ce057ee39fd88e7329b0c292aa16869b"},
]
cycler = [
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    {file = "cycler-0.11.0.tar.gz", hash = "sha256:9c87405839a19696e837b3b818fed3f5f69f16f1eec1a1ad77e043dcea9c772f"},
]
flatbuffers = [
    {file = "flatbuffers-2.0-py2.py3-none-any.whl", hash = "sha256:3751954f0604580d3219ae49a85fafec9d85eec599c0b96226e1bc0b48e57474"},
    {file = "flatbuffers-2.0.tar.gz", hash = "sha256:12158ab0272375eab8db2d663ae97370c33f152b27801fa6024e1d6105fd4dd2"},
]
fonttools = [
    {file = "fonttools-4.29.0-py3-none-any.whl", hash = "sha256:ed9496e5650b977a697c50ac99c8e8331f9eae3f99e5ae649623359103306dfe"},
    {file = "fonttools-4.29.0.zip", hash = "sha256:f4834250db2c9855c3385459579dbb5cdf74349ab059ea0e619359b65ae72037"},
]
gast = [
    {file = "gast-0.4.0-py3-none-any.whl", hash = "sha256:b7adcdd5adbebf1adf17378da5ba3f543684dbec47b1cda1f3997e573cd542c4"},
    {file = "gast-0.4.0.tar.gz", hash = "sha256:40feb7b8b8434785585ab224d1568b857edb18297e5a3047f1ba012bc83b42c1"},


@@ 896,6 1014,52 @@ keras-preprocessing = [
    {file = "Keras_Preprocessing-1.1.2-py2.py3-none-any.whl", hash = "sha256:7b82029b130ff61cc99b55f3bd27427df4838576838c5b2f65940e4fcec99a7b"},
    {file = "Keras_Preprocessing-1.1.2.tar.gz", hash = "sha256:add82567c50c8bc648c14195bf544a5ce7c1f76761536956c3d2978970179ef3"},
]
kiwisolver = [
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lazy-object-proxy = [
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@@ 947,6 1111,43 @@ markdown = [
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@@ 1083,6 1318,19 @@ pyasn1-modules = [
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@@ 1151,6 1399,9 @@ pygame-menu = [
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@@ 1166,6 1417,10 @@ pytest = [
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@@ 1178,6 1433,10 @@ rsa = [
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M pyproject.toml => pyproject.toml +3 -0
@@ 19,6 19,9 @@ pydocstyle = {extras = ["toml"], version = "^6.1.1"}
pylint = "^2.12.2"
pytest = "^6.2.5"
isort = "^5.10.1"
matplotlib = "^3.5.1"
pycairo = "^1.20.1"
PyGObject = "^3.42.0"

[tool.poetry.scripts]
sweep-ai = "sweep_ai.__main__:main"

M sweep_ai/ai.py => sweep_ai/ai.py +58 -18
@@ 3,6 3,7 @@ from typing import Optional, Tuple

import numpy as np
import tensorflow as tf
from matplotlib import pyplot as plt
from tensorflow import keras

from .logic import State


@@ 24,6 25,20 @@ class Player:
        self.fitness = -np.inf
        self.brain: Optional[keras.models.Sequential] = None

    @staticmethod
    def surround(state: State, x: int, y: int) -> np.ndarray:
        """Return an ndarray representing the 35 blocks surrounding `(x, y)`.

        If the neighbor is empty (or is beyond the border) the array contains
        0. If it's hidden the state contains -1. If it's revealed, but near a
        bomb then it's value.
        """
        surround = np.zeros((24, ), dtype=float)
        for x_n, y_n in state.neighbors(x, y, radius=2):
            if state.revealed[x_n, y_n] and state.near[x_n, y_n] > 0:
                surround[x_n + y_n] = state.near[x_n, y_n]
        return surround

    def move(self, state: State) -> Optional[Tuple[int, int]]:
        """Make a move.



@@ 33,33 48,58 @@ class Player:
        """
        if state.size != self.size:
            return None
        data = state.ai_data
        if self.brain is None:
            return None
        print(data)
        return (1, 1)

    def train(self):
        """Recrete and train the AI brain of this player."""
        size = self.size
        self.brain = keras.models.Sequential(
            [
                keras.layers.Dense(
                    size * size,
                    activation='relu',
                    input_shape=(size, size),
                ),
                keras.layers.Dense(
                    size,
                    activation='relu',
                ),
                keras.layers.Dense(
                    size * size,
                    activation='softmax',
                ),
                keras.layers.Dense(120, activation='relu', input_shape=(24, )),
                keras.layers.Dropout(0.4),
                keras.layers.Dense(60, activation='relu'),
                keras.layers.Dropout(0.2),
                keras.layers.Dense(1, activation='sigmoid'),
            ])
        self.brain.compile(
            keras.optimizers.Adam(learning_rate=0.01),
            loss='sparse_categorical_crossentropy',
            keras.optimizers.Adam(learning_rate=0.0005),
            loss='binary_crossentropy',
            metrics=['accuracy'],
        )
        self.brain.summary()

        x_train = []
        y_train = []
        weights = []
        for _ in range(100):
            state = State(self.size, 0.15)
            state.click(*state.cheat())
            state.click(*state.cheat())
            while state.won is None:
                state.click(*state.cheat())
                for x in range(self.size):
                    for y in range(self.size):
                        if state.hidden[x, y]:
                            sur = self.surround(state, x, y)
                            x_train.append(sur)
                            y_train.append(state.safe[x, y])
                            weights.append(np.sum(sur))

        x_train = np.array(x_train)
        y_train = np.array(y_train)
        weights = np.array(weights)

        history = self.brain.fit(
            x_train,
            y_train,
            sample_weight=weights,
            epochs=10,
            batch_size=50,
            verbose=1,
            validation_split=0.2,
            shuffle=True,
        )
        plt.plot(history.history['val_accuracy'])
        plt.plot(history.history['val_loss'])
        plt.show()

M sweep_ai/logic.py => sweep_ai/logic.py +25 -19
@@ 56,7 56,7 @@ class State:
        # Place bombs
        for x, y in bomb_positions:
            self.bomb[x, y] = 1
            for n_x, n_y in self._neighbors(x, y):
            for n_x, n_y in self.neighbors(x, y):
                self.near[n_x, n_y] += 1

    @property


@@ 100,20 100,6 @@ class State:
        return (self.revealed_n - self.bomb_n) / self.safe_n

    @property
    def ai_data(self) -> np.ndarray:
        """Masked state data, suitable for using in a ML algorithm.

        It's an ndarray similar to `self.near`, but with `-1` where the board
        is not revealed. The values of `self.near` are also inverted to `1 /
        value` with the exception of zeroes.
        """
        return np.where(
            self.hidden,
            -np.ones_like(self.near),    # <- hidden
            np.divide(1, self.near, where=self.near != 0),    # <- hidden
        )

    @property
    def good_move(self) -> np.ndarray:
        """Array where 1 means `(x, y)` is a "good move".



@@ 144,6 130,19 @@ class State:
        if np.array_equal(self.revealed, self.safe):
            self.won = True

    def cheat(self) -> Optional[Tuple[int, int]]:
        """Cheat the game by choosing an obviously advantageous move.

        Returns a tuple of `(x, y)` coordinates guaranteed to be a
        `self.good_move`, or `None` if there are no good moves.
        """
        moves = np.transpose(np.nonzero(self.good_move))
        if len(moves) != 0:
            idx = np.random.randint(0, len(moves))
            x, y = moves[idx]
            return x, y
        return None

    def flag(self, x: int, y: int):
        """Place a flag at `(x, y)`, or remove it if already flagged.



@@ 163,15 162,22 @@ class State:
        self.flagged[x, y] = 0
        # If there are no bombs nearby then cascade
        if self.near[x, y] == 0:
            for n_x, n_y in self._neighbors(x, y):
            for n_x, n_y in self.neighbors(x, y):
                if self.bomb[n_x, n_y] == 0 and self.revealed[n_x, n_y] == 0:
                    self.reveal(n_x, n_y)

    def _neighbors(self, x: int, y: int) -> List[Tuple[int, int]]:
    def neighbors(
        self,
        x: int,
        y: int,
        radius: int = 1,
    ) -> List[Tuple[int, int]]:
        """Return list of all valid neighbors of `(x, y)`."""
        neighbors = []
        for new_x in [x - 1, x, x + 1]:
            for new_y in [y - 1, y, y + 1]:
        for off_x in range(-radius, radius + 1):
            new_x = x + off_x
            for off_y in range(-radius, radius + 1):
                new_y = y + off_y
                if 0 <= new_x < self.size and 0 <= new_y < self.size:
                    if not (new_x == x and new_y == y):
                        neighbors.append((new_x, new_y))

M sweep_ai/window.py => sweep_ai/window.py +8 -7
@@ 1,12 1,12 @@
"""Window handling module."""
import sys
from typing import Optional, Tuple
from typing import Optional, Tuple, Dict

import pygame
import pygame_menu

from .logic import State
from . import ai
from .ai import Player

# pylint: disable=invalid-name



@@ 40,6 40,7 @@ class Game:
            (self.display_width, self.display_height),
        )
        self.events = []
        self.players: Dict[int, Player] = {}

        self.sprites = {}
        self.sprites['flag'] = pygame.image.load('assets/flag.png')


@@ 173,9 174,11 @@ class Game:

    def get_hint(self):
        """Highlight the three safest."""
        self.hint = ai.get_hint(self.state)
        if self.hint is None:
            print('NO!')
        if self.players.get(self.size) is None:
            player = Player(self.size)
            player.train()
            self.players[self.size] = player
        # self.hint = self.players[self.size].move()

    def within_board(self, pos_x: float, pos_y: float) -> bool:
        """Returns `true` if `pos_x`, `pos_y` is within the board."""


@@ 250,8 253,6 @@ class Game:

        x = int((pos_x - self.border) / self.grid_s)
        y = int((pos_y - self.border) / self.grid_s)
        print(x, y)
        print(self.state.near)
        if event.button == 1:
            self.hint = None
            self.state.click(x, y)

A tests/test_ai.py => tests/test_ai.py +15 -0
@@ 0,0 1,15 @@
"""Module for testing core game logic."""
import numpy as np

from sweep_ai.ai import Player
from sweep_ai.logic import State


def test_player():
    player = Player(4)
    data = player.produce_training_data(
        1,
        State(4, bomb_positions=[(0, 0)]),
    )
    print(data)
    assert np.allclose(data, np.array([[0] * 24] * 4))

M tests/test_logic.py => tests/test_logic.py +12 -3
@@ 35,9 35,9 @@ def test_bombs():

def test_neighbors():
    state = logic.State(4)
    assert state._neighbors(0, 0) == [(0, 1), (1, 0), (1, 1)]
    assert state._neighbors(3, 3) == [(2, 2), (2, 3), (3, 2)]
    assert state._neighbors(1, 2) == [
    assert state.neighbors(0, 0) == [(0, 1), (1, 0), (1, 1)]
    assert state.neighbors(3, 3) == [(2, 2), (2, 3), (3, 2)]
    assert state.neighbors(1, 2) == [
        (0, 1),
        (0, 2),
        (0, 3),


@@ 248,3 248,12 @@ def test_move():
            [0, 0, 0, 1],
            [0, 0, 0, 0],
        ]))


def test_cheat():
    state = logic.State(4, bomb_positions=[(0, 0), (1, 1), (3, 3)])
    while state.won is None:
        cheat = state.cheat()
        assert not state.bomb[cheat] and not state.revealed[cheat]
        state.click(*cheat)
    assert state.won is True