Prepare repo for PyPI release (#5)

* Add features to make the package more usable

* Prepare package for PyPI release

* Add Travis CI

* Add pip requirements

* Add Travis CI badge

* Fix setup.py

* Add PyPI to README

.travis.yml

@@ -0,0 +1,6 @@
+language: python
+python:
+  - "3.6"
+install:
+  - pip install -r requirements.txt
+script: pytest

README.md

@@ -1,11 +1,18 @@
 # Literature
+![Travis CI](https://travis-ci.org/neelsomani/literature.svg?branch=master)
+
 Literature card game implementation: https://en.wikipedia.org/wiki/Literature_(card_game)
 
+## Setup
+
+Install with `pip install literature`.
+
 Start with `python3 -i literature.py`. Built for Python 3.6.0.
 
 See how to train a model to play against with `python3 learning.py -h`. Play against a model that I trained with `learning.play_against_model('model_10000.out')`.
 
-Current limitations:
+## Limitations
+
 * The bots only consider asking for a `Card` that they know a `Player` does not possess in the case that there are no other possible `Moves`. I made this simplification because the initial training took too long otherwise.
 * The `Players` consider what other `Players` know about them, but they don't consider any levels further than that, e.g., the `Players` don't consider what other `Players` know that other `Players` know about them.
   * There is definitely information there that is not represented in the current state. For example, player 0 might have all of the minor hearts except for the 5 of hearts. If player 1 asks for a minor hearts, player 0 knows that player 1 does not know that player 0 knows that player 1 has the 5 of hearts. Alternately, if player 0 had previously revealed that they have every other minor heart, then player 0 would know that player 1 knows that player 0 knows what card they have.

literature/__init__.py

@@ -0,0 +1,13 @@
+from literature.actor import Actor
+from literature.card import Card, deserialize, HalfSuit, Rank, Suit
+from literature.constants import *
+from literature.knowledge import ConcreteKnowledge, Knowledge
+from literature.learning import (
+    GameHandler,
+    Model,
+    model_from_file,
+    play_against_model
+)
+from literature.literature import get_game, Literature, Team
+from literature.move import Move, Request
+from literature.player import Player

actor.py → literature/actor.py

@@ -6,7 +6,7 @@
 from functools import total_ordering
 from typing import Union, Set
 
-from card import Card
+from literature.card import Card
 
 
 @total_ordering

card.py → literature/card.py

@@ -13,7 +13,7 @@
     Union
 )
 
-from constants import (
+from literature.constants import (
     Half,
     MINOR,
     MAJOR,

constants.py → literature/constants.py

@@ -28,8 +28,8 @@ def __lt__(self, other):
 }
 
 # Convenience constants
-MINOR = SETS[Half.MINOR]
-MAJOR = SETS[Half.MAJOR]
+MINOR: Set[int] = SETS[Half.MINOR]
+MAJOR: Set[int] = SETS[Half.MAJOR]
 
 RANK_NAMES = {i: str(i) for i in range(2, 11)}
 RANK_NAMES[1] = "A"

knowledge.py → literature/knowledge.py

@@ -2,8 +2,8 @@
 Classes to express whether `Players` have or don't have particular `Cards`.
 """
 
-from card import Card
-from move import Actor
+from literature.card import Card
+from literature.move import Actor
 
 
 class ConcreteKnowledge:

learning.py → literature/learning.py

@@ -13,12 +13,12 @@
 import numpy as np
 from sklearn.neural_network import MLPRegressor
 
-from actor import Actor
-from card import Card, Suit
-from constants import Half, SETS
-from literature import get_game, Team
-from move import Move
-from player import Player
+from literature.actor import Actor
+from literature.card import Card, Suit
+from literature.constants import Half, SETS
+from literature.literature import get_game, Team
+from literature.move import Move
+from literature.player import Player
 
 # The number of elements in the serialized move
 MOVE_LENGTH = 1149

literature.py → literature/literature.py

@@ -57,19 +57,18 @@
 from typing import Callable, Dict, List
 import random
 
-from actor import Actor
-from card import (
+from literature.actor import Actor
+from literature.card import (
     Card,
     deserialize,
     get_hands,
     HalfSuit,
-    Rank,
     Suit
 )
-from constants import Half, SETS
-from move import Move
-from player import Player
-from util import PrintableDict
+from literature.constants import Half, SETS
+from literature.move import Move
+from literature.player import Player
+from literature.util import PrintableDict
 
 
 class Team(Enum):
@@ -107,14 +106,16 @@ def __init__(self,
         hands = hands_fn(n_players)
         self.players = [Player(i, hands[i], n_players)
                         for i in range(n_players)]
-
         self.turn: Player = self.players[int(turn_picker() * n_players)]
         self.logger = logging.getLogger(__name__)
         # `self.claims` maps `HalfSuits` to the `Team` who successfully made
         # the claim.
         self.claims = PrintableDict({
             HalfSuit(h, s): Team.NEITHER for h in Half for s in Suit
         })
+        # `self.actual_possessions` saves the correct mapping of cards for
+        # a `HalfSuit`
+        self.actual_possessions: Dict[HalfSuit, Dict[Card.Name, Actor]] = {}
         self.move_ledger: List[Move] = []
         self.move_success: List[bool] = []
 
@@ -226,10 +227,13 @@ def commit_claim(self,
         claimed = set()
         _random_key = list(possessions.keys())[0]
         half_suit = _random_key.half_suit()
+        if half_suit in self.actual_possessions:
+            raise ValueError('{} has already been claimed'.format(half_suit))
 
         # Once a claim is submitted, all players must show the cards they
         # have for that half suit
         actual = self._claim_for_half_suit(half_suit)
+        self.actual_possessions[half_suit] = actual
         for p in self.players:
             p.memorize_claim(actual)
 

move.py → literature/move.py

@@ -4,9 +4,9 @@
 
 from typing import List
 
-from actor import Actor
-from card import Card
-from constants import SETS
+from literature.actor import Actor
+from literature.card import Card
+from literature.constants import SETS
 
 
 class Move:

player.py → literature/player.py

@@ -12,24 +12,24 @@
     Union
 )
 
-from actor import Actor
-from card import (
+from literature.actor import Actor
+from literature.card import (
     Card,
     HalfSuit,
     Suit
 )
-from constants import (
+from literature.constants import (
     Half,
     MINOR,
     MAJOR,
     SETS
 )
-from knowledge import (
+from literature.knowledge import (
     ConcreteKnowledge,
     Knowledge
 )
-from move import Move, Request
-from util import PrintableDict
+from literature.move import Move, Request
+from literature.util import PrintableDict
 
 
 class Player(Actor):
@@ -100,6 +100,10 @@ def __init__(self,
             self._memorize(Knowledge.that(self).has(card))
             self.suit_knowledge[self][card.half_suit()] += 1
 
+    def unclaimed_cards(self) -> int:
+        """ Return the number of unclaimed cards this Player has. """
+        return len([c for c in self.hand if c.half_suit() not in self.claims])
+
     def hand_to_dict(self) -> PrintableDict:
         """ Get a `PrintableDict` of this `Player`'s hand. """
         suits: Dict[Suit, List[Card.Name]] = {s: [] for s in Suit}

test_game.py → literature/test_game.py

@@ -4,15 +4,15 @@
 
 import pytest
 
-from actor import Actor
-from card import (
+from literature.actor import Actor
+from literature.card import (
     Card,
     Suit,
     HalfSuit,
     Half
 )
-from constants import SETS
-from literature import Literature, Team
+from literature.constants import SETS
+from literature.literature import Literature, Team
 
 MISSING_CARD = Card.Name(3, Suit.CLUBS)
 
@@ -49,6 +49,8 @@ def test_turn_change(game):
     c = claims_1.pop(HalfSuit(Half.MAJOR, Suit.DIAMONDS))
     game.commit_claim(Actor(1), c)
     assert game.turn == Actor(1)
+    with pytest.raises(ValueError):
+        game.commit_claim(Actor(1), c)
 
 
 def test_end_game_condition(game):

test_player.py → literature/test_player.py

@@ -4,15 +4,15 @@
 
 import pytest
 
-from actor import Actor
-from card import (
+from literature.actor import Actor
+from literature.card import (
     Card,
     Suit,
     HalfSuit,
     Half
 )
-from constants import SETS
-from literature import Literature, Team
+from literature.constants import SETS
+from literature.literature import Literature, Team
 
 
 def get_mock_hands(_: int) -> List[List[Card.Name]]:

setup.cfg

@@ -0,0 +1,2 @@
+[metadata]
+description-file = README.md

setup.py

@@ -0,0 +1,31 @@
+from distutils.core import setup
+setup(
+    name='literature',
+    packages=['literature'],
+    version='0.1.0',
+    license='MIT',
+    description='Literature card game implementation',
+    author='Neel Somani',
+    author_email='neeljaysomani@gmail.com',
+    url='https://github.com/neelsomani/literature',
+    download_url='https://github.com/neelsomani/literature/releases',
+    keywords=[
+        'machine-learning',
+        'q-learning',
+        'neural-network',
+        'artificial-intelligence',
+        'card-game'
+    ],
+    install_requires=[
+        'numpy==1.17.0',
+        'pytest==5.0.1',
+        'scikit-learn==0.21.3'
+    ],
+    classifiers=[
+        'Development Status :: 5 - Production/Stable',
+        'Intended Audience :: Developers',
+        'Topic :: Games/Entertainment',
+        'License :: OSI Approved :: MIT License',
+        'Programming Language :: Python :: 3.6'
+    ],
+)