Merge 244cf54 into 7daf72a

axelrod/__init__.py

@@ -10,6 +10,7 @@
 from axelrod.game import DefaultGame, Game
 from axelrod.history import History, LimitedHistory
 from axelrod.player import is_basic, obey_axelrod, Player
+from axelrod.evolvable_player import EvolvablePlayer
 from axelrod.mock_player import MockPlayer
 from axelrod.match import Match
 from axelrod.moran import MoranProcess, ApproximateMoranProcess

axelrod/evolvable_player.py

@@ -0,0 +1,85 @@
+from pickle import dumps, loads
+from random import randrange
+from typing import Dict, List
+from .player import Player
+
+
+class InsufficientParametersError(Exception):
+    """Error indicating that insufficient parameters were specified to initialize an Evolvable Player."""
+    def __init__(self, *args):
+        super().__init__(*args)
+
+
+class EvolvablePlayer(Player):
+    """A class for a player that can evolve, for use in the Moran process or with reinforcement learning algorithms.
+
+    This is an abstract base class, not intended to be used directly.
+    """
+
+    name = "EvolvablePlayer"
+    parent_class = Player
+    parent_kwargs = []  # type: List[str]
+
+    def overwrite_init_kwargs(self, **kwargs):
+        """Use to overwrite parameters for proper cloning and testing."""
+        for k, v in kwargs.items():
+            self.init_kwargs[k] = v
+
+    def create_new(self, **kwargs):
+        """Creates a new variant with parameters overwritten by kwargs."""
+        init_kwargs = self.init_kwargs.copy()
+        init_kwargs.update(kwargs)
+        return self.__class__(**init_kwargs)
+
+    # Serialization and deserialization. You may overwrite to obtain more human readable serializations
+    # but you must overwrite both.
+
+    def serialize_parameters(self):
+        """Serialize parameters."""
+        return dumps(self.init_kwargs)
+
+    @classmethod
+    def deserialize_parameters(cls, serialized):
+        """Deserialize parameters to a Player instance."""
+        init_kwargs = loads(serialized)
+        return cls(**init_kwargs)
+
+    # Optional methods for evolutionary algorithms and Moran processes.
+
+    def mutate(self):
+        """Optional method to allow Player to produce a variant (not in place)."""
+        pass  # pragma: no cover
+
+    def crossover(self, other):
+        """Optional method to allow Player to produce variants in combination with another player. Returns a new
+        Player."""
+        pass  # pragma: no cover
+
+    # Optional methods for particle swarm algorithm.
+
+    def receive_vector(self, vector):
+        """Receive a vector of params and overwrite the Player."""
+        pass  # pragma: no cover
+
+    def create_vector_bounds(self):
+        """Creates the bounds for the decision variables for Particle Swarm Algorithm."""
+        pass  # pragma: no cover
+
+
+def copy_lists(lists: List[List]) -> List[List]:
+    return list(map(list, lists))
+
+
+def crossover_lists(list1: List, list2: List) -> List:
+    cross_point = randrange(len(list1))
+    new_list = list(list1[:cross_point]) + list(list2[cross_point:])
+    return new_list
+
+
+def crossover_dictionaries(table1: Dict, table2: Dict) -> Dict:
+    keys = list(table1.keys())
+    cross_point = randrange(len(keys))
+    new_items = [(k, table1[k]) for k in keys[:cross_point]]
+    new_items += [(k, table2[k]) for k in keys[cross_point:]]
+    new_table = dict(new_items)
+    return new_table

axelrod/fingerprint.py

@@ -1,10 +1,8 @@
-import csv
 import os
 from collections import namedtuple
 from tempfile import mkstemp
 from typing import Any, List, Union
 
-import dask as da
 import dask.dataframe as dd
 import matplotlib.pyplot as plt
 import numpy as np

axelrod/graph.py

@@ -66,7 +66,9 @@ def _add_edges(self, edges):
             self._add_edge(*edge)
 
     def add_loops(self):
-        """Add all loops to edges."""
+        """
+        Add all loops to edges
+        """
         self._add_edges((x, x) for x in self.vertices)
 
     @property
@@ -120,25 +122,42 @@ def cycle(length, directed=False):
     return Graph(edges=edges, directed=directed)
 
 
-def complete_graph(size, loops=True):
-    """ Produces a complete graph of specificies size.
-
-    See https://en.wikipedia.org/wiki/Complete_graph for details.
+def complete_graph(size, loops=True, directed=False):
+    """
+    Produces a complete graph of size `length`.
+    https://en.wikipedia.org/wiki/Complete_graph
 
     Parameters
     ----------
     size: int
         Number of vertices in the cycle
     loops: bool, True
-        Should the graph contain cycles?
+        attach loops at each node?
+    directed: bool, False
+        Is the graph directed?
 
     Returns
     -------
     a Graph object for the complete graph
     """
     edges = [(i, j) for i in range(size) for j in range(i + 1, size)]
-    graph = Graph(edges=edges, directed=False)
+    graph = Graph(directed=directed, edges=edges)
+    if loops:
+        graph.add_loops()
+    return graph
+
 
+def attached_complete_graphs(length, loops=True, directed=False):
+    edges = []
+    # Two complete graphs
+    for cluster in range(2):
+        for i in range(length):
+            for j in range(i + 1, length):
+                edges.append(("{}:{}".format(cluster, i),
+                              "{}:{}".format(cluster, j)))
+    # Attach at one node
+    edges.append(("0:0", "1:0"))
+    graph = Graph(directed=directed, edges=edges)
     if loops:
         graph.add_loops()
 

axelrod/moran.py

@@ -6,7 +6,7 @@
 
 import matplotlib.pyplot as plt
 import numpy as np
-from axelrod import DEFAULT_TURNS, Game, Player
+from axelrod import EvolvablePlayer, DEFAULT_TURNS, Game, Player
 
 from .deterministic_cache import DeterministicCache
 from .graph import Graph, complete_graph
@@ -56,6 +56,8 @@ def __init__(
         interaction_graph: Graph = None,
         reproduction_graph: Graph = None,
         fitness_transformation: Callable = None,
+        mutation_method="transition",
+        stop_on_fixation=True
     ) -> None:
         """
         An agent based Moran process class. In each round, each player plays a
@@ -108,6 +110,11 @@ def __init__(
             given
         fitness_transformation:
             A function mapping a score to a (non-negative) float
+        mutation_method:
+            A string indicating if the mutation method should be between original types ("transition")
+            or based on the player's mutation method, if present ("atomic").
+        stop_on_fixation:
+            A bool indicating if the process should stop on fixation
         """
         self.turns = turns
         self.prob_end = prob_end
@@ -120,6 +127,12 @@ def __init__(
         self.score_history = []  # type: List
         self.winning_strategy_name = None  # type: Optional[str]
         self.mutation_rate = mutation_rate
+        self.stop_on_fixation = stop_on_fixation
+        m = mutation_method.lower()
+        if m in ["atomic", "transition"]:
+            self.mutation_method = m
+        else:
+            raise ValueError("Invalid mutation method {}".format(mutation_method))
         assert (mutation_rate >= 0) and (mutation_rate <= 1)
         assert (noise >= 0) and (noise <= 1)
         mode = mode.lower()
@@ -159,6 +172,7 @@ def __init__(
         # Map players to graph vertices
         self.locations = sorted(interaction_graph.vertices)
         self.index = dict(zip(sorted(interaction_graph.vertices), range(len(players))))
+        self.fixated = self.fixation_check()
 
     def set_players(self) -> None:
         """Copy the initial players into the first population."""
@@ -176,17 +190,23 @@ def mutate(self, index: int) -> Player:
         index:
             The index of the player to be mutated
         """
-        # Choose another strategy at random from the initial population
-        r = random.random()
-        if r < self.mutation_rate:
-            s = str(self.players[index])
-            j = randrange(0, len(self.mutation_targets[s]))
-            p = self.mutation_targets[s][j]
-            new_player = p.clone()
-        else:
-            # Just clone the player
-            new_player = self.players[index].clone()
-        return new_player
+
+        if self.mutation_method == "atomic":
+            if not issubclass(self.players[index].__class__, EvolvablePlayer):
+                raise TypeError("Player is not evolvable. Use a subclass of EvolvablePlayer.")
+            return self.players[index].mutate()
+
+        # Assuming mutation_method == "transition"
+        if self.mutation_rate > 0:
+            # Choose another strategy at random from the initial population
+            r = random.random()
+            if r < self.mutation_rate:
+                s = str(self.players[index])
+                j = randrange(0, len(self.mutation_targets[s]))
+                p = self.mutation_targets[s][j]
+                return p.clone()
+        # Just clone the player
+        return self.players[index].clone()
 
     def death(self, index: int = None) -> int:
         """
@@ -250,14 +270,13 @@ def fixation_check(self) -> bool:
         Boolean:
             True if fixation has occurred (population all of a single type)
         """
-        if self.mutation_rate > 0:
-            return False
         classes = set(str(p) for p in self.players)
+        self.fixated = False
         if len(classes) == 1:
             # Set the winning strategy name variable
             self.winning_strategy_name = str(self.players[0])
-            return True
-        return False
+            self.fixated = True
+        return self.fixated
 
     def __next__(self) -> object:
         """
@@ -275,7 +294,7 @@ def __next__(self) -> object:
             Returns itself with a new population
         """
         # Check the exit condition, that all players are of the same type.
-        if self.fixation_check():
+        if self.stop_on_fixation and self.fixation_check():
             raise StopIteration
         if self.mode == "bd":
             # Birth then death
@@ -286,16 +305,10 @@ def __next__(self) -> object:
             i = self.death()
             self.players[i] = None
             j = self.birth(i)
-        # Mutate
-        if self.mutation_rate:
-            new_player = self.mutate(j)
-        else:
-            new_player = self.players[j].clone()
-        # Replace player i with clone of player j
-        self.players[i] = new_player
+        # Mutate and/or replace player i with clone of player j
+        self.players[i] = self.mutate(j)
+        # Record population.
         self.populations.append(self.population_distribution())
-        # Check again for fixation
-        self.fixation_check()
         return self
 
     def _matchup_indices(self) -> Set[Tuple[int, int]]:
@@ -395,10 +408,10 @@ def play(self) -> List[Counter]:
          populations:
             Returns a list of all the populations
         """
-        if self.mutation_rate != 0:
+        if not self.stop_on_fixation or self.mutation_rate != 0:
             raise ValueError(
                 "MoranProcess.play() will never exit if mutation_rate is"
-                "nonzero. Use iteration instead."
+                "nonzero or stop_on_fixation is False. Use iteration instead."
             )
         while True:
             try:

axelrod/player.py

@@ -77,7 +77,6 @@ class Player(object):
         "manipulates_state": None,
     }
 
-    # def __new__(cls, *args, history=None, **kwargs):
     def __new__(cls, *args, **kwargs):
         """Caches arguments for Player cloning."""
         obj = super().__new__(cls)

axelrod/random_.py

@@ -1,11 +1,19 @@
 import random
 
-import numpy
+import numpy as np
+from numpy.random import choice
+
 from axelrod.action import Action
 
 C, D = Action.C, Action.D
 
 
+def seed(seed_):
+    """Sets a seed"""
+    random.seed(seed_)
+    np.random.seed(seed_)
+
+
 def random_choice(p: float = 0.5) -> Action:
     """
     Return C with probability `p`, else return D
@@ -63,10 +71,10 @@ def randrange(a: int, b: int) -> int:
     return a + int(r)
 
 
-def seed(seed_):
-    """Sets a seed"""
-    random.seed(seed_)
-    numpy.random.seed(seed_)
+def random_vector(size):
+    """Create a random vector of values in [0, 1] that sums to 1."""
+    vector = np.random.random(size)
+    return vector / np.sum(vector)
 
 
 class Pdf(object):
@@ -81,7 +89,7 @@ def __init__(self, counter):
 
     def sample(self):
         """Sample from the pdf"""
-        index = numpy.random.choice(a=range(self.size), p=self.probability)
+        index = choice(a=range(self.size), p=self.probability)
         # Numpy cannot sample from a list of n dimensional objects for n > 1,
         # need to sample an index.
         return self.sample_space[index]

axelrod/result_set.py

@@ -1,18 +1,16 @@
+from collections import Counter, namedtuple
 import csv
 import itertools
-from collections import Counter, namedtuple
 from multiprocessing import cpu_count
 
-import axelrod.interaction_utils as iu
 import numpy as np
 import tqdm
-from axelrod.action import Action, str_to_actions
+from axelrod.action import Action
 
 import dask as da
 import dask.dataframe as dd
 
 from . import eigen
-from .game import Game
 
 C, D = Action.C, Action.D
 
@@ -416,7 +414,6 @@ def _build_normalised_cooperation(self):
 
     @update_progress_bar
     def _build_initial_cooperation_rate(self, interactions_series):
-        interactions_dict = interactions_series.to_dict()
         interactions_array = np.array(
             [
                 interactions_series.get(player_index, 0)