Merge pull request #1166 from Axelrod-Python/possible-v4

Rewrite the result set

Author: meatballs

.gitignore

@@ -4,7 +4,7 @@ cache.txt
 test.csv
 summary.csv
 basic_tournament.csv
-test_outputs/*csv
+test_outputs/*csv.summary
 test_outputs/*svg
 test_outputs/*cache
 
@@ -118,3 +118,12 @@ docker-compose.yml
 
 # Mypy files
 .mypy_cache/
+
+test_outputs/stochastic_tournament_0.csv
+test_outputs/stochastic_tournament_1.csv
+test_outputs/test_fingerprint.csv
+test_outputs/test_fingerprint_tmp.csv
+test_outputs/test_results_from_file.csv
+test_outputs/test_results_from_file_tmp.csv
+test_outputs/test_tournament.csv
+test_outputs/tran_fin.csv

axelrod/__init__.py

@@ -18,7 +18,7 @@
 from .deterministic_cache import DeterministicCache
 from .match_generator import *
 from .tournament import Tournament
-from .result_set import ResultSet, ResultSetFromFile
+from .result_set import ResultSet
 from .ecosystem import Ecosystem
 from .fingerprint import AshlockFingerprint, TransitiveFingerprint
 

axelrod/ecosystem.py

@@ -12,7 +12,7 @@ def __init__(self, results: ResultSet,
                  population: List[int] = None) -> None:
 
         self.results = results
-        self.nplayers = self.results.nplayers
+        self.num_players = self.results.num_players
         self.payoff_matrix = self.results.payoff_matrix
         self.payoff_stddevs = self.results.payoff_stddevs
 
@@ -27,15 +27,15 @@ def __init__(self, results: ResultSet,
             if min(population) < 0:
                 raise TypeError(
                     "Minimum value of population vector must be non-negative")
-            elif len(population) != self.nplayers:
+            elif len(population) != self.num_players:
                 raise TypeError(
                     "Population vector must be same size as number of players")
             else:
                 norm = sum(population)
                 self.population_sizes = [[p / norm for p in population]]
         else:
             self.population_sizes = [
-                [1 / self.nplayers for _ in range(self.nplayers)]]
+                [1 / self.num_players for _ in range(self.num_players)]]
 
         # This function is quite arbitrary and probably only influences the
         # kinetics for the current code.
@@ -47,7 +47,7 @@ def __init__(self, results: ResultSet,
     def reproduce(self, turns: int):
 
         for iturn in range(turns):
-            plist = list(range(self.nplayers))
+            plist = list(range(self.num_players))
             pops = self.population_sizes[-1]
 
             # The unit payoff for each player in this turn is the sum of the

axelrod/fingerprint.py

@@ -6,6 +6,8 @@
 import matplotlib.pyplot as plt
 import numpy as np
 import tqdm
+import dask.dataframe as dd
+import dask as da
 from mpl_toolkits.axes_grid1 import make_axes_locatable
 
 import axelrod as axl
@@ -266,7 +268,7 @@ def construct_tournament_elements(self, step: float,
 
     def fingerprint(
         self, turns: int = 50, repetitions: int = 10, step: float = 0.01,
-        processes: int=None, filename: str = None, in_memory: bool = False,
+        processes: int=None, filename: str = None,
         progress_bar: bool = True
 ) -> dict:
         """Build and play the spatial tournament.
@@ -290,10 +292,7 @@ def fingerprint(
             The number of processes to be used for parallel processing
         filename: str, optional
             The name of the file for self.spatial_tournament's interactions.
-            if None and in_memory=False, will auto-generate a filename.
-        in_memory: bool
-            Whether self.spatial_tournament keeps interactions_dict in memory or
-            in a file.
+            if None, will auto-generate a filename.
         progress_bar : bool
             Whether or not to create a progress bar which will be updated
 
@@ -305,7 +304,7 @@ def fingerprint(
         """
 
         temp_file_descriptor = None
-        if not in_memory and filename is None:
+        if filename is None:
             temp_file_descriptor, filename = mkstemp()
 
         edges, tourn_players = self.construct_tournament_elements(
@@ -318,13 +317,10 @@ def fingerprint(
         self.spatial_tournament.play(build_results=False,
                                      filename=filename,
                                      processes=processes,
-                                     in_memory=in_memory,
                                      progress_bar=progress_bar)
-        if in_memory:
-            self.interactions = self.spatial_tournament.interactions_dict
-        else:
-            self.interactions = read_interactions_from_file(
-                filename, progress_bar=progress_bar)
+
+        self.interactions = read_interactions_from_file(
+            filename, progress_bar=progress_bar)
 
         if temp_file_descriptor is not None:
             os.close(temp_file_descriptor)
@@ -483,17 +479,21 @@ def analyse_cooperation_ratio(filename):
             opponent in each turn. The ith row corresponds to the ith opponent
             and the jth column the jth turn.
         """
-        did_c = np.vectorize(lambda action: int(action == 'C'))
+        did_c = np.vectorize(lambda actions: [int(action == 'C')
+                                              for action in actions])
 
         cooperation_rates = {}
-        with open(filename, "r") as f:
-            reader = csv.reader(f)
-            for row in reader:
-                opponent_index, player_history = int(row[1]), list(row[4])
-                if opponent_index in cooperation_rates:
-                    cooperation_rates[opponent_index].append(did_c(player_history))
-                else:
-                    cooperation_rates[opponent_index] = [did_c(player_history)]
+        df = dd.read_csv(filename)
+        # We ignore the actions of all opponents. So we filter the dataframe to
+        # only include the results of the player with index `0`.
+        df = df[df["Player index"] == 0][["Opponent index", "Actions"]]
+
+        for _, row in df.iterrows():
+            opponent_index, player_history = row["Opponent index"], row["Actions"]
+            if opponent_index in cooperation_rates:
+                cooperation_rates[opponent_index].append(did_c(player_history))
+            else:
+                cooperation_rates[opponent_index] = [did_c(player_history)]
 
         for index, rates in cooperation_rates.items():
             cooperation_rates[index] = np.mean(rates, axis=0)

axelrod/interaction_utils.py

@@ -7,9 +7,10 @@
 This is used by both the Match class and the ResultSet class which analyse
 interactions.
 """
-from collections import Counter
+from collections import Counter, defaultdict
 import csv
 import tqdm
+import pandas as pd
 
 from axelrod.action import Action, str_to_actions
 from .game import Game
@@ -239,36 +240,22 @@ def compute_sparklines(interactions, c_symbol='█', d_symbol=' '):
         sparkline(histories[1], c_symbol, d_symbol))
 
 
-def read_interactions_from_file(filename, progress_bar=True,
-                                num_interactions=False):
+def read_interactions_from_file(filename, progress_bar=True):
     """
     Reads a file and returns a dictionary mapping tuples of player pairs to
     lists of interactions
     """
+    df = pd.read_csv(filename)[["Interaction index", "Player index",
+                                "Opponent index", "Actions"]]
+    groupby = df.groupby("Interaction index")
     if progress_bar:
-        if not num_interactions:
-            with open(filename) as f:
-                num_interactions = sum(1 for line in f)
-        progress_bar = tqdm.tqdm(total=num_interactions, desc="Loading")
-
-    pairs_to_interactions = {}
-    with open(filename, 'r') as f:
-        for row in csv.reader(f):
-            index_pair = (int(row[0]), int(row[1]))
-            p1_actions = str_to_actions(row[4])
-            p2_actions = str_to_actions(row[5])
-            interaction = list(zip(p1_actions, p2_actions))
-
-            try:
-                pairs_to_interactions[index_pair].append(interaction)
-            except KeyError:
-                pairs_to_interactions[index_pair] = [interaction]
-
-            if progress_bar:
-                progress_bar.update()
+        groupby = tqdm.tqdm(groupby)
 
-    if progress_bar:
-        progress_bar.close()
+    pairs_to_interactions = defaultdict(list)
+    for _, d in tqdm.tqdm(groupby):
+        key = tuple(d[["Player index", "Opponent index"]].iloc[0])
+        value = list(map(str_to_actions, zip(*d["Actions"])))
+        pairs_to_interactions[key].append(value)
     return pairs_to_interactions
 
 

axelrod/plot.py

@@ -25,7 +25,7 @@ def default_cmap(version: str = "2.0") -> str:
 class Plot(object):
     def __init__(self, result_set: ResultSet) -> None:
         self.result_set = result_set
-        self.nplayers = self.result_set.nplayers
+        self.num_players = self.result_set.num_players
         self.players = self.result_set.players
 
     def _violinplot(
@@ -40,16 +40,16 @@ def _violinplot(
             ax = ax
 
         figure = ax.get_figure()
-        width = max(self.nplayers / 3, 12)
+        width = max(self.num_players / 3, 12)
         height = width / 2
         spacing = 4
-        positions = spacing * arange(1, self.nplayers + 1, 1)
+        positions = spacing * arange(1, self.num_players + 1, 1)
         figure.set_size_inches(width, height)
         ax.violinplot(data, positions=positions, widths=spacing / 2,
                       showmedians=True, showextrema=False)
         ax.set_xticks(positions)
         ax.set_xticklabels(names, rotation=90)
-        ax.set_xlim([0, spacing * (self.nplayers + 1)])
+        ax.set_xlim([0, spacing * (self.num_players + 1)])
         ax.tick_params(axis='both', which='both', labelsize=8)
         if title:
             ax.set_title(title)
@@ -175,14 +175,14 @@ def _payoff_heatmap(
             ax = ax
 
         figure = ax.get_figure()
-        width = max(self.nplayers / 4, 12)
+        width = max(self.num_players / 4, 12)
         height = width
         figure.set_size_inches(width, height)
         matplotlib_version = matplotlib.__version__
         cmap = default_cmap(matplotlib_version)
         mat = ax.matshow(data, cmap=cmap)
-        ax.set_xticks(range(self.result_set.nplayers))
-        ax.set_yticks(range(self.result_set.nplayers))
+        ax.set_xticks(range(self.result_set.num_players))
+        ax.set_yticks(range(self.result_set.num_players))
         ax.set_xticklabels(names, rotation=90)
         ax.set_yticklabels(names)
         ax.tick_params(axis='both', which='both', labelsize=16)
@@ -246,7 +246,7 @@ def stackplot(
         ticks = []
         for i, n in enumerate(self.result_set.ranked_names):
             x = -0.01
-            y = (i + 0.5) * 1 / self.result_set.nplayers
+            y = (i + 0.5) * 1 / self.result_set.num_players
             ax.annotate(
                 n, xy=(x, y), xycoords=trans, clip_on=False, va='center',
                 ha='right', fontsize=5)