Merge pull request #7 from tsukuba-websci/junya/update/ga-io

Feature: GAの出力形式の修正

ga/.gitignore

@@ -9,5 +9,5 @@ results/aps/**/*
 results/twitter/**/*
 !results/twitter/.gitkeep
 
-results/synthetic/**/*
-!results/synthetic/.gitkeep
+results/synthetic_target/**/*
+!results/synthetic_target/.gitkeep

ga/ga.py

@@ -1,3 +1,4 @@
+import csv
 import logging
 from typing import Any, List, Tuple
 
@@ -12,27 +13,34 @@ class GA:
     def __init__(
         self,
         population_size: int,
-        rate: float,
+        mutation_rate: float,
         cross_rate: float,
         target: History2VecResult,
+        target_data: str,
+        num_generations: int,
         jl_main: Any,
         thread_num: int,
+        archive_dir: str,
         min_val: float = -1.0,
         max_val: float = 1.0,
         debug: bool = True,
+        is_grid_search: bool = False,
     ) -> None:
         self.population_size = population_size
         self.min_val = min_val
         self.max_val = max_val
-        self.rate = rate
+        self.mutation_rate = mutation_rate
         self.cross_rate = cross_rate
-        self.num_generations = 500
+        self.num_generations = num_generations
 
         self.target = target
+        self.target_data = target_data
         self.jl_main = jl_main
         self.thread_num = thread_num
         self.histories = [[] for _ in range(self.population_size)]
+        self.archives_dir = archive_dir
         self.debug = debug
+        self.is_grid_search = is_grid_search
 
     def tovec(self, history: List[Tuple[int, int]], interval_num: int) -> History2VecResult:
         """相互やり取りの履歴を10個の指標に変換する．
@@ -72,7 +80,7 @@ def selection(self, population: list, fitness: list) -> list:
         """ルーレット選択．適応度に比例した確率で個体を選択し，親個体にする．この親個体を用いて交叉を行う．
 
         Args:
-            population (list): 各個体のパラメータ (rho, nu, recentness, friendship) のリスト
+            population (list): 各個体のパラメータ (rho, nu, recentness, frequency) のリスト
             fitness (list): 各個体の適応度
 
         Returns:
@@ -91,8 +99,8 @@ def crossover(self, parents1: list, parents2: list, children: list) -> list:
         """交叉．親のうちランダムに選んだものを交叉させる．
 
         Args:
-            parents1 (list): 親1 (rho, nu, recentness, friendship) のリスト
-            parents2 (list): 親2 (rho, nu, recentness, friendship) のリスト
+            parents1 (list): 親1 (rho, nu, recentness, frequency) のリスト
+            parents2 (list): 親2 (rho, nu, recentness, frequency) のリスト
             children (list): 子のリスト
 
         Returns:
@@ -115,25 +123,40 @@ def mutation(self, children: list) -> list:
             children (list): 子のリスト
         """
         for i in range(self.population_size):
-            if np.random.rand() < self.rate:
+            if np.random.rand() < self.mutation_rate:
                 idx = np.random.randint(4)
                 children[i][idx] = np.random.uniform(low=self.min_val, high=self.max_val)
         return children
 
+    def dump_population(self, population: list, generation: int, fitness: list) -> None:
+        """個体群をファイルに出力する．
+
+        Args:
+            population (list): 個体群
+            generation (int): 世代数
+            fitness (list): 適応度
+        """
+        fp = f"{self.archives_dir}/{str(generation).zfill(8)}.csv"
+        with open(fp, "w") as f:
+            writer = csv.writer(f)
+            writer.writerow(["rho", "nu", "recentness", "frequency", "objective"])
+            for individual, fit in zip(population, fitness):
+                writer.writerow([individual[0], individual[1], individual[2], individual[3], -1 * fit])
+
     def plot():
         return
 
     def run_init(self) -> list:
         """GAの初期個体群を生成する．
 
         Returns:
-            population (list): 初期個体群 (rho, nu, recentness, friendship) のリスト
+            population (list): 初期個体群 (rho, nu, recentness, frequency) のリスト
         """
         rho = np.random.uniform(low=0, high=30, size=self.population_size)
         nu = np.random.uniform(low=0, high=30, size=self.population_size)
         recentness = np.random.uniform(low=self.min_val, high=self.max_val, size=self.population_size)
-        friendship = np.random.uniform(low=self.min_val, high=self.max_val, size=self.population_size)
-        population = np.array([rho, nu, recentness, friendship]).T
+        frequency = np.random.uniform(low=self.min_val, high=self.max_val, size=self.population_size)
+        population = np.array([rho, nu, recentness, frequency]).T
         return population
 
     def run(self) -> Tuple[float, History2VecResult, list]:
@@ -167,7 +190,7 @@ def run(self) -> Tuple[float, History2VecResult, list]:
                     rho=population[i][0],
                     nu=population[i][1],
                     recentness=population[i][2],
-                    friendship=population[i][3],
+                    frequency=population[i][3],
                     steps=20000,
                 )
                 self.histories[i] = run_model(params)
@@ -202,6 +225,9 @@ def run(self) -> Tuple[float, History2VecResult, list]:
                 message = f"Generation {generation}: Best fitness = {best_fitness}, Best params = {best_params}, 10Metrics = {metrics}"
                 logging.info(message)
 
+            # 個体群の出力
+            self.dump_population(population, generation, fitness)
+
         # 適応度の最小値，ターゲット，最適解，10個の指標を返す
         arg = np.argmax(fitness)
         return (

ga/history2vec.py

@@ -7,7 +7,7 @@ class Params:
     rho: float
     nu: float
     recentness: float
-    friendship: float
+    frequency: float
     steps: int
 
 

ga/io_utils.py

@@ -1,5 +1,7 @@
 import argparse
+import csv
 import json
+import os
 
 
 def parse_args(parser: argparse.ArgumentParser) -> argparse.Namespace:
@@ -11,20 +13,43 @@ def parse_args(parser: argparse.ArgumentParser) -> argparse.Namespace:
     Returns:
         argparse.Namespace: コマンドライン引数のパース結果
     """
+
     parser.add_argument("population_size", type=int, help="個体数")
-    parser.add_argument("rate", type=float, help="突然変異率")
+    parser.add_argument("mutation_rate", type=float, help="突然変異率")
     parser.add_argument("cross_rate", type=float, help="交叉率")
     parser.add_argument(
         "target_data",
         type=str,
-        choices=["twitter", "aps", "synthetic_fitting_target"],
+        choices=["twitter", "aps", "synthetic"],
         help="ターゲットデータ",
     )
+    parser.add_argument("rho", type=int, nargs="?", default=None, help="rho")
+    parser.add_argument("nu", type=int, nargs="?", default=None, help="nu")
+    parser.add_argument("s", type=str, nargs="?", default=None, choices=["SSW", "WSW"], help="strategy")
+
     parser.add_argument("-p", "--prod", action="store_true", default=False, help="本番実行用フラグ．出力先を変更する．")
+    parser.add_argument("-f", "--force", action="store_true", default=False, help="既存のファイルを上書きする．")
     args = parser.parse_args()
     return args
 
 
+def export_individual(distance: float, individual: list, fpath: str) -> None:
+    """個体をCSVファイルに出力する．
+
+    Args:
+        distance (float): ターゲットとの距離
+        individual (list): 個体を表すタプル．(rho, nu, recentness, frequency)の順
+        fpath (str): 出力先のパス
+    """
+    header = ["rho", "nu", "recentness", "frequency", "objective"]
+    objective = distance
+    row = [*individual, objective]
+    with open(fpath, "w") as f:
+        writer = csv.writer(f)
+        writer.writerow(header)
+        writer.writerow(row)
+
+
 def dump_json(result: tuple, fpath: str) -> None:
     """GAの結果をJSONファイルに出力する．
 
@@ -38,7 +63,7 @@ def dump_json(result: tuple, fpath: str) -> None:
             "rho": result[2][0],
             "nu": result[2][1],
             "recentness": result[2][2],
-            "friendship": result[2][3],
+            "frequency": result[2][3],
         },
         "target": {},
         "result": {},
@@ -50,6 +75,18 @@ def dump_json(result: tuple, fpath: str) -> None:
     json.dump(res, open(fpath, "w"), indent=4)
 
 
+def pass_run(force, fpath) -> bool:
+    """GAの実行をスキップするかどうかを判定する．forceがFalseかつすでに結果が存在する場合にTrueを返す．
+
+    Args:
+        force (bool): 既存のファイルを上書きするかどうか
+        fpath (str): 出力先のパス
+    """
+    if force:
+        return False
+    return os.path.exists(fpath)
+
+
 def validate(population_size, rate, cross_rate) -> None:
     """GAのパラメータのバリデーション．