seeds

neuralplayground/agents/domine_2023_seed.py

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+
+import argparse
+import os
+import time
+os.environ["KMP_DUPLICATE_LIB_OK"] = "True"
+from neuralplayground.agents.domine_2023_extras.class_grid_run_config import GridConfig
+
+from neuralplayground.agents import Domine2023
+from neuralplayground.agents.domine_2023_extras.class_utils import (
+    rng_sequence_from_rng,
+    set_device,
+)
+
+# @title Graph net functions
+parser = argparse.ArgumentParser()
+parser.add_argument(
+    "--config_path",
+    metavar="-C",
+    default="domine_2023_extras/Different_seed_configs/class_config_1.yaml",
+    help="path to base configuration file.",
+)
+
+args = parser.parse_args()
+set_device()
+config_class = GridConfig
+config = config_class(args.config_path)
+
+# Init environment
+arena_x_limits = [-100, 100]
+arena_y_limits = [-100, 100]
+
+
+agent_1 = Domine2023(
+    experiment_name=config.experiment_name,
+    train_on_shortest_path=config.train_on_shortest_path,
+    resample=config.resample,  # @param
+    wandb_on=config.wandb_on,
+    seed=config.seed,
+    feature_position=config.feature_position,
+    weighted=config.weighted,
+    num_hidden=config.num_hidden,  # @param
+    num_layers=config.num_layers,  # @param
+    num_message_passing_steps=config.num_message_passing_steps,  # @param
+    learning_rate=config.learning_rate,  # @param
+    num_training_steps=config.num_training_steps,  # @param
+    batch_size=config.batch_size,
+    nx_min=config.nx_min,
+    nx_max=config.nx_max,
+    batch_size_test=config.batch_size_test,
+    nx_min_test=config.nx_min_test,
+    nx_max_test=config.nx_max_test,
+    arena_y_limits=arena_y_limits,
+    arena_x_limits=arena_x_limits,
+    residual=config.residual,
+    layer_norm=config.layer_norm,
+)
+
+for n in range(config.num_training_steps):
+    agent_1.update()
+
+
+parser = argparse.ArgumentParser()
+parser.add_argument(
+    "--config_path",
+    metavar="-C",
+    default="domine_2023_extras/Different_seed_configs/class_config_2.yaml",
+    help="path to base configuration file.",
+)
+args = parser.parse_args()
+config = config_class(args.config_path)
+agent_2 = Domine2023(
+    experiment_name=config.experiment_name,
+    train_on_shortest_path=config.train_on_shortest_path,
+    resample=config.resample,  # @param
+    wandb_on=config.wandb_on,
+    seed=config.seed,
+    feature_position=config.feature_position,
+    weighted=config.weighted,
+    num_hidden=config.num_hidden,  # @param
+    num_layers=config.num_layers,  # @param
+    num_message_passing_steps=config.num_message_passing_steps,  # @param
+    learning_rate=config.learning_rate,  # @param
+    num_training_steps=config.num_training_steps,  # @param
+    batch_size=config.batch_size,
+    nx_min=config.nx_min,
+    nx_max=config.nx_max,
+    batch_size_test=config.batch_size_test,
+    nx_min_test=config.nx_min_test,
+    nx_max_test=config.nx_max_test,
+    arena_y_limits=arena_y_limits,
+    arena_x_limits=arena_x_limits,
+    residual=config.residual,
+    layer_norm=config.layer_norm,
+)
+
+for n in range(config.num_training_steps):
+    agent_2.update()
+
+
+
+import statistics
+print(statistics.mean([agent_1.roc_aucs_train,agent_2.roc_aucs_train]))
+print(statistics.stdev([agent_1.roc_aucs_train,agent_2.roc_aucs_train]))
+
+parser = argparse.ArgumentParser()
+parser.add_argument(
+    "--config_path",
+    metavar="-C",
+    default="domine_2023_extras/Different_seed_configs/class_config_3.yaml",
+    help="path to base configuration file.",
+)
+args = parser.parse_args()
+config = config_class(args.config_path)
+agent_3 = Domine2023(
+    experiment_name=config.experiment_name,
+    train_on_shortest_path=config.train_on_shortest_path,
+    resample=config.resample,  # @param
+    wandb_on=config.wandb_on,
+    seed=config.seed,
+    feature_position=config.feature_position,
+    weighted=config.weighted,
+    num_hidden=config.num_hidden,  # @param
+    num_layers=config.num_layers,  # @param
+    num_message_passing_steps=config.num_message_passing_steps,  # @param
+    learning_rate=config.learning_rate,  # @param
+    num_training_steps=config.num_training_steps,  # @param
+    batch_size=config.batch_size,
+    nx_min=config.nx_min,
+    nx_max=config.nx_max,
+    batch_size_test=config.batch_size_test,
+    nx_min_test=config.nx_min_test,
+    nx_max_test=config.nx_max_test,
+    arena_y_limits=arena_y_limits,
+    arena_x_limits=arena_x_limits,
+    residual=config.residual,
+    layer_norm=config.layer_norm,
+)
+
+for n in range(config.num_training_steps):
+    agent_3.update()
+
+parser = argparse.ArgumentParser()
+parser.add_argument(
+    "--config_path",
+    metavar="-C",
+    default="domine_2023_extras/Different_seed_configs/class_config_4.yaml",
+    help="path to base configuration file.",
+)
+args = parser.parse_args()
+config = config_class(args.config_path)
+agent_4 = Domine2023(
+    experiment_name=config.experiment_name,
+    train_on_shortest_path=config.train_on_shortest_path,
+    resample=config.resample,  # @param
+    wandb_on=config.wandb_on,
+    seed=config.seed,
+    feature_position=config.feature_position,
+    weighted=config.weighted,
+    num_hidden=config.num_hidden,  # @param
+    num_layers=config.num_layers,  # @param
+    num_message_passing_steps=config.num_message_passing_steps,  # @param
+    learning_rate=config.learning_rate,  # @param
+    num_training_steps=config.num_training_steps,  # @param
+    batch_size=config.batch_size,
+    nx_min=config.nx_min,
+    nx_max=config.nx_max,
+    batch_size_test=config.batch_size_test,
+    nx_min_test=config.nx_min_test,
+    nx_max_test=config.nx_max_test,
+    arena_y_limits=arena_y_limits,
+    arena_x_limits=arena_x_limits,
+    residual=config.residual,
+    layer_norm=config.layer_norm,
+)
+
+for n in range(config.num_training_steps):
+    agent_4.update()
+
+parser = argparse.ArgumentParser()
+parser.add_argument(
+    "--config_path",
+    metavar="-C",
+    default="domine_2023_extras/Different_seed_configs/class_config_5.yaml",
+    help="path to base configuration file.",
+)
+args = parser.parse_args()
+config = config_class(args.config_path)
+agent_5 = Domine2023(
+    experiment_name=config.experiment_name,
+    train_on_shortest_path=config.train_on_shortest_path,
+    resample=config.resample,  # @param
+    wandb_on=config.wandb_on,
+    seed=config.seed,
+    feature_position=config.feature_position,
+    weighted=config.weighted,
+    num_hidden=config.num_hidden,  # @param
+    num_layers=config.num_layers,  # @param
+    num_message_passing_steps=config.num_message_passing_steps,  # @param
+    learning_rate=config.learning_rate,  # @param
+    num_training_steps=config.num_training_steps,  # @param
+    batch_size=config.batch_size,
+    nx_min=config.nx_min,
+    nx_max=config.nx_max,
+    batch_size_test=config.batch_size_test,
+    nx_min_test=config.nx_min_test,
+    nx_max_test=config.nx_max_test,
+    arena_y_limits=arena_y_limits,
+    arena_x_limits=arena_x_limits,
+    residual=config.residual,
+    layer_norm=config.layer_norm,
+)
+
+for n in range(config.num_training_steps):
+    agent_5.update()
+
+
+
+import statistics
+print(statistics.mean([agent_1.roc_aucs_train,agent_2.roc_aucs_train,agent_3.roc_aucs_train,agent_4.roc_aucs_train,agent_5.roc_aucs_train]))
+print(statistics.stdev([agent_1.roc_aucs_train,agent_2.roc_aucs_train,agent_3.roc_aucs_train,agent_4.roc_aucs_train,agent_5.roc_aucs_train]))