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https://github.com/vale981/ray
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51 lines
1.4 KiB
Python
51 lines
1.4 KiB
Python
""" Example of using LinUCB on a recommendation environment with parametric
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actions. """
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from matplotlib import pyplot as plt
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import os
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import pandas as pd
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import time
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from ray import tune
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from ray.rllib.examples.env.bandit_envs_recommender_system import ParametricItemRecoEnv
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if __name__ == "__main__":
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# Temp fix to avoid OMP conflict
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os.environ["KMP_DUPLICATE_LIB_OK"] = "True"
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config = {
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"env": ParametricItemRecoEnv,
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}
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# Actual training_iterations will be 10 * timesteps_per_iteration
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# (100 by default) = 2,000
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training_iterations = 10
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print("Running training for %s time steps" % training_iterations)
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start_time = time.time()
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analysis = tune.run(
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"BanditLinUCB",
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config=config,
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stop={"training_iteration": training_iterations},
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num_samples=2,
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checkpoint_at_end=False,
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)
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print("The trials took", time.time() - start_time, "seconds\n")
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# Analyze cumulative regrets of the trials
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frame = pd.DataFrame()
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for key, df in analysis.trial_dataframes.items():
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frame = frame.append(df, ignore_index=True)
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x = frame.groupby("agent_timesteps_total")["episode_reward_mean"].aggregate(
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["mean", "max", "min", "std"]
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)
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plt.plot(x["mean"])
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plt.fill_between(
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x.index, x["mean"] - x["std"], x["mean"] + x["std"], color="b", alpha=0.2
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)
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plt.title("Episode reward mean")
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plt.xlabel("Training steps")
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plt.show()
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