def plot_seeds(df_in: pd.DataFrame, sweep_vars: Sequence[str] = None) -> gg.ggplot: """Plot the returns through time individually by run.""" df = df_in.copy() df['average_return'] = df.raw_return.diff() / df.episode.diff() p = plotting.plot_individual_returns( df_in=df[df.episode > 1], max_episode=NUM_EPISODES, return_column='average_return', colour_var='height_threshold', sweep_vars=sweep_vars, ) return p + gg.ylab('average episodic return')
def plot_seeds(df_in: pd.DataFrame, sweep_vars: Sequence[str] = None) -> gg.ggplot: """Plot the returns through time individually by run.""" df = dc_preprocess(df_in) df['average_return'] = 1.1 - (df.total_regret.diff() / df.episode.diff()) p = plotting.plot_individual_returns( df_in=df, max_episode=NUM_EPISODES, return_column='average_return', colour_var='optimal_horizon', yintercept=1.1, sweep_vars=sweep_vars, ) return p + gg.ylab('average episodic return')
def plot_seeds(df_in: pd.DataFrame, sweep_vars: Sequence[Text] = None, colour_var: Text = 'memory_length') -> gg.ggplot: """Plot the returns through time individually by run.""" df = df_in.copy() df['average_return'] = df.total_return.diff() / df.episode.diff() p = plotting.plot_individual_returns( df_in=df[df.episode > 10], max_episode=NUM_EPISODES, return_column='average_return', colour_var=colour_var, sweep_vars=sweep_vars, ) return p + gg.ylab('average episodic return')
def plot_seeds(df_in: pd.DataFrame, sweep_vars: Sequence[str] = None, colour_var: str = None) -> gg.ggplot: """Plot the returns through time individually by run.""" df = df_in.copy() df['average_return'] = df.raw_return.diff() / df.episode.diff() p = plotting.plot_individual_returns( df_in=df, max_episode=NUM_EPISODES, return_column='average_return', colour_var=colour_var, yintercept=-_SOLVED_STEPS, sweep_vars=sweep_vars, ) return p + gg.ylab('average episodic return')
def plot_seeds(df_in: pd.DataFrame, sweep_vars: Optional[Sequence[str]] = None, colour_var: Optional[str] = None) -> gg.ggplot: """Plot the returns through time individually by run.""" df = df_in.copy() df['average_return'] = 1.0 - (df.total_regret.diff() / df.episode.diff()) p = plotting.plot_individual_returns( df_in=df, max_episode=NUM_EPISODES, return_column='average_return', colour_var=colour_var, yintercept=1., sweep_vars=sweep_vars, ) return p + gg.ylab('average episodic return')
def plot_seeds(df_in: pd.DataFrame, sweep_vars: Sequence[Text] = None, yintercept: float = 0.99) -> gg.ggplot: """Plot the returns through time individually by run.""" df = df_in.copy() df['average_return'] = df.denoised_return.diff() / df.episode.diff() p = plotting.plot_individual_returns( df_in=df[df.episode > 0.01 * NUM_EPISODES], # First episodes very noisy max_episode=NUM_EPISODES, return_column='average_return', colour_var='size', yintercept=yintercept, sweep_vars=sweep_vars, ) return p + gg.ylab('average episodic return')
def plot_seeds(df_in: pd.DataFrame, sweep_vars: Sequence[Text] = None, colour_var: Text = None) -> gg.ggplot: """Plot the accuracy through time individually by run.""" df = df_in.copy() df['average_return'] = 1.0 - (df.total_regret.diff() / df.episode.diff()) df['average_accuracy'] = (df.average_return + 1) / 2 p = plotting.plot_individual_returns( df_in=df[df.episode >= 100], max_episode=NUM_EPISODES, return_column='average_accuracy', colour_var=colour_var, yintercept=1., sweep_vars=sweep_vars, ) return p + gg.ylab('average accuracy')