def generatePlot(ax, exp_paths, bounds): for exp_path in exp_paths: exp = ExperimentModel.load(exp_path) results = loadResults(exp, 'return_summary.npy') plotSensitivity(results, param, ax, label=exp.agent)
def generatePlotTTA(ax, exp_path, bounds): exp = loadExperiment(exp_path) results = loadResults(exp, errorfile) const, unconst = tee(results) color = colors[exp.agent] label = rename(exp.agent) if 'ReghTDC' in exp.agent: const = whereParameterEquals(const, 'ratio', 1) const = whereParameterEquals(const, 'reg_h', 1) elif 'TDRCC' in exp.agent: const = whereParameterEquals(const, 'ratio', 1) # const = whereParameterEquals(const, 'reg_h', 0.8) const = whereParameterGreaterEq(const, 'reg_h', 0.01) elif 'TDC' in exp.agent: const = whereParameterGreaterEq(const, 'ratio', 1) if show_unconst: b = plotSensitivity(unconst, param, ax, stderr=stderr, color=color, label=label + '_unc', bestBy=bestBy, dashed=True) bounds.append(b) b = plotSensitivity(const, param, ax, stderr=stderr, color=color, label=label, bestBy=bestBy) bounds.append(b)
def generatePlot(exp_paths): ax = plt.gca() bounds = [] for exp_path in exp_paths: exp = loadExperiment(exp_path) results = loadResults(exp) if exp.agent == 'TDadagrad': continue use_ideal_h = exp._d['metaParameters'].get('use_ideal_h', False) dashed = use_ideal_h color = colors[exp.agent] label = exp.agent.replace('adagrad', '') if use_ideal_h: label += '-h*' bound = plotSensitivity(results, 'h_variance', ax, label=label, color=color, dashed=dashed, bestBy='end') bounds.append(bound) lower = min(map(lambda x: x[0], bounds)) upper = max(map(lambda x: x[1], bounds)) ax.set_ylim([lower, upper]) ax.set_xscale("log", basex=2) # plt.show() save(exp, f'h_variance-sensitivity') plt.clf()
def generatePlotTTA(ax, exp_paths, bestBy, bounds): ax.set_xscale("log", basex=2) for exp_path in exp_paths: if 'amsgrad' in exp_path: continue exp = loadExperiment(exp_path) rmsve = loadResults(exp, 'errors_summary.npy') rmspbe = loadResults(exp, 'rmspbe_summary.npy') agent = exp.agent if 'SmoothTDC' in agent: average = exp._d['metaParameters']['averageType'] agent += '_' + average color = colors[agent] label = agent b = plotSensitivity(rmsve, 'ratio', ax, overStream=rmspbe, color=color, label=label, bestBy=bestBy) bounds.append(b)
def generatePlot(exp_paths): ax = plt.gca() bounds = [] i = -1 for exp_path in exp_paths: i += 1 exp = loadExperiment(exp_path) results = loadResults(exp, 'errors_summary.npy') param_dict = splitOverParameter(results, 'ratio') for key in param_dict: bound = plotSensitivity(param_dict[key], 'alpha', ax, color=colors[i], bestBy='end') bounds.append(bound) lower = min(map(lambda x: x[0], bounds)) upper = max(map(lambda x: x[1], bounds)) ax.set_ylim([lower, upper]) ax.set_xscale("log", basex=2) # plt.show() save(exp, f'alpha_over_beta_rmsve', type='svg') plt.clf()
def generatePlot(exp_paths): ax = plt.gca() bounds = [] for exp_path in exp_paths: exp = loadExperiment(exp_path) results = loadResults(exp) if exp.agent == 'TDadagrad': continue color = colors[exp.agent] label = exp.agent.replace('adagrad', '') bound = plotSensitivity(results, 'h_variance', ax, label=label, color=color, bestBy='end') bounds.append(bound) # lower = min(map(lambda x: x[0], bounds)) # upper = max(map(lambda x: x[1], bounds)) # ax.set_ylim([lower, upper]) ax.set_xscale("log", basex=2) plt.show() # save(exp, f'alpha-sensitivity') plt.clf()
def generatePlot(ax, exp_path, bounds): exp = loadExperiment(exp_path) results = loadResults(exp, errorfile) color = colors[exp.agent] label = exp.agent b = plotSensitivity(results, param, ax, color=color, label=label, bestBy=bestBy) bounds.append(b)
def generatePlot(ax, exp_path, bounds): exp = loadExperiment(exp_path) results = loadResults(exp, errorfile) results = addUpdateParam(results) color = colors[exp.agent] label = rename(exp.agent) b = plotSensitivity(results, "updates", ax, color=color, label=label, bestBy=bestBy) bounds.append(b)
def generatePlotTTA(ax, exp_paths, bestBy, bounds): ax.set_xscale("log", basex=2) for exp_path in exp_paths: exp = loadExperiment(exp_path) results = loadResults(exp, errorfile) agent = exp.agent color = colors[agent] label = agent b = plotSensitivity(results, 'reg_h', ax, reducer='best', color=color, label=label, bestBy=bestBy) bounds.append(b)
def generatePlotTTA(ax, exp_paths, bestBy, bounds): ax.set_xscale("log", basex=2) for exp_path in exp_paths: exp = loadExperiment(exp_path) results = loadResults(exp, errorfile) agent = exp.agent if 'SmoothTDC' in agent: average = exp._d['metaParameters']['averageType'] agent += '_' + average color = colors[agent] label = agent b = plotSensitivity(results, 'buffer', ax, color=color, label=label, bestBy=bestBy) bounds.append(b)
def generatePlotTTA(ax, exp_paths, bestBy, bounds): ax.set_xscale("log", basex=2) for exp_path in exp_paths: exp = loadExperiment(exp_path) results = loadResults(exp, errorfile) agent = exp.agent color = colors[agent] label = agent if exp.agent == 'ReghTDC': results = whereParameterGreaterEq(results, 'ratio', 1.0) # reducer='best' chooses the best value of other parameters *per value of 'replay'* # reducer='slice' first chooses the best parameter setting, then sweeps over 'replay' with other parameters fixed b = plotSensitivity(results, 'replay', ax, reducer='best', color=color, label=label, bestBy=bestBy) bounds.append(b)