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@ -29,6 +29,7 @@ def plot_bars(names_bars_tuple, filename='histogram_plot'):
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# catagorical
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ryb = cl.scales['10']['div']['RdYlBu']
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names, bars = names_bars_tuple
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names = ['Recurrent', 'Aggregating', 'Weightwise']# [name.split(' ')[0] for name in names]
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data_dict = {}
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for idx, name in enumerate(names):
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data_dict[name] = bars[idx]
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@ -44,11 +45,11 @@ def plot_bars(names_bars_tuple, filename='histogram_plot'):
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)
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data.append(bar)
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layout = dict(title='{} Histogram Plot'.format('Learn Severity'),
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xaxis=dict(title="Learn Severity", tilefont=dict(size=25)),
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# barmode='stack'
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layout = dict(xaxis=dict(title="Fixpoints", titlefont=dict(size=20)),
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barmode='stack',
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# height=400, width=400,
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# margin=dict(l=20, r=20, t=20, b=20)
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legend=dict(orientation="h", x=0.2)
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)
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fig = go.Figure(data=data, layout=layout)
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@ -89,5 +90,5 @@ if __name__ == '__main__':
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in_file = args.in_file[0]
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out_file = args.out_file
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search_and_apply(in_file, plot_bars, files_to_look_for=['all_data.dill'])
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search_and_apply(in_file, plot_bars, files_to_look_for=['all_counters.dill'])
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# , 'all_names.dill', 'all_notable_nets.dill'])
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@ -28,7 +28,7 @@ def line_plot(names_exp_tuple, filename='lineplot'):
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names, line_dict_list = names_exp_tuple
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names = "Weightwise Neural Network"
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names = ['Weightwise', 'Aggregating', 'Recurrent']
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data = []
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base_scale = cl.scales['10']['div']['RdYlGn']
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@ -37,14 +37,16 @@ def line_plot(names_exp_tuple, filename='lineplot'):
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trace = go.Scatter(
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x=line_dict['xs'],
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y=line_dict['ys'],
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# mode='lines',
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name=names,
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line=dict(color=scale[ld_id]),
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name=names[ld_id],
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line=dict(color=scale[ld_id],
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width=5),
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)
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data.append(trace)
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layout = dict(title='{} Weight Wise Mixed '.format(''),
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layout = dict(xaxis=dict(title='Trains per self-attack', titlefont=dict(size=20)),
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yaxis=dict(title='Average amount of fixpoints found', titlefont=dict(size=20)),
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legend=dict(orientation='h', x=0.2)
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# height=800, width=800, margin=dict(l=0, r=0, t=0, b=0)
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)
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@ -1,4 +0,0 @@
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TrainingNeuralNetworkDecorator activiation='linear' use_bias=False
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{'xs': [0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100], 'ys': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 'zs': [0.0, 0.0, 0.6, 1.8, 2.7, 5.1, 5.8, 7.8, 8.5, 9.0, 8.8]}
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@ -1,12 +0,0 @@
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WeightwiseNeuralNetwork activiation='linear' use_bias=False
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{'xs': [0, 50, 100, 150, 200, 250, 300, 350, 400, 450, 500], 'ys': [0.2, 0.3, 0.15, 0.55, 0.7, 0.85, 0.8, 0.95, 0.9, 1.0, 1.0]}
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AggregatingNeuralNetwork activiation='linear' use_bias=False
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{'xs': [0, 50, 100, 150, 200, 250, 300, 350, 400, 450, 500], 'ys': [1.0, 0.95, 1.0, 1.0, 0.95, 0.9, 0.8, 1.0, 0.85, 1.0, 0.9]}
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RecurrentNeuralNetwork activiation='linear' use_bias=False
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{'xs': [0, 50, 100, 150, 200, 250, 300, 350, 400, 450, 500], 'ys': [0.05, 0.0, 0.05, 0.0, 0.0, 0.1, 0.1, 0.05, 0.1, 0.0, 0.0]}
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@ -54,7 +54,7 @@ def count(counters, soup, notable_nets=[]):
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if __name__ == '__main__':
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with Experiment('mixed-self-fixpoints') as exp:
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with Experiment('mixed-soup') as exp:
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exp.trials = 10
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exp.soup_size = 10
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exp.soup_life = 5
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