plot-results.py 18.4 KB
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#!/usr/bin/env python
# coding: utf-8

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import os
import pandas as pd
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import numpy as np
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import plotly.io as pio
import plotly.express as px
import plotly.graph_objs as go
from itertools import combinations 
from plotly.subplots import make_subplots
import plotly.io as pio

# render figures in notebook
pio.renderers.default = "notebook_connected"

# templates figures
px.defaults.template = "simple_white"
pio.templates.default = "simple_white"

# layout for all figures
LAYOUT_FIGURES = dict(
    autosize=False,
    width = 500,
    height = 500,
    xaxis = dict(
      constrain="domain",
      mirror=True,
      showexponent="all",
      exponentformat="power"
    ),
    yaxis = dict(
      scaleanchor = "x",
      scaleratio = 1,
      mirror=True,
      showexponent="all",
      exponentformat="power"
    ),
    title = dict(
      y = 0.9,
      x = 0.5,
      xanchor = 'center',
      yanchor = 'top'
    )
)


# # Auxiliary Functions

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def create_folder(path):
    """Creates a folder if it does not exist
    
    Parameters
    ----------
    path : str
        Path of the new folder
    
    Examples
    --------
    
    >>> create_folder('./results')
    """
    if not os.path.exists(path):
        os.makedirs(path)


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def create_figure(df, model):
    """Creates a scatter figure showing the time taken by each tool to verify each property of a model
    
    Parameters
    ----------
    df : pandas.Dataframe
        Dataframe containing the results of the experiments
    model : string
        model to be plotted
    
    Returns
    -------
    plotly.graph_objects.Figure
        Scatter figure
    
    Examples
    --------
    
    >>> import os
    >>> import pandas as pd
    >>> csv_file = os.path.join("results", "output.csv")
    >>> df = pd.read_csv(csv_file)
    >>> fig = create_figure(df, 'philo10')
    """
    model_df = df[df.model == model]

    figure = px.scatter(model_df, 
                        x="formula", y="time",
                        title=model, 
                        color="tool", 
                        symbol_sequence=['x'])

    figure.update_layout(yaxis_title="time (s)", title=LAYOUT_FIGURES['title'])
    return figure


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def get_axis_title(experiment, show_strategy=True):
    """Get the axis title of a figure depending on the experiment being plotted
    
    Parameters
    ----------
    experiment : str
        String with the experiment information
    show_strategy : bool, optional
        Flag to show the information related to the strategy used by the tool
    
    Returns
    -------
    str
        axis title
        
    Examples
    --------
    
    >>> get_axis_title('pmc-sog_otfL_couv99-default_1_1', True)
    pmc-sog (Lace, strategy: couv99-default, # cores: 1)
    """
    information = experiment.split('_')
    tool_name = information[0]
    
    info = []
    library_dic = {
        'otfL': 'Lace',
        'otfP': 'Pthreads',
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        'otfC': 'Cthreads',
        'otf': 'Hybrid'
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    }
    
    if (len(information) == 5):
        info.append(library_dic[information[1]])

    if (show_strategy):
        info.append('strategy: {}'.format(information[-3]))

    nb_nodes = int(information[-2])
    if (nb_nodes > 1):
        info.append('# nodes: {}'.format(nb_nodes))

    info.append('# cores: {}'.format(information[-1]))

    title = '{} ({})'.format(tool_name, ', '.join(info))
    
    return title


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def create_log_figure(table, table_errors, model, tool_x, tool_y, show_strategy=True):
    """Creates a Scatter figure in logarithmic scale comparing the performance of two tools
    
    Parameters
    ----------
    table : pandas.Dataframe
        Dataframe with the times of each experiment
    table_errors : pandas.Dataframe
        Dataframe with the errors of each experiment
    model : string
        Model to be analyzed
    tool_x : string
        Tool to be compared and plotted on the x-axis
    tool_y : string
        Tool to be compared and plotted on the y-axis
    show_strategy : bool
        Flag to show the stretagy used by the tools
        
    Returns
    -------
    plotly.graph_objects.Figure
        Scatter figure
        
    Examples
    --------
    >>> import os
    >>> import pandas as pd
    >>> csv_file = os.path.join("results", "output.csv")
    >>> df = pd.read_csv(csv_file)
    >>> table = df.set_index(['model', 'formula', 'tool'], drop=True).unstack('tool')
    >>> fig = create_log_figure(table['time'], table['error'], 'philo10', 'pmc-sog_otfL_couv99-default_1_8', 'pmc-sog_otfP_couv99-default_1_8')
    """
    try:
        min_values = table.loc[model].min()
        max_values = table.loc[model].max()

        min_value = min(min_values[tool_x], min_values[tool_y])/2.
        max_value = max(max_values[tool_x], max_values[tool_y])

        figure = px.scatter(table.loc[model],
                            title=model,
                            x=tool_x, y=tool_y, 
                            log_x=True, log_y=True,
                            range_x=[min_value, max_value],
                            range_y=[min_value, max_value],
                            color="property",
                            hover_data=[
                                ['formula #{}'.format(i) for i in table.loc[model].index],
                                table_errors.loc[model, tool_x],
                                table_errors.loc[model, tool_y]
                                ],
                            color_discrete_map={"T": "green", "F": "red", "U": "black"},
                            symbol_sequence=["circle-open"])

        line = go.Scatter(x=[min_value, max_value], 
                            y=[min_value, max_value],
                            mode='lines', showlegend=False,
                            line=dict(color='black', width=1))

        figure.add_traces(line)
        figure.update_layout(LAYOUT_FIGURES, 
                                xaxis_title=get_axis_title(tool_x, show_strategy),
                                yaxis_title=get_axis_title(tool_y, show_strategy))
    
        return figure
    except Exception as e:
        print("Error when ploting model: {} - tool_x: {} - tool_y: {}".format(model, tool_x, tool_y))
        print(e)


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# In[6]:


def create_figure_explored_states(table_explored_states, model):
    """Creates figure showing the number of explorated states during the verification 
    
    Parameters
    ----------
    table_explored_states : pandas.Dataframe
        Dataframe with the explorated states of each experiment
    model : string
        Model to be analyzed
        
    Returns
    -------
    plotly.graph_objects.Figure
        Scatter figure
    """
    colors={'T': 'green', 'F': 'red'}
    float_formatter = "{:.2E}".format

    table_model = table_explored_states[table_explored_states.property != 'U']
    table_model = table_model[table_model.model == model]

    table_stats = table_model.groupby(['property']).agg(['mean']) 

    fig = go.Figure()
    for p in table_stats.index:
        data = table_model[table_model.property==p]
        stats = table_stats.loc[p]
        
        x_axis = np.arange(1, data['formula'].count()+1, 1)
        mean = stats['explored_states','mean']
        
        figure = px.scatter(data, 
                            x=x_axis, 
                            y="explored_states",
                            title=model, 
                            color='property',
                            color_discrete_map=colors,
                            symbol_sequence=["circle"])

        line = go.Scatter(x=[x_axis[0], x_axis[-1]], 
                          y=[mean, mean],
                          mode='lines', showlegend=False,                          
                          line=dict(color=colors[p], width=1.5))

        fig.add_trace(figure['data'][0])
        fig.add_trace(line)
        
        fig.add_annotation(x=1, 
                           y=mean,
                           font=dict(color=colors[p]),
                           text=f"mean = {float_formatter(mean)}")

    fig.update_layout(title_text=model, title=LAYOUT_FIGURES['title'], 
                      width = 500, height = 500, margin=dict(r=110))
    
    fig.update_xaxes(title="formula", range=[x_axis[0]-1, x_axis[-1]+1])
    fig.update_yaxes(title="# explored states")
    
    fig.update_annotations(dict(
        showarrow=False,
        xanchor="left",
        yanchor="middle",
        xref='paper'))
    
    return fig


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# Experiment filters

def versus_dfs(experiments):
    """Selects only experiments using DFS strategy"""
    exp1, exp2 = experiments
    strategy_exp1= exp1.split('_')[1]
    strategy_exp2= exp2.split('_')[1]
    
    return strategy_exp1 == 'dfs' or strategy_exp2 == 'dfs'

def versus_sequential(experiments):
    """Selects only experiments run sequentially """
    exp1, exp2 = experiments
    nodes_exp1, threads_exp1 = exp1.split('_')[-2:]
    nodes_exp2, threads_exp2 = exp2.split('_')[-2:]

    return (nodes_exp1 == '1' and nodes_exp2 == '1') and             (threads_exp1 == '1' or threads_exp2 == '1')

def same_tool(experiments, tool):
    """Selects only experiments comparing the same tool"""
    exp1, exp2 = experiments
    tool_exp1= exp1.split('_')[0]
    tool_exp2= exp2.split('_')[0]
    return tool_exp1.startswith(tool) and tool_exp2.startswith(tool)

def same_number_threads(experiments):
    """Selects only experiments comparing the same number of processes and cores"""
    exp1, exp2 = experiments
    nodes_exp1, threads_exp1 = exp1.split('_')[-2:]
    nodes_exp2, threads_exp2 = exp2.split('_')[-2:]
    return (nodes_exp1 == nodes_exp2) and (threads_exp1 == threads_exp2) 

def same_thread_library(experiments):
    """Selects only experiments comparing the same parallelization library"""
    exp1, exp2 = experiments
    library_exp1 = exp1.split('_')[1]
    library_exp2 = exp2.split('_')[1]
    return library_exp1 == library_exp2

def same_strategy(experiments):
    """Selects only experiments comparing the same strategy"""
    exp1, exp2 = experiments
    strategy_exp1 = exp1.split('_')[2]
    strategy_exp2 = exp2.split('_')[2]
    return strategy_exp1 == strategy_exp2

def only_couvreur_strategy(experiments):
    """Selects only experiments comparing couvreur emptiness check algorithm"""
    exp1, exp2 = experiments
    strategy_exp1 = exp1.split('_')[2]
    strategy_exp2 = exp2.split('_')[2]
    return strategy_exp1.startswith('couv99') and strategy_exp2.startswith('couv99')

def compare_threads_library(experiments):
    """Compares parallization libraries used in pmc-sog. 
    
    It selects experiments where the tool is only pmc-sog and the strategy, number of threads, 
    number of processus are the same.
    """
    return same_tool(experiments, 'pmc-sog') and             same_strategy(experiments) and             same_number_threads(experiments) and             not same_thread_library(experiments)

def compare_couvreur_strategies(experiments):
    """Compares couvreurs strategies used in pmc-sog. 
    
    It selects experiments where the tool is only pmc-sog, the strategy is couvreur, and 
    the parallelization library, number of threads, number of processus are the same.
    """
    return only_couvreur_strategy(experiments) and             same_thread_library(experiments) and             same_number_threads(experiments)

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def same_distributed_number_threads(experiments):
    """Selects only experiments where the multiplication of theirs nodes with cores are the same."""
    exp1, exp2 = experiments
    nodes_exp1, threads_exp1 = exp1.split('_')[-2:]
    nodes_exp2, threads_exp2 = exp2.split('_')[-2:]
    return (int(nodes_exp1) * int(threads_exp1)) == (int(nodes_exp2) * int(threads_exp2))

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def compare_tools(experiments):
    """Compares pmc-sog and pnml2lts-mc using the DFS algorithm. 
    
    It selects experiments where the tools are not the same, the exploration algorithm is DFS and 
    the number of processus and cores are the same.
    """
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    return not (same_tool(experiments, 'pmc-sog') or same_tool(experiments,'pnml2lts-mc')) and             versus_dfs(experiments)
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def compare_multithreading(experiments):
    """Compares the sequential and multi-core version of pmc-sog. 
    
    It selects experiments where the tools is pmc-sog, the parallelization library, the emptiness check 
    strategy are the same. Here the number of processus and cores are different.
    """
    return same_tool(experiments, 'pmc-sog') and             same_thread_library(experiments) and             same_strategy(experiments) and             versus_sequential(experiments)

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def against_hybrid(experiments):
    """Selects only experiments comparing with hybrid mode"""
    exp1, exp2 = experiments
    library_exp1 = exp1.split('_')[1]
    library_exp2 = exp2.split('_')[1]
    return (library_exp1 == 'otf') or (library_exp2 == 'otf')


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def compare_distributed(experiments):
    """Compares the hybrid version of pmc-sog"""
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    return same_tool(experiments, 'pmc-sog') and         same_strategy(experiments) and         same_distributed_number_threads(experiments) and         against_hybrid(experiments)
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# Plots to be created
plots = {
    'compare_thread_library': compare_threads_library,
    'compare_couvreur_algorithm': compare_couvreur_strategies,
    'compare_tools': compare_tools,
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    'compare_multicore': compare_multithreading,
    'compare_distributed': compare_distributed
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}


# # Load Data

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# Root folder
PROJECT_FOLDER = os.path.abspath(os.pardir)

# csv file with the output
csv_file = os.path.join(PROJECT_FOLDER, "results", "output.csv")

# Output folder
OUTPUT_FOLDER = os.path.join(PROJECT_FOLDER,"results", "figures")
create_folder(OUTPUT_FOLDER)


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# read data
df = pd.read_csv(csv_file)

# merge the information related to the experiment (# nodes, # threads, strategy) to the tool column
df['tool'] = df[['tool', 'strategy', 'num_nodes', 'num_threads']].astype(str).apply('_'.join, axis=1)
df = df.drop(columns=['strategy', 'num_nodes', 'num_threads'])

df.head()


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# ground truth for properties
p_df = pd.read_csv(csv_file)
p_df =p_df[
    (p_df.tool=='pnml2lts-mc') & 
    (p_df.strategy == 'ndfs') & 
    (p_df.num_nodes == 1) & 
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    (p_df.num_threads == 16)]
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# only property column is needed
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p_df = p_df.drop(columns=['tool', 'strategy', 'num_nodes', 'num_threads', 'time', 'explored_states', 'error'])
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p_df.fillna('U', inplace=True)
p_df.set_index(['model', 'formula'], inplace=True)
p_df.sort_index(inplace=True)

p_df.head()


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# table with times, verification output and error for each experiment
table = df.set_index(['model', 'formula', 'tool'], drop=True).unstack('tool')
table.head()


# # Preprocessing of data

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ZERO = 10e-5
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TIMEOUT = 10 * 60 # 10 minutes = 600 seconds
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# table with times for each experiment
table_time = table['time'].copy()

# replace non finished experiments with a dummy value, e.g. timeout
table_time.fillna(TIMEOUT, inplace=True)

# replace 0.00 time for 10^(-5), we cannot plot log(0)
table_time.replace(0.0, ZERO, inplace=True)

# add verification output to the table
table_time = pd.concat([table_time, p_df], axis=1)

table_time.head()


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# table with verification output for each experiment
table_property = table['property'].copy()

# replace non finished experiments with a dummy value
table_property.fillna('U', inplace=True)

# add ground truth to the table
table_property = pd.concat([table_property, p_df], axis=1)

table_property.head()


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# table with error for each experiment
table_error = table['error'].copy()

table_error.head()


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# In[16]:


# table with explored states for each experiment using ltsmin
table_explored_states = table.copy()
table_explored_states = table_explored_states['explored_states']
table_explored_states = table_explored_states[['pnml2lts-mc_dfs_1_16']]
table_explored_states = table_explored_states.rename(columns={"pnml2lts-mc_dfs_1_16": "explored_states"})

# add verification output to the table
table_explored_states = pd.concat([table_explored_states, p_df], axis=1)

# reshape
table_explored_states = table_explored_states.reset_index()

table_explored_states.head()


# In[17]:


# calculate the stats of the number of explored states

table_explored_states_stats = table_explored_states.groupby(['model', 'property']).agg(['mean', 'min', 'max'])
table_explored_states_stats = table_explored_states_stats['explored_states']
table_explored_states_stats.head()


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# # Examples

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create_figure_explored_states(table_explored_states, 'robot20')
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# In[21]:


create_figure(df, "philo10")
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# In[22]:


create_log_figure(table_time, table_error, "philo10", "pmc-sog_otf_couv99-default_2_8", "pnml2lts-mc_dfs_1_16")
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# # Generate Figures

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# models
models = df.model.unique()

# tools 
tools = df.tool.unique()


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# In[ ]:


# create all the figures of explored states

folder = os.path.join(OUTPUT_FOLDER, 'explored-states')
create_folder(folder)

for model in models:
    try:
        fig = create_figure_explored_states(table_explored_states, model)
        
        # save figures in html and pdf
        fig.write_html(os.path.join(folder, model + '.html'))
        fig.write_image(os.path.join(folder, model + '.pdf'))
    except KeyError:
        print("Error: {} was not plotted".format(model))


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# create all the figures formula vs time

folder = os.path.join(OUTPUT_FOLDER, 'time-plots')
create_folder(folder)

for model in models:
    try:
        fig = create_figure(df, model)
        
        # save figures in html and pdf
        fig.write_html(os.path.join(folder, model + '.html'))
        fig.write_image(os.path.join(folder, model + '.pdf'))
    except KeyError:
        print("Error: {} was not plotted".format(model))


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# create all the log figures

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tools_pairs = [sorted(t) for t in (combinations(tools, 2))]
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for plot, filter_method in plots.items():
    axes = list(filter(filter_method, tools_pairs))
    
    for model in models:
        folder = os.path.join(OUTPUT_FOLDER, plot, model)
        create_folder(folder)
        
        for axe in axes:
            try:
                show_strategy = plot == 'compare_couvreur_algorithm'
                fig = create_log_figure(table_time, table_error, model, axe[0], axe[1], show_strategy)
                
                # save figures in html and pdf
                figure_name = os.path.join(folder, '{}-{}-VS-{}-log'.format(model, axe[0], axe[1]))
                fig.write_html(figure_name + '.html')
                fig.write_image(figure_name + '.pdf')
            except KeyError:
                print("Error: {} was not plotted".format(model))


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