plot-results.py 22.7 KB
Newer Older
1
2
3
#!/usr/bin/env python
# coding: utf-8

4
# In[1]:
5
6
7
8


import os
import pandas as pd
Jaime Arias's avatar
Jaime Arias committed
9
import numpy as np
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
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

Jaime Arias's avatar
Jaime Arias committed
53
# In[2]:
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72


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)


Jaime Arias's avatar
Jaime Arias committed
73
# In[3]:
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111


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


Jaime Arias's avatar
Jaime Arias committed
112
# In[4]:
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142


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',
143
144
        'otfC': 'Cthreads',
        'otf': 'Hybrid'
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
    }
    
    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


Jaime Arias's avatar
Jaime Arias committed
164
# In[5]:
165
166


167
def create_log_figure(table, table_errors, model, tool_x, tool_y, callback=None, show_strategy=True):
168
169
170
171
172
173
174
175
176
177
178
179
180
181
    """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
182
183
    callback : function
        Function to be called when clicking on a point
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
    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.
206
207
        min_value_log = np.log10(min_value)

208
        max_value = max(max_values[tool_x], max_values[tool_y])
209
        max_value_log = np.log10(max_value)
210

211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
        table_model = table.loc[model]
        table_errors_model = table_error.loc[model]
        
        full_table_x = pd.concat([table_model[tool_x],table_model['property'], table_errors_model[tool_x]], axis=1)
        full_table_x.columns = ['time', 'property', 'error']

        full_table_y = pd.concat([table_model[tool_y],table_model['property'], table_errors_model[tool_y]], axis=1)
        full_table_y.columns = ['time', 'property', 'error']

        traces = [
            {"property": 'T', "color":"green"},
            {"property": 'F', "color":"red"},
            {"property": 'U', "color":"black"}
        ]

        figures = []
        for t in traces:
            # filter by verification output
            table_x = full_table_x[full_table_x.property == t['property']]
            table_y = full_table_y[full_table_y.property == t['property']]

            # custom data
            custom_data = list(zip(table_x.index, table_x.error,table_y.error))
            
            # tools
            metainfo = {
                'model': model, 
                'tools': {'x': tool_x, 'y': tool_y},
                'folder': os.path.join(os.path.abspath(os.pardir), "results")
            }

            figures.append(go.Scatter(x=table_x.time,
                                      y=table_y.time,
                                      name=t['property'],
                                      mode='markers',
                                      marker_symbol='circle-open',
                                      marker_color=t['color'],
                                      meta = metainfo,
                                      customdata=custom_data,
                                      hovertemplate =
                                        '<b>Formula # %{customdata[0]}</b><br>' +
                                        '<br><b>Times:</b><br>' +
                                        '<b>x:</b> %{x} s' +
                                        '<br><b>y:</b> %{y} s<br>' +
                                        '<br><b>Errors:</b><br>' +
                                        '<b>x:</b> %{customdata[1]}<br>' +
                                        '<b>y:</b> %{customdata[2]}',
                                        ))    

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

        # Create figure
        figure = go.FigureWidget(figures)
        figure.update_layout(LAYOUT_FIGURES,
                             title_text=model,
                             hoverlabel=dict(bgcolor="white", align='auto'),
                             legend_title_text='property',
                             xaxis=dict(type='log', autorange=False, range=[min_value_log, max_value_log]),
                             yaxis=dict(type='log', autorange=False, range=[min_value_log, max_value_log]),
                             xaxis_title=get_axis_title(tool_x, show_strategy),
                             yaxis_title=get_axis_title(tool_y, show_strategy))

        # Add event
        if callback is not None:
            for i in range(len(figure.data)):
                figure.data[i].on_click(callback)
281
282
283
284
285
286
287
    
        return figure
    except Exception as e:
        print("Error when ploting model: {} - tool_x: {} - tool_y: {}".format(model, tool_x, tool_y))
        print(e)


Jaime Arias's avatar
Jaime Arias committed
288
289
290
# In[6]:


291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
import webbrowser

def get_filename(base_path, tool, model, model_instance, formula):
    """Returns the absolute path of the experiment log
    
    Parameters
    ----------
    base_path : string
        Path of the folder where logs are saved
    tool : string
        Tool name
    model : string
        Model name
    model_instance : string
        Name of the model instance
    formula : string
        Identifier of the formula
    
    Returns
    -------
    string
        Absolute path of the log file
    
    """
    information = tool.split('_')
    
    tool_name = information[0]
    tool_configuration = '_'.join(information[:-2])
    nb_nodes = information[-2]
    nb_cores = information[-1]
    
    experiment_folder = os.path.join(base_path, tool_name, tool_configuration, model, model_instance)
    filename = f'{tool_name}_{model_instance}-n{nb_nodes}-th{nb_cores}-f{formula}'
    absolute_path = os.path.join(experiment_folder, filename)
    
    return absolute_path

def open_logs_callback(trace, points, selector):
    """Callback that open the log files when clicking on a point of the figure
    
    Parameters
    ----------
    trace : plotly.graph_objects.Figure
        the figure to attach the callback
    points : plotly.callbacks.Points 
        points of the figure selected
    selector: plotly.callbacks.InputDeviceState 
        Device information 
    """
    inds = points.point_inds
    if (inds):
        index = inds[0]

        formula, error_x, error_y = trace['customdata'][index]
        model_instance = trace['meta']['model']
        model = ''.join(c for c in model_instance if not c.isdigit())
        tools = trace['meta']['tools']
        logs_folder = trace['meta']['folder']

        filename_x = get_filename(logs_folder, tools['x'], model, model_instance, formula)
        filename_y = get_filename(logs_folder, tools['y'], model, model_instance, formula)

        for f in [filename_x, filename_y]:
            webbrowser.open(f'file://{f}.err')
            webbrowser.open(f'file://{f}.out')


# In[7]:


Jaime Arias's avatar
Jaime Arias committed
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
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


428
# In[8]:
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499


# 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)

500
501
502
503
504
505
506
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))

507
508
509
510
511
512
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.
    """
513
    return not (same_tool(experiments, 'pmc-sog') or same_tool(experiments,'pnml2lts-mc')) and             versus_dfs(experiments)
514
515
516
517
518
519
520
521
522

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)

523
524
525
526
527
528
529
530
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')


531
532
def compare_distributed(experiments):
    """Compares the hybrid version of pmc-sog"""
533
    return same_tool(experiments, 'pmc-sog') and         same_strategy(experiments) and         same_distributed_number_threads(experiments) and         against_hybrid(experiments)
534

535
536
537
538
539
# Plots to be created
plots = {
    'compare_thread_library': compare_threads_library,
    'compare_couvreur_algorithm': compare_couvreur_strategies,
    'compare_tools': compare_tools,
540
541
    'compare_multicore': compare_multithreading,
    'compare_distributed': compare_distributed
542
543
544
545
546
}


# # Load Data

547
# In[9]:
548
549
550
551
552
553
554
555
556
557
558
559
560


# 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)


561
# In[10]:
562
563
564
565
566
567
568
569
570
571
572
573


# 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()


574
# In[11]:
575
576
577
578
579
580
581
582


# 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) & 
583
    (p_df.num_threads == 16)]
584
585

# only property column is needed
Jaime Arias's avatar
Jaime Arias committed
586
p_df = p_df.drop(columns=['tool', 'strategy', 'num_nodes', 'num_threads', 'time', 'explored_states', 'error'])
587
588
589
590
591
592
593
p_df.fillna('U', inplace=True)
p_df.set_index(['model', 'formula'], inplace=True)
p_df.sort_index(inplace=True)

p_df.head()


594
# In[12]:
595
596
597
598
599
600
601
602
603


# 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

604
# In[13]:
605
606
607


ZERO = 10e-5
Jaime Arias's avatar
Jaime Arias committed
608
TIMEOUT = 10 * 60 # 10 minutes = 600 seconds
609
610


611
# In[14]:
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628


# 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()


629
# In[15]:
630
631
632
633
634
635
636
637
638
639
640
641
642
643


# 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()


644
# In[16]:
645
646
647
648
649
650
651
652


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

table_error.head()


653
# In[17]:
Jaime Arias's avatar
Jaime Arias committed
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670


# 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()


671
# In[18]:
Jaime Arias's avatar
Jaime Arias committed
672
673
674
675
676
677
678
679
680


# 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()


681
682
# # Examples

683
# In[19]:
684
685


Jaime Arias's avatar
Jaime Arias committed
686
create_figure_explored_states(table_explored_states, 'robot20')
687
688


689
# In[20]:
Jaime Arias's avatar
Jaime Arias committed
690
691
692


create_figure(df, "philo10")
693
694


695
# In[21]:
Jaime Arias's avatar
Jaime Arias committed
696
697


698
create_log_figure(table_time, table_error, "philo10", "pmc-sog_otf_couv99-default_2_8", "pnml2lts-mc_dfs_1_16", open_logs_callback)
699
700
701
702


# # Generate Figures

703
# In[ ]:
704
705
706
707
708
709
710
711
712


# models
models = df.model.unique()

# tools 
tools = df.tool.unique()


Jaime Arias's avatar
Jaime Arias committed
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
# 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))


# In[ ]:
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750


# 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))


Jaime Arias's avatar
Jaime Arias committed
751
# In[ ]:
752
753
754
755


# create all the log figures

Jaime Arias's avatar
Jaime Arias committed
756
tools_pairs = [sorted(t) for t in (combinations(tools, 2))]
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782

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))


# In[ ]: