In [10]:
# some_file.py
import sys
sys.path.insert(0, './shared_code')
import networkx as nx
import numpy as np
import matplotlib.pyplot as plt
import sqlite3 as db
import json
import pandas as pd
import importlib
from IPython.core.display import display, HTML
import os
import RHgenerate_states
import RHcomponents
import RHdisplay
import RHdistance_partition
import RHutilities as util
%matplotlib inline
In [2]:
con = db.connect('data/rush_hour.db')
cur = con.cursor()
#states.to_sql('states',con,if_exists="append")
In [7]:
importlib.reload(RHgenerate_states)
#importlib.reload(RHcomponents)
Out[7]:
<module 'RHgenerate_states' from './shared_code\\RHgenerate_states.py'>
In [38]:
num_cars = 4
num_trucks = 2
combinatorial_class = 2**num_cars * 3**num_trucks
#RHgenerate_states.generate_states(num_cars,num_trucks,con)
In [5]:
len(states) , combinatorial_class
Out[5]:
(18763346, 144)
In [ ]:
# prototype components algorithm using sqlite source for nodes
In [ ]:
comb_class = 144
solvable_count = 0
unsolvable_count = 0
cross_components_max_depth = 0
component_columns = ['comb_class','repr_board_int_s1','repr_board_int_s2',\
'repr_red_col', 'is_solvable','num_nodes',\
'density','max_solution_distance']
comb_class_columns = ['comb_class','num_nodes','num_components','num_solvable_components',\
'num_unsolvable_components','max_solution_depth']
select_node_sql = "select * from node where comb_class = 144 and included_in_component = 0 limit 1"
update_nodes_sql = ""
In [67]:
# Prime data for while condition
df = pd.read_sql_query(select_sql,con)
df
Out[67]:
id
comb_class
board_int_s1
board_int_s2
red_col
included_in_component
0
4
144
15437151837839400
2748779072896
1
0
In [32]:
In [ ]:
# create component
# seteup component data to store
# save component data
# upate node data to mark nodes as inluded in a component
# select next node not yet inlcuded in a component
total_nodes = 780 + 930 + 2400 + 1000 + 660
component_count = 5
while not df.empty:
s1 = df.iloc[0]['board_int_s1']
s2 = df.iloc[0]['board_int_s2']
red_col = df.iloc[0]['red_col']
board_int = util.combine_ints(s1,s2)
board_int,red_col
g = RHcomponents.components([(board_int,red_col)])[0]
if not g.graph['solvable']:
unsolvable_count +=1
node_dict = g.node[0]
max_soln_distance = None
else:
solvable_count +=1
RHdistance_partition.distance_partition(g)
max_soln_distance = max(g.graph['distance_partition'].keys())
if max_soln_distance > cross_components_max_depth:
cross_components_max_depth = max_soln_distance
# select a node in outermost partition
for node in g.graph['distance_partition'][max_soln_distance]:
break
node_dict = g.node[node]
s1,s2 = util.split_int( node_dict['board_int'])
df_component = pd.DataFrame( [[\
combinatorial_class
,s1
,s2
,node_dict['red_col']
,g.graph['solvable']
,len(g.nodes())
,nx.density(g)
,max_soln_distance
]],columns = component_columns)
df_component.to_sql('component',con,if_exists="append",index=False)
# Now update nodes
cur = con.cursor()
cur.executemany("update node set included_in_component = 1 where board_int_s1 = ? and board_int_s2 = ? and red_col = ?", \
[
[ int(util.split_int(g.node[x]['board_int'])[0]),\
int(util.split_int(g.node[x]['board_int'])[1]),\
int(g.node[x]['red_col'])\
] for x in g.nodes()
]
)
con.commit()
df = pd.read_sql_query(select_sql,con)
total_nodes = total_nodes + len(g.nodes())
component_count += 1
print ("%d | total nodes: %d"%(component_count,total_nodes) )
6 | total nodes: 6466
7 | total nodes: 7352
8 | total nodes: 7949
9 | total nodes: 8511
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11788 | total nodes: 8000326
11789 | total nodes: 8000522
11790 | total nodes: 8001902
11791 | total nodes: 8002136
11792 | total nodes: 8002703
11793 | total nodes: 8002868
11794 | total nodes: 8003434
11795 | total nodes: 8003647
11796 | total nodes: 8003930
11797 | total nodes: 8004337
11798 | total nodes: 8004579
11799 | total nodes: 8005267
11800 | total nodes: 8011752
11801 | total nodes: 8012050
11802 | total nodes: 8012897
11803 | total nodes: 8013712
11804 | total nodes: 8013805
11805 | total nodes: 8013885
11806 | total nodes: 8013925
11807 | total nodes: 8014174
11808 | total nodes: 8014317
11809 | total nodes: 8014440
11810 | total nodes: 8015112
11811 | total nodes: 8021517
11812 | total nodes: 8022342
11813 | total nodes: 8022522
11814 | total nodes: 8023194
11815 | total nodes: 8024006
11816 | total nodes: 8024266
11817 | total nodes: 8025394
11818 | total nodes: 8025430
11819 | total nodes: 8025590
11820 | total nodes: 8025770
11821 | total nodes: 8027156
11822 | total nodes: 8027192
11823 | total nodes: 8027336
11824 | total nodes: 8027528
11825 | total nodes: 8027724
11826 | total nodes: 8028072
11827 | total nodes: 8029120
11828 | total nodes: 8029247
11829 | total nodes: 8029436
11830 | total nodes: 8029628
11831 | total nodes: 8031104
11832 | total nodes: 8032550
11833 | total nodes: 8032748
11834 | total nodes: 8032863
11835 | total nodes: 8033700
11836 | total nodes: 8034512
11837 | total nodes: 8035299
11838 | total nodes: 8036369
11839 | total nodes: 8036492
11840 | total nodes: 8036579
11841 | total nodes: 8036645
11842 | total nodes: 8036697
11843 | total nodes: 8038161
11844 | total nodes: 8038202
11845 | total nodes: 8038910
11846 | total nodes: 8039043
11847 | total nodes: 8039084
11848 | total nodes: 8039186
11849 | total nodes: 8039309
11850 | total nodes: 8040701
11851 | total nodes: 8040890
11852 | total nodes: 8041151
11853 | total nodes: 8041630
11854 | total nodes: 8041742
11855 | total nodes: 8041793
11856 | total nodes: 8041849
11857 | total nodes: 8042253
11858 | total nodes: 8042654
11859 | total nodes: 8042793
11860 | total nodes: 8043049
11861 | total nodes: 8043101
11862 | total nodes: 8043375
11863 | total nodes: 8043789
11864 | total nodes: 8044049
11865 | total nodes: 8044105
11866 | total nodes: 8044207
11867 | total nodes: 8044289
11868 | total nodes: 8044382
11869 | total nodes: 8044624
11870 | total nodes: 8045596
11871 | total nodes: 8045862
11872 | total nodes: 8046188
11873 | total nodes: 8046481
11874 | total nodes: 8046561
11875 | total nodes: 8046663
11876 | total nodes: 8046731
11877 | total nodes: 8047571
11878 | total nodes: 8047954
11879 | total nodes: 8048791
11880 | total nodes: 8048947
11881 | total nodes: 8049268
11882 | total nodes: 8049487
11883 | total nodes: 8049926
11884 | total nodes: 8050703
11885 | total nodes: 8052986
11886 | total nodes: 8053811
11887 | total nodes: 8054087
11888 | total nodes: 8054711
11889 | total nodes: 8054763
In [70]:
len(g.nodes()), len(g.nodes()) + 780
Out[70]:
(930, 1710)
In [66]:
# now to update all 780 nodes with
cur = con.cursor()
cur.executemany("update node set included_in_component = 1 where board_int_s1 = ? and board_int_s2 = ? and red_col = ?", \
[
[ int(util.split_int(g.node[x]['board_int'])[0]),\
int(util.split_int(g.node[x]['board_int'])[1]),\
int(g.node[x]['red_col'])\
] for x in g.nodes()
]
)
con.commit()
In [ ]:
[
[ int(util.split_int(g.node[x]['board_int'])[0]),\
int(util.split_int(g.node[x]['board_int'])[1]),\
int(g.node[x]['red_col'])\
] for x in g.nodes()
]
In [52]:
node_dict,g.graph['distance_partition'][11],s1,s2
Out[52]:
({'board_int': 278140368219519116774982815580214,
'inner_nbrs': {195, 197, 626, 675},
'is_soln_state': False,
'red_col': 1,
'soln_distance': 11},
{625},
15439892043751464,
133955638)
In [ ]:
In [ ]:
In [ ]:
In [46]:
df_component
Out[46]:
comb_class
repr_board_int_s1
repr_board_int_s2
repr_red_col
is_solvable
num_nodes
density
max_solution_distance
0
144
15439892043751464
133955638
1
True
780
0.00891
11
In [35]:
g.graph
Out[35]:
{'solvable': True}
In [ ]:
In [22]:
#g = RHcomponents.components([(board_int,red_col)])[0]
if g.graph['solvable']:
RHdistance_partition.distance_partition(g)
In [25]:
max(g.graph['distance_partition'].keys()) , nx.density(g)
Out[25]:
(11, 0.008910174121984134)
In [ ]:
component
In [29]:
df_empty = pd.read_sql_query("select * from component where comb_class = 3",con)
df_empty.empty
Out[29]:
True
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [ ]:
# prep for processing generated list of components
SAVE_BATCH_SIZE = 2000 # Number of Graphs to save in batch; Equals number of rows in DataFrame
db_components = []
comb_class_max_depth = 0
solvable_count = 0
unsolvable_count = 0
save_counter = 0
component_columns = ['comb_class','repr_board_int_s1','repr_board_int_s2',\
'repr_red_col', 'is_solvable','num_nodes',\
'density','max_solution_distance']
comb_class_columns = ['comb_class','num_nodes','num_components','num_solvable_components',\
'num_unsolvable_components','max_solution_depth']
for g in RHcomponents.gen_components(states):
save_counter +=1
if save_counter >= SAVE_BATCH_SIZE:
df = pd.DataFrame([x for x in db_components if x is not None],columns = component_columns)
df.to_sql('component',con,if_exists="append")
db_components = [None]*SAVE_BATCH_SIZE
save_counter = 1
if not g.graph['solvable']:
unsolvable_count +=1
node_dict = g.node[0]
max_soln_distance = None
else:
solvable_count += 1
RHdistance_partition.distance_partition(g)
max_soln_distance = max(g.graph['distance_partition'].keys())
if max_soln_distance > comb_class_max_depth:
comb_class_max_depth = max_soln_distance
for node in g.graph['distance_partition'][max_soln_distance]:
break
node_dict = g.node[node]
s1,s2 = RHutilities.split_int( node_dict['board_int'])
db_components.append( [\
combinatorial_class
,s1
,s2
,node_dict['red_col']
,g.graph['solvable']
,len(g.nodes())
,nx.density(g)
,max_soln_distance
]
)
if db_components:
df = pd.DataFrame([x for x in db_components if x is not None],columns = component_columns)
df.to_sql('component',con,if_exists="append",index=False)
#comb_class_columns = ['comb_class','num_nodes','num_components','num_solvable_components',\
# 'num_unsolvable_components','max_solution_depth']
comb_class_data = [[combinatorial_class,num_nodes,unsolvable_count + solvable_count,\
solvable_count,unsolvable_count, \
comb_class_max_depth]]
df_class = pd.DataFrame(comb_class_data,columns = comb_class_columns)
df_class.to_sql('comb_class',con,if_exists="append",index=False)
In [17]:
df
Out[17]:
comb_class
repr_board_int_s1
repr_board_int_s2
repr_red_col
is_solvable
num_nodes
density
max_solution_distance
0
4
221184
113246208
1
True
25
0.133333
4.0
1
4
224640
0
1
False
6
0.400000
NaN
2
4
223232
140737488355328
1
True
21
0.133333
5.0
3
4
906190848
0
1
True
25
0.133333
4.0
4
4
1269764
0
1
True
23
0.138340
5.0
5
4
221184
237494511599616
1
True
25
0.133333
4.0
6
4
221184
9007233614479360
1
True
23
0.138340
4.0
7
4
67330304
0
1
True
21
0.133333
5.0
8
4
221184
432
1
True
25
0.133333
4.0
9
4
1125904202031104
0
1
True
21
0.142857
4.0
10
4
224640
0
3
True
6
0.400000
2.0
11
4
8609824
0
1
True
21
0.142857
5.0
12
4
237494511820800
0
1
True
25
0.133333
4.0
In [18]:
df2 = pd.read_sql_query("select * from comb_class",con)
df2
Out[18]:
comb_class
num_nodes
num_components
num_solvable_components
num_unsolvable_components
max_solution_depth
0
4
267
13
12
1
5
1
36
116650
1361
1024
337
19
In [69]:
if g.graph['solvable']:
RHdistance_partition.distance_partition(g)
max_distance = max(g.graph['distance_partition'].keys())
for node in g.graph['distance_partition'][max_distance]:
break
repr_node_dict = g.node[node]
else:
repr_node_dict = g.node[0]
max_distance = None
s1,s2 = RHutilities.split_int(repr_node_dict['board_int'])
red_col = repr_node_dict['red_col']
36, g.graph['solvable'],s1,s2,red_col, len(g.nodes()), nx.density(g),max_distance
Out[69]:
(36, True, 67330304, 11259042018267128, 1, 204, 0.024244180430793006, 5)
In [11]:
len(comps),sum(len(x.nodes()) for x in comps)
Out[11]:
(1361, 116650)
In [12]:
len(comps[0].nodes())
Out[12]:
308
In [14]:
solvable_comps = [g for g in comps if g.graph['solvable']==True]
unsolvable_comps = [g for g in comps if g.graph['solvable'] == False]
In [15]:
df_solvable = pd.DataFrame([len(x.nodes()) for x in solvable_comps])
df_unsolvable = pd.DataFrame([len(x.nodes()) for x in unsolvable_comps])
In [18]:
#df.describe()
#%matplotlib inline
df_solvable.hist(bins=20)
Out[18]:
array([[<matplotlib.axes._subplots.AxesSubplot object at 0x00000250BD956EB8>]], dtype=object)
In [19]:
df_unsolvable.hist(bins=20)
Out[19]:
array([[<matplotlib.axes._subplots.AxesSubplot object at 0x00000250BC07E9B0>]], dtype=object)
In [82]:
comp = comps[0]
min_int = min(comp.node[node]['board_int'] for node in comp.nodes())
min_int
Out[82]:
632587360075802098027458330624
In [16]:
df_solvable.describe()
Out[16]:
0
count
1024.000000
mean
107.013672
std
100.871674
min
1.000000
25%
20.000000
50%
60.000000
75%
178.000000
max
400.000000
In [17]:
df_unsolvable.describe()
Out[17]:
0
count
337.000000
mean
20.973294
std
20.752708
min
1.000000
25%
6.000000
50%
15.000000
75%
27.000000
max
96.000000
In [83]:
#node = 0
comp_repr_node = [comp[node] for node in comp.nodes() if comp.node[node]['board_int'] == min_int]
comp_repr_node
Out[83]:
[AtlasView({233: {}, 270: {}, 231: {}, 284: {}})]
In [113]:
len([g for g in comps if len(g.nodes())==1])
Out[113]:
18
In [37]:
n = solvable_comps[40].node[0]
#HTML(RHdisplay.svg_from_state(solvable_comps[40].nodes(0)))
HTML(RHdisplay.svg_from_state(n['board_int'],n['red_col']))
Out[37]:
In [39]:
df = pd.DataFrame( [ [len(g.nodes()),g.graph['solvable'] ] for g in comps])
In [55]:
g = solvable_comps[40]
g
Out[55]:
<networkx.classes.graph.Graph at 0x250b11594a8>
In [58]:
RHdistance_partition.distance_partition(g)
Out[58]:
{'board_int': 207885568651978971296788249575424,
'inner_nbrs': {11, 49},
'is_soln_state': False,
'outer_nbrs': {9},
'red_col': 2,
'soln_distance': 4}
In [72]:
df_dist = pd.DataFrame( [g.node[n]['soln_distance'] for n in g.nodes() ], columns = ['distance'])
In [74]:
df_dist.groupby(['distance']).size()
Out[74]:
distance
0 41
1 41
2 67
3 71
4 41
5 11
6 1
dtype: int64
In [76]:
HTML(RHdisplay.svg_from_state( g.node[0]['board_int'] , g.node[0]['red_col']))
Out[76]:
In [77]:
len(solvable_comps) , len(unsolvable_comps)
Out[77]:
(1024, 337)
In [79]:
len(solvable_comps) + len(unsolvable_comps)
Out[79]:
1361
In [85]:
importlib.reload(RHdistance_partition)
for g in solvable_comps:
RHdistance_partition.distance_partition(g)
In [142]:
for g in solvable_comps:
g.graph['max_distance'] = max(g.graph['distance_partition'].keys())
for node in g.graph['distance_partition'][g.graph['max_distance']]:
break
node_dict = g.node[node]
split_board_int = RHutilities.split_int( node_dict['board_int'])
g.graph['repr_board_int_s1'] = split_board_int[0]
g.graph['repr_board_int_s2'] = split_board_int[1]
g.graph['repr_red_col'] = node_dict['red_col']
In [162]:
df_max_dist = pd.DataFrame( \
[ [g.graph['max_distance'],\
g.graph['repr_board_int_s1'],\
g.graph['repr_board_int_s2'],\
g.graph['repr_red_col']\
,len(g.nodes())\
,g.size()\
,nx.density(g)\
]\
for g in solvable_comps], columns=['distance','int_s1','int_s2','repr_red_col','num_nodes','num_edges','density'])
In [163]:
df_max_dist.sort_values(['distance'],ascending=False)
Out[163]:
distance
int_s1
int_s2
repr_red_col
num_nodes
num_edges
density
168
19
2748789558696
179633383279104
3
56
86
0.055844
769
17
2748789558696
175922545691136
3
80
149
0.047152
642
17
343598694837
25701168185664
4
116
260
0.038981
739
16
2748789776424
25701168185664
1
102
228
0.044263
830
15
2748789558696
670828544
3
84
172
0.049340
870
15
343598694837
21990330597696
4
144
344
0.033411
387
15
2748789558696
175922531535414
3
104
214
0.039955
572
15
21990316469568
670828544
2
86
161
0.044049
408
14
2748789776424
21990330597696
1
126
299
0.037968
506
14
175922531756544
68586307588
1
112
231
0.037162
455
14
175922531756544
68652368128
1
86
161
0.044049
765
14
175922531756544
68593647648
1
64
117
0.058036
505
13
22333915357509
54
1
172
432
0.029376
402
13
24739106218344
54
1
154
388
0.032934
20
12
197912848198464
237494511599616
1
102
164
0.031838
323
12
3092388470829
3710851743744
1
174
476
0.031626
795
12
175922531756800
17996806323437568
1
66
92
0.042890
88
12
197912848198464
905969664
1
126
234
0.029714
1
12
21990316663104
34493825024
1
112
231
0.037162
135
12
3092388470829
54
1
222
634
0.025845
693
12
197912848198464
3456
1
154
320
0.027162
532
12
21990316665152
175853140967424
1
66
92
0.042890
759
12
3092388470829
14155776
1
195
544
0.028760
508
12
175922531756548
17979489015300096
1
77
121
0.041353
776
12
175922531756576
17981413160648704
1
63
89
0.045571
149
11
21990316663136
2199023256063
1
159
378
0.030093
344
11
175922531756544
8650272
1
130
270
0.032200
637
11
21990316663104
1161019854422016
1
63
89
0.045571
345
11
24739643091304
0
1
165
388
0.028677
330
11
2748789776424
133955638
1
180
492
0.030540
...
...
...
...
...
...
...
...
1012
0
21990316441974
343665803525
5
3
2
0.666667
436
0
21990316703606
17592253153280
5
3
2
0.666667
1016
0
21990330597750
21990316441920
5
1
0
0.000000
468
0
21990316605814
11276634271252480
5
12
17
0.257576
815
0
21990316444534
193514784686080
5
12
17
0.257576
1018
0
15221639058817398
21990316441920
5
2
1
1.000000
1019
0
1407380252286390
1407380252282880
5
1
0
0.000000
1020
0
21990316441974
21990324830560
5
2
1
1.000000
587
0
21990316441974
22024676311360
5
5
4
0.400000
1022
0
21990316441974
21990330597696
5
1
0
0.000000
892
0
21990316441974
21990317490500
5
2
1
1.000000
1004
0
21990316441974
21990316469568
5
2
1
1.000000
738
0
21990316441974
21990853314880
5
5
4
0.400000
944
0
24189348086134
21990316441920
5
1
0
0.000000
957
0
21990316441974
17592253414912
5
6
5
0.333333
930
0
25701168185718
21990316441920
5
1
0
0.000000
931
0
22048298500470
21990316441920
5
2
1
1.000000
992
0
18001204453835126
17592253153280
5
6
5
0.333333
993
0
21990316441974
17996806390546432
5
6
5
0.333333
994
0
21990316441974
17660838412288
5
3
2
0.666667
995
0
21990316441974
15480573963272192
5
3
2
0.666667
932
0
21990316441974
15221639058817344
5
2
1
1.000000
800
0
11259042018295798
34359869440
5
3
2
0.666667
933
0
21990316441974
1147894518251840
5
5
4
0.400000
821
0
22058901700982
17592253153280
5
3
2
0.666667
1000
0
1429370568724854
67109120
5
12
17
0.257576
1001
0
21990316441974
25701168185664
5
1
0
0.000000
1002
0
21990316441974
22048298500416
5
2
1
1.000000
1003
0
21990316441974
21990316441974
5
1
0
0.000000
997
0
21990316441974
20341042708520
5
3
2
0.666667
1024 rows × 7 columns
In [164]:
board_int = RHutilities.combine_ints(21990316605814,11276634271252480)
red_col = 5
HTML(RHdisplay.svg_from_state(board_int,red_col))
Out[164]:
In [166]:
df_max_dist.groupby(['distance']).agg(['count'])
Out[166]:
int_s1
int_s2
repr_red_col
num_nodes
num_edges
density
count
count
count
count
count
count
distance
0
34
34
34
34
34
34
1
165
165
165
165
165
165
2
73
73
73
73
73
73
3
18
18
18
18
18
18
4
221
221
221
221
221
221
5
93
93
93
93
93
93
6
55
55
55
55
55
55
7
72
72
72
72
72
72
8
110
110
110
110
110
110
9
53
53
53
53
53
53
10
55
55
55
55
55
55
11
50
50
50
50
50
50
12
11
11
11
11
11
11
13
2
2
2
2
2
2
14
4
4
4
4
4
4
15
4
4
4
4
4
4
16
1
1
1
1
1
1
17
2
2
2
2
2
2
19
1
1
1
1
1
1
In [119]:
df_max_dist.sort_values(by=['distance'] , ascending = False)
Out[119]:
distance
int_s1
int_s2
repr_red_col
168
19
175922531535792
240243301154856
4
769
17
2748789558696
175922644781568
3
642
17
343598722053
25701168185664
2
739
16
28008
1924695198793728
2
830
15
140738035933224
2749850714112
1
870
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1024 rows × 4 columns
In [135]:
df_max_dist.loc[df_max_dist['distance'] == 19]
#HTML(RHdisplay.svg_from_state(n['board_int'],n['red_col']))
Out[135]:
distance
int_s1
int_s2
repr_red_col
168
19
175922531535792
240243301154856
4
In [137]:
board_int = RHutilities.combine_ints(2748789558696,240243301154856)
red_col = 4
HTML(RHdisplay.svg_from_state(board_int,red_col))
Out[137]:
In [139]:
Out[139]:
{0: {0}, 1: {1}}
In [ ]:
Content source: crhaithcock/RushHour
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