In [1]:
import numpy as np
import pandas as pd
%matplotlib inline
In [2]:
df_uni = pd.read_hdf('../reports/large-exp-uni-feat-corr-dist.h5', 'df')
df_rea = pd.read_hdf('../reports/large-exp-rea-feat-corr-dist.h5', 'df')
In [3]:
df_uni
Out[3]:
num_norm
10
...
80
num_oot
1
...
8
num_top
1
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5
...
5
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method
feature
max_features
metric
norm_dir
oot_dir
txt_comp_dist
unigram
all
correlation
bbs152930
bbs57549
0.909091
0.090909
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0.400000
0.727273
0.272727
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1.000000
0.545455
0.454545
...
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0.766667
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mus10142
0.909091
0.090909
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0.727273
0.272727
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0.545455
0.454545
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0.727273
0.272727
0.000000
1.000000
0.545455
0.454545
...
0.058722
0.004517
0.000143
0.000001
0.000000
0.000000
0.000000
0.033333
0.300000
0.666667
mus10142
0.909091
0.090909
0.600000
0.400000
0.727273
0.272727
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...
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0.000001
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0.133333
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phy40008
0.909091
0.090909
0.300000
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0.727273
0.272727
0.033333
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0.545455
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...
0.058722
0.004517
0.000143
0.000001
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0.300000
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mus1139
bbs57549
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...
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0.133333
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mus10142
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...
0.058722
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0.545455
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0.058722
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0.000143
0.000001
0.233333
0.400000
0.100000
0.200000
0.066667
0.000000
9 rows × 116 columns
In [4]:
df_rea
Out[4]:
num_norm
10
...
80
num_oot
1
...
8
num_top
1
3
5
...
5
result
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metric
norm_dir
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txt_comp_dist
readability
all
correlation
bbs152930
bbs57549
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...
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mus10142
0.909091
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0.909091
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0.727273
0.272727
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0.058722
0.004517
0.000143
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0.166667
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0
0
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mus1139
bbs57549
0.909091
0.090909
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0.066667
0.727273
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...
0.058722
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0.909091
0.090909
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...
0.058722
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0
0
0
9 rows × 116 columns
In [5]:
df = pd.concat([df_uni, df_rea])
df
Out[5]:
num_norm
10
...
80
num_oot
1
...
8
num_top
1
3
5
...
5
result
base
perf
base
perf
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...
base
perf
k
0
1
0
1
0
1
0
1
0
1
...
2
3
4
5
0
1
2
3
4
5
method
feature
max_features
metric
norm_dir
oot_dir
txt_comp_dist
unigram
all
correlation
bbs152930
bbs57549
0.909091
0.090909
0.600000
0.400000
0.727273
0.272727
0.000000
1.000000
0.545455
0.454545
...
0.058722
0.004517
0.000143
0.000001
0.000000
0.000000
0.000000
0.233333
0.766667
0.000000
mus10142
0.909091
0.090909
0.800000
0.200000
0.727273
0.272727
0.000000
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0.545455
0.454545
...
0.058722
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phy40008
0.909091
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0.545455
0.454545
...
0.058722
0.004517
0.000143
0.000001
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0.000000
0.600000
0.400000
phy17301
bbs57549
0.909091
0.090909
0.200000
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0.727273
0.272727
0.000000
1.000000
0.545455
0.454545
...
0.058722
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0.000143
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0.000000
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0.033333
0.300000
0.666667
mus10142
0.909091
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0.272727
0.000000
1.000000
0.545455
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0.058722
0.004517
0.000143
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0.000000
0.000000
0.133333
0.233333
0.433333
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phy40008
0.909091
0.090909
0.300000
0.700000
0.727273
0.272727
0.033333
0.966667
0.545455
0.454545
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0.058722
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0.000001
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mus1139
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0.058722
0.004517
0.000143
0.000001
0.033333
0.266667
0.200000
0.266667
0.133333
0.100000
mus10142
0.909091
0.090909
0.900000
0.100000
0.727273
0.272727
0.433333
0.566667
0.545455
0.454545
...
0.058722
0.004517
0.000143
0.000001
0.433333
0.466667
0.100000
0.000000
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phy40008
0.909091
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0.727273
0.272727
0.000000
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0.545455
0.454545
...
0.058722
0.004517
0.000143
0.000001
0.233333
0.400000
0.100000
0.200000
0.066667
0.000000
readability
all
correlation
bbs152930
bbs57549
0.909091
0.090909
0.966667
0.033333
0.727273
0.272727
0.700000
0.300000
0.545455
0.454545
...
0.058722
0.004517
0.000143
0.000001
0.833333
0.133333
0.033333
0.000000
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mus10142
0.909091
0.090909
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0.727273
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0.058722
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0.000000
0.000000
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phy40008
0.909091
0.090909
0.966667
0.033333
0.727273
0.272727
0.633333
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0.545455
0.454545
...
0.058722
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phy17301
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mus1139
bbs57549
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0.090909
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0.066667
0.727273
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0.545455
0.454545
...
0.058722
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mus10142
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0.909091
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18 rows × 116 columns
In [6]:
df.index = df.index.droplevel(level=['method','max_features','metric'])
In [7]:
df
Out[7]:
num_norm
10
...
80
num_oot
1
...
8
num_top
1
3
5
...
5
result
base
perf
base
perf
base
...
base
perf
k
0
1
0
1
0
1
0
1
0
1
...
2
3
4
5
0
1
2
3
4
5
feature
norm_dir
oot_dir
unigram
bbs152930
bbs57549
0.909091
0.090909
0.600000
0.400000
0.727273
0.272727
0.000000
1.000000
0.545455
0.454545
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0.058722
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mus10142
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0.545455
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0.058722
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0.909091
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0.727273
0.272727
0.000000
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0.545455
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0.058722
0.004517
0.000143
0.000001
0.000000
0.000000
0.000000
0.033333
0.300000
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mus10142
0.909091
0.090909
0.600000
0.400000
0.727273
0.272727
0.000000
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0.058722
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0.000001
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0.133333
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phy40008
0.909091
0.090909
0.300000
0.700000
0.727273
0.272727
0.033333
0.966667
0.545455
0.454545
...
0.058722
0.004517
0.000143
0.000001
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0.166667
0.300000
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mus1139
bbs57549
0.909091
0.090909
0.300000
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0.545455
0.454545
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0.058722
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0.133333
0.100000
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0.909091
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0.100000
0.727273
0.272727
0.433333
0.566667
0.545455
0.454545
...
0.058722
0.004517
0.000143
0.000001
0.433333
0.466667
0.100000
0.000000
0.000000
0.000000
phy40008
0.909091
0.090909
0.233333
0.766667
0.727273
0.272727
0.000000
1.000000
0.545455
0.454545
...
0.058722
0.004517
0.000143
0.000001
0.233333
0.400000
0.100000
0.200000
0.066667
0.000000
readability
bbs152930
bbs57549
0.909091
0.090909
0.966667
0.033333
0.727273
0.272727
0.700000
0.300000
0.545455
0.454545
...
0.058722
0.004517
0.000143
0.000001
0.833333
0.133333
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mus10142
0.909091
0.090909
1.000000
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0.727273
0.272727
0.400000
0.600000
0.545455
0.454545
...
0.058722
0.004517
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phy40008
0.909091
0.090909
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0.033333
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0.272727
0.633333
0.366667
0.545455
0.454545
...
0.058722
0.004517
0.000143
0.000001
0.933333
0.066667
0.000000
0.000000
0.000000
0.000000
phy17301
bbs57549
0.909091
0.090909
0.966667
0.033333
0.727273
0.272727
0.700000
0.300000
0.545455
0.454545
...
0.058722
0.004517
0.000143
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mus10142
0.909091
0.090909
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0.727273
0.272727
0.566667
0.433333
0.545455
0.454545
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0.058722
0.004517
0.000143
0.000001
1.000000
0.000000
0.000000
0.000000
0.000000
0.000000
phy40008
0.909091
0.090909
0.966667
0.033333
0.727273
0.272727
0.766667
0.233333
0.545455
0.454545
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0.058722
0.004517
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0.000001
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mus1139
bbs57549
0.909091
0.090909
0.933333
0.066667
0.727273
0.272727
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0.100000
0.545455
0.454545
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0.058722
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0.000143
0.000001
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0.233333
0.033333
0.000000
0.000000
0.000000
mus10142
0.909091
0.090909
0.933333
0.066667
0.727273
0.272727
0.800000
0.200000
0.545455
0.454545
...
0.058722
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0.000000
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0.000000
0.000000
phy40008
0.909091
0.090909
0.966667
0.033333
0.727273
0.272727
0.966667
0.033333
0.545455
0.454545
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0.058722
0.004517
0.000143
0.000001
0.766667
0.233333
0.000000
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18 rows × 116 columns
In [8]:
data = np.zeros_like(df)
In [9]:
df.index.levels[1]
Out[9]:
Index(['bbs152930', 'mus1139', 'phy17301'], dtype='object')
In [10]:
df.index.levels[2]
Out[10]:
Index(['bbs57549', 'mus10142', 'phy40008'], dtype='object')
In [11]:
import itertools as it
from collections import defaultdict
In [12]:
d = defaultdict(list)
In [13]:
for t1, t2 in it.product(df.index.levels[1], df.index.levels[2]):
if t1[:3] == t2[:3]:
d['within'].append((t1, t2))
else:
d['inter'].append((t1, t2))
In [14]:
d
Out[14]:
defaultdict(<class 'list'>, {'inter': [('bbs152930', 'mus10142'), ('bbs152930', 'phy40008'), ('mus1139', 'bbs57549'), ('mus1139', 'phy40008'), ('phy17301', 'bbs57549'), ('phy17301', 'mus10142')], 'within': [('bbs152930', 'bbs57549'), ('mus1139', 'mus10142'), ('phy17301', 'phy40008')]})
In [15]:
index = []
i = 0
for feat in ['readability', 'unigram']:
for t1, t2 in d['within']:
data[i,:] = df.loc[(feat, t1, t2)].values
i += 1
index.append((feat, 'within', t1, t2))
for t1, t2 in d['inter']:
data[i,:] = df.loc[(feat, t1, t2)].values
i += 1
index.append((feat, 'inter', t1, t2))
In [16]:
data
Out[16]:
array([[ 0.90909091, 0.09090909, 0.96666667, ..., 0. ,
0. , 0. ],
[ 0.90909091, 0.09090909, 0.93333333, ..., 0. ,
0. , 0. ],
[ 0.90909091, 0.09090909, 0.96666667, ..., 0. ,
0. , 0. ],
...,
[ 0.90909091, 0.09090909, 0.23333333, ..., 0.2 ,
0.06666667, 0. ],
[ 0.90909091, 0.09090909, 0.2 , ..., 0.03333333,
0.3 , 0.66666667],
[ 0.90909091, 0.09090909, 0.6 , ..., 0.23333333,
0.43333333, 0.2 ]])
In [17]:
index
Out[17]:
[('readability', 'within', 'bbs152930', 'bbs57549'),
('readability', 'within', 'mus1139', 'mus10142'),
('readability', 'within', 'phy17301', 'phy40008'),
('readability', 'inter', 'bbs152930', 'mus10142'),
('readability', 'inter', 'bbs152930', 'phy40008'),
('readability', 'inter', 'mus1139', 'bbs57549'),
('readability', 'inter', 'mus1139', 'phy40008'),
('readability', 'inter', 'phy17301', 'bbs57549'),
('readability', 'inter', 'phy17301', 'mus10142'),
('unigram', 'within', 'bbs152930', 'bbs57549'),
('unigram', 'within', 'mus1139', 'mus10142'),
('unigram', 'within', 'phy17301', 'phy40008'),
('unigram', 'inter', 'bbs152930', 'mus10142'),
('unigram', 'inter', 'bbs152930', 'phy40008'),
('unigram', 'inter', 'mus1139', 'bbs57549'),
('unigram', 'inter', 'mus1139', 'phy40008'),
('unigram', 'inter', 'phy17301', 'bbs57549'),
('unigram', 'inter', 'phy17301', 'mus10142')]
In [18]:
index = pd.MultiIndex.from_tuples(index, names=['feature','kind','norm_dir','oot_dir'])
columns = df.columns.copy()
In [19]:
result = pd.DataFrame(data, index=index, columns=columns)
In [20]:
result
Out[20]:
num_norm
10
...
80
num_oot
1
...
8
num_top
1
3
5
...
5
result
base
perf
base
perf
base
...
base
perf
k
0
1
0
1
0
1
0
1
0
1
...
2
3
4
5
0
1
2
3
4
5
feature
kind
norm_dir
oot_dir
readability
within
bbs152930
bbs57549
0.909091
0.090909
0.966667
0.033333
0.727273
0.272727
0.700000
0.300000
0.545455
0.454545
...
0.058722
0.004517
0.000143
0.000001
0.833333
0.133333
0.033333
0.000000
0.000000
0.000000
mus1139
mus10142
0.909091
0.090909
0.933333
0.066667
0.727273
0.272727
0.800000
0.200000
0.545455
0.454545
...
0.058722
0.004517
0.000143
0.000001
1.000000
0.000000
0.000000
0.000000
0.000000
0.000000
phy17301
phy40008
0.909091
0.090909
0.966667
0.033333
0.727273
0.272727
0.766667
0.233333
0.545455
0.454545
...
0.058722
0.004517
0.000143
0.000001
0.800000
0.166667
0.033333
0.000000
0.000000
0.000000
inter
bbs152930
mus10142
0.909091
0.090909
1.000000
0.000000
0.727273
0.272727
0.400000
0.600000
0.545455
0.454545
...
0.058722
0.004517
0.000143
0.000001
1.000000
0.000000
0.000000
0.000000
0.000000
0.000000
phy40008
0.909091
0.090909
0.966667
0.033333
0.727273
0.272727
0.633333
0.366667
0.545455
0.454545
...
0.058722
0.004517
0.000143
0.000001
0.933333
0.066667
0.000000
0.000000
0.000000
0.000000
mus1139
bbs57549
0.909091
0.090909
0.933333
0.066667
0.727273
0.272727
0.900000
0.100000
0.545455
0.454545
...
0.058722
0.004517
0.000143
0.000001
0.733333
0.233333
0.033333
0.000000
0.000000
0.000000
phy40008
0.909091
0.090909
0.966667
0.033333
0.727273
0.272727
0.966667
0.033333
0.545455
0.454545
...
0.058722
0.004517
0.000143
0.000001
0.766667
0.233333
0.000000
0.000000
0.000000
0.000000
phy17301
bbs57549
0.909091
0.090909
0.966667
0.033333
0.727273
0.272727
0.700000
0.300000
0.545455
0.454545
...
0.058722
0.004517
0.000143
0.000001
0.700000
0.300000
0.000000
0.000000
0.000000
0.000000
mus10142
0.909091
0.090909
1.000000
0.000000
0.727273
0.272727
0.566667
0.433333
0.545455
0.454545
...
0.058722
0.004517
0.000143
0.000001
1.000000
0.000000
0.000000
0.000000
0.000000
0.000000
unigram
within
bbs152930
bbs57549
0.909091
0.090909
0.600000
0.400000
0.727273
0.272727
0.000000
1.000000
0.545455
0.454545
...
0.058722
0.004517
0.000143
0.000001
0.000000
0.000000
0.000000
0.233333
0.766667
0.000000
mus1139
mus10142
0.909091
0.090909
0.900000
0.100000
0.727273
0.272727
0.433333
0.566667
0.545455
0.454545
...
0.058722
0.004517
0.000143
0.000001
0.433333
0.466667
0.100000
0.000000
0.000000
0.000000
phy17301
phy40008
0.909091
0.090909
0.300000
0.700000
0.727273
0.272727
0.033333
0.966667
0.545455
0.454545
...
0.058722
0.004517
0.000143
0.000001
0.033333
0.166667
0.300000
0.400000
0.100000
0.000000
inter
bbs152930
mus10142
0.909091
0.090909
0.800000
0.200000
0.727273
0.272727
0.000000
1.000000
0.545455
0.454545
...
0.058722
0.004517
0.000143
0.000001
0.000000
0.000000
0.000000
0.000000
0.700000
0.300000
phy40008
0.909091
0.090909
0.400000
0.600000
0.727273
0.272727
0.000000
1.000000
0.545455
0.454545
...
0.058722
0.004517
0.000143
0.000001
0.000000
0.000000
0.000000
0.000000
0.600000
0.400000
mus1139
bbs57549
0.909091
0.090909
0.300000
0.700000
0.727273
0.272727
0.000000
1.000000
0.545455
0.454545
...
0.058722
0.004517
0.000143
0.000001
0.033333
0.266667
0.200000
0.266667
0.133333
0.100000
phy40008
0.909091
0.090909
0.233333
0.766667
0.727273
0.272727
0.000000
1.000000
0.545455
0.454545
...
0.058722
0.004517
0.000143
0.000001
0.233333
0.400000
0.100000
0.200000
0.066667
0.000000
phy17301
bbs57549
0.909091
0.090909
0.200000
0.800000
0.727273
0.272727
0.000000
1.000000
0.545455
0.454545
...
0.058722
0.004517
0.000143
0.000001
0.000000
0.000000
0.000000
0.033333
0.300000
0.666667
mus10142
0.909091
0.090909
0.600000
0.400000
0.727273
0.272727
0.000000
1.000000
0.545455
0.454545
...
0.058722
0.004517
0.000143
0.000001
0.000000
0.000000
0.133333
0.233333
0.433333
0.200000
18 rows × 116 columns
In [21]:
result.to_hdf('../reports/large-exp-uni-rea-feat-corr-dist-within-inter-forums.h5', 'df')
Content source: kemskems/otdet
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