1
2
3
4
5
6
7
8
9
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
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
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
112
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
|
# Copyright (c) 2011, 2012 Free Software Foundation
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as
# published by the Free Software Foundation, either version 3 of the
# License, or (at your option) any later version.
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Affero General Public License for more details.
# You should have received a copy of the GNU Affero General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
# This project incorporates work covered by the following copyright and permission notice:
# Copyright (c) 2009, Julien Fache
# All rights reserved.
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# * Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in
# the documentation and/or other materials provided with the
# distribution.
# * Neither the name of the author nor the names of other
# contributors may be used to endorse or promote products derived
# from this software without specific prior written permission.
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
# FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
# COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
# (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
# HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
# STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED
# OF THE POSSIBILITY OF SUCH DAMAGE.
"""Comparison tools for Gstudio
Based on clustered_models app"""
from math import sqrt
from gstudio.settings import F_MIN
from gstudio.settings import F_MAX
def pearson_score(list1, list2):
"""Compute the pearson score between 2 lists of vectors"""
sum1 = sum(list1)
sum2 = sum(list2)
sum_sq1 = sum([pow(l, 2) for l in list1])
sum_sq2 = sum([pow(l, 2) for l in list2])
prod_sum = sum([list1[i] * list2[i] for i in range(len(list1))])
num = prod_sum - (sum1 * sum2 / len(list1))
den = sqrt((sum_sq1 - pow(sum1, 2) / len(list1)) *
(sum_sq2 - pow(sum2, 2) / len(list2)))
if den == 0:
return 0.0
return 1.0 - num / den
class ClusteredModel(object):
"""Wrapper around Model class
building a dataset of instances"""
def __init__(self, queryset, fields=['id']):
self.fields = fields
self.queryset = queryset
def dataset(self):
"""Generate a dataset with the queryset
and specified fields"""
dataset = {}
for item in self.queryset.filter():
dataset[item] = ' '.join([unicode(item.__dict__[field])
for field in self.fields])
return dataset
class VectorBuilder(object):
"""Build a list of vectors based on datasets"""
def __init__(self, queryset, fields):
self.key = ''
self.columns = []
self.dataset = {}
self.clustered_model = ClusteredModel(queryset, fields)
self.build_dataset()
def build_dataset(self):
"""Generate whole dataset"""
data = {}
words_total = {}
model_data = self.clustered_model.dataset()
for instance, words in model_data.items():
words_item_total = {}
for word in words.split():
words_total.setdefault(word, 0)
words_item_total.setdefault(word, 0)
words_total[word] += 1
words_item_total[word] += 1
data[instance] = words_item_total
top_words = []
for word, count in words_total.items():
frequency = float(count) / len(data)
if frequency > F_MIN and frequency < F_MAX:
top_words.append(word)
self.dataset = {}
self.columns = top_words
for instance in data.keys():
self.dataset[instance] = [data[instance].get(word, 0)
for word in top_words]
self.key = self.generate_key()
def generate_key(self):
"""Generate key for this list of vectors"""
return self.clustered_model.queryset.count()
def flush(self):
"""Flush the dataset"""
if self.key != self.generate_key():
self.build_dataset()
def __call__(self):
self.flush()
return self.columns, self.dataset
|