Created
March 27, 2017 03:47
-
-
Save spencebeecher/04e79631e9725ea25889a53117319412 to your computer and use it in GitHub Desktop.
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
# coding: utf-8 | |
# In[1]: | |
import pysparnn as snn | |
# In[2]: | |
import pysparnn.matrix_distance | |
# In[3]: | |
import sklearn | |
import sklearn.metrics.pairwise | |
import scipy.sparse as sparse | |
import numpy as np | |
class UserCustomDistance(pysparnn.matrix_distance.MatrixMetricSearch): | |
def __init__(self, features, records_data): | |
super(UserCustomDistance, self).__init__(features, records_data) | |
self.matrix = self.matrix | |
self.max_overlap = self.matrix.shape[0] # for testing purpose | |
@staticmethod | |
def features_to_matrix(features): | |
return features | |
@staticmethod | |
def vstack(matrix_list): | |
return np.vstack(matrix_list) | |
def _transform_value(self, v): | |
return v | |
def user_distance_metric(self, u, v): | |
rep = sparse.csr_matrix(np.minimum(u.A, v.A)) | |
return self.max_overlap - rep.sum() | |
def _distance(self, a_matrix): | |
return sklearn.metrics.pairwise.pairwise_distances( | |
a_matrix, self.matrix, lambda u, v: self.user_distance_metric(u, v)) | |
# In[4]: | |
class SpencerUserCustomDistance(pysparnn.matrix_distance.MatrixMetricSearch): | |
def __init__(self, features, records_data): | |
super(SpencerUserCustomDistance, self).__init__(features, records_data) | |
self.matrix = self.matrix | |
self.max_overlap = self.matrix.shape[0] # for testing purpose | |
@staticmethod | |
def features_to_matrix(features): | |
return features | |
@staticmethod | |
def vstack(matrix_list): | |
return np.vstack(matrix_list) | |
@staticmethod | |
def _transform_value(self, v): | |
return v | |
def _distance(self, a_matrix): | |
return np.array(self.max_overlap - self.matrix.dot(a_matrix.transpose()).transpose().todense()) | |
# In[5]: | |
B = sparse.csr_matrix([[0, 0, 1],[0, 1, 1],[1, 1, 1]]) | |
A = sparse.csr_matrix( | |
[ | |
[0, 0, 1], | |
[0, 1, 1], | |
[1, 1, 1] | |
]) | |
# In[6]: | |
u_dist = UserCustomDistance(A, range(A.shape[0])) | |
u_dist._distance(B) | |
# In[7]: | |
su_dist = SpencerUserCustomDistance(A, range(A.shape[0])) | |
su_dist._distance(B) | |
# In[8]: | |
import scipy | |
# In[9]: | |
from scipy.sparse import rand | |
# In[10]: | |
from scipy.sparse import csr_matrix | |
# In[39]: | |
num_records = 100000 | |
matrix = csr_matrix(rand(num_records, num_records, density=0.0002, format='csr')) | |
# In[40]: | |
matrix.sum(axis=1).mean() | |
# In[41]: | |
import pysparnn.cluster_index as ci | |
# In[42]: | |
import time | |
# In[45]: | |
t0 = time.time() | |
cp2 = ci.MultiClusterIndex(matrix, np.array(range(num_records)), distance_type=SpencerUserCustomDistance) | |
t1 = time.time() | |
(t1 - t0) | |
# In[ ]: | |
t0 = time.time() | |
cp2 = ci.MultiClusterIndex(matrix, np.array(range(num_records)), distance_type=UserCustomDistance) | |
# killed after 7 min, this will take a very long time probably | |
# In[47]: | |
t1 = time.time() | |
(t1 - t0) | |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment