Evaluating association strengths of items
$begingroup$
Say I have a list of animals with their counts:
import numpy as np
import pandas as pd
from random import randint
table = np.zeros((5,1), dtype=int)
for i in range(5):
table[i]=randint(10, 20)
df1 = pd.DataFrame(columns=['Animal', 'Count'])
df1['Animal'] = animal_list
df1['Count'] = table
df1
And I have a matrix of how many times they appear together:
table = np.zeros((5,5), dtype=int)
animal_list = ['Monkey', 'Tiger', 'Cat', 'Dog', 'Lion']
for i in range(5):
for j in range(5):
table[i][j]=randint(0, 9)
df2 = pd.DataFrame(table, columns=animal_list, index=animal_list)
df2
I want to find the animals' association strength, which is defined like so - if Lion and Cat appear together 5 times, and Lion's count is 10 and Cat's count is 15, then Lion -> Cat association strength is 5/10=0.5, and Cat -> Lion association strength is 5/15=0.33.
I do it like so:
assoc_df = pd.DataFrame(columns=['Animal 1', 'Animal 2', 'Association Strength'])
for row_word in df2:
for col_word in df2:
if row_word!=col_word:
assoc_df = assoc_df.append({'Animal 1': row_word, 'Animal 2': col_word,
'Association Strength': df2[col_word][row_word]/df1[df1.Animal==row_word]['Count'].values[0]}, ignore_index=True)
assoc_df
The problem is, since there are 2 for loops, the complexity is O(n^2). This is a problem when (in my real dataset) I have ~1000 animals to loop on, which takes hours to finish computing the association strength table.
So, how to do I better optimize the creation/generation process of this association table?
python performance python-3.x community-challenge data-mining
asked 5 mins ago
Kristada673Kristada673
1463
$endgroup$
add a comment |
$begingroup$
Say I have a list of animals with their counts:
import numpy as np
import pandas as pd
from random import randint
table = np.zeros((5,1), dtype=int)
for i in range(5):
table[i]=randint(10, 20)
df1 = pd.DataFrame(columns=['Animal', 'Count'])
df1['Animal'] = animal_list
df1['Count'] = table
df1
And I have a matrix of how many times they appear together:
table = np.zeros((5,5), dtype=int)
animal_list = ['Monkey', 'Tiger', 'Cat', 'Dog', 'Lion']
for i in range(5):
for j in range(5):
table[i][j]=randint(0, 9)
df2 = pd.DataFrame(table, columns=animal_list, index=animal_list)
df2
I want to find the animals' association strength, which is defined like so - if Lion and Cat appear together 5 times, and Lion's count is 10 and Cat's count is 15, then Lion -> Cat association strength is 5/10=0.5, and Cat -> Lion association strength is 5/15=0.33.
I do it like so:
assoc_df = pd.DataFrame(columns=['Animal 1', 'Animal 2', 'Association Strength'])
for row_word in df2:
for col_word in df2:
if row_word!=col_word:
assoc_df = assoc_df.append({'Animal 1': row_word, 'Animal 2': col_word,
'Association Strength': df2[col_word][row_word]/df1[df1.Animal==row_word]['Count'].values[0]}, ignore_index=True)
assoc_df
The problem is, since there are 2 for loops, the complexity is O(n^2). This is a problem when (in my real dataset) I have ~1000 animals to loop on, which takes hours to finish computing the association strength table.
So, how to do I better optimize the creation/generation process of this association table?
python performance python-3.x community-challenge data-mining
asked 5 mins ago
Kristada673Kristada673
1463
$endgroup$
add a comment |
$begingroup$
Say I have a list of animals with their counts:
import numpy as np
import pandas as pd
from random import randint
table = np.zeros((5,1), dtype=int)
for i in range(5):
table[i]=randint(10, 20)
df1 = pd.DataFrame(columns=['Animal', 'Count'])
df1['Animal'] = animal_list
df1['Count'] = table
df1
And I have a matrix of how many times they appear together:
table = np.zeros((5,5), dtype=int)
animal_list = ['Monkey', 'Tiger', 'Cat', 'Dog', 'Lion']
for i in range(5):
for j in range(5):
table[i][j]=randint(0, 9)
df2 = pd.DataFrame(table, columns=animal_list, index=animal_list)
df2
I want to find the animals' association strength, which is defined like so - if Lion and Cat appear together 5 times, and Lion's count is 10 and Cat's count is 15, then Lion -> Cat association strength is 5/10=0.5, and Cat -> Lion association strength is 5/15=0.33.
I do it like so:
assoc_df = pd.DataFrame(columns=['Animal 1', 'Animal 2', 'Association Strength'])
for row_word in df2:
for col_word in df2:
if row_word!=col_word:
assoc_df = assoc_df.append({'Animal 1': row_word, 'Animal 2': col_word,
'Association Strength': df2[col_word][row_word]/df1[df1.Animal==row_word]['Count'].values[0]}, ignore_index=True)
assoc_df
The problem is, since there are 2 for loops, the complexity is O(n^2). This is a problem when (in my real dataset) I have ~1000 animals to loop on, which takes hours to finish computing the association strength table.
So, how to do I better optimize the creation/generation process of this association table?
python performance python-3.x community-challenge data-mining
asked 5 mins ago
Kristada673Kristada673
1463
$endgroup$
Say I have a list of animals with their counts:
import numpy as np
import pandas as pd
from random import randint
table = np.zeros((5,1), dtype=int)
for i in range(5):
table[i]=randint(10, 20)
df1 = pd.DataFrame(columns=['Animal', 'Count'])
df1['Animal'] = animal_list
df1['Count'] = table
df1
And I have a matrix of how many times they appear together:
table = np.zeros((5,5), dtype=int)
animal_list = ['Monkey', 'Tiger', 'Cat', 'Dog', 'Lion']
for i in range(5):
for j in range(5):
table[i][j]=randint(0, 9)
df2 = pd.DataFrame(table, columns=animal_list, index=animal_list)
df2
I want to find the animals' association strength, which is defined like so - if Lion and Cat appear together 5 times, and Lion's count is 10 and Cat's count is 15, then Lion -> Cat association strength is 5/10=0.5, and Cat -> Lion association strength is 5/15=0.33.
I do it like so:
assoc_df = pd.DataFrame(columns=['Animal 1', 'Animal 2', 'Association Strength'])
for row_word in df2:
for col_word in df2:
if row_word!=col_word:
assoc_df = assoc_df.append({'Animal 1': row_word, 'Animal 2': col_word,
'Association Strength': df2[col_word][row_word]/df1[df1.Animal==row_word]['Count'].values[0]}, ignore_index=True)
assoc_df
The problem is, since there are 2 for loops, the complexity is O(n^2). This is a problem when (in my real dataset) I have ~1000 animals to loop on, which takes hours to finish computing the association strength table.
So, how to do I better optimize the creation/generation process of this association table?
python performance python-3.x community-challenge data-mining
python performance python-3.x community-challenge data-mining
asked 5 mins ago
Kristada673Kristada673
1463
asked 5 mins ago
Kristada673Kristada673
1463
asked 5 mins ago
Kristada673Kristada673
1463
asked 5 mins ago
Kristada673Kristada673
1463
asked 5 mins ago
Kristada673Kristada673
1463
1463
add a comment |
add a comment |
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