deletion of unnecesary run.py
This commit is contained in:
Adrian Charkiewicz
parent
8c8f903b11
commit
b27e12d4d7
@ -1,4 +0,0 @@
|
||||
<a name="2.0.0"></a>
|
||||
## 2.0.0 (2020-05-22)
|
||||
|
||||
* Switch to probabilities as the main metric
|
||||
@ -1,6 +0,0 @@
|
||||
{
|
||||
"cells": [],
|
||||
"metadata": {},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
File diff suppressed because it is too large
Load Diff
@ -1,3 +0,0 @@
|
||||
{
|
||||
"notebooks": {}
|
||||
}
|
||||
1212
dev-0/run.ipynb
1212
dev-0/run.ipynb
File diff suppressed because it is too large
Load Diff
87
dev-0/run.py
87
dev-0/run.py
@ -1,87 +0,0 @@
|
||||
#!/usr/bin/env python
|
||||
# coding: utf-8
|
||||
|
||||
# In[90]:
|
||||
|
||||
|
||||
import pandas as pd
|
||||
import csv
|
||||
|
||||
|
||||
# In[91]:
|
||||
|
||||
|
||||
tsv_data = pd.read_csv('in.tsv', sep='\t',header=None, quoting=csv.QUOTE_NONE)[0]
|
||||
|
||||
|
||||
# In[139]:
|
||||
|
||||
|
||||
#expected = pd.read_csv('expected.tsv', sep='\t',header=None)[0]
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
# In[158]:
|
||||
|
||||
|
||||
male={'silnik', 'windows', 'gb', 'mb', 'mecz', 'pc', 'opony', 'apple', 'iphone', 'zwiastuny', 'hd', 'ubuntu', 'system', 'serwer', 'piłka', 'metal'}
|
||||
female={'ciąża', 'miesiączki', 'ciasto', 'ciąże', 'zadowolona', 'antykoncepcyjne', 'ginekologia', 'tabletki', 'porodzie', 'mąż', 'krwawienie', 'ciasta', 'narzeczony', 'ślub'}
|
||||
male = {x[:6].lower() for x in male}
|
||||
female = {x[:6].lower() for x in female}
|
||||
|
||||
|
||||
# In[159]:
|
||||
|
||||
|
||||
trimmed_docs=[]
|
||||
for document in tsv_data:
|
||||
new_doc=[]
|
||||
for word in str(document).lower().split():
|
||||
new_doc.append(word[:6])
|
||||
trimmed_docs.append(new_doc)
|
||||
|
||||
|
||||
# In[160]:
|
||||
|
||||
|
||||
male_or_female=[]
|
||||
|
||||
for doc in trimmed_docs:
|
||||
male_or_female.append((len(male&set(doc)), len(female&set(doc))))
|
||||
|
||||
doc_mean = sum(map(len, trimmed_docs))/float(len(trimmed_docs))
|
||||
# In[161]:
|
||||
|
||||
print(doc_mean)
|
||||
answers=[]
|
||||
for i in range(len(male_or_female)):
|
||||
if male_or_female[i][0]>male_or_female[i][1]:
|
||||
answers.append(1)
|
||||
elif male_or_female[i][0]<male_or_female[i][1]:
|
||||
answers.append(0)
|
||||
else:
|
||||
if len(trimmed_docs[i]) < doc_mean:
|
||||
answers.append(0)
|
||||
else:
|
||||
answers.append(1)
|
||||
|
||||
|
||||
|
||||
# In[162]:
|
||||
|
||||
"""
|
||||
result=[]
|
||||
for i in range(len(answers)):
|
||||
if answers[i]==expected[i]:
|
||||
result.append(1)
|
||||
else:
|
||||
result.append(0)
|
||||
"""
|
||||
|
||||
|
||||
|
||||
df = pd.DataFrame(answers)
|
||||
df.to_csv('out.tsv', sep = '\t')
|
||||
|
||||
87
dev-1/run.py
87
dev-1/run.py
@ -1,87 +0,0 @@
|
||||
#!/usr/bin/env python
|
||||
# coding: utf-8
|
||||
|
||||
# In[90]:
|
||||
|
||||
|
||||
import pandas as pd
|
||||
import csv
|
||||
|
||||
|
||||
# In[91]:
|
||||
|
||||
|
||||
tsv_data = pd.read_csv('in.tsv', sep='\t',header=None, quoting=csv.QUOTE_NONE)[0]
|
||||
|
||||
|
||||
# In[139]:
|
||||
|
||||
|
||||
#expected = pd.read_csv('expected.tsv', sep='\t',header=None)[0]
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
# In[158]:
|
||||
|
||||
|
||||
male={'silnik', 'windows', 'gb', 'mb', 'mecz', 'pc', 'opony', 'apple', 'iphone', 'zwiastuny', 'hd', 'ubuntu', 'system', 'serwer', 'piłka', 'metal'}
|
||||
female={'ciąża', 'miesiączki', 'ciasto', 'ciąże', 'zadowolona', 'antykoncepcyjne', 'ginekologia', 'tabletki', 'porodzie', 'mąż', 'krwawienie', 'ciasta', 'narzeczony', 'ślub'}
|
||||
male = {x[:6].lower() for x in male}
|
||||
female = {x[:6].lower() for x in female}
|
||||
|
||||
|
||||
# In[159]:
|
||||
|
||||
|
||||
trimmed_docs=[]
|
||||
for document in tsv_data:
|
||||
new_doc=[]
|
||||
for word in str(document).lower().split():
|
||||
new_doc.append(word[:6])
|
||||
trimmed_docs.append(new_doc)
|
||||
|
||||
|
||||
# In[160]:
|
||||
|
||||
|
||||
male_or_female=[]
|
||||
|
||||
for doc in trimmed_docs:
|
||||
male_or_female.append((len(male&set(doc)), len(female&set(doc))))
|
||||
|
||||
doc_mean = sum(map(len, trimmed_docs))/float(len(trimmed_docs))
|
||||
# In[161]:
|
||||
|
||||
print(doc_mean)
|
||||
answers=[]
|
||||
for i in range(len(male_or_female)):
|
||||
if male_or_female[i][0]>male_or_female[i][1]:
|
||||
answers.append(1)
|
||||
elif male_or_female[i][0]<male_or_female[i][1]:
|
||||
answers.append(0)
|
||||
else:
|
||||
if len(trimmed_docs[i]) < doc_mean:
|
||||
answers.append(0)
|
||||
else:
|
||||
answers.append(1)
|
||||
|
||||
|
||||
|
||||
# In[162]:
|
||||
|
||||
"""
|
||||
result=[]
|
||||
for i in range(len(answers)):
|
||||
if answers[i]==expected[i]:
|
||||
result.append(1)
|
||||
else:
|
||||
result.append(0)
|
||||
"""
|
||||
|
||||
|
||||
|
||||
df = pd.DataFrame(answers)
|
||||
df.to_csv('out.tsv', sep = '\t')
|
||||
|
||||
@ -1,87 +0,0 @@
|
||||
#!/usr/bin/env python
|
||||
# coding: utf-8
|
||||
|
||||
# In[90]:
|
||||
|
||||
|
||||
import pandas as pd
|
||||
import csv
|
||||
|
||||
|
||||
# In[91]:
|
||||
|
||||
|
||||
tsv_data = pd.read_csv('in.tsv', sep='\t',header=None, quoting=csv.QUOTE_NONE)[0]
|
||||
|
||||
|
||||
# In[139]:
|
||||
|
||||
|
||||
#expected = pd.read_csv('expected.tsv', sep='\t',header=None)[0]
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
# In[158]:
|
||||
|
||||
|
||||
male={'silnik', 'windows', 'gb', 'mb', 'mecz', 'pc', 'opony', 'apple', 'iphone', 'zwiastuny', 'hd', 'ubuntu', 'system', 'serwer', 'piłka', 'metal'}
|
||||
female={'ciąża', 'miesiączki', 'ciasto', 'ciąże', 'zadowolona', 'antykoncepcyjne', 'ginekologia', 'tabletki', 'porodzie', 'mąż', 'krwawienie', 'ciasta', 'narzeczony', 'ślub'}
|
||||
male = {x[:6].lower() for x in male}
|
||||
female = {x[:6].lower() for x in female}
|
||||
|
||||
|
||||
# In[159]:
|
||||
|
||||
|
||||
trimmed_docs=[]
|
||||
for document in tsv_data:
|
||||
new_doc=[]
|
||||
for word in str(document).lower().split():
|
||||
new_doc.append(word[:6])
|
||||
trimmed_docs.append(new_doc)
|
||||
|
||||
|
||||
# In[160]:
|
||||
|
||||
|
||||
male_or_female=[]
|
||||
|
||||
for doc in trimmed_docs:
|
||||
male_or_female.append((len(male&set(doc)), len(female&set(doc))))
|
||||
|
||||
doc_mean = sum(map(len, trimmed_docs))/float(len(trimmed_docs))
|
||||
# In[161]:
|
||||
|
||||
print(doc_mean)
|
||||
answers=[]
|
||||
for i in range(len(male_or_female)):
|
||||
if male_or_female[i][0]>male_or_female[i][1]:
|
||||
answers.append(1)
|
||||
elif male_or_female[i][0]<male_or_female[i][1]:
|
||||
answers.append(0)
|
||||
else:
|
||||
if len(trimmed_docs[i]) < doc_mean:
|
||||
answers.append(0)
|
||||
else:
|
||||
answers.append(1)
|
||||
|
||||
|
||||
|
||||
# In[162]:
|
||||
|
||||
"""
|
||||
result=[]
|
||||
for i in range(len(answers)):
|
||||
if answers[i]==expected[i]:
|
||||
result.append(1)
|
||||
else:
|
||||
result.append(0)
|
||||
"""
|
||||
|
||||
|
||||
|
||||
df = pd.DataFrame(answers)
|
||||
df.to_csv('out.tsv', sep = '\t')
|
||||
|
||||
Loading…
Reference in New Issue
Block a user