one column instead of 2

dev-0/run.py

@@ -0,0 +1,87 @@
+#!/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.Series(answers)
+df.to_csv('out.tsv', sep = '\t')
+

dev-1/run.py

@@ -0,0 +1,87 @@
+#!/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.Series(answers)
+df.to_csv('out.tsv', sep = '\t')
+

run.py

@@ -54,7 +54,7 @@ for doc in trimmed_docs:
 doc_mean = sum(map(len, trimmed_docs))/float(len(trimmed_docs))
 # In[161]:
 
-print(doc_mean)
+#print(doc_mean)
 answers=[]
 for i in range(len(male_or_female)):
     if male_or_female[i][0]>male_or_female[i][1]:
@@ -82,6 +82,6 @@ for i in range(len(answers)):
 
 
 
-df = pd.DataFrame(answers)
+df = pd.Series(answers)
 df.to_csv('out.tsv', sep = '\t')
 

test-A/run.py

@@ -0,0 +1,87 @@
+#!/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.Series(answers)
+df.to_csv('out.tsv', sep = '\t')
+

train/run.py

@@ -54,7 +54,7 @@ for doc in trimmed_docs:
 doc_mean = sum(map(len, trimmed_docs))/float(len(trimmed_docs))
 # In[161]:
 
-print(doc_mean)
+#print(doc_mean)
 answers=[]
 for i in range(len(male_or_female)):
     if male_or_female[i][0]>male_or_female[i][1]:
@@ -82,6 +82,6 @@ for i in range(len(answers)):
 
 
 
-df = pd.DataFrame(answers)
+df = pd.Series(answers)
 df.to_csv('out.tsv', sep = '\t')