one too many row

CHANGELOG.md

@@ -1,4 +0,0 @@
-<a name="2.0.0"></a>
-## 2.0.0 (2020-05-22)
-
-* Switch to probabilities as the main metric

dev-0/.ipynb_checkpoints/model-checkpoint.ipynb

@@ -1,6 +0,0 @@
-{
- "cells": [],
- "metadata": {},
- "nbformat": 4,
- "nbformat_minor": 5
-}

dev-0/.jovianrc

@@ -1,3 +0,0 @@
-{
-  "notebooks": {}
-}

dev-0/run.ipynb

@@ -1,175 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 90,
-   "id": "7dc5e391",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import csv"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 91,
-   "id": "a0825c64",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tsv_data = pd.read_csv('in.tsv', sep='\\t',header=None, quoting=csv.QUOTE_NONE)[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 139,
-   "id": "4b9092a6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "expected = pd.read_csv('expected.tsv', sep='\\t',header=None)[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 94,
-   "id": "56c39aa1",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "137314\n",
-      "137314\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(len(expected))\n",
-    "print(len(tsv_data))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 158,
-   "id": "d7b300ca",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "male={'silnik', 'windows', 'gb', 'mb', 'mecz', 'pc', 'opony', 'apple', 'iphone', 'zwiastuny', 'hd', 'ubuntu', 'system', 'serwer'}\n",
-    "female={'ciąża', 'miesiączki', 'ciasto', 'ciąże', 'zadowolona', 'antykoncepcyjne', 'ginekologia', 'tabletki', 'porodzie', 'mąż', 'krwawienie', 'ciasta'}\n",
-    "male = {x[:6].lower() for x in male}\n",
-    "female = {x[:6].lower() for x in female}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 159,
-   "id": "31b5864b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "trimmed_docs=[]\n",
-    "for document in tsv_data:\n",
-    "    new_doc=[]\n",
-    "    for word in str(document).lower().split():\n",
-    "        new_doc.append(word[:6])\n",
-    "    trimmed_docs.append(new_doc)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 160,
-   "id": "c1f02d77",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "male_or_female=[]\n",
-    "for doc in trimmed_docs:\n",
-    "    male_or_female.append((len(male&set(doc)), len(female&set(doc))))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 161,
-   "id": "6edfd944",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "answers=[]\n",
-    "for i in male_or_female:\n",
-    "    if i[0]>i[1]:\n",
-    "        answers.append(1)\n",
-    "    else:\n",
-    "        answers.append(0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 162,
-   "id": "40369c2b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "result=[]\n",
-    "for i in range(len(answers)):\n",
-    "    if answers[i]==expected[i]:\n",
-    "        result.append(1)\n",
-    "    else:\n",
-    "        result.append(0)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 163,
-   "id": "e296921c",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Predykcja modelu wynosi 51.007909%\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f'Predykcja modelu wynosi {sum(result)/len(result)*100:.6f}%')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 167,
-   "id": "fee431a4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df = pd.DataFrame(result)\n",
-    "df.to_csv('out.tsv', sep = '\\t')"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.7"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

dev-0/run.py

@@ -17,21 +17,17 @@ 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]
+#expected = pd.read_csv('expected.tsv', sep='\t',header=None)[0]
 
 
-# In[94]:
 
 
-print(len(expected))
-print(len(tsv_data))
-
 
 # In[158]:
 
 
-male={'silnik', 'windows', 'gb', 'mb', 'mecz', 'pc', 'opony', 'apple', 'iphone', 'zwiastuny', 'hd', 'ubuntu', 'system', 'serwer'}
-female={'ciąża', 'miesiączki', 'ciasto', 'ciąże', 'zadowolona', 'antykoncepcyjne', 'ginekologia', 'tabletki', 'porodzie', 'mąż', 'krwawienie', 'ciasta'}
+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}
 
@@ -51,41 +47,41 @@ for document in tsv_data:
 
 
 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 male_or_female:
-    if i[0]>i[1]:
+for i in range(len(male_or_female)):
+    if male_or_female[i][0]>male_or_female[i][1]:
         answers.append(1)
-    else:
+    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)
+"""
 
 
-# In[163]:
 
-
-print(f'Predykcja modelu wynosi {sum(result)/len(result)*100:.6f}%')
-
-
-# In[167]:
-
-
-df = pd.DataFrame(result)
-df.to_csv('out.tsv', sep = '\t')
+df = pd.Series(answers)
+df.to_csv('out.tsv', sep = '\t', index=False, header=False)
 

dev-1/.~lock.expected.tsv#

@@ -0,0 +1 @@
+,DESKTOP-QF2J2E3/riraa,DESKTOP-QF2J2E3,27.04.2022 01:17,file:///C:/Users/riraa/AppData/Roaming/LibreOffice/4;

dev-1/.~lock.out.tsv#

@@ -0,0 +1 @@
+,DESKTOP-QF2J2E3/riraa,DESKTOP-QF2J2E3,27.04.2022 01:16,file:///C:/Users/riraa/AppData/Roaming/LibreOffice/4;

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', index=False, header=False)
+

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', index=False, header=False)
+

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', index=False, header=False)
+

train/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', index=False, header=False)
+