finally... after platform change solution works

README.md

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+Skeptic vs paranormal subreddits
+================================
+
+Classify a reddit as either from Skeptic subreddit or one of the
+"paranormal" subreddits (Paranormal, UFOs, TheTruthIsHere, Ghosts,
+,Glitch-in-the-Matrix, conspiracytheories).
+
+Output label is the probability of a paranormal subreddit.
+
+Sources
+-------
+
+Data taken from <https://archive.org/details/2015_reddit_comments_corpus>.

bayess.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "5fcb7312",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.pipeline import make_pipeline\n",
+    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
+    "from sklearn.naive_bayes import MultinomialNB\n",
+    "import pandas as pd\n",
+    "import csv\n",
+    "import numpy as np\n",
+    "from sklearn.preprocessing import LabelEncoder"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "88ac1be8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "steps = make_pipeline(TfidfVectorizer(),MultinomialNB())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "4aa43416",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#training\n",
+    "all_train_data_in = pd.read_csv('train/in.tsv.xz', compression='xz', header=None, error_bad_lines=False, quoting=csv.QUOTE_NONE, sep='\\t', nrows=3000)\n",
+    "train_data_ex = pd.read_csv('train/expected.tsv', header=None, error_bad_lines=False, quoting=csv.QUOTE_NONE, sep='\\t', nrows=3000)\n",
+    "train_data_in = []\n",
+    "for value in all_train_data_in.values:\n",
+    "    temp = \"\"\n",
+    "    for el in value:\n",
+    "        if(temp == \"\"):\n",
+    "            temp = str(el)\n",
+    "        else:\n",
+    "            temp += '\\t' + str(el)\n",
+    "    train_data_in.append(temp)\n",
+    "    \n",
+    "nb=steps.fit(train_data_in, LabelEncoder().fit_transform(train_data_ex.values))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "15c47c24",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#dev0\n",
+    "all_dev0_data = pd.read_csv('dev-0/in.tsv.xz', compression='xz', header=None, quoting=csv.QUOTE_NONE, sep='\\t')\n",
+    "dev0_data = []\n",
+    "for value in all_dev0_data.values:\n",
+    "    temp = \"\"\n",
+    "    for el in value:\n",
+    "        if(temp == \"\"):\n",
+    "            temp = str(el)\n",
+    "        else:\n",
+    "            temp += '\\t' + str(el)\n",
+    "    dev0_data.append(temp)\n",
+    "\n",
+    "\n",
+    "dev0_y = nb.predict(dev0_data)\n",
+    "\n",
+    "#zapis wyników\n",
+    "dev0_y.tofile('dev-0/out.tsv', sep='\\n')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "822b1e29",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#test-A\n",
+    "all_testA_data = pd.read_csv('test-A/in.tsv.xz', compression='xz', header=None, quoting=csv.QUOTE_NONE, sep='\\t')\n",
+    "testA_data = []\n",
+    "for value in all_testA_data.values:\n",
+    "    temp = \"\"\n",
+    "    for el in value:\n",
+    "        if(temp == \"\"):\n",
+    "            temp = str(el)\n",
+    "        else:\n",
+    "            temp += '\\t' + str(el)\n",
+    "    testA_data.append(temp)\n",
+    "\n",
+    "\n",
+    "testA_y = nb.predict(testA_data)\n",
+    "\n",
+    "#zapis wyników\n",
+    "testA_y.tofile('test-A/out.tsv', sep='\\n')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

bayess.py

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+#!/usr/bin/env python
+# coding: utf-8
+
+# In[1]:
+
+
+from sklearn.pipeline import make_pipeline
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.naive_bayes import MultinomialNB
+import pandas as pd
+import csv
+import numpy as np
+from sklearn.preprocessing import LabelEncoder
+
+
+# In[2]:
+
+
+steps = make_pipeline(TfidfVectorizer(),MultinomialNB())
+
+
+# In[14]:
+
+
+#training
+all_train_data_in = pd.read_csv('train/in.tsv.xz', compression='xz', header=None, error_bad_lines=False, quoting=csv.QUOTE_NONE, sep='\t', nrows=3000)
+train_data_ex = pd.read_csv('train/expected.tsv', header=None, error_bad_lines=False, quoting=csv.QUOTE_NONE, sep='\t', nrows=3000)
+train_data_in = []
+for value in all_train_data_in.values:
+    temp = ""
+    for el in value:
+        if(temp == ""):
+            temp = str(el)
+        else:
+            temp += '\t' + str(el)
+    train_data_in.append(temp)
+    
+nb=steps.fit(train_data_in, LabelEncoder().fit_transform(train_data_ex.values))
+
+
+# In[17]:
+
+
+#dev0
+all_dev0_data = pd.read_csv('dev-0/in.tsv.xz', compression='xz', header=None, quoting=csv.QUOTE_NONE, sep='\t')
+dev0_data = []
+for value in all_dev0_data.values:
+    temp = ""
+    for el in value:
+        if(temp == ""):
+            temp = str(el)
+        else:
+            temp += '\t' + str(el)
+    dev0_data.append(temp)
+
+
+dev0_y = nb.predict(dev0_data)
+
+#zapis wyników
+dev0_y.tofile('dev-0/out.tsv', sep='\n')
+
+
+# In[16]:
+
+
+#test-A
+all_testA_data = pd.read_csv('test-A/in.tsv.xz', compression='xz', header=None, quoting=csv.QUOTE_NONE, sep='\t')
+testA_data = []
+for value in all_testA_data.values:
+    temp = ""
+    for el in value:
+        if(temp == ""):
+            temp = str(el)
+        else:
+            temp += '\t' + str(el)
+    testA_data.append(temp)
+
+
+testA_y = nb.predict(testA_data)
+
+#zapis wyników
+testA_y.tofile('test-A/out.tsv', sep='\n')
+

config.txt

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+--metric Likelihood --metric Accuracy --metric F1 --metric F0:N<Precision> --metric F9999999:N<Recall>  --precision 4 --in-header in-header.tsv --out-header out-header.tsv

in-header.tsv

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+PostText	Timestamp

out-header.tsv

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+Label