From 2af8e67c62e73ee864ae6d96eec31aa4216661a5 Mon Sep 17 00:00:00 2001 From: szypol Date: Thu, 8 Jul 2021 23:46:58 +0200 Subject: [PATCH] finally... after platform change solution works --- README.md | 13 + bayess.ipynb | 126 + bayess.py | 83 + config.txt | 1 + dev-0/expected.tsv | 5272 + dev-0/in.tsv.xz | Bin 0 -> 537668 bytes dev-0/out.tsv | 5272 + geval | Bin 0 -> 12638056 bytes in-header.tsv | 1 + out-header.tsv | 1 + test-A/in.tsv.xz | Bin 0 -> 540140 bytes test-A/out.tsv | 5152 + train/expected.tsv | 289579 ++++++++++++++++++++++++++++++++++++++++++ train/in.tsv.xz | Bin 0 -> 26308528 bytes 14 files changed, 305500 insertions(+) create mode 100644 README.md create mode 100644 bayess.ipynb create mode 100644 bayess.py create mode 100644 config.txt create mode 100644 dev-0/expected.tsv create mode 100644 dev-0/in.tsv.xz create mode 100644 dev-0/out.tsv create mode 100755 geval create mode 100644 in-header.tsv create mode 100644 out-header.tsv create mode 100644 test-A/in.tsv.xz create mode 100644 test-A/out.tsv create mode 100644 train/expected.tsv create mode 100644 train/in.tsv.xz diff --git a/README.md b/README.md new file mode 100644 index 0000000..8c928a6 --- /dev/null +++ b/README.md @@ -0,0 +1,13 @@ +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 . diff --git a/bayess.ipynb b/bayess.ipynb new file mode 100644 index 0000000..f8e4eb9 --- /dev/null +++ b/bayess.ipynb @@ -0,0 +1,126 @@ +{ + "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 +} diff --git a/bayess.py b/bayess.py new file mode 100644 index 0000000..4a2d86c --- /dev/null +++ b/bayess.py @@ -0,0 +1,83 @@ +#!/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') + diff --git a/config.txt b/config.txt new file mode 100644 index 0000000..f148428 --- /dev/null +++ b/config.txt @@ -0,0 +1 @@ +--metric Likelihood --metric Accuracy --metric F1 --metric F0:N --metric F9999999:N --precision 4 --in-header in-header.tsv --out-header out-header.tsv diff --git a/dev-0/expected.tsv b/dev-0/expected.tsv new file mode 100644 index 0000000..51d2bb4 --- /dev/null +++ b/dev-0/expected.tsv @@ -0,0 +1,5272 @@ +0 +1 +1 +0 +0 +0 +0 +1 +0 +0 +0 +0 +0 +1 +0 +1 +0 +0 +0 +1 +0 +0 +1 +0 +1 +0 +1 +1 +0 +1 +0 +1 +0 +0 +0 +0 +0 +0 +1 +0 +1 +0 +0 +0 +0 +0 +1 +0 +1 +1 +1 +0 +1 +0 +1 +1 +0 +0 +0 +0 +1 +1 +0 +1 +0 +1 +0 +1 +0 +1 +0 +0 +0 +1 +0 +1 +1 +0 +0 +1 +1 +0 +1 +1 +0 +0 +1 +1 +0 +1 +0 +0 +0 +0 +0 +0 +1 +1 +1 +0 +0 +1 +0 +1 +0 +1 +0 +0 +0 +1 +0 +0 +0 +0 +0 +1 +0 +0 +0 +0 +1 +1 +0 +0 +0 +1 +0 +0 +0 +0 +0 +1 +1 +1 +0 +1 +0 +0 +0 +0 +0 +0 +0 +1 +0 +0 +0 +1 +0 +1 +0 +0 +0 +0 +1 +1 +1 +0 +0 +0 +0 +1 +0 +1 +0 +0 +0 +0 +1 +1 +0 +1 +0 +0 +0 +1 +1 +0 +0 +0 +1 +0 +1 +0 +1 +0 +1 +0 +0 +0 +1 +1 +0 +0 +0 +0 +0 +0 +1 +1 +1 +1 +1 +0 +0 +0 +1 +1 +1 +1 +0 +0 +1 +0 +0 +1 +0 +0 +0 +1 +0 +1 +0 +1 +0 +0 +0 +1 +0 +0 +0 +0 +1 +1 +1 +0 +0 +0 +1 +0 +0 +0 +1 +0 +0 +1 +1 +1 +0 +0 +1 +0 +0 +1 +1 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