Sklearn multinomial bayes
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Th3NiKo
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Paranormal_or_skeptic.ipynb
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530
Paranormal_or_skeptic.ipynb
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{
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"nbformat": 4,
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"metadata": {
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"colab": {
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"name": "Paranormal or skeptic.ipynb",
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"provenance": [],
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"collapsed_sections": [],
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"toc_visible": true
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"kernelspec": {
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"name": "python3",
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"display_name": "Python 3"
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}
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},
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "x8uZz8__5sXr",
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"colab_type": "text"
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},
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"source": [
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"\n",
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"# Loading Data\n"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "NQFKg_czGeRA",
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"colab_type": "code",
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"outputId": "60d1c52a-8b42-4a26-d878-67f284589917",
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 34
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}
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},
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"source": [
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"!xzcat train/in.tsv.xz | wc -l"
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],
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"execution_count": 0,
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"outputs": [
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{
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"output_type": "stream",
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"text": [
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"289579\n"
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],
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"name": "stdout"
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}
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "GxUYlO5M6SOJ",
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"colab_type": "code",
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"colab": {}
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},
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"source": [
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"import matplotlib.pyplot as plt\n",
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"import seaborn as sns\n",
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"import pandas as pd\n",
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"import numpy as np\n",
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"from scipy.sparse import hstack\n",
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"import csv\n",
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"import datetime"
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],
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"execution_count": 0,
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"outputs": []
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "MWDzekYY6S9f",
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"colab_type": "code",
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"colab": {}
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},
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"source": [
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"from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer\n",
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"from sklearn.pipeline import Pipeline\n",
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"from sklearn.linear_model import SGDClassifier\n",
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"from sklearn.naive_bayes import MultinomialNB,ComplementNB,BernoulliNB,GaussianNB"
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],
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"execution_count": 0,
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"outputs": []
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "SrLtGV3p4pKW",
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"colab_type": "code",
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"colab": {}
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},
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"source": [
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"def load_set(path, isTest):\n",
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" dataset = pd.read_csv(path+\"/in.tsv.xz\", delimiter=\"\\t\",header=None,names=[\"text\",\"date\"],quoting=csv.QUOTE_NONE)\n",
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" dataset[\"date\"] = pd.to_datetime(dataset[\"date\"].apply(lambda x: datetime.datetime.fromtimestamp(x).isoformat()))\n",
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" if not isTest:\n",
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" expected = pd.read_csv(path+\"/expected.tsv\",header=None,names=[\"class\"],dtype=\"category\")\n",
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" return dataset, expected\n",
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" return dataset"
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],
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"execution_count": 0,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "wH70ClgjBeCO",
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"colab_type": "text"
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},
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"source": [
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"**Load all sets**"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "huOmuCrE6yCR",
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"colab_type": "code",
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"colab": {}
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},
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"source": [
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"train_set, expected_train = load_set(\"train\", False)\n",
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"dev_set, expected_dev = load_set(\"dev-0\", False)\n",
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"test_set = load_set(\"test-A\", True)"
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],
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"execution_count": 0,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "mWO1IroV6cmm",
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"colab_type": "text"
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},
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"source": [
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"# Prepare data"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "VVd7DJ1E6cOO",
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"colab_type": "code",
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"colab": {}
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},
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"source": [
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"def prepare_data(data):\n",
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" data[\"day\"] = data[\"date\"].dt.day\n",
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" data[\"month\"] = data[\"date\"].dt.month\n",
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" data[\"year\"] = data[\"date\"].dt.year\n",
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" return data"
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],
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"execution_count": 0,
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"outputs": []
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "i_k63XB5642m",
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"colab_type": "code",
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"colab": {}
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},
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"source": [
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"train_set = prepare_data(train_set)"
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],
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"execution_count": 0,
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"outputs": []
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "dcjUSa7f7Wex",
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"colab_type": "code",
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"outputId": "9fa0ca70-0516-4656-a1d5-641e5b0f41ff",
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 204
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}
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},
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"source": [
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"train_set.sample(5)"
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],
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"execution_count": 0,
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"outputs": [
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{
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"output_type": "execute_result",
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>text</th>\n",
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" <th>date</th>\n",
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" <th>day</th>\n",
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" <th>month</th>\n",
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" <th>year</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>103770</th>\n",
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" <td>Holy crap. I don't think I've seen or heard o...</td>\n",
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" <td>2010-07-16 19:27:08</td>\n",
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" <td>16</td>\n",
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" <td>7</td>\n",
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" <td>2010</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>240391</th>\n",
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" <td>You lost all pretense of civility with your ar...</td>\n",
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" <td>2010-09-30 12:18:36</td>\n",
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" <td>30</td>\n",
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" <td>9</td>\n",
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" <td>2010</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>220910</th>\n",
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" <td>What do people think of ghost adventures? Cur...</td>\n",
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" <td>2012-08-21 19:59:56</td>\n",
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" <td>21</td>\n",
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" <td>8</td>\n",
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" <td>2012</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>39644</th>\n",
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" <td>Congrats on getting the joke.</td>\n",
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" <td>2011-07-29 18:19:46</td>\n",
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" <td>29</td>\n",
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" <td>7</td>\n",
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" <td>2011</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>220867</th>\n",
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" <td>We live in a world where any media can be copi...</td>\n",
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" <td>2012-07-18 08:53:24</td>\n",
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" <td>18</td>\n",
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" <td>7</td>\n",
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" <td>2012</td>\n",
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" </tr>\n",
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||||
" </tbody>\n",
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||||
"</table>\n",
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"</div>"
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],
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"text/plain": [
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" text ... year\n",
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"103770 Holy crap. I don't think I've seen or heard o... ... 2010\n",
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"240391 You lost all pretense of civility with your ar... ... 2010\n",
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||||
"220910 What do people think of ghost adventures? Cur... ... 2012\n",
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||||
"39644 Congrats on getting the joke. ... 2011\n",
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"220867 We live in a world where any media can be copi... ... 2012\n",
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"\n",
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"[5 rows x 5 columns]"
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]
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||||
},
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"metadata": {
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||||
"tags": []
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||||
},
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||||
"execution_count": 12
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||||
}
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||||
]
|
||||
},
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||||
{
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||||
"cell_type": "markdown",
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||||
"metadata": {
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||||
"id": "hIZZ9vcu5Xx7",
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"colab_type": "text"
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},
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"source": [
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"# Train"
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]
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||||
},
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{
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||||
"cell_type": "code",
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||||
"metadata": {
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||||
"id": "yqHuHTyI8Kfz",
|
||||
"colab_type": "code",
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||||
"colab": {}
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||||
},
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"source": [
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"vectorize = CountVectorizer(stop_words='english',ngram_range=(1,3),strip_accents='ascii')\n",
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"vectorized = vectorize.fit_transform(train_set[\"text\"])"
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],
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"execution_count": 0,
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"outputs": []
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||||
},
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{
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||||
"cell_type": "code",
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||||
"metadata": {
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||||
"id": "ZaLsOdPe9aFu",
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||||
"colab_type": "code",
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"colab": {}
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},
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"source": [
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"X = vectorized\n",
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"y = expected_train[\"class\"]"
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],
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"execution_count": 0,
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"outputs": []
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},
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{
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"cell_type": "code",
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"metadata": {
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||||
"id": "CeYlhwda9Sa7",
|
||||
"colab_type": "code",
|
||||
"outputId": "607d4f8f-f632-4d41-a1ab-e5d020cc00ae",
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||||
"colab": {
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||||
"base_uri": "https://localhost:8080/",
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"height": 34
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}
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},
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"source": [
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"bayes = MultinomialNB(alpha=0.4)\n",
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"bayes.fit(X,y)"
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],
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"execution_count": 0,
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"outputs": [
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{
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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"MultinomialNB(alpha=0.4, class_prior=None, fit_prior=True)"
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]
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},
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"metadata": {
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"tags": []
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},
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||||
"execution_count": 15
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}
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]
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||||
},
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||||
{
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"cell_type": "markdown",
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"metadata": {
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||||
"id": "SaIcL28I-JCK",
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"colab_type": "text"
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},
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"source": [
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"# Predict and evaluate"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
|
||||
"id": "q34dlX_43ZoV",
|
||||
"colab_type": "code",
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"colab": {}
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},
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"source": [
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"def predict_data(data):\n",
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" prepared = prepare_data(data)\n",
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" vectorized = vectorize.transform(data[\"text\"])\n",
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" predicted = bayes.predict(vectorized)\n",
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" return predicted"
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],
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"execution_count": 0,
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"outputs": []
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},
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||||
{
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||||
"cell_type": "code",
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"metadata": {
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||||
"id": "9sLnLLEUHgoM",
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"colab_type": "code",
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"colab": {}
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},
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"source": [
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"dev_predicted = predict_data(dev_set)"
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],
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"execution_count": 0,
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"outputs": []
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},
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{
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||||
"cell_type": "code",
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||||
"metadata": {
|
||||
"id": "yigVrrVJHkob",
|
||||
"colab_type": "code",
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"outputId": "9491f926-94a3-4310-9f63-be937663489d",
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 34
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}
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},
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"source": [
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"np.mean(dev_predicted == expected_dev[\"class\"])"
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],
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"execution_count": 0,
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"outputs": [
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{
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"output_type": "execute_result",
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||||
"data": {
|
||||
"text/plain": [
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||||
"0.8201820940819423"
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||||
]
|
||||
},
|
||||
"metadata": {
|
||||
"tags": []
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},
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||||
"execution_count": 19
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||||
}
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||||
]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "gPdE2HK64aRZ",
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"colab_type": "code",
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"colab": {}
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},
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"source": [
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"test_predicted = predict_data(test_set)"
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],
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"execution_count": 0,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "QFxuvfUJ8AhJ",
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"colab_type": "text"
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},
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"source": [
|
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"**Clean output for saving**"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "zjypBm1260h1",
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"colab_type": "code",
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"colab": {}
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},
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"source": [
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"test_predicted = np.array([item.strip() for item in test_predicted])\n",
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"dev_predicted = np.array([item.strip() for item in dev_predicted])"
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],
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"execution_count": 0,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"metadata": {
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||||
"id": "baJydHEl4H7N",
|
||||
"colab_type": "text"
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},
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"source": [
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"**Save to file**\n"
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]
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},
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{
|
||||
"cell_type": "code",
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"metadata": {
|
||||
"id": "O6gyoEJf4KhS",
|
||||
"colab_type": "code",
|
||||
"colab": {}
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},
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"source": [
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"np.savetxt('test-A/out.tsv', test_predicted, '%c')\n",
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"np.savetxt('dev-0/out.tsv', dev_predicted, '%c')"
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],
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"execution_count": 0,
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"outputs": []
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},
|
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{
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"cell_type": "markdown",
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||||
"metadata": {
|
||||
"id": "jIG2Fxrm89D7",
|
||||
"colab_type": "text"
|
||||
},
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"source": [
|
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"**Check geval output**"
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]
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},
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{
|
||||
"cell_type": "code",
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"metadata": {
|
||||
"id": "mnch9uLE8vkK",
|
||||
"colab_type": "code",
|
||||
"colab": {}
|
||||
},
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"source": [
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"!wget https://gonito.net/get/bin/geval\n",
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"!chmod u+x geval"
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],
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"execution_count": 0,
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"outputs": []
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},
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||||
{
|
||||
"cell_type": "code",
|
||||
"metadata": {
|
||||
"id": "oEkjIcwe8zef",
|
||||
"colab_type": "code",
|
||||
"outputId": "cdb6473e-4eb9-48a7-cc25-25a193cc9194",
|
||||
"colab": {
|
||||
"base_uri": "https://localhost:8080/",
|
||||
"height": 34
|
||||
}
|
||||
},
|
||||
"source": [
|
||||
"!./geval -t \"dev-0\""
|
||||
],
|
||||
"execution_count": 0,
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"0.8202\n"
|
||||
],
|
||||
"name": "stdout"
|
||||
}
|
||||
]
|
||||
}
|
||||
]
|
||||
}
|
||||
5272
dev-0/out.tsv
Normal file
5272
dev-0/out.tsv
Normal file
File diff suppressed because it is too large
Load Diff
1
link_to_collab.txt
Normal file
1
link_to_collab.txt
Normal file
@ -0,0 +1 @@
|
||||
https://colab.research.google.com/drive/1JI_RWapDbABFZPc4NDhU-zQlZiIiXk58
|
||||
93
paranormal_or_skeptic.py
Normal file
93
paranormal_or_skeptic.py
Normal file
@ -0,0 +1,93 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
"""Paranormal or skeptic.ipynb
|
||||
|
||||
Automatically generated by Colaboratory.
|
||||
|
||||
Original file is located at
|
||||
https://colab.research.google.com/drive/1JI_RWapDbABFZPc4NDhU-zQlZiIiXk58
|
||||
|
||||
# Loading Data
|
||||
"""
|
||||
|
||||
!xzcat train/in.tsv.xz | wc -l
|
||||
|
||||
import matplotlib.pyplot as plt
|
||||
import seaborn as sns
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
from scipy.sparse import hstack
|
||||
import csv
|
||||
import datetime
|
||||
|
||||
from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer
|
||||
from sklearn.pipeline import Pipeline
|
||||
from sklearn.linear_model import SGDClassifier
|
||||
from sklearn.naive_bayes import MultinomialNB,ComplementNB,BernoulliNB,GaussianNB
|
||||
|
||||
def load_set(path, isTest):
|
||||
dataset = pd.read_csv(path+"/in.tsv.xz", delimiter="\t",header=None,names=["text","date"],quoting=csv.QUOTE_NONE)
|
||||
dataset["date"] = pd.to_datetime(dataset["date"].apply(lambda x: datetime.datetime.fromtimestamp(x).isoformat()))
|
||||
if not isTest:
|
||||
expected = pd.read_csv(path+"/expected.tsv",header=None,names=["class"],dtype="category")
|
||||
return dataset, expected
|
||||
return dataset
|
||||
|
||||
"""**Load all sets**"""
|
||||
|
||||
train_set, expected_train = load_set("train", False)
|
||||
dev_set, expected_dev = load_set("dev-0", False)
|
||||
test_set = load_set("test-A", True)
|
||||
|
||||
"""# Prepare data"""
|
||||
|
||||
def prepare_data(data):
|
||||
data["day"] = data["date"].dt.day
|
||||
data["month"] = data["date"].dt.month
|
||||
data["year"] = data["date"].dt.year
|
||||
return data
|
||||
|
||||
train_set = prepare_data(train_set)
|
||||
|
||||
train_set.sample(5)
|
||||
|
||||
"""# Train"""
|
||||
|
||||
vectorize = CountVectorizer(stop_words='english',ngram_range=(1,3),strip_accents='ascii')
|
||||
vectorized = vectorize.fit_transform(train_set["text"])
|
||||
|
||||
X = vectorized
|
||||
y = expected_train["class"]
|
||||
|
||||
bayes = MultinomialNB(alpha=0.4)
|
||||
bayes.fit(X,y)
|
||||
|
||||
"""# Predict and evaluate"""
|
||||
|
||||
def predict_data(data):
|
||||
prepared = prepare_data(data)
|
||||
vectorized = vectorize.transform(data["text"])
|
||||
predicted = bayes.predict(vectorized)
|
||||
return predicted
|
||||
|
||||
dev_predicted = predict_data(dev_set)
|
||||
|
||||
np.mean(dev_predicted == expected_dev["class"])
|
||||
|
||||
test_predicted = predict_data(test_set)
|
||||
|
||||
"""**Clean output for saving**"""
|
||||
|
||||
test_predicted = np.array([item.strip() for item in test_predicted])
|
||||
dev_predicted = np.array([item.strip() for item in dev_predicted])
|
||||
|
||||
"""**Save to file**"""
|
||||
|
||||
np.savetxt('test-A/out.tsv', test_predicted, '%c')
|
||||
np.savetxt('dev-0/out.tsv', dev_predicted, '%c')
|
||||
|
||||
"""**Check geval output**"""
|
||||
|
||||
!wget https://gonito.net/get/bin/geval
|
||||
!chmod u+x geval
|
||||
|
||||
!./geval -t "dev-0"
|
||||
5152
test-A/out.tsv
Normal file
5152
test-A/out.tsv
Normal file
File diff suppressed because it is too large
Load Diff
Loading…
Reference in New Issue
Block a user