add some preliminary results
This commit is contained in:
Arthur Lu
commit
85861c572e
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australian_user_reviews.json.gz
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australian_user_reviews.json.gz
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preliminary.ipynb
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preliminary.ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "494d6c25",
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"metadata": {},
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"outputs": [],
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"source": [
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"import gzip\n",
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"def parseData(fname):\n",
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" for l in gzip.open(fname):\n",
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" yield eval(l)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "ca7ea536",
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"metadata": {},
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"outputs": [],
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"source": [
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"data = list(parseData(\"australian_user_reviews.json.gz\"))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "eb772e3d",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[[29204, 21950], [964, 7187]]\n"
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]
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}
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],
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"source": [
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"import re\n",
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"\n",
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"dm = [[0,0],[0,0]]\n",
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"\n",
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"for user in data:\n",
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" for review in user[\"reviews\"]:\n",
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" funny = review[\"funny\"]\n",
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" hasfunny = int(funny != \"\")\n",
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" if funny == \"\":\n",
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" review[\"funny\"] = 0\n",
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" else:\n",
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" review[\"funny\"] = int(re.findall(\"\\d+\", funny)[0])\n",
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" \n",
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" helpful = review[\"helpful\"]\n",
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" hashelpful = int(helpful != \"No ratings yet\")\n",
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" if helpful == \"No ratings yet\":\n",
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" review[\"helpful\"] = 0\n",
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" else:\n",
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" nums = re.findall(\"\\d+\", helpful)\n",
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" review[\"helpful\"] = float(nums[0]) / float(nums[1])\n",
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" \n",
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" dm[hasfunny][hashelpful] += 1\n",
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" \n",
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"print(dm)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "72528b34",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"97248"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"from collections import defaultdict\n",
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"import string\n",
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"from nltk.stem.porter import *\n",
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"\n",
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"wordCount = defaultdict(int)\n",
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"punctuation = set(string.punctuation)\n",
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"stemmer = PorterStemmer()\n",
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"for user in data:\n",
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" for review in user[\"reviews\"]:\n",
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" r = ''.join([c for c in review['review'].lower() if not c in punctuation])\n",
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" for w in r.split():\n",
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" w = stemmer.stem(w)\n",
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" wordCount[w] += 1\n",
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" \n",
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"len(wordCount)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "834dfe92",
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"metadata": {},
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"outputs": [],
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"source": [
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"counts = [(wordCount[w], w) for w in wordCount]\n",
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"counts.sort()\n",
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"counts.reverse()\n",
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"words = [x[1] for x in counts[:1000]]\n",
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"wordId = dict(zip(words, range(len(words))))\n",
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"wordSet = set(words)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "b9b3142f",
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"metadata": {},
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"outputs": [],
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"source": [
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"def feature(datum):\n",
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" feat = [0]*len(words)\n",
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" r = ''.join([c for c in datum['review'].lower() if not c in punctuation])\n",
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" for w in r.split():\n",
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" if w in words:\n",
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" feat[wordId[w]] += 1\n",
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" feat.append(1) # offset\n",
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" return feat"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "c9b40320",
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"X = []\n",
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"Y1 = []\n",
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"Y2 = []\n",
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"for user in data:\n",
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" for review in user[\"reviews\"]:\n",
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" X.append(feature(review))\n",
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" Y1.append(review[\"funny\"])\n",
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" Y2.append(review[\"helpful\"])\n",
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"\n",
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"X = np.array(X)\n",
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"Y1 = np.array(Y1)\n",
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"Y2 = np.array(Y2)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"id": "de506b44",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"baseline 291.58597082421744 104.0410362862406\n"
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]
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}
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],
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"source": [
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"from sklearn.metrics import mean_squared_error\n",
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"guess_mean1 = np.mean(Y1)\n",
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"guess_mean2 = np.mean(Y2)\n",
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"\n",
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"print(\"baseline\", mean_squared_error(Y1, [guess_mean1]*len(Y1)), mean_squared_error(Y2, [guess_mean2]*len(Y2)))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"id": "442da10a",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"0.001 279.441162284691 111.14407506578739\n",
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"0.01 279.44116808131514 111.14220804619674\n",
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"0.1 279.4416527267009 111.12490360179714\n",
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"1 279.4563820408731 111.0088419162745\n",
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"10 279.55878360690946 110.3977031070603\n",
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"100 280.29261897219476 108.18116566648386\n",
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"1000 283.89486211897093 104.93301065452346\n"
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]
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}
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],
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"source": [
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"from sklearn import linear_model\n",
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"\n",
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"for C in [0.001, 0.01, 0.1, 1, 10, 100, 1000]:\n",
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"\n",
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" model1 = linear_model.Ridge(C, fit_intercept=True)\n",
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" model1.fit(X, Y1)\n",
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"\n",
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" model2 = linear_model.Ridge(C, fit_intercept=True)\n",
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" model2.fit(X, Y2)\n",
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"\n",
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" predictions1 = model1.predict(X)\n",
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" predictions2 = model1.predict(X)\n",
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"\n",
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" print(C, mean_squared_error(Y1, predictions1), mean_squared_error(Y2, predictions2))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"id": "90b2ad33",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"1 291.58597082421744 104.40268312757502\n",
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"10 291.58597082421744 104.40268312757502\n",
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"100 291.58597082421744 104.40268312757502\n",
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"1000 291.58597082421744 104.40268312757502\n"
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]
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}
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],
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"source": [
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"from sklearn import linear_model\n",
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"from sklearn.metrics import mean_squared_error\n",
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"\n",
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"for C in [1, 10, 100, 1000]:\n",
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"\n",
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" model1 = linear_model.Lasso(alpha=C, fit_intercept=True)\n",
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" model1.fit(X, Y1)\n",
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"\n",
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" model2 = linear_model.Lasso(alpha=C, fit_intercept=True)\n",
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" model2.fit(X, Y2)\n",
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"\n",
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" predictions1 = model1.predict(X)\n",
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" predictions2 = model1.predict(X)\n",
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"\n",
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" print(C, mean_squared_error(Y1, predictions1), mean_squared_error(Y2, predictions2))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "fafe9eec",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.6"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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