{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "accelerator": "GPU", "colab": { "name": "Ramish", "provenance": [] }, "kernelspec": { "display_name": "Python 3", "name": "python3" } }, "cells": [ { "cell_type": "code", "metadata": { "id": "RoG9rf4z4J2J" }, "source": [ "import numpy as np\n", "import pandas as pd\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "import nltk\n", "from sklearn.preprocessing import LabelBinarizer\n", "from nltk.corpus import stopwords\n", "from nltk.stem.porter import PorterStemmer\n", "from wordcloud import WordCloud,STOPWORDS\n", "from nltk.stem import WordNetLemmatizer\n", "from nltk.tokenize import word_tokenize,sent_tokenize\n", "from bs4 import BeautifulSoup\n", "import re,string,unicodedata\n", "from keras.preprocessing import text, sequence\n", "from sklearn.metrics import classification_report,confusion_matrix,accuracy_score\n", "from sklearn.model_selection import train_test_split\n", "from string import punctuation\n", "import keras\n", "from keras.models import Sequential\n", "from keras.layers import Dense,Embedding,LSTM,Dropout,Bidirectional,GRU\n", "import tensorflow as tf" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "kSn9tphp4hfu" }, "source": [ "" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "35CD2VKPMz3I", "outputId": "cc238ca0-1697-42c0-b63c-8dc3a6c3ffae" }, "source": [ "from google.colab import drive\n", "drive.mount('/content/drive')" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "Mounted at /content/drive\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "MHKLntOvJbXi", "colab": { "base_uri": "https://localhost:8080/", "height": 206 }, "outputId": "0cb177e1-d570-4d1d-cd41-581a107c4967" }, "source": [ "\n", "df1 = pd.read_json(\"//content/drive/MyDrive/Sarcasm_Headlines_Dataset_v2.json\", lines=True,orient='records')\n", "df1.head()" ], "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "
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01thirtysomething scientists unveil doomsday clo...https://www.theonion.com/thirtysomething-scien...
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20eat your veggies: 9 deliciously different recipeshttps://www.huffingtonpost.com/entry/eat-your-...
31inclement weather prevents liar from getting t...https://local.theonion.com/inclement-weather-p...
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" ], "text/plain": [ " is_sarcastic ... article_link\n", "0 1 ... https://www.theonion.com/thirtysomething-scien...\n", "1 0 ... https://www.huffingtonpost.com/entry/donna-edw...\n", "2 0 ... https://www.huffingtonpost.com/entry/eat-your-...\n", "3 1 ... https://local.theonion.com/inclement-weather-p...\n", "4 1 ... https://www.theonion.com/mother-comes-pretty-c...\n", "\n", "[5 rows x 3 columns]" ] }, "metadata": { "tags": [] }, "execution_count": 3 } ] }, { "cell_type": "code", "metadata": { "id": "a5s46eVIdRwu" }, "source": [ "" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 206 }, "id": "DsyvQyHqdjHx", "outputId": "93bd23ee-1d74-4f67-989e-33a79f56a9e5" }, "source": [ "Train = pd.read_csv(\"/content/drive/My Drive/Colab Notebooks/Sarcasm Dataset/train.csv\")\n", "Train.head()" ], "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "
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tweetsclass
0Be aware dirty step to get money #staylight ...figurative
1#sarcasm for #people who don't understand #diy...figurative
2@IminworkJeremy @medsingle #DailyMail readers ...figurative
3@wilw Why do I get the feeling you like games?...figurative
4-@TeacherArthurG @rweingarten You probably jus...figurative
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" ], "text/plain": [ " tweets class\n", "0 Be aware dirty step to get money #staylight ... figurative\n", "1 #sarcasm for #people who don't understand #diy... figurative\n", "2 @IminworkJeremy @medsingle #DailyMail readers ... figurative\n", "3 @wilw Why do I get the feeling you like games?... figurative\n", "4 -@TeacherArthurG @rweingarten You probably jus... figurative" ] }, "metadata": { "tags": [] }, "execution_count": 9 } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 206 }, "id": "g7GTLCyuFgfs", "outputId": "5f47b491-042a-4eae-f756-1cefe6ebf983" }, "source": [ "Test = pd.read_csv(\"/content/drive/My Drive/Colab Notebooks/Sarcasm Dataset/test.csv\",)\r\n", "Test.head()" ], "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "
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tweetsclass
0no one ever predicted this was going to happen...figurative
1@Stooshie its as closely related as Andrews or...figurative
2I find it ironic when Vegans say they love foo...figurative
3Quick rt that throwing money vine I've not see...figurative
4yep, keep adding me to your #devops lists.... ...figurative
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" ], "text/plain": [ " tweets class\n", "0 no one ever predicted this was going to happen... figurative\n", "1 @Stooshie its as closely related as Andrews or... figurative\n", "2 I find it ironic when Vegans say they love foo... figurative\n", "3 Quick rt that throwing money vine I've not see... figurative\n", "4 yep, keep adding me to your #devops lists.... ... figurative" ] }, "metadata": { "tags": [] }, "execution_count": 10 } ] }, { "cell_type": "code", "metadata": { "id": "GYvGqMNyIvXp" }, "source": [ "df[\"class\"].replace({'regular': 0, 'sarcasm': 1 ,'figurative': 2, 'irony': 3}, inplace = True)" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "GtsHc804oS7l" }, "source": [ "new = df.filter(['tweets','class'], axis=1)\n", "new1 = df1.filter(['headline','is_sarcastic'], axis=1)" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "fQh62L71kG02" }, "source": [ "new1 = new1.rename(columns = {'headline': 'tweets', 'is_sarcastic': 'class'}, inplace = False)" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 424 }, "id": "91Eh5HNkpkze", "outputId": "5763150b-b145-4494-8301-ef015a0b5675" }, "source": [ "frames = [new, new1]\n", "result = pd.concat(frames,ignore_index=True)\n", "result" ], "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "
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tweetsclass
0Be aware dirty step to get money #staylight ...2
1#sarcasm for #people who don't understand #diy...2
2@IminworkJeremy @medsingle #DailyMail readers ...2
3@wilw Why do I get the feeling you like games?...2
4-@TeacherArthurG @rweingarten You probably jus...2
.........
110022jews to celebrate rosh hashasha or something1
110023internal affairs investigator disappointed con...1
110024the most beautiful acceptance speech this week...0
110025mars probe destroyed by orbiting spielberg-gat...1
110026dad clarifies this not a food stop1
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" ], "text/plain": [ " tweets class\n", "0 Be aware dirty step to get money #staylight ... 2\n", "1 #sarcasm for #people who don't understand #diy... 2\n", "2 @IminworkJeremy @medsingle #DailyMail readers ... 2\n", "3 @wilw Why do I get the feeling you like games?... 2\n", "4 -@TeacherArthurG @rweingarten You probably jus... 2\n", "... ... ...\n", "110022 jews to celebrate rosh hashasha or something 1\n", "110023 internal affairs investigator disappointed con... 1\n", "110024 the most beautiful acceptance speech this week... 0\n", "110025 mars probe destroyed by orbiting spielberg-gat... 1\n", "110026 dad clarifies this not a food stop 1\n", "\n", "[110027 rows x 2 columns]" ] }, "metadata": { "tags": [] }, "execution_count": 8 } ] }, { "cell_type": "code", "metadata": { "id": "5DLRaFqNIVVB", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "7cd6f13d-e30a-4319-aba6-a636a00ca72a" }, "source": [ "from collections import Counter\n", "Counter(df1['is_sarcastic'])" ], "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "Counter({0: 14985, 1: 13634})" ] }, "metadata": { "tags": [] }, "execution_count": 9 } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "dbHVby8gdvcR", "outputId": "6608ab8f-5a61-4595-bffe-b408391ed146" }, "source": [ "from collections import Counter\n", "Counter(result['class'])" ], "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "Counter({0: 33580, 1: 34315, 2: 21238, 3: 20894})" ] }, "metadata": { "tags": [] }, "execution_count": 10 } ] }, { "cell_type": "code", "metadata": { "id": "fzKB6YOziA-k" }, "source": [ "df=result\n", "df=df.dropna()" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "R8ITB8gT4hiy", "outputId": "f5672a7b-958c-437f-9dd7-d4889ade0234" }, "source": [ "data5=Train.loc[Train['class'] == \"regular\"]\n", "print(len(data5))\n", "data4=Train.loc[Train['class'] == \"sarcasm\"]\n", "print(len(data4))" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "18595\n", "20681\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "x2ibtsdfdoI5", "outputId": "5ef1bae6-7ab3-47d3-83c0-955a92539cbb" }, "source": [ "Train=data5.append(data4,ignore_index = True) \n", "len(Train)" ], "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "39276" ] }, "metadata": { "tags": [] }, "execution_count": 12 } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Ki_6YhS_Ganp", "outputId": "a65d3c04-68a9-4dec-e587-22055b70c575" }, "source": [ "data5=Test.loc[Test['class'] == \"regular\"]\r\n", "print(len(data5))\r\n", "data4=Test.loc[Test['class'] == \"sarcasm\"]\r\n", "print(len(data4))\r\n", "Test=data5.append(data4,ignore_index = True) \r\n", "len(Test)" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "1859\n", "2105\n" ], "name": "stdout" }, { "output_type": "execute_result", "data": { "text/plain": [ "3964" ] }, "metadata": { "tags": [] }, "execution_count": 13 } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "FDSl2TkX4hmp", "outputId": "086bf3bf-f064-40d2-fcc4-32819ff574d1" }, "source": [ "\n", ">>> import nltk\n", ">>> nltk.download('stopwords')\n", ">>> import nltk\n", ">>> nltk.download('punkt')" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "[nltk_data] Downloading package stopwords to /root/nltk_data...\n", "[nltk_data] Unzipping corpora/stopwords.zip.\n", "[nltk_data] Downloading package punkt to /root/nltk_data...\n", "[nltk_data] Unzipping tokenizers/punkt.zip.\n" ], "name": "stdout" }, { "output_type": "execute_result", "data": { "text/plain": [ "True" ] }, "metadata": { "tags": [] }, "execution_count": 4 } ] }, { "cell_type": "code", "metadata": { "id": "x4C9m9NKzXYo", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "f3813cc2-1a82-4cb1-cb81-830b13a84c72" }, "source": [ "df=Train.append(Test,ignore_index = True) \r\n", "len(df)" ], "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "43240" ] }, "metadata": { "tags": [] }, "execution_count": 14 } ] }, { "cell_type": "code", "metadata": { "id": "TcTrhYiLLAE6" }, "source": [ "df.to_csv(\"/content/drive/My Drive/Colab Notebooks/Sarcasm Dataset/Dataset.csv\" , header=False, index=False)" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "ypoW7rJ9LNyV" }, "source": [ "df=pd.read_csv(\"/content/drive/My Drive/Colab Notebooks/Sarcasm Dataset/Dataset.csv\")" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "SkNk_8to5dXV", "outputId": "3bf48eb4-2642-4f31-b438-f927ed83ab8c" }, "source": [ "import re\n", "import nltk\n", "from nltk.corpus import stopwords\n", "from nltk.stem.porter import PorterStemmer\n", "from nltk.tokenize import word_tokenize\n", "from nltk.stem.porter import PorterStemmer \n", "from nltk.stem.lancaster import LancasterStemmer \n", "from nltk.stem import SnowballStemmer \n", "sb_stemmer = SnowballStemmer(\"english\",) \n", "stop_words=set(stopwords.words('english'))\n", "porter_stemmer = PorterStemmer() \n", "fcorpus=[]\n", "for i in range(0,len(df)):\n", " review = re.sub(\"(@[A-Za-z0-9]+)|([^0-9A-Za-z \\t])|(\\w+:\\/\\/\\S+)\",\" \",str(df['tweets'][i]))\n", " tokens=nltk.word_tokenize(review)\n", " words=[tokens.lower() for tokens in tokens if tokens.isalpha()]\n", " #word=[porter_stemmer.stem(wor) for wor in words]\n", " filtered_words = [w for w in words if len(w) > 2 if not w in stopwords.words('english')]\n", " wor=[w for w in filtered_words if not w in stop_words]\n", " review=' '.join(wor)\n", " fcorpus.append(review)\n", "fcorpus" ], "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "['new quote blessing curse feel much mondayinspiration late quote emotion feel emp',\n", " 'daddy uncle bubba life love loss laughter amp parkinson dreamtherapy parkie late coach basketball',\n", " 'ayvee soundcloud late night musicafterdark banger rap hip hop drake lil wayne hot',\n", " 'late starting dinner tonight prepping mushrooms redpepper',\n", " 'happy birthdayyyyy late',\n", " 'gunz late night radio favorite jamz play back back jam matches mood',\n", " 'daughter father mother happy family late post fun walk angela pic',\n", " 'photo late night eerie ladies eerielady eerie banshee sheneedstoeatyoursoul late artistoninstagram',\n", " 'coffee late cafe turning point',\n", " 'augustphotochallenge late nails need manicure really bad raleigh durham',\n", " 'mycurrentsituation late zaxbys eating wingsnthing wingsandthings yummy',\n", " 'better late never happy birthday madonna wllnp',\n", " 'two four monday kick back late night selfie date hope everyone monday',\n", " 'tautology best logic love bangalore auto autorickshaw funny lol monday late',\n", " 'forever tardona late',\n", " 'late dinner adonmagazine summer issue feat model',\n", " 'morning good time alarm becomes part dream mean one thing late',\n", " 'get yeah really really late statement',\n", " 'always love late nightwork tea oldsongs work enjoying feeling awake skyz events',\n", " 'late post late',\n", " 'railway would glad get right late',\n", " 'malam obor late post happy independenceday pic',\n", " 'happy independence day late ainun aninda rosa kania',\n", " 'epic moves usa mrjunbug late',\n", " 'truth nobody wants hangry late advice xchangesa',\n", " 'mary berry coffee walnut cake gbbo cake late',\n", " 'nigga need phones late',\n", " 'wait stuck traffic lol greektheater late',\n", " 'late post funny ass nigga kno tag cafecirca atlanta roadmanager rooftop',\n", " 'finally found make kylie jenner lip challenge work late',\n", " 'late bus publictransport fml arriva',\n", " 'least face jonfancy tomorrow late heardit',\n", " 'late post almost full team fun walk silvi lasmafransisk others pic',\n", " 'late post hdr effect snap seed',\n", " 'always time within hour appointment window receive credit annoyed late noshow',\n", " 'road info love commuting time morning bringing speed late work',\n", " 'devices run batteries let nature charge thee see clearly even smiles late',\n", " 'kaampuss late post ria pic',\n", " 'love find pics pic late icecream cookiesandcream cookie cold yummy spoon bowl night canada dutch dishes',\n", " 'need friends work late stupidme',\n", " 'photo late dinner adonmagazine summer issue feat model',\n", " 'uhhhh think know already need tell late',\n", " 'theory everything killer late',\n", " 'ellentube lays chips igdaily instalove diva diva late night snacks poodle',\n", " 'using skype coaching sessions turned productive productivity late technology',\n", " 'still kissing spiders late music lyr',\n", " 'late day kindergarten repeat late day brand new school momproblems late perpetually',\n", " 'late still worth',\n", " 'got stop getting shower laying bed dressing gown every day late',\n", " 'give middle finger big eff mondays late',\n", " 'iotnew iotwatch populating slides stage theconf late iot iot habo kibbutz',\n", " 'goodnight people video day tomorrow french late holidays',\n", " 'time place late planetshakerszxczxczxczxczxczxc',\n", " 'populating slides stage theconf late iot',\n", " 'late night hype music music stream',\n", " 'slow moving friend always makes late dinner fast slow late bud reserve',\n", " 'late breakfast spaghetti need energy watch kalyeserye later aldub puerto prinsesa city',\n", " 'late night shit lazy',\n", " 'happy indonesian independence day yesterday late',\n", " 'one asks bad thing therez wrong making realized late indian mentality',\n", " 'filmed video forever camera youtube videos handycam late night',\n", " 'good morning thank god another day late',\n", " 'late say thanks nurita nirmala others pic',\n", " 'take hour nap fall asleep phone alarm wake hours late church late',\n", " 'wisata rohani late beby ikherihulay others pic',\n", " 'late adam posted degrees',\n", " 'old love family late',\n", " 'always figure life matter figure late soon end alone',\n", " 'bart delayed tiny earthquake someone medical emergency train late happymonday bartdiaries',\n", " 'late breaking news bezerc bezercimok bridgeport famecitybridgeport',\n", " 'late',\n", " 'niggas knowing late',\n", " 'saw sfcc used many people showed change inflation late',\n", " 'late night movies heading cinema',\n", " 'train sitting city hall supposed amp mkt minutes ago late patcopetpeeve',\n", " 'looking part time late night team member harbour landing jobs',\n", " 'late transformationtuesday grind never stops',\n", " 'baking frenzy baking late brownies cupcakes cookies bored treats netflix arrow',\n", " 'still late embroidery multitask',\n", " 'sat minutes train morning bang job late',\n", " 'aaaaakkkk finished watching recently hahahah late',\n", " 'still working working hard night late nosleep new release eventtus',\n", " 'love good selfie late night boredom bored boredom letstalk aboutthatlife beauty beautiful late',\n", " 'dinner snack late dinner almostmidnight timeforcartoons happy lancaster california',\n", " 'bro even iwtf worktrainfight work fitness late donteven bootcamp nyc',\n", " 'finally work late night tyson tysontheactor dayjob follow chasingdreams',\n", " 'impressed hour late delivery amp lots substitutions please tell know cucumber courgette',\n", " 'heard hold tight majid jordan soundcloud tuesday late whoup',\n", " 'made day thankkss late',\n", " 'august late post pic',\n", " 'got stay high time weheartit tumblr night late internet sparkle cute follow',\n", " 'live periscope late firstdayofschool pro',\n", " 'forgot excruciatingly slow getting bus edinburgh fringe late',\n", " 'taken longer get huntingdon pick riggs would tontake peterborough late',\n", " 'providence rhode island late night evening time sunset',\n", " 'late post tedi pic',\n", " 'omg pova withdrew late',\n", " 'late night icecream cornerhouseicecreams corner house ice cream parlour',\n", " 'best fly dub lgw hurry flights bus main terminal immigration late',\n", " 'awk sorry late',\n", " 'hair goal wrong want right grey trend late love hair goals shouldi',\n", " 'atlanta concerts backstage late',\n", " 'yorkshire businesses worst hit late payments help',\n", " 'happy birthday mate late',\n", " 'work mode late darling bfkevinsavestheday',\n", " 'played like used getting level tonight late awesome',\n", " 'always late giving food vouchers late',\n", " 'requests eddie gunz show late night radio favorite jam tuesday evening name play',\n", " 'walk sit straight give fuck late late always selfie gayselfie',\n", " 'last train late lasttrainhome empty',\n", " 'alright good morning late',\n", " 'okay wait wait one good rows awp wants split table late awp dontwanttobeintheboondocks',\n", " 'whole doctors office losing shit got new computers technology supposed make things easier annoyed late',\n", " 'news alert needles man interstate almost hit threatens drivers zachnews late arrival',\n", " 'would give high customer service info bus late far info good service late',\n", " 'movie marathon jess haha late',\n", " 'charged yesterday late',\n", " 'real talk late night radio show days back news came man slapped beat',\n", " 'work mode late darling bfkevinsavestheday',\n", " 'jimmy fallon jimmy fallon good late night jimmy fallon animated gif',\n", " 'late nights red lights boston late night selfie',\n", " 'havent tweeted ages late',\n", " 'late post hdr effect snap seed cirebon west java',\n", " 'bravo chill long people mess anymore late',\n", " 'day coming back content exactly need restart everyday routines late',\n", " 'late night doodles late night doodle drawing diary thoughts music moon poem poetry',\n", " 'takes food plate catch shit thought late',\n", " 'irish fears late payments',\n", " 'please stop cancelling lancaster wigan late',\n", " 'yass bby late iansomerhalder',\n", " 'ran thanks walking exercise late evening',\n", " 'otw school late selfie ootd',\n", " 'late anugrah bayu',\n", " 'alarm goes get see problem getyolife wakeup gotobed sleep night morning day early late shheeesshh',\n", " 'really feel late officemode',\n", " 'way feel blessed late ziplining',\n", " 'happened loved airline delayed late travel trip',\n", " 'today challenge getting year old nursery late lazylizzie',\n", " 'guess time join twitter late',\n", " 'iyou see smiles coz know people minds stocked piles late night sleep',\n", " 'missed train work stuck waiting another hour late lirr fml',\n", " 'quickly becoming favorite person world late',\n", " 'surprising apple rival services also weening people purchasing music itunes late',\n", " 'girls late nailpolish late hurry',\n", " 'happy birthday lady rocker agustus late post novotel',\n", " 'said waiting doorstep scb late',\n", " 'late night tea time tea jaffacakes late relaxing zen summer nighttime norwood',\n", " 'late night sexy music oooweee place emonsta',\n", " 'houston traffic day keeps getting worse hurryupandwait late',\n", " 'grasshopper tacos surprisingly nutty foodadventures grasshoppers tacotuesday late',\n", " 'decide grab coffee work get new guy late',\n", " 'today definitely day even yet awesome late canigotobednow',\n", " 'late loving bigsissy always know makeup sheraton arlington hotel',\n", " 'live periscope late nite food sleep trying find something good netflix',\n", " 'opps know worked today well lookingghettoasfuck late',\n", " 'beauty beast hanyateman latepost late school friends friend friendship friendships',\n", " 'alright flight crew hell jetblue delays iwannagohome jfk late',\n", " 'starbucks forgets late',\n", " 'hard knocks television show television shows character development football practices good late take',\n", " 'going miss best friend going college love hope great year late',\n", " 'long time since last time run catch bus late',\n", " 'mayto islasmarietas relax late partyhard huloma',\n", " 'ohhh saw tweet late',\n", " 'never late new beginning life never late new beginning newchapter',\n", " 'shooting new sorrynotsorry late sot stokeontrent imgood lightwork easy fresh newmusic',\n", " 'late night shopping priiincesss',\n", " 'best part everyone work love cover late',\n", " 'dawes alarms try using alarm get job time poorservice late',\n", " 'wow love got dress coded first day late',\n", " 'unfriended fucking good late',\n", " 'power excellence late hongkong garden restaurant',\n", " 'vimes almost like merlin data gandalf rides study black speech inside ring late',\n", " 'long finally gave twitter late',\n", " 'want die young late possible feeling content',\n", " 'waiting bus morning mbta late quincy',\n", " 'scan sky looking knowing left shore poem late joyful death via',\n", " 'really telling people minute two late via',\n", " 'puncture commute late lesschwab les schwab tire centers',\n", " 'triangle stop minutes sign saying minutes til next bus late',\n", " 'late thankful god alhamdulillah want moreblessings',\n", " 'problems maturity thing late starter',\n", " 'chickenfeet hihi late lunch stalker lol',\n", " 'found berniesanders jewish oops late lol',\n", " 'get ready work late',\n", " 'know running late morning see sun way work rushhour late worklife',\n", " 'patrick stewart magic tricks late late show james corden sir patrick',\n", " 'insert everyone missing late vsco vscocam vscogrid vscoph san patricio peeps',\n", " 'mind says get ready work body says stay bed late night',\n", " 'new video still uploading late',\n", " 'late inplay independiente lanus match',\n", " 'finally pulling station home good talks webarchive repository today long long day dark late',\n", " 'sleep city koper sleeplessnights insomnia early morning late night slovenia colors',\n", " 'sleep much mind sleep late mind thinking',\n", " 'happy lunch everyone prepared cook rebecca castillo lunch late malaysia pudu ulu',\n", " 'point choosing seat change without notice worst late unitedsucks',\n", " 'real talk late night radio ntare school mbarara western uganda students suspended recently',\n", " 'late post modellingwednesday let look higher facaulties try model lives',\n", " 'kempton using dundalk watch late',\n", " 'late dinner alone alone dinner late',\n", " 'happy bday babeh inand photogrid late bday party celebrate',\n", " 'love pic drunk happy fringeandginge ginge ginger silly late drinks purple hair',\n", " 'chase perfect coffee get coffee late heart',\n", " 'italian swear words plenty today ready rub best clean see late',\n", " 'want show things feeling show much make crazy late feelings beautiful',\n", " 'decades earth thank lord late',\n", " 'love life bonjour latepost late style',\n", " 'want get asap mon thought reliable jetblue late fail',\n", " 'guess sleep schedule caught late',\n", " 'thanks finally opening doors like line people anything today late badservice',\n", " 'dapet dari semalem late post home pic',\n", " 'late wce thats thats raven voice',\n", " 'time learn tweet late',\n", " 'american behaviors considered rude places tipping cab thumbsup laugh america late pocket lefthand',\n", " 'waiting friend come pick late yesterday grab shit',\n", " 'late post ncs line pic',\n", " 'translink win ten minutes late morning traffic useless',\n", " 'people drive rain late',\n", " 'questions drugs marijuana pcp drugs marijuana questions',\n", " 'mortondowney also talked ollienorth amp drugs getting heat postmistress brooklyn giving away free drugs',\n", " 'hungry needs hurry late night geekingout tablemannerzdjs',\n", " 'late hangout grand indonesia shopping town',\n", " 'starts late fine',\n", " 'late haha saw want reply',\n", " 'bed week hours day handicap drugs brokenleg',\n", " 'time stay late late',\n", " 'interested know women put forward available asked lads always late review',\n", " 'late boy thinking millions vamping working late sleep studio',\n", " 'late wcw goes',\n", " 'sometimes get mad lil upset look mirror smile late',\n", " 'service half hour east brunswick thanksfornothing late unreliable nothappyjan',\n", " 'homie chopped new track drugs checkk shits dope fuckk',\n", " 'steve watch buy raiders snapback late',\n", " 'make primer best discovered definitely make bag staple late beauty makeup',\n", " 'congrats senior late',\n", " 'rail need get staff thorpe bay today late',\n", " 'idea north camp guildford running late announcements station late',\n", " 'check floating feat young nore cruise autotune smokesong cruiseisthename drugs reverbnation play viral',\n", " 'snoozed alarm fourth time late',\n", " 'hell bus late fine fml meetings neverending',\n", " 'music gets trials life music love gayboi gay late latenights exhausted',\n", " 'happy birthday demi lovato sorry late repost',\n", " 'back fit senses fitnesslifestyle ihop eating latenite late',\n", " 'celebrities impact drug abuse celebs drugs',\n", " 'specialty line drugs wholesale aug market growth erratic marketresearch statistics',\n", " 'everyone late sometime lateness reveal colleagues spark',\n", " 'late transfer requirements liga top teams ahead ligas',\n", " 'nofilter late upload put work sunflower half sleeve last week lot',\n", " 'lingerie brands talking hiring first black model late',\n", " 'patients fear russia restrictions could limit access western drugs medicaldevice',\n", " 'pain painkillers drugs oakland calif reuters federal appeals court thursday',\n", " 'late twitch stream saturday celebrating late mothers birthday remembrance damn years gone still believe',\n", " 'late watching wahlburgers special caring real grateful individual twug fanforlife',\n", " 'inhaled much dry ice boss comes says dont forget breathe late officefun',\n", " 'sometimes things ready late',\n", " 'late summer thursday diner amsterdam ribeye mushrooms tabledhote wine',\n", " 'finally updated ios years later serious issues change look see different skin colours late',\n", " 'late burgerking grand indonesia shopping town',\n", " 'time eat late lunch outlets pueblo verde',\n", " 'review highly entertaining star read crime mystery adventure fbi mobsters drugs sex',\n", " 'pigeon smuggling drugs prison',\n", " 'example difficult today help within athens communities enhance entrepreneurs get rid drugs',\n", " 'going phone wants work half time get messages nothing ugggh late',\n", " 'mia seized pills containing drugs psychotropic substances',\n", " 'cannabis experiment drugs science',\n", " 'slowly silently walk office late',\n", " 'gre prep books ordered late',\n", " 'cuz edwin slid tgru holla amp unk late post sincere still smh',\n", " 'little late night drawing art fineart drawing woman crown flowers pencil late jmbportfolio',\n", " 'album fucking year drake ifyourereadingthisitstoolate late beats music',\n", " 'photo sleep insomnia late night writing alvarez',\n", " 'oops bomb late',\n", " 'girls dont waste tears boys wan put tears use save send california late',\n", " 'good definition vesper vesperdresses fashion girlproblems late',\n", " 'photo nofilter late upload put work sunflower half sleeve last week',\n", " 'fucks spotify late',\n", " 'yes desk late night show',\n", " 'goodnight goodnight selfie night late sleep haha lmao omg lol cute followme follow',\n", " 'think people get arrested drug possession really issue procrastination drugs jokes',\n", " 'mps attend parliamentary debate drug laws via drugs',\n", " 'tomorrow night private gig looking top late experience high energy',\n", " 'late post selfie bareng wae freestyle soccer bandung indra jaelani others pic',\n", " 'good morning morning late hello instamod sup friends romanian photo follow',\n", " 'time guarantee make impossible file claim late due delay fraud liar badservice',\n", " 'personified weed like different ways repoman late work dog love flatbutt women need know',\n", " 'late summer sweet vacation okinawa marriott resort amp spa sweet okinawa marriott resort amp',\n", " 'always time late chicago film fujifilm',\n", " 'staring someone eyes ten minutes like tripping without drugs',\n", " 'cross bay doin dirty late ugh alllightsshouldbegreen',\n", " 'still remember nia wefie rain come late wet hair cold cloudy feel',\n", " 'light skies late loveit love sky special eve friday sun sunny',\n", " 'drugs inflammatory bowel disease powerpoint presentations powerpoint slide drugs inflammatory bowel disease',\n", " 'drugs use practin tablet side effects year old male usin dexona amp practin last',\n", " 'tentation late night draw body',\n", " 'late song',\n", " 'lonza biologics making drugs safer cost effective jesus zurdo pharmaiq',\n", " 'happy merry blueberry late shirayuki shirayukipik desert',\n", " 'valeant buy female libido drugs maker sprout sex sexual love females women viagra men',\n", " 'late night garden bit glamorous garden roses late night',\n", " 'gone wrong dude idea going literally car right back started late shame',\n", " 'store mood late night beer storage room',\n", " 'happy merry blueberry late shirayuki shirayukipik desert fruity kakigoori blueberry pic',\n", " 'taking drugs humanright declares cross party group mps peers grahamhancock warondrugs wrong',\n", " 'hey awesome people check moviereview dope dopemovie drugs',\n", " 'bed time late cute cuddles boyfriend girlfriend love curly middleparting eyelashes beauty',\n", " 'input sister waiting teacher get back class late lol',\n", " 'state highways brilliantly simple plan stop stoners stealing mile markers cars drugs marijuana',\n", " 'slippery slopes avoiding relapse addiction recovery treatment rehab drugs via',\n", " 'pain painkillers drugs reuters health mental health problems like anxiety depre',\n", " 'coalition says hair testing drugs ready prime time drugtest trucking',\n", " 'drugs must school night dead',\n", " 'twitter actually free hmmm okay thanks late',\n", " 'spot late evening golf levineee golf late evening sunset sun hillbarn',\n", " 'going party waiting apple first late',\n", " 'went denver late last fall went job yeah break law worked',\n", " 'cant sleep catching emails need replying work catchup cant sleep late qismat elgin firewire ebay',\n", " 'really telling people minute two late entrepreneur',\n", " 'american ultra one drugs filmflickers kristenstewart jesseeisenberg highlarious',\n", " 'lol busy tryna hustle nikkahs data bundles late',\n", " 'end drugs war trailer via verygood drugs important',\n", " 'benteke goal error late inthebag',\n", " 'transpo billings takes roads route misses stop pick late notthefirsttime',\n", " 'asd child amp already fed antisocial aldwyck neighbours back less amp slamming banging shouting amp yakking speedily drugs',\n", " 'put processes place deal late payers payments late',\n", " 'interesting president goes eersterust crime amp drugs amp single tweet',\n", " 'come tan come spraytan come doubledip open late till today',\n", " 'cute someone stays late talk shah',\n", " 'teach kids photography never money drugs photo fusion studio',\n", " 'official seeing straight outta compton tonight wit bae late cares lol',\n", " 'american drugs filmflickers jesseeisenberg kristenstewart smoking entertainment',\n", " 'war drugs lost meet police chief starting revolution whos',\n", " 'cowboys rams fight dunny tho lol late',\n", " 'diet works better drugs get pain',\n", " 'drugs lol drugs fail whatisdrugs whatwelearn vine happymeal',\n", " 'nigeria makes giant strides counterfeit malaria drugs',\n", " 'gettin hit type traffic onlynashville morningtraffic late',\n", " 'taking drugs human right cross party group mps peers think drugs politics',\n", " 'dual diagnosis treatment recovery addiction rehab drugs via',\n", " 'srsly watched one chance ever late',\n", " 'amp false docs bags suspected coke amphet mdma amp crystal meth seized drugs jailtime projectservator',\n", " 'express train local train way even late unbelievable subpar',\n", " 'drugs small cohort diseases historically viewed profit potential',\n", " 'sketch sketchbook illustration graphite art moleskine doodle drawing late latenight pencil',\n", " 'late wildhan mastata tahu pedes mlothot',\n", " 'better late never best policy getting yoga noon jumping yoga meditation spirit prayer late',\n", " 'tips manage drug taking impulse addiction drugs freedom',\n", " 'yeay rewind dance practiceeeee late',\n", " 'cardinal asian honor student deals drugs stanford race podcast drugs getboldtoday',\n", " 'drugs druggists sundries wholesalers pleasing aug market revenues marketresearchreports',\n", " 'matter far gone long apart liz calls answer fiction lesbian death drugs iris',\n", " 'howto heal depression without drugs',\n", " 'danielgennaoui drugs look like microscope read drugs',\n", " 'late eqao literacy test results released aug board school student results week sept oecta',\n", " 'lol likes stick face catnip container cat catnip drugs cathigh vine zachary woolf',\n", " 'stay away drugs amp cocaine kids smh worth cocaineisahellofadrug smh smfh',\n", " 'heroin bad question climatechange worse kentucky drugs',\n", " 'seven arrested drugs raid chorley read',\n", " 'uhoh dollface feeling happy today theweeknd arcticmonkeys pale sadness palegrunge drugs',\n", " 'quite sure could make time yoga train went local answer late',\n", " 'day enchanting meet late',\n", " 'ten men appear court child sex abuse childrape childsexassault evil childsextrafficking gay drugs',\n", " 'thank much braden late',\n", " 'signs loved one abusing drugs addiction treatment rehab recovery via',\n", " 'late night need someone talk',\n", " 'drugs still work gen getting kicks elsewhere drum',\n", " 'half hour late work stomach still fuckin late',\n", " 'accident route broadview stn waiting streetcars buses late whatamipayingfor',\n", " 'long get back bashing old news post kpti seems like track record late',\n", " 'writing letter explaining late payments sample',\n", " 'coffee hollywood late locally freshly roasted coffee',\n", " 'hey bro thankful born spain bro cause speak spanish bro weed live drugs',\n", " 'corrupt amp racist war drugs met reality afghanistan',\n", " 'late night drive hungry also kinda wan turnup thru drivethrough listening guilty papergoin',\n", " 'please support guys raising money help others text drugs alcohol addictionrecovery',\n", " 'turnt drugs never good good homeless dope crack pills drugs fuckedup',\n", " 'watching late night seth meyers earning great rewards viggletv viggle late night seth meyers',\n", " 'war drugs murder bomb terrorism pretty cool',\n", " 'homeboy bathong trust show northeastdistrict riverlife late bots',\n", " 'really like way amp taking drugs punjab may god give power',\n", " 'heard diss drake kill meek damn late',\n", " 'good evening late post lunch',\n", " 'popular pharmaceutical drugs auctions pharmaceuticaldrugs auctions drugs',\n", " 'piracy rewind latest world plane crashes airshow reports say news trending late',\n", " 'ridiculous overcrowding trains train weymouth enough seats opening doors people even get late',\n", " 'damn west coast times late bluejays itsnotlikeimdoinganythinganyways',\n", " 'mad mad fury road frraking incredible know took long check late worththewait',\n", " 'late post performed japex main stage purwakarta',\n", " 'photo life jobs serve drinks dribks summer late nightlife beauty',\n", " 'death door time window bool late vamp hrs slime psa brazy world',\n", " 'oldest best pharmacy arouca phone medicine pharmacy drugstore drugs trini arouca trinidad',\n", " 'signs loved one abusing drugs addiction treatment rehab recovery',\n", " 'man late night come real know come like',\n", " 'vemma mlm distributor sell drugs tea register join learn work home business lagos nigeria',\n", " 'late post performed japex stage clinic rockwell russel purwakarta',\n", " 'late eve solo pic friends crown regency hotel',\n", " 'going end night delicious curley fries yummy dinner late night fries',\n", " 'released soon pre order counter culture identity anarchy subversive drugs lgbt politics music raves',\n", " 'late watched last episode orange new black cried wait till season',\n", " 'running late like always selfie incar late killme runninglate clases violet dyedhair',\n", " 'seen helicopters overhead since drugs easternky',\n", " 'finally started watching arslan pretty excited first episodes definitely feels like fma minus magic anime late',\n", " 'diet food yummy drugs pain natural scientists found new way give tas',\n", " 'lastnite late jamsession adamavil guitars milendsingh donny house',\n", " 'know adult hours free time commit acid trip drugs adult',\n", " 'let fear shame anxiety ptsd drugs boss education students wellness teachers confidence',\n", " 'next damn late night black white baw bored thinking thinkin bogot colombia',\n", " 'broke pipe day late stillpissed',\n", " 'columbusohio food trucks nearby serving late nite munchies',\n", " 'security lady gave amp ride back fair gate make bus late',\n", " 'like years late friend lol thejonasbrothers late throwback funny hilarious haha comedy',\n", " 'roadside wild flowers caherline north lisnagry late splash colour autumnal blooms',\n", " 'tea good way start day late summer mornings',\n", " 'day foolish stay mindful time late',\n", " 'aging disasters diseases drive value pharmaceutical industry medicines drugs',\n", " 'personality changes drug dependency drugs changing dependent',\n", " 'twitter looks different last time last year late ineedtogetittogether',\n", " 'thanks adding list fda fda health consumer healthcare food drugs',\n", " 'norlmacy joke reality feels like trick misconception deluded late sad',\n", " 'flag nature indonesia years old merdeka late sabana merbabu',\n", " 'woah next single gon anotherlonelynight great choice moo late',\n", " 'late surprise problem iloveuuu mom broth little sis galih anti home pic',\n", " 'latenight creep skinny bob added vape smoke filters art aliens greys late night drawing sesh',\n", " 'drugs druggists sundries wholesale precarious aug market',\n", " 'andrew bernstein case ending drug prohibition drugs prohibition objectivism aynrand',\n", " 'howto heal depression without drugs',\n", " 'ice know comedians get jokes ice iceepidemic sydney drugs',\n", " 'watching athletics become like wwe fiction waiting bolt put gatlin table tlc drugs doping',\n", " 'ran man prayed many years good good job drugs amp church thanked praying thanks god',\n", " 'drug addict promises clean care elderly grandmother spared jail drugs addiction',\n", " 'late summer style inspiration rock signature leather legwarmers streetstyle',\n", " 'first part project escultura art late forschool',\n", " 'get early bed late unhealthy lifestyle lifestyle healthy late',\n", " 'new muppet show goes adult kermit split misspiggy infidelity drugs',\n", " 'regram rehensabhaai late night selfie lumia microsoft turbanandbeard sikh sikhism sikhlifestyle',\n", " 'late lunch popoy thanks pork sinigang teh',\n", " 'personally think one strike policy drugs athletics worldchampionships',\n", " 'saturday night outside nice enjoy safe downtown sanantonio popped lights late local',\n", " 'drugs cure mitigate symptoms cure ttac free',\n", " 'swivmatic haha actually saw reply late',\n", " 'every single southerntrainservice needed late today trains horrendous exhausted',\n", " 'faculty law late post muhammadiyah university yogyakarta',\n", " 'drugs druggists sundries wholesale aug market growth lame marketresearch data report',\n", " 'late afternoon sunshine thames london summer august saturday canary wharf',\n", " 'think thai bed time early late',\n", " 'looking late night kitchen general utility worker part time boston university jobs',\n", " 'southeastern impressed train left mins late annonce left station train would stop folkestone late',\n", " 'live late smogon tournament playlikeasaiyan',\n", " 'late post wata land waz pree yesturday farout wellbad rep anyway',\n", " 'ike days late eunike aurellia rizky others pic',\n", " 'pdp drugs amp alcohol training early break',\n", " 'best mins show far late gingeatthefringe',\n", " 'crew dblsmada late nisa asya ayfa others pic',\n", " 'excuses running late work lol excuses funny dog got stabbed friends boss',\n", " 'kind adventure missing adventures buddy missingb late night campsetup misplaced poor planning cares rollw',\n", " 'late power litttttttttttt',\n", " 'mps peers say taking drugs humanright',\n", " 'drugs rash quitting lyrica took lyrica short time years ago still suffer rash',\n", " 'happy birthday leo late post',\n", " 'slept alarm fml late firstimpressions',\n", " 'pizza time pizza late cheatday cheatnight greedy noneed',\n", " 'late post mild soundreanaline tour lapangan saburai',\n", " 'nyelfie little brother late upload',\n", " 'wonder one smashed tits drugs',\n", " 'flexin late nights early mornings nite highrollerh phhhoto',\n", " 'late post goodnight love instagood',\n", " 'drugs druggists sundries wholesale aug market growth lower marketresearch data report',\n", " 'got take senior pictures soon late',\n", " 'years stud muffin late anniversary weekend doubletree dallas',\n", " 'nobody knows exactly drugs strong goes legal highs even risky mix drugs',\n", " 'gon end getting fired one days late',\n", " 'caffeine drugs vitamins coffeelovers coffeelife',\n", " 'hey good evening late upload aldub',\n", " 'yes latenight late better thingsilove love repost phuckyoquote repostapp',\n", " 'likes whole lot drugged carnage free limited time brighton drugs dystopian party',\n", " 'funny drugs celebrated improving health frowned upon comes improving performance worldchampionships',\n", " 'check steez apex niggars came late came latest kttm',\n", " 'shoots bawling eyes ray charles movie ray trying get clean rough stuff movie drugs music',\n", " 'relaxed walks hot summer days finland summer dog flatcoat retriever late',\n", " 'breakfast late',\n", " 'got life together cab driver late',\n", " 'late adam lambert posted',\n", " 'okay taken happy pill humor drugs pills bipolardisorder bipolar depression read iartg',\n", " 'happy bday turn uhhhp late',\n", " 'get coffee take nap late post pic',\n", " 'opium drugs powerpoint presentations powerpoint slide opium drugs',\n", " 'never understand till late',\n", " 'sundaylive said jail poor true selling drugs child released watumishikwawote',\n", " 'gopdebate truths freeusa anazicoll moneychangersoros attacked thru deceit amp dem pty ohalfricanamerican smilingliar doingtreason',\n", " 'check new article gonzo music drugs',\n", " 'drugs gvk biosciences suspended germany pharma drugs medical hospitals bioinformatics biology',\n", " 'brave nah lmfao late',\n", " 'qld alcohol amp drugs discussion paper say september',\n", " 'job parent everything power keep children using abusing drugs amp alcohol',\n", " 'murder rate climbed every year prohibition amp fall repeal continue keep drugs illegal utah',\n", " 'late post cake high tea themed baby shower boxofdreamscakes babyshower itsagirl babyisbrewing',\n", " 'wow haggler hearns first round better lot full fights seen late',\n", " 'whole amp say bipolar minds logic way late represent',\n", " 'guess waking moment sunday lifestyle late wakeup afternoon',\n", " 'tutor better bring chalk slate make lecture late kttm',\n", " 'specialtypharmacy data golden nugget manufacturers pharmacy drugs',\n", " 'technobrain plans coe hyderabad pharma drugs medical hospitals bioinformatics biology life india world',\n", " 'good evening lovely equestrians late equinehour',\n", " 'hilarious badlipreading gopdebate',\n", " 'two indian movies feeling stoned emotionally physically swear like drugs',\n", " 'hood travel belgium night ufo station sncb late',\n", " 'fast easy accurate saliva drugtests ideal forensic use drugs',\n", " 'met would freak get really hot sweaty really attractive look meeting people love late',\n", " 'finally bout see straightouttacompton late doe least get bootleg lol',\n", " 'drugs effexor irritable yes effexor depression time life underwent',\n", " 'teargas pepperspray drones overpopulated drugs outofcontrol',\n", " 'ravages heroin addiction haunt friends families whole towns npr drugs overdose',\n", " 'forget morality use economical deterants drugs',\n", " 'ohhh good start week late',\n", " 'drugs tramadol also two micro discectomy last two years surgery went well',\n", " 'patients liverdisease hepatitisc drugs equal disease hcv hepc',\n", " 'look found via forsale catnip catmint drugs high cats collection',\n", " 'got mind money money mind finger trigger ganglife drugs money life',\n", " 'well case maybe ashamed perhaps would like comment nike gatlin drugs',\n", " 'four shelby county jailers plead guilty smuggling drugs aug',\n", " 'tomorrow talk state drug related crime jan combrink studio guest zarsg sabcnews drugs',\n", " 'sorry still need know america stop great gop gopdebate ccot imwithhuck tcot teaparty',\n", " 'monday traffic late feeling pissed',\n", " 'slow going bendigo morning bendigo late hopenoeightthirtymeetings monday',\n", " 'pract expect statement issue known causing widespread psychological harm austerity drugs therapy',\n", " 'got put late smh dope',\n", " 'everything right gopdebate',\n", " 'amazing isnt hahaha late',\n", " 'cancer industry becoming giant tumor find list prescription drugs cause canc',\n", " 'funny hell especially watched actual debate lool gopdebate',\n", " 'one eventually realized neither performance wellness dieting drugs starting getit thrive',\n", " 'potus creates bureau vital fetal organs foxnewsdebate gopdebate prolife foxnews',\n", " 'check californians reactions first gopdebate',\n", " 'credit via instagram nicheescapes stockholm sweden river late sky lights houses boats bridge',\n", " 'north edsa buendia took hours mins urg edsa heavyrain late',\n", " 'anxiety ptsd health education hospital spousalabuse students drugs alcohol eatingdisorder learn',\n", " 'traffic never missed late firstdayback',\n", " 'tbf late night live stream tim',\n", " 'canon powershot late afternoon beach face puertocolombia',\n", " 'random best excuse late work best excuse late work',\n", " 'conservative radio host enslave undocumented immigrants unless leave gop gopdebate feelthebern gopclowncar',\n", " 'terrible rumours neymar mufc drugs',\n", " 'sideviewapp time late work frustrated art perception clock watch sideview sanfrancisco sideview',\n", " 'service aload wank manchester aload foreigners working late bullshit',\n", " 'damn middle school crossing better figure shit late latelyfe',\n", " 'effective new drugs make organs hcv infected patients viable transplants hepc hepatitisc',\n", " 'columbusohio nearby serving late nite munchies',\n", " 'facts parents know marijuana recovery treatment drugs addiction rehab via',\n", " 'jennifer rubin conservative explains thinks trump away politics trump hillary gopdebate',\n", " 'potus creates bureau vital fetal organs foxnewsdebate gopdebate prolife foxnews',\n", " 'pain painkillers drugs london reuters british pharmaceutical company astrazeneca',\n", " 'save traffic selfie naturalhair late',\n", " 'adelefaucher saudi led coalition blamed bombing yemen world heritage site nightlife late newark',\n", " 'meet trump pretend military advisors via donaldtrumpforpresident gop gopdebate donaldtrump',\n", " 'well awkward gopdebate bobbyjindal ornot',\n", " 'heroin epidemic toll one county minutes eight overdoses pennsylvania drugs',\n", " 'howto heal depression without drugs',\n", " 'photo ulan traffic time wasted late manila edsa typhoonineng uploaded',\n", " 'get page consistent messages drugs alcohol prevent teen substance abuse',\n", " 'fuck lol drugs scapegoats',\n", " 'late night sewing session alter old dress new bra boot feel like new woman never realized could alter clo',\n", " 'mike huckabee born years ago today grandson george gop gopdebate imwithhuck ccot tcot ycot',\n", " 'late night dinner progress cooking foodies fat eat food dinner late night live voyeur',\n", " 'late tweeting great week xxxx',\n", " 'slick dab folks late',\n", " 'make hoe eat hoe cuz broke hoe late post',\n", " 'truth watch late lateonpurpose timemanagement lindatime reality lol sotrue tyrone square mall',\n", " 'utero exposure psychotropic drugs increases risk negative neonatal outcomes psychiatry obgyn',\n", " 'late night snack',\n", " 'pigeon caught flying drugs prison',\n", " 'still last week moved late',\n", " 'worst kinds late people kind late person',\n", " 'late night longboarding night longboarding',\n", " 'late night beat session stay blessed latepost latergram music 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taza grill',\n", " 'sylvia takes eighth grade amp sister vampire boyfriend bloodlust sex lots humor',\n", " 'leaked footage jared fogel new york trip submitted cdfries link comment funny jokes humor oneliner',\n", " 'hound dog happy tail always wagging pets puppy puppies cuteanimals humor funnyvine dogsofinstagram',\n", " 'weird facts brandysnoopdogg funny hiphop hiphopmusic humor',\n", " 'rejected humor satire',\n", " 'ball make happy friends give laught see holiday perfect tyoners beach late',\n", " 'harpernuit harpcity harpergif peace harcow pearl court',\n", " 'perfect computer lol via humor art bizboost',\n", " 'breaking law behind ashley madison hackjob humor ashleymadisonhack jenniferlawrence privacy',\n", " 'either way going destroy sexy romance wonderlust musicians humor rockstar contemporary',\n", " 'fishing like romance next best thing experiencing talking joe moore quotes humor quote',\n", " 'find funny humor blog',\n", " 'great mug want one punctuationsaveslives humor',\n", " 'women umbrellafish tshirt fashion humor shirt clothing',\n", " 'faces dow today humor feedly',\n", " 'ball make happy friends give laught see holiday perfect tyoners beach ball late',\n", " 'always wear kilt new situations book qotd life pirate ship run velociraptor humor',\n", " 'review life hidey hole hollow humor paranorma life memoirs',\n", " 'someone says mind almost always joe moore quotes humor',\n", " 'funny quotes pictures pict funnypictures funnytweets humor',\n", " 'good morning goodmorning coffeemorning coffeelove coffeemakestheworldgoround coffeeholic peace',\n", " 'another vampire story vampire humor mglit coming age free sample begin stripping pool',\n", " 'meeting find fun fun cute funny funnygifs funnyjpegs funnypics geeky humor',\n", " 'todays humor man man man fuck bedtime story dating adults tod',\n", " 'whitegenocidehc hopjas general wesley clark explains whitegenocide gopdebate',\n", " 'bitch likes legos tinder fails humor',\n", " 'rap rap hip hop humor',\n", " 'another vampire story prose tight simple often absent young adult vampire humor mglit',\n", " 'vicious animal attack meme humor',\n", " 'morning tuesday sunrise peace nothing better seeing sunrise know',\n", " 'colorado governor plannedparenthood humor humorous satire',\n", " 'enjoy living edge tinder fails humor',\n", " 'peace symbol guitar pick earrings original',\n", " 'keep eyes stage instead sexy romance wonderlust musicians humor rockstars',\n", " 'vacation period time people tired become exhausted joe moore quotes humor',\n", " 'dem dogs dem dogs humor dogs blog',\n", " 'rich dumbasses screwed everyone blackmonday via humor',\n", " 'careful make angry humor',\n", " 'ladies bring peace love romance best anthology sizzling heat asmsg iartg bynr',\n", " 'dogblogone fun humor dogs',\n", " 'vicious animal attack meme humor',\n", " 'best bot bio ever tinder fails humor',\n", " ...]" ] }, "metadata": { "tags": [] }, "execution_count": 23 } ] }, { "cell_type": "code", "metadata": { "id": "pn2YqHK4alZc" }, "source": [ "" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "wVyyRB-sfaDP" }, "source": [ "import os\n", "import sys\n", "import numpy as np\n", "import keras\n", "from keras.preprocessing.text import Tokenizer\n", "from keras.utils import to_categorical\n", "from keras.preprocessing.sequence import pad_sequences\n", "from keras.layers import Activation, Conv2D, Input, Embedding, Reshape, MaxPool2D, Concatenate, Flatten, Dropout, Dense, Conv1D\n", "from keras.layers import MaxPool1D\n", "from keras.models import Model\n", "from keras.callbacks import ModelCheckpoint\n", "from keras.optimizers import Adam" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "676xZCYxfaNf" }, "source": [ "\n", "# make the max word length to be constant\n", "MAX_WORDS = 10000\n", "MAX_SEQUENCE_LENGTH = 1000\n", "# the percentage of train test split to be applied\n", "VALIDATION_SPLIT = 0.20\n", "# the dimension of vectors to be used\n", "EMBEDDING_DIM = 300\n", "# filter sizes of the different conv layers \n", "filter_sizes = [3,4,5]\n", "num_filters = 512\n", "embedding_dim = 100\n", "# dropout probability\n", "drop = 0.5\n", "batch_size = 16\n", "epochs = 2" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "hMpG7lMMfaow", "outputId": "0c233652-8ce4-472a-e84f-ecde27de7ccd" }, "source": [ "tokenizer = Tokenizer(num_words = MAX_WORDS)\n", "tokenizer.fit_on_texts(df['tweets'])\n", "sequences = tokenizer.texts_to_sequences(df['tweets'])\n", "\n", "word_index = tokenizer.word_index\n", "print(\"unique words : {}\".format(len(word_index)))\n", "\n", "data = pad_sequences(sequences, maxlen=MAX_SEQUENCE_LENGTH)\n", "\n", "labels = to_categorical(np.asarray(df['class']))\n", "print('Shape of data tensor:', data.shape)\n", "print('Shape of label tensor:', labels.shape)\n", "print(labels)" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "unique words : 99199\n", "Shape of data tensor: (67895, 1000)\n", "Shape of label tensor: (67895, 2)\n", "[[1. 0.]\n", " [1. 0.]\n", " [1. 0.]\n", " ...\n", " [0. 1.]\n", " [0. 1.]\n", " [0. 1.]]\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "B-87vVY1faxL" }, "source": [ "# split the data into a training set and a validation set\n", "indices = np.arange(data.shape[0])\n", "np.random.shuffle(indices)\n", "data = data[indices]\n", "labels = labels[indices]\n", "nb_validation_samples = int(VALIDATION_SPLIT * data.shape[0])\n", "\n", "x_train = data[:-nb_validation_samples]\n", "y_train = labels[:-nb_validation_samples]\n", "x_val = data[-nb_validation_samples:]\n", "y_val = labels[-nb_validation_samples:]" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "qXWxLq7Tfa2_", "outputId": "f0462faa-ba0c-46a1-bdab-3446ee4afb4a" }, "source": [ "embeddings_index = {}\n", "embeddings_index = {}\n", "f = open('/content/drive/My Drive/toxic/glove_840B_300d_char.txt')\n", "for line in f:\n", " values = line.split(' ')\n", " word = values[0] ## The first entry is the word\n", " coefs = np.asarray(values[1:], dtype='float32') ## These are the vecotrs representing the embedding for the word\n", " embeddings_index[word] = coefs\n", "f.close()\n", "\n", "print('GloVe data loaded')\n", "\n", "print('Found %s word vectors.' % len(embeddings_index))" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "GloVe data loaded\n", "Found 94 word vectors.\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "V4TIc0yHfa0P" }, "source": [ "embedding_matrix = np.zeros((len(word_index) + 1, EMBEDDING_DIM))\n", "for word, i in word_index.items():\n", " embedding_vector = embeddings_index.get(word)\n", " if embedding_vector is not None:\n", " # words not found in embedding index will be all-zeros.\n", " embedding_matrix[i] = embedding_vector" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "QdSfGN5FouMp" }, "source": [ "\n", "\n", "from keras.layers import Embedding\n", "\n", "embedding_layer = Embedding(len(word_index) + 1,\n", " EMBEDDING_DIM,\n", " weights=[embedding_matrix],\n", " input_length=MAX_SEQUENCE_LENGTH,\n", " trainable=False)\n", "\n" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "6HQTawBCfasa" }, "source": [ "inputs = Input(shape=(MAX_SEQUENCE_LENGTH,), dtype='int32')\n", "embedding = embedding_layer(inputs)\n", "\n", "print(embedding.shape)\n", "reshape = Reshape((MAX_SEQUENCE_LENGTH,EMBEDDING_DIM,1))(embedding)\n", "print(reshape.shape)\n", "\n", "conv_0 = Conv2D(num_filters, kernel_size=(filter_sizes[0], embedding_dim), padding='valid', kernel_initializer='normal', activation='relu')(reshape)\n", "conv_1 = Conv2D(num_filters, kernel_size=(filter_sizes[1], embedding_dim), padding='valid', kernel_initializer='normal', activation='relu')(reshape)\n", "conv_2 = Conv2D(num_filters, kernel_size=(filter_sizes[2], embedding_dim), padding='valid', kernel_initializer='normal', activation='relu')(reshape)\n", "\n", "maxpool_0 = MaxPool2D(pool_size=(MAX_SEQUENCE_LENGTH - filter_sizes[0] + 1, 1), strides=(1,1), padding='valid')(conv_0)\n", "maxpool_1 = MaxPool2D(pool_size=(MAX_SEQUENCE_LENGTH - filter_sizes[1] + 1, 1), strides=(1,1), padding='valid')(conv_1)\n", "maxpool_2 = MaxPool2D(pool_size=(MAX_SEQUENCE_LENGTH - filter_sizes[2] + 1, 1), strides=(1,1), padding='valid')(conv_2)\n", "\n", "concatenated_tensor = Concatenate(axis=1)([maxpool_0, maxpool_1, maxpool_2])\n", "flatten = Flatten()(concatenated_tensor)\n", "dropout = Dropout(drop)(flatten)\n", "output = Dense(units=2, activation='softmax')(dropout)\n", "\n", "# this creates a model that includes\n", "model = Model(inputs=inputs, outputs=output)\n", "\n", "checkpoint = ModelCheckpoint('weights_cnn_sentece.hdf5', monitor='val_acc', verbose=1, save_best_only=True, mode='auto')\n", "adam = Adam(lr=1e-4, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0)\n", "\n", "model.compile(optimizer=adam, loss='categorical_crossentropy', metrics=['accuracy'])\n", "model.summary()" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "IK4AjJ2JfaJs" }, "source": [ "print(\"Traning Model...\")\n", "model.fit(x_train, y_train, batch_size=5, epochs=epochs, verbose=1, callbacks=[checkpoint], validation_data=(x_val, y_val))" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "mrFwnRdefaHZ" }, "source": [ "" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "YERWxGXg5dai" }, "source": [ "" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "KF9ESM5ISeCW" }, "source": [ "maxlen = 200\n", "training_samples = 100\n", "validation_samples = 2000\n", "max_words = 200\n", "\n", "embedding_dim = 200\n", "\n", "batch_size = 64\n", "epochs = 20" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "07sfJWs2Sd_D", "outputId": "1ad1e814-cb09-4f60-b9ec-6cb65a4932b5" }, "source": [ "from keras.preprocessing.text import Tokenizer\n", "from keras.preprocessing.sequence import pad_sequences\n", "\n", "tokenizer = Tokenizer(num_words = max_words)\n", "tokenizer.fit_on_texts(list(fcorpus))\n", "sequences = tokenizer.texts_to_sequences(list(fcorpus))\n", "word_index = tokenizer.word_index\n", "print('There are %s unique tokens.' %len(word_index))\n", "\n", "X = tokenizer.texts_to_sequences(fcorpus)\n", "X = pad_sequences(X, maxlen = maxlen)\n", "y = df['class']" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "There are 52221 unique tokens.\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "UsU0IsgRWCVK" }, "source": [ "" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "RjJ_UUkKSd81" }, "source": [ "from tensorflow.keras.callbacks import EarlyStopping\n", "from keras import layers\n", "from keras.layers import Dense, Flatten, LSTM, Conv1D, MaxPooling1D, Dropout, Activation,Embedding\n", "model = Sequential()\n", "model.add(Embedding(max_words, embedding_dim, input_length = maxlen))\n", "\n", "model.add(Flatten())\n", "model.add(Dense(100, activation=\"relu\"))\n", "model.add(Dropout(0.3))\n", "model.add(Dense(50, activation=\"relu\"))\n", "model.add(Dropout(0.3))\n", "model.add(Dense(1, activation=\"sigmoid\"))\n", "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n", "\n", "es = EarlyStopping(monitor=\"val_acc\", mode=\"max\", patience=3)\n", "history = model.fit(X, y, batch_size=batch_size, epochs=epochs, validation_split=0.2, callbacks=[es])" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "dOmy2B4wSd4R" }, "source": [ "" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "_ctJgc_aSd0J" }, "source": [ "" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "bzKYaXIpYLt2" }, "source": [ "import tensorflow.keras\n", "tokenizer = tensorflow.keras.preprocessing.text.Tokenizer(num_words=5000, lower=True,split=' ',filters='!\"#$%&()*+,-./:;<=>?@[\\\\]^_`{|}~\\t\\n')\n", "tokenizer.fit_on_texts(df[\"tweets\"])\n", "#print(tokenizer.word_index) # To see the dicstionary\n", "X = tokenizer.texts_to_sequences(df[\"tweets\"])\n", "X = tensorflow.keras.preprocessing.sequence.pad_sequences(X)\n", "\n", "\n", "from keras.layers import Dense, Flatten, LSTM, Conv1D, MaxPooling1D, Dropout, Activation,Embedding\n", "from keras.models import Sequential\n", "model_conv = Sequential()\n", "model_conv.add(Embedding(5000, 100, input_length=X.shape[1]))\n", "model_conv.add(Conv1D(128, 5, activation='relu'))\n", "model_conv.add(MaxPooling1D(pool_size=4))\n", "model_conv.add(LSTM(100))\n", "model_conv.add(Dense(1, activation='sigmoid'))\n", "model_conv.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n", "\n", "Y = df['class']\n", "\n", "\n", "X_train, X_valid, Y_train, Y_valid = train_test_split(X,Y, test_size = 0.25, random_state =2)\n", "batch_size=128\n", "#Here we train the Network.\n", "\n", "pred=model_conv.fit(X_train, Y_train, batch_size =batch_size, epochs =10, verbose =2,validation_data=(X_valid,Y_valid))\n", "pred" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "N3yiFXQiC46C", "outputId": "9d2a5ad8-e671-4c94-ebb2-60ebf1cefd02" }, "source": [ "a= model_conv.predict_classes(X_valid)" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`, if your model does multi-class classification (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`, if your model does binary classification (e.g. if it uses a `sigmoid` last-layer activation).\n", " warnings.warn('`model.predict_classes()` is deprecated and '\n" ], "name": "stderr" } ] }, { "cell_type": "code", "metadata": { "id": "aj3AAeITDQLf" }, "source": [ "v = a[:, 0]" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "R01BWtT7DpFI", "outputId": "e9788226-0876-44d7-cdf4-4e076370f48b" }, "source": [ "print(\"++++++++++++++++++++++=Testing Results++++++++++++++++++++\")\r\n", "\r\n", "print(\"Testing Accuracy\")\r\n", "print(accuracy_score(Y_valid,v))\r\n", "print(\"Traning Classifcation Report\")\r\n", "print(classification_report(Y_valid,v))\r\n", "\r\n", "print(\"Testing Confusion\")\r\n", "print(confusion_matrix(Y_valid,v))\r\n", "\r\n", "cm1 = confusion_matrix(Y_valid,v)\r\n", "\r\n", "Accuracy = cm1[0,0]/(cm1[0,1]+cm1[0,0])\r\n", "print('Accuracy 0 class : ', Accuracy)\r\n", "\r\n", "Accuracy = cm1[1,1]/(cm1[1,0]+cm1[1,1])\r\n", "print('Accuracy 1 class : ', Accuracy)\r\n", "\r\n", "total1=sum(sum(cm1))\r\n", "\r\n", "Accuracy = cm1[0,1]/(cm1[0,0]+cm1[0,1])\r\n", "print('Error Rate for 0: ', Accuracy)\r\n", "\r\n", "\r\n", "Accuracy = cm1[1,0]/(cm1[1,1]+cm1[1,0])\r\n", "print('Error Rate for 1: ', Accuracy)" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "++++++++++++++++++++++=Testing Results++++++++++++++++++++\n", "Testing Accuracy\n", "0.9200740055504163\n", "Traning Classifcation Report\n", " precision recall f1-score support\n", "\n", " 0 0.96 0.86 0.91 5077\n", " 1 0.89 0.97 0.93 5733\n", "\n", " accuracy 0.92 10810\n", " macro avg 0.93 0.92 0.92 10810\n", "weighted avg 0.92 0.92 0.92 10810\n", "\n", "Testing Confusion\n", "[[4379 698]\n", " [ 166 5567]]\n", "Accuracy 0 class : 0.8625172345873547\n", "Accuracy 1 class : 0.9710448281876853\n", "Error Rate for 0: 0.13748276541264526\n", "Error Rate for 1: 0.02895517181231467\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "eqn-tBHkocNc" }, "source": [ "from sklearn.metrics import classification_report,confusion_matrix,accuracy_score\n", "pred=model_conv.predict(X_valid)\n", "\n", "\n", "\n" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "KqGV37k0vnVX", "outputId": "e6a540d5-75ab-4419-f22d-c87b1191f93f" }, "source": [ "v = np.where(pred > 0.5, 1, 0)\n", "\n", "\n", "\n", "print(\"++++++++++++++++++++++=Testing Results++++++++++++++++++++\")\n", "\n", "print(\"Testing Accuracy\")\n", "print(accuracy_score(Y_valid,v))\n", "print(\"Traning Classifcation Report\")\n", "print(classification_report(Y_valid,v))\n", "\n", "print(\"Testing Confusion\")\n", "print(confusion_matrix(Y_valid,v))\n", "\n", "cm1 = confusion_matrix(Y_valid,v)\n", "\n", "Accuracy = cm1[0,0]/(cm1[0,1]+cm1[0,0])\n", "print('Accuracy 0 class : ', Accuracy)\n", "\n", "Accuracy = cm1[1,1]/(cm1[1,0]+cm1[1,1])\n", "print('Accuracy 1 class : ', Accuracy)\n", "\n", "total1=sum(sum(cm1))\n", "\n", "Accuracy = cm1[0,1]/(cm1[0,0]+cm1[0,1])\n", "print('Error Rate for 0: ', Accuracy)\n", "\n", "\n", "Accuracy = cm1[1,0]/(cm1[1,1]+cm1[1,0])\n", "print('Error Rate for 1: ', Accuracy)" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "++++++++++++++++++++++=Testing Results++++++++++++++++++++\n", "Testing Accuracy\n", "0.9065040650406504\n", "Traning Classifcation Report\n", " precision recall f1-score support\n", "\n", " 0 0.90 0.91 0.91 8413\n", " 1 0.91 0.90 0.91 8561\n", "\n", " accuracy 0.91 16974\n", " macro avg 0.91 0.91 0.91 16974\n", "weighted avg 0.91 0.91 0.91 16974\n", "\n", "Testing Confusion\n", "[[7689 724]\n", " [ 863 7698]]\n", "Accuracy 0 class : 0.9139427077142518\n", "Accuracy 1 class : 0.8991940193902581\n", "Error Rate for 0: 0.08605729228574825\n", "Error Rate for 1: 0.10080598060974186\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "FB4kNpGTGkzH" }, "source": [ "import matplotlib.pyplot as plt\n", "import matplotlib.pyplot as plot\n", "plt.figure(figsize=(10,7),dpi=300)\n", "\n", "plt.subplot(1,2,1)\n", "plot.plot(pred.history['accuracy'])\n", "plot.plot(pred.history['val_accuracy'])\n", "plot.title('Model accuracy')\n", "plot.ylabel('Accuracy')\n", "plot.xlabel('Epoch')\n", "plt.yticks(np.arange(0, 1, 0.1))\n", "plot.legend(['Train', 'Test'], loc='upper left')\n", "\n", "plt.subplot(1,2,2)\n", "plot.plot(pred.history['loss'])\n", "plot.plot(pred.history['val_loss'])\n", "plot.title('Model loss')\n", "plot.ylabel('Loss')\n", "\n", "plot.xlabel('Epoch')\n", "plot.legend(['Train', 'Test'], loc='upper left')\n", "plt.savefig('fig1.png')\n", "plot.show()" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "8UfGwbRi4FCi", "outputId": "109b1641-e6eb-487a-fd0f-9af8996f94b2" }, "source": [ "from sklearn.ensemble import ExtraTreesClassifier\n", "etc = ExtraTreesClassifier(n_estimators=300, random_state=5, max_depth=300)\n", "etc.fit(X_train,Y_train)\n", "etc_pred=etc.predict(X_valid)\n", "print(classification_report(Y_valid,etc_pred))" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ " precision recall f1-score support\n", "\n", " 0 0.84 0.85 0.84 5077\n", " 1 0.87 0.85 0.86 5733\n", "\n", " accuracy 0.85 10810\n", " macro avg 0.85 0.85 0.85 10810\n", "weighted avg 0.85 0.85 0.85 10810\n", "\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "4wGqfHfn4FIW" }, "source": [ "from sklearn.ensemble import RandomForestClassifier\r\n", "etc = RandomForestClassifier(n_estimators=300, random_state=5, max_depth=300)\r\n", "etc.fit(X_train,Y_train)\r\n", "etc_pred=etc.predict(X_valid)\r\n", "print(classification_report(Y_valid,etc_pred))" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "uMZ2RXln4FMc" }, "source": [ "from sklearn import tree\r\n", "etc = tree.DecisionTreeClassifier()\r\n", "etc.fit(X_train,Y_train)\r\n", "etc_pred=etc.predict(X_valid)\r\n", "print(classification_report(Y_valid,etc_pred))" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "JRd_kbWOGdWB" }, "source": [ "from sklearn.ensemble import RandomForestClassifier\r\n", "etc = RandomForestClassifier(n_estimators=300, random_state=5, max_depth=300)\r\n", "etc.fit(X_train,Y_train)\r\n", "etc_pred=etc.predict(X_valid)\r\n", "print(classification_report(Y_valid,etc_pred))" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "gysb_2SEZcYp", "outputId": "e906aefc-cf7b-4fee-da69-c339e5fa9d16" }, "source": [ "pred = model_conv.predict_classes(X_valid)\n", "print(classification_report(Y_valid, pred, target_names = ['Not Sarcastic','Sarcastic']))" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "WARNING:tensorflow:From :1: Sequential.predict_classes (from tensorflow.python.keras.engine.sequential) is deprecated and will be removed after 2021-01-01.\n", "Instructions for updating:\n", "Please use instead:* `np.argmax(model.predict(x), axis=-1)`, if your model does multi-class classification (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`, if your model does binary classification (e.g. if it uses a `sigmoid` last-layer activation).\n", " precision recall f1-score support\n", "\n", "Not Sarcastic 0.78 0.76 0.77 3713\n", " Sarcastic 0.75 0.77 0.76 3442\n", "\n", " accuracy 0.77 7155\n", " macro avg 0.77 0.77 0.77 7155\n", " weighted avg 0.77 0.77 0.77 7155\n", "\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "_TyV5Fvm4htG" }, "source": [ "\n", "\n", "max_features = 35000\n", "maxlen = 22509\n", "\n", "tokenizer = text.Tokenizer(num_words=max_features)\n", "tokenizer.fit_on_texts(x_train)\n", "tokenized_train = tokenizer.texts_to_sequences(x_train)\n", "x_train = sequence.pad_sequences(tokenized_train, maxlen=maxlen)\n", "\n" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "Y4TxMrsv4hv7" }, "source": [ "tokenized_test = tokenizer.texts_to_sequences(x_test)\n", "X_test = sequence.pad_sequences(tokenized_test, maxlen=maxlen)" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "2ZPeWbw44hzJ" }, "source": [ "EMBEDDING_FILE = '/content/drive/My Drive/toxic/glove.840B.300d-char.txt'" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "05kUahoH4h2e" }, "source": [ "\n", "\n", "def get_coefs(word, *arr): \n", " return word, np.asarray(arr, dtype='float32')\n", "embeddings_index = dict(get_coefs(*o.rstrip().rsplit(' ')) for o in open(EMBEDDING_FILE))\n", "\n" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 74 }, "id": "rDP_3K9Z4h53", "outputId": "842df2f0-d433-4f8a-dda3-a42755f56025" }, "source": [ "all_embs = np.stack(embeddings_index.values())\n", "emb_mean,emb_std = all_embs.mean(), all_embs.std()\n", "embed_size = all_embs.shape[1]\n", "\n", "word_index = tokenizer.word_index\n", "nb_words = min(max_features, len(word_index))\n", "#change below line if computing normal stats is too slow\n", "embedding_matrix = embedding_matrix = np.random.normal(emb_mean, emb_std, (nb_words, embed_size))\n", "for word, i in word_index.items():\n", " if i >= max_features: continue\n", " embedding_vector = embeddings_index.get(word)\n", " if embedding_vector is not None: embedding_matrix[i] = embedding_vector" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "/usr/local/lib/python3.6/dist-packages/IPython/core/interactiveshell.py:2822: FutureWarning: arrays to stack must be passed as a \"sequence\" type such as list or tuple. Support for non-sequence iterables such as generators is deprecated as of NumPy 1.16 and will raise an error in the future.\n", " if self.run_code(code, result):\n" ], "name": "stderr" } ] }, { "cell_type": "code", "metadata": { "id": "NAG2sI2p6s0d" }, "source": [ "batch_size = 128\n", "epochs = 2\n", "embed_size = 300" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "9CFax4P56s3m" }, "source": [ "#Defining Neural Network\n", "model = Sequential()\n", "#Non-trainable embeddidng layer\n", "model.add(Embedding(nb_words, output_dim=embed_size, weights=[embedding_matrix], input_length=300, trainable=True))\n", "#LSTM \n", "model.add(Bidirectional(LSTM(units=128 , recurrent_dropout = 0.5 , dropout = 0.5)))\n", "model.add(Dense(1, activation='sigmoid'))\n", "model.compile(optimizer=keras.optimizers.Adam(lr = 0.01), loss='binary_crossentropy', metrics=['acc'])" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "h0Z67bdh6s6b" }, "source": [ "history = model.fit(x_train, y_train, batch_size = batch_size , validation_data = (X_test,y_test) , epochs = 5)" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 54 }, "id": "KhAHrwLx7NDT", "outputId": "8e5c37a1-7f9e-49c9-bfdd-1b6fa1be4170" }, "source": [ "print(\"Accuracy of the model on Testing Data is - \" , model.evaluate(X_test_dtf,y_test)[1]*100)" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "7155/7155 [==============================] - 20s 3ms/step\n", "Accuracy of the model on Testing Data is - 53.20754647254944\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 183 }, "id": "7hNKVIpW7NGZ", "outputId": "d03eaad2-135a-42c5-f888-1fdc957f25c5" }, "source": [ "pred = model.predict_classes(X_test_dtf)\n", "print(classification_report(y_test, pred, target_names = ['Not Sarcastic','Sarcastic']))" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ " precision recall f1-score support\n", "\n", "Not Sarcastic 0.53 0.99 0.69 3808\n", " Sarcastic 0.49 0.01 0.02 3347\n", "\n", " accuracy 0.53 7155\n", " macro avg 0.51 0.50 0.36 7155\n", " weighted avg 0.51 0.53 0.38 7155\n", "\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "PZTFiWi48iG4" }, "source": [ "" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "r0XtvzZr8sE0" }, "source": [ "" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "IYiY3Rd6MPv4" }, "source": [ "" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "o_0gw2XfMPzJ" }, "source": [ "" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "ft0vLD_9MP4Q" }, "source": [ "" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "yg31eRhEMP2f", "outputId": "7bdbb1db-d1be-4280-c16f-58716b3ce7b6" }, "source": [ "from sklearn.feature_extraction.text import CountVectorizer\n", "from sklearn.feature_extraction.text import TfidfVectorizer\n", "cv=TfidfVectorizer()\n", "#transformed train reviews\n", "datacount=cv.fit_transform(fcorpus)\n", "\n", "\n", "from imblearn.over_sampling import SMOTE\n", "sm = SMOTE(random_state=2)\n", "X_restf, y_restf = sm.fit_sample(datacount, df[\"is_sarcastic\"].ravel())\n", "\n", "print('After OverSampling, the shape of train_X: {}'.format(X_restf.shape))\n", "print('After OverSampling, the shape of train_y: {} \\n'.format(y_restf.shape))" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "/usr/local/lib/python3.6/dist-packages/sklearn/externals/six.py:31: FutureWarning: The module is deprecated in version 0.21 and will be removed in version 0.23 since we've dropped support for Python 2.7. Please rely on the official version of six (https://pypi.org/project/six/).\n", " \"(https://pypi.org/project/six/).\", FutureWarning)\n", "/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:144: FutureWarning: The sklearn.neighbors.base module is deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.neighbors. Anything that cannot be imported from sklearn.neighbors is now part of the private API.\n", " warnings.warn(message, FutureWarning)\n", "/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.\n", " warnings.warn(msg, category=FutureWarning)\n" ], "name": "stderr" }, { "output_type": "stream", "text": [ "After OverSampling, the shape of train_X: (29970, 25556)\n", "After OverSampling, the shape of train_y: (29970,) \n", "\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "G2fbqrcW8sH8" }, "source": [ "" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "gvyo3DbP8sKk" }, "source": [ "X_train,X_test,y_train,y_test = train_test_split(fcorpus,df[\"class\"], test_size = 0.25 , random_state = 0)" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "faPFf0jk8iJx" }, "source": [ "from sklearn.feature_extraction.text import TfidfVectorizer\n", "from sklearn.feature_extraction.text import CountVectorizer\n", "# learn training data vocabulary, then use it to create a document-term matrix\n", "vect = TfidfVectorizer()\n", "# 3. fit\n", "X_train_tf = vect.fit_transform(X_train)\n", "X_test_tf = vect.transform(X_test)" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "KtZSl1KsEK1W" }, "source": [ ">>> from sklearn.pipeline import FeatureUnion\n", ">>> from sklearn.decomposition import PCA, TruncatedSVD\n", ">>> union = FeatureUnion([(\"TF\", TfidfVectorizer(max_features=11000)),\n", "... (\"TFIDF\", TfidfVectorizer(max_features=11000))])\n", "X_train_U=union.fit_transform(X_train)\n", "X_test_U=union.transform(X_test)" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "_KQXmgIL8iMs" }, "source": [ "" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "FJvgujXw8iPy" }, "source": [ "from sklearn.svm import SVC\n", "print(\"SVC\")\n", "svm = SVC(kernel='linear', C=3.0, random_state=2)\n", "etc3_Pre=svm.fit(X_train_tf, y_train).predict(X_train_tf)\n", "etc2_Pre=svm.fit(X_train_tf, y_train).predict(X_test_tf)\n", "\n", "print(\"Traning Accuracy\")\n", "print(accuracy_score(y_train,etc3_Pre))\n", "\n", "\n", "print(\"Traning Classifcation Report\")\n", "print(classification_report(y_train,etc3_Pre))\n", "\n", "print(\"Traning Confusion\")\n", "print(confusion_matrix(y_train,etc3_Pre))\n", "\n", "cm1 = confusion_matrix(y_train,etc3_Pre)\n", "\n", "Accuracy = cm1[0,0]/(cm1[0,1]+cm1[0,0])\n", "print('Accuracy 0 class : ', Accuracy)\n", "\n", "Accuracy = cm1[1,1]/(cm1[1,0]+cm1[1,1])\n", "print('Accuracy 1 class : ', Accuracy)\n", "\n", "total1=sum(sum(cm1))\n", "\n", "Accuracy = cm1[0,1]/(cm1[0,0]+cm1[0,1])\n", "print('Error Rate for 0: ', Accuracy)\n", "\n", "print(\"++++++++++++++++++++++=Testing Results++++++++++++++++++++\")\n", "\n", "print(\"Testing Accuracy\")\n", "print(accuracy_score(y_test,etc2_Pre))\n", "print(\"Traning Classifcation Report\")\n", "print(classification_report(y_test,etc2_Pre))\n", "\n", "print(\"Testing Confusion\")\n", "print(confusion_matrix(y_test,etc2_Pre))\n", "\n", "cm1 = confusion_matrix(y_test,etc2_Pre)\n", "\n", "Accuracy = cm1[0,0]/(cm1[0,1]+cm1[0,0])\n", "print('Accuracy 0 class : ', Accuracy)\n", "\n", "Accuracy = cm1[1,1]/(cm1[1,0]+cm1[1,1])\n", "print('Accuracy 1 class : ', Accuracy)\n", "\n", "total1=sum(sum(cm1))\n", "\n", "Accuracy = cm1[0,1]/(cm1[0,0]+cm1[0,1])\n", "print('Error Rate for 0: ', Accuracy)\n", "\n", "\n", "Accuracy = cm1[1,0]/(cm1[1,1]+cm1[1,0])\n", "print('Error Rate for 1: ', Accuracy)" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "xreA7wnt8iSm" }, "source": [ "from sklearn.ensemble import ExtraTreesClassifier\n", "etc = ExtraTreesClassifier(n_estimators=300, random_state=5, max_depth=300)\n", "etc3_Pre=etc.fit(X_train_tf, y_train).predict(X_train_tf)\n", "etc2_Pre=etc.fit(X_train_tf, y_train).predict(X_test_tf)\n", "\n", "print(\"Traning Accuracy\")\n", "print(accuracy_score(y_train,etc3_Pre))\n", "\n", "\n", "print(\"Traning Classifcation Report\")\n", "print(classification_report(y_train,etc3_Pre))\n", "\n", "print(\"Traning Confusion\")\n", "print(confusion_matrix(y_train,etc3_Pre))\n", "\n", "cm1 = confusion_matrix(y_train,etc3_Pre)\n", "\n", "Accuracy = cm1[0,0]/(cm1[0,1]+cm1[0,0])\n", "print('Accuracy 0 class : ', Accuracy)\n", "\n", "Accuracy = cm1[1,1]/(cm1[1,0]+cm1[1,1])\n", "print('Accuracy 1 class : ', Accuracy)\n", "\n", "total1=sum(sum(cm1))\n", "\n", "Accuracy = cm1[0,1]/(cm1[0,0]+cm1[0,1])\n", "print('Error Rate for 0: ', Accuracy)\n", "\n", "print(\"++++++++++++++++++++++=Testing Results++++++++++++++++++++\")\n", "\n", "print(\"Testing Accuracy\")\n", "print(accuracy_score(y_test,etc2_Pre))\n", "print(\"Traning Classifcation Report\")\n", "print(classification_report(y_test,etc2_Pre))\n", "\n", "print(\"Testing Confusion\")\n", "print(confusion_matrix(y_test,etc2_Pre))\n", "\n", "cm1 = confusion_matrix(y_test,etc2_Pre)\n", "\n", "Accuracy = cm1[0,0]/(cm1[0,1]+cm1[0,0])\n", "print('Accuracy 0 class : ', Accuracy)\n", "\n", "Accuracy = cm1[1,1]/(cm1[1,0]+cm1[1,1])\n", "print('Accuracy 1 class : ', Accuracy)\n", "\n", "total1=sum(sum(cm1))\n", "\n", "Accuracy = cm1[0,1]/(cm1[0,0]+cm1[0,1])\n", "print('Error Rate for 0: ', Accuracy)\n", "\n", "\n", "Accuracy = cm1[1,0]/(cm1[1,1]+cm1[1,0])\n", "print('Error Rate for 1: ', Accuracy)" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "AT01wohO8iWT" }, "source": [ "from sklearn import tree\n", "clf = tree.DecisionTreeClassifier()\n", "etc3_Pre=clf.fit(X_train_tf, y_train).predict(X_train_tf)\n", "etc2_Pre=clf.fit(X_train_tf, y_train).predict(X_test_tf)\n", "\n", "print(\"Traning Accuracy\")\n", "print(accuracy_score(y_train,etc3_Pre))\n", "\n", "\n", "print(\"Traning Classifcation Report\")\n", "print(classification_report(y_train,etc3_Pre))\n", "\n", "print(\"Traning Confusion\")\n", "print(confusion_matrix(y_train,etc3_Pre))\n", "\n", "cm1 = confusion_matrix(y_train,etc3_Pre)\n", "\n", "Accuracy = cm1[0,0]/(cm1[0,1]+cm1[0,0])\n", "print('Accuracy 0 class : ', Accuracy)\n", "\n", "Accuracy = cm1[1,1]/(cm1[1,0]+cm1[1,1])\n", "print('Accuracy 1 class : ', Accuracy)\n", "\n", "total1=sum(sum(cm1))\n", "\n", "Accuracy = cm1[0,1]/(cm1[0,0]+cm1[0,1])\n", "print('Error Rate for 0: ', Accuracy)\n", "\n", "print(\"++++++++++++++++++++++=Testing Results++++++++++++++++++++\")\n", "\n", "print(\"Testing Accuracy\")\n", "print(accuracy_score(y_test,etc2_Pre))\n", "print(\"Traning Classifcation Report\")\n", "print(classification_report(y_test,etc2_Pre))\n", "\n", "print(\"Testing Confusion\")\n", "print(confusion_matrix(y_test,etc2_Pre))\n", "\n", "cm1 = confusion_matrix(y_test,etc2_Pre)\n", "\n", "Accuracy = cm1[0,0]/(cm1[0,1]+cm1[0,0])\n", "print('Accuracy 0 class : ', Accuracy)\n", "\n", "Accuracy = cm1[1,1]/(cm1[1,0]+cm1[1,1])\n", "print('Accuracy 1 class : ', Accuracy)\n", "\n", "total1=sum(sum(cm1))\n", "\n", "Accuracy = cm1[0,1]/(cm1[0,0]+cm1[0,1])\n", "print('Error Rate for 0: ', Accuracy)\n", "\n", "\n", "Accuracy = cm1[1,0]/(cm1[1,1]+cm1[1,0])\n", "print('Error Rate for 1: ', Accuracy)" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "2AlSytbw8iYz" }, "source": [ "" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "3To8q4F68icC" }, "source": [ "" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "f-3TzkaJ-C2Y" }, "source": [ "" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "k6bW4wB8-C8w" }, "source": [ "from sklearn.feature_extraction.text import CountVectorizer\n", "# learn training data vocabulary, then use it to create a document-term matrix\n", "vect = CountVectorizer()\n", "# 3. fit\n", "# 4. transform training data\n", "X_train_dtf = vect.fit_transform(X_train)\n", "X_test_dtf = vect.transform(X_test)" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "Jyw97RpH-C_f" }, "source": [ "from sklearn.ensemble import RandomForestClassifier\n", "rfc = RandomForestClassifier(n_estimators=300,max_depth=200) \n", "etc3_Pre=rfc.fit(X_train_dtf, y_train).predict(X_train_dtf)\n", "etc2_Pre=rfc.fit(X_train_dtf, y_train).predict(X_test_dtf)\n", "\n", "print(\"Traning Accuracy\")\n", "print(accuracy_score(y_train,etc3_Pre))\n", "\n", "\n", "print(\"Traning Classifcation Report\")\n", "print(classification_report(y_train,etc3_Pre))\n", "\n", "print(\"Traning Confusion\")\n", "print(confusion_matrix(y_train,etc3_Pre))\n", "\n", "cm1 = confusion_matrix(y_train,etc3_Pre)\n", "\n", "Accuracy = cm1[0,0]/(cm1[0,1]+cm1[0,0])\n", "print('Accuracy 0 class : ', Accuracy)\n", "\n", "Accuracy = cm1[1,1]/(cm1[1,0]+cm1[1,1])\n", "print('Accuracy 1 class : ', Accuracy)\n", "\n", "total1=sum(sum(cm1))\n", "\n", "Accuracy = cm1[0,1]/(cm1[0,0]+cm1[0,1])\n", "print('Error Rate for 0: ', Accuracy)\n", "\n", "print(\"++++++++++++++++++++++=Testing Results++++++++++++++++++++\")\n", "\n", "print(\"Testing Accuracy\")\n", "print(accuracy_score(y_test,etc2_Pre))\n", "print(\"Traning Classifcation Report\")\n", "print(classification_report(y_test,etc2_Pre))\n", "\n", "print(\"Testing Confusion\")\n", "print(confusion_matrix(y_test,etc2_Pre))\n", "\n", "cm1 = confusion_matrix(y_test,etc2_Pre)\n", "\n", "Accuracy = cm1[0,0]/(cm1[0,1]+cm1[0,0])\n", "print('Accuracy 0 class : ', Accuracy)\n", "\n", "Accuracy = cm1[1,1]/(cm1[1,0]+cm1[1,1])\n", "print('Accuracy 1 class : ', Accuracy)\n", "\n", "total1=sum(sum(cm1))\n", "\n", "Accuracy = cm1[0,1]/(cm1[0,0]+cm1[0,1])\n", "print('Error Rate for 0: ', Accuracy)\n", "\n", "\n", "Accuracy = cm1[1,0]/(cm1[1,1]+cm1[1,0])\n", "print('Error Rate for 1: ', Accuracy)" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "NtJqK7px-C5v" }, "source": [ "from sklearn.svm import SVC\n", "print(\"SVC\")\n", "svm = SVC(kernel='linear', C=3.0, random_state=2)\n", "etc3_Pre=svm.fit(X_train_dtf, y_train).predict(X_train_dtf)\n", "etc2_Pre=svm.fit(X_train_dtf, y_train).predict(X_test_dtf)\n", "\n", "print(\"Traning Accuracy\")\n", "print(accuracy_score(y_train,etc3_Pre))\n", "\n", "\n", "print(\"Traning Classifcation Report\")\n", "print(classification_report(y_train,etc3_Pre))\n", "\n", "print(\"Traning Confusion\")\n", "print(confusion_matrix(y_train,etc3_Pre))\n", "\n", "cm1 = confusion_matrix(y_train,etc3_Pre)\n", "\n", "Accuracy = cm1[0,0]/(cm1[0,1]+cm1[0,0])\n", "print('Accuracy 0 class : ', Accuracy)\n", "\n", "Accuracy = cm1[1,1]/(cm1[1,0]+cm1[1,1])\n", "print('Accuracy 1 class : ', Accuracy)\n", "\n", "total1=sum(sum(cm1))\n", "\n", "Accuracy = cm1[0,1]/(cm1[0,0]+cm1[0,1])\n", "print('Error Rate for 0: ', Accuracy)\n", "\n", "print(\"++++++++++++++++++++++=Testing Results++++++++++++++++++++\")\n", "\n", "print(\"Testing Accuracy\")\n", "print(accuracy_score(y_test,etc2_Pre))\n", "print(\"Traning Classifcation Report\")\n", "print(classification_report(y_test,etc2_Pre))\n", "\n", "print(\"Testing Confusion\")\n", "print(confusion_matrix(y_test,etc2_Pre))\n", "\n", "cm1 = confusion_matrix(y_test,etc2_Pre)\n", "\n", "Accuracy = cm1[0,0]/(cm1[0,1]+cm1[0,0])\n", "print('Accuracy 0 class : ', Accuracy)\n", "\n", "Accuracy = cm1[1,1]/(cm1[1,0]+cm1[1,1])\n", "print('Accuracy 1 class : ', Accuracy)\n", "\n", "total1=sum(sum(cm1))\n", "\n", "Accuracy = cm1[0,1]/(cm1[0,0]+cm1[0,1])\n", "print('Error Rate for 0: ', Accuracy)\n", "\n", "\n", "Accuracy = cm1[1,0]/(cm1[1,1]+cm1[1,0])\n", "print('Error Rate for 1: ', Accuracy)" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "O3btqFYp-gSM" }, "source": [ "from sklearn.ensemble import ExtraTreesClassifier\n", "etc = ExtraTreesClassifier(n_estimators=300, random_state=5, max_depth=300)\n", "etc3_Pre=etc.fit(X_train_dtf, y_train).predict(X_train_dtf)\n", "etc2_Pre=etc.fit(X_train_dtf, y_train).predict(X_test_dtf)\n", "\n", "print(\"Traning Accuracy\")\n", "print(accuracy_score(y_train,etc3_Pre))\n", "\n", "\n", "print(\"Traning Classifcation Report\")\n", "print(classification_report(y_train,etc3_Pre))\n", "\n", "print(\"Traning Confusion\")\n", "print(confusion_matrix(y_train,etc3_Pre))\n", "\n", "cm1 = confusion_matrix(y_train,etc3_Pre)\n", "\n", "Accuracy = cm1[0,0]/(cm1[0,1]+cm1[0,0])\n", "print('Accuracy 0 class : ', Accuracy)\n", "\n", "Accuracy = cm1[1,1]/(cm1[1,0]+cm1[1,1])\n", "print('Accuracy 1 class : ', Accuracy)\n", "\n", "total1=sum(sum(cm1))\n", "\n", "Accuracy = cm1[0,1]/(cm1[0,0]+cm1[0,1])\n", "print('Error Rate for 0: ', Accuracy)\n", "\n", "print(\"++++++++++++++++++++++=Testing Results++++++++++++++++++++\")\n", "\n", "print(\"Testing Accuracy\")\n", "print(accuracy_score(y_test,etc2_Pre))\n", "print(\"Traning Classifcation Report\")\n", "print(classification_report(y_test,etc2_Pre))\n", "\n", "print(\"Testing Confusion\")\n", "print(confusion_matrix(y_test,etc2_Pre))\n", "\n", "cm1 = confusion_matrix(y_test,etc2_Pre)\n", "\n", "Accuracy = cm1[0,0]/(cm1[0,1]+cm1[0,0])\n", "print('Accuracy 0 class : ', Accuracy)\n", "\n", "Accuracy = cm1[1,1]/(cm1[1,0]+cm1[1,1])\n", "print('Accuracy 1 class : ', Accuracy)\n", "\n", "total1=sum(sum(cm1))\n", "\n", "Accuracy = cm1[0,1]/(cm1[0,0]+cm1[0,1])\n", "print('Error Rate for 0: ', Accuracy)\n", "\n", "\n", "Accuracy = cm1[1,0]/(cm1[1,1]+cm1[1,0])\n", "print('Error Rate for 1: ', Accuracy)" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "G7S5UuN7-gUu" }, "source": [ "from sklearn import tree\n", "clf = tree.DecisionTreeClassifier()\n", "etc3_Pre=clf.fit(X_train_dtf, y_train).predict(X_train_dtf)\n", "etc2_Pre=clf.fit(X_train_dtf, y_train).predict(X_test_dtf)\n", "\n", "print(\"Traning Accuracy\")\n", "print(accuracy_score(y_train,etc3_Pre))\n", "\n", "\n", "print(\"Traning Classifcation Report\")\n", "print(classification_report(y_train,etc3_Pre))\n", "\n", "print(\"Traning Confusion\")\n", "print(confusion_matrix(y_train,etc3_Pre))\n", "\n", "cm1 = confusion_matrix(y_train,etc3_Pre)\n", "\n", "Accuracy = cm1[0,0]/(cm1[0,1]+cm1[0,0])\n", "print('Accuracy 0 class : ', Accuracy)\n", "\n", "Accuracy = cm1[1,1]/(cm1[1,0]+cm1[1,1])\n", "print('Accuracy 1 class : ', Accuracy)\n", "\n", "total1=sum(sum(cm1))\n", "\n", "Accuracy = cm1[0,1]/(cm1[0,0]+cm1[0,1])\n", "print('Error Rate for 0: ', Accuracy)\n", "\n", "print(\"++++++++++++++++++++++=Testing Results++++++++++++++++++++\")\n", "\n", "print(\"Testing Accuracy\")\n", "print(accuracy_score(y_test,etc2_Pre))\n", "print(\"Traning Classifcation Report\")\n", "print(classification_report(y_test,etc2_Pre))\n", "\n", "print(\"Testing Confusion\")\n", "print(confusion_matrix(y_test,etc2_Pre))\n", "\n", "cm1 = confusion_matrix(y_test,etc2_Pre)\n", "\n", "Accuracy = cm1[0,0]/(cm1[0,1]+cm1[0,0])\n", "print('Accuracy 0 class : ', Accuracy)\n", "\n", "Accuracy = cm1[1,1]/(cm1[1,0]+cm1[1,1])\n", "print('Accuracy 1 class : ', Accuracy)\n", "\n", "total1=sum(sum(cm1))\n", "\n", "Accuracy = cm1[0,1]/(cm1[0,0]+cm1[0,1])\n", "print('Error Rate for 0: ', Accuracy)\n", "\n", "\n", "Accuracy = cm1[1,0]/(cm1[1,1]+cm1[1,0])\n", "print('Error Rate for 1: ', Accuracy)" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "NctpDpFR-gYU" }, "source": [ "" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "colab": { "background_save": true }, "id": "TFzt3K3O42XQ" }, "source": [ "pip install zeugma" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 74 }, "id": "iNfJaBU342aw", "outputId": "a26f8452-95a1-4457-9757-531a1e61c810" }, "source": [ "from zeugma.embeddings import EmbeddingTransformer\n", "glove = EmbeddingTransformer('glove')\n", "X_train_g = glove.fit_transform(x_train)\n", "X_test_g = glove.transform(x_test)" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "/usr/local/lib/python3.6/dist-packages/smart_open/smart_open_lib.py:253: UserWarning: This function is deprecated, use smart_open.open instead. See the migration notes for details: https://github.com/RaRe-Technologies/smart_open/blob/master/README.rst#migrating-to-the-new-open-function\n", " 'See the migration notes for details: %s' % _MIGRATION_NOTES_URL\n" ], "name": "stderr" } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 183 }, "id": "EOZO2hTb5Ha8", "outputId": "93af02a1-0317-4f75-a83c-731618bb6305" }, "source": [ "from sklearn.ensemble import RandomForestClassifier\n", "rfc = RandomForestClassifier(n_estimators=300,max_depth=200) \n", "rfc.fit(X_train_g, y_train)\n", "# calculate accuracy of class predictions\n", "y_pred_class = rfc.predict(X_test_g)\n", "print(classification_report(y_test, y_pred_class))" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ " precision recall f1-score support\n", "\n", " 0 0.73 0.77 0.75 3808\n", " 1 0.72 0.67 0.69 3347\n", "\n", " accuracy 0.72 7155\n", " macro avg 0.72 0.72 0.72 7155\n", "weighted avg 0.72 0.72 0.72 7155\n", "\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 202 }, "id": "iet6ff-L5Hdz", "outputId": "4f6109a0-3cab-4cab-acaa-288e059b5693" }, "source": [ "from sklearn.svm import SVC\n", "print(\"SVC\")\n", "svm = SVC(kernel='linear', C=3.0, random_state=2)\n", "svm.fit(X_train_g,y_train)\n", "y_pred=svm.predict(X_test_g)\n", "accuracy_score(y_test,y_pred)\n", "print(classification_report(y_test,y_pred))" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "SVC\n", " precision recall f1-score support\n", "\n", " 0 0.69 0.71 0.70 3808\n", " 1 0.66 0.64 0.65 3347\n", "\n", " accuracy 0.68 7155\n", " macro avg 0.68 0.68 0.68 7155\n", "weighted avg 0.68 0.68 0.68 7155\n", "\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 183 }, "id": "Da28Nz_Q5Hjl", "outputId": "c705ac82-7fce-41c4-c8b6-8d0a72ae9c2f" }, "source": [ "from sklearn.ensemble import ExtraTreesClassifier\n", "etc = ExtraTreesClassifier(n_estimators=300, random_state=5, max_depth=300)\n", "etc.fit(X_train_g,y_train)\n", "etc_pred=etc.predict(X_test_g)\n", "print(classification_report(y_test,etc_pred))" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ " precision recall f1-score support\n", "\n", " 0 0.74 0.76 0.75 3808\n", " 1 0.72 0.70 0.71 3347\n", "\n", " accuracy 0.73 7155\n", " macro avg 0.73 0.73 0.73 7155\n", "weighted avg 0.73 0.73 0.73 7155\n", "\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 183 }, "id": "B9Xw206P5HoP", "outputId": "12dec109-9ccd-40aa-c179-cb507c1c0ac8" }, "source": [ "from sklearn import tree\n", "clf = tree.DecisionTreeClassifier()\n", "clf.fit(X_train_g,y_train)\n", "y_pred=clf.predict(X_test_g)\n", "accuracy_score(y_test,y_pred)\n", "print(classification_report(y_test,y_pred))" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ " precision recall f1-score support\n", "\n", " 0 0.64 0.62 0.63 3808\n", " 1 0.58 0.61 0.59 3347\n", "\n", " accuracy 0.61 7155\n", " macro avg 0.61 0.61 0.61 7155\n", "weighted avg 0.61 0.61 0.61 7155\n", "\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "o_bNvjop5HiG" }, "source": [ "" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "8H5_fhyh42d6" }, "source": [ "" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "c9KNr1ei42gS" }, "source": [ "" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "FDOKrFhY42jV" }, "source": [ "" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "SuB7aFd542nH" }, "source": [ "" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "XNffEqBf4R9E" }, "source": [ "" ], "execution_count": null, "outputs": [] } ] }