evaluation anpassungen
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Carsten
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Carsten_Solutions/Emoji-Emoticon-Assignment_new.csv
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Carsten_Solutions/Emoji-Emoticon-Assignment_new.csv
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Icon,Icon,Icon,Icon,Icon,Icon,Icon,Icon,Icon,Icon,Icon,Emoji,Meaning
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:‑) :),:-] :],:-3 :3,:-> :>,8-) 8),:-} :},:o),:c),:^),=],=),☺️🙂😊😀😁,Smiley or happy face.[4][5][6]
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:‑D :D,8‑D 8D,x‑D xD,X‑D XD,=D,=3,B^D,,,,,😃😄😆😍,"Laughing,[4] big grin,[5][6] laugh with glasses,[7] or wide-eyed surprise[8]"
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:-)),:-)),:-)),:-)),:-)),:-)),:-)),:-)),:-)),:-)),:-)),,Very happy or double chin[7]
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:‑( :(,:‑c :c,:‑< :<,:‑[ :[,:-||,>:[,:{,:@,>:(,,,☹️🙁😠😡😞😟😣😖,"Frown,[4][5][6] sad,[9] angry,[7] pouting"
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:'‑( :'(,:'‑( :'(,:'‑( :'(,:'‑( :'(,:'‑( :'(,:'‑( :'(,:'‑( :'(,:'‑( :'(,:'‑( :'(,:'‑( :'(,:'‑( :'(,😢😭,Crying[9]
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:'‑) :'),:'‑) :'),:'‑) :'),:'‑) :'),:'‑) :'),:'‑) :'),:'‑) :'),:'‑) :'),:'‑) :'),:'‑) :'),:'‑) :'),😂,Tears of happiness[9]
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D‑':,D:<,D:,D8,D;,D=,DX,,,,,😨😧😦😱😫😩,"Horror, disgust, sadness, great dismay[5][6] (right to left)"
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:‑O :O,:‑o :o,:-0,8‑0,>:O,,,,,,,😮😯😲,"Surprise,[3] shock,[4][10] yawn[11]"
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:-* :*,:×,,,,,,,,,,😗😙😚😘😍,Kiss
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;‑) ;),*-) *),;‑] ;],;^),":‑,",;D,,,,,,😉😜😘,"Wink,[4][5][6] smirk[10][11]"
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:‑P :P,X‑P XP,x‑p xp,:‑p :p,:‑Þ :Þ,:‑þ :þ,:‑b :b,d:,=p,>:P,,😛😝😜🤑,"Tongue sticking out, cheeky/playful,[4] blowing a raspberry"
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:‑/ :/,:‑.,>:\,>:/,:\,=/,=\,:L,=L,:S,,🤔😕😟,"Skeptical, annoyed, undecided, uneasy, hesitant[4]"
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:‑| :|,,,,,,,,,,,😐😑,"Straight face[5] no expression, indecision[9]"
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:$,,,,,,,,,,,😳😞😖,"Embarrassed,[6] blushing[7]"
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:‑X :X,:‑# :#,:‑& :&,,,,,,,,,🤐😶,"Sealed lips or wearing braces,[4] tongue-tied[9]"
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O:‑) O:),0:‑3 0:3,0:‑) 0:),0;^),,,,,,,,😇👼,"Angel,[4][5][10] saint,[9] innocent"
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>:‑) >:),}:‑) }:),3:‑) 3:),>;),,,,,,,,😈,"Evil,[5] devilish[9]"
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|;‑),|‑O,,,,,,,,,,😎😪,"Cool,[9] bored/yawning[10]"
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:‑J,:‑J,:‑J,:‑J,:‑J,:‑J,:‑J,:‑J,:‑J,:‑J,:‑J,😏😒,Tongue-in-cheek[12]
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#‑),#‑),#‑),#‑),#‑),#‑),#‑),#‑),#‑),#‑),#‑),—,Partied all night[9]
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%‑) %),%‑) %),%‑) %),%‑) %),%‑) %),%‑) %),%‑) %),%‑) %),%‑) %),%‑) %),%‑) %),😵😕🤕,"Drunk,[9] confused"
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:‑###.. :###..,:‑###.. :###..,:‑###.. :###..,:‑###.. :###..,:‑###.. :###..,:‑###.. :###..,:‑###.. :###..,:‑###.. :###..,:‑###.. :###..,:‑###.. :###..,:‑###.. :###..,🤒😷🤢,Being sick[9]
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<:‑|,<:‑|,<:‑|,<:‑|,<:‑|,<:‑|,<:‑|,<:‑|,<:‑|,<:‑|,<:‑|,—,"Dumb, dunce-like[10]"
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"',:-|","',:-l",,,,,,,,,,🤨,"Scepticism, disbelief, or disapproval[13][14]"
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<_<,>_>,,,,,,,,,,,Sideways look. Devious or guilty.
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Project/Assignment_emoticon_emoji_backup.txt
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Project/Assignment_emoticon_emoji_backup.txt
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[[':c)', '☺'], [':c)', '☺'], [':-3', '😊'], [':-}', '☺'], [':>', '☺'], ['=)', '☺'], [':‑)', '☺'], [':^)', '😊'], ['8)', '☺'], [':}', '☺'], [':->', '☺'], [':-]', '☺'], [':]', '☺'], ['=]', '☺'], [':o)', '☺'], [':)', '☺'], ['8-)', '☺'], [':3', '☺'], ['xD', '😆'], ['x‑D', '😆'], ['=3', '😃'], ['B^D', '😄'], ['XD', '😆'], ['=D', '😃'], ['8‑D', '😃'], [':‑D', '😃'], ['8D', '😃'], [':D', '😃'], ['X‑D', '😆'], [':‑[', '🙁'], [':@', '😡'], [':‑(', '🙁'], [':(', '🙁'], [':{', '🙁'], [':‑<', '🙁'], ['>:(', '😡'], [':c', '☹'], [':<', '☹'], [':‑c', '☹'], ['>:[', '😡'], [':-||', '🙁'], [':[', '🙁'], [":'‑(", '😢'], [":'(", '😢'], [":')", '😂'], [":'‑)", '😂'], ['DX', '😫'], ['D:', '😧'], ['D:<', '😩'], ['D8', '😦'], ['D=', '😦'], ['D;', '😩'], ["D‑':", '😨'], [':-0', '😮'], [':‑O', '😮'], [':o', '😮'], [':O', '😮'], [':‑o', '😮'], ['>:O', '😲'], ['8‑0', '😮'], [':×', '😘'], [':*', '😘'], [':-*', '😘'], [';]', '😉'], [';)', '😉'], [';‑)', '😉'], ['*-)', '😜'], [';^)', '😜'], [';D', '😜'], [';‑]', '😉'], ['*)', '😜'], [':‑,', '😘'], [':‑Þ', '😛'], [':þ', '😛'], [':‑þ', '😛'], [':‑b', '😛'], ['x‑p', '😝'], ['>:P', '😜'], ['XP', '😝'], [':Þ', '😛'], [':P', '😛'], ['xp', '😝'], [':b', '😛'], ['=p', '😛'], ['d:', '😛'], [':p', '😛'], [':‑p', '😛'], [':‑P', '😛'], ['X‑P', '😝'], [':\\', '🤔'], ['>:/', '🤔'], [':/', '🤔'], ['=\\', '🤔'], [':L', '😕'], [':S', '😕'], [':‑/', '🤔'], ['=L', '😕'], [':‑.', '🤔'], ['=/', '😕'], ['>:\\', '🤔'], [':‑|', '😐'], [':|', '😐'], [':$', '😳'], [':&', '😶'], [':‑X', '🤐'], [':#', '🤐'], [':‑#', '🤐'], [':‑&', '🤐'], [':X', '🤐'], ['0;^)', '😇'], ['0:‑3', '😇'], ['O:‑)', '😇'], ['0:)', '😇'], ['0:3', '😇'], ['0:‑)', '😇'], ['O:)', '😇'], ['3:)', '😈'], ['>:‑)', '😈'], ['>:)', '😈'], ['>;)', '😈'], ['}:)', '😈'], ['}:‑)', '😈'], ['3:‑)', '😈'], ['|‑O', '😪'], ['|;‑)', '😪'], [':‑J', '😏'], ['%‑)', '😵'], ['%)', '😵'], [':###..', '🤢'], [':‑###..', '🤢'], ["',:-|", '\U0001f928'], ["',:-l", '\U0001f928']]
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Sentence,prediction,topic hit,sentiment hit,both,ranked
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Hi how are you?,,,,,
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do you've got time,,,,,
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I go out for party tonight,,,,,
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I'll take the bus or train,,,,,
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You look gorgeous in this dress,,,,,
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How hard was the exam,,,,,
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please can you give me some stuff,,,,,
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whats your name,,,,,
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where are you from,,,,,
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what is your favourite color,,,,,
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Do you like to play soccer this evening,,,,,
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do you have any pets,,,,,
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I watch television all day,,,,,
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there some fake news but most of the time i dont care,,,,,
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i you fucking kidding,,,,,
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i we have to hand in our report,,,,,
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is the world real,,,,,
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i am you father,,,,,
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is this a true cite,,,,,
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i like working for my phd,,,,,
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I at the end of my Master studes,,,,,
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I like chilling with my friends outside,,,,,
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are we allowed to extend our presentation time to 35min,,,,,
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yes you are because today there no other groups presenting,,,,,
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i would love if we needn't write a report,,,,,
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"no sorry, you have to wirite one.",,,,,
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"ALso Google Docs is not enough, you shell use share latex for your document",,,,,
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I'll hope we get a good grade,,,,,
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I'll really could imagine working in NLP in the feature,,,,,
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The weather today is really nice,,,,,
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I like to take my dog out for a walk,,,,,
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I am a huge soccer fan,,,,,
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I just hate bad tutorials,,,,,
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I am so glad I bought new shoes yesterday,,,,,
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My mom likes ice cream,,,,,
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This so so much work...,,,,,
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I want to have holidays,,,,,
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Please come to my birthday party,,,,,
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why are some people just not replying to emails,,,,,
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I am sick of studying,,,,,
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Living in Germany can be expensive,,,,,
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I love my new Iphone,,,,,
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Teddy bears are cute,,,,,
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The sun is shining today,,,,,
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I am really stressed out,,,,,
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Mensa food is disgusting,,,,,
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I am so disappointed of this lecture,,,,,
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I usually take my bike to work,,,,,
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"This is so sad, I am almost crying",,,,,
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My car broke down yesterday,,,,,
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What is the usual time of study in Germany?,,,,,
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I try to eat healthy,,,,,
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"Seeing people getting good marks with no effort, makes me angry",,,,,
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Live long and prosper,,,,,
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i love books about wizards,,,,,
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No one understands me,,,,,
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Why do we even have to study?,,,,,
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Tonight I will go drinking,,,,,
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Lets have a party,,,,,
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I dont think there is any bias in these sentences,,,,,
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I really like to get this freedom in our work,,,,,
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No one will care anyway,,,,,
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worth it?,,,,,
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I really thought this will be a hard semester,,,,,
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its hard for a schedule to fit all the expectations,,,,,
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dont have enough time for all the sport i want to do,,,,,
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all in all i cant imagine how we are able to stay motivated ,,,,,
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do you prefere star wars or star trek,,,,,
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Mr. Spock is the best!!,,,,,
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I would like to live in the US,,,,,
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Studying is so much fun!! ,,,,,
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I dont think so at all ...,,,,,
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i think all the effort will pay off,,,,,
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take a flight to ibiza,,,,,
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better eating a kebab or a burger,,,,,
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nothing at all i hate meat,,,,,
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jesus christ!,,,,,
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so what do you prefere to eat?,,,,,
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pizza or a different heathy meal,,,,,
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"oh dear, you kidding",,,,,
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Donald Trump met Putin outside the USA,,,,,
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Who constructed this bridge,,,,,
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I think this church is the largest in town,,,,,
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you have to lost a bet to argue why you have this horrible hair cut,,,,,
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hopefully we will have wolrd peace in feature,,,,,
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so we can focus on mor important projects in our world,,,,,
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"yes, climate change is real",,,,,
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do you will recommend this nlp lab,,,,,
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jonas have to focus on his oral exam tomorrow,,,,,
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i wish you all the best,,,,,
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happy bithday darling,,,,,
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i love mixing beer and wine with a shot of tequila,,,,,
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i love you this much my heart will broke if you leave me,,,,,
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does everybody understand my true feelings,,,,,
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i think many people will read this and will be confused later,,,,,
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buying a red car will be more expensive,,,,,
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Sentence prediction topic hit sentiment hit both ranked
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0 Hi how are you? 😁😂😌😎😅😉🙌🤟
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1 do you've got time 😌😁😎😂🙌🤸🚯🤟
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2 I go out for party tonight 😂😅😁😌🔰🔢🔣🔤
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3 I'll take the bus or train 😂😅😢😭🚆🚄🤘🤣
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4 You look gorgeous in this dress 😌🙌😀😎👗👥👤🤵
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5 How hard was the exam 😂😅😁😌😎😉🤘🤣
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6 please can you give me some stuff 😂😅😁😉😎😏🤙🤟
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7 whats your name 😂😅😁😢😌😎😉📛
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8 where are you from 😂😅😁😢😌😉😎🤟
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9 what is your favourite color 😌🙌😀😎😁😊😋😉
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10 Do you like to play soccer this evening 😅😂😢😭😁😔⚽🤟
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11 do you have any pets 😂😅😁😢😌🤸🚯🤟
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12 I watch television all day 😂😅😢😁😉😌😎⌚
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13 there some fake news but most of the time i dont care 😂😅😁😌🤘🗾🗽💩
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14 i you fucking kidding 😂😅😢😁🤟🇮🇴🇼
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15 i we have to hand in our report 😌😁😎🙌✋🤟👊👋
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16 is the world real 😂😅😁😉😎😌🤘🤣
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17 i am you father 😂😅😢😭🎅🤟🇴🇼
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18 is this a true cite 😂😅😁😌😢😘🇦💠
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19 i like working for my phd 😂😅😢😁🔢🔣🔰🇩
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20 I at the end of my Master studes 😅😂😢😭🤘🗽🍲🥩
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21 I like chilling with my friends outside 😂😅😁😉📶🥤💘💝
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22 are we allowed to extend our presentation time to 35min 😌😁😂😅😎😉🙌🎁
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23 yes you are because today there no other groups presenting 😌🙌😀😊🎁🚷⛔🚳
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24 i would love if we needn't write a report 😁😂😌😅💌🏩🤟😘
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25 no sorry, you have to wirite one. 😢😭😅😔⛔🚳🚷🔞
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26 ALso Google Docs is not enough, you shell use share latex for your document 😂😅😁😌🐚🔢🔣🔰
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27 I'll hope we get a good grade 😁😎😌😉😘🇦🙅💠
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28 I'll really could imagine working in NLP in the feature 😂😅😢😁👤⛳👥🤵
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29 The weather today is really nice 😂😅😢😭😁😉🤘🤣
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30 I like to take my dog out for a walk 😅😂😢😭🐕🐶🌭😘
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31 I am a huge soccer fan 😂😌😁😅⚽😘🇦💠
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32 I just hate bad tutorials 😢😅😂😭😔😤😁😌
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33 I am so glad I bought new shoes yesterday 😁😎😌😉🆕🥿👞👠
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34 My mom likes ice cream 😌😁😎🙌🍨🍦🍧🏒
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35 This so so much work... 😅😂😢😭😔😁😌😤
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36 I want to have holidays 😅😂😢😁😭😌😎😉
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37 Please come to my birthday party 😂😁😌😅😎😉🙌🎂
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38 why are some people just not replying to emails 😂😅😁😉😎😌😏🚯
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39 I am sick of studying 😢😅😭😂🗾🧵🧶🥛
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40 Living in Germany can be expensive 😂😅😢😭👤🤵⛳👥
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41 I love my new Iphone 😌😎😁😀🆕💌🏩🌑
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42 Teddy bears are cute 😌🙌😀😁😎😊🐻🧸
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43 The sun is shining today 😂😅😁😉⛅🌞🤘🤣
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44 I am really stressed out 😅😢😂😭😔😤😁😏
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45 Mensa food is disgusting 😂😅😁😌🥫🍲🥘😋
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46 I am so disappointed of this lecture 😂😅😁😌🗾🧵🧶🥛
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47 I usually take my bike to work 😅😂😁😌😎😉😢🙌
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48 This is so sad, I am almost crying 😢😭😅😔😂😤😩😒
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49 My car broke down yesterday 😅😂😢😭🚋🚓🚃👎
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50 What is the usual time of study in Germany? 😂😅😢😭🤘🗽🗾🤵
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51 I try to eat healthy 😂😅😢😁😌😉😎😭
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52 Seeing people getting good marks with no effort, makes me angry 😂😅😁😢🙅🚷❌🚳
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53 Live long and prosper 😂😅😢😁🏹🍴🏀🔩
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54 i love books about wizards 😁😌😎😉📚📘📖📕
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55 No one understands me 😂😅😁😢🤙🕐🤪🔉
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56 Why do we even have to study? 😅😂😢😁😭😌🤸🚯
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57 Tonight I will go drinking 😂😅😢😁😌😉😎🍹
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58 Lets have a party 😂😅😁😌😎😘🇦💠
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59 I dont think there is any bias in these sentences 😂😅😁😌👤🤵⛳👥
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60 I really like to get this freedom in our work 😅😂😢😭👤🤵⛳👥
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61 No one will care anyway 😂😁😅😉🕐🤪🔉🔞
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62 worth it? 🙌😌😀😊😎😋😁😍
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63 I really thought this will be a hard semester 😅😂😢😭💭😘🇦💠
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64 its hard for a schedule to fit all the expectations 😌😁😎🙌😘🔢🔣🔰
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65 dont have enough time for all the sport i want to do 😂😅😁😌🤸🔢🔣🚯
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66 all in all i cant imagine how we are able to stay motivated 😂😅😢😁👤🤵⛳👥
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67 do you prefere star wars or star trek 😌🙌😀😊🌟🌠⭐🌃
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68 Mr. Spock is the best!! 😌😂😁😅😎😉🤘🤣
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69 I would like to live in the US 😂😅😢😁👤⛳🤵👥
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70 Studying is so much fun!! 😂😁😉😅😎😌🙏😀
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71 I dont think so at all ... 😂😌😅😁😎🌉🌆📲
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72 i think all the effort will pay off 😂😅😢😁📴🤘🇹🤟
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73 take a flight to ibiza 😂😅😢😭😏😘🇦💠
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74 better eating a kebab or a burger 😂😅😁😢😌😘🇦💠
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75 nothing at all i hate meat 😢😭😅😂🌉🍖🌆🥩
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76 jesus christ! 😂😅😁😌😎😉🙌😀
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77 so what do you prefere to eat? 😂😅😢😁😭🤸🚯🤟
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78 pizza or a different heathy meal 😂😁😅😌🍕😘🇦💠
|
||||
79 oh dear, you kidding 😌😁😎🙌😉😀😂🤟
|
||||
80 Donald Trump met Putin outside the USA 😂😅😢😭😁😏🤘🤣
|
||||
81 Who constructed this bridge 😌🙌😁😀😎😊😋😉
|
||||
82 I think this church is the largest in town 😁😌😎🙌⛪⛳👥🤵
|
||||
83 you have to lost a bet to argue why you have this horrible hair cut 😅😂😢😭🤟🥩👱😘
|
||||
84 hopefully we will have wolrd peace in feature 😂😅😁😉👤🤵⛳👥
|
||||
85 so we can focus on mor important projects in our world 😁😌😂😎👥👤🍖⛳
|
||||
86 yes, climate change is real 😂😅😁😉😢😌😎😏
|
||||
87 do you will recommend this nlp lab 😁😎😉😌🥼🤸🚯🤟
|
||||
88 jonas have to focus on his oral exam tomorrow 😌😁😅😂🍖🚩🤣🔛
|
||||
89 i wish you all the best 😂😅😁😌🤟🤘🇹🤣
|
||||
90 happy bithday darling 😁😌😂😎😅😉🙌😀
|
||||
91 i love mixing beer and wine with a shot of tequila 😅😂😢😭🏩🍺💌🍷
|
||||
92 i love you this much my heart will broke if you leave me 😅😂😢😁🤟💜💟💛
|
||||
93 does everybody understand my true feelings 😂😁😅😉😎😌🙌😀
|
||||
94 i think many people will read this and will be confused later 😂😅😁😌🔩🏹🍴🏀
|
||||
95 buying a red car will be more expensive 😂😅😁😉🚃🍎🚋🚓
|
||||
|
||||
|
@ -102,6 +102,9 @@
|
||||
"#navigation into right path and generating classifier\n",
|
||||
"import sys\n",
|
||||
"sys.path.append(\"..\")\n",
|
||||
"sys.path.append(\"../naive_approach\")\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"import simple_approach.simple_twitter_learning as stl\n",
|
||||
"clf_advanced = stl.pipeline_manager.load_pipeline_from_files( '../simple_approach/custom_classifier', ['keras_model'], ['vectorizer', 'keras_model'])\n",
|
||||
@ -143,15 +146,13 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#sys.path.append(\"..\")\n",
|
||||
"#print(sys.path)\n",
|
||||
"\n",
|
||||
"import naive_approach.naive_approach as clf_naive"
|
||||
"import naive_approach as clf_naive"
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -248,8 +249,7 @@
|
||||
" if(current_message != \"\"):\n",
|
||||
" p = merged_prediction(msg = current_message, target_emojis=top_emojis)\n",
|
||||
"\n",
|
||||
" predictions = p\n",
|
||||
" update_descriptions()"
|
||||
" predictions = p"
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -265,103 +265,24 @@
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<style>\n",
|
||||
" .dataframe thead tr:only-child th {\n",
|
||||
" text-align: right;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe thead th {\n",
|
||||
" text-align: left;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe tbody tr th {\n",
|
||||
" vertical-align: top;\n",
|
||||
" }\n",
|
||||
"</style>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>Sentence</th>\n",
|
||||
" <th>prediction</th>\n",
|
||||
" <th>topic hit</th>\n",
|
||||
" <th>sentiment hit</th>\n",
|
||||
" <th>both</th>\n",
|
||||
" <th>ranked</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>0</th>\n",
|
||||
" <td>Hi how are you?</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>1</th>\n",
|
||||
" <td>do you've got time</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2</th>\n",
|
||||
" <td>I go out for party tonight</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>3</th>\n",
|
||||
" <td>I'll take the bus or train</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>4</th>\n",
|
||||
" <td>You look gorgeous in this dress</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" Sentence prediction topic hit sentiment hit \\\n",
|
||||
"0 Hi how are you? NaN NaN NaN \n",
|
||||
"1 do you've got time NaN NaN NaN \n",
|
||||
"2 I go out for party tonight NaN NaN NaN \n",
|
||||
"3 I'll take the bus or train NaN NaN NaN \n",
|
||||
"4 You look gorgeous in this dress NaN NaN NaN \n",
|
||||
"\n",
|
||||
" both ranked \n",
|
||||
"0 NaN NaN \n",
|
||||
"1 NaN NaN \n",
|
||||
"2 NaN NaN \n",
|
||||
"3 NaN NaN \n",
|
||||
"4 NaN NaN "
|
||||
]
|
||||
},
|
||||
"execution_count": 9,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
"ename": "ParserError",
|
||||
"evalue": "Error tokenizing data. C error: Expected 1 fields in line 27, saw 2\n",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
||||
"\u001b[0;31mParserError\u001b[0m Traceback (most recent call last)",
|
||||
"\u001b[0;32m<ipython-input-9-7e24563a7fda>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# get table\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Evaluation Sentences - Tabellenblatt1.csv\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mparser_f\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, skipfooter, skip_footer, doublequote, delim_whitespace, as_recarray, compact_ints, use_unsigned, low_memory, buffer_lines, memory_map, float_precision)\u001b[0m\n\u001b[1;32m 653\u001b[0m skip_blank_lines=skip_blank_lines)\n\u001b[1;32m 654\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 655\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 656\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 657\u001b[0m \u001b[0mparser_f\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m 409\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 410\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 411\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mparser\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnrows\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 412\u001b[0m \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 413\u001b[0m \u001b[0mparser\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mread\u001b[0;34m(self, nrows)\u001b[0m\n\u001b[1;32m 1003\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'skipfooter not supported for iteration'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1004\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1005\u001b[0;31m \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnrows\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1006\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1007\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'as_recarray'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mread\u001b[0;34m(self, nrows)\u001b[0m\n\u001b[1;32m 1746\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnrows\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1747\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1748\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnrows\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1749\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mStopIteration\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1750\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_first_chunk\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader.read (pandas/_libs/parsers.c:10862)\u001b[0;34m()\u001b[0m\n",
|
||||
"\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._read_low_memory (pandas/_libs/parsers.c:11138)\u001b[0;34m()\u001b[0m\n",
|
||||
"\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._read_rows (pandas/_libs/parsers.c:11884)\u001b[0;34m()\u001b[0m\n",
|
||||
"\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._tokenize_rows (pandas/_libs/parsers.c:11755)\u001b[0;34m()\u001b[0m\n",
|
||||
"\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.raise_parser_error (pandas/_libs/parsers.c:28765)\u001b[0;34m()\u001b[0m\n",
|
||||
"\u001b[0;31mParserError\u001b[0m: Error tokenizing data. C error: Expected 1 fields in line 27, saw 2\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
@ -373,48 +294,50 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Hi how are you?\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"ename": "FileNotFoundError",
|
||||
"evalue": "[Errno 2] No such file or directory: 'word2vec.model'",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
||||
"\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
|
||||
"\u001b[0;32m<ipython-input-11-22a65efd4496>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msentence\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mtrigger_new_prediction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall_chat\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcurrent_message\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msentence\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprediction\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32m<ipython-input-8-20fe10f899eb>\u001b[0m in \u001b[0;36mtrigger_new_prediction\u001b[0;34m(all_chat, current_message)\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0;31m#merged prediction\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0;32mif\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcurrent_message\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m\"\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m \u001b[0mp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmerged_prediction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcurrent_message\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_emojis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtop_emojis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[0mpredictions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32m<ipython-input-7-5ed291336bae>\u001b[0m in \u001b[0;36mmerged_prediction\u001b[0;34m(msg, split, number, target_emojis)\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0;31m#predict emojis with the naive approach\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mprediction_naive\u001b[0m \u001b[0;34m,\u001b[0m \u001b[0mprediction_naive_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclf_naive\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msentence\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmsg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlookup\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0mtmp_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumber_naive\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0;31m#filter 0 values\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32m~/GitRepos/NLP-LAB/Project/naive_approach/naive_approach.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(sentence, lookup, emojis_to_consider, criteria, lang, n, t)\u001b[0m\n\u001b[1;32m 98\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msentence\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlookup\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0memojis_to_consider\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"all\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcriteria\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"threshold\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlang\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'eng'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.9\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 99\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 100\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mevaluate_sentence\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msentence\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlang\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0memojis_to_consider\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0memojis_to_consider\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 101\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 102\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32m~/GitRepos/NLP-LAB/Project/naive_approach/naive_approach.py\u001b[0m in \u001b[0;36mevaluate_sentence\u001b[0;34m(sentence, description_key, lang, emojis_to_consider, stem)\u001b[0m\n\u001b[1;32m 44\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mevaluate_sentence\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msentence\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdescription_key\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'description'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlang\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'eng'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0memojis_to_consider\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"all\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstem\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 45\u001b[0m \u001b[0;31m# assumes there is a trained w2v model stored in the same directory!\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 46\u001b[0;31m \u001b[0mwv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mKeyedVectors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"word2vec.model\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmmap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'r'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 47\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 48\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mstem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/gensim/models/keyedvectors.py\u001b[0m in \u001b[0;36mload\u001b[0;34m(cls, fname_or_handle, **kwargs)\u001b[0m\n\u001b[1;32m 120\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mclassmethod\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 121\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfname_or_handle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 122\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mBaseKeyedVectors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname_or_handle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 123\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 124\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0msimilarity\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mentity1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mentity2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/gensim/utils.py\u001b[0m in \u001b[0;36mload\u001b[0;34m(cls, fname, mmap)\u001b[0m\n\u001b[1;32m 423\u001b[0m \u001b[0mcompress\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msubname\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mSaveLoad\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_adapt_by_suffix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 424\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 425\u001b[0;31m \u001b[0mobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0munpickle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 426\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_load_specials\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmmap\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcompress\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msubname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 427\u001b[0m \u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"loaded %s\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/gensim/utils.py\u001b[0m in \u001b[0;36munpickle\u001b[0;34m(fname)\u001b[0m\n\u001b[1;32m 1327\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1328\u001b[0m \"\"\"\n\u001b[0;32m-> 1329\u001b[0;31m \u001b[0;32mwith\u001b[0m \u001b[0msmart_open\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'rb'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1330\u001b[0m \u001b[0;31m# Because of loading from S3 load can't be used (missing readline in smart_open)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1331\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mversion_info\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/smart_open/smart_open_lib.py\u001b[0m in \u001b[0;36msmart_open\u001b[0;34m(uri, mode, **kw)\u001b[0m\n\u001b[1;32m 179\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'mode should be a string'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 180\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 181\u001b[0;31m \u001b[0mfobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_shortcut_open\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0muri\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 182\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mfobj\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 183\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mfobj\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/smart_open/smart_open_lib.py\u001b[0m in \u001b[0;36m_shortcut_open\u001b[0;34m(uri, mode, **kw)\u001b[0m\n\u001b[1;32m 285\u001b[0m \u001b[0mmode\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreplace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'b'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m''\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 286\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 287\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparsed_uri\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muri_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mopen_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 288\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 289\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'word2vec.model'"
|
||||
]
|
||||
}
|
||||
],
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"all_predictions = []\n",
|
||||
"\n",
|
||||
"for index, row in df.iterrows():\n",
|
||||
" sentence = row[\"Sentence\"]\n",
|
||||
" print(sentence)\n",
|
||||
" #print(sentence)\n",
|
||||
"\n",
|
||||
" trigger_new_prediction(all_chat=\"\", current_message = sentence)\n",
|
||||
" print(prediction)\n",
|
||||
" \n",
|
||||
" #print(predictions)\n",
|
||||
" \n",
|
||||
" #prediction to string\n",
|
||||
" tmp_prediction = \"\".join(predictions)\n",
|
||||
" \n",
|
||||
" #construct the preediction column\n",
|
||||
" all_predictions.append(tmp_prediction)\n",
|
||||
" "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df[\"prediction\"] = all_predictions\n",
|
||||
"\n",
|
||||
"df.head()\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df.to_csv(\"Evaluation Sentences - Tabellenblatt1.csv\", sep='\\t', encoding='utf-8')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
|
||||
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@ -102,6 +102,7 @@
|
||||
"#navigation into right path and generating classifier\n",
|
||||
"import sys\n",
|
||||
"sys.path.append(\"..\")\n",
|
||||
"sys.path.append(\"../naive_approach\")\n",
|
||||
"\n",
|
||||
"import simple_approach.simple_twitter_learning as stl\n",
|
||||
"clf_advanced = stl.pipeline_manager.load_pipeline_from_files( '../simple_approach/custom_classifier', ['keras_model'], ['vectorizer', 'keras_model'])\n",
|
||||
@ -151,7 +152,7 @@
|
||||
"#sys.path.append(\"..\")\n",
|
||||
"#print(sys.path)\n",
|
||||
"\n",
|
||||
"import naive_approach.naive_approach as clf_naive"
|
||||
"import naive_approach as clf_naive"
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -557,7 +558,7 @@
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "6cb4372b3ac5467b8eec0f4cd67f8212",
|
||||
"model_id": "6216e3a271cc4428ba568adbad2fa40c",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
|
||||
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Loading…
Reference in New Issue
Block a user