evaluation anpassungen
							
								
								
									
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								Carsten_Solutions/Emoji-Emoticon-Assignment_new.csv
									
									
									
									
									
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						| @ -0,0 +1,26 @@ | ||||
| Icon,Icon,Icon,Icon,Icon,Icon,Icon,Icon,Icon,Icon,Icon,Emoji,Meaning | ||||
| :‑) :),:-] :],:-3 :3,:-> :>,8-) 8),:-} :},:o),:c),:^),=],=),☺️🙂😊😀😁,Smiley or happy face.[4][5][6] | ||||
| :‑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]" | ||||
| :-)),:-)),:-)),:-)),:-)),:-)),:-)),:-)),:-)),:-)),:-)),,Very happy or double chin[7] | ||||
| :‑( :(,:‑c :c,:‑< :<,:‑[ :[,:-||,>:[,:{,:@,>:(,,,☹️🙁😠😡😞😟😣😖,"Frown,[4][5][6] sad,[9] angry,[7] pouting" | ||||
| :'‑( :'(,:'‑( :'(,:'‑( :'(,:'‑( :'(,:'‑( :'(,:'‑( :'(,:'‑( :'(,:'‑( :'(,:'‑( :'(,:'‑( :'(,:'‑( :'(,😢😭,Crying[9] | ||||
| :'‑) :'),:'‑) :'),:'‑) :'),:'‑) :'),:'‑) :'),:'‑) :'),:'‑) :'),:'‑) :'),:'‑) :'),:'‑) :'),:'‑) :'),😂,Tears of happiness[9] | ||||
| D‑':,D:<,D:,D8,D;,D=,DX,,,,,😨😧😦😱😫😩,"Horror, disgust, sadness, great dismay[5][6] (right to left)" | ||||
| :‑O :O,:‑o :o,:-0,8‑0,>:O,,,,,,,😮😯😲,"Surprise,[3] shock,[4][10] yawn[11]" | ||||
| :-* :*,:×,,,,,,,,,,😗😙😚😘😍,Kiss | ||||
| ;‑) ;),*-) *),;‑] ;],;^),":‑,",;D,,,,,,😉😜😘,"Wink,[4][5][6] smirk[10][11]" | ||||
| :‑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" | ||||
| :‑/ :/,:‑.,>:\,>:/,:\,=/,=\,:L,=L,:S,,🤔😕😟,"Skeptical, annoyed, undecided, uneasy, hesitant[4]" | ||||
| :‑| :|,,,,,,,,,,,😐😑,"Straight face[5] no expression, indecision[9]" | ||||
| :$,,,,,,,,,,,😳😞😖,"Embarrassed,[6] blushing[7]" | ||||
| :‑X :X,:‑# :#,:‑& :&,,,,,,,,,🤐😶,"Sealed lips or wearing braces,[4] tongue-tied[9]" | ||||
| O:‑) O:),0:‑3 0:3,0:‑) 0:),0;^),,,,,,,,😇👼,"Angel,[4][5][10] saint,[9] innocent" | ||||
| >:‑) >:),}:‑) }:),3:‑) 3:),>;),,,,,,,,😈,"Evil,[5] devilish[9]" | ||||
| |;‑),|‑O,,,,,,,,,,😎😪,"Cool,[9] bored/yawning[10]" | ||||
| :‑J,:‑J,:‑J,:‑J,:‑J,:‑J,:‑J,:‑J,:‑J,:‑J,:‑J,😏😒,Tongue-in-cheek[12] | ||||
| #‑),#‑),#‑),#‑),#‑),#‑),#‑),#‑),#‑),#‑),#‑),—,Partied all night[9] | ||||
| %‑) %),%‑) %),%‑) %),%‑) %),%‑) %),%‑) %),%‑) %),%‑) %),%‑) %),%‑) %),%‑) %),😵😕🤕,"Drunk,[9] confused" | ||||
| :‑###.. :###..,:‑###.. :###..,:‑###.. :###..,:‑###.. :###..,:‑###.. :###..,:‑###.. :###..,:‑###.. :###..,:‑###.. :###..,:‑###.. :###..,:‑###.. :###..,:‑###.. :###..,🤒😷🤢,Being sick[9] | ||||
| <:‑|,<:‑|,<:‑|,<:‑|,<:‑|,<:‑|,<:‑|,<:‑|,<:‑|,<:‑|,<:‑|,—,"Dumb, dunce-like[10]" | ||||
| "',:-|","',:-l",,,,,,,,,,🤨,"Scepticism, disbelief, or disapproval[13][14]" | ||||
| <_<,>_>,,,,,,,,,,,Sideways look. Devious or guilty. | ||||
| 
 | 
							
								
								
									
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								Project/Assignment_emoticon_emoji_backup.txt
									
									
									
									
									
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						| @ -0,0 +1 @@ | ||||
| [[':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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| @ -1,97 +1,97 @@ | ||||
| Sentence,prediction,topic hit,sentiment hit,both,ranked | ||||
| Hi how are you?,,,,, | ||||
| do you've got time,,,,, | ||||
| I go out for party tonight,,,,, | ||||
| I'll take the bus or train,,,,, | ||||
| You look gorgeous in this dress,,,,, | ||||
| How hard was the exam,,,,, | ||||
| please can you give me some stuff,,,,, | ||||
| whats your name,,,,, | ||||
| where are you from,,,,, | ||||
| what is your favourite color,,,,, | ||||
| Do you like to play soccer this evening,,,,, | ||||
| do you have any pets,,,,, | ||||
| I watch television all day,,,,, | ||||
| there some fake news but most of the time i dont care,,,,, | ||||
| i you fucking kidding,,,,, | ||||
| i we have to hand in our report,,,,, | ||||
| is the world real,,,,, | ||||
| i am you father,,,,, | ||||
| is this a true cite,,,,, | ||||
| i like working for my phd,,,,, | ||||
| I at the end of my Master studes,,,,, | ||||
| I like chilling with my friends outside,,,,, | ||||
| are we allowed to extend our presentation time to 35min,,,,, | ||||
| yes you are because today there no other groups presenting,,,,, | ||||
| i would love if we needn't write a report,,,,, | ||||
| "no sorry, you have to wirite one.",,,,, | ||||
| "ALso Google Docs is not enough, you shell use share latex for your document",,,,, | ||||
| I'll hope we get a good grade,,,,, | ||||
| I'll really could imagine working in NLP in the feature,,,,, | ||||
| The weather today is really nice,,,,, | ||||
| I like to take my dog out for a walk,,,,, | ||||
| I am a huge soccer fan,,,,, | ||||
| I just hate bad tutorials,,,,, | ||||
| I am so glad I bought new shoes yesterday,,,,, | ||||
| My mom likes ice cream,,,,, | ||||
| This so so much work...,,,,, | ||||
| I want to have holidays,,,,, | ||||
| Please come to my birthday party,,,,, | ||||
| why are some people just not replying to emails,,,,, | ||||
| I am sick of studying,,,,, | ||||
| Living in Germany can be expensive,,,,, | ||||
| I love my new Iphone,,,,, | ||||
| Teddy bears are cute,,,,, | ||||
| The sun is shining today,,,,, | ||||
| I am really stressed out,,,,, | ||||
| Mensa food is disgusting,,,,, | ||||
| I am so disappointed of this lecture,,,,, | ||||
| I usually take my bike to work,,,,, | ||||
| "This is so sad, I am almost crying",,,,, | ||||
| My car broke down yesterday,,,,, | ||||
| What is the usual time of study in Germany?,,,,, | ||||
| I try to eat healthy,,,,, | ||||
| "Seeing people getting good marks with no effort, makes me angry",,,,, | ||||
| Live long and prosper,,,,, | ||||
| i love books about wizards,,,,, | ||||
| No one understands me,,,,, | ||||
| Why do we even have to study?,,,,, | ||||
| Tonight I will go drinking,,,,, | ||||
| Lets have a party,,,,, | ||||
| I dont think there is any bias in these sentences,,,,, | ||||
| I really like to get this freedom in our work,,,,, | ||||
| No one will care anyway,,,,, | ||||
| worth it?,,,,, | ||||
| I really thought this will be a hard semester,,,,, | ||||
| its hard for a schedule to fit all the expectations,,,,, | ||||
| dont have enough time for all the sport i want to do,,,,, | ||||
| all in all i cant imagine how we are able to stay motivated ,,,,, | ||||
| do you prefere star wars or star trek,,,,, | ||||
| Mr. Spock is the best!!,,,,, | ||||
| I would like to live in the US,,,,, | ||||
| Studying is so much fun!! ,,,,, | ||||
| I dont think so at all ...,,,,, | ||||
| i think all the effort will pay off,,,,, | ||||
| take a flight to ibiza,,,,, | ||||
| better eating a kebab or a burger,,,,, | ||||
| nothing at all i hate meat,,,,, | ||||
| jesus christ!,,,,, | ||||
| so what do you prefere to eat?,,,,, | ||||
| pizza or a different heathy meal,,,,, | ||||
| "oh dear, you kidding",,,,, | ||||
| Donald Trump met Putin outside the USA,,,,, | ||||
| Who constructed this bridge,,,,, | ||||
| I think this church is the largest in town,,,,, | ||||
| you have to lost a bet to argue why you have this horrible hair cut,,,,, | ||||
| hopefully we will have wolrd peace in feature,,,,, | ||||
| so we can focus on mor important projects in our world,,,,, | ||||
| "yes, climate change is real",,,,, | ||||
| do you will recommend this nlp lab,,,,, | ||||
| jonas have to focus on his oral exam tomorrow,,,,, | ||||
| i wish you all the best,,,,, | ||||
| happy bithday darling,,,,, | ||||
| i love mixing beer and wine with a shot of tequila,,,,, | ||||
| i love you this much my heart will broke if you leave me,,,,, | ||||
| does everybody understand my true feelings,,,,, | ||||
| i think many people will read this and will be confused later,,,,, | ||||
| buying a red car will be more expensive,,,,, | ||||
| 	Sentence	prediction	topic hit	sentiment hit	both	ranked | ||||
| 0	Hi how are you?	😁😂😌😎😅😉🙌🤟				 | ||||
| 1	do you've got time	😌😁😎😂🙌🤸🚯🤟				 | ||||
| 2	I go out for party tonight	😂😅😁😌🔰🔢🔣🔤				 | ||||
| 3	I'll take the bus or train	😂😅😢😭🚆🚄🤘🤣				 | ||||
| 4	You look gorgeous in this dress	😌🙌😀😎👗👥👤🤵				 | ||||
| 5	How hard was the exam	😂😅😁😌😎😉🤘🤣				 | ||||
| 6	please can you give me some stuff	😂😅😁😉😎😏🤙🤟				 | ||||
| 7	whats your name	😂😅😁😢😌😎😉📛				 | ||||
| 8	where are you from	😂😅😁😢😌😉😎🤟				 | ||||
| 9	what is your favourite color	😌🙌😀😎😁😊😋😉				 | ||||
| 10	Do you like to play soccer this evening	😅😂😢😭😁😔⚽🤟				 | ||||
| 11	do you have any pets	😂😅😁😢😌🤸🚯🤟				 | ||||
| 12	I watch television all day	😂😅😢😁😉😌😎⌚				 | ||||
| 13	there some fake news but most of the time i dont care	😂😅😁😌🤘🗾🗽💩				 | ||||
| 14	i you fucking kidding	😂😅😢😁🤟🇮🇴🇼				 | ||||
| 15	i we have to hand in our report	😌😁😎🙌✋🤟👊👋				 | ||||
| 16	is the world real	😂😅😁😉😎😌🤘🤣				 | ||||
| 17	i am you father	😂😅😢😭🎅🤟🇴🇼				 | ||||
| 18	is this a true cite	😂😅😁😌😢😘🇦💠				 | ||||
| 19	i like working for my phd	😂😅😢😁🔢🔣🔰🇩				 | ||||
| 20	I at the end of my Master studes	😅😂😢😭🤘🗽🍲🥩				 | ||||
| 21	I like chilling with my friends outside	😂😅😁😉📶🥤💘💝				 | ||||
| 22	are we allowed to extend our presentation time to 35min	😌😁😂😅😎😉🙌🎁				 | ||||
| 23	yes you are because today there no other groups presenting	😌🙌😀😊🎁🚷⛔🚳				 | ||||
| 24	i would love if we needn't write a report	😁😂😌😅💌🏩🤟😘				 | ||||
| 25	no sorry, you have to wirite one.	😢😭😅😔⛔🚳🚷🔞				 | ||||
| 26	ALso Google Docs is not enough, you shell use share latex for your document	😂😅😁😌🐚🔢🔣🔰				 | ||||
| 27	I'll hope we get a good grade	😁😎😌😉😘🇦🙅💠				 | ||||
| 28	I'll really could imagine working in NLP in the feature	😂😅😢😁👤⛳👥🤵				 | ||||
| 29	The weather today is really nice	😂😅😢😭😁😉🤘🤣				 | ||||
| 30	I like to take my dog out for a walk	😅😂😢😭🐕🐶🌭😘				 | ||||
| 31	I am a huge soccer fan	😂😌😁😅⚽😘🇦💠				 | ||||
| 32	I just hate bad tutorials	😢😅😂😭😔😤😁😌				 | ||||
| 33	I am so glad I bought new shoes yesterday	😁😎😌😉🆕🥿👞👠				 | ||||
| 34	My mom likes ice cream	😌😁😎🙌🍨🍦🍧🏒				 | ||||
| 35	This so so much work...	😅😂😢😭😔😁😌😤				 | ||||
| 36	I want to have holidays	😅😂😢😁😭😌😎😉				 | ||||
| 37	Please come to my birthday party	😂😁😌😅😎😉🙌🎂				 | ||||
| 38	why are some people just not replying to emails	😂😅😁😉😎😌😏🚯				 | ||||
| 39	I am sick of studying	😢😅😭😂🗾🧵🧶🥛				 | ||||
| 40	Living in Germany can be expensive	😂😅😢😭👤🤵⛳👥				 | ||||
| 41	I love my new Iphone	😌😎😁😀🆕💌🏩🌑				 | ||||
| 42	Teddy bears are cute	😌🙌😀😁😎😊🐻🧸				 | ||||
| 43	The sun is shining today	😂😅😁😉⛅🌞🤘🤣				 | ||||
| 44	I am really stressed out	😅😢😂😭😔😤😁😏				 | ||||
| 45	Mensa food is disgusting	😂😅😁😌🥫🍲🥘😋				 | ||||
| 46	I am so disappointed of this lecture	😂😅😁😌🗾🧵🧶🥛				 | ||||
| 47	I usually take my bike to work	😅😂😁😌😎😉😢🙌				 | ||||
| 48	This is so sad, I am almost crying	😢😭😅😔😂😤😩😒				 | ||||
| 49	My car broke down yesterday	😅😂😢😭🚋🚓🚃👎				 | ||||
| 50	What is the usual time of study in Germany?	😂😅😢😭🤘🗽🗾🤵				 | ||||
| 51	I try to eat healthy	😂😅😢😁😌😉😎😭				 | ||||
| 52	Seeing people getting good marks with no effort, makes me angry	😂😅😁😢🙅🚷❌🚳				 | ||||
| 53	Live long and prosper	😂😅😢😁🏹🍴🏀🔩				 | ||||
| 54	i love books about wizards	😁😌😎😉📚📘📖📕				 | ||||
| 55	No one understands me	😂😅😁😢🤙🕐🤪🔉				 | ||||
| 56	Why do we even have to study?	😅😂😢😁😭😌🤸🚯				 | ||||
| 57	Tonight I will go drinking	😂😅😢😁😌😉😎🍹				 | ||||
| 58	Lets have a party	😂😅😁😌😎😘🇦💠				 | ||||
| 59	I dont think there is any bias in these sentences	😂😅😁😌👤🤵⛳👥				 | ||||
| 60	I really like to get this freedom in our work	😅😂😢😭👤🤵⛳👥				 | ||||
| 61	No one will care anyway	😂😁😅😉🕐🤪🔉🔞				 | ||||
| 62	worth it?	🙌😌😀😊😎😋😁😍				 | ||||
| 63	I really thought this will be a hard semester	😅😂😢😭💭😘🇦💠				 | ||||
| 64	its hard for a schedule to fit all the expectations	😌😁😎🙌😘🔢🔣🔰				 | ||||
| 65	dont have enough time for all the sport i want to do	😂😅😁😌🤸🔢🔣🚯				 | ||||
| 66	all in all i cant imagine how we are able to stay motivated 	😂😅😢😁👤🤵⛳👥				 | ||||
| 67	do you prefere star wars or star trek	😌🙌😀😊🌟🌠⭐🌃				 | ||||
| 68	Mr. Spock is the best!!	😌😂😁😅😎😉🤘🤣				 | ||||
| 69	I would like to live in the US	😂😅😢😁👤⛳🤵👥				 | ||||
| 70	Studying is so much fun!! 	😂😁😉😅😎😌🙏😀				 | ||||
| 71	I dont think so at all ...	😂😌😅😁😎🌉🌆📲				 | ||||
| 72	i think all the effort will pay off	😂😅😢😁📴🤘🇹🤟				 | ||||
| 73	take a flight to ibiza	😂😅😢😭😏😘🇦💠				 | ||||
| 74	better eating a kebab or a burger	😂😅😁😢😌😘🇦💠				 | ||||
| 75	nothing at all i hate meat	😢😭😅😂🌉🍖🌆🥩				 | ||||
| 76	jesus christ!	😂😅😁😌😎😉🙌😀				 | ||||
| 77	so what do you prefere to eat?	😂😅😢😁😭🤸🚯🤟				 | ||||
| 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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| Before Width: | Height: | Size: 321 KiB After Width: | Height: | Size: 99 KiB | 
| @ -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 | ||||
|       }, | ||||
|  | ||||
| Before Width: | Height: | Size: 19 KiB After Width: | Height: | Size: 37 KiB |