# Notebook from DanLeePy/Singapore-Comment-Filter
Path: 4. Classification Models/Random Forest.ipynb

<code>
from google.colab import drive

import numpy as np
import pandas as pd
import re, string
import nltk
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from nltk.stem import WordNetLemmatizer
import matplotlib.pyplot as plt
import math
import sklearn
from sklearn.model_selection import cross_val_score
lemmatizer = WordNetLemmatizer()
nltk.download('stopwords')
nltk.download('punkt')
nltk.download('wordnet')

from sklearn.ensemble import RandomForestClassifier
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer

import os[nltk_data] Downloading package stopwords to /root/nltk_data...
[nltk_data]   Unzipping corpora/stopwords.zip.
[nltk_data] Downloading package punkt to /root/nltk_data...
[nltk_data]   Unzipping tokenizers/punkt.zip.
[nltk_data] Downloading package wordnet to /root/nltk_data...
[nltk_data]   Unzipping corpora/wordnet.zip.
drive.mount('/content/drive')
Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=email%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdocs.test%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive.photos.readonly%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fpeopleapi.readonly&response_type=code

Enter your authorization code:
··········
Mounted at /content/drive
os.chdir('/content/drive/My Drive/DBA3803')
os.getcwd()_____no_output_____train = pd.read_csv("train.csv")
test = pd.read_csv("test.csv")_____no_output_____train.head() _____no_output_____print(len(train))
print(len(test))20000
1028
train.info()<class 'pandas.core.frame.DataFrame'>
RangeIndex: 20000 entries, 0 to 19999
Data columns (total 6 columns):
Comments           19999 non-null object
Insulting          20000 non-null int64
Anti Government    20000 non-null int64
Xenophobic         20000 non-null int64
Racist             20000 non-null int64
Sexual             20000 non-null int64
dtypes: int64(5), object(1)
memory usage: 937.6+ KB
def clean_text(sentences):
    print('Preprocessing Begins...')
    result = []
    symbols = '~ ! @ # $ % ^ & * < > ? /'
    symbols = symbols.split()
    for text in sentences:
        text = text.lower()
        text = str(text)
        text = re.sub(r"\'m", " am", text)
        text = re.sub(r"\'s", " is", text)
        text = re.sub(r"\'ll", " will", text)
        text = re.sub(r"\'ve", " have", text)
        text = re.sub(r"\'re", " are", text)
        text = re.sub(r"won\'t", "will not", text)
        text = re.sub(r"can\'t", "cannot", text)
        text = re.sub(r"n\'t", " not", text)
        text = re.sub(r"n\'", "ng", text)
        text = re.sub(r"\'bout", "about", text)
        text = re.sub(r"\'til", "until", text)
        #text = re.sub(r'\.+', ".", text)
        #text = re.sub(r'\!+', "!", text)
        #text = re.sub(r'\?+', "?", text)
        text = re.sub(r"""[^a-zA-Z0-9-!$%^&@*()+|=`{}\[\]:;"'<>?,.\/\s]"""," ",text)
        text = re.sub(r"\bft\b|\bforeigntalent\b","foreign talent",text)
        text = re.sub(r"\bgovt\b|\bgahment\b|\bgahmen\b|\bgov\b","government",text)
        text = re.sub(r"\bpappies\b|\bpapies\b|\bpapees\b|\bpap government\b","pap",text)
        text = re.sub(r"\bchi bai\b|\bchibai\b|\bchee bye\b|\bcheebye\b","cb",text)
        text = re.sub(r"\blky\b","lee kuan yew",text)
        if len(text.split()) == 0:
            continue
        #tokenized = nltk.word_tokenize(text)
        then = []
        sw = stopwords.words('english')
        for word in text.split():
            if word not in sw:
              word = lemmatizer.lemmatize(word)
              then.append(word)
#             else:
#               print(word)
        result.append(then)
    result = [" ".join(x) for x in result]
    print(result[:3], '\n')
    return result_____no_output_____label_cols = ['Insulting', 'Anti Government', 'Xenophobic', 'Racist', 'Sexual']
train['none'] = 1-train[label_cols].max(axis=1)
train.describe()_____no_output_____# Features

print(len(train["Comments"]))
n = train.shape[0]
vec = TfidfVectorizer(ngram_range=(1,1),
               min_df=1, max_df=0.9, strip_accents='unicode')
trn_term_doc=vec.fit_transform(train["Comments"].values.astype('U'))
print(vec.get_feature_names())
print(len(vec.get_feature_names()))
test_term_doc = vec.transform(test["Comments"])20000
['00', '000', '008no', '00pm', '00reviewsong', '01', '012014', '01this', '02', '03', '04', '0400hrs', '04all', '04pm', '05', '0507h', '05pm', '06pm', '07', '0730h', '07am', '07pm', '08', '0820hrs', '0822', '0830hrs', '09', '10', '100', '1000', '10002', '1000hrs', '1000nt', '1000pcs', '1000signed', '1000the', '100installation', '100k', '100kg', '100kmh', '100pieces', '100s', '100x', '101', '102', '103', '1030am', '1040', '105', '107', '108', '1080p', '109', '10am', '10body', '10both', '10char', '10chars', '10dance', '10face', '10girl', '10have', '10hrs', '10k', '10kkbirdnot', '10kkj', '10km', '10looks', '10m', '10married', '10min', '10mins', '10minutes', '10no', '10pax', '10pm', '10pmspent', '10real', '10s', '10saw', '10uniform', '10vssong', '10yearchallenge', '10years', '11', '110', '1100hrsall', '110v', '1111', '11110011', '112', '112019', '11229224', '114', '118', '119', '119706568', '119764585', '11am', '11am30', '11at', '11b', '11mdelays', '11min', '11pm', '11th', '12', '120', '1200', '1202pm', '120i', '120k', '120m', '120ntd', '120sqm', '120v', '120voltts', '123', '1233', '123sqm', '126', '12am', '12amsmall', '12for', '12gb', '12min', '12mm', '12pm', '12pmcheese', '12release', '12th', '13', '1300', '130mm', '131', '132', '135', '138', '13a', '13ax1', '13th', '14', '140', '140mm', '143', '146', '14mins', '14pm', '15', '150', '1500', '1500nt', '150k', '151', '152', '155', '1550', '155cm', '155kg', '157', '157kg', '158cm', '15am', '15cm', '15e', '15gb', '15k', '15l', '15m', '15min', '15mins', '15minutes', '15pm', '15th', '16', '160cm', '161', '1658hrs', '166', '168cm', '1699can', '1699got', '1699nt', '16gb', '16k', '16mm', '16seconds', '16th', '17', '170', '172', '174', '1751', '176', '178', '178k', '179', '17app', '17cm', '17cm32nononobody', '17th', '18', '180', '1800', '180000', '180cm', '180k', '182', '1831', '18am', '18btu', '18k', '18nikitanikitanikita', '18pm', '19', '190', '1900', '1930s', '194', '1944', '1964', '1965', '1965we', '1966', '1969', '1970', '1972', '198', '1981', '1983', '199', '1990', '1991424', '1996', '1998', '1999', '19th', '19yr', '1check', '1for1', '1gbps', '1h', '1hr', '1k', '1kc', '1km', '1m', '1mbps', '1mdb', '1mil', '1min', '1mo', '1month', '1oct', '1or', '1room', '1s', '1ssslegend', '1ssss', '1sssss', '1st', '1up', '1x', '1y8m', '1year', '20', '200', '2000', '2000mi', '2000s', '2003', '2005', '2006', '2006posts', '2008', '200k', '200th', '200thread', '2010', '2011', '2012', '2013', '2014', '2015', '2016', '2017', '2017a', '2018', '20181', '2018edmwer', '2018posts', '2019', '2019ether', '2019the', '2020', '2020i', '2025', '2026', '2030', '2059', '20am', '20gb', '20ish', '20k', '20km', '20m', '20mil', '20min', '20mins', '20mthose', '20pgs', '20pm', '20s', '20th', '20x10', '20year', '20yrs', '21', '2139', '217', '21hopefully', '21mins', '21st', '21yo', '22', '220', '2200', '221', '2255227', '22k', '22nd', '22t', '23', '2307', '230twd', '230v', '2388', '23cm', '23k', '23rd', '23th', '24', '240', '240284', '240374', '240v', '240volts', '24h', '24hrs', '24k', '24sweet', '25', '250', '250k', '250pm', '251', '25amtop', '25if', '25m', '25mins', '25pm', '25th', '25years', '25yo', '26', '260', '26th', '27', '273', '27am', '27th', '28', '280cm', '28th', '29', '291', '299', '29th', '2am', '2b', '2cg', '2cm', '2cumjlw', '2g', '2g20va2', '2gb', '2gvhbnu', '2hours', '2hr', '2hrs', '2k', '2kg', '2km', '2lt', '2m', '2min', '2mins', '2monly', '2nd', '2ndlinlin', '2nil', '2pin', '2pm', '2pts', '2s', '2way', '2weeks', '2will', '2wo', '2x', '2y1m', '2y3m', '2years', '2yo', '2yr', '30', '300', '3000', '300d', '300k', '300mpbs', '302', '304', '30am', '30amtop', '30b23', '30cents', '30cm', '30k', '30l', '30litres', '30min', '30mins', '30pm', '30pmlast', '30pmtop', '30s', '30secs', '30sep', '30th', '30years', '30yonothing', '30yrs', '31', '313', '31can', '31st', '31yo', '31yr', '32', '321', '32am', '32b', '32c', '32g', '32pm', '33', '330k', '335', '33x2222', '34', '34f', '34yo', '35', '350', '353', '356', '35pm', '35pm1', '35psf', '35yo', '36', '361', '369', '36pm', '37', '375', '375k', '376', '376k', '377', '377182', '37she', '38', '380', '384', '38pm', '38yo', '39', '399', '39am', '39can', '39pm', '39yo', '3am', '3br', '3cg', '3d', '3ds', '3g', '3gb', '3gp', '3k', '3kg', '3kgor', '3m', '3mar', '3mil', '3mins', '3mks50fsg', '3mm', '3mths', '3nil', '3p', '3pm', '3rd', '3room', '3some', '3x', '3yrs', '40', '400', '400000', '400k', '402', '404', '40cm', '40cmcan', '40gb', '40k', '40min', '40mins', '40s', '40sgd', '40w', '40yo', '41', '42', '4245i', '429', '42km', '42m', '43', '44', '4444', '444444ssss', '444i', '44f', '44itsme44', '44pm', '45', '450', '4500', '457', '45min', '45mins', '45pm', '45pmtop', '46', '46am', '46kg', '47', '473', '47kg', '47pm', '48', '49', '495posted', '49days', '4d', '4fs', '4ft', '4g', '4in1', '4k', '4km', '4m', '4min', '4mins', '4mths', '4p', '4pm', '4px', '4rm', '4th', '4vdk', '4x', '50', '500', '5000', '50000nt', '500gram', '500k', '500usd', '50am', '50bucks', '50cents', '50hz', '50k', '50km', '50kthese', '50linprice', '50nt', '50pm', '50w', '51', '52', '520li', '523', '528', '53', '534', '53am', '53pm', '54', '55', '55am', '55in', '55pm', '56', '56secs', '57', '5753', '588', '58am', '58pm', '5am', '5bear', '5bx', '5c', '5cm', '5days', '5e', '5g', '5gb', '5gbs', '5ghz', '5k', '5km', '5kshoe', '5kwooohoo', '5m', '5mil', '5min', '5mins', '5minsedit', '5minutes', '5mm', '5mno', '5pic', '5pm', '5ppm', '5r', '5rm', '5room', '5star', '5th', '5v', '5w', '5weeks', '5wis', '5x', '5x2gang', '5years', '5yo', '5yrs', '60', '600', '600kg', '600nt', '6076100', '60cm', '60gb', '60hz', '60k', '60kg', '60s', '60yo', '61', '62', '620', '625pm', '63', '63548154', '6389', '63892222', '640', '644am', '645am', '64sgd', '65', '650', '650mm', '65th', '66', '666nt', '68sqm', '69', '6am', '6gb', '6guest', '6k', '6min', '6mins', '6months', '6mths', '6order', '6p', '6pic', '6pm', '6pmkorean', '6pmmeat', '6pmpls', '6pts', '6s', '6t', '6th', '6times', '70', '7000', '7080k4rm', '70kg', '70s', '711', '72', '720p', '726942got', '73', '737', '745', '747', '75', '759995can', '75c', '76', '76cm', '77', '77in', '7800', '79', '799', '7am', '7early8early', '7gb', '7hwcf', '7kreno', '7m', '7min', '7pm', '7smine', '7th', '7weeks', '7yrstold', '80', '805', '8080', '80cm', '80pm', '80s', '80to', '81', '812', '815', '82', '820', '83', '830', '8310', '84', '85', '850', '85cm', '85ybbonusspoiler', '86', '87', '8767', '8769', '88', '8804', '88sgd', '89', '8am', '8amnow', '8and', '8ch', '8cmthen', '8gb', '8k', '8m', '8many', '8mil', '8mins', '8mor6', '8mths', '8pm', '8s', '8th', '90', '900', '900mm', '900redo', '901', '90d', '90kg', '90m2', '90s', '90secs', '91', '93ugany', '95', '953looks', '95kg', '96', '9696', '974', '98', '980', '983', '98mm', '99', '999', '9990', '9999', '9am', '9but', '9k', '9km', '9m', '9pm', '9pmcollection', '9t8idkimi', '9th', 'a1', 'a15', 'a2', 'a220', 'a380', 'a4', 'a6000', 'a6700', 'aa', 'aaa', 'aaaaaaaaaaaaaaaaaaaa', 'aasiiiiaaaaa', 'ab', 'abalone', 'abandon', 'abandoned', 'abandoning', 'abang', 'abangs', 'abartar', 'abbie', 'abc', 'abdomen', 'abdul', 'abdullah', 'abeilt', 'abiding', 'abigail', 'ability', 'abit', 'abksg', 'able', 'abnn', 'abnormal', 'abnormalities', 'abojf', 'abolish', 'aborigines', 'abort', 'aborted', 'aborting', 'abortion', 'about', 'above', 'abrasion', 'abroad', 'abs', 'absent', 'absolute', 'absolutely', 'absoluteunits', 'absolve', 'absorbed', 'absorbing', 'absorption', 'abstinence', 'abt', 'abu', 'abuse', 'abused', 'abuser', 'abusers', 'abuses', 'abusing', 'abv', 'ac', 'ac3100', 'ac5300', 'ac7', 'ac88', 'acacia', 'academic', 'academically', 'acc', 'acca', 'accelerate', 'accent', 'accented', 'accept', 'acceptable', 'acceptance', 'accepted', 'accepting', 'accepts', 'access', 'accessible', 'accessories', 'accessory', 'accident', 'accidentally', 'accidently', 'accidents', 'accommodate', 'accommodation', 'accompany', 'accompanying', 'accomplices', 'accomplish', 'according', 'accordingly', 'account', 'accountability', 'accountable', 'accountant', 'accountants', 'accountcross', 'accounting', 'accounts', 'accountsi', 'acct', 'accts', 'accumalation', 'accumulate', 'accumulating', 'accurate', 'accurately', 'accuse', 'accused', 'ace', 'aced', 'aceminer', 'ache', 'acherly', 'achieve', 'achieved', 'achievement', 'achievements', 'aching', 'acid', 'acidman35', 'acidman36', 'acknowledge', 'acknowledges', 'acky', 'acn', 'acquired', 'acquittal', 'across', 'acrylic', 'acs', 'act', 'acted', 'acting', 'action', 'actions', 'activate', 'activated', 'activating', 'activation', 'active', 'actively', 'activeon', 'activesg', 'activesure', 'activist', 'activists', 'activities', 'activity', 'activityedit', 'actor', 'actors', 'actress', 'actresses', 'acts', 'actual', 'actuallee', 'actually', 'actully', 'actusally', 'ad', 'ada', 'adam', 'adams', 'adapt', 'adapter', 'adaptors', 'adc', 'add', 'added', 'addicted', 'addiction', 'addictive', 'addicts', 'adding', 'addition', 'additional', 'address', 'addressee', 'ade9db25ios', 'adelphi', 'adhere', 'adheres', 'adhesive', 'adi', 'adjust', 'adjusted', 'adjusting', 'adjustment', 'admin', 'administration', 'admins', 'admiral', 'admiralty', 'admire', 'admired', 'admirers', 'admiring', 'admissions', 'admit', 'admits', 'admitted', 'admittedly', 'adopt', 'adopted', 'adopting', 'adorable', 'adore', 'adorned', 'adresssssssssssss', 'ads', 'adsthere', 'adult', 'adultery', 'adulterysary', 'adults', 'adv', 'advance', 'advanced', 'advances', 'advantage', 'advantageous', 'adventure', 'adventures', 'adventurous', 'advert', 'advertise', 'advertised', 'advertisement', 'advertisements', 'advertising', 'adverts', 'advice', 'advices', 'advisable', 'advise', 'advised', 'advisors', 'advisory', 'advocate', 'ady', 'aedes', 'aegypti', 'aerodynamics', 'aeron', 'aeroplane', 'aesthetic', 'af', 'afaik', 'afew', 'affair', 'affairs', 'affect', 'affecte', 'affected', 'affectionate', 'affects', 'affiliated', 'afford', 'affordable', 'afghanistan', 'afheng', 'afk', 'afraid', 'africa', 'african', 'africans', 'afro', 'aft', 'aftaid', 'after', 'afterall', 'afterlife', 'aftermath', 'afternoon', 'afternoonnnnnn', 'afterthat', 'afterward', 'afterwards', 'afterwardsheng', 'afterwords', 'afterworld', 'ag', 'again', 'againceo', 'againchannel', 'againdangerous', 'againheng', 'againpattern', 'againposted', 'against', 'againthink', 'agasint', 'agc', 'age', 'aged', 'ageing', 'agemono', 'agencies', 'agency', 'agencyownself', 'agenda', 'agenies', 'agent', 'agents', 'ages', 'aggravating', 'aggression', 'aggressive', 'aggressively', 'aging', 'aglophone', 'agn', 'ago', 'ago3000000still', 'agohe', 'agomoredtl', 'agomoreno', 'agony', 'agree', 'agreed', 'agreeing', 'agreement', 'agrees', 'agrifoodagrifood', 'aguero', 'ah', 'ah63892222', 'ahah', 'ahaha', 'ahahaha', 'ahahha', 'ahbear', 'ahbha', 'ahead', 'aheadbut', 'ahem', 'ahgong', 'ahgui', 'ahguihotlineah', 'ahh', 'ahhang', 'ahhh', 'ahhhh', 'ahhhhhhh', 'ahhhhhhhh', 'ahi', 'ahits', 'ahlong', 'ahlooks', 'ahma', 'ahmad', 'ahmeng', 'ahneh', 'ahoy', 'ahpek', 'ahpew', 'ahposted', 'ahtc', 'ahtiong', 'ahwhere', 'ahz', 'ai', 'ai1790x', 'aiai', 'aibee', 'aibi', 'aid', 'aids', 'aiiiiiiiiiiiiiiiiiiii', 'aileen', 'aim', 'aiming', 'ain', 'aint', 'air', 'airbags', 'airboard', 'airbot', 'aircon', 'aircraft', 'aircrafts', 'aircrash', 'aired', 'airflo', 'airflow', 'airforce', 'airi', 'airing', 'airlets', 'airline', 'airlines', 'airplane', 'airpod', 'airpods', 'airport', 'airports', 'airs', 'airsoft', 'airspace', 'airticket', 'airtime', 'aiseh', 'aiya', 'aiyah', 'aiyawhat', 'aiyo', 'aiyoh', 'aiyoyo', 'aj', 'ajajajajaj', 'ajar', 'ajax', 'ajaxspurs', 'ajunied', 'ajuuuma', 'ajuuuuuumma', 'ak', 'aka', 'akb48', 'akemi', 'akin', 'aks', 'aku', 'al', 'ala', 'alamak', 'alarm', 'albee', 'albeit', 'albert', 'albinism', 'albino', 'albirex', 'albopictus', 'album', 'albumits', 'alchemy', 'alcohol', 'alcoholic', 'aldy', 'alert', 'alerted', 'alerts', 'alex', 'alexa', 'algeria', 'algerian', 'ali', 'alibaba', 'alien', 'aliens', 'aliexpress', 'alight', 'alighted', 'align', 'aligned', 'alignment', 'alike', 'alimony', 'alittle', 'alive', 'alivehapy', 'aljunied', 'alkaline', 'all', 'allah', 'allahpuki', 'allan', 'allegation', 'allegations', 'alleged', 'allegedly', 'allergic', 'allexcept', 'allfull', 'allhope', 'alli', 'alliance', 'allied', 'alll', 'alllemonade', 'alllost', 'allmake', 'allo', 'allocate', 'allow', 'allowance', 'allowed', 'allowing', 'allows', 'allregarding', 'allrelac', 'alltmr', 'alluring', 'almos', 'almost', 'aloha', 'alone', 'alonecan', 'aloneunless', 'along', 'alongside', 'alot', 'alots', 'aloud', 'aloy', 'aloypang', 'aloypeople', 'aloys', 'aloysius', 'alpaca', 'alpha', 'alphard', 'alpinism', 'alr', 'alrd', 'alrdy', 'alrdy4', 'alreadi', 'already', 'alreadylol', 'alreadynow', 'alreadythose', 'alreadywhole', 'alreddy', 'alredi', 'alright', 'alrpoor', 'alrwady', 'alrwhen', 'als', 'also', 'alsoa', 'alsoand', 'alsobreaking', 'alsoin', 'alsospoiler', 'altar', 'alter', 'alterbaalte', 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'wuite', 'wun', 'wuss', 'wusses', 'wut', 'ww2', 'wwe', 'wwii', 'www', 'wy', 'wz', 'x1', 'x10', 'x2', 'x4', 'x8', 'xabsolut', 'xanax', 'xb', 'xd', 'xdbut', 'xdd', 'xddwish', 'xfer', 'xhl0wik4zc', 'xhz', 'xhz19993', 'xi', 'xi0ami', 'xi3', 'xia', 'xian', 'xiang', 'xiangyun', 'xiao', 'xiaohan', 'xiaohui', 'xiaolang', 'xiaomi', 'xiaosan', 'xiaozhong', 'xiaozong', 'xiasuay', 'xiaxue', 'xie', 'xies', 'xiezhi', 'xin', 'xing', 'xinmin', 'xinyi', 'xiong', 'xiongs', 'xiongxiong', 'xiuxiu', 'xjp', 'xlcliangx', 'xln', 'xm', 'xmas', 'xmassss', 'xmd', 'xmm', 'xmmjolibeeeat', 'xmms', 'xmos', 'xray', 'xs', 'xso', 'xtains', 'xtian', 'xtians', 'xtra', 'xtremeplace', 'xtt', 'xu', 'xuan', 'xuay', 'xubin', 'xue', 'xuebi', 'xueling', 'xun', 'xunli', 'xx', 'xx420noscopexx', 'xxl', 'xxx', 'y0ur', 'ya', 'yabatan', 'yah', 'yahahhahahahahahaa', 'yahoo', 'yak', 'yale', 'yall', 'yam', 'yan', 'yandao', 'yandere', 'yanderes', 'yang', 'yankaykay', 'yankee', 'yanxi', 'yao', 'yaoyao', 'yap', 'yarahsee', 'yard', 'yau', 'yauwaiching', 'yawn', 'yawnz', 'yawnzzzzzz', 'yay', 'yaya', 'yck', 'ydf40the', 'ydntkmn', 'ydr424g', 'ye', 'yea', 'yeah', 'yeahh', 'yeahhh', 'yeap', 'year', 'year1', 'yearfeels', 'yearhappy', 'yeari', 'yearly', 'yearmay', 'years', 'yearsagree', 'yearsoldiesmelancholicchinesesongs', 'yearsone', 'yee', 'yeh', 'yell', 'yelling', 'yellow', 'yeng', 'yenns', 'yeo', 'yeoh', 'yeow', 'yep', 'yes', 'yesh', 'yess', 'yessssss', 'yest', 'yesterday', 'yesteryear', 'yet', 'yet7', 'yetif', 'yew', 'yewtee', 'yeye', 'yhjmemberyhj', 'yi', 'yikes', 'yiling', 'yin', 'ying', 'yipeng', 'yishun', 'yishunadd', 'yislum', 'yislumshun', 'yitanwushui', 'yiu', 'yiyuan', 'yknow', 'yo', 'yoconfused', 'yoday', 'yoga', 'yoghurt', 'yoi', 'yok', 'yoko', 'yolo', 'yong', 'yongcan', 'yoo', 'yoona', 'york', 'you', 'youbut', 'youcantstopme', 'youd', 'youedit', 'yougot', 'youll', 'young', 'young2nd', 'younger', 'youngest', 'youngfew', 'youngsters', 'your', 'youre', 'youronatcambodia', 'yours', 'yours12', 'yourself', 'yourselfstep', 'yourselves', 'youstep', 'youth', 'youthful', 'youths', 'youtiao', 'youtube', 'youtuber', 'youtubers', 'youu', 'yoyo', 'yoyoyo', 'yoyrself', 'yp', 'yps', 'yr', 'yrs', 'yst', 'ystd', 'ysteryears', 'yt', 'ytd', 'ytdabout', 'yu', 'yua', 'yuan', 'yubari', 'yucks', 'yue', 'yuensteven', 'yui', 'yukijudai', 'yum', 'yumeko', 'yumi', 'yummeh', 'yummy', 'yummyenjoy', 'yunnam', 'yup', 'yur', 'yy', 'zai', 'zakum', 'zaley', 'zaobao', 'zap', 'zapzap', 'zarbor', 'zb', 'ze', 'zealand', 'zebra', 'zeclinelin', 'zedmaster', 'zeebai', 'zeh', 'zehnot', 'zehwhat', 'zehzeh', 'zehzehhello', 'zehzehhow', 'zehzehi', 'zehzehlong', 'zehzehmoaning', 'zehzehs', 'zehzehspoiler', 'zehzehu', 'zeidan', 'zek', 'zendesk', 'zenith', 'zepp', 'zero', 'zeusthemighty', 'zg', 'zha', 'zhai', 'zhan', 'zhang', 'zhao', 'zhaoyun', 'zharbor', 'zhe', 'zhen', 'zheng', 'zhi', 'zhihau', 'zhng', 'zhnged', 'zhong', 'zhou', 'zhu', 'zhua', 'zhun', 'zhut', 'zi', 'zijie', 'zin', 'zip', 'zipping', 'zj', 'zo', 'zoe', 'zoh', 'zombie', 'zombieland', 'zombieman', 'zombies', 'zone', 'zoo', 'zook', 'zoolong', 'zoom', 'zoomed', 'zooming', 'zouk', 'zs7', 'zsnn', 'zui', 'zurich', 'zyq', 'zz', 'zzbut', 'zzz', 'zzz80', 'zzzcab', 'zzzoriginal', 'zzzso', 'zzzz', 'zzzzspoiler', 'zzzzz', 'zzzzzz']
20590
trn_term_doc,test_term_doc_____no_output_____# Fit model
preds = np.zeros((len(test), len(label_cols)))

for i, j in enumerate(label_cols):
    print('fit', j)
    #m = RandomForestClassifier(n_estimators=1000, max_leaf_nodes=1000, random_state=21)
    m = RandomForestClassifier(n_estimators=1000, random_state=21)
    forest = m.fit(trn_term_doc, train[j].values)
    preds[:,i] = forest.predict_proba(test_term_doc)[:,1]fit Insulting
fit Anti Government
fit Xenophobic
fit Racist
fit Sexual
subm_comments = pd.DataFrame({'Comments': test["Comments"]})
submission = pd.concat([subm_comments, pd.DataFrame(preds, columns = label_cols)], axis=1)
print(submission)                                               Comments  Insulting  \
0     who can stay at home dun need to work one?if c...   0.679000
1     Jin boring..Please chat with me. I counting do...   0.013000
2     Now that Da Jie is retired and have the time, ...   0.105000
3                                                  Yes!   0.000000
4     Good enough to be ganged bang like "The Genera...   0.074000
5                                   Whacking ah gui now   0.002000
6                   Meestick korkor got tips to stream?   0.021000
7     If one day, EWL, NSL, NEL, DTL, CCL all breakd...   0.654000
8                                Worth doing. Good job.   0.010000
9                            judgment call to cover up?   0.002000
10                           She looks old and jaded...   0.024000
11    Train stuck between yishun and Khatib. Happy m...   0.019000
12                                      Wow ns line lol   0.004000
13                        Show photos of this Lassie. .   0.020000
14    Yessssss play until walk fire enter demon liaoooo   0.019544
15                               Sg police damn useless   0.237265
16                                  Delete wrong thread   0.068077
17    Train slightly delayed at katib station toward...   0.057000
18    my Monday is crazy busy, don't even have time ...   0.049000
19    Talk so much. Just Sack this so call Minister....   0.830000
20                       Some photos looks like Celeste   0.018667
21    If young still ok because u easily can find an...   0.022000
22    NSL got super packed platform.. train problems...   0.183000
23                    uzbek women in uniform. swee boh?   0.036000
24    Treasure and take our stability seriously: Cha...   0.776000
25                            Train fault at yishun now   0.018000
26                "Listen up! Very simple! Understand!"   0.020000
27                              DR NG MUST RESIGN, NOW.   0.245000
28                                             Link abv   0.005576
29    Temasick would rather invest elsewhere than gr...   0.666000
...                                                 ...        ...
998                     there is a spoon mei mei around   0.673111
999                                      Ok Liao!![NSL]   0.000000
1000                   I would choose the one in white.   0.067000
1001  On dtl when stop at hillview station, vaguely ...   0.007000
1002  NEL seems to experience some delay due to trai...   0.015000
1003            congrats to meestick korkorwhen buffet?   0.005000
1004              Ho Ho Ho is it because he's Chinese ?   0.087000
1005  My contractor till now haven come to fix the t...   0.070000
1006  Go to both the customs checkpoint in tuas and ...   0.038000
1007  CPT Anna Soo's twin brothers commissioned as o...   0.055305
1008  Can sue loh. But cannot win have to pay costs ...   0.151000
1009  Gotta love Modern journalists always looking f...   0.651000
1010              Lol so how many angbaos did u collect   0.001000
1011                     Ah Gui, got hoot TOTO boh?????   0.003000
1012  This is purely an extreme and isolated case. N...   0.056000
1013  Gd morning everybody. Finally near weekend lia...   0.000000
1014                    Greetings to Ah Gui and friends   0.000000
1015                                      Tmr good luck   0.001110
1016  This Defence Minister Ng Eng Hen cannot be tru...   0.875000
1017  23 YEARS OLD SINGAPOREAN ...ANY COMMENTS. NOT ...   0.665000
1018                           Whose fault is it today?   0.016000
1019  This morning the 1st NS train come around 10m ...   0.034000
1020  Even a rear camera will not prevent a future t...   0.032000
1021                               Wearing their boots!   0.028588
1022  which floor robot vacuum can move over floor m...   0.021000
1023  Today long q at Singapore pools....zzzzz huat ...   0.031000
1024  i need to niam, past few mths they close bukit...   0.681000
1025  They will not blame themselves but whether the...   0.105000
1026  the higher the percentage of chinese, the rich...   0.681000
1027  Why i call tt line then got a xmm answered?Jus...   0.105000

      Anti Government  Xenophobic  Racist    Sexual
0               0.001       0.003   0.005  0.004000
1               0.004       0.033   0.000  0.006000
2               0.016       0.004   0.014  0.038000
3               0.000       0.000   0.000  0.000000
4               0.019       0.000   0.001  0.733000
5               0.000       0.000   0.000  0.000000
6               0.000       0.007   0.000  0.006000
7               0.001       0.000   0.000  0.000000
8               0.004       0.000   0.000  0.000000
9               0.598       0.001   0.000  0.008000
10              0.000       0.000   0.000  0.022000
11              0.000       0.000   0.000  0.006000
12              0.004       0.004   0.000  0.003000
13              0.000       0.000   0.000  0.019000
14              0.000       0.000   0.000  0.005957
15              0.018       0.004   0.000  0.014685
16              0.000       0.000   0.000  0.001225
17              0.000       0.000   0.000  0.004000
18              0.002       0.001   0.003  0.000000
19              0.780       0.001   0.000  0.004000
20              0.000       0.001   0.000  0.016000
21              0.004       0.062   0.002  0.006000
22              0.003       0.000   0.015  0.000000
23              0.002       0.005   0.001  0.034000
24              0.774       0.003   0.007  0.015000
25              0.007       0.000   0.001  0.000000
26              0.004       0.003   0.003  0.000250
27              0.892       0.003   0.000  0.005000
28              0.000       0.000   0.000  0.006374
29              0.010       0.003   0.000  0.011000
...               ...         ...     ...       ...
998             0.001       0.000   0.000  0.654111
999             0.000       0.000   0.000  0.001000
1000            0.003       0.002   0.000  0.133000
1001            0.001       0.002   0.001  0.003000
1002            0.000       0.000   0.002  0.004000
1003            0.000       0.000   0.000  0.001095
1004            0.000       0.001   0.017  0.059000
1005            0.010       0.000   0.001  0.015000
1006            0.006       0.001   0.000  0.007000
1007            0.000       0.000   0.000  0.010929
1008            0.083       0.003   0.002  0.009000
1009            0.002       0.000   0.001  0.006000
1010            0.001       0.000   0.000  0.005000
1011            0.000       0.000   0.000  0.007000
1012            0.001       0.000   0.000  0.000000
1013            0.000       0.000   0.000  0.000000
1014            0.000       0.001   0.000  0.001000
1015            0.000       0.000   0.000  0.000000
1016            0.897       0.001   0.004  0.003000
1017            0.043       0.736   0.002  0.002000
1018            0.001       0.000   0.000  0.000000
1019            0.003       0.001   0.001  0.003000
1020            0.004       0.009   0.001  0.002000
1021            0.000       0.002   0.003  0.029447
1022            0.003       0.013   0.011  0.022000
1023            0.001       0.007   0.000  0.084000
1024            0.011       0.004   0.017  0.003000
1025            0.063       0.013   0.004  0.000000
1026            0.011       0.043   0.642  0.014000
1027            0.001       0.000   0.001  0.065000

[1028 rows x 6 columns]
#submission.to_csv('Forest_prob_subm_1.csv', index=False)_____no_output_____#import os
# from sklearn.tree import export_graphviz
# import six
# import pydot
# from sklearn import tree
# i_tree = 0
# for tree_in_forest in forest.estimators_:
#     export_graphviz(tree_in_forest,out_file='tree.dot',
#     filled=True,
#     rounded=True)
#     (graph,) = pydot.graph_from_dot_file('tree.dot')
#     name = 'tree' + str(i_tree)
#     graph.write_png(name+  '.png')
#     os.system('dot -Tpng tree.dot -o tree.png')
#     i_tree +=1_____no_output_____scores = []
sd = []
for cat in label_cols:
    train_target = train[cat]
    classifier = RandomForestClassifier(n_estimators=1000, random_state=21)

    cv_score = np.mean(cross_val_score(
        classifier, trn_term_doc, train_target, cv=5, scoring='roc_auc'))
    scores.append(cv_score)

    cv_sd = np.std(cross_val_score(
        classifier, trn_term_doc, train_target, cv=5, scoring='roc_auc'))
    sd.append(cv_sd)
    print('CV score for class {} is {}, standard deviation is {}'.format(cat, cv_score, cv_sd))

    classifier.fit(trn_term_doc, train_target)
    submission[cat] = classifier.predict_proba(test_term_doc)[:, 1]

print('Total CV score is {}'.format(np.mean(scores)))
print('The Standard Deviation of the CV score is {}'.format(np.std(scores)))CV score for class Insulting is 0.7115227352389535, standard deviation is 0.08343046822671232
test_str = "foreign whore"
test_str = clean_text([test_str])
test_com = pd.Series(test_str)
test_vec = vec.transform(pd.Series(test_com))
test_pred = np.zeros((len(test_com), len(label_cols)))

for i, j in enumerate(label_cols):
    m = RandomForestClassifier(n_estimators=1000, random_state=21)
    forest = m.fit(trn_term_doc, train[j].values)
    test_pred[:,i] = forest.predict_proba(test_vec)[:,1]
    if test_pred[:,i] >= 0.5:
        print("\n","This comment is",j)

score = pd.DataFrame(test_pred, columns = label_cols)
print("\n\n",score)_____no_output_____
</code>