Prerequisite
Run notebook week-3.0-data-prep-for-training
before.
In this excercise, you will use:
This excercise is part of the Scaling Machine Learning with Spark book available on the O'Reilly platform or on Amazon.
from pyspark.sql import SparkSession
from pyspark.ml import Pipeline
from pyspark.ml.regression import LinearRegression, LinearRegressionModel
from pyspark.ml.fpm import FPGrowth, FPGrowthModel
from pyspark.ml.evaluation import RegressionEvaluator
spark = SparkSession.builder \
.master('local[*]') \
.appName("eval_and_pipelines") \
.getOrCreate()
Load the models from previous Chapter:
lr_model = LinearRegressionModel.load('../models/linearRegression_model')
fpgrowth_model = FPGrowthModel.load('../models/fpGrowth_model')
Evaluate the models:
For evaluation, load classified test data
df_test = spark.read.parquet("../datasets/classified_test_data")
While there are many different types of classification algorithms, the evaluation of classification models all shares similar principles.
In a supervised classification problem, there exists a true output and a model-generated predicted output for each data row.
✅ Task :
Start with predicting the outcome: Use predict function
model.transform(vectorOfFeatures).select('prediction').show()
Notice that transform takes a vector of features as input.
Prediction represents if it's a bot or not. 1- bot 0- human
from pyspark.ml.feature import VectorAssembler
test = df_test.drop('description')
vecAssembler = VectorAssembler(inputCols=['screen_name','location','followers_count','friends_count','listed_count','favourites_count','verified','statuses_count','status','default_profile','name'], outputCol="features", handleInvalid = "skip")
test_df_with_vector = vecAssembler.transform(test)
test_df_with_vector.show(2)
+-----------+--------+---------------+-------------+------------+----------------+--------+--------------+------+---------------+----+---+--------------------+ |screen_name|location|followers_count|friends_count|listed_count|favourites_count|verified|statuses_count|status|default_profile|name|bot| features| +-----------+--------+---------------+-------------+------------+----------------+--------+--------------+------+---------------+----+---+--------------------+ | 1| 0| 736| 3482| 4| 22| 0| 681| 1| 0| 1| 0|[1.0,0.0,736.0,34...| | 1| 0| 3437| 2| 106| 0| 0| 4356| 1| 0| 1| 0|[1.0,0.0,3437.0,2...| +-----------+--------+---------------+-------------+------------+----------------+--------+--------------+------+---------------+----+---+--------------------+ only showing top 2 rows
model_test_prediction = lr_model.transform(test_df_with_vector)
model_test_prediction = lr_model.transform(test_df_with_vector)
model_test_prediction.select('bot','prediction').show()
+---+-------------------+ |bot| prediction| +---+-------------------+ | 0|0.11710130261697825| | 0|0.11710130261697825| | 0|0.11710130261697825| | 0|0.11710130261697825| | 0|0.11710130261697825| | 0|0.11710130261697825| | 0|0.11710130261697825| | 0|0.11710130261697825| | 0|0.11710130261697825| | 0|0.11710130261697825| | 0|0.11710130261697825| | 0|0.11710130261697825| | 0|0.11710130261697825| | 0|0.11710130261697825| | 0|0.11710130261697825| | 0|0.11710130261697825| | 0|0.11710130261697825| | 0|0.11710130261697825| | 0|0.11710130261697825| | 0|0.11710130261697825| +---+-------------------+ only showing top 20 rows
The model gave us a prediction of the chances for a specific row to be a bot. We got numbers like 0.147 and 0.1021.
It is up to us to define the threshold for classifying a bot. If it shows us 0.9? Will it satisfy us? How certain do we want to be in the classification?
RegressionEvaluator is the evaluator for regression-based models
Use regressionEvaluator to evaluate the model.
pyhon
from pyspark.ml.evaluation import RegressionEvaluator
lr_evaluator = RegressionEvaluator(predictionCol="prediction", labelCol="bot",metricName="r2")
R2 = lr_evaluator.evaluate(model_test_prediction)
Check out R2 :
R-squared is a statistical measure of how close the data are to the fitted regression line. It is also known as the coefficient of determination, or the coefficient of multiple determination for multiple regression. 100% indicates that the model explains all the variability of the response data around its mean
From: RegressionAnalysis
Notice metricName
param:
RegressionEvaluator Supports: - rmse
(default): root mean squared error - mse
: mean squared error - r2
: R Sqaure metric - mae
: mean absolute error
Notice! here we work with the train data and select both bot
and prediction
to get a feel for the classifier
test = model_test_prediction.fillna({'bot':0})
test.show()
+-----------+--------+---------------+-------------+------------+----------------+--------+--------------+------+---------------+----+---+--------------------+-------------------+ |screen_name|location|followers_count|friends_count|listed_count|favourites_count|verified|statuses_count|status|default_profile|name|bot| features| prediction| +-----------+--------+---------------+-------------+------------+----------------+--------+--------------+------+---------------+----+---+--------------------+-------------------+ | 1| 0| 736| 3482| 4| 22| 0| 681| 1| 0| 1| 0|[1.0,0.0,736.0,34...|0.11710130261697825| | 1| 0| 3437| 2| 106| 0| 0| 4356| 1| 0| 1| 0|[1.0,0.0,3437.0,2...|0.11710130261697825| | 1| 1| 150| 0| 18| 6| 0| 326| 1| 0| 1| 0|[1.0,1.0,150.0,0....|0.11710130261697825| | 1| 1| 3500781| 92| 18455| 6003| 1| 26110| 1| 0| 1| 0|[1.0,1.0,3500781....|0.11710130261697825| | 1| 1| 2039677| 0| 19842| 2| 1| 9256| 1| 0| 1| 0|[1.0,1.0,2039677....|0.11710130261697825| | 1| 0| 99| 0| 24| 0| 0| 75| 1| 0| 1| 0|(11,[0,2,4,7,8,10...|0.11710130261697825| | 1| 0| 323| 384| 4| 631| 0| 617| 1| 0| 1| 0|[1.0,0.0,323.0,38...|0.11710130261697825| | 1| 1| 4| 81| 0| 133| 0| 3| 1| 0| 1| 0|[1.0,1.0,4.0,81.0...|0.11710130261697825| | 1| 1| 5633| 5821| 223| 480| 1| 11198| 1| 0| 1| 0|[1.0,1.0,5633.0,5...|0.11710130261697825| | 1| 1| 116| 1| 14| 1| 0| 1031| 1| 0| 1| 0|[1.0,1.0,116.0,1....|0.11710130261697825| | 1| 1| 44697| 415| 814| 2530| 1| 4654| 1| 0| 1| 0|[1.0,1.0,44697.0,...|0.11710130261697825| | 1| 1| 21| 0| 7| 0| 0| 1343| 1| 0| 1| 0|[1.0,1.0,21.0,0.0...|0.11710130261697825| | 1| 1| 84| 103| 7| 107| 0| 158| 1| 0| 1| 0|[1.0,1.0,84.0,103...|0.11710130261697825| | 1| 0| 0| 28| 0| 103| 0| 145| 1| 0| 1| 0|(11,[0,3,5,7,8,10...|0.11710130261697825| | 1| 0| 347| 4| 53| 39| 0| 42526| 1| 0| 1| 0|[1.0,0.0,347.0,4....|0.11710130261697825| | 1| 1| 67937| 1644| 801| 4875| 1| 13542| 1| 0| 1| 0|[1.0,1.0,67937.0,...|0.11710130261697825| | 1| 1| 351335| 19| 1221| 395| 1| 25| 1| 0| 1| 0|[1.0,1.0,351335.0...|0.11710130261697825| | 1| 1| 1902| 94| 98| 5| 0| 21114| 1| 0| 1| 0|[1.0,1.0,1902.0,9...|0.11710130261697825| | 1| 1| 247| 0| 53| 2| 0| 17811| 1| 0| 1| 0|[1.0,1.0,247.0,0....|0.11710130261697825| | 1| 1| 56| 1| 13| 1| 0| 7118| 1| 0| 1| 0|[1.0,1.0,56.0,1.0...|0.11710130261697825| +-----------+--------+---------------+-------------+------------+----------------+--------+--------------+------+---------------+----+---+--------------------+-------------------+ only showing top 20 rows
from pyspark.ml.feature import VectorAssembler
df_train = spark.read.parquet("../datasets/classified_train_data")
train = df_train.drop('description')
vecAssemblerTrain = VectorAssembler(inputCols=['screen_name','location','followers_count','friends_count','listed_count','favourites_count','verified','statuses_count','status','default_profile','name'], outputCol="features", handleInvalid = "skip")
vecAssemblerTrain = vecAssemblerTrain.transform(train)
model_train_prediction = lr_model.transform(vecAssemblerTrain)
model_train_prediction.select('bot','prediction')
test = model_train_prediction.fillna({'bot':0})
model_train_prediction = test
lr_evaluator = RegressionEvaluator(predictionCol="prediction", labelCol="bot",metricName="r2")
R2 = lr_evaluator.evaluate(model_train_prediction)
print("R Squared (R2) on test data = %g" % R2)
R Squared (R2) on test data = 0.00281465
When looking back at the Predictions
output, we understand that they don't help us much.
Predictions
output is a number between [0,1].
However, we expect 1 or 0: bot or human. What can we do? Decide on a threshold.
For Example, every prediction above 0.8 is bot. Bellow 0.8 is human.
Or maybe every prediction above 0.14?
✅ Task :
Use model statistics params:
For example Check RMSE - Root Mean Squared Error For both train and test.
Code sample:
def getLRSummary(df):
df = df.drop('description')
vecAssembler = VectorAssembler(inputCols=['screen_name','location','followers_count','friends_count','listed_count','favourites_count','verified','statuses_count','status','default_profile','name'], outputCol="features", handleInvalid = "skip")
vecAssembler = vecAssembler.transform(df)
output_test = vecAssembler.drop('screen_name','location','followers_count','friends_count','listed_count','favourites_count','verified','statuses_count','status','default_profile','name')
output_test = output_test.selectExpr("features", "bot as label")
# evaluate function returns LinearRegressionSummary instance that holds the evaluate results
return lr_model.evaluate(output_test)
Here are function r2 docs
Check on both training and test set:
def getLRSummary(df):
df = df.drop('description')
vecAssembler = VectorAssembler(inputCols=['screen_name','location','followers_count','friends_count','listed_count','favourites_count','verified','statuses_count','status','default_profile','name'], outputCol="features", handleInvalid = "skip")
vecAssembler = vecAssembler.transform(df)
output = vecAssembler.drop('screen_name','location','followers_count','friends_count','listed_count','favourites_count','verified','statuses_count','status','default_profile','name')
output = output.selectExpr("features", "bot as label")
# evaluate function returns LinearRegressionSummary instance that holds the evaluated results
return lr_model.evaluate(output)
df_train = df_train.fillna({'bot':0})
df_test = df_test.fillna({'bot':0})
train_results = getLRSummary(df_train)
print("Root Mean Squared Error (RMSE) on train data = %g" % train_results.rootMeanSquaredError)
test_results = getLRSummary(df_test)
print("Root Mean Squared Error (RMSE) on test data = %g" % test_results.rootMeanSquaredError)
Root Mean Squared Error (RMSE) on train data = 0.451414 Root Mean Squared Error (RMSE) on test data = 0.775022
LinearRegressionSummary
gives you a summary of the statistical algorithm evaluations.
print("r2 on test data = %g" % test_results.r2)
print("r2 on train data = %g" % train_results.r2)
r2 on test data = 0.000431035 r2 on train data = 0.00281465
What more evaluating params can you get out of LinearRegressionSummary instance?
Reminder What is r2? R Square:
R-squared is a statistical measure of how close the data are to the fitted regression line. It is also known as the coefficient of determination, or the coefficient of multiple determination for multiple regression.
R Square measure how much of the variability in bot
/ label
can be explained using the model.
We must be cautious that the performance on the training set to avoid overfitting of the model to the training set.
Overrfiting can create a model that is good only for the training set and not for the test set.
What is RMSE?
Root Mean Square Error (RMSE) is the standard deviation of the residuals (prediction errors). Residuals are a measure of how far from the regression line data points are; RMSE is a measure of how spread out these residuals are. In other words, it tells you how concentrated the data is around the line of best fit.
ML Pipelines provide a uniform set of high-level APIs built on top of DataFrames that help us create and tune practical machine learning pipelines.
In the previous exercise, you learned Logistic regression.
Logistic regression is used when the dependent variable is binary. In our case, bot is binary - yes or no.
Linear regression is used to predict the continuous dependent variable. This explains the result received.
Start with a simple ML Pipelines:
from pyspark.ml import Pipeline
from pyspark.ml.classification import LogisticRegression
from pyspark.ml.feature import HashingTF, Tokenizer
Remember that in Chapter 1 we split description
into a list?
Let's do it with the Tokenizer
functionality instead!
Tokenizer
is part of the pyspark.ml.feature
. pyspark.ml.feature
give us many out of the box functionality for feature extraction. Feature extraction is the data-science way of transforming columns into a new one.
Load saved data:
data = spark.read.parquet('../datasets/train_data_only_description')
data = data.fillna({'label':0})
data.show()
(trainingData, testData) = data.randomSplit([0.7, 0.3])
+--------------------+-----+ | description|label| +--------------------+-----+ |Contributing Edit...| 1| | I live in Texas| 0| |Fresh E3 rumours ...| 0| |''The 'Hello Worl...| 0| |Proud West Belcon...| 1| |Hello, I am here ...| 0| |Meow! I want to t...| 0| |I have something ...| 0| |I have more than ...| 0| |If you have to st...| 13| |I am a twitterbot...| 1| |Designing and mak...| 0| |Host of Vleeties ...| 0| |Benefiting Refuge...| 0| |Access Hollywood ...| 0| |Producer/Songwrit...| 0| |CEO @Shapeways. I...| 0| |Two division UFC ...| 0| |Moderator of @mee...| 0| |Tweeting every le...| 0| +--------------------+-----+ only showing top 20 rows
✅ Task :
Configure an ML pipeline, which consists of three stages: tokenizer, hashingTF, and lr:
Use the next code sample and adjust it to your needs :
tokenizer = Tokenizer(inputCol="description", outputCol="words")
hashingTF = HashingTF(inputCol=tokenizer.getOutputCol(), outputCol="features")
lr = LogisticRegression(maxIter=10, regParam=0.001)
pipeline = Pipeline(stages=[tokenizer, hashingTF, lr])
After we understand that Linear Regression might not be good enough for our data science purposes, we are going to work with Logistic Regression. This is your 3rd Machine Learning model with Spark ML 🎉
# Configure an ML pipeline, which consists of three stages: tokenizer, hashingTF, and lr.
tokenizer = Tokenizer(inputCol="description", outputCol="words")
hashingTF = HashingTF(inputCol=tokenizer.getOutputCol(), outputCol="features")
lr = LogisticRegression(maxIter=10, regParam=0.001)
pipeline = Pipeline(stages=[tokenizer, hashingTF, lr])
Call fit on Pipeline to get the model:
If it fails here, validates that description
doesn't have null values.
Our HashingTF doesn't know how to handle null values.
If those exist, create a new DataFrame without them and use the new DataFrame to build the model.
In case you need it:
trainingData = trainingData.dropna('description')
Note! you might get an execption here If you do get out of memory exception here, this is because you need more memory to run this excersice. What you can do - make sure the other notebooks are closed and shutdown. If you uncertain on how to do that, ask in the chat! You can also sample the trainingData with:
trainingData = trainingData.sample(fraction=0.5, seed=3)
Feel free to play with the fraction according the the available memory.
trainingData.count()
1708
# remove this if you have enough memory on your machine / running in a distributed setting
trainingData = trainingData.sample(fraction=0.01, seed=3)
trainingData.count()
23
# Fit the pipeline to training documents.
model = pipeline.fit(trainingData)
Make predictions:
# Make predictions on test documents and print columns of interest.
prediction = model.transform(testData)
selected = prediction.select("description", "probability", "prediction")
for row in selected.collect():
description, prob, prediction = row
print("(%s) --> prob=%s, prediction=%f" % (description, str(prob), prediction))
( CA""") --> prob=[0.8773797667315911,0.12262023326840887], prediction=0.000000 ( England""") --> prob=[0.8773797667315911,0.12262023326840887], prediction=0.000000 ( NV""") --> prob=[0.8773797667315911,0.12262023326840887], prediction=0.000000 ("""Affordable & Professional Printing Services for Businesses & Individuals. For Cheapest Printing Prices Mail us sales@print365.ie #printing #Ireland""") --> prob=[0.9358968200892436,0.06410317991075642], prediction=0.000000 ("""Follow and tweet us) --> prob=[0.882717793416593,0.11728220658340704], prediction=0.000000 ("""International fanbase for Running Man ___ Variety Show. For enquires email runningmantown@gmail.com #7012""") --> prob=[0.9358968200892436,0.06410317991075642], prediction=0.000000 ("""Rare and strong PokŽmon in Las Vegas and Spring Valley. See more PokŽmon at https://t.co/GB4nYu29n3""") --> prob=[0.9249361164527934,0.07506388354720661], prediction=0.000000 (#Muslim #Arab #American #husband #Father #politicaljunkie,#Liberal #Democratic,#LoveHistory #environmentalis #Humanitarian #Businesstilldeath,#Icecream #stopwar) --> prob=[0.893121763463405,0.106878236536595], prediction=0.000000 (#RNomics, #RNA biology, RNA #bioinformatics, #RNA_World & #evolution, technologies & resources, daily papers: https://t.co/UaH05PfPIt & https://t.co/cuczvAA7dr) --> prob=[0.893121763463405,0.106878236536595], prediction=0.000000 (#bot. Retweets once every hour. @PistachioRoux and @Jonbro. Thanks to all the botmakers, bots, and @botALLY!) --> prob=[0.9404484894494282,0.05955151055057184], prediction=0.000000 ('Integrity Blues' Out Now) --> prob=[0.9532656184405969,0.046734381559403126], prediction=0.000000 ('St. Vincent' out now. Buy: http://t.co/ATBUTO9WIo | | Tour Tickets: http://t.co/WLPLabRm2d) --> prob=[0.983751865430793,0.01624813456920704], prediction=0.000000 (100k+ YouTube subs. I delete most of my tweets. https://t.co/JY3n1qms2N) --> prob=[0.6558883135464547,0.3441116864535453], prediction=0.000000 (2-time Emmy award & Grammy award-winning comedian. Order my new book, Kathy Griffin's Celebrity Run-Ins: My A-Z Index now! #GriffinTellsAll) --> prob=[0.8080917346030394,0.1919082653969606], prediction=0.000000 (@gyuterte @gyuterte @gyuterte @gyuterte @gyuterte @gyuterte @gyuterte @gyuterte @gyuterte @gyuterte @gyuterte @gyuterte @gyuterte @gyuterte @gyuterte @gyuterte) --> prob=[0.893121763463405,0.106878236536595], prediction=0.000000 (A hardcore gamer and passionate game developer! Loves to make friends!... 245F58FFE) --> prob=[0.8745749792063269,0.12542502079367313], prediction=0.000000 (A list of hitherto-unpublished Harper Lee novels. Bot by @hugovk, inspired by https://t.co/5gF8HFq0NT) --> prob=[0.8802816671974651,0.11971833280253485], prediction=0.000000 (A place for fans to connect! Tweeting from Mumbai and the world! #Bollywood #India #Parody) --> prob=[0.8286345588156474,0.1713654411843526], prediction=0.000000 (Actor, Simpsons voice guy and Determined to Succeed supporter) --> prob=[0.8808033816018553,0.11919661839814466], prediction=0.000000 (Add #Yoda to any tweet for a quote, or chat about characters in Star Wars. A twitter bot developed using Microsoft .NET by Michael Urvan at http://t.co/5AFtnH5C) --> prob=[0.9844780528532179,0.015521947146782122], prediction=0.000000 (After 11 years at TED, I'm now launching a content incubator (watch this space...). I tweet about media, technology, science, culture & ideas.) --> prob=[0.9973080884901204,0.0026919115098795743], prediction=0.000000 (American bassist, singer, record producer, music manager, and former A&R executive. He is best known as a judge on American Idol and Executive Producer.) --> prob=[0.9872865571262998,0.012713442873700243], prediction=0.000000 (Author) --> prob=[0.893121763463405,0.106878236536595], prediction=0.000000 (Author of novels PANORAMA CITY and THE INTERLOPER. Also, photog behind SLOW PAPARAZZO. And aka Gandhi Rockefeller. Stories in A Public Space, Paris Review, etc.) --> prob=[0.8383757233591841,0.1616242766408159], prediction=0.000000 (Banana Pudding Expert. Co-Creator of +ONE.) --> prob=[0.8734138872110819,0.12658611278891807], prediction=0.000000 (Because even in a sunken, dead city things can be lonely. By @polm23) --> prob=[0.9628411995879165,0.037158800412083526], prediction=0.000000 (Bridging the gap between telecommunications construction & engineering.) --> prob=[0.892267705593487,0.107732294406513], prediction=0.000000 (CDC Emergency Preparedness and Response: increasing the nation's ability to prepare for and respond to public health emergencies.) --> prob=[0.8954031601816987,0.10459683981830126], prediction=0.000000 (CEO & Science Based Sales Trainer, Sales Keynote Speaker, Sales Coach, Sales Behavioral Strategist) --> prob=[0.9227384574417433,0.07726154255825668], prediction=0.000000 (CEO @Niosocial USA. Also love art, photography, Haiku and food :) Passionate about helping women in tech) --> prob=[0.9466254638006859,0.05337453619931409], prediction=0.000000 (Curation and content @kickstarter. Writer, editor, Canadian.) --> prob=[0.932646323657362,0.067353676342638], prediction=0.000000 (Daily tips on how to stop the crisis in the humanities. Real solutions! (Machine Generated by @samplereality)) --> prob=[0.8995300905013893,0.10046990949861068], prediction=0.000000 (Dashboard Confessional Tix available NOW for our up close and personal tour in Jan and Feb of 2017: https://t.co/Bj0G7NIhy1) --> prob=[0.9344855119566707,0.06551448804332927], prediction=0.000000 (Designing and making games @nytdesign _Ò side project haver _Ò see also: @itsthisyear, @icebergdotcool, @ramsophone, @__birds__ _Ò) --> prob=[0.882717793416593,0.11728220658340704], prediction=0.000000 (DirComConnected /AlwaysOn/) --> prob=[0.893121763463405,0.106878236536595], prediction=0.000000 (Don't Panic!) --> prob=[0.893121763463405,0.106878236536595], prediction=0.000000 (Editor @deystreet. Co-host of the @killgenrenyc reading series. Editor @thescofieldmag. Poet and fiction writer.) --> prob=[0.8603212364637024,0.1396787635362976], prediction=0.000000 (Entrepreneur, mentor, angel investor (and mom of twins!) coaching women entrepreneurs and leaders.) --> prob=[0.861389210087128,0.138610789912872], prediction=0.000000 (Ever wondered what a thing is? Follow this ontography machine, powered by tweets proclaiming that something 'is apparently a thing.' By @staeiou) --> prob=[0.967274698794533,0.03272530120546702], prediction=0.000000 (Every website from the Internet International Directory, published 1995 (many dead links)) --> prob=[0.9201809937917655,0.07981900620823446], prediction=0.000000 (Exploring the future as CEO of @cmykvc, a marketing agency and community of startups, investors, brands, and people improving the state of the planet.) --> prob=[0.9488927310948588,0.05110726890514117], prediction=0.000000 (Fast and steady wins the race.) --> prob=[0.8817916016170693,0.11820839838293073], prediction=0.000000 (Fav and RT the superior word in each matchup. Goooal! A tribute to @everyword / @aparrish by @johnholdun) --> prob=[0.8585884720687204,0.14141152793127965], prediction=0.000000 (Finally, more businessmen. // bot by @ckolderup #botALLY) --> prob=[0.9386259968375767,0.06137400316242325], prediction=0.000000 (Founder & General Partner at Lakehouse Ventures.) --> prob=[0.9387585364283435,0.061241463571656496], prediction=0.000000 (Founder of the Founder Institute (https://t.co/JcT0pgQ5f8), TheFunded, 9x entrepreneur. Bio: https://t.co/OMiiJu6ukI) --> prob=[0.8724248402081877,0.12757515979181233], prediction=0.000000 (Front-end Architect) --> prob=[0.893121763463405,0.106878236536595], prediction=0.000000 (GO FOLLOW @CatarinaLiz BC SHE GAVE YOU THIS FREE FOLLOW) --> prob=[0.9929780146357263,0.007021985364273675], prediction=0.000000 (Good life, good health, good work.) --> prob=[0.893121763463405,0.106878236536595], prediction=0.000000 (Gruntled husband. Dogs of all sizes. 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Expert #blockchain / #ethereum. Founder https://t.co/ErnYAvl3U1. Cofounder @AssethFR & @laChainTech) --> prob=[0.9387585364283435,0.061241463571656496], prediction=0.000000 (Because memcached is magic. I'm a bot that updates every 3 hours. By @tinysubversions.) --> prob=[0.9779775575856997,0.02202244241430029], prediction=0.000000 (Blessed. Fortunate. Ready to Serve. https://t.co/CYt3cqFXtY) --> prob=[0.8753905218821927,0.12460947811780732], prediction=0.000000 (Book project: Towards Apparent Event Horizon) --> prob=[0.893121763463405,0.106878236536595], prediction=0.000000 (Bringing you the latest in #lncRNA scientific literature. #lincRNA #longnoncodingRNA #RNA #biochem #pubmed #science) --> prob=[0.9180274067785633,0.08197259322143668], prediction=0.000000 (CEO Rann Strategy Group. Visiting Professor King's College London. Former Australian Ambassador to Italy and High Commissioner to the UK. SA Premier 2002-2011.) --> prob=[0.9113231324598995,0.08867686754010051], prediction=0.000000 (Check out my new Plastik Magazine #cover on stands now!) --> prob=[0.9471520029608965,0.05284799703910348], prediction=0.000000 (Chief Scientist of Baidu; Chairman and Co-Founder of Coursera; Stanford CS faculty. #machinelearning, #deeplearning #MOOCs, #edtech) --> prob=[0.8368985599967691,0.1631014400032309], prediction=0.000000 (Co-Founder & CEO of Project Madison. Previously founded Stamped (acquired by Yahoo). Tried to write a screenplay once.) --> prob=[0.8901075987470296,0.1098924012529704], prediction=0.000000 (Congresswoman proudly representing Illinois's 13th district) --> prob=[0.893121763463405,0.106878236536595], prediction=0.000000 (Currently writing new music.. 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See me Tuesday nights on #SDLIVE on @USA_Network! #NeverGiveUp) --> prob=[0.964084199794078,0.035915800205922], prediction=0.000000 (The Official twitter account of Virat Kohli, Indian cricketer,gamer,car lover,loves soccer and an enthusiast) --> prob=[0.8603212364637024,0.1396787635362976], prediction=0.000000 (Tweeting the census one real american at a time. http://t.co/85PJrd9oMH) --> prob=[0.9347947974889629,0.06520520251103712], prediction=0.000000 (University of Michigan environmental engineering PhD candidate. Also a news, soccer, and policy nerd.) --> prob=[0.7574624315827984,0.24253756841720164], prediction=0.000000 (What if dinosaurs lived on exoplanets?! New exosaurs on the hour, named for random followers. Bot by @robdubbin.) --> prob=[0.990161811308929,0.009838188691071004], prediction=0.000000 (WordPress Theme and Plugin Developer. Loves programming and maths. I bite.) --> prob=[0.8876231198734573,0.11237688012654268], prediction=0.000000 (by @rainshapes) --> prob=[0.8810763324946292,0.11892366750537076], prediction=0.000000 (coagulated by @dead_cells) --> prob=[0.8810763324946292,0.11892366750537076], prediction=0.000000 (free follow courtesy of @dunblurs ‰_•ü) --> prob=[0.9035442038458593,0.09645579615414068], prediction=0.000000 (go subscribe to my channel http://t.co/tMLNHqUGp9) --> prob=[0.8100822827956369,0.18991771720436312], prediction=0.000000 (hello, i am a robot that tweets little life tips // avi from The Wise Robot Will Answer Your Questions Now by @tomgauld // bot by @thricedotted) --> prob=[0.8525032774947521,0.14749672250524792], prediction=0.000000 (hello,world) --> prob=[0.893121763463405,0.106878236536595], prediction=0.000000 (how i'd wish that were so.) --> prob=[0.8165286787184011,0.18347132128159893], prediction=0.000000 (how was the lot scene?) --> prob=[0.892267705593487,0.107732294406513], prediction=0.000000 (https://t.co/4p5lMa6faA @maxcollinsmusic @tonyfyeah @jonsiebels) --> prob=[0.893121763463405,0.106878236536595], prediction=0.000000 (https://t.co/E43z4AWe31) --> prob=[0.893121763463405,0.106878236536595], prediction=0.000000 (https://t.co/k5nleOQ50q / #MercerLaw) --> prob=[0.893121763463405,0.106878236536595], prediction=0.000000 (new to the twitter world) --> prob=[0.9381170734556583,0.06188292654434169], prediction=0.000000 (sea rise predictions / at-risk cities from the international panel on climate change. 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Please report any mistakes/misspellings/errors to @JadaManiscalco.) --> prob=[0.9502717075352324,0.04972829246476762], prediction=0.000000 (A transcriptome feed made using #Rstats. Updates made each day at 11:00 Eastern Daylight Time. Created by @davetang31. Image source https://t.co/RSExLBWZDc.) --> prob=[0.8937739490307323,0.10622605096926774], prediction=0.000000 (ATUALIZADO!!) --> prob=[0.893121763463405,0.106878236536595], prediction=0.000000 (Aggressive, spoiled, overweight and rude, antagonistic, often the catalyst for the plot, frequently insults Kyle for being Jewish and Kenny for being poor.) --> prob=[0.9389430789519572,0.061056921048042834], prediction=0.000000 (An actor, husband, dad and director living in LA. 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Followers will be picked at random to converse with dril.) --> prob=[0.9867435439277153,0.01325645607228465], prediction=0.000000 (Chief Correspondent and Editor-at-Large of Mashable, tech expert, social media commentator, amateur cartoonist and robotics fan.) --> prob=[0.8484205193214494,0.15157948067855065], prediction=0.000000 (Communications at @Reuters, Formerly @Mashable. Proud @HamiltonCollege alum. Opinions are my own.) --> prob=[0.8885097507079811,0.1114902492920189], prediction=0.000000 (Cooking and family are the greatest gifts. Love and Best Dishes, y'all!) --> prob=[0.8704446958278266,0.12955530417217342], prediction=0.000000 (Cuenta oficial del Real Madrid C.F., ENGLISH @realmadriden, FRAN‰AIS @realmadridfra, _______ @realmadridarab, ___ @realmadridjapan.) --> prob=[0.893121763463405,0.106878236536595], prediction=0.000000 (Don't click on that.) --> prob=[0.913544877195818,0.086455122804182], prediction=0.000000 (Every noun is an adjective noun under certain conditions. This bot generates jokes accordingly. Pic from: http://t.co/Gm58cmuKVv) --> prob=[0.996987900963021,0.0030120990369789657], prediction=0.000000 (FOLLOW @xx5sosvines I would really appreciate it :) and she'll follow back :)) --> prob=[0.9417395993083549,0.058260400691645065], prediction=0.000000 (Formed in Anaheim, California in 1986, No Doubt is @GwenStefani, @TomDumont, @TonyKanal and @AdrianYoungND.) --> prob=[0.9589004055286751,0.04109959447132494], prediction=0.000000 (Founder, President Udacity.) --> prob=[0.893121763463405,0.106878236536595], prediction=0.000000 (FreeWill April 29Th‰Û_•ü) --> prob=[0.893121763463405,0.106878236536595], prediction=0.000000 (Grammy award winning Writer, producer, artist and actor. I love all of my #LTTroopers #blessed) --> prob=[0.8415987354604597,0.1584012645395403], prediction=0.000000 (Here's a thing) --> prob=[0.887454789763049,0.11254521023695097], prediction=0.000000 (I dig variety acts, Pixar, puppets, prestidigitation, immersive theatre, game shows, theme parks, my family and great meals. Not necessarily in that order.) --> prob=[0.8761193099003898,0.12388069009961022], prediction=0.000000 (I read the terms that you do not) --> prob=[0.9360749232220507,0.06392507677794934], prediction=0.000000 (I'm a Markov chain bot based off almost 15 years of DRUDGE REPORT headlines. Made by @fredbenenson) --> prob=[0.9573016364407629,0.04269836355923706], prediction=0.000000 (I'm a bot that generates GIFs of the Sun's corona. Images courtesy of NASA/SDO and the AIA science team. Created by @ddbeck.) --> prob=[0.623229505331171,0.37677049466882895], prediction=0.000000 (I'm a shooting star leaping through the sky like a tiger defying the laws of gravity. There's no stopping me!) --> prob=[0.857855879147562,0.142144120852438], prediction=0.000000 (Inspired by @YesYoureRacist.) --> prob=[0.8810763324946292,0.11892366750537076], prediction=0.000000 (J-Rox Vocalist, Psychology student check J-Rox album Üó_Yume no MukaeÜó† (: https://t.co/Hv7iR1UII0) --> prob=[0.823076319183658,0.17692368081634202], prediction=0.000000 (Late adopter.) --> prob=[0.893121763463405,0.106878236536595], prediction=0.000000 (MAN THINGS THAT ONLY MEN UNDERSTAND // by @thricedotted) --> prob=[0.9049468695657119,0.09505313043428809], prediction=0.000000 (MakerBot's connected 3D printing solutions address the wider needs of professionals and educators, evolving their ideas from inspiration to innovation.) --> prob=[0.7619637055171558,0.23803629448284425], prediction=0.000000 (Making the world #PythonAwesome @ContinuumIO) --> prob=[0.892267705593487,0.107732294406513], prediction=0.000000 (Multiple award-winning journalist specializing in Middle East affairs & Islamic fundamentalism. @CNN'er for life! https://t.co/Kmt4rXLatW) --> prob=[0.916283807888,0.08371619211199999], prediction=0.000000 (NEW AARON CARTER MUSIC DOWNLOAD LINK LÌüVÌÇ EP https://t.co/aScmCVBsmN PR inquiries: Chad@emcbowery.com SnapChat | AxCarter) --> prob=[0.9795151286740217,0.020484871325978316], prediction=0.000000 (Napster, Plaxo, Facebook, Causes, Spotify & Airtime = https://t.co/pEfbUoDYae) --> prob=[0.9209868498586062,0.07901315014139376], prediction=0.000000 (Never forget the three powerful resources you always have available to you: love; prayer; and forgiveness.) --> prob=[0.9688431506677346,0.03115684933226537], prediction=0.000000 (New and improved(?) 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Sources: http://t.co/jV9djIR0Kp) --> prob=[0.914807916078424,0.08519208392157596], prediction=0.000000 (Oregon farmboy turned NY Times columnist, co-author of Half the Sky & @APathAppears, https://t.co/bcxQaJ7Po4 https://t.co/Bw42D7ya1b) --> prob=[0.6639824163438252,0.33601758365617485], prediction=0.000000 (Owner Of GLAM Beverly Hills,Model, Actress, Business Woman, Playmate, Reality Star, Brand, CEO, #Xtina Instagram @KristinaShannon1 SnapChat glamxtina) --> prob=[0.9079287795877593,0.0920712204122407], prediction=0.000000 (Private job) --> prob=[0.893121763463405,0.106878236536595], prediction=0.000000 (Producer @Buzzfeed STSF, previously @Mashable. Cat enthusiast, @marvel fanboy, master emojifier, new to LA but a New Yorker at heart. 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Records we miss u chu) --> prob=[0.927098686814238,0.07290131318576198], prediction=0.000000 (THEY'RE THERE) --> prob=[0.893121763463405,0.106878236536595], prediction=0.000000 (The Leading Applied Artificial Intelligence Summit. Built for the AI ecosystem by the AI ecosystem. #artificialintelligence #ai) --> prob=[0.8908758797761793,0.10912412022382068], prediction=0.000000 (The official feed of http://t.co/4ViY2vemvM. Find my personal tweets at @mgsiegler.) --> prob=[0.7764535417985758,0.22354645820142416], prediction=0.000000 (This is what I do. I drop truth bombs.) --> prob=[0.9960211945199332,0.003978805480066794], prediction=0.000000 (This! Is! Jeopardy! I'm your host Alex Trebek and love wrong, she catches a train, literally. (by @alicemazzy)) --> prob=[0.9107458951230925,0.08925410487690755], prediction=0.000000 (Tweet me a picture and I'll #deepdream it for you! Tweet '-me' and I'll dream your avatar! List of commands here: http://t.co/BRfLYAkWBA) --> prob=[0.21232237001984647,0.7876776299801536], prediction=1.000000 (Twitterbot of #Parasitology papers in PubMed, bioRxiv, and PeerJ PrePrints. Image from http://t.co/zYv7ygHs2U. Run by @FilipHusnik) --> prob=[0.285577759358297,0.714422240641703], prediction=1.000000 (Uma Osbourne ní£o reconhecida...) --> prob=[0.893121763463405,0.106878236536595], prediction=0.000000 (We are the music makers, and we are the dreamers of dreams...) --> prob=[0.9617000127806578,0.038299987219342246], prediction=0.000000 (We're The Fall of Troy from Mukilteo, WA. Our new album OK is available at our website for whatever you want to pay.) --> prob=[0.9933427256364186,0.0066572743635814335], prediction=0.000000 (Wife åá Mother åá Decorator åá Cook åá PartyThrower åá Singer åá Producer åá Writer åá Musician åá Whew!) --> prob=[0.893121763463405,0.106878236536595], prediction=0.000000 (With roots from beautiful Sicily, the Tantillo family brings you old world favorites. Shop online with a trusted and respected name in fine food products.) --> prob=[0.9670542635948168,0.03294573640518317], prediction=0.000000 (Working as a community to change the low, loud & frequent flight patterns from LGA that disrupt neighborhoods in NE Queens & beyond. http://queensquietskies.) --> prob=[0.7670080555370218,0.23299194446297822], prediction=0.000000 (Writer Teacher Permission Giver) --> prob=[0.893121763463405,0.106878236536595], prediction=0.000000 (Your diac No) --> prob=[0.893121763463405,0.106878236536595], prediction=0.000000 (a bot that says nice things to the people who follow it. made by @hypatiadotca at @hackerschool.) --> prob=[0.9827628267043063,0.017237173295693742], prediction=0.000000 (beep. I'm a bot. what? whose bot? Shadow's. that's whose bot I am! now go and make me a sandwich!) --> prob=[0.8882173817679297,0.11178261823207025], prediction=0.000000 (bot by @rubicon) --> prob=[0.914122803434725,0.08587719656527504], prediction=0.000000 (highly frequent flyer //åÊbotåÊbyåÊ@inky) --> prob=[0.893121763463405,0.106878236536595], prediction=0.000000 (https://t.co/giCF8ETVoY is mad about Cycling & Getting Fit from Cycling. Check us out for new & chat about Cycling, Traning & Nutrition. Lke us on Facebook!) --> prob=[0.9972254390074405,0.0027745609925594605], prediction=0.000000 (i'm a bot that mirrors the latest posts from /r/me_irl by @corbindavenport.) --> prob=[0.8620842789530636,0.1379157210469364], prediction=0.000000 (snail half dedicated to @inky // complaints to @beaugunderson) --> prob=[0.8895652291789294,0.11043477082107056], prediction=0.000000 (sound sculpture, experimental music, installation. twitter projects: @pentametron, @earwormreport, more: http://t.co/yYOdxC7R1B) --> prob=[0.893121763463405,0.106878236536595], prediction=0.000000 ( SC""") --> prob=[0.8773797667315911,0.12262023326840887], prediction=0.000000 ( SC""") --> prob=[0.8773797667315911,0.12262023326840887], prediction=0.000000 ( Switzerland""") --> prob=[0.8773797667315911,0.12262023326840887], prediction=0.000000 ("""- Official tweets and pictures from the Production Team of #Merlin -""") --> prob=[0.7652781599328708,0.23472184006712915], prediction=0.000000 ("""Running Man ___""") --> prob=[0.893121763463405,0.106878236536595], prediction=0.000000 ("""Wave Sharing Service""") --> prob=[0.893121763463405,0.106878236536595], prediction=0.000000 ('Bad As Me' Available Now. Tweets courtesy of ANTI- Records) --> prob=[0.9167645989110735,0.08323540108892646], prediction=0.000000 (??????????????? ????) --> prob=[0.893121763463405,0.106878236536595], prediction=0.000000 (@Enews Anchor &CEO @afterbuzztv.creator 2x NYTimes Best Selling Author.@siriusXM host+New book out: The EveryGirl's Guide To Cooking https://t.co/EBPveWU20P) --> prob=[0.8904898391135612,0.10951016088643883], prediction=0.000000 (@TriangleWC . WFHS Wrestling . NCUSA Wrestling) --> prob=[0.6871186856311563,0.3128813143688437], prediction=0.000000 (A bot by @tinysubversions) --> prob=[0.9094564733432482,0.0905435266567518], prediction=0.000000 (A bot created by @Gen6Games to retweet anything related to #indiedev #Unity3D) --> prob=[0.7178743312036083,0.28212566879639167], prediction=0.000000 (A feed for papers related to pooled-, population-, or evolved and re- sequencing published in NCBI, ArXiv, and bioArxiv.) --> prob=[0.5322216105339934,0.4677783894660066], prediction=0.000000 (A twitter-based port of Lights Out. Tweet play to play or help for help. Another @_ARP bot.) --> prob=[0.8941720366264239,0.10582796337357614], prediction=0.000000 (Aerie is bras, undies, swim and more! Why retouch beauty? 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Digital Journalist + Author of The Frugalista Files: How One Woman Got Out of Debt Without Giving Up the Fabulous Life @frugalista on Instagram) --> prob=[0.938051910344515,0.06194808965548504], prediction=0.000000 (Get VIP packages + tickets for our 2016 U.S.15th anniversary tour + buy our acoustic CD/DVD at https://t.co/jULQnelq5X #TheUsed15) --> prob=[0.9658261081370086,0.03417389186299136], prediction=0.000000 (Get the latest BLONDIE news at http://t.co/LIHTvKT3h7!) --> prob=[0.969099240304243,0.030900759695757007], prediction=0.000000 (Goal ciizen) --> prob=[0.893121763463405,0.106878236536595], prediction=0.000000 (Hago m̼sica y shows. (lo ̼ltimo...dentro y fuera del escenario)) --> prob=[0.893121763463405,0.106878236536595], prediction=0.000000 (Hi my name is Sydney ilove my family and friends) --> prob=[0.865273026446687,0.13472697355331298], prediction=0.000000 (I am a bot that generates lifehackish tweets. built by @mitchc2) --> prob=[0.8617455406165022,0.13825445938349779], prediction=0.000000 (I'm a robot so landscapes don't make any sense to me. Instead I will diligently sort their pixels. Much better! Tweets every two hours.) --> prob=[0.98741659796181,0.012583402038189972], prediction=0.000000 (Instagram: @robkardashian) --> prob=[0.893121763463405,0.106878236536595], prediction=0.000000 (Kevin Chin is the founder of Arowana & Co and the Managing Director at Arowana International Limited in North Sydney, Australia.) --> prob=[0.9722463144033826,0.027753685596617395], prediction=0.000000 (L5M is a provider of advanced software analytical services to the media industry. Clients include the largest firms in the industry. Please follow our progress.) --> prob=[0.9635468980882477,0.03645310191175233], prediction=0.000000 (Lip Sync Battle app: https://t.co/BNKD0JIHjm) --> prob=[0.893121763463405,0.106878236536595], prediction=0.000000 (MDGP Resident, TUTH, Maharajgunj) --> prob=[0.893121763463405,0.106878236536595], prediction=0.000000 (Melanin. Enough said!) --> prob=[0.893121763463405,0.106878236536595], prediction=0.000000 (Mom, wife, leader, thinker, doer, wave rider. CEO @pagerduty, board member @puppetize) --> prob=[0.9227384574417433,0.07726154255825668], prediction=0.000000 (Multipliquei-me para me sentir.) --> prob=[0.809269615007597,0.19073038499240302], prediction=0.000000 (Nothing witty to say the least. I try to write funny things, I work for a billion dollar fruit stand, and more please is always the correct answer.) --> prob=[0.9744242299985684,0.02557577000143163], prediction=0.000000 (PRODUCTS FOR MEN!! A bot by @NoraReed. Thanks to @zachwhalen, @GalaxyKate and @v21.) --> prob=[0.9215093233761149,0.07849067662388509], prediction=0.000000 (Papers related to the covalent DNA modification 5-hydroxymethylcytosine, from pubmed and bioR?iv) --> prob=[0.5017088437907311,0.4982911562092689], prediction=0.000000 (Play with mentions by including either droite/right/‰_Á/haut/up/‰Â /gauche/left/‰ÂÉ/bas/down/‰Âà. The longer the snake the faster the game! 7am-22pm CEST, Mon-Fri.) --> prob=[0.9195521596053351,0.0804478403946649], prediction=0.000000 (Queen's NRI Hospital, a 380 bed multi-specialty acute-cum-critical care referral hospital, is one of the most well-equipped and premier hospitals in Vizag.) --> prob=[0.9063954023760274,0.09360459762397255], prediction=0.000000 (Senior Vice President, Apple Retail. Inspired by great people, great brands and great companies.) --> prob=[0.8696709153770172,0.13032908462298276], prediction=0.000000 (Shout out to all the disambiguation pages out there! from @saranrapjs) --> prob=[0.9489882271256995,0.051011772874300476], prediction=0.000000 (Software developer, home brewer, nerd.) --> prob=[0.893121763463405,0.106878236536595], prediction=0.000000 (Streaming freshly published papers in the field of T cell biology from all relevant journals. With us you will not miss any important discovery anymore!) --> prob=[0.9434953037806147,0.05650469621938525], prediction=0.000000 (TURN ON NOTIFICATIONS‰Ï¬) --> prob=[0.913544877195818,0.086455122804182], prediction=0.000000 (Take it sleazy.) --> prob=[0.893121763463405,0.106878236536595], prediction=0.000000 (Tay Dizm Twitter) --> prob=[0.893121763463405,0.106878236536595], prediction=0.000000 (Thai freelance artist. Inspired by Japanese Manga & Anime.) --> prob=[0.8810763324946292,0.11892366750537076], prediction=0.000000 (The Web Watchdog of the Second Citizenship Industry) --> prob=[0.8714292030977909,0.12857079690220907], prediction=0.000000 (The official Stephen Cannell Twitter page features news on mystery novels & mystery books.) --> prob=[0.9376828607913436,0.06231713920865645], prediction=0.000000 (The official Twitter account of the United States Marine Corps. The appearance of links does not constitute endorsement.) --> prob=[0.8985953368832724,0.10140466311672758], prediction=0.000000 (Tweeting quotes of 1 Bitcoin on Bitstamp, BTC-e and Mt.Gox and spreads between them every 30 mins. Donations are welcome: 1FbxmPa8y8LvkgwQy6T7dbhBpq96y9LkY8) --> prob=[0.92311038095673,0.07688961904326996], prediction=0.000000 (TwitterBot for papers in ancient DNA and human genomics from arXiv, bioRxiv, & Pubmed. Curated by @bigskybioarch) --> prob=[0.5457726115411623,0.4542273884588377], prediction=0.000000 (UFC CHAMPION. Actor. Stuntman. Fox Sports Analyst. Follow T-Wood on Twitter. The New School Ninja with futuristic skills!) --> prob=[0.9915735381332184,0.008426461866781643], prediction=0.000000 (Unencumbered by time and space) --> prob=[0.8696709153770172,0.13032908462298276], prediction=0.000000 (Unofficial bot featuring a random selection of images from the Metropolitan Museum of Art's collection of Open Access images. (Maintained by @benrasmusen)) --> prob=[0.23496298408408123,0.7650370159159188], prediction=1.000000 (What if your spellbook was ridiculously precise? A project of @muffinista #botALLY) --> prob=[0.957713987570446,0.04228601242955399], prediction=0.000000 (artisanal bot by @Objelisks) --> prob=[0.914122803434725,0.08587719656527504], prediction=0.000000 (black eil bries arebae) --> prob=[0.893121763463405,0.106878236536595], prediction=0.000000 (bot by @ckolderup ‰Û¢ #botALLY) --> prob=[0.9386259968375767,0.06137400316242325], prediction=0.000000 (colors. all of 'em. | developed by @vogon; feel free to send him feedback/feature requests) --> prob=[0.90155882627647,0.09844117372353], prediction=0.000000 (hi i know things, i'll teach you some of them. bot by @cblgh) --> prob=[0.8335457077093111,0.16645429229068887], prediction=0.000000 (https://t.co/KfP3JDLj6x) --> prob=[0.893121763463405,0.106878236536595], prediction=0.000000 (im a deer and im sometimes doing deer stuff (this is a bot account!)) --> prob=[0.968150607071904,0.031849392928095965], prediction=0.000000 (it's all happening) --> prob=[0.948910155713201,0.05108984428679897], prediction=0.000000 (labor organizer _ seizing the memes of production) --> prob=[0.8724248402081877,0.12757515979181233], prediction=0.000000 (life's a beach man) --> prob=[0.887454789763049,0.11254521023695097], prediction=0.000000 (student) --> prob=[0.893121763463405,0.106878236536595], prediction=0.000000 (Κë_Κ_Ÿ©_ôî) --> prob=[0.893121763463405,0.106878236536595], prediction=0.000000 ( IN""") --> prob=[0.8773797667315911,0.12262023326840887], prediction=0.000000 ( VA""") --> prob=[0.8773797667315911,0.12262023326840887], prediction=0.000000 ("""33""") --> prob=[0.893121763463405,0.106878236536595], prediction=0.000000 ("""Every Mon-Tues at 10 PM KST | Jisung) --> prob=[0.9508409969310945,0.04915900306890553], prediction=0.000000 ("""Tengah baca Novel #KeranaKauBidadari & #CintaDuaZaman -- Tak buat tweet berbayar --""") --> prob=[0.893121763463405,0.106878236536595], prediction=0.000000 ("""there is NO FLASH without Iris West __ #love __""") --> prob=[0.8127167468180667,0.18728325318193328], prediction=0.000000 (@indexventures, @Dropbox, Startups, Physics.) --> prob=[0.893121763463405,0.106878236536595], prediction=0.000000 (@realajavolkman, @danielepand, @richk) --> prob=[0.893121763463405,0.106878236536595], prediction=0.000000 (A bot by @RandomOutput. Tweets what people on twitter think it 'Tis the Season for. Deactivated until it 'Tis the season once again.) --> prob=[0.9761170870175652,0.023882912982434834], prediction=0.000000 (A chat bot trying to become the most intelligent bot on the Internet. Can answer any What is question by looking up the answer on the Internet.) --> prob=[0.9914168661933054,0.00858313380669462], prediction=0.000000 (Actor) --> prob=[0.893121763463405,0.106878236536595], prediction=0.000000 (Actor ... well at least some are STILL saying so !!) --> prob=[0.9858268680970887,0.014173131902911251], prediction=0.000000 (Actor, Entrepreneur and an agnostic ambivert.) --> prob=[0.882717793416593,0.11728220658340704], prediction=0.000000 (Amenizando toda essa loucura!) --> prob=[0.893121763463405,0.106878236536595], prediction=0.000000 (An Outlier,Twice over!) --> prob=[0.893121763463405,0.106878236536595], prediction=0.000000 (By @zhaytee // Learn how to play here: https://t.co/a38R7wz9tO) --> prob=[0.9313219925415062,0.06867800745849384], prediction=0.000000 (CEO of https://t.co/nMwbrcvMaq and previously co-founder of https://t.co/z3ULIP6alR. Cal, Columbia, Y Combinator alum) --> prob=[0.8800018121492451,0.11999818785075489], prediction=0.000000 (CHS Senior_ô_ sc- kaitlynbrookeh) --> prob=[0.9854768312444194,0.014523168755580573], prediction=0.000000 (Cease and Desist Bot does not approve of your use of common words and phrases. created to provide Justice by @spine_cone and @nate_smith.) --> prob=[0.8100149626552063,0.18998503734479366], prediction=0.000000 (Chief Technology and Strategy Officer for Cisco. Investments and M&A. Love art, photography, Haiku and food :) Passionate about helping women in tech) --> prob=[0.9300996958380071,0.06990030416199289], prediction=0.000000 (Comedian. Writer. Been cheering up people since 2010.) --> prob=[0.952747284456767,0.04725271554323296], prediction=0.000000 (Connecting service design and customer experience to transform brands and grow businesses.) --> prob=[0.869375667979757,0.13062433202024304], prediction=0.000000 (Covering the White House for The New York Times) --> prob=[0.9657713802543938,0.034228619745606204], prediction=0.000000 (Curtindo a vida da maneira mas exata e simples!! legal simpatico e Unico Cheio de amor pra da!!!) --> prob=[0.9775477663868208,0.022452233613179162], prediction=0.000000 (Daily RSS feed for papers on Synthetic Lethality in Cancer. cancer AND (synthetic lethal OR synthetic lethality). Largely automated, don't expect responses) --> prob=[0.6417317108934748,0.3582682891065252], prediction=0.000000 (Definirse es limitarse.) --> prob=[0.893121763463405,0.106878236536595], prediction=0.000000 (Design Thinker, Dev Ops Practitioner, Lean Coach.) --> prob=[0.893121763463405,0.106878236536595], prediction=0.000000 (Disruptive Tech |Social Entrepreneurism #Empathy #Impact #Innovation #Resilience #Tolerance) --> prob=[0.8773797667315911,0.12262023326840887], prediction=0.000000 (Download 'The Promise' at https://t.co/oBMFkVGfCn) --> prob=[0.9553205201224667,0.04467947987753329], prediction=0.000000 (Everyone's looking for the Citizen Kane of games. Why is no one looking for these instead?) --> prob=[0.9741730980370173,0.02582690196298265], prediction=0.000000 (Father. Writer. Producer. Actively seeking to become wiser everyday.) --> prob=[0.8913562750574823,0.10864372494251773], prediction=0.000000 (Following the formula: I'm not X but I am the definition of X. (via @stefanhayden) Tweets every 10 minutes.) --> prob=[0.9667293566503095,0.03327064334969054], prediction=0.000000 (For over 20 years, we've been giving students in need the right resources to ensure lifelong success ‰Û¢ Home of Unexpected Champions #ReachMillions) --> prob=[0.9400582170121166,0.05994178298788344], prediction=0.000000 (Hard Rock Habibi) --> prob=[0.893121763463405,0.106878236536595], prediction=0.000000 (Hi I'm Howie D from the Backstreet Boys.) --> prob=[0.8142694589952633,0.18573054100473674], prediction=0.000000 (Hi;) My login is Duren24 on https://t.co/9qoFAsKgBy) --> prob=[0.9672868079813571,0.03271319201864287], prediction=0.000000 (I am the Sigmund Freud of cleansing tissues, the Friedrich Nietzsche of somersaulting, the Spider Man of killin') --> prob=[0.8421780281565214,0.15782197184347857], prediction=0.000000 (I live in brooklyn, I'm a bike messenger, I play in a band, I'm uniquely doing the same exact things as everyone else) --> prob=[0.9847074429865382,0.015292557013461772], prediction=0.000000 (I retweet questions followed by answers. Brought to you by @rfreebern.) --> prob=[0.9305588070199373,0.06944119298006268], prediction=0.000000 (I'm a bot Tweeting newly published papers on metagenomics. Powered by IFTTT.) --> prob=[0.8654925552288218,0.13450744477117815], prediction=0.000000 (I'm a bot that tweets images from the NASA/USGS Landsat program. Managed by @mewo2.) --> prob=[0.7372935964916221,0.26270640350837793], prediction=0.000000 (If I could stay in the shower all day, I would.) --> prob=[0.9655698099234211,0.03443019007657888], prediction=0.000000 (In case you missed any important events of the day, let's rewind a bit.) --> prob=[0.9193769064545558,0.08062309354544417], prediction=0.000000 (Instagram: @GeneSimmons) --> prob=[0.893121763463405,0.106878236536595], prediction=0.000000 (It is a thousand times better to have common sense without education than to have education without common sense.) --> prob=[0.9599941164178986,0.04000588358210144], prediction=0.000000 (Its a BOT which retweets the tweets by Hackers & Hacking News around the globe. Made to connect Hackers around the Globe. This BOT is made by @biswassudipta05) --> prob=[0.9985450177353461,0.0014549822646539035], prediction=0.000000 (Laugh as long as you breathe, love as long as you live!) --> prob=[0.963737329464425,0.03626267053557497], prediction=0.000000 (Literature bot searching arXiv, biorXiv, PeerJ & PubMed for supercoiling papers. Maintained by @red_gravel.) --> prob=[0.9336424414684805,0.06635755853151948], prediction=0.000000 (MH. 6.3.16.) --> prob=[0.893121763463405,0.106878236536595], prediction=0.000000 (Middle age is youth without levity; and age without decay.) --> prob=[0.9596139045045757,0.04038609549542427], prediction=0.000000 (Nessa sofríÈncia que í© amar vocíÈ) --> prob=[0.893121763463405,0.106878236536595], prediction=0.000000 (Oscar: Best Actress, West Wing, L Word, Switched at Birth, Spring Awakening. Speaker, Author, Advocate. Contact: https://t.co/6JRljjfPr3) --> prob=[0.7160269758497559,0.28397302415024406], prediction=0.000000 (Oxford grad student interested in all things law, finance, & tech) --> prob=[0.9657512187617415,0.034248781238258474], prediction=0.000000 (Penguin Random House Audio's BOT & Listening Library marketing team...tweeting the sweet sounds of #audiobooks to librarians & listeners of all ages.) --> prob=[0.9183090316892588,0.08169096831074119], prediction=0.000000 (Randomly generating the deaths of anyone who follows this account. By @irondavy.) --> prob=[0.9868957998507047,0.013104200149295253], prediction=0.000000 (Recent meiosis papers from pubmed RSS and bioR?iv. Maintained by @pmcarlton) --> prob=[0.3366766032593189,0.6633233967406811], prediction=1.000000 (Replacement for @FalseFlagBot R.I.P. #nWo #illuminati #falseflag) --> prob=[0.9169813604121188,0.08301863958788125], prediction=0.000000 (Rocket Ship Builder (that was Twitter's default option... how perfect)) --> prob=[0.893121763463405,0.106878236536595], prediction=0.000000 (SACE Foundation//China-Africa Analyst since 2008//Chinese national//3+ year work and research experience in Kenya, South Africa, Ghana) --> prob=[0.881769465633422,0.11823053436657804], prediction=0.000000 (Scholar alerts on transcriptional noise, made by @biochemistries) --> prob=[0.9355782651612565,0.06442173483874347], prediction=0.000000 (So in love with Jesus ? Married to the love of my life @jeffhgala, Traveler, Executive Assistant @lifestonepgh, Grad Student, TX-PA ?) --> prob=[0.8824654361887755,0.11753456381122451], prediction=0.000000 (Sociopathic straight edge atheist sweetheart with no Facebook account.) --> prob=[0.9303651277303465,0.06963487226965348], prediction=0.000000 (Technology Manager at NBC10 & Telemundo62) --> prob=[0.9387585364283435,0.061241463571656496], prediction=0.000000 (Test Practice Manager | Editor @TestingTrapeze | Co-Founder @WeTestNZ | International Speaker | Writer https://t.co/MensMzCGPj) --> prob=[0.9549224071496439,0.04507759285035606], prediction=0.000000 (The Bridge is a Brooklyn-based website dedicated to covering business in New York City's most populous and innovative borough.) --> prob=[0.9684331683538951,0.031566831646104854], prediction=0.000000 (The Official Sara Evans Twitter Page) --> prob=[0.892267705593487,0.107732294406513], prediction=0.000000 (The Place To Learn Anything, Anywhere For All) --> prob=[0.9381552131057639,0.061844786894236115], prediction=0.000000 (The artist makes artist statements out of everyone's tweets. Follow me and I might make one from yours. // by @ibogost) --> prob=[0.8024881852047513,0.1975118147952487], prediction=0.000000 (Tired of working? Hate your job? Wanna get out of debt? Want more money? Check it now! https://t.co/mLKVs1VANF) --> prob=[0.8796535097485044,0.12034649025149557], prediction=0.000000 (Tweet a selfie to this bot, get a mustached version back. Twin of @is_like_a and @thee_to.) --> prob=[0.9654797171498752,0.03452028285012476], prediction=0.000000 (Twitter API + Google Images + ImageMagick. Inspired by @akari_daisuki) --> prob=[0.7915819713828847,0.20841802861711534], prediction=0.000000 (TwitterBot collecting #synbio papers) --> prob=[0.6845683269539613,0.3154316730460387], prediction=0.000000 (Twitterbot for #Autophagy papers. Curated by @SoniaHall) --> prob=[0.8338935484857473,0.16610645151425274], prediction=0.000000 (UXer) --> prob=[0.893121763463405,0.106878236536595], prediction=0.000000 (Updates about clubs and their fluctuating baller populations. By @maxmechanic #botALLY) --> prob=[0.9410105129268387,0.05898948707316132], prediction=0.000000 (Very old person, 71, low energy, not interested in much at all, tired, worn, down and out.) --> prob=[0.9744348244245156,0.025565175575484433], prediction=0.000000 (You don't take a photograph; you make it.) --> prob=[0.835117336725671,0.164882663274329], prediction=0.000000 (ZONA OPERATIV DE DEFENSA NEGRAL NŒÁ 63) --> prob=[0.9275867035567315,0.07241329644326855], prediction=0.000000 (editor£Âfocus on auto industry) --> prob=[0.913544877195818,0.086455122804182], prediction=0.000000 (free follow from @3dclifford!! follow her if u would like :)) --> prob=[0.9356506822561801,0.06434931774381991], prediction=0.000000 (generating cards for a mysterious card game åá bot by @ckolderup åá #botALLY åá avatar by Dmitriy Ivanov from the Noun Project) --> prob=[0.8987107823532476,0.10128921764675236], prediction=0.000000 (give me food...insta ---@JERM_beats) --> prob=[0.4614789030224901,0.5385210969775098], prediction=1.000000 (https://t.co/iIaCHwWybn) --> prob=[0.893121763463405,0.106878236536595], prediction=0.000000 (likely Autechre song titles // aåÊbot byåÊ@inky) --> prob=[0.893121763463405,0.106878236536595], prediction=0.000000 (no dia em que eu sai de casa minha mae me disse que ja ia tarde) --> prob=[0.8667397665024795,0.13326023349752047], prediction=0.000000 (simple girl; lover of art; self proclaimed Queen of facial expressions; proud dork; Elephants & animated film enthusiast #Bruins #Pats _ôÖ_ô_Â_ôÖìäìë) --> prob=[0.8298770174769112,0.17012298252308877], prediction=0.000000 (they made me smile as oon as pressd play) --> prob=[0.8492157730454413,0.15078422695455873], prediction=0.000000 ("""AlolaFen""") --> prob=[0.893121763463405,0.106878236536595], prediction=0.000000 ("""Far-West""") --> prob=[0.893121763463405,0.106878236536595], prediction=0.000000 ("""PoGo Santee""") --> prob=[0.893121763463405,0.106878236536595], prediction=0.000000 ("""Scanning Newport Beach Pier to the Wedge- 100% Iv notifications) --> prob=[0.8904898391135612,0.10951016088643883], prediction=0.000000 (...) --> prob=[0.893121763463405,0.106878236536595], prediction=0.000000 (20 Something in NJ.) --> prob=[0.8922472916652341,0.10775270833476591], prediction=0.000000 (@NYUStern MBA student passionate about marketing, media, travel, and wine) --> prob=[0.9260528865693197,0.07394711343068028], prediction=0.000000 (A bot that generates random malfunctioning equipment, created by @notinventedhere using http://t.co/XpDujaprri) --> prob=[0.732787707848606,0.26721229215139397], prediction=0.000000 (A project by @brendanadkins and many, many other people. The whole sext: thing was @TriciaLockwood's idea. Be careful, be generous, be kind.) --> prob=[0.8618934493932328,0.13810655060676724], prediction=0.000000 (A twitter bot account for papers employing sequencing of single cells) --> prob=[0.8623347453433963,0.13766525465660373], prediction=0.000000 (Actor and Environmentalist) --> prob=[0.882717793416593,0.11728220658340704], prediction=0.000000 (Actor...who loves to make ppl laugh... ..https://t.co/lvWDr6NKFZ) --> prob=[0.8913562750574823,0.10864372494251773], prediction=0.000000 (All Things Leo - Start your Day off Right #Leo Zodiac #LeoHumor #LeoWisdom and #LeoFacts) --> prob=[0.9758153470694445,0.024184652930555495], prediction=0.000000 (Anak Auhn Rmbulan A2R) --> prob=[0.893121763463405,0.106878236536595], prediction=0.000000 (Cameron Crigger aka Cameron Bright. Actor - Twilight, Little Glory, X-Men, Running Scared, Birth & @CTV_Television series Motive) --> prob=[0.9293544126654003,0.07064558733459969], prediction=0.000000 (Creative, strategist, sapiens. NY native & world citizen. Convention bucker & disruption embracer. Human enthusiast, cultural connoisseur & life aficionado.) --> prob=[0.893121763463405,0.106878236536595], prediction=0.000000 (Entertaining One Crore People) --> prob=[0.952747284456767,0.04725271554323296], prediction=0.000000 (Fan club dedicado ao MC judson,nosso grande idolo. Mc judson S2) --> prob=[0.893121763463405,0.106878236536595], prediction=0.000000 (Galatians 6:9 - #DST ä_¥Ÿ - @NCATSUAggies Alumna - Morning News Producer @ABC7SWFL) --> prob=[0.9790974963855631,0.020902503614436907], prediction=0.000000 (God, Music, and Tea) --> prob=[0.882717793416593,0.11728220658340704], prediction=0.000000 (Harvard Business School professor who studies entrepreneurship) --> prob=[0.9804888820192039,0.019511117980796078], prediction=0.000000 (Head of R&D, ML/AI at Google Cloud) --> prob=[0.8664597861393795,0.13354021386062054], prediction=0.000000 (Hey imToni and im acertified Zumba Intructor.) --> prob=[0.882717793416593,0.11728220658340704], prediction=0.000000 (Husband / Father/ Backstreet Boy / Christian Artist/ Believer!) --> prob=[0.893121763463405,0.106878236536595], prediction=0.000000 (I am a bot. @vectorpoem created me to post random places from the worlds of Doom every day.) --> prob=[0.6330755216388497,0.3669244783611503], prediction=0.000000 (Investigative reporter, Washington Post) --> prob=[0.893121763463405,0.106878236536595], prediction=0.000000 (Just a single flower in the world _ôëŸ photography _ôëŸ 14 years _ôëŸ Daniela ä_) --> prob=[0.9131495413584089,0.08685045864159113], prediction=0.000000 (Keep up to date on the latest publications on the universal second messenger Cyclic di-GMP with this twitter feed of up to date publications.) --> prob=[0.9850287179247424,0.014971282075257553], prediction=0.000000 (Lead Developer @ https://t.co/2Kj4zvIKO2 | Bitcoin enthusiastic) --> prob=[0.9133771886872373,0.08662281131276273], prediction=0.000000 (My BMP converter has a bizare bug, send me pictures and you'll see. I am a strong believer in the full potential of other bots. See terms in link.) --> prob=[0.6235476699689178,0.37645233003108225], prediction=0.000000 (Name something by @irondavy) --> prob=[0.8810763324946292,0.11892366750537076], prediction=0.000000 (OFFICIAL SHAWNNA TWITTER PAGE Feature: featureshawnna2@gmail.com Booking: bookshawnna2@gmail.com New Single Getting to it https://t.co/b2XRnLTrgH) --> prob=[0.9386326548308366,0.06136734516916342], prediction=0.000000 (PROVEHITO IN ALTUM ‰Û¢ #LoveLustFaithDreams available now at https://t.co/vZ6yV8t8HA) --> prob=[0.9549292734960081,0.04507072650399191], prediction=0.000000 (Producer, Musican, Social Activist, Writer åÀTe gusta alg̼n tweet? Dale RT o hazlo un Favorito BENDICIONES. Like a tweet? RT it or Favorite. BLESSINGS) --> prob=[0.85253619457143,0.14746380542856996], prediction=0.000000 (Pubmed RSS paper feed on cohesin and its 'friends': condensin, Smc5/6, Scc2 (NIPBL), Scc4 (Mau2) and MukBEF.) --> prob=[0.7085657646660286,0.2914342353339714], prediction=0.000000 (Retweet bot for my #indiedev #gameDev bros and gals! Follow for increased retweet chance! Also follow my main account @quantomworks ! Happy coding :)) --> prob=[0.9039618342366094,0.09603816576339064], prediction=0.000000 (TV Host Fox News Channel 10 PM. Nationally Syndicated Radio Host 3-6 PM EST. https://t.co/z23FRgA02S Retweets, Follows NOT endorsements!) --> prob=[0.9426083872773988,0.05739161272260118], prediction=0.000000 (The Bee Guardian Foundation is an educational conservation organisation that aims to help all bee species and other pollinators.) --> prob=[0.9455910761872863,0.05440892381271367], prediction=0.000000 (The Official American Country Countdown Twitter Page) --> prob=[0.892267705593487,0.107732294406513], prediction=0.000000 (The complete works of composer JÌ_rgen Sebastian Bot. // by @inky) --> prob=[0.8584165944027536,0.14158340559724636], prediction=0.000000 (The coolest dude alive! Insta-ChrisBosh Snapchat: MrChrisBosh For future statements and content you gotta hit the Subscribe button at https://t.co/e7PyxmPf8e) --> prob=[0.983282259416705,0.016717740583295004], prediction=0.000000 (Tulsa Enthusiast. Accountant. Entrepreneur. Volunteer.) --> prob=[0.8773797667315911,0.12262023326840887], prediction=0.000000 (Vegan? Portuguese Girl) --> prob=[0.893121763463405,0.106878236536595], prediction=0.000000 (Verizon Team Penske driver. Race winner in F1, Indycar, NASCAR, CART, and Grand-AM.) --> prob=[0.8646088736890247,0.13539112631097527], prediction=0.000000 (You may have missed some things, I will help you find them.) --> prob=[0.9779041846826569,0.02209581531734306], prediction=0.000000 (a darker, edgier bot that appeals to today's audience // by @igowen // #botALLY) --> prob=[0.8829853125614543,0.1170146874385457], prediction=0.000000 (a little disappointed tbh) --> prob=[0.887454789763049,0.11254521023695097], prediction=0.000000 (ayang University studen) --> prob=[0.8165286787184011,0.18347132128159893], prediction=0.000000 (https://t.co/qnAibV0nJv) --> prob=[0.893121763463405,0.106878236536595], prediction=0.000000 (i like people who smile when it's raining *-*) --> prob=[0.988922225144202,0.011077774855797973], prediction=0.000000
In the text output search for
prediction=1
And write in the chat which description got classified as bot!
In the text output search for
prediction=1
And write in the chat which description got classified as bot!
# this is for clearning some memory :-)
prediction = None
selected = None
✅ Task :
CrossValidator provide us the ability to run multiple training set and testing set within one function call -
fit
.
It runs the evaluation phase and chooses the best parameters.
Read about CrossValidator
in the docs and integrate it into your pipeline.
In the docs, search for CrossValidator
python
example.
Copy the example to the notebook and adjust it to your needs.
From the docs:
CrossValidator
- K-fold cross validation performs model selection by splitting the dataset into a set of non-overlapping randomly partitioned folds which are used as separate training and test datasets e.g., with k=3 folds, K-fold cross validation will generate 3 (training, test) dataset pairs, each of which uses 2/3 of the data for training and 1/3 for testing. Each fold is used as the test set exactly once.
from pyspark.ml import Pipeline
from pyspark.ml.classification import LogisticRegression
from pyspark.ml.evaluation import BinaryClassificationEvaluator
from pyspark.ml.feature import HashingTF, Tokenizer
from pyspark.ml.tuning import CrossValidator, ParamGridBuilder
# Prepare training documents, which are labeled.
training = spark.createDataFrame([
(0, "a b c d e spark", 1.0),
(1, "b d", 0.0),
(2, "spark f g h", 1.0),
(3, "hadoop mapreduce", 0.0),
(4, "b spark who", 1.0),
(5, "g d a y", 0.0),
(6, "spark fly", 1.0),
(7, "was mapreduce", 0.0),
(8, "e spark program", 1.0),
(9, "a e c l", 0.0),
(10, "spark compile", 1.0),
(11, "hadoop software", 0.0)
], ["id", "text", "label"])
# Configure an ML pipeline, which consists of tree stages: tokenizer, hashingTF, and lr.
tokenizer = Tokenizer(inputCol="text", outputCol="words")
hashingTF = HashingTF(inputCol=tokenizer.getOutputCol(), outputCol="features")
lr = LogisticRegression(maxIter=10)
pipeline = Pipeline(stages=[tokenizer, hashingTF, lr])
# We now treat the Pipeline as an Estimator, wrapping it in a CrossValidator instance.
# This will allow us to jointly choose parameters for all Pipeline stages.
# A CrossValidator requires an Estimator, a set of Estimator ParamMaps, and an Evaluator.
# We use a ParamGridBuilder to construct a grid of parameters to search over.
# With 3 values for hashingTF.numFeatures and 2 values for lr.regParam,
# this grid will have 3 x 2 = 6 parameter settings for CrossValidator to choose from.
paramGrid = ParamGridBuilder() \
.addGrid(hashingTF.numFeatures, [10, 100, 1000]) \
.addGrid(lr.regParam, [0.1, 0.01]) \
.build()
crossval = CrossValidator(estimator=pipeline,
estimatorParamMaps=paramGrid,
evaluator=BinaryClassificationEvaluator(),
numFolds=2) # use 3+ folds in practice
# Run cross-validation, and choose the best set of parameters.
cvModel = crossval.fit(training)
# Prepare test documents, which are unlabeled.
test = spark.createDataFrame([
(4, "spark i j k"),
(5, "l m n"),
(6, "mapreduce spark"),
(7, "apache hadoop")
], ["id", "text"])
# Make predictions on test documents. cvModel uses the best model found (lrModel).
prediction = cvModel.transform(test)
selected = prediction.select("id", "text", "probability", "prediction")
for row in selected.collect():
print(row)
from pyspark.ml import Pipeline
from pyspark.ml.classification import LogisticRegression
from pyspark.ml.evaluation import BinaryClassificationEvaluator
from pyspark.ml.feature import HashingTF, Tokenizer
from pyspark.ml.tuning import CrossValidator, ParamGridBuilder
# We now treat the Pipeline as an Estimator, wrapping it in a CrossValidator instance.
# This will allow us to jointly choose parameters for all Pipeline stages.
# A CrossValidator requires an Estimator, a set of Estimator ParamMaps, and an Evaluator.
# We use a ParamGridBuilder to construct a grid of parameters to search over.
# With 3 values for hashingTF.numFeatures and 2 values for lr.regParam,
# this grid will have 3 x 2 = 6 parameter settings for CrossValidator to choose from.
paramGrid = ParamGridBuilder() \
.addGrid(hashingTF.numFeatures, [10, 100, 1000]) \
.addGrid(lr.regParam, [0.1, 0.01]) \
.build()
crossval = CrossValidator(estimator=pipeline,
estimatorParamMaps=paramGrid,
evaluator=BinaryClassificationEvaluator(),
numFolds=3) # use 3+ folds in practice
# Run cross-validation, and choose the best set of parameters.
cvModel = crossval.fit(trainingData)
prediction = cvModel.transform(testData)
selected = prediction.select("description", "probability", "prediction")
for row in selected.collect():
print(row)
from pyspark.ml import Pipeline
from pyspark.ml.classification import LogisticRegression
from pyspark.ml.evaluation import BinaryClassificationEvaluator
from pyspark.ml.feature import HashingTF, Tokenizer
from pyspark.ml.tuning import CrossValidator, ParamGridBuilder
# We now treat the Pipeline as an Estimator, wrapping it in a CrossValidator instance.
# This will allow us to jointly choose parameters for all Pipeline stages.
# A CrossValidator requires an Estimator, a set of Estimator ParamMaps, and an Evaluator.
# We use a ParamGridBuilder to construct a grid of parameters to search over.
# With 3 values for hashingTF.numFeatures and 2 values for lr.regParam,
# this grid will have 3 x 2 = 6 parameter settings for CrossValidator to choose from.
paramGrid = ParamGridBuilder() \
.addGrid(hashingTF.numFeatures, [10, 100, 1000]) \
.addGrid(lr.regParam, [0.1, 0.01]) \
.build()
crossval = CrossValidator(estimator=pipeline,
estimatorParamMaps=paramGrid,
evaluator=BinaryClassificationEvaluator(),
numFolds=3) # use 3+ folds in practice
# Run cross-validation, and choose the best set of parameters.
cvModel = crossval.fit(trainingData)
prediction = cvModel.transform(testData)
selected = prediction.select("description", "probability", "prediction")
for row in selected.collect():
print(row)
Row(description=' CA"""', probability=DenseVector([0.9915, 0.0085]), prediction=0.0) Row(description=' England"""', probability=DenseVector([0.9953, 0.0047]), prediction=0.0) Row(description=' NV"""', probability=DenseVector([0.9545, 0.0455]), prediction=0.0) Row(description='"""Affordable & Professional Printing Services for Businesses & Individuals. For Cheapest Printing Prices Mail us sales@print365.ie #printing #Ireland"""', probability=DenseVector([0.991, 0.009]), prediction=0.0) Row(description='"""Follow and tweet us', probability=DenseVector([0.936, 0.064]), prediction=0.0) Row(description='"""International fanbase for Running Man ___ Variety Show. For enquires email runningmantown@gmail.com #7012"""', probability=DenseVector([0.9998, 0.0002]), prediction=0.0) Row(description='"""Rare and strong PokŽmon in Las Vegas and Spring Valley. See more PokŽmon at https://t.co/GB4nYu29n3"""', probability=DenseVector([0.8216, 0.1784]), prediction=0.0) Row(description='#Muslim #Arab #American #husband #Father #politicaljunkie,#Liberal #Democratic,#LoveHistory #environmentalis #Humanitarian #Businesstilldeath,#Icecream #stopwar', probability=DenseVector([0.8472, 0.1528]), prediction=0.0) Row(description='#RNomics, #RNA biology, RNA #bioinformatics, #RNA_World & #evolution, technologies & resources, daily papers: https://t.co/UaH05PfPIt & https://t.co/cuczvAA7dr', probability=DenseVector([0.8835, 0.1165]), prediction=0.0) Row(description='#bot. Retweets once every hour. @PistachioRoux and @Jonbro. Thanks to all the botmakers, bots, and @botALLY!', probability=DenseVector([0.2697, 0.7303]), prediction=1.0) Row(description="'Integrity Blues' Out Now", probability=DenseVector([0.9968, 0.0032]), prediction=0.0) Row(description="'St. Vincent' out now. Buy: http://t.co/ATBUTO9WIo | | Tour Tickets: http://t.co/WLPLabRm2d", probability=DenseVector([0.999, 0.001]), prediction=0.0) Row(description='100k+ YouTube subs. I delete most of my tweets. https://t.co/JY3n1qms2N', probability=DenseVector([0.9881, 0.0119]), prediction=0.0) Row(description="2-time Emmy award & Grammy award-winning comedian. Order my new book, Kathy Griffin's Celebrity Run-Ins: My A-Z Index now! #GriffinTellsAll", probability=DenseVector([0.9834, 0.0166]), prediction=0.0) Row(description='@gyuterte @gyuterte @gyuterte @gyuterte @gyuterte @gyuterte @gyuterte @gyuterte @gyuterte @gyuterte @gyuterte @gyuterte @gyuterte @gyuterte @gyuterte @gyuterte', probability=DenseVector([1.0, 0.0]), prediction=0.0) Row(description='A hardcore gamer and passionate game developer! Loves to make friends!... 245F58FFE', probability=DenseVector([0.9979, 0.0021]), prediction=0.0) Row(description='A list of hitherto-unpublished Harper Lee novels. Bot by @hugovk, inspired by https://t.co/5gF8HFq0NT', probability=DenseVector([0.709, 0.291]), prediction=0.0) Row(description='A place for fans to connect! Tweeting from Mumbai and the world! #Bollywood #India #Parody', probability=DenseVector([0.8529, 0.1471]), prediction=0.0) Row(description='Actor, Simpsons voice guy and Determined to Succeed supporter', probability=DenseVector([0.948, 0.052]), prediction=0.0) Row(description='Add #Yoda to any tweet for a quote, or chat about characters in Star Wars. A twitter bot developed using Microsoft .NET by Michael Urvan at http://t.co/5AFtnH5C', probability=DenseVector([0.1012, 0.8988]), prediction=1.0) Row(description="After 11 years at TED, I'm now launching a content incubator (watch this space...). I tweet about media, technology, science, culture & ideas.", probability=DenseVector([0.9774, 0.0226]), prediction=0.0) Row(description='American bassist, singer, record producer, music manager, and former A&R executive. He is best known as a judge on American Idol and Executive Producer.', probability=DenseVector([0.9836, 0.0164]), prediction=0.0) Row(description='Author', probability=DenseVector([0.9922, 0.0078]), prediction=0.0) Row(description='Author of novels PANORAMA CITY and THE INTERLOPER. Also, photog behind SLOW PAPARAZZO. And aka Gandhi Rockefeller. Stories in A Public Space, Paris Review, etc.', probability=DenseVector([0.987, 0.013]), prediction=0.0) Row(description='Banana Pudding Expert. Co-Creator of +ONE.', probability=DenseVector([0.9996, 0.0004]), prediction=0.0) Row(description='Because even in a sunken, dead city things can be lonely. By @polm23', probability=DenseVector([0.8209, 0.1791]), prediction=0.0) Row(description='Bridging the gap between telecommunications construction & engineering.', probability=DenseVector([0.9427, 0.0573]), prediction=0.0) Row(description="CDC Emergency Preparedness and Response: increasing the nation's ability to prepare for and respond to public health emergencies.", probability=DenseVector([0.9462, 0.0538]), prediction=0.0) Row(description='CEO & Science Based Sales Trainer, Sales Keynote Speaker, Sales Coach, Sales Behavioral Strategist', probability=DenseVector([0.8954, 0.1046]), prediction=0.0) Row(description='CEO @Niosocial USA. Also love art, photography, Haiku and food :) Passionate about helping women in tech', probability=DenseVector([0.2406, 0.7594]), prediction=1.0) Row(description='Curation and content @kickstarter. Writer, editor, Canadian.', probability=DenseVector([0.9823, 0.0177]), prediction=0.0) Row(description='Daily tips on how to stop the crisis in the humanities. Real solutions! (Machine Generated by @samplereality)', probability=DenseVector([0.0295, 0.9705]), prediction=1.0) Row(description='Dashboard Confessional Tix available NOW for our up close and personal tour in Jan and Feb of 2017: https://t.co/Bj0G7NIhy1', probability=DenseVector([0.9936, 0.0064]), prediction=0.0) Row(description='Designing and making games @nytdesign _Ò\x8d side project haver _Ò\x8d see also: @itsthisyear, @icebergdotcool, @ramsophone, @__birds__ _Ò\x8d', probability=DenseVector([0.9664, 0.0336]), prediction=0.0) Row(description='DirComConnected /AlwaysOn/', probability=DenseVector([0.9966, 0.0034]), prediction=0.0) Row(description="Don't Panic!", probability=DenseVector([0.9127, 0.0873]), prediction=0.0) Row(description='Editor @deystreet. Co-host of the @killgenrenyc reading series. Editor @thescofieldmag. Poet and fiction writer.', probability=DenseVector([0.9808, 0.0192]), prediction=0.0) Row(description='Entrepreneur, mentor, angel investor (and mom of twins!) coaching women entrepreneurs and leaders.', probability=DenseVector([0.927, 0.073]), prediction=0.0) Row(description="Ever wondered what a thing is? Follow this ontography machine, powered by tweets proclaiming that something 'is apparently a thing.' By @staeiou", probability=DenseVector([0.638, 0.362]), prediction=0.0) Row(description='Every website from the Internet International Directory, published 1995 (many dead links)', probability=DenseVector([0.7918, 0.2082]), prediction=0.0) Row(description='Exploring the future as CEO of @cmykvc, a marketing agency and community of startups, investors, brands, and people improving the state of the planet.', probability=DenseVector([0.5399, 0.4601]), prediction=0.0) Row(description='Fast and steady wins the race.', probability=DenseVector([0.8781, 0.1219]), prediction=0.0) Row(description='Fav and RT the superior word in each matchup. Goooal! A tribute to @everyword / @aparrish by @johnholdun', probability=DenseVector([0.013, 0.987]), prediction=1.0) Row(description='Finally, more businessmen. // bot by @ckolderup #botALLY', probability=DenseVector([0.931, 0.069]), prediction=0.0) Row(description='Founder & General Partner at Lakehouse Ventures.', probability=DenseVector([0.8469, 0.1531]), prediction=0.0) Row(description='Founder of the Founder Institute (https://t.co/JcT0pgQ5f8), TheFunded, 9x entrepreneur. Bio: https://t.co/OMiiJu6ukI', probability=DenseVector([0.3336, 0.6664]), prediction=1.0) Row(description='Front-end Architect', probability=DenseVector([0.9682, 0.0318]), prediction=0.0) Row(description='GO FOLLOW @CatarinaLiz BC SHE GAVE YOU THIS FREE FOLLOW', probability=DenseVector([0.0702, 0.9298]), prediction=1.0) Row(description='Good life, good health, good work.', probability=DenseVector([0.9692, 0.0308]), prediction=0.0) Row(description='Gruntled husband. Dogs of all sizes. Whisk(e)y neat. @Monetate.', probability=DenseVector([0.3953, 0.6047]), prediction=1.0) Row(description='Hello! I am a loving friend bot! @mossdogmusic and @inurashii created me (with help from @cirne) and I love you all very much!', probability=DenseVector([0.9976, 0.0024]), prediction=0.0) Row(description='Hello, I am here https://t.co/eT03DwzhH6', probability=DenseVector([0.976, 0.024]), prediction=0.0) Row(description='His bot was nonsensical, like a completed task // by @igowen', probability=DenseVector([0.5734, 0.4266]), prediction=0.0) Row(description='Hotel Booking Bot', probability=DenseVector([0.8925, 0.1075]), prediction=0.0) Row(description='Hourly lyrics by Jason Molina ‰Û¢ bot by @ckolderup ‰Û¢ #botALLY', probability=DenseVector([0.9987, 0.0013]), prediction=0.0) Row(description="I alert Twitter users that they typed Columbian when they meant Colombian. You're welcome!", probability=DenseVector([0.9992, 0.0008]), prediction=0.0) Row(description='I am a bot @jHYtse created.', probability=DenseVector([0.9447, 0.0553]), prediction=0.0) Row(description='I cant complain but sometimes I still do', probability=DenseVector([0.9991, 0.0009]), prediction=0.0) Row(description='I destroyed Humbaba in the Cedar Forest, I slew lions in the mountain passes! _Í€__Í€__Í€__Í‹€_Í‹__Í‹__̓ñ_̓†_͉ì Bot by @PaulMMCooper that tweets in the style of the Gilgamesh Epic', probability=DenseVector([0.385, 0.615]), prediction=1.0) Row(description='I find tweets about Danny Devito.', probability=DenseVector([0.9874, 0.0126]), prediction=0.0) Row(description='I generate screenshots of old websites in old browsers. Tweets every 2 hours. Data from the Wayback Machine, bot from @muffinista #botALLY', probability=DenseVector([0.0382, 0.9618]), prediction=1.0) Row(description='I live in Texas', probability=DenseVector([0.8008, 0.1992]), prediction=0.0) Row(description='I luv Zendya. She is SO awesome. Shak it up is my favorite disney channel show. teamfollowback zswager :3', probability=DenseVector([0.9958, 0.0042]), prediction=0.0) Row(description="I make video games, and have a beautiful wife with 4 boys. It's nutty!", probability=DenseVector([0.9897, 0.0103]), prediction=0.0) Row(description="I want you to know thatI am both happy and sadand I'm still trying to fgure out how that could be.", probability=DenseVector([0.9792, 0.0208]), prediction=0.0) Row(description="I'm a PC technician who shares free apps and news about gadgets, technology & science with some humor. I don't read DMs so tweet me. Beware of mad robots!", probability=DenseVector([0.1182, 0.8818]), prediction=1.0) Row(description="I'm a professor of history at Brooklyn College and the CUNY Graduate Center. Former Fulbright at @telavivuni; former track announcer at @ScarboroDowns.", probability=DenseVector([0.6471, 0.3529]), prediction=0.0) Row(description='If you follow me and tweet a word to me, I will reply with a freestyle ‰ÛÒ RT if you like it! I am a bot by @studiomoniker', probability=DenseVector([0.763, 0.237]), prediction=0.0) Row(description="If you have to start a sentence with 'I'm not racist, but...' then chances are you're pretty racist. This isn't a bot. RTŠ_¾endorsement, obviously.", probability=DenseVector([0.0126, 0.9874]), prediction=1.0) Row(description='Instagram: @fat_mikey_7', probability=DenseVector([0.9471, 0.0529]), prediction=0.0) Row(description='Jay Sean. Singer/Songwriter. Sony Music.', probability=DenseVector([0.9995, 0.0005]), prediction=0.0) Row(description='Juan David Solarte Arana', probability=DenseVector([0.9942, 0.0058]), prediction=0.0) Row(description="Just pingin' away at @poll to make sure the whole stack is in working order. I'm bored :(", probability=DenseVector([0.4745, 0.5255]), prediction=1.0) Row(description='Legitimate reviews loved by 100% people, every 30 minutes', probability=DenseVector([0.9964, 0.0036]), prediction=0.0) Row(description='Mineirinha !', probability=DenseVector([0.997, 0.003]), prediction=0.0) Row(description='Mom, Co-Founder of ORA TV, Podcast: Back and Forth w/ Shawn & Larry King https://t.co/Af74HcYDI8 https://t.co/IvNOSD83jO.', probability=DenseVector([0.9999, 0.0001]), prediction=0.0) Row(description='New Quantitative Biology submissions to http://t.co/dc19VZjh (not affiliated with http://t.co/dc19VZjh)', probability=DenseVector([0.9884, 0.0116]), prediction=0.0) Row(description='New York based Italian Creative Director & Producer, transmedia multi-platform content', probability=DenseVector([0.9973, 0.0027]), prediction=0.0) Row(description='News Photojournalist since July 1998 Have worked for KYMA, KSWT, KECY, KTTI (radio) and City of Yuma.', probability=DenseVector([0.9977, 0.0023]), prediction=0.0) Row(description='Nine years in the NFL. Two rings.', probability=DenseVector([0.8942, 0.1058]), prediction=0.0) Row(description='Official Twitter Page of Boston Celtics star Forward/Center Al Horford', probability=DenseVector([0.9987, 0.0013]), prediction=0.0) Row(description='Old age has deformities enough of its own. It should never add to them the deformity of vice.', probability=DenseVector([0.3399, 0.6601]), prediction=1.0) Row(description="Passionate about my work, in love with my family and dedicated to spreading light. It's contagious! ;-) https://t.co/vZcUuNOKcz", probability=DenseVector([0.9913, 0.0087]), prediction=0.0) Row(description='Paz filhos da puta.', probability=DenseVector([0.9875, 0.0125]), prediction=0.0) Row(description='Platinum Recording Artist, Producer, Writer, Director, Art-Lover, Entrepreneur, 24/7 Creative Opportunist. Changing the world one song at a time..!', probability=DenseVector([0.9371, 0.0629]), prediction=0.0) Row(description='President of The Long Now Foundation--which takes no sides. In this forum, as a private person, I do take take sides occasionally.', probability=DenseVector([1.0, 0.0]), prediction=0.0) Row(description='Publication alert for genome-wide association studies @genomic_pred @mendelian_lit @wpgilks', probability=DenseVector([0.9542, 0.0458]), prediction=0.0) Row(description='Pubmed twitterbot: arealization OR patterning OR somatosensory OR thalamus OR thalamocortical OR cortical development', probability=DenseVector([0.025, 0.975]), prediction=1.0) Row(description='R&D: #ArtificialIntelligence, #AI, #DeepLearning, #NeuralNetworks, #MachineLearning, #Robotics, #Bionics, #Biometrics, #VR, #AR, etc. Founder & CEO @Rosenchild', probability=DenseVector([0.012, 0.988]), prediction=1.0) Row(description='Relationships and the Law of Attraction. Click Here:', probability=DenseVector([0.512, 0.488]), prediction=0.0) Row(description='Reminders to take your medications from a robot that swallowed a thesaurus. By @NoraReed.', probability=DenseVector([0.6648, 0.3352]), prediction=0.0) Row(description='See https://t.co/iBNSW4KCSO', probability=DenseVector([0.9964, 0.0036]), prediction=0.0) Row(description='Senior writer @Gizmodo Was @Mashable, co-host @ovrtrd @_RocketFM @bbgtl. Obsessed with media and tech. I rule. christina.warren@gizmodo.com opinions = own', probability=DenseVector([0.7748, 0.2252]), prediction=0.0) Row(description='Simple generated grids three times a day. bot by @fitnr', probability=DenseVector([0.1592, 0.8408]), prediction=1.0) Row(description="Social media made me unemployable. I'll probably be your next president. https://t.co/9ipdRVuaPX 818.486.9363 @huawei KOL cofounder @wearemidlife", probability=DenseVector([0.8493, 0.1507]), prediction=0.0) Row(description='Solve the riddle by replying only the name of the person/character described! Created by @verhoevenben, @1vangro, @fvancesco, @pedrojmmartins at #codecampcc', probability=DenseVector([0.6926, 0.3074]), prediction=0.0) Row(description='Sou senador eleito por SÌ£o Paulo. https://t.co/ZFotz8bghb https://t.co/ODpeFyVVek', probability=DenseVector([0.6691, 0.3309]), prediction=0.0) Row(description='Spoiler-free expert advice for all games. Bot.', probability=DenseVector([0.9964, 0.0036]), prediction=0.0) Row(description='Stage Manager, Writer, Leader...and more.', probability=DenseVector([0.9955, 0.0045]), prediction=0.0) Row(description='Stan Lee, Co-Creator of Spider-Man, Iron Man, Hulk, X-Men, etc.', probability=DenseVector([0.7919, 0.2081]), prediction=0.0) Row(description='Stand-upper, Zombie Therapist, Ball Dropper, Ravenclaw, @Nerdist Inventor and POINTS giver', probability=DenseVector([0.3045, 0.6955]), prediction=1.0) Row(description='Tech/scifi/data enthusiast. Loves building digital things.', probability=DenseVector([0.9934, 0.0066]), prediction=0.0) Row(description='The Mole Hole of Myrtle Beach,SC has been in business since 1978. We have the experience and selection to ensure our customers find something unique and special', probability=DenseVector([0.9837, 0.0163]), prediction=0.0) Row(description='The voice of the people. Sorry, people.', probability=DenseVector([0.9985, 0.0015]), prediction=0.0) Row(description='This bot will post when anything gets added to the WWE Network. This is not an official WWE Account. Created by tgrollo@gmail.com', probability=DenseVector([0.4941, 0.5059]), prediction=1.0) Row(description="Throw away your other Twitch bots. I'm the best Twitch bot you'll ever find. For support, visit https://t.co/MjyvbaH6TN.", probability=DenseVector([0.9895, 0.0105]), prediction=0.0) Row(description='Tweeting every letter in the english language.', probability=DenseVector([0.8711, 0.1289]), prediction=0.0) Row(description='Tweeting the descriptions of verified users with no additional context. A bot by @gangles, not affiliated with Twitter.', probability=DenseVector([0.9841, 0.0159]), prediction=0.0) Row(description='Tweets randomly 3-10 times a day between 8am-10pm PST. Lines culled from http://t.co/Qo24om4SU9. Complaints: @beaugunderson', probability=DenseVector([0.9979, 0.0021]), prediction=0.0) Row(description='Twilight fan || obeswith the Stars || I Pewdiepie || Forks ä»ôäš_ || volleyball', probability=DenseVector([1.0, 0.0]), prediction=0.0) Row(description="Twitter dedicated to revealing corruption, discrimination, harassment etc at St. John's university queens campus.", probability=DenseVector([0.8521, 0.1479]), prediction=0.0) Row(description='Twitterbot of nonribosomal or nrps or non-ribosomal in #Pubmed created and curated by jem.stach@ncl.ac.uk', probability=DenseVector([0.0601, 0.9399]), prediction=1.0) Row(description='Um blog sobre filmes, trilhas sonoras e curiosidades! Descontraí_do e eclí©tico! Siga a gente para receber as novidades!', probability=DenseVector([0.9936, 0.0064]), prediction=0.0) Row(description='Unofficial; imagery courtesy: Japan Meteorological Agency (https://t.co/lzPXaTnMCi) and CIRA (https://t.co/YksnDoJEl8). Bot by @__jcbl__', probability=DenseVector([0.546, 0.454]), prediction=0.0) Row(description='WI - CA - MA Family, Friends, Music, Sports, Shananagins, LIFE!', probability=DenseVector([0.9951, 0.0049]), prediction=0.0) Row(description='We Are Database. Listing every single twitter user in the order of their twitter ID. Created by User 143813860 | @M_PF', probability=DenseVector([0.1813, 0.8187]), prediction=1.0) Row(description='We love #innovation and are always thinking of the next #disruptive startup #idea! // a bot by @tinysubversions', probability=DenseVector([0.0018, 0.9982]), prediction=1.0) Row(description='Well-fed artist.', probability=DenseVector([0.9656, 0.0344]), prediction=0.0) Row(description='Writer. Comedian. Desi Kalakaar https://t.co/AKyYZsdoh6 Bookings: rishabh@oml.in Snapchat: kanangill', probability=DenseVector([0.9993, 0.0007]), prediction=0.0) Row(description='Young & Hungry season 5 starts Monday March 13th on Freeform.', probability=DenseVector([0.9608, 0.0392]), prediction=0.0) Row(description="[ 90.02.10 ] –¾Ô•€Ñ–ÊÒ•ÔÑ –_É–É\x81 •âš (bot) –_€•Ê_•Ê_. / I'm not real, This is Sooyoung of SNSD Bot Account : ) / •__êÔÓ •\x8d\x8f •©É–€É–\x9dÑ \x90âÑ•_Œ •âš•_†•Ð_•_Ô êÇâ •ÐÒ•__–_Ó. / –_¥•\x8f† 15. 08. 22 ~ ing", probability=DenseVector([0.6846, 0.3154]), prediction=0.0) Row(description='[WORK IN PROGRESS] reminder bot. try @mnemosynetron remind me to X at/in/on Y. // more notes in link // by and for @thricedotted', probability=DenseVector([0.0343, 0.9657]), prediction=1.0) Row(description='currently @eater newsletter editor / previously @knightlab @mymodernmet / fyi i am not the acclaimed and v cool poet jenny zhang', probability=DenseVector([0.9796, 0.0204]), prediction=0.0) Row(description='express your opinion on hopelessly mundane things with the highest-quality polls this side of Twitter. A project by @_Ninji.', probability=DenseVector([0.9966, 0.0034]), prediction=0.0) Row(description='inspiration: http://t.co/zjL2YMy52e // by @dbaker_h', probability=DenseVector([0.9692, 0.0308]), prediction=0.0) Row(description='just a litle boy from bradfordthen he smashedit', probability=DenseVector([0.3704, 0.6296]), prediction=1.0) Row(description='just a river on twitter. made by @muffinista #botALLY', probability=DenseVector([0.9931, 0.0069]), prediction=0.0) Row(description="like @TwoHeadlines but musical artists // one, two artists kneel before you, that's what i said now // by @thricedotted", probability=DenseVector([0.9998, 0.0002]), prediction=0.0) Row(description='obsessing over your apps @apple ? you look lost, follow me', probability=DenseVector([0.9282, 0.0718]), prediction=0.0) Row(description='pink team returning fire', probability=DenseVector([0.9751, 0.0249]), prediction=0.0) Row(description='principal speech architect at Voci, ex-maintainer of cmusphinx, practitioner of speech recognition, machine/deep learning, blogger of The Grand Janitor Blog ?', probability=DenseVector([0.1494, 0.8506]), prediction=1.0) Row(description='tasty recipes for robot // not for human // a bot by @inky (human) // source: https://t.co/3SFVj3be16', probability=DenseVector([0.9812, 0.0188]), prediction=0.0) Row(description="this is a free follow from @ImLeviCailes Follow me if you want and I follow those who follow my main account! DM me~ let's talk _ÙÎü", probability=DenseVector([0.1265, 0.8735]), prediction=1.0) Row(description='tweeting every word in the English language....backwards. task will complete in 2020 // by @dbaker_h in honour of @everyword', probability=DenseVector([0.2622, 0.7378]), prediction=1.0) Row(description='Î\x8fë_±_Î__Üó\x81IT\x8dî‡Üó\x81_´ÁÎ_„IC·«\x8d†ΟšÜó\x81\x8f_‰_ñ\x8d·_Î_‰\x8d†„Üó\x81_Šå·____ÎìÂÎ\x8d„__åÜó\x81_ìŠfoÎ˃Î__Üó\x81_Ÿ\x8dfo\x8f_†Î_ÇÜó‰', probability=DenseVector([0.9922, 0.0078]), prediction=0.0) Row(description='"""#ZEA #______"""', probability=DenseVector([0.9868, 0.0132]), prediction=0.0) Row(description='"""Axel Carp"""', probability=DenseVector([0.9682, 0.0318]), prediction=0.0) Row(description='"""PS4____________4___"""', probability=DenseVector([0.962, 0.038]), prediction=0.0) Row(description='"""Photographer | Los Angeles', probability=DenseVector([0.933, 0.067]), prediction=0.0) Row(description='"""Saint Seiya 4Ever"""', probability=DenseVector([0.9847, 0.0153]), prediction=0.0) Row(description='"""jogador do Chelsea FC - football player (Chelsea FC)"""', probability=DenseVector([0.9985, 0.0015]), prediction=0.0) Row(description='#DruHill20th #LastDragon', probability=DenseVector([0.8824, 0.1176]), prediction=0.0) Row(description='#IAM Visionary | Innovator | Philanthropist | Entrepreneur | Singer | Actor | Author | Investor @Browsify | Founder of @E2HoldingsLLC', probability=DenseVector([0.9266, 0.0734]), prediction=0.0) Row(description='#botALLY. by @ckolderup.', probability=DenseVector([0.9622, 0.0378]), prediction=0.0) Row(description='-As cicatrizes fazem parte da minha histí_ria,me relembrando sempre,o fato de que aquilo que ní£o me mata,sí_ me fortalece.', probability=DenseVector([1.0, 0.0]), prediction=0.0) Row(description='1', probability=DenseVector([0.962, 0.038]), prediction=0.0) Row(description='13 // Co-Owner Of DSYR // @YouTube Content Creator & Entertainer // 40+ Subscribers // Love Gaming And Basketball // G Fuel Is Life', probability=DenseVector([0.0316, 0.9684]), prediction=1.0) Row(description='21 anos, cristí£o, meio filí_sofo, apaixonado por futebol, colorado e viciado em políÈmicas. 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The longer the snake the faster the game! 7am-22pm CEST, Mon-Fri.', probability=DenseVector([0.2212, 0.7788]), prediction=1.0) Row(description="Queen's NRI Hospital, a 380 bed multi-specialty acute-cum-critical care referral hospital, is one of the most well-equipped and premier hospitals in Vizag.", probability=DenseVector([0.9814, 0.0186]), prediction=0.0) Row(description='Senior Vice President, Apple Retail. Inspired by great people, great brands and great companies.', probability=DenseVector([0.9977, 0.0023]), prediction=0.0) Row(description='Shout out to all the disambiguation pages out there! from @saranrapjs', probability=DenseVector([0.9972, 0.0028]), prediction=0.0) Row(description='Software developer, home brewer, nerd.', probability=DenseVector([0.9988, 0.0012]), prediction=0.0) Row(description='Streaming freshly published papers in the field of T cell biology from all relevant journals. With us you will not miss any important discovery anymore!', probability=DenseVector([0.9969, 0.0031]), prediction=0.0) Row(description='TURN ON NOTIFICATIONS‰Ï¬', probability=DenseVector([0.9988, 0.0012]), prediction=0.0) Row(description='Take it sleazy.', probability=DenseVector([0.9853, 0.0147]), prediction=0.0) Row(description='Tay Dizm Twitter', probability=DenseVector([0.9624, 0.0376]), prediction=0.0) Row(description='Thai freelance artist. Inspired by Japanese Manga & Anime.', probability=DenseVector([0.7634, 0.2366]), prediction=0.0) Row(description='The Web Watchdog of the Second Citizenship Industry', probability=DenseVector([0.9956, 0.0044]), prediction=0.0) Row(description='The official Stephen Cannell Twitter page features news on mystery novels & mystery books.', probability=DenseVector([0.9304, 0.0696]), prediction=0.0) Row(description='The official Twitter account of the United States Marine Corps. The appearance of links does not constitute endorsement.', probability=DenseVector([0.0111, 0.9889]), prediction=1.0) Row(description='Tweeting quotes of 1 Bitcoin on Bitstamp, BTC-e and Mt.Gox and spreads between them every 30 mins. Donations are welcome: 1FbxmPa8y8LvkgwQy6T7dbhBpq96y9LkY8', probability=DenseVector([0.7817, 0.2183]), prediction=0.0) Row(description='TwitterBot for papers in ancient DNA and human genomics from arXiv, bioRxiv, & Pubmed. Curated by @bigskybioarch', probability=DenseVector([0.9894, 0.0106]), prediction=0.0) Row(description='UFC CHAMPION. Actor. Stuntman. Fox Sports Analyst. Follow T-Wood on Twitter. The New School Ninja with futuristic skills!', probability=DenseVector([0.454, 0.546]), prediction=1.0) Row(description='Unencumbered by time and space', probability=DenseVector([0.5915, 0.4085]), prediction=0.0) Row(description="Unofficial bot featuring a random selection of images from the Metropolitan Museum of Art's collection of Open Access images. (Maintained by @benrasmusen)", probability=DenseVector([0.8774, 0.1226]), prediction=0.0) Row(description='What if your spellbook was ridiculously precise? A project of @muffinista #botALLY', probability=DenseVector([0.9739, 0.0261]), prediction=0.0) Row(description='artisanal bot by @Objelisks', probability=DenseVector([0.8636, 0.1364]), prediction=0.0) Row(description='black eil bries arebae', probability=DenseVector([0.9688, 0.0312]), prediction=0.0) Row(description='bot by @ckolderup ‰Û¢ #botALLY', probability=DenseVector([0.9946, 0.0054]), prediction=0.0) Row(description="colors. all of 'em. | developed by @vogon; feel free to send him feedback/feature requests", probability=DenseVector([0.5017, 0.4983]), prediction=0.0) Row(description="hi i know things, i'll teach you some of them. bot by @cblgh", probability=DenseVector([0.9257, 0.0743]), prediction=0.0) Row(description='https://t.co/KfP3JDLj6x', probability=DenseVector([0.9855, 0.0145]), prediction=0.0) Row(description='im a deer and im sometimes doing deer stuff (this is a bot account!)', probability=DenseVector([0.0321, 0.9679]), prediction=1.0) Row(description="it's all happening", probability=DenseVector([0.9797, 0.0203]), prediction=0.0) Row(description='labor organizer _ seizing the memes of production', probability=DenseVector([0.9824, 0.0176]), prediction=0.0) Row(description="life's a beach man", probability=DenseVector([0.9453, 0.0547]), prediction=0.0) Row(description='student', probability=DenseVector([0.9927, 0.0073]), prediction=0.0) Row(description='Î\x8fš\x8dë_Î\x8fš_Ÿ©_ôî', probability=DenseVector([0.9582, 0.0418]), prediction=0.0) Row(description=' IN"""', probability=DenseVector([0.992, 0.008]), prediction=0.0) Row(description=' VA"""', probability=DenseVector([0.9842, 0.0158]), prediction=0.0) Row(description='"""33"""', probability=DenseVector([0.9927, 0.0073]), prediction=0.0) Row(description='"""Every Mon-Tues at 10 PM KST | Jisung', probability=DenseVector([0.9609, 0.0391]), prediction=0.0) Row(description='"""Tengah baca Novel #KeranaKauBidadari & #CintaDuaZaman -- Tak buat tweet berbayar --"""', probability=DenseVector([0.9866, 0.0134]), prediction=0.0) Row(description='"""there is NO FLASH without Iris West __ #love __"""', probability=DenseVector([0.9929, 0.0071]), prediction=0.0) Row(description='@indexventures, @Dropbox, Startups, Physics.', probability=DenseVector([0.984, 0.016]), prediction=0.0) Row(description='@realajavolkman, @danielepand, @richk', probability=DenseVector([0.849, 0.151]), prediction=0.0) Row(description="A bot by @RandomOutput. Tweets what people on twitter think it 'Tis the Season for. Deactivated until it 'Tis the season once again.", probability=DenseVector([0.454, 0.546]), prediction=1.0) Row(description='A chat bot trying to become the most intelligent bot on the Internet. Can answer any What is question by looking up the answer on the Internet.', probability=DenseVector([0.392, 0.608]), prediction=1.0) Row(description='Actor', probability=DenseVector([0.9957, 0.0043]), prediction=0.0) Row(description='Actor ... well at least some are STILL saying so !!', probability=DenseVector([0.7362, 0.2638]), prediction=0.0) Row(description='Actor, Entrepreneur and an agnostic ambivert.', probability=DenseVector([0.9948, 0.0052]), prediction=0.0) Row(description='Amenizando toda essa loucura!', probability=DenseVector([0.6682, 0.3318]), prediction=0.0) Row(description='An Outlier,Twice over!', probability=DenseVector([0.9763, 0.0237]), prediction=0.0) Row(description='By @zhaytee // Learn how to play here: https://t.co/a38R7wz9tO', probability=DenseVector([0.1247, 0.8753]), prediction=1.0) Row(description='CEO of https://t.co/nMwbrcvMaq and previously co-founder of https://t.co/z3ULIP6alR. Cal, Columbia, Y Combinator alum', probability=DenseVector([0.9991, 0.0009]), prediction=0.0) Row(description='CHS Senior_ô\x90_ sc- kaitlynbrookeh', probability=DenseVector([0.9726, 0.0274]), prediction=0.0) Row(description='Cease and Desist Bot does not approve of your use of common words and phrases. created to provide Justice by @spine_cone and @nate_smith.', probability=DenseVector([0.3361, 0.6639]), prediction=1.0) Row(description='Chief Technology and Strategy Officer for Cisco. Investments and M&A. Love art, photography, Haiku and food :) Passionate about helping women in tech', probability=DenseVector([0.7608, 0.2392]), prediction=0.0) Row(description='Comedian. Writer. Been cheering up people since 2010.', probability=DenseVector([0.376, 0.624]), prediction=1.0) Row(description='Connecting service design and customer experience to transform brands and grow businesses.', probability=DenseVector([0.5054, 0.4946]), prediction=0.0) Row(description='Covering the White House for The New York Times', probability=DenseVector([0.9972, 0.0028]), prediction=0.0) Row(description='Curtindo a vida da maneira mas exata e simples!! legal simpatico e Unico Cheio de amor pra da!!!', probability=DenseVector([0.8495, 0.1505]), prediction=0.0) Row(description="Daily RSS feed for papers on Synthetic Lethality in Cancer. cancer AND (synthetic lethal OR synthetic lethality). Largely automated, don't expect responses", probability=DenseVector([0.7964, 0.2036]), prediction=0.0) Row(description='Definirse es limitarse.', probability=DenseVector([0.9808, 0.0192]), prediction=0.0) Row(description='Design Thinker, Dev Ops Practitioner, Lean Coach.', probability=DenseVector([0.9751, 0.0249]), prediction=0.0) Row(description='Disruptive Tech |Social Entrepreneurism #Empathy #Impact #Innovation #Resilience #Tolerance', probability=DenseVector([0.971, 0.029]), prediction=0.0) Row(description="Download 'The Promise' at https://t.co/oBMFkVGfCn", probability=DenseVector([0.9863, 0.0137]), prediction=0.0) Row(description="Everyone's looking for the Citizen Kane of games. Why is no one looking for these instead?", probability=DenseVector([0.9999, 0.0001]), prediction=0.0) Row(description='Father. Writer. Producer. Actively seeking to become wiser everyday.', probability=DenseVector([0.9279, 0.0721]), prediction=0.0) Row(description="Following the formula: I'm not X but I am the definition of X. (via @stefanhayden) Tweets every 10 minutes.", probability=DenseVector([0.7049, 0.2951]), prediction=0.0) Row(description="For over 20 years, we've been giving students in need the right resources to ensure lifelong success ‰Û¢ Home of Unexpected Champions #ReachMillions", probability=DenseVector([0.851, 0.149]), prediction=0.0) Row(description='Hard Rock Habibi', probability=DenseVector([0.9984, 0.0016]), prediction=0.0) Row(description="Hi I'm Howie D from the Backstreet Boys.", probability=DenseVector([0.9967, 0.0033]), prediction=0.0) Row(description='Hi;) My login is Duren24 on https://t.co/9qoFAsKgBy', probability=DenseVector([0.9128, 0.0872]), prediction=0.0) Row(description="I am the Sigmund Freud of cleansing tissues, the Friedrich Nietzsche of somersaulting, the Spider Man of killin'", probability=DenseVector([0.9994, 0.0006]), prediction=0.0) Row(description="I live in brooklyn, I'm a bike messenger, I play in a band, I'm uniquely doing the same exact things as everyone else", probability=DenseVector([0.9919, 0.0081]), prediction=0.0) Row(description='I retweet questions followed by answers. Brought to you by @rfreebern.', probability=DenseVector([0.9984, 0.0016]), prediction=0.0) Row(description="I'm a bot Tweeting newly published papers on metagenomics. Powered by IFTTT.", probability=DenseVector([0.9067, 0.0933]), prediction=0.0) Row(description="I'm a bot that tweets images from the NASA/USGS Landsat program. Managed by @mewo2.", probability=DenseVector([0.8146, 0.1854]), prediction=0.0) Row(description='If I could stay in the shower all day, I would.', probability=DenseVector([0.9982, 0.0018]), prediction=0.0) Row(description="In case you missed any important events of the day, let's rewind a bit.", probability=DenseVector([0.7922, 0.2078]), prediction=0.0) Row(description='Instagram: @GeneSimmons', probability=DenseVector([0.9935, 0.0065]), prediction=0.0) Row(description='It is a thousand times better to have common sense without education than to have education without common sense.', probability=DenseVector([0.0814, 0.9186]), prediction=1.0) Row(description='Its a BOT which retweets the tweets by Hackers & Hacking News around the globe. Made to connect Hackers around the Globe. This BOT is made by @biswassudipta05', probability=DenseVector([0.0045, 0.9955]), prediction=1.0) Row(description='Laugh as long as you breathe, love as long as you live!', probability=DenseVector([0.9988, 0.0012]), prediction=0.0) Row(description='Literature bot searching arXiv, biorXiv, PeerJ & PubMed for supercoiling papers. Maintained by @red_gravel.', probability=DenseVector([0.8414, 0.1586]), prediction=0.0) Row(description='MH. 6.3.16.', probability=DenseVector([0.9897, 0.0103]), prediction=0.0) Row(description='Middle age is youth without levity; and age without decay.', probability=DenseVector([0.0115, 0.9885]), prediction=1.0) Row(description='Nessa sofríÈncia que í© amar vocíÈ', probability=DenseVector([0.957, 0.043]), prediction=0.0) Row(description='Oscar: Best Actress, West Wing, L Word, Switched at Birth, Spring Awakening. Speaker, Author, Advocate. Contact: https://t.co/6JRljjfPr3', probability=DenseVector([0.9942, 0.0058]), prediction=0.0) Row(description='Oxford grad student interested in all things law, finance, & tech', probability=DenseVector([0.9399, 0.0601]), prediction=0.0) Row(description="Penguin Random House Audio's BOT & Listening Library marketing team...tweeting the sweet sounds of #audiobooks to librarians & listeners of all ages.", probability=DenseVector([0.9762, 0.0238]), prediction=0.0) Row(description='Randomly generating the deaths of anyone who follows this account. By @irondavy.', probability=DenseVector([0.9066, 0.0934]), prediction=0.0) Row(description='Recent meiosis papers from pubmed RSS and bioR?iv. Maintained by @pmcarlton', probability=DenseVector([0.9409, 0.0591]), prediction=0.0) Row(description='Replacement for @FalseFlagBot R.I.P. #nWo #illuminati #falseflag', probability=DenseVector([0.9834, 0.0166]), prediction=0.0) Row(description="Rocket Ship Builder (that was Twitter's default option... how perfect)", probability=DenseVector([0.4472, 0.5528]), prediction=1.0) Row(description='SACE Foundation//China-Africa Analyst since 2008//Chinese national//3+ year work and research experience in Kenya, South Africa, Ghana', probability=DenseVector([0.1242, 0.8758]), prediction=1.0) Row(description='Scholar alerts on transcriptional noise, made by @biochemistries', probability=DenseVector([0.9975, 0.0025]), prediction=0.0) Row(description='So in love with Jesus ? Married to the love of my life @jeffhgala, Traveler, Executive Assistant @lifestonepgh, Grad Student, TX-PA ?', probability=DenseVector([0.657, 0.343]), prediction=0.0) Row(description='Sociopathic straight edge atheist sweetheart with no Facebook account.', probability=DenseVector([0.6468, 0.3532]), prediction=0.0) Row(description='Technology Manager at NBC10 & Telemundo62', probability=DenseVector([0.9674, 0.0326]), prediction=0.0) Row(description='Test Practice Manager | Editor @TestingTrapeze | Co-Founder @WeTestNZ | International Speaker | Writer https://t.co/MensMzCGPj', probability=DenseVector([0.9999, 0.0001]), prediction=0.0) Row(description="The Bridge is a Brooklyn-based website dedicated to covering business in New York City's most populous and innovative borough.", probability=DenseVector([0.5618, 0.4382]), prediction=0.0) Row(description='The Official Sara Evans Twitter Page', probability=DenseVector([0.84, 0.16]), prediction=0.0) Row(description='The Place To Learn Anything, Anywhere For All', probability=DenseVector([0.92, 0.08]), prediction=0.0) Row(description="The artist makes artist statements out of everyone's tweets. Follow me and I might make one from yours. // by @ibogost", probability=DenseVector([0.5825, 0.4175]), prediction=0.0) Row(description='Tired of working? Hate your job? Wanna get out of debt? Want more money? Check it now! https://t.co/mLKVs1VANF', probability=DenseVector([0.9941, 0.0059]), prediction=0.0) Row(description='Tweet a selfie to this bot, get a mustached version back. Twin of @is_like_a and @thee_to.', probability=DenseVector([0.5727, 0.4273]), prediction=0.0) Row(description='Twitter API + Google Images + ImageMagick. Inspired by @akari_daisuki', probability=DenseVector([0.9252, 0.0748]), prediction=0.0) Row(description='TwitterBot collecting #synbio papers', probability=DenseVector([0.8731, 0.1269]), prediction=0.0) Row(description='Twitterbot for #Autophagy papers. Curated by @SoniaHall', probability=DenseVector([0.9767, 0.0233]), prediction=0.0) Row(description='UXer', probability=DenseVector([0.9855, 0.0145]), prediction=0.0) Row(description='Updates about clubs and their fluctuating baller populations. By @maxmechanic #botALLY', probability=DenseVector([0.8019, 0.1981]), prediction=0.0) Row(description='Very old person, 71, low energy, not interested in much at all, tired, worn, down and out.', probability=DenseVector([0.4699, 0.5301]), prediction=1.0) Row(description="You don't take a photograph; you make it.", probability=DenseVector([0.927, 0.073]), prediction=0.0) Row(description='ZONA OPERATIV DE DEFENSA NEGRAL NŒÁ 63', probability=DenseVector([0.9976, 0.0024]), prediction=0.0) Row(description='editor£Âfocus on auto industry', probability=DenseVector([0.9971, 0.0029]), prediction=0.0) Row(description='free follow from @3dclifford!! follow her if u would like :)', probability=DenseVector([0.2336, 0.7664]), prediction=1.0) Row(description='generating cards for a mysterious card game åá bot by @ckolderup åá #botALLY åá avatar by Dmitriy Ivanov from the Noun Project', probability=DenseVector([1.0, 0.0]), prediction=0.0) Row(description='give me food...insta ---@JERM_beats', probability=DenseVector([0.9502, 0.0498]), prediction=0.0) Row(description='https://t.co/iIaCHwWybn', probability=DenseVector([0.962, 0.038]), prediction=0.0) Row(description='likely Autechre song titles // aåÊbot byåÊ@inky', probability=DenseVector([0.9784, 0.0216]), prediction=0.0) Row(description='no dia em que eu sai de casa minha mae me disse que ja ia tarde', probability=DenseVector([0.9925, 0.0075]), prediction=0.0) Row(description='simple girl; lover of art; self proclaimed Queen of facial expressions; proud dork; Elephants & animated film enthusiast #Bruins #Pats _ô\x90Ö_ô_Â_ôÖìäìë', probability=DenseVector([0.946, 0.054]), prediction=0.0) Row(description='they made me smile as oon as pressd play', probability=DenseVector([0.7945, 0.2055]), prediction=0.0) Row(description='"""AlolaFen"""', probability=DenseVector([0.9582, 0.0418]), prediction=0.0) Row(description='"""Far-West"""', probability=DenseVector([0.9855, 0.0145]), prediction=0.0) Row(description='"""PoGo Santee"""', probability=DenseVector([0.9981, 0.0019]), prediction=0.0) Row(description='"""Scanning Newport Beach Pier to the Wedge- 100% Iv notifications', probability=DenseVector([0.8005, 0.1995]), prediction=0.0) Row(description='...', probability=DenseVector([0.9582, 0.0418]), prediction=0.0) Row(description='20 Something in NJ.', probability=DenseVector([0.9756, 0.0244]), prediction=0.0) Row(description='@NYUStern MBA student passionate about marketing, media, travel, and wine', probability=DenseVector([0.9968, 0.0032]), prediction=0.0) Row(description='A bot that generates random malfunctioning equipment, created by @notinventedhere using http://t.co/XpDujaprri', probability=DenseVector([0.9986, 0.0014]), prediction=0.0) Row(description="A project by @brendanadkins and many, many other people. The whole sext: thing was @TriciaLockwood's idea. Be careful, be generous, be kind.", probability=DenseVector([0.9434, 0.0566]), prediction=0.0) Row(description='A twitter bot account for papers employing sequencing of single cells', probability=DenseVector([0.7898, 0.2102]), prediction=0.0) Row(description='Actor and Environmentalist', probability=DenseVector([0.9831, 0.0169]), prediction=0.0) Row(description='Actor...who loves to make ppl laugh... ..https://t.co/lvWDr6NKFZ', probability=DenseVector([0.9836, 0.0164]), prediction=0.0) Row(description='All Things Leo - Start your Day off Right #Leo Zodiac #LeoHumor #LeoWisdom and #LeoFacts', probability=DenseVector([0.9998, 0.0002]), prediction=0.0) Row(description='Anak Auhn Rmbulan A2R', probability=DenseVector([0.9388, 0.0612]), prediction=0.0) Row(description='Cameron Crigger aka Cameron Bright. Actor - Twilight, Little Glory, X-Men, Running Scared, Birth & @CTV_Television series Motive', probability=DenseVector([0.0435, 0.9565]), prediction=1.0) Row(description='Creative, strategist, sapiens. NY native & world citizen. Convention bucker & disruption embracer. Human enthusiast, cultural connoisseur & life aficionado.', probability=DenseVector([0.7822, 0.2178]), prediction=0.0) Row(description='Entertaining One Crore People', probability=DenseVector([0.8526, 0.1474]), prediction=0.0) Row(description='Fan club dedicado ao MC judson,nosso grande idolo. Mc judson S2', probability=DenseVector([0.997, 0.003]), prediction=0.0) Row(description='Galatians 6:9 - #DST ä\x9d_¥Ÿ\x8f - @NCATSUAggies Alumna - Morning News Producer @ABC7SWFL', probability=DenseVector([0.9788, 0.0212]), prediction=0.0) Row(description='God, Music, and Tea', probability=DenseVector([0.9939, 0.0061]), prediction=0.0) Row(description='Harvard Business School professor who studies entrepreneurship', probability=DenseVector([0.9939, 0.0061]), prediction=0.0) Row(description='Head of R&D, ML/AI at Google Cloud', probability=DenseVector([0.9995, 0.0005]), prediction=0.0) Row(description='Hey imToni and im acertified Zumba Intructor.', probability=DenseVector([0.6717, 0.3283]), prediction=0.0) Row(description='Husband / Father/ Backstreet Boy / Christian Artist/ Believer!', probability=DenseVector([0.4792, 0.5208]), prediction=1.0) Row(description='I am a bot. @vectorpoem created me to post random places from the worlds of Doom every day.', probability=DenseVector([0.8548, 0.1452]), prediction=0.0) Row(description='Investigative reporter, Washington Post', probability=DenseVector([0.9763, 0.0237]), prediction=0.0) Row(description='Just a single flower in the world _ôëŸ photography _ôëŸ 14 years _ôëŸ Daniela ä\x9d_', probability=DenseVector([0.8806, 0.1194]), prediction=0.0) Row(description='Keep up to date on the latest publications on the universal second messenger Cyclic di-GMP with this twitter feed of up to date publications.', probability=DenseVector([0.9373, 0.0627]), prediction=0.0) Row(description='Lead Developer @ https://t.co/2Kj4zvIKO2 | Bitcoin enthusiastic', probability=DenseVector([0.9964, 0.0036]), prediction=0.0) Row(description="My BMP converter has a bizare bug, send me pictures and you'll see. I am a strong believer in the full potential of other bots. See terms in link.", probability=DenseVector([0.1835, 0.8165]), prediction=1.0) Row(description='Name something by @irondavy', probability=DenseVector([0.9684, 0.0316]), prediction=0.0) Row(description='OFFICIAL SHAWNNA TWITTER PAGE Feature: featureshawnna2@gmail.com Booking: bookshawnna2@gmail.com New Single Getting to it https://t.co/b2XRnLTrgH', probability=DenseVector([0.9951, 0.0049]), prediction=0.0) Row(description='PROVEHITO IN ALTUM ‰Û¢ #LoveLustFaithDreams available now at https://t.co/vZ6yV8t8HA', probability=DenseVector([0.9927, 0.0073]), prediction=0.0) Row(description='Producer, Musican, Social Activist, Writer åÀTe gusta alg̼n tweet? Dale RT o hazlo un Favorito BENDICIONES. Like a tweet? RT it or Favorite. BLESSINGS', probability=DenseVector([0.0685, 0.9315]), prediction=1.0) Row(description="Pubmed RSS paper feed on cohesin and its 'friends': condensin, Smc5/6, Scc2 (NIPBL), Scc4 (Mau2) and MukBEF.", probability=DenseVector([0.8689, 0.1311]), prediction=0.0) Row(description='Retweet bot for my #indiedev #gameDev bros and gals! Follow for increased retweet chance! Also follow my main account @quantomworks ! Happy coding :)', probability=DenseVector([0.6444, 0.3556]), prediction=0.0) Row(description='TV Host Fox News Channel 10 PM. Nationally Syndicated Radio Host 3-6 PM EST. https://t.co/z23FRgA02S Retweets, Follows NOT endorsements!', probability=DenseVector([0.7951, 0.2049]), prediction=0.0) Row(description='The Bee Guardian Foundation is an educational conservation organisation that aims to help all bee species and other pollinators.', probability=DenseVector([0.9681, 0.0319]), prediction=0.0) Row(description='The Official American Country Countdown Twitter Page', probability=DenseVector([0.8448, 0.1552]), prediction=0.0) Row(description='The complete works of composer JÌ_rgen Sebastian Bot. // by @inky', probability=DenseVector([0.0416, 0.9584]), prediction=1.0) Row(description='The coolest dude alive! Insta-ChrisBosh Snapchat: MrChrisBosh For future statements and content you gotta hit the Subscribe button at https://t.co/e7PyxmPf8e', probability=DenseVector([0.1151, 0.8849]), prediction=1.0) Row(description='Tulsa Enthusiast. Accountant. Entrepreneur. Volunteer.', probability=DenseVector([0.9817, 0.0183]), prediction=0.0) Row(description='Vegan? Portuguese Girl', probability=DenseVector([0.9392, 0.0608]), prediction=0.0) Row(description='Verizon Team Penske driver. Race winner in F1, Indycar, NASCAR, CART, and Grand-AM.', probability=DenseVector([0.9933, 0.0067]), prediction=0.0) Row(description='You may have missed some things, I will help you find them.', probability=DenseVector([0.9967, 0.0033]), prediction=0.0) Row(description="a darker, edgier bot that appeals to today's audience // by @igowen // #botALLY", probability=DenseVector([0.7482, 0.2518]), prediction=0.0) Row(description='a little disappointed tbh', probability=DenseVector([0.9804, 0.0196]), prediction=0.0) Row(description='ayang University studen', probability=DenseVector([0.9923, 0.0077]), prediction=0.0) Row(description='https://t.co/qnAibV0nJv', probability=DenseVector([0.9807, 0.0193]), prediction=0.0) Row(description="i like people who smile when it's raining *-*", probability=DenseVector([0.9679, 0.0321]), prediction=0.0)
In the text output search for
prediction=1
Write in the chat which description
got classified as a bot!
Notice that in some of the description
exists the word - bot
.
Meaning your algorithm found it without being told directly to search for the word bot 🤓
In the last task, you used BinaryClassificationEvaluator
since it is more accurate to our needs. It works with Binary data - bot or human.
ParamGridBuilder
is a utility that helps us construct a parameter grid for our algorithm. It helps us test out various models built with various params.
ParamGridBuilder
is part of pyspark.ml.tuning
lib.