Re: Spark Streaming: Combine MLlib Prediction and Features on Dstreams
Hi,
Anybody has any idea on below?
-Obaid
On Friday, 27 May 2016, obaidul karim <obaidc...@gmail.com> wrote:
> Hi Guys,
>
> This is my first mail to spark users mailing list.
>
> I need help on Dstream operation.
>
> In fact, I am using a MLlib randomforest model to predict using spark
> streaming. In the end, I want to combine the feature Dstream & prediction
> Dstream together for further downstream processing.
>
> I am predicting using below piece of code:
>
> predictions = texts.map( lambda x : getFeatures(x) ).map(lambda x :
> x.split(',')).map( lambda parts : [float(i) for i in parts]
> ).transform(lambda rdd: rf_model.predict(rdd))
>
> Here texts is dstream having single line of text as records
> getFeatures generates a comma separated features extracted from each record
>
>
> I want the output as below tuple:
> ("predicted value", "original text")
>
> How can I achieve that ?
> or
> at least can I perform .zip like normal RDD operation on two Dstreams,
> like below:
> output = texts.zip(predictions)
>
>
> Thanks in advance.
>
> -Obaid
>
Re: Spark Streaming: Combine MLlib Prediction and Features on Dstreams
Hi Guys,
In the end, I am using below.
The trick is using "native python map" along with "spark spreaming
transform".
May not an elegent way, however it works :).
def predictScore(texts, modelRF):
predictions = texts.map( lambda txt : (txt , getFeatures(txt)) ).\
map(lambda (txt, features) : (txt ,(features.split(','))) ).\
map( lambda (txt, features) : (txt, ([float(i) for i in features])) ).\
transform( lambda rdd: sc.parallelize(\
map( lambda x,y:(x,y), modelRF.predict(rdd.map(lambda
(x,y):y)).collect(),rdd.map(lambda (x,y):x).collect() )\
)\
)
# in the transform operation: x=text and y=features
# Retrun will be tuple of (score,'original text')
return predictions
Hope, it will help somebody who is facing same problem.
If anybody has better idea, please post it here.
-Obaid
On Mon, May 30, 2016 at 8:43 PM, nguyen duc tuan <newvalu...@gmail.com>
wrote:
> Dstream has an method foreachRDD, so you can walk through all RDDs inside
> DStream as you want.
>
>
> https://spark.apache.org/docs/1.4.0/api/java/org/apache/spark/streaming/dstream/DStream.html
>
> 2016-05-30 19:30 GMT+07:00 obaidul karim <obaidc...@gmail.com>:
>
>> Hi nguyen,
>>
>> If I am not mistaken, we cannot call "predict" on "dstream" as you have
>> suggested.
>> We have to use "transform" to be able to perform normal RDD operations on
>> dstreams and here I am trapped.
>>
>> -Obaid
>>
>>
>>
>> On Mon, May 30, 2016 at 7:58 PM, nguyen duc tuan <newvalu...@gmail.com>
>> wrote:
>>
>>> How about this ?
>>>
>>> def extract_feature(rf_model, x):
>>> text = getFeatures(x).split(',')
>>> fea = [float(i) for i in text]
>>> prediction = rf_model.predict(fea)
>>> return (prediction, x)
>>> output = texts.map(lambda x: extract_feature(rf_model, x))
>>>
>>> 2016-05-30 14:17 GMT+07:00 obaidul karim <obaidc...@gmail.com>:
>>>
>>>> Hi,
>>>>
>>>> Anybody has any idea on below?
>>>>
>>>> -Obaid
>>>>
>>>>
>>>> On Friday, 27 May 2016, obaidul karim <obaidc...@gmail.com> wrote:
>>>>
>>>>> Hi Guys,
>>>>>
>>>>> This is my first mail to spark users mailing list.
>>>>>
>>>>> I need help on Dstream operation.
>>>>>
>>>>> In fact, I am using a MLlib randomforest model to predict using spark
>>>>> streaming. In the end, I want to combine the feature Dstream & prediction
>>>>> Dstream together for further downstream processing.
>>>>>
>>>>> I am predicting using below piece of code:
>>>>>
>>>>> predictions = texts.map( lambda x : getFeatures(x) ).map(lambda x :
>>>>> x.split(',')).map( lambda parts : [float(i) for i in parts]
>>>>> ).transform(lambda rdd: rf_model.predict(rdd))
>>>>>
>>>>> Here texts is dstream having single line of text as records
>>>>> getFeatures generates a comma separated features extracted from each
>>>>> record
>>>>>
>>>>>
>>>>> I want the output as below tuple:
>>>>> ("predicted value", "original text")
>>>>>
>>>>> How can I achieve that ?
>>>>> or
>>>>> at least can I perform .zip like normal RDD operation on two Dstreams,
>>>>> like below:
>>>>> output = texts.zip(predictions)
>>>>>
>>>>>
>>>>> Thanks in advance.
>>>>>
>>>>> -Obaid
>>>>>
>>>>
>>>
>>
>
Re: Spark Streaming: Combine MLlib Prediction and Features on Dstreams
foreachRDD does not return any value. I can be used just to send result to
another place/context, like db,file etc.
I could use that but seems like over head of having another hop.
I wanted to make it simple and light.
On Tuesday, 31 May 2016, nguyen duc tuan <newvalu...@gmail.com> wrote:
> How about using foreachRDD ? I think this is much better than your trick.
>
>
> 2016-05-31 12:32 GMT+07:00 obaidul karim <obaidc...@gmail.com
> <javascript:_e(%7B%7D,'cvml','obaidc...@gmail.com');>>:
>
>> Hi Guys,
>>
>> In the end, I am using below.
>> The trick is using "native python map" along with "spark spreaming
>> transform".
>> May not an elegent way, however it works :).
>>
>> def predictScore(texts, modelRF):
>> predictions = texts.map( lambda txt : (txt , getFeatures(txt)) ).\
>> map(lambda (txt, features) : (txt ,(features.split(','))) ).\
>> map( lambda (txt, features) : (txt, ([float(i) for i in features]))
>> ).\
>> transform( lambda rdd: sc.parallelize(\
>>map( lambda x,y:(x,y), modelRF.predict(rdd.map(lambda
>> (x,y):y)).collect(),rdd.map(lambda (x,y):x).collect() )\
>>)\
>> )
>> # in the transform operation: x=text and y=features
>> # Retrun will be tuple of (score,'original text')
>> return predictions
>>
>>
>> Hope, it will help somebody who is facing same problem.
>> If anybody has better idea, please post it here.
>>
>> -Obaid
>>
>> On Mon, May 30, 2016 at 8:43 PM, nguyen duc tuan <newvalu...@gmail.com
>> <javascript:_e(%7B%7D,'cvml','newvalu...@gmail.com');>> wrote:
>>
>>> Dstream has an method foreachRDD, so you can walk through all RDDs
>>> inside DStream as you want.
>>>
>>>
>>> https://spark.apache.org/docs/1.4.0/api/java/org/apache/spark/streaming/dstream/DStream.html
>>>
>>> 2016-05-30 19:30 GMT+07:00 obaidul karim <obaidc...@gmail.com
>>> <javascript:_e(%7B%7D,'cvml','obaidc...@gmail.com');>>:
>>>
>>>> Hi nguyen,
>>>>
>>>> If I am not mistaken, we cannot call "predict" on "dstream" as you
>>>> have suggested.
>>>> We have to use "transform" to be able to perform normal RDD operations
>>>> on dstreams and here I am trapped.
>>>>
>>>> -Obaid
>>>>
>>>>
>>>>
>>>> On Mon, May 30, 2016 at 7:58 PM, nguyen duc tuan <newvalu...@gmail.com
>>>> <javascript:_e(%7B%7D,'cvml','newvalu...@gmail.com');>> wrote:
>>>>
>>>>> How about this ?
>>>>>
>>>>> def extract_feature(rf_model, x):
>>>>> text = getFeatures(x).split(',')
>>>>> fea = [float(i) for i in text]
>>>>> prediction = rf_model.predict(fea)
>>>>> return (prediction, x)
>>>>> output = texts.map(lambda x: extract_feature(rf_model, x))
>>>>>
>>>>> 2016-05-30 14:17 GMT+07:00 obaidul karim <obaidc...@gmail.com
>>>>> <javascript:_e(%7B%7D,'cvml','obaidc...@gmail.com');>>:
>>>>>
>>>>>> Hi,
>>>>>>
>>>>>> Anybody has any idea on below?
>>>>>>
>>>>>> -Obaid
>>>>>>
>>>>>>
>>>>>> On Friday, 27 May 2016, obaidul karim <obaidc...@gmail.com
>>>>>> <javascript:_e(%7B%7D,'cvml','obaidc...@gmail.com');>> wrote:
>>>>>>
>>>>>>> Hi Guys,
>>>>>>>
>>>>>>> This is my first mail to spark users mailing list.
>>>>>>>
>>>>>>> I need help on Dstream operation.
>>>>>>>
>>>>>>> In fact, I am using a MLlib randomforest model to predict using
>>>>>>> spark streaming. In the end, I want to combine the feature Dstream &
>>>>>>> prediction Dstream together for further downstream processing.
>>>>>>>
>>>>>>> I am predicting using below piece of code:
>>>>>>>
>>>>>>> predictions = texts.map( lambda x : getFeatures(x) ).map(lambda x :
>>>>>>> x.split(',')).map( lambda parts : [float(i) for i in parts]
>>>>>>> ).transform(lambda rdd: rf_model.predict(rdd))
>>>>>>>
>>>>>>> Here texts is dstream having single line of text as records
>>>>>>> getFeatures generates a comma separated features extracted from each
>>>>>>> record
>>>>>>>
>>>>>>>
>>>>>>> I want the output as below tuple:
>>>>>>> ("predicted value", "original text")
>>>>>>>
>>>>>>> How can I achieve that ?
>>>>>>> or
>>>>>>> at least can I perform .zip like normal RDD operation on two
>>>>>>> Dstreams, like below:
>>>>>>> output = texts.zip(predictions)
>>>>>>>
>>>>>>>
>>>>>>> Thanks in advance.
>>>>>>>
>>>>>>> -Obaid
>>>>>>>
>>>>>>
>>>>>
>>>>
>>>
>>
>
Re: Spark Streaming: Combine MLlib Prediction and Features on Dstreams
Hi nguyen,
Thanks a lot for your time and really appreciate good suggestions.
Please find my concerns in line below:
def extract_feature(rf_model, x):
text = getFeatures(x).split(',')
fea = [float(i) for i in text]
prediction = rf_model.predict(fea)
return (prediction, x) <<< this will return two separate list as tuple, but
i want one to one mapping (pred, text) not (predlist, textlist)
def process_rdd(rdd):
fea = rdd.map(lambda x: extract_feature(rf_model, x))
//do something as you want (saving,...) <<< I want to avoid saving to
external system(definitely not in global variable). As I said, it could be
an overhead considering streaming.
stream.foreachRDD(process_rdd) <<< As you can see here, no variable to
store the output from foreachRDD. My target is to get (pred, text) pair and
then use
Whatever it is, the output from "extract_feature" is not what I want.
I will be more than happy if you please correct my mistakes here.
-Obaid
On Tue, May 31, 2016 at 2:04 PM, nguyen duc tuan <newvalu...@gmail.com>
wrote:
> I'm not sure what do you mean by saying "does not return any value". How
> do you use this method?
> I will use this method as following :
> def extract_feature(rf_model, x):
> text = getFeatures(x).split(',')
> fea = [float(i) for i in text]
> prediction = rf_model.predict(fea)
> return (prediction, x)
>
> def process_rdd(rdd):
> fea = rdd.map(lambda x: extract_feature(rf_model, x))
> //do something as you want (saving,...)
>
> stream.foreachRDD(process_rdd)
>
> 2016-05-31 12:57 GMT+07:00 obaidul karim <obaidc...@gmail.com>:
>
>> foreachRDD does not return any value. I can be used just to send result
>> to another place/context, like db,file etc.
>> I could use that but seems like over head of having another hop.
>> I wanted to make it simple and light.
>>
>>
>> On Tuesday, 31 May 2016, nguyen duc tuan <newvalu...@gmail.com> wrote:
>>
>>> How about using foreachRDD ? I think this is much better than your trick.
>>>
>>>
>>> 2016-05-31 12:32 GMT+07:00 obaidul karim <obaidc...@gmail.com>:
>>>
>>>> Hi Guys,
>>>>
>>>> In the end, I am using below.
>>>> The trick is using "native python map" along with "spark spreaming
>>>> transform".
>>>> May not an elegent way, however it works :).
>>>>
>>>> def predictScore(texts, modelRF):
>>>> predictions = texts.map( lambda txt : (txt , getFeatures(txt)) ).\
>>>> map(lambda (txt, features) : (txt ,(features.split(','))) ).\
>>>> map( lambda (txt, features) : (txt, ([float(i) for i in
>>>> features])) ).\
>>>> transform( lambda rdd: sc.parallelize(\
>>>>map( lambda x,y:(x,y), modelRF.predict(rdd.map(lambda
>>>> (x,y):y)).collect(),rdd.map(lambda (x,y):x).collect() )\
>>>>)\
>>>> )
>>>> # in the transform operation: x=text and y=features
>>>> # Retrun will be tuple of (score,'original text')
>>>> return predictions
>>>>
>>>>
>>>> Hope, it will help somebody who is facing same problem.
>>>> If anybody has better idea, please post it here.
>>>>
>>>> -Obaid
>>>>
>>>> On Mon, May 30, 2016 at 8:43 PM, nguyen duc tuan <newvalu...@gmail.com>
>>>> wrote:
>>>>
>>>>> Dstream has an method foreachRDD, so you can walk through all RDDs
>>>>> inside DStream as you want.
>>>>>
>>>>>
>>>>> https://spark.apache.org/docs/1.4.0/api/java/org/apache/spark/streaming/dstream/DStream.html
>>>>>
>>>>> 2016-05-30 19:30 GMT+07:00 obaidul karim <obaidc...@gmail.com>:
>>>>>
>>>>>> Hi nguyen,
>>>>>>
>>>>>> If I am not mistaken, we cannot call "predict" on "dstream" as you
>>>>>> have suggested.
>>>>>> We have to use "transform" to be able to perform normal RDD
>>>>>> operations on dstreams and here I am trapped.
>>>>>>
>>>>>> -Obaid
>>>>>>
>>>>>>
>>>>>>
>>>>>> On Mon, May 30, 2016 at 7:58 PM, nguyen duc tuan <
>>>>>> newvalu...@gmail.com> wrote:
>>>>>>
>>>>>>> How about this ?
>>>>>>>
>>>>>>> def extract_feature(rf_model, x):
>>>>>
Spark Streaming: Combine MLlib Prediction and Features on Dstreams
Hi nguyen,
Thanks again.
Yes, faltMap may do the trick as well.
I may try it out.
I will let you know the result when done.
On Tue, May 31, 2016 at 3:58 PM, nguyen duc tuan <newvalu...@gmail.com
<javascript:_e(%7B%7D,'cvml','newvalu...@gmail.com');>> wrote:
> 1. RandomForest 'predict' method supports both RDD or Vector as input (
> http://spark.apache.org/docs/latest/api/scala/index.html#org.apache.spark.mllib.tree.model.RandomForestModel)
> . So, in this case, function extract_feature should return
> tuple.(prediction, rawtext). If each input text can creates a list of
> vectors, try using "flatMap" instead of "map"
> 2, 3: From spark documents: "*Discretized Stream* or *DStream* is the
> basic abstraction provided by Spark Streaming. It represents a continuous
> stream of data, either the input data stream received from source, or the
> processed data stream generated by transforming the input stream.
> Internally, it is represented by a continuous sequence of RDDs, which is
> Spark’s abstraction of an immutable, distributed dataset. Each RDD in a
> DStream contains data from a certain interval, as shown in the following
> figure."(
> https://spark.apache.org/docs/0.9.1/streaming-programming-guide.html)
>
> So, in order to handle a stream, you should handle each rdd in that
> stream. This means with everything things want to do with your new data,
> put them in 'process_rdd' function. There's nothing return in output of
> 'foreachRdd' function, of course.
>
> 2016-05-31 14:39 GMT+07:00 obaidul karim <obaidc...@gmail.com
> <javascript:_e(%7B%7D,'cvml','obaidc...@gmail.com');>>:
>
>> Hi nguyen,
>>
>> Thanks a lot for your time and really appreciate good suggestions.
>>
>> Please find my concerns in line below:
>>
>> def extract_feature(rf_model, x):
>> text = getFeatures(x).split(',')
>> fea = [float(i) for i in text]
>> prediction = rf_model.predict(fea)
>> return (prediction, x) <<< this will return two separate list as tuple,
>> but i want one to one mapping (pred, text) not (predlist, textlist)
>>
>> def process_rdd(rdd):
>> fea = rdd.map(lambda x: extract_feature(rf_model, x))
>> //do something as you want (saving,...) <<< I want to avoid saving
>> to external system(definitely not in global variable). As I said, it could
>> be an overhead considering streaming.
>>
>> stream.foreachRDD(process_rdd) <<< As you can see here, no variable to
>> store the output from foreachRDD. My target is to get (pred, text) pair and
>> then use
>>
>> Whatever it is, the output from "extract_feature" is not what I want.
>> I will be more than happy if you please correct my mistakes here.
>>
>>
>> -Obaid
>>
>> On Tue, May 31, 2016 at 2:04 PM, nguyen duc tuan <newvalu...@gmail.com
>> <javascript:_e(%7B%7D,'cvml','newvalu...@gmail.com');>> wrote:
>>
>>> I'm not sure what do you mean by saying "does not return any value".
>>> How do you use this method?
>>> I will use this method as following :
>>> def extract_feature(rf_model, x):
>>> text = getFeatures(x).split(',')
>>> fea = [float(i) for i in text]
>>> prediction = rf_model.predict(fea)
>>> return (prediction, x)
>>>
>>> def process_rdd(rdd):
>>> fea = rdd.map(lambda x: extract_feature(rf_model, x))
>>> //do something as you want (saving,...)
>>>
>>> stream.foreachRDD(process_rdd)
>>>
>>> 2016-05-31 12:57 GMT+07:00 obaidul karim <obaidc...@gmail.com
>>> <javascript:_e(%7B%7D,'cvml','obaidc...@gmail.com');>>:
>>>
>>>> foreachRDD does not return any value. I can be used just to send result
>>>> to another place/context, like db,file etc.
>>>> I could use that but seems like over head of having another hop.
>>>> I wanted to make it simple and light.
>>>>
>>>>
>>>> On Tuesday, 31 May 2016, nguyen duc tuan <newvalu...@gmail.com
>>>> <javascript:_e(%7B%7D,'cvml','newvalu...@gmail.com');>> wrote:
>>>>
>>>>> How about using foreachRDD ? I think this is much better than your
>>>>> trick.
>>>>>
>>>>>
>>>>> 2016-05-31 12:32 GMT+07:00 obaidul karim <obaidc...@gmail.com>:
>>>>>
>>>>>> Hi Guys,
>>>>>>
>>>>>> In the end, I am using below.
>>>>>> The trick is using "native python map" along with "spark spreaming
>>
Spark Streaming: Combine MLlib Prediction and Features on Dstreams
Hi Guys,
This is my first mail to spark users mailing list.
I need help on Dstream operation.
In fact, I am using a MLlib randomforest model to predict using spark
streaming. In the end, I want to combine the feature Dstream & prediction
Dstream together for further downstream processing.
I am predicting using below piece of code:
predictions = texts.map( lambda x : getFeatures(x) ).map(lambda x :
x.split(',')).map( lambda parts : [float(i) for i in parts]
).transform(lambda rdd: rf_model.predict(rdd))
Here texts is dstream having single line of text as records
getFeatures generates a comma separated features extracted from each record
I want the output as below tuple:
("predicted value", "original text")
How can I achieve that ?
or
at least can I perform .zip like normal RDD operation on two Dstreams, like
below:
output = texts.zip(predictions)
Thanks in advance.
-Obaid
Re: Spark Streaming: Combine MLlib Prediction and Features on Dstreams
Sorry for lots of typos (writing from mobile)
On Tuesday, 31 May 2016, obaidul karim <obaidc...@gmail.com> wrote:
> foreachRDD does not return any value. I can be used just to send result to
> another place/context, like db,file etc.
> I could use that but seems like over head of having another hop.
> I wanted to make it simple and light.
>
> On Tuesday, 31 May 2016, nguyen duc tuan <newvalu...@gmail.com
> <javascript:_e(%7B%7D,'cvml','newvalu...@gmail.com');>> wrote:
>
>> How about using foreachRDD ? I think this is much better than your trick.
>>
>>
>> 2016-05-31 12:32 GMT+07:00 obaidul karim <obaidc...@gmail.com>:
>>
>>> Hi Guys,
>>>
>>> In the end, I am using below.
>>> The trick is using "native python map" along with "spark spreaming
>>> transform".
>>> May not an elegent way, however it works :).
>>>
>>> def predictScore(texts, modelRF):
>>> predictions = texts.map( lambda txt : (txt , getFeatures(txt)) ).\
>>> map(lambda (txt, features) : (txt ,(features.split(','))) ).\
>>> map( lambda (txt, features) : (txt, ([float(i) for i in features]))
>>> ).\
>>> transform( lambda rdd: sc.parallelize(\
>>>map( lambda x,y:(x,y), modelRF.predict(rdd.map(lambda
>>> (x,y):y)).collect(),rdd.map(lambda (x,y):x).collect() )\
>>>)\
>>> )
>>> # in the transform operation: x=text and y=features
>>> # Retrun will be tuple of (score,'original text')
>>> return predictions
>>>
>>>
>>> Hope, it will help somebody who is facing same problem.
>>> If anybody has better idea, please post it here.
>>>
>>> -Obaid
>>>
>>> On Mon, May 30, 2016 at 8:43 PM, nguyen duc tuan <newvalu...@gmail.com>
>>> wrote:
>>>
>>>> Dstream has an method foreachRDD, so you can walk through all RDDs
>>>> inside DStream as you want.
>>>>
>>>>
>>>> https://spark.apache.org/docs/1.4.0/api/java/org/apache/spark/streaming/dstream/DStream.html
>>>>
>>>> 2016-05-30 19:30 GMT+07:00 obaidul karim <obaidc...@gmail.com>:
>>>>
>>>>> Hi nguyen,
>>>>>
>>>>> If I am not mistaken, we cannot call "predict" on "dstream" as you
>>>>> have suggested.
>>>>> We have to use "transform" to be able to perform normal RDD operations
>>>>> on dstreams and here I am trapped.
>>>>>
>>>>> -Obaid
>>>>>
>>>>>
>>>>>
>>>>> On Mon, May 30, 2016 at 7:58 PM, nguyen duc tuan <newvalu...@gmail.com
>>>>> > wrote:
>>>>>
>>>>>> How about this ?
>>>>>>
>>>>>> def extract_feature(rf_model, x):
>>>>>> text = getFeatures(x).split(',')
>>>>>> fea = [float(i) for i in text]
>>>>>> prediction = rf_model.predict(fea)
>>>>>> return (prediction, x)
>>>>>> output = texts.map(lambda x: extract_feature(rf_model, x))
>>>>>>
>>>>>> 2016-05-30 14:17 GMT+07:00 obaidul karim <obaidc...@gmail.com>:
>>>>>>
>>>>>>> Hi,
>>>>>>>
>>>>>>> Anybody has any idea on below?
>>>>>>>
>>>>>>> -Obaid
>>>>>>>
>>>>>>>
>>>>>>> On Friday, 27 May 2016, obaidul karim <obaidc...@gmail.com> wrote:
>>>>>>>
>>>>>>>> Hi Guys,
>>>>>>>>
>>>>>>>> This is my first mail to spark users mailing list.
>>>>>>>>
>>>>>>>> I need help on Dstream operation.
>>>>>>>>
>>>>>>>> In fact, I am using a MLlib randomforest model to predict using
>>>>>>>> spark streaming. In the end, I want to combine the feature Dstream &
>>>>>>>> prediction Dstream together for further downstream processing.
>>>>>>>>
>>>>>>>> I am predicting using below piece of code:
>>>>>>>>
>>>>>>>> predictions = texts.map( lambda x : getFeatures(x) ).map(lambda x :
>>>>>>>> x.split(',')).map( lambda parts : [float(i) for i in parts]
>>>>>>>> ).transform(lambda rdd: rf_model.predict(rdd))
>>>>>>>>
>>>>>>>> Here texts is dstream having single line of text as records
>>>>>>>> getFeatures generates a comma separated features extracted from
>>>>>>>> each record
>>>>>>>>
>>>>>>>>
>>>>>>>> I want the output as below tuple:
>>>>>>>> ("predicted value", "original text")
>>>>>>>>
>>>>>>>> How can I achieve that ?
>>>>>>>> or
>>>>>>>> at least can I perform .zip like normal RDD operation on two
>>>>>>>> Dstreams, like below:
>>>>>>>> output = texts.zip(predictions)
>>>>>>>>
>>>>>>>>
>>>>>>>> Thanks in advance.
>>>>>>>>
>>>>>>>> -Obaid
>>>>>>>>
>>>>>>>
>>>>>>
>>>>>
>>>>
>>>
>>
