Re: Please reply ASAP : Regarding incubator systemml/breast_cancer project

[dusenberrymw]

Hi Aishwarya,

Yes, it is quite strange that Jupyter isn't running on the PySpark kernel even 
though it's being started in that manner.  The good news is that we do use this 
everyday, so once we find the root issue with your Jupyter, it should work 
great!  Let's try temporarily removing all of the existing Jupyter/IPython 
settings & kernels and basically start fresh.  Assuming you are on OS X / macOS 
or Linux, can you do the following? (Please double check the exact paths, as 
I'm typing on a phone.)

* Stop Jupyter, and make sure that it is not running.
* Temporarily remove the Jupyter kernels.  First, you will need to see where 
they are installed, and then just rename that path.
`jupyter kernelspec list`
# look at paths above.  For example, on macOS, it may be located at 
~/Library/Jupyter/kernels, and thus to move it, you would use the following. 
Update this as needed for the exact paths listed above 
`mv ~/Library/Jupyter/kernels ~/Library/Jupyter_OLD/kernels`
* Temporarily remove the Jupyter & IPython settings:
`mv ~/.jupyter ~/.jupyter_OLD`
`mv ~/.ipython ~/.ipython_OLD`
* Make sure Jupyter is up to date:
`pip3 install -U ipython jupyter`

After that, please ensure that Jupyter is not running, then start it in the 
context of PySpark as sent previously.  Once Jupyter is started this time, 
there should only be one kernel listed, and `sc` should be available.

Can you try that?

--

Mike Dusenberry
GitHub: github.com/dusenberrymw
LinkedIn: linkedin.com/in/mikedusenberry

Sent from my iPhone.


> On Apr 26, 2017, at 2:13 AM, Aishwarya Chaurasia  
> wrote:
> 
> Hi sir,
> The sc NameError persists.
> 
> (1) There is only one jupyter server running. And that was started with the
> pyspark command in the previous mail.
> (2) Two kernels are appearing in the change kernel option - Python3 and
> Python2. Tried with both of them and the result is the same.
> 
> How is jupyter not being able to run on the pyspark kernel when we have
> started the notebook with the pyspark command only?
> 
> Is it possible to create a .py file of MachineLearning.ipynb like was done
> with preprocessing.ipynb with explicitly creating a SparkContext() ?
> 
>> On 25-Apr-2017 11:57 PM,  wrote:
>> 
>> Hi Aishwarya,
>> 
>> Unfortunately this mailing list removes all images, so I can't view your
>> screenshot.  I'm assuming that it is the same issue with the missing
>> SparkContext `sc` object, but please let me know if it is a different
>> issue.  This sounds like it could be an issue with multiple kernels
>> installed in Jupyter.  When you start the notebook, can you see if there
>> are multiple kernels listed in the "Kernel" -> "Change Kernel" menu?  If
>> so, please try one of the other kernels to see if Jupyter is starting by
>> default with a non-spark kernel.  Also, is it possible that you have more
>> than one instance of the Jupyter server running?  I.e. for this scenario,
>> we start Jupyter itself directly via pyspark using the command sent
>> previously, whereas usually Jupyter can just be started with `jupyter
>> notebook`.  In the latter case, PySpark (and thus `sc`) would *not* be
>> available (unless you've set up special PySpark kernels separately).  In
>> summary, can you (1) check for other kernels via the menus, and (2) check
>> for other running Jupyter servers that are non-PySpark?
>> 
>> As for the other inquiry, great question!  When training models, it's
>> quite useful to track the loss and other metrics (i.e. accuracy) from
>> *both* the training and validation sets.  The reasoning is that it allows
>> for a more holistic view of the overall learning process, such as
>> evaluating whether any overfitting or underfitting is occurring.  For
>> example, say that you train a model and achieve an accuracy of 80% on the
>> validation set.  Is this good?  Is this the best that can be done?  Without
>> also tracking performance on the training set, it can be difficult to make
>> these decisions.  Say that you then measure the performance on the training
>> set and find that the model achieves 100% accuracy on that data.  That
>> might be a good indication that your model is overfitting the training set,
>> and that a combination of more data, regularization, and a smaller model
>> may be helpful in raising the generalization performance, i.e. the
>> performance on the validation set and future real examples on which you
>> wish to make predictions.  If on the other hand, the model achieved an 82%
>> on the training set, this could be a good indication that the model is
>> underfitting, and that a combination of a more expressive model and better
>> data could be helpful.  In summary, tracking performance on both the
>> training and validation datasets can be useful for determining ways in
>> which to improve the overall learning process.
>> 
>> 
>> - Mike
>> 
>> --
>> 
>> Mike Dusenberry
>> GitHub: github.com/dusenberrymw
>> LinkedIn: 

Re: Please reply ASAP : Regarding incubator systemml/breast_cancer project

[Aishwarya Chaurasia]

Hi sir,
The sc NameError persists.

(1) There is only one jupyter server running. And that was started with the
pyspark command in the previous mail.
(2) Two kernels are appearing in the change kernel option - Python3 and
Python2. Tried with both of them and the result is the same.

How is jupyter not being able to run on the pyspark kernel when we have
started the notebook with the pyspark command only?

Is it possible to create a .py file of MachineLearning.ipynb like was done
with preprocessing.ipynb with explicitly creating a SparkContext() ?

On 25-Apr-2017 11:57 PM,  wrote:

> Hi Aishwarya,
>
> Unfortunately this mailing list removes all images, so I can't view your
> screenshot.  I'm assuming that it is the same issue with the missing
> SparkContext `sc` object, but please let me know if it is a different
> issue.  This sounds like it could be an issue with multiple kernels
> installed in Jupyter.  When you start the notebook, can you see if there
> are multiple kernels listed in the "Kernel" -> "Change Kernel" menu?  If
> so, please try one of the other kernels to see if Jupyter is starting by
> default with a non-spark kernel.  Also, is it possible that you have more
> than one instance of the Jupyter server running?  I.e. for this scenario,
> we start Jupyter itself directly via pyspark using the command sent
> previously, whereas usually Jupyter can just be started with `jupyter
> notebook`.  In the latter case, PySpark (and thus `sc`) would *not* be
> available (unless you've set up special PySpark kernels separately).  In
> summary, can you (1) check for other kernels via the menus, and (2) check
> for other running Jupyter servers that are non-PySpark?
>
> As for the other inquiry, great question!  When training models, it's
> quite useful to track the loss and other metrics (i.e. accuracy) from
> *both* the training and validation sets.  The reasoning is that it allows
> for a more holistic view of the overall learning process, such as
> evaluating whether any overfitting or underfitting is occurring.  For
> example, say that you train a model and achieve an accuracy of 80% on the
> validation set.  Is this good?  Is this the best that can be done?  Without
> also tracking performance on the training set, it can be difficult to make
> these decisions.  Say that you then measure the performance on the training
> set and find that the model achieves 100% accuracy on that data.  That
> might be a good indication that your model is overfitting the training set,
> and that a combination of more data, regularization, and a smaller model
> may be helpful in raising the generalization performance, i.e. the
> performance on the validation set and future real examples on which you
> wish to make predictions.  If on the other hand, the model achieved an 82%
> on the training set, this could be a good indication that the model is
> underfitting, and that a combination of a more expressive model and better
> data could be helpful.  In summary, tracking performance on both the
> training and validation datasets can be useful for determining ways in
> which to improve the overall learning process.
>
>
> - Mike
>
> --
>
> Mike Dusenberry
> GitHub: github.com/dusenberrymw
> LinkedIn: linkedin.com/in/mikedusenberry
>
> Sent from my iPhone.
>
>
> > On Apr 25, 2017, at 8:47 AM, Aishwarya Chaurasia <
> aishwarya2...@gmail.com> wrote:
> >
> > We had another query, sir. We read the entire MachineLearning.ipynb code.
> > in it the training samples and the validation samples have both been
> > evaluated separately and their respective losses and accuracies obtained.
> > Why are the training samples being evaluated again if they were used to
> > train the model in the first place? Shouldn't only the validation data
> > frames be evaluated to find out the loss and accuracy?
> >
> > Thank you
> >
> > On 25-Apr-2017 4:00 PM, "Aishwarya Chaurasia" 
> > wrote:
> >
> >> Hello sir,
> >>
> >> The NameError is occuring again sir. Why does it keep resurfacing?
> >>
> >> Attaching the screenshot of the error.
> >>
> >>> On 25-Apr-2017 2:50 AM,  wrote:
> >>>
> >>> Hi Aishwarya,
> >>>
> >>> For the error message, that just means that the SystemML jar isn't
> being
> >>> found.  Can you add a `--driver-class-path $SYSTEMML_HOME/target/
> SystemML.jar`
> >>> to the invocation of Jupyter?  I.e. `PYSPARK_PYTHON=python3
> >>> PYSPARK_DRIVER_PYTHON=jupyter PYSPARK_DRIVER_PYTHON_OPTS="notebook"
> >>> pyspark  --jars $SYSTEMML_HOME/target/SystemML.jar --driver-class-path
> >>> $SYSTEMML_HOME/target/SystemML.jar`. There was a PySpark bug that was
> >>> supposed to have been fixed in Spark 2.x, but it's possible that it is
> >>> still an issue.
> >>>
> >>> As for the output, the notebook will create SystemML `Matrix` objects
> for
> >>> all of the weights and biases of the trained models.  To save, please
> >>> convert each one to a DataFrame, i.e. `Wc1.toDF()` and 

Re: Please reply ASAP : Regarding incubator systemml/breast_cancer project

[dusenberrymw]

Hi Aishwarya,

Unfortunately this mailing list removes all images, so I can't view your 
screenshot.  I'm assuming that it is the same issue with the missing 
SparkContext `sc` object, but please let me know if it is a different issue.  
This sounds like it could be an issue with multiple kernels installed in 
Jupyter.  When you start the notebook, can you see if there are multiple 
kernels listed in the "Kernel" -> "Change Kernel" menu?  If so, please try one 
of the other kernels to see if Jupyter is starting by default with a non-spark 
kernel.  Also, is it possible that you have more than one instance of the 
Jupyter server running?  I.e. for this scenario, we start Jupyter itself 
directly via pyspark using the command sent previously, whereas usually Jupyter 
can just be started with `jupyter notebook`.  In the latter case, PySpark (and 
thus `sc`) would *not* be available (unless you've set up special PySpark 
kernels separately).  In summary, can you (1) check for other kernels via the 
menus, and (2) check for other running Jupyter servers that are non-PySpark?

As for the other inquiry, great question!  When training models, it's quite 
useful to track the loss and other metrics (i.e. accuracy) from *both* the 
training and validation sets.  The reasoning is that it allows for a more 
holistic view of the overall learning process, such as evaluating whether any 
overfitting or underfitting is occurring.  For example, say that you train a 
model and achieve an accuracy of 80% on the validation set.  Is this good?  Is 
this the best that can be done?  Without also tracking performance on the 
training set, it can be difficult to make these decisions.  Say that you then 
measure the performance on the training set and find that the model achieves 
100% accuracy on that data.  That might be a good indication that your model is 
overfitting the training set, and that a combination of more data, 
regularization, and a smaller model may be helpful in raising the 
generalization performance, i.e. the performance on the validation set and 
future real examples on which you wish to make predictions.  If on the other 
hand, the model achieved an 82% on the training set, this could be a good 
indication that the model is underfitting, and that a combination of a more 
expressive model and better data could be helpful.  In summary, tracking 
performance on both the training and validation datasets can be useful for 
determining ways in which to improve the overall learning process.


- Mike

--

Mike Dusenberry
GitHub: github.com/dusenberrymw
LinkedIn: linkedin.com/in/mikedusenberry

Sent from my iPhone.


> On Apr 25, 2017, at 8:47 AM, Aishwarya Chaurasia  
> wrote:
> 
> We had another query, sir. We read the entire MachineLearning.ipynb code.
> in it the training samples and the validation samples have both been
> evaluated separately and their respective losses and accuracies obtained.
> Why are the training samples being evaluated again if they were used to
> train the model in the first place? Shouldn't only the validation data
> frames be evaluated to find out the loss and accuracy?
> 
> Thank you
> 
> On 25-Apr-2017 4:00 PM, "Aishwarya Chaurasia" 
> wrote:
> 
>> Hello sir,
>> 
>> The NameError is occuring again sir. Why does it keep resurfacing?
>> 
>> Attaching the screenshot of the error.
>> 
>>> On 25-Apr-2017 2:50 AM,  wrote:
>>> 
>>> Hi Aishwarya,
>>> 
>>> For the error message, that just means that the SystemML jar isn't being
>>> found.  Can you add a `--driver-class-path 
>>> $SYSTEMML_HOME/target/SystemML.jar`
>>> to the invocation of Jupyter?  I.e. `PYSPARK_PYTHON=python3
>>> PYSPARK_DRIVER_PYTHON=jupyter PYSPARK_DRIVER_PYTHON_OPTS="notebook"
>>> pyspark  --jars $SYSTEMML_HOME/target/SystemML.jar --driver-class-path
>>> $SYSTEMML_HOME/target/SystemML.jar`. There was a PySpark bug that was
>>> supposed to have been fixed in Spark 2.x, but it's possible that it is
>>> still an issue.
>>> 
>>> As for the output, the notebook will create SystemML `Matrix` objects for
>>> all of the weights and biases of the trained models.  To save, please
>>> convert each one to a DataFrame, i.e. `Wc1.toDF()` and repeated for each
>>> matrix, and then simply save the DataFrames.  This could be done all at
>>> once like this for a SystemML Matrix object `Wc1`:
>>> `Wc1.toDf().write.save("path/to/save/Wc1.parquet", format="parquet")`.
>>> Just repeat for each matrix returned by the "Train" code for the
>>> algorithms.  At that point, you will have a set of saved DataFrames
>>> representing a trained SystemML model, and these can be used in downstream
>>> classification tasks in a similar manner to the "Eval" sections.
>>> 
>>> -Mike
>>> 
>>> --
>>> 
>>> Mike Dusenberry
>>> GitHub: github.com/dusenberrymw
>>> LinkedIn: linkedin.com/in/mikedusenberry
>>> 
>>> Sent from my iPhone.
>>> 
>>> 
 On Apr 24, 2017, at 3:07 AM, Aishwarya Chaurasia 

Re: Please reply ASAP : Regarding incubator systemml/breast_cancer project

[Aishwarya Chaurasia]

We had another query, sir. We read the entire MachineLearning.ipynb code.
in it the training samples and the validation samples have both been
evaluated separately and their respective losses and accuracies obtained.
Why are the training samples being evaluated again if they were used to
train the model in the first place? Shouldn't only the validation data
frames be evaluated to find out the loss and accuracy?

Thank you

On 25-Apr-2017 4:00 PM, "Aishwarya Chaurasia" 
wrote:

> Hello sir,
>
> The NameError is occuring again sir. Why does it keep resurfacing?
>
> Attaching the screenshot of the error.
>
> On 25-Apr-2017 2:50 AM,  wrote:
>
>> Hi Aishwarya,
>>
>> For the error message, that just means that the SystemML jar isn't being
>> found.  Can you add a `--driver-class-path 
>> $SYSTEMML_HOME/target/SystemML.jar`
>> to the invocation of Jupyter?  I.e. `PYSPARK_PYTHON=python3
>> PYSPARK_DRIVER_PYTHON=jupyter PYSPARK_DRIVER_PYTHON_OPTS="notebook"
>> pyspark  --jars $SYSTEMML_HOME/target/SystemML.jar --driver-class-path
>> $SYSTEMML_HOME/target/SystemML.jar`. There was a PySpark bug that was
>> supposed to have been fixed in Spark 2.x, but it's possible that it is
>> still an issue.
>>
>> As for the output, the notebook will create SystemML `Matrix` objects for
>> all of the weights and biases of the trained models.  To save, please
>> convert each one to a DataFrame, i.e. `Wc1.toDF()` and repeated for each
>> matrix, and then simply save the DataFrames.  This could be done all at
>> once like this for a SystemML Matrix object `Wc1`:
>> `Wc1.toDf().write.save("path/to/save/Wc1.parquet", format="parquet")`.
>> Just repeat for each matrix returned by the "Train" code for the
>> algorithms.  At that point, you will have a set of saved DataFrames
>> representing a trained SystemML model, and these can be used in downstream
>> classification tasks in a similar manner to the "Eval" sections.
>>
>> -Mike
>>
>> --
>>
>> Mike Dusenberry
>> GitHub: github.com/dusenberrymw
>> LinkedIn: linkedin.com/in/mikedusenberry
>>
>> Sent from my iPhone.
>>
>>
>> > On Apr 24, 2017, at 3:07 AM, Aishwarya Chaurasia <
>> aishwarya2...@gmail.com> wrote:
>> >
>> > Further more :
>> > What is the output of MachineLearning.ipynb you're obtaining sir?
>> > We are actually nearing our deadline for our problem.
>> > Thanks a lot.
>> >
>> > On 24-Apr-2017 2:58 PM, "Aishwarya Chaurasia" 
>> > wrote:
>> >
>> > Hello sir,
>> >
>> > Thanks a lot for replying sir. But unfortunately it did not work.
>> Although
>> > the NameError did not appear this time but another error came about :
>> >
>> > https://paste.fedoraproject.org/paste/TUMtSIb88Q73FYekwJmM7V
>> > 5M1UNdIGYhyRLivL9gydE=
>> >
>> > This error was obtained after executing the second block of code of
>> > MachineLearning.py in terminal. ( ml = MLContext(sc) )
>> >
>> > We have installed the bleeding-edge version of systemml only and the
>> > installation was done correctly. We are in a fix now. :/
>> > Kindly look into the matter asap
>> >
>> > On 24-Apr-2017 12:15 PM, "Mike Dusenberry" 
>> wrote:
>> >
>> > Hi Aishwarya,
>> >
>> > Glad to hear that the preprocessing stage was successful!  As for the
>> > `MachineLearning.ipynb` notebook, here is a general guide:
>> >
>> >
>> >   - The `MachineLearning.ipynb` notebook essentially (1) loads in the
>> >   training and validation DataFrames from the preprocessing step, (2)
>> >   converts them to normalized & one-hot encoded SystemML matrices for
>> >   consumption by the ML algorithms, and (3) explores training a couple
>> of
>> >   models.
>> >   - To run, you'll need to start Jupyter in the context of PySpark via
>> >   `PYSPARK_PYTHON=python3 PYSPARK_DRIVER_PYTHON=jupyter
>> >   PYSPARK_DRIVER_PYTHON_OPTS="notebook" pyspark  --jars
>> >   $SYSTEMML_HOME/target/SystemML.jar`.  Note that if you have installed
>> >   SystemML with pip from PyPy (`pip3 install systemml`), this will
>> install
>> >   our 0.13 release, and the `--jars $SYSTEMML_HOME/target/SystemML.jar`
>> > will
>> >   not be necessary.  If you instead have installed a bleeding-edge
>> version
>> > of
>> >   SystemML locally (git clone locally, maven build, `pip3 install -e
>> >   src/main/python` as listed in `projects/breast_cancer/README.md`),
>> the
>> >   `--jars $SYSTEMML_HOME/target/SystemML.jar` part *is* necessary.  We
>> are
>> >   about to release 0.14, and for this project, I *would* recommend
>> using a
>> >   bleeding edge install.
>> >   - Once Jupyter has been started in the context of PySpark, the `sc`
>> >   SparkContext object should be available.  Please let me know if you
>> >   continue to see this issue.
>> >   - The "Read in train & val data" section simply reads in the training
>> >   and validation data generated in the preprocessing stage.  Be sure
>> that
>> > the
>> >   `size` setting is the same as the preprocessing size.  The percentage
>> `p`
>> 

Re: Please reply ASAP : Regarding incubator systemml/breast_cancer project

[Aishwarya Chaurasia]

Hello sir,

The NameError is occuring again sir. Why does it keep resurfacing?

Attaching the screenshot of the error.

On 25-Apr-2017 2:50 AM,  wrote:

> Hi Aishwarya,
>
> For the error message, that just means that the SystemML jar isn't being
> found.  Can you add a `--driver-class-path $SYSTEMML_HOME/target/SystemML.jar`
> to the invocation of Jupyter?  I.e. `PYSPARK_PYTHON=python3
> PYSPARK_DRIVER_PYTHON=jupyter PYSPARK_DRIVER_PYTHON_OPTS="notebook"
> pyspark  --jars $SYSTEMML_HOME/target/SystemML.jar --driver-class-path
> $SYSTEMML_HOME/target/SystemML.jar`. There was a PySpark bug that was
> supposed to have been fixed in Spark 2.x, but it's possible that it is
> still an issue.
>
> As for the output, the notebook will create SystemML `Matrix` objects for
> all of the weights and biases of the trained models.  To save, please
> convert each one to a DataFrame, i.e. `Wc1.toDF()` and repeated for each
> matrix, and then simply save the DataFrames.  This could be done all at
> once like this for a SystemML Matrix object `Wc1`:
> `Wc1.toDf().write.save("path/to/save/Wc1.parquet", format="parquet")`.
> Just repeat for each matrix returned by the "Train" code for the
> algorithms.  At that point, you will have a set of saved DataFrames
> representing a trained SystemML model, and these can be used in downstream
> classification tasks in a similar manner to the "Eval" sections.
>
> -Mike
>
> --
>
> Mike Dusenberry
> GitHub: github.com/dusenberrymw
> LinkedIn: linkedin.com/in/mikedusenberry
>
> Sent from my iPhone.
>
>
> > On Apr 24, 2017, at 3:07 AM, Aishwarya Chaurasia <
> aishwarya2...@gmail.com> wrote:
> >
> > Further more :
> > What is the output of MachineLearning.ipynb you're obtaining sir?
> > We are actually nearing our deadline for our problem.
> > Thanks a lot.
> >
> > On 24-Apr-2017 2:58 PM, "Aishwarya Chaurasia" 
> > wrote:
> >
> > Hello sir,
> >
> > Thanks a lot for replying sir. But unfortunately it did not work.
> Although
> > the NameError did not appear this time but another error came about :
> >
> > https://paste.fedoraproject.org/paste/TUMtSIb88Q73FYekwJmM7V
> > 5M1UNdIGYhyRLivL9gydE=
> >
> > This error was obtained after executing the second block of code of
> > MachineLearning.py in terminal. ( ml = MLContext(sc) )
> >
> > We have installed the bleeding-edge version of systemml only and the
> > installation was done correctly. We are in a fix now. :/
> > Kindly look into the matter asap
> >
> > On 24-Apr-2017 12:15 PM, "Mike Dusenberry" 
> wrote:
> >
> > Hi Aishwarya,
> >
> > Glad to hear that the preprocessing stage was successful!  As for the
> > `MachineLearning.ipynb` notebook, here is a general guide:
> >
> >
> >   - The `MachineLearning.ipynb` notebook essentially (1) loads in the
> >   training and validation DataFrames from the preprocessing step, (2)
> >   converts them to normalized & one-hot encoded SystemML matrices for
> >   consumption by the ML algorithms, and (3) explores training a couple of
> >   models.
> >   - To run, you'll need to start Jupyter in the context of PySpark via
> >   `PYSPARK_PYTHON=python3 PYSPARK_DRIVER_PYTHON=jupyter
> >   PYSPARK_DRIVER_PYTHON_OPTS="notebook" pyspark  --jars
> >   $SYSTEMML_HOME/target/SystemML.jar`.  Note that if you have installed
> >   SystemML with pip from PyPy (`pip3 install systemml`), this will
> install
> >   our 0.13 release, and the `--jars $SYSTEMML_HOME/target/SystemML.jar`
> > will
> >   not be necessary.  If you instead have installed a bleeding-edge
> version
> > of
> >   SystemML locally (git clone locally, maven build, `pip3 install -e
> >   src/main/python` as listed in `projects/breast_cancer/README.md`), the
> >   `--jars $SYSTEMML_HOME/target/SystemML.jar` part *is* necessary.  We
> are
> >   about to release 0.14, and for this project, I *would* recommend using
> a
> >   bleeding edge install.
> >   - Once Jupyter has been started in the context of PySpark, the `sc`
> >   SparkContext object should be available.  Please let me know if you
> >   continue to see this issue.
> >   - The "Read in train & val data" section simply reads in the training
> >   and validation data generated in the preprocessing stage.  Be sure that
> > the
> >   `size` setting is the same as the preprocessing size.  The percentage
> `p`
> >   setting determines whether the full or sampled DataFrames are loaded.
> If
> >   you set `p = 1`, the full DataFrames will be used.  If you instead
> would
> >   prefer to use the smaller sampled DataFrames while getting started,
> > please
> >   set it to the same value as used in the preprocessing to generate the
> >   smaller sampled DataFrames.
> >   - The `Extract X & Y matrices` section splits each of the train and
> >   validation DataFrames into effectively X & Y matrices (still as
> DataFrame
> >   types), with X containing the images, and Y containing the labels.
> >   - The `Convert to SystemML Matrices` 

Re: Please reply ASAP : Regarding incubator systemml/breast_cancer project

[Aishwarya Chaurasia]

Hello sir,

Thanks a lot for replying sir. But unfortunately it did not work. Although
the NameError did not appear this time but another error came about :

https://paste.fedoraproject.org/paste/TUMtSIb88Q73FYekwJmM7V5M1UNdIG
YhyRLivL9gydE=

This error was obtained after executing the second block of code of
MachineLearning.py in terminal. ( ml = MLContext(sc) )

We have installed the bleeding-edge version of systemml only and the
installation was done correctly. We are in a fix now. :/
Kindly look into the matter asap

On 24-Apr-2017 12:15 PM, "Mike Dusenberry"  wrote:

Hi Aishwarya,

Glad to hear that the preprocessing stage was successful!  As for the
`MachineLearning.ipynb` notebook, here is a general guide:


   - The `MachineLearning.ipynb` notebook essentially (1) loads in the
   training and validation DataFrames from the preprocessing step, (2)
   converts them to normalized & one-hot encoded SystemML matrices for
   consumption by the ML algorithms, and (3) explores training a couple of
   models.
   - To run, you'll need to start Jupyter in the context of PySpark via
   `PYSPARK_PYTHON=python3 PYSPARK_DRIVER_PYTHON=jupyter
   PYSPARK_DRIVER_PYTHON_OPTS="notebook" pyspark  --jars
   $SYSTEMML_HOME/target/SystemML.jar`.  Note that if you have installed
   SystemML with pip from PyPy (`pip3 install systemml`), this will install
   our 0.13 release, and the `--jars $SYSTEMML_HOME/target/SystemML.jar`
will
   not be necessary.  If you instead have installed a bleeding-edge version
of
   SystemML locally (git clone locally, maven build, `pip3 install -e
   src/main/python` as listed in `projects/breast_cancer/README.md`), the
   `--jars $SYSTEMML_HOME/target/SystemML.jar` part *is* necessary.  We are
   about to release 0.14, and for this project, I *would* recommend using a
   bleeding edge install.
   - Once Jupyter has been started in the context of PySpark, the `sc`
   SparkContext object should be available.  Please let me know if you
   continue to see this issue.
   - The "Read in train & val data" section simply reads in the training
   and validation data generated in the preprocessing stage.  Be sure that
the
   `size` setting is the same as the preprocessing size.  The percentage `p`
   setting determines whether the full or sampled DataFrames are loaded.  If
   you set `p = 1`, the full DataFrames will be used.  If you instead would
   prefer to use the smaller sampled DataFrames while getting started,
please
   set it to the same value as used in the preprocessing to generate the
   smaller sampled DataFrames.
   - The `Extract X & Y matrices` section splits each of the train and
   validation DataFrames into effectively X & Y matrices (still as DataFrame
   types), with X containing the images, and Y containing the labels.
   - The `Convert to SystemML Matrices` section passes the X & Y DataFrames
   into a SystemML script that performs some normalization of the images &
   one-hot encoding of the labels, and then returns SystemML `Matrix` types.
   These are now ready to be passed into the subsequent algorithms.
   - The "Trigger Caching" and "Save Matrices" are experimental features,
   and not necessary to execute.
   - Next comes the two algorithms being explored in this notebook.  The
   "Softmax Classifier" is just a multi-class logistic regression model, and
   is simply there to serve as a baseline comparison with the subsequent
   convolutional neural net model.  You may wish to simply skip this softmax
   model and move to the latter convnet model further down in the notebook.
   - The actual softmax model is located at [
   https://github.com/apache/incubator-systemml/blob/
master/projects/breast_cancer/softmax_clf.dml],
   and the notebook calls functions from that file.
   - The softmax sanity check just ensures that the model is able to
   completely overfit when given a tiny sample size.  This should yield
~100%
   training accuracy if the sample size in this section is small enough.
This
   is just a check to ensure that nothing else is wrong with the math or the
   data.
   - The softmax "Train" section will train a softmax model and return the
   weights (`W`) and biases (`b`) of the model as SystemML `Matrix` objects.
   Please adjust the hyperparameters in this section to your problem.
   - The softmax "Eval" section takes the trained weights and biases and
   evaluates the training and validation performance.
   - The next model is a LeNet-like convnet model.  The actual model is
   located at [
   https://github.com/apache/incubator-systemml/blob/
master/projects/breast_cancer/convnet.dml],
   and the notebook simply calls functions from that file.
   - Once again, there is an initial sanity check for the ability to
   overfit on a small amount of data.
   - The "Hyperparameter Search" contains a script to sample different
   hyperparams for the convnet, and save the hyperparams + validation
accuracy
   of each set after a single epoch of 

Re: Regarding incubator systemml/breast_cancer project

[Mike Dusenberry]

Hi Aishwarya,

Glad to hear that the preprocessing stage was successful!  As for the
`MachineLearning.ipynb` notebook, here is a general guide:


   - The `MachineLearning.ipynb` notebook essentially (1) loads in the
   training and validation DataFrames from the preprocessing step, (2)
   converts them to normalized & one-hot encoded SystemML matrices for
   consumption by the ML algorithms, and (3) explores training a couple of
   models.
   - To run, you'll need to start Jupyter in the context of PySpark via
   `PYSPARK_PYTHON=python3 PYSPARK_DRIVER_PYTHON=jupyter
   PYSPARK_DRIVER_PYTHON_OPTS="notebook" pyspark  --jars
   $SYSTEMML_HOME/target/SystemML.jar`.  Note that if you have installed
   SystemML with pip from PyPy (`pip3 install systemml`), this will install
   our 0.13 release, and the `--jars $SYSTEMML_HOME/target/SystemML.jar` will
   not be necessary.  If you instead have installed a bleeding-edge version of
   SystemML locally (git clone locally, maven build, `pip3 install -e
   src/main/python` as listed in `projects/breast_cancer/README.md`), the
   `--jars $SYSTEMML_HOME/target/SystemML.jar` part *is* necessary.  We are
   about to release 0.14, and for this project, I *would* recommend using a
   bleeding edge install.
   - Once Jupyter has been started in the context of PySpark, the `sc`
   SparkContext object should be available.  Please let me know if you
   continue to see this issue.
   - The "Read in train & val data" section simply reads in the training
   and validation data generated in the preprocessing stage.  Be sure that the
   `size` setting is the same as the preprocessing size.  The percentage `p`
   setting determines whether the full or sampled DataFrames are loaded.  If
   you set `p = 1`, the full DataFrames will be used.  If you instead would
   prefer to use the smaller sampled DataFrames while getting started, please
   set it to the same value as used in the preprocessing to generate the
   smaller sampled DataFrames.
   - The `Extract X & Y matrices` section splits each of the train and
   validation DataFrames into effectively X & Y matrices (still as DataFrame
   types), with X containing the images, and Y containing the labels.
   - The `Convert to SystemML Matrices` section passes the X & Y DataFrames
   into a SystemML script that performs some normalization of the images &
   one-hot encoding of the labels, and then returns SystemML `Matrix` types.
   These are now ready to be passed into the subsequent algorithms.
   - The "Trigger Caching" and "Save Matrices" are experimental features,
   and not necessary to execute.
   - Next comes the two algorithms being explored in this notebook.  The
   "Softmax Classifier" is just a multi-class logistic regression model, and
   is simply there to serve as a baseline comparison with the subsequent
   convolutional neural net model.  You may wish to simply skip this softmax
   model and move to the latter convnet model further down in the notebook.
   - The actual softmax model is located at [
   
https://github.com/apache/incubator-systemml/blob/master/projects/breast_cancer/softmax_clf.dml],
   and the notebook calls functions from that file.
   - The softmax sanity check just ensures that the model is able to
   completely overfit when given a tiny sample size.  This should yield ~100%
   training accuracy if the sample size in this section is small enough.  This
   is just a check to ensure that nothing else is wrong with the math or the
   data.
   - The softmax "Train" section will train a softmax model and return the
   weights (`W`) and biases (`b`) of the model as SystemML `Matrix` objects.
   Please adjust the hyperparameters in this section to your problem.
   - The softmax "Eval" section takes the trained weights and biases and
   evaluates the training and validation performance.
   - The next model is a LeNet-like convnet model.  The actual model is
   located at [
   
https://github.com/apache/incubator-systemml/blob/master/projects/breast_cancer/convnet.dml],
   and the notebook simply calls functions from that file.
   - Once again, there is an initial sanity check for the ability to
   overfit on a small amount of data.
   - The "Hyperparameter Search" contains a script to sample different
   hyperparams for the convnet, and save the hyperparams + validation accuracy
   of each set after a single epoch of training.  These string files will be
   saved to HDFS.  Please feel free to adjust the range of the hyperparameters
   for your problem.  Please also feel free to try using the `parfor`
   (parallel for-loop) instead of the while loop to speed up this section.
   Note that this is still a work in progress.  The hyperparameter tuning in
   this section makes use of random search (as opposed to grid search), which
   has been promoted by Bengio et al. to speed up the search time.
   - The "Train" section trains the convnet and returns the weights and
   biases as SystemML `Matrix` types.  In this 

Re: Please reply asap : Regarding incubator systemml/breast_cancer project

[Aishwarya Chaurasia]

Hey,

Thank you so much for your help sir. We were finally able to run
preprocess.py without any errors. And the results obtained were
satisfactory i.e we got five set of data frames like you said we would.

But alas! when we tried to run MachineLearning.ipynb the same NameError
came : https://paste.fedoraproject.org/paste/l3LFJreg~vnYEDTSTQH7
3l5M1UNdIGYhyRLivL9gydE=

Could you guide us again as to how to proceed now?
Also, could you please provide an overview of the process
MachineLearning.ipynb is following to train the samples.
Also we have tried all possible solutions to remove the name sc error.
It would be really kind of you if you looked into the matter asap.

Thanks a lot!

On 22-Apr-2017 5:19 PM, "Aishwarya Chaurasia" 
wrote:

> Hey,
>
> Thank you so much for your help sir. We were finally able to run
> preprocess.py without any errors. And the results obtained were
> satisfactory i.e we got five set of data frames like you said we would.
>
> But alas! when we tried to run MachineLearning.ipynb the same NameError
> came : https://paste.fedoraproject.org/paste/l3LFJreg~vnYEDTSTQH7
> 3l5M1UNdIGYhyRLivL9gydE=
>
> Could you guide us again as to how to proceed now?
> Also, could you please provide an overview of the process
> MachineLearning.ipynb is following to train the samples.
>
> Thanks a lot!
>
> On 20-Apr-2017 12:16 AM,  wrote:
>
>> Hi Aishwarya,
>>
>> Looks like you've just encountered an out of memory error on one of the
>> executors.  Therefore, you just need to adjust the `spark.executor.memory`
>> and `spark.driver.memory` settings with higher amounts of RAM.  What is
>> your current setup?  I.e. are you using a cluster of machines, or a single
>> machine?  We generally use a large driver on one machine, and then a single
>> large executor on each other machine.  I would give a sizable amount of
>> memory to the driver, and about half the possible memory on the executors
>> so that the Python processes have enough memory as well.  PySpark has JVM
>> and Python components, and the Spark memory settings only pertain to the
>> JVM side, thus the need to save about half the executor memory for the
>> Python side.
>>
>> Thanks!
>>
>> - Mike
>>
>> --
>>
>> Mike Dusenberry
>> GitHub: github.com/dusenberrymw
>> LinkedIn: linkedin.com/in/mikedusenberry
>>
>> Sent from my iPhone.
>>
>>
>> > On Apr 19, 2017, at 5:53 AM, Aishwarya Chaurasia <
>> aishwarya2...@gmail.com> wrote:
>> >
>> > Hello sir,
>> >
>> > We also wanted to ensure that the spark-submit command we're using is
>> the
>> > correct one for running 'preprocess.py'.
>> > Command :  /home/new/sparks/bin/spark-submit preprocess.py
>> >
>> >
>> > Thank you.
>> > Aishwarya Chaurasia.
>> >
>> > On 19-Apr-2017 3:55 PM, "Aishwarya Chaurasia" 
>> > wrote:
>> >
>> > Hello sir,
>> > On running the file preprocess.py we are getting the following error :
>> >
>> > https://paste.fedoraproject.org/paste/IAvqiiyJChSC0V9eeETe2F5M1UNdIG
>> > YhyRLivL9gydE=
>> >
>> > Can you please help us by looking into the error and kindly tell us the
>> > solution for it.
>> > Thanks a lot.
>> > Aishwarya Chaurasia
>> >
>> >
>> >> On 19-Apr-2017 12:43 AM,  wrote:
>> >>
>> >> Hi Aishwarya,
>> >>
>> >> Certainly, here is some more detailed information about`preprocess.py`:
>> >>
>> >>  * The preprocessing Python script is located at
>> >> https://github.com/apache/incubator-systemml/blob/master/
>> >> projects/breast_cancer/preprocess.py.  Note that this is different
>> than
>> >> the library module at https://github.com/apache/incu
>> >> bator-systemml/blob/master/projects/breast_cancer/breastc
>> >> ancer/preprocessing.py.
>> >>  * This script is used to preprocess a set of histology slide images,
>> >> which are `.svs` files in our case, and `.tiff` files in your case.
>> >>  * Lines 63-79 contain "settings" such as the output image sizes,
>> folder
>> >> paths, etc.  Of particular interest, line 72 has the folder path for
>> the
>> >> original slide images that should be commonly accessible from all
>> machines
>> >> being used, and lines 74-79 contain the names of the output DataFrames
>> that
>> >> will be saved.
>> >>  * Line 82 performs the actual preprocessing and creates a Spark
>> >> DataFrame with the following columns: slide number, tumor score,
>> molecular
>> >> score, sample.  The "sample" in this case is the actual small,
>> chopped-up
>> >> section of the image that has been extracted and flattened into a row
>> >> Vector.  For test images without labels (`training=false`), only the
>> slide
>> >> number and sample will be contained in the DataFrame (i.e. no labels).
>> >> This calls the `preprocess(...)` function located on line 371 of
>> >> https://github.com/apache/incubator-systemml/blob/master/
>> >> projects/breast_cancer/breastcancer/preprocessing.py, which is a
>> >> different file.
>> >>  * Line 87 simply saves the above DataFrame to HDFS with the 

Re: Regarding incubator systemml/breast_cancer project

[Aishwarya Chaurasia]

Hey,

Thank you so much for your help sir. We were finally able to run
preprocess.py without any errors. And the results obtained were
satisfactory i.e we got five set of data frames like you said we would.

But alas! when we tried to run MachineLearning.ipynb the same NameError
came : https://paste.fedoraproject.org/paste/l3LFJreg~
vnYEDTSTQH73l5M1UNdIGYhyRLivL9gydE=

Could you guide us again as to how to proceed now?
Also, could you please provide an overview of the process
MachineLearning.ipynb is following to train the samples.

Thanks a lot!

On 20-Apr-2017 12:16 AM,  wrote:

> Hi Aishwarya,
>
> Looks like you've just encountered an out of memory error on one of the
> executors.  Therefore, you just need to adjust the `spark.executor.memory`
> and `spark.driver.memory` settings with higher amounts of RAM.  What is
> your current setup?  I.e. are you using a cluster of machines, or a single
> machine?  We generally use a large driver on one machine, and then a single
> large executor on each other machine.  I would give a sizable amount of
> memory to the driver, and about half the possible memory on the executors
> so that the Python processes have enough memory as well.  PySpark has JVM
> and Python components, and the Spark memory settings only pertain to the
> JVM side, thus the need to save about half the executor memory for the
> Python side.
>
> Thanks!
>
> - Mike
>
> --
>
> Mike Dusenberry
> GitHub: github.com/dusenberrymw
> LinkedIn: linkedin.com/in/mikedusenberry
>
> Sent from my iPhone.
>
>
> > On Apr 19, 2017, at 5:53 AM, Aishwarya Chaurasia <
> aishwarya2...@gmail.com> wrote:
> >
> > Hello sir,
> >
> > We also wanted to ensure that the spark-submit command we're using is the
> > correct one for running 'preprocess.py'.
> > Command :  /home/new/sparks/bin/spark-submit preprocess.py
> >
> >
> > Thank you.
> > Aishwarya Chaurasia.
> >
> > On 19-Apr-2017 3:55 PM, "Aishwarya Chaurasia" 
> > wrote:
> >
> > Hello sir,
> > On running the file preprocess.py we are getting the following error :
> >
> > https://paste.fedoraproject.org/paste/IAvqiiyJChSC0V9eeETe2F5M1UNdIG
> > YhyRLivL9gydE=
> >
> > Can you please help us by looking into the error and kindly tell us the
> > solution for it.
> > Thanks a lot.
> > Aishwarya Chaurasia
> >
> >
> >> On 19-Apr-2017 12:43 AM,  wrote:
> >>
> >> Hi Aishwarya,
> >>
> >> Certainly, here is some more detailed information about`preprocess.py`:
> >>
> >>  * The preprocessing Python script is located at
> >> https://github.com/apache/incubator-systemml/blob/master/
> >> projects/breast_cancer/preprocess.py.  Note that this is different than
> >> the library module at https://github.com/apache/incu
> >> bator-systemml/blob/master/projects/breast_cancer/breastc
> >> ancer/preprocessing.py.
> >>  * This script is used to preprocess a set of histology slide images,
> >> which are `.svs` files in our case, and `.tiff` files in your case.
> >>  * Lines 63-79 contain "settings" such as the output image sizes, folder
> >> paths, etc.  Of particular interest, line 72 has the folder path for the
> >> original slide images that should be commonly accessible from all
> machines
> >> being used, and lines 74-79 contain the names of the output DataFrames
> that
> >> will be saved.
> >>  * Line 82 performs the actual preprocessing and creates a Spark
> >> DataFrame with the following columns: slide number, tumor score,
> molecular
> >> score, sample.  The "sample" in this case is the actual small,
> chopped-up
> >> section of the image that has been extracted and flattened into a row
> >> Vector.  For test images without labels (`training=false`), only the
> slide
> >> number and sample will be contained in the DataFrame (i.e. no labels).
> >> This calls the `preprocess(...)` function located on line 371 of
> >> https://github.com/apache/incubator-systemml/blob/master/
> >> projects/breast_cancer/breastcancer/preprocessing.py, which is a
> >> different file.
> >>  * Line 87 simply saves the above DataFrame to HDFS with the name from
> >> line 74.
> >>  * Line 93 splits the above DataFrame row-wise into separate "training"
> >> and "validation" DataFrames, based on the split percentage from line 70
> >> (`train_frac`).  This is performed so that downstream machine learning
> >> tasks can learn from the training set, and validate performance and
> >> hyperparameter choices on the validation set.  These DataFrames will
> start
> >> with the same columns as the above DataFrame.  If `add_row_indices` from
> >> line 69 is true, then an additional row index column (`__INDEX`) will be
> >> pretended.  This is useful for SystemML in downstream machine learning
> >> tasks as it gives the DataFrame row numbers like a real matrix would
> have,
> >> and SystemML is built to operate on matrices.
> >>  * Lines 97 & 98 simply save the training and validation DataFrames
> using
> >> the names defined on lines 76 & 78.
> >>  * 

Re: Regarding incubator systemml/breast_cancer project

[dusenberrymw]

Hi Aishwarya,

Looks like you've just encountered an out of memory error on one of the 
executors.  Therefore, you just need to adjust the `spark.executor.memory` and 
`spark.driver.memory` settings with higher amounts of RAM.  What is your 
current setup?  I.e. are you using a cluster of machines, or a single machine?  
We generally use a large driver on one machine, and then a single large 
executor on each other machine.  I would give a sizable amount of memory to the 
driver, and about half the possible memory on the executors so that the Python 
processes have enough memory as well.  PySpark has JVM and Python components, 
and the Spark memory settings only pertain to the JVM side, thus the need to 
save about half the executor memory for the Python side.

Thanks!

- Mike

--

Mike Dusenberry
GitHub: github.com/dusenberrymw
LinkedIn: linkedin.com/in/mikedusenberry

Sent from my iPhone.


> On Apr 19, 2017, at 5:53 AM, Aishwarya Chaurasia  
> wrote:
> 
> Hello sir,
> 
> We also wanted to ensure that the spark-submit command we're using is the
> correct one for running 'preprocess.py'.
> Command :  /home/new/sparks/bin/spark-submit preprocess.py
> 
> 
> Thank you.
> Aishwarya Chaurasia.
> 
> On 19-Apr-2017 3:55 PM, "Aishwarya Chaurasia" 
> wrote:
> 
> Hello sir,
> On running the file preprocess.py we are getting the following error :
> 
> https://paste.fedoraproject.org/paste/IAvqiiyJChSC0V9eeETe2F5M1UNdIG
> YhyRLivL9gydE=
> 
> Can you please help us by looking into the error and kindly tell us the
> solution for it.
> Thanks a lot.
> Aishwarya Chaurasia
> 
> 
>> On 19-Apr-2017 12:43 AM,  wrote:
>> 
>> Hi Aishwarya,
>> 
>> Certainly, here is some more detailed information about`preprocess.py`:
>> 
>>  * The preprocessing Python script is located at
>> https://github.com/apache/incubator-systemml/blob/master/
>> projects/breast_cancer/preprocess.py.  Note that this is different than
>> the library module at https://github.com/apache/incu
>> bator-systemml/blob/master/projects/breast_cancer/breastc
>> ancer/preprocessing.py.
>>  * This script is used to preprocess a set of histology slide images,
>> which are `.svs` files in our case, and `.tiff` files in your case.
>>  * Lines 63-79 contain "settings" such as the output image sizes, folder
>> paths, etc.  Of particular interest, line 72 has the folder path for the
>> original slide images that should be commonly accessible from all machines
>> being used, and lines 74-79 contain the names of the output DataFrames that
>> will be saved.
>>  * Line 82 performs the actual preprocessing and creates a Spark
>> DataFrame with the following columns: slide number, tumor score, molecular
>> score, sample.  The "sample" in this case is the actual small, chopped-up
>> section of the image that has been extracted and flattened into a row
>> Vector.  For test images without labels (`training=false`), only the slide
>> number and sample will be contained in the DataFrame (i.e. no labels).
>> This calls the `preprocess(...)` function located on line 371 of
>> https://github.com/apache/incubator-systemml/blob/master/
>> projects/breast_cancer/breastcancer/preprocessing.py, which is a
>> different file.
>>  * Line 87 simply saves the above DataFrame to HDFS with the name from
>> line 74.
>>  * Line 93 splits the above DataFrame row-wise into separate "training"
>> and "validation" DataFrames, based on the split percentage from line 70
>> (`train_frac`).  This is performed so that downstream machine learning
>> tasks can learn from the training set, and validate performance and
>> hyperparameter choices on the validation set.  These DataFrames will start
>> with the same columns as the above DataFrame.  If `add_row_indices` from
>> line 69 is true, then an additional row index column (`__INDEX`) will be
>> pretended.  This is useful for SystemML in downstream machine learning
>> tasks as it gives the DataFrame row numbers like a real matrix would have,
>> and SystemML is built to operate on matrices.
>>  * Lines 97 & 98 simply save the training and validation DataFrames using
>> the names defined on lines 76 & 78.
>>  * Lines 103-137 create smaller train and validation DataFrames by taking
>> small row-wise samples of the full train and validation DataFrames.  The
>> percentage of the sample is defined on line 111 (`p=0.01` for a 1%
>> sample).  This is generally useful for quicker downstream tasks without
>> having to load in the larger DataFrames, assuming you have a large amount
>> of data.  For us, we have ~7TB of data, so having 1% sampled DataFrames is
>> useful for quicker downstream tests.  Once again, the same columns from the
>> larger train and validation DataFrames will be used.
>>  * Lines 146 & 147 simply save these sampled train and validation
>> DataFrames.
>> 
>> As a summary, after running `preprocess.py`, you will be left with the
>> following saved DataFrames in HDFS:
>>  * Full 

Re: Regarding incubator systemml/breast_cancer project

[dusenberrymw]

Hi Aishwarya,

Certainly, here is some more detailed information about`preprocess.py`:

  * The preprocessing Python script is located at 
https://github.com/apache/incubator-systemml/blob/master/projects/breast_cancer/preprocess.py.
  Note that this is different than the library module at 
https://github.com/apache/incubator-systemml/blob/master/projects/breast_cancer/breastcancer/preprocessing.py.
 
  * This script is used to preprocess a set of histology slide images, which 
are `.svs` files in our case, and `.tiff` files in your case.
  * Lines 63-79 contain "settings" such as the output image sizes, folder 
paths, etc.  Of particular interest, line 72 has the folder path for the 
original slide images that should be commonly accessible from all machines 
being used, and lines 74-79 contain the names of the output DataFrames that 
will be saved.
  * Line 82 performs the actual preprocessing and creates a Spark DataFrame 
with the following columns: slide number, tumor score, molecular score, sample. 
 The "sample" in this case is the actual small, chopped-up section of the image 
that has been extracted and flattened into a row Vector.  For test images 
without labels (`training=false`), only the slide number and sample will be 
contained in the DataFrame (i.e. no labels).  This calls the `preprocess(...)` 
function located on line 371 of 
https://github.com/apache/incubator-systemml/blob/master/projects/breast_cancer/breastcancer/preprocessing.py,
 which is a different file.
  * Line 87 simply saves the above DataFrame to HDFS with the name from line 74.
  * Line 93 splits the above DataFrame row-wise into separate "training" and 
"validation" DataFrames, based on the split percentage from line 70 
(`train_frac`).  This is performed so that downstream machine learning tasks 
can learn from the training set, and validate performance and hyperparameter 
choices on the validation set.  These DataFrames will start with the same 
columns as the above DataFrame.  If `add_row_indices` from line 69 is true, 
then an additional row index column (`__INDEX`) will be pretended.  This is 
useful for SystemML in downstream machine learning tasks as it gives the 
DataFrame row numbers like a real matrix would have, and SystemML is built to 
operate on matrices.
  * Lines 97 & 98 simply save the training and validation DataFrames using the 
names defined on lines 76 & 78.
  * Lines 103-137 create smaller train and validation DataFrames by taking 
small row-wise samples of the full train and validation DataFrames.  The 
percentage of the sample is defined on line 111 (`p=0.01` for a 1% sample).  
This is generally useful for quicker downstream tasks without having to load in 
the larger DataFrames, assuming you have a large amount of data.  For us, we 
have ~7TB of data, so having 1% sampled DataFrames is useful for quicker 
downstream tests.  Once again, the same columns from the larger train and 
validation DataFrames will be used.
  * Lines 146 & 147 simply save these sampled train and validation DataFrames.

As a summary, after running `preprocess.py`, you will be left with the 
following saved DataFrames in HDFS:
  * Full DataFrame
  * Training DataFrame
  * Validation DataFrame
  * Sampled training DataFrame
  * Sampled validation DataFrame

As for visualization, you may visualize a "sample" (i.e. small, chopped-up 
section of original image) from a DataFrame by using the 
`breastcancer.visualization.visualize_sample(...)` function.  You will need to 
do this after creating the DataFrames.  Here is a snippet to visualize the 
first row sample in a DataFrame, where `df` is one of the DataFrames from above:

```
from breastcancer.visualization import visualize_sample
visualize_sample(df.first().sample)
```

Please let me know if you have any additional questions.

Thanks!

- Mike

--

Mike Dusenberry
GitHub: github.com/dusenberrymw
LinkedIn: linkedin.com/in/mikedusenberry

Sent from my iPhone.


> On Apr 15, 2017, at 4:38 AM, Aishwarya Chaurasia  
> wrote:
> 
> Hello sir,
> Can you please elaborate more on what output we would be getting because we
> tried executing the preprocess.py file using spark submit it keeps on
> adding the tiles in rdd and while running the visualisation.py file it
> isn't showing any output. Can you please help us out asap stating the
> output we will be getting and the sequence of execution of files.
> Thank you.
> 
>> On 07-Apr-2017 5:54 AM,  wrote:
>> 
>> Hi Aishwarya,
>> 
>> Thanks for sharing more info on the issue!
>> 
>> To facilitate easier usage, I've updated the preprocessing code by pulling
>> out most of the logic into a `breastcancer/preprocessing.py` module,
>> leaving just the execution in the `Preprocessing.ipynb` notebook.  There is
>> also a `preprocess.py` script with the same contents as the notebook for
>> use with `spark-submit`.  The choice of the notebook or the script is just
>> a matter of convenience, as they 

Re: Regarding incubator systemml/breast_cancer project

[dusenberrymw]

Hi Aishwarya,

Thanks for sharing more info on the issue!

To facilitate easier usage, I've updated the preprocessing code by pulling out 
most of the logic into a `breastcancer/preprocessing.py` module, leaving just 
the execution in the `Preprocessing.ipynb` notebook.  There is also a 
`preprocess.py` script with the same contents as the notebook for use with 
`spark-submit`.  The choice of the notebook or the script is just a matter of 
convenience, as they both import from the same `breastcancer/preprocessing.py` 
package.  

As part of the updates, I've added an explicit SparkSession parameter (`spark`) 
to the `preprocess(...)` function, and updated the body to use this 
SparkSession object rather than the older SparkContext `sc` object.  
Previously, the `preprocess(...)` function accessed the `sc` object that was 
pulled in from the enclosing scope, which would work while all of the code was 
colocated within the notebook, but not if the code was extracted and imported.  
The explicit parameter now allows for the code to be imported.

Can you please try again with the latest updates?  We are currently using Spark 
2.x with Python 3.  If you use the notebook, the pyspark kernel should have a 
`spark` object available that can be supplied to the functions (as is done now 
in the notebook), and if you use the `preprocess.py` script with 
`spark-submit`, the `spark` object will be created explicitly by the script.

For a bit of context to others, Aishwarya initially reached out to find out if 
our breast cancer project could be applied to TIFF images, rather than the SVS 
images we are currently using (the answer is "yes" so long as they are "generic 
tiled TIFF images, according to the OpenSlide documentation), and then followed 
up with Spark issues related to the preprocessing code.  This conversation has 
been promptly moved to the mailing list so that others in the community can 
benefit.


Thanks!

-Mike

--

Mike Dusenberry
GitHub: github.com/dusenberrymw
LinkedIn: linkedin.com/in/mikedusenberry

Sent from my iPhone.


> On Apr 6, 2017, at 5:09 AM, Aishwarya Chaurasia  
> wrote:
> 
> Hey,
> 
> The object sc is already defined in pyspark and yet this name error keeps
> occurring. We are using spark 2.*
> 
> Here is the link to error that we are getting :
> https://paste.fedoraproject.org/paste/89iQODxzpNZVbSfgwocH8l5M1UNdIGYhyRLivL9gydE=

Regarding incubator systemml/breast_cancer project

[Aishwarya Chaurasia]

Hey,

The object sc is already defined in pyspark and yet this name error keeps
occurring. We are using spark 2.*

Here is the link to error that we are getting :
https://paste.fedoraproject.org/paste/89iQODxzpNZVbSfgwocH8l5M1UNdIGYhyRLivL9gydE=