And then maybe fixup the commits made to the same file.
Greetings everyone. I am Aryan Kenchappagol currently pursuing my 2nd year computer science engineering from International Institute of Information Technology Pune. I was going through python/ML related repositories and I found DFFML pretty interesting. I am a beginner to open source and would love to work on this repository.
Hey @aryanxk02,
I would suggest you to start by studying the documentation.
Read the contributing guide to learn how to set up and start contributing to DFFML.
Once you have a good idea how things work, you can start working on good first issues which are curated to help beginners get a kick-start :)
Later, you can make your way through the rest of the documentation to understand the code base better.
Good Luck!
I would suggest you to start by studying the documentation.
Read the contributing guide to learn how to set up and start contributing to DFFML.
Once you have a good idea how things work, you can start working on good first issues which are curated to help beginners get a kick-start :)
Later, you can make your way through the rest of the documentation to understand the code base better.
Good Luck!
issue : intel/dffml#1007
I think I can solve this. Shall I make a PR??
I think I can solve this. Shall I make a PR??
@pdxjohnny As a follow-up question from your previous answer, I am first intending to modify logistic regression model on scratch folder that already existed. I am intending to change algorithm to be more efficient, and one effort that I made (I made a PR on it) is to use "from scipy.stats import multivariate_normal as mvn" to generate dataset rather than taking dataset.sh that was prepared. My intention is to build logistic regression model from scratch into more self-sustained, but I hope your criticisms to understand if there are problems that I missed. And you previously mentioned that you wish not to take entire dataset into the memory while training the dataset, and it is my idea that we could generate "random dataset" rather than taking from dataset.sh.
Hello everyone!
This is Sanjiban Sengupta, a junior undergrad from IIIT Bhubaneswar, India, pursuing Computer Engineering. I would like to contribute to dffml.
I started with solving the issue: intel/dffml#1002
and made the PR intel/dffml#1005
For the issue, I have commented out the property decorator for the key in Record class and added the self.key= str(key) in the init function.
Currently, 8 checks are failing, some of which states having this issue within the tests,
This is Sanjiban Sengupta, a junior undergrad from IIIT Bhubaneswar, India, pursuing Computer Engineering. I would like to contribute to dffml.
I started with solving the issue: intel/dffml#1002
and made the PR intel/dffml#1005
For the issue, I have commented out the property decorator for the key in Record class and added the self.key= str(key) in the init function.
Currently, 8 checks are failing, some of which states having this issue within the tests,
File "/home/runner/work/dffml/dffml/model/daal4py/tests/test_lr.py", line 77, in test_02_predict
correct = self.test_data[i]["Y"]
TypeError: list indices must be integers or slices, not str
Hey @kahanikaar, since you are forcing the key to be str and the tests expect the key(the
So the
i in this case) to be int, it throws the error. Perhaps a bit redundant, but you could handle this by just casting the type back into int, in the tests that are failing.So the
/home/runner/work/dffml/dffml/model/daal4py/tests/test_lr.py", line 77 should be changed to: correct = self.test_data[int(i)]["Y"] and similarly in the model/scratch failing tests.
5 replies
Help Needed! while installing dffml-model-pytorch; I'm getting an error ERROR: Could not find a version that satisfies the requirement torch==1.7.1+cu110; platform_system == "Windows" and python_version == "3.7" (from dffml-model-pytorch)
ERROR: No matching distribution found for torch==1.7.1+cu110; platform_system == "Windows" and python_version == "3.7"
ERROR: No matching distribution found for torch==1.7.1+cu110; platform_system == "Windows" and python_version == "3.7"
how to resolve it?
We should document this. Can you open an issue please?
2 replies
What is cat and EOF ??
Eof is end of file delimiter
Hi all can anyone help me why I am getting the following error in the ci test checks https://github.com/intel/dffml/pull/1015/checks?check_run_id=1967979265
I the following pull I created a new test case , for that I created a file in the
test directory called test_styles
And then I inherited the base class
IntegrationCLITestCase
Hey @Pratikrocks , the failing tests on your PR can be fixed by:
1: Adding an entry in the changelog.
2: Using black to style your code as it is the format of the whole code base.
You can find more about these CI tests and fixes in the 'Contributing -> Git and Github -> How to Read the CI' documentation section.
1: Adding an entry in the changelog.
2: Using black to style your code as it is the format of the whole code base.
You can find more about these CI tests and fixes in the 'Contributing -> Git and Github -> How to Read the CI' documentation section.
Hello, I have a flask web app with an upload page where I can upload a train and a test csv file that I use to train a model and then redirect to a "trained" page that shows the accuracy. The issue is that when i go back to the upload page and upload a different set of csv files for a different problem, while the model trains, the accuracy method returns a
NoRecordsWithMatchingFeatures exception, as if the test.csv file was never changed. I checked if the files are being uploaded correctly, which they are, overwriting the old files. The train function seems to work fine but I can't seem to get the accuracy function to work unless I either restart the server, or name the test file differently (like test2.csv for example) which I don't want to. Is there any way to get this to work fine? I believe this wasn't happening while using dffml 0.3.7
I was looking at the
source of the csv file I came cross the following config params https://github.com/intel/dffml/blob/38ddb55ce0276b4590a9afc89dad47ba8cab1e5e/dffml/source/csv.py#L42 Can anyone who have worked on it give me a jist of what role does the following CSV_SOURCE_CONFIG_DEFAULT_tag CSV_SOURCE_CONFIG_DEFAULT_KEY params does?
Also, what model are you using?
Any other information that would help us re-produce the issue would be helpful
@Pratikrocks
Refer https://intel.github.io/dffml/master/api/record.html
key is a parameter that lets us access Records in the CSV file.Refer https://intel.github.io/dffml/master/api/record.html
Check it you! Things are working in colab! https://colab.research.google.com/drive/13SYqe0yQJjUYdLRvC1yajyFoyNTEASvP#scrollTo=EBy6_FQ1-ZYC I haven't used colab much before, but I think if you go to "File" -> "Save a Copy in Drive" then you can start messing around with it.
We can try tracking google colab realted stuff in this discussion: https://github.com/intel/dffml/discussions/1018 or also on Gitter as usual
Pinging @yashlamba ^
@pdxjohnny
from dffml import Features, Feature, train, accuracy, CSVSource
from dffml_model_scikit import LinearRegressionModel
import asyncio
salarymodel = LinearRegressionModel(
features=Features(
Feature("Years", int, 1),
Feature("Expertise", int, 1),
Feature("Trust", float, 1),
),
predict=Feature("Salary", int, 1),
directory="tempmodel"
)
irismodel = LinearRegressionModel(
features=Features(
Feature("sepal_length", float, 1),
Feature("sepal_width", float, 1),
Feature("petal_length", float, 1)
),
predict=Feature("petal_width", float, 1),
directory="tempmodel"
)
async def main():
s = open("data/salary.csv", "r")
salary = s.read()
s.close()
st = open("data/salarytest.csv", "r")
salarytest = st.read()
st.close()
ir = open("data/iris.csv", "r")
iris = ir.read()
ir.close()
irt = open("data/iristest.csv", "r")
iristest = irt.read()
irt.close()
# write the salary.csv and salarytest.csv files in the train.csv and test.csv files
trainfile = open("data/train.csv", "w")
trainfile.write(salary)
trainfile.close()
testfile = open("data/test.csv", "w")
testfile.write(salarytest)
testfile.close()
# train first model
await train(salarymodel, "data/train.csv")
acc = await accuracy(salarymodel, CSVSource(filename="data/test.csv"))
print("accuracy:", acc)
# rewrite the train.csv and test.csv files to contain a separate dataset
trainfile = open("data/train.csv", "w")
trainfile.write(iris)
trainfile.close()
testfile = open("data/test.csv", "w")
testfile.write(iristest)
testfile.close()
# train different model
await train(irismodel, "data/train.csv")
# NoRecordsWithMatchingFeatures exception occurs, looking for the features in the first dataset despite changing the contents of the train.csv file
acc = await accuracy(irismodel, CSVSource(filename="data/test.csv"))
print("accuracy:", acc)
if __name__ == "__main__":
asyncio.run(main())
i recreated the issue here