mljar / mljar-api-python Goto Github PK

A simple python wrapper over MLJAR API.

License: Apache License 2.0

Python 100.00%

machine-learning machine-learning-api mljar-api-python prediction-algorithm predictive-analytics predictive-modeling python

mljar-api-python's People

Contributors

Stargazers

Watchers

Forkers

vybhavk armandoayudame kamrankausar todun vishalbelsare reeftrip spencerai simoncritchell niharbhujbal arm7ai

mljar-api-python's Issues

Where can we submit issues about mljar.com app?

Hello. I see you have a github repo for mljar-api-... and mljar-docs.
Why not create a github repository called mljar-app for users to submit issues to?

`fit` on discrete features and binary target yields error

Here is what I'm doing

>>> model.fit(train_features, train_target.squeeze())
Ups, Something bad happend! There is no attributes usage defined for your dataset

When I look in mljar.com, I see that the attribute usage is not accepted.

A related point may be that this is a larger dataset than I used to upload. Previous datasets were 1 million rows and 100 columns, continuous, and around 500 MB. This one is 1 million rows and 300 columns, discrete, and around 500 MB also.

Edit 1: Looking in mljar.com, I also notice that the target is categorical with unique values True, False, "target" (string being "target"). My target in python was a ~~numpy~~ pandas array with just True/False. Maybe this was the problem with automatic acceptance of attribute usage.
This columns is also marked with "use it" and not as target. Accepting without changing it to target says something along the lines of "error, should have target". Changing it to target yields error like "binary classification target should have 2 values only."

Edit 2: target was pandas array and not numpy array .. fixed inline

mljar.com app / new Experiment / Learning Algorithms is empty

There used to be a list of checkboxes of algorithms to select from. Now it's empty.

mljar.fit returning prediction from result not belong to my current experiment

There are multiple experiments in my project.
When I ran mljar.fit(...) for my 2nd experiment,
I got results that looked like results from my 1st experiment.
Digging deeper, I found that the function ResultClient(project id).get_results(experiment id)
(link)
was not filtering the results for the experiment ID being passed.
For example, the code below


clf_mlj = Mljar(
  project='some project name',
  experiment='some experiment name',
  ...
)

from mljar.client.result import ResultClient
client = ResultClient(clf_mlj.project.hid)
results = client.get_results(clf_mlj.experiment.hid)
len(results) # returns 75
results = client.get_results(None)
len(results) # returns 75 as well

predict on `Mljar(...)` instance that was fit in a previous python session does not allow `predict`

In a previous python session, I ran model = Mljar(...) and model.fit(...) and it was fine.
Upon closing the python session and opening a new one, doing model = Mljar(...) followed by model.predict(...) returns Can not run prediction. Please run fit method first, to start models training and to retrieve them ;) eventhough the fit method had been called earlier.

columns get renamed when `predict` is called

Hi. I used the predict method on a pandas dataframe with the same column names of the dataframe used in the fit method, but the columns with the predict method got renamed from 1 to attribute_1, 2 to attribute_2, etc. Is this because the column names are numeric?

Use git tags to easily relate git commit to pypi version

Just my 2 cents:
ATM, it seems that there are no git tags in the repository,
which makes it non-straight-forward to figure out if a function parameter,
e.g. fit(..., dataset_title),
is part of the 0.0.6 release on pypi.

Perhaps for 0.0.7 and later versions, you can just run git tag 0.0.7 && git push origin 0.0.7 for the same version published to pypi

problem with running predictions on windows machines

When computing predictions on windows machine there is error:

pred = Mljar.compute_prediction(df, model_id = 'xxxxxxx', project_id = 'xxxxxxxxxxxx',keep_dataset=True)

IOError                                   Traceback (most recent call last)
<ipython-input-8-e3ccbf396609> in <module>()
----> 1 pred = Mljar.compute_prediction(df, model_id = 'xxxxxxxx', project_id = 'xxxxxxxxxx',keep_dataset=True)

c:\python27\lib\site-packages\mljar\mljar.pyc in compute_prediction(X, model_id, project_id, keep_dataset)
    328 
    329         # chack if dataset exists in mljar if not upload dataset for prediction
--> 330         dataset = DatasetClient(project_id).add_dataset_if_not_exists(X, y = None)
    331 
    332         # check if prediction is available

c:\python27\lib\site-packages\mljar\client\dataset.pyc in add_dataset_if_not_exists(self, X, y, title_prefix)
    128         if len(dataset_details) == 0:
    129             # add new dataset
--> 130             dataset_details = self.add_new_dataset(data, y, title_prefix)
    131         else:
    132             dataset_details = dataset_details[0]

c:\python27\lib\site-packages\mljar\client\dataset.pyc in add_new_dataset(self, data, y, title_prefix)
    166         prediction_only = y is None
    167         # save to local storage
--> 168         data.to_csv(file_path, index=False)
    169         # compress
    170         file_path_zip = file_path + '.zip'

c:\python27\lib\site-packages\pandas\core\frame.pyc in to_csv(self, path_or_buf, sep, na_rep, float_format, columns, header, index, index_label, mode, encoding, compression, quoting, quotechar, line_terminator, chunksize, tupleize_cols, date_format, doublequote, escapechar, decimal)
   1401                                      doublequote=doublequote,
   1402                                      escapechar=escapechar, decimal=decimal)
-> 1403         formatter.save()
   1404 
   1405         if path_or_buf is None:

c:\python27\lib\site-packages\pandas\io\formats\format.pyc in save(self)
   1569             f, handles = _get_handle(self.path_or_buf, self.mode,
   1570                                      encoding=self.encoding,
-> 1571                                      compression=self.compression)
   1572             close = True
   1573 

c:\python27\lib\site-packages\pandas\io\common.pyc in _get_handle(path_or_buf, mode, encoding, compression, memory_map, is_text)
    377         if compat.PY2:
    378             # Python 2
--> 379             f = open(path_or_buf, mode)
    380         elif encoding:
    381             # Python 3 and encoding

IOError: [Errno 2] No such file or directory: '/tmp/dataset-f8f18b2e.csv'

calling `fit` returns `Status details: Error : Unknown label type: continuous`

Hello. When I call fit from the mljar python API, it fails with the following status on each of the experiments: Status details: Error : Unknown label type: continuous

Edit: also, the fit call just hangs without returning any error

python 3 support?

Hello. It seems that mljar is python2-compatible but not python3-compatible (e.g. some print ... calls without parentheses, the from mljar import Mljar call in mljar.__init__).
Any plan on releasing a python3-compatible version?

mljar / mljar-api-python Goto Github PK

mljar-api-python's People

Contributors

Stargazers

Watchers

Forkers

mljar-api-python's Issues

Where can we submit issues about mljar.com app?

`fit` on discrete features and binary target yields error

mljar.com app / new Experiment / Learning Algorithms is empty

mljar.fit returning prediction from result not belong to my current experiment

predict on `Mljar(...)` instance that was fit in a previous python session does not allow `predict`

columns get renamed when `predict` is called

Use git tags to easily relate git commit to pypi version

problem with running predictions on windows machines

calling `fit` returns `Status details: Error : Unknown label type: continuous`

python 3 support?

Recommend Projects

React

Vue.js

Typescript

TensorFlow

Django

Laravel

D3

Recommend Topics

javascript

web

server

Machine learning

Visualization

Game

Recommend Org

Facebook

Microsoft

Google

Alibaba

D3

Tencent

Jobs