There are two things that will make the processing of your issue faster:

1. Make sure that you are using the latest version of the code,
2. In case of bug issue, it would be nice to provide more technical details such like execution command, error message or script that reproduces your bug.

Thanks!

Kamil & Jakub,

core contributors to the minerva.ml

There are two things that will make the processing of your issue faster:

1. Make sure that you are using the latest version of the code,
2. In case of bug issue, it would be nice to provide more technical details such like execution command, error message or script that reproduces your bug.

Thanks!

Kamil & Jakub,

core contributors to the minerva.ml

while I run the script python -W ignore main.py -- train_evaluate_predict_cv --pipeline_name lightGBM_stacking

it raise error like following:

2018-08-10 21:03:04 steppy >>> done: initializing experiment directories
2018-08-10 21:03:04 steppy >>> Step light_gbm_fold_0 initialized
2018-08-10 21-03-04 home-credit >>> Start pipeline fit and transform on train
2018-08-10 21:03:04 steppy >>> cleaning cache...
2018-08-10 21:03:04 steppy >>> cleaning cache done
Traceback (most recent call last):
  File "main.py", line 82, in <module>
    main()
  File "/opt/anaconda2/envs/python3/lib/python3.6/site-packages/click/core.py", line 722, in __call__
    return self.main(*args, **kwargs)
  File "/opt/anaconda2/envs/python3/lib/python3.6/site-packages/click/core.py", line 697, in main
    rv = self.invoke(ctx)
  File "/opt/anaconda2/envs/python3/lib/python3.6/site-packages/click/core.py", line 1066, in invoke
    return _process_result(sub_ctx.command.invoke(sub_ctx))
  File "/opt/anaconda2/envs/python3/lib/python3.6/site-packages/click/core.py", line 895, in invoke
    return ctx.invoke(self.callback, **ctx.params)
  File "/opt/anaconda2/envs/python3/lib/python3.6/site-packages/click/core.py", line 535, in invoke
    return callback(*args, **kwargs)
  File "main.py", line 78, in train_evaluate_predict_cv
    pipeline_manager.train_evaluate_predict_cv(pipeline_name, model_level, dev_mode, submit_predictions)
  File "/data1/huangzp/kaggle/home_risk_lightgbm/src/pipeline_manager.py", line 37, in train_evaluate_predict_cv
    train_evaluate_predict_cv(pipeline_name, model_level, dev_mode, submit_predictions)
  File "/data1/huangzp/kaggle/home_risk_lightgbm/src/pipeline_manager.py", line 173, in train_evaluate_predict_cv
    train_evaluate_predict_cv_first_level(pipeline_name, dev_mode, submit_predictions)
  File "/data1/huangzp/kaggle/home_risk_lightgbm/src/pipeline_manager.py", line 285, in train_evaluate_predict_cv_first_level
    model_level='first')
  File "/data1/huangzp/kaggle/home_risk_lightgbm/src/pipeline_manager.py", line 428, in _fold_fit_evaluate_predict_loop
    fold_id, pipeline_name, model_level)
  File "/data1/huangzp/kaggle/home_risk_lightgbm/src/pipeline_manager.py", line 517, in _fold_fit_evaluate_loop
    pipeline.fit_transform(train_data)
  File "/opt/anaconda2/envs/python3/lib/python3.6/site-packages/steppy/base.py", line 310, in fit_transform
    step_inputs[input_data_part] = data[input_data_part]
KeyError: 'input'

Dear,

Could you tell me how to export the feature correlation? I saw some features in your wiki with some correlation scores. I would like to know how can you know the score? By the way, what is the scoring math metric you used? Thanks

Hi. Neptune is a very handy tool and I'm getting to use it. However I encountered some errors.

It seems Neptune successfully read in the data, there are messages telling me the dataset has been initialized. But when the system wants to do gb training, such permission error happens.

deepsense.neptune.client_library.threads.channel_values_thread WARNING channel_values_thread.py:389 - _validate() X-coordinate 1278101.8469238281 is not greater than the previous one 1278101.8469238281. Dropping point (x=1278101.8469238281, y=PermissionError: [WinError 5] Access is denied

Besides, I'm wondering if such runtime warning is normal.

deepsense.neptune.client_library.threads.channel_values_thread WARNING channel_values_thread.py:389 - _validate() X-coordinate 1278101.8469238281 is not greater than the previous one 1278101.8469238281. Dropping point (x=1278101.8469238281, y= new_handle = steal_handle(parent_pid, pipe_handle) ) for channel stderr. X-coordinates must be strictly increasing for each channel.

This warning came out every second. Should it be like this?

Could you please tell me why there is such error?

Hello,

Thank you for sharing your project, it is interesting ! I have a question regarding the different notebook where there are some preprocessing (cleaning) and creation of new features (hand craft and aggregating). I think the notebook are not updated if we compare to the code. For instance, if we look at the application data, we see 5 cleaning in the code, but in the notebook only two are available. It is forecast to update them ? It is more easy to understand all the data engineering with a notebook than a complete code.

Pull Request template to Home Credit Default Risk Open Solution

Code contributions

Major - and most appreciated - contribution is pull request with feature or bug fix. Each pull request initiates discussion about your code contribution.

Each pull request should be provided with minimal description about its contents.

Thanks!

Jakub & Kamil,

core contributors to the minerva.ml

Solution Version: solution 5 | sunflower 🌻 Command used to run the code: neptune run --config configs/neptune.yaml main.py train_evaluate_predict_cv --pipeline_name lightGBM

Error Message: KeyError: 'NAME_EDUCATION_TYPE_CODE_GENDER_AMT_CREDIT_min' The pops up in the feature_extraction.py file, under the GroupbyAggregateDiffs class and _add_diff_features method. While iterating through self.groupby_aggregations, using this line of code for groupby_cols, specs in self.groupby_aggregations:, the contents of this - self.groupby_aggregations are:

[(['NAME_EDUCATION_TYPE', 'CODE_GENDER'], [('AMT_CREDIT', 'min'), ('AMT_CREDIT', 'mean'), ('AMT_CREDIT', 'max'), ('AMT_CREDIT', 'sum'), ('AMT_CREDIT', 'var'), ('AMT_ANNUITY', 'min'), ('AMT_ANNUITY', 'mean'), ('AMT_ANNUITY', 'max'), ('AMT_ANNUITY', 'sum'), ('AMT_ANNUITY', 'var'), ('AMT_INCOME_TOTAL', 'min'), ('AMT_INCOME_TOTAL', 'mean'), ('AMT_INCOME_TOTAL', 'max'), ('AMT_INCOME_TOTAL', 'sum'), ('AMT_INCOME_TOTAL', 'var'), ('AMT_GOODS_PRICE', 'min'), ('AMT_GOODS_PRICE', 'mean'), ('AMT_GOODS_PRICE', 'max'), ('AMT_GOODS_PRICE', 'sum'), ('AMT_GOODS_PRICE', 'var'), ('EXT_SOURCE_1', 'min'), ('EXT_SOURCE_1', 'mean'), ('EXT_SOURCE_1', 'max'), ('EXT_SOURCE_1', 'sum'), ('EXT_SOURCE_1', 'var'), ('EXT_SOURCE_2', 'min'), ('EXT_SOURCE_2', 'mean'), ('EXT_SOURCE_2', 'max'), ('EXT_SOURCE_2', 'sum'), ('EXT_SOURCE_2', 'var'), ('EXT_SOURCE_3', 'min'), ('EXT_SOURCE_3', 'mean'), ('EXT_SOURCE_3', 'max'), ('EXT_SOURCE_3', 'sum'), ('EXT_SOURCE_3', 'var'), ('OWN_CAR_AGE', 'min'), ('OWN_CAR_AGE', 'mean'), ('OWN_CAR_AGE', 'max'), ('OWN_CAR_AGE', 'sum'), ('OWN_CAR_AGE', 'var'), ('REGION_POPULATION_RELATIVE', 'min'), ('REGION_POPULATION_RELATIVE', 'mean'), ('REGION_POPULATION_RELATIVE', 'max'), ('REGION_POPULATION_RELATIVE', 'sum'), ('REGION_POPULATION_RELATIVE', 'var'), ('DAYS_REGISTRATION', 'min'), ('DAYS_REGISTRATION', 'mean'), ('DAYS_REGISTRATION', 'max'), ('DAYS_REGISTRATION', 'sum'), ('DAYS_REGISTRATION', 'var'), ('CNT_CHILDREN', 'min'), ('CNT_CHILDREN', 'mean'), ('CNT_CHILDREN', 'max'), ('CNT_CHILDREN', 'sum'), ('CNT_CHILDREN', 'var'), ('CNT_FAM_MEMBERS', 'min'), ('CNT_FAM_MEMBERS', 'mean'), ('CNT_FAM_MEMBERS', 'max'), ('CNT_FAM_MEMBERS', 'sum'), ('CNT_FAM_MEMBERS', 'var'), ('DAYS_ID_PUBLISH', 'min'), ('DAYS_ID_PUBLISH', 'mean'), ('DAYS_ID_PUBLISH', 'max'), ('DAYS_ID_PUBLISH', 'sum'), ('DAYS_ID_PUBLISH', 'var'), ('DAYS_BIRTH', 'min'), ('DAYS_BIRTH', 'mean'), ('DAYS_BIRTH', 'max'), ('DAYS_BIRTH', 'sum'), ('DAYS_BIRTH', 'var'), ('DAYS_EMPLOYED', 'min'), ('DAYS_EMPLOYED', 'mean'), ('DAYS_EMPLOYED', 'max'), ('DAYS_EMPLOYED', 'sum'), ('DAYS_EMPLOYED', 'var')]), (['NAME_FAMILY_STATUS', 'NAME_EDUCATION_TYPE'], [('AMT_CREDIT', 'min'), ('AMT_CREDIT', 'mean'), ('AMT_CREDIT', 'max'), ('AMT_CREDIT', 'sum'), ('AMT_CREDIT', 'var'), ('AMT_ANNUITY', 'min'), ('AMT_ANNUITY', 'mean'), ('AMT_ANNUITY', 'max'), ('AMT_ANNUITY', 'sum'), ('AMT_ANNUITY', 'var'), ('AMT_INCOME_TOTAL', 'min'), ('AMT_INCOME_TOTAL', 'mean'), ('AMT_INCOME_TOTAL', 'max'), ('AMT_INCOME_TOTAL', 'sum'), ('AMT_INCOME_TOTAL', 'var'), ('AMT_GOODS_PRICE', 'min'), ('AMT_GOODS_PRICE', 'mean'), ('AMT_GOODS_PRICE', 'max'), ('AMT_GOODS_PRICE', 'sum'), ('AMT_GOODS_PRICE', 'var'), ('EXT_SOURCE_1', 'min'), ('EXT_SOURCE_1', 'mean'), ('EXT_SOURCE_1', 'max'), ('EXT_SOURCE_1', 'sum'), ('EXT_SOURCE_1', 'var'), ('EXT_SOURCE_2', 'min'), ('EXT_SOURCE_2', 'mean'), ('EXT_SOURCE_2', 'max'), ('EXT_SOURCE_2', 'sum'), ('EXT_SOURCE_2', 'var'), ('EXT_SOURCE_3', 'min'), ('EXT_SOURCE_3', 'mean'), ('EXT_SOURCE_3', 'max'), ('EXT_SOURCE_3', 'sum'), ('EXT_SOURCE_3', 'var'), ('OWN_CAR_AGE', 'min'), ('OWN_CAR_AGE', 'mean'), ('OWN_CAR_AGE', 'max'), ('OWN_CAR_AGE', 'sum'), ('OWN_CAR_AGE', 'var'), ('REGION_POPULATION_RELATIVE', 'min'), ('REGION_POPULATION_RELATIVE', 'mean'), ('REGION_POPULATION_RELATIVE', 'max'), ('REGION_POPULATION_RELATIVE', 'sum'), ('REGION_POPULATION_RELATIVE', 'var'), ('DAYS_REGISTRATION', 'min'), ('DAYS_REGISTRATION', 'mean'), ('DAYS_REGISTRATION', 'max'), ('DAYS_REGISTRATION', 'sum'), ('DAYS_REGISTRATION', 'var'), ('CNT_CHILDREN', 'min'), ('CNT_CHILDREN', 'mean'), ('CNT_CHILDREN', 'max'), ('CNT_CHILDREN', 'sum'), ('CNT_CHILDREN', 'var'), ('CNT_FAM_MEMBERS', 'min'), ('CNT_FAM_MEMBERS', 'mean'), ('CNT_FAM_MEMBERS', 'max'), ('CNT_FAM_MEMBERS', 'sum'), ('CNT_FAM_MEMBERS', 'var'), ('DAYS_ID_PUBLISH', 'min'), ('DAYS_ID_PUBLISH', 'mean'), ('DAYS_ID_PUBLISH', 'max'), ('DAYS_ID_PUBLISH', 'sum'), ('DAYS_ID_PUBLISH', 'var'), ('DAYS_BIRTH', 'min'), ('DAYS_BIRTH', 'mean'), ('DAYS_BIRTH', 'max'), ('DAYS_BIRTH', 'sum'), ('DAYS_BIRTH', 'var'), ('DAYS_EMPLOYED', 'min'), ('DAYS_EMPLOYED', 'mean'), ('DAYS_EMPLOYED', 'max'), ('DAYS_EMPLOYED', 'sum'), ('DAYS_EMPLOYED', 'var')]), (['NAME_FAMILY_STATUS', 'CODE_GENDER'], [('AMT_CREDIT', 'min'), ('AMT_CREDIT', 'mean'), ('AMT_CREDIT', 'max'), ('AMT_CREDIT', 'sum'), ('AMT_CREDIT', 'var'), ('AMT_ANNUITY', 'min'), ('AMT_ANNUITY', 'mean'), ('AMT_ANNUITY', 'max'), ('AMT_ANNUITY', 'sum'), ('AMT_ANNUITY', 'var'), ('AMT_INCOME_TOTAL', 'min'), ('AMT_INCOME_TOTAL', 'mean'), ('AMT_INCOME_TOTAL', 'max'), ('AMT_INCOME_TOTAL', 'sum'), ('AMT_INCOME_TOTAL', 'var'), ('AMT_GOODS_PRICE', 'min'), ('AMT_GOODS_PRICE', 'mean'), ('AMT_GOODS_PRICE', 'max'), ('AMT_GOODS_PRICE', 'sum'), ('AMT_GOODS_PRICE', 'var'), ('EXT_SOURCE_1', 'min'), ('EXT_SOURCE_1', 'mean'), ('EXT_SOURCE_1', 'max'), ('EXT_SOURCE_1', 'sum'), ('EXT_SOURCE_1', 'var'), ('EXT_SOURCE_2', 'min'), ('EXT_SOURCE_2', 'mean'), ('EXT_SOURCE_2', 'max'), ('EXT_SOURCE_2', 'sum'), ('EXT_SOURCE_2', 'var'), ('EXT_SOURCE_3', 'min'), ('EXT_SOURCE_3', 'mean'), ('EXT_SOURCE_3', 'max'), ('EXT_SOURCE_3', 'sum'), ('EXT_SOURCE_3', 'var'), ('OWN_CAR_AGE', 'min'), ('OWN_CAR_AGE', 'mean'), ('OWN_CAR_AGE', 'max'), ('OWN_CAR_AGE', 'sum'), ('OWN_CAR_AGE', 'var'), ('REGION_POPULATION_RELATIVE', 'min'), ('REGION_POPULATION_RELATIVE', 'mean'), ('REGION_POPULATION_RELATIVE', 'max'), ('REGION_POPULATION_RELATIVE', 'sum'), ('REGION_POPULATION_RELATIVE', 'var'), ('DAYS_REGISTRATION', 'min'), ('DAYS_REGISTRATION', 'mean'), ('DAYS_REGISTRATION', 'max'), ('DAYS_REGISTRATION', 'sum'), ('DAYS_REGISTRATION', 'var'), ('CNT_CHILDREN', 'min'), ('CNT_CHILDREN', 'mean'), ('CNT_CHILDREN', 'max'), ('CNT_CHILDREN', 'sum'), ('CNT_CHILDREN', 'var'), ('CNT_FAM_MEMBERS', 'min'), ('CNT_FAM_MEMBERS', 'mean'), ('CNT_FAM_MEMBERS', 'max'), ('CNT_FAM_MEMBERS', 'sum'), ('CNT_FAM_MEMBERS', 'var'), ('DAYS_ID_PUBLISH', 'min'), ('DAYS_ID_PUBLISH', 'mean'), ('DAYS_ID_PUBLISH', 'max'), ('DAYS_ID_PUBLISH', 'sum'), ('DAYS_ID_PUBLISH', 'var'), ('DAYS_BIRTH', 'min'), ('DAYS_BIRTH', 'mean'), ('DAYS_BIRTH', 'max'), ('DAYS_BIRTH', 'sum'), ('DAYS_BIRTH', 'var'), ('DAYS_EMPLOYED', 'min'), ('DAYS_EMPLOYED', 'mean'), ('DAYS_EMPLOYED', 'max'), ('DAYS_EMPLOYED', 'sum'), ('DAYS_EMPLOYED', 'var')]), (['CODE_GENDER', 'ORGANIZATION_TYPE'], [('AMT_ANNUITY', 'mean'), ('AMT_INCOME_TOTAL', 'mean'), ('DAYS_REGISTRATION', 'mean'), ('EXT_SOURCE_1', 'mean')]), (['CODE_GENDER', 'REG_CITY_NOT_WORK_CITY'], [('AMT_ANNUITY', 'mean'), ('CNT_CHILDREN', 'mean'), ('DAYS_ID_PUBLISH', 'mean')]), (['CODE_GENDER', 'NAME_EDUCATION_TYPE', 'OCCUPATION_TYPE', 'REG_CITY_NOT_WORK_CITY'], [('EXT_SOURCE_1', 'mean'), ('EXT_SOURCE_2', 'mean')]), (['NAME_EDUCATION_TYPE', 'OCCUPATION_TYPE'], [('AMT_CREDIT', 'mean'), ('AMT_REQ_CREDIT_BUREAU_YEAR', 'mean'), ('APARTMENTS_AVG', 'mean'), ('BASEMENTAREA_AVG', 'mean'), ('EXT_SOURCE_1', 'mean'), ('EXT_SOURCE_2', 'mean'), ('EXT_SOURCE_3', 'mean'), ('NONLIVINGAREA_AVG', 'mean'), ('OWN_CAR_AGE', 'mean'), ('YEARS_BUILD_AVG', 'mean')]), (['NAME_EDUCATION_TYPE', 'OCCUPATION_TYPE', 'REG_CITY_NOT_WORK_CITY'], [('ELEVATORS_AVG', 'mean'), ('EXT_SOURCE_1', 'mean')]), (['OCCUPATION_TYPE'], [('AMT_ANNUITY', 'mean'), ('CNT_CHILDREN', 'mean'), ('CNT_FAM_MEMBERS', 'mean'), ('DAYS_BIRTH', 'mean'), ('DAYS_EMPLOYED', 'mean'), ('DAYS_ID_PUBLISH', 'mean'), ('DAYS_REGISTRATION', 'mean'), ('EXT_SOURCE_1', 'mean'), ('EXT_SOURCE_2', 'mean'), ('EXT_SOURCE_3', 'mean')])]

I am assuming that this is where it is combining 'NAME_EDUCATION_TYPE', 'CODE_GENDER', 'AMT_CREDIT', 'min' and the key is missing. I tried to isolate the error message, but the code base is quite large. I figured a community based approach to resolve the bug might be ideal.

When I run "python main.py -- train_evaluate_predict_cv --pipeline_name XGBoost", I got this error in the title. However, I can run the lightGBM pipeline successfully.

Pull Request template to Home Credit Default Risk Open Solution

Code contributions

Major - and most appreciated - contribution is pull request with feature or bug fix. Each pull request initiates discussion about your code contribution.

Each pull request should be provided with minimal description about its contents.

Thanks!

Jakub & Kamil,

core contributors to the minerva.ml

Added a general framework for a Keras-based neural network (#136)

Pull Request template to Home Credit Default Risk Open Solution

Code contributions

Major - and most appreciated - contribution is pull request with feature or bug fix. Each pull request initiates discussion about your code contribution.

Each pull request should be provided with minimal description about its contents.

Thanks!

Jakub & Kamil,

core contributors to the minerva.ml

Hi guys,

i have extracted some more features from bureau file, it improved CV from .7950 to 0.7974 ( std 0.0024) but LB drop from .804 to .802. has anyone experienced this before ? i dont use any TARGET related features, and dont think it is overfitting.

Hello, I would like to use your pipeline described as 'solution 6,' but I don't get which models were used for the 1st level as oof-predictions.

Also, I would like to know which configurations were used for the 1st level and the 2nd level respectively. In the config file, there are two configuration files (neptune_stacking.yaml and neptune.yaml) and I'm confused which one was used for each level.

Could you let me know the models and the configurations used for the 1st and the 2nd layer respectively?

Thank you!!