mljar-supervised: I have been facing this issue for 2 days. I have no Idea what's causing it.

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-2-ea67f5362246> in <module>
      3 from sklearn.model_selection import train_test_split
      4 from sklearn.metrics import mean_squared_error
----> 5 from supervised.automl import AutoML # mljar-supervised

/opt/conda/lib/python3.7/site-packages/supervised/__init__.py in <module>
      1 __version__ = "0.7.15"
      2 
----> 3 from supervised.automl import AutoML

/opt/conda/lib/python3.7/site-packages/supervised/automl.py in <module>
      1 import logging
      2 
----> 3 from supervised.base_automl import BaseAutoML
      4 
      5 from supervised.utils.config import LOG_LEVEL

/opt/conda/lib/python3.7/site-packages/supervised/base_automl.py in <module>
     17 from sklearn.metrics import r2_score, accuracy_score
     18 
---> 19 from supervised.algorithms.registry import AlgorithmsRegistry
     20 from supervised.algorithms.registry import BINARY_CLASSIFICATION
     21 from supervised.algorithms.registry import MULTICLASS_CLASSIFICATION

/opt/conda/lib/python3.7/site-packages/supervised/algorithms/registry.py in <module>
     62 # Import algorithm to be registered
     63 import supervised.algorithms.random_forest
---> 64 import supervised.algorithms.xgboost
     65 import supervised.algorithms.decision_tree
     66 import supervised.algorithms.baseline

/opt/conda/lib/python3.7/site-packages/supervised/algorithms/xgboost.py in <module>
      4 import pandas as pd
      5 import os
----> 6 import xgboost as xgb
      7 
      8 from supervised.algorithms.algorithm import BaseAlgorithm

/opt/conda/lib/python3.7/site-packages/xgboost/__init__.py in <module>
      7 import os
      8 
----> 9 from .core import DMatrix, DeviceQuantileDMatrix, Booster
     10 from .training import train, cv
     11 from . import rabit  # noqa

/opt/conda/lib/python3.7/site-packages/xgboost/core.py in <module>
     17 import scipy.sparse
     18 
---> 19 from .compat import (
     20     STRING_TYPES, DataFrame, py_str,
     21     PANDAS_INSTALLED,

/opt/conda/lib/python3.7/site-packages/xgboost/compat.py in <module>
    106 # cudf
    107 try:
--> 108     from cudf import concat as CUDF_concat
    109 except ImportError:
    110     CUDF_concat = None

/opt/conda/lib/python3.7/site-packages/cudf/__init__.py in <module>
      9 import rmm
     10 
---> 11 from cudf import core, datasets, testing
     12 from cudf._version import get_versions
     13 from cudf.core import (

/opt/conda/lib/python3.7/site-packages/cudf/core/__init__.py in <module>
      1 # Copyright (c) 2018-2019, NVIDIA CORPORATION.
----> 2 from cudf.core import buffer, column
      3 from cudf.core.buffer import Buffer
      4 from cudf.core.dataframe import DataFrame, from_pandas, merge
      5 from cudf.core.index import (

/opt/conda/lib/python3.7/site-packages/cudf/core/column/__init__.py in <module>
      1 # Copyright (c) 2020, NVIDIA CORPORATION.
      2 
----> 3 from cudf.core.column.categorical import CategoricalColumn
      4 from cudf.core.column.column import (
      5     ColumnBase,

/opt/conda/lib/python3.7/site-packages/cudf/core/column/categorical.py in <module>
      6 
      7 import cudf
----> 8 from cudf import _lib as libcudf
      9 from cudf._lib.transform import bools_to_mask
     10 from cudf.core.buffer import Buffer

/opt/conda/lib/python3.7/site-packages/cudf/_lib/__init__.py in <module>
      2 import numpy as np
      3 
----> 4 from . import (
      5     avro,
      6     binaryop,

cudf/_lib/gpuarrow.pyx in init cudf._lib.gpuarrow()

AttributeError: module 'pyarrow.lib' has no attribute 'IpcWriteOptions'

About this issue

Original URL
State: closed
Created 3 years ago
Reactions: 1
Comments: 29 (14 by maintainers)

Most upvoted comments

ThankYou problem solved

kingabzpro on Jan 13, 2021

Your AutoMlJar is working wonders in classification. If I publish my notebook with a few simple codes, it will ruin the leader board in another competition. I just want to thank you for your wonderful work. @pplonski

kingabzpro on Jan 23, 2021

I was able to install mljar from github in kaggle notebook and successfully run it. It was without GPU. The link to notebook: https://www.kaggle.com/mt77pp/mljar-autoeda-automl-prediction

Additionally, I have added report() method, so user get an interactive report from models. Just click on the model to see its details.

this works amazing

kingabzpro on Jan 23, 2021

Thanks you its working magically

AutoML directory: AutoML_1
The task is regression with evaluation metric rmse
AutoML will use algorithms: ['Baseline', 'Linear', 'Decision Tree', 'Random Forest', 'Xgboost', 'Neural Network']
AutoML will ensemble availabe models
AutoML steps: ['simple_algorithms', 'default_algorithms', 'ensemble']
* Step simple_algorithms will try to check up to 3 models
There was an error during 1_Baseline training.
Please check AutoML_1/errors.md for details.
Exception while producing SHAP explanations. cannot import name '_png' from 'matplotlib' (/opt/conda/lib/python3.7/site-packages/matplotlib/__init__.py)
Continuing ...

kingabzpro on Jan 13, 2021

I was able to install mljar from github in kaggle notebook and successfully run it. It was without GPU. The link to notebook: https://www.kaggle.com/mt77pp/mljar-autoeda-automl-prediction

Additionally, I have added report() method, so user get an interactive report from models. Just click on the model to see its details.

pplonski on Jan 21, 2021

Kaggle Competition

Right now I am on 4th position by using the average output of multiple models especially LGB and Catboost. I think using optuna improved my params automatically. H20 Auto ml and Auto Keras perform worst than your Auto ml, I used fast Auto ml but that was just light version of your model.

kingabzpro on Jan 13, 2021

I’ve added an issue in kaggle docker repo https://github.com/Kaggle/docker-python/issues/936

pplonski on Jan 12, 2021