tvdboom/atom

Automated Tool for Optimized Modelling

atom's Issues

Can't run deeplearning example due to AttributeError

Contribution guidelines

• I've read the contribution guidelines.
• The documentation does not mention anything about my problem.
• There are no open or closed issues that are related to my problem.

Description

Wehn trying to build the AtomClassifier, I get an attribute error because some string is empty and can't be split

Expected behaviour

No response

Actual behaviour

When I run the deep learning example on the atom page, I get the following error message

  File "C:\Users\i13500020\.spyder-py3\IST\untitled0.py", line 74, in <module>
    atom = ATOMClassifier(*data, n_rows=0.1, n_jobs=6, warnings=False, verbose=2)

  File "C:\Users\i13500020\AppData\Roaming\Python\Python38\site-packages\typeguard\__init__.py", line 1033, in wrapper
    retval = func(*args, **kwargs)

  File "C:\Users\i13500020\AppData\Roaming\Python\Python38\site-packages\atom\api.py", line 316, in __init__
    super().__init__(

  File "C:\Users\i13500020\AppData\Roaming\Python\Python38\site-packages\atom\basetransformer.py", line 63, in __init__
    setattr(self, key, value)

  File "C:\Users\i13500020\AppData\Roaming\Python\Python38\site-packages\atom\basepredictor.py", line 58, in __setattr__
    super().__setattr__(item, value)

  File "C:\Users\i13500020\AppData\Roaming\Python\Python38\site-packages\atom\basetransformer.py", line 139, in experiment
    mlflow.sklearn.autolog(disable=True)

  File "C:\Users\i13500020\Anaconda3\lib\site-packages\mlflow\utils\autologging_utils\__init__.py", line 468, in autolog
    return _autolog(*args, **kwargs)

  File "C:\Users\i13500020\Anaconda3\lib\site-packages\mlflow\sklearn\__init__.py", line 943, in autolog
    _, estimators_to_patch = zip(*_all_estimators())

  File "C:\Users\i13500020\Anaconda3\lib\site-packages\mlflow\sklearn\utils.py", line 728, in _all_estimators
    return all_estimators()

  File "C:\Users\i13500020\AppData\Roaming\Python\Python38\site-packages\sklearn\utils\__init__.py", line 1174, in all_estimators
    from ._testing import ignore_warnings

  File "C:\Users\i13500020\AppData\Roaming\Python\Python38\site-packages\sklearn\utils\_testing.py", line 531, in <module>
    _in_unstable_openblas_configuration(),

  File "C:\Users\i13500020\AppData\Roaming\Python\Python38\site-packages\sklearn\utils\__init__.py", line 93, in _in_unstable_openblas_configuration
    modules_info = threadpool_info()

  File "C:\Users\i13500020\AppData\Roaming\Python\Python38\site-packages\sklearn\utils\fixes.py", line 162, in threadpool_info
    return threadpoolctl.threadpool_info()

  File "C:\Users\i13500020\Anaconda3\lib\site-packages\threadpoolctl.py", line 124, in threadpool_info
    return _ThreadpoolInfo(user_api=_ALL_USER_APIS).todicts()

  File "C:\Users\i13500020\Anaconda3\lib\site-packages\threadpoolctl.py", line 340, in __init__
    self._load_modules()

  File "C:\Users\i13500020\Anaconda3\lib\site-packages\threadpoolctl.py", line 373, in _load_modules
    self._find_modules_with_enum_process_module_ex()

  File "C:\Users\i13500020\Anaconda3\lib\site-packages\threadpoolctl.py", line 485, in _find_modules_with_enum_process_module_ex
    self._make_module_from_path(filepath)

  File "C:\Users\i13500020\Anaconda3\lib\site-packages\threadpoolctl.py", line 515, in _make_module_from_path
    module = module_class(filepath, prefix, user_api, internal_api)

  File "C:\Users\i13500020\Anaconda3\lib\site-packages\threadpoolctl.py", line 606, in __init__
    self.version = self.get_version()

  File "C:\Users\i13500020\Anaconda3\lib\site-packages\threadpoolctl.py", line 646, in get_version
    config = get_config().split()

AttributeError: 'NoneType' object has no attribute 'split'

I get this error message every time I try to build a regressor or classfier. I had a bunch of issues installing atom-ml, so maybe that's where this comes from? Any guidance would be very welcome

Steps to reproduce

No response

Python and package version

• Python: 3.8.5
• ATOM: 4.13.1

Multilabel Classification method give the same results

Contribution guidelines

• I've read the contribution guidelines.
• The documentation does not mention anything about my problem.
• There are no open or closed issues that are related to my problem.

Description

I've tried to compare three methods of Multilabel Classification by Random Forest. I wanted to check wich method will be the best MultiOutputClassifier, ClassifierChain or native multilabel RandomForestClassifier. To my surprise, all the results were identical. What is wrong then, since when I do the same calculations using sklearn I get different results. Could you help me.

test.pdf

Expected behaviour

No response

Actual behaviour

No response

Steps to reproduce

No response

Python and package version

• Python: import sys; sys.version
• ATOM: import atom; atom.__version__

Atom process keeps getting "Killed"

I am trying to run atom-ml with LR and LSVM on a big dataset. The full dataset has about 350K rows, and about the same number of cols (NLP problem, with tf-idf on the words)

The problem is that with a very small dataset it all works fine, but as soon as the size of the dataset increases, the ATOM process gets killed. I noticed the memory consumption (using top), and it keeps increasing. At one point the process was using about 40GB of memory and kept increasing.

Question: How to better work with very large datasets using atom-ml?

Using "top"

ATOM process Killed, with only 50K rows dataset

Branching breaks voting

Hi, first of all, I would like to thank you for developing such a great library! Being able to simply add models, branch off of it, experiment new models and techniques and compare all of it with few lines of code is something I was searching for some time and this lib looks really promising!!!

So here is my model against a custom dataset:

Thing is, if I try to use .voting() outside the master branch, I get this error:

Feature request: Support SKLearn pipelines

It would be really nice if you could also include sklearn pipelines as input to atom.

Keep up the good work. Cheers! :D

Error when running the "getting started" example

Hi tvdboom,

The package that you made looks really nice! However, for some reason I am not able to run the simple example that you mention on your page (see error message below).

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
~/My_data/ATOM_model.py in 
      26 
      27 X, y = load_breast_cancer(return_X_y = True)
----> 28 atom = ATOMClassifier(X, y, logger="auto", n_jobs=2, verbose=2)

~/miniconda3/envs/lib/python3.7/site-packages/typeguard/__init__.py in wrapper(*args, **kwargs)
    921         memo = _CallMemo(python_func, _localns, args=args, kwargs=kwargs)
    922         check_argument_types(memo)
--> 923         retval = func(*args, **kwargs)
    924         try:
    925             check_return_type(retval, memo)

~/miniconda3/envs/lib/python3.7/site-packages/atom/api.py in __init__(self, y, n_rows, test_size, n_jobs, verbose, warnings, logger, random_state, *arrays)
    262 
    263         self.goal = "classification"
--> 264         ATOM.__init__(self, arrays, y=y, n_rows=n_rows, test_size=test_size)
    265 
    266 

~/miniconda3/envs/lib/python3.7/site-packages/atom/utils.py in wrapper(*args, **kwargs)
   1047                     logger.exception("Exception encountered:")
   1048 
-> 1049                 raise exception  # Always raise it
   1050         else:
   1051             return f(*args, **kwargs)

~/miniconda3/envs/lib/python3.7/site-packages/atom/utils.py in wrapper(*args, **kwargs)
   1039         if logger is not None:
   1040             try:  # Run the function
-> 1041                 return f(*args, **kwargs)
   1042 
   1043             except Exception as exception:

~/miniconda3/envs/lib/python3.7/site-packages/atom/utils.py in wrapper(*args, **kwargs)
   1066             logger.info(f"{args[0].__class__.__name__}.{f.__name__}()")
   1067 
-> 1068         result = f(*args, **kwargs)
   1069         return result
   1070 

~/miniconda3/envs/lib/python3.7/site-packages/atom/atom.py in __init__(self, arrays, y, n_rows, test_size)
    100 
    101         self.log('', 1)  # Add empty rows around stats for cleaner look
--> 102         self.stats(1)
    103         self.log('', 1)
    104 

~/miniconda3/envs/lib/python3.7/site-packages/atom/utils.py in wrapper(*args, **kwargs)
   1047                     logger.exception("Exception encountered:")
   1048 
-> 1049                 raise exception  # Always raise it
   1050         else:
   1051             return f(*args, **kwargs)

~/miniconda3/envs/lib/python3.7/site-packages/atom/utils.py in wrapper(*args, **kwargs)
   1039         if logger is not None:
   1040             try:  # Run the function
-> 1041                 return f(*args, **kwargs)
   1042 
   1043             except Exception as exception:

~/miniconda3/envs/lib/python3.7/site-packages/atom/utils.py in wrapper(*args, **kwargs)
   1066             logger.info(f"{args[0].__class__.__name__}.{f.__name__}()")
   1067 
-> 1068         result = f(*args, **kwargs)
   1069         return result
   1070 

~/miniconda3/envs/lib/python3.7/site-packages/atom/atom.py in stats(self, _vb)
    269             nans = self.nans.sum()
    270             n_categorical = self.n_categorical
--> 271             outliers = self.outliers.sum()
    272             duplicates = self.dataset.duplicated().sum()
    273 

~/miniconda3/envs/lib/python3.7/site-packages/atom/atom.py in outliers(self)
    217         if not check_deep(self.X):
    218             num_and_target = self.dataset.select_dtypes(include=["number"]).columns
--> 219             z_scores = stats.zscore(self.train[num_and_target], nan_policy="propagate")
    220             srs = pd.Series((np.abs(z_scores) > 3).sum(axis=0), index=num_and_target)
    221             return srs[srs > 0]

TypeError: zscore() got an unexpected keyword argument 'nan_policy'

Do you know how to solve this problem?

Kind regards,
JvdHoogen

Jupyter kernel dies with njobs > 1

Describe the bug

Jupyter kernel dies with njobs > 1 in classification task.

To Reproduce

import pandas as pd
from sklearn.datasets import load_breast_cancer
from atom import ATOMClassifier

X, y = load_breast_cancer(return_X_y=True)
atom = ATOMClassifier(X, y, logger="auto", n_jobs=2, verbose=2)
atom.impute(strat_num="knn", strat_cat="most_frequent", max_nan_rows=0.1)  
atom.encode(strategy="LeaveOneOut", max_onehot=8, frac_to_other=0.05)  
atom.feature_selection(strategy="PCA", n_features=12)

atom.run(
    models=["LR","XGB"],#  "lSVM", "LDA"
    metric="f1",
    n_calls=25,
    n_initial_points=10,
    n_bootstrap=4
)

Expected behavior

As described above. Works fine with njobs=1.

Additional context

Ubuntu 20.04. Python 3.9.7. Separate conda environment with packages installed
from atom requirements.txt.

Version

4.9.1

Adding Zoofs into the feature selection options

Is your feature request related to a problem?

Adding Zoofs into the feature selection options

Suggest a new feature or potential alternative

Either adding zoofs to ATOM.feature_selection or creating a separate module

Additional context

Reload Error

Contribution guidelines

• I've read the contribution guidelines.
• The documentation does not mention anything about my problem.
• There are no open or closed issues that are related to my problem.

Description

I saved the model with save_data=True/False, but when I reloaded the file this error occurred:

AttributeError: Can't get attribute 'sqrt' on <module 'featuretools.primitives.base.transform_primitive_base' from '/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/featuretools/primitives/base/transform_primitive_base.py'>

Expected behaviour

No response

Actual behaviour

No response

Steps to reproduce

No response

Python and package version

• Python: 3.7.4
• ATOM: 4.13.0

How to save the LightGBM Regressor trained using ATOM Pipeline to get prediction intervals using MAPIE

Contribution guidelines

• I've read the contribution guidelines.
• The documentation does not mention anything about this feature.
• There are no open or closed issues that are related to this feature.

Description

Hi, I read that we can use joblib to dump the entire pipeline created using ATOM-ML in an existing issue. How can I save only the trained model (XGB or LGB) to use MAPIE to get a prediction interval for regression rather than a single-point prediction?

https://mapie.readthedocs.io/en/latest/, or other libraries for other tasks (like explainability, etc).

Use Cases

• Conformal Regression
• Feature Importance
• Interoperability of the trained models

Screenshots / Mockups

No response

Support multi-dimensional y data

Contribution guidelines

• I've read the contribution guidelines.
• The documentation does not mention anything about this feature.
• There are no open or closed issues that are related to this feature.

Description

I have a lot of project where I am trying to predict multiple targets at once. Atom has a bunch of nice features that I'd like to use, but I'm not sure its worth it if I have to build a separate model for each target dimension

Use Cases

No response

Screenshots / Mockups

No response

Keras & Atom incompatibility?

Hi, I'm trying to add a Keras model to my atom instance so I can compare it with my other models, but it always recognizes the score as 0.5, very different from what Keras is printing out:

Any ideas if this is something I'm not configuring properly or a bug?

Here is the snippet of how I build the Keras model:

input_shape = atom.X_train.shape[1]

def neural_network():
    keras_model = Sequential()
    keras_model.add(Dense(70,input_shape=(input_shape,),activation='relu'))
    keras_model.add(Dropout(0.3))
    keras_model.add(Dense(35, activation='relu'))
    keras_model.add(Dropout(0.3))
    keras_model.add(Dense(2, activation='sigmoid'))
    
    keras_model.compile(loss='categorical_crossentropy',
                  optimizer='Adam',
                  metrics=[AUC(), Recall()])
    
    return keras_model

from keras.wrappers.scikit_learn import KerasClassifier
model = KerasClassifier(neural_network, 
                        epochs=50, 
                        batch_size=128, 
                        verbose=1)

atom.run(model, # Keras
         metric="roc_auc", # ROC AUC
         needs_proba=True,
         random_state=random_state)

Running into issues with binary classification example

Hi,

Pretty excited about the library but ran into several issues with the binary classification example. I tried rerunning the binary classification example and got this error ValueError: Columns to be encoded can not contain new values while running atom.encode(strategy="CatBoost", max_onehot=10, frac_to_other=0.04).

Then I tried the library on my own data that had no categorical data. However on calling atom.run , all my models failed because they were not serializable (PickleError).

[Error when setting custom BO params]

Describe the bug

Jupyter notebook returns an error: "NameError: name 'Integer' is not defined"

To Reproduce

atom.run(
    models=["GBM", "XGB", "CATB", "LGB"],
    metric=["roc_auc_ovr", "f1_weighted"],
    n_calls=25,
    n_initial_points=10,
    bo_params={"dimensions":[Integer(100, 1000, name="n_estimators"),  
                            Integer(3, 6, name="max_depth"),   
                            Real(0.01, 1, "log-uniform", name="learning_rate")],  
                            "base_estimator":"RF", "max_time":10000},  
    n_bootstrap=5,
)```



<br>

## Expected behavior
<!--
A clear and concise description of what you expected to happen.
-->
Expected to go ahead with running atom


<br>

## Additional context
<!--
Add any other context or screenshots about the problem here.
-->

<img width="1083" alt="Screenshot 2021-12-17 at 23 31 30" src="https://user-images.githubusercontent.com/73858914/146605243-9dcc5464-7c54-4c33-b66e-b5aae109ec4e.png">


<br>

## Version
<!--
Please run the following code snippet and paste the output here:
 
import atom
print(atom.__version__)
-->
The latest

Setting up

Contribution guidelines

• I've read the contribution guidelines.
• The documentation does not mention anything about my problem.
• There are no open or closed issues that are related to my problem.

Description

I cant install ATOM-ml and use it in a jupyter notebook

It is listed in pip list in my environment. However I can't access it nor is it listed in a jupyter notebook in the same environment,

Expected behaviour

I expect
import atom
to work

Actual behaviour

Tried installing from in Jupyter

import sys
!{sys.executable} -m pip install atom-ml[full]

import atom

Output below

Collecting atom-ml[full]
Using cached atom_ml-5.1.1-py3-none-any.whl (222 kB)
Collecting numpy>=1.23.5
Using cached numpy-1.24.2-cp39-cp39-win_amd64.whl (14.9 MB)
Collecting dill>=0.3.6
Using cached dill-0.3.6-py3-none-any.whl (110 kB)
Requirement already satisfied: joblib>=1.1.0 in c:\users\user\anaconda3\lib\site-packages (from atom-ml[full]) (1.2.0)
Requirement already satisfied: shap>=0.41.0 in c:\users\user\anaconda3\lib\site-packages (from atom-ml[full]) (0.41.0)
Collecting scikit-learn>=1.2.1
Using cached scikit_learn-1.2.2-cp39-cp39-win_amd64.whl (8.4 MB)
Collecting matplotlib>=3.6.3
Using cached matplotlib-3.7.1-cp39-cp39-win_amd64.whl (7.6 MB)
Collecting ray[serve]>=2.3.0
Using cached ray-2.3.0-cp39-cp39-win_amd64.whl (21.7 MB)
Collecting plotly>=5.13.1
Using cached plotly-5.13.1-py2.py3-none-any.whl (15.2 MB)
Requirement already satisfied: imbalanced-learn>=0.10.1 in c:\users\user\anaconda3\lib\site-packages (from atom-ml[full]) (0.10.1)
Collecting modin[ray]>=0.18.1
Using cached modin-0.19.0-py3-none-any.whl (1.0 MB)
Collecting zoofs>=0.1.26
Using cached zoofs-0.1.26-py3-none-any.whl
Collecting scikit-learn-intelex>=2023.0.1
Using cached scikit_learn_intelex-2023.0.1-py39-none-win_amd64.whl (89 kB)
Collecting pandas>=1.5.3
Using cached pandas-1.5.3-cp39-cp39-win_amd64.whl (10.9 MB)
Collecting mlflow>=2.2.0
Using cached mlflow-2.2.2-py3-none-any.whl (17.6 MB)
Requirement already satisfied: optuna>=3.1.0 in c:\users\user\anaconda3\lib\site-packages (from atom-ml[full]) (3.1.0)
Collecting dagshub<=0.2.10
Using cached dagshub-0.2.10-py3-none-any.whl (46 kB)
Collecting featuretools>=1.23.0
Using cached featuretools-1.23.0-py3-none-any.whl (555 kB)
Collecting nltk>=3.8.1
Using cached nltk-3.8.1-py3-none-any.whl (1.5 MB)
Collecting gplearn>=0.4.2
Using cached gplearn-0.4.2-py3-none-any.whl (25 kB)
Collecting scipy>=1.9.3
Using cached scipy-1.10.1-cp39-cp39-win_amd64.whl (42.5 MB)
Requirement already satisfied: category-encoders>=2.5.1 in c:\users\user\anaconda3\lib\site-packages (from atom-ml[full]) (2.6.0)
Collecting ydata-profiling>=4.1.0
Using cached ydata_profiling-4.1.1-py2.py3-none-any.whl (344 kB)
Collecting gradio>=3.19.1
Using cached gradio-3.22.1-py3-none-any.whl (15.8 MB)
Requirement already satisfied: lightgbm>=3.3.5 in c:\users\user\anaconda3\lib\site-packages (from atom-ml[full]) (3.3.5)
Collecting evalml>=0.68.0
Using cached evalml-0.71.0-py3-none-any.whl (6.5 MB)
Requirement already satisfied: catboost>=1.1.1 in c:\users\user\anaconda3\lib\site-packages (from atom-ml[full]) (1.1.1)
Collecting xgboost>=1.7.4
Using cached xgboost-1.7.4-py3-none-win_amd64.whl (89.1 MB)
Requirement already satisfied: schemdraw>=0.15 in c:\users\user\anaconda3\lib\site-packages (from atom-ml[full]) (0.15)
Requirement already satisfied: wordcloud>=1.8.2 in c:\users\user\anaconda3\lib\site-packages (from atom-ml[full]) (1.8.2.2)
Collecting explainerdashboard>=0.4.2
Using cached explainerdashboard-0.4.2.1-py3-none-any.whl (286 kB)
Requirement already satisfied: six in c:\users\user\anaconda3\lib\site-packages (from catboost>=1.1.1->atom-ml[full]) (1.16.0)
Requirement already satisfied: graphviz in c:\users\user\anaconda3\lib\site-packages (from catboost>=1.1.1->atom-ml[full]) (0.20.1)
Requirement already satisfied: statsmodels>=0.9.0 in c:\users\user\anaconda3\lib\site-packages (from category-encoders>=2.5.1->atom-ml[full]) (0.13.2)
Requirement already satisfied: patsy>=0.5.1 in c:\users\user\anaconda3\lib\site-packages (from category-encoders>=2.5.1->atom-ml[full]) (0.5.2)
Collecting GitPython>=3.1.29
Using cached GitPython-3.1.31-py3-none-any.whl (184 kB)
Collecting httpx==0.22.0
Using cached httpx-0.22.0-py3-none-any.whl (84 kB)
Collecting fusepy>=3
Using cached fusepy-3.0.1-py3-none-any.whl
Requirement already satisfied: PyYAML>=5 in c:\users\user\anaconda3\lib\site-packages (from dagshub<=0.2.10->atom-ml[full]) (6.0)
Requirement already satisfied: appdirs>=1.4.4 in c:\users\user\anaconda3\lib\site-packages (from dagshub<=0.2.10->atom-ml[full]) (1.4.4)
Requirement already satisfied: click>=8.0.4 in c:\users\user\anaconda3\lib\site-packages (from dagshub<=0.2.10->atom-ml[full]) (8.0.4)
Collecting rfc3986[idna2008]<2,>=1.3
Using cached rfc3986-1.5.0-py2.py3-none-any.whl (31 kB)
Requirement already satisfied: charset-normalizer in c:\users\user\anaconda3\lib\site-packages (from httpx==0.22.0->dagshub<=0.2.10->atom-ml[full]) (2.0.4)
Collecting httpcore<0.15.0,>=0.14.5
Using cached httpcore-0.14.7-py3-none-any.whl (68 kB)
Requirement already satisfied: sniffio in c:\users\user\anaconda3\lib\site-packages (from httpx==0.22.0->dagshub<=0.2.10->atom-ml[full]) (1.2.0)
Requirement already satisfied: certifi in c:\users\user\anaconda3\lib\site-packages (from httpx==0.22.0->dagshub<=0.2.10->atom-ml[full]) (2022.9.14)
Collecting texttable>=1.6.2
Using cached texttable-1.6.7-py2.py3-none-any.whl (10 kB)
Requirement already satisfied: black[jupyter]>=22.3.0 in c:\users\user\anaconda3\lib\site-packages (from evalml>=0.68.0->atom-ml[full]) (22.6.0)
Requirement already satisfied: scikit-optimize>=0.9.0 in c:\users\user\anaconda3\lib\site-packages (from evalml>=0.68.0->atom-ml[full]) (0.9.0)
Requirement already satisfied: pmdarima>=1.8.5 in c:\users\user\anaconda3\lib\site-packages (from evalml>=0.68.0->atom-ml[full]) (2.0.3)
Requirement already satisfied: pyzmq>=20.0.0 in c:\users\user\anaconda3\lib\site-packages (from evalml>=0.68.0->atom-ml[full]) (23.2.0)
Requirement already satisfied: sktime>=0.15.0 in c:\users\user\anaconda3\lib\site-packages (from evalml>=0.68.0->atom-ml[full]) (0.16.1)
Collecting category-encoders>=2.5.1
Using cached category_encoders-2.5.1.post0-py2.py3-none-any.whl (72 kB)
Collecting lime>=0.2.0.1
Using cached lime-0.2.0.1-py3-none-any.whl
Requirement already satisfied: networkx>=2.6 in c:\users\user\anaconda3\lib\site-packages (from evalml>=0.68.0->atom-ml[full]) (2.8.4)
Collecting nlp-primitives>=2.9.0
Using cached nlp_primitives-2.10.0-py3-none-any.whl (44.7 MB)
Collecting packaging>=23.0
Using cached packaging-23.0-py3-none-any.whl (42 kB)
Requirement already satisfied: kaleido>=0.1.0 in c:\users\user\anaconda3\lib\site-packages (from evalml>=0.68.0->atom-ml[full]) (0.2.1)
Collecting woodwork>=0.22.0
Using cached woodwork-0.22.0-py3-none-any.whl (232 kB)
Collecting holidays<0.21,>=0.13
Using cached holidays-0.20-py3-none-any.whl (226 kB)
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Installing collected packages: texttable, tbb, rfc3986, pywin32, PyMeeus, pydub, py-spy, opencensus-context, nvidia-ml-py, korean-lunar-calendar, htmlmin, fusepy, ffmpy, distlib, ansicon, WTForms, websockets, waitress, vowpalwabbit, virtualenv, uc-micro-py, typeguard, tokenize-rt, tangled-up-in-unicode, sqlparse, smmap, querystring-parser, python-multipart, pydantic, plotly, packaging, oyaml, numpy, multimethod, multidict, mdurl, llvmlite, jinxed, jinja2, hijri-converter, h11, googleapis-common-protos, frozenlist, dill, daal, convertdate, colorful, async-timeout, aiorwlock, aiofiles, yarl, uvicorn, starlette, scipy, pyarrow, pandas, numba, nltk, markdown-it-py, linkify-it-py, huggingface-hub, httpcore, holidays, gitdb, docker, databricks-cli, daal4py, contourpy, blessed, aiosignal, zoofs, xgboost, visions, scikit-learn, ray, modin, mdit-py-plugins, matplotlib, imagehash, httpx, gpustat, google-api-core, GitPython, Flask-WTF, fastapi, altair, aiohttp, woodwork, scikit-learn-intelex, phik, opencensus, lime, gradio, gplearn, flask-simplelogin, dash-bootstrap-components, dash-auth, dagshub, category-encoders, aiohttp-cors, ydata-profiling, mlflow, explainerdashboard, featuretools, nlp-primitives, atom-ml, evalml
Attempting uninstall: tbb
Found existing installation: TBB 0.2

ERROR: Cannot uninstall 'TBB'. It is a distutils installed project and thus we cannot accurately determine which files belong to it which would lead to only a partial uninstall.

---

ModuleNotFoundError Traceback (most recent call last)
~\AppData\Local\Temp\ipykernel_9756\2535248663.py in
2 get_ipython().system('{sys.executable} -m pip install atom-ml[full]')
3
----> 4 import atom

ModuleNotFoundError: No module named 'atom'

Steps to reproduce

No response

Python and package version

• Python: import sys; sys.version 3.10.9
• ATOM: import atom; atom.__version__ 5.1.1

PicklingError: Could not pickle the task to send it to the workers.

Hi, great library! It really helped me to accelerate some tasks. Keep it up!

I have encountered this error whenever I put n_jobs different than 1. I am aware this is a common error coming from pickle, but the problem is I am not finding a way to have more information from this error in order to fix it.

I have run this, kept it really simple:

import pandas as pd
import atom as atomml

X_train = pd.read_csv('X_train.csv', header=0)
X_test = pd.read_csv('X_test.csv', header=0)
y_train = pd.read_csv('y_train.csv', header=0, squeeze=True)
y_test = pd.read_csv('y_test.csv', header=0, squeeze=True)

atom = atomml.ATOMClassifier(X_train, X_test, y_train, y_test)

atom.run(n_calls=25, n_initial_points=10, verbose=2, n_jobs=2, warnings=False, logger="test2.log", experiment=None, random_state=1)

And this is the log:

DirectClassifier.run()

Training ===================================== >>
Models: Dummy, GP, GNB, MNB, BNB, CatNB, CNB, Ridge, LR, LDA, QDA, KNN, RNN, Tree, Bag, ET, RF, AdaB, GBM, XGB, LGB, CatB, lSVM, kSVM, PA, SGD, MLP
Metric: f1_weighted


Running BO for Dummy Classification...
Initial point 1 ---------------------------------
Parameters --> {'strategy': 'stratified'}

Exception encountered while running the Dummy model. Removing model from pipeline. 
PicklingError: Could not pickle the task to send it to the workers.

...

The log continues and skips almost every model and only manages to finish Gaussian Process and Gaussian Naive Bayes

How can I save model with feature_generation

Contribution guidelines

• I've read the contribution guidelines.
• The documentation does not mention anything about my problem.
• There are no open or closed issues that are related to my problem.

Description

I tried feature generation with atom, and I met this error when I wanted to save model .

PicklingError: Can't pickle <class 'featuretools.primitives.base.transform_primitive_base.sqrt'>: it's not found as featuretools.primitives.base.transform_primitive_base.sqrt

Expected behaviour

No response

Actual behaviour

No response

Steps to reproduce

No response

Python and package version

• Python: 3.7.4
• ATOM: 4.12.0

Hyperparameter tuning - IndexError: positional indexers are out-of-bounds

Contribution guidelines

• I've read the contribution guidelines.
• The documentation does not mention anything about my problem.
• There are no open or closed issues that are related to my problem.

Description

When trying to tune hyperparameters using the below code

atom.run(
    models=["ET"],
    n_calls=30,
    bo_params={"dimensions": {"all": "n_estimators"}},
)

Actual behaviour

returns IndexError: positional indexers are out-of-bounds on Kaggle notebook.

Python and package version

• Python: 3.7.12
• ATOM: 4.12.0