Describe the bug

I am trying to train model, and get the results, but I keep getting tuple model failed to execute.

Expected behavior

It's supposed to be trained, and give me all predictions and info.

Screenshots

Desktop (please complete the following information):

• OS: Linux
• Browser Firefox
• Version - Latest

Additional context
Add any other context about the problem here.

From the above picture, it is clear that lazypredict is successfully installed, however, if I try to access it, it says No module named lazypredict. Why?

Also README.rst doesn't mention the steps to install this package and directly jumps to USAGE. Does it mean that it should work without pre-installation?

CONTRIBUTING.rst explains how to install using setup.py but doesn't mention that one should install requirements_dev.txt which is essential when working on virtual environments.

Is it fine if I open a PR regarding Documentation?

CodeFactor found an issue: Trailing spaces

It's currently on:
.travis.yml:5

Custom_metric is used for results evalutaion only?

Or it's also used in each model as loss for optimization?

Is it a personal choice or is there any technical reason where reST is better than Markdown? Asking this only because I rarely .rst file extensions.

Is your feature request related to a problem? Please describe.
I am trying to do a multi class output model. Not working as the code expects a 1D array not 2D.

Describe the solution you'd like
Ideally I'd love to just add as lazypredict arguments an train_Y and test_Y as 2D arrays.

• Lazy Predict version: 0.2.9
• Python version: 3.7 (on google colab)
• Operating System: Windows 10, and I use python on google colab.

Description

-I wish I could do 5 or 10 fold Cross-validation and get RMSE and STD results in the table.
-Also, I wish there was built in predict function.

What I Did

Paste the command(s) you ran and the output.
If there was a crash, please include the traceback here.

• Lazy Predict version: lazypredict-0.2.7
• Python version: 3.8
• Operating System: Windows 10 64-bit

Description

I'm trying to make predictions but keep getting the same error both locally and on colab

What I Did

AttributeError                            Traceback (most recent call last)
<ipython-input-22-cb325c4d69cb> in <module>
      1 from lazypredict.Supervised import LazyClassifier
      2 clf = LazyClassifier(verbose=0,ignore_warnings=True, custom_metric=None)
----> 3 models,predictions = clf.fit(X_train, X_test, y_train, y_test)
      4 models

~\AppData\Local\Continuum\anaconda3\lib\site-packages\lazypredict\Supervised.py in fit(self, X_train, X_test, y_train, y_test)
    203             X_test = pd.DataFrame(X_test)
    204 
--> 205         numeric_features = X_train.select_dtypes(
    206             include=['int64', 'float64', 'int32', 'float32']).columns
    207         categorical_features = X_train.select_dtypes(

~\AppData\Local\Continuum\anaconda3\lib\site-packages\scipy\sparse\base.py in __getattr__(self, attr)
    689             return self.getnnz()
    690         else:
--> 691             raise AttributeError(attr + " not found")
    692 
    693     def transpose(self, axes=None, copy=False):

AttributeError: select_dtypes not found.

Refer to https://scikit-learn.org/stable/whats_new/v0.22.html :

API Change The following utils have been deprecated and are now private:

mocking.CheckingClassifier

CodeFactor found an issue: Using type() instead of isinstance() for a typecheck.

It's currently on:
lazypredict\Supervised.py:442

Describe the bug
LazyRegressor output (prediction) array produces a single near-constant value for all items within a model. This does not begin to describe the data. Data input is (very loosely) sinusoidal in shape. This occurs with both real and standardized values.

To Reproduce
Steps to reproduce the behaviour:
following the regression example within the documentation

Expected behaviour
Different values within the prediction data frame should not all have the same prediction value.

Desktop (please complete the following information):

• OS: MAC

Hi,

I wanted to give your package a try, but can't.
A bit of description of my setup:

• Operating System: Google Colab Notebook (Linux)
• Python 3.6.9
• lazypredict==0.2.7
• scikit-learn==0.24.0

I'm getting an error just by importing your package.

from lazypredict.Supervised import LazyRegressor

---------------------------------------------------------------------------
ModuleNotFoundError                       Traceback (most recent call last)
<ipython-input-85-a096f5bd7a1d> in <module>()
----> 1 from lazypredict.Supervised import LazyRegressor

/usr/local/lib/python3.6/dist-packages/lazypredict/Supervised.py in <module>()
     14 from sklearn.preprocessing import StandardScaler, OneHotEncoder
     15 from sklearn.compose import ColumnTransformer
---> 16 from sklearn.utils.testing import all_estimators
     17 from sklearn.base import RegressorMixin
     18 from sklearn.base import ClassifierMixin

ModuleNotFoundError: No module named 'sklearn.utils.testing'

---------------------------------------------------------------------------
NOTE: If your import is failing due to a missing package, you can
manually install dependencies using either !pip or !apt.

To view examples of installing some common dependencies, click the
"Open Examples" button below.
---------------------------------------------------------------------------

It seems to be a discrepancy between the version of SciKit-Learn I have and the one that LazyPredict expects, since the architecture of sklearn is different.

Shouldn't you try to make sure that the expected versions of dependency packages are installed ?

Is your feature request related to a problem? Please describe.
Recently I saw a review of this library where the user suggested that we should include hyperparameter tuning. I do realize it will increase the train time significantly, but it does sound like a good feature.

Describe the solution you'd like
Optional parameter to tune the hyperparameters of the models while being fitted lazyclassifier or lazyregressor. We definitely need #114 and #65 to be implemented before this to efficiently run the operation. We will need to look into efficient parallel processing and threading also to reduce the overall time.
Additional context
Full review of the library which is motivation for this issue can be seen here.

CodeFactor found an issue: Trailing spaces

It's currently on:
.travis.yml:10

CodeFactor found an issue: Trailing newlines

It's currently on:
docs\conf.py:164

Describe the bug
catboost is not installing and it shows sys is not defined
module not found error is coming

Describe the bug
Using a dataset with 500k rows and 27 features, I ran into a huge memory issue on iteration 12/30. Screenshot included so you can see how much memory was being used.

Desktop (please complete the following information):

• OS: OSX Catalina 10.15.5

Additional context
Other packages installed

awswrangler==2.4.0
pandas==1.2.1
numpy==1.20.0
scikit-learn==0.23.1
sqlalchemy==1.3.23
psycopg2-binary==2.8.6
lazypredict==0.2.7
tqdm==4.56.0
xgboost==1.3.3
lightgbm==3.1.1
pytest==6.2.2
imblearn
shap==0.38.1
matplotlib==3.3.4
ipython

• Lazy Predict version: lazypredict-0.2.7
• Python version: 3
• Operating System: Windows

Description

I am trying to run lazypredict code for a classification use-case and it has been stuck at 3% for over 2 hours. the data size is around 15,000 observations. Any way to speed up the code? Thanks!

What I Did

from lazypredict.Supervised import LazyClassifier
from sklearn.model_selection import train_test_split
import numpy as np

X=data['text']
y=data['category_id']
X_train, X_test, y_train, y_test = train_test_split(X, y,test_size=.3,random_state =1)
clf = LazyClassifier(verbose=0,ignore_warnings=True, custom_metric=None)
models,predictions = clf.fit(np.array(X_train), np.array(X_test), np.array(y_train), np.array(y_test))
models

Is your feature request related to a problem? Please describe.
I run the demo code and find the models and predictions are the same.

Describe the solution you'd like

How can I get the per case predictions?

Kindest regards,
Jun

Describe the bug

when using from lazypredict.Supervised import LazyClassifier in jupyter notebook:

--------------------------------------------------------------------------
ModuleNotFoundError                       Traceback (most recent call last)
<ipython-input-778-0601eec82da4> in <module>
----> 1 from lazypredict.Supervised import LazyClassifier

~\AppData\Local\Continuum\anaconda3\lib\site-packages\lazypredict\Supervised.py in <module>
     14 from sklearn.preprocessing import StandardScaler, OneHotEncoder
     15 from sklearn.compose import ColumnTransformer
---> 16 from sklearn.utils.testing import all_estimators
     17 from sklearn.base import RegressorMixin
     18 from sklearn.base import ClassifierMixin

ModuleNotFoundError: No module named 'sklearn.utils.testing' 

Versions
sklearn version: '0.24.0'
Jupyter:
jupyter core : 4.6.3
jupyter-notebook : 6.1.1
qtconsole : 4.7.7
ipython : 7.19.0
ipykernel : 5.3.4
jupyter client : 6.1.7
jupyter lab : 2.2.6
nbconvert : 5.6.1
ipywidgets : 7.5.1
nbformat : 5.0.8
traitlets : 4.3.3

Expected behavior
Expected Lazyclassifier to import, not have modulenotfound error - I have noticed that sklearn.utils.testing doesn't seem have to testing listed for all_estimators, but rather just sklearn.utils

Note: this is my first time opening an issue, if i can be more clear or helpful, please indicate how and i will be obliged to do so.

Is your feature request related to a problem? Please describe.
User should be able to choose categorical encoder apart from the default one-hot encoder

Describe alternatives you've considered
Tried Already encoded data as input. Solves the issue for time being.

CodeFactor found an issue: Using type() instead of isinstance() for a typecheck.

It's currently on:
lazypredict\Supervised.py:233

Hello,

Facing this issue.

lazypredict 0.2.9 requires scikit-learn==0.23.1, but you have scikit-learn 0.24.2 which is incompatible.

lazypredict has to be restricted to scikit older version?

thanks

Since the goal of lazypredict is to run the models quickly and give the results quickly to get insights in a high level, visualizing these results of models or predictions would be much easier to gather insights and draw some conclusion rather than looking at a table.

Or do you think users can use matplotlib or other libraries easily and why to bother writing a function for it?

Any views regarding this?

When we select the custom classifiers, the corresponding libraries are not selected. Here is where the code has a bug (line 286-297 in the Supervised.py

if self.classifiers == "all": self.classifiers = CLASSIFIERS else: try: temp_list = [] for classifier in self.classifiers: full_name = (classifier.__class__.__name__, classifier) temp_list.append(full_name) self.classifiers = temp_list except Exception as exception: print(exception) print("Invalid Classifier(s)")

The temp_list contains a list of tuples which has the name of the classifier and 'str'. Indeed, it should be the name of the classifier and the corresponding library in the tuples.

• Lazy Predict version: '0.2.7'
• Python version: 3.8.0
• Sklearn version : 0.21.3 / 0.23.1 / 0.24.1
• Operating System: Windows 10

Description

I'm trying to Import LazyRegressor from lazypredict.Supervised

But while importing I'm getting an error " ModuleNotFoundError: No module named 'sklearn.utils.testing' ". I try it by installing Sklearn version: 0.21.3 / 0.23.1 / 0.24.1 but the same error is raised everytime ( I did all this in new env )

What I Did

import lazypredict
from lazypredict.Supervised import LazyRegressor

Output :
---------------------------------------------------------------------------
ModuleNotFoundError                       Traceback (most recent call last)
<ipython-input-12-340b76a5a8e6> in <module>
      1 import lazypredict
----> 2 from lazypredict.Supervised import LazyRegressor

d:\ds env\python\lib\site-packages\lazypredict\Supervised.py in <module>
     14 from sklearn.preprocessing import StandardScaler, OneHotEncoder
     15 from sklearn.compose import ColumnTransformer
---> 16 from sklearn.utils.testing import all_estimators
     17 from sklearn.base import RegressorMixin
     18 from sklearn.base import ClassifierMixin

ModuleNotFoundError: No module named 'sklearn.utils.testing'

Hi,

I have copied the sample code for the regression comparison (Boston dataset).

It starts running and then stops with the following error message

StackingRegressor model failed to execute
init() missing 1 required positional argument: 'estimators'

Context
I am using sklearn = '0.23.1'
Using jupyter lab
Browser = Firefox

Implement Random State for consistent results

How to i choose a specific subset of algorithms when run Lazypredict ??

Describe the bug
I haven't got anything complex... the same code as the tutorial

from lazypredict.Supervised import LazyClassifier, LazyRegressor
from sklearn.model_selection import train_test_split

clf = LazyClassifier(predictions=True)
models, predictions = clf.fit(X_train, x_test, Y_train, y_test)

I'm getting this error message. Could be my config. Please help

ImportError                               Traceback (most recent call last)
<ipython-input-9-f370d18ec53e> in <module>
----> 1 from lazypredict.Supervised import LazyClassifier, LazyRegressor
      2 from sklearn.model_selection import train_test_split
      3 
      4 clf = LazyClassifier(predictions=True)
      5 models, predictions = clf.fit(X_train, x_test, Y_train, y_test)

~\AppData\Roaming\Python\Python38\site-packages\lazypredict\Supervised.py in <module>
     14 from sklearn.preprocessing import StandardScaler, OneHotEncoder, OrdinalEncoder
     15 from sklearn.compose import ColumnTransformer
---> 16 from sklearn.utils.testing import all_estimators
     17 from sklearn.base import RegressorMixin
     18 from sklearn.base import ClassifierMixin

~\Anaconda3\lib\site-packages\sklearn\utils\testing.py in <module>
      5 from . import _testing  # type: ignore
      6 from ..externals._pep562 import Pep562
----> 7 from ..utils.deprecation import _raise_dep_warning_if_not_pytest
      8 
      9 deprecated_path = 'sklearn.utils.testing'

ImportError: cannot import name '_raise_dep_warning_if_not_pytest' from 'sklearn.utils.deprecation' (C:\Users\Anaconda3\lib\site-packages\sklearn\utils\deprecation.py)

Python version : 3.7.4
pip check lazypredict
instapy 0.6.10 has requirement jsonschema<3,>=2.6.0, but you have jsonschema 3.2.0.
clarifai 2.6.2 has requirement configparser<4,>=3.5, but you have configparser 5.0.0.
clarifai 2.6.2 has requirement jsonschema<3,>=2.5, but you have jsonschema 3.2.0.

Describe the bug
The current dependencies mentioned are not enough. User has to manually download multiple other libraries such as tqdm,xgboost, lightgbm, pytest. So please add these to the dependency/ requirements so that it automatically downloads these files when lazypredict is installed in an environment.

To Reproduce
Steps to reproduce the behavior:

1. Create a new python environment.
2. install lazypredict.
3. try and run lazypredict from python using the following lines:
   

import lazypredict
from lazypredict.Supervised import LazyClassifier
Traceback (most recent call last):
File "", line 1, in
File "/home/shyambhu.mukherjee/spacy_lazypredict/new_app/lib/python3.6/site-packages/lazypredict/Supervised.py", line 8, in
from tqdm import tqdm
ModuleNotFoundError: No module named 'tqdm'

4. See that module not found error will come for tqdm,xgboost, lightgbm and pytest.

Expected behavior
While pip installing lazypredict, it should download all the things.

Desktop:

• OS: ubuntu 18.04 bionic
• Version: lazypredict==0.2.7
  python==3.6.9.

Describe the bug
When fitting the LazyRegressor, i get following output for all algorithms.

To Reproduce
Steps to reproduce the behavior:
Check out this notebook
https://colab.research.google.com/drive/1CZa8rL2N4PxUqhCKAyzO3incPFfGwwIM?usp=sharing

Expected behavior
I expect the table of the results of the regressors

• Lazy Predict version:
• Python version:
• Operating System:

Description

Im running libeary on google colab

What I Did

clf = LazyClassifier(verbose=0, ignore_warnings=True, custom_metric=None)
models, predictions = clf.fit(X_train, X_test, y_train, y_test)
models

Output =>
---------------------------------------------------------------------------
ImportError                               Traceback (most recent call last)
/usr/local/lib/python3.7/dist-packages/pyforest/__init__.py in <module>()
      1 clf = LazyClassifier(verbose=0, ignore_warnings=True, custom_metric=None)
----> 2 models, predictions = clf.fit(X_train, X_test, y_train, y_test)
      3 models

6 frames
/usr/local/lib/python3.7/dist-packages/pandas/core/computation/expressions.py in <module>()
     13 from pandas._config import get_option
     14 
---> 15 from pandas._libs.lib import values_from_object
     16 
     17 from pandas.core.dtypes.generic import ABCDataFrame

ImportError: cannot import name 'values_from_object' from 'pandas._libs.lib' (/usr/local/lib/python3.7/dist-packages/pandas/_libs/lib.cpython-37m-x86_64-linux-gnu.so)

---------------------------------------------------------------------------
NOTE: If your import is failing due to a missing package, you can
manually install dependencies using either !pip or !apt.

To view examples of installing some common dependencies, click the
"Open Examples" button below.
---------------------------------------------------------------------------

Description:
Setting custom_metric of LazyClassifier returns a ValueError: arrays must all be same length.

Steps to reproduce the behavior:
Set the value of custom_metric in LazyClassifer to sklearn.metrics log_loss

Expected behavior:
Log Loss metric column should be returned in the scores dataframe

Screenshot:

Desktop:

• OS: Windows 10 Pro
• Browser: Chrome
• Version: -NA-

Additional Content:
-> Error points to TIME list when constructing the scores dataframe
-> Also the issue happens irrespective of verbose value i.e. 0 or 1

Hii
I'm using LazyPredict to get performance of Regression models on a dataset (with LazyRegressor), and finally I am getting the performance table and prediction values from this

regr=LazyRegressor(verbose=0,predictions=True)   
models_r,predictions_r=regr.fit(X_train, X_test, Y_train, Y_test)

But now I want to know the prediction values on new input features.

So, how I can use this model_r to get the prediction table? or is there other way to get it ?

AdaBoostRegressor model failed to execute
all features must be in [0, 83] or [-84, 0]
BaggingRegressor model failed to execute
all features must be in [0, 83] or [-84, 0]
BayesianRidge model failed to execute
all features must be in [0, 83] or [-84, 0]
DecisionTreeRegressor model failed to execute
all features must be in [0, 83] or [-84, 0]
DummyRegressor model failed to execute
all features must be in [0, 83] or [-84, 0]
ElasticNet model failed to execute
all features must be in [0, 83] or [-84, 0]
ElasticNetCV model failed to execute
all features must be in [0, 83] or [-84, 0]
ExtraTreeRegressor model failed to execute
all features must be in [0, 83] or [-84, 0]
ExtraTreesRegressor model failed to execute
all features must be in [0, 83] or [-84, 0]
GammaRegressor model failed to execute
all features must be in [0, 83] or [-84, 0]
GaussianProcessRegressor model failed to execute
all features must be in [0, 83] or [-84, 0]
GeneralizedLinearRegressor model failed to execute
all features must be in [0, 83] or [-84, 0]
GradientBoostingRegressor model failed to execute
all features must be in [0, 83] or [-84, 0]
HistGradientBoostingRegressor model failed to execute
all features must be in [0, 83] or [-84, 0]
HuberRegressor model failed to execute
all features must be in [0, 83] or [-84, 0]
KNeighborsRegressor model failed to execute
all features must be in [0, 83] or [-84, 0]
KernelRidge model failed to execute
all features must be in [0, 83] or [-84, 0]
Lars model failed to execute
all features must be in [0, 83] or [-84, 0]
LarsCV model failed to execute
all features must be in [0, 83] or [-84, 0]
Lasso model failed to execute
all features must be in [0, 83] or [-84, 0]
LassoCV model failed to execute
all features must be in [0, 83] or [-84, 0]
LassoLars model failed to execute
all features must be in [0, 83] or [-84, 0]
LassoLarsCV model failed to execute
all features must be in [0, 83] or [-84, 0]
LassoLarsIC model failed to execute
all features must be in [0, 83] or [-84, 0]
LinearRegression model failed to execute
all features must be in [0, 83] or [-84, 0]
LinearSVR model failed to execute
all features must be in [0, 83] or [-84, 0]
MLPRegressor model failed to execute
all features must be in [0, 83] or [-84, 0]
NuSVR model failed to execute
all features must be in [0, 83] or [-84, 0]
OrthogonalMatchingPursuit model failed to execute
all features must be in [0, 83] or [-84, 0]
OrthogonalMatchingPursuitCV model failed to execute
all features must be in [0, 83] or [-84, 0]
PassiveAggressiveRegressor model failed to execute
all features must be in [0, 83] or [-84, 0]
PoissonRegressor model failed to execute
all features must be in [0, 83] or [-84, 0]
RANSACRegressor model failed to execute
all features must be in [0, 83] or [-84, 0]
RandomForestRegressor model failed to execute
all features must be in [0, 83] or [-84, 0]
Ridge model failed to execute
all features must be in [0, 83] or [-84, 0]
RidgeCV model failed to execute
all features must be in [0, 83] or [-84, 0]
SGDRegressor model failed to execute
all features must be in [0, 83] or [-84, 0]
SVR model failed to execute
all features must be in [0, 83] or [-84, 0]
StackingRegressor model failed to execute
init() missing 1 required positional argument: 'estimators'
TransformedTargetRegressor model failed to execute
all features must be in [0, 83] or [-84, 0]
TweedieRegressor model failed to execute
all features must be in [0, 83] or [-84, 0]
XGBRegressor model failed to execute
all features must be in [0, 83] or [-84, 0]
LGBMRegressor model failed to execute
all features must be in [0, 83] or [-84, 0]
100%|██████████| 43/43 [00:00<00:00, 1954.43it/s]
Empty DataFrame
Columns: [R-Squared, RMSE, Time Taken]
Index: []

• Lazy Predict version: latest
• Python version:
• Operating System:

Description

Describe what you were trying to get done.
I have one feature. This feature is a
<1044374x15537 sparse matrix of type '<class 'numpy.float64'>' with 10514625 stored elements in Compressed Sparse Row format>
I'm guessing that's where the problem comes from, I'm getting :

`~\AppData\Roaming\Python\Python38\site-packages\lazypredict\Supervised.py in fit(self, X_train, X_test, y_train, y_test)
269 X_test = pd.DataFrame(X_test)
270
--> 271 numeric_features = X_train.select_dtypes(include=[np.number]).columns
272 categorical_features = X_train.select_dtypes(include=["object"]).columns
273

~\AppData\Roaming\Python\Python38\site-packages\scipy\sparse\base.py in getattr(self, attr)
685 return self.getnnz()
686 else:
--> 687 raise AttributeError(attr + " not found")
688
689 def transpose(self, axes=None, copy=False):

AttributeError: select_dtypes not found`

Is your feature request related to a problem? Please describe.
I noticed you used the weighted F1 score as the default metric for F1.
Sometimes, you don't want to just use the weighted version. There is no example of how should the users add their customized scorer function.

Describe the solution you'd like
Can you provide an example of how to add a custom scorer to the classifier or the regressor for using lazypredict?

I couldn't find any methods to return the model objects that are trained.

Does lazypredict allow that? If yes, how ?

ERROR: Could not find a version that satisfies the requirement scipy==1.6.0 (from lazypredict) (from versions: 0.8.0, 0.9.0, 0.10.0, 0.10.1, 0.11.0, 0.12.0, 0.12.1, 0.13.0, 0.13.1, 0.13.2, 0.13.3, 0.14.0, 0.14.1, 0.15.0, 0.15.1, 0.16.0, 0.16.1, 0.17.0, 0.17.1, 0.18.0rc2, 0.18.0, 0.18.1, 0.19.0, 0.19.1, 1.0.0b1, 1.0.0rc1, 1.0.0rc2, 1.0.0, 1.0.1, 1.1.0rc1, 1.1.0, 1.2.0rc1, 1.2.0rc2, 1.2.0, 1.2.1, 1.2.2, 1.2.3, 1.3.0rc1, 1.3.0rc2, 1.3.0, 1.3.1, 1.3.2, 1.3.3, 1.4.0rc1, 1.4.0rc2, 1.4.0, 1.4.1, 1.5.0rc1, 1.5.0rc2, 1.5.0, 1.5.1, 1.5.2, 1.5.3, 1.5.4)
ERROR: No matching distribution found for scipy==1.6.0 (from lazypredict)

Is your feature request related to a problem? Please describe.
R-square is not a correct metric when we have multiple features

Describe the solution you'd like
Adjusted R-square metric needs to be included for Regression

Describe the bug
the lazy regressor is not working

To Reproduce
Steps to reproduce the behavior:

1. run the lazy regressor code
   2.you'll see there is no output displayed only the learning rates are available
2. no r-square value displayed
3. there is a problem in the stackregressor
4. see an error

ran on google colab

Hi, I am able to call LazyClassifier and everything displays as expected.

However, when passing in a list to "classifiers" for specific algorithms, they have been showing up as either "ABCMeta" or "Type".
I've attached an example passing in two classifiers but this also affects larger lists.

Support for xgboost, lightgbm and catboost

I would like to be able to pass an argument called timeout which skips a model if building the models crosses the timeout seconds

@shankarpandala : i really liked this library and will help in future, reporting a bug / issue

Describe the bug
I have literally copy pasted the entire code in Jupyter notebook. I am getting an error

ModuleNotFoundError: No module named 'xgboost'

Expected behavior
A clear and concise description of what you expected to happen.

Screenshots
I am attaching screenshot for your reference

Desktop (please complete the following information):

• OS: Windows
• Browser Chrome

AttributeError Traceback (most recent call last)
in
----> 1 from lazypredict.Supervised import LazyClassifier, LazyRegressor
2 from sklearn.model_selection import train_test_split
3 from sklearn import datasets
4 from lightgbm import *

~/opt/anaconda3/lib/python3.7/site-packages/lazypredict/Supervised.py in
77
78 REGRESSORS.append(('XGBRegressor', xgboost.XGBRegressor))
---> 79 REGRESSORS.append(('LGBMRegressor',lightgbm.LGBMRegressor))
80 # REGRESSORS.append(('CatBoostRegressor',catboost.CatBoostRegressor))
81

AttributeError: module 'lightgbm' has no attribute 'LGBMRegressor'

MacOs 10.15.7
lightgbm 3.1.1
lazypredict 0.2.7

it show this error why ?

Describe the bug
A clear and concise description of what the bug is.

Thanks for the great work.

I have the following errors when installing the latest lazypredict.

ERROR: astroid 2.3.1 requires typed-ast<1.5,>=1.4.0; implementation_name == "cpython" and python_version < "3.8", which is not installed.
ERROR: auto-sklearn 0.12.3 has requirement scikit-learn<0.25.0,>=0.24.0, but you'll have scikit-learn 0.23.1 which is incompatible.
ERROR: astroid 2.3.1 has requirement six==1.12, but you'll have six 1.15.0 which is incompatible.
Installing collected packages: scikit-learn, pytest, six, xgboost, pandas, tqdm, click, lightgbm, PyYAML, lazypredict
  Found existing installation: scikit-learn 0.24.1
    Uninstalling scikit-learn-0.24.1:
      Successfully uninstalled scikit-learn-0.24.1
  Found existing installation: pytest 5.2.1
    Uninstalling pytest-5.2.1:
      Successfully uninstalled pytest-5.2.1
  Found existing installation: six 1.12.0
    Uninstalling six-1.12.0:
      Successfully uninstalled six-1.12.0
  Found existing installation: pandas 1.2.2
    Uninstalling pandas-1.2.2:
      Successfully uninstalled pandas-1.2.2
  Found existing installation: tqdm 4.36.1
    Uninstalling tqdm-4.36.1:
      Successfully uninstalled tqdm-4.36.1
  Found existing installation: Click 7.0
    Uninstalling Click-7.0:
      Successfully uninstalled Click-7.0
  Found existing installation: PyYAML 5.1.2
ERROR: Cannot uninstall 'PyYAML'. 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.

To Reproduce
Steps to reproduce the behavior:

1. pip install lazypredict

Screenshots

Desktop (please complete the following information):

Any help will be highly appreciated.

Best regards,
Jun

• Lazy Predict version:
• Python version:
• Operating System:

Description

I tried to run lazypredict regressor on a black friday sales train dataset n it gets stuck on 60 %-63% . dataset has 55,000 rows.

What I Did

 60%|█████████████████████████████████████████████████▌                                | 26/43 [33:11<04:11, 14.82s/it]

CodeFactor found an issue: Redefining built-in 'copyright'

It's currently on:
docs\conf.py:52