UnboundLocalError: local variable 'sp' referenced before assignment

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👋 Welcome to the AutoViML Fan Club Page!
We just hit 3300 stars collectively for all AutoViML libraries on Github!!

AutoViML creates innovative Open Source libraries to make data scientists' and machine learning engineers' lives easier and more productive!

Our innovative libraries so far:

🤝 AutoViz Automatically Visualizes any dataset, any size with a single line of code. Now with Bokeh and Holoviews it can make your charts and dashboards interactive!
🤝 Auto_ViML Automatically builds multiple ML models with a single line of code. Uses scikit-learn, XGBoost and CatBoost.
🤝 Auto_TS Automatically builds ARIMA, SARIMAX, VAR, FB Prophet and XGBoost Models on Time Series data sets with a Single Line of Code. Now updated with DASK to handle millions of rows.
🤝 Featurewiz Uses advanced feature engineering strategies and select the best features from your data set fast with a single line of code. Now updated with DASK to handle millions of rows.
🤝 Deep_AutoViML Builds tensorflow keras models and pipelines for any data set, any size with text, image and tabular data, with a single line of code.
🤝 lazytransform Automatically transform all categorical, date-time, NLP variables to numeric in a single line of code, for any data, set any size.
🤝 pandas_dq Automatically find and fix data quality issues in your dataset with a single line of code, for pandas.

Feb-2024: Added "Auto Encoders" for automatic feature extraction to featurewiz library for #feature-extraction

On Feb 8, 2024, we released a major update to our popular "featurewiz" library that will transform your input into a latent space with a dimension of latent_dim. This lower dimension (similar to PCA) will enable you to extract the best patterns in your data for the toughest imbalanced class and multi-class problems. Try it and let us know!
how to use autoencoders in featurewiz

April-2023: Released a major new python library "pandas_dq" #data_quality #dataengineering

On April 2, 2023, we released a major new Python library called "pandas_dq" that will automatically find and fix data quality issuesin your train and test dataframes in a single line of code, for any data, set any size.
how many pixels wide is my screen

April-2022: Released a major new python library "lazytransform" #featureengineering #featureselection

On April 3, 2022, we released a major new Python library called "lazytransform" that will automatically transform all categorical, date-time, NLP variables to numeric in a single line of code, for any data, set any size.

Jan-2022: Major upgrade to featurewiz: you can now perform feature selection thru fit and transform #MLOps #featureselection

As of version 0.0.90, featurewiz has a scikit-learn compatible feature selection transformer called FeatureWiz. You can use it to perform fit and predict as follows. You will get a Scikit-Learn Transformer object that you can add it to other data pipelines in MLops to select the top variables from your dataset.

Dec-23-2021 Update: AutoViz now does Wordclouds! #autoviz #wordcloud

AutoViz can now create Wordclouds automatically for your NLP variables in data. It detects NLP variables automatically and creates wordclouds for them.

Dec 21, 2021: AutoViz now runs on Docker containers as part of MLOps pipelines. Check out Orchest.io

We are excited to announce that AutoViz and Deep_AutoViML are now available as containerized applications on Docker. This means that you can build data pipelines using a fantastic tool like orchest.io to build MLOps pipelines visually. Here are two sample pipelines we have created:

AutoViz pipeline: https://lnkd.in/g5uC-z66 Deep_AutoViML pipeline: https://lnkd.in/gdnWTqCG

You can find more examples and a wonderful video on orchest's web site

Dec-17-2021 AutoViz now uses HoloViews to display dashboards with Bokeh and save them as Dynamic HTML for web serving #HTML #Bokeh #Holoviews

Now you can use AutoViz to create Interactive Bokeh charts and dashboards (see below) either in Jupyter Notebooks or in the browser. Use chart_format as follows:

chart_format='bokeh': interactive Bokeh dashboards are plotted in Jupyter Notebooks.
chart_format='server', dashboards will pop up for each kind of chart on your web browser.
chart_format='html', interactive Bokeh charts will be silently saved as Dynamic HTML files under AutoViz_Plots directory

Date	parameter1	parameter2	…	parameter8	EventDetection
20221001	xx	xx	xx	xx	no
20221002	xx	xx	xx	xx	no
20221003	xx	xx	xx	xx	no
20221004	xx	xx	xx	xx	no
20221005	xx	xx	xx	xx	no
20221006	xx	xx	xx	xx	no
20221007	xx	xx	xx	xx	yes
20221008	xx	xx	xx	xx	no
20221009	xx	xx	xx	xx	no
20221010	xx	xx	xx	xx	no
20221011	xx	xx	xx	xx	yes
20221012	xx	xx	xx	xx	yes
20221013	xx	xx	xx	xx	yes
20221014	xx	xx	xx	xx	no

Sample#	Date	parameters	EventDetection
1	20221001	xx	no
1	20221002	xx	no
1	20221003	xx	no
1	20221004	xx	no
1	20221005	xx	no
1	20221006	xx	no
1	20221007	xx	yes
1	20221008	xx	no
1	20221009	xx	no
1	20221010	xx	no
1	20221011	xx	yes
1	20221012	xx	yes
1	20221013	xx	yes
1	20221014	xx	no
2	20221001	xx	no
2	20221002	xx	no
2	20221003	xx	yes
2	20221004	xx	no
2	20221005	xx	no
2	20221006	xx	no
2	20221007	xx	no
2	20221008	xx	no
2	20221009	xx	no
2	20221010	xx	no
2	20221011	xx	yes
2	20221012	xx	no
2	20221013	xx	yes
2	20221014	xx	no
3	20221001	xx	no
3	20221002	xx	no
3	20221003	xx	yes
3	20221004	xx	yes
3	20221005	xx	yes
3	20221006	xx	no
3	20221007	xx	no
3	20221008	xx	no
3	20221009	xx	yes
3	20221010	xx	no
3	20221011	xx	no
3	20221012	xx	no
3	20221013	xx	yes
3	20221014	xx	no
…	…	…	…

Sample#	Date	parameters	EventDetection
1_1	20221001	xx	no
1_1	20221002	xx	no
1_1	20221003	xx	no
1_1	20221004	xx	no
1_1	20221005	xx	no
1_2	20221002	xx	no
1_2	20221003	xx	no
1_2	20221004	xx	no
1_2	20221005	xx	no
1_2	20221006	xx	no
1_3	20221003	xx	no
1_3	20221004	xx	no
1_3	20221005	xx	no
1_3	20221006	xx	no
1_3	20221007	xx	yes
1_4	20221004	xx	no
1_4	20221005	xx	no
1_4	20221006	xx	no
1_4	20221007	xx	yes
1_4	20221008	xx	no
…	…	…	…
1_9	20221009	xx	no
1_9	20221010	xx	no
1_9	20221011	xx	yes
1_9	20221012	xx	yes
1_9	20221013	xx	yes
…	…	…	…
3_10	20221010	xx	no
3_10	20221011	xx	no
3_10	20221012	xx	no
3_10	20221013	xx	yes
3_10	20221014	xx	no

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auto_viml's Introduction

👋 Welcome to the AutoViML Fan Club Page! We just hit 3300 stars collectively for all AutoViML libraries on Github!!

AutoViML creates innovative Open Source libraries to make data scientists' and machine learning engineers' lives easier and more productive!

Our innovative libraries so far:

Feb-2024: Added "Auto Encoders" for automatic feature extraction to featurewiz library for #feature-extraction

April-2023: Released a major new python library "pandas_dq" #data_quality #dataengineering

April-2022: Released a major new python library "lazytransform" #featureengineering #featureselection

Jan-2022: Major upgrade to featurewiz: you can now perform feature selection thru fit and transform #MLOps #featureselection

Dec-23-2021 Update: AutoViz now does Wordclouds! #autoviz #wordcloud

Dec 21, 2021: AutoViz now runs on Docker containers as part of MLOps pipelines. Check out Orchest.io

Dec-17-2021 AutoViz now uses HoloViews to display dashboards with Bokeh and save them as Dynamic HTML for web serving #HTML #Bokeh #Holoviews

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auto_viml's Issues

`Filling missing values with "missing" placeholder and adding a column for missing_flags

After this, y_pred is a Series from now on. You need y_pred.values

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👋 Welcome to the AutoViML Fan Club Page!
We just hit 3300 stars collectively for all AutoViML libraries on Github!!