GML

Creators

Muhammad Ahmed
Naman Tuli

Contributors

Mehran Kamal
Rafey Iqbal Rahman

Tired of doing Data Science manually? GML is here for you!

GML is an automatic data science library in python built on top of multiple Python packages. Complete features which we offer are listed as:

Installation:

python pip install GML

https://pypi.org/project/GML
If you are facing any pytorch related issue during installation, kindly refer to following solution: https://github.com/Muhammad4hmed/GML/issues/6#issuecomment-735912557

Features:

Auto Feature Engineering

```python from GML import FeatureEngineering

fe = FeatureEngineering(Data, 'target', fillmissingdata=True, encodedata=True, normalize=True, removeoutliers=True, newfeatures=True, featengsteps=2 ) # feateng_steps = 0 for features selection without feature creation

Xnew, y, test = fe.getnew_data() ```

Click Here for complete DEMO

Auto EDA (Powered by Sweetviz)

```python from GML import sweetviz

result1 = sweetviz.compare([train,'train'],[test,'test'],'target') result2 = sweetviz.analyze([train,'train'])

result.showhtml() result2.showhtml() ```

Click Here for complete DEMO

Auto Machine Learning

```python from GML import AutoML

gml_ml = AutoML()

gmlml.GMLClassifier(X, y, metric = accuracyscore, folds = 10) ```

Click Here for complete DEMO

Auto Text Cleaning

```python from GML import AutoNLP

nlp = AutoNLP()

cleanX = X.apply(lambda x: nlp.clean(x)) ```

Click Here for complete DEMO

Auto Text Classification using transformers

```python from GML import AutoNLP

nlp = AutoNLP()

nlp.setparams(cleanX, tokenizername='roberta-large-mnli', BATCHSIZE=4, modelname='roberta-large-mnli', MAX_LEN=200)

model = nlp.train_model(tokenizedX, y) ```

Click Here for complete DEMO

Auto Image Classification with Augmentation

```python from GML import AutoImageProcessing

gmlimageprocessing = AutoImageProcessing()

model = gmlimageprocessing.imgClassificationcsv(imgpath = './covidimagedata/train', trainpath = './covidimagedata/Trainingsetcovid.csv', modellist = models, tfms = True, advanceaugmentation = True, epochs=1) ```

Click Here for complete DEMO

Text Augmentation using transformers: GPT-2

```python from GML import AutoNLP

nlp = AutoNLP()

nlp.augmentation_train('./data.csv')

nlp.set_params(X['Text'])

newText = nlp.augmentationgenerate(y = y, SENTENCES = 100) ```

Click Here for complete DEMO

More cool features and handling of different data types like audio data etc will be added in future.
Feel free to give suggestions, report bugs and contribute.