ML Regression Model Building using Feature Input

‘ML Regression Model Building using Feature Input’ is a floe that train multiple Neural Network Regression models on custom feature input provided by the user. Every input training record needs to contain a feature vector as FloatVec which will be used to train the models. These feature vectors will be normalized before training. It builds Machine Learning models for all possible combination of neural network hyperparameters provided below. Generates floe report containing details of the best models built. User can pick any model and use it to predict properties of other molecules in a separate floe (Predict Physical Properties). The floe report presents detailed statistics on the hyperparameters so as to tweak them and build better models (See documentation). In addition to prediction, the built models provide explanation of predictions and confidence interval. NOTE: This floe by default, builds about 1k machine learning models. Although cheaper than generating fingerprints, on a large dataset, this maybe pricey. Refer to documentations on how to build a cheaper version of the same

Inputs

Name	Description	Type
Input Small Molecules to train machine learning models on.	Input dataset file with each record containing molecule and response value(float) to train on	Molecule Dataset

Options

Name	Description	Type
Response Value Field	Name of the field containing the primary data being trained on and predicted.	Float
Custom Feature	Field containing feature vector to train model on. Must be a float vector	FloatVec
Number of Models to show in Floe report	How many best models to provide in FloeReport. By default, keeps best 20 models (based on r2 score) such that it meet memory requirement	Int
Preprocess Molecule	Preprocess by Neutral Ph, Largest Mol, Blockbuster Filter	Bool
Apply Blockbuster filter	For every molecule, stores only largest component, adjusts ionization to Neutral Ph	Bool
Negative Log	Transform Learning Value to Negative Log. Only for Regression. False: Build Tensorflow Neural Network Model for prediction and explanation (Deterministic Model)	Bool
Number of Top features to explain	Number of top features to provide LIME votes results for. These are the most important features that determine property prediction based on trained ML Model	List

Neural Network Hyperparameter Options: Build models for all possible combination of Hyperparameters

Name	Description	Type
Dropouts	List of dropout hyperparameters.	FloatVec
Sets of Hidden Layers	list(s) of hidden layers separated by -1. Input and output layer will be determined by data. Eg: 150,100,50 will create NN with 3 hidden layers of size 150, 100, 50.	IntVec
Sets of Regularisation Layers	list(s) of regularisation layers separated by -1. No regularisation on Input and output layer.	FloatVec
Learning Rates	List of all the learning rate hyperparameters to train model.	FloatVec
Max Epochs	Maximum number of epochs to train model.	Int
Activation	Activation Functions: ReLU, LeakyReLU, PReLU, tanh, SELU, ELU	List
Batch Size	Batch size for training regressor	Int

Outputs

Name	Description	Type
Models Built	Output of Generated Models	Dataset
Failure Output	Output of Failure	Dataset