Plan - WIP

[ardunn]

CODING TODOS, IN ORDER OF IMPORTANCE/STATE OF NEGLECT - 10.2.2018

1. (tie) Heuristic-based Featurizer Selection - Qi

[given df, return a set of featurizers]

• A scheme to abandon featurizers unworkable for a given composition dataset.
• A scheme to abandon featurizers unworkable for a given structure dataset.

1. (tie) Top level class - Alex D

[given dataframe, give reports and final models] (+ generating final reports)

• A method for benchmarking
• A method for predicting
• Way to determine regression or classification

2. (tie) Featurization - Alex G

[given df and set of featurizers, featurize a df robustly]

• Having good sets of featurizers, working on transfer learning part, cached features, etc.

2. (tie) Preprocessing - Alireza

[given featurized df, return an ML ready df]
Coming up with good methods for preprocessing and and feature reduction

• preprocess should not have a new method: separation of concerns
• adding RandomForest feature selection
• adding sensitivity analysis selection as an option

3. Data - Daniel

[given a request for data, return a nice dataframe, citations, etc.]
Moving the datasets to matminer, adding to figshare, making sure all columns are numeric, making sure correct citation data is present, having all in json format

• update matminer to support seaborn style dataset loading including tests
• convert existing dataset metadata dict to json file and make a gui or cli interface to update it
• write functions to interface with dataset metadata and give user info
• convert existing matminer datasets to json
• remove deprecated matminer functions
• convert matbench datasets to json, add to figshare, add to matminer
• update matbench interface with datasets to use the matminer interface
• ensure all datasets have their dataframes formatted properly, (numeric data, etc.)

4. ML pipeline - Qi?

[given an ML ready df (or just X and y), return a model]
Checking defaults, adding or testing Neural network?

• Check Tpot defaults
• Add other adaptor classes for other backends?

5. Analysis + Visualization + Interpretability - Daniel

[given a model and dataframe, return cool informative stats (and graphs)]

[Doppe1g4nger]

I'm working on Data, can dive into whatever else once that's finished. Maybe Tpot ML.

[albalu]

I'm working on Preprocessing

[Qi-max]

I will work on Metalearning, maybe also optimizing Tpot ML.

[ardunn]

I'll work on the top level class, and Analysis and Visualization. I'll also be dabbling in each sub package to help the top level class work with them

[ardunn]

gonna close this issue in favor of issues + the project board