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# Signaturization
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The main feature of the CC is the automatic processing of virtually any compound-related data into a homogeneous, standard-format data ready-to-be-used in machine learning tasks. |
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The main feature of the CC is the automatic processing of virtually any compound-related data into a homogeneous, standard-format data ready-to-be-used in machine learning tasks.
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To discuss with @mbertoni and @oguitart (I've put Martino as assignee, because I think he is the most experienced in python classes).
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As you know, we have most of the CC data stored as `HDF5` files. I think `HDF5` format is good and we have to stick to this file format. However, I think that these files should be accessible through some classes. These classes must not load all data into memory.
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**Signature classes**
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* Applies to:
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* Signatures Type 0
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* Signatures Type 1
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* Signatures Type 2
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* Signatures Type 3
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* 2D projections
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* Every class must have **at least** the following attributes:
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* `V`: the values
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* `keys`: the keys, sorted alphabetically
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* `metric`: the distance used
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* `pvalues`: (distance, p-value) array
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* `PATH`: the path where everything is stored
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* Every class must have **at least** the following methods:
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* `__iter__`: smart, batch iteration, if necessary
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* `__getattr__`: returns the vector corresponding to the key. Works fast with `bisect`, but should return `None` if the key is not in `keys` (ideally, keep a set to do this).
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* `fit`: Not sure this is necessary... Maybe we can just do it as part of the pipeline.
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* `predict`: For the new samples, we should be able to produce the corresponding `V` vectors. This will be, by far, the most tricky part. One should access the `models` folder and use them correspondingly. To increase speed in this part, probably one should just predict for the ones that are not already in the reference. Sometimes, it will be necessary to learn a mapping functions, for instance via AdaNet; for example, in the case of Signature Type 2, as node2vec does not allow for out-of-sample mapping.
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* `validate`: I'm thinking of a folder where we have validation files (for now, MoA and ATC), and then automatically outputting AUROC and KS metrics, among others.
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* `background`: Not sure this is necessary... Just like fit, this has to be done only at initiation.
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**Other classes**
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* We have other data types, such as the nearest neighbors produced by Oriol and the clusters produced by myself.
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* These must also have **at least** the following methods:
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* `__iter__`
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* `__getattr__`
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* `predict`: As always, we want to be able to predict for new molecules using the models stored.
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* `validate`: Here we will not use AUROC and KS, but other statistics, depending on the case.
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Here I put a scheme of the first part of the Chemical Checker pipeline:
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