chemicalchecker.core.sign1.sign1

class sign1(signature_path, dataset, **kwargs)

Bases: BaseSignature, DataSignature

Signature type 1 class.

Initialize a Signature.

Parameters:

• signature_path (str) – the path to the signature directory.

• model_path (str) – Where the persistent model is.

Methods

add_datasets	Add dataset to a H5
apply_mappings	Map signature throught mappings.
as_dataframe
available	This signature data is available.
background_distances	Return the background distances according to the selected metric.
check_mappings
chunk_iter	Iterator on chunks of data
chunker	Iterate on signatures.
clear	Remove everything from this signature.
clear_all	Remove everything from this signature for both referene and full.
close_hdf5
compute_distance_pvalues	Compute the distance pvalues according to the selected metric.
consistency_check	Check that signature is valid.
copy_from	Copy dataset 'key' to current signature.
copy_sign0_to_sign1	Copy from sign0 to sign1
dataloader	Return a pytorch DataLoader object for quick signature iteration.
delete_tmp
diagnosis
duplicate
export_features
filter_h5_dataset	Apply a maks to a dataset, dropping columns or rows.
fit	Fit signature 1 given signature 0
fit_end	Conclude fit method.
fit_hpc	Execute the fit method on the configured HPC.
func_hpc	Execute the any method on the configured HPC.
generator_fn	Return the generator function that we can query for batches.
get_cc	Return the CC where the signature is present
get_h5_dataset	Get a specific dataset in the signature.
get_intersection	Return the intersection between two signatures.
get_molset	Return a signature from a different molset
get_neig	Return the neighbors signature, given a signature
get_non_redundant_intersection	Return the non redundant intersection between two signatures.
get_self_triplets	Get triplets of signatures only looking at itself
get_sign	Return the signature type for current dataset
get_status_stack
get_triplets	Read triplets of signature across the CC
get_vectors	Get vectors for a list of keys, sorted by default.
get_vectors_lite	Iterate on signatures.
h5_str
hstack_signatures	Merge horizontally a list of signatures.
index	Give the index according to the key.
is_fit
is_valid
load_model
make_filtered_copy	Make a copy of applying a filtering mask on rows.
mark_ready
neighbors	Neighbors
open_hdf5
optimal_t	Find optimal (recommended) number of neighbors.
pipeline_file
predict	Use the learned model to predict the signature.
refresh	Refresh all cached properties
save_full	Map the non redundant signature in explicit full molset.
save_reference	Save a non redundant signature in reference molset.
score	Score based on triplets.
string_dtype
subsample	Subsample from a signature without replacement.
to_csv	Write smiles to h5.
update_status
validate	Perform validations.
vstack_signatures	Merge vertically a list of signatures.
was_sparse	Guess if the matrix was sparse

Attributes

info_h5	Get the dictionary of dataset and shapes.
qualified_name	Signature qualified name (e.g.
shape	Get the V matrix shape.
size	Get the V matrix size.
status

__getitem__(key)

Return the vector corresponding to the key.

The key can be a string (then it’s mapped though self.keys) or and int. Works fast with bisect, but should return None if the key is not in keys (ideally, keep a set to do this).

__iter__()

By default iterate on signatures V.

__repr__()

String representig the signature.

add_datasets(data_dict, overwrite=True, chunks=None, compression=None)

Add dataset to a H5

apply_mappings(out_file, mappings=None)

Map signature throught mappings.

available()

This signature data is available.

background_distances(metric, limit_inks=None, name=None)

Return the background distances according to the selected metric.

Parameters:

metric (str) – the metric name (cosine or euclidean).

chunk_iter(key, chunk_size, axis=0, chunk=False, bar=True)

Iterator on chunks of data

chunker(size=2000, n=None)

Iterate on signatures.

clear()

Remove everything from this signature.

clear_all()

Remove everything from this signature for both referene and full.

compute_distance_pvalues(bg_file, metric, sample_pairs=None, unflat=True, memory_safe=False, limit_inks=None)

Compute the distance pvalues according to the selected metric.

Parameters:

• bg_file (Str) – The file where to store the distances.

• metric (str) – the metric name (cosine or euclidean).

• sample_pairs (int) – Amount of pairs for distance calculation.

• unflat (bool) – Remove flat regions whenever we observe them.

• memory_safe (bool) – Computing distances is much faster if we can load the full matrix in memory.

• limit_inks (list) – Compute distances only for this subset on inchikeys.

Returns:

Dictionary with distances and Pvalues

Return type:

bg_distances(dict)

consistency_check()

Check that signature is valid.

copy_from(sign, key, chunk=None)

Copy dataset ‘key’ to current signature.

Parameters:

• sign (SignatureBase) – The source signature.

• key (str) – The dataset to copy from.

copy_sign0_to_sign1(s0, s1, just_data=False)

Copy from sign0 to sign1

dataloader(batch_size=32, num_workers=1, shuffle=False, weak_shuffle=False, drop_last=False)

Return a pytorch DataLoader object for quick signature iteration.

filter_h5_dataset(key, mask, axis, chunk_size=1000)

Apply a maks to a dataset, dropping columns or rows.

key (str): The H5 dataset to filter. mask (np.array): A bool one dimensional mask array. True values will

be kept.

axis (int): Wether the mask refers to rows (0) or columns (1).

fit(sign0=None, latent=True, scale=True, metric_learning=False, semisupervised=False, scale_kwargs={}, pca_kwargs={}, lsi_kwargs={}, **kwargs)

Fit signature 1 given signature 0

Parameters:

sign0 – A signature 0.

fit_end(**kwargs)

Conclude fit method.

We compute background distances, run validations (including diagnostic) and finally marking the signature as ready.

fit_hpc(*args, **kwargs)

Execute the fit method on the configured HPC.

Parameters:

• args (tuple) – the arguments for of the fit method

• kwargs (dict) – arguments for the HPC method.

func_hpc(func_name, *args, **kwargs)

Execute the any method on the configured HPC.

Parameters:

• args (tuple) – the arguments for of the fit method

• kwargs (dict) – arguments for the HPC method.

generator_fn(weak_shuffle=False, batch_size=None)

Return the generator function that we can query for batches.

get_cc(cc_root=None)

Return the CC where the signature is present

get_h5_dataset(h5_dataset_name, mask=None)

Get a specific dataset in the signature.

get_intersection(sign)

Return the intersection between two signatures.

get_molset(molset)

Return a signature from a different molset

get_neig()

Return the neighbors signature, given a signature

get_non_redundant_intersection(sign)

Return the non redundant intersection between two signatures.

(i.e. keys and vectors that are common to both signatures.) N.B: to maximize overlap it’s better to use signatures of type ‘full’. N.B: Near duplicates are found in the first signature.

get_self_triplets(local_neig_path, num_triplets=10000000)

Get triplets of signatures only looking at itself

get_sign(sign_type)

Return the signature type for current dataset

get_triplets(reference)

Read triplets of signature across the CC

get_vectors(keys, include_nan=False, dataset_name='V', output_missing=False)

Get vectors for a list of keys, sorted by default.

Parameters:

• keys (list) – a List of string, only the overlapping subset to the signature keys is considered.

• include_nan (bool) – whether to include requested but absent molecule signatures as NaNs.

• dataset_name (str) – return any dataset in the h5 which is organized by sorted keys.

get_vectors_lite(keys, chunk_size=2000, chunk_above=10000)

Iterate on signatures.

static hstack_signatures(sign_list, destination, chunk_size=1000, aggregate_keys=None)

Merge horizontally a list of signatures.

index(key)

Give the index according to the key.

Parameters:

key (str) – the key to search index in the matrix.

Returns:

Index in the matrix

Return type:

index(int)

property info_h5

Get the dictionary of dataset and shapes.

make_filtered_copy(destination, mask, include_all=False, data_file=None, datasets=None, dst_datasets=None, chunk_size=1000, compression=None)

Make a copy of applying a filtering mask on rows.

destination (str): The destination file path. mask (bool array): A numpy mask array (e.g. result of np.isin) include_all (bool): Whether to copy other dataset (e.g. features,

date, name…)

data_file (str): A specific file to copy (by default is the signature

h5)

neighbors(tmp, metric='cosine', k_neig=1000, cpu=4)

Neighbors

optimal_t(max_triplets=10000, min_triplets=1000, local_neig_path=False, save=True)

Find optimal (recommended) number of neighbors.

Based on the accuracy of triplets across the CC. Neighbors class needs to be precomputed. Only done for the reference set (it doesn’t really make sense to do it for the full).

Parameters:

• max_triplets (int) – Maximum number of triplets to consider (default=10000).

• save (bool) – Store an opt_t.h5 file (default=True).

predict(sign0, destination)

Use the learned model to predict the signature.

Parameters:

• sign1 (signature) – A valid Signature type 1

• destination (None|path|signature) – If None the prediction results are returned as dictionary, if str then is used as path for H5 data, if empty Signature type 2 its data_path is used as destination.

property qualified_name

Signature qualified name (e.g. ‘B1.001-sign1-full’).

refresh()

Refresh all cached properties

save_full(overwrite=False)

Map the non redundant signature in explicit full molset.

It generates a new signature in the full folders.

Parameters:

overwrite (bool) – Overwrite existing (default=False).

save_reference(cpu=4, overwrite=False)

Save a non redundant signature in reference molset.

It generates a new signature in the references folders.

Parameters:

• cpu (int) – Number of CPUs (default=4),

• overwrite (bool) – Overwrite existing (default=False).

score(reference, max_triplets=10000)

Score based on triplets.

Parameters:

max_triplets (int) – Maximum number of triplets to consider.

property shape

Get the V matrix shape.

property size

Get the V matrix size.

subsample(n, seed=42)

Subsample from a signature without replacement.

Parameters:

n (int) – Maximum number of samples (default=10000).

Returns:

A (samples, features) matrix. keys(array): The list of keys.

Return type:

V(matrix)

to_csv(filename, smiles=None)

Write smiles to h5.

At the moment this is done quering the Structure table for inchikey inchi mapping and then converting via Converter.

validate(apply_mappings=True, metric='cosine', diagnostics=False)

Perform validations.

A validation file is an external resource basically presenting pairs of molecules and whether they share or not a given property (i.e the file format is inchikey inchikey 0/1). Current test are performed on MOA (Mode Of Action) and ATC (Anatomical Therapeutic Chemical) corresponding to B1.001 and E1.001 dataset.

Parameters:

apply_mappings (bool) – Whether to use mappings to compute validation. Signature which have been redundancy-reduced (i.e. reference) have fewer molecules. The key are moleules from the full signature and values are moleules from the reference set.

static vstack_signatures(sign_list, destination, chunk_size=10000, vchunk_size=100)

Merge vertically a list of signatures.

was_sparse(max_keys=1000, zero_prop=0.5)

Guess if the matrix was sparse