chemfp.diversity module¶

This module contains interfaces to chemfp’s diversity selection algorithms.

Terminology¶

The selection algorithms uses different concepts of “dissimilar” to iteratively pick one or more dissimilar fingerprints from an arena containing candidate fingerprints.

The picked fingerprints are dissimilar to all other candidate fingerprints, and optionally also dissimilar to fingerprints in an arena of “reference” fingerprints.

This latter case may be used to select diverse fingerprints from a vendor catalog (“the candidates”) which are also dissimilar to an in-house compound library (“the references”).

To create a given picker, use one of the get_*_picker functions or, alternatively one of the picker class’s from_ methods. Do not call the class constructor directly.

Each picker implements a pick_n() method, along with some variations, to pick an additional n items. They also implement several iter_*() methods to iteratively get the next pick.

MaxMin picker¶

The MaxMinPicker implements the MaxMin algorithm[1][2]. This algorithm iteratively picks fingerprints from a set of candidates such that the newly picked fingerprint has the smallest Tanimoto similarity compared to any previously picked fingerprint, and optionally also the smallest Tanimoto similarity to the reference fingerprints.

The MaxMin diversity score for a given pick is the maximum Tanimoto score between that pick and all previous picks and the reference arena. If there is no reference arena then the diversity score of the first pick is 0.0.

HeapSweep picker¶

The HeapSweepPicker implements a sweep-based algorithm to pick fingerprints based on their maximum Tanimoto similarity to any other fingerprint in the arena, from least maximum similarity to most. This method uses a heap to track the current highest-known score for each fingerprints. Each sweep compares a fingerprint with the smallest score to all other fingerprints, while also updating the highest-known score for each other fingerprint.

The heapsweep algorithm is used to find the initial pick for the MaxMin picker if references fingerprints or an initial pick are not specified. This algorithm is significantly slower than MaxMin (over 100-fold!), and is mostly here to find all initial picks with same minimum maximum score. While it can be used to find the diversity score for all fingerprints, a k=1 NxN nearest-neighbor search will be faster and can make use of multiple cores.

The heapsweep diversity score for a given pick is the maximum Tanimoto score between that pick and all other fingerprints in the arena.

The heapsweep algorithm appears to be novel to chemfp. It is strongly influenced by the “Sweep” family of algorithms. See the SumSweep paper [3] for a description of many of those heuristics.

Sphere exclusion picker¶

The SphereExclusionPicker implements the sphere exclusion algorithm[4] with optional ranking for directed sphere exclusion[5]. This method iteratively picks fingerprints from a set of candidates such that the fingerprint is not within a given threshold of similarity to any previously selected fingerprint.

By default it picks fingerprints with the smallest number of set bits. It can also be configured to pick a fingerprint, or to pick a fingerprint by the smallest associated rank (again, either by the smallest number of set bits or randomly).

The DISERanker class implements the Gobbi and Lee[5] ranking algorithm to generate ranks that can be passed to the SphereExclusionPicker.

[1] Ashton M., Barnard J., Casset F., Charlton M., Downs G., Gorse D., Holliday J., Lahana R., Willett P. (2002). Identification of diverse database subsets using property-based and fragment-based molecular descriptions. Quantitative Structure-Activity Relationships 21 (6) 598-604. https://doi.org/10.1002/qsar.200290002

[2] Sayle, R. (2017). Recent Improvements to the RDKit. https://github.com/rdkit/UGM_2017/blob/master/Presentations/Sayle_RDKitDiversity_Berlin17.pdf

[3] Borassi, M., Crescenzi, P., Habib, M., Kosters, W. A., Marino, A., and Takes, F. W. (2015). Fast diameter and radius BFS-based computation in (weakly connected) real-world graphs: With an application to the six degrees of separation games. Theoretical Computer Science 586 (2015) 59-80. http://dx.doi.org/10.1016/j.tcs.2015.02.033

[4] Hudson, B. D., Hyde, R. M., Rahr, E., Wood, J., Osman, J. (1996). Parameter based methods for compound selection from chemical databases. Quantitative Structure-Activity Relationships, 15(4), 285-289. https://doi.org/10.1002/qsar.19960150402

[5] Gobbi, A., Lee, M. L. (2003). DISE: directed sphere exclusion. Journal of Chemical Information and Computer Sciences, 43(1), 317-323. https://doi.org/10.1021/ci025554v

class chemfp.diversity.BaseMaxMinPicker¶

Bases: object

The base class for the MaxMin and HeapSweep pickers

Its candidate_arena attribute is the FingerprintArena used for picking.

candidate_arena¶

candidates¶

Get access to the remaining candidates as a chemfp.diversity.MaxMinCandidates

NOTE: This is not part of the public API.

iter_ids(max_similarity=1.0)¶: Iteratively make a pick, yielding the candidate id each time

iter_ids_and_scores(max_similarity=1.0)¶: Iteratively make a pick, yielding (candidate id, diversity score) each time

iter_indices(max_similarity=1.0)¶: Iteratively make a pick, yielding the candidate index each time

iter_indices_and_scores(max_similarity=1.0)¶: Iteratively make a pick, yielding (candidate index, diversity score) each time

picks¶: Get access to all of the picks so far (including initial picks) as a chemfp.diversity.Picks

class chemfp.diversity.BasePicks¶

Bases: object

Information about the picks (ids and indices).

Do not modify its values.

as_ctypes()¶

Return a ctypes view of the underlying pick data

The view is a Pick array with attributes named “candidate_idx” and “popcount”.

as_numpy()¶

Return a NumPy view of the underlying pick data

The view has a structured dtype with fields named “candidate_idx” and “popcount”.

get_ids()¶: Return a list of ids for each pick

get_indices()¶: Return a list of indices into the candidates arena for each pick

to_pandas(*, column='pick_id')¶

Return the pick ids as a pandas DataFrame

The default column header is “pick_id”. Use column to specify an alternate header.

Parameters:: column (a string) – the column header for the pick ids
Returns:: a pandas DataFrame

class chemfp.diversity.Candidate¶

Bases: Structure

A view of a candidate fingerprint in the picker.

Do not modify its values.

c¶: Structure/Union member

candidate_idx¶: Structure/Union member

d¶: Structure/Union member

depth¶: Structure/Union member

popcount¶: Structure/Union member

reference_popcount¶: Structure/Union member

class chemfp.diversity.DISERanker(dise_arena)¶

Bases: object

Generate a fingerprint ranking based on the method in the DISE paper.

The next pick is the candidate fingerprint closest to the first fingerprint in the input dise_arena, with ties broken by the similarity to the second fingerprint, etc.

This class can be used to generate values passed to SphereExclusionPicker’s ranks parameter.

The class variable DISE_SMILES_LIST contains the SMILES strings for the three reference compounds used in the DISE paper by Gobbi and Lee.

The public attributes are:

dise_arena¶: The reference FingerprintArena used for ranking.

DISE_SMILES_LIST = ['CCCC1=NN(C2=C1N=C(NC2=O)C3=C(C=CC(=C3)S(=O)(=O)N4CCN(CC4)C)OCC)C.O=C(O)CC(O)(C(O)=O)CC(O)=O', 'O=C(OC)\\C3=C(\\N\\C(=C(\\C(=O)OC(C)C)C3c1cccc2nonc12)C)C', 'O=C(OCC)[C@@H](N[C@@H]2C(=O)N(c1ccccc1CC2)CC(=O)O)CCc3ccccc3']¶

static from_dise_paper(fptype, reader_args=None)¶

Use the structures from the DISE paper to create a DISERanker for a given fingerprint type

The structures are the SMILES strings in DISE_SMILES_LIST.

Parameters:

fptype (a string or a chemfp.types.FingerprintType) – the fingerprint type used to process the SMILES strings
reader_args (None, or a dictionary) – optional reader arguments for SMILES processing

Returns:

a chemfp.diversity.DISERanker

static from_fingerprints(fingerprints, metadata=None)¶

Use a list of fingerprints to create a DISERanker

This is a short-hand for:

arena = load_fingerprints(fingerprints, metadata=metadata, reorder=False)
return DISERanker(arena)

See chemfp.load_fingerprints() for full details.

Parameters:

fingerprints – the fingerprints to use
metadata (a chemfp.Metata) – the metadata used if fingerprints is an (id, fp) iterator

Returns:

a chemfp.diversity.DISERanker

static from_smiles_list(fptype, smiles_list, reader_args=None)¶

Use a list of SMILES string to create a DISERanker for a given fingerprint type

Parameters:

fptype (a string or a chemfp.types.FingerprintType) – the fingerprint type used to process the SMILES strings
smiles_list (a list of strings) – the list of SMILES strings
reader_args (None, or a dictionary) – optional reader arguments for SMILES processing

Returns:

a chemfp.diversity.DISERanker

rank_arena(arena, rng=None)¶

Return an array of ranks, one for each fingerprint.

The algorithm starts by ranking each arena fingerprint to the first reference fingerprint. Fingerprints with a low rank value are more similar to the reference fingerprint than fingerprints with a high rank value.

Ties are broken by similarity to each successive reference fingerprint (in self.dise_arena).

If rng is None then any final ties are left as-is, otherwise ties are broken by the passed-in rng using its rng.shuffle() method.

If rng is an integer then use Python’s random.Random(rng) to create the rng.

Parameters:

arena (a chemfp.arena.FingerprintArena) – a fingerprint arena
rng (None, an integer, or an object with a shuffle method.) – an RNG used to break any final ties

Returns:

an array.array of ranks, one for each arena fingerprint

class chemfp.diversity.HeapSweepPicker¶

Bases: BaseMaxMinPicker

An implementation of the heapsweep picker algorithm

The constructor must not be called directly. Instead, use HeapSweepPicker.from_candidates().

Once you have a picker, use HeapSweepPicker.pick_n() or HeapSweepPicker.pick_n_with_scores() to pick the next n candidates, optionally also with its heapsweep score.

Alternatively, use iter_indices() or iter_ids(), to pick the next candidate, yielding either the pick index or pick id; or use iter_indices_and_scores(), or iter_ids_and_scores() to also include the heapsweep diversity score.

static from_candidates(candidate_arena, *, randomize=True, seed=-1)¶

Use heapsweep to pick diverse fingerprints from the candidate arena

The heapsweep diversity score for a fingerprint is the maximum Tanimoto score between that fingerprint and all other fingerprints in the candidate_arena. The heapsweep method iteratively picks fingerprints from most diverse (smallest maximum Tanimoto) to least.

If randomize is True (the default), the candidates are shuffled before the heapsweep algorithm starts. Shuffling should only affect the ordering of fingerprints with identical diversity scores. It is True by default so the first picked fingerprint is the same as MaxMinPicker.from_candidates(). Setting to False should generally be slightly faster.

The shuffle and heapsweep methods depend on a (shared) RNG, which requires an initial seed. If seed is -1 (the default) then use Python’s own RNG to generate the initial seed, otherwise use the value as the seed.

Parameters:

candidate_arena (a chemfp.arena.FingerprintArena with popcount indices and at least one non-empty fingerprint) – an arena containing the candidate fingerprints to pick from
randomize (True to shuffle, False to leave as-is) – shuffle the candidates before picking?
seed (a value between 0 and 2**64-1, or -1) – initial RNG seed, or -1 (the default) to seed from Python’s RNG

Returns:

a chemfp.diversity.HeapSweepPicker

pick_n(n, max_similarity=1.0, timeout=None)¶

Pick up to n candidates with a globally maximum similarity of no more than max_similarity

The picks are appended to the MaxMinPicker’s self.picks and the pick information (picked candidate fingerprint indices and corresponding ids) is returned in a :class:.Picks instance.

Use HeapSweepPicker.pick_n_with_scores() if you also need the maximum similarity score.

n may zero, in which case an empty Picks instance is returned.

Use timeout to stop picking after the given number of seconds has elapsed. This is primarily meant for interactive use like progress bars and status updates.

Parameters:

n (an integer) – the maximum number of remaining candidates to pick
max_similarity (a float) – the maximum allowed pick similarity
timeout (None for no maximum time, or a non-negative float) – stop picking after the given number of seconds

Returns:

a chemfp.diversity.Picks

pick_n_with_scores(n, max_similarity=1.0, result=None, timeout=None)¶

Pick up to n candidates

The picks are appended to the HeapSweepPicker’s self.picks and the pick information (picked candidate fingerprint indices, maximum score, and corresponding ids) is returned in a PicksAndScores instance.

Use HeapSweepPicker.pick_n() if you do not need the maximum similarity score.

n may zero, in which case an empty PicksAndScores is returned. This may be useful in combination with the result parameter to accumulate successive picks.

If result is a PicksAndScores returned from a previous HeapSweepPicker.pick_n_with_scores() call then the pick information will be stored in that instance instead of creating a new one.

Use timeout to stop picking after the given number of seconds has elapsed. This is primarily meant for interactive use like progress bars and status updates.

Parameters:

n (an integer) – the maximum number of remaining candidates to pick
max_similarity (a float) – the maximum allowed pick similarity
result – store picks in the given object instead of creating a new result object
timeout (None for no maximum time, or a non-negative float) – stop picking after the given number of seconds

Returns:

a chemfp.diversity.PicksAndScores

class chemfp.diversity.MaxMinCandidates¶

Bases: object

Get access to the remaining MaxMin or HeapSweep candidates.

NOTE: This is an internal API used for testing and not part of the public API. Do not modify any values.

If you find it useful, let me know.

as_ctypes()¶: Get a ctypes view of the underlying Candidate data

as_numpy()¶: Get a numpy view of the underlying Candidate data

get_indices()¶: Return a list of indices into the candidates arena

class chemfp.diversity.MaxMinPicker¶

Bases: BaseMaxMinPicker

An implementation of the MaxMin picker algorithm (Ashton, et al.)

The constructor must not be called directly. Instead, use one of:

MaxMinPicker.from_candidates()
MaxMinPicker.from_candidates_and_initial_pick()
MaxMinPicker.from_candidates_and_references()

Once you have a picker, use MaxMinPicker.pick_n() or MaxMinPicker.pick_n_with_scores() to pick the the next n candidates, optionally also with its MaxMin diversity score.

Alternatively, use iter_indices() or iter_ids() to pick the next candidate, yielding either the pick index or pick id; or use iter_indices_and_scores(), or iter_ids_and_scores() to also include the MaxMin score.

static from_candidates(candidate_arena, *, randomize=True, seed=-1)¶

Use MaxMin to pick diverse fingerprints from the candidate arena

The initial pick is determined by the heapsweep algorithm, which selects a fingerprint with the globally smallest maximum Tanimoto score to any other fingerprint. This may take a few seconds so use MaxMinPicker.from_candidates_and_initial_pick() if you know the initial pick.

If randomize is True (the default), the candidates are shuffled before the MaxMin algorithm starts. Shuffling gives a sense of how MaxMin is affected by arbitrary tie-breaking.

The heapsweep and shuffle methods depend on a (shared) RNG, which requires an initial seed. If seed is -1 (the default) then use Python’s own RNG to generate the initial seed, otherwise use the value as the seed.

Parameters:

candidate_arena (a chemfp.arena.FingerprintArena with popcount indices and at least one non-empty fingerprint) – an arena containing the candidate fingerprints to pick from
randomize (True to shuffle, False to leave as-is) – shuffle the candidates before picking?
seed (a value between 0 and 2**64-1, or -1) – initial RNG seed, or -1 (the default) to seed from Python’s RNG

Returns:

a chemfp.diversity.MaxMinPicker

static from_candidates_and_initial_pick(candidate_arena, initial_pick, *, randomize=True, seed=-1)¶

Use MaxMin to pick diverse fingerprints from the candidate arena, starting with an initial pick

This method lets you specify the initial pick as an initial_pick index into the candidate arena.

There are several strategies for the initial MaxMin pick: use the “middle” fingerprint, use a randomly selected fingerprint, or, if heapsweep identifies that multiple fingerprints have the same smallest maximum Tanimoto score, then try each of those as starting point.

If randomize is True (the default), the candidates are shuffled before the MaxMin algorithm starts. Shuffling gives a sense of how MaxMin is affected by arbitrary tie-breaking.

Shuffling depends on a RNG, which requires an initial seed. If seed is -1 (the default) then use Python’s own RNG to generate the initial seed, otherwise use the value as the seed.

Parameters:

candidate_arena (a chemfp.arena.FingerprintArena with popcount indices and at least one non-empty fingerprint) – an arena containing the candidate fingerprints to pick from
initial_pick (an integer) – the index of the initial pick, which must be a non-empty fingerprint
randomize (True to shuffle, False to leave as-is) – shuffle the candidates before picking?
seed (a value between 0 and 2**64-1, or -1) – initial RNG seed, or -1 (the default) to seed from Python’s RNG

Returns:

a chemfp.diversity.MaxMinPicker

static from_candidates_and_references(candidate_arena, reference_arena, *, randomize=True, seed=-1)¶

Use MaxMin to pick diverse fingerprints from the candidate arena, which are also diverse from the reference arena

The fingerprints in candidate_arena are ranked according to their most similar fingerprint in reference_arena. A fingerprint with the the smallest maximum score is used as the initial pick when applying the MaxMin algorithm to the remaining fingerprint in the candidate_arena.

If randomize is True (the default), the candidates are shuffled before the MaxMin algorithm starts. Shuffling gives a sense of how MaxMin is affected by arbitrary tie-breaking.

Shuffling depends on a RNG, which requires an initial seed. If seed is -1 (the default) then use Python’s own RNG to generate the initial seed, otherwise use the value as the seed.

Parameters:

candidate_arena (a chemfp.arena.FingerprintArena with popcount indices and at least one non-empty fingerprint) – an arena containing the candidate fingerprints to pick from
reference_arena (a chemfp.arena.FingerprintArena with popcount indices and at least one non-empty fingerprint) – an arena containing reference fingerprints
randomize (True to shuffle, False to leave as-is) – shuffle the candidates before picking?
seed (a value between 0 and 2**64-1, or -1) – initial RNG seed, or -1 (the default) to seed from Python’s RNG

Returns:

a chemfp.diversity.MaxMinPicker

pick_n(n, max_similarity=1.0, timeout=None)¶

Pick up to n candidates with a maximum similarity of max_similarity to any previous pick

The picks are appended to the MaxMinPicker’s self.picks data and the pick information (picked candidate fingerprint indices and corresponding ids) is returned in a Picks instance.

Use MaxMinPicker.pick_n_with_scores() if you also need the maximum similarity score.

n may zero, in which case an empty Picks instance is returned.

Use timeout to stop picking after the given number of seconds has elapsed. This is primarily meant for interactive use like progess bars and status updates.

Parameters:

n (an integer) – the maximum number of remaining candidates to pick
max_similarity (a float) – the maximum allowed pick similarity
timeout (None for no maximum time, or a non-negative float) – stop picking after the given number of seconds

Returns:

a chemfp.diversity.Picks

pick_n_with_scores(n, max_similarity=1.0, result=None, timeout=None)¶

Pick up to n candidates with a maximum similarity of max_similarity to any previous pick

The picks are appended to the MaxMinPicker’s self.picks data and the pick information (picked candidate fingerprint indices, maximum score, and corresponding ids) is returned in a PicksAndScores instance.

Use MaxMinPicker.pick_n() if you do not need the maximum similarity score.

n may zero, in which case an empty PicksAndScores is returned. This may be useful in combination with the result parameter to accumulate successive picks.

If result is a PicksAndScores returned from a previous MaxMinPicker.pick_n_with_scores() call then the pick information will be stored in that instance instead of creating a new one.

Use timeout to stop picking after the given number of seconds has elapsed. This is primarily meant for interactive use like progess bars and status updates.

Parameters:

n (an integer) – the maximum number of remaining candidates to pick
max_similarity (a float) – the maximum allowed pick similarity
result (a chemfp.diversity.PicksAndScores) – store picks in the given object instead of creating a new result object
timeout (None for no maximum time, or a non-negative float) – stop picking after the given number of seconds

Returns:

a chemfp.diversity.PicksAndScores

class chemfp.diversity.Neighbors¶

Bases: PicksAndScores

Access the sphere exclusion neighbor indices, score, and ids

as_ctypes()¶

Return a ctypes view of the underlying neighbor data

The view is a PickAndScore array with attributes named candidate_idx and score.

as_numpy()¶

Return a numpy view of the underlying neighbor data

The view has a structure dtype with fields named “candidate_idx” and “score”.

get_ids()¶: Return a list of neighbor ids for the exclusion sphere

get_ids_and_scores()¶: Return a tuple of (id, score) for the neighbors in the exclusion sphere

get_indices()¶: Return a list of indices into the candidate arena for the neighbors

get_indices_and_scores()¶: Return a tuple of (arena indices, score) for the neighbors

get_scores()¶: Return a list of scores for the neighbors in the exclusion sphere

reorder(ordering='decreasing-score-plus')¶

Reorder the neighbors based on the requested ordering.

The available orderings are:

increasing-score - sort by increasing score
decreasing-score - sort by decreasing score
increasing-score-plus - sort by increasing score, break ties by increasing index
decreasing-score-plus - sort by decreasing score, break ties by increasing index
increasing-index - sort by increasing index
decreasing-index - sort by decreasing index
move-closest-first - move the neighbor with the highest score to the first position
reverse - reverse the current ordering

Parameters:: ordering (string) – the name of the ordering to use

to_pandas(*, columns=['neighbor_id', 'score'])¶

Return a pandas DataFrame with the sphere neighbor ids and scores

The first column contains the ids, the second column contains the ids. The default columns headers are “neighbor_id” and “score”. Use columns to specify different headers.

Parameters:: columns (a list of two strings) – column names for the returned DataFrame
Returns:: a pandas DataFrame

class chemfp.diversity.Pick¶

Bases: Structure

A view of a picked fingerprint in the picker.

Do not modify its values.

candidate_idx¶: Structure/Union member

popcount¶: Structure/Union member

class chemfp.diversity.PickAndScore¶

Bases: Structure

A picked fingerprint index and score.

Do not modify its values.

candidate_idx¶: Structure/Union member

score¶: Structure/Union member

class chemfp.diversity.Picks¶

Bases: BasePicks

A list-like container of picked identifiers

class chemfp.diversity.PicksAndCounts¶

Bases: BasePicks

Information about the sphere exclusion picks (ids and indices) and counts. Do not modify its values.

get_counts()¶: Return the array of counts for the picks

get_ids()¶: Return a list of pick ids for each pick

get_ids_and_counts()¶: Return a list of (pick id, count) for each pick

get_indices_and_counts()¶: Return a list of (arena index, count) for each pick

to_pandas(*, columns=['pick_id', 'count'])¶

Return a pandas DataFrame with the pick ids and sphere exclusion counts.

The first column contains the ids, the second column contains the sphere exclusion counts. The default columns headers are “pick_id” and “count”. Use columns to specify different headers.

Parameters:: columns (a list of two strings) – column names for the returned DataFrame
Returns:: a pandas DataFrame

class chemfp.diversity.PicksAndNeighbors¶

Bases: BasePicks

Information about the sphere exclusion picks (ids and indices) neighbors.

Do not modify its values.

A “neighbor” is a candidate index within the pick’s sphere similarity threshold, and may include the pick.

get_all_neighbors()¶: Return the list of all neighbors for each pick

get_counts()¶: Return the array of counts for the picks

get_ids_and_counts()¶: Return a list of (pick id, count) for each pick

get_ids_and_neighbors()¶: Return a tuple of (pick id, neighbors) for each pick

get_indices_and_counts()¶: Return a list of (pick index, count) for each pick

get_indices_and_neighbors()¶: Return a tuple of (candidate arena index, neighbors) for each pick

to_pandas(*, columns=['pick_id', 'neighbor_id', 'score'], empty=('*', None))¶

Return a pandas DataFrame with pick id and its sphere neighbor ids and scores

Each pick has zero or more neighbors. Each neighbor becomes a row in the output table, with the pick id in the first column, the neighbor id in the second, and the hit score in the third.

The default columns headers are “pick_id”, “neighbor_id” and “score”. Use columns to specify different headers.

If a pick has no neighbors then by default a row is added with the query id, ‘*’ as the target id, and None as the score (which pandas will treat as a NA value).

Use empty to specify different behavior for queries with no hits. If empty is None then no row is added to the table. If empty is a 2-element tuple the first element is used as the target id and the second is used as the score.

Parameters:: columns (a list of three strings) – column names for the returned DataFrame
Returns:: a pandas DataFrame

class chemfp.diversity.PicksAndScores¶

Bases: object

Access the pick indices, scores, and ids

as_ctypes()¶

Return a ctypes view of the underlying hit data

The view is a PickAndScore array with attributes named candidate_idx and score.

as_numpy()¶

Return a numpy view of the underlying hit data

The view has a structure dtype with fields named “candidate_idx” and “score”.

get_ids()¶: Return a list of identifiers for the picks

get_ids_and_scores()¶: Return a tuple of (id, score) for the picks

get_indices()¶: Return a list of indices into the candidate arena for the picks

get_indices_and_scores()¶: Return a tuple of (arena indices, score) for the picks

get_scores()¶: Return a list of scores for the picks

move_pick_index_to_first(pick_index)¶

Move the pick with the given index to the first position in the list

raises IndexError if the pick_index does not exist.

This lets spherex output always have the center as the first member.

reorder(ordering='decreasing-score-plus')¶

Reorder the picks based on the requested ordering.

The available orderings are:

increasing-score - sort by increasing score
decreasing-score - sort by decreasing score
increasing-score-plus - sort by increasing score, break ties by increasing index
decreasing-score-plus - sort by decreasing score, break ties by increasing index
increasing-index - sort by increasing pick index
decreasing-index - sort by decreasing pick index
move-closest-first - move the pick with the highest score to the first position
reverse - reverse the current ordering

Parameters:: ordering (string) – the name of the ordering to use

to_pandas(*, columns=['pick_id', 'score'])¶

Return a pandas DataFrame with the pick ids and scores

The first column contains the ids, the second column contains the ids. The default columns headers are “pick_id” and “score”. Use columns to specify different headers.

Parameters:: columns (a list of two strings) – column names for the returned DataFrame
Returns:: a pandas DataFrame

class chemfp.diversity.SphereExclusionCandidates¶

Bases: object

Get access to the remaining sphere exclusion candidates.

NOTE: This is an internal API used for testing and not part of the public API. Do not modify any of its values.

If you find it useful, let me know.

get_indices()¶: Return the candidate indices as an array.array of integers

get_ranks()¶: Return the candidate ranks as an array.array of integers

class chemfp.diversity.SphereExclusionPicker¶

Bases: object

An implementation of the sphere picker algorithm, optionally directed

The constructor must not be called directly. Instead, use one of:

SphereExclusionPicker.from_candidates()
SphereExclusionPicker.from_candidates_and_initial_pick()
SphereExclusionPicker.from_candidates_and_initial_picks()
SphereExclusionPicker.from_candidates_and_references()

Once you have a picker, use pick_n(), pick_n_with_counts() or pick_n_with_neighbors() to pick the next n candidates, optionally also with the number of fingerprints within its sphere, or with the information about those fingerprints stored in a Neighbors object.

Alternatively, use SphereExclusionPicker.iter_indices() or iter_ids(), to pick the next candidate, yielding either the pick index or pick id; or use iter_indices_and_counts() or iter_ids_and_counts() to also include the counts; or use iter_indices_and_neighbors() or iter_ids_and_neighbors() to also include the Neighbors for each sphere.

The sphere picker uses OpenMP to parallelize neighbor identification with one thread per popcount bin. I’ve found that the default number of threads is likely too small, and something like 30 or more threads can be faster.

candidates¶

Get access to the remaining candidates as a chemfp.diversity.SphereExclusionCandidates

NOTE: This is not part of the public API.

static from_candidates(candidate_arena, *, threshold=0.4, randomize=None, seed=-1, ranks=None, num_threads=-1)¶

Use sphere exclusion to pick diverse fingerprints from the candidate arena

Each new pick from candidate_arena will be less than threshold similar to any previous pick. The effective sphere radius is 1 - threshold

By default randomize is None because the appropriate default value depends on if ranks is specified. If ranks is None the randomize = None is interpreted as randomize = True. If ranks is not None then randomize is interpreted as False.

The default method (with ranks = None and randomize = None or randomize = True) picks the next fingerprint at random from the remaining candidates. This is undirected sphere picking.

If ranks = None and randomize = False then the next pick is the available candidate with the smallest index in the arena. Since the candidate arena is ordered by popcount, this directs sphere picking to select fingerprints with the smallest number of on bits. (In practice this does not seem that useful.)

If ranks is specified then it must be an array of unsigned integers, with one rank value for each fingerprint. The ranks are used for directed sphere exclusion; a candidate with a lower rank is chosen before one with a higher rank.

If ranks is not None and randomize = None or randomize = False then the next pick is the fingerprint with the lowest rank, with ties broken by the smallest index in the candidate arena.

If ranks is not None and randomize = True then the next pick is chosen at random from all of the fingerprints with the same lowest rank. The current implementation assumes ranks are nearly all distinct, and takes O(number of duplicates) time if there are duplicates, which may take quadratic time if there are only a few distinct ranks.

The random methods require an initial seed for the RNG. If seed is -1 (the default) then use Python’s own RNG to generate the initial seed, otherwise use the value as the seed.

Use num_threads to specify the number of threads to use. The default of -1 means to use the value of chemfp.get_num_threads(), otherwise it must be a positive integer.

Parameters:

candidate_arena (a chemfp.arena.FingerprintArena with popcount indices and at least one non-empty fingerprint) – an arena containing the candidate fingerprints to pick from
threshold (a double between 0.0 and 1.0, inclusive) – the Tanimoto similarity threshold used to identify sphere exclusion
seed (a value between 0 and 2**64-1, or -1) – initial RNG seed, or -1 (the default) to seed from Python’s RNG
ranks (None, or an array of unsigned 32-bit integers) – rank values for each candidate (optional)
num_threads (int) – the number of threads to use

Returns:

a chemfp.diversity.SphereExclusionPicker

static from_candidates_and_initial_pick(candidate_arena, initial_pick, *, threshold=0.4, randomize=None, seed=-1, ranks=None, num_threads=-1)¶

Use sphere exclusion to pick diverse fingerprints from the candidate arena, starting with an intial pick

This is a short-cut for:

from_candidates_and_initial_picks(candidate_arena, [initial_pick], ...)

See SphereExclusionPicker.from_candidates_and_initial_picks() for full details.

Parameters:

candidate_arena (a chemfp.arena.FingerprintArena with popcount indices and at least one non-empty fingerprint) – an arena containing the candidate fingerprints to pick from
initial_pick (an integer) – the initial pick, as an index into the candidate arena
threshold (a double between 0.0 and 1.0, inclusive) – the Tanimoto similarity threshold used to identify sphere exclusion
seed (a value between 0 and 2**64-1, or -1) – initial RNG seed, or -1 (the default) to seed from Python’s RNG
ranks (None, or an array of unsigned 32-bit integers) – rank values for each candidate (optional)
num_threads (int) – the number of threads to use

Returns:

a chemfp.diversity.SphereExclusionPicker

static from_candidates_and_initial_picks(candidate_arena, initial_picks, *, threshold=0.4, randomize=None, seed=-1, ranks=None, num_threads=-1)¶

Use sphere exclusion to pick diverse fingerprints from the candidate arena, starting with an intial pick list

Each new pick from candidate_arena will be less than threshold similar to any previous pick. The effective sphere radius = 1 - threshold

Use initial_picks to specify the initial picks. If a specified candidate index was picked by an ealier candidate index then pick will still occur but the new candidate index will not be included in the count nor the neighbors.

By default randomize = None because the appropriate default value depends on if ranks is specified. If ranks is None the randomize = None is interpreted as randomize = True. If ranks is not None then randomize is interpreted as False.

The default method (with ranks = None and randomize = None or randomize = True) picks the next fingerprint at random from the remaining candidates. This is undirected sphere picking.

If ranks = None and randomize = False then the next pick is the available candidate with the smallest index in the arena. Since the candidate arena is ordered by popcount, this directs sphere picking to select fingerprints with the smallest number of on bits. (In practice this does not seem that useful.)

If ranks is specified then it must be an array of unsigned integers, with one rank value for each fingerprint. The ranks are used for directed sphere exclusion; a candidate with a lower rank is chosen before one with a higher rank.

If ranks is not None and randomize = None or randomize = False then the next pick is the fingerprint with the lowest rank, with ties broken by the smallest index in the candidate arena.

If ranks is not None and randomize = True then the next pick is chosen at random from all of the fingerprints with the same lowest rank. The current implementation assumes ranks are nearly all distinct, and takes O(number of duplicates) time if there are duplicates, which may take quadratic time if there are only a few distinct ranks.

The random methods require an initial seed for the RNG. If seed is -1 (the default) then use Python’s own RNG to generate the initial seed, otherwise use the value as the seed.

Use num_threads to specify the number of threads to use. The default of -1 means to use the value of chemfp.get_num_threads(), otherwise it must be a positive integer.

Parameters:

candidate_arena (a chemfp.arena.FingerprintArena with popcount indices and at least one non-empty fingerprint) – an arena containing the candidate fingerprints to pick from
initial_picks (a list or array of integers) – the initial picks, as indicies into the candidate arena (duplicates are ignored)
threshold (a double between 0.0 and 1.0, inclusive) – the Tanimoto similarity threshold used to identify sphere exclusion
seed (a value between 0 and 2**64-1, or -1) – initial RNG seed, or -1 (the default) to seed from Python’s RNG
ranks (None, or an array of unsigned 32-bit integers) – rank values for each candidate (optional)
num_threads (int) – the number of threads to use

Returns:

a chemfp.diversity.SphereExclusionPicker

static from_candidates_and_references(candidate_arena, reference_arena, *, threshold=0.4, randomize=None, seed=-1, ranks=None, num_threads=-1)¶

Use sphere exclusion to pick diverse fingerprints from the candidate arena which are also diverse from the reference arena

Each new pick from candidate_arena will be less than threshold similar any previous pick and any fingerprint in reference_arena. The effective sphere radius = 1 - threshold.