repo_id stringclasses 400
values | commit_sha stringclasses 400
values | commit_index int32 0 951 | in_repo_split stringclasses 1
value | cross_repo_split stringclasses 1
value | test_file stringlengths 7 121 | test_function stringlengths 1 108 | assertion_type stringclasses 32
values | difficulty stringclasses 8
values | context_lines int32 3 600 | prefix large_stringlengths 44 113k | target large_stringlengths 1 498 | anchor_sha stringclasses 400
values | anchor_index int32 0 951 | qna_source stringclasses 1
value |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/filters/lint.py | test_lint | assert | func_call | 10 | from rdkit.Chem import Mol
from skfp.filters import LINTFilter
def test_lint(mols_list):
pains = LINTFilter()
mols_filtered = pains.transform(mols_list)
assert all(isinstance(x, Mol) for x in mols_filtered)
assert len(mols_filtered) <= | len(mols_list) | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/filters/mlsmr.py | test_mlsmr | assert | func_call | 10 | from rdkit.Chem import Mol
from skfp.filters import MLSMRFilter
def test_mlsmr(mols_list):
pains = MLSMRFilter()
mols_filtered = pains.transform(mols_list)
assert all(isinstance(x, Mol) for x in mols_filtered)
assert len(mols_filtered) <= | len(mols_list) | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/filters/nih.py | test_nih | assert | func_call | 10 | from rdkit.Chem import Mol
from skfp.filters import NIHFilter
def test_nih(mols_list):
pains = NIHFilter()
mols_filtered = pains.transform(mols_list)
assert all(isinstance(x, Mol) for x in mols_filtered)
assert len(mols_filtered) <= | len(mols_list) | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/filters/pains.py | test_basic_pains | assert | func_call | 11 | import pytest
from rdkit.Chem import Mol
from skfp.filters import PAINSFilter
def test_basic_pains(mols_list):
filt = PAINSFilter()
mols_filtered = filt.transform(mols_list)
assert all(isinstance(x, Mol) for x in mols_filtered)
assert len(mols_filtered) <= | len(mols_list) | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/filters/surechembl.py | test_surechembl | assert | func_call | 10 | from rdkit.Chem import Mol
from skfp.filters import SureChEMBLFilter
def test_surechembl(mols_list):
pains = SureChEMBLFilter()
mols_filtered = pains.transform(mols_list)
assert all(isinstance(x, Mol) for x in mols_filtered)
assert len(mols_filtered) <= | len(mols_list) | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/filters/zinc_basic.py | test_zinc_basic | assert | func_call | 10 | from rdkit.Chem import Mol
from skfp.filters import ZINCBasicFilter
def test_zinc_basic(mols_list):
pains = ZINCBasicFilter()
mols_filtered = pains.transform(mols_list)
assert all(isinstance(x, Mol) for x in mols_filtered)
assert len(mols_filtered) <= | len(mols_list) | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/filters/zinc_druglike.py | test_zinc_druglike | assert | func_call | 10 | from rdkit.Chem import Mol
from skfp.filters import ZINCDruglikeFilter
def test_zinc_druglike(mols_list):
pains = ZINCDruglikeFilter()
mols_filtered = pains.transform(mols_list)
assert all(isinstance(x, Mol) for x in mols_filtered)
assert len(mols_filtered) <= | len(mols_list) | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/preprocessing/input_output/inchi.py | test_mol_to_inchi | assert | func_call | 14 | import pytest
from rdkit.Chem import Mol, MolFromSmiles, MolToInchi
from skfp.preprocessing import MolFromInchiTransformer, MolToInchiTransformer
def inchi_list(smiles_list):
return [MolToInchi(MolFromSmiles(smi)) for smi in smiles_list]
def test_mol_to_inchi(mols_list):
mol_to_inchi = MolToInchiTransformer(... | len(mols_list) | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/preprocessing/input_output/sdf.py | test_mol_to_and_from_sdf | assert | func_call | 24 | import os
import pytest
from rdkit.Chem import Mol
from skfp.preprocessing import MolFromSDFTransformer, MolToSDFTransformer
def sdf_in_file_path():
# L-alanine
# https://www.molinstincts.com/sdf-mol-file/L-alanine-sdf-CT1000647025.html
return _get_sdf_file_path("mol_in.sdf")
def sdf_out_file_path():
... | len(mols_list) | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/preprocessing/input_output/smiles.py | test_mol_to_smiles | assert | func_call | 11 | import numpy as np
from rdkit.Chem import Mol
from skfp.preprocessing import MolFromSmilesTransformer, MolToSmilesTransformer
def test_mol_to_smiles(mols_list):
mol_to_smiles = MolToSmilesTransformer()
smiles_list = mol_to_smiles.transform(mols_list)
assert len(smiles_list) == | len(mols_list) | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/filters/beyond_ro5.py | test_bro5_return_indicators | assert | func_call | 47 | import numpy as np
import pytest
from rdkit.Chem import Mol
from skfp.filters import BeyondRo5Filter, LipinskiFilter
def smiles_passing_ro5() -> list[str]:
return [
# paracetamol
"CC(=O)Nc1ccc(O)cc1",
# caffeine
"CN1C=NC2=C1C(=O)N(C(=O)N2C)C",
# nicotine
"c1ncccc1[C... | len(all_smiles) | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/filters/nibr.py | test_nibr_return_indicators | assert | func_call | 33 | import numpy as np
import pytest
from rdkit.Chem import Mol
from skfp.filters import NIBRFilter
def smiles_passing_nibr() -> list[str]:
return [
# paracetamol
"CC(=O)Nc1ccc(O)cc1",
# caffeine
"CN1C=NC2=C1C(=O)N(C(=O)N2C)C",
# nicotine
"c1ncccc1[C@@H]2CCCN2C",
]
... | len(all_smiles) | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/model_selection/splitters/butina_split.py | test_test_split_smaller_than_train_split | assert | func_call | 44 | import pytest
from rdkit import Chem
from rdkit.Chem import Mol
from skfp.model_selection.splitters.butina_split import (
_create_clusters,
butina_train_test_split,
butina_train_valid_test_split,
)
from skfp.preprocessing import MolFromSmilesTransformer
def varied_mols() -> list[str]:
# those molecule... | len(test_split) | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/preprocessing/input_output/aminoseq.py | test_mol_from_fasta | assert | func_call | 21 | import numpy as np
import pytest
from rdkit.Chem import Mol, MolFromFASTA, MolToSmiles
from skfp.preprocessing import MolFromAminoseqTransformer
def sequence_list(fasta_list):
return [fst.split("\n")[1] for fst in fasta_list]
def peptide_list(fasta_list):
return [MolFromFASTA(fst) for fst in fasta_list]
def... | len(fasta_list) | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/preprocessing/input_output/inchi.py | test_mol_from_inchi | assert | func_call | 14 | import pytest
from rdkit.Chem import Mol, MolFromSmiles, MolToInchi
from skfp.preprocessing import MolFromInchiTransformer, MolToInchiTransformer
def inchi_list(smiles_list):
return [MolToInchi(MolFromSmiles(smi)) for smi in smiles_list]
def test_mol_from_inchi(inchi_list):
mol_from_inchi = MolFromInchiTrans... | len(inchi_list) | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/model_selection/splitters/butina_split.py | test_train_test_split_total_molecule_count | assert | func_call | 43 | import pytest
from rdkit import Chem
from rdkit.Chem import Mol
from skfp.model_selection.splitters.butina_split import (
_create_clusters,
butina_train_test_split,
butina_train_valid_test_split,
)
from skfp.preprocessing import MolFromSmilesTransformer
def varied_mols() -> list[str]:
# those molecule... | len(varied_mols) | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/model_selection/splitters/pubchem_split.py | test_pubchem_train_test_split_default | assert | func_call | 43 | from typing import Union
from unittest.mock import patch
import pytest
from rdkit import Chem
from rdkit.Chem import Mol
from skfp.model_selection.splitters.pubchem_split import (
_get_cid_for_smiles,
_get_earliest_publication_date,
pubchem_train_test_split,
pubchem_train_valid_test_split,
)
def get_... | len(varied_mols) | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/preprocessing/input_output/smiles.py | test_mol_from_smiles | assert | func_call | 11 | import numpy as np
from rdkit.Chem import Mol
from skfp.preprocessing import MolFromSmilesTransformer, MolToSmilesTransformer
def test_mol_from_smiles(smiles_list):
mol_from_smiles = MolFromSmilesTransformer()
mols_list = mol_from_smiles.transform(smiles_list)
assert len(mols_list) == | len(smiles_list) | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/model_selection/hyperparam_search/grid_search.py | test_fp_estimator_grid_search | assert | func_call | 27 | import numpy as np
from sklearn.dummy import DummyClassifier
from sklearn.model_selection import GridSearchCV
from skfp.fingerprints import AtomPairFingerprint
from skfp.model_selection import FingerprintEstimatorGridSearch
def test_fp_estimator_grid_search(smallest_mols_list):
num_mols = len(smallest_mols_list)
... | len(y_pred_proba) | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/bases/base_substructure_fp.py | test_substructure_bit_fingerprint | assert | complex_expr | 35 | import numpy as np
import pytest
from scipy.sparse import csr_array
from sklearn.utils._param_validation import InvalidParameterError
from skfp.bases.base_substructure_fp import BaseSubstructureFingerprint
def substructure_smiles_list() -> list[str]:
return [
"CCOC",
"CCOCCO",
"CC(=O)O",
... | np.uint8 | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/fingerprints/atom_pair.py | test_atom_pair_bit_fingerprint | assert | complex_expr | 22 | import numpy as np
import pytest
from rdkit.Chem.rdFingerprintGenerator import (
GetAtomPairGenerator,
GetMorganFeatureAtomInvGen,
)
from scipy.sparse import csr_array
from sklearn.utils._param_validation import InvalidParameterError
from skfp.fingerprints import AtomPairFingerprint
def test_atom_pair_bit_fin... | np.uint8 | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/fingerprints/avalon.py | test_avalon_bit_fingerprint | assert | complex_expr | 15 | import numpy as np
from rdkit.Avalon.pyAvalonTools import GetAvalonCountFP, GetAvalonFP
from scipy.sparse import csr_array
from skfp.fingerprints import AvalonFingerprint
def test_avalon_bit_fingerprint(smiles_list, mols_list):
avalon_fp = AvalonFingerprint(n_jobs=-1)
X_skfp = avalon_fp.transform(smiles_list)... | np.uint8 | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/fingerprints/e3fp_fp.py | test_e3fp_bit_fingerprint | assert | complex_expr | 19 | import numpy as np
import pytest
import scipy.sparse
from sklearn.utils._param_validation import InvalidParameterError
from skfp.fingerprints import E3FPFingerprint
def test_e3fp_bit_fingerprint(mols_conformers_list):
e3fp_fp = E3FPFingerprint(n_jobs=-1)
X_skfp = e3fp_fp.transform(mols_conformers_list)
X... | np.uint8 | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/fingerprints/ecfp.py | test_ecfp_bit_fingerprint | assert | complex_expr | 20 | import numpy as np
from rdkit.Chem.rdFingerprintGenerator import (
GetMorganFeatureAtomInvGen,
GetMorganGenerator,
)
from scipy.sparse import csr_array
from skfp.fingerprints import ECFPFingerprint
def test_ecfp_bit_fingerprint(smiles_list, mols_list):
ecfp_fp = ECFPFingerprint(n_jobs=-1)
X_skfp = ecf... | np.uint8 | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/fingerprints/erg.py | test_erg_bit_fingerprint | assert | complex_expr | 21 | import numpy as np
import pytest
from rdkit.Chem import MolFromSmiles
from rdkit.Chem.rdReducedGraphs import GetErGFingerprint
from scipy.sparse import csr_array
from sklearn.utils._param_validation import InvalidParameterError
from skfp.fingerprints import ERGFingerprint
def test_erg_bit_fingerprint(smiles_list):
... | np.uint8 | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/fingerprints/estate.py | test_estate_bit_fingerprint | assert | complex_expr | 17 | import numpy as np
from rdkit.Chem.EState.Fingerprinter import FingerprintMol
from scipy.sparse import csr_array
from skfp.fingerprints import EStateFingerprint
def test_estate_bit_fingerprint(smiles_list, mols_list):
estate_fp = EStateFingerprint(variant="bit", n_jobs=-1)
X_skfp = estate_fp.transform(smiles_... | np.uint8 | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/fingerprints/functional_groups.py | test_functional_groups_bit_fingerprint | assert | complex_expr | 27 | from inspect import getmembers, isfunction
import numpy as np
import rdkit.Chem.Fragments
from scipy.sparse import csr_array
from skfp.fingerprints import FunctionalGroupsFingerprint
def test_functional_groups_bit_fingerprint(smiles_list, mols_list):
fg_fp = FunctionalGroupsFingerprint(n_jobs=-1)
X_skfp = fg... | np.uint8 | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/fingerprints/ghose_crippen.py | test_ghose_crippen_bit_fingerprint | assert | complex_expr | 12 | import numpy as np
from scipy.sparse import csr_array
from skfp.fingerprints import GhoseCrippenFingerprint
def test_ghose_crippen_bit_fingerprint(smiles_list):
gc_fp = GhoseCrippenFingerprint(n_jobs=-1)
X = gc_fp.transform(smiles_list)
assert isinstance(X, np.ndarray)
assert X.dtype == | np.uint8 | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/fingerprints/klekota_roth.py | test_klekota_roth_bit_fingerprint | assert | complex_expr | 12 | import numpy as np
from scipy.sparse import csr_array
from skfp.fingerprints import KlekotaRothFingerprint
def test_klekota_roth_bit_fingerprint(smiles_list):
kr_fp = KlekotaRothFingerprint(n_jobs=-1)
X = kr_fp.transform(smiles_list)
assert isinstance(X, np.ndarray)
assert X.dtype == | np.uint8 | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/fingerprints/laggner.py | test_laggner_bit_fingerprint | assert | complex_expr | 12 | import numpy as np
from scipy.sparse import csr_array
from skfp.fingerprints import LaggnerFingerprint
def test_laggner_bit_fingerprint(smiles_list):
laggner_fp = LaggnerFingerprint(n_jobs=-1)
X = laggner_fp.transform(smiles_list)
assert isinstance(X, np.ndarray)
assert X.dtype == | np.uint8 | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/fingerprints/layered.py | test_layered_fingerprint | assert | complex_expr | 17 | import numpy as np
import pytest
from rdkit.Chem.rdmolops import LayeredFingerprint as RDKitLayeredFingerprint
from scipy.sparse import csr_array
from sklearn.utils._param_validation import InvalidParameterError
from skfp.fingerprints import LayeredFingerprint
def test_layered_fingerprint(smiles_list, mols_list):
... | np.uint8 | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/fingerprints/lingo.py | test_lingo_fingerprint_bit | assert | complex_expr | 19 | import os
from typing import Union
import numpy as np
from scipy.sparse import csr_array, load_npz
from skfp.fingerprints import LingoFingerprint
def test_lingo_fingerprint_bit():
smiles = ["CC(=O)NCCC1=CNC2=C1C=C(C=C2)OC", "C[n]1cnc2N(C)C(=O)N(C)C(=O)c12"]
lingo_fp = LingoFingerprint()
X_skfp = lingo_fp... | np.uint8 | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/fingerprints/maccs.py | test_maccs_bit_fingerprint | assert | complex_expr | 15 | import numpy as np
from rdkit.Chem.rdMolDescriptors import GetMACCSKeysFingerprint
from scipy.sparse import csr_array
from skfp.fingerprints import MACCSFingerprint
def test_maccs_bit_fingerprint(smiles_list, mols_list):
maccs_fp = MACCSFingerprint(n_jobs=-1)
X_skfp = maccs_fp.transform(smiles_list)
X_rdk... | np.uint8 | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/fingerprints/map.py | test_map_bit_fingerprint | assert | complex_expr | 22 | import numpy as np
from scipy.sparse import csr_array
from skfp.fingerprints import MAPFingerprint
def test_map_bit_fingerprint(smallest_smiles_list, smallest_mols_list):
map_fp = MAPFingerprint(
n_jobs=-1,
)
X_skfp = map_fp.transform(smallest_smiles_list)
X_map = np.stack(
[
... | np.uint8 | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/fingerprints/mhfp.py | test_mhfp_bit_fingerprint | assert | complex_expr | 26 | import numpy as np
import pytest
from rdkit.Chem.rdMHFPFingerprint import MHFPEncoder
from scipy.sparse import csr_array
from sklearn.utils._param_validation import InvalidParameterError
from skfp.fingerprints import MHFPFingerprint
def test_mhfp_bit_fingerprint(smiles_list, mols_list):
mhfp_fp = MHFPFingerprint(... | np.uint8 | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/fingerprints/mqns.py | test_mqns_bit_fingerprint | assert | complex_expr | 12 | import numpy as np
from scipy.sparse import csr_array
from skfp.fingerprints import MQNsFingerprint
def test_mqns_bit_fingerprint(smiles_list):
mqn_fp = MQNsFingerprint(count=False, n_jobs=-1)
X = mqn_fp.transform(smiles_list)
assert isinstance(X, np.ndarray)
assert X.dtype == | np.uint8 | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/fingerprints/pattern.py | test_pattern_fingerprint | assert | complex_expr | 15 | import numpy as np
from rdkit.Chem.rdmolops import PatternFingerprint as RDKitPatternFingerprint
from scipy.sparse import csr_array
from skfp.fingerprints import PatternFingerprint
def test_pattern_fingerprint(smiles_list, mols_list):
pattern_fp = PatternFingerprint(n_jobs=-1)
X_skfp = pattern_fp.transform(sm... | np.uint8 | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/fingerprints/pharmacophore.py | test_pharmacophore_raw_bits_fingerprint | assert | complex_expr | 22 | import numpy as np
import pytest
from rdkit.Chem import Get3DDistanceMatrix, Mol
from rdkit.Chem.ChemicalFeatures import BuildFeatureFactoryFromString
from rdkit.Chem.Pharm2D import Gobbi_Pharm2D
from rdkit.Chem.Pharm2D.Generate import Gen2DFingerprint
from rdkit.Chem.Pharm2D.SigFactory import SigFactory
from scipy.spa... | np.uint8 | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/fingerprints/physiochemical_properties.py | test_physiochemical_properties_bp_bit_fingerprint | assert | complex_expr | 13 | import numpy as np
from scipy.sparse import csr_array
from skfp.fingerprints import PhysiochemicalPropertiesFingerprint
def test_physiochemical_properties_bp_bit_fingerprint(smiles_list):
pp_fp = PhysiochemicalPropertiesFingerprint(variant="BP", n_jobs=-1)
X_skfp = pp_fp.transform(smiles_list)
assert isi... | np.uint8 | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/fingerprints/pubchem.py | test_pubchem_bit_fingerprint | assert | complex_expr | 11 | import numpy as np
from skfp.fingerprints import PubChemFingerprint
def test_pubchem_bit_fingerprint(smiles_list, mols_list):
pubchem_fp = PubChemFingerprint(n_jobs=-1)
X_skfp = pubchem_fp.transform(smiles_list)
assert X_skfp.shape == (len(mols_list), 881)
assert X_skfp.dtype == | np.uint8 | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/fingerprints/rdkit_fp.py | test_rdkit_bit_fingerprint | assert | complex_expr | 22 | import numpy as np
import pytest
from rdkit.Chem.rdFingerprintGenerator import (
GetMorganFeatureAtomInvGen,
GetRDKitFPGenerator,
)
from scipy.sparse import csr_array
from sklearn.utils._param_validation import InvalidParameterError
from skfp.fingerprints import RDKitFingerprint
def test_rdkit_bit_fingerprint... | np.uint8 | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/fingerprints/secfp.py | test_secfp_fingerprint | assert | complex_expr | 19 | import numpy as np
import pytest
from rdkit.Chem.rdMHFPFingerprint import MHFPEncoder
from scipy.sparse import csr_array
from sklearn.utils._param_validation import InvalidParameterError
from skfp.fingerprints import SECFPFingerprint
def test_secfp_fingerprint(smiles_list, mols_list):
secfp_fp = SECFPFingerprint(... | np.uint8 | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/fingerprints/topological_torsion.py | test_topological_torsion_bit_fingerprint | assert | complex_expr | 20 | import numpy as np
from rdkit.Chem.rdFingerprintGenerator import (
GetMorganFeatureAtomInvGen,
GetTopologicalTorsionGenerator,
)
from scipy.sparse import csr_array
from skfp.fingerprints import TopologicalTorsionFingerprint
def test_topological_torsion_bit_fingerprint(smiles_list, mols_list):
tt_fp = Topo... | np.uint8 | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/bases/base_substructure_fp.py | test_substructure_count_fingerprint | assert | complex_expr | 35 | import numpy as np
import pytest
from scipy.sparse import csr_array
from sklearn.utils._param_validation import InvalidParameterError
from skfp.bases.base_substructure_fp import BaseSubstructureFingerprint
def substructure_smiles_list() -> list[str]:
return [
"CCOC",
"CCOCCO",
"CC(=O)O",
... | np.uint32 | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/fingerprints/atom_pair.py | test_atom_pair_count_fingerprint | assert | complex_expr | 22 | import numpy as np
import pytest
from rdkit.Chem.rdFingerprintGenerator import (
GetAtomPairGenerator,
GetMorganFeatureAtomInvGen,
)
from scipy.sparse import csr_array
from sklearn.utils._param_validation import InvalidParameterError
from skfp.fingerprints import AtomPairFingerprint
def test_atom_pair_count_f... | np.uint32 | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/fingerprints/avalon.py | test_avalon_count_fingerprint | assert | complex_expr | 15 | import numpy as np
from rdkit.Avalon.pyAvalonTools import GetAvalonCountFP, GetAvalonFP
from scipy.sparse import csr_array
from skfp.fingerprints import AvalonFingerprint
def test_avalon_count_fingerprint(smiles_list, mols_list):
avalon_fp = AvalonFingerprint(count=True, n_jobs=-1)
X_skfp = avalon_fp.transfor... | np.uint32 | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/datasets/lrgb.py | test_load_lrgb_splits | assert | numeric_literal | 19 | import pytest
from skfp.datasets.lrgb import (
load_lrgb_mol_benchmark,
load_lrgb_mol_splits,
load_peptides_func,
load_peptides_struct,
)
from tests.datasets.test_utils import run_basic_dataset_checks
def get_dataset_names() -> list[str]:
return ["Peptides-func", "Peptides-struct"]
@pytest.mark.p... | 0 | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/datasets/moleculenet.py | test_load_ogb_splits | assert | numeric_literal | 43 | import pytest
from sklearn.utils._param_validation import InvalidParameterError
from skfp.datasets.moleculenet import (
load_bace,
load_bbbp,
load_clintox,
load_esol,
load_freesolv,
load_hiv,
load_lipophilicity,
load_moleculenet_benchmark,
load_muv,
load_ogb_splits,
load_pcb... | 0 | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/datasets/utils.py | test_get_smiles_and_labels | assert | numeric_literal | 14 | import os
import shutil
import numpy as np
import pandas as pd
from skfp.datasets.utils import get_data_home_dir, get_mol_strings_and_labels
def test_get_smiles_and_labels():
df = pd.DataFrame({"SMILES": ["a", "b", "c"], "label": [0, 0, 1]})
smiles_list, y = get_mol_strings_and_labels(df)
assert smiles_l... | 1 | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/datasets/utils.py | test_get_smiles_and_labels | assert | numeric_literal | 22 | import os
import shutil
import numpy as np
import pandas as pd
from skfp.datasets.utils import get_data_home_dir, get_mol_strings_and_labels
def test_get_smiles_and_labels():
df = pd.DataFrame({"SMILES": ["a", "b", "c"], "label": [0, 0, 1]})
smiles_list, y = get_mol_strings_and_labels(df)
assert smiles_l... | 2 | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/filters/beyond_ro5.py | test_mols_ro5_vs_bro5 | assert | numeric_literal | 43 | import numpy as np
import pytest
from rdkit.Chem import Mol
from skfp.filters import BeyondRo5Filter, LipinskiFilter
def smiles_passing_ro5() -> list[str]:
return [
# paracetamol
"CC(=O)Nc1ccc(O)cc1",
# caffeine
"CN1C=NC2=C1C(=O)N(C(=O)N2C)C",
# nicotine
"c1ncccc1[C... | 3 | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/filters/beyond_ro5.py | test_mols_ro5_vs_bro5 | assert | numeric_literal | 44 | import numpy as np
import pytest
from rdkit.Chem import Mol
from skfp.filters import BeyondRo5Filter, LipinskiFilter
def smiles_passing_ro5() -> list[str]:
return [
# paracetamol
"CC(=O)Nc1ccc(O)cc1",
# caffeine
"CN1C=NC2=C1C(=O)N(C(=O)N2C)C",
# nicotine
"c1ncccc1[C... | 0 | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/filters/faf4_druglike.py | test_mols_failing_faf4_druglike | assert | numeric_literal | 40 | import numpy as np
import pytest
from skfp.filters import FAF4DruglikeFilter
def smiles_passing_faf4_druglike() -> list[str]:
return [
# paracetamol
"CC(=O)Nc1ccc(O)cc1",
# Ibuprofen
"CC(C)CC1=CC=C(C=C1)C(C)C(=O)O",
# caffeine
"CN1C=NC2=C1C(=O)N(C(=O)N2C)C",
... | 0 | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/filters/faf4_leadlike.py | test_mols_failing_faf4_leadlike | assert | numeric_literal | 34 | import numpy as np
import pytest
from skfp.filters import FAF4LeadlikeFilter
def smiles_passing_faf4_leadlike() -> list[str]:
return [
# paracetamol
"CC(=O)Nc1ccc(O)cc1",
# Ibuprofen
"CC(C)CC1=CC=C(C=C1)C(C)C(=O)O",
# caffeine
"CN1C=NC2=C1C(=O)N(C(=O)N2C)C",
... | 0 | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/filters/ghose.py | test_mols_failing_ghose | assert | numeric_literal | 32 | import numpy as np
import pytest
from skfp.filters import GhoseFilter
def smiles_passing_ghose() -> list[str]:
return [
"CC(=O)C1=C(O)C(=O)N(CCc2c[nH]c3ccccc23)C1c1ccc(C)cc1",
r"CC(=O)C1C(=O)c2c(cccc2[N+](=O)[O-])/C1=N\c1ccccc1C",
"CC(=O)c1c(C)n(CC2CCCO2)c2ccc(O)cc12",
]
def smiles_fa... | 0 | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/filters/ghose.py | test_mols_passing_with_violation_ghose | assert | numeric_literal | 33 | import numpy as np
import pytest
from skfp.filters import GhoseFilter
def smiles_passing_ghose() -> list[str]:
return [
"CC(=O)C1=C(O)C(=O)N(CCc2c[nH]c3ccccc23)C1c1ccc(C)cc1",
r"CC(=O)C1C(=O)c2c(cccc2[N+](=O)[O-])/C1=N\c1ccccc1C",
"CC(=O)c1c(C)n(CC2CCCO2)c2ccc(O)cc12",
]
def smiles_fa... | 3 | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/filters/gsk.py | test_mols_failing_gsk | assert | numeric_literal | 30 | import numpy as np
import pytest
from skfp.filters import GSKFilter
def smiles_passing_gsk() -> list[str]:
return [
"C1CC1N2C=C(C(=O)C3=CC(=C(C=C32)N4CCNCC4)F)C(=O)O", # Ciprofloxacin
"CC(=O)CC(C1=CC=CC=C1)C2=C(C3=CC=CC=C3OC2=O)O", # Warfarin
]
def smiles_passing_one_fail() -> list[str]:
... | 0 | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/filters/hao.py | test_mols_failing_hao | assert | numeric_literal | 32 | import numpy as np
import pytest
from skfp.filters import HaoFilter
def smiles_passing_hao() -> list[str]:
return [
"CCOC(=O)Nc1ccc(C(=O)C=Cc2ccc(N(CC)CC)cc2)cc1",
"CN(C)c1ccc(C=Cc2cc[n+](C)c3ccccc23)cc1",
"c1cnc2c(c1)ccc1cccnc12",
]
def smiles_failing_hao() -> list[str]:
return [... | 0 | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/filters/hao.py | test_mols_passing_with_violation_hao | assert | numeric_literal | 33 | import numpy as np
import pytest
from skfp.filters import HaoFilter
def smiles_passing_hao() -> list[str]:
return [
"CCOC(=O)Nc1ccc(C(=O)C=Cc2ccc(N(CC)CC)cc2)cc1",
"CN(C)c1ccc(C=Cc2cc[n+](C)c3ccccc23)cc1",
"c1cnc2c(c1)ccc1cccnc12",
]
def smiles_failing_hao() -> list[str]:
return [... | 3 | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/filters/lipinski.py | test_mols_failing_lipinski | assert | numeric_literal | 43 | import numpy as np
import pytest
from rdkit.Chem import Mol
from skfp.filters import LipinskiFilter
def smiles_passing_lipinski() -> list[str]:
return [
# paracetamol
"CC(=O)Nc1ccc(O)cc1",
# caffeine
"CN1C=NC2=C1C(=O)N(C(=O)N2C)C",
# nicotine
"c1ncccc1[C@@H]2CCCN2C"... | 0 | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/filters/lipinski.py | test_mols_failing_strict_lipinski | assert | numeric_literal | 42 | import numpy as np
import pytest
from rdkit.Chem import Mol
from skfp.filters import LipinskiFilter
def smiles_passing_lipinski() -> list[str]:
return [
# paracetamol
"CC(=O)Nc1ccc(O)cc1",
# caffeine
"CN1C=NC2=C1C(=O)N(C(=O)N2C)C",
# nicotine
"c1ncccc1[C@@H]2CCCN2C"... | 3 | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/filters/lipinski.py | test_mols_lipinski_various_conditions | assert | numeric_literal | 50 | import numpy as np
import pytest
from rdkit.Chem import Mol
from skfp.filters import LipinskiFilter
def smiles_passing_lipinski() -> list[str]:
return [
# paracetamol
"CC(=O)Nc1ccc(O)cc1",
# caffeine
"CN1C=NC2=C1C(=O)N(C(=O)N2C)C",
# nicotine
"c1ncccc1[C@@H]2CCCN2C"... | 6 | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/filters/mol_weight.py | test_mol_weight_thresholds | assert | numeric_literal | 61 | import numpy as np
import pytest
from rdkit.Chem import Mol
from sklearn.utils._param_validation import InvalidParameterError
from skfp.filters import MolecularWeightFilter
from skfp.preprocessing import MolFromSmilesTransformer
def smiles_light_mols() -> list[str]:
# less than 200 daltons
return [
# ... | 3 | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/filters/mol_weight.py | test_mol_weight_thresholds | assert | numeric_literal | 69 | import numpy as np
import pytest
from rdkit.Chem import Mol
from sklearn.utils._param_validation import InvalidParameterError
from skfp.filters import MolecularWeightFilter
from skfp.preprocessing import MolFromSmilesTransformer
def smiles_light_mols() -> list[str]:
# less than 200 daltons
return [
# ... | 0 | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/filters/mol_weight.py | test_mol_weight_thresholds | assert | numeric_literal | 73 | import numpy as np
import pytest
from rdkit.Chem import Mol
from sklearn.utils._param_validation import InvalidParameterError
from skfp.filters import MolecularWeightFilter
from skfp.preprocessing import MolFromSmilesTransformer
def smiles_light_mols() -> list[str]:
# less than 200 daltons
return [
# ... | 6 | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/filters/nibr.py | test_mols_failing_nibr | assert | numeric_literal | 31 | import numpy as np
import pytest
from rdkit.Chem import Mol
from skfp.filters import NIBRFilter
def smiles_passing_nibr() -> list[str]:
return [
# paracetamol
"CC(=O)Nc1ccc(O)cc1",
# caffeine
"CN1C=NC2=C1C(=O)N(C(=O)N2C)C",
# nicotine
"c1ncccc1[C@@H]2CCCN2C",
]
... | 0 | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/filters/oprea.py | test_mols_failing_oprea_filter | assert | numeric_literal | 29 | import numpy as np
import pytest
from skfp.filters import OpreaFilter
def smiles_passing_oprea() -> list[str]:
return [
"C1CC1N2C=C(C(=O)C3=CC(=C(C=C32)N4CCNCC4)F)C(=O)O", # Ciprofloxacin
"CC(=O)CC(C1=CC=CC=C1)C2=C(C3=CC=CC=C3OC2=O)O", # Warfarin
]
def smiles_passing_oprea_one_fail() -> lis... | 0 | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/filters/pfizer.py | test_mols_failing_pfizer | assert | numeric_literal | 32 | import numpy as np
import pytest
from skfp.filters import PfizerFilter
def smiles_passing_pfizer() -> list[str]:
return [
"COC(=O)c1ccccc1NC(=O)CSc1nc(O)c(-c2ccccc2)c(=O)[nH]1",
"CS(=O)(=O)NCc1nnc(SCc2ccccc2C(F)(F)F)o1",
"COCCCn1c(C)nnc1SCC(=O)NCc1ccco1",
]
def smiles_failing_pfizer()... | 0 | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/filters/pfizer.py | test_mols_passing_with_violation_pfizer | assert | numeric_literal | 33 | import numpy as np
import pytest
from skfp.filters import PfizerFilter
def smiles_passing_pfizer() -> list[str]:
return [
"COC(=O)c1ccccc1NC(=O)CSc1nc(O)c(-c2ccccc2)c(=O)[nH]1",
"CS(=O)(=O)NCc1nnc(SCc2ccccc2C(F)(F)F)o1",
"COCCCn1c(C)nnc1SCC(=O)NCc1ccco1",
]
def smiles_failing_pfizer()... | 3 | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/filters/reos.py | test_mols_failing_reos | assert | numeric_literal | 30 | import numpy as np
import pytest
from skfp.filters import REOSFilter
def smiles_passing_reos() -> list[str]:
return [
"CC(C)CC1=CC=C(C=C1)C(C)C(=O)O", # Ibuprofren
"CN1CC[C@@]23CCCC[C@@H]2[C@@H]1CC4=C3C=C(C=C4)OC", # Dextromethorphan
]
def smiles_passing_one_fail() -> list[str]:
return ... | 0 | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/filters/rule_of_four.py | test_mols_failing_rule_of_four | assert | numeric_literal | 32 | import numpy as np
import pytest
from skfp.filters import RuleOfFourFilter
def smiles_passing_rule_of_four() -> list[str]:
return [
"c1ccc2oc(-c3ccc(Nc4nc(N5CCCCC5)nc(N5CCOCC5)n4)cc3)nc2c1",
"c1csc(-c2csc3nc(CN4CCOCC4)nc(NCc4ccc5c(c4)OCO5)c23)c1",
"CC(=O)C1=C(C)NC(SCC(=O)c2ccc(-c3ccccc3)cc... | 0 | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/filters/rule_of_four.py | test_mols_passing_with_violation_rule_of_four | assert | numeric_literal | 33 | import numpy as np
import pytest
from skfp.filters import RuleOfFourFilter
def smiles_passing_rule_of_four() -> list[str]:
return [
"c1ccc2oc(-c3ccc(Nc4nc(N5CCCCC5)nc(N5CCOCC5)n4)cc3)nc2c1",
"c1csc(-c2csc3nc(CN4CCOCC4)nc(NCc4ccc5c(c4)OCO5)c23)c1",
"CC(=O)C1=C(C)NC(SCC(=O)c2ccc(-c3ccccc3)cc... | 3 | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/filters/rule_of_three.py | test_mols_failing_basic_rule_of_three | assert | numeric_literal | 45 | import numpy as np
import pytest
from skfp.filters import RuleOfThreeFilter
def smiles_passing_basic_rule_of_three() -> list[str]:
return [
"C=CCNC(=S)NCc1ccccc1OC",
"C=CCOc1ccc(Br)cc1/C=N/O",
"c1ccc(-c2nnc3n2CCCC3)cc1",
]
def smiles_failing_basic_rule_of_three() -> list[str]:
ret... | 0 | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/filters/rule_of_three.py | test_mols_passing_with_violation_basic_rule_of_three | assert | numeric_literal | 48 | import numpy as np
import pytest
from skfp.filters import RuleOfThreeFilter
def smiles_passing_basic_rule_of_three() -> list[str]:
return [
"C=CCNC(=S)NCc1ccccc1OC",
"C=CCOc1ccc(Br)cc1/C=N/O",
"c1ccc(-c2nnc3n2CCCC3)cc1",
]
def smiles_failing_basic_rule_of_three() -> list[str]:
ret... | 3 | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/filters/rule_of_two.py | test_mols_failing_rule_of_two | assert | numeric_literal | 24 | import numpy as np
import pytest
from skfp.filters import RuleOfTwoFilter
def smiles_passing_rule_of_two() -> list[str]:
return ["[C-]#N", "CC=O", "C=CCc1c(C)[nH]c(N)nc1=O", "C=CCNC(=O)c1ccncc1"]
def smiles_failing_rule_of_two() -> list[str]:
return [
"O=C(O)c1ccccc1c2ccc(cc2)Cn3c4cc(cc(c4nc3CCC)C)c5... | 0 | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/filters/rule_of_two.py | test_mols_passing_with_violation_rule_of_two | assert | numeric_literal | 25 | import numpy as np
import pytest
from skfp.filters import RuleOfTwoFilter
def smiles_passing_rule_of_two() -> list[str]:
return ["[C-]#N", "CC=O", "C=CCc1c(C)[nH]c(N)nc1=O", "C=CCNC(=O)c1ccncc1"]
def smiles_failing_rule_of_two() -> list[str]:
return [
"O=C(O)c1ccccc1c2ccc(cc2)Cn3c4cc(cc(c4nc3CCC)C)c5... | 3 | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/filters/rule_of_veber.py | test_mols_failing_rule_of_veber | assert | numeric_literal | 26 | import numpy as np
import pytest
from skfp.filters import RuleOfVeberFilter
def smiles_passing_rule_of_veber() -> list[str]:
return ["[C-]#N", "CC=O"]
def smiles_passing_one_fail() -> list[str]:
return [
"CC(C)C1=C(C(=C(N1CC[C@H](C[C@H](CC(=O)O)O)O)C2=CC=C(C=C2)F)C3=CC=CC=C3)C(=O)NC4=CC=CC=C4", # At... | 0 | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/model_selection/splitters/utils.py | test_get_data_from_indices_empty | assert | collection | 22 | from typing import Union
import pytest
from skfp.model_selection.splitters.utils import (
ensure_nonempty_subset,
split_additional_data,
validate_train_test_split_sizes,
validate_train_valid_test_split_sizes,
)
from skfp.utils.functions import get_data_from_indices
def smiles_data() -> list[str]:
... | [] | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/model_selection/splitters/utils.py | test_validate_train_test_split_sizes_both_provided | assert | collection | 21 | from typing import Union
import pytest
from skfp.model_selection.splitters.utils import (
ensure_nonempty_subset,
split_additional_data,
validate_train_test_split_sizes,
validate_train_valid_test_split_sizes,
)
from skfp.utils.functions import get_data_from_indices
def smiles_data() -> list[str]:
... | (7, 3) | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/model_selection/splitters/utils.py | test_validate_train_test_split_sizes_test_missing | assert | collection | 21 | from typing import Union
import pytest
from skfp.model_selection.splitters.utils import (
ensure_nonempty_subset,
split_additional_data,
validate_train_test_split_sizes,
validate_train_valid_test_split_sizes,
)
from skfp.utils.functions import get_data_from_indices
def smiles_data() -> list[str]:
... | (6, 4) | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/model_selection/splitters/utils.py | test_validate_train_test_split_sizes_both_missing | assert | collection | 21 | from typing import Union
import pytest
from skfp.model_selection.splitters.utils import (
ensure_nonempty_subset,
split_additional_data,
validate_train_test_split_sizes,
validate_train_valid_test_split_sizes,
)
from skfp.utils.functions import get_data_from_indices
def smiles_data() -> list[str]:
... | (8, 2) | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/fingerprints/lingo.py | test_lingo_fingerprint_bit | assert | collection | 18 | import os
from typing import Union
import numpy as np
from scipy.sparse import csr_array, load_npz
from skfp.fingerprints import LingoFingerprint
def test_lingo_fingerprint_bit():
smiles = ["CC(=O)NCCC1=CNC2=C1C=C(C=C2)OC", "C[n]1cnc2N(C)C(=O)N(C)C(=O)c12"]
lingo_fp = LingoFingerprint()
X_skfp = lingo_fp... | (2, 1024) | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/model_selection/splitters/utils.py | test_validate_train_valid_test_split_sizes_all_provided | assert | collection | 22 | from typing import Union
import pytest
from skfp.model_selection.splitters.utils import (
ensure_nonempty_subset,
split_additional_data,
validate_train_test_split_sizes,
validate_train_valid_test_split_sizes,
)
from skfp.utils.functions import get_data_from_indices
def smiles_data() -> list[str]:
... | (7, 2, 1) | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/model_selection/splitters/utils.py | test_validate_train_valid_test_split_sizes_all_missing | assert | collection | 22 | from typing import Union
import pytest
from skfp.model_selection.splitters.utils import (
ensure_nonempty_subset,
split_additional_data,
validate_train_test_split_sizes,
validate_train_valid_test_split_sizes,
)
from skfp.utils.functions import get_data_from_indices
def smiles_data() -> list[str]:
... | (8, 1, 1) | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/model_selection/splitters/utils.py | test_validate_train_valid_test_split_sizes_different_type | assert | collection | 22 | from typing import Union
import pytest
from skfp.model_selection.splitters.utils import (
ensure_nonempty_subset,
split_additional_data,
validate_train_test_split_sizes,
validate_train_valid_test_split_sizes,
)
from skfp.utils.functions import get_data_from_indices
def smiles_data() -> list[str]:
... | (6, 3, 1) | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/metrics/utils.py | test_extract_pos_proba_single_task | assert | collection | 12 | import numpy as np
from skfp.metrics import extract_pos_proba
def test_extract_pos_proba_single_task():
n_samples = 10
predictions = np.random.rand(n_samples, 2)
predictions = extract_pos_proba(predictions)
assert predictions.shape == | (n_samples,) | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/model_selection/splitters/utils.py | test_get_data_from_indices_valid | assert | collection | 22 | from typing import Union
import pytest
from skfp.model_selection.splitters.utils import (
ensure_nonempty_subset,
split_additional_data,
validate_train_test_split_sizes,
validate_train_valid_test_split_sizes,
)
from skfp.utils.functions import get_data_from_indices
def smiles_data() -> list[str]:
... | ["CCC", "CCO"] | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/datasets/utils.py | test_get_smiles_and_labels | assert | collection | 13 | import os
import shutil
import numpy as np
import pandas as pd
from skfp.datasets.utils import get_data_home_dir, get_mol_strings_and_labels
def test_get_smiles_and_labels():
df = pd.DataFrame({"SMILES": ["a", "b", "c"], "label": [0, 0, 1]})
smiles_list, y = get_mol_strings_and_labels(df)
assert smiles_... | ["a", "b", "c"] | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/datasets/test_utils.py | assert_valid_labels | assert | collection | 58 | from typing import Literal
import numpy as np
import pandas as pd
from rdkit.Chem import Mol
from skfp.preprocessing import MolFromSmilesTransformer
def run_basic_dataset_checks(
smiles_list: list[str],
y: np.ndarray,
df: pd.DataFrame,
expected_length: int,
num_tasks: int,
task_type: Literal[... | (expected_length,) | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/fingerprints/vsa.py | test_vsa_fingerprint | assert | collection | 20 | import numpy as np
from rdkit.Chem.EState.EState_VSA import EState_VSA_
from rdkit.Chem.rdMolDescriptors import PEOE_VSA_, SMR_VSA_, SlogP_VSA_
from scipy.sparse import csr_array
from skfp.fingerprints import VSAFingerprint
def test_vsa_fingerprint(mols_list):
vsa_fp = VSAFingerprint(n_jobs=-1)
X_skfp = vsa_f... | (len(mols_list), 36) | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/fingerprints/vsa.py | test_vsa_fingerprint_with_estate | assert | collection | 21 | import numpy as np
from rdkit.Chem.EState.EState_VSA import EState_VSA_
from rdkit.Chem.rdMolDescriptors import PEOE_VSA_, SMR_VSA_, SlogP_VSA_
from scipy.sparse import csr_array
from skfp.fingerprints import VSAFingerprint
def test_vsa_fingerprint_with_estate(mols_list):
vsa_fp = VSAFingerprint(variant="all", n_... | (len(mols_list), 47) | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/metrics/utils.py | test_extract_pos_proba_multioutput | assert | collection | 13 | import numpy as np
from skfp.metrics import extract_pos_proba
def test_extract_pos_proba_multioutput():
n_samples = 10
n_tasks = 5
predictions = [np.random.rand(n_samples, 2) for _ in range(n_tasks)]
predictions = extract_pos_proba(predictions)
assert predictions.shape == | (n_samples, n_tasks) | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/fingerprints/pubchem.py | test_pubchem_bit_fingerprint | assert | collection | 10 | import numpy as np
from skfp.fingerprints import PubChemFingerprint
def test_pubchem_bit_fingerprint(smiles_list, mols_list):
pubchem_fp = PubChemFingerprint(n_jobs=-1)
X_skfp = pubchem_fp.transform(smiles_list)
assert X_skfp.shape == | (len(mols_list), 881) | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/fingerprints/pubchem.py | test_pubchem_count_fingerprint | assert | collection | 10 | import numpy as np
from skfp.fingerprints import PubChemFingerprint
def test_pubchem_count_fingerprint(smiles_list, mols_list):
pubchem_fp = PubChemFingerprint(count=True, n_jobs=-1)
X_skfp = pubchem_fp.transform(smiles_list)
assert X_skfp.shape == | (len(mols_list), 757) | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/fingerprints/estate.py | test_estate_bit_fingerprint | assert | collection | 16 | import numpy as np
from rdkit.Chem.EState.Fingerprinter import FingerprintMol
from scipy.sparse import csr_array
from skfp.fingerprints import EStateFingerprint
def test_estate_bit_fingerprint(smiles_list, mols_list):
estate_fp = EStateFingerprint(variant="bit", n_jobs=-1)
X_skfp = estate_fp.transform(smiles_... | (len(smiles_list), 79) | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/fingerprints/functional_groups.py | test_functional_groups_bit_fingerprint | assert | collection | 26 | from inspect import getmembers, isfunction
import numpy as np
import rdkit.Chem.Fragments
from scipy.sparse import csr_array
from skfp.fingerprints import FunctionalGroupsFingerprint
def test_functional_groups_bit_fingerprint(smiles_list, mols_list):
fg_fp = FunctionalGroupsFingerprint(n_jobs=-1)
X_skfp = fg... | (len(smiles_list), 85) | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/fingerprints/mqns.py | test_mqns_bit_fingerprint | assert | collection | 13 | import numpy as np
from scipy.sparse import csr_array
from skfp.fingerprints import MQNsFingerprint
def test_mqns_bit_fingerprint(smiles_list):
mqn_fp = MQNsFingerprint(count=False, n_jobs=-1)
X = mqn_fp.transform(smiles_list)
assert isinstance(X, np.ndarray)
assert X.dtype == np.uint8
assert X.... | (len(smiles_list), 42) | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/fingerprints/autocorr.py | test_autocorr_fingerprint | assert | collection | 13 | import numpy as np
from rdkit.Chem.rdMolDescriptors import CalcAUTOCORR2D, CalcAUTOCORR3D
from skfp.fingerprints import AutocorrFingerprint
def test_autocorr_fingerprint(smiles_list, mols_list):
autocorr_fp = AutocorrFingerprint(use_3D=False, n_jobs=-1)
X_skfp = autocorr_fp.transform(smiles_list)
X_rdkit ... | (len(smiles_list), 192) | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/fingerprints/erg.py | test_erg_fuzzy_fingerprint | assert | collection | 19 | import numpy as np
import pytest
from rdkit.Chem import MolFromSmiles
from rdkit.Chem.rdReducedGraphs import GetErGFingerprint
from scipy.sparse import csr_array
from sklearn.utils._param_validation import InvalidParameterError
from skfp.fingerprints import ERGFingerprint
def test_erg_fuzzy_fingerprint(smiles_list):
... | (len(smiles_list), 315) | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
scikit-fingerprints/scikit-fingerprints | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | train | train | tests/fingerprints/ghose_crippen.py | test_ghose_crippen_bit_fingerprint | assert | collection | 13 | import numpy as np
from scipy.sparse import csr_array
from skfp.fingerprints import GhoseCrippenFingerprint
def test_ghose_crippen_bit_fingerprint(smiles_list):
gc_fp = GhoseCrippenFingerprint(n_jobs=-1)
X = gc_fp.transform(smiles_list)
assert isinstance(X, np.ndarray)
assert X.dtype == np.uint8
... | (len(smiles_list), 110) | ba59309f45598918c7bd11b0bbd393fb9695ec9f | 150 | v2_extractor_at_anchor |
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