repo_id stringclasses 409
values | prefix large_stringlengths 34 36.3k | target large_stringlengths 1 498 | assertion_type stringclasses 31
values | difficulty stringclasses 8
values | test_file stringlengths 10 121 | test_function stringlengths 1 104 | test_class stringlengths 0 51 | lineno int32 2 11.3k | commit_idx int32 |
|---|---|---|---|---|---|---|---|---|---|
scikit-fingerprints/scikit-fingerprints | import numpy as np
from numpy.testing import assert_equal
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_... | np.uint8 | assert | complex_expr | tests/fingerprints/avalon.py | test_avalon_bit_fingerprint | 16 | null | |
scikit-fingerprints/scikit-fingerprints | import numpy as np
from numpy.testing import assert_allclose, assert_equal
from rdkit.Chem.EState.Fingerprinter import FingerprintMol
from scipy.sparse import csr_array
from skfp.fingerprints import EStateFingerprint
def test_estate_feature_names():
estate_fp = EStateFingerprint()
feature_names = estate_fp.ge... | "[LiD1]-*") | assert_* | string_literal | tests/fingerprints/estate.py | test_estate_feature_names | 89 | null | |
scikit-fingerprints/scikit-fingerprints | import pytest
from rdkit.Chem import MolFromSmiles
from skfp.fingerprints import AtomPairFingerprint
from skfp.utils.validators import (
ensure_mols,
ensure_smiles,
require_atoms,
require_mols,
require_mols_with_conf_ids,
require_strings,
)
@pytest.mark.parametrize(
"min_atoms, only_explic... | True | assert | bool_literal | tests/utils/validators.py | test_require_atoms_decorator | 98 | null | |
scikit-fingerprints/scikit-fingerprints | import numpy as np
import pytest
from numpy.testing import assert_equal
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)c1c... | (4,)) | assert_* | collection | tests/filters/rule_of_two.py | test_rule_of_two_condition_names | 119 | null | |
scikit-fingerprints/scikit-fingerprints | import pytest
from numpy.testing import assert_equal
from sklearn.utils._param_validation import InvalidParameterError
from skfp.datasets.tdc import load_tdc_benchmark, load_tdc_splits
from skfp.datasets.tdc.adme import (
load_b3db_classification,
load_b3db_regression,
load_bioavailability_ma,
load_cac... | len(test) | assert | func_call | tests/datasets/tdc.py | test_load_tdc_splits | 117 | null | |
scikit-fingerprints/scikit-fingerprints | import pytest
from skfp.model_selection.splitters.utils import (
_check_subset_size,
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]:
r... | (7, 2, 1) | assert | collection | tests/model_selection/splitters/utils.py | test_validate_train_valid_test_split_sizes_all_provided | 102 | null | |
scikit-fingerprints/scikit-fingerprints | import numpy as np
from numpy.testing import assert_equal
from rdkit.Chem import MolFromSmiles
from rdkit.Chem.rdMolDescriptors import GetMACCSKeysFingerprint
from scipy.sparse import csr_array
from skfp.fingerprints import MACCSFingerprint
def test_maccs_count_feature_names():
# we check a few selected feature n... | "QCH2A") | assert_* | string_literal | tests/fingerprints/maccs.py | test_maccs_count_feature_names | 79 | null | |
scikit-fingerprints/scikit-fingerprints | import numpy as np
import pytest
from numpy.testing import assert_allclose, assert_equal
from rdkit.Chem.rdMolDescriptors import CalcWHIM
from scipy.sparse import csr_array
from skfp.fingerprints import WHIMFingerprint
def mols_conformers_3_plus_atoms(mols_conformers_list):
return [mol for mol in mols_conformers_... | X_rdkit) | assert_* | variable | tests/fingerprints/whim.py | test_whim_fingerprint | 27 | null | |
scikit-fingerprints/scikit-fingerprints | import numpy as np
from numpy.testing import assert_equal
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.ndarr... | np.uint8 | assert | complex_expr | tests/fingerprints/ghose_crippen.py | test_ghose_crippen_bit_fingerprint | 14 | null | |
scikit-fingerprints/scikit-fingerprints | import numpy as np
from numpy.testing import assert_equal
from rdkit.Chem import MolFromSmiles
from rdkit.Chem.rdMolDescriptors import GetMACCSKeysFingerprint
from scipy.sparse import csr_array
from skfp.fingerprints import MACCSFingerprint
def test_maccs_feature_names():
# we check a few selected feature names
... | "ISOTOPE") | assert_* | string_literal | tests/fingerprints/maccs.py | test_maccs_feature_names | 57 | null | |
scikit-fingerprints/scikit-fingerprints | import numpy as np
import pytest
from numpy.testing import assert_array_equal, assert_equal
from rdkit.Chem import AddHs, MolFromSmiles
from skfp.preprocessing import ConformerGenerator, MolFromSmilesTransformer
def test_conformer_generator_error_handling(smallest_mols_list):
y = np.zeros(len(smallest_mols_list))... | len(mols)) | assert_* | func_call | tests/preprocessing/conformer_generator.py | test_conformer_generator_error_handling | 89 | null | |
scikit-fingerprints/scikit-fingerprints | from pathlib import Path
import numpy as np
import pytest
from numpy.testing import assert_equal
from rdkit import Chem
from scipy.sparse import csr_array
from skfp.fingerprints import KlekotaRothFingerprint
def test_klekota_roth_count_fingerprint(smiles_list):
kr_fp = KlekotaRothFingerprint(count=True, n_jobs=-... | np.uint32 | assert | complex_expr | tests/fingerprints/klekota_roth.py | test_klekota_roth_count_fingerprint | 28 | null | |
scikit-fingerprints/scikit-fingerprints | import pytest
from numpy.testing import assert_equal
from skfp.datasets.lrgb import (
load_lrgb_mol_benchmark,
load_lrgb_mol_dataset,
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... | test | assert | variable | tests/datasets/lrgb.py | test_load_lrgb_splits_as_dict | 71 | null | |
scikit-fingerprints/scikit-fingerprints | import numpy as np
import pytest
from numpy.testing import assert_equal
from skfp.filters import ValenceDiscoveryFilter
def smiles_passing_valence_discovery() -> list[str]:
return [
# chlordiazepoxide
"ClC1=CC2=C(N=C(NC)C[N+]([O-])=C2C3=CC=CC=C3)C=C1",
# cortisol
r"O=C4\C=C2/[C@]([... | (16,)) | assert_* | collection | tests/filters/valence_discovery.py | test_valence_discovery_condition_names | 138 | null | |
scikit-fingerprints/scikit-fingerprints | import numpy as np
import pytest
from numpy.testing import assert_allclose, assert_equal
from rdkit.Chem.rdFingerprintGenerator import (
GetAtomPairGenerator,
GetMorganFeatureAtomInvGen,
)
from scipy.sparse import csr_array
from sklearn.utils._param_validation import InvalidParameterError
from skfp.fingerprint... | np.uint8 | assert | complex_expr | tests/fingerprints/atom_pair.py | test_atom_pair_bit_fingerprint | 23 | null | |
scikit-fingerprints/scikit-fingerprints | import os
import shutil
import numpy as np
import pandas as pd
import pytest
from numpy.testing import assert_equal
from skfp.datasets.utils import (
fetch_splits,
get_data_home_dir,
get_mol_strings_and_labels,
)
@pytest.mark.parametrize("mol_type", ["SMILES", "aminoseq"])
def test_get_smiles_and_labels(... | 1) | assert_* | numeric_literal | tests/datasets/utils.py | test_get_smiles_and_labels | 45 | null | |
scikit-fingerprints/scikit-fingerprints | import pytest
from numpy.testing import assert_equal
from skfp.datasets.lrgb import (
load_lrgb_mol_benchmark,
load_lrgb_mol_dataset,
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... | 0 | assert | numeric_literal | tests/datasets/lrgb.py | test_load_lrgb_splits | 43 | null | |
scikit-fingerprints/scikit-fingerprints | import numpy as np
import pytest
from numpy.testing import assert_equal
from skfp.filters import TiceHerbicidesFilter
def smiles_passing_tice_herbicides() -> list[str]:
return [
"CCC(C)NC(=O)NC(CCSC)C(=O)OC",
"OCCNc1nc2ccc(Cl)cc2[nH]1",
"Nc1nnc(-c2ccco2)s1",
]
def smiles_failing_tice_... | (5,)) | assert_* | collection | tests/filters/tice_herbicides.py | test_tice_herbicides_condition_names | 130 | null | |
scikit-fingerprints/scikit-fingerprints | from inspect import getmembers, isfunction
import numpy as np
import rdkit.Chem.Fragments
from numpy.testing import assert_equal
from scipy.sparse import csr_array
from skfp.fingerprints import FunctionalGroupsFingerprint
def test_functional_groups_count_fingerprint(smiles_list, mols_list):
fg_fp = FunctionalGro... | np.uint32 | assert | complex_expr | tests/fingerprints/functional_groups.py | test_functional_groups_count_fingerprint | 48 | null | |
scikit-fingerprints/scikit-fingerprints | import numpy as np
import pytest
from numpy.testing import assert_equal
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_passi... | (7,)) | assert_* | collection | tests/filters/reos.py | test_reos_condition_names | 112 | null | |
scikit-fingerprints/scikit-fingerprints | import numpy as np
from numpy.testing import assert_equal
from rdkit.Chem import MolFromSmiles
from rdkit.Chem.rdMolDescriptors import GetMACCSKeysFingerprint
from scipy.sparse import csr_array
from skfp.fingerprints import MACCSFingerprint
def test_maccs_feature_names():
# we check a few selected feature names
... | "O") | assert_* | string_literal | tests/fingerprints/maccs.py | test_maccs_feature_names | 62 | null | |
scikit-fingerprints/scikit-fingerprints | import numpy as np
import pytest
from numpy.testing import assert_equal
from sklearn.datasets import (
make_blobs,
make_classification,
make_multilabel_classification,
make_regression,
)
from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor
from sklearn.model_selection import train_... | (len(X),) | assert | collection | tests/applicability_domain/prob_std.py | test_ptobstd_ad_checker_with_classifier | 104 | null | |
scikit-fingerprints/scikit-fingerprints | import numpy as np
import pytest
from numpy.testing import assert_equal
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(CC2CC... | (4,)) | assert_* | collection | tests/filters/ghose.py | test_ghose_condition_names | 123 | null | |
scikit-fingerprints/scikit-fingerprints | from unittest.mock import patch
import numpy as np
import pytest
from numpy.testing import assert_equal
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_tr... | "25058138") | assert_* | string_literal | tests/model_selection/splitters/pubchem_split.py | test_get_cid_for_smiles_with_proper_smiles | 50 | null | |
scikit-fingerprints/scikit-fingerprints | import numpy as np
import pytest
from numpy.testing import assert_allclose, assert_equal
from rdkit.Chem.rdMolDescriptors import BCUT2D
from skfp.fingerprints import BCUT2DFingerprint
def gasteiger_allowed_mols(mols_list):
# Gasteiger partial charge model does not work for metals
# allowed elements: https://g... | X_rdkit) | assert_* | variable | tests/fingerprints/bcut2d.py | test_bcut2d_fingerprint_gasteiger | 31 | null | |
scikit-fingerprints/scikit-fingerprints | import numpy as np
import pytest
from numpy.testing import assert_equal
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(CC2CC... | 0) | assert_* | numeric_literal | tests/filters/ghose.py | test_mols_failing_ghose | 48 | null | |
scikit-fingerprints/scikit-fingerprints | import numpy as np
from numpy.testing import assert_allclose, assert_equal
from rdkit.Chem.rdMolDescriptors import CalcMORSE
from scipy.sparse import csr_array
from skfp.fingerprints import MORSEFingerprint
def test_morse_fingerprint(mols_conformers_list):
morse_fp = MORSEFingerprint(n_jobs=-1)
X_skfp = morse... | X_rdkit) | assert_* | variable | tests/fingerprints/morse.py | test_morse_fingerprint | 20 | null | |
scikit-fingerprints/scikit-fingerprints | import numpy as np
import pytest
from numpy.testing import assert_equal
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_f... | 0) | assert_* | numeric_literal | tests/filters/hao.py | test_mols_failing_hao | 48 | null | |
scikit-fingerprints/scikit-fingerprints | import numpy as np
from numpy.testing import assert_equal
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_j... | np.uint32 | assert | complex_expr | tests/fingerprints/avalon.py | test_avalon_count_fingerprint | 27 | null | |
scikit-fingerprints/scikit-fingerprints | import numpy as np
import pytest
from numpy.testing import assert_equal
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_f... | 3) | assert_* | numeric_literal | tests/filters/hao.py | test_mols_passing_with_violation_hao | 56 | null | |
scikit-fingerprints/scikit-fingerprints | import numpy as np
import pytest
from numpy.testing import assert_equal
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)NCc1ccc... | 0) | assert_* | numeric_literal | tests/filters/pfizer.py | test_mols_failing_pfizer | 46 | null | |
scikit-fingerprints/scikit-fingerprints | import numpy as np
import pytest
from numpy.testing import assert_equal
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... | (4,)) | assert_* | collection | tests/filters/oprea.py | test_oprea_condition_names | 113 | null | |
scikit-fingerprints/scikit-fingerprints | import pytest
from numpy.testing import assert_equal
from skfp.datasets.lrgb import (
load_lrgb_mol_benchmark,
load_lrgb_mol_dataset,
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... | len(test) | assert | func_call | tests/datasets/lrgb.py | test_load_lrgb_splits | 55 | null | |
scikit-fingerprints/scikit-fingerprints | from pathlib import Path
import numpy as np
import pytest
from numpy.testing import assert_equal
from rdkit import Chem
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_... | np.uint8 | assert | complex_expr | tests/fingerprints/klekota_roth.py | test_klekota_roth_bit_fingerprint | 18 | null | |
scikit-fingerprints/scikit-fingerprints | import re
import pytest
from numpy.testing import assert_equal
from sklearn.utils.parallel import delayed
from skfp.utils.parallel import ProgressParallel, run_in_parallel
def test_run_in_parallel_verbose_dict(capsys):
func = lambda x: x + 1
data = list(range(100))
run_in_parallel(func, data, n_jobs=-1, ... | stderr | assert | variable | tests/utils/parallel.py | test_run_in_parallel_verbose_dict | 73 | null | |
scikit-fingerprints/scikit-fingerprints | import numpy as np
from numpy.testing import assert_allclose, assert_equal
from rdkit.Chem.rdMolDescriptors import CalcRDF
from scipy.sparse import csr_array
from skfp.fingerprints import RDFFingerprint
def test_rdf_fingerprint(mols_conformers_list):
rdf_fp = RDFFingerprint(n_jobs=-1)
X_skfp = rdf_fp.transfor... | X_rdkit) | assert_* | variable | tests/fingerprints/rdf.py | test_rdf_fingerprint | 17 | null | |
scikit-fingerprints/scikit-fingerprints | import numpy as np
import pytest
from numpy.testing import assert_equal
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)NCc1ccc... | len(y)) | assert_* | func_call | tests/filters/pfizer.py | test_pfizer_return_condition_indicators_transform_x_y | 155 | null | |
scikit-fingerprints/scikit-fingerprints | import numpy as np
import pytest
from numpy.testing import assert_allclose, assert_equal
from rdkit.Chem.rdMolDescriptors import GetUSR
from skfp.fingerprints import USRFingerprint
def mols_conformers_3_plus_atoms(mols_conformers_list):
return [mol for mol in mols_conformers_list if mol.GetNumAtoms() >= 3]
def t... | y_rdkit) | assert_* | variable | tests/fingerprints/usr.py | test_usr_bit_fingerprint_transform_x_y | 45 | null | |
scikit-fingerprints/scikit-fingerprints | import numpy as np
from numpy.testing import assert_equal
from scipy.sparse import csr_array
from skfp.fingerprints import GhoseCrippenFingerprint
def test_ghose_crippen_count_fingerprint(smiles_list):
gc_fp = GhoseCrippenFingerprint(count=True, n_jobs=-1)
X = gc_fp.transform(smiles_list)
assert isinstan... | np.uint32 | assert | complex_expr | tests/fingerprints/ghose_crippen.py | test_ghose_crippen_count_fingerprint | 24 | null | |
scikit-fingerprints/scikit-fingerprints | from rdkit.Chem import MolFromSmiles
from rdkit.rdBase import LogToPythonStderr
from skfp.utils import no_rdkit_logs
def test_no_rdkit_logs(capsys):
LogToPythonStderr()
MolFromSmiles("X")
assert "SMILES Parse Error" in capsys.readouterr().err
assert capsys.readouterr().out == | "" | assert | string_literal | tests/utils/rdkit_logging.py | test_no_rdkit_logs | 12 | null | |
scikit-fingerprints/scikit-fingerprints | import numpy as np
import pytest
from numpy.testing import assert_allclose, assert_equal
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 ERGFi... | np.uint8 | assert | complex_expr | tests/fingerprints/erg.py | test_erg_bit_fingerprint | 41 | null | |
scikit-fingerprints/scikit-fingerprints | import numpy as np
import pytest
from numpy.testing import assert_array_equal, assert_equal
from rdkit.Chem import AddHs, MolFromSmiles
from skfp.preprocessing import ConformerGenerator, MolFromSmilesTransformer
def test_conformer_generator_copy_y():
mols = [MolFromSmiles("O")]
labels = np.array([1])
conf... | labels | assert | variable | tests/preprocessing/conformer_generator.py | test_conformer_generator_copy_y | 99 | null | |
scikit-fingerprints/scikit-fingerprints | import numpy as np
import pytest
from numpy.testing import assert_equal
from skfp.filters import ValenceDiscoveryFilter
def smiles_passing_valence_discovery() -> list[str]:
return [
# chlordiazepoxide
"ClC1=CC2=C(N=C(NC)C[N+]([O-])=C2C3=CC=CC=C3)C=C1",
# cortisol
r"O=C4\C=C2/[C@]([... | len(y)) | assert_* | func_call | tests/filters/valence_discovery.py | test_valence_discovery_return_condition_indicators_transform_x_y | 171 | null | |
scikit-fingerprints/scikit-fingerprints | import pytest
from numpy.testing import assert_equal
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... | len(test) | assert | func_call | tests/datasets/moleculenet.py | test_load_ogb_splits | 90 | null | |
scikit-fingerprints/scikit-fingerprints | import numpy as np
import pytest
from numpy.testing import assert_equal
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",... | len(y)) | assert_* | func_call | tests/filters/beyond_ro5.py | test_bro5_return_condition_indicators_transform_x_y | 191 | null | |
scikit-fingerprints/scikit-fingerprints | import numpy as np
import pytest
from numpy.testing import assert_equal
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_f... | np.uint8 | assert | complex_expr | tests/fingerprints/layered.py | test_layered_fingerprint | 18 | null | |
scikit-fingerprints/scikit-fingerprints | import numpy as np
from numpy.testing import assert_allclose, assert_equal
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_rdkit) | assert_* | variable | tests/fingerprints/autocorr.py | test_autocorr_fingerprint | 13 | null | |
scikit-fingerprints/scikit-fingerprints | import numpy as np
import pytest
from numpy.testing import assert_allclose, assert_equal
from rdkit.Chem.rdMolDescriptors import GetUSRCAT
from skfp.fingerprints import USRCATFingerprint
def mols_conformers_3_plus_atoms(mols_conformers_list):
# molecules with 1 or 2 atoms can be numerically unstable with WHIM
... | X_rdkit) | assert_* | variable | tests/fingerprints/usrcat.py | test_usrcat_bit_fingerprint | 26 | null | |
scikit-fingerprints/scikit-fingerprints | import numpy as np
import pytest
from numpy.testing import assert_equal
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 smi... | (2,)) | assert_* | collection | tests/filters/gsk.py | test_gsk_condition_names | 112 | null | |
scikit-fingerprints/scikit-fingerprints | import pytest
from skfp.model_selection.splitters.utils import (
_check_subset_size,
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]:
r... | (8, 1, 1) | assert | collection | tests/model_selection/splitters/utils.py | test_validate_train_valid_test_split_sizes_all_missing | 123 | null | |
scikit-fingerprints/scikit-fingerprints | import numpy as np
import pytest
from numpy.testing import assert_equal
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 smi... | len(y)) | assert_* | func_call | tests/filters/gsk.py | test_gsk_return_condition_indicators_transform_x_y | 140 | null | |
scikit-fingerprints/scikit-fingerprints | import numpy as np
import pytest
from numpy.testing import assert_equal
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 t... | 6) | assert_* | numeric_literal | tests/filters/mol_weight.py | test_mol_weight_thresholds | 80 | null | |
scikit-fingerprints/scikit-fingerprints | import numpy as np
from numpy.testing import assert_equal
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)
a... | np.uint8 | assert | complex_expr | tests/fingerprints/mqns.py | test_mqns_bit_fingerprint | 13 | null | |
scikit-fingerprints/scikit-fingerprints | import os
import numpy as np
from numpy.testing import assert_equal
from scipy.sparse import csr_array, load_npz
from skfp.fingerprints import LingoFingerprint
def test_lingo_fingerprint_smiles_to_dict():
smiles = ["CC(=O)NCCC1=CNC2=C1C=C(C=C2)OC"]
lingo_fp = LingoFingerprint()
X_skfp = lingo_fp.smiles_t... | expected | assert | variable | tests/fingerprints/lingo.py | test_lingo_fingerprint_smiles_to_dict | 44 | null | |
scikit-fingerprints/scikit-fingerprints | import numpy as np
import pytest
from numpy.testing import assert_equal
from skfp.filters import RuleOfXuFilter
def smiles_passing_rule_of_xu() -> 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
... | len(y)) | assert_* | func_call | tests/filters/rule_of_xu.py | test_rule_of_xu_return_condition_indicators_transform_x_y | 148 | null | |
scikit-fingerprints/scikit-fingerprints | import numpy as np
import pytest
from numpy.testing import assert_equal
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 smi... | 0) | assert_* | numeric_literal | tests/filters/gsk.py | test_mols_failing_gsk | 51 | null | |
scikit-fingerprints/scikit-fingerprints | import numpy as np
import pytest
from numpy.testing import assert_equal
from skfp.filters import TiceInsecticidesFilter
def smiles_passing_tice_insecticides() -> list[str]:
return [
"O=C(CC1COc2ccccc2O1)NCCc1ccccc1",
"Cc1cc(C)c(C)c(S(=O)(=O)Nc2ccc(OCC(=O)O)cc2)c1C",
"O=C(Nc1cccc(Cl)c1)N1CC... | len(y)) | assert_* | func_call | tests/filters/tice_insecticides.py | test_tice_insecticides_return_condition_indicators_transform_x_y | 167 | null | |
scikit-fingerprints/scikit-fingerprints | import numpy as np
from numpy.testing import assert_equal
from rdkit.Chem import Mol
from skfp.filters import ZINCDruglikeFilter
def test_zinc_druglike_return_condition_indicators_transform_x_y(mols_list):
labels = np.ones(len(mols_list))
filt = ZINCDruglikeFilter(return_type="condition_indicators")
cond... | len(y)) | assert_* | func_call | tests/filters/zinc_druglike.py | test_zinc_druglike_return_condition_indicators_transform_x_y | 72 | null | |
scikit-fingerprints/scikit-fingerprints | import pytest
from numpy.testing import assert_equal
from sklearn.utils._param_validation import InvalidParameterError
from skfp.datasets.tdc import load_tdc_benchmark, load_tdc_splits
from skfp.datasets.tdc.adme import (
load_b3db_classification,
load_b3db_regression,
load_bioavailability_ma,
load_cac... | train | assert | variable | tests/datasets/tdc.py | test_load_ogb_splits_as_dict | 131 | null | |
scikit-fingerprints/scikit-fingerprints | from inspect import getmembers, isfunction
import numpy as np
import rdkit.Chem.Fragments
from numpy.testing import assert_equal
from scipy.sparse import csr_array
from skfp.fingerprints import FunctionalGroupsFingerprint
def test_functional_groups_bit_fingerprint(smiles_list, mols_list):
fg_fp = FunctionalGroup... | np.uint8 | assert | complex_expr | tests/fingerprints/functional_groups.py | test_functional_groups_bit_fingerprint | 28 | null | |
scikit-fingerprints/scikit-fingerprints | import numpy as np
from numpy.testing import assert_equal
from rdkit.Chem import MolFromSmiles
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 = MACCSF... | np.uint8 | assert | complex_expr | tests/fingerprints/maccs.py | test_maccs_bit_fingerprint | 17 | null | |
scikit-fingerprints/scikit-fingerprints | import numpy as np
import pytest
from numpy.testing import assert_equal
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",... | 3) | assert_* | numeric_literal | tests/filters/beyond_ro5.py | test_mols_ro5_vs_bro5 | 81 | null | |
scikit-fingerprints/scikit-fingerprints | import numpy as np
from numpy.testing import assert_equal
from rdkit.Chem import MolFromSmiles
from rdkit.Chem.rdMolDescriptors import GetMACCSKeysFingerprint
from scipy.sparse import csr_array
from skfp.fingerprints import MACCSFingerprint
def test_maccs_feature_names():
# we check a few selected feature names
... | "6M Ring") | assert_* | string_literal | tests/fingerprints/maccs.py | test_maccs_feature_names | 61 | null | |
scikit-fingerprints/scikit-fingerprints | import numpy as np
import pytest
from numpy.testing import assert_allclose, assert_equal
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... | X_rdkit) | assert_* | variable | tests/fingerprints/vsa.py | test_vsa_fingerprint | 21 | null | |
scikit-fingerprints/scikit-fingerprints | import pytest
from numpy.testing import assert_equal
from sklearn.utils._param_validation import InvalidParameterError
from skfp.filters import LipinskiFilter
@pytest.mark.parametrize("n_jobs", [1, 2])
def test_base_verbose(n_jobs, smiles_list, capsys):
filt = LipinskiFilter(n_jobs=n_jobs, verbose=True)
filt.... | output | assert | variable | tests/bases/base_filter.py | test_base_verbose | 38 | null | |
scikit-fingerprints/scikit-fingerprints | import numpy as np
from numpy.testing import assert_allclose, assert_equal
from sklearn.dummy import DummyClassifier
from sklearn.model_selection import GridSearchCV
from skfp.fingerprints import AtomPairFingerprint
from skfp.model_selection import FingerprintEstimatorGridSearch
def test_best_fp_caching(smallest_mols... | None | assert | none_literal | tests/model_selection/hyperparam_search/grid_search.py | test_best_fp_caching | 98 | null | |
scikit-fingerprints/scikit-fingerprints | import numpy as np
from numpy.testing import assert_equal
from scipy.sparse import csr_array
from skfp.fingerprints import LaggnerFingerprint
def test_laggner_feature_names():
# we check a few selected feature names
laggner_fp = LaggnerFingerprint()
feature_names = laggner_fp.get_feature_names_out()
... | "CH-acidic") | assert_* | string_literal | tests/fingerprints/laggner.py | test_laggner_feature_names | 82 | null | |
scikit-fingerprints/scikit-fingerprints | import numpy as np
import pytest
from numpy.testing import assert_equal
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(CC2CC... | len(y)) | assert_* | func_call | tests/filters/ghose.py | test_ghose_return_condition_indicators_transform_x_y | 151 | null | |
scikit-fingerprints/scikit-fingerprints | import pytest
from numpy.testing import assert_equal
from skfp.datasets.lrgb import (
load_lrgb_mol_benchmark,
load_lrgb_mol_dataset,
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... | valid | assert | variable | tests/datasets/lrgb.py | test_load_lrgb_splits_as_dict | 70 | null | |
scikit-fingerprints/scikit-fingerprints | import numpy as np
from numpy.testing import assert_equal
from rdkit.Chem.rdFingerprintGenerator import (
GetMorganFeatureAtomInvGen,
GetTopologicalTorsionGenerator,
)
from scipy.sparse import csr_array
from skfp.fingerprints import TopologicalTorsionFingerprint
def test_topological_torsion_bit_fingerprint(sm... | X_rdkit) | assert_* | variable | tests/fingerprints/topological_torsion.py | test_topological_torsion_bit_fingerprint | 19 | null | |
scikit-fingerprints/scikit-fingerprints | import numpy as np
import pytest
from numpy.testing import assert_equal
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_f... | len(y)) | assert_* | func_call | tests/filters/hao.py | test_hao_return_condition_indicators_transform_x_y | 151 | null | |
scikit-fingerprints/scikit-fingerprints | import numpy as np
from numpy.testing import assert_allclose, assert_equal
from sklearn.dummy import DummyClassifier
from sklearn.model_selection import GridSearchCV
from skfp.fingerprints import AtomPairFingerprint
from skfp.model_selection import FingerprintEstimatorRandomizedSearch
def test_fp_estimator_randomized... | output | assert | variable | tests/model_selection/hyperparam_search/randomized_selection.py | test_fp_estimator_randomized_search_verbose | 73 | null | |
scikit-fingerprints/scikit-fingerprints | import pytest
from numpy.testing import assert_equal
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... | 0 | assert | numeric_literal | tests/datasets/moleculenet.py | test_load_ogb_splits | 78 | null | |
scikit-fingerprints/scikit-fingerprints | import numpy as np
from numpy.testing import assert_equal
from rdkit.Chem import MolFromSmiles
from rdkit.Chem.rdMolDescriptors import GetMACCSKeysFingerprint
from scipy.sparse import csr_array
from skfp.fingerprints import MACCSFingerprint
def test_maccs_count_feature_names():
# we check a few selected feature n... | "F") | assert_* | string_literal | tests/fingerprints/maccs.py | test_maccs_count_feature_names | 77 | null | |
scikit-fingerprints/scikit-fingerprints | import os
import numpy as np
from numpy.testing import assert_equal
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_s... | np.uint8 | assert | complex_expr | tests/fingerprints/lingo.py | test_lingo_fingerprint_bit | 56 | null | |
scikit-fingerprints/scikit-fingerprints | import os
import shutil
import numpy as np
import pandas as pd
import pytest
from numpy.testing import assert_equal
from skfp.datasets.utils import (
fetch_splits,
get_data_home_dir,
get_mol_strings_and_labels,
)
def test_fetch_splits(capsys):
fetch_splits(
None,
dataset_name="Molecul... | stdout | assert | variable | tests/datasets/utils.py | test_fetch_splits | 36 | null | |
scikit-fingerprints/scikit-fingerprints | import numpy as np
from numpy.testing import assert_allclose
from skfp.metrics import spearman_correlation
def test_spearman_perfect_correlation():
y_true = list(range(5))
y_pred = list(range(1, 6))
corr = spearman_correlation(y_true, y_pred)
np_corr = spearman_correlation(np.array(y_true), np.array(y... | np_corr) | assert_* | variable | tests/metrics/spearman.py | test_spearman_perfect_correlation | 12 | null | |
scikit-fingerprints/scikit-fingerprints | import numpy as np
import pytest
from numpy.testing import assert_equal
from rdkit.Chem.rdFingerprintGenerator import (
GetMorganFeatureAtomInvGen,
GetRDKitFPGenerator,
)
from scipy.sparse import csr_array
from sklearn.utils._param_validation import InvalidParameterError
from skfp.fingerprints import RDKitFing... | X_rdkit) | assert_* | variable | tests/fingerprints/rdkit_fp.py | test_rdkit_bit_fingerprint | 21 | null | |
scikit-fingerprints/scikit-fingerprints | import numpy as np
from numpy.testing import assert_equal
from rdkit.Chem import MolFromSmiles
from rdkit.Chem.rdMolDescriptors import GetMACCSKeysFingerprint
from scipy.sparse import csr_array
from skfp.fingerprints import MACCSFingerprint
def test_maccs_feature_names():
# we check a few selected feature names
... | "Ring") | assert_* | string_literal | tests/fingerprints/maccs.py | test_maccs_feature_names | 63 | null | |
scikit-fingerprints/scikit-fingerprints | import numpy as np
from numpy.testing import assert_equal
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.s... | np.uint8 | assert | complex_expr | tests/fingerprints/map.py | test_map_bit_fingerprint | 21 | null | |
scikit-fingerprints/scikit-fingerprints | from collections.abc import Callable
import numpy as np
import pytest
from numpy.testing import assert_allclose
from sklearn.datasets import make_classification
from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor
from sklearn.metrics import (
accuracy_score,
average_precision_score,
... | str(error) | assert | func_call | tests/metrics/multioutput.py | test_metrics_inputs_shapes | 285 | null | |
scikit-fingerprints/scikit-fingerprints | import numpy as np
from numpy.testing import assert_equal
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.s... | X_map) | assert_* | variable | tests/fingerprints/map.py | test_map_bit_fingerprint | 19 | null | |
scikit-fingerprints/scikit-fingerprints | import pytest
from numpy.testing import assert_equal
from sklearn.utils._param_validation import InvalidParameterError
from skfp.datasets.tdc import load_tdc_benchmark, load_tdc_splits
from skfp.datasets.tdc.adme import (
load_b3db_classification,
load_b3db_regression,
load_bioavailability_ma,
load_cac... | 0 | assert | numeric_literal | tests/datasets/tdc.py | test_load_tdc_splits | 105 | null | |
scikit-fingerprints/scikit-fingerprints | from typing import Literal
import numpy as np
import pandas as pd
from numpy.testing import assert_allclose, assert_equal
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_... | y) | assert_* | variable | tests/datasets/test_utils.py | assert_valid_dataframe | 120 | null | |
scikit-fingerprints/scikit-fingerprints | import numpy as np
import pytest
import scipy.sparse
from numpy.testing import assert_equal
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... | X_e3fp) | assert_* | variable | tests/fingerprints/e3fp_fp.py | test_e3fp_bit_fingerprint | 18 | null | |
scikit-fingerprints/scikit-fingerprints | import numpy as np
import pytest
from numpy.testing import assert_equal
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
... | 0) | assert_* | numeric_literal | tests/filters/faf4_druglike.py | test_mols_failing_faf4_druglike | 62 | null | |
scikit-fingerprints/scikit-fingerprints | import numpy as np
import pytest
import scipy.sparse
from numpy.testing import assert_equal
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... | np.uint8 | assert | complex_expr | tests/fingerprints/e3fp_fp.py | test_e3fp_bit_fingerprint | 20 | null | |
scikit-fingerprints/scikit-fingerprints | import numpy as np
import pytest
from numpy.testing import assert_equal
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)... | (2,)) | assert_* | collection | tests/filters/rule_of_veber.py | test_rule_of_veber_condition_names | 117 | null | |
scikit-fingerprints/scikit-fingerprints | import os
import shutil
import numpy as np
import pandas as pd
import pytest
from numpy.testing import assert_equal
from skfp.datasets.utils import (
fetch_splits,
get_data_home_dir,
get_mol_strings_and_labels,
)
@pytest.mark.parametrize("mol_type", ["SMILES", "aminoseq"])
def test_get_smiles_and_labels(... | 2) | assert_* | numeric_literal | tests/datasets/utils.py | test_get_smiles_and_labels | 54 | null | |
scikit-fingerprints/scikit-fingerprints | import numpy as np
import pytest
from numpy.testing import assert_equal
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_... | X_rdkit) | assert_* | variable | tests/fingerprints/secfp.py | test_secfp_fingerprint | 18 | null | |
scikit-fingerprints/scikit-fingerprints | import numpy as np
from numpy.testing import assert_equal
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=-... | X_rdkit) | assert_* | variable | tests/fingerprints/pattern.py | test_pattern_fingerprint | 14 | null | |
scikit-fingerprints/scikit-fingerprints | import pytest
from skfp.model_selection.splitters.utils import (
_check_subset_size,
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]:
r... | (7, 3) | assert | collection | tests/model_selection/splitters/utils.py | test_validate_train_test_split_sizes_both_provided | 33 | null | |
scikit-fingerprints/scikit-fingerprints | import pytest
from numpy.testing import assert_equal
from skfp.datasets.lrgb import (
load_lrgb_mol_benchmark,
load_lrgb_mol_dataset,
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... | train | assert | variable | tests/datasets/lrgb.py | test_load_lrgb_splits_as_dict | 69 | null | |
scikit-fingerprints/scikit-fingerprints | import numpy as np
import pytest
from numpy.testing import assert_allclose, assert_equal
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 ERGFi... | np.uint32 | assert | complex_expr | tests/fingerprints/erg.py | test_erg_count_fingerprint | 55 | null | |
scikit-fingerprints/scikit-fingerprints | import numpy as np
import pytest
from numpy.testing import assert_allclose, assert_equal
from rdkit import Chem
from rdkit.Chem import Mol
from skfp.model_selection.splitters.maxmin_split import (
maxmin_stratified_train_test_split,
maxmin_stratified_train_valid_test_split,
maxmin_train_test_split,
max... | t2) | assert_* | variable | tests/model_selection/splitters/maxmin_split.py | test_seed_consistency_train_valid_test_split | 64 | null | |
scikit-fingerprints/scikit-fingerprints | import numpy as np
import pytest
from numpy.testing import assert_equal
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 t... | 3) | assert_* | numeric_literal | tests/filters/mol_weight.py | test_mol_weight_thresholds | 68 | null | |
scikit-fingerprints/scikit-fingerprints | import numpy as np
import pytest
from numpy.testing import assert_equal
from rdkit.Chem import Mol
from skfp.filters import (
BMSFilter,
BrenkFilter,
GlaxoFilter,
InpharmaticaFilter,
LINTFilter,
MLSMRFilter,
NIBRFilter,
NIHFilter,
PAINSFilter,
SureChEMBLFilter,
ZINCBasicFilt... | len(y)) | assert_* | func_call | tests/filters/substructural_filters.py | test_substructural_filter_return_condition_indicators_transform_x_y | 150 | null | |
scikit-fingerprints/scikit-fingerprints | import numpy as np
from numpy.testing import assert_equal
from scipy.sparse import csr_array
from skfp.fingerprints import LaggnerFingerprint
def test_laggner_feature_names():
# we check a few selected feature names
laggner_fp = LaggnerFingerprint()
feature_names = laggner_fp.get_feature_names_out()
... | "Alkyne") | assert_* | string_literal | tests/fingerprints/laggner.py | test_laggner_feature_names | 78 | null | |
scikit-fingerprints/scikit-fingerprints | import numpy as np
from numpy.testing import assert_allclose, assert_equal
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_verb... | output | assert | variable | tests/model_selection/hyperparam_search/grid_search.py | test_fp_estimator_grid_search_verbose | 68 | null | |
scikit-fingerprints/scikit-fingerprints | import numpy as np
import pytest
from numpy.testing import assert_equal
from sklearn.datasets import (
make_classification,
make_multilabel_classification,
make_regression,
)
from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor
from sklearn.utils._param_validation import InvalidParamet... | (len(X),)) | assert_* | collection | tests/applicability_domain/standard_deviation.py | test_std_ad_checker_with_classifier | 97 | null |
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