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"""
Needed objects for tests
"""
#---------------------------------------------------------------------------------------
# Dependencies
import pytest
import pandas as pd
import pickle
import numpy as np
import torch
from data_preprocessing.create_descriptors import create_cleaned_mol_objects
#---------------------------------------------------------------------------------------
# Define fixtures
#---------------------------------------------------------------------------------------
# Data preprocessing
@pytest.fixture(scope="session")
def input_molecule_formats():
class Formats:
smiles = "CCO"
smiles_coma = "CCO, CCN"
smiles_list = ["CCO", "CCN"]
smiles_df = pd.DataFrame({"smiLES": ["CCO", "CCN"]})
smiles_df_wrong_key = pd.DataFrame({"notSMILES": ["CCO", "CCN"]})
return Formats()
@pytest.fixture(scope="session")
def input_smiles():
current_loc = __file__.rsplit("/",3)[0]
with open(current_loc + "/assets/test_reference_data/smiles.pkl", "rb") as fl:
input_smiles = pickle.load(fl)
return input_smiles
@pytest.fixture(scope="session")
def input_mols_from_smiles():
current_loc = __file__.rsplit("/",3)[0]
with open(current_loc + "/assets/test_reference_data/smiles.pkl", "rb") as fl:
input_smiles = pickle.load(fl)
input_molecules = create_cleaned_mol_objects(input_smiles)
return input_molecules
@pytest.fixture(scope="session")
def ecfps_from_smiles():
current_loc = __file__.rsplit("/",3)[0]
ecfps = np.load(current_loc + "/assets/test_reference_data/ecfps.npy")
return ecfps
@pytest.fixture(scope="session")
def rdkit_descrs_from_smiles():
current_loc = __file__.rsplit("/",3)[0]
rdkit_descrs = np.load(current_loc + "/assets/test_reference_data/rdkit_descrs.npy")
return rdkit_descrs
@pytest.fixture(scope="session")
def rdkit_descr_quantils():
current_loc = __file__.rsplit("/",3)[0]
rdkit_descr_quantils = np.load(
current_loc + "/assets/test_reference_data/rdkit_descr_quantils.npy")
return rdkit_descr_quantils
@pytest.fixture(scope="session")
def preprocessed_features():
current_loc = __file__.rsplit("/",3)[0]
preprocessed_features = np.load(
current_loc + "/assets/test_reference_data/preprocessed_features.npy")
return preprocessed_features
#---------------------------------------------------------------------------------------
# Model
@pytest.fixture(scope="session")
def model_input_query():
current_loc = __file__.rsplit("/",3)[0]
model_input_query = torch.load(
current_loc + "/assets/test_reference_data/model_input_query.pt")
return model_input_query
@pytest.fixture(scope="session")
def model_input_support_actives():
current_loc = __file__.rsplit("/",3)[0]
model_input_support_actives = torch.load(
current_loc + "/assets/test_reference_data/model_input_support_actives.pt")
return model_input_support_actives
@pytest.fixture(scope="session")
def model_input_support_inactives():
current_loc = __file__.rsplit("/",3)[0]
model_input_support_inactives = torch.load(
current_loc + "/assets/test_reference_data/model_input_support_inactives.pt")
return model_input_support_inactives
@pytest.fixture(scope="session")
def model_predictions():
current_loc = __file__.rsplit("/",3)[0]
model_predictions = torch.load(
current_loc + "/assets/test_reference_data/model_predictions.pt")
return model_predictions |