def test_predictions():
opts = test_predict_args
logging.basicConfig(format="DFPL-%(levelname)s: %(message)s", level=logging.INFO)
logging.info(f"Predicting compounds in the input file {opts.inputFile} for association with target {opts.target}")
df = fp.importDataFile(opts.inputFile, import_function=fp.importSmilesCSV, fp_size=opts.fpSize)
use_compressed = False
if opts.acFile:
use_compressed = True
# load trained model for autoencoder
(_, encoder) = ac.define_ac_model(input_size=opts.fpSize, encoding_dim=opts.encFPSize)
encoder.load_weights(opts.acFile)
# compress the fingerprints using the autoencoder
df = ac.compress_fingerprints(df, encoder)
# predict
df2 = p.predict_values(df=df,
opts=opts,
use_compressed=use_compressed)
names_columns = [c for c in df2.columns if c not in ['fp', 'fpcompressed']]
output_file = path.join(opts.outputDir,
path.basename(path.splitext(opts.inputFile)[0]) + ".predictions.csv")
df2[names_columns].to_csv(path_or_buf=output_file)
logging.info(f"Predictions done.\nResults written to '{output_file}'.")
def predict(opts: options.PredictOptions) -> None:
"""
Run prediction given specific options
:param opts: Options defining the details of the prediction
"""
df = fp.importDataFile(opts.inputFile, import_function=fp.importSmilesCSV, fp_size=opts.fpSize)
# Create output dir if it doesn't exist
createDirectory(opts.outputDir)
use_compressed = False
if opts.ecWeightsFile:
logging.info(f"Using fingerprint compression with AC {opts.ecWeightsFile}")
use_compressed = True
# load trained model for autoencoder
(_, encoder) = ac.define_ac_model(input_size=opts.fpSize, encoding_dim=opts.encFPSize)
encoder.load_weights(opts.ecWeightsFile)
# compress the fingerprints using the autoencoder
df = ac.compress_fingerprints(df, encoder)
# predict
df2 = predictions.predict_values(df=df,
opts=opts,
use_compressed=use_compressed)
names_columns = [c for c in df2.columns if c not in ['fp', 'fpcompressed']]
output_file = path.join(opts.outputDir,
path.basename(path.splitext(opts.inputFile)[0]) + ".predictions.csv")
df2[names_columns].to_csv(path_or_buf=output_file)
outputDir=f"/home/hertelj/tmp/",
model=
f"/home/hertelj/git-hertelj/deepFPlearn_CODE/validation/case_03/results/ER_compressed-True_sampled-None.best.FNN.model.hdf5",
target="ER",
fpSize=2048,
type="smiles",
fpType="topological")
(_, encoder) = ac.define_ac_model(input_size=2048, encoding_dim=256)
encoder.load_weights(
"/home/hertelj/git-hertelj/deepFPlearn_CODE/modeltraining/Sun_etal_dataset.encoder.hdf5"
)
data = ac.compress_fingerprints(dfS, encoder)
s_compressed = data[data['cid'].isin(cid_of_interest)]['fpcompressed']
df2 = predictions.predict_values(df=data, opts=opts, use_compressed=True)
s_predictions = df2[df2['cid'].isin(cid_of_interest)][['cid', 'trained']]
data2 = ac.compress_fingerprints(dfD, encoder)
d_compressed = data2[data2['toxid'].isin(toxid_of_interest)]['fpcompressed']
df3 = predictions.predict_values(df=data2, opts=opts, use_compressed=True)
d_predictions = df3[df3['toxid'].isin(toxid_of_interest)][['toxid', 'trained']]
fp_matrix_S = np.array(df['fpSbool'].to_list(), dtype=bool, copy=False)
predictions_S = encoder.predict(fp_matrix_S)
fp_matrix_D = np.array(df['fpDbool'].to_list(), dtype=bool, copy=False)
predictions_D = encoder.predict(fp_matrix_D)
df['fpcompressedS'] = [s for s in predictions_S]
df['fpcompressedD'] = [s for s in predictions_D]
# compressed fp equal?