- Create a conda environment, with rdkit, and install the package.
conda create -n qctime -c rdkit rdkit
conda activate qctime
cd models/qc_time_estimator
python setup.py install
# or for development
pip install -e .- Train the model to generate and save the model's pkl file (used for prediction and tests)
python models/qc_time_estimator/qc_time_estimator/train_pipeline.py - Next, after generating the model, you can run tests (optional)
cd models/qc_time_estimator
pytest- Finally, you can run predictions in Python:
Input file example:
nthreads,driver,method,restricted,cpu_clock_speed,cpu_launch_year
12,gradient,b3lyp,True,2500.0,2014
4,energy,wb97x-d,True,2100.0,2016
8,gradient,b3lyp,True,2200.0,2016
4,energy,pbe,True,2500.0,2014
2,energy,hf,True,2600.0,2013
4,energy,wb97x-d,True,2100.0,2016
16,energy,pbe,True,2100.0,2016
4,gradient,b3lyp,True,2500.0,2014
from qc_time_estimator.predict import make_prediction
import pandas as pd
test_input = pd.read_csv('path/to/file.csv')
predictions = make_prediction(input_data=test_input)