recover changes

#!/usr/bin/env python3

# -*- coding: utf-8 -*-

"""

Created on Tue Oct 18 15:53:27 2016

@author: Michael Wu

Benchmark test:

Giving classification performances of: 

    Random forest(rf), MultitaskDNN(tf), 

    RobustMultitaskDNN(tf_robust),

    Logistic regression(logreg), IRV(irv)

    Graph convolution(graphconv)                 

on datasets: bace_c, bbbp, clintox, hiv, muv, pcba, sider, tox21, toxcast  

Giving regression performances of:

    MultitaskDNN(tf_regression),

    Fit Transformer MultitaskDNN(tf_regression_ft),

    Random forest(rf_regression),

    Graph convolution regression(graphconvreg)

on datasets: bace_r, chembl, clearance, delaney(ESOL), hopv, kaggle, lipo,

             nci, pdbbind, ppb, qm7, qm7b, qm8, qm9, sampl(FreeSolv)

time estimation listed in README file

"""

from __future__ import print_function

from __future__ import division

from __future__ import unicode_literals

import os

import numpy as np

import deepchem as dc

import argparse

np.random.seed(123)

parser = argparse.ArgumentParser(

    description='Deepchem benchmark: ' +

    'giving performances of different learning models on datasets')

parser.add_argument(

    '-s',

    action='append',

    dest='splitter_args',

    default=[],

    help='Choice of splitting function: index, random, scaffold, stratified')

parser.add_argument(

    '-m',

    action='append',

    dest='model_args',

    default=[],

    help='Choice of model: tf, tf_robust, logreg, rf, irv, graphconv, ' +

    'tf_regression, tf_regression_ft, rf_regression, graphconvreg')

parser.add_argument(

    '-d',

    action='append',

    dest='dataset_args',

    default=[],

    help='Choice of dataset: bace_c, bace_r, bbbp, chembl, clearance, ' +

    'clintox, delaney, hiv, hopv, kaggle, lipo, muv, nci, pcba, ' +

    'pdbbind, ppb, qm7, qm7b, qm8, qm9, sampl, sider, tox21, toxcast')

parser.add_argument(

    '-t',

    action='store_true',

    dest='test',

    default=False,

    help='Evalute performance on test set')

args = parser.parse_args()

#Datasets and models used in the benchmark test

splitters = args.splitter_args

models = args.model_args

datasets = args.dataset_args

test = args.test

if len(splitters) == 0:

  splitters = ['index', 'random', 'scaffold']

if len(models) == 0:

  models = [

      'tf', 'tf_robust', 'logreg', 'graphconv', 'tf_regression',

      'tf_regression_ft', 'graphconvreg'

  ]

  #irv, rf, rf_regression should be assigned manually

if len(datasets) == 0:

  datasets = [

      'bace_c', 'bace_r', 'bbbp', 'clearance', 'clintox', 'delaney', 'hiv',

      'hopv', 'lipo', 'muv', 'pcba', 'pdbbind', 'ppb', 'qm7b', 'qm8', 'qm9',

      'sampl', 'sider', 'tox21', 'toxcast'

  ]

for split in splitters:

  for dataset in datasets:

    for model in models:

      dc.molnet.run_benchmark([dataset], str(model), split=split, test=test)