Commit 228863b3 authored by miaecle's avatar miaecle
Browse files

update

parent c1cf3673

deepchem/models/models.py

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@@ -167,7 +167,7 @@ class Model(BaseEstimator): 
      y_pred = np.reshape(y_pred, (n_samples,))

    return y_pred



  def evaluate(self, dataset, metrics, transformers=[], per_task_metrics=True):

  def evaluate(self, dataset, metrics, transformers=[], per_task_metrics=False):

    """

    Evaluates the performance of this model on specified dataset.

deepchem/models/tensorgraph/.graph_layers.py.swp

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−16 KiB

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examples/benchmark.py

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Original line number Diff line number Diff line
@@ -126,4 +126,4 @@ for dataset in datasets: 
      np.random.seed(seed)

      dc.molnet.run_benchmark(

          [dataset], str(model), split=split, metric=metrics[dataset][0],

          direction=metrics[dataset][1], hyper_param_search=True, max_iter=5, test=test, seed=seed)
 
          direction=metrics[dataset][1], hyper_param_search=True, max_iter=20, test=test, seed=seed)

examples/benchmark_10.py

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Original line number Diff line number Diff line 
#!/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), xgboost(xgb),

    Directed acyclic graph(dag), Weave(weave)

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),

    xgboost(xgb_regression), Deep tensor neural net(dtnn),

    Directed acyclic graph(dag_regression),

    Weave(weave_regression)

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



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, xgb,' + \

         ' dag, weave, tf_regression, tf_regression_ft, rf_regression, ' + \

         'graphconvreg, xgb_regression, dtnn, dag_regression, weave_regression')

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')

parser.add_argument(

    '--seed',

    action='append',

    dest='seed_args',

    default=[],

    help='Choice of random seed')

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(args.seed_args) > 0:

  seed = int(args.seed_args[0])

else:

  seed = 123



if len(splitters) == 0:

  splitters = ['random']

if len(models) == 0:

  models = [

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

      'tf_regression_ft', 'graphconvreg', 'weave', 'weave_regression', 'dtnn'

  ]

  #irv, rf, rf_regression should be assigned manually

if len(datasets) == 0:

  datasets = [

      'clintox', 'delaney', 'lipo', 'qm7b', 'qm8', 'sampl',

      'sider', 'tox21', 'toxcast', 'muv'

  ]



metrics = {

    'qm7': [[dc.metrics.Metric(dc.metrics.mean_absolute_error, np.mean, mode='regression')], False],

    'qm7b': [[dc.metrics.Metric(dc.metrics.mean_absolute_error, np.mean, mode='regression')], False],

    'qm8': [[dc.metrics.Metric(dc.metrics.mean_absolute_error, np.mean, mode='regression')], False],

    'qm9': [[dc.metrics.Metric(dc.metrics.mean_absolute_error, np.mean, mode='regression')], False],

    'delaney': [[dc.metrics.Metric(dc.metrics.rms_score, np.mean, mode='regression')], False],

    'sampl': [[dc.metrics.Metric(dc.metrics.rms_score, np.mean, mode='regression')], False],

    'lipo': [[dc.metrics.Metric(dc.metrics.rms_score, np.mean, mode='regression')], False],

    'pdbbind': [[dc.metrics.Metric(dc.metrics.rms_score, np.mean, mode='regression')], False],

    'pcba': [[dc.metrics.Metric(dc.metrics.prc_auc_score, np.mean, mode='classification')], True],

    'muv': [[dc.metrics.Metric(dc.metrics.prc_auc_score, np.mean, mode='classification')], True],

    'hiv': [[dc.metrics.Metric(dc.metrics.roc_auc_score, np.mean, mode='classification')], True],

    'tox21': [[dc.metrics.Metric(dc.metrics.roc_auc_score, np.mean, mode='classification')], True],

    'toxcast': [[dc.metrics.Metric(dc.metrics.roc_auc_score, np.mean, mode='classification')], True],

    'sider': [[dc.metrics.Metric(dc.metrics.roc_auc_score, np.mean, mode='classification')], True],

    'clintox': [[dc.metrics.Metric(dc.metrics.roc_auc_score, np.mean, mode='classification')], True],

    'bace_c': [[dc.metrics.Metric(dc.metrics.roc_auc_score, np.mean, mode='classification')], True],

    'bbbp': [[dc.metrics.Metric(dc.metrics.roc_auc_score, np.mean, mode='classification')], True]

    }

for dataset in datasets:

  for split in splitters:

    for model in models:

      np.random.seed(seed)

      dc.molnet.run_benchmark(

          [dataset], str(model), split=split, metric=metrics[dataset][0],

          direction=metrics[dataset][1], hyper_param_search=True, max_iter=10, test=test, seed=seed)

examples/benchmark_20.py

deleted100644 → 0
+0 −129
Original line number Diff line number Diff line 
#!/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), xgboost(xgb),

    Directed acyclic graph(dag), Weave(weave)

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),

    xgboost(xgb_regression), Deep tensor neural net(dtnn),

    Directed acyclic graph(dag_regression),

    Weave(weave_regression)

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



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, xgb,' + \

         ' dag, weave, tf_regression, tf_regression_ft, rf_regression, ' + \

         'graphconvreg, xgb_regression, dtnn, dag_regression, weave_regression')

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')

parser.add_argument(

    '--seed',

    action='append',

    dest='seed_args',

    default=[],

    help='Choice of random seed')

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(args.seed_args) > 0:

  seed = int(args.seed_args[0])

else:

  seed = 123



if len(splitters) == 0:

  splitters = ['random']

if len(models) == 0:

  models = [

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

      'tf_regression_ft', 'graphconvreg', 'weave', 'weave_regression', 'dtnn'

  ]

  #irv, rf, rf_regression should be assigned manually

if len(datasets) == 0:

  datasets = [

      'clintox', 'delaney', 'lipo', 'qm7b', 'qm8', 'sampl',

      'sider', 'tox21', 'toxcast', 'muv'

  ]



metrics = {

    'qm7': [[dc.metrics.Metric(dc.metrics.mean_absolute_error, np.mean, mode='regression')], False],

    'qm7b': [[dc.metrics.Metric(dc.metrics.mean_absolute_error, np.mean, mode='regression')], False],

    'qm8': [[dc.metrics.Metric(dc.metrics.mean_absolute_error, np.mean, mode='regression')], False],

    'qm9': [[dc.metrics.Metric(dc.metrics.mean_absolute_error, np.mean, mode='regression')], False],

    'delaney': [[dc.metrics.Metric(dc.metrics.rms_score, np.mean, mode='regression')], False],

    'sampl': [[dc.metrics.Metric(dc.metrics.rms_score, np.mean, mode='regression')], False],

    'lipo': [[dc.metrics.Metric(dc.metrics.rms_score, np.mean, mode='regression')], False],

    'pdbbind': [[dc.metrics.Metric(dc.metrics.rms_score, np.mean, mode='regression')], False],

    'pcba': [[dc.metrics.Metric(dc.metrics.prc_auc_score, np.mean, mode='classification')], True],

    'muv': [[dc.metrics.Metric(dc.metrics.prc_auc_score, np.mean, mode='classification')], True],

    'hiv': [[dc.metrics.Metric(dc.metrics.roc_auc_score, np.mean, mode='classification')], True],

    'tox21': [[dc.metrics.Metric(dc.metrics.roc_auc_score, np.mean, mode='classification')], True],

    'toxcast': [[dc.metrics.Metric(dc.metrics.roc_auc_score, np.mean, mode='classification')], True],

    'sider': [[dc.metrics.Metric(dc.metrics.roc_auc_score, np.mean, mode='classification')], True],

    'clintox': [[dc.metrics.Metric(dc.metrics.roc_auc_score, np.mean, mode='classification')], True],

    'bace_c': [[dc.metrics.Metric(dc.metrics.roc_auc_score, np.mean, mode='classification')], True],

    'bbbp': [[dc.metrics.Metric(dc.metrics.roc_auc_score, np.mean, mode='classification')], True]

    }

for dataset in datasets:

  for split in splitters:

    for model in models:

      np.random.seed(seed)

      dc.molnet.run_benchmark(

          [dataset], str(model), split=split, metric=metrics[dataset][0],

          direction=metrics[dataset][1], hyper_param_search=True, max_iter=20, test=test, seed=seed)

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