update plotting script

        else:

          shard_batch_size = batch_size

        num_local_batches = math.ceil(n_shard_samples / shard_batch_size)

        if n_shard_samples == 0:

          cur_shard += 1

          if batch_size is None:

            cur_global_batch += 1

          continue

        num_local_batches = math.ceil(n_shard_samples / shard_batch_size)

        if not deterministic:

          sample_perm = np.random.permutation(n_shard_samples)

        else:

import csv

import os

import numpy as np

import matplotlib.pyplot as plt

import pickle

TODO = {

  ('tox21', 'random'): ['weave', 'graphconv', 'tf', 'tf_robust', 'irv', 'xgb', 'logreg', 'textcnn'],

  ('clintox', 'random'): ['weave', 'graphconv', 'tf', 'tf_robust', 'irv', 'xgb', 'logreg', 'textcnn'],

  ('sider', 'random'): ['weave', 'graphconv', 'tf', 'tf_robust', 'irv', 'xgb', 'logreg', 'textcnn'], 

  ('bbbp', 'scaffold'): ['weave', 'graphconv', 'tf', 'irv', 'xgb', 'logreg', 'textcnn'], 

  ('bace_c', 'scaffold'): ['weave', 'graphconv', 'tf', 'irv', 'xgb', 'logreg', 'textcnn'], 

  ('hiv', 'scaffold'): ['weave', 'graphconv', 'tf', 'irv', 'xgb', 'logreg', 'textcnn'], 

  ('muv', 'random'): ['graphconv', 'tf', 'tf_robust', 'irv', 'xgb', 'logreg'], 

  ('delaney', 'random'): ['weave_regression', 'graphconvreg', 'tf_regression', 'xgb_regression', 'krr', 'textcnn_regression', 'dag_regression', 'mpnn'],

  ('sampl', 'random'): ['weave_regression', 'graphconvreg', 'tf_regression', 'xgb_regression', 'krr', 'textcnn_regression', 'dag_regression', 'mpnn'],

  ('lipo', 'random'): ['weave_regression', 'graphconvreg', 'tf_regression', 'xgb_regression', 'krr', 'textcnn_regression', 'dag_regression', 'mpnn'],

  ('qm7', 'stratified'): ['dtnn', 'graphconvreg', 'tf_regression_ft', 'krr_ft'],

  ('qm7b', 'random'): ['dtnn', 'tf_regression_ft', 'krr_ft'],

  ('qm8', 'random'): ['dtnn', 'graphconvreg', 'mpnn', 'tf_regression', 'tf_regression_ft'],

}

ORDER = [

  'logreg', 'rf', 'rf_regression', 'xgb', 'xgb_regression', 'kernelsvm', 'krr', 

  'krr_ft', 'tf', 'tf_regression', 'tf_regression_ft', 'tf_robust', 'irv', 

  'textcnn', 'textcnn_regression', 'graphconv', 'graphconvreg', 'dag', 

  'dag_regression', 'ani', 'weave', 'weave_regression', 'dtnn', 'mpnn']

COLOR = {

  'logreg': '#3F3F3F', 

  'rf': '#67AD4F', 

  'rf_regression': '#67AD4F', 

  'xgb': '#0E766C', 

  'xgb_regression': '#0E766C', 

  'kernelsvm': '#FC926B', 

  'krr': '#FC926B', 

  'krr_ft': '#5A372A', 

  'tf': '#2B6596', 

  'tf_regression': '#2B6596', 

  'tf_regression_ft': '#162939', 

  'tf_robust': '#775183', 

  'irv': '#D9D9D9', 

  'graphconv': '#A4D192',

  'graphconvreg': '#A4D192', 

  'dag': '#D06329', 

  'dag_regression': '#D06329', 

  'ani': '#D9D9D9', 

  'weave': '#8196AE',

  'weave_regression': '#8196AE', 

  'textcnn': '#811B18', 

  'textcnn_regression': '#811B18', 

  'dtnn': '#D06329', 

  'mpnn': '#7B0A48'

}

TODO_list = set()

for key in TODO.keys():

  for val in TODO[key]:

    TODO_list.add((key[0], key[1], val))

def read_results(path):

  Results = set()

  with open(path, 'r') as f:

    reader = csv.reader(f)

    for line in reader:

      Results.add((line[0], line[1], line[3]))

  return Results

def run_benchmark(path):

  finished = read_results(path)

  while len(TODO_list - finished) > 0:

    todo = TODO_list - finished

    for p in todo:

      os.system('python benchmark.py -seed 123 -d '+p[0]+' -s '+p[1]+' -m '+p[2])

def plot(dataset, split, path, out_path):

  if dataset in [

      'bace_c', 'bbbp', 'clintox', 'hiv', 'muv', 'pcba', 'pcba_146',

      'pcba_2475', 'sider', 'tox21', 'toxcast'

  ]:

    mode = 'classification'

  else:

    mode = 'regression'

  data = {}

  with open(path, 'r') as f:

    reader = csv.reader(f)

    for line in reader:

      if line[0] == dataset and line[1] == split:

        data[line[3]] = line[8]

  labels = []

  values = []

  colors = []

  for model in ORDER:

    if model in data.keys():

      labels.append(model)

      colors.append(COLOR[model])

      values.append(float(data[model]))

  y_pos = np.arange(len(labels))

  plt.rcdefaults()

  fig, ax = plt.subplots()

  ax.barh(y_pos, values, align='center', color='green')

  ax.set_yticks(y_pos)

  ax.set_yticklabels(labels)

  ax.invert_yaxis()

  if mode == 'regression':

    ax.set_xlabel('R square')

    ax.set_xlim(left=0., right=1.)

  else:

    ax.set_xlabel('ROC-AUC')

    ax.set_xlim(left=0.4, right=1.)

  ax.set_title("Performance on %s (%s split)" % (dataset, split))

  for i in range(len(colors)):

    ax.get_children()[i].set_color(colors[i])

    ax.text(values[i]-0.1, y_pos[i]+0.1, str("%.3f" % values[i]), color='white')

  fig.savefig(os.path.join(out_path, dataset+'_'+split+'.png'))

  plt.show()

if __name__ == '__main__':

  FILE = '/home/zqwu/deepchem/examples/results.csv'

  run_benchmark(FILE)

  for pair in TODO.keys():

    plot(pair[0], pair[1], FILE, os.environ['DEEPCHEM_DATA_DIR'])

 No newline at end of file

#!/usr/bin/env python2

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

"""

Created on Wed Jan 10 16:49:01 2018

@author: zqwu

"""

import csv

import matplotlib.pyplot as plt

import numpy as np

FILE = '/home/zqwu/deepchem/examples/results.csv'

path = FILE

dataset = 'delaney'

split = 'random'

ORDER = ['logreg', 'rf', 'rf_regression', 'xgb', 'xgb_regression', 

         'kernelsvm', 'krr', 'krr_ft', 'tf', 'tf_regression', 

         'tf_regression_ft', 'tf_robust', 'irv', 'graphconv',

         'graphconvreg', 'dag', 'dag_regression', 'ani', 'weave',

         'weave_regression', 'textcnn', 'textcnn_regression', 'dtnn', 'mpnn']

COLOR = {

  'logreg'

    }

def plot(dataset, split, path):

  data = {}

  with open(path, 'r') as f:

    reader = csv.reader(f)

    for line in reader:

      if line[0] == dataset and line[1] == split:

        data[line[3]] = line[8]

  labels = []

  values = []

  for model in ORDER:

    if model in data.keys():

      labels.append(model)

      values.append(float(data[model]))

  y_pos = np.arange(len(labels))

  plt.rcdefaults()

  fig, ax = plt.subplots()

  ax.barh(y_pos, values, align='center', color='green')

  ax.set_yticks(y_pos)

  ax.set_yticklabels(labels)

  ax.invert_yaxis()

  ax.set_xlabel('R square')

  ax.set_xlim(left=0., right=1.)

  plt.show()

 No newline at end of file