changes

import sys

import numpy as np

import pandas as pd

import deepchem as dc

from sklearn.ensemble import RandomForestClassifier

from deepchem.models.multitask import SingletaskToMultitask

from deepchem import metrics

from deepchem.metrics import Metric

from deepchem.models.sklearn_models import SklearnModel

from deepchem.splits import StratifiedSplitter, RandomSplitter

#from deepchem.splits import StratifiedSplitter, RandomSplitter

#from sweetlead_datasets import load_sweet

#sys.path.append('./../toxcast')

#

#base_tox_data_dir = "/home/apappu/deepchem-models/toxcast_models/toxcast/toxcast_data"

tox_tasks, tox_dataset, tox_transformers = dc.molnet.load_toxcast()

tox_tasks, (tox_train, tox_valid, tox_test), tox_transformers = dc.molnet.load_toxcast()

#removes directory if present -- warning

#base_tox_dir = "/home/apappu/deepchem-models/toxcast_models/toxcast/toxcast_analysis"

#tox_splitter = StratifiedSplitter()

#default split is 80-10-10 train-valid-test split

tox_train_dataset, tox_valid_dataset, tox_test_dataset = tox_splitter.train_valid_test_split(

  tox_dataset, tox_train_dir, tox_valid_dir, tox_test_dir)

#tox_train_dataset, tox_valid_dataset, tox_test_dataset = tox_splitter.train_valid_test_split(

#  tox_dataset, tox_train_dir, tox_valid_dir, tox_test_dir)

## Fit Logistic Regression models

#tox_task_types = {task: "classification" for task in tox_tasks}

classification_metric = Metric(metrics.roc_auc_score, np.mean, mode="classification")

params_dict = {

    "batch_size": None,

    "data_shape": tox_train_dataset.get_data_shape(),

}

def model_builder(tasks, task_types, model_params, model_dir, verbosity=None):

  return SklearnModel(tasks, task_types, model_params, model_dir,

                      model_instance=RandomForestClassifier(

#params_dict = {

#    "batch_size": None,

#    "data_shape": tox_train_dataset.get_data_shape(),

#}

#def model_builder(tasks, task_types, model_params, model_dir, verbosity=None):

#  return SklearnModel(tasks, task_types, model_params, model_dir,

#                      model_instance=RandomForestClassifier(

#                          class_weight="balanced",

#                          n_estimators=500,

#                          n_jobs=-1),

#                      verbosity=verbosity)

def model_builder(model_dir):

  sklearn_model = RandomForestClassifier(

                          class_weight="balanced",

                          n_estimators=500,

                          n_jobs=-1),

                      verbosity=verbosity)

tox_model = SingletaskToMultitask(tox_tasks, tox_task_types, params_dict, tox_model_dir,

                              model_builder, verbosity=verbosity)

                          n_jobs=-1)

  return dc.models.SklearnModel(sklearn_model, model_dir)

tox_model = SingletaskToMultitask(tox_tasks, model_builder)

tox_model.reload()

"""