More fixes to examples

metric = dc.metrics.Metric(

    dc.metrics.roc_auc_score, np.mean, mode="classification")

rate = dc.models.tensorgraph.optimizers.ExponentialDecay(0.001, 0.8, 1000)

rate = dc.models.optimizers.ExponentialDecay(0.001, 0.8, 1000)

model = dc.models.MultitaskClassifier(

    len(muv_tasks),

    n_features=1024,

import os

import numpy as np

import shutil

from nci_datasets import load_nci

from deepchem.molnet import load_nci

from sklearn.linear_model import LogisticRegression

from sklearn.ensemble import RandomForestRegressor

from deepchem.data import Dataset

# Set some global variables up top

verbosity = "high"

base_dir = "/tmp/nci_rf"

model_dir = os.path.join(base_dir, "model")

if os.path.exists(base_dir):

  shutil.rmtree(base_dir)

os.makedirs(base_dir)

nci_tasks, nci_dataset, transformers = load_nci(

    base_dir)

nci_tasks, nci_dataset, transformers = load_nci()

(train_dataset, valid_dataset, test_dataset) = nci_dataset

classification_metric = Metric(metrics.roc_auc_score, np.mean,

                               verbosity=verbosity,

                               mode="classification")

def model_builder(model_dir):

  sklearn_model = RandomForestRegressor(n_estimators=500)

  return SklearnModel(sklearn_model, model_dir)

model = SingletaskToMultitask(nci_tasks, model_builder, model_dir)

model = SingletaskToMultitask(nci_tasks, model_builder)

# Fit trained model

model.fit(train_dataset)

model.save()

train_evaluator = Evaluator(model, train_dataset, transformers, verbosity=verbosity)

train_scores = train_evaluator.compute_model_performance([classification_metric])

# Fit trained model

model.fit(train_dataset)

model.save()

train_evaluator = Evaluator(model, train_dataset, transformers)

train_scores = train_evaluator.compute_model_performance([metric])

frag1_num_atoms = 70  # for ligand atoms

frag2_num_atoms = 24000  # for protein atoms

complex_num_atoms = frag1_num_atoms + frag2_num_atoms

atomic_convnet = atomic_conv.AtomicConvModel(

    batch_size=batch_size,

model = dc.models.AtomicConvModel(

    frag1_num_atoms=frag1_num_atoms,

    frag2_num_atoms=frag2_num_atoms,

    complex_num_atoms=complex_num_atoms)

metric = dc.metrics.Metric(dc.metrics.pearson_r2_score)

current_dir = os.path.dirname(os.path.realpath(__file__))

model_dir = os.path.join(current_dir, "%s_%s_RF" % (split, subset))

sklearn_model = RandomForestRegressor(n_estimators=500)

model = dc.models.SklearnModel(sklearn_model, model_dir=model_dir)

model = dc.models.SklearnModel(sklearn_model)

# Fit trained model

print("Fitting model on train dataset")

model.fit(train_dataset)

model.save()

print("Evaluating model")

train_scores = model.evaluate(train_dataset, [metric], transformers)

print("Validation scores")

print(valid_scores)