Merge pull request #1305 from rbharath/qm7sklearn

# IDE files

.idea

*.iml

# Vim swap

*.swp

from __future__ import division

from __future__ import unicode_literals

import os

import deepchem as dc

import numpy as np

import shutil

from sklearn.kernel_ridge import KernelRidge

np.random.seed(123)

import tensorflow as tf

tf.set_random_seed(123)

import deepchem as dc

from sklearn.kernel_ridge import KernelRidge

base_dir = "/tmp/gdb7_sklearn"

data_dir = os.path.join(base_dir, "dataset")

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

train_dir = os.path.join(base_dir, "train")

test_dir = os.path.join(base_dir, "test")

if os.path.exists(base_dir):

  shutil.rmtree(base_dir)

os.makedirs(base_dir)

max_num_atoms = 23

featurizers = dc.feat.CoulombMatrixEig(max_num_atoms)

input_file = "gdb7.sdf"

tasks = ["u0_atom"]

smiles_field = "smiles"

mol_field = "mol"

tasks, datasets, transformers = dc.molnet.load_qm7(

    featurizer='CoulombMatrix', split='stratified', move_mean=False)

featurizer = dc.data.SDFLoader(tasks, smiles_field=smiles_field, mol_field=mol_field, featurizer=featurizers)

dataset = featurizer.featurize(input_file, data_dir)

random_splitter = dc.splits.RandomSplitter()

train_dataset, test_dataset = random_splitter.train_test_split(dataset, train_dir, test_dir, frac_train=0.8)

#transformers = [dc.trans.NormalizationTransformer(transform_X=True, dataset=train_dataset), dc.trans.NormalizationTransformer(transform_y=True, dataset=train_dataset)]

transformers = [dc.trans.NormalizationTransformer(transform_y=True, dataset=train_dataset)]

train, valid, test = datasets

for transformer in transformers:

    train_dataset = transformer.transform(train_dataset)

for transformer in transformers:

    test_dataset = transformer.transform(test_dataset)

regression_metric = dc.metrics.Metric(

    dc.metrics.mean_absolute_error, mode="regression")

regression_metric = dc.metrics.Metric(dc.metrics.mean_absolute_error, mode="regression")

def model_builder(model_dir):

  sklearn_model = KernelRidge(

      kernel="rbf", alpha=5e-4, gamma=0.008)

  sklearn_model = KernelRidge(kernel="rbf", alpha=5e-4, gamma=0.008)

  return dc.models.SklearnModel(sklearn_model, model_dir)

model = dc.models.SingletaskToMultitask(tasks, model_builder, model_dir)

model = dc.models.SingletaskToMultitask(tasks, model_builder)

# Fit trained model

model.fit(train_dataset)

model.fit(train)

model.save()

train_evaluator = dc.utils.evaluate.Evaluator(model, train_dataset, transformers)

train_evaluator = dc.utils.evaluate.Evaluator(model, train, transformers)

train_scores = train_evaluator.compute_model_performance([regression_metric])

print("Train scores [kcal/mol]")

print(train_scores)

test_evaluator = dc.utils.evaluate.Evaluator(model, test_dataset, transformers)

test_evaluator = dc.utils.evaluate.Evaluator(model, test, transformers)

test_scores = test_evaluator.compute_model_performance([regression_metric])

print("Validation scores [kcal/mol]")