fix common env tests (9340cdfd) · Commits · 钟慕尧 / deepchem

deepchem/data/tests/test_datasets.py

+1 −0

Original line number	Diff line number	Diff line
		@@ -676,6 +676,7 @@ def test_merge():
		assert len(new_data.tasks) == len(datasets[0].tasks)


		@pytest.mark.tensorflow
		def test_make_tf_dataset():
		"""Test creating a Tensorflow Iterator from a Dataset."""
		X = np.random.random((100, 5))

+3 −0

Original line number	Diff line number	Diff line
		@@ -2,6 +2,8 @@
		import os
		import unittest
		from unittest import TestCase
		import pytest

		try:
		from transformers import RobertaForMaskedLM
		from deepchem.feat.smiles_tokenizer import SmilesTokenizer
		@@ -14,6 +16,7 @@ class TestSmilesTokenizer(TestCase):
		"""Tests the SmilesTokenizer to load the USPTO vocab file and a ChemBERTa Masked LM model with pre-trained weights.."""

		@unittest.skipIf(not has_transformers, 'transformers are not installed')
		@pytest.mark.torch
		def test_tokenize(self):
		current_dir = os.path.dirname(os.path.realpath(__file__))
		vocab_path = os.path.join(current_dir, 'data', 'vocab.txt')

+4 −0

Original line number	Diff line number	Diff line
		@@ -10,6 +10,7 @@ import sklearn
		import sklearn.ensemble
		import deepchem as dc
		import unittest
		import pytest
		import tempfile
		from flaky import flaky

		@@ -93,6 +94,7 @@ class TestGaussianHyperparamOpt(unittest.TestCase):
		assert valid_score["pearson_r2_score"] > 0

		@flaky
		@pytest.mark.torch
		def test_multitask_example(self):
		"""Test a simple example of optimizing a multitask model with a gaussian process search."""
		# Generate dummy dataset
		@@ -128,6 +130,7 @@ class TestGaussianHyperparamOpt(unittest.TestCase):
		assert valid_score["mean-mean_squared_error"] > 0

		@flaky
		@pytest.mark.torch
		def test_multitask_example_different_search_range(self):
		"""Test a simple example of optimizing a multitask model with a gaussian process search with per-parameter search range."""
		# Generate dummy dataset
		@@ -174,6 +177,7 @@ class TestGaussianHyperparamOpt(unittest.TestCase):
		assert valid_score["mean-mean_squared_error"] > 0

		@flaky
		@pytest.mark.torch
		def test_multitask_example_nb_epoch(self):
		"""Test a simple example of optimizing a multitask model with a gaussian process search with a different number of training epochs."""
		# Generate dummy dataset

+4 −0

Original line number	Diff line number	Diff line
		@@ -4,6 +4,7 @@ Tests for hyperparam optimization.
		import unittest
		import tempfile
		import numpy as np
		import pytest
		import deepchem as dc
		import sklearn
		import sklearn.ensemble
		@@ -84,6 +85,7 @@ class TestGridHyperparamOpt(unittest.TestCase):
		assert valid_score["pearson_r2_score"] == max(all_results.values())
		assert valid_score["pearson_r2_score"] > 0

		@pytest.mark.torch
		def test_multitask_example(self):
		"""Test a simple example of optimizing a multitask model with a grid search."""
		# Generate dummy dataset
		@@ -117,6 +119,7 @@ class TestGridHyperparamOpt(unittest.TestCase):
		assert valid_score["mean-mean_squared_error"] == min(all_results.values())
		assert valid_score["mean-mean_squared_error"] > 0

		@pytest.mark.torch
		def test_multitask_example_multiple_params(self):
		"""Test a simple example of optimizing a multitask model with a grid search with multiple parameters to optimize."""
		# Generate dummy dataset
		@@ -160,6 +163,7 @@ class TestGridHyperparamOpt(unittest.TestCase):
		assert valid_score["mean-mean_squared_error"] == min(all_results.values())
		assert valid_score["mean-mean_squared_error"] > 0

		@pytest.mark.torch
		def test_multitask_nb_epoch(self):
		"""Test a simple example of optimizing a multitask model with a grid search with a different number of training epochs."""
		# Generate dummy dataset

+1 −1

Original line number	Diff line number	Diff line
		@@ -3,12 +3,12 @@ Gathers all models in one place for convenient imports
		"""
		# flake8: noqa
		from deepchem.models.models import Model
		from deepchem.models.multitask import SingletaskToMultitask
		from deepchem.models.wandblogger import WandbLogger

		# Tensorflow Depedency Models
		try:
		from deepchem.models.keras_model import KerasModel
		from deepchem.models.multitask import SingletaskToMultitask
		from deepchem.models.callbacks import ValidationCallback

		from deepchem.models.IRV import MultitaskIRVClassifier