:ok_hand: fix for reveiw (e3c96d90) · Commits · 钟慕尧 / deepchem

deepchem/models/sklearn_models/sklearn_model.py

+1 −1

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		@@ -35,7 +35,7 @@ class SklearnModel(Model):
		perhaps you want to use the hyperparameter tuning capabilities in
		`dc.hyper`. The `SklearnModel` class provides a wrapper around scikit-learn
		models that allows scikit-learn models to be trained on `Dataset` objects
		and evaluated with the same metrics as other DeepChem models.`
		and evaluated with the same metrics as other DeepChem models.

		Notes
		-----

+3 −3

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		@@ -93,7 +93,7 @@ WeaveFeaturizer
		:members:

		MACCSKeysFingerprint
		^^^^^^^^^^^^^^^^^^^
		^^^^^^^^^^^^^^^^^^^^

		.. autoclass:: deepchem.feat.MACCSKeysFingerprint
		:members:
		@@ -105,13 +105,13 @@ CircularFingerprint
		:members:

		PubChemFingerprint
		^^^^^^^^^^^^^^^^^^^
		^^^^^^^^^^^^^^^^^^

		.. autoclass:: deepchem.feat.PubChemFingerprint
		:members:

		Mol2VecFingerprint
		^^^^^^^^^^^^^^^^^^^
		^^^^^^^^^^^^^^^^^^

		.. autoclass:: deepchem.feat.Mol2VecFingerprint
		:members:

+0 −6

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		@@ -196,9 +196,6 @@ Losses
		.. autoclass:: deepchem.models.losses.SparseSoftmaxCrossEntropy
		:members:

		.. autoclass:: deepchem.models.losses.SparseSoftmaxCrossEntropy
		:members:

		.. autoclass:: deepchem.models.losses.VAE_ELBO
		:members:

		@@ -238,9 +235,6 @@ Optimizers
		.. autoclass:: deepchem.models.optimizers.LinearCosineDecay
		:members:

		.. autoclass:: deepchem.models.optimizers.LinearCosineDecay
		:members:


		Keras Models
		============

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		@@ -46,7 +46,7 @@ extensions = [
		autodoc_default_options = {
		'member-order': 'bysource',
		'special-members': True,
		'exclude-members': '__repr__, __str__, __weakref__',
		'exclude-members': '__repr__, __str__, __weakref__, __hash__, __eq__',
		}

		# How to represents typehints.

+7 −6

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		@@ -75,6 +75,7 @@ your model can learn this datasest with high accuracy. For regression
		and classification task, this should correspond to low training error
		on the dataset. For generative tasks, this should correspond to low
		training loss on the dataset.

		- Reloading test: Check that a trained model can be saved to disk and
		reloaded correctly. This should involve checking that predictions from
		the saved and reloaded models matching exactly.