Changes (fe7bdb7b) · Commits · 钟慕尧 / deepchem

deepchem/hyper/base_classes.py

+6 −2

Original line number	Diff line number	Diff line
		@@ -53,9 +53,13 @@ class HyperparamOpt(object):
		Parameters
		----------
		params_dict: dict
		Dictionary mapping strings to Ints/Floats/Lists. Note that the
		Dictionary mapping strings to values. Note that the
		precise semantics of `params_dict` will change depending on the
		optimizer that you're using.
		optimizer that you're using. Depending on the type of
		hyperparameter optimization, these values can be
		ints/floats/strings/lists/etc. Read the documentation for the
		concrete hyperparameter optimization subclass you're using to
		learn more about what's expected.
		train_dataset: `dc.data.Dataset`
		dataset used for training
		valid_dataset: `dc.data.Dataset`

deepchem/hyper/gaussian_process.py

+29 −7

Original line number	Diff line number	Diff line
		@@ -18,7 +18,7 @@ def _convert_hyperparam_dict_to_filename(hyper_params):
		Parameters
		----------
		hyper_params: dict
		Maps string of hyperparameter name to int/float/list.
		Maps string of hyperparameter name to int/float.

		Returns
		-------
		@@ -32,8 +32,12 @@ def _convert_hyperparam_dict_to_filename(hyper_params):
		value = hyper_params[key]
		if isinstance(value, int):
		filename += "_%s" % str(value)
		else:
		elif isinstance(value, float):
		filename += "_%.2f" % value
		else:
		raise ValueError(
		"Hyperparameters to search must be specified as ints/floats since GaussianProcessHyperparamOpt searches over a range of numbers around the specified point."
		)
		return filename


		@@ -43,9 +47,10 @@ def compute_parameter_range(params_dict, search_range):
		Parameters
		----------
		params_dict: dict
		Dictionary mapping strings to Ints/Floats/Lists. For those
		parameters in which int/float is specified, an explicit list of
		parameters is computed with `search_range`.
		Dictionary mapping strings to Ints/Floats. An explicit list of
		parameters is computed with `search_range`. The optimization range
		computed is specified in the documentation for `search_range`
		below.
		search_range: int(float) (default 4)
		For int/float values in `params_dict`, computes optimization range
		on `[initial values / search_range, initial values *
		@@ -57,7 +62,9 @@ def compute_parameter_range(params_dict, search_range):
		Dictionary mapping hyperparameter names to tuples. Each tuple is
		of form `(value_type, value_range)` where `value_type` is a string
		that is either "int" or "cont" and `value_range` is a list of two
		elements of the form `[low, hi]`
		elements of the form `[low, hi]`. This format is expected by
		pyGPGO which `GaussianProcessHyperparamOpt` uses to perform
		optimization.
		"""
		# Range of optimization
		param_range = {}
		@@ -94,6 +101,20 @@ class GaussianProcessHyperparamOpt(HyperparamOpt):
		>>> import deepchem as dc
		>>> optimizer = dc.hyper.GaussianProcessHyperparamOpt(lambda p: dc.models.GraphConvModel(p))

		Here's a more sophisticated example that shows how to optimize only
		some parameters of a model

		>>> def model_builder(**model_params):
		... n_layers = model_params['layers']
		... layer_width = model_params['width']
		... dropout = model_params['dropout']
		... return dc.models.MultitaskClassifier(
		... n_tasks=5,
		... n_features=100,
		... layer_sizes=[layer_width]*n_layers,
		... dropouts=dropout
		... )
		>> optimizer = dc.hyper.GaussianProcessHyperparamOpt(model_builder)
		"""

		def hyperparam_search(self,
		@@ -116,7 +137,8 @@ class GaussianProcessHyperparamOpt(HyperparamOpt):
		values. The semantics of this list are different than for
		`GridHyperparamOpt`. `params_dict[hp]` must map to an int/float,
		which is used as the center of a search with radius
		`search_range`.
		`search_range` since pyGPGO can only optimize numerical
		hyperparameters.
		train_dataset: `dc.data.Dataset`
		dataset used for training
		valid_dataset: `dc.data.Dataset`

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