Merge pull request #289 from rbharath/tf_simplification (dccc7555) · Commits · 钟慕尧 / deepchem

deepchem/hyper/init.py

+1 −1

Original line number	Diff line number	Diff line
		@@ -17,7 +17,7 @@ class HyperparamOpt(object):
		Provides simple hyperparameter search capabilities.
		"""

		def __init__(self, model_class, verbosity=None):
		def __init__(self, model_class, verbosity="high"):
		self.model_class = model_class
		assert verbosity in [None, "low", "high"]
		self.verbosity = verbosity

+2 −3

Original line number	Diff line number	Diff line
		@@ -165,9 +165,8 @@ class TestHyperparamOptAPI(unittest.TestCase):
		params_dict = {"layer_sizes": [(10,), (100,)]}

		def model_builder(model_params, model_dir):
		tensorflow_model = dc.models.TensorflowMultiTaskClassifier(
		return dc.models.TensorflowMultiTaskClassifier(
		len(tasks), n_features, model_dir, **model_params)
		return dc.models.TensorflowModel(tensorflow_model)
		optimizer = dc.hyper.HyperparamOpt(model_builder)
		best_model, best_hyperparams, all_results = optimizer.hyperparam_search(
		params_dict, train_dataset, valid_dataset, transformers, metric,

+8 −6

Original line number	Diff line number	Diff line
		@@ -104,7 +104,7 @@ class Metric(object):
		"""Wrapper class for computing user-defined metrics."""

		def __init__(self, metric, task_averager=None, name=None, threshold=None,
		verbosity=None, mode=None, compute_energy_metric=False):
		verbosity="high", mode=None, compute_energy_metric=False):
		"""
		Args:
		metric: function that takes args y_true, y_pred (in that order) and
		@@ -127,10 +127,12 @@ class Metric(object):
		self.verbosity = verbosity
		self.threshold = threshold
		if mode is None:
		if self.metric.__name__ in ["roc_auc_score", "matthews_corrcoef", "recall_score",
		"accuracy_score", "kappa_score", "precision_score"]:
		if self.metric.__name__ in ["roc_auc_score", "matthews_corrcoef",
		"recall_score", "accuracy_score",
		"kappa_score", "precision_score"]:
		mode = "classification"
		elif self.metric.__name__ in ["pearson_r2_score", "r2_score", "mean_squared_error",
		elif self.metric.__name__ in ["pearson_r2_score", "r2_score",
		"mean_squared_error",
		"mean_absolute_error", "rms_score",
		"mae_score"]:
		mode = "regression"

+5 −6

Original line number	Diff line number	Diff line
		@@ -10,15 +10,14 @@ from deepchem.models.sklearn_models import SklearnModel
		from deepchem.models.keras_models import KerasModel
		from deepchem.models.tf_keras_models.multitask_classifier import MultitaskGraphClassifier
		from deepchem.models.tf_keras_models.support_classifier import SupportGraphClassifier
		from deepchem.models.tensorflow_models import TensorflowModel
		from deepchem.models.multitask import SingletaskToMultitask

		# TODO(rbharath): I'm not sure if these belong here or in deepchem.nn
		# The issue is that these are not valid deepchem models. The solution might be
		# to make inherit from Model class
		from deepchem.models.keras_models.fcnet import MultiTaskDNN
		from deepchem.models.tensorflow_models.fcnet import TensorflowMultiTaskRegressor
		from deepchem.models.tensorflow_models.fcnet import TensorflowMultiTaskClassifier
		from deepchem.models.tensorflow_models.robust_multitask import RobustMultitaskRegressor
		from deepchem.models.tensorflow_models.robust_multitask import RobustMultitaskClassifier
		from deepchem.models.tensorflow_models.lr import TensorflowLogisticRegression

		# TODO(rbharath): I'm not sure if this model should be exposed. Not in
		# benchmark suite for example.
		from deepchem.models.keras_models.fcnet import MultiTaskDNN

+2 −1

Original line number	Diff line number	Diff line
		@@ -162,7 +162,8 @@ class Model(object):
		"""
		y_preds = []
		n_tasks = self.get_num_tasks()
		for (X_batch, y_batch, w_batch, ids_batch) in dataset.iterbatches(
		ind = 0
		for (X_batch, _, _, ids_batch) in dataset.iterbatches(
		batch_size, deterministic=True):
		n_samples = len(X_batch)
		y_pred_batch = self.predict_on_batch(X_batch, pad_batch=pad_batches)