Moved back to eval_models. (c5778d0e) · Commits · 钟慕尧 / deepchem

deep_chem/models/deep.py

+19 −22

Original line number	Diff line number	Diff line
		@@ -16,7 +16,6 @@ from deep_chem.utils.preprocess import train_test_scaffold_split
		from deep_chem.utils.preprocess import dataset_to_numpy
		from deep_chem.utils.preprocess import to_one_hot
		from deep_chem.utils.evaluate import eval_model
		from deep_chem.utils.evaluate import size_eval_model
		from deep_chem.utils.evaluate import compute_r2_scores
		from deep_chem.utils.evaluate import compute_rms_scores
		from deep_chem.utils.evaluate import compute_roc_auc_scores
		@@ -169,28 +168,26 @@ def fit_singletask_mlp(paths, task_types, task_transforms,
		singletasks[target])
		model = train_multitask_model(X_train, y_train, W_train,
		{target: task_types[target]}, **training_params)
		#results = eval_model(test, model, {target: task_types[target]},
		# # We run singletask models as special cases of
		# # multitask.
		# modeltype="keras_multitask")
		results = size_eval_model(test, model, {target: task_types[target]},
		results = eval_model(test, model, {target: task_types[target]},
		# We run singletask models as special cases of
		# multitask.
		modeltype="keras_multitask")
		# target_aucs = compute_roc_auc_scores(results, task_types)
		# target_r2s = compute_r2_scores(results, task_types)
		# target_rms = compute_rms_scores(results, task_types)
		target_aucs = compute_roc_auc_scores(results, task_types)
		target_r2s = compute_r2_scores(results, task_types)
		target_rms = compute_rms_scores(results, task_types)

		# aucs.update(target_aucs)
		# r2s.update(target_r2s)
		# rms.update(target_rms)
		#if aucs:
		# print aucs
		# print "Mean AUC: %f" % np.mean(np.array(aucs.values()))
		#if r2s:
		# print r2s
		# print "Mean R^2: %f" % np.mean(np.array(r2s.values()))
		#if rms:
		# print rms
		# print "Mean RMS: %f" % np.mean(np.array(rms.values()))
		aucs.update(target_aucs)
		r2s.update(target_r2s)
		rms.update(target_rms)
		if aucs:
		print aucs
		print "Mean AUC: %f" % np.mean(np.array(aucs.values()))
		if r2s:
		print r2s
		print "Mean R^2: %f" % np.mean(np.array(r2s.values()))
		if rms:
		print rms
		print "Mean RMS: %f" % np.mean(np.array(rms.values()))

		def train_multitask_model(X, y, W, task_types,
		learning_rate=0.01, decay=1e-6, momentum=0.9, nesterov=True, activation="relu",

deep_chem/models/standard.py

+17 −20

Original line number	Diff line number	Diff line
		@@ -9,7 +9,6 @@ from deep_chem.utils.preprocess import train_test_random_split
		from deep_chem.utils.preprocess import train_test_scaffold_split
		from deep_chem.utils.preprocess import dataset_to_numpy
		from deep_chem.utils.evaluate import eval_model
		from deep_chem.utils.evaluate import size_eval_model
		from deep_chem.utils.evaluate import compute_r2_scores
		from deep_chem.utils.evaluate import compute_rms_scores
		from deep_chem.utils.evaluate import compute_roc_auc_scores
		@@ -83,27 +82,25 @@ def fit_singletask_models(paths, modeltype, task_types, task_transforms,
		else:
		raise ValueError("Invalid model type provided.")
		model.fit(X_train, y_train.ravel())
		#results = eval_model(test, model, {target: task_types[target]},
		# modeltype="sklearn")
		results = size_eval_model(test, model, {target: task_types[target]},
		results = eval_model(test, model, {target: task_types[target]},
		modeltype="sklearn")

		# target_aucs = compute_roc_auc_scores(results, task_types)
		# target_r2s = compute_r2_scores(results, task_types)
		# target_rms = compute_rms_scores(results, task_types)
		#
		# aucs.update(target_aucs)
		# r2s.update(target_r2s)
		# rms.update(target_rms)
		#if aucs:
		# print results_to_csv(aucs)
		# print "Mean AUC: %f" % np.mean(np.array(aucs.values()))
		#if r2s:
		# print results_to_csv(r2s)
		# print "Mean R^2: %f" % np.mean(np.array(r2s.values()))
		#if rms:
		# print results_to_csv(rms)
		# print "Mean RMS: %f" % np.mean(np.array(rms.values()))
		target_aucs = compute_roc_auc_scores(results, task_types)
		target_r2s = compute_r2_scores(results, task_types)
		target_rms = compute_rms_scores(results, task_types)

		aucs.update(target_aucs)
		r2s.update(target_r2s)
		rms.update(target_rms)
		if aucs:
		print results_to_csv(aucs)
		print "Mean AUC: %f" % np.mean(np.array(aucs.values()))
		if r2s:
		print results_to_csv(r2s)
		print "Mean R^2: %f" % np.mean(np.array(r2s.values()))
		if rms:
		print results_to_csv(rms)
		print "Mean RMS: %f" % np.mean(np.array(rms.values()))


		def fit_multitask_rf(dataset, splittype="random"):

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