Merge pull request #796 from lilleswing/sklearn (93e1e761) · Commits · 钟慕尧 / deepchem

deepchem/metalearning/tests/test_maml.py

+3 −0

Original line number	Diff line number	Diff line
		from flaky import flaky

		import deepchem as dc
		from deepchem.models.tensorgraph.layers import Feature, Label, Dense, L2Loss
		import numpy as np
		@@ -7,6 +9,7 @@ import unittest

		class TestMAML(unittest.TestCase):

		@flaky
		def test_sine(self):
		"""Test meta-learning for sine function."""

deepchem/models/sklearn_models/init.py

+39 −6

Original line number	Diff line number	Diff line
		@@ -2,9 +2,11 @@
		Code for processing datasets using scikit-learn.
		"""
		import numpy as np
		from sklearn.cross_decomposition import PLSRegression
		from sklearn.ensemble import RandomForestClassifier
		from sklearn.ensemble import RandomForestRegressor
		from sklearn.linear_model import LogisticRegression
		from sklearn.gaussian_process import GaussianProcessRegressor
		from sklearn.linear_model import LogisticRegression, BayesianRidge
		from sklearn.linear_model import LinearRegression
		from sklearn.linear_model import RidgeCV
		from sklearn.linear_model import LassoCV
		@@ -14,11 +16,42 @@ from deepchem.models import Model
		from deepchem.utils.save import load_from_disk
		from deepchem.utils.save import save_to_disk

		NON_WEIGHTED_MODELS = [
		LogisticRegression, PLSRegression, GaussianProcessRegressor, ElasticNetCV,
		LassoCV, BayesianRidge
		]


		class SklearnModel(Model):
		"""
		Abstract base class for different ML models.
		"""

		def __init__(self,
		model_instance=None,
		model_dir=None,
		verbose=True,
		**kwargs):
		"""
		Parameters
		----------
		model_instance: sklearn model
		model_dir: str
		verbose: bool
		kwargs: dict
		kwargs['use_weights'] is a bool which determines if we pass weights into
		self.model_instance.fit()
		"""
		super(SklearnModel, self).__init__(model_instance, model_dir, verbose,
		**kwargs)
		if 'use_weights' in kwargs:
		self.use_weights = kwargs['use_weights']
		else:
		self.use_weights = True
		for model_instance in NON_WEIGHTED_MODELS:
		if isinstance(self.model_instance, model_instance):
		self.use_weights = False

		def fit(self, dataset, **kwargs):
		"""
		Fits SKLearn model to data.
		@@ -27,11 +60,10 @@ class SklearnModel(Model):
		y = np.squeeze(dataset.y)
		w = np.squeeze(dataset.w)
		# Logistic regression doesn't support weights
		if not isinstance(self.model_instance, LogisticRegression):
		if self.use_weights:
		self.model_instance.fit(X, y, w)
		else:
		return
		self.model_instance.fit(X, y)
		y_pred_raw = self.model_instance.predict(X)

		def predict_on_batch(self, X, pad_batch=False):
		"""
		@@ -73,7 +105,8 @@ class SklearnModel(Model):

		def reload(self):
		"""Loads sklearn model from joblib file on disk."""
		self.model_instance = load_from_disk(Model.get_model_filename(self.model_dir))
		self.model_instance = load_from_disk(
		Model.get_model_filename(self.model_dir))

		def get_num_tasks(self):
		"""Number of tasks for this model. Defaults to 1"""

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