Merge branch 'xgboost-again' into tg-atomconv-cr-out

    tg.set_loss(loss)

    tg.fit(dataset, nb_epoch=10)

    prediction = np.squeeze(tg.predict_proba_on_batch(X))

    assert_true(np.all(np.isclose(prediction, y, atol=0.2)))

    assert_true(np.all(np.isclose(prediction, y, atol=0.4)))

  def test_multi_task_classifier(self):

    n_data_points = 20

    # Eval model on train

    scores = model.evaluate(dataset, [regression_metric])

    assert scores[regression_metric.name] > .9

    assert scores[regression_metric.name] > .8

  def test_siamese_singletask_classification_overfit(self):

    """Test siamese singletask model overfits tiny data."""

import numpy as np

import tensorflow as tf

import deepchem

import xgboost

from deepchem.utils.dependencies import xgboost

from deepchem.molnet.preset_hyper_parameters import hps

from sklearn.ensemble import RandomForestClassifier

from sklearn.ensemble import RandomForestRegressor