a092a5612c48a8a61c7d3deadf3749338ea4b64d,tests/test_regression.py,,check_linear_models,#,160

Before Change

    // catastrophic happened. Note though that we cannot
    // test feature weights if we are using feature hashing
    // since model_params is not defined with a featurehasher.
    if not use_feature_hashing:

        // get the weights for this trained model
        learned_weights = learner.model_params[0]

        for feature_name in learned_weights:
            learned_w = math.ceil(learned_weights[feature_name])
            given_w = math.ceil(weightdict[feature_name])
            eq_(learned_w, given_w)

    // now generate the predictions on the test FeatureSet
    predictions = learner.predict(test_fs)

    // now make sure that the predictions are close to
    // the actual test FeatureSet labels that we generated

After Change

    // get the weights for this trained model
    learned_weights = learner.model_params[0]

    for feature_name in learned_weights:
        learned_w_ceil = math.ceil(learned_weights[feature_name])
        given_w_ceil = math.ceil(weightdict[feature_name])
        learned_w_round = round(learned_weights[feature_name], 0)
        given_w_round = round(weightdict[feature_name], 0)
        ceil_equal = learned_w_ceil == given_w_ceil
        round_equal = learned_w_round == given_w_round
        either_equal = ceil_equal or round_equal
        assert either_equal

    // now generate the predictions on the test FeatureSet
    predictions = learner.predict(test_fs)

    // now make sure that the predictions are close to
    // the actual test FeatureSet labels that we generated

Instances