Python sklearn.metrics.get_scorer() Examples

def test_regression_scorers():
    # Test regression scorers.
    diabetes = load_diabetes()
    X, y = diabetes.data, diabetes.target
    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
    clf = Ridge()
    clf.fit(X_train, y_train)
    score1 = get_scorer('r2')(clf, X_test, y_test)
    score2 = r2_score(y_test, clf.predict(X_test))
    assert_almost_equal(score1, score2) 

def test_thresholded_scorers_multilabel_indicator_data():
    # Test that the scorer work with multilabel-indicator format
    # for multilabel and multi-output multi-class classifier
    X, y = make_multilabel_classification(allow_unlabeled=False,
                                          random_state=0)
    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)

    # Multi-output multi-class predict_proba
    clf = DecisionTreeClassifier()
    clf.fit(X_train, y_train)
    y_proba = clf.predict_proba(X_test)
    score1 = get_scorer('roc_auc')(clf, X_test, y_test)
    score2 = roc_auc_score(y_test, np.vstack([p[:, -1] for p in y_proba]).T)
    assert_almost_equal(score1, score2)

    # Multi-output multi-class decision_function
    # TODO Is there any yet?
    clf = DecisionTreeClassifier()
    clf.fit(X_train, y_train)
    clf._predict_proba = clf.predict_proba
    clf.predict_proba = None
    clf.decision_function = lambda X: [p[:, 1] for p in clf._predict_proba(X)]

    y_proba = clf.decision_function(X_test)
    score1 = get_scorer('roc_auc')(clf, X_test, y_test)
    score2 = roc_auc_score(y_test, np.vstack([p for p in y_proba]).T)
    assert_almost_equal(score1, score2)

    # Multilabel predict_proba
    clf = OneVsRestClassifier(DecisionTreeClassifier())
    clf.fit(X_train, y_train)
    score1 = get_scorer('roc_auc')(clf, X_test, y_test)
    score2 = roc_auc_score(y_test, clf.predict_proba(X_test))
    assert_almost_equal(score1, score2)

    # Multilabel decision function
    clf = OneVsRestClassifier(LinearSVC(random_state=0))
    clf.fit(X_train, y_train)
    score1 = get_scorer('roc_auc')(clf, X_test, y_test)
    score2 = roc_auc_score(y_test, clf.decision_function(X_test))
    assert_almost_equal(score1, score2) 

def test_supervised_cluster_scorers():
    # Test clustering scorers against gold standard labeling.
    X, y = make_blobs(random_state=0, centers=2)
    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
    km = KMeans(n_clusters=3)
    km.fit(X_train)
    for name in CLUSTER_SCORERS:
        score1 = get_scorer(name)(km, X_test, y_test)
        score2 = getattr(cluster_module, name)(y_test, km.predict(X_test))
        assert_almost_equal(score1, score2) 

def test(dataset_loader_test, model_persister,
         scoring=None, model_version=None):

    with timer(logger.info, "Loading data"):
        X, y = dataset_loader_test()

    with timer(logger.info, "Reading model"):
        model = model_persister.read(version=model_version)

    logger.info(
        'Loaded model version {}'.format(model.__metadata__['version']))

    if not (hasattr(model, 'score') or scoring is not None):
        raise ValueError(
            "Your model doesn't seem to implement a 'score' method.  You may "
            "want to define a 'scoring' option in the configuration."
            )

    with timer(logger.info, "Applying model"):
        scores = []
        if scoring is not None:
            if not isinstance(scoring, dict):
                scoring = {'score': scoring}
            for key, scorer in scoring.items():
                scorer = get_scorer(scorer)
                scores.append("{}: {}".format(key, scorer(model, X, y)))
        else:
            scores.append("score: {}".format(model.score(X, y)))

    logger.info("Score: {}.".format('\n       '.join(scores)))
    return scores 

def test_regression_scorers():
    # Test regression scorers.
    diabetes = load_diabetes()
    X, y = diabetes.data, diabetes.target
    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
    clf = Ridge()
    clf.fit(X_train, y_train)
    score1 = get_scorer('r2')(clf, X_test, y_test)
    score2 = r2_score(y_test, clf.predict(X_test))
    assert_almost_equal(score1, score2) 

def test_thresholded_scorers():
    # Test scorers that take thresholds.
    X, y = make_blobs(random_state=0, centers=2)
    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
    clf = LogisticRegression(random_state=0)
    clf.fit(X_train, y_train)
    score1 = get_scorer('roc_auc')(clf, X_test, y_test)
    score2 = roc_auc_score(y_test, clf.decision_function(X_test))
    score3 = roc_auc_score(y_test, clf.predict_proba(X_test)[:, 1])
    assert_almost_equal(score1, score2)
    assert_almost_equal(score1, score3)

    logscore = get_scorer('neg_log_loss')(clf, X_test, y_test)
    logloss = log_loss(y_test, clf.predict_proba(X_test))
    assert_almost_equal(-logscore, logloss)

    # same for an estimator without decision_function
    clf = DecisionTreeClassifier()
    clf.fit(X_train, y_train)
    score1 = get_scorer('roc_auc')(clf, X_test, y_test)
    score2 = roc_auc_score(y_test, clf.predict_proba(X_test)[:, 1])
    assert_almost_equal(score1, score2)

    # test with a regressor (no decision_function)
    reg = DecisionTreeRegressor()
    reg.fit(X_train, y_train)
    score1 = get_scorer('roc_auc')(reg, X_test, y_test)
    score2 = roc_auc_score(y_test, reg.predict(X_test))
    assert_almost_equal(score1, score2)

    # Test that an exception is raised on more than two classes
    X, y = make_blobs(random_state=0, centers=3)
    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
    clf.fit(X_train, y_train)
    assert_raises(ValueError, get_scorer('roc_auc'), clf, X_test, y_test) 

def test_thresholded_scorers_multilabel_indicator_data():
    # Test that the scorer work with multilabel-indicator format
    # for multilabel and multi-output multi-class classifier
    X, y = make_multilabel_classification(allow_unlabeled=False,
                                          random_state=0)
    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)

    # Multi-output multi-class predict_proba
    clf = DecisionTreeClassifier()
    clf.fit(X_train, y_train)
    y_proba = clf.predict_proba(X_test)
    score1 = get_scorer('roc_auc')(clf, X_test, y_test)
    score2 = roc_auc_score(y_test, np.vstack(p[:, -1] for p in y_proba).T)
    assert_almost_equal(score1, score2)

    # Multi-output multi-class decision_function
    # TODO Is there any yet?
    clf = DecisionTreeClassifier()
    clf.fit(X_train, y_train)
    clf._predict_proba = clf.predict_proba
    clf.predict_proba = None
    clf.decision_function = lambda X: [p[:, 1] for p in clf._predict_proba(X)]

    y_proba = clf.decision_function(X_test)
    score1 = get_scorer('roc_auc')(clf, X_test, y_test)
    score2 = roc_auc_score(y_test, np.vstack(p for p in y_proba).T)
    assert_almost_equal(score1, score2)

    # Multilabel predict_proba
    clf = OneVsRestClassifier(DecisionTreeClassifier())
    clf.fit(X_train, y_train)
    score1 = get_scorer('roc_auc')(clf, X_test, y_test)
    score2 = roc_auc_score(y_test, clf.predict_proba(X_test))
    assert_almost_equal(score1, score2)

    # Multilabel decision function
    clf = OneVsRestClassifier(LinearSVC(random_state=0))
    clf.fit(X_train, y_train)
    score1 = get_scorer('roc_auc')(clf, X_test, y_test)
    score2 = roc_auc_score(y_test, clf.decision_function(X_test))
    assert_almost_equal(score1, score2) 

def test_supervised_cluster_scorers():
    # Test clustering scorers against gold standard labeling.
    X, y = make_blobs(random_state=0, centers=2)
    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
    km = KMeans(n_clusters=3)
    km.fit(X_train)
    for name in CLUSTER_SCORERS:
        score1 = get_scorer(name)(km, X_test, y_test)
        score2 = getattr(cluster_module, name)(y_test, km.predict(X_test))
        assert_almost_equal(score1, score2) 

def test_classification_scores():
    # Test classification scorers.
    X, y = make_blobs(random_state=0, centers=2)
    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
    clf = LinearSVC(random_state=0)
    clf.fit(X_train, y_train)

    for prefix, metric in [('f1', f1_score), ('precision', precision_score),
                           ('recall', recall_score),
                           ('jaccard', jaccard_score)]:

        score1 = get_scorer('%s_weighted' % prefix)(clf, X_test, y_test)
        score2 = metric(y_test, clf.predict(X_test), pos_label=None,
                        average='weighted')
        assert_almost_equal(score1, score2)

        score1 = get_scorer('%s_macro' % prefix)(clf, X_test, y_test)
        score2 = metric(y_test, clf.predict(X_test), pos_label=None,
                        average='macro')
        assert_almost_equal(score1, score2)

        score1 = get_scorer('%s_micro' % prefix)(clf, X_test, y_test)
        score2 = metric(y_test, clf.predict(X_test), pos_label=None,
                        average='micro')
        assert_almost_equal(score1, score2)

        score1 = get_scorer('%s' % prefix)(clf, X_test, y_test)
        score2 = metric(y_test, clf.predict(X_test), pos_label=1)
        assert_almost_equal(score1, score2)

    # test fbeta score that takes an argument
    scorer = make_scorer(fbeta_score, beta=2)
    score1 = scorer(clf, X_test, y_test)
    score2 = fbeta_score(y_test, clf.predict(X_test), beta=2)
    assert_almost_equal(score1, score2)

    # test that custom scorer can be pickled
    unpickled_scorer = pickle.loads(pickle.dumps(scorer))
    score3 = unpickled_scorer(clf, X_test, y_test)
    assert_almost_equal(score1, score3)

    # smoke test the repr:
    repr(fbeta_score) 

def test_thresholded_scorers():
    # Test scorers that take thresholds.
    X, y = make_blobs(random_state=0, centers=2)
    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
    clf = LogisticRegression(random_state=0)
    clf.fit(X_train, y_train)
    score1 = get_scorer('roc_auc')(clf, X_test, y_test)
    score2 = roc_auc_score(y_test, clf.decision_function(X_test))
    score3 = roc_auc_score(y_test, clf.predict_proba(X_test)[:, 1])
    assert_almost_equal(score1, score2)
    assert_almost_equal(score1, score3)

    logscore = get_scorer('neg_log_loss')(clf, X_test, y_test)
    logloss = log_loss(y_test, clf.predict_proba(X_test))
    assert_almost_equal(-logscore, logloss)

    # same for an estimator without decision_function
    clf = DecisionTreeClassifier()
    clf.fit(X_train, y_train)
    score1 = get_scorer('roc_auc')(clf, X_test, y_test)
    score2 = roc_auc_score(y_test, clf.predict_proba(X_test)[:, 1])
    assert_almost_equal(score1, score2)

    # test with a regressor (no decision_function)
    reg = DecisionTreeRegressor()
    reg.fit(X_train, y_train)
    score1 = get_scorer('roc_auc')(reg, X_test, y_test)
    score2 = roc_auc_score(y_test, reg.predict(X_test))
    assert_almost_equal(score1, score2)

    # Test that an exception is raised on more than two classes
    X, y = make_blobs(random_state=0, centers=3)
    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
    clf.fit(X_train, y_train)
    with pytest.raises(ValueError, match="multiclass format is not supported"):
        get_scorer('roc_auc')(clf, X_test, y_test)

    # test error is raised with a single class present in model
    # (predict_proba shape is not suitable for binary auc)
    X, y = make_blobs(random_state=0, centers=2)
    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
    clf = DecisionTreeClassifier()
    clf.fit(X_train, np.zeros_like(y_train))
    with pytest.raises(ValueError, match="need classifier with two classes"):
        get_scorer('roc_auc')(clf, X_test, y_test)

    # for proba scorers
    with pytest.raises(ValueError, match="need classifier with two classes"):
        get_scorer('neg_log_loss')(clf, X_test, y_test) 

def fit(dataset_loader_train, model, model_persister, persist=True,
        activate=True, dataset_loader_test=None, evaluate=False,
        persist_if_better_than=None, scoring=None):

    if persist_if_better_than is not None:
        evaluate = True
        if dataset_loader_test is None:
            raise ValueError(
                "When using 'persist_if_better_than', make sure you also "
                "provide a 'dataset_loader_test'."
                )

    if evaluate and not (hasattr(model, 'score') or scoring is not None):
        raise ValueError(
            "Your model doesn't seem to implement a 'score' method.  You may "
            "want to define a 'scoring' option in the configuration."
            )

    if scoring is not None:
        scorer = get_scorer(scoring)
    else:
        def scorer(model, X, y):
            return model.score(X, y)

    with timer(logger.info, "Loading data"):
        X, y = dataset_loader_train()

    with timer(logger.info, "Fitting model"):
        model.fit(X, y)

    if evaluate:
        with timer(logger.debug, "Evaluating model on train set"):
            score_train = scorer(model, X, y)
            annotate(model, {'score_train': score_train})
            logger.info("Train score: {}".format(score_train))

    X, y = None, None
    gc.collect()

    score_test = None
    if evaluate and dataset_loader_test is not None:
        with timer(logger.info, "Loading test data"):
            X_test, y_test = dataset_loader_test()
        with timer(logger.debug, "Evaluating model on test set"):
            score_test = scorer(model, X_test, y_test)
            annotate(model, {'score_test': score_test})
            logger.info("Test score:  {}".format(score_test))

    if persist:
        if (persist_if_better_than is not None and
            score_test < persist_if_better_than):
            logger.info("Not persisting model that has a test score "
                        "{} < {}".format(score_test, persist_if_better_than))
        else:
            _persist_model(model, model_persister, activate=activate)

    return model 

def __init__(self, model, dataset, metric, shuffle_seed=0, data_root=None):
        """Build class that wraps sklearn classifier/regressor CV score for use as an objective function.

        Parameters
        ----------
        model : str
            Which classifier to use, must be key in `MODELS_CLF` or `MODELS_REG` dict depending on if dataset is
            classification or regression.
        dataset : str
            Which data set to use, must be key in `DATA_LOADERS` dict, or name of custom csv file.
        metric : str
            Which sklearn scoring metric to use, in `SCORERS_CLF` list or `SCORERS_REG` dict depending on if dataset is
            classification or regression.
        shuffle_seed : int
            Random seed to use when splitting the data into train and validation in the cross-validation splits. This
            is needed in order to keep the split constant across calls. Otherwise there would be extra noise in the
            objective function for varying splits.
        data_root : str
            Root directory to look for all custom csv files.
        """
        TestFunction.__init__(self)
        data, target, problem_type = load_data(dataset, data_root=data_root)
        assert problem_type in (ProblemType.clf, ProblemType.reg)
        self.is_classifier = problem_type == ProblemType.clf

        # Do some validation on loaded data
        assert isinstance(data, np.ndarray)
        assert isinstance(target, np.ndarray)
        assert data.ndim == 2 and target.ndim == 1
        assert data.shape[0] == target.shape[0]
        assert data.size > 0
        assert data.dtype == np.float_
        assert np.all(np.isfinite(data))  # also catch nan
        assert target.dtype == (np.int_ if self.is_classifier else np.float_)
        assert np.all(np.isfinite(target))  # also catch nan

        model_lookup = MODELS_CLF if self.is_classifier else MODELS_REG
        base_model, fixed_params, api_config = model_lookup[model]

        # New members for model
        self.base_model = base_model
        self.fixed_params = fixed_params
        self.api_config = api_config

        # Always shuffle your data to be safe. Use fixed seed for reprod.
        self.data_X, self.data_Xt, self.data_y, self.data_yt = train_test_split(
            data, target, test_size=0.2, random_state=shuffle_seed, shuffle=True
        )

        assert metric in METRICS, "Unknown metric %s" % metric
        assert metric in METRICS_LOOKUP[problem_type], "Incompatible metric %s with problem type %s" % (
            metric,
            problem_type,
        )
        self.scorer = get_scorer(SklearnModel._METRIC_MAP[metric]) 

def evaluate(self, params):
        """Evaluate the sklearn CV objective at a particular parameter setting.

        Parameters
        ----------
        params : dict(str, object)
            The varying (non-fixed) parameter dict to the sklearn model.

        Returns
        -------
        cv_loss : float
            Average loss over CV splits for sklearn model when tested using the settings in params.
        """
        params = dict(params)  # copy to avoid modification of original
        params.update(self.fixed_params)  # add in fixed params

        # now build the skl object
        clf = self.base_model(**params)

        assert np.all(np.isfinite(self.data_X)), "all features must be finite"
        assert np.all(np.isfinite(self.data_y)), "all targets must be finite"

        # Do the x-val, ignore user warn since we expect BO to try weird stuff
        with warnings.catch_warnings():
            warnings.filterwarnings("ignore", category=UserWarning)
            S = cross_val_score(clf, self.data_X, self.data_y, scoring=self.scorer, cv=CV_SPLITS)
        # Take the mean score across all x-val splits
        cv_score = np.mean(S)

        # Now let's get the generalization error for same hypers
        clf = self.base_model(**params)
        clf.fit(self.data_X, self.data_y)
        generalization_score = self.scorer(clf, self.data_Xt, self.data_yt)

        # get_scorer makes everything a score not a loss, so we need to negate to get the loss back
        cv_loss = -cv_score
        assert np.isfinite(cv_loss), "loss not even finite"
        generalization_loss = -generalization_score
        assert np.isfinite(generalization_loss), "loss not even finite"

        # Unbox to basic float to keep it simple
        cv_loss = cv_loss.item()
        assert isinstance(cv_loss, float)
        generalization_loss = generalization_loss.item()
        assert isinstance(generalization_loss, float)

        # For now, score with same objective. We can later add generalization error
        return cv_loss, generalization_loss 

def test_classification_scores():
    # Test classification scorers.
    X, y = make_blobs(random_state=0, centers=2)
    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
    clf = LinearSVC(random_state=0)
    clf.fit(X_train, y_train)

    for prefix, metric in [('f1', f1_score), ('precision', precision_score),
                           ('recall', recall_score)]:

        score1 = get_scorer('%s_weighted' % prefix)(clf, X_test, y_test)
        score2 = metric(y_test, clf.predict(X_test), pos_label=None,
                        average='weighted')
        assert_almost_equal(score1, score2)

        score1 = get_scorer('%s_macro' % prefix)(clf, X_test, y_test)
        score2 = metric(y_test, clf.predict(X_test), pos_label=None,
                        average='macro')
        assert_almost_equal(score1, score2)

        score1 = get_scorer('%s_micro' % prefix)(clf, X_test, y_test)
        score2 = metric(y_test, clf.predict(X_test), pos_label=None,
                        average='micro')
        assert_almost_equal(score1, score2)

        score1 = get_scorer('%s' % prefix)(clf, X_test, y_test)
        score2 = metric(y_test, clf.predict(X_test), pos_label=1)
        assert_almost_equal(score1, score2)

    # test fbeta score that takes an argument
    scorer = make_scorer(fbeta_score, beta=2)
    score1 = scorer(clf, X_test, y_test)
    score2 = fbeta_score(y_test, clf.predict(X_test), beta=2)
    assert_almost_equal(score1, score2)

    # test that custom scorer can be pickled
    unpickled_scorer = pickle.loads(pickle.dumps(scorer))
    score3 = unpickled_scorer(clf, X_test, y_test)
    assert_almost_equal(score1, score3)

    # smoke test the repr:
    repr(fbeta_score)