Python sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis() Examples

def test_qda_regularization():
    # the default is reg_param=0. and will cause issues
    # when there is a constant variable
    clf = QuadraticDiscriminantAnalysis()
    with ignore_warnings():
        y_pred = clf.fit(X2, y6).predict(X2)
    assert_true(np.any(y_pred != y6))

    # adding a little regularization fixes the problem
    clf = QuadraticDiscriminantAnalysis(reg_param=0.01)
    with ignore_warnings():
        clf.fit(X2, y6)
    y_pred = clf.predict(X2)
    assert_array_equal(y_pred, y6)

    # Case n_samples_in_a_class < n_features
    clf = QuadraticDiscriminantAnalysis(reg_param=0.1)
    with ignore_warnings():
        clf.fit(X5, y5)
    y_pred5 = clf.predict(X5)
    assert_array_equal(y_pred5, y5) 

def test_qda_store_covariance():
    # The default is to not set the covariances_ attribute
    clf = QuadraticDiscriminantAnalysis().fit(X6, y6)
    assert not hasattr(clf, 'covariance_')

    # Test the actual attribute:
    clf = QuadraticDiscriminantAnalysis(store_covariance=True).fit(X6, y6)
    assert hasattr(clf, 'covariance_')

    assert_array_almost_equal(
        clf.covariance_[0],
        np.array([[0.7, 0.45], [0.45, 0.7]])
    )

    assert_array_almost_equal(
        clf.covariance_[1],
        np.array([[0.33333333, -0.33333333], [-0.33333333, 0.66666667]])
    ) 

def test_qda_regularization():
    # the default is reg_param=0. and will cause issues
    # when there is a constant variable
    clf = QuadraticDiscriminantAnalysis()
    with ignore_warnings():
        y_pred = clf.fit(X2, y6).predict(X2)
    assert np.any(y_pred != y6)

    # adding a little regularization fixes the problem
    clf = QuadraticDiscriminantAnalysis(reg_param=0.01)
    with ignore_warnings():
        clf.fit(X2, y6)
    y_pred = clf.predict(X2)
    assert_array_equal(y_pred, y6)

    # Case n_samples_in_a_class < n_features
    clf = QuadraticDiscriminantAnalysis(reg_param=0.1)
    with ignore_warnings():
        clf.fit(X5, y5)
    y_pred5 = clf.predict(X5)
    assert_array_equal(y_pred5, y5) 

def __init__(self, classifier=FaceClassifierModels.DEFAULT):
        self._clf = None
        if classifier == FaceClassifierModels.LINEAR_SVM:
            self._clf = SVC(C=1.0, kernel="linear", probability=True)
        elif classifier == FaceClassifierModels.NAIVE_BAYES:
            self._clf = GaussianNB()
        elif classifier == FaceClassifierModels.RBF_SVM:
            self._clf = SVC(C=1, kernel='rbf', probability=True, gamma=2)
        elif classifier == FaceClassifierModels.NEAREST_NEIGHBORS:
            self._clf = KNeighborsClassifier(1)
        elif classifier == FaceClassifierModels.DECISION_TREE:
            self._clf = DecisionTreeClassifier(max_depth=5)
        elif classifier == FaceClassifierModels.RANDOM_FOREST:
            self._clf = RandomForestClassifier(max_depth=5, n_estimators=10, max_features=1)
        elif classifier == FaceClassifierModels.NEURAL_NET:
            self._clf = MLPClassifier(alpha=1)
        elif classifier == FaceClassifierModels.ADABOOST:
            self._clf = AdaBoostClassifier()
        elif classifier == FaceClassifierModels.QDA:
            self._clf = QuadraticDiscriminantAnalysis()
        print("classifier={}".format(FaceClassifierModels(classifier))) 

def test_qda_store_covariance():
    # The default is to not set the covariances_ attribute
    clf = QuadraticDiscriminantAnalysis().fit(X6, y6)
    assert_false(hasattr(clf, 'covariance_'))

    # Test the actual attribute:
    clf = QuadraticDiscriminantAnalysis(store_covariance=True).fit(X6, y6)
    assert_true(hasattr(clf, 'covariance_'))

    assert_array_almost_equal(
        clf.covariance_[0],
        np.array([[0.7, 0.45], [0.45, 0.7]])
    )

    assert_array_almost_equal(
        clf.covariance_[1],
        np.array([[0.33333333, -0.33333333], [-0.33333333, 0.66666667]])
    ) 

def main():
	# prepare data
	trainingSet=[]
	testSet=[]
	accuracy = 0.0
	split = 0.25
	loadDataset('../Dataset/combined.csv', split, trainingSet, testSet)
	print 'Train set: ' + repr(len(trainingSet))
	print 'Test set: ' + repr(len(testSet))
	# generate predictions
	predictions=[]
	trainData = np.array(trainingSet)[:,0:np.array(trainingSet).shape[1] - 1]
  	columns = trainData.shape[1] 
	X = np.array(trainData)
	y = np.array(trainingSet)[:,columns]
	clf = BaggingClassifier(QDA())
	clf.fit(X, y)
	testData = np.array(testSet)[:,0:np.array(trainingSet).shape[1] - 1]
	X_test = np.array(testData)
	y_test = np.array(testSet)[:,columns]
	accuracy = clf.score(X_test,y_test)
	accuracy *= 100
	print("Accuracy %:",accuracy) 

def test_qda():
    # QDA classification.
    # This checks that QDA implements fit and predict and returns
    # correct values for a simple toy dataset.
    clf = QuadraticDiscriminantAnalysis()
    y_pred = clf.fit(X6, y6).predict(X6)
    assert_array_equal(y_pred, y6)

    # Assure that it works with 1D data
    y_pred1 = clf.fit(X7, y6).predict(X7)
    assert_array_equal(y_pred1, y6)

    # Test probas estimates
    y_proba_pred1 = clf.predict_proba(X7)
    assert_array_equal((y_proba_pred1[:, 1] > 0.5) + 1, y6)
    y_log_proba_pred1 = clf.predict_log_proba(X7)
    assert_array_almost_equal(np.exp(y_log_proba_pred1), y_proba_pred1, 8)

    y_pred3 = clf.fit(X6, y7).predict(X6)
    # QDA shouldn't be able to separate those
    assert np.any(y_pred3 != y7)

    # Classes should have at least 2 elements
    assert_raises(ValueError, clf.fit, X6, y4) 

def test_qda():
    # QDA classification.
    # This checks that QDA implements fit and predict and returns
    # correct values for a simple toy dataset.
    clf = QuadraticDiscriminantAnalysis()
    y_pred = clf.fit(X6, y6).predict(X6)
    assert_array_equal(y_pred, y6)

    # Assure that it works with 1D data
    y_pred1 = clf.fit(X7, y6).predict(X7)
    assert_array_equal(y_pred1, y6)

    # Test probas estimates
    y_proba_pred1 = clf.predict_proba(X7)
    assert_array_equal((y_proba_pred1[:, 1] > 0.5) + 1, y6)
    y_log_proba_pred1 = clf.predict_log_proba(X7)
    assert_array_almost_equal(np.exp(y_log_proba_pred1), y_proba_pred1, 8)

    y_pred3 = clf.fit(X6, y7).predict(X6)
    # QDA shouldn't be able to separate those
    assert_true(np.any(y_pred3 != y7))

    # Classes should have at least 2 elements
    assert_raises(ValueError, clf.fit, X6, y4) 

def __init__(self, **kwargs):
        super(QuadraticLDA, self).__init__()
        self.clf = _QuadraticDiscriminantAnalysis(**kwargs) 

def test_qda_deprecation():
    # Test the deprecation
    clf = QuadraticDiscriminantAnalysis(store_covariances=True)
    assert_warns_message(DeprecationWarning, "'store_covariances' was renamed"
                         " to store_covariance in version 0.19 and will be "
                         "removed in 0.21.", clf.fit, X, y)

    # check that covariance_ (and covariances_ with warning) is stored
    assert_warns_message(DeprecationWarning, "Attribute covariances_ was "
                         "deprecated in version 0.19 and will be removed "
                         "in 0.21. Use covariance_ instead", getattr, clf,
                         'covariances_') 

def test_qda_priors():
    clf = QuadraticDiscriminantAnalysis()
    y_pred = clf.fit(X6, y6).predict(X6)
    n_pos = np.sum(y_pred == 2)

    neg = 1e-10
    clf = QuadraticDiscriminantAnalysis(priors=np.array([neg, 1 - neg]))
    y_pred = clf.fit(X6, y6).predict(X6)
    n_pos2 = np.sum(y_pred == 2)

    assert_greater(n_pos2, n_pos) 

def test_objectmapper_deprecated(self):
        df = pdml.ModelFrame([])
        with tm.assert_produces_warning(FutureWarning):
            self.assertIs(df.lda.LinearDiscriminantAnalysis,
                          da.LinearDiscriminantAnalysis)
        with tm.assert_produces_warning(FutureWarning):
            self.assertIs(df.qda.QuadraticDiscriminantAnalysis,
                          da.QuadraticDiscriminantAnalysis) 

def test_objectmapper(self):
        df = pdml.ModelFrame([])
        self.assertIs(df.discriminant_analysis.LinearDiscriminantAnalysis,
                      da.LinearDiscriminantAnalysis)
        self.assertIs(df.discriminant_analysis.QuadraticDiscriminantAnalysis,
                      da.QuadraticDiscriminantAnalysis)

        self.assertIs(df.da.LinearDiscriminantAnalysis,
                      da.LinearDiscriminantAnalysis)
        self.assertIs(df.da.QuadraticDiscriminantAnalysis,
                      da.QuadraticDiscriminantAnalysis) 

def __init__(self, priors=None, reg_param=0.0, store_covariance=False, tol=0.0001, store_covariances=None):
        self._hyperparams = {
            'priors': priors,
            'reg_param': reg_param,
            'store_covariance': store_covariance,
            'tol': tol,
            'store_covariances': store_covariances}
        self._wrapped_model = Op(**self._hyperparams) 

def getModels():
    result = []
    result.append("LinearRegression")
    result.append("BayesianRidge")
    result.append("ARDRegression")
    result.append("ElasticNet")
    result.append("HuberRegressor")
    result.append("Lasso")
    result.append("LassoLars")
    result.append("Rigid")
    result.append("SGDRegressor")
    result.append("SVR")
    result.append("MLPClassifier")
    result.append("KNeighborsClassifier")
    result.append("SVC")
    result.append("GaussianProcessClassifier")
    result.append("DecisionTreeClassifier")
    result.append("RandomForestClassifier")
    result.append("AdaBoostClassifier")
    result.append("GaussianNB")
    result.append("LogisticRegression")
    result.append("QuadraticDiscriminantAnalysis")
    return result 

def test_qda_priors():
    clf = QuadraticDiscriminantAnalysis()
    y_pred = clf.fit(X6, y6).predict(X6)
    n_pos = np.sum(y_pred == 2)

    neg = 1e-10
    clf = QuadraticDiscriminantAnalysis(priors=np.array([neg, 1 - neg]))
    y_pred = clf.fit(X6, y6).predict(X6)
    n_pos2 = np.sum(y_pred == 2)

    assert_greater(n_pos2, n_pos) 

def __init__(self, cal_results: Union[Result, List[Result]],
                 qubit_mask: List[int], expected_states: List[str] = None,
                 standardize: bool = False,
                 schedules: Union[List[str], List[Schedule]] = None,
                 discriminator_parameters: dict = None):
        """
        Args:
            cal_results (Union[Result, List[Result]]): calibration results,
                Result or list of Result used to fit the discriminator.
            qubit_mask (List[int]): determines which qubit's level 1 data to
                use in the discrimination process.
            expected_states (List[str]): a list that should have the same
                length as schedules. All results in cal_results are used if
                schedules is None. expected_states must have the corresponding
                length.
            standardize (bool): if true the discriminator will standardize the
                xdata using the internal method _scale_data.
            schedules (Union[List[str], List[Schedule]]): The schedules or a
                subset of schedules in cal_results used to train the
                discriminator. The user may also pass the name of the schedules
                instead of the schedules. If schedules is None, then all the
                schedules in cal_results are used.
            discriminator_parameters (dict): parameters for Sklearn's LDA.
        """
        if not discriminator_parameters:
            discriminator_parameters = {}

        store_cov = discriminator_parameters.get('store_covariance', False)
        tol = discriminator_parameters.get('tol', 1.0e-4)

        self._qda = QuadraticDiscriminantAnalysis(store_covariance=store_cov,
                                                  tol=tol)

        # Also sets the x and y data.
        IQDiscriminationFitter.__init__(self, cal_results, qubit_mask,
                                        expected_states, standardize,
                                        schedules)

        self._description = 'Quadratic IQ discriminator for measurement ' \
                            'level 1.'

        self.fit() 

def getSKLearnModel(modelName):
    if modelName == 'LinearRegression':
        model = linear_model.LinearRegression()
    elif modelName == 'BayesianRidge':
        model = linear_model.BayesianRidge()
    elif modelName == 'ARDRegression':
        model = linear_model.ARDRegression()
    elif modelName == 'ElasticNet':
        model = linear_model.ElasticNet()
    elif modelName == 'HuberRegressor':
        model = linear_model.HuberRegressor()
    elif modelName == 'Lasso':
        model = linear_model.Lasso()
    elif modelName == 'LassoLars':
        model = linear_model.LassoLars()
    elif modelName == 'Rigid':
        model = linear_model.Ridge()
    elif modelName == 'SGDRegressor':
        model = linear_model.SGDRegressor()
    elif modelName == 'SVR':
        model = SVR()
    elif modelName=='MLPClassifier':
        model = MLPClassifier()
    elif modelName=='KNeighborsClassifier':
        model = KNeighborsClassifier()
    elif modelName=='SVC':
        model = SVC()
    elif modelName=='GaussianProcessClassifier':
        model = GaussianProcessClassifier()
    elif modelName=='DecisionTreeClassifier':
        model = DecisionTreeClassifier()
    elif modelName=='RandomForestClassifier':
        model = RandomForestClassifier()
    elif modelName=='AdaBoostClassifier':
        model = AdaBoostClassifier()
    elif modelName=='GaussianNB':
        model = GaussianNB()
    elif modelName=='LogisticRegression':
        model = linear_model.LogisticRegression()
    elif modelName=='QuadraticDiscriminantAnalysis':
        model = QuadraticDiscriminantAnalysis()

    return model