示例#1
0
def test_rest(x, y,c=0,ratio='auto'):
    c=c
    if(c==0):
        print('Random under-sampling')
        US = UnderSampler(indices_support=indices_support, verbose=verbose,ratio=ratio)
        x, y, idx_tmp = US.fit_transform(x, y)
        print ('Indices selected')
        print(idx_tmp)
    elif(c==1):
        print('Tomek links')
        TL = TomekLinks(verbose=verbose,ratio=ratio)
        x, y = TL.fit_transform(x, y)
    elif(c==2):
        print('Clustering centroids')
        CC = ClusterCentroids(verbose=verbose,ratio=ratio)
        x, y = CC.fit_transform(x, y)
    elif(c==3):
        print('NearMiss-1')
        NM1 = NearMiss(version=1, indices_support=indices_support, verbose=verbose,ratio=ratio)
        x, y, idx_tmp = NM1.fit_transform(x, y)
        print ('Indices selected')
        print(idx_tmp)
    elif(c==4):
        print('NearMiss-2')
        NM2 = NearMiss(version=2, indices_support=indices_support, verbose=verbose,ratio=ratio)
        x, y, idx_tmp = NM2.fit_transform(x, y)
        print ('Indices selected')
        print(idx_tmp)
    elif(c==5):
        print('NearMiss-3')
        NM3 = NearMiss(version=3, indices_support=indices_support, verbose=verbose,ratio=ratio)
        x, y, idx_tmp = NM3.fit_transform(x, y)
        print ('Indices selected')
        print(idx_tmp)
    elif(c==6):
        print('Neighboorhood Cleaning Rule')
        NCR = NeighbourhoodCleaningRule(indices_support=indices_support, verbose=verbose,ratio=ratio)
        x, y, idx_tmp = NCR.fit_transform(x, y)
        print ('Indices selected')
        print(idx_tmp)
    elif(c==7):
        print('Random over-sampling')
        OS = OverSampler(verbose=verbose,ratio=ratio)
        x, y = OS.fit_transform(x, y)
    elif(c==8):
        print('SMOTE Tomek links')
        STK = SMOTETomek(verbose=verbose,ratio=ratio)
        x, y = STK.fit_transform(x, y)
    elif(c==9):
        print('SMOTE ENN')
        SENN = SMOTEENN(verbose=verbose,ratio=ratio)
        x, y = SENN.fit_transform(x, y)
    else:
        print('EasyEnsemble')
        EE = EasyEnsemble(verbose=verbose,ratio=ratio)
        x, y = EE.fit_transform(x, y)
    return x, y
示例#2
0
def test_rest(x, y):

    print('Random under-sampling')
    US = UnderSampler(indices_support=indices_support, verbose=verbose)
    usx, usy, idx_tmp = US.fit_transform(x, y)
    print ('Indices selected')
    print(idx_tmp)

    print('Tomek links')
    TL = TomekLinks(verbose=verbose)
    tlx, tly = TL.fit_transform(x, y)

    print('Clustering centroids')
    CC = ClusterCentroids(verbose=verbose)
    ccx, ccy = CC.fit_transform(x, y)

    print('NearMiss-1')
    NM1 = NearMiss(version=1, indices_support=indices_support, verbose=verbose)
    nm1x, nm1y, idx_tmp = NM1.fit_transform(x, y)
    print ('Indices selected')
    print(idx_tmp)

    print('NearMiss-2')
    NM2 = NearMiss(version=2, indices_support=indices_support, verbose=verbose)
    nm2x, nm2y, idx_tmp = NM2.fit_transform(x, y)
    print ('Indices selected')
    print(idx_tmp)

    print('NearMiss-3')
    NM3 = NearMiss(version=3, indices_support=indices_support, verbose=verbose)
    nm3x, nm3y, idx_tmp = NM3.fit_transform(x, y)
    print ('Indices selected')
    print(idx_tmp)

    print('Neighboorhood Cleaning Rule')
    NCR = NeighbourhoodCleaningRule(indices_support=indices_support, verbose=verbose)
    ncrx, ncry, idx_tmp = NCR.fit_transform(x, y)
    print ('Indices selected')
    print(idx_tmp)

    print('Random over-sampling')
    OS = OverSampler(verbose=verbose)
    ox, oy = OS.fit_transform(x, y)

    print('SMOTE Tomek links')
    STK = SMOTETomek(verbose=verbose)
    stkx, stky = STK.fit_transform(x, y)

    print('SMOTE ENN')
    SENN = SMOTEENN(verbose=verbose)
    sennx, senny = SENN.fit_transform(x, y)

    print('EasyEnsemble')
    EE = EasyEnsemble(verbose=verbose)
    eex, eey = EE.fit_transform(x, y)
def test_ncr_fit_transform():
    """Test the fit transform routine"""

    # Resample the data
    ncr = NeighbourhoodCleaningRule(random_state=RND_SEED)
    X_resampled, y_resampled = ncr.fit_transform(X, Y)

    currdir = os.path.dirname(os.path.abspath(__file__))
    X_gt = np.load(os.path.join(currdir, 'data', 'ncr_x.npy'))
    y_gt = np.load(os.path.join(currdir, 'data', 'ncr_y.npy'))
    assert_array_equal(X_resampled, X_gt)
    assert_array_equal(y_resampled, y_gt)
def test_ncr_fit_transform():
    """Test the fit transform routine"""

    # Resample the data
    ncr = NeighbourhoodCleaningRule(random_state=RND_SEED)
    X_resampled, y_resampled = ncr.fit_transform(X, Y)

    currdir = os.path.dirname(os.path.abspath(__file__))
    X_gt = np.load(os.path.join(currdir, 'data', 'ncr_x.npy'))
    y_gt = np.load(os.path.join(currdir, 'data', 'ncr_y.npy'))
    assert_array_equal(X_resampled, X_gt)
    assert_array_equal(y_resampled, y_gt)
def test_ncr_fit():
    """Test the fitting method"""

    # Create the object
    ncr = NeighbourhoodCleaningRule(random_state=RND_SEED)
    # Fit the data
    ncr.fit(X, Y)

    # Check if the data information have been computed
    assert_equal(ncr.min_c_, 0)
    assert_equal(ncr.maj_c_, 1)
    assert_equal(ncr.stats_c_[0], 500)
    assert_equal(ncr.stats_c_[1], 4500)
def test_ncr_fit():
    """Test the fitting method"""

    # Create the object
    ncr = NeighbourhoodCleaningRule(random_state=RND_SEED)
    # Fit the data
    ncr.fit(X, Y)

    # Check if the data information have been computed
    assert_equal(ncr.min_c_, 0)
    assert_equal(ncr.maj_c_, 1)
    assert_equal(ncr.stats_c_[0], 500)
    assert_equal(ncr.stats_c_[1], 4500)
def test_ncr_fit_transform_with_indices():
    """Test the fit transform routine with indices support"""

    # Resample the data
    ncr = NeighbourhoodCleaningRule(return_indices=True, random_state=RND_SEED)
    X_resampled, y_resampled, idx_under = ncr.fit_transform(X, Y)

    currdir = os.path.dirname(os.path.abspath(__file__))
    X_gt = np.load(os.path.join(currdir, 'data', 'ncr_x.npy'))
    y_gt = np.load(os.path.join(currdir, 'data', 'ncr_y.npy'))
    idx_gt = np.load(os.path.join(currdir, 'data', 'ncr_idx.npy'))
    assert_array_equal(X_resampled, X_gt)
    assert_array_equal(y_resampled, y_gt)
    assert_array_equal(idx_under, idx_gt)
def test_ncr_fit_transform_with_indices():
    """Test the fit transform routine with indices support"""

    # Resample the data
    ncr = NeighbourhoodCleaningRule(return_indices=True, random_state=RND_SEED)
    X_resampled, y_resampled, idx_under = ncr.fit_transform(X, Y)

    currdir = os.path.dirname(os.path.abspath(__file__))
    X_gt = np.load(os.path.join(currdir, 'data', 'ncr_x.npy'))
    y_gt = np.load(os.path.join(currdir, 'data', 'ncr_y.npy'))
    idx_gt = np.load(os.path.join(currdir, 'data', 'ncr_idx.npy'))
    assert_array_equal(X_resampled, X_gt)
    assert_array_equal(y_resampled, y_gt)
    assert_array_equal(idx_under, idx_gt)
def ncl_undersampling(X, y):
    """
	Perform the Neighbourhood Cleaning Rule undersampling

	Keyword arguments:
	X -- The feature vectors
	y -- The target vector
	"""

    if verbose:
        print '\nUndersampling with the Neighbourhood Cleaning Rule ...'

    undersampler = NeighbourhoodCleaningRule(verbose=verbose)
    X_undersampled, y_undersampled = undersampler.fit_transform(X, y)
    return X_undersampled, y_undersampled
def ncl_undersampling(X,y):
	"""
	Perform the Neighbourhood Cleaning Rule undersampling

	Keyword arguments:
	X -- The feature vectors
	y -- The target vector
	"""

	if verbose:
		print '\nUndersampling with the Neighbourhood Cleaning Rule ...'

	undersampler=NeighbourhoodCleaningRule(verbose=verbose)
	X_undersampled,y_undersampled = undersampler.fit_transform(X,y)
	return X_undersampled,y_undersampled
def test_ncr_transform_wt_fit():
    """Test either if an error is raised when transform is called before
    fitting"""

    # Create the object
    ncr = NeighbourhoodCleaningRule(random_state=RND_SEED)
    assert_raises(RuntimeError, ncr.transform, X, Y)
def test_ncr_fit_single_class():
    """Test either if an error when there is a single class"""

    # Create the object
    ncr = NeighbourhoodCleaningRule(random_state=RND_SEED)
    # Resample the data
    # Create a wrong y
    y_single_class = np.zeros((X.shape[0], ))
    assert_raises(RuntimeError, ncr.fit, X, y_single_class)
def test_ncr_init():
    """Test the initialisation of the object"""

    # Define a ratio
    verbose = True
    ncr = NeighbourhoodCleaningRule(random_state=RND_SEED, verbose=verbose)

    assert_equal(ncr.size_ngh, 3)
    assert_equal(ncr.n_jobs, -1)
    assert_equal(ncr.rs_, RND_SEED)
    assert_equal(ncr.verbose, verbose)
    assert_equal(ncr.min_c_, None)
    assert_equal(ncr.maj_c_, None)
    assert_equal(ncr.stats_c_, {})
                           weights=[0.1, 0.9],
                           n_informative=3,
                           n_redundant=1,
                           flip_y=0,
                           n_features=20,
                           n_clusters_per_class=1,
                           n_samples=5000,
                           random_state=10)

# Instanciate a PCA object for the sake of easy visualisation
pca = PCA(n_components=2)
# Fit and transform x to visualise inside a 2D feature space
X_vis = pca.fit_transform(X)

# Apply the random under-sampling
ncl = NeighbourhoodCleaningRule()
X_resampled, y_resampled = ncl.fit_transform(X, y)
X_res_vis = pca.transform(X_resampled)

# Two subplots, unpack the axes array immediately
f, (ax1, ax2) = plt.subplots(1, 2)

ax1.scatter(X_vis[y == 0, 0],
            X_vis[y == 0, 1],
            label="Class #0",
            alpha=0.5,
            edgecolor=almost_black,
            facecolor=palette[0],
            linewidth=0.15)
ax1.scatter(X_vis[y == 1, 0],
            X_vis[y == 1, 1],