def test_bad_verbose(self):
m = np.array([[8., 4., 6., 7.], [3., 6., 5., 2.], [9., 11., 3., 1.]], )
xip = np.array([20., 18., 22.])
xpj = np.array([18., 16., 12., 14.])
aggregates = [xip, xpj]
dimensions = [[0], [1]]
with pytest.raises(ValueError):
IPF = ipfn.ipfn(m, aggregates, dimensions, convergence_rate=1e-5, verbose=4)
def test_bad_types(self):
m = [[8., 4., 6., 7.], [3., 6., 5., 2.], [9., 11., 3., 1.]] # not a np.array
xip = np.array([20., 18., 22.])
xpj = np.array([18., 16., 12., 14.])
aggregates = [xip, xpj]
dimensions = [[0], [1]]
IPF = ipfn.ipfn(m, aggregates, dimensions, convergence_rate=1e-5)
with pytest.raises(ValueError):
m = IPF.iteration()
def test_numpy_2D(self):
m = np.array([[8., 4., 6., 7.], [3., 6., 5., 2.], [9., 11., 3., 1.]], )
xip = np.array([20., 18., 22.])
xpj = np.array([18., 16., 12., 14.])
aggregates = [xip, xpj]
dimensions = [[0], [1]]
IPF = ipfn.ipfn(m, aggregates, dimensions, convergence_rate=1e-5)
m = IPF.iteration()
marginals1D = [xip, xpj]
m_inc = 0
for marginal in marginals1D:
nb_dim = marginal.shape[0]
for dim in range(nb_dim):
if m_inc == 0:
ipfn_number = np.sum(m[dim, :])
if m_inc == 1:
ipfn_number = np.sum(m[:, dim])
truth_number = marginal[dim]
assert round(ipfn_number, 2) == round(truth_number, 2)
m_inc += 1
def test_numpy_4D(self):
m = np.random.rand(2, 5, 4, 3) * 200
m_new = np.random.rand(2, 5, 4, 3) * 200
xijkp = np.random.rand(2, 5, 4) * 200
xpjkl = np.random.rand(5, 4, 3) * 200
xipkl = np.random.rand(2, 4, 3) * 200
xijpl = np.random.rand(2, 5, 3) * 200
xippp = np.random.rand(2) * 200
xpjpp = np.random.rand(5) * 200
xppkp = np.random.rand(4) * 200
xpppl = np.random.rand(3) * 200
xijpp = np.random.rand(2, 5) * 200
xpjkp = np.random.rand(5, 4) * 200
xppkl = np.random.rand(4, 3) * 200
xippl = np.random.rand(2, 3) * 200
for i in range(2):
for j in range(5):
for k in range(4):
xijkp[i, j, k] = m_new[i, j, k, :].sum()
for j in range(5):
for k in range(4):
for l in range(3):
xpjkl[j, k, l] = m_new[:, j, k, l].sum()
for i in range(2):
for k in range(4):
for l in range(3):
xipkl[i, k, l] = m_new[i, :, k, l].sum()
for i in range(2):
for j in range(5):
for l in range(3):
xijpl[i, j, l] = m_new[i, j, :, l].sum()
for i in range(2):
xippp[i] = m_new[i, :, :, :].sum()
for j in range(5):
xpjpp[j] = m_new[:, j, :, :].sum()
for k in range(4):
xppkp[k] = m_new[:, :, k, :].sum()
for l in range(3):
xpppl[l] = m_new[:, :, :, l].sum()
for i in range(2):
for j in range(5):
xijpp[i, j] = m_new[i, j, :, :].sum()
for j in range(5):
for k in range(4):
xpjkp[j, k] = m_new[:, j, k, :].sum()
for k in range(4):
for l in range(3):
xppkl[k, l] = m_new[:, :, k, l].sum()
for i in range(2):
for l in range(3):
xippl[i, l] = m_new[i, :, :, l].sum()
aggregates = [
xijkp, xpjkl, xipkl, xijpl, xippp, xpjpp, xppkp, xpppl, xijpp,
xpjkp, xppkl, xippl
]
dimensions = [[0, 1, 2], [1, 2, 3], [0, 2, 3], [0, 1, 3], [0], [1],
[2], [3], [0, 1], [1, 2], [2, 3], [0, 3]]
IPF = ipfn.ipfn(m, aggregates, dimensions, convergence_rate=1e-6)
m = IPF.iteration()
marginals1D = [xippp, xpjpp, xppkp, xpppl]
m_inc = 0
for marginal in marginals1D:
nb_dim = marginal.shape[0]
for dim in range(nb_dim):
if m_inc == 0:
ipfn_number = np.sum(m[dim, :, :, :])
if m_inc == 1:
ipfn_number = np.sum(m[:, dim, :, :])
if m_inc == 2:
ipfn_number = np.sum(m[:, :, dim, :])
if m_inc == 3:
ipfn_number = np.sum(m[:, :, :, dim])
truth_number = marginal[dim]
assert round(ipfn_number, 2) == round(truth_number, 2)
m_inc += 1
marginals2D = [xijpp, xpjkp, xppkl, xippl]
m_inc = 0
for marginal in marginals2D:
nb_dim1, nb_dim2 = marginal.shape
for dim1 in range(nb_dim1):
for dim2 in range(nb_dim2):
if m_inc == 0:
ipfn_number = np.sum(m[dim1, dim2, :, :])
if m_inc == 1:
ipfn_number = np.sum(m[:, dim1, dim2, :])
if m_inc == 2:
ipfn_number = np.sum(m[:, :, dim1, dim2])
if m_inc == 3:
ipfn_number = np.sum(m[dim1, :, :, dim2])
truth_number = marginal[dim1, dim2]
assert round(ipfn_number, 2) == round(truth_number, 2)
m_inc += 1
def test_numpy_3D(self):
m = np.zeros((2, 4, 3))
m[0, 0, 0] = 1
m[0, 0, 1] = 2
m[0, 0, 2] = 1
m[0, 1, 0] = 3
m[0, 1, 1] = 5
m[0, 1, 2] = 5
m[0, 2, 0] = 6
m[0, 2, 1] = 2
m[0, 2, 2] = 2
m[0, 3, 0] = 1
m[0, 3, 1] = 7
m[0, 3, 2] = 2
m[1, 0, 0] = 5
m[1, 0, 1] = 4
m[1, 0, 2] = 2
m[1, 1, 0] = 5
m[1, 1, 1] = 5
m[1, 1, 2] = 5
m[1, 2, 0] = 3
m[1, 2, 1] = 8
m[1, 2, 2] = 7
m[1, 3, 0] = 2
m[1, 3, 1] = 7
m[1, 3, 2] = 6
xipp = np.array([52, 48])
xpjp = np.array([20, 30, 35, 15])
xppk = np.array([35, 40, 25])
xijp = np.array([[9, 17, 19, 7], [11, 13, 16, 8]])
xpjk = np.array([[7, 9, 4], [8, 12, 10], [15, 12, 8], [5, 7, 3]])
aggregates = [xipp, xpjp, xppk, xijp, xpjk]
dimensions = [[0], [1], [2], [0, 1], [1, 2]]
IPF = ipfn.ipfn(m, aggregates, dimensions, convergence_rate=0.0001)
m = IPF.iteration()
marginals1D = [xipp, xpjp, xppk]
m_inc = 0
for marginal in marginals1D:
nb_dim = marginal.shape[0]
for dim in range(nb_dim):
if m_inc == 0:
ipfn_number = np.sum(m[dim, :, :])
if m_inc == 1:
ipfn_number = np.sum(m[:, dim, :])
if m_inc == 2:
ipfn_number = np.sum(m[:, :, dim])
truth_number = marginal[dim]
assert round(ipfn_number, 2) == round(truth_number, 2)
m_inc += 1
marginals2D = [xijp, xpjk]
m_inc = 0
for marginal in marginals2D:
nb_dim1, nb_dim2 = marginal.shape
for dim1 in range(nb_dim1):
for dim2 in range(nb_dim2):
if m_inc == 0:
ipfn_number = np.sum(m[dim1, dim2, :])
if m_inc == 1:
ipfn_number = np.sum(m[:, dim1, dim2])
truth_number = marginal[dim1, dim2]
assert round(ipfn_number, 2) == round(truth_number, 2)
m_inc += 1
def test_pandas_3D(self):
m = np.array([
1., 2., 1., 3., 5., 5., 6., 2., 2., 1., 7., 2., 5., 4., 2., 5., 5.,
5., 3., 8., 7., 2., 7., 6.
], )
dma_l = [
501, 501, 501, 501, 501, 501, 501, 501, 501, 501, 501, 501, 502,
502, 502, 502, 502, 502, 502, 502, 502, 502, 502, 502
]
size_l = [
1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4,
4, 4
]
age_l = [
'20-25', '30-35', '40-45', '20-25', '30-35', '40-45', '20-25',
'30-35', '40-45', '20-25', '30-35', '40-45', '20-25', '30-35',
'40-45', '20-25', '30-35', '40-45', '20-25', '30-35', '40-45',
'20-25', '30-35', '40-45'
]
df = pd.DataFrame()
df['dma'] = dma_l
df['size'] = size_l
df['age'] = age_l
df['total'] = m
xipp = df.groupby('dma')['total'].sum()
xpjp = df.groupby('size')['total'].sum()
xppk = df.groupby('age')['total'].sum()
xijp = df.groupby(['dma', 'size'])['total'].sum()
xpjk = df.groupby(['size', 'age'])['total'].sum()
# xppk = df.groupby('age')['total'].sum()
xipp.loc[501] = 52
xipp.loc[502] = 48
xpjp.loc[1] = 20
xpjp.loc[2] = 30
xpjp.loc[3] = 35
xpjp.loc[4] = 15
xppk.loc['20-25'] = 35
xppk.loc['30-35'] = 40
xppk.loc['40-45'] = 25
xijp.loc[501] = [9, 17, 19, 7]
xijp.loc[502] = [11, 13, 16, 8]
xpjk.loc[1] = [7, 9, 4]
xpjk.loc[2] = [8, 12, 10]
xpjk.loc[3] = [15, 12, 8]
xpjk.loc[4] = [5, 7, 3]
aggregates = [xipp, xpjp, xppk, xijp, xpjk]
dimensions = [['dma'], ['size'], ['age'], ['dma', 'size'],
['size', 'age']]
IPF = ipfn.ipfn(df, aggregates, dimensions, convergence_rate=1e-5)
df = IPF.iteration()
marginals1D = [(xipp, ['dma']), (xpjp, ['size']), (xppk, 'age')]
m_inc = 0
for marginal, vertical in marginals1D:
features = marginal.index.tolist()
for feature in features:
assert round(
df.groupby(vertical)['total'].sum().loc[feature],
2) == round(marginal.loc[feature], 2)
m_inc += 1
marginals2D = [(xijp, ['dma', 'size']), (xpjk, ['size', 'age'])]
m_inc = 0
for marginal, vertical in marginals2D:
features = marginal.index.tolist()
for feature in features:
assert round(
df.groupby(vertical)['total'].sum().loc[feature],
2) == round(marginal.loc[feature], 2)
m_inc += 1
