Python GLRMの例

コード例 #1

ファイル: pca_test.py プロジェクト: JuergenNeubauer/GLRM

def GLRMfit(A, k, missing=None):
    loss = QuadraticLoss
    regX, regY = LinearReg(0.001), LinearReg(0.001)
    model = GLRM(A, loss, regX, regY, k, missing)
    model.fit(eps=1e-4, max_iters=1000)
    model.converge.plot()
    return model.factors()

コード例 #2

def GLRMfit(A, k, missing=None):
    loss = QuadraticLoss
    regX, regY = LinearReg(0.001), LinearReg(0.001)
    model = GLRM(A, loss, regX, regY, k, missing)
    model.fit(eps=1e-4, max_iters=1000)
    model.converge.plot()
    return model.factors()

コード例 #3

ファイル: likert.py プロジェクト: JuergenNeubauer/GLRM

from glrm import GLRM
from glrm.convergence import Convergence
from glrm.util import pplot
from numpy.random import randn, choice, seed
from numpy import sign
from itertools import product
from math import ceil
seed(1)

# Generate problem data
m, n, k = 100, 100, 10
data = randn(m,k).dot(randn(k,n))
data = data - data.min()
data = (data/data.max()*6).round() + 1 # approx rank k
#data = choice(range(7), (m,n)) + 1 # not inherently rank k

# Initialize model
A = data
loss = OrdinalLoss
regX, regY = QuadraticReg(0.1), QuadraticReg(0.1)
glrm_ord = GLRM(A, loss, regX, regY, k)

# Fit
glrm_ord.fit(eps=1e-3, max_iters=1000)

# Results
X, Y = glrm_ord.factors()
A_hat = glrm_ord.predict() # glrm_pca.predict(X, Y) works too; returns decode(XY)
ch = glrm_ord.convergence() # convergence history
pplot([A, A_hat, A-A_hat], ["original", "glrm", "error"])

コード例 #4

# Generate problem data (draw smiley with -1's, 1's)
m, n, k = 500, 500, 8
data = -ones((m, n))
for i,j in product(list(range(120, 190)), list(range(120, 190))): 
    d = (155-i)**2 + (155-j)**2
    if d <= 35**2: 
        data[i,j] = 1
        data[i, m-j] = 1
for i,j in product(list(range(300, 451)), list(range(100, 251))):
    d = (250 - i)**2 + (250-j)**2
    if d <= 200**2 and d >= 150**2: 
        data[i,j] = 1
        data[i,m-j] = 1

# Initialize model
A = data
loss = HingeLoss
regX, regY = QuadraticReg(0.1), QuadraticReg(0.1)
converge = Convergence(TOL = 1e-2)
glrm_binary = GLRM(A, loss, regX, regY, k, converge = converge)

# Fit
glrm_binary.fit()

# Results
X, Y = glrm_binary.factors()
A_hat = glrm_binary.predict() # glrm_pca.predict(X, Y) works too; returns decode(XY)
ch = glrm_binary.convergence() # convergence history
pplot([A, A_hat, A - A_hat], ["original", "glrm", "error"])

コード例 #5

ファイル: hinge.py プロジェクト: JuergenNeubauer/GLRM

from numpy.random import choice
from itertools import product
from numpy import sign

# Generate problem data
m, n, k = 100, 100, 10
eta = 0.1 # noise power
X_true, Y_true = randn(m,k), randn(k,n)
data = sign(X_true.dot(Y_true) + eta*randn(m,n)) # noisy rank k

# Initialize model
A = data
loss = HingeLoss
regX, regY = QuadraticReg(0.01), QuadraticReg(0.01)
c = Convergence(TOL=1e-2)
model = GLRM(A, loss, regX, regY, k, converge=c)

# Fit
model.fit(eps=1e-4, max_iters = 1000) # want more precision for hinge loss problem

# Results
X, Y = model.factors()
A_hat = model.predict() # glrm_pca.predict(X, Y) works too; returns decode(XY)
ch = model.convergence() # convergence history
pplot([A, A_hat, A - A_hat], ["original", "glrm", "error"])
# 
# # Now with missing data
# missing = list(product(range(int(0.25*m), int(0.75*m)), range(int(0.25*n), int(0.75*n))))
# glrm_nn_missing = GLRM(A, loss, regX, regY, k, missing)
# glrm_nn_missing.fit()
# A_hat = glrm_nn_missing.predict()

コード例 #6

ファイル: pca_nonneg.py プロジェクト: pmnyc/Source_Codes_Collected

from numpy.random import randn, choice, seed
from numpy.random import choice
from itertools import product
from numpy import sign

# Generate problem data
m, n, k = 20, 20, 5
eta = 0.1  # noise power
X_true, Y_true = abs(randn(m, k)), abs(randn(k, n))
data = X_true.dot(Y_true) + eta * randn(m, n)  # noisy rank k

# Initialize model
A = data
loss = QuadraticLoss
regX, regY = NonnegativeReg(0.1), NonnegativeReg(0.1)
glrm_nn = GLRM(A, loss, regX, regY, k)

# Fit
glrm_nn.fit()

# Results
X, Y = glrm_nn.factors()
A_hat = glrm_nn.predict(
)  # glrm_pca.predict(X, Y) works too; returns decode(XY)
ch = glrm_nn.convergence()  # convergence history
pplot([A, A_hat, A - A_hat], ["original", "glrm", "error"])

# Now with missing data
missing = list(
    product(range(int(0.25 * m), int(0.75 * m)),
            range(int(0.25 * n), int(0.75 * n))))

コード例 #7

ファイル: TEST__sandbox_for_testing_things.py プロジェクト: KnowledgeLab/GSS_code

from glrm import GLRM
from glrm.loss import QuadraticLoss, HingeLoss
from glrm.reg import QuadraticReg
from glrm.convergence import Convergence


# In[80]:

get_ipython().magic(u'pinfo GLRM')


# In[5]:

regX, regY = QuadraticReg(0.01), QuadraticReg(0.01)

converge = Convergence(TOL = 1e-5, max_iters = 100)

model = GLRM(df.values, QuadraticLoss, regX, regY, k=2, converge=converge)
model.fit()

X, Y = model.factors()
A_hat = model.predict() # a horizontally concatenated matrix, not a list

norm(A_hat - hstack(A_list)) # by hand


# In[ ]:

コード例 #8

ファイル: pca_nonneg.py プロジェクト: wlattner/GLRM

from numpy.random import randn, choice, seed
from numpy.random import choice
from itertools import product
from numpy import sign

# Generate problem data
m, n, k = 20, 20, 5
eta = 0.1 # noise power
X_true, Y_true = abs(randn(m,k)), abs(randn(k,n))
data = X_true.dot(Y_true) + eta*randn(m,n) # noisy rank k

# Initialize model
A = data
loss = QuadraticLoss
regX, regY = NonnegativeReg(0.1), NonnegativeReg(0.1)
glrm_nn = GLRM(A, loss, regX, regY, k)

# Fit
glrm_nn.fit()

# Results
X, Y = glrm_nn.factors()
A_hat = glrm_nn.predict() # glrm_pca.predict(X, Y) works too; returns decode(XY)
ch = glrm_nn.convergence() # convergence history
pplot([A, A_hat, A - A_hat], ["original", "glrm", "error"])

# Now with missing data
missing = list(product(list(range(int(0.25*m), int(0.75*m))), list(range(int(0.25*n), int(0.75*n)))))
glrm_nn_missing = GLRM(A, loss, regX, regY, k, missing)
glrm_nn_missing.fit()
A_hat = glrm_nn_missing.predict()

コード例 #9

ファイル: mf_glrm.py プロジェクト: mufan-li/sg

k = 5 # decomposition rank
N, M = sgdata_matrix.shape

from glrm.loss import QuadraticLoss
from glrm.reg import QuadraticReg

loss = [QuadraticLoss]
regX, regY = [QuadraticReg(regC1), QuadraticReg(regC2)]

A, A_miss, v_miss = find_missing_entries(sgdata_matrix)
A_list = [A]
miss = [A_miss]

start_time = time.time()

model = GLRM(A_list, loss, regX, regY, k, miss)
model.fit()

end_time = time.time()
print 'time:' + str(round(end_time-start_time,1)) + 'seconds'

X, Y = model.factors()
A_hat = model.predict()

error = fbnorm(A_hat - np.hstack(A_list), v_miss)
print 'Frobenius Error: ' + str(round(error,2))
error2 = rmse(A, A_hat, v_miss)
print 'RMSE: ' + str(round(error2,2))

print A[~v_miss][:10]
print A_hat[~v_miss][:10].round()

コード例 #10

ファイル: fractional_pca.py プロジェクト: pmnyc/Source_Codes_Collected

from glrm import GLRM
from glrm.util import pplot
from numpy.random import randn, choice, seed
from numpy import sign, exp
seed(2)

# Generate problem data
m, n, k = 50, 50, 5
eta = 0.1 # noise power
data = exp(randn(m,k).dot(randn(k,n)) + eta*randn(m,n))+eta*randn(m,n) # noisy rank k

# Initialize model
A = data
loss = FractionalLoss
regX, regY = QuadraticReg(0.1), QuadraticReg(0.1)
glrm_frac = GLRM(A, loss, regX, regY, k)

# Fit
glrm_frac.fit()

# Results
X, Y = glrm_frac.factors()
A_hat = glrm_frac.predict() # glrm_pca.predict(X, Y) works too; returns decode(XY)
ch = glrm_frac.convergence() # convergence history
pplot([A, A_hat, A-A_hat], ["original", "glrm", "error"])

# Now with missing data
# from numpy.random import choice
# from itertools import product
# missing = list(product(range(int(0.25*m), int(0.75*m)), range(int(0.25*n), int(0.75*n))))
# 

コード例 #11

from numpy.random import randn, choice, seed
from numpy.random import choice
from itertools import product
from numpy import sign
seed(1)

# Generate problem data
m, n, k = 50, 50, 10
eta = 0.1 # noise power
data = randn(m,k).dot(randn(k,n)) + eta*randn(m,n) # noisy rank k

# Initialize model
A = data
loss = QuadraticLoss
regX, regY = QuadraticReg(0.0001), QuadraticReg(0.0001)
glrm_nn = GLRM(A, loss, regX, regY, k)

# Fit
glrm_nn.fit(eps=1e-4, max_iters=1000)

# Results
X, Y = glrm_nn.factors()
A_hat = glrm_nn.predict() # glrm_pca.predict(X, Y) works too; returns decode(XY)
ch = glrm_nn.convergence() # convergence history
pplot([A, A_hat, A - A_hat], ["original", "glrm", "error"])

# # Now with missing data
# missing = list(product(range(int(0.25*m), int(0.75*m)), range(int(0.25*n), int(0.75*n))))
# glrm_nn_missing = GLRM(A, loss, regX, regY, k, missing)
# glrm_nn_missing.fit()
# A_hat = glrm_nn_missing.predict()

コード例 #12

from numpy.random import choice
from itertools import product
from numpy import sign

# Generate problem data
m, n, k = 100, 100, 10
eta = 0.1  # noise power
X_true, Y_true = randn(m, k), randn(k, n)
data = sign(X_true.dot(Y_true) + eta * randn(m, n))  # noisy rank k

# Initialize model
A = data
loss = HingeLoss
regX, regY = QuadraticReg(0.01), QuadraticReg(0.01)
c = Convergence(TOL=1e-2)
model = GLRM(A, loss, regX, regY, k, converge=c)

# Fit
model.fit(eps=1e-4,
          max_iters=1000)  # want more precision for hinge loss problem

# Results
X, Y = model.factors()
A_hat = model.predict()  # glrm_pca.predict(X, Y) works too; returns decode(XY)
ch = model.convergence()  # convergence history
pplot([A, A_hat, A - A_hat], ["original", "glrm", "error"])
#
# # Now with missing data
# missing = list(product(range(int(0.25*m), int(0.75*m)), range(int(0.25*n), int(0.75*n))))
# glrm_nn_missing = GLRM(A, loss, regX, regY, k, missing)
# glrm_nn_missing.fit()