def matdecomp(imregion, method):
"""Compute matrix decomposition
Parameters
----------
imregion : 2D array
The image region data
method : str
Options for method ('eigen', 'NMF')
"""
if method == 'eigen':
## columns are eigen vectors
e_vals, e_vecs = LA.eig(imregion)
return e_vecs
if method == 'NMF':
model = ProjectedGradientNMF(n_components=2, init='random',random_state=0)
model.fit(imregion)
comp = model.components_
err = model.reconstruction_err_
return comp
def extract_codes(self,
X,
n_components=16,
log_amplitude=True,
**nmf_args):
"""Given a spectrogram, learn a dictionary of 2D patch atoms from spectrogram data
inputs:
X - spectrogram data (frequency x time)
n_components - how many components to extract [16]
log_amplitude - weather to apply log amplitude scaling log(1+X)
**nmf_args - keyword arguments for ProjectedGradientNMF(...) [None]
outputs:
self.data - 2D patches of input spectrogram
self.D.components_ - dictionary of 2D NMF components
"""
zscore = False
self._extract_data_patches(X, zscore, log_amplitude)
self.n_components = n_components
nmf_args.setdefault('sparseness', 'components')
nmf_args.setdefault('init', 'nndsvd')
nmf_args.setdefault('beta', 0.5)
print("NMF...")
self.model = ProjectedGradientNMF(n_components=self.n_components,
**nmf_args)
self.model.fit(self.data)
self.D = self.model
def fit(self, trainSamples, trainTargets):
self.dataModel = MemeryDataModel(trainSamples, trainTargets)
#print 'train user:' + str(self.dataModel.getUsersNum())
V = self.dataModel.getData()
model = ProjectedGradientNMF(n_components=self.factors, max_iter=1000, nls_max_iter=1000)
self.pu = model.fit_transform(V)
self.qi = model.fit(V).components_.transpose()
class NMF(method.Method): def __init__(self, params): self.params = params self.dec = ProjectedGradientNMF(**params) def __str__(self): return "Non-Negative matrix factorization by Projected Gradient (NMF)" def train(self, data): """ Train the NMF on the withened data :param data: whitened data, ready to use """ self.dec.fit(data) def encode(self, data): """ Encodes the ready to use data :returns: encoded data with dimension n_components """ return self.dec.transform(data) def decode(self, components): """ Decode the data to return whitened reconstructed data :returns: reconstructed data """ return self.dec.inverse_transform(components)
def init_rois(self, n_components=100, show=False):
Ain, Cin, center = greedyROI2d(self.Y,
nr=n_components,
gSig=[2, 2],
gSiz=[7, 7],
use_median=False)
Cn = np.mean(self.Y, axis=-1)
if show:
pl1 = pl.imshow(Cn, interpolation='none')
pl.colorbar()
pl.scatter(x=center[:, 1], y=center[:, 0], c='m', s=40)
pl.axis((-0.5, self.Y.shape[1] - 0.5, -0.5, self.Y.shape[0] - 0.5))
pl.gca().invert_yaxis()
active_pixels = np.squeeze(np.nonzero(np.sum(Ain, axis=1)))
Yr = np.reshape(self.Y,
(self.Y.shape[0] * self.Y.shape[1], self.Y.shape[2]),
order='F')
P = arpfit(Yr, p=2, pixels=active_pixels)
Y_res = Yr - np.dot(Ain, Cin)
model = ProjectedGradientNMF(n_components=1,
init='random',
random_state=0)
model.fit(np.maximum(Y_res, 0))
fin = model.components_.squeeze()
self.Yr, self.Cin, self.fin, self.Ain, self.P, self.Cn = Yr, Cin, fin, Ain, P, Cn
def get_cluster_membership(self):
""" Determine the cluster number that each sample is associated with. """
model = ProjectedGradientNMF(n_components=self._num_clusters,
init='random',
beta=.3,
eta=.5,
max_iter=5000)
w = model.fit_transform(self._matrix)
h = model.components_
# convert the 'H' matrix, which represents weights for our data matrix W, into
# an array representing cluster membership. Index of biggest value in each
# col of matrix H is the cluster
clusters = []
model_width = len(h[0])
for col_idx in range(model_width):
max_val = dict()
for row_idx in range(self._num_clusters):
h_val = h[row_idx][col_idx]
if not max_val or h_val > max_val['val']:
max_val = {'row_idx': row_idx, 'val': h_val}
clusters.append(max_val['row_idx'])
# clusters array, w, h
return (clusters, w, h)
def _nmf(X, K):
nmf = ProjectedGradientNMF(n_components=K, max_iter=1000)
nmf.fit(X)
B = nmf.components_
A = np.dot(X, np.linalg.pinv(B))
return (A, B)
def nmfModel(matrix, nTopics):
t=time()
print "Starting Factorization"
nmf = ProjectedGradientNMF(nTopics, max_iter=220, sparseness='data', init='nndsvd')
W = nmf.fit_transform(matrix)
H = nmf.components_
print "Factorization took %s minutes"%(round((time()-t)/60., 2))
return W, H, nmf
def fit(self, trainSamples, trainTargets):
self.dataModel = MemeryDataModel(trainSamples, trainTargets)
#print 'train user:' + str(self.dataModel.getUsersNum())
V = self.dataModel.getData()
model = ProjectedGradientNMF(n_components=self.factors,
max_iter=1000,
nls_max_iter=1000)
self.pu = model.fit_transform(V)
self.qi = model.fit(V).components_.transpose()
def nmf(self, k):
nmf = ProjectedGradientNMF(n_components=k, max_iter=200)
P = nmf.fit_transform(self.tdm)
Q = nmf.components_.T
self.P = P
self.Q = Q
self.er = nmf.reconstruction_err_
#print "\tError: ", self.er
return P, Q
def extract_codes(self, X, **kwargs):
self.standardize=False
self._extract_data_patches(X)
kwargs.setdefault('sparseness','components')
kwargs.setdefault('init','nndsvd')
kwargs.setdefault('beta',0.5)
print("NMF...")
self.model = ProjectedGradientNMF(n_components=self.n_components, **kwargs)
self.model.fit(self.data)
self.D = self.model
return self
def __init__(self,model,beta=1, eta=0.1, init='nndsvd', max_iter=500,
n_components=100, nls_max_iter=2000, random_state=0, sparseness=None,tol=0.0001):
self.check_non_negtive(model)
self.model = model
super(NMFpredictor,self).__init__()
self.nmf = ProjectedGradientNMF(beta=beta, eta=eta, init=init, max_iter=max_iter,
n_components=n_components, nls_max_iter=nls_max_iter, random_state=random_state,
sparseness=sparseness,tol=tol)
self.user_latent_M, self.item_latent_M = self.construct_latent_matrics()
def calcNMF(delta_data, components):
data = preprocess(delta_data)
nmf = ProjectedGradientNMF(n_components=components)
x_nmf = nmf.fit_transform(data['cleanMatrix'])
nmf_fill = np.ones((delta_data.shape[0],components))*np.nan
nmf_fill[data['cleanind']] = x_nmf
nmf_weights = nmf.components_.T
delta_nmf = {'transform':nmf_fill,
'weights' : nmf_weights,
}
return delta_nmf
def __nmf_initialization(A, ncomms):
try:
from sklearn.decomposition import ProjectedGradientNMF
except ImportError:
print("sklearn module is missing.")
return
model = ProjectedGradientNMF(n_components=ncomms, init='nndsvd')
Uin = np.asmatrix(model.fit_transform(A))
Vin = np.asmatrix(model.components_)
Vin = Vin.T
init_dict = {'U': Uin, 'V': Vin}
return init_dict
class SparseApproxSpectrumNonNegative(SparseApproxSpectrum):
"""Non-negative sparse dictionary learning from 2D spectrogram patches
initialization:
patch_size=(12,12) - size of time-frequency 2D patches in spectrogram units (freq,time)
max_samples=1000000 - if num audio patches exceeds this threshold, randomly sample spectrum
"""
def __init__(self, patch_size=(12, 12), max_samples=1000000):
self.patch_size = patch_size
self.max_samples = max_samples
self.D = None
self.data = None
self.components = None
self.zscore = False
self.log_amplitude = False
def extract_codes(self,
X,
n_components=16,
log_amplitude=True,
**nmf_args):
"""Given a spectrogram, learn a dictionary of 2D patch atoms from spectrogram data
inputs:
X - spectrogram data (frequency x time)
n_components - how many components to extract [16]
log_amplitude - weather to apply log amplitude scaling log(1+X)
**nmf_args - keyword arguments for ProjectedGradientNMF(...) [None]
outputs:
self.data - 2D patches of input spectrogram
self.D.components_ - dictionary of 2D NMF components
"""
zscore = False
self._extract_data_patches(X, zscore, log_amplitude)
self.n_components = n_components
nmf_args.setdefault('sparseness', 'components')
nmf_args.setdefault('init', 'nndsvd')
nmf_args.setdefault('beta', 0.5)
print("NMF...")
self.model = ProjectedGradientNMF(n_components=self.n_components,
**nmf_args)
self.model.fit(self.data)
self.D = self.model
def reconstruct_spectrum(self, w=None, randomize=False):
"reconstruct by fitting current NMF 2D dictionary to self.data"
if w is None:
self.w = self.model.transform(self.data)
w = self.w
return SparseApproxSpectrum.reconstruct_spectrum(self,
w=w,
randomize=randomize)
def test_projgrad_nmf_sparseness():
# Test sparseness
# Test that sparsity constraints actually increase sparseness in the
# part where they are applied.
tol = 1e-2
A = np.abs(random_state.randn(10, 10))
m = ProjectedGradientNMF(n_components=5, random_state=0, tol=tol).fit(A)
data_sp = ProjectedGradientNMF(n_components=5, sparseness='data',
random_state=0,
tol=tol).fit(A).data_sparseness_
comp_sp = ProjectedGradientNMF(n_components=5, sparseness='components',
random_state=0,
tol=tol).fit(A).comp_sparseness_
assert_greater(data_sp, m.data_sparseness_)
assert_greater(comp_sp, m.comp_sparseness_)
def reducedim_nmf(self,factors):
print "Number of factors is "+str(factors)
model = ProjectedGradientNMF(n_components=factors,init='random',random_state=0)
self.reducedmatrix= model.fit_transform(self.fullmatrix) #left factor w (n*k)
h= model.components_ #right factor h (k*d)
if self.testing:
print self.fullmatrix
print self.reducedmatrix
print h
v = numpy.dot(self.reducedmatrix,h)
print v
print "Completed NMF routine"
for vector in self.vectordict.values():
vector.array=sparse.csc_matrix(self.reducedmatrix[vector.rowindex])
print "Stored individual vectors"
def train_model(self):
print 'begin'
RATE_MATRIX = np.zeros((9238, 7973))
for line in self.train.values:
print line
uid = int(float(line[1]))
iid = int(float(line[2]))
RATE_MATRIX[uid][iid] = int(float(line[3]))
V = spr.csr_matrix(RATE_MATRIX)
model = ProjectedGradientNMF(n_components=self.n_features, max_iter=1000, nls_max_iter=10000)
self.pu = model.fit_transform(V)
self.qi = model.fit(V).components_.transpose()
print model.reconstruction_err_
self.ValidateF1()
t = pd.DataFrame(np.array(self.pu))
t.to_csv('50pu')
t = pd.DataFrame(np.array(self.qi))
t.to_csv('50qi')
print("model generation over")
def recommend(matrix_3filled, matrix_raw, user, numOfNeighbors=5):
# The following 3 lines uses Scikit-learn. For more information, refer to the documentation link in README.
model = ProjectedGradientNMF(n_components=2, init='random', random_state=0)
model.fit(matrix_3filled)
# transformed matrix is the result of non-negative matrix factorization, and we will use this for the recommendations
transformed = np.dot(model.fit_transform(matrix_3filled), model.components_)
neighbors=[]
# Calculate distances from the current user to every other users.
distances = np.sum((transformed-transformed[user])**2, axis=1)
# Find nearest neighbors.
for x in xrange(numOfNeighbors):
distances[np.argmin(distances)] = sys.float_info.max
neighbors.append(np.argmin(distances))
# Get an average for nearest neighbors. average is a vector containing the average rating for each humor.
average=[0.0]*transformed.shape[1]
for x in xrange(numOfNeighbors):
average += transformed[neighbors[x]]
average = average/numOfNeighbors
# Find the unrated items for current users.
unratedItems=[]
for x in xrange(np.shape(matrix_raw)[1]):
if matrix_raw[user][x] == 0:
unratedItems.append(x)
# If there are no unrated items, just return an item with max average rating.
if len(unratedItems) is 0:
item = np.argmax(average)
return item
# Else, return an unrated item with max average rating.
else:
maxAverage = 0
item = np.argmax(average)
for x in xrange(len(unratedItems)):
if average[unratedItems[x]] > maxAverage:
maxAverage = average[unratedItems[x]]
item = unratedItems[x]
return item
def matrixFactorization(inmatrix, p_components=False): from sklearn.decomposition import PCA from sklearn.decomposition import ProjectedGradientNMF import pdb if p_components: p_comp = p_components else: pca = PCA(n_components=inmatrix.shape[1]) pca.fit(inmatrix) explained_variance = pca.explained_variance_ratio_.cumsum() explained_variance = explained_variance[explained_variance <= .9] p_comp = len(explained_variance) model = ProjectedGradientNMF(n_components=p_comp, init='nndsvd', beta=1, sparseness=None) #pdb.set_trace() model.fit(inmatrix) return model
class SparseApproxSpectrumNonNegative(SparseApproxSpectrum):
"""Non-negative sparse dictionary learning from 2D spectrogram patches
initialization:
patch_size=(12,12) - size of time-frequency 2D patches in spectrogram units (freq,time)
max_samples=1000000 - if num audio patches exceeds this threshold, randomly sample spectrum
"""
def __init__(self, patch_size=(12,12), max_samples=1000000):
self.patch_size = patch_size
self.max_samples = max_samples
self.D = None
self.data = None
self.components = None
self.zscore=False
self.log_amplitude=False
def extract_codes(self, X, n_components=16, log_amplitude=True, **nmf_args):
"""Given a spectrogram, learn a dictionary of 2D patch atoms from spectrogram data
inputs:
X - spectrogram data (frequency x time)
n_components - how many components to extract [16]
log_amplitude - weather to apply log amplitude scaling log(1+X)
**nmf_args - keyword arguments for ProjectedGradientNMF(...) [None]
outputs:
self.data - 2D patches of input spectrogram
self.D.components_ - dictionary of 2D NMF components
"""
zscore=False
self._extract_data_patches(X, zscore, log_amplitude)
self.n_components=n_components
nmf_args.setdefault('sparseness','components')
nmf_args.setdefault('init','nndsvd')
nmf_args.setdefault('beta',0.5)
print "NMF..."
self.model = ProjectedGradientNMF(n_components=self.n_components, **nmf_args)
self.model.fit(self.data)
self.D = self.model
def reconstruct_spectrum(self, w=None, randomize=False):
"reconstruct by fitting current NMF 2D dictionary to self.data"
if w is None:
self.w = self.model.transform(self.data)
w = self.w
return SparseApproxSpectrum.reconstruct_spectrum(self, w=w, randomize=randomize)
def extract_codes(self, X, **kwargs):
self.standardize=False
self._extract_data_patches(X)
kwargs.setdefault('sparseness','components')
kwargs.setdefault('init','nndsvd')
kwargs.setdefault('beta',0.5)
print "NMF..."
self.model = ProjectedGradientNMF(n_components=self.n_components, **kwargs)
self.model.fit(self.data)
self.D = self.model
return self
def init_rois(self, n_components=100, show=False):
Ain,Cin,center = greedyROI2d(self.Y, nr=n_components, gSig=[2,2], gSiz=[7,7], use_median=False)
Cn = np.mean(self.Y, axis=-1)
if show:
pl1 = pl.imshow(Cn,interpolation='none')
pl.colorbar()
pl.scatter(x=center[:,1], y=center[:,0], c='m', s=40)
pl.axis((-0.5,self.Y.shape[1]-0.5,-0.5,self.Y.shape[0]-0.5))
pl.gca().invert_yaxis()
active_pixels = np.squeeze(np.nonzero(np.sum(Ain,axis=1)))
Yr = np.reshape(self.Y,(self.Y.shape[0]*self.Y.shape[1],self.Y.shape[2]),order='F')
P = arpfit(Yr, p=2, pixels=active_pixels)
Y_res = Yr - np.dot(Ain,Cin)
model = ProjectedGradientNMF(n_components=1, init='random', random_state=0)
model.fit(np.maximum(Y_res,0))
fin = model.components_.squeeze()
self.Yr,self.Cin,self.fin,self.Ain,self.P,self.Cn = Yr,Cin,fin,Ain,P,Cn
def reducedim_nmf(self, factors):
print "Number of factors is " + str(factors)
model = ProjectedGradientNMF(n_components=factors,
init='random',
random_state=0)
self.reducedmatrix = model.fit_transform(
self.fullmatrix) #left factor w (n*k)
h = model.components_ #right factor h (k*d)
if self.testing:
print self.fullmatrix
print self.reducedmatrix
print h
v = numpy.dot(self.reducedmatrix, h)
print v
print "Completed NMF routine"
for vector in self.vectordict.values():
vector.array = sparse.csc_matrix(
self.reducedmatrix[vector.rowindex])
print "Stored individual vectors"
class NMFSpectrum(SparseApproxSpectrum):
def __init__(self, **kwargs):
SparseApproxSpectrum.__init__(self,**kwargs)
def extract_codes(self, X, **kwargs):
self.standardize=False
self._extract_data_patches(X)
kwargs.setdefault('sparseness','components')
kwargs.setdefault('init','nndsvd')
kwargs.setdefault('beta',0.5)
print("NMF...")
self.model = ProjectedGradientNMF(n_components=self.n_components, **kwargs)
self.model.fit(self.data)
self.D = self.model
return self
def reconstruct_spectrum(self, w=None, randomize=False):
if w is None:
self.w = self.model.transform(self.data)
w = self.w
return SparseApproxSpectrum.reconstruct_spectrum(self, w=w, randomize=randomize)
def decomposition(V, W, H, n_components, solver='mu', update_H=True):
if solver != 'project':
W, H, _ = non_negative_factorization(V,
W=W,
H=H,
n_components=n_components,
update_H=update_H,
max_iter=1000,
solver=solver)
#regularization='transformation', l1_ratio=0.1)
else:
model = ProjectedGradientNMF(n_components=n_components,
init='random',
random_state=0,
sparseness='data',
beta=0,
max_iter=100000)
model.fit(V)
H = model.components_
W = model.fit_transform(V)
return W, H
class NMFSpectrum(SparseApproxSpectrum):
def __init__(self, **kwargs):
SparseApproxSpectrum.__init__(self,**kwargs)
def extract_codes(self, X, **kwargs):
self.standardize=False
self._extract_data_patches(X)
kwargs.setdefault('sparseness','components')
kwargs.setdefault('init','nndsvd')
kwargs.setdefault('beta',0.5)
print "NMF..."
self.model = ProjectedGradientNMF(n_components=self.n_components, **kwargs)
self.model.fit(self.data)
self.D = self.model
return self
def reconstruct_spectrum(self, w=None, randomize=False):
if w is None:
self.w = self.model.transform(self.data)
w = self.w
return SparseApproxSpectrum.reconstruct_spectrum(self, w=w, randomize=randomize)
def _nmf_fixed_component(self, i, X):
"""
Uses sklearn to make the non negative factorization
input: i, number of clusters for this NMF instance
author: Arthur Desjardins
"""
model = ProjectedGradientNMF(n_components=i, init='nndsvd')
model.fit(X)
# H-matrix (clusters x words)
H = model.components_
# W-matrix (documents x clusters)
W = model.transform(X)
# word matrix
words = open(attributFile).read().split()
# processing extremely basic cluster bush
most_relevant_words = np.argmax(H, axis=1)
docs_per_cluster = [0]*i
for tweet in W:
most_relevant_cluster = np.argmax(tweet)
docs_per_cluster[most_relevant_cluster] += 1
clusters = dict(((words[most_relevant_words[i]], docs_per_cluster[i])
for i in range(0, i)))
return clusters
def _nonNegativeFactorization(self):
"""
Uses sklearn to make the non negative factorization
"""
print 'Loading data..'
X = np.asmatrix(np.loadtxt(dataFile))
print 'Data loaded. Making model..'
model = ProjectedGradientNMF(init='nndsvd')
print 'Fitting model..'
model.fit(X)
print 'Model fit'
print 'Error rate is', model.reconstruction_err_
# H-matrix
outFile1 = open(factoredHMatrix, 'w')
np.savetxt(outFile1, model.components_, fmt='%i')
outFile1.close
# W-matrix
outFile2 = open(factoredWMatrix, 'w')
np.savetxt(outFile2, model.transform(X), fmt='%i')
outFile2.close
def perform_nmf(X, w_dir):
# factorize composition into components
print "Performing NMF..."
n_com = 48
model = ProjectedGradientNMF(n_components=n_com,
sparseness='data',
beta=1,
eta=0.9,
tol=0.000001,
max_iter=2000,
nls_max_iter=5000,
random_state=None)
model.fit(X)
print model.reconstruction_err_
nmf_components = model.components_
print "done."
# visualize Base Rules
# nmf_components = project_data(nmf_components)
f_name = w_dir + "base_rules_48.png"
visualize_base_rules(nmf_components, n_com, f_name)
return model
def extract_codes(self, X, n_components=16, log_amplitude=True, **nmf_args):
"""Given a spectrogram, learn a dictionary of 2D patch atoms from spectrogram data
inputs:
X - spectrogram data (frequency x time)
n_components - how many components to extract [16]
log_amplitude - weather to apply log amplitude scaling log(1+X)
**nmf_args - keyword arguments for ProjectedGradientNMF(...) [None]
outputs:
self.data - 2D patches of input spectrogram
self.D.components_ - dictionary of 2D NMF components
"""
zscore=False
self._extract_data_patches(X, zscore, log_amplitude)
self.n_components=n_components
nmf_args.setdefault('sparseness','components')
nmf_args.setdefault('init','nndsvd')
nmf_args.setdefault('beta',0.5)
print "NMF..."
self.model = ProjectedGradientNMF(n_components=self.n_components, **nmf_args)
self.model.fit(self.data)
self.D = self.model
class NMFpredictor(Predictor):
def __init__(self,model,beta=1, eta=0.1, init='nndsvd', max_iter=500,
n_components=100, nls_max_iter=2000, random_state=0, sparseness=None,tol=0.0001):
self.check_non_negtive(model)
self.model = model
super(NMFpredictor,self).__init__()
self.nmf = ProjectedGradientNMF(beta=beta, eta=eta, init=init, max_iter=max_iter,
n_components=n_components, nls_max_iter=nls_max_iter, random_state=random_state,
sparseness=sparseness,tol=tol)
self.user_latent_M, self.item_latent_M = self.construct_latent_matrics()
def construct_latent_matrics(self):
start = time.time()
data_matrix = self.model.get_data_matrix()
user_latent_M = self.nmf.fit_transform(data_matrix)
item_latent_M = self.nmf.components_
print "use time: ", time.time() - start
return user_latent_M, item_latent_M
def predict(self,user_id, item_id):
user_no = self.model.user_id_to_no[user_id]
item_no = self.model.item_id_to_no[item_id]
pref = np.dot(self.user_latent_M[user_no,:], self.item_latent_M[:,item_no])
if pref > self.model.max_pref:
pref = self.model.max_pref
if pref < self.model.min_pref:
pref = self.model.min_pref
return pref
def check_non_negtive(self,model):
if model.min_pref < 0:
raise NotImplementedError("non_negtive!")
import numpy
client = MongoClient('mongodb://localhost:27017/')
mydb = client['movie_database']
movies = mydb.movies.find()
i = 1
for movie in movies:
print str(i)+" >> "+movie.get("title") +"--"+ movie.get("_id")
i = i + 1
users = mydb.users.find()
i = 1
for user in users:
print str(i) + " >>" + user.get("_id") + "--" + user.get("password")
activities = mydb.activity.find()
i = 1
for activity in activities:
print str(i) + " >>" + str(activity)
A = numpy.random.uniform(size = [40, 30])
nmf_model = ProjectedGradientNMF(n_components = 5, init='random', random_state=0)
W = nmf_model.fit_transform(A);
H = nmf_model.components_;
print W
print H
fr = frame.drop('Email', 1)
#NMF will not use email or total score
fr = fr.drop('Total Score', 1)
feature_names = fr.columns
X = np.array(fr.astype(float))
'''for i in range(60): #Test error as a function of number of topics
model = ProjectedGradientNMF(n_components=i, init='nndsvda',random_state=0,max_iter=500)
model.fit(X)
print (i,model.reconstruction_err_);'''
model = ProjectedGradientNMF(n_components=11,
init='nndsvda',
random_state=0,
max_iter=500) #Perform the NMF
Xtrans = model.fit_transform(X)
for topic_idx, topic in enumerate(
model.components_
): #Print the rubric items with strongest contribution in topics
sorte = np.sort(topic)[::-1]
sorteargs = np.argsort(topic)[::-1]
i = 0
print("Topic #%d:" % topic_idx)
while (sorte[i] > 1.5
): #Only show things where contribution is large (1.5 is arbitrary)
print feature_names[sorteargs[i]], np.mean(
np.transpose(X)[sorteargs[i]]) / ptvals[feature_names[
sorteargs[i]]]
if ans != "y":
exit()
from sklearn.cluster import MiniBatchKMeans, KMeans
km = MiniBatchKMeans(n_clusters=k,
init='k-means++',
n_init=1,
init_size=1000,
batch_size=1000,
verbose=1)
km2 = KMeans(n_clusters=k, init='k-means++', verbose=1)
y2 = km2.fit_transform(X)
topics5 = [[(km.cluster_centers_[l][i], feature_names[i])
for i in np.argsort(-np.abs(km.cluster_centers_[l]))[:10]]
for l in range(k)]
print topics5
### NMF #######################
ans = raw_input("Start NMF with Scikit ? ")
if ans != "y":
exit()
from sklearn.decomposition import ProjectedGradientNMF
# BEWARE : THIS IS COMPUTATIONNALY INTENSIVE
nmf = ProjectedGradientNMF(n_components=k, max_iter=10, nls_max_iter=100)
nmf.fit(X)
topics6 = [[(nmf.components_[l][i], feature_names[i])
for i in np.argsort(-np.abs(nmf.components_[l]))[:10]]
for l in range(k)]
def select_features_nmf(train_X, train_y, test_X, k):
selector = ProjectedGradientNMF(n_components=k, init='nndsvd', random_state=42)
selector.fit(train_X)
train_X = selector.transform(train_X)
test_X = selector.transform(test_X)
return train_X, test_X
def main():
es_client = Elasticsearch(hosts = [{ "host" : "localhost", "port" : 9200 }])
index_name = "slclusters"
if es_client.indices.exists(index_name):
print("deleting '%s' index..." % (index_name))
print(es_client.indices.delete(index = index_name, ignore=[400, 404]))
print("creating '%s' index..." % (index_name))
print(es_client.indices.create(index = index_name))
import re
rr=re.compile(r"[\w']+")
tok=lambda a:rr.findall(a)
ff1=open('../docker/syslog.csv').readlines()
aa=[]
for d in ff1:
#print(d)
try:
aa.append(json.loads(d))
except:
continue
print(len(aa))
# ff='\n'.join(ff1)
docs=[]
other=[]
# aa=json.loads(ff)
#print(aa)
for iii,row in enumerate(aa):
if len(tok(row['syslog_message']))>3:
doc={}
doc['created_at']=datetime.strptime(row["@timestamp"], "%Y-%m-%dT%H:%M:%S.000Z")
doc['text']=row['syslog_message']
docs.append(doc['text'])
other.append( doc['created_at'] )
print(doc['text'])
print(tok(doc['text']))
print()
if len(docs)>=100000:
break
cv=CountVectorizer(tokenizer=tok, max_df=0.5,min_df=5)
# for iii,t in enumerate(tc):
# print(iii,t)
# if iii>100:
# break
M=cv.fit_transform(docs).astype(np.float)
M2=Normalizer(copy=False).fit_transform(M)
km=KMeans(n_clusters=30, init='k-means++', max_iter=200, n_init=5,\
verbose=True)
km.fit_transform(M2)
clusters=km.labels_
sortInds=[i[0] for i in sorted(enumerate(clusters), key=lambda x:x[1])]
nmf=ProjectedGradientNMF(n_components=30)
M3=nmf.fit_transform(M2)
print(M3.shape)
tDict={}
maxInd=0
esDocs=[]
for iii in sortInds:
dd={}
dd['message']=docs[iii]
dd['cluster']=int(clusters[iii])
c2=tuple(np.argsort(M3[iii,:])[-1:])
if c2 in tDict:
cc=tDict[c2]
else:
cc=maxInd
tDict[c2]=maxInd
maxInd=maxInd+1
dd['cluster2']=cc
dd['created_at']=other[iii]
esDocs.append(dd)
#print(clusters[iii],other[iii],other[iii])
res = helpers.bulk(es_client, esDocs, index=index_name, doc_type='syslogmsg', refresh=True)
def main():
es_client = Elasticsearch(hosts = [{ "host" : "localhost", "port" : 9200 }])
index_name = "twclusters"
if es_client.indices.exists(index_name):
print("deleting '%s' index..." % (index_name))
print(es_client.indices.delete(index = index_name, ignore=[400, 404]))
print("creating '%s' index..." % (index_name))
print(es_client.indices.create(index = index_name))
from tokenizers import tokenize_nor,get_nor_stopwords
tok=lambda a:tokenize_nor(a,get_nor_stopwords())
docs=[]
other=[]
conn=sqlite3.connect('../data/tweets.sqlite')
cur=conn.execute('select * from T')
for iii,row in enumerate(cur):
doc={}
doc['_id']=row[3]
doc['created_at']=datetime.strptime(row[0], "%Y-%m-%d %H:%M:%S")
doc['author_id']=row[1]
doc['text']=row[4]
doc['language']=row[5]
if len(tok(doc['text']))>2:
docs.append(doc['text'])
other.append( (doc['created_at'],doc['author_id']) )
if len(docs)>=100000:
break
cur.close()
cv=CountVectorizer(tokenizer=tok, max_df=0.5,min_df=5)
# for iii,t in enumerate(tc):
# print(iii,t)
# if iii>100:
# break
M=cv.fit_transform(docs).astype(np.float)
M2=Normalizer(copy=False).fit_transform(M)
km=KMeans(n_clusters=20, init='k-means++', max_iter=200, n_init=5,\
verbose=True)
km.fit_transform(M2)
clusters=km.labels_
sortInds=[i[0] for i in sorted(enumerate(clusters), key=lambda x:x[1])]
nmf=ProjectedGradientNMF(n_components=10)
M3=nmf.fit_transform(M2)
print(M3.shape)
tDict={}
maxInd=0
esDocs=[]
for iii in sortInds:
dd={}
dd['tweet']=docs[iii]
dd['cluster']=int(clusters[iii])
c2=tuple(np.argsort(M3[iii,:])[-2:])
if c2 in tDict:
cc=tDict[c2]
else:
cc=maxInd
tDict[c2]=maxInd
maxInd=maxInd+2
dd['cluster2']=cc
dd['created_at']=other[iii][1]
dd['author_id']=other[iii][0]
esDocs.append(dd)
#print(clusters[iii],other[iii],other[iii])
res = helpers.bulk(es_client, esDocs, index=index_name, doc_type='tweet', refresh=True)
et = ExtraTreesClassifier()
ab = AdaBoostClassifier()
clf2 = svm.LinearSVC(penalty='l1', loss='l2', C=100, dual=False)
clf = svm.SVC(kernel='rbf')
logreg = linear_model.LogisticRegression(C=100, penalty='l2')
knn = KNeighborsClassifier(n_neighbors=5)
sgdc = SGDClassifier()
gnb = GaussianNB()
mnb = MultinomialNB()
bnb = BernoulliNB()
prcp = Perceptron()
rbm = BernoulliRBM(random_state=0, verbose=True)
rbm.learning_rate = 0.02
rbm.n_iter = 20
rbm.n_components = 1000
NMF = ProjectedGradientNMF(n_components=2, init='random', random_state=0)
PCA = PCA()
LDA = LDA()
#ICA = ICA()
classifier = Pipeline(steps=[('rbm', rbm), ('logreg', logreg)])
file_handler_features = open('feature_vectors_heroes.csv', 'r')
def unique(training_data, test_data):
for item in training_data:
if item in test_data:
print 'Item in test'
def hold_out(training_data, results):
model=ProjectedGradientNMF(n_components=latentFactorNum,init='nndsvd',tol=tol,max_iter=max_iter);
print "nnmf start:",datetime.now();
W,H=model.fit_transform(mat);
print "nnmf end:",datetime.now();
return W,H;
if __name__=="__main__":
if len(argv)!=3:
print "usage:",argv[0],"datafile_prefix threshold";
else:
t,users=load_index_map(argv[1]+".user");
t,brands=load_index_map(argv[1]+".brand");
clickMat=convert(argv[1]+".clk.lbm",len(users),len(brands));
buyMat=convert(argv[1]+".buy.lbm",len(users),len(brands));
testUCMat=convert("data/8.clk.lbm",len(users),len(brands)).todense();
testUBMat=convert("data/8.clk.lbm",len(users),len(brands)).todense();
model=ProjectedGradientNMF(n_components=50,init='nndsvd',tol=1e-8,max_iter=1000);
print "nnmf start:",datetime.now();
#W,H=model.fit_transform(clickMat);
W,H=model.fit_transform(buyMat);
print "nnmf end:",datetime.now();
Y=np.dot(W,H); # prediction
#cuMat=np.transpose(clickMat).todense();
#cbMat=cuMat.dot(buyMat.todense());
#buyPredict=np.dot(Y,cbMat);
buyPredict=Y;
#print "error=",norm(clickMat-Y);
fout=open("/tmp/score","w");
for i in range(len(users)):
content=users[i];
for j in range(len(brands)):
def nmf(mat,latentFactorNum=50,tol=1e-8,max_iter=1000): model=ProjectedGradientNMF(n_components=latentFactorNum,init='nndsvd',tol=tol,max_iter=max_iter); print "nnmf start:",datetime.now(); W,H=model.fit_transform(mat); print "nnmf end:",datetime.now(); return W,H;
# Split into training and test
#answers_train, answers_test, cats_train, cats_test = train_test_split(answers, cats, test_size = 0.3)#, random_state=42)
# Word counts
count_vect = CountVectorizer(stop_words = 'english')
answers_train = count_vect.fit_transform(answers_train)
answers_test = count_vect.transform(answers_test)
# Tf-idf
tfidf_transformer = TfidfTransformer()
answers_train = tfidf_transformer.fit_transform(answers_train)
answers_test = tfidf_transformer.transform(answers_test)
# NMF fit on training set
print("Fitting NMF on training word count matrix with shape" + str(answers_train.shape))
nmf = ProjectedGradientNMF(n_components = 100, max_iter=200)
answers_train = nmf.fit_transform(answers_train)
answers_test = nmf.transform(answers_test)
# Fit SVM classifier
print("Fitting SVM classifier on matrix with shape" + str(answers_train.shape))
svc = svm.LinearSVC()
svc.fit(answers_train, cats_train)
print("SVM train classification %: " + str(svc.score(answers_train, cats_train) * 100))
print("SVM test classification %: " + str(svc.score(answers_test, cats_test) * 100))
mc_label = Counter(cats_train).most_common(1)[0][0]
print("Best guess % = " + str( float(Counter(cats_test)[mc_label]) / len(cats_test) * 100))
# Metrics
np.set_printoptions(linewidth=200, precision=3)
Cn = local_correlations(Y) plt1 = plt.imshow(Cn,interpolation='none') plt.colorbar() plt.scatter(x=center[:,1], y=center[:,0], c='m', s=40) plt.axis((-0.5,d2-0.5,-0.5,d1-0.5)) plt.gca().invert_yaxis() #%% crd = plot_contours(coo_matrix(Ain[:,::-1]),Cn,thr=0.9) #%% active_pixels = np.squeeze(np.nonzero(np.sum(Ain,axis=1))) Yr = np.reshape(Y,(d1*d2,T),order='F') p = 2; P = arpfit(Yr,p=1,pixels = active_pixels) Y_res = Yr - np.dot(Ain,Cin) model = ProjectedGradientNMF(n_components=1, init='random', random_state=0) model.fit(np.maximum(Y_res,0)) fin = model.components_.squeeze() #%% t1 = time() A,b,Cin = update_spatial_components(Yr, Cin, fin, Ain, d1=d1, d2=d2, sn = P['sn'],dist=2,max_size=8,min_size=3) t_elSPATIAL = time() - t1 #%% crd = plot_contours(A,Cn2,thr=0.9,cmap=pl.cm.gray) #%% t1 = time() C,f,Y_res,Pnew = update_temporal_components(Yr,A,b,Cin,fin,ITER=2,deconv_method = 'spgl1') t_elTEMPORAL2 = time() - t1 #%% t1 = time()
import numpy as np X = np.array([[1,1,2,3], [2, 1,4,5], [3, 2,4,5], [4, 1,2,1], [5, 4,3,1], [6, 1,4,3]]) from sklearn.decomposition import ProjectedGradientNMF model = ProjectedGradientNMF(n_components=2, init='random', random_state=0) print model.fit(X) #ProjectedGradientNMF(beta=1, eta=0.1, init='random', max_iter=200, # n_components=2, nls_max_iter=2000, random_state=0, sparseness=None, # tol=0.0001) print model.components_ #array([[ 0.77032744, 0.11118662], # [ 0.38526873, 0.38228063]]) print model.reconstruction_err_ #0.00746... W = model.fit_transform(X); H = model.components_; print 'w: ' + str(W) print 'h: ' + str(H) model = ProjectedGradientNMF(n_components=2, sparseness='components', init='random', random_state=0) print model.fit(X) #ProjectedGradientNMF(beta=1, eta=0.1, init='random', max_iter=200, # n_components=2, nls_max_iter=2000, random_state=0, # sparseness='components', tol=0.0001)
global indexes
parser = argparse.ArgumentParser(description='Compute Non-negative Matrix Factorization')
parser.add_argument('data_matrix', help='path to data file, should be readable by numpy')
parser.add_argument('k', type=int, help='number of components to keep')
parser.add_argument('feature_list', help='path to file containing list of feature names')
parser.add_argument('index_file', help='path to array_index for this dataset')
args = vars(parser.parse_args())
data = np.loadtxt(args['data_matrix'])
k = args['k']
with open(args['feature_list']) as f:
feature_list = map(str.rstrip, f.readlines())
indexes = np.loadtxt(args['index_file'])
model = ProjectedGradientNMF(n_components=k, init='random', random_state=0)
H = model.fit_transform(data) # H is submissions(row) by factors(cols)
W = model.components_ # W is factors(row) by features(cols)
magnitude = np.prod([np.sum(H, axis = 0), np.sum(W, axis = 1)], axis = 0)
savetxt_3d(np.array(sort_by_row(W))[:, 0:20, :], 'nmf/factors_and_sorted_features.np', "factor")
show_feature_name('nmf/factors_and_sorted_features.np', feature_list)
subs_and_sorted_factors = sort_by_row(H)
for sub in subs_and_sorted_factors:
for factor in sub:
factor[0] += 1
savetxt_3d(subs_and_sorted_factors, 'nmf/subs_and_sorted_factors.np', "submission")
print "\n-------------- pattern of dominating factors ----------------\n"
import numpy as np
X = np.array([[1, 1], [2, 1], [3, 1.2], [4, 1], [5, 0.8], [6, 1]])
from sklearn.decomposition import ProjectedGradientNMF
model = ProjectedGradientNMF(n_components=10, init='random', random_state=0)
model.fit(X)
print model.components_
U = X.dot(model.components_.T)
print U
print U.dot(model.components_)
model.reconstruction_err_
model = ProjectedGradientNMF(
n_components=2, sparseness='components', init='random', random_state=0)
model.fit(X)
ProjectedGradientNMF(beta=1, eta=0.1, init='random', max_iter=200,
n_components=2, nls_max_iter=2000, random_state=0,
sparseness='components', tol=0.0001)
model.components_
model.reconstruction_err_
####THEIRS- not needed
# Example data matrix X
###MINE
X = DataFrame(matrix)
X_imputed = X.copy()
X = pa.DataFrame(matrix)# DataFrame(toy_vals, index = range(nrows), columns = range(ncols))
###use some way to mask only a few vals.... thst too either 0 or 1
msk = (X.values + np.random.randn(*X.shape) - X.values) < 0.8
X_imputed.values[~msk] = 0
##THEIRS
# Hiding values to test imputation
# Initializing model
nmf_model = ProjectedGradientNMF(n_components = 600, init='nndsvda', random_state=0,max_iter=300, eta=0.01, alpha = 0.01)
nmf_model.fit(X_imputed.values)
# iterate model
#while nmf_model.reconstruction_err_**2 > 10:
#nmf_model = NMF( n_components = 600, init='nndsvda', random_state=0,max_iter=300, eta=0.01, alpha = 0.01)
W = nmf_model.fit_transform(X_imputed.values)
X_imputed.values[~msk] = W.dot(nmf_model.components_)[~msk]
print nmf_model.reconstruction_err_
H = nmf_model.components_
rHat = np.dot(W,H)
np.savetxt("rHat.txt" ,rHat)
import numpy as np
X = np.array([[1, 1], [2, 1], [3, 1.2], [4, 1], [5, 0.8], [6, 1]])
from sklearn.decomposition import ProjectedGradientNMF
model = ProjectedGradientNMF(n_components=10, init='random', random_state=0)
model.fit(X)
print model.components_
U = X.dot(model.components_.T)
print U
print U.dot(model.components_)
model.reconstruction_err_
model = ProjectedGradientNMF(n_components=2,
sparseness='components',
init='random',
random_state=0)
model.fit(X)
ProjectedGradientNMF(beta=1,
eta=0.1,
init='random',
max_iter=200,
n_components=2,
nls_max_iter=2000,
random_state=0,
sparseness='components',
tol=0.0001)
model.components_
model.reconstruction_err_
def driver_movie_data_test_sklearn(train_filename,test_filename,k):
(A,movie_ids,user_ids,m_count,u_count) = read_data(train_filename)
# Do nnmf
#(U1,V1) = hack_nmf_iter(A,k,.07,16*A.nnz)
model = ProjectedGradientNMF(n_components=k)
model.fit(A)
V1 = model.components_
U1 = model.transform(A)
print A.shape
print U1.shape
print V1.shape
# Read test data
(A,movie_ids,user_ids,m_count,u_count) = read_data(test_filename,movie_ids,user_ids,m_count,u_count,discard=True)
(error,del_U,del_V,random_pairs) = evaluate_gradients(A,U1,V1,.07,16*A.nnz,hard=True)
reverse_user = inverse_map(user_ids)
reverse_movie = inverse_map(movie_ids)
# Test on Ratings!
outfile = open("test.sklearn.predictions","w")
print ("Doing %d test ratings" % A.nnz)
(n,m) = A.shape
for row in xrange(n):
for row_col_index in xrange(A.indptr[row],A.indptr[row+1]):
col = A.indices[row_col_index]
elt = A.data[row_col_index]
print >> outfile, "%s,%s,%0.5f" % (reverse_movie[row],reverse_user[col], nd.dot(U1[row,:],V1[:,col]))
# Test on completely random pairs
outfile = open("test.sklearn.rndpairs.predictions","w")
for n_pairs in xrange(1000):
row = r.randint(0,n-1)
col = r.randint(0,m)
print >> outfile, "%s,%s,%0.5f" % (reverse_movie[row],reverse_user[col], nd.dot(U1[row,:],V1[:,col]))
# Test on difficult distribution that ephasizes non-rated pairs where movies and users
# are chosen based on rating count.
outfile = open("test.sklearn.hard.rndpairs.predictions","w")
for n_pairs in xrange(1000):
i = r.randint(0,A.nnz -1)
row = find_index(A.indptr,i)
j = r.randint(0,A.nnz -1)
col = A.indices[j]
if (row > A.shape[0]-1):
print row, A.shape, "what is going on"
continue
if (col > A.shape[1]-1):
print col, A.shape, "what is going on"
continue
#print "shape,row,col", A.shape,row,col
# if (A[row][col] > 0):
# continue
print >> outfile, "%s,%s,%0.5f" % (reverse_movie[row],reverse_user[col], nd.dot(U1[row,:],V1[:,col]))
print ("test rsme", math.sqrt(error))
for i in xrange(k):
print ("Factor:", i)
print_movie_factor(U1,reverse_movie, i)
return(U1,V1,reverse_movie,reverse_user)
def filter_1sigma_nmf_new(dma, iter_date, df, header_df):
print 'Get the 1-sigma filtered data'
print df.shape[1]
idx_vt = df.shape[1] - 1
mean_viewtime = df[idx_vt].mean()
std_viewtime = df[idx_vt].std()
print mean_viewtime / 3600.0, std_viewtime / 3600.0
reduced_df = df[(df[idx_vt] >= LOW_LIMIT)
& (df[idx_vt] <= HIGH_LIMIT)].reset_index()
print reduced_df.shape
reduced_df[range(1, idx_vt)] = reduced_df[range(1, idx_vt)].div(
1.0 * reduced_df[idx_vt], 'index')
dev_id_list = reduced_df[0]
reduced_df_vsum = reduced_df[range(1, idx_vt)].sum()
reduced_df_vsum = reduced_df_vsum[reduced_df_vsum > 0.00]
idx_list = reduced_df_vsum.index.tolist()
reduced_df_1 = reduced_df[range(1, idx_vt)][reduced_df_vsum.index.tolist()]
# Select the header accordingly
reduced_header_df = header_df[idx_list]
#program_viewtime_array = np.array(reduced_df[range(1,idx_vt)].astype(np.float))
program_viewtime_array = np.array(reduced_df_1.astype(np.float))
program_name_array = np.array(reduced_header_df)
t_program_viewtime_array = program_viewtime_array.transpose()
cluster_num = 14
# Non-negative Matrix Factorization
model = ProjectedGradientNMF(n_components=cluster_num,
sparseness='data',
init='nndsvd',
max_iter=400,
random_state=0)
WW = model.fit_transform(t_program_viewtime_array)
t_WW = WW.transpose()
HH = model.components_
t_HH = HH.transpose()
#print t_HH.shape
#print pd.DataFrame(t_HH).head()
membership = [-1 for item in range(0, t_HH.shape[0])]
# Assign the membership
for i in range(0, t_HH.shape[0]):
membership[i] = np.argmax(t_HH[i])
dd = reduced_header_df
print dd.shape
print program_name_array.shape
print program_viewtime_array.shape
file = open(
'decompose_results_clusters_%s_%s_%s.csv' %
(iter_date.month, iter_date.day, dma), 'w')
file.write(
'Cluster_id,Dev_num,Household_num,Feature_val,Feature_fraction,Program_name\n'
)
file.write(
'-1,%s,%s,,,\n' %
(len(dev_id_list), get_household_num(dma, dev_id_list.tolist())))
cluster_num = t_WW.shape[0]
for i in range(0, cluster_num):
dev_indices = [index for index, v in enumerate(membership) if v == i]
dev_in_cluster = dev_id_list[dev_indices]
dev_num = len(dev_in_cluster)
household_num = get_household_num(dma, dev_in_cluster.tolist())
#print heapq.nlargest(10,t_WW[i])
feature_val = np.sort(t_WW[i])
feature_val = feature_val[::-1]
#print 't_WW:',t_WW[i]
#print 'sorted t_WW:',feature_val
val_sum = np.sum(feature_val)
feature_frac = feature_val * 1.0 / val_sum
accumulated_frac = 0
cut_ind = 0
for frac in feature_frac:
accumulated_frac += frac
cut_ind += 1
if accumulated_frac > 0.6:
break
idx_list = np.argsort(t_WW[i])[::-1][:cut_ind]
program_list = program_name_array[0][idx_list]
for j in range(0, cut_ind):
file.write('%s,%s,%s,%s,%s,%s\n' %
(i, dev_num, household_num, feature_val[j],
feature_frac[j], program_list[j]))
#file.write(' '.join(program_name_array[0][idx_list]))
#file.write('\n')
file.close()
#income_analysis(dma, dev_id_list, cluster_num, membership)
#child_present_analysis(dma, dev_id_list, cluster_num, membership)
#age_analysis(dma, dev_id_list, cluster_num, membership)
clusters_obj = all_clusters(dma, cluster_num, dev_id_list, membership)
return clusters_obj
row_info = train_data.iloc[row]
curr_q, curr_u, label = row_info[0], row_info[1], row_info[2]
#print curr_q, curr_u
question_index = question_list.index(curr_q)
user_index = expert_list.index(curr_u)
matrix[question_index][user_index] = label
#print matrix
# In[57]:
print 'running model...'
model = ProjectedGradientNMF(n_components=50,
init='nndsvda',
random_state=0,
max_iter=300,
eta=0.01,
alpha=0.01)
W = model.fit_transform(matrix)
H = model.components_
rHat = np.dot(W, H)
print 'recon error: ', model.reconstruction_err_
#np.savetxt("rHat.txt",rHat)
#pickle.dump(question_list, 'qList.txt')
# np.savetxt("qList.txt",question_list)
#np.savetxt( user_list,"uList.txt")
# In[61]:
LC = out.tolist()
X = []
Y = []
for i in LC:
X.append(i[0])
Y.append(i[1])
cpmC = pca.components_
for i in range(len(cpmC[1])):
if cpmC[1][i] * cpmC[1][i] > 0.04:
print app[i]
print i
from sklearn.decomposition import ProjectedGradientNMF
pca = ProjectedGradientNMF(n_components=2)
out = pca.fit_transform(catBarr)
LC = out.tolist()
X = []
Y = []
Z = []
for i in LC:
X.append(i[0])
Y.append(i[1])
cpmC = pca.components_
lis1 = cpmC[0].tolist()
for i in range(len(lis1)):
def driver_movie_data_test_sklearn(train_filename, test_filename, k):
(A, movie_ids, user_ids, m_count, u_count) = read_data(train_filename)
# Do nnmf
#(U1,V1) = hack_nmf_iter(A,k,.07,16*A.nnz)
model = ProjectedGradientNMF(n_components=k)
model.fit(A)
V1 = model.components_
U1 = model.transform(A)
print A.shape
print U1.shape
print V1.shape
# Read test data
(A, movie_ids, user_ids, m_count, u_count) = read_data(test_filename,
movie_ids,
user_ids,
m_count,
u_count,
discard=True)
(error, del_U, del_V, random_pairs) = evaluate_gradients(A,
U1,
V1,
.07,
16 * A.nnz,
hard=True)
reverse_user = inverse_map(user_ids)
reverse_movie = inverse_map(movie_ids)
# Test on Ratings!
outfile = open("test.sklearn.predictions", "w")
print("Doing %d test ratings" % A.nnz)
(n, m) = A.shape
for row in xrange(n):
for row_col_index in xrange(A.indptr[row], A.indptr[row + 1]):
col = A.indices[row_col_index]
elt = A.data[row_col_index]
print >> outfile, "%s,%s,%0.5f" % (reverse_movie[row],
reverse_user[col],
nd.dot(U1[row, :], V1[:, col]))
# Test on completely random pairs
outfile = open("test.sklearn.rndpairs.predictions", "w")
for n_pairs in xrange(1000):
row = r.randint(0, n - 1)
col = r.randint(0, m)
print >> outfile, "%s,%s,%0.5f" % (reverse_movie[row],
reverse_user[col],
nd.dot(U1[row, :], V1[:, col]))
# Test on difficult distribution that ephasizes non-rated pairs where movies and users
# are chosen based on rating count.
outfile = open("test.sklearn.hard.rndpairs.predictions", "w")
for n_pairs in xrange(1000):
i = r.randint(0, A.nnz - 1)
row = find_index(A.indptr, i)
j = r.randint(0, A.nnz - 1)
col = A.indices[j]
if (row > A.shape[0] - 1):
print row, A.shape, "what is going on"
continue
if (col > A.shape[1] - 1):
print col, A.shape, "what is going on"
continue
#print "shape,row,col", A.shape,row,col
# if (A[row][col] > 0):
# continue
print >> outfile, "%s,%s,%0.5f" % (reverse_movie[row],
reverse_user[col],
nd.dot(U1[row, :], V1[:, col]))
print("test rsme", math.sqrt(error))
for i in xrange(k):
print("Factor:", i)
print_movie_factor(U1, reverse_movie, i)
return (U1, V1, reverse_movie, reverse_user)
def __init__(self, params): self.params = params self.dec = ProjectedGradientNMF(**params)
genreMat4 = np.vstack(genreMat4)
print genreMat4
index = filmsbygenre['Action']
E = y[index, :]
### K-Means ###################
ans = raw_input("Start K-Means with Scikit ? ")
if ans != "y":
exit()
from sklearn.cluster import MiniBatchKMeans, KMeans
km = MiniBatchKMeans(n_clusters=k, init='k-means++', n_init=1, init_size=1000, batch_size=1000, verbose=1)
km2 = KMeans(n_clusters = k, init='k-means++', verbose=1)
y2 = km2.fit_transform(X)
topics5 = [[(km.cluster_centers_[l][i], feature_names[i]) for i in np.argsort(-np.abs(km.cluster_centers_[l]))[:10]] for l in range(k)]
print topics5
### NMF #######################
ans = raw_input("Start NMF with Scikit ? ")
if ans != "y":
exit()
from sklearn.decomposition import ProjectedGradientNMF
# BEWARE : THIS IS COMPUTATIONNALY INTENSIVE
nmf = ProjectedGradientNMF(n_components=k, max_iter = 10, nls_max_iter=100)
nmf.fit(X)
topics6 = [[(nmf.components_[l][i], feature_names[i]) for i in np.argsort(-np.abs(nmf.components_[l]))[:10]] for l in range(k)]
__author__ = 'juliewe' import numpy as np #import sklearn.decomposition.NMF as NMF #implements C.J.Lin's projected gradient methods for NMF X = np.array([[1,1],[2,1],[3,1.2],[4,1],[5,0.8],[6,1]]) #n*d from sklearn.decomposition import ProjectedGradientNMF model = ProjectedGradientNMF(n_components=2,init='random',random_state=0) w= model.fit_transform(X) #left factor w (n*k) h= model.components_ #right factor h (k*d) print w print h v = np.dot(w,h) print v
t1 = time() Ain,Cin,center = greedyROI2d(Y, nr = nr, gSig = [4,4], gSiz = [9,9]) t_elGREEDY = time()-t1 #%% arpfit active_pixels = np.squeeze(np.nonzero(np.sum(Ain,axis=1))) Yr = np.reshape(Y,(d1*d2,T),order='F') p = 2; P = arpfit(Yr,p=2,pixels = active_pixels) #%% nmf Y_res = Yr - np.dot(Ain,Cin) model = ProjectedGradientNMF(n_components=1, init='random', random_state=0) model.fit(np.maximum(Y_res,0)) fin = model.components_.squeeze() #%% update spatial components t1 = time() A,b = update_spatial_components(Yr, Cin, fin, Ain, d1=d1, d2=d2, sn = P['sn']) t_elSPATIAL = time() - t1 #%% t1 = time() C,f,Y_res,Pnew = update_temporal_components(Yr,A,b,Cin,fin,ITER=2) t_elTEMPORAL1 = time() - t1
__author__ = 'juliewe' import numpy as np #import sklearn.decomposition.NMF as NMF #implements C.J.Lin's projected gradient methods for NMF X = np.array([[1, 1], [2, 1], [3, 1.2], [4, 1], [5, 0.8], [6, 1]]) #n*d from sklearn.decomposition import ProjectedGradientNMF model = ProjectedGradientNMF(n_components=2, init='random', random_state=0) w = model.fit_transform(X) #left factor w (n*k) h = model.components_ #right factor h (k*d) print w print h v = np.dot(w, h) print v