def generate_training_validating_rt(version, r_to_i, u_to_i, r_u_t_fn,
split, is_test=False):
"""Function called to generate training.mtx, validating.mtx and
recommendation_times.npy
"""
if is_test:
data_processed_dir = join(PROCESSED_DATA_DIR, "test")
else:
data_processed_dir = PROCESSED_DATA_DIR
u_r_times = mmread(r_u_t_fn).transpose().tolil()
nu, nr = u_r_times.shape
training_matrix = lil_matrix((nu,nr), dtype=np.int_)
validating_matrix = lil_matrix((nu,nr), dtype=np.int_)
recommendation_times = np.zeros(nu, dtype=np.int_)
valid_repositories_table = version+"_repositories"
cursor = getDB(is_test=is_test).cursor()
for uidx in xrange(nu):
cursor.execute("""SELECT vr.id
FROM repositories as r,
{} as vr
WHERE vr.id = r.id AND r.owner_id = %s
""".format(valid_repositories_table), (u_to_i.r(uidx),))
owned_rs = np.array([r_to_i[r[0]] for r in cursor])
interests = u_r_times.getrowview(uidx)
interested_rs = np.unique(interests.tocoo().col)
ext_rs = np.setdiff1d(interested_rs, owned_rs, assume_unique=True)
times = interests[0,ext_rs].toarray()[0]
sorted_indices = times.argsort()
threshold = int(floor(split*len(ext_rs)))
training = [ext_rs[i] for i in sorted_indices[:threshold]]
threshold_time = times[sorted_indices[threshold]]
training += [r for r in owned_rs if interests[0,r] < threshold_time]
validating = [ext_rs[i] for i in sorted_indices[threshold:]]
for t in training:
training_matrix[uidx,t] = 1
for v in validating:
validating_matrix[uidx,v] = 1
recommendation_times[uidx] = threshold_time
comment="""
Training interests are before validating interests.
The split is as follows:
Training: all internals before first last 1/3 externals + first 2/3 externals
Testing: last 1/3 externals"""
version_dir = join(data_processed_dir, version)
tfn = join(version_dir, TRAINING_FN)
vfn = join(version_dir, VALIDATING_FN)
rtfn = join(version_dir, RECOMMENDATION_TIMES_FN)
mmwrite(tfn, training_matrix, comment=comment)
mmwrite(vfn, validating_matrix, comment=comment)
np.save(rtfn, recommendation_times)
return (tfn, vfn, rtfn)
def gen_app_pop_count(dev_app, ga_train, ga_test, base_dir='/data'):
start_time = time.time()
print('generating popularity weighted app count per device')
app_popularity = dev_app.groupby(['app_id'])['device_id'].agg(
{'popularity': lambda x: x.nunique()})
app_pop_count = dev_app.groupby(['device_id'])['app_id'].agg(
{'app_pop_count': lambda x: app_popularity.loc[x.unique(), 'popularity'].sum()})
app_count_train = ga_train['device_id'].map(
app_pop_count['app_pop_count']).fillna(0)
app_count_train = app_count_train / app_count_train.max()
app_count_train = csr_matrix(app_count_train.values).transpose()
app_count_test = ga_test['device_id'].map(app_pop_count['app_pop_count']).fillna(0)
app_count_test = app_count_test / app_count_test.max()
app_count_test = csr_matrix(app_count_test.values).transpose()
print('train set shape: ', app_count_train.shape)
io.mmwrite(base_dir + "train_apppopcount.mtx", app_count_train)
print('test set shape: ', app_count_test.shape)
io.mmwrite(base_dir + "test_apppopcount.mtx", app_count_test)
print('Time generating app pop count: ', (time.time() - start_time) / 60)
def encode() :
"""
Generate extra features from pairs, triplets, and common
quadruplets of the existing features and then save those features
in a sparse matrix to disk.
"""
dftrain = load_dataframe('train')
dftest = load_dataframe('test')
lentrain = len(dftrain)
all_data = np.vstack((dftrain.ix[:,1:-1], dftest.ix[:,1:-1]))
np.array(dftrain.ACTION).dump('{}/train_truth.dat'.format(ddir))
dp = group_data(all_data, degree=2, remove_unique=True)
dt = group_data(all_data, degree=3, remove_unique=True)
dq = group_data(all_data, degree=4, remove_unique=True)
dq = remove_rare(dq, 15)
X = all_data[:lentrain]
X_2 = dp[:lentrain]
X_3 = dt[:lentrain]
X_4 = dq[:lentrain]
X_train_all = np.hstack((X, X_2, X_3, X_4))
mmwrite('{}/train_encoded'.format(ddir), X_train_all)
X_test = all_data[lentrain:]
X_test_2 = dp[lentrain:]
X_test_3 = dt[lentrain:]
X_test_4 = dq[lentrain:]
X_test_all = np.hstack((X_test, X_test_2, X_test_3, X_test_4))
mmwrite('{}/test_encoded'.format(ddir), X_test_all)
def build_nontext_vector(fin, colname, colidx, normalize):
"""
Handles the specified column as a categorical variable.
"""
print "Building category vector for %s" % (colname)
fout = str.replace(fin, ".csv", "." + colname + ".mtx")
if os.path.isfile(fout):
return
ftmp = str.replace(fin, ".csv", ".tmp")
reader = csv.reader(open(fin, 'rb'))
tmpwriter = open(ftmp, 'wb')
ln = 0
for row in reader:
ln += 1
if ln <= 1:
continue
if ln % 1000 == 0:
print "...(processed %d lines)" % (ln)
colval = str.lower(row[colidx])
if normalize:
colval = str.replace(colval, " ", "_")
if len(colval.rstrip()) == 0:
colval = "UNK"
tmpwriter.write(colval + "\n")
tmpwriter.close()
tmpreader = open(ftmp, 'rb')
vectorizer = sft.CountVectorizer(max_features=100)
catmatrix = vectorizer.fit_transform(tmpreader)
os.remove(ftmp)
writer = open(fout, 'wb')
sio.mmwrite(writer, catmatrix)
writer.close()
def __init__(self, programEntities):
nusers = len(programEntities.userIndex.keys())
self.numFriends = np.zeros((nusers))
self.userFriends = ss.dok_matrix((nusers, nusers))
fin = open("../Data/user_friends.csv", 'rb')
fin.readline() # skip header
ln = 0
for line in fin:
# if ln % 100 == 0:
# print "Loading line: ", ln
cols = line.strip().split(",")
user = cols[0]
if programEntities.userIndex.has_key(user):
friends = cols[1].split(" ")
i = programEntities.userIndex[user]
self.numFriends[i] = len(friends)
for friend in friends:
if programEntities.userIndex.has_key(friend):
j = programEntities.userIndex[friend]
# the objective of this score is to infer the degree to
# and direction in which this friend will influence the
# user's decision, so we sum the user/event score for
# this user across all training events.
eventsForUser = programEntities.userEventScores.getrow(j).todense()
score = eventsForUser.sum() / np.shape(eventsForUser)[1]
self.userFriends[i, j] += score
self.userFriends[j, i] += score
ln += 1
fin.close()
# normalize the arrays
sumNumFriends = self.numFriends.sum(axis=0)
self.numFriends = self.numFriends / sumNumFriends
sio.mmwrite("../Models/UF_numFriends", np.matrix(self.numFriends))
self.userFriends = normalize(self.userFriends, norm="l1", axis=0, copy=False)
sio.mmwrite("../Models/UF_userFriends", self.userFriends)
def __init__(self, programEvents):
nevents = len(programEvents.eventIndex.keys())
self.eventPopularity = ss.dok_matrix((nevents, 5))
self.eventAttendees = collections.defaultdict(list)
f = open("/users/chaitanya/PyCharmProjects/EventRec/data/event_attendees.csv", 'rb')
f.readline() # skip header
for line in f:
cols = line.strip().split(",")
eventId = cols[0]
if programEvents.eventIndex.has_key(eventId):
i = programEvents.eventIndex[eventId]
self.eventPopularity[i, 0] = len(cols[1].split(" ")) - len(cols[4].split(" ")) # number of yes-no
self.eventPopularity[i, 1] = len(cols[3].split(" ")) # number of invited folks
self.eventAttendees[i].append(cols[1].split(" "))
#list of yes folks
self.eventAttendees[i].append(cols[2].split(" ")) #list of no folks
self.eventAttendees[i].append(cols[3].split(" ")) #list of invited folks
f.close()
self.eventPopularity = normalize(self.eventPopularity, norm="l1",axis=0, copy=False)
sio.mmwrite("/users/chaitanya/PyCharmProjects/EventRec/Models/EA_eventPopularity", self.eventPopularity)
cPickle.dump(self.eventAttendees, open("/users/chaitanya/PyCharmProjects/EventRec/Models/PE_eventAttendees.pkl", 'wb'))
def genJaccard(feature_matrix):
jaccard_matrix_pre = []
#jaccard_matrix_pre is a list of arrays that contain non-zero indicies of each article in the corpus
for i in feature_matrix[0:test_num]:
indicies = np.flatnonzero(i)
jaccard_matrix_pre.append(indicies)
S=sparse.dok_matrix((test_num, test_num))
t0=time.time()
numi=0
for i in jaccard_matrix_pre:
jnum=0
for j in jaccard_matrix_pre[0:numi+1]: #decrease number of calculations to n choose 2 instead of n^2
diviser = float(len(set(i).union(set(j))))
if diviser != 0:
actual_jaccard = float(len(set(i).intersection(set(j))))/diviser
if actual_jaccard != 0 and actual_jaccard!=1:
S[numi,jnum] = actual_jaccard
jnum=jnum+1
numi = numi+1
with open('pickled_minhash/actual_jaccard_matrix_small.mtx', 'wb') as f:
#size of feature_matrix_large: 1261 x 19043
io.mmwrite(f, S)
print("TIME to generate jaccard_matrix: {}".format(time.time()-t0))
def store_matrix(matrix='',
output_dir_path='',
out_file_name='',
output_format=''):
"""store_matrix."""
if not os.path.exists(output_dir_path):
os.mkdir(output_dir_path)
full_out_file_name = os.path.join(output_dir_path, out_file_name)
if output_format == "MatrixMarket":
if len(matrix.shape) == 1:
raise Exception(
"'MatrixMarket' format supports only 2D dimensional array\
and not vectors")
else:
io.mmwrite(full_out_file_name, matrix, precision=None)
elif output_format == "numpy":
np.save(full_out_file_name, matrix)
elif output_format == "joblib":
joblib.dump(matrix, full_out_file_name)
elif output_format == "text":
with open(full_out_file_name, "w") as f:
if len(matrix.shape) == 1:
for x in matrix:
f.write("%s\n" % (x))
else:
raise Exception(
"'text' format supports only mono dimensional array\
and not matrices")
logger.info("Written file: %s" % full_out_file_name)
def get_content_similarity_scores(readmes, dataset_dir, profile="tfidf",
similarity="cos"):
"""Return CSR matrix of similarity_{r,r} for all r in `readmes`.
`dataset_dir` the directory where the similarity scores are
`profile` bool or tfidf
`similarity` cos or ijd (inverse Jacquard Distance)
"""
if profile == "tfidf":
sim_fn = join(dataset_dir, TF_IDF_FN)
if exists(sim_fn):
return mmread(sim_fn).tocsr()
if profile == "bool":
#readme_words = COUNTVECTORIZER readmes
pass
else:
tfidf = TfidfVectorizer(input='file', #sublinear_tf=True,
max_df=0.5, stop_words='english',
decode_error="ignore")
#max_df=0.5: if a word occurs in more than half of the readmes it is
# ignored
readme_words = tfidf.fit_transform(readmes)
if similarity == "cos":
similarity_scores = csr_matrix(cosine_similarity(readme_words))
else:
# similarity_scores = csr_matrix(ijd(readme_words))
pass
mmwrite(sim_fn, similarity_scores, comment=profile+"_"+similarity+"_similarity_{r,r}")
return similarity_scores
def SOP(alpha, teta, nbBasket, nbReco):
data = load()
###############################################################
# CREATE MODELS
###############################################################
print 'Create the model based on the training set'
modelSOP = processing.SOPRecoModel(data.getUserItemMatrix(), alpha, teta)
modelSOP.launch()
###############################################################
# SET RECOMMENDATION
###############################################################
if nbBasket == -1:
evalSOP = processing.Evaluation(modelSOP, data.getBasketItemList(), nbReco)
else :
evalSOP = processing.Evaluation(modelSOP, data.getBasketItemList()[:nbBasket], nbReco)
###############################################################
# LAUNCH RECOMMENDATION + SAVE RESULTS
###############################################################
t = time.time()
evalSOP.newEval()
SOPTime = time.time()-t
mmwrite('SOPPerf_a%s_t%s_nb%s_nr%s'%(alpha,teta,nbBasket,nbReco),evalSOP.perf)
print 'SOP Execution time:', SOPTime
print 'Performances : '
print evalSOP.testNames
print evalSOP.meanPerf()
evalSOP.savePerf('SOPPerf_a%s_t%s_nb%s_nr%s.txt'%(alpha,teta,nbBasket,nbReco))
return evalSOP
def RWWR(alpha, nbBasket, nbReco):
data = load()
###############################################################
# CREATE MODELS
###############################################################
print 'Create the model based on the training set'
modelRWWR = processing.RandomWalkWithRestartRecoModel(data.getUserItemMatrix(), alpha)
###############################################################
# SET RECOMMENDATION
###############################################################
if nbBasket == -1:
evalRWWR = processing.Evaluation(modelRWWR, data.getBasketItemList(), nbReco)
else :
evalRWWR = processing.Evaluation(modelRWWR, data.getBasketItemList()[:nbBasket], nbReco)
###############################################################
# LAUNCH RECOMMENDATION + SAVE RESULTS
###############################################################
t = time.time()
evalRWWR.newEval()
RWWRTime = time.time()-t
mmwrite('RWWR_a%s_nb%s'%(alpha, nbBasket),evalRWWR.perf)
print 'RWWR Execution time:', RWWRTime
print 'Performances :'
print evalRWWR.testNames
print evalRWWR.meanPerf()
evalRWWR.savePerf('RWWR_a%s_nb%s'%(alpha, nbBasket))
return evalRWWR
def Cosine(nbBasket, nbReco):
data = load()
###############################################################
# CREATE MODELS
###############################################################
print 'Create the model based on the training set'
modelCosine = processing.CosineRecoModel(data.getUserItemMatrix())
###############################################################
# SET RECOMMENDATION
###############################################################
if nbBasket == -1:
evalCosine = processing.Evaluation(modelCosine, data.getBasketItemList(), nbReco)
else :
evalCosine = processing.Evaluation(modelCosine, data.getBasketItemList()[:nbBasket], nbReco)
###############################################################
# LAUNCH RECOMMENDATION + SAVE RESULTS
###############################################################
t = time.time()
evalCosine.newEval()
CosineTime = time.time()-t
mmwrite('Cosine_nb%s'%nbBasket,evalCosine.perf)
print 'Cosine Execution time:', CosineTime
print 'Performances :'
print evalCosine.testNames
print evalCosine.meanPerf()
evalCosine.savePerf('Cosine_nb%s.txt'%nbBasket)
return evalCosine
def make_doc_vectors(fname_pat, out_fname):
fnames = glob(fname_pat)
labels = [splitext(basename(fn))[0] for fn in fnames]
stop_words = frozenset(list(ENGLISH_STOP_WORDS) + OTHER_STOPWORDS)
vectorizer = CountVectorizer(input="filename",
ngram_range=(1,3),
min_df=5,
max_df=0.7,
stop_words=stop_words,
token_pattern=r"(?u)\b[A-Za-z]\w+\b")
vectors = vectorizer.fit_transform(fnames)
log.info("saving matrix in Numpy format to " + out_fname)
np.savez(out_fname,
vectorizer=vectorizer,
vectors=vectors,
labels=labels)
base_fname = splitext(out_fname)[0]
mm_fname = base_fname + ".mtx"
log.info("saving matrix in Matrix Market format to " + mm_fname)
mmwrite(mm_fname, vectors, "IDIScape document vectors", "integer")
feat_fname = base_fname + "_features.txt"
log.info("saving features to " + feat_fname)
feat_names = vectorizer.get_feature_names()
open(feat_fname, "w", "utf8").write(u"\n".join(feat_names))
label_fname = base_fname + "_labels.txt"
log.info("saving labels to " + label_fname)
open(label_fname, "w", "utf8").write(u"\n".join(labels))
def nearest_neighbor_degree_analysis(self, target, method="online"):
"""nearest neighbors' degree
ref Empirical analysis of web-based user-object bipartite networks, Ming-Sheng Shang et al. 17 June 2010.
target = 'user' means nearest neighbors' degree for users,
target = 'item' means nearest neighbors' degree for items.
method = 'online' means online calculation,
method = 'offline' means using offline results.
"""
filepath = "./offline_results/nn_degree"
if method == "online":# online calculation
tinynum = 0.00000001
if target == "user":# for user
self.ui_matrix = self.ui_matrix.tocsc()
degree = sparse.csc_matrix(self.ui_matrix.sum(0))
# degree = degree + sparse.csc_matrix(np.ones([1, self.usernum]))*tinynum# to avoid zero division
nn_degree = sparse.csc_matrix(self.ui_matrix.sum(1).transpose())\
.dot(self.ui_matrix)/degree
elif target == "item":# for item
self.ui_matrix = self.ui_matrix.tocsr()
degree = sparse.csr_matrix(self.ui_matrix.sum(1))
# degree = degree + sparse.csr_matrix(np.ones([self.itemnum, 1]))*tinynum# to avoid zero division
nn_degree = self.ui_matrix.dot(sparse.csr_matrix(self.ui_matrix.sum(0).transpose()))/degree
else:
print "target arg error !"
sys.exit()
if target == "user" or target == "item":
try:
io.mmwrite(filepath+"_%s"%target, nn_degree)
except Exception,e:
print e
sys.exit()
return nn_degree
def run(self, ratio, input_db, output_mat):
db = sqlite3.connect(input_db)
# assume no empty users
users = db.execute("""SELECT Users.[Id] FROM Users""").fetchall()
# pick <ratio> of them for training db, pick <ratio/10> of them for test db
train_ids = []
test_ids = []
test_threshold = ratio/10
train_threshold = test_threshold + ratio
for u in users:
rnd = random.random()
if (rnd <= test_threshold):
test_ids.append(u[0])
elif (rnd <= train_threshold):
train_ids.append(u[0])
train_matrix = self.data_to_matrix(db, train_ids).tocsc()
test_matrix = self.data_to_matrix(db, test_ids).tocsc()
(train_matrix, test_matrix) = self.trim_matrices(train_matrix, test_matrix)
savemat(output_mat, {'train' : train_matrix,'test' : test_matrix}, oned_as = 'row')
mmwrite(output_mat + '.train', train_matrix)
mmwrite(output_mat + '.test', test_matrix)
print("Done!")
def main():
"""
Main entry point to script to perform kmeans.
Returns:
- `0` or `1` on success or failure respectively.
- Saves `centroids`, `centroiddict`, and `clusters` in working dir.
"""
parser = gen_args()
args = parser.parse_args()
sessionid = args.sessionid
data = spio.mmread(args.data).tocsc()
logger = logging.getLogger(__name__)
logger.addHandler(logging.StreamHandler())
if args.verbose:
logger.setLevel(logging.DEBUG)
if args.k:
k = args.k
kmeans = KMeans(data, k, args.n, args.delta, args.randomcentroids, \
args.classical, args.verbose)
result = kmeans.run()
clusters = result['clusters']
centroids = result['centroids']
centroiddict = result['centroiddict']
cPickle.dump(clusters, open("data_clusters_" + sessionid + '.pck', 'w'))
cPickle.dump(centroiddict, open("centroid_dict_" + \
sessionid + '.pck', 'w'))
spio.mmwrite(open("data_centroids_" + sessionid + '.mtx', 'w'), \
centroids, comment="CSC Matrix", field='real')
logger.info(" %d Clusters Generated ", len(clusters))
return 0
def __init__(self, programEntities, sim=ssd.correlation):
cleaner = DataCleaner()
nusers = len(programEntities.userIndex.keys())
fin = open("../Data/users.csv", 'rb')
colnames = fin.readline().strip().split(",")
self.userMatrix = ss.dok_matrix((nusers, len(colnames) - 1))
for line in fin:
cols = line.strip().split(",")
# consider the user only if he exists in train.csv
if programEntities.userIndex.has_key(cols[0]):
i = programEntities.userIndex[cols[0]]
self.userMatrix[i, 0] = cleaner.getLocaleId(cols[1])
self.userMatrix[i, 1] = cleaner.getBirthYearInt(cols[2])
self.userMatrix[i, 2] = cleaner.getGenderId(cols[3])
self.userMatrix[i, 3] = cleaner.getJoinedYearMonth(cols[4])
self.userMatrix[i, 4] = cleaner.getCountryId(cols[5])
self.userMatrix[i, 5] = cleaner.getTimezoneInt(cols[6])
fin.close()
# normalize the user matrix
self.userMatrix = normalize(self.userMatrix, norm="l1", axis=0, copy=False)
sio.mmwrite("../Models/US_userMatrix", self.userMatrix)
# calculate the user similarity matrix and save it for later
self.userSimMatrix = ss.dok_matrix((nusers, nusers))
for i in range(0, nusers):
self.userSimMatrix[i, i] = 1.0
for u1, u2 in programEntities.uniqueUserPairs:
i = programEntities.userIndex[u1]
j = programEntities.userIndex[u2]
if not self.userSimMatrix.has_key((i, j)):
usim = sim(self.userMatrix.getrow(i).todense(),
self.userMatrix.getrow(j).todense())
self.userSimMatrix[i, j] = usim
self.userSimMatrix[j, i] = usim
sio.mmwrite("../Models/US_userSimMatrix", self.userSimMatrix)
def main(argv):
inputfile = ''
outputfile = ''
try:
opts, args = getopt.getopt(argv,"hi:o:",["ifile=","ofile="])
except getopt.GetoptError:
print 'python <script name> -i <inputfile> -o <outputfile>'
sys.exit(2)
for opt, arg in opts:
if opt == '-h':
print 'python <script name> -i <inputfile> -o <outputfile>'
sys.exit()
elif opt in ("-i"):
inputfile = arg
elif opt in ("-o"):
outputfile = arg
print 'Reading images from "', inputfile
print 'Writing vectors to "', outputfile
input_file_name = inputfile
""" ################### START PROGRAM ############################ """
print "--------------STRAT-----------------"
running_time = time.now()
data_matrix = process(input_file_name)
print "Total time:" + str(time.now() - running_time)
print "Writing to file."
io.mmwrite(outputfile, data_matrix)
def test4():
n = 3 ; p = 2
geo = periodic_square(n=[n,n], p=[p,p])
geo_ref, list_lmatrices = geo.bezier_extract()
from scipy.io import mmwrite
M = list_lmatrices[0][0]
mmwrite("M_conversion_test_4.mtx", M)
def RW_POP(alpha, nbBasket, nbReco): data = load() ############################################################### # CREATE MODELS ############################################################### print 'Create the model based on the training set' modelRW = processing.BasketRandomWalk_POP(data.getUserItemMatrix(), alpha) ############################################################### # SET RECOMMENDATION ############################################################### if nbBasket == -1: evalRW = processing.Evaluation(modelRW, data.getBasketItemList(), nbReco) else : evalRW = processing.Evaluation(modelRW, data.getBasketItemList()[:nbBasket], nbReco) ############################################################### # LAUNCH RECOMMENDATION + SAVE RESULTS ############################################################### t = time.time() evalRW.newEval() RWTime = time.time()-t mmwrite(resultFolder+'RW_POP_a%s_nb%s'%(alpha, nbBasket),evalRW.perf) print 'RW_POP Execution time:', RWTime print 'Performances :' print evalRW.testNames print evalRW.computePerf() evalRW.savePerf(resultFolder+'RW_POP_a%s_nb%s.txt'%(alpha, nbBasket)) return evalRW
def save_results(results, name, version="1_1"):
expt_action_mean, expt_action_std, \
expt_reward_mean, expt_reward_std, epsilon = results
if expt_action_mean is not None:
mmwrite("./out/ex%s_%s_action_mean.mtx" % (version, name.lower()),
expt_action_mean,
"Experiment %s %s actions mean." % (version, name))
if expt_action_std is not None:
mmwrite("./out/ex%s_%s_action_std.mtx" % (version, name.lower()),
expt_action_std,
"Experiment %s %s actions SD." % (version, name))
if expt_reward_mean is not None:
mmwrite("./out/ex%s_%s_reward_mean.mtx" % (version, name.lower()),
expt_reward_mean,
"Experiment %s %s rewards mean." % (version, name))
if expt_reward_std is not None:
mmwrite("./out/ex%s_%s_reward_std.mtx" % (version, name.lower()),
expt_reward_std,
"Experiment %s %s rewards SD." % (version, name))
if epsilon is not None:
mmwrite("./out/ex%s_%s_epsilon.mtx" % (version, name.lower()),
epsilon,
"Experiment %s %s exploration rates." % (version, name))
def buildBaseDB(self, verticesPath):
mainDir = os.path.dirname(verticesPath)
verticesFileName = os.path.basename(verticesPath)
targetsDBPath = os.path.join(mainDir,self.targetDBFolder)
if "_vertices" not in verticesFileName:
print "Error: the vertices file name must have the form of *_vertices.dat"
return
if not os.path.isdir(targetsDBPath):
print "Error: targets folder %s not found"%(targetsDBPath)
return
base_file = os.path.join(mainDir, verticesFileName.replace("_vertices","_base"))
vertices,lookup = buildbase.read_vertices(verticesPath)
print "Loading targets..."
rb,target_names = buildbase.load_targets(targetsDBPath,vertices,lookup)
print "Making base..."
base,back = buildbase.make_base(rb,self.cutOff)
del rb
print "Saving base..."
f = open(base_file,'w')
mmwrite(f,base)
f.close()
del base
print "Saved in %s"%(base_file)
def make_author_vectors(crawl_fname, doc_vec_fname, auth_vec_fname):
docs = np.load(doc_vec_fname)
doc_vecs = docs["vectors"][()]
# Convert to LIL, because modifying CSR is slow
doc_vecs = doc_vecs.tolil()
# Create mapping from label (=DOI) to row number (=doc vector)
doi2n = dict((l,i) for i,l in enumerate(docs["labels"]))
# Collect authors
tree = etree.parse(crawl_fname)
authors = np.array(list(set(tree.xpath("//author/text()"))))
# Create empty author vectors
shape = (len(authors), doc_vecs.shape[1])
auth_vecs = sp.lil_matrix(shape)
# Create mapping from authors to row number (=author vector)
auth2n = dict((a,i) for i,a in enumerate(authors))
## author to group mapping
##auth2group = {}
# Fill author vectors by adding doc vectors
for item in tree.findall("//item"):
author = item.find("author").text
##group = item.find("group")
##auth2group[author] = group
url = item.find("url").text
query = urlparse.urlparse(url).query
doi = urlparse.parse_qs(query)["doi"][0]
log.debug(u"DOI={} author={}".format(doi, author))
try:
auth_vecs[auth2n[author]] += doc_vecs[doi2n[doi]]
except KeyError:
log.warning(u"No document with DOI={} for author {}".format(
doi, author))
auth_vecs = auth_vecs.tocsr()
##group_labels = [auth2group[auth] for auth in authors]
log.info("saving matrix in Numpy format to " + auth_vec_fname)
np.savez(auth_vec_fname,
vectorizer=docs["vectorizer"],
vectors=auth_vecs,
author_labels=authors,
##group_labels=group_labels
)
base_fname = splitext(auth_vec_fname)[0]
mm_fname = base_fname + ".mtx"
log.info("saving matrix in Matrix Market format to " + mm_fname)
mmwrite(mm_fname, auth_vecs, "IDIScape document vectors", "integer")
label_fname = base_fname + "_labels.txt"
log.info("saving labels to " + label_fname)
open(label_fname, "w", "utf8").write(u"\n".join(authors))
def save_new_ref(filename, data):
""" Saves a new version of the reference data, and backs up the old """
ext = filename.split('.')[-1]
if (data == None):
print("WARNING: Error generating file: %s" % filename)
print("Skipped... try again.")
return
if os.path.exists(filename):
os.system( 'mv %s %s' % (filename, BACKUP_DIR) )
if ext in ['h5', 'lh5']:
if scipy.sparse.issparse(data):
data = data.toarray()
Serializer.SaveData(filename, data)
elif ext == 'mtx':
io.mmwrite(filename, data)
elif ext == 'pkl':
f = open(filename, 'w')
pickle.dump(f, data)
f.close()
else:
raise ValueError('Could not understand extension (.%s) for %s' % (ext, filename))
return
def test1D2():
spl = splineRefMat(DIM_1D)
# list_r = list(np.random.random(20))
list_r = [0.1,0.2,0.3]
nx = 3
px = 2
geo = line(n=[nx], p=[px])
nrb = geo[0]
knots = nrb.knots[0]
n = nrb.shape[0]
p = nrb.degree[0]
P = nrb.points
M = spl.construct(list_r, p, n, knots)
from scipy.io import mmwrite
mmwrite('M.mtx', M)
R = M.dot(nrb.points[:,0])
geo = line(n=[nx], p=[px])
geo.refine(id=0, list_t=[list_r])
nrb = geo[0]
P = np.asarray(nrb.points[:,0])
assert(np.allclose(P,R))
print("test1D2: OK")
def main111():
if 1:
G = nx.read_edgelist(infname)
print nx.info(G)
# Graph adj matix
A = nx.to_scipy_sparse_matrix(G)
print type(A)
from scipy import sparse, io
io.mmwrite("Results/test.mtx", A)
exit()
# write to disk clustering coeffs for this graph
snm.get_clust_coeff([G], 'orig', 'mmonth')
# write to disk egienvalue
snm.network_value_distribution([G], [], 'origMmonth')
if 0:
edgelist = np.loadtxt(infname, dtype=str, delimiter='\t')
print edgelist[:4]
idx = np.arange(len(edgelist))
np.random.shuffle(idx)
subsamp_edgelist = edgelist[idx[:100]]
G = nx.Graph()
G.add_edges_from([(long(x), long(y)) for x, y in subsamp_edgelist])
# visualize this graph
# visualize_graph(G)
exit()
G = nx.Graph()
G.add_edges_from([(long(x), long(y)) for x, y in edgelist])
print nx.info(G)
print 'Done'
def Pop(nbBasket, nbReco): data = load() ############################################################### # CREATE MODELS ############################################################### print 'Create the model based on the training set' modelPop = processing.PopRecoModel(data.getUserItemMatrix()) ############################################################### # SET RECOMMENDATION ############################################################### if nbBasket == -1: evalPop = processing.Evaluation(modelPop, data.getBasketItemList(), nbReco) else : evalPop = processing.Evaluation(modelPop, data.getBasketItemList()[:nbBasket], nbReco) ############################################################### # LAUNCH RECOMMENDATION + SAVE RESULTS ############################################################### t = time.time() evalPop.newEval() PopTime = time.time()-t mmwrite(resultFolder+'Pop',evalPop.perf) print 'Pop Execution time:', PopTime print 'Performances :' print evalPop.testNames print evalPop.computePerf() evalPop.savePerf(resultFolder+'Pop.txt') return evalPop
def process_dataset(name):
prefix = 'dataset/' + name + '/' + name
fea = np.loadtxt(prefix + '.fea')
# transform link
link_data = np.loadtxt(prefix + '.link')
link_data = link_data - 1
reverse_link_data = np.append(link_data[:, 1][:,np.newaxis], link_data[:, 0][:,np.newaxis], axis=1)
link_data = np.append(link_data, reverse_link_data, axis=0)
weight = [1]*link_data.shape[0]
num_inst = fea.shape[0]
link = sparse.csr_matrix((weight, link_data.transpose()), shape=(num_inst, num_inst))
# transform label
gnd = np.loadtxt(prefix + '.gnd')
lb = preprocessing.LabelBinarizer()
label = lb.fit_transform(gnd)
label = label.astype(np.float)
# use max component
g = nx.Graph(link)
mc = nx.connected_components(g)[0]
link = link[mc, :][:, mc]
label = label[mc, :]
fea = fea[mc, :]
# save
np.save(prefix + '_fea', fea)
mmwrite(prefix + '_link', link)
np.save(prefix + '_label', label)
np.save(prefix + '_mc', mc)
return fea, link, label
def df_to_sparse (jour,df_in, Fam, col):
# INPUTS:
# num = jour considéré
# df_in = nom du dataframe d'entrée à traiter
# Fam = famille considéré
# col = colonne à conserver du data frame d'entrée vers celui de sortie (si
# col = 0 on prend tout le dataframe)
# OUTPUT:
# Il n'y a pas d'output, le fichier est enregistré sous format de sparse
# matrix
# FONCTION:
dossier = str(jour) +'.03.2013'
path_entree = 'C:\Users\lbn\Documents\\data_frames\\'
path_sortie = 'C:\Users\lbn\Documents\\data_frames\\sparse_data\\'
doc_entree = path_entree + dossier +'\\'+df_in + str(jour) +'.pickle'
doc_sortie = path_sortie + dossier +'\\'+df_in+ str(jour)+Fam
data = read_pickle(doc_entree)
if type(col) == list :
data = csr_matrix(data.iloc[:,col], dtype=np.int8)
else :
data = csr_matrix(data, dtype=np.int8)
io.mmwrite(doc_sortie, data)
print('Traduction effectuee de:', df_in+ str(jour)+Fam)
def CondProb(alpha, nbBasket, nbReco): data = load() ############################################################### # CREATE MODELS ############################################################### print 'Create the model based on the training set' modelCondProb = processing.CondProbRecoModel(data.getUserItemMatrix(), alpha) ############################################################### # SET RECOMMENDATION ############################################################### if nbBasket == -1: evalCondProb = processing.Evaluation(modelCondProb, data.getBasketItemList(), nbReco) else : evalCondProb = processing.Evaluation(modelCondProb, data.getBasketItemList()[:nbBasket], nbReco) ############################################################### # LAUNCH RECOMMENDATION + SAVE RESULTS ############################################################### t = time.time() evalCondProb.newEval() CondProbTime = time.time()-t mmwrite(resultFolder+'CondProb_a%s_nb%s'%(alpha, nbBasket),evalCondProb.perf) print 'Cond Prob Execution time:', CondProbTime print 'Performances :' print evalCondProb.testNames print evalCondProb.computePerf() evalCondProb.savePerf(resultFolder+'CondProb_a%s_nb%s.txt'%(alpha, nbBasket)) return evalCondProb
def process_SVD2(inputFileName, outputFileName, n, p, showError):
"""
Perform SVD2.
"""
mat, rowids = loadMatrix(inputFileName)
X = mat.tocsc()
ut, s, vt = sparsesvd(X, n)
A = np.dot(np.dot(ut.T, np.diag(s**p)), vt)
saveMatrix(A, rowids, outputFileName)
mmwrite("%s.ut" % inputFileName, ut)
np.savetxt("%s.s" % inputFileName, s)
mmwrite("%s.vt" % inputFileName, vt)
if showError:
Xnorm = np.linalg.norm(X.todense(), ord='fro')
Error = np.linalg.norm((X - A), ord='fro')
rate = (100 * Error) / Xnorm
print "Approximation Error Percentage = %f%%" % rate
print "Frobenius norm of the original matrix =", Xnorm
print "Frobenius norm of the error matrix =", Error
pass
def peak_count_matrix(atac_inter_bed, out_prefix):
# first three columns is 'chr','start','end'
# last column is barcode
inter_peak = pd.read_csv(atac_inter_bed, header=None, sep='\t')
inter_peak.columns = ['chr', 'start', 'end'
] + [''] * (inter_peak.shape[1] - 4) + ['barcode']
#inter_peak['peak']=inter_peak['name'].apply(lambda x: x.split('/')[-1])
inter_peak['peak'] = inter_peak.apply(
lambda x: x['chr'] + ':' + str(x['start']) + '-' + str(x['end']),
axis=1)
bc_peak_counts = inter_peak.groupby(['barcode', 'peak']).size()
df = pd.DataFrame(bc_peak_counts)
df.reset_index(inplace=True)
new_df = df.pivot(index='peak', columns='barcode', values=0)
new_df.columns.name = None
new_df.index.name = None
new_df = new_df.fillna(0)
new_df = new_df.astype('int')
mmwrite(out_prefix + 'count.mtx', csr_matrix(new_df))
np.savetxt(out_prefix + 'peaks.txt', new_df.index.values, fmt="%s")
np.savetxt(out_prefix + 'barcodes.txt', new_df.columns.values, fmt="%s")
def prepare_bow_matrix(labeled) -> None:
"""Function to prepare the BOW matrix."""
savedir = Path(config['data']['save_path'])
cvect = CountVectorizer(strip_accents='ascii', min_df=2)
cvect.fit((savedir / 'train.txt').read_text().splitlines())
if labeled:
savedir = savedir / 'labeled'
train_dbyw = cvect.transform(
(savedir / 'train.txt').read_text().splitlines(),
)
valid_dbyw = cvect.transform(
(savedir / 'valid.txt').read_text().splitlines(),
)
test_dbyw = cvect.transform(
(savedir / 'test.txt').read_text().splitlines(),
)
mmwrite(str(savedir / 'train.mtx'), train_dbyw)
mmwrite(str(savedir / 'valid.mtx'), valid_dbyw)
mmwrite(str(savedir / 'test.mtx'), test_dbyw)
with (savedir / 'vocab').open('w', encoding='utf-8') as fvpw:
fvpw.write('\n'.join(cvect.vocabulary_))
with (savedir / 'mtx.flist').open('w') as flpw:
flpw.write('{0}\n'.format(savedir.resolve() / 'train.mtx'))
flpw.write('{0}\n'.format(savedir.resolve() / 'valid.mtx'))
flpw.write('{0}\n'.format(savedir.resolve() / 'test.mtx'))
def expression_matrix(df, validated_barcodes, outdir, sample, gtf_file):
matrix_10X_dir = f"{outdir}/{sample}_matrix_10X/"
matrix_table_file = f"{outdir}/{sample}_matrix.tsv.gz"
if not os.path.exists(matrix_10X_dir):
os.mkdir(matrix_10X_dir)
df.loc[:, 'mark'] = 'UB'
df.loc[df['Barcode'].isin(validated_barcodes), 'mark'] = 'CB'
CB_total_Genes = df.loc[df['mark'] == 'CB', 'geneID'].nunique()
CB_reads_count = df.loc[df['mark'] == 'CB', 'count'].sum()
reads_mapped_to_transcriptome = df['count'].sum()
table = df.loc[df['mark'] == 'CB', :].pivot_table(
index='geneID', columns='Barcode', values='UMI',
aggfunc=len).fillna(0).astype(int)
id_name = gene_convert(gtf_file)
id = table.index.to_series()
name = id.apply(lambda x: id_name[x])
genes = pd.concat([id, name], axis=1)
genes.columns = ['gene_id', 'gene_name']
# write 10X matrix
table.columns.to_series().to_csv(f'{matrix_10X_dir}/barcodes.tsv',
index=False,
sep='\t')
genes.to_csv(f'{matrix_10X_dir}/genes.tsv',
index=False,
header=False,
sep='\t')
mmwrite(f'{matrix_10X_dir}/matrix', csr_matrix(table))
# convert id to name; write table matrix
table.index = name
table.index.name = ""
table.to_csv(matrix_table_file, sep="\t", compression='gzip')
return (CB_total_Genes, CB_reads_count, reads_mapped_to_transcriptome)
def load_graph(name):
dir = 'data/'
try:
if isinstance(name, str):
meshname = dir + name
else:
meshname = dir + names[name]
mesh = loadmat(meshname)
except IOError as e:
print 'Matrix market file : %s.mtx not available...downloading' % meshname
url = base % (meshname, meshname)
response = urllib2.urlopen(url)
graph = response.read()
adj_lists = [map(int, a.split()) for a in graph.splitlines() if a]
num_nodes, num_edges = adj_lists[0]
vertex_degrees = [len(edges) for edges in adj_lists[1:]]
node_lists = [
itertools.repeat(i, n)
for i, n in zip(range(num_nodes), vertex_degrees)
]
I = numpy.array(list(itertools.chain(*node_lists)))
J = numpy.array(list(itertools.chain(*adj_lists[1:]))) - 1
V = numpy.ones(2 * num_edges)
G = coo_matrix((V, (I, J)), shape=(num_nodes, num_nodes))
mmwrite(dir + meshname, G)
G_nx = make_graph(G)
pos = nx.spring_layout(G_nx, iterations=200)
x = numpy.array([pos[i][0] for i in range(G.shape[0])])
y = numpy.array([pos[i][1] for i in range(G.shape[0])])
V = numpy.vstack((x, y)).T
E = numpy.vstack((G.row, G.col)).T
mesh = {'V': V, 'E': E}
savemat(dir + meshname, mesh)
return mesh
def inference(dataloader, net, criterion, opt, OutputDir):
net.eval()
for i, (sample_idx, annotation, adj_matrix, label, mask) in enumerate(dataloader, 0):
padding = torch.zeros(opt.batchSize, opt.n_node, opt.L, opt.state_dim - opt.annotation_dim).double()
init_input = torch.cat((annotation, padding), 3)
if opt.cuda:
adj_matrix = adj_matrix.cuda()
annotation = annotation.cuda()
init_input = init_input.cuda()
label = label.cuda()
mask = mask.cuda()
adj_matrix = Variable(adj_matrix)
annotation = Variable(annotation)
init_input = Variable(init_input)
target = Variable(label)
mask = Variable(mask)
output = net(init_input)
output = output.argmax(axis=2)[:, :, np.newaxis]
# 予測結果とラベルを保存
os.makedirs(OutputDir + "/output", exist_ok=True)
for batch in range(opt.batchSize):
p = output.detach().numpy()[batch]
t = target[batch].numpy()
m = mask[batch].numpy()
mmwrite(OutputDir + "/output/pred" + str(sample_idx.numpy()[batch]), lil_matrix(p))
mmwrite(OutputDir + "/output/true" + str(sample_idx.numpy()[batch]), lil_matrix(t))
mmwrite(OutputDir + "/output/mask" + str(sample_idx.numpy()[batch]), lil_matrix(m))
def cover(socp_data, N):
"""stacks the socp data and partitions it into N
local dicts describing constraints R <= s"""
if not settings.paths['mondriaan']:
raise Exception(
"Please provide a path to mondriaan: settings.paths['mondriaan'] = PATH.")
n = socp_data['c'].shape[0]
# form the Laplacian and use pymetis to partition
L = form_laplacian(socp_data)
io.mmwrite("mondriaan.mtx", L)
import subprocess
outpath = "mondriaan.mtx-P%d" % N
proc = subprocess.Popen(
[settings.paths['mondriaan'], "mondriaan.mtx", str(N), "0.05"])
proc.wait()
with open(outpath, "r") as f:
f.readline() # ignore comments
f.readline() # ignore comments
# basic info about the matrix
m, _, _, _ = f.readline().strip().split(" ")
pstart = []
# read the starting index of the partition
for i in xrange(N + 1):
pstart.append(int(f.readline()))
part_vert = np.zeros(int(m), dtype=np.int)
count = 0
part = 0
for i in xrange(N):
while count < pstart[i + 1]:
(row, col, val) = f.readline().strip().split(" ")
part_vert[int(row) - 1] = part
count += 1
part += 1
return part_vert[n:]
def inference(dataloader, opt, OutputDir, Attribute_idx):
for i, (sample_idx, annotation, adj_matrix, label,
mask) in enumerate(dataloader, 0):
target = Variable(label)
mask = Variable(mask)
output = annotation[:, :, -1][:, :, Attribute_idx]
output = output.argmax(axis=2)[:, :, np.newaxis]
for batch in range(opt.batchSize):
ts = int(sample_idx[batch].numpy())
output[batch][pred_binary[ts] > threshold] = torch.LongTensor(
pred_transfer[ts][pred_binary[ts] > threshold])
# 予測結果とラベルを保存
os.makedirs(OutputDir + "/output", exist_ok=True)
for batch in range(opt.batchSize):
p = output.detach().numpy()[batch]
t = target[batch].numpy()
m = mask[batch].numpy()
mmwrite(
OutputDir + "/output/pred" + str(sample_idx.numpy()[batch]),
lil_matrix(p))
mmwrite(
OutputDir + "/output/true" + str(sample_idx.numpy()[batch]),
lil_matrix(t))
mmwrite(
OutputDir + "/output/mask" + str(sample_idx.numpy()[batch]),
lil_matrix(m))
def __init__(self, programEntities):
nusers = len(programEntities.userIndex.keys())
self.numFriends = np.zeros((nusers))
self.userFriends = ss.dok_matrix((nusers, nusers))
fin = open("user_friends.csv", 'rb')
fin.readline() # skip header
ln = 0
for line in fin:
if ln % 200 == 0:
print("Loading line: ", ln)
cols = line.strip().split(",")
user = cols[0]
if programEntities.userIndex.has_key(user):
friends = cols[1].split(" ")
i = programEntities.userIndex[user]
self.numFriends[i] = len(friends)
for friend in friends:
if programEntities.userIndex.has_key(friend):
j = programEntities.userIndex[friend]
# the objective of this score is to infer the degree to
# and direction in which this friend will influence the
# user's decision, so we sum the user/event score for
# this user across all training events.
eventsForUser = programEntities.userEventScores.getrow(
j).todense()
score = eventsForUser.sum() / np.shape(
eventsForUser)[1]
self.userFriends[i, j] += score
self.userFriends[j, i] += score
ln += 1
fin.close()
# 归一化数组
sumNumFriends = self.numFriends.sum(axis=0)
self.numFriends = self.numFriends / sumNumFriends
sio.mmwrite("UF_numFriends", np.matrix(self.numFriends))
self.userFriends = normalize(self.userFriends,
norm="l1",
axis=0,
copy=False)
sio.mmwrite("UF_userFriends", self.userFriends)
def vector_word():
with open(
'I:\MeachineLearnProject\SpamMessage-LR-Twt/RawData/train_content.json',
'r') as f:
content = json.load(f)
with open(
'I:\MeachineLearnProject\SpamMessage-LR-Twt/RawData/train_label.json',
'r') as f:
label = json.load(f)
'''
vec_count = MessageCountVectorizer(min_df=2, max_df=0.8)
data_count = vec_count.fit_transform(content)
name_count_feature = vec_count.get_feature_names()
'''
content_sub = content[0:100000]
label_sub = label[0:100000]
return content_sub, label_sub
#print(content_sub)
#vec_tfidf = TfidfVectorizer(min_df=2, max_df=0.8)
#vec_tfidf = TfidfVectorizer()
#data_tfidf = vec_tfidf.fit_transform(content_sub)
#weight = vec_tfidf.fit_transform(content_sub).toarray()
#print(data_tfidf)
#name_tfidf_feature = vec_tfidf.get_feature_names()
#print(name_tfidf_feature)
#DecisionTreeClassifyTfidf(data_tfidf,,name_tfidf_feature)
io.mmwrite(
'I:\MeachineLearnProject\SpamMessage-LR-Twt/Data/word_vector_sub.mtx',
data_tfidf)
with open(
'I:\MeachineLearnProject\SpamMessage-LR-Twt/Data/train_label_sub.json',
'w') as f:
json.dump(label, f)
with open(
'I:\MeachineLearnProject\SpamMessage-LR-Twt/Data/vector_type_sub.json',
'w') as f:
json.dump(name_tfidf_feature, f)
def get_debug(data):
full_train = sio.mmread('data/%s_train.mtx' % data).tocsr()
(nu, nm) = full_train.shape
print 'sampling'
debug_mids = sample(range(nm), nm / 5)
debug_uids = sample(range(nu), nu / 5)
debug = full_train[debug_uids][:, debug_mids].tocoo()
nr = debug.nnz
train_ids, _, test_ids = sample_split(nr)
# build matrix from given indices
print 'writing debug_train'
debug_train = coo_matrix(
(debug.data[train_ids], (debug.row[train_ids], debug.col[train_ids])),
debug.shape)
sio.mmwrite('data/%s_debug_train.mtx' % data, debug_train)
print 'writing debug_test'
debug_test = coo_matrix(
(debug.data[test_ids], (debug.row[test_ids], debug.col[test_ids])),
debug.shape)
sio.mmwrite('data/%s_debug_test.mtx' % data, debug_test)
# build movie mtx from debug_mids
print 'movie debug'
movies = sio.mmread('data/movies.mtx').tocsr()
movies_debug = movies[debug_mids]
sio.mmwrite('data/movies_%s_debug.mtx' % data, movies_debug)
return debug, debug_train, debug_test, movies_debug
def __init__(self, programEntities, psim=ssd.correlation, csim=ssd.cosine):
cleaner = DataCleaner()
fin = open("../Data/events.csv", 'rb')
fin.readline() # skip header
nevents = len(programEntities.eventIndex.keys())
self.eventPropMatrix = ss.dok_matrix((nevents, 7))
self.eventContMatrix = ss.dok_matrix((nevents, 100))
ln = 0
for line in fin.readlines():
# if ln > 10:
# break
cols = line.strip().split(",")
eventId = cols[0]
if programEntities.eventIndex.has_key(eventId):
i = programEntities.eventIndex[eventId]
self.eventPropMatrix[i, 0] = cleaner.getJoinedYearMonth(cols[2]) # start_time
self.eventPropMatrix[i, 1] = cleaner.getFeatureHash(cols[3]) # city
self.eventPropMatrix[i, 2] = cleaner.getFeatureHash(cols[4]) # state
self.eventPropMatrix[i, 3] = cleaner.getFeatureHash(cols[5]) # zip
self.eventPropMatrix[i, 4] = cleaner.getFeatureHash(cols[6]) # country
self.eventPropMatrix[i, 5] = cleaner.getFloatValue(cols[7]) # lat
self.eventPropMatrix[i, 6] = cleaner.getFloatValue(cols[8]) # lon
for j in range(9, 109):
self.eventContMatrix[i, j-9] = cols[j]
ln += 1
fin.close()
self.eventPropMatrix = normalize(self.eventPropMatrix,
norm="l1", axis=0, copy=False)
sio.mmwrite("../Models/EV_eventPropMatrix", self.eventPropMatrix)
self.eventContMatrix = normalize(self.eventContMatrix,
norm="l1", axis=0, copy=False)
sio.mmwrite("../Models/EV_eventContMatrix", self.eventContMatrix)
# calculate similarity between event pairs based on the two matrices
self.eventPropSim = ss.dok_matrix((nevents, nevents))
self.eventContSim = ss.dok_matrix((nevents, nevents))
for e1, e2 in programEntities.uniqueEventPairs:
i = programEntities.eventIndex[e1]
j = programEntities.eventIndex[e2]
if not self.eventPropSim.has_key((i,j)):
epsim = psim(self.eventPropMatrix.getrow(i).todense(),
self.eventPropMatrix.getrow(j).todense())
self.eventPropSim[i, j] = epsim
self.eventPropSim[j, i] = epsim
if not self.eventContSim.has_key((i,j)):
ecsim = csim(self.eventContMatrix.getrow(i).todense(),
self.eventContMatrix.getrow(j).todense())
self.eventContSim[i, j] = epsim
self.eventContSim[j, i] = epsim
sio.mmwrite("../Models/EV_eventPropSim", self.eventPropSim)
sio.mmwrite("../Models/EV_eventContSim", self.eventContSim)
def load_stats(stats_f, vocab_f):
""" Validate and load the input stats """
stats = sio.mmread(stats_f)
if vocab_f:
vocab = read_simple_flist(vocab_f)
# Check the compatibility of stats
if stats.shape[1] == len(vocab):
stats = stats.T
print("Transposed the stats to make them word-by-doc.")
sio.mmwrite(os.path.realpath(stats_f), stats)
if stats.shape[0] != len(vocab):
print(
"Number of rows in stats should match with length of vocabulary."
)
print("Given stats:", stats.shape[0], "vocab. length:", len(vocab))
sys.exit()
return stats.tocsc()
def __init__(self, programEntities=None, isClean=True):
print("统计活跃度初始化开始...")
if isClean == False:
raise ImportError("不进行统计活跃度会导致结果不准确!请改成True或不赋值成False")
self.programEntities = programEntities
self.num_events = len(self.programEntities.eventIndex.keys())
self.eventPopularity = ss.dok_matrix((self.num_events, 1))
with open('event_attendees.csv', 'rb') as reader:
reader = str(reader.readline())
for line in reader:
cols = line.strip().split(",")
eventId = cols[0]
if eventId in self.programEntities.eventIndex:
i = self.programEntities.eventIndex[eventId]
self.eventPopularity[i, 0] = len(cols[1].split(" ")) - len(
cols[4].split(" "))
self.eventPopularity = normalize(self.eventPopularity,
norm="l1",
axis=0,
copy=False)
sio.mmwrite("Event_Popularity", self.eventPopularity)
print("统计活跃度结束...\n\n{}\n".format("*" * 200))
def build_text_vector(fin, stopwords_pattern):
"""
Create temporary fields by concatenating text columns to form
a new column and generate a vector of term frequencies.
"""
print "Building text vector..."
fout = str.replace(fin, ".csv", ".text.mtx")
if os.path.isfile(fout):
return
ftmp = str.replace(fin, ".csv", ".tmp")
reader = csv.reader(open(fin, 'rb'))
tmpwriter = open(ftmp, 'wb')
ln = 0
for row in reader:
ln += 1
if ln <= 1:
continue # skip header
if ln % 1000 == 0:
print "...(processed %d lines)" % (ln)
title = row[1]
full_description = extract_keywords(row[2], stopwords_pattern)
loc_raw = row[3]
tmpwriter.write(" ".join(
[title, title, title, title, full_description, loc_raw, loc_raw]) +
"\n")
tmpwriter.close()
vectorizer = sft.CountVectorizer(max_features=1000)
# vectorizer = sft.TfidfVectorizer(
# charset_error="ignore",
# strip_accents="ascii",
# stop_words="english",
# max_features=100,
# use_idf=False)
tmpreader = open(ftmp, 'rb')
tdmatrix = vectorizer.fit_transform(tmpreader)
os.remove(ftmp)
writer = open(fout, 'wb')
sio.mmwrite(writer, tdmatrix)
writer.close()
def save_randomforest_path(model_path, w2v_path):
if os.path.exists(model_path):
print("the model already exists.")
clf = joblib.load(model_path)
else:
print("the model doesn't exists.")
return None
w2v_list = list()
for root, dirs, files in os.walk(w2v_path):
for file in files:
if os.path.splitext(file)[1] == '.txt':
w2v_list.append(file)
w2v_list.sort()
for w2v_name in w2v_list:
filename = BasePath + "/w2v_corpus/" + w2v_name
w2v_vec = np.loadtxt(filename)
print(w2v_vec.shape)
path_of_sample, _ = clf.decision_path(w2v_vec)
save_file_path = BasePath + "/rf_path/" + "path_" + w2v_name.split(
'.')[0] + ".mtx"
io.mmwrite(save_file_path, path_of_sample)
def __init__(self, programEntities, sim=ssd.correlation):
cleaner = DataCleaner()
nusers = len(programEntities.userIndex.keys())
fin = open("../data/users.csv", 'rb')
colnames = fin.readline().strip().split(",")
self.userMatrix = ss.dok_matrix((nusers, len(colnames) - 1))
for line in fin:
cols = line.strip().split(",")
# 只考虑train.csv中出现的用户
if programEntities.userIndex.has_key(cols[0]):
i = programEntities.userIndex[cols[0]]
##将数据进行预处理再放进userMatrix矩阵中
self.userMatrix[i, 0] = cleaner.getLocaleId(cols[1])
self.userMatrix[i, 1] = cleaner.getBirthYearInt(cols[2])
self.userMatrix[i, 2] = cleaner.getGenderId(cols[3])
self.userMatrix[i, 3] = cleaner.getJoinedYearMonth(cols[4])
self.userMatrix[i, 4] = cleaner.getCountryId(cols[5])
self.userMatrix[i, 5] = cleaner.getTimezoneInt(cols[6])
fin.close()
# 归一化用户矩阵
self.userMatrix = normalize(self.userMatrix,
norm="l1",
axis=0,
copy=False)
sio.mmwrite("US_userMatrix", self.userMatrix)
# 计算用户相似度矩阵，之后会用到
self.userSimMatrix = ss.dok_matrix((nusers, nusers))
for i in range(0, nusers):
self.userSimMatrix[i, i] = 1.0
for u1, u2 in programEntities.uniqueUserPairs:
i = programEntities.userIndex[u1]
j = programEntities.userIndex[u2]
if not self.userSimMatrix.has_key((i, j)):
usim = sim(
self.userMatrix.getrow(i).todense(),
self.userMatrix.getrow(j).todense())
self.userSimMatrix[i, j] = usim
self.userSimMatrix[j, i] = usim
sio.mmwrite("US_userSimMatrix", self.userSimMatrix)
def Users():
pr = ProgramEntities()
nusers = len(pr.userIndex)
sim = ssd.correlation
cleaner = DataCleaner()
fin = open(r"D:\kaggle_data\event_recommendation\users.csv", 'r')
colnames = fin.readline().strip().split(",")
userMatrix = sparse.dok_matrix((nusers, len(colnames)-1)) # 稀疏矩阵
for line in fin:
cols = line.strip().split(",")
# 只考虑train.csv中出现的用户
if cols[0] in pr.userIndex.keys():
i = pr.userIndex[cols[0]]
userMatrix[i, 0] = cleaner.getLocaleId(cols[1])
userMatrix[i, 1] = cleaner.getBirthYearInt(cols[2])
userMatrix[i, 2] = cleaner.getGenderId(cols[3])
userMatrix[i, 3] = cleaner.getJoinedYearMonth(cols[4])
userMatrix[i, 4] = cleaner.getCountryId(cols[5])
userMatrix[i, 5] = cleaner.getTimezoneInt(cols[6])
fin.close()
# print(userMatrix)
# 归一化用户矩阵，并存起来
userMatrix_N = normalize(userMatrix, norm='l1', axis=0) # axis=0 沿着列归一化
# sio.mmwrite("US_userMatrix", userMatrix_N)
# 生成一个用户相似度矩阵
userSimMatrix = sparse.dok_matrix((nusers, nusers))
for i in range(nusers):
userSimMatrix[i, i] = 1.0
for u1,u2 in pr.uniqueUserPairs:
i = pr.userIndex[u1]
j = pr.userIndex[u2]
if (i not in userSimMatrix.keys() and j not in userSimMatrix.keys()):
usim = sim(userMatrix_N.getrow(i).todense(), userMatrix_N.getrow(j).todense())
userSimMatrix[i, j] = usim
userSimMatrix[j, i] = usim
sio.mmwrite("US_userSimMatrix", userSimMatrix)
def main():
"""
Main entry point to script to perform spectral co-clustering.
Returns:
- `0` or `1` on success or failure respectively.
- Saves `centroids`, `centroiddict`, `clusters` and `clusterdict` in \
working dir.
"""
parser = gen_args()
args = parser.parse_args()
sessionid = args.sessionid
A = spio.mmread(args.A).tocsc()
logger = logging.getLogger(__name__)
logger.addHandler(logging.StreamHandler())
if args.verbose:
logger.setLevel(logging.DEBUG)
if args.k:
k = args.k
spcc = SpectralCoClusterer(A, k, args.n, args.delta, \
args.randomcentroids, \
args.classical, args.verbose)
result = spcc.run()
clusters = result['clusters']
centroids = result['centroids']
centroid_dict = result['centroiddict']
cluster_dict = result['clusterdict']
cPickle.dump(clusters, open("clusters_" + sessionid + '.pck', 'w'))
cPickle.dump(centroid_dict, open("centroid_dict_" + \
sessionid + '.pck', 'w'))
cPickle.dump(cluster_dict, open("cluster_dict_" + \
sessionid + '.pck', 'w'))
spio.mmwrite(open("centroids_" + sessionid + '.mtx', 'w'), \
centroids, comment="CSC Matrix", field='real')
logger.info(" %d Clusters Generated ", len(clusters))
return 0
def genHashes():
with open('pickled_minhash/feature_matrix_binary_sample.npy', 'rb') as f:
#size of feature_matrix_large: 1261 x 19043
feature_matrix = np.load(f)
with open('pickled_minhash/actual_jaccard_matrix_small.mtx', 'rb') as f:
baseline = io.mmread(f)
baseline = baseline.todok()
jaccard_matrix = []
#jaccard_matrix_pre is a list of arrays that contain non-zero indicies of each article in the corpus
for i in feature_matrix[0:test_num]:
indicies = np.flatnonzero(i)
jaccard_matrix.append(indicies)
k_vals = [16, 32, 64, 128, 256]
for k in k_vals:
#calculate minHash with k =16
S = sparse.dok_matrix((len(jaccard_matrix), len(jaccard_matrix)))
t0 = time.time()
hashmap = {}
for i in range(0, len(jaccard_matrix)):
mh = MinHash(num_perm=k)
for d in jaccard_matrix[i]:
mh.digest(sha1(struct.pack("!I", d)))
hashmap[i] = mh
for i in range(0, len(jaccard_matrix)):
m8_1 = hashmap[i]
for j in range(0, i + 1):
m8_2 = hashmap[j]
estj = MinHash.jaccard(m8_1, m8_2)
if estj != 0 and estj != 1:
S[i, j] = estj
print("Time to calculate first %s estj (k=%s): %f" %
(len(jaccard_matrix), k, time.time() - t0))
with open('pickled_minhash/estj_' + str(k) + '_small.mtx', 'wb') as f:
#size of feature_matrix_large: 1261 x 19043
io.mmwrite(f, S)
def save_sparse_matrix(data, fmt, filepath):
"""
Save a scipy sparse matrix in the specified format. Row and column
indices will be converted to 1-indexed if you specify a plain text
format (tsv, csv, mm). Note that zero entries are guaranteed to be
saved in tsv or csv format.
Parameters
----------
data : scipy sparse matrix to save
fmt : str
Specifies the file format to write:
- tsv
- csv
- mm (MatrixMarket)
- npz (save as npz archive of numpy arrays)
- fsm (mrec.sparse.fast_sparse_matrix)
filepath : str
The file to load.
"""
if fmt == 'tsv':
m = data.tocoo()
with open(filepath, 'w') as out:
for u, i, v in izip(m.row, m.col, m.data):
print >> out, '{0}\t{1}\t{2}'.format(u + 1, i + 1, v)
elif fmt == 'csv':
m = data.tocoo()
with open(filepath, 'w') as out:
for u, i, v in izip(m.row, m.col, m.data):
print >> out, '{0},{1},{2}'.format(u + 1, i + 1, v)
elif fmt == 'mm':
mmwrite(filepath, data)
elif fmt == 'npz':
savez(data.tocoo(), filepath)
elif fmt == 'fsm':
fast_sparse_matrix(data).save(filepath)
else:
raise ValueError('unknown output format: {0}'.format(fmt))
def index(search_dir, index_dir):
cmd = 'pdftotext "%s" %s/loog.txt'
dirs, files = rsync.ls(search_dir)
files = [(f, size) for (f, size) in files if '.pdf' in f]
N = len(files)
A = sps.lil_matrix((N, cols))
print A.shape
df_files = []
for i, (f, size) in enumerate(files):
file = f.replace("\\", "/")
print file
if ".pdf" in file:
cmd2 = cmd % (f, os.environ['TEMP'])
os.system(cmd2)
lowers = open(
"%s/loog.txt" %
os.environ['TEMP']).read().decode("ISO-8859-1").lower()
tokens = nltk.word_tokenize(lowers)
tokens = stem_tokens(tokens)
print tokens[:30]
for token in tokens:
A[i, hash(token) % cols] += 1
df_files.append([file, size])
df = A.copy()
df[df > 0] = 1.
df = np.array(df.sum(axis=0))
idf = df.copy()
idf[df.nonzero()] = np.log(N / df[df.nonzero()])
io.mmwrite(index_dir + "/loogle_idf.mtx", idf)
tf = A.copy().tocoo()
tf.data = 1 + np.log(tf.data)
tfidf = sps.csr_matrix(tf.multiply(idf))
tfidf = normalize(tfidf, norm='l2', axis=1)
io.mmwrite(index_dir + "/loogle_tfidf.mtx", tfidf)
df_files = pd.DataFrame(df_files, columns=['file', 'size'])
df_files.to_csv(index_dir + "/loogle_files.csv", index=None)
def main():
usage = "" # TODO
parser = OptionParser(usage=usage)
#parser.add_option("-a", "--a_descrip", action="store_true", help="This is a flat")
parser.add_option("-o", "--out_dir", help="Output directory")
(options, args) = parser.parse_args()
dataset = args[0]
out_dir = options.out_dir
r = load_dataset.load_dataset(dataset, 'counts')
the_exps = r[3]
data_matrix = r[10]
gene_ids = r[11]
genes_df = pd.read_csv('genes.tsv', sep='\t', index_col=0, header=None)
genes_df = genes_df.loc[gene_ids]
with open(join(out_dir, 'matrix.mtx'), 'wb') as f:
mmwrite(f, coo_matrix(data_matrix.T))
with open(join(out_dir, 'barcodes.tsv'), 'w') as f:
f.write('\n'.join(the_exps))
genes_df.to_csv(join(out_dir, 'genes.tsv'), sep='\t', header=False)
def calculate(words, feature, fname):
e = np.zeros((len(words), len(words)))
for row in range(0, len(words)):
for column in range(row, len(words)):
if row < column:
tem = (np.dot(feature[row], feature[column]))\
/ (np.linalg.norm(feature[row])*np.linalg.norm(feature[column]))
e[row][column] = np.arccos(-tem)
e[column][row] = e[row][column]
for i in range(0, len(words)):
kth_max = np.sort(e[i])
zero_list = [0]
e_row = e[i]
e_row = np.where(e_row < kth_max[len(words) - 300], zero_list, e_row)
e[i] = e_row
for row in range(0, len(words)):
for column in range(row, len(words)):
if e[row][column] != e[column][row]:
e[row][column] = 0
e[column][row] = 0
sparse_e = sparse.csr_matrix(e)
io.mmwrite(fname, sparse_e)
return
def save_data(sco, outputpath):
mmf = os.path.join(outputpath, 'expr_m.mtx')
# with open(outputpath + 'expr_m.mtx', 'w') as f:
# mmwrite(f, sco.expression_matrix)
mmwrite(mmf, csc_matrix(sco.expression_matrix))
sc_info = []
sc_info.append(['meta_info'])
meta_info = sco.meta_info
p = sco.processed
p.append('save data')
meta_info['processed'] = p
meta_info = list([list(meta_info.keys()), list(meta_info.values())])
sc_info.append(meta_info)
sc_info[0].append("genes_list")
sc_info.append(list(sco.gene_ref.get_list()))
for i in sco.cell_info.data_names:
sc_info[0].append(i)
sc_info.append(list(sco.cell_info[i]))
with open(os.path.join(outputpath, 'info.json'), 'w') as f:
json.dump(sc_info, f)
def test_sparse_formats(self):
mats = []
I = array([0, 0, 1, 2, 3, 3, 3, 4])
J = array([0, 3, 1, 2, 1, 3, 4, 4])
V = array([1.0, 6.0, 10.5, 0.015, 250.5, -280.0, 33.32, 12.0])
mats.append(scipy.sparse.coo_matrix((V, (I, J)), shape=(5, 5)))
V = array([
1.0 + 3j, 6.0 + 2j, 10.50 + 0.9j, 0.015 + -4.4j, 250.5 + 0j,
-280.0 + 5j, 33.32 + 6.4j, 12.00 + 0.8j
])
mats.append(scipy.sparse.coo_matrix((V, (I, J)), shape=(5, 5)))
for mat in mats:
expected = mat.toarray()
for fmt in ['csr', 'csc', 'coo']:
fn = mktemp(dir=self.tmpdir) # safe, we own tmpdir
mmwrite(fn, mat.asformat(fmt))
result = mmread(fn).toarray()
assert_array_almost_equal(result, expected)
def test_gzip_py3(self):
# test if fix for #2152 works
try:
# gzip module can be missing from Python installation
import gzip
except ImportError:
return
I = array([0, 0, 1, 2, 3, 3, 3, 4])
J = array([0, 3, 1, 2, 1, 3, 4, 4])
V = array([1.0, 6.0, 10.5, 0.015, 250.5, -280.0, 33.32, 12.0])
b = scipy.sparse.coo_matrix((V, (I, J)), shape=(5, 5))
mmwrite(self.fn, b)
fn_gzip = "%s.gz" % self.fn
with open(self.fn, 'rb') as f_in:
f_out = gzip.open(fn_gzip, 'wb')
f_out.write(f_in.read())
f_out.close()
a = mmread(fn_gzip).toarray()
assert_array_almost_equal(a, b.toarray())
def test_bzip2_py3(self):
# test if fix for #2152 works
try:
# bz2 module isn't always built when building Python.
import bz2
except ImportError:
return
I = array([0, 0, 1, 2, 3, 3, 3, 4])
J = array([0, 3, 1, 2, 1, 3, 4, 4])
V = array([1.0, 6.0, 10.5, 0.015, 250.5, -280.0, 33.32, 12.0])
b = scipy.sparse.coo_matrix((V, (I, J)), shape=(5, 5))
mmwrite(self.fn, b)
fn_bzip2 = "%s.bz2" % self.fn
with open(self.fn, 'rb') as f_in:
f_out = bz2.BZ2File(fn_bzip2, 'wb')
f_out.write(f_in.read())
f_out.close()
a = mmread(fn_bzip2).toarray()
assert_array_almost_equal(a, b.toarray())
def export_matrix(self, filename, matrix_name=None, output_format='matlab', mu=None):
"""Save the matrix of the operator to a file.
Parameters
----------
filename
Name of output file.
matrix_name
The name, the output matrix is given. (Comment field is used in
case of Matrix Market output_format.) If `None`, the |Operator|'s `name`
is used.
output_format
Output file format. Either `matlab` or `matrixmarket`.
mu
The |Parameter| to assemble the to be exported matrix for.
"""
assert output_format in {'matlab', 'matrixmarket'}
matrix = self.assemble(mu).matrix
matrix_name = matrix_name or self.name
if output_format == 'matlab':
savemat(filename, {matrix_name: matrix})
else:
mmwrite(filename, matrix, comment=matrix_name)
def write_matrix_10X(self, df, matrix_dir):
if not os.path.exists(matrix_dir):
os.mkdir(matrix_dir)
df_UMI = df.groupby(['geneID', 'Barcode']).agg({'UMI': 'count'})
mtx = coo_matrix(
(df_UMI.UMI, (df_UMI.index.codes[0], df_UMI.index.codes[1])))
gene_id = df_UMI.index.levels[0].to_series()
# add gene symbol
gene_name = gene_id.apply(lambda x: self.gtf_dict[x])
genes = pd.concat([gene_id, gene_name], axis=1)
genes.columns = ['gene_id', 'gene_name']
barcodes = df_UMI.index.levels[1].to_series()
genes.to_csv(f'{matrix_dir}/{FEATURE_FILE_NAME}',
index=False,
sep='\t',
header=False)
barcodes.to_csv(f'{matrix_dir}/{BARCODE_FILE_NAME}',
index=False,
sep='\t',
header=False)
mmwrite(f'{matrix_dir}/{MATRIX_FILE_NAME}', mtx)