def feat_importance_ip(row_id_str, ds_id, hdfs_feat_dir, local_score_file,
score_file_IT, sp_master, spark_rdd_compress,
spark_driver_maxResultSize, sp_exe_memory, sp_core_max,
zipout_dir, zipcode_dir, zip_file_name, mongo_tuples,
jobname, uploadtype):
# zip func in other files for Spark workers ================= ================
zip_file_path = ml_util.ml_build_zip_file(zipout_dir,
zipcode_dir,
zip_file_name,
prefix='zip_feature_util')
print "INFO: zip_file_path=", zip_file_path
# get_spark_context
sc = ml_util.ml_get_spark_context(sp_master, spark_rdd_compress,
spark_driver_maxResultSize,
sp_exe_memory, sp_core_max, jobname,
[zip_file_path])
'''
SparkContext.setSystemProperty('spark.rdd.compress', config.get('spark', 'spark_rdd_compress'))
SparkContext.setSystemProperty('spark.driver.maxResultSize', config.get('spark', 'spark_driver_maxResultSize'))
#SparkContext.setSystemProperty('spark.kryoserializer.buffer.mb', config.get('spark', 'spark_kryoserializer_buffer_mb'))
SparkContext.setSystemProperty('spark.executor.memory', args.exe_memory)
SparkContext.setSystemProperty('spark.cores.max', args.core_max)
sc = SparkContext(args.sp_master, 'feature_importance_2ways:'+str(args.row_id))
'''
t0 = time()
# get folder list (labels) from hdfs data_out/<id>/metadata ==============
dirFile_loc = os.path.join(hdfs_feat_dir, "metadata")
dirFolders = sc.textFile(dirFile_loc)
hash_Folders = dirFolders.collect()
print "INFO: dirFile_loc=", dirFile_loc, ", hash_Folders=", hash_Folders
folder_list = [x.encode('UTF8') for x in hash_Folders]
print "INFO: folder_list=", folder_list
# get feature seq : ngram hash mapping ==================================
key = "dic_seq_hashes" #{"123":"136,345"}
jstr_filter = '{"rid":' + row_id_str + ',"key":"' + key + '"}'
jstr_proj = '{"value":1}'
# get parent dataset's data
if ds_id != row_id_str:
jstr_filter = '{"rid":' + ds_id + ',"key":"' + key + '"}'
doc = query_mongo.find_one_t(mongo_tuples, jstr_filter, jstr_proj)
dic_list = doc['value']
dic_all_columns = dic_list
feature_count = len(dic_list)
#print "INFO: feature_count=",feature_count
#print "dic_list=",dic_list #{u'123,345':u'136'}
#print "INFO: dic_all_columns=",dic_all_columns # {1: u'8215,8216'}
# end
# get hash : raw string mapping ==================================
key = "dic_hash_str" #{"123":"openFile"}
jstr_filter = '{"rid":' + row_id_str + ',"key":"' + key + '"}'
jstr_proj = '{"value":1}'
# get parent dataset's data
if ds_id != row_id_str:
jstr_filter = '{"rid":' + ds_id + ',"key":"' + key + '"}'
doc = query_mongo.find_one_t(mongo_tuples, jstr_filter, jstr_proj)
dic_hash_str = doc['value']
'''
# get folder list (labels) from hdfs data_out/<id>/libsvm ==============
libsvm_loc = os.path.join(hdfs_feat_dir , "libsvm_data")
# based on label, divide RDD into arrays
f_rdd = sc.textFile(libsvm_loc).map(lambda x: libsvm2tuple_arr(x))
arr_libsvm=sorted(f_rdd.collect(), key=lambda x:x[0]) # sorted by label
'''
# filename for featured data
libsvm_data_file = os.path.join(hdfs_feat_dir, "libsvm_data")
print "INFO: libsvm_data_file=", libsvm_data_file
print "INFO: feature_count=",feature_count\
# get sample array from hdfs
arr_libsvm = zip_feature_util.get_sample_tuple_arr(sc, libsvm_data_file)
# sorted by label
arr_libsvm = sorted(arr_libsvm, key=lambda x: x[0])
# convert libsvm to features_list, row_list, col_list, sample count, col_num
lbl_flag = -1
row_num_training = 0
sparse_mtx_list = [] # for feat impor calculation
features_list = [] # for csc_matrix
row_list = [] # for csc_matrix
col_list = [] # for csc_matrix
sample_numbers = [] # for csc_matrix
feature_arr = None
for idx, i in enumerate(arr_libsvm):
#print "idx=",idx,",l=",i[0],",d=",i[1:]
if lbl_flag != i[0]:
if feature_arr and len(feature_arr) > 0:
features_list.append(np.array(feature_arr))
row_list.append(np.array(row_arr))
col_list.append(np.array(col_arr))
sample_numbers.append(cnt)
row_arr = []
col_arr = []
feature_arr = []
cnt = 0
lbl_flag += 1
for j in i[1:]:
row_arr.append(cnt)
col_arr.append(j[0] - 1)
feature_arr.append(j[1])
cnt += 1
# for last part
if len(feature_arr) > 0:
features_list.append(np.array(feature_arr))
row_list.append(np.array(row_arr))
col_list.append(np.array(col_arr))
sample_numbers.append(cnt)
#print ",features_list=",features_list
#print ",row_list=",row_list
#print ",col_list=",col_list
print "INFO: sample_numbers=", sample_numbers
col_num = len(dic_list)
print "INFO: column number: ", col_num #, ",len(max_feat_list)=",len(max_feat_list)
for i in range(0, len(features_list)):
#print "i=",i
#print "features_list=",features_list[i]
#print "row_list=",row_list[i]
#print "col_list=",col_list[i]
#print "sample_numbers=",sample_numbers[i]
sparse_mtx = csc_matrix((features_list[i], (row_list[i], col_list[i])),
shape=(sample_numbers[i], col_num))
sparse_mtx_list.append(sparse_mtx)
#print sparse_mtx_list[0]
print "INFO: sparse_mtx_list[0].shape=", sparse_mtx_list[0].shape
#print sparse_mtx_list[1]
print "INFO: sparse_mtx_list[1].shape=", sparse_mtx_list[1].shape
exclusive_feature_set_mal = []
exclusive_feature_set_clean = []
dic_feature_cnt_mal = {}
dic_feature_cnt_clean = {}
dic_score = {}
dic_cnt_mal = {}
dic_cnt_clean = {}
dic_IT_grain = {}
####################################################
####feature importance algorithms: 2 methods ####### # Only for 2 classes ???
####################################################
if len(sample_numbers) == 2:
###################################################
################## calculate probability ############
###################################################
print "INFO: =======Feature Importance(probability) ================ "
for j in range(0, col_num):
curr_col_dirty = sparse_mtx_list[0].getcol(j)
sum_col = curr_col_dirty.sum(0)
cnt_mal = sum_col.tolist()[0][0]
curr_col_clean = sparse_mtx_list[1].getcol(j)
sum_col = curr_col_clean.sum(0)
cnt_clean = sum_col.tolist()[0][0]
percnt_mal = cnt_mal / float(sample_numbers[0])
percnt_clean = cnt_clean / float(sample_numbers[1])
score_j = (percnt_mal + 1 - percnt_clean) / 2
dic_score[j + 1] = score_j
dic_cnt_clean[j + 1] = cnt_clean
dic_cnt_mal[j + 1] = cnt_mal
sorted_score = sorted(dic_score.items(),
key=operator.itemgetter(1),
reverse=True)
#print "sorted_score:", sorted_score
#print "dic_cnt_clean", dic_cnt_clean
#print "dic_cnt_mal", dic_cnt_mal
############output result########################
if os.path.exists(local_score_file):
try:
os.remove(local_score_file)
except OSError, e:
print("Error: %s - %s." % (e.local_score_file, e.strerror))
for ii in range(0, len(sorted_score)):
(feat, score) = sorted_score[ii]
#print feat, score, dic_all_columns[feat]
if dic_hash_str:
description_str = feats2strs(dic_all_columns[str(feat)],
dic_hash_str)
else:
description_str = "N/A"
print "Warning: No mapping found for feature number"
str01 = str(feat) + "\t" + str(
score) + "\t" + description_str + "\n"
with open(local_score_file, "a") as f:
f.write(str01)
########################################################
##################Information Gain (entropy)############
########################################################
print "INFO: =======Information Gain================ "
for j in range(0, col_num):
cnt_mal = dic_cnt_mal[j + 1]
cnt_clean = dic_cnt_clean[j + 1]
total_samples = sample_numbers[0] + sample_numbers[1]
p0 = float(sample_numbers[0]) / total_samples
p1 = 1 - p0
if p0 == 0 or p1 == 0:
parent_entropy = 0
else:
parent_entropy = 0 - p0 * np.log2(p0) - p1 * np.log2(p1)
if cnt_clean + cnt_mal == 0:
information_gain = 0
elif total_samples - cnt_clean - cnt_mal == 0:
information_gain = 0
else:
p0 = float(cnt_mal) / (cnt_clean + cnt_mal)
p1 = 1 - p0
if p0 == 0 or p1 == 0:
child_left_entropy = 0
else:
child_left_entropy = 0 - p0 * np.log2(p0) - p1 * np.log2(
p1)
p0 = float(sample_numbers[0] - cnt_mal) / (total_samples -
cnt_clean - cnt_mal)
p1 = 1 - p0
if p0 == 0 or p1 == 0:
child_right_entropy = 0
else:
child_right_entropy = 0 - p0 * np.log2(p0) - p1 * np.log2(
p1)
weighted_child_entropy = child_left_entropy * float(
cnt_clean +
cnt_mal) / total_samples + child_right_entropy * float(
total_samples - cnt_clean - cnt_mal) / total_samples
information_gain = parent_entropy - weighted_child_entropy
dic_IT_grain[j + 1] = information_gain
sorted_IT_gain = sorted(dic_IT_grain.items(),
key=operator.itemgetter(1),
reverse=True)
if os.path.exists(score_file_IT):
try:
os.remove(score_file_IT)
except OSError, e:
print("Error: %s - %s." % (e.score_file_IT, e.strerror))
def feat_importance_firm(row_id_str, ds_id, hdfs_feat_dir, local_score_file,
sp_master, spark_rdd_compress,
spark_driver_maxResultSize, sp_exe_memory,
sp_core_max, zipout_dir, zipcode_dir, zip_file_name,
mongo_tuples, training_fraction, jobname, uploadtype,
description_file):
# zip func in other files for Spark workers ================= ================
zip_file_path = ml_util.ml_build_zip_file(zipout_dir,
zipcode_dir,
zip_file_name,
prefix='zip_feature_util')
print "INFO: zip_file_path=", zip_file_path
# get_spark_context
sc = ml_util.ml_get_spark_context(sp_master, spark_rdd_compress,
spark_driver_maxResultSize,
sp_exe_memory, sp_core_max, jobname,
[zip_file_path])
t0 = time()
# get feature seq mapping from mongo
if uploadtype == "MD5 List IN-dynamic":
### connect to database to get the column list which contains all column number of the corresponding feature
key = "dict_dynamic"
jstr_filter = '{"rid":' + row_id_str + ',"key":"' + key + '"}'
jstr_proj = '{"value":1}'
# get parent dataset's data
if ds_id != row_id_str:
jstr_filter = '{"rid":' + ds_id + ',"key":"' + key + '"}'
doc = query_mongo.find_one_t(mongo_tuples, jstr_filter, jstr_proj)
dic_list = doc['value']
dic_all_columns = {}
max_feature = 0
# reverse dict{hashes:sequence number} ======
for i in range(0, len(dic_list)):
for key in dic_list[i]:
dic_all_columns[eval(dic_list[i][key])] = key
if eval(dic_list[i][key]) > max_feature:
max_feature = eval(dic_list[i][key])
print "INFO: max_feature=", max_feature
#print "dic_all_columns=",dic_all_columns # fid:numb,numb
dirFile_loc = os.path.join(hdfs_feat_dir, "metadata")
dirFolders = sc.textFile(dirFile_loc)
hash_Folders = dirFolders.collect()
#print "INFO: dirFile_loc=",dirFile_loc,", hash_Folders=",hash_Folders
folder_list = [x.encode('UTF8') for x in hash_Folders]
print "INFO: hdfs folder_list=", folder_list #['dirty/', 'clean/']
features_training = []
labels_training = []
names_training = []
row_training = []
col_training = []
max_feat_training = 0
row_num_training = 0
features_testing = []
labels_testing = []
names_testing = []
row_testing = []
col_testing = []
max_feat_testing = 0
row_num_testing = 0
# loop through hdfs folders; TBD
for folder in folder_list:
print "INFO: folder=", folder
label = folder_list.index(folder) + 1
print 'INFO: label=', label
logFile_name = os.path.join(hdfs_feat_dir, folder, mtx_name_list)
#print "logFile_name=",logFile_name
logFile_data = os.path.join(hdfs_feat_dir, folder, mtx_libsvm)
#print "logFile_data=",logFile_data
logNames = sc.textFile(logFile_name).cache()
logData = sc.textFile(logFile_data).cache()
names = logNames.collect()
data = logData.collect()
name_l = [x.encode('UTF8') for x in names]
feature_l = [x.encode('UTF8') for x in data]
name_list = [names.strip() for names in name_l]
feature_list = [features.strip() for features in feature_l]
##########data seperation######
id_perm = data_seperation_random(name_list)
num_names = len(name_list)
print 'INFO: num of samples=', num_names
num_train = int(training_portion * num_names)
print 'INFO: num_train = ', num_train
########generate training data#########
i = 0
#print "INFO: generate training data"
#print "INFO: len(id_perm)=",len(id_perm)
while i < num_train:
#print i, id_perm[i]
features = feature_list[id_perm[i]]
features = features.strip()
feature_array = features.split(' ')
labels_training.append(label)
length = len(feature_array)
j = 0
while j < length:
feature = feature_array[j]
feat, value = feature.split(':', 2)
row_training.append(i + row_num_training)
col_training.append(int(feat) - 1)
features_training.append(int(value))
max_feat_training = max(max_feat_training, int(feat))
j = j + 1
i = i + 1
row_num_training = row_num_training + num_train
i = num_train
########generate testing data#########
while i < num_names:
####for generating testing data folder####
test_file_name = name_list[id_perm[i]]
features = feature_list[id_perm[i]]
features = features.strip()
feature_array = features.split(' ')
labels_testing.append(label)
length = len(feature_array)
j = 0
while j < length:
feature = feature_array[j]
feat, value = feature.split(':', 2)
row_testing.append(i - num_train + row_num_testing)
col_testing.append(int(feat) - 1)
features_testing.append(int(value))
max_feat_testing = max(max_feat_testing, int(feat))
j = j + 1
i = i + 1
row_num_testing = row_num_testing + (num_names - num_train)
# end for loop here ========================
col_num = max(max_feat_training, max_feat_testing)
if max_feat_training < col_num:
for i in range(0, row_num_training):
for j in range(max_feat_training, col_num):
features_training.append(0)
row_training.append(i)
col_training.append(j)
elif max_feat_testing < col_num:
for i in range(0, row_num_testing):
for j in range(max_feat_testing, col_num):
features_testing.append(0)
row_testing.append(i)
col_testing.append(j)
features_training = array(features_training)
row_training = array(row_training)
col_training = array(col_training)
#print "row_training:", row_training
#print "INFO: col_training:", col_training
len_col = len(col_training)
print "INFO: col_num:", col_num
labels_training = array(labels_training)
features_testing = array(features_testing)
row_testing = array(row_testing)
col_testing = array(col_testing)
labels_testing = array(labels_testing)
sparse_mtx = csc_matrix((features_training, (row_training, col_training)),
shape=(row_num_training, col_num))
#print "sparse_mtx.todense(), sparse_mtx.shape=",sparse_mtx.todense(), sparse_mtx.shape
sparse_test = csc_matrix((features_testing, (row_testing, col_testing)),
shape=(row_num_testing, col_num))
#print " sparse_test.todense(), sparse_test.shape=",sparse_test.todense(), sparse_test.shape
clf = svm.LinearSVC()
#clf = svm.SVC(C=0.1, kernel='rbf', degree=3, gamma=0.05, coef0=0.0, shrinking=True, probability=False, tol=0.001, cache_size=200, class_weight=None, verbose=False, max_iter=-1, random_state=None)
#clf = svm.NuSVC(nu=0.3, kernel='rbf', degree=3, gamma=0.05, coef0=0.0, shrinking=True, probability=False, tol=0.001, cache_size=200, verbose=False, max_iter=-1, random_state=None)
#print "labels_training=",labels_training
#print "sparse_mtx=",sparse_mtx
clf.fit(sparse_mtx, labels_training)
#print "INFO: model:intercept=",clf.intercept_
#print "INFO: model:coef=",clf.coef_
labels_pred = clf.predict(sparse_test)
#print "labels_pred:", labels_pred
accuracy = clf.score(sparse_test, labels_testing)
#print "INFO: data folder=", hdfs_feat_dir
print "INFO: accuracy=", accuracy
#####################################################################
##################calculate feature importance with predication labels#######################
#####################################################################
AA = sparse_mtx.todense()
BB = sparse_test.todense()
labels_train_pred = clf.predict(sparse_mtx)
labels_test_pred = labels_pred
#print "###################################################################################"
print "INFO: ======= Calculate feature importance with predication labels =================="
#print "###################################################################################"
dic_importance_label = {}
for j in range(0, col_num): ###for all features in the loop
##############################
#print "====new way with sparse matrix========="
curr_col_train = sparse_mtx.getcol(j)
sum_col = curr_col_train.sum(0)
positive_feature_number = int(sum_col.tolist()[0][0])
labels_value = 3 - labels_train_pred
dot_product = csr_matrix(np.array(labels_value)).dot(curr_col_train)
sum_product = dot_product.sum(1)
labels_positive_sum = int(sum_product.tolist()[0][0])
sum_label_values = sum(labels_value)
labels_negitive_sum = sum_label_values - labels_positive_sum
##############################
#print "====new way with sparse matrix========="
curr_col_test = sparse_test.getcol(j)
sum_col = curr_col_test.sum(0)
positive_feature_number = positive_feature_number + int(
sum_col.tolist()[0][0])
labels_value = 3 - labels_test_pred
dot_product = csr_matrix(np.array(labels_value)).dot(curr_col_test)
sum_product = dot_product.sum(1)
labels_positive_sum = labels_positive_sum + int(
sum_product.tolist()[0][0])
sum_label_values = sum(labels_value)
labels_negitive_sum = labels_negitive_sum + sum_label_values - int(
sum_product.tolist()[0][0])
n_total = row_num_training + row_num_testing
negitive_feature_number = n_total - positive_feature_number
if positive_feature_number == 0:
#print "feature ", j+1, "all 0s!"
dic_importance_label[j + 1] = -100
elif negitive_feature_number == 0:
#print "feature ", j+1, "all 1s!"
dic_importance_label[j + 1] = -200
else:
q_positive = float(labels_positive_sum) / positive_feature_number
q_negitive = float(labels_negitive_sum) / negitive_feature_number
Q = (q_positive - q_negitive) * sqrt(
float(q_positive) * q_negitive / float(n_total) /
float(n_total))
dic_importance_label[j + 1] = Q
sorted_importance = sorted(dic_importance_label.items(),
key=operator.itemgetter(1),
reverse=True)
print "INFO: ======= Feature Importance(FIRM score) ================"
if os.path.exists(local_score_file):
try:
os.remove(local_score_file)
except OSError, e:
print("ERROR: %s - %s." % (e.local_score_file, e.strerror))
def feat_importance_2way(row_id_str, ds_id, hdfs_feat_dir, score_file_IT,
score_file_prob, sp_master, spark_rdd_compress,
spark_driver_maxResultSize, sp_exe_memory,
sp_core_max, zipout_dir, zipcode_dir, zip_file_name,
mongo_tuples, jobname, uploadtype, description_file):
# zip func in other files for Spark workers ================= ================
zip_file_path = ml_util.ml_build_zip_file(zipout_dir,
zipcode_dir,
zip_file_name,
prefix='zip_feature_util')
print "INFO: zip_file_path=", zip_file_path
# get_spark_context
sc = ml_util.ml_get_spark_context(sp_master, spark_rdd_compress,
spark_driver_maxResultSize,
sp_exe_memory, sp_core_max, jobname,
[zip_file_path])
t0 = time()
# get folder list (labels) from hdfs data_out/<id>/metadata
dirFile_loc = os.path.join(hdfs_feat_dir, "metadata")
dirFolders = sc.textFile(dirFile_loc)
hash_Folders = dirFolders.collect()
#print "hash_Folders=",hash_Folders
folder_list = [x.encode('UTF8') for x in hash_Folders]
#print "INFO: dirFile_loc=",dirFile_loc,", folder_list=",folder_list
row_num_training = 0
sample_numbers = []
sparse_mtx_list = []
features_list = []
row_list = []
col_list = []
max_feat_list = []
for folder in folder_list:
print "INFO: folder:", folder
label = folder_list.index(folder) + 1
print 'INFO: label=', label
# md5 list
logFile_name = os.path.join(hdfs_feat_dir, folder, mtx_name_list)
# libsvm data
logFile_data = os.path.join(hdfs_feat_dir, folder, mtx_libsvm)
#logFile_data = hdfs_feat_dir + folder + mtx_stat
#print "INFO: logFile_name=",logFile_name
#print "INFO: logFile_data=",logFile_data
logNames = sc.textFile(logFile_name).cache()
logData = sc.textFile(logFile_data).cache()
names = logNames.collect()
data = logData.collect()
name_l = [x.encode('UTF8') for x in names]
feature_l = [x.encode('UTF8') for x in data]
name_list = [names.strip() for names in name_l]
feature_list = [features.strip() for features in feature_l]
num_names = len(name_list)
sample_numbers.append(num_names)
print 'INFO: sample_numbers=', sample_numbers
print 'INFO: name_list count=', num_names
########generate list for csc_matrix creation #########
i = 0
features_training = []
row_training = []
col_training = []
labels_training = []
max_feat = 0
while i < num_names:
features = feature_list[i]
features = features.strip()
feature_array = features.split(' ')
labels_training.append(label)
length = len(feature_array)
j = 0
while j < length:
feature = feature_array[j]
feat, value = feature.split(':', 2)
row_training.append(i)
col_training.append(int(feat) - 1)
features_training.append(int(value))
max_feat = max(max_feat, int(feat))
j = j + 1
i = i + 1
#print "features_training=",features_training
#print "row_training=",row_training
#print "col_training=",col_training
features_training = array(features_training)
row_training = array(row_training)
col_training = array(col_training)
#print "type(row_training)=",type(row_training),",row_training=",row_training
#print "type(col_training)=",type(col_training),",col_training=",col_training
max_feat_list.append(max_feat)
features_list.append(features_training)
row_list.append(row_training)
col_list.append(col_training)
row_num_training = row_num_training + num_names
#print " END for folder in folder_list: "
col_num = max(max_feat_list)
print "INFO: column number=", col_num, ", len(max_feat_list)=", len(
max_feat_list)
for i in range(0, len(max_feat_list)):
sparse_mtx = csc_matrix((features_list[i], (row_list[i], col_list[i])),
shape=(sample_numbers[i], col_num))
sparse_mtx_list.append(sparse_mtx)
#print sparse_mtx_list[0]
#print "sparse_mtx_list[0].shape=",sparse_mtx_list[0].shape
#print sparse_mtx_list[1]
#print "sparse_mtx_list[1].shape=",sparse_mtx_list[1].shape
exclusive_feature_set_mal = []
exclusive_feature_set_clean = []
dic_feature_cnt_mal = {}
dic_feature_cnt_clean = {}
dic_score = {}
dic_cnt_mal = {}
dic_cnt_clean = {}
dic_IT_grain = {}
####################################################
####feature importance algorithms: 2 methods ####### # Only for 2 classes ???
####################################################
if len(sample_numbers) == 2:
###################################################
################## calculate probability ############
###################################################
print "INFO: ======= Feature Importance(probability) ================"
for j in range(0, col_num):
curr_col_dirty = sparse_mtx_list[0].getcol(j)
sum_col = curr_col_dirty.sum(0)
cnt_mal = sum_col.tolist()[0][0]
curr_col_clean = sparse_mtx_list[1].getcol(j)
sum_col = curr_col_clean.sum(0)
cnt_clean = sum_col.tolist()[0][0]
percnt_mal = cnt_mal / float(sample_numbers[0])
percnt_clean = cnt_clean / float(sample_numbers[1])
score_j = (percnt_mal + 1 - percnt_clean) / 2
dic_score[j + 1] = score_j
dic_cnt_clean[j + 1] = cnt_clean
dic_cnt_mal[j + 1] = cnt_mal
sorted_score = sorted(dic_score.items(),
key=operator.itemgetter(1),
reverse=True)
#print "sorted_score:", sorted_score
#print "dic_cnt_clean", dic_cnt_clean
#print "dic_cnt_mal", dic_cnt_mal
if os.path.exists(score_file_prob):
try:
os.remove(score_file_prob)
except OSError, e:
print("Error: %s - %s." % (e.score_file_prob, e.strerror))
for ii in range(0, len(sorted_score)):
(feat, score) = sorted_score[ii]
#print feat, score, dic_all_columns[feat]
str01 = str(feat) + "\t" + str(
score) + "\t" + description_str + "\n"
with open(score_file_prob, "a") as f:
f.write(str01)
########################################################
##################Information Gain (entropy)############
########################################################
print "INFO: ======= Information Gain ================"
for j in range(0, col_num):
cnt_mal = dic_cnt_mal[j + 1]
cnt_clean = dic_cnt_clean[j + 1]
total_samples = sample_numbers[0] + sample_numbers[1]
p0 = float(sample_numbers[0]) / total_samples
p1 = 1 - p0
if p0 == 0 or p1 == 0:
parent_entropy = 0
else:
parent_entropy = 0 - p0 * np.log2(p0) - p1 * np.log2(p1)
if cnt_clean + cnt_mal == 0:
information_gain = 0
elif total_samples - cnt_clean - cnt_mal == 0:
information_gain = 0
else:
p0 = float(cnt_mal) / (cnt_clean + cnt_mal)
p1 = 1 - p0
if p0 == 0 or p1 == 0:
child_left_entropy = 0
else:
child_left_entropy = 0 - p0 * np.log2(p0) - p1 * np.log2(
p1)
p0 = float(sample_numbers[0] - cnt_mal) / (total_samples -
cnt_clean - cnt_mal)
p1 = 1 - p0
if p0 == 0 or p1 == 0:
child_right_entropy = 0
else:
child_right_entropy = 0 - p0 * np.log2(p0) - p1 * np.log2(
p1)
weighted_child_entropy = child_left_entropy * float(
cnt_clean +
cnt_mal) / total_samples + child_right_entropy * float(
total_samples - cnt_clean - cnt_mal) / total_samples
information_gain = parent_entropy - weighted_child_entropy
dic_IT_grain[j + 1] = information_gain
sorted_IT_gain = sorted(dic_IT_grain.items(),
key=operator.itemgetter(1),
reverse=True)
if os.path.exists(score_file_IT):
try:
os.remove(score_file_IT)
except OSError, e:
print("ERROR: %s - %s." % (e.score_file_IT, e.strerror))
def train(row_id_str,
ds_id,
hdfs_feat_dir,
local_out_dir,
ml_opts_jstr,
excluded_feat_cslist,
sp_master,
spark_rdd_compress,
spark_driver_maxResultSize,
sp_exe_memory,
sp_core_max,
zipout_dir,
zipcode_dir,
zip_file_name,
mongo_tuples,
labelnameflag,
fromweb,
training_fraction,
jobname,
random_seed=None):
### generate data folder and out folder, clean up if needed
#local_out_dir = local_out_dir + "/"
#if os.path.exists(local_out_dir):
# shutil.rmtree(local_out_dir) # to keep smaplelist file
if not os.path.exists(local_out_dir):
os.makedirs(local_out_dir)
# create zip files for Spark workers ================= ================
zip_file_path = ml_util.ml_build_zip_file(zipout_dir,
zipcode_dir,
zip_file_name,
prefix='zip_feature_util')
print "INFO: zip_file_path=", zip_file_path
# get_spark_context
sc = ml_util.ml_get_spark_context(sp_master, spark_rdd_compress,
spark_driver_maxResultSize,
sp_exe_memory, sp_core_max, jobname,
[zip_file_path])
t0 = time()
# check if ml_opts.has_excluded_feat ==1 ===================================
has_excluded_feat = 0
ml_opts = {}
if not ml_opts_jstr is None:
ml_opts = json.loads(ml_opts_jstr)
if "has_excluded_feat" in ml_opts:
has_excluded_feat = ml_opts["has_excluded_feat"]
#print "has_excluded_feat=",has_excluded_feat,",excluded_feat_cslist=",excluded_feat_cslist
# get excluded feature list from mongo ========== ===
if str(has_excluded_feat) == "1" and excluded_feat_cslist is None:
excluded_feat_cslist = ml_util.ml_get_excluded_feat(
row_id_str, mongo_tuples)
print "INFO: excluded_feat_cslist=", excluded_feat_cslist
# filename for featured data
libsvm_data_file = os.path.join(hdfs_feat_dir, "libsvm_data")
print "INFO: libsvm_data_file:", libsvm_data_file
# load feature count file
feat_count_file = libsvm_data_file + "_feat_count"
feature_count = zip_feature_util.get_feature_count(sc, feat_count_file)
print "INFO: feature_count=", feature_count
# load sample RDD from text file
# also exclude selected features in sample ================ =====
# format (LabeledPoint,hash) from str2LabeledPoint_hash()
#samples_rdd = MLUtils.loadLibSVMFile(sc, libsvm_data_file)
samples_rdd, feature_count = zip_feature_util.get_sample_rdd(
sc, libsvm_data_file, feature_count, excluded_feat_cslist)
# get distinct label list
labels_list_all = samples_rdd.map(
lambda p: p[0].label).distinct().collect()
# split samples to training and testing data, format (LabeledPoint,hash)
training_rdd, testing_rdd = samples_rdd.randomSplit(
[training_fraction, 1 - training_fraction], seed=int(random_seed))
training_rdd = training_rdd.map(lambda p: p[0]) # keep LabeledPoint only
training_rdd.cache()
training_sample_count = training_rdd.count()
training_lbl_cnt_list = training_rdd.map(
lambda p: (p.label, 1)).reduceByKey(add).collect()
testing_rdd.cache()
testing_sample_count = testing_rdd.count()
testing_lbl_cnt_list = testing_rdd.map(
lambda p: (p[0].label, 1)).reduceByKey(add).collect()
sample_count = training_sample_count + testing_sample_count
# random_seed testing
if not random_seed is None:
all_t = testing_rdd.collect()
all_t = sorted(all_t, key=lambda x: x[1])
cnt = 0
for i in all_t:
print i[1]
cnt = cnt + 1
if cnt > 3:
break
t1 = time()
print "INFO: training sample count=", training_sample_count, ", testing sample count=", testing_sample_count
print "INFO: training label list=", training_lbl_cnt_list, ", testing label list=", testing_lbl_cnt_list
print "INFO: labels_list_all=", labels_list_all
print "INFO: training and testing samples generated!"
print 'INFO: running time: %f' % (t1 - t0)
t0 = t1
###############################################
###########build learning model################
###############################################
### get the parameters###
print "INFO: ======Learning Algorithm and Parameters============="
#ml_opts = json.loads(ml_opts_jstr)
model_name = ml_opts[
'learning_algorithm'] # 1: linear_svm_with_sgd; 2: logistic_regression_with_lbfgs; 3: logistic_regression_with_sgd
iteration_num = 0
if 'iterations' in ml_opts:
iteration_num = ml_opts['iterations']
C = 0
if 'c' in ml_opts:
C = eval(ml_opts['c'])
regularization = ""
if 'regularization' in ml_opts:
regularization = ml_opts['regularization']
print "INFO: Learning Algorithm: ", model_name
print "INFO: C = ", C
print "INFO: iterations = ", iteration_num
print "INFO: regType = ", regularization
regP = C / float(training_sample_count)
print "INFO: Calculated: regParam = ", regP
### generate label names (family names) #####
### connect to database to get the column list which contains all column number of the corresponding feature####
if labelnameflag == 1:
'''
key = "dic_name_label"
jstr_filter='{"rid":'+row_id_str+',"key":"'+key+'"}'
jstr_proj='{"value":1}'
# get parent dataset's data
if ds_id != row_id_str:
jstr_filter='{"rid":'+ds_id+',"key":"'+key+'"}'
doc=query_mongo.find_one_t(mongo_tuples, jstr_filter, jstr_proj)
dic_list = doc['value']
print "INFO: dic_list=",dic_list
label_dic = {}
for i in range(0, len(dic_list)):
for key in dic_list[i]:
label_dic[dic_list[i][key]] = key.encode('UTF8')
'''
label_dic = ml_util.ml_get_label_dict(row_id_str, mongo_tuples, ds_id)
print "INFO: label_dic:", label_dic
else:
label_dic = {}
label_set = set(labels_list_all)
for label_value in label_set:
label_dic[int(label_value)] = str(int(label_value))
print "INFO: generated label_dic:", label_dic
labels_list = []
for key in sorted(label_dic):
labels_list.append(label_dic[key])
print "INFO: labels:", labels_list
class_num = len(labels_list)
if class_num > 2:
print "INFO: Multi-class classification! Number of classes = ", class_num
### build model ###
if model_name == "linear_svm_with_sgd":
### 1: linearSVM
print "INFO: ====================1: Linear SVM============="
model_classification = SVMWithSGD.train(
training_rdd,
regParam=regP,
iterations=iteration_num,
regType=regularization) # regParam = 1/(sample_number*C)
#print model_classification
elif model_name == "logistic_regression_with_lbfgs":
### 2: LogisticRegressionWithLBFGS
print "INFO: ====================2: LogisticRegressionWithLBFGS============="
model_classification = LogisticRegressionWithLBFGS.train(
training_rdd,
regParam=regP,
iterations=iteration_num,
regType=regularization,
numClasses=class_num) # regParam = 1/(sample_number*C)
elif model_name == "logistic_regression_with_sgd":
### 3: LogisticRegressionWithSGD
print "INFO: ====================3: LogisticRegressionWithSGD============="
model_classification = LogisticRegressionWithSGD.train(
training_rdd,
regParam=regP,
iterations=iteration_num,
regType=regularization) # regParam = 1/(sample_number*C)
else:
print "INFO: Training model selection error: no valid ML model selected!"
return
print "INFO: model type=", type(model_classification)
# create feature coefficient file ================================
coef_arr = None
intercept = None
if model_classification.weights is None:
print "WARNING: model weights not found!"
else:
coef_weights = model_classification.weights
#print "coef_weights=",coef_weights
#print type(coef_weights),coef_weights.shape
coef_arr = coef_weights.toArray().tolist()
# save coef_arr to mongo
key = "coef_arr"
ret = ml_util.save_json_t(row_id_str, key, coef_arr, mongo_tuples)
# save coef_arr to local file
if ret == 0:
# drop old record in mongo
filter = '{"rid":' + row_id_str + ',"key":"coef_arr"}'
ret = query_mongo.delete_many(mongo_tuples, None, filter)
if not os.path.exists(local_out_dir):
os.makedirs(local_out_dir)
fn_ca = os.path.join(local_out_dir, row_id_str,
row_id_str + "_coef_arr.pkl")
print
ml_util.ml_pickle_save(coef_arr, fn_ca)
# save intercept to mongo
intercept = model_classification.intercept
key = "coef_intercept"
ret = ml_util.save_json_t(row_id_str, key, intercept, mongo_tuples)
# feature list + coef file =============
feat_filename = os.path.join(local_out_dir,
row_id_str + "_feat_coef.json")
print "INFO: feat_filename=", feat_filename
# create feature, coef & raw string file =============================================== ============
# expect a dict of {"fid":(coef, feature_raw_string)}
jret = ml_util.build_feat_list_t(row_id_str, feat_filename, None, None,
coef_arr, ds_id, mongo_tuples)
# special featuring for IN or libsvm
if jret is None:
jret = ml_util.build_feat_coef_raw_list_t(row_id_str,
feat_filename, coef_arr,
ds_id, mongo_tuples)
if jret is None:
print "WARNING: Cannot create sample list for testing dataset. "
jfeat_coef_dict = jret
print "INFO: coef_arr len=", len(
coef_arr), ", feature_count=", feature_count
# for multi-class
if len(coef_arr) != feature_count:
jfeat_coef_dict = {}
print "WARNING: coef count didn't match feature count. multi-class classification was not supported"
# Calculate prediction and Save testing dataset
bt_coef_arr = sc.broadcast(coef_arr)
bt_intercept = sc.broadcast(intercept)
bt_jfeat_coef_dict = sc.broadcast(jfeat_coef_dict)
### Evaluating the model on testing dataset: label, predict label, score, feature list
print "INFO: intercept=", intercept
print "INFO: coef_arr len=", len(coef_arr), type(coef_arr)
print "INFO: jfeat_coef_dict len=", len(
jfeat_coef_dict) #, jfeat_coef_dict
# get prediction of testing dataset : (tlabel, plabel, score, libsvm, raw feat str, hash) ==============================
if len(coef_arr) == feature_count:
testing_pred_rdd = testing_rdd.map(lambda p: (
p[0].label \
,model_classification.predict(p[0].features) \
,zip_feature_util.calculate_hypothesis(p[0].features, bt_coef_arr.value, bt_intercept.value, model_name) \
,p[0].features \
,p[1] \
) ).cache()
else: # for multi-class, no prediction score; TBD for better solution: how to display multiple weights for each class
testing_pred_rdd = testing_rdd.map(lambda p: (
p[0].label \
,model_classification.predict(p[0].features) \
,"-" \
,p[0].features \
,p[1] \
) ).cache()
''',p[0].features.dot(bt_coef_arr.value)+bt_intercept.value \
# Save testing dataset for analysis
libsvm_testing_output = hdfs_feat_dir + "libsvm_testing_output_"+row_id_str
print "INFO: libsvm_testing_output=", libsvm_testing_output
try:
hdfs.rmr(libsvm_testing_output)
except IOError as e:
print "WARNING: I/O error({0}): {1}".format(e.errno, e.strerror)
except:
print "WARNING: Unexpected error at libsvm_testing_output file clean up:", sys.exc_info()[0]
# save only false prediction?
#testing_pred_rdd.filter(lambda p: p[0] != p[1]).saveAsTextFile(libsvm_testing_output)
testing_pred_rdd.saveAsTextFile(libsvm_testing_output)
'''
#test_tmp=testing_pred_rdd.collect()
# save false prediction to local file
false_pred_fname = os.path.join(local_out_dir,
row_id_str + "_false_pred.json")
print "INFO: false_pred_fname=", false_pred_fname
false_pred_data=testing_pred_rdd.filter(lambda p: p[0] != p[1])\
.map(lambda p: (p[0],p[1],p[2] \
,zip_feature_util.get_dict_coef_raw4feat(zip_feature_util.sparseVector2dict(p[3]), bt_jfeat_coef_dict.value)
,p[4] ) ) \
.collect()
print "INFO: false predicted count=", len(false_pred_data)
false_pred_arr = []
with open(false_pred_fname, "w") as fp:
for sp in false_pred_data:
jsp = {
"tlabel": sp[0],
"plabel": sp[1],
"score": sp[2],
"feat": sp[3],
"hash": sp[4]
}
#print "jsp=",jsp
false_pred_arr.append(jsp)
fp.write(json.dumps(false_pred_arr))
# save prediction results, format: label, prediction, hash
pred_ofname = os.path.join(local_out_dir,
row_id_str + "_pred_output.pkl")
print "INFO: pred_ofname=", pred_ofname
pred_out_arr = testing_pred_rdd.map(lambda p:
(p[0], p[1], p[4])).collect()
ml_util.ml_pickle_save(pred_out_arr, pred_ofname)
'''
one_item= testing_pred_rdd.first()
print "one_item=",one_item
sparse_arr=one_item[3]
dict_feat=zip_feature_util.sparseVector2dict(sparse_arr)
print "len=",len(dict_feat),"dict_feat=",dict_feat
dict_weit=zip_feature_util.add_coef2dict(coef_arr,dict_feat)
print "len=",len(dict_weit),"dict_weit=",dict_weit
'''
# Calculate Accuracy. labelsAndPreds = (true_label,predict_label)
labelsAndPreds = testing_pred_rdd.map(lambda p: (p[0], p[1]))
labelsAndPreds.cache()
testing_sample_number = testing_rdd.count()
testErr = labelsAndPreds.filter(lambda (v, p): v != p).count() / float(
testing_sample_number)
accuracy = 1 - testErr
print "INFO: Accuracy = ", accuracy
### Save model
#save_dir = config.get('app', 'HADOOP_MASTER')+'/user/hadoop/yigai/row_6/'
#save_dir = config.get('app', 'HADOOP_MASTER')+config.get('app', 'HDFS_MODEL_DIR')+'/'+row_id_str
save_dir = os.path.join(config.get('app', 'HADOOP_MASTER'),
config.get('app', 'HDFS_MODEL_DIR'), row_id_str)
try:
hdfs.ls(save_dir)
#print "find hdfs folder"
hdfs.rmr(save_dir)
#print "all files removed"
except IOError as e:
print "WARNING: I/O error({0}): {1}".format(
e.errno, e.strerror), ". At HDFS=", save_dir
except:
print "WARNING: Unexpected error:", sys.exc_info(
)[0], ". At HDFS=", save_dir
model_classification.save(sc, save_dir)
###load model if needed
#sameModel = SVMModel.load(sc, save_dir)
t1 = time()
print 'INFO: training run time: %f' % (t1 - t0)
t0 = t1
###############################################
###########plot prediction result figure ==================================================== ===============
###############################################
labels = labelsAndPreds.collect()
true_label_list = [x for x, _ in labels]
pred_label_list = [x for _, x in labels]
pred_fname = os.path.join(local_out_dir, row_id_str + "_1" + ".png")
true_fname = os.path.join(local_out_dir, row_id_str + "_2" + ".png")
pred_xlabel = 'Prediction (Single Run)'
true_xlabel = 'True Labels (Single Run)'
test_cnt_dic = ml_util.ml_plot_predict_figures(
pred_label_list, true_label_list, labels_list, label_dic,
testing_sample_count, pred_xlabel, pred_fname, true_xlabel, true_fname)
print "INFO: figure files: ", pred_fname, true_fname
#print "INFO: Number of samples in each label is=", test_cnt_dic
roc_auc = None
perf_measures = None
dataset_info = {
"training_fraction": training_fraction,
"class_count": class_num,
"dataset_count": sample_count
}
#############################################################
###################for 2 class only (plot ROC curve) ==================================================== ===============
#############################################################
if len(labels_list) == 2:
do_ROC = True
reverse_label_dic = dict((v, k) for k, v in label_dic.items())
if 'clean' in reverse_label_dic:
flag_clean = reverse_label_dic['clean']
elif 'benign' in reverse_label_dic:
flag_clean = reverse_label_dic['benign']
elif '0' in reverse_label_dic:
flag_clean = 0
else:
print "INFO: No ROC curve generated: 'clean','benign' or '0' must be a label for indicating negative class!"
do_ROC = False
# build data file for score graph
score_graph_fname = os.path.join(local_out_dir,
row_id_str + "_score_graph.json")
print "INFO: score_graph_fname=", score_graph_fname
# build score_arr_0, score_arr_1
# format: tlabel, plabel, score, libsvm, raw feat str, hash
graph_arr = testing_pred_rdd.map(lambda p:
(int(p[0]), float(p[2]))).collect()
score_arr_0 = []
score_arr_1 = []
max_score = 0
min_score = 0
for p in graph_arr:
if p[0] == 0:
score_arr_0.append(p[1])
else:
score_arr_1.append(p[1])
# save max,min score
if p[1] > max_score:
max_score = p[1]
elif p[1] < min_score:
min_score = p[1]
ml_build_pred_score_graph(score_arr_0, score_arr_1, model_name,
score_graph_fname, max_score, min_score)
if do_ROC:
perf_measures = ml_util.calculate_fscore(true_label_list,
pred_label_list)
print "RESULT: perf_measures=", perf_measures
'''
# calculate fscore ==========
tp = labelsAndPreds.filter(lambda (v, p): v == 1 and p==1 ).count()
fp = labelsAndPreds.filter(lambda (v, p): v == 0 and p==1 ).count()
fn = labelsAndPreds.filter(lambda (v, p): v == 1 and p==0 ).count()
tn = labelsAndPreds.filter(lambda (v, p): v == 0 and p==0 ).count()
print "RESULT: tp=",tp,",fp=",fp,",fn=",fn,",tn=",tn
precision=float(tp)/(tp+fp)
recall=float(tp)/(tp+fn)
print "RESULT: precision=",precision,",recall=",recall
acc=(tp+tn)/(float(testing_sample_number))
fscore=2*((precision*recall)/(precision+recall))
print "RESULT: fscore=",fscore,",acc=",acc
'''
model_classification.clearThreshold()
scoreAndLabels = testing_rdd.map(lambda p: (
model_classification.predict(p[0].features), int(p[0].label)))
#metrics = BinaryClassificationMetrics(scoreAndLabels)
#areROC = metrics.areaUnderROC
#print areROC
scoreAndLabels_list = scoreAndLabels.collect()
if flag_clean == 0:
scores = [x for x, _ in scoreAndLabels_list]
s_labels = [x for _, x in scoreAndLabels_list]
testing_N = test_cnt_dic[0]
testing_P = test_cnt_dic[1]
else:
scores = [-x for x, _ in scoreAndLabels_list]
s_labels = [1 - x for _, x in scoreAndLabels_list]
testing_N = test_cnt_dic[1]
testing_P = test_cnt_dic[0]
# create ROC data file ======== ====
roc_auc = ml_create_roc_files(row_id_str, scores, s_labels,
testing_N, testing_P, local_out_dir,
row_id_str)
#, local_out_dir, file_name_given)
perf_measures["roc_auc"] = roc_auc
# only update db for web request ==================================================== ===============
if fromweb == "1":
#print "database update"
str_sql="UPDATE atdml_document set "+"accuracy = '"+str(accuracy*100)+"%" \
+"', status = 'learned', processed_date ='"+str(datetime.datetime.now()) \
+"', perf_measures='"+json.dumps(perf_measures) \
+"', dataset_info='"+json.dumps(dataset_info) \
+"' where id="+row_id_str
ret = exec_sqlite.exec_sql(str_sql)
print "INFO: Data update done! ret=", str(ret)
else:
print "INFO: accuracy = '" + str(accuracy * 100) + "%"
print 'INFO: Finished!'
return 0
def feat_importance_firm(row_id_str, ds_id, hdfs_feat_dir, local_score_file,
sp_master, spark_rdd_compress,
spark_driver_maxResultSize, sp_exe_memory,
sp_core_max, zipout_dir, zipcode_dir, zip_file_name,
mongo_tuples, training_fraction, jobname, uploadtype):
# zip func in other files for Spark workers ================= ================
zip_file_path = ml_util.ml_build_zip_file(zipout_dir,
zipcode_dir,
zip_file_name,
prefix='zip_feature_util')
print "INFO: zip_file_path=", zip_file_path
# get_spark_context
sc = ml_util.ml_get_spark_context(sp_master, spark_rdd_compress,
spark_driver_maxResultSize,
sp_exe_memory, sp_core_max, jobname,
[zip_file_path])
'''
SparkContext.setSystemProperty('spark.rdd.compress', config.get('spark', 'spark_rdd_compress'))
SparkContext.setSystemProperty('spark.driver.maxResultSize', config.get('spark', 'spark_driver_maxResultSize'))
SparkContext.setSystemProperty('spark.executor.memory', args.exe_memory)
SparkContext.setSystemProperty('spark.cores.max', args.core_max)
sc = SparkContext(args.sp_master, 'feature_importance_FRIM:'+str(args.row_id))
'''
t0 = time()
# get folder list (labels) from hdfs data_out/<id>/metadata ==============
dirFile_loc = os.path.join(hdfs_feat_dir, "metadata")
dirFolders = sc.textFile(dirFile_loc)
hash_Folders = dirFolders.collect()
print "INFO: dirFile_loc=", dirFile_loc, ", hash_Folders=", hash_Folders
folder_list = [x.encode('UTF8') for x in hash_Folders]
print "INFO: folder_list=", folder_list #['dirty/', 'clean/']
# get feature seq : ngram hash mapping ==================================
key = "dic_seq_hashes" #{"123":"136,345"}
jstr_filter = '{"rid":' + row_id_str + ',"key":"' + key + '"}'
jstr_proj = '{"value":1}'
# get parent dataset's data
if ds_id != row_id_str:
jstr_filter = '{"rid":' + ds_id + ',"key":"' + key + '"}'
doc = query_mongo.find_one_t(mongo_tuples, jstr_filter, jstr_proj)
dic_list = doc['value']
dic_all_columns = dic_list
feature_count = len(dic_list)
#print "INFO: feature_count=",feature_count
#print "dic_list=",dic_list #{u'123,345':u'136'}
#print "dic_all_columns=",dic_all_columns # {1: u'8215,8216'}
# end
# get {hash : raw string} mapping ==================================
key = "dic_hash_str" #{"123":"openFile"}
jstr_filter = '{"rid":' + row_id_str + ',"key":"' + key + '"}'
jstr_proj = '{"value":1}'
# get parent dataset's data
if ds_id != row_id_str:
jstr_filter = '{"rid":' + ds_id + ',"key":"' + key + '"}'
doc = query_mongo.find_one_t(mongo_tuples, jstr_filter, jstr_proj)
dic_hash_str = doc['value']
'''
# get folder list (labels) from hdfs data_out/<id>/libsvm ==============
libsvm_loc = os.path.join(hdfs_feat_dir , "libsvm_data")
print "INFO: libsvm_loc=", libsvm_loc
samples_rdd = MLUtils.loadLibSVMFile(sc, libsvm_loc)
'''
# filename for featured data
libsvm_data_file = os.path.join(hdfs_feat_dir, "libsvm_data")
print "INFO: libsvm_data_file=", libsvm_data_file
# load feature count file
#feat_count_file=libsvm_data_file+"_feat_count"
#feature_count=zip_feature_util.get_feature_count(sc,feat_count_file)
print "INFO: feature_count=", feature_count
#samples_rdd = MLUtils.loadLibSVMFile(sc, libsvm_data_file)
# load sample RDD from text file
# also exclude selected features in sample ================ =====
# format (LabeledPoint,hash) from str2LabeledPoint_hash()
samples_rdd, feature_count = zip_feature_util.get_sample_rdd(
sc, libsvm_data_file, feature_count, excluded_feat_cslist=None)
labels_and_features_rdd = samples_rdd.map(lambda p:
(p[0].label, p[0].features))
all_data = labels_and_features_rdd.collect()
features_list = [x.toArray() for _, x in all_data]
labels_list_all = [x for x, _ in all_data]
labels_list_all = np.array(labels_list_all)
features_array = np.array(features_list)
### generate sparse matrix (csr) for all samples
features_sparse_mtx = csr_matrix(features_array)
### randomly split the samples into training and testing data
sparse_mtx, sparse_test, labels_training, labels_testing = \
cross_validation.train_test_split(features_sparse_mtx, labels_list_all, test_size=(1-training_fraction))
#print "INFO: sparse_mtx.shape=",sparse_mtx.shape
#print "INFO: sparse_test.shape=",sparse_test.shape
row_num_training = (sparse_mtx.shape)[0]
row_num_testing = (sparse_test.shape)[0]
# why use LinearSVC ?
clf = svm.LinearSVC()
#clf = svm.SVC(C=0.1, kernel='rbf', degree=3, gamma=0.05, coef0=0.0, shrinking=True, probability=False, tol=0.001, cache_size=200, class_weight=None, verbose=False, max_iter=-1, random_state=None)
#clf = svm.NuSVC(nu=0.3, kernel='rbf', degree=3, gamma=0.05, coef0=0.0, shrinking=True, probability=False, tol=0.001, cache_size=200, verbose=False, max_iter=-1, random_state=None)
#print "labels_training=",labels_training
#print "sparse_mtx=",sparse_mtx
clf.fit(sparse_mtx, labels_training)
#print "**model:intercept***"
#print clf.intercept_
#print "**model:coef***"
#print clf.coef_
col_num = len(clf.coef_[0]) # for n_classes==2
print "INFO: col_num=", col_num
labels_pred = clf.predict(sparse_test)
#print "labels_pred:", labels_pred
accuracy = clf.score(sparse_test, labels_testing)
print "INFO: data folder:", hdfs_feat_dir
print "INFO: accuracy: ", accuracy
#####################################################################
##################calculate feature importance with predication labels#######################
#####################################################################
AA = sparse_mtx.todense()
BB = sparse_test.todense()
labels_train_pred = clf.predict(sparse_mtx)
labels_test_pred = labels_pred
print "INFO: ###################################################################################"
print "INFO: ############calculate feature importance with predication labels###################"
print "INFO: ###################################################################################"
dic_importance_label = {}
for j in range(0, col_num): ###for all features in the loop
##############################
#print "====new way with sparse matrix========="
curr_col_train = sparse_mtx.getcol(j)
sum_col = curr_col_train.sum(0)
positive_feature_number = int(sum_col.tolist()[0][0])
labels_value = 3 - labels_train_pred
dot_product = csr_matrix(np.array(labels_value)).dot(curr_col_train)
sum_product = dot_product.sum(1)
labels_positive_sum = int(sum_product.tolist()[0][0])
sum_label_values = sum(labels_value)
labels_negitive_sum = sum_label_values - labels_positive_sum
##############################
#print "====new way with sparse matrix========="
curr_col_test = sparse_test.getcol(j)
sum_col = curr_col_test.sum(0)
positive_feature_number = positive_feature_number + int(
sum_col.tolist()[0][0])
labels_value = 3 - labels_test_pred
dot_product = csr_matrix(np.array(labels_value)).dot(curr_col_test)
sum_product = dot_product.sum(1)
labels_positive_sum = labels_positive_sum + int(
sum_product.tolist()[0][0])
sum_label_values = sum(labels_value)
labels_negitive_sum = labels_negitive_sum + sum_label_values - int(
sum_product.tolist()[0][0])
n_total = row_num_training + row_num_testing
negitive_feature_number = n_total - positive_feature_number
if positive_feature_number == 0:
#print "feature ", j+1, "all 0s!"
dic_importance_label[j + 1] = -100
elif negitive_feature_number == 0:
#print "feature ", j+1, "all 1s!"
dic_importance_label[j + 1] = -200
else:
q_positive = float(labels_positive_sum) / positive_feature_number
q_negitive = float(labels_negitive_sum) / negitive_feature_number
Q = (q_positive - q_negitive) * sqrt(
float(q_positive) * q_negitive / float(n_total) /
float(n_total))
dic_importance_label[j + 1] = Q
sorted_importance = sorted(dic_importance_label.items(),
key=operator.itemgetter(1),
reverse=True)
print "INFO: =======Feature Importance(FIRM score)================"
if os.path.exists(local_score_file):
try:
os.remove(local_score_file)
except OSError, e:
print("ERROR: %s - %s." % (e.local_score_file, e.strerror))
def feat_extraction(row_id_str,
hdfs_dir_list,
hdfs_feat_dir,
model_data_folder,
sp_master,
spark_rdd_compress,
spark_driver_maxResultSize,
sp_exe_memory,
sp_core_max,
zipout_dir,
zipcode_dir,
zip_file_name,
mongo_tuples,
fromweb,
label_arr,
metadata_count,
label_idx,
data_idx,
pattern_str,
ln_delimitor,
data_field_list,
jkey_dict,
jobname,
num_gram,
feature_count_threshold,
token_dict=None,
HDFS_RETR_DIR=None,
remove_duplicated="N",
cust_featuring=None,
cust_featuring_params=None,
local_out_dir=None,
filter_ratio=None,
binary_flag=False):
# zip func in other files for Spark workers ================= ================
zip_file_path = ml_util.ml_build_zip_file(zipout_dir,
zipcode_dir,
zip_file_name,
user_custom=cust_featuring)
# get_spark_context
spark = ml_util.ml_get_spark_session(sp_master, spark_rdd_compress,
spark_driver_maxResultSize,
sp_exe_memory, sp_core_max, jobname,
zip_file_path)
if spark:
sc = spark.sparkContext
# log time ================================================================ ================
t0 = time()
# input filename
input_filename = "*"
ext_type = '.gz'
gz_list = None
# single hdfs file
if not ',' in hdfs_dir_list: # single dir having *.gz ==== =========
# read raw data from HDFS as .gz format ==========
hdfs_files = os.path.join(hdfs_dir_list, input_filename + ext_type)
# check if gz files in hdfs ============
try:
gz_list = hdfs.ls(hdfs_dir_list)
print "INFO: check hdfs folder=", hdfs_dir_list
except IOError as e:
print "WARNING: I/O error({0}): {1}".format(e.errno, e.strerror)
except:
print "WARNING: Error at checking HDFS file:", sys.exc_info()[0]
# use whole folder
#print "gz_list",gz_list
if gz_list is None or len(gz_list) == 0:
print "ERROR: No file found by ", input_filename + ext_type #,", use",hdfs_dir_list,"instead"
return -2
elif len(gz_list) == 1:
# use dir as filename
hdfs_files = hdfs_dir_list[0:-1]
else: # multiple dirs ==== =========
hdfs_files = ""
cnt = 0
temp_lbl_list = []
comma = ""
print "INFO: before label_arr=", label_arr
# check each folder
for dr in hdfs_dir_list.split(','):
#print "****=",dr
if not len(dr) > 0:
continue
try:
# remove space etc.
dr = dr.strip()
fdr = os.path.join(HDFS_RETR_DIR, dr)
# ls didn't like "*"
if '*' in fdr:
#gz_list=hdfs.ls(fdr.replace("*",""))
dn = os.path.dirname(fdr).strip()
bn = os.path.basename(
fdr).strip() #print "dn=",dn,",bn=",bn
# get all names under folder and do filtering
gz_list = fnmatch.filter(hdfs.ls(dn), '*' + bn)
else:
gz_list = hdfs.ls(fdr)
cnt = cnt + len(gz_list)
if len(gz_list) > 0:
hdfs_files = hdfs_files + comma + fdr
comma = ","
except IOError as e:
print "WARNING: I/O error({0}): {1}".format(
e.errno, e.strerror)
except:
print "WARNING: Error at checking HDFS file:", sys.exc_info(
)[0]
# use whole folder
if cnt is None or cnt == 0:
print "ERROR: No file found at", hdfs_files
return -2
else:
print "INFO: total file count=", cnt
# set convert flag only when multiple dir and label_arr has dirty label
if not label_arr is None and len(
label_arr) == 2 and label_arr[1] == "dirty":
convert2dirty = "Y"
print "INFO: hdfs_dir_list=", hdfs_dir_list
print "INFO: hdfs_files=", hdfs_files
cust_featuring_jparams = None
# custom featuring
if not cust_featuring is None and len(cust_featuring) > 0:
# load user module =======
user_func, cust_featuring_jparams = get_user_custom_func(
cust_featuring, cust_featuring_params)
# TBD apply user_func
all_hashes_cnt_dic = None
all_hash_str_dic = None
all_hashes_seq_dic = None
else:
print "ERROR: custom featuring type is needed"
print "INFO: cust_featuring=", cust_featuring, "cust_featuring_jparams=", cust_featuring_jparams
dnn_flag = False
has_header = None
label_col = None
label_index = None
# get featuring params
if cust_featuring_jparams:
if 'label_index' in cust_featuring_jparams: # idx number for label, 0 based
label_index = cust_featuring_jparams['label_index']
if 'has_header' in cust_featuring_jparams: # True/False
has_header = eval(cust_featuring_jparams['has_header'])
if has_header == 1:
has_header = True
if 'dnn_flag' in cust_featuring_jparams: # True/False
dnn_flag = cust_featuring_jparams['dnn_flag']
if dnn_flag == 1:
dnn_flag = True
elif dnn_flag == 0:
dnn_flag = False
if label_index is None:
label_index = 0
elif not isinstance(label_index, int):
label_index = eval(label_index)
print "INFO: label_index=", label_index, ",has_header=", has_header, ",dnn_flag=", dnn_flag
# read as DataFrame ===============================================
df = spark.read.csv(hdfs_files, header=has_header)
df.show()
print "INFO: col names=", df.columns
# get column name for label
label_col = None
for i, v in enumerate(df.columns):
if i == label_index:
label_col = v
# get all distinct labels into an array =============== provided by parameter?
if label_arr is None and not label_col is None:
label_arr = sorted([
rw[label_col] for rw in df.select(label_col).distinct().collect()
])
print "INFO: label_arr=", label_arr
label_dic = {}
# convert label_arr to dict; {label:number|
for idx, label in enumerate(sorted(label_arr)):
if not label in label_dic:
label_dic[
label] = idx #starting from 0, value = idx, e.g., clean:0, dirty:1
# add params for dataframe conversion
cust_featuring_jparams["label_dict"] = label_dic
# convert to int
cust_featuring_jparams["label_index"] = label_index
featuring_params = json.dumps(cust_featuring_jparams)
# convert DataFrame row to libsvm string
libsvm_rdd = df.rdd.map(lambda x: user_func(list(x), featuring_params))
print "INFO: sample df row=", (libsvm_rdd.collect()[0])
print "INFO: featuring_params=", featuring_params
total_input_count = df.count()
print "INFO: Total input sample count=", total_input_count
#print "INFO: feature_count_threshold=",feature_count_threshold
#get all hashes and total occurring count ===============
# all_hashes_cnt_dic: {'col index': total count,... }
# build all_hashes_cnt_dic
cnt_df = df.select(
[count(when(~isnull(c), c)).alias(c) for c in df.columns])
#cnt_df.show()
cnt_arr = cnt_df.rdd.map(lambda x: list(x)).collect()
feat_sample_count_arr = cnt_arr[0]
#print "feat_sample_count_arr=",feat_sample_count_arr
if all_hashes_cnt_dic is None:
all_hashes_cnt_dic = {}
idx = 1
for i, v in enumerate(feat_sample_count_arr):
if i != label_index:
all_hashes_cnt_dic[idx] = v
idx += 1
#print "all_hashes_cnt_dic=",all_hashes_cnt_dic
#get all hashes and their extracted string ===============
# all_hash_str_dic: {hash:'str1', ...
if all_hash_str_dic is None:
# convert header to dict=index:string; excude label column
all_hash_str_dic = {}
idx = 1
for i, v in enumerate(df.schema.names):
if i != label_index:
all_hash_str_dic[idx] = v
idx += 1
#print "all_hash_str_dic=",all_hash_str_dic
# save labels to hdfs as text file==================================== ============
hdfs_folder = hdfs_feat_dir #+ "/" # "/" is needed to create the folder correctly
print "INFO: hdfs_folder=", hdfs_folder
try:
hdfs.mkdir(hdfs_folder)
except IOError as e:
print "WARNING: I/O error({0}): {1}".format(e.errno, e.strerror)
except:
print "WARNING: Unexpected error at mkdir:", sys.exc_info()[0]
# clean up metadata_file
metadata_file = os.path.join(hdfs_folder, metadata) #"metadata"
print "INFO: metadata_file=", metadata_file
try:
hdfs.rmr(metadata_file)
except IOError as e:
print "WARNING: I/O error({0}): {1}".format(e.errno, e.strerror)
except:
print "WARNING: Unexpected error at rmr():", sys.exc_info()[0]
sc.parallelize(label_arr, 1).saveAsTextFile(metadata_file)
#remap all hash values to continuous key/feature number ==============
# all_hashes_seq_dic: { hash : sequential_numb }
if all_hashes_seq_dic is None:
all_hashes_seq_dic = {}
# csv column index as sequentail number
remap2seq(all_hash_str_dic, all_hashes_seq_dic)
#print "all_hashes_seq_dic=",all_hashes_seq_dic
total_feature_numb = len(all_hashes_seq_dic)
print "INFO: Total feature count=", len(all_hashes_seq_dic)
# save feat_sample_count_arr data ==================================== ============
filter = '{"rid":' + row_id_str + ',"key":"feat_sample_count_arr"}'
upsert_flag = True
jo_insert = {}
jo_insert["rid"] = eval(row_id_str)
jo_insert["key"] = "feat_sample_count_arr"
jo_insert["value"] = feat_sample_count_arr
jstr_insert = json.dumps(jo_insert)
ret = query_mongo.upsert_doc_t(mongo_tuples, filter, jstr_insert,
upsert_flag)
print "INFO: Upsert count for feat_sample_count_arr=", ret
# insert failed, save to local
if ret == 0:
# drop old record in mongo
ret = query_mongo.delete_many(mongo_tuples, None, filter)
if not os.path.exists(local_out_dir):
os.makedirs(local_out_dir)
fsca_hs = os.path.join(local_out_dir, row_id_str,
row_id_str + "_feat_sample_count_arr.pkl")
print "WARNING: save feat_sample_count_arr to local"
ml_util.ml_pickle_save(feat_sample_count_arr, fsca_hs)
# get rdd statistics info
# remove duplicated libsvm string; only keep the first duplicated item, assume space following key_idx
if remove_duplicated == "Y":
libsvm_rdd=libsvm_rdd \
.map(lambda x: ( ','.join(x.split(' ')[metadata_count:]), x)) \
.groupByKey().map(lambda x: list(x[1])[0] ) \
.cache()
cnt_list = libsvm_rdd.map(lambda x: (x.split(' ')[1], 1)).reduceByKey(
add).collect()
stats = libsvm_rdd.map(
lambda x: len(x.split(' ')[metadata_count:])).stats()
feat_count_max = stats.max()
feat_count_stdev = stats.stdev()
feat_count_mean = stats.mean()
sample_count = stats.count()
print "INFO: Non-Duplicated libsvm data: sample count=", sample_count, ",Feat count mean=", feat_count_mean, ",Stdev=", feat_count_stdev
print "INFO: ,max feature count=", feat_count_max
print "INFO: Non-Duplicated Label count list=", cnt_list
# clean up libsvm data ==================================== ============
libsvm_data_file = os.path.join(hdfs_folder,
libsvm_alldata_filename) #"libsvm_data"
print "INFO: libsvm_data_file=", libsvm_data_file
try:
hdfs.rmr(libsvm_data_file)
except IOError as e:
print "WARNING: I/O error({0}): {1} at libsvm_data_file clean up".format(
e.errno, e.strerror)
except:
print "WARNING: Unexpected error at libsvm file clean up:", sys.exc_info(
)[0]
#codec = "org.apache.hadoop.io.compress.GzipCodec"
#libsvm_rdd.saveAsTextFile(libsvm_data_file, codec)
libsvm_rdd.saveAsTextFile(libsvm_data_file) # TBD encrypted
feat_count_file = libsvm_data_file + "_feat_count"
print "INFO: feat_count_file=", feat_count_file
try:
hdfs.rmr(feat_count_file)
except IOError as e:
print "WARNING: I/O error({0}): {1} at feat_count clean up".format(
e.errno, e.strerror)
except:
print "WARNING: Unexpected error at libsvm feature count clean up:", sys.exc_info(
)[0]
sc.parallelize([total_feature_numb], 1).saveAsTextFile(feat_count_file)
# TBD ??? output text for DNN:[meta-data1,meta-data2,..., [feature tokens]] ================= DNN ===========
if dnn_flag: # special flag to tokenize and keep input orders
print "INFO: processing data for DNN..."
# create token dict
# str_hash_dict: string to hash
# all_hashes_seq_dic: hash to seq id
if token_dict is None or len(token_dict) == 0:
token_dict = {}
str_hash_dict = {v: k for k, v in all_hash_str_dic.iteritems()}
for k, v in str_hash_dict.iteritems():
token_dict[k] = int(all_hashes_seq_dic[str(v)])
#print "token_dict=",len(token_dict),token_dict
# TBD here: need to implement non-binary feature
dnn_rdd=df.rdd \
.map(lambda x: tokenize_by_dict(x, data_idx, token_dict,label_idx, label_dic)) \
.filter(lambda x: len(x) > metadata_count) \
.filter(lambda x: type(x[metadata_count]) is list)
#.cache()
# filter duplication here
#print dnn_rdd.take(3)
dnn_data_file = os.path.join(hdfs_folder,
dnn_alldata_filename) #"dnn_data"
print "INFO: dnn_data_file=", dnn_data_file
try:
hdfs.rmr(dnn_data_file)
except IOError as e:
print "WARNING: I/O error({0}): {1} at dnn_data_file clean up".format(
e.errno, e.strerror)
except:
print "WARNING: Unexpected error at libsvm file clean up:", sys.exc_info(
)[0]
# clean up data
dnn_npy_gz_file = os.path.join(hdfs_folder, row_id_str + "_dnn_")
print "INFO: dnn_npy_gz_file=", dnn_npy_gz_file
try:
hdfs.rmr(dnn_npy_gz_file + "data.npy.gz")
hdfs.rmr(dnn_npy_gz_file + "label.npy.gz")
hdfs.rmr(dnn_npy_gz_file + "info.npy.gz")
except IOError as e:
print "WARNING: I/O error({0}): {1} at dnn_npy clean up".format(
e.errno, e.strerror)
except:
print "WARNING: Unexpected error at dnn_npy file clean up:", sys.exc_info(
)[0]
# save new data
try:
dnn_rdd.saveAsTextFile(dnn_data_file)
except:
print "WARNING: Unexpected error at saving dnn data:", sys.exc_info(
)[0]
# show data statistics
try:
stats = dnn_rdd.map(lambda p: len(p[metadata_count])).stats()
feat_count_max = stats.max()
feat_count_stdev = stats.stdev()
feat_count_mean = stats.mean()
sample_count = stats.count()
print "INFO: DNN data: sample count=", sample_count, ",Feat count mean=", feat_count_mean, ",Stdev=", feat_count_stdev
print "INFO: ,max feature count=", feat_count_max
except:
print "WARNING: Unexpected error at getting stats of dnn_rdd:", sys.exc_info(
)[0]
# clean up pca data in hdfs ============ ========================
pca_files = '*' + libsvm_alldata_filename + "_pca_*"
#print "INFO: pca_files=", pca_files
try:
f_list = hdfs.ls(hdfs_folder)
if len(f_list) > 0:
df_list = fnmatch.filter(f_list, pca_files)
for f in df_list:
print "INFO: rm ", f
hdfs.rmr(f)
except IOError as e:
print "WARNING: I/O error({0}): {1}".format(e.errno, e.strerror)
except:
print "WARNING: Unexpected error at libsvm pca file clean up:", sys.exc_info(
)[0]
# clean up pca data in web local ============ ========================
pca_fname = os.path.join(model_data_folder, row_id_str + '_pca_*.pkl*')
print "INFO: pca_fname=", pca_fname
try:
for fl in glob.glob(pca_fname):
print "INFO: remove ", fl
os.remove(fl)
except OSError, e:
print("Error: %s - %s." % (e.pca_fname, e.strerror))
def preprocess(
row_id_str,
ds_id,
hdfs_feat_dir,
local_out_dir,
ml_opts_jstr #, excluded_feat_cslist
,
sp_master,
spark_rdd_compress,
spark_driver_maxResultSize,
sp_exe_memory,
sp_core_max,
zipout_dir,
zipcode_dir,
zip_file_name,
data_fname
#, mongo_tuples, labelnameflag, fromweb
,
jobname,
dnn_data_suffix=config.get("machine_learning", "dnn_data_suffix"),
dnn_label_suffix=config.get("machine_learning", "dnn_label_suffix"),
dnn_info_suffix=config.get("machine_learning", "dnn_info_suffix")):
### generate data folder and out folder, clean up if needed
if not os.path.exists(local_out_dir):
os.makedirs(local_out_dir)
# create zip files for Spark workers ================= ================
zip_file_path = ml_util.ml_build_zip_file(zipout_dir,
zipcode_dir,
zip_file_name,
prefix='zip_feature_util')
print "INFO: zip_file_path=", zip_file_path, ",ml_opts_jstr=", ml_opts_jstr
# get_spark_context
sc = ml_util.ml_get_spark_context(sp_master, spark_rdd_compress,
spark_driver_maxResultSize,
sp_exe_memory, sp_core_max, jobname,
[zip_file_path])
t0 = time()
# Check option
ml_opts = {}
try:
ml_opts = json.loads(ml_opts_jstr)
except:
print "ERROR: string ml_opts is invalid!"
return -1
learning_algorithm = ml_opts["learning_algorithm"]
#return
# create 3 arrays
feat_list = []
label_list = []
info_list = []
feature_count = None
sample_count = None
if learning_algorithm == "cnn": #================================================== ==========
# libsvm_data for featured data, not use "dnn_data" here
libsvm_data_file = os.path.join(hdfs_feat_dir, "libsvm_data")
print "INFO: libsvm_data_file=", libsvm_data_file
# load sample RDD from libsvm file
# output format: [([features], label, info)] , feature_count, feat_max, feat_min
all_list, feature_count, feat_max, feat_min =\
zip_feature_util.get_sample_as_arr(sc, libsvm_data_file, None, None)
c = 0
# convert to 3 list ========================= ==========
for i in all_list:
#print type(i[0]),type(i[1]), type(i[2])
if c % 10000 == 0:
print ".",
sys.stdout.flush()
c = c + 1
# convert to float32 np array and normalize by max value (0-1.0)
feat_list.append(np.array(i[0], dtype=np.float32) / feat_max)
label_list.append(i[1])
info_list.append(i[2])
print "INFO: feat_list t=", type(feat_list), type(feat_list[0]), type(
feat_list[0][0])
elif learning_algorithm == "lstm": #================================================== ==========
# filename for featured data
dnn_data_file = os.path.join(hdfs_feat_dir, data_fname)
print "INFO: dnn_data_file=", dnn_data_file
# output format: [([features], label, info)] , chucked_sample_count
all_list, chucked_sample_count=\
zip_feature_util.get_sample_as_chunk(sc, dnn_data_file, None, None)
c = 0
# convert to 3 list ========================= ==========
for i in all_list:
#print type(i[0]),type(i[1]), type(i[2])
if c % 10000 == 0:
print ".",
sys.stdout.flush()
c = c + 1
# convert to float32 np array and normalize by max value (0-1.0)
feat_list.append(np.array(i[0], dtype=np.int32))
label_list.append(i[1])
info_list.append(i[2])
print "INFO: feat_list t=", type(feat_list), type(
feat_list[0]), feat_list[0][0]
print "INFO: label_list t=", type(label_list), label_list[0]
print "INFO: info_list t=", type(info_list), info_list[0]
if not all_list is None and len(all_list) > 0:
sample_count = len(all_list)
else:
sample_count = None
print "INFO: sample_count=", sample_count
tgt_prefix = row_id_str
if (row_id_str != ds_id):
tgt_prefix = ds_id
# save data file ========================= ==========
data_fname = os.path.join(local_out_dir, tgt_prefix + dnn_data_suffix)
print "INFO: data_fname=", data_fname
# reshape for image
#if not feature_count is None and feature_count >0 and not sample_count is None and sample_count > 0:
# nparr_feat=np.asarray(feat_list,dtype=np.float32).reshape(sample_count,feature_count)
#else: # lstm didn't need to reshape
nparr_feat = np.asarray(feat_list, dtype=np.int32)
print "INFO: nparr_feat s=", nparr_feat.shape, nparr_feat[0].shape, type(
nparr_feat[0][0]), nparr_feat[0][0]
with gzip.open(data_fname, "wb") as fp:
#pickle.dump(nparr_feat,fp, 2) # may overflow in pickle; limited to 2B elements; use numpy.save()
np.save(fp, nparr_feat, allow_pickle=False) #62.9M for (465724, 2967)
# save label file ========================= ==========
lbl_fname = os.path.join(local_out_dir, tgt_prefix + dnn_label_suffix)
print "INFO: lbl_fname=", lbl_fname
# convert to numpy array and int32 to save space
nparr_lbl = np.asarray(label_list, dtype=np.int32)
#print "label=",nparr_lbl[0]
with gzip.open(lbl_fname, "wb") as fp:
#pickle.dump(nparr_lbl,fp, 2)
np.save(fp, nparr_lbl, allow_pickle=False)
# save info file ========================= ==========
info_fname = os.path.join(local_out_dir, tgt_prefix + dnn_info_suffix)
print "INFO: info_fname=", info_fname
nparr_info = np.asarray(info_list)
with gzip.open(info_fname, "wb") as fp:
#pickle.dump(nparr_info,fp,2)
np.save(fp, nparr_info, allow_pickle=False)
t1 = time()
print 'INFO: running time: %f' % (t1 - t0)
print 'INFO: Finished!'
return 0
def feat_extr_ngram(row_id_str,
hdfs_dir_list,
hdfs_feat_dir,
model_data_folder,
sp_master,
spark_rdd_compress,
spark_driver_maxResultSize,
sp_exe_memory,
sp_core_max,
zipout_dir,
zipcode_dir,
zip_file_name,
mongo_tuples,
fromweb,
label_arr,
metadata_count,
label_idx,
data_idx,
pattern_str,
ln_delimitor,
data_field_list,
jkey_dict,
jobname,
num_gram,
feature_count_threshold,
token_dict=None,
HDFS_RETR_DIR=None,
remove_duplicated="N",
cust_featuring=None,
cust_featuring_params=None,
local_out_dir=None,
filter_ratio=None,
binary_flag=True):
# zip func in other files for Spark workers ================= ================
zip_file_path = ml_util.ml_build_zip_file(zipout_dir,
zipcode_dir,
zip_file_name,
user_custom=cust_featuring)
# get_spark_context
sc = ml_util.ml_get_spark_context(sp_master, spark_rdd_compress,
spark_driver_maxResultSize,
sp_exe_memory, sp_core_max, jobname,
[zip_file_path])
# log time ================================================================ ================
t0 = time()
# input filename
input_filename = "*"
ext_type = '.gz'
gz_list = None
convert2dirty = "N"
if not ',' in hdfs_dir_list: # single dir having *.gz ==== =========
# read raw data from HDFS as .gz format ==========
rdd_files = os.path.join(hdfs_dir_list, input_filename + ext_type)
# check if gz files in hdfs ============
try:
gz_list = hdfs.ls(hdfs_dir_list)
print "INFO: check hdfs folder=", hdfs_dir_list
except IOError as e:
print "WARNING: I/O error({0}): {1}".format(e.errno, e.strerror)
except:
print "WARNING: Error at checking HDFS file:", sys.exc_info()[0]
# use whole folder
if gz_list is None or len(gz_list) == 0:
print "ERROR: No file found by ", input_filename + ext_type #,", use",hdfs_dir_list,"instead"
return -2
elif len(gz_list) == 1:
# use dir as filename
rdd_files = hdfs_dir_list[0:-1]
else: # multiple dirs ==== =========
rdd_files = ""
cnt = 0
temp_lbl_list = []
comma = ""
print "INFO: before label_arr=", label_arr
# check each folder
for dr in hdfs_dir_list.split(','):
#print "****=",dr
if not len(dr) > 0:
continue
try:
# remove space etc.
dr = dr.strip()
fdr = os.path.join(HDFS_RETR_DIR, dr)
#print "fdr=",fdr
# ls didn't like "*"
if '*' in fdr:
#gz_list=hdfs.ls(fdr.replace("*",""))
dn = os.path.dirname(fdr).strip()
bn = os.path.basename(fdr).strip()
#print "dn=",dn,",bn=",bn
# get all names under folder and do filtering
gz_list = fnmatch.filter(hdfs.ls(dn), '*' + bn)
#print "gz_list=",gz_list
else:
gz_list = hdfs.ls(fdr)
cnt = cnt + len(gz_list)
if len(gz_list) > 0:
rdd_files = rdd_files + comma + fdr
comma = ","
except IOError as e:
print "WARNING: I/O error({0}): {1}".format(
e.errno, e.strerror)
except:
print "WARNING: Error at checking HDFS file:", sys.exc_info(
)[0]
# use whole folder
if cnt is None or cnt == 0:
print "ERROR: No file found at", rdd_files
return -2
else:
print "INFO: total file count=", cnt
# set convert flag only when multiple dir and label_arr has dirty label
#if label_arr is None: # create label arr if None
# label_arr=temp_lbl_list
if not label_arr is None and len(
label_arr) == 2 and label_arr[1] == "dirty":
convert2dirty = "Y"
print "INFO: rdd_files=", rdd_files
txt_rdd = sc.textFile(rdd_files) #, use_unicode=False
total_input_count = txt_rdd.count()
print "INFO: Total input sample count=", total_input_count
# debug only
#for x in txt_rdd.collect():
# print "t=",x
print "INFO: hdfs_dir_list=", hdfs_dir_list
print "INFO: label_arr=", label_arr
print "INFO: feature_count_threshold=", feature_count_threshold
#jkey_dict={"meta_list":["label","md5","mdate"], "data_key":"logs"}
# this dict depends on the format of input data
if not data_field_list is None:
jkey_dict = json.loads(jkey_dict)
data_key = jkey_dict["data_key"]
meta_list = jkey_dict["meta_list"]
metadata_count = len(meta_list)
data_idx = metadata_count
print "INFO: jkey_dict=", jkey_dict
print "INFO: meta_list=", meta_list
print "INFO: data_key=", data_key
print "INFO: data_field_list=", data_field_list
print "INFO: metadata_count=", metadata_count
featured_rdd = txt_rdd \
.map(lambda x: preprocess_json(x,meta_list,data_key,data_field_list)) \
.filter(lambda x: len(x) > metadata_count) \
.filter(lambda x: type(x[metadata_count]) is list) \
.map(lambda x: feature_extraction_ngram(x, data_idx, MAX_FEATURES, num_gram)) \
.filter(lambda x: len(x) > metadata_count) \
.filter(lambda x: type(x[metadata_count]) is dict) \
.filter(lambda x: type(x[metadata_count+1]) is dict) \
.filter(lambda x: len(x[metadata_count])> int(feature_count_threshold) ) \
.cache()
#print "INFO: featured_rdd="
#for x in featured_rdd.collect():
# print "INFO: **** f=",x
# user custom code for featuring ============================================= ==========
# input txt_rdd format (string): each text row for each sample
# output featured_rdd format (list):[meta-data1,meta-data2,..., hash_cnt_dic, hash_str_dic]
elif not cust_featuring is None and len(cust_featuring) > 0:
user_module = None
user_func = None
user_func_dnn = None
# load user module =======
try:
modules = map(__import__, [CUSTOM_PREFIX + cust_featuring])
user_module = modules[0]
user_func = getattr(user_module, CUSTOM_FUNC)
except Exception as e:
print "ERROR: module=", CUSTOM_PREFIX + cust_featuring
print "ERROR: user module error.", e.__doc__, e.message
return -101
try:
jparams = json.loads(cust_featuring_params)
if jparams and 'n-gram' in jparams:
num_gram = jparams['n-gram']
elif jparams and 'ngram' in jparams:
num_gram = jparams['ngram']
if jparams and 'binary_flag' in jparams:
binary_flag = eval(jparams['binary_flag'])
except Exception as e:
print "ERROR: user params error.", e.__doc__, e.message
return -200
# convert feast into array. output format: [ meta1,meta2,..., [feat1,feat2,...]]
tmp_rdd = txt_rdd.map(lambda x: user_func(x, cust_featuring_params)) \
.filter(lambda x: len(x) > metadata_count) \
.filter(lambda x: type(x[metadata_count]) is list).cache()
print " tmp_rdd cnt=", tmp_rdd.count(
), ",ix=", data_idx, ",max f=", MAX_FEATURES, "ngram=", num_gram
print "take(1) rdd=", tmp_rdd.take(1)
# TBD for multivariant output format: [ meta1,meta2,..., [[feat1,feat2,...],[feat1,feat2,...],...]]
# TBD only for num_gram available
# for traditional ML, feat in a dict
# output format: [ meta1,meta2,..., [[feat1,feat2,...],[feat1,feat2,...],...]]
featured_rdd = tmp_rdd \
.map(lambda x: feature_extraction_ngram(x, data_idx, MAX_FEATURES, num_gram)) \
.filter(lambda x: len(x) > metadata_count) \
.filter(lambda x: type(x[metadata_count]) is dict) \
.filter(lambda x: type(x[metadata_count+1]) is dict) \
.filter(lambda x: len(x[metadata_count])> int(feature_count_threshold) ) \
.cache()
all_hashes_cnt_dic = None
all_hash_str_dic = None
all_hashes_seq_dic = None
else:
print "INFO: pattern_str=", pattern_str + "<--"
print "INFO: ln_delimitor=", ln_delimitor + "<--"
print "INFO: label_idx=", label_idx
print "INFO: data_idx=", data_idx
print "INFO: metadata_count=", metadata_count
print "INFO: filter_ratio=", filter_ratio
# filter top and least percentage of feature
if not filter_ratio is None and filter_ratio > 0 and filter_ratio < 1:
# check total count here before continue
upper_cnt = total_input_count * (1 - filter_ratio)
lower_cnt = total_input_count * filter_ratio
# set limit for lower bound. if total count is large, lower_cnt may exclude all features...
# max lower count = min( MAX_FILTER_LOWER_CNT, total_input_count/100 )
if not MAX_FILTER_LOWER_CNT is None and lower_cnt > MAX_FILTER_LOWER_CNT:
if MAX_FILTER_LOWER_CNT > total_input_count / 100:
lower_cnt = total_input_count / 100
else:
lower_cnt = MAX_FILTER_LOWER_CNT
print "INFO: filtering by count, upper bound=", upper_cnt, ",lower bound=", lower_cnt
# find unique feature, count them, remove them if in highest and lowest % and then create a dict
f_feat_set = Set (txt_rdd.map(lambda x:x.split(ln_delimitor)).flatMap(lambda x:Set(x[metadata_count:])) \
.map(lambda x:(x,1)).reduceByKey(lambda a, b: a + b) \
.filter(lambda x:x[1]<= upper_cnt and x[1]>= lower_cnt) \
.map(lambda x:x[0]).collect() )
print "INFO: f_feat_set len=", len(f_feat_set)
broadcast_f_set = sc.broadcast(f_feat_set)
#txt_rdd=txt_rdd.map(lambda x: filter_by_list(x, metadata_count,ln_delimitor, broadcast_f_list.value ))
txt_rdd=txt_rdd.map(lambda x: x.split(ln_delimitor)) \
.map(lambda x: x[:metadata_count]+ [w for w in x[metadata_count:] if w and w in broadcast_f_set.value]) \
.map(lambda x: ln_delimitor.join(x))
# preprocess by pattern matching and then extract n-gram features #.encode('UTF8')
# input txt_rdd format (string): meta-data1\tmeta-data2\t...\tdataline1\tdataline2\t...datalineN\n
# output featured_rdd format (list):[meta-data1,meta-data2,..., hash_cnt_dic, hash_str_dic]
# hash_cnt_dic: {hash,hash:count,...} hash_str_dic: {hash: 'str1',... }
tmp_rdd = txt_rdd \
.map(lambda x: preprocess_pattern(x, metadata_count, pattern_str, ln_delimitor \
, label_idx, label_arr, convert2dirty )) \
.filter(lambda x: len(x) > metadata_count) \
.filter(lambda x: type(x[metadata_count]) is list) #.cache() memory issue...
#tmp_rdd_count=tmp_rdd.count()
#print "INFO: After preprocessing count=",tmp_rdd_count
featured_rdd = tmp_rdd \
.map(lambda x: feature_extraction_ngram(x, data_idx, MAX_FEATURES, num_gram)) \
.filter(lambda x: len(x) > metadata_count) \
.filter(lambda x: type(x[metadata_count]) is dict) \
.filter(lambda x: type(x[metadata_count+1]) is dict) \
.filter(lambda x: len(x[metadata_count])> int(feature_count_threshold) ) \
.cache()
#feat_rdd_count=featured_rdd.count()
#print "INFO: After featuring count=",feat_rdd_count
all_hashes_cnt_dic = None
all_hash_str_dic = None
all_hashes_seq_dic = None
#get all hashes and total occurring count ===============
# all_hashes_cnt_dic: {'hash,hash': total count,... }
if all_hashes_cnt_dic is None:
#all_hashes_cnt_dic = featured_rdd.map(lambda x: x[metadata_count]).reduce(lambda a, b: combine_dic_cnt(a, b))
all_hashes_cnt_dic = dict(
featured_rdd.flatMap(lambda x: x[metadata_count].items()).
reduceByKey(lambda a, b: a + b).collect())
#get all hashes and their extracted string ===============
# all_hash_str_dic: {hash:'str1', ...
if all_hash_str_dic is None:
#all_hash_str_dic = featured_rdd.map(lambda x: x[metadata_count+1]).reduce(lambda a, b: combine_dic(a, b))
all_hash_str_dic = dict(
featured_rdd.flatMap(
lambda x: x[metadata_count + 1].items()).distinct().collect())
# get all labels into an array =============== provided by parameter?
if label_arr is None:
# will force "clean" be 0 here
label_arr = sorted(
featured_rdd.map(
lambda x: x[label_idx].lower()).distinct().collect())
# debug only
print "INFO: label_arr.=", json.dumps(sorted(label_arr))
# save labels to hdfs as text file==================================== ============
hdfs_folder = hdfs_feat_dir #+ "/" # "/" is needed to create the folder correctly
print "INFO: hdfs_folder=", hdfs_folder
try:
hdfs.mkdir(hdfs_folder)
except IOError as e:
print "WARNING: I/O error({0}): {1}".format(e.errno, e.strerror)
except:
print "WARNING: Unexpected error at mkdir:", sys.exc_info()[0]
# clean up metadata_file
metadata_file = os.path.join(hdfs_folder, metadata) #"metadata"
print "INFO: metadata_file=", metadata_file
try:
hdfs.rmr(metadata_file)
except IOError as e:
print "WARNING: I/O error({0}): {1}".format(e.errno, e.strerror)
except:
print "WARNING: Unexpected error at rmr():", sys.exc_info()[0]
sc.parallelize(label_arr, 1).saveAsTextFile(metadata_file)
#remap all hash values to continuous key/feature number ==============
# all_hashes_seq_dic: { hash : sequential_numb }
if all_hashes_seq_dic is None:
all_hashes_seq_dic = {}
remap2seq(
all_hashes_cnt_dic,
all_hashes_seq_dic) #all_hashes_seq_dic has continuous key number
#print "all_hashes_seq_dic=",all_hashes_seq_dic
total_feature_numb = len(all_hashes_seq_dic)
print "INFO: Total feature count=", len(all_hashes_seq_dic)
# featured_rdd (list): [meta-data1,meta-data2,..., hash_cnt_dic, hash_str_dic]
# seq_featured_rdd(list): [meta-data1,meta-data2,..., hash_cnthsh_dict, hash_str_dic] (feat id in sorted sequence)
# hash_cnt_dic: {hash: count} hash_str_dic: {hash: 'str1,str2...' }
# set binary_flag to True, all feature:value will be 1
broadcast_dic = sc.broadcast(all_hashes_seq_dic)
seq_featured_rdd = featured_rdd.map(lambda x: convert2seq(
x, label_idx, data_idx, broadcast_dic.value, binary_flag=binary_flag)
).cache()
# get hash_cnthsh_dict then flatMap and reduce to (feat id, count)
ct_rdd = seq_featured_rdd.flatMap(lambda x: [(i[0], i[1]) for i in x[
data_idx].iteritems()]).reduceByKey(lambda a, b: a + b)
# sorted by feature id as int
feat_sample_count_arr = ct_rdd.sortBy(lambda x: int(x[0])).map(
lambda x: x[1]).collect()
# sort after collect may fail when rdd is huge
#feat_sample_count_arr=[]
#for i in sorted(ct_rdd.collect(), key=lambda t: int(t[0])):
# feat_sample_count_arr.append(i[1])
print "INFO: feat_sample_count_arr len=", len(feat_sample_count_arr)
# save feat_sample_count_arr data ==================================== ============
filter = '{"rid":' + row_id_str + ',"key":"feat_sample_count_arr"}'
upsert_flag = True
jo_insert = {}
jo_insert["rid"] = eval(row_id_str)
jo_insert["key"] = "feat_sample_count_arr"
jo_insert["value"] = feat_sample_count_arr
jstr_insert = json.dumps(jo_insert)
ret = query_mongo.upsert_doc_t(mongo_tuples, filter, jstr_insert,
upsert_flag)
print "INFO: Upsert count for feat_sample_count_arr=", ret
# insert failed, save to local
if ret == 0:
# drop old record in mongo
ret = query_mongo.delete_many(mongo_tuples, None, filter)
if not os.path.exists(local_out_dir):
os.makedirs(local_out_dir)
fsca_hs = os.path.join(local_out_dir, row_id_str,
row_id_str + "_feat_sample_count_arr.pkl")
print "WARNING: save feat_sample_count_arr to local"
ml_util.ml_pickle_save(feat_sample_count_arr, fsca_hs)
# save feature data; TBD. not used. ==================================== ============
#libsvm_rdd=seq_featured_rdd.map(lambda x: convert2libsvm(x,label_idx,data_idx,label_arr))
# put hash to the front of each row, assume hash is after label
libsvm_rdd = seq_featured_rdd.map(
lambda x: x[label_idx + 1] + " " + convert2libsvm(
x, label_idx, data_idx, label_arr))
# debug only
#print "libsvm_rdd="
#for i in libsvm_rdd.collect():
# print i
# get rdd statistics info
stats = featured_rdd.map(lambda p: len(p[metadata_count])).stats()
feat_count_max = stats.max()
feat_count_stdev = stats.stdev()
feat_count_mean = stats.mean()
sample_count = stats.count()
print "INFO: libsvm data: sample count=", sample_count, ",Feat count mean=", feat_count_mean, ",Stdev=", feat_count_stdev
print "INFO: ,max feature count=", feat_count_max
# find sample count
lbl_arr = featured_rdd.map(lambda x: (x[label_idx], 1)).reduceByKey(
add).collect()
print "INFO: Sample count by label=", lbl_arr
# remove duplicated libsvm string; only keep the first duplicated item, assume space following key_idx
if remove_duplicated == "Y":
libsvm_rdd=libsvm_rdd \
.map(lambda x: ( ','.join(x.split(' ')[metadata_count:]), x)) \
.groupByKey().map(lambda x: list(x[1])[0] ) \
.cache()
cnt_list = libsvm_rdd.map(lambda x: (x.split(' ')[1], 1)).reduceByKey(
add).collect()
stats = libsvm_rdd.map(
lambda x: len(x.split(' ')[metadata_count:])).stats()
feat_count_max = stats.max()
feat_count_stdev = stats.stdev()
feat_count_mean = stats.mean()
sample_count = stats.count()
print "INFO: Non-Duplicated libsvm data: sample count=", sample_count, ",Feat count mean=", feat_count_mean, ",Stdev=", feat_count_stdev
print "INFO: ,max feature count=", feat_count_max
print "INFO: Non-Duplicated Label count list=", cnt_list
# clean up libsvm data ==================================== ============
libsvm_data_file = os.path.join(hdfs_folder,
libsvm_alldata_filename) #"libsvm_data"
print "INFO: libsvm_data_file=", libsvm_data_file
try:
#hdfs.ls(save_dir)
#print "find hdfs folder"
hdfs.rmr(libsvm_data_file)
#if num_gram == 1:
# hdfs.rmr(dnn_data_file)
#print "all files removed"
except IOError as e:
print "WARNING: I/O error({0}): {1} at libsvm_data_file clean up".format(
e.errno, e.strerror)
except:
print "WARNING: Unexpected error at libsvm file clean up:", sys.exc_info(
)[0]
#codec = "org.apache.hadoop.io.compress.GzipCodec"
#libsvm_rdd.saveAsTextFile(libsvm_data_file, codec)
libsvm_rdd.saveAsTextFile(libsvm_data_file) # TBD encrypted
feat_count_file = libsvm_data_file + "_feat_count"
print "INFO: feat_count_file=", feat_count_file
try:
hdfs.rmr(feat_count_file)
except IOError as e:
print "WARNING: I/O error({0}): {1} at feat_count clean up".format(
e.errno, e.strerror)
except:
print "WARNING: Unexpected error at libsvm feature count clean up:", sys.exc_info(
)[0]
sc.parallelize([total_feature_numb], 1).saveAsTextFile(feat_count_file)
label_dic = {}
# assign label a number
for idx, label in enumerate(sorted(label_arr)):
if not label in label_dic:
label_dic[
label] = idx #starting from 0, value = idx, e.g., clean:0, dirty:1
# output text for DNN:[meta-data1,meta-data2,..., [feature tokens]] ================= DNN ===========
if num_gram == 1: # special flag to tokenize and keep input orders
print "INFO: processing data for DNN..."
# create token dict
# str_hash_dict: string to hash
# all_hashes_seq_dic: hash to seq id
if token_dict is None or len(token_dict) == 0:
token_dict = {}
str_hash_dict = {v: k for k, v in all_hash_str_dic.iteritems()}
for k, v in str_hash_dict.iteritems():
token_dict[k] = int(all_hashes_seq_dic[str(v)])
#print "token_dict=",len(token_dict),token_dict
dnn_rdd = tmp_rdd \
.map(lambda x: tokenize_by_dict(x, data_idx, token_dict,label_idx, label_dic)) \
.filter(lambda x: len(x) > metadata_count) \
.filter(lambda x: type(x[metadata_count]) is list)
#.cache()
# filter duplication here
#print dnn_rdd.take(3)
dnn_data_file = os.path.join(hdfs_folder,
dnn_alldata_filename) #"dnn_data"
print "INFO: dnn_data_file=", dnn_data_file
try:
hdfs.rmr(dnn_data_file)
except IOError as e:
print "WARNING: I/O error({0}): {1} at dnn_data_file clean up".format(
e.errno, e.strerror)
except:
print "WARNING: Unexpected error at libsvm file clean up:", sys.exc_info(
)[0]
# clean up data
dnn_npy_gz_file = os.path.join(hdfs_folder, row_id_str + "_dnn_")
print "INFO: dnn_npy_gz_file=", dnn_npy_gz_file
try:
hdfs.rmr(dnn_npy_gz_file + "data.npy.gz")
hdfs.rmr(dnn_npy_gz_file + "label.npy.gz")
hdfs.rmr(dnn_npy_gz_file + "info.npy.gz")
except IOError as e:
print "WARNING: I/O error({0}): {1} at dnn_npy clean up".format(
e.errno, e.strerror)
except:
print "WARNING: Unexpected error at dnn_npy file clean up:", sys.exc_info(
)[0]
# save new data
try:
dnn_rdd.saveAsTextFile(dnn_data_file)
except:
print "WARNING: Unexpected error at saving dnn data:", sys.exc_info(
)[0]
# show data statistics
try:
stats = dnn_rdd.map(lambda p: len(p[metadata_count])).stats()
feat_count_max = stats.max()
feat_count_stdev = stats.stdev()
feat_count_mean = stats.mean()
sample_count = stats.count()
print "INFO: DNN data: sample count=", sample_count, ",Feat count mean=", feat_count_mean, ",Stdev=", feat_count_stdev
print "INFO: ,max feature count=", feat_count_max
except:
print "WARNING: Unexpected error at getting stats of dnn_rdd:", sys.exc_info(
)[0]
# clean up pca data in hdfs ============ ========================
pca_files = '*' + libsvm_alldata_filename + "_pca_*"
#print "INFO: pca_files=", pca_files
try:
f_list = hdfs.ls(hdfs_folder)
if len(f_list) > 0:
df_list = fnmatch.filter(f_list, pca_files)
for f in df_list:
print "INFO: rm ", f
hdfs.rmr(f)
except IOError as e:
print "WARNING: I/O error({0}): {1}".format(e.errno, e.strerror)
except:
print "WARNING: Unexpected error at libsvm pca file clean up:", sys.exc_info(
)[0]
# clean up pca data in web local ============ ========================
pca_fname = os.path.join(model_data_folder, row_id_str + '_pca_*.pkl*')
print "INFO: pca_fname=", pca_fname
try:
for fl in glob.glob(pca_fname):
print "INFO: remove ", fl
os.remove(fl)
except OSError, e:
print("Error: %s - %s." % (e.pca_fname, e.strerror))