def train(row_id_str, ds_id, hdfs_feat_dir, local_out_dir, ml_opts_jstr
, 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, src_filename
, jobname ):
# create zip files for Spark workers ================= ================
zip_file_path = ml_build_zip_file(zipout_dir, zipcode_dir, zip_file_name, prefix='zip_feature_util')
print "INFO: zip_file_path=",zip_file_path
#data_folder = hdfs_feat_dir + "/"
#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)
# init 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])
# start here =================================================================== ===============
t0 = time()
### Need to check if PCA available here ===========================
libsvm_data_file = os.path.join(hdfs_feat_dir , src_filename) # need to set k numb in filename somehow
print "INFO: libsvm_data_file=", libsvm_data_file
#samples_rdd = MLUtils.loadLibSVMFile(sc, libsvm_data_file).cache()
# load sample RDD from text file
# format (LabeledPoint,hash) from str2LabeledPoint_hash()
feature_count=0
samples_rdd, feature_count = zip_feature_util.get_sample_rdd(sc, libsvm_data_file, feature_count, '')
# get label as a list
labels_list_all = samples_rdd.map(lambda p: int(p[0].label)).collect()
total_sample_count=len(labels_list_all)
parsedData =samples_rdd.map(lambda p: p[0].features).cache()
#for i in parsedData.collect(): #p.features: pyspark.mllib.linalg.SparseVector
# print "pd=",type(i),",i=",i
t1 = time()
print 'INFO: running time: %f' %(t1-t0)
t0 = t1
###############################################
########## build learning model ###############
###############################################
### get the parameters###
print "INFO: ============Learning Algorithm and Parameters============="
para_dict = json.loads(ml_opts_jstr)
flag_model = para_dict['learning_algorithm'] # kmeans
iteration_num = eval(para_dict['iterations'])
k=2
if 'k' in para_dict:
k = eval(para_dict['k'])
print "INFO: Learning Algorithm:", flag_model
print "INFO: iterations=", iteration_num
#print "training_sample_number=", training_sample_number
### 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']
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')
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_list=", labels_list
### build model ###
if flag_model == "kmeans":
print "=================== Kmeans ============"
model = KMeans.train(parsedData, k, maxIterations=iteration_num)
t_cost= model.computeCost(parsedData)
print "INFO: cost for training set =", str(t_cost)
clusterCenters=model.clusterCenters
print "INFO: clusterCenters t=", type(clusterCenters) #list
elif flag_model == "gaussian_mixture_model": # didn't work some native lib issue
print "=================== Gaussian_Mixture_Model ============"
model = GaussianMixture.train(parsedData, k, maxIterations=iteration_num)
print "INFO: model.weights =", model.weights
else:
print "INFO: Training model selection error: no valid ML model selected!"
return
### Save model
save_dir = 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 "ERROR: I/O error({0}): {1}".format(e.errno, e.strerror)
except:
print "ERROR: Unexpected error:", sys.exc_info()[0]
print "INFO: model saved at hdfs=",save_dir
print "INFO: model type=",type(model)," model=",model
model.save(sc, save_dir)
###load model if needed
#sameModel = SVMModel.load(sc, save_dir)
###
# (true label, keams label, features list, hash)
all_data=samples_rdd.map(lambda t: ( t[0].label, model.predict(t[0].features), t[0].features, t[1] ) ).collect()
true_label_arr = np.asarray([int(x) for x,_,_,_ in all_data])
labels_kmeans = np.asarray([int(x) for _,x,_,_ in all_data])
hash_list = np.asarray([x for _,_,_,x in all_data])
print "INFO: all_data len=",len(all_data),"all_data t=",type(labels_list_all)
print "INFO: true_label_arr.shape=",true_label_arr.shape,"labels_kmeans.shape=",labels_kmeans.shape
print "INFO: true_label_arr t=",type(true_label_arr),"labels_kmeans t=",type(labels_kmeans)
mtx_center=np.asarray(clusterCenters)
features_array_reduced=np.asarray([x.toArray() for _,_,x,_ in all_data])
print "INFO: mtx_center t=",type(mtx_center),"mtx_center.shape=",mtx_center.shape
print "INFO: features_array_reduced t=",type(features_array_reduced),"features_array_reduced.shape",features_array_reduced.shape
#Adjusted Mutual Information between two clusterings
amis=adjusted_mutual_info_score(labels_list_all,labels_kmeans)
print "INFO: Adjusted_mutual_info_score=", amis
#Similarity measure between two clusterings
ars=adjusted_rand_score(labels_list_all,labels_kmeans)
print "INFO: Adjusted_rand_score=", ars
accuracy=0.0
t1 = time()
print 'INFO: training run time: %f' %(t1-t0)
t0 = t1
###############################################
########## plot histogram ######
###############################################
n_clusters=k
plot_col_num = int(math.ceil(math.sqrt(n_clusters)))
figsize = (4*plot_col_num, 3*int(math.ceil(n_clusters*1.0/plot_col_num)))
print "INFO: n_clusters=",n_clusters,",label_dic=",label_dic
print "INFO: plot_col_num=",plot_col_num,",figsize=",figsize,",local_out_dir=",local_out_dir
# kmeans histogram
_, p_true = ml_plot_kmeans_histogram_subfigures(true_label_arr, labels_kmeans, n_clusters, names = label_dic
, plot_col_num = plot_col_num, figsize=figsize, folder = local_out_dir, rid=row_id_str)
# normalized kmeans histogram
_, p_true_norm = ml_plot_kmeans_histogram_subfigures(true_label_arr, labels_kmeans, n_clusters, names = label_dic
, plot_col_num = plot_col_num, figsize=figsize, normalize = True, folder = local_out_dir, rid=row_id_str)
####plot "reverse" histogram with labels ####
num_bars = max(true_label_arr) + 1
figsize = (4*plot_col_num, 3*int(math.ceil(num_bars*1.0/plot_col_num)))
_, p_cluster = ml_plot_kmeans_histogram_subfigures(labels_kmeans, true_label_arr, num_bars, names = label_dic
, plot_col_num = plot_col_num, figsize=figsize, reverse = True, folder = local_out_dir, rid=row_id_str)
#### plot dot figures ####
# dot plot for Kmeans ===========
filename=os.path.join(local_out_dir ,row_id_str+'_cluster.png')
filename_3d=os.path.join(local_out_dir ,row_id_str+'_cluster_3d.json')
ml_plot_kmeans_dot_graph_save_file(features_array_reduced, labels_kmeans, mtx_center, n_clusters, figsize=(10,7), filename=filename
, title='KMeans', filename_3d=filename_3d)
# dot plot for True Labels ===========
filename=os.path.join(local_out_dir ,row_id_str+'_cluster_tl.png')
filename_3d=os.path.join(local_out_dir ,row_id_str+'_cluster_3d_tl.json')
ml_plot_kmeans_dot_graph_save_file(features_array_reduced, true_label_arr, mtx_center, n_clusters, figsize=(10,7), filename=filename
, title='True Labels', filename_3d=filename_3d)
dataset_info={"training_fraction":1, "class_count":n_clusters,"dataset_count":total_sample_count}
# only update db for web request
if fromweb=="1":
#print "database update"
str_sql="UPDATE atdml_document set "+"accuracy = '" \
+"', status = 'learned', processed_date ='"+str(datetime.datetime.now()) \
+"', total_feature_numb='"+str(feature_count) \
+"', 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 mrun(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, fromweb
, training_fraction, jobname, run_number, bin_number ):
### generate data folder and out folder, clean up if needed
if not os.path.exists(local_out_dir):
os.makedirs(local_out_dir)
# zip func in other files for Spark workers ================= ================
zip_file_path = 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
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:
key = "feature_excluded"
jstr_filter='{"rid":'+row_id_str+',"key":"'+key+'"}'
jstr_proj='{"value":1}'
# get from own id (not from parent dataset id)
#print "jstr_filter=",jstr_filter,",jstr_proj=",jstr_proj
doc=query_mongo.find_one(args.ip_address, args.port, args.db_name, args.tb_name, username, password, jstr_filter, jstr_proj)
#print "feature_excluded=",doc
if not doc is None and 'value' in doc:
excluded_feat_cslist = ','.join(str(i) for i in doc['value'])
print "INFO: excluded_feat_cslist=",excluded_feat_cslist
### generate Labeled point
#libsvm_data_file = data_folder + "libsvm_data"
# 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)
#samples_rdd = MLUtils.loadLibSVMFile(sc, libsvm_data_file)
# get distinct label list
labels_list_all = samples_rdd.map(lambda p: p[0].label).distinct().collect()
#labels_list_all = samples_rdd.map(lambda p: p.label).collect()
t1 = time()
print "INFO: labels_list_all=",labels_list_all
#print "INFO: training and testing samples generated!"
print 'INFO: data generating time: %f' %(t1-t0)
t0 = t1
### generate label names (family names) #####
### connect to database to get the column list which contains all column number of the corresponding feature####
label_set = set(labels_list_all)
class_num = len(label_set)
#class_num = len(labels_list)
if class_num > 2:
print "INFO:Number of classes=", class_num
###############################################
###########build learning model################
###############################################
### get the parameters###
print "INFO: ============Learning Algorithm and Parameters============="
#param_dict = json.loads(ml_opts_jstr)
flag_model = ml_opts['learning_algorithm'] # 1: linear_svm_with_sgd; 2: logistic_regression_with_lbfgs; 3: logistic_regression_with_sgd
C = eval(ml_opts['c'])
iteration_num = ml_opts['iterations']
regularization = ml_opts['regularization']
print "INFO: Learning Algorithm: ", flag_model
print "INFO: C = ", C
print "INFO: iteration = ", iteration_num
print "INFO: regType = ", regularization
t0 = time()
accuracy_array = np.zeros(run_number)
for rnd in range (0, run_number):
### generate training and testing data
training_rdd, testing_rdd = samples_rdd.randomSplit([training_fraction, 1-training_fraction])
training_rdd=training_rdd.map(lambda p:p[0])# keep LabeledPoint only
training_rdd.cache()
testing_rdd.cache()
training_sample_count = training_rdd.count()
regP = C/float(training_sample_count)
print "INFO: Calculated: regParam = ", regP
### build model ###
if flag_model == "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 flag_model == "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 flag_model == "logistic_regression_with_sgd":
### 3: LogisticRegressionWithLBFGS
print "INFO: ====================3: LogisticRegressionWithSGD============="
model_classification = LogisticRegressionWithSGD.train(training_rdd, regParam=regP, iterations=iteration_num, regType=regularization) # regParam = 1/(sample_number*C)
else:
print "ERROR: Training model selection error: no valid ML model selected!"
return
### Evaluating the model on testing data
labelsAndPreds = testing_rdd.map(lambda p: (p[0].label, model_classification.predict(p[0].features)))
labelsAndPreds.cache()
testing_sample_number = testing_rdd.count()
testErr = labelsAndPreds.filter(lambda (v, p): v != p).count() / float(testing_sample_number)
accuracy = 1 - testErr
accuracy_array[rnd] = accuracy
print "INFO: current round=", rnd
print "INFO: Accuracy=", accuracy
########################below: same as train_skLean_multi_run.py#####################################
###############################################
#######plot distribution and variance##########
###############################################
plt.figure(1)
num_bins = bin_number ####10 is default
n, bins, patches = plt.hist(accuracy_array, num_bins, normed=1, facecolor='green', alpha=0.5)
ave = np.mean(accuracy_array)
print "INFO: Accuracy mean=", ave
variance = np.std(accuracy_array)
print "INFO: Accuracy variance=", variance
#print "INFO: bins: ", bins
# add a 'best fit' line
y = mlab.normpdf(bins, ave, variance)
#print "INFO: y: ", y
plt.plot(bins, y, 'r--')
plt.title('Accuracy distribution of '+str(run_number)+' runs:')
plt.xlabel('Accuracy Values')
plt.ylabel('Probability / Accuracy bar width')
#plt.savefig(local_out_dir+file_name_given+"_var_"+str(run_number)+".png")
plt.savefig(os.path.join(local_out_dir, row_id_str+"_var_"+str(run_number)+".png"))
# create ROC data for graph ====================
all_json=[]
barp_arr=[] #n
disp_arr=[] #y
last_idx=0
for idx,ht in enumerate(n): # n is bar height
#print "INFO: mrun bar=",idx, bins[idx], bins[idx+1],((bins[idx]+bins[idx+1])/2)
barp_arr.append([ ((bins[idx]+bins[idx+1])/2.0),n[idx]]) # mid point for x axis
if not math.isnan(y[idx]):
disp_arr.append([bins[idx],y[idx]])
last_idx=idx
#print "INFO: ",last_idx+1, bins[last_idx+1], y[last_idx+1]
if not math.isnan(y[last_idx+1]):
disp_arr.append([bins[last_idx+1],y[last_idx+1]])
#print "barp_arr=", barp_arr
#print "disp_arr=", disp_arr
#bar
bar_json={}
bar_json["values"]=barp_arr
bar_json["key"]='Mutil-Run Accuracy' #
bar_json["type"]="bar" # light blue
bar_json["yAxis"]=1
all_json.append(bar_json)
#distribution
if len(disp_arr)>0:
dis_json={}
dis_json["values"]=disp_arr
dis_json["key"]='Normal Distribution' #
dis_json["type"]="line" # light blue
dis_json["yAxis"]=1
all_json.append(dis_json)
mrun_jfile = os.path.join(local_out_dir, row_id_str+"_mrun.json")
#print "INFO: all_json=",all_json
print "INFO: mrun_jfile=",mrun_jfile
if os.path.exists(mrun_jfile):
try:
os.remove(mrun_jfile)
except OSError, e:
print ("Error: %s - %s." % (e.mrun_jfile,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,
model_data_folder,
random_seed=None):
# create zip files for Spark workers ================= ================
zip_file_path = ml_build_zip_file(zipout_dir,
zipcode_dir,
zip_file_name,
prefix='zip_feature_util')
print "INFO: zip_file_path=", zip_file_path
# ML model filename ====
model_fname = os.path.join(model_data_folder, row_id_str + '.pkl')
print "INFO: model_data_folder=", model_data_folder
# create out folders and clean up old model files ====
ml_util.ml_prepare_output_dirs(row_id_str, local_out_dir,
model_data_folder, model_fname)
# init 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])
# start here =================================================================== ===============
t0 = time()
# check if ml_opts.has_excluded_feat ==1 ===================================
has_excluded_feat = 0
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"]
# 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
# source libsvm filename
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)
# collect all data to local for processing ===============
all_data = samples_rdd.collect()
sample_count = len(all_data)
if not random_seed is None and int(random_seed) > 0:
np.random.seed(int(random_seed))
all_data = sorted(all_data, key=lambda x: x[1])
# 2-D array
features_list = [x.features.toArray() for x, _ in all_data]
# label array
labels_list_all = [x.label for x, _ in all_data]
# hash array
hash_list_all = [x for _, x in all_data]
# convert to np array
labels_list_all = array(labels_list_all)
features_array = np.array(features_list)
hash_list_all = np.array(hash_list_all)
#print "features_list=",features_list
# generate sparse matrix (csr) for all samples
features_sparse_mtx = csr_matrix(features_array)
# if ensamble is on, do special split here
### randomly split the samples into training and testing data ===============
X_train_sparse, X_test_sparse, labels_train, labels_test, train_hash_list, test_hash_list = \
cross_validation.train_test_split(features_sparse_mtx, labels_list_all, hash_list_all, test_size=(1-training_fraction) )
# X_test_sparse is scipy.sparse.csr.csr_matrix
testing_sample_count = len(labels_test)
training_sample_count = len(labels_train)
training_lbl_cnt_list = Counter(labels_train)
testing_lbl_cnt_list = Counter(labels_test)
print "INFO: training sample count=", training_sample_count, ", testing sample count=", testing_sample_count, ",sample_count=", sample_count
print "INFO: training label list=", training_lbl_cnt_list, ", testing label list=", testing_lbl_cnt_list
print "INFO: train_hash_list count=", len(
train_hash_list), ", test_hash_list count=", len(test_hash_list)
# random_seed testing
if not random_seed is None:
cnt = 0
for i in train_hash_list:
print i
cnt = cnt + 1
if cnt > 3:
break
#print "INFO: labels_list_all=",labels_list_all # too big
t1 = time()
print 'INFO: running time: %f' % (t1 - t0)
###############################################
###########build learning model ==================================================== ===============
###############################################
### parse parameters and generate the model ###
(clf, model_name) = parse_param_and_get_model(ml_opts)
if model_name == "none":
print "ERROR: model name not found!"
return -1
#####fit the model to training dataset ===============
try:
clf.fit(X_train_sparse, labels_train)
except:
print "ERROR: clf.fit(): clf=", clf
print "ERROR: sys.exc_info:", sys.exc_info()[0]
return -1
print "INFO: model type=", type(clf), " clf=", clf
#### save clf for future use ================== ===============
joblib.dump(clf, model_fname)
# get data from model ================================
coef = None
intercept = None
# get column size =====
try:
if type(clf) in (classes.SVC, classes.NuSVC): # svm didn't have coef_
col_num = clf.support_vectors_.shape[1]
else: #linear only
# coef_ is only available when using a linear kernel
col_num = len(clf.coef_[0])
coef = clf.coef_[0]
intercept = clf.intercept_[0] # only get 1st item?
#print "**model:clf.coef_[0] =",clf.coef_[0]
# save coef_ to Mongo
except Exception as e:
print "Warning: Can't get clf.coef_[0]. e=", e, ", get total features from meta-data"
col_num = 0 #how to get feature number for sparse array?
print "INFO: total feature # in the model: ", col_num
jfeat_coef_dict = {}
# create feature coefficient file ================================
if coef is None:
print "WARNING: model weights not found!"
else:
feat_filename = os.path.join(local_out_dir,
row_id_str + "_feat_coef.json")
print "INFO: feat_filename=", feat_filename
# save coef_arr to mongo ===
#jfeat_coef_dict=save_coef2db(row_id_str, mongo_tuples, coef, intercept, feat_filename, ds_id)
jfeat_coef_dict = ml_util.ml_save_coef_build_feat_coef(
row_id_str, mongo_tuples, coef, intercept, feat_filename, ds_id)
#print "INFO: jfeat_coef_dict=", jfeat_coef_dict
print "INFO: jfeat_coef_dict len=", len(jfeat_coef_dict)
### Evaluating the model on testing dataset ===============
labels_pred = clf.predict(X_test_sparse)
accuracy = clf.score(X_test_sparse, labels_test)
print "INFO: Accuracy = ", accuracy
# filename for false prediction samples ===============
false_pred_fname = os.path.join(local_out_dir,
row_id_str + "_false_pred.json")
print "INFO: false_pred_fname=", false_pred_fname
# build files for false pred & score graph
(score_arr_0, score_arr_1, max_score,
min_score) = ml_build_false_pred(X_test_sparse,
coef,
intercept,
labels_test,
labels_pred,
test_hash_list,
model_name,
jfeat_coef_dict,
false_pred_fname,
row_id_str=row_id_str,
ds_id=ds_id,
mongo_tuples=mongo_tuples)
# save pred output
pred_out_arr = []
for i in range(0, len(labels_test)):
pred_out_arr.append(
(labels_test[i], labels_pred[i], test_hash_list[i]))
pred_ofname = os.path.join(local_out_dir, row_id_str + "_pred_output.pkl")
print "INFO: pred_ofname=", pred_ofname
ml_util.ml_pickle_save(pred_out_arr, pred_ofname)
###################################################
### generate label names (family names) ==================================================== ===============
###################################################
if labelnameflag == 1:
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: label_dic=", label_dic
labels_list = []
for key in sorted(label_dic):
labels_list.append(label_dic[key])
###############################################
###########plot prediction result figures ==================================================== ===============
###############################################
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(labels_pred.tolist(),
labels_test.tolist(),
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
#fscore=None
perf_measures = None
class_count = len(labels_list)
dataset_info = {
"training_fraction": training_fraction,
"class_count": class_count,
"dataset_count": sample_count
}
#############################################################
###################for 2 class only (plot ROC curve) ==================================================== ===============
#############################################################
if class_count == 2:
# 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
ml_build_pred_score_graph(score_arr_0, score_arr_1, model_name,
score_graph_fname, max_score, min_score)
do_ROC = True
# clean is 0; dirty is 1
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 "WARNING: No ROC curve generated: 'clean' or '0' must be a label for indicating negative class!"
do_ROC = False
if do_ROC:
# calculate fscore ==========
perf_measures = ml_util.calculate_fscore(labels_test, labels_pred)
#fscore=perf_measures["fscore"]
#acc=perf_measures["accuracy"]
#phi=perf_measures["phi"]
print "INFO: perf_measures=", perf_measures
confidence_score = clf.decision_function(X_test_sparse)
#print "INFO:confidence_score=",confidence_score
if flag_clean == 0:
scores = [x for x in confidence_score]
s_labels = [x for x in labels_test]
testing_N = test_cnt_dic[0]
testing_P = test_cnt_dic[1]
else:
scores = [-x for x in confidence_score]
s_labels = [1 - x for x in labels_test]
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)
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: Sqlite update done! ret=", str(ret)
else:
print "INFO: accuracy = '" + str(accuracy * 100) + "%"
t1 = time()
print 'INFO: running time: %f' % (t1 - t0)
print 'INFO: Train Finished!'
return 0
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):
if not os.path.exists(local_out_dir):
os.makedirs(local_out_dir)
# zip func in other files for Spark workers ================= ================
zip_file_path = 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()
t00 = t0
# 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
### generate Labeled point
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)
#samples_rdd = MLUtils.loadLibSVMFile(sc, libsvm_data_file)
# get distinct label list
labels_list_all = samples_rdd.map(
lambda p: p[0].label).distinct().collect()
### generate training and testing data
training_rdd, testing_rdd = samples_rdd.randomSplit(
[training_fraction, 1 - training_fraction])
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
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
##############################################
########### Grid Search with CV ##############
##############################################
### get the parameters for cross validation and grid search ###
(cv, model_name, param_dict) = generate_param(ml_opts)
### 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:
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 "labels:", labels_list
class_num = len(labels_list)
if class_num > 2:
print "INFO: Multi-class classification! Number of classes = ", class_num
#### generate training and testing rdd(s) for CV#####
split_prob = 1.0 / float(cv)
split_prob_list = []
for i in range(0, cv):
split_prob_list.append(split_prob)
list_rdd = training_rdd.randomSplit(split_prob_list)
list_train_rdd = []
list_test_rdd = []
for i in range(0, cv):
list_rdd[i].cache()
for i in range(0, cv):
tr_rdd = sc.emptyRDD()
for j in range(0, cv):
if j == i:
pass
else:
tr_rdd = tr_rdd + list_rdd[j]
tr_rdd.cache()
list_train_rdd.append(tr_rdd)
list_test_rdd.append(list_rdd[i])
all_comb_list_of_dic = get_all_combination_list_of_dic(param_dict)
print "INFO: Total number of searching combinations:", len(
all_comb_list_of_dic)
### loop for all parameter combinations and search the best parameters with CV###
results = []
for p in range(0, len(all_comb_list_of_dic)):
params = all_comb_list_of_dic[p]
C = params['C']
iteration_num = params['iterations']
regularization = params['regType']
scores = []
for i in range(0, cv):
train_rdd = list_train_rdd[i]
test_rdd = list_test_rdd[i]
train_number = train_rdd.count()
regP = C / float(train_number)
### build model ###
if model_name == "linear_svm_with_sgd":
#print "====================1: Linear SVM============="
model_classification = SVMWithSGD.train(
train_rdd,
regParam=regP,
iterations=iteration_num,
regType=regularization) # regParam = 1/(sample_number*C)
elif model_name == "logistic_regression_with_lbfgs":
#print "====================2: LogisticRegressionWithLBFGS============="
model_classification = LogisticRegressionWithLBFGS.train(
train_rdd,
regParam=regP,
iterations=iteration_num,
regType=regularization,
numClasses=class_num) # regParam = 1/(sample_number*C)
elif model_name == "logistic_regression_with_sgd":
#print "====================3: LogisticRegressionWithSGD============="
model_classification = LogisticRegressionWithSGD.train(
train_rdd,
regParam=regP,
iterations=iteration_num,
regType=regularization) # regParam = 1/(sample_number*C)
else:
print "ERROR: Training model selection error: no valid ML model selected!"
return
### Evaluating the model on testing data
labelsAndPreds = test_rdd.map(
lambda p: (p.label, model_classification.predict(p.features)))
labelsAndPreds.cache()
test_sample_number = test_rdd.count()
testErr = labelsAndPreds.filter(
lambda (v, p): v != p).count() / float(test_sample_number)
accuracy = 1 - testErr
#print "Accuracy = ", accuracy
scores.append(accuracy)
ss = np.asarray(scores)
#print "%0.3f (+/-%0.03f) for " % (ss.mean(), ss.std() * 2), params
results.append((ss.mean(), ss.std() * 2, params))
sorted_results = sorted(results, key=lambda x: x[0], reverse=1)
(best_accuracy, best_std2, best_param) = sorted_results[0]
print "INFO: ml_opts_jstr=", ml_opts_jstr
print "INFO: best_param=", best_param
#ml_opts=json.loads(ml_opts_jstr);
print "INFO: ml_opts=", ml_opts
##############################################
######output Grid Search results##############
##############################################
json2save = {}
json2save["rid"] = int(row_id_str)
json2save["key"] = "cv_result"
#json2save["param_str"]=ml_opts_jstr
json2save["param_dic"] = param_dict
cv_grid = []
print ""
print "INFO: =====Grid Search Results for SPARK ======"
print "INFO: Best parameters set found for ", model_name, " is: "
for key in best_param:
print "INFO:", key, "=", best_param[key]
if key.lower() == "regtype":
ml_opts['regularization'] = str(best_param[key])
else:
ml_opts[key.lower()] = str(best_param[key]) # add best param to
ml_opts_jstr = json.dumps(ml_opts)
json2save["param_str"] = ml_opts_jstr
print "INFO: Average accuracy with CV = ", cv, ": ", best_accuracy
print ""
print "INFO: Grid scores on development set:"
for i in range(0, len(sorted_results)):
(ave_accu_i, std2_i, param_i) = sorted_results[i]
print "%0.3f (+/-%0.03f) for " % (ave_accu_i, std2_i), param_i
#outstr='%s,%0.3f,%0.03f,%s' % (param_i,ave_accu_i, std2_i,"Selected" if param_i==best_param else "")
outj = {}
outj["param"] = param_i
outj["average_accuracy"] = "%0.3f" % (ave_accu_i)
outj["std_deviation"] = "%0.3f" % (std2_i)
outj["selected"] = "%s" % ("Selected" if param_i == best_param else "")
cv_grid.append(outj)
print " "
t1 = time()
print 'INFO: Grid Search with CV run time: %f' % (t1 - t0)
t0 = time()
##################################################################################
json2save["cv_grid_data"] = cv_grid
cv_result = json.dumps(json2save)
print "INFO: cv_result=", cv_result
filter = '{"rid":' + row_id_str + ',"key":"cv_result"}'
upsert_flag = True
## write to mongoDB.myml.dataset_info, ignore doc with duplicated key
# db.dataset_info.createIndex({"rid":1,"key":1},{unique:true})
ret = query_mongo.upsert_doc_t(mongo_tuples, filter, cv_result,
upsert_flag)
print "INFO: Upsert count for mllib cv_result: ret=", ret
############################################################################################
########### retrain with all training data and generate the final model with results #######
############################################################################################
C = best_param['C']
iteration_num = best_param['iterations']
regularization = best_param['regType']
regP = C / float(training_sample_count)
######################################the rest of the code is the same as train_MLlib.py #####################################################################
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_arr = model_classification.weights.toArray().tolist()
# save to mongo
key = "coef_arr"
ret = ml_util.save_json_t(row_id_str, key, coef_arr, mongo_tuples)
# save intercept to mongo
key = "coef_intercept"
intercept = model_classification.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 list + coef 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: feature list can't be shown for multi-class classification"
# 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)
print "INFO: jfeat_coef_dict len=", len(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) \
,0 \
,p[0].features \
,p[1] \
) ).cache()
# 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)
### Evaluating the model on testing data
#labelsAndPreds = testing_rdd.map(lambda p: (p.label, model_classification.predict(p.features)))
labelsAndPreds = testing_pred_rdd.map(lambda p: (p[0], p[1]))
labelsAndPreds.cache()
#testing_sample_count = testing_rdd.count()
testErr = labelsAndPreds.filter(lambda (v, p): v != p).count() / float(
testing_sample_count)
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)
plt.show()
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 "WARNING: No ROC curve generated: 'clean' 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)
#print "score_arr_0=",score_arr_0
#print "score_arr_1=",score_arr_1
#print "max_score=",max_score
#print "min_score=",min_score
if do_ROC:
perf_measures = ml_util.calculate_fscore(true_label_list,
pred_label_list)
print "RESULT: perf_measures=", perf_measures
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]
#print scores
#print s_labels
# 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)
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()) \
+"',ml_opts='"+ml_opts_jstr \
+"', 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) + "%"
t1 = time()
print 'INFO: total run time: %f' % (t1 - t00)
print 'INFO: Finished!'
return 0
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, model_data_folder ):
# zip func in other files for Spark workers ================= ================
zip_file_path = ml_build_zip_file(zipout_dir, zipcode_dir, zip_file_name, prefix='zip_feature_util')
print "INFO: zip_file_path=",zip_file_path
# ML model filename ====
model_fname=os.path.join(model_data_folder, row_id_str+'.pkl')
print "INFO: model_data_folder=",model_data_folder
# create out folders and clean up old model files ====
ml_util.ml_prepare_output_dirs(row_id_str,local_out_dir,model_data_folder,model_fname)
# init 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()
t00 = t0
# check if ml_opts.has_excluded_feat ==1 ===================================
has_excluded_feat=0
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"]
# 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
# source libsvm filename
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)
all_data = samples_rdd.collect()
sample_count=len(all_data)
# 2-D array
features_list = [x.features.toArray() for x,_ in all_data]
# label array
labels_list_all = [x.label for x,_ in all_data]
# hash array
hash_list_all = [x for _,x in all_data]
# convert to np array
labels_list_all = array(labels_list_all)
features_array = np.array(features_list)
hash_list_all=np.array(hash_list_all)
# generate sparse matrix (csr) for all samples
features_sparse_mtx = csr_matrix(features_array)
### randomly split the samples into training and testing data ===============
X_train_sparse, X_test_sparse, labels_train, labels_test, train_hash_list, test_hash_list = \
cross_validation.train_test_split(features_sparse_mtx, labels_list_all, hash_list_all, test_size=(1-training_fraction) )
# X_test_sparse is scipy.sparse.csr.csr_matrix
testing_sample_count = len(labels_test)
training_sample_count=len(labels_train)
training_lbl_cnt_list=Counter(labels_train)
testing_lbl_cnt_list=Counter(labels_test)
print "INFO: training sample count=",training_sample_count,", testing sample count=",testing_sample_count,",sample_count=",sample_count
print "INFO: training label list=",training_lbl_cnt_list,", testing label list=",testing_lbl_cnt_list
print "INFO: train_hash_list count=",len(train_hash_list),", test_hash_list count=",len(test_hash_list)
t1 = time()
print 'INFO: running time: %f' %(t1-t0)
###############################################
###########build learning model################
###############################################
### parse parameters and generate the model ###
(clf, model_name, api, cv, param_dic) = parse_param_and_get_model(ml_opts)
if model_name == "none":
print "ERROR: model name not found!"
return -1
#param_jobj=json.loads(ml_opts_jstr);
#print "param_jobj=",param_jobj
########################################################
##########Grid Search with cross validation#############
########################################################
json2save={}
json2save["rid"]=int(row_id_str)
json2save["key"]="cv_result"
#json2save["param_str"]=ml_opts_jstr
json2save["param_dic"]=param_dic
cv_grid=[]
if api == "centralized":
#########run with Scikit-learn API (for comparison)######
print "INFO: ******************Grid Search with Scikit-learn API************"
t0 = time()
# Set the parameters by cross-validation
#tuned_parameters = [{'C': [0.0001, 0.001, 0.01, 0.1, 1, 10, 100, 1000, 10000, 100000]}]
#tuned_parameters = [{'kernel': ['rbf'], 'gamma': [1e-3, 1e-4], \
# 'C': [1, 10, 100, 1000]}, \
# {'kernel': ['linear'], 'C': [1, 10, 100, 1000]}]
scores = ['accuracy']
json2save["scores"]=scores
#print json2save
for score in scores: # for one item only? score=accuracy
print("INFO: # Tuning hyper-parameters for %s" % score)
#print()
grid = grid_search.GridSearchCV(estimator = clf, param_grid = param_dic, cv=cv, scoring= score)
grid.fit(X_train_sparse, labels_train)
print "INFO: Best parameters set found on development set:"
print "INFO: grid.best_params_=",grid.best_params_
print "INFO: Grid scores on development set:"
for key in grid.best_params_:
print "INFO: best_params["+key+"]=", grid.best_params_[key]
if key.lower()=="regtype":
ml_opts['regularization']=str(grid.best_params_[key]) # add best param to
else:
ml_opts[key.lower()]=str(grid.best_params_[key]) # add best param to
# save best param to db as json string
j_str=json.dumps(ml_opts);
json2save["param_str"]=j_str;
print "INFO: grid_scores_ with params:"
for params, mean_score, scores in grid.grid_scores_:
print "INFO: %0.3f (+/-%0.03f) for %r" % (mean_score, scores.std() * 2, params)
#outstr='%s,%0.3f,%0.03f,%s' % (params,mean_score, scores.std() * 2,"Selected" if params==grid.best_params_ else "")
outj={}
outj["param"]=params
outj["average_accuracy"]="%0.3f" % (mean_score)
outj["std_deviation"]="%0.3f" % (scores.std() * 2)
outj["selected"]="%s" % ("Selected" if params==grid.best_params_ else "")
cv_grid.append(outj)
clf_best = grid.best_estimator_
t1 = time()
############# END run with SKlearn ######
print 'INFO: Grid Search with SKlearn running time: %f' %(t1-t0)
t0 = time()
else:
#############run with SPARK######
print "INFO: ******************Grid Search with SPARK************"
all_comb_list_of_dic = get_all_combination_list_of_dic(param_dic)
print "INFO: Total number of searching combinations=", len(all_comb_list_of_dic)
#print "all_comb_list_of_dic: ", all_comb_list_of_dic
params_rdd = sc.parallelize(all_comb_list_of_dic)
###broad cast clf, traning data, testing data to all workers###
X_broadcast = sc.broadcast(X_train_sparse)
y_broadcast = sc.broadcast(labels_train)
clf_broadcast = sc.broadcast(clf)
### Grid Search with CV in multiple workers ###
models = params_rdd.map(lambda x: learn_with_params(clf_broadcast.value, X_broadcast.value, y_broadcast.value, cv, x)).sortByKey(ascending = False).cache()
(ave_accuracy, (clf_best, p_dic_best, std2)) = models.first()
# output results #
print "INFO: Best parameters set found for ", model_name, " is: "
print "INFO: ",
for key in p_dic_best:
print key, " = ", p_dic_best[key],
if key.lower()=="regtype":
ml_opts['regularization']=str(p_dic_best[key])
else:
ml_opts[key.lower()]=str(p_dic_best[key]) # add best param to
# save best param to db as json string
print ""
j_str=json.dumps(ml_opts);
json2save["param_str"]=j_str;
print "INFO: Average accuracy with CV = ", cv, ": ", ave_accuracy
######## print complete report #######
print "INFO: Grid scores on development set:"
all_results = models.collect()
for i in range(0, len(all_results)):
(ave_accu_i, (clf_i, p_dic_i, std2_i)) = all_results[i]
print "INFO: ",ave_accu_i, " for ", p_dic_i
print "INFO: %0.3f (+/-%0.03f) for " % (ave_accu_i, std2_i), p_dic_i
#outstr='%s,%0.3f,%0.03f,%s' % ( p_dic_i, ave_accu_i, std2_i, "Selected" if p_dic_i==p_dic_best else "")
outj={}
outj["param"]=p_dic_i
outj["average_accuracy"]="%0.3f" % (ave_accu_i)
outj["std_deviation"]="%0.3f" % (std2_i)
outj["selected"]="%s" % ("Selected" if p_dic_i==p_dic_best else "")
cv_grid.append(outj)
print " "
t1 = time()
############# END run with SPARK######
print 'INFO: Grid search with SPARK running time: %f' %(t1-t0)
##################################################################################
#print "cv_grid=",cv_grid
#json2save["cv_grid_title"]='param,average_accuracy,std_deviation,selected'
json2save["cv_grid_data"]=cv_grid
json2save['clf_best']=str(clf_best).replace("\n","").replace(" ","")
cv_result=json.dumps(json2save)
#print "INFO: cv_result=",cv_result
filter='{"rid":'+row_id_str+',"key":"cv_result"}'
upsert_flag=True
## write to mongoDB.myml.dataset_info, ignore doc with duplicated key
# db.dataset_info.createIndex({"rid":1,"key":1},{unique:true})
ret=query_mongo.upsert_doc_t(mongo_tuples,filter,cv_result,upsert_flag)
print "INFO: Upsert count for cv_result: ret=",ret
##################################################################################
##########Retrain with best model for training set and output results#############
##################################################################################
print "INFO: **********Retrain with best model for training set and output results************"
clf_best.fit(X_train_sparse, labels_train)
#### save clf_best for future use ####
#joblib.dump(clf_best, model_data_folder + row_id_str+'.pkl')
joblib.dump(clf_best, model_fname)
### Evaluating the model on testing data
labels_pred = clf_best.predict(X_test_sparse)
accuracy = clf_best.score(X_test_sparse, labels_test)
print "INFO: Accuracy = ", accuracy
######################################the rest of the code is the same as train_sklean.py (replace clf with clf_best)#####################################################################
clf=clf_best
print "INFO: model type=",type(clf)," clf=",clf
# get data from model ================================
coef=None
intercept=None
try:
if type(clf) in ( classes.SVC , classes.NuSVC) :# svm didn't have coef_
col_num=clf.support_vectors_.shape[1]
else: #linear only
# coef_ is only available when using a linear kernel
col_num = len(clf.coef_[0])
coef=clf.coef_[0]
intercept=clf.intercept_[0] # only get 1st item?
#print "**model:clf.coef_[0] =",clf.coef_[0]
except Exception as e:
print "WARNING: Can't get clf.coef_[0]. e=",e,", get total features from meta-data"
col_num = 0 #how to get feature number for sparse array?
print "INFO: total feature # in the model: ", col_num
jfeat_coef_dict={}
# create feature coefficient file ================================
if coef is None:
print "WARNING: model weights not found!"
else:
feat_filename=os.path.join(local_out_dir,row_id_str+"_feat_coef.json")
print "INFO: feat_filename=",feat_filename
# save coef_arr to mongo & create jfeat_coef_dict===
jfeat_coef_dict=ml_util.ml_save_coef_build_feat_coef(row_id_str, mongo_tuples, coef, intercept, feat_filename, ds_id)
#print "INFO: jfeat_coef_dict=", jfeat_coef_dict
print "INFO: jfeat_coef_dict len=", len(jfeat_coef_dict )
# filename for false pred
false_pred_fname=os.path.join(local_out_dir,row_id_str+"_false_pred.json")
print "INFO: false_pred_fname=", false_pred_fname
# build files for false pred & score graph
(score_arr_0, score_arr_1, max_score,min_score)=ml_build_false_pred(X_test_sparse,coef,intercept
, labels_test, labels_pred, test_hash_list, model_name, jfeat_coef_dict, false_pred_fname)
# save pred output
pred_out_arr=[]
for i in range(0,len(labels_test)):
pred_out_arr.append((labels_test[i], labels_pred[i], test_hash_list[i]))
pred_ofname=os.path.join(local_out_dir,row_id_str+"_pred_output.pkl")
print "INFO: pred_ofname=", pred_ofname
ml_util.ml_pickle_save(pred_out_arr,pred_ofname)
###################################################
### 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']
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')
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])
### generate sample numbers of each family in testing data###
testing_sample_number = len(labels_test)
print "INFO: testing_sample_number=", testing_sample_number
test_cnt_dic = {}
for key in label_dic:
test_cnt_dic[key] = 0
for i in range (0, testing_sample_number):
for key in label_dic:
if labels_test[i] == key:
test_cnt_dic[key] = test_cnt_dic[key] + 1
print "INFO: Number of samples in each label is=", test_cnt_dic
###############################################
###########plot prediction result figure#######
###############################################
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(labels_pred.tolist(), labels_test.tolist(), 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
#fscore=None
perf_measures=None
class_count=len(labels_list)
dataset_info={"training_fraction":training_fraction, "class_count":class_count,"dataset_count":sample_count}
#############################################################
###################for 2 class only (plot ROC curve)#########
#############################################################
if len(labels_list) == 2:
# 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
ml_build_pred_score_graph(score_arr_0,score_arr_1,model_name, score_graph_fname,max_score,min_score)
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 "No ROC curve generated: 'clean' or '0' must be a label for indicating negative class!"
do_ROC=False
if do_ROC:
# calculate fscore ==========
perf_measures=ml_util.calculate_fscore(labels_test, labels_pred)
print "INFO: perf_measures=",perf_measures
confidence_score = clf_best.decision_function(X_test_sparse)
if flag_clean == 0:
scores = [x for x in confidence_score]
s_labels = [x for x in labels_test]
testing_N = test_cnt_dic[0]
testing_P = test_cnt_dic[1]
else:
scores = [-x for x in confidence_score]
s_labels = [1-x for x in labels_test]
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)
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()) \
+"',ml_opts='"+j_str \
+"', 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: total running time: %f' %(t1-t00)
print 'INFO: Finished!'
return 0
def mrun(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, fromweb, training_fraction,
jobname, run_number, bin_number):
if not os.path.exists(local_out_dir):
os.makedirs(local_out_dir)
# zip func in other files for Spark workers ================= ================
zip_file_path = ml_build_zip_file(zipout_dir,
zipcode_dir,
zip_file_name,
prefix='zip_feature_util')
print "INFO: zip_file_path=", zip_file_path
# init 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
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"]
# 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
### load libsvm file ###
#libsvm_data_file = data_folder + "libsvm_data"
# source libsvm filename
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)
#samples_rdd = MLUtils.loadLibSVMFile(sc, libsvm_data_file)
#print samples_rdd.count()
# collect all data to local for processing ===============
all_data = samples_rdd.map(
lambda p: p[0]).collect() # keep LabeledPoint only
# 2-D array
features_list = [x.features.toArray() for x in all_data]
# label array
labels_list_all = [x.label for x in all_data]
# convert to np array
labels_list_all = array(labels_list_all)
features_array = np.array(features_list)
### generate sparse matrix (csr) for all samples
features_sparse_mtx = csr_matrix(features_array)
t1 = time()
print "INFO: labels_list_all=", labels_list_all
print 'INFO: data generating time: %f' % (t1 - t0)
label_set = set(labels_list_all)
class_num = len(label_set)
#class_num = len(labels_list)
if class_num > 2:
print "INFO: Number of classes =", class_num
###############################################
###########build learning model################
###############################################
### parse parameters and generate the model ###
(clf, model_name) = parse_para_and_get_model(ml_opts)
if model_name == "none":
return
t0 = time()
accuracy_array = np.zeros(run_number)
for rnd in range(0, run_number):
### randomly split the samples into training and testing data
X_train_sparse, X_test_sparse, labels_train, labels_test = \
cross_validation.train_test_split(features_sparse_mtx, labels_list_all, test_size=(1-training_fraction))
#####fit the model to training dataset ####
try:
clf.fit(X_train_sparse, labels_train)
except ValueError as v:
print "INFO: ValueError:", v
#raise v didn't work
return -1
### Evaluating the model on testing data
labels_pred = clf.predict(X_test_sparse)
accuracy = clf.score(X_test_sparse, labels_test)
accuracy_array[rnd] = accuracy
print "INFO: current round: ", rnd
print "INFO: Accuracy = ", accuracy
###############################################
#######plot distribution and variance##########
###############################################
plt.figure(1)
num_bins = bin_number ####10 is default
n, bins, patches = plt.hist(accuracy_array,
num_bins,
normed=1,
facecolor='green',
alpha=0.5)
ave = np.mean(accuracy_array)
print "INFO: num_bins=", num_bins
print "INFO: accuracy_array=", accuracy_array
print "INFO: Accuracy mean: ", ave
variance = np.std(accuracy_array)
print "INFO: Accuracy variance: ", variance
# add a 'best fit' line
y = mlab.normpdf(bins, ave, variance)
#print "INFO: y: ", y
plt.plot(bins, y, 'r--')
plt.title('Accuracy distribution of ' + str(run_number) + ' runs:')
plt.xlabel('Accuracy Values')
plt.ylabel('Probability / Accuracy bar width')
mrun_fname = os.path.join(local_out_dir,
row_id_str + "_var_" + str(run_number) + ".png")
plt.savefig(mrun_fname)
# create data for graph ====================
all_json = []
barp_arr = [] #n
disp_arr = [] #y
last_idx = 0
for idx, ht in enumerate(n): # n is bar height
#print "mrun bar=",idx, bins[idx], bins[idx+1],((bins[idx]+bins[idx+1])/2)
barp_arr.append([((bins[idx] + bins[idx + 1]) / 2.0),
n[idx]]) # mid point for x axis, may shift 100% bar
if not math.isnan(y[idx]):
disp_arr.append([bins[idx], y[idx]])
last_idx = idx
#print last_idx+1, bins[last_idx+1], y[last_idx+1]
if not math.isnan(y[last_idx + 1]):
disp_arr.append([bins[last_idx + 1], y[last_idx + 1]])
#print "barp_arr=", barp_arr
#print "disp_arr=", disp_arr
#bar
bar_json = {}
bar_json["values"] = barp_arr
bar_json["key"] = 'Mutil-Run Accuracy' #
bar_json["type"] = "bar" # light blue
bar_json["yAxis"] = 1
all_json.append(bar_json)
#distribution
if len(disp_arr) > 0:
dis_json = {}
dis_json["values"] = disp_arr
dis_json["key"] = 'Normal Distribution' #
dis_json["type"] = "line" # light blue
dis_json["yAxis"] = 1
all_json.append(dis_json)
mrun_jfile = os.path.join(local_out_dir, row_id_str + "_mrun.json")
#mrun_jfile = local_out_dir+row_id_str+"_mrun.json"
#print "INFO: all_json=",all_json
print "INFO: mrun_jfile=", mrun_jfile
if os.path.exists(mrun_jfile):
try:
os.remove(mrun_jfile)
except OSError, e:
print("ERROR: %s - %s." % (e.mrun_jfile, 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 pca(row_id_str, ds_id, hdfs_feat_dir, local_out_dir, sp_master,
spark_rdd_compress, spark_driver_maxResultSize, sp_exe_memory,
sp_core_max, zipout_dir, zipcode_dir, zip_file_name, mongo_tuples,
fromweb, pca_jstr, jobname, model_data_folder):
# create zip files for Spark workers ================= ================
zip_file_path = ml_build_zip_file(zipout_dir,
zipcode_dir,
zip_file_name,
prefix='zip_feature_util')
print "INFO: zip_file_path=", zip_file_path
# init 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, 'pca_sklearn:'+str(args.row_id))
'''
pca_param = json.loads(pca_jstr)
if "k" in pca_param:
k = pca_param["k"]
else:
k = None
if "threshold" in pca_param:
threshold = pca_param["threshold"]
else:
threshold = None
if "lib" in pca_param:
lib = pca_param["lib"]
else:
lib = 'sklearn'
#if "recreate" in pca_param:
# recreate=pca_param["recreate"]
#else:
# recreate='0'
ret = -1
# start here =================================================================== ===============
t0 = time()
# source libsvm filename
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
# format (LabeledPoint,hash) from str2LabeledPoint_hash()
samples_rdd, feature_count = zip_feature_util.get_sample_rdd(
sc, libsvm_data_file, feature_count, '')
# convert labeled point to tuple (label,features)
#labels_and_features_rdd = samples_rdd.map(lambda p: (p.label, p.features))
all_data = samples_rdd.collect()
sample_count = len(all_data)
# 2-D array
features_list = [x.features.toArray() for x, _ in all_data]
# label array
labels_list_all = [x.label for x, _ in all_data]
# hash array
hash_list_all = [x for _, x in all_data]
# convert to int to speed up
features_array = np.array(features_list, dtype=np.int32)
true_label_array = np.array(labels_list_all, dtype=np.int8)
hash_list_all = np.array(hash_list_all)
# PCA here, TBD for removal
print "INFO: features_array.shape=", features_array.shape
print "INFO: true_label_array.shape=", true_label_array.shape
print "INFO: hash_list_all.shape=", hash_list_all.shape
print "INFO: Doing PCA... threshold=", threshold, ",k=", k
(features_array_reduced, k,
pca) = sklearn_PCA_transform(features_array, threshold, k)
#print "features_array_reduced t=", type(features_array_reduced), ",features_array_reduced=", features_array_reduced
print "INFO: features_array_reduced.shape", features_array_reduced.shape, ",k=", k
pca_fname3 = ""
if not threshold is None:
pca_fname = os.path.join(
model_data_folder, row_id_str + '_pca_' + str(threshold) + '.pkl')
pca_fname3 = os.path.join(
model_data_folder, row_id_str + '_pca3_' + str(threshold) + '.pkl')
else:
pca_fname = os.path.join(model_data_folder,
row_id_str + '_pca_' + str(k) + '.pkl')
#pc_3=features_array_reduced[:,0:3]
#print "type=",type(pc_3),"shape=",pc_3.shape,"pca_fname3=",pca_fname3
#np.save(pca_fname3,pc_3)
if not os.path.exists(model_data_folder):
os.makedirs(model_data_folder)
if os.path.exists(pca_fname): # remove file if exists
try:
for fl in glob.glob(pca_fname + "*"):
os.remove(fl)
except OSError, e:
print("Error: %s - %s." % (e.pca_fname, e.strerror))
def train(row_id_str, ds_id, hdfs_feat_dir, local_out_dir, ml_opts_jstr,
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, src_filename, jobname,
model_data_folder):
# create zip files for Spark workers ================= ================
zip_file_path = ml_build_zip_file(zipout_dir,
zipcode_dir,
zip_file_name,
prefix='zip_feature_util')
print "INFO: zip_file_path=", zip_file_path
#data_folder = hdfs_feat_dir + "/"
#local_out_dir = local_out_dir + "/"
if not os.path.exists(local_out_dir):
os.makedirs(local_out_dir)
# ML model filename ====
model_fname = os.path.join(model_data_folder, row_id_str + '.pkl')
print "INFO: model_data_folder=", model_data_folder
# create out folders and clean up old model files ====
ml_util.ml_prepare_output_dirs(row_id_str, local_out_dir,
model_data_folder, model_fname)
# init 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])
# start here =================================================================== ===============
t0 = time()
### load libsvm file: may or may not be PCA-ed ###
libsvm_data_file = os.path.join(hdfs_feat_dir, src_filename)
print "INFO: libsvm_data_file=", libsvm_data_file
# feature count is a variable if PCA
feature_count = 0
# samples_rdd may be from PCAed data
# load sample RDD from text file
# format (LabeledPoint,hash) from str2LabeledPoint_hash()
samples_rdd, feature_count = zip_feature_util.get_sample_rdd(
sc, libsvm_data_file, feature_count, '')
# collect all data to local for processing ===============
all_data = samples_rdd.collect()
total_sample_count = len(all_data)
# 2-D array, may be PCAed
features_list = [x.features.toArray() for x, _ in all_data]
# label array
labels_list_all = [x.label for x, _ in all_data]
# hash array
hash_list_all = [x for _, x in all_data]
# convert to np array
features_array_reduced = np.array(features_list)
hash_list_all = np.array(hash_list_all)
labels_list_all = np.array(labels_list_all)
true_label_array = np.array(labels_list_all, dtype=np.int8)
print "INFO: total_sample_count=", total_sample_count
print "INFO: features_array_reduced.shape=", features_array_reduced.shape
print "INFO: labels_list_all.shape=", labels_list_all.shape
print "INFO: true_label_array.shape=", true_label_array.shape
t1 = time()
print 'INFO: data generating time: %f' % (t1 - t0)
###############################################
########## build learning model ###############
###############################################
### parse parameters and generate the model ###
(model, alg, n_clusters) = parse_para_and_get_model(ml_opts_jstr)
if model is None:
return
labels_kmeans = None
#### fit the model to training dataset ####
try:
model.fit(features_array_reduced)
labels_kmeans = model.labels_ #'numpy.ndarray'
except:
print "ERROR: Error in model.fit(): ", "model=", model, ", sys.exc_info:", sys.exc_info(
)[0]
return
#### save clf for future use ####
#joblib.dump(model, model_data_folder + row_id_str+'.pkl')
joblib.dump(model, model_fname)
#print "**model:intercept***"
#print clf.intercept_
print "INFO: model type=", type(model), " model=", model
###################################################
### 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']
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')
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_list=", labels_list
#Adjusted Mutual Information between two clusterings
amis = adjusted_mutual_info_score(labels_list_all, labels_kmeans)
print "INFO: Adjusted_mutual_info_score=", amis
#Similarity measure between two clusterings
ars = adjusted_rand_score(labels_list_all, labels_kmeans)
print "INFO: Adjusted_rand_score=", ars
###################################################
#######plot histogram ####
###################################################
plot_col_num = int(math.ceil(math.sqrt(n_clusters)))
figsize = (4 * plot_col_num,
3 * int(math.ceil(n_clusters * 1.0 / plot_col_num)))
print "INFO: labels_list_all.shape=", labels_list_all.shape, "labels_kmeans.shape=", labels_kmeans.shape
print "INFO: labels_list_all t=", type(
labels_list_all), "labels_kmeans t=", type(labels_kmeans)
print "INFO: n_clusters=", n_clusters, ",label_dic=", label_dic
print "INFO: plot_col_num=", plot_col_num, ",figsize=", figsize, ",local_out_dir=", local_out_dir
# kmeans histogram
_, p_true = ml_plot_kmeans_histogram_subfigures(labels_list_all,
labels_kmeans,
n_clusters,
names=label_dic,
plot_col_num=plot_col_num,
figsize=figsize,
folder=local_out_dir,
rid=row_id_str)
# normalized kmeans histogram
_, p_true_norm = ml_plot_kmeans_histogram_subfigures(
labels_list_all,
labels_kmeans,
n_clusters,
names=label_dic,
plot_col_num=plot_col_num,
figsize=figsize,
normalize=True,
folder=local_out_dir,
rid=row_id_str)
####plot "reverse" histogram with labels ####
#num_bars = len(np.unique(labels_list_all))
num_bars = max(labels_list_all) + 1
figsize = (4 * plot_col_num,
3 * int(math.ceil(num_bars * 1.0 / plot_col_num)))
_, p_cluster = ml_plot_kmeans_histogram_subfigures(
labels_kmeans,
labels_list_all,
num_bars,
names=label_dic,
plot_col_num=plot_col_num,
figsize=figsize,
reverse=True,
folder=local_out_dir,
rid=row_id_str)
#### plot dot figures ####
#mtx_label = model.labels_
mtx_center = model.cluster_centers_
# dot plot for Kmeans ===========
filename = os.path.join(local_out_dir, row_id_str + '_cluster.png')
filename_3d = os.path.join(local_out_dir, row_id_str + '_cluster_3d.json')
ml_plot_kmeans_dot_graph_save_file(features_array_reduced,
labels_kmeans,
mtx_center,
n_clusters,
figsize=(10, 7),
filename=filename,
title='KMeans',
filename_3d=filename_3d)
#print "features_array_reduced s=",features_array_reduced.shape
# dot plot for True Labels ===========
filename = os.path.join(local_out_dir, row_id_str + '_cluster_tl.png')
filename_3d = os.path.join(local_out_dir,
row_id_str + '_cluster_3d_tl.json')
ml_plot_kmeans_dot_graph_save_file(features_array_reduced,
true_label_array,
mtx_center,
n_clusters,
figsize=(10, 7),
filename=filename,
title='True Labels',
filename_3d=filename_3d)
dataset_info = {
"training_fraction": 1,
"class_count": n_clusters,
"dataset_count": total_sample_count
}
# only update db for web request ===========
if fromweb == "1":
#print "database update"
str_sql="UPDATE atdml_document set accuracy = '" \
+"', status = 'learned', processed_date ='"+str(datetime.datetime.now()) \
+"', total_feature_numb='"+str(feature_count) \
+"', 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) + "%"
t1 = time()
print 'INFO: running time: %f' % (t1 - t0)
#print 'Finished!'
return 0