def ml_input(request): # obviously, it has to be added id record and everything concerning db
if (request.method == 'POST'):
template = "machine_learning/results.html"
mat =[[0.12347442045527879, 0.8094406486883253],
[0.13438271020294834, 0.9195616568032954],
[0.7340808740690876, 0.501292876257899],
[0.15205183424532076, 0.7723196374025724],
[0.15305657903122016, 0.02967990232224793],
[0.751312493253797, 0.15057926746395178],
[0.3325655818985571, 0.8545431696554671],
[0.388049400727121, 0.6359039900354648],
[0.7656376483351357, 0.011118993319648052],
[0.3030715521728802, 0.3478716425630006]]
#print "culoculoculoculo" # GET THE POST, ELABORATE AND GO TO THE DB OR THE PLOT
#print request.POST
mydict = dict(request.POST.iterlists())
# for key in request.POST.iterkeys(): # "for key in request.GET" works too.
# # Add filtering logic here.
#
# print key, request.POST.getlist(key)
#print mydict
#print '-' * 60
#localdir = 'PhysioWat/preproc/scripts/processing_scripts/output/'
#input_data = pd.DataFrame.from_csv(path=localdir + 'feat_claire_labeled.csv') # , index_col=None, sep=',')
exprecid = mydict['choose_id']
#exprecid = [18]
input_data = pddbload.load_file_pd_db(exprecid[0])
num_feat = -1 # set to -1 because of
percentage = mydict['test_percentage'][0]
percentage = float(percentage) / 100.0
list_of_feat = list(input_data.columns)
num_iteration = mydict['number_of_iterations'][0]
ft.iterations = int(num_iteration)
algorithm = mydict['alg_choice'][0]
flag = True
if 'viewf' in mydict:
if 'norm' in mydict['viewf']:
input_data = ft.normalize(input_data)
#print input_data
train_data, test_data = ft.split(input_data, percentage)
flag = False
if 'sel' in mydict['viewf']:
# print "i have selected the first stuff!"
if 'k_selected' in mydict['FeatChoose']:
num_feat = mydict['feat_num']
if (num_feat <= 0):
return render(request, "machine_learning/form_error.html")
train_data, test_data, list_of_feat = ft.getfeatnumber(train_data, test_data, k) #RETURNS 2 SUBSET DF GIVEN IN INPUT THE TRAIN DATA, THE TEST DATA, AND THE NUMBER OF FEATS
if ('k_auto' in mydict['FeatChoose']):
train_data, test_data, best_feat_n_mat, list_of_feat = ft.bestfeatn(train_data, test_data)
if(flag == True):
train_data, test_data = ft.split(input_data, percentage)
print "dopo il case del viewf"
if (algorithm == 'ALL') and ('auto' not in mydict['parameter_choiche']):
return render(request, "machine_learning/form_error.html")
if 'def' in mydict['parameter_choiche']:
clf, score, error = ft.quick_crossvalidate(train_data, alg=algorithm)
if 'pers' in mydict['parameter_choiche']:
if (algorithm == 'KNN'):
k_neighbour = mydict['k_neighbour'][0]
print(k_neighbour)
clf, score, error = ft.pers_crossvalidation1(train_data, algorithm, k_neighbour)
if (algorithm == 'DCT'):
max_features = mydict['max_features'][0]
#print(type(max_features)) #IT'S A STRING!!!!
clf, score, error = ft.pers_crossvalidation1(train_data, algorithm, max_features)
if (algorithm == 'SVM'):
kernel = mydict['kernel']
C = mydict['C']
clf, score, error = ft.pers_crossvalidation2(train_data, algorithm, kernel, C)
if (algorithm == 'RFC'):
max_features = mydict['max_features']
number_estimators = mydict['number_estimators']
clf, score, error = ft.pers_crossvalidation2(train_data, algorithm, max_features, number_estimators)
if (algorithm == 'ADA'):
number_estimators = mydict['number_estimators']
learning_rate = mydict['learning_rate']
clf, score, error = ft.pers_crossvalidation2(train_data, algorithm, number_estimators, learning_rate)
if (algorithm == 'LDA'):
solver = mydict['solver']
clf, score, error = ft.pers_crossvalidation1(train_data, algorithm, solver)
if 'auto' in mydict['parameter_choiche']:
metrics = mydict['maximize'][0]
#print metrics
clf, result_mat = ft.bestAlg(train_data, metrics)
dic_metric, conf_mat = ft.test_learning(clf, test_data)
print dic_metric, conf_mat
h = heatmap()
data = h.get_data(conf_mat)
context = {'datac': json.dumps(data)}
# TODO check the 'matrix for'
return render(request, template, context)
#CALL OTHER FUNCTIONS / GET OTHER DATAS/
#final_ml_page(request, result_dict=dic_metric, conf_mat=conf_mat)
# ---------------------------------------------------------------------------------------
# TODO HERE STARTS THE FINAL PART OF THE MACHINE LEARNING, WHICH IS NO MORE PROCESSING BUT JUST RENDERING THE FORM (and getting the json)
# -------------------------------------------------------------------------------
else:
template = "machine_learning/ml_input.html"
form_viewf = viewFeatures()
form_f_par = FeatPar()
form_test_par = TestParam()
form_alg_choose = AlgChoose()
form_alg_param = AlgParam()
# form_knn = KnnParam()
form_svm = SvmParam()
form_knear = KNearParam()
form_dectree = DecTreeParam()
form_rndfor = RndForParam()
form_adaboost = AdaBoostParam()
form_lda = LatDirAssParam()
form_autoParam = autoFitParam()
form_list = [form_svm, form_knear, form_dectree, form_rndfor, form_adaboost, form_lda]
id_list=getprocessedrecordid()
print id_list
id_list=[(i, str(i)) for i in id_list ]
print id_list
form_list_id = id_choose(choices=id_list)
print form_list_id
#print(form_viewf)
#print form_f_par
context = {'viewf': form_viewf,
'FPar': FeatPar,
'TPar': form_test_par,
'AlgChoose': form_alg_choose,
'AlgParamChoose': form_alg_param,
'forms': {'form_SVM': form_svm,
'form_KNN': form_knear,
'form_DCT': form_dectree,
'form_RFC': form_rndfor,
'form_ADA': form_adaboost,
'form_LDA': form_lda, },
'autoParam': form_autoParam,
'formListId':form_list_id
}
print '-' * 60
#print context['forms']
#print '-' * 60
return render(request, template, context)
def ml_input(
request, id_record
): # obviously, it has to be added id record and everything concerning db
if (request.method == 'POST'):
# print "culoculoculoculo" # GET THE POST, ELABORATE AND GO TO THE DB OR THE PLOT
# print request.POST
mydict = dict(request.POST.iterlists())
# for key in request.POST.iterkeys(): # "for key in request.GET" works too.
# # Add filtering logic here.
#
# print key, request.POST.getlist(key)
print mydict
# print '-' * 60
# localdir = '/home/emanuele/wv_physio/PhysioWat/PhysioWat/preproc/scripts/processing_scripts/output/'
# input_data = pd.DataFrame.from_csv(path=localdir + 'feat_claire_labeled.csv') # , index_col=None, sep=','
exprecid = mydict['choose_id']
print exprecid
input_data = pddbload.load_file_pd_db(int(exprecid[0]))
num_feat = -1 # set to -1 because of
print 'Ciao'
print input_data.shape
print 'hola'
percentage = mydict['test_percentage'][0]
percentage = float(percentage) / 100.0
num_iteration = mydict['number_of_iterations'][0]
ft.iterations = int(num_iteration)
algorithm = mydict['alg_choice'][0]
flag = True
flag_has_selected_auto_feat = False
list_of_feat = None
best_feat_n_mat = []
num_feat = -1
auto_alg_result_mat = None
best_feat_json = None
if 'viewf' in mydict:
print "hellp"
if 'norm' in mydict['viewf']:
input_data = ft.normalize(input_data)
print input_data.shape
train_data, test_data = ft.split(input_data, percentage)
print train_data.shape, test_data.shape
flag = False
if 'sel' in mydict['viewf']:
# print "i have selected the first stuff!"
if 'k_selected' in mydict['FeatChoose']:
num_feat = int(mydict['feat_num'][0])
if (num_feat <= 0):
return render(request,
"machine_learning/form_error.html")
train_data, test_data, list_of_feat = ft.getfeatnumber(
train_data, test_data, num_feat
) # RETURNS 2 SUBSET DF GIVEN IN INPUT THE TRAIN DATA, THE TEST DATA, AND THE NUMBER OF FEATS
if ('k_auto' in mydict['FeatChoose']):
print "hi"
train_data, test_data, best_feat_n_mat, list_of_feat = ft.bestfeatn(
train_data, test_data)
flag_has_selected_auto_feat = True
list_of_feat = list_of_feat[4]
if (flag == True):
train_data, test_data = ft.split(input_data, percentage)
flag = False
if (algorithm == 'ALL') and ('auto' not in mydict['parameter_choice']):
return render(request, "machine_learning/form_error.html")
if 'def' in mydict['parameter_choice']:
clf, score, error = ft.quick_crossvalidate(train_data,
alg=algorithm)
if 'pers' in mydict['parameter_choice']:
if (algorithm == 'KNN'):
k_neighbour = mydict['k_neighbour'][0]
print(k_neighbour)
clf, score, error = ft.pers_crossvalidation1(
train_data, algorithm, k_neighbour)
if (algorithm == 'DCT'):
max_features = mydict['max_features'][0]
clf, score, error = ft.pers_crossvalidation1(
train_data, algorithm, max_features)
if (algorithm == 'SVM'):
kernel = mydict['kernel']
C = mydict['C']
clf, score, error = ft.pers_crossvalidation2(
train_data, algorithm, kernel, C)
if (algorithm == 'RFC'):
max_features = mydict['max_features']
number_estimators = mydict['number_estimators']
clf, score, error = ft.pers_crossvalidation2(
train_data, algorithm, max_features, number_estimators)
if (algorithm == 'ADA'):
number_estimators = mydict['number_estimators']
learning_rate = mydict['learning_rate']
clf, score, error = ft.pers_crossvalidation2(
train_data, algorithm, number_estimators, learning_rate)
if (algorithm == 'LDA'):
solver = mydict['solver']
clf, score, error = ft.pers_crossvalidation1(
train_data, algorithm, solver)
if 'auto' in mydict['parameter_choice']:
metrics = mydict['maximize'][0]
# print metrics
if (algorithm == 'ALL'):
clf, auto_alg_result_mat = ft.bestAlg(train_data, metrics)
else:
clf, loc_metric, loc_error, loc_mat = ft.bestfit(
train_data, algorithm, metrics)
dic_metric, conf_mat = ft.test_learning(clf, test_data)
# print dic_metric, conf_mat
# print best_feat_n_mat
# print "BEST FEAT NUMBER: PRECISION IN FUNFCION OF THE UMBER", type(best_feat_n_mat), best_feat_n_mat
# print "CONFUSION MATRIX" ,conf_mat
# print "DICTIONARY OF THE METRICS", dic_metric
best_feat_json = best_feat_n_mat # see if it's needed
# categories = #PICK ALGORITHM CATEGORIES
if best_feat_n_mat != []:
s = linegraph3()
best_feat_json = s.get_data(
data_tmp=best_feat_n_mat.tolist()) # xcategories = categories)
best_feat_json = json.dumps(best_feat_json)
print "LA MATRICE DELLE FEATURES, CHE SUL TEMPLATE FUNZIONA ", best_feat_json
# PART OF THE BEST ALGORITHM
if auto_alg_result_mat != None:
s = linegraph3()
auto_alg_result_mat = list(np.array(auto_alg_result_mat[:, 1:]))
algorithm_categories = [
'KNN', 'SVM', 'DCT', 'RND', 'ADA', 'QDA', 'LDA'
] # TODO PEDOT FUNCTION!!!!
auto_alg_result_mat = s.get_data(
data_tmp=auto_alg_result_mat,
xcategories=algorithm_categories,
tipo="errorbar",
title="precision of the various algorithms")
auto_alg_result_mat = json.dumps(auto_alg_result_mat)
# PART OF THE METRICS VALUE!!!
dict_sigla_parola = {
'ACC': 'accuracy %',
'F1M': 'F-Test macro',
'F1m': 'F-Test micro',
'F1W': 'F-Test weighted',
'WHM': 'Weighted Harmonic Mean of precision and recall',
'PRM': 'Precision Score Macro',
'PRm': 'Precision Score Micro',
'PRW': 'Precision Score Weighted',
'REM': 'Recall Score Macro',
'REm': 'Recall Score Micro',
'REW': 'Recall Score Weighted'
}
print "dizionario -------", dic_metric
s = linegraph3()
xcate = []
for i in dic_metric.keys():
xcate.append(dict_sigla_parola[i])
metrics = s.get_data(data_tmp=dic_metric.values(),
title="metrics accuracy",
tipo="scatter",
xcategories=xcate)
metrics = json.dumps(metrics)
print "metriche --->", metrics # todo delete line
# PART OF THE CONFUSION MATRIX
h = heatmap()
# conf_mat = conf_mat.tolist() #!!!important
conf_data = h.get_data(conf_mat)
conf_data = json.dumps(conf_data)
context = {
'auto_alg_result_mat':
auto_alg_result_mat, # boxplot with the algorithms cosres
'conf_mat': conf_data,
'metrics': metrics,
'list_of_feat': list_of_feat,
# second part, with the list of features and the function score vs nfeat
'best_feat_scores': best_feat_n_mat,
}
if best_feat_n_mat != []:
best_feat_n_mat[:, 1] *= 100.0
# best_feat_n_mat = best_feat_n_mat.tolist() #LIST: CONTAINS THE PRECISION IN FUNCTIONM OF THE NUMBER OF FEATURES
# TODO check the 'matrix for'
# todo best n feat NAMES?
if list_of_feat:
list_of_feat = [i.replace('_', ' ') for i in list_of_feat]
template = "machine_learning/results.html"
# context = {'conf_mat': conf_data,
# 'dic_result':dic_metric, # essential part, the last one (conf.matrix)
# 'list_of_feat': list_of_feat, #second part, with the list of features and the function score vs nfeat
# 'best_feat_scores': best_feat_n_mat,
# 'dict_sigla_parola': dict_sigla_parola,
# 'best_feat_scores_json': best_feat_json,
#
# 'auto_alg_result_mat':auto_alg_result_mat, #boxplot with the algorithms cosres
# }
return render(request, template, context)
# CALL OTHER FUNCTIONS / GET OTHER DATAS/
# final_ml_page(request, result_dict=dic_metric, conf_mat=conf_mat)
# ---------------------------------------------------------------------------------------
# TODO HERE STARTS THE FINAL PART OF THE MACHINE LEARNING, WHICH IS NO MORE PROCESSING BUT JUST RENDERING THE FORM (and getting the json)
# -------------------------------------------------------------------------------
else:
template = "machine_learning/ml_input.html"
form_viewf = viewFeatures()
form_f_par = FeatPar()
form_test_par = TestParam()
form_alg_choose = AlgChoose()
form_alg_param = AlgParam()
# form_knn = KnnParam()
form_svm = SvmParam()
form_knear = KNearParam()
form_dectree = DecTreeParam()
form_rndfor = RndForParam()
form_adaboost = AdaBoostParam()
form_lda = LatDirAssParam()
form_autoParam = autoFitParam()
form_list = [
form_svm, form_knear, form_dectree, form_rndfor, form_adaboost,
form_lda
]
id_list = getprocessedrecordid(id_record)
# print id_list
# id_list=[(i, str(i)) for i in id_list ]
# print id_list
form_list_id = id_choose(choices=id_list)
print '###############'
print form_list_id
print '###############'
# print(form_viewf)
# print form_f_par
context = {
'viewf': form_viewf,
'FPar': FeatPar,
'TPar': form_test_par,
'AlgChoose': form_alg_choose,
'AlgParamChoose': form_alg_param,
'forms': {
'form_SVM': form_svm,
'form_KNN': form_knear,
'form_DCT': form_dectree,
'form_RFC': form_rndfor,
'form_ADA': form_adaboost,
'form_LDA': form_lda,
},
'autoParam': form_autoParam,
'formListId': form_list_id
}
print '-' * 60
# print context['forms']
# print '-' * 60
return render(request, template, context)
def ml_input(request, id_record): # obviously, it has to be added id record and everything concerning db
if (request.method == 'POST'):
# print "culoculoculoculo" # GET THE POST, ELABORATE AND GO TO THE DB OR THE PLOT
# print request.POST
mydict = dict(request.POST.iterlists())
# for key in request.POST.iterkeys(): # "for key in request.GET" works too.
# # Add filtering logic here.
#
# print key, request.POST.getlist(key)
print mydict
# print '-' * 60
# localdir = '/home/emanuele/wv_physio/PhysioWat/PhysioWat/preproc/scripts/processing_scripts/output/'
# input_data = pd.DataFrame.from_csv(path=localdir + 'feat_claire_labeled.csv') # , index_col=None, sep=','
exprecid = mydict['choose_id']
print exprecid
input_data = pddbload.load_file_pd_db(int(exprecid[0]))
num_feat = -1 # set to -1 because of
print 'Ciao'
print input_data.shape
print 'hola'
percentage = mydict['test_percentage'][0]
percentage = float(percentage) / 100.0
num_iteration = mydict['number_of_iterations'][0]
ft.iterations = int(num_iteration)
algorithm = mydict['alg_choice'][0]
flag = True
flag_has_selected_auto_feat = False
list_of_feat = None
best_feat_n_mat = []
num_feat = -1
auto_alg_result_mat = None
best_feat_json = None
if 'viewf' in mydict:
print "hellp"
if 'norm' in mydict['viewf']:
input_data = ft.normalize(input_data)
print input_data.shape
train_data, test_data = ft.split(input_data, percentage)
print train_data.shape, test_data.shape
flag = False
if 'sel' in mydict['viewf']:
# print "i have selected the first stuff!"
if 'k_selected' in mydict['FeatChoose']:
num_feat = int(mydict['feat_num'][0])
if (num_feat <= 0):
return render(request, "machine_learning/form_error.html")
train_data, test_data, list_of_feat = ft.getfeatnumber(train_data, test_data,
num_feat) # RETURNS 2 SUBSET DF GIVEN IN INPUT THE TRAIN DATA, THE TEST DATA, AND THE NUMBER OF FEATS
if ('k_auto' in mydict['FeatChoose']):
print "hi"
train_data, test_data, best_feat_n_mat, list_of_feat = ft.bestfeatn(train_data, test_data)
flag_has_selected_auto_feat = True
list_of_feat = list_of_feat[4]
if (flag == True):
train_data, test_data = ft.split(input_data, percentage)
flag = False
if (algorithm == 'ALL') and ('auto' not in mydict['parameter_choice']):
return render(request, "machine_learning/form_error.html")
if 'def' in mydict['parameter_choice']:
clf, score, error = ft.quick_crossvalidate(train_data, alg=algorithm)
if 'pers' in mydict['parameter_choice']:
if (algorithm == 'KNN'):
k_neighbour = mydict['k_neighbour'][0]
print(k_neighbour)
clf, score, error = ft.pers_crossvalidation1(train_data, algorithm, k_neighbour)
if (algorithm == 'DCT'):
max_features = mydict['max_features'][0]
clf, score, error = ft.pers_crossvalidation1(train_data, algorithm, max_features)
if (algorithm == 'SVM'):
kernel = mydict['kernel']
C = mydict['C']
clf, score, error = ft.pers_crossvalidation2(train_data, algorithm, kernel, C)
if (algorithm == 'RFC'):
max_features = mydict['max_features']
number_estimators = mydict['number_estimators']
clf, score, error = ft.pers_crossvalidation2(train_data, algorithm, max_features, number_estimators)
if (algorithm == 'ADA'):
number_estimators = mydict['number_estimators']
learning_rate = mydict['learning_rate']
clf, score, error = ft.pers_crossvalidation2(train_data, algorithm, number_estimators, learning_rate)
if (algorithm == 'LDA'):
solver = mydict['solver']
clf, score, error = ft.pers_crossvalidation1(train_data, algorithm, solver)
if 'auto' in mydict['parameter_choice']:
metrics = mydict['maximize'][0]
# print metrics
if (algorithm == 'ALL'):
clf, auto_alg_result_mat = ft.bestAlg(train_data, metrics)
else:
clf, loc_metric, loc_error, loc_mat = ft.bestfit(train_data, algorithm, metrics)
dic_metric, conf_mat = ft.test_learning(clf, test_data)
# print dic_metric, conf_mat
# print best_feat_n_mat
# print "BEST FEAT NUMBER: PRECISION IN FUNFCION OF THE UMBER", type(best_feat_n_mat), best_feat_n_mat
# print "CONFUSION MATRIX" ,conf_mat
# print "DICTIONARY OF THE METRICS", dic_metric
best_feat_json = best_feat_n_mat # see if it's needed
# categories = #PICK ALGORITHM CATEGORIES
if best_feat_n_mat != []:
s = linegraph3()
best_feat_json = s.get_data(data_tmp=best_feat_n_mat.tolist()) # xcategories = categories)
best_feat_json = json.dumps(best_feat_json)
print "LA MATRICE DELLE FEATURES, CHE SUL TEMPLATE FUNZIONA ", best_feat_json
# PART OF THE BEST ALGORITHM
if auto_alg_result_mat != None:
s = linegraph3()
auto_alg_result_mat = list(np.array(auto_alg_result_mat[:, 1:]))
algorithm_categories = ['KNN', 'SVM', 'DCT', 'RND', 'ADA', 'QDA', 'LDA'] # TODO PEDOT FUNCTION!!!!
auto_alg_result_mat = s.get_data(data_tmp=auto_alg_result_mat, xcategories=algorithm_categories,
tipo="errorbar", title="precision of the various algorithms")
auto_alg_result_mat = json.dumps(auto_alg_result_mat)
# PART OF THE METRICS VALUE!!!
dict_sigla_parola = {'ACC': 'accuracy %',
'F1M': 'F-Test macro',
'F1m': 'F-Test micro',
'F1W': 'F-Test weighted',
'WHM': 'Weighted Harmonic Mean of precision and recall',
'PRM': 'Precision Score Macro',
'PRm': 'Precision Score Micro',
'PRW': 'Precision Score Weighted',
'REM': 'Recall Score Macro',
'REm': 'Recall Score Micro',
'REW': 'Recall Score Weighted'}
print "dizionario -------", dic_metric
s = linegraph3()
xcate = []
for i in dic_metric.keys():
xcate.append(dict_sigla_parola[i])
metrics = s.get_data(data_tmp=dic_metric.values(), title="metrics accuracy", tipo="scatter", xcategories=xcate)
metrics = json.dumps(metrics)
print "metriche --->", metrics # todo delete line
# PART OF THE CONFUSION MATRIX
h = heatmap()
# conf_mat = conf_mat.tolist() #!!!important
conf_data = h.get_data(conf_mat)
conf_data = json.dumps(conf_data)
context = {'auto_alg_result_mat': auto_alg_result_mat, # boxplot with the algorithms cosres
'conf_mat': conf_data,
'metrics': metrics,
'list_of_feat': list_of_feat,
# second part, with the list of features and the function score vs nfeat
'best_feat_scores': best_feat_n_mat,
}
if best_feat_n_mat != []:
best_feat_n_mat[:, 1] *= 100.0
# best_feat_n_mat = best_feat_n_mat.tolist() #LIST: CONTAINS THE PRECISION IN FUNCTIONM OF THE NUMBER OF FEATURES
# TODO check the 'matrix for'
# todo best n feat NAMES?
if list_of_feat:
list_of_feat = [i.replace('_', ' ') for i in list_of_feat]
template = "machine_learning/results.html"
# context = {'conf_mat': conf_data,
# 'dic_result':dic_metric, # essential part, the last one (conf.matrix)
# 'list_of_feat': list_of_feat, #second part, with the list of features and the function score vs nfeat
# 'best_feat_scores': best_feat_n_mat,
# 'dict_sigla_parola': dict_sigla_parola,
# 'best_feat_scores_json': best_feat_json,
#
# 'auto_alg_result_mat':auto_alg_result_mat, #boxplot with the algorithms cosres
# }
return render(request, template, context)
# CALL OTHER FUNCTIONS / GET OTHER DATAS/
# final_ml_page(request, result_dict=dic_metric, conf_mat=conf_mat)
# ---------------------------------------------------------------------------------------
# TODO HERE STARTS THE FINAL PART OF THE MACHINE LEARNING, WHICH IS NO MORE PROCESSING BUT JUST RENDERING THE FORM (and getting the json)
# -------------------------------------------------------------------------------
else:
template = "machine_learning/ml_input.html"
form_viewf = viewFeatures()
form_f_par = FeatPar()
form_test_par = TestParam()
form_alg_choose = AlgChoose()
form_alg_param = AlgParam()
# form_knn = KnnParam()
form_svm = SvmParam()
form_knear = KNearParam()
form_dectree = DecTreeParam()
form_rndfor = RndForParam()
form_adaboost = AdaBoostParam()
form_lda = LatDirAssParam()
form_autoParam = autoFitParam()
form_list = [form_svm, form_knear, form_dectree, form_rndfor, form_adaboost, form_lda]
id_list = getprocessedrecordid(id_record)
# print id_list
# id_list=[(i, str(i)) for i in id_list ]
# print id_list
form_list_id = id_choose(choices=id_list)
print '###############'
print form_list_id
print '###############'
# print(form_viewf)
# print form_f_par
context = {'viewf': form_viewf,
'FPar': FeatPar,
'TPar': form_test_par,
'AlgChoose': form_alg_choose,
'AlgParamChoose': form_alg_param,
'forms': {'form_SVM': form_svm,
'form_KNN': form_knear,
'form_DCT': form_dectree,
'form_RFC': form_rndfor,
'form_ADA': form_adaboost,
'form_LDA': form_lda, },
'autoParam': form_autoParam,
'formListId': form_list_id
}
print '-' * 60
# print context['forms']
# print '-' * 60
return render(request, template, context)