'''
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|  \/  |         | |    | |  / _ \ | ___ \_   _|
| .  . | ___   __| | ___| | / /_\ \| |_/ / | |  
| |\/| |/ _ \ / _` |/ _ \ | |  _  ||  __/  | |  
| |  | | (_) | (_| |  __/ | | | | || |    _| |_ 
\_|  |_/\___/ \__,_|\___|_| \_| |_/\_|    \___/ 

This is Allie's modeling API to help build classification or regression models.

All you need to do is run the model.py script and you will be guided through the 
modeling process.

Usage: python3 model.py

Alternative CLI Usage: python3 model.py audio 2 c gender males females
- audio = audio file type
- 2 = 2 classes
- c = classification (r for regression)
- gender = common name of model
- male = first class
- female = second class [via N number of classes]

For addditional documentation, check out 
https://github.com/jim-schwoebel/allie/tree/master/training                                                                          
'''
###############################################################
##                  IMPORT STATEMENTS                        ##
###############################################################
import os, sys, pickle, json, random, shutil, time, itertools, uuid, datetime, uuid, psutil, json, platform
from pyfiglet import Figlet
f=Figlet(font='doh')
print(f.renderText('Allie'))
f=Figlet(font='doom')
import pandas as pd
import matplotlib.pyplot as plt

###############################################################
##               CREATE HELPER FUNCTIONS                     ##
###############################################################

def most_common(lst):
	'''
	get most common item in a list
	'''
	return max(set(lst), key=lst.count)

def prev_dir(directory):
	g=directory.split('/')
	dir_=''
	for i in range(len(g)):
		if i != len(g)-1:
			if i==0:
				dir_=dir_+g[i]
			else:
				dir_=dir_+'/'+g[i]
	# print(dir_)
	return dir_

def get_folders(listdir):
	folders=list()
	for i in range(len(listdir)):
		if listdir[i].find('.') < 0:
			folders.append(listdir[i])

	return folders 

def classifyfolder(listdir):
	filetypes=list()
	for i in range(len(listdir)):
		if listdir[i].endswith(('.mp3', '.wav')):
			filetypes.append('audio')
		elif listdir[i].endswith(('.png', '.jpg')):
			filetypes.append('image')
		elif listdir[i].endswith(('.txt')):
			filetypes.append('text')
		elif listdir[i].endswith(('.mp4', '.avi')):
			filetypes.append('video')
		elif listdir[i].endswith(('.csv')):
			filetypes.append('csv')

	counts={'audio': filetypes.count('audio'),
			'image': filetypes.count('image'),
			'text': filetypes.count('text'),
			'video': filetypes.count('video'),
			'csv': filetypes.count('csv')}

	# get back the type of folder (main file type)
	countlist=list(counts)
	countvalues=list(counts.values())
	maxvalue=max(countvalues)
	maxind=countvalues.index(maxvalue)
	return countlist[maxind]

def pull_element(mylist, element):
	pull_=list()
	for i in range(len(mylist)):
		pull_.append(mylist[i][element])
	return pull_

def convert_csv(X_train, y_train, labels, mtype, classes):
	'''
	Take in a array of features and labels and output a 
	pandas DataFrame format for easy .CSV expor and for model training.

	This is important to make sure all machine learning training sessions
	use the same dataset (so they can be benchmarked appropriately).
	'''
	# from pandas merging guide https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html
	feature_list=labels
	data=list()
	for i in tqdm(range(len(X_train)), desc='converting csv...'):
		
		newlist=list()
		for j in range(len(X_train[i])):
			newlist.append([X_train[i][j]])

		temp=pd.DataFrame(dict(zip(feature_list,newlist)), index=[i])
		# print(temp)
		data.append(temp)
		

	data = pd.concat(data)

	if mtype == 'c':
		data['class_']=y_train
	elif mtype == 'r':
		if len(classes) == 1:
			data[classes[0]]=y_train
		else:
			for j in range(len(classes)):
				newy=pull_element(y_train, j)
				data[classes[j]]=newy

	data=pd.DataFrame(data, columns = list(data))
	# print this because in pretty much every case you will write the .CSV file afterwards
	print('writing csv file...')

	return data

def device_info():
	cpu_data={'memory':psutil.virtual_memory(),
			   'cpu percent':psutil.cpu_percent(),
			   'cpu times':psutil.cpu_times(),
			   'cpu count':psutil.cpu_count(),
			   'cpu stats':psutil.cpu_stats(),
			   'cpu swap':psutil.swap_memory(),
			   'partitions':psutil.disk_partitions(),
			   'disk usage':psutil.disk_usage('/'),
			   'disk io counters':psutil.disk_io_counters(),
			   'battery':psutil.sensors_battery(),
			   'boot time':psutil.boot_time(),
			   }
	data={'time':datetime.datetime.now().strftime("%Y-%m-%d %H:%M"),
		  'timezone':time.tzname,
		  'operating system': platform.system(),
		  'os release':platform.release(),
		  'os version':platform.version(),
		  'cpu data':cpu_data,
		  'space left': list(psutil.disk_usage('/'))[2]/1000000000}

	return data

def get_metrics(clf, problemtype, mtype, default_training_script, common_name, X_test, y_test, classes, modelname, settings, model_session, transformer_name, created_csv_files, test_data, model_start_time):
	'''
	get the metrics associated iwth a classification and regression problem
	and output a .JSON file with the training session.
	'''
	metrics_=dict()
	y_true=y_test

	if default_training_script not in ['autogluon', 'autokeras', 'autopytorch', 'alphapy', 'atm', 'keras', 'devol', 'ludwig', 'safe', 'neuraxle']:
		y_pred=clf.predict(X_test)
	elif default_training_script=='alphapy':
		# go to the right folder 
		curdir=os.getcwd()
		print(os.listdir())
		os.chdir(common_name+'_alphapy_session')
		alphapy_dir=os.getcwd()
		os.chdir('input')
		os.rename('test.csv', 'predict.csv')
		os.chdir(alphapy_dir)
		os.system('alphapy --predict')
		os.chdir('output')
		listdir=os.listdir()
		for k in range(len(listdir)):
			if listdir[k].startswith('predictions'):
				csvfile=listdir[k]
		y_pred=pd.read_csv(csvfile)['prediction']
		os.chdir(curdir)
	elif default_training_script == 'autogluon':
		from autogluon import TabularPrediction as task
		test_data=test_data.drop(labels=['class'],axis=1)
		y_pred=clf.predict(test_data)
	elif default_training_script == 'autokeras':
		y_pred=clf.predict(X_test).flatten()
	elif default_training_script == 'autopytorch':
		y_pred=clf.predict(X_test).flatten()
	elif default_training_script == 'atm':
		curdir=os.getcwd()
		os.chdir('atm_temp')
		data = pd.read_csv('test.csv').drop(labels=['class_'], axis=1)
		y_pred = clf.predict(data)
		os.chdir(curdir)
	elif default_training_script == 'ludwig':
		data=pd.read_csv('test.csv').drop(labels=['class_'], axis=1)
		pred=clf.predict(data)['class__predictions']
		y_pred=np.array(list(pred), dtype=np.int64)
	elif default_training_script == 'devol':
		X_test=X_test.reshape(X_test.shape+ (1,)+ (1,))
		y_pred=clf.predict_classes(X_test).flatten()
	elif default_training_script=='keras':
		if mtype == 'c':
		    y_pred=clf.predict_classes(X_test).flatten()
		elif mtype == 'r':
			y_pred=clf.predict(X_test).flatten()
	elif default_training_script=='neuraxle':
		y_pred=clf.transform(X_test)
	elif default_training_script=='safe':
		# have to make into a pandas dataframe
		test_data=pd.read_csv('test.csv').drop(columns=['class_'], axis=1)
		y_pred=clf.predict(test_data)
	
	print(y_pred)
	
	# get classification or regression metrics
	if mtype in ['c', 'classification']:
		# now get all classification metrics
		mtype='classification'
		metrics_['accuracy']=metrics.accuracy_score(y_true, y_pred)
		metrics_['balanced_accuracy']=metrics.balanced_accuracy_score(y_true, y_pred)
		try:
			metrics_['precision']=metrics.precision_score(y_true, y_pred)
		except:
			metrics_['precision']='n/a'
		try:
			metrics_['recall']=metrics.recall_score(y_true, y_pred)
		except:
			metrics_['recall']='n/a'
		try:
			metrics_['f1_score']=metrics.f1_score (y_true, y_pred, pos_label=1)
		except:
			metrics_['f1_score']='n/a'
		try:
			metrics_['f1_micro']=metrics.f1_score(y_true, y_pred, average='micro')
		except:
			metrics_['f1_micro']='n/a'
		try:
			metrics_['f1_macro']=metrics.f1_score(y_true, y_pred, average='macro')
		except:
			metrics_['f1_macro']='n/a'
		try:
			metrics_['roc_auc']=metrics.roc_auc_score(y_true, y_pred)
		except:
			metrics_['roc_auc']='n/a'
		try:
			metrics_['roc_auc_micro']=metrics.roc_auc_score(y_true, y_pred, average='micro')
		except:
			metrics_['roc_auc_micro']='n/a'
		try:
			metrics_['roc_auc_macro']=metrics.roc_auc_score(y_true, y_pred, average='macro')
		except:
			metrics_['roc_auc_micro']='n/a'
	
		metrics_['confusion_matrix']=metrics.confusion_matrix(y_true, y_pred).tolist()
		metrics_['classification_report']=metrics.classification_report(y_true, y_pred, target_names=classes)

		plot_confusion_matrix(np.array(metrics_['confusion_matrix']), classes)
		try:
			# predict_proba only works for or log loss and modified Huber loss.
			# https://stackoverflow.com/questions/47788981/sgdclassifier-with-predict-proba
			try:
				y_probas = clf.predict_proba(X_test)[:, 1]
			except:
				try:
					y_probas = clf.decision_function(X_test)[:, 1]
				except:
					print('error making y_probas')
				
			plot_roc_curve(y_test, [y_probas], [default_training_script])
		except:
			print('error plotting ROC curve')
			print('predict_proba only works for or log loss and modified Huber loss.')

	elif mtype in ['r', 'regression']:
		# now get all regression metrics
		mtype='regression'
		metrics_['mean_absolute_error'] = metrics.mean_absolute_error(y_true, y_pred)
		metrics_['mean_squared_error'] = metrics.mean_squared_error(y_true, y_pred)
		metrics_['median_absolute_error'] = metrics.median_absolute_error(y_true, y_pred)
		metrics_['r2_score'] = metrics.r2_score(y_true, y_pred)

		plot_regressor(clf, classes, X_test, y_test)

	data={'sample type': problemtype,
		  'training time': time.time()-model_start_time,
		  'created date': str(datetime.datetime.now()),
		  'device info': device_info(),
		  'session id': model_session,
		  'classes': classes,
	      'problem type': mtype,
	      'model name': modelname, 
	      'model type': default_training_script,
	      'metrics': metrics_,
	      'settings': settings,
	      'transformer name': transformer_name,
	      'training data': created_csv_files,
	      'sample X_test': X_test[0].tolist(),
	      'sample y_test': y_test[0].tolist()}

	if modelname.endswith('.pickle'):
		jsonfilename=modelname[0:-7]+'.json'
	elif modelname.endswith('.h5'):
		jsonfilename=modelname[0:-3]+'.json'
	else:
		jsonfilename=modelname+'.json'

	jsonfile=open(jsonfilename,'w')
	json.dump(data,jsonfile)
	jsonfile.close()

	# also output requirements.txt for reproducibilty purposes
	curdir=os.getcwd()
	basedir=prev_dir(curdir)
	os.chdir(basedir)
	os.system('pip3 freeze -> requirements.txt')

	# FUTURE - add in optional copy of cleaning, augmentation, and feature libraries contextually
	# try:
	# 	shutil.copytree(prev_dir(prev_dir(basedir))+'/features', basedir+'/features')
	# except:
	# 	print('error copying features')
	# try:
	# 	shutil.copytree(prev_dir(prev_dir(basedir))+'/cleaning', basedir+'/cleaning')
	# except:
	# 	print('error copying cleaning techniques')
	# 	shutil.copytree(prev_dir(prev_dir(basedir))+'/augmentation', basedir+'/augmentation')
	# except:
	# 	print('error copying augmentation techniques')

	os.chdir(curdir)

def plot_roc_curve(y_test, probs, clf_names):  
	'''
	This function plots an ROC curve with the appropriate 
	list of classifiers.
	'''
	cycol = itertools.cycle('bgrcmyk')

	for i in range(len(probs)):
		print(y_test)
		print(probs[i])
		try:
			fper, tper, thresholds = roc_curve(y_test, probs[i]) 
			plt.plot(fper, tper, color=next(cycol), label=clf_names[i]+' = %s'%(str(round(metrics.auc(fper, tper), 3))))
			plt.plot([0, 1], [0, 1], color='darkblue', linestyle='--')
		except:
			print('passing %s'%(clf_names[i]))

	plt.xlabel('False Positive Rate')
	plt.ylabel('True Positive Rate')
	plt.title('Receiver Operating Characteristic (ROC) Curve')
	plt.legend()
	plt.tight_layout()
	plt.savefig('roc_curve.png')
	plt.close()

def plot_confusion_matrix(cm, classes, normalize=True, title='Confusion matrix', cmap=plt.cm.Blues):
	"""
	This function prints and plots the confusion matrix.
	Normalization can be applied by setting `normalize=True`.
	"""
	if normalize:
		cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
		print("\nNormalized confusion matrix")
	else:
		print('\nConfusion matrix, without normalization')

	plt.imshow(cm, interpolation='nearest', cmap=cmap)
	plt.title(title)
	plt.colorbar()
	tick_marks = np.arange(len(classes))
	plt.xticks(tick_marks, classes, rotation=45)
	plt.yticks(tick_marks, classes)

	fmt = '.2f' if normalize else 'd'
	thresh = cm.max() / 2.
	for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
		plt.text(j, i, format(cm[i, j], fmt),
				 horizontalalignment="center",
				 color="white" if cm[i, j] > thresh else "black")

	plt.tight_layout()
	plt.ylabel('True label')
	plt.xlabel('Predicted label')
	plt.tight_layout()
	plt.savefig('confusion_matrix.png')
	plt.close()

def plot_regressor(regressor, classes, X_test, y_test):
	'''
	plot regression models with a bar chart.
	'''

	try:
		y_pred = regressor.predict(X_test)

		# plot the first 25 records
		if len(classes) == 2:
			df = pd.DataFrame({'Actual': y_test.flatten(), 'Predicted': y_pred.flatten()})
			df1 = df.head(25)
			df1.plot(kind='bar',figsize=(16,10))
			plt.grid(which='major', linestyle='-', linewidth='0.5', color='green')
			plt.grid(which='minor', linestyle=':', linewidth='0.5', color='black')
			plt.tight_layout()
			plt.savefig('bar_graph_predictions.png')
			plt.close()

			# plot a straight line on the data
			plt.scatter(X_test, y_test,  color='gray')
			plt.plot(X_test, y_pred, color='red', linewidth=2)
			plt.tight_layout()
			plt.savefig('straight_line_predictions.png')
			plt.close()
		else:
			# multi-dimensional generalization 
			df = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred})
			df1 = df.head(25)

			df1.plot(kind='bar',figsize=(10,8))
			plt.grid(which='major', linestyle='-', linewidth='0.5', color='green')
			plt.grid(which='minor', linestyle=':', linewidth='0.5', color='black')
			plt.tight_layout()
			plt.savefig('bar_graph_predictions.png')
			plt.close()
	except:
		print('error plotting regressor')

def pursue_modeling(mtype, model_dir, problemtype, default_training_script,common_name_model):
	'''
	simple script to decide whether or not to continue modeling the data.
	'''
	try:
		model_listdir=os.listdir(model_dir+'/'+problemtype+'_models')
	except:	
		model_listdir=list()

	# note that these are tpot definitions
	model_exists=False
	if default_training_script == 'tpot':
		if common_name_model + '_classifier' in model_listdir and mtype == 'c':
			model_exists=True
		elif common_name_model +'_regression' in model_listdir and mtype == 'r':
			model_exists=True
	else:
		# only look for naming conflicts with TPOT for now, can expand into the future.
		model_exists=False

	return model_exists, model_listdir
	
def get_csvfiles(listdir):
	csvfiles=list()
	for i in range(len(listdir)):
		if listdir[i].endswith('.csv'):
			csvfiles.append(listdir[i])
	return csvfiles

###############################################################
##                    LOADING SETTINGS                       ##
###############################################################

# load the default feature set 
cur_dir = os.getcwd()
prevdir= prev_dir(cur_dir)
sys.path.append(prevdir+'/train_dir')
settings=json.load(open(prevdir+'/settings.json'))

# get all the default feature arrays 
default_audio_features=settings['default_audio_features']
default_text_features=settings['default_text_features']
default_image_features=settings['default_image_features']
default_video_features=settings['default_video_features']
default_csv_features=settings['default_csv_features']
create_csv=settings['create_csv']

# prepare training and testing data (should have been already featurized) - # of classes/folders
os.chdir(prevdir+'/train_dir')

data_dir=os.getcwd()
listdir=os.listdir()
folders=get_folders(listdir)
csvfiles=get_csvfiles(listdir)

# now assess folders by content type 
data=dict()
for i in range(len(folders)):
	os.chdir(folders[i])
	listdir=os.listdir()
	filetype=classifyfolder(listdir)
	data[folders[i]]=filetype 
	os.chdir(data_dir)

###############################################################
##                  INITIALIZE CLASSES                       ##
###############################################################

# get all information from sys.argv, and if not, 
# go through asking user for the proper parameters 
try:
	problemtype=sys.argv[1]
	mtype=sys.argv[3]
	if mtype == 'c':
		classnum=sys.argv[2]
		common_name=sys.argv[4]
		classes=list()
		for i in range(int(classnum)):
			classes.append(sys.argv[i+5])
	else:

		classnum=1
		problemtype='csv'
		mtype=sys.argv[1]
		csvfile=sys.argv[2]
		classes=[sys.argv[3]]
		common_name=csvfile[0:-4]
except:
	# now ask user what type of problem they are trying to solve 
	mtype=input('is this a classification (c) or regression (r) problem? \n')
	while mtype not in ['c','r']:
		print('input not recognized...')
		mtype=input('is this a classification (c) or regression (r) problem? \n')

	if mtype == 'c':
		problemtype=input('what problem are you solving? (1-audio, 2-text, 3-image, 4-video, 5-csv)\n')
		while problemtype not in ['1','2','3','4','5']:
			print('answer not recognized...')
			problemtype=input('what problem are you solving? (1-audio, 2-text, 3-image, 4-video, 5-csv)\n')

		if problemtype=='1':
			problemtype='audio'
		elif problemtype=='2':
			problemtype='text'
		elif problemtype=='3':
			problemtype='image'
		elif problemtype=='4':
			problemtype='video'
		elif problemtype=='5':
			problemtype='csv'

		if problemtype != 'csv':

			print('\n OK cool, we got you modeling %s files \n'%(problemtype))
			count=0
			availableclasses=list()
			for i in range(len(folders)):
				if data[folders[i]]==problemtype:
					availableclasses.append(folders[i])
					count=count+1

			classnum=input('how many classes would you like to model? (%s available) \n'%(str(count)))
			print('these are the available classes: ')
			print(availableclasses)
			# get all if all (good for many classes)
			classes=list()
			if classnum=='all':
				for i in range(len(availableclasses)):
					classes.append(availableclasses[i])
			else:
						
				stillavailable=list()
				for i in range(int(classnum)):
					class_=input('what is class #%s \n'%(str(i+1)))

					while class_ not in availableclasses and class_ not in '' or class_ in classes:
						print('\n')
						print('------------------ERROR------------------')
						print('the input class does not exist (for %s files).'%(problemtype))
						print('these are the available classes: ')
						if len(stillavailable)==0:
							print(availableclasses)
						else:
							print(stillavailable)
						print('------------------------------------')
						class_=input('what is class #%s \n'%(str(i+1)))
					for j in range(len(availableclasses)):
						stillavailable=list()
						if availableclasses[j] not in classes:
							stillavailable.append(availableclasses[j])
					if class_ == '':
						class_=stillavailable[0]

					classes.append(class_)

		elif problemtype == 'csv':
			print('\n OK cool, we got you modeling %s files \n'%(problemtype))
			print('csv file options are: %s \n'%(csvfiles))
			csvfile=input('which csvfile would you like to use for classification? \n')
			g=pd.read_csv(csvfile)
			columns=list(g)
			print('potential targets include: %s'%(columns))
			target=input('what target would you like to use? \n')
			csv_labels=g[target]
			csv_features=g.drop([target], axis=1)
			
	elif mtype =='r':
		# for regression problems we need a target column to predict / classes from a .CSV 
		problemtype='csv'
		# assumes the .CSV file is in the train dir
		os.chdir(prevdir+'/train_dir')
		listdir=os.listdir()
		csvfiles=list()
		for i in range(len(listdir)):
			if listdir[i].endswith('.csv'):
				csvfiles.append(listdir[i])

		csvfile=input('what is the name of the spreadsheet (in ./train_dir) used for prediction? \n\n available: %s\n\n'%(str(csvfiles)))
		while csvfile not in csvfiles:
			print('answer not recognized...')
			csvfile=input('what is the name of the spreadsheet (in ./train_dir) used for prediction? \n\n available: %s\n\n'%(str(csvfiles)))

		# the available classes are only the numeric columns from the spreadsheet
		data = pd.read_csv(csvfile)
		columns = list(data)
		availableclasses=list()

		for i in range(len(columns)):
			# look at filetype extension in each column
			coldata=data[columns[i]]
			sampletypes=list()
			for j in range(len(coldata)):
				try:
					values=float(coldata[j])
					sampletypes.append('numerical')
				except:
					if coldata[j].endswith('.wav'):
						sampletypes.append('audio')
					elif coldata[j].endswith('.txt'):
						sampletypes.append('text')
					elif coldata[j].endswith('.png'):
						sampletypes.append('image')
					elif coldata[j].endswith('.mp4'):
						sampletypes.append('video')
					else:
						sampletypes.append('other')

			coltype=most_common(sampletypes)

			# correct the other category if needed
			if coltype == 'other':
				# if coltype.endswith('.csv'):
					# coltype='csv'
				if len(set(list(coldata))) < 10:
					coltype='categorical'
				else:
					# if less than 5 unique answers then we can interpret this as text input
					coltype='typedtext'

			if coltype == 'numerical':
				availableclasses.append(columns[i])

		if len(availableclasses) > 0:
			classnum=input('how many classes would you like to model? (%s available) \n'%(str(len(availableclasses))))
			print('these are the available classes: %s'%(str(availableclasses)))

			classes=list()	
			stillavailable=list()
			for i in range(int(classnum)):
				class_=input('what is class #%s \n'%(str(i+1)))

				while class_ not in availableclasses and class_ not in '' or class_ in classes:
					print('\n')
					print('------------------ERROR------------------')
					print('the input class does not exist (for %s files).'%(problemtype))
					print('these are the available classes: ')
					if len(stillavailable)==0:
						print(availableclasses)
					else:
						print(stillavailable)
					print('------------------------------------')
					class_=input('what is class #%s \n'%(str(i+1)))
				for j in range(len(availableclasses)):
					stillavailable=list()
					if availableclasses[j] not in classes:
						stillavailable.append(availableclasses[j])
				if class_ == '':
					class_=stillavailable[0]

				classes.append(class_)

		else:
			print('no classes available... ending session')
			sys.exit()

	common_name=input('what is the 1-word common name for the problem you are working on? (e.g. gender for male/female classification) \n')

###############################################################
##      	      UPGRADE MODULES / LOAD MODULES             ##
###############################################################

print('-----------------------------------')
print('          LOADING MODULES          ')
print('-----------------------------------')
# upgrade to have the proper scikit-learn version later
os.chdir(cur_dir)
os.system('python3 upgrade.py')
import pandas as pd
from sklearn.model_selection import train_test_split
from tqdm import tqdm
import numpy as np
from sklearn import metrics
from sklearn.metrics import roc_curve

###############################################################
##                    CLEAN THE DATA                        ##
###############################################################
clean_data=settings['clean_data']
clean_dir=prevdir+'/cleaning'

if clean_data == True and mtype == 'c':
	
	# only pursue augmentation strategies on directories of files and classification problems
	print('-----------------------------------')
	print(f.renderText('CLEANING DATA'))
	print('-----------------------------------')
	for i in range(len(classes)):
		if problemtype == 'audio':
			# clean audio via default_audio_cleaners 
			os.chdir(clean_dir+'/audio_cleaning')
		elif problemtype == 'text':
			# clean text via default_text_cleaners 
			os.chdir(clean_dir+'/text_cleaning')
		elif problemtype == 'image':
			# clean images via default_image_cleaners 
			os.chdir(clean_dir+'/image_cleaning')
		elif problemtype == 'video':
			# clean video via default_video_cleaners 
			os.chdir(clean_dir+'/video_cleaning')
		elif problemtype == 'csv':
			# clean .CSV via default_csv_cleaners 
			os.chdir(clean_dir+'/csv_cleaning')
		os.system('python3 clean.py "%s"'%(data_dir+'/'+classes[i]))

elif clean_data == True and mtype == 'r':
	for i in range(len(classes)):
		if problemtype == 'csv':
			# clean .CSV via default_csv_cleaners 
			os.chdir(clean_dir+'/csv_cleaning')
		os.system('python3 clean.py "%s"'%(data_dir+'/'+classes[i]))

###############################################################
##                    AUGMENT THE DATA                       ##
###############################################################
augment_data=settings['augment_data']
augment_dir=prevdir+'/augmentation'

if augment_data == True and mtype == 'c':

	# only pursue augmentation strategies on directories of files and classification problems
	print('-----------------------------------')
	print(f.renderText('AUGMENTING DATA'))
	print('-----------------------------------')
	for i in range(len(classes)):
		if problemtype == 'audio':
			# augment audio via default_audio_augmenters
			os.chdir(augment_dir+'/audio_augmentation')
		elif problemtype == 'text':
			# augment text via default_text_augmenters
			os.chdir(augment_dir+'/text_augmentation')
		elif problemtype == 'image':
			# augment images via default_image_augmenters
			os.chdir(augment_dir+'/image_augmentation')
		elif problemtype == 'video':
			# augment video via default_video_augmenters
			os.chdir(augment_dir+'/video_augmentation')
		elif problemtype == 'csv':
			# augment .CSV via default_csv_augmenters
			os.chdir(augment_dir+'/csv_augmentation')
		os.system('python3 augment.py "%s"'%(data_dir+'/'+classes[i]))

elif augment_data == True and mtype == 'r':
	for i in range(len(classes)):
		if problemtype == 'csv':
			# featurize .CSV via default_csv_augmenters
			os.chdir(augment_dir+'/csv_augmentation')
		os.system('python3 augment.py "%s"'%(data_dir+'/'+classes[i]))

###############################################################
##                    FEATURIZE FILES                        ##
###############################################################

# now featurize each class (in proper folder)
if mtype == 'c':
	data={}
	print('-----------------------------------')
	print(f.renderText('FEATURIZING DATA'))
	print('-----------------------------------')

	if problemtype == 'csv':
		# csv features should have already been defined
		# need to separate into number of unique classes
		csv_labels=g[target]
		csv_features=g.drop([target], axis=1)
		csv_feature_labels=list(csv_features)
		classes=list(set(list(csv_labels)))
		for i in range(len(classes)):
			class_type = classes[i]

			feature_list=list()
			label_list=list()
			
			for i in range(len(csv_features)):
				if csv_labels[i] == class_type:
					feature_list.append(list(csv_features.iloc[i,:]))
					label_list.append(csv_feature_labels)

			data[class_type]=feature_list
	else:
		# 
		for i in range(len(classes)):
			class_type=classes[i]
			if problemtype == 'audio':
				# featurize audio 
				os.chdir(prevdir+'/features/audio_features')
				default_features=default_audio_features
			elif problemtype == 'text':
				# featurize text
				os.chdir(prevdir+'/features/text_features')
				default_features=default_text_features
			elif problemtype == 'image':
				# featurize images
				os.chdir(prevdir+'/features/image_features')
				default_features=default_image_features
			elif problemtype == 'video':
				# featurize video 
				os.chdir(prevdir+'/features/video_features')
				default_features=default_video_features

			print('-----------------------------------')
			print('           FEATURIZING %s'%(classes[i].upper()))
			print('-----------------------------------')
			
			os.system('python3 featurize.py "%s"'%(data_dir+'/'+classes[i]))
			os.chdir(data_dir+'/'+classes[i])
			# load audio features 
			listdir=os.listdir()
			feature_list=list()
			label_list=list()
			for j in range(len(listdir)):
				if listdir[j][-5:]=='.json':
					try:
						g=json.load(open(listdir[j]))
						# consolidate all features into one array (if featurizing with multiple featurizers)
						default_feature=list()
						default_label=list()
						for k in range(len(default_features)):
							default_feature=default_feature+g['features'][problemtype][default_features[k]]['features']
							default_label=default_label+g['features'][problemtype][default_features[k]]['labels']

						feature_list.append(default_feature)
						label_list.append(default_label)
					except:
						print('ERROR - skipping ' + listdir[j])
			
			data[class_type]=feature_list


elif mtype == 'r':
	# featurize .CSV 
	os.chdir(prevdir+'/features/csv_features')
	output_file=str(uuid.uuid1())+'.csv'
	os.system('python3 featurize_csv_regression.py -i "%s" -o "%s" -t "%s"'%(prevdir+'/train_dir/'+csvfile, prevdir+'/train_dir/'+output_file, classes[0]))
	csvfile=output_file
	default_features=['csv_regression']

###############################################################
##                  GENERATE TRAINING DATA                   ##
###############################################################

print('-----------------------------------')
print(f.renderText('CREATING TRAINING DATA'))
print('-----------------------------------')

# perform class balance such that both classes have the same number
# of members (true by default, but can also be false)
os.chdir(prevdir+'/training/')
model_dir=prevdir+'/models'
balance=settings['balance_data']
remove_outliers=settings['remove_outliers']
outlier_types=settings['default_outlier_detector']

if mtype == 'c':

	if problemtype != 'csv':
		jsonfile=''
		for i in range(len(classes)):
			if i==0:
				jsonfile=classes[i]
			else:
				jsonfile=jsonfile+'_'+classes[i]

		jsonfile=jsonfile+'.json'

		#try:
		g=data
		alldata=list()
		labels=list()
		lengths=list()

		# check to see all classes are same length and reshape if necessary
		for i in range(len(classes)):
			class_=g[classes[i]]
			lengths.append(len(class_))

		lengths=np.array(lengths)
		minlength=np.amin(lengths)

		# now load all the classes
		for i in range(len(classes)):
			class_=g[classes[i]]
			random.shuffle(class_)

			# only balance if specified in settings
			if balance==True:
				if len(class_) > minlength:
					print('%s greater than minlength (%s) by %s, equalizing...'%(classes[i], str(minlength), str(len(class_)-minlength)))
					class_=class_[0:minlength]

			for j in range(len(class_)):
				alldata.append(class_[j])
				labels.append(i)

		# load features file and get feature labels by loading in classes
		labels_dir=prevdir+'/train_dir/'+classes[0]
		os.chdir(labels_dir)
		listdir=os.listdir()
		features_file=''
		for i in range(len(listdir)):
			if listdir[i].endswith('.json'):
				features_file=listdir[i]

		labels_=list()

		for i in range(len(default_features)):
			tlabel=json.load(open(features_file))['features'][problemtype][default_features[i]]['labels']
			labels_=labels_+tlabel

	elif problemtype == 'csv':
		# format data appropriately 
		jsonfile=target+'.json'

		#try:
		g=data
		alldata=list()
		labels=list()
		lengths=list()

		# check to see all classes are same length and reshape if necessary
		for i in range(len(classes)):
			class_=g[classes[i]]
			lengths.append(len(class_))

		lengths=np.array(lengths)
		minlength=np.amin(lengths)

		# now load all the classes
		for i in range(len(classes)):
			class_=g[classes[i]]
			random.shuffle(class_)

			# only balance if specified in settings
			if balance==True:
				if len(class_) > minlength:
					print('%s greater than minlength (%s) by %s, equalizing...'%(classes[i], str(minlength), str(len(class_)-minlength)))
					class_=class_[0:minlength]

			for j in range(len(class_)):
				alldata.append(class_[j])
				labels.append(i)

		# load features file and get feature labels by loading in classes
		labels_=csv_feature_labels

elif mtype == 'r':
	regression_data=pd.read_csv(prevdir+'/train_dir/'+csvfile)
	print(csvfile)
	# get features and labels
	features_=regression_data.drop(columns=classes, axis=1)
	labels_=list(features_)
	labels_csv=regression_data.drop(columns=list(features_), axis=1)
	# iterate through each column and make into proper features and labels
	features=list()
	labels=list()

	# testing
	# print(len(features_))
	# print(len(labels_))
	# print(features_)
	# print(labels_)
	# print(features_.iloc[0,:])
	# print(labels_.iloc[0,:])

	# get features and labels 
	for i in range(len(features_)):
		features.append(list(features_.iloc[i,:]))
		labels.append(list(labels_csv.iloc[i,:]))

	# convert to name alldata just to be consistent
	alldata=features
	# print(alldata[0])
	# print(labels[0])

# print(labels_)
os.chdir(model_dir)

# get the split from the settings.json
try:
	test_size=settings['test_size']
except:
	test_size=0.25

# error checking around lengths of arrays and deleting as necessary
lengths=list()
for i in range(len(alldata)):
	lengths.append(len(alldata[i]))

# CLEAN IF DIMENSIONS DO NOT MATCH!!
maxval=max(lengths)
minval=min(lengths)
delete_ind=list()
inds=list()
alldata=np.array(alldata)
labels=np.array(labels)

if maxval != minval:
	if lengths.count(maxval) > lengths.count(minval):
		for i in range(len(lengths)):
			# this means that additional column has been removed
			if lengths[i] == minval:
				delete_ind.append(i)

	elif lengths.count(maxval) < lengths.count(minval):
		for i in range(len(lengths)):
			# this means that additional column has been added
			if lengths[i] == maxval:
				delete_ind.append(i)

	print('DELETING THESE INDICES: %s'%(str(delete_ind)))
	print(alldata.shape)
	print(labels.shape)
	alldata=np.delete(alldata, tuple(delete_ind), axis=0)
	labels=np.delete(labels, tuple(delete_ind))
	print(alldata.shape)
	print(labels.shape)
	
# # now see if any element in the array is a NaN and do not include if so in alldata or labels
# for i in range(len(alldata)):
# 	try:
# 		array_has_nan = list(np.isnan(np.array(alldata[i]))).count(True)
# 		array_has_string=list(np.char.isnumeric(np.array(alldata[i]))).count(False)
# 	except:
# 		array_has_string=1
# 	if array_has_nan > 0 or array_has_string > 0:
# 		inds.append(i)
# 		print(alldata[i])

# if len(inds) > 0:
# 	print('DELETING THESE INDICES: %s'%(str(inds)))
# 	alldata=np.delete(alldata, tuple(inds))
# 	labels=np.delete(labels, tuple(inds))

# REMOVE OUTLIERS IF SETTING IS TRUE
alldata=np.array(alldata)
labels=np.array(labels)

if remove_outliers==True:
	print('-----------------------------------')
	print('			REMOVING OUTLIERS')
	print('-----------------------------------')
	for i in range(len(outlier_types)):
		outlier_type=outlier_types[i]
		if outlier_type =='isolationforest':
		    from sklearn.ensemble import IsolationForest
		    clf = IsolationForest(random_state=0).fit(alldata)
		    y_pred = clf.predict(alldata)

		    inlier_ind=list(np.where(y_pred==1))
		    outlier_ind=list(np.where(y_pred==-1))
		    y_pred = y_pred.tolist()

		    print(type(y_pred))
		    print(type(y_pred[0]))
		    n_inliers = y_pred.count(1)
		    n_outliers = y_pred.count(-1)
		    print(n_inliers)
		    print(n_outliers)

		    # shape before 
		    print(alldata.shape)
		    print(labels.shape)
		    # delete outliers
		    alldata=np.delete(alldata, tuple(outlier_ind), axis=0)
		    labels=np.delete(labels, tuple(outlier_ind))
		    print(alldata.shape)
		    print(labels.shape)

		elif outlier_type=='zscore':
			os.system('pip3 install statsmodels==0.11.1')
			from scipy import stats
			from statsmodels.formula.api import ols
			# https://towardsdatascience.com/ways-to-detect-and-remove-the-outliers-404d16608dba
			z = np.abs(stats.zscore(alldata))
			# print(z)
			threshold = 3
			inds=list(set(np.where(z>threshold)[0]))
			print(len(inds))
			print(tuple(inds))
			print(alldata.shape)
			print('-->')
			alldata = np.delete(alldata, tuple(inds), axis=0)
			print(alldata.shape)
			labels = np.delete(labels, tuple(inds))
			print(len(alldata))
			print(len(labels))

	# rebalance data to all be the same length
	newlabels=list(labels)
	outlier_class=list()
	for i in range(len(classes)):
		outlier_class.append(newlabels.count(i))

	lengths=np.array(outlier_class)
	minlength=np.amin(outlier_class)
	# now load all the classes
	for i in range(len(classes)):
		# only balance if specified in settings
		if balance==True:
			count2=newlabels.count(i)
			while count2 > minlength:
				count2=newlabels.count(i)
				print('%s greater than minlength (%s) by %s, equalizing...'%(classes[i], str(minlength), str(count2-minlength)))
				ind=list(labels).index(i)
				alldata=np.delete(alldata, tuple([ind]), axis=0)
				labels=np.delete(labels, tuple([ind]))
				newlabels=list(labels)

alldata=list(alldata)
labels=list(labels)

# split the data 
X_train, X_test, y_train, y_test = train_test_split(alldata, labels, test_size=test_size)

# convert everything to numpy arrays (for testing later)
X_train=np.array(X_train)
X_test=np.array(X_test)
y_train=np.array(y_train)
y_test=np.array(y_test)

# create list of created csv files
created_csv_files=list()

# create training and testing datasets and save to a .CSV file for archive purposes
# this ensures that all machine learning training methods use the same training data
basefile=common_name
temp_listdir=os.listdir()

if create_csv == True:
	try:
		print(basefile+'_all.csv'.upper())
		if basefile+'_all.csv' not in temp_listdir:
			all_data = convert_csv(alldata, labels, labels_, mtype, classes)
			all_data.to_csv(basefile+'_all.csv',index=False)
		created_csv_files.append(basefile+'_all.csv')
	except:
		print('error exporting data into excel sheet %s'%(basefile+'_all.csv'))
	try:
		print(basefile+'_train.csv'.upper())
		if basefile+'_train.csv' not in temp_listdir:
			train_data= convert_csv(X_train, y_train, labels_, mtype, classes)
			train_data.to_csv(basefile+'_train.csv',index=False)
		created_csv_files.append(basefile+'_train.csv')
	except:
		print('error exporting data into excel sheet %s'%(basefile+'_train.csv'))
	try:
		print(basefile+'_test.csv'.upper())
		if basefile+'_test.csv' not in temp_listdir:
			test_data= convert_csv(X_test, y_test, labels_, mtype, classes)
			test_data.to_csv(basefile+'_test.csv',index=False)
		created_csv_files.append(basefile+'_test.csv')
	except:
		print('error exporting data into excel sheet %s'%(basefile+'_test.csv'))

############################################################
## 			        DATA TRANSFORMATION 			      ##
############################################################

'''
Scale features via scalers, dimensionality reduction techniques,
and feature selection strategies per the settings.json document.
'''
preprocess_dir=prevdir+'/preprocessing'
os.chdir(preprocess_dir)

# get all the important settings for the transformations 
scale_features=settings['scale_features']
reduce_dimensions=settings['reduce_dimensions']
select_features=settings['select_features']
default_scalers=settings['default_scaler']
default_reducers=settings['default_dimensionality_reducer']
default_selectors=settings['default_feature_selector']

# get command for terminal
transform_command=''
if problemtype == 'csv' and mtype == 'c':
	transform_command=transform_command+' "'+'Class'+'"'
else:
	for i in range(len(classes)):
		transform_command=transform_command+' "'+classes[i]+'"'

# get filename / create a unique file name
if mtype=='r':
	t_filename='r_'+common_name
elif mtype=='c':
	t_filename='c_'+common_name

# only add names in if True 
if scale_features == True:
	for i in range(len(default_scalers)):
		t_filename=t_filename+'_'+default_scalers[i]
if reduce_dimensions == True:
	for i in range(len(default_reducers)):
		t_filename=t_filename+'_'+default_reducers[i]
if select_features == True:
	for i in range(len(default_selectors)):
		t_filename=t_filename+'_'+default_selectors[i]

transform_file=t_filename+'.pickle'

if scale_features == True or reduce_dimensions == True or select_features == True:
	print('----------------------------------')
	print(f.renderText('TRANSFORMING DATA'))
	print('----------------------------------')
	# go to proper transformer directory
	try:
		os.chdir(problemtype+'_transformer')
	except:
		os.mkdir(problemtype+'_transformer')
		os.chdir(problemtype+'_transformer')
	# train transformer if it doesn't already exist
	os.system('pip3 install scikit-learn==0.22.2.post1')
	if transform_file in os.listdir():
		# remove file if in listdir to avoid conflicts with naming
		os.remove(transform_file)

	print('making transformer...')
	alldata=np.asarray(alldata)
	labels=np.asarray(labels)
	os.chdir(preprocess_dir)
	if mtype == 'c':
		print('python3 transform.py "%s" "%s" "%s" %s'%(problemtype, 'c', common_name, transform_command))
		os.system('python3 transform.py "%s" "%s" "%s" %s'%(problemtype, 'c', common_name, transform_command))
		os.chdir(problemtype+'_transformer')
		print(transform_file)
		transform_model=pickle.load(open(transform_file,'rb'))
		alldata=transform_model.transform(np.array(alldata))

	elif mtype == 'r':
		command='python3 transform.py "%s" "%s" "%s" "%s" "%s" "%s"'%('csv', 'r', classes[0], csvfile, prevdir+'/train_dir/', common_name)
		print(command)
		os.system(command)
		os.chdir(problemtype+'_transformer')
		transform_model=pickle.load(open(transform_file,'rb'))
		alldata=transform_model.transform(alldata)

	os.chdir(preprocess_dir)
	os.system('python3 load_transformer.py "%s" "%s"'%(problemtype, transform_file))

	# now make new files as .CSV
	os.chdir(model_dir)

	# split the data 
	X_train, X_test, y_train, y_test = train_test_split(alldata, labels, test_size=test_size)

	# convert to numpy arrays 
	X_train=np.array(X_train)
	X_test=np.array(X_test)
	y_train=np.array(y_train)
	y_test=np.array(y_test)
	
	# get new labels_ array 
	labels_=list()
	for i in range(len(alldata[0].tolist())):
		labels_.append('transformed_feature_%s'%(str(i)))

	# now create transformed excel sheets
	temp_listdir=os.listdir()
	if create_csv == True:
		try:
			print(basefile+'_all_transformed.csv'.upper())
			if basefile+'_all_transformed.csv' not in temp_listdir:
				all_data = convert_csv(alldata, labels, labels_, mtype, classes)
				all_data.to_csv(basefile+'_all_transformed.csv',index=False)
			created_csv_files.append(basefile+'_all_transformed.csv')
		except:
			print('error exporting data into excel sheet %s'%(basefile+'_all_transformed.csv'))
		try:
			print(basefile+'_train_transformed.csv'.upper())
			if basefile+'_train_transformed.csv' not in temp_listdir:
				train_data= convert_csv(X_train, y_train, labels_, mtype, classes)
				train_data.to_csv(basefile+'_train_transformed.csv',index=False)
			created_csv_files.append(basefile+'_train_transformed.csv')
		except:
			print('error exporting data into excel sheet %s'%(basefile+'_train_transformed.csv'))

		try:
			print(basefile+'_test_transformed.csv'.upper())
			if basefile+'_test_transformed.csv' not in temp_listdir:
				test_data= convert_csv(X_test, y_test, labels_, mtype, classes)
				test_data.to_csv(basefile+'_test_transformed.csv',index=False)
			created_csv_files.append(basefile+'_test_transformed.csv')
		except:
			print('error exporting data into excel sheet %s'%(basefile+'_test_transformed.csv'))
else:
	# make a transform model == '' so that later during model training this can be skipped
	transform_model=''

############################################################
## 					VISUALIZE DATA  					  ##
############################################################

visualize_data=settings['visualize_data']
visual_dir=prevdir+'/visualize'
model_session=str(uuid.uuid1())
os.chdir(visual_dir)

if visualize_data == True and mtype == 'c':
	print('----------------------------------')
	print(f.renderText('VISUALIZING DATA'))
	print('----------------------------------')

	command='python3 visualize.py %s'%(problemtype)
	for i in range(len(classes)):
		command=command+' "'+classes[i]+'"'
	os.system(command)

	# restructure the visualization directory 
	os.chdir(visual_dir+'/visualization_session')
	os.mkdir('visualizations')
	vizdir=os.getcwd()

	# move directories so that visualization is separate from main model directory 
	shutil.move(vizdir+'/clustering', vizdir+'/visualizations/clustering')
	shutil.move(vizdir+'/feature_ranking', vizdir+'/visualizations/feature_ranking')
	shutil.move(vizdir+'/model_selection', vizdir+'/visualizations/model_selection')
	
	# go back to main direcotry
	os.chdir(visual_dir)

	# now copy over the visualization directory to 
	try:
		shutil.copytree(visual_dir+'/visualization_session', model_dir+'/'+model_session)
	except:
		shutil.rmtree(model_dir+'/'+model_session)
		shutil.copytree(visual_dir+'/visualization_session', model_dir+'/'+model_session)
	# copy over settings.json 
	shutil.copy(prevdir+'/settings.json',model_dir+'/%s/settings.json'%(model_session))

else:
	# make a model session for next section if it doesn't exist from visualization directory
	os.chdir(model_dir)
	try:
		os.mkdir(model_session)
	except:
		shutil.rmtree(model_session)
		os.mkdir(model_session)
	# copy over settings.json
	shutil.copy(prevdir+'/settings.json', model_dir+'/%s/settings.json'%(model_session))

############################################################
## 					TRAIN THE MODEL 					  ##
############################################################

'''
Now we can train the machine learning model via the default_training script.
Note you can specify multiple training scripts and it will consecutively model the 
files appropriately. 

#^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^#
#  Here is what all the variables below mean:
#^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^#

# alldata = list of features in an array for model training  
	# [[39.0, 112.15384615384616, 70.98195453650514, 248.0, 14.0, 103.0, 143.5546875...],
		...
	   [39.0, 112.15384615384616, 70.98195453650514, 248.0, 14.0, 103.0, 143.5546875,...]]
# labels = list of labels in an array for model training 
	# ['males','females',...,'males','females']
# mtype = classification or regression problem?
	# 'c' --> classification
	# 'r' --> regression
# jsonfile = filename of the .JSON document seprating classes
	#  males_females.json
# problemtype = type of problem selected
	# 'audio' --> audio files
	# 'image' --> images files
	# 'text' --> text files
	# 'video' --> video files
	# 'csv' --> csv files 
# default_featurenames = default feature array(s) to use for modeling 
	# ['librosa_features']
# settings = overall settings currenty used for model training 
	# output of the settings.json document 

-----

# transform_model = transformer model if applicable 
	# useful for data transformation as part of the model initialization process (if pickle file)
	# uses scikit-learn pipeline 

# X_train, X_test, y_train, y_test
	# training datasets used in the .CSV documents 
	# also can use pandas dataframe if applicable (loading in the model dir)
'''
print('----------------------------------')
print(f.renderText('MODELING DATA'))
print('----------------------------------')

# get defaults 
default_training_scripts=settings['default_training_script']
model_compress=settings['model_compress']
default_featurenames=''

if problemtype != 'csv' and mtype == 'c':
	for i in range(len(default_features)):
		if i ==0:
			default_featurenames=default_features[i]
		else:
			default_featurenames=default_featurenames+'_|_'+default_features[i] 

else:
	default_featurenames='csv_classification'

# just move all created .csv files into model_session directory
os.chdir(model_dir)
os.chdir(model_session)
os.mkdir('data')
for i in range(len(created_csv_files)):
	shutil.move(model_dir+'/'+created_csv_files[i], os.getcwd()+'/data/'+created_csv_files[i])

# initialize i (for tqdm) and go through all model training scripts 
i=0
for i in tqdm(range(len(default_training_scripts)), desc=default_training_scripts[i]):

	try:

		model_start_time=time.time()
		# go to model directory 
		os.chdir(model_dir)

		# get common name and default training script to select proper model trainer
		default_training_script=default_training_scripts[i]
		common_name_model=common_name+'_'+default_training_script
		model_exists, model_listdir = pursue_modeling(mtype, model_dir, problemtype, default_training_script, common_name_model)
		if model_exists == False:	

			print('----------------------------------')
			print('       .... training %s           '%(default_training_script.upper()))
			print('----------------------------------')

			if default_training_script=='adanet':
				print('Adanet training is coming soon! Please use a different model setting for now.') 
				# import train_adanet as ta 
				# ta.train_adanet(mtype, classes, jsonfile, alldata, labels, feature_labels, problemtype, default_featurenames)
			elif default_training_script=='alphapy':
				import train_alphapy as talpy
				modelname, modeldir, files=talpy.train_alphapy(X_train,X_test,y_train,y_test,mtype,common_name_model,problemtype,classes,default_featurenames,transform_model,settings,model_session)
			elif default_training_script=='atm':
				import train_atm as tatm
				modelname, modeldir, files=tatm.train_atm(X_train,X_test,y_train,y_test,mtype,common_name_model,problemtype,classes,default_featurenames,transform_model,settings,model_session)
			elif default_training_script=='autobazaar':
				import train_autobazaar as autobzr
				modelname, modeldir, files=autobzr.train_autobazaar(X_train,X_test,y_train,y_test,mtype,common_name_model,problemtype,classes,default_featurenames,transform_model,settings,model_session)
			elif default_training_script=='autogbt':
				import train_autogbt as tautogbt
				modelname, modeldir, files=tautogbt.train_autogbt(X_train,X_test,y_train,y_test,mtype,common_name_model,problemtype,classes,default_featurenames,transform_model,settings,model_session)
			elif default_training_script=='autogluon':
				import train_autogluon as tautg
				modelname, modeldir, files, test_data=tautg.train_autogluon(X_train,X_test,y_train,y_test,mtype,common_name_model,problemtype,classes,default_featurenames,transform_model,settings,model_session)
			elif default_training_script=='autokaggle':
				import train_autokaggle as autokag
				modelname, modeldir, files=autokag.train_autokaggle(X_train,X_test,y_train,y_test,mtype,common_name_model,problemtype,classes,default_featurenames,transform_model,settings,model_session)
			elif default_training_script=='autokeras':
				import train_autokeras as autokeras_
				modelname, modeldir, files=autokeras_.train_autokeras(X_train,X_test,y_train,y_test,mtype,common_name_model,problemtype,classes,default_featurenames,transform_model,settings,model_session)
			elif default_training_script=='automl':
				import train_automl as auto_ml
				modelname, modeldir, files=auto_ml.train_automl(X_train,X_test,y_train,y_test,mtype,common_name_model,problemtype,classes,default_featurenames,transform_model,settings,model_session)
			elif default_training_script=='autosklearn':
				print('Autosklearn training is unstable! Please use a different model setting for now.') 
				# import train_autosklearn as taskl
				# taskl.train_autosklearn(alldata, labels, mtype, jsonfile, problemtype, default_featurenames)
			elif default_training_script=='autopytorch':
				import train_autopytorch as autotorch_
				modelname, modeldir, files=autotorch_.train_autopytorch(X_train,X_test,y_train,y_test,mtype,common_name_model,problemtype,classes,default_featurenames,transform_model,settings,model_session)
			elif default_training_script=='btb':
				import train_btb as tbtb
				modelname, modeldir, files=tbtb.train_btb(X_train,X_test,y_train,y_test,mtype,common_name_model,problemtype,classes,default_featurenames,transform_model,settings,model_session)
			elif default_training_script=='cvopt':
				import train_cvopt as tcvopt
				modelname, modeldir, files = tcvopt.train_cvopt(X_train,X_test,y_train,y_test,mtype,common_name_model,problemtype,classes,default_featurenames,transform_model,settings,model_session)
			elif default_training_script=='devol':
				import train_devol as td 
				modelname, modeldir, files=td.train_devol(X_train,X_test,y_train,y_test,mtype,common_name_model,problemtype,classes,default_featurenames,transform_model,settings,model_session)
			elif default_training_script=='gama':
				import train_gama as tgama
				modelname, modeldir, files=tgama.train_gama(X_train,X_test,y_train,y_test,mtype,common_name_model,problemtype,classes,default_featurenames,transform_model,settings,model_session)
			elif default_training_script=='gentun':
				import train_gentun as tgentun 
				modelname, modeldir, files=tgentun.train_gentun(X_train,X_test,y_train,y_test,mtype,common_name_model,problemtype,classes,default_featurenames,transform_model,settings,model_session)
			elif default_training_script=='hyperband':
				import train_hyperband as thband
				modelname, modeldir, files = thband.train_hyperband(X_train,X_test,y_train,y_test,mtype,common_name_model,problemtype,classes,default_featurenames,transform_model,settings,model_session)
			elif default_training_script=='hypsklearn':
				import train_hypsklearn as th 
				modelname, modeldir, files=th.train_hypsklearn(X_train,X_test,y_train,y_test,mtype,common_name_model,problemtype,classes,default_featurenames,transform_model,settings,model_session)
			elif default_training_script=='hungabunga':
				import train_hungabunga as thung
				modelname, modeldir, files=thung.train_hungabunga(X_train,X_test,y_train,y_test,mtype,common_name_model,problemtype,classes,default_featurenames,transform_model,settings,model_session)
			elif default_training_script=='imbalance':
				import train_imbalance as timb
				modelname, modeldir, files=timb.train_imbalance(X_train,X_test,y_train,y_test,mtype,common_name_model,problemtype,classes,default_featurenames,transform_model,settings,model_session)
			elif default_training_script=='keras':
				import train_keras as tk
				modelname, modeldir, files=tk.train_keras(X_train,X_test,y_train,y_test,mtype,common_name_model,problemtype,classes,default_featurenames,transform_model,settings,model_session)
			elif default_training_script=='ludwig':
				import train_ludwig as tl
				modelname, modeldir, files=tl.train_ludwig(X_train,X_test,y_train,y_test,mtype,common_name_model,problemtype,classes,default_featurenames,transform_model,settings,model_session)
			elif default_training_script=='mlblocks':
				import train_mlblocks as mlb
				modelname, modeldir, files=mlb.train_mlblocks(X_train,X_test,y_train,y_test,mtype,common_name_model,problemtype,classes,default_featurenames,transform_model,settings,model_session)
			elif default_training_script=='mlbox':
				import train_mlbox as mlbox_
				modelname, modeldir, files=mlbox_.train_mlbox(X_train,X_test,y_train,y_test,mtype,common_name_model,problemtype,classes,default_featurenames,transform_model,settings,model_session)
			elif default_training_script=='neuraxle':
				if mtype=='c':
					print('Neuraxle does not support classification at this time. Please use a different model training script')
					break
				else:
					import train_neuraxle as tneuraxle
					modelname, modeldir, files=tneuraxle.train_neuraxle(X_train,X_test,y_train,y_test,mtype,common_name_model,problemtype,classes,default_featurenames,transform_model,settings,model_session)
			elif default_training_script=='plda':
				print('PLDA training is unstable! Please use a different model setting for now.') 
				# import train_pLDA as tp
				# tp.train_pLDA(alldata,labels)
			elif default_training_script=='pytorch':
				import train_pytorch as t_pytorch
				modelname, modeldir, files = t_pytorch.train_pytorch(X_train,X_test,y_train,y_test,mtype,common_name_model,problemtype,classes,default_featurenames,transform_model,settings,model_session)
			elif default_training_script=='safe':
				import train_safe as tsafe
				modelname, modeldir, files=tsafe.train_safe(X_train,X_test,y_train,y_test,mtype,common_name_model,problemtype,classes,default_featurenames,transform_model,settings,model_session)
			elif default_training_script=='scsr':
				import train_scsr as scsr
				if mtype == 'c':
					modelname, modeldir, files=scsr.train_sc(X_train,X_test,y_train,y_test,mtype,common_name_model,problemtype,classes,default_featurenames,transform_model,settings,minlength)
				elif mtype == 'r':
					modelname, modeldir, files=scsr.train_sr(X_train,X_test,y_train,y_test,common_name_model,problemtype,classes,default_featurenames,transform_model,model_dir,settings)
			elif default_training_script=='tpot':
				import train_TPOT as tt
				modelname, modeldir, files=tt.train_TPOT(X_train,X_test,y_train,y_test,mtype,common_name_model,problemtype,classes,default_featurenames,transform_model,settings,model_session)

			############################################################
			## 		  CALCULATE METRICS / PLOT ROC CURVE        	  ##
			############################################################

			if modelname.endswith('.pickle'):
				foldername=modelname[0:-7]
			elif modelname.endswith('.h5'):
				foldername=modelname[0:-3]
			else:
				foldername=common_name_model

			# copy the folder in case there are multiple models being trained 
			try:
				shutil.copytree(model_session, foldername)
			except:
				shutil.rmtree(foldername)
				shutil.copytree(model_session, foldername)
				
			cur_dir2=os.getcwd()
			os.chdir(foldername)
			os.mkdir('model')
			os.chdir('model')
			model_dir_temp=os.getcwd()

			# dump transform model to the models directory if necessary
			if transform_model == '':
				transformer_name=''
			else:
				# dump the tranform model into the current working directory
				transformer_name=modelname.split('.')[0]+'_transform.pickle'
				tmodel=open(transformer_name,'wb')
				pickle.dump(transform_model, tmodel)
				tmodel.close()

			# move all supplementary files into model folder
			for j in range(len(files)):
				shutil.move(modeldir+'/'+files[j], model_dir_temp+'/'+files[j])
			
			# load model for getting metrics
			if default_training_script not in ['alphapy', 'atm', 'autokeras', 'autopytorch', 'ludwig', 'keras', 'devol']:
				loadmodel=open(modelname, 'rb')
				clf=pickle.load(loadmodel)
				loadmodel.close()
			elif default_training_script == 'atm':
				from atm import Model
				clf=Model.load(modelname)
			elif default_training_script == 'autokeras':
				import tensorflow as tf
				import autokeras as ak
				clf = pickle.load(open(modelname, 'rb'))
			elif default_training_script=='autopytorch':
				import torch
				clf=torch.load(modelname)
			elif default_training_script == 'ludwig':
				from ludwig.api import LudwigModel
				clf=LudwigModel.load('ludwig_files/experiment_run/model/')
			elif default_training_script in ['devol', 'keras']: 
				from keras.models import load_model
				clf = load_model(modelname)
			else: 
				clf=''

			# create test_data variable for anything other than autogluon
			if default_training_script != 'autogluon':
				test_data=''

			# now make main .JSON file for the session summary with metrics
			get_metrics(clf, problemtype, mtype, default_training_script, common_name, X_test, y_test, classes, modelname, settings, model_session, transformer_name, created_csv_files, test_data, model_start_time)
			
			# now move to the proper models directory
			os.chdir(model_dir)
			os.system('python3 create_readme.py "%s"'%(os.getcwd()+'/'+foldername))

			try:
				os.chdir(problemtype+'_models')
			except:
				os.mkdir(problemtype+'_models')
				os.chdir(problemtype+'_models')

			shutil.move(model_dir+'/'+foldername, os.getcwd()+'/'+foldername)

			############################################################
			## 					COMPRESS MODELS 					  ##
			############################################################

			if model_compress == True:
				print(f.renderText('COMPRESSING MODEL'))
				# now compress the model according to model type 
				if default_training_script in ['hypsklearn', 'scsr', 'tpot']:
					# all .pickle files and can compress via scikit-small-ensemble
					from sklearn.externals import joblib

					# open up model 
					loadmodel=open(modelname, 'rb')
					model = pickle.load(loadmodel)
					loadmodel.close()

					# compress - from 0 to 9. Higher value means more compression, but also slower read and write times. 
					# Using a value of 3 is often a good compromise.
					joblib.dump(model, modelname[0:-7]+'_compressed.joblib',compress=3)

					# can now load compressed models as such
					# thenewmodel=joblib.load(modelname[0:-7]+'_compressed.joblib')
					# leads to up to 10x reduction in model size and .72 sec - 0.23 secoon (3-4x faster loading model)
					# note may note work in sklearn and python versions are different from saving and loading environments. 

				elif default_training_script in ['devol', 'keras']: 
					# can compress with keras_compressor 
					import logging
					from keras.models import load_model
					from keras_compressor.compressor import compress

					logging.basicConfig(
						level=logging.INFO,
					)

					try:
						print('compressing model!!')
						model = load_model(modelname)
						model = compress(model, 7e-1)
						model.save(modelname[0:-3]+'_compressed.h5')
					except:
						print('error compressing model!!')

				else:
					# for everything else, we can compress pocketflow models in the future.
					print('We cannot currently compress %s models. We are working on this!! \n\n The model will remain uncompressed for now'%(default_training_script))

		else:
			if mtype == 'r':
				print('SKIPPING MODELTYPE - %s already exists in the %s folder: %s'%(common_name_model+'_regression', problemtype+'_models', str(model_listdir)))
			elif mtype == 'c':
				print('SKIPPING MODELTYPE - %s already exists in the %s folder: %s'%(common_name_model+'_classifier', problemtype+'_models', str(model_listdir)))
		############################################################
		## 					PRODUCTIONIZING MODELS				  ##
		############################################################

		# TO BE COMPLETED IN THE FUTURE!

	except:
		print('ERROR - error in modeling session')

'''
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              A:::A              l:::::l l:::::l  i::::i                     
             A:::::A             l:::::l l:::::l   iiii                      
            A:::::::A            l:::::l l:::::l                             
           A:::::::::A            l::::l  l::::l iiiiiii     eeeeeeeeeeee    
          A:::::A:::::A           l::::l  l::::l i:::::i   ee::::::::::::ee  
         A:::::A A:::::A          l::::l  l::::l  i::::i  e::::::eeeee:::::ee
        A:::::A   A:::::A         l::::l  l::::l  i::::i e::::::e     e:::::e
       A:::::A     A:::::A        l::::l  l::::l  i::::i e:::::::eeeee::::::e
      A:::::AAAAAAAAA:::::A       l::::l  l::::l  i::::i e:::::::::::::::::e 
     A:::::::::::::::::::::A      l::::l  l::::l  i::::i e::::::eeeeeeeeeee  
    A:::::AAAAAAAAAAAAA:::::A     l::::l  l::::l  i::::i e:::::::e           
   A:::::A             A:::::A   l::::::ll::::::li::::::ie::::::::e          
  A:::::A               A:::::A  l::::::ll::::::li::::::i e::::::::eeeeeeee  
 A:::::A                 A:::::A l::::::ll::::::li::::::i  ee:::::::::::::e  
AAAAAAA                   AAAAAAAlllllllllllllllliiiiiiii    eeeeeeeeeeeeee  
                                                                             
|  \/  |         | |    | |  / _ \ | ___ \_   _|
| .  . | ___   __| | ___| | / /_\ \| |_/ / | |  
| |\/| |/ _ \ / _` |/ _ \ | |  _  ||  __/  | |  
| |  | | (_) | (_| |  __/ | | | | || |    _| |_ 
\_|  |_/\___/ \__,_|\___|_| \_| |_/\_|    \___/ 

This is Allie's modeling API to help build classification or regression models.

All you need to do is run the model.py script and you will be guided through the 
modeling process.

Usage: python3 model.py

Alternative CLI Usage: python3 model.py audio 2 c gender males females
- audio = audio file type
- 2 = 2 classes
- c = classification (r for regression)
- gender = common name of model
- male = first class
- female = second class [via N number of classes]

For addditional documentation, check out 
https://github.com/jim-schwoebel/allie/tree/master/training                                                                          
'''
###############################################################
##                  IMPORT STATEMENTS                        ##
###############################################################
import os, sys, pickle, json, random, shutil, time, itertools, uuid, datetime, uuid, psutil, json, platform
from pyfiglet import Figlet
f=Figlet(font='doh')
print(f.renderText('Allie'))
f=Figlet(font='doom')
import pandas as pd
import matplotlib.pyplot as plt

###############################################################
##               CREATE HELPER FUNCTIONS                     ##
###############################################################

def most_common(lst):
	'''
	get most common item in a list
	'''
	return max(set(lst), key=lst.count)

def prev_dir(directory):
	g=directory.split('/')
	dir_=''
	for i in range(len(g)):
		if i != len(g)-1:
			if i==0:
				dir_=dir_+g[i]
			else:
				dir_=dir_+'/'+g[i]
	# print(dir_)
	return dir_

def get_folders(listdir):
	folders=list()
	for i in range(len(listdir)):
		if listdir[i].find('.') < 0:
			folders.append(listdir[i])

	return folders 

def classifyfolder(listdir):
	filetypes=list()
	for i in range(len(listdir)):
		if listdir[i].endswith(('.mp3', '.wav')):
			filetypes.append('audio')
		elif listdir[i].endswith(('.png', '.jpg')):
			filetypes.append('image')
		elif listdir[i].endswith(('.txt')):
			filetypes.append('text')
		elif listdir[i].endswith(('.mp4', '.avi')):
			filetypes.append('video')
		elif listdir[i].endswith(('.csv')):
			filetypes.append('csv')

	counts={'audio': filetypes.count('audio'),
			'image': filetypes.count('image'),
			'text': filetypes.count('text'),
			'video': filetypes.count('video'),
			'csv': filetypes.count('csv')}

	# get back the type of folder (main file type)
	countlist=list(counts)
	countvalues=list(counts.values())
	maxvalue=max(countvalues)
	maxind=countvalues.index(maxvalue)
	return countlist[maxind]

def pull_element(mylist, element):
	pull_=list()
	for i in range(len(mylist)):
		pull_.append(mylist[i][element])
	return pull_

def convert_csv(X_train, y_train, labels, mtype, classes):
	'''
	Take in a array of features and labels and output a 
	pandas DataFrame format for easy .CSV expor and for model training.

	This is important to make sure all machine learning training sessions
	use the same dataset (so they can be benchmarked appropriately).
	'''
	# from pandas merging guide https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html
	feature_list=labels
	data=list()
	for i in tqdm(range(len(X_train)), desc='converting csv...'):
		
		newlist=list()
		for j in range(len(X_train[i])):
			newlist.append([X_train[i][j]])

		temp=pd.DataFrame(dict(zip(feature_list,newlist)), index=[i])
		# print(temp)
		data.append(temp)
		

	data = pd.concat(data)

	if mtype == 'c':
		data['class_']=y_train
	elif mtype == 'r':
		if len(classes) == 1:
			data[classes[0]]=y_train
		else:
			for j in range(len(classes)):
				newy=pull_element(y_train, j)
				data[classes[j]]=newy

	data=pd.DataFrame(data, columns = list(data))
	# print this because in pretty much every case you will write the .CSV file afterwards
	print('writing csv file...')

	return data

def device_info():
	cpu_data={'memory':psutil.virtual_memory(),
			   'cpu percent':psutil.cpu_percent(),
			   'cpu times':psutil.cpu_times(),
			   'cpu count':psutil.cpu_count(),
			   'cpu stats':psutil.cpu_stats(),
			   'cpu swap':psutil.swap_memory(),
			   'partitions':psutil.disk_partitions(),
			   'disk usage':psutil.disk_usage('/'),
			   'disk io counters':psutil.disk_io_counters(),
			   'battery':psutil.sensors_battery(),
			   'boot time':psutil.boot_time(),
			   }
	data={'time':datetime.datetime.now().strftime("%Y-%m-%d %H:%M"),
		  'timezone':time.tzname,
		  'operating system': platform.system(),
		  'os release':platform.release(),
		  'os version':platform.version(),
		  'cpu data':cpu_data,
		  'space left': list(psutil.disk_usage('/'))[2]/1000000000}

	return data

def get_metrics(clf, problemtype, mtype, default_training_script, common_name, X_test, y_test, classes, modelname, settings, model_session, transformer_name, created_csv_files, test_data, model_start_time):
	'''
	get the metrics associated iwth a classification and regression problem
	and output a .JSON file with the training session.
	'''
	metrics_=dict()
	y_true=y_test

	if default_training_script not in ['autogluon', 'autokeras', 'autopytorch', 'alphapy', 'atm', 'keras', 'devol', 'ludwig', 'safe', 'neuraxle']:
		y_pred=clf.predict(X_test)
	elif default_training_script=='alphapy':
		# go to the right folder 
		curdir=os.getcwd()
		print(os.listdir())
		os.chdir(common_name+'_alphapy_session')
		alphapy_dir=os.getcwd()
		os.chdir('input')
		os.rename('test.csv', 'predict.csv')
		os.chdir(alphapy_dir)
		os.system('alphapy --predict')
		os.chdir('output')
		listdir=os.listdir()
		for k in range(len(listdir)):
			if listdir[k].startswith('predictions'):
				csvfile=listdir[k]
		y_pred=pd.read_csv(csvfile)['prediction']
		os.chdir(curdir)
	elif default_training_script == 'autogluon':
		from autogluon import TabularPrediction as task
		test_data=test_data.drop(labels=['class'],axis=1)
		y_pred=clf.predict(test_data)
	elif default_training_script == 'autokeras':
		y_pred=clf.predict(X_test).flatten()
	elif default_training_script == 'autopytorch':
		y_pred=clf.predict(X_test).flatten()
	elif default_training_script == 'atm':
		curdir=os.getcwd()
		os.chdir('atm_temp')
		data = pd.read_csv('test.csv').drop(labels=['class_'], axis=1)
		y_pred = clf.predict(data)
		os.chdir(curdir)
	elif default_training_script == 'ludwig':
		data=pd.read_csv('test.csv').drop(labels=['class_'], axis=1)
		pred=clf.predict(data)['class__predictions']
		y_pred=np.array(list(pred), dtype=np.int64)
	elif default_training_script == 'devol':
		X_test=X_test.reshape(X_test.shape+ (1,)+ (1,))
		y_pred=clf.predict_classes(X_test).flatten()
	elif default_training_script=='keras':
		if mtype == 'c':
		    y_pred=clf.predict_classes(X_test).flatten()
		elif mtype == 'r':
			y_pred=clf.predict(X_test).flatten()
	elif default_training_script=='neuraxle':
		y_pred=clf.transform(X_test)
	elif default_training_script=='safe':
		# have to make into a pandas dataframe
		test_data=pd.read_csv('test.csv').drop(columns=['class_'], axis=1)
		y_pred=clf.predict(test_data)
	
	print(y_pred)
	
	# get classification or regression metrics
	if mtype in ['c', 'classification']:
		# now get all classification metrics
		mtype='classification'
		metrics_['accuracy']=metrics.accuracy_score(y_true, y_pred)
		metrics_['balanced_accuracy']=metrics.balanced_accuracy_score(y_true, y_pred)
		try:
			metrics_['precision']=metrics.precision_score(y_true, y_pred)
		except:
			metrics_['precision']='n/a'
		try:
			metrics_['recall']=metrics.recall_score(y_true, y_pred)
		except:
			metrics_['recall']='n/a'
		try:
			metrics_['f1_score']=metrics.f1_score (y_true, y_pred, pos_label=1)
		except:
			metrics_['f1_score']='n/a'
		try:
			metrics_['f1_micro']=metrics.f1_score(y_true, y_pred, average='micro')
		except:
			metrics_['f1_micro']='n/a'
		try:
			metrics_['f1_macro']=metrics.f1_score(y_true, y_pred, average='macro')
		except:
			metrics_['f1_macro']='n/a'
		try:
			metrics_['roc_auc']=metrics.roc_auc_score(y_true, y_pred)
		except:
			metrics_['roc_auc']='n/a'
		try:
			metrics_['roc_auc_micro']=metrics.roc_auc_score(y_true, y_pred, average='micro')
		except:
			metrics_['roc_auc_micro']='n/a'
		try:
			metrics_['roc_auc_macro']=metrics.roc_auc_score(y_true, y_pred, average='macro')
		except:
			metrics_['roc_auc_micro']='n/a'
	
		metrics_['confusion_matrix']=metrics.confusion_matrix(y_true, y_pred).tolist()
		metrics_['classification_report']=metrics.classification_report(y_true, y_pred, target_names=classes)

		plot_confusion_matrix(np.array(metrics_['confusion_matrix']), classes)
		try:
			# predict_proba only works for or log loss and modified Huber loss.
			# https://stackoverflow.com/questions/47788981/sgdclassifier-with-predict-proba
			try:
				y_probas = clf.predict_proba(X_test)[:, 1]
			except:
				try:
					y_probas = clf.decision_function(X_test)[:, 1]
				except:
					print('error making y_probas')
				
			plot_roc_curve(y_test, [y_probas], [default_training_script])
		except:
			print('error plotting ROC curve')
			print('predict_proba only works for or log loss and modified Huber loss.')

	elif mtype in ['r', 'regression']:
		# now get all regression metrics
		mtype='regression'
		metrics_['mean_absolute_error'] = metrics.mean_absolute_error(y_true, y_pred)
		metrics_['mean_squared_error'] = metrics.mean_squared_error(y_true, y_pred)
		metrics_['median_absolute_error'] = metrics.median_absolute_error(y_true, y_pred)
		metrics_['r2_score'] = metrics.r2_score(y_true, y_pred)

		plot_regressor(clf, classes, X_test, y_test)

	data={'sample type': problemtype,
		  'training time': time.time()-model_start_time,
		  'created date': str(datetime.datetime.now()),
		  'device info': device_info(),
		  'session id': model_session,
		  'classes': classes,
	      'problem type': mtype,
	      'model name': modelname, 
	      'model type': default_training_script,
	      'metrics': metrics_,
	      'settings': settings,
	      'transformer name': transformer_name,
	      'training data': created_csv_files,
	      'sample X_test': X_test[0].tolist(),
	      'sample y_test': y_test[0].tolist()}

	if modelname.endswith('.pickle'):
		jsonfilename=modelname[0:-7]+'.json'
	elif modelname.endswith('.h5'):
		jsonfilename=modelname[0:-3]+'.json'
	else:
		jsonfilename=modelname+'.json'

	jsonfile=open(jsonfilename,'w')
	json.dump(data,jsonfile)
	jsonfile.close()

	# also output requirements.txt for reproducibilty purposes
	curdir=os.getcwd()
	basedir=prev_dir(curdir)
	os.chdir(basedir)
	os.system('pip3 freeze -> requirements.txt')

	# FUTURE - add in optional copy of cleaning, augmentation, and feature libraries contextually
	# try:
	# 	shutil.copytree(prev_dir(prev_dir(basedir))+'/features', basedir+'/features')
	# except:
	# 	print('error copying features')
	# try:
	# 	shutil.copytree(prev_dir(prev_dir(basedir))+'/cleaning', basedir+'/cleaning')
	# except:
	# 	print('error copying cleaning techniques')
	# 	shutil.copytree(prev_dir(prev_dir(basedir))+'/augmentation', basedir+'/augmentation')
	# except:
	# 	print('error copying augmentation techniques')

	os.chdir(curdir)

def plot_roc_curve(y_test, probs, clf_names):  
	'''
	This function plots an ROC curve with the appropriate 
	list of classifiers.
	'''
	cycol = itertools.cycle('bgrcmyk')

	for i in range(len(probs)):
		print(y_test)
		print(probs[i])
		try:
			fper, tper, thresholds = roc_curve(y_test, probs[i]) 
			plt.plot(fper, tper, color=next(cycol), label=clf_names[i]+' = %s'%(str(round(metrics.auc(fper, tper), 3))))
			plt.plot([0, 1], [0, 1], color='darkblue', linestyle='--')
		except:
			print('passing %s'%(clf_names[i]))

	plt.xlabel('False Positive Rate')
	plt.ylabel('True Positive Rate')
	plt.title('Receiver Operating Characteristic (ROC) Curve')
	plt.legend()
	plt.tight_layout()
	plt.savefig('roc_curve.png')
	plt.close()

def plot_confusion_matrix(cm, classes, normalize=True, title='Confusion matrix', cmap=plt.cm.Blues):
	"""
	This function prints and plots the confusion matrix.
	Normalization can be applied by setting `normalize=True`.
	"""
	if normalize:
		cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
		print("\nNormalized confusion matrix")
	else:
		print('\nConfusion matrix, without normalization')

	plt.imshow(cm, interpolation='nearest', cmap=cmap)
	plt.title(title)
	plt.colorbar()
	tick_marks = np.arange(len(classes))
	plt.xticks(tick_marks, classes, rotation=45)
	plt.yticks(tick_marks, classes)

	fmt = '.2f' if normalize else 'd'
	thresh = cm.max() / 2.
	for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
		plt.text(j, i, format(cm[i, j], fmt),
				 horizontalalignment="center",
				 color="white" if cm[i, j] > thresh else "black")

	plt.tight_layout()
	plt.ylabel('True label')
	plt.xlabel('Predicted label')
	plt.tight_layout()
	plt.savefig('confusion_matrix.png')
	plt.close()

def plot_regressor(regressor, classes, X_test, y_test):
	'''
	plot regression models with a bar chart.
	'''

	try:
		y_pred = regressor.predict(X_test)

		# plot the first 25 records
		if len(classes) == 2:
			df = pd.DataFrame({'Actual': y_test.flatten(), 'Predicted': y_pred.flatten()})
			df1 = df.head(25)
			df1.plot(kind='bar',figsize=(16,10))
			plt.grid(which='major', linestyle='-', linewidth='0.5', color='green')
			plt.grid(which='minor', linestyle=':', linewidth='0.5', color='black')
			plt.tight_layout()
			plt.savefig('bar_graph_predictions.png')
			plt.close()

			# plot a straight line on the data
			plt.scatter(X_test, y_test,  color='gray')
			plt.plot(X_test, y_pred, color='red', linewidth=2)
			plt.tight_layout()
			plt.savefig('straight_line_predictions.png')
			plt.close()
		else:
			# multi-dimensional generalization 
			df = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred})
			df1 = df.head(25)

			df1.plot(kind='bar',figsize=(10,8))
			plt.grid(which='major', linestyle='-', linewidth='0.5', color='green')
			plt.grid(which='minor', linestyle=':', linewidth='0.5', color='black')
			plt.tight_layout()
			plt.savefig('bar_graph_predictions.png')
			plt.close()
	except:
		print('error plotting regressor')

def pursue_modeling(mtype, model_dir, problemtype, default_training_script,common_name_model):
	'''
	simple script to decide whether or not to continue modeling the data.
	'''
	try:
		model_listdir=os.listdir(model_dir+'/'+problemtype+'_models')
	except:	
		model_listdir=list()

	# note that these are tpot definitions
	model_exists=False
	if default_training_script == 'tpot':
		if common_name_model + '_classifier' in model_listdir and mtype == 'c':
			model_exists=True
		elif common_name_model +'_regression' in model_listdir and mtype == 'r':
			model_exists=True
	else:
		# only look for naming conflicts with TPOT for now, can expand into the future.
		model_exists=False

	return model_exists, model_listdir
	
def get_csvfiles(listdir):
	csvfiles=list()
	for i in range(len(listdir)):
		if listdir[i].endswith('.csv'):
			csvfiles.append(listdir[i])
	return csvfiles

###############################################################
##                    LOADING SETTINGS                       ##
###############################################################

# load the default feature set 
cur_dir = os.getcwd()
prevdir= prev_dir(cur_dir)
sys.path.append(prevdir+'/train_dir')
settings=json.load(open(prevdir+'/settings.json'))

# get all the default feature arrays 
default_audio_features=settings['default_audio_features']
default_text_features=settings['default_text_features']
default_image_features=settings['default_image_features']
default_video_features=settings['default_video_features']
default_csv_features=settings['default_csv_features']
create_csv=settings['create_csv']

# prepare training and testing data (should have been already featurized) - # of classes/folders
os.chdir(prevdir+'/train_dir')

data_dir=os.getcwd()
listdir=os.listdir()
folders=get_folders(listdir)
csvfiles=get_csvfiles(listdir)

# now assess folders by content type 
data=dict()
for i in range(len(folders)):
	os.chdir(folders[i])
	listdir=os.listdir()
	filetype=classifyfolder(listdir)
	data[folders[i]]=filetype 
	os.chdir(data_dir)

###############################################################
##                  INITIALIZE CLASSES                       ##
###############################################################

# get all information from sys.argv, and if not, 
# go through asking user for the proper parameters 
try:
	problemtype=sys.argv[1]
	mtype=sys.argv[3]
	if mtype == 'c':
		classnum=sys.argv[2]
		common_name=sys.argv[4]
		classes=list()
		for i in range(int(classnum)):
			classes.append(sys.argv[i+5])
	else:

		classnum=1
		problemtype='csv'
		mtype=sys.argv[1]
		csvfile=sys.argv[2]
		classes=[sys.argv[3]]
		common_name=csvfile[0:-4]
except:
	# now ask user what type of problem they are trying to solve 
	mtype=input('is this a classification (c) or regression (r) problem? \n')
	while mtype not in ['c','r']:
		print('input not recognized...')
		mtype=input('is this a classification (c) or regression (r) problem? \n')

	if mtype == 'c':
		problemtype=input('what problem are you solving? (1-audio, 2-text, 3-image, 4-video, 5-csv)\n')
		while problemtype not in ['1','2','3','4','5']:
			print('answer not recognized...')
			problemtype=input('what problem are you solving? (1-audio, 2-text, 3-image, 4-video, 5-csv)\n')

		if problemtype=='1':
			problemtype='audio'
		elif problemtype=='2':
			problemtype='text'
		elif problemtype=='3':
			problemtype='image'
		elif problemtype=='4':
			problemtype='video'
		elif problemtype=='5':
			problemtype='csv'

		if problemtype != 'csv':

			print('\n OK cool, we got you modeling %s files \n'%(problemtype))
			count=0
			availableclasses=list()
			for i in range(len(folders)):
				if data[folders[i]]==problemtype:
					availableclasses.append(folders[i])
					count=count+1

			classnum=input('how many classes would you like to model? (%s available) \n'%(str(count)))
			print('these are the available classes: ')
			print(availableclasses)
			# get all if all (good for many classes)
			classes=list()
			if classnum=='all':
				for i in range(len(availableclasses)):
					classes.append(availableclasses[i])
			else:
						
				stillavailable=list()
				for i in range(int(classnum)):
					class_=input('what is class #%s \n'%(str(i+1)))

					while class_ not in availableclasses and class_ not in '' or class_ in classes:
						print('\n')
						print('------------------ERROR------------------')
						print('the input class does not exist (for %s files).'%(problemtype))
						print('these are the available classes: ')
						if len(stillavailable)==0:
							print(availableclasses)
						else:
							print(stillavailable)
						print('------------------------------------')
						class_=input('what is class #%s \n'%(str(i+1)))
					for j in range(len(availableclasses)):
						stillavailable=list()
						if availableclasses[j] not in classes:
							stillavailable.append(availableclasses[j])
					if class_ == '':
						class_=stillavailable[0]

					classes.append(class_)

		elif problemtype == 'csv':
			print('\n OK cool, we got you modeling %s files \n'%(problemtype))
			print('csv file options are: %s \n'%(csvfiles))
			csvfile=input('which csvfile would you like to use for classification? \n')
			g=pd.read_csv(csvfile)
			columns=list(g)
			print('potential targets include: %s'%(columns))
			target=input('what target would you like to use? \n')
			csv_labels=g[target]
			csv_features=g.drop([target], axis=1)
			
	elif mtype =='r':
		# for regression problems we need a target column to predict / classes from a .CSV 
		problemtype='csv'
		# assumes the .CSV file is in the train dir
		os.chdir(prevdir+'/train_dir')
		listdir=os.listdir()
		csvfiles=list()
		for i in range(len(listdir)):
			if listdir[i].endswith('.csv'):
				csvfiles.append(listdir[i])

		csvfile=input('what is the name of the spreadsheet (in ./train_dir) used for prediction? \n\n available: %s\n\n'%(str(csvfiles)))
		while csvfile not in csvfiles:
			print('answer not recognized...')
			csvfile=input('what is the name of the spreadsheet (in ./train_dir) used for prediction? \n\n available: %s\n\n'%(str(csvfiles)))

		# the available classes are only the numeric columns from the spreadsheet
		data = pd.read_csv(csvfile)
		columns = list(data)
		availableclasses=list()

		for i in range(len(columns)):
			# look at filetype extension in each column
			coldata=data[columns[i]]
			sampletypes=list()
			for j in range(len(coldata)):
				try:
					values=float(coldata[j])
					sampletypes.append('numerical')
				except:
					if coldata[j].endswith('.wav'):
						sampletypes.append('audio')
					elif coldata[j].endswith('.txt'):
						sampletypes.append('text')
					elif coldata[j].endswith('.png'):
						sampletypes.append('image')
					elif coldata[j].endswith('.mp4'):
						sampletypes.append('video')
					else:
						sampletypes.append('other')

			coltype=most_common(sampletypes)

			# correct the other category if needed
			if coltype == 'other':
				# if coltype.endswith('.csv'):
					# coltype='csv'
				if len(set(list(coldata))) < 10:
					coltype='categorical'
				else:
					# if less than 5 unique answers then we can interpret this as text input
					coltype='typedtext'

			if coltype == 'numerical':
				availableclasses.append(columns[i])

		if len(availableclasses) > 0:
			classnum=input('how many classes would you like to model? (%s available) \n'%(str(len(availableclasses))))
			print('these are the available classes: %s'%(str(availableclasses)))

			classes=list()	
			stillavailable=list()
			for i in range(int(classnum)):
				class_=input('what is class #%s \n'%(str(i+1)))

				while class_ not in availableclasses and class_ not in '' or class_ in classes:
					print('\n')
					print('------------------ERROR------------------')
					print('the input class does not exist (for %s files).'%(problemtype))
					print('these are the available classes: ')
					if len(stillavailable)==0:
						print(availableclasses)
					else:
						print(stillavailable)
					print('------------------------------------')
					class_=input('what is class #%s \n'%(str(i+1)))
				for j in range(len(availableclasses)):
					stillavailable=list()
					if availableclasses[j] not in classes:
						stillavailable.append(availableclasses[j])
				if class_ == '':
					class_=stillavailable[0]

				classes.append(class_)

		else:
			print('no classes available... ending session')
			sys.exit()

	common_name=input('what is the 1-word common name for the problem you are working on? (e.g. gender for male/female classification) \n')

###############################################################
##      	      UPGRADE MODULES / LOAD MODULES             ##
###############################################################

print('-----------------------------------')
print('          LOADING MODULES          ')
print('-----------------------------------')
# upgrade to have the proper scikit-learn version later
os.chdir(cur_dir)
os.system('python3 upgrade.py')
import pandas as pd
from sklearn.model_selection import train_test_split
from tqdm import tqdm
import numpy as np
from sklearn import metrics
from sklearn.metrics import roc_curve

###############################################################
##                    CLEAN THE DATA                        ##
###############################################################
clean_data=settings['clean_data']
clean_dir=prevdir+'/cleaning'

if clean_data == True and mtype == 'c':
	
	# only pursue augmentation strategies on directories of files and classification problems
	print('-----------------------------------')
	print(f.renderText('CLEANING DATA'))
	print('-----------------------------------')
	for i in range(len(classes)):
		if problemtype == 'audio':
			# clean audio via default_audio_cleaners 
			os.chdir(clean_dir+'/audio_cleaning')
		elif problemtype == 'text':
			# clean text via default_text_cleaners 
			os.chdir(clean_dir+'/text_cleaning')
		elif problemtype == 'image':
			# clean images via default_image_cleaners 
			os.chdir(clean_dir+'/image_cleaning')
		elif problemtype == 'video':
			# clean video via default_video_cleaners 
			os.chdir(clean_dir+'/video_cleaning')
		elif problemtype == 'csv':
			# clean .CSV via default_csv_cleaners 
			os.chdir(clean_dir+'/csv_cleaning')
		os.system('python3 clean.py "%s"'%(data_dir+'/'+classes[i]))

elif clean_data == True and mtype == 'r':
	for i in range(len(classes)):
		if problemtype == 'csv':
			# clean .CSV via default_csv_cleaners 
			os.chdir(clean_dir+'/csv_cleaning')
		os.system('python3 clean.py "%s"'%(data_dir+'/'+classes[i]))

###############################################################
##                    AUGMENT THE DATA                       ##
###############################################################
augment_data=settings['augment_data']
augment_dir=prevdir+'/augmentation'

if augment_data == True and mtype == 'c':

	# only pursue augmentation strategies on directories of files and classification problems
	print('-----------------------------------')
	print(f.renderText('AUGMENTING DATA'))
	print('-----------------------------------')
	for i in range(len(classes)):
		if problemtype == 'audio':
			# augment audio via default_audio_augmenters
			os.chdir(augment_dir+'/audio_augmentation')
		elif problemtype == 'text':
			# augment text via default_text_augmenters
			os.chdir(augment_dir+'/text_augmentation')
		elif problemtype == 'image':
			# augment images via default_image_augmenters
			os.chdir(augment_dir+'/image_augmentation')
		elif problemtype == 'video':
			# augment video via default_video_augmenters
			os.chdir(augment_dir+'/video_augmentation')
		elif problemtype == 'csv':
			# augment .CSV via default_csv_augmenters
			os.chdir(augment_dir+'/csv_augmentation')
		os.system('python3 augment.py "%s"'%(data_dir+'/'+classes[i]))

elif augment_data == True and mtype == 'r':
	for i in range(len(classes)):
		if problemtype == 'csv':
			# featurize .CSV via default_csv_augmenters
			os.chdir(augment_dir+'/csv_augmentation')
		os.system('python3 augment.py "%s"'%(data_dir+'/'+classes[i]))

###############################################################
##                    FEATURIZE FILES                        ##
###############################################################

# now featurize each class (in proper folder)
if mtype == 'c':
	data={}
	print('-----------------------------------')
	print(f.renderText('FEATURIZING DATA'))
	print('-----------------------------------')

	if problemtype == 'csv':
		# csv features should have already been defined
		# need to separate into number of unique classes
		csv_labels=g[target]
		csv_features=g.drop([target], axis=1)
		csv_feature_labels=list(csv_features)
		classes=list(set(list(csv_labels)))
		for i in range(len(classes)):
			class_type = classes[i]

			feature_list=list()
			label_list=list()
			
			for i in range(len(csv_features)):
				if csv_labels[i] == class_type:
					feature_list.append(list(csv_features.iloc[i,:]))
					label_list.append(csv_feature_labels)

			data[class_type]=feature_list
	else:
		# 
		for i in range(len(classes)):
			class_type=classes[i]
			if problemtype == 'audio':
				# featurize audio 
				os.chdir(prevdir+'/features/audio_features')
				default_features=default_audio_features
			elif problemtype == 'text':
				# featurize text
				os.chdir(prevdir+'/features/text_features')
				default_features=default_text_features
			elif problemtype == 'image':
				# featurize images
				os.chdir(prevdir+'/features/image_features')
				default_features=default_image_features
			elif problemtype == 'video':
				# featurize video 
				os.chdir(prevdir+'/features/video_features')
				default_features=default_video_features

			print('-----------------------------------')
			print('           FEATURIZING %s'%(classes[i].upper()))
			print('-----------------------------------')
			
			os.system('python3 featurize.py "%s"'%(data_dir+'/'+classes[i]))
			os.chdir(data_dir+'/'+classes[i])
			# load audio features 
			listdir=os.listdir()
			feature_list=list()
			label_list=list()
			for j in range(len(listdir)):
				if listdir[j][-5:]=='.json':
					try:
						g=json.load(open(listdir[j]))
						# consolidate all features into one array (if featurizing with multiple featurizers)
						default_feature=list()
						default_label=list()
						for k in range(len(default_features)):
							default_feature=default_feature+g['features'][problemtype][default_features[k]]['features']
							default_label=default_label+g['features'][problemtype][default_features[k]]['labels']

						feature_list.append(default_feature)
						label_list.append(default_label)
					except:
						print('ERROR - skipping ' + listdir[j])
			
			data[class_type]=feature_list


elif mtype == 'r':
	# featurize .CSV 
	os.chdir(prevdir+'/features/csv_features')
	output_file=str(uuid.uuid1())+'.csv'
	os.system('python3 featurize_csv_regression.py -i "%s" -o "%s" -t "%s"'%(prevdir+'/train_dir/'+csvfile, prevdir+'/train_dir/'+output_file, classes[0]))
	csvfile=output_file
	default_features=['csv_regression']

###############################################################
##                  GENERATE TRAINING DATA                   ##
###############################################################

print('-----------------------------------')
print(f.renderText('CREATING TRAINING DATA'))
print('-----------------------------------')

# perform class balance such that both classes have the same number
# of members (true by default, but can also be false)
os.chdir(prevdir+'/training/')
model_dir=prevdir+'/models'
balance=settings['balance_data']
remove_outliers=settings['remove_outliers']
outlier_types=settings['default_outlier_detector']

if mtype == 'c':

	if problemtype != 'csv':
		jsonfile=''
		for i in range(len(classes)):
			if i==0:
				jsonfile=classes[i]
			else:
				jsonfile=jsonfile+'_'+classes[i]

		jsonfile=jsonfile+'.json'

		#try:
		g=data
		alldata=list()
		labels=list()
		lengths=list()

		# check to see all classes are same length and reshape if necessary
		for i in range(len(classes)):
			class_=g[classes[i]]
			lengths.append(len(class_))

		lengths=np.array(lengths)
		minlength=np.amin(lengths)

		# now load all the classes
		for i in range(len(classes)):
			class_=g[classes[i]]
			random.shuffle(class_)

			# only balance if specified in settings
			if balance==True:
				if len(class_) > minlength:
					print('%s greater than minlength (%s) by %s, equalizing...'%(classes[i], str(minlength), str(len(class_)-minlength)))
					class_=class_[0:minlength]

			for j in range(len(class_)):
				alldata.append(class_[j])
				labels.append(i)

		# load features file and get feature labels by loading in classes
		labels_dir=prevdir+'/train_dir/'+classes[0]
		os.chdir(labels_dir)
		listdir=os.listdir()
		features_file=''
		for i in range(len(listdir)):
			if listdir[i].endswith('.json'):
				features_file=listdir[i]

		labels_=list()

		for i in range(len(default_features)):
			tlabel=json.load(open(features_file))['features'][problemtype][default_features[i]]['labels']
			labels_=labels_+tlabel

	elif problemtype == 'csv':
		# format data appropriately 
		jsonfile=target+'.json'

		#try:
		g=data
		alldata=list()
		labels=list()
		lengths=list()

		# check to see all classes are same length and reshape if necessary
		for i in range(len(classes)):
			class_=g[classes[i]]
			lengths.append(len(class_))

		lengths=np.array(lengths)
		minlength=np.amin(lengths)

		# now load all the classes
		for i in range(len(classes)):
			class_=g[classes[i]]
			random.shuffle(class_)

			# only balance if specified in settings
			if balance==True:
				if len(class_) > minlength:
					print('%s greater than minlength (%s) by %s, equalizing...'%(classes[i], str(minlength), str(len(class_)-minlength)))
					class_=class_[0:minlength]

			for j in range(len(class_)):
				alldata.append(class_[j])
				labels.append(i)

		# load features file and get feature labels by loading in classes
		labels_=csv_feature_labels

elif mtype == 'r':
	regression_data=pd.read_csv(prevdir+'/train_dir/'+csvfile)
	print(csvfile)
	# get features and labels
	features_=regression_data.drop(columns=classes, axis=1)
	labels_=list(features_)
	labels_csv=regression_data.drop(columns=list(features_), axis=1)
	# iterate through each column and make into proper features and labels
	features=list()
	labels=list()

	# testing
	# print(len(features_))
	# print(len(labels_))
	# print(features_)
	# print(labels_)
	# print(features_.iloc[0,:])
	# print(labels_.iloc[0,:])

	# get features and labels 
	for i in range(len(features_)):
		features.append(list(features_.iloc[i,:]))
		labels.append(list(labels_csv.iloc[i,:]))

	# convert to name alldata just to be consistent
	alldata=features
	# print(alldata[0])
	# print(labels[0])

# print(labels_)
os.chdir(model_dir)

# get the split from the settings.json
try:
	test_size=settings['test_size']
except:
	test_size=0.25

# error checking around lengths of arrays and deleting as necessary
lengths=list()
for i in range(len(alldata)):
	lengths.append(len(alldata[i]))

# CLEAN IF DIMENSIONS DO NOT MATCH!!
maxval=max(lengths)
minval=min(lengths)
delete_ind=list()
inds=list()
alldata=np.array(alldata)
labels=np.array(labels)

if maxval != minval:
	if lengths.count(maxval) > lengths.count(minval):
		for i in range(len(lengths)):
			# this means that additional column has been removed
			if lengths[i] == minval:
				delete_ind.append(i)

	elif lengths.count(maxval) < lengths.count(minval):
		for i in range(len(lengths)):
			# this means that additional column has been added
			if lengths[i] == maxval:
				delete_ind.append(i)

	print('DELETING THESE INDICES: %s'%(str(delete_ind)))
	print(alldata.shape)
	print(labels.shape)
	alldata=np.delete(alldata, tuple(delete_ind), axis=0)
	labels=np.delete(labels, tuple(delete_ind))
	print(alldata.shape)
	print(labels.shape)
	
# # now see if any element in the array is a NaN and do not include if so in alldata or labels
# for i in range(len(alldata)):
# 	try:
# 		array_has_nan = list(np.isnan(np.array(alldata[i]))).count(True)
# 		array_has_string=list(np.char.isnumeric(np.array(alldata[i]))).count(False)
# 	except:
# 		array_has_string=1
# 	if array_has_nan > 0 or array_has_string > 0:
# 		inds.append(i)
# 		print(alldata[i])

# if len(inds) > 0:
# 	print('DELETING THESE INDICES: %s'%(str(inds)))
# 	alldata=np.delete(alldata, tuple(inds))
# 	labels=np.delete(labels, tuple(inds))

# REMOVE OUTLIERS IF SETTING IS TRUE
alldata=np.array(alldata)
labels=np.array(labels)

if remove_outliers==True:
	print('-----------------------------------')
	print('			REMOVING OUTLIERS')
	print('-----------------------------------')
	for i in range(len(outlier_types)):
		outlier_type=outlier_types[i]
		if outlier_type =='isolationforest':
		    from sklearn.ensemble import IsolationForest
		    clf = IsolationForest(random_state=0).fit(alldata)
		    y_pred = clf.predict(alldata)

		    inlier_ind=list(np.where(y_pred==1))
		    outlier_ind=list(np.where(y_pred==-1))
		    y_pred = y_pred.tolist()

		    print(type(y_pred))
		    print(type(y_pred[0]))
		    n_inliers = y_pred.count(1)
		    n_outliers = y_pred.count(-1)
		    print(n_inliers)
		    print(n_outliers)

		    # shape before 
		    print(alldata.shape)
		    print(labels.shape)
		    # delete outliers
		    alldata=np.delete(alldata, tuple(outlier_ind), axis=0)
		    labels=np.delete(labels, tuple(outlier_ind))
		    print(alldata.shape)
		    print(labels.shape)

		elif outlier_type=='zscore':
			os.system('pip3 install statsmodels==0.11.1')
			from scipy import stats
			from statsmodels.formula.api import ols
			# https://towardsdatascience.com/ways-to-detect-and-remove-the-outliers-404d16608dba
			z = np.abs(stats.zscore(alldata))
			# print(z)
			threshold = 3
			inds=list(set(np.where(z>threshold)[0]))
			print(len(inds))
			print(tuple(inds))
			print(alldata.shape)
			print('-->')
			alldata = np.delete(alldata, tuple(inds), axis=0)
			print(alldata.shape)
			labels = np.delete(labels, tuple(inds))
			print(len(alldata))
			print(len(labels))

	# rebalance data to all be the same length
	newlabels=list(labels)
	outlier_class=list()
	for i in range(len(classes)):
		outlier_class.append(newlabels.count(i))

	lengths=np.array(outlier_class)
	minlength=np.amin(outlier_class)
	# now load all the classes
	for i in range(len(classes)):
		# only balance if specified in settings
		if balance==True:
			count2=newlabels.count(i)
			while count2 > minlength:
				count2=newlabels.count(i)
				print('%s greater than minlength (%s) by %s, equalizing...'%(classes[i], str(minlength), str(count2-minlength)))
				ind=list(labels).index(i)
				alldata=np.delete(alldata, tuple([ind]), axis=0)
				labels=np.delete(labels, tuple([ind]))
				newlabels=list(labels)

alldata=list(alldata)
labels=list(labels)

# split the data 
X_train, X_test, y_train, y_test = train_test_split(alldata, labels, test_size=test_size)

# convert everything to numpy arrays (for testing later)
X_train=np.array(X_train)
X_test=np.array(X_test)
y_train=np.array(y_train)
y_test=np.array(y_test)

# create list of created csv files
created_csv_files=list()

# create training and testing datasets and save to a .CSV file for archive purposes
# this ensures that all machine learning training methods use the same training data
basefile=common_name
temp_listdir=os.listdir()

if create_csv == True:
	try:
		print(basefile+'_all.csv'.upper())
		if basefile+'_all.csv' not in temp_listdir:
			all_data = convert_csv(alldata, labels, labels_, mtype, classes)
			all_data.to_csv(basefile+'_all.csv',index=False)
		created_csv_files.append(basefile+'_all.csv')
	except:
		print('error exporting data into excel sheet %s'%(basefile+'_all.csv'))
	try:
		print(basefile+'_train.csv'.upper())
		if basefile+'_train.csv' not in temp_listdir:
			train_data= convert_csv(X_train, y_train, labels_, mtype, classes)
			train_data.to_csv(basefile+'_train.csv',index=False)
		created_csv_files.append(basefile+'_train.csv')
	except:
		print('error exporting data into excel sheet %s'%(basefile+'_train.csv'))
	try:
		print(basefile+'_test.csv'.upper())
		if basefile+'_test.csv' not in temp_listdir:
			test_data= convert_csv(X_test, y_test, labels_, mtype, classes)
			test_data.to_csv(basefile+'_test.csv',index=False)
		created_csv_files.append(basefile+'_test.csv')
	except:
		print('error exporting data into excel sheet %s'%(basefile+'_test.csv'))

############################################################
## 			        DATA TRANSFORMATION 			      ##
############################################################

'''
Scale features via scalers, dimensionality reduction techniques,
and feature selection strategies per the settings.json document.
'''
preprocess_dir=prevdir+'/preprocessing'
os.chdir(preprocess_dir)

# get all the important settings for the transformations 
scale_features=settings['scale_features']
reduce_dimensions=settings['reduce_dimensions']
select_features=settings['select_features']
default_scalers=settings['default_scaler']
default_reducers=settings['default_dimensionality_reducer']
default_selectors=settings['default_feature_selector']

# get command for terminal
transform_command=''
if problemtype == 'csv' and mtype == 'c':
	transform_command=transform_command+' "'+'Class'+'"'
else:
	for i in range(len(classes)):
		transform_command=transform_command+' "'+classes[i]+'"'

# get filename / create a unique file name
if mtype=='r':
	t_filename='r_'+common_name
elif mtype=='c':
	t_filename='c_'+common_name

# only add names in if True 
if scale_features == True:
	for i in range(len(default_scalers)):
		t_filename=t_filename+'_'+default_scalers[i]
if reduce_dimensions == True:
	for i in range(len(default_reducers)):
		t_filename=t_filename+'_'+default_reducers[i]
if select_features == True:
	for i in range(len(default_selectors)):
		t_filename=t_filename+'_'+default_selectors[i]

transform_file=t_filename+'.pickle'

if scale_features == True or reduce_dimensions == True or select_features == True:
	print('----------------------------------')
	print(f.renderText('TRANSFORMING DATA'))
	print('----------------------------------')
	# go to proper transformer directory
	try:
		os.chdir(problemtype+'_transformer')
	except:
		os.mkdir(problemtype+'_transformer')
		os.chdir(problemtype+'_transformer')
	# train transformer if it doesn't already exist
	os.system('pip3 install scikit-learn==0.22.2.post1')
	if transform_file in os.listdir():
		# remove file if in listdir to avoid conflicts with naming
		os.remove(transform_file)

	print('making transformer...')
	alldata=np.asarray(alldata)
	labels=np.asarray(labels)
	os.chdir(preprocess_dir)
	if mtype == 'c':
		print('python3 transform.py "%s" "%s" "%s" %s'%(problemtype, 'c', common_name, transform_command))
		os.system('python3 transform.py "%s" "%s" "%s" %s'%(problemtype, 'c', common_name, transform_command))
		os.chdir(problemtype+'_transformer')
		print(transform_file)
		transform_model=pickle.load(open(transform_file,'rb'))
		alldata=transform_model.transform(np.array(alldata))

	elif mtype == 'r':
		command='python3 transform.py "%s" "%s" "%s" "%s" "%s" "%s"'%('csv', 'r', classes[0], csvfile, prevdir+'/train_dir/', common_name)
		print(command)
		os.system(command)
		os.chdir(problemtype+'_transformer')
		transform_model=pickle.load(open(transform_file,'rb'))
		alldata=transform_model.transform(alldata)

	os.chdir(preprocess_dir)
	os.system('python3 load_transformer.py "%s" "%s"'%(problemtype, transform_file))

	# now make new files as .CSV
	os.chdir(model_dir)

	# split the data 
	X_train, X_test, y_train, y_test = train_test_split(alldata, labels, test_size=test_size)

	# convert to numpy arrays 
	X_train=np.array(X_train)
	X_test=np.array(X_test)
	y_train=np.array(y_train)
	y_test=np.array(y_test)
	
	# get new labels_ array 
	labels_=list()
	for i in range(len(alldata[0].tolist())):
		labels_.append('transformed_feature_%s'%(str(i)))

	# now create transformed excel sheets
	temp_listdir=os.listdir()
	if create_csv == True:
		try:
			print(basefile+'_all_transformed.csv'.upper())
			if basefile+'_all_transformed.csv' not in temp_listdir:
				all_data = convert_csv(alldata, labels, labels_, mtype, classes)
				all_data.to_csv(basefile+'_all_transformed.csv',index=False)
			created_csv_files.append(basefile+'_all_transformed.csv')
		except:
			print('error exporting data into excel sheet %s'%(basefile+'_all_transformed.csv'))
		try:
			print(basefile+'_train_transformed.csv'.upper())
			if basefile+'_train_transformed.csv' not in temp_listdir:
				train_data= convert_csv(X_train, y_train, labels_, mtype, classes)
				train_data.to_csv(basefile+'_train_transformed.csv',index=False)
			created_csv_files.append(basefile+'_train_transformed.csv')
		except:
			print('error exporting data into excel sheet %s'%(basefile+'_train_transformed.csv'))

		try:
			print(basefile+'_test_transformed.csv'.upper())
			if basefile+'_test_transformed.csv' not in temp_listdir:
				test_data= convert_csv(X_test, y_test, labels_, mtype, classes)
				test_data.to_csv(basefile+'_test_transformed.csv',index=False)
			created_csv_files.append(basefile+'_test_transformed.csv')
		except:
			print('error exporting data into excel sheet %s'%(basefile+'_test_transformed.csv'))
else:
	# make a transform model == '' so that later during model training this can be skipped
	transform_model=''

############################################################
## 					VISUALIZE DATA  					  ##
############################################################

visualize_data=settings['visualize_data']
visual_dir=prevdir+'/visualize'
model_session=str(uuid.uuid1())
os.chdir(visual_dir)

if visualize_data == True and mtype == 'c':
	print('----------------------------------')
	print(f.renderText('VISUALIZING DATA'))
	print('----------------------------------')

	command='python3 visualize.py %s'%(problemtype)
	for i in range(len(classes)):
		command=command+' "'+classes[i]+'"'
	os.system(command)

	# restructure the visualization directory 
	os.chdir(visual_dir+'/visualization_session')
	os.mkdir('visualizations')
	vizdir=os.getcwd()

	# move directories so that visualization is separate from main model directory 
	shutil.move(vizdir+'/clustering', vizdir+'/visualizations/clustering')
	shutil.move(vizdir+'/feature_ranking', vizdir+'/visualizations/feature_ranking')
	shutil.move(vizdir+'/model_selection', vizdir+'/visualizations/model_selection')
	
	# go back to main direcotry
	os.chdir(visual_dir)

	# now copy over the visualization directory to 
	try:
		shutil.copytree(visual_dir+'/visualization_session', model_dir+'/'+model_session)
	except:
		shutil.rmtree(model_dir+'/'+model_session)
		shutil.copytree(visual_dir+'/visualization_session', model_dir+'/'+model_session)
	# copy over settings.json 
	shutil.copy(prevdir+'/settings.json',model_dir+'/%s/settings.json'%(model_session))

else:
	# make a model session for next section if it doesn't exist from visualization directory
	os.chdir(model_dir)
	try:
		os.mkdir(model_session)
	except:
		shutil.rmtree(model_session)
		os.mkdir(model_session)
	# copy over settings.json
	shutil.copy(prevdir+'/settings.json', model_dir+'/%s/settings.json'%(model_session))

############################################################
## 					TRAIN THE MODEL 					  ##
############################################################

'''
Now we can train the machine learning model via the default_training script.
Note you can specify multiple training scripts and it will consecutively model the 
files appropriately. 

#^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^#
#  Here is what all the variables below mean:
#^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^#

# alldata = list of features in an array for model training  
	# [[39.0, 112.15384615384616, 70.98195453650514, 248.0, 14.0, 103.0, 143.5546875...],
		...
	   [39.0, 112.15384615384616, 70.98195453650514, 248.0, 14.0, 103.0, 143.5546875,...]]
# labels = list of labels in an array for model training 
	# ['males','females',...,'males','females']
# mtype = classification or regression problem?
	# 'c' --> classification
	# 'r' --> regression
# jsonfile = filename of the .JSON document seprating classes
	#  males_females.json
# problemtype = type of problem selected
	# 'audio' --> audio files
	# 'image' --> images files
	# 'text' --> text files
	# 'video' --> video files
	# 'csv' --> csv files 
# default_featurenames = default feature array(s) to use for modeling 
	# ['librosa_features']
# settings = overall settings currenty used for model training 
	# output of the settings.json document 

-----

# transform_model = transformer model if applicable 
	# useful for data transformation as part of the model initialization process (if pickle file)
	# uses scikit-learn pipeline 

# X_train, X_test, y_train, y_test
	# training datasets used in the .CSV documents 
	# also can use pandas dataframe if applicable (loading in the model dir)
'''
print('----------------------------------')
print(f.renderText('MODELING DATA'))
print('----------------------------------')

# get defaults 
default_training_scripts=settings['default_training_script']
model_compress=settings['model_compress']
default_featurenames=''

if problemtype != 'csv' and mtype == 'c':
	for i in range(len(default_features)):
		if i ==0:
			default_featurenames=default_features[i]
		else:
			default_featurenames=default_featurenames+'_|_'+default_features[i] 

else:
	default_featurenames='csv_classification'

# just move all created .csv files into model_session directory
os.chdir(model_dir)
os.chdir(model_session)
os.mkdir('data')
for i in range(len(created_csv_files)):
	shutil.move(model_dir+'/'+created_csv_files[i], os.getcwd()+'/data/'+created_csv_files[i])

# initialize i (for tqdm) and go through all model training scripts 
i=0
for i in tqdm(range(len(default_training_scripts)), desc=default_training_scripts[i]):

	try:

		model_start_time=time.time()
		# go to model directory 
		os.chdir(model_dir)

		# get common name and default training script to select proper model trainer
		default_training_script=default_training_scripts[i]
		common_name_model=common_name+'_'+default_training_script
		model_exists, model_listdir = pursue_modeling(mtype, model_dir, problemtype, default_training_script, common_name_model)
		if model_exists == False:	

			print('----------------------------------')
			print('       .... training %s           '%(default_training_script.upper()))
			print('----------------------------------')

			if default_training_script=='adanet':
				print('Adanet training is coming soon! Please use a different model setting for now.') 
				# import train_adanet as ta 
				# ta.train_adanet(mtype, classes, jsonfile, alldata, labels, feature_labels, problemtype, default_featurenames)
			elif default_training_script=='alphapy':
				import train_alphapy as talpy
				modelname, modeldir, files=talpy.train_alphapy(X_train,X_test,y_train,y_test,mtype,common_name_model,problemtype,classes,default_featurenames,transform_model,settings,model_session)
			elif default_training_script=='atm':
				import train_atm as tatm
				modelname, modeldir, files=tatm.train_atm(X_train,X_test,y_train,y_test,mtype,common_name_model,problemtype,classes,default_featurenames,transform_model,settings,model_session)
			elif default_training_script=='autobazaar':
				import train_autobazaar as autobzr
				modelname, modeldir, files=autobzr.train_autobazaar(X_train,X_test,y_train,y_test,mtype,common_name_model,problemtype,classes,default_featurenames,transform_model,settings,model_session)
			elif default_training_script=='autogbt':
				import train_autogbt as tautogbt
				modelname, modeldir, files=tautogbt.train_autogbt(X_train,X_test,y_train,y_test,mtype,common_name_model,problemtype,classes,default_featurenames,transform_model,settings,model_session)
			elif default_training_script=='autogluon':
				import train_autogluon as tautg
				modelname, modeldir, files, test_data=tautg.train_autogluon(X_train,X_test,y_train,y_test,mtype,common_name_model,problemtype,classes,default_featurenames,transform_model,settings,model_session)
			elif default_training_script=='autokaggle':
				import train_autokaggle as autokag
				modelname, modeldir, files=autokag.train_autokaggle(X_train,X_test,y_train,y_test,mtype,common_name_model,problemtype,classes,default_featurenames,transform_model,settings,model_session)
			elif default_training_script=='autokeras':
				import train_autokeras as autokeras_
				modelname, modeldir, files=autokeras_.train_autokeras(X_train,X_test,y_train,y_test,mtype,common_name_model,problemtype,classes,default_featurenames,transform_model,settings,model_session)
			elif default_training_script=='automl':
				import train_automl as auto_ml
				modelname, modeldir, files=auto_ml.train_automl(X_train,X_test,y_train,y_test,mtype,common_name_model,problemtype,classes,default_featurenames,transform_model,settings,model_session)
			elif default_training_script=='autosklearn':
				print('Autosklearn training is unstable! Please use a different model setting for now.') 
				# import train_autosklearn as taskl
				# taskl.train_autosklearn(alldata, labels, mtype, jsonfile, problemtype, default_featurenames)
			elif default_training_script=='autopytorch':
				import train_autopytorch as autotorch_
				modelname, modeldir, files=autotorch_.train_autopytorch(X_train,X_test,y_train,y_test,mtype,common_name_model,problemtype,classes,default_featurenames,transform_model,settings,model_session)
			elif default_training_script=='btb':
				import train_btb as tbtb
				modelname, modeldir, files=tbtb.train_btb(X_train,X_test,y_train,y_test,mtype,common_name_model,problemtype,classes,default_featurenames,transform_model,settings,model_session)
			elif default_training_script=='cvopt':
				import train_cvopt as tcvopt
				modelname, modeldir, files = tcvopt.train_cvopt(X_train,X_test,y_train,y_test,mtype,common_name_model,problemtype,classes,default_featurenames,transform_model,settings,model_session)
			elif default_training_script=='devol':
				import train_devol as td 
				modelname, modeldir, files=td.train_devol(X_train,X_test,y_train,y_test,mtype,common_name_model,problemtype,classes,default_featurenames,transform_model,settings,model_session)
			elif default_training_script=='gama':
				import train_gama as tgama
				modelname, modeldir, files=tgama.train_gama(X_train,X_test,y_train,y_test,mtype,common_name_model,problemtype,classes,default_featurenames,transform_model,settings,model_session)
			elif default_training_script=='gentun':
				import train_gentun as tgentun 
				modelname, modeldir, files=tgentun.train_gentun(X_train,X_test,y_train,y_test,mtype,common_name_model,problemtype,classes,default_featurenames,transform_model,settings,model_session)
			elif default_training_script=='hyperband':
				import train_hyperband as thband
				modelname, modeldir, files = thband.train_hyperband(X_train,X_test,y_train,y_test,mtype,common_name_model,problemtype,classes,default_featurenames,transform_model,settings,model_session)
			elif default_training_script=='hypsklearn':
				import train_hypsklearn as th 
				modelname, modeldir, files=th.train_hypsklearn(X_train,X_test,y_train,y_test,mtype,common_name_model,problemtype,classes,default_featurenames,transform_model,settings,model_session)
			elif default_training_script=='hungabunga':
				import train_hungabunga as thung
				modelname, modeldir, files=thung.train_hungabunga(X_train,X_test,y_train,y_test,mtype,common_name_model,problemtype,classes,default_featurenames,transform_model,settings,model_session)
			elif default_training_script=='imbalance':
				import train_imbalance as timb
				modelname, modeldir, files=timb.train_imbalance(X_train,X_test,y_train,y_test,mtype,common_name_model,problemtype,classes,default_featurenames,transform_model,settings,model_session)
			elif default_training_script=='keras':
				import train_keras as tk
				modelname, modeldir, files=tk.train_keras(X_train,X_test,y_train,y_test,mtype,common_name_model,problemtype,classes,default_featurenames,transform_model,settings,model_session)
			elif default_training_script=='ludwig':
				import train_ludwig as tl
				modelname, modeldir, files=tl.train_ludwig(X_train,X_test,y_train,y_test,mtype,common_name_model,problemtype,classes,default_featurenames,transform_model,settings,model_session)
			elif default_training_script=='mlblocks':
				import train_mlblocks as mlb
				modelname, modeldir, files=mlb.train_mlblocks(X_train,X_test,y_train,y_test,mtype,common_name_model,problemtype,classes,default_featurenames,transform_model,settings,model_session)
			elif default_training_script=='mlbox':
				import train_mlbox as mlbox_
				modelname, modeldir, files=mlbox_.train_mlbox(X_train,X_test,y_train,y_test,mtype,common_name_model,problemtype,classes,default_featurenames,transform_model,settings,model_session)
			elif default_training_script=='neuraxle':
				if mtype=='c':
					print('Neuraxle does not support classification at this time. Please use a different model training script')
					break
				else:
					import train_neuraxle as tneuraxle
					modelname, modeldir, files=tneuraxle.train_neuraxle(X_train,X_test,y_train,y_test,mtype,common_name_model,problemtype,classes,default_featurenames,transform_model,settings,model_session)
			elif default_training_script=='plda':
				print('PLDA training is unstable! Please use a different model setting for now.') 
				# import train_pLDA as tp
				# tp.train_pLDA(alldata,labels)
			elif default_training_script=='pytorch':
				import train_pytorch as t_pytorch
				modelname, modeldir, files = t_pytorch.train_pytorch(X_train,X_test,y_train,y_test,mtype,common_name_model,problemtype,classes,default_featurenames,transform_model,settings,model_session)
			elif default_training_script=='safe':
				import train_safe as tsafe
				modelname, modeldir, files=tsafe.train_safe(X_train,X_test,y_train,y_test,mtype,common_name_model,problemtype,classes,default_featurenames,transform_model,settings,model_session)
			elif default_training_script=='scsr':
				import train_scsr as scsr
				if mtype == 'c':
					modelname, modeldir, files=scsr.train_sc(X_train,X_test,y_train,y_test,mtype,common_name_model,problemtype,classes,default_featurenames,transform_model,settings,minlength)
				elif mtype == 'r':
					modelname, modeldir, files=scsr.train_sr(X_train,X_test,y_train,y_test,common_name_model,problemtype,classes,default_featurenames,transform_model,model_dir,settings)
			elif default_training_script=='tpot':
				import train_TPOT as tt
				modelname, modeldir, files=tt.train_TPOT(X_train,X_test,y_train,y_test,mtype,common_name_model,problemtype,classes,default_featurenames,transform_model,settings,model_session)

			############################################################
			## 		  CALCULATE METRICS / PLOT ROC CURVE        	  ##
			############################################################

			if modelname.endswith('.pickle'):
				foldername=modelname[0:-7]
			elif modelname.endswith('.h5'):
				foldername=modelname[0:-3]
			else:
				foldername=common_name_model

			# copy the folder in case there are multiple models being trained 
			try:
				shutil.copytree(model_session, foldername)
			except:
				shutil.rmtree(foldername)
				shutil.copytree(model_session, foldername)
				
			cur_dir2=os.getcwd()
			os.chdir(foldername)
			os.mkdir('model')
			os.chdir('model')
			model_dir_temp=os.getcwd()

			# dump transform model to the models directory if necessary
			if transform_model == '':
				transformer_name=''
			else:
				# dump the tranform model into the current working directory
				transformer_name=modelname.split('.')[0]+'_transform.pickle'
				tmodel=open(transformer_name,'wb')
				pickle.dump(transform_model, tmodel)
				tmodel.close()

			# move all supplementary files into model folder
			for j in range(len(files)):
				shutil.move(modeldir+'/'+files[j], model_dir_temp+'/'+files[j])
			
			# load model for getting metrics
			if default_training_script not in ['alphapy', 'atm', 'autokeras', 'autopytorch', 'ludwig', 'keras', 'devol']:
				loadmodel=open(modelname, 'rb')
				clf=pickle.load(loadmodel)
				loadmodel.close()
			elif default_training_script == 'atm':
				from atm import Model
				clf=Model.load(modelname)
			elif default_training_script == 'autokeras':
				import tensorflow as tf
				import autokeras as ak
				clf = pickle.load(open(modelname, 'rb'))
			elif default_training_script=='autopytorch':
				import torch
				clf=torch.load(modelname)
			elif default_training_script == 'ludwig':
				from ludwig.api import LudwigModel
				clf=LudwigModel.load('ludwig_files/experiment_run/model/')
			elif default_training_script in ['devol', 'keras']: 
				from keras.models import load_model
				clf = load_model(modelname)
			else: 
				clf=''

			# create test_data variable for anything other than autogluon
			if default_training_script != 'autogluon':
				test_data=''

			# now make main .JSON file for the session summary with metrics
			get_metrics(clf, problemtype, mtype, default_training_script, common_name, X_test, y_test, classes, modelname, settings, model_session, transformer_name, created_csv_files, test_data, model_start_time)
			
			# now move to the proper models directory
			os.chdir(model_dir)
			os.system('python3 create_readme.py "%s"'%(os.getcwd()+'/'+foldername))

			try:
				os.chdir(problemtype+'_models')
			except:
				os.mkdir(problemtype+'_models')
				os.chdir(problemtype+'_models')

			shutil.move(model_dir+'/'+foldername, os.getcwd()+'/'+foldername)

			############################################################
			## 					COMPRESS MODELS 					  ##
			############################################################

			if model_compress == True:
				print(f.renderText('COMPRESSING MODEL'))
				# now compress the model according to model type 
				if default_training_script in ['hypsklearn', 'scsr', 'tpot']:
					# all .pickle files and can compress via scikit-small-ensemble
					from sklearn.externals import joblib

					# open up model 
					loadmodel=open(modelname, 'rb')
					model = pickle.load(loadmodel)
					loadmodel.close()

					# compress - from 0 to 9. Higher value means more compression, but also slower read and write times. 
					# Using a value of 3 is often a good compromise.
					joblib.dump(model, modelname[0:-7]+'_compressed.joblib',compress=3)

					# can now load compressed models as such
					# thenewmodel=joblib.load(modelname[0:-7]+'_compressed.joblib')
					# leads to up to 10x reduction in model size and .72 sec - 0.23 secoon (3-4x faster loading model)
					# note may note work in sklearn and python versions are different from saving and loading environments. 

				elif default_training_script in ['devol', 'keras']: 
					# can compress with keras_compressor 
					import logging
					from keras.models import load_model
					from keras_compressor.compressor import compress

					logging.basicConfig(
						level=logging.INFO,
					)

					try:
						print('compressing model!!')
						model = load_model(modelname)
						model = compress(model, 7e-1)
						model.save(modelname[0:-3]+'_compressed.h5')
					except:
						print('error compressing model!!')

				else:
					# for everything else, we can compress pocketflow models in the future.
					print('We cannot currently compress %s models. We are working on this!! \n\n The model will remain uncompressed for now'%(default_training_script))

		else:
			if mtype == 'r':
				print('SKIPPING MODELTYPE - %s already exists in the %s folder: %s'%(common_name_model+'_regression', problemtype+'_models', str(model_listdir)))
			elif mtype == 'c':
				print('SKIPPING MODELTYPE - %s already exists in the %s folder: %s'%(common_name_model+'_classifier', problemtype+'_models', str(model_listdir)))
		############################################################
		## 					PRODUCTIONIZING MODELS				  ##
		############################################################

		# TO BE COMPLETED IN THE FUTURE!

	except:
		print('ERROR - error in modeling session')

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All you need to do is run the model.py script and you will be guided through the  modeling process.  Usage: python3 model.py  Alternative CLI Usage: python3 model.py audio 2 c gender males females - audio = audio file type - 2 = 2 classes - c = classification (r for regression) - gender = common name of model - male = first class - female = second class [via N number of classes]  For addditional documentation, check out  https://github.com/jim-schwoebel/allie/tree/master/training ############################################################### ##                  IMPORT STATEMENTS                        ## ############################################################### ############################################################### ##               CREATE HELPER FUNCTIONS                     ## ############################################################### get most common item in a list # print(dir_) # get back the type of folder (main file type) Take in a array of features and labels and output a  	pandas DataFrame format for easy .CSV expor and for model training.  	This is important to make sure all machine learning training sessions 	use the same dataset (so they can be benchmarked appropriately). # from pandas merging guide https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html # print(temp) # print this because in pretty much every case you will write the .CSV file afterwards get the metrics associated iwth a classification and regression problem 	and output a .JSON file with the training session. # go to the right folder # have to make into a pandas dataframe # get classification or regression metrics # now get all classification metrics # predict_proba only works for or log loss and modified Huber loss. # https://stackoverflow.com/questions/47788981/sgdclassifier-with-predict-proba # now get all regression metrics # also output requirements.txt for reproducibilty purposes # FUTURE - add in optional copy of cleaning, augmentation, and feature libraries contextually # try: # 	shutil.copytree(prev_dir(prev_dir(basedir))+'/features', basedir+'/features') # except: # 	print('error copying features') # try: # 	shutil.copytree(prev_dir(prev_dir(basedir))+'/cleaning', basedir+'/cleaning') # except: # 	print('error copying cleaning techniques') # 	shutil.copytree(prev_dir(prev_dir(basedir))+'/augmentation', basedir+'/augmentation') # except: # 	print('error copying augmentation techniques') This function plots an ROC curve with the appropriate  	list of classifiers. This function prints and plots the confusion matrix. 	Normalization can be applied by setting `normalize=True`. plot regression models with a bar chart. # plot the first 25 records # plot a straight line on the data # multi-dimensional generalization simple script to decide whether or not to continue modeling the data. # note that these are tpot definitions # only look for naming conflicts with TPOT for now, can expand into the future. ############################################################### ##                    LOADING SETTINGS                       ## ############################################################### # load the default feature set # get all the default feature arrays # prepare training and testing data (should have been already featurized) - # of classes/folders # now assess folders by content type ############################################################### ##                  INITIALIZE CLASSES                       ## ############################################################### # get all information from sys.argv, and if not, # go through asking user for the proper parameters # now ask user what type of problem they are trying to solve # get all if all (good for many classes) #%s \n'%(str(i+1))) #%s \n'%(str(i+1))) # for regression problems we need a target column to predict / classes from a .CSV # assumes the .CSV file is in the train dir # the available classes are only the numeric columns from the spreadsheet # look at filetype extension in each column # correct the other category if needed # if coltype.endswith('.csv'): # coltype='csv' # if less than 5 unique answers then we can interpret this as text input #%s \n'%(str(i+1))) #%s \n'%(str(i+1))) ############################################################### ##      	      UPGRADE MODULES / LOAD MODULES             ## ############################################################### # upgrade to have the proper scikit-learn version later ############################################################### ##                    CLEAN THE DATA                        ## ############################################################### # only pursue augmentation strategies on directories of files and classification problems # clean audio via default_audio_cleaners # clean text via default_text_cleaners # clean images via default_image_cleaners # clean video via default_video_cleaners # clean .CSV via default_csv_cleaners # clean .CSV via default_csv_cleaners ############################################################### ##                    AUGMENT THE DATA                       ## ############################################################### # only pursue augmentation strategies on directories of files and classification problems # augment audio via default_audio_augmenters # augment text via default_text_augmenters # augment images via default_image_augmenters # augment video via default_video_augmenters # augment .CSV via default_csv_augmenters # featurize .CSV via default_csv_augmenters ############################################################### ##                    FEATURIZE FILES                        ## ############################################################### # now featurize each class (in proper folder) # csv features should have already been defined # need to separate into number of unique classes # # featurize audio # featurize text # featurize images # featurize video # load audio features # consolidate all features into one array (if featurizing with multiple featurizers) # featurize .CSV ############################################################### ##                  GENERATE TRAINING DATA                   ## ############################################################### # perform class balance such that both classes have the same number # of members (true by default, but can also be false) #try: # check to see all classes are same length and reshape if necessary # now load all the classes # only balance if specified in settings # load features file and get feature labels by loading in classes # format data appropriately #try: # check to see all classes are same length and reshape if necessary # now load all the classes # only balance if specified in settings # load features file and get feature labels by loading in classes # get features and labels # iterate through each column and make into proper features and labels # testing # print(len(features_)) # print(len(labels_)) # print(features_) # print(labels_) # print(features_.iloc[0,:]) # print(labels_.iloc[0,:]) # get features and labels # convert to name alldata just to be consistent # print(alldata[0]) # print(labels[0]) # print(labels_) # get the split from the settings.json # error checking around lengths of arrays and deleting as necessary # CLEAN IF DIMENSIONS DO NOT MATCH!! # this means that additional column has been removed # this means that additional column has been added # # now see if any element in the array is a NaN and do not include if so in alldata or labels # for i in range(len(alldata)): # 	try: # 		array_has_nan = list(np.isnan(np.array(alldata[i]))).count(True) # 		array_has_string=list(np.char.isnumeric(np.array(alldata[i]))).count(False) # 	except: # 		array_has_string=1 # 	if array_has_nan > 0 or array_has_string > 0: # 		inds.append(i) # 		print(alldata[i]) # if len(inds) > 0: # 	print('DELETING THESE INDICES: %s'%(str(inds))) # 	alldata=np.delete(alldata, tuple(inds)) # 	labels=np.delete(labels, tuple(inds)) # REMOVE OUTLIERS IF SETTING IS TRUE # shape before # delete outliers # https://towardsdatascience.com/ways-to-detect-and-remove-the-outliers-404d16608dba # print(z) # rebalance data to all be the same length # now load all the classes # only balance if specified in settings # split the data # convert everything to numpy arrays (for testing later) # create list of created csv files # create training and testing datasets and save to a .CSV file for archive purposes # this ensures that all machine learning training methods use the same training data ############################################################ ## 			        DATA TRANSFORMATION 			      ## ############################################################ Scale features via scalers, dimensionality reduction techniques, and feature selection strategies per the settings.json document. # get all the important settings for the transformations # get command for terminal # get filename / create a unique file name # only add names in if True # go to proper transformer directory # train transformer if it doesn't already exist # remove file if in listdir to avoid conflicts with naming # now make new files as .CSV # split the data # convert to numpy arrays # get new labels_ array # now create transformed excel sheets # make a transform model == '' so that later during model training this can be skipped ############################################################ ## 					VISUALIZE DATA  					  ## ############################################################ # restructure the visualization directory # move directories so that visualization is separate from main model directory # go back to main direcotry # now copy over the visualization directory to # copy over settings.json # make a model session for next section if it doesn't exist from visualization directory # copy over settings.json ############################################################ ## 					TRAIN THE MODEL 					  ## ############################################################ Now we can train the machine learning model via the default_training script. Note you can specify multiple training scripts and it will consecutively model the  files appropriately.   #^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^# #  Here is what all the variables below mean: #^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^#  # alldata = list of features in an array for model training   	# [[39.0, 112.15384615384616, 70.98195453650514, 248.0, 14.0, 103.0, 143.5546875...], 		... 	   [39.0, 112.15384615384616, 70.98195453650514, 248.0, 14.0, 103.0, 143.5546875,...]] # labels = list of labels in an array for model training  	# ['males','females',...,'males','females'] # mtype = classification or regression problem? 	# 'c' --> classification 	# 'r' --> regression # jsonfile = filename of the .JSON document seprating classes 	#  males_females.json # problemtype = type of problem selected 	# 'audio' --> audio files 	# 'image' --> images files 	# 'text' --> text files 	# 'video' --> video files 	# 'csv' --> csv files  # default_featurenames = default feature array(s) to use for modeling  	# ['librosa_features'] # settings = overall settings currenty used for model training  	# output of the settings.json document   -----  # transform_model = transformer model if applicable  	# useful for data transformation as part of the model initialization process (if pickle file) 	# uses scikit-learn pipeline   # X_train, X_test, y_train, y_test 	# training datasets used in the .CSV documents  	# also can use pandas dataframe if applicable (loading in the model dir) # get defaults # just move all created .csv files into model_session directory # initialize i (for tqdm) and go through all model training scripts # go to model directory # get common name and default training script to select proper model trainer # import train_adanet as ta # ta.train_adanet(mtype, classes, jsonfile, alldata, labels, feature_labels, problemtype, default_featurenames) # import train_autosklearn as taskl # taskl.train_autosklearn(alldata, labels, mtype, jsonfile, problemtype, default_featurenames) # import train_pLDA as tp # tp.train_pLDA(alldata,labels) ############################################################ ## 		  CALCULATE METRICS / PLOT ROC CURVE        	  ## ############################################################ # copy the folder in case there are multiple models being trained # dump transform model to the models directory if necessary # dump the tranform model into the current working directory # move all supplementary files into model folder # load model for getting metrics # create test_data variable for anything other than autogluon # now make main .JSON file for the session summary with metrics # now move to the proper models directory ############################################################ ## 					COMPRESS MODELS 					  ## ############################################################ # now compress the model according to model type # all .pickle files and can compress via scikit-small-ensemble # open up model # compress - from 0 to 9. Higher value means more compression, but also slower read and write times. # Using a value of 3 is often a good compromise. # can now load compressed models as such # thenewmodel=joblib.load(modelname[0:-7]+'_compressed.joblib') # leads to up to 10x reduction in model size and .72 sec - 0.23 secoon (3-4x faster loading model) # note may note work in sklearn and python versions are different from saving and loading environments. # can compress with keras_compressor # for everything else, we can compress pocketflow models in the future. ############################################################ ## 					PRODUCTIONIZING MODELS				  ## ############################################################ # TO BE COMPLETED IN THE FUTURE!