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How to use prepare_data method in lettuce-tools

Best Python code snippet using lettuce-tools_python

search_indexes.py

Source:search_indexes.py

1from haystack import indexes2from anarapp.models import Yacimiento, Piedra3##################################################4# Piedra Index5##################################################6class PiedraIndex(indexes.SearchIndex, indexes.Indexable):7	#Busqueda General8	text = indexes.CharField(document=True, use_template=True)9	10	#Piedra11	codigo 			    = indexes.CharField(model_attr='codigo')12	nombre 			= indexes.CharField(model_attr='nombre')13	figuras 		        = indexes.CharField(model_attr='nombreFiguras')14	def get_model(self):15		return Piedra16	def index_queryset(self, using=None):17		return self.get_model().objects.all()18##################################################19# Yacimiento Index20##################################################21class YacimientoIndex(indexes.SearchIndex, indexes.Indexable):22	#Busqueda General23	text = indexes.CharField(document=True, use_template=True)24	25	#Yacimiento26	codigo 			    = indexes.CharField(model_attr='codigo')27	pais                  = indexes.CharField(model_attr='pais')28	municipio 		= indexes.CharField(model_attr='municipio')29	estado 			= indexes.CharField(model_attr='estado')30	nombre 			= indexes.CharField(model_attr='nombre')31	localidad 		    = indexes.CharField()32	fotografia 		= indexes.CharField()33	tipo 			        = indexes.MultiValueField() 34	exposicion 		= indexes.MultiValueField() 35	manifestacion 	= indexes.MultiValueField() 	36	ubicacion 		= indexes.MultiValueField() 37	material 		= indexes.MultiValueField() 38	conservacion 	= indexes.MultiValueField() 39	40	manifasociadas 	= indexes.MultiValueField()41	carasurcopetrotipo  = indexes.MultiValueField()42	carasurcopetroancho = indexes.MultiValueField()43	carasurcopetroprofun = indexes.MultiValueField()44	45	def get_model(self):46		return Yacimiento47	def index_queryset(self, using=None):48		return self.get_model().objects.all()49	def prepare(self, obj):50		self.prepare_data = super(YacimientoIndex, self).prepare(obj)51		52		#Localidad Yacimiento53		try:54			localidad = obj.LocalidadYacimiento55			self.prepare_data['localidad'] = localidad.nombrePoblado + ' ' + localidad.nombreNoPoblado56		except:57			pass58		59		#Fotografias	60		fotografias = obj.FotografiaYac.all()61		self.prepare_data['fotografia'] = 'true' if fotografias.count() > 0 else 'false'62		63		#Tipo Yacimiento64		try:65			tipo = obj.TipoYacimiento66			self.prepare_data['tipo'] = []67			68			if tipo.esParedRocosa:69				self.prepare_data['tipo'].append(1)70			if tipo.esRoca:71				self.prepare_data['tipo'].append(2)72			if tipo.esDolmen:73				self.prepare_data['tipo'].append(3)74			if tipo.esAbrigo:75				self.prepare_data['tipo'].append(4)76			if tipo.esCueva:77				self.prepare_data['tipo'].append(5)78			if tipo.esCuevadeRec:79				self.prepare_data['tipo'].append(6)80			if tipo.esTerrenoSup:81				self.prepare_data['tipo'].append(7)82			if tipo.esTerrenoPro:83				self.prepare_data['tipo'].append(8)84		except:85			pass86			87		#Exposicion88		try:89			exposicion = obj.TipoExposicionYac90			self.prepare_data['exposicion'] = []			91			92			if exposicion.expuesto:93				self.prepare_data['exposicion'].append(1)94			#if exposicion.noExpuesto:95			#	self.prepare_data['exposicion'].append(2)96			if exposicion.expuestoperiodicamente:97				self.prepare_data['exposicion'].append(3)98		except:99			pass100			101		#Manifestaciones102		manifestaciones = obj.ManifestacionYacimiento.all()			103		self.prepare_data['manifestacion'] = []104		105		for m in manifestaciones:106			self.prepare_data['manifestacion'].append(m.tipoManifestacion)107		#Ubicacion de la manifestacion108		ubicaciones = obj.UbicacionYacimiento.all()109		self.prepare_data['ubicacion'] = []110		for u in ubicaciones:111			self.prepare_data['ubicacion'].append(u.ubicacionManifestacion)112		#Material113		try:114			material = obj.MaterialYacimiento115			self.prepare_data['material'] = []			116			117			if material.esRoca and material.esIgnea :118				self.prepare_data['material'].append(1)119			if material.esRoca and material.esMetamor:120				self.prepare_data['material'].append(2)121			if material.esRoca and materia.esSedimentaria:122				self.prepare_data['material'].append(3)123			if material.esTierra:124				self.prepare_data['material'].append(4)125			if material.esHueso:126				self.prepare_data['material'].append(5)127			if material.esCorteza:128				self.prepare_data['material'].append(6)129			if material.esPiel:130				self.prepare_data['material'].append(7)	131		except:132			pass133		#Conservacion134		try:135			conservacion = obj.EstadoConserYac136			self.prepare_data['conservacion'] = []137						138			if conservacion.enBuenEstado:139				self.prepare_data['conservacion'].append(1)140			if exposicion.estadoModificado:141				self.prepare_data['conservacion'].append(2)142			if conservacion.porErosion and conservacion.porErosionParModerada:143				self.prepare_data['conservacion'].append(3)144			if conservacion.porErosion and conservacion.porErosionParSevere:145				self.prepare_data['conservacion'].append(4)146			if conservacion.porErosion and conservacion.porErosionExtModerada:147				self.prepare_data['conservacion'].append(5)148			if conservacion.porErosion and conservacion.porErosionExtSevere:149				self.prepare_data['conservacion'].append(6)150		except:151			pass152			153		154		#Manifestaciones Asociadas155		try:156			asociada = obj.ManifestacionesAsociadas157			self.prepare_data['manifasociadas'] = []158			159			if asociada.esLitica:160				self.prepare_data['manifasociadas'].append(1)161			if asociada.esCeramica:162				self.prepare_data['manifasociadas'].append(2)163			if asociada.esOseo:164				self.prepare_data['manifasociadas'].append(3)165			if asociada.esConcha:166				self.prepare_data['manifasociadas'].append(4)167			if asociada.esCarbon:168				self.prepare_data['manifasociadas'].append(5)169			if asociada.esMito:170				self.prepare_data['manifasociadas'].append(6)171			if asociada.esCementerio:172				self.prepare_data['manifasociadas'].append(7)173			if asociada.esMonticulo:174				self.prepare_data['manifasociadas'].append(8)175		except:176			pass177		#CaraSurcoPetroglifo178		try:179			caracpetro = obj.CaracSurcoPetroglifo180			self.prepare_data['carasurcopetroancho'] = caracpetro.anchoDe + ' ' +caracpetro.anchoA181			self.prepare_data['carasurcopetroprofun'] = caracpetro.produndidadDe + ' '+caracpetro.profundidadA182			self.prepare_data['carasurcopetrotipo'] = []183			if caracpetro.esBase:184				self.prepare_data['carasurcopetrotipo'].append(1)185			if caracpetro.esBaseRedonda:186				self.prepare_data['carasurcopetrotipo'].append(2)187			if caracpetro.esBaseAguda:188				self.prepare_data['carasurcopetrotipo'].append(3)189			if caracpetro.esBajoRelieve:190				self.prepare_data['carasurcopetrotipo'].append(4)191			if caracpetro.esBajoRelieveLineal:192				self.prepare_data['carasurcopetrotipo'].append(5)193			if caracpetro.esBajoRelievePlanar:194				self.prepare_data['carasurcopetrotipo'].append(6)195			if caracpetro.esAltoRelieve:196				self.prepare_data['carasurcopetrotipo'].append(7)197			if caracpetro.esAltoRelieveLineal:198				self.prepare_data['carasurcopetrotipo'].append(8)199			if caracpetro.esAltoRelievePlanar:200				self.prepare_data['carasurcopetrotipo'].append(9)201			if caracpetro.esAreaInterlineal:202				self.prepare_data['carasurcopetrotipo'].append(10)203			if caracpetro.esAreaInterlinealPulida:204				self.prepare_data['carasurcopetrotipo'].append(11)205			if caracpetro.esAreaInterlinealRebajada:206				self.prepare_data['carasurcopetrotipo'].append(12)207			if caracpetro.esGrabadoSuperpuesto:208				self.prepare_data['carasurcopetrotipo'].append(13)209			if caracpetro.esGrabadoRebajado:210				self.prepare_data['carasurcopetrotipo'].append(14)211		except:212			pass213		return self.prepare_data	214"""215class BaseIndex(indexes.SearchIndex, indexes.Indexable):216	#Busqueda General217	text = indexes.CharField(document=True, use_template=True)	218	def get_model(self):219		return Yacimiento220	def index_queryset(self, using=None):221		return self.get_model().objects.all()222		223	def prepare(self, instance):224		self.prepare_data = super(BaseIndex, self).prepare(instance)225		226		#Listando todas las piedras una sola vez227		piedras = None228		try:229			piedras = instance.Piedra.all()230		except:231			pass232		233		#Recorriendo todos los modelos de anarapp234		for mname, model in dynamic.get_models(anarapp.models):235			if mname == 'Yacimiento':236				continue237			238			foreign = None239			elem 	= None240			elems	= None241			242			#Se relaciona con Piedra y existe al menos una piedra243			if dynamic.has_attr(model, 'piedra') and piedras != None:244				try:245					elems = [getattr(piedra, name) for piedra in piedras]246				except:247					continue248				249				for fname, ftype, name in dynamic.get_attrs(model):250			 		if ftype == 'OneToOneField' or ftype == 'ForeignKey':251						continue252						253					self.prepare_data[fname] = [getattr(e, name) for e in elems]254				continue255				256			#Se relaciona con Yacimiento257			if dynamic.has_attr(model, 'yacimiento'):258				foreign = dynamic.get_type(model, 'yacimiento')259				try:260					elem = getattr(instance, mname)261				except:262					continue263			264			#Relaciones uno a uno: un campo por modelo	265			if foreign == 'OneToOneField':266				for fname, ftype, name in dynamic.get_attrs(model):267					if ftype == 'OneToOneField' or ftype == 'ForeignKey':268						continue269					270					value = getattr(elem, name)271					272					#Troll Attribute273					if fname.endswith('cantidad'):274						value = unicode(value)275						276					#Troll Type277					if ftype == 'BooleanField':278						value = 'true' if getattr(elem, name) else 'false'279						280					self.prepare_data[fname] = value281			 282			#Relaciones muchos a muchos: todos los campos multivalue283			elif foreign == 'ForeignKey':284				elems = elem.all()285				286				for fname, ftype, name in dynamic.get_attrs(model):287			 		if ftype == 'OneToOneField' or ftype == 'ForeignKey':288						continue289					290					values = []291					292					#Handling Troll Type293					if ftype == 'BooleanField':294						for e in elems:295							values.append('true' if getattr(e,name) else 'false')296					else:297						values = [getattr(e, name) for e in elems]298					299					self.prepare_data[fname] = values300						301		302		#print self.prepare_data			303		return self.prepare_data304def crear_yacimiento_index():305	attrs = {}306	#Recorriendo todos los modelos de anarapp307	for mname, model in dynamic.get_models(anarapp.models):308		foreign = None309			310		#Se relaciona con Yacimiento311		if dynamic.has_attr(model, 'yacimiento'):312			foreign = dynamic.get_type(model, 'yacimiento')313		314		#Se relaciona con Piedra	315		elif dynamic.has_attr(model, 'piedra'):316			foreign = dynamic.get_type(model, 'piedra')317		318		#Es la clase principal319		elif mname == 'Yacimiento':320			for fname, ftype, name in dynamic.get_attrs(model):321				attrs[fname] = indexes.CharField(model_attr=name)322			continue323		324		#Relacion uno a uno: un campo por modelo	325		if foreign == 'OneToOneField':326			for fname, ftype, name in dynamic.get_attrs(model):327				if ftype == 'CharField':328					attrs[fname] = indexes.CharField()329				elif ftype == 'IntegerField':330					attrs[fname] = indexes.IntegerField(null=True)331				elif ftype == 'BooleanField':332					attrs[fname] = indexes.CharField()					333				elif ftype == 'DateField':334					attrs[fname] = indexes.DateField()335		336		#Relacion muchos a muchos: todos los campos con multivalue			337		elif foreign == 'ForeignKey':338			for fname, ftype, name in dynamic.get_attrs(model):339				if ftype == 'OneToOneField' or ftype == 'ForeignKey':340					continue341				attrs[fname] = indexes.MultiValueField()		 342	return type("YacimientoIndex", (BaseIndex, indexes.Indexable), attrs)343########################344# Creando Index	345########################346YacimientoIndex = crear_yacimiento_index()...

main.py

Source:main.py

1import torch2import torch.nn as nn3import torch.optim as optim4from torch.utils.data import DataLoader, WeightedRandomSampler5import numpy as np6import pickle7import gensim8import fasttext9import math10import utils.prepare_data as prepare_data11from model import Encoder, Transformer12from config.config import *13from train import train14from get_predictions import get_predictions15torch.manual_seed(0)16device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")17print(device)18# load features and labels19print('Loading data...')20# Lahjoita Puhetta data21#features_train = prepare_data.load_features_combined('data/gender_modeling/features/train.npy', max_len)22#trn_train, topic_train = prepare_data.load_transcripts('data/gender_modeling/transcripts_gender/train.txt')23#24#features_dev = prepare_data.load_features_combined('data/gender_modeling/features/dev.npy', max_len)25#trn_dev, topic_dev = prepare_data.load_transcripts('data/gender_modeling/transcripts_gender/dev.txt')26# test Lahjoita Puhetta27features_train = prepare_data.load_features_combined('data/gender_modeling/features/test.npy', max_len)28trn_train, topic_train = prepare_data.load_transcripts('data/gender_modeling/transcripts_gender/test.txt')29features_train = features_train30trn_train = trn_train31topic_train = topic_train32features_dev = features_train33trn_dev = trn_train34topic_dev = topic_train35print('Done...')36print('Loading embeddings...')37embeddings = fasttext.load_model('weights/embeddings/cc.fi.300.bin')38print('Done...')39# generate index dictionaries40#char2idx, idx2char = prepare_data.encode_data(target_train)41#topic2idx = {}42#for i in topic_train:43#    i = i[0]44#    if i not in topic2idx.keys():45#        topic2idx[i] = len(topic2idx) + 146#47#idx2topic = {v: k for k, v in topic2idx.items()}48# generate index dictionaries49#with open('weights/topic2idx.pkl', 'wb') as f:50#    pickle.dump(topic2idx, f, protocol=pickle.HIGHEST_PROTOCOL)51#52#with open('weights/idx2topic.pkl', 'wb') as f:53#    pickle.dump(idx2topic, f, protocol=pickle.HIGHEST_PROTOCOL)54# For topic detection55#with open('weights/topic2idx.pkl', 'rb') as f:56#    topic2idx = pickle.load(f)57#with open('weights/idx2topic.pkl', 'rb') as f:58#    idx2topic = pickle.load(f)59# for gender detection60topic2idx = {'Mies': 0, 'Nainen': 1}61idx2topic = {0: 'Mies', 1: 'Nainen'}62# for age detection63#topic2idx = {'1-10': 1, '11-20': 2, '21-30': 3, '31-40': 4, '41-50': 5, '51-60': 6, '61-70': 7, '71-80': 8, '81-90': 9, '91-100': 10, '101+': 11}64#topic2idx = {'1-20': 0, '21-60': 1, '61-101+': 2}65#idx2topic = {v: k for k, v in topic2idx.items()}66with open('weights/char2idx.pkl', 'rb') as f:67    char2idx = pickle.load(f)68with open('weights/idx2char.pkl', 'rb') as f:69    idx2char = pickle.load(f)70# convert topics to indices71indexed_topic_train = prepare_data.topic_to_idx(topic_train, topic2idx)72indexed_topic_dev = prepare_data.topic_to_idx(topic_dev, topic2idx)73# convert words to vectors74indexed_word_train = prepare_data.word_to_idx(trn_train, embeddings)75indexed_word_dev = prepare_data.word_to_idx(trn_dev, embeddings)76# combine features and topics in a tuple77train_data = prepare_data.combine_data(features_train, indexed_topic_train, indexed_word_train)78dev_data = prepare_data.combine_data(features_dev, indexed_topic_dev, indexed_word_dev)79# remove extra data that doesn't fit in batch80train_data = prepare_data.remove_extra(train_data, batch_size)81dev_data = prepare_data.remove_extra(dev_data, batch_size)82class_1 = 083class_2 = 084class_3 = 085indexed_topic_train = indexed_topic_train[:3520]86for sample in indexed_topic_train:87    if sample.item() == 0:88        class_1 += 189    if sample.item() == 1:90        class_2 += 191    if sample.item() == 2:92        class_3 += 193class_sample_counts = [class_1, class_2, class_3]94weights = 1. / torch.tensor(class_sample_counts, dtype=torch.float)95sample_weights = np.array([weights[t.item() - 1] for t in indexed_topic_train])96sample_weights = torch.from_numpy(sample_weights)97sampler = WeightedRandomSampler(sample_weights, len(sample_weights), replacement=True)98pairs_batch_train = DataLoader(dataset=train_data,99                    batch_size=batch_size,100                    shuffle=True,101                    collate_fn=prepare_data.collate,102                    pin_memory=True)103pairs_batch_dev = DataLoader(dataset=dev_data,104                    batch_size=batch_size,105                    shuffle=True,106                    collate_fn=prepare_data.collate,107                    pin_memory=True)108transformer = Transformer(features_train[0].size(1), len(topic2idx), n_head_encoder, d_model, n_layers_encoder, max_len).to(device)109for p in transformer.parameters():110    if p.dim() > 1:111        nn.init.xavier_uniform_(p)112total_trainable_params = sum(p.numel() for p in transformer.parameters() if p.requires_grad)113print('The number of trainable parameters is: %d' % (total_trainable_params))114# train115if skip_training == False:116    print('Training...')117    #weight_1 = len(indexed_topic_train) / (3 * class_1)118    #weight_2 = len(indexed_topic_train) / (3 * class_2)119    #weight_3 = len(indexed_topic_train) / (3 * class_3)120    121    #weight_1 = 1 / class_1122    #weight_2 = 1 / class_2123    #weight_3 = 1 / class_3124    #weight = torch.Tensor([weight_1, weight_2, weight_3]).to(device)125    criterion = nn.CrossEntropyLoss(reduction='mean')126    optimizer = torch.optim.AdamW(transformer.parameters(), lr=lr) 127    #checkpoint = torch.load('weights/gender_new/state_dict_26.pt', map_location=torch.device('cpu'))128    #transformer.load_state_dict(checkpoint['transformer'])129    #optimizer.load_state_dict(checkpoint['optimizer'])130    train(pairs_batch_train, 131            pairs_batch_dev,132            transformer,133            criterion,134            optimizer,135            num_epochs,136            batch_size,137            len(features_train),138            len(features_dev),139            device) 140else:141    checkpoint = torch.load('weights/gender/state_dict_34.pt', map_location=torch.device('cpu'))142    transformer.load_state_dict(checkpoint['transformer'])143batch_size = 1144pairs_batch_train = DataLoader(dataset=dev_data,145                    batch_size=batch_size,146                    shuffle=False,147                    collate_fn=prepare_data.collate,148                    pin_memory=True)...

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