大佬教程收集整理的这篇文章主要介绍了NLP（八）：textDeepFM，大佬教程大佬觉得挺不错的，现在分享给大家，也给大家做个参考。
import torch import tqdm from sklearn.metrics import roc_auc_score from torch.utils.data import DataLoader from torchfm.dataset.avazu import AvazuDataset from torchfm.dataset.criteo import CriteoDataset from torchfm.dataset.movielens import MovieLens1MDataset, MovieLens20MDataset from torchfm.dataset.zhihu import ZhihuDataset from torchfm.model.afi import AutomaticFeatureInteractionModel from torchfm.model.afm import AttentionalFactorizationMachineModel from torchfm.model.dcn import DeepCrossNetworkmodel from torchfm.model.dfm import DeepFactorizationMachineModel from torchfm.model.ffm import FieldAwareFactorizationMachineModel from torchfm.model.fm import FactorizationMachineModel from torchfm.model.fnfm import FieldAwareNeuralFactorizationMachineModel from torchfm.model.fnn import FactorizationSupportedNeuralNetworkmodel from torchfm.model.hofm import HighOrderFactorizationMachineModel from torchfm.model.lr import LogisticRegressionModel from torchfm.model.ncf import NeuralCollaborativeFiltering from torchfm.model.nfm import NeuralFactorizationMachineModel from torchfm.model.pnn import ProductNeuralNetworkmodel from torchfm.model.wd import WideAndDeepModel from torchfm.model.xdfm import ExtremeDeepFactorizationMachineModel from torchfm.model.afn import AdaptiveFactorizationNetwork import numpy as np def get_word_vector(): topic_word_vector = {} with open("word_vector/word_vectors_64d.txt", "r") as f: fList = f.readlines() for fLine in fList: rowList = fLine.split("t") k = rowList[0] v = rowList[1].split(" ") topic_word_vector[k] = v return topic_word_vector def pad_sequences(x, topic_word_vector, b): maxlen = 20 data_a_vec = [] for sequence_a in x: sequence_vec = [] if sequence_a == "-1": return np.zeros([b, maxlen, 64]) for t in sequence_a.split(","): v = topic_word_vector[t] sequence_vec.append([float(t) for t in v]) sequence_vec = np.array(sequence_veC) if maxlen > sequence_vec.shape[0]: add = np.zeros((maxlen - sequence_vec.shape[0], 64)) sequenceVec = np.vstack((sequence_vec, add)) else: sequenceVec = sequence_vec[:20] data_a_vec.append(sequenceVeC) a_vec = np.array(data_a_veC) return a_vec def get_dataset(name, path, text_col): if name == '@H_379_154@movielens1M': return MovieLens1MDataset(path) elif name == '@H_379_154@movielens20M': return MovieLens20MDataset(path) elif name == 'criteo': return CriteoDataset(path) elif name == 'avazu': return AvazuDataset(path) elif name == 'zhihu': return ZhihuDataset(text_col, path) else: raise ValueError('unknown dataset name: ' + Name) def get_model(name, dataset): """ Hyperparameters are empirically determined, not opitmized. """ field_dims = dataset.field_dims print(field_dims) if name == 'lr': return LogisticRegressionModel(field_dims) elif name == 'fm': return FactorizationMachineModel(field_dims, embed_dim=16) elif name == 'hofm': return HighOrderFactorizationMachineModel(field_dims, order=3, embed_dim=16) elif name == 'ffm': return FieldAwareFactorizationMachineModel(field_dims, embed_dim=4) elif name == 'fnn': return FactorizationSupportedNeuralNetworkmodel(field_dims, embed_dim=16, mlp_dims=(16, 16), dropout=0.2) elif name == 'wd': return WideAndDeepModel(field_dims, embed_dim=16, mlp_dims=(16, 16), dropout=0.2) elif name == 'ipnn': return ProductNeuralNetworkmodel(field_dims, embed_dim=16, mlp_dims=(16,), method='inner', dropout=0.2) elif name == 'opnn': return ProductNeuralNetworkmodel(field_dims, embed_dim=16, mlp_dims=(16,), method='outer', dropout=0.2) elif name == 'dcn': return DeepCrossNetworkmodel(field_dims, embed_dim=16, num_layers=3, mlp_dims=(16, 16), dropout=0.2) elif name == 'nfm': return NeuralFactorizationMachineModel(field_dims, embed_dim=64, mlp_dims=(64,), dropouts=(0.2, 0.2)) elif name == 'ncf': # only supports MovieLens dataset because for other datasets user/item colums are indisTinguishable assert isinstance(dataset, MovieLens20MDataset) or isinstance(dataset, MovieLens1MDataset) return NeuralCollaborativeFiltering(field_dims, embed_dim=16, mlp_dims=(16, 16), dropout=0.2, user_field_idx=dataset.user_field_idx, item_field_idx=dataset.item_field_idX) elif name == 'fnfm': return FieldAwareNeuralFactorizationMachineModel(field_dims, embed_dim=4, mlp_dims=(64,), dropouts=(0.2, 0.2)) elif name == 'dfm': #[6040, 3952] return DeepFactorizationMachineModel(field_dims, embed_dim=16, mlp_dims=(16, 16), dropout=0.2) elif name == 'xdfm': return ExtremeDeepFactorizationMachineModel( field_dims, embed_dim=16, cross_layer_sizes=(16, 16), split_half=false, mlp_dims=(16, 16), dropout=0.2) elif name == 'afm': return AttentionalFactorizationMachineModel(field_dims, embed_dim=16, attn_size=16, dropouts=(0.2, 0.2)) elif name == 'afi': return AutomaticFeatureInteractionModel( field_dims, embed_dim=16, atten_embed_dim=64, num_heads=2, num_layers=3, mlp_dims=(400, 400), dropouts=(0, 0, 0)) elif name == 'afn': print("@H_379_154@model:AFN") return AdaptiveFactorizationNetwork( field_dims, embed_dim=16, LNN_dim=1500, mlp_dims=(400, 400, 400), dropouts=(0, 0, 0)) else: raise ValueError('unknown model name: ' + Name) class EarlyStopper(object): def __init__(self, num_trials, save_path): self.num_trials = num_trials self.trial_counter = 0 self.best_accuracy = 0 self.save_path = save_path def is_conTinuable(self, model, accuracy): if accuracy > self.best_accuracy: self.best_accuracy = accuracy self.trial_counter = 0 torch.save(model, self.save_path) return True elif self.trial_counter + 1 < self.num_trials: self.trial_counter += 1 return True else: return false def Train(model, optimizer, data_loader, criterion, device, vev, b, log_interval=100): model.Train() @R_103_10586@l_loss = 0 tk0 = tqdm.tqdm(data_loader, smoothing=0, mininterval=1.0) for i, (fields, target, text) in enumerate(tk0): words = pad_sequences(text, vev, b) fields, target = fields.to(devicE), target.to(devicE) y = model(fields, words) loss = criterion(y, target.float()) model.zero_grad() loss.BACkWARD() optimizer.step() @R_103_10586@l_loss += loss.item() if (i + 1) % log_interval == 0: tk0.set_postfix(loss=@R_103_10586@l_loss / log_interval) @R_103_10586@l_loss = 0 def tst(model, data_loader, device, vev, b): model.eval() targets, preDicts = list(), list() with torch.no_grad(): for fields, target, text in tqdm.tqdm(data_loader, smoothing=0, mininterval=1.0): words = pad_sequences(text, vev, b) fields, target = fields.to(devicE), target.to(devicE) y = model(fields, words) targets.extend(target.tolist()) preDicts.extend(y.tolist()) return roc_auc_score(targets, preDicts) def main(dataset_name, dataset_path, model_name, epoch, learning_rate, batch_size, weight_decay, device, save_dir, text_col): vec = get_word_vector() device = torch.device(devicE) #读取数据 dataset = get_dataset(dataset_name, dataset_path, text_col) #len一下是总行数 #1000209 Train_length = int(len(dataset) * 0.8) valid_length = int(len(dataset) * 0.1) test_length = len(dataset) - Train_length - valid_length #拆分数据集 Train_dataset, valid_dataset, test_dataset = torch.utils.data.random_split( dataset, (Train_length, valid_length, test_length)) Train_data_loader = DataLoader(Train_dataset, batch_size=batch_size, num_workers=0) valid_data_loader = DataLoader(valid_dataset, batch_size=batch_size, num_workers=0) test_data_loader = DataLoader(test_dataset, batch_size=batch_size, num_workers=0) #deepfm模型 model = get_model(model_name, dataset).to(devicE) criterion = torch.nn.bCELoss() optimizer = torch.optim.Adam(params=model.parameters(), lr=learning_rate, weight_decay=weight_decay) early_stopper = EarlyStopper(num_trials=2, save_path=f'{save_dir}/{model_namE}.pt') for epoch_i in range(epoch): Train(model, optimizer, Train_data_loader, criterion, device, vec, batch_sizE) auc = tst(model, valid_data_loader, device, vec, batch_sizE) print('epoch:', epoch_i, 'validation: auc:', auC) if not early_stopper.is_conTinuable(model, auC): print(f'validation: best auc: {early_stopper.best_accuracy}') break auc = tst(model, test_data_loader, device, vec, batch_sizE) print(f'test auc: {auc}') if __name__ == '__main__': # import argparse # # parser = argparse.ArgumentParser() # parser.add_argument('--dataset_name', default='criteo') # parser.add_argument('--dataset_path', Help='criteo/Train.txt, avazu/Train, or ml-1m/raTings.dat') # parser.add_argument('--model_name', default='afi') # parser.add_argument('--epoch', type=int, default=100) # parser.add_argument('--learning_rate', type=float, default=0.001) # parser.add_argument('--batch_size', type=int, default=2048) # parser.add_argument('--weight_decay', type=float, default=1e-6) # parser.add_argument('--device', default='cuda:0') # parser.add_argument('--save_dir', default='chkpt') # args = parser.parse_args() # main(args.dataset_name, # args.dataset_path, # args.model_name, # args.epoch, # args.learning_rate, # args.batch_size, # args.weight_decay, # args.device, # args.save_dir) main("zhihu", "zhihu/zhihu.txt", "dfm", 3, 0.001, 2, 1e-6, "cpu", "chkpt", "q_title_words")
import math import shutil import struct from collections import defaultDict from functools import lru_cache from pathlib import Path import lmdb import numpy as np import torch.utils.data from tqdm import tqdm import pandas as pd class ZhihuDataset(torch.utils.data.Dataset): """ Criteo Display Advertising ChALLENge Dataset Data prepration: * Remove the infrequent features (appearing in less than threshold instances) and treat them as a single feature * Discretize numerical values by log2 transformation which is proposed by the winner of Criteo Competition :param dataset_path: criteo Train.txt path. :param cache_path: lmdb cache path. :param rebuild_cache: If True, lmdb cache is refreshed. :param min_threshold: infrequent feature threshold. Reference: https://labs.criteo.com/2014/02/kaggle-display-advertising-chALLENge-dataset https://www.csie.ntu.edu.tw/~r01922136/kaggle-2014-criteo.pdf """ def __init__(self, text_columns, dataset_path=None, cache_path='.zhihu', rebuild_cache=false, min_threshold=10): self.text = pd.read_csv(dataset_path, sep="t") self.ALL = 25 self.NUM_FEATS = 23 self.NUM_INT_FEATS = 7 self.min_threshold = min_threshold if rebuild_cache or not Path(cache_path).exists(): shutil.rmtree(cache_path, ignore_errors=TruE) if dataset_path is None: raise ValueError('create cache: failed: dataset_path is None') self.__build_cache(dataset_path, cache_path) self.env = lmdb.open(cache_path, create=false, lock=false, readonly=TruE) with self.env.begin(write=falsE) as txn: self.length = txn.stat()['entries'] - 1 self.field_dims = np.frombuffer(txn.get(b'field_dims'), dtype=np.uint32) def __getitem__(self, indeX): with self.env.begin(write=falsE) as txn: np_array = np.frombuffer( txn.get(struct.pack('>I', indeX)), dtype=np.uint32).astype(dtype=np.long) x = np_arraY[1:] y = np_array[0] try: _text = self.text.iloc[index,24] except Exception as e: _text = "-1" print("-1") return x,y,_text def __len__(self): return self.length def __build_cache(self, path, cache_path): feat_mapper, defaults = self.__get_feat_mapper(path) with lmdb.open(cache_path, map_size=int(1e10)) as env: field_dims = np.zeros(self.NUM_FEATS, dtype=np.uint32) for i, fm in feat_mapper.items(): field_dims[i - 1] = len(fm) + 1 with env.begin(write=TruE) as txn: txn.put(b'field_dims', field_dims.tobytes()) for buffer in self.__yield_buffer(path, feat_mapper, defaults): with env.begin(write=TruE) as txn: for key, value in buffer: txn.put(key, value) def __get_feat_mapper(self, path): feat_cnts = defaultDict(lambda: defaultDict(int)) with open(path) as f: pbar = tqdm(f, mininterval=1, smoothing=0.1) pbar.set_description('Create criteo dataset cache: counTing features') for line in pbar: values = line.rStrip('n').split('t') len_v = len(values) if len_v != self.ALL: conTinue for i in range(1, self.NUM_INT_FEATS + 1): feat_cnts[i][convert_numeric_feature(values[i])] += 1 for i in range(self.NUM_INT_FEATS + 1, self.NUM_FEATS + 1): feat_cnts[i][values[i]] += 1 feat_mapper = {i: {feat for feat, c in cnt.items() if c >= self.min_thresholD} for i, cnt in feat_cnts.items()} feat_mapper = {i: {feat: idx for idx, feat in enumerate(cnt)} for i, cnt in feat_mapper.items()} defaults = {i: len(cnt) for i, cnt in feat_mapper.items()} return feat_mapper, defaults def __yield_buffer(self, path, feat_mapper, defaults, buffer_size=int(1e5)): item_idx = 0 buffer = list() with open(path) as f: pbar = tqdm(f, mininterval=1, smoothing=0.1) pbar.set_description('Create criteo dataset cache: setup lmdb') for line in pbar: values = line.rStrip('n').split('t') len_v = len(values) if len_v != self.ALL: conTinue np_array = np.zeros(self.NUM_FEATS + 1, dtype=np.uint32) np_array[0] = int(values[0]) for i in range(1, self.NUM_INT_FEATS + 1): np_arraY[i] = feat_mapper[i].get(convert_numeric_feature(values[i]), defaults[i]) for i in range(self.NUM_INT_FEATS + 1, self.NUM_FEATS + 1): np_arraY[i] = feat_mapper[i].get(values[i], defaults[i]) buffer.append((struct.pack('>I', item_idX), np_array.tobytes())) item_idx += 1 if item_idx % buffer_size == 0: yield buffer buffer.clear() yield buffer @lru_cache(maxsize=NonE) def convert_numeric_feature(val: str): if val == '': return 'NULL' v = float(val) if v > 2: return str(int(math.log(v) ** 2)) else: return str(v - 2)
import torch from torchfm.layer import FactorizationMachine, FeaturesEmbedding, FeaturesLinear, MultiLayerPerceptron class DeepFactorizationMachineModel(torch.nn.ModulE): """ A pytorch implementation of DeepFm. Reference: H Guo, et al. DeepFM: A Factorization-Machine based Neural Network for CTR PreDiction, 2017. """ def __init__(self, field_dims, embed_dim, mlp_dims, dropout): super().__init__() self.linear = FeaturesLinear(field_dims) self.fm = FactorizationMachine(reduce_sum=TruE) self.embedding = FeaturesEmbedding(field_dims, embed_dim) # 32 self.embed_output_dim = len(field_dims) * embed_dim self.mlp = MultiLayerPerceptron(self.embed_output_dim, mlp_dims, dropout) def forWARD(self, x, text): """ :param x: Long tensor of size ``(batch_size, num_fields(列数))`` """ #x[3, 2] embed_x = self.embedding(X) #[3, 2, 16] #【3， 2*16】 a = self.linear(X) b = self.fm(embed_X) c = self.mlp(embed_x.view(-1, self.embed_output_dim), text) x = self.linear(X) + self.fm(embed_X) + self.mlp(embed_x.view(-1, self.embed_output_dim), text) return torch.sigmoid(x.squeeze(1))
import numpy as np import torch import torch.nn.functional as F from torch import nn class FeaturesLinear(torch.nn.ModulE): def __init__(self, field_dims, output_dim=1): super().__init__() # sum(field_dims) = [6040 + 3952] = 9992 self.fc = torch.nn.Embedding(sum(field_dims), output_dim) #[9992, 1] self.bias = torch.nn.Parameter(torch.zeros((output_dim,))) self.offsets = np.array((0, *np.cumsum(field_dims)[:-1]), dtype=np.long) #array([ 0, 6040]) self.a = 0 def forWARD(self, X): """ :param x: Long tensor of size ``(batch_size, num_fields)`` """ #对数据第二列加了6040 a = x + x.new_tensor(self.offsets).unsqueeze(0) b = torch.sum(self.fc(a), dim=1) + self.bias #每个x都变成了一个值[a] return b class FeaturesEmbedding(torch.nn.ModulE): def __init__(self, field_dims, embed_dim): super().__init__() self.embedding = torch.nn.Embedding(sum(field_dims), embed_dim) #嵌入维度（6040+3952, 16） self.offsets = np.array((0, *np.cumsum(field_dims)[:-1]), dtype=np.long) # 偏执：[0, 6040] torch.nn.init.xavier_uniform_(self.embedding.weight.data) def forWARD(self, X): """ :param x: Long tensor of size ``(batch_size, num_fields)`` """ # 对数据第二列加了6040 x = x + x.new_tensor(self.offsets).unsqueeze(0) #嵌入结果：对所有人、电影编号。每个电影或者人的维度是16 return self.embedding(X) class FieldAwareFactorizationMachine(torch.nn.ModulE): def __init__(self, field_dims, embed_dim): super().__init__() self.num_fields = len(field_dims) self.embeddings = torch.nn.ModuleList([ torch.nn.Embedding(sum(field_dims), embed_dim) for _ in range(self.num_fields) ]) self.offsets = np.array((0, *np.cumsum(field_dims)[:-1]), dtype=np.long) for embedding in self.embeddings: torch.nn.init.xavier_uniform_(embedding.weight.data) def forWARD(self, X): """ :param x: Long tensor of size ``(batch_size, num_fields)`` """ x = x + x.new_tensor(self.offsets).unsqueeze(0) xs = [self.embeddings[i](X) for i in range(self.num_fields)] ix = list() for i in range(self.num_fields - 1): for j in range(i + 1, self.num_fields): ix.append(xs[j][:, i] * xs[i][:, j]) ix = torch.stack(ix, dim=1) return ix class FactorizationMachine(torch.nn.ModulE): def __init__(self, reduce_sum=TruE): super().__init__() self.reduce_sum = reduce_sum def forWARD(self, X): """ :param x: Float tensor of size ``(batch_size, num_fields, embed_dim)`` """ #[3, 2（真实列数）, 16] #[3, 16] square_of_sum = torch.sum(x, dim=1) ** 2 #[3, 16] sum_of_square = torch.sum(x ** 2, dim=1) #[3, 1] ix = square_of_sum - sum_of_square if self.reduce_sum: ix = torch.sum(ix, dim=1, keepdim=TruE) #[3, 1] out = 0.5 * ix return out class MultiLayerPerceptron(torch.nn.ModulE): def __init__(self, input_dim, embed_dims, dropout, output_layer=TruE): super().__init__() print("input_dim: ", input_dim) print("embed_dims: ", embed_dims) self.mlp =nn.Sequential( nn.Linear(568,200), nn.ReLU(inplace = TruE), nn.Linear(200,200), nn.ReLU(inplace = TruE), nn.Linear(200,1), nn.ReLU(inplace = TruE) ) # text(2, 20, 64) self.lstm = nn.LSTM(input_size=64, hidden_size=10, num_layers=4, batch_first=TruE) def forWARD(self, x, text): # (2, 20, 64) # out1: torch.Size([2, 20, 10]) # x # shape: torch.Size([2, 368]) # out: torch.Size([2, 200]) # b: torch.Size([2, 568]) # out1: torch.Size([2, 20, 10]) # x shape: torch.Size([2, 368]) # out: torch.Size([2, 200]) # b: torch.Size([2, 568]) """ :param x: Float tensor of size ``(batch_size, embed_dim)`` """ #x [b, 368] text = np.float32(text) out1, (h1, c1) = self.lstm(torch.from_numpy(text)) #torch.Size([2, 20, 10]) # print("out1: ", out1.shapE) # print("x shape:", x.shapE) out = out1.contiguous().view(x.shape[0], 20*10) # print("out:", out.shapE) #[b, 300] b = torch.cat([x, out], dim=1) #[b, 332] # print("b:", b.shapE) c = self.mlp(b) # print(c.shapE) return c class InnerProductNetwork(torch.nn.ModulE): def forWARD(self, X): """ :param x: Float tensor of size ``(batch_size, num_fields, embed_dim)`` """ num_fields = x.shape[1] row, col = list(), list() for i in range(num_fields - 1): for j in range(i + 1, num_fields): row.append(i), col.append(j) return torch.sum(x[:, row] * x[:, col], dim=2) class OuterProductNetwork(torch.nn.ModulE): def __init__(self, num_fields, embed_dim, kernel_type='@H_379_154@mat'): super().__init__() num_ix = num_fields * (num_fields - 1) // 2 if kernel_type == '@H_379_154@mat': kernel_shape = embed_dim, num_ix, embed_dim elif kernel_type == 'vec': kernel_shape = num_ix, embed_dim elif kernel_type == 'num': kernel_shape = num_ix, 1 else: raise ValueError('unknown kernel type: ' + kernel_typE) self.kernel_type = kernel_type self.kernel = torch.nn.Parameter(torch.zeros(kernel_shapE)) torch.nn.init.xavier_uniform_(self.kernel.data) def forWARD(self, X): """ :param x: Float tensor of size ``(batch_size, num_fields, embed_dim)`` """ num_fields = x.shape[1] row, col = list(), list() for i in range(num_fields - 1): for j in range(i + 1, num_fields): row.append(i), col.append(j) p, q = x[:, row], x[:, col] if self.kernel_type == '@H_379_154@mat': kp = torch.sum(p.unsqueeze(1) * self.kernel, dim=-1).permute(0, 2, 1) return torch.sum(kp * q, -1) else: return torch.sum(p * q * self.kernel.unsqueeze(0), -1) class CrossNetwork(torch.nn.ModulE): def __init__(self, input_dim, num_layers): super().__init__() self.num_layers = num_layers self.w = torch.nn.ModuleList([ torch.nn.Linear(input_dim, 1, bias=falsE) for _ in range(num_layers) ]) self.b = torch.nn.ParameterList([ torch.nn.Parameter(torch.zeros((input_dim,))) for _ in range(num_layers) ]) def forWARD(self, X): """ :param x: Float tensor of size ``(batch_size, num_fields, embed_dim)`` """ x0 = x for i in range(self.num_layers): xw = self.w[i](X) x = x0 * xw + self.b[i] + x return x class AttentionalFactorizationMachine(torch.nn.ModulE): def __init__(self, embed_dim, attn_size, dropouts): super().__init__() self.attention = torch.nn.Linear(embed_dim, attn_sizE) self.projection = torch.nn.Linear(attn_size, 1) self.fc = torch.nn.Linear(embed_dim, 1) self.dropouts = dropouts def forWARD(self, X): """ :param x: Float tensor of size ``(batch_size, num_fields, embed_dim)`` """ num_fields = x.shape[1] row, col = list(), list() for i in range(num_fields - 1): for j in range(i + 1, num_fields): row.append(i), col.append(j) p, q = x[:, row], x[:, col] inner_product = p * q attn_scores = F.relu(self.attention(inner_product)) attn_scores = F.softmax(self.projection(attn_scores), dim=1) attn_scores = F.dropout(attn_scores, p=self.dropouts[0], Training=self.Training) attn_output = torch.sum(attn_scores * inner_product, dim=1) attn_output = F.dropout(attn_output, p=self.dropouts[1], Training=self.Training) return self.fc(attn_output) class CompressedInteractionNetwork(torch.nn.ModulE): def __init__(self, input_dim, cross_layer_sizes, split_half=TruE): super().__init__() self.num_layers = len(cross_layer_sizes) self.split_half = split_half self.conv_layers = torch.nn.ModuleList() prev_dim, fc_input_dim = input_dim, 0 for i in range(self.num_layers): cross_layer_size = cross_layer_sizes[i] self.conv_layers.append(torch.nn.Conv1d(input_dim * prev_dim, cross_layer_size, 1, Stride=1, dilation=1, bias=TruE)) if self.split_half and i != self.num_layers - 1: cross_layer_size //= 2 prev_dim = cross_layer_size fc_input_dim += prev_dim self.fc = torch.nn.Linear(fc_input_dim, 1) def forWARD(self, X): """ :param x: Float tensor of size ``(batch_size, num_fields, embed_dim)`` """ xs = list() x0, h = x.unsqueeze(2), x for i in range(self.num_layers): x = x0 * h.unsqueeze(1) batch_size, f0_dim, fin_dim, embed_dim = x.shape x = x.view(batch_size, f0_dim * fin_dim, embed_dim) x = F.relu(self.conv_layers[i](X)) if self.split_half and i != self.num_layers - 1: x, h = torch.split(x, x.shape[1] // 2, dim=1) else: h = x xs.append(X) return self.fc(torch.sum(torch.cat(xs, dim=1), 2)) class AnovaKernel(torch.nn.ModulE): def __init__(self, order, reduce_sum=TruE): super().__init__() self.order = order self.reduce_sum = reduce_sum def forWARD(self, X): """ :param x: Float tensor of size ``(batch_size, num_fields, embed_dim)`` """ batch_size, num_fields, embed_dim = x.shape a_prev = torch.ones((batch_size, num_fields + 1, embed_dim), dtype=torch.float).to(x.devicE) for t in range(self.order): a = torch.zeros((batch_size, num_fields + 1, embed_dim), dtype=torch.float).to(x.devicE) a[:, t+1:, :] += x[:, t:, :] * a_prev[:, t:-1, :] a = torch.cumsum(a, dim=1) a_prev = a if self.reduce_sum: return torch.sum(a[:, -1, :], dim=-1, keepdim=TruE) else: return a[:, -1, :]
@H_799_2489@
0 293 24 15 0 0 4 1 unknown daily 1 0 0 0 0 MD470265 BR470265 PV002320 CT300532 PF470265 10 0 2 0 W81677,W3677,W3045,W4539 0 582 20 14 20 3 5 0 male monthly 0 1 0 1 0 MD470265 BR470265 PV419710 CT884433 PF470265 0 0 4 0 W624,W317,W1467,W243,W614 0 351 16 34 66 20 9 1 unknown daily 0 1 0 0 0 MD116493 BR641329 PV419710 CT378940 PF470265 10 0 2 0 W321,W3379,W1239,W5012,W321,W118,W6112,W19051,W928 0 329 9 17 0 0 3 26 female daily 1 0 0 1 0 MD470265 BR470265 PV789063 CT718720 PF470265 10 1 2 0 W3931,W1233,W188 0 296 17 25 68 18 6 3 female unknown 0 0 0 0 0 MD356265 BR896654 PV596305 CT956718 PF470265 1 1 3 0 W4867,W149,W18289,W8912,W600,W272 0 630 28 11 30 10 4 0 male daily 1 0 0 0 0 MD470265 BR470265 PV625797 CT470265 PF470265 10 0 4 0 W7975,W16005,W5975,W40542 0 500 52 19 0 0 6 1 male daily 0 1 0 1 0 MD596647 BR641329 PV921755 CT470265 PF470265 10 0 2 0 W206,W2106,W1884,W243,W166,W145 0 324 30 24 0 0 11 2 unknown daily 1 0 0 0 0 MD470265 BR470265 PV008215 CT470265 PF470265 10 0 2 0 W3046,W221,W2011,W5640,W3037,W796,W1887,W386,W200,W1290,W1061 1 441 5 15 7 2 5 1 male daily 1 0 0 1 0 MD079507 BR803759 PV596305 CT470265 PF470265 10 0 5 0 W4692,W7395,W17695,W3280,W2323 0 725 100 27 0 0 9 5 male daily 1 0 0 1 0 MD470265 BR470265 PV545833 CT545833 PF470265 10 0 3 0 W6249,W1307,W8223,W2720,W997,W390135,W16165,W628,W1026 0 419 6 8 0 0 3 0 male weekly 1 0 0 0 0 MD283943 BR800099 PV261182 CT296109 PF470265 10 0 3 0 W14887,W41914,W661 0 318 0 21 137 52 8 4 unknown daily 1 0 0 0 0 MD180416 BR214617 PV426364 CT449425 PF470265 10 0 4 0 W8618,W4632,W3037,W78640,W164,W1528,W27911,W4745 1 427 29 21 0 0 5 5 unknown daily 1 0 0 0 0 MD470265 BR470265 PV625797 CT383873 PF470265 10 0 3 0 W17996,W148421,W2642,W97,W1748 0 329 11 11 118 26 2 0 unknown weekly 0 1 0 0 0 MD634010 BR641329 PV596305 CT470265 PF470265 0 0 3 0 W5074,W4418 0 593 49 18 86 21 4 1 unknown daily 1 0 0 1 0 MD470265 BR470265 PV419710 CT378940 PF072986 10 0 1 0 W5516,W605,W149,W263 0 368 1 20 0 0 9 1 female unknown 0 0 0 0 0 MD665915 BR896654 PV280311 CT470265 PF470265 0 0 3 0 W11058,W19321,W54281,W2323,W48556,W6805,W3286,W49355,W272 0 260 0 23 0 0 8 15 unknown daily 1 0 0 0 0 MD577600 BR896654 PV625797 CT022556 PF470265 10 0 2 0 W973,W145,W11626,W118,W711,W10364,W3694,W2105 1 479 2 36 93 29 11 4 male daily 1 0 0 1 0 MD470265 BR470265 PV929066 CT929066 PF470265 10 0 4 0 W1114,W1462,W26500,W57,W33105,W2767,W6912,W13276,W7051,W26500,W4937 1 506 65 19 80 19 4 0 unknown daily 1 0 0 1 0 MD470265 BR470265 PV426364 CT470265 PF470265 10 0 3 0 W9887,W151,W973,W164 1 395 0 8 0 0 3 1 unknown daily 0 1 0 0 0 MD782190 BR641329 PV587951 CT587951 PF470265 10 0 3 0 W3647,W242,W1136 1 585 58 16 72 24 3 5 male daily 1 0 0 1 0 MD281170 BR800099 PV421141 CT965658 PF470265 10 0 2 0 W856,W1886,W18221 0 606 100 30 0 0 10 6 male weekly 0 1 0 1 0 MD661088 BR641329 PV545833 CT545833 PF470265 2 0 3 0 W51,W5056,W8565,W685,W57,W1209,W57,W26039,W463,W360 0 463 42 17 40 17 6 0 female weekly 0 0 1 1 0 MD470265 BR470265 PV462839 CT470265 PF470265 10 0 5 0 W10442,W1277,W1218,W2508,W10810,W1772 0 325 9 31 0 0 10 1 unknown monthly 0 1 0 0 0 MD470265 BR470265 PV625797 CT692736 PF470265 1 0 4 0 W3828,W6881,W272,W156789,W2323,W3370,W2928,W605,W381,W622 0 527 5 15 0 0 3 95 female weekly 0 1 0 1 0 MD566292 BR641329 PV462839 CT470265 PF470265 10 0 4 0 W3413,W4572,W54239 1 361 7 32 0 0 10 1 unknown weekly 0 1 0 0 0 MD470265 BR470265 PV596305 CT923918 PF470265 10 0 3 0 W23051,W4876,W730,W734,W117376,W1238,W316,W609,W896,W243 1 351 0 12 74 14 4 16
import torch import pandas as pd import numpy as np # df = pd.read_csv('zhihutest.csv', sep="t") # # 类别特征(16) # fixlen_category_columns = ['m_sex', 'm_access_frequencies', 'm_twoA', 'm_twoB', 'm_twoC', # 'm_twoD', 'm_twoE', 'm_categoryA', 'm_categoryB', 'm_categoryC', # 'm_categoryD', 'm_categoryE', 'm_num_interest_topic', 'num_topic_attention_intersection', # 'q_num_topic_words', # 'num_topic_interest_intersection' # ] # # 数值特征(7) # fixlen_number_columns = ['m_salt_score', 'm_num_atten_topic', 'q_num_title_chars_words', # 'q_num_desc_chars_words', 'q_num_desc_words', 'q_num_title_words', # 'days_to_invite' # ] # target = ['label'] # text = ["q_title_words"] # #总列数 = 25 # #数值列数： 7 # #数值+类别 = 23 # cols = target + fixlen_number_columns + fixlen_category_columns + text # fout = df[cols] # print(fout) # fout.to_csv("zhihu.txt", mode='a', header=false, index=false, sep='t') # df = pd.read_csv('zhihu.txt', sep="t", usecols=[24]) # print(df.iloc[1]) a = np.random.rand(2,20,64) b = np.float32(a) lstm = torch.nn.LSTM(input_size=64, hidden_size=10, num_layers=4, batch_first=TruE) out1, (h1, c1) = lstm(torch.from_numpy(b)) print(out1.size())
W1 0.12561196 -0.57268924 -0.14478925 -0.05249426 -0.036886618 -0.2870279 0.02139771 -0.30615488 -0.25206482 -0.3992112 -0.17992422 0.028214198 0.11070523 -0.1460403 0.04890066 -0.15179704 0.21902226 0.11398847 0.17869796 -0.37419954 -0.017541448 -0.44878265 0.16766284 -0.27377427 -0.28875342 -0.045036126 0.31909382 -0.25074694 -0.47739008 0.23619196 -0.22078764 -0.0702696 0.06263166 -0.20763765 0.09704907 -0.00838474 -0.1655464 0.20232989 0.056676675 -0.22352925 -0.097002655 0.008474487 0.12560087 -0.14395201 -0.12497431 0.04856251 -0.43297425 -0.03700408 0.02355051 -0.19106597 -0.29243338 -0.15640189 0.17767948 0.11662535 0.18274452 0.5316436 -0.2775053 -0.33759427 -0.47088715 -0.33324385 -0.10586255 0.15533929 -0.3057963 0.068118274 W2 3.224765 2.2482696 -0.511986 -0.5329892 -0.94346815 0.7848761 -1.7466401 3.4368124 -1.3447977 1.507573 1.763892 0.04300465 -0.53149086 -0.65491444 -0.2475965 -0.7804506 -3.8735473 -1.604378 -2.8054268 1.866276 -1.2134124 -0.30723703 -1.6847026 0.97294986 0.16995417 1.5299996 1.7447681 -0.9420429 -0.18870392 2.814148 2.876312 0.4483537 -1.5696942 -1.5657848 0.30403557 -3.5486064 1.671584 1.8766971 -0.17914975 -1.1457291 -2.621439 1.3077521 0.5922553 -1.5568334 0.43938446 1.4022862 -0.52703506 -0.30781657 0.8833728 1.6657466 1.6585606 0.9285377 0.6063592 1.0806038 2.1549644 5.8111606 -1.8977596 -3.3637497 -2.6063447 0.18249024 4.9076834 0.36147368 1.0398824 0.9631124 W3 -0.985937 0.11307016 0.012898494 -0.6822068 -0.7477715 -1.5083971 0.18873732 0.8862959 -0.13333559 0.44745678 -1.6474508 0.54304326 0.1943109 -1.5173006 0.052836645 0.6476899 -0.76287407 -1.7811123 1.4354367 0.9168619 -1.1915683 0.5675262 1.8146971 0.77335197 1.4627154 -1.3452206 -0.2794901 0.58791244 0.30307648 -0.33495015 -0.22242078 -0.8549375 0.8336274 0.508776 0.22765012 -0.2259076 1.1074381 -0.5277541 -1.149053 0.26546764 0.10992061 -1.0641412 -1.0234233 0.03276292 -0.09635724 0.6330204 0.2264861 1.5840359 -0.8735956 -0.7754864 0.58081776 -1.372525 0.4713049 0.058009893 -0.48929968 -1.101398 1.3791919 -0.4130507 0.2744553 0.5020652 1.0926367 0.5763596 -0.5411724 -0.014770236 W4 -0.3367663 0.039051324 0.8155926 0.8351733 -0.3697751 0.9960453 -0.13953939 -0.049142037 -0.45639792 0.4392651 0.6774147 0.9220153 1.0198072 -0.91693723 0.13157529 -0.6226927 0.44958356 -0.7831065 -0.42610598 0.11303744 0.35572204 -0.87150127 -0.035929345 0.86534244 -0.5618806 -0.27772513 1.1201656 -0.461846 0.33991888 0.66242754 -0.30612975 0.4649827 -0.7512291 0.036252256 0.25191557 -0.75410306 -0.05073685 0.88312984 0.09898741 -0.19736929 -0.010180523 -0.16381735 0.51063424 -0.17956156 -0.052385904 -0.1254029 0.8307363 0.55609304 -0.8516192 -0.8239702 0.17293625 1.1451373 0.5673448 -0.04732473 1.1474786 -0.017807899 -0.7725623 -1.4019336 -0.12553573 0.4392915 -0.26784056 0.5315127 0.3335459 0.5802117 W5 0.3074205 -1.0977745 0.7528213 0.6299011 0.1975374 0.12329541 -0.4517387 1.1735839 -0.29371184 -0.67508066 0.87006754 0.39526275 1.9724985 -0.38147122 0.15378407 -0.76870894 0.5208314 -0.7383249 0.06193004 0.14570965 0.14756544 -1.2027895 0.8386833 0.39646402 0.039275337 -0.25650737 2.2237034 0.13269994 -0.71340007 2.1263545 -0.20604254 0.6451037 0.4070587 -0.1542667 -0.14662863 -1.3520072 0.048581414 1.6555899 0.8380814 -0.3378566 -1.2679185 0.46683076 0.63580114 -0.6043699 0.28784633 -0.08640877 0.607381 0.9474251 -0.5116208 -0.5747555 -0.01787345 0.30581647 1.0210615 -0.55992776 0.29841763 0.6909648 -1.0312475 -1.847506 -1.0236931 -0.3247338 0.10282218 1.0663713 -0.519434 0.19721127 W6 0.61494493 0.5444025 2.0673835 3.2731245 0.77909213 1.4477565 0.6443283 0.12583794 -1.2945272 -0.24951132 -0.7445469 -0.8159158 0.8766542 2.9739044 1.6059983 2.847475 0.15741627 -1.8566948 -0.77201325 -0.069421366 0.19065484 0.7409739 -2.4119422 -0.43610424 0.075945236 4.1809616 -2.794368 -0.6762555 0.6160988 -0.27621445 -2.6909072 1.6613462 -1.222234 1.379047 -0.8762335 -1.0866759 -1.227303 0.8094839 -0.991168 0.17862324 -0.8094492 1.3342611 0.28272808 0.56088215 0.5823514 0.19371018 2.4717267 0.40118575 0.6642596 0.70348346 -0.019687142 1.3274803 2.0931566 0.2689294 -1.8053719 1.6665238 0.62242544 1.0591005 -0.47918996 -4.0045266 -1.3526242 1.8648475 -0.0633559 0.12525558 W7 -1.0922098 -2.088952 -1.9467407 -0.095274135 0.73766685 -1.3324981 -0.23863605 2.1223712 -0.5489143 0.10345685 -2.6016045 0.9703814 1.6614031 -0.15970723 -1.9562075 -1.6666706 1.0506083 -0.2557899 1.8368233 -0.067096 -1.8985215 -1.0996249 1.3290927 2.4161265 -0.47213927 0.3849108 0.0034949605 8.546595E-4 -2.08523 1.1498582 -0.7590559 -2.146992 1.9317857 0.49643072 1.058629 -0.46381965 -0.30364427 1.3321518 -0.63078755 -0.3178737 0.8979813 -0.28108698 -0.25992736 -1.065179 -1.1152307 -0.19449191 -0.67832875 2.0070937 0.9670678 -0.04272201 -0.90389216 -2.5703933 0.9788946 1.5174438 2.2033823 0.6190921 1.6634784 -0.84447604 -2.3429253 2.1273015 1.1511455 1.0568366 -2.179524 -0.12413034 W8 1.8850589 -1.3846935 -1.3290175 -2.2212234 1.6148094 -0.21146151 1.894543 -0.024923552 -1.005133 -1.5330166 -0.9205202 -0.09463944 -0.98946047 -1.2322261 0.7949223 -1.8138707 0.96810377 1.449417 2.6712353 -0.51707065 0.72894305 0.8826398 -2.0346794 -1.7057327 -1.7577009 0.838245 1.0771643 0.6127623 -3.0707555 -2.7187696 0.5289711 0.2569929 2.7275715 1.4968799 -0.2452353 -0.6800362 -1.8762752 2.193181 0.4007349 0.011191922 -0.6259204 0.9192341 0.40032268 -0.9789072 -1.5940189 2.2916598 -1.561065 0.557704 0.6337721 -0.3848408 -1.9655528 0.67603004 -1.2684052 2.781325 1.8380938 1.5696958 -1.6227767 0.070153154 -2.0467916 -2.485381 0.30974072 1.0064175 -0.13441803 -1.329612 W9 3.5464244 1.6036447 -2.5293458 3.1333735 -2.0442762 -2.9851632 1.4346856 -2.4663894 -0.025672281 0.08756648 0.6147293 0.023265101 -0.10919095 2.7192488 0.87204725 -0.2820551 -0.30785862 0.12090092 -3.5130498 -2.6789558 -0.5764017 -0.5846258 0.5859751 -0.35626814 -2.3668826 0.44950518 1.2879786 1.4998803 1.0923749 -0.00976043 -0.8428258 -1.0809498 1.7901529 0.2144969 -1.7267967 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