diff --git a/.DS_Store b/.DS_Store new file mode 100644 index 0000000..bc26776 Binary files /dev/null and b/.DS_Store differ diff --git a/DBSCAN/dbscan.py b/DBSCAN/dbscan.py new file mode 100644 index 0000000..3efc858 --- /dev/null +++ b/DBSCAN/dbscan.py @@ -0,0 +1,201 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- + +# 实现了DBSCAN算法 + +__author__ = 'ZYC@BUPT' +import jieba +import os +import sys +import json +jieba.load_userdict("newword.dict") +sys.setdefaultencoding("utf-8") +from sklearn import feature_extraction +from sklearn.feature_extraction.text import TfidfTransformer +from sklearn.feature_extraction.text import CountVectorizer +import numpy as np +import matplotlib.pyplot as plt +import math +import time +UNCLASSIFIED = False +NOISE = 0 + +def Test2(rootDir): + for lists in os.listdir(rootDir): + path = os.path.join(rootDir, lists) + # print path.decode('gb2312') + if path.find(".txt")!=-1: + Participle(path) + if os.path.isdir(path): + Test2(path) + +def Participle(path): + try: + fp = open(path, "r") + ad = fp.readline().strip('\n') + na = fp.readline().strip('\n') + ti = fp.readline().strip('\n')#time + si = fp.readline().strip('\n') + cont = na+fp.read() + fp.close() + except IOError: + return 0 + + try: + insi = {} + insi['time'] = ti + print(ti) + insi['url'] = ad + insi['title'] = na + insi['site'] = si#decode("gb2312").encode("utf-8") + global fnum + global segcont + global doc + seg_list = jieba.lcut(cont, cut_all=False) + stline = "" + for word in seg_list: + if ((word in d) is False) and word != '\n': + stline = stline + " " + word + segcont.append(stline) + print (str(fnum) + " 分词") + doc[fnum] = insi + fnum = fnum + 1 + except UnicodeError: + return 0 + +def loadDataSet(splitChar=','): + dataSet = [] + global we + dataSet=we + del we + return dataSet + +def region_query(data, pointId, eps): + nPoints = data.shape[1] + seeds = [] + for i in range(nPoints): + if eps_neighbor(data[:, pointId], data[:, i], eps): + seeds.append(i) + return seeds + +def tstore(clusters,clusterNum):#测试使用 + global doc + fpath="./test_res/" + i=0 + wr=[] + while i<=clusterNum: + path=fpath+str(i)+".txt" + fp=open(path,'w') + wr.append(fp) + i+=1 + i=1 + for cl in clusters: + enstr="" + enstr=doc[i]['title']+doc[i]['url'] + wr[cl].write(enstr+'\n') + i+=1 + +def expand_cluster(data, clusterResult, pointId, clusterId, eps, minPts): + seeds = region_query(data, pointId, eps) + if len(seeds) < minPts: # 不满足minPts条件的为噪声点 + clusterResult[pointId] = NOISE + return False + else: + clusterResult[pointId] = clusterId # 划分到该簇 + for seedId in seeds: + clusterResult[seedId] = clusterId + while len(seeds) > 0: # 扩张 + currentPoint = seeds[0] + queryResults = region_query(data, currentPoint, eps) + if len(queryResults) >= minPts: + for i in range(len(queryResults)): + resultPoint = queryResults[i] + if clusterResult[resultPoint] == UNCLASSIFIED: + seeds.append(resultPoint) + clusterResult[resultPoint] = clusterId + elif clusterResult[resultPoint] == NOISE: + clusterResult[resultPoint] = clusterId + seeds = seeds[1:] + + return True + +def dbscan(data, eps, minPts): + clusterId = 1 + nPoints = data.shape[1] + clusterResult = [UNCLASSIFIED] * nPoints + for pointId in range(nPoints): + # print "point :"+str(pointId) + point = data[:, pointId] + if clusterResult[pointId] == UNCLASSIFIED: + if expand_cluster(data, clusterResult, pointId, clusterId, eps, minPts): + clusterId = clusterId + 1 + return clusterResult, clusterId - 1 + + +def eps_neighbor(a, b, eps): + dis=math.sqrt(np.power(a - b, 2).sum()) + print(dis) + return dis < eps + +def main(): + dataSet = loadDataSet(splitChar=',') + dataSet = np.mat(dataSet).transpose() + # print(dataSet) + clusters, clusterNum = dbscan(dataSet, 1.37, 5)################################# + print("cluster Numbers = ", clusterNum) + # print(clusters) + #store(clusters, clusterNum) + tstore(clusters, clusterNum) + + +def TFIDF(): + global segcont + global weight + vectorizer = CountVectorizer() + transformer = TfidfTransformer() + tfidf = transformer.fit_transform(vectorizer.fit_transform(segcont)) + word = vectorizer.get_feature_names() # 所有文本的关键字 + weight = tfidf.toarray() # 对应的tfidf矩阵 + del segcont + + seg = [] + for i in range(len(weight)): + enstr = "" + for j in range(len(word)): + if weight[i][j] >= 0.1:##################################### + enstr = enstr + " " + word[j] + seg.append(enstr) + + del weight + vec = CountVectorizer() + tra = TfidfTransformer() + tidf = tra.fit_transform(vec.fit_transform(seg)) + wo = vec.get_feature_names() + we = tidf.toarray() + + global we + +def dbs(): + global fnum,doc,segcont,d + fnum = 1 + segcont = [] + doc = {} + stfp = open("stop.txt", "r") + stcont = stfp.read() + list_a = jieba.lcut(stcont, cut_all=False) + d = set([]) + for li in list_a: + d.add(li) + stfp.close() + Test2('./sou1') + TFIDF() + start = time.clock() + main() + end = time.clock() + print('finish all in %s' % str(end - start)) + +dbs() + + + + diff --git a/README.md b/README.md index a74bc1b..7831611 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ ## MachineLearning -机器学习算法代码及个人总结整理，对于算法实现部分，在相应目录中都包含有源码和数据以及测试实例，内容正在不断完善中！如有错误，望不吝指教。PS:所有代码均符合我们整理出来的这份[编码规范](https://github.com/csuldw/MachineLearning/blob/master/Python-coding-standards.md). +机器学习算法代码及个人总结整理，对于算法实现部分，在相应目录中都包含有源码和数据以及测试实例，内容正在不断完善中！如有错误，还望读者指出，非常感谢，若您觉得对你有帮助，可以在右上角给个star哈(#^.^#)。PS:所有代码均符合我们整理出来的这份[编码规范](https://github.com/csuldw/MachineLearning/blob/master/Python-coding-standards.md). ## Contents @@ -38,10 +38,19 @@ ## Contributor -- 刘帝伟, 中南大学2014级硕士，[HomePage](http://www.csuldw.com). +- 刘帝伟, CSU硕士毕业，关注AI、机器学习、深度学习方向，[HomePage](http://www.csuldw.com). ## Contact -- QQ: 466454368 +如果有任何疑问，可在我的微信公众号后台留言： + + + +
+ +
+ +或是发邮件吧： + - E-mail: csu.ldw@csu.edu.cn diff --git a/Recommendation System/data_process/user_keywords.csv b/Recommendation System/data_process/user_keywords.csv new file mode 100644 index 0000000..915df37 --- /dev/null +++ b/Recommendation System/data_process/user_keywords.csv @@ -0,0 +1,9 @@ +user_id,keywords +113,新闻推荐|资讯推荐|内容推荐|文本分类|人工分类|自然语言处理|聚类|分类|冷启动 +143,网络|睡眠|精神衰弱|声音|人工分类 +123,新年愿望|梦想|2018|辞旧迎新 +234,父母|肩头|饺子|蔬菜块|青春叛逆期|声音 +117,新闻推荐|内容推荐|文本分类|人工分类|自然语言处理|聚类|分类|冷启动 +119,新闻推荐|资讯推荐|人工分类|自然语言处理|聚类|分类|冷启动 +12,新闻推荐|资讯推荐|内容推荐|文本分类|聚类|分类|冷启动 +122,机器学习|新闻推荐|梦想|人工分类|自然语言处理 \ No newline at end of file diff --git a/Recommendation System/pyfm_demo.py b/Recommendation System/pyfm_demo.py new file mode 100644 index 0000000..7c634f5 --- /dev/null +++ b/Recommendation System/pyfm_demo.py @@ -0,0 +1,47 @@ +# -*- coding: utf-8 -*- +""" +Created on Sun Jan 27 23:49:24 2019 + +pyfm install：将https://github.com/coreylynch/pyFM 下载到本地，去掉setup.py里面的 +libraries=["m"],然后安装即可. + +@author: liudiwei +""" +from sklearn.datasets import load_iris +from sklearn.model_selection import train_test_split +from pyfm import pylibfm +from sklearn.feature_extraction import DictVectorizer + +def load_data(): + """ + 调用sklearn的iris数据集，筛选正负样本并构造切分训练测试数据集 + """ + iris_data = load_iris() + X = iris_data['data'] + y = iris_data['target'] == 2 + data = [ {v: k for k, v in dict(zip(i, range(len(i)))).items()} for i in X] + + X_train,X_test,y_train, y_test = train_test_split(data, y, test_size=0.3, random_state=0) + return X_train,X_test,y_train, y_test + +X_train,X_test,y_train, y_test = load_data() + +v = DictVectorizer() +X_train = v.fit_transform(X_train) +X_test = v.transform(X_test) + +fm = pylibfm.FM(num_factors=2, + num_iter=200, + verbose=True, + task="classification", + initial_learning_rate=0.001, + learning_rate_schedule="optimal") + +fm.fit(X_train, y_train) + +y_preds = fm.predict(X_test) +y_preds_label = y_preds > 0.5 +from sklearn.metrics import log_loss,accuracy_score +print ("Validation log loss: %.4f" % log_loss(y_test, y_preds)) +print ("accuracy: %.4f" % accuracy_score(y_test, y_preds_label)) + diff --git a/Recommendation System/recommend.py b/Recommendation System/recommend.py new file mode 100644 index 0000000..110e9a9 --- /dev/null +++ b/Recommendation System/recommend.py @@ -0,0 +1,173 @@ +# -*- coding: utf-8 -*- +""" +Created on Sat Jan 5 15:34:34 2019 + +@author: liudiwei +""" +import pandas as pd +import numpy as np +import random + +from keras.layers import Input, Embedding, Dot, Reshape, Dense +from keras.models import Model +random.seed(100) + +#load dataset +user_keywords = pd.read_csv("../data_process/user_keywords.csv") + +""" + user_id keywords +0 113 新闻推荐|资讯推荐|内容推荐|文本分类|人工分类|自然语言处理|聚类|分类|冷启动 +1 143 网络|睡眠|精神衰弱|声音|人工分类 +2 123 新年愿望|梦想|2018|辞旧迎新 +3 234 父母|肩头|饺子|蔬菜块|青春叛逆期|声音 +4 117 新闻推荐|内容推荐|文本分类|人工分类|自然语言处理|聚类|分类|冷启动 +5 119 新闻推荐|资讯推荐|人工分类|自然语言处理|聚类|分类|冷启动 +6 12 新闻推荐|资讯推荐|内容推荐|文本分类|聚类|分类|冷启动 +7 122 机器学习|新闻推荐|梦想|人工分类|自然语言处理 +""" + +def date_process(user_item): + """user_item is a DataFrame, column=[user_id, keywords] + 1. user_item: user and item information, user_id, keywords, keyword_index + 2. user_index: user to index + 3. item_index：item to index + """ + user_item["keywords"] = user_item["keywords"].apply(lambda x: x.split("|")) + keyword_list = [] + for i in user_item["keywords"]: + keyword_list.extend(i) + + #word count + item_count = pd.DataFrame(pd.Series(keyword_list).value_counts()) + # add index to word_count + item_count['id'] = list(range(0, len(item_count))) + + #将word的id对应起来 + map_index = lambda x: list(item_count['id'][x]) + user_item['keyword_index'] = user_item['keywords'].apply(map_index) #速度太慢 + #create user_index, item_index + user_index = { v:k for k,v in user_item["user_id"].to_dict().items()} + item_index = item_count["id"].to_dict() + return user_item, user_index, item_index + +user_keywords, user_index, keyword_index = date_process(user_keywords) + +def create_pairs(user_keywords, user_index): + """ + generate user, keyword pair list + """ + pairs = [] + def doc2tag(pairs, x): + for index in x["keyword_index"]: + pairs.append((user_index[x["user_id"]], index)) + user_keywords.apply(lambda x: doc2tag(pairs, x), axis=1) #速度太慢 + return pairs + +pairs = create_pairs(user_keywords, user_index) + + +def build_embedding_model(embedding_size = 50, classification = False): + """Model to embed users and keywords using the Keras functional API. + Trained to discern if a keyword is clicked by user""" + + # Both inputs are 1-dimensional + user = Input(name = 'user', shape = [1]) + keyword = Input(name = 'keyword', shape = [1]) + + # Embedding the user default: (shape will be (None, 1, 50)) + user_embedding = Embedding(name = 'user_embedding', + input_dim = len(user_index), + output_dim = embedding_size)(user) + + # Embedding the keyword default: (shape will be (None, 1, 50)) + keyword_embedding = Embedding(name = 'keyword_embedding', + input_dim = len(keyword_index), + output_dim = embedding_size)(keyword) + + # Merge the layers with a dot product along the second axis + # (shape will be (None, 1, 1)) + merged = Dot(name = 'dot_product', normalize = True, + axes = 2)([user_embedding, keyword_embedding]) + + # Reshape to be a single number (shape will be (None, 1)) + merged = Reshape(target_shape = [1])(merged) + + # Squash outputs for classification + out = Dense(1, activation = 'sigmoid')(merged) + model = Model(inputs = [user, keyword], outputs = out) + + # Compile using specified optimizer and loss + model.compile(optimizer = 'Adam', loss = 'binary_crossentropy', + metrics = ['accuracy']) + #print(model.summary()) + return model + +model = build_embedding_model(embedding_size = 20, classification = False) + + +def generate_batch(pairs, n_positive = 50, negative_ratio = 1.0): + """Generate batches of samples for training. + Random select positive samples + from pairs and randomly select negatives.""" + + # Create empty array to hold batch + batch_size = n_positive * (1 + negative_ratio) + batch = np.zeros((batch_size, 3)) + + # Continue to yield samples + while True: + # Randomly choose positive examples + for idx, (user_id, keyword_id) in enumerate(random.sample(pairs, n_positive)): + batch[idx, :] = (user_id, keyword_id, 1) + idx += 1 + + # Add negative examples until reach batch size + while idx < batch_size: + + # Random selection + random_user = random.randrange(len(user_index)) + random_keyword = random.randrange(len(keyword_index)) + #print(random_user, random_keyword) + + # Check to make sure this is not a positive example + if (random_user, random_keyword) not in pairs: + + # Add to batch and increment index + batch[idx, :] = (random_user, random_keyword, 0) + idx += 1 + + # Make sure to shuffle order + np.random.shuffle(batch) + yield {'user': batch[:, 0], 'keyword': batch[:, 1]}, batch[:, 2] + + +n_positive = len(pairs) +gen = generate_batch(pairs, n_positive, negative_ratio = 1) +# Train +h = model.fit_generator(gen, epochs = 100, steps_per_epoch = len(pairs) // n_positive) + + +# Extract embeddings +user_layer = model.get_layer('user_embedding') +user_weights = user_layer.get_weights()[0] + + +keyword_layer = model.get_layer('keyword_embedding') +keyword_weights = keyword_layer.get_weights()[0] + +from sklearn.decomposition import PCA +import seaborn as sns + +#PCA可视化 +def pca_show(): + pca = PCA(n_components=2) + pca_result = pca.fit_transform(user_weights) + sns.jointplot(x=pca_result[:,0], y=pca_result[:,1]) +pca_show() + + +#calculate cosine similarity +from sklearn.metrics.pairwise import cosine_similarity +cos = cosine_similarity(user_weights[0:1], user_weights) +recommendations = cos[0].argsort()[-4:][::-1] diff --git a/bp/activators.py b/bp/activators.py new file mode 100644 index 0000000..3f3c34c --- /dev/null +++ b/bp/activators.py @@ -0,0 +1,40 @@ +# -*- coding: utf-8 -*- +""" +Created on Wed Apr 04 09:43:53 2018 + +@author: l00424630 +""" + +import numpy as np + +class ReluActivator(object): + def forward(self, weighted_input): + #return weighted_input + return max(0, weighted_input) + + def backward(self, output): + return 1 if output > 0 else 0 + + +class LinearActivator(object): + def forward(self, weighted_input): + return weighted_input + + def backward(self, output): + return 1 + + +class SigmoidActivator(object): + def forward(self, weighted_input): + return 1.0 / (1.0 + np.exp(-weighted_input)) + + def backward(self, output): + return output * (1 - output) + + +class TanhActivator(object): + def forward(self, weighted_input): + return 2.0 / (1.0 + np.exp(-2 * weighted_input)) - 1.0 + + def backward(self, output): + return 1 - output * output diff --git a/bp/activators.pyc b/bp/activators.pyc new file mode 100644 index 0000000..b8dd021 Binary files /dev/null and b/bp/activators.pyc differ diff --git a/bp/bp.py b/bp/bp.py new file mode 100644 index 0000000..8104657 --- /dev/null +++ b/bp/bp.py @@ -0,0 +1,120 @@ +# -*- coding: utf-8 -*- +""" +Created on Wed Apr 04 09:33:57 2018 + +@author: liudiwei +""" + +import numpy as np +import time + +from activators import SigmoidActivator + +class FullConnectedLayer(object): + """ + 定义全连接层 + """ + def __init__(self, input_size, output_size, activator): + self.input_size = input_size + self.output_size = output_size + self.activator = activator + + # 权重向量W + self.W = np.random.uniform(-0.1, 0.1, (output_size, input_size)) + + # 偏置项b + self.b = np.zeros(output_size) + # 输出向量 + self.output = np.zeros(output_size) + + #内部变量 + self._input = None + self._delta = None + self._W_grad = None + self._b_grad = None + + def _network(self, input_sample, weight, bias): + return np.dot(weight, input_sample) + bias + + def forward(self, input_sample): + #前向传播计算输出 + self._input = input_sample + self.output = self.activator.forward( + self._network(self._input, self.W, self.b)) + + def backward(self, delta_array): + #后向传播计算权值和偏置 计算前一层delta + self._delta = self.activator.backward(self._input) * np.dot( + self.W.T, delta_array) + + #传入的数据有点不一样，因此需要加入.reshape(-1,1) + self._W_grad = np.dot(delta_array.reshape(-1,1), self._input.reshape(-1,1).T) + self._b_grad = delta_array + + def update(self, learning_rate): + #更新权重值 + self.W += learning_rate * self._W_grad + self.b += learning_rate * self._b_grad + + +class Network(object): + """ + layers: 一个二维数组，表示神经网络每层的节点数 + """ + def __init__(self, layers): + + self.layers = [] + + for i in range(len(layers) - 1): + self.layers.append( + FullConnectedLayer( + layers[i], + layers[i+1], + SigmoidActivator() + ) + ) + + def predict(self, sample): + #预测某个样本 + output = sample + for layer in self.layers: + layer.forward(output) + output = layer.output + return output + + def train(self, labels, train_sample, learning_rate, epoch): + ''' + labels: 样本标签 + train_sample: 输入样本 + learning_rate: 学习速率 + epoch: 训练轮数 + ''' + for i in range(epoch): + print '%s epoch %d begin.' % (time.ctime(), i+1) + for j in range(len(train_sample)): + self._train_one_sample(labels[j], train_sample[j], learning_rate) + print '%s epoch %d finished' % (time.ctime(), i+1) + + + def _train_one_sample(self, label, sample, learning_rate): + self.predict(sample) + self._get_gradient(label) + self._update_weight(learning_rate) + + + def _get_gradient(self, label): + #计算输出层delta + delta = self.layers[-1].activator.backward(self.layers[-1].output) * ( + label - self.layers[-1].output) + + for layer in self.layers[::-1]: + layer.backward(delta) + delta = layer._delta + return delta + + def _update_weight(self, rate): + for layer in self.layers: + layer.update(rate) + + + diff --git a/bp/bp.pyc b/bp/bp.pyc new file mode 100644 index 0000000..3a8d025 Binary files /dev/null and b/bp/bp.pyc differ diff --git a/bp/data/sample_submission.csv b/bp/data/sample_submission.csv new file mode 100644 index 0000000..7ea007b --- /dev/null +++ b/bp/data/sample_submission.csv @@ -0,0 +1,28001 @@ +ImageId,Label +1,0 +2,0 +3,0 +4,0 +5,0 +6,0 +7,0 +8,0 +9,0 +10,0 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+27976,3 +27977,6 +27978,0 +27979,1 +27980,9 +27981,3 +27982,1 +27983,1 +27984,0 +27985,4 +27986,5 +27987,2 +27988,2 +27989,9 +27990,6 +27991,7 +27992,6 +27993,1 +27994,9 +27995,7 +27996,9 +27997,7 +27998,3 +27999,9 +28000,2 diff --git a/bp/test.py b/bp/test.py new file mode 100644 index 0000000..a74145b --- /dev/null +++ b/bp/test.py @@ -0,0 +1,79 @@ +# -*- coding: utf-8 -*- +""" +Created on Wed Apr 04 17:12:30 2018 + +@author: liudiwei +""" +import numpy as np +from bp import Network +import pandas as pd + +def output_value(labelVec): + max_value_index = 0 + max_value = 0 + for i in range(len(labelVec)): + if labelVec[i] > max_value: + max_value = labelVec[i] + max_value_index = i + return max_value_index + +def evaluate(network, test_data_set, test_labels): + error = 0 + total = len(test_data_set) + for i in range(total): + label = output_value(test_labels[i]) + predict = output_value(network.predict(test_data_set[i])) + + if label != predict: + error += 1 + print "label is %s" %label + print "predict value is %s" %predict + + return float(error) / float(total) + +# one_hot encoder +#error ratio is 0.053167 +#error ratio is 0.063917 +def onehot_encoder(label, classes): + label_size = label.shape[0] + label_onehot = np.zeros((label_size, classes)) + 0.1 + for i in range(label_size): + label_onehot[i][int(label[i])] = 0.9 + return label_onehot + +if __name__ == "__main__": + + train_data = pd.read_csv('./data/train.csv') + testData = pd.read_csv("./data/test.csv") + + train_input = train_data.iloc[:, 1:].values.astype(np.float) + data = np.multiply(train_input, 1.0/255.0) + label = train_data.iloc[:, 0].values.astype(np.float) + + label = onehot_encoder(label, 10) + + train_data = data[:30000] + train_labels = label[:30000] + + test_data = data[30000:] + test_labels = label[30000:] + + network = Network([784, 300, 10]) + learning_rate = 0.3 + epoch = 5 + network.train(label, data, learning_rate, epoch) + + #error_ratio = evaluate(network, test_data, test_labels) + # print "error ratio is %f" %error_ratio + + ########################################################################### + test_input = testData.values.astype(np.float) + test_input = np.multiply(test_input, 1.0/255.0) + + predicts = np.zeros(len(test_input)) + for i in range(len(test_input)): + predict = output_value(network.predict(test_input[i])) + predicts[i] = predict + + submissions = pd.DataFrame({'ImageId': np.arange(1 , 1 + test_input.shape[0]), 'Label': predicts.astype(int)}) + submissions.to_csv('./submission.csv', index=False) diff --git a/doc/boostingexperiments.pdf b/doc/1996 Experiments with a New Boosting Algorithm.pdf similarity index 100% rename from doc/boostingexperiments.pdf rename to doc/1996 Experiments with a New Boosting Algorithm.pdf diff --git a/draft/UserItemEmbeddingModel.py b/draft/UserItemEmbeddingModel.py new file mode 100644 index 0000000..82525b6 --- /dev/null +++ b/draft/UserItemEmbeddingModel.py @@ -0,0 +1,183 @@ +# -*- coding: utf-8 -*- +""" +Created on Sat Jan 5 15:03:51 2019 + +@author: liudiwei +""" + +import os +import pandas as pd +import numpy as np +import keras +import random + +from keras.preprocessing import sequence +from sklearn.cross_validation import train_test_split +from keras.models import Sequential +from keras.layers.embeddings import Embedding +from keras.layers.recurrent import LSTM +from keras.layers.core import Dense, Dropout,Activation +from keras.models import model_from_yaml +from keras.layers import Input, Embedding, Flatten, Dot, Dense, Reshape +from keras.models import Model +from sklearn.decomposition import PCA +import seaborn as sns +from sklearn.manifold import TSNE +random.seed(100) + +os.chdir(r"../data") + + +dataset = pd.read_csv('item_rating.csv') + +users = list(set(dataset.user_id.tolist())) +items = list(set(dataset.item_id.tolist())) + +#获取item的下标和item_id关系 +item_index = {item: idx for idx, item in enumerate(items)} +index_item = {idx: item for item, idx in item_index.items()} + +#获取user的下标和user_id关系 +user_index = {user: idx for idx, user in enumerate(users)} +index_user = {idx: user for user, idx in user_index.items()} + +dataset["user_index"] = dataset["user_id"].apply(lambda x: user_index[x]) +dataset["item_index"] = dataset["item_id"].apply(lambda x: item_index[x]) + +def load_data(): + train, test = train_test_split(dataset, test_size=0.2, random_state=42) + n_users = len(dataset.user_id.unique()) + n_items = len(dataset.item_id.unique()) + print(dataset.head()) + return dataset, train, test, n_users, n_items + +dataset, train, test, n_users, n_items = load_data() + + +def build_model(n_users, n_items, embedding_size = 10, classification=False): + # Both inputs are 1-dimensional + user_input = Input(shape=[1], name="user") + item_input = Input(shape=[1], name="item") + + user_embedding = Embedding(n_users, embedding_size, name="user-Embedding")(user_input) + item_embedding = Embedding(n_items, embedding_size, name="item-Embedding")(item_input) + + user_vec = Flatten(name="Flatten-Users")(user_embedding) + item_vec = Flatten(name="Flatten-items")(item_embedding) + + prod = Dot(name="Dot-Product", axes=1)([item_vec, user_vec]) + # Reshape to be a single number (shape will be (None, 1)) + merged = Reshape(target_shape = [1])(prod) + + if classification: + out = Dense(1, activation = 'sigmoid')(merged) + model = Model(inputs = [user_input, item_input], outputs = out) + model.compile(optimizer = 'Adam', loss = 'binary_crossentropy', metrics = ['accuracy']) + + # Otherwise loss function is mean squared error + else: + model = Model(inputs = [user_input, item_input], outputs = merged) + model.compile(optimizer = 'Adam', loss = 'mse') + #model.compile('adam', 'mean_squared_error') + + model.summary() + return model + +model = build_model(n_users, n_items, embedding_size=20) + +def generate_batch(train, + n_positive = 50, + negative_ratio = 1.0): + """Generate batches of samples for training. + Random select samples.""" + # Create empty array to hold batch + batch_size = n_positive * negative_ratio + batch = np.zeros((batch_size, 3)) + print(batch_size, batch) + # Continue to yield samples + while True: + # Randomly choose positive examples + batch_train = train.sample(batch_size) + batch_train["idx"] = [i for i in range(batch_train.shape[0])] + def getBatch(x): + batch[x["idx"], :] = (x["user_index"], x["item_index"], int(x["rating"])) + batch_train.apply(lambda x: getBatch(x), axis=1) + + + # Make sure to shuffle order + np.random.shuffle(batch) + del batch_train + yield {'user': batch[:, 0], 'item': batch[:, 1]}, batch[:, 2] + + +#train a regression model with fit +def train_model(model, train, epochs=5): + model.fit([train.user_index, train.item_index], train.rating, epochs=5, verbose=1) + model.save('regression_model.h5') + + # Extract embeddings + item_em = model.get_layer('item-Embedding') + item_em_weights = item_em.get_weights()[0] + item_em_weights + return model, item_em_weights + +def train_model_batch(model, train, epochs=5, batch_size_pos=8000): + n_positive = batch_size_pos + gen = generate_batch(train, n_positive, negative_ratio = 1) + # Train + model.fit_generator(gen, epochs = 5, steps_per_epoch = train.shape[0] // n_positive) + model.save('regression_model.h5') + return model + +#训练模型Method1 +model, item_em_weights = train_model(model, train) +#训练模型Method2 +model = train_model_batch(model, train, batch_size_pos=1000) + +# Extract item embeddings +item_layer = model.get_layer('item-Embedding') +item_weights = item_layer.get_weights()[0] +item_weights.shape + +# Extract user embeddings +user_layer = model.get_layer('user-Embedding') +user_weights = user_layer.get_weights()[0] +user_weights.shape + + +#PCA visualization +def pca_show(weights): + pca = PCA(n_components=2) + pca_result = pca.fit_transform(weights) + sns.jointplot(x=pca_result[:,0], y=pca_result[:,1]) +pca_show(item_em_weights) + + + +def tsne_show(weights): + tsne = TSNE(n_components=2, verbose=1, perplexity=40, n_iter=300) + tnse_results = tsne.fit_transform(weights) + sns.jointplot(x=tnse_results[:,0], y=tnse_results[:,1]) +tsne_show(item_em_weights) + + +#recommendation for the user +def recommend_for_user(user_index, dataset): + item_data = np.array(list(set(dataset.item_index))) + user = np.array([user_index for i in range(len(item_data))]) + predictions = model.predict([user, item_data]) + predictions = np.array([a[0] for a in predictions]) + recommended_item_ids = (-predictions).argsort()[:30] + print(recommended_item_ids) + return recommended_item_ids + #print(predictions[recommended_item_ids]) + +aaa = recommend_for_user(0,dataset) +aaa = recommend_for_user(3,dataset) +aaa = recommend_for_user(4,dataset) + +#calculate cosine similarity +from sklearn.metrics.pairwise import cosine_similarity +cos = cosine_similarity(item_em_weights[0:1], item_em_weights) +recommendations = cos[0].argsort()[-4:][::-1] + diff --git a/draft/book_recommendation.py b/draft/book_recommendation.py new file mode 100644 index 0000000..c10bfda --- /dev/null +++ b/draft/book_recommendation.py @@ -0,0 +1,96 @@ +# -*- coding: utf-8 -*- +""" +Created on Sat Jan 5 15:03:51 2019 + +@author: liudiwei +""" + +import os +import pandas as pd +import numpy as np +import keras + +from keras.preprocessing import sequence +from sklearn.cross_validation import train_test_split +from keras.models import Sequential +from keras.layers.embeddings import Embedding +from keras.layers.recurrent import LSTM +from keras.layers.core import Dense, Dropout,Activation +from keras.models import model_from_yaml +from keras.layers import Input, Embedding, Flatten, Dot, Dense +from keras.models import Model +from sklearn.decomposition import PCA +import seaborn as sns +from sklearn.manifold import TSNE + +os.chdir(r"../data") + +def load_data(): + dataset = pd.read_csv('ratings.csv') + train, test = train_test_split(dataset, test_size=0.2, random_state=42) + n_users = len(dataset.user_id.unique()) + n_books = len(dataset.book_id.unique()) + print(dataset.head()) + return dataset, train, test, n_users, n_books + +dataset, train, test, n_users, n_books = load_data() + +def build_model(n_users, n_books): + book_input = Input(shape=[1], name="Book-Input") + book_embedding = Embedding(n_books+1, 5, name="Book-Embedding")(book_input) + book_vec = Flatten(name="Flatten-Books")(book_embedding) + + user_input = Input(shape=[1], name="User-Input") + user_embedding = Embedding(n_users+1, 5, name="User-Embedding")(user_input) + user_vec = Flatten(name="Flatten-Users")(user_embedding) + + prod = Dot(name="Dot-Product", axes=1)([book_vec, user_vec]) + model = Model([user_input, book_input], prod) + model.compile('adam', 'mean_squared_error') + + model.summary() + return model + +model = build_model(n_users, n_books) + +#train a regression model +def train_model(model, train, epochs=5): + model.fit([train.user_id, train.book_id], train.rating, epochs=5, verbose=1) + model.save('regression_model.h5') + + # Extract embeddings + book_em = model.get_layer('Book-Embedding') + book_em_weights = book_em.get_weights()[0] + book_em_weights + return model, book_em_weights + +model, book_em_weights = train_model(model, train) + +#PCA visualization +def pca_show(weights): + pca = PCA(n_components=2) + pca_result = pca.fit_transform(weights) + sns.jointplot(x=pca_result[:,0], y=pca_result[:,1]) +pca_show(book_em_weights) + +def tsne_show(weights): + tsne = TSNE(n_components=2, verbose=1, perplexity=40, n_iter=300) + tnse_results = tsne.fit_transform(weights) + sns.jointplot(x=tnse_results[:,0], y=tnse_results[:,1]) +tsne_show(book_em_weights) + +#recommendation for the user +def recommend_for_user(user_index, dataset): + book_data = np.array(list(set(dataset.book_id))) + user = np.array([user_index for i in range(len(book_data))]) + predictions = model.predict([user, book_data]) + predictions = np.array([a[0] for a in predictions]) + recommended_book_ids = (-predictions).argsort()[:30] + print(recommended_book_ids) + #print(predictions[recommended_book_ids]) + +aaa = recommend_for_user(2,dataset) +aaa = recommend_for_user(3,dataset) +aaa = recommend_for_user(4,dataset) + + diff --git a/draft/contentBasedRec.py b/draft/contentBasedRec.py new file mode 100644 index 0000000..55e7b1d --- /dev/null +++ b/draft/contentBasedRec.py @@ -0,0 +1,37 @@ +# -*- coding: utf-8 -*- +""" +Created on Sun Jan 13 23:18:00 2019 + +@author: liudiwei +""" +import numpy as np +from sklearn import feature_extraction +from sklearn.feature_extraction.text import TfidfTransformer +from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer +import pandas as pd + +user_items = pd.read_csv("./data_process/user_keywords.csv") +user_items["items"] = user_items["keywords"].apply(lambda x: x.replace("|", " ")) +corpus = user_items["items"].tolist() + +tfidf_vectorizer = TfidfVectorizer(max_df=0.95, min_df=2) + +transformer=TfidfTransformer()#该类会统计每个词语的tf-idf权值 + +vectorizer=CountVectorizer()#该类会将文本中的词语转换为词频矩阵，矩阵元素a[i][j] 表示j词在i类文本下的词频 + +tfidf=transformer.fit_transform(tfidf_vectorizer.fit_transform(corpus))#第一个fit_transform是计算tf-idf，第二个fit_transform是将文本转为词频矩阵 + +word=tfidf_vectorizer.get_feature_names()#获取词袋模型中的所有词语 +weight=tfidf.toarray()#将tf-idf矩阵抽取出来，元素a[i][j]表示j词在i类文本中的tf-idf权重 + + +#calculate cosine similarity +from sklearn.metrics.pairwise import cosine_similarity +cos = cosine_similarity(weight[0:1], weight) +recommendations = cos[0].argsort()[-4:][::-1] + + +a = list(weight[0:1][0]) +aaa = pd.DataFrame(weight) +cosine_similarity(np.array([a]), aaa) \ No newline at end of file diff --git a/draft/data/artice1.txt b/draft/data/artice1.txt new file mode 100644 index 0000000..9769e91 --- /dev/null +++ b/draft/data/artice1.txt @@ -0,0 +1,2 @@ +然而，即使上述模型对词向量进行平均处理，我们仍然忽略了单词之间的排列顺序对情感分析的影响。即上述的word2vec只是基于词的维度进行"语义分析"的，而并不具有上下文的"语义分析"能力。 +作为一个处理可变长度文本的总结性方法，Quoc Le 和 Tomas Mikolov 提出了 Doc2Vec方法。除了增加一个段落向量以外，这个方法几乎等同于 Word2Vec。和 Word2Vec 一样，该模型也存在两种方法：Distributed Memory(DM) 和 Distributed Bag of Words(DBOW)。DM 试图在给定上下文和段落向量的情况下预测单词的概率。在一个句子或者文档的训练过程中，段落 ID 保持不变，共享着同一个段落向量。DBOW 则在仅给定段落向量的情况下预测段落中一组随机单词的概率。 \ No newline at end of file diff --git a/draft/data/article2.txt b/draft/data/article2.txt new file mode 100644 index 0000000..3d264a8 --- /dev/null +++ b/draft/data/article2.txt @@ -0,0 +1 @@ +训练过程中新增了paragraph id，即训练语料中每个句子都有一个唯一的id。paragraph id和普通的word一样，也是先映射成一个向量，即paragraph vector。paragraph vector与word vector的维数虽一样，但是来自于两个不同的向量空间。在之后的计算里，paragraph vector和word vector累加或者连接起来，作为输出层softmax的输入。在一个句子或者文档的训练过程中，paragraph id保持不变，共享着同一个paragraph vector，相当于每次在预测单词的概率时，都利用了整个句子的语义。 \ No newline at end of file diff --git a/draft/data/corpus_test/t1.txt b/draft/data/corpus_test/t1.txt new file mode 100644 index 0000000..9769e91 --- /dev/null +++ b/draft/data/corpus_test/t1.txt @@ -0,0 +1,2 @@ +然而，即使上述模型对词向量进行平均处理，我们仍然忽略了单词之间的排列顺序对情感分析的影响。即上述的word2vec只是基于词的维度进行"语义分析"的，而并不具有上下文的"语义分析"能力。 +作为一个处理可变长度文本的总结性方法，Quoc Le 和 Tomas Mikolov 提出了 Doc2Vec方法。除了增加一个段落向量以外，这个方法几乎等同于 Word2Vec。和 Word2Vec 一样，该模型也存在两种方法：Distributed Memory(DM) 和 Distributed Bag of Words(DBOW)。DM 试图在给定上下文和段落向量的情况下预测单词的概率。在一个句子或者文档的训练过程中，段落 ID 保持不变，共享着同一个段落向量。DBOW 则在仅给定段落向量的情况下预测段落中一组随机单词的概率。 \ No newline at end of file diff --git a/draft/data/corpus_test/t2.txt b/draft/data/corpus_test/t2.txt new file mode 100644 index 0000000..3d264a8 --- /dev/null +++ b/draft/data/corpus_test/t2.txt @@ -0,0 +1 @@ +训练过程中新增了paragraph id，即训练语料中每个句子都有一个唯一的id。paragraph id和普通的word一样，也是先映射成一个向量，即paragraph vector。paragraph vector与word vector的维数虽一样，但是来自于两个不同的向量空间。在之后的计算里，paragraph vector和word vector累加或者连接起来，作为输出层softmax的输入。在一个句子或者文档的训练过程中，paragraph id保持不变，共享着同一个paragraph vector，相当于每次在预测单词的概率时，都利用了整个句子的语义。 \ No newline at end of file diff --git a/draft/data/corpus_words/artice1.txt b/draft/data/corpus_words/artice1.txt new file mode 100644 index 0000000..4b65fdd --- /dev/null +++ b/draft/data/corpus_words/artice1.txt @@ -0,0 +1 @@ +然而 ， 即使 上述 模型 对词 向量 进行 平均 处理 ， 我们 仍然 忽略 了 单词 之间 的 排列 顺序 对 情感 分析 的 影响 。 即 上述 的 word2vec 只是 基于 词 的 维度 进行 " 语义 分析 " 的 ， 而 并 不 具有 上下文 的 " 语义 分析 " 能力 。 作为 一个 处理 可变 长度 文本 的 总结性 方法 ， QuocLe 和 TomasMikolov 提出 了 Doc2Vec 方法 。 除了 增加 一个 段落 向量 以外 ， 这个 方法 几乎 等同于 Word2Vec 。 和 Word2Vec 一样 ， 该 模型 也 存在 两种 方法 ： DistributedMemory ( DM ) 和 DistributedBagofWords ( DBOW ) 。 DM 试图 在 给定 上下文 和 段落 向量 的 情况 下 预测 单词 的 概率 。 在 一个 句子 或者 文档 的 训练 过程 中 ， 段落 ID 保持 不变 ， 共享 着 同一个 段落 向量 。 DBOW 则 在 仅 给定 段落 向量 的 情况 下 预测 段落 中 一组 随机 单词 的 概率 。 然而 ， 即使 上述 模型 对词 向量 进行 平均 处理 ， 我们 仍然 忽略 了 单词 之间 的 排列 顺序 对 情感 分析 的 影响 。 即 上述 的 word2vec 只是 基于 词 的 维度 进行 " 语义 分析 " 的 ， 而 并 不 具有 上下文 的 " 语义 分析 " 能力 。 作为 一个 处理 可变 长度 文本 的 总结性 方法 ， QuocLe 和 TomasMikolov 提出 了 Doc2Vec 方法 。 除了 增加 一个 段落 向量 以外 ， 这个 方法 几乎 等同于 Word2Vec 。 和 Word2Vec 一样 ， 该 模型 也 存在 两种 方法 ： DistributedMemory ( DM ) 和 DistributedBagofWords ( DBOW ) 。 DM 试图 在 给定 上下文 和 段落 向量 的 情况 下 预测 单词 的 概率 。 在 一个 句子 或者 文档 的 训练 过程 中 ， 段落 ID 保持 不变 ， 共享 着 同一个 段落 向量 。 DBOW 则 在 仅 给定 段落 向量 的 情况 下 预测 段落 中 一组 随机 单词 的 概率 。 \ No newline at end of file diff --git a/draft/data/corpus_words/article2.txt b/draft/data/corpus_words/article2.txt new file mode 100644 index 0000000..a34839c --- /dev/null +++ b/draft/data/corpus_words/article2.txt @@ -0,0 +1 @@ +训练 过程 中 新增 了 paragraphid ， 即 训练 语料 中 每个 句子 都 有 一个 唯一 的 id 。 paragraphid 和 普通 的 word 一样 ， 也 是 先 映射 成 一个 向量 ， 即 paragraphvector 。 paragraphvector 与 wordvector 的 维数 虽 一样 ， 但是 来自 于 两个 不同 的 向量 空间 。 在 之后 的 计算 里 ， paragraphvector 和 wordvector 累加 或者 连接起来 ， 作为 输出 层 softmax 的 输入 。 在 一个 句子 或者 文档 的 训练 过程 中 ， paragraphid 保持 不变 ， 共享 着 同一个 paragraphvector ， 相当于 每次 在 预测 单词 的 概率 时 ， 都 利用 了 整个 句子 的 语义 。 \ No newline at end of file diff --git a/draft/doc2vector.py b/draft/doc2vector.py new file mode 100644 index 0000000..0492268 --- /dev/null +++ b/draft/doc2vector.py @@ -0,0 +1,203 @@ +# -*- coding: utf-8 -*- +""" +Created on Sun Jan 27 14:20:16 2019 + +@author: liudiwei +""" + +# -*- coding: utf-8 -*- +import codecs +import re +from os import listdir + +import gensim +import jieba +import numpy as np +import pandas as pd +import os + +def doc_segment(doc_path, corpus_path): + """save word segment + """ + # 先把所有文档的路径存进一个 array 中，docLabels： + doc_lists = [file for file in listdir(doc_path) if file.endswith('.txt')] + + for doc in doc_lists: + try: + ws = codecs.open(doc_path + "/" + doc, encoding="utf8").read() + doc_words = segment(ws) + if not os.path.exists(corpus_path): + os.mkdir(corpus_path) + with codecs.open(corpus_path + "/{}".format(doc), "a", encoding="UTF-8") as f: + f.write(" ".join(doc_words)) + except: + print(doc) + +def segment(doc: str, stopword_file=None): + """中文分词 + parameter: + doc : str, input text + return: + [type] --- [description] + """ + # 停用词 + if stopword_file != None: + stop_words = pd.read_csv(stopword_file, + index_col=False, + quoting=3, + names=['stopword'], + sep="\n", + encoding='utf-8') + + stop_words = list(stop_words.stopword) + else: + stop_words = [] + reg_html = re.compile(r'<[^>]+>', re.S) + doc = reg_html.sub('', doc) + doc = re.sub('[0-9]', '', doc) + doc = re.sub('\s', '', doc) + word_list = list(jieba.cut(doc)) + out_str = '' + for word in word_list: + if word not in stop_words: + out_str += word + out_str += ' ' + segments = out_str.split(sep=" ") + return segments + +def build_corpus(corpus_path): + """build word corpus: list of list + """ + doc_labels = [f for f in os.listdir(corpus_path) if f.endswith('.txt')] + + corpus = [] + for doc in doc_labels: + ws = open(corpus_path + "/" + doc, 'r', encoding='UTF-8').read() + corpus.append(ws) + + print("corpus size: ", len(corpus)) + return corpus, doc_labels + +############################## build model #################################### + +def train_model(corpus, doc_labels, model_path, model_name="doc2vec.model"): + """training model + parameter: + - courpus: [[...], [....]] + - doc_labels: [...] + - model_path + - model_name: default value "doc2vec.model" + return: + - model: model + - model_file: model_path + "/" + model_name + """ + # training doc2vec model and save model to local disk： + sentences = LabeledLineSentence(corpus, doc_labels) + # an empty model + model = gensim.models.Doc2Vec(vector_size=256, + window=10, + min_count=5, + workers=4, + alpha=0.025, + min_alpha=0.025, + epochs=12) + model.build_vocab(sentences) + + print("start training...") + model.train(sentences, total_examples = model.corpus_count, epochs=12) + + if not os.path.exists(model_path): + os.mkdir(model_path) + model_file = model_path + "/" + model_name + model.save(model_file) + print("Model saved") + return model, model_file + +def test_model(model_file, file1, file2): + print("Loading Model.") + model = gensim.models.Doc2Vec.load(model_file) + + sentence1 = open(file1, 'r', encoding='UTF-8').read() + sentence2 = open(file2, 'r', encoding='UTF-8').read() + + # 分词 + print("start to segment") + words1 = segment(sentence1) + words2 = segment(sentence2) + + # 转成句子向量 + vector1 = sent2vec(model, words1) + vector2 = sent2vec(model, words2) + + import sys + print(sys.getsizeof(vector1)) + print(sys.getsizeof(vector2)) + + cos = similarity(vector1, vector2) + print("相似度：{:.4f}".format(cos)) + + +def similarity(a_vect, b_vect): + """计算两个向量余弦值 + parameter: + a_vect {[type]} -- a 向量 + b_vect {[type]} -- b 向量 + + return: + [type] -- [description] + """ + + dot_val = 0.0 + a_norm = 0.0 + b_norm = 0.0 + cos = None + for a, b in zip(a_vect, b_vect): + dot_val += a*b + a_norm += a**2 + b_norm += b**2 + if a_norm == 0.0 or b_norm == 0.0: + cos = -1 + else: + cos = dot_val / ((a_norm*b_norm)**0.5) + + return cos + + +def sent2vec(model, words): + """sentence2vector + parameter: + model {[type]} -- Doc2Vec 模型 + words {[type]} -- 分词后的文本 + return: + [type] -- 向量数组 + """ + vect_list = [] + for w in words: + try: + vect_list.append(model.wv[w]) + except: + continue + vect_list = np.array(vect_list) + vect = vect_list.sum(axis=0) + return vect / np.sqrt((vect ** 2).sum()) + + + +class LabeledLineSentence(object): + def __init__(self, doc_list, labels_list): + self.labels_list = labels_list + self.doc_list = doc_list + + def __iter__(self): + for idx, doc in enumerate(self.doc_list): + yield gensim.models.doc2vec.TaggedDocument(words=doc.split(), tags=[self.labels_list[idx]]) + + +if __name__ == '__main__': + doc_path = "./data/" + corpus_path = "data/corpus_words" + model_path = "./models" + #doc_segment(data_dir) + corpus, doc_labels = build_corpus(corpus_path) + model, model_file = train_model(corpus, doc_labels, model_path) + test_model(model_file, './data/corpus_test/t2.txt', './data/corpus_test/t1.txt') diff --git a/draft/jupyter/fm_demo.ipynb b/draft/jupyter/fm_demo.ipynb new file mode 100644 index 0000000..f288396 --- /dev/null +++ b/draft/jupyter/fm_demo.ipynb @@ -0,0 +1,555 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "os.chdir(r\"F:\\CSU\\Github\\nlp_exp\\fm_exp\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "from itertools import count \n", + "from collections import defaultdict\n", + "from scipy.sparse import csr \n", + "from __future__ import print_function \n", + "\n", + "def vectorize_dic(dic, ix=None, p=None):\n", + " \"\"\" \n", + " Creates a scipy csr matrix from a list of lists (each inner list is a set of values corresponding to a feature) \n", + " \n", + " parameters:\n", + " -----------\n", + " dic -- dictionary of feature lists. Keys are the name of features\n", + " ix -- index generator (default None)\n", + " p -- dimension of featrure space (number of columns in the sparse matrix) (default None)\n", + " \"\"\"\n", + " if (ix == None):\n", + " d = count(0)\n", + " ix = defaultdict(lambda: next(d)) \n", + " \n", + " n = len(list(dic.values())[0]) # num samples\n", + " g = len(list(dic.keys())) # num groups\n", + " nz = n * g # number of non-zeros\n", + "\n", + " col_ix = np.empty(nz, dtype=int) \n", + " \n", + " i = 0\n", + " for k, lis in dic.items(): \n", + " # append index el with k in order to prevet mapping different columns with same id to same index\n", + " col_ix[i::g] = [ix[str(el) + str(k)] for el in lis]\n", + " i += 1\n", + " \n", + " row_ix = np.repeat(np.arange(0, n), g) \n", + " data = np.ones(nz)\n", + " \n", + " if (p == None):\n", + " p = len(ix)\n", + " \n", + " ixx = np.where(col_ix < p)\n", + "\n", + " return csr.csr_matrix((data[ixx],(row_ix[ixx], col_ix[ixx])), shape=(n, p)), ix" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 加载数据集" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " user item rating timestamp\n", + "0 1 1 5 874965758\n", + "1 1 2 3 876893171\n", + "2 1 3 4 878542960\n", + "3 1 4 3 876893119\n", + "4 1 5 3 889751712\n", + "5 1 6 5 887431973\n", + "6 1 7 4 875071561\n", + "7 1 8 1 875072484\n", + "8 1 9 5 878543541\n", + "9 1 10 3 875693118" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "from sklearn.feature_extraction import DictVectorizer\n", + "\n", + "# laod data with pandas\n", + "cols = ['user', 'item', 'rating', 'timestamp']\n", + "train = pd.read_csv('data/ua.base', delimiter='\\t', names=cols)\n", + "test = pd.read_csv('data/ua.test', delimiter='\\t', names=cols)\n", + "train.head(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### vectorize data and convert them to csr matrix" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "X_train, ix = vectorize_dic({'users': train.user.values, 'items': train.item.values})\n", + "X_test, ix = vectorize_dic({'users': test.user.values, 'items': test.item.values}, ix, X_train.shape[1])\n", + "y_train = train.rating.values\n", + "y_test= test.rating.values" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(90570, 2623)\n", + "(9430, 2623)\n" + ] + } + ], + "source": [ + "X_train = X_train.todense()\n", + "X_test = X_test.todense()\n", + "\n", + "# print shape of data\n", + "print(X_train.shape)\n", + "print(X_test.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Define FM Model with tensorflow" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "import tensorflow as tf\n", + "\n", + "#get x_train shape\n", + "n, p = X_train.shape\n", + "\n", + "# number of latent factors\n", + "k = 10\n", + "\n", + "# design matrix\n", + "X = tf.placeholder('float', shape=[None, p])\n", + "# target vector\n", + "y = tf.placeholder('float', shape=[None, 1])\n", + "\n", + "# bias and weights\n", + "w0 = tf.Variable(tf.zeros([1]))\n", + "W = tf.Variable(tf.zeros([p]))\n", + "\n", + "# interaction factors, randomly initialized \n", + "V = tf.Variable(tf.random_normal([k, p], stddev=0.01))\n", + "\n", + "# estimate of y, initialized to 0.\n", + "y_hat = tf.Variable(tf.zeros([n, 1]))" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$$\\hat{y}(\\mathbf{x}) = w_0 + \\sum_{j=1}^{p}w_jx_j + \\frac{1}{2} \\sum_{f=1}^{k} ((\\sum_{j=1}^{p}v_{j,f}x_j)^2-\\sum_{j=1}^{p}v_{j,f}^2 x_j^2)$$" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from IPython.display import display, Math, Latex\n", + "\n", + "display(Math(r'\\hat{y}(\\mathbf{x}) = w_0 + \\sum_{j=1}^{p}w_jx_j + \\frac{1}{2} \\sum_{f=1}^{k} ((\\sum_{j=1}^{p}v_{j,f}x_j)^2-\\sum_{j=1}^{p}v_{j,f}^2 x_j^2)'))" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "# Calculate output with FM equation\n", + "linear_terms = tf.add(w0, tf.reduce_sum(tf.multiply(W, X), 1, keep_dims=True))\n", + "pair_interactions = (tf.multiply(0.5,\n", + " tf.reduce_sum(\n", + " tf.subtract(\n", + " tf.pow( tf.matmul(X, tf.transpose(V)), 2),\n", + " tf.matmul(tf.pow(X, 2), tf.transpose(tf.pow(V, 2)))),\n", + " 1, keep_dims=True)))\n", + "y_hat = tf.add(linear_terms, pair_interactions)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$$L = \\sum_{i=1}^{n} (y_i - \\hat{y}_i)^2 + \\lambda_w ||W||^2 + \\lambda_v ||V||^2$$" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(Math(r'L = \\sum_{i=1}^{n} (y_i - \\hat{y}_i)^2 + \\lambda_w ||W||^2 + \\lambda_v ||V||^2'))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "# L2 regularized sum of squares loss function over W and V\n", + "lambda_w = tf.constant(0.001, name='lambda_w')\n", + "lambda_v = tf.constant(0.001, name='lambda_v')\n", + "\n", + "l2_norm = (tf.reduce_sum(\n", + " tf.add(\n", + " tf.multiply(lambda_w, tf.pow(W, 2)),\n", + " tf.multiply(lambda_v, tf.pow(V, 2)))))\n", + "\n", + "error = tf.reduce_mean(tf.square(tf.subtract(y, y_hat)))\n", + "loss = tf.add(error, l2_norm)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$$\\Theta_{i+1} = \\Theta_{i} - \\eta \\frac{\\delta L}{\\delta \\Theta}$$" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(Math(r'\\Theta_{i+1} = \\Theta_{i} - \\eta \\frac{\\delta L}{\\delta \\Theta}'))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.01).minimize(loss)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "def batcher(X_, y_=None, batch_size=-1):\n", + " n_samples = X_.shape[0]\n", + "\n", + " if batch_size == -1:\n", + " batch_size = n_samples\n", + " if batch_size < 1:\n", + " raise ValueError('Parameter batch_size={} is unsupported'.format(batch_size))\n", + "\n", + " for i in range(0, n_samples, batch_size):\n", + " upper_bound = min(i + batch_size, n_samples)\n", + " ret_x = X_[i:upper_bound]\n", + " ret_y = None\n", + " if y_ is not None:\n", + " ret_y = y_[i:i + batch_size]\n", + " yield (ret_x, ret_y)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "a8247d26eae64d09b463eab3875f13c5", + "version_major": 2, + "version_minor": 0 + }, + "text/html": [ + "

Failed to display Jupyter Widget of type HBox.

\n", + "

\n", + " If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n", + " that the widgets JavaScript is still loading. If this message persists, it\n", + " likely means that the widgets JavaScript library is either not installed or\n", + " not enabled. See the Jupyter\n", + " Widgets Documentation for setup instructions.\n", + "

\n", + "

\n", + " If you're reading this message in another frontend (for example, a static\n", + " rendering on GitHub or NBViewer),\n", + " it may mean that your frontend doesn't currently support widgets.\n", + "

\n" + ], + "text/plain": [ + "HBox(children=(IntProgress(value=0, max=10), HTML(value='')))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "from tqdm import tqdm_notebook as tqdm\n", + "\n", + "epochs = 10\n", + "batch_size = 1000\n", + "\n", + "# Launch the graph\n", + "init = tf.global_variables_initializer()\n", + "sess = tf.Session()\n", + "\n", + "sess.run(init)\n", + "\n", + "for epoch in tqdm(range(epochs), unit='epoch'):\n", + " perm = np.random.permutation(X_train.shape[0])\n", + " # iterate over batches\n", + " for bX, bY in batcher(X_train[perm], y_train[perm], batch_size):\n", + " sess.run(optimizer, feed_dict={X: bX.reshape(-1, p), y: bY.reshape(-1, 1)})" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.113785\n" + ] + } + ], + "source": [ + "errors = []\n", + "for bX, bY in batcher(X_test, y_test):\n", + " errors.append(sess.run(error, feed_dict={X: bX.reshape(-1, p), y: bY.reshape(-1, 1)}))\n", + "\n", + "RMSE = np.sqrt(np.array(errors).mean())\n", + "print(RMSE)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "sess.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[1.2405171]" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "errors" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/draft/stacking.py b/draft/stacking.py new file mode 100644 index 0000000..915d778 --- /dev/null +++ b/draft/stacking.py @@ -0,0 +1,181 @@ +from sklearn.model_selection import KFold +from sklearn.model_selection import train_test_split +from sklearn.datasets import load_digits +import numpy as np +from sklearn.svm import SVC +from sklearn import metrics +from sklearn.ensemble import RandomForestClassifier +from sklearn import preprocessing +import pandas as pd +from functools import reduce +from sklearn.metrics import confusion_matrix, classification_report + +class StackingClassifier(object): + + def __init__(self, modellist=[], meta_classifier=None): + self.modellist = modellist + if meta_classifier == None: + from sklearn.linear_model import LogisticRegression + meta_classifier = LogisticRegression() + self.meta_classifier = meta_classifier + + def SelectModel(self, modelname): + + if modelname == "SVM": + from sklearn.svm import SVC + model = SVC(kernel='rbf', C=16, gamma=0.125,probability=True) + + elif modelname == "lr": + from sklearn.linear_model import LogisticRegression + model = LogisticRegression() + + elif modelname == "GBDT": + from sklearn.ensemble import GradientBoostingClassifier + model = GradientBoostingClassifier() + + elif modelname == "RF": + from sklearn.ensemble import RandomForestClassifier + model = RandomForestClassifier() + + elif modelname == "xgboost": + from xgboost import XGBClassifier + model = XGBClassifier( + learning_rate=0.01, + n_estimators=1000, + max_depth=4, + min_child_weight=3, + gamma=0.1, + subsample=0.8, + colsample_bytree=0.8, + reg_alpha=1, + objective='binary:logistic', #multi:softmax + nthread=8, + scale_pos_weight=1, + seed=27, + random_state=27 + ) + elif modelname == "KNN": + from sklearn.neighbors import KNeighborsClassifier as knn + model = knn() + + elif modelname == "MNB": + from sklearn.naive_bayes import MultinomialNB + model = MultinomialNB() + else: + pass + return model + + def get_oof(self, clf, n_folds, X_train, y_train, X_test): + ntrain = X_train.shape[0] + ntest = X_test.shape[0] + print("kfolds: ", ntrain, ntest) + classnum = len(np.unique(y_train)) + kf = KFold(n_splits=n_folds,random_state=1) + oof_train = np.zeros((ntrain,classnum)) + oof_test = np.zeros((ntest,classnum)) + + for i,(train_index, test_index) in enumerate(kf.split(X_train)): + kf_X_train = X_train[train_index] # 数据 + kf_y_train = y_train[train_index] # 标签 + + kf_X_test = X_train[test_index] # k-fold的验证集 + + clf.fit(kf_X_train, kf_y_train) + oof_train[test_index] = clf.predict_proba(kf_X_test) + # print("shape of oof_train:", oof_train[test_index].shape) + + print("fold{i}: oof_train: {a}, oof_test:{b}".format(i=i, a=oof_train.shape, b=oof_test.shape)) + oof_test += clf.predict_proba(X_test) + oof_test = oof_test/float(n_folds) + print("oof_train: {a}, oof_test:{b}".format(a=oof_train.shape, b=oof_test.shape)) + return oof_train, oof_test + + def first_layer(self, X_train, y_train, X_test, modellist=None): + """modellist 需要重新修改 + """ + newfeature_list = [] + newtestdata_list = [] + for modelname in self.modellist: + sub_clf = self.SelectModel(modelname) + oof_train_, oof_test_= self.get_oof(clf=sub_clf, + n_folds=5, + X_train=X_train, + y_train=y_train, + X_test=X_test) + print("oof_train: ", oof_train_.shape) + print("model-{}".format(modelname),len(oof_train_), len(oof_test_)) + newfeature_list.append(oof_train_) + print("newfeature_list: ", len(newfeature_list)) + newtestdata_list.append(oof_test_) + + # 特征组合 + X_train_stacking = reduce(lambda x,y:np.concatenate((x,y),axis=1),newfeature_list) + X_test_stacking = reduce(lambda x,y:np.concatenate((x,y),axis=1),newtestdata_list) + + return X_train_stacking, X_test_stacking + + def fit(self, X_train, y_train, clf=None): + if clf != None: + self.meta_classifier = clf + self.meta_classifier.fit(X_train, y_train) + return self.meta_classifier + + #second_layer + def second_layer(self, X_train, y_train, clf=None): + return self.fit(X_train, y_train, clf) + + def predict(self, X_test, clf=None, type="label"): + if clf == None: + clf = self.meta_classifier + if type == "proba": + return clf.predict_proba(X_test) + elif type == "label": + return clf.predict(X_test) + + def get_accuracy(self, y_true, y_pred): + accuracy = metrics.accuracy_score(y_true, y_pred)*100 + return accuracy + + def performance(self, y_true, y_pred): + accuracy = self.get_accuracy(y_true, y_pred) + confusion = confusion_matrix(y_true, y_pred) + report = classification_report(y_true, y_pred) + print("多模型融合预测accuracy：{}".format(accuracy)) + print("混淆矩阵：\n{}".format(confusion)) + print("预测结果：\n{}".format(report)) + return confusion, report + + +# 使用stacking方法的时候 +# 第一级，重构特征当做第二级的训练集 +if __name__ == "__main__": + # 导入数据集切割训练与测试数据 + data = load_digits() + data_D = preprocessing.StandardScaler().fit_transform(data.data) + data_L = data.target + X_train, X_test, y_train, y_test = train_test_split(data_D,data_L,random_state=100,test_size=0.7) + print(set(y_train)) + + # 单纯使用一个分类器的时候 + clf_meta = RandomForestClassifier() + clf_meta.fit(X_train, y_train) + pred = clf_meta.predict(X_test) + accuracy = metrics.accuracy_score(y_test, pred)*100 + print ("====================", accuracy) + # 91.0969793323 + + #layer 1：多模型融合 + modelist = ['SVM', 'GBDT', 'RF', 'KNN'] + stacking_clf = StackingClassifier(modelist) + X_train_stacking, X_test_stacking = stacking_clf.first_layer(X_train, y_train, X_test) + print("shape of X_train_stacking {}".format(X_train_stacking.shape)) + print("shape of X_test_stacking {}".format(X_test_stacking.shape)) + + #layer 2： 单模型训练 + RF = stacking_clf.SelectModel(modelname="RF") + clf = stacking_clf.second_layer(X_train_stacking, y_train, clf=RF) + pred = stacking_clf.predict(X_test_stacking) + + #模型评估 + stacking_clf.performance(y_test, pred) + # 96.4228934817 diff --git a/stacking/stacking.py b/stacking/stacking.py new file mode 100644 index 0000000..4091365 --- /dev/null +++ b/stacking/stacking.py @@ -0,0 +1,241 @@ +from sklearn.model_selection import KFold +from sklearn.model_selection import train_test_split +from sklearn.datasets import load_digits +import numpy as np +from sklearn.svm import SVC +from sklearn import metrics +from sklearn.ensemble import RandomForestClassifier +from sklearn import preprocessing +import pandas as pd +from functools import reduce +from sklearn.metrics import confusion_matrix, classification_report +from sklearn.linear_model import LogisticRegression +from sklearn.base import clone +import xgboost as xgb + +class SubClassifier(object): + def __init__(self): + # import lightgbm as lgb + # import xgboost as xgb + # from sklearn.svm import SVC + # from sklearn.ensemble import BaggingClassifier, RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier + # from sklearn.linear_model import LogisticRegression + # from sklearn.svm import LinearSVC + # clfs = { + # 'lr': LogisticRegression(penalty='l1', C=0.1, tol=0.0001), + # 'svm': LinearSVC(C=0.05, penalty='l2', dual=True), + # 'svm_linear': SVC(kernel='linear', probability=True), + # 'svm_ploy': SVC(kernel='poly', probability=True), + # 'bagging': BaggingClassifier(base_estimator=base_clf, n_estimators=60, max_samples=1.0, max_features=1.0, + # random_state=1, n_jobs=1, verbose=1), + # 'rf': RandomForestClassifier(n_estimators=40, criterion='gini', max_depth=9), + # 'adaboost': AdaBoostClassifier(base_estimator=base_clf, n_estimators=50, algorithm='SAMME'), + # 'gbdt': GradientBoostingClassifier(), + # 'xgb': xgb.XGBClassifier(learning_rate=0.1, max_depth=3, n_estimators=50), + # 'lgb': lgb.LGBMClassifier(boosting_type='gbdt', learning_rate=0.01, max_depth=5, n_estimators=250, num_leaves=90) + # } + pass + + def SelectModel(self, modelname): + if modelname == "SVM": + from sklearn.svm import SVC + clf = SVC(kernel='rbf', C=16, gamma=0.125,probability=True) + + elif modelname == "lr": + from sklearn.linear_model import LogisticRegression + clf = LogisticRegression() + + elif modelname == "GBDT": + from sklearn.ensemble import GradientBoostingClassifier + clf = GradientBoostingClassifier() + + elif modelname == "RF": + from sklearn.ensemble import RandomForestClassifier + clf = RandomForestClassifier(n_estimators=100) + + elif modelname == "xgboost": + from xgboost import XGBClassifier + clf = XGBClassifier( + learning_rate=0.01, + n_estimators=1000, + max_depth=4, + min_child_weight=3, + gamma=0.1, + subsample=0.8, + colsample_bytree=0.8, + reg_alpha=1, + objective='binary:logistic', #multi:softmax + nthread=8, + scale_pos_weight=1, + seed=27, + random_state=27 + ) + elif modelname == "KNN": + from sklearn.neighbors import KNeighborsClassifier as knn + clf = knn() + + elif modelname == "MNB": + from sklearn.naive_bayes import MultinomialNB + clf = MultinomialNB() + else: + pass + return clf + + def performance(self, y_true, y_pred, modelname=""): + accuracy = metrics.accuracy_score(y_true, y_pred)*100 + confusion = confusion_matrix(y_true, y_pred) + report = classification_report(y_true, y_pred) + print("模型{}预测accuracy：{}".format(modelname, accuracy)) + print("混淆矩阵：\n{}".format(confusion)) + print("预测结果：\n{}".format(report)) + return confusion, report + + +class StackingClassifier(object): + + def __init__(self, classifiers, meta_classifier, + use_clones=True, n_folds=2, + n_classes=2, random_state=100, + sample_weight=None, use_probas=True): + + self.classifiers = classifiers + self.meta_classifier = meta_classifier + self.use_clones=use_clones + self.n_folds = n_folds + self.n_classes = n_classes + self.random_state = random_state + self.sample_weight = sample_weight + self.use_probas = use_probas + + def cross_valid_oof(self, clf, X, y, n_folds): + """返回CV预测结果 + """ + ntrain = X.shape[0] + n_classes = self.n_classes + random_state = self.random_state + oof_features = np.zeros((ntrain, n_classes)) + oof_pred = np.zeros(ntrain) + kf = KFold(n_splits=n_folds, random_state=random_state) + for i,(train_index, test_index) in enumerate(kf.split(X)): + kf_X_train = X[train_index] # 数据 + kf_y_train = y[train_index] # 标签 + + kf_X_test = X[test_index] # k-fold的验证集 + + clf.fit(kf_X_train, kf_y_train) + if not self.use_probas: + oof_features[test_index] = clf.predict(kf_X_test) + else: + oof_features[test_index] = clf.predict_proba(kf_X_test) + oof_pred[test_index] = clf.predict(kf_X_test) + print("fold-{i}: oof_features: {a}, cv-oof accuracy:{c}".format(i=i, + a=oof_features.shape, + c=self.get_accuracy(y[test_index], oof_pred[test_index]))) + return oof_features + + def fit(self, X, y): + self.clfs_ = self.classifiers + self.meta_clf_ = self.meta_classifier + + n_folds = self.n_folds + sample_weight = self.sample_weight + meta_features = None + + #feature layer + for name, sub_clf in self.clfs_.items(): + print("feature layer, current model: {}".format(name)) + meta_prediction = self.cross_valid_oof(sub_clf, X, y, n_folds) + if meta_features is None: + meta_features = meta_prediction + else: + meta_features = np.column_stack((meta_features, meta_prediction)) + + for name, model in self.clfs_.items(): + print("fit base model using all train set: {}".format(name)) + if sample_weight is None: + model.fit(X, y) + else: + model.fit(X, y, sample_weight=sample_weight) + + #meta layer + if sample_weight is None: + self.meta_clf_.fit(meta_features, y) + else: + self.meta_clf_.fit(meta_features, y, sample_weight=sample_weight) + + return self + + def predict_meta_features(self, X): + """ Get meta-features of test-data. + Parameters + ------- + X : numpy array, shape = [n_samples, n_features] + + Returns: + ------- + meta-features : numpy array, shape = [n_samples, n_classifiers] + """ + per_model_preds = [] + + for name, model in self.clfs_.items(): + print("model {} predict_meta_features".format(name)) + if not self.use_probas: + pred_score = model.predict(X) + else: + pred_score = model.predict_proba(X) + + per_model_preds.append(pred_score) + + return np.hstack(per_model_preds) + + + def predict(self, X): + """ Predict class label for X.""" + meta_features = self.predict_meta_features(X) + return self.meta_clf_.predict(meta_features) + + def predict_prob(self, X): + """ Predict class probabilities for X.""" + meta_features = self.predict_meta_features(X) + return self.meta_clf_.predict_proba(meta_features) + + def get_accuracy(self, y_true, y_pred): + accuracy = round(metrics.accuracy_score(y_true, y_pred)*100,3) + return accuracy + + def performance(self, y_true, y_pred): + accuracy = self.get_accuracy(y_true, y_pred) + confusion = confusion_matrix(y_true, y_pred) + report = classification_report(y_true, y_pred) + print("多模型融合预测accuracy：{}".format(accuracy)) + print("混淆矩阵：\n{}".format(confusion)) + print("预测结果：\n{}".format(report)) + return confusion, report + +# 使用stacking方法的时候 +if __name__ == "__main__": + # 导入数据集切割训练与测试数据 + data = load_digits() + data_D = preprocessing.StandardScaler().fit_transform(data.data) + data_L = data.target + X_train, X_test, y_train, y_test = train_test_split(data_D,data_L,random_state=100,test_size=0.7) + print(set(y_train)) + + #layer 1：多模型融合 + classifiers = { + 'KNN': SubClassifier().SelectModel(modelname="KNN"), + 'rf': SubClassifier().SelectModel(modelname="RF"), + 'svm': SubClassifier().SelectModel(modelname="SVM"), + 'GBDT': SubClassifier().SelectModel(modelname="GBDT") + } + + meta_classifier = SubClassifier().SelectModel(modelname="RF") + + stacking_clf = StackingClassifier(classifiers, meta_classifier, n_classes=10,n_folds=5) + + stacking_clf.fit(X_train, y_train) + pred = stacking_clf.predict(X_test) + + #模型评估 + stacking_clf.performance(y_test, pred) + # 96.4228934817 \ No newline at end of file