├── .gitignore ├── .ipynb_checkpoints └── speedmodel-checkpoint.ipynb ├── README.md ├── speedmodel.ipynb ├── kmean_vectors.ipynb ├── titanic ├── gender_submission.csv ├── test.csv └── train.csv └── gen_features.ipynb /.gitignore: -------------------------------------------------------------------------------- 1 | .ipynb_checkpoints -------------------------------------------------------------------------------- /.ipynb_checkpoints/speedmodel-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [], 3 | "metadata": {}, 4 | "nbformat": 4, 5 | "nbformat_minor": 2 6 | } 7 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # data_analysis 2 | #### get_feature_importance 机器学习数据预处理：包括浮点型数据转化降低内存、画数据分布图、多种特征筛选、多种调参技巧 3 | #### gen_features 各种数据衍生方式 4 | #### 树模型部署：treelite模型加速 5 | #### 文本聚类打标签 6 | -------------------------------------------------------------------------------- /speedmodel.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "# https://treelite.readthedocs.io/en/latest/tutorials/first.html" 10 | ] 11 | }, 12 | { 13 | "cell_type": "code", 14 | "execution_count": null, 15 | "metadata": {}, 16 | "outputs": [], 17 | "source": [] 18 | } 19 | ], 20 | "metadata": { 21 | "kernelspec": { 22 | "display_name": "Python 3", 23 | "language": "python", 24 | "name": "python3" 25 | }, 26 | "language_info": { 27 | "codemirror_mode": { 28 | "name": "ipython", 29 | "version": 3 30 | }, 31 | "file_extension": ".py", 32 | "mimetype": "text/x-python", 33 | "name": "python", 34 | "nbconvert_exporter": "python", 35 | "pygments_lexer": "ipython3", 36 | "version": "3.7.4" 37 | }, 38 | "toc": { 39 | "base_numbering": 1, 40 | "nav_menu": {}, 41 | "number_sections": true, 42 | "sideBar": true, 43 | "skip_h1_title": false, 44 | "title_cell": "Table of Contents", 45 | "title_sidebar": "Contents", 46 | "toc_cell": false, 47 | "toc_position": {}, 48 | "toc_section_display": true, 49 | "toc_window_display": false 50 | } 51 | }, 52 | "nbformat": 4, 53 | "nbformat_minor": 2 54 | } 55 | -------------------------------------------------------------------------------- /kmean_vectors.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "id": "139c210d", 7 | "metadata": {}, 8 | "outputs": [], 9 | "source": [ 10 | "import pandas as pd\n", 11 | "from sklearn.cluster import KMeans\n", 12 | "import numpy as np\n", 13 | "import matplotlib.pyplot as plt\n", 14 | "import torch\n", 15 | "from sklearn.metrics import silhouette_score\n", 16 | "from transformers import AutoTokenizer, AutoModelForMaskedLM" 17 | ] 18 | }, 19 | { 20 | "cell_type": "code", 21 | "execution_count": null, 22 | "id": "bfbcf944", 23 | "metadata": {}, 24 | "outputs": [], 25 | "source": [ 26 | "def cosine_similarity(vector1, vector2):\n", 27 | " dot_product = np.dot(vector1, vector2)\n", 28 | " norm_vector1 = np.linalg.norm(vector1)\n", 29 | " norm_vector2 = np.linalg.norm(vector2)\n", 30 | " cosine_similarity = dot_product / (norm_vector1 * norm_vector2)\n", 31 | " return cosine_similarity\n", 32 | "\n", 33 | "\n", 34 | "def get_emb(text):\n", 35 | " inputs = tokenizer(text, return_tensors=\"pt\")\n", 36 | " outputs = model(**inputs, output_hidden_states=True)\n", 37 | " text_embedding = outputs.hidden_states[-1][:, 0, :]\n", 38 | " vector = torch.Tensor(text_embedding).tolist()[0]\n", 39 | " return vector" 40 | ] 41 | }, 42 | { 43 | "cell_type": "code", 44 | "execution_count": null, 45 | "id": "f4466060", 46 | "metadata": {}, 47 | "outputs": [], 48 | "source": [ 49 | "# 文本向量化\n", 50 | "model = AutoModelForMaskedLM.from_pretrained(r'./Erlangshen-SimCSE-110M-Chinese')\n", 51 | "tokenizer = AutoTokenizer.from_pretrained(r'./Erlangshen-SimCSE-110M-Chinese')\n", 52 | "data = pd.read_csv(r\"./kmean_test.csv\", encoding='gbk')\n", 53 | "data['emb'] = data['text'].apply(lambda x: get_emb(x))\n", 54 | "vector_list = data['emb'].values.tolist()" 55 | ] 56 | }, 57 | { 58 | "cell_type": "code", 59 | "execution_count": null, 60 | "id": "7faf0b3e", 61 | "metadata": {}, 62 | "outputs": [], 63 | "source": [ 64 | "# 寻找最佳聚类k值的两种方法\n", 65 | "# 手肘法 利用SSE选择k，取拐点时的k\n", 66 | "SSE = [] # 存放每次结果的误差平方和\n", 67 | "for k in range(2, 9):\n", 68 | " estimator = KMeans(n_clusters=k) # 构造聚类器\n", 69 | " estimator.fit(vector_list)\n", 70 | " SSE.append(estimator.inertia_)\n", 71 | "X = range(2, 9)\n", 72 | "plt.xlabel('k')\n", 73 | "plt.ylabel('SSE')\n", 74 | "plt.plot(X, SSE, 'o-')\n", 75 | "plt.show()\n", 76 | "\n", 77 | "# 轮廓系数法 取系数最大时的k\n", 78 | "Scores = [] # 存放轮廓系数\n", 79 | "for k in range(2, 9):\n", 80 | " estimator = KMeans(n_clusters=k) # 构造聚类器\n", 81 | " estimator.fit(vector_list)\n", 82 | " Scores.append(silhouette_score(vector_list, estimator.labels_, metric='euclidean'))\n", 83 | "X = range(2, 9)\n", 84 | "plt.xlabel('k')\n", 85 | "plt.ylabel('轮廓系数')\n", 86 | "plt.plot(X, Scores, 'o-')\n", 87 | "plt.show()" 88 | ] 89 | }, 90 | { 91 | "cell_type": "code", 92 | "execution_count": null, 93 | "id": "cbe7596a", 94 | "metadata": {}, 95 | "outputs": [], 96 | "source": [ 97 | "# 确定k后开始聚类\n", 98 | "clf = KMeans(n_clusters=6)\n", 99 | "clf.fit(vector_list)" 100 | ] 101 | }, 102 | { 103 | "cell_type": "code", 104 | "execution_count": null, 105 | "id": "6dcafd2a", 106 | "metadata": {}, 107 | "outputs": [], 108 | "source": [ 109 | "# 获取所有训练样本的簇标签[0,2,1,0,2,2,1,..]\n", 110 | "labels = clf.labels_\n", 111 | "data['label'] = labels\n", 112 | "\n", 113 | "# 也可以推理新的样本\n", 114 | "# clf.predict()\n", 115 | "\n", 116 | "# 获取中心点，并构建簇中心点与簇标签的映射\n", 117 | "centers = clf.cluster_centers_\n", 118 | "center_map = dict(zip([i for i in range(len(centers))], centers))\n", 119 | "data['center'] = data['label'].apply(lambda x: center_map.get(x))\n", 120 | "\n", 121 | "# 计算样本到簇心的距离\n", 122 | "data['cosine'] = data.apply(lambda x: cosine_similarity(x['center'], x['emb']), axis=1)\n", 123 | "data.to_csv(r\"./kmean_test_res.csv\", index=False)\n", 124 | "\n", 125 | "# todo 挑选每个簇最靠近簇心的样本，人为观察打标或丢给大模型打标" 126 | ] 127 | } 128 | ], 129 | "metadata": { 130 | "kernelspec": { 131 | "display_name": "Python 3", 132 | "language": "python", 133 | "name": "python3" 134 | }, 135 | "language_info": { 136 | "codemirror_mode": { 137 | "name": "ipython", 138 | "version": 3 139 | }, 140 | "file_extension": ".py", 141 | "mimetype": "text/x-python", 142 | "name": "python", 143 | "nbconvert_exporter": "python", 144 | "pygments_lexer": "ipython3", 145 | "version": "3.8.5" 146 | } 147 | }, 148 | "nbformat": 4, 149 | "nbformat_minor": 5 150 | } 151 | 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Mary Conover",female,16,0,1,PC 17592,39.4,S 856 | 855,0,2,"Carter, Mrs. Ernest Courtenay (Lilian Hughes)",female,44,1,0,244252,26,S 857 | 856,1,3,"Aks, Mrs. Sam (Leah Rosen)",female,18,0,1,392091,9.35,S 858 | 857,1,1,"Wick, Mrs. George Dennick (Mary Hitchcock)",female,45,1,1,36928,164.8667,S 859 | 858,1,1,"Daly, Mr. Peter Denis ",male,51,0,0,113055,26.55,S 860 | 859,1,3,"Baclini, Mrs. Solomon (Latifa Qurban)",female,24,0,3,2666,19.2583,C 861 | 860,0,3,"Razi, Mr. Raihed",male,,0,0,2629,7.2292,C 862 | 861,0,3,"Hansen, Mr. Claus Peter",male,41,2,0,350026,14.1083,S 863 | 862,0,2,"Giles, Mr. Frederick Edward",male,21,1,0,28134,11.5,S 864 | 863,1,1,"Swift, Mrs. Frederick Joel (Margaret Welles Barron)",female,48,0,0,17466,25.9292,S 865 | 864,0,3,"Sage, Miss. Dorothy Edith ""Dolly""",female,,8,2,CA. 2343,69.55,S 866 | 865,0,2,"Gill, Mr. John William",male,24,0,0,233866,13,S 867 | 866,1,2,"Bystrom, Mrs. (Karolina)",female,42,0,0,236852,13,S 868 | 867,1,2,"Duran y More, Miss. Asuncion",female,27,1,0,SC/PARIS 2149,13.8583,C 869 | 868,0,1,"Roebling, Mr. Washington Augustus II",male,31,0,0,PC 17590,50.4958,S 870 | 869,0,3,"van Melkebeke, Mr. Philemon",male,,0,0,345777,9.5,S 871 | 870,1,3,"Johnson, Master. Harold Theodor",male,4,1,1,347742,11.1333,S 872 | 871,0,3,"Balkic, Mr. Cerin",male,26,0,0,349248,7.8958,S 873 | 872,1,1,"Beckwith, Mrs. Richard Leonard (Sallie Monypeny)",female,47,1,1,11751,52.5542,S 874 | 873,0,1,"Carlsson, Mr. Frans Olof",male,33,0,0,695,5,S 875 | 874,0,3,"Vander Cruyssen, Mr. Victor",male,47,0,0,345765,9,S 876 | 875,1,2,"Abelson, Mrs. Samuel (Hannah Wizosky)",female,28,1,0,P/PP 3381,24,C 877 | 876,1,3,"Najib, Miss. Adele Kiamie ""Jane""",female,15,0,0,2667,7.225,C 878 | 877,0,3,"Gustafsson, Mr. Alfred Ossian",male,20,0,0,7534,9.8458,S 879 | 878,0,3,"Petroff, Mr. Nedelio",male,19,0,0,349212,7.8958,S 880 | 879,0,3,"Laleff, Mr. Kristo",male,,0,0,349217,7.8958,S 881 | 880,1,1,"Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)",female,56,0,1,11767,83.1583,C 882 | 881,1,2,"Shelley, Mrs. William (Imanita Parrish Hall)",female,25,0,1,230433,26,S 883 | 882,0,3,"Markun, Mr. Johann",male,33,0,0,349257,7.8958,S 884 | 883,0,3,"Dahlberg, Miss. Gerda Ulrika",female,22,0,0,7552,10.5167,S 885 | 884,0,2,"Banfield, Mr. Frederick James",male,28,0,0,C.A./SOTON 34068,10.5,S 886 | 885,0,3,"Sutehall, Mr. Henry Jr",male,25,0,0,SOTON/OQ 392076,7.05,S 887 | 886,0,3,"Rice, Mrs. William (Margaret Norton)",female,39,0,5,382652,29.125,Q 888 | 887,0,2,"Montvila, Rev. Juozas",male,27,0,0,211536,13,S 889 | 888,1,1,"Graham, Miss. Margaret Edith",female,19,0,0,112053,30,S 890 | 889,0,3,"Johnston, Miss. Catherine Helen ""Carrie""",female,,1,2,W./C. 6607,23.45,S 891 | 890,1,1,"Behr, Mr. Karl Howell",male,26,0,0,111369,30,C 892 | 891,0,3,"Dooley, Mr. Patrick",male,32,0,0,370376,7.75,Q 893 | -------------------------------------------------------------------------------- /gen_features.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "from sklearn.model_selection import KFold,StratifiedKFold\n", 10 | "import pandas as pd\n", 11 | "import numpy as np\n", 12 | "from tqdm import tqdm\n", 13 | "import warnings\n", 14 | "warnings.filterwarnings(\"ignore\")" 15 | ] 16 | }, 17 | { 18 | "cell_type": "code", 19 | "execution_count": 2, 20 | "metadata": {}, 21 | "outputs": [], 22 | "source": [ 23 | "train = pd.read_csv('./titanic/train.csv')\n", 24 | "test = pd.read_csv('./titanic/test.csv')" 25 | ] 26 | }, 27 | { 28 | "cell_type": "code", 29 | "execution_count": 3, 30 | "metadata": {}, 31 | "outputs": [], 32 | "source": [ 33 | "train['Age'] = train['Age'].fillna(train['Age'].mode())\n", 34 | "test['Age'] = test['Age'].fillna(test['Age'].mode())\n", 35 | "\n", 36 | "train['Embarked'] = train['Embarked'].fillna(train['Embarked'].mode())\n", 37 | "\n", 38 | "train.rename(columns={'Survived':'label'},inplace=True)\n", 39 | "test.rename(columns={'Survived':'label'},inplace=True)\n", 40 | "\n", 41 | "train = train.drop(['Name','Ticket'],axis=1)\n", 42 | "test = test.drop(['Name','Ticket'],axis=1)" 43 | ] 44 | }, 45 | { 46 | "cell_type": "code", 47 | "execution_count": 4, 48 | "metadata": {}, 49 | "outputs": [ 50 | { 51 | "data": { 52 | "text/html": [ 53 | "
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PassengerIdlabelPclassSexAgeSibSpParchFareEmbarked
0103male22.0107.2500S
1211female38.01071.2833C
2313female26.0007.9250S
3411female35.01053.1000S
4503male35.0008.0500S
\n", 145 | "
" 146 | ], 147 | "text/plain": [ 148 | " PassengerId label Pclass Sex Age SibSp Parch Fare Embarked\n", 149 | "0 1 0 3 male 22.0 1 0 7.2500 S\n", 150 | "1 2 1 1 female 38.0 1 0 71.2833 C\n", 151 | "2 3 1 3 female 26.0 0 0 7.9250 S\n", 152 | "3 4 1 1 female 35.0 1 0 53.1000 S\n", 153 | "4 5 0 3 male 35.0 0 0 8.0500 S" 154 | ] 155 | }, 156 | "execution_count": 4, 157 | "metadata": {}, 158 | "output_type": "execute_result" 159 | } 160 | ], 161 | "source": [ 162 | "train.head()" 163 | ] 164 | }, 165 | { 166 | "cell_type": "code", 167 | "execution_count": 5, 168 | "metadata": {}, 169 | "outputs": [ 170 | { 171 | "data": { 172 | "text/plain": [ 173 | "Index(['PassengerId', 'label', 'Pclass', 'Sex', 'Age', 'SibSp', 'Parch',\n", 174 | " 'Fare', 'Embarked'],\n", 175 | " dtype='object')" 176 | ] 177 | }, 178 | "execution_count": 5, 179 | "metadata": {}, 180 | "output_type": "execute_result" 181 | } 182 | ], 183 | "source": [ 184 | "train.columns" 185 | ] 186 | }, 187 | { 188 | "cell_type": "code", 189 | "execution_count": 6, 190 | "metadata": {}, 191 | "outputs": [], 192 | "source": [ 193 | "#疑问：分组统计特征，要在train和test中单独求吗？" 194 | ] 195 | }, 196 | { 197 | "cell_type": "code", 198 | "execution_count": 7, 199 | "metadata": {}, 200 | "outputs": [ 201 | { 202 | "name": "stderr", 203 | "output_type": "stream", 204 | "text": [ 205 | "5it [00:00, 333.45it/s]\n" 206 | ] 207 | }, 208 | { 209 | "data": { 210 | "text/html": [ 211 | "
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PassengerIdlabelPclassSexAgeSibSpParchFareEmbarkedSex_target_encEmbarked_target_enc
010.03male22.0107.2500S0.1952790.335271
121.01female38.01071.2833C0.7398370.589552
231.03female26.0007.9250S0.7398370.335271
341.01female35.01053.1000S0.7398370.335271
450.03male35.0008.0500S0.1952790.335271
\n", 315 | "
" 316 | ], 317 | "text/plain": [ 318 | " PassengerId label Pclass Sex Age SibSp Parch Fare Embarked \\\n", 319 | "0 1 0.0 3 male 22.0 1 0 7.2500 S \n", 320 | "1 2 1.0 1 female 38.0 1 0 71.2833 C \n", 321 | "2 3 1.0 3 female 26.0 0 0 7.9250 S \n", 322 | "3 4 1.0 1 female 35.0 1 0 53.1000 S \n", 323 | "4 5 0.0 3 male 35.0 0 0 8.0500 S \n", 324 | "\n", 325 | " Sex_target_enc Embarked_target_enc \n", 326 | "0 0.195279 0.335271 \n", 327 | "1 0.739837 0.589552 \n", 328 | "2 0.739837 0.335271 \n", 329 | "3 0.739837 0.335271 \n", 330 | "4 0.195279 0.335271 " 331 | ] 332 | }, 333 | "execution_count": 7, 334 | "metadata": {}, 335 | "output_type": "execute_result" 336 | } 337 | ], 338 | "source": [ 339 | "# 目标编码\n", 340 | "# https://mp.weixin.qq.com/s/taMj-x-qLz5sw-7zld5BmA\n", 341 | "# target encoding，可以理解为转化率，就是聚合id后对label求mean、sum、count(lable=1)/count(all)，\n", 342 | "# 这里需要考虑到数据穿越造成线下严重过拟合的情况，一般需要通过交叉平滑处理，如划分为5折，用其中4折去统计剩下的一折，\n", 343 | "# 然后对过大或过小的进行平滑，对测试集直接用训练集来做统计，若数据集有时间因素存在，则需要根据时间顺序来处理；\n", 344 | "def kfold_mean(df_train, df_test, target, target_mean_list):\n", 345 | " folds = StratifiedKFold(n_splits=5)\n", 346 | "\n", 347 | " mean_of_target = df_train[target].mean()\n", 348 | "\n", 349 | " for fold_, (trn_idx, val_idx) in tqdm(enumerate(folds.split(df_train, y=df_train[target]))):\n", 350 | " tr_x = df_train.iloc[trn_idx, :]\n", 351 | " vl_x = df_train.iloc[val_idx, :]\n", 352 | "\n", 353 | " for col in target_mean_list:\n", 354 | " df_train.loc[vl_x.index, f'{col}_target_enc'] = vl_x[col].map(tr_x.groupby(col)[target].mean())\n", 355 | "\n", 356 | " for col in target_mean_list:\n", 357 | " df_train[f'{col}_target_enc'].fillna(mean_of_target, inplace=True)\n", 358 | "\n", 359 | " df_test[f'{col}_target_enc'] = df_test[col].map(df_train.groupby(col)[f'{col}_target_enc'].mean())\n", 360 | "\n", 361 | " df_test[f'{col}_target_enc'].fillna(mean_of_target, inplace=True)\n", 362 | " return pd.concat([df_train, df_test], ignore_index=True)\n", 363 | "\n", 364 | "feature_list = ['Sex','Embarked']\n", 365 | "data = pd.concat([train, test], ignore_index=True)\n", 366 | "data = kfold_mean(data[~data['label'].isna()], data[data['label'].isna()],'label',feature_list)\n", 367 | "\n", 368 | "data.head()" 369 | ] 370 | }, 371 | { 372 | "cell_type": "code", 373 | "execution_count": 8, 374 | "metadata": {}, 375 | "outputs": [ 376 | { 377 | "name": "stderr", 378 | "output_type": "stream", 379 | "text": [ 380 | "count_feas 基本交叉特征: 100%|██████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 499.26it/s]\n" 381 | ] 382 | }, 383 | { 384 | "data": { 385 | "text/html": [ 386 | "
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PassengerIdlabelPclassSexAgeSibSpParchFareEmbarkedSex_target_enc...Pclass_Age_addPclass_Age_diffAge_Fare_ratioAge_Fare_multiAge_Fare_addAge_Fare_diffAge_Pclass_ratioAge_Pclass_multiAge_Pclass_addAge_Pclass_diff
010.03male22.0107.2500S0.195279...25.0-19.03.034483159.500029.250014.75007.33333366.025.019.0
121.01female38.01071.2833C0.739837...39.0-37.00.5330842708.7654109.2833-33.283338.00000038.039.037.0
231.03female26.0007.9250S0.739837...29.0-23.03.280757206.050033.925018.07508.66666778.029.023.0
341.01female35.01053.1000S0.739837...36.0-34.00.6591341858.500088.1000-18.100035.00000035.036.034.0
450.03male35.0008.0500S0.195279...38.0-32.04.347826281.750043.050026.950011.666667105.038.032.0
\n", 550 | "

5 rows × 35 columns

\n", 551 | "
" 552 | ], 553 | "text/plain": [ 554 | " PassengerId label Pclass Sex Age SibSp Parch Fare Embarked \\\n", 555 | "0 1 0.0 3 male 22.0 1 0 7.2500 S \n", 556 | "1 2 1.0 1 female 38.0 1 0 71.2833 C \n", 557 | "2 3 1.0 3 female 26.0 0 0 7.9250 S \n", 558 | "3 4 1.0 1 female 35.0 1 0 53.1000 S \n", 559 | "4 5 0.0 3 male 35.0 0 0 8.0500 S \n", 560 | "\n", 561 | " Sex_target_enc ... Pclass_Age_add Pclass_Age_diff Age_Fare_ratio \\\n", 562 | "0 0.195279 ... 25.0 -19.0 3.034483 \n", 563 | "1 0.739837 ... 39.0 -37.0 0.533084 \n", 564 | "2 0.739837 ... 29.0 -23.0 3.280757 \n", 565 | "3 0.739837 ... 36.0 -34.0 0.659134 \n", 566 | "4 0.195279 ... 38.0 -32.0 4.347826 \n", 567 | "\n", 568 | " Age_Fare_multi Age_Fare_add Age_Fare_diff Age_Pclass_ratio \\\n", 569 | "0 159.5000 29.2500 14.7500 7.333333 \n", 570 | "1 2708.7654 109.2833 -33.2833 38.000000 \n", 571 | "2 206.0500 33.9250 18.0750 8.666667 \n", 572 | "3 1858.5000 88.1000 -18.1000 35.000000 \n", 573 | "4 281.7500 43.0500 26.9500 11.666667 \n", 574 | "\n", 575 | " Age_Pclass_multi Age_Pclass_add Age_Pclass_diff \n", 576 | "0 66.0 25.0 19.0 \n", 577 | "1 38.0 39.0 37.0 \n", 578 | "2 78.0 29.0 23.0 \n", 579 | "3 35.0 36.0 34.0 \n", 580 | "4 105.0 38.0 32.0 \n", 581 | "\n", 582 | "[5 rows x 35 columns]" 583 | ] 584 | }, 585 | "execution_count": 8, 586 | "metadata": {}, 587 | "output_type": "execute_result" 588 | } 589 | ], 590 | "source": [ 591 | "#数值交叉特征\n", 592 | "countfea = ['Fare','Pclass','Age']\n", 593 | "#数值特征与数值特征之间做加减乘除\n", 594 | "for f1 in tqdm(countfea, desc=\"count_feas 基本交叉特征\"):\n", 595 | " for f2 in countfea:\n", 596 | " if f1 != f2:\n", 597 | " data['{}_{}_ratio'.format(f1, f2)] = data[f1].values / data[f2].values\n", 598 | " data['{}_{}_multi'.format(f1, f2)] = data[f1].values * data[f2].values\n", 599 | " data['{}_{}_add'.format(f1, f2)] = data[f1].values + data[f2].values\n", 600 | " data['{}_{}_diff'.format(f1, f2)] = data[f1].values - data[f2].values\n", 601 | "data.head()" 602 | ] 603 | }, 604 | { 605 | "cell_type": "code", 606 | "execution_count": 9, 607 | "metadata": {}, 608 | "outputs": [ 609 | { 610 | "name": "stderr", 611 | "output_type": "stream", 612 | "text": [ 613 | "count_feas 基本聚合特征: 100%|██████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 111.11it/s]\n" 614 | ] 615 | }, 616 | { 617 | "data": { 618 | "text/html": [ 619 | "
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PassengerIdlabelPclassSexAgeSibSpParchFareEmbarkedSex_target_enc...Age_Sex_meanAge_Sex_medianAge_Sex_maxAge_Sex_minAge_Sex_stdAge_Embarked_meanAge_Embarked_medianAge_Embarked_maxAge_Embarked_minAge_Embarked_std
010.03male22.0107.2500S0.195279...30.58522828.080.00.3314.28058129.24520528.080.00.1714.047507
121.01female38.01071.2833C0.739837...28.68708827.076.00.1714.57696232.33217030.071.00.4215.258092
231.03female26.0007.9250S0.739837...28.68708827.076.00.1714.57696229.24520528.080.00.1714.047507
341.01female35.01053.1000S0.739837...28.68708827.076.00.1714.57696229.24520528.080.00.1714.047507
450.03male35.0008.0500S0.195279...30.58522828.080.00.3314.28058129.24520528.080.00.1714.047507
\n", 783 | "

5 rows × 55 columns

\n", 784 | "
" 785 | ], 786 | "text/plain": [ 787 | " PassengerId label Pclass Sex Age SibSp Parch Fare Embarked \\\n", 788 | "0 1 0.0 3 male 22.0 1 0 7.2500 S \n", 789 | "1 2 1.0 1 female 38.0 1 0 71.2833 C \n", 790 | "2 3 1.0 3 female 26.0 0 0 7.9250 S \n", 791 | "3 4 1.0 1 female 35.0 1 0 53.1000 S \n", 792 | "4 5 0.0 3 male 35.0 0 0 8.0500 S \n", 793 | "\n", 794 | " Sex_target_enc ... Age_Sex_mean Age_Sex_median Age_Sex_max \\\n", 795 | "0 0.195279 ... 30.585228 28.0 80.0 \n", 796 | "1 0.739837 ... 28.687088 27.0 76.0 \n", 797 | "2 0.739837 ... 28.687088 27.0 76.0 \n", 798 | "3 0.739837 ... 28.687088 27.0 76.0 \n", 799 | "4 0.195279 ... 30.585228 28.0 80.0 \n", 800 | "\n", 801 | " Age_Sex_min Age_Sex_std Age_Embarked_mean Age_Embarked_median \\\n", 802 | "0 0.33 14.280581 29.245205 28.0 \n", 803 | "1 0.17 14.576962 32.332170 30.0 \n", 804 | "2 0.17 14.576962 29.245205 28.0 \n", 805 | "3 0.17 14.576962 29.245205 28.0 \n", 806 | "4 0.33 14.280581 29.245205 28.0 \n", 807 | "\n", 808 | " Age_Embarked_max Age_Embarked_min Age_Embarked_std \n", 809 | "0 80.0 0.17 14.047507 \n", 810 | "1 71.0 0.42 15.258092 \n", 811 | "2 80.0 0.17 14.047507 \n", 812 | "3 80.0 0.17 14.047507 \n", 813 | "4 80.0 0.17 14.047507 \n", 814 | "\n", 815 | "[5 rows x 55 columns]" 816 | ] 817 | }, 818 | "execution_count": 9, 819 | "metadata": {}, 820 | "output_type": "execute_result" 821 | } 822 | ], 823 | "source": [ 824 | "#类别特征与数值特征之间\n", 825 | "catefea = ['Sex','Embarked']\n", 826 | "countfea = ['Fare','Age']\n", 827 | "#例如男性购买的船票票价均值、最大值、最小值中位数。\n", 828 | "for i in tqdm(countfea,desc=\"count_feas 基本聚合特征\"):\n", 829 | " for j in catefea:\n", 830 | " data['{}_{}_mean'.format(i,j)] = data.groupby(j)[i].transform('mean')\n", 831 | " data['{}_{}_median'.format(i,j)] = data.groupby(j)[i].transform('median')\n", 832 | " data['{}_{}_max'.format(i,j)] = data.groupby(j)[i].transform('max')\n", 833 | " data['{}_{}_min'.format(i,j)] = data.groupby(j)[i].transform('min')\n", 834 | " data['{}_{}_std'.format(i,j)] = data.groupby(j)[i].transform('std')\n", 835 | " \n", 836 | "data.head()" 837 | ] 838 | }, 839 | { 840 | "cell_type": "code", 841 | "execution_count": 10, 842 | "metadata": {}, 843 | "outputs": [ 844 | { 845 | "data": { 846 | "text/html": [ 847 | "
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PassengerIdlabelPclassSexAgeSibSpParchFareEmbarkedSex_target_enc...Age-max_gb_SexAge/sum_gb_SexFare-mean_gb_EmbarkedFare-min_gb_EmbarkedFare-max_gb_EmbarkedFare/sum_gb_EmbarkedAge-mean_gb_EmbarkedAge-min_gb_EmbarkedAge-max_gb_EmbarkedAge/sum_gb_Embarked
010.03male22.0107.2500S0.195279...-58.00.001093-20.1688247.2500-255.75000.000290-7.24520521.83-58.00.000962
121.01female38.01071.2833C0.739837...-38.00.0034148.94703367.2708-441.04590.0042355.66783037.58-33.00.005544
231.03female26.0007.9250S0.739837...-50.00.002336-19.4938247.9250-255.07500.000317-3.24520525.83-54.00.001137
341.01female35.01053.1000S0.739837...-41.00.00314425.68117653.1000-209.90000.0021215.75479534.83-45.00.001530
450.03male35.0008.0500S0.195279...-45.00.001739-19.3688248.0500-254.95000.0003225.75479534.83-45.00.001530
\n", 1011 | "

5 rows × 71 columns

\n", 1012 | "
" 1013 | ], 1014 | "text/plain": [ 1015 | " PassengerId label Pclass Sex Age SibSp Parch Fare Embarked \\\n", 1016 | "0 1 0.0 3 male 22.0 1 0 7.2500 S \n", 1017 | "1 2 1.0 1 female 38.0 1 0 71.2833 C \n", 1018 | "2 3 1.0 3 female 26.0 0 0 7.9250 S \n", 1019 | "3 4 1.0 1 female 35.0 1 0 53.1000 S \n", 1020 | "4 5 0.0 3 male 35.0 0 0 8.0500 S \n", 1021 | "\n", 1022 | " Sex_target_enc ... Age-max_gb_Sex Age/sum_gb_Sex Fare-mean_gb_Embarked \\\n", 1023 | "0 0.195279 ... -58.0 0.001093 -20.168824 \n", 1024 | "1 0.739837 ... -38.0 0.003414 8.947033 \n", 1025 | "2 0.739837 ... -50.0 0.002336 -19.493824 \n", 1026 | "3 0.739837 ... -41.0 0.003144 25.681176 \n", 1027 | "4 0.195279 ... -45.0 0.001739 -19.368824 \n", 1028 | "\n", 1029 | " Fare-min_gb_Embarked Fare-max_gb_Embarked Fare/sum_gb_Embarked \\\n", 1030 | "0 7.2500 -255.7500 0.000290 \n", 1031 | "1 67.2708 -441.0459 0.004235 \n", 1032 | "2 7.9250 -255.0750 0.000317 \n", 1033 | "3 53.1000 -209.9000 0.002121 \n", 1034 | "4 8.0500 -254.9500 0.000322 \n", 1035 | "\n", 1036 | " Age-mean_gb_Embarked Age-min_gb_Embarked Age-max_gb_Embarked \\\n", 1037 | "0 -7.245205 21.83 -58.0 \n", 1038 | "1 5.667830 37.58 -33.0 \n", 1039 | "2 -3.245205 25.83 -54.0 \n", 1040 | "3 5.754795 34.83 -45.0 \n", 1041 | "4 5.754795 34.83 -45.0 \n", 1042 | "\n", 1043 | " Age/sum_gb_Embarked \n", 1044 | "0 0.000962 \n", 1045 | "1 0.005544 \n", 1046 | "2 0.001137 \n", 1047 | "3 0.001530 \n", 1048 | "4 0.001530 \n", 1049 | "\n", 1050 | "[5 rows x 71 columns]" 1051 | ] 1052 | }, 1053 | "execution_count": 10, 1054 | "metadata": {}, 1055 | "output_type": "execute_result" 1056 | } 1057 | ], 1058 | "source": [ 1059 | "#偏离值特征,顾名思义，偏离均值、最大值、最小值的误差值\n", 1060 | "catefea = ['Sex','Embarked']\n", 1061 | "countfea = ['Fare','Age'] \n", 1062 | "for group in catefea:\n", 1063 | " for feature in countfea:\n", 1064 | " tmp = data.groupby(group)[feature].agg([sum, min, max, np.mean]).reset_index()\n", 1065 | " tmp = pd.merge(data, tmp, on=group, how='left')\n", 1066 | " data['{}-mean_gb_{}'.format(feature, group)] = data[feature] - tmp['mean']\n", 1067 | " data['{}-min_gb_{}'.format(feature, group)] = data[feature] - tmp['min']\n", 1068 | " data['{}-max_gb_{}'.format(feature, group)] = data[feature] - tmp['max']\n", 1069 | " data['{}/sum_gb_{}'.format(feature, group)] = data[feature] / tmp['sum']\n", 1070 | "data.head()" 1071 | ] 1072 | }, 1073 | { 1074 | "cell_type": "code", 1075 | "execution_count": 11, 1076 | "metadata": {}, 1077 | "outputs": [], 1078 | "source": [ 1079 | "# 特征unique count特征，针对类别型特征\n", 1080 | "# for index, col1 in enumerate(['age', 'province', 'city', 'model']):\n", 1081 | "# for col2 in ['age', 'province', 'city', 'model'][index:]:\n", 1082 | "# data['{}_in_{}_count'.format(col1, col2)] = data.groupby(col1)[col2].transform('count')\n", 1083 | "# data['{}_in_{}_nunique'.format(col1, col2)] = data.groupby(col1)[col2].transform('nunique')\n", 1084 | "# data['{}_in_{}_nunique/{}_in_{}_count'.format(col1, col2, col1, col2)] = data['{}_in_{}_nunique'.format(col1,col2)] /data['{}_in_{}_count'.format(col1,col2)]\n", 1085 | "\n", 1086 | "# data['{}_in_{}_count'.format(col2, col1)] = data.groupby(col2)[col1].transform('count')\n", 1087 | "# data['{}_in_{}_nunique'.format(col2, col1)] = data.groupby(col2)[col1].transform('nunique')\n", 1088 | "# data['{}_in_{}_nunique/{}_in_{}_count'.format(col2, col1, col2, col1)] = data['{}_in_{}_nunique'.format(col2,col1)] / data['{}_in_{}_count'.format(col2, col1)]\n" 1089 | ] 1090 | }, 1091 | { 1092 | "cell_type": "code", 1093 | "execution_count": 12, 1094 | "metadata": {}, 1095 | "outputs": [ 1096 | { 1097 | "name": "stderr", 1098 | "output_type": "stream", 1099 | "text": [ 1100 | "100%|██████████████████████████████████████████████████████████████████████████| 1309/1309 [00:00<00:00, 163549.12it/s]\n" 1101 | ] 1102 | }, 1103 | { 1104 | "data": { 1105 | "text/html": [ 1106 | "
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PassengerIdlabelPclassSexAgeSibSpParchFareEmbarkedSex_target_enc...Age/sum_gb_SexFare-mean_gb_EmbarkedFare-min_gb_EmbarkedFare-max_gb_EmbarkedFare/sum_gb_EmbarkedAge-mean_gb_EmbarkedAge-min_gb_EmbarkedAge-max_gb_EmbarkedAge/sum_gb_EmbarkedPclass_Sex
010.03male22.0107.2500S0.195279...0.001093-20.1688247.2500-255.75000.000290-7.24520521.83-58.00.0009620
121.01female38.01071.2833C0.739837...0.0034148.94703367.2708-441.04590.0042355.66783037.58-33.00.0055441
231.03female26.0007.9250S0.739837...0.002336-19.4938247.9250-255.07500.000317-3.24520525.83-54.00.0011372
341.01female35.01053.1000S0.739837...0.00314425.68117653.1000-209.90000.0021215.75479534.83-45.00.0015301
450.03male35.0008.0500S0.195279...0.001739-19.3688248.0500-254.95000.0003225.75479534.83-45.00.0015300
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5 rows × 72 columns

\n", 1271 | "
" 1272 | ], 1273 | "text/plain": [ 1274 | " PassengerId label Pclass Sex Age SibSp Parch Fare Embarked \\\n", 1275 | "0 1 0.0 3 male 22.0 1 0 7.2500 S \n", 1276 | "1 2 1.0 1 female 38.0 1 0 71.2833 C \n", 1277 | "2 3 1.0 3 female 26.0 0 0 7.9250 S \n", 1278 | "3 4 1.0 1 female 35.0 1 0 53.1000 S \n", 1279 | "4 5 0.0 3 male 35.0 0 0 8.0500 S \n", 1280 | "\n", 1281 | " Sex_target_enc ... Age/sum_gb_Sex Fare-mean_gb_Embarked \\\n", 1282 | "0 0.195279 ... 0.001093 -20.168824 \n", 1283 | "1 0.739837 ... 0.003414 8.947033 \n", 1284 | "2 0.739837 ... 0.002336 -19.493824 \n", 1285 | "3 0.739837 ... 0.003144 25.681176 \n", 1286 | "4 0.195279 ... 0.001739 -19.368824 \n", 1287 | "\n", 1288 | " Fare-min_gb_Embarked Fare-max_gb_Embarked Fare/sum_gb_Embarked \\\n", 1289 | "0 7.2500 -255.7500 0.000290 \n", 1290 | "1 67.2708 -441.0459 0.004235 \n", 1291 | "2 7.9250 -255.0750 0.000317 \n", 1292 | "3 53.1000 -209.9000 0.002121 \n", 1293 | "4 8.0500 -254.9500 0.000322 \n", 1294 | "\n", 1295 | " Age-mean_gb_Embarked Age-min_gb_Embarked Age-max_gb_Embarked \\\n", 1296 | "0 -7.245205 21.83 -58.0 \n", 1297 | "1 5.667830 37.58 -33.0 \n", 1298 | "2 -3.245205 25.83 -54.0 \n", 1299 | "3 5.754795 34.83 -45.0 \n", 1300 | "4 5.754795 34.83 -45.0 \n", 1301 | "\n", 1302 | " Age/sum_gb_Embarked Pclass_Sex \n", 1303 | "0 0.000962 0 \n", 1304 | "1 0.005544 1 \n", 1305 | "2 0.001137 2 \n", 1306 | "3 0.001530 1 \n", 1307 | "4 0.001530 0 \n", 1308 | "\n", 1309 | "[5 rows x 72 columns]" 1310 | ] 1311 | }, 1312 | "execution_count": 12, 1313 | "metadata": {}, 1314 | "output_type": "execute_result" 1315 | } 1316 | ], 1317 | "source": [ 1318 | "#类别与类别特征逻辑上的交叉，特征值的排列组合\n", 1319 | "#例如：即是男性，pclass又是1的样本。\n", 1320 | "def cross_two(base_info,name_1,name_2):\n", 1321 | " new_col=[]\n", 1322 | " encode=0\n", 1323 | " dic={}\n", 1324 | " val_1=base_info[name_1]\n", 1325 | " val_2=base_info[name_2]\n", 1326 | " for i in tqdm(range(len(val_1))):\n", 1327 | " tmp=str(val_1[i])+'_'+str(val_2[i])\n", 1328 | " if tmp in dic:\n", 1329 | " new_col.append(dic[tmp])\n", 1330 | " else:\n", 1331 | " dic[tmp]=encode\n", 1332 | " new_col.append(encode)\n", 1333 | " encode+=1\n", 1334 | " return new_col\n", 1335 | "\n", 1336 | "new_col=cross_two(data,'Pclass','Sex')#作企业类型-小类的交叉特征\n", 1337 | "data['Pclass_Sex']=new_col\n", 1338 | "data.head()" 1339 | ] 1340 | }, 1341 | { 1342 | "cell_type": "code", 1343 | "execution_count": 13, 1344 | "metadata": {}, 1345 | "outputs": [ 1346 | { 1347 | "data": { 1348 | "text/html": [ 1349 | "
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PassengerIdlabelPclassSexAgeSibSpParchFareEmbarkedSex_target_enc...Fare-max_gb_EmbarkedFare/sum_gb_EmbarkedAge-mean_gb_EmbarkedAge-min_gb_EmbarkedAge-max_gb_EmbarkedAge/sum_gb_EmbarkedPclass_SexSex_COUNTEmbarked_COUNTPclass_COUNT
010.03male22.0107.2500S0.195279...-255.75000.000290-7.24520521.83-58.00.0009620843914.0709
121.01female38.01071.2833C0.739837...-441.04590.0042355.66783037.58-33.00.0055441466270.0323
231.03female26.0007.9250S0.739837...-255.07500.000317-3.24520525.83-54.00.0011372466914.0709
341.01female35.01053.1000S0.739837...-209.90000.0021215.75479534.83-45.00.0015301466914.0323
450.03male35.0008.0500S0.195279...-254.95000.0003225.75479534.83-45.00.0015300843914.0709
\n", 1513 | "

5 rows × 75 columns

\n", 1514 | "
" 1515 | ], 1516 | "text/plain": [ 1517 | " PassengerId label Pclass Sex Age SibSp Parch Fare Embarked \\\n", 1518 | "0 1 0.0 3 male 22.0 1 0 7.2500 S \n", 1519 | "1 2 1.0 1 female 38.0 1 0 71.2833 C \n", 1520 | "2 3 1.0 3 female 26.0 0 0 7.9250 S \n", 1521 | "3 4 1.0 1 female 35.0 1 0 53.1000 S \n", 1522 | "4 5 0.0 3 male 35.0 0 0 8.0500 S \n", 1523 | "\n", 1524 | " Sex_target_enc ... Fare-max_gb_Embarked Fare/sum_gb_Embarked \\\n", 1525 | "0 0.195279 ... -255.7500 0.000290 \n", 1526 | "1 0.739837 ... -441.0459 0.004235 \n", 1527 | "2 0.739837 ... -255.0750 0.000317 \n", 1528 | "3 0.739837 ... -209.9000 0.002121 \n", 1529 | "4 0.195279 ... -254.9500 0.000322 \n", 1530 | "\n", 1531 | " Age-mean_gb_Embarked Age-min_gb_Embarked Age-max_gb_Embarked \\\n", 1532 | "0 -7.245205 21.83 -58.0 \n", 1533 | "1 5.667830 37.58 -33.0 \n", 1534 | "2 -3.245205 25.83 -54.0 \n", 1535 | "3 5.754795 34.83 -45.0 \n", 1536 | "4 5.754795 34.83 -45.0 \n", 1537 | "\n", 1538 | " Age/sum_gb_Embarked Pclass_Sex Sex_COUNT Embarked_COUNT Pclass_COUNT \n", 1539 | "0 0.000962 0 843 914.0 709 \n", 1540 | "1 0.005544 1 466 270.0 323 \n", 1541 | "2 0.001137 2 466 914.0 709 \n", 1542 | "3 0.001530 1 466 914.0 323 \n", 1543 | "4 0.001530 0 843 914.0 709 \n", 1544 | "\n", 1545 | "[5 rows x 75 columns]" 1546 | ] 1547 | }, 1548 | "execution_count": 13, 1549 | "metadata": {}, 1550 | "output_type": "execute_result" 1551 | } 1552 | ], 1553 | "source": [ 1554 | "# 频数统计,计算类别特征每一类的个数，例如 男性的count是843，那男性对应的衍生特征值是843\n", 1555 | "cat_col = ['Sex','Embarked','Pclass']\n", 1556 | "for col in cat_col:\n", 1557 | " data[col + '_COUNT'] = data[col].map(data[col].value_counts())\n", 1558 | " col_idx = data[col].value_counts()\n", 1559 | "\n", 1560 | "data.head()" 1561 | ] 1562 | }, 1563 | { 1564 | "cell_type": "code", 1565 | "execution_count": 14, 1566 | "metadata": {}, 1567 | "outputs": [ 1568 | { 1569 | "name": "stderr", 1570 | "output_type": "stream", 1571 | "text": [ 1572 | "分箱特征: 100%|█████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 996.75it/s]\n" 1573 | ] 1574 | }, 1575 | { 1576 | "data": { 1577 | "text/html": [ 1578 | "
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PassengerIdlabelPclassSexAgeSibSpParchFareEmbarkedSex_target_enc...Fare/sum_gb_EmbarkedAge-mean_gb_EmbarkedAge-min_gb_EmbarkedAge-max_gb_EmbarkedAge/sum_gb_EmbarkedPclass_SexSex_COUNTEmbarked_COUNTPclass_COUNTAge_bin
010.03male22.0107.2500S0.195279...0.000290-7.24520521.83-58.00.0009620843914.07094.0
121.01female38.01071.2833C0.739837...0.0042355.66783037.58-33.00.0055441466270.03237.0
231.03female26.0007.9250S0.739837...0.000317-3.24520525.83-54.00.0011372466914.07095.0
341.01female35.01053.1000S0.739837...0.0021215.75479534.83-45.00.0015301466914.03237.0
450.03male35.0008.0500S0.195279...0.0003225.75479534.83-45.00.0015300843914.07097.0
\n", 1742 | "

5 rows × 76 columns

\n", 1743 | "
" 1744 | ], 1745 | "text/plain": [ 1746 | " PassengerId label Pclass Sex Age SibSp Parch Fare Embarked \\\n", 1747 | "0 1 0.0 3 male 22.0 1 0 7.2500 S \n", 1748 | "1 2 1.0 1 female 38.0 1 0 71.2833 C \n", 1749 | "2 3 1.0 3 female 26.0 0 0 7.9250 S \n", 1750 | "3 4 1.0 1 female 35.0 1 0 53.1000 S \n", 1751 | "4 5 0.0 3 male 35.0 0 0 8.0500 S \n", 1752 | "\n", 1753 | " Sex_target_enc ... Fare/sum_gb_Embarked Age-mean_gb_Embarked \\\n", 1754 | "0 0.195279 ... 0.000290 -7.245205 \n", 1755 | "1 0.739837 ... 0.004235 5.667830 \n", 1756 | "2 0.739837 ... 0.000317 -3.245205 \n", 1757 | "3 0.739837 ... 0.002121 5.754795 \n", 1758 | "4 0.195279 ... 0.000322 5.754795 \n", 1759 | "\n", 1760 | " Age-min_gb_Embarked Age-max_gb_Embarked Age/sum_gb_Embarked Pclass_Sex \\\n", 1761 | "0 21.83 -58.0 0.000962 0 \n", 1762 | "1 37.58 -33.0 0.005544 1 \n", 1763 | "2 25.83 -54.0 0.001137 2 \n", 1764 | "3 34.83 -45.0 0.001530 1 \n", 1765 | "4 34.83 -45.0 0.001530 0 \n", 1766 | "\n", 1767 | " Sex_COUNT Embarked_COUNT Pclass_COUNT Age_bin \n", 1768 | "0 843 914.0 709 4.0 \n", 1769 | "1 466 270.0 323 7.0 \n", 1770 | "2 466 914.0 709 5.0 \n", 1771 | "3 466 914.0 323 7.0 \n", 1772 | "4 843 914.0 709 7.0 \n", 1773 | "\n", 1774 | "[5 rows x 76 columns]" 1775 | ] 1776 | }, 1777 | "execution_count": 14, 1778 | "metadata": {}, 1779 | "output_type": "execute_result" 1780 | } 1781 | ], 1782 | "source": [ 1783 | "#分箱，等频、等宽\n", 1784 | "def bucket(data,count_feas,bucket_len):\n", 1785 | " #通过除法映射到间隔均匀的分箱中，每个分箱的取值范围都是Age/10,相当于宽度10等宽分箱\n", 1786 | " for fea in tqdm(count_feas,desc='分箱特征'):\n", 1787 | " data['{}_bin'.format(fea)] = np.floor_divide(data[fea], bucket_len)\n", 1788 | "\n", 1789 | "count_feas = ['Age']\n", 1790 | "bucket(data,count_feas,5)\n", 1791 | "data.head()" 1792 | ] 1793 | }, 1794 | { 1795 | "cell_type": "code", 1796 | "execution_count": 15, 1797 | "metadata": {}, 1798 | "outputs": [ 1799 | { 1800 | "name": "stderr", 1801 | "output_type": "stream", 1802 | "text": [ 1803 | "100%|██████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1001.27it/s]\n" 1804 | ] 1805 | }, 1806 | { 1807 | "data": { 1808 | "text/html": [ 1809 | "
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PassengerIdlabelPclassSexAgeSibSpParchFareEmbarkedSex_target_enc...Age-mean_gb_EmbarkedAge-min_gb_EmbarkedAge-max_gb_EmbarkedAge/sum_gb_EmbarkedPclass_SexSex_COUNTEmbarked_COUNTPclass_COUNTAge_binSex_label_encoder
010.03male22.0107.2500S0.195279...-7.24520521.83-58.00.0009620843914.07094.01
121.01female38.01071.2833C0.739837...5.66783037.58-33.00.0055441466270.03237.00
231.03female26.0007.9250S0.739837...-3.24520525.83-54.00.0011372466914.07095.00
341.01female35.01053.1000S0.739837...5.75479534.83-45.00.0015301466914.03237.00
450.03male35.0008.0500S0.195279...5.75479534.83-45.00.0015300843914.07097.01
\n", 1973 | "

5 rows × 77 columns

\n", 1974 | "
" 1975 | ], 1976 | "text/plain": [ 1977 | " PassengerId label Pclass Sex Age SibSp Parch Fare Embarked \\\n", 1978 | "0 1 0.0 3 male 22.0 1 0 7.2500 S \n", 1979 | "1 2 1.0 1 female 38.0 1 0 71.2833 C \n", 1980 | "2 3 1.0 3 female 26.0 0 0 7.9250 S \n", 1981 | "3 4 1.0 1 female 35.0 1 0 53.1000 S \n", 1982 | "4 5 0.0 3 male 35.0 0 0 8.0500 S \n", 1983 | "\n", 1984 | " Sex_target_enc ... Age-mean_gb_Embarked Age-min_gb_Embarked \\\n", 1985 | "0 0.195279 ... -7.245205 21.83 \n", 1986 | "1 0.739837 ... 5.667830 37.58 \n", 1987 | "2 0.739837 ... -3.245205 25.83 \n", 1988 | "3 0.739837 ... 5.754795 34.83 \n", 1989 | "4 0.195279 ... 5.754795 34.83 \n", 1990 | "\n", 1991 | " Age-max_gb_Embarked Age/sum_gb_Embarked Pclass_Sex Sex_COUNT \\\n", 1992 | "0 -58.0 0.000962 0 843 \n", 1993 | "1 -33.0 0.005544 1 466 \n", 1994 | "2 -54.0 0.001137 2 466 \n", 1995 | "3 -45.0 0.001530 1 466 \n", 1996 | "4 -45.0 0.001530 0 843 \n", 1997 | "\n", 1998 | " Embarked_COUNT Pclass_COUNT Age_bin Sex_label_encoder \n", 1999 | "0 914.0 709 4.0 1 \n", 2000 | "1 270.0 323 7.0 0 \n", 2001 | "2 914.0 709 5.0 0 \n", 2002 | "3 914.0 323 7.0 0 \n", 2003 | "4 914.0 709 7.0 1 \n", 2004 | "\n", 2005 | "[5 rows x 77 columns]" 2006 | ] 2007 | }, 2008 | "execution_count": 15, 2009 | "metadata": {}, 2010 | "output_type": "execute_result" 2011 | } 2012 | ], 2013 | "source": [ 2014 | "# 基本的类别特征转换方法label_encode\n", 2015 | "from sklearn.preprocessing import LabelEncoder\n", 2016 | "for col in tqdm(['Sex']):\n", 2017 | " le = LabelEncoder()\n", 2018 | " le.fit(data[col])\n", 2019 | " data[col+'_label_encoder'] = le.transform(data[col])\n", 2020 | " # test[col] = le.transform(test[col])\n", 2021 | "data.head()\n" 2022 | ] 2023 | }, 2024 | { 2025 | "cell_type": "code", 2026 | "execution_count": null, 2027 | "metadata": {}, 2028 | "outputs": [], 2029 | "source": [ 2030 | "#构建序列特征，例如构建每个用户的登录行为序列\n", 2031 | "#例如一个用户会存在多个样本，对用户groupby获取登录序列，拼起来作为新特征\n", 2032 | "launch_grp = pd.DataFrame()\n", 2033 | "\n", 2034 | "user_id = []\n", 2035 | "launch_date_str = []\n", 2036 | "for i in launch.groupby('user_id'):\n", 2037 | " launch_date = []\n", 2038 | " user_id.append(i[0])\n", 2039 | " for j in i[1]['date']:\n", 2040 | " launch_date.append(j)\n", 2041 | " launch_date_str.append(str(launch_date))\n", 2042 | "launch_grp['user_id'] = list(user_id)\n", 2043 | "launch_grp['launch_date_str'] = list(launch_date_str)\n", 2044 | "launch_grp.head()" 2045 | ] 2046 | }, 2047 | { 2048 | "cell_type": "code", 2049 | "execution_count": null, 2050 | "metadata": {}, 2051 | "outputs": [], 2052 | "source": [ 2053 | "#对上述序列或文本特征进行w2v，构建embedding特征\n", 2054 | "from gensim.models.word2vec import Word2Vec\n", 2055 | "\n", 2056 | "data['tagid'] = data['tagid'].apply(lambda x: eval(x))\n", 2057 | "sentences = data['tagid'].values.tolist()\n", 2058 | "for i in range(len(sentences)):\n", 2059 | " sentences[i] = [str(x) for x in sentences[i]] #将每个tagid转化成str格式\n", 2060 | "\n", 2061 | "#训练数据格式如下\n", 2062 | "#sentences=[['外形', '外观', '好看', '屏幕', '特别'], ['手机', '好看', '段时间'], ['手机', '很漂亮', '评价']]\n", 2063 | "\n", 2064 | "emb_size = 32\n", 2065 | "#model = Word2Vec(sentences,vector_size=emb_size, window=6, min_count=5, sg=0, hs=0, seed=1,epochs=5)\n", 2066 | "model = Word2Vec.load('./w2vmodel/w2vmodel.model')\n", 2067 | "emb_matrix = []\n", 2068 | "for seq in sentences:\n", 2069 | " vec = []\n", 2070 | " for w in seq:\n", 2071 | "# if w in model.wv.vocab:\n", 2072 | "# vec.append(model.wv[w])\n", 2073 | " try:\n", 2074 | " vec.append(model.wv[w])\n", 2075 | " except KeyError:\n", 2076 | " continue\n", 2077 | " \n", 2078 | " if len(vec) > 0:\n", 2079 | " emb_matrix.append(np.mean(vec, axis=0))\n", 2080 | " else:\n", 2081 | " emb_matrix.append([0] * emb_size)\n", 2082 | "emb_matrix = np.array(emb_matrix)\n", 2083 | "for i in range(emb_size):\n", 2084 | " data['tag_emb_{}'.format(i)] = emb_matrix[:, i]" 2085 | ] 2086 | }, 2087 | { 2088 | "cell_type": "code", 2089 | "execution_count": null, 2090 | "metadata": {}, 2091 | "outputs": [], 2092 | "source": [ 2093 | "#想象力特征" 2094 | ] 2095 | } 2096 | ], 2097 | "metadata": { 2098 | "kernelspec": { 2099 | "display_name": "Python 3", 2100 | "language": "python", 2101 | "name": "python3" 2102 | }, 2103 | "language_info": { 2104 | "codemirror_mode": { 2105 | "name": "ipython", 2106 | "version": 3 2107 | }, 2108 | "file_extension": ".py", 2109 | "mimetype": "text/x-python", 2110 | "name": "python", 2111 | "nbconvert_exporter": "python", 2112 | "pygments_lexer": "ipython3", 2113 | "version": "3.8.5" 2114 | }, 2115 | "toc": { 2116 | "base_numbering": 1, 2117 | "nav_menu": {}, 2118 | "number_sections": true, 2119 | "sideBar": true, 2120 | "skip_h1_title": false, 2121 | "title_cell": "Table of Contents", 2122 | "title_sidebar": "Contents", 2123 | "toc_cell": false, 2124 | "toc_position": {}, 2125 | "toc_section_display": true, 2126 | "toc_window_display": false 2127 | } 2128 | }, 2129 | "nbformat": 4, 2130 | "nbformat_minor": 2 2131 | } 2132 | --------------------------------------------------------------------------------