{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.7.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":38760,"databundleVersionId":4493939,"sourceType":"competition"},{"sourceId":4994829,"sourceType":"datasetVersion","datasetId":2897176}],"dockerImageVersionId":30350,"isInternetEnabled":false,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# 3rd Place - Team G & B & D & T Final Solution - LB 0.604!\nThis notebook is the 3rd place final submission for Team G&B&D&T for Kaggle's OTTO RecSys competition. Team members are Giba ( @titericz ), Benny ( @benediktschifferer ), Chris Deotte ( @cdeotte ), and Theo ( @theoviel ). This solution is an ensemble of 3 single XGB reranker models. Discussion explaining this solution is [here][1], [here][2], and [here][3].\n\n[1]: https://www.kaggle.com/competitions/otto-recommender-system/discussion/386497\n[2]: https://www.kaggle.com/competitions/otto-recommender-system/discussion/383013\n[3]: https://www.kaggle.com/competitions/otto-recommender-system/discussion/382975","metadata":{}},{"cell_type":"markdown","source":"# explained by wenzhe tian","metadata":{}},{"cell_type":"code","source":"import gc\nimport numpy as np\nimport pandas as pd\nfrom tqdm import tqdm\nfrom collections import Counter\n\npd.options.display.max_colwidth = 500","metadata":{"_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","execution":{"iopub.status.busy":"2024-12-18T12:50:04.065237Z","iopub.execute_input":"2024-12-18T12:50:04.065744Z","iopub.status.idle":"2024-12-18T12:50:04.096256Z","shell.execute_reply.started":"2024-12-18T12:50:04.065622Z","shell.execute_reply":"2024-12-18T12:50:04.095110Z"},"papermill":{"duration":0.021588,"end_time":"2023-01-31T17:15:22.397756","exception":false,"start_time":"2023-01-31T17:15:22.376168","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"FILES = [\n    \"/kaggle/input/otto-comp-single-models/submission_chris_v186v406v412.csv\", # Chris的方案，得分为0.601\n    \"/kaggle/input/otto-comp-single-models/submission_benny_601.csv\",      # Benny的方案，得分为 0.601\n    \"/kaggle/input/otto-comp-single-models/submission_theo_6029.csv\"       # Theo的方案，得分为 0.603\n]\n\nWEIGHTS = [1, 1, 3]      # 三个方案对应的权重 ","metadata":{"execution":{"iopub.status.busy":"2024-12-18T12:50:07.164902Z","iopub.execute_input":"2024-12-18T12:50:07.165281Z","iopub.status.idle":"2024-12-18T12:50:07.171097Z","shell.execute_reply.started":"2024-12-18T12:50:07.165249Z","shell.execute_reply":"2024-12-18T12:50:07.169684Z"},"papermill":{"duration":0.012207,"end_time":"2023-01-31T17:15:22.413271","exception":false,"start_time":"2023-01-31T17:15:22.401064","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"    subs = [\n        pd.read_csv(f).sort_values(['session_type']).reset_index(drop=True) for f in FILES    # 针对每个文件，先按照session排好序，方便在后面对这三个文件比较个相似度\n    ]         # 这三个文件的同一行的session是一样的\n    \n    for s in subs:\n        assert len(s) == len(subs[0])  # 断言关键字，用于声明一个条件必须为真。如果条件为真，程序继续执行；如果条件为假，程序将抛出AssertionError异常，并停止执行","metadata":{"execution":{"iopub.status.busy":"2024-12-18T12:50:10.095084Z","iopub.execute_input":"2024-12-18T12:50:10.095512Z","iopub.status.idle":"2024-12-18T12:51:40.847291Z","shell.execute_reply.started":"2024-12-18T12:50:10.095477Z","shell.execute_reply":"2024-12-18T12:51:40.845975Z"},"papermill":{"duration":130.159027,"end_time":"2023-01-31T17:17:32.575337","exception":false,"start_time":"2023-01-31T17:15:22.41631","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"display(subs[0])","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T11:28:04.991379Z","iopub.execute_input":"2024-12-18T11:28:04.992352Z","iopub.status.idle":"2024-12-18T11:28:05.009298Z","shell.execute_reply.started":"2024-12-18T11:28:04.992307Z","shell.execute_reply":"2024-12-18T11:28:05.007863Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"for s, f in zip(subs, FILES):\n    print(f.split('/')[-1])\n    assert len(s) == len(subs[0])\n    display(s.head(3))\n    print()","metadata":{"execution":{"iopub.status.busy":"2024-12-18T12:51:40.849488Z","iopub.execute_input":"2024-12-18T12:51:40.849841Z","iopub.status.idle":"2024-12-18T12:51:40.882781Z","shell.execute_reply.started":"2024-12-18T12:51:40.849808Z","shell.execute_reply":"2024-12-18T12:51:40.881609Z"},"papermill":{"duration":0.051056,"end_time":"2023-01-31T17:17:32.62971","exception":false,"start_time":"2023-01-31T17:17:32.578654","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"subs[0]['labels'][100].split(' ')   # 每一个session都有20个labels","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T11:48:49.050378Z","iopub.execute_input":"2024-12-18T11:48:49.050955Z","iopub.status.idle":"2024-12-18T11:48:49.059868Z","shell.execute_reply.started":"2024-12-18T11:48:49.050912Z","shell.execute_reply":"2024-12-18T11:48:49.058576Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"for idx in range(9):\n    for j in range(len(subs)):\n        for k in range(j):\n            p1 = np.array(sorted(subs[j]['labels'][idx].split(' '))).astype(int)\n            p2 = np.array(sorted(subs[k]['labels'][idx].split(' '))).astype(int)\n            sim = len((set(p1).intersection(set(p2)))) / 20     # 用来计算p1与p2的相似程度，\n            print(f'Similarity of row {subs[0][\"session_type\"][idx]} between subs {j} & {k} : {sim :.3f}')\n    print()","metadata":{"execution":{"iopub.status.busy":"2024-12-18T12:51:40.884429Z","iopub.execute_input":"2024-12-18T12:51:40.884863Z","iopub.status.idle":"2024-12-18T12:51:40.896449Z","shell.execute_reply.started":"2024-12-18T12:51:40.884819Z","shell.execute_reply":"2024-12-18T12:51:40.895281Z"},"papermill":{"duration":0.020269,"end_time":"2023-01-31T17:17:32.654143","exception":false,"start_time":"2023-01-31T17:17:32.633874","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"display(subs[0])","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T12:55:03.623490Z","iopub.execute_input":"2024-12-18T12:55:03.623943Z","iopub.status.idle":"2024-12-18T12:55:03.638101Z","shell.execute_reply.started":"2024-12-18T12:55:03.623885Z","shell.execute_reply":"2024-12-18T12:55:03.636810Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"![image.png](attachment:d8a51bc7-8c3b-46d0-b1d4-fa41aa400d1d.png)","metadata":{},"attachments":{"d8a51bc7-8c3b-46d0-b1d4-fa41aa400d1d.png":{"image/png":"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"}}},{"cell_type":"code","source":"amap = Counter()\nsub = subs[0]\nccccc = 0\nfor w, i in enumerate(sub[\"labels\"][idx].split(' ')):  # sub[\"labels\"][idx].split(' ')结果是一行中，所有的标签'954951',‘111124’，‘111135’...这种，i是value， w是index，对应现在是枚举第w项了\n    amap[i] += (1 * (20 - w))               \n    print(w,i,amap[i])\n\n\n    \n    ccccc +=1\n    if ccccc == 3:\n        break","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T12:53:46.289168Z","iopub.execute_input":"2024-12-18T12:53:46.289606Z","iopub.status.idle":"2024-12-18T12:53:46.297915Z","shell.execute_reply.started":"2024-12-18T12:53:46.289570Z","shell.execute_reply":"2024-12-18T12:53:46.296630Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"![image.png](attachment:4ff00a7e-e766-4c9a-ad01-e17e5a307721.png)","metadata":{},"attachments":{"4ff00a7e-e766-4c9a-ad01-e17e5a307721.png":{"image/png":"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"}}},{"cell_type":"code","source":"blend = [] \nfor idx in tqdm(range(len(subs[0]))):    # 遍历这5015409个session         # range(len(subs[0]))   len(subs[0])只是为了获取session的数量，本身和subs[0]无任何关系\n    amap = Counter()\n    for sub, sub_w in zip(subs, WEIGHTS):     # 去遍历所有的sub，有相同index的就是相同的session，然后通过amap[i] += (sub_w * (20 - w))  计算其重要程度，把他加到amap里，统计数量\n        for w, i in enumerate(sub[\"labels\"][idx].split(' ')):  # sub[\"labels\"][idx].split(' ')结果是一行中，所有的标签'954951',‘111124’，‘111135’...\n            #   w是index，i是value，也就是具体的‘1123412’，即物品的label\n            amap[i] += (sub_w * (20 - w))  # 对于每个标签i，根据其在列表中的位置w（位置越靠前，权重越大，因为20 - w会更大），计算加权分数，并更新到amap中。\n            # 针对物品i，统计他在sub表中出现的分数\n    \n    aid = ' '.join([aid_ for aid_, _ in amap.most_common(20)])   # 使用amap.most_common(20)获取加权计数最高的20个标签。 join方法将这20个标签连接成一个字符串，中间用空格分隔。\n    #   每一个session,我们给其添加\n    blend.append(aid)  # blend里面存的每一项是 当前sub对应的数量最多的20个标签    # 注意，这个blend是对三个表进行加权汇总，得到的一个总表，对应每个session和它对应的对应的最多的物品label","metadata":{"execution":{"iopub.status.busy":"2024-12-18T13:14:08.681040Z","iopub.execute_input":"2024-12-18T13:14:08.681495Z","iopub.status.idle":"2024-12-18T13:20:21.877395Z","shell.execute_reply.started":"2024-12-18T13:14:08.681459Z","shell.execute_reply":"2024-12-18T13:20:21.876252Z"},"papermill":{"duration":540.427811,"end_time":"2023-01-31T17:26:33.086154","exception":false,"start_time":"2023-01-31T17:17:32.658343","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"len(blend)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T13:37:11.768500Z","iopub.execute_input":"2024-12-18T13:37:11.768907Z","iopub.status.idle":"2024-12-18T13:37:11.775746Z","shell.execute_reply.started":"2024-12-18T13:37:11.768871Z","shell.execute_reply":"2024-12-18T13:37:11.774703Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"sub = subs[0].copy()\nsub","metadata":{"execution":{"iopub.status.busy":"2024-12-18T13:24:24.409316Z","iopub.execute_input":"2024-12-18T13:24:24.409884Z","iopub.status.idle":"2024-12-18T13:24:25.633037Z","shell.execute_reply.started":"2024-12-18T13:24:24.409831Z","shell.execute_reply":"2024-12-18T13:24:25.631976Z"},"papermill":{"duration":21.482596,"end_time":"2023-01-31T17:26:54.948903","exception":false,"start_time":"2023-01-31T17:26:33.466307","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"sub['labels'] = blend\ndisplay(sub)\n# 噫，快哉快哉！","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T13:24:43.423560Z","iopub.execute_input":"2024-12-18T13:24:43.423991Z","iopub.status.idle":"2024-12-18T13:24:44.216809Z","shell.execute_reply.started":"2024-12-18T13:24:43.423943Z","shell.execute_reply":"2024-12-18T13:24:44.215630Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"\nsub.to_csv('submission.csv', index=False)\n\nsub.head(12)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T13:38:03.122581Z","iopub.execute_input":"2024-12-18T13:38:03.123020Z","iopub.status.idle":"2024-12-18T13:38:26.570428Z","shell.execute_reply.started":"2024-12-18T13:38:03.122983Z","shell.execute_reply":"2024-12-18T13:38:26.569280Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"接下来我们再次来统计其相似度，和上面那个统计相似度类似，不多解释","metadata":{}},{"cell_type":"code","source":"for idx in range(3):   # 我们针对前三个session，统计其相似度\n    for j in range(len(subs)):\n        p1 = np.array(sorted(subs[j]['labels'][idx].split(' '))).astype(int)\n        p2 = np.array(sorted(sub['labels'][idx].split(' '))).astype(int)\n        sim = len((set(p1).intersection(set(p2)))) / 20\n        print(f'Similarity of row {subs[0][\"session_type\"][idx]} between sub {j} & blend : {sim :.3f}')\n    print()","metadata":{"execution":{"iopub.status.busy":"2024-12-18T13:40:43.155686Z","iopub.execute_input":"2024-12-18T13:40:43.156101Z","iopub.status.idle":"2024-12-18T13:40:43.166030Z","shell.execute_reply.started":"2024-12-18T13:40:43.156066Z","shell.execute_reply":"2024-12-18T13:40:43.164844Z"},"papermill":{"duration":0.333482,"end_time":"2023-01-31T17:26:55.599998","exception":false,"start_time":"2023-01-31T17:26:55.266516","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"Done ! ","metadata":{"papermill":{"duration":0.316415,"end_time":"2023-01-31T17:26:56.297275","exception":false,"start_time":"2023-01-31T17:26:55.98086","status":"completed"},"tags":[]}}]}