{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('../input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2021-07-31T05:14:05.192351Z","iopub.execute_input":"2021-07-31T05:14:05.192892Z","iopub.status.idle":"2021-07-31T05:14:05.228653Z","shell.execute_reply.started":"2021-07-31T05:14:05.192820Z","shell.execute_reply":"2021-07-31T05:14:05.227450Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import mlb\nimport json\nimport gc","metadata":{"execution":{"iopub.status.busy":"2021-07-31T05:14:05.235246Z","iopub.execute_input":"2021-07-31T05:14:05.235720Z","iopub.status.idle":"2021-07-31T05:14:05.247802Z","shell.execute_reply.started":"2021-07-31T05:14:05.235671Z","shell.execute_reply":"2021-07-31T05:14:05.246563Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# load model\nimport joblib\nmodel_path = '../input/new-model/'\nmodel1 = joblib.load(model_path+'new_model1.pkl')\nmodel2 = joblib.load(model_path+'new_model2.pkl')\nmodel3 = joblib.load(model_path+'new_model3.pkl')\nmodel4 = joblib.load(model_path+'new_model4.pkl')","metadata":{"execution":{"iopub.status.busy":"2021-07-31T05:14:05.249704Z","iopub.execute_input":"2021-07-31T05:14:05.250137Z","iopub.status.idle":"2021-07-31T05:14:06.399355Z","shell.execute_reply.started":"2021-07-31T05:14:05.250088Z","shell.execute_reply":"2021-07-31T05:14:06.398306Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"\ndef merge_nested_form(form, column):\n  result = pd.DataFrame()\n  for index, value in form[column].items():\n    value = str(value)\n    if (value!='nan') and (value is not np.nan):\n      unpack = pd.read_json(value)\n      result = pd.concat([result,unpack], axis=0)\n  index = pd.Series(list(range(0,result.shape[0])))       # 给整张表重置一下index\n  result = result.set_index(index)\n  return result\n\n\n# def merge_nested_form(form, name):\n#   l = []\n#   rows = len(form[name])\n#   print(name)\n#   print('rows:',rows)\n#   for i in range(rows):\n#     # for now if nan, we skip, NEED TO CHANGE later\n#     print('type:',type(form[name].iloc[i]))\n# #     print('ele:',form[name].iloc[i])\n\n    \n#     json_str = str(form[name].iloc[i])\n#     print(json_str == 'nan')\n# #     print('str:',json_str)\n#     if(json_str=='nan' or pd.isna(json_str)):\n#       continue\n#     l.append(pd.read_json(json_str))\n#   mergedDF = pd.concat(l, ignore_index = True)\n#   return mergedDF\n\ndef unpack_certain_column_and_concat_date(form, column):\n  form = form.copy()\n  result = pd.DataFrame()                              #先创建一个空白表格\n  for index, value in form[column].iteritems():        #把这一列逐行遍历\n    if pd.isnull(index):                               #注意判断方法\n      continue\n    else:\n      unpack = pd.read_json(value)                     #把有内容的单元解压缩成df\n      date = form['date'].loc[index]                   #找到 unpack 出的这个表格对应的日期是多少\n      rows_amount = unpack.shape[0]                    #看一下这个 unpack 的表格一共有多少行\n      unpack['date'] = pd.Series(data = [int(date)] * rows_amount)\n          #因为整张 unpack 表都对应一个日期，所以用乘法的方法，把这个日期广播到 date 那一列的每一行\n      result = pd.concat([result, unpack], axis=0)     #把解压缩出来的内容和每个拼在一起\n  #刚才为了实现广播这个操作，date的格式是int\n  #现在需要把 date 那一列的格式改成 str，因为其他表 date 那一列都是 str，不改的话后面 merge 很麻烦\n  #但是需要先转成 datetime 格式，因为年份月份和日期之间需要加上分界线\n  result['date'] = pd.to_datetime(result['date'],format='%Y%m%d')\n  result['date'] = result['date'].dt.date\n  result['date'] = result['date'].astype(str)          \n  index = pd.Series(list(range(0,result.shape[0])))    # 给整张表重置一下index\n  result = result.set_index(index)\n  return result","metadata":{"execution":{"iopub.status.busy":"2021-07-31T05:14:06.401013Z","iopub.execute_input":"2021-07-31T05:14:06.401675Z","iopub.status.idle":"2021-07-31T05:14:06.416085Z","shell.execute_reply.started":"2021-07-31T05:14:06.401624Z","shell.execute_reply":"2021-07-31T05:14:06.414910Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def unique_record(data,column1,column2,new_column):\n  data = data.copy()\n  data[column1] = data[column1].astype(str)\n  data[column2] = data[column2].astype(str)\n  data[new_column] = data[column1]+data[column2]\n  # 到此为止，给原表重新创造了一列，后面要以这一列为基准\n\n  temp = data.groupby(new_column).max()             # 这里应该是 groupby().max()，这个函数好像是没保存，跑一下如果报错了就是这个有问题\n\n  # 把一天之内参加了多场比赛的球队/队员的信息保留最大值\n  # 之所以保留最大值，是因为这样也能保留下字符串的列\n  # 但是这一步会导致 index 的错乱，同时 string 的列会被自动删除\n  temp[new_column] = temp.index\n  reset_index = pd.Series(list(range(0,temp.shape[0])))    # 给整张表重置一下index\n  temp = temp.set_index(reset_index)\n  # 因为刚才的 groupby 计算搞乱了 index，需要重置一下\n  '''\n  categorical_columns = data[data.columns[data.dtypes == object]] \n  # 这一步就很牛逼了：把数据格式为 object 的列拎出来\n  # 注意，被拎出来的列中一定包括 columns1，column2，和 new_column\n  result = pd.merge(temp,categorical_columns,on=new_column,how='left')\n  '''\n  # 因为刚才的 groupby 函数使用的是 max，不会对字符串的列产生影响，否则还要想办法处理这些列\n  # 不过写在这里的处理方法也不是完美的，会导致一部分的数据被迫丢失\n  result = temp\n  return result","metadata":{"execution":{"iopub.status.busy":"2021-07-31T05:14:06.417561Z","iopub.execute_input":"2021-07-31T05:14:06.418257Z","iopub.status.idle":"2021-07-31T05:14:06.439005Z","shell.execute_reply.started":"2021-07-31T05:14:06.418204Z","shell.execute_reply":"2021-07-31T05:14:06.437772Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def merge_two_nested_form(primary_form,secondary_form,key):\n  primary_form = primary_form.copy()\n  secondary_form = secondary_form.copy()\n  result = pd.DataFrame()\n  merge_area = round(primary_form.shape[0]/100) # 每次 merge 1%\n  start = 0\n  for n in range(0,100):\n    start = n*merge_area\n    end = min(start+merge_area,primary_form.shape[0])\n    temp = pd.merge(primary_form.iloc[start:end], secondary_form, \n                    on=key, #加这个参数很重要，不然的话电脑需要一个一个去匹配，内存会爆\n                    how=\"left\")\n    result = result.append(temp)\n    gc.collect()\n  return result","metadata":{"execution":{"iopub.status.busy":"2021-07-31T05:14:06.440654Z","iopub.execute_input":"2021-07-31T05:14:06.441410Z","iopub.status.idle":"2021-07-31T05:14:06.452696Z","shell.execute_reply.started":"2021-07-31T05:14:06.441359Z","shell.execute_reply":"2021-07-31T05:14:06.451755Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def target_encode(data,y, a=0, p=0):\n  # 输入的 data 格式全部都是 category value\n  data = data.copy()\n  columns = data.columns.drop(y)                                                        \n  for n in columns:\n    temp = data.dropna(subset=[n])   \n        # 必须先复制一个表（没用copy是因为dropna默认inpalce为否），然后把groupby函数括号中的那一列（也就是n那一列）中的nan全都给删掉，然后再按照n那一列groupby\n        # 不然会报错，因为groupby的时候是自动dropna的，transform 返回结果的行数就少了，和原来的列表行数不匹配\n        # 因为pandas要求transform返回的series行数必须和原表的行数相同，即使不把transform的结果插入原表，系统也这么要求自己\n        # 注意，是 transform 结果的长度不匹配，不是插入新列的时候长度不匹配。所以我们先得到transform的结果，在按照index插入原表就不存在行数不匹配的问题                                  \n    data[f'{n}(target)'] = temp.groupby(n)[y].transform(lambda x: (np.sum(x)+a*p)/(x.count()+a))\n        # 按照每一列 groupby，然后把每一种值对应的y给整理出来，求和 和 计数 分别作为分子分母\n        # a 和 p 是两个超参数，用于避免 overfitting，默认为0\n  data.drop(columns = list(columns), inplace=True)\n  return data","metadata":{"execution":{"iopub.status.busy":"2021-07-31T05:14:06.454188Z","iopub.execute_input":"2021-07-31T05:14:06.454983Z","iopub.status.idle":"2021-07-31T05:14:06.470984Z","shell.execute_reply.started":"2021-07-31T05:14:06.454895Z","shell.execute_reply":"2021-07-31T05:14:06.469878Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# dict_path = '/kaggle/input/encoded-dict/'\n# encode_dict = json.load(open(dict_path+\"dict.txt\"))\nencode_dict = {'playerId': {'628317': 3.572679062519813, '547989': 4.097064433099979, '519317': 14.799762667739191, '607625': 1.273158865961782, '592547': 0.3053678858514164, '641553': 0.9898237558571998, '606959': 0.18773746492715238, '435263': 2.200154062292159, '502179': 0.1294632727704282, '493200': 0.8820140120494955, '605490': 1.046546757676012, '517369': 0.537131564478753, '543939': 3.3166009508202747, '460077': 0.6123053095786506, '543768': 2.6527629427243973, '476454': 2.77548442961736, '542979': 1.3553361054030415, '461865': 0.7643633088436447, '641857': 1.5878816259261341, '664208': 0.39034472189832853, '572998': 0.1692301843471732, '519203': 7.80484160404877, '453568': 3.2681546445941403, '608701': 0.6326696045756744, '593700': 0.7168771149646599, '425772': 0.78833201810647, '592200': 0.8191229316204246, '571760': 1.6232633171161306, '592811': 0.31578419568314386, '542583': 2.661135411536607, '593144': 0.5088780682020392, '594795': 0.4038038171314761, '544928': 0.5345141124238157, '641531': 1.5710002382056245, '641490': 0.14864774030827752, '642180': 1.8938715905504047, '641432': 1.318481888299282, '570799': 0.76077051302014, '622097': 0.2441641741078269, '477132': 12.384353706874833, '446334': 4.125371511673067, '452104': 0.6293713976340802, '622761': 0.9900137626569121, '501571': 1.1159718773487715, '608349': 1.1125237506793504, '459429': 0.6460249279468859, '571718': 1.187751204860829, '642162': 0.5901590649515736, '614177': 2.6353957261975975, '457918': 4.804424508190058, '605195': 0.5902616512153042, '594992': 0.0799368390254118, '448281': 1.6622556864190603, '434538': 1.3127389385651638, '435079': 2.809047330163991, '516782': 2.8692113308230867, '592879': 0.7391042456616465, '502671': 6.30724763413386, '548389': 2.177472626976539, '488681': 0.28692344629087385, '623364': 0.40030465901010265, '592340': 0.2752096605598718, '553878': 1.377804853154436, '461314': 5.290460807558067, '592387': 1.1818175188798532, '627894': 0.5081541815622093, '643338': 1.6867843210940363, '642571': 0.12413138834921157, '425877': 6.64505751082315, '605508': 0.36608805124714555, '501659': 0.5622350193190392, '460269': 0.4007671175484125, '571946': 1.2567710153054448, '593423': 1.360678762327474, '465657': 1.073031202746122, '592169': 0.6843900975296582, '547004': 1.0616989617533723, '501817': 0.38503709538718206, '605538': 0.842415380925716, '453943': 4.646258535485707, '570240': 0.8472663593688581, '545338': 0.19592388778308764, '500743': 1.264426175884453, '605253': 0.7870372199550424, '641645': 0.962365504747945, '543807': 7.187688533689595, '624577': 8.277249969926455, '622491': 2.966402311811155, '607455': 0.3083179710346354, '488671': 1.2883698196845919, '581527': 0.8034668020432588, '624133': 0.3615185284474311, '660623': 0.5254284076277331, '519455': 1.5656770406121445, '455119': 0.8219048144177732, '645261': 1.6483611335385209, '607680': 3.6917326862484012, '605164': 2.313868630379781, '543228': 1.6041238830239974, '622713': 0.14320754179048634, '474832': 2.197335879019556, '453172': 0.8958163408529556, '571666': 2.9293900313201853, '606299': 1.5869254383810143, '605288': 0.952480897652225, '592454': 1.5814773285011545, '433587': 4.287589006931669, '592863': 0.3222774802603094, '444432': 1.1487422546726695, '460026': 0.7576407993968194, '596001': 1.104563750704543, '518516': 7.485926363808945, '592791': 2.3408501532627137, '596115': 3.2878414874440964, '571918': 0.3536831023672568, '501985': 2.3190386816702677, '596057': 1.3047048895904434, '504379': 0.507086928735178, '570663': 0.7415560488892567, '553869': 0.6296931180308978, '543243': 4.173815409733851, '657205': 0.16228600277368854, '570482': 4.413664895625509, '493247': 0.4107266168885861, '518466': 0.45718999075458067, '502239': 1.3890136102637571, '594807': 2.2758345399530295, '592518': 14.001575890270207, '547912': 0.58133816814815, '502190': 1.637294687411225, '543148': 0.44234990004735336, '608648': 0.5258751186643374, '430935': 4.890517173482893, '543557': 1.3836610028762697, '448609': 0.4236346961359337, '543308': 0.6260319767410539, '547973': 5.889538007382979, '600303': 2.190704913479175, '462382': 0.3124684802198503, '592130': 0.23589264917889566, '606424': 0.5819888833500371, '542947': 0.5389187702013144, '606160': 0.5805083941311812, '624585': 2.7216803804881557, '596019': 8.031303048531493, '543339': 1.4385143466152022, '645302': 2.1311183935963895, '592426': 0.8181608285072812, '621453': 0.9691457057614059, '457759': 6.369322615961911, '435622': 1.7243296517186593, '543685': 6.3830581175109815, '519390': 1.1195243491901135, '596112': 0.59200077138031, '475582': 2.5039842678345834, '527038': 2.1200518793832335, '446386': 0.43330159339283564, '640461': 0.9705594146262712, '642008': 0.3722367899504179, '451661': 0.4101769871376477, '572008': 0.8315203476102793, '571951': 0.42356916814879003, '664056': 2.8869054197144632, '543302': 0.517486895094054, '571467': 0.16109001112470786, '607043': 2.802664302185517, '607219': 0.6109062610659076, '656941': 5.253489724313521, '571875': 1.436375100270803, '543877': 2.0738247047140597, '543935': 0.5321638224388935, '592773': 1.046632814122266, '570560': 1.3278584518178413, '641835': 0.37469746514822233, '664948': 0.09638265464784693, '467008': 1.2712767083280665, '572761': 4.581361335723047, '500208': 1.5589972603138018, '571561': 0.4258509964546902, '643289': 1.2317064651204697, '493316': 3.569231101273552, '592351': 1.6359804461782135, '621112': 0.3955673761907123, '622497': 0.13680297069561168, '641838': 0.39094458755777556, '542364': 0.4791339719246233, '453064': 2.875992571880854, '605486': 0.60112358828682, '621261': 0.7495488186736179, '518792': 4.046984697937929, '458675': 0.6756482175601828, '656945': 0.39670081335026125, '643327': 1.0567327122123134, '621043': 4.962219902990795, '502381': 0.5278671331481135, '501981': 3.7832580470021626, '622534': 1.47456924154081, '570731': 2.6602548821055834, '445213': 0.9222224382740736, '489446': 1.6601545423448585, '605244': 0.5574998870502317, '475253': 2.239462544314813, '450203': 4.313288190340479, '642086': 2.1875509420120074, '600986': 0.1922667590732682, '660422': 0.24321245716057732, '594694': 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'BAMMILBFCOFF': 0.2617796753750205, 'BAMMILBSCRP': 0.0021616947686985003, 'WDPOY': 12.68894025781956, 'WMLBDPOY': 19.083956621715515, 'ALSS': 23.63471545162515, 'NLSS': 25.225803158981858, 'HEARTANDHUSTLE': 12.912337453616505, 'MLBPCALOR': 6.910062994544954, 'MLBPCNLOR': 6.944634394507531, 'MLBPCALPIT': 7.5196521484550845, 'MLBPCALOP': 9.325210926692787, 'MLBPCNLCOM': 5.068351133102865, 'MLBPCNLPIT': 3.838980936629659, 'BABERUTH': 17.284700254984184, 'ALPG': 33.42425232317987, 'MLBPCNLOP': 12.032937890987144, 'NLPG': 42.86499357889655, 'MLBPCMOY': 2.4910434626335394, 'MLBPCPOY': 7.117293470422436, 'MLBPCALCOM': 3.6342993253467504, 'NLROY': 59.99477077719751, 'ALROY': 70.8657703567912, 'WARRENSPAHN': 42.858690167484774, 'NLCY': 70.7877034370738, 'ALCY': 43.64246833293699, 'NLMVP': 65.24154083114907, 'ALMVP': 79.86562791443212, 'AFLSPORT': 0.5537360790830101, 'AFLMVP': 8.178582294344483, 'ALCPOY': 11.657269198277433, 'NLCPOY': 18.290758969932103, 'DHOY': 8.561568673243846, 'HUTCH': 0.5696831557814007, 'OAKSERVICE': 0.2958518262847838, 'CLETOMAHAWK': 1.9603163851276837, 'TORMIP': 0.15429428337790652, 'TORROY': 1.0275894514051116, 'TORMVP': 1.2597718342458555, 'TORPOY': 6.93437080068439, 'AFLPSAS': 0.12917200529636716, 'TONYCONIGLIARO': 3.1760479414640077, 'DWLROY': 0.6294583617086337, 'PWLROY': 0.02720348204570175, 'CSMVP': 8.914168801341793, 'MLBPLAYOW': 12.238690679319664, 'RETIREDUNI_140': 22.259193538814692, 'RETIREDUNI_142': 17.77295643932962, 'MEXMSAS': 0.0568521150033235, 'SLTOPS': 1.2995437111156054, 'ILROY': 13.582406582391577, 'AAACMVP': 0.016544421772459, 'SDGCHAIRMAN': 0.06966489198252575, 'BOSGOODGUY': 0.3320166504143708, 'MINPOHLAD': 1.161602384230651, 'MINGOODGUY': 0.21641274616492728, 'MILMVP': 15.244491701145991, 'STLKILE': 1.9910184119712326, 'CINGOODGUY': 1.9020511034634664, 'MLBSECOND': 3.062592976942929, 'MLBAFIRST': 14.111473840050884, 'MEXPOY': 0.03876245497794726, 'DWLMVP': 0.18421811714047212, 'DWLPOY': 0.0220171624282905, 'VWLRLOY': 0.03908839852346388, 'PWLPSAS': 0.1659698381392969, 'MLBLEGEND': 8.833490280749821, 'VWLMVP': 0.11014893160811574}}\n","metadata":{"execution":{"iopub.status.busy":"2021-07-31T05:14:06.474323Z","iopub.execute_input":"2021-07-31T05:14:06.474961Z","iopub.status.idle":"2021-07-31T05:14:06.873651Z","shell.execute_reply.started":"2021-07-31T05:14:06.474890Z","shell.execute_reply":"2021-07-31T05:14:06.872227Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# tt_dict_path = '/kaggle/input/twitter-encoded-dict/'\n# ptt_dict = json.load(open(tt_dict_path+\"ptt_dict.txt\"))\n# ttt_dict = json.load(open(tt_dict_path+\"ttt_dict.txt\"))\n# twitter_info = pd.read_csv(\"../input/twitter-info-csv/twitter_info.csv\")\n# ptt_dict = pd.Series(twitter_info['playerId(target)'].values,index=twitter_info.player_twitter).to_dict()\n# ttt_dict = pd.Series(twitter_info['playerId(target)'].values,index=twitter_info.team_twitter).to_dict()\n\nptt_dict = {-0.2156475279878565: 0.1647886026860004, -0.1839866339458649: 0.8096977214792735, -0.1668713858383491: 0.4156895415950615, -0.2068661247658759: 0.5370960700191922, -0.1712312213571707: 1.0845394334444294, 0.0548243211629715: 1.7757206311242053, -0.0812642792086013: 0.8286663970884277, -0.200878102885158: 0.3302444830215153, -0.1702898055460269: 0.2655648127073787, -0.1564309301623036: 1.1603439405374842, -0.1711540561267491: 1.037740777845348, -0.1233964950188071: 0.7095264151888652, -0.1763704257032508: 0.3728876558747225, 0.7171875095330503: 4.069933009946518, -0.1358200971166883: 1.747474497268857, 0.0008935416212998: 2.981440868640378, 1.9365447641324829: 7.714159745977281, -0.2146212304232489: 0.7230599371611379, -0.1978918084678412: 0.5912139107370784, 0.1410333165900079: 4.279324094250846, -0.21048517407265: 0.043209635054345, -0.1300327048350666: 0.8095270067723545, -0.1260355458992266: 1.7195345632021446, 1.707093951473789: 1.956884476480288, -0.0450737861408605: 1.1114746875534791, 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0.4643242455609596: 1.376346461961503, 0.5333486042992874: 0.3336814024986965, 0.3202992689811427: 0.4831856911888746}","metadata":{"execution":{"iopub.status.busy":"2021-07-31T05:14:06.876601Z","iopub.execute_input":"2021-07-31T05:14:06.877126Z","iopub.status.idle":"2021-07-31T05:14:07.056056Z","shell.execute_reply.started":"2021-07-31T05:14:06.877073Z","shell.execute_reply":"2021-07-31T05:14:07.054365Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def twitter_fillna(data,player_twitter,team_twitter,playerId,teamId):\n  data = data.copy()\n  # 先把所有的 player_twitter 项给 fillna 一下\n  # 对于那些有记录的 palyer 来说，由于记录发生在每月的1号，所以这一个月的 player_twitter 全部按照1号的填充\n  # 可能有些人有的月份没有 player_twitter 记录，那么就按上一个月的记录延续，如果是开头几个月没有，那么就按最早的记录填充\n  # 所以先 forward fill，在 back fill\n  data[player_twitter] = data.groupby(playerId)[player_twitter].fillna(method='ffill')\n  data[player_twitter] = data.groupby(playerId)[player_twitter].fillna(method='bfill')\n  # 但是这么操作一圈以后，有些人他们一条 player_twitter 记录都没有，只能全部-1了\n  data[player_twitter] = data[player_twitter].fillna(-1)\n\n  # 接下来处理 team_twitter\n  # 对于那些有 teamId 的行，team_twitter 的缺失主要是由于 team_twitter 的记录都在每个月的1号\n  data[team_twitter] = data.groupby([teamId],)[team_twitter].fillna(method='ffill')\n  # 然后把没有 teamId 记录的 player_date，全部 team_twitter =-1\n  data[team_twitter] = data[team_twitter].loc[data[teamId].isnull()==True] = -1\n  return data","metadata":{"execution":{"iopub.status.busy":"2021-07-31T05:14:07.057771Z","iopub.execute_input":"2021-07-31T05:14:07.058231Z","iopub.status.idle":"2021-07-31T05:14:07.066892Z","shell.execute_reply.started":"2021-07-31T05:14:07.058180Z","shell.execute_reply":"2021-07-31T05:14:07.065220Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def var_type_judge(data):\n  data = data.copy()\n  result = pd.DataFrame(index=data.columns,\n                        columns=['variable type'])\n  for c,n in data.iteritems():\n    if data[c].unique().shape[0] == data[c].shape[0]:\n      result.loc[c]='ID'\n    elif data[c].dtype == 'int64' or data[c].dtype == 'float64':\n      result.loc[c]='numerical'\n    elif data[c].dtype == 'O':\n      result.loc[c]='catagorical'\n    else:\n      result.loc[c]='?????????'\n  return result","metadata":{"execution":{"iopub.status.busy":"2021-07-31T05:14:07.068808Z","iopub.execute_input":"2021-07-31T05:14:07.069344Z","iopub.status.idle":"2021-07-31T05:14:07.087708Z","shell.execute_reply.started":"2021-07-31T05:14:07.069280Z","shell.execute_reply":"2021-07-31T05:14:07.086540Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def trim_fraction(text):\n    text = str(text)\n    if '.0' in text:\n        return text[:text.rfind('.0')]\n    return text","metadata":{"execution":{"iopub.status.busy":"2021-07-31T05:14:07.089198Z","iopub.execute_input":"2021-07-31T05:14:07.089671Z","iopub.status.idle":"2021-07-31T05:14:07.104206Z","shell.execute_reply.started":"2021-07-31T05:14:07.089620Z","shell.execute_reply":"2021-07-31T05:14:07.102666Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"X_columns = ['playerId', 'teamId',\"player_twitter\",\"team_twitter\",\n           'status', 'awardId', 'seriesDescription',\n           'Current_winning_percentage', 'Score', 'home', 'jerseyNum',\n           'positionCode', 'battingOrder', 'gamesPlayedBatting', 'flyOuts',\n           'groundOuts', 'runsScored', 'doubles', 'triples', 'homeRuns',\n           'strikeOuts', 'baseOnBalls', 'intentionalWalks', 'hits', 'hitByPitch',\n           'atBats', 'caughtStealing', 'stolenBases', 'groundIntoDoublePlay',\n           'groundIntoTriplePlay', 'plateAppearances', 'totalBases', 'rbi',\n           'leftOnBase', 'sacBunts', 'sacFlies', 'catchersInterference',\n           'pickoffs', 'gamesPlayedPitching', 'gamesStartedPitching',\n           'completeGamesPitching', 'shutoutsPitching', 'winsPitching',\n           'lossesPitching', 'flyOutsPitching', 'airOutsPitching',\n           'groundOutsPitching', 'runsPitching', 'doublesPitching',\n           'triplesPitching', 'homeRunsPitching', 'strikeOutsPitching',\n           'baseOnBallsPitching', 'intentionalWalksPitching', 'hitsPitching',\n           'hitByPitchPitching', 'atBatsPitching', 'caughtStealingPitching',\n           'stolenBasesPitching', 'inningsPitched', 'saveOpportunities',\n           'earnedRuns', 'battersFaced', 'outsPitching', 'pitchesThrown', 'balls',\n           'strikes', 'hitBatsmen', 'balks', 'wildPitches', 'pickoffsPitching',\n           'rbiPitching', 'gamesFinishedPitching', 'inheritedRunners',\n           'inheritedRunnersScored', 'catchersInterferencePitching',\n           'sacBuntsPitching', 'sacFliesPitching', 'saves', 'holds', 'blownSaves',\n           'assists', 'putOuts', 'errors', 'chances', 'divisionRank', 'leagueRank',\n           'totalWins_onTHEseason', 'totalLosses_onTHEseason',\n           'homeWins_onTHEseason', 'homeLosses_onTHEseason',\n           'awayWins_onTHEseason', 'awayLosses_onTHEseason',\n           'positionName', 'positionType']\n\ndf_X = ['playerId', 'teamId', 'status', 'seriesDescription',\n       'Current_winning_percentage', 'Score', 'home', 'jerseyNum',\n       'positionCode', 'positionName', 'positionType', 'battingOrder',\n       'gamesPlayedBatting', 'flyOuts', 'groundOuts', 'runsScored', 'doubles',\n       'triples', 'homeRuns', 'strikeOuts', 'baseOnBalls', 'intentionalWalks',\n       'hits', 'hitByPitch', 'atBats', 'caughtStealing', 'stolenBases',\n       'groundIntoDoublePlay', 'groundIntoTriplePlay', 'plateAppearances',\n       'totalBases', 'rbi', 'leftOnBase', 'sacBunts', 'sacFlies',\n       'catchersInterference', 'pickoffs', 'gamesPlayedPitching',\n       'gamesStartedPitching', 'completeGamesPitching', 'shutoutsPitching',\n       'winsPitching', 'lossesPitching', 'flyOutsPitching', 'airOutsPitching',\n       'groundOutsPitching', 'runsPitching', 'doublesPitching',\n       'triplesPitching', 'homeRunsPitching', 'strikeOutsPitching',\n       'baseOnBallsPitching', 'intentionalWalksPitching', 'hitsPitching',\n       'hitByPitchPitching', 'atBatsPitching', 'caughtStealingPitching',\n       'stolenBasesPitching', 'inningsPitched', 'saveOpportunities',\n       'earnedRuns', 'battersFaced', 'outsPitching', 'pitchesThrown', 'balls',\n       'strikes', 'hitBatsmen', 'balks', 'wildPitches', 'pickoffsPitching',\n       'rbiPitching', 'gamesFinishedPitching', 'inheritedRunners',\n       'inheritedRunnersScored', 'catchersInterferencePitching',\n       'sacBuntsPitching', 'sacFliesPitching', 'saves', 'holds', 'blownSaves',\n       'assists', 'putOuts', 'errors', 'chances', 'divisionRank', 'leagueRank',\n       'totalWins_onTHEseason', 'totalLosses_onTHEseason',\n       'homeWins_onTHEseason', 'homeLosses_onTHEseason',\n       'awayWins_onTHEseason', 'awayLosses_onTHEseason', 'awardId',\"player_twitter\",\"team_twitter\"]\nset(X_columns)==set(df_X)","metadata":{"execution":{"iopub.status.busy":"2021-07-31T05:14:07.106201Z","iopub.execute_input":"2021-07-31T05:14:07.106691Z","iopub.status.idle":"2021-07-31T05:14:07.130094Z","shell.execute_reply.started":"2021-07-31T05:14:07.106639Z","shell.execute_reply":"2021-07-31T05:14:07.128602Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def preprocess(data, test_y):\n    #preprocess submission y\n    merge_start = test_y.copy()\n    merge_start.drop(columns=[\"date_playerId\"], inplace=True)\n    merge_start[\"engagementMetricsDate\"] = merge_start['date'].copy()\n    merge_start[\"date\"] = pd.to_datetime(merge_start['date'], format=\"%Y%m%d\")\n    merge_start[\"date\"] = pd.DatetimeIndex(merge_start[\"date\"]) - pd.DateOffset(1)\n    merge_start['date'] = merge_start['date'].astype(str)\n    merge_start['engagementMetricsDate'] = pd.to_datetime(merge_start['engagementMetricsDate'],format='%Y%m%d')\n    merge_start['engagementMetricsDate'] = merge_start['engagementMetricsDate'].astype(str)\n    \n    rosters = merge_nested_form(data, 'rosters')\n\n    # rosters 原本一共有6列：\n    # playerId\tgameDate\tteamId\tstatusCode\tstatus\n    # 现在仅保留有用的4列\n    rosters = rosters[['playerId','gameDate','teamId','status']]\n\n    # 并且 pd.isnull(train['rosters']).sum() 的结果为0，也就表示，train['rosters'] 这一列每一行都有 value\n    # 所以，gameDate 应该就是 date\n    rosters.rename(columns={'gameDate':'date'},inplace=True)\n    \n    #     playerTwitterFollowers = merge_nested_form(data, 'playerTwitterFollowers')\n#     print(playerTwitterFollowers)\n#     # playerTwitterFollowers 原本一共有6列：\n#     # date\tplayerId\tplayerName\taccountName\ttwitterHandle\tnumberOfFollowers\n#     # 现在仅保留有用的3列\n#     playerTwitterFollowers = playerTwitterFollowers[['date','playerId','numberOfFollowers']]\n\n#     # 嵌套表格以'date'为列名称的，数据格式好像全部都是 datetime，需要全部转换成 str\n#     # 比如 transactions，playerTwitterFollowers，teamTwitterFollowers 表\n#     # 但是以'gameDate'为列名称的，数据格式好像全部都是 str\n#     playerTwitterFollowers['date'] = playerTwitterFollowers['date'].astype(str)    \n\n#     playerTwitterFollowers.rename(columns={'numberOfFollowers':'player_twitter'},inplace=True)\n    \n#     teamTwitterFollowers = merge_nested_form(data, 'teamTwitterFollowers')\n\n#     # teamTwitterFollowers 原本一共有6列：\n#     # date\tteamId\tteamName\taccountName\ttwitterHandle\tnumberOfFollowers\n#     # 现在仅保留有用的3列\n#     teamTwitterFollowers = teamTwitterFollowers[['date','teamId','numberOfFollowers']]\n\n#     # 嵌套表格以'date'为列名称的，数据格式好像全部都是 datetime，需要全部转换成 str\n#     # 比如 transactions，playerTwitterFollowers，teamTwitterFollowers 表\n#     # 但是以'gameDate'为列名称的，数据格式好像全部都是 str\n#     teamTwitterFollowers['date'] = teamTwitterFollowers['date'].astype(str) \n\n#     teamTwitterFollowers.rename(columns={'numberOfFollowers':'team_twitter'},inplace=True)\n\n    games = merge_nested_form(data, 'games')\n\n    # gameType\n    '''\n    [{\"id\":\"S\",\"description\":\"Spring Training\"},\n     {\"id\":\"R\",\"description\":\"Regular Season\"},\n     {\"id\":\"F\",\"description\":\"Wild Card Game\"},\n     {\"id\":\"D\",\"description\":\"Division Series\"},\n     {\"id\":\"L\",\"description\":\"League Championship Series\"},\n     {\"id\":\"W\",\"description\":\"World Series\"},\n     {\"id\":\"C\",\"description\":\"Championship\"},\n     {\"id\":\"N\",\"description\":\"Nineteenth Century Series\"},\n     {\"id\":\"P\",\"description\":\"Playoffs\"},\n     {\"id\":\"A\",\"description\":\"All-Star Game\"},\n     {\"id\":\"I\",\"description\":\"Intrasquad\"},\n     {\"id\":\"E\",\"description\":\"Exhibition\"}]\n    '''\n\n    # 只有10场比赛存在resume，所以相关变量可以删除了\n    # 只有116场比赛 istie 那一列是1，可以删除\n\n    # 改一下日期的 column name，方便后面 merge\n    games.rename(columns={'gameDate':'date'},inplace=True)\n    \n    # 把 game 表格分程两部分，主场球队的信息，和客场球队的信息\n    # 两部分都包括比赛的编号和类型\n    # 仅摘取我们认为比较有用的信息\n    games_home = games[['seriesDescription','date','homeId','homeWinPct','homeScore']]\n    games_away = games[['seriesDescription','date','awayId','awayWinPct','awayScore']]\n\n    # 创建一个 feature 用于判断是主场还是客场\n    games_home['home'] = pd.Series()\n    games_home['home'] = games_home['home'].fillna(1).astype(int)\n    games_away['home'] = pd.Series()\n    games_away['home'] = games_away['home'].fillna(0).astype(int)\n\n    #为了后面主客场两站表的融合，改一下 columns name\n    games_home.rename(columns={'homeId':'teamId',\n                               'homeWinner':'Win',\n                               'homeWinPct':'Current_winning_percentage',\n                               'homeScore':'Score'},\n                      inplace = True)\n    games_away.rename(columns={'awayId':'teamId',\n                               'awayWinner':'Win',\n                               'awayWinPct':'Current_winning_percentage',\n                               'awayScore':'Score'},\n                      inplace = True)\n\n    games_new = pd.concat([games_home,games_away],axis=0)\n\n    index = pd.Series(list(range(0,games_new.shape[0])))       # 给整张表重置一下index\n    games_new = games_new.set_index(index)\n    \n    playerBoxScores = merge_nested_form(data, 'playerBoxScores')\n\n    # 改一下日期的 column name，方便后面 merge\n    playerBoxScores.rename(columns={'gameDate':'date'},inplace=True)\n\n    # 因为这张表计划大部分内容保留，所以只把没用的删除就好\n    playerBoxScores.drop(columns = ['gameTimeUTC','teamName','playerName','home','gamePk'], inplace=True)\n    \n    teamBoxScores = merge_nested_form(data, 'teamBoxScores')\n\n    # 改一下日期的 column name，方便后面 merge\n    teamBoxScores.rename(columns={'gameDate':'date'},inplace=True)\n\n    teamBoxScores.drop(['home','gamePk'], axis=1, inplace=True)\n    \n    standings = merge_nested_form(data, 'standings')\n\n    # 改一下日期的 column name，方便后面 merge\n    standings.rename(columns={'gameDate':'date'},inplace=True)\n    \n    standings_new = standings[['date','teamId','divisionRank','leagueRank',\n                           'wins','losses','homeWins','homeLosses','awayWins','awayLosses']]\n\n    standings_new.rename(columns={'wins':'totalWins_onTHEseason',\n                                  'losses':'totalLosses_onTHEseason',\n                                  'homeWins':'homeWins_onTHEseason',\n                                  'homeLosses':'homeLosses_onTHEseason',\n                                  'awayWins':'awayWins_onTHEseason',\n                                  'awayLosses':'awayLosses_onTHEseason'},\n                         inplace=True)\n    \n    awards = merge_nested_form(data, 'awards')\n    # awards['awardId'].unique().shape[0] = 399\n    # 一共有399种不同的奖项\n\n    # 改一下日期的 column name，方便后面 merge\n    awards.rename(columns={'awardDate':'date'},inplace=True)\n\n    # 仅保留有用的几列\n    if(awards.empty):\n        awards['awardId'] = np.nan\n        awards['date'] = merge_start['date']\n        awards['playerId'] = merge_start['playerId']\n    else:\n        awards = awards[['awardId','date','playerId']]\n    \n#     transactions = merge_nested_form(data, 'transactions')\n#     print(\"transactions:\", transactions)\n#     # transactions['typeDesc'].unique().shape[0] = 16\n#     # 一共有16种不同的 transactions\n\n#     # 嵌套表格以'date'为列名称的，数据格式好像全部都是 datetime，需要全部转换成 str\n#     # 比如 transactions，playerTwitterFollowers，teamTwitterFollowers 表\n#     # 但是以'gameDate'为列名称的，数据格式好像全部都是 str\n#     transactions['date'] = transactions['date'].astype(str) \n\n#     # 有375行里，playerId 是 nan，就尼玛离谱，需要全部删除，然后再重置一下 index\n#     delete_index = transactions.loc[transactions['playerId'].isnull()==True].index\n#     transactions.drop(index = delete_index, inplace = True)\n#     reset_index = pd.Series(list(range(0,transactions.shape[0])))    # 给整张表重置一下index\n#     transactions = transactions.set_index(reset_index)\n\n#     # 这个 playerId 默认有小数点就你妈离谱\n#     transactions['playerId'] = transactions['playerId'].astype(int)\n\n#     # transactions['toTeamId'].isnull().sum()==0\n#     # 我们可以认为，没有球员被解聘，只存在两种情况：从别的队挖人or招募新人\n\n#     transactions.drop(columns=['transactionId','playerName','fromTeamName','toTeamName','typeCode','description'],inplace=True)\n    \n    games_new_new = unique_record(games_new,'date','teamId','date_teamId')\n    games_new_new.drop(columns='date_teamId',inplace=True)\n    games_new_new['teamId'] = games_new_new['teamId'].astype(int)\n    \n    playerBoxScores_new = unique_record(playerBoxScores,'date','playerId','date_playerId')\n    playerBoxScores_new.drop(columns='date_playerId',inplace=True)\n    playerBoxScores_new['playerId'] = playerBoxScores_new['playerId'].astype(int)\n    \n    teamBoxScores_new = unique_record(teamBoxScores,'date','teamId','date_teamId')\n    teamBoxScores_new.drop(columns='date_teamId',inplace=True)\n    teamBoxScores_new['teamId'] = playerBoxScores_new['teamId'].astype(int)\n    \n    awards_new = unique_record(awards,'date','playerId','date_playerId')\n    awards_new.drop(columns='date_playerId',inplace=True)\n    awards_new['playerId'] = awards_new['playerId'].astype(int)\n    \n    gc.collect()\n#     merge all\n    merge_rosters = merge_two_nested_form(merge_start,rosters,key=['playerId','date'])\n    gc.collect()\n    merge_games = merge_two_nested_form(merge_rosters,games_new_new,key=['teamId','date'])\n    gc.collect()\n    merge_PScores = merge_two_nested_form(merge_games,playerBoxScores_new,key=['playerId','teamId','date'])\n    gc.collect()\n    merge_standings = merge_two_nested_form(merge_PScores,standings_new,key=['teamId','date'])\n    gc.collect()\n    merge_awards = merge_two_nested_form(merge_standings,awards_new,key=['playerId','date'])\n    gc.collect()\n    merge_final = merge_awards.copy()\n    gc.collect()\n    merge = merge_final\n    \n    merge['date'] = pd.to_datetime(merge['date'],format='%Y-%m-%d')\n    merge['engagementMetricsDate'] = pd.to_datetime(merge['engagementMetricsDate'],format='%Y-%m-%d')\n    \n    df = merge.copy()\n    df[\"playerId\"] = df[\"playerId\"].astype('str')\n    df['playerId'] = df['playerId'].map(encode_dict[\"playerId\"])\n    df[\"teamId\"] = df[\"teamId\"].apply(trim_fraction)\n    df['teamId'] = df['teamId'].map(encode_dict[\"teamId\"])\n    df[\"status\"] = df[\"status\"].astype('str')\n    df['status'] = df['status'].map(encode_dict[\"status\"])\n    df[\"seriesDescription\"] = df[\"seriesDescription\"].astype('str')\n    df['seriesDescription'] = df['seriesDescription'].map(encode_dict[\"seriesDescription\"])\n    df[\"positionName\"] = df[\"positionName\"].astype('str')\n    df['positionName'] = df['positionName'].map(encode_dict[\"positionName\"])\n    df[\"positionType\"] = df[\"positionType\"].astype('str')\n    df['positionType'] = df['positionType'].map(encode_dict[\"positionType\"])\n    df[\"awardId\"] = df[\"awardId\"].astype('str')\n    df['awardId'] = df['awardId'].map(encode_dict[\"awardId\"])\n#     df.playerId.update(pd.Series(encode_dict[\"playerId\"]))\n    df[\"player_twitter\"] = df[\"playerId\"].map(ptt_dict)\n    df[\"team_twitter\"] = df[\"playerId\"].map(ttt_dict)\n    \n#     dict_keys(['playerId', 'teamId', 'status', 'seriesDescription', 'positionName', 'positionType', 'awardId'])\n    df = df.fillna(-1)\n    \n    df.drop(columns=[\"target1\",\"target2\",\"target3\",\"target4\",\"engagementMetricsDate\",\"date\"],inplace=True)\n    pd.set_option('display.max_columns', None)\n#     print(df.columns)\n    df = df[X_columns]\n    \n#     print(\"df\",df)\n    return df","metadata":{"execution":{"iopub.status.busy":"2021-07-31T05:14:07.134082Z","iopub.execute_input":"2021-07-31T05:14:07.134536Z","iopub.status.idle":"2021-07-31T05:14:07.174482Z","shell.execute_reply.started":"2021-07-31T05:14:07.134495Z","shell.execute_reply":"2021-07-31T05:14:07.172881Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"env = mlb.make_env() # initialize the environment\niter_test = env.iter_test() # iterator which loops over each date in test set\ntest_data = pd.DataFrame()\nsub_sample = pd.DataFrame()","metadata":{"execution":{"iopub.status.busy":"2021-07-31T05:14:07.175833Z","iopub.execute_input":"2021-07-31T05:14:07.176269Z","iopub.status.idle":"2021-07-31T05:14:07.192056Z","shell.execute_reply.started":"2021-07-31T05:14:07.176167Z","shell.execute_reply":"2021-07-31T05:14:07.191001Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# for (test_df, sample_prediction_df) in iter_test: # make predictions here\n#     sample_prediction_df = sample_prediction_df.reset_index(drop=True)\n#     # creat dataset\n#     sample_prediction_df['playerId'] = sample_prediction_df['date_playerId']\\\n#                                         .map(lambda x: int(x.split('_')[1]))\n#     sample_prediction_df['date'] = sample_prediction_df['date_playerId']\\\n#                                         .map(lambda x: int(x.split('_')[0]))\n#     print(sample_prediction_df)","metadata":{"execution":{"iopub.status.busy":"2021-07-31T05:14:07.193575Z","iopub.execute_input":"2021-07-31T05:14:07.194175Z","iopub.status.idle":"2021-07-31T05:14:07.205864Z","shell.execute_reply.started":"2021-07-31T05:14:07.194123Z","shell.execute_reply":"2021-07-31T05:14:07.204994Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"for (test_df, sample_prediction_df) in iter_test: # make predictions here\n    \n    sample_prediction_df = sample_prediction_df.reset_index(drop=True)\n    # creat dataset\n    sample_prediction_df['playerId'] = sample_prediction_df['date_playerId']\\\n                                        .map(lambda x: int(x.split('_')[1]))\n    sample_prediction_df['date'] = sample_prediction_df['date_playerId']\\\n                                        .map(lambda x: int(x.split('_')[0]))\n    \n    \n    # Dealing with missing values\n    \n    test_X = preprocess(test_df,sample_prediction_df)\n#     test_data = pd.concat([test_data,test_df], axis=0)\n#     sub_sample = pd.concat([sub_sample,sample_prediction_df], axis=0)\n#     env.predict(sample_prediction_df)\n    \n#     # predict\n    pred1 = model1.predict(test_X)\n    pred2 = model2.predict(test_X)\n    pred3 = model3.predict(test_X)\n    pred4 = model4.predict(test_X)\n    \n    # merge submission\n    sample_prediction_df['target1'] = np.clip(pred1, 0, 100)\n    sample_prediction_df['target2'] = np.clip(pred2, 0, 100)\n    sample_prediction_df['target3'] = np.clip(pred3, 0, 100)\n    sample_prediction_df['target4'] = np.clip(pred4, 0, 100)\n    sample_prediction_df = sample_prediction_df.fillna(0.)\n\n#     sample_prediction_df[\"date\"] = sample_prediction_df[\"date\"].replace({\"-\":\"\"}, regex=True)\n#     sample_prediction_df[\"playerId\"] = sample_prediction_df[\"playerId\"].astype(str)\n#     sample_prediction_df[\"date_playerId\"] = sample_prediction_df[\"date\"]+\"_\"+sample_prediction_df[\"playerId\"]\n    del sample_prediction_df['playerId']\n    del sample_prediction_df['date']\n#     sample_prediction_df = sample_prediction_df[[\"date_playerId\",\"target1\",\"target2\",\"target3\",\"target4\"]]\n    final_headers = [\"date_playerId\", \"target1\", \"target2\", \"target3\", \"target4\"] \n    sample_prediction_df = sample_prediction_df[final_headers].copy() \n#     sample_prediction_df[\"target1\"] = sample_prediction_df[\"target1\"].round(6)\n#     sample_prediction_df[\"target2\"] = sample_prediction_df[\"target2\"].round(6)\n#     sample_prediction_df[\"target3\"] = sample_prediction_df[\"target3\"].round(6)\n#     sample_prediction_df[\"target4\"] = sample_prediction_df[\"target4\"].round(6)\n    env.predict(sample_prediction_df)","metadata":{"execution":{"iopub.status.busy":"2021-07-31T05:14:07.207220Z","iopub.execute_input":"2021-07-31T05:14:07.207707Z","iopub.status.idle":"2021-07-31T05:19:34.311861Z","shell.execute_reply.started":"2021-07-31T05:14:07.207663Z","shell.execute_reply":"2021-07-31T05:19:34.310579Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# test_data.to_csv(\"test_data2.csv\",index = False)","metadata":{"execution":{"iopub.status.busy":"2021-07-31T05:19:34.314805Z","iopub.execute_input":"2021-07-31T05:19:34.315311Z","iopub.status.idle":"2021-07-31T05:19:34.320314Z","shell.execute_reply.started":"2021-07-31T05:19:34.315259Z","shell.execute_reply":"2021-07-31T05:19:34.319144Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# sub_sample.to_csv(\"sub_sample.csv\", index = False)","metadata":{"execution":{"iopub.status.busy":"2021-07-31T05:19:34.322056Z","iopub.execute_input":"2021-07-31T05:19:34.322412Z","iopub.status.idle":"2021-07-31T05:19:34.334745Z","shell.execute_reply.started":"2021-07-31T05:19:34.322379Z","shell.execute_reply":"2021-07-31T05:19:34.333511Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sub = pd.read_csv(\"./submission.csv\")\nsub","metadata":{"execution":{"iopub.status.busy":"2021-07-31T05:19:34.336267Z","iopub.execute_input":"2021-07-31T05:19:34.336691Z","iopub.status.idle":"2021-07-31T05:19:34.376658Z","shell.execute_reply.started":"2021-07-31T05:19:34.336645Z","shell.execute_reply":"2021-07-31T05:19:34.375539Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# sub.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2021-07-31T05:19:34.378009Z","iopub.execute_input":"2021-07-31T05:19:34.378330Z","iopub.status.idle":"2021-07-31T05:19:34.382269Z","shell.execute_reply.started":"2021-07-31T05:19:34.378299Z","shell.execute_reply":"2021-07-31T05:19:34.381457Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# sub.dtypes","metadata":{"execution":{"iopub.status.busy":"2021-07-31T05:19:34.383781Z","iopub.execute_input":"2021-07-31T05:19:34.384370Z","iopub.status.idle":"2021-07-31T05:19:34.397544Z","shell.execute_reply.started":"2021-07-31T05:19:34.384334Z","shell.execute_reply":"2021-07-31T05:19:34.396119Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# tmpsub","metadata":{"execution":{"iopub.status.busy":"2021-07-31T05:19:34.398937Z","iopub.execute_input":"2021-07-31T05:19:34.399390Z","iopub.status.idle":"2021-07-31T05:19:34.412055Z","shell.execute_reply.started":"2021-07-31T05:19:34.399355Z","shell.execute_reply":"2021-07-31T05:19:34.410882Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# for i, v in sub.iterrows():\n#     tmpsub.loc[tmpsub[\"date_playerId\"]==v['date_playerId'],\"target1\"] = v[\"target1\"]\n#     tmpsub.loc[tmpsub[\"date_playerId\"]==v['date_playerId'],\"target2\"] = v[\"target2\"]\n#     tmpsub.loc[tmpsub[\"date_playerId\"]==v['date_playerId'],\"target3\"] = v[\"target3\"]\n#     tmpsub.loc[tmpsub[\"date_playerId\"]==v['date_playerId'],\"target4\"] = v[\"target4\"]\n\n# tmpsub","metadata":{"execution":{"iopub.status.busy":"2021-07-31T05:19:34.415337Z","iopub.execute_input":"2021-07-31T05:19:34.415712Z","iopub.status.idle":"2021-07-31T05:19:34.424911Z","shell.execute_reply.started":"2021-07-31T05:19:34.415683Z","shell.execute_reply":"2021-07-31T05:19:34.423784Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# tmpsub.to_csv('./submission.csv')","metadata":{"execution":{"iopub.status.busy":"2021-07-31T05:19:34.426202Z","iopub.execute_input":"2021-07-31T05:19:34.426522Z","iopub.status.idle":"2021-07-31T05:19:34.437688Z","shell.execute_reply.started":"2021-07-31T05:19:34.426492Z","shell.execute_reply":"2021-07-31T05:19:34.436648Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}