本文主要是介绍Kaggle竞赛项目--推荐系统之便利店销量预测，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！
视频地址：https://www.bilibili.com/video/av53701517
源码地址： 链接：https://pan.baidu.com/s/1r-ncwIDU92ZFMQaBY5sBaQ 提取码：peeq
Forecast sales using store, promotion, and competitor data
Rossmann operates over 3,000 drug stores in 7 European countries. Currently,
Rossmann store managers are tasked with predicting their daily sales for up to six weeks in advance. Store sales are influenced by many factors, including promotions, competition, school and state holidays, seasonality, and locality. With thousands of individual managers predicting sales based on their unique circumstances, the accuracy of results can be quite varied.
In their first Kaggle competition, Rossmann is challenging you to predict 6 weeks of daily sales for 1,115 stores located across Germany. Reliable sales forecasts enable store managers to create effective staff schedules that increase productivity and motivation. By helping Rossmann create a robust prediction model, you will help store managers stay focused on what’s most important to them: their customers and their teams!If you are interested in joining Rossmann at their headquarters near Hanover, Germany, please contact Mr. Frank König (Frank.Koenig {at} rossmann.de) Rossmann is currently recruiting data scientists at senior and entry-level positions.
数据
You are provided with historical sales data for 1,115 Rossmann stores. The task is to forecast the "Sales" column for the test set. Note that some stores in the dataset were temporarily closed for refurbishment.
Files
- train.csv - historical data including Sales
- test.csv - historical data excluding Sales
- sample_submission.csv - a sample submission file in the correct format
- store.csv - supplemental information about the stores
Data fields
Most of the fields are self-explanatory. The following are descriptions for those that aren't.
- Id - an Id that represents a (Store, Date) duple within the test set
- Store - a unique Id for each store
- Sales - the turnover for any given day (this is what you are predicting)
- Customers - the number of customers on a given day
- Open - an indicator for whether the store was open: 0 = closed, 1 = open
- StateHoliday - indicates a state holiday. Normally all stores, with few exceptions, are closed on state holidays. Note that all schools are closed on public holidays and weekends. a = public holiday, b = Easter holiday, c = Christmas, 0 = None
- SchoolHoliday - indicates if the (Store, Date) was affected by the closure of public schools
- StoreType - differentiates between 4 different store models: a, b, c, d
- Assortment - describes an assortment level: a = basic, b = extra, c = extended
- CompetitionDistance - distance in meters to the nearest competitor store
- CompetitionOpenSince[Month/Year] - gives the approximate year and month of the time the nearest competitor was opened
- Promo - indicates whether a store is running a promo on that day
- Promo2 - Promo2 is a continuing and consecutive promotion for some stores: 0 = store is not participating, 1 = store is participating
- Promo2Since[Year/Week] - describes the year and calendar week when the store started participating in Promo2
- PromoInterval - describes the consecutive intervals Promo2 is started, naming the months the promotion is started anew. E.g. "Feb,May,Aug,Nov" means each round starts in February, May, August, November of any given year for that store
引入所需的库
import pandas as pd
import datetime
import csv
import numpy as np
import os
import scipy as sp
import xgboost as xgb
import itertools
import operator
import warnings
warnings.filterwarnings("ignore")from sklearn.preprocessing import StandardScaler, LabelEncoder
from sklearn.base import TransformerMixin
from sklearn import cross_validation
from matplotlib import pylab as plt
plot = Truegoal = 'Sales'
myid = 'Id'定义一些变换和评判准则
使用不同的loss function的时候要特别注意这个
def ToWeight(y):w = np.zeros(y.shape, dtype=float)ind = y != 0w[ind] = 1./(y[ind]**2)return wdef rmspe(yhat, y):w = ToWeight(y)rmspe = np.sqrt(np.mean( w * (y - yhat)**2 ))return rmspedef rmspe_xg(yhat, y):# y = y.valuesy = y.get_label()y = np.exp(y) - 1yhat = np.exp(yhat) - 1w = ToWeight(y)rmspe = np.sqrt(np.mean(w * (y - yhat)**2))return "rmspe", rmspestore = pd.read_csv('./data/store.csv')
store.head()
train_df = pd.read_csv('./data/train.csv')
train_df.head()
test_df = pd.read_csv('./data/test.csv')
test_df.head()
加载数据
def load_data():"""加载数据，设定数值型和非数值型数据"""store = pd.read_csv('./data/store.csv')train_org = pd.read_csv('./data/train.csv',dtype={'StateHoliday':pd.np.string_})test_org = pd.read_csv('./data/test.csv',dtype={'StateHoliday':pd.np.string_})train = pd.merge(train_org,store, on='Store', how='left')test = pd.merge(test_org,store, on='Store', how='left')features = test.columns.tolist()numerics = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64']features_numeric = test.select_dtypes(include=numerics).columns.tolist()features_non_numeric = [f for f in features if f not in features_numeric]return (train,test,features,features_non_numeric)数据与特征处理
def process_data(train,test,features,features_non_numeric):"""Feature engineering and selection."""# # FEATURE ENGINEERINGtrain = train[train['Sales'] > 0]for data in [train,test]:# year month daydata['year'] = data.Date.apply(lambda x: x.split('-')[0])data['year'] = data['year'].astype(float)data['month'] = data.Date.apply(lambda x: x.split('-')[1])data['month'] = data['month'].astype(float)data['day'] = data.Date.apply(lambda x: x.split('-')[2])data['day'] = data['day'].astype(float)# promo interval "Jan,Apr,Jul,Oct"data['promojan'] = data.PromoInterval.apply(lambda x: 0 if isinstance(x, float) else 1 if "Jan" in x else 0)data['promofeb'] = data.PromoInterval.apply(lambda x: 0 if isinstance(x, float) else 1 if "Feb" in x else 0)data['promomar'] = data.PromoInterval.apply(lambda x: 0 if isinstance(x, float) else 1 if "Mar" in x else 0)data['promoapr'] = data.PromoInterval.apply(lambda x: 0 if isinstance(x, float) else 1 if "Apr" in x else 0)data['promomay'] = data.PromoInterval.apply(lambda x: 0 if isinstance(x, float) else 1 if "May" in x else 0)data['promojun'] = data.PromoInterval.apply(lambda x: 0 if isinstance(x, float) else 1 if "Jun" in x else 0)data['promojul'] = data.PromoInterval.apply(lambda x: 0 if isinstance(x, float) else 1 if "Jul" in x else 0)data['promoaug'] = data.PromoInterval.apply(lambda x: 0 if isinstance(x, float) else 1 if "Aug" in x else 0)data['promosep'] = data.PromoInterval.apply(lambda x: 0 if isinstance(x, float) else 1 if "Sep" in x else 0)data['promooct'] = data.PromoInterval.apply(lambda x: 0 if isinstance(x, float) else 1 if "Oct" in x else 0)data['promonov'] = data.PromoInterval.apply(lambda x: 0 if isinstance(x, float) else 1 if "Nov" in x else 0)data['promodec'] = data.PromoInterval.apply(lambda x: 0 if isinstance(x, float) else 1 if "Dec" in x else 0)# # Features set.noisy_features = [myid,'Date']features = [c for c in features if c not in noisy_features]features_non_numeric = [c for c in features_non_numeric if c not in noisy_features]features.extend(['year','month','day'])# Fill NAclass DataFrameImputer(TransformerMixin):# http://stackoverflow.com/questions/25239958/impute-categorical-missing-values-in-scikit-learndef __init__(self):"""Impute missing values.Columns of dtype object are imputed with the most frequent valuein column.Columns of other types are imputed with mean of column."""def fit(self, X, y=None):self.fill = pd.Series([X[c].value_counts().index[0] # modeif X[c].dtype == np.dtype('O') else X[c].mean() for c in X], # meanindex=X.columns)return selfdef transform(self, X, y=None):return X.fillna(self.fill)train = DataFrameImputer().fit_transform(train)test = DataFrameImputer().fit_transform(test)# Pre-processing non-numberic valuesle = LabelEncoder()for col in features_non_numeric:le.fit(list(train[col])+list(test[col]))train[col] = le.transform(train[col])test[col] = le.transform(test[col])# LR和神经网络这种模型都对输入数据的幅度极度敏感，请先做归一化操作scaler = StandardScaler()for col in set(features) - set(features_non_numeric) - \set([]): # TODO: add what not to scalescaler.fit(list(train[col])+list(test[col]))train[col] = scaler.transform(train[col])test[col] = scaler.transform(test[col])return (train,test,features,features_non_numeric)训练与分析
def XGB_native(train,test,features,features_non_numeric):depth = 13eta = 0.01ntrees = 8000mcw = 3params = {"objective": "reg:linear","booster": "gbtree","eta": eta,"max_depth": depth,"min_child_weight": mcw,"subsample": 0.9,"colsample_bytree": 0.7,"silent": 1}print "Running with params: " + str(params)print "Running with ntrees: " + str(ntrees)print "Running with features: " + str(features)# Train model with local splittsize = 0.05X_train, X_test = cross_validation.train_test_split(train, test_size=tsize)dtrain = xgb.DMatrix(X_train[features], np.log(X_train[goal] + 1))dvalid = xgb.DMatrix(X_test[features], np.log(X_test[goal] + 1))watchlist = [(dvalid, 'eval'), (dtrain, 'train')]gbm = xgb.train(params, dtrain, ntrees, evals=watchlist, early_stopping_rounds=100, feval=rmspe_xg, verbose_eval=True)train_probs = gbm.predict(xgb.DMatrix(X_test[features]))indices = train_probs < 0train_probs[indices] = 0error = rmspe(np.exp(train_probs) - 1, X_test[goal].values)print error# Predict and Exporttest_probs = gbm.predict(xgb.DMatrix(test[features]))indices = test_probs < 0test_probs[indices] = 0submission = pd.DataFrame({myid: test[myid], goal: np.exp(test_probs) - 1})if not os.path.exists('result/'):os.makedirs('result/')submission.to_csv("./result/dat-xgb_d%s_eta%s_ntree%s_mcw%s_tsize%s.csv" % (str(depth),str(eta),str(ntrees),str(mcw),str(tsize)) , index=False)# Feature importanceif plot:outfile = open('xgb.fmap', 'w')i = 0for feat in features:outfile.write('{0}\t{1}\tq\n'.format(i, feat))i = i + 1outfile.close()importance = gbm.get_fscore(fmap='xgb.fmap')importance = sorted(importance.items(), key=operator.itemgetter(1))df = pd.DataFrame(importance, columns=['feature', 'fscore'])df['fscore'] = df['fscore'] / df['fscore'].sum()# Plotitupplt.figure()df.plot()df.plot(kind='barh', x='feature', y='fscore', legend=False, figsize=(25, 15))plt.title('XGBoost Feature Importance')plt.xlabel('relative importance')plt.gcf().savefig('Feature_Importance_xgb_d%s_eta%s_ntree%s_mcw%s_tsize%s.png' % (str(depth),str(eta),str(ntrees),str(mcw),str(tsize)))print "=> 载入数据中..."
train,test,features,features_non_numeric = load_data()
print "=> 处理数据与特征工程..."
train,test,features,features_non_numeric = process_data(train,test,features,features_non_numeric)
print "=> 使用XGBoost建模..."
XGB_native(train,test,features,features_non_numeric)
Will train until train error hasn't decreased in 100 rounds.
[0] eval-rmspe:0.999864 train-rmspe:0.999864
[1] eval-rmspe:0.999838 train-rmspe:0.999838
[2] eval-rmspe:0.999810 train-rmspe:0.999810
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[5] eval-rmspe:0.999713 train-rmspe:0.999713
[6] eval-rmspe:0.999676 train-rmspe:0.999676
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[10] eval-rmspe:0.999502 train-rmspe:0.999502
[11] eval-rmspe:0.999451 train-rmspe:0.999451
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[13] eval-rmspe:0.999339 train-rmspe:0.999339
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[16] eval-rmspe:0.999143 train-rmspe:0.999143
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[20] eval-rmspe:0.998821 train-rmspe:0.998822
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[22] eval-rmspe:0.998630 train-rmspe:0.998631
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[24] eval-rmspe:0.998418 train-rmspe:0.998418
[25] eval-rmspe:0.998303 train-rmspe:0.998303
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[27] eval-rmspe:0.998053 train-rmspe:0.998053
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[150] eval-rmspe:0.831933 train-rmspe:0.832115
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[171] eval-rmspe:0.764459 train-rmspe:0.764921
[172] eval-rmspe:0.761063 train-rmspe:0.761544
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[181] eval-rmspe:0.729911 train-rmspe:0.730592
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[195] eval-rmspe:0.680163 train-rmspe:0.681267
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[197] eval-rmspe:0.672995 train-rmspe:0.674167
[198] eval-rmspe:0.669403 train-rmspe:0.670615
[199] eval-rmspe:0.665808 train-rmspe:0.667054
[200] eval-rmspe:0.662204 train-rmspe:0.663490
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[202] eval-rmspe:0.655009 train-rmspe:0.656374
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[204] eval-rmspe:0.647823 train-rmspe:0.649273
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[206] eval-rmspe:0.640633 train-rmspe:0.642163
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[208] eval-rmspe:0.633434 train-rmspe:0.635059
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[213] eval-rmspe:0.615533 train-rmspe:0.617381
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[220] eval-rmspe:0.590670 train-rmspe:0.592772
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[226] eval-rmspe:0.569634 train-rmspe:0.572083
[227] eval-rmspe:0.566160 train-rmspe:0.568666
[228] eval-rmspe:0.562700 train-rmspe:0.565268
[229] eval-rmspe:0.559248 train-rmspe:0.561880
[230] eval-rmspe:0.555806 train-rmspe:0.558472
[231] eval-rmspe:0.552370 train-rmspe:0.555089
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[233] eval-rmspe:0.545530 train-rmspe:0.548373
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[238] eval-rmspe:0.528642 train-rmspe:0.531675
[239] eval-rmspe:0.525300 train-rmspe:0.528395
[240] eval-rmspe:0.521970 train-rmspe:0.525119
[241] eval-rmspe:0.518657 train-rmspe:0.521875
[242] eval-rmspe:0.515360 train-rmspe:0.518650
[243] eval-rmspe:0.512069 train-rmspe:0.515436
[244] eval-rmspe:0.508801 train-rmspe:0.512158
[245] eval-rmspe:0.505542 train-rmspe:0.508967
[246] eval-rmspe:0.502301 train-rmspe:0.505802
[247] eval-rmspe:0.499072 train-rmspe:0.502568
[248] eval-rmspe:0.495861 train-rmspe:0.499409
[249] eval-rmspe:0.492660 train-rmspe:0.496207
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[251] eval-rmspe:0.486301 train-rmspe:0.489997
[252] eval-rmspe:0.483143 train-rmspe:0.486908
[253] eval-rmspe:0.480005 train-rmspe:0.483849
[254] eval-rmspe:0.476886 train-rmspe:0.480812
[255] eval-rmspe:0.473782 train-rmspe:0.477789
[256] eval-rmspe:0.470686 train-rmspe:0.474778
[257] eval-rmspe:0.467605 train-rmspe:0.471782
[258] eval-rmspe:0.464537 train-rmspe:0.468798
[259] eval-rmspe:0.461504 train-rmspe:0.465849
[260] eval-rmspe:0.458486 train-rmspe:0.462883
[261] eval-rmspe:0.455457 train-rmspe:0.459945
[262] eval-rmspe:0.452470 train-rmspe:0.457031
[263] eval-rmspe:0.449508 train-rmspe:0.454157
[264] eval-rmspe:0.446550 train-rmspe:0.451285
[265] eval-rmspe:0.443623 train-rmspe:0.448448
[266] eval-rmspe:0.440713 train-rmspe:0.445626
[267] eval-rmspe:0.437818 train-rmspe:0.442742
[268] eval-rmspe:0.434943 train-rmspe:0.439899
[269] eval-rmspe:0.432090 train-rmspe:0.437131
[270] eval-rmspe:0.429257 train-rmspe:0.434280
[271] eval-rmspe:0.426442 train-rmspe:0.431552
[272] eval-rmspe:0.423644 train-rmspe:0.428818
[273] eval-rmspe:0.420857 train-rmspe:0.426133
[274] eval-rmspe:0.418090 train-rmspe:0.423427
[275] eval-rmspe:0.415340 train-rmspe:0.420774
[276] eval-rmspe:0.412621 train-rmspe:0.418084
[277] eval-rmspe:0.409919 train-rmspe:0.415451
[278] eval-rmspe:0.407238 train-rmspe:0.412865
[279] eval-rmspe:0.404579 train-rmspe:0.410286
[280] eval-rmspe:0.401938 train-rmspe:0.407717
[281] eval-rmspe:0.399287 train-rmspe:0.405173
[282] eval-rmspe:0.396676 train-rmspe:0.402659
[283] eval-rmspe:0.394083 train-rmspe:0.400166
[284] eval-rmspe:0.391519 train-rmspe:0.397708
[285] eval-rmspe:0.388986 train-rmspe:0.395199
[286] eval-rmspe:0.386444 train-rmspe:0.392762
[287] eval-rmspe:0.383928 train-rmspe:0.390350
[288] eval-rmspe:0.381446 train-rmspe:0.387937
[289] eval-rmspe:0.378978 train-rmspe:0.385567
[290] eval-rmspe:0.376485 train-rmspe:0.383176
[291] eval-rmspe:0.374064 train-rmspe:0.380707
[292] eval-rmspe:0.371648 train-rmspe:0.378380
[293] eval-rmspe:0.369268 train-rmspe:0.375973
[294] eval-rmspe:0.366876 train-rmspe:0.373680
[295] eval-rmspe:0.364530 train-rmspe:0.371400
[296] eval-rmspe:0.362199 train-rmspe:0.369171
[297] eval-rmspe:0.359884 train-rmspe:0.366944
[298] eval-rmspe:0.357610 train-rmspe:0.364654
[299] eval-rmspe:0.355357 train-rmspe:0.362500
[300] eval-rmspe:0.353066 train-rmspe:0.360315
[301] eval-rmspe:0.350850 train-rmspe:0.358120
[302] eval-rmspe:0.348654 train-rmspe:0.355849
[303] eval-rmspe:0.346417 train-rmspe:0.353714
[304] eval-rmspe:0.344271 train-rmspe:0.351512
[305] eval-rmspe:0.342142 train-rmspe:0.349361
[306] eval-rmspe:0.339941 train-rmspe:0.347270
[307] eval-rmspe:0.337833 train-rmspe:0.345234
[308] eval-rmspe:0.335749 train-rmspe:0.343239
[309] eval-rmspe:0.333685 train-rmspe:0.341267
[310] eval-rmspe:0.331629 train-rmspe:0.339320
[311] eval-rmspe:0.329614 train-rmspe:0.337300
[312] eval-rmspe:0.327619 train-rmspe:0.335254
[313] eval-rmspe:0.325638 train-rmspe:0.333346
[314] eval-rmspe:0.323649 train-rmspe:0.331452
[315] eval-rmspe:0.321682 train-rmspe:0.329566
[316] eval-rmspe:0.319734 train-rmspe:0.327706
[317] eval-rmspe:0.317831 train-rmspe:0.325903
[318] eval-rmspe:0.315936 train-rmspe:0.324120
[319] eval-rmspe:0.314067 train-rmspe:0.322258
[320] eval-rmspe:0.312201 train-rmspe:0.320489
[321] eval-rmspe:0.310397 train-rmspe:0.318799
[322] eval-rmspe:0.308579 train-rmspe:0.317072
[323] eval-rmspe:0.306776 train-rmspe:0.315366
[324] eval-rmspe:0.305025 train-rmspe:0.313709
[325] eval-rmspe:0.303214 train-rmspe:0.311992
[326] eval-rmspe:0.301484 train-rmspe:0.310345
[327] eval-rmspe:0.299785 train-rmspe:0.308697
[328] eval-rmspe:0.298099 train-rmspe:0.307095
[329] eval-rmspe:0.296425 train-rmspe:0.305521
[330] eval-rmspe:0.294744 train-rmspe:0.303948
[331] eval-rmspe:0.293088 train-rmspe:0.302402
[332] eval-rmspe:0.291456 train-rmspe:0.300756
[333] eval-rmspe:0.289895 train-rmspe:0.299292
[334] eval-rmspe:0.288335 train-rmspe:0.297665
[335] eval-rmspe:0.286746 train-rmspe:0.296190
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