1.探索性数据分析:
在这个时间序列的 "入门 "比赛中，我们被要求预测来自Corporación Favorita的商店销售数据，这是一家位于厄瓜多尔的大型杂货零售商。我们需要一个能够预测不同商店所销售的数千种商品的单位销售额的模型。在这次比赛中，我们有不同的数据集，描述了厄瓜多尔2013年至2017年期间的销售、商店、假期数据等。
目标
预测未来16天每个产品、每个商店的销售情况。
衡量标准
本次比赛使用的评估指标是均方根误差（RMSLE）。（取对数意味着预测大的销售数字和小的销售数字的误差将更均匀地影响结果）。
In [ ]:
import numpy as np
import pandas as pd
import random
import datetime as dt
import seaborn as sns
from re import search
random.seed(333)
pd.options.mode.chained_assignment = None
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename))
- 探索性数据分析：
1.1 数据框架和NAs
作为EDA的第一步，我们想知道我们的数据是什么样子的，以及数据集中是否有任何NAs。
我们有不同的数据集供我们使用：
假日_事件：一个包含所有厄瓜多尔假日和事件的列表；
石油：一个石油价格的列表，旨在作为厄瓜多尔的经济指标；
商店：一个包含我们商店信息的数据集：包括城市、州、类型和其他；
交易：一个数据集，包含每个商店每天的交易总数；
测试：我们需要预测的16天销售额的一般测试集；
训练：一个巨大的训练集，包含大约4年的数据，用于预测我们的测试销售数据。
这个笔记本使用了所有的数据集，除了石油价格，因为从表面上看，它似乎并没有给建模结果带来任何改善。如果它确实有用的话，有可能被添加到本笔记本的未来更新中。
In [ ]:
# Read train/test data and check colnames & NA's:
original_train = pd.read_csv('/kaggle/input/store-sales-time-series-forecasting/train.csv')
original_test = pd.read_csv('/kaggle/input/store-sales-time-series-forecasting/test.csv')
# Dataframe info:
print(original_train.info())
# Check NAs:
original_train.isna().any()
我们有ID，日期，商店号码，（产品）系列，售价和促销列。我们的训练和测试数据集有零缺失值。
# Find out how many stores, products and dates are in our data:
original_train['store_nbr'].unique().__len__() # 54 stores
original_train['family'].unique().__len__() # 33 products
len(original_train) / 54 / 33 # 1684 days (between 4 and 5 years)
original_train['date'].iloc[0] # 2013-01-01 is start
original_train['date'].iloc[-1] # 2017-08-15 is end
len(original_test) / 54 / 33 # 16 days
original_test['date'].iloc[0] # 2017-08-16 is test start
original_test['date'].iloc[-1] # 2017-08-31 is test end
我们的主要数据集包括：：
54家商店
33个产品组
我们被要求预测每个产品组（33）在每个商店（54）连续16天的销售情况。这意味着我们必须进行335416=28512的预测。为了使其更易于管理，我们可以为每个产品组创建一个数据框架和预测。这意味着我们将创建33个数据框架，每个框架做864个预测。
为了更快的运行时间和更好的监督，我们将首先创建不同的数据框架，然后通过管道将其合并到33个单独的产品数据框架。
1.2 可视化
由于我们要为每个产品创建一个数据框架，所以最好是创建一个图表来查看每个系列的销售在这些年的演变。我们想要每个月、每年、每个系列的销售总和，所以我们要进行相应的汇总。
original_train['date'] = pd.to_datetime(original_train['date'])
original_train['year'] = original_train['date'].dt.year
original_train['month'] = original_train['date'].dt.month
monthly_sales = original_train.groupby(['family', 'year','month']).agg({"sales" : "sum"}).reset_index()
# The value of the last month (for each 33 products) we change to nan, as otherwise it will distort
# the graph since this month's data is incomplete:
for x in range(33):
z = 55+(x*56)
monthly_sales.at[z,'sales'] = np.nan
# We use seaborn's FacetGrid with a col_wrap of 3 to show all the graphs in rows of three.
# We also need sharey = False so that the y axis of all the graphs is not shared but individual.
product_lineplots = sns.FacetGrid(monthly_sales, col="family", hue='year', sharey=False, height=3.5, col_wrap=3, palette='rocket_r')
product_lineplots.map(sns.lineplot, "month", 'sales')
product_lineplots.add_legend()
product_lineplots.set(xlim=(1, 12), ylim=(0, None), xticks=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])
一些观察：
一些月份对我们的一些产品显示出奇怪的行为（例如宠物用品、农产品......）：
2014年: 二月(2), 四月(4), 五月(5), 六月(6), 八月(8)
2015年: 一月(1), 二月(2), 三月(3), 四月(4), 五月(5)
12月：
似乎是许多产品销售最好的月份，可能是因为它是一个有圣诞节和新年前夕的假期。
书籍：
似乎 "书籍 "类别正处于被淘汰的末期，把这个预测值设为零可能是个好主意。
学校和办公用品：
似乎在4月达到高峰，但在今年之前更多是在8-9月。这些日期对我们来说特别有趣，因为我们的预测也发生在这个高峰的中间（8月底）。
# Create a graph for allsales:
total_monthly_sales = original_train.groupby(['year','month']).agg({"sales" : "sum"}).reset_index()
total_monthly_sales.at[55,'sales'] = np.nan
total_plot = sns.lineplot(x='month', y='sales', hue='year', palette='rocket_r', data=total_monthly_sales)
total_plot.set(xlim=(1, 12), ylim=(0, None), xticks=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])
1.3 创建变量
我们将创建：
一个独立的*数据框架：一天=一行，独立于商店编号和产品组，在这里我们创建取决于日期的变量，与商店无关（例如，一周中的一天）。
一个所有商店的数据框架：我们创建一个数据框架，在这里我们汇总所有54家商店，并添加取决于商店的变量（例如：商店_关闭）。
一个产品组数据框：我们在主数据框中汇总每个产品，然后添加之前从独立df和所有商店df创建的数据。
1.3.1 独立数据框架
对于独立的数据框架，我们创建了以下函数：
create_date_df：一个小函数，用于将数据框架聚合成一个按日期分组的小框架。
create_paydays：对于政府工作（厄瓜多尔最大的雇主），工资是在每月的第一天和15日支付。这个函数创建了发薪日列：两个有效的发薪日，作为一个刻度，向上计数，有一个var显示人们多久前发过工资。
onehotencode：将选择的列变成不同的二进制列的函数（我们也将对我们的所有商店df使用这个函数）。
independant pipeline：合并所有之前的函数以创建一个数据框架的管道。在这里，我们还添加了额外的变量：常规的日期变量，一个表示地震发生时间的变量，以及一个表示学校开始时间的变量（以增加我们的学校和办公用品得分）。
def create_date_df(df, store_nr):
single_store_df = df[df['store_nbr'] == store_nr]
single_store_series = single_store_df.groupby(["date"]).sum(numeric_only=True)
return single_store_series
def create_payday_anchors(df):
df.reset_index(inplace=True)
df['Payday'] = 0
for id, row in df.iterrows():
if search('-01$', row['date']):
df.at[id - 1, 'Payday'] = 1
if search('-15$', row['date']):
df.at[id, 'Payday'] = 1
df = df[:-1]
return df
def onehotencode(df, list_of_variables):
column_name_list = list()
my_category_list = list()
for column in list_of_variables:
categories = df[column].unique().tolist()
for i in categories:
this_list = ((df[column] == i) * 1).tolist()
column_name_list.append(column + str(i))
my_category_list.append(this_list)
print('Finished ' + str(i))
print(str(column) + ' is done.')
onehotencode_df = pd.DataFrame(my_category_list).transpose()
onehotencode_df.columns = np.asarray(column_name_list)
return onehotencode_df
def independant_pipeline():
original_train = pd.read_csv('/kaggle/input/store-sales-time-series-forecasting/train.csv')
original_test = pd.read_csv('/kaggle/input/store-sales-time-series-forecasting/test.csv')
# Get one store one product DF:
one_store_df = create_date_df(original_train, 1)
one_store_df_test = create_date_df(original_test, 1)
one_store_df.drop('sales', axis=1, inplace=True)
one_store_df = pd.concat([one_store_df, one_store_df_test])
del original_train
del original_test
########################
# Add Paydays #
########################
one_store_df = create_payday_anchors(one_store_df)
payday_series = one_store_df['Payday']
payday_count = 0
payday_scale_list = list()
for x in range(payday_series.__len__()):
if payday_series[x] == 1:
payday_count = 0
payday_scale_list.append(payday_count)
else:
payday_count += 1
payday_scale_list.append(payday_count)
one_store_df['Payday_Scale'] = payday_scale_list
one_store_df.drop(['id'], axis=1, inplace=True)
######################
# Add Date Variables #
######################
dayoftheweek_list = list()
dayoftheyear_list = list()
monthoftheyear_list = list()
year_list = list()
for x in range(1700): # because 1700 different days
thisdate = one_store_df['date'][x]
thisdayoftheweek = dt.datetime.strptime(thisdate, '%Y-%m-%d').strftime('%A')
thisdayoftheyear = dt.datetime.strptime(thisdate, '%Y-%m-%d').strftime('%j')
thismonthoftheyear = dt.datetime.strptime(thisdate, '%Y-%m-%d').strftime('%B')
thisyear = dt.datetime.strptime(thisdate, '%Y-%m-%d').strftime('%Y')
dayoftheweek_list.append(thisdayoftheweek)
dayoftheyear_list.append(thisdayoftheyear)
monthoftheyear_list.append(thismonthoftheyear)
year_list.append(thisyear)
one_store_df['DayOfTheWeek'] = dayoftheweek_list
one_store_df['DayOfTheYear'] = dayoftheyear_list
one_store_df['MonthOfTheYear'] = monthoftheyear_list
one_store_df['Year'] = year_list
one_store_df['DayOfTheYear'] = pd.to_numeric(one_store_df['DayOfTheYear'])
# Convert DayOfTheWeek to numeric:
dayoftheweek_scale_dict = {'Monday': 1, 'Tuesday': 2, 'Wednesday': 3, 'Thursday': 4,
'Friday': 5, 'Saturday': 6, 'Sunday': 7}
one_store_df['dayoftheweek_scale'] = one_store_df['DayOfTheWeek'].map(dayoftheweek_scale_dict)
########################
# OneHotEncode #
########################
onehotcolumnlist = ('DayOfTheWeek', 'MonthOfTheYear', 'Year')
onehotencode_df = onehotencode(one_store_df, onehotcolumnlist)
one_store_df = pd.concat([one_store_df, onehotencode_df], axis=1)
########################
# Drop Some Cols #
########################
one_store_df.drop(['store_nbr', 'DayOfTheWeek', 'onpromotion'], axis=1, inplace=True)
return one_store_df
# --- Execute Pipeline --- #
independant_df = independant_pipeline()
1.3.2 所有商店数据框架
对于我们的所有商店数据框架，我们创建了以下函数：
create_multi_store_one_product_df: 一个小函数，用于创建一个聚集在商店和过滤一个产品的数据框架。
create_holiday_variables: 创建所有不同节日变量的函数（取决于商店的位置）。
create_location_variables: 函数，根据商店位置创建一些额外的变量。这是我们添加的外部数据，是我们在kaggle上的初始数据框架中没有的。我们特别关注海拔高度（厄瓜多尔是一个多山的国家）和城市密度（在大城市的商店和在人口稀少的城镇的商店在某些时候可能有非常不同的效果）。
all_stores_pipeline：管道函数，合并前面的函数（也执行onehotencode）以创建我们的所有商店df。
def create_multi_store_one_product_df(df, product_name):
multistore_single_product = df[df['family'] == product_name]
return multistore_single_product
def create_holiday_variables(df):
holidays = pd.read_csv('/kaggle/input/store-sales-time-series-forecasting/holidays_events.csv')
holidays = holidays[holidays['transferred'] == False]
holidays['holiday_type'] = holidays['type']
holidays.drop(['transferred', 'description', 'type'], axis=1, inplace=True)
national_holidays = holidays[holidays['locale'] == 'National']
national_holidays['national_holiday_type'] = national_holidays['holiday_type']
national_holidays.drop(['locale', 'locale_name', 'holiday_type'], axis=1, inplace=True)
national_holidays.drop_duplicates(subset='date', keep="first", inplace=True)
df = pd.merge(df, national_holidays, how='left', on=['date'])
state_holidays = holidays[holidays['locale'] == 'Regional']
state_holidays['state'] = state_holidays['locale_name']
state_holidays['state_holiday_type'] = state_holidays['holiday_type']
state_holidays.drop(['locale', 'locale_name', 'holiday_type'], axis=1, inplace=True)
df = pd.merge(df, state_holidays, how='left', on=['date', 'state'])
city_holidays = holidays[holidays['locale'] == 'Local']
city_holidays['city'] = city_holidays['locale_name']
city_holidays['city_holiday_type'] = city_holidays['holiday_type']
city_holidays.drop(['locale', 'locale_name', 'holiday_type'], axis=1, inplace=True)
city_holidays.drop([265], axis=0, inplace=True)
df = pd.merge(df, city_holidays, how='left', on=['date', 'city'])
df['holiday_type'] = np.nan
df['holiday_type'] = df['holiday_type'].fillna(df['national_holiday_type'])
df['holiday_type'] = df['holiday_type'].fillna(df['state_holiday_type'])
df['holiday_type'] = df['holiday_type'].fillna(df['city_holiday_type'])
df.drop(['national_holiday_type', 'state_holiday_type', 'city_holiday_type'], axis=1, inplace=True)
return df
def create_location_variables(df):
stores = pd.read_csv('/kaggle/input/store-sales-time-series-forecasting/stores.csv')
# stores['city'].unique()
# ['Quito', 'Santo Domingo', 'Cayambe', 'Latacunga', 'Riobamba',
# 'Ibarra', 'Guaranda', 'Puyo', 'Ambato', 'Guayaquil', 'Salinas',
# 'Daule', 'Babahoyo', 'Quevedo', 'Playas', 'Libertad', 'Cuenca',
# 'Loja', 'Machala', 'Esmeraldas', 'Manta', 'El Carmen']
# Height dict:
Height = {'Quito': 2850, 'Santo Domingo': 550, 'Cayambe': 2830, 'Latacunga': 2860,
'Riobamba': 2754, 'Ibarra': 2225, 'Guaranda': 2668, 'Puyo': 950,
'Ambato': 2577, 'Guayaquil': 0, 'Salinas': 0, 'Daule': 0,
'Babahoyo': 0, 'Quevedo': 75, 'Playas': 0, 'Libertad': 36,
'Cuenca': 2560, 'Loja': 2060, 'Machala': 0, 'Esmeraldas': 15,
'Manta': 0, 'El Carmen': 250}
# Elevation:
# 0 = 0 - 200 (10)
# 1 = 200-700 (2)
# 2 = 700-1500 (1)
# 3 = 1500-2300 (2)
# 4 = 2300-3000 (7)
Population = {'Quito': 2000000, 'Santo Domingo': 460000, 'Cayambe': 40000, 'Latacunga': 100000,
'Riobamba': 157000, 'Ibarra': 150000, 'Guaranda': 35000, 'Puyo': 40000,
'Ambato': 350000, 'Guayaquil': 2750000, 'Salinas': 50000, 'Daule': 130000,
'Babahoyo': 105000, 'Quevedo': 200000, 'Playas': 40000, 'Libertad': 105000,
'Cuenca': 445000, 'Loja': 200000, 'Machala': 260000, 'Esmeraldas': 200000,
'Manta': 240000, 'El Carmen': 120000}
# Population:
# 0 = 0-60000 (5)
# 1 = 60000-160000 (12)
# 2 = 160000-280000 (3)
# 3 = 280000+ (2)
Size = {'Quito': 372, 'Santo Domingo': 60, 'Cayambe': 378, 'Latacunga': 370,
'Riobamba': 59, 'Ibarra': 242, 'Guaranda': 520, 'Puyo': 88,
'Ambato': 47, 'Guayaquil': 345, 'Salinas': 27, 'Daule': 475,
'Babahoyo': 175, 'Quevedo': 300, 'Playas': 280, 'Libertad': 28,
'Cuenca': 71, 'Loja': 44, 'Machala': 67, 'Esmeraldas': 70,
'Manta': 60, 'El Carmen': 1250}
stores["City_Population"] = stores['city'].map(Population)
stores["City_Elevation"] = stores['city'].map(Height)
stores["City_Size"] = stores['city'].map(Size)
stores["City_Density"] = round(stores["City_Population"] / stores["City_Size"],0)
stores["City_Population_Category"] = 0
stores["City_Elevation_Category"] = 0
stores["City_Size_Category"] = 0
stores["City_Density_Category"] = 0
for id, row in stores.iterrows():
if row['City_Elevation'] < 200:
stores.at[id, 'City_Elevation_Category'] = 0
elif row['City_Elevation'] < 700:
stores.at[id, 'City_Elevation_Category'] = 1
elif row['City_Elevation'] < 1500:
stores.at[id, 'City_Elevation_Category'] = 2
elif row['City_Elevation'] < 2300:
stores.at[id, 'City_Elevation_Category'] = 3
else:
stores.at[id, 'City_Elevation_Category'] = 4
if row['City_Population'] < 60000:
stores.at[id, 'City_Population_Category'] = 0
elif row['City_Population'] < 160000:
stores.at[id, 'City_Population_Category'] = 1
elif row['City_Population'] < 280000:
stores.at[id, 'City_Population_Category'] = 2
else:
stores.at[id, 'City_Population_Category'] = 3
if row['City_Size'] < 150:
stores.at[id, 'City_Size_Category'] = 0
elif row['City_Size'] < 325:
stores.at[id, 'City_Size_Category'] = 1
elif row['City_Size'] < 1000:
stores.at[id, 'City_Size_Category'] = 2
else:
stores.at[id, 'City_Size_Category'] = 3
if row['City_Density'] < 150:
stores.at[id, 'City_Density_Category'] = 0
elif row['City_Density'] < 325:
stores.at[id, 'City_Density_Category'] = 1
elif row['City_Density'] < 1000:
stores.at[id, 'City_Density_Category'] = 2
elif row['City_Density'] < 3000:
stores.at[id, 'City_Density_Category'] = 3
elif row['City_Density'] < 7000:
stores.at[id, 'City_Density_Category'] = 4
else:
stores.at[id, 'City_Density_Category'] = 5
city_variables_df = stores[['store_nbr', 'City_Elevation_Category', 'City_Population_Category', 'City_Size_Category',
'City_Density_Category', 'City_Density']]
df = pd.merge(df, city_variables_df, how='left', on='store_nbr')
df.drop(['city','state'], axis=1, inplace=True)
return df
def all_stores_pipeline():
originaltrainFull = pd.read_csv('/kaggle/input/store-sales-time-series-forecasting/train.csv')
originaltest = pd.read_csv('/kaggle/input/store-sales-time-series-forecasting/test.csv')
stores = pd.read_csv('/kaggle/input/store-sales-time-series-forecasting/stores.csv')
transactions = pd.read_csv('/kaggle/input/store-sales-time-series-forecasting/transactions.csv')
all_stores_df = create_multi_store_one_product_df(originaltrainFull, 'AUTOMOTIVE')
all_stores_df_test = create_multi_store_one_product_df(originaltest, 'AUTOMOTIVE')
all_stores_df.drop('sales', axis=1, inplace=True)
all_stores_df = pd.concat([all_stores_df, all_stores_df_test])
all_stores_df.drop(['id', 'family', 'onpromotion'], axis=1, inplace=True)
all_stores_df = pd.merge(all_stores_df, stores, how='left', on=['store_nbr'])
del originaltest
del originaltrainFull
#########################
# Add Holiday Variables #
#########################
all_stores_df = create_holiday_variables(all_stores_df)
##########################
# Add Location Variables #
##########################
all_stores_df = create_location_variables(all_stores_df)
################################
# Create Store Closed Variable #
################################
all_stores_df = pd.merge(all_stores_df, transactions, how='left', on=['date', 'store_nbr'])
all_stores_df['transactions'].fillna(0, inplace=True)
store_closed = [1 if x == 0 else 0 for x in all_stores_df['transactions']]
all_stores_df['store_closed'] = store_closed
all_stores_df['store_closed'].iloc[-864:] = 0
all_stores_df.drop('transactions', axis=1, inplace=True)
###################
# OneHotEncode #
###################
all_stores_df['isholiday'] = 1
thislist = all_stores_df['holiday_type'].isna()
all_stores_df.loc[thislist,'isholiday'] = 0
onehotcolumnlist = ('store_nbr', 'type', 'cluster', 'holiday_type', 'City_Elevation_Category',
'City_Population_Category', 'City_Density_Category', 'City_Size_Category')
onehotencode_df = onehotencode(all_stores_df, onehotcolumnlist)
all_stores_df = pd.concat([all_stores_df, onehotencode_df], axis=1)
###################
# Drop Some Cols #
###################
all_stores_df.drop(['type', 'cluster', 'holiday_type'], axis=1, inplace=True)
return all_stores_df
# --- Execute Pipeline --- #
all_stores_df = all_stores_pipeline()
1.3.3 产品数据框
变量创建阶段的最后一步，是创建33个产品数据框架。我们创建了一个管道，整合了我们的独立和所有商店的数据框架。
def full_product_pipeline(family, independant_df=independant_df, all_stores_df=all_stores_df):
originaltrainFull = pd.read_csv('/kaggle/input/store-sales-time-series-forecasting/train.csv')
originaltest = pd.read_csv('/kaggle/input/store-sales-time-series-forecasting/test.csv')
multistore_product = create_multi_store_one_product_df(originaltrainFull, family)
# merge with test:
multistore_product_test = create_multi_store_one_product_df(originaltest, family)
multistore_product_test['sales'] = np.nan
del originaltrainFull
del originaltest
# take log of sales:
multistore_product['sales'] = np.log1p(multistore_product['sales']+1)
msp_full = pd.concat([multistore_product, multistore_product_test])
# reset index:
msp_full.reset_index(inplace=True, drop=True)
######################
# Add Independant DF #
######################
msp_full = pd.merge(msp_full, independant_df, how='left', on=['date'])
#####################
# Add All Stores DF #
#####################
msp_full = pd.merge(msp_full, all_stores_df, how='left', on=['date', 'store_nbr'])
############################
# Add Earthquake Info #
############################
earthquake_day = [1 if x == '2016-04-16' else 0 for x in msp_full['date']]
earthquake_impact = [1 if (x > '2016-04-16') & (x < '2016-05-16') else 0 for x in msp_full['date']]
msp_full['earthquake_day'] = earthquake_day
msp_full['earthquake_impact'] = earthquake_impact
############################
# Add School Info #
############################
school_preparation = [1 if (x > '2014-09-15') & (x < '2014-10-15') or (x > '2015-09-15') & (x < '2015-10-15')
or (x > '2016-09-15') & (x < '2016-10-15') or (x > '2017-09-15') & (x < '2017-10-15')
else 0 for x in msp_full['date']]
msp_full['school_preparation'] = school_preparation
#############################
# Clean DF before modelling #
#############################
msp_full.drop(['family', 'MonthOfTheYear'], axis=1, inplace=True)
return msp_full
# --- Execute Full Product Pipeline for each product --- #
# List all product families:
list_of_families = ['AUTOMOTIVE', 'BABY CARE', 'BEAUTY', 'BEVERAGES', 'BOOKS',
'BREAD/BAKERY', 'CELEBRATION', 'CLEANING', 'DAIRY', 'DELI', 'EGGS',
'FROZEN FOODS', 'GROCERY I', 'GROCERY II', 'HARDWARE',
'HOME AND KITCHEN I', 'HOME AND KITCHEN II', 'HOME APPLIANCES',
'HOME CARE', 'LADIESWEAR', 'LAWN AND GARDEN', 'LINGERIE',
'LIQUOR,WINE,BEER', 'MAGAZINES', 'MEATS', 'PERSONAL CARE',
'PET SUPPLIES', 'PLAYERS AND ELECTRONICS', 'POULTRY',
'PREPARED FOODS', 'PRODUCE', 'SCHOOL AND OFFICE SUPPLIES',
'SEAFOOD']
# Create new .csv for each product family:
for x in list_of_families:
this_df = full_product_pipeline(x)
if x == 'BREAD/BAKERY':
x = 'BREADBAKERY'
print('Completed eda for ' + str(x))
this_df.to_csv('/kaggle/working/'+str(x)+'.csv', index=False)
2. Modelling
对于建模来说，LGBMR似乎给出了最好的结果。
你可以在一个验证集或几个交叉验证褶皱上测试该模型。主要是为了速度，我选择在实际预测期前16天只测试一个验证集。这并不理想，可能会有点过度拟合，因为它只是一个测试集，但由于验证集接近真实集，它应该在一定程度上代表我们的kaggle测试。
我们创建了一些辅助函数来支持我们的建模之旅：
scorethis_rmsle：这个函数以kaggle对比赛的评分方式（使用rmsle）对预测与一组基础事实进行评分。
create_validation：这个函数创建了我们的验证测试集和训练集，以及基础事实。分别是train、train_y、test和test_y。如果validation=False，那么这个函数将创建train、train_y和test，我们可以在提交时使用。
lgbmr_run：这个函数运行我们选择的lgbmr模型。它有两种模式：如果你想在验证集上运行，就用 "验证"；如果你想在提交的kaggle中运行，就用 "提交"。
execute_validation：在每个产品数据帧上执行所选模型。
from sklearn.preprocessing import MinMaxScaler
from lightgbm import LGBMRegressor
sample_submission = pd.read_csv('/kaggle/input/store-sales-time-series-forecasting/sample_submission.csv')
def scorethis_rmsle(prediction_list, y_list):
scorelist = list()
for x in range(prediction_list.__len__()):
log_score_x = np.abs(np.abs(prediction_list[x]) - np.abs(y_list[x]))
try:
[scorelist.append(y) for y in log_score_x.values]
except:
scorelist.append(log_score_x)
score_array = np.array(scorelist)
rmsle = np.sqrt(np.mean(score_array**2)) # sqrt of mean of power of difference of the logs
rmsle = np.round(rmsle, 3)
return rmsle
def create_validation(this_family_df, validation=True):
if validation is True:
this_family_df = this_family_df[:-864]
# Remove the 864 top submission rows if it is for validation
this_family_sales = this_family_df['sales']
this_family_df.drop(['sales', 'date'], axis=1, inplace=True)
########################
# Scale Data #
########################
scaler = MinMaxScaler()
this_family_df[this_family_df.columns] = scaler.fit_transform(this_family_df[this_family_df.columns])
########################
# Split Train and Test #
########################
test = this_family_df.iloc[-864:]
test_y = this_family_sales.iloc[-864:]
train = this_family_df.iloc[:-864]
train_y = this_family_sales.iloc[:-864]
return train, train_y, test, test_y
def lgbmr_run(train, train_y, test, test_y,
validation=True):
#################
# Create Model #
#################
lgbmr_model = LGBMRegressor(
colsample_bytree=0.7,
learning_rate=0.055,
min_child_samples=10,
num_leaves=19,
objective='regression',
n_estimators=1000,
n_jobs=4,
random_state=337)
#################
# Execute LGBMR #
#################
lgbmr_model.fit(train, train_y)
lgbmr_pred = lgbmr_model.predict(test).tolist()
lgbmr_pred = [round(x, 2) for x in lgbmr_pred]
if validation == True:
# validation set also has ground truths:
test_y = test_y.to_list()
return lgbmr_pred, test_y
else:
return lgbmr_pred
def execute_validation(thisfunc):
double_list_of_predictions = []
double_list_of_ground_truths = []
for x in list_of_families: # 33
if x == 'BREAD/BAKERY':
x = 'BREADBAKERY'
# Otherwise would create an error searching for the BREAD/ directory instead of the file
print('Evaluating '+str(x)+'...')
this_df = pd.read_csv('/kaggle/working/' + str(x) + '.csv')
train, train_y, test, test_y = create_validation(this_df)
pred, y = thisfunc(train, train_y, test, test_y, validation=True)
if x == 'BOOKS':
zero_list = []
for g in range(864):
zero_list.append(0.6931471805599453)
# this will be exactly 0 when we transform our predictions again
# to before we did log(sales +1)
double_list_of_predictions.append(zero_list)
double_list_of_ground_truths.append(y)
else:
double_list_of_predictions.append(pred) # 33 * [864]
double_list_of_ground_truths.append(y) # 33 * [864]
list_of_predictions = list()
list_of_ground_truths = list()
for x in double_list_of_predictions:
for y in x:
list_of_predictions.append(y) # unpack 33 * 864
for x in double_list_of_ground_truths:
for z in x:
list_of_ground_truths.append(z) # unpack 33 * 864
return list_of_predictions, list_of_ground_truths
# --- Execute LGBMR Model On Validation Set --- #
# Run this code if you want to do a validation test + see the score:
# list_of_lgbmr_predictions, list_of_ground_truths = execute_validation(lgbmr_run)
# scorethis_rmsle(list_of_lgbmr_predictions, list_of_ground_truths)
经过更多的测试，一些观察结果：
这个分数为0.337，是我在验证集上得到的最好分数。
同一模型的其他超参数得分+0.338
其他模型在验证中的得分是0.358（XGB）到0.423（Lasso）。
简单的堆叠并没有改善结果
在没有任何变量选择方法的情况下，SVR需要很长时间，所以还没有在这个数据集上进行测试
提交:
现在我们要在我们的kaggle提交集上执行我们已经测试过的同样的LGBMR模型
def execute_submission(thisfunc):
list_of_predictions = []
for x in list_of_families:
if x == 'BREAD/BAKERY':
x = 'BREADBAKERY'
# Otherwise would create an error searching for the BREAD/ directory instead of the file
print('Evaluating '+str(x)+'...')
this_df = pd.read_csv('/kaggle/working/' + str(x) + '.csv')
if x == 'BOOKS':
zero_list = []
for g in range(864):
zero_list.append(0.6931471805599453)
# this will be exactly 0 when we transform our predictions again
# to before we did log(sales +1)
list_of_predictions.append(zero_list)
else:
train, train_y, test, test_y = create_validation(this_df, validation=False)
pred = thisfunc(train, train_y, test, test_y=None, validation=False)
list_of_predictions.append(pred)
###############################
# Put Back In Submission Form #
###############################
restructured_predictions = list()
for y in range(864):
for z in range(33):
restructured_predictions.append(list_of_predictions[z][y])
restructured_predictions = np.expm1(restructured_predictions) - 1
return restructured_predictions
# --- Execute Submission --- #
restructured_predictions = execute_submission(lgbmr_run)
sample_submission['sales'] = restructured_predictions
# Convert some (slightly) negative predictions to a zero prediction:
sample_submission['sales'] = [0 if x < 0 else x for x in sample_submission['sales']]
sample_submission.to_csv('/kaggle/working/submission.csv', index=False)
标签：City,Forecasting,stores,df,Series,list,Time,test,store From： https://www.cnblogs.com/furiyo/p/17289259.html