Market Basket Analysis using Instacart Online Grocery Dataset
Which products will an Instacart consumer purchase again?
To showcase the concept of market basket analysis with the Databricks Unified Analytics Platform, we will use the Instacart's 3 Million Instacart Orders, Open Sourced dataset.
“The Instacart Online Grocery Shopping Dataset 2017”, Accessed from https://www.instacart.com/datasets/grocery-shopping-2017 on 01/17/2018. This anonymized dataset contains a sample of over 3 million grocery orders from more than 200,000 Instacart users. For each user, we provide between 4 and 100 of their orders, with the sequence of products purchased in each order. We also provide the week and hour of day the order was placed, and a relative measure of time between orders.
Whether you shop from meticulously planned grocery lists or let whimsy guide your grazing, our unique food rituals define who we are. Instacart's grocery ordering and delivery app aims to make it easy to fill your refrigerator and pantry with your personal favorites and staples when you need them. After selecting products through the Instacart app, personal shoppers review your order and do the in-store shopping and delivery for you.
This notebook will show how you can predict which items a shopper will purchase whether they buy it again or recommend to try for the first time.
Data Engineering Pipeline
Data engineering pipelines are commonly comprised of these components:
- Ingest Data: Bringing in the data from your source systems; often involving ETL processes (though we will skip this step in this demo for brevity)
- Explore Data: Now that you have cleansed data, explore it so you can get some business insight
- Train ML Model: Execute FP-growth for frequent pattern mining
- Review Association Rules: Review the generated association rules
%sh wget https://s3.amazonaws.com/instacart-datasets/instacart_online_grocery_shopping_2017_05_01.tar.gz
--2019-05-31 05:58:37-- https://s3.amazonaws.com/instacart-datasets/instacart_online_grocery_shopping_2017_05_01.tar.gz
Resolving s3.amazonaws.com (s3.amazonaws.com)... 52.216.114.29
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*** WARNING: skipped 256163 bytes of output ***
184400K .......... .......... .......... .......... .......... 91% 219M 1s
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197800K .......... .......... .......... .......... .......... 98% 206M 0s
197850K .......... .......... .......... .......... .......... 98% 1.76M 0s
197900K .......... .......... .......... .......... .......... 98% 153M 0s
197950K .......... .......... .......... .......... .......... 98% 167M 0s
198000K .......... .......... .......... .......... .......... 98% 9.20M 0s
198050K .......... .......... .......... .......... .......... 98% 212M 0s
198100K .......... .......... .......... .......... .......... 98% 140M 0s
198150K .......... .......... .......... .......... .......... 98% 55.6M 0s
198200K .......... .......... .......... .......... .......... 98% 118M 0s
198250K .......... .......... .......... .......... .......... 98% 198M 0s
198300K .......... .......... .......... .......... .......... 98% 3.13M 0s
198350K .......... .......... .......... .......... .......... 98% 26.3M 0s
198400K .......... .......... .......... .......... .......... 98% 39.9M 0s
198450K .......... .......... .......... .......... .......... 98% 66.3M 0s
198500K .......... .......... .......... .......... .......... 98% 43.8M 0s
198550K .......... .......... .......... .......... .......... 98% 121M 0s
198600K .......... .......... .......... .......... .......... 98% 205M 0s
198650K .......... .......... .......... .......... .......... 98% 194M 0s
198700K .......... .......... .......... .......... .......... 99% 206M 0s
198750K .......... .......... .......... .......... .......... 99% 164M 0s
198800K .......... .......... .......... .......... .......... 99% 209M 0s
198850K .......... .......... .......... .......... .......... 99% 203M 0s
198900K .......... .......... .......... .......... .......... 99% 204M 0s
198950K .......... .......... .......... .......... .......... 99% 179M 0s
199000K .......... .......... .......... .......... .......... 99% 201M 0s
199050K .......... .......... .......... .......... .......... 99% 207M 0s
199100K .......... .......... .......... .......... .......... 99% 201M 0s
199150K .......... .......... .......... .......... .......... 99% 171M 0s
199200K .......... .......... .......... .......... .......... 99% 207M 0s
199250K .......... .......... .......... .......... .......... 99% 201M 0s
199300K .......... .......... .......... .......... .......... 99% 205M 0s
199350K .......... .......... .......... .......... .......... 99% 187M 0s
199400K .......... .......... .......... .......... .......... 99% 201M 0s
199450K .......... .......... .......... .......... .......... 99% 1.73M 0s
199500K .......... .......... .......... .......... .......... 99% 205M 0s
199550K .......... .......... .......... .......... .......... 99% 10.5M 0s
199600K .......... .......... .......... .......... .......... 99% 72.1M 0s
199650K .......... .......... .......... .......... .......... 99% 205M 0s
199700K .......... .......... .......... .......... .......... 99% 122M 0s
199750K .......... .......... .......... .......... .......... 99% 49.1M 0s
199800K .......... .......... .......... .......... .......... 99% 199M 0s
199850K .......... .......... .......... .......... .......... 99% 207M 0s
199900K .......... .......... .......... .......... .......... 99% 3.19M 0s
199950K .......... .......... .......... .......... .......... 99% 25.3M 0s
200000K .......... .......... .......... .......... .......... 99% 63.5M 0s
200050K .......... .......... .......... .......... .......... 99% 71.1M 0s
200100K .......... .......... .......... .......... .......... 99% 213M 0s
200150K .......... .......... .......... .......... .......... 99% 183M 0s
200200K .......... .......... .......... .......... .......... 99% 206M 0s
200250K .......... .......... .......... .......... .......... 99% 202M 0s
200300K .......... .......... .......... .......... .......... 99% 187M 0s
200350K .......... .......... .......... .......... .......... 99% 170M 0s
200400K .......... .......... .......... .......... .......... 99% 211M 0s
200450K .......... .......... .......... .......... .......... 99% 200M 0s
200500K .......... .......... .......... .......... .......... 99% 203M 0s
200550K .......... .......... .......... .......... .......... 99% 178M 0s
200600K .......... .......... .......... .......... .......... 99% 207M 0s
200650K .......... .......... .......... .......... .......... 99% 204M 0s
200700K .......... .......... .......... 100% 150M=8.0s
2019-05-31 05:58:45 (24.5 MB/s) - ‘instacart_online_grocery_shopping_2017_05_01.tar.gz’ saved [205548478/205548478]
%sh tar zxvf instacart_online_grocery_shopping_2017_05_01.tar.gz
instacart_2017_05_01/
instacart_2017_05_01/._aisles.csv
instacart_2017_05_01/aisles.csv
instacart_2017_05_01/._departments.csv
instacart_2017_05_01/departments.csv
instacart_2017_05_01/._order_products__prior.csv
instacart_2017_05_01/order_products__prior.csv
instacart_2017_05_01/._order_products__train.csv
instacart_2017_05_01/order_products__train.csv
instacart_2017_05_01/._orders.csv
instacart_2017_05_01/orders.csv
instacart_2017_05_01/._products.csv
instacart_2017_05_01/products.csv
%fs head dbfs:/datasets/instacart_2017_05_01/orders.csv
[Truncated to first 65536 bytes]
order_id,user_id,eval_set,order_number,order_dow,order_hour_of_day,days_since_prior_order
2539329,1,prior,1,2,08,
2398795,1,prior,2,3,07,15.0
473747,1,prior,3,3,12,21.0
2254736,1,prior,4,4,07,29.0
431534,1,prior,5,4,15,28.0
3367565,1,prior,6,2,07,19.0
550135,1,prior,7,1,09,20.0
3108588,1,prior,8,1,14,14.0
2295261,1,prior,9,1,16,0.0
2550362,1,prior,10,4,08,30.0
1187899,1,train,11,4,08,14.0
2168274,2,prior,1,2,11,
1501582,2,prior,2,5,10,10.0
1901567,2,prior,3,1,10,3.0
738281,2,prior,4,2,10,8.0
1673511,2,prior,5,3,11,8.0
1199898,2,prior,6,2,09,13.0
3194192,2,prior,7,2,12,14.0
788338,2,prior,8,1,15,27.0
1718559,2,prior,9,2,09,8.0
1447487,2,prior,10,1,11,6.0
1402090,2,prior,11,1,10,30.0
3186735,2,prior,12,1,09,28.0
3268552,2,prior,13,4,11,30.0
839880,2,prior,14,3,10,13.0
1492625,2,train,15,1,11,30.0
1374495,3,prior,1,1,14,
444309,3,prior,2,3,19,9.0
3002854,3,prior,3,3,16,21.0
2037211,3,prior,4,2,18,20.0
2710558,3,prior,5,0,17,12.0
1972919,3,prior,6,0,16,7.0
1839752,3,prior,7,0,15,7.0
3225766,3,prior,8,0,17,7.0
3160850,3,prior,9,0,16,7.0
676467,3,prior,10,3,16,17.0
521107,3,prior,11,0,18,11.0
1402502,3,prior,12,1,15,15.0
2774568,3,test,13,5,15,11.0
3343014,4,prior,1,6,11,
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691089,4,prior,3,4,15,21.0
94891,4,prior,4,5,13,15.0
2557754,4,prior,5,5,13,0.0
329954,4,test,6,3,12,30.0
2717275,5,prior,1,3,12,
1909121,5,prior,2,0,16,11.0
2267326,5,prior,3,3,18,10.0
157374,5,prior,4,1,18,19.0
2196797,5,train,5,0,11,6.0
2086598,6,prior,1,5,18,
298250,6,prior,2,4,16,6.0
998866,6,prior,3,2,18,12.0
1528013,6,test,4,3,16,22.0
2565571,7,prior,1,3,09,
2402008,7,prior,2,1,19,30.0
121053,7,prior,3,0,18,30.0
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525192,7,train,21,2,11,6.0
600894,8,prior,1,6,00,
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880375,8,train,4,1,14,10.0
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1224907,10,prior,1,2,14,
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1376945,11,test,8,6,11,8.0
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1356845,12,test,6,1,20,30.0
2618231,13,prior,1,6,12,
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1870022,15,prior,17,2,16,8.0
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2345592,15,prior,19,5,11,3.0
2994589,15,prior,20,1,13,10.0
2715276,15,prior,21,1,09,7.0
487368,15,prior,22,1,10,14.0
2161313,15,test,23,1,09,7.0
1273676,16,prior,1,2,08,
875885,16,prior,2,1,13,20.0
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2704482,16,prior,4,6,14,12.0
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2648319,17,prior,7,1,13,4.0
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*** WARNING: skipped 15593 bytes of output ***
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1671477,146,prior,36,0,12,7.0
3092707,146,prior,37,1,11,1.0
2689637,146,prior,38,0,14,6.0
582965,146,prior,39,0,13,14.0
431025,146,prior,40,5,09,19.0
673776,146,train,41,2,19,25.0
1317868,147,prior,1,0,19,
2190345,147,prior,2,0,18,30.0
3252043,147,prior,3,0,13,28.0
1637674,147,prior,4,6,10,6.0
764956,147,prior,5,0,16,8.0
2477065,147,prior,6,0,14,14.0
1772177,147,prior,7,0,19,14.0
657707,147,prior,8,1,16,15.0
1109225,147,prior,9,6,16,12.0
3266859,147,prior,10,6,15,7.0
1561904,147,prior,11,0,17,8.0
614915,147,prior,12,5,12,19.0
1191676,147,prior,13,5,17,30.0
318441,147,train,14,0,11,30.0
3266887,148,prior,1,5,16,
3169439,148,prior,2,1,07,3.0
2175302,148,prior,3,4,05,3.0
3221240,148,prior,4,6,13,2.0
415062,148,prior,5,2,13,3.0
1759549,148,prior,6,3,12,1.0
3300653,148,prior,7,6,15,30.0
3348216,148,train,8,5,23,27.0
1025331,149,prior,1,0,20,
2756395,149,prior,2,6,10,13.0
970597,149,prior,3,0,16,15.0
1386107,149,prior,4,0,10,14.0
1185790,149,prior,5,6,10,13.0
936948,149,prior,6,0,21,29.0
2618740,149,prior,7,6,07,13.0
1174012,149,prior,8,6,17,30.0
1073005,149,prior,9,6,08,14.0
2576952,149,prior,10,5,23,13.0
3387714,149,prior,11,2,12,18.0
2503718,149,prior,12,6,14,11.0
3328310,149,prior,13,6,13,30.0
2585753,149,prior,14,6,17,14.0
1180289,149,prior,15,4,22,12.0
2567075,149,test,16,5,14,15.0
3139566,150,prior,1,6,06,
242667,150,prior,2,1,06,9.0
2789394,150,prior,3,4,18,10.0
309416,150,prior,4,2,17,12.0
3123164,150,prior,5,5,18,10.0
1462796,150,prior,6,2,16,11.0
2519716,150,prior,7,2,16,14.0
1743975,150,prior,8,5,08,10.0
1279359,150,prior,9,2,15,4.0
759564,150,prior,10,2,12,7.0
2515579,150,prior,11,0,13,12.0
1228126,150,prior,12,5,14,5.0
3024068,150,prior,13,3,07,26.0
3164014,150,prior,14,4,13,30.0
2003778,150,prior,15,0,04,10.0
3182724,150,prior,16,0,16,7.0
2207448,150,prior,17,6,17,6.0
2822984,150,prior,18,5,19,6.0
2346394,150,prior,19,1,16,3.0
1600895,150,prior,20,1,17,7.0
2520237,150,prior,21,4,08,3.0
1785347,150,prior,22,4,16,14.0
1157021,150,prior,23,6,13,2.0
2982817,150,prior,24,4,17,5.0
12278,150,prior,25,3,17,6.0
1297781,150,prior,26,5,19,2.0
2199,150,prior,27,4,14,6.0
887483,150,prior,28,6,11,9.0
2461899,150,prior,29,1,16,23.0
1525078,150,prior,30,1,15,30.0
2375858,150,prior,31,0,11,6.0
1575396,150,train,32,6,14,20.0
2302343,151,prior,1,3,20,
581168,151,prior,2,3,18,7.0
2338135,151,prior,3,1,19,5.0
3088590,151,prior,4,5,17,25.0
3198855,151,prior,5,3,19,5.0
1116436,151,prior,6,0,08,4.0
1506911,151,prior,7,2,15,2.0
1923876,151,prior,8,6,10,4.0
2109329,151,prior,9,2,17,3.0
1637544,151,prior,10,0,09,5.0
442514,151,prior,11,2,20,2.0
1855063,151,test,12,6,15,11.0
2772293,152,prior,1,0,1
// Import Data val aisles = spark.read.format("csv").option("header", "true").option("inferSchema", "true").load("/datasets/instacart_2017_05_01/aisles.csv") val departments = spark.read.format("csv").option("header", "true").option("inferSchema", "true").load("/datasets/instacart_2017_05_01/departments.csv") val order_products_prior = spark.read.format("csv").option("header", "true").option("inferSchema", "true").load("/datasets/instacart_2017_05_01/order_products__prior.csv") val order_products_train = spark.read.format("csv").option("header", "true").option("inferSchema", "true").load("/datasets/instacart_2017_05_01/order_products__train.csv") val orders = spark.read.format("csv").option("header", "true").option("inferSchema", "true").load("/datasets/instacart_2017_05_01/orders.csv") val products = spark.read.format("csv").option("header", "true").option("inferSchema", "true").load("/datasets/instacart_2017_05_01/products.csv") // Create Temporary Tables aisles.createOrReplaceTempView("aisles") departments.createOrReplaceTempView("departments") order_products_prior.createOrReplaceTempView("order_products_prior") order_products_train.createOrReplaceTempView("order_products_train") orders.createOrReplaceTempView("orders") products.createOrReplaceTempView("products")
aisles: org.apache.spark.sql.DataFrame = [aisle_id: int, aisle: string]
departments: org.apache.spark.sql.DataFrame = [department_id: int, department: string]
order_products_prior: org.apache.spark.sql.DataFrame = [order_id: int, product_id: int ... 2 more fields]
order_products_train: org.apache.spark.sql.DataFrame = [order_id: int, product_id: int ... 2 more fields]
orders: org.apache.spark.sql.DataFrame = [order_id: int, user_id: int ... 5 more fields]
products: org.apache.spark.sql.DataFrame = [product_id: int, product_name: string ... 2 more fields]
%fs head /mnt/aciaOregon/instacart_2017_05_01/orders.csv
[Truncated to first 65536 bytes]
order_id,user_id,eval_set,order_number,order_dow,order_hour_of_day,days_since_prior_order
2539329,1,prior,1,2,08,
2398795,1,prior,2,3,07,15.0
473747,1,prior,3,3,12,21.0
2254736,1,prior,4,4,07,29.0
431534,1,prior,5,4,15,28.0
3367565,1,prior,6,2,07,19.0
550135,1,prior,7,1,09,20.0
3108588,1,prior,8,1,14,14.0
2295261,1,prior,9,1,16,0.0
2550362,1,prior,10,4,08,30.0
1187899,1,train,11,4,08,14.0
2168274,2,prior,1,2,11,
1501582,2,prior,2,5,10,10.0
1901567,2,prior,3,1,10,3.0
738281,2,prior,4,2,10,8.0
1673511,2,prior,5,3,11,8.0
1199898,2,prior,6,2,09,13.0
3194192,2,prior,7,2,12,14.0
788338,2,prior,8,1,15,27.0
1718559,2,prior,9,2,09,8.0
1447487,2,prior,10,1,11,6.0
1402090,2,prior,11,1,10,30.0
3186735,2,prior,12,1,09,28.0
3268552,2,prior,13,4,11,30.0
839880,2,prior,14,3,10,13.0
1492625,2,train,15,1,11,30.0
1374495,3,prior,1,1,14,
444309,3,prior,2,3,19,9.0
3002854,3,prior,3,3,16,21.0
2037211,3,prior,4,2,18,20.0
2710558,3,prior,5,0,17,12.0
1972919,3,prior,6,0,16,7.0
1839752,3,prior,7,0,15,7.0
3225766,3,prior,8,0,17,7.0
3160850,3,prior,9,0,16,7.0
676467,3,prior,10,3,16,17.0
521107,3,prior,11,0,18,11.0
1402502,3,prior,12,1,15,15.0
2774568,3,test,13,5,15,11.0
3343014,4,prior,1,6,11,
2030307,4,prior,2,4,11,19.0
691089,4,prior,3,4,15,21.0
94891,4,prior,4,5,13,15.0
2557754,4,prior,5,5,13,0.0
329954,4,test,6,3,12,30.0
2717275,5,prior,1,3,12,
1909121,5,prior,2,0,16,11.0
2267326,5,prior,3,3,18,10.0
157374,5,prior,4,1,18,19.0
2196797,5,train,5,0,11,6.0
2086598,6,prior,1,5,18,
298250,6,prior,2,4,16,6.0
998866,6,prior,3,2,18,12.0
1528013,6,test,4,3,16,22.0
2565571,7,prior,1,3,09,
2402008,7,prior,2,1,19,30.0
121053,7,prior,3,0,18,30.0
1695742,7,prior,4,2,10,9.0
3321109,7,prior,5,5,18,3.0
1682003,7,prior,6,1,18,10.0
1014379,7,prior,7,1,15,7.0
776265,7,prior,8,2,12,8.0
2507052,7,prior,9,6,07,4.0
1396119,7,prior,10,1,20,9.0
565870,7,prior,11,0,15,6.0
3239072,7,prior,12,0,14,7.0
1175028,7,prior,13,5,15,5.0
3060817,7,prior,14,1,13,17.0
3120740,7,prior,15,3,16,2.0
2425840,7,prior,16,0,13,11.0
23391,7,prior,17,0,10,28.0
2984707,7,prior,18,0,09,7.0
369015,7,prior,19,3,12,3.0
2452257,7,prior,20,3,09,7.0
525192,7,train,21,2,11,6.0
600894,8,prior,1,6,00,
2979257,8,prior,2,1,08,30.0
2570360,8,prior,3,5,00,30.0
880375,8,train,4,1,14,10.0
280530,9,prior,1,1,17,
2762092,9,prior,2,0,14,6.0
1830137,9,prior,3,5,12,30.0
1094988,9,train,4,6,10,30.0
1224907,10,prior,1,2,14,
68288,10,prior,2,5,15,30.0
2115522,10,prior,3,3,19,12.0
83395,10,prior,4,3,15,14.0
1353310,10,prior,5,5,20,23.0
1822501,10,train,6,0,19,30.0
2411567,11,prior,1,0,11,
1170872,11,prior,2,5,11,12.0
2840752,11,prior,3,5,10,14.0
1611810,11,prior,4,5,13,30.0
2921164,11,prior,5,5,11,30.0
790927,11,prior,6,5,11,7.0
1468214,11,prior,7,5,09,30.0
1376945,11,test,8,6,11,8.0
1483446,12,prior,1,1,12,
1247527,12,prior,2,5,09,30.0
1916106,12,prior,3,5,08,14.0
1057378,12,prior,4,3,09,26.0
221248,12,prior,5,1,09,30.0
1356845,12,test,6,1,20,30.0
2618231,13,prior,1,6,12,
2560699,13,prior,2,0,11,8.0
2288946,13,prior,3,6,21,6.0
19256,13,prior,4,1,12,9.0
1378982,13,prior,5,0,13,6.0
2363981,13,prior,6,0,18,7.0
1906169,13,prior,7,0,08,7.0
1757094,13,prior,8,6,18,13.0
519471,13,prior,9,6,17,7.0
2298068,13,prior,10,6,22,7.0
1472646,13,prior,11,0,14,8.0
1789302,13,prior,12,6,13,6.0
1827621,13,train,13,0,21,8.0
2906263,14,prior,1,2,18,
3206747,14,prior,2,5,08,30.0
1366559,14,prior,3,0,14,2.0
1385564,14,prior,4,6,08,20.0
1691859,14,prior,5,5,08,30.0
2127409,14,prior,6,1,08,30.0
2505309,14,prior,7,5,10,30.0
3067644,14,prior,8,4,08,27.0
2109666,14,prior,9,5,09,30.0
1625325,14,prior,10,4,21,30.0
1335295,14,prior,11,1,05,25.0
2045336,14,prior,12,5,09,4.0
3394109,14,prior,13,5,05,7.0
2316178,14,train,14,2,19,11.0
2514324,15,prior,1,5,13,
3135826,15,prior,2,6,12,15.0
2564002,15,prior,3,6,12,7.0
714991,15,prior,4,1,11,16.0
3317979,15,prior,5,4,15,17.0
876095,15,prior,6,5,11,8.0
2685110,15,prior,7,1,11,17.0
3119091,15,prior,8,2,17,8.0
887727,15,prior,9,2,13,7.0
2570697,15,prior,10,2,10,21.0
2600170,15,prior,11,2,09,14.0
2883598,15,prior,12,1,08,6.0
2332087,15,prior,13,1,10,14.0
113728,15,prior,14,1,10,14.0
2968173,15,prior,15,1,09,7.0
3097485,15,prior,16,1,09,7.0
1870022,15,prior,17,2,16,8.0
1911383,15,prior,18,2,11,7.0
2345592,15,prior,19,5,11,3.0
2994589,15,prior,20,1,13,10.0
2715276,15,prior,21,1,09,7.0
487368,15,prior,22,1,10,14.0
2161313,15,test,23,1,09,7.0
1273676,16,prior,1,2,08,
875885,16,prior,2,1,13,20.0
1491849,16,prior,3,1,20,21.0
2704482,16,prior,4,6,14,12.0
3192400,16,prior,5,2,14,30.0
2000615,16,prior,6,0,14,26.0
1416320,16,test,7,0,13,7.0
1737705,17,prior,1,2,13,
1681401,17,prior,2,5,10,3.0
2680214,17,prior,3,3,10,5.0
3197376,17,prior,4,1,14,5.0
3237467,17,prior,5,6,17,5.0
2616505,17,prior,6,4,17,5.0
2648319,17,prior,7,1,13,4.0
2430354,17,prior,8,0,15,6.0
2373492,17,prior,9,3,13,3.0
900554,17,prior,10,0,15,4.0
805025,17,prior,11,4,15,4.0
912404,17,prior,12,2,14,5.0
1389057,17,prior,13,1,13,6.0
603534,17,prior,14,6,17,5.0
2660930,17,prior,15,2,16,3.0
1719551,17,prior,16,1,12,6.0
601444,17,prior,17,5,21,4.0
2956367,17,prior,18,6,12,8.0
2508834,17,prior,19,5,11,6.0
415389,17,prior,20,5,13,7.0
3141030,17,prior,21,2,12,4.0
603376,17,prior,22,6,16,4.0
3069802,17,prior,23,3,23,4.0
1683378,17,prior,24,1,10,5.0
2795254,17,prior,25,0,11,6.0
343199,17,prior,26,5,17,5.0
1385847,17,prior,27,4,10,6.0
3314059,17,prior,28,1,09,4.0
697863,17,prior,29,6,14,5.0
2148135,17,prior,30,6,15,0.0
3327122,17,prior,31,4,12,5.0
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2553089,17,prior,33,3,11,9.0
791858,17,prior,34,5,12,16.0
2773605,17,prior,35,6,13,8.0
674977,17,prior,36,3,12,30.0
2831726,17,prior,37,2,12,13.0
1385397,17,prior,38,1,15,30.0
348137,17,prior,39,5,16,11.0
3350883,17,prior,40,4,11,27.0
2180313,17,train,41,3,10,30.0
2780889,18,prior,1,6,18,
3106101,18,prior,2,0,17,1.0
1860960,18,prior,3,1,19,8.0
3133044,18,prior,4,0,16,6.0
1020460,18,prior,5,2,16,9.0
441977,18,prior,6,6,12,4.0
2461523,18,train,7,6,09,7.0
1321974,19,prior,1,6,13,
2293453,19,prior,2,5,14,6.0
2208892,19,prior,3,0,14,16.0
2808909,19,prior,4,5,12,5.0
1973799,19,prior,5,6,12,8.0
2474845,19,prior,6,5,13,20.0
532817,19,prior,7,4,17,6.0
2483716,19,prior,8,4,04,7.0
86918,19,prior,9,5,16,8.0
1735923,19,test,10,6,17,8.0
947722,20,prior,1,1,10,
157550,20,prior,2,3,16,2.0
3408319,20,prior,3,2,11,6.0
2741696,20,prior,4,2,11,7.0
1980631,20,test,5,1,11,30.0
2625565,21,prior,1,1,10,
2698037,21,prior,2,1,09,7.0
1166994,21,prior,3,1,13,7.0
129152,21,prior,4,1,14,14.0
62373,21,prior,5,1,14,7.0
2984916,21,prior,6,5,06,4.0
1716848,21,prior,7,5,09,28.0
3288630,21,prior,8,2,13,18.0
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1573906,21,prior,10,3,10,6.0
2102674,21,prior,11,1,15,12.0
534401,21,prior,12,1,09,21.0
2510446,21,prior,13,5,11,4.0
1082944,21,prior,14,1,09,10.0
3257456,21,prior,15,1,09,7.0
774475,21,prior,16,1,11,7.0
1953572,21,prior,17,2,15,1.0
1674891,21,prior,18,2,11,21.0
77791,21,prior,19,3,09,8.0
1413723,21,prior,20,1,10,12.0
1806047,21,prior,21,1,09,7.0
1431584,21,prior,22,3,09,9.0
2941505,21,prior,23,2,13,6.0
1322450,21,prior,24,5,08,3.0
1776467,21,prior,25,5,08,7.0
497940,21,prior,26,1,11,10.0
1735495,21,prior,27,3,08,9.0
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1110781,21,prior,29,2,09,8.0
2097221,21,prior,30,1,11,6.0
2057208,21,prior,31,3,15,9.0
865324,21,prior,32,1,13,12.0
3341737,21,prior,33,1,12,30.0
1854765,21,train,34,1,12,28.0
2300537,22,prior,1,3,07,
144358,22,prior,2,3,07,30.0
1219654,22,prior,3,0,21,4.0
2922708,22,prior,4,3,11,30.0
3393222,22,prior,5,5,21,16.0
2327987,22,prior,6,1,19,17.0
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1014994,22,prior,9,5,15,28.0
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*** WARNING: skipped 15593 bytes of output ***
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//s"/mnt/$MountName/instacart_2017_05_01/ // Import Data val aisles = spark.read.format("csv").option("header", "true").option("inferSchema", "true").load(s"/mnt/$MountNameOregon/instacart_2017_05_01/aisles.csv") val departments = spark.read.format("csv").option("header", "true").option("inferSchema", "true").load(s"/mnt/$MountNameOregon/instacart_2017_05_01/departments.csv") val order_products_prior = spark.read.format("csv").option("header", "true").option("inferSchema", "true").load(s"/mnt/$MountNameOregon/instacart_2017_05_01/order_products__prior.csv") val order_products_train = spark.read.format("csv").option("header", "true").option("inferSchema", "true").load(s"/mnt/$MountNameOregon/instacart_2017_05_01/order_products__train.csv") val orders = spark.read.format("csv").option("header", "true").option("inferSchema", "true").load(s"/mnt/$MountNameOregon/instacart_2017_05_01/orders.csv") val products = spark.read.format("csv").option("header", "true").option("inferSchema", "true").load(s"/mnt/$MountNameOregon/instacart_2017_05_01/products.csv") // Create Temporary Tables aisles.createOrReplaceTempView("aisles") departments.createOrReplaceTempView("departments") order_products_prior.createOrReplaceTempView("order_products_prior") order_products_train.createOrReplaceTempView("order_products_train") orders.createOrReplaceTempView("orders") products.createOrReplaceTempView("products")
aisles: org.apache.spark.sql.DataFrame = [aisle_id: int, aisle: string]
departments: org.apache.spark.sql.DataFrame = [department_id: int, department: string]
order_products_prior: org.apache.spark.sql.DataFrame = [order_id: int, product_id: int ... 2 more fields]
order_products_train: org.apache.spark.sql.DataFrame = [order_id: int, product_id: int ... 2 more fields]
orders: org.apache.spark.sql.DataFrame = [order_id: int, user_id: int ... 5 more fields]
products: org.apache.spark.sql.DataFrame = [product_id: int, product_name: string ... 2 more fields]
%sql select count(order_id) as total_orders, (case when order_dow = '0' then 'Sunday' when order_dow = '1' then 'Monday' when order_dow = '2' then 'Tuesday' when order_dow = '3' then 'Wednesday' when order_dow = '4' then 'Thursday' when order_dow = '5' then 'Friday' when order_dow = '6' then 'Saturday' end) as day_of_week from orders group by order_dow order by total_orders desc
%sql select countbydept.* from ( -- from product table, let's count number of records per dept -- and then sort it by count (highest to lowest) select department_id, count(1) as counter from products group by department_id order by counter asc ) as maxcount inner join ( -- let's repeat the exercise, but this time let's join -- products and departments tables to get a full list of dept and -- prod count select d.department_id, d.department, count(1) as products from departments d inner join products p on p.department_id = d.department_id group by d.department_id, d.department order by products desc ) countbydept -- combine the two queries's results by matching the product count on countbydept.products = maxcount.counter
// Organize the data by shopping basket import org.apache.spark.sql.functions._ //collect_set, col, count val rawData = spark.sql("select p.product_name, o.order_id from products p inner join order_products_train o where o.product_id = p.product_id") val baskets = rawData.groupBy("order_id").agg(collect_set("product_name").alias("items")) baskets.createOrReplaceTempView("baskets")
import org.apache.spark.sql.functions._
rawData: org.apache.spark.sql.DataFrame = [product_name: string, order_id: int]
baskets: org.apache.spark.sql.DataFrame = [order_id: int, items: array<string>]
import org.apache.spark.ml.fpm.FPGrowth // Extract out the items val baskets_ds = spark.sql("select items from baskets").as[Array[String]].toDF("items") // Use FPGrowth val fpgrowth = new FPGrowth().setItemsCol("items").setMinSupport(0.001).setMinConfidence(0) val model = fpgrowth.fit(baskets_ds)
import org.apache.spark.ml.fpm.FPGrowth
baskets_ds: org.apache.spark.sql.DataFrame = [items: array<string>]
fpgrowth: org.apache.spark.ml.fpm.FPGrowth = fpgrowth_7a03077ab5d9
model: org.apache.spark.ml.fpm.FPGrowthModel = fpgrowth_7a03077ab5d9
SDS-2.x, Scalable Data Engineering Science
This is a Scala version of the notebook in databricks engineering blog:
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