6: APPLY TableVectorizer

This step used:

5: GETITEM ...

This step was used by:

7: CALLMETHOD 'groupby'

Where was this step created?

                
  File "/home/jerome/workspace/scratch/2025/01/expr-presentation/fraud_skrub.py", line 18, in <module>
    vectorized_products = products.skb.apply(

Estimator fitted in this step:

OnSubFrame(cols=(all() - cols('basket_ID')),
           transformer=TableVectorizer(high_cardinality=StringEncoder(n_components=5)))

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Output of this step

Column	Column name	dtype	Unique values	Mean	Std	Min	Median	Max
0	basket_ID	Int64DType	92790 (56.8%)	5.59e+04	3.46e+04	0	54,665	115,985
1	item_0	Float32DType	173 (0.1%)	0.358	0.440	-1.04e-16	0.00232	0.988
2	item_1	Float32DType	173 (0.1%)	0.156	0.359	-2.44e-05	6.12e-05	1.00
3	item_2	Float32DType	173 (0.1%)	0.0770	0.327	-0.153	0.000128	0.911
4	item_3	Float32DType	173 (0.1%)	0.0954	0.287	-0.00900	0.00131	0.999
5	item_4	Float32DType	173 (0.1%)	0.0800	0.244	-0.0140	0.000215	0.959
6	cash_price	Float32DType	1594 (1.0%)	701.	742.	0.00	549.	2.20e+04
7	make_0	Float32DType	830 (0.5%)	0.468	0.499	-2.06e-19	1.29e-05	1.00
8	make_1	Float32DType	830 (0.5%)	0.323	0.463	-1.29e-05	1.81e-08	1.00
9	make_2	Float32DType	830 (0.5%)	0.0518	0.221	-1.26e-07	-3.71e-11	1.00
10	make_3	Float32DType	830 (0.5%)	0.0313	0.174	-1.13e-06	-1.12e-07	1.00
11	make_4	Float32DType	830 (0.5%)	0.0202	0.138	-5.40e-05	-2.27e-07	1.00
12	model_0	Float32DType	9669 (5.9%)	0.289	0.427	-1.92e-28	0.0156	1.00
13	model_1	Float32DType	9670 (5.9%)	0.199	0.288	-0.0262	0.0512	0.811
14	model_2	Float32DType	9669 (5.9%)	0.0432	0.237	-0.541	0.0113	0.655
15	model_3	Float32DType	9669 (5.9%)	0.0500	0.200	-0.0829	-0.000969	0.854
16	model_4	Float32DType	9670 (5.9%)	0.0510	0.187	-0.0695	-0.000472	0.905
17	goods_code_0	Float32DType	14876 (9.1%)	0.153	0.360	-2.14e-08	4.74e-11	1.00
18	goods_code_1	Float32DType	14868 (9.1%)	0.117	0.253	-2.95e-13	0.00972	0.900
19	goods_code_2	Float32DType	14879 (9.1%)	0.0814	0.164	-0.0113	0.00649	0.623
20	goods_code_3	Float32DType	14873 (9.1%)	0.0426	0.152	-0.0887	0.00820	0.881
21	goods_code_4	Float32DType	14879 (9.1%)	0.00142	0.155	-0.375	-3.95e-05	0.766
22	Nbr_of_prod_purchas	Float32DType	20 (< 0.1%)	1.05	0.427	1.00	1.00	40.0

Column 1	Column 2	Cramér's V
item_1	goods_code_0	1.00
make_0	model_1	0.961
item_2	make_0	0.900
item_0	make_0	0.884
item_0	item_2	0.817
item_0	model_1	0.719
make_0	model_2	0.712
item_1	model_0	0.710
model_0	goods_code_0	0.710
item_3	model_4	0.697
model_1	model_2	0.696
make_0	model_0	0.690
make_2	model_4	0.669
make_0	make_1	0.666
item_1	make_1	0.638
make_1	goods_code_0	0.638
make_4	model_4	0.626
make_3	model_4	0.616
make_1	model_0	0.576
goods_code_2	goods_code_4	0.534

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	basket_ID	item_0	item_1	item_2	item_3	item_4	cash_price	make_0	make_1	make_2	make_3	make_4	model_0	model_1	model_2	model_3	model_4	goods_code_0	goods_code_1	goods_code_2	goods_code_3	goods_code_4	Nbr_of_prod_purchas
	basket_ID	item_0	item_1	item_2	item_3	item_4	cash_price	make_0	make_1	make_2	make_3	make_4	model_0	model_1	model_2	model_3	model_4	goods_code_0	goods_code_1	goods_code_2	goods_code_3	goods_code_4	Nbr_of_prod_purchas
0	85517	0.9882413148880005	-2.4426024538115598e-05	-0.15276144444942474	0.0013054301962256432	0.00021473383822012693	889.0	1.0	-1.2925240298500285e-05	-3.7126069579729304e-11	-1.1239455233180706e-07	-2.273910126859846e-07	0.08248666673898697	0.8109883069992065	-0.5407345294952393	-0.017663072794675827	0.022470692172646523	-9.597581618336903e-10	0.8996102809906006	-0.0113435173407197	-0.01964709348976612	0.008371813222765923	1.0
1	51113	0.40892505645751953	9.8394688393455e-05	0.9109643697738647	-0.009002331644296646	-0.002758502494543791	409.0	1.0	-1.2925240298500285e-05	-3.7126069579729304e-11	-1.1239455233180706e-07	-2.273910126859846e-07	0.0023703970946371555	0.0855584666132927	0.05529085546731949	0.6650562286376953	0.03614027053117752	-8.343496560314634e-09	0.08153810352087021	0.007707663346081972	0.07294385880231857	-0.006642509717494249	1.0
2	83008	0.0001210441259900108	3.0023331419215538e-05	0.00901311356574297	0.9992326498031616	-0.002600299194455147	1399.0	1.0806103034610715e-07	8.4619199469671e-07	1.5519016072218506e-09	0.9999983310699463	-5.39734355697874e-05	0.0008999390993267298	0.04100307449698448	0.0609818771481514	-0.030578967183828354	0.4235115349292755	-8.209698892924777e-11	0.05250881612300873	0.022454867139458656	0.01849365234375	-0.0005260583129711449	1.0
3	78712	0.9882413148880005	-2.4426024538115598e-05	-0.15276144444942474	0.0013054301962256432	0.00021473383822012693	689.0	1.0	-1.2925240298500285e-05	-3.7126069579729304e-11	-1.1239455233180706e-07	-2.273910126859846e-07	0.008394350297749043	0.446507066488266	0.32407060265541077	-0.034273091703653336	-0.06064724177122116	2.0538091227706445e-09	0.07498347014188766	0.008810597471892834	0.0667758509516716	-0.007886180654168129	1.0
4	78712	0.40892505645751953	9.8394688393455e-05	0.9109643697738647	-0.009002331644296646	-0.002758502494543791	119.0	1.0	-1.2925240298500285e-05	-3.7126069579729304e-11	-1.1239455233180706e-07	-2.273910126859846e-07	0.002580079482868314	0.07965049147605896	0.02265028841793537	0.05735518038272858	0.005055511835962534	8.191194944551228e-10	0.017666136845946312	0.013678669929504395	0.06772863119840622	0.028541866689920425	1.0

163352	42613	0.00011322547652525827	0.026742402464151382	0.0020137073006480932	0.0017230769153684378	0.6012616157531738	259.0	3.5110249996250786e-07	0.016211308538913727	-2.0389521182551107e-09	1.7992266293731518e-05	3.6068363442609552e-06	0.00914500281214714	0.0056508504785597324	0.010372740216553211	0.0728520005941391	0.005781204905360937	-3.3578564928582466e-10	0.0088573656976223	0.0037883520126342773	0.07680531591176987	-0.006582336500287056	1.0
163353	42613	0.00018370886391494423	0.02186746522784233	0.0033177072182297707	0.0007147646974772215	0.43649590015411377	949.0	1.2706952823293705e-08	4.0153212466975674e-05	0.22918562591075897	4.195377414362156e-07	0.0001343405747320503	0.010308784432709217	0.010172782465815544	0.009103742428123951	0.010674724355340004	0.025372087955474854	-4.511379811389382e-11	0.039065033197402954	0.002710816916078329	0.012030630372464657	-0.0009758408996276557	1.0
163354	43567	0.9882413148880005	-2.4426024538115598e-05	-0.15276144444942474	0.0013054301962256432	0.00021473383822012693	1099.0	1.0	-1.2925240298500285e-05	-3.7126069579729304e-11	-1.1239455233180706e-07	-2.273910126859846e-07	0.0079492861405015	0.5506052374839783	0.6548560261726379	-0.08286207169294357	-0.06570319831371307	4.741710149525069e-11	0.019254770129919052	0.6082489490509033	-0.0864415392279625	-0.3626284897327423	1.0
163355	43567	0.9882413148880005	-2.4426024538115598e-05	-0.15276144444942474	0.0013054301962256432	0.00021473383822012693	2099.0	1.0	-1.2925240298500285e-05	-3.7126069579729304e-11	-1.1239455233180706e-07	-2.273910126859846e-07	0.0035209052730351686	0.15411490201950073	0.04496346041560173	0.03920045867562294	0.01731499284505844	-8.823670683000273e-09	0.012577343732118607	0.002423470839858055	0.01685507409274578	-0.0010179224191233516	1.0
163356	68268	0.0001210441259900108	3.0023331419215538e-05	0.00901311356574297	0.9992326498031616	-0.002600299194455147	799.0	4.063430147915881e-12	1.9998083189420868e-08	0.9999967813491821	-2.7157458504234455e-09	-5.545936687667563e-07	0.0009588065440766513	0.05045400932431221	0.08625345677137375	-0.05629198998212814	0.7716756463050842	-6.971144772549565e-10	0.059858307242393494	0.007471682969480753	0.026486705988645554	-0.002832273952662945	1.0

6: APPLY TableVectorizer

This step used:

This step was used by:

Where was this step created?

Estimator fitted in this step:

Output of this step

basket_ID

item_0

item_1

item_2

item_3

item_4

cash_price

make_0

make_1

make_2

make_3

make_4

model_0

model_1

model_2

model_3

model_4

goods_code_0

goods_code_1

goods_code_2

goods_code_3

goods_code_4

Nbr_of_prod_purchas