Correlation Coefficient Matrix - Model Training| Alibaba Cloud Documentation Center

This topic describes the Correlation Coefficient Matrix component provided by Machine Learning Studio.

The correlation coefficient indicates the correlation between columns in a matrix. The coefficient is in the range of [-1,1]. The count parameter is measured when the value is the number of non-zero elements in two consecutive columns.

Configure the component

You can configure the component by using one of the following methods:

Machine Learning Platform for AI console


Tab	Parameter	Description
Fields Setting	All Selected by Default	N/A
Tuning	Cores	This parameter is used with Memory Size.
Tuning	Memory Size	This parameter is used with Cores.

PAI command

PAI -name corrcoef
    -project algo_public
    -DinputTableName=maple_test_corrcoef_basic12x10_input
    -DoutputTableName=maple_test_corrcoef_basic12x10_output
    -DcoreNum=1
    -DmemSizePerCore=110;


Parameter	Required	Description	Default value
inputTableName	Yes	The name of the input table.	No default value
inputTablePartitions	No	The partitions selected from the input table for training. The system supports the following formats: Partition_name=value name1=value1/name2=value2: multi-level partitions Note If you specify multiple partitions, separate them with commas (,).	No default value
outputTableName	Yes	The names of output tables.	No default value
selectedColNames	No	The columns selected from the input table.	All columns are selected by default.
lifecycle	No	The lifecycle of the output table.	No default value
coreNum	No	This parameter is used with memSizePerCore. The value must be a positive integer. Valid values: [1, 9999].	Automatically calculated
memSizePerCore	No	The memory size of each core. Unit: MB. A positive integer in the range of [1024, 64 × 1024]	Automatically calculated

Example

Input


col0:double	col1:bigint	col2:double	col3:bigint	col4:double	col5:bigint	col6:double	col7:bigint	col8:double	col9:double
19	95	33	52	115	43	32	98	76	40
114	26	101	69	56	59	116	23	109	105
103	89	7	9	65	118	73	50	55	81
79	20	63	71	5	24	77	31	21	75
87	16	66	47	25	14	42	99	108	57
11	104	38	37	106	51	3	91	80	97
84	30	70	46	8	6	94	22	45	48
35	17	107	64	10	112	53	34	90	96
13	61	39	1	29	117	112	2	82	28
62	4	102	88	100	36	67	54	12	85
49	27	44	93	68	110	60	72	86	58
92	119	0	113	41	15	74	83	18	111

PAI command

PAI -name corrcoef
    -project algo_public
    -DinputTableName=maple_test_corrcoef_basic12x10_input
    -DoutputTableName=maple_test_corrcoef_basic12x10_output
    -DcoreNum=1
    -DmemSizePerCore=110;

Output


columnsnames	col0	col1	col2	col3	col4	col5	col6	col7	col8	col9
col0	1	-0.2115657251820724	0.0598306259706561	0.2599903570684693	-0.3483249188225586	-0.28716254396809926	0.47880162127435116	-0.13646519484213326	-0.19500158764680092	0.3897390240949085
col1	-0.2115657251820724	1	-0.8444477377898585	-0.17507636221594533	0.40943384150571377	0.09135976026101403	-0.3018506374626574	0.40733726912808044	-0.11827739124590071	0.12433851389455183
col2	0.0598306259706561	-0.8444477377898585	1	0.18518346647293102	-0.20934839228057014	-0.1896417512389659	0.1799377498863213	-0.3858885676469948	0.20254569203773892	0.13476160753756655
col3	0.2599903570684693	-0.17507636221594533	0.18518346647293102	1	0.03988018649854009	-0.43737887418329147	-0.053818296425267184	0.2900856441586986	-0.3607547910075688	0.4912019074930449
col4	-0.3483249188225586	0.40943384150571377	-0.20934839228057014	0.03988018649854009	1	0.1465605209246875	-0.5016030364347955	0.5496024325711117	0.013743256115394122	0.07497231559184887
col5	-0.28716254396809926	0.09135976026101403	-0.1896417512389659	-0.43737887418329147	0.1465605209246875	1	0.16729809310873522	-0.29890655828796964	0.3618518101014617	-0.1713960957286885
col6	0.47880162127435116	-0.3018506374626574	0.1799377498863213	-0.053818296425267184	-0.5016030364347955	0.16729809310873522	1	-0.8165019880156462	-0.11173420918721436	-0.10363860378347944
col7	-0.13646519484213326	0.40733726912808044	-0.3858885676469948	0.2900856441586986	0.5496024325711117	-0.29890655828796964	-0.8165019880156462	1	0.07435907471544469	0.11711976051999162
col8	-0.19500158764680092	-0.11827739124590071	0.20254569203773892	-0.3607547910075688	0.013743256115394122	0.3618518101014617	-0.11173420918721436	0.07435907471544469	1	-0.18463012549540175
col9	0.3897390240949085	0.12433851389455183	0.13476160753756655	0.4912019074930449	0.07497231559184887	-0.1713960957286885	-0.10363860378347944	0.11711976051999162	-0.18463012549540175	1

col0:double	col1:bigint	col2:double	col3:bigint	col4:double	col5:bigint	col6:double	col7:bigint	col8:double	col9:double
19	95	33	52	115	43	32	98	76	40
114	26	101	69	56	59	116	23	109	105
103	89	7	9	65	118	73	50	55	81
79	20	63	71	5	24	77	31	21	75
87	16	66	47	25	14	42	99	108	57
11	104	38	37	106	51	3	91	80	97
84	30	70	46	8	6	94	22	45	48
35	17	107	64	10	112	53	34	90	96
13	61	39	1	29	117	112	2	82	28
62	4	102	88	100	36	67	54	12	85
49	27	44	93	68	110	60	72	86	58
92	119	0	113	41	15	74	83	18	111

col0:double	col1:bigint	col2:double	col3:bigint	col4:double	col5:bigint	col6:double	col7:bigint	col8:double	col9:double
19	95	33	52	115	43	32	98	76	40
114	26	101	69	56	59	116	23	109	105
103	89	7	9	65	118	73	50	55	81
79	20	63	71	5	24	77	31	21	75
87	16	66	47	25	14	42	99	108	57
11	104	38	37	106	51	3	91	80	97
84	30	70	46	8	6	94	22	45	48
35	17	107	64	10	112	53	34	90	96
13	61	39	1	29	117	112	2	82	28
62	4	102	88	100	36	67	54	12	85
49	27	44	93	68	110	60	72	86	58
92	119	0	113	41	15	74	83	18	111

col0:double	col1:bigint	col2:double	col3:bigint	col4:double	col5:bigint	col6:double	col7:bigint	col8:double	col9:double
19	95	33	52	115	43	32	98	76	40
114	26	101	69	56	59	116	23	109	105
103	89	7	9	65	118	73	50	55	81
79	20	63	71	5	24	77	31	21	75
87	16	66	47	25	14	42	99	108	57
11	104	38	37	106	51	3	91	80	97
84	30	70	46	8	6	94	22	45	48
35	17	107	64	10	112	53	34	90	96
13	61	39	1	29	117	112	2	82	28
62	4	102	88	100	36	67	54	12	85
49	27	44	93	68	110	60	72	86	58
92	119	0	113	41	15	74	83	18	111