Skip to content
GitLab
Projects
Groups
Snippets
Help
Loading...
Help
What's new
7
Help
Support
Community forum
Keyboard shortcuts
?
Submit feedback
Contribute to GitLab
Switch to GitLab Next
Sign in / Register
Toggle navigation
Open sidebar
datadrivendiscovery
primitives
Commits
84210a25
Commit
84210a25
authored
Jan 14, 2020
by
Mitar
Browse files
Merge branch 'master' into 'master'
Cornell updated pipelines See merge request
!89
parents
b04e9f8e
c01ebc47
Pipeline
#109352903
passed with stages
in 47 minutes and 47 seconds
Changes
7
Pipelines
1
Expand all
Hide whitespace changes
Inline
Side-by-side
Showing
7 changed files
with
1436 additions
and
0 deletions
+1436
-0
v2020.1.9/Cornell/d3m.primitives.collaborative_filtering.high_rank_imputer.Cornell/v0.1.1/pipeline_runs/07e848ee-756e-4fb5-b724-ef18cd130fec_run.yaml.gz
...ine_runs/07e848ee-756e-4fb5-b724-ef18cd130fec_run.yaml.gz
+0
-0
v2020.1.9/Cornell/d3m.primitives.collaborative_filtering.high_rank_imputer.Cornell/v0.1.1/pipelines/07e848ee-756e-4fb5-b724-ef18cd130fec.json
...0.1.1/pipelines/07e848ee-756e-4fb5-b724-ef18cd130fec.json
+371
-0
v2020.1.9/Cornell/d3m.primitives.collaborative_filtering.high_rank_imputer.Cornell/v0.1.1/primitive.json
...filtering.high_rank_imputer.Cornell/v0.1.1/primitive.json
+224
-0
v2020.1.9/Cornell/d3m.primitives.data_preprocessing.low_rank_imputer.Cornell/v0.1.1/pipeline_runs/204f67b3-4baa-4bc0-ae55-5e02bfd04b72_run.yaml.gz
...ine_runs/204f67b3-4baa-4bc0-ae55-5e02bfd04b72_run.yaml.gz
+0
-0
v2020.1.9/Cornell/d3m.primitives.data_preprocessing.low_rank_imputer.Cornell/v0.1.1/pipelines/204f67b3-4baa-4bc0-ae55-5e02bfd04b72.json
...0.1.1/pipelines/204f67b3-4baa-4bc0-ae55-5e02bfd04b72.json
+399
-0
v2020.1.9/Cornell/d3m.primitives.data_preprocessing.low_rank_imputer.Cornell/v0.1.1/primitive.json
...processing.low_rank_imputer.Cornell/v0.1.1/primitive.json
+214
-0
v2020.1.9/Cornell/d3m.primitives.feature_extraction.huber_pca.Cornell/v0.1.1/primitive.json
...eature_extraction.huber_pca.Cornell/v0.1.1/primitive.json
+228
-0
No files found.
v2020.1.9/Cornell/d3m.primitives.collaborative_filtering.high_rank_imputer.Cornell/v0.1.1/pipeline_runs/07e848ee-756e-4fb5-b724-ef18cd130fec_run.yaml.gz
0 → 100644
View file @
84210a25
File added
v2020.1.9/Cornell/d3m.primitives.collaborative_filtering.high_rank_imputer.Cornell/v0.1.1/pipelines/07e848ee-756e-4fb5-b724-ef18cd130fec.json
0 → 100644
View file @
84210a25
{
"created"
:
"2020-01-13T17:34:39.847681Z"
,
"digest"
:
"52e453eceebc9446fa51668ae80876a7dc77dd2e0ee97bd521cf6e8576fa39e3"
,
"id"
:
"07e848ee-756e-4fb5-b724-ef18cd130fec"
,
"inputs"
:
[
{
"name"
:
"inputs"
}
],
"outputs"
:
[
{
"data"
:
"steps.9.produce"
,
"name"
:
"output"
}
],
"schema"
:
"https://metadata.datadrivendiscovery.org/schemas/v0/pipeline.json"
,
"steps"
:
[
{
"arguments"
:
{
"inputs"
:
{
"data"
:
"inputs.0"
,
"type"
:
"CONTAINER"
}
},
"outputs"
:
[
{
"id"
:
"produce"
}
],
"primitive"
:
{
"digest"
:
"2029603954acb505cb894594ca20a91af5f2b7bba931553f12fad544071ec911"
,
"id"
:
"4b42ce1e-9b98-4a25-b68e-fad13311eb65"
,
"name"
:
"Extract a DataFrame from a Dataset"
,
"python_path"
:
"d3m.primitives.data_transformation.dataset_to_dataframe.Common"
,
"version"
:
"0.3.0"
},
"type"
:
"PRIMITIVE"
},
{
"arguments"
:
{
"inputs"
:
{
"data"
:
"steps.0.produce"
,
"type"
:
"CONTAINER"
}
},
"hyperparams"
:
{
"semantic_types"
:
{
"data"
:
[
"https://metadata.datadrivendiscovery.org/types/Attribute"
],
"type"
:
"VALUE"
}
},
"outputs"
:
[
{
"id"
:
"produce"
}
],
"primitive"
:
{
"digest"
:
"356d1b0c08c5046167866de7a45d926f9b8dee661b773e8040cf3b69bc3eace6"
,
"id"
:
"4503a4c6-42f7-45a1-a1d4-ed69699cf5e1"
,
"name"
:
"Extracts columns by semantic type"
,
"python_path"
:
"d3m.primitives.data_transformation.extract_columns_by_semantic_types.Common"
,
"version"
:
"0.3.0"
},
"type"
:
"PRIMITIVE"
},
{
"arguments"
:
{
"inputs"
:
{
"data"
:
"steps.1.produce"
,
"type"
:
"CONTAINER"
}
},
"hyperparams"
:
{
"columns"
:
{
"data"
:
[
1
,
2
,
3
,
4
,
5
,
6
,
7
,
8
,
9
,
10
,
11
,
12
,
13
,
14
,
15
,
16
,
17
,
18
,
19
,
20
,
21
,
22
,
23
,
24
,
25
,
26
,
27
,
28
,
29
,
30
,
31
,
32
,
33
,
34
,
35
,
36
,
37
,
38
,
39
,
40
,
41
,
42
,
43
,
44
,
45
,
46
,
47
,
48
,
49
,
50
,
51
,
52
,
53
,
54
,
55
,
56
,
57
,
58
,
59
,
60
,
61
,
62
,
63
,
64
,
65
,
66
,
67
,
68
,
69
,
70
,
71
,
72
,
73
,
74
,
75
,
76
,
77
],
"type"
:
"VALUE"
}
},
"outputs"
:
[
{
"id"
:
"produce"
}
],
"primitive"
:
{
"digest"
:
"101bb426df45a58c1b5f57a0c477f6535dc217fcf2334930e9c65253b1876722"
,
"id"
:
"81d7e261-e25b-4721-b091-a31cd46e99ae"
,
"name"
:
"Extracts columns"
,
"python_path"
:
"d3m.primitives.data_transformation.extract_columns.Common"
,
"version"
:
"0.1.0"
},
"type"
:
"PRIMITIVE"
},
{
"arguments"
:
{
"inputs"
:
{
"data"
:
"steps.2.produce"
,
"type"
:
"CONTAINER"
}
},
"outputs"
:
[
{
"id"
:
"produce"
}
],
"primitive"
:
{
"digest"
:
"d9fe7ea33c426b78a03f39219342b319be88358b09091912177e0f4faa6bc552"
,
"id"
:
"d510cb7a-1782-4f51-b44c-58f0236e47c7"
,
"name"
:
"Parses strings into their types"
,
"python_path"
:
"d3m.primitives.data_transformation.column_parser.Common"
,
"version"
:
"0.5.0"
},
"type"
:
"PRIMITIVE"
},
{
"arguments"
:
{
"inputs"
:
{
"data"
:
"steps.0.produce"
,
"type"
:
"CONTAINER"
}
},
"hyperparams"
:
{
"semantic_types"
:
{
"data"
:
[
"https://metadata.datadrivendiscovery.org/types/TrueTarget"
],
"type"
:
"VALUE"
}
},
"outputs"
:
[
{
"id"
:
"produce"
}
],
"primitive"
:
{
"digest"
:
"356d1b0c08c5046167866de7a45d926f9b8dee661b773e8040cf3b69bc3eace6"
,
"id"
:
"4503a4c6-42f7-45a1-a1d4-ed69699cf5e1"
,
"name"
:
"Extracts columns by semantic type"
,
"python_path"
:
"d3m.primitives.data_transformation.extract_columns_by_semantic_types.Common"
,
"version"
:
"0.3.0"
},
"type"
:
"PRIMITIVE"
},
{
"arguments"
:
{
"inputs"
:
{
"data"
:
"steps.4.produce"
,
"type"
:
"CONTAINER"
}
},
"outputs"
:
[
{
"id"
:
"produce"
}
],
"primitive"
:
{
"digest"
:
"12cfcf2f6f39b88ee8c45cf4da240235add9e6c817d00f32f93d0d065ade3832"
,
"id"
:
"26fc8fd3-f6b2-4c65-8afb-edb54ed2a3e4"
,
"name"
:
"Label encoder with an unseen category"
,
"python_path"
:
"d3m.primitives.data_preprocessing.label_encoder.Common"
,
"version"
:
"0.2.0"
},
"type"
:
"PRIMITIVE"
},
{
"arguments"
:
{
"inputs"
:
{
"data"
:
"steps.3.produce"
,
"type"
:
"CONTAINER"
},
"outputs"
:
{
"data"
:
"steps.3.produce"
,
"type"
:
"CONTAINER"
}
},
"hyperparams"
:
{
"alpha"
:
{
"data"
:
0.01
,
"type"
:
"VALUE"
},
"beta"
:
{
"data"
:
0.001
,
"type"
:
"VALUE"
},
"d"
:
{
"data"
:
50
,
"type"
:
"VALUE"
},
"maxiter"
:
{
"data"
:
1000
,
"type"
:
"VALUE"
}
},
"outputs"
:
[
{
"id"
:
"produce"
}
],
"primitive"
:
{
"digest"
:
"200124f95be4cceb8cead9aa722dc58c2d09c3410c41dab75e2e7e729b55d8b1"
,
"id"
:
"e6ee30fa-af68-4bfe-9234-5ca7e7ac8e93"
,
"name"
:
"Matrix Completion via Sparse Factorization"
,
"python_path"
:
"d3m.primitives.collaborative_filtering.high_rank_imputer.Cornell"
,
"version"
:
"v0.1.1"
},
"type"
:
"PRIMITIVE"
},
{
"arguments"
:
{
"inputs"
:
{
"data"
:
"steps.6.produce"
,
"type"
:
"CONTAINER"
},
"outputs"
:
{
"data"
:
"steps.5.produce"
,
"type"
:
"CONTAINER"
}
},
"hyperparams"
:
{
"C"
:
{
"data"
:
100
,
"type"
:
"VALUE"
}
},
"outputs"
:
[
{
"id"
:
"produce"
}
],
"primitive"
:
{
"digest"
:
"0bc31dcee8ad75441208bb3550ea527fe64eabbc84dc9a97b59e28c9d27bc20b"
,
"id"
:
"0ae7d42d-f765-3348-a28c-57d94880aa6a"
,
"name"
:
"sklearn.svm.classes.SVC"
,
"python_path"
:
"d3m.primitives.classification.svc.SKlearn"
,
"version"
:
"2019.6.7"
},
"type"
:
"PRIMITIVE"
},
{
"arguments"
:
{
"inputs"
:
{
"data"
:
"steps.7.produce"
,
"type"
:
"CONTAINER"
}
},
"hyperparams"
:
{
"encoder"
:
{
"data"
:
5
,
"type"
:
"PRIMITIVE"
}
},
"outputs"
:
[
{
"id"
:
"produce"
}
],
"primitive"
:
{
"digest"
:
"de5bbb80e57426221055205f46441d69034383e2ed4fc4eb90ecfb980f4971ee"
,
"id"
:
"39ae30f7-39ed-40af-8679-5cf108499605"
,
"name"
:
"Label decoder for UnseenLabelEncoderPrimitive"
,
"python_path"
:
"d3m.primitives.data_preprocessing.label_decoder.Common"
,
"version"
:
"0.1.0"
},
"type"
:
"PRIMITIVE"
},
{
"arguments"
:
{
"inputs"
:
{
"data"
:
"steps.8.produce"
,
"type"
:
"CONTAINER"
},
"reference"
:
{
"data"
:
"steps.0.produce"
,
"type"
:
"CONTAINER"
}
},
"outputs"
:
[
{
"id"
:
"produce"
}
],
"primitive"
:
{
"digest"
:
"4f49e6abf7eb7ea3247eeb48f7b2fdd5fdb42f7ecc734fb3e37efada43895c6d"
,
"id"
:
"8d38b340-f83f-4877-baaa-162f8e551736"
,
"name"
:
"Construct pipeline predictions output"
,
"python_path"
:
"d3m.primitives.data_transformation.construct_predictions.Common"
,
"version"
:
"0.3.0"
},
"type"
:
"PRIMITIVE"
}
]
}
\ No newline at end of file
v2020.1.9/Cornell/d3m.primitives.collaborative_filtering.high_rank_imputer.Cornell/v0.1.1/primitive.json
0 → 100644
View file @
84210a25
This diff is collapsed.
Click to expand it.
v2020.1.9/Cornell/d3m.primitives.data_preprocessing.low_rank_imputer.Cornell/v0.1.1/pipeline_runs/204f67b3-4baa-4bc0-ae55-5e02bfd04b72_run.yaml.gz
0 → 100644
View file @
84210a25
File added
v2020.1.9/Cornell/d3m.primitives.data_preprocessing.low_rank_imputer.Cornell/v0.1.1/pipelines/204f67b3-4baa-4bc0-ae55-5e02bfd04b72.json
0 → 100644
View file @
84210a25
{
"created"
:
"2020-01-13T17:33:39.231457Z"
,
"digest"
:
"167fdc76716e167fe3cfa5068e7d6fb405a96d201fbc6d4942b5fb32eabc9c9e"
,
"id"
:
"204f67b3-4baa-4bc0-ae55-5e02bfd04b72"
,
"inputs"
:
[
{
"name"
:
"inputs"
}
],
"outputs"
:
[
{
"data"
:
"steps.10.produce"
,
"name"
:
"output"
}
],
"schema"
:
"https://metadata.datadrivendiscovery.org/schemas/v0/pipeline.json"
,
"steps"
:
[
{
"arguments"
:
{
"inputs"
:
{
"data"
:
"inputs.0"
,
"type"
:
"CONTAINER"
}
},
"outputs"
:
[
{
"id"
:
"produce"
}
],
"primitive"
:
{
"digest"
:
"b1ac4776f0e916f707a6b6a74f3a36c56ea2eb37c6a255d986faf21855446073"
,
"id"
:
"4b42ce1e-9b98-4a25-b68e-fad13311eb65"
,
"name"
:
"Extract a DataFrame from a Dataset"
,
"python_path"
:
"d3m.primitives.data_transformation.dataset_to_dataframe.Common"
,
"version"
:
"0.3.0"
},
"type"
:
"PRIMITIVE"
},
{
"arguments"
:
{
"inputs"
:
{
"data"
:
"steps.0.produce"
,
"type"
:
"CONTAINER"
}
},
"hyperparams"
:
{
"semantic_types"
:
{
"data"
:
[
"https://metadata.datadrivendiscovery.org/types/Attribute"
],
"type"
:
"VALUE"
}
},
"outputs"
:
[
{
"id"
:
"produce"
}
],
"primitive"
:
{
"digest"
:
"f828f4fbd3ce894ef26a264e53ac98c79d689b653b0eda497b069e42ddfebef4"
,
"id"
:
"4503a4c6-42f7-45a1-a1d4-ed69699cf5e1"
,
"name"
:
"Extracts columns by semantic type"
,
"python_path"
:
"d3m.primitives.data_transformation.extract_columns_by_semantic_types.Common"
,
"version"
:
"0.3.0"
},
"type"
:
"PRIMITIVE"
},
{
"arguments"
:
{
"inputs"
:
{
"data"
:
"steps.1.produce"
,
"type"
:
"CONTAINER"
}
},
"hyperparams"
:
{
"columns"
:
{
"data"
:
[
2
,
3
,
4
,
5
,
6
,
7
,
8
,
9
,
10
,
11
,
12
,
13
,
14
,
15
,
16
,
17
,
18
,
19
,
20
,
21
,
22
,
23
,
24
,
25
,
26
,
27
,
28
,
29
,
30
,
31
,
32
,
33
,
34
,
35
,
36
,
37
,
38
,
39
,
40
,
41
,
42
,
43
,
44
,
45
,
46
,
47
,
48
,
49
,
50
,
51
,
52
,
53
,
54
,
55
,
56
,
57
,
58
,
59
,
60
,
61
,
62
,
63
,
64
,
65
,
66
,
67
,
68
,
69
,
70
,
71
,
72
,
73
,
74
,
75
,