[jira] [Updated] (MADLIB-1490) Can You add XGBOOST?
[ https://issues.apache.org/jira/browse/MADLIB-1490?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Orhan Kislal updated MADLIB-1490: - Fix Version/s: v1.20.0 > Can You add XGBOOST? > > > Key: MADLIB-1490 > URL: https://issues.apache.org/jira/browse/MADLIB-1490 > Project: Apache MADlib > Issue Type: Request >Reporter: Ameer Ul Islam >Priority: Major > Fix For: v1.20.0 > > > Can You add XGBOOST module? -- This message was sent by Atlassian Jira (v8.20.10#820010)
[jira] [Updated] (MADLIB-1498) dbconnector interface
[ https://issues.apache.org/jira/browse/MADLIB-1498?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Orhan Kislal updated MADLIB-1498: - Fix Version/s: v1.20.0 > dbconnector interface > - > > Key: MADLIB-1498 > URL: https://issues.apache.org/jira/browse/MADLIB-1498 > Project: Apache MADlib > Issue Type: Question > Components: DB Abstraction Layer, Documentation >Reporter: Sabina Dayanova >Priority: Major > Fix For: v1.20.0 > > > Dear MADlib contributors! > My name is Sabina, and I am currently working on integrating MADlib library > into another DBMS - > [ClickHouse|[https://clickhouse.com|https://clickhouse.com/]] I am trying to > understand the dbconnector interface, so that I can adapt it to the DBMS that > I am working with. Unfortunately, I am having a hard time doing that, since > there is no explanation of what the dbconnector.hpp file should exactly do. > I was wondering if you could give me some helpful information about it. > Thanks! > -- This message was sent by Atlassian Jira (v8.20.10#820010)
[jira] [Updated] (MADLIB-1503) Add multi column support for PageRank
[ https://issues.apache.org/jira/browse/MADLIB-1503?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Orhan Kislal updated MADLIB-1503: - Fix Version/s: v1.20.0 > Add multi column support for PageRank > - > > Key: MADLIB-1503 > URL: https://issues.apache.org/jira/browse/MADLIB-1503 > Project: Apache MADlib > Issue Type: New Feature >Reporter: Orhan Kislal >Priority: Major > Fix For: v1.20.0 > > > PageRank should support multiple columns as vertex identifiers -- This message was sent by Atlassian Jira (v8.20.10#820010)
[jira] [Updated] (MADLIB-1502) Add multi column support for wcc
[ https://issues.apache.org/jira/browse/MADLIB-1502?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Orhan Kislal updated MADLIB-1502: - Fix Version/s: v1.20.0 > Add multi column support for wcc > > > Key: MADLIB-1502 > URL: https://issues.apache.org/jira/browse/MADLIB-1502 > Project: Apache MADlib > Issue Type: New Feature >Reporter: Orhan Kislal >Priority: Major > Fix For: v1.20.0 > > > WCC should support multiple columns as vertex identifiers -- This message was sent by Atlassian Jira (v8.20.10#820010)
[jira] [Updated] (MADLIB-1455) compile params doesn't accept double quoted strings
[ https://issues.apache.org/jira/browse/MADLIB-1455?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Orhan Kislal updated MADLIB-1455: - Fix Version/s: v1.20.0 (was: v1.19.0) > compile params doesn't accept double quoted strings > --- > > Key: MADLIB-1455 > URL: https://issues.apache.org/jira/browse/MADLIB-1455 > Project: Apache MADlib > Issue Type: Bug > Components: Deep Learning >Reporter: Domino Valdano >Priority: Minor > Fix For: v1.20.0 > > > When I try to train with this mst table: > mnist=# select * from mst_table_mnist; > > ┌─┬──┬─┬──┐ > │ mst_key │ model_id │ compile_params │ fit_params │ > > ├─┼──┼─┼──┤ > │ 4 │ 1 │ loss="categorical_crossentropy", optimizer="SGD", > metrics=["accuracy"] │ epochs=1, batch_size=200 │ > │ 2 │ 1 │ loss="categorical_crossentropy", optimizer="Adam(lr=0.1)", > metrics=["accuracy"] │ epochs=1, batch_size=100 │ > │ 3 │ 1 │ loss="categorical_crossentropy", optimizer="Adam(lr=0.001)", > metrics=["accuracy"] │ epochs=1, batch_size=200 │ > │ 1 │ 1 │ loss="categorical_crossentropy", optimizer="Adam(lr=0.0001)", > metrics=["accuracy"] │ epochs=1, batch_size=50 │ > > └─┴──┴─┴──┘ > I get this error message: > ERROR: XX000: spiexceptions.InternalError: plpy.Error: model_keras error: > invalid optimizer name: "Adam (plpy_elog.c:121) (seg2 127.0.0.1:6004 > pid=33258) (plpy_elog.c:121) > CONTEXT: Traceback (most recent call last): > PL/Python function "madlib_keras_fit_multiple_model", line 24, in > fit_obj.fit_multiple_model() > PL/Python function "madlib_keras_fit_multiple_model", line 233, in > fit_multiple_model > PL/Python function "madlib_keras_fit_multiple_model", line 265, in > train_multiple_model > PL/Python function "madlib_keras_fit_multiple_model", line 912, in > run_training > PL/Python function "madlib_keras_fit_multiple_model" > It works if I replace the double quotes with single quotes, even though these > are supposed to mean the same thing in a python expression. -- This message was sent by Atlassian Jira (v8.20.10#820010)
[jira] [Updated] (MADLIB-1423) How to seed the environment (for reproducible results)
[ https://issues.apache.org/jira/browse/MADLIB-1423?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Orhan Kislal updated MADLIB-1423: - Fix Version/s: v1.20.0 (was: v1.19.0) > How to seed the environment (for reproducible results) > -- > > Key: MADLIB-1423 > URL: https://issues.apache.org/jira/browse/MADLIB-1423 > Project: Apache MADlib > Issue Type: Question > Components: Design, k-NN >Reporter: Pranas Baliuka >Priority: Minor > Fix For: v1.20.0 > > > How to seed the environment (for reproducible results)? > I'd love to have reproducible reults for PCA/k-NN algos. > > Thanks! -- This message was sent by Atlassian Jira (v8.20.10#820010)
[jira] [Updated] (MADLIB-1481) DL: Passing null or temporal for sample_weight_mode errors out
[
https://issues.apache.org/jira/browse/MADLIB-1481?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Orhan Kislal updated MADLIB-1481:
-
Fix Version/s: v1.20.0
(was: v1.19.0)
> DL: Passing null or temporal for sample_weight_mode errors out
> --
>
> Key: MADLIB-1481
> URL: https://issues.apache.org/jira/browse/MADLIB-1481
> Project: Apache MADlib
> Issue Type: Bug
> Components: Deep Learning
>Reporter: Frank McQuillan
>Priority: Minor
> Fix For: v1.20.0
>
>
> Passing sample_weight_mode as 'temporal' or NULL fails with the following
> error although both these are valid values.
> Also we don't support sample_weight as a compile param, so maybe supporting
> sample_weight_mode makes doesn't really add value
> {code}
> ERROR: spiexceptions.InternalError: plpy.Error: invalid input value for
> parameter sample_weight_mode=temporal, please refer to the documentation
> {code}
> The actual failure for sample_weight_mode = 'temporal' should be something
> like
> {code}
> use of '
> 'sample_weight_mode="temporal") is restricted to '
> 'outputs that are at least 3D, i.e. that have '
> 'a time dimension
> {code}
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[jira] [Updated] (MADLIB-1280) Add GROUP BY to ARIMA
[ https://issues.apache.org/jira/browse/MADLIB-1280?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Orhan Kislal updated MADLIB-1280: - Fix Version/s: v1.20.0 (was: v1.19.0) > Add GROUP BY to ARIMA > - > > Key: MADLIB-1280 > URL: https://issues.apache.org/jira/browse/MADLIB-1280 > Project: Apache MADlib > Issue Type: Improvement >Reporter: Frank McQuillan >Priority: Major > Fix For: v1.20.0 > > > Currently ARIMA does not support GROUP BY > http://madlib.apache.org/docs/latest/group__grp__arima.html > This JIRA is to add GROUP BY in an efficient way to ARIMA. -- This message was sent by Atlassian Jira (v8.20.10#820010)
[jira] [Updated] (MADLIB-1478) DL: Passing in a custom function table to fit along with built in loss and metrics functions errors out
[
https://issues.apache.org/jira/browse/MADLIB-1478?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Orhan Kislal updated MADLIB-1478:
-
Fix Version/s: v1.20.0
(was: v1.19.0)
> DL: Passing in a custom function table to fit along with built in loss and
> metrics functions errors out
> ---
>
> Key: MADLIB-1478
> URL: https://issues.apache.org/jira/browse/MADLIB-1478
> Project: Apache MADlib
> Issue Type: Bug
> Components: Deep Learning
>Reporter: Frank McQuillan
>Priority: Minor
> Fix For: v1.20.0
>
>
> Passing in a custom function table to fit along with built in loss and
> metrics functions errors out
> {code}
> SELECT madlib.load_top_k_accuracy_function('custom_function_table',
>3);
> SELECT madlib.madlib_keras_fit('iris_data_packed',
> 'iris_model','iris_model_arch',1,
> $$loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']$$,
> $$batch_size=10,epochs=1$$,1, FALSE, NULL, NULL,
> FALSE,NULL,NULL,'custom_function_table');
> DETAIL: Traceback (most recent call last):\n File "", line 11, in
> __plpython_procedure_fit_transition_wide_359961\n File
> "/Users/nikhilkak/workspace/madlib/build/src/ports/greenplum/6/modules/deep_learning/madlib_keras.py",
> line 579, in fit_transition_wide\n File
> "/Users/nikhilkak/workspace/madlib/build/src/ports/greenplum/6/modules/deep_learning/madlib_keras.py",
> line 618, in fit_transition\n File
> "/Users/nikhilkak/workspace/madlib/build/src/ports/greenplum/6/modules/deep_learning/madlib_keras.py",
> line 91, in get_init_model_and_sess\n File
> "/Users/nikhilkak/workspace/madlib/build/src/ports/greenplum/6/modules/deep_learning/madlib_keras.py",
> line 551, in init_model\n File
> "/Users/nikhilkak/workspace/madlib/build/src/ports/greenplum/6/modules/deep_learning/madlib_keras_wrapper.py",
> line 347, in compile_model\n File
> "/Library/Python/2.7/site-packages/dill/_dill.py", line 283, in loads\n
> return load(file, ignore, **kwds)\n File
> "/Library/Python/2.7/site-packages/dill/_dill.py", line 278, in load\n
> return Unpickler(file, ignore=ignore, **kwds).load()\n File
> "/Library/Python/2.7/site-packages/dill/_dill.py", line 481, in load\nobj
> = StockUnpickler.load(self)\n File
> "/System/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/pickle.py",
> line 864, in load\ndispatch[key](self)\nKeyError: \'{\'\n'
> (plpy_elog.c:121) (seg0 slice1 127.0.0.1:6002 pid=85404)
> {code}
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[jira] [Updated] (MADLIB-1459) DL: Add Multiple input/output support to multi-model functions
[ https://issues.apache.org/jira/browse/MADLIB-1459?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Orhan Kislal updated MADLIB-1459: - Fix Version/s: v1.20.0 (was: v1.19.0) > DL: Add Multiple input/output support to multi-model functions > -- > > Key: MADLIB-1459 > URL: https://issues.apache.org/jira/browse/MADLIB-1459 > Project: Apache MADlib > Issue Type: New Feature > Components: Deep Learning >Reporter: Orhan Kislal >Priority: Major > Fix For: v1.20.0 > > > This is a follow-up to MADLIB-1457 > We should add support for multiple input and outputs to the multi-model > methods. > # fit multiple > # automl > # hyperband > -- This message was sent by Atlassian Jira (v8.20.10#820010)
[jira] [Updated] (MADLIB-1485) DL: Unhelpful error message when loss/accuracy does not exist
[
https://issues.apache.org/jira/browse/MADLIB-1485?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Orhan Kislal updated MADLIB-1485:
-
Fix Version/s: v1.20.0
(was: v1.19.0)
> DL: Unhelpful error message when loss/accuracy does not exist
> -
>
> Key: MADLIB-1485
> URL: https://issues.apache.org/jira/browse/MADLIB-1485
> Project: Apache MADlib
> Issue Type: Bug
> Components: Deep Learning
>Reporter: Frank McQuillan
>Priority: Minor
> Fix For: v1.20.0
>
>
> We throw an unhelpful error message when either the loss or the metrics
> argument is invalid. We should check if this happens for any other argument
> {code}
> SELECT madlib.madlib_keras_fit('mnist_train_batched', 'mnist_lstm_model',
> 'model_arch_mnist_lstm', 1,
> $$ optimizer=SGD(lr=0.01, decay=1e-6, nesterov=True), loss='does_not_exist',
> metrics=['accuracy']$$::text,
> $$ batch_size=25, epochs=1, verbose=0 $$::text, 5, TRUE, NULL, NULL);
> ERROR: plpy.Error: Object table not specified for function
> ['does_not_exist'] in compile_params (plpy_elog.c:121)
> {code}
> {code}
> SELECT madlib.madlib_keras_fit('mnist_train_batched', 'mnist_lstm_model',
> 'model_arch_mnist_lstm', 1,
> $$ optimizer=SGD(lr=0.01, decay=1e-6, nesterov=True),
> loss='categorical_crossentropy', metrics=['foobar']$$::text,
> $$ batch_size=25, epochs=1, verbose=0 $$::text, 5, TRUE, NULL, NULL);
> ERROR: plpy.Error: Object table not specified for function ['foobar'] in
> compile_params (plpy_elog.c:121)
> {code}
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[jira] [Updated] (MADLIB-1482) DL: metrics compute frequency should be >=1 only
[
https://issues.apache.org/jira/browse/MADLIB-1482?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Orhan Kislal updated MADLIB-1482:
-
Fix Version/s: v1.20.0
(was: v1.19.0)
> DL: metrics compute frequency should be >=1 only
>
>
> Key: MADLIB-1482
> URL: https://issues.apache.org/jira/browse/MADLIB-1482
> Project: Apache MADlib
> Issue Type: Bug
> Components: Deep Learning
>Reporter: Frank McQuillan
>Priority: Minor
> Fix For: v1.20.0
>
>
> metrics_compute_frequency should be >= 1 only. Currently it allows neg
> numbers:
> {code}
> SELECT madlib.madlib_keras_fit('balanced2_train_packed', -- source table
>'model1', -- model output table
>'model_arch_library', -- model arch table
> 1,-- model arch id
> $$ loss='categorical_crossentropy',
> optimizer='adam', metrics=['accuracy'] $$, -- compile_params
> $$ batch_size=64, epochs=1 $$, -- fit_params
> 10, -- num_iterations
> FALSE,-- use GPUs
> 'balanced2_test_packed', -- validation
> dataset
> -3,-- metrics compute
> frequency
> FALSE,-- warm start
>'Frank', -- name
>'Network test run' -- description
> );
> {code}
> produces
> {code}
> SELECT * FROM model1_summary;
> -[ RECORD 1
> ]-+-
> source_table | balanced2_train_packed
> model | model1
> dependent_varname | {y}
> independent_varname | {feature_vector}
> model_arch_table | model_arch_library
> model_id | 1
> compile_params| loss='categorical_crossentropy',
> optimizer='adam', metrics=['accuracy']
> fit_params| batch_size=64, epochs=1
> num_iterations| 10
> validation_table | balanced2_test_packed
> object_table |
> metrics_compute_frequency | -3
> name | Frank
> description | Network test run
> model_type| madlib_keras
> model_size| 5.9853515625
> start_training_time | 2021-03-12 20:24:27.74585
> end_training_time | 2021-03-12 20:24:30.012898
> metrics_elapsed_time |
> {1.13839101791382,1.57564496994019,2.02039790153503,2.26697182655334}
> madlib_version| 1.18.0-dev
> num_classes | {23}
> dependent_vartype | {text}
> normalizing_const | 1
> metrics_type | {accuracy}
> loss_type | categorical_crossentropy
> training_metrics_final| 0.529687523841858
> training_loss_final | 470.371795654297
> training_metrics |
> {0.283894240856171,0.344591349363327,0.489182680845261,0.529687523841858}
> training_loss |
> {3195.37939453125,1194.63610839844,508.576507568359,470.371795654297}
> validation_metrics_final | 0.52836537361145
> validation_loss_final | 11892.33203125
> validation_metrics|
> {0.289903849363327,0.35432693362236,0.482692301273346,0.52836537361145}
> validation_loss |
> {35025.0390625,23250.08984375,12720.3359375,11892.33203125}
> metrics_iters | {3,6,9,10}
> y_class_values|
> {class01,class02,class03,class04,class05,class06,class07,class08,class09,class10,class11,class12,class13,class14,class15,class16,class17,class18,class19,class20,class21,class22,normal}
> {code}
> Also if you make it 0 you get this cryptic error
> {code}
> InternalError: (psycopg2.errors.InternalError_) ZeroDivisionError: integer
> division or modulo by zero (plpython.c:5038)
> CONTEXT: Traceback (most recent call last):
> PL/Python function "madlib_keras_fit", line 23, in
> madlib_keras.fit(**globals())
> PL/Python function "madlib_keras_fit", line 42, in wrapper
> PL/Python function "madlib_keras_fit", line 273, in fit
> PL/Python function "madlib_keras_fit", line 542, in
> should_compute_metrics_this_iter
> PL/Python function "madlib_keras_fit"
> [SQL: SELECT madlib.madlib_keras_fit('balanced2_train_packed', -- source
> table
>'model1', -- model output table
>'model_arch_library', -- model arch table
>
[jira] [Updated] (MADLIB-1476) Support PG 13
[ https://issues.apache.org/jira/browse/MADLIB-1476?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Orhan Kislal updated MADLIB-1476: - Fix Version/s: v1.20.0 (was: v1.19.0) > Support PG 13 > - > > Key: MADLIB-1476 > URL: https://issues.apache.org/jira/browse/MADLIB-1476 > Project: Apache MADlib > Issue Type: New Feature >Reporter: Frank McQuillan >Priority: Major > Fix For: v1.20.0 > > > Please update to support PG 13 which is the latest version. -- This message was sent by Atlassian Jira (v8.20.10#820010)
[jira] [Updated] (MADLIB-1480) DL: Passing in weighted_metrics fails
[
https://issues.apache.org/jira/browse/MADLIB-1480?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Orhan Kislal updated MADLIB-1480:
-
Fix Version/s: v1.20.0
(was: v1.19.0)
> DL: Passing in weighted_metrics fails
> -
>
> Key: MADLIB-1480
> URL: https://issues.apache.org/jira/browse/MADLIB-1480
> Project: Apache MADlib
> Issue Type: Bug
> Components: Deep Learning
>Reporter: Frank McQuillan
>Priority: Minor
> Fix For: v1.20.0
>
>
> Passing in weighted_metrics fails, not sure if we even support it
> {code}
> SELECT madlib.madlib_keras_fit( 'iris_data_packed', 'iris_model',
> 'iris_model_arch',
> 1,
> $$loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy'],weighted_metrics=['accuracy']$$,$$batch_size=10,epochs=1$$,1);
> ERROR: spiexceptions.InternalError: ValueError: too many values to unpack
> (plpy_elog.c:121) (seg0 slice1 127.0.0.1:6002 pid=84135) (plpy_elog.c:121)
> CONTEXT: Traceback (most recent call last):
> PL/Python function "madlib_keras_fit", line 23, in
> madlib_keras.fit(**globals())
> PL/Python function "madlib_keras_fit", line 42, in wrapper
> PL/Python function "madlib_keras_fit", line 298, in fit
> PL/Python function "madlib_keras_fit", line 520, in compute_loss_and_metrics
> PL/Python function "madlib_keras_fit", line 1001, in
> get_loss_metric_from_keras_eval
> PL/Python function "madlib_keras_fit"
> {code}
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[jira] [Commented] (MADLIB-1504) Does madlib support arm platform?
[ https://issues.apache.org/jira/browse/MADLIB-1504?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17568136#comment-17568136 ] Orhan Kislal commented on MADLIB-1504: -- Hi, We haven't tested with ARM but as long as you have the necessary dependencies, it should be possible to compile. Please feel free to post any errors here for us to take a look at. Officially supporting ARM would be great! Thanks > Does madlib support arm platform? > -- > > Key: MADLIB-1504 > URL: https://issues.apache.org/jira/browse/MADLIB-1504 > Project: Apache MADlib > Issue Type: Question >Reporter: seth.qiang >Priority: Major > > I looked up the documentation and didn't see anything about the arm platform > does anyone know about this? > https://cwiki.apache.org/confluence/display/MADLIB/Database+and+OS+Support -- This message was sent by Atlassian Jira (v8.20.10#820010)
[jira] [Commented] (MADLIB-1505) Support for Windows Platform
[ https://issues.apache.org/jira/browse/MADLIB-1505?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17568133#comment-17568133 ] Orhan Kislal commented on MADLIB-1505: -- Hi, Unfortunately, we don't officially support Windows. You might want to try compiling from source but I don't think it will be an easy task. Thanks > Support for Windows Platform > > > Key: MADLIB-1505 > URL: https://issues.apache.org/jira/browse/MADLIB-1505 > Project: Apache MADlib > Issue Type: New Feature >Reporter: Poorna Chandra Raju >Priority: Major > > *Is madlib available for Windows platform at least for Postgres ?* > If not, is it possible to build existing code on Windows or is it already in > the road map ? > Let me know the possibilities and challenges on achieving this task. > Thanks. -- This message was sent by Atlassian Jira (v8.20.10#820010)
