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Removing quotes from udf_metadata_key #1026

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merged 14 commits into from
Sep 5, 2023
Merged

Removing quotes from udf_metadata_key #1026

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adbd3f5
Replacing udf_metadata_key from string_literal to ID_LITERAL
hershd23 Sep 1, 2023
0bdeb71
Fixing typos
hershd23 Sep 1, 2023
a4c3f01
Replaced metadata key back to KEY in test
hershd23 Sep 1, 2023
fcb8e89
Replacing udf_metadata_key in docs
hershd23 Sep 1, 2023
0011642
Fixing failed unit test
hershd23 Sep 1, 2023
82c66d8
Merge branch 'staging' into remove_quotes_udf_metadata_key
hershd23 Sep 4, 2023
b30a204
Replacing <'metadata_key'> with <METADATA_KEY>
hershd23 Sep 4, 2023
801ddb6
Increased underline length in at line 75 in text_summarization.rst
hershd23 Sep 4, 2023
af6c447
Merging staging into branch
hershd23 Sep 5, 2023
ec050b5
Deleted .lock_preprocessing file
hershd23 Sep 5, 2023
9968033
Removed quotes from udf_metadata_key and converted to upper case for
hershd23 Sep 5, 2023
1889c38
Merge branch 'staging' into remove_quotes_udf_metadata_key
hershd23 Sep 5, 2023
8d3668a
Merge branch 'staging' into remove_quotes_udf_metadata_key
xzdandy Sep 5, 2023
49a2df8
Sync with staging
xzdandy Sep 5, 2023
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4 changes: 2 additions & 2 deletions README.md
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Expand Up @@ -185,8 +185,8 @@ SELECT ChatGPT('Is this video summary related to Ukraine russia war', text)
CREATE UDF IF NOT EXISTS PredictHouseRent FROM
( SELECT * FROM HomeRentals )
TYPE Ludwig
'predict' 'rental_price'
'time_limit' 120;
PREDICT 'rental_price'
TIME_LIMIT 120;
```

</details>
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6 changes: 3 additions & 3 deletions docs/source/reference/evaql/create.rst
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Expand Up @@ -44,9 +44,9 @@ To register an user-defined function by training a predication model.
CREATE UDF IF NOT EXISTS PredictHouseRent FROM
(SELECT * FROM HomeRentals)
TYPE Ludwig
'predict' 'rental_price'
'time_list' 120;
'tune_for_memory' False;
PREDICT 'rental_price'
TIME_LIST 120;
TUNE_FOR_MEMORY False;

CREATE MATERIALIZED VIEW
------------------------
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8 changes: 4 additions & 4 deletions docs/source/reference/udfs/model-train.rst
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Expand Up @@ -12,8 +12,8 @@ Training and Finetuning
CREATE UDF IF NOT EXISTS PredictHouseRent FROM
( SELECT sqft, location, rental_price FROM HomeRentals )
TYPE Ludwig
'predict' 'rental_price'
'time_limit' 120;
PREDICT 'rental_price'
TIME_LIMIT 120;

In the above query, you are creating a new customized UDF by automatically training a model from the `HomeRentals` table. The `rental_price` column will be the target column for predication, while `sqft` and `location` are the inputs.

Expand All @@ -24,8 +24,8 @@ You can also simply give all other columns in `HomeRentals` as inputs and let th
CREATE UDF IF NOT EXISTS PredictHouseRent FROM
( SELECT * FROM HomeRentals )
TYPE Ludwig
'predict' 'rental_price'
'time_limit' 120;
PREDICT 'rental_price'
TIME_LIMIT 120;

.. note::

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2 changes: 1 addition & 1 deletion evadb/parser/evadb.lark
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Expand Up @@ -49,7 +49,7 @@ udf_impl: string_literal

udf_metadata: udf_metadata_key udf_metadata_value

udf_metadata_key: string_literal
udf_metadata_key: uid

udf_metadata_value: string_literal | decimal_literal

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4 changes: 3 additions & 1 deletion evadb/parser/lark_visitor/_functions.py
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Expand Up @@ -97,7 +97,9 @@ def create_udf(self, tree):
value = key_value_pair[1]
if isinstance(value, ConstantValueExpression):
value = value.value
metadata.append((key_value_pair[0].value, value)),
# Removing .value from key_value_pair[0] since key is now an ID_LITERAL
# Adding lower() to ensure the key is in lowercase
metadata.append((key_value_pair[0].lower(), value)),

return CreateUDFStatement(
udf_name,
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4 changes: 2 additions & 2 deletions test/integration_tests/long/test_model_train.py
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Expand Up @@ -60,8 +60,8 @@ def test_ludwig_automl(self):
CREATE UDF IF NOT EXISTS PredictHouseRent FROM
( SELECT * FROM HomeRentals )
TYPE Ludwig
'predict' 'rental_price'
'time_limit' 120;
PREDICT 'rental_price'
TIME_LIMIT 120;
"""
execute_query_fetch_all(self.evadb, create_predict_udf)

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4 changes: 2 additions & 2 deletions test/unit_tests/parser/test_parser.py
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Expand Up @@ -662,7 +662,7 @@ def test_create_udf_statement(self):
OUTPUT (Labels NDARRAY STR(10), Bbox NDARRAY UINT8(10, 4))
TYPE Classification
IMPL 'data/fastrcnn.py'
"KEY" "VALUE";
PREDICT "VALUE";
"""

expected_cci = ColConstraintInfo()
Expand Down Expand Up @@ -690,7 +690,7 @@ def test_create_udf_statement(self):
],
"Classification",
None,
[("KEY", "VALUE")],
[("predict", "VALUE")],
)
evadb_statement_list = parser.parse(create_udf_query)
self.assertIsInstance(evadb_statement_list, list)
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