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feat: add support for aggregates and toxicity classification #551

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merged 39 commits into from
Jan 7, 2023
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@jarulraj jarulraj commented Jan 2, 2023

  1. Based on Add support for COUNT, SUM, AVG, MIN, MAX  #519
  2. Brings back pip package testing in CI
  3. Reduces verbosity of YOLO model
  4. Update links in readme to point to the stable version in read-the-docs
  5. Add support for toxicity detection based on feat: Add toxic meme detection via UDF #516
  6. Add support for querying based on video timestamps (based on feat: Support timestamps and querying for timestamps #520)

agg_func_name = self.visit(child).value
elif isinstance(child, Token):
token = child.value
# Support for COUNT(*)
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I don't understand this logic. Are we hardcoding * to id in the parser? If yes, I guess even though it is hacky, it saves us from handling this corner case in the binder. We could change it to IDENTIFIER_COLUMN, which is supposed to be a unique row id in all the tables.

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Yes. This query currently works. I was also worried if "id" will be always present. How should I change it to IDENTIFIER_COLUMN?

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The unit tests do not create tables using IDENTIFIER_COLUMN -- so the test case fails.

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Yeah, I verified we don't support projecting IDENTIFIER_COLUMN, which causes the binder to fail.
id won't work for images or other tables. Ideally, the binder should take care of it.
An if condition here should fix it.

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@@ -378,7 +380,14 @@ def aggregate(self, method: str) -> None:
Arguments:
method: string with one of the five above options
"""
self._frames = self._frames.agg([method])
# Aggregate ndarray
if isinstance(self._frames.iat[0, 0], np.ndarray):
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Is it aggregating each row of the array? If yes, I suspect that will break the execution logic.

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Yes, how will it break the execution logic?

The NDARRAY case is for object detection array etc. The normal case is the one that existed earlier -- self._frames = self._frames.agg([method])

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I have reverted back the NDARRAY case as it does not make sense.

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Aggregate on ndarray and primitive column will result in different row counts.

We have a different set of aggregate operators to apply row-wise aggregates likeArray_Count

self.assertEqual(actual_batch.frames.iat[0, 0], 10)
self.assertEqual(actual_batch.frames.iat[0, 1], 4.5)

complex_aggregate_query = """SELECT SUM(id), COUNT(label)
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We should add a test case with aggregate on ndarray column.

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@jarulraj jarulraj Jan 3, 2023

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When the query operates on an ndarray column, it does not reduce it to a single row. It actually keeps as many rows around as the number of the rows in the input column.

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I have reverted back the NDARRAY case as it does not make sense.

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Do we raise an error if the query tries to aggregate on an array column? We should add a test case to verify it. Thanks!

@jarulraj jarulraj changed the title feat: add support for aggregates feat: add support for aggregates and toxicity classification Jan 3, 2023
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jarulraj commented Jan 3, 2023

I just added a ToxicityClassifier UDF -- that runs on top of OCR labels.

@@ -55,9 +55,14 @@ def evaluate(self, *args, **kwargs):
elif self.etype == ExpressionType.AGGREGATION_MAX:
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We miss an else condition to raise an error. Right now, we silently ignore it and return the origin batch.

eva/readers/opencv_reader.py Show resolved Hide resolved
@@ -0,0 +1,49 @@
# coding=utf-8
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What is this for?

single_result = self.model.predict(text)
toxicity_score = single_result["toxicity"][0]
if toxicity_score >= self.threshold:
outcome = outcome.append({"labels": "toxic"}, ignore_index=True)
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I changed it to use list for the append operation. DataFrame throws a lot of warnings. You can refer to other udfs.

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Did you push your change?

if len(inp.columns) != 1:
raise ValueError("input must only contain one column (seconds)")

seconds = pd.DataFrame(inp[inp.columns[0]])
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Isn't it no-op?

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This is a timestamp UDF.

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SELECT id, seconds, Timestamp(seconds) FROM MyVideo WHERE Timestamp(seconds) <= "00:00:01";

@@ -76,6 +76,7 @@ def test_create_multimedia_table_catalog_entry(self, mock):
ColumnDefinition(
"data", ColumnType.NDARRAY, NdArrayType.UINT8, [None, None, None]
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array_dimension should be a tuple.

@gaurav274
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I just added a ToxicityClassifier UDF -- that runs on top of OCR labels.

Thanks! This will be a fun example to showcase 💯

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3 participants