def test_aggregation_max(self):
columnName = TupleValueExpression(col_name=0)
aggr_expr = AggregationExpression(
ExpressionType.AGGREGATION_MAX,
None,
columnName
)
tuples = Batch(pd.DataFrame({0: [1, 2, 3], 1: [2, 3, 4], 2: [3, 4, 5]}))
self.assertEqual(3, aggr_expr.evaluate(tuples, None))
def test_aggregation_min(self):
columnName = TupleValueExpression(col_name=0)
aggr_expr = AggregationExpression(
ExpressionType.AGGREGATION_MIN,
None,
columnName
)
tuples = Batch(pd.DataFrame(
{0: [1, 2, 3], 1: [2, 3, 4], 2: [3, 4, 5]}))
batch = aggr_expr.evaluate(tuples, None)
self.assertEqual(1, batch.frames.iloc[0][0])
def test_aggregation_min(self):
columnName = TupleValueExpression(0)
aggr_expr = AggregationExpression(
ExpressionType.AGGREGATION_MIN,
None,
columnName
)
frame_1 = Frame(1, np.ones((1, 1)), None)
frame_2 = Frame(2, 2 * np.ones((1, 1)), None)
frame_3 = Frame(3, 3 * np.ones((1, 1)), None)
outcome_1 = Prediction(frame_1, ["car", "bus"], [0.5, 0.6])
outcome_2 = Prediction(frame_2, ["bus"], [0.5, 0.6])
outcome_3 = Prediction(frame_3, ["car", "train"], [0.5, 0.6])
input_batch = FrameBatch(frames=[
frame_1,
frame_2,
frame_3,
], info=None)
expected_value = 1
output_value = aggr_expr.evaluate(input_batch)
self.assertEqual(expected_value, output_value)
def test_aggregation_max(self):
columnName = TupleValueExpression(col_idx=0)
aggr_expr = AggregationExpression(ExpressionType.AGGREGATION_MAX, None,
columnName)
tuples = [[1, 2, 3], [2, 3, 4], [3, 4, 5]]
self.assertEqual(3, aggr_expr.evaluate(tuples, None))
