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26 in github.com/amazon-science/chronos-forecasting
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26
Route
test_attention_implementations_with_output_attentions
pytest.mark.parametrize("output_attentions", [False, True])
test/test_chronos2.py:None
Route
test_pipeline_can_be_finetuned_with_empty_future_covariates
pytest.mark.parametrize( "inputs, prediction_length, expected_output_shapes", [ # Homogenous l
test/test_chronos2.py:None
Route
test_pipeline_can_be_finetuned_with_validation
pytest.mark.parametrize( "inputs, prediction_length, expected_output_shapes", [ # Homogenous l
test/test_chronos2.py:None
Route
test_pipeline_can_evaluate_on_dummy_fev_task
pytest.mark.parametrize( "task_kwargs", [ {"dataset_path": "autogluon/chronos_datasets", "data
test/test_chronos2.py:None
Route
test_pipeline_embed
pytest.mark.parametrize("input_dtype", [torch.float32, torch.bfloat16, torch.int64])
test/test_chronos_bolt.py:None
Route
test_pipeline_embed
pytest.mark.parametrize("input_dtype", [torch.float32, torch.bfloat16, torch.int64])
test/test_chronos.py:None
Route
test_pipeline_predict
pytest.mark.parametrize("input_dtype", [torch.float32, torch.bfloat16, torch.int64])
test/test_chronos_bolt.py:None
Route
test_pipeline_predict
pytest.mark.parametrize("input_dtype", [torch.float32, torch.bfloat16, torch.int64])
test/test_chronos.py:None
Route
test_pipeline_predict_can_handle_different_model_and_input_dtypes
pytest.mark.parametrize("input_dtype", [torch.float32, torch.bfloat16, torch.int64])
test/test_chronos2.py:None
Route
test_pipeline_predict_quantiles
pytest.mark.parametrize("input_dtype", [torch.float32, torch.bfloat16, torch.int64])
test/test_chronos_bolt.py:None
Route
test_pipeline_predict_quantiles
pytest.mark.parametrize("input_dtype", [torch.float32, torch.bfloat16, torch.int64])
test/test_chronos.py:None
Route
test_predict_df_df_validation_errors
pytest.mark.parametrize( "context_data, error_match", [ # Missing timestamp column ({"
test/test_chronos2.py:None
Route
test_predict_df_future_df_validation_errors
pytest.mark.parametrize( "future_data, error_match", [ # Missing timestamp column ({"i
test/test_chronos2.py:None
Route
test_predict_df_outputs_different_results_with_cross_learning_enabled
pytest.mark.parametrize( "context_setup, future_setup", [ # Targets only ({}, None),
test/test_chronos2.py:None
Route
test_predict_df_with_non_uniform_timestamps_raises_error
pytest.mark.parametrize("validate_inputs", [True, False])
test/test_chronos2.py:None
Route
test_predict_df_works_for_valid_inputs
pytest.mark.parametrize( "context_setup, expected_rows", [ # Targets only ({}, 6), #
test/test_chronos_bolt.py:None
Route
test_predict_df_works_for_valid_inputs
pytest.mark.parametrize( "context_setup, expected_rows", [ # Targets only ({}, 6), #
test/test_chronos.py:None
Route
test_predict_df_works_for_valid_inputs
pytest.mark.parametrize( "context_setup, future_setup", [ # Targets only ({}, None),
test/test_chronos2.py:None
Route
test_two_step_finetuning_with_df_input_works
pytest.mark.parametrize( "context_setup, future_setup", [ # Targets only ({}, None),
test/test_chronos2.py:None
Route
test_validate_df_raises_for_malformed_future_df
pytest.mark.parametrize( "future_data, error_match", [ # Missing timestamp column ({"i
test/test_df_utils.py:None
Route
test_when_input_is_invalid_then_predict_raises_value_error
pytest.mark.parametrize( "inputs, error_match_string", [ (torch.rand(16), "Expected 3-d tensor
test/test_chronos2.py:None
Route
test_when_input_is_valid_then_pipeline_can_be_finetuned
pytest.mark.parametrize( "inputs, prediction_length, expected_output_shapes", [ # Homogenous u
test/test_chronos2.py:None
Route
test_when_input_is_valid_then_pipeline_can_embed
pytest.mark.parametrize( "inputs, expected_output_shapes", [ # NOTE: d_model for the dummy mod
test/test_chronos2.py:None
Route
test_when_input_is_valid_then_pipeline_can_predict
pytest.mark.parametrize( "inputs, prediction_length, expected_output_shapes", [ # Homogenous u
test/test_chronos2.py:None
Route
test_when_input_is_valid_then_pipeline_can_predict_quantiles
pytest.mark.parametrize( "inputs, prediction_length, quantile_levels, expected_output_shapes", [
test/test_chronos2.py:None
Route
test_when_input_time_series_are_too_short_then_finetuning_raises_error
pytest.mark.parametrize( "inputs, prediction_length", [ # Homogenous univariate task (
test/test_chronos2.py:None