[BUG] best_tft.plot_prediction(new_x, new_raw_predictions, idx=idx, show_future_observed=False) is not working for me

[ash1407]

Describe the bug

To Reproduce

Build TimeSeriesDataSet

predicting = TimeSeriesDataSet(
pred,
time_idx="time_idx",
target="DEPARTURE_DELAY",
group_ids=['IATA_CODE'],
min_encoder_length=max_prediction_length, # years, length to encode (can be far longer than the decoder length but does not have to be)
max_encoder_length=max_encoder_length, # its in years, max_encoder_length
max_prediction_length=max_prediction_length,
static_categoricals=static_cats,
static_reals=static_reals,
time_varying_known_categoricals=time_varying_cats,
time_varying_known_reals=[
"SCHEDULED_DEPARTURE", "DISTANCE", "SCHEDULED_ARRIVAL",
"SCHEDULED_TIME", "ARRIVAL_TIME", "time_idx"
],
time_varying_unknown_reals=[
"DEPARTURE_TIME", "ARRIVAL_DELAY", "SCHEDULED_TIME",
"WIND_GUST_SPEED", "VIS_DIST", "TMP_CELSIUS", "SLP_PRESSURE",
"DEW_CELSIUS", "CEILING_QUALITY", "CIG_HEIGHT", "WIND_DIRECTION",
"SECURITY_DELAY", "AIRLINE_DELAY", "LATE_AIRCRAFT_DELAY",
"WEATHER_DELAY", "AIR_SYSTEM_DELAY", "DIVERTED", "CANCELLED",
"TAXI_IN", "TAXI_OUT", "WHEELS_OFF",
"WHEELS_ON", "ELAPSED_TIME", "AIR_TIME", "DEPARTURE_DELAY"
],
target_normalizer=GroupNormalizer(groups=['IATA_CODE'], transformation="softplus"), # use softplus and normalize by group
categorical_encoders=categorical_encoders, # Apply NaN encoders
add_relative_time_idx=True,
add_target_scales=True,
add_encoder_length=True,
allow_missing_timesteps=True,
)

pred_dataloader = predicting.to_dataloader(train=True, batch_size=128, num_workers=4)

best_tft.plot_prediction(x, raw_predictions, idx=0, show_future_observed=False);

AttributeError Traceback (most recent call last)
Cell In[231], line 1
----> 1 best_tft.plot_prediction(x, raw_predictions, idx=0, show_future_observed=False);

File c:\Users\qj771f\AppData\Local\Programs\Python\Python310\lib\site-packages\pytorch_forecasting\models\temporal_fusion_transformer_init_.py:719, in TemporalFusionTransformer.plot_prediction(self, x, out, idx, plot_attention, add_loss_to_title, show_future_observed, ax, **kwargs)
717 # add attention on secondary axis
718 if plot_attention:
--> 719 interpretation = self.interpret_output(out.iget(slice(idx, idx + 1)))
720 for f in to_list(fig):
721 ax = f.axes[0]

File c:\Users\qj771f\AppData\Local\Programs\Python\Python310\lib\site-packages\pytorch_forecasting\models\temporal_fusion_transformer_init_.py:597, in TemporalFusionTransformer.interpret_output(self, out, reduction, attention_prediction_horizon)
595 # roll encoder attention (so start last encoder value is on the right)
596 encoder_attention = out["encoder_attention"]
--> 597 shifts = encoder_attention.size(3) - out["encoder_lengths"]
598 new_index = (
599 torch.arange(encoder_attention.size(3), device=encoder_attention.device)[None, None, None].expand_as(
600 encoder_attention
601 )
602 - shifts[:, None, None, None]
603 ) % encoder_attention.size(3)
604 encoder_attention = torch.gather(encoder_attention, dim=3, index=new_index)

AttributeError: 'list' object has no attribute 'size'

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[fkiraly]

@ash1407, this example is not fully reproducible since it does not include the data.

Would you be able to produce some dummy data and add all the import, so the bug report is self-contained?

We can then use this for debugging.

[ash1407]

There are some negative data points in data ex- for temperature, departure delay, dew temp
using
pytorch_forecasting: 0.10.1
pandas: 1.5.0
scikit-learn: 1.0.2
matplotlib: 3.7.1
numpy: 1.26.4
seaborn: 0.12.2
pytorch_lightning: 1.9.5
torch: 1.13.1+cu116

<style> </style>

time_idx	DATE	YEAR	MONTH	DAY	IATA_CODE	AIRPORT	CITY	STATE	COUNTRY	LATITUDE	LONGITUDE	DAY_OF_WEEK	AIRLINE	FLIGHT_NUMBER	TAIL_NUMBER	ORIGIN_AIRPORT	DESTINATION_AIRPORT	SCHEDULED_DEPARTURE	DEPARTURE_TIME	DEPARTURE_DELAY	TAXI_OUT	WHEELS_OFF	SCHEDULED_TIME	ELAPSED_TIME	AIR_TIME	DISTANCE	WHEELS_ON	TAXI_IN	SCHEDULED_ARRIVAL	ARRIVAL_TIME	ARRIVAL_DELAY	DIVERTED	CANCELLED	CANCELLATION_REASON	AIR_SYSTEM_DELAY	SECURITY_DELAY	AIRLINE_DELAY	LATE_AIRCRAFT_DELAY	WEATHER_DELAY	SCHEDULED_DEPARTURE_NEW_MIN	WIND_DIRECTION	WIND_SPEED	WIND_CHAR	WIND_GUST_SPEED	WIND_QUALITY	CIG_HEIGHT	CEILING_QUALITY	CIG_TYPE	CLOUD_DETECTION	VIS_DIST	VIS_QUAL	VIS_OBSTRUCT	VIS_QUALITY2	TMP_CELSIUS	TEMP_QUALITY	DEW_CELSIUS	DEW_FLAG	SLP_PRESSURE	SLP_QUALITY
0	1/5/2015	2015	1	5	IAH	George Bush Intercontinental Airport	Houston	TX	USA	29.98047	95.33972	1	EV	4298	N12160	IAH	DTW	1015	1013	2	14	1027	169	155	133	1075	1340	8	1404	1348	16	0	0	B	3	0	5	1	0	1015	40	5	N	36	5	7620	5	M	N	16093	5	N	5	28	5	39	5	10387	5
0	1/9/2015	2015	1	9	DAL	Dallas Love Field	Dallas	TX	USA	32.84711	96.85177	5	WN	1614	N413WN	DAL	DEN	1710	1751	41	6	1757	125	112	94	651	1831	12	1815	1843	28	0	0	B	0	0	28	0	0	1710	240	5	N	36	5	7620	5	M	N	16093	5	N	5	61	5	17	5	10221	5
0	1/2/2015	2015	1	2	DTW	Detroit Metropolitan Airport	Detroit	MI	USA	42.21206	83.34884	5	EV	5086	N852AS	DTW	MLI	1525	1522	3	10	1532	91	77	64	373	1536	3	1556	1539	17	0	0	B	3	0	5	1	0	1525	240	5	N	36	5	7620	5	M	N	16093	5	N	5	61	5	17	5	10221	5
0	1/6/2015	2015	1	6	SLC	Salt Lake City International Airport	Salt Lake City	UT	USA	40.78839	111.9778	2	OO	4755	N549CA	SLC	PHX	1651	1736	45	12	1748	100	84	69	507	1857	3	1831	1900	29	0	0	B	0	0	27	2	0	1651	240	5	N	36	5	7620	5	M	N	16093	5	N	5	61	5	17	5	10221	5
0	########	2015	1	22	MSP	Minneapolis-Saint Paul International Airport	Minneapolis	MN	USA	44.88055	93.21692	4	DL	2628	N896AT	MSP	DSM	1310	1308	2	10	1318	74	56	43	232	1401	3	1424	1404	20	0	0	B	3	0	5	1	0	1310	220	5	N	62	5	427	5	M	N	16093	5	N	5	-56	5	89	5	99999	9
0	1/9/2015	2015	1	9	EWR	Newark Liberty International Airport	Newark	NJ	USA	40.6925	74.16866	5	EV	4300	N15572	EWR	RIC	1550	1602	12	16	1618	82	87	59	277	1717	12	1712	1729	17	0	0	B	5	0	3	9	0	1550	240	5	N	36	5	7620	5	M	N	16093	5	N	5	61	5	17	5	10221	5
0	1/3/2015	2015	1	3	MCO	Orlando International Airport	Orlando	FL	USA	28.42889	81.31603	6	B6	2228	N354JB	MCO	EWR	550	548	2	19	607	149	143	116	937	803	8	819	811	8	0	0	B	3	0	5	1	0	550	90	5	N	15	5	61	5	M	N	805	5	N	5	200	5	200	5	99999	9
0	1/5/2015	2015	1	5	LAX	Los Angeles International Airport	Los Angeles	CA	USA	33.94254	118.4081	1	AA	1691	N851AA	LAX	LAS	1755	1806	11	14	1820	75	65	42	236	1902	9	1910	1911	1	0	0	B	3	0	5	1	0	1755	240	5	N	36	5	7620	5	M	N	16093	5	N	5	61	5	17	5	10221	5
0	########	2015	1	29	SLC	Salt Lake City International Airport	Salt Lake City	UT	USA	40.78839	111.9778	4	DL	1074	N367NW	SLC	MSP	1511	1517	6	12	1529	149	150	133	991	1842	5	1840	1847	7	0	0	B	3	0	5	1	0	1511	240	5	N	36	5	7620	5	M	N	16093	5	N	5	61	5	17	5	10221	5
0	########	2015	1	17	IAH	George Bush Intercontinental Airport	Houston	TX	USA	29.98047	95.33972	6	EV	4184	N12921	IAH	LIT	1301	1257	4	16	1313	79	77	56	374	1409	5	1420	1414	6	0	0	B	3	0	5	1	0	1301	220	5	N	46	5	7620	5	M	N	16093	5	N	5	200	5	72	5	10175	5
0	########	2015	1	22	CLT	Charlotte Douglas International Airport	Charlotte	NC	USA	35.21401	80.94313	4	US	1798	N250AY	CLT	PHL	2010	2010	0	15	2025	97	89	70	449	2135	4	2147	2139	8	0	0	B	3	0	5	1	0	2010	240	5	N	36	5	7620	5	M	N	16093	5	N	5	61	5	17	5	10221	5
0	1/2/2015	2015	1	2	DAL	Dallas Love Field	Dallas	TX	USA	32.84711	96.85177	5	WN	3300	N638SW	DAL	STL	1335	1355	20	14	1409	90	89	72	546	1521	3	1505	1524	19	0	0	B	0	0	14	5	0	1335	30	5	N	26	5	213	5	M	N	2414	5	N	5	50	5	39	5	99999	9
0	########	2015	1	28	ATL	Hartsfield-Jackson Atlanta International Airport	Atlanta	GA	USA	33.64044	84.42694	3	DL	1803	N947DN	ATL	RDU	1120	1120	0	19	1139	80	80	56	356	1235	5	1240	1240	0	0	0	B	3	0	5	1	0	1120	60	5	N	26	5	22000	5	9	N	16093	5	N	5	89	5	50	5	10250	5
0	########	2015	1	29	LAX	Los Angeles International Airport	Los Angeles	CA	USA	33.94254	118.4081	4	WN	3944	N354SW	LAX	SFO	1105	1101	4	15	1116	85	74	53	337	1209	6	1230	1215	15	0	0	B	3	0	5	1	0	1105	80	5	N	21	5	5486	5	M	N	16093	5	N	5	194	5	67	5	10180	5

[fkiraly]

@ash1407, thanks - could you put that in a single python code block?

You can myke python code blocks with three backticks, like this:

a = 42
print(a)