Merge branch 'main' into main

examples/README.md

@@ -1,6 +1,6 @@
 # Requirements
 
-Here is the minimal hardware requirements to run the example.
+Here is the minimal hardware requirements to run the `workflow_by_code` example.
 - Memory: 16G
 - Free Disk: 5G
 

examples/benchmarks/README.md

@@ -29,4 +29,4 @@ The numbers shown below demonstrate the performance of the entire `workflow` of
 | GRU (Kyunghyun Cho, et al.) | Alpha158 (with selected 20 features) | 0.0311±0.00 | 0.2418±0.04| 0.0425±0.00 | 0.3434±0.02 | 0.0330±0.02 | 0.4805±0.30| -0.1021±0.02 |
 | LSTM (Sepp Hochreiter, et al.) | Alpha158 (with selected 20 features) | 0.0312±0.00 | 0.2394±0.04| 0.0418±0.00 | 0.3324±0.03 | 0.0298±0.02 | 0.4198±0.33| -0.1348±0.03 |
 | ALSTM (Yao Qin, et al.) | Alpha158 (with selected 20 features) | 0.0385±0.01 | 0.3022±0.06| 0.0478±0.00 | 0.3874±0.04 | 0.0486±0.03 | 0.7141±0.45| -0.1088±0.03 |
-| GATs (Petar Velickovic, et al.) | Alpha158 (with selected 20 features) | 0.0349±0.00 | 0.2511±0.01| 0.0457±0.00 | 0.3537±0.01 | 0.0578±0.02 | 0.8221±0.25| -0.0824±0.02 |
+| GATs (Petar Velickovic, et al.) | Alpha158 (with selected 20 features) | 0.0349±0.00 | 0.2511±0.01| 0.0457±0.00 | 0.3537±0.01 | 0.0578±0.02 | 0.8221±0.25| -0.0824±0.02 |

examples/benchmarks/SFM/README.md

@@ -1,3 +1,3 @@
 # State-Frequency-Memory
 - State Frequency Memory (SFM) is a novel recurrent network that uses Discrete Fourier Transform to decompose the hidden states of memory cells and capture the multi-frequency trading patterns from past market data to make stock price predictions. 
-- Paper: Stock Price Prediction via Discovering Multi-Frequency Trading Patterns. [https://www.cs.ucf.edu/~gqi/publications/kdd2017_stock.pdf.](https://www.cs.ucf.edu/~gqi/publications/kdd2017_stock.pdf.)
+- Paper: Stock Price Prediction via Discovering Multi-Frequency Trading Patterns. [http://www.eecs.ucf.edu/~gqi/publications/kdd2017_stock.pdf.](http://www.eecs.ucf.edu/~gqi/publications/kdd2017_stock.pdf)

examples/benchmarks/TFT/expt_settings/configs.py

@@ -25,7 +25,7 @@ import os
 import data_formatters.qlib_Alpha158
 
 
-class ExperimentConfig(object):
+class ExperimentConfig:
     """Defines experiment configs and paths to outputs.
 
     Attributes:

examples/benchmarks/TFT/libs/tft_model.py

@@ -320,7 +320,7 @@ class InterpretableMultiHeadAttention:
         return outputs, attn
 
 
-class TFTDataCache(object):
+class TFTDataCache:
     """Caches data for the TFT."""
 
     _data_cache = {}
@@ -348,7 +348,7 @@ class TFTDataCache(object):
 
 
 # TFT model definitions.
-class TemporalFusionTransformer(object):
+class TemporalFusionTransformer:
     """Defines Temporal Fusion Transformer.
 
     Attributes:
@@ -972,7 +972,7 @@ class TemporalFusionTransformer(object):
             valid_quantiles = self.quantiles
             output_size = self.output_size
 
-            class QuantileLossCalculator(object):
+            class QuantileLossCalculator:
                 """Computes the combined quantile loss for prespecified quantiles.
 
                 Attributes:

examples/run_all_model.py

@@ -69,9 +69,9 @@ def handler(signum, frame):
     os.system("kill -9 %d" % os.getpid())
 
 
-signal.signal(signal.SIGTSTP, handler)
 signal.signal(signal.SIGINT, handler)
 
+
 # function to calculate the mean and std of a list in the results dictionary
 def cal_mean_std(results) -> dict:
     mean_std = dict()