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* fix isort & black & toml-sort & sphinx error

* fix ci error

* fix ci error

* add comments

* Update Makefile

* change sphinx build command

* add auto-lint

* add black args

* format with black

* Auto Linting document

* fix ci error

---------

Co-authored-by: you-n-g <you-n-g@users.noreply.github.com>
Co-authored-by: Young <afe.young@gmail.com>
This commit is contained in:
Linlang
2024-07-26 12:12:16 +08:00
committed by GitHub
parent 45b7a169fe
commit c7cfd397ca
56 changed files with 604 additions and 475 deletions
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rdagent/app/quant_factor_benchmark/analysis.py
+14 -15
View File
@@ -1,12 +1,14 @@
import json
import pickle
import pandas as pd
from pathlib import Path
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
from rdagent.components.benchmark.eval_method import FactorImplementEval
from rdagent.components.benchmark.conf import BenchmarkSettings
from rdagent.components.benchmark.eval_method import FactorImplementEval
class BenchmarkAnalyzer:
def __init__(self, settings):
@@ -25,10 +27,10 @@ class BenchmarkAnalyzer:
file_path = Path(file_path)
if not (file_path.is_file() and file_path.suffix == ".pkl"):
raise ValueError("Invalid file path")
with file_path.open("rb") as f:
res = pickle.load(f)
return res
def process_results(self, results):
@@ -39,7 +41,7 @@ class BenchmarkAnalyzer:
processed_data = self.analyze_data(summarized_data)
final_res[experiment] = processed_data.iloc[-1, :]
return final_res
def reformat_succ_rate(self, display_df):
new_idx = []
display_df = display_df[display_df.index.isin(self.index_map.keys())]
@@ -52,8 +54,10 @@ class BenchmarkAnalyzer:
)
display_df = display_df.swaplevel(0, 2).swaplevel(0, 1).sort_index(axis=0)
return display_df.sort_index(key=lambda x: [{"Easy": 0, "Medium": 1, "Hard": 2, "New Discovery": 3}.get(i, i) for i in x])
return display_df.sort_index(
key=lambda x: [{"Easy": 0, "Medium": 1, "Hard": 2, "New Discovery": 3}.get(i, i) for i in x]
)
def result_all_key_order(self, x):
order_v = []
for i in x:
@@ -92,9 +96,7 @@ class BenchmarkAnalyzer:
sum_df_clean["FactorRowCountEvaluator"]
format_issue = (
sum_df_clean["FactorRowCountEvaluator"] & sum_df_clean["FactorIndexEvaluator"]
)
format_issue = sum_df_clean["FactorRowCountEvaluator"] & sum_df_clean["FactorIndexEvaluator"]
eval_series = format_issue.unstack()
succ_rate = eval_series.T.fillna(False).astype(bool) # false indicate failure
format_succ_rate = succ_rate.mean(axis=0).to_frame("success rate")
@@ -113,10 +115,7 @@ class BenchmarkAnalyzer:
value_max_res = self.reformat_succ_rate(value_max)
value_avg = (
(sum_df_clean["FactorMissingValuesEvaluator"] * format_issue)
.unstack()
.T.mean(axis=0)
.to_frame("avg_value")
(sum_df_clean["FactorMissingValuesEvaluator"] * format_issue).unstack().T.mean(axis=0).to_frame("avg_value")
)
value_avg_res = self.reformat_succ_rate(value_avg)
@@ -148,7 +147,6 @@ class BenchmarkAnalyzer:
return df_w_mean
class Plotter:
@staticmethod
def change_fs(font_size):
@@ -169,6 +167,7 @@ class Plotter:
plt.title("Comparison of Different Methods")
plt.savefig(file_name)
if __name__ == "__main__":
settings = BenchmarkSettings()
benchmark = BenchmarkAnalyzer(settings)