From bc56c7e1adc0cc3ee8bc98495f45c99e349b4edb Mon Sep 17 00:00:00 2001 From: sanyhe Date: Tue, 27 Feb 2024 21:13:08 +0800 Subject: [PATCH] perf: optimze the loading of built-in application data. --- geochemistrypi/data_mining/cli_pipeline.py | 11 ++++++----- ...lsx => ApplicationData_Classification.xlsx} | Bin ...on.xlsx => ApplicationData_Regression.xlsx} | Bin .../data/dataset/InferenceData_Clustering.xlsx | Bin 225363 -> 0 bytes .../dataset/InferenceData_Decomposition.xlsx | Bin 44562 -> 0 bytes geochemistrypi/data_mining/model/regression.py | 4 ++-- 6 files changed, 8 insertions(+), 7 deletions(-) rename geochemistrypi/data_mining/data/dataset/{InferenceData_Classification.xlsx => ApplicationData_Classification.xlsx} (100%) rename geochemistrypi/data_mining/data/dataset/{InferenceData_Regression.xlsx => ApplicationData_Regression.xlsx} (100%) delete mode 100644 geochemistrypi/data_mining/data/dataset/InferenceData_Clustering.xlsx delete mode 100644 geochemistrypi/data_mining/data/dataset/InferenceData_Decomposition.xlsx diff --git a/geochemistrypi/data_mining/cli_pipeline.py b/geochemistrypi/data_mining/cli_pipeline.py index eda6de6d..db44495d 100644 --- a/geochemistrypi/data_mining/cli_pipeline.py +++ b/geochemistrypi/data_mining/cli_pipeline.py @@ -202,20 +202,21 @@ def cli_pipeline(training_data_path: str, application_data_path: Optional[str] = logger.debug("Built-in Application Data Loading") # If the user doesn't provide training data path and inference data path, then use the built-in inference data. if is_built_in_inference_data and built_in_training_data_num == 1: - application_data_path = "InferenceData_Regression.xlsx" + application_data_path = "ApplicationData_Regression.xlsx" inference_data = read_data(file_path=application_data_path) - print(f"Successfully loading the built-in inference data set '{application_data_path}'.") + print(f"Successfully loading the built-in application data set '{application_data_path}'.") show_data_columns(inference_data.columns) + clear_output() elif is_built_in_inference_data and built_in_training_data_num == 2: - application_data_path = "InferenceData_Classification.xlsx" + application_data_path = "ApplicationData_Classification.xlsx" inference_data = read_data(file_path=application_data_path) - print(f"Successfully loading the built-in inference data set '{application_data_path}'.") + print(f"Successfully loading the built-in application data set '{application_data_path}'.") show_data_columns(inference_data.columns) + clear_output() elif is_built_in_inference_data and built_in_training_data_num == 3: inference_data = None elif is_built_in_inference_data and built_in_training_data_num == 4: inference_data = None - clear_output() # <--- World Map Projection ---> logger.debug("World Map Projection") diff --git a/geochemistrypi/data_mining/data/dataset/InferenceData_Classification.xlsx b/geochemistrypi/data_mining/data/dataset/ApplicationData_Classification.xlsx similarity index 100% rename from geochemistrypi/data_mining/data/dataset/InferenceData_Classification.xlsx rename to geochemistrypi/data_mining/data/dataset/ApplicationData_Classification.xlsx diff --git a/geochemistrypi/data_mining/data/dataset/InferenceData_Regression.xlsx b/geochemistrypi/data_mining/data/dataset/ApplicationData_Regression.xlsx similarity index 100% rename from geochemistrypi/data_mining/data/dataset/InferenceData_Regression.xlsx rename to geochemistrypi/data_mining/data/dataset/ApplicationData_Regression.xlsx diff --git a/geochemistrypi/data_mining/data/dataset/InferenceData_Clustering.xlsx b/geochemistrypi/data_mining/data/dataset/InferenceData_Clustering.xlsx deleted file mode 100644 index 5ac75b7341cf97a78602af7b28768eafa223ae42..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 225363 zcmZs>bzGBS`#ubV(d`IP8j&vPZWJU$I!A-FA|WwGcc_HYB_b^;IgnD2F6r*h5!>F| z=llFVpWpX=-~F>O?(IHzT<3Wl$9Z0tt|ksH5!Sy)l=iB^zvut+AOv36dD!Z{_V9co 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zi0snfvIxb2#1Ig$qTSFT6ywb{fl|0ka7>V?Osp2p)mvXv(^p><%7w6qRp0$lQ$Chl zwkcx3D8t6$UlRC5que81gFgxT8TMqy$U+!EyL@$YfZR8V)sJHyzV1P!K>UX2-mOFv zmjf#q9S}zVo?ZWkdHBz$1OulB{N*7Da|TS=f-XWr{l`Q4OA-G#(iXJ?iO1GiU&YuTp{ZDeO>{l~NaE05i6-TX6%;{=34`5OzUy&xwL#PfHCe^+Aiv|}d# z)btok07xi*Pn3v%f}Jc39ZgM?ok4nH?)2Zg@DCh4E+MPq+2IilUh>R)RSc02o#U2jKpL zPyzejgnv=}TPFW@n*Snd%>0w(-!AmO3H~`_`3nvJc;o{B|DM168~sm9@prUP(cjSj o%WnJ|{_jEl7eRl?pSAzL8S+3GD3CS*0EnOuC8%>3OaCbS4|SwN5dZ)H diff --git a/geochemistrypi/data_mining/model/regression.py b/geochemistrypi/data_mining/model/regression.py index b621bdbd..e78cda95 100644 --- a/geochemistrypi/data_mining/model/regression.py +++ b/geochemistrypi/data_mining/model/regression.py @@ -4115,7 +4115,7 @@ def __init__( @property def settings(self) -> Dict: - """The configuration of Lasso to implement AutoML by FLAML framework.""" + """The configuration of Ridge to implement AutoML by FLAML framework.""" configuration = { "time_budget": 10, # total running time in seconds "metric": "r2", @@ -4128,7 +4128,7 @@ def settings(self) -> Dict: @property def customization(self) -> object: - """The customized Lasso of FLAML framework.""" + """The customized Ridge of FLAML framework.""" from flaml import tune from flaml.data import REGRESSION from flaml.model import SKLearnEstimator