From 7cb6a6623d78f09b87593575cb0f89c5ce1cac88 Mon Sep 17 00:00:00 2001
From: jin <571979568@qq.com>
Date: Thu, 26 Sep 2024 22:12:29 +0800
Subject: [PATCH] perf:Optimized the code to build the APP
---
geochemistrypi/data_mining/cli_pipeline.py | 318 ++++++++++++++++--
geochemistrypi/data_mining/constants.py | 3 +
geochemistrypi/data_mining/update/_base.py | 0
.../data_mining/update/anaconda_checker.py | 17 +
geochemistrypi/data_mining/update/base.py | 96 ++++++
5 files changed, 398 insertions(+), 36 deletions(-)
delete mode 100644 geochemistrypi/data_mining/update/_base.py
create mode 100644 geochemistrypi/data_mining/update/base.py
diff --git a/geochemistrypi/data_mining/cli_pipeline.py b/geochemistrypi/data_mining/cli_pipeline.py
index 2fdde4e8..a11ebd7f 100644
--- a/geochemistrypi/data_mining/cli_pipeline.py
+++ b/geochemistrypi/data_mining/cli_pipeline.py
@@ -27,8 +27,10 @@
NON_AUTOML_MODELS,
OPTION,
OUTPUT_PATH,
+ PACKAGEDIR,
REGRESSION_MODELS,
REGRESSION_MODELS_WITH_MISSING_VALUES,
+ RELEASE_VERSION,
SECTION,
TEST_DATA_OPTION,
TOGGLE_ADDRESS_STATUS,
@@ -61,6 +63,8 @@
from .process.decompose import DecompositionModelSelection
from .process.detect import AnomalyDetectionModelSelection
from .process.regress import RegressionModelSelection
+from .update.anaconda_checker import anoconda_installer
+from .update.base import get_latest_release_version, run_bat_file
from .utils.base import check_package, clear_output, copy_files, create_geopi_output_dir, get_os, install_package, log, save_data, show_warning
from .utils.mlflow_utils import retrieve_previous_experiment_id
from .utils.toggle_address_status import toggle_address_status
@@ -83,6 +87,18 @@ def cli_pipeline(training_data_path: str, application_data_path: Optional[str] =
The path of the application data, by default None
"""
+ # Check if Anaconda is installed on the computer.
+ anoconda_installer()
+
+ # Check if it is the latest version, if not, download it.
+ repo_owner = "ZJUEarthData"
+ repo_name = "Geochemistrypi"
+ version = get_latest_release_version(repo_owner, repo_name)
+ if str(version) != RELEASE_VERSION:
+ update_path = os.path.join(PACKAGEDIR, "bat", "latestversion_installer.bat")
+ update_path = "{} '{}'".format(update_path, str(version))
+ run_bat_file(update_path)
+
# Local test: Uncomment the following line to utilize built-in datasets to test the pipeline. Don't forget to modify the path value to be consistent with your own location.
# training_data_path = "/Users/can/Documents/github/work/geo_ml/geochemistrypi/geochemistrypi/data_mining/data/dataset/Data_Classification.xlsx"
# application_data_path = "/Users/can/Documents/github/work/geo_ml/geochemistrypi/geochemistrypi/data_mining/data/dataset/Data_Classification.xlsx"
@@ -104,7 +120,11 @@ def cli_pipeline(training_data_path: str, application_data_path: Optional[str] =
sleep(0.75)
# Call toggle_address_status and pass status and training_data_path as parameters to obtain the address of the training data
- training_data_path = toggle_address_status(status=TOGGLE_ADDRESS_STATUS, training_data_path=training_data_path, user_conformation=1)[0]
+ training_data_path = toggle_address_status(
+ status=TOGGLE_ADDRESS_STATUS,
+ training_data_path=training_data_path,
+ user_conformation=1,
+ )[0]
# Check if the length of training_data_path is greater than 1
if len(training_data_path) > 1:
@@ -288,8 +308,20 @@ def cli_pipeline(training_data_path: str, application_data_path: Optional[str] =
distribution_plot(data_selected.columns, data_selected, name_column_select)
log_distribution_plot(data_selected.columns, data_selected, name_column_select)
GEOPI_OUTPUT_ARTIFACTS_DATA_PATH = os.getenv("GEOPI_OUTPUT_ARTIFACTS_DATA_PATH")
- save_data(data, name_column_origin, "Data Original", GEOPI_OUTPUT_ARTIFACTS_DATA_PATH, MLFLOW_ARTIFACT_DATA_PATH)
- save_data(data_selected, name_column_select, "Data Selected", GEOPI_OUTPUT_ARTIFACTS_DATA_PATH, MLFLOW_ARTIFACT_DATA_PATH)
+ save_data(
+ data,
+ name_column_origin,
+ "Data Original",
+ GEOPI_OUTPUT_ARTIFACTS_DATA_PATH,
+ MLFLOW_ARTIFACT_DATA_PATH,
+ )
+ save_data(
+ data_selected,
+ name_column_select,
+ "Data Selected",
+ GEOPI_OUTPUT_ARTIFACTS_DATA_PATH,
+ MLFLOW_ARTIFACT_DATA_PATH,
+ )
data_selected_name = pd.concat([name_column_select, data_selected], axis=1)
clear_output()
@@ -353,7 +385,13 @@ def cli_pipeline(training_data_path: str, application_data_path: Optional[str] =
print(data_selected_dropped)
print("Basic Statistical Information:")
drop_name_column = data_selected_dropped_name.iloc[:, 0]
- save_data(data_selected_dropped, drop_name_column, "Data Selected Dropped-Imputed", GEOPI_OUTPUT_ARTIFACTS_DATA_PATH, MLFLOW_ARTIFACT_DATA_PATH)
+ save_data(
+ data_selected_dropped,
+ drop_name_column,
+ "Data Selected Dropped-Imputed",
+ GEOPI_OUTPUT_ARTIFACTS_DATA_PATH,
+ MLFLOW_ARTIFACT_DATA_PATH,
+ )
drop_rows_with_missing_value_flag = True
imputed_flag = False
elif drop_missing_value_strategy_num == 2:
@@ -377,7 +415,13 @@ def cli_pipeline(training_data_path: str, application_data_path: Optional[str] =
print(data_selected_dropped)
print("Basic Statistical Information:")
drop_name_column = data_selected_dropped_name.iloc[:, 0]
- save_data(data_selected_dropped, drop_name_column, "Data Selected Dropped-Imputed", GEOPI_OUTPUT_ARTIFACTS_DATA_PATH, MLFLOW_ARTIFACT_DATA_PATH)
+ save_data(
+ data_selected_dropped,
+ drop_name_column,
+ "Data Selected Dropped-Imputed",
+ GEOPI_OUTPUT_ARTIFACTS_DATA_PATH,
+ MLFLOW_ARTIFACT_DATA_PATH,
+ )
drop_rows_with_missing_value_flag = True
imputed_flag = False
missing_value_flag = check_missing_value(data_selected_dropped)
@@ -391,12 +435,24 @@ def cli_pipeline(training_data_path: str, application_data_path: Optional[str] =
else:
# Don't deal with the missing values, which means neither drop the rows with missing values nor use imputation techniques.
imputed_flag = False
- save_data(data_selected, name_column_select, "Data Selected Dropped-Imputed", GEOPI_OUTPUT_ARTIFACTS_DATA_PATH, MLFLOW_ARTIFACT_DATA_PATH)
+ save_data(
+ data_selected,
+ name_column_select,
+ "Data Selected Dropped-Imputed",
+ GEOPI_OUTPUT_ARTIFACTS_DATA_PATH,
+ MLFLOW_ARTIFACT_DATA_PATH,
+ )
clear_output()
else:
# If the selected data set doesn't have missing values, then don't deal with the missing values.
imputed_flag = False
- save_data(data_selected, name_column_select, "Data Selected Dropped-Imputed", GEOPI_OUTPUT_ARTIFACTS_DATA_PATH, MLFLOW_ARTIFACT_DATA_PATH)
+ save_data(
+ data_selected,
+ name_column_select,
+ "Data Selected Dropped-Imputed",
+ GEOPI_OUTPUT_ARTIFACTS_DATA_PATH,
+ MLFLOW_ARTIFACT_DATA_PATH,
+ )
clear_output()
data_selected = data_selected_dropped if drop_rows_with_missing_value_flag else data_selected
drop_name_column = data_selected_dropped_name.iloc[:, 0] if drop_rows_with_missing_value_flag else name_column_select
@@ -426,7 +482,13 @@ def cli_pipeline(training_data_path: str, application_data_path: Optional[str] =
probability_plot(data_selected.columns, data_selected, data_selected_imputed)
basic_info(data_selected_imputed)
basic_statistic(data_selected_imputed)
- save_data(data_selected_imputed, drop_name_column, "Data Selected Dropped-Imputed", GEOPI_OUTPUT_ARTIFACTS_DATA_PATH, MLFLOW_ARTIFACT_DATA_PATH)
+ save_data(
+ data_selected_imputed,
+ drop_name_column,
+ "Data Selected Dropped-Imputed",
+ GEOPI_OUTPUT_ARTIFACTS_DATA_PATH,
+ MLFLOW_ARTIFACT_DATA_PATH,
+ )
del data_selected
clear_output()
else:
@@ -483,7 +545,13 @@ def cli_pipeline(training_data_path: str, application_data_path: Optional[str] =
print(X)
print("Basic Statistical Information: ")
basic_statistic(X)
- save_data(X, name_all, "X Without Scaling", GEOPI_OUTPUT_ARTIFACTS_DATA_PATH, MLFLOW_ARTIFACT_DATA_PATH)
+ save_data(
+ X,
+ name_all,
+ "X Without Scaling",
+ GEOPI_OUTPUT_ARTIFACTS_DATA_PATH,
+ MLFLOW_ARTIFACT_DATA_PATH,
+ )
clear_output()
# Create Y data set
@@ -499,7 +567,13 @@ def cli_pipeline(training_data_path: str, application_data_path: Optional[str] =
print(y)
print("Basic Statistical Information: ")
basic_statistic(y)
- save_data(y, name_all, "Y", GEOPI_OUTPUT_ARTIFACTS_DATA_PATH, MLFLOW_ARTIFACT_DATA_PATH)
+ save_data(
+ y,
+ name_all,
+ "Y",
+ GEOPI_OUTPUT_ARTIFACTS_DATA_PATH,
+ MLFLOW_ARTIFACT_DATA_PATH,
+ )
clear_output()
# <--- Feature Scaling --->
@@ -517,7 +591,13 @@ def cli_pipeline(training_data_path: str, application_data_path: Optional[str] =
print(X)
print("Basic Statistical Information: ")
basic_statistic(X)
- save_data(X, name_all, "X With Scaling", GEOPI_OUTPUT_ARTIFACTS_DATA_PATH, MLFLOW_ARTIFACT_DATA_PATH)
+ save_data(
+ X,
+ name_all,
+ "X With Scaling",
+ GEOPI_OUTPUT_ARTIFACTS_DATA_PATH,
+ MLFLOW_ARTIFACT_DATA_PATH,
+ )
else:
feature_scaling_config = {}
clear_output()
@@ -533,7 +613,13 @@ def cli_pipeline(training_data_path: str, application_data_path: Optional[str] =
feature_selection_config, X = feature_selector(X, y, mode_num, FEATURE_SELECTION_STRATEGY, feature_selection_num - 1)
print("--Selected Features-")
show_data_columns(X.columns)
- save_data(X, name_all, "X After Feature Selection", GEOPI_OUTPUT_ARTIFACTS_DATA_PATH, MLFLOW_ARTIFACT_DATA_PATH)
+ save_data(
+ X,
+ name_all,
+ "X After Feature Selection",
+ GEOPI_OUTPUT_ARTIFACTS_DATA_PATH,
+ MLFLOW_ARTIFACT_DATA_PATH,
+ )
else:
feature_selection_config = {}
clear_output()
@@ -555,10 +641,19 @@ def cli_pipeline(training_data_path: str, application_data_path: Optional[str] =
data_name_column = train_test_data["Name Train"]
else:
data_name_column = train_test_data["Name Test"]
- save_data(value, data_name_column, key, GEOPI_OUTPUT_ARTIFACTS_DATA_PATH, MLFLOW_ARTIFACT_DATA_PATH)
+ save_data(
+ value,
+ data_name_column,
+ key,
+ GEOPI_OUTPUT_ARTIFACTS_DATA_PATH,
+ MLFLOW_ARTIFACT_DATA_PATH,
+ )
X_train, X_test = train_test_data["X Train"], train_test_data["X Test"]
y_train, y_test = train_test_data["Y Train"], train_test_data["Y Test"]
- name_train, name_test = train_test_data["Name Train"], train_test_data["Name Test"]
+ name_train, name_test = (
+ train_test_data["Name Train"],
+ train_test_data["Name Test"],
+ )
del data_selected_imputed_fe
clear_output()
else:
@@ -580,7 +675,13 @@ def cli_pipeline(training_data_path: str, application_data_path: Optional[str] =
print(X)
print("Basic Statistical Information: ")
basic_statistic(X)
- save_data(X, name_all, "X With Scaling", GEOPI_OUTPUT_ARTIFACTS_DATA_PATH, MLFLOW_ARTIFACT_DATA_PATH)
+ save_data(
+ X,
+ name_all,
+ "X With Scaling",
+ GEOPI_OUTPUT_ARTIFACTS_DATA_PATH,
+ MLFLOW_ARTIFACT_DATA_PATH,
+ )
else:
feature_scaling_config = {}
clear_output()
@@ -588,7 +689,14 @@ def cli_pipeline(training_data_path: str, application_data_path: Optional[str] =
feature_selection_config = {}
# Create training data without data split because it is unsupervised learning
X_train = X
- y, X_test, y_train, y_test, name_train, name_test = None, None, None, None, None, None
+ y, X_test, y_train, y_test, name_train, name_test = (
+ None,
+ None,
+ None,
+ None,
+ None,
+ None,
+ )
name_all = drop_name_column
# <--- Model Selection --->
logger.debug("Model Selection")
@@ -604,10 +712,24 @@ def cli_pipeline(training_data_path: str, application_data_path: Optional[str] =
# If the selected data set is with missing values and is not been imputed, then only allow the user to choose regression, classification and clustering models.
# Otherwise, allow the user to choose decomposition models.
if missing_value_flag and not process_missing_value_flag:
- Modes2Models = {1: REGRESSION_MODELS_WITH_MISSING_VALUES, 2: CLASSIFICATION_MODELS_WITH_MISSING_VALUES, 3: CLUSTERING_MODELS_WITH_MISSING_VALUES}
- Modes2Initiators = {1: RegressionModelSelection, 2: ClassificationModelSelection, 3: ClusteringModelSelection}
+ Modes2Models = {
+ 1: REGRESSION_MODELS_WITH_MISSING_VALUES,
+ 2: CLASSIFICATION_MODELS_WITH_MISSING_VALUES,
+ 3: CLUSTERING_MODELS_WITH_MISSING_VALUES,
+ }
+ Modes2Initiators = {
+ 1: RegressionModelSelection,
+ 2: ClassificationModelSelection,
+ 3: ClusteringModelSelection,
+ }
else:
- Modes2Models = {1: REGRESSION_MODELS, 2: CLASSIFICATION_MODELS, 3: CLUSTERING_MODELS, 4: DECOMPOSITION_MODELS, 5: ANOMALYDETECTION_MODELS}
+ Modes2Models = {
+ 1: REGRESSION_MODELS,
+ 2: CLASSIFICATION_MODELS,
+ 3: CLUSTERING_MODELS,
+ 4: DECOMPOSITION_MODELS,
+ 5: ANOMALYDETECTION_MODELS,
+ }
Modes2Initiators = {
1: RegressionModelSelection,
2: ClassificationModelSelection,
@@ -654,16 +776,46 @@ def cli_pipeline(training_data_path: str, application_data_path: Optional[str] =
inference_data_fe = new_feature_builder.batch_build(feature_engineering_config)
inference_data_fe_selected = inference_data_fe[selected_columns]
inference_name_column = inference_data[NAME]
- save_data(inference_data, name_column_origin, "Application Data Original", GEOPI_OUTPUT_ARTIFACTS_DATA_PATH, MLFLOW_ARTIFACT_DATA_PATH)
- save_data(inference_data_fe, inference_name_column, "Application Data Feature-Engineering", GEOPI_OUTPUT_ARTIFACTS_DATA_PATH, MLFLOW_ARTIFACT_DATA_PATH)
- save_data(inference_data_fe_selected, inference_name_column, "Application Data Feature-Engineering Selected", GEOPI_OUTPUT_ARTIFACTS_DATA_PATH, MLFLOW_ARTIFACT_DATA_PATH)
+ save_data(
+ inference_data,
+ name_column_origin,
+ "Application Data Original",
+ GEOPI_OUTPUT_ARTIFACTS_DATA_PATH,
+ MLFLOW_ARTIFACT_DATA_PATH,
+ )
+ save_data(
+ inference_data_fe,
+ inference_name_column,
+ "Application Data Feature-Engineering",
+ GEOPI_OUTPUT_ARTIFACTS_DATA_PATH,
+ MLFLOW_ARTIFACT_DATA_PATH,
+ )
+ save_data(
+ inference_data_fe_selected,
+ inference_name_column,
+ "Application Data Feature-Engineering Selected",
+ GEOPI_OUTPUT_ARTIFACTS_DATA_PATH,
+ MLFLOW_ARTIFACT_DATA_PATH,
+ )
else:
print("You have not applied feature engineering to the training data.")
print("Hence, no feature engineering operation will be applied to the inference data.")
inference_data_fe_selected = inference_data[selected_columns]
inference_name_column = inference_data[NAME]
- save_data(inference_data, name_column_origin, "Application Data Original", GEOPI_OUTPUT_ARTIFACTS_DATA_PATH, MLFLOW_ARTIFACT_DATA_PATH)
- save_data(inference_data_fe_selected, inference_name_column, "Application Data Selected", GEOPI_OUTPUT_ARTIFACTS_DATA_PATH, MLFLOW_ARTIFACT_DATA_PATH)
+ save_data(
+ inference_data,
+ name_column_origin,
+ "Application Data Original",
+ GEOPI_OUTPUT_ARTIFACTS_DATA_PATH,
+ MLFLOW_ARTIFACT_DATA_PATH,
+ )
+ save_data(
+ inference_data_fe_selected,
+ inference_name_column,
+ "Application Data Selected",
+ GEOPI_OUTPUT_ARTIFACTS_DATA_PATH,
+ MLFLOW_ARTIFACT_DATA_PATH,
+ )
else:
# If the user doesn't provide the inference data path, it means that the user doesn't want to run the model inference.
print("You did not enter inference data.")
@@ -687,14 +839,32 @@ def cli_pipeline(training_data_path: str, application_data_path: Optional[str] =
if not is_automl:
run.activate(X, y, X_train, X_test, y_train, y_test, name_train, name_test, name_all)
else:
- run.activate(X, y, X_train, X_test, y_train, y_test, name_train, name_test, name_all, is_automl)
+ run.activate(
+ X,
+ y,
+ X_train,
+ X_test,
+ y_train,
+ y_test,
+ name_train,
+ name_test,
+ name_all,
+ is_automl,
+ )
clear_output()
# <--- Transform Pipeline --->
# Construct the transform pipeline using sklearn.pipeline.make_pipeline method.
logger.debug("Transform Pipeline")
print("-*-*- Transform Pipeline Construction -*-*-")
- transformer_config, transform_pipeline = build_transform_pipeline(imputation_config, feature_scaling_config, feature_selection_config, run, X_train, y_train)
+ transformer_config, transform_pipeline = build_transform_pipeline(
+ imputation_config,
+ feature_scaling_config,
+ feature_selection_config,
+ run,
+ X_train,
+ y_train,
+ )
clear_output()
# <--- Model Inference --->
@@ -706,7 +876,14 @@ def cli_pipeline(training_data_path: str, application_data_path: Optional[str] =
if drop_rows_with_missing_value_flag:
inference_data_fe_selected_dropped = inference_data_fe_selected.dropna()
inference_name_column = inference_data[NAME]
- model_inference(inference_data_fe_selected_dropped, inference_name_column, is_inference, run, transformer_config, transform_pipeline)
+ model_inference(
+ inference_data_fe_selected_dropped,
+ inference_name_column,
+ is_inference,
+ run,
+ transformer_config,
+ transform_pipeline,
+ )
save_data(
inference_data_fe_selected_dropped,
inference_name_column,
@@ -716,7 +893,14 @@ def cli_pipeline(training_data_path: str, application_data_path: Optional[str] =
)
else:
inference_name_column = inference_data[NAME]
- model_inference(inference_data_fe_selected, inference_name_column, is_inference, run, transformer_config, transform_pipeline)
+ model_inference(
+ inference_data_fe_selected,
+ inference_name_column,
+ is_inference,
+ run,
+ transformer_config,
+ transform_pipeline,
+ )
clear_output()
# <--- Data Dumping --->
@@ -725,7 +909,12 @@ def cli_pipeline(training_data_path: str, application_data_path: Optional[str] =
GEOPI_OUTPUT_ARTIFACTS_PATH = os.getenv("GEOPI_OUTPUT_ARTIFACTS_PATH")
GEOPI_OUTPUT_METRICS_PATH = os.getenv("GEOPI_OUTPUT_METRICS_PATH")
GEOPI_OUTPUT_PARAMETERS_PATH = os.getenv("GEOPI_OUTPUT_PARAMETERS_PATH")
- copy_files(GEOPI_OUTPUT_ARTIFACTS_PATH, GEOPI_OUTPUT_METRICS_PATH, GEOPI_OUTPUT_PARAMETERS_PATH, GEOPI_OUTPUT_SUMMARY_PATH)
+ copy_files(
+ GEOPI_OUTPUT_ARTIFACTS_PATH,
+ GEOPI_OUTPUT_METRICS_PATH,
+ GEOPI_OUTPUT_PARAMETERS_PATH,
+ GEOPI_OUTPUT_SUMMARY_PATH,
+ )
else:
# Run all models
@@ -736,20 +925,58 @@ def cli_pipeline(training_data_path: str, application_data_path: Optional[str] =
run = Modes2Initiators[mode_num](MODELS[i])
# If is_automl is False, then run all models without AutoML.
if not is_automl:
- run.activate(X, y, X_train, X_test, y_train, y_test, name_train, name_test, name_all)
+ run.activate(
+ X,
+ y,
+ X_train,
+ X_test,
+ y_train,
+ y_test,
+ name_train,
+ name_test,
+ name_all,
+ )
else:
# If is_automl is True, but MODELS[i] is in the NON_AUTOML_MODELS, then run the model without AutoML.
if MODELS[i] in NON_AUTOML_MODELS:
- run.activate(X, y, X_train, X_test, y_train, y_test, name_train, name_test, name_all)
+ run.activate(
+ X,
+ y,
+ X_train,
+ X_test,
+ y_train,
+ y_test,
+ name_train,
+ name_test,
+ name_all,
+ )
else:
# If is_automl is True, and MODELS[i] is not in the NON_AUTOML_MODELS, then run the model with AutoML.
- run.activate(X, y, X_train, X_test, y_train, y_test, name_train, name_test, name_all, is_automl)
+ run.activate(
+ X,
+ y,
+ X_train,
+ X_test,
+ y_train,
+ y_test,
+ name_train,
+ name_test,
+ name_all,
+ is_automl,
+ )
# <--- Transform Pipeline --->
# Construct the transform pipeline using sklearn.pipeline.make_pipeline method.
logger.debug("Transform Pipeline")
print("-*-*- Transform Pipeline Construction -*-*-")
- transformer_config, transform_pipeline = build_transform_pipeline(imputation_config, feature_scaling_config, feature_selection_config, run, X_train, y_train)
+ transformer_config, transform_pipeline = build_transform_pipeline(
+ imputation_config,
+ feature_scaling_config,
+ feature_selection_config,
+ run,
+ X_train,
+ y_train,
+ )
# <--- Model Inference --->
# If the user provides the inference data, then run the model inference.
@@ -760,7 +987,14 @@ def cli_pipeline(training_data_path: str, application_data_path: Optional[str] =
if drop_rows_with_missing_value_flag:
inference_data_fe_selected_dropped = inference_data_fe_selected.dropna()
inference_name_column = inference_data[NAME]
- model_inference(inference_data_fe_selected_dropped, inference_name_column, is_inference, run, transformer_config, transform_pipeline)
+ model_inference(
+ inference_data_fe_selected_dropped,
+ inference_name_column,
+ is_inference,
+ run,
+ transformer_config,
+ transform_pipeline,
+ )
save_data(
inference_data_fe_selected_dropped,
inference_name_column,
@@ -770,7 +1004,14 @@ def cli_pipeline(training_data_path: str, application_data_path: Optional[str] =
)
else:
inference_name_column = inference_data[NAME]
- model_inference(inference_data_fe_selected, inference_name_column, is_inference, run, transformer_config, transform_pipeline)
+ model_inference(
+ inference_data_fe_selected,
+ inference_name_column,
+ is_inference,
+ run,
+ transformer_config,
+ transform_pipeline,
+ )
clear_output()
# <--- Data Dumping --->
@@ -779,6 +1020,11 @@ def cli_pipeline(training_data_path: str, application_data_path: Optional[str] =
GEOPI_OUTPUT_ARTIFACTS_PATH = os.getenv("GEOPI_OUTPUT_ARTIFACTS_PATH")
GEOPI_OUTPUT_METRICS_PATH = os.getenv("GEOPI_OUTPUT_METRICS_PATH")
GEOPI_OUTPUT_PARAMETERS_PATH = os.getenv("GEOPI_OUTPUT_PARAMETERS_PATH")
- copy_files(GEOPI_OUTPUT_ARTIFACTS_PATH, GEOPI_OUTPUT_METRICS_PATH, GEOPI_OUTPUT_PARAMETERS_PATH, GEOPI_OUTPUT_SUMMARY_PATH)
+ copy_files(
+ GEOPI_OUTPUT_ARTIFACTS_PATH,
+ GEOPI_OUTPUT_METRICS_PATH,
+ GEOPI_OUTPUT_PARAMETERS_PATH,
+ GEOPI_OUTPUT_SUMMARY_PATH,
+ )
mlflow.end_run()
diff --git a/geochemistrypi/data_mining/constants.py b/geochemistrypi/data_mining/constants.py
index 0ed654c0..4191607b 100644
--- a/geochemistrypi/data_mining/constants.py
+++ b/geochemistrypi/data_mining/constants.py
@@ -2,6 +2,9 @@
from .utils.toggle_address_status import toggle_address_status
+# Release version
+RELEASE_VERSION = "0.5.0"
+
# Adjust the path of project data flow: The number 1 indicates standard mode, and the number 2 indicates APP mode.
TOGGLE_ADDRESS_STATUS = 1
diff --git a/geochemistrypi/data_mining/update/_base.py b/geochemistrypi/data_mining/update/_base.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/geochemistrypi/data_mining/update/anaconda_checker.py b/geochemistrypi/data_mining/update/anaconda_checker.py
index e69de29b..a571adef 100644
--- a/geochemistrypi/data_mining/update/anaconda_checker.py
+++ b/geochemistrypi/data_mining/update/anaconda_checker.py
@@ -0,0 +1,17 @@
+import os
+
+from ..constants import PACKAGEDIR
+from .base import is_software_installed, run_bat_file
+
+
+def anoconda_installer() -> None:
+ """
+ Install Anaconda if it is not already installed.
+ """
+
+ detection = is_software_installed("conda.exe")
+ if detection:
+ pass
+ else:
+ conda_installer_path = os.path.join(PACKAGEDIR, "bat", "pre-installer.bat")
+ run_bat_file(conda_installer_path)
diff --git a/geochemistrypi/data_mining/update/base.py b/geochemistrypi/data_mining/update/base.py
new file mode 100644
index 00000000..058dcc74
--- /dev/null
+++ b/geochemistrypi/data_mining/update/base.py
@@ -0,0 +1,96 @@
+import subprocess
+from typing import Optional
+
+import requests
+
+
+def is_software_installed(software_name: str) -> bool:
+ """
+ Check if a specified software is installed on the system.
+
+ Parameters
+ ----------
+ software_name : str
+ The name of the software to check for installation.
+
+ Returns
+ -------
+ bool
+ True if the software is installed, False otherwise.
+
+ """
+
+ try:
+ # Use the where command to find the executable file of the software
+ result = subprocess.run(["where", software_name], capture_output=True, text=True)
+
+ # If the where command returns 0, it means the software was found
+ if result.returncode == 0:
+ print(f"{software_name} is installed.")
+ return True
+ else:
+ print(f"{software_name} is not installed.")
+ return False
+ except Exception as e:
+ print(f"An error occurred: {e}")
+ return False
+
+
+def run_bat_file(bat_file_path: Optional[list]) -> None:
+ """
+ Execute a specified batch file.
+
+ Parameters
+ ----------
+ bat_file_path : list
+ The path to the batch file to be executed.
+ """
+
+ try:
+ subprocess.run([bat_file_path], check=True, shell=True)
+ print(f"Bat file executed successfully: {bat_file_path}")
+ except subprocess.CalledProcessError as e:
+ print(f"An error occurred while executing the bat file: {e}")
+ except Exception as e:
+ print(f"An unexpected error occurred: {e}")
+
+
+def get_latest_release_version(repo_owner: str, repo_name: str) -> str:
+ """
+ Retrieve the latest release version of a GitHub repository.
+
+ Parameters
+ ----------
+ repo_owner : str
+ The owner of the GitHub repository.
+ repo_name : str
+ The name of the GitHub repository.
+
+ Returns
+ -------
+ str
+ The latest release version number, or None if the version cannot be retrieved.
+ """
+
+ # GitHub API URL for the latest release
+ api_url = f"https://api.github.com/repos/{repo_owner}/{repo_name}/releases/latest"
+
+ try:
+ # Send a GET request to the GitHub API
+ response = requests.get(api_url)
+ response.raise_for_status() # Raise an exception for HTTP errors
+
+ # Parse the JSON response
+ release_info = response.json()
+
+ # Extract the version number from the tag_name
+ if "tag_name" in release_info:
+ version_number = release_info["tag_name"]
+ print(f"Latest release version: {version_number}")
+ return version_number
+ else:
+ print("No tag_name found in the latest release.")
+ return None
+ except requests.exceptions.RequestException as e:
+ print(f"An error occurred: {e}")
+ return None