From 175985230b99ab170b9cd94ed343577f9f3bd326 Mon Sep 17 00:00:00 2001
From: Andri Joos <andri@joos.io>
Date: Fri, 11 Oct 2024 20:29:54 +0200
Subject: [PATCH] add multi feature regression
---
.gitlab-ci.yml | 19 ++++++++++++++
.vscode/launch.json | 38 +++++++++++++++++++++++++++
src/app.py | 64 +++++++++++++++++++++++++++++++++++++--------
3 files changed, 110 insertions(+), 11 deletions(-)
diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml
index 540ac63..87a75ee 100644
--- a/.gitlab-ci.yml
+++ b/.gitlab-ci.yml
@@ -28,3 +28,22 @@ best_single_feature_regression:
- out/
script:
- vqcfim best-single-feature-regression --train-data dataset/InjectionMolding_Train.csv --out out --target 'mass' --p-value-threshold 0.05
+
+multi_feature_regression_p_value:
+ extends: .run_script
+ artifacts:
+ expire_in: 1d
+ paths:
+ - out/
+ script:
+ - vqcfim multi-feature-regression --train-data dataset/InjectionMolding_Train.csv --out out --target 'mass' --p-value-threshold 0.05
+
+multi_feature_regression_manual_features:
+ extends: .run_script
+ artifacts:
+ expire_in: 1d
+ paths:
+ - out/
+ script:
+ - vqcfim multi-feature-regression --train-data dataset/InjectionMolding_Train.csv --out out --target 'mass'
+ --features Inj1PosVolAct_Var Inj1PrsAct_meanOfInjPhase ClpFceAct_1stPCscore
diff --git a/.vscode/launch.json b/.vscode/launch.json
index 1c202b6..7095acd 100644
--- a/.vscode/launch.json
+++ b/.vscode/launch.json
@@ -37,6 +37,44 @@
"--p-value-threshold",
"0.05"
]
+ },
+ {
+ "name": "Python Debugger: Multi Feature Regression with p-value",
+ "type": "debugpy",
+ "request": "launch",
+ "program": "${workspaceFolder}/src/app.py",
+ "console": "integratedTerminal",
+ "args": [
+ "multi-feature-regression",
+ "-t",
+ "dataset/InjectionMolding_Train.csv",
+ "--target",
+ "mass",
+ "-o",
+ "out",
+ "--p-value-threshold",
+ "0.05"
+ ]
+ },
+ {
+ "name": "Python Debugger: Multi Feature Regression with features",
+ "type": "debugpy",
+ "request": "launch",
+ "program": "${workspaceFolder}/src/app.py",
+ "console": "integratedTerminal",
+ "args": [
+ "multi-feature-regression",
+ "-t",
+ "dataset/InjectionMolding_Train.csv",
+ "--target",
+ "mass",
+ "-o",
+ "out",
+ "-f",
+ "Inj1PosVolAct_Var",
+ "Inj1PrsAct_meanOfInjPhase",
+ "ClpFceAct_1stPCscore"
+ ]
}
]
}
diff --git a/src/app.py b/src/app.py
index 328a6be..8502fb2 100644
--- a/src/app.py
+++ b/src/app.py
@@ -18,6 +18,8 @@ DEFAULT_CORRELATION_THRESHOLD = 0.9
TARGET_ARG = '--target'
P_VALUE_THRESHOLD_ARG = '--p-value-threshold'
DEFAULT_P_VALUE_THRESHOLD = 0.05
+FEATURES_ARG = '--features'
+FEATURES_ARG_SHORT = '-f'
PVALUE_COLUMN_NAME = 'p-value'
RSQUARED_COLUMN_NAME = 'R^2'
@@ -25,6 +27,34 @@ RSQUARED_COLUMN_NAME = 'R^2'
def ensure_directory(directory: Path):
directory.mkdir(parents=True, exist_ok=True)
+def get_possible_features_from_p_value(data: pd.DataFrame, target: str, p_value_threshold: float) -> pd.DataFrame:
+ features = data.columns
+ features = features.drop(target)
+ possible_features = pd.DataFrame({
+ PVALUE_COLUMN_NAME: pd.Series(dtype='float'),
+ RSQUARED_COLUMN_NAME: pd.Series(dtype='float'),
+ })
+ for feature in features:
+ pvalue, rsquared = single_feature_regression(data, feature, target)
+ possible_features.loc[feature] = {PVALUE_COLUMN_NAME: pvalue, RSQUARED_COLUMN_NAME: rsquared}
+
+ return possible_features.where(possible_features[PVALUE_COLUMN_NAME] < p_value_threshold).dropna()
+
+def multi_feature_regression_model(train_data: pd.DataFrame, selected_features: List[str] | None, p_value_threshold: float | None, target: str) -> sm.OLS:
+ features: List[str] = None
+ if selected_features is not None and p_value_threshold is None:
+ features = selected_features
+ elif p_value_threshold is not None and selected_features is None:
+ features = list(get_possible_features_from_p_value(train_data, target, p_value_threshold).index)
+ else:
+ raise ValueError(f'selected_features is {selected_features} and p_value_threshold is {p_value_threshold}, but expected exactly one to be set')
+
+ X = sm.add_constant(train_data[features])
+ y = train_data[target]
+ model = sm.OLS(y, X).fit()
+
+ return model, features
+
def correlation_analysis(train_data_file: Path, out_dir: Path, correlation_threshold: float):
# Load training and test data
train_data = pd.read_csv(train_data_file)
@@ -62,7 +92,7 @@ def correlation_analysis(train_data_file: Path, out_dir: Path, correlation_thres
with open(correlations_file, 'w') as f:
f.writelines(correlations)
-def single_feature_regression(data: pd.DataFrame, feature: str, target: str):
+def single_feature_regression(data: pd.DataFrame, feature: str, target: str) -> Tuple[float, float]:
X = sm.add_constant(data[[feature]]) # Add constant for intercept
y = data[target]
model = sm.OLS(y, X).fit()
@@ -70,16 +100,7 @@ def single_feature_regression(data: pd.DataFrame, feature: str, target: str):
def best_single_feature_regression(train_data_file: Path, target: str, p_value_threshold: float, out_dir: Path):
train_data = pd.read_csv(train_data_file)
- features = train_data.columns
- features = features.drop(target)
-
- evaluated_features = pd.DataFrame({
- PVALUE_COLUMN_NAME: pd.Series(dtype='float'),
- RSQUARED_COLUMN_NAME: pd.Series(dtype='float'),
- })
- for feature in features:
- pvalue, rsquared = single_feature_regression(train_data, feature, target)
- evaluated_features.loc[feature] = {PVALUE_COLUMN_NAME: pvalue, RSQUARED_COLUMN_NAME: rsquared}
+ evaluated_features = get_possible_features_from_p_value(train_data, target, p_value_threshold)
print('Evaluated features')
print(evaluated_features)
@@ -125,6 +146,18 @@ def best_single_feature_regression(train_data_file: Path, target: str, p_value_t
p-value: {best_feature[PVALUE_COLUMN_NAME]}
R^2: {best_feature[RSQUARED_COLUMN_NAME]}
''')
+
+def multi_feature_regression(train_data_file: Path, target: str, selected_features: List[str] | None, p_value_threshold: float | None, out_dir: Path):
+ train_data = pd.read_csv(train_data_file)
+ model, features = multi_feature_regression_model(train_data, selected_features, p_value_threshold, target)
+ print(model.summary())
+
+ ensure_directory(out_dir)
+ multi_feature_regression_results_file = out_dir / 'multi_feature_regression_results.txt'
+ with open(multi_feature_regression_results_file, 'w') as f:
+ f.write(f'''features: {features}
+rsquared: {model.rsquared}
+''')
def main():
argument_parser = ArgumentParser('vqcfim', description='Virtual Quality Control for Injection Molding')
@@ -143,6 +176,15 @@ def main():
best_single_feature_regression_subparser.add_argument(P_VALUE_THRESHOLD_ARG, action='store', type=float, required=False, default=DEFAULT_P_VALUE_THRESHOLD)
best_single_feature_regression_subparser.set_defaults(func=lambda train_data, target, out, p_value_threshold, func: best_single_feature_regression(train_data, target, p_value_threshold, out))
+ multi_feature_regression_subparser = subparsers.add_parser('multi-feature-regression', aliases=['mfr'], description='Performs a linear regression using multiple features')
+ multi_feature_regression_subparser.add_argument(TRAIN_DATA_ARG, TRAIN_DATA_ARG_SHORT, action='store', type=Path, required=True)
+ multi_feature_regression_subparser.add_argument(TARGET_ARG, action='store', type=str, required=True)
+ multi_feature_regression_subparser.add_argument(OUT_DIR_ARG, OUT_DIR_ARG_SHORT, action='store', type=Path, required=False, default=DEFAULT_OUT_DIR)
+ multi_feature_regression_features_group = multi_feature_regression_subparser.add_mutually_exclusive_group(required=True)
+ multi_feature_regression_features_group.add_argument(FEATURES_ARG, FEATURES_ARG_SHORT, action='store', type=str, required=False, nargs='+')
+ multi_feature_regression_features_group.add_argument(P_VALUE_THRESHOLD_ARG, action='store', type=float, required=False, default=None)
+ multi_feature_regression_subparser.set_defaults(func=lambda train_data, target, out, features, p_value_threshold, func: multi_feature_regression(train_data, target, features, p_value_threshold, out))
+
parsed_args = argument_parser.parse_args()
args = vars(parsed_args)
parsed_args.func(**args)
--
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