.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

.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"
+            ]
         }
     ]
 }

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)