diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml
index 60a71e69a0ef8c27c13e2f9be1d0e2e8a5b895b6..156899f384734b871abcd8c869c94d27406fa211 100644
--- a/.gitlab-ci.yml
+++ b/.gitlab-ci.yml
@@ -60,3 +60,13 @@ model_evaluation:
script:
- vqcfim model-evaluation --model out/multi_feature_regression_model.pickle --test-data dataset/InjectionMolding_Test.csv --target mass --out out
--features Inj1PosVolAct_Var Inj1PrsAct_meanOfInjPhase Inj1HtgEd3Act_1stPCscore ClpFceAct_1stPCscore
+
+quadratic_term:
+ extends: .run_script
+ artifacts:
+ expire_in: 1d
+ paths:
+ - out/
+ script:
+ - vqcfim higher-order-terms --train-data dataset/InjectionMolding_Train.csv --test-data dataset/InjectionMolding_Test.csv --target mass --out out
+ --features Inj1PosVolAct_Var Inj1PrsAct_meanOfInjPhase Inj1HtgEd3Act_1stPCscore ClpFceAct_1stPCscore --degree 2
diff --git a/.vscode/launch.json b/.vscode/launch.json
index dc0c266e3137de7a184e21d8ab0969c99353307d..0a103a2083f2b83200829e500c88b61b0be222fe 100644
--- a/.vscode/launch.json
+++ b/.vscode/launch.json
@@ -98,6 +98,58 @@
"Inj1HtgEd3Act_1stPCscore",
"ClpFceAct_1stPCscore"
]
+ },
+ {
+ "name": "Python Debugger: Higher Order Terms",
+ "type": "debugpy",
+ "request": "launch",
+ "program": "${workspaceFolder}/src/app.py",
+ "console": "integratedTerminal",
+ "args": [
+ "higher-order-terms",
+ "--train-data",
+ "dataset/InjectionMolding_Train.csv",
+ "--test-data",
+ "dataset/InjectionMolding_Test.csv",
+ "--target",
+ "mass",
+ "-o",
+ "out",
+ "-f",
+ "Inj1PosVolAct_Var",
+ "Inj1PrsAct_meanOfInjPhase",
+ "Inj1HtgEd3Act_1stPCscore",
+ "ClpFceAct_1stPCscore",
+ "--degree",
+ "2"
+ ]
+ },
+ {
+ "name": "Python Debugger: Best Higher Order Terms",
+ "type": "debugpy",
+ "request": "launch",
+ "program": "${workspaceFolder}/src/app.py",
+ "console": "integratedTerminal",
+ "args": [
+ "best-higher-order-terms",
+ "--train-data",
+ "dataset/InjectionMolding_Train.csv",
+ "--test-data",
+ "dataset/InjectionMolding_Test.csv",
+ "--target",
+ "mass",
+ "-o",
+ "out/higher-order-terms",
+ "-f",
+ "Inj1PosVolAct_Var",
+ "Inj1PrsAct_meanOfInjPhase",
+ "Inj1HtgEd3Act_1stPCscore",
+ "ClpFceAct_1stPCscore",
+ "--degree-bottom-boundary",
+ "1",
+ "--degree-top-boundary",
+ "50"
+ ]
}
]
}
diff --git a/src/app.py b/src/app.py
index 248c8d42119ec8e52d51a7c5c3c7cbcc6cacfabc..6fc71bdce151d33a4673f2cfa7946ebf53fedfc7 100644
--- a/src/app.py
+++ b/src/app.py
@@ -7,6 +7,9 @@ import math
from typing import List, Tuple
import statsmodels.api as sm
from sklearn.metrics import mean_squared_error # is not deprecated, only squared param
+from sklearn.linear_model import LinearRegression
+from sklearn.preprocessing import PolynomialFeatures
+from sklearn.pipeline import Pipeline
TRAIN_DATA_ARG = '--train-data'
TRAIN_DATA_ARG_SHORT = '-t'
@@ -24,9 +27,17 @@ FEATURES_ARG_SHORT = '-f'
MODEL_ARG = '--model'
MODEL_ARG_SHORT = '-m'
TEST_DATA_ARG = '--test-data'
+DEGREE_ARG = '--degree'
+INTERACTION_ONLY_ARG = '--interaction-only'
+DEGREE_BOTTOM_BOUNDARY_ARG = '--degree-bottom-boundary'
+DEGREE_TOP_ARG = '--degree-top-boundary'
PVALUE_COLUMN_NAME = 'p-value'
RSQUARED_COLUMN_NAME = 'R^2'
+DEGREE_COLUMN_NAME = 'degree'
+INTERACTION_ONLY_COLUMN_NAME = 'interaction_only'
+TRAIN_MSE_COLUMN_NAME = 'train_mse'
+TEST_MSE_COLUMN_NAME = 'test_mse'
def ensure_directory(directory: Path):
directory.mkdir(parents=True, exist_ok=True)
@@ -185,6 +196,79 @@ def model_evaluation(model_file: Path, test_data_file: Path, features: List[str]
with open(model_evaluation_file, 'w') as f:
f.write(f'''test_MSE: {test_mse}
''')
+
+def higher_order_terms(train_data_file: Path, test_data_file: Path, features: List[str], target: str, interaction_only: bool, degree: int, out_dir: Path):
+ train_data = pd.read_csv(train_data_file)
+ test_data = pd.read_csv(test_data_file)
+
+ X_train = train_data[features]
+ y_train = train_data[target]
+
+ X_test = test_data[features]
+ y_test = test_data[target]
+
+ pipeline = Pipeline([
+ ('poly', PolynomialFeatures(degree=degree, include_bias=False, interaction_only=interaction_only)),
+ ('linear', LinearRegression())
+ ])
+
+ pipeline.fit(X_train, y_train)
+ y_pred = pipeline.predict(X_train)
+ train_mse = mean_squared_error(y_train, y_pred)
+ rsquared = pipeline.score(X_train, y_train)
+
+ y_pred = pipeline.predict(X_test)
+ test_mse = mean_squared_error(y_test, y_pred)
+
+ print(f'train MSE: {train_mse}, test mse: {test_mse}, R^2: {rsquared}')
+
+ ensure_directory(out_dir)
+ output_file = out_dir / f'higher_order_terms_{degree}_{interaction_only}.txt'
+ with open(output_file, 'w') as f:
+ f.write(f'''train MSE: {train_mse}
+test MSE: {test_mse}
+rsquared: {rsquared}
+''')
+
+ return train_mse, test_mse, rsquared
+
+def best_higher_order_terms(train_data_file: Path, test_data_file: Path, features: List[str], target: str, degree_bottom_boundary: int, degree_top_boundary: int, out_dir: Path):
+ if degree_bottom_boundary > degree_top_boundary:
+ raise ValueError('degree bottom boundary must be smaller or equal than degree top boundary')
+
+ configurations = pd.DataFrame({
+ DEGREE_COLUMN_NAME: pd.Series(dtype=int),
+ INTERACTION_ONLY_COLUMN_NAME: pd.Series(dtype=bool),
+ TRAIN_MSE_COLUMN_NAME: pd.Series(dtype=float),
+ TEST_MSE_COLUMN_NAME: pd.Series(dtype=float),
+ RSQUARED_COLUMN_NAME: pd.Series(dtype='float'),
+ })
+ for degree in range(degree_bottom_boundary, degree_top_boundary + 1):
+ interaction_only = False
+ train_mse, test_mse, rsquared = higher_order_terms(train_data_file, test_data_file, features, target, interaction_only, degree, out_dir)
+ configurations.loc[-1] = {
+ DEGREE_COLUMN_NAME: degree,
+ INTERACTION_ONLY_COLUMN_NAME: interaction_only,
+ TRAIN_MSE_COLUMN_NAME: train_mse,
+ TEST_MSE_COLUMN_NAME: test_mse,
+ RSQUARED_COLUMN_NAME: rsquared,
+ }
+ configurations.index = configurations.index + 1
+
+ interaction_only = True
+ train_mse, test_mse, rsquared = higher_order_terms(train_data_file, test_data_file, features, target, interaction_only, degree, out_dir)
+ configurations.loc[-1] = {
+ DEGREE_COLUMN_NAME: degree,
+ INTERACTION_ONLY_COLUMN_NAME: interaction_only,
+ TRAIN_MSE_COLUMN_NAME: train_mse,
+ TEST_MSE_COLUMN_NAME: test_mse,
+ RSQUARED_COLUMN_NAME: rsquared,
+ }
+ configurations.index = configurations.index + 1
+
+ configurations = configurations.sort_values(RSQUARED_COLUMN_NAME, ascending=False)
+
+ print(configurations)
def main():
argument_parser = ArgumentParser('vqcfim', description='Virtual Quality Control for Injection Molding')
@@ -220,6 +304,26 @@ def main():
model_evaluation_subparser.add_argument(FEATURES_ARG, FEATURES_ARG_SHORT, action='store', type=str, required=True, nargs='+')
model_evaluation_subparser.set_defaults(func=lambda model, test_data, target, out, features, func: model_evaluation(model, test_data, features, target, out))
+ higher_order_terms_subparser = subparsers.add_parser('higher-order-terms', aliases=['hot'], description='Creates and evaluates a model with higher order terms')
+ higher_order_terms_subparser.add_argument(TRAIN_DATA_ARG, TRAIN_DATA_ARG_SHORT, action='store', type=Path, required=True)
+ higher_order_terms_subparser.add_argument(TEST_DATA_ARG, action='store', type=Path, required=True)
+ higher_order_terms_subparser.add_argument(FEATURES_ARG, FEATURES_ARG_SHORT, action='store', type=str, required=True, nargs='+')
+ higher_order_terms_subparser.add_argument(TARGET_ARG, action='store', type=str, required=True)
+ higher_order_terms_subparser.add_argument(OUT_DIR_ARG, OUT_DIR_ARG_SHORT, action='store', type=Path, required=False, default=DEFAULT_OUT_DIR)
+ higher_order_terms_subparser.add_argument(DEGREE_ARG, action='store', type=int, required=True)
+ higher_order_terms_subparser.add_argument(INTERACTION_ONLY_ARG, action='store_true', required=False)
+ higher_order_terms_subparser.set_defaults(func=lambda train_data, test_data, features, target, degree, interaction_only, out, func: higher_order_terms(train_data, test_data, features, target, interaction_only, degree, out))
+
+ best_higher_order_terms_subparser = subparsers.add_parser('best-higher-order-terms', aliases=['bhot'], description='Creates and evaluates models with higher order terms')
+ best_higher_order_terms_subparser.add_argument(TRAIN_DATA_ARG, TRAIN_DATA_ARG_SHORT, action='store', type=Path, required=True)
+ best_higher_order_terms_subparser.add_argument(TEST_DATA_ARG, action='store', type=Path, required=True)
+ best_higher_order_terms_subparser.add_argument(FEATURES_ARG, FEATURES_ARG_SHORT, action='store', type=str, required=True, nargs='+')
+ best_higher_order_terms_subparser.add_argument(TARGET_ARG, action='store', type=str, required=True)
+ best_higher_order_terms_subparser.add_argument(OUT_DIR_ARG, OUT_DIR_ARG_SHORT, action='store', type=Path, required=False, default=DEFAULT_OUT_DIR)
+ best_higher_order_terms_subparser.add_argument(DEGREE_BOTTOM_BOUNDARY_ARG, action='store', type=int, required=True)
+ best_higher_order_terms_subparser.add_argument(DEGREE_TOP_ARG, action='store', type=int, required=True)
+ best_higher_order_terms_subparser.set_defaults(func=lambda train_data, test_data, features, degree_bottom_boundary, degree_top_boundary, target, out, func: best_higher_order_terms(train_data, test_data, features, target, degree_bottom_boundary, degree_top_boundary, out))
+
parsed_args = argument_parser.parse_args()
args = vars(parsed_args)
parsed_args.func(**args)