--- a
+++ b/baseline/Coxph_baseline.py
@@ -0,0 +1,52 @@
+"""
+Cox-Ph regression. Baseline.
+Leon Zheng
+"""
+
+from Coxph_regression import CoxPhRegression
+from sklearn.model_selection import GridSearchCV
+import preprocessing
+import numpy as np
+
+"""
+Feature selection
+"""
+radiomics_features = ['original_shape_Sphericity', 'original_shape_SurfaceVolumeRatio',
+                      'original_shape_Maximum3DDiameter', 'original_glcm_JointEntropy', 'original_glcm_Id',
+                      'original_glcm_Idm']
+clinical_features = ['SourceDataset', 'Nstage']
+features = radiomics_features + clinical_features
+
+"""
+Reading data
+"""
+# Read clean data
+input_train, output_train, input_test = preprocessing.load_owkin_data()
+input_train = input_train[features]
+input_test = input_test[features]
+
+# Normalization
+input_train, input_test = preprocessing.normalizing_input(input_train, input_test)
+print(input_train)
+print(output_train)
+
+""" 
+Grid search
+"""
+tuned_params = {"alpha": np.logspace(-8, -3, 10),
+                "threshold": np.linspace(0.85, 0.95, 10)}
+grid_search = GridSearchCV(CoxPhRegression(), tuned_params, cv=5, n_jobs=4)
+grid_search.fit(input_train, output_train)
+print(grid_search.best_score_)
+best_params = grid_search.best_params_
+print(best_params)
+
+"""
+Create submission
+"""
+# Create submission
+print(input_test)
+coxph = CoxPhRegression(**best_params)
+coxph.fit(input_train, output_train)
+y_pred = coxph.predict(input_test)
+y_pred.to_csv('submission.csv')