--- a
+++ b/coxnet/coxnet_baseline.py
@@ -0,0 +1,97 @@
+"""
+Coxnet: CoxPH with Lasso penalty. On Owkin dataset.
+Leon Zheng
+"""
+
+import preprocessing
+from sksurv.linear_model import CoxnetSurvivalAnalysis
+from sklearn.model_selection import cross_validate, RandomizedSearchCV
+from sksurv.util import Surv
+import numpy as np
+import pandas as pd
+
+# Features
+# features = ['original_shape_Compactness2',
+#  'original_shape_SphericalDisproportion',
+#  'original_shape_SurfaceVolumeRatio',
+#  'original_firstorder_Kurtosis',
+#  'original_firstorder_MeanAbsoluteDeviation',
+#  'original_firstorder_Minimum',
+#  'original_glcm_ClusterProminence',
+#  'original_glcm_Contrast',
+#  'original_glcm_DifferenceEntropy',
+#  'original_glcm_DifferenceAverage',
+#  'original_glcm_JointEnergy',
+#  'original_glcm_Id',
+#  'original_glcm_Idm',
+#  'original_glcm_Imc1',
+#  'original_glcm_Imc2',
+#  'original_glcm_Idmn',
+#  'original_glcm_Idn',
+#  'original_glrlm_ShortRunEmphasis',
+#  'original_glrlm_LongRunEmphasis',
+#  'original_glrlm_GrayLevelNonUniformity',
+#  'original_glrlm_RunPercentage',
+#  'original_glrlm_ShortRunLowGrayLevelEmphasis',
+#  'original_glrlm_LongRunLowGrayLevelEmphasis',
+#  'original_glrlm_LongRunHighGrayLevelEmphasis',
+#  'Nstage',
+#  'age',
+#  'SourceDataset']
+
+# 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
+
+features = ['Mstage',
+            'Nstage',
+            'SourceDataset',
+            'age',
+            'original_shape_VoxelVolume',
+            'original_firstorder_Maximum',
+            'original_firstorder_Mean',
+            'original_glcm_ClusterProminence',
+            'original_glcm_Idm',
+            'original_glcm_Idn',
+            'original_glrlm_RunPercentage']
+
+# Read data
+input_train, output_train, input_test = preprocessing.load_owkin_data()
+input_train = input_train[features]
+input_test = input_test[features]
+input_train, input_test = preprocessing.normalizing_input(input_train, input_test)
+structured_y = Surv.from_dataframe('Event', 'SurvivalTime', output_train)
+
+# Coxnet
+# coxnet = CoxnetSurvivalAnalysis()
+# print(cross_validate(coxnet, input_train, structured_y, cv=5))
+
+# Grid search
+tuned_params = {"l1_ratio": np.linspace(0.01, 0.02, 100),
+                "n_alphas": range(140, 160, 1),
+                }
+grid_search = RandomizedSearchCV(CoxnetSurvivalAnalysis(), tuned_params, cv=5, n_jobs=4, n_iter=1000)
+grid_search.fit(input_train, structured_y)
+print(grid_search.best_score_)
+best_params = grid_search.best_params_
+print(best_params)
+
+# Prediction
+def predict(model, X, threshold=0.9):
+    prediction = model.predict_survival_function(X)
+    y_pred = []
+    for pred in prediction:
+        time = pred.x
+        survival_prob = pred.y
+        i_pred = 0
+        while i_pred < len(survival_prob) - 1 and survival_prob[i_pred] > threshold:
+            i_pred += 1
+        y_pred.append(time[i_pred])
+    return pd.DataFrame(np.array([[y, np.nan] for y in y_pred]), index=X.index, columns=['SurvivalTime', 'Event'])
+
+coxph = CoxnetSurvivalAnalysis(**best_params, fit_baseline_model=True)
+coxph.fit(input_train, structured_y)
+y_pred = predict(coxph, input_test)
+y_pred.to_csv('submission.csv')