diff --git a/p_2/code/pdp.py b/p_2/code/pdp.py
new file mode 100644
index 0000000000000000000000000000000000000000..90d3a70f37e253b70edc30699f07d7766f31a6ff
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+++ b/p_2/code/pdp.py
@@ -0,0 +1,133 @@
+
+import pandas as pd
+pd.set_option('display.max_columns', 500)
+
+v = pd.read_excel(r"data(7).xlsx")
+v_list=[]
+for index,name in (enumerate(v['variant'].value_counts().index.tolist())):
+ v_list.append(name)
+v_list.append('not_sequenced')
+month_data_encoded=pd.read_excel(r"month_data_encoded.xlsx")
+data_encoded=pd.read_excel(r"data_encoded.xlsx")
+import tensorflow as tf
+import tensorflow_probability as tfp
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+import time
+from sklearn.model_selection import train_test_split
+model = tf.keras.models.load_model(r"convmodel")
+def accuracy_score(preds, labels):
+ #return np.mean(np.argmax(preds, axis=1) == np.argmax(labels, axis=1))
+ #predictednumbers=np.argmax(preds, axis=1)
+ #realnumbers=np.argmax(labels, axis=1)
+ diff=np.subtract(preds, labels)
+ return np.sqrt(sum(np.power(diff, 2))/len(diff))
+month_codes=[]
+for index,name in (enumerate(data_encoded['month'].value_counts().index.tolist())):
+ #for name in['Germany']:
+ month=name
+ data_encoded_2=data_encoded.loc[(data_encoded['month'] == month)]
+ month_codes.append([month,[data_encoded_2.iloc[0]['month_0'],data_encoded_2.iloc[0]['month_1'] ,data_encoded_2.iloc[0]['month_2'] ,data_encoded_2.iloc[0]['month_3'] ] ])
+
+
+
+from statistics import mean
+from Big_list import Big_list
+filtered = []
+
+for i in Big_list:
+ if mean(i[1]) >= 6.3:
+ filtered.append(i)
+
+
+vv=[[i[0],mean(i[1])] for i in list(filtered)]
+def Sort(sub_li):
+
+ l = len(sub_li)
+
+ for i in range(0, l):
+ for j in range(0, l-i-1):
+
+ if (sub_li[j][1] > sub_li[j + 1][1]):
+ tempo = sub_li[j]
+ sub_li[j] = sub_li[j + 1]
+ sub_li[j + 1] = tempo
+
+ return sub_li
+ff=Sort(vv)
+ff=[ff[i][0] for i in range(0,(len(ff)-2))]
+
+
+
+feature_list=ff
+xxx_list=[]
+cat_nr=100
+def valuation_formula(step):
+ return step
+for index,name in (enumerate(month_data_encoded['countriesAndTerritories'].value_counts().index.tolist())):
+ #for name in['Germany']:
+ land=name
+ print(land)
+ month_data_encoded_land=month_data_encoded.loc[(month_data_encoded['countriesAndTerritories'] == land)]
+ mde=month_data_encoded_land
+ mde=mde.drop(['dateRep', 'cases' , 'Rescd' , '7days_before_mean','7days_after_mean', 'week' ,'year' , 'index', 'month', 'countriesAndTerritories', 'va' , 'vaccin'], axis=1)
+ mde = mde.reindex(sorted(mde.columns), axis=1)
+ percent_list=[]
+ number_list=[-10000]
+
+
+
+ test_origin=mde
+ xx_list=[]
+ test=test_origin.copy(deep=True)
+ for step in [i[1] for i in month_codes]:
+ test['month_0'] = test.apply(lambda row: step[0], axis=1)
+ test['month_1'] = test.apply(lambda row: step[1], axis=1)
+ test['month_2'] = test.apply(lambda row: step[2], axis=1)
+ test['month_3'] = test.apply(lambda row: step[3], axis=1)
+
+ X_test = test.drop(labels = ["rp_zeitraum"],axis = 1)
+ X_test = X_test.values.reshape(-1,1,111,1)
+
+ n_mc_run = 100
+ #med_prob_thres = 0.35
+
+ y_pred_logits_list = [model(X_test) for _ in range(n_mc_run)] # a list of predicted logits
+ y_pred_prob_all = np.concatenate([tf.nn.softmax(y, axis=-1)[:, :, np.newaxis] for y in y_pred_logits_list], axis=-1)
+ y_predicted_list=[np.argmax([np.mean(y_pred_prob_all[idx][i]) for i in range (0,len(y_pred_prob_all[idx]))],axis=-1) for idx in range (0,len(X_test)) ]
+
+ mean_predicted=np.mean(y_predicted_list)
+ xx_list.append(mean_predicted)
+ xxx_list.append([land,'month',[[i[0] for i in month_codes],xx_list]])
+ print([land,'month',[[i[0] for i in month_codes],xx_list]])
+ print(xx_list)
+ for xx in feature_list:
+ print(xx)
+ xx_list=[]
+ test=test_origin.copy(deep=True)
+ for step in [0,1.0]:
+ test[xx] = test.apply(lambda row: valuation_formula(step), axis=1)
+ if xx in v_list:
+ vv_list=v_list.copy()
+ vv_list.remove(xx)
+ for i in vv_list:
+ test[i] = test[i].multiply(1-step)
+
+ X_test = test.drop(labels = ["rp_zeitraum"],axis = 1)
+ X_test = X_test.values.reshape(-1,1,111,1)
+
+ n_mc_run = 100
+ #med_prob_thres = 0.35
+
+ y_pred_logits_list = [model(X_test) for _ in range(n_mc_run)] # a list of predicted logits
+ y_pred_prob_all = np.concatenate([tf.nn.softmax(y, axis=-1)[:, :, np.newaxis] for y in y_pred_logits_list], axis=-1)
+ y_predicted_list=[np.argmax([np.mean(y_pred_prob_all[idx][i]) for i in range (0,len(y_pred_prob_all[idx]))],axis=-1) for idx in range (0,len(X_test)) ]
+
+ mean_predicted=np.mean(y_predicted_list)
+ xx_list.append(mean_predicted)
+ xxx_list.append([land,xx,xx_list])
+ print([land,xx,xx_list])
+
+with open('xxx_list_r.py', 'w') as w:
+ w.write('xxx_list_r = %s' % xxx_list)
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