.

.gitignore

@@ -1,3 +1,6 @@
 __pycache__
 .ipynb_checkpoints
-suicide_model.h5
+suicide_model.h5
+test.csv
+train.csv
+validate.csv

eval.Jenkinsfile

@@ -1,5 +1,7 @@
 pipeline {
-    agent any;
+    agent {
+        dockerfile true
+    }
        parameters{
       buildSelector(
             defaultSelector: lastSuccessful(),

evaluation.py

@@ -5,22 +5,21 @@ import matplotlib.pyplot as plt
 from keras import backend as K
 from sklearn.metrics import mean_squared_error
 
-model = 'suicide_model.h5'
-model = keras.models.load_model(model)
+model = keras.models.load_model('suicide_model.h5')
 
-train = pd.read_csv('train.csv')
-test = pd.read_csv('test.csv')
-validate = pd.read_csv('validate.csv')
+# train = pd.read_csv('train.csv')
+# test = pd.read_csv('test.csv')
+# validate = pd.read_csv('validate.csv')
 
-# podział train set
-X_train = train.loc[:, train.columns != 'suicides_no']
-y_train = train[['suicides_no']]
-X_test = test.loc[:, train.columns != 'suicides_no']
-y_test = test[['suicides_no']]
+# # podział train set
+# X_train = train.loc[:, train.columns != 'suicides_no']
+# y_train = train[['suicides_no']]
+# X_test = test.loc[:, train.columns != 'suicides_no']
+# y_test = test[['suicides_no']]
 
-predictions = model.predict(X_test)
+# predictions = model.predict(X_test)
 
-error = mean_squared_error(y_test, predictions)
+# error = mean_squared_error(y_test, predictions)
 
-with open('eval_results.txt', 'a') as f:
-    f.write(str(error) + "\n")
+# with open('eval_results.txt', 'a') as f:
+#     f.write(str(error) + "\n")