From 8fa7f68080698396dc9688dbe192cb9dddeba94f Mon Sep 17 00:00:00 2001
From: AdamOsiowy123 <adaosi@st.amu.edu.pl>
Date: Tue, 3 May 2022 00:04:19 +0200
Subject: [PATCH] evaluate

---
 Jenkins/Jenkinsfile.evaluation     | 48 ++++++++++++++++
 Scripts/evaluate_neural_network.py | 88 ++++++++++++++++++++++++++++++
 2 files changed, 136 insertions(+)
 create mode 100644 Jenkins/Jenkinsfile.evaluation
 create mode 100644 Scripts/evaluate_neural_network.py

diff --git a/Jenkins/Jenkinsfile.evaluation b/Jenkins/Jenkinsfile.evaluation
new file mode 100644
index 0000000..6276cfb
--- /dev/null
+++ b/Jenkins/Jenkinsfile.evaluation
@@ -0,0 +1,48 @@
+node {
+    checkout scm
+    try {
+        docker.image('s444452/ium:1.3').inside {
+        stage('Preparation') {
+            properties([
+                pipelineTriggers([upstream(threshold: hudson.model.Result.SUCCESS, upstreamProjects: "s444452-training")]),
+                parameters([
+                string(
+                 defaultValue: ".,14000,100",
+                 description: 'Test params: data_path,num_words,pad_length',
+                 name: 'TEST_PARAMS'
+                )
+                ])
+            ])
+        }
+        stage('Copy artifacts') {
+            copyArtifacts filter: 'train_data.csv', fingerprintArtifacts: true, projectName: 's444452-create-dataset'
+            copyArtifacts filter: 'test_data.csv', fingerprintArtifacts: true, projectName: 's444452-create-dataset'
+        }
+        stage('Run script') {
+            withEnv(["TEST_PARAMS=${params.TEST_PARAMS}"]) {
+                sh "python3 Scripts/evaluate_neural_network.py $TEST_PARAMS"
+            }
+        }
+        stage('Archive artifacts') {
+            archiveArtifacts "neural_network_evaluation.txt"
+        }
+        }
+    } catch (e) {
+        currentBuild.result = "FAILED"
+        throw e
+    } finally {
+        notifyBuild(currentBuild.result)
+    }
+}
+def notifyBuild(String buildStatus = 'STARTED') {
+    buildStatus = buildStatus ?: 'SUCCESS'
+
+    def subject = "Job: ${env.JOB_NAME}"
+    def details = "Build nr: ${env.BUILD_NUMBER}, status: ${buildStatus} \n url: ${env.BUILD_URL} \n build params: ${params.TRAIN_PARAMS}"
+
+    emailext (
+        subject: subject,
+        body: details,
+        to: 'e19191c5.uam.onmicrosoft.com@emea.teams.ms'
+    )
+}
\ No newline at end of file
diff --git a/Scripts/evaluate_neural_network.py b/Scripts/evaluate_neural_network.py
new file mode 100644
index 0000000..6d814a6
--- /dev/null
+++ b/Scripts/evaluate_neural_network.py
@@ -0,0 +1,88 @@
+#!/usr/bin/python
+import datetime
+import glob
+import os
+import pprint
+import sys
+import pandas as pd
+from keras.models import Sequential, load_model
+from keras import layers
+from keras.preprocessing.text import Tokenizer
+from keras.preprocessing.sequence import pad_sequences
+from sklearn.metrics import precision_score, recall_score, f1_score, accuracy_score
+import logging
+
+logging.getLogger("tensorflow").setLevel(logging.ERROR)
+
+data_path = ''
+num_words = 0
+pad_length = 0
+
+
+def tokenize(x, x_test):
+    global pad_length, num_words
+    tokenizer = Tokenizer(num_words=num_words)
+    tokenizer.fit_on_texts(x)
+    test_x = tokenizer.texts_to_sequences(x_test)
+    vocabulary_length = len(tokenizer.word_index) + 1
+    test_x = pad_sequences(test_x, padding='post', maxlen=pad_length)
+    return test_x, vocabulary_length
+
+
+def evaluate_and_save(model, x, y, abs_path):
+    loss, accuracy = model.evaluate(x, y, verbose=False)
+    y_predicted = (model.predict(x) >= 0.5).astype(int)
+
+    if os.path.exists(os.path.join(abs_path, 'neural_network_evaluation.txt')):
+        with open(os.path.join(abs_path, 'neural_network_evaluation.txt'), "a") as log_file:
+            for obj in (
+                    ('Accuracy: ', accuracy), ('Loss: ', loss), ('Precision: ', precision_score(y, y_predicted)),
+                    ('Recall: ', recall_score(y, y_predicted)), ('F1: ', f1_score(y, y_predicted)),
+                    ('Accuracy: ', accuracy_score(y, y_predicted))):
+                log_file.write(str(obj) + '\n')
+    else:
+        with open(os.path.join(abs_path, 'neural_network_evaluation.txt'), "w") as log_file:
+            for obj in (
+                    ('Accuracy: ', accuracy), ('Loss: ', loss), ('Precision: ', precision_score(y, y_predicted)),
+                    ('Recall: ', recall_score(y, y_predicted)), ('F1: ', f1_score(y, y_predicted)),
+                    ('Accuracy: ', accuracy_score(y, y_predicted))):
+                log_file.write(str(obj) + '\n')
+
+
+def load_trained_model(abs_path):
+    model_name = glob.glob('neural_net_*')[0]
+    return load_model(os.path.join(abs_path, model_name))
+
+
+def split_data(data):
+    x = data['tokens']
+    y = data['fraudulent']
+    return x, y
+
+
+def load_data(data_path, filename) -> pd.DataFrame:
+    return pd.read_csv(os.path.join(data_path, filename))
+
+
+def read_params():
+    global data_path, num_words, pad_length
+    data_path, num_words, pad_length = sys.argv[1].split(',')
+    num_words = int(num_words)
+    pad_length = int(pad_length)
+
+
+def main():
+    read_params()
+    global data_path
+    abs_data_path = os.path.abspath(data_path)
+    train_data = load_data(abs_data_path, 'train_data.csv')
+    test_data = load_data(abs_data_path, 'test_data.csv')
+    x_train, _ = split_data(train_data)
+    x_test, y_test = split_data(test_data)
+    x_test, _ = tokenize(pd.concat([x_train, x_test]), x_test)
+    model = load_trained_model(abs_data_path)
+    evaluate_and_save(model, x_test, y_test, abs_data_path)
+
+
+if __name__ == '__main__':
+    main()