train neural network

Jenkinsfile.create_dataset

@@ -29,11 +29,11 @@ node {
             withEnv(["KAGGLE_USERNAME=${params.KAGGLE_USERNAME}",
                 "KAGGLE_KEY=${params.KAGGLE_KEY}","CUTOFF=${params.CUTOFF}"]) {
                 sh "python3 download_dataset.py '.' 'dataset.csv'"
-                sh "ls"
+                sh "python3 train_neural_network.py '.'"
             }
         }
         stage('Archive artifacts') {
-            archiveArtifacts "dataset.csv, train_data.csv, test_data.csv, dev_data.csv"
+            archiveArtifacts "dataset.csv, train_data.csv, test_data.csv, dev_data.csv, neural_network_evaluation.txt"
         }
     }
 }

download_dataset.py

@@ -3,9 +3,11 @@ import os.path
 import sys
 
 import pandas as pd
+import numpy as np
 from kaggle import api
 from pandas import read_csv
 from sklearn.model_selection import train_test_split
+from nltk.tokenize import RegexpTokenizer
 
 
 def download_and_save_dataset(data_path, dataset_name):
@@ -17,8 +19,21 @@ def download_and_save_dataset(data_path, dataset_name):
 
 
 def preprocess_dataset(data):
-    # drop columns with many nulls
-    return data.drop(['job_id', 'department', 'salary_range', 'benefits'], axis=1)
+    data = data.replace(np.nan, '', regex=True)
+
+    data['description'] = data['description'].str.replace(r"\W+", " ", regex=True)
+    data['description'] = data['description'].str.replace(r"url_\w+", " ", regex=True)
+    data['description'] = data['description'].str.replace(r"\s+", " ", regex=True)
+
+    data['text'] = data[['title', 'department', 'company_profile', 'description', 'requirements', 'benefits']].apply(
+        lambda x: ' '.join(x), axis=1)
+    data['text'] = data['text'].str.lower()
+
+    tokenizer = RegexpTokenizer(r'\w+')
+    data['tokens'] = data['text'].apply(tokenizer.tokenize)
+
+    return data.drop(['job_id', 'department', 'company_profile', 'description', 'requirements', 'benefits', 'text'],
+                     axis=1)
 
 
 def split_dataset(data_path, dataset_name):
@@ -38,7 +53,7 @@ def split_dataset(data_path, dataset_name):
 
 
 def save_dataset(data_path, data, name):
-    data.to_csv(os.path.join(data_path, name))
+    data.to_csv(os.path.join(data_path, name), index=False)
 
 
 def main():

requirements.txt

@@ -1,19 +1,55 @@
+absl-py==1.0.0
+astunparse==1.6.3
+cachetools==5.0.0
 certifi==2021.10.8
 charset-normalizer==2.0.12
+click==8.1.2
+flatbuffers==2.0
+gast==0.5.3
+google-auth==2.6.6
+google-auth-oauthlib==0.4.6
+google-pasta==0.2.0
+grpcio==1.44.0
+h5py==3.6.0
 idna==3.3
+importlib-metadata==4.11.3
 joblib==1.1.0
 kaggle==1.5.12
+keras==2.8.0
+Keras-Preprocessing==1.1.2
+libclang==14.0.1
+Markdown==3.3.6
+nltk==3.7
 numpy==1.22.3
-pandas==1.4.1
+oauthlib==3.2.0
+opt-einsum==3.3.0
+pandas==1.4.2
+protobuf==3.20.1
+pyasn1==0.4.8
+pyasn1-modules==0.2.8
 python-dateutil==2.8.2
 python-slugify==6.1.1
 pytz==2022.1
+regex==2022.3.15
 requests==2.27.1
+requests-oauthlib==1.3.1
+rsa==4.8
 scikit-learn==1.0.2
 scipy==1.8.0
 six==1.16.0
 sklearn==0.0
+tensorboard==2.8.0
+tensorboard-data-server==0.6.1
+tensorboard-plugin-wit==1.8.1
+tensorflow==2.8.0
+tensorflow-io-gcs-filesystem==0.25.0
+termcolor==1.1.0
 text-unidecode==1.3
+tf-estimator-nightly==2.8.0.dev2021122109
 threadpoolctl==3.1.0
-tqdm==4.63.1
+tqdm==4.64.0
+typing_extensions==4.2.0
 urllib3==1.26.9
+Werkzeug==2.1.1
+wrapt==1.14.0
+zipp==3.8.0

train_neural_network.py

@@ -0,0 +1,90 @@
+#!/usr/bin/python
+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)
+
+
+def tokenize(x, x_train, x_test, max_len):
+    tokenizer = Tokenizer(num_words=14000)
+    tokenizer.fit_on_texts(x)
+    train_x = tokenizer.texts_to_sequences(x_train)
+    test_x = tokenizer.texts_to_sequences(x_test)
+    vocabulary_length = len(tokenizer.word_index) + 1
+
+    train_x = pad_sequences(train_x, padding='post', maxlen=max_len)
+    test_x = pad_sequences(test_x, padding='post', maxlen=max_len)
+    return train_x, 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)
+    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))):
+            pprint.pprint(obj, log_file)
+
+
+def load_trained_model(abs_path, model_name):
+    return load_model(os.path.join(abs_path, model_name))
+
+
+def save_model(model, abs_path, model_name):
+    model.save(os.path.join(abs_path, model_name))
+
+
+def train_model(model, x_train, y_train):
+    model.fit(x_train, y_train, epochs=1, verbose=False, batch_size=50)
+
+
+def get_model(output_dim, vocabulary_length):
+    model = Sequential()
+    model.add(layers.Embedding(input_dim=vocabulary_length,
+                               output_dim=output_dim,
+                               input_length=100))
+    model.add(layers.Flatten())
+    model.add(layers.Dense(10, activation='relu'))
+    model.add(layers.Dense(1, activation='sigmoid'))
+    model.compile(optimizer='adam',
+                  loss='binary_crossentropy',
+                  metrics=['accuracy'])
+    return model
+
+
+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 main():
+    data_path = sys.argv[1]
+    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, y_train = split_data(train_data)
+    x_test, y_test = split_data(test_data)
+    x_train, x_test, vocab_size = tokenize(pd.concat([x_train, x_test]), x_train, x_test, 100)
+    model = get_model(50, vocab_size)
+    train_model(model, x_train, y_train)
+    # save_model(model, abs_data_path, 'neural_network')
+    evaluate_and_save(model, x_test, y_test, abs_data_path)
+
+
+if __name__ == '__main__':
+    main()