Added DVC with remote storage

.dvc/.gitignore

@@ -0,0 +1,3 @@
+/config.local
+/tmp
+/cache

.dvc/config

@@ -0,0 +1,2 @@
+['remote "gdrive_remote"']
+    url = gdrive://1NY8yEl6N1ZhE-q9jnEt6G6cqIHyEiCKc

.dvcignore

@@ -0,0 +1,3 @@
+# Add patterns of files dvc should ignore, which could improve
+# the performance. Learn more at
+# https://dvc.org/doc/user-guide/dvcignore

.pylintrc

@@ -193,7 +193,7 @@ evaluation=10.0 - ((float(5 * error + warning + refactor + convention) / stateme
 # Set the output format. Available formats are text, parseable, colorized, json
 # and msvs (visual studio). You can also give a reporter class, e.g.
 # mypackage.mymodule.MyReporterClass.
-output-format=text:data/reports/report.txt,colorized
+output-format=text:reports/pylint_report.txt,colorized
 
 # Tells whether to display a full report or only the messages.
 reports=y

data/external/.gitignore

@@ -0,0 +1 @@
+*.tsv

data/models/.gitignore

@@ -0,0 +1,2 @@
+c1_BoW_Sentiment_Model.pkl
+c2_Classifier_Sentiment_Model

data/processed/.gitignore

@@ -0,0 +1 @@
+*.joblib

dvc.lock

@@ -0,0 +1,50 @@
+schema: '2.0'
+stages:
+  preprocessing:
+    cmd: python src/preprocessing.py
+    deps:
+    - path: data/external/a1_RestaurantReviews_HistoricDump.tsv
+      md5: 102f1f4193e0bdebdd6cce7f13e0a839
+      size: 54686
+    - path: src/preprocessing.py
+      md5: b45d76ab50b20ccabfb50d591ee7ef02
+      size: 2034
+    outs:
+    - path: data/processed/corpus.joblib
+      md5: 243212bb05cce5e3fdc72bfd2826d329
+      size: 31612
+  load_data:
+    cmd: python src/load_data.py
+    deps:
+    - path: src/load_data.py
+      md5: e579b1f5296f89c5f22d8ac4af92e1c0
+      size: 913
+    outs:
+    - path: data/external/a1_RestaurantReviews_HistoricDump.tsv
+      md5: 102f1f4193e0bdebdd6cce7f13e0a839
+      size: 54686
+  training:
+    cmd: python src/training.py
+    deps:
+    - path: data/external/a1_RestaurantReviews_HistoricDump.tsv
+      md5: 102f1f4193e0bdebdd6cce7f13e0a839
+      size: 54686
+    - path: data/processed/corpus.joblib
+      md5: 243212bb05cce5e3fdc72bfd2826d329
+      size: 31612
+    - path: src/evaluation.py
+      md5: 96c08113733680243cbc537a93cc128d
+      size: 396
+    - path: src/training.py
+      md5: 81ddde09ae93959e83afb4bae0ddd90a
+      size: 2073
+    outs:
+    - path: data/models/c1_BoW_Sentiment_Model.pkl
+      md5: 47e4584e52d616cbb5af92f988648e27
+      size: 39823
+    - path: data/models/c2_Classifier_Sentiment_Model
+      md5: e6e6744062a1d370a585d15df7f45934
+      size: 46127
+    - path: reports/model_evaluation.txt
+      md5: 35b131f5c189995225c586a8ae7025d9
+      size: 67

dvc.yaml

@@ -0,0 +1,25 @@
+stages:
+  load_data:
+    cmd: python src/load_data.py
+    deps:
+    - src/load_data.py
+    outs:
+    - data/external/a1_RestaurantReviews_HistoricDump.tsv
+  preprocessing:
+    cmd: python src/preprocessing.py
+    deps:
+    - src/preprocessing.py
+    - data/external/a1_RestaurantReviews_HistoricDump.tsv
+    outs:
+    - data/processed/corpus.joblib
+  training:
+    cmd: python src/training.py
+    deps:
+    - src/training.py
+    - src/evaluation.py
+    - data/external/a1_RestaurantReviews_HistoricDump.tsv
+    - data/processed/corpus.joblib
+    outs:
+    - data/models/c1_BoW_Sentiment_Model.pkl
+    - data/models/c2_Classifier_Sentiment_Model
+    - reports/model_evaluation.txt

reports/.gitignore

@@ -0,0 +1 @@
+/model_evaluation.txt

reports/pylint_report.txt

@@ -0,0 +1,102 @@
+
+
+Report
+======
+109 statements analysed.
+
+Statistics by type
+------------------
+
++---------+-------+-----------+-----------+------------+---------+
+|type     |number |old number |difference |%documented |%badname |
++=========+=======+===========+===========+============+=========+
+|module   |7      |7          |=          |100.00      |0.00     |
++---------+-------+-----------+-----------+------------+---------+
+|class    |1      |1          |=          |100.00      |0.00     |
++---------+-------+-----------+-----------+------------+---------+
+|method   |3      |3          |=          |100.00      |0.00     |
++---------+-------+-----------+-----------+------------+---------+
+|function |3      |3          |=          |100.00      |0.00     |
++---------+-------+-----------+-----------+------------+---------+
+
+
+
+External dependencies
+---------------------
+::
+
+    joblib (src.main,src.preprocessing,src.training)
+    nltk (src.preprocessing)
+      \-corpus (src.preprocessing)
+      \-stem 
+        \-porter (src.preprocessing)
+    pandas (src.preprocessing,src.training)
+    sklearn 
+      \-feature_extraction 
+      | \-text (src.training)
+      \-metrics (src.evaluation)
+      \-model_selection (src.classification,src.training)
+      \-naive_bayes (src.classification,src.training)
+
+
+
+Raw metrics
+-----------
+
++----------+-------+------+---------+-----------+
+|type      |number |%     |previous |difference |
++==========+=======+======+=========+===========+
+|code      |122    |49.19 |122      |=          |
++----------+-------+------+---------+-----------+
+|docstring |32     |12.90 |32       |=          |
++----------+-------+------+---------+-----------+
+|comment   |37     |14.92 |37       |=          |
++----------+-------+------+---------+-----------+
+|empty     |57     |22.98 |57       |=          |
++----------+-------+------+---------+-----------+
+
+
+
+Duplication
+-----------
+
++-------------------------+------+---------+-----------+
+|                         |now   |previous |difference |
++=========================+======+=========+===========+
+|nb duplicated lines      |0     |0        |0          |
++-------------------------+------+---------+-----------+
+|percent duplicated lines |0.000 |0.000    |=          |
++-------------------------+------+---------+-----------+
+
+
+
+Messages by category
+--------------------
+
++-----------+-------+---------+-----------+
+|type       |number |previous |difference |
++===========+=======+=========+===========+
+|convention |0      |1        |1          |
++-----------+-------+---------+-----------+
+|refactor   |0      |0        |0          |
++-----------+-------+---------+-----------+
+|warning    |0      |0        |0          |
++-----------+-------+---------+-----------+
+|error      |0      |0        |0          |
++-----------+-------+---------+-----------+
+
+
+
+Messages
+--------
+
++-----------+------------+
+|message id |occurrences |
++===========+============+
+
+
+
+
+-------------------------------------------------------------------
+Your code has been rated at 10.00/10 (previous run: 9.91/10, +0.09)
+

requirements.txt

@@ -2,4 +2,8 @@ joblib==1.1.1
 nltk==3.7
 scikit_learn==1.2.2
 setuptools==45.2.0
-
+dvc==2.58.1
+dvc_gdrive==2.19.2
+pylint==2.12.2
+mllint==0.12.2
+dslinter==2.0.9

setup.py

@@ -4,10 +4,6 @@
 
 from setuptools import setup, find_packages
 
-requirements = [ ]
-
-test_requirements = [ ]
-
 setup(
     author="Team 08",
     python_requires='>=3.6',
@@ -21,15 +17,13 @@
         'Programming Language :: Python :: 3.7',
         'Programming Language :: Python :: 3.8',
     ],
-    description="The model-training repository of Team 08 for the Release Engineering for Machine Learning (CS4295) course at the TU Delft.",
-    install_requires=requirements,
+    description="The model-training repository of Team 08 for the CS4295 course at the TU Delft.",
     license="MIT license",
     include_package_data=True,
     keywords='model_training',
     name='model_training',
     packages=find_packages(include=['model_training', 'model_training.*']),
     test_suite='tests',
-    tests_require=test_requirements,
     url='https://github.com/remla23-team08/model-training',
     version='0.2.0',
     zip_safe=False,

src/evaluation.py

@@ -1,16 +1,17 @@
 #! /usr/bin/env
 
 """
-Functions related to model evaluation
+Evaluate the model and return results
 """
 
 from sklearn.metrics import confusion_matrix, accuracy_score
 
 
 def model_eval(classifier, X_test, y_test):
     """
-    Prints model evaluation metrics
+    Returns model evaluation metrics
     """
     y_pred = classifier.predict(X_test)
     conf_matrix = confusion_matrix(y_test, y_pred)
-    print(conf_matrix, accuracy_score(y_test, y_pred))
+    acc_score = accuracy_score(y_test, y_pred)
+    return conf_matrix, acc_score

src/load_data.py

@@ -1,13 +1,30 @@
-#! /usr/bin/env
+# #! /usr/bin/env
 
 """
 This script loads data from the dataset_path into a pandas dataset.
 """
 
-import pandas as pd
+import os
+import urllib.request
+import zipfile
 
-def load_data(dataset_path):
-    """Function loading data from dataset_path into pandas dataset"""
-    dataset = pd.read_csv(dataset_path, delimiter = '\t', quoting = 3, dtype={'Review': object, 'Liked': int})[:]
 
-    return dataset
+if __name__ == "__main__":
+       # Specify the relative path to data tsv
+    root_path = os.path.dirname(os.path.abspath(__file__))
+    dataset_path = os.path.join(root_path, '..', 'data', 'external', 'a1_RestaurantReviews_HistoricDump.tsv')
+
+    # Import the data from external source
+    print("Importing external dataset..")
+    URL = r'https://drive.google.com/uc?export=download&id=1G7rLkSloPUzkK4zCzb9lLR0zSYygu8mK'
+    zip_path, _ = urllib.request.urlretrieve(URL)
+
+    # Define export path for dataset
+    export_path = os.path.dirname(os.path.abspath(dataset_path))
+
+    # Unzip at export path
+    with zipfile.ZipFile(zip_path, "r") as f:
+        f.extractall(export_path)
+
+    # Print success to console
+    print("External dataset sucessfully imported!")

src/preprocessing.py

@@ -5,43 +5,56 @@
 """
 
 import os
-import pickle
 import re
 import nltk
 from nltk.corpus import stopwords
 from nltk.stem.porter import PorterStemmer
-from sklearn.feature_extraction.text import CountVectorizer
+import pandas as pd
+import joblib
 
-def data_preprocessing(dataset):
-    """
-    Main preprocessing steps for ML data
-    """
-    nltk.download('stopwords')
-    porter_stem = PorterStemmer()
 
-    all_stopwords = stopwords.words('english')
-    all_stopwords.remove('not')
+class Preprocessing:
+    """Class to easily preprocess datasets"""
 
-    corpus=[]
-    for i in range(0, len(dataset)):
-        review = re.sub('[^a-zA-Z]', ' ', dataset['Review'][i])
+    def __init__(self):
+        """Initialize preprocess class"""
+        nltk.download('stopwords')
+        self.porter_stem = PorterStemmer()
+        self.all_stopwords = stopwords.words('english')
+        self.all_stopwords.remove('not')
+
+        self.dataset = None
+        self.count_vectorizer = None
+
+    def preprocess_dataset(self, dataset):
+        """Loop over entire dataset to preprocess"""
+        corpus = []
+        for i in range(0, len(dataset)):
+            corpus.append(self.preprocess_review(dataset['Review'][i]))
+        return corpus
+
+    def preprocess_review(self, review):
+        """Processing a single review"""
+        review = re.sub('[^a-zA-Z]', ' ', review)
         review = review.lower()
         review = review.split()
-        review = [porter_stem.stem(word) for word in review if not word in set(all_stopwords)]
+        review = [self.porter_stem.stem(word) for word in review if not word in set(self.all_stopwords)]
         review = ' '.join(review)
-        corpus.append(review)
+        return review
 
-    # Use count vectoriser to transform dataset
-    count_vectoriser = CountVectorizer(max_features = 1420)
-    X = count_vectoriser.fit_transform(corpus).toarray()
-    y = dataset.iloc[:, -1].values
 
-    # Get the root path of the current script, and bow path to save dictionary later
+if __name__ == "__main__":
+    # Specify the relative path to data tsv
     root_path = os.path.dirname(os.path.abspath(__file__))
-    bow_path = os.path.join(root_path, '..', 'data', 'models', 'c1_BoW_Sentiment_Model.pkl')
-
-    # Saving BoW dictionary to later use in prediction
-    with open(bow_path, "wb") as file:
-        pickle.dump(count_vectoriser, file)
-
-    return X, y
+    dataset_path = os.path.join(root_path, '..', 'data', 'external', 'a1_RestaurantReviews_HistoricDump.tsv')
+
+    # Load data from file
+    load_dataset = pd.read_csv(dataset_path, delimiter = '\t', quoting = 3, dtype={'Review': object, 'Liked': int})[:]
+
+    # Preprocess and store processed corpus in joblib
+    print("Preprocessing the dataset...")
+    preprocess_class = Preprocessing()
+    save_corpus = preprocess_class.preprocess_dataset(load_dataset)
+    corpus_path = os.path.join(root_path, '..', 'data/processed/corpus.joblib')
+    joblib.dump(save_corpus, corpus_path)
+    print(f"Processed dataset (corpus) is saved to: {corpus_path}")