Create spam classification tutorial

spam/tutorial.md

@@ -0,0 +1,271 @@
+# Spam Classification with ML-Pack on the command line
+
+## Introduction
+
+In this tutorial, the ML-Pack command line interface will
+be used to train a machine learning model to classify
+SMS spam. It will be assumed that ML-Pack has been
+successfully installed on your machine. The tutorial has
+been tested in a linux environment.
+
+
+## Example
+
+As an example, we will train some machine learning models to classify spam SMS messages. We will use an example spam dataset in Indonesian provided by Yudi Wibisono
+
+
+We will try to classify a message as spam or ham by the number of occurences of a word in a message. We first change the file line endings, remove line 243 which is missing a label and then remove the header from the dataset. Then, we split our data into two files, labels and messages. Since the labels are at the end of the message, the message is reversed and then the label removed and placed in one file. The message is then removed and placed in another file.
+
+```
+tr '\r' '\n' < dataset_sms_spam_v1.csv > dataset.txt
+sed '243d' dataset.txt > dataset1.csv
+sed '1d' dataset1.csv > dataset.csv
+rev dataset.csv | cut -c1  | rev > labels.txt
+rev dataset.csv | cut -c2- | rev > messages.txt
+rm dataset.csv
+rm dataset1.csv
+rm dataset.txt
+```
+
+Machine learning works on numeric data, so we will use labels to 1 for ham and 0 for spam. The dataset contains thre labels, 0, normal sms (ham), 1, fraud (spam) and  2, promotion (spam). We will label all spam as 1, so promotions
+and fraud will be labelled as 1.
+
+
+```
+tr '2' '1' < labels.txt > labels.csv
+rm labels.txt
+```
+
+The next step is to convert all text in the messages to lower case and for simplicity remove punctuation and any symbols that are not spaces, line endings or in the range a-z (one would need expand this range of symbols for production use) 
+
+```
+tr '[:upper:]' '[:lower:]' < messages.txt > messagesLower.txt
+tr -Cd 'abcdefghijklmnopqrstuvwxyz \n' < messagesLower.txt > messagesLetters.txt
+rm messagesLower.txt
+```
+
+We now obtain a sorted list of unique words used (this step may take a few minutes, so use nice to give it a low priority while you continue with other tasks on your computer).
+
+```
+nice -20 xargs -n1 < messagesLetters.txt > temp.txt
+sort temp.txt > temp2.txt
+uniq temp2.txt > words.txt
+rm temp.txt
+rm temp2.txt
+```
+
+We then create a matrix, where for each message, the frequency of word occurences is counted (more on this on Wikipedia, [here](https://en.wikipedia.org/wiki/Tf–idf) and [here](https://en.wikipedia.org/wiki/Document-term_matrix)). This requires a few lines of code, so the full script, which should be saved as 'makematrix.sh' is below
+
+
+```
+#!/bin/bash
+declare -a words=()
+declare -a letterstartind=()
+declare -a letterstart=()
+letter=" "
+i=0
+lettercount=0
+while IFS= read -r line; do
+        labels[$((i))]=$line
+        let "i++"
+done < labels.csv
+i=0
+while IFS= read -r line; do
+        words[$((i))]=$line
+        firstletter="$( echo $line | head -c 1 )"
+        if [ "$firstletter" != "$letter" ]
+        then
+                letterstartind[$((lettercount))]=$((i))
+                letterstart[$((lettercount))]=$firstletter
+                letter=$firstletter
+                let "lettercount++"
+        fi
+        let "i++"
+done < words.txt
+letterstartind[$((lettercount))]=$((i))
+echo "Created list of letters"
+
+touch wordfrequency.txt
+rm wordfrequency.txt
+touch wordfrequency.txt
+messagecount=0
+messagenum=0
+messages="$( wc -l messages.txt )"
+i=0
+while IFS= read -r line; do
+        let "messagenum++"
+        declare -a wordcount=()
+        declare -a wordarray=()
+        read -r -a wordarray <<< "$line"
+        let "messagecount++"
+        words=${#wordarray[@]}
+        for word in "${wordarray[@]}"; do
+                startletter="$( echo $word | head -c 1 )"
+                j=-1
+                while [ $((j)) -lt $((lettercount)) ]; do
+                        let "j++"
+                        if [ "$startletter" == "${letterstart[$((j))]}" ]
+                        then
+                                mystart=$((j))
+                        fi
+                done
+                myend=$((mystart))+1
+                j=${letterstartind[$((mystart))]}
+                jend=${letterstartind[$((myend))]}
+                while [ $((j)) -le $((jend)) ]; do
+                        wordcount[$((j))]=0
+                        if [ "$word" == "${words[$((j))]}" ]
+                        then
+                                wordcount[$((j))]="$( echo $line | grep -o $word | wc -l )"
+                        fi
+                        let "j++"
+                done
+        done
+        for j in "${!wordcount[@]}"; do
+                wordcount[$((j))]=$(echo " scale=4; $((${wordcount[$((j))]})) / $((words))" | bc)
+        done
+        wordcount[$((words))+1]=$((words))
+        echo "${wordcount[*]}" >> wordfrequency.txt
+        echo "Processed message ""$messagenum"
+        let "i++"
+done < messagesLetters.txt
+# Create csv file
+tr ' '  ',' < wordfrequency.txt > data.csv
+```
+
+Since [Bash](https://www.gnu.org/software/bash/) is an interpreted language, this simple implementation can take upto 30 minutes to complete. If using the above Bash script on your primary workstation, run it as a task with low priority so that you can continue with other work while you wait:
+
+```
+nice -20 bash makematrix.sh
+```
+
+Once the script has finished running, split the data into testing (30%) and training (70%) sets:
+
+```
+mlpack_preprocess_split                                       \
+    --input_file data.csv                     \
+    --input_labels_file labels.csv            \
+    --training_file train.data.csv                 \
+    --training_labels_file train.labels.csv   \
+    --test_file test.data.csv                      \
+    --test_labels_file test.labels.csv        \
+    --test_ratio 0.3                                          \
+    --verbose
+```
+
+Now train a [Logistic regression model](https://mlpack.org/doc/mlpack-3.3.1/cli_documentation.html#logistic_regression):
+
+```
+mlpack_logistic_regression --training_file train.data.csv --labels_file train.labels.csv --lambda 0.1 --output_model_file lr_model.bin
+```
+
+Finally we test our model by producing predictions,
+
+```
+mlpack_logistic_regression --input_model_file lr_model.bin --test_file test.data.csv --output_file lr_predictions.csv
+```
+
+and comparing the predictions with the exact results,
+
+```
+export incorrect=$(diff -U 0 lr_predictions.csv test.labels.csv | grep '^@@' | wc -l)
+export tests=$(wc -l < lr_predictions.csv)
+echo "scale=2;  100 * ( 1 - $((incorrect)) / $((tests)))"  | bc
+```
+
+This gives approximately 90% validation rate, similar to that obtained [here](https://towardsdatascience.com/spam-detection-with-logistic-regression-23e3709e522).
+
+The dataset is composed of approximately 50% spam messages, so the validation rates are quite good without doing much parameter tuning.
+In typical cases, datasets are unbalanced with many more entries in some categories than in others. In these cases a good validation
+rate can be obtained by mispredicting the class with a few entries. 
+Thus to better evaluate these models, one can compare the number of misclassifications of spam, and the number of misclassifications of ham. 
+Of particular importance in applications is the number of false positive spam results as these are typically not transmitted. The script below produces a confusion matrix which gives a better indication of misclassification.
+Save it as 'confusion.sh'
+
+```
+#!/bin/bash
+declare -a labels
+declare -a lr
+i=0
+while IFS= read -r line; do
+        labels[i]=$line
+        let "i++"
+done < test.labels.csv
+i=0
+while IFS= read -r line; do
+        lr[i]=$line
+        let "i++"
+done < lr_predictions.csv
+TruePositiveLR=0
+FalsePositiveLR=0
+TrueZerpLR=0
+FalseZeroLR=0
+Positive=0
+Zero=0
+for i in "${!labels[@]}"; do
+        if [ "${labels[$i]}" == "1" ]
+        then
+                let "Positive++"
+                if [ "${lr[$i]}" == "1" ] 
+                then
+                        let "TruePositiveLR++"
+                else
+                        let "FalseZeroLR++"
+                fi
+        fi
+        if [ "${labels[$i]}" == "0" ]
+        then
+                let "Zero++"
+                if [ "${lr[$i]}" == "0" ]
+                then
+                        let "TrueZeroLR++"
+                else
+                        let "FalsePositiveLR++"
+                fi
+        fi
+
+done
+echo "Logistic Regression"
+echo "Total spam"  $Positive
+echo "Total ham"  $Zero
+echo "Confusion matrix"
+echo "             Predicted class"
+echo "                 Ham | Spam "
+echo "              ---------------"
+echo " Actual| Ham  | " $TrueZeroLR "|" $FalseZeroLR
+echo " class | Spam | " $FalsePositiveLR " |" $TruePositiveLR
+echo ""
+
+```
+
+then run the script
+
+```
+bash confusion.sh
+```
+
+You should get output similar to
+
+Logistic Regression
+Total spam 183
+Total ham 159
+Confusion matrix
+             Predicted class
+                 Ham | Spam 
+              ---------------
+ Actual| Ham  |  128 | 26
+ class | Spam |  31  | 157
+
+
+which indicates a reasonable level of classification.
+Other methods you can try in ML-Pack for this problem include
+* [Naive Bayes](https://mlpack.org/doc/mlpack-3.3.1/cli_documentation.html#nbc)
+* [Random forest](https://mlpack.org/doc/mlpack-3.3.1/cli_documentation.html#random_forest) 
+* [Decision tree](https://mlpack.org/doc/mlpack-3.3.1/cli_documentation.html#decision_tree)
+* [AdaBoost](https://mlpack.org/doc/mlpack-3.3.1/cli_documentation.html#adaboost)
+* [Perceptron](https://mlpack.org/doc/mlpack-3.3.1/cli_documentation.html#perceptron)
+
+To improve the error rating, you can try other pre-processing methods on the initial data set.
+Neural networks can give upto 99.95% validation rates, see for example [here](https://thesai.org/Downloads/Volume11No1/Paper_67-The_Impact_of_Deep_Learning_Techniques.pdf), [here](https://www.kaggle.com/kredy10/simple-lstm-for-text-classification) and [here](https://www.kaggle.com/xiu0714/sms-spam-detection-bert-acc-0-993). However, using these techniques with ML-Pack is best covered in another tutorial.
+
+This tutorial is an adaptation of one that first appeared in the [Fedora Magazine](https://fedoramagazine.org/spam-classification-with-ml-pack/).

tools/download_data_set.py

@@ -133,7 +133,16 @@ def iris_dataset():
   tar.extractall()
   tar.close()
   clean()
-
+
+def spam_dataset():
+    print("Downloading spam dataset...")
+    spam = requests.get("https://www.mlpack.org/datasets/dataset_sms_spam_bhs_indonesia_v1.tar.gz")
+    progress_bar("dataset_sms_spam_bhs_indonesia_v1.tar.gz", spam)
+    tar = tarfile.open("dataset_sms_spam_bhs_indonesia_v1.tar.gz", "r:gz")
+    tar.extractall()
+    tar.close()
+    clean()
+
 def all_datasets():
   mnist_dataset()
   electricity_consumption_dataset()