
%pip install pandas scikit-learn numpy matplotlib seaborn nltk wordcloud xgboost lightgbm

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# For data manipulation and analysis
import pandas as pd
import numpy as np
import string
from collections import Counter

# For plotting
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline

# For text processing and feature extraction
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from sklearn.feature_extraction.text import TfidfVectorizer

# For text preprocessing and feature extraction
import nltk
from nltk import bigrams
from nltk.corpus import stopwords
from nltk.stem.porter import PorterStemmer
from nltk.tokenize import word_tokenize, sent_tokenize

# Machine learning models and evaluation metrics
from sklearn.naive_bayes import MultinomialNB
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import RandomForestClassifier

from xgboost import XGBClassifier
from lightgbm.sklearn import LGBMClassifier

# Machine learning evaluation metrics
from sklearn.metrics import accuracy_score,confusion_matrix, precision_score, roc_auc_score, f1_score, recall_score

from wordcloud import WordCloud

import warnings
warnings.filterwarnings("ignore")
nltk.download('punkt')
nltk.download('stopwords')

[nltk_data] Downloading package punkt to /root/nltk_data...
[nltk_data]   Unzipping tokenizers/punkt.zip.
[nltk_data] Downloading package stopwords to /root/nltk_data...
[nltk_data]   Unzipping corpora/stopwords.zip.

True


# DATA_SET = "./data/spam_dataset.csv"
DATA_SET = "https://raw.githubusercontent.com/qualitia-cdev/hands-on-data/main/spam_dataset.csv"

df = pd.read_csv(DATA_SET, encoding='latin-1')
df.head()


# basic info about the dataset
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5572 entries, 0 to 5571
Data columns (total 5 columns):
 #   Column      Non-Null Count  Dtype 
---  ------      --------------  ----- 
 0   v1          5572 non-null   object
 1   v2          5572 non-null   object
 2   Unnamed: 2  50 non-null     object
 3   Unnamed: 3  12 non-null     object
 4   Unnamed: 4  6 non-null      object
dtypes: object(5)
memory usage: 217.8+ KB


# last 3 columns are unnecessary. So we are going to drop them.
df.drop(['Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4'], axis=1, inplace=True)


df.rename(columns={'v1': 'label', 'v2': 'message'}, inplace=True)
df.head(10)


target_cat = df.label.value_counts()

#create pie chart using seaborn
color1 = sns.color_palette("viridis")[0]
color2 = sns.color_palette("viridis")[3]

plt.pie(target_cat, labels=target_cat.index, colors=[color1, color2], autopct='%1.1f%%', shadow=True, startangle=90, explode=(0, 0.1))
plt.title('Target Variable Distribution (Spam and Ham)')
plt.show()

print(target_cat)

ham     4825
spam     747
Name: label, dtype: int64


num_spam = target_cat['spam']

# Randomly sampling the majority class to match the count of the minority class
df = pd.concat(
    [
        df[df['label'] == 'ham'].sample(n=num_spam, random_state=42),
        df[df['label'] == 'spam']
    ]
)

#check the target variable distribution again
target_cat = df.label.value_counts()

#set the colors
color1 = sns.color_palette("viridis")[0]
color2 = sns.color_palette("viridis")[3]

#plot the pie chart
plt.pie(target_cat, labels=target_cat.index, colors=[color1, color2], autopct='%1.1f%%', shadow=True, startangle=90, explode=(0, 0.1))
plt.title('Target Variable Distribution after dropping ham messages')
plt.show()

print(target_cat)

ham     747
spam    747
Name: label, dtype: int64


# We'll use a predefined list of stopwords as we can't download them directly here
# stop_words = set([
#     "ourselves", "hers", "between", "yourself", "but",
#     "again", "there", "about", "once", "during", "out",
#     "very", "having", "with", "they", "own", "an", "be",
#     "some", "for", "do", "its", "yours", "such", "into",
#     "of", "most", "itself", "other", "off", "is", "s", "am",
#     "or", "who", "as", "from", "him", "each", "the", "themselves",
#     "until", "below", "are", "we", "these", "your", "his", "through",
#     "don", "nor", "me", "were", "her", "more", "himself", "this",
#     "down", "should", "our", "their", "while", "above", "both", "up",
#     "to", "ours", "had", "she", "all", "no", "when", "at", "any", "before",
#     "them", "same", "and", "been", "have", "in", "will", "on", "does",
#     "yourselves", "then", "that", "because", "what", "over", "why", "so",
#     "can", "did", "not", "now", "under", "he", "you", "herself", "has",
#     "just", "where", "too", "only", "myself", "which", "those", "i",
#     "after", "few", "whom", "t", "being", "if", "theirs", "my", "against",
#     "a", "by", "doing", "it", "how", "further", "was", "here", "than"
# ])

#get the stopwords from nltk
stop_words = set(stopwords.words('english'))

def get_most_common_words(texts, n=10):
    words = []
    for text in texts:
        tokens = word_tokenize(text)
        words.extend([word.lower() for word in tokens if word.isalpha() and word.lower() not in stop_words])
    # Count the frequency of each word
    word_freq = Counter(words)

    return word_freq.most_common(n)

# Get the 10 most common words for spam and ham messages
common_words_ham = get_most_common_words(df[df['label'] == 'ham']['message'], 10)
common_words_spam = get_most_common_words(df[df['label'] == 'spam']['message'], 10)


#create table to for common_words_ham, common_words_spam
common_words_ham_df = pd.DataFrame(common_words_ham, columns=['word', 'count'])
common_words_spam_df = pd.DataFrame(common_words_spam, columns=['word', 'count'])

common_words_spam_df.head(10)


def generate_wordcloud(texts_ham, texts_spam):

    # Join all the text messages together
    all_text_ham = ' '.join(texts_ham)
    all_text_spam = ' '.join(texts_spam)

    # Create a word cloud for ham and spam messages
    wordcloud_ham = WordCloud(stopwords=stop_words, background_color="white", width=800, height=400).generate(all_text_ham)
    wordcloud_spam = WordCloud(stopwords=stop_words, background_color="white", width=800, height=400).generate(all_text_spam)

    # Plot the word clouds
    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(20, 5))
    ax1.imshow(wordcloud_ham, interpolation='bilinear')
    ax1.axis('off')
    ax1.set_title("Ham Messages Word Cloud", fontsize=25)

    ax2.imshow(wordcloud_spam, interpolation='bilinear')
    ax2.axis('off')
    ax2.set_title("Spam Messages Word Cloud", fontsize=25)

    # Adjust layout
    plt.tight_layout()
    plt.show()

# Generate word clouds for ham and spam messages side by side
generate_wordcloud(df[df['label'] == 'ham']['message'].tolist(), df[df['label'] == 'spam']['message'].tolist())


def remove_punctuation(message):
    return ''.join([char for char in message if char not in string.punctuation])


def tokenize(message):
    return [word.lower() for word in message.split() if word.lower() not in stop_words]


def generate_bigrams(tokens):
    return list(bigrams(tokens))


def preprocess_message(message):
    message = remove_punctuation(message)
    tokens = tokenize(message)
    return generate_bigrams(tokens)


def get_top_bigrams(series, n=10):
    # Flatten the list of bigrams and count occurrences
    all_bigrams = [bigram for sublist in series for bigram in sublist]
    bigram_counts = Counter(all_bigrams)
    return bigram_counts.most_common(n)

def plot_top_bigrams(df, column, title, ax, palette="viridis"):
    sns.barplot(data=df, y=column, x='Count', ax=ax, palette=palette)
    ax.set_title(title)
    ax.set_xlabel('Count')
    ax.set_ylabel(column)

# Assuming your original DataFrame is loaded as 'df'
df["bigrams"] = df["message"].apply(preprocess_message)

# Extract top bigrams
top_spam_bigrams = get_top_bigrams(df[df['label'] == 'spam']['bigrams'])
top_ham_bigrams = get_top_bigrams(df[df['label'] == 'ham']['bigrams'])

# Create dataframes for visualization
spam_df = pd.DataFrame(top_spam_bigrams, columns=['Bigrams', 'Count'])
ham_df = pd.DataFrame(top_ham_bigrams, columns=['Bigrams', 'Count'])

# Adjust bigram representation for readability
spam_df['Bigrams'] = spam_df['Bigrams'].apply(lambda x: ' '.join(x))
ham_df['Bigrams'] = ham_df['Bigrams'].apply(lambda x: ' '.join(x))

# Create subplots
fig, axes = plt.subplots(1, 2, figsize=(16, 8), sharex=True)
plot_top_bigrams(spam_df, 'Bigrams', 'Top Bigrams in Spam Messages', axes[0])
plot_top_bigrams(ham_df, 'Bigrams', 'Top Bigrams in Ham Messages', axes[1])

plt.tight_layout()
plt.show()


# create a new column for message length and calculate the length of each message
df["msg_len"] = df["message"].apply(len)

# Plot the distribution of message lengths
sns.distplot(df[df.label == 'ham'].msg_len, label='Ham', color=sns.color_palette("viridis")[0])
sns.distplot(df[df.label == 'spam'].msg_len, label='Spam', color=sns.color_palette("viridis")[3])
plt.title('Distribution Plot for Length of Messages')
plt.xlabel('Length of Messages')
plt.legend()
plt.show()

print('Average message length for ham messages: ', df[df['label'] == 'ham']['message'].str.len().mean())
print('Average message length for spam messages: ', df[df['label'] == 'spam']['message'].str.len().mean())

Average message length for ham messages:  69.11780455153949
Average message length for spam messages:  138.8661311914324


# Create new column for number of punctuations, and calculate the number of punctuations in each message
df['num_punctuations'] = df['message'].apply(lambda x: sum([1 for char in x if char in string.punctuation]))

# Create new column for number of exclamation marks, and calculate the number of exclamation marks in each message
df['num_exclamation_marks'] = df['message'].apply(lambda x: sum([1 for char in x if char == '!']))

# Calculate the  number of uppercase letters in each message and create a new column for it
df['num_upper_case'] = df['message'].apply(lambda x: sum([1 for char in x if char.isupper()]))

# Count the number of numeric characters in each message and create a new column for it
df['num_numeric'] = df['message'].apply(lambda x: sum([1 for char in x if char.isdigit()]))

# Tokenize messages and compute the number of sentences in each message and create a new column for it
df['num_sentences'] = df['message'].apply(lambda x: len(sent_tokenize(x)))

# Create a new column for number of words in each message and calculate the number of words in each message
df['num_words'] = df['message'].apply(lambda x: len(word_tokenize(x)))


df.columns

Index(['label', 'message', 'bigrams', 'msg_len', 'num_punctuations',
       'num_exclamation_marks', 'num_upper_case', 'num_numeric',
       'num_sentences', 'num_words'],
      dtype='object')


X = df[['msg_len', 'num_exclamation_marks', 'num_punctuations', 'num_upper_case', 'num_numeric', 'num_words', 'num_sentences']]


encoder = LabelEncoder()
encoded_labels = encoder.fit_transform(df['label'])
df['encoded_label'] = encoded_labels
df.tail()


# Display classes and their encoded values
for original, encoded in zip(encoder.classes_, range(len(encoder.classes_))):
    print(f"'{original}' is encoded as {encoded}")

'ham' is encoded as 0
'spam' is encoded as 1


y = df["encoded_label"]


X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)


num_feat_models = {
    "Random Forest": RandomForestClassifier(n_estimators=1000, random_state=42),
    "Decision Tree": DecisionTreeClassifier(random_state=42),
    "Logistic Regression": LogisticRegression(random_state=42),
    "SVC": SVC(kernel="linear", random_state=42),
    "KNN" : KNeighborsClassifier(),
    "XGB": XGBClassifier(objective='binary:hinge', random_state=42),
    "Multinomial NB": MultinomialNB(),
    "LGBM" : LGBMClassifier(boosting_type='gbdt',objective='binary', max_depth=4, random_state=42, verbose=-1)

}

Comparing predictions on new data using numeric features model
Predicting for: Congratulations! You have won a $1000 Walmart gift card. Click here to claim: https://bit.ly/3kceqyh
Random Forest predicted: spam
Decision Tree predicted: spam
Logistic Regression predicted: spam
SVC predicted: spam
KNN predicted: ham
XGB predicted: spam
Multinomial NB predicted: spam
LGBM predicted: spam
Models made different predictions.
----------
Predicting for: Hey, how are you doing? Let's meet up soon!
Random Forest predicted: ham
Decision Tree predicted: ham
Logistic Regression predicted: ham
SVC predicted: ham
KNN predicted: ham
XGB predicted: ham
Multinomial NB predicted: ham
LGBM predicted: ham
All models agree on the prediction.
----------


num_feat_models = {
    "Random Forest": RandomForestClassifier(n_estimators=1000, random_state=42),
    "Decision Tree": DecisionTreeClassifier(random_state=42),
    "Logistic Regression": LogisticRegression(random_state=42),
    "SVC": SVC(kernel="linear", random_state=42),
    "KNN" : KNeighborsClassifier(),
    "XGB": XGBClassifier(objective='binary:hinge', random_state=42),
    "Multinomial NB": MultinomialNB(),
    "LGBM" : LGBMClassifier(boosting_type='gbdt',objective='binary', max_depth=4, random_state=42, verbose=-1)

}


def train_clf(model, X_train, y_train, X_test, y_test):
    # Train the model
    model.fit(X_train, y_train)

    # Make predictions and get accuracy and precision
    y_pred = model.predict(X_test)

    results = {
        'Accuracy': accuracy_score(y_test, y_pred),
        'Precision': precision_score(y_test, y_pred),
        'Recall': recall_score(y_test, y_pred),
        'F1 Score': f1_score(y_test, y_pred),
        'Roc Auc Score': roc_auc_score(y_test, y_pred),
        'Confusion Matrix': confusion_matrix(y_test, y_pred)
    }

    return results

# Store scores for each model
model_scores = {}

for name, model in num_feat_models.items():
    model_scores[name] = train_clf(model, X_train, y_train, X_test, y_test)

# Convert scores dictionary to a DataFrame
scores_df = pd.DataFrame(model_scores).T
scores_df.reset_index(inplace=True)
scores_df.rename(columns={'index': 'Algorithm'}, inplace=True)
scores_df = scores_df.sort_values(by="F1 Score", ascending=False)


scores_df


# Plot
classes = ['ham', 'spam']

fig, axes = plt.subplots(nrows=2, ncols=4, figsize=(20, 10))
for idx, ax in enumerate(axes.flatten()):
    algo = scores_df.iloc[idx]['Algorithm']
    cm = scores_df.iloc[idx]['Confusion Matrix']
    # Prepare the labels for each cell
    labels = np.array([["TP: " + str(cm[0, 0]), "FP: " + str(cm[0, 1])],
                       ["FN: " + str(cm[1, 0]), "TN: " + str(cm[1, 1])]])

    sns.heatmap(cm, annot=labels, fmt="", cmap="Blues", ax=ax, cbar=False)

    ax.set_title(algo)

    ax.set_xticklabels(classes)
    ax.set_yticklabels(classes, rotation=0)

    ax.set_xlabel('Predicted labels')
    ax.set_ylabel('True labels')

plt.tight_layout()
plt.show()


def extract_numerical_features(message):
    features = {
        'msg_len': len(message),
        'num_exclamation_marks': message.count('!'),
        'num_punctuations': sum(1 for char in message if char in string.punctuation),
        'num_upper_case': sum(1 if char.isupper() else 0 for char in message),
        'num_numeric': sum(1 if char.isdigit() else 0 for char in message),
        'num_words': len(word_tokenize(message)),
        'num_sentences': len(sent_tokenize(message))
    }
    return features

def predict_new_data(message, num_feat_models, encoder):
    # Extract features
    features = extract_numerical_features(message)

    # Convert to dataframe
    df = pd.DataFrame(features, index=[0])

    # Dictionary to store the results
    predictions = {}

    for model_name, model in num_feat_models.items():
        pred = model.predict(df)
        # Assuming encoder.inverse_transform(pred) returns a list of class labels
        predicted_class = encoder.inverse_transform(pred)[0]
        predictions[model_name] = predicted_class

    return predictions

def compare_predictions(text_samples, num_feat_models, encoder):
    for text in text_samples:
        print(f"Predicting for: {text}")
        predictions = predict_new_data(text, num_feat_models, encoder)

        # Print individual model predictions
        for model, pred in predictions.items():
            print(f"{model} predicted: {pred}")

        # Check if all model predictions are the same; if not, compare the results
        unique_predictions = set(predictions.values())

        if len(unique_predictions) == 1:
            print("All models agree on the prediction.")
        else:
            print("Models made different predictions.")

        print("----------")

# Usage:
text_samples = [
    "Congratulations! You have won a $1000 Walmart gift card. Click here to claim: https://bit.ly/3kceqyh",
    "Hey, how are you doing? Let's meet up soon!"
]

# Assuming 'num_feat_models' is a dictionary of your models and 'encoder' is your label encoder
print("Comparing predictions on new data using numeric features model")
compare_predictions(text_samples, num_feat_models, encoder)

Comparing predictions on new data using numeric features model
Predicting for: Congratulations! You have won a $1000 Walmart gift card. Click here to claim: https://bit.ly/3kceqyh
Random Forest predicted: spam
Decision Tree predicted: spam
Logistic Regression predicted: spam
SVC predicted: spam
KNN predicted: ham
XGB predicted: spam
Multinomial NB predicted: spam
LGBM predicted: spam
Models made different predictions.
----------
Predicting for: Hey, how are you doing? Let's meet up soon!
Random Forest predicted: ham
Decision Tree predicted: ham
Logistic Regression predicted: ham
SVC predicted: ham
KNN predicted: ham
XGB predicted: ham
Multinomial NB predicted: ham
LGBM predicted: ham
All models agree on the prediction.
----------

	v1	v2	Unnamed: 2	Unnamed: 3	Unnamed: 4
0	ham	Go until jurong point, crazy.. Available only ...	NaN	NaN	NaN
1	ham	Ok lar... Joking wif u oni...	NaN	NaN	NaN
2	spam	Free entry in 2 a wkly comp to win FA Cup fina...	NaN	NaN	NaN
3	ham	U dun say so early hor... U c already then say...	NaN	NaN	NaN
4	ham	Nah I don't think he goes to usf, he lives aro...	NaN	NaN	NaN

	word	count
0	call	346
1	free	219
2	txt	156
3	u	144
4	ur	144
5	mobile	123
6	text	121
7	stop	114
8	claim	113
9	reply	104

	Algorithm	Accuracy	Precision	Recall	F1 Score	Roc Auc Score	Confusion Matrix
0	Random Forest	0.973274	0.986726	0.961207	0.973799	0.973691	[[214, 3], [9, 223]]
7	LGBM	0.966592	0.977974	0.956897	0.96732	0.966928	[[212, 5], [10, 222]]
5	XGB	0.962138	0.969432	0.956897	0.963124	0.962319	[[210, 7], [10, 222]]
1	Decision Tree	0.957684	0.965066	0.952586	0.958785	0.95786	[[209, 8], [11, 221]]
4	KNN	0.955457	0.969027	0.943966	0.956332	0.955854	[[210, 7], [13, 219]]
2	Logistic Regression	0.953229	0.968889	0.939655	0.954048	0.953699	[[210, 7], [14, 218]]
3	SVC	0.951002	0.972973	0.931034	0.951542	0.951692	[[211, 6], [16, 216]]
6	Multinomial NB	0.919822	0.962264	0.87931	0.918919	0.921222	[[209, 8], [28, 204]]

基本的な機械学習テクニックによるSPAMフィルタリング¶

はじめに¶

機械学習入門¶

機械学習とは何か？¶

機械学習の種類¶

教師あり学習¶

教師あり学習 (例)¶

Google Colab.¶

1. ライブラリのインストール¶

2. ライブラリのインポート¶

3. データの読み込みと概要

3.1. データのロード¶

3.2. データセットの概要¶

4. EDA（探索的データ分析）

4.1. EDAとは¶

4.2. 対象変数の分布¶

ターゲット変数とは何か？¶

4.3. スパムメッセージとハムメッセージで最もよく使われる単語（ユニグラム）¶

4.4. スパムとハムメッセージで最もよく使われるバイグラム¶

バイグラムとは？¶

5. 特徴量エンジニアリング¶

特徴量エンジニアリングとは？¶

5.1. メッセージの長さ¶

5.2 その他の特徴量¶

6 モデルのトレーニング¶

6.1. 特徴量の選択¶

6.2. ラベルのエンコード¶

6.3. データの分割¶

トレーニングセットとテストセットとは？¶

6.4. 機械学習モデル¶

6.5. モデルのトレーニング¶

6.6. モデルの評価¶

7.6. 新しいデータの予測¶

予測の流れ¶

8. モデルのパフォーマンスを向上させるには¶

9. 結論¶

	label	message	bigrams	msg_len	num_punctuations	num_exclamation_marks	num_upper_case	num_numeric	num_sentences	num_words	encoded_label
5537	spam	Want explicit SEX in 30 secs? Ring 02073162414...	[(want, explicit), (explicit, sex), (sex, 30),...	90	3	1	17	21	3	18	1
5540	spam	ASKED 3MOBILE IF 0870 CHATLINES INCLU IN FREE ...	[(asked, 3mobile), (3mobile, 0870), (0870, cha...	160	5	0	104	14	6	38	1
5547	spam	Had your contract mobile 11 Mnths? Latest Moto...	[(contract, mobile), (mobile, 11), (11, mnths)...	160	8	1	20	2	5	35	1
5566	spam	REMINDER FROM O2: To get 2.50 pounds free call...	[(reminder, o2), (o2, get), (get, 250), (250, ...	147	3	0	14	5	1	30	1
5567	spam	This is the 2nd time we have tried 2 contact u...	[(2nd, time), (time, tried), (tried, 2), (2, c...	161	8	1	9	21	4	35	1