train_classifier.py

import joblib
import matplotlib.pyplot as plt
import pandas as pd
import re
import seaborn as sns
from nltk.corpus import stopwords
from nltk.stem import PorterStemmer
from sklearn import metrics
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.neighbors import KNeighborsClassifier, NeighborhoodComponentsAnalysis
from sklearn.preprocessing import LabelEncoder

file_path = '~/Projects/hau/csstudy/resume-screening-and-classification/knn-trial/datasets/dataset_hr_edited.csv'

resumeDataSet = pd.read_csv(file_path)

stop_words = set(stopwords.words('english'))
stemmer = PorterStemmer()

print (resumeDataSet['Category'].value_counts())

def cleanResume(resumeText):
    resumeText = re.sub('http\S+\s*', ' ', resumeText)  # remove URLs
    resumeText = re.sub('RT|cc', ' ', resumeText)  # remove RT and cc
    resumeText = re.sub('#\S+', '', resumeText)  # remove hashtags
    resumeText = re.sub('@\S+', '  ', resumeText)  # remove mentions
    resumeText = re.sub('[%s]' % re.escape("""!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~"""), ' ', resumeText)  # remove punctuations
    resumeText = re.sub(r'[^\x00-\x7f]',r' ', resumeText) 
    resumeText = re.sub('\s+', ' ', resumeText)  # remove extra whitespace

    words = resumeText.split()
    words = [word for word in words if word.lower() not in stop_words]
    words = [stemmer.stem(word.lower()) for word in words if word.lower() not in stop_words]
    resumeText = ' '.join(words)
    return resumeText

resumeDataSet['cleaned_resume'] = resumeDataSet.Resume.apply(lambda x: cleanResume(x))

le = LabelEncoder()
resumeDataSet['Category'] = le.fit_transform(resumeDataSet['Category'])
le_filename = f'label_encoder.joblib'
joblib.dump(le, le_filename)

requiredText = resumeDataSet['cleaned_resume'].values
requiredTarget = resumeDataSet['Category'].values

word_vectorizer = TfidfVectorizer(
    stop_words='english',
    sublinear_tf=True,
    max_features=18038
)

word_vectorizer.fit(requiredText)
joblib.dump(word_vectorizer, 'tfidf_vectorizer.joblib')
WordFeatures = word_vectorizer.transform(requiredText)

nca = NeighborhoodComponentsAnalysis(n_components=300, random_state=42)
WordFeatures = nca.fit_transform(WordFeatures.toarray(), requiredTarget)
nca_filename = f'nca_model.joblib'
joblib.dump(nca, nca_filename)

X_train,X_test,y_train,y_test = train_test_split(WordFeatures,requiredTarget,random_state=42, test_size=0.2,shuffle=True, stratify=requiredTarget)
print(X_train.shape)
print(X_test.shape)

# n_neighbors_values = [1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 43, 45, 47, 49, 51, 53, 55, 57, 59, 61, 63, 65, 67, 69, 71, 73, 75, 77, 79, 81, 83, 85, 87, 89, 91, 93, 95, 97, 99]
# weights = ["uniform", "distance"]
# metric = ["euclidean", "manhattan", "minkowski", "cosine"]
# algorithm = ['ball_tree', 'kd_tree', 'brute', 'auto']
# param_grid = dict(n_neighbors=n_neighbors_values, weights=weights, metric=metric, algorithm=algorithm)
# knn = KNeighborsClassifier()
# gs = GridSearchCV(estimator=knn, param_grid=param_grid, scoring="accuracy", verbose=1, cv=10, n_jobs=3)
# grid_search = gs.fit(X_train, y_train)
# best_score = grid_search.best_score_
# best_parameters = grid_search.best_params_
# print("Best Score:", best_score)
# print("Best Parameters:", best_parameters)

knn = KNeighborsClassifier(n_neighbors=1, 
                           metric='manhattan',
                           weights='uniform',
                           algorithm='ball_tree',
                           )
knn.fit(X_train, y_train)

knnModel_filename = f'knn_model.joblib'
joblib.dump(knn, knnModel_filename)

prediction = knn.predict(X_test)
print('Accuracy of KNeighbors Classifier on training set: {:.2f}'.format(knn.score(X_train, y_train)))
print('Accuracy of KNeighbors Classifier on test set: {:.2f}'.format(knn.score(X_test, y_test)))
print("\n Classification report for classifier %s:\n%s\n" % (knn, metrics.classification_report(y_test, prediction)))

confusion_matrix = metrics.confusion_matrix(y_test, prediction)

plt.figure(figsize=(10, 10))
sns.heatmap(confusion_matrix, annot=True, fmt='d', cmap='Blues', xticklabels=le.classes_, yticklabels=le.classes_)
plt.xlabel('Predicted')
plt.ylabel('True')
plt.title('Confusion Matrix')
plt.show()