ProteinSeq_Classifier/train_NB.py at master · chethiyagalkaduwa/ProteinSeq_Classifier · GitHub

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'''
- This program was build for academic purpose - ProteinSeq - Protein Sequence Classifier Copyright (C) 2023  Chethiya Galkaduwa
'''

from sklearn.feature_extraction.text import CountVectorizer
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
from data_filter import data
from sklearn.naive_bayes import MultinomialNB
from sklearn.ensemble import AdaBoostClassifier
from tqdm import tqdm
import matplotlib.pyplot as plt
import numpy as np

# ------ Train Test Split ------------------------------------------------------------------------

# Split Data
#X_train, X_test, y_train, y_test = train_test_split(data['sequence'], data['classification'], test_size = 0.2, random_state = 42)

X_train, X_test, y_train, y_test = train_test_split(data['sequence'], data['classification'], test_size=0.05, random_state=42)
X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.05, random_state=42)


# After splitting the data, it's important to utilize the CountVectorizer to create a dictionary composed from the training dataset.
vect = CountVectorizer( analyzer = 'char_wb', ngram_range = (4 , 4) )

# Fit and Transform CountVectorizer
vect.fit(X_train)
X_train_df = vect.transform(X_train)
X_test_df = vect.transform(X_test)
X_val_df = vect.transform(X_val)

# Print a few of the features
print(vect.get_feature_names_out()[-20:])


# ------ Machine Learning Model ------

# Make a prediction dictionary to store accuracies
prediction = dict()

# set the number of epochs
epochs = 10

# fit the model for each epoch and update the progress bar
train_acc, val_acc = [], []

# Set a unique random seed for each epoch
for epoch in range(epochs):
    np.random.seed(epoch)

    model = MultinomialNB()
    model.fit(X_train_df, y_train)

    NB_pred_train = model.predict(X_train_df)
    acc_train = accuracy_score(NB_pred_train, y_train)
    train_acc.append(acc_train)

    NB_pred_val = model.predict(X_val_df)
    acc_val = accuracy_score(NB_pred_val, y_val)
    val_acc.append(acc_val)

    NB_pred_test = model.predict(X_test_df)
    acc_test = accuracy_score(NB_pred_test, y_test)
    prediction["MultinomialNB"] = acc_test
    print(f"Epoch {epoch+1}: Test Accuracy = {acc_test:.4f}")

# --------------Plot learning curves----------------------
plt.plot(train_acc, label='Training accuracy')
plt.plot(val_acc, label='Validation accuracy')
plt.title('Learning curves')
plt.xlabel('Epochs')
plt.ylabel('Accuracy')
plt.legend()
plt.show()

'''# ------ Machine Learning Model ------

# Make a prediction dictionary to store accuracies
prediction = dict()

# set the number of epochs
epochs = 2

# fit the model for each epoch and update the progress bar
for epoch in range(epochs):
    model = MultinomialNB()
    model.fit(X_train_df, y_train)
    NB_pred = model.predict(X_test_df)
    acc = accuracy_score(NB_pred, y_test)
    prediction["MultinomialNB"] = acc
    print (acc)'''

'''    # update progress bar
    tqdm.write(f"Epoch {epoch+1}/{epochs} - Accuracy: {acc}")

# -------------------------------------------------------------------

# Adaptive boosting

model = AdaBoostClassifier()
model.fit(X_train_df,y_train)
ADA_pred = model.predict(X_test_df)
prediction["Adaboost"] = accuracy_score(ADA_pred , y_test)
print(prediction["Adaboost"]) '''