# Install MathFeature via pip
# -m flag added to ensure pip is run as a module
# -q flag added to suppress output

pip install -m -q mathfeature

# Transform data using RF algorithm with CN and AAC descriptors
# -o flag added to specify output file
# -i flag added to specify input file
# -a flag added to specify algorithm (RF)
# -d flag added to specify descriptors (CN and AAC)
# -t flag added to specify transformation
mathfeature -o output.csv -i input.csv -a RF -d CN,AAC -t

# Perform 10-fold cross-validation for model assessment
# -c flag added to specify cross-validation
# -f flag added to specify number of folds (10)
mathfeature -o output.csv -i input.csv -a RF -d CN,AAC -c -f 10

# Import necessary libraries
import pandas as pd # Importing pandas library and assigning it an alias "pd"
from sklearn.model_selection import train_test_split # Importing train_test_split function from sklearn.model_selection library
from sklearn.metrics import accuracy_score, f1_score, matthews_corrcoef # Importing accuracy_score, f1_score, and matthews_corrcoef functions from sklearn.metrics library
from mathfeature.preprocessing import Preprocessor # Importing Preprocessor class from mathfeature.preprocessing module
from mathfeature.extraction import Extractor # Importing Extractor class from mathfeature.extraction module
import mathfeature as mf # Importing mathfeature library and assigning it an alias "mf"

# Explanation: The above code imports necessary libraries and functions for data preprocessing, feature extraction, and performance evaluation.

# Next, let's define a function to load and preprocess the data
def load_and_preprocess_data(data_path): # Defining a function named "load_and_preprocess_data" that takes in a parameter "data_path"
    # Load data using pandas
    data = pd.read_csv(data_path) # Using the read_csv function from pandas library to load data from the specified data path and assigning it to a variable "data"
    
    # Preprocess data using Preprocessor class
    preprocessor = Preprocessor() # Creating an instance of the Preprocessor class and assigning it to a variable "preprocessor"
    preprocessed_data = preprocessor.preprocess(data) # Using the preprocess method from the Preprocessor class to preprocess the data and assigning it to a variable "preprocessed_data"
    
    return preprocessed_data # Returning the preprocessed data

# Explanation: The above code defines a function to load and preprocess data using the Preprocessor class.

# Now, let's extract features from the preprocessed data
def extract_features(data): # Defining a function named "extract_features" that takes in a parameter "data"
    # Extract features using Extractor class
    extractor = Extractor() # Creating an instance of the Extractor class and assigning it to a variable "extractor"
    features = extractor.extract(data) # Using the extract method from the Extractor class to extract features from the data and assigning it to a variable "features"
    
    return features # Returning the extracted features

# Explanation: The above code defines a function to extract features from the preprocessed data using the Extractor class.

# Now, let's split the data into training and testing sets
def split_data(features, labels): # Defining a function named "split_data" that takes in two parameters "features" and "labels"
    # Split data using train_test_split function
    X_train, X_test, y_train, y_test = train_test_split(features, labels, test_size=0.2, random_state=42) # Using the train_test_split function to split the features and labels into training and testing sets with a test size of 20% and a random state of 42
    
    return X_train, X_test, y_train, y_test # Returning the training and testing sets

# Explanation: The above code defines a function to split the data into training and testing sets using the train_test_split function.

# Now, let's train a model and make predictions on the testing set
def train_and_predict(X_train, X_test, y_train, y_test): # Defining a function named "train_and_predict" that takes in four parameters "X_train", "X_test", "y_train", and "y_test"
    # Train a model using mathfeature library
    model = mf.train(X_train, y_train) # Using the train function from the mathfeature library to train a model using the training data and assigning it to a variable "model"
    
    # Make predictions on the testing set
    y_pred = model.predict(X_test) # Using the predict method from the trained model to make predictions on the testing set and assigning it to a variable "y_pred"
    
    # Evaluate model performance using accuracy, f1-score, and Matthews correlation coefficient
    accuracy = accuracy_score(y_test, y_pred) # Using the accuracy_score function to calculate the accuracy of the model's predictions and assigning it to a variable "accuracy"
    f1 = f1_score(y_test, y_pred) # Using the f1_score function to calculate the f1-score of the model's predictions and assigning it to a variable "f1"
    mcc = matthews_corrcoef(y_test, y_pred) # Using the matthews_corrcoef function to calculate the Matthews correlation coefficient of the model's predictions and assigning it to a variable "mcc"
    
    return accuracy, f1, mcc # Returning the model's performance metrics

# Explanation: The above code defines a function to train a model, make predictions on the testing set, and evaluate its performance using accuracy, f1-score, and Matthews correlation coefficient.

# Finally, let's run the entire process
if __name__ == "__main__": # Checking if the script is being run directly
    # Load and preprocess data
    data_path = "data.csv" # Assigning the data path to a variable "data_path"
    preprocessed_data = load_and_preprocess_data(data_path) # Calling the load_and_preprocess_data function with the data path as the parameter and assigning the preprocessed data to a variable "preprocessed_data"
    
    # Extract features from preprocessed data
    features = extract_features(preprocessed_data) # Calling the extract_features function with the preprocessed data as the parameter and assigning the extracted features to a variable "features"
    
    # Split data into training and testing sets
    X_train, X_test, y_train, y_test = split_data(features, preprocessed_data["label"]) # Calling the split_data function with the extracted features and labels as parameters and assigning the training and testing sets to variables "X_train", "X_test", "y_train", and "y_test"
    
    # Train a model and make predictions on the testing set
    accuracy, f1, mcc = train_and_predict(X_train, X_test, y_train, y_test) # Calling the train_and_predict function with the training and testing sets as parameters and assigning the model's performance metrics to variables "accuracy", "f1", and "mcc"
    
    # Print model performance
    print("Accuracy:", accuracy) # Printing the accuracy of the model's predictions
    print("F1-score:", f1) # Printing the f1-score of the model's predictions
    print("Matthews correlation coefficient:", mcc) # Printing the Matthews correlation coefficient of the model's predictions

# Explanation: The above code runs the entire process of loading and preprocessing data, extracting features, splitting the data into training and testing sets, training a model, making predictions on the testing set, and evaluating its performance.

# Import the necessary libraries
import pandas as pd
from MathFeature import Preprocessor

# Load the dataset from a text file, using tab as the separator
df = pd.read_csv('dataset.txt', sep='\t')

# Create an instance of the Preprocessor class
preprocessor = Preprocessor()

# Use the fit_transform method to preprocess the dataset and assign the transformed data to variables X and y
X, y = preprocessor.fit_transform(df)

# The fit_transform method performs the following steps:
# 1. Fit the preprocessor to the dataset, learning the necessary transformations
# 2. Transform the dataset using the learned transformations
# 3. Assign the transformed data to variables X and y for further use

# Import the necessary libraries
import MathFeature as mf

# Create an instance of the Extractor class and pass in the CN and AAC descriptors as arguments
extractor = mf.Extractor([mf.descriptors.CN(), mf.descriptors.AAC()])

# Use the fit_transform method to extract the descriptors from the given data X and store the results in X_new and y_new
X_new, y_new = extractor.fit_transform(X)

# The Extractor class is used to extract features from data using different descriptors. 
# Here, we are passing in the CN and AAC descriptors to extract the features. 
# The fit_transform method is used to fit the data and transform it into the desired format. 
# The results are then stored in X_new and y_new variables.

# Import the necessary library
import sklearn

# Split the data into training and testing sets using sklearn's train_test_split function
# X_new and y_new are the features and labels of the dataset respectively
# test_size=0.2 indicates that 20% of the data will be used for testing and 80% for training
train_x, test_x, train_y, test_y = sklearn.model_selection.train_test_split(X_new, y_new, test_size=0.2)

# Import the RandomForestClassifier from sklearn.ensemble library
from sklearn.ensemble import RandomForestClassifier

# Create an instance of the RandomForestClassifier and assign it to the variable clf
clf = RandomForestClassifier()

# Train the classifier using the train_x and train_y data
clf.fit(train_x, train_y)

# Use the trained classifier to make predictions on the test_x data and assign it to the variable predictions
predictions = clf.predict(test_x)

# The RandomForestClassifier is a machine learning algorithm used for classification tasks. 
# It is imported from the sklearn.ensemble library.
# An instance of the classifier is created and assigned to the variable clf.
# The classifier is then trained using the train_x and train_y data.
# Finally, the trained classifier is used to make predictions on the test_x data and the results are stored in the predictions variable.

# Assessing predictive performance using accuracy score, F1-score, and Matthews correlation coefficient

# Import necessary libraries
import numpy as np
from sklearn.metrics import accuracy_score, f1_score, matthews_corrcoef

# Calculate and print accuracy score
acc = accuracy_score(test_y, predictions) # Calculate accuracy score using test_y and predictions
acc = round(acc*100, 2) # Round the accuracy score to 2 decimal places
print('Accuracy:', acc) # Print the accuracy score

# Calculate and print F1-score
f1 = f1_score(test_y, predictions) # Calculate F1-score using test_y and predictions
f1 = round(f1*100, 2) # Round the F1-score to 2 decimal places
print('F1-Score:', f1) # Print the F1-score

# Calculate and print Matthews correlation coefficient
mcc = matthews_corrcoef(test_y, predictions) # Calculate Matthews correlation coefficient using test_y and predictions
mcc = round(mcc, 4) # Round the MCC to 4 decimal places
print('MCC:', mcc) # Print the MCC