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# PROJECT: DEPRESSION-DETECTION-USING-TWEETS
# AUTHORS: AMEY THAKUR & MEGA SATISH
# GITHUB (AMEY): https://github.com/Amey-Thakur
# GITHUB (MEGA): https://github.com/msatmod
# REPOSITORY: https://github.com/Amey-Thakur/DEPRESSION-DETECTION-USING-TWEETS
# RELEASE DATE: June 5, 2022
# LICENSE: MIT License
# DESCRIPTION: Script for training machine learning models for tweet analysis.
# ==============================================================================
import argparse
import warnings
import train_utilities as TU
# Suppression of non-critical runtime warnings to ensure output clarity during training
warnings.filterwarnings("ignore")
def main():
"""
Primary execution routine for the model training utility.
This script facilitates the training of various machine learning
architectures by providing a standardized interface for:
1. Dataset Ingestion: Loading and splitting training data.
2. Hyperparameter Configuration: Setting up model-specific parameters.
3. Algorithmic Training: Executing the training process via train_utilities.
4. Model Serialization: Persisting the resulting model for future inference.
"""
# Initialize the CLI argument parser
parser = argparse.ArgumentParser(
description="Twitter Depression Detection: Model Training Utility"
)
# Positional argument for the training dataset path (CSV format)
parser.add_argument(
'filename',
help="Path to the training dataset (TSV/CSV format with 'label' and 'clean_text')"
)
# Positional argument for the classification model architecture
# Supported: 'DT', 'LR', 'kNN', 'SVM', 'RF', 'NN', 'LSTM'
parser.add_argument(
'model',
help="Target model architecture for training"
)
# Execution of the parsing logic
args = parser.parse_args()
# Deployment of the selected training pipeline based on the 'model' parameter
model_type = args.model
dataset_path = args.filename
# Pipeline selection logic
if model_type in ["DT", "LR", "kNN", "SVM", "RF", "NN"]:
# Logic for standardized Scikit-learn architectures
print(f"Initializing {model_type} training pipeline...")
# Step 1: Data Acquisition and Validation Splitting
X_train, X_test, Y_train, Y_test = TU.load_prepare_split_df(dataset_path)
# Step 2: Algorithmic Training and Parameter Optimization
# The 'classification' method handles instantiation and fitting
trained_model = TU.classification(X_train=X_train, Y_train=Y_train, model=model_type)
print(f"Training for {model_type} successful.")
elif model_type == "LSTM":
# Specialized logic for Long Short-Term Memory (LSTM) Neural Networks
# LSTMs are utilized here to capture long-range temporal dependencies in text
print("Initializing LSTM deep learning pipeline...")
TU.LSTM(dataset_path)
else:
print(f"Error: Model architecture '{model_type}' is not currently recognized.")
print("Supported architectures: DT, LR, kNN, SVM, RF, NN, LSTM")
if __name__ == '__main__':
main() |