Model Card for Infinitode/PSPM-OPEN-ARC

Repository: https://github.com/Infinitode/OPEN-ARC/

Model Description

OPEN-ARC-PSP is a straightforward XGBClassifier model developed as part of Infinitode's OPEN-ARC initiative. It was designed to potentially identify plants experiencing high stress caused by external factors.

Architecture:

• XGBClassifier: n_estimators=100, learning_rate=0.1, max_depth=6, subsample=0.8, colsample_bytree=0.8, random_state=42.
• Framework: XGBoost
• Training Setup: Trained with the default training params.

Uses

• Identifying crops experiencing significant stress.
• Improving crop production by mitigating major stressors affecting plants.
• Performing experimental studies on plant behavior and yield outcomes influenced by stress levels.

Limitations

• May generate implausible or inappropriate results when influenced by extreme outlier values.
• Could provide inaccurate plant stress levels; caution is advised when relying on these outputs.

Training Data

• Dataset: Plant-Health-Data dataset from Kaggle.
• Source URL: https://www.kaggle.com/datasets/ziya07/plant-health-data
• Content: Soil characteristics, moisture levels, and various agricultural metrics, combined with the anticipated stress level of the plant.
• Size: 1200 entries of plant stress levels.
• Preprocessing: Dropped unnecessary features like the Timestamp and Plant_ID. Stress levels were manually mapped to three distinct numerical values.

Training Procedure

• Metrics: accuracy, precision, recall, F1
• Train/Testing Split: 80% train, 20% testing.

Evaluation Results

Metric	Value
Testing Accuracy	99.1%
Testing Weighted Average Precision	99%
Testing Weighted Average Recall	99%
Testing Weighted Average F1	99%

How to Use

import random

def test_random_samples(model, X_test, y_test, n_samples=5):
    """
    Selects random samples from the test set, makes predictions, and compares with actual values.
    
    Parameters:
    - model: Trained XGBoost classifier.
    - X_test: Feature set for testing.
    - y_test: True labels for testing.
    - n_samples: Number of random samples to test.
    
    Returns:
    None
    """
    # Convert X_test and y_test to DataFrame for easier indexing
    X_test_df = X_test.reset_index(drop=True)
    y_test_df = y_test.reset_index(drop=True)

    # Pick random indices
    random_indices = random.sample(range(len(X_test)), n_samples)
    
    print("Testing on Random Samples:")
    for idx in random_indices:
        sample = X_test_df.iloc[idx]
        true_label = y_test_df.iloc[idx]
        
        # Predict using the model
        prediction = model.predict(sample.values.reshape(1, -1))

        # Reverse the health mapping
        reverse_health_mapping = {v: k for k, v in health_mapping.items()}

        # Map true and predicted labels
        true_label_description = reverse_health_mapping[true_label]
        predicted_label_description = reverse_health_mapping[prediction[0]]
        
        # Output results
        print(f"Sample Index: {idx}")
        print(f"Features: {sample.values}")
        print(f"True Label: {true_label}, Predicted Label: {prediction[0]}")
        print(f"True Label (Description): {true_label_description}, Predicted Label (Description): {predicted_label_description}")
        print("-" * 40)

# Example usage
test_random_samples(xgb, X_test, y_test)

Contact

For questions or issues, open a GitHub issue or reach out at https://infinitode.netlify.app/forms/contact.