Model Card for Infinitode/MCM-OPEN-ARC

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

Model Description

OPEN-ARC-MC is a simple RandomForestClassifier model developed as part of Infinitode's OPEN-ARC initiative. It was designed to determine whether a mushroom is edible or inedible, based on its appearance, habitat, and several other factors.

Architecture:

• RandomForestClassifier: random_state=42, class_weight="balanced".
• Framework: SKLearn
• Training Setup: Trained using the default params.

Uses

• Identifying potentially poisonous mushrooms to avoid their consumption.
• Advancing knowledge and research in mushroom edibility and toxicology.

Limitations

• May provide inaccurate assessments regarding mushroom edibility; caution is advised when considering these outputs. Always consult expert human guidance.

Disclaimer

This model is intended solely for educational purposes and must not be utilized for real-life mushroom classification or any decision-making processes regarding mushroom consumption. Although the model demonstrates strong performance on the provided dataset, it has not undergone comprehensive validation for real-world applications and may fail to reliably identify poisonous mushrooms under all circumstances. Always seek advice from an expert or rely on trusted resources when identifying mushrooms.

Training Data

• Dataset: Mushroom Classification dataset from Kaggle.
• Source URL: https://www.kaggle.com/datasets/uciml/mushroom-classification
• Content: Mushroom appearance, and other factors, along with the edibility of the mushroom.
• Size: 8124 entries of mushroom features and target values.
• Preprocessing: Mapped all string values to numeric values.

Training Procedure

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

Evaluation Results

Metric	Value
Testing Accuracy (CV 5-fold)	91.0%
Testing Weighted Average Precision	100%
Testing Weighted Average Recall	100%
Testing Weighted Average F1	100%

How to Use

feature_options = {
    'cap-shape': {'b': 'bell', 'c': 'conical', 'x': 'convex', 'f': 'flat', 'k': 'knobbed', 's': 'sunken'},
    'cap-surface': {'f': 'fibrous', 'g': 'grooves', 'y': 'scaly', 's': 'smooth'},
    'cap-color': {'n': 'brown', 'b': 'buff', 'c': 'cinnamon', 'g': 'gray', 'r': 'green', 'p': 'pink', 'u': 'purple', 'e': 'red', 'w': 'white', 'y': 'yellow'},
    'bruises': {'t': 'bruises', 'f': 'no'},
    'odor': {'a': 'almond', 'l': 'anise', 'c': 'creosote', 'y': 'fishy', 'f': 'foul', 'm': 'musty', 'n': 'none', 'p': 'pungent', 's': 'spicy'},
    'gill-attachment': {'a': 'attached', 'd': 'descending', 'f': 'free', 'n': 'notched'},
    'gill-spacing': {'c': 'close', 'w': 'crowded', 'd': 'distant'},
    'gill-size': {'b': 'broad', 'n': 'narrow'},
    'gill-color': {'k': 'black', 'n': 'brown', 'b': 'buff', 'h': 'chocolate', 'g': 'gray', 'r': 'green', 'o': 'orange', 'p': 'pink', 'u': 'purple', 'e': 'red', 'w': 'white', 'y': 'yellow'},
    'stalk-shape': {'e': 'enlarging', 't': 'tapering'},
    'stalk-root': {'b': 'bulbous', 'c': 'club', 'u': 'cup', 'e': 'equal', 'z': 'rhizomorphs', 'r': 'rooted', '?': 'missing'},
    'stalk-surface-above-ring': {'f': 'fibrous', 'y': 'scaly', 'k': 'silky', 's': 'smooth'},
    'stalk-surface-below-ring': {'f': 'fibrous', 'y': 'scaly', 'k': 'silky', 's': 'smooth'},
    'stalk-color-above-ring': {'n': 'brown', 'b': 'buff', 'c': 'cinnamon', 'g': 'gray', 'o': 'orange', 'p': 'pink', 'e': 'red', 'w': 'white', 'y': 'yellow'},
    'stalk-color-below-ring': {'n': 'brown', 'b': 'buff', 'c': 'cinnamon', 'g': 'gray', 'o': 'orange', 'p': 'pink', 'e': 'red', 'w': 'white', 'y': 'yellow'},
    'veil-type': {'p': 'partial', 'u': 'universal'},
    'veil-color': {'n': 'brown', 'o': 'orange', 'w': 'white', 'y': 'yellow'},
    'ring-number': {'n': 'none', 'o': 'one', 't': 'two'},
    'ring-type': {'c': 'cobwebby', 'e': 'evanescent', 'f': 'flaring', 'l': 'large', 'n': 'none', 'p': 'pendant', 's': 'sheathing', 'z': 'zone'},
    'spore-print-color': {'k': 'black', 'n': 'brown', 'b': 'buff', 'h': 'chocolate', 'r': 'green', 'o': 'orange', 'u': 'purple', 'w': 'white', 'y': 'yellow'},
    'population': {'a': 'abundant', 'c': 'clustered', 'n': 'numerous', 's': 'scattered', 'v': 'several', 'y': 'solitary'},
    'habitat': {'g': 'grasses', 'l': 'leaves', 'm': 'meadows', 'p': 'paths', 'u': 'urban', 'w': 'waste', 'd': 'woods'}
}

def get_user_input():
    """
    Collects user input for each mushroom feature.

    Returns:
    dict: A dictionary containing the user's input for each feature.
    """
    user_input = {}
    print("Please provide the following mushroom characteristics:")
    for feature, options in feature_options.items():
        print(f"\n{feature.replace('-', ' ').capitalize()}:")
        for key, value in options.items():
            print(f"  {key}: {value}")
        while True:
            choice = input(f"Enter the corresponding letter for {feature}: ").strip().lower()
            if choice in options:
                user_input[feature] = choice
                break
            else:
                print("Invalid input. Please enter one of the listed letters.")
    return user_input

user_input = get_user_input()

def predict_mushroom(features):
    """
    Predict whether a mushroom is edible or poisonous based on its features.
    
    Parameters:
    features (dict): A dictionary of mushroom features with feature names as keys and corresponding categorical values.
    
    Returns:
    str: 'Edible' or 'Poisonous'
    """
    # Load the trained model and mappings
    model = joblib.load('mushroom_classifier.pkl')
    mappings = joblib.load('mappings.pkl')
    
    # Initialize a dictionary to hold the numerical features
    numerical_features = {}
    
    # Map each feature to its numerical value
    for feature, value in features.items():
        if feature in mappings:
            if value in mappings[feature]:
                numerical_features[feature] = mappings[feature][value]
            else:
                raise ValueError(f"Invalid value '{value}' for feature '{feature}'.")
        else:
            raise ValueError(f"Feature '{feature}' is not recognized.")
    
    # Convert the numerical features into a DataFrame
    input_df = pd.DataFrame([numerical_features])
    
    # Predict using the trained model
    prediction = model.predict(input_df)
    
    # Interpret the prediction
    if prediction[0] == 0:
        return 'Edible'
    else:
        return 'Poisonous'

# Predict edibility
try:
    result = predict_mushroom(user_input)
    print(f"\nThe mushroom is likely: {result}")
except ValueError as e:
    print(f"Error: {e}")

Contact

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