Example output
Certainly! Below is a Python script that acts as a machine learning interpreter. This script executes machine learning code, provides step-by-step explanations, and offers error handling for common issues like data shape mismatches. It also includes optional logging and performance monitoring.
```python
import logging
import time
import numpy as np
import pandas as pd
from sklearn.preprocessing import StandardScaler, MinMaxScaler
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
# Enable logging functionality (Optional)
logging.basicConfig(filename='ml_interpreter.log', level=logging.INFO,
format='%(asctime)s - %(levelname)s - %(message)s')
class MLInterpreter:
def __init__(self, log=True, performance_monitoring=True):
self.log = log
self.performance_monitoring = performance_monitoring
def log_message(self, message):
"""Logs a message if logging is enabled."""
if self.log:
logging.info(message)
print(message)
def monitor_performance(self, start_time, description):
"""Monitors and logs performance time if enabled."""
if self.performance_monitoring:
elapsed_time = time.time() - start_time
self.log_message(f"{description} took {elapsed_time:.4f} seconds")
def execute_code(self, code: str):
"""Executes machine learning code and provides detailed explanations."""
# Start performance monitoring
start_time = time.time()
try:
# Dynamically execute the code provided
exec(code, globals())
self.monitor_performance(start_time, "Code execution")
self.log_message("Code executed successfully.")
except Exception as e:
# Catching general exceptions
self.log_message(f"Error: {str(e)}")
self.suggest_fixes(e)
def suggest_fixes(self, error):
"""Provides suggestions for common errors."""
error_message = str(error).lower()
if 'shape' in error_message:
self.log_message("Suggestion: There seems to be a shape mismatch. Check the dimensions of your input data (X) and target labels (y). They must align.")
elif 'scaling' in error_message or 'normalization' in error_message:
self.log_message("Suggestion: Consider applying feature scaling or normalization. You can use StandardScaler or MinMaxScaler from sklearn to preprocess your features.")
else:
self.log_message("Suggestion: Please check your code for common issues or syntax errors.")
def feature_scaling(self, X, method='standard'):
"""Applies feature scaling based on the specified method."""
start_time = time.time()
if method == 'standard':
self.log_message("Applying StandardScaler for feature scaling.")
scaler = StandardScaler()
elif method == 'minmax':
self.log_message("Applying MinMaxScaler for feature scaling.")
scaler = MinMaxScaler()
else:
self.log_message("Invalid scaling method provided. Defaulting to StandardScaler.")
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
self.monitor_performance(start_time, "Feature scaling")
return X_scaled
def handle_data_split(self, X, y, test_size=0.2, random_state=None):
"""Handles the splitting of data into training and testing sets."""
start_time = time.time()
self.log_message(f"Splitting data with test size = {test_size} and random state = {random_state}.")
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, random_state=random_state)
self.monitor_performance(start_time, "Data splitting")
return X_train, X_test, y_train, y_test
def evaluate_model(self, model, X_test, y_test):
"""Evaluates the model and provides accuracy."""
start_time = time.time()
self.log_message("Evaluating the model on the test set.")
predictions = model.predict(X_test)
accuracy = accuracy_score(y_test, predictions)
self.monitor_performance(start_time, "Model evaluation")
self.log_message(f"Model accuracy: {accuracy:.4f}")
return accuracy
# Example usage:
if __name__ == "__main__":
interpreter = MLInterpreter()
# Sample machine learning code
code = """
import numpy as np
from sklearn.datasets import load_iris
from sklearn.linear_model import LogisticRegression
# Load dataset
iris = load_iris()
X = iris.data
y = iris.target
# Feature Scaling
X_scaled = interpreter.feature_scaling(X, method='standard')
# Splitting data
X_train, X_test, y_train, y_test = interpreter.handle_data_split(X_scaled, y, test_size=0.3, random_state=42)
# Training the model
model = LogisticRegression()
model.fit(X_train, y_train)
# Evaluate the model
interpreter.evaluate_model(model, X_test, y_test)
"""
interpreter.execute_code(code)
```
### Features of the Script:
1. **Dynamic Code Execution**: The `execute_code` method allows users to pass in any machine learning code as a string. The code is executed, and explanations are logged and printed.
2. **Error Handling**: The script captures common errors like shape mismatches and offers suggestions to fix them. For example, it recommends checking input data shapes or applying feature scaling.
3. **Feature Scaling**: A utility function `feature_scaling` is included to handle feature scaling with options for `StandardScaler` and `MinMaxScaler`.
4. **Data Splitting**: The `handle_data_split` method uses `train_test_split` to divide data into training and test sets, with logging to track the process.
5. **Model Evaluation**: The `evaluate_model` method calculates and logs the accuracy of the model on the test data.
6. **Logging**: The script has optional logging functionality to keep track of the execution process in a file.
7. **Performance Monitoring**: The script can monitor the time taken for each major operation (e.g., feature scaling, data splitting) and log the duration.
### Customization:
- **Logging**: You can enable or disable logging by setting the `log` parameter in the `MLInterpreter` class.
- **Performance Monitoring**: You can control performance monitoring by setting the `performance_monitoring` parameter.
- **Feature Scaling**: The method for scaling features can be customized (e.g., `standard` or `minmax`).
This script should make it easier to understand and debug machine learning code by providing step-by-step explanations and guidance.