无法从pandas_ml导入ConfusionMatrix及相关报错的解决咨询

Hey there! Let's resolve this issue you're having with calculating confusion matrices and classification metrics. The root problem here is that pandas_ml is an outdated, unmaintained library, and the imbaccessors module it's trying to reference no longer exists (and was never available as a standalone PyPI package). Instead of chasing deprecated tools, let's use the industry-standard approach with scikit-learn.

Solution 1: Use Scikit-Learn (Recommended) #

Scikit-learn has all the tools you need to generate confusion matrices and compute key classification metrics, and it's actively maintained. Here's how to implement it:

1. First, ensure scikit-learn is installed (if not already):

   pip install scikit-learn
   

2. Use this code to analyze your actual and predicted values from the DataFrame:

   import pandas as pd
   from sklearn.metrics import confusion_matrix, accuracy_score, precision_score, recall_score, f1_score
   
   # Assume your DataFrame has columns named 'actual' and 'predicted'
   actual_values = df['actual']
   predicted_values = df['predicted']
   
   # Generate confusion matrix
   conf_matrix = confusion_matrix(actual_values, predicted_values)
   print("Confusion Matrix:")
   print(conf_matrix)
   
   # Calculate key classification metrics
   accuracy = accuracy_score(actual_values, predicted_values)
   precision = precision_score(actual_values, predicted_values, average='weighted')  # Adjust 'average' as needed for your use case
   recall = recall_score(actual_values, predicted_values, average='weighted')
   f1 = f1_score(actual_values, predicted_values, average='weighted')
   
   print(f"\nAccuracy: {accuracy:.4f}")
   print(f"Precision: {precision:.4f}")
   print(f"Recall: {recall:.4f}")
   print(f"F1 Score: {f1:.4f}")
   

Solution 2: Convert Confusion Matrix to a DataFrame (Optional) #

If you want a DataFrame-formatted confusion matrix (similar to what pandas_ml offered), you can easily convert scikit-learn's output:

import pandas as pd
from sklearn.metrics import confusion_matrix

conf_matrix = confusion_matrix(actual_values, predicted_values)
class_labels = actual_values.unique()  # Extract unique class names from your actual data

# Convert matrix to DataFrame with class labels as index and columns
conf_matrix_df = pd.DataFrame(conf_matrix, index=class_labels, columns=class_labels)
print("Confusion Matrix (DataFrame Format):")
print(conf_matrix_df)

Why Your Original Approach Failed #

• The pandas_ml library hasn't been updated since 2018, so it's incompatible with modern versions of pandas and other dependencies.
• The imbaccessors module was either an internal component of pandas_ml or a deprecated package that's no longer hosted on PyPI—this is why pip install pandas-imbaccessors throws an error.

内容的提问来源于stack exchange，提问作者Ashay Shukla