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import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
import plotly.graph_objects as go
from sklearn.metrics import roc_curve, precision_recall_curve
import shap
class Visualizer:
def __init__(self):
pass
def plot_class_distribution(self, df, target_col='Class'):
"""Plot the distribution of fraud vs non-fraud transactions"""
plt.figure(figsize=(10, 6))
sns.countplot(x=target_col, data=df)
plt.title('Class Distribution (Fraud vs Non-Fraud)')
plt.xlabel('Class (0: Normal, 1: Fraud)')
plt.ylabel('Count')
# Add percentage labels
total = len(df)
for p in plt.gca().patches:
height = p.get_height()
plt.text(p.get_x() + p.get_width()/2.,
height + 3,
'{:.2f}%'.format(100 * height/total),
ha="center")
return plt
def plot_feature_distributions(self, df, target_col='Class', n_features=5):
"""Plot distributions of top features by class"""
# Select numerical columns only
num_cols = df.select_dtypes(include=['int64', 'float64']).columns
num_cols = [col for col in num_cols if col != target_col]
# If there are too many features, select a subset
if len(num_cols) > n_features:
num_cols = num_cols[:n_features]
# Create subplots
fig, axes = plt.subplots(len(num_cols), 1, figsize=(12, 4*len(num_cols)))
# If there's only one feature, axes won't be an array
if len(num_cols) == 1:
axes = [axes]
for i, col in enumerate(num_cols):
sns.histplot(data=df, x=col, hue=target_col, bins=50, ax=axes[i], kde=True)
axes[i].set_title(f'Distribution of {col} by Class')
plt.tight_layout()
return fig
def plot_correlation_matrix(self, df, target_col='Class'):
"""Plot correlation matrix of features"""
# Calculate correlation matrix
corr_matrix = df.corr()
# Create heatmap
plt.figure(figsize=(12, 10))
mask = np.triu(np.ones_like(corr_matrix, dtype=bool))
sns.heatmap(corr_matrix, mask=mask, annot=False, cmap='coolwarm',
linewidths=0.5, vmin=-1, vmax=1)
plt.title('Feature Correlation Matrix')
return plt
def plot_feature_importance(self, model, feature_names, model_name="Model"):
"""Plot feature importance for tree-based models"""
if hasattr(model, 'feature_importances_'):
# Get feature importances
importances = model.feature_importances_
# Sort feature importances in descending order
indices = np.argsort(importances)[::-1]
# Rearrange feature names so they match the sorted feature importances
names = [feature_names[i] for i in indices]
# Create plot
plt.figure(figsize=(12, 8))
plt.title(f"Feature Importance ({model_name})")
plt.bar(range(len(importances)), importances[indices])
plt.xticks(range(len(importances)), names, rotation=90)
plt.tight_layout()
return plt
else:
print(f"Model {model_name} doesn't have feature_importances_ attribute")
return None
def plot_roc_curve(self, models_results):
"""Plot ROC curves for multiple models"""
plt.figure(figsize=(10, 8))
for result in models_results:
model_name = result['model_name']
y_test = result['y_test']
y_pred_proba = result['y_pred_proba']
fpr, tpr, _ = roc_curve(y_test, y_pred_proba)
auc = result['auc']
plt.plot(fpr, tpr, label=f'{model_name} (AUC = {auc:.3f})')
plt.plot([0, 1], [0, 1], 'k--')
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('ROC Curve')
plt.legend(loc='best')
return plt
def plot_precision_recall_curve(self, models_results):
"""Plot Precision-Recall curves for multiple models"""
plt.figure(figsize=(10, 8))
for result in models_results:
model_name = result['model_name']
y_test = result['y_test']
y_pred_proba = result['y_pred_proba']
precision, recall, _ = precision_recall_curve(y_test, y_pred_proba)
plt.plot(recall, precision, label=f'{model_name}')
plt.xlabel('Recall')
plt.ylabel('Precision')
plt.title('Precision-Recall Curve')
plt.legend(loc='best')
return plt
def plot_confusion_matrix(self, cm, model_name="Model"):
"""Plot confusion matrix"""
plt.figure(figsize=(8, 6))
sns.heatmap(cm, annot=True, fmt='d', cmap='Blues')
plt.title(f'Confusion Matrix - {model_name}')
plt.ylabel('Actual')
plt.xlabel('Predicted')
return plt
def plot_shap_values(self, model, X_test, feature_names, model_name="Model"):
"""Plot SHAP values to explain model predictions"""
# Create explainer
if model_name == "XGBoost":
explainer = shap.TreeExplainer(model)
else:
explainer = shap.Explainer(model)
# Calculate SHAP values
shap_values = explainer.shap_values(X_test)
# Summary plot
plt.figure(figsize=(12, 8))
shap.summary_plot(shap_values, X_test, feature_names=feature_names)
return plt |