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import numpy as np
import pandas as pd
from sklearn.datasets import make_classification
from sklearn.ensemble import IsolationForest
import shap
import matplotlib.pyplot as plt
from itertools import combinations
# Generate synthetic data with 20 features
np.random.seed(42)
X, _ = make_classification(
n_samples=500,
n_features=20,
n_informative=10,
n_redundant=5,
n_clusters_per_class=1,
random_state=42
)
outliers = np.random.uniform(low=-6, high=6, size=(50, 20)) # Add outliers
X = np.vstack([X, outliers])
# Convert to DataFrame
columns = [f"Feature{i+1}" for i in range(20)]
df = pd.DataFrame(X, columns=columns)
# Fit Isolation Forest
iso_forest = IsolationForest(
n_estimators=100,
max_samples=256,
contamination=0.1,
random_state=42
)
iso_forest.fit(df)
# Predict anomaly scores
anomaly_scores = iso_forest.decision_function(df) # Negative values indicate anomalies
anomaly_labels = iso_forest.predict(df) # -1 for anomaly, 1 for normal
# Add results to DataFrame
df["Anomaly_Score"] = anomaly_scores
df["Anomaly_Label"] = np.where(anomaly_labels == -1, "Anomaly", "Normal")
# SHAP Explainability
explainer = shap.Explainer(iso_forest, df[columns])
shap_values = explainer(df[columns])
# SHAP Summary Plot (Global Explainability)
shap.summary_plot(shap_values, df[columns], feature_names=columns)
# SHAP Waterfall Plot for a Specific Data Point (Local Explainability)
specific_index = df[df["Anomaly_Label"] == "Anomaly"].index[0] # Select first anomaly
shap.waterfall_plot(
shap.Explanation(
values=shap_values.values[specific_index],
base_values=shap_values.base_values[specific_index],
data=df.iloc[specific_index],
feature_names=columns
)
)
# Scatter plots for pairwise combinations of features
feature_combinations = list(combinations(columns[:5], 2)) # Use first 5 features for simplicity
for feature1, feature2 in feature_combinations:
plt.figure(figsize=(8, 6))
plt.scatter(
df[feature1],
df[feature2],
c=(df["Anomaly_Label"] == "Anomaly"),
cmap="coolwarm",
edgecolor="k",
alpha=0.7
)
plt.title(f"Isolation Forest - {feature1} vs {feature2}")
plt.xlabel(feature1)
plt.ylabel(feature2)
plt.show()
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