LOFDetector Code Review and Fixes

Detailed Code Inspection

Code Review

The provided code defines the LOFDetector class used for anomaly detection based on the Local Outlier Factor (LOF) algorithm. It also includes methods for preprocessing logs, detecting anomalies, explaining instances with LIME, visualizing anomalies, and explaining results with SHAP.

Detected Bugs and Issues

Below are the identified bugs and issues in the provided code along with the suggested fixes:

1. Import Order and Usage

   • There should be a consistent import order for readability.
   • Import statements should be grouped logically based on their purposes (e.g., standard libraries, third-party packages, local modules).

2. Incorrect Backslash in visualize_anomalies Method

   • The line plt.title(f'Comparison of Anomaly Scores with {feature\}') contains an erroneous backslash.

3. Deprecated Pandas Data Type Conversion

   • The use of .astype(np.int64) for converting datetime to integers is deprecated. Use .view('int64').

4. Variable Not Used in lime_explain_instance Method

   • The variable instance_vector_2d is defined but not necessary in the given context.

5. Empy List Interpretation in human_readable_insights Method

   • The variable explanation should be correctly unpacked assuming it's a list of tuples or list of lists.

Suggested Fixes

Below is the fixed code with structured sections and detailed comments:

# Fixed Code
import json
import numpy as np
import pandas as pd
from sklearn.neighbors import LocalOutlierFactor
from sklearn.feature_extraction.text import TfidfVectorizer
import matplotlib.pyplot as plt
import seaborn as sns
import lime.lime_tabular
import shap
from shap import KernelExplainer
from logai.algorithms.algo_interfaces import AnomalyDetectionAlgo
from logai.config_interfaces import Config
from logai.algorithms.factory import factory
from idia_logai.LOFParams import LOFParams 

@factory.register("detection", "lof", LOFParams)
class LOFDetector(AnomalyDetectionAlgo):
    def __init__(self, params: LOFParams):
        self.model = LocalOutlierFactor(
            n_neighbors=params.n_neighbors,
            algorithm=params.algorithm,
            leaf_size=params.leaf_size,
            metric=params.metric,
            p=params.p,
            metric_params=params.metric_params,
            contamination=params.contamination,
            novelty=params.novelty,
            n_jobs=params.n_jobs,
        )
        self.vectorizer = TfidfVectorizer(max_features=100)

    def get_vector_df(self, log_features):
        tfidf_matrix = self.vectorizer.fit_transform(log_features).toarray()
        tfidf_df = pd.DataFrame(tfidf_matrix, columns=self.vectorizer.get_feature_names_out())
        return tfidf_df

    def preprocess_idia_logs(self, log_data: pd.DataFrame) -> pd.DataFrame:
        log_features = log_data[["eventTime", "severity"]].copy()
        log_features["eventTime"] = pd.to_datetime(log_features["eventTime"], errors='coerce')
        log_features = log_features.dropna(subset=["eventTime"])
        log_features["eventTime"] = log_features["eventTime"].view('int64') // 10**6  # Updated for pandas datetime compatibility
        return log_features

    def detect_anomalies(self, log_data: pd.DataFrame) -> pd.DataFrame:
        preprocessed_data = self.preprocess_idia_logs(log_data)
        log_vectors = self.get_vector_df(preprocessed_data.astype(str))
        log_data["anom_score"] = self.model.fit_predict(log_vectors)
        return log_data

    def lime_explain_instance(self, instance: pd.DataFrame):
        preprocessed_instance = self.preprocess_idia_logs(instance)
        instance_vector = self.get_vector_df(preprocessed_instance.astype(str))
        training_data = self.vectorizer.transform(preprocessed_instance.astype(str)).toarray()
        
        def predict_func(X):
            return self.model.decision_function(X)
        
        explainer = lime.lime_tabular.LimeTabularExplainer(
            training_data=training_data,
            feature_names=self.vectorizer.get_feature_names_out(),
            verbose=True,
            mode="regression"
        )
        
        explanation = explainer.explain_instance(
            data_row=instance_vector.values[0],  # Corrected variable usage
            predict_fn=predict_func
        )
        
        return explanation

    def visualize_anomalies(self, log_data: pd.DataFrame, feature: str):
        plt.figure(figsize=(10, 6))
        sns.boxplot(x=log_data[feature], y=log_data['anom_score'])
        plt.title(f'Comparison of Anomaly Scores with {feature}')  # Fixed erroneous backslash
        return plt.gcf()

    def human_readable_insights(self, explanation: list):
        insights = ["Feature: {}, Contribution: {}".format(feature, contribution) for feature, contribution in explanation]
        return insights


def shap_explain(model, log_data: pd.DataFrame):
    sample_data = log_data.sample(n=100)
    log_features = model.preprocess_idia_logs(sample_data)
    log_vectors = model.get_vector_df(log_features.astype(str))
    
    def predict_func(X):
        return model.model.decision_function(X)

    explainer = KernelExplainer(predict_func, log_vectors)
    shap_values = explainer.shap_values(log_vectors)

    return shap_values, log_vectors


def plot_shap_values(shap_values, log_vectors):
    shap.summary_plot(shap_values, log_vectors, feature_names=log_vectors.columns)
    return plt.gcf()

Additional Insights

• Logging and Exception Handling: It's recommended to add logging and exception handling for robust error tracking.
• Unit Testing: Implementing unit tests for methods can ensure functional integrity.

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