Bug Analysis and Fixes for Python Anomaly Detection Code

Detected Bugs in the Code

Below is a detailed inspection of the provided Python code, identifying bugs present in the code snippet along with suggested fixes.

1. Incorrect Use of Escape Characters

Section: visualize_anomalies Method

plt.title(f'Comparison of Anomaly Scores with {feature\}')

Bug:

• The escape character \ before the closing brace } is not correct and will cause a syntax error.

Fix:

• Remove the incorrect escape character to properly format the f-string.

plt.title(f'Comparison of Anomaly Scores with {feature}')

2. Incorrect Format String Usage

Section: human_readable_insights Method

insights = ["Feature: {\}, Contribution: {\}".format(feature, contribution) for feature, contribution in explanation]

Bug:

• The curly braces {} are not formatted correctly; they appear escaped which would cause a syntax error.

Fix:

• Properly format the string using f-strings for more readability.

insights = [f"Feature: {feature}, Contribution: {contribution}" for feature, contribution in explanation]

3. Typo in Import Statement

Section: Import Statements

from idia_logai.LOFParams import LOFParams

Bug:

• The module name idia_logai likely contains a typo and should be corrected based on the package structure. Assuming the correct name is logai.

Fix:

• Update the import statement accordingly if the package name is logai.

from logai.LOFParams import LOFParams

4. Incorrect Time Conversion

Section: preprocess_idia_logs Method

log_features["eventTime"] = log_features["eventTime"].astype(np.int64) // 10**6

Bug:

• Converting Datetime to integer with np.int64 might raise a future warning or error. Use .view('int64') instead.

Fix:

• Implement date conversion using .view('int64').

log_features["eventTime"] = log_features["eventTime"].view('int64') // 10**6 

5. Model Prediction Usage

Section: lime_explain_instance Method

def predict_func(X):
    return self.model.decision_function(X)

Bug:

• LocalOutlierFactor does not have a decision_function, it has negative_outlier_factor_. Use appropriate function or attributes for prediction.

Fix:

• Adjust the predict function accordingly.

def predict_func(X):
    return self.model.fit_predict(X)

6. Incorrect GCF Method

Section: visualize_anomalies and plot_shap_values Methods

return plt.gcf()$0

Bug:

• The $0 appears to be a typo.

Fix:

• Remove $0.

return plt.gcf()

7. Missing Self in Class Method

Section: shap_explain Method

def shap_explain(model, log_data: pd.DataFrame):

Bug:

• This should be a method under the LOFDetector class. It is essential for object encapsulation.

Fix:

• Add self to maintain consistency.

def shap_explain(self, log_data: pd.DataFrame):

Overall Assessment

Fix these bugs to ensure the code executes without errors. Here is the fixed version of the provided code snippet:

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 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
        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))
        instance_vector_2d = instance_vector.values.reshape(1, -1)
        training_data = self.vectorizer.transform(preprocessed_instance.astype(str)).toarray()
        
        def predict_func(X):
            return self.model.fit_predict(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_2d[0],
            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}')
        return plt.gcf()

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


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

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