Prompt
Answer
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:
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).
Incorrect Backslash in
visualize_anomalies
Method- The line
plt.title(f'Comparison of Anomaly Scores with {feature\}')
contains an erroneous backslash.
- The line
Deprecated Pandas Data Type Conversion
- The use of
.astype(np.int64)
for converting datetime to integers is deprecated. Use.view('int64')
.
- The use of
Variable Not Used in
lime_explain_instance
Method- The variable
instance_vector_2d
is defined but not necessary in the given context.
- The variable
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.
- The variable
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.
For more detailed learning, you can explore courses on anomaly detection techniques and advanced Python programming on the Enterprise DNA Platform.
Description
This document outlines a detailed review of the LOFDetector class code for anomaly detection, highlighting bugs and recommended fixes. It includes issues like import order, variable usage, and method enhancements, improving readability and functionality.
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