Bug Detector | Python
Bug Analysis and Fixes for Python Anomaly Detection Code
This document details the bugs identified in a Python code snippet for anomaly detection, providing specific fixes for issues like incorrect escape characters, format string usage, import statement typos, and method definitions.
![Empty image or helper icon](https://fs.enterprisedna.co/library/data-mentor/cover-images/UL14a4hvkbCv95qZicKkSIJVhOi2KYPqelzTW3BL.webp)
Prompt
Answer
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 islogai
.
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 withnp.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 adecision_function
, it hasnegative_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()
For further in-depth learning about data science and anomaly detection algorithms, consider courses available on the Enterprise DNA platform.
Description
This document details the bugs identified in a Python code snippet for anomaly detection, providing specific fixes for issues like incorrect escape characters, format string usage, import statement typos, and method definitions.
More Bug Detectors
![Apache Flink Bug Detector](https://fs.enterprisedna.co/library/icons/flink.png)
![Apache Pig Bug Detector](https://fs.enterprisedna.co/library/icons/apache_pig.png)
![Azure Data Factory Bug Detector](https://fs.enterprisedna.co/library/icons/azure-data-factory.png)
![C/C++ Bug Detector](https://fs.enterprisedna.co/library/icons/c_c_plus.png)
![CouchDB Bug Detector](https://fs.enterprisedna.co/library/icons/couchdb.png)
![DAX Bug Detector](https://fs.enterprisedna.co/library/icons/power-bi.png)
![Excel Bug Detector](https://fs.enterprisedna.co/library/icons/excel.png)
![Firebase Bug Detector](https://fs.enterprisedna.co/library/icons/firebase.png)
![Google BigQuery Bug Detector](https://fs.enterprisedna.co/library/icons/google_bigquery.png)
![Google Sheets Bug Detector](https://fs.enterprisedna.co/library/icons/google_sheets.png)
![GraphQL Bug Detector](https://fs.enterprisedna.co/library/icons/graphql.png)
![Hive Bug Detector](https://fs.enterprisedna.co/library/icons/hive.png)
![Java Bug Detector](https://fs.enterprisedna.co/library/icons/java.png)
![JavaScript Bug Detector](https://fs.enterprisedna.co/library/icons/javascript.png)
![Julia Bug Detector](https://fs.enterprisedna.co/library/icons/julia.png)
![Lua Bug Detector](https://fs.enterprisedna.co/library/icons/lua.png)
![M (Power Query) Bug Detector](https://fs.enterprisedna.co/library/icons/m.png)
![MATLAB Bug Detector](https://fs.enterprisedna.co/library/icons/matlab.png)
![MongoDB Bug Detector](https://fs.enterprisedna.co/library/icons/mongodb.png)
![Oracle Bug Detector](https://fs.enterprisedna.co/library/icons/oracle.png)
![PostgreSQL Bug Detector](https://fs.enterprisedna.co/library/icons/postgresql.png)
![Power BI Bug Detector](https://fs.enterprisedna.co/library/icons/power-bi.png)
![Python Bug Detector](https://fs.enterprisedna.co/library/icons/python.png)
![R Bug Detector](https://fs.enterprisedna.co/library/icons/r.png)
![Redis Bug Detector](https://fs.enterprisedna.co/library/icons/redis.png)
![Regex Bug Detector](https://fs.enterprisedna.co/library/icons/regex.png)
![Ruby Bug Detector](https://fs.enterprisedna.co/library/icons/ruby.png)
![SAS Bug Detector](https://fs.enterprisedna.co/library/icons/sas.png)
![Scala Bug Detector](https://fs.enterprisedna.co/library/icons/scala.png)
![Shell Bug Detector](https://fs.enterprisedna.co/library/icons/shell.png)
![SPSS Bug Detector](https://fs.enterprisedna.co/library/icons/spss.png)
![SQL Bug Detector](https://fs.enterprisedna.co/library/icons/sql.png)
![SQLite Bug Detector](https://fs.enterprisedna.co/library/icons/sqlite.png)
![Stata Bug Detector](https://fs.enterprisedna.co/library/icons/stata.png)
![Tableau Bug Detector](https://fs.enterprisedna.co/library/icons/tableau.png)
![VBA Bug Detector](https://fs.enterprisedna.co/library/icons/vba.png)