pip install dtw
import pandas as pd
import numpy as np
from scipy.stats import t
from sklearn.decomposition import PCA
from sklearn.svm import OneClassSVM
from sklearn.ensemble import IsolationForest
from sklearn.neighbors import LocalOutlierFactor
import matplotlib.pyplot as plt
from sklearn.cluster import DBSCAN
from dtw import dtw # Install the 'dtw' module using pip
from statsmodels.tsa.seasonal import seasonal_decompose
# Read the data
train_data = pd.read_csv("train.csv")
test_data = pd.read_csv("test.csv")
test_labels = pd.read_csv("test_label.csv")
PCA
# Task 1: Principal Component Analysis (PCA)
def anomaly_detection_pca(train_data, test_data):
# Handle NaN values by imputing or dropping them
train_data.dropna(inplace=True)
test_data.dropna(inplace=True)
# Apply PCA
pca = PCA(n_components=2) # You can adjust the number of components
pca.fit(train_data)
train_pca = pca.transform(train_data)
test_pca = pca.transform(test_data)
return train_pca, test_pca
# Perform anomaly detection for each task
train_pca, test_pca = anomaly_detection_pca(train_data, test_data)
# Visualize the PCA results
plt.figure(figsize=(10, 6))
plt.scatter(train_pca[:, 0], train_pca[:, 1], label='Train Data')
plt.scatter(test_pca[:, 0], test_pca[:, 1], label='Test Data')
plt.xlabel('Principal Component 1')
plt.ylabel('Principal Component 2')
plt.title('PCA Visualization')
plt.legend()
# Highlight anomalies in the test data
anomaly_indices = test_labels[test_labels['label'] == 1].index
valid_indices = anomaly_indices[anomaly_indices < len(test_pca)]
plt.scatter(test_pca[valid_indices, 0], test_pca[valid_indices, 1], color='red', label='Anomalies')
plt.legend()
plt.show()
One-Class SVM
# One-Class SVM
def anomaly_detection_oneclasssvm(train_data, test_data):
# Reshape the data to 2D if it's 1D
if len(train_data.shape) == 1:
train_data = train_data.reshape(-1, 1)
if len(test_data.shape) == 1:
test_data = test_data.reshape(-1, 1)
svm = OneClassSVM()
svm.fit(train_data)
test_pred = svm.predict(test_data)
return test_pred
test_pred_svm = anomaly_detection_oneclasssvm(train_data, test_data)
# Visualize the results of One-Class SVM anomaly detection
plt.figure(figsize=(10, 6))
# Plot train data
plt.plot(train_data, label='Train Data', color='blue')
# Highlight anomalies in test data
anomalies_indices = [i for i, pred in enumerate(test_pred_svm) if pred == -1]
anomalies_values = [test_data[i] for i in anomalies_indices]
plt.scatter(anomalies_indices, anomalies_values, color='red', label='Anomalies')
plt.xlabel('Index')
plt.ylabel('Value')
plt.title('One-Class SVM Anomaly Detection Results')
plt.legend()
plt.show()
Isolation Forest
# Isolation Forest
def anomaly_detection_isolationforest(train_data, test_data):
forest = IsolationForest()
forest.fit(train_data)
test_pred = forest.predict(test_data)
return test_pred
test_pred_forest = anomaly_detection_isolationforest(train_data, test_data)
# Visualization for Isolation Forest
plt.figure(figsize=(10, 6))
plt.scatter(train_pca[:, 0], train_pca[:, 1], label='Train Data')
plt.scatter(test_pca[:, 0], test_pca[:, 1], label='Test Data')
plt.xlabel('Principal Component 1')
plt.ylabel('Principal Component 2')
plt.title('Isolation Forest Anomaly Detection')
plt.legend()
# Highlight anomalies in the test data detected by Isolation Forest
plt.scatter(test_pca[test_pred_forest == -1][:, 0], test_pca[test_pred_forest == -1][:, 1], color='red', label='Anomalies')
plt.legend()
plt.show()
Local Outlier Factor
# Local Outlier Factor
def anomaly_detection_lof(train_data, test_data):
lof = LocalOutlierFactor()
test_pred = lof.fit_predict(test_data)
return test_pred
test_pred_lof = anomaly_detection_lof(train_data, test_data)
# Visualization for Local Outlier Factor (LOF)
plt.figure(figsize=(10, 6))
plt.scatter(train_pca[:, 0], train_pca[:, 1], label='Train Data')
plt.scatter(test_pca[:, 0], test_pca[:, 1], label='Test Data')
plt.xlabel('Principal Component 1')
plt.ylabel('Principal Component 2')
plt.title('LOF Anomaly Detection')
plt.legend()
# Highlight anomalies in the test data detected by LOF
plt.scatter(test_pca[test_pred_lof == -1][:, 0], test_pca[test_pred_lof == -1][:, 1], color='red', label='Anomalies')
plt.legend()
plt.show()
DBSCAN
# DBSCAN
def anomaly_detection_dbscan(train_data, test_data):
dbscan = DBSCAN()
dbscan.fit(train_data)
test_pred = dbscan.fit_predict(test_data)
return test_pred
# Visualization for DBSCAN
plt.figure(figsize=(10, 6))
plt.scatter(train_pca[:, 0], train_pca[:, 1], label='Train Data')
plt.scatter(test_pca[:, 0], test_pca[:, 1], label='Test Data')
plt.xlabel('Principal Component 1')
plt.ylabel('Principal Component 2')
plt.title('DBSCAN Anomaly Detection')
plt.legend()
# Highlight anomalies in the test data detected by DBSCAN
plt.scatter(test_pca[test_pred_dbscan == -1][:, 0], test_pca[test_pred_dbscan == -1][:, 1], color='red', label='Anomalies')
plt.legend()
plt.show()
Shesd
def anomaly_detection_shesd(train_data, test_data, period=24, alpha=0.05, max_anomalies=None):
# Decompose the time series
decomposition = seasonal_decompose(train_data, period=period)
seasonal = decomposition.seasonal
resid = decomposition.resid
# Calculate the residuals for the test data
test_decomposition = seasonal_decompose(test_data, period=period)
test_residuals = test_decomposition.resid
# Calculate the anomalies using S-H-ESD
n = len(test_residuals)
anomalies = []
for i in range(n):
# Calculate the mean and standard deviation of the residuals up to time i
mean_residuals = np.mean(resid[:i+1])
std_residuals = np.std(resid[:i+1])
# Calculate the test statistic
test_statistic = (test_residuals[i] - mean_residuals) / std_residuals
# Calculate the critical value
critical_value = t.ppf(1 - alpha / (2 * (n - i)), n - i - 1)
# Check if the test statistic exceeds the critical value
if np.abs(test_statistic) > critical_value:
anomalies.append(i)
# Stop if the maximum number of anomalies is reached
if max_anomalies and len(anomalies) >= max_anomalies:
break
return anomalies
# Load the data
train_data = pd.read_csv("train.csv")["timestamp_(min)"].values
test_data = pd.read_csv("test.csv")["timestamp_(min)"].values
# Perform anomaly detection using S-H-ESD
anomalies_shesd = anomaly_detection_shesd(train_data, test_data)
print("Detected anomalies at indices:", anomalies_shesd)
Detected anomalies at indices: []
# Plot the time series data
plt.figure(figsize=(10, 6))
plt.plot(test_data, label='Test Data')
plt.xlabel('Time')
plt.ylabel('Value')
plt.title('Time Series Data with Anomalies Detected by S-H-ESD')
# Highlight the detected anomalies
for anomaly_index in anomalies_shesd:
plt.scatter(anomaly_index, test_data[anomaly_index], color='red', label='Anomaly')
plt.legend()
plt.show()
import numpy as np
import pandas as pd
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import classification_report
# Step 1: Data Preprocessing
train_data = pd.read_csv("train.csv")
test_data = pd.read_csv("test.csv")
# Drop rows with NaN values
train_data.dropna(inplace=True)
test_data.dropna(inplace=True)
# Assuming the target column is not included in the training data
X_train = train_data.drop(columns=["timestamp_(min)"]) # Remove timestamp column
X_test = test_data.drop(columns=["timestamp_(min)"])
# Scale the features
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)
# Step 2: PCA Model
pca = PCA(n_components=0.95) # Retain 95% of variance
pca.fit(X_train_scaled)
# Step 3: Anomaly Detection
train_reconstructed = pca.inverse_transform(pca.transform(X_train_scaled))
test_reconstructed = pca.inverse_transform(pca.transform(X_test_scaled))
train_mse = np.mean(np.square(X_train_scaled - train_reconstructed), axis=1)
test_mse = np.mean(np.square(X_test_scaled - test_reconstructed), axis=1)
# Step 4: Threshold Selection (e.g., using statistical methods or domain knowledge)
# Step 5: Evaluate Performance
# Assuming you have labels for the test data (test_label.csv)
test_labels = pd.read_csv("test_label.csv")["label"]
# Determine anomalies based on threshold
threshold = 0.001 # Set your threshold here
anomalies = test_mse > threshold
# Calculate metrics
print(classification_report(test_labels, anomalies))
precision recall f1-score support
0 0.00 0.00 0.00 63460
1 0.28 1.00 0.43 24381
accuracy 0.28 87841
macro avg 0.14 0.50 0.22 87841
weighted avg 0.08 0.28 0.12 87841
# Step 1: Handle missing values
# Option 1: Drop rows with missing values
X_train.dropna(inplace=True)
# Option 2: Impute missing values
# You can use SimpleImputer from sklearn.preprocessing to fill missing values with mean, median, etc.
# Example:
from sklearn.impute import SimpleImputer
imputer = SimpleImputer(strategy='mean')
X_train_imputed = imputer.fit_transform(X_train)
# Step 2: Define and Train the Isolation Forest model
model = IsolationForest(n_estimators=100, contamination=0.1) # Adjust parameters as needed
model.fit(X_train_imputed)
# Step 3: Generate predictions
predictions = model.predict(X_test)
predictions_binary = (predictions == -1).astype(int) # -1 indicates anomaly, 1 indicates normal
# Step 4: Evaluate performance
print(classification_report(test_labels, predictions_binary))
precision recall f1-score support
0 0.75 0.94 0.83 63460
1 0.51 0.18 0.26 24381
accuracy 0.72 87841
macro avg 0.63 0.56 0.55 87841
weighted avg 0.68 0.72 0.67 87841
from sklearn.impute import SimpleImputer
from sklearn.metrics import classification_report, roc_auc_score
from sklearn.ensemble import IsolationForest
import time
# Step 1: Handle missing values
# Option 1: Drop rows with missing values
X_train.dropna(inplace=True)
# Option 2: Impute missing values
# You can use SimpleImputer from sklearn.preprocessing to fill missing values with mean, median, etc.
# Example:
imputer = SimpleImputer(strategy='mean')
X_train_imputed = imputer.fit_transform(X_train)
# Step 2: Define and Train the Isolation Forest model
start_time = time.time()
model = IsolationForest(n_estimators=100, contamination=0.1) # Adjust parameters as needed
model.fit(X_train_imputed)
train_time = time.time() - start_time
# Step 3: Generate predictions
start_time = time.time()
predictions = model.predict(X_test)
prediction_time = time.time() - start_time
predictions_binary = (predictions == -1).astype(int) # -1 indicates anomaly, 1 indicates normal
# Step 4: Evaluate performance
f1 = classification_report(test_labels, predictions_binary)
auc = roc_auc_score(test_labels, predictions_binary)
print("Training Time:", train_time)
print("Prediction Time:", prediction_time)
print("F1 Score:", f1)
print("AUC Score:", auc)
Training Time: 3.0592174530029297
Prediction Time: 2.5410304069519043
F1 Score: precision recall f1-score support
0 0.75 0.92 0.83 63460
1 0.52 0.22 0.31 24381
accuracy 0.73 87841
macro avg 0.64 0.57 0.57 87841
weighted avg 0.69 0.73 0.69 87841
AUC Score: 0.5715618170121648
# Plot
plt.figure(figsize=(10, 6))
plt.plot(metrics, values, marker='o', linestyle='-', color='b')
plt.title('Performance Metrics')
plt.xlabel('Metrics')
plt.ylabel('Values')
plt.grid(True)
plt.show()
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