Prompt Title: Features outlier detn

To find outliers in the provided data where the timestamp is in the index using LocalOutlierFactor, One-class SVM, and IsolationForest, and create a plot for each method, you can use Python's scikit-learn library. Here's a step-by-step code to do this:

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import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.ensemble import IsolationForest
from sklearn.svm import OneClassSVM
from sklearn.neighbors import LocalOutlierFactor

# Load your data
data = pd.read_csv('your_data.csv', delimiter='\t')
# Assuming the timestamp is in the index, no need to convert it to datetime

# Select the columns to be used for outlier detection
columns_for_detection = ['line1VoltageAvg', 'line2VoltageAvg', 'line1CurrentAvg', 'line2CurrentAvg', 'oilTemperatureAvg', 'pressureAvg', 'oilLevelAvg']

# Define the outlier detection methods
methods = {
    "LocalOutlierFactor": LocalOutlierFactor(n_neighbors=20, contamination=0.1),
    "OneClassSVM": OneClassSVM(nu=0.1),
    "IsolationForest": IsolationForest(contamination=0.1, random_state=42)
}

# Create a new DataFrame to store the outlier predictions
outliers = pd.DataFrame(index=data.index)

# Detect outliers and store predictions in the 'outliers' DataFrame for each column
for column in columns_for_detection:
    column_data = data[column].values.reshape(-1, 1)
    for method_name, method in methods.items():
        outlier_predictions = method.fit_predict(column_data)
        outliers[f'{method_name}_{column}'] = outlier_predictions

# Create a plot for each method for each column
for method_name in methods.keys():
    plt.figure(figsize=(10, 5))
    plt.title(f'Outliers Detection using {method_name}')
    for column in columns_for_detection:
        plt.scatter(data.index, data[column], c=outliers[f'{method_name}_{column}'], cmap='coolwarm', label=column)
    plt.xlabel('Timestamp')
    plt.ylabel('Values')
    plt.colorbar(label='Outlier Prediction')
    plt.legend()
    plt.show()

# Save the predictions back to the original DataFrame
data = pd.concat([data, outliers], axis=1)

# Display the data with outlier predictions
print(data)

Make sure to replace 'your_data.csv' with the path to your data file. This code will detect outliers for each feature separately using LocalOutlierFactor, One-class SVM, and IsolationForest and create separate plots for each method and feature combination. The outlier predictions are stored in new columns in the outliers DataFrame and are also added back to the original DataFrame.