Isolation Forest के हाइपरपैरामीटर ट्यूनिंग

Python में Anomaly Detection

Bekhruz (Bex) Tuychiev

Kaggle Master, Data Science Content Creator

Contamination ट्यून करना

ट्यूनिंग का कोई तय तरीका नहीं
इन पर निर्भर रहें:
- अंतर्ज्ञान
- EDA से मिली समझ
- डोमेन नॉलेज
- बिज़नेस अपेक्षाएँ

सर्वे उदाहरण

समान सर्वे पर रिसर्च करें
सबसे गरीब और सबसे अमीर का अनुपात जानें
यूँही मान चुनने से रिसर्च बेहतर है

Big Mart बिक्री डेटा

import pandas as pd

big_mart = pd.read_csv("big_mart_sales.csv")
big_mart.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 7060 entries, 0 to 7059
Data columns (total 5 columns):
 #   Column            Non-Null Count  Dtype  
 0   weight            7060 non-null   float64
 1   fat_content       7060 non-null   object 
 2   type              7060 non-null   object 
 3   max_retail_price  7060 non-null   float64
 4   sales             7060 non-null   float64
dtypes: float64(3), object(2)

Categoricals एन्कोड करें

big_mart = pd.get_dummies(big_mart)

   weight  max_retail_price      sales  fat_content_low_fat    fat_content_regular
0    9.30          249.8092  3735.1380                    1                      0
1    5.92           48.2692   443.4228                    0                      1
2   17.50          141.6180  2097.2700                    1                      0
3   19.20          182.0950   732.3800                    0                      1
4    8.93           53.8614   994.7052                    1                      0

evaluate_outlier_classifier

def evaluate_outlier_classifier(model, data):
    # Get labels
    labels = model.fit_predict(data)

    # Return inliers
    return data[labels == 0]

evaluate_regressor

from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import root_mean_squared_error

evaluate_regressor

def evaluate_regressor(inliers):
    X = inliers.drop("sales", axis=1) 
    y = inliers[['sales']]


    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=10)

    lr = LinearRegression()
    lr.fit(X_train, y_train)


    preds = lr.predict(X_test)
    rmse = root_mean_squared_error(y_test, preds)

    return round(rmse, 3)

Contamination ट्यून करना

contaminations = [0.05, 0.1, 0.2, 0.3]
scores = dict()


for c in contaminations:
    # Instantiate IForest with the current c
    iforest = IForest(contamination=c, random_state=10)


    # Get inliers with the current IForest
    inliers = evaluate_outlier_classifier(iforest, big_mart)


    # Calculate and store RMSE into scores
    scores[c] = evaluate_regressor(inliers)

आउटपुट देखें

print(scores)

{0.05: 1148.555, 0.1: 1147.48, 0.2: 1082.307, 0.3: 1029.33}

एकाधिक हाइपरपैरामीटर ट्यून करना

estimators = [100, 200, 300,]
max_samples = [0.6, 0.8, 1]
scores = dict()

Cartesian product

from itertools import product

list(product(estimators, max_samples))

[(100, 0.6),
 (100, 0.8),
 (100, 1),
 (200, 0.6),
 (200, 0.8),
 (200, 1),
 (300, 0.6),
 (300, 0.8),
 (300, 1)]

लूप के अंदर

estimators = [100, 200, 300,]
max_samples = [0.6, 0.8, 1]
scores = dict()


for e, m in product(estimators, max_samples):

    # Instantiate an IForest
    iforest = IForest(n_estimators=e, max_samples=m, contamination=.3)


    # Get the inliers with the current IForest
    inliers = evaluate_outlier_classifier(iforest, big_mart)

    # Calculate and store RMSE into scores
    scores[(e, m)] = evaluate_regressor(inliers)

आउटपुट देखना

print(scores)

{(100, 0.6): 959.398,
 (100, 0.8): 986.056,
 (100, 1): 1195.875,
 (200, 0.6): 947.628,
 (200, 0.8): 933.115,
 (200, 1): 1195.875,
 (300, 0.6): 949.412,
 (300, 0.8): 935.962,
 (300, 1): 1195.875}

पैरालेल execution

# Faster compuation with n_jobs=-1
iforest = IForest(n_estimators=1000, n_jobs=-1)

iforest.fit(big_mart)

अभ्यास करते हैं!

Python में Anomaly Detection