Is it possible to get empty anchors?

[pramitchoudhary]

Still in the process of understanding the core algo but from the current understanding found it weird for the explainer to be returning empty anchors. Is that by design?

Mentioned below is the sample code used for experimenting,

import numpy as np
import pandas as pd 
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.decomposition import PCA, TruncatedSVD
import time

# Classifier Libraries
from sklearn.ensemble import RandomForestClassifier
import collections


# Other Libraries
from sklearn.model_selection import train_test_split
from sklearn.pipeline import make_pipeline
from imblearn.pipeline import make_pipeline as imbalanced_make_pipeline
from imblearn.over_sampling import SMOTE
from imblearn.under_sampling import NearMiss
from imblearn.metrics import classification_report_imbalanced
from sklearn.metrics import precision_score, recall_score, f1_score, roc_auc_score, accuracy_score, classification_report
from collections import Counter
from sklearn.model_selection import KFold, StratifiedKFold
import warnings
warnings.filterwarnings("ignore")

# data source: https://www.kaggle.com/mlg-ulb/creditcardfraud
# All continuous features
credit_data = pd.read_csv("../creditcard.csv")


colors = ["#0101DF", "#DF0101"]

sns.countplot('Class', data=df, palette=colors)
plt.title('Class Distributions \n (0: No Fraud || 1: Fraud)', fontsize=14)

print('No Frauds', round(df['Class'].value_counts()[0]/len(df) * 100,2), '% of the dataset')
print('Frauds', round(df['Class'].value_counts()[1]/len(df) * 100,2), '% of the dataset')

from sklearn.preprocessing import StandardScaler, RobustScaler


# RobustScaler is less prone to outliers
rob_scaler = RobustScaler()

df['scaled_amount'] = rob_scaler.fit_transform(df['Amount'].values.reshape(-1,1))
df['scaled_time'] = rob_scaler.fit_transform(df['Time'].values.reshape(-1,1))

df.drop(['Time','Amount'], axis=1, inplace=True)


from sklearn.model_selection import StratifiedShuffleSplit

X = df.drop('Class', axis=1)
y = df['Class']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, stratify=y, random_state=42)
print('No Frauds', round(y_train.value_counts()[0]/len(y_train) * 100,2), '% of the dataset')
print('Frauds', round(y_train.value_counts()[1]/len(y_train) * 100,2), '% of the dataset')

# Oversample under represented class
sm = SMOTE(sampling_strategy='minority', random_state=42)

Xsm_train, ysm_train = sm.fit_resample(X_train, y_train)
print('No Frauds', round(ysm_train.value_counts()[0]/len(ysm_train) * 100,2), '% of the dataset')
print('Frauds', round(ysm_train.value_counts()[1]/len(ysm_train) * 100,2), '% of the dataset')


from xgboost import XGBClassifier
classifiers = {
#     "BoostingTrees": XGBClassifier(objective='reg:logistic')
    "RF": RandomForestClassifier(n_estimators=50, n_jobs=5)
}
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import StratifiedKFold

cv = StratifiedKFold(random_state=42, shuffle=True)
for key, classifier in classifiers.items():
    classifier.fit(Xsm_train.to_numpy(), ysm_train.to_numpy())
    training_score = cross_val_score(classifier, Xsm_train, ysm_train, cv=cv)
    print("Classifiers: ", classifier.__class__.__name__, "Has a training score of", round(training_score.mean(), 2) * 100, "% accuracy score")
    
X_test = X_test.reset_index(drop=True)
y_test = y_test.reset_index(drop=True)

from sklearn.metrics import classification_report
y_pred = classifiers['RF'].predict(X_test)
target_names = ['No Fraud', 'Fraud']
print(classification_report(y_test, y_pred, target_names=target_names))


# A random data row
idx = 20
print(X_test[idx:idx+1])
print(f"Ground Truth: {y_test[idx]}")
print(f"Predicted Label: {classifiers['RF'].predict(X_test[idx:idx+1])}")

from anchor import utils
from anchor import anchor_tabular

class_names = ['Not Fraud', 'Fraud']
feature_names = list(X_test.columns)

# Also tried with 'quartile` discretizer
anchor = anchor_tabular.AnchorTabularExplainer(class_names, 
                                               feature_names, X_test.to_numpy(), {}, discretizer='decile')
                                               
np.random.seed(1)
c = classifiers['RF']
print('Prediction: ', anchor.class_names[c.predict(X_test.iloc[idx:idx+1])[0]])
exp = anchor.explain_instance(X_test.loc[idx, feature_names].values, c.predict, threshold=0.67, batch_size=5000)

print('Anchor: %s' % (' AND '.join(exp.names())))
print('Precision: %.2f' % exp.precision())
print('Coverage: %.2f' % exp.coverage())

Output:

Anchor: 
Precision: 1.00
Coverage: 1.00

Output of exp_map in-case helpful

{'feature': [],
 'mean': [],
 'precision': [],
 'coverage': [],
 'examples': [],
 'all_precision': 0.999000199960008,
 'num_preds': 5001,
 'names': [],
 'instance': array([-0.72322052, -0.29332573,  2.47559774, -2.5954306 , -1.13671485,
        -0.53469756, -0.30401351, -0.13267745, -2.23157085,  1.32801337,
         1.28661056, -0.4945589 ,  0.43808759, -0.74606032, -0.42314735,
         0.0285372 , -0.14005478,  0.76874186, -0.74326881, -0.11283696,
        -0.03239568,  0.45303638, -0.29160164,  0.51677789,  0.33426919,
        -0.23986655,  0.01333135, -0.09690853, -0.12017047, -0.66296597]),
 'prediction': 0}

Note: One gets an output if we lower the threshold to 0.2.

[pramitchoudhary]

might be related to #7

[marcotcr]

Is it the case that there is extreme class imbalance? If so, the empty anchor just means that almost all perturbations we try have no impact in the prediction, even if we don't fix any feature values.

[pramitchoudhary]

Ah yes there is a class imbalance,

No Frauds: 99.83 %
Frauds 0.17 %

So, if I understand correctly it means that the perturbation engine couldn't pick a signal that overlaps with the feature space with target as Fraud?

• Then does it makes sense to make threshold a hyper-param as well so that if one is not able to find anchors
  for a particular threshold, it lowers the threshold and looks further?
• or when building the candidate set, should the perturbation engine not sample from the imbalance class as well?

Sorry, not sure, if the above thought made sense? As you give more thought to it, will revisit the algo as well.

[marcotcr]

The threshold is a hyperparameter (threshold), with default value 0.95. I think the empty anchor is the explanation I want in this case though, as it tells me that something to the effect of 'unless there is strong evidence otherwise, the model predicts 'No Fraud'' with high probability. That is, nothing in the example at hand is 'necessary' for a high likelihood of 'no fraud' prediction.