แนวคิดเปลี่ยนแปลง: เมื่อความสัมพันธ์ระหว่าง X และ Y เปลี่ยนไป

import numpy as np
import pandas as pd

# Scenario: credit scoring model
# Feature: income. Label: 1 = defaults, 0 = repays

# Training period: incomes 30-50k correlate with defaults
np.random.seed(42)
train_income = np.random.normal(40000, 8000, 1000)
train_default = (train_income < 35000).astype(int)  # low income -> default

# Concept drift: inflation shifts threshold; now defaults start at 50k
prod_income = np.random.normal(40000, 8000, 500)  # SAME distribution (no data drift)
prod_default = (prod_income < 50000).astype(int)  # new relationship

print('Train default rate:', train_default.mean().round(3))
print('Prod default rate:', prod_default.mean().round(3))
print('Feature distribution same (no data drift).',
      'But Y|X relationship has changed (concept drift).')

import numpy as np
import matplotlib.pyplot as plt

t = np.linspace(0, 100, 1000)

# Sudden drift: step function at t=50
sudden = np.where(t < 50, 0.8, 0.3)  # accuracy drops at t=50

# Gradual drift: linear transition
gradual = np.where(t < 40, 0.8,
          np.where(t > 60, 0.4,
          0.8 - (t - 40) * 0.02))

# Recurring drift: seasonal pattern
recurring = 0.6 + 0.2 * np.sin(2 * np.pi * t / 20)

for name, series in [('Sudden', sudden), ('Gradual', gradual), ('Recurring', recurring)]:
    print(f'{name} drift -- min acc: {series.min():.2f}, max: {series.max():.2f}')

import numpy as np
import pandas as pd

np.random.seed(42)

# Simulate weekly model accuracy tracking
weekly_accuracy = [
    0.92, 0.91, 0.90, 0.89, 0.88,  # slight decline
    0.86, 0.83, 0.79, 0.74, 0.68,  # accelerating decline = concept drift
    0.65, 0.61
]

df = pd.DataFrame({'week': range(1, 13), 'accuracy': weekly_accuracy})
df['rolling_mean'] = df['accuracy'].rolling(3).mean()
df['alert'] = df['accuracy'] < 0.75  # threshold

print(df.to_string(index=False))
print('Weeks with alerts:', df[df['alert']]['week'].tolist())

import numpy as np

# Monitoring prediction distributions as a proxy for concept drift
# Period 1 (training distribution): ~30% positive predictions
period1_probs = np.random.beta(2, 5, 1000)  # right-skewed, ~30% above 0.5
period1_pos_rate = (period1_probs > 0.5).mean()

# Period 2 (concept drifted): only ~5% positive predictions (model confused)
period2_probs = np.random.beta(1, 15, 500)  # very right-skewed
period2_pos_rate = (period2_probs > 0.5).mean()

print(f'Period 1 positive rate: {period1_pos_rate:.2%}')
print(f'Period 2 positive rate: {period2_pos_rate:.2%}')
print(f'Rate drop: {(period1_pos_rate - period2_pos_rate):.2%}')
print('Possible concept drift detected!')

import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score

np.random.seed(42)

def simulate_concept_drift(n, t):
    X = np.random.normal(0, 1, (n, 2))
    # Decision boundary rotates over time
    y = ((X[:, 0] * np.cos(t) - X[:, 1] * np.sin(t)) > 0).astype(int)
    return X, y

# Full retrain vs sliding window retrain
for window_size in [100, 500, None]:  # None = all data
    all_X, all_y = [], []
    accs = []
    for t in np.linspace(0, np.pi, 10):
        X, y = simulate_concept_drift(200, t)
        all_X.append(X)
        all_y.append(y)
        if window_size:
            train_X = np.vstack(all_X[-window_size//200:])
            train_y = np.hstack(all_y[-window_size//200:])
        else:
            train_X = np.vstack(all_X)
            train_y = np.hstack(all_y)
        clf = LogisticRegression().fit(train_X, train_y)
        accs.append(clf.score(X, y))
    print(f'Window={window_size}: avg accuracy={np.mean(accs):.3f}')

import numpy as np
from sklearn.ensemble import GradientBoostingClassifier

np.random.seed(42)
# Simulate 1000 training examples collected over 10 weeks
X = np.random.normal(0, 1, (1000, 5))
y = (X[:, 0] + np.random.normal(0, 0.5, 1000) > 0).astype(int)
weeks_ago = np.linspace(10, 0, 1000)  # example 0 is oldest, 999 is newest

# Exponential decay: half-life of 3 weeks
half_life = 3.0
sample_weights = np.exp(-weeks_ago * np.log(2) / half_life)
sample_weights /= sample_weights.sum()

clf = GradientBoostingClassifier(n_estimators=100, random_state=42)
clf.fit(X, y, sample_weight=sample_weights * len(y))  # scale up
print('Model trained with recency-weighted samples.')
print('Weight ratio new/old:', round(sample_weights[-1] / sample_weights[0], 2))

# Using the river library for streaming drift detection
# pip install river

# from river.drift import ADWIN

# adwin = ADWIN(delta=0.002)  # delta controls sensitivity
# error_sequence = [0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1]  # errors over time
#
# for i, error in enumerate(error_sequence):
#     adwin.update(error)
#     if adwin.drift_detected:
#         print(f'ADWIN detected drift at step {i}')
#         adwin = ADWIN(delta=0.002)  # reset detector

# Manual CUSUM example:
def cusum(errors, threshold=5, drift=0.01):
    cum_sum, alerts = 0, []
    for i, e in enumerate(errors):
        cum_sum = max(0, cum_sum + e - drift)
        if cum_sum > threshold:
            alerts.append(i)
            cum_sum = 0
    return alerts

errors = [0]*10 + [1]*15  # errors spike after step 10
print('CUSUM drift alerts at steps:', cusum(errors))

import numpy as np
from scipy.stats import ks_2samp

np.random.seed(42)

# Training: July 2023 data
july_2023 = np.random.normal(100, 20, 1000)  # low activity (summer)

# Monitoring: July 2024 (seasonal -- same month last year)
july_2024 = np.random.normal(102, 21, 500)   # similar to last July

# Monitoring: December 2024 (seasonal spike -- same as Dec 2023)
dec_2024 = np.random.normal(180, 30, 500)    # high activity (Christmas)

stat_july, p_july = ks_2samp(july_2023, july_2024)
stat_dec, p_dec = ks_2samp(july_2023, dec_2024)

print(f'July 2024 vs July 2023: KS={stat_july:.3f} p={p_july:.3f} -> {"drift" if p_july < 0.05 else "seasonal OK"}')
print(f'Dec 2024 vs July 2023:  KS={stat_dec:.3f} p={p_dec:.3f} -> Expected seasonal shift')

def concept_drift_response(current_accuracy, baseline_accuracy,
                            data_drift_detected, prediction_rate_shift):
    relative_drop = (baseline_accuracy - current_accuracy) / baseline_accuracy

    if relative_drop > 0.10:
        return ('TIER 4 ESCALATION: >10% accuracy drop. '
                'Manual feature engineering review required.')
    elif relative_drop > 0.05:
        return ('TIER 3 ACTION: 5-10% accuracy drop. '
                'Trigger windowed retraining immediately.')
    elif data_drift_detected or prediction_rate_shift > 0.15:
        return ('TIER 2 MONITOR: Early warning signals present. '
                'Increase monitoring to daily. Queue retraining.')
    else:
        return 'TIER 1 OK: No significant concept drift detected.'

print(concept_drift_response(0.84, 0.92, False, 0.05))   # TIER 3
print(concept_drift_response(0.91, 0.92, True, 0.18))    # TIER 2
print(concept_drift_response(0.91, 0.92, False, 0.03))   # TIER 1

import numpy as np
from sklearn.ensemble import RandomForestClassifier

# Measure inter-tree disagreement on current data as drift signal
def inter_tree_disagreement(forest, X):
    predictions = np.array([tree.predict(X) for tree in forest.estimators_])
    # Fraction of pairs of trees that disagree per sample, averaged over samples
    n_trees = len(forest.estimators_)
    disagreement_per_sample = []
    for i in range(X.shape[0]):
        preds = predictions[:, i]
        disagree = (preds != preds[0]).sum() / n_trees
        disagreement_per_sample.append(disagree)
    return np.mean(disagreement_per_sample)

# Higher disagreement = more concept drift
# print(inter_tree_disagreement(model, X_current))