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d3b385b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 | import pandas as pd
import numpy as np
from typing import Dict, List, Tuple, Optional
def generate_job_failure_data(n_samples: int = 1000, seed: int = 42) -> pd.DataFrame:
"""
Generates synthetic SAP Job failure data (TBTCO/TBTCP style).
"""
np.random.seed(seed)
records = []
job_classes = ['A', 'B', 'C']
job_names = ['Z_FIN_POST', 'Z_SALES_EXTRACT', 'Z_INV_RECON', 'Z_HR_SYNC', 'Z_MRP_RUN']
for i in range(n_samples):
job_name = np.random.choice(job_names)
job_class = np.random.choice(job_classes, p=[0.1, 0.3, 0.6])
# Features
duration_sec = np.random.gamma(shape=2, scale=300) # Avg 600s
delay_sec = np.random.exponential(scale=100)
step_count = np.random.randint(1, 15)
concurrent_jobs = np.random.randint(0, 50)
mem_usage_pct = np.random.uniform(10, 95)
cpu_load_pct = np.random.uniform(5, 90)
has_variant = np.random.choice([0, 1], p=[0.2, 0.8])
hist_fail_rate = np.random.uniform(0, 0.15)
# Non-linear risk formula
# Risk increases with high concurrency, high memory, and high delay
risk_score = (
(concurrent_jobs / 50) * 1.5 +
(mem_usage_pct / 100) * 2.0 +
(delay_sec / 500) * 1.2 +
(1 if job_class == 'A' else 0) * 0.5 +
hist_fail_rate * 5.0
)
risk_score += np.random.normal(0, 0.2)
# Determine class
if risk_score > 3.5:
status = 'Cancelled'
risk_label = 'HIGH'
elif risk_score > 2.2:
status = 'Finished' # But risky
risk_label = 'MEDIUM'
else:
status = 'Finished'
risk_label = 'LOW'
records.append({
'JOBNAME': job_name,
'JOBCOUNT': f'{i:08d}',
'JOBCLASS': job_class,
'DURATION_SEC': round(duration_sec, 1),
'DELAY_SEC': round(delay_sec, 1),
'STEP_COUNT': step_count,
'CONCURRENT_JOBS': concurrent_jobs,
'MEM_USAGE_PCT': round(mem_usage_pct, 1),
'CPU_LOAD_PCT': round(cpu_load_pct, 1),
'HAS_VARIANT': has_variant,
'HIST_FAIL_RATE': round(hist_fail_rate, 3),
'STATUS': status,
'RISK_SCORE': round(risk_score, 2),
'RISK_LABEL': risk_label
})
return pd.DataFrame(records)
def generate_transport_failure_data(n_samples: int = 1000, seed: int = 42) -> pd.DataFrame:
"""
Generates synthetic SAP Transport failure data (E070/E071 style).
"""
np.random.seed(seed)
records = []
users = ['DEV_ALAL', 'DEV_JDOE', 'DEV_BSMITH', 'DEV_KLEE']
systems = ['DEV', 'QAS', 'PRD']
for i in range(n_samples):
user = np.random.choice(users)
obj_count = np.random.randint(1, 500)
table_obj_pct = np.random.uniform(0, 0.8)
prog_obj_pct = 1.0 - table_obj_pct
cross_sys_dep = np.random.randint(0, 10)
author_success_rate = np.random.uniform(0.7, 0.99)
target_sys_load = np.random.uniform(10, 90)
network_latency = np.random.uniform(5, 200)
# Risk formula
risk_score = (
(obj_count / 500) * 2.0 +
table_obj_pct * 1.5 +
cross_sys_dep * 0.5 +
(1 - author_success_rate) * 4.0 +
(target_sys_load / 100) * 1.0 +
(network_latency / 200) * 0.8
)
risk_score += np.random.normal(0, 0.3)
if risk_score > 4.0:
risk_label = 'HIGH'
result = 'Error'
elif risk_score > 2.5:
risk_label = 'MEDIUM'
result = 'Warning'
else:
risk_label = 'LOW'
result = 'Success'
records.append({
'TRKORR': f'SIDK9{i:05d}',
'AS4USER': user,
'OBJ_COUNT': obj_count,
'TABLE_OBJ_PCT': round(table_obj_pct, 3),
'PROG_OBJ_PCT': round(prog_obj_pct, 3),
'CROSS_SYS_DEP': cross_sys_dep,
'AUTHOR_SUCCESS_RATE': round(author_success_rate, 3),
'TARGET_SYS_LOAD': round(target_sys_load, 1),
'NETWORK_LATENCY': round(network_latency, 1),
'RESULT': result,
'RISK_SCORE': round(risk_score, 2),
'RISK_LABEL': risk_label
})
return pd.DataFrame(records)
def generate_interface_failure_data(n_samples: int = 1000, seed: int = 42) -> pd.DataFrame:
"""
Generates synthetic SAP Interface failure data (IDoc/RFC style).
"""
np.random.seed(seed)
records = []
msg_types = ['ORDERS', 'INVOIC', 'MATMAS', 'DEBMAS']
partners = ['CUST_A', 'VEND_B', 'SYS_X', 'EXT_Y']
for i in range(n_samples):
msg_type = np.random.choice(msg_types)
partner = np.random.choice(partners)
payload_size_kb = np.random.lognormal(mean=4, sigma=1)
queue_depth = np.random.randint(0, 1000)
partner_reliability = np.random.uniform(0.6, 0.99)
retry_count = np.random.randint(0, 5)
sys_load_idx = np.random.uniform(0.1, 0.9)
dest_availability = np.random.uniform(0.5, 1.0)
# Risk formula
risk_score = (
(payload_size_kb / 500) * 1.0 +
(queue_depth / 1000) * 2.0 +
(1 - partner_reliability) * 3.0 +
retry_count * 0.8 +
sys_load_idx * 1.5 +
(1 - dest_availability) * 2.5
)
risk_score += np.random.normal(0, 0.25)
if risk_score > 4.5:
risk_label = 'HIGH'
status = 'Error'
elif risk_score > 2.8:
risk_label = 'MEDIUM'
status = 'Warning'
else:
risk_label = 'LOW'
status = 'Success'
records.append({
'MESTYP': msg_type,
'PARTNER': partner,
'PAYLOAD_SIZE_KB': round(payload_size_kb, 1),
'QUEUE_DEPTH': queue_depth,
'PARTNER_RELIABILITY': round(partner_reliability, 3),
'RETRY_COUNT': retry_count,
'SYS_LOAD_IDX': round(sys_load_idx, 2),
'DEST_AVAILABILITY': round(dest_availability, 3),
'STATUS': status,
'RISK_SCORE': round(risk_score, 2),
'RISK_LABEL': risk_label
})
return pd.DataFrame(records)
def detect_drift(df1: pd.DataFrame, df2: pd.DataFrame, column: str) -> float:
"""
Simple drift detection using mean difference percentage.
"""
if column not in df1.columns or column not in df2.columns:
return 0.0
m1 = df1[column].mean()
m2 = df2[column].mean()
if m1 == 0: return 0.0
return abs(m1 - m2) / m1
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