diff --git a/.github/CODEOWNERS b/.github/CODEOWNERS index e69de29..3dc347e 100644 --- a/.github/CODEOWNERS +++ b/.github/CODEOWNERS @@ -0,0 +1,3 @@ +# Global owners +* @cherry-aggarwal @Brijesh-Thakkar @coderTanisha22 + diff --git a/.github/PULL_REQUEST_TEMPLATE.md b/.github/PULL_REQUEST_TEMPLATE.md index e69de29..6515727 100644 --- a/.github/PULL_REQUEST_TEMPLATE.md +++ b/.github/PULL_REQUEST_TEMPLATE.md @@ -0,0 +1,96 @@ +# Pull Request + +## Related Issue + +Closes # + +--- + +## Summary + +Provide a brief description of the changes made in this Pull Request. + +Example: + +* Implemented process telemetry collection +* Updated module documentation + +--- + +## Type of Change + +**Instructions:** Mark the applicable option(s) by replacing the space inside the brackets with an **`x`**. + +Example: + +```text +- [x] New feature +- [ ] Bug fix +``` + +Select all that apply: + +* [ ] New feature +* [ ] Bug fix +* [ ] Documentation update +* [ ] Refactoring +* [ ] Performance improvement +* [ ] Test addition +* [ ] Other (please specify) + +--- + +## Changes Made + +Describe the main changes introduced by this PR. + +* +* +* + +--- + +## Testing + +Describe how you tested your changes. + +* [ ] Project builds successfully +* [ ] Existing functionality verified +* [ ] New functionality tested +* [ ] Tests added/updated (if applicable) + +Additional testing details: + +--- + +## Documentation + +* [ ] Documentation updated +* [ ] No documentation changes required + +If updated, specify the affected documentation: + +--- + +## Checklist + +Before requesting a review, confirm the following: + +* [ ] My code follows the project's coding standards. +* [ ] I have reviewed my own code. +* [ ] I have updated relevant documentation. +* [ ] My changes do not introduce unnecessary files or dependencies. +* [ ] The project builds successfully. +* [ ] This Pull Request addresses a single feature or bug. + +--- + +## Screenshots / Logs (if applicable) + +Attach screenshots, terminal output, or logs that help demonstrate your changes. + +--- + +## Additional Notes + +Include any information reviewers should know before reviewing this Pull Request. diff --git a/.gitignore b/.gitignore index 6b08a83..68b46d7 100644 --- a/.gitignore +++ b/.gitignore @@ -7,11 +7,29 @@ __pycache__/ .venv/ venv/ +# Environment & Config (IMPORTANT) +.env +config.ini + # Local data and generated artifacts -data/cognios.db +cognios_telemetry.db +*.db +*.db-shm +*.db-wal data/models/*.pt data/traces/* # Keep data directories present in git if marker files are added. -!data/models/.gitkeep !data/traces/.gitkeep + +<<<<<<< HEAD +# OS / IDE +.DS_Store +.vscode/ +.idea/ +======= +# Ignore SQLite database files +*.db +*.sqlite3 +*.sqlite +>>>>>>> start_script diff --git a/README.md b/README.md index 044fb97..8a6efbd 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,41 @@ +# CogniOS - Archietecture diagram +```mermaid +graph TD + subgraph System Environment + proc[/proc Filesystem/] + psutil[psutil wrapper] + end + + subgraph CogniOS Core + DB[(Central SQLite DB)] + TC[Module 1: Telemetry Collector\nBackground Daemon] + + TC -->|Writes real-time data| DB + proc --> TC + psutil --> TC + end + + subgraph Intelligent Modules + OD[Module 2: OS Doctor\nIsolation Forest] + FO[Module 3: FocusOS\nCNN Classifier] + BB[Module 4: BlackBox\nRolling Window / Replay] + RE[Module 5: Research Engine\nRL & Simulators] + end + + DB <-->|Reads Data / Writes Alerts| OD + DB <-->|Reads Heatmap / Writes Configs| FO + DB <-->|Reads Trace / Writes Narrative| BB + DB <-->|Reads Traces for Simulation| RE + + subgraph User Interface + DASH[Streamlit Dashboard\nUnified GUI] + end + + OD --> DASH + FO --> DASH + BB --> DASH + RE --> DASH +``` # CogniOS ### Intelligent OS Observability & Adaptive Workload Optimization Platform diff --git a/cognios_as_daemon.py b/cognios_as_daemon.py index 03d43a7..4e68d68 100644 --- a/cognios_as_daemon.py +++ b/cognios_as_daemon.py @@ -1 +1,81 @@ """Daemon entry point for CogniOS.""" +import time +import json +import logging +from db import create_connection, write_layer1 +from collectors.layer1_system import collect_layer1_metrics +from config import DB_PATH + +# Set up basic logging +logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') + +def run_daemon(): + logging.info(f"Starting CogniOS Daemon. Saving metrics to '{DB_PATH}' every 1 second.") + + # Initialize database connection + conn = create_connection(DB_PATH) + + try: + while True: + try: + metrics = collect_layer1_metrics() + + write_layer1( + conn, + metrics['timestamp'], + metrics['cpu_usage_percent'], + metrics['cpu_current_freq'], + metrics['cpu_user_time'], + metrics['cpu_system_time'], + metrics['cpu_idle_time'], + metrics['cpu_iowait_time'], + metrics['cpu_busy_time'], + metrics['cpu_ctx_switches'], + metrics['memory_percent'], + metrics['memory_used'], + metrics['memory_available'], + metrics['memory_cached'], + metrics['memory_buffers'], + metrics['swap_percent'], + metrics['swap_sin'], + metrics['swap_sout'], + metrics['disk_usage_percent'], + metrics['disk_read'], + metrics['disk_write'], + metrics['disk_read_time'], + metrics['disk_write_time'], + metrics['load_avg1'], + metrics['load_avg5'], + metrics['load_avg15'], + metrics['total_processes'], + metrics['running_processes'], + metrics['sleeping_processes'], + metrics['zombie_processes'], + metrics['avg_temp'], + metrics['max_temp'], + metrics['battery_percent'], + metrics['net_rate_mb_s'], + metrics['net_bytes_sent'], + metrics['net_bytes_received'], + metrics['net_packets_sent'], + metrics['net_packets_received'], + metrics['net_errs'], + metrics['net_drops'], + json.dumps(metrics['process_data']), + json.dumps(metrics['num_threads']) + ) + logging.info(f"Successfully saved metrics for timestamp: {metrics['timestamp']}") + + except Exception as e: + logging.error(f"Error collecting or writing metrics: {e}") + + # Wait for 1 second before the next collection + time.sleep(1) + + except KeyboardInterrupt: + logging.info("Stopping CogniOS Daemon. Shutting down gracefully...") + finally: + conn.close() + +if __name__ == "__main__": + run_daemon() diff --git a/collectors/layer1_system.py b/collectors/layer1_system.py index 291e147..a4592e3 100644 --- a/collectors/layer1_system.py +++ b/collectors/layer1_system.py @@ -1 +1,283 @@ -"""Layer 1 system telemetry collection.""" +import enum +import enum +import psutil +import time +from datetime import datetime,timezone +import sys +import os +#adding path to locate the utils +sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) +import utils +from utils.helpers import rate_mb_s + +# a dictionary to store previous values +_last = { + "time": None, + "disk_read_bytes": None, + "disk_write_bytes": None, + "net_bytes_sent": None, + "net_bytes_recv": None, +} +# collects metriics +def collect_layer1_metrics(): + now = time.time() + timestamp = datetime.now(timezone.utc).isoformat() + #process + try: + procs = {} + for p in psutil.process_iter(["name", "memory_percent"]): + try: + p.cpu_percent() + procs[p.pid] = p + except (psutil.NoSuchProcess, psutil.AccessDenied): + pass + time.sleep(0.1) + process_data = [] + num_threads=[] + for pid, p in procs.items(): + try: + cpu = round(p.cpu_percent(), 2) + mem = round(p.info.get("memory_percent") or 0.0, 2) + if cpu > 0.5 or mem > 0.5: + process_data.append((p.info.get("name"), cpu, mem)) + num_threads.append(p.num_threads()) + except (psutil.NoSuchProcess, psutil.AccessDenied): + pass + except Exception: + process_data = [] + num_threads= [] + # CPU Metrics + cpu_usage_percent = psutil.cpu_percent(interval=None) + cpu_times = psutil.cpu_times() + cpu_user_time = cpu_times.user + cpu_system_time = cpu_times.system + cpu_idle_time = cpu_times.idle + cpu_iowait_time = getattr(cpu_times, 'iowait', None) # iowait for Linux only + cpu_busy_time=cpu_user_time + cpu_system_time + + # freq , ctx_switches are not available on all systems + try: + freq=psutil.cpu_freq() + cpu_current_freq=freq.current if freq else None + except Exception: + cpu_current_freq=None + + try: + cpu_ctx_switches=psutil.cpu_stats().ctx_switches + except Exception: + cpu_ctx_switches=None + + # Memory Metrics + + vmem=psutil.virtual_memory() + memory_percent=vmem.percent + memory_used=vmem.used + memory_available=vmem.available + memory_cached=getattr(vmem, 'cached', None) #cached and buffers not available in the mac and windows + memory_buffers=getattr(vmem, 'buffers', None) # None- like value nahi mili aur 0- ek valid value hai + + swap=psutil.swap_memory() + swap_percent=swap.percent + swap_sin=swap.sin + swap_sout=swap.sout + + # Disk Metrics + + # same reason, in windows "/" is not valid + try: + disk_usage=psutil.disk_usage('/').percent + except Exception: + disk_usage=None + + try: + disk_io = psutil.disk_io_counters() + except Exception: + disk_io=None + + disk_read=None + disk_write=None + disk_read_time=None + disk_write_time=None + + if disk_io is not None: + elapsed_time = (now - _last["time"]) if _last["time"] else 0 + disk_read = rate_mb_s(disk_io.read_bytes, _last["disk_read_bytes"], elapsed_time) + disk_write = rate_mb_s(disk_io.write_bytes, _last["disk_write_bytes"], elapsed_time) + disk_read_time = getattr(disk_io, 'read_time', None) + disk_write_time = getattr(disk_io, 'write_time', None) + + _last["disk_read_bytes"] = disk_io.read_bytes + _last["disk_write_bytes"] = disk_io.write_bytes + + + # Network Metrics + net_io=psutil.net_io_counters() + net_bytes_sent=net_io.bytes_sent if net_io else 0 + net_bytes_received=net_io.bytes_recv if net_io else 0 + net_packets_sent=net_io.packets_sent if net_io else 0 + net_packets_received=net_io.packets_recv if net_io else 0 + net_errs=(net_io.errin + net_io.errout) if net_io else 0 + net_drops=(net_io.dropin + net_io.dropout) if net_io else 0 + + net_rate_mb_s=None + if _last["time"]: + elapsed_time = now - _last["time"] + sent_rate = rate_mb_s(net_bytes_sent, _last["net_bytes_sent"], elapsed_time) + recv_rate = rate_mb_s(net_bytes_received, _last["net_bytes_recv"], elapsed_time) + if sent_rate is not None and recv_rate is not None: + net_rate_mb_s = sent_rate + recv_rate + + _last["net_bytes_sent"] = net_bytes_sent + _last["net_bytes_recv"] = net_bytes_received + _last["time"] = now + + + #b Load Average Metrics + try: + load_avg1, load_avg5, load_avg15=psutil.getloadavg() + + # except AttributeError: # catches specific error when getloadavg is not support + except Exception: + load_avg1=load_avg5=load_avg15=None + + #initialising the process_type_count variables + total_processes, running_processes, sleeping_processes, zombie_processes = 0, 0, 0, 0 + for p in psutil.process_iter(['status']): + total_processes+= 1 + try: + status=p.info['status'] + if status==psutil.STATUS_RUNNING: + running_processes+=1 + elif status==psutil.STATUS_SLEEPING: + sleeping_processes+=1 + elif status==psutil.STATUS_ZOMBIE: + zombie_processes+=1 + except (psutil.NoSuchProcess,psutil.AccessDenied): + pass + + + # Temperature and Battery Metrics (if available) + temp_avg, temp_max = None, None + try: + temps=psutil.sensors_temperatures() + all_temps=[t.current for sensors in temps.values() for t in sensors] + if all_temps: + temp_avg = sum(all_temps) / len(all_temps) + temp_max = max(all_temps) + except Exception: + pass + battery_percent=None + try: + battery=psutil.sensors_battery() + if battery: + battery_percent=battery.percent + except Exception: + pass + + return { + "timestamp": timestamp, + "cpu_usage_percent": cpu_usage_percent, + "cpu_current_freq": cpu_current_freq, + "cpu_user_time": cpu_user_time, + "cpu_system_time": cpu_system_time, + "cpu_idle_time": cpu_idle_time, + "cpu_iowait_time": cpu_iowait_time, + "cpu_busy_time": cpu_busy_time, + "cpu_ctx_switches": cpu_ctx_switches, + + "memory_percent": memory_percent, + "memory_used":memory_used, + "memory_available":memory_available, + "memory_cached":memory_cached, + "memory_buffers":memory_buffers, + "swap_percent":swap_percent, + "swap_sin":swap_sin, + "swap_sout":swap_sout, + + "disk_usage_percent":disk_usage, + "disk_read":disk_read, + "disk_write":disk_write, + "disk_read_time":disk_read_time, + "disk_write_time":disk_write_time, + + "net_rate_mb_s":net_rate_mb_s, + "net_bytes_sent":net_bytes_sent, + "net_bytes_received":net_bytes_received, + "net_packets_sent":net_packets_sent, + "net_packets_received":net_packets_received, + "net_errs":net_errs, + "net_drops":net_drops, + + "load_avg1":load_avg1, + "load_avg5":load_avg5, + "load_avg15":load_avg15, + "total_processes":total_processes, + "running_processes":running_processes, + "sleeping_processes":sleeping_processes, + "zombie_processes":zombie_processes, + "avg_temp":temp_avg, + "max_temp":temp_max, + "battery_percent":battery_percent, + "process_data":process_data, + "num_threads":num_threads + } + +if __name__ == "__main__": + import json + from db import create_connection, write_layer1 + from config import DB_PATH + + metrics = collect_layer1_metrics() + print("Gathered metrics:") + print(json.dumps(metrics, indent=4)) + + print("\nSaving metrics to database...") + try: + conn = create_connection(DB_PATH) + write_layer1( + conn, + metrics['timestamp'], + metrics['cpu_usage_percent'], + metrics['cpu_current_freq'], + metrics['cpu_user_time'], + metrics['cpu_system_time'], + metrics['cpu_idle_time'], + metrics['cpu_iowait_time'], + metrics['cpu_busy_time'], + metrics['cpu_ctx_switches'], + metrics['memory_percent'], + metrics['memory_used'], + metrics['memory_available'], + metrics['memory_cached'], + metrics['memory_buffers'], + metrics['swap_percent'], + metrics['swap_sin'], + metrics['swap_sout'], + metrics['disk_usage_percent'], + metrics['disk_read'], + metrics['disk_write'], + metrics['disk_read_time'], + metrics['disk_write_time'], + metrics['load_avg1'], + metrics['load_avg5'], + metrics['load_avg15'], + metrics['total_processes'], + metrics['running_processes'], + metrics['sleeping_processes'], + metrics['zombie_processes'], + metrics['avg_temp'], + metrics['max_temp'], + metrics['battery_percent'], + metrics['net_rate_mb_s'], + metrics['net_bytes_sent'], + metrics['net_bytes_received'], + metrics['net_packets_sent'], + metrics['net_packets_received'], + metrics['net_errs'], + metrics['net_drops'], + json.dumps(metrics['process_data']), + json.dumps(metrics["num_threads"]) + ) + print(f"Successfully saved Layer 1 metrics to database table 'layer1_sys' in {DB_PATH}!") + except Exception as e: + print(f"Error saving to database: {e}") \ No newline at end of file diff --git a/collectors/layer2_process.py b/collectors/layer2_process.py index 87632ca..58ea9ee 100644 --- a/collectors/layer2_process.py +++ b/collectors/layer2_process.py @@ -1 +1,159 @@ """Layer 2 process telemetry collection.""" +import time +import psutil + + +def collect_process_telemetry(prev_states=None): + if prev_states is None: + prev_states = {} + + # --- Phase 0: CPU prime pass (t=0) --- + # First cpu_percent call per Process object always returns 0.0 or a stale + # cumulative rate. Discarding it gives the 5 sampling passes a clean 1-second + # baseline each. + for proc in psutil.process_iter(['pid']): + try: + proc.cpu_percent(interval=None) + except (psutil.NoSuchProcess, psutil.AccessDenied, psutil.ZombieProcess): + continue + + # --- Phase 1: 5 × 1-second CPU + RAM sampling --- + cpu_samples = {} # pid -> [cpu_percent, ...] + ram_samples = {} # pid -> [rss_mb, ...] + for _ in range(5): + time.sleep(1.0) + for proc in psutil.process_iter(['pid']): + try: + pid = proc.pid + cpu_samples.setdefault(pid, []).append(proc.cpu_percent(interval=None)) + try: + ram_samples.setdefault(pid, []).append( + round(proc.memory_info().rss / (1024 * 1024), 3) + ) + except (psutil.AccessDenied, AttributeError): + pass + except (psutil.NoSuchProcess, psutil.AccessDenied, psutil.ZombieProcess): + continue + + # --- Phase 2: Full metadata pass + aggregation --- + current_time = time.time() + current_states = {} + processed_snapshots = [] + + for proc in psutil.process_iter(['pid', 'name', 'num_threads', 'status', 'create_time']): + try: + info = proc.info + pid = info['pid'] + + if pid not in cpu_samples: + continue + + mem_info = proc.memory_info() + mem_percent = proc.memory_percent() + + try: + io = proc.io_counters() + read_bytes, write_bytes = io.read_bytes, io.write_bytes + except (psutil.AccessDenied, AttributeError): + read_bytes, write_bytes = 0, 0 + + try: + ctx = proc.num_ctx_switches() + ctx_vol = ctx.voluntary + ctx_invol = ctx.involuntary + except (psutil.AccessDenied, AttributeError): + ctx_vol, ctx_invol = 0, 0 + + try: + open_fds = proc.num_fds() + except (psutil.AccessDenied, AttributeError): + open_fds = 0 + + try: + net_conn_count = len(proc.net_connections()) + except (psutil.AccessDenied, AttributeError): + net_conn_count = 0 + + calc_read_rate = 0.0 + calc_write_rate = 0.0 + calc_ctx_vol_sec = 0.0 + calc_ctx_invol_sec = 0.0 + if pid in prev_states: + prev = prev_states[pid] + dt = current_time - prev['timestamp'] + if dt > 0: + calc_read_rate = max(0.0, (read_bytes - prev['read_bytes']) / dt) + calc_write_rate = max(0.0, (write_bytes - prev['write_bytes']) / dt) + calc_ctx_vol_sec = max(0.0, (ctx_vol - prev['ctx_vol']) / dt) + calc_ctx_invol_sec = max(0.0, (ctx_invol - prev['ctx_invol']) / dt) + + current_states[pid] = { + 'read_bytes': read_bytes, + 'write_bytes': write_bytes, + 'ctx_vol': ctx_vol, + 'ctx_invol': ctx_invol, + 'timestamp': current_time, + } + + cpu_s = cpu_samples[pid] + cpu_avg = round(sum(cpu_s) / len(cpu_s), 2) + cpu_peak = round(max(cpu_s), 2) + cpu_score = round(0.6 * cpu_peak + 0.4 * cpu_avg, 2) + + ram_s = ram_samples.get(pid, []) + if ram_s: + ram_avg = round(sum(ram_s) / len(ram_s), 3) + ram_peak = round(max(ram_s), 3) + ram_score = round(0.6 * ram_peak + 0.4 * ram_avg, 3) + rss_mb_now = ram_s[-1] + else: + rss_mb_now = round(mem_info.rss / (1024 * 1024), 3) + ram_avg = ram_peak = ram_score = rss_mb_now + + processed_snapshots.append({ + 'pid': pid, + 'name': info['name'] or 'Unknown', + 'cpu_percent': round(cpu_s[-1], 2), + 'cpu_avg': cpu_avg, + 'cpu_peak': cpu_peak, + 'cpu_score': cpu_score, + 'memory_percent': round(mem_percent, 2), + 'rss_mb': rss_mb_now, + 'ram_avg': ram_avg, + 'ram_peak': ram_peak, + 'ram_score': ram_score, + 'vms_gb': round(mem_info.vms / (1024 * 1024 * 1024), 4), + 'thread_count': info['num_threads'] or 1, + 'read_bytes_sec': round(calc_read_rate, 2), + 'write_bytes_sec': round(calc_write_rate, 2), + 'status': info['status'] or 'unknown', + 'age_sec': round(current_time - (info['create_time'] or current_time), 1), + 'open_fds': open_fds, + 'ctx_vol_sec': round(calc_ctx_vol_sec, 2), + 'ctx_invol_sec': round(calc_ctx_invol_sec, 2), + 'net_conn_count': net_conn_count, + }) + + except (psutil.NoSuchProcess, psutil.AccessDenied, psutil.ZombieProcess): + continue + + top_cpu = sorted(processed_snapshots, key=lambda x: x['cpu_score'], reverse=True)[:5] + top_mem = sorted(processed_snapshots, key=lambda x: x['ram_score'], reverse=True)[:5] + + return top_cpu, top_mem, current_states + + +if __name__ == '__main__': + from db import init_db, insert_process_snapshot + + init_db() + baselines = {} + print("CogniOS Telemetry Daemon started. Press Ctrl+C to stop.") + + try: + while True: + top_cpu, top_mem, baselines = collect_process_telemetry(baselines) + insert_process_snapshot(top_cpu, top_mem) + print(f"Snapshot committed at t={time.time():.0f} | top_cpu={top_cpu[0]['name']} score={top_cpu[0]['cpu_score']}") + except KeyboardInterrupt: + print("\nDaemon safely terminated.") diff --git a/config.py b/config.py index be02e34..ec1f9db 100644 --- a/config.py +++ b/config.py @@ -1 +1,4 @@ -"""CogniOS configuration.""" +import os +BASE_DIR = os.path.dirname(os.path.abspath(__file__)) +DB_PATH = os.path.join(BASE_DIR, "cognios_telemetry.db") +SLIDING_WIND_N = 120 diff --git a/db.py b/db.py index e5c2395..8b19ace 100644 --- a/db.py +++ b/db.py @@ -1 +1,169 @@ """Shared database schema and read/write interface.""" +import json +import sqlite3 +import time +from config import DB_PATH + +# layer 2 db code starts here + + +def init_db(): + """Initializes the unified process_snapshot table for Layer 2 telemetry.""" + with sqlite3.connect(DB_PATH,timeout=10.0) as conn: + conn.execute(""" + CREATE TABLE IF NOT EXISTS process_snapshot ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + timestamp REAL NOT NULL, + top_cpu_processes TEXT NOT NULL, + top_ram_processes TEXT NOT NULL + ) + """) + conn.execute(""" + CREATE INDEX IF NOT EXISTS idx_snapshot_ts + ON process_snapshot (timestamp) + """) + conn.commit() + + +def insert_process_snapshot(top_cpu, top_ram): + """Writes one unified row per 5-second poll — timestamp + two compact JSON arrays.""" + row = ( + time.time(), + json.dumps(top_cpu, separators=(',', ':')), + json.dumps(top_ram, separators=(',', ':')) + ) + with sqlite3.connect(DB_PATH,timeout=10.0) as conn: + conn.execute( + "INSERT INTO process_snapshot (timestamp, top_cpu_processes, top_ram_processes) VALUES (?, ?, ?)", + row + ) + conn.commit() + +# layer 2 db code ends here + +# layer 1 db code starts here + + +db_path = DB_PATH + +# Creating table for all the metrics collected from the system + +def create_connection(db_path): + conn = sqlite3.connect(db_path,timeout=10.0) + conn.execute("PRAGMA journal_mode=WAL") + cursor = conn.cursor() + cursor.execute('''CREATE TABLE IF NOT EXISTS layer1_sys ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + timestamp TEXT NOT NULL, + + --CPU Metrics + cpu_usage_percent REAL, + cpu_freq REAL, + cpu_user_time REAL, + cpu_system_time REAL, + cpu_idle_time REAL, + cpu_iowait_time REAL, + cpu_busy_time REAL, + cpu_ctx_switches REAL, + + --Memory Metrics + memory_percent REAL, + memory_used INTEGER, + memory_available INTEGER, + memory_cached INTEGER, + memory_buffers INTEGER, + swap_percent REAL, + swap_sin INTEGER, + swap_sout INTEGER, + + --Disk Metrics + disk_usage_percent REAL, + disk_read_mb_s REAL, + disk_write_mb_s REAL, + disk_read_time INTEGER, + disk_write_time INTEGER, + + --Network Metrics + net_rate_mb_s REAL, + net_bytes_sent INTEGER, + net_bytes_recv INTEGER, + net_packets_sent INTEGER, + net_packets_recv INTEGER, + net_errs INTEGER, + net_drops INTEGER, + + --System Metrics + load_avg_1 REAL, + load_avg_5 REAL, + load_avg_15 REAL, + total_processes INTEGER, + running_processes INTEGER, + sleeping_processes INTEGER, + zombie_processes INTEGER, + + --Hardware Metrics + avg_temp REAL, + max_temp REAL, + battery_percent REAL, + process_data TEXT, + num_threads INTEGER + ) + ''') + + # Ensure num_threads column exists for older database instances + cursor.execute("PRAGMA table_info(layer1_sys)") + columns = [row[1] for row in cursor.fetchall()] + if 'num_threads' not in columns: + cursor.execute("ALTER TABLE layer1_sys ADD COLUMN num_threads INTEGER") + + conn.commit() + return conn + +# Function to write the collected metrics into the database + +def write_layer1(conn, timestamp, cpu_usage_percent, cpu_freq, cpu_user_time, cpu_system_time, cpu_idle_time, cpu_iowait_time, cpu_busy_time, cpu_ctx_switches, memory_percent, memory_used, memory_available, memory_cached, memory_buffers, swap_percent, swap_sin, swap_sout, disk_usage_percent, disk_read_mb_s, disk_write_mb_s, disk_read_time, disk_write_time, load_avg_1, load_avg_5, load_avg_15, total_processes, running_processes, sleeping_processes, zombie_processes, avg_temp, max_temp, battery_percent, net_rate_mb_s, net_bytes_sent, net_bytes_recv, net_packets_sent, net_packets_recv, net_errs, net_drops, process_data,num_threads): + cursor = conn.cursor() + cursor.execute(''' + INSERT INTO layer1_sys (timestamp, cpu_usage_percent, cpu_freq, cpu_user_time, cpu_system_time, cpu_idle_time, cpu_iowait_time, cpu_busy_time, cpu_ctx_switches, memory_percent, memory_used, memory_available, memory_cached, memory_buffers, swap_percent, swap_sin, swap_sout, disk_usage_percent, disk_read_mb_s, disk_write_mb_s, disk_read_time, disk_write_time, net_rate_mb_s, net_bytes_sent, net_bytes_recv, net_packets_sent, net_packets_recv, net_errs, net_drops, load_avg_1, load_avg_5, load_avg_15, total_processes, running_processes, sleeping_processes, zombie_processes, avg_temp, max_temp, battery_percent, process_data, num_threads) + VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?,?, ?, ?, ?, ?, ?, ?, ?) + ''', (timestamp, + cpu_usage_percent, + cpu_freq, cpu_user_time, + cpu_system_time, + cpu_idle_time, + cpu_iowait_time, + cpu_busy_time, + cpu_ctx_switches, + memory_percent, + memory_used, + memory_available, + memory_cached, + memory_buffers, + swap_percent, + swap_sin, + swap_sout, + disk_usage_percent, + disk_read_mb_s, + disk_write_mb_s, + disk_read_time, + disk_write_time, + net_rate_mb_s, + net_bytes_sent, + net_bytes_recv, + net_packets_sent, + net_packets_recv, + net_errs, + net_drops, + load_avg_1, + load_avg_5, + load_avg_15, + total_processes, + running_processes, + sleeping_processes, + zombie_processes, + avg_temp, + max_temp, + battery_percent, + process_data, + num_threads)) + conn.commit() diff --git a/focusos/cnn_classifier.py b/focusos/cnn_classifier.py deleted file mode 100644 index ce6b6da..0000000 --- a/focusos/cnn_classifier.py +++ /dev/null @@ -1 +0,0 @@ -"""CNN classifier for FocusOS heatmaps.""" diff --git a/focusos/collector.py b/focusos/collector.py new file mode 100644 index 0000000..c64af60 --- /dev/null +++ b/focusos/collector.py @@ -0,0 +1,115 @@ +import sys +import os +sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) + +import collectors.layer1_system as layer1_system +import collectors.layer2_process as layer2_process +from utils import helpers +import db +import sqlite3 +import subprocess +import json + +DB_PATH = "cognios_telemetry.db" +conn = db.create_connection(DB_PATH) + +#collects relevant telemetry data from layer 1 table and returns it as a dictionary +def collect_snapshot(conn): + conn.row_factory = sqlite3.Row + cursor = conn.cursor() + + cursor.execute("SELECT timestamp, cpu_usage_percent, memory_percent, net_rate_mb_s, total_processes, process_data, num_threads FROM layer1_sys ORDER BY timestamp DESC LIMIT 1") + row = cursor.fetchone() + result_dict = {} + if row is not None: + result_dict = dict(row) + + # 1. Safely decode the JSON string from SQLite into a Python list + if result_dict.get('process_data'): + try: + processes = json.loads(result_dict['process_data']) + + #Sorting the list and retaining top 5 entries + result_dict['process_data'] = sorted(processes, key=lambda x: x['cpu'], reverse=True)[:5] + except (json.JSONDecodeError, TypeError): + result_dict['process_data'] = [] + else: + result_dict['process_data'] = [] + + if result_dict.get('num_threads'): + try: + result_dict['num_threads'] = json.loads(result_dict['num_threads']) + except (json.JSONDecodeError, TypeError): + result_dict['num_threads'] = [] + else: + result_dict['num_threads'] = [] + + return result_dict + + +#returns the active window name in lowercase, example: 'firefox' +def get_foreground_app(): + try: + window_id = subprocess.check_output(["xdotool", "getactivewindow"]).decode().strip() + temp_window = subprocess.check_output(["xdotool", "getwindowname", window_id]) + window = temp_window.decode("utf-8").strip().lower() + if " - " in window: + window = window.split(" - ")[-1] + return window + except subprocess.CalledProcessError: + window = "unknown" + return window + + +#returns 2 list[dict], one for top cpu and one for top memory +def get_top_processes(n = 5): + top_cpu, top_mem, _ = layer2_process.collect_process_telemetry() + + top_cpu_filtered = [] + top_mem_filtered = [] + + cpu_keys = ['pid', 'name', 'cpu_percent', 'thread_count', 'status'] + mem_keys = ['pid', 'name', 'memory_percent', 'thread_count', 'status'] + + top_cpu_filtered = [{key: d[key] for key in cpu_keys if key in d} for d in top_cpu] + top_mem_filtered = [{key: d[key] for key in mem_keys if key in d} for d in top_mem] + + return top_cpu_filtered, top_mem_filtered + + +#layer 1 must also be run as a daemon to get all outputs +if __name__ == "__main__": + + import asyncio + async def collector_test(): + print("Testing collector as sandbox\nPress Ctrl + C to finish test run\n") + count = 0 + try: + while True: + start_time = asyncio.get_event_loop().time() + count+=1 + + snapshot = collect_snapshot(conn) + print(snapshot) + foreground_app = get_foreground_app() + print(foreground_app) + + """if count%5 == 0: + top_cpu, top_mem = get_top_processes(n = 5) + print(top_cpu) + print(top_mem)""" + + print("_"*30) + execution_time = asyncio.get_event_loop().time() - start_time + sleep_time = max(0, 1.0 - execution_time) + await asyncio.sleep(sleep_time) + except asyncio.CancelledError: + print("Task cancelled") + finally: + conn.close() + print("Finished execution") + + try: + asyncio.run(collector_test()) + except KeyboardInterrupt: + print("Finished collector test run") \ No newline at end of file diff --git a/focusos/feature_engineer.py b/focusos/feature_engineer.py new file mode 100644 index 0000000..e84c7b8 --- /dev/null +++ b/focusos/feature_engineer.py @@ -0,0 +1,98 @@ +import pandas as pd +import sys +import os +sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) +from config import DB_PATH + +# Import the stateless database window function +from focusos.sliding_window import get_window_from_db + +# sliding window fetching all the 120 rows via the sliding_window module +def extract_features(df: pd.DataFrame): + try: + if df is None or df.empty: + return None + + # reversing the index so that 0 is oldest and -1 is newest + #df = df.iloc[::-1].reset_index(drop=True) + #already reversed in the sliding window + # cpu + cpu_mean = df["cpu_usage_percent"].mean() + cpu_max = df["cpu_usage_percent"].max() + cpu_variance = df["cpu_usage_percent"].var() + + # mem feature engineering + ram_mean = df["memory_percent"].mean() + ram_growth_rate = (df["memory_percent"].iloc[-1] - df["memory_percent"].iloc[0]) if len(df) > 0 else 0 + + # network + net_combined = df["net_bytes_sent"] + df["net_bytes_recv"] + net_mean = net_combined.mean() + if net_mean > 0: + # captures trend: positive means rising traffic, negative means falling traffic + net_mean = float((net_combined.iloc[-1] - net_combined.iloc[0]) / net_mean) + else: + net_mean = 0.0 + #here i am calculating the coeffiecient of var = std/mean + # 10x spike on ssd == 10x spike on hdd + disk_combined = df["disk_write_mb_s"] + df["disk_read_mb_s"] + disk_mean_raw = disk_combined.mean() + if disk_mean_raw > 0: + disk_io_mean = float(disk_combined.std() / disk_mean_raw) + else: + disk_io_mean = 0.0 + # processes + process_count_mean = int(df["total_processes"].mean()) + #calculating the normalised value: threads per core + cpu_cores = os.cpu_count() or 4 + if "num_threads" in df.columns: + import json + def safe_sum_threads(val): + if isinstance(val, str) and val.strip(): + try: + lst = json.loads(val) + if isinstance(lst, list): + return sum(lst) + except Exception: + pass + return 0 + thread_count_mean = df["num_threads"].apply(safe_sum_threads).mean() / cpu_cores + else: + thread_count_mean = (df["total_processes"].mean() * 2.5) / cpu_cores + + + # We use fillna('') so .str doesn't crash on missing process names + process_col = df["process_data"].fillna("").str.lower() + + vscode_active = int(process_col.str.contains("code|code-insiders|vsls-agent|antigravity|sublime", regex=True).any()) + browser_active = int(process_col.str.contains("chrome|firefox|brave|msedge", regex=True).any()) + compiler_active = int(process_col.str.contains("gcc|g\\+\\+|clang|rustc|javac|make", regex=True).any()) + + features = { + "cpu_mean": cpu_mean, + "cpu_max": cpu_max, + "cpu_variance": cpu_variance, + "ram_mean": ram_mean, + "ram_growth_rate": ram_growth_rate, + "network_mean": net_mean, + "disk_io_mean": disk_io_mean, + "process_count_mean": process_count_mean, + "thread_count_mean": thread_count_mean, + "vscode_active": vscode_active, + "browser_active": browser_active, + "compiler_active": compiler_active, + } + features_df = pd.DataFrame([features]) + return features_df + + except Exception as e: + print(f"Window Extraction Error: {e}") + return None + +if __name__ == "__main__": + print("Fetching window from database...") + df = get_window_from_db() + if df is not None: + extract_features(df) + else: + print("Not enough data or database empty.") \ No newline at end of file diff --git a/focusos/heatmap.py b/focusos/heatmap.py deleted file mode 100644 index 452374b..0000000 --- a/focusos/heatmap.py +++ /dev/null @@ -1 +0,0 @@ -"""Telemetry to 2D heatmap conversion.""" diff --git a/focusos/models/classifier.py b/focusos/models/classifier.py new file mode 100644 index 0000000..baa5791 --- /dev/null +++ b/focusos/models/classifier.py @@ -0,0 +1,125 @@ +import os +import joblib +import pandas as pd +import numpy as np +from sklearn.model_selection import train_test_split +from sklearn.preprocessing import LabelEncoder +from sklearn.metrics import classification_report, confusion_matrix +import xgboost as xgb + +CURRENT_FILE = os.path.abspath(__file__) +MODELS_DIR = os.path.join(os.path.dirname(os.path.dirname(CURRENT_FILE)), "models_saved") +DATASET_PATH = os.path.join(MODELS_DIR, "pseudo_labeled_dataset.csv") + +FEATURE_COLUMNS = [ + "cpu_mean", + "cpu_max", + "cpu_variance", + "ram_mean", + "ram_growth_rate", + "network_mean", + "disk_io_mean", + "process_count_mean", + "thread_count_mean", + "vscode_active", + "browser_active", + "compiler_active", +] +class WorkloadPredictor: + def __init__(self, models_dir=MODELS_DIR): + self.models_dir = models_dir + self.scaler = joblib.load(os.path.join(models_dir, "scaler.pkl")) + self.label_encoder = joblib.load(os.path.join(models_dir, "label_encoder.pkl")) + self.kmeans = joblib.load(os.path.join(models_dir, "kmeans_model.pkl")) + self.xgb = xgb.XGBClassifier() + self.xgb.load_model(os.path.join(models_dir, "xgboost_model.json")) + + def predict(self, features_df: pd.DataFrame) -> dict: + if features_df is None or features_df.empty: + return None + + # scaling the incoming feature_vector + scaled = self.scaler.transform(features_df) + + # predicting the workload using xgboost + probs = self.xgb.predict_proba(scaled)[0] + pred_idx = int(np.argmax(probs)) + workload_name = str(self.label_encoder.inverse_transform([pred_idx])[0]) + + # calculating the confidence + confidence = float(probs[pred_idx] * 100) + + # predicting the cluster id + cluster_id = int(self.kmeans.predict(scaled)[0]) + + return { + "workload": workload_name, + "confidence": round(confidence, 2), + "cluster_id": cluster_id, + } + + + +def train_classifier(): + print("\n" + "═"*60) + print(" FocusOS — XGBoost Classifier Training") + print("═"*60) + + if not os.path.exists(DATASET_PATH): + print(f"[classifier] Error: Pseudo-labeled dataset not found at {DATASET_PATH}.") + print("[classifier] Please run cluster_trainer(ref).py first to generate it.") + return + + df = pd.read_csv(DATASET_PATH) + print(f"[classifier] Loaded training dataset with {len(df)} samples.") + + # split features and labels + X = df[FEATURE_COLUMNS].values + y_raw = df["workload_label"].values + + # encode categorical labels to integers + le = LabelEncoder() + y = le.fit_transform(y_raw) + + # save the label encoder + le_path = os.path.join(MODELS_DIR, "label_encoder.pkl") + joblib.dump(le, le_path) + print(f"[classifier] Saved LabelEncoder to {le_path}") + + # split into train and test sets (80% train, 20% test) + X_train, X_test, y_train, y_test = train_test_split( + X, y, test_size=0.2, random_state=42, stratify=y + ) + + print(f"[classifier] Split data into {len(X_train)} training and {len(X_test)} testing samples.") + + # train XGBoost classifier + print("[classifier] Fitting XGBClassifier...") + model = xgb.XGBClassifier( + n_estimators=100, + max_depth=5, + learning_rate=0.1, + random_state=42, + eval_metric='mlogloss' + ) + model.fit(X_train, y_train) + + # evaluate classifier + y_pred = model.predict(X_test) + accuracy = np.mean(y_pred == y_test) + print(f"[classifier] Test Set Accuracy: {accuracy*100:.2f}%") + + print("\n[classifier] Classification Report:") + target_names = [str(c) for c in le.classes_] + print(classification_report(y_test, y_pred, target_names=target_names)) + + print("\n[classifier] Confusion Matrix:") + print(confusion_matrix(y_test, y_pred)) + + # saving trained model + model_path = os.path.join(MODELS_DIR, "xgboost_model.json") + model.save_model(model_path) + print(f"\n[classifier] Trained XGBoost model saved to {model_path}") + +if __name__ == "__main__": + train_classifier() diff --git a/focusos/models/cluster_trainer.py b/focusos/models/cluster_trainer.py new file mode 100644 index 0000000..8de90d9 --- /dev/null +++ b/focusos/models/cluster_trainer.py @@ -0,0 +1,616 @@ +import os +import sys +import time +import sqlite3 +import numpy as np +import pandas as pd +import joblib +from sklearn.cluster import KMeans +from sklearn.preprocessing import StandardScaler +import matplotlib.pyplot as plt + +#path setup so imports work from anywhere + +CURRENT_FILE = os.path.abspath(__file__) # Path to the current file + +MODELS_DIR = os.path.dirname(CURRENT_FILE) # Directory containing cluster_trainer.py + +FOCUSOS_DIR = os.path.dirname(MODELS_DIR) # FocusOS package directory + + +PROJECT_ROOT = os.path.dirname(FOCUSOS_DIR) # Project root directory (contains config.py) + +if PROJECT_ROOT not in sys.path: + sys.path.append(PROJECT_ROOT) # Add project root to Python module search path + +from config import DB_PATH +from focusos.sliding_window import get_window_from_db +from focusos.feature_engineer import extract_features + +N_CLUSTERS = 6 # Matches: Coding, Gaming, Compiling, Video_Call, Idle,browsing + +MIN_SAMPLES_REQUIRED = 500 # Minimum number of samples required to train the model + +MODELS_DIR = os.path.join( + os.path.dirname(os.path.dirname(os.path.abspath(__file__))), + "models_saved" +) +FEATURE_COLUMNS = [ + "cpu_mean", + "cpu_max", + "cpu_variance", + "ram_mean", + "ram_growth_rate", + "network_mean", + "disk_io_mean", + "process_count_mean", + "thread_count_mean", + "vscode_active", + "browser_active", + "compiler_active", +] + +def collect_feature_vectors( + db_path: str = DB_PATH, + n_samples: int = MIN_SAMPLES_REQUIRED, + sample_interval_sec: int = 30, +) -> pd.DataFrame: + """ + Collects feature vectors by repeatedly calling the sliding window + and feature engineering pipeline over time. + + PURPOSE: + KMeans needs many data points (feature vectors) to find meaningful + clusters. One call to get_window_from_db() + extract_features() + gives us ONE vector (one 2-minute summary). We need hundreds. + + RETURNS: + DataFrame of shape (n_collected, 12) — one row per feature vector. + May return fewer than n_samples if the DB doesn't have enough history. + """ + print(f"\n[cluster_trainer] Collecting feature vectors from DB: {db_path}") + print(f"[cluster_trainer] Target: {n_samples} samples") + + feature_rows = [] + + try: + with sqlite3.connect(db_path) as conn: + # ── count total rows available in the DB ────────────────────── + total_rows_query = "SELECT COUNT(*) FROM layer1_sys" + total_rows = conn.execute(total_rows_query).fetchone()[0] + print(f"[cluster_trainer] Total rows in layer1_sys: {total_rows}") + + + WINDOW_SIZE = 120 # matches SLIDING_WIND_N in config + + if total_rows < WINDOW_SIZE: + print(f"[cluster_trainer] ERROR: Need at least {WINDOW_SIZE} rows, " + f"only have {total_rows}. Run telemetry collector longer.") + return pd.DataFrame() + + # ── fetch ALL timestamps in order ───────────────────────────── + # We'll slide a window of 120 rows, stepping by STEP rows each time. + # STEP = 30 means each sample is offset by 30 seconds from the last. + # This gives temporal diversity — we sample from different moments. + timestamps_df = pd.read_sql_query( + "SELECT rowid, timestamp FROM layer1_sys ORDER BY timestamp ASC", + conn + ) + all_rowids = timestamps_df["id"].tolist() + + STEP = 30 # rows between window starts (= 30 seconds diversity) + + # ── slide through history, extract one feature vector per window ── + windows_collected = 0 + start_idx = 0 + + while start_idx + WINDOW_SIZE <= len(all_rowids) and windows_collected < n_samples: + + window_rowids = all_rowids[start_idx : start_idx + WINDOW_SIZE] + rowid_list = ",".join(str(r) for r in window_rowids) + + # Fetch exactly those rows from the DB + window_df = pd.read_sql_query( + f"SELECT * FROM layer1_sys WHERE rowid IN ({rowid_list}) " + f"ORDER BY timestamp ASC", + conn + ) + + if len(window_df) < WINDOW_SIZE: #to check the required size of window + + start_idx += STEP + continue + + + feature_vec = extract_features(window_df) + + if feature_vec is not None and not feature_vec.empty: + + feature_rows.append(feature_vec) + windows_collected += 1 + + if windows_collected % 50 == 0: + print(f"[cluster_trainer] Collected {windows_collected}/{n_samples} vectors...") + + start_idx += STEP # advance window by STEP rows + + except sqlite3.OperationalError as e: + print(f"[cluster_trainer] DB error: {e}") + return pd.DataFrame() + + if not feature_rows: + print("[cluster_trainer] No feature vectors collected. Check DB.") + return pd.DataFrame() + + # Stack all 1-row DataFrames into one big DataFrame + all_features = pd.concat(feature_rows, ignore_index=True) + + + available_cols = [c for c in FEATURE_COLUMNS if c in all_features.columns] + all_features = all_features[available_cols] + + print(f"[cluster_trainer] Collection complete: {len(all_features)} feature vectors, " + f"{len(available_cols)} features each") + return all_features + + + +# SCALING DOWN OF FEATURE +def scale_features(df: pd.DataFrame): + """ + Applies StandardScaler to the feature matrix. + + StandardScaler: transforms each feature to mean=0, std=1. + This is CRITICAL — without it, high-range features (thread_count) + dominate Euclidean distance and clustering becomes meaningless. + + WHY WE SAVE THE SCALER: + Because every time the cluster scaling will be different. + + ARGS: + df: DataFrame of shape (n_samples, 12) — raw feature values + + RETURNS: + X_scaled: numpy array (n_samples, 12) — scaled features + scaler: fitted StandardScaler object (save this!) + """ + print("\n[cluster_trainer] Scaling features with StandardScaler...") + + + n_before = len(df) + df_clean = df.dropna() #this will help to handle the NaN values for k means training. + n_after = len(df_clean) + if n_before != n_after: + print(f"[cluster_trainer] Dropped {n_before - n_after} rows with NaN values") + + if df_clean.empty: + raise ValueError("No valid feature vectors after dropping NaN rows.") + + scaler = StandardScaler() + X_scaled = scaler.fit_transform(df_clean.values.astype(float)) + + + print(f"[cluster_trainer] Scaled {X_scaled.shape[0]} samples × {X_scaled.shape[1]} features") + print(f"[cluster_trainer] Feature means (should all be ~0): " + f"{X_scaled.mean(axis=0).round(3)}") + print(f"[cluster_trainer] Feature stds (should all be ~1): " + f"{X_scaled.std(axis=0).round(3)}") + + return X_scaled, scaler, df_clean # return df_clean too (NaN rows dropped) + + +#ELBOW METHOD +def find_optimal_k(X_scaled: np.ndarray, k_range=range(2, 11)) -> list: + """ + The "elbow" — where the curve bends — is the optimal k. + """ + print("\n[cluster_trainer] Running elbow method...") + results = [] + + for k in k_range: + km = KMeans( + n_clusters=k, + init='k-means++', + n_init=10, # fewer runs here — just for diagnosis + max_iter=200, + random_state=42 + ) + km.fit(X_scaled) + results.append((k, km.inertia_)) + print(f" k={k:2d} inertia={km.inertia_:10.1f}") + + # Find the elbow programmatically (largest drop in inertia reduction rate) + inertias = [r[1] for r in results] + drops = [inertias[i] - inertias[i+1] for i in range(len(inertias)-1)] + drop_reduction = [drops[i] - drops[i+1] for i in range(len(drops)-1)] + best_k_idx = drop_reduction.index(max(drop_reduction)) + 2 # +2 offset + best_k = list(k_range)[best_k_idx] + print(f"\n[cluster_trainer] Elbow suggests k={best_k} (verify visually)") + return results,best_k + + +# Collect features +df_features = collect_feature_vectors(db_path=DB_PATH) + +# Scale features +X_scaled, scaler, df_clean = scale_features(df_features) + +results, best_k = find_optimal_k(X_scaled ) +ks = [r[0] for r in results] +inertias = [r[1] for r in results] + +plt.figure(figsize=(8,5)) + +plt.plot(ks, inertias, marker='o', linewidth=2) + +plt.scatter(best_k, + inertias[ks.index(best_k)], + s=120, + color='red', + label=f"Suggested k={best_k}") + +plt.xlabel("Number of Clusters (k)") +plt.ylabel("Inertia") +plt.title("Elbow Method") +plt.xticks(ks) +plt.grid(True) +plt.legend() + +plt.show() + +#train k means +def train_kmeans(X_scaled: np.ndarray, n_clusters: int = N_CLUSTERS) -> KMeans: + """ + Fits KMeans clustering on the scaled feature matrix. + + WHAT IT PRODUCES: + model.labels_ → array of shape (n_samples,) + value 0-4: which cluster each window belongs to + model.cluster_centers_ → array of shape (5, 12) + centroid of each cluster in scaled feature space + model.inertia_ → float: total within-cluster sum of squares + lower = tighter, more distinct clusters + model.n_iter_ → how many iterations it actually took + + ARGS: + X_scaled: scaled feature matrix (n_samples, n_features) + n_clusters: number of clusters (default 5) + + RETURNS: + Fitted KMeans model. + """ + print(f"\n[cluster_trainer] Training KMeans with n_clusters={n_clusters}...") + print(f"[cluster_trainer] Input shape: {X_scaled.shape}") + + model = KMeans( + n_clusters=n_clusters, + init='k-means++', + n_init=20, + max_iter=200, + tol=1e-4, + random_state=42, + algorithm='lloyd', + verbose=0, + ) + + model.fit(X_scaled) + + print(f"[cluster_trainer] Converged in {model.n_iter_} iterations") + print(f"[cluster_trainer] Final inertia: {model.inertia_:.2f}") + + unique, counts = np.unique(model.labels_, return_counts=True) + print(f"\n[cluster_trainer] Cluster size distribution:") #this will give the bar graph representation for checking + for cid, count in zip(unique, counts): + pct = 100 * count / len(model.labels_) + bar = "█" * int(pct / 2) + print(f" Cluster {cid}: {count:4d} samples ({pct:5.1f}%) {bar}") + + return model + +#cluster_inspect + +def inspect_clusters( + model: KMeans, + df_clean: pd.DataFrame, + scaler: StandardScaler +) -> None: + """ + Prints a detailed summary of each cluster's characteristics. + + After reading this output, update CLUSTER_TO_WORKLOAD + in label_mapper.py with your findings. + + ARGS: + model: fitted KMeans model + df_clean: original (unscaled) feature DataFrame (NaN rows dropped) + scaler: the fitted StandardScaler (to inverse-transform centroids) + """ + print("\n" + "="*70) + print(" CLUSTER INSPECTION — Read this to assign workload labels") + print("="*70) + + df_labeled = df_clean.copy() + df_labeled["cluster_id"] = model.labels_ + + centroids_original = scaler.inverse_transform(model.cluster_centers_) #show the centroid in unscaled + centroids_df = pd.DataFrame( + centroids_original, + columns=df_clean.columns, + index=[f"Cluster_{i}" for i in range(model.n_clusters)] + ) + + for cid in range(model.n_clusters): + subset = df_labeled[df_labeled["cluster_id"] == cid] + n = len(subset) + pct = 100 * n / len(df_labeled) + + print(f"\n{'─'*60}") + print(f" CLUSTER {cid} ({n} samples, {pct:.1f}%)") + print(f"{'─'*60}") + print(f" {'Feature':<25} {'Mean':>10} {'Hint'}") + print(f" {'─'*25} {'─'*10} {'─'*20}") + + # Print each feature with an interpretation hint + hints = { + "cpu_mean": "high → CPU-heavy workload", + "cpu_max": "high → peak CPU spikes", + "cpu_variance": "high → bursty CPU usage", + "ram_mean": "high → memory-heavy app", + "ram_growth_rate": "positive → memory growing (possible leak or load)", + "network_mean": "high → network activity (video call, browsing)", + "disk_io_mean": "high → disk-heavy (compiling, large file ops)", + "process_count_mean":"high → many processes running", + "thread_count_mean": "high → heavily threaded app", + "vscode_active": "1 = VSCode detected → likely Coding", + "browser_active": "1 = browser open → Browsing or Video Call", + "compiler_active": "1 = compiler (gcc/make) → Compiling", + } + + for col in df_clean.columns: + val = subset[col].mean() + hint = hints.get(col, "") + print(f" {col:<25} {val:>10.3f} {hint}") + + print("\n" + "="*70) + print(" ACTION REQUIRED:") + print(" Look at the output above. For each cluster, decide:") + print(" - Which workload type does it most resemble?") + print(" - Update CLUSTER_TO_WORKLOAD in label_mapper.py") + print("\n Example mapping (YOURS WILL DIFFER):") + print(" CLUSTER_TO_WORKLOAD = {") + print(" 0: 'Coding', # vscode_active≈1, moderate cpu") + print(" 1: 'Compiling', # compiler_active≈1, high cpu") + print(" 2: 'Idle', # low everything") + print(" 3: 'Video_Call', # high network, browser_active≈1") + print(" 4: 'Gaming', # high cpu, no vscode/compiler") + print(" }") + print("="*70) + + #pseudo code generation + +def generate_pseudo_labels( + model: KMeans, + df_clean: pd.DataFrame, + cluster_to_workload: dict +) -> pd.DataFrame: + """ + Attaches cluster IDs and workload names to the feature DataFrame. + + PURPOSE: + This is the BRIDGE between unsupervised (KMeans) and supervised (XGBoost). + + KMeans discovered groupings in unlabeled data. + You gave those groups human-readable names (in cluster_to_workload). + Now we attach those names to every feature vector. + + The resulting DataFrame is the TRAINING DATA for XGBoost: + - features (cpu_mean, vscode_active, etc.) are the X + - workload_label (Coding, Gaming, etc.) is the y + + ARGS: + model: fitted KMeans model (has .labels_ attribute) + df_clean: feature DataFrame used to train KMeans + cluster_to_workload: dict mapping {0: 'Coding', 1: 'Gaming', ...} + (comes from label_mapper.py — YOU fill this in) + + RETURNS: + DataFrame with all original features + 'cluster_id' + 'workload_label' + """ + df_out = df_clean.copy() + df_out["cluster_id"] = model.labels_ + df_out["workload_label"] = [cluster_to_workload.get(c, f"Unknown_{c}") + for c in model.labels_] + + print(f"\n[cluster_trainer] Pseudo-label distribution:") + print(df_out["workload_label"].value_counts().to_string()) + + return df_out + + + + # SAVE MODELS + +def save_models(model: KMeans, scaler: StandardScaler, save_dir: str = MODELS_DIR) -> None: + """ + Saves the trained KMeans model and StandardScaler to disk. + + PURPOSE: + Both the model and scaler must be saved together. + At inference time (every 30 sec), we: + 1. Load the SAME scaler → apply to new feature vector + 2. Load the SAME KMeans → predict cluster (used for pseudo-label + generation if retraining; NOT used in runtime inference path) + + If you save the model but not the scaler, and retrain the scaler + later on different data, the cluster assignments will be meaningless. + + FILES SAVED: + models_saved/kmeans_model.pkl → the KMeans model + models_saved/feature_scaler.pkl → the StandardScaler + models_saved/feature_columns.pkl → list of feature column names in order + """ + os.makedirs(save_dir, exist_ok=True) + + model_path = os.path.join(save_dir, "kmeans_model.pkl") + scaler_path = os.path.join(save_dir, "feature_scaler.pkl") + cols_path = os.path.join(save_dir, "feature_columns.pkl") + + joblib.dump(model, model_path) + joblib.dump(scaler, scaler_path) + joblib.dump(FEATURE_COLUMNS, cols_path) + + print(f"\n[cluster_trainer] Models saved:") + print(f" KMeans → {model_path}") + print(f" Scaler → {scaler_path}") + print(f" Columns → {cols_path}") + +# utils load (used by optimizer and classifier) + +def load_scaler(save_dir: str = MODELS_DIR) -> StandardScaler: + """ + Loads the saved StandardScaler. + + Returns: fitted StandardScaler, or None if not found. + """ + scaler_path = os.path.join(save_dir, "feature_scaler.pkl") + if not os.path.exists(scaler_path): + print(f"[cluster_trainer] Scaler not found at {scaler_path}. Run training first.") + return None + return joblib.load(scaler_path) + + +def load_kmeans(save_dir: str = MODELS_DIR) -> KMeans: + """ + Loads the saved KMeans model. + + Returns: fitted KMeans, or None if not found. + """ + model_path = os.path.join(save_dir, "kmeans_model.pkl") + if not os.path.exists(model_path): + print(f"[cluster_trainer] KMeans not found at {model_path}. Run training first.") + return None + return joblib.load(model_path) + + +def predict_cluster( + feature_vector: np.ndarray, + model: KMeans = None, + scaler: StandardScaler = None +) -> int: + """ + Predicts the cluster ID for a single new feature vector. + + ARGS: + feature_vector: 1D array of shape (12,) — one window's features + model: KMeans model (loaded from disk if None) + scaler: StandardScaler (loaded from disk if None) + + RETURNS: + cluster_id (int 0–4) + """ + if model is None: + model = load_kmeans() + if scaler is None: + scaler = load_scaler() + if model is None or scaler is None: + return -1 + + vec = np.array(feature_vector).reshape(1, -1) # shape (1, 12) + vec_scaled = scaler.transform(vec) # apply same scaler as training + cluster_id = model.predict(vec_scaled)[0] + return int(cluster_id) + +# main pipeline + +def run_training_pipeline( + db_path: str = DB_PATH, + n_samples: int = MIN_SAMPLES_REQUIRED, + run_elbow: bool = False, + cluster_to_workload: dict = None, +) -> pd.DataFrame: + """ + Full KMeans training pipeline from raw DB to pseudo-labeled dataset. + + ARGS: + db_path: path to SQLite DB with telemetry + n_samples: how many feature windows to collect + run_elbow: if True, runs elbow method to suggest optimal k + cluster_to_workload: optional mapping {0: 'Coding', ...} + if None, trains and inspects only (you fill mapping later) + + RETURNS: + pseudo_labeled_df if cluster_to_workload provided, else None + """ + print("\n" + "═"*60) + print(" FocusOS — KMeans Cluster Training Pipeline") + print("═"*60) + + # ── 1. Collect feature vectors + df_features = collect_feature_vectors(db_path=db_path, n_samples=n_samples) + + if df_features.empty: + print("[cluster_trainer] ABORT: Not enough data. Collect more telemetry.") + return None + + if len(df_features) < 50: + print(f"[cluster_trainer] WARNING: Only {len(df_features)} samples. " + f"Results may be poor. Recommended minimum: 200.") + + # ── 2. Scale features + X_scaled, scaler, df_clean = scale_features(df_features) + + # ── 3. Elbow method + if run_elbow: + find_optimal_k(X_scaled) + + # ── 4. Train KMeans + model = train_kmeans(X_scaled, n_clusters=N_CLUSTERS) + + # ── 5. Inspect clusters + inspect_clusters(model, df_clean, scaler) + + # ── 6. Save models + save_models(model, scaler) + + # ── 7. Generate pseudo-labels (only if mapping provided) + if cluster_to_workload is not None: + df_labeled = generate_pseudo_labels(model, df_clean, cluster_to_workload) + + # Save labeled dataset for XGBoost training + out_path = os.path.join(MODELS_DIR, "pseudo_labeled_dataset.csv") + df_labeled.to_csv(out_path, index=False) + print(f"\n[cluster_trainer] Pseudo-labeled dataset saved → {out_path}") + print(f"[cluster_trainer] Feed this into classifier.py to train XGBoost") + return df_labeled + + print("\n[cluster_trainer] Training complete.") + print("[cluster_trainer] Next step: read the cluster inspection above,") + print("[cluster_trainer] update CLUSTER_TO_WORKLOAD in label_mapper.py,") + print("[cluster_trainer] then re-run with cluster_to_workload argument.") + return None + + +if __name__ == "__main__": + """ + TYPICAL WORKFLOW: + + FIRST RUN (no mapping yet): + python -m focusos.models.cluster_trainer + → Collects data, trains, prints cluster inspection + → You read the output and fill in CLUSTER_TO_WORKLOAD below + + SECOND RUN (with your mapping): + → Uncomment and fill MY_MAPPING below based on first run output + → Re-run to generate pseudo_labeled_dataset.csv + → Then run classifier.py to train XGBoost on it + """ + + # ── Fill this in after first run, based on inspect_clusters() output ──,Numbers on the LEFT (0–4) come from KMeans,Strings on the RIGHT are what you name each cluster after reading output.,Your mapping WILL differ from this example — read inspect_clusters() output! + MY_MAPPING = None # ← set to None on first run; fill in after inspection + + run_training_pipeline( + db_path=DB_PATH, + n_samples=MIN_SAMPLES_REQUIRED, + run_elbow=False, # set True on first run to verify k=5 + cluster_to_workload=MY_MAPPING, + ) \ No newline at end of file diff --git a/focusos/models/label_mapper.py b/focusos/models/label_mapper.py new file mode 100644 index 0000000..ef4be53 --- /dev/null +++ b/focusos/models/label_mapper.py @@ -0,0 +1,159 @@ +# Fill this in after reading inspect_clusters() output from cluster_trainer.py. +# Keys are cluster IDs from KMeans (always 0 to N_CLUSTERS-1 = 0 to 4) +# Values are the workload name that we will decided from reading the cluster summary + +CLUSTER_TO_WORKLOAD = { + 0: "Coding", # ← replace this based on our cluster_trainer output + 1: "Compiling", + 2: "Idle", + 3: "Video_Call", + 4: "Gaming", +} + +#FUNCTION to check the correct mapping here +# Reverse mapping — useful for debugging +# e.g. WORKLOAD_TO_CLUSTER["Coding"] → 0 +WORKLOAD_TO_CLUSTER = {v: k for k, v in CLUSTER_TO_WORKLOAD.items()} + + +VALID_WORKLOADS = list(CLUSTER_TO_WORKLOAD.values())# All valid workload names as a list + + + +# OPTIMIZATION PROFILES + +WORKLOAD_OPTIMIZATION_PROFILE = { + "Coding": { + "process_keywords": ["code", "code-insiders", "vsls-agent", "sublime", "vim", "nvim"], + "deprioritize_others": True, + "nice_value": -5, + "description": "Boosting editor and language server priority", + }, + "Compiling": { + "process_keywords": ["gcc", "g++", "clang", "make", "ninja", "rustc", "javac", "cc1"], + "deprioritize_others": True, + "nice_value": -10, + # Compiling benefits most from CPU priority + "description": "Boosting compiler processes for faster build times", + }, + "Video_Call": { + "process_keywords": ["zoom", "teams", "chrome", "firefox", "brave", "msedge"], + "deprioritize_others": True, + "nice_value": -5, + "description": "Boosting video call app for smooth audio and video", + }, + "Gaming": { + "process_keywords": [], + "deprioritize_others": True, + "nice_value": 0, + "description": "Deprioritizing background processes for smoother gameplay", + }, + "Idle": { + "process_keywords": [], + "deprioritize_others": False, + "nice_value": 0, + # System is idle — no point changing priorities + "description": "System idle — no optimization applied", + }, +} + + +# ───────────────────────────────────────────────────────────────────────────── +# HELPER FUNCTIONS — imported by other modules +# ───────────────────────────────────────────────────────────────────────────── + +def get_workload_name(cluster_id: int) -> str: + """ + Converts a KMeans cluster ID (integer) to a workload name (string). + RETURNS: + Workload name string e.g. "Coding" + Returns "Unknown_" if cluster_id not in mapping + """ + return CLUSTER_TO_WORKLOAD.get(int(cluster_id), f"Unknown_{cluster_id}") + + +def get_cluster_id(workload_name: str) -> int: + """ + Reverse of get_workload_name — converts workload name to cluster ID. + RETURNS: + cluster_id integer, or -1 if not found + """ + return WORKLOAD_TO_CLUSTER.get(workload_name, -1) + + +def get_optimization_profile(workload_name: str) -> dict: + """ + Returns the optimization profile for a given workload name. + RETURNS: + dict with keys: process_keywords, deprioritize_others, + nice_value, description + Returns a safe no-op profile for unknown workloads. + """ + return WORKLOAD_OPTIMIZATION_PROFILE.get(workload_name, { + "process_keywords": [], + "deprioritize_others": False, + "nice_value": 0, + "description": f"Unknown workload '{workload_name}' — no optimization", + }) + + +def validate_mapping() -> bool: + """ + Sanity check that your CLUSTER_TO_WORKLOAD is properly filled in. + RETURNS: + True if everything looks good, False if there are issues. + """ + # Import here to avoid circular import issues at module load time + try: + from focusos.models.cluster_trainer import N_CLUSTERS + except ImportError: + N_CLUSTERS = 5 # fallback + + all_ok = True + + # Check 1: correct cluster IDs present + expected_ids = set(range(N_CLUSTERS)) + actual_ids = set(CLUSTER_TO_WORKLOAD.keys()) + if expected_ids != actual_ids: + print(f"[label_mapper] ERROR: Expected IDs {expected_ids}, got {actual_ids}") + print(f" Missing: {expected_ids - actual_ids}") + print(f" Extra: {actual_ids - expected_ids}") + all_ok = False + + # Check 2: no duplicate workload names + names = list(CLUSTER_TO_WORKLOAD.values()) + if len(names) != len(set(names)): + duplicates = [n for n in names if names.count(n) > 1] + print(f"[label_mapper] ERROR: Duplicate workload names: {duplicates}") + all_ok = False + + # Check 3: all workloads have optimization profiles + for name in names: + if name not in WORKLOAD_OPTIMIZATION_PROFILE: + print(f"[label_mapper] WARNING: '{name}' has no optimization profile. " + f"Optimizer will apply no-op for this workload.") + + # Check 4: no placeholder names + placeholder_check = [n for n in names if n.startswith("Unknown") or n == ""] + if placeholder_check: + print(f"[label_mapper] WARNING: Placeholder names found: {placeholder_check}") + print(f" Did you forget to fill in CLUSTER_TO_WORKLOAD?") + all_ok = False + + if all_ok: + print(f"[label_mapper] Mapping OK: {CLUSTER_TO_WORKLOAD}") + + return all_ok + + +#running the file +if __name__ == "__main__": + print("Validating label_mapper.py...\n") + validate_mapping() + + print("\nTesting get_workload_name():") + for cid in range(5): + name = get_workload_name(cid) + profile = get_optimization_profile(name) + print(f" Cluster {cid} → '{name}' | nice={profile['nice_value']} " + f"| keywords={profile['process_keywords'][:2]}...") diff --git a/focusos/models_saved/kmeans_model.pkl b/focusos/models_saved/kmeans_model.pkl new file mode 100644 index 0000000..ac3a9aa Binary files /dev/null and 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\ No newline at end of file diff --git a/focusos/optimisation.py b/focusos/optimisation.py new file mode 100644 index 0000000..2714063 --- /dev/null +++ b/focusos/optimisation.py @@ -0,0 +1,150 @@ +import psutil +import os +import sqlite3 +import time +import sys +import json +sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) +from config import DB_PATH +from config import COMPILERS # yet not written in the config files- > will update soon +from config import BROWSERS # yet not written in the config files- > will update soon +from config import CALLS # yet not written in the config files- > will update soon +from config import IDES # yet not written in the config files- > will update soon +def apply_optimization(workload: str, confidence: float) -> bool: + if confidence < 0.80: + print(f"Optimisation aborted: Confidence for {workload} is less than 80%") + return False + actions = [] + total_cores = os.cpu_count() or 4 + # isolating backgrounds tasks by allocating them unto the last core + background_cores=[total_cores-1] + foreground_cores=list(range(0,total_cores-1)) if total_cores > 1 else [0] + if workload.lower()=="compiler": + #deprioritising the compilers + count_nice=sum(prioritise_process(compiler,15) for compiler in COMPILERS) + count_affinity=sum(pin_process_to_cores(compiler,background_cores) for compiler in COMPILERS) + count_io = sum(set_io_priority(compiler, 3) for compiler in COMPILERS) + if count_nice > 0: + actions.append(f"Deprioritised {count_nice} compiler(s), pinned {count_affinity} to core(s) {background_cores}, set {count_io} to idle IO") + + + elif workload.lower() == "gaming": + #will reset the affinity of the bg processes to free up gaming cores + count_nice=prioritise_process("steam",-5) + count_nice+=prioritise_process("game-process",-10) + count_affinity=pin_process_to_cores("game-process",list(range(total_cores))) + if count_nice > 0: + actions.append(f"Prioritised {count_nice} game process(es), granted {count_affinity} full core affinity") + + + elif workload.lower() == "coding": + count_nice = sum(prioritise_process(ide, -5) for ide in IDES) + count_affinity = sum(pin_process_to_cores(browser, background_cores) for browser in BROWSERS) + if count_nice > 0: + actions.append(f"Prioritised {count_nice} IDE process(es), pinned {count_affinity} browser(s) to cores {background_cores}") + + + elif workload.lower()=="browsing": + #reset all the process to normal priority and spread across all cores + count_nice=sum(prioritise_process(browser,0) for browser in BROWSERS) + count_affinity = sum(pin_process_to_cores(browser, list(range(total_cores))) for browser in BROWSERS) + if count_nice > 0: + actions.append(f"Browsing: Restored {count_nice} browser(s) to normal nice priority across {count_affinity} core(s)") + + + elif workload.lower()=="video call": + #prioritising the communication + count_nice=sum(prioritise_process(call,-5) for call in CALLS) + actions.append(f"Video calling :Prioritising {count_nice} communication process") + + elif workload.lower() == "idle": + # restore IDEs, browsers, and communication tools to baseline priority and full affinity + count_nice = sum(prioritise_process(ide, 0) for ide in IDES) + count_nice += sum(prioritise_process(browser, 0) for browser in BROWSERS) + count_nice += sum(prioritise_process(call, 0) for call in CALLS) + + count_aff = sum(pin_process_to_cores(browser, list(range(total_cores))) for browser in BROWSERS) + actions.append( + f"Idle: Restored {count_nice} processes to normal scheduling and granted {count_aff} full core affinity." + ) + # + if len(actions) > 0: + try: + log_optimization_result(workload, confidence, actions) + print(f"Optimization Successful: {actions[-1]}") + return True + except Exception as e: + print(f"Database logging error :{e}") + return True + +def pin_process_to_cores(proc_name: str,cores: list[int]) -> int: + optimised_count=0 + for proc in psutil.process_iter(attrs=['pid','name']): + try: + current_name=proc.info['name'] or "" + if proc_name.lower() in current_name.lower(): + pid=proc.info['pid'] + process_obj=psutil.Process(pid) + + process_obj.cpu_affinity(cores) + optimised_count+=1 + except(psutil.NoSuchProcess,psutil.AccessDenied,psutil.ZombieProcess): + continue + except Exception: + continue + return optimised_count +#it safely adjusts the cpu scheduling priority 19 to -20(nicest) +def prioritise_process(proc_name: str, nice_val: int) -> int: + """Safely adjusts the CPU scheduling priority (nice: -20 highest to 19 lowest).""" + optimised_count = 0 + for proc in psutil.process_iter(attrs=['pid', 'name']): + try: + current_name = proc.info['name'] or "" + if proc_name.lower() in current_name.lower(): + pid = proc.info['pid'] + process_obj = psutil.Process(pid) + process_obj.nice(nice_val) + optimised_count += 1 + except (psutil.NoSuchProcess, psutil.AccessDenied, psutil.ZombieProcess): + continue + except Exception: + continue + return optimised_count +def set_io_priority(proc_name: str,io_class: int) -> int: + optimised_count=0 + for proc in psutil.process_iter(attrs=["pid","name"]): + current_pid=proc.info["pid"] + try: + current_name=proc.info["name"] or "" + if proc_name.lower() in current_name.lower(): + process_obj=psutil.Process(current_pid) + process_obj.ionice(io_class) + optimised_count += 1 + except(psutil.NoSuchProcess,psutil.ZombieProcess,psutil.AccessDenied): + continue + return optimised_count + + + + +def log_optimization_result(workload: str, confidence: float, actions: list[str]): + """Log all optimisation events to the focusos_events database table.""" + try: + with sqlite3.connect(DB_PATH) as conn: + cur = conn.cursor() + cur.execute(""" + CREATE TABLE IF NOT EXISTS focusos_events ( + timestamp REAL, + workload TEXT, + confidence REAL, + actions TEXT + ) + """) + cur.execute( + """INSERT INTO focusos_events (timestamp, workload, confidence, actions) + VALUES (?, ?, ?, ?)""", + (time.time(), workload, confidence, json.dumps(actions)) + ) + # `with sqlite3.connect(...) as conn` auto-commits on exit + except Exception as e: + print(f"Database logging error: {e}") diff --git a/focusos/priority_engine.py b/focusos/priority_engine.py deleted file mode 100644 index 8fcfc7d..0000000 --- a/focusos/priority_engine.py +++ /dev/null @@ -1 +0,0 @@ -"""Process priority, affinity, and IO adjustment engine.""" diff --git a/focusos/readme.md b/focusos/readme.md new file mode 100644 index 0000000..48ecaad --- /dev/null +++ b/focusos/readme.md @@ -0,0 +1,547 @@ +# FocusOS — Module Documentation + +**Part of CogniOS | Adaptive Workload Understanding and System Optimization Engine** + +--- + +## Table of Contents + +1. [Module Overview](#1-module-overview) +2. [Workflow](#2-workflow) +3. [File Structure](#3-file-structure) +4. [Function Definitions](#4-function-definitions) +5. [Useful Telemetry Data](#5-useful-telemetry-data) +6. [Additional Information](#6-additional-information) +7. [Module Diagram](#7-module-diagram) +8. [Limitations](#8-limitations) + +--- + +## 1. Module Overview + +### What is FocusOS? + +FocusOS is the **adaptive optimization layer** of CogniOS. Its core responsibility is to understand *what kind of work the user is currently doing* — not just which app is open — and dynamically tune the operating system's resource allocation to make that work feel faster and smoother. + +Traditional OS schedulers are workload-agnostic. They don't know if the user is in a deep coding session, compiling a project, gaming, or on a video call. FocusOS bridges this gap by continuously inferring workload context from system telemetry and applying intelligent, context-aware optimizations. + +> Instead of: *"What app is open?"* +> FocusOS asks: *"What kind of workload is happening right now?"* + +### Workload Categories Detected + +| Workload | Description | +|----------|-------------| +| `Coding` | IDE active, moderate CPU, low network, Python/Node processes | +| `Compiling` | Compiler active, high CPU, high disk I/O | +| `Gaming` | Game process dominant, high GPU/CPU, low network variance | +| `Video Call` | Zoom/Meet active, high network, moderate CPU | +| `Browsing` | Browser dominant, variable network, moderate CPU | +| `Idle` | Low CPU/RAM/disk, no dominant foreground process | + +--- + +## 2. Workflow + +### Training Phase + +**Step 1 — Collect telemetry every second** + +- **Metric Sourcing:** Relevant metrics are collected from Layer 1 and Layer 2. +- **In-Memory Buffer Storage:** A buffer may be implemented to eliminate the overhead of writing directly to the database tables every second. +- **Feature Classification:** + - System features + - Process features — a dedicated function is defined to fetch process metrics. To accurately compute the process creation rate, `asyncio` is used. This function accepts the local database path as its primary input argument. + - Context features + +**Step 2 — Generation of time-averaged statistics and final feature vector** + +- **Sliding window execution:** A dedicated function is defined to calculate rolling statistical metrics. +- **Execution Interval:** This function is triggered strictly every 30 seconds utilizing `asyncio`. +- **Dataset Creation:** A 2D array encompassing all compiled feature vectors is generated to assemble the training dataset for the clustering model. +- **Feature Scaling:** Prior to passing the dataset to KMeans, the data is normalized using `StandardScaler` from `sklearn.preprocessing`. This ensures that all metrics carry a mathematically similar weightage during distance calculations. +- **Unsupervised Clustering:** The output of this training phase is a tracking array (`trained_array`) that assigns a definitive cluster integer to every input feature vector row. +- **Pseudo-Labeling:** To perform pseudo-labeling, the centroid of each individual cluster is evaluated. This is achieved by calculating the mathematical mean of every feature vector that has been mapped to that same cluster number within the `trained_array`. +- **Supervised Mapping Database:** A "trained database" is then constructed to store the original structural feature vectors, along with a column storing their corresponding mapped workload labels. +- **XGBoost Dataset Formulation:** For training the supervised XGBoost model, `x_train` is designated as the initial 2D telemetry array, while `y_train` is fed the calculated `trained_array` labels from KMeans. + +### Testing with Actual Data + +- On live telemetry data, the module evaluates the system state using `model.predict_proba()` to compute a probability distribution across the different workload categories. +- **Label Dictionary Mapping:** The mapping from cluster number to workload category is done via a predefined dictionary mapping. +- **Threshold:** If the prediction confidence score for any individual workload category exceeds **80%**, the optimization layer triggers to actively prioritize that specific process. + +--- + +## 3. File Structure + +``` +focusos/ +│ +├── telemetry/ +│ └── collector.py # Reads psutil + /proc, emits 1-sec snapshots +│ +├── windowing/ +│ └── sliding_window.py # Maintains a 120-sample rolling buffer +│ +├── features/ +│ └── feature_engineer.py # Converts raw window → statistical feature vector +│ +├── models/ +│ ├── cluster_trainer.py # KMeans training for unsupervised discovery +│ ├── label_mapper.py # Maps cluster IDs → workload names (human step) +│ └── classifier.py # XGBoost train + inference +│ +├── optimization/ +│ └── optimizer.py # Applies nice(), cpu_affinity(), ionice() via psutil +│ +├── explanation/ +│ └── llm_explainer.py # Builds LLM prompt from prediction + feature importance +│ +├── dashboard/ +│ └── dashboard_server.py # Serves data to React frontend +│ +├── utils/ +│ └── process_utils.py # Helper: get foreground app, detect compiler, etc. +│ +├── models_saved/ +│ ├── kmeans_model.pkl # Saved KMeans model +│ └── xgboost_model.pkl # Saved XGBoost classifier +│ +└── main.py # Entry point: starts daemon loop +``` + +--- + +## 4. Function Definitions + +### `collector.py` + +```python +def collect_snapshot(conn) -> dict +``` + +**Purpose:** Collects one complete telemetry snapshot from `psutil` and `/proc` every second. Stores the snapshot into the shared SQLite database and returns it as a dict for the sliding window buffer. This is the heartbeat of FocusOS — everything downstream depends on this data. + +```python +timestamp < (strftime('%s','now') - 1) +``` + +--- + +```python +def get_foreground_app() -> str +``` + +**Purpose:** Detects the currently focused window title using `xdotool`. Returns the lowercased application name string. Falls back to `'unknown'` if no window manager is available or the call fails. + +```python +subprocess.check_output(['xdotool', 'getactivewindow']) +``` + +--- + +```python +def get_top_processes(n: int = 5) -> list[dict] +``` + +**Purpose:** Returns the top N processes sorted by CPU usage, and separately by memory usage. Each entry contains `pid`, `name`, `cpu_percent`, `memory_percent`, `num_threads`, `status`. Called every 5 seconds (Layer 2 telemetry). Only top-5 are stored to keep the database lean. + +```python +psutil.process_iter(['pid','name','cpu_percent','memory_percent','num_threads','status']) +``` + +--- + +### `sliding_window.py` + +```python +class SlidingWindowBuffer(maxlen: int = 120) +``` + +**Purpose:** Maintains a fixed-size FIFO deque of telemetry snapshots representing the last 2 minutes of system state (120 samples at 1/sec). When the buffer is full, the oldest entry is automatically dropped on each new addition. This is the temporal memory FocusOS reasons over. + +```python +self.buffer = deque(maxlen=120) +``` + +--- + +```python +def add(self, snapshot: dict) -> None +``` + +**Purpose:** Appends a new telemetry snapshot to the buffer. If the buffer is already at `maxlen`, the oldest snapshot is evicted automatically by the deque. Called once per second by the daemon loop. + +```python +self.buffer.append(snapshot) +``` + +--- + +```python +def is_ready(self) -> bool +``` + +**Purpose:** Returns `True` only when the buffer has accumulated exactly `maxlen` snapshots. FocusOS waits for a full 2-minute window before running its first inference. Prevents classification on incomplete data at startup. + +```python +return len(self.buffer) == self.buffer.maxlen +``` + +--- + +```python +def get_window(self) -> list[dict] +``` + +**Purpose:** Returns a plain list copy of the current buffer contents, ordered oldest to newest. This copy is passed to `feature_engineer.py` for statistical feature extraction every 30 seconds. + +```python +return list(self.buffer) +``` + +--- + +### `feature_engineer.py` + +```python +def extract_features(window: list[dict]) -> dict +``` + +**Purpose:** Converts the 120-snapshot sliding window into a flat statistical feature vector. Computes mean, max, and variance for CPU, RAM, network, and process signals. Also sets binary context flags (`vscode_active`, `compiler_active`). The output dict is the direct input to the XGBoost classifier. + +--- + +```python +def categorize_app(app_name: str) -> int +``` + +**Purpose:** Maps a raw foreground application name (window title string) to an integer category code used as a numerical feature in the ML model. Categories: `IDE=0`, `Browser=1`, `Game=2`, `Call=3`, `Terminal=4`, `Unknown=5`. Called inside `extract_features()`. + +```python +if any(k in app_name for k in ['code', 'pycharm', 'vim']): + return 0 # IDE +``` + +--- + +```python +def get_compiler_flag(window: list[dict]) -> int +``` + +**Purpose:** Scans the top process names across all 120 snapshots and returns `1` if any known compiler or build tool (`gcc`, `g++`, `make`, `clang`, `rustc`, `javac`, `cargo`) appeared in the top-5 CPU processes during the window. Returns `0` otherwise. + +```python +compiler_tools = ['gcc', 'g++', 'make', 'clang', 'rustc', 'javac', 'cargo'] +``` + +--- + +### `cluster_trainer.py` + +```python +def train_kmeans(X: np.ndarray, n_clusters: int = 5) -> KMeans +``` + +**Purpose:** Trains a KMeans clustering model on collected feature vectors to discover natural workload behavior groups. Called once during the initial bootstrapping phase before any labels exist. Saves the fitted model and scaler to `models_saved/`. This is the unsupervised first step of the semi-supervised pipeline. + +```python +model = KMeans(n_clusters=5, random_state=42, n_init=10) +model.fit(X_scaled) +``` + +--- + +```python +def assign_pseudo_labels(cluster_ids: np.ndarray, label_map: dict) -> list[str] +``` + +**Purpose:** Converts raw integer cluster IDs from KMeans output into human-readable workload label strings using a manually defined mapping (e.g. `{0: 'coding', 1: 'video_call', ...}`). This human labelling step is the bridge from unsupervised clustering to supervised classification. + +```python +return [label_map.get(cid, 'unknown') for cid in cluster_ids] +``` + +--- + +```python +def save_pseudo_labeled_dataset(X: np.ndarray, labels: list[str], path: str) -> None +``` + +**Purpose:** Persists the pseudo-labeled feature matrix to disk as a CSV file. Each row is one 2-minute window's feature vector, with its assigned workload label as the final column. This dataset is later loaded by `classifier.py` to train XGBoost. + +```python +df = pd.DataFrame(X, columns=FEATURE_NAMES) +df['label'] = labels +df.to_csv(path, index=False) +``` + +--- + +### `classifier.py` + +```python +def train_classifier(X_train: np.ndarray, y_train: np.ndarray) -> XGBClassifier +``` + +**Purpose:** Trains an XGBoost multi-class classifier on the pseudo-labeled workload dataset. Uses 100 estimators, max depth 6, and learning rate 0.1. Saves the fitted model to `models_saved/xgboost_model.pkl`. Called once after pseudo-labels are generated, and can be retrained periodically. + +```python +model = XGBClassifier(n_estimators=100, max_depth=6, learning_rate=0.1) +model.fit(X_train, y_train) +``` + +--- + +```python +def predict_workload(features: dict) -> tuple[str, float, dict] +``` + +**Purpose:** Runs a single real-time inference pass on a feature vector extracted from the current sliding window. Returns the predicted workload label, the confidence score for that label, and a dict of all class probabilities. Called every 30 seconds by the daemon loop. + +```python +probs = model.predict_proba(X)[0] +label = WORKLOAD_LABELS[np.argmax(probs)] +confidence = probs[np.argmax(probs)] +``` + +--- + +```python +def get_feature_importances() -> dict +``` + +**Purpose:** Extracts and returns the feature importance scores from the trained XGBoost model as a `{feature_name: score}` dict. The top 3 features by importance are passed to the LLM explanation layer to generate a human-readable reason for the current workload prediction. + +```python +return dict(zip(FEATURE_NAMES, model.feature_importances_)) +``` + +--- + +### `optimizer.py` + +```python +def apply_optimization(workload: str, confidence: float) -> None +``` + +**Purpose:** The action layer of FocusOS. Reads the detected workload and — only if confidence exceeds 80% — adjusts OS-level process scheduling by calling `nice()`, `cpu_affinity()`, and `ionice()` via psutil. Prioritizes relevant processes and deprioritizes background tasks. Logs all actions taken. + +--- + +```python +def prioritize_process(proc_name: str, nice_val: int) -> bool +``` + +**Purpose:** Finds a running process by name substring match and sets its CPU scheduling priority using `psutil.Process.nice()`. Returns `True` if the process was found and updated, `False` if the process was not running or access was denied. Called internally by `apply_optimization()`. + +```python +p = psutil.Process(pid) +p.nice(nice_val) +``` + +--- + +```python +def log_optimization_result(workload: str, confidence: float, actions: list[str]) -> None +``` + +**Purpose:** Writes the optimization event — workload label, confidence score, timestamp, and list of actions applied — to the `focusos_events` table in the shared CogniOS SQLite database. This log is consumed by the dashboard and by BlackBox for pre-crash workload reconstruction. + +```python +cur.execute(""" + INSERT INTO focusos_events (timestamp, workload, confidence, actions_json) + VALUES (?, ?, ?, ?) +""", (time.time(), workload, confidence, json.dumps(actions))) +``` + +--- + +### `llm_explainer.py` + +```python +def generate_explanation(prediction: str, confidence: float, top_features: dict) -> str +``` + +**Purpose:** Builds a structured JSON prompt from the workload prediction and top XGBoost feature importances, then sends it to the configured LLM (Ollama local or API). Returns a 2-sentence plain-English explanation of why the current workload was detected. Result is displayed on the dashboard. + +```python +prompt = { + "prediction": prediction, + "confidence": f"{confidence:.0%}", + "important_features": [...top 3 features...] +} +return call_llm(system_prompt, json.dumps(prompt)) +``` + +--- + +## 5. Useful Telemetry Data + +FocusOS reads from the shared CogniOS Telemetry Collector. Below are the signals most relevant to FocusOS and the reasoning behind each. + +### Layer 1: System Telemetry (Every 1 Second) + +| Signal | Why FocusOS Needs It | +|--------|---------------------| +| `cpu_percent` | Primary indicator: coding ~30-50%, compiling ~80-100% | +| `cpu_per_core` | Distribution matters: compiler uses all cores, game pins 2-4 | +| `cpu_user` / `cpu_system` | System-heavy = kernel work; user-heavy = application | +| `cpu_iowait` | High iowait = compiling or heavy disk-bound workload | +| `mem_percent` | Gaming and video editing push RAM high; coding is moderate | +| `mem_cached` | High cache = file-heavy workload (compiling, video) | +| `disk_read_mbps` | Compiling reads many source files | +| `disk_write_mbps` | Build artifacts being written = strong compiling signal | +| `net_bytes_sent` / `net_bytes_recv` | High recv = streaming/video call; near-zero = offline coding | +| `process_count` | Many new processes = build system spawning (make, cargo) | +| `context_switches` | High = many competing threads (compiling, game engine) | +| `load_1m` / `load_5m` | Sustained high load = compile or gaming; spike = anomaly | + +### Layer 2: Top Process Telemetry (Every 5 Seconds) + +| Signal | Why FocusOS Needs It | +|--------|---------------------| +| Top-5 process `name` | gcc/g++/make = compiling; zoom = video call; code = coding | +| Top process `cpu_percent` | One process at 90% = game; distributed = compile | +| Top process `memory_percent` | Memory-hungry process reveals workload type | +| `num_threads` | Many threads = parallel compile jobs or game engine | +| `read_bytes` / `write_bytes` | Compiler writes output; game engine streams assets | +| `status` | Zombie accumulation signals build system issues | + +### Layer 3: Process Metadata (Store Once per New Top-5 Entry) + +| Signal | Why FocusOS Needs It | +|--------|---------------------| +| `exe` (executable path) | `/usr/bin/gcc` is unambiguous compiling; `/opt/zoom/zoom` = video call | +| `cmdline` | `python train.py` = ML work; `make -j8` = compiling | +| `nice` (priority) | Know baseline before FocusOS modifies it (needed for rollback) | +| `create_time` | Long-lived compiler process = active sustained build | + +### Layer 4: Deep Diagnostics (Anomaly Only — from OS Doctor) + +| Signal | Relevance | +|--------|-----------| +| `cpu_affinity` | Avoid double-setting if process is already pinned | +| `io_priority` | Know existing I/O class before overriding | +| `open_files` | Compiler opens many `.c`/`.h` files — confirming signal | +| `net_connections` | Zoom/Teams keeps multiple open sockets — video call signal | + +### Final Feature Vector Passed to XGBoost + +```python +[ + mean_cpu, # Statistical mean of CPU over 2 min window + max_cpu, # Peak CPU during window + cpu_variance, # Low variance = sustained load; high = bursty + mean_ram, # Mean memory usage % + ram_growth_rate, # RAM trending up → memory-intensive workload + mean_net_upload, # Video call = high upload + mean_net_download, # Streaming = high download + net_variance, # Low = steady call; high = burst downloads + mean_process_count, # Build systems spawn many processes + mean_thread_count, # Parallel compiler = many threads + top_proc_cpu_share, # 1 process at 90% = game; distributed = compile + foreground_app_category, # IDE=0, Browser=1, Game=2, Call=3, Terminal=4 + compiler_active, # 1 if gcc/make/cargo seen in top processes + vscode_active, # 1 if VSCode is foreground app +] +``` + +--- + +## 6. Additional Information + +### 6.1 Why Semi-Supervised (KMeans → XGBoost)? + +| Approach | Problem | +|----------|---------| +| Pure supervised | Needs labeled data — hard to collect at scale | +| CNN on heatmap | Needs GPU, hard to explain, overkill for tabular data | +| Pure KMeans | Cannot generalize to new unseen patterns at inference time | +| **KMeans → Pseudo-labels → XGBoost** | No GPU, fast inference, explainable, works on small data | + +### 6.2 Why XGBoost Over Other Classifiers? + +| Property | Why It Matters for FocusOS | +|----------|---------------------------| +| Tabular data performance | Features are statistical — XGBoost excels here | +| Small dataset | Works well with hundreds to thousands of samples | +| Fast inference | Less than 1ms prediction, critical for 30-second real-time cycle | +| No GPU required | Runs on any Linux machine in the CogniOS target environment | + +### 6.3 Integration with Other CogniOS Modules + +| Module | Integration Point | +|--------|------------------| +| **Telemetry Collector** | FocusOS reads from the shared SQLite telemetry database (Layers 1-3) | +| **OS Doctor** | OS Doctor flags CPU/Memory > 90% anomalies so FocusOS avoids misclassifying a runaway process as "compiling" | +| **BlackBox** | FocusOS writes its workload timeline to the events table; BlackBox uses this to reconstruct pre-crash workload context | +| **Dashboard** | FocusOS pushes workload, confidence, probabilities, actions, and LLM explanation via WebSocket to the React frontend | + +### 7. Module Diagram +```mermaid +graph TD + subgraph Phase1["Phase 1: Telemetry Collection"] + A1["psutil (CPU, RAM, Disk, Net)"] --> B1["Telemetry Collector Daemon"] + A2["/proc filesystem (Process/thread stats)"] --> B1 + A3["Window Manager APIs (xprop / xdotool / compositor)"] --> B1 + B1 -->|Sample every 1 sec| C1["Raw Metrics Buffer"] + end + + subgraph Phase2["Phase 2: Sliding Window Generator"] + C1 --> D1["120-Second FIFO Queue"] + D1 --> E1["Matrix Representation (120 x Feature_Count)"] + end + + subgraph Phase3["Phase 3: Feature Engineering"] + E1 --> F1["Compute Statistical Metrics"] + F1 --> G1["CPU Stats (Mean, Max, Variance)"] + F1 --> G2["RAM Stats (Mean, Growth Rate)"] + F1 --> G3["Network Stats (Mean Up/Down, Variance)"] + F1 --> G4["Process Stats (Count, Threads, Top Share)"] + F1 --> G5["Context Stats (One-Hot Encoded Categories)"] + G1 & G2 & G3 & G4 & G5 --> H1["Final Feature Vector"] + end + + subgraph Phase4["Phase 4: Unsupervised Clustering"] + H1 -->|Batch Historical Data| I1["KMeans Clustering (k=5)"] + I1 --> J1["Discover 5 Resource Profiles"] + J1 --> K1["Analyze Profile Rules (e.g. High Net + Zoom)"] + K1 --> L1["Apply Human Pseudo-Labels (Coding, Compiling, etc.)"] + end + + subgraph Phase5["Phase 5: Supervised Classification"] + L1 --> M1["Labeled Tabular Dataset"] + M1 --> N1["Train XGBoost Classifier"] + H1 -->|Live Vector Every 30 sec| O1["XGBoost Real-Time Inference"] + N1 --> O1 + O1 --> P1{"Confidence > 80%?"} + end + + subgraph Phase6["Phase 6: Optimization"] + P1 -->|Yes| Q1["Identify Target Process"] + P1 -->|No| Q2["Maintain Current State"] + Q1 --> R1["Apply nice() / renice priority"] + Q1 --> R2["Configure sched_setaffinity() (CPU core separation)"] + end + + subgraph Phase7["Phase 7: Explanation & UI"] + O1 --> S1["Extract XGBoost Feature Importances"] + S1 --> T1["Build Context JSON Payload"] + T1 --> U1["Query Local LLM Explanation Layer"] + U1 --> V1["Generate Natural Language Explanation"] + V1 --> W1["Streamlit Dashboard Display"] + R1 --> W1 + R2 --> W1 + end +``` + +### 8. Limitations + +| Limitation | Description | +|------------|------------| +| **Optimisation Delay** | FocusOS relies on a 120 sample buffer to represent the last 2 minutes performance as a single feature vector. The module may not be able to apply optimisations for the the first 120 seconds after the dameon starts. | +| **Rigidity with Labels** | The pseudo labelling structure maps the system state from cluster numbers to definite workload categories. In case of complex or mixed workflows, system metrics may not fit one particular category, which may get misclassified or classified as “unknown”. | +| **Threshold for Optimisation** | If the confidence for a particular workload category is more than 80%, the optimisation layer is deployed. In case of a mixed workload, such as 55% compiling and 35% coding, the threshold will not be met, and the optimisation layer will be skipped. | +| **Dependence on OS Doctor for Anomaly Detection** | FocusOS is not able to distinguish between heavy workload and a broken application that is monopolising a core. If OS Doctor is unable to detect the anomaly, FocusOS may incorrectly prioritise this rogue application with system hardware. | \ No newline at end of file diff --git a/focusos/sliding_window.py b/focusos/sliding_window.py new file mode 100644 index 0000000..24d7908 --- /dev/null +++ b/focusos/sliding_window.py @@ -0,0 +1,41 @@ +import os +import sys +import sqlite3 +import pandas as pd +from collections import deque +sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) +from config import DB_PATH +from config import SLIDING_WIND_N +#commenting some part of the sliding window file +#other modules will suffer because as buffer implementation will cause other modules to work on the stale +# class SlidingWindowBuffer: +# def __init__(self, maxlen: int = SLIDING_WIND_N): +# # it initialises the deque with a maxlen=SLIDING_WIND_N +# self.buffer = deque(maxlen=maxlen) + +# def add(self, snapshot: dict) -> None: +# # this checks the deque if full then drops the oldest adds newest +# self.buffer.append(snapshot) + +# def is_ready(self) -> bool: +# # keeps check if maxlen is achieved or not +# return len(self.buffer) == self.buffer.maxlen + +# def get_window(self) -> list[dict]: +# # Returns a copy of the current buffer as a standard Python list. +# return list(self.buffer) +def get_window_from_db(db_path=DB_PATH, limit=SLIDING_WIND_N): + try: + with sqlite3.connect(db_path,timeout=10.0) as conn: + # it will fetch last nth rows n=limit from the db + query = f"select * from layer1_sys order by timestamp desc limit {limit}" + df = pd.read_sql(query, conn) + if df.empty: + return None + + # Reverses the dataframe so the oldest data is first, newest is last + df = df[::-1].reset_index(drop=True) + return df + except Exception as e: + print(f"Window Extraction Error: {e}") + return None diff --git a/focusos/train_cnn.py b/focusos/train_cnn.py deleted file mode 100644 index 0e8e332..0000000 --- a/focusos/train_cnn.py +++ /dev/null @@ -1 +0,0 @@ -"""Training entry point for the FocusOS CNN.""" diff --git a/main.py b/main.py index 6e2b21e..38b114c 100644 --- a/main.py +++ b/main.py @@ -1 +1,50 @@ """Single startup script for CogniOS.""" +import subprocess +import sys + +print("Starting CogniOS System...") + +# Start the Telemetry Daemon in the background +# Output is routed to telemetry_daemon.log +telemetry_log = open('telemetry_daemon.log', 'w') +telemetry_process = subprocess.Popen( + [sys.executable, "cognios_as_daemon.py"], + stdout=telemetry_log, + stderr=telemetry_log +) +print(f"Telemetry Daemon started! (PID: {telemetry_process.pid})") +print("To view the live logs anytime, run: tail -f telemetry_daemon.log") +print("To stop the daemon, run: pkill -f cognios_as_daemon.py") +# Start the FocusOS Daemon in the background +# Output is routed to focusos.log +# focusos_log = open('focusos.log', 'w') +# focusos_process = subprocess.Popen( +# [sys.executable, "focusos_daemon.py"], +# stdout=focusos_log, +# stderr=focusos_log +#) +#print(f"FocusOS Daemon started! (PID: {focusos_process.pid})") + +#print("\nAll CogniOS modules are now running silently in the background!") +print("Your terminal is free to use.") + +from focusos.models.classifier import WorkloadPredictor +from focusos.feature_engineer import extract_features +from focusos.sliding_window import get_window_from_db +import time + +def run_focusos(): + predictor = WorkloadPredictor() + print("FocusOS model inference testing...") + while True: + df_window = get_window_from_db() + if df_window is not None: + features = extract_features(df_window) + if features is not None: + result = predictor.predict(features) + if result: + print(f"[{time.strftime('%H:%M:%S')}] Detected: {result['workload']} ({result['confidence']}%)") + time.sleep(2) + +if __name__ == "__main__": + run_focusos() diff --git a/os_doctor/README.md b/os_doctor/README.md new file mode 100644 index 0000000..abd2aeb --- /dev/null +++ b/os_doctor/README.md @@ -0,0 +1,142 @@ +OSDoctor +Real-Time Intelligent Operating System Monitoring & Anomaly Detection System + +Project Documentation + +1. Introduction + +OSDoctor is an intelligent observability system that continuously monitors operating system telemetry, detects abnormal system behavior using machine learning, identifies the likely root cause, and explains the issue in natural language. Its objective is to answer the question: “Why is my laptop slow right now?” + +2. Objectives + +- Monitor real-time system telemetry. +- Detect system anomalies automatically. +- Identify causes of performance issues. +- Detect CPU, memory, disk, and process-related faults. +- Explain issues in simple, human-readable language. +- Provide actionable alerts and recommendations. + + +3. System Architecture + +```text +CogniOS Telemetry Collector + │ + ▼ +OSDoctor (Data Extraction) + │ + ▼ +System Metrics + Process Metrics + │ + ▼ +SQLite Database + │ + ▼ +Feature Engineering + │ + ▼ +Isolation Forest + │ + ├── No Anomaly + │ │ + │ ▼ + │ Continue Monitoring + │ + └── Anomaly Detected + │ + ▼ + Alerts Table + │ + ▼ + LLM Explanation Layer + │ + ▼ + Streamlit Dashboard +``` + +4. File Structure + +```text +os_doctor/ +│── _init_.py # Marks the directory as a Python package +│── featuring.py # Feature engineering and preprocessing +│── i_forest.py # Isolation Forest model implementation +│── llm_layer.py # LLM integration and response generation +│── streamlit.py # Streamlit web application +│── README.md # Project overview and setup guide +``` + + +5. Function Documentation +6. Feature Engineering + +## Module Overview +The `feature.py` script serves as the core mathematical transformation layer for the **OS Doctor Anomaly Engine**. Its primary responsibility is to bridge the gap between low-frequency/high-frequency transactional data stored in the local SQLite database and the high-dimensional, uniform matrices required by the **Isolation Forest** machine learning pipeline. + +The module continuously processes a rolling **2-minute sliding window** of system and process performance telemetry, cleans missing data, applies advanced statistical transformations (rolling averages, gradients, and rates of change), and generates a unified vector payload for real-time anomaly detection. + +--- + +## Libraries Used + +### 1 pandas +We use pandas for faster vector calculations leveraging it's built in functions like rolling(), shift() and many more. + +### 2 sqlite3 +We use sqlite3 library to handle the telemetry database created prior. + +### 3 json +We use json library to parse the json object created in layer2. + +## Core Telemetry Pipeline Alignment Matrix + +Because our telemetry collection infrastructure drops metrics down at asynchronous cadences, this script enforces chronological structure across distinct dimensional boundaries: + +| Data Layer | Source Table | DB CADENCE | Target Window Scope | Base Matrix Shape | +| :--- | :--- | :--- | :--- | :--- | +| **Layer 1: System-Wide** | `system_telemetry` | Every 1 Second | Last 120 Seconds | $120 \times \text{metrics}$ | +| **Layer 2: Top Processes**| `process_telemetry`| Every 5 Seconds | Last 120 Seconds | $24 \times \text{metrics}$ | + +--- + +## Detailed Function Breakdown + +The feature engineering layer executes its operations deterministically through four functions: + +### 1. `extract_and_engineer_system(db_path)` +* **Intent:** Pulls the high-frequency global system indicators (Layer 1) and translates flat numbers into directional trends. +* **Mechanism:** * Queries the last **120 rows** from the `system_telemetry` table ordered by timestamp, reversing them in memory to form a clean chronological left-to-right timeline. + * Utilizes `pandas` to calculate moving windows (e.g., 30-second rolling averages for CPU consumption) to smooth out short-term spikes. + * Calculates **I/O Storm Rates** and **Scheduler Context Switch Acceleration** by taking the difference between the most current metric entry and past rows (`df['metric'] - df['metric'].shift(1)`). +* **Output Shape:** A flat, single-row pandas DataFrame: `[1 × num_system_features]`. + +### 2. `extract_and_engineer_processes(db_path)` +* **Intent:** Unpacks the denormalized JSON arrays for the Top 5 CPU and Top 5 RAM consumers, correcting the shape mismatch and handling the "cold start" baseline issue. +* **Mechanism:** + * Queries the last **24 rows** (representing 120 seconds of 5-second steps) from the `process_telemetry` table. + * **The Chronological Upsampling Transform:** Because the database contains 24 rows but the final matrix requires a 1-second grid alignment, it upsamples the process rows into a 120-second array using forward-filling (`.ffill()`). This ensures data points match at every single second ticker. + * **Zero-Imputation (Cold Start Fix):** If a rogue process bursts into the Top 5 list halfway through the window, its missing history blocks are auto-populated with `0.0` instead of letting `NaN` values break the mathematical tracking loops. + * Computes process acceleration curves and resource gradients ($\Delta \text{Metric} / \Delta t$). +* **Output Shape:** Two independent flat, single-row arrays: `[1 × num_cpu_features]` and `[1 × num_ram_features]`. + +### 3. `build_unified_vector(sys_vec, cpu_vec, ram_vec)` +* **Intent:** Combines separate feature spaces into a single high-dimensional coordinate vector. +* **Mechanism:** + * Takes the horizontal outputs generated by the system and process computation layers. + * Executes an optimized columnar concatenation step (`pandas.concat(axis=1)`) to stitch the arrays together. + * Enforces a rigid, static schema definition so that column indexes never drift, ensuring the input dimensions exactly match what the downstream Isolation Forest expects. +* **Output Shape:** A single, high-dimensional flat vector row: `[1 × total_combined_features]`. + +### 4. `get_inference_payload(db_path)` +* **Intent:** Serves as the centralized public orchestrator function called directly by the core daemon script loop. +* **Mechanism:** + * Acts as a non-blocking execution gatekeeper. + * First runs a safety health check on the database capacity. If the background collectors haven't yet logged the baseline buffer of 120 system records, this function gracefully exits returning `None`, preventing the ML models from calculating false positives on partial windows. + * Executes functions 1, 2, and 3 sequentially completely in-memory, then passes the finalized ready vector row directly down to the machine learning execution loop (`i_forest.py`). +* **Output:** An inference-ready `pandas.DataFrame` row or `None`. + +7. Machine Learning Model +8. LLM Explanation Layer +9. Dashboard +10. Installation +11. Future Scope \ No newline at end of file diff --git a/os_doctor/alerts.py b/os_doctor/alerts.py deleted file mode 100644 index f19881c..0000000 --- a/os_doctor/alerts.py +++ /dev/null @@ -1 +0,0 @@ -"""Alert helpers for OS Doctor.""" diff --git a/os_doctor/documentation.md b/os_doctor/documentation.md new file mode 100644 index 0000000..e749e36 --- /dev/null +++ b/os_doctor/documentation.md @@ -0,0 +1,76 @@ +OSDoctor +Real-Time Intelligent Operating System Monitoring & Anomaly Detection System + +Project Documentation + +1. Introduction + +OSDoctor is an intelligent observability system that continuously monitors operating system telemetry, detects abnormal system behavior using machine learning, identifies the likely root cause, and explains the issue in natural language. Its objective is to answer the question: “Why is my laptop slow right now?” + +2. Objectives + +- Monitor real-time system telemetry. +- Detect system anomalies automatically. +- Identify causes of performance issues. +- Detect CPU, memory, disk, and process-related faults. +- Explain issues in simple, human-readable language. +- Provide actionable alerts and recommendations. + + +3. System Architecture + +```text +CogniOS Telemetry Collector + │ + ▼ +OSDoctor (Data Extraction) + │ + ▼ +System Metrics + Process Metrics + │ + ▼ +SQLite Database + │ + ▼ +Feature Engineering + │ + ▼ +Isolation Forest + │ + ├── No Anomaly + │ │ + │ ▼ + │ Continue Monitoring + │ + └── Anomaly Detected + │ + ▼ + Alerts Table + │ + ▼ + LLM Explanation Layer + │ + ▼ + Streamlit Dashboard +``` + +4. File Structure + +```text +os_doctor/ +│── _init_.py # Marks the directory as a Python package +│── featuring.py # Feature engineering and preprocessing +│── i_forest.py # Isolation Forest model implementation +│── llm_layer.py # LLM integration and response generation +│── streamlit.py # Streamlit web application +│── README.md # Project overview and setup guide +``` + + +5. Function Documentation +6. Feature Engineering +7. Machine Learning Model +8. LLM Explanation Layer +9. Dashboard +10. Installation +11. Future Scope diff --git a/os_doctor/featuring.py b/os_doctor/featuring.py new file mode 100644 index 0000000..1034c7c --- /dev/null +++ b/os_doctor/featuring.py @@ -0,0 +1,2 @@ +""" featuring raw data to useful data.""" + diff --git a/os_doctor/anomaly_model.py b/os_doctor/i_forest.py similarity index 100% rename from os_doctor/anomaly_model.py rename to os_doctor/i_forest.py diff --git a/os_doctor/diagnostics.py b/os_doctor/llm_layer.py similarity index 100% rename from os_doctor/diagnostics.py rename to os_doctor/llm_layer.py diff --git a/os_doctor/streamlit.py b/os_doctor/streamlit.py new file mode 100644 index 0000000..9752436 --- /dev/null +++ b/os_doctor/streamlit.py @@ -0,0 +1 @@ +"""Streamlit Dashboard code goes here""" \ No newline at end of file diff --git a/telemetry_daemon.log b/telemetry_daemon.log new file mode 100644 index 0000000..48e0b65 --- /dev/null +++ b/telemetry_daemon.log @@ -0,0 +1,370 @@ +2026-07-10 21:15:51,985 - INFO - Starting CogniOS Daemon. 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Press Ctrl+C to stop.") + + try: + while True: + top_cpu, top_mem, baselines = collect_process_telemetry(baselines) + insert_process_snapshot(top_cpu, top_mem) + print( + f"Snapshot committed at t={time.time():.0f} | " + f"top_cpu={top_cpu[0]['name']} score={top_cpu[0]['cpu_score']} | " + f"top_ram={top_mem[0]['name']} score={top_mem[0]['ram_score']}" + ) + except KeyboardInterrupt: + print("\nDaemon safely terminated.") \ No newline at end of file diff --git a/utils/helpers.py b/utils/helpers.py new file mode 100644 index 0000000..7a92516 --- /dev/null +++ b/utils/helpers.py @@ -0,0 +1,9 @@ + + +def rate_mb_s(current_bytes, last_bytes, elapsed_sec): + + if last_bytes is None or elapsed_sec <= 0: + return None + # how many bytes have been sent/received since the last check, and convert to MB/s by dividing by 1 MB + delta_bytes = current_bytes - last_bytes + return (delta_bytes / elapsed_sec) / (1024 * 1024) \ No newline at end of file