You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
78 lines
2.5 KiB
78 lines
2.5 KiB
import logging as log |
|
|
|
import pandas as pd |
|
from typing import Optional |
|
|
|
from detectors import Detector |
|
from models import ModelCache |
|
from time import time |
|
from utils import convert_sec_to_ms, convert_pd_timestamp_to_ms |
|
|
|
|
|
logger = log.getLogger('THRESHOLD_DETECTOR') |
|
|
|
|
|
class ThresholdDetector(Detector): |
|
|
|
WINDOW_SIZE = 3 |
|
|
|
def __init__(self): |
|
pass |
|
|
|
def train(self, dataframe: pd.DataFrame, threshold: dict, cache: Optional[ModelCache]) -> ModelCache: |
|
return { |
|
'cache': { |
|
'value': threshold['value'], |
|
'condition': threshold['condition'] |
|
} |
|
} |
|
|
|
def detect(self, dataframe: pd.DataFrame, cache: ModelCache) -> dict: |
|
if cache is None or cache == {}: |
|
raise ValueError('Threshold detector error: cannot detect before learning') |
|
value = cache['value'] |
|
condition = cache['condition'] |
|
|
|
now = convert_sec_to_ms(time()) |
|
segments = [] |
|
|
|
dataframe_without_nans = dataframe.dropna() |
|
if len(dataframe_without_nans) == 0: |
|
if condition == 'NO_DATA': |
|
segments.append({ 'from': now, 'to': now , 'params': { value: 'NO_DATA' } }) |
|
else: |
|
return None |
|
else: |
|
last_entry = dataframe_without_nans.iloc[-1] |
|
last_time = convert_pd_timestamp_to_ms(last_entry['timestamp']) |
|
last_value = float(last_entry['value']) |
|
segment = { 'from': last_time, 'to': last_time, 'params': { value: last_value } } |
|
|
|
if condition == '>': |
|
if last_value > value: |
|
segments.append(segment) |
|
elif condition == '>=': |
|
if last_value >= value: |
|
segments.append(segment) |
|
elif condition == '=': |
|
if last_value == value: |
|
segments.append(segment) |
|
elif condition == '<=': |
|
if last_value <= value: |
|
segments.append(segment) |
|
elif condition == '<': |
|
if last_value < value: |
|
segments.append(segment) |
|
|
|
return { |
|
'cache': cache, |
|
'segments': segments, |
|
'lastDetectionTime': now |
|
} |
|
|
|
def consume_data(self, data: pd.DataFrame, cache: Optional[ModelCache]) -> Optional[dict]: |
|
result = self.detect(data, cache) |
|
return result if result else None |
|
|
|
def get_window_size(self, cache: Optional[ModelCache]) -> int: |
|
return self.WINDOW_SIZE
|
|
|