Spaces:
Running
Running
File size: 6,056 Bytes
f568a4f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 | import { useState, useEffect } from 'react';
import { supabase } from '@/integrations/supabase/client';
export interface PredictedWindow {
startTime: Date;
endTime: Date;
confidence: number;
expectedCoherence: number;
expectedLHECount: number;
reasoning: string;
dayOfWeek: string;
hourRange: string;
}
export interface HistoricalPattern {
dayOfWeek: number;
hour: number;
activationRate: number;
avgCoherence: number;
avgLHECount: number;
sampleSize: number;
}
export function usePredictiveWindows(daysAhead = 7) {
const [predictions, setPredictions] = useState<PredictedWindow[]>([]);
const [patterns, setPatterns] = useState<HistoricalPattern[]>([]);
const [isAnalyzing, setIsAnalyzing] = useState(true);
const [error, setError] = useState<string | null>(null);
useEffect(() => {
const analyzePredictiveWindows = async () => {
setIsAnalyzing(true);
setError(null);
try {
// Fetch last 30 days of scheduler history
const thirtyDaysAgo = new Date();
thirtyDaysAgo.setDate(thirtyDaysAgo.getDate() - 30);
const { data: history, error: historyError } = await supabase
.from('scheduler_history')
.select('*')
.gte('timestamp', thirtyDaysAgo.toISOString())
.order('timestamp', { ascending: true });
if (historyError) throw historyError;
if (!history || history.length === 0) {
setPredictions([]);
setPatterns([]);
setIsAnalyzing(false);
return;
}
// Analyze patterns by day of week and hour
const patternMap = new Map<string, {
activations: number;
totalSamples: number;
coherenceSum: number;
lheSum: number;
}>();
history.forEach(record => {
const date = new Date(record.timestamp);
const dayOfWeek = date.getDay();
const hour = date.getHours();
const key = `${dayOfWeek}-${hour}`;
const existing = patternMap.get(key) || {
activations: 0,
totalSamples: 0,
coherenceSum: 0,
lheSum: 0,
};
existing.totalSamples++;
existing.coherenceSum += record.coherence_at_action;
existing.lheSum += record.lighthouse_events_count;
if (record.action === 'enable') {
existing.activations++;
}
patternMap.set(key, existing);
});
// Convert to patterns array
const analyzedPatterns: HistoricalPattern[] = [];
patternMap.forEach((value, key) => {
const [dayOfWeek, hour] = key.split('-').map(Number);
analyzedPatterns.push({
dayOfWeek,
hour,
activationRate: value.activations / value.totalSamples,
avgCoherence: value.coherenceSum / value.totalSamples,
avgLHECount: value.lheSum / value.totalSamples,
sampleSize: value.totalSamples,
});
});
setPatterns(analyzedPatterns);
// Generate predictions for next N days
const now = new Date();
const predictedWindows: PredictedWindow[] = [];
for (let dayOffset = 0; dayOffset < daysAhead; dayOffset++) {
const targetDate = new Date(now);
targetDate.setDate(targetDate.getDate() + dayOffset);
const dayOfWeek = targetDate.getDay();
// Find patterns for this day of week
const dayPatterns = analyzedPatterns.filter(p => p.dayOfWeek === dayOfWeek);
if (dayPatterns.length === 0) continue;
// Find high-probability windows (activation rate > 0.5 and sufficient samples)
const strongPatterns = dayPatterns
.filter(p => p.activationRate > 0.5 && p.sampleSize >= 3)
.sort((a, b) => b.activationRate - a.activationRate);
strongPatterns.forEach(pattern => {
const windowStart = new Date(targetDate);
windowStart.setHours(pattern.hour, 0, 0, 0);
const windowEnd = new Date(windowStart);
windowEnd.setHours(windowStart.getHours() + 1);
// Skip past windows
if (windowEnd < now) return;
const confidence = Math.min(
pattern.activationRate * (Math.min(pattern.sampleSize, 10) / 10),
1
);
const dayNames = ['Sunday', 'Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday'];
predictedWindows.push({
startTime: windowStart,
endTime: windowEnd,
confidence,
expectedCoherence: pattern.avgCoherence,
expectedLHECount: pattern.avgLHECount,
reasoning: `Historical data shows ${(pattern.activationRate * 100).toFixed(0)}% activation rate during this window (${pattern.sampleSize} samples)`,
dayOfWeek: dayNames[pattern.dayOfWeek],
hourRange: `${String(pattern.hour).padStart(2, '0')}:00-${String(pattern.hour + 1).padStart(2, '0')}:00`,
});
});
}
// Sort by start time
predictedWindows.sort((a, b) => a.startTime.getTime() - b.startTime.getTime());
setPredictions(predictedWindows);
} catch (err) {
const errorMessage = err instanceof Error ? err.message : 'Analysis failed';
setError(errorMessage);
console.error('Predictive windows analysis error:', err);
} finally {
setIsAnalyzing(false);
}
};
analyzePredictiveWindows();
// Refresh every hour
const interval = setInterval(analyzePredictiveWindows, 60 * 60 * 1000);
return () => clearInterval(interval);
}, [daysAhead]);
return {
predictions,
patterns,
isAnalyzing,
error,
nextOptimalWindow: predictions[0],
};
}
|