Spaces:
Sleeping
Sleeping
File size: 8,417 Bytes
b95e73a e4711a6 b95e73a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 |
"""Pattern analysis for government spending trends."""
from typing import Dict, List, Optional
from collections import defaultdict, Counter
from datetime import datetime
from ..core.base_models import MLModel
class PatternAnalyzer(MLModel):
"""Analyzes patterns in government spending data."""
def __init__(self):
super().__init__("pattern_analyzer")
self._patterns = {}
async def train(self, data: List[Dict], **kwargs) -> Dict:
"""Train pattern analysis model."""
self._patterns = await self._extract_patterns(data)
self._is_trained = True
return {
"status": "trained",
"samples": len(data),
"patterns_found": len(self._patterns),
"model": self.model_name
}
async def predict(self, data: List[Dict]) -> List[Dict]:
"""Analyze patterns in new data."""
patterns = await self._extract_patterns(data)
pattern_analysis = []
for pattern_type, pattern_data in patterns.items():
pattern_analysis.append({
"pattern_type": pattern_type,
"pattern_data": pattern_data,
"confidence": self._calculate_confidence(pattern_data),
"significance": self._calculate_significance(pattern_data)
})
return pattern_analysis
async def evaluate(self, data: List[Dict]) -> Dict:
"""Evaluate pattern analysis."""
patterns = await self.predict(data)
return {
"total_patterns": len(patterns),
"high_confidence_patterns": len([p for p in patterns if p["confidence"] > 0.7]),
"significant_patterns": len([p for p in patterns if p["significance"] > 0.6])
}
async def _extract_patterns(self, data: List[Dict]) -> Dict:
"""Extract spending patterns from data."""
patterns = {
"temporal": self._analyze_temporal_patterns(data),
"supplier": self._analyze_supplier_patterns(data),
"value": self._analyze_value_patterns(data),
"category": self._analyze_category_patterns(data)
}
return patterns
def _analyze_temporal_patterns(self, data: List[Dict]) -> Dict:
"""Analyze temporal spending patterns."""
monthly_spending = defaultdict(float)
for item in data:
# Extract month from date (simplified)
date_str = item.get("data", "")
if date_str:
try:
# Assume format YYYY-MM-DD or similar
month = date_str[:7] # YYYY-MM
value = float(item.get("valor", 0))
monthly_spending[month] += value
except (ValueError, TypeError):
continue
return {
"monthly_totals": dict(monthly_spending),
"peak_months": self._find_peak_periods(monthly_spending),
"seasonal_trends": self._detect_seasonal_trends(monthly_spending)
}
def _analyze_supplier_patterns(self, data: List[Dict]) -> Dict:
"""Analyze supplier patterns."""
supplier_counts = Counter()
supplier_values = defaultdict(float)
for item in data:
supplier = item.get("fornecedor", {}).get("nome", "Unknown")
value = float(item.get("valor", 0))
supplier_counts[supplier] += 1
supplier_values[supplier] += value
return {
"top_suppliers_by_count": supplier_counts.most_common(10),
"top_suppliers_by_value": sorted(
supplier_values.items(),
key=lambda x: x[1],
reverse=True
)[:10],
"supplier_concentration": self._calculate_concentration(supplier_values)
}
def _analyze_value_patterns(self, data: List[Dict]) -> Dict:
"""Analyze value distribution patterns."""
values = [float(item.get("valor", 0)) for item in data if item.get("valor")]
if not values:
return {"error": "No value data available"}
values.sort()
n = len(values)
return {
"total_count": n,
"total_value": sum(values),
"mean_value": sum(values) / n,
"median_value": values[n // 2],
"quartiles": {
"q1": values[n // 4],
"q3": values[3 * n // 4]
},
"outliers": self._detect_value_outliers(values)
}
def _analyze_category_patterns(self, data: List[Dict]) -> Dict:
"""Analyze spending by category."""
category_spending = defaultdict(float)
for item in data:
# Extract category from object description (simplified)
obj_desc = item.get("objeto", "").lower()
category = self._categorize_spending(obj_desc)
value = float(item.get("valor", 0))
category_spending[category] += value
return {
"category_totals": dict(category_spending),
"category_distribution": self._calculate_distribution(category_spending)
}
def _categorize_spending(self, description: str) -> str:
"""Categorize spending based on description."""
categories = {
"technology": ["software", "hardware", "sistema", "tecnologia"],
"services": ["serviço", "consultoria", "manutenção"],
"infrastructure": ["obra", "construção", "reforma"],
"supplies": ["material", "equipamento", "mobiliário"],
"other": []
}
description_lower = description.lower()
for category, keywords in categories.items():
if any(keyword in description_lower for keyword in keywords):
return category
return "other"
def _find_peak_periods(self, monthly_data: Dict) -> List[str]:
"""Find peak spending periods."""
if not monthly_data:
return []
avg_spending = sum(monthly_data.values()) / len(monthly_data)
return [month for month, value in monthly_data.items() if value > avg_spending * 1.5]
def _detect_seasonal_trends(self, monthly_data: Dict) -> Dict:
"""Detect seasonal spending trends."""
# Simplified seasonal analysis
return {"trend": "stable", "seasonality": "low"}
def _calculate_concentration(self, supplier_values: Dict) -> float:
"""Calculate supplier concentration (simplified Herfindahl index)."""
total_value = sum(supplier_values.values())
if total_value == 0:
return 0
concentration = sum((value / total_value) ** 2 for value in supplier_values.values())
return concentration
def _detect_value_outliers(self, sorted_values: List[float]) -> List[float]:
"""Detect value outliers using IQR method."""
n = len(sorted_values)
if n < 4:
return []
q1 = sorted_values[n // 4]
q3 = sorted_values[3 * n // 4]
iqr = q3 - q1
lower_bound = q1 - 1.5 * iqr
upper_bound = q3 + 1.5 * iqr
return [value for value in sorted_values if value < lower_bound or value > upper_bound]
def _calculate_distribution(self, category_data: Dict) -> Dict:
"""Calculate percentage distribution."""
total = sum(category_data.values())
if total == 0:
return {}
return {category: (value / total) * 100 for category, value in category_data.items()}
def _calculate_confidence(self, pattern_data: Dict) -> float:
"""Calculate confidence score for pattern."""
# Simplified confidence calculation
if not pattern_data or isinstance(pattern_data, dict) and not pattern_data:
return 0.0
return 0.8 # Default high confidence for stub
def _calculate_significance(self, pattern_data: Dict) -> float:
"""Calculate significance score for pattern."""
# Simplified significance calculation
if not pattern_data:
return 0.0
return 0.7 # Default medium significance for stub |