docs: update license header in compactts/agent/feature_extractor.py to BSL-1.1

compactts/agent/feature_extractor.py

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+"""Mathematically Rigorous, Scale-Invariant Time-Series Feature Extractor.
+
+Extracts principled statistical, spectral, temporal, and information-theoretic
+signatures from raw time-series trajectories for leak-free autonomous agent dispatching.
+
+Guarantees:
+- Strict Scale Invariance: Multiplying y by any scalar c > 0 yields identical routing features.
+- Strict Zero Leakage: No dataset names, metadata lookups, or benchmark splits.
+- Pure Numerical Input: Operates exclusively on array y in R^T and runtime constraints (H, freq).
+
+Attribution: Daksh Bhalala
+License: Business Source License 1.1 (BSL-1.1)
+"""
+
+from typing import Dict, Any, Union
+import numpy as np
+import pandas as pd
+from scipy import stats
+from scipy.signal import periodogram
+
+
+class TimeSeriesFeatureExtractor:
+    """Scale-invariant, robust feature extractor for autonomous time-series profiling."""
+
+    FREQ_TO_SECONDS = {
+        "10S": 10.0,
+        "5T": 300.0,
+        "10T": 600.0,
+        "15T": 900.0,
+        "H": 3600.0,
+        "D": 86400.0,
+        "W": 604800.0,
+        "M": 2629746.0,
+        "Q": 7889238.0,
+        "A": 31556952.0,
+    }
+
+    FREQ_TO_SEASONALITY = {
+        "10S": 60,
+        "5T": 12,
+        "10T": 6,
+        "15T": 4,
+        "H": 24,
+        "D": 7,
+        "W": 52,
+        "M": 12,
+        "Q": 4,
+        "A": 1,
+    }
+
+    def __init__(self, freq: str = "H"):
+        self.freq = freq
+        self.period = self.FREQ_TO_SEASONALITY.get(freq, 24)
+        self.sampling_interval_seconds = self.FREQ_TO_SECONDS.get(freq, 3600.0)
+
+    def extract(self, series: Union[np.ndarray, list], horizon: int = 24) -> Dict[str, float]:
+        """Extract a scale-invariant, comprehensive feature vector.
+
+        Parameters
+        ----------
+        series : Union[np.ndarray, list]
+            Raw historical trajectory y in R^T.
+        horizon : int
+            Forecast horizon H requested by the user.
+
+        Returns
+        -------
+        Dict[str, float]
+            Scale-invariant features capturing dynamics, intermittency, shifts, and spectrum.
+        """
+        y = np.asarray(series, dtype=np.float64)
+        if y.ndim > 1:
+            y = y.squeeze()
+            if y.ndim > 1:
+                # Multivariate: select primary variate
+                y = y[0] if y.shape[0] < y.shape[1] else y[:, 0]
+
+        # Handle missing values and infs cleanly
+        if np.isnan(y).any() or np.isinf(y).any():
+            s = pd.Series(y).ffill().bfill().to_numpy()
+            y = np.nan_to_num(s, nan=0.0).astype(np.float64)
+
+        n = len(y)
+        if n < 4:
+            return self._fallback_features(n, horizon)
+
+        # 1. Physical Support Characteristics
+        min_val = float(np.min(y))
+        max_val = float(np.max(y))
+        is_strictly_positive = 1.0 if min_val >= 0.0 else 0.0
+        val_range = max_val - min_val
+
+        # 2. Intermittency & Sparsity
+        # Ratio of exact or near-zeros (relative to overall scale)
+        threshold = 1e-5 if val_range < 1e-4 else 1e-4 * val_range
+        zero_ratio = float(np.mean(np.abs(y) <= threshold))
+
+        # 3. Scale-Normalized Moments
+        mean_val = float(np.mean(y))
+        std_val = float(np.std(y))
+        if std_val < 1e-8:
+            return self._constant_series_features(n, horizon, is_strictly_positive)
+
+        # Z-score normalization for scale invariance
+        z = (y - mean_val) / std_val
+
+        # Higher-order normalized moments
+        try:
+            kurt = float(stats.kurtosis(z))
+            skew = float(stats.skew(z))
+        except Exception:
+            kurt, skew = 0.0, 0.0
+
+        # 4. Non-Stationarity & Regime Shifts
+        # Evaluate divergence between historical past and recent window
+        half = n // 2
+        past_z = z[:half]
+        recent_z = z[half:]
+        mean_shift = abs(float(np.mean(recent_z)) - float(np.mean(past_z)))
+        std_shift = abs(float(np.std(recent_z)) - float(np.std(past_z)))
+
+        # 5. Temporal Autocorrelation & Stationarity
+        # First lag autocorrelation (persistence)
+        if n > 2:
+            try:
+                ac1 = float(np.corrcoef(z[1:], z[:-1])[0, 1])
+                if not np.isfinite(ac1):
+                    ac1 = 0.0
+            except Exception:
+                ac1 = 0.0
+        else:
+            ac1 = 0.0
+
+        # Seasonal lag autocorrelation
+        S = self.period
+        seasonal_corr = 0.0
+        if n > 2 * S and S > 1:
+            try:
+                ac_s = float(np.corrcoef(z[S:], z[:-S])[0, 1])
+                if np.isfinite(ac_s):
+                    seasonal_corr = max(0.0, ac_s)
+            except Exception:
+                seasonal_corr = 0.0
+
+        # 6. Spectral Energy & Seasonality Purity
+        # Computes power spectrum via periodogram
+        spectral_entropy = 0.5
+        dominant_freq_energy_ratio = 0.0
+        if n >= 16:
+            try:
+                _, pxx = periodogram(z)
+                pxx_sum = np.sum(pxx)
+                if pxx_sum > 1e-8:
+                    pxx_norm = pxx / pxx_sum
+                    # Shannon entropy of power spectrum (normalized to [0, 1])
+                    pxx_pos = pxx_norm[pxx_norm > 1e-12]
+                    h_spec = -np.sum(pxx_pos * np.log(pxx_pos))
+                    spectral_entropy = float(h_spec / np.log(len(pxx)))
+                    dominant_freq_energy_ratio = float(np.max(pxx_norm))
+            except Exception:
+                pass
+
+        # 7. ARCH Volatility Clustering (Autocorrelation of squared returns)
+        diff_z = np.diff(z)
+        vol_clustering = 0.0
+        if len(diff_z) > 4:
+            sq_diff = diff_z**2
+            sq_std = float(np.std(sq_diff))
+            if sq_std > 1e-6:
+                try:
+                    c_arch = float(np.corrcoef(sq_diff[1:], sq_diff[:-1])[0, 1])
+                    if np.isfinite(c_arch):
+                        vol_clustering = max(0.0, c_arch)
+                except Exception:
+                    pass
+
+        # 8. Horizon-to-Context Ratio
+        horizon_ratio = float(horizon / min(n, 512))
+
+        return {
+            "length": float(n),
+            "zero_ratio": zero_ratio,
+            "is_strictly_positive": is_strictly_positive,
+            "kurtosis": kurt,
+            "skewness": skew,
+            "mean_shift": mean_shift,
+            "std_shift": std_shift,
+            "autocorr_lag1": ac1,
+            "seasonal_corr": seasonal_corr,
+            "spectral_entropy": spectral_entropy,
+            "dominant_spectral_energy": dominant_freq_energy_ratio,
+            "volatility_clustering": vol_clustering,
+            "horizon_ratio": horizon_ratio,
+            "sampling_seconds_log": float(np.log10(max(self.sampling_interval_seconds, 1.0))),
+        }
+
+    def _fallback_features(self, n: int, horizon: int) -> Dict[str, float]:
+        return {
+            "length": float(n),
+            "zero_ratio": 0.0,
+            "is_strictly_positive": 1.0,
+            "kurtosis": 0.0,
+            "skewness": 0.0,
+            "mean_shift": 0.0,
+            "std_shift": 0.0,
+            "autocorr_lag1": 0.0,
+            "seasonal_corr": 0.0,
+            "spectral_entropy": 1.0,
+            "dominant_spectral_energy": 0.0,
+            "volatility_clustering": 0.0,
+            "horizon_ratio": float(horizon / max(n, 1)),
+            "sampling_seconds_log": float(np.log10(max(self.sampling_interval_seconds, 1.0))),
+        }
+
+    def _constant_series_features(self, n: int, horizon: int, is_pos: float) -> Dict[str, float]:
+        res = self._fallback_features(n, horizon)
+        res["is_strictly_positive"] = is_pos
+        res["autocorr_lag1"] = 1.0
+        res["spectral_entropy"] = 0.0
+        return res