Regenerate R parity cache for NNS 13.1

.github/workflows/pr118-live-r-debug.yml

@@ -0,0 +1,66 @@
+name: PR118 live R internal diagnostics
+on:
+  pull_request:
+    branches: [main]
+permissions:
+  contents: read
+jobs:
+  diagnose:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - uses: actions/setup-python@v5
+        with:
+          python-version: '3.11'
+      - uses: r-lib/actions/setup-r@v2
+        with:
+          r-version: release
+          use-public-rspm: true
+      - run: |
+          sudo apt-get update
+          sudo apt-get install -y libgsl-dev libjpeg-dev libpng-dev libtiff5-dev libfreetype6-dev libharfbuzz-dev libfribidi-dev xorg-dev
+          curl -fL https://raw.githubusercontent.com/OVVO-Financial/NNS/1d4d1e9ccc872ccc25eacfc7153a5ff8c4abd172/NNS_13.1.tar.gz -o /tmp/NNS_13.1.tar.gz
+          mkdir -p /tmp/nns && tar -xzf /tmp/NNS_13.1.tar.gz -C /tmp/nns --strip-components=1
+          Rscript -e "options(repos=c(CRAN='https://cloud.r-project.org')); install.packages(c('remotes','jsonlite')); remotes::install_deps('/tmp/nns', dependencies=NA, upgrade='never')"
+          R CMD INSTALL /tmp/NNS_13.1.tar.gz
+          python -m pip install -U pip
+          python -m pip install build scikit-build-core nanobind pytest 'numpy<2.5' scipy hypothesis pytest-benchmark pytest-xdist
+          python -m pip install -e . --force-reinstall --no-deps
+      - name: Compare unresolved internals
+        continue-on-error: true
+        run: |
+          Rscript - <<'RS' > live-r-debug.txt 2>&1
+          library(NNS)
+          a <- c(1,2,3,4,5); b <- c(1,2,1,4,3); cc <- c(2,1,3,5,4)
+          cat('R copula3 continuous',NNS.copula(cbind(a,b,cc),continuous=TRUE,plot=FALSE),'\n')
+          cat('R copula3 discrete',NNS.copula(cbind(a,b,cc),continuous=FALSE,plot=FALSE),'\n')
+          dx <- cbind(c(-2,-1,0,1,2),c(1,3,5,7,9)); dy <- dx[,1]^2 + sin(dx[,2])
+          cat('R dy1 first',dy.d_(dx,dy,wrt=1,eval.points='mean',messages=FALSE)$First,'\n')
+          cat('R dy1 second',dy.d_(dx,dy,wrt=1,eval.points='mean',messages=FALSE)$Second,'\n')
+          cat('R dy2 first',dy.d_(dx,dy,wrt=2,eval.points='mean',messages=FALSE)$First,'\n')
+          cat('R dy2 second',dy.d_(dx,dy,wrt=2,eval.points='mean',messages=FALSE)$Second,'\n')
+          t <- 1:70; x <- sin(2*pi*t/7); y <- c(tail(x,1),head(x,-1)) + .05*cos(t/3); tau <- 7
+          make_tau <- function(v,tau){n<-length(v);z<-vector('list',tau+1);for(i in 0:tau)z[[i+1]]<-v[(tau-i+1):(n-i)];do.call(cbind,z)}
+          xn <- unlist(NNS.norm(make_tau(x,tau))[,1]); yn <- unlist(NNS.norm(make_tau(y,tau))[,1]); xy <- NNS.norm(cbind(xn,yn))
+          cat('R xn head',head(xn,8),'\n'); cat('R yn head',head(yn,8),'\n'); cat('R xy head',as.numeric(head(xy,4)),'\n')
+          cat('R Uni xy',NNS:::Uni.caus(x,y,tau,plot=FALSE),'\n'); cat('R Uni yx',NNS:::Uni.caus(y,x,tau,plot=FALSE),'\n'); cat('R caus',NNS.caus(x,y,tau='ts',plot=FALSE),'\n')
+          RS
+          python - <<'PY' >> live-r-debug.txt 2>&1
+          import numpy as np
+          from nns import dy_d,nns_causation,nns_copula,nns_norm
+          from nns.causation import _tau_normalized,_uni_caus
+          a=np.array([1,2,3,4,5.]); b=np.array([1,2,1,4,3.]); c=np.array([2,1,3,5,4.]); a3=np.column_stack((a,b,c))
+          print('PY copula3 continuous',nns_copula(a3,continuous=True)); print('PY copula3 discrete',nns_copula(a3,continuous=False))
+          dx=np.column_stack((np.array([-2,-1,0,1,2.]),np.array([1,3,5,7,9.]))); dy=dx[:,0]**2+np.sin(dx[:,1])
+          print('PY dy1',dy_d(dx,dy,wrt=1,eval_points='mean')); print('PY dy2',dy_d(dx,dy,wrt=2,eval_points='mean'))
+          t=np.arange(1,71,dtype=float); x=np.sin(2*np.pi*t/7); y=np.roll(x,1)+.05*np.cos(t/3)
+          xn,yn=_tau_normalized(x,y,7); xy=nns_norm(np.column_stack((xn,yn)))
+          print('PY xn head',xn[:8].tolist()); print('PY yn head',yn[:8].tolist()); print('PY xy head',xy[:4].ravel(order='F').tolist())
+          print('PY Uni xy',_uni_caus(x,y,7)); print('PY Uni yx',_uni_caus(y,x,7)); print('PY caus',list(nns_causation(x,y,tau='ts').values()))
+          PY
+      - uses: actions/upload-artifact@v4
+        if: always()
+        with:
+          name: pr118-live-r-debug
+          path: live-r-debug.txt
+          if-no-files-found: warn

.github/workflows/pr118-targeted-diagnostics.yml

@@ -0,0 +1,55 @@
+name: PR118 targeted parity diagnostics
+on:
+  pull_request:
+    branches: [main]
+  push:
+    branches: [automation/r-cache-nns-13.1]
+permissions:
+  contents: read
+jobs:
+  diagnose:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - uses: actions/setup-python@v5
+        with:
+          python-version: '3.11'
+      - run: |
+          python -m pip install -U pip
+          python -m pip install build scikit-build-core nanobind pytest 'numpy<2.5' scipy hypothesis pytest-benchmark pytest-xdist
+          python -m pip install -e . --force-reinstall --no-deps
+      - name: Run targeted parity
+        continue-on-error: true
+        run: |
+          NNS_R_CACHE_ONLY=1 python -m pytest -q \
+            tests/parity/test_original_dependence.py \
+            tests/parity/test_diff.py \
+            tests/parity/test_r13_smoke.py \
+            tests/parity/test_var.py \
+            tests/parity/test_causation.py > pr118-targeted.log 2>&1
+      - name: Record seasonal lag diagnostics
+        run: |
+          python - <<'PY' > pr118-seasonal.txt
+          import numpy as np
+          from nns import nns_causation
+          from nns.seasonality import nns_seas
+          t=np.arange(1,71,dtype=float)
+          x=np.sin(2*np.pi*t/7)
+          y=np.roll(x,1)+0.05*np.cos(t/3)
+          print('x periods',nns_seas(x,plot=False)['periods'].tolist())
+          print('y periods',nns_seas(y,plot=False)['periods'].tolist())
+          for tx in range(1,9):
+            for ty in range(1,9):
+              a=nns_causation(x,y,tau=ty)
+              b=nns_causation(y,x,tau=tx)
+              print('same_tau',tx,ty,list(a.values()),list(b.values()))
+          t=np.arange(1,81,dtype=float)
+          z=[np.sin(2*np.pi*t/5),np.sin(2*np.pi*t/6+0.2),np.sin(2*np.pi*t/7+0.5)]
+          print('matrix periods',[nns_seas(v,plot=False)['periods'].tolist() for v in z])
+          PY
+      - uses: actions/upload-artifact@v4
+        with:
+          name: pr118-targeted-diagnostics
+          path: |
+            pr118-targeted.log
+            pr118-seasonal.txt

src/nns/diff.py

@@ -556,7 +556,7 @@ def _nns_copula_matrix(values: NDArray[np.float64]) -> float:
 
     discrete_d_pm = _dpm_nd(data, target, 0.0, norm=True)
     continuous_d_pm = _dpm_nd(data, target, 1.0, norm=True)
-    independent_d_pm = 1.0 - (0.5**n_cols)
+    independent_d_pm = 1.0 - 2.0 * (0.5**n_cols)
     n_dim_discrete_dep = abs(discrete_d_pm - independent_d_pm) / independent_d_pm
     n_dim_continuous_dep = abs(continuous_d_pm - independent_d_pm) / independent_d_pm
 

src/nns/norm.py

@@ -48,7 +48,7 @@ def nns_norm(
         if len({item.size for item in series}) > 1:
             return _norm_unequal_series(series)
         values = _as_matrix(np.column_stack(series), "x")
-    means = np.mean(values, axis=0)
+    means = np.mean(values.astype(np.longdouble), axis=0).astype(np.float64)
     means = means.copy()
     means[means == 0.0] = 1e-10
     ratio_grid = means[:, np.newaxis] * (1.0 / means[np.newaxis, :])

src/nns/seasonality.py

@@ -225,10 +225,7 @@ def _cv_stat(values: NDArray[np.float64], var_cov: float, exact_cv: bool) -> flo
 
 
 def _mean(values: NDArray[np.float64]) -> float:
-    if values.size >= 16:
-        mean = float(np.sum(values)) / float(values.size)
-        if abs(mean) > 1e-12:
-            return mean
+    # Match R NNS_seas_cpp::vec_mean exactly: sequential accumulation.
     total = 0.0
     for value in values:
         total += float(value)
@@ -243,15 +240,12 @@ def _mean_exact(values: NDArray[np.float64]) -> float:
 
 
 def _sample_sd_from_mean(values: NDArray[np.float64], mean: float, *, exact: bool = False) -> float:
-    if not exact and values.size >= 16 and abs(mean) > 1e-12:
-        ss = float(np.dot(values, values)) - float(values.size) * mean * mean
-        if ss < 0.0:
-            ss = 0.0
-    else:
-        ss = 0.0
-        for value in values:
-            delta = float(value) - mean
-            ss += delta * delta
+    del exact
+    # Match R NNS_seas_cpp::vec_sd exactly: sequential squared deviations.
+    ss = 0.0
+    for value in values:
+        delta = float(value) - mean
+        ss += delta * delta
     return math.sqrt(ss / float(values.size - 1))
 
 

src/nns/stack.py

@@ -77,7 +77,7 @@ def _spearman_scores(x: NDArray[np.float64], y: NDArray[np.float64]) -> NDArray[
     y_rank = _rank_average(y)
     scores = np.empty(x.shape[1], dtype=np.float64)
     for col in range(x.shape[1]):
-        scores[col] = abs(round(_pearson(_rank_average(x[:, col]), y_rank), 2))
+        scores[col] = abs(_pearson(_rank_average(x[:, col]), y_rank))
     scores[~np.isfinite(scores)] = 0.0
     return scores
 
@@ -645,7 +645,7 @@ def caus_coef() -> NDArray[np.float64]:
             coef = np.ones(p, dtype=np.float64)
         elif dim_red_value == "all":
             coef = np.mean(
-                np.column_stack((caus_coef(), dep_coef(), cor_coef(), np.ones(p))), axis=1
+                np.column_stack((caus_coef(), dep_coef(), cor_coef())), axis=1
             )
         else:
             raise ValueError("Unsupported [dim.red.method].")

sync/nns_source.json

@@ -1,8 +1,8 @@
 {
   "r_repo": "OVVO-Financial/NNS",
-  "r_commit": "905b8bbd42b3236bf88aba7f18df7a9a378dbd7b",
-  "r_version": "13.0",
-  "r_src_tree_hash": "654e411bd4e8caabfd57a1a4190eb1d97411e059",
+  "r_commit": "1d4d1e9ccc872ccc25eacfc7153a5ff8c4abd172",
+  "r_version": "13.1",
+  "r_src_tree_hash": "1c82fa36a4af3ce6359cc34b3d841afa6bdd1815",
   "core_repo": "OVVO-Financial/NNS-core",
   "core_commit": "cfc25a3469df6460f9224fb976fcb58de9d58068",
   "python_repo": "OVVO-Financial/NNS-python",

sync/pr118-ci-trigger.txt

@@ -0,0 +1 @@
+Temporary CI trigger for PR #118 after VAR and cache-backed test fixes. Remove after validation.

tests/_r.py

@@ -16,8 +16,7 @@
 _CACHE_PATH = Path(__file__).with_name("_r_cache.json")
 _LOCK_PATH = _CACHE_PATH.with_suffix(".lock")
 _SCHEMA_VERSION = 1
-_NNS_VERSION = "13.1-racine-hastie-r2"
-
+_NNS_VERSION = '13.1'
 JsonValue: TypeAlias = None | str | float | list["JsonValue"] | dict[str, "JsonValue"]
 RValue: TypeAlias = (
     None | float | str | list[str | None] | NDArray[np.float64] | dict[str, "RValue"]