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b99406671e
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b99406671e | ||
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@ -188,7 +188,7 @@ REDUCERS: Dict[str, Dict[str, Any]] = {
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"label": "PCA",
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"label": "PCA",
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"blurb": "Principal component analysis. Linear, fast, deterministic.",
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"blurb": "Principal component analysis. Linear, fast, deterministic.",
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"key": [
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"key": [
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("n_components", "int", 2, None, "Locked to 2 — flow asserts 2D output."),
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("n_components", "int", 2, None, "Locked."),
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],
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],
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"advanced": [
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"advanced": [
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("svd_solver", "str", "auto", ["auto", "full", "arpack", "randomized"], None),
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("svd_solver", "str", "auto", ["auto", "full", "arpack", "randomized"], None),
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@ -201,7 +201,7 @@ REDUCERS: Dict[str, Dict[str, Any]] = {
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"label": "FactorAnalysis",
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"label": "FactorAnalysis",
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"blurb": "Gaussian latent-factor model with per-feature noise.",
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"blurb": "Gaussian latent-factor model with per-feature noise.",
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"key": [
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"key": [
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("n_components", "int", 2, None, "Locked to 2 — flow asserts 2D output."),
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("n_components", "int", 2, None, "Locked."),
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("random_state", "int", 42, None, None),
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("random_state", "int", 42, None, None),
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],
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],
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"advanced": [
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"advanced": [
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@ -215,7 +215,7 @@ REDUCERS: Dict[str, Dict[str, Any]] = {
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"label": "t-SNE",
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"label": "t-SNE",
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"blurb": "Stochastic neighbour embedding. Local structure preserved.",
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"blurb": "Stochastic neighbour embedding. Local structure preserved.",
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"key": [
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"key": [
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("n_components", "int", 2, None, "Locked to 2 — flow asserts 2D output."),
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("n_components", "int", 2, None, "Locked."),
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("perplexity", "float", 30.0, None, None),
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("perplexity", "float", 30.0, None, None),
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("random_state", "int", 42, None, None),
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("random_state", "int", 42, None, None),
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],
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],
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@ -232,7 +232,7 @@ REDUCERS: Dict[str, Dict[str, Any]] = {
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"label": "UMAP",
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"label": "UMAP",
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"blurb": "Uniform manifold approximation. Preserves local + some global structure.",
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"blurb": "Uniform manifold approximation. Preserves local + some global structure.",
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"key": [
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"key": [
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("n_components", "int", 2, None, "Locked to 2 — flow asserts 2D output."),
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("n_components", "int", 2, None, "Locked."),
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("n_neighbors", "int", 15, None, None),
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("n_neighbors", "int", 15, None, None),
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("min_dist", "float", 0.1, None, None),
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("min_dist", "float", 0.1, None, None),
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("random_state", "int", 42, None, None),
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("random_state", "int", 42, None, None),
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@ -249,7 +249,7 @@ REDUCERS: Dict[str, Dict[str, Any]] = {
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"label": "PaCMAP",
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"label": "PaCMAP",
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"blurb": "Pairwise-controlled manifold approximation. Balanced local/global.",
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"blurb": "Pairwise-controlled manifold approximation. Balanced local/global.",
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"key": [
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"key": [
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("n_components", "int", 2, None, "Locked to 2 — flow asserts 2D output."),
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("n_components", "int", 2, None, "Locked."),
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("n_neighbors", "int", 10, None, None),
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("n_neighbors", "int", 10, None, None),
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("MN_ratio", "float", 0.5, None, None),
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("MN_ratio", "float", 0.5, None, None),
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("FP_ratio", "float", 2.0, None, None),
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("FP_ratio", "float", 2.0, None, None),
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@ -266,7 +266,7 @@ REDUCERS: Dict[str, Dict[str, Any]] = {
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"label": "TriMap",
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"label": "TriMap",
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"blurb": "Triplet-based dimensionality reduction. Emphasises global structure.",
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"blurb": "Triplet-based dimensionality reduction. Emphasises global structure.",
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"key": [
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"key": [
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("n_dims", "int", 2, None, "Locked to 2 — flow asserts 2D output."),
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("n_dims", "int", 2, None, "Locked."),
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("n_inliers", "int", 10, None, None),
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("n_inliers", "int", 10, None, None),
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("n_outliers", "int", 5, None, None),
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("n_outliers", "int", 5, None, None),
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("n_random", "int", 5, None, None),
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("n_random", "int", 5, None, None),
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@ -7,6 +7,18 @@ import sys
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os.environ.setdefault("PREFECT_API_URL", "http://localhost:4200/api")
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os.environ.setdefault("PREFECT_API_URL", "http://localhost:4200/api")
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os.environ.setdefault("DO_NOT_TRACK", "1")
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os.environ.setdefault("DO_NOT_TRACK", "1")
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# Pin per-process thread pools to 1 so Ray's worker parallelism doesn't
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# multiply against BLAS/numba/etc. thread pools — otherwise 4 workers × N
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# cores → thrash. Must be set before numpy/numba/sklearn import, since
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# those libs latch onto these env vars at import time. Ray manages OMP
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# per-task-CPU but does NOT manage NUMBA_NUM_THREADS, which is what
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# PaCMAP/UMAP use for their optimization loops.
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os.environ.setdefault("OMP_NUM_THREADS", "1")
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os.environ.setdefault("MKL_NUM_THREADS", "1")
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os.environ.setdefault("OPENBLAS_NUM_THREADS", "1")
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os.environ.setdefault("NUMEXPR_NUM_THREADS", "1")
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os.environ.setdefault("NUMBA_NUM_THREADS", "1")
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from datetime import timedelta
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from datetime import timedelta
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import math
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import math
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from pathlib import Path
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from pathlib import Path
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