Updated LKJ notebook to PyMC 6#875
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View / edit / reply to this conversation on ReviewNB aloctavodia commented on 2026-05-11T06:41:18Z This should also work:
az.add_lines(pc, Rho_actual[0, 1]); |
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* update variational_api_quickstart for PyMC 6 Part of #862. Five distinct breakages addressed with minimal source changes; full Restart & Run All produced fresh outputs and execution metadata. - Cells 9, 21, 50, 53: Approximation.sample(...) now requires an active model context in PyMC 6. Wrapped each call in 'with <model>:'. - Cell 21: az.plot_posterior was removed in ArviZ 1.0. Replaced with az.plot_dist(idata, var_names=[...]), matching the migration pattern used in #875. - Cells 28, 40, 84: pm.callbacks was removed; Tracker and CheckParametersConvergence now live in pm.variational.callbacks. - Cell 100: passing total_size=data.shape (tuple) to a multi-dim observed RV triggers an IndexError in get_scaling under symbolic_normalizing_constant. Changed to total_size=data.shape[0] (int), which is simpler and the intended API regardless. Verified under pymc 6.0.1, pytensor 3.0.3, arviz 1.1.0. All 105 cells execute cleanly under Restart & Run All; pre-commit passes. * address review: migrate to ArviZ 1 plot API Per @aloctavodia's review: - Drop %matplotlib inline; apply az.style.use("arviz-variat") (cell 1). - Install ipywidgets locally; progress-bar warnings cleared from outputs (cell 5 thread). - Replace sns.kdeplot model comparisons with az.plot_dist dict form (cells 10, 50, 53). - az.plot_trace -> az.plot_trace_dist (cell 16). Kept plot_trace_dist over plot_rank_dist to stay consistent with the surrounding "Above are traces for x^2 and sin(x)" narrative. - Update markdown around repeated sample_node.eval(): in modern PyTensor the random graph is compiled, so repeated .eval() returns the cached value. Note this and point to sample_node(size=N) for fresh samples (cell 60). - Replace sns.kdeplot of raw numpy with az.convert_to_dataset + az.plot_dist; the first plot also switches to sample_node(size=N) so it reflects independent samples (cells 61, 64). * address 2nd-round review: legends + visual cleanup Per @aloctavodia's followup: - Cell 10: add legend via pc.add_legend("model") and disable credible_interval/point_estimate visuals to keep only the KDE. - Cell 34: add trailing semicolon to advi.approx to suppress the bare-repr output. Applied the same legend + visuals pattern to the other two multi-model plot_dist calls (cells 50 and 53) for consistency. * strip trailing newline from cell sources (pre-commit fixup) CI's black-jupyter wanted the trailing newline removed from the last line of three code cells; local hooks now match. * fix narrative: sample_node().eval() returns the same draw because PyTensor's compiled function reads the shared RNG state without advancing it, not because the value is "cached" — the previous phrasing was inaccurate. Verified by inspecting RNG state across .eval() calls (state identical) and by compiling an explicit function with updates={rng_shared: next_rng}, which does advance and produces fresh samples. --------- Co-authored-by: Yicheng Yang <[email protected]>
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Pretty straightforward update:
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