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andrewherrenandrewherren
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Updated python demos
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5 files changed

+36
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‎demo/debug/causal_inference.py

Lines changed: 2 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -87,8 +87,8 @@
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plt.axline((0, 0), slope=1, color="black", linestyle=(0, (3,3)))
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plt.show()
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90-
sigma_df_mcmc = pd.DataFrame(np.concatenate((np.expand_dims(np.arange(bcf_model.num_samples),axis=1), np.expand_dims(bcf_model.global_var_samples,axis=1)), axis = 1), columns=["Sample", "Sigma"])
91-
sns.scatterplot(data=sigma_df_mcmc, x="Sample", y="Sigma")
90+
sigma_df_mcmc = pd.DataFrame(np.concatenate((np.expand_dims(np.arange(bcf_model.num_samples),axis=1), np.expand_dims(bcf_model.global_var_samples,axis=1)), axis = 1), columns=["Sample", "Sigma^2"])
91+
sns.scatterplot(data=sigma_df_mcmc, x="Sample", y="Sigma^2")
9292
plt.show()
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9494
b_df_mcmc = pd.DataFrame(np.concatenate((np.expand_dims(np.arange(bcf_model.num_samples),axis=1), np.expand_dims(bcf_model.b0_samples,axis=1), np.expand_dims(bcf_model.b1_samples,axis=1)), axis = 1), columns=["Sample", "Beta_0", "Beta_1"])

‎demo/debug/supervised_learning.py

Lines changed: 6 additions & 6 deletions
Original file line numberDiff line numberDiff line change
@@ -66,8 +66,8 @@ def outcome_mean(X, W):
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plt.axline((0, 0), slope=1, color="black", linestyle=(0, (3,3)))
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plt.show()
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69-
sigma_df_mcmc = pd.DataFrame(np.concatenate((np.expand_dims(np.arange(bart_model.num_samples),axis=1), np.expand_dims(bart_model.global_var_samples,axis=1)), axis = 1), columns=["Sample", "Sigma"])
70-
sns.scatterplot(data=sigma_df_mcmc, x="Sample", y="Sigma")
69+
sigma_df_mcmc = pd.DataFrame(np.concatenate((np.expand_dims(np.arange(bart_model.num_samples),axis=1), np.expand_dims(bart_model.global_var_samples,axis=1)), axis = 1), columns=["Sample", "Sigma^2"])
70+
sns.scatterplot(data=sigma_df_mcmc, x="Sample", y="Sigma^2")
7171
plt.show()
7272

7373
# Compute the test set RMSE
@@ -89,8 +89,8 @@ def outcome_mean(X, W):
8989
plt.axline((0, 0), slope=1, color="black", linestyle=(0, (3,3)))
9090
plt.show()
9191

92-
sigma_df_mcmc = pd.DataFrame(np.concatenate((np.expand_dims(np.arange(bart_model.num_samples),axis=1), np.expand_dims(bart_model.global_var_samples,axis=1)), axis = 1), columns=["Sample", "Sigma"])
93-
sns.scatterplot(data=sigma_df_mcmc, x="Sample", y="Sigma")
92+
sigma_df_mcmc = pd.DataFrame(np.concatenate((np.expand_dims(np.arange(bart_model.num_samples),axis=1), np.expand_dims(bart_model.global_var_samples,axis=1)), axis = 1), columns=["Sample", "Sigma^2"])
93+
sns.scatterplot(data=sigma_df_mcmc, x="Sample", y="Sigma^2")
9494
plt.show()
9595

9696
# Compute the test set RMSE
@@ -110,8 +110,8 @@ def outcome_mean(X, W):
110110
plt.axline((0, 0), slope=1, color="black", linestyle=(0, (3,3)))
111111
plt.show()
112112

113-
sigma_df_mcmc = pd.DataFrame(np.concatenate((np.expand_dims(np.arange(bart_model.num_samples),axis=1), np.expand_dims(bart_model.global_var_samples,axis=1)), axis = 1), columns=["Sample", "Sigma"])
114-
sns.scatterplot(data=sigma_df_mcmc, x="Sample", y="Sigma")
113+
sigma_df_mcmc = pd.DataFrame(np.concatenate((np.expand_dims(np.arange(bart_model.num_samples),axis=1), np.expand_dims(bart_model.global_var_samples,axis=1)), axis = 1), columns=["Sample", "Sigma^2"])
114+
sns.scatterplot(data=sigma_df_mcmc, x="Sample", y="Sigma^2")
115115
plt.show()
116116

117117
# Compute the test set RMSE

‎demo/notebooks/prototype_interface.ipynb

Lines changed: 18 additions & 18 deletions
Original file line numberDiff line numberDiff line change
@@ -346,9 +346,9 @@
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"forest_preds_mcmc = forest_preds[:, num_warmstart:num_samples]\n",
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"\n",
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"# Global error variance\n",
349-
"sigma_samples = np.sqrt(global_var_samples) * y_std\n",
350-
"sigma_samples_gfr = sigma_samples[:num_warmstart]\n",
351-
"sigma_samples_mcmc = sigma_samples[num_warmstart:num_samples]"
349+
"sigma2_samples = global_var_samples * y_std * y_std\n",
350+
"sigma2_samples_gfr = sigma2_samples[:num_warmstart]\n",
351+
"sigma2_samples_mcmc = sigma2_samples[num_warmstart:num_samples]"
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]
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},
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{
@@ -384,13 +384,13 @@
384384
" np.concatenate(\n",
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" (\n",
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" np.expand_dims(np.arange(num_warmstart), axis=1),\n",
387-
" np.expand_dims(sigma_samples_gfr, axis=1),\n",
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" np.expand_dims(sigma2_samples_gfr, axis=1),\n",
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" ),\n",
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" axis=1,\n",
390390
" ),\n",
391-
" columns=[\"Sample\", \"Sigma\"],\n",
391+
" columns=[\"Sample\", \"Sigma^2\"],\n",
392392
")\n",
393-
"sns.scatterplot(data=sigma_df_gfr, x=\"Sample\", y=\"Sigma\")\n",
393+
"sns.scatterplot(data=sigma_df_gfr, x=\"Sample\", y=\"Sigma^2\")\n",
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"plt.show()"
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]
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},
@@ -427,13 +427,13 @@
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" np.concatenate(\n",
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" (\n",
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" np.expand_dims(np.arange(num_samples - num_warmstart), axis=1),\n",
430-
" np.expand_dims(sigma_samples_mcmc, axis=1),\n",
430+
" np.expand_dims(sigma2_samples_mcmc, axis=1),\n",
431431
" ),\n",
432432
" axis=1,\n",
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" ),\n",
434-
" columns=[\"Sample\", \"Sigma\"],\n",
434+
" columns=[\"Sample\", \"Sigma^2\"],\n",
435435
")\n",
436-
"sns.scatterplot(data=sigma_df_mcmc, x=\"Sample\", y=\"Sigma\")\n",
436+
"sns.scatterplot(data=sigma_df_mcmc, x=\"Sample\", y=\"Sigma^2\")\n",
437437
"plt.show()"
438438
]
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},
@@ -909,9 +909,9 @@
909909
"forest_preds_tau_mcmc = forest_preds_tau[:, num_warmstart:num_samples]\n",
910910
"\n",
911911
"# Global error variance\n",
912-
"sigma_samples = np.sqrt(global_var_samples) * y_std\n",
913-
"sigma_samples_gfr = sigma_samples[:num_warmstart]\n",
914-
"sigma_samples_mcmc = sigma_samples[num_warmstart:num_samples]\n",
912+
"sigma2_samples = global_var_samples * y_std * y_std\n",
913+
"sigma2_samples_gfr = sigma2_samples[:num_warmstart]\n",
914+
"sigma2_samples_mcmc = sigma2_samples[num_warmstart:num_samples]\n",
915915
"\n",
916916
"# Adaptive coding parameters\n",
917917
"b_1_samples_gfr = b_1_samples[:num_warmstart] * y_std\n",
@@ -969,13 +969,13 @@
969969
" np.concatenate(\n",
970970
" (\n",
971971
" np.expand_dims(np.arange(num_warmstart), axis=1),\n",
972-
" np.expand_dims(sigma_samples_gfr, axis=1),\n",
972+
" np.expand_dims(sigma2_samples_gfr, axis=1),\n",
973973
" ),\n",
974974
" axis=1,\n",
975975
" ),\n",
976-
" columns=[\"Sample\", \"Sigma\"],\n",
976+
" columns=[\"Sample\", \"Sigma^2\"],\n",
977977
")\n",
978-
"sns.scatterplot(data=sigma_df_gfr, x=\"Sample\", y=\"Sigma\")\n",
978+
"sns.scatterplot(data=sigma_df_gfr, x=\"Sample\", y=\"Sigma^2\")\n",
979979
"plt.show()"
980980
]
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},
@@ -1050,13 +1050,13 @@
10501050
" np.concatenate(\n",
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" (\n",
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" np.expand_dims(np.arange(num_samples - num_warmstart), axis=1),\n",
1053-
" np.expand_dims(sigma_samples_mcmc, axis=1),\n",
1053+
" np.expand_dims(sigma2_samples_mcmc, axis=1),\n",
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" ),\n",
10551055
" axis=1,\n",
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" ),\n",
1057-
" columns=[\"Sample\", \"Sigma\"],\n",
1057+
" columns=[\"Sample\", \"Sigma^2\"],\n",
10581058
")\n",
1059-
"sns.scatterplot(data=sigma_df_mcmc, x=\"Sample\", y=\"Sigma\")\n",
1059+
"sns.scatterplot(data=sigma_df_mcmc, x=\"Sample\", y=\"Sigma^2\")\n",
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"plt.show()"
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]
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},

‎demo/notebooks/serialization.ipynb

Lines changed: 2 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -173,9 +173,9 @@
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" ),\n",
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" axis=1,\n",
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" ),\n",
176-
" columns=[\"Sample\", \"Sigma\"],\n",
176+
" columns=[\"Sample\", \"Sigma^2\"],\n",
177177
")\n",
178-
"sns.scatterplot(data=sigma_df_mcmc, x=\"Sample\", y=\"Sigma\")\n",
178+
"sns.scatterplot(data=sigma_df_mcmc, x=\"Sample\", y=\"Sigma^2\")\n",
179179
"plt.show()"
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]
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},

‎demo/notebooks/supervised_learning.ipynb

Lines changed: 8 additions & 8 deletions
Original file line numberDiff line numberDiff line change
@@ -172,9 +172,9 @@
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" ),\n",
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" axis=1,\n",
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" ),\n",
175-
" columns=[\"Sample\", \"Sigma\"],\n",
175+
" columns=[\"Sample\", \"Sigma^2\"],\n",
176176
")\n",
177-
"sns.scatterplot(data=sigma_df_mcmc, x=\"Sample\", y=\"Sigma\")\n",
177+
"sns.scatterplot(data=sigma_df_mcmc, x=\"Sample\", y=\"Sigma^2\")\n",
178178
"plt.show()"
179179
]
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},
@@ -260,9 +260,9 @@
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" ),\n",
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" axis=1,\n",
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" ),\n",
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" columns=[\"Sample\", \"Sigma\"],\n",
263+
" columns=[\"Sample\", \"Sigma^2\"],\n",
264264
")\n",
265-
"sns.scatterplot(data=sigma_df_mcmc, x=\"Sample\", y=\"Sigma\")\n",
265+
"sns.scatterplot(data=sigma_df_mcmc, x=\"Sample\", y=\"Sigma^2\")\n",
266266
"plt.show()"
267267
]
268268
},
@@ -346,9 +346,9 @@
346346
" ),\n",
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" axis=1,\n",
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" ),\n",
349-
" columns=[\"Sample\", \"Sigma\"],\n",
349+
" columns=[\"Sample\", \"Sigma^2\"],\n",
350350
")\n",
351-
"sns.scatterplot(data=sigma_df_mcmc, x=\"Sample\", y=\"Sigma\")\n",
351+
"sns.scatterplot(data=sigma_df_mcmc, x=\"Sample\", y=\"Sigma^2\")\n",
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"plt.show()"
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]
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},
@@ -371,7 +371,7 @@
371371
],
372372
"metadata": {
373373
"kernelspec": {
374-
"display_name": "venv",
374+
"display_name": "stochtree-dev",
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"language": "python",
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"name": "python3"
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},
@@ -385,7 +385,7 @@
385385
"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
388-
"version": "3.12.9"
388+
"version": "3.10.16"
389389
}
390390
},
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"nbformat": 4,

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