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Commit 5de2a7e

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s-mishras-mishra
committedSep 2, 2021
Adding code for question 2
1 parent bed0fbb commit 5de2a7e

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2 files changed

+1605
-271
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‎data/happiness.csv

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1,0,2

‎week4/swapnil_solutions.ipynb

+304-271
Original file line numberDiff line numberDiff line change
@@ -26,7 +26,8 @@
2626
"numpyro.set_platform(\"cpu\")\n",
2727
"numpyro.set_host_device_count(4)\n",
2828
"rng_key = random.PRNGKey(0)\n",
29-
"%config InlineBackend.figure_format ='retina'"
29+
"%config InlineBackend.figure_format ='retina'\n",
30+
"from causalgraphicalmodels import CausalGraphicalModel"
3031
]
3132
},
3233
{
@@ -272,12 +273,302 @@
272273
},
273274
{
274275
"cell_type": "code",
275-
"execution_count": null,
276+
"execution_count": 5,
276277
"id": "c43e55ab-abba-438a-89c1-29f611251244",
277278
"metadata": {},
279+
"outputs": [
280+
{
281+
"data": {
282+
"image/svg+xml": [
283+
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284+
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285+
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286+
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287+
" -->\n",
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" viewBox=\"0.00 0.00 173.80 116.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
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"<!-- happiness -->\n",
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"<g id=\"node1\" class=\"node\">\n",
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"<title>happiness</title>\n",
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"<text text-anchor=\"middle\" x=\"46.8\" y=\"-86.3\" font-family=\"Times,serif\" font-size=\"14.00\">happiness</text>\n",
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"</g>\n",
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"<text text-anchor=\"middle\" x=\"92.8\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">marriage</text>\n",
304+
"</g>\n",
305+
"<!-- happiness&#45;&gt;marriage -->\n",
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"<g id=\"edge1\" class=\"edge\">\n",
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"<title>happiness&#45;&gt;marriage</title>\n",
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"</g>\n",
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"<!-- age -->\n",
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"<g id=\"node3\" class=\"node\">\n",
313+
"<title>age</title>\n",
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"<ellipse fill=\"none\" stroke=\"black\" cx=\"138.8\" cy=\"-90\" rx=\"27\" ry=\"18\"/>\n",
315+
"<text text-anchor=\"middle\" x=\"138.8\" y=\"-86.3\" font-family=\"Times,serif\" font-size=\"14.00\">age</text>\n",
316+
"</g>\n",
317+
"<!-- age&#45;&gt;marriage -->\n",
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"<g id=\"edge2\" class=\"edge\">\n",
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"<title>age&#45;&gt;marriage</title>\n",
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331+
"metadata": {},
332+
"output_type": "execute_result"
333+
}
334+
],
335+
"source": [
336+
"happiness = CausalGraphicalModel(\n",
337+
" nodes=[\"happiness\", \"marriage\", \"age\"],\n",
338+
" edges=[\n",
339+
" (\"happiness\", \"marriage\"), \n",
340+
" (\"age\", \"marriage\")\n",
341+
" ]\n",
342+
")\n",
343+
"happiness.draw()"
344+
]
345+
},
346+
{
347+
"cell_type": "markdown",
348+
"id": "80322ad5-1849-4665-9bbe-eb7072b31fb4",
349+
"metadata": {},
350+
"source": [
351+
"I have ended up getting data from R package and saving it in the data folder; reading it now and performing the edits done in the code from book"
352+
]
353+
},
354+
{
355+
"cell_type": "code",
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"execution_count": 6,
357+
"id": "9676a1a8-960b-4bc8-a85e-dbfd1ede0097",
358+
"metadata": {},
359+
"outputs": [
360+
{
361+
"name": "stdout",
362+
"output_type": "stream",
363+
"text": [
364+
" age married happiness\n",
365+
"count 1300.000000 1300.000000 1.300000e+03\n",
366+
"mean 33.000000 0.300769 -9.018427e-17\n",
367+
"std 18.768883 0.458769 1.214421e+00\n",
368+
"min 1.000000 0.000000 -2.000000e+00\n",
369+
"25% 17.000000 0.000000 -1.000000e+00\n",
370+
"50% 33.000000 0.000000 -1.110223e-16\n",
371+
"75% 49.000000 1.000000 1.000000e+00\n",
372+
"max 65.000000 1.000000 2.000000e+00\n",
373+
" age married happiness modified_age\n",
374+
"count 960.00000 960.000000 9.600000e+02 960.000000\n",
375+
"mean 41.50000 0.407292 -8.881784e-17 0.500000\n",
376+
"std 13.86062 0.491586 1.214587e+00 0.294907\n",
377+
"min 18.00000 0.000000 -2.000000e+00 0.000000\n",
378+
"25% 29.75000 0.000000 -1.000000e+00 0.250000\n",
379+
"50% 41.50000 0.000000 -1.110223e-16 0.500000\n",
380+
"75% 53.25000 1.000000 1.000000e+00 0.750000\n",
381+
"max 65.00000 1.000000 2.000000e+00 1.000000\n"
382+
]
383+
}
384+
],
385+
"source": [
386+
"df_data = pd.read_csv(\"../data/happiness.csv\")\n",
387+
"print(df_data.describe())\n",
388+
"df_data = df_data[df_data.age>17] # getting adults\n",
389+
"df_data = df_data.assign(modified_age = (df_data.age - 18)/(65-18))\n",
390+
"print(df_data.describe())"
391+
]
392+
},
393+
{
394+
"cell_type": "code",
395+
"execution_count": 7,
396+
"id": "ce5f937e-935c-479d-8337-58d802a3ab08",
397+
"metadata": {},
278398
"outputs": [],
279399
"source": [
280-
"## Need to do this but it is basically same as question 3, I was just lazy"
400+
"def model6_9(age, married, happiness=None):\n",
401+
" n = len(set(married))\n",
402+
" alpha = numpyro.sample(\"alpha\", dist.Normal(0,1), sample_shape=(n,))\n",
403+
" beta = numpyro.sample(\"beta\", dist.Normal(0, 2))\n",
404+
" sigma = numpyro.sample(\"sigma\", dist.Exponential(1))\n",
405+
" mu = numpyro.deterministic('mu', alpha[married] + beta * age)\n",
406+
" numpyro.sample(\"happiness\", dist.Normal(mu, sigma), obs=happiness)\n",
407+
"def model6_10(age, happiness=None):\n",
408+
" alpha = numpyro.sample(\"alpha\", dist.Normal(0,1))\n",
409+
" beta = numpyro.sample(\"beta\", dist.Normal(0, 2))\n",
410+
" sigma = numpyro.sample(\"sigma\", dist.Exponential(1))\n",
411+
" mu = numpyro.deterministic('mu', alpha + beta * age)\n",
412+
" numpyro.sample(\"happiness\", dist.Normal(mu, sigma), obs=happiness)"
413+
]
414+
},
415+
{
416+
"cell_type": "code",
417+
"execution_count": 8,
418+
"id": "354c7223-00dc-4332-acee-dd6f120b2ba7",
419+
"metadata": {},
420+
"outputs": [
421+
{
422+
"name": "stdout",
423+
"output_type": "stream",
424+
"text": [
425+
"\n",
426+
" mean std median 5.0% 95.0% n_eff r_hat\n",
427+
" alpha[0] -0.24 0.06 -0.24 -0.33 -0.12 2601.73 1.00\n",
428+
" alpha[1] 1.26 0.09 1.26 1.12 1.39 2582.98 1.00\n",
429+
" beta -0.75 0.11 -0.75 -0.93 -0.56 2264.58 1.00\n",
430+
" sigma 0.99 0.02 0.99 0.96 1.03 4972.01 1.00\n",
431+
"\n",
432+
"Number of divergences: 0\n"
433+
]
434+
}
435+
],
436+
"source": [
437+
"kernel6_9 = NUTS(model6_9)\n",
438+
"m6_9 = MCMC(kernel6_9, num_warmup=1000, num_samples=2000,num_chains=4,progress_bar=False)\n",
439+
"m6_9.run(rng_key, age = df_data.modified_age.values, married = df_data.married.values, happiness=df_data.happiness.values)\n",
440+
"m6_9.print_summary()"
441+
]
442+
},
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{
444+
"cell_type": "code",
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"execution_count": 9,
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"id": "1833082a-a33d-4d3c-8750-de4226067156",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
453+
"\n",
454+
" mean std median 5.0% 95.0% n_eff r_hat\n",
455+
" alpha -0.00 0.08 -0.00 -0.13 0.13 2429.78 1.00\n",
456+
" beta 0.00 0.13 0.00 -0.23 0.21 2489.96 1.00\n",
457+
" sigma 1.22 0.03 1.22 1.17 1.26 4679.18 1.00\n",
458+
"\n",
459+
"Number of divergences: 0\n"
460+
]
461+
}
462+
],
463+
"source": [
464+
"kernel6_10 = NUTS(model6_10)\n",
465+
"m6_10 = MCMC(kernel6_10, num_warmup=1000, num_samples=2000,num_chains=4,progress_bar=False)\n",
466+
"m6_10.run(rng_key, age = df_data.modified_age.values, happiness=df_data.happiness.values)\n",
467+
"m6_10.print_summary()"
468+
]
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"Basically we have model with wrong causal inference doing better prediction, it is expected here if you look at causal graph knowing marriage is useful for predicting happiness but that doesn't give us right causal effect for age. To get causal effect of age if we condition on marriage in our model then path gets openend up as it is a collider."
281572
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" rank loo p_loo d_loo weight se \\\n",
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642-
"\n",
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" dse warning loo_scale \n",
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" warnings.warn(\n",
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"/Users/smishra/anaconda3/envs/statrethinking/lib/python3.8/site-packages/arviz/stats/stats.py:1405: UserWarning: For one or more samples the posterior variance of the log predictive densities exceeds 0.4. This could be indication of WAIC starting to fail. \n",
677-
"See http://arxiv.org/abs/1507.04544 for details\n",
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" warnings.warn(\n",
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"/Users/smishra/anaconda3/envs/statrethinking/lib/python3.8/site-packages/arviz/stats/stats.py:1405: UserWarning: For one or more samples the posterior variance of the log predictive densities exceeds 0.4. This could be indication of WAIC starting to fail. \n",
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" rank waic p_waic d_waic weight se dse \\\n",
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788-
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"source": [
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"az.compare({'model_1': m1,\n",
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