week-05.ipynb
{"cells":[{"cell_type":"code","execution_count":1,"source":["# INFO: IPython extension for auto reloading modified custom packages\"\"\"\n","%load_ext autoreload\n","%autoreload 2\n","\n","\n","# INFO: IPython extension for package/system spec output\n","%load_ext watermark\n","\n","\n","# INFO: Core imports for practicaly any data science activity\n","import numpy as np\n","import pandas as pd\n","\n","\n","# INFO: Customize settings for Pandas\n","pd.options.display.max_columns = 500\n","pd.options.display.max_rows = 500\n","pd.options.display.max_colwidth = 500\n","\n","# INFO: to display dataframes as tables on call\n","from IPython.display import display\n","\n","\n","# INFO: Plotting setup (matplotlib is only for compatibility with legacy code)\n","# import matplotlib.pyplot as plt\n","%matplotlib inline\n","import plotly.io as pio\n","import plotly.express as px\n","import plotly.graph_objects as go\n","\n","\n","# INFO: Customize plotting backend for Pandas (matplotlib for compat)\n","pd.options.plotting.backend = \"plotly\"\n","# pd.options.plotting.backend = \"matplotlib\"\n","\n","\n","# INFO: Customize Plotly theme\n","pio.templates.default = \"plotly_dark\"\n","# pio.templates.default = \"plotly\"\n","\n","\n","# INFO: Logging setup (replaces 'print' in development & seamlessly transitions to production code)\n","import logging\n","import sys\n","\n","root = logging.getLogger()\n","root.setLevel(logging.INFO)\n","\n","handler = logging.StreamHandler(sys.stdout)\n","handler.setLevel(logging.INFO)\n","formatter = logging.Formatter(\"%(asctime)s - %(name)s - %(levelname)s - %(message)s\")\n","handler.setFormatter(formatter)\n","root.addHandler(handler)\n","\n","# INFO: Use logging like this:\n","logging.info(\"Logging is set!\")\n","\n","\n","# INFO: Call for package/system spec output\n","%watermark --iversions"],"outputs":[{"output_type":"stream","name":"stdout","text":["The autoreload extension is already loaded. To reload it, use:\n"," %reload_ext autoreload\n","2022-04-25 23:58:24,713 - root - INFO - Logging is set!\n","matplotlib: 3.5.1\n","logging : 0.5.1.2\n","numpy : 1.22.3\n","plotly : 5.6.0\n","pandas : 1.4.1\n","sys : 3.10.4 (main, Mar 25 2022, 00:00:00) [GCC 11.2.1 20220127 (Red Hat 11.2.1-9)]\n","\n"]}],"metadata":{}},{"cell_type":"code","execution_count":2,"source":["# INFO: PPL specific imports\n","\n","import jax.numpy as jnp\n","from jax import random\n","\n","import numpyro\n","import numpyro.distributions as dist\n","import numpyro.optim as optim\n","from numpyro.infer import SVI, Trace_ELBO, Predictive\n","from numpyro.infer import MCMC, NUTS\n","from numpyro.infer.autoguide import AutoLaplaceApproximation, AutoNormal\n","\n","from jax import lax, random\n","from jax.scipy.special import expit\n","\n","import arviz as az"],"outputs":[],"metadata":{}},{"cell_type":"code","execution_count":3,"source":["data_uri = \"https://raw.githubusercontent.com/rmcelreath/rethinking/master/data/NWOGrants.csv\"\n","df_dev = pd.read_csv(data_uri, sep=\";\")\n","df_dev.head()\n","df_dev[\"gender\"] = df_dev[\"gender\"] == \"m\"\n","df_dev[\"gender\"] = df_dev[\"gender\"].astype(int)"],"outputs":[],"metadata":{}},{"cell_type":"code","execution_count":4,"source":["# INFO: total effect for starters; DAG synced with answers (coz my personal DAG is different)\n","\n","\n","def model(data: pd.DataFrame, observed=True):\n"," applications = data[\"applications\"].values\n"," awards = data[\"awards\"].values\n","\n"," discipline = data[\"discipline\"].values\n"," discipline_card = np.unique(discipline).shape[0]\n"," gender = data[\"gender\"].values\n"," gender_card = np.unique(gender).shape[0]\n","\n"," alpha_gender = numpyro.sample(\"alpha_gender\", dist.Normal(-1, 1).expand([gender_card]))\n"," logit_p = numpyro.deterministic(\"logit_p\", alpha_gender[gender_card])\n","\n"," # alpha_gender = numpyro.sample(\"alpha_gender\", dist.Normal(-1, 1).expand([gender_card, discipline_card]))\n"," # logit_p = numpyro.deterministic(\"logit_p\", alpha_gender[gender_card, discipline_card])\n","\n"," numpyro.sample(\"awards\", dist.Binomial(total_count=applications, logits=logit_p), obs=awards if observed else None)\n"," # numpyro.sample(\"awards\", dist.Binomial(total_count=applications, probs=logit_p), obs=awards if observed else None)\n"," # numpyro.sample(\n"," # \"awards\", dist.Binomial(total_count=applications, logits=expit(logit_p)), obs=awards if observed else None\n"," # )\n","\n"," # dist.Binomial(applications, logits=logits)\n","\n"," # with numpyro.plate(\"applications\", applications):\n"," # alpha_gender = numpyro.sample(\"alpha_gender\", dist.Normal(-1, 1).expand([gender_card]))\n"," # logit_p = numpyro.deterministic(\"logit_p\", alpha_gender[gender_card])\n","\n"," # numpyro.sample(\"obs\", dist.Bernoulli(logit_p), obs=data)\n","\n","\n","kernel = NUTS(model)\n","mcmc = MCMC(\n"," kernel,\n"," # num_warmup=500,\n"," num_warmup=1000,\n"," # num_warmup=2000,\n"," # num_samples=2000,\n"," num_samples=5000,\n"," # num_samples=10_000,\n"," num_chains=1,\n"," # num_chains=4,\n"," progress_bar=True,\n",")\n","mcmc.run(random.PRNGKey(0), df_dev)\n","samples = mcmc.get_samples()\n","\n","numpyro.diagnostics.print_summary(samples, prob=0.89, group_by_chain=False)"],"outputs":[{"output_type":"stream","name":"stdout","text":["2022-04-25 23:58:57,453 - absl - INFO - Unable to initialize backend 'tpu_driver': NOT_FOUND: Unable to find driver in registry given worker: \n","2022-04-25 23:58:57,454 - absl - INFO - Unable to initialize backend 'gpu': NOT_FOUND: Could not find registered platform with name: \"cuda\". Available platform names are: Host Interpreter\n","2022-04-25 23:58:57,455 - absl - INFO - Unable to initialize backend 'tpu': INVALID_ARGUMENT: TpuPlatform is not available.\n"]},{"output_type":"stream","name":"stderr","text":["sample: 100%|██████████| 6000/6000 [00:10<00:00, 584.37it/s, 1023 steps of size 1.63e-03. acc. prob=0.84]\n"]},{"output_type":"stream","name":"stdout","text":["\n"," mean std median 5.5% 94.5% n_eff r_hat\n","alpha_gender[0] -0.98 0.99 -0.99 -2.50 0.62 952.90 1.00\n","alpha_gender[1] -1.62 0.05 -1.62 -1.71 -1.54 586.28 1.00\n"," logit_p -1.62 0.05 -1.62 -1.71 -1.54 586.28 1.00\n","\n"]}],"metadata":{}},{"cell_type":"code","execution_count":5,"source":["az.plot_trace(az.from_numpyro(mcmc))"],"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[<AxesSubplot:title={'center':'alpha_gender'}>,\n"," <AxesSubplot:title={'center':'alpha_gender'}>],\n"," [<AxesSubplot:title={'center':'logit_p'}>,\n"," <AxesSubplot:title={'center':'logit_p'}>]], dtype=object)"]},"metadata":{},"execution_count":5},{"output_type":"display_data","data":{"image/png":"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","text/plain":["<Figure size 864x288 with 4 Axes>"]},"metadata":{"needs_background":"dark"}}],"metadata":{}}],"nbformat":4,"nbformat_minor":2,"metadata":{"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":3},"orig_nbformat":4}}