Move tests/ to project root

Author: jessegrabowski

.github/workflows/test.yml

@@ -7,6 +7,7 @@ on:
     paths:
       - ".github/workflows/*"
       - "pymc_experimental/**"
+      - "tests/**"
       - "setup.py"
       - "pyproject.toml"
       - "buildosx"
@@ -20,7 +21,7 @@ jobs:
         os: [ubuntu-latest]
         python-version: ["3.10"]
         test-subset:
-          - pymc_experimental/tests
+          - tests
       fail-fast: false
     runs-on: ${{ matrix.os }}
     env:
@@ -58,7 +59,7 @@ jobs:
         os: [windows-latest]
         python-version: ["3.12"]
         test-subset:
-          - pymc_experimental/tests
+          - tests
       fail-fast: false
     runs-on: ${{ matrix.os }}
     env:

codecov.yml

@@ -22,7 +22,7 @@ coverage:
         base: auto
 
 ignore:
-  - "pymc_experimental/tests/*"
+  - "tests/*"
 
 comment:
   layout: "reach, diff, flags, files"

notebooks/SARMA Example.ipynb

@@ -2221,7 +2221,7 @@
    ],
    "source": [
     "airpass = pd.read_csv(\n",
-    "    \"../pymc_experimental/tests/statespace/test_data/airpass.csv\",\n",
+    "    \"../tests/statespace/test_data/airpass.csv\",\n",
     "    parse_dates=True,\n",
     "    date_format=\"%Y %b\",\n",
     "    index_col=0,\n",

notebooks/discrete_markov_chain.ipynb

@@ -2,6 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
+   "id": "e1e16d35",
    "metadata": {},
    "source": [
     "# Discrete Markov Chain Distribution"
@@ -10,6 +11,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
+   "id": "cc15e4cd",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -20,8 +22,21 @@
   {
    "cell_type": "code",
    "execution_count": 2,
+   "id": "7c983797",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'pymc_experimental'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[2], line 11\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyplot\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mplt\u001b[39;00m\n\u001b[1;32m      9\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m ticker \u001b[38;5;28;01mas\u001b[39;00m mtick\n\u001b[0;32m---> 11\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpymc_experimental\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdistributions\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtimeseries\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m DiscreteMarkovChain\n",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'pymc_experimental'"
+     ]
+    }
+   ],
    "source": [
     "import arviz as az\n",
     "import numpy as np\n",
@@ -38,25 +53,18 @@
   },
   {
    "cell_type": "markdown",
+   "id": "a36d5e56",
    "metadata": {},
    "source": [
     "## Demonstration of API "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
+   "id": "05d16797",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/ricardo/Documents/Projects/pymc-experimental/pymc_experimental/distributions/timeseries.py:159: UserWarning: Initial distribution not specified, defaulting to `Categorical.dist(p=pt.full((k_states, ), 1/k_states), shape=...)`. You can specify an init_dist manually to suppress this warning.\n",
-      "  warnings.warn(\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "with pm.Model() as model:\n",
     "    logit_P = pm.Normal(\"logit_P\", sigma=0.1, size=(3, 3))\n",
@@ -66,6 +74,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
+   "id": "911fc178",
    "metadata": {},
    "outputs": [
     {
@@ -131,6 +140,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "5d74be4c",
    "metadata": {},
    "source": [
     "Dims of the output are `(batch, time)`."
@@ -139,6 +149,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
+   "id": "88acfab3",
    "metadata": {},
    "outputs": [
     {
@@ -167,6 +178,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "2d67e342",
    "metadata": {},
    "source": [
     "## Parameter Recovery\n",
@@ -177,6 +189,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
+   "id": "33081e17",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -201,6 +214,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
+   "id": "f92d551a",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -210,6 +224,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
+   "id": "31751f8c",
    "metadata": {
     "scrolled": false
    },
@@ -287,6 +302,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
+   "id": "87bdb1e0",
    "metadata": {},
    "outputs": [
     {
@@ -306,6 +322,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "610b3596",
    "metadata": {},
    "source": [
     "## Hidden markov model\n",
@@ -318,6 +335,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
+   "id": "dc1f9e25",
    "metadata": {},
    "outputs": [
     {
@@ -348,6 +366,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
+   "id": "67f7e824",
    "metadata": {},
    "outputs": [
     {
@@ -524,6 +543,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
+   "id": "1b5d3444",
    "metadata": {
     "scrolled": false
    },
@@ -594,6 +614,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
+   "id": "04de50a9",
    "metadata": {},
    "outputs": [
     {
@@ -758,6 +779,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "44c1f25b",
    "metadata": {},
    "source": [
     "It's quite difficult to sample this model, so I use a very high `target_accept` and a very large number of draws. It seems to be hard to get a sufficient number of effective samples for rare rates without a lot of draws. "
@@ -766,6 +788,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
+   "id": "e382b4d4",
    "metadata": {},
    "outputs": [
     {
@@ -848,6 +871,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "f778d068",
    "metadata": {},
    "source": [
     "### Post-Estimation Diagnostics\n",
@@ -858,6 +882,7 @@
   {
    "cell_type": "code",
    "execution_count": 15,
+   "id": "c45b726c",
    "metadata": {},
    "outputs": [
     {
@@ -887,6 +912,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "b057e84c",
    "metadata": {},
    "source": [
     "...but the trace plots look great! "
@@ -895,6 +921,7 @@
   {
    "cell_type": "code",
    "execution_count": 16,
+   "id": "34bb9eb9",
    "metadata": {},
    "outputs": [
     {
@@ -915,6 +942,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "65bb1c27",
    "metadata": {},
    "source": [
     "Even after 20,000 draws, we only have about 500 samples for the transition probabilities to the more rare state 1."
@@ -923,6 +951,7 @@
   {
    "cell_type": "code",
    "execution_count": 17,
+   "id": "2d050b4e",
    "metadata": {},
    "outputs": [
     {
@@ -1133,6 +1162,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "aa017801",
    "metadata": {},
    "source": [
     "## Comparison with Statsmodels"
@@ -1141,6 +1171,7 @@
   {
    "cell_type": "code",
    "execution_count": 18,
+   "id": "8200aae3",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1155,6 +1186,7 @@
   {
    "cell_type": "code",
    "execution_count": 19,
+   "id": "febd7972",
    "metadata": {},
    "outputs": [
     {
@@ -1196,6 +1228,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "5588a4f6",
    "metadata": {},
    "source": [
     "## Posterior Prediction"
@@ -1204,6 +1237,7 @@
   {
    "cell_type": "code",
    "execution_count": 20,
+   "id": "c4399802",
    "metadata": {},
    "outputs": [
     {
@@ -1266,6 +1300,7 @@
   {
    "cell_type": "code",
    "execution_count": 21,
+   "id": "47c8ce2a",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1279,6 +1314,7 @@
   {
    "cell_type": "code",
    "execution_count": 22,
+   "id": "9c047eb0",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1288,6 +1324,7 @@
   {
    "cell_type": "code",
    "execution_count": 23,
+   "id": "9a8b3055",
    "metadata": {},
    "outputs": [
     {
@@ -1317,6 +1354,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "92eb9a8f",
    "metadata": {},
    "source": [
     "## Regime Inference\n",
@@ -1327,6 +1365,7 @@
   {
    "cell_type": "code",
    "execution_count": 24,
+   "id": "4487098b",
    "metadata": {},
    "outputs": [
     {
@@ -1364,6 +1403,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "7f037fb9",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -1372,9 +1412,9 @@
  "metadata": {
   "hide_input": false,
   "kernelspec": {
-   "display_name": "pymc-experimental",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
-   "name": "pymc-experimental"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -1386,7 +1426,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.11.9"
   },
   "toc": {
    "base_numbering": 1,