{ "cells": [ { "cell_type": "markdown", "id": "91f05010-b82d-42f3-b882-4f689eaa946c", "metadata": {}, "source": [ "(migration_guide)=\n", "# ArviZ migration guide\n", "\n", "We have been working on refactoring ArviZ to allow more flexibility and extensibility of its elements\n", "while keeping as much as possible a friendly user-interface that gives sensible results with little to no arguments.\n", "\n", "One important change is enhanced modularity. Everything will still be available through a common namespace `arviz`,\n", "but ArviZ will now be composed of 3 smaller libraries:\n", "\n", "* [arviz-base](https://arviz-base.readthedocs.io/en/latest/) data related functionality, including converters from different PPLs.\n", "* [arviz-stats](https://arviz-stats.readthedocs.io/en/latest/) for statistical functions and diagnostics.\n", "* [arviz-plots](https://arviz-plots.readthedocs.io/en/latest/) for visual checks built on top of arviz-stats and arviz-base.\n", "\n", "Each library has a minimal set of dependencies, with a lot of functionality built on top of optional dependencies.\n", "This keeps ArviZ smaller and easier to install as you can install only the components you really need. The main examples are:\n", "\n", "* `arviz-base` has no I/O library as a dependency, but you can use `netcdf4`, `h5netcdf` or `zarr` to read and write your data, allowing you to install only the one you need.\n", "* `arviz-plots` has no plotting library as a dependency, but it can generate plots with `matplotlib`, `bokeh` or `plotly` if they are installed." ] }, { "cell_type": "code", "execution_count": 1, "id": "f41bc2a7-3694-4cb1-97b1-8379515da0d6", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [ "remove-cell" ] }, "outputs": [], "source": [ "import xarray as xr\n", "xr.set_options(display_expand_attrs=False, display_expand_coords=False);" ] }, { "cell_type": "code", "execution_count": 2, "id": "4074f836-233b-4b10-8483-d2177cad7424", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "outputs": [], "source": [ "import arviz as az" ] }, { "cell_type": "markdown", "id": "a28ab6bd-d3c1-4f71-981f-3444f39ee249", "metadata": {}, "source": [ "Check all 3 libraries have been exposed correctly:" ] }, { "cell_type": "code", "execution_count": 3, "id": "7c792e9f-9a22-4ba0-ad11-138fbc510784", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Status information for ArviZ 1.0.0rc0\n", "\n", "arviz_base 0.9.0.dev0 available, exposing its functions as part of the `arviz` namespace\n", "arviz_stats 0.9.0.dev available, exposing its functions as part of the `arviz` namespace\n", "arviz_plots 0.9.0.dev0 available, exposing its functions as part of the `arviz` namespace\n" ] } ], "source": [ "print(az.info)" ] }, { "cell_type": "markdown", "id": "dba357f5-cc19-4fe3-918c-6a540723c3e9", "metadata": {}, "source": [ "## `arviz-base`" ] }, { "cell_type": "markdown", "id": "f9f0a3a9", "metadata": {}, "source": [ "### Credible intervals and rcParams\n", "\n", "Some global configuration settings have changed. For example, the default credible interval probability (`ci_prob`) has been updated from 0.94 to 0.89. Using 0.89 produces intervals with lower variability, leading to more stable summaries. At the same time, keeping a non-standard value (rather than 0.90 or 0.95) serves as a friendly reminder that the choice of interval can depend on the problem at hand.\n", "\n", "In addition, a new setting `ci_kind` has been introduced, which defaults to \"eti\" (equal-tailed interval). This controls the method used to compute credible intervals. The alternative is \"hdi\" (highest density interval), which was previously the default.\n", "\n", "\n", "Defaults set via `rcParams` are not fixed rules, they’re meant to be adjusted to fit the needs of your analysis. `rcParams` offers a convenient way to establish global defaults for your workflow, while most functions that compute credible intervals also provide `ci_prob` and `ci_kind` arguments to override these settings locally.\n", "\n", "\n", "You can check all default settings with:" ] }, { "cell_type": "code", "execution_count": 4, "id": "6e2e42a9", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "RcParams({'data.http_protocol': 'https',\n", " 'data.index_origin': 0,\n", " 'data.sample_dims': ('chain', 'draw'),\n", " 'data.save_warmup': False,\n", " 'plot.backend': 'matplotlib',\n", " 'plot.density_kind': 'kde',\n", " 'plot.max_subplots': 40,\n", " 'stats.ci_kind': 'eti',\n", " 'stats.ci_prob': 0.89,\n", " 'stats.envelope_prob': 0.99,\n", " 'stats.ic_compare_method': 'stacking',\n", " 'stats.ic_pointwise': True,\n", " 'stats.ic_scale': 'log',\n", " 'stats.module': 'base',\n", " 'stats.point_estimate': 'mean',\n", " 'stats.round_to': '2g'})" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "az.rcParams" ] }, { "cell_type": "markdown", "id": "b73c5b99-234c-4b5d-ab3e-adfce2fb2edc", "metadata": {}, "source": [ "### `DataTree`\n", "One of the main differences is that the `arviz.InferenceData` object doesn't exist anymore.\n", "`arviz-base` uses {class}`xarray.DataTree` instead. This is a new data structure in xarray,\n", "so it might still have some rough edges, but it is much more flexible and powerful.\n", "To give some examples, I/O will now be more flexible, and any format supported by\n", "xarray is automatically available to you, no need to add wrappers on top of them within ArviZ.\n", "It is also possible to have arbitrary nesting of variables within groups and subgroups.\n", "\n", ":::{important}\n", "Not all the functionality on `xarray.DataTree` will be compatible with ArviZ as it would be too much\n", "work for us to cover and maintain. If there are things you have always wanted to do but\n", "were not possible with `InferenceData` and are now possible with `DataTree` please try\n", "them out, give feedback on them and on desired behaviour for things that still don't work.\n", "After a couple releases the \"ArviZverse\" will stabilize much more, and it might not be\n", "possible to add support for that anymore.\n", ":::" ] }, { "cell_type": "markdown", "id": "236f1871-444a-4771-ab5b-ca95c78a80f2", "metadata": {}, "source": [ "#### What about my existing netcdf/zarr files?\n", "**They are still valid. There have been no changes on this end and we don't plan to make any.**\n", "The underlying functions handling I/O operations have changed, but the effect on your workflows\n", "should be minimal; the arguments continue to be mostly the same, and only some duplicated aliases have been removed:\n", "\n", "| Function in legacy ArviZ | New equivalent in xarray |\n", "|--------------------------|--------------------------|\n", "| arviz.from_netcdf | {func}`arviz.from_netcdf`[^1] |\n", "| arviz.from_zarr | {func}`arviz.from_zarr`[^1] |\n", "| arviz.to_netcdf | - |\n", "| arviz.to_zarr | - |\n", "| arviz.InferenceData.from_netcdf | - |\n", "| arviz.InferenceData.from_zarr | - |\n", "| arviz.InferenceData.to_netcdf | {meth}`xarray.DataTree.to_netcdf` |\n", "| arviz.InferenceData.to_zarr | {meth}`xarray.DataTree.to_zarr` |\n", "\n", "[^1]: In addition to exposing top level functions from the three arviz-xyz libraries,\n", " the main ArviZ library also includes two aliases to {func}`xarray.open_datatree`.\n", "\n", " * `from_zarr` is a `functools.partial` wrapper of `open_datatree` with `engine=\"zarr\"`\n", " already set\n", " * `from_netcdf` is exactly `open_datatree` so you can use the `engine`\n", " keyword to choose explicitly between `netcdf4`, `h5netcdf`\n", " or leave it to xarray's default behaviour and {func}`netcdf_engine_order ` setting.\n", "\n", "Here is an example where we read a file that was saved from an `InferenceData` object using `idata.to_netcdf(\"example.nc\")`." ] }, { "cell_type": "code", "execution_count": 5, "id": "d4f961f4-0997-47d6-9f89-4ab2c1b0ca50", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [ "remove-cell" ] }, "outputs": [], "source": [ "# the example dataset centered_eight was saved as netcdf from an InferenceData object\n", "# Here we load it as DataTree and save again as `example.nc` to allow running the notebook from top to bottom\n", "# This cell is removed from the rendered docs to keep the story clear though\n", "# If reading old netcdf files as DataTree failed this cell would fail, so the only way for the notebook to run\n", "# is for the statement right before this code cell to be true.\n", "az.load_arviz_data(\"centered_eight\").to_netcdf(\"example.nc\", engine=\"h5netcdf\")" ] }, { "cell_type": "code", "execution_count": 6, "id": "64abc25b-2111-4c18-837c-41a8dac2c9b1", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ "\n", "Group: /\n", "β”œβ”€β”€ Group: /posterior\n", "β”‚ Dimensions: (chain: 4, draw: 500, school: 8)\n", "β”‚ Coordinates: (3)\n", "β”‚ Data variables:\n", "β”‚ mu (chain, draw) float64 16kB ...\n", "β”‚ theta (chain, draw, school) float64 128kB ...\n", "β”‚ tau (chain, draw) float64 16kB ...\n", "β”‚ Attributes: (6)\n", "β”œβ”€β”€ Group: /posterior_predictive\n", "β”‚ Dimensions: (chain: 4, draw: 500, school: 8)\n", "β”‚ Coordinates: (3)\n", "β”‚ Data variables:\n", "β”‚ obs (chain, draw, school) float64 128kB ...\n", "β”‚ Attributes: (4)\n", "β”œβ”€β”€ Group: /log_likelihood\n", "β”‚ Dimensions: (chain: 4, draw: 500, school: 8)\n", "β”‚ Coordinates: (3)\n", "β”‚ Data variables:\n", "β”‚ obs (chain, draw, school) float64 128kB ...\n", "β”‚ Attributes: (4)\n", "...\n", "β”œβ”€β”€ Group: /prior_predictive\n", "β”‚ Dimensions: (chain: 1, draw: 500, school: 8)\n", "β”‚ Coordinates: (3)\n", "β”‚ Data variables:\n", "β”‚ obs (chain, draw, school) float64 32kB ...\n", "β”‚ Attributes: (4)\n", "β”œβ”€β”€ Group: /observed_data\n", "β”‚ Dimensions: (school: 8)\n", "β”‚ Coordinates: (1)\n", "β”‚ Data variables:\n", "β”‚ obs (school) float64 64B ...\n", "β”‚ Attributes: (4)\n", "└── Group: /constant_data\n", " Dimensions: (school: 8)\n", " Coordinates: (1)\n", " Data variables:\n", " sigma (school) float64 64B ...\n", " Attributes: (4)" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dt = az.from_netcdf(\"example.nc\")\n", "dt" ] }, { "cell_type": "markdown", "id": "708795ba-6d1c-41a2-bf87-ae92fe15076c", "metadata": {}, "source": [ "#### Other key differences\n", "Because `DataTree` is an xarray object intended for a broader audience; its methods differ from those of `InferenceData`.\n", "\n", "This section goes over the main differences to help migrate code that used `InferenceData` to now use `DataTree`.\n", "\n", "`DataTree` supports an arbitrary level of nesting (as opposed to the exactly 1 level of nesting in\n", "`InferenceData`). To stay consistent, accessing a group always returns a `DataTree`,\n", "even when the group is a leaf (that is, it contains no further subgroups).\n", "\n", "This means that `dt[\"posterior\"]` will now return a `DataTree`.\n", "In many cases this is irrelevant, but there will be some cases where you'll want the\n", "group as a `Dataset` instead. You can achieve this with either `dt[\"posterior\"].dataset` if you only need a view,\n", "or `dt[\"posterior\"].to_dataset()` to get a new copy if you want a mutable Dataset.\n", "\n", "There are no changes at the variable/`DataArray` level. Thus, `dt[\"posterior\"][\"theta\"]` is still\n", "a `DataArray`, accessing its variables is one of the cases where having either `DataTree`\n", "or `Dataset` is irrelevant." ] }, { "cell_type": "markdown", "id": "59d18eb9-17a3-48b1-8982-1cd334d76f8c", "metadata": {}, "source": [ "##### `InferenceData.extend`\n", "\n", "Another extremely common method of `InferenceData` was `.extend`. In this case, the same behaviour can be replicated with {meth}`xarray.DataTree.update` which behaves like the method of the same name in `dict` objects. These are the two equivalences:\n", "\n", "```python\n", "idata.extend(idata_new)\n", "idata_new.update(idata)\n", "# or\n", "idata.extend(idata_new, how=\"right\")\n", "idata.update(idata_new)\n", "```\n", "\n", "The default behaviour in `.extend` was to do a \"left-like merge\". That is, if both `idata` and `idata_new` have an `observed_data` group, `.extend` preserved the one in `idata`\n", "and ignored that group in `idata_new`. Using `.update` with the switched order we get the same behaviour as any repeated groups in `idata` will overwrite the ones in `idata_new`.\n", "For cases that explicitly set `how=\"right\"` then `.update` should use the same order as `.extend` did.\n" ] }, { "cell_type": "markdown", "id": "525db088-1047-4700-a312-717f27ceb470", "metadata": {}, "source": [ "##### `InferenceData.map`\n", "The `.map` method is very similar to {meth}`xarray.DataTree.map_over_datasets`. The main difference is the lack of `groups`, `filter_groups` and `inplace` arguments.\n", "In order to achieve this we need to combine `.map_over_datasets` with either {meth}`~xarray.DataTree.filter` or {meth}`~xarray.DataTree.match`.\n", "\n", "For example, applying a function to only the posterior_predictive and prior_predictive group which used to be\n", "\n", "```python\n", "idata.map(lambda ds: ds + 3, groups=\"_predictive\", filter_groups=\"like\")\n", "```\n", "\n", "can now be _partially_ achieved with (we'll see the the full equivalence later on):" ] }, { "cell_type": "code", "execution_count": 7, "id": "143bfb73-4cc0-49af-91bd-14ac940a2434", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ "\n", "Group: /\n", "β”œβ”€β”€ Group: /posterior_predictive\n", "β”‚ Dimensions: (chain: 4, draw: 500, school: 8)\n", "β”‚ Coordinates: (3)\n", "β”‚ Data variables:\n", "β”‚ obs (chain, draw, school) float64 128kB 41.88 -11.98 ... 30.05 23.99\n", "β”‚ Attributes: (4)\n", "└── Group: /prior_predictive\n", " Dimensions: (chain: 1, draw: 500, school: 8)\n", " Coordinates: (3)\n", " Data variables:\n", " obs (chain, draw, school) float64 32kB 25.03 29.95 ... 61.23 42.78\n", " Attributes: (4)" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dt.match(\"*_predictive\").map_over_datasets(lambda ds: ds + 3)" ] }, { "cell_type": "markdown", "id": "0e1baed1-5bd9-40c0-9d27-0cfae9a2a2ae", "metadata": {}, "source": [ "If we instead want to apply it also to the observed_data group, it is no longer as easy to use glob-like patterns. We can use filter instead to check against a list, which is similar to using a list/tuple as the `groups` argument:" ] }, { "cell_type": "code", "execution_count": 8, "id": "0da8110d-c05a-4e04-8daa-ebee2aef9dd8", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ "\n", "Group: /\n", "β”œβ”€β”€ Group: /posterior_predictive\n", "β”‚ Dimensions: (chain: 4, draw: 500, school: 8)\n", "β”‚ Coordinates: (3)\n", "β”‚ Data variables:\n", "β”‚ obs (chain, draw, school) float64 128kB 41.88 -11.98 ... 30.05 23.99\n", "β”‚ Attributes: (4)\n", "β”œβ”€β”€ Group: /prior_predictive\n", "β”‚ Dimensions: (chain: 1, draw: 500, school: 8)\n", "β”‚ Coordinates: (3)\n", "β”‚ Data variables:\n", "β”‚ obs (chain, draw, school) float64 32kB 25.03 29.95 ... 61.23 42.78\n", "β”‚ Attributes: (4)\n", "└── Group: /observed_data\n", " Dimensions: (school: 8)\n", " Coordinates: (1)\n", " Data variables:\n", " obs (school) float64 64B 31.0 11.0 0.0 10.0 2.0 4.0 21.0 15.0\n", " Attributes: (4)" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dt.filter(\n", " lambda node: node.name in (\"posterior_predictive\", \"prior_predictive\", \"observed_data\")\n", ").map_over_datasets(lambda ds: ds + 3)" ] }, { "cell_type": "markdown", "id": "71f57589-ebb9-48a0-bf90-6f1677a6f4c7", "metadata": {}, "source": [ "In both cases we have created a whole new `DataTree` with only the groups we have filtered and applied functions to.\n", "This is often not what we want when working with `DataTree` objects that follow the InferenceData schema.\n", "The default behaviour of `.map` (or any `InferenceData` method that took a `groups` argument) was to act on the selected groups,\n", "leave the rest untouched and return _all_ groups in the output. We can achieve this and fully reproduce `.map` using also `.update`." ] }, { "cell_type": "code", "execution_count": 9, "id": "50567832-bd9f-4544-9b61-34fee1a24c3c", "metadata": {}, "outputs": [], "source": [ "shifted_dt = dt.copy()\n", "shifted_dt.update(dt.match(\"*_predictive\").map_over_datasets(lambda ds: ds + 3))" ] }, { "cell_type": "code", "execution_count": 10, "id": "adf36a86-a4f2-4188-a920-16138003e825", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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       "        Data variables:\n",
       "            sigma    (school) float64 64B ...\n",
       "        Attributes: (4)
" ], "text/plain": [ "\n", "Group: /\n", "β”œβ”€β”€ Group: /posterior\n", "β”‚ Dimensions: (chain: 4, draw: 500, school: 8)\n", "β”‚ Coordinates: (3)\n", "β”‚ Data variables:\n", "β”‚ mu (chain, draw) float64 16kB ...\n", "β”‚ theta (chain, draw, school) float64 128kB ...\n", "β”‚ tau (chain, draw) float64 16kB ...\n", "β”‚ Attributes: (6)\n", "β”œβ”€β”€ Group: /posterior_predictive\n", "β”‚ Dimensions: (chain: 4, draw: 500, school: 8)\n", "β”‚ Coordinates: (3)\n", "β”‚ Data variables:\n", "β”‚ obs (chain, draw, school) float64 128kB 41.88 -11.98 ... 30.05 23.99\n", "β”‚ Attributes: (4)\n", "β”œβ”€β”€ Group: /log_likelihood\n", "β”‚ Dimensions: (chain: 4, draw: 500, school: 8)\n", "β”‚ Coordinates: (3)\n", "β”‚ Data variables:\n", "β”‚ obs (chain, draw, school) float64 128kB ...\n", "β”‚ Attributes: (4)\n", "...\n", "β”œβ”€β”€ Group: /prior_predictive\n", "β”‚ Dimensions: (chain: 1, draw: 500, school: 8)\n", "β”‚ Coordinates: (3)\n", "β”‚ Data variables:\n", "β”‚ obs (chain, draw, school) float64 32kB 25.03 29.95 ... 61.23 42.78\n", "β”‚ Attributes: (4)\n", "β”œβ”€β”€ Group: /observed_data\n", "β”‚ Dimensions: (school: 8)\n", "β”‚ Coordinates: (1)\n", "β”‚ Data variables:\n", "β”‚ obs (school) float64 64B ...\n", "β”‚ Attributes: (4)\n", "└── Group: /constant_data\n", " Dimensions: (school: 8)\n", " Coordinates: (1)\n", " Data variables:\n", " sigma (school) float64 64B ...\n", " Attributes: (4)" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "shifted_dt" ] }, { "cell_type": "markdown", "id": "fb55d868-d73b-4230-8e37-75f6168e57de", "metadata": {}, "source": [ "In order to replicate the `inplace=True` behaviour you can skip the `.copy` part.\n", "\n", ":::{tip}\n", "Other methods like `.sel` are already present in `DataTree` and generally serve as drop-in replacements.\n", "But there is also the difference of `groups`, `filter_groups` and `inplace`.\n", "The patterns shown here for `.map_over_datasets` can be used with any method we want to apply to a subset of groups.\n", ":::" ] }, { "cell_type": "markdown", "id": "87c27c8f-6712-43c3-99fe-4016c41f3976", "metadata": {}, "source": [ "##### `InferenceData.groups`\n", "`DataTree` continues to have a `.groups` attribute, but due to its support for arbitrary nesting, the groups are returned as unix directory paths:" ] }, { "cell_type": "code", "execution_count": 11, "id": "abe6d584-03b5-4150-ad95-5e78a36126a0", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "('/',\n", " '/posterior',\n", " '/posterior_predictive',\n", " '/log_likelihood',\n", " '/sample_stats',\n", " '/prior',\n", " '/prior_predictive',\n", " '/observed_data',\n", " '/constant_data')" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dt.groups" ] }, { "cell_type": "markdown", "id": "3e9c639a-20dc-4704-93e6-ebf0ef0d3341", "metadata": {}, "source": [ "To check against `.groups` we'd need do something like `f\"/{group}\" in dt.groups` which might be annoying (but necessary if we want to test for groups nested more than one level).\n", "In our case, we usually restrict ourselves to a single level of nesting in which case it can be more convenient to check things against `.children`" ] }, { "cell_type": "code", "execution_count": 12, "id": "037b8ac8-f5c9-4853-b080-2d244a2e4421", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\"posterior\" in dt.children" ] }, { "cell_type": "markdown", "id": "87c4abab-694b-4e21-b478-0f27d11310c7", "metadata": {}, "source": [ "The `.children` attribute is a dict-like view of the nodes at the immediately lower level in the hierarchy. When checking for presence of groups this doesn't matter as we have seen, but to get a list of groups like the old `InferenceData.groups` you need to convert it explicitly:" ] }, { "cell_type": "code", "execution_count": 13, "id": "24430f50-e617-497d-a05f-0d68a716b050", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['posterior',\n", " 'posterior_predictive',\n", " 'log_likelihood',\n", " 'sample_stats',\n", " 'prior',\n", " 'prior_predictive',\n", " 'observed_data',\n", " 'constant_data']" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "list(dt.children)" ] }, { "cell_type": "markdown", "id": "eb0c6f1f", "metadata": {}, "source": [ "### Enhanced converter flexibility\n", "Were you constantly needing to add an extra axis to your data because it didn't have any `chain` dimension? No more!" ] }, { "cell_type": "code", "execution_count": 14, "id": "a4defb20-059e-4f1e-b848-41251b176d04", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "rng = np.random.default_rng()\n", "data = rng.normal(size=1000)" ] }, { "cell_type": "code", "execution_count": 15, "id": "32a32f5d-c811-4277-b254-53b924ce61dc", "metadata": {}, "outputs": [], "source": [ "# arviz_legacy.from_dict({\"posterior\": {\"mu\": data}}) would fail\n", "# unless you did data[None, :] to add the chain dimension\n", "az.rcParams[\"data.sample_dims\"] = \"sample\"" ] }, { "cell_type": "code", "execution_count": 16, "id": "2bee6e26-0601-4cb2-8f3a-7e17d849a47d", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.DataTree>\n",
       "Group: /\n",
       "└── Group: /posterior\n",
       "        Dimensions:  (sample: 1000)\n",
       "        Coordinates: (1)\n",
       "        Data variables:\n",
       "            mu       (sample) float64 8kB 0.4879 -0.4405 0.4089 ... 1.147 -0.3469 0.4506\n",
       "        Attributes: (4)
" ], "text/plain": [ "\n", "Group: /\n", "└── Group: /posterior\n", " Dimensions: (sample: 1000)\n", " Coordinates: (1)\n", " Data variables:\n", " mu (sample) float64 8kB 0.4879 -0.4405 0.4089 ... 1.147 -0.3469 0.4506\n", " Attributes: (4)" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dt = az.from_dict({\"posterior\": {\"mu\": data}})\n", "dt" ] }, { "cell_type": "code", "execution_count": 17, "id": "f5816596-a974-4fbb-aee8-88efaf7a84c7", "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# arviz-stats and arviz-plots also take it into account\n", "az.plot_dist(dt);" ] }, { "cell_type": "markdown", "id": "488e6ecd-1269-4d98-8ae5-1a5e65531d32", "metadata": {}, "source": [ ":::{note}\n", "It is also possible to modify `sample_dims` through arguments to the different functions.\n", ":::\n", "\n", "### New data wrangling features\n", "We have also added multiple functions to help with common data wrangling tasks,\n", "mostly from and to `xarray.Dataset`. For example, you can convert a dataset\n", "to a wide format dataframe with unique combinations of `sample_dims` as its rows,\n", "with {func}`~arviz_base.dataset_to_dataframe`:" ] }, { "cell_type": "code", "execution_count": 18, "id": "e59bc6d4", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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mutheta[Choate]theta[Deerfield]theta[Phillips Andover]theta[Phillips Exeter]theta[Hotchkiss]theta[Lawrenceville]theta[St. Paul's]theta[Mt. Hermon]tau
(0, 0)1.7157232.3173911.4501742.0855502.2270763.0715072.7129723.0837641.4604480.877494
(0, 1)1.9034810.8891700.7429493.1258692.7795242.8347051.5589392.4875031.9843790.802714
(0, 2)1.9034810.8891700.7429493.1258692.7795242.8347051.5589392.4875031.9843790.802714
(0, 3)1.9034810.8891700.7429493.1258692.7795242.8347051.5589392.4875031.9843790.802714
(0, 4)2.0174971.1091200.8188932.7506201.9286701.9831621.0296203.6627442.1675740.767934
.................................
(3, 495)7.75062511.4775895.5783279.3215315.8120955.4370993.0961429.7314097.9483213.020477
(3, 496)6.9223682.7107638.6461363.8078447.5436696.7888816.5950364.0030425.2750162.704639
(3, 497)5.40883611.4063904.4469379.2107756.3310744.1507784.8123029.6932574.9146562.236486
(3, 498)7.7214407.08613912.3118896.58430110.28609310.05016711.8599387.9522689.7544682.989656
(3, 499)10.23715710.46439013.71430610.26166615.18009810.91603015.07090014.92321014.0231293.051559
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2000 rows Γ— 10 columns

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" ], "text/plain": [ " mu theta[Choate] theta[Deerfield] theta[Phillips Andover] \\\n", "(0, 0) 1.715723 2.317391 1.450174 2.085550 \n", "(0, 1) 1.903481 0.889170 0.742949 3.125869 \n", "(0, 2) 1.903481 0.889170 0.742949 3.125869 \n", "(0, 3) 1.903481 0.889170 0.742949 3.125869 \n", "(0, 4) 2.017497 1.109120 0.818893 2.750620 \n", "... ... ... ... ... \n", "(3, 495) 7.750625 11.477589 5.578327 9.321531 \n", "(3, 496) 6.922368 2.710763 8.646136 3.807844 \n", "(3, 497) 5.408836 11.406390 4.446937 9.210775 \n", "(3, 498) 7.721440 7.086139 12.311889 6.584301 \n", "(3, 499) 10.237157 10.464390 13.714306 10.261666 \n", "\n", " theta[Phillips Exeter] theta[Hotchkiss] theta[Lawrenceville] \\\n", "(0, 0) 2.227076 3.071507 2.712972 \n", "(0, 1) 2.779524 2.834705 1.558939 \n", "(0, 2) 2.779524 2.834705 1.558939 \n", "(0, 3) 2.779524 2.834705 1.558939 \n", "(0, 4) 1.928670 1.983162 1.029620 \n", "... ... ... ... \n", "(3, 495) 5.812095 5.437099 3.096142 \n", "(3, 496) 7.543669 6.788881 6.595036 \n", "(3, 497) 6.331074 4.150778 4.812302 \n", "(3, 498) 10.286093 10.050167 11.859938 \n", "(3, 499) 15.180098 10.916030 15.070900 \n", "\n", " theta[St. Paul's] theta[Mt. Hermon] tau \n", "(0, 0) 3.083764 1.460448 0.877494 \n", "(0, 1) 2.487503 1.984379 0.802714 \n", "(0, 2) 2.487503 1.984379 0.802714 \n", "(0, 3) 2.487503 1.984379 0.802714 \n", "(0, 4) 3.662744 2.167574 0.767934 \n", "... ... ... ... \n", "(3, 495) 9.731409 7.948321 3.020477 \n", "(3, 496) 4.003042 5.275016 2.704639 \n", "(3, 497) 9.693257 4.914656 2.236486 \n", "(3, 498) 7.952268 9.754468 2.989656 \n", "(3, 499) 14.923210 14.023129 3.051559 \n", "\n", "[2000 rows x 10 columns]" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# back to default behaviour\n", "az.rcParams[\"data.sample_dims\"] = [\"chain\", \"draw\"]\n", "dt = az.load_arviz_data(\"centered_eight\")\n", "az.dataset_to_dataframe(dt.posterior.dataset)" ] }, { "cell_type": "markdown", "id": "d116a72b-a46b-4cac-959b-0b669ce7012f", "metadata": {}, "source": [ "Note it is also aware of ArviZ naming conventions in addition to using\n", "the `sample_dims` `rcParam`. It can be further customized through a `labeller` argument.\n", "\n", ":::{tip}\n", "If you want to convert to a long format dataframe, you should use\n", "{meth}`xarray.Dataset.to_dataframe` instead.\n", ":::\n", "\n", "## `arviz-stats`\n", "Stats and diagnostics related functionality have also had some changes,\n", "and it should also be noted that out of the 3 new modular libraries it is\n", "currently the one lagging behind a bit more. At the same time,\n", "it does already have several new features that won't be added to legacy ArviZ at any point,\n", "check out its {doc}`arviz_stats:api/index` page for the complete and up to date list\n", "of available functions.\n", "\n", "### Model comparison\n", "For a long time we have been recommending using PSIS-LOO-CV (`loo`) over WAIC.\n", "PSIS-LOO-CV is more robust, has better theoretical properties, and offers diagnostics\n", "to assess the reliability of the estimates. For these reasons, we have decided to remove WAIC\n", "from `arviz-stats`, and instead focus exclusively on PSIS-LOO-CV for model comparison.\n", "We now we offer many new features related to PSIS-LOO-CV. Including:\n", "- Compute weighted expectations, including mean, variance, quantiles, etc. See {func}`~arviz_stats.loo_expectations`.\n", "- Compute predictive metrics such as RMSE, MAE, etc. See {func}`~arviz_stats.loo_metrics`.\n", "- Compute LOO-R2, see {func}`~arviz_stats.loo_r2`.\n", "- Compute CRPS/SCRPS, see {func}`~arviz_stats.loo_score`.\n", "- Compute PSIS-LOO-CV for approximate posteriors. See {func}`~arviz_stats.loo_approximate_posterior`.\n", "\n", "\n", "For a complete list check {doc}`arviz_stats:api/index` and in particular {doc}`arviz_stats:api/index#model-comparison`\n", "\n", "\n", "### `dim` and `sample_dims`\n", "Similarly to the rest of the libraries, most functions take an argument to indicate\n", "which dimensions should be reduced (or considered core dims) in the different computations.\n", "Given `arviz-stats` is the one with behaviour and API closest to xarray itself,\n", "this argument can either be `dim` or `sample_dims` as a way to keep the APIs of ArviZ\n", "and xarray similar.\n", "\n", "Let's see the differences in action. `ess` uses `sample_dims`. This means we can do:" ] }, { "cell_type": "code", "execution_count": 19, "id": "b8417aac", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.DataTree 'posterior'>\n",
       "Group: /posterior\n",
       "    Dimensions:  (school: 8)\n",
       "    Coordinates: (1)\n",
       "    Data variables:\n",
       "        mu       float64 8B 2.115e+03\n",
       "        theta_t  (school) float64 64B 2.25e+03 2.638e+03 ... 1.981e+03 2.42e+03\n",
       "        tau      float64 8B 833.8\n",
       "        theta    (school) float64 64B 2.196e+03 2.322e+03 ... 1.431e+03 2.188e+03
" ], "text/plain": [ "\n", "Group: /posterior\n", " Dimensions: (school: 8)\n", " Coordinates: (1)\n", " Data variables:\n", " mu float64 8B 2.115e+03\n", " theta_t (school) float64 64B 2.25e+03 2.638e+03 ... 1.981e+03 2.42e+03\n", " tau float64 8B 833.8\n", " theta (school) float64 64B 2.196e+03 2.322e+03 ... 1.431e+03 2.188e+03" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dt = az.load_arviz_data(\"non_centered_eight\")\n", "az.ess(dt, sample_dims=[\"chain\", \"draw\"])" ] }, { "cell_type": "markdown", "id": "7324dc47", "metadata": {}, "source": [ "but we can't do:" ] }, { "cell_type": "code", "execution_count": 20, "id": "d1985a11", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Traceback (most recent call last):\n", " File \"/tmp/ipykernel_22572/2159531800.py\", line 2, in \n", " az.ess(dt, sample_dims=[\"school\", \"draw\"])\n", " File \"/home/oriol/Documents/repos_oss/arviz-stats/src/arviz_stats/sampling_diagnostics.py\", line 141, in ess\n", " return data.azstats.ess(\n", " ^^^^^^^^^^^^^^^^^\n", " File \"/home/oriol/Documents/repos_oss/arviz-stats/src/arviz_stats/accessors.py\", line 96, in ess\n", " return self._apply(\n", " ^^^^^^^^^^^^\n", " File \"/home/oriol/Documents/repos_oss/arviz-stats/src/arviz_stats/accessors.py\", line 444, in _apply\n", " group_i: apply_function_to_dataset(\n", " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n", " File \"/home/oriol/Documents/repos_oss/arviz-stats/src/arviz_stats/accessors.py\", line 56, in apply_function_to_dataset\n", " result = func(da, **subset_kwargs)\n", " ^^^^^^^^^^^^^^^^^^^^^^^^^\n", " File \"/home/oriol/Documents/repos_oss/arviz-stats/src/arviz_stats/base/dataarray.py\", line 77, in ess\n", " return apply_ufunc(\n", " ^^^^^^^^^^^^\n", " File \"/home/oriol/bin/miniforge3/envs/general/lib/python3.12/site-packages/xarray/computation/apply_ufunc.py\", line 1267, in apply_ufunc\n", " return apply_dataarray_vfunc(\n", " ^^^^^^^^^^^^^^^^^^^^^^\n", " File \"/home/oriol/bin/miniforge3/envs/general/lib/python3.12/site-packages/xarray/computation/apply_ufunc.py\", line 310, in apply_dataarray_vfunc\n", " result_var = func(*data_vars)\n", " ^^^^^^^^^^^^^^^^\n", " File \"/home/oriol/bin/miniforge3/envs/general/lib/python3.12/site-packages/xarray/computation/apply_ufunc.py\", line 730, in apply_variable_ufunc\n", " broadcast_compat_data(arg, broadcast_dims, core_dims)\n", " File \"/home/oriol/bin/miniforge3/envs/general/lib/python3.12/site-packages/xarray/computation/apply_ufunc.py\", line 675, in broadcast_compat_data\n", " order = tuple(old_dims.index(d) for d in reordered_dims)\n", " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", " File \"/home/oriol/bin/miniforge3/envs/general/lib/python3.12/site-packages/xarray/computation/apply_ufunc.py\", line 675, in \n", " order = tuple(old_dims.index(d) for d in reordered_dims)\n", " ^^^^^^^^^^^^^^^^^\n", "ValueError: tuple.index(x): x not in tuple\n" ] } ], "source": [ "try:\n", " az.ess(dt, sample_dims=[\"school\", \"draw\"])\n", "except Exception as err:\n", " import traceback\n", " traceback.print_exception(err)" ] }, { "cell_type": "markdown", "id": "632daeae", "metadata": {}, "source": [ "This limitation doesn't come from the fact that interpreting the \"school\" dimension as \"chain\"\n", "makes no sense but from the fact that when using `ess` on multiple variables (aka on a Dataset)\n", "all dimensions in `sample_dims` must be present in all variables.\n", "Consequently, the following cell is technically valid even if it still makes no sense conceptually:" ] }, { "cell_type": "code", "execution_count": 21, "id": "8eef0abd", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.DataTree 'posterior'>\n",
       "Group: /posterior\n",
       "    Dimensions:  (chain: 4)\n",
       "    Coordinates: (1)\n",
       "    Data variables:\n",
       "        theta    (chain) float64 32B 1.26e+03 3.788e+03 2.048e+03 357.3\n",
       "        theta_t  (chain) float64 32B 1.72e+03 595.5 711.2 873.5
" ], "text/plain": [ "\n", "Group: /posterior\n", " Dimensions: (chain: 4)\n", " Coordinates: (1)\n", " Data variables:\n", " theta (chain) float64 32B 1.26e+03 3.788e+03 2.048e+03 357.3\n", " theta_t (chain) float64 32B 1.72e+03 595.5 711.2 873.5" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "az.ess(dt, var_names=[\"theta\", \"theta_t\"], sample_dims=[\"school\", \"draw\"])" ] }, { "cell_type": "markdown", "id": "bdca7fec", "metadata": {}, "source": [ "When we restrict the target variables to only \"theta\" and \"theta_t\" we make it so\n", "all variables have both \"school\" and \"draw\" dimension.\n", "\n", "Whenever a computation requires all input variables to share the same set of dimensions, it uses`sample_dims`.\n", "On ArviZ's side this includes `ess`, `rhat` or `mcse`. Xarray only has an example of this: {meth}`~xarray.Dataset.to_stacked_array`.\n", "\n", "On the other hand, `hdi` uses `dim`. This means that both examples we attempted for `ess` and `sample_dims` will work without caveats:" ] }, { "cell_type": "code", "execution_count": 22, "id": "9095c5e7", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.DataTree 'posterior'>\n",
       "Group: /posterior\n",
       "    Dimensions:   (ci_bound: 2, school: 8)\n",
       "    Coordinates: (2)\n",
       "    Data variables:\n",
       "        mu        (ci_bound) float64 16B -0.418 9.684\n",
       "        theta_t   (school, ci_bound) float64 128B -1.166 2.063 ... -1.499 1.626\n",
       "        tau       (ci_bound) float64 16B 0.00136 7.453\n",
       "        theta     (school, ci_bound) float64 128B -1.611 14.14 -2.92 ... -2.91 12.36
" ], "text/plain": [ "\n", "Group: /posterior\n", " Dimensions: (ci_bound: 2, school: 8)\n", " Coordinates: (2)\n", " Data variables:\n", " mu (ci_bound) float64 16B -0.418 9.684\n", " theta_t (school, ci_bound) float64 128B -1.166 2.063 ... -1.499 1.626\n", " tau (ci_bound) float64 16B 0.00136 7.453\n", " theta (school, ci_bound) float64 128B -1.611 14.14 -2.92 ... -2.91 12.36" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dt.azstats.hdi(dim=[\"chain\", \"draw\"])" ] }, { "cell_type": "markdown", "id": "b59f7a66", "metadata": {}, "source": [ "here we have reduced both \"chain\" and \"draw\" dimensions like we did in `ess`.\n", "The only difference is `hdi` also adds a \"ci_bound\" dimension, so instead\n", "of ending up with scalars and variables with a \"school\" dimension only,\n", "we end up with variables that have either \"ci_bound\" or (\"ci_bound\", \"school\") dimensionality.\n", "\n", "Let's continue with the other example:" ] }, { "cell_type": "code", "execution_count": 23, "id": "1e8cf6ad", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.DataTree 'posterior'>\n",
       "Group: /posterior\n",
       "    Dimensions:   (chain: 4, ci_bound: 2)\n",
       "    Coordinates: (2)\n",
       "    Data variables:\n",
       "        mu        (chain, ci_bound) float64 64B -0.5564 9.508 ... -0.918 8.548\n",
       "        theta_t   (chain, ci_bound) float64 64B -1.561 1.58 -1.506 ... -1.485 1.63\n",
       "        tau       (chain, ci_bound) float64 64B 0.04483 7.851 ... 0.006411 7.781\n",
       "        theta     (chain, ci_bound) float64 64B -2.993 12.83 -2.522 ... -3.421 12.13
" ], "text/plain": [ "\n", "Group: /posterior\n", " Dimensions: (chain: 4, ci_bound: 2)\n", " Coordinates: (2)\n", " Data variables:\n", " mu (chain, ci_bound) float64 64B -0.5564 9.508 ... -0.918 8.548\n", " theta_t (chain, ci_bound) float64 64B -1.561 1.58 -1.506 ... -1.485 1.63\n", " tau (chain, ci_bound) float64 64B 0.04483 7.851 ... 0.006411 7.781\n", " theta (chain, ci_bound) float64 64B -2.993 12.83 -2.522 ... -3.421 12.13" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dt.azstats.hdi(dim=[\"school\", \"draw\"])" ] }, { "cell_type": "markdown", "id": "4082a639", "metadata": {}, "source": [ "We are now reducing the subset of `dim` present in each variable. That means\n", "that `mu` and `tau` only have the \"draw\" dimension reduced, whereas `theta` and `theta_t`\n", "have both \"draw\" and \"school\" reduced. Consequently, all variables end up with \n", "(\"chain\", \"ci_bound\") dimensions.\n", "\n", "Computations that can operate over different subsets of the given dimensions use `dim`.\n", "On ArviZ's side this includes functions like `hdi`, `eti` or `kde`. Most xarray functions fall in this category too, some examples are {meth}`~xarray.Dataset.mean`, {meth}`~xarray.Dataset.quantile`, {meth}`~xarray.Dataset.std` or {meth}`~xarray.Dataset.cumsum`." ] }, { "cell_type": "markdown", "id": "29a96aab", "metadata": {}, "source": [ "### Accessors on xarray objects\n", "\n", "We are also taking advantage of the fact that xarray allows third party libraries to register\n", "accessors on its object. This means that _after importing `arviz_stats`_ (or a library that imports\n", "it like `arviz.preview`) DataArrays, Datasets and DataTrees get a new attribute, `azstats`.\n", "This attribute is called accessor and exposes ArviZ functions that act on the object from which\n", "the accessor is used.\n", "\n", "We plan to have most functions available as both top level functions and accessors to help\n", "with discoverability of ArviZ functions. But not all functions can be implemented as\n", "accessors to all objects. Mainly, functions that need multiple groups can be available\n", "on the DataTree accessor, but not on Dataset or DataArray ones. Moreover, at the time of\n", "writing, some functions are only available as one of the two options but should be extended soon.\n", "\n", "We have already used the `azstats` accessor to compute the HDI, now we can check that\n", "we get the same result when using `ess` through the accessor than what we got when using\n", "the top level function:" ] }, { "cell_type": "code", "execution_count": 24, "id": "bb6e5b59", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.DataTree 'posterior'>\n",
       "Group: /posterior\n",
       "    Dimensions:  (school: 8)\n",
       "    Coordinates: (1)\n",
       "    Data variables:\n",
       "        mu       float64 8B 2.115e+03\n",
       "        theta_t  (school) float64 64B 2.25e+03 2.638e+03 ... 1.981e+03 2.42e+03\n",
       "        tau      float64 8B 833.8\n",
       "        theta    (school) float64 64B 2.196e+03 2.322e+03 ... 1.431e+03 2.188e+03
" ], "text/plain": [ "\n", "Group: /posterior\n", " Dimensions: (school: 8)\n", " Coordinates: (1)\n", " Data variables:\n", " mu float64 8B 2.115e+03\n", " theta_t (school) float64 64B 2.25e+03 2.638e+03 ... 1.981e+03 2.42e+03\n", " tau float64 8B 833.8\n", " theta (school) float64 64B 2.196e+03 2.322e+03 ... 1.431e+03 2.188e+03" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dt.azstats.ess()" ] }, { "cell_type": "markdown", "id": "a6328b1f", "metadata": {}, "source": [ "### Computational backends\n", "\n", "We have also modified a bit how computations accelerated by optional dependencies are handled.\n", "There are no longer dedicated \"flag classes\" like we had for Numba and Dask. Instead,\n", "low level stats functions are implemented in classes so we can subclass and reimplement only\n", "bottleneck computations (with the rest of the computations being inherited from the base class).\n", "\n", "The default computational backend is controlled by `rcParams[\"stats.module\"]` which can be\n", "\"base\", \"numba\" or a user defined custom computational module[^2]. \n", "\n", "[^2]: User defined modules are valid when doing `rcParams[\"stats.module\"] = module` but can't\n", " can't be set as the default through the `arvizrc` configuration file." ] }, { "cell_type": "code", "execution_count": 25, "id": "04dfa78d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "165 ms Β± 6.68 ms per loop (mean Β± std. dev. of 7 runs, 10 loops each)\n" ] } ], "source": [ "dt = az.load_arviz_data(\"radon\")\n", "az.rcParams[\"stats.module\"] = \"base\"\n", "%timeit dt.azstats.histogram(dim=\"draw\")" ] }, { "cell_type": "code", "execution_count": 26, "id": "1c4ae822", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "92.7 ms Β± 4.04 ms per loop (mean Β± std. dev. of 7 runs, 1 loop each)\n" ] } ], "source": [ "az.rcParams[\"stats.module\"] = \"numba\"\n", "%timeit dt.azstats.histogram(dim=\"draw\")" ] }, { "cell_type": "code", "execution_count": 27, "id": "860cbf1d", "metadata": {}, "outputs": [], "source": [ "az.rcParams[\"stats.module\"] = \"base\"" ] }, { "cell_type": "markdown", "id": "8360d49d", "metadata": {}, "source": [ "The histogram method is one of the re-implemented ones, mostly so it scales better to larger data.\n", "However, it should be noted that we haven't really done much profiling nor in-depth optimization\n", "efforts. Please open issues if you notice performance regressions or open issues/PRs to \n", "discuss and implement faster versions of the bottleneck methods.\n", "\n", "### Array interface\n", "It is also possible to install `arviz-stats` without xarray or `arviz-base` in which case,\n", "only a subset of the functionality is available, and through an array only API.\n", "This API has little to no defaults or assumptions baked into it, leaving all the choices\n", "to the user who has to be explicit in every call.\n", "\n", "Due to the dependencies\n", "needed to install this minimal version of `arviz-stats` being only NumPy and SciPy\n", "we hope it will be particularly useful to other developers.\n", "PPL developers can for example use `arviz-stats` for MCMC diagnostics without having to add\n", "xarray or pandas as dependencies of their library. This will ensure they are using\n", "tested and up to date versions of the diagnostics without having to implement or maintain\n", "them as part of the PPL itself.\n", "\n", "The array interface is covered in detail at the {ref}`arviz_stats:array_interface` page." ] }, { "cell_type": "markdown", "id": "13df89cf", "metadata": {}, "source": [ "## `arviz-plots`\n", "\n", "Out of the 3 libraries, `arviz-plots` is the one with the most changes at all levels,\n", "breaking changes, new features more layers to explore.\n", "\n", "### More and better supported backends!\n", "One of they key efforts of the refactor has been simplifying the way we interface\n", "with the different plotting backends supported.\n", "arviz-plots has more backends: matplotlib, bokeh and plotly are all supported now,\n", "with (mostly) feature parity among them. All while having less backend related code!\n", "\n", "This also means that `az.style` is no longer an alias to `matplotlib.style` but its own\n", "module with similar (reduced API) that sets the style for all compatible and installed\n", "backends (unless a backend is requested explicitly):" ] }, { "cell_type": "code", "execution_count": 28, "id": "0d1da7bd", "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "az.style.use(\"arviz-vibrant\")\n", "dt = az.load_arviz_data(\"centered_eight\")\n", "az.plot_rank(dt, var_names=[\"mu\", \"tau\"], backend=\"matplotlib\");" ] }, { "cell_type": "code", "execution_count": 29, "id": "cffc0108", "metadata": {}, "outputs": [ { "data": { "text/html": [ " \n", " \n", " " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import plotly.io as pio\n", "pio.renderers.default = \"notebook\"\n", "pc = az.plot_rank(dt, var_names=[\"mu\", \"tau\"], backend=\"plotly\")\n", "pc.show()" ] }, { "cell_type": "markdown", "id": "b202d560", "metadata": {}, "source": [ "At the time of writing, there are three cross-backend themes defined by ArviZ:\n", "`arviz-variat`, `arviz-vibrant` and `arviz-cetrino`." ] }, { "cell_type": "markdown", "id": "ff109e20-842f-4d76-a833-e475164475b9", "metadata": {}, "source": [ "### Plotting function inventory\n", "\n", "The following functions have been renamed or restructured:\n", "\n", "| ArviZ <1 | ArviZ >=1 |\n", "|------------------|-------------------|\n", "| plot_bpv | plot_ppc_pit, plot_ppc_tstat |\n", "| plot_dist_comparison | plot_prior_posterior |\n", "| plot_ecdf | plot_dist, plot_ecdf_pit |\n", "| plot_ess | plot_ess, plot_ess_evolution |\n", "| plot_forest | plot_forest, plot_ridge |\n", "| plot_ppc | plot_ppc_dist |\n", "| plot_posterior, plot_density | plot_dist |\n", "| plot_trace | plot_trace_dist, plot_trace_rank |\n", "\n", "Others have had their code rewritten and their arguments updated to some extent,\n", "but kept the same name:\n", "\n", "* plot_autocorr\n", "* plot_bf\n", "* plot_compare\n", "* plot_energy\n", "* plot_khat\n", "* plot_lm\n", "* plot_loo_pit\n", "* plot_mcse\n", "* plot_pair\n", "* plot_parallel\n", "* plot_rank\n", "\n", "The following functions have been added:\n", "\n", "* {func}`~arviz_plots.combine_plots`\n", "* {func}`~arviz_plots.plot_convergence_dist`\n", "* {func}`~arviz_plots.plot_dgof`\n", "* {func}`~arviz_plots.plot_dgof_dist`\n", "* {func}`~arviz_plots.plot_pair_focus`\n", "* {func}`~arviz_plots.plot_ppc_censored`\n", "* {func}`~arviz_plots.plot_ppc_interval`\n", "* {func}`~arviz_plots.plot_ppc_pava`\n", "* {func}`~arviz_plots.plot_ppc_pava_residuals`\n", "* {func}`~arviz_plots.plot_ppc_pit`\n", "* {func}`~arviz_plots.plot_ppc_rootogram`\n", "* {func}`~arviz_plots.plot_psense_dist`\n", "* {func}`~arviz_plots.plot_psense_quantities`\n", "* {func}`~arviz_plots.plot_trace`\n", "\n", "Some functions have been removed and we don't plan to add them:\n", "\n", "* plot_dist (notice we have `plot_dist` but it is a different function)\n", "* plot_kde (this is now part of `plot_dist`)\n", "* plot_violin\n", "\n", "And there are also functions we plan to add but aren't available yet.\n", "\n", "* plot_elpd\n", "* plot_ppc_residuals\n", "* plot_ts\n", "\n", ":::{note}\n", "For now, the documentation for arviz-plots defaults to `latest` which is built\n", "from GitHub with each commit. If you see some of the functions in the last block already\n", "on the example gallery you should be able to try them, but only if you install\n", "the development version! See {ref}`arviz_plots:installation`\n", ":::\n", "\n", "You can see all of them at the {ref}`arviz-plots gallery `." ] }, { "cell_type": "markdown", "id": "d9416392", "metadata": {}, "source": [ "### What to expect from the new plotting functions\n", "\n", "There are two main differences with the plotting functions here in legacy ArviZ:\n", "\n", "1. The way of forwarding arguments to the plotting backends.\n", "2. The return type is now {class}`PlotCollection`, one of the key features of `arviz-plots`.\n", " A quick overview in the context of `plot_xyz` is given here but it later has a section of\n", " its own.\n", "\n", "Other than that, some arguments have been renamed or gotten different defaults,\n", "but nothing major. Note, however, that we have incorporated elements\n", "from grammar of graphics into `arviz-plots`, now that we'll cover the internals\n", "of `plot_xyz` in passing we'll use some terms from grammar of graphics.\n", "If you have never heard about grammar of graphics we recommend you take\n", "a look at {ref}`arviz_plots:overview_plots` before continuing.\n", "\n", "#### kwarg forwarding\n", "Most `plot_xyz` functions now have a `visuals` and a `stats` argument. These arguments\n", "are dictionaries whose keys define where their values are forwarded too. The values\n", "are also dictionaries representing keyword arguments that will be passed downstream\n", "via `**kwargs`. This allows you to send arbitrary keyword arguments to all the different\n", "visual elements or statistical computations that are part of a plot without\n", "bloating the call signature with endless `xyz_kwargs` arguments like in legacy ArviZ.\n", "\n", "These same arguments also allow indicating a visual element should not be added to the plot,\n", "or providing pre computed statistical summaries for faster re-rendering of plots (at the time\n", "of writing pre-computed inputs are only working in `plot_forest` but should be extended soon).\n", "\n", "In addition, the call signature of new plotting functions is `plot_xyz(..., **pc_kwargs)`,\n", "with these `pc_kwargs` being forwarded to the initialization of {class}`PlotCollection`.\n", "This argument allows controlling the layout of the {term}`arviz_plots:figure` as well\n", "as any {term}`arviz_plots:aesthetic mappings` that might be used by the plotting function.\n", "\n", "For a complete notebook introduction on this see {ref}`arviz_plots:plots_intro`\n", "\n", "#### New return type: `PlotCollection`\n", "All `plot_xyz` functions now return a \"plotting manager class\". At the time of writing\n", "this means either {class}`~arviz_plots.PlotCollection` (vast majority of plots) or\n", "{class}`PlotMatrix` (for upcoming `plot_pair` for example).\n", "\n", "These classes are the ones that handle {term}`arviz_plots:faceting` and\n", "{term}`arviz_plots:aesthetic mappings` and allow the `plot_xyz` functions to\n", "focus on the {term}`arviz_plots:visuals` and not on the plot layout or encodings.\n", "\n", "See {ref}`arviz_plots:use_plotcollection` for more details on how to work with\n", "existing `PlotCollection` instances.\n", "\n", "### Plotting manager classes\n", "As we have just mentioned, `plot_xyz` use these plotting manager classes and then return them\n", "as their output. In addition, we hope users will use these classes directly to help them\n", "write custom plotting functions more easily and with more flexibility.\n", "\n", "By using these classes, users should be able to focus on writing smaller functions that take\n", "care of a \"unit of plotting\". You can then use their `.map` methods to apply these plotting\n", "functions as many times as needed given the faceting and aesthetic mappings defined by the user.\n", "Different layouts and different mappings will generally not require changes to these plotting\n", "functions, only to the arguments that define aesthetic mappings and the faceting strategy.\n", "\n", "Take a look at {ref}`arviz_plots:compose_own_plot` if that sounds interesting!" ] }, { "cell_type": "markdown", "id": "4d08e071", "metadata": {}, "source": [ "### Other arviz-plots features\n", "There are also helper functions to help compose or extend existing plotting functions.\n", "For example, we can create a new plot, with a similar layout to that of `plot_trace_dist`\n", "or `plot_rank_dist` but custom diagnostics in each column: distribution, rank and ess evolution:" ] }, { "cell_type": "code", "execution_count": 30, "id": "a862f196-0315-4d55-baf3-9e76fd86df8d", "metadata": { "lines_to_next_cell": 0 }, "outputs": [ 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1RLS0H2Nd8TPCiYhooQinIba1tgda7+fZoRIAEOns2CM0AHvS3miWL1/uGu7WrZtH70BN0Qqbo0ePdg3rgatdek6LNnY74QT705BV9+7dfapsxToAgVi1apXrvp5sHDRokM+v9d7WuI8rECtXrvQYdg8c+jtN3uMComlZCSSQ5a6lxgdAtC4vd999tysgog1xtBNAINaXFe8GSywXsAM7Lit6jKJVCZx++OEHn16nnXuuWbPGNawdV7k3LAGiDcf3AGKdP+f5I60NQiRUJYlkobruw/wQPu+//74JKDrpcY5Wxgk35onwiKZlnurp4dWvXz9b7Xco5onIwnoieul+h+5/2K1jBMV6IvLaj7Gu+BnhRAAIQg+0WtbXvWfxr7/+mp73gSjsERqAfXugdu+tZsSIET6/1nvd4r3uARA+3j2UVVdXN/o81gEIpAe63bt3u4a1MyR/Gsbm5+d7DOt+pxXcx6M93hUUFIR9mhDb7LqstEZhYaFH43ntPMH7YjUQC8uL9p46f/58c79NmzZy6623BuV9gEhbVpYuXeox7LxorUFe7VX5iiuukKOPPtoEsw455BA56aST5I477jCdozXXiy4QbcuKbjtOOOEEj2Dv6tWrW3zdG2+8YQKKThqOB6IZx/cA4Pt5/lCoqqoybXmctI2Prx3DeV9P9K6OC2uF4roP80N4zZgxw3bhAOaJ8ImmZZ7q6eHl3WF2OPc7nJgnIgfrieimle7cO0YYOHCg9O7dW+yA9URktR9jXeGJcCIANKGsrMwjwKQ952uJeF/oAWv//v1dw5WVlbJhwwa+ayDKeoQGEBk9UGunAb5ifQHYk54s2rRpk8dj2gizMawD0Frex2zayYU/cnNzmx1fa2jD4+LiYo/Ks+4d4YRjmgA7Liut9dJLLzW4IJGZmRm26UH0iYTlRYMuDzzwgGv4hhtukA4dOlj+PkAkLivuPeoq7Y130aJFpof83/72t6YHYw266/n/vXv3mnOOr732mlx11VVyxhlnNAg3AtG6rKibb77ZVYG6trZWrr/+etMzdVO+/PJLeeihhzw+y0UXXWTZ9AB2w/E9APh3nj8U1q9f79HJuLbx8fXcq3b64Nz3Ufq5tBMTBEcorvswP4SPts1zD4lpVXbt/CfcmCfCJ5qWeaqnh5f3eQ/33y1cmCciB+uJ2OoYYfLkyWIXrCciq/0Y6wpPnt0CAAA8eq90b6DmT3UK54GoexlfHd9BBx3ENwzYpEfosWPH8lsAUcq713cqTAGRb8GCBaaxr/u+QFMdh7AOQGu5hwCdQUB/tGvXzmO4qKgo7NPk/Xwrpgmw47LSGlrNR8OJ7mgQj1hcXjQYsnPnTlfPsOedd57l7wFE6rLiXDaUNlDTartaLdG98VpTvv/+e7nwwgvlL3/5ixx11FGWTA9g12VFdezYUf75z3/K1VdfLRs3bjQNPyZNmiTnnHOOHH744eb/2oO1NtZ4//335b///a/U1NS4jvGfeuopycjIsGx6ALvh+B4A/DvP3xjdh5g5c6bZz9C2Axpg0v2bbt26yejRo2XixIl+tckJ5FqCsw2C85hB2xatW7dO+vXr59c4otl9990n3377rQme6XZQ9/V0/1U7odbK88cff7ykp6f7NK5QXPdhfgift99+26N9nh5Dex+7+IJ1RPSIpmWe6unhNWfOHI9hfwohKN2GaQdl2gZ4x44dUlFRIW3btjXnOA4++GA54ogjZNy4cabIgq+YJ4InUrcDzBOht2XLFss6RmA9EXp2az/GusIT4UTAT3rC5M477zS942qPn1rWNysry6zcNCV92GGHyYQJE/yqZIDI6DnFu0fZlnTq1KnZ8SH8vvrqK9Og5Mcff5Rdu3aZA0U9gMzLy5NRo0bJ0UcfLYMHDw73ZMLmPUIjOi5oILpoA6jWrjO8n6sNphD5rD4JidB78cUXPYb1JH9cXFyjz2UdgNYqKSnxGE5OTvbr9SkpKR7Der4gUN7j8Pdcg/dnsGKaADsuK/7SClc33XST+et06KGHyjHHHBPyaUF0s/vysnDhQnnjjTfMfT0vdvfdd/vVkAKI9mXFPbiVkJAg1113nSuYqMeTGubVhkht2rSR7du3m0qKL7/8suv9y8rK5Ne//rVZznr16mXJNCG22XVZcerTp49pWKwdQEyfPt00unryySfNrTEOh0NOOOEEueWWWxpcUwOiDcf3AODfef7GfPLJJx7Del5H94+0asZnn30mjz/+uLnec9ttt/l0bdDqNkF6TZFwYtO/9549e8xNOwzTfcYHH3xQLrvsMrn88stbPBcRius+zA/h89Zbb3kMn3baaa0aD+uI6BEtyzzV08NL1wnu81JaWpq5DuQP3cfwrs6lIUW9acdkuq3r2bOnud6kbcZbwjwRXJG4HWCeCA/dF62trXUNjx8/vlUdIyjWE6Fnt/ZjrCs8cZUZaEX1rldffVVWrFhh7uuFaO3d4KeffpLXXnvNXGjWHZjXX3+d7zbCefcYS4WK6PPNN9+YHnIKCwulvLzcXBjUkJM+Pm3aNDnzzDPlkksuMcs7wsvOPUIjvAcaerJHL2Lo9tj9Ysbvfvc7EzB++umnPQ4mERu8l3F/TiBoVVb3hlq6bWAeio6TkJ9++qls3bq1wQlIrWJx8sknm0am2jsW7OeLL74wAVMnPan0y1/+ssnnsw5Aa2njcSuDgN7js6Lxor+NjwknIlaWFX/dddddprMiJ+3URDs/AWJpedHjaO2Ez9kz/S9+8QsZMmSIZeMHIn1Z0Z7Q3UPsul+2f/9+c1/DVO+9955cfPHFZrnp0aOH6Sjr5ptvlnfffVe6du3qMS3a4AWI1mXFm/M8WkvTpg3Qzz//fLnxxhsJJiImcHwPINb5e56/tfsh+h6nn366fPnlly0+nzZB4aXVTh555BETUNT2d80JxXUf5ofw0OIQ2qmJe1ugsWPHBuW9WEdEjmhZ5qmeHj56Xu/+++/3eEzPf3t32mSFNWvWyDXXXCN//vOfParANoZ5IrzsuB1gnojsjhF8xXoiutuPsa7wROVEIAi00fPtt99udmB0J5cqirF5kcgOlQAQuM8//9wcnP7pT3+SE088ka80TOzeIzTsfUFDD0oee+wxU+kYscGKbbiG1t3Hp5U5Ef0nIb/++muzvvC3xzwEjwbPb731Vo/HJk+eLP3792/yNawDYBV/eu1u7PktXYAKxzQBsbKsNOeZZ54xlXzcp0eDiQUFBSGdDsQmOy0vzz77rKxatcp1kVzDIYBd2GFZaaqjIu3d+qGHHpLExMRG/5+fn2+qxGmDBme4ccmSJeZ6EceaiMZlxd3cuXPNMbz2+u7LMvbCCy+YaqPnnHOOCfcGo5EgYFcc3wOIJa05z++ubdu2Jqw0YsQIU5Hc2aBUx/vdd9/Jf//7X9Ohrfv7XXXVVfKf//yn2UqGtAkKDv2NtPrMwIEDTcV5vcaqnWJoR+FfffWVzJgxwyOMqG1yfvWrX8k//vEPU7E+XNd+mR/CQ+cHd6ecckqTx9tNYR0RfaJlmad6evhoO0/v4LPuG/jK4XDIwQcfLGPGjDH7K1oBT0Mq2nGZhozmzZsn//vf/6S6utr1Gt2O6Xx0ww03NDle5ongiOTtAPNE6C1evFjWrl3rsX7Qynv+Yj0RenZtP8a6whPhRMCPCwR64kQ3QgMGDJAuXbqYXtW15wIt+6onS9555x2PXka151zdAD344IN8zxEoEnqgReukpaXJEUccYRqD9O7d2+xg6klO3XlZvny5fPDBB+akqPtv95vf/EYyMzPNQSdCj+URwb6ggeheZ/h7YOm9zSecGLmCdRISoVFTU2MayWvnL065ubkNTjZ5Yx2A1tKLSt69avrD/aSk87gjUN7j8H6PcEwTYMdlxVdaZV07MHGnx/t0RoRYW17Wr18vf//7313Dt9xyCx36IKzsuKzoNGllN++Qoh6jtNRQ8qCDDpIzzzzThK6c9PiTcCKsmC/ttqw4zZ49W66//npzLO80ePBgueCCC2TkyJHSsWNHU7V306ZN8umnn8q//vUv2b59u2nI99JLL8myZcvkueeeo4MwRC2O7wHEqtae51c5OTmmCpGet2mqvY7uZ2jlPa1gfuedd7oahupfvT48c+bMJvffA72WQJsgT0ceeaTZ9xs0aFCj35dedzv66KPN73Lvvfd6VKzRa/zTpk2T6667LmzXfpkfQk879Jk1a5bHY1rNylesI6JXtCzzVE8PD20z9sorr3g8dvfdd5u2I7646KKL5PjjjzeBxMbouY5TTz3VhBD1pucznPSc+yGHHCKHHXZYo69lnrBWNGwHmCfC3zHCySef7HfHCKwnQs/O7cdYV3iidTbgg0mTJskf/vAH6dGjR6P/13CE7pDqRTdtyDF//nyPRk+6w3nGGWdE3XetjViCXX2sa9euJgRqB3brgRatu/CnBxl6Mquxi+66jGuvN3rCVMNM2kBx165dHjs3Glz09WAVwcPyGJuCeUED0Y11RuwJ9klIhIauy3WfzEl/j0cffdR0GOEP1gHwlfcxgr8Nfb2fb1WjeHfO6jvhnCbAjsuKr5V8fve733mcn7niiivk8ssvD8n7IzbZdXm55557XOMeNWqUXw2/gFhaVrRXXPdrIFlZWeb8lC+02oN7OPGbb76xZJoQ2+y6rGzbtk1uu+02j2Ci7mPddNNNJuTrpOdo+vbta26/+MUv5JprrpGvv/7a1Wv5XXfd1aAjCSBacHwPIFYFcp5fO/3Qm6/739o49ZJLLjEdIqgNGzbIm2++aao0h0Kstwk66aSTfHqeNubVa3jaMPjVV191Pf7Pf/5TLrzwQldHo5F+3SfW5wdfzJkzx1Qhc9LjBF8rqirWEbEjWpZ5qqcH34IFC0zbD3fnnXeeHHfccX6FjnxRUFBgOlvS8WsxDKfHHnusyXCiN+aJwETjdoB5wt4dIzixngi9WGo/Vhfh6wrCiQhIrITTzjrrLJ+e1759e3nmmWfkyiuvNKW7naZOnWp2bvytvGd3mjj/9ttvg/oeevLp8MMPl3Cwcw+0kSrc6wytkHj++ef7NB6d71588UU5++yzpaioyDymldi05169oI7QYnlEqC9oIPLXGc6TSc5tsj/7k97b/HDviyL6T0KiIe1VUCtYOmljRq1Ir1UwW8I6AK3l3cOZVlP1x+7duz2G27RpE/CP4T0Of6fJ+/lWTBNgx2XFlwvS2lGJVuZx0gbx2ikREGvLi3am99lnn5n7CQkJJggChJsdlxXneNzPZ2tHle5Bq+bocx0OhyuspefGtQqjr68HImlZef75513XUdS4cePk5ptvbvY12nDkiSeekBNOOEF27txpHnvvvfdM454hQ4ZYMl2AnXB8DyAWBXKevzW0851LL71UnnrqKddjr7/+epPXegJtg0DHcIG5/fbbTef/hYWFZliPvbTj0Mba9ITiug/zQ+i5dzatJk+eHNT3Yx0ROaJlmad6emitWLFCrrrqKo+OXrWDe93eBIvORw8//LCpvKbn/ZydL61cuVJ69+7d4PnME+Flx+0A80RoffTRR6YNuFOfPn1kwIABQX1P1hPR336MdYUnwokISLSH01rDudI75phjpKSkxDy2efNmk9geP358uCcPUdADbSSLtHWGhhr0Arp7jzpvvPGGqaAYyp4EwPKI4F/QQHTRgz73XhZ1mxzIgSXb8Ojn70lIBNdrr71mehR0d8cdd5hKmL5gHYDW6tatm8fwli1b/Hr91q1bG/SYGSjtYEUbIBcXF5threyuF9V87fzI+zNYMU2AHZeV5miPtVOmTPHoREobwWvlOCAWl5cnn3zSdV8bTei+06ZNm/w6RvKeLu20z/viGxDpy4pzurQinFNOTo7Pr9VOszSIsnfvXjOsDZT0XEXbtm0tmTbEJrsuK++//77H8P/93//59DoNKGqFgb/+9a+ux9555x3CiYhKHN8DiDWBnudvrYsvvth0Ku8MCOh5Id0nb2w/nDBaeOk57gsuuMB0OuykbeuaCicG+9ov80NoaQcl2pbDSTvQ0vNUwcY6IjJEyzJP9fTQ0fPbl112mcd8M3r0aLMvop2HBZO2L9VOmj7++GOP7Vlj4UTmifCz23aAeSK6O0ZwYj0R3e3HWFd4ontOIAi0IpN3qV/3crKIDHbtgRahpTugWVlZHr/rTz/9xM8QYiyPCOSChju2x7EhkF6oy8rKzM39oJKqBrFzEtL9t3aehERozZo1q0H1nl//+temsaKvWAegtXS/XxsLul8cd98mtMQ72NGzZ09LfowePXq47tfV1bUYIAnFNCG22XVZacyaNWvMBWn3Sj5HHnmkPPTQQ+zjIWaXF/cLaXohVjvZa+n23XffeYxDGwq6/99ZiRGIpmXF2WjAna8dRDT1fPee24FoWVa0o1Zn53DO+X748OE+v/6QQw7xGF62bFnA0wTYFcf3AGKFFef5W0v3lfr27esa1obnq1evDkpVW9oEBc6743GtNBWu6z7MD6H17rvvSnV1tcc5W386BGot1hGRIVqWeaqnh4aeG9HrQNu3b3c9NnDgQFNpSzsPC4XDDjss6Nuzxp5Pe+TI3w4wT4SOdgL96aefenSMcMopp4Ts/VlPRG/7MdYVnqicCARxQ/LSSy+1uMMZybQnU397g/BXhw4dJFy6du1qaQ+03uNDZEhMTDTVlD788EPXYytWrJB+/fqFdbpijV17hEb0XNBAdNFlfNWqVR7rAO8GhU3xXr+w/Y69k5A//PCDx0nIESNGhHvSYoaeCNSq1c5e6pRWtNRqV/5gHYBA9OrVS77++mtzX+dFbRirxwO+8A5u6LisoD1rLl261DW8ZMkSnxsRB2uaADsuK942b95stiPuF/9GjhwpU6dONcf6QKhEwvIC2IEdlxXvHs7dw+6+8H4+VRMRjcuKs8q7+3zuT1UCrb4bSEMvIJJwfA8gFlh1nj8QeXl5rms9ze1f0CYo/PS3aq4Rfyiv+zA/hNaMGTM8hk877bSQvTfrCPuLlmWe6unBp9WwNJi4bt0612N6DfXZZ59tUAjBDtsz5gl7sNN2gHkifB0jjBkzJiQdIzixnoje9mOsKzwRTkRAoj2cFoj8/HyfdjgjWaT+Nv70XhkXF2cqUyh/qlM09nz33jBjVaSuM3zdMUTwe4R2fvfOHqG9S2I3hYoxsYvlNzbpycaPP/7YNbxx40afX8v6Irb5ehIS1lu4cKH86le/kqqqKtdjZ555pvz2t7/1e1ysAxBoxwbOhr5qwYIFPjX01ZOY7tVC9PivS5culnV+NH36dI9p8uVCvR57uVcdSU9Pl6FDh1oyTYAdlxXv3i8vueQSj4sG2lPuU089JSkpKfyACCm7Ly+AXdhxWRk7dqzHcFO9aDdGp8m9V10NbPlbeRGIhGVFjzPc+VPJsbHnaw/UQLTi+B5AtLPyPH8gvM/9lJeXN/o87w7gAmkTpG2Lunfv7tfrIQ0qWjX3WwX72i/zQ+j8+OOP8tNPP3kcLx999NEhe3/WEfYXTcu8Hns7OyDVNqj6Ol87IKXtSvNKS0vl//7v/8w6xb29xz//+U/TxjCUvNcrzbWPZZ4IP7ttB5gnQuOtt97yGD799NMllFhPRG/7MdYVnhrWlgT8DAFpCC+Yt1CV1g7XCRTYl14Adb8wu2PHDtPrvi8qKys9GrZrj/xUXorcdYY/O4YIHvdenJ09QvuKSgOxi+1xbPKubOC9DmiOVqNy52tvOYitk5Cw1vLly+XKK6/0aJB4wgknyL333tuq8bEOQCC8L4BrD3q+eOedd5odTyDGjRsnCQk/9y/2wQcf+HRMotXf9cKc05FHHkmDeET1suJepcq7p1zdpwt1T7mAXZeXjz76yDQA8+c2evRoj3HMmTPH4/8TJkywZNoQ2+y2rDh71e3Tp49reM2aNQ16vG7K559/7jF88MEHWzZdiG12W1Z0/8o9UKj7Yr5eS1Pu19Iaq6QIRBOO7wFEM6vP8wfCu+PJpiqYd+vWzbTlcf8M2tbHF9rpg3ao7B6G8LVjZfj/W4Xiug/zQ/iqJp544okhvXbBOsL+ommZ9/4s3tPXHNq6NU1/q2uvvVYWLVrk0S5Vg4m5ubkSar6uVxTzRPjZbTvAPBF8GmJ2PwephVJC2TGCYj0Rve3HWFd4IpwIhGhD0q5dO77rCO2B1p32QOvrRsl9R/SQQw7x2FFFZPFnxxD2Wx6pNBDbWH5jk/f6QnvU8ZX3umXMmDGWTRfsj3VG6GnjXg2QaONF9+okDz30kMTHt+6UBesABKJv374eDdC1Os7cuXObfY0GmV955RWPx04++WTLfgg9Oa7BQqd9+/bJm2++2exrtPfVf/3rXx6PnXLKKZZNE2DHZcX5HnrBwv0Ck3ZkFI6ecgG7Ly+A3dh1WTn11FM9Okx76aWXWnxNTU1Ng+dpIAWI1mVl5MiRHsPuld9b4v1cgryIZhzfA4hWwTjP31q6z/799997PNaxY8dGn6tteNwrUGsbH2d1K3+vJx5xxBGtmt5Y5/19N/VbheK6D/NDaFRXV8t7770XtspFrCMiQzQt81o9vbnXNkU7KXXvtD89PV2GDh3q02ujnZ53u+mmm+Szzz7zONZ67rnnTEDEztszxTwRXnbcDjBPhL5qYqg7RlCsJ6K3/RjrCk+EEwEbbEhgX969I3j3LBvOSgAIHZZne7Bbj9CIDCy/salTp04ycOBA1/D69et96vlm27Zt8vXXX7uGc3JyOMEbQ/w5CQlrbNmyRS699FLZvXu36zE9ufu3v/0toI49WAcgUNrTpjvthU0DgU155JFHTO+HTlo9asCAAc02vtUGxc7bhRde2OI0XXPNNR7Df/nLXzze05sGE923fTo9xxxzTIvvA0TyslJVVSW/+tWvPC4q6Lb8+eefN9sGIJzstrwAdmXHZeX88883Pa876XalpQvYU6dONb0xu1eCO+2001p8LyBSl5Vjjz3WY/iZZ57xqRqEHre4N/KIi4vjuAURheN7AAjeef7W0grme/fu9eh4ulevXkFvE8S519aZNWuWx7B7ACAc132YH4Lv008/9agkpRVohgwZIqHCOiIyRNMyT/V0a2nnrLfffrt88MEHrsfS0tLMeQg9fxEOem3qww8/bLYTJ3fME+Flx+0A80TwA83e7YwnT54socR6Ivrbj7Gu+BnhRCBEJ1Ca2+GEfWlq3r1nfe1xZcWKFc2+ZseOHfLf//7XNZycnNzgwiwih/Y67N6QxOFw0HNvmNixR2hEzwUNRB/vZV2r5fjSIEpPSjiddNJJplEUYoO/JyERGD2hdMkll5gTTE6DBg2SJ598UlJSUgL+elkHIBB6/DZ8+HDX8MaNG+WCCy6Qn376yeN52mObNgJ+4YUXPI7/brjhBst/gMGDB5vtktP+/fvlvPPOa9Bbm/ak+ve//10eeOAB12O6Lbv55pvZpiHql5Vbb71V5s2b5xrW7cl9991n5v1Nmzb5fNPlC4j25QWwKzsuK6mpqaY3dveGBFdccYW8/vrrHucQlG5DdLqmTZvm8fhvfvMbS45zALsuKxq+7dq1q8d5+Ysuukj+/e9/m2OUxq6j3XPPPfKnP/3J4/Hjjz8+bI0JEb183e/fs2dPo8/T+dVKHN8DiCbBPs/vL61m8+ijj3o8ppU2mquyoftV7lVTtKKbe3CqMbrPpdeU3Buqele8Qcu0M4uZM2d6PDZ+/PiwXvdhfgi+GTNmeAyHsiMf1hGRJVqWeaqnW+vPf/6z6STG/RyHnocLZ1XJF1980aNDKD2XeMghhzT5fOaJ8LHrdoB5IrQdI/Ts2TOkHSMo1hPR336MdcXPEtzuA7DI+++/L4sWLXIN6wpJezdA5NENmzY00AMbZ0WdO+64w+wsNFbWWf+vF1SLi4tdj51zzjkePSsjsnrb0ZLQ+tdp2LBh5oAA4esR+rrrrnMNa8OK5n4Tf3uERmxf0EB0Offcc+W5555znWDQoKoebOp6oKn5RQ8s3fcBLr/88pB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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "az.combine_plots(\n", " dt,\n", " [\n", " (az.plot_dist, {\"kind\": \"ecdf\"}),\n", " (az.plot_rank, {}),\n", " (az.plot_ess_evolution, {}),\n", " ],\n", " var_names=[\"theta\", \"mu\", \"tau\"],\n", " coords={\"school\": [\"Hotchkiss\", \"St. Paul's\"]},\n", ");" ] }, { "cell_type": "markdown", "id": "1b4e2e85", "metadata": {}, "source": [ "## Other nice features\n", "\n", "### Citation helper\n", "\n", "We have also added a helper to cite ArviZ in your publications and also the methods implemented in it.\n", "You can get the citation in BibTeX format through {func}`~arviz.citation`:\n", "\n", "### Extended documentation\n", "\n", "One recurring feedback we have received is that the documentation was OK for people very familiar with Bayesian statistics and probabilistic programming,\n", "but not so much for newcomers. Thus, we have added more introductory material and examples to the documentation, including a separated resource that show how to use ArviZ \"in-context\", see [EABM](https://arviz-devs.github.io/EABM/). And we attempted to make the documentation easier to navigate and understand for everyone.\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.12" } }, "nbformat": 4, "nbformat_minor": 5 }