New documentation

birdman/inference.py

@@ -16,7 +16,36 @@ def fit_to_inference(
     dims: dict,
     posterior_predictive: str = None,
     log_likelihood: str = None,
-):
+) -> az.InferenceData:
+    """Convert a fitted model to an arviz InferenceData object.
+
+    :param fit: Fitted CmdStan model
+    :type fit: Either CmdStanMCMC or CmdStanVB
+
+    :param chains: Number of chains
+    :type chains: int
+
+    :param draws: Number of draws
+    :type draws: int
+
+    :param params: Parameters to include in inference
+    :type params: Sequence[str]
+
+    :param coords: Coordinates for InferenceData
+    :type coords: dict
+
+    :param dims: Dimensions for InferenceData
+    :type dims: dict
+
+    :param posterior_predictive: Name of posterior predictive var in model
+    :type posterior_predictive: str
+
+    :param log_likelihood: Name of log likelihood var in model
+    :type log_likelihood: str
+
+    :returns: Model converted to InferenceData
+    :rtype: az.InferenceData
+    """
     if log_likelihood is not None and log_likelihood not in dims:
         raise KeyError("Must include dimensions for log-likelihood!")
     if posterior_predictive is not None and posterior_predictive not in dims:
@@ -120,15 +149,33 @@ def concatenate_inferences(
     return az.concat(*all_group_inferences)
 
 
-# TODO: Fix docstring
 def stan_var_to_da(
     data: np.ndarray,
     coords: dict,
     dims: dict,
     chains: int,
     draws: int
-):
-    """Convert Stan variable draws to xr.DataArray."""
+) -> xr.DataArray:
+    """Convert results of stan_var to DataArray.
+
+    :params data: Result of stan_var
+    :type data: np.ndarray
+
+    :params coords: Coordinates of variables
+    :type coords: dict
+
+    :params dims: Dimensions of variables
+    :type dims: dict
+
+    :params chains: Number of chains
+    :type chains: int
+
+    :params draws: Number of draws
+    :type draws: int
+
+    :returns: DataArray representation of stan variables
+    :rtype: xr.DataArray
+    """
     data = np.stack(np.split(data, chains))
 
     coords["draw"] = np.arange(draws)

docs/custom_model.rst

@@ -25,14 +25,19 @@ We then import the data into Python so we can use BIRDMAn.
 
     import biom
     import pandas as pd
+    import glob
+
+    fpath = glob.glob("templates/*.txt")[0]
 
     table = biom.load_table("BIOM/94270/reference-hit.biom")
     metadata = pd.read_csv(
-        "templates/11913_20191016-112545.txt",
+        fpath,
         sep="\t",
         index_col=0
     )
 
+    metadata.head()
+
 Processing metadata
 -------------------
 
@@ -224,9 +229,6 @@ We will now pass this file along with our table, metadata, and formula into BIRD
     nb_lme = birdman.TableModel(
         table=filt_tbl,
         model_path="negative_binomial_re.stan",
-        num_iter=500,
-        chains=4,
-        seed=42
     )
     nb_lme.create_regression(
         metadata=metadata_model.loc[samps_to_keep],
@@ -272,6 +274,7 @@ Now we can add all the necessary parameters to BIRDMAn with the ``add_parameters
         "depth": np.log(filt_tbl.sum(axis="sample")),
         "B_p": 3.0,
         "inv_disp_sd": 3.0,
+        "A": np.log(1 / filt_tbl.shape[0]),
         "u_p": 1.0
     }
     nb_lme.add_parameters(param_dict)
@@ -294,8 +297,8 @@ We pass all these arguments into the ``specify_model`` method of the ``Model`` o
         dims={
             "beta_var": ["covariate", "feature_alr"],
             "inv_disp": ["feature"],
-            "subj_int": ["subject"],
-            "log_lik": ["tbl_sample", "feature"],
+            "subj_int": ["subject", "feature_alr"],
+            "log_lhood": ["tbl_sample", "feature"],
             "y_predict": ["tbl_sample", "feature"]
         },
         coords={
@@ -306,7 +309,7 @@ We pass all these arguments into the ``specify_model`` method of the ``Model`` o
             "tbl_sample": nb_lme.sample_names
         },
         posterior_predictive="y_predict",
-        log_likelihood="log_lik",
+        log_likelihood="log_lhood",
         include_observed_data=True
     )
 
@@ -319,7 +322,7 @@ Finally, we compile and fit the model.
 .. code-block:: python
 
     nb_lme.compile_model()
-    nb_lme.fit_model()
+    nb_lme.fit_model(method="vi", num_draws=500)
 
 Converting to ``InferenceData``
 -------------------------------
@@ -329,7 +332,13 @@ When the model has finished fitting, you can convert to an inference data assumi
 .. code-block:: python
 
     from birdman.transform import posterior_alr_to_clr
+
     inference = nb_lme.to_inference()
-    inference.posterior = posterior_alr_to_clr(inference.posterior)
+    inference.posterior = posterior_alr_to_clr(
+        inference.posterior,
+        alr_params=["subj_int", "beta_var"],
+        dim_replacement={"feature_alr": "feature"},
+        new_labels=filt_tbl.ids("observation")
+    )
 
 With this you can use the rest of the BIRDMAn suite as usual or directly interact with the ``arviz`` library!

docs/default_model_example.rst

@@ -23,13 +23,16 @@ Next, we want to import the data into Python so we can run BIRDMAn.
 
     from birdman import NegativeBinomial
 
-    table = biom.load_table("BIOM/44773/otu_table.biom")
+    fpath = glob.glob("templates/*.txt")[0]
+    table = biom.load_table("BIOM/44773/reference-hit.biom")
     metadata = pd.read_csv(
-        "templates/107_20180101-113755.txt",
+        fpath,
         sep="\t",
         index_col=0
     )
 
+    metadata.head()
+
 This table has nearly 2000 features, many of which are likely lowly prevalent. We are going to filter to only features that are present in at least 5 samples.
 
 .. code-block:: python
@@ -44,11 +47,12 @@ For this example we're going to use a simple formula that only takes ``diet`` in
 
 .. code-block:: python
 
+   from birdman import NegativeBinomial
+
     nb = NegativeBinomial(
         table=table_filt,
         formula="diet",
         metadata=metadata,
-        num_iter=1000,
     )
 
 We then have to compile and fit our model. This is very straightforward in BIRDMAn.
@@ -68,6 +72,7 @@ Now we have our parameter estimates which we can use in downstream analyses. Man
 .. code-block:: python
 
     from birdman.transform import posterior_alr_to_clr
+
     inference = nb.to_inference()
     inference.posterior = posterior_alr_to_clr(
         inference.posterior,
@@ -85,7 +90,7 @@ Finally, we'll plot the feature differentials and their standard deviations. We
     ax = viz.plot_parameter_estimates(
         inference,
         parameter="beta_var",
-        coord={"covariate": "diet[T.DIO]"},
+        coords={"covariate": "diet[T.DIO]"},
     )
 
 .. image:: imgs/example_differentials.png

docs/index.rst

@@ -14,14 +14,14 @@ We provide several default models but also allow users to create their own stati
 Installation
 ------------
 
-.. note:: BIRDMAn requires Python >= 3.7
+.. note:: BIRDMAn requires Python >= 3.8
 
 There are several dependencies you must install to use BIRDMAn.
 The easiest way to install the required dependencies is through ``conda`` or ``mamba``.
 
 .. code:: bash
 
-    conda install -c conda-forge biom-format patsy xarray arviz cmdstanpy
+    mamba install -c conda-forge biom-format patsy xarray arviz cmdstanpy
     pip install birdman
 
 If you are planning on contributing to BIRDMAn you must also install the following packages:
@@ -53,6 +53,7 @@ If you are planning on contributing to BIRDMAn you must also install the followi
     :caption: API
 
     models
+    inference
     diagnostics
     summary
     transform

docs/inference.rst

@@ -0,0 +1,6 @@
+Inference Functions
+===================
+
+.. automodule:: birdman.inference
+    :members:
+

docs/models.rst

@@ -14,6 +14,8 @@ These are the default models that are included in BIRDMAn. They should be usable
     :members:
 .. autoclass:: birdman.default_models.NegativeBinomialLME
     :members:
+.. autoclass:: birdman.default_models.NegativeBinomialLMESingle
+    :members:
 
 Table Model
 -----------

environment.yml

@@ -6,7 +6,7 @@ channels:
 dependencies:
   - pandas
   - numpy
-  - python=3.7
+  - python=3.8
   - python-language-server
   - xarray
   - patsy
@@ -16,4 +16,3 @@ dependencies:
   - docutils==0.16
   - pip:
     - sphinx-rtd-theme==0.5.1
-prefix: /Users/gibs/miniconda3/envs/birdman