Align casing of NumPyro with canonical spelling

README.md

@@ -30,7 +30,7 @@ Taking a Bayesian approach to MMM allows an advertiser to integrate prior inform
 - Report on both parameter and model uncertainty and propagate it to your budget optimisation.
 - Construct hierarchical models, with generally tighter credible intervals, using breakout dimensions such as geography.
 
-The LightweightMMM package (built using [Numpyro](https://github.com/pyro-ppl/numpyro) and [JAX](https://github.com/google/jax)) helps advertisers easily build Bayesian MMM models by providing the functionality to appropriately scale data, evaluate models, optimise budget allocations and plot common graphs used in the field.
+The LightweightMMM package (built using [NumPyro](https://github.com/pyro-ppl/numpyro) and [JAX](https://github.com/google/jax)) helps advertisers easily build Bayesian MMM models by providing the functionality to appropriately scale data, evaluate models, optimise budget allocations and plot common graphs used in the field.
 
 ## Theory
 

docs/index.rst

@@ -8,7 +8,7 @@ library that allows users to easily train MMMs and obtain channel attribution
 information. The library also includes capabilities for optimizing media 
 allocation as well as plotting common graphs in the field.
 
-It is built in python3 and makes use of Numpyro and JAX.
+It is built in python3 and makes use of NumPyro and JAX.
 
 Installation
 ------------

lightweight_mmm/core/transformations/lagging.py

@@ -95,7 +95,7 @@ def carryover(
     custom_priors: The custom priors we want the model to take instead of the
       default ones.
     number_lags: Number of lags for the carryover function.
-    prefix: Prefix to use in the variable name for Numpyro.
+    prefix: Prefix to use in the variable name for NumPyro.
 
   Returns:
     The transformed media data.
@@ -178,7 +178,7 @@ def adstock(
       default ones. The possible names of parameters for adstock and exponent
       are "lag_weight" and "exponent".
     normalise: Whether to normalise the output values.
-    prefix: Prefix to use in the variable name for Numpyro.
+    prefix: Prefix to use in the variable name for NumPyro.
 
   Returns:
     The transformed media data.

lightweight_mmm/core/transformations/saturation.py

@@ -62,7 +62,7 @@ def hill(
     custom_priors: The custom priors we want the model to take instead of the
       default ones. The possible names of parameters for hill_adstock and
       exponent are "lag_weight", "half_max_effective_concentration" and "slope".
-    prefix: Prefix to use in the variable name for Numpyro.
+    prefix: Prefix to use in the variable name for NumPyro.
 
   Returns:
     The transformed media data.
@@ -112,7 +112,7 @@ def exponent(
       national models and 3 for geo models.
     custom_priors: The custom priors we want the model to take instead of the
       default ones.
-    prefix: Prefix to use in the variable name for Numpyro.
+    prefix: Prefix to use in the variable name for NumPyro.
 
   Returns:
     The transformed media data.

lightweight_mmm/lightweight_mmm.py

@@ -214,17 +214,17 @@ def _create_list_of_attributes_to_compare(
   def _preprocess_custom_priors(
       self,
       custom_priors: Dict[str, Prior]) -> MutableMapping[str, Prior]:
-    """Preprocesses the user input custom priors to Numpyro distributions.
+    """Preprocesses the user input custom priors to NumPyro distributions.
 
-    If numpyro distributions are given they remains untouched, however if any
+    If NumPyro distributions are given they remains untouched, however if any
     other option is passed, it is passed to the default distribution to alter
     its constructor values.
 
     Args:
       custom_priors: Mapping of the name of the prior to its custom value.
 
     Returns:
-      A mapping of names to numpyro distributions based on user input and
+      A mapping of names to NumPyro distributions based on user input and
         default values.
     """
     default_priors = {
@@ -246,8 +246,8 @@ def _preprocess_custom_priors(
             **custom_priors[prior_name])
       elif not isinstance(custom_priors[prior_name], dist.Distribution):
         raise ValueError(
-            "Priors given must be a Numpyro distribution or one of the "
-            "following to fit in the constructor of our default Numpyro "
+            "Priors given must be a NumPyro distribution or one of the "
+            "following to fit in the constructor of our default NumPyro "
             "distribution. It could be given as args or kwargs as long as it "
             "is the correct format for such object. Please refer to our "
             "documentation on custom priors to know more.")
@@ -295,7 +295,7 @@ def fit(
       number_chains: Number of chains to sample. Default is 2.
       target_accept_prob: Target acceptance probability for step size in the
         NUTS sampler. Default is .85.
-      init_strategy: Initialization function for numpyro NUTS. The available
+      init_strategy: Initialization function for NumPyro NUTS. The available
         options can be found in
         https://num.pyro.ai/en/stable/utilities.html#initialization-strategies.
         Default is numpyro.infer.init_to_median.
@@ -395,7 +395,7 @@ def fit(
     logging.info("Model has been fitted")
 
   def print_summary(self) -> None:
-    """Calls print_summary function from numpyro to print parameters summary.
+    """Calls print_summary function from NumPyro to print parameters summary.
     """
     # TODO(): add name selection for print.
     self._mcmc.print_summary()
@@ -431,7 +431,7 @@ def _predict(
       frequency: Frequency of the seasonality.
       transform_function: Media transform function to use within the model.
       weekday_seasonality: Allow daily weekday estimation.
-      model: Numpyro model to use for numpyro.infer.Predictive.
+      model: NumPyro model to use for numpyro.infer.Predictive.
       posterior_samples: Mapping of the posterior samples.
       custom_priors: The custom priors we want the model to take instead of the
         default ones. Refer to the full documentation on custom priors for

lightweight_mmm/lightweight_mmm_test.py

@@ -210,7 +210,7 @@ def test_fit_with_custom_prior_raises_valueerror_if_wrong_format(
 
     with self.assertRaisesRegex(
         ValueError,
-        "Priors given must be a Numpyro distribution or one of the "):
+        "Priors given must be a NumPyro distribution or one of the "):
       mmm_object.fit(
           media=media,
           target=target,