Remove sampler category

README.md

@@ -1,6 +1,6 @@
-# AutoRA Uncertainty Sampler
+# AutoRA Uncertainty Experimentalist
 
-The uncertainty sampler identifies experimental conditions $\vec{x}' \in X'$ with respect model uncertainty. Within the uncertainty sampler, there are three methods to determine uncertainty:
+The uncertainty experimentalist identifies experimental conditions $\vec{x}' \in X'$ with respect model uncertainty. Within the uncertainty sampler, there are three methods to determine uncertainty:
 
 ## Least Confident
 $$
@@ -25,7 +25,7 @@ $$
 # Example Code
 
 ```
-from autora.experimentalist.sampler.uncertainty import uncertainty_sample
+from autora.experimentalist.uncertainty import uncertainty_sample
 from sklearn.linear_model import LogisticRegression
 import numpy as np
 

docs/Basic Usage.ipynb

@@ -15,7 +15,7 @@
    "outputs": [],
    "source": [
     "# Uncomment the following line when running on Google Colab\n",
-    "# !pip install \"autora[experimentalist-sampler-uncertainty]\""
+    "# !pip install \"autora[experimentalist-uncertainty]\""
    ]
   },
   {
@@ -27,7 +27,7 @@
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
     "from sklearn.linear_model import LogisticRegression\n",
-    "from autora.experimentalist.sampler.uncertainty import uncertainty_sample"
+    "from autora.experimentalist.uncertainty import uncertainty_sample"
    ]
   },
   {

docs/index.md

@@ -1,4 +1,4 @@
-# Uncertainty Sampler
+# Uncertainty Experimentalist
 
 The uncertainty sampler identifies experimental conditions $\vec{x}' \in X'$ with respect model uncertainty. Within the uncertainty sampler, there are three methods to determine uncertainty:
 
@@ -25,7 +25,7 @@ $$
 # Example Code
 
 ```
-from autora.experimentalist.sampler.uncertainty import uncertainty_sample
+from autora.experimentalist.uncertainty import uncertainty_sample
 from sklearn.linear_model import LogisticRegression
 import numpy as np
 

docs/quickstart.md

@@ -7,11 +7,11 @@ You will need:
 *Uncertainty-Sampler is a part of the `autora` package:
 
 ```shell
-pip install -U autora["experimentalist-sampler-uncertainty"]
+pip install -U autora["experimentalist-uncertainty"]
 ```
 
 
 Check your installation by running:
 ```shell
-python -c "from autora.experimentalist.sampler.uncertainty import uncertainty_sample"
+python -c "from autora.experimentalist.uncertainty import uncertainty_sample"
 ```

mkdocs.yml

@@ -1,8 +1,8 @@
 # yaml-language-server: $schema=https://squidfunk.github.io/mkdocs-material/schema.json
 INHERIT: mkdocs/base.yml  # use the shared AutoRA configuration by default
 
-site_name: AutoRA Uncertainty Sampler
-repo_url: 'https://github.com/AutoResearch/autora-experimentalist-sampler-uncertainty'
+site_name: AutoRA Uncertainty
+repo_url: 'https://github.com/AutoResearch/autora-experimentalist-uncertainty'
 
 nav:
 - Home: 'index.md'

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 # UPDATE THIS BEFORE PUBLISHING
-name = "autora-experimentalist-sampler-uncertainty"
+name = "autora-experimentalist-uncertainty"
 description = "Sampler based on where the model is least certain."
 authors = [{ name = "Built by Sebastian Musslick, restructured by Chad Williams", email = "[email protected]" }]
 dynamic = ["version"]
@@ -22,7 +22,7 @@ dev = [
 
 [project.urls]
 homepage = "http://www.empiricalresearch.ai"
-repository = "https://github.com/AutoResearch/autora-experimentalist-sampler-uncertainty"
+repository = "https://github.com/AutoResearch/autora-experimentalist-uncertainty"
 documentation = "https://autoresearch.github.io/autora/"
 
 

src/autora/experimentalist/sampler/uncertainty/__init__.py → src/autora/experimentalist/uncertainty/__init__.py

@@ -1,10 +1,12 @@
 from typing import Iterable
+
 import numpy as np
 from scipy.stats import entropy
 
 from autora.utils.deprecation import deprecated_alias
 
-def uncertainty_sample(X, model, num_samples, measure="least_confident"):
+
+def sample(X, model, num_samples, measure="least_confident"):
     """
 
     Args:
@@ -60,4 +62,6 @@ class labels under the model, respectively.
 
     return X[idx]
 
+
+uncertainty_sample = sample
 uncertainty_sampler = deprecated_alias(uncertainty_sample, "uncertainty_sampler")

tests/test_uncertainty_sampler.py

@@ -1,19 +1,21 @@
-from autora.experimentalist.sampler.uncertainty import uncertainty_sample
-from sklearn.linear_model import LogisticRegression
 import numpy as np
+from sklearn.linear_model import LogisticRegression
+
+from autora.experimentalist.uncertainty import uncertainty_sample
+
 
 def test_output_dimensions():
-    #Meta-Setup
+    # Meta-Setup
     X = np.linspace(start=-3, stop=6, num=10).reshape(-1, 1)
     y = (X**2).reshape(-1)
     n = 5
-    
-    #Theorists
+
+    # Theorists
     lr_theorist = LogisticRegression()
-
-    lr_theorist.fit(X,y)
 
-    #Sampler
+    lr_theorist.fit(X, y)
+
+    # Sampler
     X_new = uncertainty_sample(X, lr_theorist, n)
 
     # Check that the sampler returns n experiment conditions