adding example to readme, cleaning up code a bit

Signed-off-by: vsoch <vsoch@users.noreply.github.com>

CHANGELOG.md

@@ -14,4 +14,5 @@ and **Merged pull requests**. Critical items to know are:
 The versions coincide with releases on pip. Only major versions will be released as tags on Github.
 
 ## [0.0.x](https://github.com/vsoch/riverapi/tree/main) (0.0.x)
+ - addition of link to examples (0.0.11)
  - skeleton release (0.0.1)

Dockerfile

@@ -15,4 +15,3 @@ RUN pip install ipython
 WORKDIR /code
 COPY . /code
 RUN pip install -e .[all]
-ENTRYPOINT ["/opt/conda/bin/riverapi"]

README.md

@@ -1,15 +1,61 @@
 # River API Client
 
+[![PyPI version](https://badge.fury.io/py/riverapi.svg)](https://badge.fury.io/py/riverapi)
+
 This is an API client created for [django-river-ml](https://pypi.org/project/django-river-ml/)
 that is intended to make it easy to interact with an online ML server providing river models (learning, predicting, etc.).
 It currently does not provide a terminal  or command line client and is intended to be used
 from Python, but if there is a good use case for a command line set of interactions
 this can be added.
 
-**under development**
+## Quick Start
+
+Given that you have a server running that implements the same space as django-river-ml:
+
+```python
+from river import datasets
+from river import linear_model
+from river import preprocessing
+
+from riverapi.main import Client
+
+
+def main():
+
+    # This is the default, just to show how to customize
+    cli = Client("http://localhost:8000")
+
+    # Upload a model
+    model = preprocessing.StandardScaler() | linear_model.LinearRegression()
+
+    # Save the model name for other endpoint interaction
+    model_name = cli.upload_model(model, "regression")
+    print("Created model %s" % model_name)
+
+    # Train on some data
+    for x, y in datasets.TrumpApproval().take(100):
+        cli.learn(model_name, x=x, y=y)
+
+    # Get the model (this is a json representation)
+    model_json = cli.get_model_json(model_name)
+    model_json
+
+    # Saves to model-name>.pkl in pwd unless you provide a second arg, dest
+    cli.download_model(model_name)
+
+    # Make predictions
+    for x, y in datasets.TrumpApproval().take(10):
+        print(cli.predict(model_name, x=x))
+
+    # Get all models
+    print(cli.models())
+
 
-not ready for use!
+if __name__ == "__main__":
+    main()
+```
 
+**under development** more coming soon!
 
 ## Contributors
 

riverapi/defaults.py

@@ -1,8 +1,2 @@
-__author__ = "Vanessa Sochat"
-__copyright__ = "Copyright 2022, Vanessa Sochat"
-__license__ = "MPL 2.0"
-
-import os
-
 # Assumes local host for development
 baseurl = "http://127.0.0.1:8000"

riverapi/client.py → riverapi/main.py

@@ -53,8 +53,11 @@ def print_response(self, r):
     def do_request(
         self, typ, url, data=None, json=None, headers=None, return_json=True
     ):
+        """
+        Do a request (get, post, etc)
+        """
         if not self.quiet:
-            logger.info("%s %s" % (typ.uppercase(), url))
+            logger.info("%s %s" % (typ.upper(), url))
 
         # The first post when you upload the model defines the flavor (regression)
         if json:
@@ -71,11 +74,17 @@ def do_request(
         return r
 
     def post(self, url, data=None, json=None, headers=None, return_json=True):
+        """
+        Perform a POST request
+        """
         return self.do_request(
             "post", url, data=data, json=json, headers=headers, return_json=return_json
         )
 
     def get(self, url, data=None, json=None, headers=None, return_json=True):
+        """
+        Perform a GET request
+        """
         return self.do_request(
             "get", url, data=data, json=json, headers=headers, return_json=return_json
         )
@@ -92,7 +101,7 @@ def upload_model(self, model, flavor):
         logger.info("Created model %s" % model_name)
         return model_name
 
-    def train(self, model_name, x, y=None):
+    def learn(self, model_name, x, y=None):
         """
         Train on some data. You are required to provide at least the model
         name known to the server and x (data).
@@ -121,21 +130,24 @@ def download_model(self, model_name, dest=None):
         r = self.get("/api/model/download/%s/" % model_name, return_json=False)
 
         # Default to pickle in PWD
-        dest = dest or "%s.pkg" % model_name
+        dest = dest or "%s.pkl" % model_name
 
         # Save to dest file
         with open(dest, "wb") as f:
             for chunk in r:
                 f.write(chunk)
         return dest
 
-    def predict(self, model_name, x, y):
+    def predict(self, model_name, x):
         """
         Make a prediction
         """
         return self.post("/api/predict/", json={"model": model_name, "features": x})
 
     def models(self):
+        """
+        Get a listing of known models
+        """
         return self.get("/api/models/")
 
     # TODO need to add streaming events/other endpoints and authentication

riverapi/version.py

@@ -2,7 +2,7 @@
 __copyright__ = "Copyright 2022, Vanessa Sochat"
 __license__ = "MPL 2.0"
 
-__version__ = "0.0.1"
+__version__ = "0.0.11"
 AUTHOR = "Vanessa Sochat"
 EMAIL = "vsoch@users.noreply.github.com"
 NAME = "riverapi"