rename pins, write app, write docker

Author: isabelizimm

vetiver/handlers/sklearn.py

@@ -1,8 +1,9 @@
 import pandas as pd
 import sklearn
 
+from ..meta import _model_meta
 from .base import VetiverHandler
-from ..meta import vetiver_meta
+
 
 class SKLearnHandler(VetiverHandler):
     """Handler class for creating VetiverModels with sklearn.
@@ -12,14 +13,14 @@ class SKLearnHandler(VetiverHandler):
     model : sklearn.base.BaseEstimator
         a trained sklearn model
     """
+
     base_class = sklearn.base.BaseEstimator
-    
+
     def __init__(self, model, ptype_data):
         super().__init__(model, ptype_data)
 
     def describe(self):
-        """Create description for sklearn model
-        """
+        """Create description for sklearn model"""
         desc = f"Scikit-learn {self.model.__class__} model"
         return desc
 
@@ -31,11 +32,10 @@ def construct_meta(
     ):
         """Create metadata for sklearn model"""
         required_pkgs = required_pkgs + ["scikit-learn"]
-        meta = vetiver_meta(user, version, url, required_pkgs)
+        meta = _model_meta(user, version, url, required_pkgs)
 
         return meta
 
-
     def handler_predict(self, input_data, check_ptype):
         """Generates method for /predict endpoint in VetiverAPI
 
@@ -58,7 +58,7 @@ def handler_predict(self, input_data, check_ptype):
             if isinstance(input_data, pd.DataFrame):
                 prediction = self.model.predict(input_data)
             else:
-               prediction = self.model.predict([input_data])
+                prediction = self.model.predict([input_data])
 
         # do not check ptype
         else:

vetiver/handlers/torch.py

@@ -1,7 +1,7 @@
 import numpy as np
 
+from ..meta import _model_meta
 from .base import VetiverHandler
-from ..meta import vetiver_meta
 
 torch_exists = True
 try:
@@ -18,13 +18,14 @@ class TorchHandler(VetiverHandler):
     model : nn.Module
         a trained torch model
     """
+
     base_class = torch.nn.Module
+
     def __init__(self, model, ptype_data):
         super().__init__(model, ptype_data)
 
     def describe(self):
-        """Create description for torch model
-        """
+        """Create description for torch model"""
         desc = f"Pytorch model of type {type(self.model)}"
         return desc
 
@@ -34,10 +35,9 @@ def create_meta(
         url: str = None,
         required_pkgs: list = [],
     ):
-        """Create metadata for torch model
-        """
+        """Create metadata for torch model"""
         required_pkgs = required_pkgs + ["torch"]
-        meta = vetiver_meta(user, version, url, required_pkgs)
+        meta = _model_meta(user, version, url, required_pkgs)
 
         return meta
 
@@ -62,9 +62,9 @@ def handler_predict(self, input_data, check_ptype):
             if check_ptype == True:
                 input_data = np.array(input_data, dtype=np.array(self.ptype_data).dtype)
                 prediction = self.model(torch.from_numpy(input_data))
-            
+
             # do not check ptype
-            else:    
+            else:
                 input_data = torch.tensor(input_data)
                 prediction = self.model(input_data)
 

vetiver/pin_read_write.py

@@ -1,4 +1,3 @@
-import pydantic
 import pins
 import warnings
 import json

vetiver/tests/test_build_vetiver_model.py

@@ -1,8 +1,9 @@
+from numpy import isin
 import pytest
+import sklearn
 
-from vetiver.vetiver_model import VetiverModel
+import vetiver as vt
 from vetiver.mock import get_mock_data, get_mock_model
-from vetiver.pin_read_write import pin_write
 
 import pandas as pd
 import pydantic
@@ -16,7 +17,7 @@
 
 def test_vetiver_model_array_ptype():
     # build VetiverModel, no ptype
-    vt1 = VetiverModel(
+    vt1 = vt.VetiverModel(
         model=model,
         ptype_data=X_array,
         model_name="iris",
@@ -32,7 +33,7 @@ def test_vetiver_model_array_ptype():
 
 def test_vetiver_model_df_ptype():
     # build VetiverModel, df ptype_data
-    vt2 = VetiverModel(
+    vt2 = vt.VetiverModel(
         model=model,
         ptype_data=X_df,
         model_name="iris",
@@ -48,7 +49,7 @@ def test_vetiver_model_df_ptype():
 
 def test_vetiver_model_dict_ptype():
     dict_data = {"B": 0, "C": 0, "D": 0}
-    vt3 = VetiverModel(
+    vt3 = vt.VetiverModel(
         model=model,
         ptype_data=dict_data,
         model_name="iris",
@@ -64,7 +65,7 @@ def test_vetiver_model_dict_ptype():
 
 def test_vetiver_model_no_ptype():
     # build VetiverModel, no ptype
-    vt4 = VetiverModel(
+    vt4 = vt.VetiverModel(
         model=model,
         ptype_data=None,
         model_name="iris",
@@ -78,18 +79,18 @@ def test_vetiver_model_no_ptype():
 
 def test_vetiver_model_from_pin():
 
-    v = VetiverModel(
+    v = vt.VetiverModel(
         model=model,
-        ptype_data=None,
-        model_name="iris",
+        ptype_data=X_df,
+        model_name="model",
         versioned=None,
         description=None,
         metadata=None,
     )
     board = pins.board_temp(allow_pickle_read=True)
-    vetiver_pin_write(board=board, model=v)
-    v = VetiverModel.from_pin(board, "model")
-    assert isinstance(v, VetiverModel) 
-    assert vt5.model == model
-    assert isinstance(vt5.ptype.construct(), pydantic.BaseModel)
+    vt.vetiver_pin_write(board=board, model=v)
+    v2 = vt.VetiverModel.from_pin(board, "model")
+    assert isinstance(v2, vt.VetiverModel) 
+    assert isinstance(v2.model, sklearn.base.BaseEstimator)
+    assert isinstance(v2.ptype.construct(), pydantic.BaseModel)
     board.pin_delete("model")

vetiver/tests/test_pin_read.py

@@ -1,7 +1,7 @@
 import pytest 
 
 from vetiver.mock import get_mock_data, get_mock_model
-from vetiver.pin_read_write import vetiver_pin_read, vetiver_pin_write
+from vetiver.pin_read_write import vetiver_pin_write
 from vetiver.vetiver_model import VetiverModel
 import sklearn
 import pins