More example updates

Author: smlindauer

examples/data/MLFlowModels/Model1/MLFlowModel.pickle

@@ -0,0 +1,17 @@
+{
+    "name": "MLFlowModel",
+    "description": "MLFlow Model",
+    "scoreCodeType": "python",
+    "trainTable": "",
+    "trainCodeType": "Python",
+    "algorithm": "",
+    "function": "Classification",
+    "targetVariable": "tensor",
+    "targetEvent": "1",
+    "targetLevel": "BINARY",
+    "eventProbVar": "P_1",
+    "modeler": "sasdemo",
+    "tool": "Python 3",
+    "toolVersion": "3.8.16",
+    "properties": []
+}

examples/data/MLFlowModels/Model1/MLFlowModel.zip

@@ -0,0 +1,18 @@
+[
+    {
+        "role": "inputVariables",
+        "name": "inputVar.json"
+    },
+    {
+        "role": "outputVariables",
+        "name": "outputVar.json"
+    },
+    {
+        "role": "score",
+        "name": "score_MLFlowModel.py"
+    },
+    {
+        "role": "scoreResource",
+        "name": "MLFlowModel.pickle"
+    }
+]

examples/data/MLFlowModels/Model1/ModelProperties.json

@@ -0,0 +1,68 @@
+[
+    {
+        "name": "fixedacidity",
+        "level": "interval",
+        "type": "decimal",
+        "length": 8
+    },
+    {
+        "name": "volatileacidity",
+        "level": "interval",
+        "type": "decimal",
+        "length": 8
+    },
+    {
+        "name": "citricacid",
+        "level": "interval",
+        "type": "decimal",
+        "length": 8
+    },
+    {
+        "name": "residualsugar",
+        "level": "interval",
+        "type": "decimal",
+        "length": 8
+    },
+    {
+        "name": "chlorides",
+        "level": "interval",
+        "type": "decimal",
+        "length": 8
+    },
+    {
+        "name": "freesulfurdioxide",
+        "level": "interval",
+        "type": "decimal",
+        "length": 8
+    },
+    {
+        "name": "totalsulfurdioxide",
+        "level": "interval",
+        "type": "decimal",
+        "length": 8
+    },
+    {
+        "name": "density",
+        "level": "interval",
+        "type": "decimal",
+        "length": 8
+    },
+    {
+        "name": "pH",
+        "level": "interval",
+        "type": "decimal",
+        "length": 8
+    },
+    {
+        "name": "sulphates",
+        "level": "interval",
+        "type": "decimal",
+        "length": 8
+    },
+    {
+        "name": "alcohol",
+        "level": "interval",
+        "type": "decimal",
+        "length": 8
+    }
+]

examples/data/MLFlowModels/Model1/fileMetadata.json

@@ -0,0 +1,8 @@
+[
+    {
+        "name": "tensor",
+        "level": "interval",
+        "type": "decimal",
+        "length": 8
+    }
+]

examples/data/MLFlowModels/Model1/inputVar.json

@@ -0,0 +1,32 @@
+import math
+import cloudpickle
+import pandas as pd
+import numpy as np
+from pathlib import Path
+
+import settings
+
+with open(Path(settings.pickle_path) / "MLFlowTest.pickle", "rb") as pickle_model:
+    model = cloudpickle.load(pickle_model)
+
+def score(fixedacidity, volatileacidity, citricacid, residualsugar, chlorides, freesulfurdioxide, totalsulfurdioxide, density, pH, sulphates, alcohol):
+    "Output: tensor"
+
+    try:
+        global model
+    except NameError:
+        with open(Path(settings.pickle_path) / "MLFlowTest.pickle", "rb") as pickle_model:
+                model = cloudpickle.load(pickle_model)
+
+    input_array = pd.DataFrame([[fixedacidity, volatileacidity, citricacid, residualsugar, chlorides, freesulfurdioxide, totalsulfurdioxide, density, pH, sulphates, alcohol]],
+                              columns=["fixedacidity", "volatileacidity", "citricacid", "residualsugar", "chlorides", "freesulfurdioxide", "totalsulfurdioxide", "density", "pH", "sulphates", "alcohol"],
+                              dtype=float)
+    prediction = model.predict(input_array)
+
+    # Check for numpy values and convert to a CAS readable representation
+    if isinstance(prediction, np.ndarray):
+        prediction = prediction.tolist()[0]
+
+    tensor = prediction
+
+    return tensor