Alibi deployment example - Anchor Tabular

doc/source/examples/alibi_anchor_tabular.nblink

@@ -0,0 +1,3 @@
+{
+ "path": "../../../examples/explainers/alibi_anchor_tabular/alibi_anchor_tabular_seldon_deployment.ipynb"
+}

doc/source/examples/notebooks.rst

@@ -5,6 +5,7 @@ Notebooks
 .. toctree::
    :maxdepth: 1
 
+   Ambassador Canary <ambassador_canary>
    Ambassador Canary <ambassador_canary>
    Ambassador Shadow <ambassador_shadow>
    Ambassador Headers <ambassador_headers>
@@ -15,6 +16,7 @@ Notebooks
    Combiner Example <combiner>
    Custom Endpoints <custom_endpoints>
    Example Helm Deployments <helm_examples>
+   Explainer Alibi Anchor Tabular <alibi_anchor_tabular>
    Go Model <go_example>
    H2O Java MoJo <h2o_mojo>
    Istio Canary <istio_canary>

examples/explainers/alibi_anchor_tabular/pipeline/pipeline_steps/loanclassifier-explainer/.s2i/environment

@@ -0,0 +1,4 @@
+MODEL_NAME=Explainer
+API_TYPE=REST
+SERVICE_TYPE=MODEL
+PERSISTENCE=0

examples/explainers/alibi_anchor_tabular/pipeline/pipeline_steps/loanclassifier-explainer/Explainer.py

@@ -0,0 +1,26 @@
+import dill
+import json
+import numpy as np
+
+class Explainer:
+    def __init__(self, *args, **kwargs):
+
+        with open("explainer.dill", "rb") as x_f:
+            self.explainer = dill.load(x_f)
+
+    def predict(self, X, feature_names=[]):
+        print("Received: " + str(X))
+        explanation = self.explainer.explain(X)
+        print("Predicted: " + str(explanation))
+        return json.dumps(explanation, cls=NumpyEncoder)
+
+class NumpyEncoder(json.JSONEncoder):
+    def default(self, obj):
+        if isinstance(obj, (
+        np.int_, np.intc, np.intp, np.int8, np.int16, np.int32, np.int64, np.uint8, np.uint16, np.uint32, np.uint64)):
+            return int(obj)
+        elif isinstance(obj, (np.float_, np.float16, np.float32, np.float64)):
+            return float(obj)
+        elif isinstance(obj, (np.ndarray,)):
+            return obj.tolist()
+        return json.JSONEncoder.default(self, obj)

examples/explainers/alibi_anchor_tabular/pipeline/pipeline_steps/loanclassifier-explainer/loanclassifiermodel-explainer.yaml

@@ -0,0 +1,22 @@
+apiVersion: machinelearning.seldon.io/v1alpha2
+kind: SeldonDeployment
+metadata:
+  labels:
+    app: seldon
+  name: loanclassifier-explainer
+spec:
+  name: loanclassifier-explainer
+  predictors:
+  - componentSpecs:
+    - spec:
+        containers:
+        - image: loanclassifier-explainer:0.1
+          name: model-explainer
+    graph:
+      children: []
+      name: model-explainer
+      type: MODEL
+      endpoint:
+        type: REST
+    name: loanclassifier-explainer
+    replicas: 1

examples/explainers/alibi_anchor_tabular/pipeline/pipeline_steps/loanclassifier-explainer/requirements.txt

@@ -0,0 +1,4 @@
+scikit-learn==0.20.1
+dill==0.2.9
+alibi==0.2.0
+seldon-core==0.3.0

examples/explainers/alibi_anchor_tabular/pipeline/pipeline_steps/loanclassifier/.s2i/environment

@@ -0,0 +1,4 @@
+MODEL_NAME=Model
+API_TYPE=REST
+SERVICE_TYPE=MODEL
+PERSISTENCE=0

examples/explainers/alibi_anchor_tabular/pipeline/pipeline_steps/loanclassifier/Model.py

@@ -0,0 +1,16 @@
+import dill
+
+class Model:
+    def __init__(self, *args, **kwargs):
+
+        with open("preprocessor.dill", "rb") as prep_f:
+            self.preprocessor = dill.load(prep_f)
+        with open("model.dill", "rb") as model_f:
+            self.clf = dill.load(model_f)
+
+    def predict(self, X, feature_names=[]):
+        print("Received: " + str(X))
+        X_prep = self.preprocessor.transform(X)
+        proba = self.clf.predict_proba(X_prep)
+        print("Predicted: " + str(proba))
+        return proba

examples/explainers/alibi_anchor_tabular/pipeline/pipeline_steps/loanclassifier/loanclassifiermodel.yaml

@@ -0,0 +1,22 @@
+apiVersion: machinelearning.seldon.io/v1alpha2
+kind: SeldonDeployment
+metadata:
+  labels:
+    app: seldon
+  name: loanclassifier
+spec:
+  name: loanclassifier
+  predictors:
+  - componentSpecs:
+    - spec:
+        containers:
+        - image: loanclassifier:0.1
+          name: model
+    graph:
+      children: []
+      name: model
+      type: MODEL
+      endpoint:
+        type: REST
+    name: loanclassifier
+    replicas: 1

examples/explainers/alibi_anchor_tabular/pipeline/pipeline_steps/loanclassifier/requirements.txt

@@ -0,0 +1,2 @@
+scikit-learn==0.20.1
+dill==0.2.9

examples/explainers/alibi_anchor_tabular/requirements-dev.txt

@@ -0,0 +1,7 @@
+xai==0.0.5
+python-dateutil==2.8.0
+seldon_core==0.3.0
+alibi==0.2.0
+jupyter==1.0.0
+dill==0.2.9
+scikit-learn==0.20.1