Example using Seldon for text classification with SpaCy tokenizer

examples/models/sklearn_spacy_text/RedditClassifier.py

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+import dill
+
+from ml_utils import CleanTextTransformer, SpacyTokenTransformer
+
+class RedditClassifier(object):
+    def __init__(self):
+
+        self._clean_text_transformer = CleanTextTransformer()
+        self._spacy_tokenizer = SpacyTokenTransformer()
+
+        with open('tfidf_vectorizer.model', 'rb') as model_file:
+            self._tfidf_vectorizer = dill.load(model_file)
+
+        with open('lr.model', 'rb') as model_file:
+            self._lr_model = dill.load(model_file)
+
+    def predict(self, X, feature_names):
+        clean_text = self._clean_text_transformer.transform(X)
+        spacy_tokens = self._spacy_tokenizer.transform(clean_text)
+        tfidf_features = self._tfidf_vectorizer.transform(spacy_tokens)
+        predictions = self._lr_model.predict_proba(tfidf_features)
+        return predictions
+

examples/models/sklearn_spacy_text/ml_utils.py

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+import spacy
+from spacy.cli import download
+import re 
+import numpy as np
+from sklearn.base import TransformerMixin
+from html.parser import HTMLParser
+import dill
+import sys, os
+
+download("en_core_web_sm")
+nlp = spacy.load('en_core_web_sm', parser=False, entity=False)
+
+class SpacyTokenTransformer(TransformerMixin):
+    __symbols = set("!$%^&*()_+|~-=`{}[]:\";'<>?,./-")
+
+    def transform(self, X, **kwargs):
+        f = np.vectorize(SpacyTokenTransformer.transform_to_token, otypes=[object])
+        X_tokenized = f(X)
+        return X_tokenized
+
+    def fit(self, X, y=None, **fit_params):
+        return self
+
+    @staticmethod
+    def transform_to_token(text):
+        str_text = str(text)
+        doc = nlp(str_text, disable=['parser', 'tagger', 'ner'])
+        tokens = []
+        for token in doc:
+            if token.like_url:
+                clean_token = "URL"
+            else:
+                clean_token = token.lemma_.lower().strip()
+                if len(clean_token) < 1 or clean_token in \
+                        SpacyTokenTransformer.__symbols: 
+                    continue
+            tokens.append(clean_token)
+        return tokens
+
+class CleanTextTransformer(TransformerMixin):
+    __html_parser = HTMLParser()
+    __uplus_pattern = \
+        re.compile("\<[uU]\+(?P<digit>[a-zA-Z0-9]+)\>")
+    __markup_link_pattern = \
+        re.compile("\[(.*)\]\((.*)\)")
+
+    def transform(self, X, **kwargs):
+        f = np.vectorize(CleanTextTransformer.transform_clean_text)
+        X_clean = f(X)
+        return X_clean
+
+    def fit(self, X, y=None, **fit_params):
+        return self
+
+    @staticmethod
+    def transform_clean_text(raw_text):
+        try:
+            decoded = raw_text.encode("ISO-8859-1").decode("utf-8")
+        except:
+            decoded = raw_text.encode("ISO-8859-1").decode("cp1252")
+        html_unescaped = CleanTextTransformer.\
+            __html_parser.unescape(decoded) 
+        html_unescaped = re.sub(r"\r\n", " ", html_unescaped)
+        html_unescaped = re.sub(r"\r\r\n", " ", html_unescaped)
+        html_unescaped = re.sub(r"\r", " ", html_unescaped)
+        html_unescaped = html_unescaped.replace("&gt;", " > ")
+        html_unescaped = html_unescaped.replace("&lt;", " < ")
+        html_unescaped = html_unescaped.replace("--", " - ")
+        html_unescaped = CleanTextTransformer.__uplus_pattern.sub(
+            " U\g<digit> ", html_unescaped)
+        html_unescaped = CleanTextTransformer.__markup_link_pattern.sub(
+            " \1 \2 ", html_unescaped)
+        html_unescaped = html_unescaped.replace("\\", "")
+        return html_unescaped
+

examples/models/sklearn_spacy_text/reddit_clf.json

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+{
+    "apiVersion": "machinelearning.seldon.io/v1alpha2",
+    "kind": "SeldonDeployment",
+    "metadata": {
+        "labels": {
+            "app": "seldon"
+        },
+        "name": "reddit-classifier"
+    },
+    "spec": {
+        "annotations": {
+            "project_name": "Reddit classifier",
+            "deployment_version": "v1"
+        },
+        "name": "reddit-classifier",
+        "oauth_key": "oauth-key",
+        "oauth_secret": "oauth-secret",
+        "predictors": [
+            {
+                "componentSpecs": [{
+                    "spec": {
+                        "containers": [
+                            {
+                                "image": "reddit-classifier:0.1",
+                                "imagePullPolicy": "IfNotPresent",
+                                "name": "classifier",
+                                "resources": {
+                                    "requests": {
+                                        "memory": "1Mi"
+                                    }
+                                }
+                            }
+                        ],
+                        "terminationGracePeriodSeconds": 20
+                    }
+                }],
+                "graph": {
+                    "children": [],
+                    "name": "classifier",
+                    "endpoint": {
+            "type" : "REST"
+            },
+                    "type": "MODEL"
+                },
+                "name": "single-model",
+                "replicas": 1,
+        "annotations": {
+            "predictor_version" : "v1"
+        }
+            }
+        ]
+    }
+}

examples/models/sklearn_spacy_text/requirements.txt

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+scipy>= 0.13.3
+scikit-learn>=0.18
+spacy==2.0.18
+dill==0.2.9