{"action":"created","comment":{"url":"https://api.github.com/repos/usc-isi-i2/dig-business-type-extractor/pulls/comments/84195634","pull_request_review_id":4987654,"id":84195634,"diff_hunk":"@@ -0,0 +1,202 @@\n+# -*- coding: utf-8 -*-\n+# @Author: ZwEin\n+# @Date:   2016-10-06 23:36:45\n+# @Last Modified by:   ZwEin\n+# @Last Modified time: 2016-10-12 16:14:07\n+# -*- coding: utf-8 -*-\n+# @Author: ZwEin\n+# @Date:   2016-09-23 12:58:37\n+# @Last Modified by:   ZwEin\n+# @Last Modified time: 2016-09-24 20:29:20\n+\n+import os\n+import sys\n+import csv\n+import numpy as np\n+from sklearn.externals import joblib\n+from sklearn.neighbors import NearestNeighbors\n+from sklearn.neighbors import KNeighborsClassifier\n+from sklearn.feature_extraction.text import CountVectorizer\n+from sklearn.feature_extraction.text import TfidfVectorizer\n+from os.path import expanduser\n+import shutil\n+\n+\n+##################################################################\n+# Constant\n+##################################################################\n+\n+STOP_WORDS = set([u'all', u'just', u'being', u'over', u'both', u'through', u'yourselves', u'its', u'before', u'o', u'hadn', u'herself', u'll', u'had', u'should', u'to', u'only', u'won', u'under', u'ours', u'has', u'do', u'them', u'his', u'very', u'they', u'not', u'during', u'now', u'him', u'nor', u'd', u'did', u'didn', u'this', u'she', u'each', u'further', u'where', u'few', u'because', u'doing', u'some', u'hasn', u'are', u'our', u'ourselves', u'out', u'what', u'for', u'while', u're', u'does', u'above', u'between', u'mustn', u't', u'be', u'we', u'who', u'were', u'here', u'shouldn', u'hers', u'by', u'on', u'about', u'couldn', u'of', u'against', u's', u'isn', u'or', u'own', u'into', u'yourself', u'down', u'mightn', u'wasn', u'your', u'from', u'her', u'their', u'aren', u'there', u'been', u'whom', u'too', u'wouldn', u'themselves', u'weren', u'was', u'until', u'more', u'himself', u'that', u'but', u'don', u'with', u'than', u'those', u'he', u'me', u'myself', u'ma', u'these', u'up', u'will', u'below', u'ain', u'can', u'theirs', u'my', u'and', u've', u'then', u'is', u'am', u'it', u'doesn', u'an', u'as', u'itself', u'at', u'have', u'in', u'any', u'if', u'again', u'no', u'when', u'same', u'how', u'other', u'which', u'you', u'shan', u'needn', u'haven', u'after', u'most', u'such', u'why', u'a', u'off', u'i', u'm', u'yours', u'so', u'y', u'the', u'having', u'once'])\n+\n+DC_CATEGORY_NAMES = [\n+    'unknown',\n+    'others',\n+    'massage',\n+    'escort',\n+    'job_ads'\n+]\n+DC_CATEGORY_DICT = {\n+    'unknown': -1,\n+    'others': 1,\n+    'massage': 2,\n+    'escort': 3,\n+    'job_ads': 4\n+}\n+\n+USER_HOME_PATH = expanduser(\"~\")\n+\n+APP_DIR = os.path.join(USER_HOME_PATH, 'WEDC_DATA')\n+DATA_DIR = os.path.join(APP_DIR, 'data')\n+MODEL_DIR = os.path.join(APP_DIR, 'model')\n+MODEL_CLASSIFIER_DIR = os.path.join(MODEL_DIR, 'classifier')\n+MODEL_VECTORIZER_DIR = os.path.join(MODEL_DIR, 'vectorizer')\n+\n+DC_DEFAULT_DATASET_PATH = os.path.join(DATA_DIR, 'dataset.csv')\n+DC_DEFAULT_CLASSIFIER_MODEL_PATH = os.path.join(MODEL_CLASSIFIER_DIR, 'wedc_classifier.pkl')\n+DC_DEFAULT_VECTORIZER_MODEL_PATH = os.path.join(MODEL_VECTORIZER_DIR, 'wedc_vectorizer.pkl')\n+\n+def init_env():\n+    for path in [DATA_DIR, MODEL_CLASSIFIER_DIR, MODEL_VECTORIZER_DIR]:\n+        if not os.path.exists(path):\n+            os.makedirs(path)\n+\n+    default_data_path = os.path.join(os.path.dirname(__file__), 'data', 'dataset.csv')\n+    if not os.path.exists(DC_DEFAULT_DATASET_PATH):\n+        shutil.copy(default_data_path, DC_DEFAULT_DATASET_PATH)\n+        \n+init_env()\n+\n+\n+class WEDC(object):\n+\n+    ##################################################################\n+    # Basic Methods\n+    ##################################################################\n+\n+    def __init__(self, data_path=DC_DEFAULT_DATASET_PATH, vectorizer_model_path=None, vectorizer_type='tfidf', classifier_model_path=None, classifier_type='knn', classifier_algorithm='brute', metrix='cosine'):\n+        self.corpus = []\n+        self.labels = []\n+        self.size = 0\n+\n+        if data_path:\n+            new_corpus, new_labels = self.load_data(filepath=data_path)\n+            self.corpus += new_corpus\n+            self.labels += new_labels\n+            self.size += len(new_labels)\n+\n+        self.vectorizer = self.load_vectorizer(handler_type=vectorizer_type, binary=True)\n+        self.classifier = self.load_classifier(handler_type=classifier_type, algorithm=classifier_algorithm, weights='distance', n_neighbors=5, metric=metrix)\n+\n+        if not classifier_model_path:\n+            self.classifier_model_path = DC_DEFAULT_CLASSIFIER_MODEL_PATH\n+\n+        if not vectorizer_model_path:\n+            self.vectorizer_model_path = DC_DEFAULT_VECTORIZER_MODEL_PATH\n+\n+    def load_data(self, filepath=None):\n+\n+        def clean_content(content):\n+            content = content.lower()\n+\n+            # remove digits and punctuations\n+            content = ''.join([_ for _ in content if (_ >= 'a' and _ <= 'z') or (_ in ' \\t')])\n+            \n+            # remove stopwords\n+            # content = ' '.join([_ for _ in content.split() if _ not in STOP_WORDS])\n+            # print content\n+            return content\n+\n+        dataset = []\n+        labels = []\n+        with open(filepath, 'rb') as csvfile:\n+            reader = csv.reader(csvfile)\n+            header = next(reader)\n+            for row in reader:\n+                label =row[0]\n+                content = row[1].decode('utf-8', 'ignore').encode('ascii', 'ignore')\n+                dataset.append(clean_content(content))\n+                labels.append(label)\n+        return dataset, labels\n+\n+    def load_vectorizer(self, handler_type='count', **kwargs):\n+        vectorizers = {\n+            'count': CountVectorizer(binary=kwargs.get('binary', True)),\n+            'tfidf': TfidfVectorizer(min_df=kwargs.get('min_df', 1))\n+        } \n+        return vectorizers[handler_type]\n+\n+    def load_classifier(self, handler_type='knn', **kwargs):\n+        classifiers = {\n+            'knn': KNeighborsClassifier( \\\n+                        algorithm=kwargs.get('algorithm', 'auto'), \\\n+                        weights=kwargs.get('weights', 'distance'), \\\n+                        n_neighbors=kwargs.get('n_neighbors', 5), \\\n+                        metric=kwargs.get('metric', 'jaccard'))\n+        }\n+        return classifiers[handler_type]\n+\n+\n+    def train(self, data_path=None):\n+        if data_path:\n+            new_corpus, new_labels = self.load_data(filepath=data_path)\n+            self.corpus += new_corpus\n+            self.labels += new_labels\n+            self.size += len(new_labels)\n+\n+        # full matrix\n+        # vectors = self.vectorizer.fit_transform(self.corpus).toarray()\n+        # self.classifier.fit(vectors, self.labels)\n+        \n+        # sparse matrix\n+        vectors = self.vectorizer.fit_transform(self.corpus)\n+        self.classifier.fit(vectors, self.labels)\n+\n+        joblib.dump(self.classifier, self.classifier_model_path) \n+        joblib.dump(self.vectorizer, self.vectorizer_model_path) \n+        \n+        return self.classifier, self.vectorizer\n+\n+    def predict(self, data, classifier_model_path=None, vectorizer_model_path=None):\n+        if not data :\n+            return None\n+        if not classifier_model_path:\n+            classifier_model_path = self.classifier_model_path\n+        if not vectorizer_model_path:\n+            vectorizer_model_path = self.vectorizer_model_path\n+\n+        data_corpus = [data] if isinstance(data, basestring) else data\n+\n+        # load trained classifier and vectorizer models\n+        classifier = vectorizer = None\n+        try:\n+            classifier = joblib.load(classifier_model_path) 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