--- a
+++ b/doc2vec_transformer.py
@@ -0,0 +1,80 @@
+import datetime
+
+from gensim.models import Doc2Vec
+import numpy as np
+from sklearn.base import TransformerMixin
+
+import loader
+import extract_data
+from structured_data_extractor import get_diagnoses, get_chronic_diagnoses
+import language_processing
+
+class Doc2Vec_Note_Transformer(TransformerMixin):
+    """
+
+    """ 
+    def __init__(self, note_type, model_file, max_notes, dbow_file=None):
+        self.note_type = note_type
+        self.max_notes = max_notes
+        self.model = Doc2Vec.load(model_file)
+        if dbow_file:
+            self.dbow = Doc2Vec.load(dbow_file)
+        else:
+            self.dbow = None
+
+    def fit(self, X, y=None, **fit_params):
+        return self
+
+    def transform(self, X, **transform_params):
+        transformed_X = map(self.get_sent_vector, X)
+        return np.array(transformed_X)
+
+    def get_sent_vector(self, empi):
+        patient = loader.get_patient_by_EMPI(empi)
+        operation_date = extract_data.get_operation_date(patient)
+        diagnoses = get_diagnoses(empi)
+
+        date_key = extract_data.get_date_key(self.note_type)
+        notes = []
+        if self.note_type in patient.keys() and date_key != None:
+            # Get sorted list of notes before procedure
+            time_idx_pairs = []
+            for i in range(len(patient[self.note_type])):
+                doc = patient[self.note_type][i]
+                date = extract_data.parse_date(doc[date_key])
+                if date != None and date < operation_date:
+                    time_idx_pairs.append((operation_date - date, i))
+            time_idx_pairs.sort()
+
+            for time,idx in time_idx_pairs[:self.max_notes]:
+                doc = patient[self.note_type][idx]
+                notes.append(doc['free_text'])
+
+        # ensure that notes vector length is equal to max_notes
+        if len(notes) < self.max_notes:
+            delta = self.max_notes - len(notes)
+            for i in range(delta):
+                notes.append('')  
+
+        # Turn notes into Doc Vectors
+        vectors = map(self.get_sent_vector_from_doc, notes)
+        return np.array(vectors).flatten()
+
+    def get_sent_vector_from_doc(self, doc):
+        split_doc = doc.split()
+        vec = self.model.infer_vector(split_doc)
+        if self.dbow:
+            vec = np.concatenate((vec, self.dbow.infer_vector(split_doc)))
+        return vec
+
+    def get_feature_names(self):
+        feature_names = []
+        for i in range(self.max_notes):
+            for j in range(self.model.vector_size):
+                feature_names.append(self.note_type 
+                                     + '_doc:' + str(i) + '_dim:' + str(j))
+            if self.dbow:
+                for j in range(self.model.vector_size):
+                    feature_names.append(self.note_type 
+                                         + '_doc:' + str(i) + 'dbow_dim:' + str(j))
+        return feature_names