--- a
+++ b/mimic_icd9_coding/utils/classifier.py
@@ -0,0 +1,40 @@
+
+from __future__ import unicode_literals
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.multioutput import MultiOutputClassifier
+from collections import Counter
+import pandas as pd
+import numpy as np
+
+
+
+class Classifier():
+    def __init__(self):
+        #Multi label classifier
+        forest = RandomForestClassifier(n_estimators=100, random_state=1)
+        self.clf = MultiOutputClassifier(forest, n_jobs=-1)
+
+    def fit(self, X, y):
+
+        self.clf.fit(X, y)
+
+    def predict(self, X):
+        y_pred = np.array(self.clf.predict(X))
+        return y_pred
+
+    def predict_proba(self, X):
+        raise NotImplemented
+        """
+        Compte the probailities for each label
+        Important: this class needs to return an 2D array with 2 columns per label, so 109*2 columns. """
+        proba = self.clf.predict_proba(X)
+        #Proba is a list of size 109, one for each label, each element is an array of size n_samples * 2,
+        #except some times when it is n_sample*1 so a little work is needed to reshape the array
+        y_proba = proba[0]
+        for x in proba[1 : ] :
+            if x.shape[1] == 2 : 
+                y_proba = np.hstack((y_proba,x))
+            else:
+                y_proba = np.hstack((y_proba,x,np.zeros_like(x)))
+                             
+        return y_proba