From 0cd549d91f9bcede5b6b653925eef158e5b9f227 Mon Sep 17 00:00:00 2001
From: Fize Jacques <jacques.fize@cirad.fr>
Date: Thu, 17 May 2018 16:26:06 +0200
Subject: [PATCH] - Cythonize Gmatch4py

- Debug disambiguisation

- Debug Spacy NER API and StanfordNER Api

- Add Notebooks for Evaluations
---
 generate_data_csv.py                          |   26 +-
 .../helpers/__init__.py                       |    0
 gmatch4py/helpers/networkx_parser.py          |  148 ++
 gmatch4py/jaccard.py                          |    2 +-
 gmatch4py_cython/__init__.pyx                 |    1 -
 gmatch4py_cython/gmatch4py/__init__.py        |    1 +
 .../{ => gmatch4py}/bag_of_cliques.pyx        |   22 +-
 gmatch4py_cython/{ => gmatch4py}/deltacon.pyx |    0
 .../gmatch4py/exception/__init__.py           |    1 +
 gmatch4py_cython/gmatch4py/ged/__init__.py    |    2 +
 .../{ => gmatch4py}/ged/algorithm/__init__.py |    0
 .../algorithm/abstract_graph_edit_dist.pyx    |   32 +-
 .../ged/algorithm/edge_edit_dist.pyx          |    4 +-
 .../ged/algorithm/graph_edit_dist.pyx         |    7 +-
 .../gmatch4py/ged/approximate_ged.pyx         |   20 +
 .../ged/bipartite_graph_matching_2.pyx        |  150 ++
 .../gmatch4py/ged/graph/__init__.py           |    1 +
 .../{ => gmatch4py}/ged/graph/__init__.pyx    |    0
 .../{ => gmatch4py}/ged/graph/edge_graph.pyx  |    0
 .../gmatch4py/ged/greedy_edit_distance.pyx    |   44 +
 .../gmatch4py/ged/hausdorff_edit_distance.pyx |  156 ++
 gmatch4py_cython/{ => gmatch4py}/jaccard.pyx  |   24 +-
 .../gmatch4py/kernels/__init__.py             |    1 +
 gmatch4py_cython/{ => gmatch4py}/mcs.pyx      |   10 +-
 .../{ => gmatch4py}/vertex_edge_overlap.pyx   |   17 +-
 .../{ => gmatch4py}/vertex_ranking.pyx        |   12 +-
 gmatch4py_cython/setup.py                     |   47 +
 gmatch4py_cython/utils.pyx                    |   94 -
 helpers/classic.py                            |   26 +
 helpers/gazeteer_helpers.py                   |   18 +
 nlp/disambiguator/disambiguator.py            |    4 +-
 nlp/disambiguator/geodict_gaurav.py           |   41 +-
 nlp/disambiguator/most_common.py              |   59 +
 nlp/disambiguator/pagerank.py                 |    4 +-
 nlp/ner/spacy.py                              |   56 +-
 nlp/ner/stanford_ner.py                       |   13 +-
 notebooks/Cython Enhancement on HED.ipynb     | 1311 ++++++++++
 notebooks/EvalDesambiguisationMada.ipynb      |  311 +++
 notebooks/EvalDesambiguisationPADIWEB.ipynb   |  378 +++
 notebooks/EvalTopoMadagascar.ipynb            |  719 ++++++
 notebooks/NER Evaluation.ipynb                |  612 +++--
 notebooks/StanfordMadaAgro.ipynb              |  950 +++++++
 notebooks/corpusmadahard.ipynb                | 2285 ++++++++++++++---
 pipeline.py                                   |    1 +
 temp.py                                       |  181 ++
 45 files changed, 7073 insertions(+), 718 deletions(-)
 rename gmatch4py_cython/ged/__init__.pyx => gmatch4py/helpers/__init__.py (100%)
 create mode 100644 gmatch4py/helpers/networkx_parser.py
 delete mode 100644 gmatch4py_cython/__init__.pyx
 create mode 100644 gmatch4py_cython/gmatch4py/__init__.py
 rename gmatch4py_cython/{ => gmatch4py}/bag_of_cliques.pyx (79%)
 rename gmatch4py_cython/{ => gmatch4py}/deltacon.pyx (100%)
 create mode 100644 gmatch4py_cython/gmatch4py/exception/__init__.py
 create mode 100644 gmatch4py_cython/gmatch4py/ged/__init__.py
 rename gmatch4py_cython/{ => gmatch4py}/ged/algorithm/__init__.py (100%)
 rename gmatch4py_cython/{ => gmatch4py}/ged/algorithm/abstract_graph_edit_dist.pyx (84%)
 rename gmatch4py_cython/{ => gmatch4py}/ged/algorithm/edge_edit_dist.pyx (88%)
 rename gmatch4py_cython/{ => gmatch4py}/ged/algorithm/graph_edit_dist.pyx (93%)
 create mode 100644 gmatch4py_cython/gmatch4py/ged/approximate_ged.pyx
 create mode 100644 gmatch4py_cython/gmatch4py/ged/bipartite_graph_matching_2.pyx
 create mode 100644 gmatch4py_cython/gmatch4py/ged/graph/__init__.py
 rename gmatch4py_cython/{ => gmatch4py}/ged/graph/__init__.pyx (100%)
 rename gmatch4py_cython/{ => gmatch4py}/ged/graph/edge_graph.pyx (100%)
 create mode 100644 gmatch4py_cython/gmatch4py/ged/greedy_edit_distance.pyx
 create mode 100644 gmatch4py_cython/gmatch4py/ged/hausdorff_edit_distance.pyx
 rename gmatch4py_cython/{ => gmatch4py}/jaccard.pyx (77%)
 create mode 100644 gmatch4py_cython/gmatch4py/kernels/__init__.py
 rename gmatch4py_cython/{ => gmatch4py}/mcs.pyx (86%)
 rename gmatch4py_cython/{ => gmatch4py}/vertex_edge_overlap.pyx (79%)
 rename gmatch4py_cython/{ => gmatch4py}/vertex_ranking.pyx (76%)
 create mode 100644 gmatch4py_cython/setup.py
 delete mode 100644 gmatch4py_cython/utils.pyx
 create mode 100644 helpers/classic.py
 create mode 100644 nlp/disambiguator/most_common.py
 create mode 100644 notebooks/Cython Enhancement on HED.ipynb
 create mode 100644 notebooks/EvalDesambiguisationMada.ipynb
 create mode 100644 notebooks/EvalDesambiguisationPADIWEB.ipynb
 create mode 100644 notebooks/EvalTopoMadagascar.ipynb
 create mode 100644 notebooks/StanfordMadaAgro.ipynb
 create mode 100644 temp.py

diff --git a/generate_data_csv.py b/generate_data_csv.py
index 141a0f0..fffe0ff 100644
--- a/generate_data_csv.py
+++ b/generate_data_csv.py
@@ -81,10 +81,7 @@ with ProgressBar(max_value=len(files_glob),widgets=[' [', Timer(), '] ',Bar(),'(
 
         id_=int(re.findall("\d+", fn)[-1])
         df=pd.read_csv(fn)
-        try:
-            df=df[(df["GID"]!='O') & (df.GID.notnull())]
-        except:
-            df = df[(df.GID.notnull())]
+        df = df[-df["GID"].isin(['0', 'o', 'NR', 'O'])]
         try:
             count_per_doc[id_]=json.loads(df.groupby("GID").GID.count().to_json())
             associated_es[id_] = df[["GID","text"]].groupby("GID",as_index=False).max().set_index('GID').to_dict()["text"]
@@ -98,22 +95,29 @@ all_es=set([])
 for k,v in associated_es.items():
     for k2 in v:
         all_es.add(k2)
+
 logging.info("Get All Shapes from Database for all ES")
 all_shapes=get_all_shapes(list(all_es))
-#print(all_shapes.keys())
+
 i=0
+def foo_(x):
+    try:
+        return get_data(x)["en"]
+    except:
+        print(x)
 with ProgressBar(max_value=len(files_glob),
                  widgets=[' [', Timer(), '] ', Bar(), '(', Counter(), ')', '(', ETA(), ')']) as pg:
     for fn in files_glob:
 
         id_ = int(re.findall("\d+", fn)[-1])
         df = pd.read_csv(fn)
-        try:
-            df = df[(df["GID"] != 'O') & (df.GID.notnull())]
-        except:
-            df = df[(df.GID.notnull())]
-
-        df["label"]=df.GID.apply(lambda x:get_data(x)["en"])
+        # try:
+        df= df[-df["GID"].isin(['0','o','NR','O'])]
+        #print(df)
+        # except:
+        #     df = df[(df.GID.notnull())]
+        #     print("BUG",df)
+        df["label"]=df.GID.apply(foo_)
         df = df.rename(columns={"GID": "id"})
         str_=STR.from_pandas(df,[],all_shapes).build()
         nx.write_gexf(str_, args.graphs_output_dir + "/{0}.gexf".format(id_))
diff --git a/gmatch4py_cython/ged/__init__.pyx b/gmatch4py/helpers/__init__.py
similarity index 100%
rename from gmatch4py_cython/ged/__init__.pyx
rename to gmatch4py/helpers/__init__.py
diff --git a/gmatch4py/helpers/networkx_parser.py b/gmatch4py/helpers/networkx_parser.py
new file mode 100644
index 0000000..d67049a
--- /dev/null
+++ b/gmatch4py/helpers/networkx_parser.py
@@ -0,0 +1,148 @@
+# coding = utf-8
+
+import networkx as nx
+import graph_tool as gt
+
+
+
+def get_prop_type(value, key=None):
+    """
+    Performs typing and value conversion for the graph_tool PropertyMap class.
+    If a key is provided, it also ensures the key is in a format that can be
+    used with the PropertyMap. Returns a tuple, (type name, value, key)
+    """
+    # Deal with the value
+    if isinstance(value, bool):
+        tname = 'bool'
+
+    elif isinstance(value, int):
+        tname = 'float'
+        value = float(value)
+
+    elif isinstance(value, float):
+        tname = 'float'
+
+    elif isinstance(value, str):
+        tname = 'string'
+        value = str(value)
+
+    elif isinstance(value, dict):
+        tname = 'object'
+
+    else:
+        tname = 'string'
+        value = str(value)
+
+    return tname, value, key
+
+
+def nx2gt(nxG):
+    """
+    Converts a networkx graph to a graph-tool graph.
+    """
+    # Phase 0: Create a directed or undirected graph-tool Graph
+    gtG = gt.Graph(directed=nxG.is_directed())
+
+    # Add the Graph properties as "internal properties"
+    for key, value in nxG.graph.items():
+        # Convert the value and key into a type for graph-tool
+        tname, value, key = get_prop_type(value, key)
+
+        prop = gtG.new_graph_property(tname) # Create the PropertyMap
+        gtG.graph_properties[key] = prop     # Set the PropertyMap
+        gtG.graph_properties[key] = value    # Set the actual value
+
+    # Phase 1: Add the vertex and edge property maps
+    # Go through all nodes and edges and add seen properties
+    # Add the node properties first
+    nprops = set() # cache keys to only add properties once
+    for node, data in nxG.nodes_iter(data=True):
+
+        # Go through all the properties if not seen and add them.
+        for key, val in data.items():
+            if key in nprops: continue # Skip properties already added
+
+            # Convert the value and key into a type for graph-tool
+            tname, _, key  = get_prop_type(val, key)
+
+            prop = gtG.new_vertex_property(tname) # Create the PropertyMap
+            gtG.vertex_properties[key] = prop     # Set the PropertyMap
+
+            # Add the key to the already seen properties
+            nprops.add(key)
+
+    # Also add the node id: in NetworkX a node can be any hashable type, but
+    # in graph-tool node are defined as indices. So we capture any strings
+    # in a special PropertyMap called 'id' -- modify as needed!
+    gtG.vertex_properties['id'] = gtG.new_vertex_property('string')
+
+    # Add the edge properties second
+    eprops = set() # cache keys to only add properties once
+    for src, dst, data in nxG.edges_iter(data=True):
+
+        # Go through all the edge properties if not seen and add them.
+        for key, val in data.items():
+            if key in eprops: continue # Skip properties already added
+
+            # Convert the value and key into a type for graph-tool
+            tname, _, key = get_prop_type(val, key)
+
+            prop = gtG.new_edge_property(tname) # Create the PropertyMap
+            gtG.edge_properties[key] = prop     # Set the PropertyMap
+
+            # Add the key to the already seen properties
+            eprops.add(key)
+
+    # Phase 2: Actually add all the nodes and vertices with their properties
+    # Add the nodes
+    vertices = {} # vertex mapping for tracking edges later
+    for node, data in nxG.nodes_iter(data=True):
+
+        # Create the vertex and annotate for our edges later
+        v = gtG.add_vertex()
+        vertices[node] = v
+
+        # Set the vertex properties, not forgetting the id property
+        data['id'] = str(node)
+        for key, value in data.items():
+            gtG.vp[key][v] = value # vp is short for vertex_properties
+
+    # Add the edges
+    for src, dst, data in nxG.edges_iter(data=True):
+
+        # Look up the vertex structs from our vertices mapping and add edge.
+        e = gtG.add_edge(vertices[src], vertices[dst])
+
+        # Add the edge properties
+        for key, value in data.items():
+            gtG.ep[key][e] = value # ep is short for edge_properties
+
+    # Done, finally!
+    return gtG
+
+
+if __name__ == '__main__':
+
+    # Create the networkx graph
+    nxG = nx.Graph(name="Undirected Graph")
+    nxG.add_node("v1", name="alpha", color="red")
+    nxG.add_node("v2", name="bravo", color="blue")
+    nxG.add_node("v3", name="charlie", color="blue")
+    nxG.add_node("v4", name="hub", color="purple")
+    nxG.add_node("v5", name="delta", color="red")
+    nxG.add_node("v6", name="echo", color="red")
+
+    nxG.add_edge("v1", "v2", weight=0.5, label="follows")
+    nxG.add_edge("v1", "v3", weight=0.25, label="follows")
+    nxG.add_edge("v2", "v4", weight=0.05, label="follows")
+    nxG.add_edge("v3", "v4", weight=0.35, label="follows")
+    nxG.add_edge("v5", "v4", weight=0.65, label="follows")
+    nxG.add_edge("v6", "v4", weight=0.53, label="follows")
+    nxG.add_edge("v5", "v6", weight=0.21, label="follows")
+
+    for item in nxG.edges_iter(data=True):
+        print(item)
+
+    # Convert to graph-tool graph
+    gtG = nx2gt(nxG)
+    gtG.list_properties()
\ No newline at end of file
diff --git a/gmatch4py/jaccard.py b/gmatch4py/jaccard.py
index bb4c61b..1676ff1 100644
--- a/gmatch4py/jaccard.py
+++ b/gmatch4py/jaccard.py
@@ -44,7 +44,7 @@ class Jaccard():
     def union_nodes(g1, g2):
         union=set([])
         for n in g1.nodes():union.add(n)
-        for n in g2.nodes(): union.add(n)
+        for n in g2.nodes():union.add(n)
         return union
 
     @staticmethod
diff --git a/gmatch4py_cython/__init__.pyx b/gmatch4py_cython/__init__.pyx
deleted file mode 100644
index a4e2017..0000000
--- a/gmatch4py_cython/__init__.pyx
+++ /dev/null
@@ -1 +0,0 @@
-__version__ = "0.1"
diff --git a/gmatch4py_cython/gmatch4py/__init__.py b/gmatch4py_cython/gmatch4py/__init__.py
new file mode 100644
index 0000000..950f635
--- /dev/null
+++ b/gmatch4py_cython/gmatch4py/__init__.py
@@ -0,0 +1 @@
+# coding = utf-8
\ No newline at end of file
diff --git a/gmatch4py_cython/bag_of_cliques.pyx b/gmatch4py_cython/gmatch4py/bag_of_cliques.pyx
similarity index 79%
rename from gmatch4py_cython/bag_of_cliques.pyx
rename to gmatch4py_cython/gmatch4py/bag_of_cliques.pyx
index a91827c..f297507 100644
--- a/gmatch4py_cython/bag_of_cliques.pyx
+++ b/gmatch4py_cython/gmatch4py/bag_of_cliques.pyx
@@ -5,7 +5,7 @@ from typing import Sequence
 
 import networkx as nx
 import numpy as np
-
+cimport numpy as np
 
 class BagOfCliques():
 
@@ -14,7 +14,7 @@ class BagOfCliques():
         b=BagOfCliques()
         bog=b.getBagOfCliques(graphs)
         #Compute cosine similarity
-        scores=np.dot(bog,bog.T)
+        cdef np.ndarray scores=np.dot(bog,bog.T)
         for i in range(len(scores)):
             for j in range(len(scores)):
                 scores[i,j]/=(np.sqrt(np.sum(bog[i]**2))*np.sqrt(np.sum(bog[j]**2))) # Can be computed in one line
@@ -27,9 +27,11 @@ class BagOfCliques():
         """
         tree = {}
         c_ = 0
-        clique_vocab = []
+        cdef list clique_vocab = []
+        cdef list cli_temp
+        cdef list cliques
         for g in graphs:
-            cliques = list(nx.algorithms.clique.find_cliques(nx.Graph(g)))
+            cliques = list(nx.find_cliques(nx.Graph(g)))
             for clique in cliques:
                 t = tree
                 cli_temp = copy.deepcopy(clique)
@@ -55,7 +57,7 @@ class BagOfCliques():
         return clique_vocab
 
 
-    def ifHaveMinor(self,G: nx.Graph, H: list):
+    def ifHaveMinor(self,G, list H):
         """
         If a clique (minor) H belong to a graph G
         :param H:
@@ -66,16 +68,18 @@ class BagOfCliques():
         return 0
 
 
-    def getBagOfCliques(self,graphs : Sequence[nx.Graph]):
+    def  getBagOfCliques(self,graphs ):
         """
 
         :param clique_vocab:
         :return:
         """
-        clique_vocab=self.getUniqueCliques(graphs)
+        cdef list clique_vocab=self.getUniqueCliques(graphs)
+
+        cdef int l_v=len(clique_vocab)
+        cdef np.ndarray boc = np.zeros((len(graphs), l_v))
+        cdef np.ndarray vector
 
-        l_v=len(clique_vocab)
-        boc = np.zeros((len(graphs), l_v))
         for g in range(len(graphs)):
             gr = graphs[g]
             vector = np.zeros(l_v)
diff --git a/gmatch4py_cython/deltacon.pyx b/gmatch4py_cython/gmatch4py/deltacon.pyx
similarity index 100%
rename from gmatch4py_cython/deltacon.pyx
rename to gmatch4py_cython/gmatch4py/deltacon.pyx
diff --git a/gmatch4py_cython/gmatch4py/exception/__init__.py b/gmatch4py_cython/gmatch4py/exception/__init__.py
new file mode 100644
index 0000000..950f635
--- /dev/null
+++ b/gmatch4py_cython/gmatch4py/exception/__init__.py
@@ -0,0 +1 @@
+# coding = utf-8
\ No newline at end of file
diff --git a/gmatch4py_cython/gmatch4py/ged/__init__.py b/gmatch4py_cython/gmatch4py/ged/__init__.py
new file mode 100644
index 0000000..e5c6c3c
--- /dev/null
+++ b/gmatch4py_cython/gmatch4py/ged/__init__.py
@@ -0,0 +1,2 @@
+# coding = utf-8
+
diff --git a/gmatch4py_cython/ged/algorithm/__init__.py b/gmatch4py_cython/gmatch4py/ged/algorithm/__init__.py
similarity index 100%
rename from gmatch4py_cython/ged/algorithm/__init__.py
rename to gmatch4py_cython/gmatch4py/ged/algorithm/__init__.py
diff --git a/gmatch4py_cython/ged/algorithm/abstract_graph_edit_dist.pyx b/gmatch4py_cython/gmatch4py/ged/algorithm/abstract_graph_edit_dist.pyx
similarity index 84%
rename from gmatch4py_cython/ged/algorithm/abstract_graph_edit_dist.pyx
rename to gmatch4py_cython/gmatch4py/ged/algorithm/abstract_graph_edit_dist.pyx
index e0a1d3b..481ec69 100644
--- a/gmatch4py_cython/ged/algorithm/abstract_graph_edit_dist.pyx
+++ b/gmatch4py_cython/gmatch4py/ged/algorithm/abstract_graph_edit_dist.pyx
@@ -5,9 +5,12 @@ import sys
 
 import numpy as np
 from scipy.optimize import linear_sum_assignment
+cimport numpy as np
 
 
 class AbstractGraphEditDistance(object):
+
+
     def __init__(self, g1, g2,debug=False,**kwargs):
         self.g1 = g1
         self.g2 = g2
@@ -26,12 +29,14 @@ class AbstractGraphEditDistance(object):
         return sum(opt_path)
 
     def print_operations(self,cost_matrix,row_ind,col_ind):
-        nodes1 = self.g1.nodes()
-        nodes2 = self.g2.nodes()
+        cdef list nodes1 = self.g1.nodes()
+        cdef list nodes2 = self.g2.nodes()
         dn1 = self.g1.node
         dn2 = self.g2.node
 
-        n,m=len(nodes1),len(nodes2)
+        cdef int n=len(nodes1)
+        cdef int m=len(nodes2)
+        cdef int x,y,i
         for i in range(len(row_ind)):
             y,x=row_ind[i],col_ind[i]
             val=cost_matrix[row_ind[i]][col_ind[i]]
@@ -43,7 +48,7 @@ class AbstractGraphEditDistance(object):
                 print("DEL {0} cost = {1}".format(dn1[nodes1[m-x]]["label"],val))
 
     def edit_costs(self):
-        cost_matrix = self.create_cost_matrix()
+        cdef np.ndarray cost_matrix = self.create_cost_matrix()
         if self.debug:
             np.set_printoptions(precision=3)
             print("Cost Matrix for ",str(self.__class__.__name__),"\n",cost_matrix)
@@ -51,7 +56,8 @@ class AbstractGraphEditDistance(object):
         row_ind,col_ind = linear_sum_assignment(cost_matrix)
         if self.debug:
             self.print_operations(cost_matrix,row_ind,col_ind)
-        return [cost_matrix[row_ind[i]][col_ind[i]] for i in range(len(row_ind))]
+        cdef int f=len(row_ind)
+        return [cost_matrix[row_ind[i]][col_ind[i]] for i in range(f)]
 
     def create_cost_matrix(self):
         """
@@ -67,13 +73,13 @@ class AbstractGraphEditDistance(object):
 
         The delete -> delete region is filled with zeros
         """
-        n = len(self.g1)
-        m = len(self.g2)
-        cost_matrix = np.zeros((n+m,n+m))
+        cdef int n = len(self.g1)
+        cdef int m = len(self.g2)
+        cdef np.ndarray cost_matrix = np.zeros((n+m,n+m))
         #cost_matrix = [[0 for i in range(n + m)] for j in range(n + m)]
-        nodes1 = self.g1.nodes()
-        nodes2 = self.g2.nodes()
-
+        cdef list nodes1 = self.g1.nodes()
+        cdef list nodes2 = self.g2.nodes()
+        cdef int i,j
         for i in range(n):
             for j in range(m):
                 cost_matrix[i,j] = self.substitute_cost(nodes1[i], nodes2[j])
@@ -89,10 +95,10 @@ class AbstractGraphEditDistance(object):
         self.cost_matrix = cost_matrix
         return cost_matrix
 
-    def insert_cost(self, i, j):
+    def insert_cost(self, int i, int j):
         raise NotImplementedError
 
-    def delete_cost(self, i, j):
+    def delete_cost(self, int i, int j):
         raise NotImplementedError
 
     def substitute_cost(self, nodes1, nodes2):
diff --git a/gmatch4py_cython/ged/algorithm/edge_edit_dist.pyx b/gmatch4py_cython/gmatch4py/ged/algorithm/edge_edit_dist.pyx
similarity index 88%
rename from gmatch4py_cython/ged/algorithm/edge_edit_dist.pyx
rename to gmatch4py_cython/gmatch4py/ged/algorithm/edge_edit_dist.pyx
index 684dc09..80f24e7 100644
--- a/gmatch4py_cython/ged/algorithm/edge_edit_dist.pyx
+++ b/gmatch4py_cython/gmatch4py/ged/algorithm/edge_edit_dist.pyx
@@ -13,12 +13,12 @@ class EdgeEditDistance(AbstractGraphEditDistance):
     def __init__(self, g1, g2,**kwargs):
         AbstractGraphEditDistance.__init__(self, g1, g2,**kwargs)
 
-    def insert_cost(self, i, j, nodes2):
+    def insert_cost(self, int i, int j, nodes2):
         if i == j:
             return self.edge_ins
         return sys.maxsize
 
-    def delete_cost(self, i, j, nodes1):
+    def delete_cost(self, int i, int j, nodes1):
         if i == j:
             return self.edge_del
         return sys.maxsize
diff --git a/gmatch4py_cython/ged/algorithm/graph_edit_dist.pyx b/gmatch4py_cython/gmatch4py/ged/algorithm/graph_edit_dist.pyx
similarity index 93%
rename from gmatch4py_cython/ged/algorithm/graph_edit_dist.pyx
rename to gmatch4py_cython/gmatch4py/ged/algorithm/graph_edit_dist.pyx
index 2c23758..cc38d08 100644
--- a/gmatch4py_cython/ged/algorithm/graph_edit_dist.pyx
+++ b/gmatch4py_cython/gmatch4py/ged/algorithm/graph_edit_dist.pyx
@@ -30,12 +30,12 @@ class GraphEditDistance(AbstractGraphEditDistance):
         else:
             return self.node_ins+self.node_del
 
-    def delete_cost(self, i, j, nodes1):
+    def delete_cost(self, int i, int j, nodes1):
         if i == j:
             return self.node_del+self.g1.degree(nodes1[i]) # Deleting a node implicate to delete in and out edges
         return sys.maxsize
 
-    def insert_cost(self, i, j, nodes2):
+    def insert_cost(self, int i, int j, nodes2):
         if i == j:
             deg=self.g2.degree(nodes2[j])
             if isinstance(deg,dict):deg=0
@@ -44,7 +44,7 @@ class GraphEditDistance(AbstractGraphEditDistance):
             return sys.maxsize
 
     def get_edge_multigraph(self,g,node):
-        edges=[]
+        cdef list edges=[]
         for id_,val in g.edge[node].items():
             if not 0 in val:
                 edges.append(str(id_) + val["color"])
@@ -54,6 +54,7 @@ class GraphEditDistance(AbstractGraphEditDistance):
         return edges
 
     def edge_diff(self, node1, node2):
+        cdef list edges1,edges2
         if isinstance(self.g1,nx.MultiDiGraph):
             edges1 = self.get_edge_multigraph(self.g1,node1)
             edges2 = self.get_edge_multigraph(self.g2,node2)
diff --git a/gmatch4py_cython/gmatch4py/ged/approximate_ged.pyx b/gmatch4py_cython/gmatch4py/ged/approximate_ged.pyx
new file mode 100644
index 0000000..d77f522
--- /dev/null
+++ b/gmatch4py_cython/gmatch4py/ged/approximate_ged.pyx
@@ -0,0 +1,20 @@
+# coding = utf-8
+
+import numpy as np
+
+from .algorithm.graph_edit_dist import GraphEditDistance
+
+
+class ApproximateGraphEditDistance():
+    __type__ = "dist"
+
+    @staticmethod
+    def compare(listgs,c_del_node=1,c_del_edge=1,c_ins_node=1,c_ins_edge=1):
+        n= len(listgs)
+        comparison_matrix = np.zeros((n,n))
+        for i in range(n):
+            for j in range(i,n):
+                comparison_matrix[i,j]= GraphEditDistance(listgs[i],listgs[j],False,node_del=c_del_node,node_ins=c_ins_node,edge_del=c_del_edge,edge_ins=c_ins_edge).distance()
+                comparison_matrix[j,i]= comparison_matrix[i,j] # Unethical ! Since AGED is not a symmetric similarity measure !
+
+        return comparison_matrix
\ No newline at end of file
diff --git a/gmatch4py_cython/gmatch4py/ged/bipartite_graph_matching_2.pyx b/gmatch4py_cython/gmatch4py/ged/bipartite_graph_matching_2.pyx
new file mode 100644
index 0000000..a09627a
--- /dev/null
+++ b/gmatch4py_cython/gmatch4py/ged/bipartite_graph_matching_2.pyx
@@ -0,0 +1,150 @@
+# coding = utf-8
+import numpy as np
+cimport numpy as np
+
+cdef class BP_2():
+    """
+
+    """
+    __type__="dist"
+
+    cdef int node_del
+    cdef int node_ins
+    cdef int edge_del
+    cdef int edge_ins
+
+    @staticmethod
+    def compare(listgs, c_del_node=1, c_del_edge=1, c_ins_node=1, c_ins_edge=1):
+        cdef int n = len(listgs)
+        comparator = BP_2(c_del_node, c_ins_node, c_del_edge, c_ins_edge)
+        cdef np.ndarray comparison_matrix = np.zeros((n, n))
+        for i in range(n):
+            for j in range(i, n):
+                comparison_matrix[i, j] = comparator.bp2(listgs[i], listgs[j])
+                comparison_matrix[j, i] = comparison_matrix[i, j]
+
+        return comparison_matrix
+
+    def __init__(self, node_del=1, node_ins=1, edge_del=1, edge_ins=1):
+        """Constructor for HED"""
+        self.node_del = node_del
+        self.node_ins = node_ins
+        self.edge_del = edge_del
+        self.edge_ins = edge_ins
+
+    def bp2(self, g1, g2):
+        """
+        Compute de Hausdorff Edit Distance
+        :param g1: first graph
+        :param g2: second graph
+        :return:
+        """
+        return min(self.distance(self.psi(g1,g2)),self.distance(self.psi(g2,g1)))
+
+    def distance(self,e):
+        return np.sum(e)
+
+    def psi(self,g1,g2):
+        cdef list psi_=[]
+        cdef list nodes1 = g1.nodes()
+        cdef list nodes2 = g2.nodes()
+        for u in nodes1:
+            v=None
+            for w in nodes2:
+                if 2*self.fuv(g1,g2,u,w) < self.fuv(g1,g2,u,None) + self.fuv(g1,g2,None,w)\
+                     and self.fuv(g1,g2,u,w) < self.fuv(g1,g2,u,v):
+                    v=w
+                psi_.append(self.fuv(g1,g2,u,v))
+            if u:
+                nodes1= list(set(nodes1).difference(set([u])))
+            if v:
+                nodes2= list(set(nodes2).difference(set([v])))
+        for v in nodes2:
+            psi_.append(self.fuv(g1,g2,None,v))
+        return  psi_
+
+
+    def fuv(self, g1, g2, n1, n2):
+        """
+        Compute the Node Distance function
+        :param g1: first graph
+        :param g2: second graph
+        :param n1: node of the first graph
+        :param n2: node of the second graph
+        :return:
+        """
+        if n2 == None:  # Del
+            return self.node_del + ((self.edge_del / 2) * g1.degree(n1))
+        if n1 == None:  # Insert
+            return self.node_ins + ((self.edge_ins / 2) * g2.degree(n2))
+        else:
+            if n1 == n2:
+                return 0.
+            return (self.node_del + self.node_ins + self.hed_edge(g1, g2, n1, n2)) / 2
+
+    def hed_edge(self, g1, g2, n1, n2):
+        """
+        Compute HEDistance between edges of n1 and n2, respectively in g1 and g2
+        :param g1: first graph
+        :param g2: second graph
+        :param n1: node of the first graph
+        :param n2: node of the second graph
+        :return:
+        """
+        return self.sum_gpq(g1, n1, g2, n2) + self.sum_gpq(g1, n1, g2, n2)
+
+    def get_edge_multigraph(self, g, node):
+        """
+        Get list of edge around a node in a Multigraph
+        :param g: multigraph
+        :param node: node in the multigraph
+        :return:
+        """
+        edges = []
+        for edge in g.edges(data=True):
+            if node == edge[0] or node == edge[1]:
+                edges.append("{0}-{1}-{2}".format(edge[0],edge[1],edge[2]["color"]))
+        return edges
+
+    def sum_gpq(self, g1, n1, g2, n2):
+        """
+        Compute Nearest Neighbour Distance between edges around n1 in G1  and edges around n2 in G2
+        :param g1: first graph
+        :param n1: node in the first graph
+        :param g2: second graph
+        :param n2: node in the second graph
+        :return:
+        """
+
+        #if isinstance(g1, nx.MultiDiGraph):
+        cdef list edges1 = self.get_edge_multigraph(g1, n1)
+        cdef list edges2 = self.get_edge_multigraph(g2, n2)
+        #else:
+            #print(1)
+            #edges1 = [str(n1 + "-" + ef) for ef in list(g1.edge[n1].keys())]
+            #edges2 = [str(n2 + "-" + ef) for ef in list(g2.edge[n2].keys())]
+        edges2.extend([None])
+        cdef np.ndarray min_sum = np.zeros(len(edges1))
+        for i in range(len(edges1)):
+            min_i = np.zeros(len(edges2))
+            for j in range(len(edges2)):
+                min_i[j] = self.gpq(edges1[i], edges2[j])
+            min_sum[i] = np.min(min_i)
+        return np.sum(min_sum)
+
+    def gpq(self, e1, e2):
+        """
+        Compute the edge distance function
+        :param e1: edge1
+        :param e2: edge2
+        :return:
+        """
+        if e2 == None:  # Del
+            return self.edge_del
+        if e1 == None:  # Insert
+            return self.edge_ins
+        else:
+            if e1 == e2:
+                return 0.
+            return (self.edge_del + self.edge_ins) / 2
+
diff --git a/gmatch4py_cython/gmatch4py/ged/graph/__init__.py b/gmatch4py_cython/gmatch4py/ged/graph/__init__.py
new file mode 100644
index 0000000..950f635
--- /dev/null
+++ b/gmatch4py_cython/gmatch4py/ged/graph/__init__.py
@@ -0,0 +1 @@
+# coding = utf-8
\ No newline at end of file
diff --git a/gmatch4py_cython/ged/graph/__init__.pyx b/gmatch4py_cython/gmatch4py/ged/graph/__init__.pyx
similarity index 100%
rename from gmatch4py_cython/ged/graph/__init__.pyx
rename to gmatch4py_cython/gmatch4py/ged/graph/__init__.pyx
diff --git a/gmatch4py_cython/ged/graph/edge_graph.pyx b/gmatch4py_cython/gmatch4py/ged/graph/edge_graph.pyx
similarity index 100%
rename from gmatch4py_cython/ged/graph/edge_graph.pyx
rename to gmatch4py_cython/gmatch4py/ged/graph/edge_graph.pyx
diff --git a/gmatch4py_cython/gmatch4py/ged/greedy_edit_distance.pyx b/gmatch4py_cython/gmatch4py/ged/greedy_edit_distance.pyx
new file mode 100644
index 0000000..96478dd
--- /dev/null
+++ b/gmatch4py_cython/gmatch4py/ged/greedy_edit_distance.pyx
@@ -0,0 +1,44 @@
+# coding = utf-8
+import numpy as np
+
+from .algorithm.graph_edit_dist import GraphEditDistance
+cimport numpy as np
+
+class GreedyEditDistance(GraphEditDistance):
+    """
+    Implementation of the Greedy Edit Distance presented in :
+
+    Improved quadratic time approximation of graph edit distance by Hausdorff matching and greedy assignement
+    Andreas Fischer, Kaspar Riesen, Horst Bunke
+    2016
+    """
+    __type__ = "dist"
+    @staticmethod
+    def compare(listgs, c_del_node=1, c_del_edge=1, c_ins_node=1, c_ins_edge=1):
+        cdef int n = len(listgs)
+        cdef np.ndarray comparison_matrix = np.zeros((n, n))
+        for i in range(n):
+            for j in range(i, n):
+                comparison_matrix[i, j] = GreedyEditDistance(listgs[i], listgs[j],False, node_del=c_del_node,
+                                                            node_ins=c_ins_node, edge_del=c_del_edge,
+                                                            edge_ins=c_ins_edge).distance()
+                comparison_matrix[j, i] = comparison_matrix[i, j]
+
+
+        return comparison_matrix
+
+    def __init__(self,g1,g2,debug=False,**kwargs):
+        """Constructor for GreedyEditDistance"""
+        super().__init__(g1,g2,debug,**kwargs)
+
+
+    def edit_costs(self):
+        cdef np.ndarray cost_matrix=self.create_cost_matrix()
+        cdef np.ndarray cost_matrix_2=cost_matrix.copy()
+        cdef list psi=[]
+        for i in range(len(cost_matrix)):
+            phi_i=np.argmin((cost_matrix[i]))
+            cost_matrix=np.delete(cost_matrix,phi_i,1)
+            psi.append([i,phi_i+i]) #+i to compensate the previous column deletion
+        return [cost_matrix_2[psi[i][0]][psi[i][1]] for i in range(len(psi))]
+
diff --git a/gmatch4py_cython/gmatch4py/ged/hausdorff_edit_distance.pyx b/gmatch4py_cython/gmatch4py/ged/hausdorff_edit_distance.pyx
new file mode 100644
index 0000000..11eb6c5
--- /dev/null
+++ b/gmatch4py_cython/gmatch4py/ged/hausdorff_edit_distance.pyx
@@ -0,0 +1,156 @@
+# coding = utf-8
+
+
+import numpy as np
+cimport numpy as np
+cdef class HED:
+    """
+    Implementation of Hausdorff Edit Distance described in
+
+    Improved quadratic time approximation of graph edit distance by Hausdorff matching and greedy assignement
+    Andreas Fischer, Kaspar Riesen, Horst Bunke
+    2016
+    """
+
+    cdef int node_del
+    cdef int node_ins
+    cdef int edge_del
+    cdef int edge_ins
+
+    __type__ = "dist"
+    @staticmethod
+    def compare(list listgs, int c_del_node=1, int c_del_edge=1, int c_ins_node=1, int c_ins_edge=1):
+        cdef int n = len(listgs)
+        comparator = HED(c_del_node, c_ins_node, c_del_edge, c_ins_edge)
+        cdef np.ndarray comparison_matrix = np.zeros((n, n))
+        for i in range(n):
+            for j in range(i, n):
+                comparison_matrix[i, j] = comparator.hed(listgs[i], listgs[j])
+                comparison_matrix[j, i] = comparison_matrix[i, j]
+
+        return comparison_matrix
+
+
+    def __init__(self, int node_del=1, int node_ins=1, int edge_del=1, int edge_ins=1):
+        """Constructor for HED"""
+        self.node_del = node_del
+        self.node_ins = node_ins
+        self.edge_del = edge_del
+        self.edge_ins = edge_ins
+
+    cpdef float hed(self, g1, g2):
+        """
+        Compute de Hausdorff Edit Distance
+        :param g1: first graph
+        :param g2: second graph
+        :return:
+        """
+        return self.sum_fuv(g1, g2) + self.sum_fuv(g2, g1)
+
+    cdef float sum_fuv(self, g1, g2):
+        """
+        Compute Nearest Neighbour Distance between G1 and G2
+        :param g1: First Graph
+        :param g2: Second Graph
+        :return:
+        """
+        cdef np.ndarray min_sum = np.zeros(len(g1))
+        nodes1 = g1.nodes()
+        nodes2 = g2.nodes()
+        nodes2.extend([None])
+        cdef np.ndarray min_i
+        for i in range(len(nodes1)):
+            min_i = np.zeros(len(nodes2))
+            for j in range(len(nodes2)):
+                min_i[j] = self.fuv(g1, g2, nodes1[i], nodes2[j])
+            min_sum[i] = np.min(min_i)
+        return np.sum(min_sum)
+
+    cdef float fuv(self, g1, g2, n1, n2):
+        """
+        Compute the Node Distance function
+        :param g1: first graph
+        :param g2: second graph
+        :param n1: node of the first graph
+        :param n2: node of the second graph
+        :return:
+        """
+        if n2 == None:  # Del
+            return self.node_del + ((self.edge_del / 2) * g1.degree(n1))
+        if n1 == None:  # Insert
+            return self.node_ins + ((self.edge_ins / 2) * g2.degree(n2))
+        else:
+            if n1 == n2:
+                return 0
+            return (self.node_del + self.node_ins + self.hed_edge(g1, g2, n1, n2)) / 2
+
+    cdef float hed_edge(self, g1, g2, n1, n2):
+        """
+        Compute HEDistance between edges of n1 and n2, respectively in g1 and g2
+        :param g1: first graph
+        :param g2: second graph
+        :param n1: node of the first graph
+        :param n2: node of the second graph
+        :return:
+        """
+        return self.sum_gpq(g1, n1, g2, n2) + self.sum_gpq(g1, n1, g2, n2)
+
+    cdef list get_edge_multigraph(self, g, node):
+        """
+        Get list of edge around a node in a Multigraph
+        :param g: multigraph
+        :param node: node in the multigraph
+        :return:
+        """
+        cdef list edges = []
+        for edge in g.edges(data=True):
+            if node == edge[0] or node == edge[1]:
+                try:
+                    edges.append("{0}-{1}-{2}".format(edge[0],edge[1],edge[2]["color"]))
+                except:
+                    edges.append("{0}-{1}".format(edge[0],edge[1]))
+        return edges
+
+    cdef float  sum_gpq(self, g1, n1, g2, n2):
+        """
+        Compute Nearest Neighbour Distance between edges around n1 in G1  and edges around n2 in G2
+        :param g1: first graph
+        :param n1: node in the first graph
+        :param g2: second graph
+        :param n2: node in the second graph
+        :return:
+        """
+
+        #if isinstance(g1, nx.MultiDiGraph):
+        cdef list edges1 = self.get_edge_multigraph(g1, n1)
+        cdef list edges2 = self.get_edge_multigraph(g2, n2)
+
+        #else:
+            #edges1 = [str(n1 + "-" + ef) for ef in list(g1.edge[n1].keys())]
+            #edges2 = [str(n2 + "-" + ef) for ef in list(g2.edge[n2].keys())]
+
+        cdef np.ndarray min_sum = np.zeros(len(edges1))
+        edges2.extend([None])
+        cdef np.ndarray min_i
+        for i in range(len(edges1)):
+            min_i = np.zeros(len(edges2))
+            for j in range(len(edges2)):
+                min_i[j] = self.gpq(edges1[i], edges2[j])
+            min_sum[i] = np.min(min_i)
+        return np.sum(min_sum)
+
+    cdef float gpq(self, str e1, str e2):
+        """
+        Compute the edge distance function
+        :param e1: edge1
+        :param e2: edge2
+        :return:
+        """
+        if e2 == None:  # Del
+            return self.edge_del
+        if e1 == None:  # Insert
+            return self.edge_ins
+        else:
+            if e1 == e2:
+                return 0
+            return (self.edge_del + self.edge_ins) / 2
\ No newline at end of file
diff --git a/gmatch4py_cython/jaccard.pyx b/gmatch4py_cython/gmatch4py/jaccard.pyx
similarity index 77%
rename from gmatch4py_cython/jaccard.pyx
rename to gmatch4py_cython/gmatch4py/jaccard.pyx
index bb4c61b..237acd3 100644
--- a/gmatch4py_cython/jaccard.pyx
+++ b/gmatch4py_cython/gmatch4py/jaccard.pyx
@@ -3,7 +3,7 @@
 # coding = utf-8
 
 import numpy as np
-
+cimport numpy as np
 
 def intersect(a, b):
     return list(set(a) & set(b))
@@ -13,8 +13,10 @@ class Jaccard():
 
     @staticmethod
     def compare(listgs):
-        n = len(listgs)
-        comparison_matrix = np.zeros((n, n))
+        cdef int n = len(listgs)
+        cdef np.ndarray comparison_matrix = np.zeros((n, n))
+        cdef i=0
+        cdef j=0
         for i in range(n):
             for j in range(i,n):
                 g1 = listgs[i]
@@ -31,9 +33,9 @@ class Jaccard():
 
     @staticmethod
     def intersect_edges(g1,g2):
-        ed1 = Jaccard.transform_edges(g1.edges(data=True))
-        ed2 = Jaccard.transform_edges(g2.edges(data=True))
-        inter_ed=[]
+        cdef list ed1 = Jaccard.transform_edges(g1.edges(data=True))
+        cdef list ed2 = Jaccard.transform_edges(g2.edges(data=True))
+        cdef list inter_ed=[]
         for e1 in ed1:
             for e2 in ed2:
                 if e1 == e2:
@@ -42,17 +44,17 @@ class Jaccard():
 
     @staticmethod
     def union_nodes(g1, g2):
-        union=set([])
+        cdef set union=set([])
         for n in g1.nodes():union.add(n)
         for n in g2.nodes(): union.add(n)
         return union
 
     @staticmethod
     def union_edges(g1, g2):
-        ed1 = Jaccard.transform_edges(g1.edges(data=True))
-        ed2 = Jaccard.transform_edges(g2.edges(data=True))
-        union = []
-        register=set([])
+        cdef list ed1 = Jaccard.transform_edges(g1.edges(data=True))
+        cdef list ed2 = Jaccard.transform_edges(g2.edges(data=True))
+        cdef list union = []
+        cdef set register=set([])
         trans_=lambda x : "{0}-{1}:{2}".format(x[0],x[1],x[2]["color"])
         for e1 in ed1:
             if not trans_(e1) in register:
diff --git a/gmatch4py_cython/gmatch4py/kernels/__init__.py b/gmatch4py_cython/gmatch4py/kernels/__init__.py
new file mode 100644
index 0000000..950f635
--- /dev/null
+++ b/gmatch4py_cython/gmatch4py/kernels/__init__.py
@@ -0,0 +1 @@
+# coding = utf-8
\ No newline at end of file
diff --git a/gmatch4py_cython/mcs.pyx b/gmatch4py_cython/gmatch4py/mcs.pyx
similarity index 86%
rename from gmatch4py_cython/mcs.pyx
rename to gmatch4py_cython/gmatch4py/mcs.pyx
index 4b902a3..17a8fd0 100644
--- a/gmatch4py_cython/mcs.pyx
+++ b/gmatch4py_cython/gmatch4py/mcs.pyx
@@ -1,17 +1,17 @@
 # coding = utf-8
 import networkx as nx
 import numpy as np
+cimport numpy as np
 
 class MCS():
     """
     *A graph distance metric based on the maximal common subgraph, H. Bunke and K. Shearer,
     Pattern Recognition Letters, 1998*
     """
-
     @staticmethod
     def compare(listgs):
-        n = len(listgs)
-        comparison_matrix = np.zeros((n, n))
+        cdef int n = len(listgs)
+        cdef np.ndarray comparison_matrix = np.zeros((n, n))
         for i in range(n):
             for j in range(i, n):
                 g1 = listgs[i]
@@ -36,8 +36,8 @@ class MCS():
 
     @staticmethod
     def intersect_edges(g1, g2):
-        ed1 = MCS.transform_edges(g1.edges(data=True))
-        ed2 = MCS.transform_edges(g2.edges(data=True))
+        cdef list ed1 = MCS.transform_edges(g1.edges(data=True))
+        cdef list ed2 = MCS.transform_edges(g2.edges(data=True))
         inter_ed = []
         for e1 in ed1:
             for e2 in ed2:
diff --git a/gmatch4py_cython/vertex_edge_overlap.pyx b/gmatch4py_cython/gmatch4py/vertex_edge_overlap.pyx
similarity index 79%
rename from gmatch4py_cython/vertex_edge_overlap.pyx
rename to gmatch4py_cython/gmatch4py/vertex_edge_overlap.pyx
index 3fe4fc6..f0856b1 100644
--- a/gmatch4py_cython/vertex_edge_overlap.pyx
+++ b/gmatch4py_cython/gmatch4py/vertex_edge_overlap.pyx
@@ -1,9 +1,11 @@
 # coding = utf-8
 
 import numpy as np
+cimport numpy as np
 
 
-def intersect(a, b):
+
+cdef list intersect(a, b):
     return list(set(a) & set(b))
 class VertexEdgeOverlap():
     __type__ = "sim"
@@ -17,9 +19,12 @@ class VertexEdgeOverlap():
     """
 
     @staticmethod
-    def compare(listgs):
+    def compare(list listgs):
         n = len(listgs)
-        comparison_matrix = np.zeros((n, n))
+        cdef np.ndarray comparison_matrix = np.zeros((n, n))
+        cdef list inter_ver
+        cdef list inter_ed
+        cdef int denom
         for i in range(n):
             for j in range(i,n):
                 g1 = listgs[i]
@@ -36,9 +41,9 @@ class VertexEdgeOverlap():
 
     @staticmethod
     def intersect_edges(g1,g2):
-        ed1 = VertexEdgeOverlap.transform_edges(g1.edges(data=True))
-        ed2 = VertexEdgeOverlap.transform_edges(g2.edges(data=True))
-        inter_ed=[]
+        cdef list ed1 = VertexEdgeOverlap.transform_edges(g1.edges(data=True))
+        cdef list ed2 = VertexEdgeOverlap.transform_edges(g2.edges(data=True))
+        cdef list inter_ed=[]
         for e1 in ed1:
             for e2 in ed2:
                 if e1 == e2:
diff --git a/gmatch4py_cython/vertex_ranking.pyx b/gmatch4py_cython/gmatch4py/vertex_ranking.pyx
similarity index 76%
rename from gmatch4py_cython/vertex_ranking.pyx
rename to gmatch4py_cython/gmatch4py/vertex_ranking.pyx
index 5b8c8ef..8f72a4d 100644
--- a/gmatch4py_cython/vertex_ranking.pyx
+++ b/gmatch4py_cython/gmatch4py/vertex_ranking.pyx
@@ -2,6 +2,7 @@
 
 import networkx as nx
 import numpy as np
+cimport numpy as np
 from scipy.stats import spearmanr
 
 
@@ -19,10 +20,13 @@ class VertexRanking():
     """
     __type__ = "sim"
     @staticmethod
-    def compare(listgs):
-        n = len(listgs)
-        comparison_matrix = np.zeros((n,n))
-        page_r=[nx.pagerank(nx.DiGraph(g)) for g in listgs]
+    def  compare(listgs):
+        cdef int n = len(listgs)
+        cdef np.ndarray comparison_matrix = np.zeros((n,n))
+        cdef list page_r=[nx.pagerank(nx.DiGraph(g)) for g in listgs]
+        cdef list node_intersection
+        cdef list X
+        cdef list Y
         for i in range(n):
             for j in range(i,n):
                 node_intersection=intersect(list(page_r[i].keys()),list(page_r[j].keys()))
diff --git a/gmatch4py_cython/setup.py b/gmatch4py_cython/setup.py
new file mode 100644
index 0000000..c8df67b
--- /dev/null
+++ b/gmatch4py_cython/setup.py
@@ -0,0 +1,47 @@
+import sys, os
+from distutils.core import setup
+from distutils.extension import Extension
+
+# we'd better have Cython installed, or it's a no-go
+try:
+    from Cython.Distutils import build_ext
+except:
+    print("You don't seem to have Cython installed. Please get a")
+    print("copy from www.cython.org and install it")
+    sys.exit(1)
+
+
+# scan the 'dvedit' directory for extension files, converting
+# them to extension names in dotted notation
+def scandir(dir, files=[]):
+    for file in os.listdir(dir):
+        path = os.path.join(dir, file)
+        if os.path.isfile(path) and path.endswith(".pyx"):
+            files.append(path.replace(os.path.sep, ".")[:-4])
+        elif os.path.isdir(path):
+            scandir(path, files)
+    return files
+
+
+# generate an Extension object from its dotted name
+def makeExtension(extName):
+    extPath = extName.replace(".", os.path.sep)+".pyx"
+    return Extension(
+        extName,
+        [extPath],
+        extra_compile_args = ["-O3", "-Wall"]
+        )
+
+# get the list of extensions
+extNames = scandir("gmatch4py")
+
+# and build up the set of Extension objects
+extensions = [makeExtension(name) for name in extNames]
+
+# finally, we can pass all this to distutils
+setup(
+  name="gmatch4py_test",
+  packages=["gmatch4py", "gmatch4py.ged","gmatch4py.kernels"],
+  ext_modules=extensions,
+  cmdclass = {'build_ext': build_ext},
+)
\ No newline at end of file
diff --git a/gmatch4py_cython/utils.pyx b/gmatch4py_cython/utils.pyx
deleted file mode 100644
index 656e072..0000000
--- a/gmatch4py_cython/utils.pyx
+++ /dev/null
@@ -1,94 +0,0 @@
-# coding = utf-8
-
-import numpy as np
-from shapely.geometry import Point
-
-from helpers.collision_with_gazetteer_data import collisionTwoSEBoundaries
-from helpers.gazeteer_helpers import get_data
-from models.str import get_inclusion_chain
-
-_cache_distance={}
-def get_nodes_geolocalization(graph):
-    info = {}
-    for node in graph.nodes():
-        if not node in info:
-            info[node] = get_data(node)
-    return info
-
-def is_included_in(se1_id,se2_id):
-    inc_chain_P131 = get_inclusion_chain(se1_id, "P131")
-    inc_chain_P706 = get_inclusion_chain(se1_id, "P706")
-
-    print("mixDEB")
-    inc_chain = inc_chain_P131
-    inc_chain.extend(inc_chain_P706)
-    inc_chain = set(inc_chain)
-    print("mixFIN")
-    if se2_id in inc_chain:
-        return True
-    return False
-
-def are_adjacent(se1,se2):
-    if "P47" in se1:
-        if se2["id"] in se1["P47"]:
-            return True
-    elif collisionTwoSEBoundaries(se1["id"], se2["id"]):
-        return True
-    return False
-
-def geoDistance(info1,info2,n1,n2):
-    if n1 in _cache_distance:
-        if n2 in _cache_distance[n1]:
-            return _cache_distance[n1][n2]
-    if n2 in _cache_distance:
-        if n1 in _cache_distance[n2]:
-            return _cache_distance[n2][n1]
-
-
-    coord1 = (info1["coord"]["lon"], info1["coord"]["lat"])
-    coord2 = (info2["coord"]["lon"], info2["coord"]["lat"])
-    dist=Point(coord1).distance(Point(coord2))
-    if not n1 in _cache_distance:_cache_distance[n1]={}
-    if not n2 in _cache_distance[n1]:_cache_distance[n1][n2]=0.
-    _cache_distance[n1][n2] = dist
-    return dist
-
-def get_score_distance(n1,n2,all_info1,all_info2):
-    if n1 == n2 :
-        return 0
-    score = geoDistance(all_info1[n1],all_info2[n2],n1,n2)
-    return score
-    avg=[]
-    for ni in all_info1:
-        if ni != n1:
-            avg.append(geoDistance(all_info1[ni],all_info1[n1],ni,n1))
-    if len(avg)>0:
-        return score/np.mean(avg)
-    return 0
-
-def get_distance_two_entity(n1,n2,info1,info2):
-    if n1 == None or n2 == None:
-        return 0
-    score = 0
-    try:
-        dist=get_score_distance(n1,n2,info1,info2)
-    except:
-        dist=0
-    if  dist >1 and dist < 10 :
-        #print(n1,info1[n1]["fr"],info2[n2]["fr"])
-        score+=0.5
-    else:
-        score+=1
-    #if set(info1[n1]["class"]) and info2[n2]["class"]:
-     #   score-=1
-
-    # included=is_included_in(n1,n2)
-    # if not included:
-    #     included = is_included_in(n2, n1)
-    # if not included:
-    #     if not are_adjacent(info1[n1],info2[n2]):
-    #         score+=1
-    return score
-
-
-
diff --git a/helpers/classic.py b/helpers/classic.py
new file mode 100644
index 0000000..a6f269d
--- /dev/null
+++ b/helpers/classic.py
@@ -0,0 +1,26 @@
+# coding = utf-8
+import string
+
+
+def flatten(lis):
+    """Given a list, possibly nested to any level, return it flattened."""
+    new_lis = []
+    for item in lis:
+        if type(item) == type([]):
+            new_lis.extend(flatten(item))
+        else:
+            new_lis.append(item)
+    return new_lis
+
+
+def join_string(tokens):
+    ch = ""
+    for i in range(len(tokens)):
+        if i == 0:
+            ch += tokens[i]
+            continue
+        if not tokens[i - 1][-1] in string.punctuation and tokens[i] not in string.punctuation:
+            ch += " " + tokens[i]
+        else:
+            ch += tokens[i]
+    return ch
diff --git a/helpers/gazeteer_helpers.py b/helpers/gazeteer_helpers.py
index 4deab05..6de2bb1 100644
--- a/helpers/gazeteer_helpers.py
+++ b/helpers/gazeteer_helpers.py
@@ -151,3 +151,21 @@ def get_by_alias(alias, lang):
         return response['hits']['hits']
     return None
 
+
+
+def get_most_common_id_v3(label,lang='fr'):
+    id_,score=get_most_common_id_v2(label,lang)
+    if id_:
+        return id_,score
+    if not id_ and lang !='en':
+        id_,score=get_most_common_id_v2(label,'en')
+        if id_:
+            return id_,score
+    id_,score=get_most_common_id_alias_v2(label,lang)
+    if not id_ and lang !='en':
+        id_,score=get_most_common_id_v2(label,'en')
+        if id_:
+            return id_,score
+    return None,-1
+
+
diff --git a/nlp/disambiguator/disambiguator.py b/nlp/disambiguator/disambiguator.py
index e4e3bd1..95e4d85 100644
--- a/nlp/disambiguator/disambiguator.py
+++ b/nlp/disambiguator/disambiguator.py
@@ -1,10 +1,12 @@
 # coding = utf-8
 
 import copy
+import string
 
 import numpy as np
 
 from nlp.ner.ner import NER
+from helpers.classic import join_string
 
 
 class Disambiguator(object):
@@ -40,7 +42,7 @@ class Disambiguator(object):
                     t += 1
                     while corpus[t][1] == "END-" + placeTag or corpus[t][1] == placeTag:
                         tag = copy.copy(corpus[t])
-                        if tag[0].endswith("-") or compound_tag.endswith("-"):
+                        if tag[0][-1] in string.punctuation or compound_tag[-1] in string.punctuation:
                             compound_tag += tag[0]
                         else:
                             compound_tag += " " + tag[0]
diff --git a/nlp/disambiguator/geodict_gaurav.py b/nlp/disambiguator/geodict_gaurav.py
index a650dff..0f6e906 100644
--- a/nlp/disambiguator/geodict_gaurav.py
+++ b/nlp/disambiguator/geodict_gaurav.py
@@ -5,7 +5,7 @@ from helpers.collision_with_gazetteer_data import *
 from helpers.gazeteer_helpers import *
 from .disambiguator import Disambiguator
 
-
+from models.str import get_inclusion_chain
 class GauravGeodict(Disambiguator):
 
     def __init__(self):
@@ -18,6 +18,8 @@ class GauravGeodict(Disambiguator):
         return int(round(val))
 
     def inclusion_log(self, x, alpha=0.2):
+        if x==0:
+            return 1
         return math.log(x)
 
     def get_inclusion_tree(self, id_, prop):
@@ -36,11 +38,11 @@ class GauravGeodict(Disambiguator):
         return arr
 
     def get_inclusion_score(self, id1, id2):  # is it really inclusion ? :)
-        list1 = self.get_inclusion_tree(id1, 'P131')
-        list2 = self.get_inclusion_tree(id2, 'P131')
+        list1 = get_inclusion_chain(id1, 'P131')
+        list2 = get_inclusion_chain(id2, 'P131')
         interP131 = len(list(set(list1).intersection(list2)))
-        list1 = self.get_inclusion_tree(id1, 'P706')
-        list2 = self.get_inclusion_tree(id2, 'P706')
+        list1 = get_inclusion_chain(id1, 'P706')
+        list2 = get_inclusion_chain(id2, 'P706')
         interP706 = len(list(set(list1).intersection(list2)))
         # return fib_no[interP131]+fib_no[interP706]
         return self.inclusion_log(interP131) + self.inclusion_log(interP706)
@@ -104,3 +106,32 @@ class GauravGeodict(Disambiguator):
             fixed_entities[k] = v
 
         return count, fixed_entities
+
+    def eval(self,se_,lang):
+        selected_en = {}
+
+        fixed_entities = {}
+        ambiguous_entities = {}
+        for en in se_:
+            request = get_by_label(en, lang)
+            if len(request) == 0:
+                request = get_by_alias(en, lang)
+
+            if len(request) > 1:
+                ambiguous_entities[en] = [r["_source"] for r in request]
+            elif len(request) == 1:
+                fixed_entities[en] = request[0]["_source"]
+
+        d_amb_results = {}
+        for amb_ent in ambiguous_entities:
+            d = self.disambiguateOne(ambiguous_entities[amb_ent], fixed_entities)
+            if not d:
+                d_amb_results[amb_ent] = get_most_common_id_v2(amb_ent, lang)[0]
+            else:
+                d_amb_results[amb_ent] = d
+        for k, v in fixed_entities.items():
+            fixed_entities[k] = v["id"]
+        for k, v in d_amb_results.items():
+            fixed_entities[k] = v
+
+        return fixed_entities
\ No newline at end of file
diff --git a/nlp/disambiguator/most_common.py b/nlp/disambiguator/most_common.py
new file mode 100644
index 0000000..affd138
--- /dev/null
+++ b/nlp/disambiguator/most_common.py
@@ -0,0 +1,59 @@
+# coding = utf-8
+
+
+from helpers.gazeteer_helpers import label_exists, alias_exists, get_most_common_id_v2,get_most_common_id_v3, get_most_common_id_alias_v2
+from .disambiguator import Disambiguator
+import re, json, os
+
+stop_words = {
+    "fr": set(open("/Users/jacquesfize/LOD_DATASETS/language_resources/stop_words_fr.txt").read().split("\n")),
+    "en": set(open("/Users/jacquesfize/LOD_DATASETS/language_resources/stop_words_en.txt").read().split("\n"))
+}
+
+common_words = {
+    # "fr":set(open("/Users/jacquesfize/LOD_DATASETS/language_resources/french_common_words.txt").read().split("\n")),
+    "fr": set(json.load(open("/Users/jacquesfize/LOD_DATASETS/language_resources/dic_fr.json"))),
+    "en": set(
+        open("/Users/jacquesfize/LOD_DATASETS/language_resources/english_common_words_filtered.txt").read().split("\n"))
+}
+
+
+class MostCommonDisambiguator(Disambiguator):
+
+    def __init__(self):
+        Disambiguator.__init__(self)
+
+    def disambiguate(self, ner_result, lang="en"):
+        count, se_ = self.extract_se_entities(ner_result)
+        new_count = {}
+        selected_en = {}
+        for en in se_:
+            id_,score=self.disambiguate_(en,lang)
+            if not id_ =="O":
+                selected_en[id_] = en
+                new_count[id_] = count[en]
+
+        return new_count, selected_en
+
+    def disambiguate_(self, label, lang='fr'):
+        if re.match("^\d+$", label):
+            return 'O', -1
+        if label.lower() in stop_words[lang] or label.lower() in common_words[lang]:
+            return 'O', -1
+
+        plural = label.rstrip("s") + "s"
+        if plural.lower() in stop_words[lang] or plural.lower() in common_words[lang]:
+            return 'O', -1
+
+        id_, score = get_most_common_id_v3(label, lang)
+        if id_:
+            id_en, score_en = get_most_common_id_v3(label, "en")
+            if id_en and score_en:
+                if score_en > score:
+                    id_, score = id_en, score_en
+            id_alias, score_alias = get_most_common_id_alias_v2(label, lang)
+            if id_alias and score_alias:
+                if score_alias > score:
+                    id_, score = id_alias, score_alias
+
+        return id_, score
diff --git a/nlp/disambiguator/pagerank.py b/nlp/disambiguator/pagerank.py
index 05c6490..b0a7423 100644
--- a/nlp/disambiguator/pagerank.py
+++ b/nlp/disambiguator/pagerank.py
@@ -23,7 +23,7 @@ class PageRankDisambiguator(Disambiguator):
                 selected_en[en_most_common] = en
                 new_count[en_most_common] = count[en]
             else:
-                selected_en[id_] = en
-                new_count[id_] = count[en]
+                selected_en[en_most_common] = en
+                new_count[en_most_common] = count[en]
 
         return new_count, selected_en
diff --git a/nlp/ner/spacy.py b/nlp/ner/spacy.py
index f6133be..e7dcde2 100644
--- a/nlp/ner/spacy.py
+++ b/nlp/ner/spacy.py
@@ -1,23 +1,21 @@
-# coding = utf-8
-
 # coding=utf-8
 
 import spacy
 
 from helpers.deprecated import deprecated
+from helpers.classic import flatten
 from .ner import NER
 from ..exception.language import LanguageNotAvailable
 
-_spacy_available_language = ["fr", "en", "es", "de"]
+_spacy_available_language = ["fr", "en","es","de"]
 
 _tag_spacy = {
     "place": ["GPE", "LOC"],  # Petite particularité
-    "person": "PERSON",
+    "pers": "PERSON",
     "org": "ORG"
 }
 
 
-@deprecated("Not finished yet !")
 class Spacy(NER):
     """
     Python wrapper for StanfordNER
@@ -31,29 +29,43 @@ class Spacy(NER):
 
         self._ner = spacy.load(self._lang)
 
-    def identify(self, text=None):
+    def split_text(self,text,maxlen=50000):
+        texts=text.split(".")
+        phrases_given=[]
+        c=0
+        current_phrase=""
+        for t in texts:
+            if c + len(t)+1 <maxlen:
+                current_phrase+="."+t
+                c+=len(t)+1
+            elif c + len(t) > maxlen:
+                phrases_given.append(current_phrase)
+                current_phrase, c ="",0
+        if not phrases_given:
+            phrases_given=[text]
+        return phrases_given
 
-        output_ = [[token, token.pos_, token.type_ent_] for token in self.ner(text)]
-        new_output_ = []
-        for o in output_:
-            if o[-1]:
-                o[-2] = o[-1]
-                new_output_.append(o[:-1])
-        return self.parse_output(new_output_, [])
+    def identify(self, text=None):
+        if len(text) > 1000000:
+            output_=[]
+            for t in self.split_text(text,1000000):
+                output_.extend([[token.text, token.pos_, token.ent_type_] for token in self._ner(t)])
+            return self.parse_output(output_, [])
+        else:
+            output_ = [[token.text, token.pos_, token.ent_type_] for token in self._ner(text)]
+            return self.parse_output(output_, [])
 
     def parse_output(self, output, pos_tags):
         # Pre-Treatment on the output
         # print(1)
         tagged_ = []
-        _tag_entity = list(_tag_spacy.values())
-
-        for sentence in output["sentences"]:
-            # print(sentence.keys())
-            for w in sentence["tokens"]:
-                if w["ner"] in _tag_entity:
-                    tagged_.append([w["originalText"], self.translate_tag(w["ner"])])
-                else:
-                    tagged_.append([w["originalText"], w["pos"]])
+        _tag_entity = flatten(list(_tag_spacy.values()))
+
+        for token in output:
+            if token[-1] in _tag_entity:
+                tagged_.append([token[0], self.translate_tag(token[-1])])
+            else:
+                tagged_.append([token[0], token[-2]])
 
         return self.add_beg_ending_to_tag(tagged_)
 
diff --git a/nlp/ner/stanford_ner.py b/nlp/ner/stanford_ner.py
index 4a3a77f..82b382b 100644
--- a/nlp/ner/stanford_ner.py
+++ b/nlp/ner/stanford_ner.py
@@ -2,7 +2,7 @@
 
 from queue import Queue
 from threading import Thread
-
+import json
 from pycorenlp import StanfordCoreNLP as RestStanford
 
 from config.configuration import config
@@ -38,6 +38,8 @@ class NERWorker(Thread):
             self.outputs.append((id_,self.ner.annotate(text, properties={'annotators': 'tokenize,ssplit,pos,ner',
                                                         'outputFormat': 'json',"tokenize.untokenizable": "noneDelete"})))
             self.queue.task_done()
+            if self.queue.empty():
+                break
 class StanfordNER(NER):
     """
     Python wrapper for StanfordNER
@@ -69,6 +71,8 @@ class StanfordNER(NER):
             elif c + len(t) > maxlen:
                 phrases_given.append(current_phrase)
                 current_phrase, c ="",0
+        if not phrases_given:
+            phrases_given=[text]
         return phrases_given
 
     def identify(self, text=None):
@@ -97,8 +101,13 @@ class StanfordNER(NER):
                 worker=NERWorker(self._ner,queue,self._lang)
                 list_worker.append(worker)
                 list_worker[-1].daemon=True
-                list_worker[-1].start()
+                #print(type(list_worker[-1]))
+                try:
+                    list_worker[-1].start()
+                except:
+                    print("Worker {0} couldn't be activated !".format(t))
             queue.join()
+            queue.queue.clear()
             outputs=["" for i in range(len(texts)-1)]
             for worker in list_worker:
                 for id,out in worker.outputs:
diff --git a/notebooks/Cython Enhancement on HED.ipynb b/notebooks/Cython Enhancement on HED.ipynb
new file mode 100644
index 0000000..b651169
--- /dev/null
+++ b/notebooks/Cython Enhancement on HED.ipynb	
@@ -0,0 +1,1311 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-04-20T07:03:06.266539Z",
+     "start_time": "2018-04-20T07:03:05.937225Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "%load_ext Cython"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-04-20T07:03:06.281912Z",
+     "start_time": "2018-04-20T07:03:06.268809Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<!DOCTYPE html>\n",
+       "<!-- Generated by Cython 0.25.2 -->\n",
+       "<html>\n",
+       "<head>\n",
+       "    <meta http-equiv=\"Content-Type\" content=\"text/html; charset=utf-8\" />\n",
+       "    <title>Cython: _cython_magic_fcaa11e6595f50de5667d45b47247f97.pyx</title>\n",
+       "    <style type=\"text/css\">\n",
+       "    \n",
+       "body.cython { font-family: courier; font-size: 12; }\n",
+       "\n",
+       ".cython.tag  {  }\n",
+       ".cython.line { margin: 0em }\n",
+       ".cython.code { font-size: 9; color: #444444; display: none; margin: 0px 0px 0px 8px; border-left: 8px none; }\n",
+       "\n",
+       ".cython.line .run { background-color: #B0FFB0; }\n",
+       ".cython.line .mis { background-color: #FFB0B0; }\n",
+       ".cython.code.run  { border-left: 8px solid #B0FFB0; }\n",
+       ".cython.code.mis  { border-left: 8px solid #FFB0B0; }\n",
+       "\n",
+       ".cython.code .py_c_api  { color: red; }\n",
+       ".cython.code .py_macro_api  { color: #FF7000; }\n",
+       ".cython.code .pyx_c_api  { color: #FF3000; }\n",
+       ".cython.code .pyx_macro_api  { color: #FF7000; }\n",
+       ".cython.code .refnanny  { color: #FFA000; }\n",
+       ".cython.code .trace  { color: #FFA000; }\n",
+       ".cython.code .error_goto  { color: #FFA000; }\n",
+       "\n",
+       ".cython.code .coerce  { color: #008000; border: 1px dotted #008000 }\n",
+       ".cython.code .py_attr { color: #FF0000; font-weight: bold; }\n",
+       ".cython.code .c_attr  { color: #0000FF; }\n",
+       ".cython.code .py_call { color: #FF0000; font-weight: bold; }\n",
+       ".cython.code .c_call  { color: #0000FF; }\n",
+       "\n",
+       ".cython.score-0 {background-color: #FFFFff;}\n",
+       ".cython.score-1 {background-color: #FFFFe7;}\n",
+       ".cython.score-2 {background-color: #FFFFd4;}\n",
+       ".cython.score-3 {background-color: #FFFFc4;}\n",
+       ".cython.score-4 {background-color: #FFFFb6;}\n",
+       ".cython.score-5 {background-color: #FFFFaa;}\n",
+       ".cython.score-6 {background-color: #FFFF9f;}\n",
+       ".cython.score-7 {background-color: #FFFF96;}\n",
+       ".cython.score-8 {background-color: #FFFF8d;}\n",
+       ".cython.score-9 {background-color: #FFFF86;}\n",
+       ".cython.score-10 {background-color: #FFFF7f;}\n",
+       ".cython.score-11 {background-color: #FFFF79;}\n",
+       ".cython.score-12 {background-color: #FFFF73;}\n",
+       ".cython.score-13 {background-color: #FFFF6e;}\n",
+       ".cython.score-14 {background-color: #FFFF6a;}\n",
+       ".cython.score-15 {background-color: #FFFF66;}\n",
+       ".cython.score-16 {background-color: #FFFF62;}\n",
+       ".cython.score-17 {background-color: #FFFF5e;}\n",
+       ".cython.score-18 {background-color: #FFFF5b;}\n",
+       ".cython.score-19 {background-color: #FFFF57;}\n",
+       ".cython.score-20 {background-color: #FFFF55;}\n",
+       ".cython.score-21 {background-color: #FFFF52;}\n",
+       ".cython.score-22 {background-color: #FFFF4f;}\n",
+       ".cython.score-23 {background-color: #FFFF4d;}\n",
+       ".cython.score-24 {background-color: #FFFF4b;}\n",
+       ".cython.score-25 {background-color: #FFFF48;}\n",
+       ".cython.score-26 {background-color: #FFFF46;}\n",
+       ".cython.score-27 {background-color: #FFFF44;}\n",
+       ".cython.score-28 {background-color: #FFFF43;}\n",
+       ".cython.score-29 {background-color: #FFFF41;}\n",
+       ".cython.score-30 {background-color: #FFFF3f;}\n",
+       ".cython.score-31 {background-color: #FFFF3e;}\n",
+       ".cython.score-32 {background-color: #FFFF3c;}\n",
+       ".cython.score-33 {background-color: #FFFF3b;}\n",
+       ".cython.score-34 {background-color: #FFFF39;}\n",
+       ".cython.score-35 {background-color: #FFFF38;}\n",
+       ".cython.score-36 {background-color: #FFFF37;}\n",
+       ".cython.score-37 {background-color: #FFFF36;}\n",
+       ".cython.score-38 {background-color: #FFFF35;}\n",
+       ".cython.score-39 {background-color: #FFFF34;}\n",
+       ".cython.score-40 {background-color: #FFFF33;}\n",
+       ".cython.score-41 {background-color: #FFFF32;}\n",
+       ".cython.score-42 {background-color: #FFFF31;}\n",
+       ".cython.score-43 {background-color: #FFFF30;}\n",
+       ".cython.score-44 {background-color: #FFFF2f;}\n",
+       ".cython.score-45 {background-color: #FFFF2e;}\n",
+       ".cython.score-46 {background-color: #FFFF2d;}\n",
+       ".cython.score-47 {background-color: #FFFF2c;}\n",
+       ".cython.score-48 {background-color: #FFFF2b;}\n",
+       ".cython.score-49 {background-color: #FFFF2b;}\n",
+       ".cython.score-50 {background-color: #FFFF2a;}\n",
+       ".cython.score-51 {background-color: #FFFF29;}\n",
+       ".cython.score-52 {background-color: #FFFF29;}\n",
+       ".cython.score-53 {background-color: #FFFF28;}\n",
+       ".cython.score-54 {background-color: #FFFF27;}\n",
+       ".cython.score-55 {background-color: #FFFF27;}\n",
+       ".cython.score-56 {background-color: #FFFF26;}\n",
+       ".cython.score-57 {background-color: #FFFF26;}\n",
+       ".cython.score-58 {background-color: #FFFF25;}\n",
+       ".cython.score-59 {background-color: #FFFF24;}\n",
+       ".cython.score-60 {background-color: #FFFF24;}\n",
+       ".cython.score-61 {background-color: #FFFF23;}\n",
+       ".cython.score-62 {background-color: #FFFF23;}\n",
+       ".cython.score-63 {background-color: #FFFF22;}\n",
+       ".cython.score-64 {background-color: #FFFF22;}\n",
+       ".cython.score-65 {background-color: #FFFF22;}\n",
+       ".cython.score-66 {background-color: #FFFF21;}\n",
+       ".cython.score-67 {background-color: #FFFF21;}\n",
+       ".cython.score-68 {background-color: #FFFF20;}\n",
+       ".cython.score-69 {background-color: #FFFF20;}\n",
+       ".cython.score-70 {background-color: #FFFF1f;}\n",
+       ".cython.score-71 {background-color: #FFFF1f;}\n",
+       ".cython.score-72 {background-color: #FFFF1f;}\n",
+       ".cython.score-73 {background-color: #FFFF1e;}\n",
+       ".cython.score-74 {background-color: #FFFF1e;}\n",
+       ".cython.score-75 {background-color: #FFFF1e;}\n",
+       ".cython.score-76 {background-color: #FFFF1d;}\n",
+       ".cython.score-77 {background-color: #FFFF1d;}\n",
+       ".cython.score-78 {background-color: #FFFF1c;}\n",
+       ".cython.score-79 {background-color: #FFFF1c;}\n",
+       ".cython.score-80 {background-color: #FFFF1c;}\n",
+       ".cython.score-81 {background-color: #FFFF1c;}\n",
+       ".cython.score-82 {background-color: #FFFF1b;}\n",
+       ".cython.score-83 {background-color: #FFFF1b;}\n",
+       ".cython.score-84 {background-color: #FFFF1b;}\n",
+       ".cython.score-85 {background-color: #FFFF1a;}\n",
+       ".cython.score-86 {background-color: #FFFF1a;}\n",
+       ".cython.score-87 {background-color: #FFFF1a;}\n",
+       ".cython.score-88 {background-color: #FFFF1a;}\n",
+       ".cython.score-89 {background-color: #FFFF19;}\n",
+       ".cython.score-90 {background-color: #FFFF19;}\n",
+       ".cython.score-91 {background-color: #FFFF19;}\n",
+       ".cython.score-92 {background-color: #FFFF19;}\n",
+       ".cython.score-93 {background-color: #FFFF18;}\n",
+       ".cython.score-94 {background-color: #FFFF18;}\n",
+       ".cython.score-95 {background-color: #FFFF18;}\n",
+       ".cython.score-96 {background-color: #FFFF18;}\n",
+       ".cython.score-97 {background-color: #FFFF17;}\n",
+       ".cython.score-98 {background-color: #FFFF17;}\n",
+       ".cython.score-99 {background-color: #FFFF17;}\n",
+       ".cython.score-100 {background-color: #FFFF17;}\n",
+       ".cython.score-101 {background-color: #FFFF16;}\n",
+       ".cython.score-102 {background-color: #FFFF16;}\n",
+       ".cython.score-103 {background-color: #FFFF16;}\n",
+       ".cython.score-104 {background-color: #FFFF16;}\n",
+       ".cython.score-105 {background-color: #FFFF16;}\n",
+       ".cython.score-106 {background-color: #FFFF15;}\n",
+       ".cython.score-107 {background-color: #FFFF15;}\n",
+       ".cython.score-108 {background-color: #FFFF15;}\n",
+       ".cython.score-109 {background-color: #FFFF15;}\n",
+       ".cython.score-110 {background-color: #FFFF15;}\n",
+       ".cython.score-111 {background-color: #FFFF15;}\n",
+       ".cython.score-112 {background-color: #FFFF14;}\n",
+       ".cython.score-113 {background-color: #FFFF14;}\n",
+       ".cython.score-114 {background-color: #FFFF14;}\n",
+       ".cython.score-115 {background-color: #FFFF14;}\n",
+       ".cython.score-116 {background-color: #FFFF14;}\n",
+       ".cython.score-117 {background-color: #FFFF14;}\n",
+       ".cython.score-118 {background-color: #FFFF13;}\n",
+       ".cython.score-119 {background-color: #FFFF13;}\n",
+       ".cython.score-120 {background-color: #FFFF13;}\n",
+       ".cython.score-121 {background-color: #FFFF13;}\n",
+       ".cython.score-122 {background-color: #FFFF13;}\n",
+       ".cython.score-123 {background-color: #FFFF13;}\n",
+       ".cython.score-124 {background-color: #FFFF13;}\n",
+       ".cython.score-125 {background-color: #FFFF12;}\n",
+       ".cython.score-126 {background-color: #FFFF12;}\n",
+       ".cython.score-127 {background-color: #FFFF12;}\n",
+       ".cython.score-128 {background-color: #FFFF12;}\n",
+       ".cython.score-129 {background-color: #FFFF12;}\n",
+       ".cython.score-130 {background-color: #FFFF12;}\n",
+       ".cython.score-131 {background-color: #FFFF12;}\n",
+       ".cython.score-132 {background-color: #FFFF11;}\n",
+       ".cython.score-133 {background-color: #FFFF11;}\n",
+       ".cython.score-134 {background-color: #FFFF11;}\n",
+       ".cython.score-135 {background-color: #FFFF11;}\n",
+       ".cython.score-136 {background-color: #FFFF11;}\n",
+       ".cython.score-137 {background-color: #FFFF11;}\n",
+       ".cython.score-138 {background-color: #FFFF11;}\n",
+       ".cython.score-139 {background-color: #FFFF11;}\n",
+       ".cython.score-140 {background-color: #FFFF11;}\n",
+       ".cython.score-141 {background-color: #FFFF10;}\n",
+       ".cython.score-142 {background-color: #FFFF10;}\n",
+       ".cython.score-143 {background-color: #FFFF10;}\n",
+       ".cython.score-144 {background-color: #FFFF10;}\n",
+       ".cython.score-145 {background-color: #FFFF10;}\n",
+       ".cython.score-146 {background-color: #FFFF10;}\n",
+       ".cython.score-147 {background-color: #FFFF10;}\n",
+       ".cython.score-148 {background-color: #FFFF10;}\n",
+       ".cython.score-149 {background-color: #FFFF10;}\n",
+       ".cython.score-150 {background-color: #FFFF0f;}\n",
+       ".cython.score-151 {background-color: #FFFF0f;}\n",
+       ".cython.score-152 {background-color: #FFFF0f;}\n",
+       ".cython.score-153 {background-color: #FFFF0f;}\n",
+       ".cython.score-154 {background-color: #FFFF0f;}\n",
+       ".cython.score-155 {background-color: #FFFF0f;}\n",
+       ".cython.score-156 {background-color: #FFFF0f;}\n",
+       ".cython.score-157 {background-color: #FFFF0f;}\n",
+       ".cython.score-158 {background-color: #FFFF0f;}\n",
+       ".cython.score-159 {background-color: #FFFF0f;}\n",
+       ".cython.score-160 {background-color: #FFFF0f;}\n",
+       ".cython.score-161 {background-color: #FFFF0e;}\n",
+       ".cython.score-162 {background-color: #FFFF0e;}\n",
+       ".cython.score-163 {background-color: #FFFF0e;}\n",
+       ".cython.score-164 {background-color: #FFFF0e;}\n",
+       ".cython.score-165 {background-color: #FFFF0e;}\n",
+       ".cython.score-166 {background-color: #FFFF0e;}\n",
+       ".cython.score-167 {background-color: #FFFF0e;}\n",
+       ".cython.score-168 {background-color: #FFFF0e;}\n",
+       ".cython.score-169 {background-color: #FFFF0e;}\n",
+       ".cython.score-170 {background-color: #FFFF0e;}\n",
+       ".cython.score-171 {background-color: #FFFF0e;}\n",
+       ".cython.score-172 {background-color: #FFFF0e;}\n",
+       ".cython.score-173 {background-color: #FFFF0d;}\n",
+       ".cython.score-174 {background-color: #FFFF0d;}\n",
+       ".cython.score-175 {background-color: #FFFF0d;}\n",
+       ".cython.score-176 {background-color: #FFFF0d;}\n",
+       ".cython.score-177 {background-color: #FFFF0d;}\n",
+       ".cython.score-178 {background-color: #FFFF0d;}\n",
+       ".cython.score-179 {background-color: #FFFF0d;}\n",
+       ".cython.score-180 {background-color: #FFFF0d;}\n",
+       ".cython.score-181 {background-color: #FFFF0d;}\n",
+       ".cython.score-182 {background-color: #FFFF0d;}\n",
+       ".cython.score-183 {background-color: #FFFF0d;}\n",
+       ".cython.score-184 {background-color: #FFFF0d;}\n",
+       ".cython.score-185 {background-color: #FFFF0d;}\n",
+       ".cython.score-186 {background-color: #FFFF0d;}\n",
+       ".cython.score-187 {background-color: #FFFF0c;}\n",
+       ".cython.score-188 {background-color: #FFFF0c;}\n",
+       ".cython.score-189 {background-color: #FFFF0c;}\n",
+       ".cython.score-190 {background-color: #FFFF0c;}\n",
+       ".cython.score-191 {background-color: #FFFF0c;}\n",
+       ".cython.score-192 {background-color: #FFFF0c;}\n",
+       ".cython.score-193 {background-color: #FFFF0c;}\n",
+       ".cython.score-194 {background-color: #FFFF0c;}\n",
+       ".cython.score-195 {background-color: #FFFF0c;}\n",
+       ".cython.score-196 {background-color: #FFFF0c;}\n",
+       ".cython.score-197 {background-color: #FFFF0c;}\n",
+       ".cython.score-198 {background-color: #FFFF0c;}\n",
+       ".cython.score-199 {background-color: #FFFF0c;}\n",
+       ".cython.score-200 {background-color: #FFFF0c;}\n",
+       ".cython.score-201 {background-color: #FFFF0c;}\n",
+       ".cython.score-202 {background-color: #FFFF0c;}\n",
+       ".cython.score-203 {background-color: #FFFF0b;}\n",
+       ".cython.score-204 {background-color: #FFFF0b;}\n",
+       ".cython.score-205 {background-color: #FFFF0b;}\n",
+       ".cython.score-206 {background-color: #FFFF0b;}\n",
+       ".cython.score-207 {background-color: #FFFF0b;}\n",
+       ".cython.score-208 {background-color: #FFFF0b;}\n",
+       ".cython.score-209 {background-color: #FFFF0b;}\n",
+       ".cython.score-210 {background-color: #FFFF0b;}\n",
+       ".cython.score-211 {background-color: #FFFF0b;}\n",
+       ".cython.score-212 {background-color: #FFFF0b;}\n",
+       ".cython.score-213 {background-color: #FFFF0b;}\n",
+       ".cython.score-214 {background-color: #FFFF0b;}\n",
+       ".cython.score-215 {background-color: #FFFF0b;}\n",
+       ".cython.score-216 {background-color: #FFFF0b;}\n",
+       ".cython.score-217 {background-color: #FFFF0b;}\n",
+       ".cython.score-218 {background-color: #FFFF0b;}\n",
+       ".cython.score-219 {background-color: #FFFF0b;}\n",
+       ".cython.score-220 {background-color: #FFFF0b;}\n",
+       ".cython.score-221 {background-color: #FFFF0b;}\n",
+       ".cython.score-222 {background-color: #FFFF0a;}\n",
+       ".cython.score-223 {background-color: #FFFF0a;}\n",
+       ".cython.score-224 {background-color: #FFFF0a;}\n",
+       ".cython.score-225 {background-color: #FFFF0a;}\n",
+       ".cython.score-226 {background-color: #FFFF0a;}\n",
+       ".cython.score-227 {background-color: #FFFF0a;}\n",
+       ".cython.score-228 {background-color: #FFFF0a;}\n",
+       ".cython.score-229 {background-color: #FFFF0a;}\n",
+       ".cython.score-230 {background-color: #FFFF0a;}\n",
+       ".cython.score-231 {background-color: #FFFF0a;}\n",
+       ".cython.score-232 {background-color: #FFFF0a;}\n",
+       ".cython.score-233 {background-color: #FFFF0a;}\n",
+       ".cython.score-234 {background-color: #FFFF0a;}\n",
+       ".cython.score-235 {background-color: #FFFF0a;}\n",
+       ".cython.score-236 {background-color: #FFFF0a;}\n",
+       ".cython.score-237 {background-color: #FFFF0a;}\n",
+       ".cython.score-238 {background-color: #FFFF0a;}\n",
+       ".cython.score-239 {background-color: #FFFF0a;}\n",
+       ".cython.score-240 {background-color: #FFFF0a;}\n",
+       ".cython.score-241 {background-color: #FFFF0a;}\n",
+       ".cython.score-242 {background-color: #FFFF0a;}\n",
+       ".cython.score-243 {background-color: #FFFF0a;}\n",
+       ".cython.score-244 {background-color: #FFFF0a;}\n",
+       ".cython.score-245 {background-color: #FFFF0a;}\n",
+       ".cython.score-246 {background-color: #FFFF09;}\n",
+       ".cython.score-247 {background-color: #FFFF09;}\n",
+       ".cython.score-248 {background-color: #FFFF09;}\n",
+       ".cython.score-249 {background-color: #FFFF09;}\n",
+       ".cython.score-250 {background-color: #FFFF09;}\n",
+       ".cython.score-251 {background-color: #FFFF09;}\n",
+       ".cython.score-252 {background-color: #FFFF09;}\n",
+       ".cython.score-253 {background-color: #FFFF09;}\n",
+       ".cython.score-254 {background-color: #FFFF09;}\n",
+       ".cython .hll { background-color: #ffffcc }\n",
+       ".cython  { background: #f8f8f8; }\n",
+       ".cython .c { color: #408080; font-style: italic } /* Comment */\n",
+       ".cython .err { border: 1px solid #FF0000 } /* Error */\n",
+       ".cython .k { color: #008000; font-weight: bold } /* Keyword */\n",
+       ".cython .o { color: #666666 } /* Operator */\n",
+       ".cython .ch { color: #408080; font-style: italic } /* Comment.Hashbang */\n",
+       ".cython .cm { color: #408080; font-style: italic } /* Comment.Multiline */\n",
+       ".cython .cp { color: #BC7A00 } /* Comment.Preproc */\n",
+       ".cython .cpf { color: #408080; font-style: italic } /* Comment.PreprocFile */\n",
+       ".cython .c1 { color: #408080; font-style: italic } /* Comment.Single */\n",
+       ".cython .cs { color: #408080; font-style: italic } /* Comment.Special */\n",
+       ".cython .gd { color: #A00000 } /* Generic.Deleted */\n",
+       ".cython .ge { font-style: italic } /* Generic.Emph */\n",
+       ".cython .gr { color: #FF0000 } /* Generic.Error */\n",
+       ".cython .gh { color: #000080; font-weight: bold } /* Generic.Heading */\n",
+       ".cython .gi { color: #00A000 } /* Generic.Inserted */\n",
+       ".cython .go { color: #888888 } /* Generic.Output */\n",
+       ".cython .gp { color: #000080; font-weight: bold } /* Generic.Prompt */\n",
+       ".cython .gs { font-weight: bold } /* Generic.Strong */\n",
+       ".cython .gu { color: #800080; font-weight: bold } /* Generic.Subheading */\n",
+       ".cython .gt { color: #0044DD } /* Generic.Traceback */\n",
+       ".cython .kc { color: #008000; font-weight: bold } /* Keyword.Constant */\n",
+       ".cython .kd { color: #008000; font-weight: bold } /* Keyword.Declaration */\n",
+       ".cython .kn { color: #008000; font-weight: bold } /* Keyword.Namespace */\n",
+       ".cython .kp { color: #008000 } /* Keyword.Pseudo */\n",
+       ".cython .kr { color: #008000; font-weight: bold } /* Keyword.Reserved */\n",
+       ".cython .kt { color: #B00040 } /* Keyword.Type */\n",
+       ".cython .m { color: #666666 } /* Literal.Number */\n",
+       ".cython .s { color: #BA2121 } /* Literal.String */\n",
+       ".cython .na { color: #7D9029 } /* Name.Attribute */\n",
+       ".cython .nb { color: #008000 } /* Name.Builtin */\n",
+       ".cython .nc { color: #0000FF; font-weight: bold } /* Name.Class */\n",
+       ".cython .no { color: #880000 } /* Name.Constant */\n",
+       ".cython .nd { color: #AA22FF } /* Name.Decorator */\n",
+       ".cython .ni { color: #999999; font-weight: bold } /* Name.Entity */\n",
+       ".cython .ne { color: #D2413A; font-weight: bold } /* Name.Exception */\n",
+       ".cython .nf { color: #0000FF } /* Name.Function */\n",
+       ".cython .nl { color: #A0A000 } /* Name.Label */\n",
+       ".cython .nn { color: #0000FF; font-weight: bold } /* Name.Namespace */\n",
+       ".cython .nt { color: #008000; font-weight: bold } /* Name.Tag */\n",
+       ".cython .nv { color: #19177C } /* Name.Variable */\n",
+       ".cython .ow { color: #AA22FF; font-weight: bold } /* Operator.Word */\n",
+       ".cython .w { color: #bbbbbb } /* Text.Whitespace */\n",
+       ".cython .mb { color: #666666 } /* Literal.Number.Bin */\n",
+       ".cython .mf { color: #666666 } /* Literal.Number.Float */\n",
+       ".cython .mh { color: #666666 } /* Literal.Number.Hex */\n",
+       ".cython .mi { color: #666666 } /* Literal.Number.Integer */\n",
+       ".cython .mo { color: #666666 } /* Literal.Number.Oct */\n",
+       ".cython .sa { color: #BA2121 } /* Literal.String.Affix */\n",
+       ".cython .sb { color: #BA2121 } /* Literal.String.Backtick */\n",
+       ".cython .sc { color: #BA2121 } /* Literal.String.Char */\n",
+       ".cython .dl { color: #BA2121 } /* Literal.String.Delimiter */\n",
+       ".cython .sd { color: #BA2121; font-style: italic } /* Literal.String.Doc */\n",
+       ".cython .s2 { color: #BA2121 } /* Literal.String.Double */\n",
+       ".cython .se { color: #BB6622; font-weight: bold } /* Literal.String.Escape */\n",
+       ".cython .sh { color: #BA2121 } /* Literal.String.Heredoc */\n",
+       ".cython .si { color: #BB6688; font-weight: bold } /* Literal.String.Interpol */\n",
+       ".cython .sx { color: #008000 } /* Literal.String.Other */\n",
+       ".cython .sr { color: #BB6688 } /* Literal.String.Regex */\n",
+       ".cython .s1 { color: #BA2121 } /* Literal.String.Single */\n",
+       ".cython .ss { color: #19177C } /* Literal.String.Symbol */\n",
+       ".cython .bp { color: #008000 } /* Name.Builtin.Pseudo */\n",
+       ".cython .fm { color: #0000FF } /* Name.Function.Magic */\n",
+       ".cython .vc { color: #19177C } /* Name.Variable.Class */\n",
+       ".cython .vg { color: #19177C } /* Name.Variable.Global */\n",
+       ".cython .vi { color: #19177C } /* Name.Variable.Instance */\n",
+       ".cython .vm { color: #19177C } /* Name.Variable.Magic */\n",
+       ".cython .il { color: #666666 } /* Literal.Number.Integer.Long */\n",
+       "    </style>\n",
+       "    <script>\n",
+       "    function toggleDiv(id) {\n",
+       "        theDiv = id.nextElementSibling\n",
+       "        if (theDiv.style.display != 'block') theDiv.style.display = 'block';\n",
+       "        else theDiv.style.display = 'none';\n",
+       "    }\n",
+       "    </script>\n",
+       "</head>\n",
+       "<body class=\"cython\">\n",
+       "<p><span style=\"border-bottom: solid 1px grey;\">Generated by Cython 0.25.2</span></p>\n",
+       "<p>\n",
+       "    <span style=\"background-color: #FFFF00\">Yellow lines</span> hint at Python interaction.<br />\n",
+       "    Click on a line that starts with a \"<code>+</code>\" to see the C code that Cython generated for it.\n",
+       "</p>\n",
+       "<div class=\"cython\"><pre class=\"cython line score-17\" onclick='toggleDiv(this)'>+<span class=\"\">1</span>: <span class=\"k\">def</span> <span class=\"nf\">foo</span><span class=\"p\">():</span></pre>\n",
+       "<pre class='cython code score-17 '>/* Python wrapper */\n",
+       "static PyObject *__pyx_pw_46_cython_magic_fcaa11e6595f50de5667d45b47247f97_1foo(PyObject *__pyx_self, CYTHON_UNUSED PyObject *unused); /*proto*/\n",
+       "static PyMethodDef __pyx_mdef_46_cython_magic_fcaa11e6595f50de5667d45b47247f97_1foo = {\"foo\", (PyCFunction)__pyx_pw_46_cython_magic_fcaa11e6595f50de5667d45b47247f97_1foo, METH_NOARGS, 0};\n",
+       "static PyObject *__pyx_pw_46_cython_magic_fcaa11e6595f50de5667d45b47247f97_1foo(PyObject *__pyx_self, CYTHON_UNUSED PyObject *unused) {\n",
+       "  PyObject *__pyx_r = 0;\n",
+       "  <span class='refnanny'>__Pyx_RefNannyDeclarations</span>\n",
+       "  <span class='refnanny'>__Pyx_RefNannySetupContext</span>(\"foo (wrapper)\", 0);\n",
+       "  __pyx_r = __pyx_pf_46_cython_magic_fcaa11e6595f50de5667d45b47247f97_foo(__pyx_self);\n",
+       "\n",
+       "  /* function exit code */\n",
+       "  <span class='refnanny'>__Pyx_RefNannyFinishContext</span>();\n",
+       "  return __pyx_r;\n",
+       "}\n",
+       "\n",
+       "static PyObject *__pyx_pf_46_cython_magic_fcaa11e6595f50de5667d45b47247f97_foo(CYTHON_UNUSED PyObject *__pyx_self) {\n",
+       "  PyObject *__pyx_v_i = NULL;\n",
+       "  PyObject *__pyx_r = NULL;\n",
+       "  <span class='refnanny'>__Pyx_RefNannyDeclarations</span>\n",
+       "  <span class='refnanny'>__Pyx_RefNannySetupContext</span>(\"foo\", 0);\n",
+       "/* … */\n",
+       "  /* function exit code */\n",
+       "  __pyx_r = Py_None; <span class='pyx_macro_api'>__Pyx_INCREF</span>(Py_None);\n",
+       "  goto __pyx_L0;\n",
+       "  __pyx_L1_error:;\n",
+       "  <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_t_1);\n",
+       "  <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_t_2);\n",
+       "  <span class='pyx_c_api'>__Pyx_AddTraceback</span>(\"_cython_magic_fcaa11e6595f50de5667d45b47247f97.foo\", __pyx_clineno, __pyx_lineno, __pyx_filename);\n",
+       "  __pyx_r = NULL;\n",
+       "  __pyx_L0:;\n",
+       "  <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_v_i);\n",
+       "  <span class='refnanny'>__Pyx_XGIVEREF</span>(__pyx_r);\n",
+       "  <span class='refnanny'>__Pyx_RefNannyFinishContext</span>();\n",
+       "  return __pyx_r;\n",
+       "}\n",
+       "/* … */\n",
+       "  __pyx_tuple__2 = <span class='py_c_api'>PyTuple_Pack</span>(1, __pyx_n_s_i); if (unlikely(!__pyx_tuple__2)) __PYX_ERR(0, 1, __pyx_L1_error)\n",
+       "  <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_tuple__2);\n",
+       "  <span class='refnanny'>__Pyx_GIVEREF</span>(__pyx_tuple__2);\n",
+       "/* … */\n",
+       "  __pyx_t_1 = PyCFunction_NewEx(&amp;__pyx_mdef_46_cython_magic_fcaa11e6595f50de5667d45b47247f97_1foo, NULL, __pyx_n_s_cython_magic_fcaa11e6595f50de56); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 1, __pyx_L1_error)\n",
+       "  <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_1);\n",
+       "  if (<span class='py_c_api'>PyDict_SetItem</span>(__pyx_d, __pyx_n_s_foo, __pyx_t_1) &lt; 0) __PYX_ERR(0, 1, __pyx_L1_error)\n",
+       "  <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_1); __pyx_t_1 = 0;\n",
+       "</pre><pre class=\"cython line score-54\" onclick='toggleDiv(this)'>+<span class=\"\">2</span>:     <span class=\"k\">for</span> <span class=\"n\">i</span> <span class=\"ow\">in</span> <span class=\"nb\">range</span><span class=\"p\">(</span><span class=\"mf\">50000</span><span class=\"p\">):</span></pre>\n",
+       "<pre class='cython code score-54 '>  __pyx_t_1 = <span class='pyx_c_api'>__Pyx_PyObject_Call</span>(__pyx_builtin_range, __pyx_tuple_, NULL); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 2, __pyx_L1_error)\n",
+       "  <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_1);\n",
+       "  if (likely(<span class='py_c_api'>PyList_CheckExact</span>(__pyx_t_1)) || <span class='py_c_api'>PyTuple_CheckExact</span>(__pyx_t_1)) {\n",
+       "    __pyx_t_2 = __pyx_t_1; <span class='pyx_macro_api'>__Pyx_INCREF</span>(__pyx_t_2); __pyx_t_3 = 0;\n",
+       "    __pyx_t_4 = NULL;\n",
+       "  } else {\n",
+       "    __pyx_t_3 = -1; __pyx_t_2 = <span class='py_c_api'>PyObject_GetIter</span>(__pyx_t_1); if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 2, __pyx_L1_error)\n",
+       "    <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_2);\n",
+       "    __pyx_t_4 = Py_TYPE(__pyx_t_2)-&gt;tp_iternext; if (unlikely(!__pyx_t_4)) __PYX_ERR(0, 2, __pyx_L1_error)\n",
+       "  }\n",
+       "  <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_1); __pyx_t_1 = 0;\n",
+       "  for (;;) {\n",
+       "    if (likely(!__pyx_t_4)) {\n",
+       "      if (likely(<span class='py_c_api'>PyList_CheckExact</span>(__pyx_t_2))) {\n",
+       "        if (__pyx_t_3 &gt;= <span class='py_macro_api'>PyList_GET_SIZE</span>(__pyx_t_2)) break;\n",
+       "        #if CYTHON_ASSUME_SAFE_MACROS &amp;&amp; !CYTHON_AVOID_BORROWED_REFS\n",
+       "        __pyx_t_1 = <span class='py_macro_api'>PyList_GET_ITEM</span>(__pyx_t_2, __pyx_t_3); <span class='pyx_macro_api'>__Pyx_INCREF</span>(__pyx_t_1); __pyx_t_3++; if (unlikely(0 &lt; 0)) __PYX_ERR(0, 2, __pyx_L1_error)\n",
+       "        #else\n",
+       "        __pyx_t_1 = <span class='py_macro_api'>PySequence_ITEM</span>(__pyx_t_2, __pyx_t_3); __pyx_t_3++; if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 2, __pyx_L1_error)\n",
+       "        <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_1);\n",
+       "        #endif\n",
+       "      } else {\n",
+       "        if (__pyx_t_3 &gt;= <span class='py_macro_api'>PyTuple_GET_SIZE</span>(__pyx_t_2)) break;\n",
+       "        #if CYTHON_ASSUME_SAFE_MACROS &amp;&amp; !CYTHON_AVOID_BORROWED_REFS\n",
+       "        __pyx_t_1 = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_t_2, __pyx_t_3); <span class='pyx_macro_api'>__Pyx_INCREF</span>(__pyx_t_1); __pyx_t_3++; if (unlikely(0 &lt; 0)) __PYX_ERR(0, 2, __pyx_L1_error)\n",
+       "        #else\n",
+       "        __pyx_t_1 = <span class='py_macro_api'>PySequence_ITEM</span>(__pyx_t_2, __pyx_t_3); __pyx_t_3++; if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 2, __pyx_L1_error)\n",
+       "        <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_1);\n",
+       "        #endif\n",
+       "      }\n",
+       "    } else {\n",
+       "      __pyx_t_1 = __pyx_t_4(__pyx_t_2);\n",
+       "      if (unlikely(!__pyx_t_1)) {\n",
+       "        PyObject* exc_type = <span class='py_c_api'>PyErr_Occurred</span>();\n",
+       "        if (exc_type) {\n",
+       "          if (likely(exc_type == PyExc_StopIteration || <span class='py_c_api'>PyErr_GivenExceptionMatches</span>(exc_type, PyExc_StopIteration))) <span class='py_c_api'>PyErr_Clear</span>();\n",
+       "          else __PYX_ERR(0, 2, __pyx_L1_error)\n",
+       "        }\n",
+       "        break;\n",
+       "      }\n",
+       "      <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_1);\n",
+       "    }\n",
+       "    <span class='pyx_macro_api'>__Pyx_XDECREF_SET</span>(__pyx_v_i, __pyx_t_1);\n",
+       "    __pyx_t_1 = 0;\n",
+       "/* … */\n",
+       "  }\n",
+       "  <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_2); __pyx_t_2 = 0;\n",
+       "/* … */\n",
+       "  __pyx_tuple_ = <span class='py_c_api'>PyTuple_Pack</span>(1, __pyx_int_50000); if (unlikely(!__pyx_tuple_)) __PYX_ERR(0, 2, __pyx_L1_error)\n",
+       "  <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_tuple_);\n",
+       "  <span class='refnanny'>__Pyx_GIVEREF</span>(__pyx_tuple_);\n",
+       "</pre><pre class=\"cython line score-6\" onclick='toggleDiv(this)'>+<span class=\"\">3</span>:         <span class=\"n\">i</span><span class=\"o\">*</span><span class=\"n\">i</span></pre>\n",
+       "<pre class='cython code score-6 '>    __pyx_t_1 = <span class='py_c_api'>PyNumber_Multiply</span>(__pyx_v_i, __pyx_v_i); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 3, __pyx_L1_error)\n",
+       "    <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_1);\n",
+       "    <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_1); __pyx_t_1 = 0;\n",
+       "</pre></div></body></html>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%cython --annotate\n",
+    "def foo():\n",
+    "    for i in range(50000):\n",
+    "        i*i"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-04-20T07:03:06.289592Z",
+     "start_time": "2018-04-20T07:03:06.283966Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 1.91 ms, sys: 3 µs, total: 1.92 ms\n",
+      "Wall time: 1.93 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "%time foo()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-04-20T07:03:06.296109Z",
+     "start_time": "2018-04-20T07:03:06.292000Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "%%cython\n",
+    "\n",
+    "cdef list ch=[\"Le\",\"pont\",\"d'\",\"avignon\",\"est\",\"-\",\"sympa\"]\n",
+    "def foo():\n",
+    "    print([c+(\"\" if c[-1] in [\"\\'\",\"-\"] else \" \") for c in ch])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-04-20T07:03:06.302224Z",
+     "start_time": "2018-04-20T07:03:06.298061Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['Le ', 'pont ', \"d'\", 'avignon ', 'est ', '-', 'sympa ']\n",
+      "CPU times: user 70 µs, sys: 29 µs, total: 99 µs\n",
+      "Wall time: 83.9 µs\n"
+     ]
+    }
+   ],
+   "source": [
+    "%time foo()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-04-20T07:03:06.307291Z",
+     "start_time": "2018-04-20T07:03:06.304153Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "ch=[\"Le\",\"pont\",\"d'\",\"avignon\",\"est\",\"-\",\"sympa\"]\n",
+    "def foo():\n",
+    "    print([c+(\"\" if c[-1] in [\"\\'\",\"-\"] else \" \") for c in ch])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-04-20T07:03:06.312937Z",
+     "start_time": "2018-04-20T07:03:06.309325Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['Le ', 'pont ', \"d'\", 'avignon ', 'est ', '-', 'sympa ']\n",
+      "CPU times: user 49 µs, sys: 20 µs, total: 69 µs\n",
+      "Wall time: 59.8 µs\n"
+     ]
+    }
+   ],
+   "source": [
+    "%time foo()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-04-20T07:03:06.320412Z",
+     "start_time": "2018-04-20T07:03:06.315555Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "%%cython\n",
+    "\n",
+    "def cyfac(n):\n",
+    "    if n <= 1:\n",
+    "        return 1\n",
+    "    return n * cyfac(n - 1)\n",
+    "\n",
+    "def cyfac_double(double n):\n",
+    "    if n <= 1:\n",
+    "        return 1.0\n",
+    "    return n * cyfac_double(n - 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-04-20T07:03:19.194254Z",
+     "start_time": "2018-04-20T07:03:06.323051Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.58 µs ± 15.5 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "2.43290200817664e+18"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "\n",
+    "%timeit cyfac(20.0)\n",
+    "cyfac(20.0)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-04-20T07:03:27.738161Z",
+     "start_time": "2018-04-20T07:03:19.196269Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.05 µs ± 6.72 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "2.43290200817664e+18"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%timeit cyfac_double(20.0)\n",
+    "cyfac_double(20.0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-04-20T07:03:27.745810Z",
+     "start_time": "2018-04-20T07:03:27.740673Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "%%cython\n",
+    "\n",
+    "cpdef double cyfac_double_fast(double n):\n",
+    "    if n <= 1:\n",
+    "        return 1.0\n",
+    "    return n * cyfac_double_fast(n - 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-04-20T07:03:37.326307Z",
+     "start_time": "2018-04-20T07:03:27.748079Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "117 ns ± 4.03 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "2.43290200817664e+18"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%timeit cyfac_double_fast(20.0)\n",
+    "cyfac_double_fast(20.0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-04-20T07:03:37.332486Z",
+     "start_time": "2018-04-20T07:03:37.328492Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/Users/jacquesfize/nas_cloud/Code/str-python\n"
+     ]
+    }
+   ],
+   "source": [
+    "cd .."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-04-20T07:03:37.421880Z",
+     "start_time": "2018-04-20T07:03:37.334723Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "class PureHED():\n",
+    "    \"\"\"\n",
+    "    Implementation of Hausdorff Edit Distance described in\n",
+    "\n",
+    "    Improved quadratic time approximation of graph edit distance by Hausdorff matching and greedy assignement\n",
+    "    Andreas Fischer, Kaspar Riesen, Horst Bunke\n",
+    "    2016\n",
+    "    \"\"\"\n",
+    "    __type__ = \"dist\"\n",
+    "    @staticmethod\n",
+    "    def compare(listgs, c_del_node=1, c_del_edge=1, c_ins_node=1, c_ins_edge=1):\n",
+    "        n = len(listgs)\n",
+    "        comparator = PureHED(c_del_node, c_ins_node, c_del_edge, c_ins_edge)\n",
+    "        comparison_matrix = np.zeros((n, n))\n",
+    "        for i in range(n):\n",
+    "            for j in range(i, n):\n",
+    "                comparison_matrix[i, j] = comparator.hed(listgs[i], listgs[j])\n",
+    "                comparison_matrix[j, i] = comparison_matrix[i, j]\n",
+    "\n",
+    "        return comparison_matrix\n",
+    "\n",
+    "\n",
+    "    def __init__(self, node_del=1, node_ins=1, edge_del=1, edge_ins=1):\n",
+    "        \"\"\"Constructor for HED\"\"\"\n",
+    "        self.node_del = node_del\n",
+    "        self.node_ins = node_ins\n",
+    "        self.edge_del = edge_del\n",
+    "        self.edge_ins = edge_ins\n",
+    "\n",
+    "    def hed(self, g1, g2):\n",
+    "        \"\"\"\n",
+    "        Compute de Hausdorff Edit Distance\n",
+    "        :param g1: first graph\n",
+    "        :param g2: second graph\n",
+    "        :return:\n",
+    "        \"\"\"\n",
+    "        return self.sum_fuv(g1, g2) + self.sum_fuv(g2, g1)\n",
+    "\n",
+    "    def sum_fuv(self, g1, g2):\n",
+    "        \"\"\"\n",
+    "        Compute Nearest Neighbour Distance between G1 and G2\n",
+    "        :param g1: First Graph\n",
+    "        :param g2: Second Graph\n",
+    "        :return:\n",
+    "        \"\"\"\n",
+    "        min_sum = np.zeros(len(g1))\n",
+    "        nodes1 = g1.nodes()\n",
+    "        nodes2 = g2.nodes()\n",
+    "        nodes2.extend([None])\n",
+    "        for i in range(len(nodes1)):\n",
+    "            min_i = np.zeros(len(nodes2))\n",
+    "            for j in range(len(nodes2)):\n",
+    "                min_i[j] = self.fuv(g1, g2, nodes1[i], nodes2[j])\n",
+    "            min_sum[i] = np.min(min_i)\n",
+    "        return np.sum(min_sum)\n",
+    "\n",
+    "    def fuv(self, g1, g2, n1, n2):\n",
+    "        \"\"\"\n",
+    "        Compute the Node Distance function\n",
+    "        :param g1: first graph\n",
+    "        :param g2: second graph\n",
+    "        :param n1: node of the first graph\n",
+    "        :param n2: node of the second graph\n",
+    "        :return:\n",
+    "        \"\"\"\n",
+    "        if n2 == None:  # Del\n",
+    "            return self.node_del + ((self.edge_del / 2) * g1.degree(n1))\n",
+    "        if n1 == None:  # Insert\n",
+    "            return self.node_ins + ((self.edge_ins / 2) * g2.degree(n2))\n",
+    "        else:\n",
+    "            if n1 == n2:\n",
+    "                return 0.\n",
+    "            return (self.node_del + self.node_ins + self.hed_edge(g1, g2, n1, n2)) / 2\n",
+    "\n",
+    "    def hed_edge(self, g1, g2, n1, n2):\n",
+    "        \"\"\"\n",
+    "        Compute HEDistance between edges of n1 and n2, respectively in g1 and g2\n",
+    "        :param g1: first graph\n",
+    "        :param g2: second graph\n",
+    "        :param n1: node of the first graph\n",
+    "        :param n2: node of the second graph\n",
+    "        :return:\n",
+    "        \"\"\"\n",
+    "        return self.sum_gpq(g1, n1, g2, n2) + self.sum_gpq(g1, n1, g2, n2)\n",
+    "\n",
+    "    def get_edge_multigraph(self, g, node):\n",
+    "        \"\"\"\n",
+    "        Get list of edge around a node in a Multigraph\n",
+    "        :param g: multigraph\n",
+    "        :param node: node in the multigraph\n",
+    "        :return:\n",
+    "        \"\"\"\n",
+    "        edges = []\n",
+    "        for edge in g.edges(data=True):\n",
+    "            if node == edge[0] or node == edge[1]:\n",
+    "                edges.append(\"{0}-{1}\".format(edge[0],edge[1]))\n",
+    "        return edges\n",
+    "\n",
+    "    def sum_gpq(self, g1, n1, g2, n2):\n",
+    "        \"\"\"\n",
+    "        Compute Nearest Neighbour Distance between edges around n1 in G1  and edges around n2 in G2\n",
+    "        :param g1: first graph\n",
+    "        :param n1: node in the first graph\n",
+    "        :param g2: second graph\n",
+    "        :param n2: node in the second graph\n",
+    "        :return:\n",
+    "        \"\"\"\n",
+    "\n",
+    "        #if isinstance(g1, nx.MultiDiGraph):\n",
+    "        edges1 = self.get_edge_multigraph(g1, n1)\n",
+    "        edges2 = self.get_edge_multigraph(g2, n2)\n",
+    "\n",
+    "        #else:\n",
+    "            #edges1 = [str(n1 + \"-\" + ef) for ef in list(g1.edge[n1].keys())]\n",
+    "            #edges2 = [str(n2 + \"-\" + ef) for ef in list(g2.edge[n2].keys())]\n",
+    "\n",
+    "        min_sum = np.zeros(len(edges1))\n",
+    "        edges2.extend([None])\n",
+    "        for i in range(len(edges1)):\n",
+    "            min_i = np.zeros(len(edges2))\n",
+    "            for j in range(len(edges2)):\n",
+    "                min_i[j] = self.gpq(edges1[i], edges2[j])\n",
+    "            min_sum[i] = np.min(min_i)\n",
+    "        return np.sum(min_sum)\n",
+    "\n",
+    "    def gpq(self, e1, e2):\n",
+    "        \"\"\"\n",
+    "        Compute the edge distance function\n",
+    "        :param e1: edge1\n",
+    "        :param e2: edge2\n",
+    "        :return:\n",
+    "        \"\"\"\n",
+    "        if e2 == None:  # Del\n",
+    "            return self.edge_del\n",
+    "        if e1 == None:  # Insert\n",
+    "            return self.edge_ins\n",
+    "        else:\n",
+    "            if e1 == e2:\n",
+    "                return 0\n",
+    "            return (self.edge_del + self.edge_ins) / 2\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-04-20T07:03:37.430098Z",
+     "start_time": "2018-04-20T07:03:37.424291Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "%%cython \n",
+    "import numpy as np\n",
+    "cimport numpy as np\n",
+    "cdef class HED:\n",
+    "    \"\"\"\n",
+    "    Implementation of Hausdorff Edit Distance described in\n",
+    "\n",
+    "    Improved quadratic time approximation of graph edit distance by Hausdorff matching and greedy assignement\n",
+    "    Andreas Fischer, Kaspar Riesen, Horst Bunke\n",
+    "    2016\n",
+    "    \"\"\"\n",
+    "    \n",
+    "    cdef int node_del \n",
+    "    cdef int node_ins \n",
+    "    cdef int edge_del \n",
+    "    cdef int edge_ins \n",
+    "    \n",
+    "    __type__ = \"dist\"\n",
+    "    @staticmethod\n",
+    "    def compare(list listgs, int c_del_node=1, int c_del_edge=1, int c_ins_node=1, int c_ins_edge=1):\n",
+    "        cdef int n = len(listgs)\n",
+    "        comparator = HED(c_del_node, c_ins_node, c_del_edge, c_ins_edge)\n",
+    "        cdef np.ndarray comparison_matrix = np.zeros((n, n))\n",
+    "        for i in range(n):\n",
+    "            for j in range(i, n):\n",
+    "                comparison_matrix[i, j] = comparator.hed(listgs[i], listgs[j])\n",
+    "                comparison_matrix[j, i] = comparison_matrix[i, j]\n",
+    "\n",
+    "        return comparison_matrix\n",
+    "\n",
+    "\n",
+    "    def __init__(self, int node_del=1, int node_ins=1, int edge_del=1, int edge_ins=1):\n",
+    "        \"\"\"Constructor for HED\"\"\"\n",
+    "        self.node_del = node_del\n",
+    "        self.node_ins = node_ins\n",
+    "        self.edge_del = edge_del\n",
+    "        self.edge_ins = edge_ins\n",
+    "\n",
+    "    cpdef float hed(self, g1, g2):\n",
+    "        \"\"\"\n",
+    "        Compute de Hausdorff Edit Distance\n",
+    "        :param g1: first graph\n",
+    "        :param g2: second graph\n",
+    "        :return:\n",
+    "        \"\"\"\n",
+    "        return self.sum_fuv(g1, g2) + self.sum_fuv(g2, g1)\n",
+    "\n",
+    "    cpdef float sum_fuv(self, g1, g2):\n",
+    "        \"\"\"\n",
+    "        Compute Nearest Neighbour Distance between G1 and G2\n",
+    "        :param g1: First Graph\n",
+    "        :param g2: Second Graph\n",
+    "        :return:\n",
+    "        \"\"\"\n",
+    "        cdef np.ndarray min_sum = np.zeros(len(g1))\n",
+    "        nodes1 = g1.nodes()\n",
+    "        nodes2 = g2.nodes()\n",
+    "        nodes2.extend([None])\n",
+    "        cdef np.ndarray min_i\n",
+    "        for i in range(len(nodes1)):\n",
+    "            min_i = np.zeros(len(nodes2))\n",
+    "            for j in range(len(nodes2)):\n",
+    "                min_i[j] = self.fuv(g1, g2, nodes1[i], nodes2[j])\n",
+    "            min_sum[i] = np.min(min_i)\n",
+    "        return np.sum(min_sum)\n",
+    "\n",
+    "    cpdef float fuv(self, g1, g2, n1, n2):\n",
+    "        \"\"\"\n",
+    "        Compute the Node Distance function\n",
+    "        :param g1: first graph\n",
+    "        :param g2: second graph\n",
+    "        :param n1: node of the first graph\n",
+    "        :param n2: node of the second graph\n",
+    "        :return:\n",
+    "        \"\"\"\n",
+    "        if n2 == None:  # Del\n",
+    "            return self.node_del + ((self.edge_del / 2) * g1.degree(n1))\n",
+    "        if n1 == None:  # Insert\n",
+    "            return self.node_ins + ((self.edge_ins / 2) * g2.degree(n2))\n",
+    "        else:\n",
+    "            if n1 == n2:\n",
+    "                return 0\n",
+    "            return (self.node_del + self.node_ins + self.hed_edge(g1, g2, n1, n2)) / 2\n",
+    "\n",
+    "    cpdef float hed_edge(self, g1, g2, n1, n2):\n",
+    "        \"\"\"\n",
+    "        Compute HEDistance between edges of n1 and n2, respectively in g1 and g2\n",
+    "        :param g1: first graph\n",
+    "        :param g2: second graph\n",
+    "        :param n1: node of the first graph\n",
+    "        :param n2: node of the second graph\n",
+    "        :return:\n",
+    "        \"\"\"\n",
+    "        return self.sum_gpq(g1, n1, g2, n2) + self.sum_gpq(g1, n1, g2, n2)\n",
+    "\n",
+    "    cpdef list get_edge_multigraph(self, g, node):\n",
+    "        \"\"\"\n",
+    "        Get list of edge around a node in a Multigraph\n",
+    "        :param g: multigraph\n",
+    "        :param node: node in the multigraph\n",
+    "        :return:\n",
+    "        \"\"\"\n",
+    "        cdef list edges = []\n",
+    "        for edge in g.edges(data=True):\n",
+    "            if node == edge[0] or node == edge[1]:\n",
+    "                edges.append(\"{0}-{1}\".format(edge[0],edge[1]))\n",
+    "        return edges\n",
+    "\n",
+    "    cpdef float  sum_gpq(self, g1, n1, g2, n2):\n",
+    "        \"\"\"\n",
+    "        Compute Nearest Neighbour Distance between edges around n1 in G1  and edges around n2 in G2\n",
+    "        :param g1: first graph\n",
+    "        :param n1: node in the first graph\n",
+    "        :param g2: second graph\n",
+    "        :param n2: node in the second graph\n",
+    "        :return:\n",
+    "        \"\"\"\n",
+    "\n",
+    "        #if isinstance(g1, nx.MultiDiGraph):\n",
+    "        cdef list edges1 = self.get_edge_multigraph(g1, n1)\n",
+    "        cdef list edges2 = self.get_edge_multigraph(g2, n2)\n",
+    "\n",
+    "        #else:\n",
+    "            #edges1 = [str(n1 + \"-\" + ef) for ef in list(g1.edge[n1].keys())]\n",
+    "            #edges2 = [str(n2 + \"-\" + ef) for ef in list(g2.edge[n2].keys())]\n",
+    "\n",
+    "        cdef np.ndarray min_sum = np.zeros(len(edges1))\n",
+    "        edges2.extend([None])\n",
+    "        cdef np.ndarray min_i\n",
+    "        for i in range(len(edges1)):\n",
+    "            min_i = np.zeros(len(edges2))\n",
+    "            for j in range(len(edges2)):\n",
+    "                min_i[j] = self.gpq(edges1[i], edges2[j])\n",
+    "            min_sum[i] = np.min(min_i)\n",
+    "        return np.sum(min_sum)\n",
+    "\n",
+    "    cpdef float gpq(self, str e1, str e2):\n",
+    "        \"\"\"\n",
+    "        Compute the edge distance function\n",
+    "        :param e1: edge1\n",
+    "        :param e2: edge2\n",
+    "        :return:\n",
+    "        \"\"\"\n",
+    "        if e2 == None:  # Del\n",
+    "            return self.edge_del\n",
+    "        if e1 == None:  # Insert\n",
+    "            return self.edge_ins\n",
+    "        else:\n",
+    "            if e1 == e2:\n",
+    "                return 0\n",
+    "            return (self.edge_del + self.edge_ins) / 2\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-04-20T07:03:37.434790Z",
+     "start_time": "2018-04-20T07:03:37.432514Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-04-20T07:03:37.736648Z",
+     "start_time": "2018-04-20T07:03:37.437089Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import networkx as nx"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-04-20T07:03:37.750336Z",
+     "start_time": "2018-04-20T07:03:37.738699Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "g1=nx.read_gexf(\"/Users/jacquesfize/LOD_DATASETS/exp_17_avr18/normal/2000.gexf\")\n",
+    "g2=nx.read_gexf(\"/Users/jacquesfize/LOD_DATASETS/exp_17_avr18/normal/4620.gexf\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-04-20T07:03:37.763916Z",
+     "start_time": "2018-04-20T07:03:37.753086Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "g1=nx.random_graphs.barabasi_albert_graph(50,25)\n",
+    "g2=nx.random_graphs.barabasi_albert_graph(50,25)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-04-20T07:04:01.654836Z",
+     "start_time": "2018-04-20T07:03:37.766300Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 23.8 s, sys: 191 ms, total: 24 s\n",
+      "Wall time: 23.9 s\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "array([[0., 0.],\n",
+       "       [0., 0.]])"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%time PureHED.compare([g1,g2])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-04-20T07:04:14.050637Z",
+     "start_time": "2018-04-20T07:04:01.657104Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 12.4 s, sys: 41.7 ms, total: 12.4 s\n",
+      "Wall time: 12.4 s\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "array([[0., 0.],\n",
+       "       [0., 0.]])"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%time HED.compare([g1,g2])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.5"
+  },
+  "toc": {
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": "block",
+   "toc_window_display": false
+  },
+  "varInspector": {
+   "cols": {
+    "lenName": 16,
+    "lenType": 16,
+    "lenVar": 40
+   },
+   "kernels_config": {
+    "python": {
+     "delete_cmd_postfix": "",
+     "delete_cmd_prefix": "del ",
+     "library": "var_list.py",
+     "varRefreshCmd": "print(var_dic_list())"
+    },
+    "r": {
+     "delete_cmd_postfix": ") ",
+     "delete_cmd_prefix": "rm(",
+     "library": "var_list.r",
+     "varRefreshCmd": "cat(var_dic_list()) "
+    }
+   },
+   "types_to_exclude": [
+    "module",
+    "function",
+    "builtin_function_or_method",
+    "instance",
+    "_Feature"
+   ],
+   "window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/EvalDesambiguisationMada.ipynb b/notebooks/EvalDesambiguisationMada.ipynb
new file mode 100644
index 0000000..4cb419e
--- /dev/null
+++ b/notebooks/EvalDesambiguisationMada.ipynb
@@ -0,0 +1,311 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-05-16T23:58:48.134280Z",
+     "start_time": "2018-05-16T23:58:47.729327Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-05-16T23:58:48.140894Z",
+     "start_time": "2018-05-16T23:58:48.136384Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/Users/jacquesfize/nas_cloud/Code/str-python\n"
+     ]
+    }
+   ],
+   "source": [
+    "cd .."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-05-16T23:58:48.150739Z",
+     "start_time": "2018-05-16T23:58:48.143107Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import glob,re,sys\n",
+    "fns=glob.glob(\"data/mada_disambiguisation/*.csv\")\n",
+    "ids_list=[int(re.findall(r\"\\d+\",fn)[-1]) for fn in fns]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-05-16T23:58:48.173363Z",
+     "start_time": "2018-05-16T23:58:48.153066Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "data_lang=json.load(open(\"/Users/jacquesfize/LOD_DATASETS/raw_bvlac/associated_lang.json\"))\n",
+    "data_lang={int(k):v for k,v in data_lang.items()}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-05-16T23:58:48.864223Z",
+     "start_time": "2018-05-16T23:58:48.177516Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from nlp.disambiguator.geodict_gaurav import GauravGeodict\n",
+    "from nlp.disambiguator.most_common import MostCommonDisambiguator\n",
+    "disMost_common=MostCommonDisambiguator()\n",
+    "disGaurav=GauravGeodict()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-05-17T00:59:53.695102Z",
+     "start_time": "2018-05-17T00:59:53.685756Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "df=pd.read_csv(\"data/mada_disambiguisation/11.csv\")\n",
+    "\n",
+    "def accuracyMostCommon(df,lang):\n",
+    "    df2=df[-df[\"GID\"].isin([\"O\",\"NR\",\"o\"])][[\"text\",\"GID\"]]\n",
+    "    df2[\"disambiguation\"]=df2.text.apply(lambda x:disMost_common.disambiguate_(x,lang)[0])\n",
+    "    return (df2.GID == df2.disambiguation).sum()/len(df2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-05-17T00:01:52.885111Z",
+     "start_time": "2018-05-17T00:01:52.850434Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-05-17T01:00:09.181696Z",
+     "start_time": "2018-05-17T01:00:09.178578Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import re"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-05-17T00:59:55.445531Z",
+     "start_time": "2018-05-17T00:59:55.407867Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "%autoreload\n",
+    "def accuracyGeodict(df,lang):\n",
+    "    df2=df[-df[\"GID\"].isin([\"O\",\"NR\",\"o\"])][[\"text\",\"GID\"]]\n",
+    "    res_dis=disGaurav.eval(df2[\"text\"].unique(),lang)\n",
+    "    df2[\"disambiguation\"]=df2.text.apply(lambda x:res_dis[x] if x in res_dis else \"0\")\n",
+    "    return (df2.GID == df2.disambiguation).sum()/len(df2)\n",
+    "#df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-05-17T01:22:15.528864Z",
+     "start_time": "2018-05-17T01:01:01.373760Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.6/site-packages/ipykernel_launcher.py:6: RuntimeWarning: invalid value encountered in long_scalars\n",
+      "  \n"
+     ]
+    }
+   ],
+   "source": [
+    "acc_MC,acc_GEO=[],[]\n",
+    "for fn in fns:\n",
+    "    id_=int(re.findall(r\"\\d+\",fn)[-1])\n",
+    "    \n",
+    "    df=pd.read_csv(fn)\n",
+    "    acc_MC.append(accuracyMostCommon(df,data_lang[id_]))\n",
+    "    acc_GEO.append(accuracyGeodict(df,data_lang[id_]))\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-05-17T01:22:15.574548Z",
+     "start_time": "2018-05-17T01:22:15.567387Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.6118508350166977"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "np.mean(np.nan_to_num(acc_GEO))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-05-17T01:22:15.618633Z",
+     "start_time": "2018-05-17T01:22:15.612431Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.7694373020389706"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.mean(np.nan_to_num(acc_MC))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.5"
+  },
+  "toc": {
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": "block",
+   "toc_window_display": false
+  },
+  "varInspector": {
+   "cols": {
+    "lenName": 16,
+    "lenType": 16,
+    "lenVar": 40
+   },
+   "kernels_config": {
+    "python": {
+     "delete_cmd_postfix": "",
+     "delete_cmd_prefix": "del ",
+     "library": "var_list.py",
+     "varRefreshCmd": "print(var_dic_list())"
+    },
+    "r": {
+     "delete_cmd_postfix": ") ",
+     "delete_cmd_prefix": "rm(",
+     "library": "var_list.r",
+     "varRefreshCmd": "cat(var_dic_list()) "
+    }
+   },
+   "position": {
+    "height": "297px",
+    "left": "914px",
+    "right": "20px",
+    "top": "120px",
+    "width": "350px"
+   },
+   "types_to_exclude": [
+    "module",
+    "function",
+    "builtin_function_or_method",
+    "instance",
+    "_Feature"
+   ],
+   "window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/EvalDesambiguisationPADIWEB.ipynb b/notebooks/EvalDesambiguisationPADIWEB.ipynb
new file mode 100644
index 0000000..ba763a8
--- /dev/null
+++ b/notebooks/EvalDesambiguisationPADIWEB.ipynb
@@ -0,0 +1,378 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-05-17T00:50:38.399698Z",
+     "start_time": "2018-05-17T00:50:38.396888Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import json"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-05-17T00:48:13.001356Z",
+     "start_time": "2018-05-17T00:48:12.994569Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/Users/jacquesfize/nas_cloud/Code/str-python\n"
+     ]
+    }
+   ],
+   "source": [
+    "cd .."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-05-17T00:54:11.406691Z",
+     "start_time": "2018-05-17T00:54:11.400933Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from elasticsearch import Elasticsearch\n",
+    "\n",
+    "from config.configuration import config\n",
+    "\n",
+    "es = Elasticsearch(config.es_server)\n",
+    "def get_data_by_geoname_id(id):\n",
+    "    res = es.search(\"gazetteer\", \"place\",\n",
+    "                    body={\"query\": {\"bool\": {\"must\": [{\"term\": {\"geonameID\": id}}], \"must_not\": [], \"should\": []}}, \"from\": 0,\n",
+    "                          \"size\": 10, \"sort\": [], \"aggs\": {}})\n",
+    "    if res[\"hits\"][\"total\"] > 0:\n",
+    "        res = res[\"hits\"][\"hits\"][0][\"_source\"]\n",
+    "    return res\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-05-17T01:25:05.006779Z",
+     "start_time": "2018-05-17T01:25:05.000357Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def parse_file(fn):\n",
+    "    id_=int(re.findall(r\"\\d+\",fn)[-1])\n",
+    "    lang=langdetect.detect(open(\"data/EPI_ELENA/raw_text/{0}.txt\".format(id_)).read())\n",
+    "    df=pd.read_json(fn,orient=\"index\")\n",
+    "    try:\n",
+    "        df=df[(df[\"type\"]==\"location\") & (df[\"annotation\"]==\"correct\")]\n",
+    "    except:\n",
+    "        return\n",
+    "    df[\"GID\"]=df[\"info\"].apply(lambda x:get_data_by_geoname_id(x[\"id\"])[\"id\"])\n",
+    "    df[\"content\"]=df[\"content\"].apply(lambda x:re.sub(r\"\\s+\",\" \",x.strip()))\n",
+    "    return df,lang\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-05-17T00:56:26.195260Z",
+     "start_time": "2018-05-17T00:56:26.185713Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import glob,re,sys\n",
+    "fns=glob.glob(\"data/EPI_ELENA/final_annotations/*.json\")\n",
+    "ids_list=[int(re.findall(r\"\\d+\",fn)[-1]) for fn in fns]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-05-17T01:05:10.917961Z",
+     "start_time": "2018-05-17T01:05:10.915317Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import langdetect"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-05-17T00:57:28.905930Z",
+     "start_time": "2018-05-17T00:57:28.346854Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from nlp.disambiguator.geodict_gaurav import GauravGeodict\n",
+    "from nlp.disambiguator.most_common import MostCommonDisambiguator\n",
+    "disMost_common=MostCommonDisambiguator()\n",
+    "disGaurav=GauravGeodict()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-05-17T01:10:19.593778Z",
+     "start_time": "2018-05-17T01:10:19.585332Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "df=pd.read_csv(\"data/mada_disambiguisation/11.csv\")\n",
+    "\n",
+    "def accuracyMostCommon(df,lang):\n",
+    "    df2=df[-df[\"GID\"].isin([\"O\",\"NR\",\"o\"])][[\"content\",\"GID\"]]\n",
+    "    df2[\"disambiguation\"]=df2.content.apply(lambda x:disMost_common.disambiguate_(x,lang)[0])\n",
+    "    return (df2.GID == df2.disambiguation).sum()/len(df2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-05-17T00:01:52.885111Z",
+     "start_time": "2018-05-17T00:01:52.850434Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-05-17T01:10:21.463216Z",
+     "start_time": "2018-05-17T01:10:21.098003Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.6666666666666666"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df,lang=parse_file(fns[0])\n",
+    "accuracyMostCommon(df,lang)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-05-17T01:06:38.089187Z",
+     "start_time": "2018-05-17T01:06:38.080846Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "%autoreload\n",
+    "def accuracyGeodict(df,lang):\n",
+    "    df2=df[-df[\"GID\"].isin([\"O\",\"NR\",\"o\"])][[\"content\",\"GID\"]]\n",
+    "    res_dis=disGaurav.eval(df2[\"content\"].unique(),lang)\n",
+    "    df2[\"disambiguation\"]=df2.content.apply(lambda x:res_dis[x] if x in res_dis else \"0\")\n",
+    "    return (df2.GID == df2.disambiguation).sum()/len(df2)\n",
+    "#df\n",
+    "#df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-05-17T01:37:02.165192Z",
+     "start_time": "2018-05-17T01:25:31.325566Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.6/site-packages/pandas/core/ops.py:816: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
+      "  result = getattr(x, name)(y)\n",
+      "/Users/jacquesfize/nas_cloud/Code/str-python/helpers/collision.py:30: RuntimeWarning: invalid value encountered in double_scalars\n",
+      "  d_over_o_squared = d/np.dot(o, o) + 1e-10\n",
+      "/usr/local/lib/python3.6/site-packages/ipykernel_launcher.py:6: RuntimeWarning: invalid value encountered in long_scalars\n",
+      "  \n"
+     ]
+    }
+   ],
+   "source": [
+    "acc_MC,acc_GEO=[],[]\n",
+    "for fn in fns:\n",
+    "    \n",
+    "    try:\n",
+    "        df,lang=parse_file(fn)\n",
+    "        acc_MC.append(accuracyMostCommon(df,lang))\n",
+    "        acc_GEO.append(accuracyGeodict(df,lang))\n",
+    "    except:\n",
+    "        pass\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-05-17T01:37:02.209200Z",
+     "start_time": "2018-05-17T01:37:02.200462Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.5139891137064413"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "np.mean(np.nan_to_num(acc_GEO))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-05-17T01:37:02.250591Z",
+     "start_time": "2018-05-17T01:37:02.246260Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.5267050989770068"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.mean(np.nan_to_num(acc_MC))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.5"
+  },
+  "toc": {
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": "block",
+   "toc_window_display": false
+  },
+  "varInspector": {
+   "cols": {
+    "lenName": 16,
+    "lenType": 16,
+    "lenVar": 40
+   },
+   "kernels_config": {
+    "python": {
+     "delete_cmd_postfix": "",
+     "delete_cmd_prefix": "del ",
+     "library": "var_list.py",
+     "varRefreshCmd": "print(var_dic_list())"
+    },
+    "r": {
+     "delete_cmd_postfix": ") ",
+     "delete_cmd_prefix": "rm(",
+     "library": "var_list.r",
+     "varRefreshCmd": "cat(var_dic_list()) "
+    }
+   },
+   "position": {
+    "height": "297px",
+    "left": "914px",
+    "right": "20px",
+    "top": "120px",
+    "width": "350px"
+   },
+   "types_to_exclude": [
+    "module",
+    "function",
+    "builtin_function_or_method",
+    "instance",
+    "_Feature"
+   ],
+   "window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/EvalTopoMadagascar.ipynb b/notebooks/EvalTopoMadagascar.ipynb
new file mode 100644
index 0000000..9f6a358
--- /dev/null
+++ b/notebooks/EvalTopoMadagascar.ipynb
@@ -0,0 +1,719 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-05-17T06:15:39.543009Z",
+     "start_time": "2018-05-17T06:15:39.538598Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/Users/jacquesfize/nas_cloud/Code/str-python\n"
+     ]
+    }
+   ],
+   "source": [
+    "cd .."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-05-17T06:15:39.906690Z",
+     "start_time": "2018-05-17T06:15:39.545042Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from nlp.disambiguator.disambiguator import Disambiguator\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-05-17T06:15:41.165016Z",
+     "start_time": "2018-05-17T06:15:39.908807Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from pipeline import *\n",
+    "from nlp.pos_tagger.tagger import Tagger\n",
+    "from nlp.disambiguator.pagerank import *\n",
+    "from nlp.disambiguator.geodict_gaurav import *\n",
+    "from nlp.pos_tagger.treetagger import TreeTagger\n",
+    "from nlp.ner.stanford_ner import StanfordNER\n",
+    "from nlp.ner.polyglot import Polyglot\n",
+    "from nlp.ner.nltk import NLTK\n",
+    "from nlp.ner.gate_annie import GateAnnie\n",
+    "from nlp.ner.spacy import Spacy\n",
+    "from nlp.ner.ner import NER\n",
+    "from progressbar import ProgressBar\n",
+    "from polyglot.text import Text"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-05-17T06:15:50.113793Z",
+     "start_time": "2018-05-17T06:15:41.167223Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Language may not be supported by NTLK !\n"
+     ]
+    }
+   ],
+   "source": [
+    "pipStanford={\n",
+    "    \"en\":Pipeline(lang=\"english\",tagger=Tagger(),ner=StanfordNER(lang=\"en\")),\n",
+    "    \"fr\":Pipeline(lang=\"french\",tagger=Tagger(),ner=StanfordNER(lang=\"fr\"))\n",
+    "}\n",
+    "\n",
+    "pipNLTK={\n",
+    "    \"en\":Pipeline(lang=\"english\",tagger=Tagger(),ner=NLTK(lang=\"en\")),\n",
+    "    \"fr\":Pipeline(lang=\"french\",tagger=Tagger(),ner=NLTK(lang=\"fr\"))\n",
+    "}\n",
+    "\n",
+    "pipPolyglot={\n",
+    "    \"en\":Pipeline(lang=\"english\",tagger=Tagger(),ner=Polyglot(lang=\"en\")),\n",
+    "    \"fr\":Pipeline(lang=\"english\",tagger=Tagger(),ner=Polyglot(lang=\"fr\"))\n",
+    "}\n",
+    "\n",
+    "pipGate={\n",
+    "    \"en\":Pipeline(lang=\"english\",tagger=Tagger(),ner=GateAnnie(lang=\"en\")),\n",
+    "    \"fr\":Pipeline(lang=\"french\",tagger=Tagger(),ner=GateAnnie(lang=\"fr\"))\n",
+    "}\n",
+    "\n",
+    "pipSpacy={\n",
+    "    \"en\":Pipeline(lang=\"english\",tagger=Tagger(),ner=Spacy(lang=\"en\")),\n",
+    "    \"fr\":Pipeline(lang=\"french\",tagger=Tagger(),ner=Spacy(lang=\"fr\"))\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-05-17T06:15:50.130340Z",
+     "start_time": "2018-05-17T06:15:50.115895Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "data_lang=json.load(open(\"/Users/jacquesfize/LOD_DATASETS/raw_bvlac/associated_lang.json\"))\n",
+    "data_lang={int(k):v for k,v in data_lang.items()}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-05-17T06:15:50.138305Z",
+     "start_time": "2018-05-17T06:15:50.132448Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import glob,re,sys\n",
+    "fns=glob.glob(\"data/mada_disambiguisation/*.csv\")\n",
+    "ids_list=[int(re.findall(r\"\\d+\",fn)[-1]) for fn in fns]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-05-17T06:15:50.143454Z",
+     "start_time": "2018-05-17T06:15:50.139829Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from ipywidgets import IntProgress\n",
+    "from IPython.display import display\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-05-17T06:15:50.169663Z",
+     "start_time": "2018-05-17T06:15:50.145641Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "input_dir=\"/Users/jacquesfize/LOD_DATASETS/raw_bvlac/\"\n",
+    "\n",
+    "def compute_precision_recall(pipeline):\n",
+    "    precision=[]\n",
+    "    recall=[]\n",
+    "    co=0\n",
+    "    for i in ids_list:\n",
+    "        sys.stdout.write(\"\\r{0}/{1}\".format(co,len(ids_list)))\n",
+    "        lang=data_lang[i]\n",
+    "        data_real=pd.read_csv(\"data/mada_disambiguisation/{0}.csv\".format(i))\n",
+    "        data_real=data_real[-data_real[\"GID\"].isin([\"O\",\"NR\",\"o\"])][[\"text\",\"GID\"]]\n",
+    "        text=open(\"{0}/{1}.txt\".format(input_dir.rstrip(\"/\"),i)).read()\n",
+    "        \n",
+    "        try:\n",
+    "            res_ner=pipeline[lang].ner.identify(text)\n",
+    "            res_ner=Disambiguator.parse_corpus(res_ner)\n",
+    "        except Exception as e:\n",
+    "            print(e)\n",
+    "            continue\n",
+    "        system_data=pd.DataFrame(res_ner,columns=[\"text\",\"pos\"])\n",
+    "        system_data=system_data[system_data[\"pos\"]==\"LOC\"]\n",
+    "        #count_tp=system_data[\"text\"].str.lower().isin(data_real[\"text\"].str.lower()).sum()\n",
+    "        count_tp=len(set(data_real[\"text\"].str.lower().unique())&(set(system_data[\"text\"].str.lower().unique())))\n",
+    "        count_fp=len(system_data)-count_tp\n",
+    "        try:\n",
+    "            precision.append(count_tp/len(system_data[\"text\"].unique()))\n",
+    "        except:\n",
+    "            print(1)\n",
+    "            precision.append(0)\n",
+    "        try:\n",
+    "            recall.append(count_tp/len(data_real[\"text\"].unique()))\n",
+    "        except:\n",
+    "            print(2)\n",
+    "            recall.append(0)\n",
+    "        co+=1\n",
+    "    return precision,recall\n",
+    "        #pd.DataFrame(res_ner,columns=[\"text\",\"pos\"])\n",
+    "#compute_precision_recall(pipSpacy)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-05-17T06:15:50.201209Z",
+     "start_time": "2018-05-17T06:15:50.171396Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-05-17T06:27:25.917340Z",
+     "start_time": "2018-05-17T06:17:25.038572Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "117/2322\n",
+      "231/232"
+     ]
+    }
+   ],
+   "source": [
+    "%autoreload\n",
+    "prec_sp,rec_sp=compute_precision_recall(pipSpacy)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-05-17T06:43:36.230684Z",
+     "start_time": "2018-05-17T06:27:55.927495Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3/2321\n",
+      "4/2321\n",
+      "41/2321\n",
+      "42/2321\n",
+      "43/2321\n",
+      "44/2321\n",
+      "46/2321\n",
+      "48/2321\n",
+      "51/232list index out of range\n",
+      "54/2321\n",
+      "61/2321\n",
+      "65/2321\n",
+      "76/2321\n",
+      "78/2321\n",
+      "79/2321\n",
+      "82/2321\n",
+      "83/2321\n",
+      "114/2321\n",
+      "116/2321\n",
+      "2\n",
+      "117/2321\n",
+      "156/2321\n",
+      "157/2321\n",
+      "174/2321\n",
+      "193/2321\n",
+      "194/2321\n",
+      "205/2321\n",
+      "211/2321\n",
+      "214/2321\n",
+      "215/2321\n",
+      "220/232list index out of range\n",
+      "222/2321\n",
+      "223/2321\n",
+      "229/232"
+     ]
+    }
+   ],
+   "source": [
+    "%autoreload\n",
+    "prec_st,rec_st=compute_precision_recall(pipStanford)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-05-17T06:56:10.536873Z",
+     "start_time": "2018-05-17T06:43:36.284258Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "117/2322\n",
+      "231/232"
+     ]
+    }
+   ],
+   "source": [
+    "prec_nl,rec_nl=compute_precision_recall(pipNLTK)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-05-17T07:05:03.304819Z",
+     "start_time": "2018-05-17T06:56:10.591028Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "41/232Package 'ner2.mg' not found in index\n",
+      "41/232Package 'ner2.mg' not found in index\n",
+      "67/232Package 'ner2.mg' not found in index\n",
+      "114/2321\n",
+      "2\n",
+      "228/232"
+     ]
+    }
+   ],
+   "source": [
+    "prec_po,rec_po=compute_precision_recall(pipPolyglot)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-05-17T07:19:35.445903Z",
+     "start_time": "2018-05-17T07:05:03.362992Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2/232list index out of range\n",
+      "3/232list index out of range\n",
+      "5/232list index out of range\n",
+      "8/232list index out of range\n",
+      "15/232list index out of range\n",
+      "15/232list index out of range\n",
+      "15/232list index out of range\n",
+      "15/232list index out of range\n",
+      "15/232list index out of range\n",
+      "15/232list index out of range\n",
+      "15/232list index out of range\n",
+      "16/232list index out of range\n",
+      "21/232list index out of range\n",
+      "27/232list index out of range\n",
+      "27/232list index out of range\n",
+      "27/232list index out of range\n",
+      "27/232list index out of range\n",
+      "27/232list index out of range\n",
+      "27/232list index out of range\n",
+      "28/232list index out of range\n",
+      "28/2321\n",
+      "29/232list index out of range\n",
+      "29/232list index out of range\n",
+      "34/232list index out of range\n",
+      "34/232list index out of range\n",
+      "34/232list index out of range\n",
+      "34/232list index out of range\n",
+      "34/232list index out of range\n",
+      "35/232list index out of range\n",
+      "36/232list index out of range\n",
+      "38/232list index out of range\n",
+      "38/232list index out of range\n",
+      "38/232list index out of range\n",
+      "38/232list index out of range\n",
+      "38/232list index out of range\n",
+      "44/232list index out of range\n",
+      "49/232list index out of range\n",
+      "50/232list index out of range\n",
+      "51/232list index out of range\n",
+      "51/232list index out of range\n",
+      "52/232list index out of range\n",
+      "52/232list index out of range\n",
+      "53/232list index out of range\n",
+      "54/232list index out of range\n",
+      "56/232list index out of range\n",
+      "58/232list index out of range\n",
+      "58/232list index out of range\n",
+      "60/232list index out of range\n",
+      "60/232list index out of range\n",
+      "61/2321\n",
+      "62/2321\n",
+      "63/232list index out of range\n",
+      "63/232list index out of range\n",
+      "63/232list index out of range\n",
+      "63/232list index out of range\n",
+      "64/232list index out of range\n",
+      "64/2321\n",
+      "2\n",
+      "65/2321\n",
+      "66/232list index out of range\n",
+      "66/232list index out of range\n",
+      "66/232list index out of range\n",
+      "66/232list index out of range\n",
+      "66/232list index out of range\n",
+      "72/232list index out of range\n",
+      "72/232list index out of range\n",
+      "72/232list index out of range\n",
+      "72/232list index out of range\n",
+      "72/232list index out of range\n",
+      "72/232list index out of range\n",
+      "72/232list index out of range\n",
+      "72/232list index out of range\n",
+      "73/232list index out of range\n",
+      "73/232list index out of range\n",
+      "73/232list index out of range\n",
+      "73/232list index out of range\n",
+      "73/232list index out of range\n",
+      "74/232list index out of range\n",
+      "77/232list index out of range\n",
+      "80/232list index out of range\n",
+      "80/232list index out of range\n",
+      "82/232list index out of range\n",
+      "84/232list index out of range\n",
+      "84/232list index out of range\n",
+      "89/232list index out of range\n",
+      "89/232list index out of range\n",
+      "89/232list index out of range\n",
+      "89/232list index out of range\n",
+      "89/232list index out of range\n",
+      "89/232list index out of range\n",
+      "95/232list index out of range\n",
+      "95/2321\n",
+      "96/232list index out of range\n",
+      "100/232list index out of range\n",
+      "101/232list index out of range\n",
+      "102/232list index out of range\n",
+      "102/232list index out of range\n",
+      "102/232list index out of range\n",
+      "105/232list index out of range\n",
+      "108/232"
+     ]
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m/usr/local/lib/python3.6/site-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36m_make_request\u001b[0;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)\u001b[0m\n\u001b[1;32m    379\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# Python 2.7, use buffering of HTTP responses\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 380\u001b[0;31m                 \u001b[0mhttplib_response\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetresponse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbuffering\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    381\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# Python 2.6 and older, Python 3\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mTypeError\u001b[0m: getresponse() got an unexpected keyword argument 'buffering'",
+      "\nDuring handling of the above exception, another exception occurred:\n",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-15-ddd472848dde>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprec_ga\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mrec_ga\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcompute_precision_recall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpipGate\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-8-f7e3a40e4d49>\u001b[0m in \u001b[0;36mcompute_precision_recall\u001b[0;34m(pipeline)\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m             \u001b[0mres_ner\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpipeline\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mlang\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mner\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0midentify\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m             \u001b[0mres_ner\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mDisambiguator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparse_corpus\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mres_ner\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/Users/jacquesfize/nas_cloud/Code/str-python/nlp/ner/gate_annie.py\u001b[0m in \u001b[0;36midentify\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m     18\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 20\u001b[0;31m         \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrequests\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpost\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhost\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m\"/ner\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"utf-8\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontent\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     21\u001b[0m         \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"utf-8\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"\\n\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     22\u001b[0m         \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"\\t\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mr\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/site-packages/requests/api.py\u001b[0m in \u001b[0;36mpost\u001b[0;34m(url, data, json, **kwargs)\u001b[0m\n\u001b[1;32m    110\u001b[0m     \"\"\"\n\u001b[1;32m    111\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 112\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mrequest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'post'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mjson\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mjson\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    113\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    114\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/site-packages/requests/api.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(method, url, **kwargs)\u001b[0m\n\u001b[1;32m     56\u001b[0m     \u001b[0;31m# cases, and look like a memory leak in others.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0msessions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSession\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0msession\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 58\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0msession\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     59\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     60\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/site-packages/requests/sessions.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[0m\n\u001b[1;32m    506\u001b[0m         }\n\u001b[1;32m    507\u001b[0m         \u001b[0msend_kwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msettings\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 508\u001b[0;31m         \u001b[0mresp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprep\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0msend_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    509\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    510\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mresp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/site-packages/requests/sessions.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, request, **kwargs)\u001b[0m\n\u001b[1;32m    616\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    617\u001b[0m         \u001b[0;31m# Send the request\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 618\u001b[0;31m         \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0madapter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    619\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    620\u001b[0m         \u001b[0;31m# Total elapsed time of the request (approximately)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/site-packages/requests/adapters.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[1;32m    438\u001b[0m                     \u001b[0mdecode_content\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    439\u001b[0m                     \u001b[0mretries\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax_retries\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 440\u001b[0;31m                     \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    441\u001b[0m                 )\n\u001b[1;32m    442\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/site-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36murlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[1;32m    599\u001b[0m                                                   \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout_obj\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    600\u001b[0m                                                   \u001b[0mbody\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mheaders\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 601\u001b[0;31m                                                   chunked=chunked)\n\u001b[0m\u001b[1;32m    602\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    603\u001b[0m             \u001b[0;31m# If we're going to release the connection in ``finally:``, then\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/site-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36m_make_request\u001b[0;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)\u001b[0m\n\u001b[1;32m    381\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# Python 2.6 and older, Python 3\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    382\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 383\u001b[0;31m                     \u001b[0mhttplib_response\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetresponse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    384\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    385\u001b[0m                     \u001b[0;31m# Remove the TypeError from the exception chain in Python 3;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/Cellar/python/3.6.5/Frameworks/Python.framework/Versions/3.6/lib/python3.6/http/client.py\u001b[0m in \u001b[0;36mgetresponse\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1329\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1330\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1331\u001b[0;31m                 \u001b[0mresponse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbegin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1332\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mConnectionError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1333\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/Cellar/python/3.6.5/Frameworks/Python.framework/Versions/3.6/lib/python3.6/http/client.py\u001b[0m in \u001b[0;36mbegin\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    295\u001b[0m         \u001b[0;31m# read until we get a non-100 response\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    296\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 297\u001b[0;31m             \u001b[0mversion\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatus\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreason\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_read_status\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    298\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mstatus\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mCONTINUE\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    299\u001b[0m                 \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/Cellar/python/3.6.5/Frameworks/Python.framework/Versions/3.6/lib/python3.6/http/client.py\u001b[0m in \u001b[0;36m_read_status\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    256\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    257\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_read_status\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 258\u001b[0;31m         \u001b[0mline\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreadline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_MAXLINE\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"iso-8859-1\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    259\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0m_MAXLINE\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    260\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mLineTooLong\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"status line\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/Cellar/python/3.6.5/Frameworks/Python.framework/Versions/3.6/lib/python3.6/socket.py\u001b[0m in \u001b[0;36mreadinto\u001b[0;34m(self, b)\u001b[0m\n\u001b[1;32m    584\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    585\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 586\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv_into\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    587\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    588\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_timeout_occurred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "prec_ga,rec_ga=compute_precision_recall(pipGate)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-05-17T07:25:36.506464Z",
+     "start_time": "2018-05-17T07:25:36.496991Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "def m(x):\n",
+    "    return np.mean(np.nan_to_num(x))\n",
+    "cols=[\"NER\",\"P\",\"R\"]\n",
+    "df=pd.DataFrame(columns=cols)\n",
+    "df=pd.DataFrame([[\"StanfordNER\",m(prec_st),m(rec_st)],\n",
+    "                        [\"Polyglot\",m(prec_po),m(rec_po)],[\"NLTK\",m(prec_nl),m(rec_nl)],\n",
+    "                       [\"Spacy\",m(prec_sp),m(rec_sp)]],columns=cols)\n",
+    "df[\"F\"]= df.apply(lambda x: 2*((x[\"P\"]*x[\"R\"])/(x[\"P\"]+x[\"R\"])), axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-05-17T07:25:37.723293Z",
+     "start_time": "2018-05-17T07:25:37.713231Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>NER</th>\n",
+       "      <th>P</th>\n",
+       "      <th>R</th>\n",
+       "      <th>F</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>StanfordNER</td>\n",
+       "      <td>0.319804</td>\n",
+       "      <td>0.169799</td>\n",
+       "      <td>0.221822</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Polyglot</td>\n",
+       "      <td>0.207006</td>\n",
+       "      <td>0.356064</td>\n",
+       "      <td>0.261805</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>NLTK</td>\n",
+       "      <td>0.137581</td>\n",
+       "      <td>0.158004</td>\n",
+       "      <td>0.147087</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Spacy</td>\n",
+       "      <td>0.147053</td>\n",
+       "      <td>0.849829</td>\n",
+       "      <td>0.250722</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           NER         P         R         F\n",
+       "0  StanfordNER  0.319804  0.169799  0.221822\n",
+       "1     Polyglot  0.207006  0.356064  0.261805\n",
+       "2         NLTK  0.137581  0.158004  0.147087\n",
+       "3        Spacy  0.147053  0.849829  0.250722"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-05-17T07:51:46.198366Z",
+     "start_time": "2018-05-17T07:51:46.192160Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\\begin{tabular}{llrrr}\n",
+      "\\toprule\n",
+      "{} &          NER &         P &         R &         F \\\\\n",
+      "\\midrule\n",
+      "0 &  StanfordNER &  0.319804 &  0.169799 &  0.221822 \\\\\n",
+      "1 &     Polyglot &  0.207006 &  0.356064 &  0.261805 \\\\\n",
+      "2 &         NLTK &  0.137581 &  0.158004 &  0.147087 \\\\\n",
+      "3 &        Spacy &  0.147053 &  0.849829 &  0.250722 \\\\\n",
+      "\\bottomrule\n",
+      "\\end{tabular}\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df.to_latex())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.5"
+  },
+  "toc": {
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": "block",
+   "toc_window_display": false
+  },
+  "varInspector": {
+   "cols": {
+    "lenName": 16,
+    "lenType": 16,
+    "lenVar": 40
+   },
+   "kernels_config": {
+    "python": {
+     "delete_cmd_postfix": "",
+     "delete_cmd_prefix": "del ",
+     "library": "var_list.py",
+     "varRefreshCmd": "print(var_dic_list())"
+    },
+    "r": {
+     "delete_cmd_postfix": ") ",
+     "delete_cmd_prefix": "rm(",
+     "library": "var_list.r",
+     "varRefreshCmd": "cat(var_dic_list()) "
+    }
+   },
+   "position": {
+    "height": "217px",
+    "left": "915px",
+    "right": "28px",
+    "top": "120px",
+    "width": "341px"
+   },
+   "types_to_exclude": [
+    "module",
+    "function",
+    "builtin_function_or_method",
+    "instance",
+    "_Feature"
+   ],
+   "window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/NER Evaluation.ipynb b/notebooks/NER Evaluation.ipynb
index 67d847a..85610ef 100644
--- a/notebooks/NER Evaluation.ipynb	
+++ b/notebooks/NER Evaluation.ipynb	
@@ -4,16 +4,17 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
+    "ExecuteTime": {
+     "end_time": "2018-05-08T15:19:37.341397Z",
+     "start_time": "2018-05-08T15:19:37.337211Z"
+    }
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "/Users/jacquesfize/ownCloud/THESE/Code/str-python\n"
+      "/Users/jacquesfize/nas_cloud/Code/str-python\n"
      ]
     }
    ],
@@ -23,28 +24,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 2,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
+    "ExecuteTime": {
+     "end_time": "2018-05-08T15:19:37.412267Z",
+     "start_time": "2018-05-08T15:19:37.343429Z"
+    }
    },
    "outputs": [],
    "source": [
     "import json\n",
     "import os,sys,re,glob\n",
     "from elasticsearch import Elasticsearch\n",
-    "\n",
-    "es_client=Elasticsearch(hosts=\"172.16.50.33:32773\")"
+    "es_client=Elasticsearch()"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
+    "ExecuteTime": {
+     "end_time": "2018-05-08T15:19:37.738728Z",
+     "start_time": "2018-05-08T15:19:37.414740Z"
+    },
     "scrolled": false
    },
    "outputs": [],
@@ -91,9 +93,10 @@
    "cell_type": "code",
    "execution_count": 4,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
+    "ExecuteTime": {
+     "end_time": "2018-05-08T15:19:37.750366Z",
+     "start_time": "2018-05-08T15:19:37.740586Z"
+    }
    },
    "outputs": [
     {
@@ -142,20 +145,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Chargement des Données"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "# Transformation des données\n",
     "\n",
@@ -164,11 +161,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 5,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
+    "ExecuteTime": {
+     "end_time": "2018-05-08T15:19:37.754389Z",
+     "start_time": "2018-05-08T15:19:37.752283Z"
+    }
    },
    "outputs": [],
    "source": [
@@ -177,11 +175,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 6,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
+    "ExecuteTime": {
+     "end_time": "2018-05-08T15:19:37.768916Z",
+     "start_time": "2018-05-08T15:19:37.756469Z"
+    }
    },
    "outputs": [],
    "source": [
@@ -212,11 +211,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
+    "ExecuteTime": {
+     "end_time": "2018-05-08T15:19:41.186582Z",
+     "start_time": "2018-05-08T15:19:37.771576Z"
+    }
    },
    "outputs": [],
    "source": [
@@ -225,45 +225,62 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
+    "ExecuteTime": {
+     "end_time": "2018-05-08T15:19:42.471682Z",
+     "start_time": "2018-05-08T15:19:41.188081Z"
+    }
    },
    "outputs": [],
    "source": [
     "from pipeline import *\n",
-    "from pos_tagger.tagger import Tagger\n",
-    "from disambiguator.pagerank import *\n",
-    "from disambiguator.geodict_gaurav import *\n",
-    "from pos_tagger.treetagger import TreeTagger\n",
-    "from ner.stanford_ner import StanfordNER\n",
-    "from ner.polyglot import Polyglot\n",
-    "from ner.nltk import NLTK\n",
-    "from ner.gate_annie import GateAnnie\n",
-    "from ner.ner import NER\n",
+    "from nlp.pos_tagger.tagger import Tagger\n",
+    "from nlp.disambiguator.pagerank import *\n",
+    "from nlp.disambiguator.geodict_gaurav import *\n",
+    "from nlp.pos_tagger.treetagger import TreeTagger\n",
+    "from nlp.ner.stanford_ner import StanfordNER\n",
+    "from nlp.ner.polyglot import Polyglot\n",
+    "from nlp.ner.nltk import NLTK\n",
+    "from nlp.ner.gate_annie import GateAnnie\n",
+    "from nlp.ner.spacy import Spacy\n",
+    "from nlp.ner.ner import NER\n",
     "from progressbar import ProgressBar\n",
     "from polyglot.text import Text\n",
     "\n",
     "\n",
-    "from disambiguator.disambiguator import Disambiguator"
+    "from nlp.disambiguator.disambiguator import Disambiguator"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
+    "ExecuteTime": {
+     "end_time": "2018-05-08T15:19:43.131232Z",
+     "start_time": "2018-05-08T15:19:42.473854Z"
+    }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.6/site-packages/ipykernel_launcher.py:5: DeprecationWarning: Call to deprecated class Spacy (Not finished yet !).\n",
+      "  \"\"\"\n",
+      "/usr/local/lib/python3.6/site-packages/msgpack_numpy.py:84: DeprecationWarning: The binary mode of fromstring is deprecated, as it behaves surprisingly on unicode inputs. Use frombuffer instead\n",
+      "  dtype=np.dtype(descr)).reshape(obj[b'shape'])\n",
+      "/usr/local/lib/python3.6/site-packages/msgpack_numpy.py:88: DeprecationWarning: The binary mode of fromstring is deprecated, as it behaves surprisingly on unicode inputs. Use frombuffer instead\n",
+      "  dtype=np.dtype(descr))[0]\n"
+     ]
+    }
+   ],
    "source": [
     "pipStanford=Pipeline(lang=\"english\",tagger=Tagger(),ner=StanfordNER(lang=\"en\"))\n",
     "pipNLTK=Pipeline(lang=\"english\",tagger=Tagger(),ner=NLTK(lang=\"en\"))\n",
     "pipPolyglot=Pipeline(lang=\"english\",tagger=Tagger(),ner=Polyglot())\n",
-    "pipGate=Pipeline(lang=\"english\",tagger=Tagger(),ner=GateAnnie(lang=\"en\"))\n"
+    "pipGate=Pipeline(lang=\"english\",tagger=Tagger(),ner=GateAnnie(lang=\"en\"))\n",
+    "pipSpacy=Pipeline(lang=\"english\",tagger=Tagger(),ner=Spacy(lang=\"en\"))"
    ]
   },
   {
@@ -281,13 +298,140 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 13,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
+    "ExecuteTime": {
+     "end_time": "2018-05-08T15:23:08.238218Z",
+     "start_time": "2018-05-08T15:23:08.179062Z"
+    }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "<ipython-input-13-b29052c515c1>:2: DeprecationWarning: invalid escape sequence \\s\n",
+      "  return re.sub(\"[!]+\",\" \",re.sub(\"\\s\",\"!!\",label.strip()))\n"
+     ]
+    }
+   ],
    "source": [
     "def parse_epi_labels(label):\n",
     "    return re.sub(\"[!]+\",\" \",re.sub(\"\\s\",\"!!\",label.strip()))\n",
@@ -302,7 +446,7 @@
     "        for i in texts:\n",
     "            if not i in ann_data or not texts[i]:continue\n",
     "            try:\n",
-    "                output,spat_entities=pipeline.parse(texts[i])\n",
+    "                _,output,spat_entities=pipeline.parse(texts[i])\n",
     "            except:\n",
     "                #print(texts[i])\n",
     "                continue\n",
@@ -328,7 +472,7 @@
     "        for i in texts:\n",
     "            if not i in ann_data or not texts[i]:continue\n",
     "            try:\n",
-    "                output,spat_entities=pipeline.parse(texts[i])\n",
+    "                _,output,spat_entities=pipeline.parse(texts[i])\n",
     "            except:\n",
     "                #print(texts[i])\n",
     "                continue\n",
@@ -359,9 +503,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {
-    "collapsed": false
+    "ExecuteTime": {
+     "end_time": "2018-05-08T15:19:43.278565Z",
+     "start_time": "2018-05-08T15:19:43.190533Z"
+    }
    },
    "outputs": [],
    "source": [
@@ -383,7 +530,7 @@
     "            if not i in ann_data or not texts[i]:\n",
     "                continue\n",
     "            try:\n",
-    "                output, spat_entities = pipeline.parse(texts[i])\n",
+    "                _,output, spat_entities = pipeline.parse(texts[i])\n",
     "            except:\n",
     "                # print(texts[i])\n",
     "                continue\n",
@@ -431,9 +578,9 @@
     "            if not i in ann_data or not texts[i]:\n",
     "                continue\n",
     "            try:\n",
-    "                output, spat_entities = pipeline.parse(texts[i])\n",
+    "                _,output, spat_entities = pipeline.parse(texts[i])\n",
     "            except:\n",
-    "                # print(texts[i])\n",
+    "                #print(texts[i])\n",
     "                continue\n",
     "            out = Disambiguator.parse_corpus(output)\n",
     "\n",
@@ -461,17 +608,65 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-05-08T15:25:06.163620Z",
+     "start_time": "2018-05-08T15:23:14.468336Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100% (532 of 532) |######################| Elapsed Time: 0:00:55 Time:  0:00:55\n",
+      "N/A% (0 of 532) |                        | Elapsed Time: 0:00:00 ETA:  --:--:--"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "464 464\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100% (532 of 532) |######################| Elapsed Time: 0:00:55 Time:  0:00:55\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "464 501\n"
+     ]
+    }
+   ],
+   "source": [
+    "rec_spacy = ner_recall_epi(texts, ann_data, pipSpacy)\n",
+    "prec_spacy = ner_precision_epi(texts, ann_data, pipSpacy)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
    "metadata": {
-    "collapsed": false
+    "ExecuteTime": {
+     "end_time": "2018-05-08T15:37:02.477840Z",
+     "start_time": "2018-05-08T15:25:25.959366Z"
+    }
    },
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100% (532 of 532) |##############################################################################| Elapsed Time: 0:02:27 Time: 0:02:27\n",
-      "  0% (1 of 532) |                                                                                 | Elapsed Time: 0:00:00 ETA: 0:01:38"
+      "100% (532 of 532) |######################| Elapsed Time: 0:02:15 Time:  0:02:15\n",
+      "N/A% (0 of 532) |                        | Elapsed Time: 0:00:00 ETA:  --:--:--"
      ]
     },
     {
@@ -485,25 +680,22 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100% (532 of 532) |##############################################################################| Elapsed Time: 0:02:28 Time: 0:02:28\n",
-      "  0% (2 of 532) |                                                                                 | Elapsed Time: 0:00:00 ETA: 0:00:29"
+      "100% (532 of 532) |######################| Elapsed Time: 0:02:25 Time:  0:02:25\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "457 494\n"
+      "463 500\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "  3% (21 of 532) |###                                                                             | Elapsed Time: 0:00:02 ETA: 0:01:01Detector is not able to detect the language reliably.\n",
-      " 67% (361 of 532) |#####################################################                          | Elapsed Time: 0:00:42 ETA: 0:00:19Detector is not able to detect the language reliably.\n",
-      "100% (532 of 532) |##############################################################################| Elapsed Time: 0:00:56 Time: 0:00:56\n",
-      "  0% (2 of 532) |                                                                                 | Elapsed Time: 0:00:00 ETA: 0:00:28"
+      "100% (532 of 532) |######################| Elapsed Time: 0:01:06 Time:  0:01:06\n",
+      "N/A% (0 of 532) |                        | Elapsed Time: 0:00:00 ETA:  --:--:--"
      ]
     },
     {
@@ -517,25 +709,22 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "  3% (21 of 532) |###                                                                             | Elapsed Time: 0:00:02 ETA: 0:01:04Detector is not able to detect the language reliably.\n",
-      " 70% (374 of 532) |#######################################################                        | Elapsed Time: 0:00:43 ETA: 0:00:19Detector is not able to detect the language reliably.\n",
-      "100% (532 of 532) |##############################################################################| Elapsed Time: 0:00:56 Time: 0:00:56\n",
-      "N/A% (0 of 532) |                                                                               | Elapsed Time: 0:00:00 ETA:  --:--:--"
+      "100% (532 of 532) |######################| Elapsed Time: 0:00:56 Time:  0:00:56\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "444 479\n"
+      "455 490\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100% (532 of 532) |##############################################################################| Elapsed Time: 0:01:23 Time: 0:01:23\n",
-      "  0% (1 of 532) |                                                                                 | Elapsed Time: 0:00:00 ETA: 0:01:01"
+      "100% (532 of 532) |######################| Elapsed Time: 0:01:24 Time:  0:01:24\n",
+      "N/A% (0 of 532) |                        | Elapsed Time: 0:00:00 ETA:  --:--:--"
      ]
     },
     {
@@ -549,25 +738,23 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100% (532 of 532) |##############################################################################| Elapsed Time: 0:01:25 Time: 0:01:25\n",
-      "  0% (1 of 532) |                                                                                 | Elapsed Time: 0:00:00 ETA: 0:01:05"
+      "100% (532 of 532) |######################| Elapsed Time: 0:01:24 Time:  0:01:24\n",
+      "N/A% (0 of 532) |                        | Elapsed Time: 0:00:00 ETA:  --:--:--"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "454 491\n"
+      "463 500\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "  3% (21 of 532) |###                                                                             | Elapsed Time: 0:00:02 ETA: 0:01:04Detector is not able to detect the language reliably.\n",
-      " 66% (355 of 532) |####################################################                           | Elapsed Time: 0:00:36 ETA: 0:00:19Detector is not able to detect the language reliably.\n",
-      "100% (532 of 532) |##############################################################################| Elapsed Time: 0:00:47 Time: 0:00:47\n",
-      "  0% (2 of 532) |                                                                                 | Elapsed Time: 0:00:00 ETA: 0:00:32"
+      "100% (532 of 532) |######################| Elapsed Time: 0:01:00 Time:  0:01:00\n",
+      "N/A% (0 of 532) |                        | Elapsed Time: 0:00:00 ETA:  --:--:--"
      ]
     },
     {
@@ -581,16 +768,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "  3% (21 of 532) |###                                                                             | Elapsed Time: 0:00:02 ETA: 0:00:51Detector is not able to detect the language reliably.\n",
-      " 68% (365 of 532) |######################################################                         | Elapsed Time: 0:00:35 ETA: 0:00:17Detector is not able to detect the language reliably.\n",
-      "100% (532 of 532) |##############################################################################| Elapsed Time: 0:00:46 Time: 0:00:46\n"
+      "100% (532 of 532) |######################| Elapsed Time: 0:00:56 Time:  0:00:56\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "430 465\n"
+      "443 478\n"
      ]
     }
    ],
@@ -605,22 +790,24 @@
     "prec_nltk = ner_precision_epi(texts, ann_data, pipNLTK)\n",
     "\n",
     "rec_gate = ner_recall_epi(texts, ann_data, pipGate)\n",
-    "prec_gate = ner_precision_epi(texts, ann_data, pipGate)"
+    "prec_gate = ner_precision_epi(texts, ann_data, pipGate)\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 16,
    "metadata": {
-    "collapsed": false
+    "ExecuteTime": {
+     "end_time": "2018-05-08T15:48:32.390113Z",
+     "start_time": "2018-05-08T15:37:09.480442Z"
+    }
    },
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100% (532 of 532) |##############################################################################| Elapsed Time: 0:02:19 Time: 0:02:19\n",
-      "  0% (1 of 532) |                                                                                 | Elapsed Time: 0:00:00 ETA: 0:01:37"
+      "100% (532 of 532) |######################| Elapsed Time: 0:02:17 Time:  0:02:17\n"
      ]
     },
     {
@@ -634,8 +821,8 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100% (532 of 532) |##############################################################################| Elapsed Time: 0:02:18 Time: 0:02:18\n",
-      "  0% (2 of 532) |                                                                                 | Elapsed Time: 0:00:00 ETA: 0:00:27"
+      "100% (532 of 532) |######################| Elapsed Time: 0:02:20 Time:  0:02:20\n",
+      "N/A% (0 of 532) |                        | Elapsed Time: 0:00:00 ETA:  --:--:--"
      ]
     },
     {
@@ -649,10 +836,8 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "  3% (21 of 532) |###                                                                             | Elapsed Time: 0:00:02 ETA: 0:00:59Detector is not able to detect the language reliably.\n",
-      " 67% (361 of 532) |#####################################################                          | Elapsed Time: 0:00:41 ETA: 0:00:19Detector is not able to detect the language reliably.\n",
-      "100% (532 of 532) |##############################################################################| Elapsed Time: 0:00:53 Time: 0:00:53\n",
-      "  0% (2 of 532) |                                                                                 | Elapsed Time: 0:00:00 ETA: 0:00:29"
+      "100% (532 of 532) |######################| Elapsed Time: 0:00:57 Time:  0:00:57\n",
+      "N/A% (0 of 532) |                        | Elapsed Time: 0:00:00 ETA:  --:--:--"
      ]
     },
     {
@@ -666,10 +851,8 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "  4% (24 of 532) |###                                                                             | Elapsed Time: 0:00:02 ETA: 0:00:54Detector is not able to detect the language reliably.\n",
-      " 71% (383 of 532) |########################################################                       | Elapsed Time: 0:00:41 ETA: 0:00:17Detector is not able to detect the language reliably.\n",
-      "100% (532 of 532) |##############################################################################| Elapsed Time: 0:00:53 Time: 0:00:53\n",
-      "  0% (1 of 532) |                                                                                 | Elapsed Time: 0:00:00 ETA: 0:01:02"
+      "100% (532 of 532) |######################| Elapsed Time: 0:00:57 Time:  0:00:57\n",
+      "N/A% (0 of 532) |                        | Elapsed Time: 0:00:00 ETA:  --:--:--"
      ]
     },
     {
@@ -683,8 +866,8 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100% (532 of 532) |##############################################################################| Elapsed Time: 0:01:20 Time: 0:01:20\n",
-      "  0% (1 of 532) |                                                                                 | Elapsed Time: 0:00:00 ETA: 0:00:57"
+      "100% (532 of 532) |######################| Elapsed Time: 0:01:25 Time:  0:01:25\n",
+      "  0% (3 of 532) |                        | Elapsed Time: 0:00:00 ETA:   0:00:18"
      ]
     },
     {
@@ -698,8 +881,8 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100% (532 of 532) |##############################################################################| Elapsed Time: 0:01:20 Time: 0:01:20\n",
-      "  0% (4 of 532) |                                                                                 | Elapsed Time: 0:00:00 ETA: 0:00:21"
+      "100% (532 of 532) |######################| Elapsed Time: 0:01:25 Time:  0:01:25\n",
+      "  0% (4 of 532) |                        | Elapsed Time: 0:00:00 ETA:   0:00:18"
      ]
     },
     {
@@ -713,10 +896,8 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "  3% (21 of 532) |###                                                                             | Elapsed Time: 0:00:01 ETA: 0:00:47Detector is not able to detect the language reliably.\n",
-      " 66% (355 of 532) |####################################################                           | Elapsed Time: 0:00:34 ETA: 0:00:17Detector is not able to detect the language reliably.\n",
-      "100% (532 of 532) |##############################################################################| Elapsed Time: 0:00:45 Time: 0:00:45\n",
-      "  0% (2 of 532) |                                                                                 | Elapsed Time: 0:00:00 ETA: 0:00:32"
+      "100% (532 of 532) |######################| Elapsed Time: 0:00:57 Time:  0:00:57\n",
+      "N/A% (0 of 532) |                        | Elapsed Time: 0:00:00 ETA:  --:--:--"
      ]
     },
     {
@@ -730,9 +911,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "  4% (22 of 532) |###                                                                             | Elapsed Time: 0:00:01 ETA: 0:00:47Detector is not able to detect the language reliably.\n",
-      " 70% (374 of 532) |#######################################################                        | Elapsed Time: 0:00:34 ETA: 0:00:15Detector is not able to detect the language reliably.\n",
-      "100% (532 of 532) |##############################################################################| Elapsed Time: 0:00:45 Time: 0:00:45\n"
+      "100% (532 of 532) |######################| Elapsed Time: 0:01:00 Time:  0:01:00\n"
      ]
     },
     {
@@ -759,9 +938,57 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 17,
    "metadata": {
-    "collapsed": false
+    "ExecuteTime": {
+     "end_time": "2018-05-08T15:50:37.503408Z",
+     "start_time": "2018-05-08T15:48:45.036943Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100% (532 of 532) |######################| Elapsed Time: 0:00:56 Time:  0:00:56\n",
+      "N/A% (0 of 532) |                        | Elapsed Time: 0:00:00 ETA:  --:--:--"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "464 464\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100% (532 of 532) |######################| Elapsed Time: 0:00:56 Time:  0:00:56\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "464 501\n"
+     ]
+    }
+   ],
+   "source": [
+    "rec_spacy_all = ner_recall_epi_all(texts, ann_data, pipSpacy)\n",
+    "prec_spacy_all = ner_precision_epi_all(texts, ann_data, pipSpacy)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-05-08T15:51:07.306398Z",
+     "start_time": "2018-05-08T15:51:07.295232Z"
+    }
    },
    "outputs": [],
    "source": [
@@ -769,22 +996,39 @@
     "df=pd.DataFrame(columns=cols)\n",
     "df=pd.DataFrame([[\"StanfordNER\",prec_SFNER,rec_SFNER,prec_SFNER_all,rec_SFNER_all],\n",
     "                        [\"Polyglot\",prec_poly,rec_poly,prec_poly_all,rec_poly_all],[\"NLTK\",prec_nltk,rec_nltk,prec_nltk_all,rec_nltk_all],\n",
-    "                       [\"GATE\",prec_gate,rec_gate,prec_gate_all,rec_gate_all]],columns=cols)\n",
+    "                       [\"GATE\",prec_gate,rec_gate,prec_gate_all,rec_gate_all],\n",
+    "                [\"Spacy\",prec_spacy,rec_spacy,prec_spacy_all,rec_spacy_all]],columns=cols)\n",
     "df[\"F-Measure(D)\"]= df.apply(lambda x: 2*((x[\"Precision(ID)\"]*x[\"Recall(ID)\"])/(x[\"Precision(ID)\"]+x[\"Recall(ID)\"])), axis=1)\n",
     "df[\"F-Measure(ALL)\"]= df.apply(lambda x: 2*((x[\"Precision(ALL)\"]*x[\"Recall(ALL)\"])/(x[\"Precision(ALL)\"]+x[\"Recall(ALL)\"])), axis=1)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 19,
    "metadata": {
-    "collapsed": false
+    "ExecuteTime": {
+     "end_time": "2018-05-08T15:51:08.070983Z",
+     "start_time": "2018-05-08T15:51:08.058901Z"
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -802,90 +1046,92 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>StanfordNER</td>\n",
-       "      <td>0.689301</td>\n",
-       "      <td>0.737253</td>\n",
-       "      <td>0.667065</td>\n",
-       "      <td>0.720500</td>\n",
-       "      <td>0.712471</td>\n",
-       "      <td>0.692754</td>\n",
+       "      <td>0.594245</td>\n",
+       "      <td>0.771514</td>\n",
+       "      <td>0.666652</td>\n",
+       "      <td>0.718504</td>\n",
+       "      <td>0.671375</td>\n",
+       "      <td>0.691608</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>Polyglot</td>\n",
-       "      <td>0.669563</td>\n",
-       "      <td>0.703822</td>\n",
+       "      <td>0.532444</td>\n",
+       "      <td>0.724127</td>\n",
        "      <td>0.608216</td>\n",
        "      <td>0.666334</td>\n",
-       "      <td>0.686265</td>\n",
+       "      <td>0.613666</td>\n",
        "      <td>0.635950</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>NLTK</td>\n",
-       "      <td>0.561466</td>\n",
-       "      <td>0.635291</td>\n",
-       "      <td>0.488915</td>\n",
-       "      <td>0.609152</td>\n",
-       "      <td>0.596101</td>\n",
-       "      <td>0.542451</td>\n",
+       "      <td>0.429511</td>\n",
+       "      <td>0.665637</td>\n",
+       "      <td>0.497519</td>\n",
+       "      <td>0.617828</td>\n",
+       "      <td>0.522119</td>\n",
+       "      <td>0.551185</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>GATE</td>\n",
-       "      <td>0.701907</td>\n",
-       "      <td>0.617005</td>\n",
+       "      <td>0.578102</td>\n",
+       "      <td>0.626061</td>\n",
        "      <td>0.633567</td>\n",
        "      <td>0.585320</td>\n",
-       "      <td>0.656724</td>\n",
+       "      <td>0.601126</td>\n",
        "      <td>0.608488</td>\n",
        "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Spacy</td>\n",
+       "      <td>0.406803</td>\n",
+       "      <td>0.652530</td>\n",
+       "      <td>0.404245</td>\n",
+       "      <td>0.616491</td>\n",
+       "      <td>0.501167</td>\n",
+       "      <td>0.488301</td>\n",
+       "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
        "           NER  Precision(ID)  Recall(ID)  Precision(ALL)  Recall(ALL)  \\\n",
-       "0  StanfordNER       0.689301    0.737253        0.667065     0.720500   \n",
-       "1     Polyglot       0.669563    0.703822        0.608216     0.666334   \n",
-       "2         NLTK       0.561466    0.635291        0.488915     0.609152   \n",
-       "3         GATE       0.701907    0.617005        0.633567     0.585320   \n",
+       "0  StanfordNER       0.594245    0.771514        0.666652     0.718504   \n",
+       "1     Polyglot       0.532444    0.724127        0.608216     0.666334   \n",
+       "2         NLTK       0.429511    0.665637        0.497519     0.617828   \n",
+       "3         GATE       0.578102    0.626061        0.633567     0.585320   \n",
+       "4        Spacy       0.406803    0.652530        0.404245     0.616491   \n",
        "\n",
        "   F-Measure(D)  F-Measure(ALL)  \n",
-       "0      0.712471        0.692754  \n",
-       "1      0.686265        0.635950  \n",
-       "2      0.596101        0.542451  \n",
-       "3      0.656724        0.608488  "
+       "0      0.671375        0.691608  \n",
+       "1      0.613666        0.635950  \n",
+       "2      0.522119        0.551185  \n",
+       "3      0.601126        0.608488  \n",
+       "4      0.501167        0.488301  "
       ]
      },
-     "execution_count": 28,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df.to"
+    "df"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": null,
    "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "|    | NER         |   Precision(ID) |   Recall(ID) |   Precision(ALL) |   Recall(ALL) |   F-Measure(D) |   F-Measure(ALL) |\n",
-      "|---:|:------------|----------------:|-------------:|-----------------:|--------------:|---------------:|-----------------:|\n",
-      "|  0 | StanfordNER |        0.689301 |     0.737253 |         0.667065 |      0.7205   |       0.712471 |         0.692754 |\n",
-      "|  1 | Polyglot    |        0.669563 |     0.703822 |         0.608216 |      0.666334 |       0.686265 |         0.63595  |\n",
-      "|  2 | NLTK        |        0.561466 |     0.635291 |         0.488915 |      0.609152 |       0.596101 |         0.542451 |\n",
-      "|  3 | GATE        |        0.701907 |     0.617005 |         0.633567 |      0.58532  |       0.656724 |         0.608488 |\n"
-     ]
+    "ExecuteTime": {
+     "end_time": "2018-05-08T15:20:22.410670Z",
+     "start_time": "2018-05-08T15:19:38.062Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "from tabulate import tabulate\n",
     "print(tabulate(df, headers='keys', tablefmt='pipe'))"
@@ -894,9 +1140,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": []
   }
@@ -917,13 +1161,23 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.0"
+   "version": "3.6.5"
+  },
+  "toc": {
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": "block",
+   "toc_window_display": false
   },
   "varInspector": {
    "cols": {
-    "lenName": 16.0,
-    "lenType": 16.0,
-    "lenVar": 40.0
+    "lenName": 16,
+    "lenType": 16,
+    "lenVar": 40
    },
    "kernels_config": {
     "python": {
diff --git a/notebooks/StanfordMadaAgro.ipynb b/notebooks/StanfordMadaAgro.ipynb
new file mode 100644
index 0000000..faedc5b
--- /dev/null
+++ b/notebooks/StanfordMadaAgro.ipynb
@@ -0,0 +1,950 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-05-15T05:07:11.698091Z",
+     "start_time": "2018-05-15T05:07:11.253243Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-05-15T05:07:12.270692Z",
+     "start_time": "2018-05-15T05:07:12.257655Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "selected=pd.read_csv(\"/Users/jacquesfize/LOD_DATASETS/selected_mada.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-05-15T05:07:12.613016Z",
+     "start_time": "2018-05-15T05:07:12.610457Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "base_dir='/Users/jacquesfize/LOD_DATASETS/raw_bvlac/'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-05-15T05:07:12.920064Z",
+     "start_time": "2018-05-15T05:07:12.914272Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/Users/jacquesfize/nas_cloud/Code/str-python\n"
+     ]
+    }
+   ],
+   "source": [
+    "cd .."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-05-15T05:07:14.617383Z",
+     "start_time": "2018-05-15T05:07:13.522309Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from pipeline import *\n",
+    "from nlp.ner.stanford_ner import StanfordNER"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-05-15T05:07:14.850467Z",
+     "start_time": "2018-05-15T05:07:14.760004Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "data_lang=pd.DataFrame(data=list(\n",
+    "    json.load(open(\"/Users/jacquesfize/LOD_DATASETS/raw_bvlac/associated_lang.json\")).items()),\n",
+    "    columns=[\"id_doc\",\"lang\"]\n",
+    ")\n",
+    "data_lang[\"id_doc\"]=data_lang[\"id_doc\"].astype(int)\n",
+    "selected[\"id_doc\"]=selected[\"id_doc\"].astype(int)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-05-08T17:11:00.213069Z",
+     "start_time": "2018-05-08T17:11:00.208408Z"
+    }
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-05-15T05:07:15.813223Z",
+     "start_time": "2018-05-15T05:07:15.808854Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "data_lang=data_lang[data_lang[\"id_doc\"].isin(selected[\"id_doc\"])]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-05-15T05:07:16.101978Z",
+     "start_time": "2018-05-15T05:07:16.096855Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "data_lang=data_lang.set_index(\"id_doc\")\n",
+    "selected=selected.set_index(\"id_doc\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-05-15T05:07:16.371936Z",
+     "start_time": "2018-05-15T05:07:16.368656Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "selected[\"lang\"]=data_lang[\"lang\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-05-15T05:07:16.794152Z",
+     "start_time": "2018-05-15T05:07:16.775373Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['fr', 'en'], dtype=object)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "selected[\"lang\"].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-05-15T05:07:17.265106Z",
+     "start_time": "2018-05-15T05:07:17.261840Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "pipeline= {\n",
+    "    \"en\":Pipeline(lang=\"english\",tagger=Tagger(),ner=StanfordNER(lang=\"en\")),\n",
+    "    \"fr\":Pipeline(lang=\"french\",tagger=Tagger(),ner=StanfordNER(lang=\"fr\"))\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-05-15T05:07:17.802814Z",
+     "start_time": "2018-05-15T05:07:17.795266Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "dfEn=selected[selected.lang == \"en\"]\n",
+    "dfFr=selected[selected.lang == \"fr\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-05-15T05:07:18.417499Z",
+     "start_time": "2018-05-15T05:07:18.395886Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Unnamed: 0</th>\n",
+       "      <th>count</th>\n",
+       "      <th>format</th>\n",
+       "      <th>lang</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id_doc</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>12</td>\n",
+       "      <td>txt</td>\n",
+       "      <td>fr</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1000</th>\n",
+       "      <td>3</td>\n",
+       "      <td>6</td>\n",
+       "      <td>txt</td>\n",
+       "      <td>fr</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1001</th>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "      <td>pdf</td>\n",
+       "      <td>fr</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1002</th>\n",
+       "      <td>15</td>\n",
+       "      <td>5</td>\n",
+       "      <td>docx</td>\n",
+       "      <td>fr</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1003</th>\n",
+       "      <td>26</td>\n",
+       "      <td>11</td>\n",
+       "      <td>doc</td>\n",
+       "      <td>fr</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1004</th>\n",
+       "      <td>37</td>\n",
+       "      <td>11</td>\n",
+       "      <td>xls</td>\n",
+       "      <td>fr</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10044</th>\n",
+       "      <td>41</td>\n",
+       "      <td>5</td>\n",
+       "      <td>pdf</td>\n",
+       "      <td>fr</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1005</th>\n",
+       "      <td>47</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>fr</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10052</th>\n",
+       "      <td>50</td>\n",
+       "      <td>4</td>\n",
+       "      <td>docx</td>\n",
+       "      <td>fr</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1006</th>\n",
+       "      <td>58</td>\n",
+       "      <td>6</td>\n",
+       "      <td>doc</td>\n",
+       "      <td>fr</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10060</th>\n",
+       "      <td>59</td>\n",
+       "      <td>5</td>\n",
+       "      <td>docx</td>\n",
+       "      <td>fr</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10062</th>\n",
+       "      <td>61</td>\n",
+       "      <td>4</td>\n",
+       "      <td>xls</td>\n",
+       "      <td>fr</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10065</th>\n",
+       "      <td>64</td>\n",
+       "      <td>5</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>fr</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1007</th>\n",
+       "      <td>69</td>\n",
+       "      <td>7</td>\n",
+       "      <td>doc</td>\n",
+       "      <td>fr</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10070</th>\n",
+       "      <td>70</td>\n",
+       "      <td>5</td>\n",
+       "      <td>doc</td>\n",
+       "      <td>fr</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10073</th>\n",
+       "      <td>73</td>\n",
+       "      <td>4</td>\n",
+       "      <td>doc</td>\n",
+       "      <td>fr</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10074</th>\n",
+       "      <td>74</td>\n",
+       "      <td>4</td>\n",
+       "      <td>doc</td>\n",
+       "      <td>fr</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10078</th>\n",
+       "      <td>78</td>\n",
+       "      <td>6</td>\n",
+       "      <td>doc</td>\n",
+       "      <td>fr</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1008</th>\n",
+       "      <td>80</td>\n",
+       "      <td>7</td>\n",
+       "      <td>xls</td>\n",
+       "      <td>fr</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10084</th>\n",
+       "      <td>85</td>\n",
+       "      <td>5</td>\n",
+       "      <td>doc</td>\n",
+       "      <td>fr</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10086</th>\n",
+       "      <td>87</td>\n",
+       "      <td>5</td>\n",
+       "      <td>doc</td>\n",
+       "      <td>fr</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10087</th>\n",
+       "      <td>88</td>\n",
+       "      <td>4</td>\n",
+       "      <td>doc</td>\n",
+       "      <td>fr</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1009</th>\n",
+       "      <td>91</td>\n",
+       "      <td>7</td>\n",
+       "      <td>doc</td>\n",
+       "      <td>fr</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10092</th>\n",
+       "      <td>94</td>\n",
+       "      <td>5</td>\n",
+       "      <td>doc</td>\n",
+       "      <td>fr</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10094</th>\n",
+       "      <td>96</td>\n",
+       "      <td>4</td>\n",
+       "      <td>xls</td>\n",
+       "      <td>fr</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10099</th>\n",
+       "      <td>101</td>\n",
+       "      <td>4</td>\n",
+       "      <td>doc</td>\n",
+       "      <td>fr</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1010</th>\n",
+       "      <td>103</td>\n",
+       "      <td>7</td>\n",
+       "      <td>docx</td>\n",
+       "      <td>fr</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1011</th>\n",
+       "      <td>114</td>\n",
+       "      <td>4</td>\n",
+       "      <td>doc</td>\n",
+       "      <td>fr</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1012</th>\n",
+       "      <td>125</td>\n",
+       "      <td>4</td>\n",
+       "      <td>doc</td>\n",
+       "      <td>fr</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10129</th>\n",
+       "      <td>135</td>\n",
+       "      <td>4</td>\n",
+       "      <td>doc</td>\n",
+       "      <td>fr</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9709</th>\n",
+       "      <td>13323</td>\n",
+       "      <td>4</td>\n",
+       "      <td>pdf</td>\n",
+       "      <td>fr</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9710</th>\n",
+       "      <td>13325</td>\n",
+       "      <td>4</td>\n",
+       "      <td>pdf</td>\n",
+       "      <td>fr</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9711</th>\n",
+       "      <td>13326</td>\n",
+       "      <td>4</td>\n",
+       "      <td>pdf</td>\n",
+       "      <td>fr</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>973</th>\n",
+       "      <td>13340</td>\n",
+       "      <td>4</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>fr</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>974</th>\n",
+       "      <td>13351</td>\n",
+       "      <td>4</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>fr</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>975</th>\n",
+       "      <td>13360</td>\n",
+       "      <td>7</td>\n",
+       "      <td>pdf</td>\n",
+       "      <td>fr</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>976</th>\n",
+       "      <td>13371</td>\n",
+       "      <td>7</td>\n",
+       "      <td>doc</td>\n",
+       "      <td>fr</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>977</th>\n",
+       "      <td>13380</td>\n",
+       "      <td>9</td>\n",
+       "      <td>xls</td>\n",
+       "      <td>fr</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>978</th>\n",
+       "      <td>13389</td>\n",
+       "      <td>9</td>\n",
+       "      <td>pdf</td>\n",
+       "      <td>fr</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>979</th>\n",
+       "      <td>13400</td>\n",
+       "      <td>4</td>\n",
+       "      <td>pdf</td>\n",
+       "      <td>fr</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>980</th>\n",
+       "      <td>13411</td>\n",
+       "      <td>4</td>\n",
+       "      <td>docx</td>\n",
+       "      <td>fr</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>981</th>\n",
+       "      <td>13418</td>\n",
+       "      <td>6</td>\n",
+       "      <td>pdf</td>\n",
+       "      <td>fr</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>982</th>\n",
+       "      <td>13428</td>\n",
+       "      <td>6</td>\n",
+       "      <td>doc</td>\n",
+       "      <td>fr</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>983</th>\n",
+       "      <td>13438</td>\n",
+       "      <td>6</td>\n",
+       "      <td>doc</td>\n",
+       "      <td>fr</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>984</th>\n",
+       "      <td>13448</td>\n",
+       "      <td>6</td>\n",
+       "      <td>doc</td>\n",
+       "      <td>fr</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>985</th>\n",
+       "      <td>13458</td>\n",
+       "      <td>5</td>\n",
+       "      <td>pdf</td>\n",
+       "      <td>fr</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>986</th>\n",
+       "      <td>13469</td>\n",
+       "      <td>5</td>\n",
+       "      <td>pdf</td>\n",
+       "      <td>fr</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>987</th>\n",
+       "      <td>13480</td>\n",
+       "      <td>4</td>\n",
+       "      <td>pdf</td>\n",
+       "      <td>fr</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>988</th>\n",
+       "      <td>13489</td>\n",
+       "      <td>4</td>\n",
+       "      <td>pdf</td>\n",
+       "      <td>fr</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>989</th>\n",
+       "      <td>13499</td>\n",
+       "      <td>6</td>\n",
+       "      <td>pdf</td>\n",
+       "      <td>fr</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>990</th>\n",
+       "      <td>13511</td>\n",
+       "      <td>6</td>\n",
+       "      <td>doc</td>\n",
+       "      <td>fr</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>991</th>\n",
+       "      <td>13522</td>\n",
+       "      <td>10</td>\n",
+       "      <td>doc</td>\n",
+       "      <td>fr</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>992</th>\n",
+       "      <td>13531</td>\n",
+       "      <td>10</td>\n",
+       "      <td>doc</td>\n",
+       "      <td>fr</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>993</th>\n",
+       "      <td>13542</td>\n",
+       "      <td>7</td>\n",
+       "      <td>pdf</td>\n",
+       "      <td>fr</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>994</th>\n",
+       "      <td>13549</td>\n",
+       "      <td>7</td>\n",
+       "      <td>doc</td>\n",
+       "      <td>fr</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>995</th>\n",
+       "      <td>13560</td>\n",
+       "      <td>11</td>\n",
+       "      <td>docx</td>\n",
+       "      <td>fr</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>996</th>\n",
+       "      <td>13569</td>\n",
+       "      <td>11</td>\n",
+       "      <td>doc</td>\n",
+       "      <td>fr</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>997</th>\n",
+       "      <td>13578</td>\n",
+       "      <td>6</td>\n",
+       "      <td>doc</td>\n",
+       "      <td>fr</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>998</th>\n",
+       "      <td>13586</td>\n",
+       "      <td>6</td>\n",
+       "      <td>doc</td>\n",
+       "      <td>fr</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>999</th>\n",
+       "      <td>13597</td>\n",
+       "      <td>6</td>\n",
+       "      <td>doc</td>\n",
+       "      <td>fr</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5273 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        Unnamed: 0  count format lang\n",
+       "id_doc                               \n",
+       "1                1     12    txt   fr\n",
+       "1000             3      6    txt   fr\n",
+       "1001             9      5    pdf   fr\n",
+       "1002            15      5   docx   fr\n",
+       "1003            26     11    doc   fr\n",
+       "1004            37     11    xls   fr\n",
+       "10044           41      5    pdf   fr\n",
+       "1005            47      6    NaN   fr\n",
+       "10052           50      4   docx   fr\n",
+       "1006            58      6    doc   fr\n",
+       "10060           59      5   docx   fr\n",
+       "10062           61      4    xls   fr\n",
+       "10065           64      5    NaN   fr\n",
+       "1007            69      7    doc   fr\n",
+       "10070           70      5    doc   fr\n",
+       "10073           73      4    doc   fr\n",
+       "10074           74      4    doc   fr\n",
+       "10078           78      6    doc   fr\n",
+       "1008            80      7    xls   fr\n",
+       "10084           85      5    doc   fr\n",
+       "10086           87      5    doc   fr\n",
+       "10087           88      4    doc   fr\n",
+       "1009            91      7    doc   fr\n",
+       "10092           94      5    doc   fr\n",
+       "10094           96      4    xls   fr\n",
+       "10099          101      4    doc   fr\n",
+       "1010           103      7   docx   fr\n",
+       "1011           114      4    doc   fr\n",
+       "1012           125      4    doc   fr\n",
+       "10129          135      4    doc   fr\n",
+       "...            ...    ...    ...  ...\n",
+       "9709         13323      4    pdf   fr\n",
+       "9710         13325      4    pdf   fr\n",
+       "9711         13326      4    pdf   fr\n",
+       "973          13340      4    NaN   fr\n",
+       "974          13351      4    NaN   fr\n",
+       "975          13360      7    pdf   fr\n",
+       "976          13371      7    doc   fr\n",
+       "977          13380      9    xls   fr\n",
+       "978          13389      9    pdf   fr\n",
+       "979          13400      4    pdf   fr\n",
+       "980          13411      4   docx   fr\n",
+       "981          13418      6    pdf   fr\n",
+       "982          13428      6    doc   fr\n",
+       "983          13438      6    doc   fr\n",
+       "984          13448      6    doc   fr\n",
+       "985          13458      5    pdf   fr\n",
+       "986          13469      5    pdf   fr\n",
+       "987          13480      4    pdf   fr\n",
+       "988          13489      4    pdf   fr\n",
+       "989          13499      6    pdf   fr\n",
+       "990          13511      6    doc   fr\n",
+       "991          13522     10    doc   fr\n",
+       "992          13531     10    doc   fr\n",
+       "993          13542      7    pdf   fr\n",
+       "994          13549      7    doc   fr\n",
+       "995          13560     11   docx   fr\n",
+       "996          13569     11    doc   fr\n",
+       "997          13578      6    doc   fr\n",
+       "998          13586      6    doc   fr\n",
+       "999          13597      6    doc   fr\n",
+       "\n",
+       "[5273 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dfFr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "start_time": "2018-05-15T05:07:19.146Z"
+    },
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9cf91ec038374d759ada26870b7760df",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "IntProgress(value=0, description='Processing', max=5273)"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import json,os\n",
+    "from ipywidgets import IntProgress\n",
+    "from IPython.display import display\n",
+    "p=IntProgress(description=\"Processing\",max=len(dfFr))\n",
+    "display(p)\n",
+    "\n",
+    "for row in dfFr.itertuples():\n",
+    "    p.value+=1\n",
+    "    id_doc=row[0]\n",
+    "    if not os.path.exists(\"/Users/jacquesfize/LOD_DATASETS/raw_bvlac_ner/{0}.csv\".format(id_doc)):\n",
+    "        try:\n",
+    "        #print(len(open(base_dir+str(id_doc)+\".txt\").read()))\n",
+    "            test=pipeline[\"fr\"].ner.identify(open(base_dir+str(id_doc)+\".txt\").read())\n",
+    "            pd.DataFrame(test,columns=[\"text\",\"pos\"]).to_csv(\"/Users/jacquesfize/LOD_DATASETS/raw_bvlac_ner/{0}.csv\".format(id_doc))\n",
+    "\n",
+    "        except Exception as e:\n",
+    "            print(e)\n",
+    "            print(id_doc,row[-2],row[-1])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-05-08T19:18:48.737157Z",
+     "start_time": "2018-05-08T18:43:55.338361Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5accc27c2ee9432fa8f38afe70125a7d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "IntProgress(value=0, description='Processing', max=279)"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import json\n",
+    "from ipywidgets import IntProgress\n",
+    "from IPython.display import display\n",
+    "p=IntProgress(description=\"Processing\",max=len(dfEn))\n",
+    "display(p)\n",
+    "\n",
+    "for row in dfEn.itertuples():\n",
+    "    p.value+=1\n",
+    "    id_doc=row[0]\n",
+    "    try:\n",
+    "        test=pipeline[\"en\"].ner.identify(open(base_dir+str(id_doc)+\".txt\").read())\n",
+    "        pd.DataFrame(test,columns=[\"text\",\"pos\"]).to_csv(\"/Users/jacquesfize/LOD_DATASETS/raw_bvlac_ner/{0}.csv\".format(id_doc))\n",
+    "        \n",
+    "    except:\n",
+    "        print(id_doc,row[-2],row[-1])\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-05-09T07:52:26.414920Z",
+     "start_time": "2018-05-09T07:52:26.410473Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10014"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(open(base_dir+str(id_doc)+\".txt\").read())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.5"
+  },
+  "toc": {
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": "block",
+   "toc_window_display": false
+  },
+  "varInspector": {
+   "cols": {
+    "lenName": 16,
+    "lenType": 16,
+    "lenVar": 40
+   },
+   "kernels_config": {
+    "python": {
+     "delete_cmd_postfix": "",
+     "delete_cmd_prefix": "del ",
+     "library": "var_list.py",
+     "varRefreshCmd": "print(var_dic_list())"
+    },
+    "r": {
+     "delete_cmd_postfix": ") ",
+     "delete_cmd_prefix": "rm(",
+     "library": "var_list.r",
+     "varRefreshCmd": "cat(var_dic_list()) "
+    }
+   },
+   "types_to_exclude": [
+    "module",
+    "function",
+    "builtin_function_or_method",
+    "instance",
+    "_Feature"
+   ],
+   "window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/corpusmadahard.ipynb b/notebooks/corpusmadahard.ipynb
index 6f580bc..7bac11d 100644
--- a/notebooks/corpusmadahard.ipynb
+++ b/notebooks/corpusmadahard.ipynb
@@ -5,8 +5,8 @@
    "execution_count": 1,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2018-04-19T16:00:10.161616Z",
-     "start_time": "2018-04-19T16:00:10.155255Z"
+     "end_time": "2018-05-16T10:00:15.686303Z",
+     "start_time": "2018-05-16T10:00:15.680340Z"
     }
    },
    "outputs": [
@@ -24,11 +24,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 2,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2018-04-20T06:04:58.583630Z",
-     "start_time": "2018-04-20T06:04:58.202679Z"
+     "end_time": "2018-05-16T10:00:15.895344Z",
+     "start_time": "2018-05-16T10:00:15.892752Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from ipywidgets import IntProgress\n",
+    "from IPython.display import display"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-05-16T10:00:16.511473Z",
+     "start_time": "2018-05-16T10:00:16.102310Z"
     }
    },
    "outputs": [],
@@ -38,28 +53,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2018-04-19T16:00:10.589362Z",
-     "start_time": "2018-04-19T16:00:10.164034Z"
+     "end_time": "2018-05-16T10:00:17.122139Z",
+     "start_time": "2018-05-16T10:00:16.556517Z"
     }
    },
    "outputs": [],
    "source": [
     "import pandas as pd\n",
     "import os,re\n",
-    "output_dir=\"/Users/jacquesfize/LOD_DATASETS/raw_bvlac_ner/\"\n",
+    "import numpy as np\n",
+    "output_dir=\"/Users/jacquesfize/LOD_DATASETS/raw_bvlac_ner_spacy/\"\n",
     "%matplotlib inline"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2018-04-19T16:00:10.694797Z",
-     "start_time": "2018-04-19T16:00:10.591777Z"
+     "end_time": "2018-05-16T10:00:17.284662Z",
+     "start_time": "2018-05-16T10:00:17.167430Z"
     }
    },
    "outputs": [],
@@ -69,21 +85,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 22,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2018-04-19T16:00:10.712869Z",
-     "start_time": "2018-04-19T16:00:10.697137Z"
+     "end_time": "2018-05-16T20:55:10.902547Z",
+     "start_time": "2018-05-16T20:55:10.890232Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "('GD2373613', 2363.0420701386847)"
+       "('GD13263662', -1)"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -103,16 +119,16 @@
     "        if id_:\n",
     "            return id_,score\n",
     "    return None,-1\n",
-    "get_most_common_id_v3(\"Berlin\")"
+    "get_most_common_id_v3(\"Tibet\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 7,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2018-04-19T16:00:10.913147Z",
-     "start_time": "2018-04-19T16:00:10.715134Z"
+     "end_time": "2018-05-16T10:00:17.624018Z",
+     "start_time": "2018-05-16T10:00:17.395144Z"
     }
    },
    "outputs": [],
@@ -158,11 +174,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 8,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2018-04-20T06:05:03.276532Z",
-     "start_time": "2018-04-20T06:05:02.384996Z"
+     "end_time": "2018-05-16T10:00:19.411617Z",
+     "start_time": "2018-05-16T10:00:19.403105Z"
     }
    },
    "outputs": [],
@@ -170,17 +186,17 @@
     "%%cython\n",
     "\n",
     "#cdef list ch=[\"Le\",\"pont\",\"d'\",\"avignon\",\"est\",\"-\",\"sympa\"]\n",
-    "def foo(list ch):\n",
-    "    print([c+(\"\" if c[-1] in [\"\\'\",\"-\"] else \" \") for c in ch])"
+    "def foo2(list ch):\n",
+    "    return [c+(\"\" if c[-1] in [\"\\'\",\"’\",\"-\"] else \" \") for c in ch]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 9,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2018-04-20T06:06:07.432292Z",
-     "start_time": "2018-04-20T06:06:07.426838Z"
+     "end_time": "2018-05-16T10:00:21.268761Z",
+     "start_time": "2018-05-16T10:00:21.226880Z"
     },
     "format": "row"
    },
@@ -194,12 +210,12 @@
     "    data[\"diff2\"]=(data[(data[\"ent_type_\"]==\"LOC\")][\"diff\"]>1).cumsum()\n",
     "    mx_=data[\"diff2\"].notnull().max()\n",
     "    def foo(x):\n",
-    "        if np.isnan(x):\n",
+    "        if pd.isnull(x).any():\n",
     "            mx_+=1\n",
     "            return mx_\n",
     "        return x\n",
     "    f={\n",
-    "        'text':lambda x: \" \".join(foo(list(map(str,x)))),\n",
+    "        'text':lambda x: \"\".join(foo2(list(map(str,x)))).rstrip(),\n",
     "        'pos_':'max',\n",
     "        'ent_type_':'max'\n",
     "\n",
@@ -210,11 +226,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 10,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2018-04-19T16:00:11.070182Z",
-     "start_time": "2018-04-19T16:00:10.922999Z"
+     "end_time": "2018-05-16T10:00:23.252597Z",
+     "start_time": "2018-05-16T10:00:23.056531Z"
     }
    },
    "outputs": [
@@ -235,11 +251,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 11,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2018-04-19T16:00:11.250132Z",
-     "start_time": "2018-04-19T16:00:11.073331Z"
+     "end_time": "2018-05-16T10:00:25.134319Z",
+     "start_time": "2018-05-16T10:00:25.080059Z"
     }
    },
    "outputs": [],
@@ -256,26 +272,45 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 12,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2018-04-19T16:00:11.265256Z",
-     "start_time": "2018-04-19T16:00:11.252226Z"
+     "end_time": "2018-05-16T10:00:27.099706Z",
+     "start_time": "2018-05-16T10:00:27.062820Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "data_ext=pd.DataFrame(data=list(\n",
+    "    json.load(open(\"/Users/jacquesfize/LOD_DATASETS/raw_bvlac/association.json\")).items()),\n",
+    "    columns=[\"id_doc\",\"format\"]\n",
+    ")\n",
+    "data_ext[\"format\"]=data_ext[\"format\"].apply(lambda x : x.split(\".\")[-1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-05-16T10:00:28.928246Z",
+     "start_time": "2018-05-16T10:00:28.908037Z"
     }
    },
    "outputs": [],
    "source": [
     "good_lang=data_lang[(data_lang[\"lang\"] == \"fr\") | (data_lang[\"lang\"] == \"en\")]\n",
-    "selected=data_count_agro[data_count_agro[\"id_doc\"].isin(good_lang[\"id_doc\"])]"
+    "selected=data_count_agro[data_count_agro[\"id_doc\"].isin(good_lang[\"id_doc\"])]\n",
+    "selected[\"format\"]=data_ext[data_ext[\"id_doc\"].isin(good_lang[\"id_doc\"])][\"format\"]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 14,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2018-04-19T16:00:11.271793Z",
-     "start_time": "2018-04-19T16:00:11.267370Z"
+     "end_time": "2018-05-16T10:00:30.954784Z",
+     "start_time": "2018-05-16T10:00:30.949524Z"
     }
    },
    "outputs": [],
@@ -285,29 +320,119 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 15,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2018-04-19T16:00:11.690969Z",
-     "start_time": "2018-04-19T16:00:11.273812Z"
+     "end_time": "2018-05-16T09:10:31.246298Z",
+     "start_time": "2018-05-16T09:10:31.220975Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x10af5d4a8>"
+       "90848"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "open(\"selected_mada.json\",'w').write(selected.to_csv())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-05-16T10:00:33.484417Z",
+     "start_time": "2018-05-16T10:00:33.477182Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "format,id_doc,count\n",
+      "doc,2163,2163\n",
+      "docx,194,194\n",
+      "html,791,791\n",
+      "pdf,598,598\n",
+      "txt,43,43\n",
+      "xls,826,826\n",
+      "xlsx,35,35\n",
+      "xml,7,7\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(selected.groupby('format').count().to_csv())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-05-16T10:00:35.621805Z",
+     "start_time": "2018-05-16T10:00:35.615322Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "format,id_doc\n",
+      "doc,6651\n",
+      "docx,838\n",
+      "html,3465\n",
+      "pdf,1931\n",
+      "ppt,228\n",
+      "pptx,40\n",
+      "sql,1\n",
+      "txt,157\n",
+      "xls,2544\n",
+      "xlsx,126\n",
+      "xml,29\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(data_ext.groupby('format').count().to_csv())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-05-16T09:10:31.667687Z",
+     "start_time": "2018-05-16T09:10:31.266005Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x114914d68>"
+      ]
+     },
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10aafbd30>"
+       "<matplotlib.figure.Figure at 0x114914550>"
       ]
      },
      "metadata": {},
@@ -320,11 +445,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 17,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2018-04-19T16:00:11.781858Z",
-     "start_time": "2018-04-19T16:00:11.693274Z"
+     "end_time": "2018-05-16T10:01:04.739745Z",
+     "start_time": "2018-05-16T10:01:04.644633Z"
     }
    },
    "outputs": [],
@@ -337,11 +462,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 18,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2018-04-19T16:00:11.787422Z",
-     "start_time": "2018-04-19T16:00:11.784106Z"
+     "end_time": "2018-05-16T10:01:22.697656Z",
+     "start_time": "2018-05-16T10:01:22.693785Z"
     }
    },
    "outputs": [],
@@ -354,33 +479,49 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 19,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2018-04-19T16:00:11.914635Z",
-     "start_time": "2018-04-19T16:00:11.789825Z"
-    }
+     "end_time": "2018-05-16T10:01:24.995281Z",
+     "start_time": "2018-05-16T10:01:24.869933Z"
+    },
+    "scrolled": true
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "mkdir: /Users/jacquesfize/LOD_DATASETS/bv_lac_pos_ner_disambiguate_3: File exists\r\n"
+      "mkdir: /Users/jacquesfize/LOD_DATASETS/bv_lac_pos_ner_disambiguate_5: File exists\r\n"
      ]
     }
    ],
    "source": [
-    "!mkdir /Users/jacquesfize/LOD_DATASETS/bv_lac_pos_ner_disambiguate_3"
+    "!mkdir /Users/jacquesfize/LOD_DATASETS/bv_lac_pos_ner_disambiguate_5"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 22,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2018-04-20T06:16:37.554432Z",
-     "start_time": "2018-04-20T06:06:13.862194Z"
+     "end_time": "2018-05-16T09:10:31.904875Z",
+     "start_time": "2018-05-16T09:10:31.901715Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "skip=0\n",
+    "skipPercentage=0.10"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-05-16T09:10:32.031609Z",
+     "start_time": "2018-05-16T09:10:31.907100Z"
     }
    },
    "outputs": [
@@ -388,23 +529,1520 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\r",
-      "64/5552"
+      "mkdir: /Users/jacquesfize/LOD_DATASETS/disambiguate_1: File exists\r\n"
      ]
+    }
+   ],
+   "source": [
+    "%mkdir /Users/jacquesfize/LOD_DATASETS/disambiguate_1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-05-16T11:46:45.348959Z",
+     "start_time": "2018-05-16T10:02:19.180944Z"
+    },
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e1ea34fcab9149af988ed0c3327c1828",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "IntProgress(value=0, description='Processing', max=5552)"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-     "name": "stderr",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
-      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/pandas/core/ops.py:792: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
-      "  result = getattr(x, name)(y)\n"
+      "64/5552\n",
+      "Empty 1029\n",
+      "110/5552\n",
+      " 1077\n",
+      "111/5552\n",
+      " 1078\n",
+      "127/5552\n",
+      " 1099\n",
+      "130/5552\n",
+      " 1100\n",
+      "145/5552\n",
+      " 1121\n",
+      "146/5552\n",
+      " 1122\n",
+      "154/5552\n",
+      " 1135\n",
+      "155/5552\n",
+      " 1136\n",
+      "165/5552\n",
+      " 11438\n",
+      "168/5552\n",
+      " 11440\n",
+      "175/5552\n",
+      " 11493\n",
+      "180/5552\n",
+      " 11500\n",
+      "246/5552\n",
+      " 11682\n",
+      "251/5552\n",
+      " 117\n",
+      "262/5552\n",
+      "Empty 11724\n",
+      "279/5552\n",
+      "Empty 11770\n",
+      "294/5552\n",
+      " 118\n",
+      "298/5552\n",
+      "Empty 11806\n",
+      "331/5552\n",
+      " 11899\n",
+      "334/5552\n",
+      " 11903\n",
+      "342/5552\n",
+      " 11910\n",
+      "368/5552\n",
+      " 11952\n",
+      "403/5552\n",
+      " 12009\n",
+      "405/5552\n",
+      " 12013\n",
+      "412/5552\n",
+      " 12020\n",
+      "437/5552\n",
+      " 12061\n",
+      "457/5552\n",
+      " 12095\n",
+      "470/5552\n",
+      " 12137\n",
+      "477/5552\n",
+      " 1217\n",
+      "478/5552\n",
+      " 1218\n",
+      "481/5552\n",
+      "Empty 12193\n",
+      "\n",
+      " 12194\n",
+      "482/5552\n",
+      "Empty 12194\n",
+      "491/5552\n",
+      " 12205\n",
+      "496/5552\n",
+      " 12211\n",
+      "508/5552\n",
+      " 12223\n",
+      "513/5552\n",
+      " 1223\n",
+      "516/5552\n",
+      " 1224\n",
+      "522/5552\n",
+      " 12247\n",
+      "541/5552\n",
+      " 12264\n",
+      "542/5552\n",
+      " 12265\n",
+      "543/5552\n",
+      " 12266\n",
+      "545/5552\n",
+      " 12268\n",
+      "553/5552\n",
+      " 12275\n",
+      "558/5552\n",
+      " 12282\n",
+      "576/5552\n",
+      " 1231\n",
+      "581/5552\n",
+      " 1232\n",
+      "586/5552\n",
+      " 12372\n",
+      "589/5552\n",
+      " 12375\n",
+      "603/5552\n",
+      " 12392\n",
+      "607/5552\n",
+      " 12407\n",
+      "615/5552\n",
+      " 12422\n",
+      "619/5552\n",
+      " 12427\n",
+      "620/5552\n",
+      " 12428\n",
+      "639/5552\n",
+      " 12496\n",
+      "646/5552\n",
+      " 12510\n",
+      "647/5552\n",
+      " 12511\n",
+      "649/5552\n",
+      " 12513\n",
+      "651/5552\n",
+      " 12515\n",
+      "652/5552\n",
+      " 12516\n",
+      "656/5552\n",
+      " 12520\n",
+      "657/5552\n",
+      " 12521\n",
+      "670/5552\n",
+      " 12547\n",
+      "672/5552\n",
+      " 12554\n",
+      "673/5552\n",
+      " 12557\n",
+      "675/5552\n",
+      " 12559\n",
+      "678/5552\n",
+      " 12562\n",
+      "679/5552\n",
+      " 12563\n",
+      "681/5552\n",
+      " 12572\n",
+      "682/5552\n",
+      " 12575\n",
+      "687/5552\n",
+      " 12595\n",
+      "688/5552\n",
+      " 12598\n",
+      "693/5552\n",
+      "Empty 12601\n",
+      "694/5552\n",
+      "Empty 12602\n",
+      "\n",
+      " 12603\n",
+      "695/5552\n",
+      "Empty 12603\n",
+      "696/5552\n",
+      " 12606\n",
+      "704/5552\n",
+      " 12614\n",
+      "708/5552\n",
+      " 12618\n",
+      "709/5552\n",
+      " 12619\n",
+      "713/5552\n",
+      " 12623\n",
+      "730/5552\n",
+      " 12651\n",
+      "732/5552\n",
+      " 12660\n",
+      "738/5552\n",
+      "Empty 12678\n",
+      "742/5552\n",
+      " 12687\n",
+      "744/5552\n",
+      "Empty 12688\n",
+      "746/5552\n",
+      " 12690\n",
+      "748/5552\n",
+      " 12692\n",
+      "752/5552\n",
+      " 12699\n",
+      "765/5552\n",
+      " 1271\n",
+      "776/5552\n",
+      " 1272\n",
+      "874/5552\n",
+      " 12810\n",
+      "876/5552\n",
+      " 12812\n",
+      "884/5552\n",
+      " 12841\n",
+      "922/5552\n",
+      " 1293\n",
+      "924/5552\n",
+      " 1294\n",
+      "928/5552\n",
+      " 1295\n",
+      "933/5552\n",
+      " 1296\n",
+      "934/5552\n",
+      " 12960\n",
+      "937/5552\n",
+      " 12964\n",
+      "939/5552\n",
+      " 12968\n",
+      "944/5552\n",
+      " 12976\n",
+      "946/5552\n",
+      " 12980\n",
+      "949/5552\n",
+      " 12985\n",
+      "950/5552\n",
+      " 12986\n",
+      "957/5552\n",
+      " 12994\n",
+      "967/5552\n",
+      " 13004\n",
+      "969/5552\n",
+      " 13006\n",
+      "970/5552\n",
+      " 13007\n",
+      "979/5552\n",
+      " 13016\n",
+      "980/5552\n",
+      " 13017\n",
+      "1002/5552\n",
+      " 13043\n",
+      "1009/5552\n",
+      " 1305\n",
+      "1015/5552\n",
+      " 1306\n",
+      "1019/5552\n",
+      " 13069\n",
+      "1024/5552\n",
+      " 13073\n",
+      "1027/5552\n",
+      " 13076\n",
+      "1028/5552\n",
+      " 13077\n",
+      "1034/5552\n",
+      "Empty 13088\n",
+      "1038/5552\n",
+      " 13096\n",
+      "1040/5552\n",
+      " 13098\n",
+      "1041/5552\n",
+      " 13099\n",
+      "1043/5552\n",
+      " 13100\n",
+      "1044/5552\n",
+      " 13101\n",
+      "1046/5552\n",
+      " 13103\n",
+      "1049/5552\n",
+      " 1311\n",
+      "1053/5552\n",
+      "Empty 13115\n",
+      "1054/5552\n",
+      " 1312\n",
+      "1055/5552\n",
+      " 13122\n",
+      "1057/5552\n",
+      " 13124\n",
+      "1059/5552\n",
+      " 13126\n",
+      "1060/5552\n",
+      " 13127\n",
+      "1062/5552\n",
+      " 13129\n",
+      "1066/5552\n",
+      " 13133\n",
+      "1070/5552\n",
+      " 13140\n",
+      "1073/5552\n",
+      " 13143\n",
+      "1076/5552\n",
+      " 13147\n",
+      "1077/5552\n",
+      " 13148\n",
+      "1082/5552\n",
+      " 13152\n",
+      "1083/5552\n",
+      " 13153\n",
+      "1085/5552\n",
+      " 13155\n",
+      "1092/5552\n",
+      " 13161\n",
+      "1096/5552\n",
+      " 13167\n",
+      "1103/5552\n",
+      " 13173\n",
+      "1105/5552\n",
+      " 13177\n",
+      "1106/5552\n",
+      " 13178\n",
+      "1172/5552\n",
+      "Empty 13293\n",
+      "1175/5552\n",
+      "Empty 13303\n",
+      "1176/5552\n",
+      "Empty 13304\n",
+      "1183/5552\n",
+      " 13319\n",
+      "1185/5552\n",
+      " 13320\n",
+      "1194/5552\n",
+      " 13336\n",
+      "1216/5552\n",
+      " 13372\n",
+      "1220/5552\n",
+      " 13377\n",
+      "1260/5552\n",
+      " 13426\n",
+      "1264/5552\n",
+      " 13430\n",
+      "1266/5552\n",
+      " 13432\n",
+      "1268/5552\n",
+      " 13434\n",
+      "1270/5552\n",
+      " 13436\n",
+      "1275/5552\n",
+      " 13440\n",
+      "1277/5552\n",
+      " 13442\n",
+      "1278/5552\n",
+      " 13443\n",
+      "1312/5552\n",
+      " 13493\n",
+      "1317/5552\n",
+      " 13499\n",
+      "1321/5552\n",
+      " 13502\n",
+      "1326/5552\n",
+      " 13507\n",
+      "1327/5552\n",
+      " 13508\n",
+      "1330/5552\n",
+      " 13511\n",
+      "1331/5552\n",
+      " 13512\n",
+      "1332/5552\n",
+      " 13513\n",
+      "1333/5552\n",
+      " 13514\n",
+      "1334/5552\n",
+      " 13517\n",
+      "1337/5552\n",
+      " 13520\n",
+      "1338/5552\n",
+      " 13521\n",
+      "1339/5552\n",
+      " 13522\n",
+      "1340/5552\n",
+      " 13523\n",
+      "1343/5552\n",
+      " 13526\n",
+      "1344/5552\n",
+      " 13527\n",
+      "1347/5552\n",
+      " 13530\n",
+      "1348/5552\n",
+      " 13531\n",
+      "1350/5552\n",
+      " 13534\n",
+      "1352/5552\n",
+      " 13536\n",
+      "1354/5552\n",
+      " 13538\n",
+      "1356/5552\n",
+      " 13540\n",
+      "1364/5552\n",
+      " 13549\n",
+      "1376/5552\n",
+      " 13561\n",
+      "1390/5552\n",
+      " 13576\n",
+      "1397/5552\n",
+      " 13582\n",
+      "1398/5552\n",
+      " 13583\n",
+      "1402/5552\n",
+      " 1359\n",
+      "1408/5552\n",
+      " 1360\n",
+      "1411/5552\n",
+      " 13613\n",
+      "1507/5552\n",
+      " 13721\n",
+      "1523/5552\n",
+      " 13742\n",
+      "1525/5552\n",
+      " 13744\n",
+      "1532/5552\n",
+      " 13750\n",
+      "1535/5552\n",
+      " 13753\n",
+      "1537/5552\n",
+      " 13756\n",
+      "1539/5552\n",
+      " 13758\n",
+      "1548/5552\n",
+      " 13766\n",
+      "1549/5552\n",
+      " 13767\n",
+      "1551/5552\n",
+      " 13769\n",
+      "1552/5552\n",
+      " 1377\n",
+      "1568/5552\n",
+      " 13786\n",
+      "1569/5552\n",
+      " 13787\n",
+      "1576/5552\n",
+      " 13794\n",
+      "1586/5552\n",
+      " 13803\n",
+      "1589/5552\n",
+      " 13808\n",
+      "1592/5552\n",
+      " 13811\n",
+      "1593/5552\n",
+      " 13812\n",
+      "1603/5552\n",
+      " 13821\n",
+      "1668/5552\n",
+      " 1391\n",
+      "1687/5552\n",
+      " 1397\n",
+      "1688/5552\n",
+      " 13975\n",
+      "1690/5552\n",
+      " 13977\n",
+      "1702/5552\n",
+      " 13997\n",
+      "1710/5552\n",
+      " 14010\n",
+      "1786/5552\n",
+      " 14165\n",
+      "1789/5552\n",
+      "Empty 14167\n",
+      "1810/5552\n",
+      " 14201\n",
+      "1813/5552\n",
+      " 14210\n",
+      "1835/5552\n",
+      "Empty 14241\n",
+      "1845/5552\n",
+      " 14258\n",
+      "1851/5552\n",
+      " 14269\n",
+      "1858/5552\n",
+      " 14280\n",
+      "1862/5552\n",
+      " 14286\n",
+      "1867/5552\n",
+      " 14294\n",
+      "1877/5552\n",
+      " 14306\n",
+      "1888/5552\n",
+      " 14324\n",
+      "1895/5552\n",
+      " 14339\n",
+      "1918/5552\n",
+      " 14376\n",
+      "1924/5552\n",
+      " 14396\n",
+      "1930/5552\n",
+      " 14402\n",
+      "1933/5552\n",
+      " 14409\n",
+      "1943/5552\n",
+      " 14423\n",
+      "1952/5552\n",
+      " 14438\n",
+      "1957/5552\n",
+      " 14446\n",
+      "1961/5552\n",
+      " 14457\n",
+      "1963/5552\n",
+      " 14460\n",
+      "1969/5552\n",
+      " 14474\n",
+      "1976/5552\n",
+      " 14486\n",
+      "1988/5552\n",
+      " 14505\n",
+      "1994/5552\n",
+      " 14517\n",
+      "1995/5552\n",
+      " 14519\n",
+      "2004/5552\n",
+      " 14536\n",
+      "2008/5552\n",
+      " 14541\n",
+      "2010/5552\n",
+      " 14547\n",
+      "2021/5552\n",
+      " 14570\n",
+      "2028/5552\n",
+      " 14584\n",
+      "2037/5552\n",
+      " 14598\n",
+      "2043/5552\n",
+      " 14610\n",
+      "2051/5552\n",
+      " 14619\n",
+      "2058/5552\n",
+      " 14634\n",
+      "2065/5552\n",
+      " 14642\n",
+      "2072/5552\n",
+      " 14659\n",
+      "2078/5552\n",
+      " 14671\n",
+      "2086/5552\n",
+      " 14683\n",
+      "2092/5552\n",
+      " 14694\n",
+      "2128/5552\n",
+      " 14754\n",
+      "2133/5552\n",
+      " 14768\n",
+      "2144/5552\n",
+      " 14791\n",
+      "2145/5552\n",
+      " 14792\n",
+      "2158/5552\n",
+      " 14831\n",
+      "2163/5552\n",
+      " 14842\n",
+      "2174/5552\n",
+      " 14862\n",
+      "2178/5552\n",
+      " 1487\n",
+      "2182/5552\n",
+      " 14874\n",
+      "2186/5552\n",
+      " 1488\n",
+      "2192/5552\n",
+      " 14888\n",
+      "2199/5552\n",
+      " 14898\n",
+      "2205/5552\n",
+      " 14904\n",
+      "2210/5552\n",
+      " 14909\n",
+      "2212/5552\n",
+      " 14910\n",
+      "2214/5552\n",
+      " 14914\n",
+      "2218/5552\n",
+      " 14919\n",
+      "2222/5552\n",
+      " 14924\n",
+      "2241/5552\n",
+      " 14945\n",
+      "2244/5552\n",
+      " 14949\n",
+      "2255/5552\n",
+      " 14961\n",
+      "2257/5552\n",
+      " 14963\n",
+      "2259/5552\n",
+      " 1497\n",
+      "2279/5552\n",
+      "Empty 14998\n",
+      "\n",
+      " 14999\n",
+      "2280/5552\n",
+      "Empty 14999\n",
+      "2283/5552\n",
+      " 15002\n",
+      "2285/5552\n",
+      " 15007\n",
+      "2298/5552\n",
+      " 1502\n",
+      "2318/5552\n",
+      " 15049\n",
+      "2338/5552\n",
+      " 15082\n",
+      "2341/5552\n",
+      " 15086\n",
+      "2342/5552\n",
+      " 15087\n",
+      "2343/5552\n",
+      " 15088\n",
+      "2351/5552\n",
+      " 15107\n",
+      "2383/5552\n",
+      " 15202\n",
+      "2399/5552\n",
+      " 15218\n",
+      "2401/5552\n",
+      " 1522\n",
+      "2411/5552\n",
+      " 15230\n",
+      "2415/5552\n",
+      " 15235\n",
+      "2418/5552\n",
+      " 15238\n",
+      "2425/5552\n",
+      " 15260\n",
+      "2426/5552\n",
+      " 15263\n",
+      "2431/5552\n",
+      " 15270\n",
+      "2444/5552\n",
+      " 15284\n",
+      "2492/5552\n",
+      " 15376\n",
+      "2493/5552\n",
+      " 15377\n",
+      "2503/5552\n",
+      " 15387\n",
+      "2518/5552\n",
+      " 15415\n",
+      "2519/5552\n",
+      " 15416\n",
+      "2552/5552\n",
+      " 15449\n",
+      "2557/5552\n",
+      " 15457\n",
+      "2560/5552\n",
+      " 15460\n",
+      "2561/5552\n",
+      " 15461\n",
+      "2562/5552\n",
+      " 15462\n",
+      "2567/5552\n",
+      " 15472\n",
+      "2573/5552\n",
+      " 15488\n",
+      "2575/5552\n",
+      " 15490\n",
+      "2582/5552\n",
+      " 15511\n",
+      "2585/5552\n",
+      " 15515\n",
+      "2588/5552\n",
+      " 15518\n",
+      "2604/5552\n",
+      " 15549\n",
+      "2605/5552\n",
+      " 1555\n",
+      "2606/5552\n",
+      " 15550\n",
+      "2616/5552\n",
+      " 15563\n",
+      "2617/5552\n",
+      " 15564\n",
+      "2623/5552\n",
+      "Empty 15577\n",
+      "2625/5552\n",
+      " 15587\n",
+      "2626/5552\n",
+      " 15588\n",
+      "2627/5552\n",
+      " 15589\n",
+      "2629/5552\n",
+      " 15591\n",
+      "2634/5552\n",
+      " 15617\n",
+      "2640/5552\n",
+      " 15630\n",
+      "2641/5552\n",
+      " 15631\n",
+      "2653/5552\n",
+      " 15643\n",
+      "2657/5552\n",
+      " 15647\n",
+      "2664/5552\n",
+      " 1566\n",
+      "2678/5552\n",
+      " 15677\n",
+      "2685/5552\n",
+      " 15690\n",
+      "2687/5552\n",
+      " 15693\n",
+      "2688/5552\n",
+      " 15694\n",
+      "2689/5552\n",
+      " 15695\n",
+      "2691/5552\n",
+      " 15697\n",
+      "2704/5552\n",
+      " 15709\n",
+      "2711/5552\n",
+      " 1572\n",
+      "2718/5552\n",
+      "Empty 15737\n",
+      "2721/5552\n",
+      " 1575\n",
+      "2729/5552\n",
+      " 1588\n",
+      "2732/5552\n",
+      " 1592\n",
+      "2735/5552\n",
+      " 1602\n",
+      "2736/5552\n",
+      " 1603\n",
+      "2737/5552\n",
+      " 1604\n",
+      "2738/5552\n",
+      " 1605\n",
+      "2748/5552\n",
+      " 1623\n",
+      "2750/5552\n",
+      " 1625\n",
+      "2755/5552\n",
+      " 1633\n",
+      "2756/5552\n",
+      " 1634\n",
+      "2757/5552\n",
+      " 1635\n",
+      "2758/5552\n",
+      " 1636\n",
+      "2759/5552\n",
+      " 1637\n",
+      "2760/5552\n",
+      " 1638\n",
+      "2768/5552\n",
+      " 1661\n",
+      "2770/5552\n",
+      " 1663\n",
+      "2775/5552\n",
+      " 1668\n",
+      "2776/5552\n",
+      " 1669\n",
+      "2785/5552\n",
+      " 1678\n",
+      "2786/5552\n",
+      " 1679\n",
+      "2793/5552\n",
+      " 1686\n",
+      "2794/5552\n",
+      " 1687\n",
+      "2806/5552\n",
+      " 17\n",
+      "2825/5552\n",
+      " 1721\n",
+      "2842/5552\n",
+      " 1748\n",
+      "2843/5552\n",
+      " 1749\n",
+      "2850/5552\n",
+      " 1762\n",
+      "2851/5552\n",
+      " 1763\n",
+      "2910/5552\n",
+      " 1892\n",
+      "2911/5552\n",
+      " 1893\n",
+      "2977/5552\n",
+      " 2038\n",
+      "2978/5552\n",
+      " 2039\n",
+      "2993/5552\n",
+      " 2068\n",
+      "2994/5552\n",
+      " 2069\n",
+      "2995/5552\n",
+      " 2072\n",
+      "2996/5552\n",
+      " 2073\n",
+      "3016/5552\n",
+      " 2116\n",
+      "3017/5552\n",
+      " 2117\n",
+      "3024/5552\n",
+      " 2128\n",
+      "3025/5552\n",
+      " 2129\n",
+      "3036/5552\n",
+      " 2144\n",
+      "3037/5552\n",
+      " 2145\n",
+      "3052/5552\n",
+      " 2168\n",
+      "3053/5552\n",
+      " 2169\n",
+      "3056/5552\n",
+      " 2176\n",
+      "3057/5552\n",
+      " 2177\n",
+      "3062/5552\n",
+      " 2184\n",
+      "3063/5552\n",
+      " 2185\n",
+      "3107/5552\n",
+      " 2280\n",
+      "3108/5552\n",
+      " 2281\n",
+      "3114/5552\n",
+      " 2296\n",
+      "3115/5552\n",
+      " 2297\n",
+      "3128/5552\n",
+      " 2317\n",
+      "3170/5552\n",
+      " 2365\n",
+      "3171/5552\n",
+      " 2366\n",
+      "3174/5552\n",
+      " 2371\n",
+      "3175/5552\n",
+      " 2372\n",
+      "3180/5552\n",
+      " 2379\n",
+      "3181/5552\n",
+      " 2380\n",
+      "3184/5552\n",
+      " 2385\n",
+      "3185/5552\n",
+      " 2386\n",
+      "3235/5552\n",
+      " 2451\n",
+      "3236/5552\n",
+      " 2452\n",
+      "3237/5552\n",
+      " 2453\n",
+      "3238/5552\n",
+      " 2454\n",
+      "3250/5552\n",
+      " 2481\n",
+      "3251/5552\n",
+      " 2482\n",
+      "3272/5552\n",
+      " 2505\n",
+      "3273/5552\n",
+      " 2506\n",
+      "3292/5552\n",
+      "Empty 2538\n",
+      "3309/5552\n",
+      " 2589\n",
+      "3311/5552\n",
+      " 2590\n",
+      "3319/5552\n",
+      "Empty 2641\n",
+      "3320/5552\n",
+      "Empty 2645\n",
+      "3321/5552\n",
+      "Empty 2646\n",
+      "3325/5552\n",
+      "Empty 2665\n",
+      "3326/5552\n",
+      "Empty 2666\n",
+      "3327/5552\n",
+      "Empty 2667\n",
+      "3328/5552\n",
+      "Empty 2668\n",
+      "3338/5552\n",
+      "Empty 2697\n",
+      "3339/5552\n",
+      "Empty 2698\n",
+      "3381/5552\n",
+      "Empty 2849\n",
+      "3383/5552\n",
+      "Empty 2854\n",
+      "3384/5552\n",
+      "Empty 2856\n",
+      "3385/5552\n",
+      "Empty 2862\n",
+      "3386/5552\n",
+      "Empty 2867\n",
+      "3390/5552\n",
+      " 2893\n",
+      "3391/5552\n",
+      " 2894\n",
+      "3392/5552\n",
+      " 2895\n",
+      "3411/5552\n",
+      " 2924\n",
+      "3414/5552\n",
+      "Empty 2936\n",
+      "3415/5552\n",
+      "Empty 2937\n",
+      "\n",
+      " 2943\n",
+      "3416/5552\n",
+      "Empty 2943\n",
+      "3417/5552\n",
+      " 2948\n",
+      "3418/5552\n",
+      " 2950\n",
+      "3419/5552\n",
+      " 2951\n",
+      "3424/5552\n",
+      "Empty 2961\n",
+      "\n",
+      " 2963\n",
+      "3425/5552\n",
+      "Empty 2963\n",
+      "3426/5552\n",
+      " 2967\n",
+      "3429/5552\n",
+      "Empty 2970\n",
+      "3430/5552\n",
+      " 2977\n",
+      "3432/5552\n",
+      " 2981\n",
+      "3435/5552\n",
+      " 2986\n",
+      "3443/5552\n",
+      " 2995\n",
+      "3444/5552\n",
+      " 2996\n",
+      "3445/5552\n",
+      " 2999\n",
+      "3448/5552\n",
+      " 3000\n",
+      "3449/5552\n",
+      " 3001\n",
+      "3450/5552\n",
+      " 3003\n",
+      "3451/5552\n",
+      " 3004\n",
+      "3452/5552\n",
+      " 3005\n",
+      "3453/5552\n",
+      " 3006\n",
+      "3454/5552\n",
+      " 3008\n",
+      "3456/5552\n",
+      " 3010\n",
+      "3457/5552\n",
+      " 3011\n",
+      "3468/5552\n",
+      " 3023\n",
+      "3478/5552\n",
+      "Empty 3035\n",
+      "3482/5552\n",
+      " 3040\n",
+      "3484/5552\n",
+      " 3042\n",
+      "3492/5552\n",
+      " 3050\n",
+      "3493/5552\n",
+      " 3051\n",
+      "3498/5552\n",
+      " 3060\n",
+      "3532/5552\n",
+      " 3266\n",
+      "3534/5552\n",
+      "Empty 3267\n",
+      "3541/5552\n",
+      " 3331\n",
+      "3573/5552\n",
+      " 3424\n",
+      "3579/5552\n",
+      "Empty 3453\n",
+      "3588/5552\n",
+      "Empty 3486\n",
+      "3620/5552\n",
+      " 3556\n",
+      "3621/5552\n",
+      " 3559\n",
+      "3649/5552\n",
+      " 3614\n",
+      "3652/5552\n",
+      " 3619\n",
+      "3663/5552\n",
+      " 3644\n",
+      "3666/5552\n",
+      "Empty 3653\n",
+      "3673/5552\n",
+      " 3677\n",
+      "3676/5552\n",
+      " 3680\n",
+      "3685/5552\n",
+      " 3690\n",
+      "3687/5552\n",
+      "Empty 3691\n",
+      "\n",
+      " 3692\n",
+      "3688/5552\n",
+      "Empty 3692\n",
+      "\n",
+      " 3693\n",
+      "3689/5552\n",
+      "Empty 3693\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "5525/5552"
+      "3696/5552\n",
+      " 3706\n",
+      "3701/5552\n",
+      " 3723\n",
+      "3704/5552\n",
+      " 3732\n",
+      "3739/5552\n",
+      " 3860\n",
+      "3740/5552\n",
+      " 3861\n",
+      "3749/5552\n",
+      "Empty 3886\n",
+      "\n",
+      " 389\n",
+      "3750/5552\n",
+      "Empty 389\n",
+      "3751/5552\n",
+      " 3891\n",
+      "3754/5552\n",
+      " 390\n",
+      "3766/5552\n",
+      " 393\n",
+      "3773/5552\n",
+      " 394\n",
+      "3777/5552\n",
+      " 3945\n",
+      "3789/5552\n",
+      " 3964\n",
+      "3796/5552\n",
+      " 3984\n",
+      "3799/5552\n",
+      "Empty 3998\n",
+      "3802/5552\n",
+      " 401\n",
+      "3808/5552\n",
+      " 402\n",
+      "3809/5552\n",
+      " 4021\n",
+      "3811/5552\n",
+      "Empty 4023\n",
+      "3812/5552\n",
+      "Empty 4025\n",
+      "3813/5552\n",
+      "Empty 4026\n",
+      "3814/5552\n",
+      " 403\n",
+      "3816/5552\n",
+      "Empty 4032\n",
+      "3817/5552\n",
+      "Empty 4034\n",
+      "3818/5552\n",
+      "Empty 4035\n",
+      "3820/5552\n",
+      " 404\n",
+      "3822/5552\n",
+      "Empty 4042\n",
+      "3827/5552\n",
+      " 4050\n",
+      "3914/5552\n",
+      " 4358\n",
+      "3921/5552\n",
+      " 4372\n",
+      "3922/5552\n",
+      " 4373\n",
+      "3956/5552\n",
+      " 4554\n",
+      "3958/5552\n",
+      "Empty 4555\n",
+      "3970/5552\n",
+      " 4621\n",
+      "3994/5552\n",
+      "Empty 4699\n",
+      "4001/5552\n",
+      " 4714\n",
+      "4010/5552\n",
+      "Empty 4743\n",
+      "4014/5552\n",
+      " 476\n",
+      "4018/5552\n",
+      " 477\n",
+      "4022/5552\n",
+      "Empty 4776\n",
+      "4051/5552\n",
+      " 4845\n",
+      "4052/5552\n",
+      " 4849\n",
+      "4078/5552\n",
+      " 4904\n",
+      "4081/5552\n",
+      " 4909\n",
+      "4090/5552\n",
+      " 4934\n",
+      "4093/5552\n",
+      "Empty 4943\n",
+      "4100/5552\n",
+      " 4967\n",
+      "4103/5552\n",
+      " 4970\n",
+      "4112/5552\n",
+      " 4980\n",
+      "4114/5552\n",
+      "Empty 4981\n",
+      "\n",
+      " 4982\n",
+      "4115/5552\n",
+      "Empty 4982\n",
+      "\n",
+      " 4983\n",
+      "4116/5552\n",
+      "Empty 4983\n",
+      "4123/5552\n",
+      " 4996\n",
+      "4131/5552\n",
+      " 5013\n",
+      "4135/5552\n",
+      " 5022\n",
+      "4145/5552\n",
+      " 504\n",
+      "4147/5552\n",
+      " 505\n",
+      "4161/5552\n",
+      " 509\n",
+      "4166/5552\n",
+      " 510\n",
+      "4176/5552\n",
+      " 5150\n",
+      "4177/5552\n",
+      " 5151\n",
+      "4186/5552\n",
+      "Empty 5176\n",
+      "4188/5552\n",
+      " 5181\n",
+      "4209/5552\n",
+      " 5235\n",
+      "4219/5552\n",
+      " 5254\n",
+      "4224/5552\n",
+      " 5274\n",
+      "4227/5552\n",
+      "Empty 5288\n",
+      "4237/5552\n",
+      " 5311\n",
+      "4239/5552\n",
+      "Empty 5315\n",
+      "4240/5552\n",
+      "Empty 5316\n",
+      "4244/5552\n",
+      "Empty 5325\n",
+      "4250/5552\n",
+      " 5333\n",
+      "4277/5552\n",
+      "Empty 539\n",
+      "4319/5552\n",
+      " 555\n",
+      "4323/5552\n",
+      "Empty 5554\n",
+      "4325/5552\n",
+      " 556\n",
+      "4350/5552\n",
+      " 5641\n",
+      "4358/5552\n",
+      " 5655\n",
+      "4359/5552\n",
+      " 5656\n",
+      "4362/5552\n",
+      " 5661\n",
+      "4363/5552\n",
+      " 5662\n",
+      "4364/5552\n",
+      " 5665\n",
+      "4366/5552\n",
+      " 5667\n",
+      "4367/5552\n",
+      " 5668\n",
+      "4368/5552\n",
+      " 5669\n",
+      "4369/5552\n",
+      " 569\n",
+      "4371/5552\n",
+      " 570\n",
+      "4380/5552\n",
+      " 571\n",
+      "4391/5552\n",
+      "Empty 5740\n",
+      "4393/5552\n",
+      " 5744\n",
+      "4405/5552\n",
+      " 5784\n",
+      "4406/5552\n",
+      " 5786\n",
+      "4413/5552\n",
+      " 5804\n",
+      "4424/5552\n",
+      " 5829\n",
+      "4428/5552\n",
+      "Empty 5838\n",
+      "4449/5552\n",
+      " 5953\n",
+      "4450/5552\n",
+      " 5955\n",
+      "4451/5552\n",
+      " 5956\n",
+      "4452/5552\n",
+      " 5957\n",
+      "4453/5552\n",
+      " 5958\n",
+      "4457/5552\n",
+      " 5966\n",
+      "4460/5552\n",
+      " 597\n",
+      "4462/5552\n",
+      " 5972\n",
+      "4463/5552\n",
+      " 5973\n",
+      "4467/5552\n",
+      " 598\n",
+      "4468/5552\n",
+      " 5980\n",
+      "4470/5552\n",
+      " 5983\n",
+      "4471/5552\n",
+      " 5985\n",
+      "4492/5552\n",
+      "Empty 6039\n",
+      "4506/5552\n",
+      "Empty 6112\n",
+      "4507/5552\n",
+      " 6118\n",
+      "4538/5552\n",
+      "Empty 6244\n",
+      "4543/5552\n",
+      " 6259\n",
+      "4550/5552\n",
+      "Empty 6288\n",
+      "4560/5552\n",
+      "Empty 6321\n",
+      "4574/5552\n",
+      "Empty 6366\n",
+      "4575/5552\n",
+      " 6370\n",
+      "4578/5552\n",
+      "Empty 6372\n",
+      "4579/5552\n",
+      " 6374\n",
+      "4586/5552\n",
+      " 6391\n",
+      "4590/5552\n",
+      " 6400\n",
+      "4609/5552\n",
+      "Empty 6469\n",
+      "4610/5552\n",
+      "Empty 6472\n",
+      "4613/5552\n",
+      " 6488\n",
+      "4614/5552\n",
+      " 6489\n",
+      "4622/5552\n",
+      " 6503\n",
+      "4624/5552\n",
+      " 6505\n",
+      "4625/5552\n",
+      " 6508\n",
+      "4626/5552\n",
+      " 6509\n",
+      "4628/5552\n",
+      " 6511\n",
+      "4644/5552\n",
+      " 6535\n",
+      "4646/5552\n",
+      " 6537\n",
+      "4656/5552\n",
+      " 6551\n",
+      "4664/5552\n",
+      " 6565\n",
+      "4669/5552\n",
+      " 6570\n",
+      "4678/5552\n",
+      " 6584\n",
+      "4692/5552\n",
+      " 6605\n",
+      "4715/5552\n",
+      " 6659\n",
+      "4719/5552\n",
+      " 6663\n",
+      "4720/5552\n",
+      " 6664\n",
+      "4727/5552\n",
+      "Empty 6681\n",
+      "4736/5552\n",
+      "Empty 6692\n",
+      "4739/5552\n",
+      "Empty 6697\n",
+      "4743/5552\n",
+      " 6706\n",
+      "4747/5552\n",
+      "Empty 6712\n",
+      "4748/5552\n",
+      " 6716\n",
+      "4764/5552\n",
+      " 675\n",
+      "4767/5552\n",
+      "Empty 6752\n",
+      "4770/5552\n",
+      " 676\n",
+      "4796/5552\n",
+      "Empty 6816\n",
+      "4799/5552\n",
+      "Empty 6821\n",
+      "4802/5552\n",
+      " 6830\n",
+      "4805/5552\n",
+      "Empty 6836\n",
+      "4807/5552\n",
+      " 6840\n",
+      "4826/5552\n",
+      "Empty 6876\n",
+      "4838/5552\n",
+      "Empty 6904\n",
+      "4851/5552\n",
+      "Empty 6932\n",
+      "4856/5552\n",
+      " 6943\n",
+      "4857/5552\n",
+      " 6944\n",
+      "4858/5552\n",
+      " 6945\n",
+      "4872/5552\n",
+      " 6961\n",
+      "4932/5552\n",
+      " 7100\n",
+      "4943/5552\n",
+      " 7152\n",
+      "4947/5552\n",
+      " 7156\n",
+      "4950/5552\n",
+      " 7159\n",
+      "4960/5552\n",
+      " 720\n",
+      "4961/5552\n",
+      " 721\n",
+      "4971/5552\n",
+      " 723\n",
+      "5005/5552\n",
+      " 728\n",
+      "5013/5552\n",
+      " 729\n",
+      "5018/5552\n",
+      " 7294\n",
+      "5019/5552\n",
+      " 7295\n",
+      "5027/5552\n",
+      " 7308\n",
+      "5036/5552\n",
+      " 733\n",
+      "5044/5552\n",
+      " 734\n",
+      "5076/5552\n",
+      "Empty 7389\n",
+      "5081/5552\n",
+      " 7401\n",
+      "5094/5552\n",
+      " 7432\n",
+      "5101/5552\n",
+      " 7439\n",
+      "5110/5552\n",
+      " 7451\n",
+      "5111/5552\n",
+      " 7452\n",
+      "5112/5552\n",
+      " 7453\n",
+      "5114/5552\n",
+      " 7455\n",
+      "5125/5552\n",
+      " 7471\n",
+      "5127/5552\n",
+      " 7473\n",
+      "5134/5552\n",
+      " 7482\n",
+      "5135/5552\n",
+      " 7483\n",
+      "5137/5552\n",
+      " 7489\n",
+      "5142/5552\n",
+      " 7493\n",
+      "5155/5552\n",
+      " 7505\n",
+      "5156/5552\n",
+      " 7506\n",
+      "5160/5552\n",
+      " 751\n",
+      "5165/5552\n",
+      " 7518\n",
+      "5166/5552\n",
+      " 7519\n",
+      "5167/5552\n",
+      " 7521\n",
+      "5168/5552\n",
+      " 7522\n",
+      "5196/5552\n",
+      " 7555\n",
+      "5223/5552\n",
+      " 7584\n",
+      "5247/5552\n",
+      " 7698\n",
+      "5248/5552\n",
+      " 7699\n",
+      "5255/5552\n",
+      "Empty 7721\n",
+      "5258/5552\n",
+      " 7758\n",
+      "5268/5552\n",
+      " 7770\n",
+      "5271/5552\n",
+      " 7774\n",
+      "5272/5552\n",
+      " 7775\n",
+      "5284/5552\n",
+      " 7799\n",
+      "5292/5552\n",
+      " 7808\n",
+      "5299/5552\n",
+      "Empty 7825\n",
+      "5303/5552\n",
+      "Empty 7843\n",
+      "5307/5552\n",
+      " 7866\n",
+      "5312/5552\n",
+      " 7878\n",
+      "5314/5552\n",
+      " 7880\n",
+      "5316/5552\n",
+      " 7884\n",
+      "5317/5552\n",
+      " 7888\n",
+      "5322/5552\n",
+      " 7894\n",
+      "5325/5552\n",
+      " 7898\n",
+      "5332/5552\n",
+      " 7909\n",
+      "5342/5552\n",
+      " 794\n",
+      "5343/5552\n",
+      " 795\n",
+      "5350/5552\n",
+      "Empty 8027\n",
+      "5351/5552\n",
+      "Empty 8028\n",
+      "5353/5552\n",
+      "Empty 8031\n",
+      "5354/5552\n",
+      "Empty 8032\n",
+      "5355/5552\n",
+      " 805\n",
+      "5373/5552\n",
+      "Empty 8216\n",
+      "5419/5552\n",
+      " 888\n",
+      "5423/5552\n",
+      "Empty 8892\n",
+      "5426/5552\n",
+      "Empty 8971\n",
+      "5427/5552\n",
+      " 901\n",
+      "5438/5552\n",
+      "Empty 9104\n",
+      "5469/5552\n",
+      " 930\n",
+      "5501/5552\n",
+      " 949\n",
+      "5503/5552\n",
+      " 950\n",
+      "5515/5552\n",
+      "Empty 9689\n",
+      "5518/5552\n",
+      "Empty 9703\n",
+      "5519/5552\n",
+      "Empty 9704\n",
+      "5520/5552\n",
+      "Empty 9705\n",
+      "5521/5552\n",
+      "Empty 9706\n",
+      "5522/5552\n",
+      "Empty 9707\n",
+      "5523/5552\n",
+      "Empty 9709\n",
+      "5524/5552\n",
+      "Empty 9710\n",
+      "5525/5552\n",
+      "Empty 9711\n",
+      "5552/5552"
      ]
     }
    ],
@@ -412,111 +2050,98 @@
     "import sys\n",
     "i=0\n",
     "n=len(selected)\n",
+    "import time\n",
+    "p=IntProgress(description=\"Processing\",max=n)\n",
+    "display(p)\n",
+    "\n",
     "for id_,row in selected.iterrows():\n",
+    "    p.value+=1\n",
     "    i+=1\n",
     "    try:\n",
     "        df=reformat_data(read_csv_ner(output_dir+\"{0}.csv\".format(row[\"id_doc\"])))\n",
-    "        sys.stdout.write(\"\\r{0}/{1}\".format(i,n))\n",
+    "    except Exception as e:\n",
+    "        print(\"\\n\",row[\"id_doc\"])\n",
+    "    #df=read_csv_ner(output_dir+\"{0}.csv\".format(row[\"id_doc\"]))\n",
+    "    #df=df[skip:]\n",
+    "    #df=reformat_data(df)\n",
+    "    #skip=int(skipPercentage*len(df))\n",
+    "    #df=df[skip:]\n",
+    "    sys.stdout.write(\"\\r{0}/{1}\".format(i,n))\n",
+    "    if df.empty:\n",
+    "        print(\"\\nEmpty\",row[\"id_doc\"])\n",
+    "        df.to_csv(\"/Users/jacquesfize/LOD_DATASETS/disambiguate_1/{0}.csv\".format(row[\"id_doc\"]))\n",
+    "        continue\n",
+    "    df[\"GID\"]=df[df[\"ent_type_\"] == \"LOC\"][\"text\"].apply(\n",
+    "        lambda x: disambiguate(x,lang=data_lang[data_lang[\"id_doc\"] == row[\"id_doc\"]][\"lang\"].values[0])[0]\n",
+    "    )\n",
+    "    df.to_csv(\"/Users/jacquesfize/LOD_DATASETS/disambiguate_1/{0}.csv\".format(row[\"id_doc\"]))\n",
     "\n",
-    "        df[\"GID\"]=df[df[\"ent_type_\"] == \"LOC\"][\"text\"].apply(\n",
-    "            lambda x: disambiguate(x,lang=data_lang[data_lang[\"id_doc\"] == row[\"id_doc\"]][\"lang\"].values[0])[0]\n",
-    "        )\n",
-    "    except:\n",
-    "        df[\"GID\"]='O'\n",
-    "    df.to_csv(\"/Users/jacquesfize/LOD_DATASETS/bv_lac_pos_ner_disambiguate_3/{0}.csv\".format(id_))\n"
+    "    \n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 27,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2018-04-19T17:19:13.241583Z",
-     "start_time": "2018-04-19T17:19:12.415531Z"
+     "end_time": "2018-05-16T09:13:16.096156Z",
+     "start_time": "2018-05-16T09:13:15.843266Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><iframe src=\"data:text/html;charset=utf-8;base64,<!DOCTYPE html>
<head>    
    <meta http-equiv="content-type" content="text/html; charset=UTF-8" />
    <script>L_PREFER_CANVAS = false; L_NO_TOUCH = false; L_DISABLE_3D = false;</script>
    <script src="https://cdn.jsdelivr.net/npm/leaflet@1.2.0/dist/leaflet.js"></script>
    <script src="https://ajax.googleapis.com/ajax/libs/jquery/1.11.1/jquery.min.js"></script>
    <script src="https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/js/bootstrap.min.js"></script>
    <script src="https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.js"></script>
    <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/leaflet@1.2.0/dist/leaflet.css" />
    <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap.min.css" />
    <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap-theme.min.css" />
    <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" />
    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.css" />
    <link rel="stylesheet" href="https://rawgit.com/python-visualization/folium/master/folium/templates/leaflet.awesome.rotate.css" />
    <style>html, body {width: 100%;height: 100%;margin: 0;padding: 0;}</style>
    <style>#map {position:absolute;top:0;bottom:0;right:0;left:0;}</style>
    
            <style> #map_30af0140330d485194b8136e60eab790 {
                position : relative;
                width : 100.0%;
                height: 100.0%;
                left: 0.0%;
                top: 0.0%;
                }
            </style>
        
</head>
<body>    
    
            <div class="folium-map" id="map_30af0140330d485194b8136e60eab790" ></div>
        
</body>
<script>    
    

            
                var bounds = null;
            

            var map_30af0140330d485194b8136e60eab790 = L.map(
                                  'map_30af0140330d485194b8136e60eab790',
                                  {center: [0,0],
                                  zoom: 1,
                                  maxBounds: bounds,
                                  layers: [],
                                  worldCopyJump: false,
                                  crs: L.CRS.EPSG3857
                                 });
            
        
    
            var tile_layer_cace2050b93748b98a302084dab01ca4 = L.tileLayer(
                'https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png',
                {
  "attribution": null,
  "detectRetina": false,
  "maxZoom": 18,
  "minZoom": 1,
  "noWrap": false,
  "subdomains": "abc"
}
                ).addTo(map_30af0140330d485194b8136e60eab790);
        
    

            var marker_fb10afd3e5b8465f9b5122397362994f = L.marker(
                [-17.833333333333,48.416666666667],
                {
                    icon: new L.Icon.Default()
                    }
                )
                .addTo(map_30af0140330d485194b8136e60eab790);
            
    
            var popup_6163d5b8267b430e9722d45e486f3d6a = L.popup({maxWidth: '300'});

            
                var html_4b84ac3149f24b2c99cb6510774cabd4 = $('<div id="html_4b84ac3149f24b2c99cb6510774cabd4" style="width: 100.0%; height: 100.0%;">Ambatondrazaka</div>')[0];
                popup_6163d5b8267b430e9722d45e486f3d6a.setContent(html_4b84ac3149f24b2c99cb6510774cabd4);
            

            marker_fb10afd3e5b8465f9b5122397362994f.bindPopup(popup_6163d5b8267b430e9722d45e486f3d6a);

            
        
    

            var marker_4838b37d18be4314954212e93ba87220 = L.marker(
                [-17.833333333333,48.416666666667],
                {
                    icon: new L.Icon.Default()
                    }
                )
                .addTo(map_30af0140330d485194b8136e60eab790);
            
    
            var popup_8fde1166acf34a41b2c5d631bcd1c73d = L.popup({maxWidth: '300'});

            
                var html_f0d47b802158437b88c6556c54f6d81c = $('<div id="html_f0d47b802158437b88c6556c54f6d81c" style="width: 100.0%; height: 100.0%;">Ambatondrazaka</div>')[0];
                popup_8fde1166acf34a41b2c5d631bcd1c73d.setContent(html_f0d47b802158437b88c6556c54f6d81c);
            

            marker_4838b37d18be4314954212e93ba87220.bindPopup(popup_8fde1166acf34a41b2c5d631bcd1c73d);

            
        
    

            var marker_f4e17d9a2c9b4502bdb89fc2298aaeb7 = L.marker(
                [-17.18333,48.36667],
                {
                    icon: new L.Icon.Default()
                    }
                )
                .addTo(map_30af0140330d485194b8136e60eab790);
            
    
            var popup_2531282b0f8e4847abbedaf21bf912b3 = L.popup({maxWidth: '300'});

            
                var html_994ab1670a7e491382b8b4c2a66eb48c = $('<div id="html_994ab1670a7e491382b8b4c2a66eb48c" style="width: 100.0%; height: 100.0%;">Bemanjato</div>')[0];
                popup_2531282b0f8e4847abbedaf21bf912b3.setContent(html_994ab1670a7e491382b8b4c2a66eb48c);
            

            marker_f4e17d9a2c9b4502bdb89fc2298aaeb7.bindPopup(popup_2531282b0f8e4847abbedaf21bf912b3);

            
        
    

            var marker_67e3cea687ac40909ebe059dc4d5f1ab = L.marker(
                [-23.76667,45.66667],
                {
                    icon: new L.Icon.Default()
                    }
                )
                .addTo(map_30af0140330d485194b8136e60eab790);
            
    
            var popup_e27133b5950a488bbc141fa2276ce560 = L.popup({maxWidth: '300'});

            
                var html_b71e061a15b6467ea5950235d31d8c6c = $('<div id="html_b71e061a15b6467ea5950235d31d8c6c" style="width: 100.0%; height: 100.0%;">Maromaniry</div>')[0];
                popup_e27133b5950a488bbc141fa2276ce560.setContent(html_b71e061a15b6467ea5950235d31d8c6c);
            

            marker_67e3cea687ac40909ebe059dc4d5f1ab.bindPopup(popup_e27133b5950a488bbc141fa2276ce560);

            
        
    

            var marker_301b318f852b4df783b1bbbc9cce6d9d = L.marker(
                [-16.7167,46.8167],
                {
                    icon: new L.Icon.Default()
                    }
                )
                .addTo(map_30af0140330d485194b8136e60eab790);
            
    
            var popup_eadd9b641ffa4c5984f78990ea41176e = L.popup({maxWidth: '300'});

            
                var html_b944d99801b74c4fbd40b5d936d8938b = $('<div id="html_b944d99801b74c4fbd40b5d936d8938b" style="width: 100.0%; height: 100.0%;">Mangabe</div>')[0];
                popup_eadd9b641ffa4c5984f78990ea41176e.setContent(html_b944d99801b74c4fbd40b5d936d8938b);
            

            marker_301b318f852b4df783b1bbbc9cce6d9d.bindPopup(popup_eadd9b641ffa4c5984f78990ea41176e);

            
        
    

            var marker_51edbbf4d30e4d16bd9c17b6791f1071 = L.marker(
                [-17.8833,48.4167],
                {
                    icon: new L.Icon.Default()
                    }
                )
                .addTo(map_30af0140330d485194b8136e60eab790);
            
    
            var popup_72c69cf69a364b11bb9e20a63e36d36d = L.popup({maxWidth: '300'});

            
                var html_ff0a2b3c14f3452a8182dccf064f2591 = $('<div id="html_ff0a2b3c14f3452a8182dccf064f2591" style="width: 100.0%; height: 100.0%;">Ilafy</div>')[0];
                popup_72c69cf69a364b11bb9e20a63e36d36d.setContent(html_ff0a2b3c14f3452a8182dccf064f2591);
            

            marker_51edbbf4d30e4d16bd9c17b6791f1071.bindPopup(popup_72c69cf69a364b11bb9e20a63e36d36d);

            
        
    

            var marker_32791796fe60489cb32ba318fb2c47ca = L.marker(
                [70.196389,28.186667],
                {
                    icon: new L.Icon.Default()
                    }
                )
                .addTo(map_30af0140330d485194b8136e60eab790);
            
    
            var popup_78ec80518333400193e45a18d15a59bd = L.popup({maxWidth: '300'});

            
                var html_791e2df6da29491fac7a7ff8b33dc630 = $('<div id="html_791e2df6da29491fac7a7ff8b33dc630" style="width: 100.0%; height: 100.0%;">Tana</div>')[0];
                popup_78ec80518333400193e45a18d15a59bd.setContent(html_791e2df6da29491fac7a7ff8b33dc630);
            

            marker_32791796fe60489cb32ba318fb2c47ca.bindPopup(popup_78ec80518333400193e45a18d15a59bd);

            
        
    

            var marker_5222b407340f42e9b3bf4dda573da0d9 = L.marker(
                [-17.833333333333,48.416666666667],
                {
                    icon: new L.Icon.Default()
                    }
                )
                .addTo(map_30af0140330d485194b8136e60eab790);
            
    
            var popup_82ef013011484f5ba8561834c1c9f113 = L.popup({maxWidth: '300'});

            
                var html_353ae3d0bf7344eca37b58d35949fd3d = $('<div id="html_353ae3d0bf7344eca37b58d35949fd3d" style="width: 100.0%; height: 100.0%;">Ambatondrazaka</div>')[0];
                popup_82ef013011484f5ba8561834c1c9f113.setContent(html_353ae3d0bf7344eca37b58d35949fd3d);
            

            marker_5222b407340f42e9b3bf4dda573da0d9.bindPopup(popup_82ef013011484f5ba8561834c1c9f113);

            
        
</script>\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
-      ],
-      "text/plain": [
-       "<folium.folium.Map at 0x1106b3128>"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "import folium\n",
-    "m = folium.Map()\n",
-    "for id,row in df[df[\"ent_type_\"] == \"LOC\"].iterrows():\n",
-    "    if not row[\"GID\"] or row[\"GID\"] == \"O\":\n",
-    "        continue\n",
-    "    data=pd.Series(get_data(row[\"GID\"]))\n",
-    "    if \"coord\" in data:\n",
-    "        folium.Marker([data[\"coord\"][\"lat\"], data[\"coord\"][\"lon\"]], popup=data[\"fr\"]).add_to(m)\n",
-    "#folium.Marker([45.3311, -121.7113], popup='<b>Timberline Lodge</b>').add_to(m)\n",
-    "m"
+    "df=reformat_data(read_csv_ner(output_dir+\"{0}.csv\".format(2)))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2018-04-19T17:19:13.246441Z",
-     "start_time": "2018-04-19T17:19:13.243633Z"
+     "end_time": "2018-05-16T09:11:35.133829Z",
+     "start_time": "2018-05-16T09:10:29.700Z"
     }
    },
    "outputs": [],
    "source": [
     "from glob import glob\n",
     "import numpy as np\n",
-    "import sys"
+    "import sys\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2018-04-19T17:19:13.297655Z",
-     "start_time": "2018-04-19T17:19:13.249152Z"
+     "end_time": "2018-05-16T09:11:35.135250Z",
+     "start_time": "2018-05-16T09:10:29.702Z"
     }
    },
    "outputs": [],
    "source": [
-    "files=glob(\"/Users/jacquesfize/LOD_DATASETS/bv_lac_pos_ner_disambiguate_2/*.csv\")"
+    "files=glob(\"/Users/jacquesfize/LOD_DATASETS/bv_lac_pos_ner_disambiguate_5/*.csv\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2018-04-19T17:21:12.711563Z",
-     "start_time": "2018-04-19T17:19:13.300567Z"
+     "end_time": "2018-05-16T09:11:35.136229Z",
+     "start_time": "2018-05-16T09:10:29.704Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "5552/5552"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "i=0\n",
     "n=len(files)\n",
     "points={}\n",
+    "p=IntProgress(description=\"Processing\",max=n)\n",
+    "display(p)\n",
     "for fn in files:\n",
     "    i+=1\n",
+    "    p.value+=1\n",
     "    sys.stdout.write(\"\\r{0}/{1}\".format(i,n))\n",
     "    df=pd.read_csv(fn)\n",
     "    df=df.fillna(\"O\")\n",
@@ -531,65 +2156,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-04-19T17:21:12.844337Z",
-     "start_time": "2018-04-19T17:21:12.713671Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "README.md                      \u001b[1m\u001b[36mgraphs\u001b[m\u001b[m/\r\n",
-      "\u001b[1m\u001b[36m__pycache__\u001b[m\u001b[m/                   \u001b[1m\u001b[36mgui_graph_viewer\u001b[m\u001b[m/\r\n",
-      "\u001b[1m\u001b[36mconfig\u001b[m\u001b[m/                        \u001b[1m\u001b[36mhelpers\u001b[m\u001b[m/\r\n",
-      "\u001b[1m\u001b[36mdata\u001b[m\u001b[m/                          \u001b[1m\u001b[36mmodels\u001b[m\u001b[m/\r\n",
-      "\u001b[1m\u001b[36meval\u001b[m\u001b[m/                          \u001b[1m\u001b[36mnlp\u001b[m\u001b[m/\r\n",
-      "eval.py                        \u001b[1m\u001b[36mnotebooks\u001b[m\u001b[m/\r\n",
-      "\u001b[31mexp_17_avril.sh\u001b[m\u001b[m*               pipeline.py\r\n",
-      "\u001b[31mexp_30mars.sh\u001b[m\u001b[m*                 points_dump.txt\r\n",
-      "\u001b[31mexp_fev_18.sh\u001b[m\u001b[m*                 \u001b[31mrequirements.txt\u001b[m\u001b[m*\r\n",
-      "\u001b[31mexp_mar_12.sh\u001b[m\u001b[m*                 \u001b[1m\u001b[36mresources\u001b[m\u001b[m/\r\n",
-      "extract_log                    temp.py\r\n",
-      "generate_data.py               test.py\r\n",
-      "generate_data_csv.py           test_gmatch4py.py\r\n",
-      "generate_selected_document.py  \u001b[1m\u001b[36mtests\u001b[m\u001b[m/\r\n",
-      "generate_transform.py          tools.py\r\n",
-      "\u001b[1m\u001b[36mgmatch4py\u001b[m\u001b[m/                     \u001b[1m\u001b[36mtt4py\u001b[m\u001b[m/\r\n"
-     ]
-    }
-   ],
-   "source": [
-    "ls"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2018-04-19T17:22:50.320185Z",
-     "start_time": "2018-04-19T17:21:12.847627Z"
+     "end_time": "2018-05-16T09:11:35.137502Z",
+     "start_time": "2018-05-16T09:10:29.706Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "5552/5552"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "i=0\n",
     "n=len(files)\n",
     "count={}\n",
+    "p=IntProgress(description=\"Processing\",max=n)\n",
+    "display(p)\n",
     "for fn in files:\n",
     "    i+=1\n",
+    "    p.value+=1\n",
     "    sys.stdout.write(\"\\r{0}/{1}\".format(i,n))\n",
     "    df=pd.read_csv(fn)\n",
     "    df=df.fillna(\"O\")\n",
@@ -603,28 +2186,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2018-04-19T17:23:02.158693Z",
-     "start_time": "2018-04-19T17:22:50.322656Z"
+     "end_time": "2018-05-16T09:11:35.139281Z",
+     "start_time": "2018-05-16T09:10:29.708Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "5552/5552"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "i=0\n",
     "n=len(files)\n",
     "count_idf={}\n",
+    "p=IntProgress(description=\"Processing\",max=n)\n",
+    "display(p)\n",
     "for fn in files:\n",
     "    i+=1\n",
+    "    p.value+=1\n",
     "    sys.stdout.write(\"\\r{0}/{1}\".format(i,n))\n",
     "    df=pd.read_csv(fn)\n",
     "    df=df.fillna(\"O\")\n",
@@ -638,11 +2216,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2018-04-19T17:23:02.183240Z",
-     "start_time": "2018-04-19T17:23:02.160929Z"
+     "end_time": "2018-05-16T09:11:35.140723Z",
+     "start_time": "2018-05-16T09:10:29.708Z"
     }
    },
    "outputs": [],
@@ -656,107 +2234,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2018-04-19T17:23:02.200130Z",
-     "start_time": "2018-04-19T17:23:02.187288Z"
+     "end_time": "2018-05-16T09:11:35.142191Z",
+     "start_time": "2018-05-16T09:10:29.710Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>lat</th>\n",
-       "      <th>lon</th>\n",
-       "      <th>count</th>\n",
-       "      <th>idf</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>GD3404996</th>\n",
-       "      <td>-20.00000</td>\n",
-       "      <td>47.00000</td>\n",
-       "      <td>16408</td>\n",
-       "      <td>-3.574217</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>GD4803039</th>\n",
-       "      <td>9.80000</td>\n",
-       "      <td>38.73330</td>\n",
-       "      <td>1774</td>\n",
-       "      <td>-1.901458</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>GD4160491</th>\n",
-       "      <td>40.86677</td>\n",
-       "      <td>-74.31626</td>\n",
-       "      <td>1335</td>\n",
-       "      <td>-1.935504</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>GD12293461</th>\n",
-       "      <td>52.41000</td>\n",
-       "      <td>16.83000</td>\n",
-       "      <td>75</td>\n",
-       "      <td>0.938270</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>GD11540371</th>\n",
-       "      <td>30.47187</td>\n",
-       "      <td>-97.27472</td>\n",
-       "      <td>501</td>\n",
-       "      <td>-1.246532</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                 lat       lon  count       idf\n",
-       "GD3404996  -20.00000  47.00000  16408 -3.574217\n",
-       "GD4803039    9.80000  38.73330   1774 -1.901458\n",
-       "GD4160491   40.86677 -74.31626   1335 -1.935504\n",
-       "GD12293461  52.41000  16.83000     75  0.938270\n",
-       "GD11540371  30.47187 -97.27472    501 -1.246532"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.head(5)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2018-04-19T17:23:07.914581Z",
-     "start_time": "2018-04-19T17:23:02.202900Z"
+     "end_time": "2018-05-16T09:11:35.143577Z",
+     "start_time": "2018-05-16T09:10:29.712Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x10b99d5f8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Libraries\n",
     "from mpl_toolkits.basemap import Basemap\n",
@@ -780,30 +2279,19 @@
     "m.colorbar()\n",
     "plt.title(\"Spatial Entities Occurrence in BVLAC Corpus (World Scale)\",fontdict={\"fontsize\":24})\n",
     "# Save as png\n",
-    "plt.savefig('SE_Dispersion_World.pdf', bbox_inches='tight')"
+    "plt.savefig('SE_Dispersion_World_{0}Per.pdf'.format(skipPercentage), bbox_inches='tight')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2018-04-20T06:03:14.313187Z",
-     "start_time": "2018-04-20T06:03:10.007003Z"
+     "end_time": "2018-05-16T09:11:35.145171Z",
+     "start_time": "2018-05-16T09:10:29.714Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x10f361da0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Libraries\n",
     "from mpl_toolkits.basemap import Basemap\n",
@@ -831,38 +2319,19 @@
     "# Save as png\n",
     "m.colorbar(location='bottom')\n",
     "plt.title(\"Spatial Entities Occurrence in BVLAC Corpus (Madagascar Scale)\",fontdict={\"fontsize\":15})\n",
-    "plt.savefig('SE_Dispersion_MADA.pdf', bbox_inches='tight')"
+    "plt.savefig('SE_Dispersion_MADA_{0}Per.pdf'.format(skipPercentage), bbox_inches='tight')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2018-04-19T17:23:15.457718Z",
-     "start_time": "2018-04-19T17:23:10.235688Z"
+     "end_time": "2018-05-16T09:11:35.146513Z",
+     "start_time": "2018-05-16T09:10:29.716Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/matplotlib/collections.py:877: RuntimeWarning: invalid value encountered in sqrt\n",
-      "  scale = np.sqrt(self._sizes) * dpi / 72.0 * self._factor\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x10aee8ef0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Libraries\n",
     "from mpl_toolkits.basemap import Basemap\n",
@@ -886,38 +2355,19 @@
     "m.colorbar()\n",
     "plt.title(\"Spatial Entities IDF in BVLAC Corpus (World Scale)\",fontdict={\"fontsize\":24})\n",
     "# Save as png\n",
-    "plt.savefig('SE_Dispersion_IDF_World.pdf', bbox_inches='tight')"
+    "plt.savefig('SE_Dispersion_IDF_World_{0}Per.pdf'.format(skipPercentage), bbox_inches='tight')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2018-04-20T05:55:33.963110Z",
-     "start_time": "2018-04-20T05:55:29.673733Z"
+     "end_time": "2018-05-16T09:11:35.147911Z",
+     "start_time": "2018-05-16T09:10:29.718Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/matplotlib/collections.py:877: RuntimeWarning: invalid value encountered in sqrt\n",
-      "  scale = np.sqrt(self._sizes) * dpi / 72.0 * self._factor\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x110b92390>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Libraries\n",
     "from mpl_toolkits.basemap import Basemap\n",
@@ -945,16 +2395,16 @@
     "# Save as png\n",
     "m.colorbar(location='bottom')\n",
     "plt.title(\"Spatial Entities IDF in BVLAC Corpus (Madagascar Scale)\",fontdict={\"fontsize\":15})\n",
-    "plt.savefig('SE_Dispersion_IDF_MADA.pdf', bbox_inches='tight')"
+    "plt.savefig('SE_Dispersion_IDF_MADA_{0}Per.pdf'.format(skipPercentage), bbox_inches='tight')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2018-04-19T17:23:17.645281Z",
-     "start_time": "2018-04-19T17:23:17.642734Z"
+     "end_time": "2018-05-16T09:11:35.149138Z",
+     "start_time": "2018-05-16T09:10:29.720Z"
     }
    },
    "outputs": [],
@@ -963,6 +2413,147 @@
     "[c+(\"\" if c[-1] in [\"\\'\",\"-\"] else \" \") for c in ch]"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-05-16T09:11:35.150112Z",
+     "start_time": "2018-05-16T09:10:29.722Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "files=glob(\"/Users/jacquesfize/LOD_DATASETS/bv_lac_pos_ner_disambiguate_3/*.csv\")\n",
+    "i=0\n",
+    "n=len(files)\n",
+    "old_points={}\n",
+    "for fn in files:\n",
+    "    i+=1\n",
+    "    sys.stdout.write(\"\\r{0}/{1}\".format(i,n))\n",
+    "    df=pd.read_csv(fn)\n",
+    "    df=df.fillna(\"O\")\n",
+    "    for id,row in df.iterrows():\n",
+    "        if not row[\"GID\"] or row[\"GID\"] == \"O\":\n",
+    "            continue\n",
+    "        if not row[\"GID\"] in old_points:\n",
+    "            data=pd.Series(get_data(row[\"GID\"]))\n",
+    "            if \"coord\" in data:\n",
+    "                old_points[row[\"GID\"]]=[data[\"coord\"][\"lat\"], data[\"coord\"][\"lon\"]]\n",
+    "\n",
+    "        "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-05-16T09:11:35.151756Z",
+     "start_time": "2018-05-16T09:10:29.722Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "len(points),len(old_points)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-05-16T09:11:35.153353Z",
+     "start_time": "2018-05-16T09:10:29.724Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "diff={}\n",
+    "new_keys=list(old_points.keys())\n",
+    "for k in new_keys:\n",
+    "    if not k in points:\n",
+    "        diff[k]=old_points[k]\n",
+    "len(diff)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-05-16T09:11:35.155215Z",
+     "start_time": "2018-05-16T09:10:29.726Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Libraries\n",
+    "# Set the dimension of the figure\n",
+    "\n",
+    "df=pd.DataFrame.from_dict(diff, orient='index')\n",
+    "df=df.rename(columns={0:\"lat\",1:\"lon\"})\n",
+    "\n",
+    "my_dpi=96\n",
+    "plt.figure(figsize=(2600/my_dpi, 1800/my_dpi), dpi=my_dpi)\n",
+    " \n",
+    "# Make the background map\n",
+    "m=Basemap(llcrnrlon=-180, llcrnrlat=-65,urcrnrlon=180,urcrnrlat=80)\n",
+    "#m.drawmapboundary(fill_color='#A6CAE0', linewidth=0)\n",
+    "m.fillcontinents(color='grey', alpha=0.3)\n",
+    "m.drawcoastlines(linewidth=0.1, color=\"#666666\")\n",
+    "#m.arcgisimage(service='ESRI_Imagery_World_2D', xpixels = 1500)\n",
+    "# Add a point per position\n",
+    "m.scatter(df['lon'], df['lat'], s=1 ,cmap=\"autumn\")\n",
+    " \n",
+    "# copyright and source data info\n",
+    "#plt.text( -170, -58,\"Répartition des entités spatiales dans le corpus BVLAC (5500 documents)\", ha='left', va='bottom', size=9, color='#555555' )\n",
+    "#m.colorbar()\n",
+    "plt.title(\"Spatial Entities IDF in BVLAC Corpus (World Scale)\",fontdict={\"fontsize\":24})\n",
+    "# Save as png\n",
+    "plt.savefig('SE_Dispersion_Diff_World_{0}Per.pdf'.format(skipPercentage), bbox_inches='tight')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-05-16T09:11:35.156455Z",
+     "start_time": "2018-05-16T09:10:29.728Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Libraries\n",
+    "from mpl_toolkits.basemap import Basemap\n",
+    "import matplotlib.pyplot as plt\n",
+    " \n",
+    "# Set the dimension of the figure\n",
+    "my_dpi=96\n",
+    "plt.figure(figsize=(2600/my_dpi, 1800/my_dpi), dpi=my_dpi)\n",
+    " \n",
+    "# Make the background map 43.2541870461, -25.6014344215, 50.4765368996, -12.0405567359)\n",
+    "m=Basemap(llcrnrlon=43.2541870461, llcrnrlat=-25.6,urcrnrlon=50.4765368996,urcrnrlat=-11.5,resolution=\"h\")\n",
+    "#m.drawmapboundary(fill_color='#A6CAE0', linewidth=0)\n",
+    "m.fillcontinents(color='grey', alpha=0.3)\n",
+    "m.drawcoastlines(linewidth=0.1, color=\"#666666\")\n",
+    "#m.arcgisimage(service='ESRI_Imagery_World_2D', xpixels = 1500)\n",
+    "\n",
+    "df2=df[(df['lon'] > 43.5) & (df['lon'] < 50.47) & (df['lat'] > -25.6) & (df['lat'] < -12.04) ]\n",
+    "\n",
+    "# Add a point per position\n",
+    "#m.scatter(df2['lon'], df2['lat'], s=df2['count']/6, alpha=0.4,  cmap=\"autumn\")\n",
+    "m.scatter(df2['lon'], df2['lat'],s=1,  cmap=\"YlOrRd\")\n",
+    "# copyright and source data info\n",
+    "#plt.text( -170, -58,\"Répartition des entités spatiales dans le corpus BVLAC (5500 documents)\", ha='left', va='bottom', size=9, color='#555555' )\n",
+    " \n",
+    "# Save as png\n",
+    "#m.colorbar(location='bottom')\n",
+    "plt.title(\"Spatial Entities IDF in BVLAC Corpus (Madagascar Scale)\",fontdict={\"fontsize\":15})\n",
+    "plt.savefig('SE_Dispersion_Diff_MADA_{0}Per.pdf'.format(skipPercentage), bbox_inches='tight')"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -987,7 +2578,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.0"
+   "version": "3.6.5"
   },
   "toc": {
    "nav_menu": {},
diff --git a/pipeline.py b/pipeline.py
index 8c47af0..264f4a0 100644
--- a/pipeline.py
+++ b/pipeline.py
@@ -12,6 +12,7 @@ from nlp.ner.ner import NER
 from nlp.ner.stanford_ner import StanfordNER
 from nlp.pos_tagger.tagger import Tagger
 from nlp.pos_tagger.treetagger import TreeTagger
+import json
 
 
 class Pipeline(object):
diff --git a/temp.py b/temp.py
new file mode 100644
index 0000000..37b907c
--- /dev/null
+++ b/temp.py
@@ -0,0 +1,181 @@
+# coding = utf-8
+import argparse
+import glob
+import logging
+import string
+import time
+from concurrent.futures import ThreadPoolExecutor
+
+from langdetect import detect
+from progressbar import ProgressBar, Timer, Bar, ETA, Counter
+
+from nlp.disambiguator.geodict_gaurav import *
+from pipeline import *
+
+
+logging.basicConfig(format='%(asctime)s %(message)s')
+
+def filter_nonprintable(text):
+    # Get the difference of all ASCII characters from the set of printable characters
+    nonprintable = set([chr(i) for i in range(128)]).difference(string.printable)
+    # Use translate to remove all non-printable characters
+    return text.translate({ord(character):None for character in nonprintable})
+
+parser = argparse.ArgumentParser()
+parser.add_argument("texts_input_dir")
+parser.add_argument("graphs_output_dir")
+parser.add_argument("metadata_output_fn")
+
+subparsers = parser.add_subparsers(help='commands')
+
+normal = subparsers.add_parser(
+    'normal', help='Basic STR generation. No argument are necessary !')
+normal.set_defaults(which="norm")
+
+
+gen_parser = subparsers.add_parser(
+    'generalisation', help='Apply a generalisation transformation on the generated STRs')
+gen_parser.set_defaults(which="gene")
+gen_parser.add_argument(
+    '-t','--type_gen', help='Type of generalisation',default="all")
+gen_parser.add_argument(
+    '-n', help='Language',default=1)
+gen_parser.add_argument(
+    '-b','--bound', help='If Generalisation is bounded, this arg. correspond'
+                         'to the maximal ',default="country")
+
+ext_parser = subparsers.add_parser(
+    'extension', help='Apply a extension process on the STRs')
+ext_parser.set_defaults(which="ext")
+ext_parser.add_argument(
+    '-d','--distance', help='radius distance',default=150)
+ext_parser.add_argument(
+    '-u','--unit', help='unit used for the radius distance',default="km")
+ext_parser.add_argument(
+    '-a','--adjacent_count', help='number of adjacent SE add to the STR',default=1)
+
+args = parser.parse_args()
+if "which" in args:
+    if args.which =="gene":
+        args.type_trans="gen"
+    elif args.which =="ext":
+        args.type_trans="ext"
+
+print("Parameters entered : ",args)
+
+
+start = time.time()
+class_=StanfordNER
+# Initialise Graphs Transformers
+pipeline= {
+    "en":Pipeline(lang="english",tagger=Tagger(),ner=class_(lang="en")),
+    "fr":Pipeline(lang="french",tagger=Tagger(),ner=class_(lang="fr")),
+    "es":Pipeline(lang="espagnol",tagger=Tagger(),ner=class_(lang="es"))
+}
+
+
+
+# Read Input Files
+import re
+texts_=[]
+if os.path.exists(args.texts_input_dir):
+    files_glob= glob.glob(args.texts_input_dir+"/*.txt")
+    files_=["" ]* len(files_glob)
+    for fn in files_glob:
+        id = int(re.findall("\d+", fn)[-1])
+        files_[id]=fn
+    if not files_:
+        print("No .txt files found in {0}".format(args.texts_input_dir))
+        exit()
+    for fn in files_:
+        try:
+            tex=open(fn).read()
+            #lang = detect(tex) #for bug encoding
+            texts_.append(tex)
+        except:
+            print("{0} could'nt be read ! Add Lorem Ipsum instead".format(fn))
+            texts_.append("Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.")
+
+
+# If output Dir doesn't exists
+if not os.path.exists(args.graphs_output_dir):
+    os.makedirs(args.graphs_output_dir)
+
+if not texts_:
+    print("No text files were loaded !")
+    exit()
+
+
+
+
+data={}
+n=0
+logging.info("Identify Document(s) language(s)")
+with ProgressBar(max_value=len(texts_),widgets=[' [', Timer(), '] ',Bar(),'(', Counter(),')','(', ETA(), ')']) as pg:
+    for text in range(len(texts_)):
+        pg.update(text)
+        if not text:
+            lang="en"
+        else:
+            try:
+                lang=detect(texts_[text])
+
+            except Exception as e:
+                lang="en"
+            #print(lang, text)
+        if not lang in data and lang in pipeline:
+            data[lang]=[]
+        if lang in pipeline:
+            data[lang].append(text)
+        else:
+            if not "en" in data:data["en"]=[] # Ca peut arriver :s :s :s !!!
+            data["en"].append(text)
+    # except:
+        #     n+=1 # encoding error
+
+associated_es={}
+count_per_doc={}
+list_gs=[]
+i=0
+
+
+
+def workSTR(id_doc,text,count_per_doc,associated_es, list_gs,pg,lang):
+    global i
+    if not text:
+        count_per_doc[id_doc] = {}
+        associated_es[id_doc] = {}
+        g = nx.MultiDiGraph()
+        list_gs.append(g)
+
+    else:
+        t = filter_nonprintable(text)
+        # try:
+        str, count, se_identified = pipeline[lang].build(t, None, **vars(args))
+        list_gs.append(str.graph)
+        # Save Metadata
+        count_per_doc[id_doc] = count
+        associated_es[id_doc] = se_identified
+
+
+    # Save Graph structure
+    nx.write_gexf(list_gs[-1], args.graphs_output_dir + "/{0}.gexf".format(id_doc))
+    i+=1
+    pg.update(i)
+
+
+logging.info("Extracting Toponyms and Building STR...")
+queue=[]
+with  ThreadPoolExecutor(max_workers=4) as executor:
+    with ProgressBar(max_value=len(texts_),widgets=[' [', Timer(), '] ',Bar(),'(', Counter(),')','(', ETA(), ')']) as pg:
+        pg.start()
+        for lang in data:
+            for id_doc in data[lang]:
+                future = executor.submit(workSTR,id_doc,texts_[id_doc],count_per_doc,associated_es, list_gs,pg,lang)
+
+
+# Save Metadata
+open(os.path.join(args.graphs_output_dir,args.metadata_output_fn),'w').write(json.dumps([associated_es,count_per_doc],indent=4))
+
+
+print("--- %s seconds ---" % (time.time() - start))
\ No newline at end of file
-- 
GitLab