--- a
+++ b/Features/FeatureParserThread.py
@@ -0,0 +1,115 @@
+#!/usr/bin/env python
+# -*- coding: UTF-8 -*-
+#
+# Copyright 2017 University of Westminster. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+""" It reads and parses the variables, then it generate features, in threaded batches.
+"""
+
+from typing import List, TypeVar, Dict
+import numpy as np
+import statistics
+from scipy.stats import itemfreq
+
+PandasDataFrame = TypeVar('DataFrame')
+NumpyNdarray = TypeVar('ndarray')
+
+__author__ = "Mohsen Mesgarpour"
+__copyright__ = "Copyright 2016, https://github.com/mesgarpour"
+__credits__ = ["Mohsen Mesgarpour"]
+__license__ = "GPL"
+__version__ = "1.1"
+__maintainer__ = "Mohsen Mesgarpour"
+__email__ = "mohsen.mesgarpour@gmail.com"
+__status__ = "Release"
+
+
+class FeatureParserThread:
+
+    @staticmethod
+    def aggregate_cell(postfixes: str,
+                       variable_type: str,
+                       prevalence: Dict,
+                       variable_cell: str) -> NumpyNdarray:
+        """Aggregate the variable value, based on the selected aggregated functions.
+        :param postfixes: the aggregated variable.
+        :param variable_type: the type of the input variable.
+        :param prevalence: the prevalence dictionary of values for all the variables.
+        :param variable_cell: the variable value (a single row) to aggregate.
+        :return: the aggregated value (a single row).
+        """
+        features_temp = np.zeros([len(postfixes)])
+
+        # if null or empty
+        if variable_cell is None or variable_cell == "":
+            return features_temp
+
+        # parse variables
+        # Note: replace None with '0'
+        variable_cell = variable_cell.split('|')
+        variable_cell = [v2 for v1 in variable_cell for v2 in set(v1.split(','))]
+        variable_cell = [v if v != "" else 0 for v in variable_cell]
+        if variable_type == "INT":
+            variable_cell = list(map(int, variable_cell))
+
+        # generate freq. table
+        freq = itemfreq(variable_cell)
+        freq = np.array([tuple(row) for row in freq if row[1] != 0], dtype=[('value', 'int'), ('freq', 'int')])
+        freq_sorted = np.sort(freq, order=['freq', 'value'])[::-1]['value']
+        freq_dic = dict(zip(map(str, freq['value']), freq['freq']))
+
+        # set
+        for p in range(len(postfixes)):
+            if len(postfixes[p]) > 11 and postfixes[p][0:11] == "prevalence_":
+                index = int(postfixes[p].split('_')[1]) - 1
+                if index < len(prevalence):
+                    value = prevalence[index]
+                    if str(value) in freq_dic.keys():
+                        features_temp[p] = freq_dic[value]
+            elif len(postfixes[p]) > 9 and postfixes[p][0:9] == "max_freq_":
+                index = int(postfixes[p][9:]) - 1
+                if len(freq_sorted) > index:
+                    features_temp[p] = freq_sorted[index]
+            elif postfixes[p] == "others_cnt":
+                features_temp[p] = len(freq_sorted)  # np.count_nonzero(variable_cell)
+            elif postfixes[p] == "max":
+                features_temp[p] = max(variable_cell)
+            elif postfixes[p] == "avg":
+                features_temp[p] = statistics.mean(variable_cell)
+            elif postfixes[p] == "min":
+                features_temp[p] = min(variable_cell)
+            elif postfixes[p] == "median":
+                features_temp[p] = statistics.median(variable_cell)
+            else:
+                raise ValueError(postfixes)
+        return features_temp
+
+    @staticmethod
+    def prevalence_cell(variable_cell: str) -> List:
+        """Parse the inputted variable value (a single row), to a list of value.
+        :param variable_cell: the variable value (a single row), to calculate the prevalence.
+        :return: the list of values of the current variable value.
+        """
+        # if null or empty
+        if variable_cell is None or variable_cell == "":
+            return []
+        else:
+            # parse variables
+            # Note: replace None with '0'
+            variable_cell = variable_cell.split('|')
+            variable_cell = [v2 for v1 in variable_cell for v2 in set(v1.split(','))]
+            variable_cell = [v if v != "" else 0 for v in variable_cell]
+            variable_cell = list(map(str, variable_cell))
+            return variable_cell