abhijeet-talaulikar · August 21, 2023 14:19 · Aug 21, 2023
diff --git a/mta-credit-card-transition.py b/mta-credit-card-transition.py
@@ -0,0 +1,52 @@
+# Function to create intermediate path strings
+def transition_states(paths):
+    unique_channels = set(x for element in paths for x in element)
+    transition_states = {x + '>' + y: 0 for x in unique_channels for y in unique_channels}
+
+    for possible_state in unique_channels:
+        if possible_state not in ['Activation', 'Null']:
+            for user_path in paths:
+                if possible_state in user_path:
+                    indices = [i for i, s in enumerate(user_path) if possible_state in s]
+                    for col in indices:
+                        transition_states[user_path[col] + '>' + user_path[col + 1]] += 1
+
+    return transition_states
+
+# Function to calculate transition probabilities
+def transition_prob(trans_dict, paths):
+    unique_channels = set(x for element in paths for x in element)
+    trans_prob = dict()
+    for state in unique_channels:
+        if state not in ['Activation', 'Null']:
+            counter = 0
+            index = [i for i, s in enumerate(trans_dict) if state + '>' in s]
+            for col in index:
+                if trans_dict[list(trans_dict)[col]] > 0:
+                    counter += trans_dict[list(trans_dict)[col]]
+            for col in index:
+                if trans_dict[list(trans_dict)[col]] > 0:
+                    state_prob = float((trans_dict[list(trans_dict)[col]])) / float(counter)
+                    trans_prob[list(trans_dict)[col]] = state_prob
+
+    return trans_prob
+
+# Function to create transition matrix
+def transition_matrix(list_of_paths, transition_probabilities):
+    trans_matrix = pd.DataFrame()
+    unique_channels = set(x for element in list_of_paths for x in element)
+
+    for channel in unique_channels:
+        trans_matrix[channel] = 0.00
+        trans_matrix.loc[channel] = 0.00
+        trans_matrix.loc[channel][channel] = 1.0 if channel in ['Activation', 'Null'] else 0.0
+
+    for key, value in transition_probabilities.items():
+        origin, destination = key.split('>')
+        trans_matrix.at[origin, destination] = value
+
+    return trans_matrix
+
+trans_states = transition_states(journeys['path'])
+trans_prob = transition_prob(trans_states, journeys['path'])
+trans_matrix = transition_matrix(journeys['path'], trans_prob)