Fix Target contains only one unique value error (Issue #1)

.vscode/launch.json

@@ -12,6 +12,221 @@
             "program": "${file}",
             "console": "integratedTerminal",
             "justMyCode": true
+        },{
+            "name": "Train \"latin\" 4000",
+            "type": "python",
+            "request": "launch",
+            "program": "transmorgrify.py",
+            "console": "integratedTerminal",
+            "justMyCode": true,
+            "args": [
+                "--train",
+                "--in_csv", "./examples/latin/training_latin.txt",
+                "--a_header", "original",
+                "--b_header", "split",
+                "--device", "0:1",
+                "--model", "latin_forward.tm",
+                "--train_percentage", "50",
+            ]
+        },{
+            "name": "Train \"latin\" 100",
+            "type": "python",
+            "request": "launch",
+            "program": "transmorgrify.py",
+            "console": "integratedTerminal",
+            "justMyCode": true,
+            "args": [
+                "--train",
+                "--in_csv", "./examples/latin/training_latin.txt",
+                "--a_header", "original",
+                "--b_header", "split",
+                "--device", "0:1",
+                "--model", "latin_forward_100.tm",
+                "-n", "100",
+                "--train_percentage", "50",
+            ]
+        },{
+            "name": "Train \"latin\" 10000",
+            "type": "python",
+            "request": "launch",
+            "program": "transmorgrify.py",
+            "console": "integratedTerminal",
+            "justMyCode": true,
+            "args": [
+                "--train",
+                "--in_csv", "./examples/latin/training_latin.txt",
+                "--a_header", "original",
+                "--b_header", "split",
+                "--device", "0:1",
+                "--model", "latin_forward_10000.tm",
+                "-n", "10000",
+                "--train_percentage", "50",
+            ]
+        },{
+            "name": "Execute \"latin\" 4000",
+            "type": "python",
+            "request": "launch",
+            "program": "transmorgrify.py",
+            "console": "integratedTerminal",
+            "justMyCode": true,
+            "args": [
+                "--execute",
+                "--in_csv", "./examples/latin/training_latin.txt",
+                "--a_header", "original",
+                "--b_header", "split",
+                "--device", "0:1",
+                "--model", "latin_forward.tm",
+                "--verbose",
+                "--include_stats",
+                "--out_csv", "latin_4000.csv",
+                "--train_percentage", "50",
+            ]
+        },{
+            "name": "Execute \"latin\" 100",
+            "type": "python",
+            "request": "launch",
+            "program": "transmorgrify.py",
+            "console": "integratedTerminal",
+            "justMyCode": true,
+            "args": [
+                "--execute",
+                "--in_csv", "./examples/latin/training_latin.txt",
+                "--a_header", "original",
+                "--b_header", "split",
+                "--device", "0:1",
+                "--model", "latin_forward_100.tm",
+                "--verbose",
+                "--include_stats",
+                "--out_csv", "latin_100.csv",
+                "--train_percentage", "50",
+            ]
+        },{
+            "name": "Execute \"latin\" 10000",
+            "type": "python",
+            "request": "launch",
+            "program": "transmorgrify.py",
+            "console": "integratedTerminal",
+            "justMyCode": true,
+            "args": [
+                "--execute",
+                "--in_csv", "./examples/latin/training_latin.txt",
+                "--a_header", "original",
+                "--b_header", "split",
+                "--device", "0:1",
+                "--model", "latin_forward_10000.tm",
+                "--verbose",
+                "--include_stats",
+                "--out_csv", "latin_10000.csv",
+                "--train_percentage", "50",
+            ]
+        },{
+            "name": "Demo \"latin\" 4000",
+            "type": "python",
+            "request": "launch",
+            "program": "transmorgrify.py",
+            "console": "integratedTerminal",
+            "justMyCode": true,
+            "args": [
+                "--gradio",
+                "--in_csv", "./examples/latin/training_latin.txt",
+                "--a_header", "original",
+                //"--b_header", "split",
+                //"--device", "0:1",
+                "--model", "latin_forward.tm"
+            ]
+        },{
+            "name": "Demo \"latin\" 100",
+            "type": "python",
+            "request": "launch",
+            "program": "transmorgrify.py",
+            "console": "integratedTerminal",
+            "justMyCode": true,
+            "args": [
+                "--gradio",
+                "--in_csv", "./examples/latin/training_latin.txt",
+                "--a_header", "original",
+                //"--b_header", "split",
+                //"--device", "0:1",
+                "--model", "latin_forward_100.tm"
+            ]
+        },{
+            "name": "Demo \"latin\" 10000",
+            "type": "python",
+            "request": "launch",
+            "program": "transmorgrify.py",
+            "console": "integratedTerminal",
+            "justMyCode": true,
+            "args": [
+                "--gradio",
+                "--in_csv", "./examples/latin/training_latin.txt",
+                "--a_header", "original",
+                //"--b_header", "split",
+                //"--device", "0:1",
+                "--model", "latin_forward_10000.tm"
+            ]
+        },{
+            "name": "Train \"latin\" mod",
+            "type": "python",
+            "request": "launch",
+            "program": "transmorgrify.py",
+            "console": "integratedTerminal",
+            "justMyCode": true,
+            "args": [
+                "--train",
+                "--in_csv", "./examples/latin/training_latin_mod.csv",
+                "--a_header", "original",
+                "--b_header", "split",
+                "--device", "0:1",
+                "--model", "latin_mod_forward.tm"
+            ]
+        },{
+            "name": "Execute \"latin\" mod",
+            "type": "python",
+            "request": "launch",
+            "program": "transmorgrify.py",
+            "console": "integratedTerminal",
+            "justMyCode": true,
+            "args": [
+                "--execute",
+                "--in_csv", "./examples/latin/training_latin_mod.csv",
+                "--a_header", "original",
+                //"--b_header", "split",
+                "--device", "0:1",
+                "--model", "latin_mod_forward.tm",
+                "--out_csv", "latin_mod_out.csv",
+            ]
+        },{
+            "name": "Execute mixup",
+            "type": "python",
+            "request": "launch",
+            "program": "transmorgrify.py",
+            "console": "integratedTerminal",
+            "justMyCode": true,
+            "args": [
+                "--execute",
+                "--in_csv", "./examples/latin/training_latin_mod.csv",
+                "--a_header", "original",
+                //"--b_header", "split",
+                "--device", "0:1",
+                //"--model", "latin_mod_forward.tm",
+                "--model", "phonetics_small.tm",
+                "--out_csv", "mixum_out.csv",
+            ]
+        },{
+            "name": "Demo \"latin\" mod",
+            "type": "python",
+            "request": "launch",
+            "program": "transmorgrify.py",
+            "console": "integratedTerminal",
+            "justMyCode": true,
+            "args": [
+                "--gradio",
+                "--in_csv", "./examples/latin/training_latin_mod.csv",
+                "--a_header", "original",
+                //"--b_header", "split",
+                //"--device", "0:1",
+                "--model", "latin_mod_forward.tm"
+            ]
         },{
             "name": "Train phonetic 4000 gpu",
             "type": "python",
@@ -21,29 +236,14 @@
             "justMyCode": true,
             "args": [
                 "--train",
-                "--in_csv", "/home/lansford/Sync/projects/tf_over/sentence_transmogrifier/examples/phonetic/phonetic.csv",
+                "--in_csv", "./examples/phonetic/phonetic.csv",
                 "--a_header", "English",
                 "--b_header", "Phonetic",
                 "--device", "0:1",
                 "--model", "phonetics_forward.tm"
             ]
-            },{
-                "name": "Train reverse phonetic 4000 gpu",
-                "type": "python",
-                "request": "launch",
-                "program": "transmorgrify.py",
-                "console": "integratedTerminal",
-                "justMyCode": true,
-                "args": [
-                    "--train",
-                    "--in_csv", "/home/lansford/Sync/projects/tf_over/sentence_transmogrifier/examples/phonetic/phonetic.csv",
-                    "--b_header", "English",
-                    "--a_header", "Phonetic",
-                    "--device", "0:1",
-                    "--model", "phonetics_backwards.tm"
-                ]
         },{
-            "name": "Train short phonetic 4000 gpu",
+            "name": "Train reverse phonetic 4000 gpu",
             "type": "python",
             "request": "launch",
             "program": "transmorgrify.py",
@@ -51,31 +251,30 @@
             "justMyCode": true,
             "args": [
                 "--train",
-                "--in_csv", "/home/lansford/Sync/projects/tf_over/sentence_transmogrifier/examples/phonetic/phonetic_short.csv",
-                "--a_header", "English",
-                "--b_header", "Phonetic",
+                "--in_csv", "./examples/phonetic/phonetic.csv",
+                "--b_header", "English",
+                "--a_header", "Phonetic",
                 "--device", "0:1",
-                "--model", "phonetics_small.tm"
+                "--model", "phonetics_backwards.tm"
             ]
         },{
-            "name": "Execute phonetic gpu",
+            "name": "Train short phonetic 4000 gpu",
             "type": "python",
             "request": "launch",
             "program": "transmorgrify.py",
             "console": "integratedTerminal",
             "justMyCode": true,
             "args": [
-                "--execute",
-                "--in_csv", "/home/lansford/Sync/projects/tf_over/sentence_transmogrifier/examples/phonetic/phonetic.csv",
-                "--out_csv", "./phonetic_out.csv",
+                "--train",
+                "--in_csv", "./examples/phonetic/phonetic_short.csv",
                 "--a_header", "English",
                 "--b_header", "Phonetic",
                 "--device", "0:1",
-                "--model", "phonetics_forward.tm",
-                "--verbose",
+                "-n", "100",
+                "--model", "phonetics_small.tm"
             ]
         },{
-            "name": "short Execute phonetic",
+            "name": "Execute phonetic gpu",
             "type": "python",
             "request": "launch",
             "program": "transmorgrify.py",
@@ -83,16 +282,13 @@
             "justMyCode": true,
             "args": [
                 "--execute",
-                "--in_csv", "/home/lansford/Sync/projects/tf_over/sentence_transmogrifier/examples/phonetic/phonetic_short.csv",
-                "--out_csv", "./phonetic_out.csv",
-                "--a_header", "English",
-                "--b_header", "Phonetic",
-                "--model", "phonetics_forward.tm",
+                "--in_csv", "./examples/phonetic/phonetic.csv",
+                "--a_header", "original",
+                //"--b_header", "split",
                 "--verbose",
-                "--include_stats",
             ]
         },{
-            "name": "short Execute reverse phonetic",
+            "name": "Execute short phonetic",
             "type": "python",
             "request": "launch",
             "program": "transmorgrify.py",
@@ -100,11 +296,12 @@
             "justMyCode": true,
             "args": [
                 "--execute",
-                "--in_csv", "/home/lansford/Sync/projects/tf_over/sentence_transmogrifier/examples/phonetic/phonetic_short.csv",
-                "--out_csv", "./reverse_phonetic_out.csv",
-                "--b_header", "English",
-                "--a_header", "Phonetic",
-                "--model", "phonetics_backwards.tm",
+                "--in_csv", "./examples/phonetic/phonetic_short.csv",
+                "--out_csv", "./phonetic_out.csv",
+                "--a_header", "English",
+                "--b_header", "Phonetic",
+                "--model", "phonetics_small.tm",
+                "--device", "0:1",
                 "--verbose",
                 "--include_stats",
             ]

README.md

@@ -13,10 +13,10 @@ license: apache-2.0
 ## Sentence Transmorgrifier
 
 # What is the Sentence Transmorgrifier?
-- The Sentence Transmorgrifier is a framework to make text to text conversion models which uses a categorical gradiant boost library, [catboost](https://catboost.ai/), as its back end.
+- The Sentence Transmorgrifier is a framework to make text to text conversion models which uses a categorical gradient boost library, [catboost](https://catboost.ai/), as its back end.
 - This library does not use neural net or word embeddings but does the transformation on the character level.
 - For Sentence Transmorgrifier to work, there has to be some common characters between the from and two conversion.
-- The model uses a modified form of the [logest common subsequence algorithm](https://en.wikipedia.org/wiki/Longest_common_subsequence_problem) to transform the sentence conversion into a sequence of three types of operations:
+- The model uses a modified form of the [longest common subsequence algorithm](https://en.wikipedia.org/wiki/Longest_common_subsequence_problem) to transform the sentence conversion into a sequence of three types of operations:
   1. Match: Pass the character from input to output
   2. Drop: Remove the incoming character from the input.
   3. Insert: Generate a character and add it to the output.
@@ -31,7 +31,7 @@ license: apache-2.0
 - The project has been configured to be able to be used in two different ways.
 
 ## Shell access
-- The transmorgrify.py script can be called directly with arguments specifying an input csv file, what lables are from and to and what to save the resulting model as or to process the input csv to an output.  Here is an example:
+- The transmorgrify.py script can be called directly with arguments specifying an input csv file, what labels are from and to and what to save the resulting model as or to process the input csv to an output.  Here is an example:
 
 ```sh
 python transmorgrify.py \
@@ -49,7 +49,7 @@ python transmorgrify.py \
  - `--b_header` This indicates the to column
  - `--device` This specifies the gpu if you have one or type `cpu` if you do not have a gpu.
  - `--model` This indicates where to save the model
- - `--verbose` Self explanitory
+ - `--verbose` Self explanatory
  - `--iterations` This indicates how many catboost iterations should be executed on your input data.
  - `--train_percentage` If you are going to use the same file for testing as well as the training, giving a train percentage will only use the percentage specified for training.
 
@@ -71,7 +71,7 @@ python transmorgrify.py \
  - `--b_header` This indicates the to column.  The to column must be specified if `--include_stats` is also specified.
  - `--device` This specifies the gpu if you have one or type `cpu` if you do not have a gpu.
  - `--model` This indicates where to load the model
- - `--verbose` Self explanitory
+ - `--verbose` Self explanatory
  - `--include_stats` This adds editing distance to the output csv so that you can sort and graph how well the model did.  It reports the Levenshtein Distance from input to output before and after transformation and the percent improvement.
  - `--out_csv` This indicates where the data should be saved after being processed by the model.
  - `--train_percentage` If you are going to use the same file for testing as well as the training, give the same train percentage as was given for training and the execution will only use the remaining data not used for training.
@@ -107,7 +107,7 @@ model -- The filename of the model to load. (default my_model.tm)
 ```
   - `execute`
 ```
-Runs the data from from_sentaces.  The results are returned 
+Runs the data from from_sentences.  The results are returned 
 using yield so you need to wrap this in list() if you want 
 to index it.  from_sentences can be an array or a generator.
 
@@ -150,4 +150,4 @@ my_model.load( "my_model.tm" )
 results = list( my_model.execute( inference_data["from_header"] ) )
 ```
 # What is the license?
-- The licence has been set to apache-2.0 to match catboost so I don't have to think about compatibility issues.
+- The license has been set to apache-2.0 to match catboost so I don't have to think about compatibility issues.

transmorgrify.py

@@ -36,7 +36,21 @@ class Transmorgrifier:
         #and the char model
         #slice through where only the action is insert.
         insert_indexes = Y['action'] == INSERT_TO
-        self.char_model = _train_catboost( X[insert_indexes], Y['char'][insert_indexes], iterations, verbose=verbose, device=device, model_piece='char' )
+
+        #if there is only one char to insert, we can't train the second model and need to handle that as a boundary case.
+        if Y['char'][insert_indexes].nunique() > 1:
+            self.char_model = _train_catboost( X[insert_indexes], Y['char'][insert_indexes], iterations, verbose=verbose, device=device, model_piece='char' )
+            self.constant_output = None
+        else:
+            self.char_model = None
+            if Y['char'][insert_indexes].nunique() == 1:
+                self.constant_output = Y['char'][insert_indexes].unique()[0]
+            else:
+                #If there is never an insertion handle it as always inserting a space,
+                #because it will never insert, but it handles the boundary case so the saving and loading code works.
+                self.constant_output = ' '
+            
+
 
         self.trailing_context = trailing_context
         self.leading_context = leading_context
@@ -52,19 +66,24 @@ class Transmorgrifier:
         model -- The pathname to save the model such as "my_model.tm" (default my_model.tm)
         """
         self.name = model
-        with zipfile.ZipFile( model, mode="w", compression=zipfile.ZIP_DEFLATED, compresslevel=9 ) as myzip:
-            with myzip.open( 'params.json', mode='w' ) as out:
-                out.write( json.dumps({
+        with zipfile.ZipFile( model, mode="w", compression=zipfile.ZIP_DEFLATED, compresslevel=9 ) as my_zip:
+            with my_zip.open( 'params.json', mode='w' ) as out:
+                params = {
                     'version': FILE_VERSION,
                     'leading_context': self.leading_context,
                     'trailing_context': self.trailing_context,
                     'iterations': self.iterations,
-                }).encode())
+                }
+                if self.constant_output is not None:
+                    params['constant_output'] = self.constant_output
+
+                out.write( json.dumps(params).encode())
             temp_filename = _mktemp()
             self.action_model.save_model( temp_filename )
-            myzip.write( temp_filename, "action.cb" )
-            self.char_model.save_model( temp_filename )
-            myzip.write( temp_filename,   "char.cb" )
+            my_zip.write( temp_filename, "action.cb" )
+            if not self.char_model is None:
+                self.char_model.save_model( temp_filename )
+                my_zip.write( temp_filename,   "char.cb" )
             os.unlink( temp_filename )
 
         return self
@@ -78,21 +97,26 @@ class Transmorgrifier:
         """
         self.name = model
         with zipfile.ZipFile( model, mode='r' ) as zip:
-            with zip.open( 'params.json' ) as fin:
-                params = json.loads( fin.read().decode() )
+            with zip.open( 'params.json' ) as f_in:
+                params = json.loads( f_in.read().decode() )
                 if params['version'] > FILE_VERSION: raise Exception( f"Version {params['version']} greater than {FILE_VERSION}" )
                 self.leading_context = int(params['leading_context'])
                 self.trailing_context = int(params['trailing_context'])
                 self.iterations = int(params['iterations'])
             temp_filename = _mktemp()
-            with zip.open( 'action.cb' ) as fin:
-                with open( temp_filename, "wb" ) as fout:
-                    fout.write( fin.read() )
+            with zip.open( 'action.cb' ) as f_in:
+                with open( temp_filename, "wb" ) as f_out:
+                    f_out.write( f_in.read() )
             self.action_model = CatBoostClassifier().load_model( temp_filename )
-            with zip.open( 'char.cb' ) as fin:
-                with open( temp_filename, "wb" ) as fout:
-                    fout.write( fin.read() )
-            self.char_model   = CatBoostClassifier().load_model(  temp_filename   )
+            if 'constant_output' not in params:
+                with zip.open( 'char.cb' ) as f_in:
+                    with open( temp_filename, "wb" ) as f_out:
+                        f_out.write( f_in.read() )
+                self.char_model   = CatBoostClassifier().load_model(  temp_filename   )
+                self.constant_output = None
+            else:
+                self.constant_output = params['constant_output']
+                self.char_model = None
 
         os.unlink( temp_filename)
 
@@ -101,7 +125,7 @@ class Transmorgrifier:
     
     def execute( self, from_sentences, verbose=False ):
         """
-        Runs the data from from_sentaces.  The results are returned 
+        Runs the data from from_sentences.  The results are returned 
         using yield so you need to wrap this in list() if you want 
         to index it.  from_sentences can be an array or a generator.
 
@@ -113,6 +137,7 @@ class Transmorgrifier:
             yield _do_reconstruct( 
                 action_model=self.action_model, 
                 char_model=self.char_model, 
+                constant_output=self.constant_output,
                 text=from_sentence, 
                 num_pre_context_chars=self.leading_context, 
                 num_post_context_chars=self.trailing_context  )
@@ -133,15 +158,15 @@ class Transmorgrifier:
         demo.launch( share=share )
 
 def _list_trace( trace ):
-    if trace.parrent is None:
+    if trace.parent is None:
         result = [trace]
     else:
-        result = _list_trace( trace.parrent )
+        result = _list_trace( trace.parent )
         result.append( trace )
     return result
 
 class _edit_trace_hop():
-    parrent = None
+    parent = None
     edit_distance = None
     char = None
     from_row_i = None
@@ -182,7 +207,7 @@ def _trace_edits( from_sentence, to_sentence, print_debug=False ):
             #root case.
             if from_row_i == 0 and to_column_i == 0:
                 best_option = _edit_trace_hop()
-                best_option.parrent = None
+                best_option.parent = None
                 best_option.edit_distance = 0
                 best_option.char = ""
                 best_option.from_row_i = from_row_i
@@ -193,8 +218,8 @@ def _trace_edits( from_sentence, to_sentence, print_debug=False ):
             if to_column_i > 0:
                 if best_option is None or current_row[to_column_i-1].edit_distance + 1 < best_option.edit_distance:
                     best_option = _edit_trace_hop()
-                    best_option.parrent = current_row[to_column_i-1]
-                    best_option.edit_distance = best_option.parrent.edit_distance + 1
+                    best_option.parent = current_row[to_column_i-1]
+                    best_option.edit_distance = best_option.parent.edit_distance + 1
                     best_option.char = to_sentence[to_column_i-1]
                     best_option.from_row_i = from_row_i
                     best_option.to_column_i = to_column_i
@@ -204,8 +229,8 @@ def _trace_edits( from_sentence, to_sentence, print_debug=False ):
             if from_row_i > 0:
                 if best_option is None or last_row[to_column_i].edit_distance + 1 < best_option.edit_distance:
                     best_option = _edit_trace_hop()
-                    best_option.parrent = last_row[to_column_i]
-                    best_option.edit_distance = best_option.parrent.edit_distance + 1
+                    best_option.parent = last_row[to_column_i]
+                    best_option.edit_distance = best_option.parent.edit_distance + 1
                     best_option.char = from_sentence[from_row_i-1]
                     best_option.from_row_i = from_row_i
                     best_option.to_column_i = to_column_i
@@ -216,8 +241,8 @@ def _trace_edits( from_sentence, to_sentence, print_debug=False ):
                     if to_sentence[to_column_i-1] == from_sentence[from_row_i-1]:
                         if best_option is None or last_row[to_column_i-1].edit_distance <= best_option.edit_distance: #prefer match so use <= than <
                             best_option = _edit_trace_hop()
-                            best_option.parrent = last_row[to_column_i-1]
-                            best_option.edit_distance = best_option.parrent.edit_distance + 1
+                            best_option.parent = last_row[to_column_i-1]
+                            best_option.edit_distance = best_option.parent.edit_distance + 1
                             best_option.char = from_sentence[from_row_i-1]
                             best_option.from_row_i = from_row_i
                             best_option.to_column_i = to_column_i
@@ -231,8 +256,8 @@ def _trace_edits( from_sentence, to_sentence, print_debug=False ):
 
     if print_debug:
         def print_diffs( current_node ):
-            if current_node.parrent is not None:
-                print_diffs( current_node.parrent )
+            if current_node.parent is not None:
+                print_diffs( current_node.parent )
             
             if current_node.action == START:
                 print( "start" )
@@ -344,7 +369,7 @@ def _parse_single_for_training( from_sentence, to_sentence, num_pre_context_char
     result_split_into_dict['action'] = action_slice
     result_split_into_dict['char']   = char_slice
         
-    #now return it as a dataframe.
+    #now return it as a data_frame.
     return pd.DataFrame( context_split_into_dict ), pd.DataFrame( result_split_into_dict )
 
 
@@ -391,8 +416,8 @@ def _train_catboost( X, y, iterations, device, verbose, model_piece, learning_ra
 
 
 def _mktemp():
-    #I know mktemp exists in the library but it has been depricated suggesting using
-    #mkstemp but catboost can't write to a filehandle yet, so I need an actual
+    #I know mktemp exists in the library but it has been deprecated suggesting using
+    #mkstemp but catboost can't write to a file handle yet, so I need an actual
     #filename.
     number = 0
     while os.path.exists( f".temp_{number}~" ):
@@ -400,24 +425,21 @@ def _mktemp():
     return f".temp_{number}~"
 
 
-def _do_reconstruct( action_model, char_model, text, num_pre_context_chars, num_post_context_chars  ):
-    # result = ""
-    # for i in range(len(text)):
-    #     pre_context = ( (" " * num_pre_context_chars) + result[max(0,len(result)-num_pre_context_chars):])[-num_pre_context_chars:]
-    #     post_context = (text[i:min(len(text),i+num_post_context_chars)] + (" " * num_post_context_chars))[:num_post_context_chars]
-    #     full_context = pre_context + post_context
-    #     context_as_dictionary = { 'c'+str(c):[full_context[c]] for c in range(len(full_context)) }
-    #     context_as_pd = pd.DataFrame( context_as_dictionary )
-
-    #     model_result = model.predict( context_as_pd )[0]
-
-    #     if not quite and len( result ) % 500 == 0: print( "%" + str(i*100/len(text))[:4] + " " + result[-100:])
-
-    #     if model_result: result += " "
-    #     result += text[i]
+def predict_wrapper( model, model_input ):
+    #Big hack.  Catboost has shown itself to be unstable on producing
+    #either a single value or an array with a single value in it.
+    #I traced it back to the saved model, and then the model to what
+    #data it is trained on.  But I could figure out what it was
+    #in the data which would make the saved model be one way or the other
+    #so I am going to use the results this way so that it works either way.
+    result = model.predict( model_input )[0]
+    try:
+        result = result[0]
+    except:
+        pass
+    return result
 
-    #     pass
-    # return result
+def _do_reconstruct( action_model, char_model, constant_output, text, num_pre_context_chars, num_post_context_chars  ):
 
     #test for nan.
     if text != text: text = ''
@@ -451,7 +473,7 @@ def _do_reconstruct( action_model, char_model, text, num_pre_context_chars, num_
         context_as_pd = pd.DataFrame( context_as_dictionary )
 
         #run the model
-        action_model_result = action_model.predict( context_as_pd )[0][0]
+        action_model_result = predict_wrapper(action_model,context_as_pd )
 
         #stop run away.  If we have added more chars then our context, nothing is going to change.
         if action_model_result == INSERT_TO and continuous_added >= num_post_context_chars:
@@ -461,8 +483,11 @@ def _do_reconstruct( action_model, char_model, text, num_pre_context_chars, num_
         if action_model_result == START:
             pass
         elif action_model_result == INSERT_TO:
-            #for an insert ask the char model what to insert
-            char_model_result = char_model.predict( context_as_pd )[0][0]
+            if constant_output is None:
+                #for an insert ask the char model what to insert
+                char_model_result = predict_wrapper(char_model, context_as_pd )
+            else:
+                char_model_result = constant_output
 
             working_to += char_model_result
             continuous_added += 1
@@ -507,7 +532,7 @@ def train( in_csv, a_header, b_header, model, iterations, device, leading_contex
     split_index = int( train_percentage/100*len(full_data) )
     train_data = full_data.iloc[:split_index,:].reset_index(drop=True)
 
-    if verbose: print( "parcing data for training" )
+    if verbose: print( "parsing data for training" )
 
 
     tm = Transmorgrifier()
@@ -645,7 +670,7 @@ def main():
         tm = Transmorgrifier()
         tm.load( args.model )
 
-        tm.demo( args.share is not None )
+        tm.demo( share=args.share )
 
 
 if __name__ == '__main__':