Add parameter "progress file"

docs_sources/api.md

@@ -14,7 +14,9 @@
                           <strong>periodic_checkpoint_folder</strong>=None,
                           <strong>early_stop</strong>=None,
                           <strong>verbosity</strong>=0,
-                          <strong>disable_update_check</strong>=False</em>)</pre>
+                          <strong>disable_update_check</strong>=False,
+                          <strong>log_file</strong>=None
+                          </em>)</pre>
 <div align="right"><a href="https://github.com/EpistasisLab/tpot/blob/master/tpot/base.py">source</a></div>
 
 Automated machine learning for supervised classification tasks.
@@ -222,6 +224,13 @@ Flag indicating whether the TPOT version checker should be disabled.
 <br /><br />
 The update checker will tell you when a new version of TPOT has been released.
 </blockquote>
+
+<strong>log_file</strong>: io.TextIOWrapper or io.StringIO, optional (defaul: sys.stdout)
+<br /><br />
+<blockquote>
+Save progress content to a file.
+</blockquote>
+
 </td>
 </tr>
 

tests/test_log_file.py

@@ -0,0 +1,72 @@
+# -*- coding: utf-8 -*-
+
+"""This file is part of the TPOT library.
+
+TPOT was primarily developed at the University of Pennsylvania by:
+    - Randal S. Olson (rso@randalolson.com)
+    - Weixuan Fu (weixuanf@upenn.edu)
+    - Daniel Angell (dpa34@drexel.edu)
+    - and many more generous open source contributors
+
+TPOT is free software: you can redistribute it and/or modify
+it under the terms of the GNU Lesser General Public License as
+published by the Free Software Foundation, either version 3 of
+the License, or (at your option) any later version.
+
+TPOT is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+GNU Lesser General Public License for more details.
+
+You should have received a copy of the GNU Lesser General Public
+License along with TPOT. If not, see <http://www.gnu.org/licenses/>.
+
+"""
+
+from tpot import TPOTClassifier
+from sklearn.datasets import load_iris
+from nose.tools import assert_equal
+import os
+
+data = load_iris()
+X = data['data']
+y = data['target']
+
+def test_log_file_verbosity_1():
+  """ Set verbosity as 1. Assert log_file parameter to generate log file. """
+  file_name = "progress_verbose_1.log"
+  tracking_progress_file = open(file_name, "w")
+  tpot_obj = TPOTClassifier(
+                population_size=10,
+                generations=10,
+                verbosity=1, 
+                log_file=tracking_progress_file
+            )
+  tpot_obj.fit(X, y)
+  assert_equal(os.path.getsize(file_name), 0)
+
+def test_log_file_verbosity_2():
+  """ Set verbosity as 2. Assert log_file parameter to generate log file. """
+  file_name = "progress_verbose_2.log"
+  tracking_progress_file = open(file_name, "w")
+  tpot_obj = TPOTClassifier(
+                population_size=10,
+                generations=10,
+                verbosity=2, 
+                log_file=tracking_progress_file
+            )
+  tpot_obj.fit(X, y)
+  assert_equal(os.path.getsize(file_name) > 0,  True)
+
+def test_log_file_verbose_3():
+  """ Set verbosity as 3. Assert log_file parameter to generate log file. """
+  file_name = "progress_verbosity_3.log"
+  tracking_progress_file = open(file_name, "w")
+  tpot_obj = TPOTClassifier(
+                population_size=10,
+                generations=10,
+                verbosity=3, 
+                log_file=tracking_progress_file
+            )
+  tpot_obj.fit(X, y)
+  assert_equal(os.path.getsize(file_name) > 0,  True)

tests/tpot_tests.py

@@ -47,6 +47,7 @@
 import warnings
 from multiprocessing import cpu_count
 import os
+import sys
 from re import search
 from datetime import datetime
 from time import sleep
@@ -155,7 +156,8 @@ def test_init_custom_parameters():
         verbosity=1,
         random_state=42,
         disable_update_check=True,
-        warm_start=True
+        warm_start=True,
+        log_file=None
     )
 
     assert tpot_obj.population_size == 500
@@ -168,6 +170,7 @@ def test_init_custom_parameters():
     assert tpot_obj.max_time_mins is None
     assert tpot_obj.warm_start is True
     assert tpot_obj.verbosity == 1
+    assert tpot_obj.log_file == None
 
     tpot_obj._fit_init()
 
@@ -179,7 +182,14 @@ def test_init_custom_parameters():
     assert tpot_obj._optimized_pipeline_score == None
     assert tpot_obj.fitted_pipeline_ == None
     assert tpot_obj._exported_pipeline_text == []
+    assert tpot_obj.log_file == sys.stdout
 
+def test_init_custom_progress_file():
+    """ Assert that TPOT has right file handler to save progress. """
+    file_name = "progress.txt"
+    file_handle = open(file_name, "w")
+    tpot_obj = TPOTClassifier(log_file=file_handle)
+    assert tpot_obj.log_file == file_handle
 
 def test_init_default_scoring():
     """Assert that TPOT intitializes with the correct default scoring function."""
@@ -1196,7 +1206,7 @@ def test_check_periodic_pipeline():
     )
     tpot_obj.fit(training_features, training_target)
     with closing(StringIO()) as our_file:
-        tpot_obj._file = our_file
+        tpot_obj.log_file = our_file
         tpot_obj.verbosity = 3
         tpot_obj._last_pipeline_write = datetime.now()
         sleep(0.11)
@@ -1240,7 +1250,7 @@ def test_save_periodic_pipeline():
     )
     tpot_obj.fit(training_features, training_target)
     with closing(StringIO()) as our_file:
-        tpot_obj._file = our_file
+        tpot_obj.log_file = our_file
         tpot_obj.verbosity = 3
         tpot_obj._last_pipeline_write = datetime.now()
         sleep(0.11)
@@ -1270,7 +1280,7 @@ def test_save_periodic_pipeline_2():
     )
     tpot_obj.fit(training_features, training_target)
     with closing(StringIO()) as our_file:
-        tpot_obj._file = our_file
+        tpot_obj.log_file = our_file
         tpot_obj.verbosity = 3
         tpot_obj._last_pipeline_write = datetime.now()
         sleep(0.11)
@@ -1301,7 +1311,7 @@ def test_check_periodic_pipeline_3():
     )
     tpot_obj.fit(training_features, training_target)
     with closing(StringIO()) as our_file:
-        tpot_obj._file = our_file
+        tpot_obj.log_file = our_file
         tpot_obj.verbosity = 3
         tpot_obj._exported_pipeline_text = []
         tpot_obj._last_pipeline_write = datetime.now()
@@ -1544,7 +1554,7 @@ def test_update_pbar():
     # reset verbosity = 3 for checking pbar message
     tpot_obj.verbosity = 3
     with closing(StringIO()) as our_file:
-        tpot_obj._file=our_file
+        tpot_obj.log_file=our_file
         tpot_obj._pbar = tqdm(total=10, disable=False, file=our_file)
         tpot_obj._update_pbar(pbar_num=2, pbar_msg="Test Warning Message")
         our_file.seek(0)
@@ -1563,7 +1573,7 @@ def test_update_val():
     # reset verbosity = 3 for checking pbar message
     tpot_obj.verbosity = 3
     with closing(StringIO()) as our_file:
-        tpot_obj._file=our_file
+        tpot_obj.log_file=our_file
         tpot_obj._pbar = tqdm(total=10, disable=False, file=our_file)
         result_score_list = []
         result_score_list = tpot_obj._update_val(0.9999, result_score_list)
@@ -1610,7 +1620,7 @@ def test_preprocess_individuals():
     # reset verbosity = 3 for checking pbar message
     tpot_obj.verbosity = 3
     with closing(StringIO()) as our_file:
-        tpot_obj._file=our_file
+        tpot_obj.log_file=our_file
         tpot_obj._pbar = tqdm(total=2, disable=False, file=our_file)
         operator_counts, eval_individuals_str, sklearn_pipeline_list, stats_dicts = \
                                 tpot_obj._preprocess_individuals(individuals)
@@ -1656,7 +1666,7 @@ def test_preprocess_individuals_2():
     # reset verbosity = 3 for checking pbar message
     tpot_obj.verbosity = 3
     with closing(StringIO()) as our_file:
-        tpot_obj._file=our_file
+        tpot_obj.log_file=our_file
         tpot_obj._pbar = tqdm(total=3, disable=False, file=our_file)
         operator_counts, eval_individuals_str, sklearn_pipeline_list, stats_dicts = \
                                 tpot_obj._preprocess_individuals(individuals)
@@ -1703,7 +1713,7 @@ def test_preprocess_individuals_3():
     # reset verbosity = 3 for checking pbar message
 
     with closing(StringIO()) as our_file:
-        tpot_obj._file=our_file
+        tpot_obj.log_file=our_file
         tpot_obj._lambda=4
         tpot_obj._pbar = tqdm(total=2, disable=False, file=our_file)
         tpot_obj._pbar.n = 2

tpot/base.py

@@ -91,7 +91,8 @@ def __init__(self, generations=100, population_size=100, offspring_size=None,
                  random_state=None, config_dict=None, template=None,
                  warm_start=False, memory=None, use_dask=False,
                  periodic_checkpoint_folder=None, early_stop=None,
-                 verbosity=0, disable_update_check=False):
+                 verbosity=0, disable_update_check=False,
+                 log_file=None):
         """Set up the genetic programming algorithm for pipeline optimization.
 
         Parameters
@@ -235,7 +236,8 @@ def __init__(self, generations=100, population_size=100, offspring_size=None,
             A setting of 2 or higher will add a progress bar during the optimization procedure.
         disable_update_check: bool, optional (default: False)
             Flag indicating whether the TPOT version checker should be disabled.
-
+        log_file: io.TextIOWrapper or io.StringIO, optional (defaul: sys.stdout)
+            Save progress content to a file.
 
         Returns
         -------
@@ -266,6 +268,7 @@ def __init__(self, generations=100, population_size=100, offspring_size=None,
         self.verbosity = verbosity
         self.disable_update_check = disable_update_check
         self.random_state = random_state
+        self.log_file = log_file
 
 
     def _setup_template(self, template):
@@ -566,9 +569,9 @@ def _fit_init(self):
             )
 
         self._pbar = None
-        # Specifies where to output the progress messages (default: sys.stdout).
-        # Maybe open this API in future version of TPOT.(io.TextIOWrapper or io.StringIO)
-        self._file = sys.stdout
+
+        if not self.log_file:
+            self.log_file = sys.stdout
 
         self._setup_scoring_function(self.scoring)
 
@@ -693,7 +696,7 @@ def pareto_eq(ind1, ind2):
         else:
             total_evals = self._lambda * self.generations + self.population_size
 
-        self._pbar = tqdm(total=total_evals, unit='pipeline', leave=False,
+        self._pbar = tqdm(total=total_evals, unit='pipeline', leave=False, file=self.log_file,
                           disable=not (self.verbosity >= 2), desc='Optimization Progress')
 
         try:
@@ -717,9 +720,9 @@ def pareto_eq(ind1, ind2):
         # Allow for certain exceptions to signal a premature fit() cancellation
         except (KeyboardInterrupt, SystemExit, StopIteration) as e:
             if self.verbosity > 0:
-                self._pbar.write('', file=self._file)
+                self._pbar.write('', file=self.log_file)
                 self._pbar.write('{}\nTPOT closed prematurely. Will use the current best pipeline.'.format(e),
-                                 file=self._file)
+                                 file=self.log_file)
         finally:
             # clean population for the next call if warm_start=False
             if not self.warm_start:
@@ -1331,10 +1334,11 @@ def _evaluate_individuals(self, population, features, target, sample_weight=None
 
         except (KeyboardInterrupt, SystemExit, StopIteration) as e:
             if self.verbosity > 0:
-                self._pbar.write('', file=self._file)
+                self._pbar.write('', file=self.log_file)
                 self._pbar.write('{}\nTPOT closed during evaluation in one generation.\n'
-                                    'WARNING: TPOT may not provide a good pipeline if TPOT is stopped/interrupted in an early generation.'.format(e),
-                                 file=self._file)
+                                    'WARNING: TPOT may not provide a good pipeline if TPOT is stopped/interrupted in a early generation.'.format(e),
+                                 file=self.log_file)
+
             # number of individuals already evaluated in this generation
             num_eval_ind = len(result_score_list)
             self._update_evaluated_individuals_(result_score_list,
@@ -1482,7 +1486,7 @@ def _update_pbar(self, pbar_num=1, pbar_msg=None):
         """
         if not isinstance(self._pbar, type(None)):
             if self.verbosity > 2 and pbar_msg is not None:
-                self._pbar.write(pbar_msg, file=self._file)
+                self._pbar.write(pbar_msg, file=self.log_file)
             if not self._pbar.disable:
                 self._pbar.update(pbar_num)