Merge pull request #409 from EducationalTestingService/fix-output-dir…

…ectory-creation

Fix output directory creation

skll/config.py

@@ -481,10 +481,11 @@ def _parse_config_file(config_path, log_level=logging.INFO):
     # next, get the log path before anything else since we need to
     # save all logging messages to a log file in addition to displaying
     # them on the console
-    log_path = _locate_file(config.get("Output", "log"), config_dir)
-    if log_path:
-        log_path = join(config_dir, log_path)
-        if not exists(log_path):
+    try:
+        log_path = _locate_file(config.get("Output", "log"), config_dir)
+    except IOError as e:
+        if e.errno == errno.ENOENT:
+            log_path = e.filename
             os.makedirs(log_path)
 
     # Create a top-level log file under the log path
@@ -731,24 +732,29 @@ def _parse_config_file(config_path, log_level=logging.INFO):
     probability = config.getboolean("Output", "probability")
 
     # do we want to keep the predictions?
-    prediction_dir = _locate_file(config.get("Output", "predictions"),
-                                  config_dir)
-    if prediction_dir:
-        if not exists(prediction_dir):
+    # make sure the predictions path exists and if not create it
+    try:
+        prediction_dir = _locate_file(config.get("Output", "predictions"),
+                                      config_dir)
+    except IOError as e:
+        if e.errno == errno.ENOENT:
+            prediction_dir = e.filename
             os.makedirs(prediction_dir)
 
-    # make sure model path exists
-    model_path = _locate_file(config.get("Output", "models"), config_dir)
-    if model_path:
-        model_path = join(config_dir, model_path)
-        if not exists(model_path):
+    # make sure model path exists and if not, create it
+    try:
+        model_path = _locate_file(config.get("Output", "models"), config_dir)
+    except IOError as e:
+        if e.errno == errno.ENOENT:
+            model_path = e.filename
             os.makedirs(model_path)
 
     # make sure results path exists
-    results_path = _locate_file(config.get("Output", "results"), config_dir)
-    if results_path:
-        results_path = join(config_dir, results_path)
-        if not exists(results_path):
+    try:
+        results_path = _locate_file(config.get("Output", "results"), config_dir)
+    except IOError as e:
+        if e.errno == errno.ENOENT:
+            results_path = e.filename
             os.makedirs(results_path)
 
     # what are the output metrics?

tests/test_input.py

@@ -62,6 +62,10 @@ def tearDown():
     config_dir = join(_my_dir, 'configs')
     for config_file in glob(join(config_dir, 'test_config_parsing_*.cfg')):
         os.unlink(config_file)
+    for auto_dir in glob(join(_my_dir, 'auto*')):
+        for auto_dir_file in os.listdir(auto_dir):
+            os.unlink(join(auto_dir, auto_dir_file))
+        os.rmdir(auto_dir)
 
 
 def check_safe_float_conversion(converted_val, expected_val):
@@ -1119,6 +1123,60 @@ def test_config_parsing_relative_input_paths():
      learning_curve_train_sizes, output_metrics) = _parse_config_file(config_path)
 
 
+def test_config_parsing_automatic_output_directory_creation():
+
+    train_dir = '../train'
+    train_file = join(train_dir, 'f0.jsonlines')
+    test_file = join(train_dir, 'f1.jsonlines')
+    output_dir = '../output'
+
+    # make a simple config file that has new directories that should
+    # be automatically created
+    new_log_path = join(_my_dir, 'autolog')
+    new_results_path = join(_my_dir, 'autoresults')
+    new_models_path = join(_my_dir, 'automodels')
+    new_predictions_path = join(_my_dir, 'autopredictions')
+
+    ok_(not(exists(new_log_path)))
+    ok_(not(exists(new_results_path)))
+    ok_(not(exists(new_models_path)))
+    ok_(not(exists(new_predictions_path)))
+
+    values_to_fill_dict = {'experiment_name': 'auto_dir_creation',
+                           'task': 'evaluate',
+                           'train_file': train_file,
+                           'test_file': test_file,
+                           'learners': "['LogisticRegression']",
+                           'log': new_log_path,
+                           'results': new_results_path,
+                           'models': new_models_path,
+                           'predictions': new_predictions_path,
+                           'objective': 'f1_score_micro'}
+
+    config_template_path = join(_my_dir, 'configs',
+                                'test_relative_paths.template.cfg')
+    config_path = fill_in_config_options(config_template_path,
+                                         values_to_fill_dict,
+                                         'auto_dir_creation')
+
+    (experiment_name, task, sampler, fixed_sampler_parameters,
+     feature_hasher, hasher_features, id_col, label_col, train_set_name,
+     test_set_name, suffix, featuresets, do_shuffle, model_path,
+     do_grid_search, grid_objective, probability, results_path,
+     pos_label_str, feature_scaling, min_feature_count, folds_file,
+     grid_search_jobs, grid_search_folds, cv_folds, save_cv_folds,
+     use_folds_file_for_grid_search, do_stratified_folds,
+     fixed_parameter_list, param_grid_list, featureset_names, learners,
+     prediction_dir, log_path, train_path, test_path, ids_to_floats,
+     class_map, custom_learner_path, learning_curve_cv_folds_list,
+     learning_curve_train_sizes, output_metrics) = _parse_config_file(config_path)
+
+    ok_(exists(new_log_path))
+    ok_(exists(new_results_path))
+    ok_(exists(new_models_path))
+    ok_(exists(new_predictions_path))
+
+
 def check_config_parsing_metrics_and_objectives_overlap(task,
                                                         metrics,
                                                         objectives):