Use scikit-learn in requirements instead of deprecated sklearn (O…

…asisLMF#1186) * use scikit-learn in requirements * more PEP8
sambles · Dec 20, 2022 · 413187a · 413187a
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commit 413187a
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Showing 7 changed files with 10 additions and 10 deletions.
diff --git a/oasislmf/computation/generate/losses.py b/oasislmf/computation/generate/losses.py
@@ -749,7 +749,7 @@ def run(self):
         guls_fp = os.path.join(output_dir, "raw_guls.csv")
         guls.to_csv(guls_fp, index=False)
 
-        #il_stream_type = 2 if self.fmpy else 1
+        # il_stream_type = 2 if self.fmpy else 1
         ils_fp = os.path.join(output_dir, 'raw_ils.csv')
 
         # Create IL fmpy financial structures

diff --git a/oasislmf/lookup/builtin.py b/oasislmf/lookup/builtin.py
@@ -351,7 +351,7 @@ def build_rtree(self, file_path, file_type, id_columns, nearest_neighbor_min_dis
 
         if nearest_neighbor_min_distance > 0:
             if BallTree is None:
-                raise OasisException(f"sklearn modules are needed for rtree with nearest_neighbor_min_distance, {OPT_INSTALL_MESSAGE}")
+                raise OasisException(f"scikit-learn modules are needed for rtree with nearest_neighbor_min_distance, {OPT_INSTALL_MESSAGE}")
             gdf_area_peril['center'] = gdf_area_peril.centroid
             base_geometry_name = gdf_area_peril.geometry.name
 

diff --git a/oasislmf/pytools/fm/stream.py b/oasislmf/pytools/fm/stream.py
@@ -28,7 +28,7 @@
 logger = logging.getLogger(__name__)
 
 
-#gul_header = np.int32(1 << 24).tobytes()
+# gul_header = np.int32(1 << 24).tobytes()
 fm_header = np.int32(1 | 2 << 24).tobytes()
 
 
@@ -215,7 +215,7 @@ def load_event(event_agg, sidx_loss, event_id, nodes_array, losses, loss_indexes
             output_id = output_array[node['output_ids'] + layer]
             if output_id:  # if output is not in xref output_id is 0
                 loss_index = loss_indexes[node['ba'] + layer]
-                #print(computes[compute_i], output_id, loss_index, losses[loss_index])
+                # print(computes[compute_i], output_id, loss_index, losses[loss_index])
                 while cursor < nb_values:
                     if i_index == 0:
                         event_agg[cursor]['event_id'], event_agg[cursor]['agg_id'] = event_id, output_id

diff --git a/optional-package.in b/optional-package.in
@@ -1,6 +1,6 @@
 shapely>=1.3.0
 geopandas>=0.8.0
-sklearn
+scikit-learn
 pyarrow>=3.0.0
 rtree>=0.8.3
 cookiecutter>=1.6.0

diff --git a/setup.py b/setup.py
@@ -200,7 +200,7 @@ def try_get_bin_install_kwargs(self):
                 OS = None
 
         # ENV OVERRIDE TO install a localy copy of ktools
-        #TAR_DIR = os.path.abspath(os.getenv('KTOOLS_TAR_FILE_DIR', ''))
+        # TAR_DIR = os.path.abspath(os.getenv('KTOOLS_TAR_FILE_DIR', ''))
 
         if os.getenv('KTOOLS_TAR_FILE_DIR', None):
             TAR_OVERRIDE = os.path.join(os.path.abspath(os.getenv('KTOOLS_TAR_FILE_DIR')), '{}_{}.tar.gz'.format(OS, ARCH))

diff --git a/tests/model_execution/test_bash.py b/tests/model_execution/test_bash.py
@@ -138,7 +138,7 @@ def gen_chunked_bash(self, name,
         )
 
         # debug
-        #print(json.dumps(params, indent=4))
+        # print(json.dumps(params, indent=4))
 
         fifo_tmp_dir = params['fifo_tmp_dir']
         for process_id in range(num_partitions):

diff --git a/tests/model_preparation/test_summaries.py b/tests/model_preparation/test_summaries.py
@@ -152,12 +152,12 @@ def test_single_peril__totals_correct(self, data):
 
         # Check number of not-modelled
         # WARNING: current assumption is that all cov types must be covered to be modelled
-        #moddeled = 0
-        #moddeld_loc_ids = gul_inputs[gul_inputs['status'] == 'success'].loc_id.unique()
+        # moddeled = 0
+        # moddeld_loc_ids = gul_inputs[gul_inputs['status'] == 'success'].loc_id.unique()
         # for loc_id in moddeld_loc_ids:
         #    if len(gul_inputs[gul_inputs.loc_id == loc_id].coverage_type_id.unique()) == 4:
         #        moddeled+=1
-        #self.assertEqual(len(loc_df) - moddeled, exp_summary['total']['not-modelled']['number_of_locations'])
+        # self.assertEqual(len(loc_df) - moddeled, exp_summary['total']['not-modelled']['number_of_locations'])
 
     @given(st.data())
     @settings(max_examples=10, deadline=None)