Merge remote-tracking branch 'origin/master'

simba/mixins/train_model_mixin.py

@@ -58,7 +58,9 @@
 from simba.plotting.shap_agg_stats_visualizer import \
     ShapAggregateStatisticsVisualizer
 from simba.ui.tkinter_functions import TwoOptionQuestionPopUp
-from simba.utils.checks import (check_file_exist_and_readable, check_float,
+from simba.utils.checks import (check_all_dfs_in_list_has_same_cols,
+                                check_file_exist_and_readable,
+                                check_filepaths_in_iterable_exist, check_float,
                                 check_if_dir_exists, check_if_valid_input,
                                 check_instance, check_int, check_str,
                                 check_that_column_exist, check_valid_array,
@@ -115,52 +117,38 @@ def read_all_files_in_folder(self,
         :examples:
         >>> self.read_all_files_in_folder(file_paths=['targets_inserted/Video_1.csv', 'targets_inserted/Video_2.csv'], file_type='csv', classifier_names=['Attack'])
         """
-
+        check_filepaths_in_iterable_exist(file_paths=file_paths, name=self.__class__.__name__)
         timer = SimbaTimer(start=True)
-        frm_number_lst = []
-        df_concat = pd.DataFrame()
+        frm_number_lst, dfs = [], []
+        if len(file_paths) == 0:
+            raise NoDataError(msg="SimBA found 0 annotated frames in the project_folder/csv/targets_inserted directory", source=self.__class__.__name__)
         for file_cnt, file in enumerate(file_paths):
-            print(f"Reading in file {str(file_cnt + 1)}/{str(len(file_paths))}...")
             _, vid_name, _ = get_fn_ext(file)
-            df = (
-                read_df(file, file_type)
-                .dropna(axis=0, how="all")
-                .fillna(0)
-                .astype(np.float32)
-            )
-            frm_number_lst.extend((df.index))
+            print(f"Reading in {vid_name} (file {str(file_cnt + 1)}/{str(len(file_paths))})...")
+            df = (read_df(file, file_type).dropna(axis=0, how="all").fillna(0).astype(np.float32))
+            frm_number_lst.extend(list(df.index))
             df.index = [vid_name] * len(df)
             if classifier_names != None:
                 for clf_name in classifier_names:
                     if not clf_name in df.columns:
                         raise MissingColumnsError(msg=f"Data for video {vid_name} does not contain any annotations for behavior {clf_name}. Delete classifier {clf_name} from the SimBA project, or add annotations for behavior {clf_name} to the video {vid_name}", source=self.__class__.__name__,)
                     elif (len(set(df[clf_name].unique()) - {0, 1}) > 0 and raise_bool_clf_error):
                         raise InvalidInputError(msg=f"The annotation column for a classifier should contain only 0 or 1 values. However, in file {file} the {clf_name} field contains additional value(s): {list(set(df[clf_name].unique()) - {0, 1})}.", source=self.__class__.__name__)
-                    else:
-                        df_concat = pd.concat([df_concat, df], axis=0)
-            else:
-                df_concat = pd.concat([df_concat, df], axis=0)
-        try:
-            df_concat = df_concat.set_index("scorer")
-        except KeyError:
-            pass
-        if len(df_concat) == 0:
-            raise NoDataError(msg="SimBA found 0 annotated frames in the project_folder/csv/targets_inserted directory", source=self.__class__.__name__)
-        df_concat = df_concat.loc[
-                    :, ~df_concat.columns.str.contains("^Unnamed")
-                    ].fillna(0)
+            dfs.append(df)
+
+        check_all_dfs_in_list_has_same_cols(dfs=dfs, source='/project_folder/csv/targets_inserted', raise_error=True)
+        col_headers = [list(x.columns) for x in dfs]
+        dfs = [x[col_headers[0]] for x in dfs]
+        dfs = pd.concat(dfs, axis=0)
+        if 'scorer' in dfs.columns:
+            dfs = dfs.set_index("scorer")
+        dfs = dfs.loc[:, ~dfs.columns.str.contains("^Unnamed")].fillna(0)
         timer.stop_timer()
-        memory_size = get_memory_usage_of_df(df=df_concat)
-        print(
-            f'Dataset size: {memory_size["megabytes"]}MB / {memory_size["gigabytes"]}GB'
-        )
-        print(
-            "{} file(s) read (elapsed time: {}s) ...".format(
-                str(len(file_paths)), timer.elapsed_time_str
-            )
-        )
+        memory_size = get_memory_usage_of_df(df=dfs)
+        print(f'Dataset size: {memory_size["megabytes"]}MB / {memory_size["gigabytes"]}GB')
+        print(f"{len(file_paths)} file(s) read (elapsed time: {timer.elapsed_time_str}s) ...")
 
-        return df_concat.astype(np.float32), frm_number_lst
+        return dfs.astype(np.float32), frm_number_lst
 
     def read_in_all_model_names_to_remove(self, config: configparser.ConfigParser, model_cnt: int, clf_name: str) -> List[str]:
         """
@@ -1580,24 +1568,29 @@ def read_all_files_in_folder_mp_futures(self,
         """
 
         THREADSAFE_CORE_COUNT = 16
+        check_filepaths_in_iterable_exist(file_paths=annotations_file_paths, name=self.__class__.__name__)
         try:
             if (platform.system() == "Darwin") and (multiprocessing.get_start_method() != "spawn"):
                 multiprocessing.set_start_method("spawn", force=True)
             cpu_cnt, _ = find_core_cnt()
             if (cpu_cnt > THREADSAFE_CORE_COUNT) and (platform.system() == OS.WINDOWS.value):
                 cpu_cnt = THREADSAFE_CORE_COUNT
-            df_lst, frm_number_list = [], []
+            dfs, frm_number_list = [], []
             with concurrent.futures.ProcessPoolExecutor(max_workers=cpu_cnt) as executor:
                 results = [executor.submit(self._read_data_file_helper_futures, data, file_type, classifier_names, raise_bool_clf_error) for data in annotations_file_paths]
                 for result in concurrent.futures.as_completed(results):
-                    df_lst.append(result.result()[0])
+                    dfs.append(result.result()[0])
                     frm_number_list.extend((result.result()[-1]))
                     print(f"Reading complete {result.result()[1]} (elapsed time: {result.result()[2]}s)...")
-            df_concat = pd.concat(df_lst, axis=0).round(4)
-            if "scorer" in df_concat.columns:
-                df_concat = df_concat.drop(["scorer"], axis=1)
 
-            return df_concat, frm_number_list
+            check_all_dfs_in_list_has_same_cols(dfs=dfs, source='/project_folder/csv/targets_inserted', raise_error=True)
+            col_headers = [list(x.columns) for x in dfs]
+            dfs = [x[col_headers[0]] for x in dfs]
+            dfs = pd.concat(dfs, axis=0).round(4)
+            if "scorer" in dfs.columns: dfs = dfs.drop(["scorer"], axis=1)
+            memory_size = get_memory_usage_of_df(df=dfs)
+            print(f'Dataset size: {memory_size["megabytes"]}MB / {memory_size["gigabytes"]}GB')
+            return dfs, frm_number_list
 
         except Exception as e:
             MultiProcessingFailedWarning(msg=f"Multi-processing file read failed, reverting to single core (increased run-time on large datasets). Exception: {e.args}")

simba/sandbox/check_dfs_in_lst.py

@@ -0,0 +1,19 @@
+from typing import List
+
+import pandas as pd
+
+from simba.utils.checks import check_valid_lst
+
+
+def check_all_dfs_in_list_has_same_cols(dfs: List[pd.DataFrame], raise_error: bool = True) -> bool:
+    check_valid_lst(data=dfs, source=check_all_dfs_in_list_has_same_cols.__name__, valid_dtypes=(pd.DataFrame,), min_len=1)
+    col_headers = [list(x.columns) for x in dfs]
+    common_headers = set(col_headers[0]).intersection(*col_headers[1:])
+    all_headers = set(item for sublist in col_headers for item in sublist)
+    missing_headers = list(all_headers - common_headers)
+    if len(missing_headers) > 0:
+        if raise_error:
+            raise MissingColumnsError(msg=f"The data in project_folder/csv/targets_inserted directory do not contain the same headers. Some files are missing the headers: {missing_headers}", source=self.__class__.__name__)
+        else:
+            return False
+    return True

simba/utils/checks.py

@@ -24,7 +24,8 @@
                                 DirectoryNotEmptyError, FFMPEGNotFoundError,
                                 FloatError, FrameRangeError, IntegerError,
                                 InvalidFilepathError, InvalidInputError,
-                                NoDataError, NoFilesFoundError, NoROIDataError,
+                                MissingColumnsError, NoDataError,
+                                NoFilesFoundError, NoROIDataError,
                                 NotDirectoryError, ParametersFileError,
                                 StringError)
 from simba.utils.warnings import (CorruptedFileWarning, FrameRangeWarning,
@@ -1478,3 +1479,17 @@ def check_filepaths_in_iterable_exist(file_paths: Iterable[str],
         check_str(name=f'{check_filepaths_in_iterable_exist.__name__} {file_path} {name}', value=file_path)
         if not os.path.isfile(file_path):
             raise NoFilesFoundError(msg=f'{name} {file_path} is not a valid file path')
+
+def check_all_dfs_in_list_has_same_cols(dfs: List[pd.DataFrame], raise_error: bool = True, source: str = '') -> bool:
+    """ Checks that all dataframes in list has the same column names"""
+    check_valid_lst(data=dfs, source=check_all_dfs_in_list_has_same_cols.__name__, valid_dtypes=(pd.DataFrame,), min_len=1)
+    col_headers = [list(x.columns) for x in dfs]
+    common_headers = set(col_headers[0]).intersection(*col_headers[1:])
+    all_headers = set(item for sublist in col_headers for item in sublist)
+    missing_headers = list(all_headers - common_headers)
+    if len(missing_headers) > 0:
+        if raise_error:
+            raise MissingColumnsError(msg=f"The data in {source} directory do not contain the same headers. Some files are missing the headers: {missing_headers}", source=check_all_dfs_in_list_has_same_cols.__name__)
+        else:
+            return False
+    return True