Description added

Signed-off-by: Sasha Meister <[email protected]>

tools/speech_data_explorer/sde/dataloader/engines/cudf_engine.py

@@ -1,48 +1,89 @@
 import cudf.pandas
-
 cudf.pandas.install()
 import pandas as pd
 
 
 class cuDF:
+    """
+    A class representing a data engine utilizing cuDF for processing and analysis.
+
+    Methods:
+    - load_samples_chunk(samples: list[dict]) -> pd.DataFrame:
+        Loads a chunk of samples into a cuDF DataFrame.
+
+    - concat_samples_chunks(samples_chunks: list) -> pd.DataFrame:
+        Concatenates a list of sample chunks into a single cuDF DataFrame.
+
+    - process_vocabulary(words_frequencies: dict, hypotheses_metrics: list[object]) -> pd.DataFrame:
+        Processes vocabulary information, including word frequencies and match frequencies for hypotheses.
+
+    """
+
     def __init__(self):
         pass
-
+    
     def load_samples_chunk(self, samples: list[dict]):
+        """
+        Loads a chunk of samples into a cuDF DataFrame.
+
+        Parameters:
+        - samples (list[dict]): List of dictionaries representing samples.
+
+        Returns:
+        - pd.DataFrame: cuDF DataFrame containing the loaded samples.
+        """
+
         chunk = pd.DataFrame(samples)
         return chunk
-
+    
     def concat_samples_chunks(self, samples_chunks: list):
+        """
+        Concatenates a list of sample chunks into a single cuDF DataFrame.
+
+        Parameters:
+        - samples_chunks (list): List of cuDF DataFrames representing sample chunks.
+
+        Returns:
+        - pd.DataFrame: Concatenated cuDF DataFrame containing all samples.
+        """
+
         samples_datatable = pd.concat(samples_chunks).reset_index(drop=True)
         return samples_datatable
-
+    
     def process_vocabulary(self, words_frequencies: dict, hypotheses_metrics: list[object]):
-        vocabulary_dfs = []
+        """
+        Processes vocabulary information, including word frequencies and match frequencies for hypotheses.
 
+        Parameters:
+        - words_frequencies (dict): Dictionary containing word frequencies.
+        - hypotheses_metrics (list[object]): List of HypothesisMetrics objects representing hypotheses.
+
+        Returns:
+        - pd.DataFrame: Processed vocabulary datatable containing word and match frequencies.
+        """
+
+        vocabulary_dfs = []
+
         words_frequencies_df = pd.DataFrame(words_frequencies.items(), columns=["Word", "Amount"]).set_index("Word")
         vocabulary_dfs.append(words_frequencies_df)
-
+                
         for hypothesis_metrics_obj in hypotheses_metrics:
             label = hypothesis_metrics_obj.hypothesis_label
             match_words_frequencies = hypothesis_metrics_obj.match_words_frequencies
-            match_words_frequencies_df = pd.DataFrame(
-                match_words_frequencies.items(), columns=["Word", f"Match_{hypothesis_metrics_obj.hypothesis_label}"]
-            ).set_index("Word")
+            match_words_frequencies_df = pd.DataFrame(match_words_frequencies.items(), columns=["Word", f"Match_{hypothesis_metrics_obj.hypothesis_label}"]).set_index("Word")
             vocabulary_dfs.append(match_words_frequencies_df)
-
-        vocabulary_datatable = pd.concat(vocabulary_dfs, axis=1, join="outer").reset_index().fillna(0)
-
+        
+        vocabulary_datatable = pd.concat(vocabulary_dfs, axis = 1, join = "outer").reset_index().fillna(0)
+        
         for hypothesis_metrics_obj in hypotheses_metrics:
             label = hypothesis_metrics_obj.hypothesis_label
             postfix = ""
             if label != "":
                 postfix = f"_{label}"
-
-            vocabulary_datatable[f"Accuracy{postfix}"] = (
-                vocabulary_datatable[f"Match_{label}"] / vocabulary_datatable["Amount"] * 100
-            )
+
+            vocabulary_datatable[f"Accuracy{postfix}"] = vocabulary_datatable[f"Match_{label}"] / vocabulary_datatable["Amount"] * 100
             vocabulary_datatable[f"Accuracy{postfix}"] = vocabulary_datatable[f"Accuracy{postfix}"].round(2)
-            vocabulary_datatable = vocabulary_datatable.drop(f"Match_{label}", axis=1)
+            vocabulary_datatable = vocabulary_datatable.drop(f"Match_{label}", axis = 1)
             hypothesis_metrics_obj.mwa = round(vocabulary_datatable[f"Accuracy{postfix}"].mean(), 2)
-
+        
         return vocabulary_datatable