make Preprocessing a dedicated class

alphastats/DataSet.py

@@ -34,7 +34,7 @@
 plotly.io.templates.default = "simple_white+alphastats_colors"
 
 
-class DataSet(Preprocess, Statistics, Plot, Enrichment):
+class DataSet(Statistics, Plot, Enrichment):
     """Analysis Object"""
 
     def __init__(self, loader, metadata_path=None, sample_column=None):
@@ -83,6 +83,25 @@ def __init__(self, loader, metadata_path=None, sample_column=None):
         print("DataSet has been created.")
         self.overview()
 
+    def preprocess(self, **kwargs):
+        pp = Preprocess(
+            self.filter_columns,
+            self.rawinput,
+            self.index_column,
+            self.sample,
+            self.metadata,
+            self.preprocessing_info,
+            self.mat,
+        )
+
+        self.mat, self.metadata, self.preprocessing_info = pp.preprocess(**kwargs)
+        self.preprocessed = True
+
+    def reset_preprocessing(self):
+        """Reset all preprocessing steps"""
+        self.create_matrix()
+        print("All preprocessing steps are reset.")
+
     def _create_metadata(self):
         samples = list(self.mat.index)
         self.metadata = pd.DataFrame({"sample": samples})

alphastats/DataSet_Preprocess.py

@@ -1,4 +1,3 @@
-import itertools
 import logging
 
 import numpy as np
@@ -9,7 +8,6 @@
 import streamlit as st
 
 from sklearn.experimental import enable_iterative_imputer  # noqa
-from tqdm import tqdm
 
 from alphastats.utils import ignore_warning
 
@@ -18,7 +16,27 @@ class Preprocess:
     imputation_methods = ["mean", "median", "knn", "randomforest"]
     normalization_methods = ["vst", "zscore", "quantile"]
 
-    def _remove_sampels(self, sample_list: list):
+    def __init__(
+        self,
+        filter_columns,
+        rawinput,
+        index_column,
+        sample,
+        metadata,
+        preprocessing_info,
+        mat,
+    ):
+        self.filter_columns = filter_columns
+
+        self.rawinput = rawinput
+        self.index_column = index_column
+        self.sample = sample
+
+        self.metadata = metadata  # changed
+        self.preprocessing_info = preprocessing_info  # changed
+        self.mat = mat  # changed
+
+    def _remove_samples(self, sample_list: list):
         # exclude samples for analysis
         self.mat = self.mat.drop(sample_list)
         self.metadata = self.metadata[~self.metadata[self.sample].isin(sample_list)]
@@ -31,10 +49,6 @@ def _subset(self):
         )
         return self.mat[self.mat.index.isin(self.metadata[self.sample].tolist())]
 
-    def preprocess_print_info(self):
-        """Print summary of preprocessing steps"""
-        print(pd.DataFrame(self.preprocessing_info.items()))
-
     def _remove_na_values(self, cut_off):
         if (
             self.preprocessing_info.get("Missing values were removed")
@@ -215,42 +229,6 @@ def _normalization(self, method: str):
 
         self.preprocessing_info.update({"Normalization": method})
 
-    def reset_preprocessing(self):
-        """Reset all preprocessing steps"""
-        self.create_matrix()
-        print("All preprocessing steps are reset.")
-
-    @ignore_warning(RuntimeWarning)
-    def _compare_preprocessing_modes(self, func, params_for_func) -> list:
-        dataset = self
-
-        preprocessing_modes = list(
-            itertools.product(self.normalization_methods, self.imputation_methods)
-        )
-
-        results_list = []
-
-        del params_for_func["compare_preprocessing_modes"]
-        params_for_func["dataset"] = params_for_func.pop("self")
-
-        # TODO: make this progress transparent in GUI
-        for preprocessing_mode in tqdm(preprocessing_modes):
-            # reset preprocessing
-            dataset.reset_preprocessing()
-            print(
-                f"Normalization {preprocessing_mode[0]}, Imputation {str(preprocessing_mode[1])}"
-            )
-            dataset.mat.replace([np.inf, -np.inf], np.nan, inplace=True)
-
-            dataset.preprocess(
-                subset=True,
-                normalization=preprocessing_mode[0],
-                imputation=preprocessing_mode[1],
-            )
-
-            res = func(**params_for_func)
-            results_list.append(res)
-
     # TODO this needs to be reimplemented
     # @ignore_warning(RuntimeWarning)
     # def _compare_preprocessing_modes(self, func, params_for_func) -> list:
@@ -370,7 +348,7 @@ def preprocess(
             self._filter()
 
         if remove_samples is not None:
-            self._remove_sampels(sample_list=remove_samples)
+            self._remove_samples(sample_list=remove_samples)
 
         if subset:
             self.mat = self._subset()
@@ -394,4 +372,5 @@ def preprocess(
                 "Matrix: Number of ProteinIDs/ProteinGroups": self.mat.shape[1],
             }
         )
-        self.preprocessed = True
+
+        return self.mat, self.metadata, self.preprocessing_info