error in veda resolve

src/VEDA/Veda.py

@@ -1,8 +1,9 @@
-import BalanceData
-import DimensionReducer
-import FeatureSelector
-import OutlierHandler
-import Preprocessor
+from .BalanceData import AdaptiveBalancer
+from .DimensionReducer import DimensionReducer
+from .FeatureSelector import FeatureSelection
+from .OutlierHandler import OutlierPreprocessor
+from .Preprocessor import DataPreprocessor
+
 import pandas as pd
 
 from sklearn.pipeline import Pipeline
@@ -12,11 +13,11 @@ class Veda(BaseEstimator, TransformerMixin):
     def __init__(self, classification=None):
         if classification is None:
             raise ValueError("Parameter classification can't be empty")
-        self.preprocess_data = Preprocessor.DataPreprocessor()
-        self.outlierhandler = OutlierHandler.OutlierPreprocessor()
-        self.feature_selection = FeatureSelector.FeatureSelection()
-        self.reduced_dim = DimensionReducer.DimensionReducer()
-        self.data_balancing = BalanceData.AdaptiveBalancer(classification=classification)
+        self.preprocess_data = DataPreprocessor()
+        self.outlierhandler = OutlierPreprocessor()
+        self.feature_selection = FeatureSelection()
+        self.reduced_dim = DimensionReducer()
+        self.data_balancing = AdaptiveBalancer(classification=classification)
         self.X = None
         self.y = None
         self.outliers = None

src/tests/test1.py → tests/test1.py

@@ -1,10 +1,13 @@
-from VEDA import BalanceData
 import pytest
 import pandas as pd
 from sklearn.base import BaseEstimator
+import sys
+sys.path.insert(0, '../src')
+from VEDA import BalanceData
+
 
 def test():
-    df = pd.read_csv('src\\tests\sample_data\sample.csv')
+    df = pd.read_csv('tests\sample_data\sample.csv')
     X = df.drop(['price'], axis=1)
     y = df['price']
 

src/tests/test2.py → tests/test2.py

@@ -1,9 +1,12 @@
-from VEDA import DimensionReducer
 import pytest
 import pandas as pd
+from sklearn.base import BaseEstimator
+import sys
+sys.path.insert(0, '../src')
+from VEDA import DimensionReducer
 
 def test():
-    df = pd.read_csv('src\\tests\sample_data\sample.csv')
+    df = pd.read_csv('tests\sample_data\sample.csv')
     X = df.drop(['price'], axis=1)
     y = df['price']
 

src/tests/test3.py → tests/test3.py

@@ -1,9 +1,13 @@
-from VEDA import FeatureSelector
 import pytest
 import pandas as pd
+from sklearn.base import BaseEstimator
+import sys
+sys.path.insert(0, '../src')
+from VEDA import FeatureSelector
 
 def test():
-    df = pd.read_csv('src\\tests\sample_data\sample.csv')
+    df = pd.read_csv('tests\sample_data\sample.csv')
+
     X = df.drop(['price'], axis=1)
     y = df['price']
 

src/tests/test4.py → tests/test4.py

@@ -1,9 +1,13 @@
-from VEDA import OutlierHandler
 import pytest
 import pandas as pd
+from sklearn.base import BaseEstimator
+import sys
+sys.path.insert(0, '../src')
+from VEDA import OutlierHandler
 
 def test():
-    df = pd.read_csv('src\\tests\sample_data\sample.csv')
+    df = pd.read_csv('tests\sample_data\sample.csv')
+
     X = df.drop(['price'], axis=1)
     y = df['price']
 

src/tests/test5.py → tests/test5.py

@@ -1,9 +1,13 @@
-from VEDA import Preprocessor
 import pytest
 import pandas as pd
+from sklearn.base import BaseEstimator
+import sys
+sys.path.insert(0, '../src')
+from VEDA import Preprocessor
 
 def test():
-    df = pd.read_csv('src\\tests\sample_data\sample.csv')
+    df = pd.read_csv('tests\sample_data\sample.csv')
+
     X = df.drop(['price'], axis=1)
     y = df['price']
 

src/tests/test6.py → tests/test6.py

@@ -1,10 +1,12 @@
-from VEDA import Veda
 import pytest
 import pandas as pd
 from sklearn.base import BaseEstimator
+import sys
+sys.path.insert(0, '../src')
+from VEDA import Veda
 
 def test():
-    df = pd.read_csv('src\\tests\sample_data\sample.csv')
+    df = pd.read_csv('tests\sample_data\sample.csv')
     X = df.drop(['price'], axis=1)
     y = df['price']