Add new feautre decomposition and preprocessing

pipelineservice/decomposition/_decomposition.py

@@ -1,6 +1,13 @@
 import numpy as np
 import pandas as pd
-from sklearn.decomposition import PCA, FastICA, KernelPCA, TruncatedSVD, IncrementalPCA, LatentDirichletAllocation, MiniBatchDictionaryLearning, MiniBatchSparsePCA, NMF, SparsePCA, SparseCoder
+from sklearn.decomposition import PCA, FastICA, KernelPCA, TruncatedSVD, IncrementalPCA, LatentDirichletAllocation, MiniBatchDictionaryLearning, MiniBatchSparsePCA, NMF, SparsePCA, SparseCoder, FactorAnalysis
+
+class factorAnalysis(FactorAnalysis):
+    feature_name = 'factoranalysis'
+    def transform(self, X):
+        data = super().transform(X)
+        cols = [f'{self.feature_name}_{i}' for i in range(data.shape[1])]
+        return pd.DataFrame(data, columns = cols, index = X.index)
 
 
 class pCA(PCA):

pipelineservice/preprocessing/_data.py

@@ -1,7 +1,7 @@
 import numpy as np
 import pandas as pd
 from sklearn.base import BaseEstimator, TransformerMixin
-from sklearn.preprocessing import PolynomialFeatures
+from sklearn.preprocessing import PolynomialFeatures, QuantileTransformer, PowerTransformer, KBinsDiscretizer, KernelCenterer
 from sklearn.preprocessing import StandardScaler, RobustScaler
 
 class polynomialFeatures(PolynomialFeatures):
@@ -29,3 +29,22 @@ class robustScaler(RobustScaler):
     def transform(self, X):
         data = super().transform(X)
         return pd.DataFrame(data, columns = X.columns, index = X.index)
+
+
+
+class quantileTransformer(QuantileTransformer):
+    def transform(self, X):
+        data = super().transform(X)
+        return pd.DataFrame(data, columns = X.columns, index = X.index)
+class powerTransformer(PowerTransformer):
+    def transform(self, X):
+        data = super().transform(X)
+        return pd.DataFrame(dapa, columns = X.columns, index = X.index)
+class kBinsDiscretizer(KBinsDiscretizer):
+    def transform(self, X):
+        data = super().transform(X)
+        return pd.DataFrame(dapa, columns = X.columns, index = X.index)
+class KernelCenterer(KernelCenterer):
+    def transform(self, X):
+        data = super().transform(X)
+        return pd.DataFrame(dapa, columns = X.columns, index = X.index)