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README.md

@@ -2,10 +2,10 @@
 PyCaret is end-to-end open source machine learning library for python programming language. Its primary objective is to reduce the cycle time of hypothesis to insights by providing an easy to use high level unified API. PyCaret's vision is to become defacto standard for teaching machine learning and data science. Our strength is in our easy to use unified interface for both supervised and unsupervised learning. It saves time and effort that citizen data scientists, students and researchers spent on coding or learning to code using different interfaces, so that now they can focus on business problem.
 
 ## Current Release
-The current release is beta 0.0.29 (as of 30/01/2020). A full release is targetted in the first week of February 2020.
+The current release is beta 0.0.30 (as of 31/01/2020). A full release is targetted in the first week of February 2020.
 
 ## Features Currently Available
-As per beta 0.0.29 following modules are generally available:
+As per beta 0.0.30 following modules are generally available:
 * pycaret.datasets <br/>
 * pycaret.classification (binary and multiclass) <br/>
 * pycaret.regression <br/>
@@ -31,7 +31,7 @@ pip install pycaret
 ```
 
 ## Quick Start
-As of beta 0.0.29 classification, regression, nlp, arules, anomaly and clustering modules are available.
+As of beta 0.0.30 classification, regression, nlp, arules, anomaly and clustering modules are available.
 
 ### Classification / Regression
 

preprocess.py

@@ -16,6 +16,7 @@ from sklearn.preprocessing import MaxAbsScaler
 from sklearn.preprocessing import PowerTransformer
 from sklearn.preprocessing import QuantileTransformer
 from sklearn.preprocessing import OneHotEncoder
+from sklearn.preprocessing import OrdinalEncoder
 from sklearn.decomposition import PCA
 from sklearn.decomposition import KernelPCA
 from sklearn.cross_decomposition import PLSRegression
@@ -1060,7 +1061,45 @@ class Make_Time_Features(BaseEstimator,TransformerMixin):
 
     return(data)
 
+ #____________________________________________________________________________________________________________________________________________________________________
+# Ordinal transformer
+class Ordinal(BaseEstimator,TransformerMixin):
+  '''
+    - converts categorical features into ordinal values 
+    - takes a dataframe , and information about column names and ordered categories as dict
+    - returns float panda data frame
+  '''
+
+  def __init__(self, info_as_dict):
+    self.info_as_dict = info_as_dict
+    return(None)
+
+  def fit(self,data,y=None):
+    return(None)
+
+  def transform(self,dataset,y=None):
+    data = dataset.copy()
+    new_data_test = pd.DataFrame(self.enc.transform(data[self.info_as_dict.keys()]),columns= self.info_as_dict.keys(),index= data.index)
+    for i in self.info_as_dict.keys():
+      data[i] = new_data_test[i]
+    return(data)
 
+  def fit_transform(self,dataset,y=None):
+    data = dataset.copy()
+    # creat categories from given keys in the data set
+    cat_list = []
+    for i in self.info_as_dict.values():
+      i = [np.array(i)]
+      cat_list = cat_list + i
+    
+    # now do fit transform 
+    self.enc = OrdinalEncoder(cat_list)
+    new_data_train = pd.DataFrame(self.enc.fit_transform(data.loc[:,self.info_as_dict.keys()]),columns=self.info_as_dict,index= data.index )
+    # new_data = pd.DataFrame(self.enc.fit_transform(data.loc[:,self.info_as_dict.keys()]))
+    for i in self.info_as_dict.keys():
+      data[i] = new_data_train[i]
+      
+    return(data)
 # _______________________________________________________________________________________________________________________
 
 # make dummy variables
@@ -1482,8 +1521,8 @@ class Advanced_Feature_Selection_Classic(BaseEstimator,TransformerMixin):
   def transform(self,dataset,y=None):
     # return the data with onlys specific columns
     data= dataset.copy()
-    self.selected_columns.remove(self.target)
-    return(data[self.selected_columns])
+    # self.selected_columns.remove(self.target)
+    return(data[self.selected_columns_test])
     # return(data)
 
   def fit_transform(self,dataset,y=None):
@@ -1547,7 +1586,7 @@ class Advanced_Feature_Selection_Classic(BaseEstimator,TransformerMixin):
 
     self.selected_columns = list(set([self.target]+list(dummy_all_columns_RF) + list(corr) +list(dummy_all_columns_LGBM)))
     
-    del(dummy_all)
+    self.selected_columns_test = dataset[self.selected_columns].drop(self.target,axis=1).columns
     return(dataset[self.selected_columns])
 #_
 
@@ -2034,7 +2073,8 @@ def Preprocess_Path_One(train_data,target_variable,ml_usecase=None,test_data =No
                                 imputation_type = "simple imputer" ,numeric_imputation_strategy='mean',categorical_imputation_strategy='not_available',
                                 apply_zero_nearZero_variance = False,
                                 club_rare_levels = False, rara_level_threshold_percentage =0.05,
-                                apply_untrained_levels_treatment= False,untrained_levels_treatment_method = 'least frequent', 
+                                apply_untrained_levels_treatment= False,untrained_levels_treatment_method = 'least frequent',
+                                apply_ordinal_encoding = False, ordinal_columns_and_categories= {},
                                 apply_binning=False, features_to_binn =[],
                                 apply_grouping= False , group_name=[] , features_to_group_ListofList=[[]],
                                 apply_polynomial_trigonometry_features = False, max_polynomial=2,trigonometry_calculations=['sin','cos','tan'], top_poly_trig_features_to_select_percentage=.20,
@@ -2118,6 +2158,13 @@ def Preprocess_Path_One(train_data,target_variable,ml_usecase=None,test_data =No
   else:
     new_levels= Empty()
 
+  # ordinal coding
+  if apply_ordinal_encoding == True:
+    global ordinal
+    ordinal = Ordinal(info_as_dict=ordinal_columns_and_categories)
+  else:
+    ordinal = Empty()
+
   # grouping
   if apply_grouping == True:
     global group
@@ -2221,6 +2268,7 @@ def Preprocess_Path_One(train_data,target_variable,ml_usecase=None,test_data =No
                  ('znz',znz),
                  ('club_R_L',club_R_L),
                  ('new_levels',new_levels),
+                 ('ordinal',ordinal),
                  ('feature_time',feature_time),
                  ('group',group),
                  ('nonliner',nonliner),
@@ -2251,7 +2299,8 @@ def Preprocess_Path_Two(train_data,ml_usecase=None,test_data =None,categorical_f
                                 imputation_type = "simple imputer" ,numeric_imputation_strategy='mean',categorical_imputation_strategy='not_available',
                                 apply_zero_nearZero_variance = False,
                                 club_rare_levels = False, rara_level_threshold_percentage =0.05,
-                                apply_untrained_levels_treatment= False,untrained_levels_treatment_method = 'least frequent', 
+                                apply_untrained_levels_treatment= False,untrained_levels_treatment_method = 'least frequent',
+                                apply_ordinal_encoding = False, ordinal_columns_and_categories= {}, 
                                 apply_binning=False, features_to_binn =[],
                                 apply_grouping= False , group_name=[] , features_to_group_ListofList=[[]],
                                 scale_data= False, scaling_method='zscore',
@@ -2328,6 +2377,13 @@ def Preprocess_Path_Two(train_data,ml_usecase=None,test_data =None,categorical_f
   else:
     new_levels= Empty()
   
+  # ordinal coding
+  if apply_ordinal_encoding == True:
+    global ordinal
+    ordinal = Ordinal(info_as_dict=ordinal_columns_and_categories)
+  else:
+    ordinal = Empty()
+  
   # grouping
   if apply_grouping == True:
     global group
@@ -2394,6 +2450,7 @@ def Preprocess_Path_Two(train_data,ml_usecase=None,test_data =None,categorical_f
                  ('znz',znz),
                  ('club_R_L',club_R_L),
                  ('new_levels',new_levels),
+                 ('ordinal',ordinal),
                  ('feature_time',feature_time),
                  ('group',group),
                  ('scaling',scaling),
@@ -2412,4 +2469,4 @@ def Preprocess_Path_Two(train_data,ml_usecase=None,test_data =None,categorical_f
     return(train_t.drop(target_variable,axis=1),test_t)
   else:
     train_t = pipe.fit_transform(train_data)
-    return(train_t.drop(target_variable,axis=1))
\ No newline at end of file
+    return(train_t.drop(target_variable,axis=1))

setup.py

@@ -27,7 +27,7 @@ def readme():
 
 setup(
     name="pycaret",
-    version="0.0.29",
+    version="0.0.30",
     description="A Python package for supervised and unsupervised machine learning.",
     long_description=readme(),
     long_description_content_type="text/markdown",