diff --git a/pipeline/fairprep.py b/pipeline/fairprep.py
new file mode 100644
index 0000000..32f629b
--- /dev/null
+++ b/pipeline/fairprep.py
@@ -0,0 +1,344 @@
+"""
+    Class to run the pipeline
+
+"""
+
+
+import os
+import numpy as np
+import pandas as pd
+
+from time import time
+from pathlib import Path
+from datetime import datetime
+from itertools import islice
+
+
+import warnings
+warnings.filterwarnings("ignore")
+from pipeline.preprocess.splitters import *
+from pipeline.preprocess.samplers import *
+from pipeline.preprocess.imputers import *
+from pipeline.preprocess.scalers import *
+from pipeline.preprocess.categorizers import *
+from pipeline.preprocess.encoders import *
+from pipeline.preprocess.fair_preprocessors import *
+from pipeline.model.classifiers import *
+from pipeline.model.fair_classifiers import *
+from pipeline.postprocess.fair_postprocessors import *
+
+import json
+
+PRINT_SPLIT = "\n===================================================\n"
+# for integrity check of user inputs
+PREREQUISITE_STEPS = {"Splitter": [], "Sampler": [], "Imputer": ["Splitter"], "Scaler": ["Imputer"],
+                    "Categorizer": ["Imputer"], "Encoder": ["Imputer"], "SpecialEncoder": ["Imputer"],
+                    "FairPreprocessor": ["Splitter", "Imputer", "Scaler", "Categorizer", "Encoder", "SpecialEncoder"],
+                    "Model": ["Splitter", "Imputer", "Scaler", "Categorizer", "Encoder", "SensitiveAttEncoder"],
+                    "FairPostprocessor": ["Splitter", "Imputer", "Scaler", "Categorizer", "Encoder", "SpecialEncoder"]}
+
+
+
+class FairPipeline():
+    def __init__(self, data_file_name, target_col, target_positive_values, sensitive_atts, value_mapping, sep_flag=None, na_mark=None, verbose=True):
+        """
+
+        :param data_file_name: str, file name that stores the data.
+        :param *fixed_random_seed: int, the fixed random seed that will be used through the pipeline.
+        :param target_col: str, the name of the target variable in above data.
+        :param target_positive_values: list of str, each str is the value of the target variable in above data that represents the positive outcome.
+        :param sensitive_atts: list, stores the user specified sensitive attributes in above data. Optional.
+        :param protected_values: dict, stores the user-specified protected values for above sensitive attributes. Optional.
+                                Key is the name in sensitive_atts, value is a list of str, representing the values of the attribute.
+                                 Order should mapping to the order in the sensitive_atts.
+        """
+
+        # TODO: move integrity check to a separate function
+        self.data_file_name = data_file_name
+        self.target_col = target_col
+        self.target_positive_values = target_positive_values
+        self.sensitive_atts = sensitive_atts
+        self.value_mapping = value_mapping
+        self.sep_flag = sep_flag
+        self.na_mark = na_mark
+        self.verbose = verbose
+
+        self.data_name = None
+        self.raw_data = None
+
+        self.attributes = None
+        self.metadata_desp = None
+        self.num_atts = None
+        self.cate_atts = None
+        self.pred_target = "pred_" + target_col
+
+        self.log_path = 'logs/'
+        self.exec_timestamp = self.generate_timestamp()
+        self.log_dir_name = None
+
+    def validate_input_parameters(self):
+        if not os.path.exists(os.path.realpath(self.data_file_name)):
+            print("The data you specified doesn't exist!")
+            raise ValueError
+        if ".csv" not in self.data_file_name:
+            print("The data you specified is not valid! Only support .csv file.")
+            raise ValueError
+        data_name = self.data_file_name.replace(".csv", "")
+        self.data_name = data_name[data_name.rfind("/")+1:]
+
+        if self.sep_flag:
+            df = pd.read_csv(self.data_file_name, sep=self.sep_flag)
+        else: # default ',' separated data
+            df = pd.read_csv(self.data_file_name)
+
+        if not df.shape[0]:
+            print("Uploaded data is empty!")
+            raise ValueError
+
+        # np.random.seed(fixed_random_seed)
+        # self.fixed_random_seed = fixed_random_seed
+
+        # integrity check for target_col
+        if self.target_col is None or self.target_positive_values is None:
+            print("Need to specify target_col and target_positive_value!")
+            raise ValueError
+
+        if self.target_col not in df.columns:
+            print("Need to specify a valid target attribute to be predicted!")
+            raise ValueError
+        target_values = df[self.target_col].unique()
+        if len(target_values) != 2:
+            print("Only support binary target feature now!")
+            raise ValueError
+        if len(set(self.target_positive_values).intersection(target_values)) == 0:
+            print("Need to specify a valid target positive value!")
+            raise ValueError
+
+        # TODO: update the below code
+        # # integrity check for sensitive_atts and protected_values
+        # input_indicator = sum([len(x)== 0 for x in [self.sensitive_atts, self.protected_values]])
+        # if input_indicator == 0: # both are specified
+        #     if len(self.sensitive_atts) != len(self.protected_values):
+        #         print("Different size of input sensitive attributes and protected values!")
+        #         raise ValueError
+        #     if sum([len(set(self.protected_values[x]).difference(df[x].unique())) > 0 for x in self.protected_values]) > 0:
+        #         print("Some specified protected values do not appear in the column specified in sensitive_atts!")
+        #         raise ValueError
+        # elif input_indicator == 1: # one of parameter is empty
+        #     print("Need to specify both sensitive_atts and protected_values!")
+        #     raise ValueError
+        # else: # both are empty
+        #     print("Need to specify both sensitive_atts and protected_values!")
+        #     raise ValueError
+
+        self.raw_data = df
+        self.attributes = df.columns
+        return self
+
+    def get_meta_information(self):
+        # infer the numerical and categorical attributes first
+        if self.raw_data.describe().shape[0] == 8:# DataFrame.describe() usually returns 8 rows.
+            num_atts = set(self.raw_data.describe().columns)
+        else:# DataFrame.describe() returns less than 8 rows when there is no numerical attribute.
+            num_atts = set()
+
+        cate_atts = set(self.attributes).difference(num_atts)
+
+
+        self.metadata_desp = {"size": self.raw_data.shape[0], "features": self.raw_data.shape[1],
+                              "categorical features": list(cate_atts), "numerical features": list(num_atts)}
+        # record the domain of the attributes
+        feature_domain = {}
+        for attri in self.attributes:
+            if attri in cate_atts:
+                all_values = self.raw_data[attri].unique()
+                feature_domain[attri] = (len(all_values), list(all_values))
+            else:
+                feature_domain[attri] = (min(self.raw_data[attri]), max(self.raw_data[attri]))
+        self.metadata_desp.update({"domain": feature_domain})
+
+        # remove the sensitive and target attributes from the inferred list since these attributes are treated separately in the pipeline
+        if self.target_col in cate_atts:
+            cate_atts.remove(self.target_col)
+        if self.target_col in num_atts:
+            num_atts.remove(self.target_col)
+        for si in self.sensitive_atts:
+            cate_atts.remove(si)
+
+        self.num_atts = list(num_atts)
+        self.cate_atts = list(cate_atts)
+
+        log_dir_name = os.path.join(os.path.dirname(os.path.realpath(__file__)), self.generate_file_path())
+        if not os.path.exists(log_dir_name):
+            os.makedirs(log_dir_name)
+
+        self.log_dir_name = log_dir_name
+        print("**"*10, log_dir_name)
+        # print(self.metadata_desp)
+
+        with open(log_dir_name+"/"+self.data_name+'.json', 'w') as outfile:
+            json.dump(dict(self.metadata_desp), outfile)
+
+        return self
+
+    def iter_steps(self, steps):
+        # TODO: optimize with the input data.
+        """
+        Generator function to iterate steps.
+        :param steps: list of objects that represent the steps user want to perform on the input data.
+                      Supported steps are listed in STEPS.md.
+        :return:  the pandas dataframes that are returned by applying a step on the input data.
+        """
+        # islice(steps, 0, len(steps))
+        for idx, stepi in enumerate(steps):
+            yield idx, stepi
+
+    def validate_input_steps(self, steps):
+
+        if not steps or not isinstance(steps, list):
+            print("Require non-empty list of steps as input!")
+            raise ValueError
+
+        # TODO: integrity check for steps using pre-requisite order
+
+        return self
+
+    def fill_zero_to_dummy_data(self, input_step, df):
+        raw_df = pd.get_dummies(self.raw_data, columns=self.cate_atts, prefix_sep='=')
+        if input_step.step_name() in ["FairPreprocessor", "Model", "FairInprocessor", "FairPostprocessor"]: # these steps return non-encoded data
+            encoded_df = pd.get_dummies(df, columns=self.cate_atts, prefix_sep='=')
+        else:
+            encoded_df = df
+        # print("**" * 10, raw_df.shape, encoded_df.shape)
+        if raw_df.shape[1] != encoded_df.shape[1]:
+            for coli in set(raw_df.columns):
+                if "=" in coli and coli[:coli.find("=")] in self.cate_atts and coli not in encoded_df.columns:
+                    encoded_df.loc[:, coli] = 0
+
+        # print("**" * 10, raw_df.shape, encoded_df.shape)
+        return encoded_df
+
+    def run_pipeline(self, steps, save_interdata=False):
+        """
+
+        :param df: pandas dataframe, the data on which steps are applied.
+        :param steps: list of classes that represent the steps user want to perform on the above data.
+                      Supported steps are listed in STEPS.md.
+        :return:  two pandas dataframes: before_data and after_data. after_data is the data after applied the input steps.
+
+        """
+        self.validate_input_parameters()
+        self.get_meta_information()
+
+        self.validate_input_steps(steps)
+
+        # first run the splitter
+        print(self.print_log_message(steps, 0))
+        train_df, val_df, test_df = steps[0].apply(self.raw_data)
+        print(PRINT_SPLIT)
+
+        # for step_idx, train_fitted_step, train_df in self.iter_steps(steps, train_df):
+        # for step_idx, stepi in enumerate(steps):
+        for step_idx, stepi in self.iter_steps(steps):
+            if step_idx == 0:
+                continue
+
+            print(self.print_log_message(steps, step_idx))
+
+            # run the same step on validation data, if multiple methods are specified at one step, the one with optimal performance on validation set is selected
+            if isinstance(steps[step_idx], list): # multiple methods specified at one step
+                # TODO: add the selection on validation data
+                pass
+
+            else: # single method specified at a step
+                stepi.fit(train_df)
+                train_df = stepi.apply(train_df)
+
+                if stepi.fit_only_on_train(): # the steps that fit only on train
+                    val_df = stepi.apply(val_df)
+                    test_df = stepi.apply(test_df)
+
+                else: # the steps that treat train, val, test independently through the same rule
+                    stepi.fit(val_df)
+                    val_df = stepi.apply(val_df)
+
+                    stepi.fit(test_df)
+                    test_df = stepi.apply(test_df)
+
+            # autofill encoded columns after the steps that might affect the dimension of the encoded data. Only for categorical attributes that are not sensitive and target column
+            if stepi.input_encoded_data() != stepi.output_encoded_data():
+                train_df = self.fill_zero_to_dummy_data(stepi, train_df)
+                val_df = self.fill_zero_to_dummy_data(stepi, val_df)
+                test_df = self.fill_zero_to_dummy_data(stepi, test_df)
+
+            if save_interdata: # save intermediate data on the disc
+                for file_i, df_i in zip(["train", "val", "test"], [train_df, val_df, test_df]):
+                    self.save_inter_data(file_i, df_i, steps[:step_idx+1])
+
+            print(PRINT_SPLIT)
+
+        # # TODO: move to other location
+        # # transfer back to original values for encoded sensitive and target columns
+        # for dfi in [train_df, val_df, test_df]:
+        #     for atti in [self.target_col] + self.sensitive_atts:
+        #         df_i[atti] = df_i[atti].apply(lambda x: self.sensi_target_value_mapping[atti][x])
+        #
+        #     if self.pred_target in df_i.columns:
+        #         # TODO: check whether to keep the line for score prediction
+        #         df_i[self.pred_target] = df_i[self.pred_target].apply(lambda x: int(x >= 0.5)) # for the model that returns a score instead of labels
+        #         df_i[self.pred_target] = df_i[self.pred_target].apply(lambda x: self.sensi_target_value_mapping[self.target_col][x])
+
+        return train_df, val_df, test_df
+
+    def print_log_message(self, steps, step_idx):
+        if not self.verbose:
+            return None
+        return '(step %d of %d) running %s' % (step_idx + 1, len(steps), steps[step_idx].abbr_name()) + PRINT_SPLIT
+
+    def get_executed_steps_name(self, executed_steps):
+        return "_".join([x.abbr_name() for x in executed_steps])
+
+    def generate_file_path(self, file_name=''):
+        dir_name = '{}__{}/'.format(self.exec_timestamp, self.data_name)
+        return self.log_path + dir_name + file_name
+
+    def generate_timestamp(self):
+        return datetime.fromtimestamp(time()).strftime('%Y-%m-%d_%H-%M-%S-%f')[:-3]
+
+
+    def save_inter_data(self, file_name, df: pd.DataFrame, executed_steps):
+        data_file_name = '{}-{}.csv'.format(self.get_executed_steps_name(executed_steps), file_name)
+        data_file_path = os.path.join(self.log_dir_name, data_file_name)
+
+        df.to_csv(data_file_path, index=False)
+        if self.verbose:
+            print ("Data is saved to ", data_file_path)
+
+    
+
+if __name__ == '__main__':
+
+    data_file = "../data/german_AIF_test.csv"
+    y_col = "credit"
+    y_posi = ["good"]
+    sensi_atts = ["age", "sex"]
+
+    value_mapping = {"female": 0, "male": 1, "good": 1, "bad": 0, "young": 0, "old": 1}
+
+    debias_focus_att = "sex"
+    global_seed = 0
+
+    numerical_atts = ["month", "credit_amount"]
+    categorical_atts = ["status", "employment", "housing"]
+
+    pipeline = [RandomSplitter([0.5, 0.3, 0.2], global_seed), NoSampler(), NoImputer(), SK_MinMaxScaler(numerical_atts),
+                  NoBinarizer(),  OneHotEncoder(categorical_atts), MappingEncoder([y_col] + sensi_atts, value_mapping),
+                  AIF_Reweighing(y_col, debias_focus_att), OPT_LogisticRegression(y_col, global_seed), NoFairPostprocessor()]
+
+
+
+    cur_pip = FairPipeline(data_file, y_col, y_posi, sensi_atts, value_mapping)
+
+
+    train_now, val_now, test_now = cur_pip.run_pipeline(pipeline, save_interdata=True)
+
diff --git a/pipeline/logs/2020-07-30_18-09-09-739__german_AIF_test/RS_NS-test.csv b/pipeline/logs/2020-07-30_18-09-09-739__german_AIF_test/RS_NS-test.csv
new file mode 100644
index 0000000..37c539e
--- /dev/null
+++ b/pipeline/logs/2020-07-30_18-09-09-739__german_AIF_test/RS_NS-test.csv
@@ -0,0 +1,201 @@
+status,month,credit_amount,employment,housing,sex,age,credit
+A13,36,5848,A73,A152,male,young,good
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diff --git a/pipeline/logs/2020-07-30_18-09-09-739__german_AIF_test/RS_NS-train.csv b/pipeline/logs/2020-07-30_18-09-09-739__german_AIF_test/RS_NS-train.csv
new file mode 100644
index 0000000..1045c8d
--- /dev/null
+++ b/pipeline/logs/2020-07-30_18-09-09-739__german_AIF_test/RS_NS-train.csv
@@ -0,0 +1,501 @@
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diff --git a/pipeline/logs/2020-07-30_18-09-09-739__german_AIF_test/RS_NS-val.csv b/pipeline/logs/2020-07-30_18-09-09-739__german_AIF_test/RS_NS-val.csv
new file mode 100644
index 0000000..e0b67a7
--- /dev/null
+++ b/pipeline/logs/2020-07-30_18-09-09-739__german_AIF_test/RS_NS-val.csv
@@ -0,0 +1,301 @@
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diff --git a/pipeline/logs/2020-07-30_18-09-09-739__german_AIF_test/RS_NS_NI-test.csv b/pipeline/logs/2020-07-30_18-09-09-739__german_AIF_test/RS_NS_NI-test.csv
new file mode 100644
index 0000000..37c539e
--- /dev/null
+++ b/pipeline/logs/2020-07-30_18-09-09-739__german_AIF_test/RS_NS_NI-test.csv
@@ -0,0 +1,201 @@
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diff --git a/pipeline/logs/2020-07-30_18-09-09-739__german_AIF_test/RS_NS_NI-train.csv b/pipeline/logs/2020-07-30_18-09-09-739__german_AIF_test/RS_NS_NI-train.csv
new file mode 100644
index 0000000..1045c8d
--- /dev/null
+++ b/pipeline/logs/2020-07-30_18-09-09-739__german_AIF_test/RS_NS_NI-train.csv
@@ -0,0 +1,501 @@
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diff --git a/pipeline/logs/2020-07-30_18-09-09-739__german_AIF_test/RS_NS_NI-val.csv b/pipeline/logs/2020-07-30_18-09-09-739__german_AIF_test/RS_NS_NI-val.csv
new file mode 100644
index 0000000..e0b67a7
--- /dev/null
+++ b/pipeline/logs/2020-07-30_18-09-09-739__german_AIF_test/RS_NS_NI-val.csv
@@ -0,0 +1,301 @@
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diff --git a/pipeline/logs/2020-07-30_18-09-09-739__german_AIF_test/RS_NS_NI_MS-test.csv b/pipeline/logs/2020-07-30_18-09-09-739__german_AIF_test/RS_NS_NI_MS-test.csv
new file mode 100644
index 0000000..1b64b92
--- /dev/null
+++ b/pipeline/logs/2020-07-30_18-09-09-739__german_AIF_test/RS_NS_NI_MS-test.csv
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diff --git a/pipeline/logs/2020-07-30_18-09-09-739__german_AIF_test/RS_NS_NI_MS-train.csv b/pipeline/logs/2020-07-30_18-09-09-739__german_AIF_test/RS_NS_NI_MS-train.csv
new file mode 100644
index 0000000..b15c2c9
--- /dev/null
+++ b/pipeline/logs/2020-07-30_18-09-09-739__german_AIF_test/RS_NS_NI_MS-train.csv
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diff --git a/pipeline/logs/2020-07-30_18-09-09-739__german_AIF_test/RS_NS_NI_MS-val.csv b/pipeline/logs/2020-07-30_18-09-09-739__german_AIF_test/RS_NS_NI_MS-val.csv
new file mode 100644
index 0000000..757c07f
--- /dev/null
+++ b/pipeline/logs/2020-07-30_18-09-09-739__german_AIF_test/RS_NS_NI_MS-val.csv
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diff --git a/pipeline/logs/2020-07-30_18-09-09-739__german_AIF_test/RS_NS_NI_MS_NB-test.csv b/pipeline/logs/2020-07-30_18-09-09-739__german_AIF_test/RS_NS_NI_MS_NB-test.csv
new file mode 100644
index 0000000..1b64b92
--- /dev/null
+++ b/pipeline/logs/2020-07-30_18-09-09-739__german_AIF_test/RS_NS_NI_MS_NB-test.csv
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diff --git a/pipeline/logs/2020-07-30_18-09-09-739__german_AIF_test/RS_NS_NI_MS_NB-train.csv b/pipeline/logs/2020-07-30_18-09-09-739__german_AIF_test/RS_NS_NI_MS_NB-train.csv
new file mode 100644
index 0000000..b15c2c9
--- /dev/null
+++ b/pipeline/logs/2020-07-30_18-09-09-739__german_AIF_test/RS_NS_NI_MS_NB-train.csv
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diff --git a/pipeline/logs/2020-07-30_18-09-09-739__german_AIF_test/RS_NS_NI_MS_NB-val.csv b/pipeline/logs/2020-07-30_18-09-09-739__german_AIF_test/RS_NS_NI_MS_NB-val.csv
new file mode 100644
index 0000000..757c07f
--- /dev/null
+++ b/pipeline/logs/2020-07-30_18-09-09-739__german_AIF_test/RS_NS_NI_MS_NB-val.csv
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diff --git a/pipeline/logs/2020-07-30_18-09-09-739__german_AIF_test/RS_NS_NI_MS_NB_HE-test.csv b/pipeline/logs/2020-07-30_18-09-09-739__german_AIF_test/RS_NS_NI_MS_NB_HE-test.csv
new file mode 100644
index 0000000..4fbf0ba
--- /dev/null
+++ b/pipeline/logs/2020-07-30_18-09-09-739__german_AIF_test/RS_NS_NI_MS_NB_HE-test.csv
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diff --git a/pipeline/logs/2020-07-30_18-09-09-739__german_AIF_test/RS_NS_NI_MS_NB_HE-train.csv b/pipeline/logs/2020-07-30_18-09-09-739__german_AIF_test/RS_NS_NI_MS_NB_HE-train.csv
new file mode 100644
index 0000000..7fa0cc9
--- /dev/null
+++ b/pipeline/logs/2020-07-30_18-09-09-739__german_AIF_test/RS_NS_NI_MS_NB_HE-train.csv
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diff --git a/pipeline/logs/2020-07-30_18-09-09-739__german_AIF_test/RS_NS_NI_MS_NB_HE-val.csv b/pipeline/logs/2020-07-30_18-09-09-739__german_AIF_test/RS_NS_NI_MS_NB_HE-val.csv
new file mode 100644
index 0000000..3907229
--- /dev/null
+++ b/pipeline/logs/2020-07-30_18-09-09-739__german_AIF_test/RS_NS_NI_MS_NB_HE-val.csv
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diff --git a/pipeline/logs/2020-07-30_18-09-09-739__german_AIF_test/RS_NS_NI_MS_NB_HE_ME-test.csv b/pipeline/logs/2020-07-30_18-09-09-739__german_AIF_test/RS_NS_NI_MS_NB_HE_ME-test.csv
new file mode 100644
index 0000000..912f248
--- /dev/null
+++ b/pipeline/logs/2020-07-30_18-09-09-739__german_AIF_test/RS_NS_NI_MS_NB_HE_ME-test.csv
@@ -0,0 +1,201 @@
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diff --git a/pipeline/logs/2020-07-30_18-09-09-739__german_AIF_test/RS_NS_NI_MS_NB_HE_ME-train.csv b/pipeline/logs/2020-07-30_18-09-09-739__german_AIF_test/RS_NS_NI_MS_NB_HE_ME-train.csv
new file mode 100644
index 0000000..cd248b0
--- /dev/null
+++ b/pipeline/logs/2020-07-30_18-09-09-739__german_AIF_test/RS_NS_NI_MS_NB_HE_ME-train.csv
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diff --git a/pipeline/logs/2020-07-30_18-09-09-739__german_AIF_test/RS_NS_NI_MS_NB_HE_ME-val.csv b/pipeline/logs/2020-07-30_18-09-09-739__german_AIF_test/RS_NS_NI_MS_NB_HE_ME-val.csv
new file mode 100644
index 0000000..ac5e47f
--- /dev/null
+++ b/pipeline/logs/2020-07-30_18-09-09-739__german_AIF_test/RS_NS_NI_MS_NB_HE_ME-val.csv
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diff --git a/pipeline/logs/2020-07-30_18-09-09-739__german_AIF_test/RS_NS_NI_MS_NB_HE_ME_RW-test.csv b/pipeline/logs/2020-07-30_18-09-09-739__german_AIF_test/RS_NS_NI_MS_NB_HE_ME_RW-test.csv
new file mode 100644
index 0000000..2714c97
--- /dev/null
+++ b/pipeline/logs/2020-07-30_18-09-09-739__german_AIF_test/RS_NS_NI_MS_NB_HE_ME_RW-test.csv
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diff --git a/pipeline/logs/2020-07-30_18-09-09-739__german_AIF_test/RS_NS_NI_MS_NB_HE_ME_RW-train.csv b/pipeline/logs/2020-07-30_18-09-09-739__german_AIF_test/RS_NS_NI_MS_NB_HE_ME_RW-train.csv
new file mode 100644
index 0000000..9e3b099
--- /dev/null
+++ b/pipeline/logs/2020-07-30_18-09-09-739__german_AIF_test/RS_NS_NI_MS_NB_HE_ME_RW-train.csv
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diff --git a/pipeline/logs/2020-07-30_18-09-09-739__german_AIF_test/RS_NS_NI_MS_NB_HE_ME_RW-val.csv b/pipeline/logs/2020-07-30_18-09-09-739__german_AIF_test/RS_NS_NI_MS_NB_HE_ME_RW-val.csv
new file mode 100644
index 0000000..d26a4e0
--- /dev/null
+++ b/pipeline/logs/2020-07-30_18-09-09-739__german_AIF_test/RS_NS_NI_MS_NB_HE_ME_RW-val.csv
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diff --git a/pipeline/logs/2020-07-30_18-09-09-739__german_AIF_test/RS_NS_NI_MS_NB_HE_ME_RW_OLR-test.csv b/pipeline/logs/2020-07-30_18-09-09-739__german_AIF_test/RS_NS_NI_MS_NB_HE_ME_RW_OLR-test.csv
new file mode 100644
index 0000000..5e83fb1
--- /dev/null
+++ b/pipeline/logs/2020-07-30_18-09-09-739__german_AIF_test/RS_NS_NI_MS_NB_HE_ME_RW_OLR-test.csv
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diff --git a/pipeline/logs/2020-07-30_18-09-09-739__german_AIF_test/RS_NS_NI_MS_NB_HE_ME_RW_OLR-train.csv b/pipeline/logs/2020-07-30_18-09-09-739__german_AIF_test/RS_NS_NI_MS_NB_HE_ME_RW_OLR-train.csv
new file mode 100644
index 0000000..1627cd1
--- /dev/null
+++ b/pipeline/logs/2020-07-30_18-09-09-739__german_AIF_test/RS_NS_NI_MS_NB_HE_ME_RW_OLR-train.csv
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diff --git a/pipeline/logs/2020-07-30_18-09-09-739__german_AIF_test/RS_NS_NI_MS_NB_HE_ME_RW_OLR-val.csv b/pipeline/logs/2020-07-30_18-09-09-739__german_AIF_test/RS_NS_NI_MS_NB_HE_ME_RW_OLR-val.csv
new file mode 100644
index 0000000..4856718
--- /dev/null
+++ b/pipeline/logs/2020-07-30_18-09-09-739__german_AIF_test/RS_NS_NI_MS_NB_HE_ME_RW_OLR-val.csv
@@ -0,0 +1,301 @@
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diff --git a/pipeline/logs/2020-07-30_18-09-09-739__german_AIF_test/RS_NS_NI_MS_NB_HE_ME_RW_OLR_NP-test.csv b/pipeline/logs/2020-07-30_18-09-09-739__german_AIF_test/RS_NS_NI_MS_NB_HE_ME_RW_OLR_NP-test.csv
new file mode 100644
index 0000000..5e83fb1
--- /dev/null
+++ b/pipeline/logs/2020-07-30_18-09-09-739__german_AIF_test/RS_NS_NI_MS_NB_HE_ME_RW_OLR_NP-test.csv
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diff --git a/pipeline/logs/2020-07-30_18-09-09-739__german_AIF_test/RS_NS_NI_MS_NB_HE_ME_RW_OLR_NP-train.csv b/pipeline/logs/2020-07-30_18-09-09-739__german_AIF_test/RS_NS_NI_MS_NB_HE_ME_RW_OLR_NP-train.csv
new file mode 100644
index 0000000..1627cd1
--- /dev/null
+++ b/pipeline/logs/2020-07-30_18-09-09-739__german_AIF_test/RS_NS_NI_MS_NB_HE_ME_RW_OLR_NP-train.csv
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diff --git a/pipeline/logs/2020-07-30_18-09-09-739__german_AIF_test/RS_NS_NI_MS_NB_HE_ME_RW_OLR_NP-val.csv b/pipeline/logs/2020-07-30_18-09-09-739__german_AIF_test/RS_NS_NI_MS_NB_HE_ME_RW_OLR_NP-val.csv
new file mode 100644
index 0000000..4856718
--- /dev/null
+++ b/pipeline/logs/2020-07-30_18-09-09-739__german_AIF_test/RS_NS_NI_MS_NB_HE_ME_RW_OLR_NP-val.csv
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+0.03571428571428571,0.06184659403543523,1.0,1.0,1.0,0.5231864157229879,0,0,1,0,0,0,1,0,1,0,0,0
+0.03571428571428571,0.06597336854847584,1.0,1.0,1.0,0.7623117828116139,0,0,0,0,1,0,1,0,0,1,0,0
+0.24999999999999997,0.07329151535160118,1.0,1.0,1.0,0.9073342365921735,0,0,0,0,1,0,1,0,0,0,0,1
diff --git a/pipeline/logs/2020-07-30_18-09-09-739__german_AIF_test/german_AIF_test.json b/pipeline/logs/2020-07-30_18-09-09-739__german_AIF_test/german_AIF_test.json
new file mode 100644
index 0000000..1e56c7a
--- /dev/null
+++ b/pipeline/logs/2020-07-30_18-09-09-739__german_AIF_test/german_AIF_test.json
@@ -0,0 +1 @@
+{"size": 1000, "features": 8, "categorical features": ["credit", "age", "employment", "housing", "sex", "status"], "numerical features": ["credit_amount", "month"], "domain": {"status": [4, ["A11", "A12", "A14", "A13"]], "month": [4, 72], "credit_amount": [250, 18424], "employment": [5, ["A75", "A73", "A74", "A71", "A72"]], "housing": [3, ["A152", "A153", "A151"]], "sex": [2, ["male", "female"]], "age": [2, ["old", "young"]], "credit": [2, ["good", "bad"]]}}
\ No newline at end of file
diff --git a/pipeline/model/classifiers.py b/pipeline/model/classifiers.py
new file mode 100644
index 0000000..27e514b
--- /dev/null
+++ b/pipeline/model/classifiers.py
@@ -0,0 +1,254 @@
+"""
+    Classes of supervised binary classifiers.
+"""
+
+import pandas as pd
+import numpy as np
+from aif360.datasets import BinaryLabelDataset
+from sklearn.linear_model import SGDClassifier
+from sklearn.tree import DecisionTreeClassifier
+
+from sklearn.model_selection import GridSearchCV
+from sklearn.pipeline import Pipeline
+from pipeline.step import Step
+
+
+class SK_LogisticRegression(Step):
+    def __init__(self, target_col, seed, loss_func="log", instance_weights=[], target_positive=1):
+        """
+        :param target_col: str, the name of the target variable in above data.
+        :param seed: integer, the seed for random state.
+        :param loss_func: str, the name of the loss function used in linear model. Same as the loss parameter in sklearn.linear_model.SGDClassifier.
+                         The possible options are ‘hinge’, ‘log’, ‘modified_huber’, ‘squared_hinge’, ‘perceptron’, or a regression loss: ‘squared_loss’, ‘huber’, ‘epsilon_insensitive’, or ‘squared_epsilon_insensitive’.
+        :param instance_weights: list of float, each number represents the weight of the sample in above data.
+        :param target_positive: integer, 0 or 1, represents the positive value of the target attribute. Default is 1.
+
+        """
+        self.target_col = target_col
+        self.pred_target_col = "pred_" + target_col  # store the predicted score (probability) column using this fixed name
+        self.seed = seed
+        self.loss_func = loss_func
+        self.instance_weights = instance_weights
+        self.target_positive = target_positive
+        self.fitted_step = None
+
+    def fit(self, df):
+        if len(self.instance_weights) == 0:
+            self.instance_weights = [1 for _ in range(1, df.shape[0] + 1)]
+
+        self.fitted_step = SGDClassifier(loss=self.loss_func, random_state=self.seed).fit(np.array(df.drop(columns=[self.target_col])), np.array(df[self.target_col]), sample_weight=self.instance_weights)
+
+        return self
+
+    def apply(self, df):
+        aif_pred_df = BinaryLabelDataset(df=df, label_names=[self.target_col], protected_attribute_names=[])
+        after_df, _ = aif_pred_df.convert_to_dataframe(de_dummy_code=True, sep='=', set_category=True)
+
+        favorable_class_idx = list(self.fitted_step.classes_).index(self.target_positive)
+        after_df[self.pred_target_col] = [x[favorable_class_idx] for x in self.fitted_step.predict_proba(np.array(df.drop(columns=[self.target_col])))]
+
+        return after_df
+
+    def name(self):
+        return "SK_LogisticRegression"
+
+    def abbr_name(self):
+        return "LR"
+
+    def step_name(self):
+        return "Model"
+
+    def input_encoded_data(self):
+        return True
+
+    def output_encoded_data(self):
+        return False
+
+    def fit_only_on_train(self):
+        return True
+
+
+class SK_DecisionTree(Step):
+    def __init__(self, target_col, seed, instance_weights=[], target_positive=1):
+        """
+        :param target_col: str, the name of the target variable in above data.
+        :param seed: integer, the seed for random state.
+        :param instance_weights: list of float, each number represents the weight of the sample in above data.
+        :param target_positive: integer, 0 or 1, represents the positive value of the target attribute. Default is 1.
+
+        """
+
+        self.target_col = target_col
+        self.pred_target_col = "pred_" + target_col  # store the predicted score (probability) column using this fixed name
+        self.seed = seed
+        self.instance_weights = instance_weights
+        self.target_positive = target_positive
+        self.fitted_step = None
+
+    def fit(self, df):
+        if len(self.instance_weights) == 0:
+            self.instance_weights = [1 for _ in range(1, df.shape[0] + 1)]
+
+        self.fitted_step = DecisionTreeClassifier(random_state=self.seed).fit(np.array(df.drop(columns=[self.target_col])), np.array(df[self.target_col]), sample_weight=self.instance_weights)
+
+        return self
+
+    def apply(self, df):
+        aif_pred_df = BinaryLabelDataset(df=df, label_names=[self.target_col], protected_attribute_names=[])
+        after_df, _ = aif_pred_df.convert_to_dataframe(de_dummy_code=True, sep='=', set_category=True)
+
+        favorable_class_idx = list(self.fitted_step.classes_).index(self.target_positive)
+        after_df[self.pred_target_col] = [x[favorable_class_idx] for x in self.fitted_step.predict_proba(np.array(df.drop(columns=[self.target_col])))]
+
+        return after_df
+
+    def name(self):
+        return "SK_DecisionTree"
+
+    def abbr_name(self):
+        return "DT"
+
+    def step_name(self):
+        return "Model"
+
+    def input_encoded_data(self):
+        return True
+
+    def output_encoded_data(self):
+        return False
+
+    def fit_only_on_train(self):
+        return True
+
+class OPT_LogisticRegression(Step):
+    def __init__(self, target_col, seed, loss_func="log", max_iter=1000, instance_weights=[], target_positive=1):
+        """
+        :param target_col: str, the name of the target variable in above data.
+        :param seed: integer, random seed.
+        :param loss_func: str, the name of the loss function used in linear model. Same as the loss parameter in sklearn.linear_model.SGDClassifier.
+                         The possible options are ‘hinge’, ‘log’, ‘modified_huber’, ‘squared_hinge’, ‘perceptron’, or a regression loss: ‘squared_loss’, ‘huber’, ‘epsilon_insensitive’, or ‘squared_epsilon_insensitive’.
+        :param max_iter: integer, max number of iterations of the model.
+        :param instance_weights: list of float, each number represents the weight of the sample in above data.
+        :param target_positive: integer, 0 or 1, represents the positive value of the target attribute. Default is 1.
+        """
+        self.target_col = target_col
+        self.pred_target_col = "pred_" + target_col  # store the predicted score (probability) column using this fixed name
+        self.seed = seed
+        self.loss_func = loss_func
+        self.max_iter = max_iter
+        self.instance_weights = instance_weights
+        self.target_positive = target_positive
+        self.fitted_step = None
+
+        self.param_grid = {
+            'learner__loss': [loss_func],
+            'learner__penalty': ['l2', 'l1', 'elasticnet'],
+            'learner__alpha': [0.00005, 0.0001, 0.005, 0.001]
+        }
+
+    def fit(self, df):
+        if len(self.instance_weights) == 0:
+            self.instance_weights = [1 for _ in range(1, df.shape[0] + 1)]
+
+        search = GridSearchCV(Pipeline([('learner', SGDClassifier(max_iter=self.max_iter, random_state=self.seed))]), self.param_grid, scoring='roc_auc', cv=5, verbose=1, n_jobs=-1)
+        self.fitted_step = search.fit(np.array(df.drop(columns=[self.target_col])), np.array(df[self.target_col]), None,
+                                 **{'learner__sample_weight': self.instance_weights})
+
+        return self
+
+    def apply(self, df):
+        aif_pred_df = BinaryLabelDataset(df=df, label_names=[self.target_col], protected_attribute_names=[])
+        after_df, _ = aif_pred_df.convert_to_dataframe(de_dummy_code=True, sep='=', set_category=True)
+
+        favorable_class_idx = list(self.fitted_step.classes_).index(self.target_positive)
+        after_df[self.pred_target_col] = [x[favorable_class_idx] for x in self.fitted_step.predict_proba(np.array(df.drop(columns=[self.target_col])))]
+
+        return after_df
+
+    def name(self):
+        return "OPT_LogisticRegression"
+
+    def abbr_name(self):
+        return "OLR"
+
+    def step_name(self):
+        return "Model"
+
+    def input_encoded_data(self):
+        return True
+
+    def output_encoded_data(self):
+        return False
+
+    def fit_only_on_train(self):
+        return True
+
+class OPT_DecisionTree(Step):
+    def __init__(self, target_col, seed, instance_weights=[], target_positive=1):
+        """
+        :param target_col: str, the name of the target variable in above data.
+        :param seed: integer, random seed.
+        :param instance_weights: list of float, each number represents the weight of the sample in above data.
+        :param target_positive: integer, 0 or 1, represents the positive value of the target attribute. Default is 1.
+        """
+        self.target_col = target_col
+        self.pred_target_col = "pred_" + target_col  # store the predicted score (probability) column using this fixed name
+        self.seed = seed
+        self.instance_weights = instance_weights
+        self.target_positive = target_positive
+        self.fitted_step = None
+
+        self.param_grid = {
+            'learner__min_samples_split': range(20, 500, 10),
+            'learner__max_depth': range(15, 30, 2),
+            'learner__min_samples_leaf': [3, 4, 5, 10],
+            "learner__criterion": ["gini", "entropy"]
+        }
+
+    def fit(self, df):
+        if len(self.instance_weights) == 0:
+            self.instance_weights = [1 for _ in range(1, df.shape[0] + 1)]
+
+        search = GridSearchCV(Pipeline([('learner', DecisionTreeClassifier(random_state=self.seed))]), self.param_grid, scoring='roc_auc', cv=5, verbose=1, n_jobs=-1)
+        self.fitted_step = search.fit(np.array(df.drop(columns=[self.target_col])), np.array(df[self.target_col]), None,
+                                 **{'learner__sample_weight': self.instance_weights})
+
+        return self
+
+    def apply(self, df):
+        aif_pred_df = BinaryLabelDataset(df=df, label_names=[self.target_col], protected_attribute_names=[])
+        after_df, _ = aif_pred_df.convert_to_dataframe(de_dummy_code=True, sep='=', set_category=True)
+
+        favorable_class_idx = list(self.fitted_step.classes_).index(self.target_positive)
+        after_df[self.pred_target_col] = [x[favorable_class_idx] for x in self.fitted_step.predict_proba(np.array(df.drop(columns=[self.target_col])))]
+
+        return after_df
+
+    def name(self):
+        return "OPT_DecisionTree"
+
+    def abbr_name(self):
+        return "ODT"
+
+    def step_name(self):
+        return "Model"
+
+    def input_encoded_data(self):
+        return True
+
+    def output_encoded_data(self):
+        return False
+
+    def fit_only_on_train(self):
+        return True
+
+if __name__ == '__main__':
+    data = pd.read_csv("../../data/german_pre_encoded.csv")
+    # cur_o = SK_LogisticRegression("credit", 0)
+    cur_o = SK_DecisionTree("credit", 0)
+    # cur_o = OPT_LogisticRegression("credit", 0)
+    # cur_o = OPT_DecisionTree("credit", 0)
+
+    cur_o.fit(data)
+    after_data = cur_o.apply(data)
+    after_data.to_csv("../../data/german_after_" + cur_o.name() + ".csv", index=False)
diff --git a/pipeline/model/fair_classifiers.py b/pipeline/model/fair_classifiers.py
new file mode 100644
index 0000000..f92c5ad
--- /dev/null
+++ b/pipeline/model/fair_classifiers.py
@@ -0,0 +1,187 @@
+"""
+    Classes of fair supervised binary classifiers.
+"""
+
+import pandas as pd
+from aif360.datasets import BinaryLabelDataset
+from aif360.algorithms.inprocessing import AdversarialDebiasing
+from aif360.algorithms.inprocessing import MetaFairClassifier
+from aif360.algorithms.inprocessing import PrejudiceRemover
+from pipeline.step import Step
+
+import warnings
+warnings.filterwarnings("ignore")
+
+class AIF_AdversarialDebiasing(Step):
+
+    def __init__(self, target_col, sensitive_att, seed):
+        """
+        :param target_col: str, the name of the target variable in above data. Assume 1 represents the favorable class.
+        :param sensitive_att: str, the name of a sensitive attribute in above data. If none, call auto_detection to update. Value 0 represent protected.
+        :param seed: integer, the seed for random process.
+        """
+
+        import tensorflow as tf
+        self.sess = tf.Session()
+
+        self.sensitive_att = sensitive_att
+        self.target_col = target_col
+        self.pred_target_col = "pred_" + target_col  # store the predicted score (probability) column using this fixed name
+        self.seed = seed
+        self.fitted_step = None
+
+    def fit(self, df):
+        # wrap the input dataframe with AIF 360 object "BinaryLabelDataset"
+        aif_df = BinaryLabelDataset(df=df, label_names=[self.target_col], protected_attribute_names=[self.sensitive_att])
+        self.fitted_step = AdversarialDebiasing(unprivileged_groups=[{self.sensitive_att: 0}], privileged_groups=[{self.sensitive_att: 1}], scope_name='debiased_classifier', debias=True, sess=self.sess, seed=self.seed).fit(aif_df)
+
+        return self
+
+    def apply(self, df):
+        aif_pred_df = BinaryLabelDataset(df=df, label_names=[self.target_col], protected_attribute_names=[self.sensitive_att])
+
+        after_aif_df = self.fitted_step.predict(aif_pred_df)
+
+        after_df, _ = after_aif_df.convert_to_dataframe(de_dummy_code=True, sep='=', set_category=True)
+        after_df[self.pred_target_col] = after_aif_df.labels
+
+        return after_df
+
+    def name(self):
+        return "AdversarialDebiasing"
+
+    def abbr_name(self):
+        return "AD"
+
+    def step_name(self):
+        return "FairInprocessor"
+
+    def input_encoded_data(self):
+        return True
+
+    def output_encoded_data(self):
+        return False
+
+    def fit_only_on_train(self):
+        return True
+
+class AIF_MetaFairClassifier(Step):
+
+    def __init__(self, target_col, sensitive_att, fairness_penalty=0.8, fair_metric="sr"):
+        """
+        :param target_col: str, the name of the target variable in above data.
+        :param sensitive_att: str, the name of a sensitive attribute in above data. If none, call auto_detection to update. Value 0 represent protected.
+        :param fairness_penalty: float in [0,1], fairness penalty parameter. default is 0.8. The same parameter in aif360.algorithms.inprocessing.MetaFairClassifier.
+        :param fair_metric: str, fairness metric used in this method. Value from ["fdr" (false discovery rate ratio), "sr" (statistical rate/disparate impact)].
+                            The same parameter in aif360.algorithms.inprocessing.MetaFairClassifier.
+        """
+
+        self.sensitive_att = sensitive_att
+        self.target_col = target_col
+        self.pred_target_col = "pred_" + target_col  # store the predicted score (probability) column using this fixed name
+        self.fairness_penalty = fairness_penalty
+        self.fair_metric = fair_metric
+
+        self.fitted_step = None
+
+
+    def fit(self, df):
+        # wrap the input dataframe with AIF 360 object "BinaryLabelDataset"
+        aif_df = BinaryLabelDataset(df=df, label_names=[self.target_col],
+                                    protected_attribute_names=[self.sensitive_att])
+        self.fitted_step = MetaFairClassifier(tau=self.fairness_penalty, sensitive_attr=self.sensitive_att, type=self.fair_metric).fit(aif_df)
+
+        return self
+
+    def apply(self, df):
+        aif_pred_df = BinaryLabelDataset(df=df, label_names=[self.target_col],
+                                         protected_attribute_names=[self.sensitive_att])
+
+        after_aif_df = self.fitted_step.predict(aif_pred_df)
+
+        after_df, _ = after_aif_df.convert_to_dataframe(de_dummy_code=True, sep='=', set_category=True)
+        after_df[self.pred_target_col] = after_aif_df.labels
+
+        return after_df
+
+    def name(self):
+        return "MetaFairClassifier"
+
+    def abbr_name(self):
+        return "MFC"
+
+    def step_name(self):
+        return "FairInprocessor"
+
+    def input_encoded_data(self):
+        return True
+
+    def output_encoded_data(self):
+        return False
+
+    def fit_only_on_train(self):
+        return True
+
+class AIF_PrejudiceRemover(Step):
+
+    def __init__(self, target_col, sensitive_att, fairness_penalty=1.0):
+        """
+        :param target_col: str, the name of the target variable in above data.
+        :param sensitive_att: str, the name of a sensitive attribute in above data. If none, call auto_detection to update. Value 0 represent protected.
+        :param fairness_penalty: float in [0,1], fairness penalty parameter. default is 1. The same parameter in aif360.algorithms.inprocessing.PrejudiceRemover.
+
+        """
+        # TODO: fix the bug that cannot import lib of 'getoutput'
+        self.sensitive_att = sensitive_att
+        self.target_col = target_col
+        self.pred_target_col = "pred_" + target_col  # store the predicted score (probability) column using this fixed name
+        self.fairness_penalty = fairness_penalty
+
+        self.fitted_step = None
+
+    def fit(self, df):
+        # wrap the input dataframe with AIF 360 object "BinaryLabelDataset"
+        aif_df = BinaryLabelDataset(df=df, label_names=[self.target_col],
+                                    protected_attribute_names=[self.sensitive_att])
+        self.fitted_step = PrejudiceRemover(eta=self.fairness_penalty, sensitive_attr=self.sensitive_att, class_attr=self.target_col).fit(aif_df)
+
+        return self
+
+    def apply(self, df):
+        aif_pred_df = BinaryLabelDataset(df=df, label_names=[self.target_col],
+                                         protected_attribute_names=[self.sensitive_att])
+
+        after_aif_df = self.fitted_step.predict(aif_pred_df)
+
+        after_df, _ = after_aif_df.convert_to_dataframe(de_dummy_code=True, sep='=', set_category=True)
+        after_df[self.pred_target_col] = after_aif_df.labels
+
+        return after_df
+
+    def name(self):
+        return "PrejudiceRemover"
+
+    def abbr_name(self):
+        return "PR"
+
+    def step_name(self):
+        return "FairInprocessor"
+
+    def input_encoded_data(self):
+        return True
+
+    def output_encoded_data(self):
+        return False
+
+    def fit_only_on_train(self):
+        return True
+
+if __name__ == '__main__':
+    data = pd.read_csv("../../data/german_pre_encoded.csv")
+    # cur_o = AIF_AdversarialDebiasing("credit", "sex")
+    cur_o = AIF_MetaFairClassifier("credit", "sex")
+    # cur_o = AIF_PrejudiceRemover("credit", "sex") # not working TODO: fix
+
+    cur_o.fit(data)
+    after_data = cur_o.apply(data)
+    after_data.to_csv("../../data/german_after_" + cur_o.name() + ".csv", index=False)
diff --git a/pipeline/postprocess/fair_postprocessors.py b/pipeline/postprocess/fair_postprocessors.py
new file mode 100644
index 0000000..412fdc6
--- /dev/null
+++ b/pipeline/postprocess/fair_postprocessors.py
@@ -0,0 +1,219 @@
+"""
+    Classes for post-process data and model outcomes
+"""
+
+import pandas as pd
+import numpy as np
+from aif360.datasets import BinaryLabelDataset
+from aif360.algorithms.postprocessing import CalibratedEqOddsPostprocessing
+from aif360.algorithms.postprocessing import EqOddsPostprocessing
+from aif360.algorithms.postprocessing import RejectOptionClassification
+from pipeline.step import Step
+
+class NoFairPostprocessor(Step):
+    def __init__(self):
+        self.fitted_step = None
+
+    def fit(self, df):
+        pass
+
+    def apply(self, df):
+        return df
+
+    def name(self):
+        return "NoPostprocessor"
+
+    def abbr_name(self):
+        return "NP"
+
+    def step_name(self):
+        return "FairPostprocessor"
+
+    def input_encoded_data(self):
+        return False
+
+    def output_encoded_data(self):
+        return False
+
+    def fit_only_on_train(self):
+        return False
+
+class AIF_EqOddsPostprocessing(Step):
+
+    def __init__(self, target_col, sensitive_att, seed, threshold=0.5):
+        """
+        :param target_col: str, the name of the target variable in above data. Assume 1 represents the favorable class.
+        :param sensitive_att: str, the name of a sensitive attribute in above data. If none, call auto_detection to update. Value 0 represent protected.
+        :param seed: integer, the seed for random process.
+        :param threshold: float in [0, 1], the classification threshold to generate the predicted class label.
+        """
+        self.sensitive_att = sensitive_att
+        self.target_col = target_col
+        self.pred_target_col = "pred_" + target_col  # store the predicted score (probability) column using this fixed name
+        self.seed = seed
+        self.clf_threshold = threshold
+        self.fitted_step = None
+
+
+    def fit(self, df):
+        # wrap the input dataframe with AIF 360 object "BinaryLabelDataset"
+        aif_true_df = BinaryLabelDataset(df=df.drop(columns=[self.pred_target_col]), label_names=[self.target_col],
+                                         protected_attribute_names=[self.sensitive_att])
+
+        aif_pred_df = aif_true_df.copy()
+        aif_pred_df.labels = np.array([int(x >= self.clf_threshold) for x in df[self.pred_target_col]])
+
+        self.fitted_step = EqOddsPostprocessing([{self.sensitive_att: 0}], [{self.sensitive_att: 1}], self.seed).fit(aif_true_df, aif_pred_df)
+
+        return self
+
+    def apply(self, df):
+        # wrap the input dataframe with AIF 360 object "BinaryLabelDataset"
+        df["pred_label_" + self.target_col] = [int(x >= self.clf_threshold) for x in df[self.pred_target_col]]
+        aif_pred_df = BinaryLabelDataset(df=df.drop(columns=[self.pred_target_col]),
+                                             label_names=["pred_label_" + self.target_col],
+                                             protected_attribute_names=[self.sensitive_att])
+
+        after_aif_df = self.fitted_step.predict(aif_pred_df)
+
+        after_df, _ = after_aif_df.convert_to_dataframe(de_dummy_code=True, sep='=', set_category=True)
+        after_df[self.pred_target_col] = after_aif_df.labels
+
+        return after_df
+
+    def name(self):
+        return "Eq_odds"
+
+    def abbr_name(self):
+        return "EQ"
+
+    def step_name(self):
+        return "FairPostprocessor"
+
+    def input_encoded_data(self):
+        return True
+
+    def output_encoded_data(self):
+        return False
+
+    def fit_only_on_train(self):
+        return True
+
+class AIF_CalibratedEqOddsPostprocessing(Step):
+
+    def __init__(self, target_col, sensitive_att, seed, cost_constraint='weighted'):
+        """
+        :param df: pandas dataframe, stores the data to fit the postprocessor.
+        :param target_col: str, the name of the target variable in above data. Assume 1 represents the favorable class.
+        :param sensitive_att: str, the name of a sensitive attribute in above data. If none, call auto_detection to update. Value 0 represent protected.
+        :param seed: integer, the seed for random process.
+        :param cost_constraint: str, the fairness constraints format, value from [fpr, fnr, weighted].
+                                The same parameter as in aif360.algorithms.postprocessing.CalibratedEqOddsPostprocessing.
+        """
+        self.sensitive_att = sensitive_att
+        self.target_col = target_col
+        self.pred_target_col = "pred_" + target_col  # store the predicted score (probability) column using this fixed name
+        self.seed = seed
+        self.cost_constraint = cost_constraint
+        self.fitted_step = None
+
+    def fit(self, df):
+
+        aif_true_df = BinaryLabelDataset(df=df.drop(columns=[self.pred_target_col]), label_names=[self.target_col],
+                                         protected_attribute_names=[self.sensitive_att])
+        aif_pred_df = aif_true_df.copy()
+        aif_pred_df.scores = df[self.pred_target_col]
+        self.fitted_step = CalibratedEqOddsPostprocessing([{self.sensitive_att: 0}], [{self.sensitive_att: 1}], cost_constraint=self.cost_constraint, seed=self.seed).fit(aif_true_df, aif_pred_df)
+        return self
+
+    def apply(self, df):
+        aif_pred_df = BinaryLabelDataset(df=df, label_names=[self.target_col], scores_names=[self.pred_target_col],
+                                             protected_attribute_names=[self.sensitive_att])
+
+        after_aif_df = self.fitted_step.predict(aif_pred_df)
+        after_df, _ = after_aif_df.convert_to_dataframe(de_dummy_code=True, sep='=', set_category=True)
+        after_df[self.pred_target_col] = after_aif_df.labels
+
+        return after_df
+
+    def name(self):
+        return "Calibrated_eq_odds"
+
+    def abbr_name(self):
+        return "CEQ"
+
+    def step_name(self):
+        return "FairPostprocessor"
+
+    def input_encoded_data(self):
+        return True
+
+    def output_encoded_data(self):
+        return False
+
+    def fit_only_on_train(self):
+        return True
+
+class AIF_RejectOptionPostprocessing(Step):
+
+    def __init__(self, target_col, sensitive_att):
+        """
+        :param df: pandas dataframe, stores the data to fit the postprocessor.
+        :param target_col: str, the name of the target variable in above data. Assume 1 represents the favorable class.
+        :param sensitive_att: str, the name of a sensitive attribute in above data. If none, call auto_detection to update. Value 0 represent protected.
+        """
+        # TODO: fix the bug that reject option outputs error
+
+        self.sensitive_att = sensitive_att
+        self.target_col = target_col
+        self.pred_target_col = "pred_" + target_col  # store the predicted score (probability) column using this fixed name
+
+        self.fitted_step = None
+
+    def fit(self, df):
+        aif_true_df = BinaryLabelDataset(df=df.drop(columns=[self.pred_target_col]), label_names=[self.target_col],
+                                         protected_attribute_names=[self.sensitive_att])
+        aif_pred_df = aif_true_df.copy()
+        aif_pred_df.scores = df[self.pred_target_col]
+        self.fitted_step = RejectOptionClassification([{self.sensitive_att: 0}], [{self.sensitive_att: 1}]).fit(aif_true_df, aif_pred_df)
+        return self
+
+    def apply(self, df):
+        aif_pred_df = BinaryLabelDataset(df=df, label_names=[self.target_col], scores_names=[self.pred_target_col],
+                                         protected_attribute_names=[self.sensitive_att])
+
+        after_aif_df = self.fitted_step.predict(aif_pred_df)
+        after_df, _ = after_aif_df.convert_to_dataframe(de_dummy_code=True, sep='=', set_category=True)
+        after_df[self.pred_target_col] = after_aif_df.labels
+
+        return after_df
+
+    def name(self):
+        return "Reject_option"
+
+    def abbr_name(self):
+        return "RO"
+
+    def step_name(self):
+        return "FairPostprocessor"
+
+    def input_encoded_data(self):
+        return True
+
+    def output_encoded_data(self):
+        return False
+
+    def fit_only_on_train(self):
+        return True
+
+if __name__ == '__main__':
+    data = pd.read_csv("../../data/german_pre_encoded_after_model.csv")
+    # cur_o = AIF_RejectOptionPostprocessing("credit", "sex")
+    # cur_o = AIF_EqOddsPostprocessing("credit", "sex", 0)
+    cur_o = AIF_CalibratedEqOddsPostprocessing("credit", "sex", 0)
+
+
+    cur_o.fit(data)
+    after_data = cur_o.apply(data)
+
+    after_data.to_csv("../../data/german_after_" + cur_o.name() + ".csv", index=False)
diff --git a/pipeline/preprocess/categorizers.py b/pipeline/preprocess/categorizers.py
new file mode 100644
index 0000000..f0afb99
--- /dev/null
+++ b/pipeline/preprocess/categorizers.py
@@ -0,0 +1,139 @@
+"""
+    Classes to discretize numerical attributes into categorical attributes.
+"""
+import pandas as pd
+import numpy as np
+from sklearn.preprocessing import KBinsDiscretizer
+from sklearn.preprocessing import Binarizer
+from pipeline.step import Step
+
+class NoBinarizer(Step):
+    def __init__(self):
+        self.fitted_step = None
+
+    def fit(self, df):
+        pass
+
+    def apply(self, df):
+        return df
+
+    def name(self):
+        return "NoBinarizer"
+
+    def abbr_name(self):
+        return "NB"
+
+    def step_name(self):
+        return "Categorizer"
+
+    def input_encoded_data(self):
+        return False
+
+    def output_encoded_data(self):
+        return False
+
+    def fit_only_on_train(self):
+        return False
+
+class SK_Discretizer(Step):
+    def __init__(self, num_atts, bin_size, encode='ordinal', strategy='kmeans'):
+        """
+        :param num_atts: list of str, each str represents the name of a numerical attribute in above data.
+        :param bin_size: list of integer, each integer represents the number of bins to categorize the corresponding numerical attribute.
+        :param encode: same parameter with sklearn KBinsDiscretizer
+        :param strategy: same parameter with sklearn KBinsDiscretizer
+        """
+        self.focus_atts = num_atts
+        self.bin_size = bin_size
+        self.encode = encode
+        self.strategy = strategy
+
+        self.fitted_step = None
+
+    def fit(self, df):
+        fitted_step = {}
+        for idx, ai in enumerate(self.focus_atts):
+            fitted_step[ai] = KBinsDiscretizer(n_bins=self.bin_size[idx], encode=self.encode, strategy=self.strategy).fit(np.array(df[ai]).reshape(-1, 1))
+        self.fitted_step = fitted_step
+        return self
+
+    def apply(self, df):
+        after_df = df.copy()
+        for ai in self.focus_atts:
+            after_df[ai] = self.fitted_step[ai].transform(np.array(after_df[ai]).reshape(-1, 1))
+
+        return after_df
+
+    def name(self):
+        return "SK_Discretizer"
+
+    def abbr_name(self):
+        return "DS"
+
+    def step_name(self):
+        return "Categorizer"
+
+    def input_encoded_data(self):
+        return False
+
+    def output_encoded_data(self):
+        return False
+
+    def fit_only_on_train(self):
+        return True
+
+class SK_Binarizer(Step):
+    def __init__(self, num_atts, bin_thresholds, copy=True):
+        """
+        :param num_atts: list of str, each str represents the name of a numerical attribute in above data.
+        :param bin_thresholds: list of float, each float represents the value to binarize the corresponding numerical attributes.
+                               Values below or equal to this threshold are replaced by 0, above it by 1.
+        :param copy: same parameter with sklearn Binarizer
+        """
+        self.focus_atts = num_atts
+        self.bin_thresholds = bin_thresholds
+        self.copy = copy
+
+        self.fitted_step = None
+
+    def fit(self, df):
+        fitted_step = {}
+        for idx, ai in enumerate(self.focus_atts):
+            fitted_step[ai] = Binarizer(threshold=self.bin_thresholds[idx], copy=self.copy).fit(np.array(df[ai]).reshape(-1, 1))
+        self.fitted_step = fitted_step
+        return self
+
+    def apply(self, df):
+        after_df = df.copy()
+        for ai in self.focus_atts:
+            after_df[ai] = self.fitted_step[ai].transform(np.array(after_df[ai]).reshape(-1, 1))
+
+        return after_df
+
+    def name(self):
+        return "SK_Binarizer"
+
+    def abbr_name(self):
+        return "BI"
+
+    def step_name(self):
+        return "Categorizer"
+
+    def input_encoded_data(self):
+        return False
+
+    def output_encoded_data(self):
+        return False
+
+    def fit_only_on_train(self):
+        return True
+
+if __name__ == '__main__':
+
+    data = pd.read_csv("../../data/german.csv")
+    # cur_o = SK_Discretizer(["month", "age"], [2, 3])
+    cur_o = SK_Binarizer(["month", "age"], [24, 30])
+
+    cur_o.fit(data)
+    after_data = cur_o.apply(data)
+    after_data.to_csv("../../data/german_"+cur_o.name()+".csv", index=False)
diff --git a/pipeline/preprocess/encoders.py b/pipeline/preprocess/encoders.py
new file mode 100644
index 0000000..e81ab60
--- /dev/null
+++ b/pipeline/preprocess/encoders.py
@@ -0,0 +1,144 @@
+"""
+    Classes to encode the string values for categorical attributes.
+"""
+
+import numpy as np
+import pandas as pd
+from sklearn.preprocessing import OrdinalEncoder
+from pipeline.step import Step
+
+class SK_OrdinalEncoder(Step):
+    def __init__(self, cate_atts, sort_label, sort_positive_value):
+        """
+        :param cate_atts: list of str, each str represents the name of a categorical attribute in above data.
+        :param sort_label: str, name of the target varible to determine the order of ordinal encodings.
+        :param sort_positive_value: str, value of the target varible to determine the order of ordinal encodings.
+
+        """
+
+        self.focus_atts = cate_atts
+        self.sort_label = sort_label
+        self.sort_positive_value = sort_positive_value
+
+        self.fitted_step = None
+
+    def fit(self, df):
+        fitted_step = {}
+        for idx, ai in enumerate(self.focus_atts):
+            value_counts = {}
+            for vi in df[ai].unique():
+                value_counts[vi] = df[(df[ai] == vi) & (df[self.sort_label] == self.sort_positive_value)].shape[0]
+            value_orders = sorted(value_counts.keys(), key=lambda x: value_counts[x])
+            fitted_step[ai] = OrdinalEncoder(categories=[value_orders]).fit(np.array(df[ai]).reshape(-1, 1))
+        self.fitted_step = fitted_step
+        return self
+
+    def apply(self, df):
+        after_df = df.copy()
+        for ai in self.focus_atts:
+            after_df[ai] = self.fitted_step[ai].transform(np.array(after_df[ai]).reshape(-1, 1))
+
+        return after_df
+
+    def name(self):
+        return "SK_OrdinalEncoder"
+
+    def abbr_name(self):
+        return "OE"
+
+    def step_name(self):
+        return "Encoder"
+
+    def input_encoded_data(self):
+        return True
+
+    def output_encoded_data(self):
+        return True
+
+    def fit_only_on_train(self):
+        return True
+
+
+class OneHotEncoder(Step):
+    def __init__(self, cate_atts):
+        """
+        :param cate_atts: list of str, each str represents the name of a categorical attribute in above data.
+        """
+        self.focus_atts = cate_atts
+
+    def fit(self, df):
+        pass
+
+    def apply(self, df):
+        after_df = pd.get_dummies(df, columns=self.focus_atts, prefix_sep='=')
+        return after_df
+
+    def name(self):
+        return "OneHotEncoder"
+
+    def abbr_name(self):
+        return "HE"
+
+    def step_name(self):
+        return "Encoder"
+
+    def input_encoded_data(self):
+        return False
+
+    def output_encoded_data(self):
+        return True
+
+    def fit_only_on_train(self):
+        return False
+
+# That can only apply in SensitiveAttributeEncoder
+class MappingEncoder(Step):
+    def __init__(self, focus_atts, mapping_dict):
+        """ Encode sensitive attribute and target feature through mapping string to numberical values according to the input dictionary.
+        :param focus_atts: list of str, each str represents the name of an attribute.
+        :param mapping_dict: dict, key is the value (str) of the attribute in focus_atts, value is int that encode the value. E.g. {"female": 0, "male": 1} for the values of attribute gender.
+        """
+
+        self.focus_atts = focus_atts
+        self.mapping_dict = mapping_dict
+
+    def fit(self, df):
+        pass
+
+
+    def apply(self, df):
+        after_df = df.copy()
+        for si in self.focus_atts:
+            after_df[si] = after_df[si].apply(lambda x: self.mapping_dict[x])
+        return after_df
+
+    def name(self):
+        return "MappingEncoder"
+
+    def abbr_name(self):
+        return "ME"
+
+    def step_name(self):
+        return "SpecialEncoder"
+
+    def input_encoded_data(self):
+        return True
+
+    def output_encoded_data(self):
+        return True
+
+    def fit_only_on_train(self):
+        return False
+
+
+if __name__ == '__main__':
+    data = pd.read_csv("../../data/german_AIF.csv")
+
+    cur_o = SK_OrdinalEncoder(["sex"], "credit", "good")
+    # cur_o = OneHotEncoder(["sex", "credit", "age"])
+
+    # cur_o = MappingEncoder(["sex", "credit"], {"female": 0, "male": 1, "good": 1, "bad": 0})
+
+    cur_o.fit(data)
+    after_data = cur_o.apply(data)
+    after_data.to_csv("../../data/german_AIF_"+cur_o.name()+".csv", index=False)
diff --git a/pipeline/preprocess/fair_preprocessors.py b/pipeline/preprocess/fair_preprocessors.py
new file mode 100644
index 0000000..908ad19
--- /dev/null
+++ b/pipeline/preprocess/fair_preprocessors.py
@@ -0,0 +1,186 @@
+"""
+    Class of fairness preprocessing interventions
+"""
+
+import pandas as pd
+from aif360.datasets import BinaryLabelDataset
+from aif360.algorithms.preprocessing import Reweighing as Reweighing
+from aif360.algorithms.preprocessing import LFR as LFR
+from aif360.algorithms.preprocessing import DisparateImpactRemover
+from pipeline.step import Step
+
+class AIF_Reweighing(Step):
+
+    def __init__(self, target_col, sensitive_att):
+        """
+        :param target_col: str, the name of the target variable in above data.
+        :param sensitive_att: str, the name of a sensitive attribute in above data.
+
+        """
+        self.sensitive_att = sensitive_att
+        self.target_col = target_col
+        self.pred_target_col = "pred_" + target_col  # store the predicted score (probability) column using this fixed name
+
+        self.fitted_step = None
+
+    def fit(self, df):
+        # wrap the input dataframe with AIF 360 object "BinaryLabelDataset"
+        aif_df = BinaryLabelDataset(df=df, label_names=[self.target_col], protected_attribute_names=[self.sensitive_att])
+        self.fitted_step = Reweighing([{self.sensitive_att: 0}], [{self.sensitive_att: 1}]).fit(aif_df)
+        return self
+
+    def apply(self, df):
+        # wrap the input dataframe with AIF 360 object "BinaryLabelDataset"
+        aif_df = BinaryLabelDataset(df=df, label_names=[self.target_col], protected_attribute_names=[self.sensitive_att])
+        after_aif_df = self.fitted_step.transform(aif_df)
+        # TODO: double check whether to return weights
+        preprocessed_weights = after_aif_df.instance_weights
+
+        after_df, _ = after_aif_df.convert_to_dataframe(de_dummy_code=True, sep='=', set_category=True)
+        
+        return after_df
+
+    def name(self):
+        return "Reweighing"
+
+    def abbr_name(self):
+        return "RW"
+
+    def step_name(self):
+        return "FairPreprocessor"
+
+    def input_encoded_data(self):
+        return True
+
+    def output_encoded_data(self):
+        return False
+
+    def fit_only_on_train(self):
+        return True
+
+class AIF_DIRemover(Step):
+    def __init__(self, target_col, sensitive_att, repair_level):
+        """
+        :param target_col: str, the name of the target variable in above data.
+        :param sensitive_att: str, the name of a sensitive attribute in above data.
+
+        """
+        self.sensitive_att = sensitive_att
+        self.target_col = target_col
+        self.pred_target_col = "pred_" + target_col  # store the predicted score (probability) column using this fixed name
+
+        self.fitted_step = None
+
+        if repair_level is None or not isinstance(repair_level, float):
+            print("Input repair_level is not valid! Should be float within [0,1]!")
+            raise ValueError
+        else:
+            if repair_level < 0 or repair_level > 1:
+                print("Input repair_level is not valid! Should be float within [0,1]!")
+                raise ValueError
+
+        self.repair_level = repair_level
+
+
+    def fit(self, df):
+        # TODO: Rewrite the AIF DIRemover to afford the separation of train, validation, and test.
+        # aif_df = BinaryLabelDataset(df=df, label_names=[self.target_col], protected_attribute_names=[self.sensitive_att])
+        # self.fitted_step = DisparateImpactRemover(repair_level=self.repair_level, sensitive_attribute=self.sensitive_att).fit(aif_df)
+        #
+        pass
+
+    def apply(self, df):
+        # wrap the input dataframe with AIF 360 object "BinaryLabelDataset"
+        aif_df = BinaryLabelDataset(df=df, label_names=[self.target_col], protected_attribute_names=[self.sensitive_att])
+        after_aif_df = DisparateImpactRemover(repair_level=self.repair_level, sensitive_attribute=self.sensitive_att).fit_transform(aif_df)
+
+        after_df, _ = after_aif_df.convert_to_dataframe(de_dummy_code=True, sep='=', set_category=True)
+        return after_df
+
+    def name(self):
+        return "DIRemover"
+
+    def abbr_name(self):
+        return "RI"
+
+    def step_name(self):
+        return "FairPreprocessor"
+
+    def input_encoded_data(self):
+        return True
+
+    def output_encoded_data(self):
+        return False
+
+    def fit_only_on_train(self):
+        return False
+
+class AIF_LFR(Step):
+
+    def __init__(self, target_col, sensitive_att):
+        """ NOTE: very sensitive to input data, refer the example in AIF 360 for this preprocessor
+        :param target_col: str, the name of the target variable in above data.
+        :param sensitive_att: str, the name of a sensitive attribute in above data.
+
+        """
+        # TODO: fix the bug of LFR for not returning categorical atts
+        # TODO: experiment with the same data used by AIF360 tutorial to compare whether the categorical atts are returned
+        self.sensitive_att = sensitive_att
+        self.target_col = target_col
+        self.pred_target_col = "pred_" + target_col  # store the predicted score (probability) column using this fixed name
+
+        self.fitted_step = None
+
+    def fit(self, df):
+        # wrap the input dataframe with AIF 360 object "BinaryLabelDataset"
+        aif_df = BinaryLabelDataset(df=df, label_names=[self.target_col], protected_attribute_names=[self.sensitive_att])
+        self.fitted_step = LFR([{self.sensitive_att: 0}], [{self.sensitive_att: 1}]).fit(aif_df)
+        return self
+
+    def apply(self, df):
+        # wrap the input dataframe with AIF 360 object "BinaryLabelDataset"
+        aif_df = BinaryLabelDataset(df=df, label_names=[self.target_col], protected_attribute_names=[self.sensitive_att])
+        after_aif_df = self.fitted_step.transform(aif_df)
+
+        after_df, _ = after_aif_df.convert_to_dataframe(de_dummy_code=True, sep='=', set_category=True)
+        return after_df
+
+    def name(self):
+        return "LFR"
+
+    def abbr_name(self):
+        return "LR"
+
+    def step_name(self):
+        return "FairPreprocessor"
+
+    def input_encoded_data(self):
+        return True
+
+    def output_encoded_data(self):
+        return False
+
+    def fit_only_on_train(self):
+        return True
+
+if __name__ == '__main__':
+    data = pd.read_csv("../../data/german_pre_encoded.csv")
+    # name_mapping = {"female": 0, "male": 1, "young": 0, "old": 1, "bad": 0, "good": 1}
+    # for atti in ["credit", "sex", "age"]:
+    #     data[atti] = data[atti].apply(lambda x: name_mapping[x])
+    #
+    # data.to_csv("../../data/german_pre_encoded.csv")
+
+    # cur_o = AIF_Reweighing("credit", "sex")
+    # cur_o = AIF_LFR("credit", "sex") # TODO: bug not working for this dataset, test another dataset
+    cur_o = AIF_DIRemover("credit", "sex", 0.8)
+    cur_o.fit(data)
+    after_data = cur_o.apply(data)
+    # # for Reweighing
+    # after_data, new_weights = cur_o.apply(data)
+    after_data.to_csv("../../data/german_after_"+cur_o.name()+".csv", index=False)
+
+    # print(cur_o.get_name())
+    # for Reweighing
+    # print(len(new_weights))
+    # print(new_weights)
\ No newline at end of file
diff --git a/pipeline/preprocess/filters.py b/pipeline/preprocess/filters.py
new file mode 100644
index 0000000..264deb4
--- /dev/null
+++ b/pipeline/preprocess/filters.py
@@ -0,0 +1,94 @@
+"""
+    Classes to filter slice of data.
+"""
+import pandas as pd
+from pipeline.step import Step
+
+
+# TODO: add multiple filter class
+class RowFilter(Step):
+
+    def __init__(self, column, value):
+        """
+        :param column: str, name of the column name to be focused.
+        :param value: str, integer, float, the value of the column to keep.
+        """
+
+        self.column = column
+        self.value = value
+
+    def fit(self, df):
+        pass
+
+    # utility functions to wrap the input filter to a df query
+    def wrap_filter(self):
+        if isinstance(self.value, str):  # string or null
+            if self.value in ["?", ""]:  # for null
+                return '{}!={}'.format(self.column, self.column)
+            else:
+                return '{}=="{}"'.format(self.column, self.value)
+        else:  # numerical value
+            return '{}=={}'.format(self.column, self.value)
+
+    def apply(self, df):
+        return df.query(self.wrap_filter())
+
+    def name(self):
+        return "RowFilter"
+
+    def abbr_name(self):
+        return "RF"
+
+    def step_name(self):
+        return "Filter"
+
+    def input_encoded_data(self):
+        return False
+
+    def output_encoded_data(self):
+        return False
+
+    def fit_only_on_train(self):
+        return False
+
+class ColumnFilter(Step):
+    def __init__(self, remove_cols):
+        """
+        :param exclude_cols: list of string, each string represents the name of the column to be filtered out.
+        """
+        self.remove_cols = remove_cols
+
+    def fit(self, df):
+        pass
+
+    def apply(self, df):
+        return df.drop(columns=self.remove_cols)
+
+    def name(self):
+        return "ColumnFilter"
+
+    def abbr_name(self):
+        return "CF"
+
+    def step_name(self):
+        return "Filter"
+
+    def input_encoded_data(self):
+        return False
+
+    def output_encoded_data(self):
+        return False
+
+    def fit_only_on_train(self):
+        return False
+
+if __name__ == '__main__':
+
+    data = pd.read_csv("../../data/german_AIF.csv")
+    cur_o = RowFilter("sex", "female")
+    # cur_o = ColumnFilter(["sex", "age"])
+
+    cur_o.fit(data)
+    after_data = cur_o.apply(data)
+    after_data.to_csv("../../data/german_AIF_" + cur_o.name() + ".csv", index=False)
+
diff --git a/pipeline/preprocess/imputers.py b/pipeline/preprocess/imputers.py
new file mode 100644
index 0000000..66cfc57
--- /dev/null
+++ b/pipeline/preprocess/imputers.py
@@ -0,0 +1,183 @@
+"""
+    Classes to impute missing values in data.
+"""
+import numpy as np
+import pandas as pd
+import datawig
+from sklearn.impute import SimpleImputer
+from pipeline.step import Step
+
+class NoImputer(Step):
+    def __init__(self):
+        self.fitted_step = None
+
+    def fit(self, df):
+        pass
+
+    def apply(self, df):
+        return df
+
+    def name(self):
+        return "NoImputer"
+
+    def abbr_name(self):
+        return "NI"
+
+    def step_name(self):
+        return "Imputer"
+
+    def input_encoded_data(self):
+        return False
+
+    def output_encoded_data(self):
+        return False
+
+    def fit_only_on_train(self):
+        return False
+
+class DropNAImputer(Step):
+    def __init__(self, na_mark=None):
+        """
+        :param na_mark: str, represents the symbol of missing values. Default is None, i.e. NaN represents the missing values.
+        """
+        self.na_mark = na_mark
+
+    def fit(self, df):
+        pass
+
+    def apply(self, df):
+        if self.na_mark:
+            df = df.replace({self.na_mark:np.nan})
+        return df.dropna()
+
+    def name(self):
+        return "DropNAImputer"
+
+    def abbr_name(self):
+        return "DN"
+
+    def step_name(self):
+        return "Imputer"
+
+    def input_encoded_data(self):
+        return False
+
+    def output_encoded_data(self):
+        return False
+
+    def fit_only_on_train(self):
+        return False
+
+class ModeImputer(Step):
+    def __init__(self, num_atts, cate_atts, na_mark=None):
+        """
+        :param num_atts: list of str, each str represents the name of numerical column to be imputed using the mean value.
+        :param cate_atts: list of str, each str represents the name of categorical column to be imputed using the most frequent value.
+        :param na_mark: str, represents the symbol of missing values. Default is None, i.e. NaN represents the missing values.
+        """
+        self.num_atts = num_atts
+        self.cate_atts = cate_atts
+        self.na_mark = na_mark
+
+        self.fitted_step = None
+
+    def fit(self, df):
+        fitted_step = {}
+        if len(self.cate_atts) > 0:
+            for ci in self.cate_atts:
+                fitted_step[ci] = SimpleImputer(strategy='most_frequent').fit(np.array(df[ci]).reshape(-1, 1))
+        if len(self.num_atts) > 0:
+            for ni in self.num_atts:
+                fitted_step[ni] = SimpleImputer(strategy='mean').fit(np.array(df[ni]).reshape(-1, 1))
+        self.fitted_step = fitted_step
+
+        return self
+
+    def apply(self, df):
+        if self.na_mark:
+            df = df.replace({self.na_mark:np.nan})
+        after_df = df.copy()
+        for ai in self.num_atts + self.cate_atts:
+            after_df[ai] = self.fitted_step[ai].transform(np.array(after_df[ai]).reshape(-1, 1))
+
+        return after_df
+
+    def name(self):
+        return "SK_ModeImputer"
+
+    def abbr_name(self):
+        return "MI"
+
+    def step_name(self):
+        return "Imputer"
+
+    def input_encoded_data(self):
+        return False
+
+    def output_encoded_data(self):
+        return False
+
+    def fit_only_on_train(self):
+        return True
+
+class DatawigImputer(Step):
+    def __init__(self, impute_atts, na_mark=None, output_path="datawig/", num_epochs=50):
+        """
+        :param impute_atts: list of str, each str represents the name of column to be imputed using datawig model. Column can be categorical or numerical.
+        :param na_mark: str, represents the symbol of missing values. Default is None, i.e. NaN represents the missing values.
+        :param output_path: str, the path to store the learned datawig model.
+        :param num_epochs: integer, the maximum iteration of datawig model.
+        """
+        self.focus_atts = impute_atts
+        self.na_mark = na_mark
+        self.output_path = output_path
+        self.num_epochs = num_epochs
+
+        self.fitted_step = None
+
+    def fit(self, df):
+        learned_imputers = {}
+        for ai in self.focus_atts:
+            learned_imputers[ai] = datawig.SimpleImputer(input_columns=list(set(df.columns).difference(ai)), output_column=ai, output_path=self.output_path).fit(train_df=df, num_epochs=self.num_epochs)
+        self.fitted_step = learned_imputers
+        return self
+
+    def apply(self, df):
+        """
+        :param df: pandas dataframe, stores the data to apply the learned imputer.
+        :return: pandas dataframe, stores the data after impute.
+        """
+        if self.na_mark:
+            df = df.replace({self.na_mark:np.nan})
+        after_df = df.copy()
+        for ai in self.focus_atts:
+            after_df[ai] = self.fitted_step[ai].predict(df)[ai + '_imputed']
+        return after_df
+
+    def name(self):
+        return "DatawigImputer"
+
+    def abbr_name(self):
+        return "DW"
+
+    def step_name(self):
+        return "Imputer"
+
+    def input_encoded_data(self):
+        return False
+
+    def output_encoded_data(self):
+        return False
+
+    def fit_only_on_train(self):
+        return True
+
+if __name__ == '__main__':
+    data = pd.read_csv("../../data/adult_AIF.csv")
+    # cur_o = DropNAImputer(na_mark="?")
+    cur_o = ModeImputer([],["workclass"], na_mark="?")
+    # cur_o = DatawigImputer(["workclass"], na_mark="?") # TODO: test after fix dependency issue
+
+    cur_o.fit(data)
+    after_data = cur_o.apply(data)
+    after_data.to_csv("../../data/adult_AIF_"+cur_o.name()+".csv", index=False)
diff --git a/pipeline/preprocess/samplers.py b/pipeline/preprocess/samplers.py
new file mode 100644
index 0000000..d19e390
--- /dev/null
+++ b/pipeline/preprocess/samplers.py
@@ -0,0 +1,150 @@
+"""
+    Classes to sample a subset of data.
+"""
+
+import numpy as np
+import pandas as pd
+from pipeline.step import Step
+
+
+class NoSampler(Step):
+    def __init__(self):
+        self.fitted_step = None
+
+    def fit(self, df):
+        pass
+
+    def apply(self, df):
+        return df
+
+    def name(self):
+        return "NoSampler"
+
+    def abbr_name(self):
+        return "NS"
+
+    def step_name(self):
+        return "Sampler"
+
+    def input_encoded_data(self):
+        return False
+
+    def output_encoded_data(self):
+        return False
+
+    def fit_only_on_train(self):
+        return False
+
+class RandomSampler(Step):
+    def __init__(self, sample_n, seed):
+        """
+        :param sample_n: integer, the size of the sampled subset of data
+        :param seed: integer, the seed for random process.
+        """
+        self.sample_n = sample_n
+        self.seed = seed
+
+    def fit(self, df):
+        pass
+
+    def apply(self, df):
+        return df.sample(n=self.sample_n, random_state=self.seed)
+
+    def name(self):
+        return "RandomSampler"
+
+    def abbr_name(self):
+        return "RS"
+
+    def step_name(self):
+        return "Sampler"
+
+    def input_encoded_data(self):
+        return False
+
+    def output_encoded_data(self):
+        return False
+
+    def fit_only_on_train(self):
+        return False
+
+
+class BalancePopulationSampler(Step):
+    def __init__(self, sample_n, balance_col, seed):
+        """
+        :param sample_n: integer, the size of the sampled subset of data
+        :param balance_col: str, the name of a categorical column that the population of groups within this column are balanced in the sampled subset.
+        :param seed: integer, the seed for random process.
+
+        """
+
+        self.sample_n = sample_n
+        self.balance_col = balance_col
+        self.seed = seed
+
+    def fit(self, df):
+        pass
+
+    def apply(self, df):
+        # TODO: update to minimum sample set and remove print statement
+        balance_groups = list(df[self.balance_col].unique())
+        n_group = int(np.ceil(self.sample_n/len(balance_groups)))
+
+        sampled_df = {}
+        small_groups = []
+        for gi in balance_groups:
+            gi_data = df[df[self.balance_col]==gi]
+            if gi_data.shape[0] < n_group: # for small group, accept all in the balanced samples
+                sampled_df[gi] = gi_data
+                small_groups.append(gi)
+            else:
+                sampled_df[gi] = df[df[self.balance_col]==gi].sample(n=n_group, random_state=self.seed)
+
+        after_df = pd.DataFrame()
+        if not self.sample_n % len(balance_groups): # for even groups
+            for gi in balance_groups:
+                after_df = pd.concat([after_df, sampled_df[gi]])
+        else: # for odd groups, remove extra sampled items
+            remove_cates = list(set(balance_groups).difference(small_groups))
+            print(len(balance_groups)*n_group-self.sample_n)
+            for gi in np.random.choice(remove_cates, len(balance_groups)*n_group-self.sample_n, False):
+                after_df = pd.concat([after_df, sampled_df[gi].head(sampled_df[gi].shape[0]-1)])
+                print(gi, after_df.shape[0])
+                del sampled_df[gi] # remove the group already added into the final sample
+            print()
+            print(sampled_df.keys())
+            for gi in sampled_df:
+                after_df = pd.concat([after_df, sampled_df[gi]])
+                print(gi, after_df.shape[0])
+        print()
+        print(after_df.groupby(self.balance_col).count())
+        return after_df
+
+    def name(self):
+        return "BalanceSampler"
+
+    def abbr_name(self):
+        return "BS"
+
+    def step_name(self):
+        return "Sampler"
+
+    def input_encoded_data(self):
+        return False
+
+    def output_encoded_data(self):
+        return False
+
+    def fit_only_on_train(self):
+        return False
+
+if __name__ == '__main__':
+
+    data = pd.read_csv("../../data/german_AIF.csv")
+    # cur_o = RandomSampler(200, 0)
+
+    cur_o = BalancePopulationSampler(200, "sex", 0)
+
+    cur_o.fit(data)
+    after_data = cur_o.apply(data)
+    after_data.to_csv("../../data/german_AIF_" + cur_o.name() + ".csv", index=False)
\ No newline at end of file
diff --git a/pipeline/preprocess/scalers.py b/pipeline/preprocess/scalers.py
new file mode 100644
index 0000000..8ad585a
--- /dev/null
+++ b/pipeline/preprocess/scalers.py
@@ -0,0 +1,114 @@
+"""
+    Classes to scale data.
+"""
+import pandas as pd
+import numpy as np
+from sklearn.preprocessing import StandardScaler, MinMaxScaler
+from pipeline.step import Step
+
+class SK_StandardScaler(Step):
+
+    def __init__(self, num_atts, copy=True, with_mean=True, with_std=True):
+        """
+        :param num_atts: list of str, each str represents the name of a numerical attribute in above data.
+        :param copy: same parameter with sklearn StandardScaler
+        :param with_mean: same parameter with sklearn StandardScaler
+        :param with_std: same parameter with sklearn StandardScaler
+        """
+        self.focus_atts = num_atts
+        self.copy = copy
+        self.with_mean = with_mean
+        self.with_std = with_std
+
+        self.fitted_step = None
+
+    def fit(self, df):
+        fitted_step = {}
+        for ai in self.focus_atts:
+            fitted_step[ai] = StandardScaler(copy=self.copy, with_mean=self.with_mean, with_std=self.with_std).fit(np.array(df[ai]).reshape(-1, 1))
+
+        self.fitted_step = fitted_step
+
+        return self
+
+    def apply(self, df):
+        after_df = df.copy()
+        for ai in self.focus_atts:
+            after_df[ai] = self.fitted_step[ai].transform(np.array(after_df[ai]).reshape(-1, 1))
+
+        return after_df
+
+    def name(self):
+        return "StandardScaler"
+
+    def abbr_name(self):
+        return "SS"
+
+    def step_name(self):
+        return "Scaler"
+
+    def input_encoded_data(self):
+        return False
+
+    def output_encoded_data(self):
+        return False
+
+    def fit_only_on_train(self):
+        return True
+
+
+class SK_MinMaxScaler(Step):
+    def __init__(self, num_atts, feature_range=(0, 1), copy=True):
+        """
+        :param num_atts: list of str, each str represents the name of a numerical attribute in above data.
+        :param feature_range: same parameter with sklearn MinMaxScaler
+        :param copy: same parameter with sklearn MinMaxScaler
+        """
+        self.focus_atts = num_atts
+        self.feature_range = feature_range
+        self.copy = copy
+
+        self.fitted_step = None
+
+    def fit(self, df):
+        fitted_step = {}
+        for ai in self.focus_atts:
+            fitted_step[ai] = MinMaxScaler(feature_range=self.feature_range, copy=self.copy).fit(np.array(df[ai]).reshape(-1, 1))
+
+        self.fitted_step = fitted_step
+
+        return self
+
+    def apply(self, df):
+        after_df = df.copy()
+        for ai in self.focus_atts:
+            after_df[ai] = self.fitted_step[ai].transform(np.array(after_df[ai]).reshape(-1, 1))
+
+        return after_df
+
+    def name(self):
+        return "MinMaxScaler"
+
+    def abbr_name(self):
+        return "MS"
+
+    def step_name(self):
+        return "Scaler"
+
+    def input_encoded_data(self):
+        return False
+
+    def output_encoded_data(self):
+        return False
+
+    def fit_only_on_train(self):
+        return True
+
+if __name__ == '__main__':
+    data = pd.read_csv("../../data/german_AIF.csv")
+    # cur_o = SK_StandardScaler(["month", "credit_amount"])
+    cur_o = SK_MinMaxScaler(["month", "credit_amount"])
+
+    cur_o.fit(data)
+    after_data = cur_o.apply(data)
+    after_data.to_csv("../../data/german_AIF_"+cur_o.name()+".csv", index=False)
diff --git a/pipeline/preprocess/splitters.py b/pipeline/preprocess/splitters.py
new file mode 100644
index 0000000..9a725dc
--- /dev/null
+++ b/pipeline/preprocess/splitters.py
@@ -0,0 +1,168 @@
+"""
+    Classes to split data into train, validation, and test set.
+"""
+import numpy as np
+import pandas as pd
+from sklearn.model_selection import StratifiedShuffleSplit
+from pipeline.step import Step
+
+# TODO: optimize the below function
+def split_to_df(splitter, input_df, y_label):
+    after_df_1 = pd.DataFrame()
+    after_df_2 = pd.DataFrame()
+    X = np.array(input_df.drop(columns=[y_label]))
+    y = np.array(input_df[y_label])
+    for index_1, index_2 in splitter.split(X, y):
+        X_1, X_2 = X[index_1], X[index_2]
+        y_1, y_2 = y[index_1], y[index_2]
+
+        after_df_1 = pd.concat(
+            [after_df_1, pd.DataFrame(data=np.hstack((X_1, y_1.reshape(-1, 1))), columns=input_df.columns)])
+        after_df_2 = pd.concat(
+            [after_df_2, pd.DataFrame(data=np.hstack((X_2, y_2.reshape(-1, 1))), columns=input_df.columns)])
+    return after_df_1, after_df_2
+
+def valid_split_ratio(split_ratio):
+    if split_ratio is None:
+        print("Need to specify split_ratio!")
+        raise ValueError
+    else:
+        if len(split_ratio) == 1:  # not valid, at least two for train and test
+            print(
+                "split_ratio should have at least 2 values for train and test sets and at most 3 values for train, validation and test sets!")
+            raise ValueError
+        if sum([not isinstance(x, float) for x in split_ratio]) > 0:
+            print("split_ratio includes non float value!")
+            raise ValueError
+        for x in split_ratio:
+            if not isinstance(x, float):
+                print("split_ratio includes non float value!")
+                raise ValueError
+            else:
+                if x < 0 or x > 1:
+                    print("split_ratio includes not valid value! Value should between 0 and 1.")
+                    raise ValueError
+                if sum(split_ratio) != 1:
+                    print("The sum of split_ratio does not equal to 1!")
+                    raise ValueError
+
+    return True
+
+class BalanceTargetSplitter(Step):
+    # TODO: fix the bug of not returning enough item
+    def __init__(self, split_ratio, target_col, seed):
+        """
+        :param split_ratio: list of float, each float represents the size-ratio of splitted data. Corresponding order maps to the size of the train, [validataion], and test set.
+                            Value ranges in [0,1]. Sum of the values in this list should be equal to 1.
+                            e.g. [0.7, 0.2, 0.1] means 70% train, 20% validation, and 10% test set.
+        :param target_col: str, the name of the target variable in above data.
+        :param seed: integer, seed to be used to generate random state. Same as 'random_state' in sklearn.model_selection.StratifiedKFold. Default is 0.
+        """
+        self.split_ratio = split_ratio
+        self.target_col = target_col
+        self.seed = seed
+
+        self.fitted_step = None
+
+
+    def fit(self, df):
+        if valid_split_ratio(self.split_ratio):
+            train_size = self.split_ratio[0]
+            validation_size = self.split_ratio[1]
+            test_size = self.split_ratio[2]
+
+            self.fitted_step = [StratifiedShuffleSplit(n_splits=1, test_size=test_size+validation_size, train_size=train_size, random_state=self.seed),
+                              StratifiedShuffleSplit(n_splits=1, test_size=test_size, train_size=validation_size, random_state=self.seed)]
+        else:
+            print("Invalid inputs!")
+            raise ValueError
+        return self
+
+    def apply(self, df):
+        after_train_df, rest_df = split_to_df(self.fitted_step[0], df, self.target_col)
+        after_val_df, after_test_df = split_to_df(self.fitted_step[1], rest_df, self.target_col)
+        return after_train_df, after_val_df, after_test_df
+
+    def name(self):
+        return "BalanceTargetSplitter"
+
+    def abbr_name(self):
+        return "BS"
+
+    def step_name(self):
+        return "Splitter"
+
+    def input_encoded_data(self):
+        return False
+
+    def output_encoded_data(self):
+        return False
+
+    def fit_only_on_train(self):
+        return True
+
+class RandomSplitter(Step):
+    def __init__(self, split_ratio, seed):
+        """
+        :param split_ratio: list of float, each float represents the size-ratio of splitted data. Corresponding order maps to the size of the train, [validataion], and test set.
+                            Value ranges in [0,1]. Sum of the values in this list should be equal to 1.
+                            e.g. [0.7, 0.2, 0.1] means 70% train, 20% validation, and 10% test set.
+        :param seed: integer, seed to be used to generate random state. Same as 'random_state' in sklearn.model_selection.StratifiedKFold. Default is 0.
+        """
+        self.split_ratio = split_ratio
+        self.seed = seed
+        self.fitted_step = None
+
+    def fit(self, df):
+        if valid_split_ratio(self.split_ratio):
+            pass
+        else:
+            print("Invalid inputs!")
+            raise ValueError
+
+    def apply(self, df):
+        df = df.sample(frac=1, random_state=self.seed).reset_index(drop=True)
+        split_idx_1 = int(self.split_ratio[0] * df.shape[0])
+        split_idx_2 = split_idx_1 + int(self.split_ratio[1] * df.shape[0])
+
+        return df.iloc[:split_idx_1], df.iloc[split_idx_1:split_idx_2], df.iloc[split_idx_2:]
+
+    def name(self):
+        return "RandomSplitter"
+
+    def abbr_name(self):
+        return "RS"
+
+    def step_name(self):
+        return "Splitter"
+
+    def input_encoded_data(self):
+        return False
+
+    def output_encoded_data(self):
+        return False
+
+    def fit_only_on_train(self):
+        return False
+
+if __name__ == '__main__':
+    data = pd.read_csv("../../data/german_AIF.csv")
+    print(data.shape)
+    # cur_o = BalanceTargetSplitter([0.5, 0.3, 0.2], "credit", 0) # bug: returned splitted data's total size does not equal to the input data
+    cur_o = RandomSplitter([0.5, 0.3, 0.2], 0)
+
+    cur_o.fit(data)
+    after_train, after_val, after_test = cur_o.apply(data)
+    # after_train, after_test = cur_o.apply(data)
+    print(after_train.shape)
+    print(after_val.shape)
+    print(after_test.shape)
+
+    after_train.to_csv("../../data/german_AIF_train_"+cur_o.name()+".csv", index=False)
+    after_val.to_csv("../../data/german_AIF_val_"+cur_o.name()+".csv", index=False)
+    after_test.to_csv("../../data/german_AIF_test_"+cur_o.name()+".csv", index=False)
+
+
+
+
+
diff --git a/pipeline/preprocess/transformers.py b/pipeline/preprocess/transformers.py
new file mode 100644
index 0000000..63919cc
--- /dev/null
+++ b/pipeline/preprocess/transformers.py
@@ -0,0 +1,24 @@
+"""
+    Classes to transform data for multiple columns.
+"""
+
+import numpy as np
+import pandas as pd
+from sklearn.compose import ColumnTransformer
+from pipeline.preprocess.preprocessor import Preprocessor
+
+class SK_ColumnTransformer(Preprocessor):
+    def __init__(self, df, cols):
+        """
+        :param df: pandas dataframe, stores the data to fit the discretizer.
+        :param cols: list of str, each str represents the name of an attribute in above data to be transformed.
+        """
+        pass
+    # TODO: add column transformer
+
+    def apply(self, df):
+        """
+        :param df: pandas dataframe, stores the data to apply the learned discretizer.
+        :return: pandas dataframe, stores the data after discretize.
+        """
+        pass
\ No newline at end of file
diff --git a/pipeline/step.py b/pipeline/step.py
new file mode 100644
index 0000000..e353bfa
--- /dev/null
+++ b/pipeline/step.py
@@ -0,0 +1,55 @@
+"""
+    Base abstract class for every step supported in this system.
+"""
+METHOD_NAME_MAPPING = {"RandomSplitter": "RSP", "BalanceTargetSplitter": "BTSP",
+             "RandomSampler": "RSM", "BalancePopulationSampler": "BPSM",
+             "DropNAImputer": "DNIM", "ModeImputer": "MIM", "DatawigImputer": "DWIM",
+             "SK_StandardScaler": "SSC", "SK_MinMaxScaler": "MMSC",
+             "SK_Discretizer": "DCA", "SK_Binarizer": "BCA",
+             "SK_OrdinalEncoder": "OREN", "SK_OneHotEncoder": "OHEN",
+             "CustomCateAttsEncoder": "CCSN",
+             "AIF_Reweighing": "RWFB", "AIF_DIRemover": "DIRFB",
+             "SK_LogisticRegression": "LRRM", "SK_DecisionTree": "DTRM", "OPT_LogisticRegression": "OLRRM", "OPT_DecisionTree": "ODTRM", "AIF_AdversarialDebiasing": "ADFM",
+             "AIF_EqOddsPostprocessing": "EQFA", "AIF_CalibratedEqOddsPostprocessing": "CEQFA"}
+
+SUPPORT_STEPS = {"SP": "Splitter", "SM": "Sampler", "IM": "Imputer", "SC": "Scaler",
+                 "CA": "Categorizer", "EN": "Encoder", "SN": "SensitiveAttEncoder",
+                 "FB": "FairPreprocessor", "RM": "model", "FM": "model", "RA": "FairPostprocessor"}
+
+class Step():
+    def fit(self, df):
+        """
+        :param df: pandas dataframe, stores the data to learn the step.
+        :return: self: the fitted step is updated.
+        """
+        raise NotImplementedError
+
+    def apply(self, df):
+        """
+        :param df: pandas dataframe, stores the data to apply the step.
+        :return: pandas dataframe, stores the data after the step.
+        """
+        raise NotImplementedError
+
+    def name(self):
+        raise NotImplementedError
+
+    def abbr_name(self):
+        raise NotImplementedError
+
+    def step_name(self):
+        raise NotImplementedError
+
+    def input_encoded_data(self):
+        # use to detect the change of the dimension of the dataset
+        raise NotImplementedError
+
+    def output_encoded_data(self):
+        # use to detect the change of the dimension of the dataset
+        raise NotImplementedError
+
+    def fit_only_on_train(self):
+        # indicate whether the step fit on the input data (return False) or use the fitted model (return True)
+        raise NotImplementedError
+
+