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[Validator] (feat&fix) New Exceptions added and fixed previous #1

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[Validator] (feat&fix) New Exceptions added and fixed previous #1

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33 changes: 27 additions & 6 deletions src/SupervisedLearning/LogisticRegression/Validator.py
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Original file line number Diff line number Diff line change
@@ -1,21 +1,32 @@
import numpy as np
from pandas.api.types import is_numeric_dtype


class TargetValueError(Exception):
def __init__(self, seen, expected):
message = "".join(map(str,["Invalid nb of target classes, seen: ",seen ," expected ", expected]))
def __init__(self, received, expected):
message = "".join(map(str,["Invalid number of target classes, received: ", received ," expected: ", expected]))
super(TargetValueError, self).__init__(message)


class DataDimError(Exception):
def __init__(self, seen, expected):
message = "".join(map(str, ["Invalid number of features, seen: ", seen," expected ", expected]))
def __init__(self, expected, received):
message = "".join(map(str, ["Invalid number of features, expected: ", expected, " received: ", received]))
super(DataDimError, self).__init__(message)

class DataTargetMissmatch(Exception):
def __init__(self, data, target):
message = "".join(map(str, ["Number of data examples ", data ," does not match target ", target," examples"]))
message = "".join(map(str, ["Number of data examples: ", data ," does not match target: ", target," examples"]))
super(DataTargetMissmatch, self).__init__(message)

class TargetDataCatError(Exception):
def __init__(self, target):
message = "".join(map(str,["Invalid type of target classes, received: ", target ," expected cat {0, 1} "]))
super(TargetDataCatError, self).__init__(message)

class DataTypeError(Exception):
def __init__(self):
message = "".join(map(str,["Invalid type of data, expected numerical."]))
super(DataTypeError, self).__init__(message)


class LogRegValidator:
Expand All @@ -25,16 +36,26 @@ def __init__(self, n_features, n_classes):

def validate_training(self, data, target):
self.__validate_target(target)
self.__validate_target_type(target)
self.__validate_data(data)
self.__validate_data_type(data)
self.__check_if_data_and_target_match(data, target)

def __validate_target(self, target):
if np.unique(target).shape[0] > self.n_classes:
raise TargetValueError(np.unique(target).shape[0], self.n_classes)

def __validate_target_type(self, target):
if set(target) != {0, 1}:
raise TargetDataCatError(set(target))

def __validate_data(self, data):
if data.shape[1] != self.n_features:
raise DataDimError(data.shape[0], self.n_features)
raise DataDimError(data.shape[1], self.n_features)

def __validate_data_type(self, data):
if set([is_numeric_dtype(data[e]) for e in data.columns]) != {True}:
raise DataTypeError()

def __check_if_data_and_target_match(self, data, target):
if data.shape[0] != target.shape[0]:
Expand Down