def transform(self,X):
X=X.copy()
#Check to make sure none of the values are non-negative
if not (X[self.variables] > 0).all().all():
vars_ = self.variables[(X[self.variables]<=0).any()]
raise InvalidModelInputError(
f'Variables Contain Non Negative or Zero values,'
f"can't apply log for vars:{vars_}")
for feature in self.variables:
X[feature] = np.log(X[feature])
return X
def transform(self, X):
X = X.copy()
if not (X[self.variables] > 0).all().all():
vars_ = self.variables[(X[self.variables] <= 0).any()]
raise InvalidModelInputError(
f"Variables contain zero or negative values,"
f"cant apply log for vars: {vars_}")
for feature in self.variables:
X[feature] = np.log(X[feature])
return X
def transform(self, X):
X = X.copy()
# check that the values are positive for log transform
if not (X[self.variables] > 0).all().all():
vars_ = self.variables[(X[self.variables] <= 0).any()]
raise InvalidModelInputError(
f'Variables contain zero or negative values, '
f'can not apply log for vars: {_vars_}')
for feature in self.variables:
X[feature] = np.log(X[feature])
return X
def transform(self, X):
# Encode Labels
X = X.copy()
for feature in self.variables:
X[feature] = X[feature].map(self.encoder_dict_[feature])
# Check if transformer introduces NaN
if X[self.variables].isnull().any().any():
null_counts = X[self.variables].isnull().any()
vars_ = {key: value for (key, value)
in null_counts.items() if value is True}
raise InvalidModelInputError(
f"Categorical encoder has introduced NaN when " f"transforming categorical variables: {vars_.keys()}")
return X
def transform(self, X):
X = X.copy()
# check that the values are non-negative for log transform
if not (X[self.variables] > 0).all().all():
vars_ = self.variables[(X[self.variables] <= 0).any()]
#this is used to show custom errors
raise InvalidModelInputError(
f"Variables contain zero or negative values, "
f"can't apply log for vars: {vars_}")
for feature in self.variables:
X[feature] = np.log(X[feature])
return X
def transform(self,X):
X=X.copy()
for var in self.variables:
X[var] = X[var].map(self.encoder_dict_[var])
#Check if transformer introduces NAN
if X[self.variables].isnull().any().any():
null_counts = X[self.variables].isnull().any()
vars_ = {key: value for(key,value) in null_counts.item()
if value is True}
raise InvalidModelInputError(
f'Categorical encoder has returned NAN when'
f'Transforming categorical variables: {vars_.keys()}')
return X
def transform(self, x: pd.DataFrame) -> 'pd.DataFrame':
x = x.copy()
temp = x[self.variables] < 0
if temp.any(axis=None):
neg_columns = [
column for column in x[self.variables] if x[column].min() <= 0
]
raise InvalidModelInputError(
f'Log transformation cannot happen'
f'in these columns{neg_columns} because they have negative values'
)
for var in self.variables:
x[var] = np.log(x[var])
return x
def transform(self, X: pd.DataFrame) -> pd.DataFrame:
X = X.copy()
for feature in self.variables:
X[feature] = X[feature].map(self.encoder_dict_[feature])
if X[self.variables].isnull().any().any():
null_counts = X[self.variables].isnull().any()
vars_ = {
key: value
for (key, value) in null_counts.items() if value is True
}
raise InvalidModelInputError(
f"Categorical Variables has introduced NaN when transforming cateforical variables: {vars_.keys()}"
)
return X
def transform(self, X: pd.DataFrame) -> pd.DataFrame:
X = X.copy()
for var in self.variables:
X[var] = X[var].map(self.encoder_dict[var])
if X[self.variables].isnull().any(axis=None):
null_columns = [
index
for index, value in X[self.variables].isnull().any().items()
if value
]
raise InvalidModelInputError(
f'Categorical encoding has created nulls in these {null_columns} columns'
)
return X
return self
def transform(self, X):
# encode labels
X = X.copy()
for feature in self.variables:
X[feature] = X[feature].map(self.encoder_dict_[feature])
# check if transformer introduces NaN
if X[self.variables].isnull().any().any():
null_counts = X[self.variables].isnull().any()
<<<<<<< HEAD
vars_ = {key: value for (key, value) in null_counts.items()
if value is True}
raise InvalidModelInputError(
f'Categorical encoder has introduced NaN when '
f'transforming categorical variables: {vars_.keys()}')
=======
vars_ = {
key: value for (key, value) in null_counts.items() if value is True
}
raise InvalidModelInputError(
f"Categorical encoder has introduced NaN when "
f"transforming categorical variables: {vars_.keys()}"
)
>>>>>>> upstream/master
return X
class DropUnecessaryFeatures(BaseEstimator, TransformerMixin):