class frst3ChrsCVTE(CustomTransformer):
_testing_can_skip_failure = False # ensure tested as if shouldn't fail
@staticmethod
def get_default_properties():
return dict(col_type="text", min_cols=1, max_cols=1, relative_importance=1)
def fit_transform(self, X: dt.Frame, y: np.array = None):
self.binner = firstNChars()
X = self.binner.fit_transform(X, 3)
# Compute mean target (out of fold) per same string
self.cvte = CVTargetEncodeTransformer(cat_cols=X.names)
if self.labels is not None:
# for classification, always turn y into numeric form, even if already integer
y = dt.Frame(LabelEncoder().fit(self.labels).transform(y))
X = self.cvte.fit_transform(X, y)
return X
def transform(self, X: dt.Frame):
X = self.binner.transform(X, 3)
X = self.cvte.transform(X)
return X
class LogScaleTargetEncodingTransformer(CustomTransformer):
@staticmethod
def get_default_properties():
return dict(col_type="numeric",
min_cols=1,
max_cols=1,
relative_importance=1)
def fit_transform(self, X: dt.Frame, y: np.array = None):
# Roughly: Convert numbers to a string of their exponent
self.binner = LogScaleBinner()
X = self.binner.fit_transform(X)
# Compute mean target (out of fold) per same string
self.cvte = CVTargetEncodeTransformer(cat_cols=X.names)
if self.labels is not None:
# for classification, always turn y into numeric form, even if already integer
y = dt.Frame(LabelEncoder().fit(self.labels).transform(y))
X = self.cvte.fit_transform(X, y)
return X
def transform(self, X: dt.Frame):
X = self.binner.transform(X)
X = self.cvte.transform(X)
return X
def fit_transform(self, X: dt.Frame, y: np.array = None):
self.binner = firstNChars()
X = self.binner.fit_transform(X, 3)
# Compute mean target (out of fold) per same string
self.cvte = CVTargetEncodeTransformer(cat_cols=X.names)
if self.labels is not None:
# for classification, always turn y into numeric form, even if already integer
y = dt.Frame(LabelEncoder().fit(self.labels).transform(y))
X = self.cvte.fit_transform(X, y)
return X
def fit_transform(self, X: dt.Frame, y: np.array = None):
# Roughly: Convert numbers to a string of their exponent
self.binner = LogScaleBinner()
X = self.binner.fit_transform(X)
# Compute mean target (out of fold) per same string
self.cvte = CVTargetEncodeTransformer(cat_cols=X.names)
if self.labels is not None:
# for classification, always turn y into numeric form, even if already integer
y = dt.Frame(LabelEncoder().fit(self.labels).transform(y))
X = self.cvte.fit_transform(X, y)
return X
class LogScaleTargetEncodingTransformer(CustomTransformer):
_testing_can_skip_failure = False # ensure tested as if shouldn't fail
@staticmethod
def get_default_properties():
return dict(col_type="numeric",
min_cols=1,
max_cols=1,
relative_importance=1)
def fit_transform(self, X: dt.Frame, y: np.array = None):
# Roughly: Convert numbers to a string of their exponent
self.binner = LogScaleBinner()
X = self.binner.fit_transform(X)
# Compute mean target (out of fold) per same string
self.cvte = CVTargetEncodeTransformer(cat_cols=X.names)
if self.labels is not None:
# for classification, always turn y into numeric form, even if already integer
y = dt.Frame(LabelEncoder().fit(self.labels).transform(y))
X = dt.Frame(self.cvte.fit_transform(X, y))
# ensure no inf
# Don't leave inf/-inf
for i in range(X.ncols):
X.replace([math.inf, -math.inf], None)
return X
def transform(self, X: dt.Frame):
X = self.binner.transform(X)
X = dt.Frame(self.cvte.transform(X))
# Don't leave inf/-inf
for i in range(X.ncols):
X.replace([math.inf, -math.inf], None)
return X