def make_si(feat: pd.DataFrame, cols: list, transform: bool = True):
# Imports
from src.utils import assertions as a
from sklearn.impute import SimpleImputer
import numpy as np
# Assertions
assert a.all_dataframe(feat)
if isinstance(cols, str): cols = [cols]
assert a.all_str(cols)
assert a.all_in(cols, feat.columns)
assert a.all_bool(transform)
# Instantiations
si = SimpleImputer(missing_values=np.nan,
strategy="constant",
fill_value="Other")
# Do work
if transform:
feat[cols] = si.fit_transform(feat[cols])
else:
si.fit(feat[cols])
# Return
return feat, si
def make_ohe(feat: pd.DataFrame, cols: list, transform: bool = True):
# Imports
from src.utils import assertions as a
from sklearn.preprocessing import OneHotEncoder
# Assertions
assert a.all_dataframe(feat)
assert a.all_str(cols)
if isinstance(cols, str): cols = [cols]
assert a.all_in(cols, feat.columns)
assert a.all_bool(transform)
# Instantiations
ohe = OneHotEncoder(sparse=False)
# Do work
data = feat[cols]
if transform:
data = ohe.fit_transform(data)
data = pd.DataFrame(data)
data.columns = ohe.get_feature_names(cols)
feat.drop(cols, axis=1, inplace=True)
feat = pd.concat([feat, data], axis=1)
else:
ohe.fit(data)
# Return
return feat, ohe
def scale_features(feat: pd.DataFrame,
cols: list = None,
transform: bool = True):
# Imports
from src.utils import assertions as a
from sklearn.preprocessing import StandardScaler
# Assertions
assert a.all_dataframe(feat)
if cols:
if isinstance(cols, str): cols = [cols]
assert a.all_str(cols)
assert a.all_in(cols, feat.columns)
assert a.all_bool(transform)
# Get cols
if not cols:
cols = feat.columns
# Instantiations
sc = StandardScaler()
# Do work
if transform:
feat[cols] = sc.fit_transform(feat[cols])
else:
sc.fit(feat[cols])
# Return
return feat, sc
def pop_target(data:pd.DataFrame, targ:str):
"""
Pop the target column off the data set.
Args:
data (pd.DataFrame): The data set, from which the target will be removed.
targ (str): The name of the feature to be removed. Must be a valid and existing column in `data`.
Returns:
data (pd.DataFrame): The updated `data` object, having had the `feat` column removed.
targ (pd.Series): The feature that has been removed from the `data` object.
"""
# Imports
from src.utils import assertions as a
# Assertions
assert a.all_dataframe(data)
assert a.all_str(targ)
assert targ in data.columns
# Do work
# targ = data.pop(targ)
feat = data.drop([targ], axis=1)
targ = data[[targ]]
# Return
return feat, targ
def sel_feat_cols(data:pd.DataFrame, feats:list):
# Imports
from src.utils import assertions as a
# Assertions
assert a.all_dataframe(data)
assert isinstance(feats, (str, list))
assert a.all_str(feats)
assert a.all_in(feats, data.columns)
# Do work
data = data[feats]
return data
def rem_features(data:pd.DataFrame, feats:list):
# Imports
from src.utils import assertions as a
# Assertions
assert a.all_dataframe(data)
assert a.all_str(feats, (str, list))
assert a.all_str(feats)
assert a.all_in(feats, data.columns)
# Do work
data = data.drop(columns=feats)
# Return
return data
def make_le(feat: pd.DataFrame, cols: list, transform: bool = True):
# Imports
from src.utils import assertions as a
from sklearn.preprocessing import LabelEncoder
# Assertions
assert a.all_dataframe(feat)
assert a.all_str(cols)
if isinstance(cols, str): cols = [cols]
assert a.all_in(cols, feat.columns)
assert a.all_bool(transform)
# Instantiations
le = LabelEncoder()
# Do work
if transform:
feat[cols] = le.fit_transform(feat[cols])
else:
le.fit(feat[cols])
# Return
return feat, le
def encode_features(feat: pd.DataFrame,
cols=list,
type: str = "ordinal",
transform: bool = True):
# Imports
from src.utils import assertions as a
from src.data.prep_data import make_oe, make_ohe
# Assertions
assert a.all_dataframe(feat)
assert isinstance(cols, (str, list))
assert a.all_str(cols)
assert a.all_str(type)
# Do work & return
if type in ["oe", "ord", "ordinal", "ordinalencoder", "ordinal encoder"]:
return make_oe(feat=feat, cols=cols, transform=transform)
elif type in ["ohe", "one", "onehotencoder", "one hot encoder"]:
return make_ohe(feat=feat, cols=cols, transform=transform)
elif type in ["le", "label", "label encoder", "labelencoder"]:
return make_le(feat=feat, cols=cols, transform=transform)
else:
return feat, None