def _fit_js(self, df, y, target, parameter):
js_encoder = ce.JamesSteinEncoder()
js_encoder.fit(df[target].map(to_str), df[y])
name = ['continuous_' + remove_continuous_discrete_prefix(x) + '_js' for x in
js_encoder.get_feature_names()]
self.trans_ls.append(('catboost', name, target, js_encoder))
def test_continuous_target_beta(self):
X = np.array(['a', 'b', 'b', 'c'])
y = np.array([-10, 0, 0, 10])
out = encoders.JamesSteinEncoder(return_df=False,
model='beta').fit_transform(X, y)
self.assertEqual(
[-2, 0, 0, 2], list(out),
'The model assumes normal distribution -> we support real numbers')
def target_encoder_jamesstein(df, train_df, cols, target):
ce_jse = ce.JamesSteinEncoder(cols=cols, drop_invariant=True)
ce_jse.fit(X=train_df[cols], y=train_df[target])
_df = ce_jse.transform(df[cols])
# カラム名の変更
for col in cols:
_df = _df.rename({col: f'{col}_targetenc_ce_jse'}, axis=1)
return pd.concat([df, _df], axis=1)
def test_zero_variance(self):
X = np.array(['a', 'b', 'c', 'd', 'd'])
y = np.array([0, 1, 1, 1, 1])
out = encoders.JamesSteinEncoder(return_df=False,
model='independent').fit_transform(
X, y)
self.assertEqual([0, 1, 1, 1, 1], list(out),
'Should not result into division by zero')
def test_ids_large_pooled(self):
X = np.array(['a', 'b', 'c', 'd', 'e'])
y = np.array([1, 0, 1, 0, 1])
out = encoders.JamesSteinEncoder(model='pooled').fit_transform(X, y)
self.assertTrue(
all(np.var(out) == 0),
'This is not a standard behaviour of James-Stein estimator. But it helps a lot if we treat id-like attributes as non-predictive.'
)
def test_large_samples_binary(self):
X = np.array(['a', 'b', 'b', 'c', 'd'])
y = np.array([1, 0, 1, 0, 0])
out = encoders.JamesSteinEncoder(return_df=False,
model='binary').fit_transform(X, y)
self.assertNotEqual(
[1, 0.5, 0.5, 0, 0], list(out),
'Shrinkage should kick in with 4 or more unique values')
def fit(self, input_df, y=None):
train_df = input_df[~(input_df.likes.isnull())]
self.cbe = ce.JamesSteinEncoder(cols=self.column, drop_invariant=True)
self.cbe.fit(train_df[self.column], train_df['likes'])
return
def category_encode(dataframe):
global category_columns
global category_target
x = dataframe[category_columns]
y = dataframe[target]
ce_ord = ce.JamesSteinEncoder(cols=category_columns)
dataframe[category_columns] = ce_ord.fit_transform(x, y)
return dataframe
def test_large_samples_beta(self):
X = np.array(['a', 'b', 'b', 'c', 'd'])
y = np.array([1, 0, 1, 0, 0])
out = encoders.JamesSteinEncoder(return_df=False,
model='beta').fit_transform(X, y)
self.assertNotEqual(
[1, 0.5, 0.5, 0, 0], list(out),
'Shrinkage should kick in with 4 or more unique values')
self.assertTrue(np.max(out) <= 1, 'This should still be a probability')
self.assertTrue(np.min(out) >= 0, 'This should still be a probability')
def test_small_samples_independent(self):
X = np.array(['a', 'b', 'b'])
y = np.array([1, 0, 1])
out = encoders.JamesSteinEncoder(return_df=False,
model='independent').fit_transform(
X, y)
self.assertEqual([1, 0.5, 0.5], list(
out
), 'When the count of unique values in the column is <4 (here it is 2), James-Stein estimator returns (unbiased) sample means'
)
def encode_df(X, y, cat_features, cat_encoding):
ENCODERS = {
'leave_one_out':
ce.LeaveOneOutEncoder(cols=cat_features, handle_missing='return_nan'),
'james_stein':
ce.JamesSteinEncoder(cols=cat_features, handle_missing='return_nan'),
'target':
ce.TargetEncoder(cols=cat_features, handle_missing='return_nan')
}
X = ENCODERS[cat_encoding].fit_transform(X, y)
return X
def __init__(self, encoder_type, columns_name=None):
"""
:param encoder_type:
:param columns_name: list， 特征名组成的列表名
"""
if encoder_type == "BackwardDe": # 反向差分编码
self.encoder = ce.BackwardDifferenceEncoder(cols=columns_name)
elif encoder_type == "BaseN": # BaseN编码
self.encoder = ce.BaseNEncoder(cols=columns_name)
elif encoder_type == "Binary": # 二值编码
self.encoder = ce.BinaryEncoder(cols=columns_name)
elif encoder_type == "Catboost":
self.encoder = ce.CatBoostEncoder(cols=columns_name)
elif encoder_type == "Hash":
self.encoder = ce.HashingEncoder(cols=columns_name)
elif encoder_type == "Helmert":
self.encoder = ce.HelmertEncoder(cols=columns_name)
elif encoder_type == "JamesStein":
self.encoder = ce.JamesSteinEncoder(cols=columns_name)
elif encoder_type == "LOO": # LeaveOneOutEncoder 编码
self.encoder = ce.LeaveOneOutEncoder(cols=columns_name)
elif encoder_type == "ME":
self.encoder = ce.MEstimateEncoder(cols=columns_name) # M估计编码器
elif encoder_type == "OneHot":
self.encoder = ce.OneHotEncoder(cols=columns_name)
elif encoder_type == "OridinalEncoder": # 原始编码
self.encoder = ce.OrdinalEncoder(cols=columns_name)
elif encoder_type == "Sum": # 求和编码
self.encoder = ce.SumEncoder(cols=columns_name)
elif encoder_type == "Polynomial": # 多项式编码
self.encoder = ce.PolynomialEncoder(cols=columns_name)
elif encoder_type == "Target": # 目标编码
self.encoder = ce.TargetEncoder(cols=columns_name)
elif encoder_type == "WOE": # WOE 编码器
self.encoder = ce.WOEEncoder(cols=columns_name)
else:
raise ValueError("请选择正确的编码方式")
def get_encoder_dict():
encoder_dict = {
'OneHotEncoder': ce.OneHotEncoder(),
'BinaryEncoder': ce.BinaryEncoder(),
'HashingEncoder': ce.HashingEncoder(),
'LabelEncoder': le.MultiColumnLabelEncoder(),
'FrequencyEncoder': fe.FrequencyEncoder(),
'TargetEncoder': ce.TargetEncoder(),
'HelmertEncoder': ce.HelmertEncoder(),
'JamesSteinEncoder': ce.JamesSteinEncoder(),
'BaseNEncoder': ce.BaseNEncoder(),
'SumEncoder': ce.SumEncoder(),
}
return encoder_dict
def test_ids_large_pooled(self):
X = np.array(['a', 'b', 'c', 'd', 'e'])
y = np.array([1, 0, 1, 0, 1])
out = encoders.JamesSteinEncoder(model='pooled').fit_transform(X, y)
self.assertTrue(
all(np.var(out) == 0),
'This is not a standard behaviour of James-Stein estimator. But it helps a lot if we treat id-like attributes as non-predictive.'
)
####### Beta
def test_continuous_target_pooled(self):
X = np.array(['a', 'b', 'b', 'c'])
y = np.array([-10, 0, 0, 10])
out = encoders.JamesSteinEncoder(return_df=False,
model='beta').fit_transform(X, y)
self.assertEqual([-10, 0, 0, 10], list(
out
), 'The model assumes normal distribution -> we support real numbers'
)
def test_large_samples_pooled(self):
X = np.array(['a', 'b', 'b', 'c', 'd'])
y = np.array([1, 0, 1, 0, 0])
out = encoders.JamesSteinEncoder(return_df=False,
model='beta').fit_transform(X, y)
self.assertNotEqual(
[1, 0.5, 0.5, 0, 0], list(out),
'Shrinkage should kick in with 4 or more unique values')
self.assertTrue(
np.max(out) <= 1, 'This should still be a probability')
self.assertTrue(
np.min(out) >= 0, 'This should still be a probability')
def test_ids_small_pooled(self):
X = np.array(['a', 'b', 'c'])
y = np.array([1, 0, 1])
out = encoders.JamesSteinEncoder(model='beta').fit_transform(X, y)
self.assertTrue(
all(np.var(out) == 0),
'This is not a standard behaviour of James-Stein estimator. But it helps a lot if we treat id-like attributes as non-predictive.'
)
def test_ids_large_pooled(self):
X = np.array(['a', 'b', 'c', 'd', 'e'])
y = np.array([1, 0, 1, 0, 1])
out = encoders.JamesSteinEncoder(model='beta').fit_transform(X, y)
self.assertTrue(
all(np.var(out) == 0),
'This is not a standard behaviour of James-Stein estimator. But it helps a lot if we treat id-like attributes as non-predictive.'
)
def test_small_samples_binary(self):
X = np.array(['a', 'b', 'b'])
y = np.array([1, 0, 1])
out = encoders.JamesSteinEncoder(return_df=False,
model='binary').fit_transform(X, y)
self.assertTrue(
np.sum(
np.abs([
np.log((1.5 * 1.5) / (0.5 * 1.5)),
np.log((0.5 * 1.5) / (1.5 * 1.5)),
np.log((0.5 * 1.5) / (1.5 * 1.5))
] - np.transpose(out))) < 0.001,
'When the count of unique values in the column is <4 (here it is 2), James-Stein estimator returns (unbiased) sample means'
)
def js_encoding(X_fit, y_fit, cols, X_test=None, model='independent'):
"""
只针对continuous target
X_fit: 用来计算encoding的df, 包含cols
y_fit: encoding的target
X_test: 需要transform的对象
cols: 需要encoding的列
model: 'pooled' or 'independent'；pooled是假设所有个体具有相同的方差，与casi书中定义一致
"""
if X_test is None:
X_test = X_fit
encoder = ce.JamesSteinEncoder(cols=cols, model=model)
encoder.fit(X_fit, y_fit)
result = encoder.transform(X_test)
return result
def mean_absolute_percentage_error(y_true, y_pred):
y_true, y_pred = np.array(y_true), np.array(y_pred)
return np.mean(np.abs((y_true - y_pred) / y_true)) * 100
df = pd.read_csv("BigDataV5.csv")
y = df['price']
x = df.drop('price', axis=1)
X_train, X_test, y_train, y_test = train_test_split(x,
y,
test_size=0.2,
random_state=0)
encoder = ce.JamesSteinEncoder(
cols=['make', 'model', 'city', 'color', 'trans'])
X_train_tran = encoder.fit_transform(X_train, y_train)
X_test_tran = encoder.transform(X_test, y_test)
# Create the model
model = ensemble.GradientBoostingRegressor()
# Parameters we want to try
param_grid = {
'n_estimators': np.arange(1000, 5000, 100),
'max_depth': np.arange(5, 10),
'min_samples_leaf': np.arange(3, 20),
'learning_rate': np.arange(0.01, 0.1),
'max_features': np.arange(0.1, 1),
'loss': ['ls', 'lad', 'huber']
def get_encoder(self) -> BaseEstimator:
return ce.JamesSteinEncoder(cols=self.target_columns)
'colic.arff', 'credit.a.arff', 'credit.g.arff', 'cylinder.bands.arff', 'flags.arff', 'heart.c.arff', 'heart.h.arff',
'hepatitis.arff', 'hypothyroid.arff', 'kr.vs.kp.arff', 'labor.arff', 'lymph.arff', 'mushroom.arff', 'nursery.arff',
'postoperative.patient.data.arff', 'primary.tumor.arff', 'sick.arff', 'solar.flare1.arff', 'solar.flare2.arff',
'soybean.arff', 'spectrometer.arff', 'sponge.arff', 'tic-tac-toe.arff', 'trains.arff', 'vote.arff', 'vowel.arff']
# datasets = ['carvana.csv', 'erasmus.csv', 'internetusage.csv', 'ipumsla97small.csv', 'kobe.csv', 'pbcseq.csv', 'phpvcoG8S.csv', 'westnile.csv'] # amazon is too large...
# We painstakingly initialize each encoder here because that gives us the freedom to initialize the
# encoders with any setting we want.
encoders = [ #category_encoders.BackwardDifferenceEncoder(),
category_encoders.BaseNEncoder(),
category_encoders.BinaryEncoder(),
category_encoders.HashingEncoder(),
# category_encoders.HelmertEncoder(),
category_encoders.JamesSteinEncoder(),
category_encoders.LeaveOneOutEncoder(),
category_encoders.MEstimateEncoder(),
category_encoders.OneHotEncoder(),
category_encoders.OrdinalEncoder(),
# category_encoders.PolynomialEncoder(),
# category_encoders.SumEncoder(),
category_encoders.TargetEncoder(),
category_encoders.WOEEncoder()]
encoders = [ #category_encoders.BackwardDifferenceEncoder(),
category_encoders.BaseNEncoder(handle_missing='value'),
category_encoders.BaseNEncoder(handle_missing='indicator'),
category_encoders.BinaryEncoder(handle_missing='value'),
category_encoders.BinaryEncoder(handle_missing='indicator'),
# category_encoders.HashingEncoder(handle_missing='value'),
def encode_all(df,dfv,dfk,encoder_to_use,handle_missing='return_nan'):
encoders_used = {}
for col in encoder_to_use:
if encoder_to_use[col] == 'ColumnDropper':
df = df.drop(columns = col)
dfv = dfv.drop(columns = col)
dfk = dfk.drop(columns = col)
encoders_used[col] = 'ColumnDropper'
if encoder_to_use[col]=='BackwardDifferenceEncoder':
encoder=ce.BackwardDifferenceEncoder(cols=[col],return_df=1,drop_invariant=1,handle_missing=handle_missing)
encoder.fit(X=df,y=df['set_clicked'])
df=encoder.transform(df)
dfv=encoder.transform(dfv)
dfk=encoder.transform(dfk)
encoders_used[col]=encoder
if encoder_to_use[col]=='BaseNEncoder':
encoder=ce.BaseNEncoder(cols=[col],return_df=1,drop_invariant=1,handle_missing=handle_missing,base=3)
encoder.fit(X=df,y=df['set_clicked'])
df=encoder.transform(df)
dfv=encoder.transform(dfv)
dfk=encoder.transform(dfk)
encoders_used[col]=encoder
if encoder_to_use[col]=='BinaryEncoder':
encoder=ce.BinaryEncoder(cols=[col],return_df=1,drop_invariant=1,handle_missing=handle_missing)
encoder.fit(X=df,y=df['set_clicked'])
df=encoder.transform(df)
dfv=encoder.transform(dfv)
dfk=encoder.transform(dfk)
encoders_used[col]=encoder
if encoder_to_use[col]=='CatBoostEncoder':
encoder=ce.CatBoostEncoder(cols=[col],return_df=1,drop_invariant=1,handle_missing=handle_missing,sigma=None,a=2)
encoder.fit(X=df,y=df['set_clicked'])
df=encoder.transform(df)
dfv=encoder.transform(dfv)
dfk=encoder.transform(dfk)
encoders_used[col]=encoder
# if encoder_to_use[col]=='HashingEncoder':
# encoder=ce.HashingEncoder(cols=[col],return_df=1,drop_invariant=1,handle_missing=handle_missing)
# encoder.fit(X=df,y=df['set_clicked'])
# df=encoder.transform(df)
# encoders_used[col]=encoder
if encoder_to_use[col]=='HelmertEncoder':
encoder=ce.HelmertEncoder(cols=[col],return_df=1,drop_invariant=1,handle_missing=handle_missing)
encoder.fit(X=df,y=df['set_clicked'])
df=encoder.transform(df)
dfv=encoder.transform(dfv)
dfk=encoder.transform(dfk)
encoders_used[col]=encoder
if encoder_to_use[col]=='JamesSteinEncoder':
encoder=ce.JamesSteinEncoder(cols=[col],return_df=1,drop_invariant=1,handle_missing=handle_missing, model='binary')
encoder.fit(X=df,y=df['set_clicked'])
df=encoder.transform(df)
dfv=encoder.transform(dfv)
dfk=encoder.transform(dfk)
encoders_used[col]=encoder
if encoder_to_use[col]=='LeaveOneOutEncoder':
encoder=ce.LeaveOneOutEncoder(cols=[col],return_df=1,drop_invariant=1,handle_missing=handle_missing,sigma=None)
encoder.fit(X=df,y=df['set_clicked'])
df=encoder.transform(df)
dfv=encoder.transform(dfv)
dfk=encoder.transform(dfk)
encoders_used[col]=encoder
if encoder_to_use[col]=='MEstimateEncoder':
encoder=ce.MEstimateEncoder(cols=[col],return_df=1,drop_invariant=1,handle_missing=handle_missing,randomized=True,sigma=None,m=2)
encoder.fit(X=df,y=df['set_clicked'])
df=encoder.transform(df)
dfv=encoder.transform(dfv)
encoders_used[col]=encoder
if encoder_to_use[col]=='OneHotEncoder':
encoder=ce.OneHotEncoder(cols=[col],return_df=1,drop_invariant=1,handle_missing=handle_missing,use_cat_names=True)
encoder.fit(X=df,y=df['set_clicked'])
df=encoder.transform(df)
dfv=encoder.transform(dfv)
dfk=encoder.transform(dfk)
encoders_used[col]=encoder
if encoder_to_use[col]=='OrdinalEncoder':
encoder=ce.OrdinalEncoder(cols=[col],return_df=1,drop_invariant=1,handle_missing=handle_missing)
encoder.fit(X=df,y=df['set_clicked'])
df=encoder.transform(df)
dfv=encoder.transform(dfv)
dfk=encoder.transform(dfk)
encoders_used[col]=encoder
if encoder_to_use[col]=='SumEncoder':
encoder=ce.SumEncoder(cols=[col],return_df=1,drop_invariant=1,handle_missing=handle_missing)
encoder.fit(X=df,y=df['set_clicked'])
df=encoder.transform(df)
dfv=encoder.transform(dfv)
dfk=encoder.transform(dfk)
encoders_used[col]=encoder
if encoder_to_use[col]=='PolynomialEncoder':
encoder=ce.PolynomialEncoder(cols=[col],return_df=1,drop_invariant=1,handle_missing=handle_missing)
encoder.fit(X=df,y=df['set_clicked'])
df=encoder.transform(df)
dfv=encoder.transform(dfv)
dfk=encoder.transform(dfk)
encoders_used[col]=encoder
if encoder_to_use[col]=='TargetEncoder':
encoder=ce.TargetEncoder(cols=[col],return_df=1,drop_invariant=1,handle_missing=handle_missing,min_samples_leaf=10, smoothing=5)
encoder.fit(X=df,y=df['set_clicked'])
df=encoder.transform(df)
dfv=encoder.transform(dfv)
dfk=encoder.transform(dfk)
encoders_used[col]=encoder
if encoder_to_use[col]=='WOEEncoder':
encoder=ce.WOEEncoder(cols=[col],return_df=1,drop_invariant=1,handle_missing=handle_missing,randomized=True,sigma=None)
encoder.fit(X=df,y=df['set_clicked'])
df=encoder.transform(df)
dfv=encoder.transform(dfv)
dfk=encoder.transform(dfk)
encoders_used[col]=encoder
# print("Encoding done for - ",col)
print("Completed encoder - ",datetime.datetime.now())
return df, dfv, dfk, encoders_used
# tic-tac-toe.arff
# trains.arff Medium impact (tiny dataset -> with high variance)
datasets = ['audiology.arff', 'autos.arff', 'breast.cancer.arff', 'bridges.version1.arff', 'bridges.version2.arff', 'car.arff',
'colic.arff', 'credit.a.arff', 'credit.g.arff', 'cylinder.bands.arff', 'flags.arff', 'heart.c.arff', 'heart.h.arff',
'hepatitis.arff', 'hypothyroid.arff', 'kr.vs.kp.arff', 'labor.arff', 'lymph.arff', 'mushroom.arff', 'nursery.arff',
'postoperative.patient.data.arff', 'primary.tumor.arff', 'sick.arff', 'solar.flare1.arff', 'solar.flare2.arff',
'soybean.arff', 'spectrometer.arff', 'sponge.arff', 'tic-tac-toe.arff', 'trains.arff', 'vote.arff', 'vowel.arff']
# We painstakingly initialize each encoder here because that gives us the freedom to initialize the
# encoders with any setting we want.
encoders = [category_encoders.BackwardDifferenceEncoder(),
category_encoders.BaseNEncoder(),
category_encoders.BinaryEncoder(),
category_encoders.HashingEncoder(),
category_encoders.HelmertEncoder(),
category_encoders.JamesSteinEncoder(),
category_encoders.LeaveOneOutEncoder(),
category_encoders.MEstimateEncoder(),
category_encoders.OneHotEncoder(),
category_encoders.OrdinalEncoder(),
category_encoders.PolynomialEncoder(),
category_encoders.SumEncoder(),
category_encoders.TargetEncoder(),
category_encoders.WOEEncoder()]
# Initialization
if os.path.isfile('./output/result.csv'):
os.remove('./output/result.csv')
# Loop over datasets, then over encoders, and finally, over the models
for dataset_name in datasets:
def get_model(PARAMS):
"""return model for provided params
:param PARAMS: dictionary with model params
:type PARAMS: dicr
:return: model pipeline
:rtype: sklearn pipeline
"""
try:
te_dict = {
'CatBoostEncoder': ce.CatBoostEncoder(),
'HashingEncoder': ce.HashingEncoder(),
'HelmertEncoder': ce.HelmertEncoder(),
'LeaveOneOutEncoder': ce.LeaveOneOutEncoder(),
'OneHotEncoder': ce.OneHotEncoder(),
'TargetEncoder': ce.TargetEncoder(),
'WOEEncoder': ce.WOEEncoder(),
'BackwardDifferenceEncoder': ce.BackwardDifferenceEncoder(),
'BaseNEncoder': ce.BaseNEncoder(),
'BinaryEncoder': ce.BinaryEncoder(),
'CountEncoder': ce.CountEncoder(),
'JamesSteinEncoder': ce.JamesSteinEncoder(),
'MEstimateEncoder': ce.MEstimateEncoder(),
'PolynomialEncoder': ce.PolynomialEncoder(),
'SumEncoder': ce.SumEncoder()
}
pipe = make_pipeline(
helpers.PrepareData(extraxt_year=True, unicode_text=True),
ColumnTransformer([
('num', helpers.PassThroughOrReplace(), [
'flat_size', 'rooms', 'floor', 'number_of_floors',
'year_of_building', 'GC_latitude', 'GC_longitude'
]),
('te_producer', te_dict.get(PARAMS['te_producer']),
'producer_name'),
('te_road', te_dict.get(PARAMS['te_road']), 'GC_addr_road'),
('te_neighbourhood', te_dict.get(PARAMS['te_neighbourhood']),
'GC_addr_neighbourhood'),
('te_suburb', te_dict.get(PARAMS['te_suburb']),
'GC_addr_suburb'),
('te_postcode', te_dict.get(PARAMS['te_postcode']),
'GC_addr_postcode'),
('txt_name',
TfidfVectorizer(lowercase=True,
ngram_range=(1,
PARAMS['txt_name__ngram_range']),
max_features=PARAMS['txt_name__max_features'],
dtype=np.float32,
binary=PARAMS['txt_name__binary'],
use_idf=PARAMS['txt_name__use_idf']), 'name'),
('txt_dscr',
TfidfVectorizer(lowercase=True,
ngram_range=(1,
PARAMS['txt_dscr__ngram_range']),
max_features=PARAMS['txt_dscr__max_features'],
dtype=np.float32,
binary=PARAMS['txt_dscr__binary'],
use_idf=PARAMS['txt_dscr__use_idf']),
'description'),
]),
TransformedTargetRegressor(
regressor=lgb.LGBMRegressor(**PARAMS, random_state=seed),
func=np.log1p,
inverse_func=np.expm1))
return pipe
except BaseException as e:
LOG.error(e)
return None
'colic.arff', 'credit.a.arff', 'credit.g.arff', 'cylinder.bands.arff', 'flags.arff', 'heart.c.arff', 'heart.h.arff',
'hepatitis.arff', 'hypothyroid.arff', 'kr.vs.kp.arff', 'labor.arff', 'lymph.arff', 'mushroom.arff', 'nursery.arff',
'postoperative.patient.data.arff', 'primary.tumor.arff', 'sick.arff', 'solar.flare1.arff', 'solar.flare2.arff',
'soybean.arff', 'spectrometer.arff', 'sponge.arff', 'tic-tac-toe.arff', 'trains.arff', 'vote.arff', 'vowel.arff']
datasets = ['carvana.csv', 'erasmus.csv', 'internetusage.csv', 'ipumsla97small.csv', 'kobe.csv', 'pbcseq.csv', 'phpvcoG8S.csv', 'westnile.csv'] # amazon is too large...
# We painstakingly initialize each encoder here because that gives us the freedom to initialize the
# encoders with any setting we want.
encoders = [ #category_encoders.BackwardDifferenceEncoder(),
category_encoders.BaseNEncoder(),
category_encoders.BinaryEncoder(),
category_encoders.HashingEncoder(),
# category_encoders.HelmertEncoder(),
category_encoders.JamesSteinEncoder(),
category_encoders.LeaveOneOutEncoder(),
category_encoders.MEstimateEncoder(),
category_encoders.OneHotEncoder(),
category_encoders.OrdinalEncoder(),
# category_encoders.PolynomialEncoder(),
# category_encoders.SumEncoder(),
category_encoders.TargetEncoder(),
category_encoders.WOEEncoder()]
encoders = [category_encoders.TargetEncoder(), category_encoders.JamesSteinEncoder(), category_encoders.WOEEncoder()]
# Initialization
if os.path.isfile('./output/result.csv'):
os.remove('./output/result.csv')
def __init__(self, variables=None):
if not isinstance(variables, list):
self.variables = [variables]
else:
self.variables = variables
self.encoder = ce.JamesSteinEncoder(cols=self.variables)
def frc_runner(df_eng_all, base_product_number_std,
categoricalVars, numerical_vars,
num_neighbours, validation_test_size,
num_iterations, learning_rate, depth,
feat_importance_keyword = 'feature_importances_',
experiment_label = 'grocery',
responseVar = 'wk1_sales_all_stores',
identifierVar = 'promo_identifier_latex',
doVisualisation=True,
doSaveExcel = True):
# Create an identifier
df_eng_all['promo_identifier'] = df_eng_all.base_product_number_std + \
' (' + df_eng_all.offer_description + ')'
# for latex
#all_bpns = df_eng_all.base_product_number_std.unique().tolist()
all_bpns = [*set(df_eng_all.base_product_number_std)-set(base_product_number_std)]
all_bpns_as_ids = ['product_' + str(idx) for idx in range(0, len(all_bpns))]
dict_bpns = dict(zip(all_bpns, all_bpns_as_ids))
dict_bpns.update({base_product_number_std: 'new_product'})
df_eng_all['promo_identifier_latex'] = df_eng_all.base_product_number_std.map(dict_bpns)
prefix = experiment_label + '_' + base_product_number_std + '_' + dt.datetime.today().strftime('%d_%m_%Y_%HH')
idx_product = df_eng_all.base_product_number_std == base_product_number_std
df_test = df_eng_all[idx_product].copy()
df_test.reset_index(inplace=True)
print(df_test.promo_identifier.iloc[0])
# From the product, find the PSGC and exclude the product itself
this_psgc = df_test.product_sub_group_code.iloc[0]
idx_psgc = df_eng_all.product_sub_group_code.str.contains(this_psgc)
df = df_eng_all[idx_psgc & ~ idx_product].copy()
df.reset_index(inplace=True)
# NUMERICAL:
# Make sure the numericals are encoded as such.
vTypes = pt.infer_variable_type(df)
# Numerical vars
#numerical_vars = [*set(inputVars) - set(categoricalVars)]
# Make sure all of them are coded as numerical ones
missing_vars = [*set(numerical_vars)-set(vTypes['numerical'])]
if missing_vars:
print(f'List of missing vars {missing_vars}')
# CATEGORICAL: Save a copy of the categorical variables as
# this encoder overwrites them
enc_postfix = '_encoded'
enc_categoricalVars = []
for varName in categoricalVars:
currentVarName = varName + enc_postfix
enc_categoricalVars.append(currentVarName)
df[currentVarName] = df[varName]
df_test[currentVarName] = df_test[varName]
# For quick remapping
catVarsMapping = dict(zip(enc_categoricalVars, categoricalVars))
# get the index of the categorical variables for CatBoost
inputVars = numerical_vars + enc_categoricalVars
# JamesSteinEncoder
inputVars_encoder = inputVars + categoricalVars
encoder_js = ce.JamesSteinEncoder(cols=enc_categoricalVars, verbose=1)
# fit training
df_A_enc = encoder_js.fit_transform(df[inputVars_encoder], df[responseVar])
df_A_enc[responseVar] = df[responseVar]
df_A_enc[identifierVar] = df[identifierVar]
# fit test
df_test_enc = encoder_js.transform(df_test[inputVars_encoder])
df_test_enc[responseVar] = df_test[responseVar]
df_test_enc[identifierVar] = df_test[identifierVar]
'''
Train, val and test
'''
num_inputVars = len(inputVars)
X_train = df_A_enc[inputVars].values
y_train = df_A_enc[responseVar].values
id_train = df_A_enc[identifierVar].values
X_test = df_test_enc[inputVars].values
y_test = df_test_enc[responseVar].values
'''
Model. Using CatBoost here
'''
# Create the forecaster
contrastiveReg = contrastiveRegressor(num_neighbours = num_neighbours,
validation_test_size = validation_test_size)
# CatBoost
cb_model = CatBoostRegressor(iterations=num_iterations, learning_rate=learning_rate,
depth=depth, loss_function='RMSE', cat_features=None, silent=True)
# Set the regressor
contrastiveReg.set_regressor(cb_model, feat_importance_keyword, inputVars)
# fit the regressor
contrastiveReg.fit(X_train, y_train)
# eval results
contrastiveReg.predict_eval_test()
eval_results = contrastiveReg.get_results()
# Predict
contrastiveReg.predict(X_test, categorical_mapping = catVarsMapping)
cold_start_results = contrastiveReg.get_results()
# Sort by importance
df_feature_importances = cold_start_results.get('df_feat_importances', None)
print(df_feature_importances)
# Arrange the results in a DF so we can easily plot them
df_frc = df_test.copy()
df_frc['y_hat'] = cold_start_results['y_hat_weighted']
# review the cold-start forecast
all_cold_forecast = []
all_frc_latex = []
model_vars = ['y_actual', 'y_forecast', \
'y_train', 'delta_y_train', \
'y_train_plus_delta', 'y_train_distances']
vars_latex = ['y_train', 'delta_y_train', \
'y_train_plus_delta', 'y_train_distances']
# Annonymise
dict_feature_importances = df_feature_importances.to_dict(orient='dict').get(0, None)
vars_model = numerical_vars + categoricalVars
inputObfuscated = []
for idx, iVar in enumerate(vars_model,1):
str_feat_weight = f' (vi:{dict_feature_importances.get(iVar, 0):3.2f})'
inputObfuscated.append('v_' + str(idx) + str_feat_weight)
mapObfuscatedVars = dict(zip(vars_model, inputObfuscated))
mapObfuscatedVars[responseVar] = 'response'
list_vars = [iVar for iVar in df_feature_importances.index.tolist() if 'ref_' not in iVar]
for idx_review in range(df_test.shape[0]):
print('Running {idx_review}...')
df_forecast_ext = contrastiveReg.arrange_regressor_results(idx_review, df_A_enc, \
y_train, id_train, list_vars, \
identifierVar, df_test_enc, y_test, num_inputVars)
df_forecast_ext.reset_index(inplace=True)
df_forecast_ext['y_train'].iloc[-2] = \
df_forecast_ext['y_weighted_forecast'].iloc[-2]
print(df_forecast_ext)
all_cold_forecast.append(df_forecast_ext)
y_actual = df_forecast_ext['y_actual'].iloc[-2]
y_forecast = df_forecast_ext['y_weighted_forecast'].iloc[-2]
print(f'(actual: {y_actual:3.2f}, forecast: {y_forecast:3.2f})')
all_frc_latex.append(df_forecast_ext[0:-1])
# Append them all
df_all_cold_forecast = pd.concat(all_cold_forecast)
df_all_latex = pd.concat(all_frc_latex)
top_n_features = 4
if top_n_features < num_inputVars:
list_vars_LaTeX = list_vars[0:top_n_features]
else:
list_vars_LaTeX = list_vars
if doSaveExcel:
# Created an obfuscated LaTeX version
df_latex = df_all_latex[[identifierVar] + list_vars_LaTeX + vars_latex].copy()
# Obfuscate the TSR so we don't get into problems
tsr_col = 'total_store_revenue'
if tsr_col in df_latex.columns.tolist():
df_latex[tsr_col] = df_latex[tsr_col].apply(lambda x: np.log10(x))
df_latex.rename(columns=mapObfuscatedVars, inplace=True)
str_latex = fhelp.prepareTableLaTeX(df_latex)
tex_file_name = _p.join('tex', prefix + '_table.tex')
fhelp.writeTextFile(str_latex, tex_file_name)
# Excel
list_vars.extend(model_vars)
vars_xls = list_vars + [identifierVar]
xlsx_file_name = _p.join('results', prefix + '_table.xlsx')
fhelp.to_excel_file(df_all_cold_forecast[vars_xls], xlsx_file_name)
'''
Visualise
'''
if doVisualisation:
varX = list_vars[0]
varY = list_vars[1]
varZ = responseVar
if varX in categoricalVars:
df[varX] = df[varX].astype(int)
df_frc[varX] = df_frc[varX].astype(int)
if varY in categoricalVars:
df[varY] = df[varY].astype(int)
df_frc[varY] = df_frc[varY].astype(int)
_alpha = 0.75
fig = plt.figure()
ax = plt.axes(projection='3d')
# All the subgroup
ax.scatter(df[varX], df[varY], df[varZ], alpha=0.15)
# Also plot the test points
ax.scatter(df_frc[varX], df_frc[varY], df_frc['y_hat'], alpha=1.0, label='cold-forecast', color='red', s=75)
# plot the selected products
for idx_forecast in range(0, len(cold_start_results['y_hat'])):
idx_closest_promos = cold_start_results['y_idx_closest_promos'][idx_forecast]
df_A = df.iloc[idx_closest_promos].copy()
ax.scatter(df_A[varX], df_A[varY], df_A[varZ], alpha=_alpha, label='neighbours_' + str(idx_forecast), s=50)
for idx, row in df_frc.iterrows():
#point_name = f'F{(1+idx)} ({row[varX]}, {row[varY]:2.0f}, {row.y_hat:3.2f})'
point_name = f'Frc_{idx}'
ax.text(row[varX]+2.5,row[varY],row['y_hat'], point_name, color='black', fontsize=9)
ax.set_xlabel(mapObfuscatedVars[varX])
ax.set_ylabel(mapObfuscatedVars[varY])
ax.set_zlabel(mapObfuscatedVars[varZ])
ax.view_init(elev=32, azim=-50)
ax.legend()
ax.grid(True)
plt.tight_layout()
plt.show(block = True)
pfg_file_name = _p.join('figs', prefix + '_plot_3D.png')
plt.savefig(pfg_file_name)
d = {'eval_results': eval_results, \
'cold_start_results': cold_start_results, \
'contrastiveRegressor': contrastiveReg,
'X_test': X_test, 'y_test': y_test,
'df_train': df, 'df_test': df_test}
return d
def generate_candidates(adjusted_cols):
return [
(
"bsplitz_method",
Pipeline([
("cate", category_encoders.OrdinalEncoder(cols=adjusted_cols)),
(
"ordinal_encoder",
BsplitZClassifier(adjusted_cols,
random_state=10,
num_samples=100),
),
]),
),
(
"target_encoder",
Pipeline([
(
"target_encoder",
category_encoders.TargetEncoder(cols=adjusted_cols),
),
("clf", BsplitZClassifier()),
]),
),
# ('clf', DecisionTreeClassifier(max_depth=1))])),
(
"m_encoder",
Pipeline([
(
"m_encoder",
category_encoders.MEstimateEncoder(cols=adjusted_cols),
),
("clf", BsplitZClassifier()),
]),
),
# ('clf', DecisionTreeClassifier(max_depth=1))])),
(
"cat_encoder",
Pipeline([
(
"m_encoder",
category_encoders.CatBoostEncoder(cols=adjusted_cols),
),
("clf", BsplitZClassifier()),
]),
),
# ('clf', DecisionTreeClassifier(max_depth=1))])),
# ('backward_encoder', Pipeline([
# ('backward_encoder', category_encoders.BackwardDifferenceEncoder(
# cols=adjusted_cols)),
# ('clf', BsplitZClassifier())])), #skip because of too slow
(
"basen_encoder",
Pipeline([
(
"basen_encoder",
category_encoders.BaseNEncoder(cols=adjusted_cols),
),
("clf", BsplitZClassifier()),
]),
),
# ('clf', DecisionTreeClassifier(max_depth=1))])),
(
"binary_encoder",
Pipeline([
(
"basen_encoder",
category_encoders.BinaryEncoder(cols=adjusted_cols),
),
("clf", BsplitZClassifier()),
]),
),
# ('clf', DecisionTreeClassifier(max_depth=1))])),
(
"count_encoder",
Pipeline([
(
"basen_encoder",
category_encoders.CountEncoder(cols=adjusted_cols),
),
("clf", BsplitZClassifier()),
]),
),
# ('clf', DecisionTreeClassifier(max_depth=1))])),
# ('hashing_encoder', Pipeline([
# ('basen_encoder', category_encoders.HashingEncoder(
# cols=adjusted_cols)),
# ('clf', BsplitZClassifier())])), #skip because of too slow
# ('woe_encoder', Pipeline([
# ('woe_encoder', category_encoders.WOEEncoder(
# cols=adjusted_cols)),
# ('clf', BsplitZClassifier())])), #skip because of binary target only
(
"jamesstein_encoder",
Pipeline([
(
"js_encoder",
category_encoders.JamesSteinEncoder(cols=adjusted_cols),
),
("clf", BsplitZClassifier()),
]),
),
# ('clf', DecisionTreeClassifier(max_depth=1))])),
# ('helmert_encoder', Pipeline([
# ('helmert_encoder', category_encoders.HelmertEncoder(
# cols=adjusted_cols)),
# ('clf', BsplitZClassifier())])), #skip because of too slow
]
def encode_categorical_features(dataframe,
strategy,
list_of_features,
list_of_features_to_skip=None):
"""
This function will take a dataframe as input and perform the following:
Encode the features passed as a list using the strategy selected. This
function uses category_encoder library.
For ordinal features, the functional will use ordinal_encoder
For non-ordinal features, the function will use JamesStein Encoder
:param dataframe: dataframe object
:param strategy: this is the parameter that holds how the categorical features should be encoded
strategy types available: 'ordinal', 'non_ordinal'
:param list_of_features: pass a list object containing features to be encoded using strategy selected
:param list_of_features_to_skip: pass a list object containing features to be omitted from encoding
:return: dataframe with categorical features encoded.
"""
if not isinstance(dataframe, pd.DataFrame):
raise ValueError("Object passed is not a dataframe")
if strategy is None:
raise ValueError("Please select a strategy to use")
if list_of_features is None:
raise ValueError("Please pass a list of features to be encoded")
if not isinstance(list_of_features, list):
raise ValueError("Object passed is not a dataframe")
encoder_type = "james_stein"
# split dataframe into features and target variable
y = dataframe['loan_status']
x = dataframe.drop("loan_status", axis=1)
if strategy == 'ordinal':
# create an ordinal encoder object
ordinal_encoder = ce.OrdinalEncoder(cols=list_of_features)
# transform the dataframe - returns a df with categorical features encoded
dataframe = ordinal_encoder.fit_transform(x, y)
# merge back the dataframe with the y target variable
dataframe = dataframe.merge(y, on=y.index)
# drop the index feature key_0
dataframe.drop("key_0", axis=1, inplace=True)
# convert the categorical features back to the category data type
dataframe[list_of_features] = dataframe[list_of_features].astype(
'category')
elif strategy == 'non_ordinal':
# select all the features in the dataset
non_ordinal = dataframe.select_dtypes(
include='category').columns.tolist()
# filter out non-ordinal features using ordinal features passed
non_ordinal_features = [
value for value in non_ordinal if value not in list_of_features
]
# create encoder object
encoder = ce.JamesSteinEncoder(cols=non_ordinal_features)
# transform the dataframe - returns a df with categorical features encoded
dataframe = encoder.fit_transform(x, y)
# merge back the target variable to the dataframe(df)
dataframe = dataframe.merge(y, on=y.index)
# drop the index feature key_0
dataframe.drop("key_0", axis=1, inplace=True)
# convert non_ordinal_features back to category data type
dataframe[non_ordinal_features] = dataframe[
non_ordinal_features].astype('category')
return dataframe
