def get_signals(X_train, y_train, X_test, threshold):
"""
Used to predict buy and sell signals. The function itself has no awareness
what it is predicting. It is just a helper function used by
get_possible_trades().
Target is the column that contains the target. The other columns are
considered to be features to be used for training and prediction.
The function uses a balanced weight of evidence scorecard to predict the
signals. It returns the signals array.
Note that the function uses 70% for training and 30% for testing. The
date where the split happens is dependent on how much data the hist
dataframe contains. So, the caller will not see a single split date for
all tickers.
"""
log(f"- Building model with features: {X_train.columns}")
scaler = StandardScaler()
encoder = WOEEncoder()
binner = KBinsDiscretizer(n_bins=5, encode='ordinal')
objectify = FunctionTransformer(func=stringify, check_inverse=False, validate=False)
imputer = SimpleImputer(strategy='constant', fill_value=0.0)
clf = LogisticRegression(class_weight='balanced', random_state=42)
pipe = make_pipeline(scaler, binner, objectify, encoder, imputer, clf)
pipe.fit(X_train, y_train.values)
test_signals = (pipe.predict_proba(X_test) > threshold).astype(int)[:,1]
return y_train.values, test_signals.copy()
def WOE_Encoding(self,
regularization: float = 1.0,
sigma: float = 0.05,
randomized: bool = False):
"""
woe编码
:param regularization:
:param sigma:
:param randomized:
:return:
"""
self.encoder = WOEEncoder(cols=self.cols,
regularization=regularization,
randomized=randomized,
sigma=sigma)
def fit(self, X, y):
self.woe_cols = []
self.dummy_cols = []
self.thresh = self.thresh_func(len(X))
cat_cols = X.dtypes[X.dtypes == "object"].index
for i in cat_cols:
if len(X[i].unique()) > self.thresh:
self.woe_cols.append(i)
else:
self.dummy_cols.append(i)
self.woe = WOEEncoder(drop_invariant=True,
random_state=1234,
cols=self.woe_cols)
self.woe.fit(X, y)
return self
def CatEncoder(X, cat_cols, tags, estimator_name, objective_type, trial, n_classes, random_state):
if tags["handles categorical"] == False:
large_threshold = 6
#TODO: handle numpy arrays with categorical?
#TODO: handle multiclass / Regression
if isinstance(X, pd.DataFrame) and isinstance(cat_cols[0], str):
large_cardinal_cats = [col for col in X[cat_cols].columns if X[col].nunique() > large_threshold]
small_cardinal_cats = [col for col in X[cat_cols].columns if X[col].nunique() <= large_threshold]
elif isinstance(X, pd.DataFrame):
large_cardinal_cats = [col for col in cat_cols if len(np.unique(X.iloc[:,col])) > large_threshold]
small_cardinal_cats = [col for col in cat_cols if len(np.unique(X.iloc[:,col])) <= large_threshold]
else:
large_cardinal_cats = [col for col in cat_cols if len(np.unique(X[:,col])) > large_threshold]
small_cardinal_cats = [col for col in cat_cols if len(np.unique(X[:,col])) <= large_threshold]
enc_pipe = None
cat_enc_types = ["target", "binary", "catboost"]
if len(small_cardinal_cats) > 0:
enc_pipe = add_to_pipe(enc_pipe, "ohe", OneHotEncoder(cols=small_cardinal_cats, drop_invariant=True))
if len(large_cardinal_cats) > 0:
if (objective_type == "classification" and n_classes == 1):
cat_enc_types.append("woe")
cat_enc_type = trial.suggest_categorical(estimator_name + " cat_enc_type", cat_enc_types)
if cat_enc_type == "binary":
# mapping = get_mapping(X, large_cardinal_cats)
enc = BinaryEncoder(cols=large_cardinal_cats,
# mapping=mapping
)
elif cat_enc_type == "woe":
enc = WOEEncoder(cols=large_cardinal_cats, drop_invariant=True)
elif cat_enc_type == "target":
min_samples_leaf = 6 # TODO: calculate percentage or something else
enc = TargetEncoder(min_samples_leaf=min_samples_leaf,
cols=large_cardinal_cats)
else: # catboost
enc = CatBoostEncoder(cols=large_cardinal_cats,
random_state=random_state) # TODO: replace SEED
# TODO: permute to the dataset beforehand
enc_pipe = add_to_pipe(enc_pipe, cat_enc_type + "_encoder", enc)
return enc_pipe
def __init__(self, kind, **kwargs):
self.kind = kind
if kind not in ['OHE', 'TE', 'LOOE', 'WOE', 'LE']:
raise Exception(
"Encoder type not supported, choose one of ('OHE','TE','LOOE','WOE', 'LE')"
)
else:
if kind == 'OHE':
self.encoder = OneHotEncoder(**kwargs)
elif kind == 'TE':
self.encoder = TargetEncoder(**kwargs)
elif kind == 'LOOE':
self.encoder = LeaveOneOutEncoder(**kwargs)
elif kind == 'WOE':
self.encoder = WOEEncoder(**kwargs)
elif kind == 'LE':
self.encoder = MultiColumnTransformer(LabelEncoder)
def CatEncoder(X, cat_cols, tags, objective_type, trial, n_classes, random_state):
if tags["handles categorical"] == False:
large_threshold = 6
#TODO: handle numpy arrays with categorical?
large_cardinal_cats = [col for col in X[cat_cols].columns if X[col].nunique() > large_threshold]
small_cardinal_cats = [col for col in X[cat_cols].columns if X[col].nunique() <= large_threshold]
enc_pipe = None
cat_enc_types = ["binary", "catboost", "woe", "target"]
if small_cardinal_cats is not None:
enc_pipe = add_to_pipe(enc_pipe, "ohe", OneHotEncoder(cols=small_cardinal_cats, drop_invariant=True))
if large_cardinal_cats is not None:
if (objective_type == "classification" and n_classes > 2): #multiclass
cat_enc_types = ["binary"]
cat_enc_type = trial.suggest_categorical("cat_enc_type", cat_enc_types)
if cat_enc_type == "binary":
# mapping = get_mapping(X, large_cardinal_cats)
enc = BinaryEncoder(cols=large_cardinal_cats,
drop_invariant=True,
# mapping=mapping
)
elif cat_enc_type == "woe":
enc = WOEEncoder(cols=large_cardinal_cats, drop_invariant=True)
elif cat_enc_type == "target":
min_samples_leaf = 10 # TODO: calculate percentage or something else
enc = TargetEncoder(min_samples_leaf=min_samples_leaf,
cols=large_cardinal_cats,
drop_invariant=True)
else: # catboost
enc = CatBoostEncoder(cols=large_cardinal_cats,
drop_invariant=True,
random_state=random_state) # TODO: replace SEED
# TODO: permute to the dataset beforehand
enc_pipe = add_to_pipe(enc_pipe, cat_enc_type + "_encoder", enc)
return enc_pipe
def fit_transform(self, df, colname, targetname):
'''
Fit encoder, transform column in df, save attributes for transform(/inverse_transform().
Variable is encoded with adding minor noize to reduce the risk of overfitting.
Parameters
----------
df : pd.DataFrame
Data containing the colname to transform.
colname : str
Column name in df to be transformed.
targetname : str
column name for extracting the mean values for each colname category.
Returns
-------
transformed_df : pd.DataFrame
Data with the column transformed.
'''
assert_fit_transform_args(df, colname)
assert_binary_target(df, targetname)
encoded_df = df.copy()
self._colname = colname
from category_encoders import WOEEncoder
generic_encoder = WOEEncoder(**self._params)
encoded_column = generic_encoder.fit_transform(df[colname],
df[targetname])
self.__generic_encoder = generic_encoder
encoded_df[self._colname] = encoded_column
# save inverse_transform pattern for test set (without noize)
woe_vals_no_noize = self.transform(df)[self._colname].unique()
original_vals = df[self._colname].unique()
self.__pattern = dict(zip(woe_vals_no_noize, original_vals))
return encoded_df
def get_signals(hist, target, threshold):
"""
Used to predict buy and sell signals. The function itself has no awareness
what it is predicting. It is just a helper function used by
get_possible_trades().
Target is the column that contains the target. The other columns are
considered to be features to be used for training and prection.
The function uses a balanced weight of evidence scorecard to predict the
signals. It returns the signals array.
Note that the function uses 70% for training and 30% for testing. The
date where the split happens is dependent on how much data the hist
dataframe contains. So, the caller will not see a single split date for
all tickers.
"""
# NB: we do not include smooth in data!
data = hist[['Close', 'Open', 'Low', 'High']]
data = features(data, hist, target)
used_cols = [c for c in data.columns.tolist() if c not in [target]]
X, y, X_train, X_test, y_train, y_test = split_data(
data, used_cols, target, 0.7)
encoder = WOEEncoder()
binner = KBinsDiscretizer(n_bins=5, encode='ordinal')
objectify = FunctionTransformer(func=stringify,
check_inverse=False,
validate=False)
imputer = SimpleImputer(strategy='constant', fill_value=0.0)
clf = LogisticRegression(class_weight='balanced', random_state=42)
pipe = make_pipeline(binner, objectify, encoder, imputer, clf)
pipe.fit(X_train, y_train.values)
test_signals = (pipe.predict_proba(X_test) > threshold).astype(int)[:, 1]
return y_train.values, test_signals
def doCleanupEncode(X,
y=None,
cat=None,
oh=None,
binary=None,
loo=None,
woe=None,
lp_cols=None,
NoData=True):
from enrich import replaceCVs
from enrich import one_hot_encode
from category_encoders import BinaryEncoder
from category_encoders import OneHotEncoder
from category_encoders import WOEEncoder
from category_encoders import LeaveOneOutEncoder
if NoData is False:
if cat is not None | oh is not None:
# translate associated columns' null, NaN, blank and 9 values to zero
X = replaceCVs(X, cat + oh, [np.nan, 9, "", " "], 0)
if oh is not None:
if NoData:
ec = OneHotEncoder(cols=oh,
use_cat_names=True,
return_df=True,
handle_unknown='indicator',
handle_missing='indicator').fit(X)
X = ec.fit_transform(X)
# dropping these columns did not help performance
# for o in oh:
# stem = o.split("_")[1]
# d1 = "L_" + stem + "_-1"
# d2 = "L_" + stem + "_nan"
# print("DROPPING ", d1, " ", d2, "\n")
# X.drop(d1, axis=1, errors='ignore', inplace=True)
# X.drop(d2, axis=1, errors='ignore', inplace=True)
else:
# one-hot encode, then drop 0 if created
for oh_c in oh:
X = one_hot_encode(X, oh_c, False)
X.drop(0, axis=1, errors='ignore', inplace=True)
if binary is not None:
# binary encode binary columns
if NoData:
enc = BinaryEncoder(cols=binary,
drop_invariant=True,
return_df=True,
handle_unknown='indicator').fit(X)
X = enc.transform(X)
else:
enc = BinaryEncoder(cols=binary,
drop_invariant=True,
return_df=True).fit(X)
X = enc.transform(X)
if woe is not None:
# use weight of evidence on woe columns
for w in woe:
X[w] = X[w].fillna('NoData')
wenc = WOEEncoder(cols=woe).fit(X, y)
X = wenc.transform(X).round(2)
if loo is not None:
# use leave one out on loo columns
for l in loo:
X[l] = X[l].fillna('NoData')
lenc = LeaveOneOutEncoder(cols=loo, return_df=True).fit(X, y)
X = lenc.transform(X).round(2)
# Cast all to int64
# X = X.astype("int64")
if lp_cols is not None:
# drop least predictive
X.drop(lp_cols, axis=1, errors="ignore", inplace=True)
X.reset_index(drop=True, inplace=True)
return X
def __init__(self, columns=None, **kwargs):
self.columns = columns
self.model = WOEEncoder(**kwargs)
self.transform_cols = None
def Convert_to_numeric(df):
#Ordinal features
map_ord = {
'Novice': 0,
'Contributor': 1,
'Expert': 2,
'Master': 3,
'Grandmaster': 4,
'Freezing': 0,
'Cold': 1,
'Warm': 2,
'Hot': 3,
'Boiling Hot': 4,
'Lava Hot': 5
}
scii_letters_list = list(string.ascii_letters)
map_ord_hex = dict(zip(scii_letters_list, range(0,
len(scii_letters_list))))
df['ord_0'] = df['ord_0']
df['ord_1'] = df['ord_1'].replace(map_ord)
df['ord_2'] = df['ord_2'].replace(map_ord)
df['ord_3'] = df['ord_3'].replace(map_ord_hex)
df['ord_4'] = df['ord_4'].replace(map_ord_hex)
df[features_ord] = df[features_ord].fillna(df[features_ord].mean())
StandardScaler_Encoder = preprocessing.StandardScaler()
df[features_ord] = StandardScaler_Encoder.fit_transform(
df[features_ord].astype(float))
#Binary, Low nominal and time features WOE encoder.
n_splits = 5
WOE_features = features_bin + features_low_nom + features_cyc
# for col in WOE_features:
# df[f'{col}_Encode']=0
# for tr_idx, tst_idx in StratifiedKFold(n_splits=n_splits, random_state=2020, shuffle=True).split(df[:600000], y_train):
# WOE_encoder = WOEEncoder(cols=col)
# WOE_encoder.fit(df[:600000].iloc[tr_idx, :], y_train.iloc[tr_idx])
# col_df=WOE_encoder.transform(df)[col]/n_splits
# df[f'{col}_Encode']= df[f'{col}_Encode']+col_df
WOE_features_encode = [w + '_Encode' for w in WOE_features]
for c in WOE_features_encode:
df[c] = 0
for tr_idx, tst_idx in StratifiedKFold(n_splits=n_splits,
random_state=2020,
shuffle=True).split(
df[:600000], y_train):
WOE_encoder = WOEEncoder(cols=WOE_features)
WOE_encoder.fit(df[:600000].loc[tr_idx, WOE_features],
y_train.iloc[tr_idx])
col_df = WOE_encoder.transform(df[WOE_features]) / n_splits
df.loc[:, WOE_features_encode] += col_df
df = df.drop(WOE_features, axis=1)
#High Nominal Features Label encoder.
Label_col = features_hi_nom + features_hi_ord
for col in Label_col:
Label_Encoder = preprocessing.LabelEncoder()
df[col] = Label_Encoder.fit_transform(
df[col].fillna("-1").astype(str).values)
return df
# 单词特征的特征散列化
def hash_features(word_list, m):
output = [0] * m
for word in word_list:
index = hash_fcn(word) % m
output[index] += 1
return output
# 带符号的特征散列化
def hash_features(word_list, m):
output = [0] * m
for word in word_list:
index = hash_fcn(word) % m
sign_bit = sign_hash(word) % 2
if sign_bit == 0:
output[index] -= 1
else:
output[index] += 1
return output
h = FeatureHasher(n_features=m, input_type="string")
f = h.trasnform(df["feat"])
enc = TargetEncoder(cols=['Name_of_col', 'Another_name'])
training_set = enc.fit_transform(X_train, y_train)
enc = LeaveOneOutEncoder(cols=['Name_of_col', 'Another_name'])
training_set = enc.fit_transform(X_train, y_train)
enc = WOEEncoder(cols=['Name_of_col', 'Another_name'])
training_set = enc.fit_transform(X_train, y_train)
from sklearn.preprocessing import OrdinalEncoder as SklOrdinalEncoder
from category_encoders import WOEEncoder, OrdinalEncoder
from skl2onnx import update_registered_converter, to_onnx, get_model_alias
from skl2onnx.common.data_types import FloatTensorType
from skl2onnx.common.utils import check_input_and_output_numbers
from skl2onnx.algebra.onnx_ops import OnnxCast
from skl2onnx.algebra.onnx_operator import OnnxSubEstimator
from skl2onnx.sklapi import WOETransformer
import skl2onnx.sklapi.register # noqa
data = load_iris()
X, y = data.data, data.target
X = X.astype(np.int64)[:, :2]
y = (y == 2).astype(np.int64)
woe = WOEEncoder(cols=[0]).fit(X, y)
print(woe.transform(X[:5]))
########################################
# Let's look into the trained parameters of the model.
# It appears that WOEEncoder uses an OrdinalEncoder
# but not the one from scikit-learn. We need to add a
# converter for this model tool.
print("encoder", type(woe.ordinal_encoder), woe.ordinal_encoder)
print("mapping", woe.mapping)
print("encoder.mapping", woe.ordinal_encoder.mapping)
print("encoder.cols", woe.ordinal_encoder.cols)
######################################
# Custom converter for OrdinalEncoder
def add_woe_encoding(self):
self.pipeline.append(("WOEncoder", WOEEncoder()))