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FeatureGenerator.py
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FeatureGenerator.py
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from cmath import nan
import numpy as np
import pandas as pd
all_operators = ["freq"]
num_operators = ["abs", "log", "sqrt", "square", "sigmoid", "round", "residual"]
num_num_operators = ["min", "max", "+", "-", "*", "/"]
cat_num_operators = ["GroupByThenMin", "GroupByThenMax", "GroupByThenMean",
"GroupByThenMedian", "GroupByThenStd", "GroupByThenRank"]
cat_cat_operators = ["Combine", "CombineThenFreq", "GroupByThenNUnique"]
symmetry_operators = ["min", "max", "+", "-", "*", "/", "Combine", "CombineThenFreq"]
cal_all_operators = ["freq",
"GroupByThenMin", "GroupByThenMax", "GroupByThenMean",
"GroupByThenMedian", "GroupByThenStd", "GroupByThenRank",
"Combine", "CombineThenFreq", "GroupByThenNUnique"]
# 有必要把numerical中的discrete单独划分出来吗(ordinal features)?
# 感觉是有必要的,这些特征既可以当做numerical也可以当做categorical
# ordinal features可以出现在任何运算符的任何位置
def _reduce_memory(df):
if str(df.dtypes) in ['object', 'category']:
return df
cmin = df.min()
cmax = df.max()
if str(df.dtypes)[:3] == 'int':
# Can use unsigned int here too
if cmin > np.iinfo(np.int8).min and cmax < np.iinfo(np.int8).max:
df = df.astype(np.int8)
elif cmin > np.iinfo(np.int16).min and cmax < np.iinfo(np.int16).max:
df = df.astype(np.int16)
elif cmin > np.iinfo(np.int32).min and cmax < np.iinfo(np.int32).max:
df = df.astype(np.int32)
elif cmin > np.iinfo(np.int64).min and cmax < np.iinfo(np.int64).max:
df = df.astype(np.int64)
else:
if cmin > np.finfo(np.float16).min and cmax < np.finfo(np.float16).max:
df = df.astype(np.float16)
elif cmin > np.finfo(np.float32).min and cmax < np.finfo(np.float32).max:
df = df.astype(np.float32)
else:
df = df.astype(np.float64)
return df
class Node(object):
def __init__(self, op, children):
self.name = op
self.children = children
self.data = None
self.train_idx = []
self.val_idx = []
def get_fnode(self):
fnode_list = []
for child in self.children:
fnode_list.extend(child.get_fnode())
return fnode_list
def delete(self):
self.data = None
for child in self.children:
child.delete()
def f_delete(self):
for child in self.children:
child.f_delete()
def calculate(self, data, is_root=False):
# update: 0: 用新数据计算,可以作为自动的初始化。 1:update,但是当children有group_by的时候更高阶的都要全部计算
if self.name in all_operators+num_operators:
d = self.children[0].calculate(data)
if self.name == "abs":
new_data = d.abs()
elif self.name == "log":
new_data = np.log(np.abs(d))
elif self.name == "sqrt":
new_data = np.sqrt(np.abs(d))
elif self.name == "square":
new_data = np.square(d)
elif self.name == "sigmoid":
new_data = 1 / (1 + np.exp(-d))
elif self.name == "freq":
value_counts = d.value_counts()
value_counts.loc[np.nan] = np.nan
new_data = d.apply(lambda x: value_counts.loc[x]) # 如果category是int,就必须用.loc[]而非[]
elif self.name == "round":
new_data = np.floor(d)
elif self.name == "residual":
new_data = d - np.floor(d)
else:
raise NotImplementedError(f"Unrecognize operator {self.name}.")
elif self.name in num_num_operators:
d1 = self.children[0].calculate(data)
d2 = self.children[1].calculate(data)
if self.name == "max":
new_data = np.maximum(d1, d2)
elif self.name == "min":
new_data = np.minimum(d1, d2)
elif self.name == "+":
new_data = d1 + d2
elif self.name == "-":
new_data = d1 - d2
elif self.name == "*":
new_data = d1 * d2
elif self.name == "/":
new_data = d1 / d2.replace(0, np.nan)
else:
d1 = self.children[0].calculate(data)
d2 = self.children[1].calculate(data)
if self.name == "GroupByThenMin":
temp = d1.groupby(d2).min()
temp.loc[np.nan] = np.nan
new_data = d2.apply(lambda x: temp.loc[x])
elif self.name == "GroupByThenMax":
temp = d1.groupby(d2).max()
temp.loc[np.nan] = np.nan
new_data = d2.apply(lambda x: temp.loc[x])
elif self.name == "GroupByThenMean":
temp = d1.groupby(d2).mean()
temp.loc[np.nan] = np.nan
new_data = d2.apply(lambda x: temp.loc[x])
elif self.name == "GroupByThenMedian":
temp = d1.groupby(d2).median()
temp.loc[np.nan] = np.nan
new_data = d2.apply(lambda x: temp.loc[x])
elif self.name == "GroupByThenStd":
temp = d1.groupby(d2).std()
temp.loc[np.nan] = np.nan
new_data = d2.apply(lambda x: temp.loc[x])
elif self.name == 'GroupByThenRank':
new_data = d1.groupby(d2).rank(ascending=True, pct=True)
elif self.name == "GroupByThenFreq":
def _f(x):
value_counts = x.value_counts()
value_counts.loc[np.nan] = np.nan
return x.apply(lambda x: value_counts.loc[x])
new_data = d1.groupby(d2).apply(_f)
elif self.name == "GroupByThenNUnique":
nunique = d1.groupby(d2).nunique()
nunique.loc[np.nan] = np.nan
new_data = d2.apply(lambda x: nunique.loc[x])
elif self.name == "Combine":
temp = d1.astype(str) + '_' + d2.astype(str)
temp[d1.isna() | d2.isna()] = np.nan
temp, _ = temp.factorize()
new_data = pd.Series(temp, index=d1.index).astype("float64")
elif self.name == "CombineThenFreq":
temp = d1.astype(str) + '_' + d2.astype(str)
temp[d1.isna() | d2.isna()] = np.nan
value_counts = temp.value_counts()
value_counts.loc[np.nan] = np.nan
new_data = temp.apply(lambda x: value_counts.loc[x])
else:
raise NotImplementedError(f"Unrecognized operator {self.name}.")
if self.name == 'Combine':
new_data = new_data.astype('category')
else:
new_data = new_data.astype('float')
# new_data = new_data.replace([np.inf, -np.inf], np.nan)
if is_root:
self.data = new_data
# self.data = _reduce_memory(self.data)
return new_data
class FNode(object):
def __init__(self, name):
self.name = name
self.data = None
self.calculate_all = False
def delete(self):
self.data = None
def f_delete(self):
self.data = None
def get_fnode(self):
return [self.name]
def calculate(self, data):
self.data = data[self.name]
return self.data