如何建立RF（Random Forest）和PSO（Particle Swarm Optimizer）的混合模型来寻找产品的最优折扣？
How to build hybrid model of RF(Random Forest) and PSO(Particle Swarm Optimizer) to find optimal discount of products?
我需要为每种产品（例如 A、B、C）找到最佳折扣，以便最大化总销售额。对于将折扣和季节映射到销售的每种产品，我都有现有的随机森林模型。我如何组合这些模型并将它们提供给优化器以找到每个产品的最佳折扣？
选型原因：
- RF：它能够提供更好的（w.r.t 线性模型）预测变量和响应（sales_uplift_norm）之间的关系。
- PSO：在许多白皮书中建议（可在 researchgate/IEEE 获得），也可在 python here and here.
中获得该包
输入数据：sample data用于在产品级别构建模型。数据一览如下：
Idea/Steps 后面是我：
- 为每个产品构建 RF 模型
# pre-processed data
products_pre_processed_data = {key:pre_process_data(df, key) for key, df in df_basepack_dict.items()}
# rf models
products_rf_model = {key:rf_fit(df) for key, df in products_pre_processed_data .items()}
- 将模型传递给优化器
- Objective函数：最大化sales_uplift_norm（RF模型的响应变量）
- 约束：
- 总支出（A + B + C <= 20），支出 = total_units_sold_of_products * discount_percentage * mrp_of_products
- 产品的下限(A, B, C): [0.0, 0.0, 0.0] # 折扣百分比下限
- 产品上限(A, B, C): [0.3, 0.4, 0.4] # discount percentage upper bounds
sudo/sample code # 因为我找不到将 product_models 传递给优化器的方法。
from pyswarm import pso
def obj(x):
model1 = products_rf_model.get('A')
model2 = products_rf_model.get('B')
model3 = products_rf_model.get('C')
return -(model1 + model2 + model3) # -ve sign as to maximize
def con(x):
x1 = x[0]
x2 = x[1]
x3 = x[2]
return np.sum(units_A*x*mrp_A + units_B*x*mrp_B + units_C* x *spend_C)-20 # spend budget
lb = [0.0, 0.0, 0.0]
ub = [0.3, 0.4, 0.4]
xopt, fopt = pso(obj, lb, ub, f_ieqcons=con)
尊敬的 SO 专家，请求您提供有关如何使用 PSO 优化器（或任何其他优化器）的指导（几周以来一直在努力寻找任何指导）如果我没有跟随正确的）与 RF.
添加用于模型的函数：
def pre_process_data(df,product):
data = df.copy().reset_index()
# print(data)
bp = product
print("----------product: {}----------".format(bp))
# Pre-processing steps
print("pre process df.shape {}".format(df.shape))
#1. Reponse var transformation
response = data.sales_uplift_norm # already transformed
#2. predictor numeric var transformation
numeric_vars = ['discount_percentage'] # may include mrp, depth
df_numeric = data[numeric_vars]
df_norm = df_numeric.apply(lambda x: scale(x), axis = 0) # center and scale
#3. char fields dummification
#select category fields
cat_cols = data.select_dtypes('category').columns
#select string fields
str_to_cat_cols = data.drop(['product'], axis = 1).select_dtypes('object').astype('category').columns
# combine all categorical fields
all_cat_cols = [*cat_cols,*str_to_cat_cols]
# print(all_cat_cols)
#convert cat to dummies
df_dummies = pd.get_dummies(data[all_cat_cols])
#4. combine num and char df together
df_combined = pd.concat([df_dummies.reset_index(drop=True), df_norm.reset_index(drop=True)], axis=1)
df_combined['sales_uplift_norm'] = response
df_processed = df_combined.copy()
print("post process df.shape {}".format(df_processed.shape))
# print("model fields: {}".format(df_processed.columns))
return(df_processed)
def rf_fit(df, random_state = 12):
train_features = df.drop('sales_uplift_norm', axis = 1)
train_labels = df['sales_uplift_norm']
# Random Forest Regressor
rf = RandomForestRegressor(n_estimators = 500,
random_state = random_state,
bootstrap = True,
oob_score=True)
# RF model
rf_fit = rf.fit(train_features, train_labels)
return(rf_fit)
编辑： 将数据集更新为简化版本。
您可以在下面找到完整的解决方案！
与您的方法的根本区别如下：
- 由于随机森林模型将
season 特征作为输入，因此必须为每个季节计算最佳折扣。
- 检查 pyswarm 的文档，
con 函数产生的输出必须符合 con(x) >= 0.0。因此，正确的约束是 20 - sum(...) 而不是相反。另外，units 和 mrp 变量没有给出；我假设值为 1，您可能想要更改这些值。
对原始代码的其他修改包括：
sklearn 的预处理和管道包装器，以简化预处理步骤。- 最佳参数存储在输出
.xlsx 文件中。
- PSO 的
maxiter 参数已设置为 5 到 speed-up 调试，您可能需要将其值设置为另一个值（默认 = 100 ).
因此代码为：
import pandas as pd
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import OneHotEncoder, StandardScaler
from sklearn.compose import ColumnTransformer
from sklearn.ensemble import RandomForestRegressor
from sklearn.base import clone
# ====================== RF TRAINING ======================
# Preprocessing
def build_sample(season, discount_percentage):
return pd.DataFrame({
'season': [season],
'discount_percentage': [discount_percentage]
})
columns_to_encode = ["season"]
columns_to_scale = ["discount_percentage"]
encoder = OneHotEncoder()
scaler = StandardScaler()
preproc = ColumnTransformer(
transformers=[
("encoder", Pipeline([("OneHotEncoder", encoder)]), columns_to_encode),
("scaler", Pipeline([("StandardScaler", scaler)]), columns_to_scale)
]
)
# Model
myRFClassifier = RandomForestRegressor(
n_estimators = 500,
random_state = 12,
bootstrap = True,
oob_score = True)
pipeline_list = [
('preproc', preproc),
('clf', myRFClassifier)
]
pipe = Pipeline(pipeline_list)
# Dataset
df_tot = pd.read_excel("so_data.xlsx")
df_dict = {
product: df_tot[df_tot['product'] == product].drop(columns=['product']) for product in pd.unique(df_tot['product'])
}
# Fit
print("Training ...")
pipe_dict = {
product: clone(pipe) for product in df_dict.keys()
}
for product, df in df_dict.items():
X = df.drop(columns=["sales_uplift_norm"])
y = df["sales_uplift_norm"]
pipe_dict[product].fit(X,y)
# ====================== OPTIMIZATION ======================
from pyswarm import pso
# Parameter of PSO
maxiter = 5
n_product = len(pipe_dict.keys())
# Constraints
budget = 20
units = [1, 1, 1]
mrp = [1, 1, 1]
lb = [0.0, 0.0, 0.0]
ub = [0.3, 0.4, 0.4]
# Must always remain >= 0
def con(x):
s = 0
for i in range(n_product):
s += units[i] * mrp[i] * x[i]
return budget - s
print("Optimization ...")
# Save optimal discounts for every product and every season
df_opti = pd.DataFrame(data=None, columns=df_tot.columns)
for season in pd.unique(df_tot['season']):
# Objective function to minimize
def obj(x):
s = 0
for i, product in enumerate(pipe_dict.keys()):
s += pipe_dict[product].predict(build_sample(season, x[i]))
return -s
# PSO
xopt, fopt = pso(obj, lb, ub, f_ieqcons=con, maxiter=maxiter)
print("Season: {}\t xopt: {}".format(season, xopt))
# Store result
df_opti = pd.concat([
df_opti,
pd.DataFrame({
'product': list(pipe_dict.keys()),
'season': [season] * n_product,
'discount_percentage': xopt,
'sales_uplift_norm': [
pipe_dict[product].predict(build_sample(season, xopt[i]))[0] for i, product in enumerate(pipe_dict.keys())
]
})
])
# Save result
df_opti = df_opti.reset_index().drop(columns=['index'])
df_opti.to_excel("so_result.xlsx")
print("Summary")
print(df_opti)
它给出：
Training ...
Optimization ...
Stopping search: maximum iterations reached --> 5
Season: summer xopt: [0.1941521 0.11233673 0.36548761]
Stopping search: maximum iterations reached --> 5
Season: winter xopt: [0.18670604 0.37829516 0.21857777]
Stopping search: maximum iterations reached --> 5
Season: monsoon xopt: [0.14898102 0.39847885 0.18889792]
Summary
product season discount_percentage sales_uplift_norm
0 A summer 0.194152 0.175973
1 B summer 0.112337 0.229735
2 C summer 0.365488 0.374510
3 A winter 0.186706 -0.028205
4 B winter 0.378295 0.266675
5 C winter 0.218578 0.146012
6 A monsoon 0.148981 0.199073
7 B monsoon 0.398479 0.307632
8 C monsoon 0.188898 0.210134
我需要为每种产品（例如 A、B、C）找到最佳折扣，以便最大化总销售额。对于将折扣和季节映射到销售的每种产品，我都有现有的随机森林模型。我如何组合这些模型并将它们提供给优化器以找到每个产品的最佳折扣？
选型原因：
- RF：它能够提供更好的（w.r.t 线性模型）预测变量和响应（sales_uplift_norm）之间的关系。
- PSO：在许多白皮书中建议（可在 researchgate/IEEE 获得），也可在 python here and here. 中获得该包
输入数据：sample data用于在产品级别构建模型。数据一览如下：
Idea/Steps 后面是我：
- 为每个产品构建 RF 模型
# pre-processed data
products_pre_processed_data = {key:pre_process_data(df, key) for key, df in df_basepack_dict.items()}
# rf models
products_rf_model = {key:rf_fit(df) for key, df in products_pre_processed_data .items()}
- 将模型传递给优化器
- Objective函数：最大化sales_uplift_norm（RF模型的响应变量）
- 约束：
- 总支出（A + B + C <= 20），支出 = total_units_sold_of_products * discount_percentage * mrp_of_products
- 产品的下限(A, B, C): [0.0, 0.0, 0.0] # 折扣百分比下限
- 产品上限(A, B, C): [0.3, 0.4, 0.4] # discount percentage upper bounds
sudo/sample code # 因为我找不到将 product_models 传递给优化器的方法。
from pyswarm import pso
def obj(x):
model1 = products_rf_model.get('A')
model2 = products_rf_model.get('B')
model3 = products_rf_model.get('C')
return -(model1 + model2 + model3) # -ve sign as to maximize
def con(x):
x1 = x[0]
x2 = x[1]
x3 = x[2]
return np.sum(units_A*x*mrp_A + units_B*x*mrp_B + units_C* x *spend_C)-20 # spend budget
lb = [0.0, 0.0, 0.0]
ub = [0.3, 0.4, 0.4]
xopt, fopt = pso(obj, lb, ub, f_ieqcons=con)
尊敬的 SO 专家，请求您提供有关如何使用 PSO 优化器（或任何其他优化器）的指导（几周以来一直在努力寻找任何指导）如果我没有跟随正确的）与 RF.
添加用于模型的函数：
def pre_process_data(df,product):
data = df.copy().reset_index()
# print(data)
bp = product
print("----------product: {}----------".format(bp))
# Pre-processing steps
print("pre process df.shape {}".format(df.shape))
#1. Reponse var transformation
response = data.sales_uplift_norm # already transformed
#2. predictor numeric var transformation
numeric_vars = ['discount_percentage'] # may include mrp, depth
df_numeric = data[numeric_vars]
df_norm = df_numeric.apply(lambda x: scale(x), axis = 0) # center and scale
#3. char fields dummification
#select category fields
cat_cols = data.select_dtypes('category').columns
#select string fields
str_to_cat_cols = data.drop(['product'], axis = 1).select_dtypes('object').astype('category').columns
# combine all categorical fields
all_cat_cols = [*cat_cols,*str_to_cat_cols]
# print(all_cat_cols)
#convert cat to dummies
df_dummies = pd.get_dummies(data[all_cat_cols])
#4. combine num and char df together
df_combined = pd.concat([df_dummies.reset_index(drop=True), df_norm.reset_index(drop=True)], axis=1)
df_combined['sales_uplift_norm'] = response
df_processed = df_combined.copy()
print("post process df.shape {}".format(df_processed.shape))
# print("model fields: {}".format(df_processed.columns))
return(df_processed)
def rf_fit(df, random_state = 12):
train_features = df.drop('sales_uplift_norm', axis = 1)
train_labels = df['sales_uplift_norm']
# Random Forest Regressor
rf = RandomForestRegressor(n_estimators = 500,
random_state = random_state,
bootstrap = True,
oob_score=True)
# RF model
rf_fit = rf.fit(train_features, train_labels)
return(rf_fit)
编辑： 将数据集更新为简化版本。
您可以在下面找到完整的解决方案！
与您的方法的根本区别如下：
- 由于随机森林模型将
season特征作为输入，因此必须为每个季节计算最佳折扣。 - 检查 pyswarm 的文档，
con函数产生的输出必须符合con(x) >= 0.0。因此，正确的约束是20 - sum(...)而不是相反。另外，units和mrp变量没有给出；我假设值为 1，您可能想要更改这些值。
对原始代码的其他修改包括：
sklearn的预处理和管道包装器，以简化预处理步骤。- 最佳参数存储在输出
.xlsx文件中。 - PSO 的
maxiter参数已设置为5到 speed-up 调试，您可能需要将其值设置为另一个值（默认 =100).
因此代码为：
import pandas as pd
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import OneHotEncoder, StandardScaler
from sklearn.compose import ColumnTransformer
from sklearn.ensemble import RandomForestRegressor
from sklearn.base import clone
# ====================== RF TRAINING ======================
# Preprocessing
def build_sample(season, discount_percentage):
return pd.DataFrame({
'season': [season],
'discount_percentage': [discount_percentage]
})
columns_to_encode = ["season"]
columns_to_scale = ["discount_percentage"]
encoder = OneHotEncoder()
scaler = StandardScaler()
preproc = ColumnTransformer(
transformers=[
("encoder", Pipeline([("OneHotEncoder", encoder)]), columns_to_encode),
("scaler", Pipeline([("StandardScaler", scaler)]), columns_to_scale)
]
)
# Model
myRFClassifier = RandomForestRegressor(
n_estimators = 500,
random_state = 12,
bootstrap = True,
oob_score = True)
pipeline_list = [
('preproc', preproc),
('clf', myRFClassifier)
]
pipe = Pipeline(pipeline_list)
# Dataset
df_tot = pd.read_excel("so_data.xlsx")
df_dict = {
product: df_tot[df_tot['product'] == product].drop(columns=['product']) for product in pd.unique(df_tot['product'])
}
# Fit
print("Training ...")
pipe_dict = {
product: clone(pipe) for product in df_dict.keys()
}
for product, df in df_dict.items():
X = df.drop(columns=["sales_uplift_norm"])
y = df["sales_uplift_norm"]
pipe_dict[product].fit(X,y)
# ====================== OPTIMIZATION ======================
from pyswarm import pso
# Parameter of PSO
maxiter = 5
n_product = len(pipe_dict.keys())
# Constraints
budget = 20
units = [1, 1, 1]
mrp = [1, 1, 1]
lb = [0.0, 0.0, 0.0]
ub = [0.3, 0.4, 0.4]
# Must always remain >= 0
def con(x):
s = 0
for i in range(n_product):
s += units[i] * mrp[i] * x[i]
return budget - s
print("Optimization ...")
# Save optimal discounts for every product and every season
df_opti = pd.DataFrame(data=None, columns=df_tot.columns)
for season in pd.unique(df_tot['season']):
# Objective function to minimize
def obj(x):
s = 0
for i, product in enumerate(pipe_dict.keys()):
s += pipe_dict[product].predict(build_sample(season, x[i]))
return -s
# PSO
xopt, fopt = pso(obj, lb, ub, f_ieqcons=con, maxiter=maxiter)
print("Season: {}\t xopt: {}".format(season, xopt))
# Store result
df_opti = pd.concat([
df_opti,
pd.DataFrame({
'product': list(pipe_dict.keys()),
'season': [season] * n_product,
'discount_percentage': xopt,
'sales_uplift_norm': [
pipe_dict[product].predict(build_sample(season, xopt[i]))[0] for i, product in enumerate(pipe_dict.keys())
]
})
])
# Save result
df_opti = df_opti.reset_index().drop(columns=['index'])
df_opti.to_excel("so_result.xlsx")
print("Summary")
print(df_opti)
它给出：
Training ...
Optimization ...
Stopping search: maximum iterations reached --> 5
Season: summer xopt: [0.1941521 0.11233673 0.36548761]
Stopping search: maximum iterations reached --> 5
Season: winter xopt: [0.18670604 0.37829516 0.21857777]
Stopping search: maximum iterations reached --> 5
Season: monsoon xopt: [0.14898102 0.39847885 0.18889792]
Summary
product season discount_percentage sales_uplift_norm
0 A summer 0.194152 0.175973
1 B summer 0.112337 0.229735
2 C summer 0.365488 0.374510
3 A winter 0.186706 -0.028205
4 B winter 0.378295 0.266675
5 C winter 0.218578 0.146012
6 A monsoon 0.148981 0.199073
7 B monsoon 0.398479 0.307632
8 C monsoon 0.188898 0.210134