I need to find optimal discount for each product (in e.g. A, B, C) so that I can maximize total sales. I have existing Random Forest models for each product that map discount and season to sales. How do I combine these models and feed them to an optimiser to find the optimum discount per product?
Reason for model selection:
Input data: sample data used to build model at product level. Glance of the data as below:
Idea/Steps followed by me:
Build RF model per products
# 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()}
sudo/sample code # as I am unable to find a way to pass the product_models into optimizer.
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)
How to use the PSO optimizer (or any other optimizer if I am not following right one) with RF?
Adding functions used for model:
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)
you can find a complete solution below !
The fundamental differences with your approach are the following :
season
feature, optimal discounts must be computed for every season.con
function yields an output that must comply with con(x) >= 0.0
. The correct constraint is therefore 20 - sum(...)
and not the other way around. In addition, the units
and mrp
variable were not given ; I just assumed a value of 1, you might want to change those values.Additional modifications to your original code include :
sklearn
in order to simplify the preprocessing steps..xlsx
file.maxiter
parameter of the PSO has been set to 5
to speed-up debugging, you might want to set its value to another one (default = 100
).The code is therefore :
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)
It gives :
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