base.py

import pandas as pd
from utils import load_data, expected_profit_campaign_predicted, expected_profit_campaign_actual
from utils import expected_profit_campaign_predicted_actual, matches_count
from csv_prediction_model import CsvPredictionModel
from prediction_system import PredictionModel
from expert_system import ExpertSystem


# Read the data frame
#df = load_data("data/trialPromoResults.csv").drop(axis=1, labels="decision")
df = load_data("data/custdatabase.csv")

# model = CsvPredictionModel("data/neural_network_pred_v1.csv")
model = PredictionModel()
ga_cutpoints = {
    "age": [30, 57, 85],
    "income": [2500, 14551, 17670],
    "avbal": [14000, 20000, 27000],
    "avtrans": [6312, 6811, 8403],
    "cip": [3, 10, 10]
}
# expert = ExpertSystem(df)
expert = ExpertSystem(df, ga_cutpoints)

expected_profit_campaign_pred, predicted_index = expected_profit_campaign_predicted(model, expert, df, save_csv=True)
print("Predicted:", expected_profit_campaign_pred)

df_actual = pd.read_csv("data/Cust_Actual.csv")
expected_profit_campaign_act, actual_index = expected_profit_campaign_actual(df_actual)
print("Actual:", expected_profit_campaign_act)

df_actual = pd.read_csv("data/Cust_Actual.csv", index_col=['index'])
expected_profit_campaign_pred_act = expected_profit_campaign_predicted_actual(df_actual, predicted_index)
print("Predicted Actual:", expected_profit_campaign_pred_act)

matches = matches_count(actual_index, predicted_index)
print("Number of Matches:", matches)