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prudential_preprocess.py
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prudential_preprocess.py
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import csv
import itertools
import pandas as pd
# Note: paths to datasets and files are removed
def creating_dataset(root):
# read in the csv as a dataframe
prudential_data = pd.read_csv(root)
original_categorical_featurenames = [
"Product_Info_1",
"Product_Info_2",
"Product_Info_3",
"Product_Info_5",
"Product_Info_6",
"Product_Info_7",
"Employment_Info_2",
"Employment_Info_3",
"Employment_Info_5",
"InsuredInfo_1",
"InsuredInfo_2",
"InsuredInfo_3",
"InsuredInfo_4",
"InsuredInfo_5",
"InsuredInfo_6",
"InsuredInfo_7",
"Insurance_History_1",
"Insurance_History_2",
"Insurance_History_3",
"Insurance_History_4",
"Insurance_History_7",
"Insurance_History_8",
"Insurance_History_9",
"Family_Hist_1",
"Medical_History_2",
"Medical_History_3",
"Medical_History_4",
"Medical_History_5",
"Medical_History_6",
"Medical_History_7",
"Medical_History_8",
"Medical_History_9",
"Medical_History_11",
"Medical_History_12",
"Medical_History_13",
"Medical_History_14",
"Medical_History_16",
"Medical_History_17",
"Medical_History_18",
"Medical_History_19",
"Medical_History_20",
"Medical_History_21",
"Medical_History_22",
"Medical_History_23",
"Medical_History_25",
"Medical_History_26",
"Medical_History_27",
"Medical_History_28",
"Medical_History_29",
"Medical_History_30",
"Medical_History_31",
"Medical_History_33",
"Medical_History_34",
"Medical_History_35",
"Medical_History_36",
"Medical_History_37",
"Medical_History_38",
"Medical_History_39",
"Medical_History_40",
"Medical_History_41",
"Medical_Keyword_1",
"Medical_Keyword_2",
"Medical_Keyword_3",
"Medical_Keyword_4",
"Medical_Keyword_5",
"Medical_Keyword_6",
"Medical_Keyword_7",
"Medical_Keyword_8",
"Medical_Keyword_9",
"Medical_Keyword_10",
"Medical_Keyword_11",
"Medical_Keyword_12",
"Medical_Keyword_13",
"Medical_Keyword_14",
"Medical_Keyword_15",
"Medical_Keyword_16",
"Medical_Keyword_17",
"Medical_Keyword_18",
"Medical_Keyword_19",
"Medical_Keyword_20",
"Medical_Keyword_21",
"Medical_Keyword_22",
"Medical_Keyword_23",
"Medical_Keyword_24",
"Medical_Keyword_25",
"Medical_Keyword_26",
"Medical_Keyword_27",
"Medical_Keyword_28",
"Medical_Keyword_29",
"Medical_Keyword_30",
"Medical_Keyword_31",
"Medical_Keyword_32",
"Medical_Keyword_33",
"Medical_Keyword_34",
"Medical_Keyword_35",
"Medical_Keyword_36",
"Medical_Keyword_37",
"Medical_Keyword_38",
"Medical_Keyword_39",
"Medical_Keyword_40",
"Medical_Keyword_41",
"Medical_Keyword_42",
"Medical_Keyword_43",
"Medical_Keyword_44",
"Medical_Keyword_45",
"Medical_Keyword_46",
"Medical_Keyword_47",
"Medical_Keyword_48",
]
categorical_featurenames = []
num_featurenames = [
"Product_Info_4",
"Ins_Age",
"Ht",
"Wt",
"BMI",
"Employment_Info_1",
"Employment_Info_4",
"Employment_Info_6",
"Insurance_History_5",
"Family_Hist_2",
"Family_Hist_3",
"Family_Hist_4",
"Family_Hist_5",
]
disc_featurenames = [
"Medical_History_1",
"Medical_History_10",
"Medical_History_15",
"Medical_History_24",
"Medical_History_32",
]
for category in original_categorical_featurenames:
encoded = pd.get_dummies(prudential_data[category], dtype=float)
encoded = encoded.add_suffix("_" + category)
prudential_data = pd.concat([prudential_data, encoded], axis=1).drop(
category, axis=1
)
new_features = list(encoded.columns.values)
categorical_featurenames.extend(new_features)
for feature in num_featurenames:
prudential_data[feature] = (
prudential_data[feature] - (prudential_data[feature]).mean()
) / prudential_data[feature].std()
# fill in with desired csv name
prudential_data.to_csv()
# Much of the logic and code in this section is from "Conditionally Risk-Averse Contextual Bandits" [Farsang et al 2022]
# Paper Link: https://arxiv.org/pdf/2210.13573.pdf
# fill in with the csv that prudential_data was just used to create
with open("", encoding="utf-8") as csvfile:
rows = csv.reader(csvfile, delimiter=",")
rows_header = next(rows, None)
rows_iter = itertools.islice(rows, 0, None)
headers_dict = {k: v for v, k in enumerate(rows_header)}
reverse_headers_dict = {v: k for v, k in enumerate(rows_header)}
categorical_indices = [headers_dict[name] for name in categorical_featurenames]
numeric_indices = [headers_dict[name] for name in num_featurenames]
discrete_indices = [headers_dict[name] for name in disc_featurenames]
context = {}
for row in rows_iter:
for i in categorical_indices:
if row[i] != "":
if "cat_" + str(i) in context:
context["cat_" + str(i)].append(row[i])
context["cat_" + str(i) + "_NA"].append(0)
else:
context["cat_" + str(i)] = [row[i]]
context["cat_" + str(i) + "_NA"] = [0]
for i in numeric_indices:
curr_mean = prudential_data[reverse_headers_dict[i]].mean()
if row[i] != "":
normalized = (float(row[i]) - curr_mean) / (
prudential_data[reverse_headers_dict[i]].std()
)
# filled in default values with 0
if "num_" + str(i) in context:
context["num_" + str(i)].append(float(normalized))
context["num_" + str(i) + "_NA"].append(0)
else:
context["num_" + str(i)] = [float(normalized)]
context["num_" + str(i) + "_NA"] = [0]
else:
if "num_" + str(i) in context:
context["num_" + str(i) + "_NA"].append(1)
context["num_" + str(i)].append(0.0)
else:
context["num_" + str(i) + "_NA"] = [1]
context["num_" + str(i)] = [0.0]
for i in discrete_indices:
curr_mean = prudential_data[reverse_headers_dict[i]].mean()
if row[i] != "":
normalized = (float(row[i]) - curr_mean) / (
prudential_data[reverse_headers_dict[i]].std()
)
if "disc_" + str(i) in context:
context["disc_" + str(i)].append((normalized))
context["disc_" + str(i) + "_NA"].append(0)
else:
context["disc_" + str(i)] = []
context["disc_" + str(i)].append((normalized))
context["disc_" + str(i) + "_NA"] = [0]
else:
if "disc_" + str(i) in context:
context["disc_" + str(i) + "_NA"].append(1)
context["disc_" + str(i)].append(0.0)
else:
context["disc_" + str(i) + "_NA"] = [1]
context["disc_" + str(i)] = [0.0]
context_df = pd.DataFrame.from_dict(context)
# fill in with desired name of csv
context_df.to_csv()
# call creating_dataset function with path to downloaded Prudential dataset csv from Kaggle
# creating_dataset()