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78 lines
2.6 KiB
78 lines
2.6 KiB
import pandas, seaborn
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from requests import get
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from sys import argv, exit
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# chack args
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cmds = ["and", "max"]
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if len(argv) < 2 or argv[1] not in cmds:
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print(f"""USAGE:\npython3 main.py <{'|'.join(cmds)}>
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max = guess fraudulent if the heuristic for the best column finds it
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and = guess fraudulent if ALL the heuristics determined to be good find it""")
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exit(-1)
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# download dataset if it isn't already present (too large for github)
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try: open("creditcard.csv").read()
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except FileNotFoundError:
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print("downloading dataset...")
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txt = get("https://the.silly.computer/creditcard.csv").text
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open("creditcard.csv", "w").write(txt)
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# read dataset into dataframe
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data = pandas.read_csv("creditcard.csv")
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data['mean'] = data.mean(axis=1)
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# isolate fraud & legitimate sets
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fraud_set = data.loc[data["Class"] == 1]
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legit_set = data.loc[data["Class"] == 0]
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#find the best columns for determining fraud
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good_heuristics = []
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for col_name in fraud_set.columns:
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fm = fraud_set[col_name].mean()
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lm = legit_set[col_name].mean()
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corr = 0
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incorr = 0
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for r in data.iterrows():
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if abs(r[1][col_name] - fm) < abs(r[1][col_name] - lm):
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if r[1]["Class"] == 1: corr += 1
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else: incorr += 1
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elif abs(r[1][col_name] - fm) > abs(r[1][col_name] - lm):
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if r[1]["Class"] == 0: corr += 1
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else: incorr += 1
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print(col_name)
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print(fm)
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print(lm)
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accuracy = corr/(corr+incorr)
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print(accuracy)
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if (accuracy > .95) and col_name != "Class":
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print("good heuristic!")
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good_heuristics.append({"name": col_name, "fraud_mean": fm, "legit_mean": lm, "accuracy": accuracy})
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print("")
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print(good_heuristics)
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# create new dataframe with guesses based on found heuristics and chosen type (max = best column, and = all good columns must match)
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guessed_class = []
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best_heuristic = None
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best_acc = 0
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for h in good_heuristics:
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if h["accuracy"] > best_acc:
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best_acc = h["accuracy"]
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best_heuristic = h
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print(f"using heuristic: {best_heuristic['name']}")
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for r in data.iterrows():
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if argv[1] == "and":
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bools = []
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for h in good_heuristics:
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bools.append(abs(r[1][h["name"]] - h["fraud_mean"]) < abs(r[1][h["name"]] - h["legit_mean"]))
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fraud = all(bools)
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guessed_class.append(1 if fraud else 0)
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elif argv[1] == "max":
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good = True
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if abs(r[1][best_heuristic["name"]] - best_heuristic["fraud_mean"]) < abs(r[1][best_heuristic["name"]] - best_heuristic["legit_mean"]):
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good = False
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guessed_class.append(0 if good else 1)
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data["guess"] = guessed_class
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print(data.head(10))
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data.to_csv("woo.csv") |