Berack96
b4bd976a9d
- cleaner app.py - fixed pandas Warning - better learning method -power-plant csv fixed
79 lines
2.5 KiB
Python
79 lines
2.5 KiB
Python
import pandas as pd
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import numpy as np
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from typing_extensions import Self
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class Dataset:
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def __init__(self, csv:str, target:str) -> None:
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data = pd.read_csv(csv)
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# move target to the start
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col_target = data.pop(target)
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data.insert(0, target, col_target)
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data.insert(1, "Bias", 1.0)
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self.original = data
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self.data = data
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self.target = target
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self.classification = (data[target].dtype == object)
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def regularize(self, excepts:list[str]=[]) -> Self:
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excepts.append(self.target)
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excepts.append("Bias")
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for col in self.data:
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if col not in excepts:
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index = self.data.columns.get_loc(col)
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datacol = self.data.pop(col)
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datacol = (datacol - datacol.mean()) / datacol.std()
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self.data.insert(index, col, datacol)
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return self
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def factorize(self, columns:list[str]=[]) -> Self:
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data = self.data
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for col in columns:
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data[col] = pd.factorize(data[col])[0]
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return self
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def to_numbers(self, columns:list[str]=[]) -> Self:
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data = self.data
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for col in columns:
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if data[col].dtype == object:
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data[col] = pd.to_numeric(data[col], errors='coerce')
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return self
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def handle_na(self) -> Self:
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self.data = self.data.dropna()
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return self
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def shuffle(self) -> Self:
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self.data = self.data.sample(frac=1)
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return self
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def as_ndarray(self) -> np.ndarray:
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return self.data.to_numpy()
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def get_index(self, column:str) -> int:
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return self.data.columns.get_loc(column)
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class PrincipalComponentAnalisys:
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def __init__(self, data:np.ndarray) -> None:
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self.data = data
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def reduce(self, total:int=0, threshold:float=1) -> Self:
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columns = self.data.shape[1]
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if total > columns or total <= 0:
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total = columns
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if threshold <= 0 or threshold > 1:
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threshold = 1
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if __name__ == "__main__":
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df = Dataset("datasets\\regression\\automobile.csv", "symboling")
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attributes_to_modify = ["fuel-system", "engine-type", "drive-wheels", "body-style", "make", "engine-location", "aspiration", "fuel-type", "num-of-cylinders", "num-of-doors"]
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df.factorize(attributes_to_modify)
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df.to_numbers(["normalized-losses", "bore", "stroke", "horsepower", "peak-rpm", "price"])
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df.handle_na()
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df.regularize(excepts=attributes_to_modify)
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print(df.data.dtypes)
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