In [25]:
import matplotlib.pyplot as plt import numpy as np import torch import pandas as pd import tqdm import sklearn.utils import sklearn.datasets from typing import cast import math import scipy.optimize
In [26]:
sklearn_iris = cast(sklearn.utils.Bunch, sklearn.datasets.load_iris()) iris = pd.DataFrame() iris["sepal_length"] = sklearn_iris.data[:, 0] iris["sepal_width"] = sklearn_iris.data[:, 1] iris["petal_length"] = sklearn_iris.data[:, 2] iris["petal_width"] = sklearn_iris.data[:, 3] iris["is_setosa"] = (sklearn_iris.target == 0).astype(np.float32) iris["is_versicolor"] = (sklearn_iris.target == 1).astype(np.float32) iris["is_virginica"] = (sklearn_iris.target == 2).astype(np.float32) iris
Out [26]:
sepal_length sepal_width petal_length petal_width is_setosa is_versicolor is_virginica 0 5.1 3.5 1.4 0.2 1.0 0.0 0.0 1 4.9 3.0 1.4 0.2 1.0 0.0 0.0 2 4.7 3.2 1.3 0.2 1.0 0.0 0.0 3 4.6 3.1 1.5 0.2 1.0 0.0 0.0 4 5.0 3.6 1.4 0.2 1.0 0.0 0.0 ... ... ... ... ... ... ... ... 145 6.7 3.0 5.2 2.3 0.0 0.0 1.0 146 6.3 2.5 5.0 1.9 0.0 0.0 1.0 147 6.5 3.0 5.2 2.0 0.0 0.0 1.0 148 6.2 3.4 5.4 2.3 0.0 0.0 1.0 149 5.9 3.0 5.1 1.8 0.0 0.0 1.0 150 rows × 7 columns
In [27]:
FEATURES = ["sepal_length", "sepal_width", "petal_length", "petal_width"] TARGETS = ["is_setosa", "is_versicolor", "is_virginica"]
In [28]:
# Let's scale the data from sklearn.preprocessing import StandardScaler scaler = StandardScaler() iris[FEATURES] = scaler.fit_transform(iris[FEATURES]) iris
Out [28]:
sepal_length sepal_width petal_length petal_width is_setosa is_versicolor is_virginica 0 -0.900681 1.019004 -1.340227 -1.315444 1.0 0.0 0.0 1 -1.143017 -0.131979 -1.340227 -1.315444 1.0 0.0 0.0 2 -1.385353 0.328414 -1.397064 -1.315444 1.0 0.0 0.0 3 -1.506521 0.098217 -1.283389 -1.315444 1.0 0.0 0.0 4 -1.021849 1.249201 -1.340227 -1.315444 1.0 0.0 0.0 ... ... ... ... ... ... ... ... 145 1.038005 -0.131979 0.819596 1.448832 0.0 0.0 1.0 146 0.553333 -1.282963 0.705921 0.922303 0.0 0.0 1.0 147 0.795669 -0.131979 0.819596 1.053935 0.0 0.0 1.0 148 0.432165 0.788808 0.933271 1.448832 0.0 0.0 1.0 149 0.068662 -0.131979 0.762758 0.790671 0.0 0.0 1.0 150 rows × 7 columns
In [29]:
# Drop class iris_orig = iris.copy() iris = iris.drop(TARGETS, axis=1) iris
Out [29]:
sepal_length sepal_width petal_length petal_width 0 -0.900681 1.019004 -1.340227 -1.315444 1 -1.143017 -0.131979 -1.340227 -1.315444 2 -1.385353 0.328414 -1.397064 -1.315444 3 -1.506521 0.098217 -1.283389 -1.315444 4 -1.021849 1.249201 -1.340227 -1.315444 ... ... ... ... ... 145 1.038005 -0.131979 0.819596 1.448832 146 0.553333 -1.282963 0.705921 0.922303 147 0.795669 -0.131979 0.819596 1.053935 148 0.432165 0.788808 0.933271 1.448832 149 0.068662 -0.131979 0.762758 0.790671 150 rows × 4 columns
