import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

from sklearn.datasets import load_iris

%matplotlib inline

Data-preparatie

ds = load_iris()
ds.DESCR

'.. _iris_dataset:\n\nIris plants dataset\n--------------------\n\n**Data Set Characteristics:**\n\n:Number of Instances: 150 (50 in each of three classes)\n:Number of Attributes: 4 numeric, predictive attributes and the class\n:Attribute Information:\n    - sepal length in cm\n    - sepal width in cm\n    - petal length in cm\n    - petal width in cm\n    - class:\n            - Iris-Setosa\n            - Iris-Versicolour\n            - Iris-Virginica\n\n:Summary Statistics:\n\n============== ==== ==== ======= ===== ====================\n                Min  Max   Mean    SD   Class Correlation\n============== ==== ==== ======= ===== ====================\nsepal length:   4.3  7.9   5.84   0.83    0.7826\nsepal width:    2.0  4.4   3.05   0.43   -0.4194\npetal length:   1.0  6.9   3.76   1.76    0.9490  (high!)\npetal width:    0.1  2.5   1.20   0.76    0.9565  (high!)\n============== ==== ==== ======= ===== ====================\n\n:Missing Attribute Values: None\n:Class Distribution: 33.3% for each of 3 classes.\n:Creator: R.A. Fisher\n:Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\n:Date: July, 1988\n\nThe famous Iris database, first used by Sir R.A. Fisher. The dataset is taken\nfrom Fisher\'s paper. Note that it\'s the same as in R, but not as in the UCI\nMachine Learning Repository, which has two wrong data points.\n\nThis is perhaps the best known database to be found in the\npattern recognition literature.  Fisher\'s paper is a classic in the field and\nis referenced frequently to this day.  (See Duda & Hart, for example.)  The\ndata set contains 3 classes of 50 instances each, where each class refers to a\ntype of iris plant.  One class is linearly separable from the other 2; the\nlatter are NOT linearly separable from each other.\n\n.. dropdown:: References\n\n  - Fisher, R.A. "The use of multiple measurements in taxonomic problems"\n    Annual Eugenics, 7, Part II, 179-188 (1936); also in "Contributions to\n    Mathematical Statistics" (John Wiley, NY, 1950).\n  - Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis.\n    (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.\n  - Dasarathy, B.V. (1980) "Nosing Around the Neighborhood: A New System\n    Structure and Classification Rule for Recognition in Partially Exposed\n    Environments".  IEEE Transactions on Pattern Analysis and Machine\n    Intelligence, Vol. PAMI-2, No. 1, 67-71.\n  - Gates, G.W. (1972) "The Reduced Nearest Neighbor Rule".  IEEE Transactions\n    on Information Theory, May 1972, 431-433.\n  - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al"s AUTOCLASS II\n    conceptual clustering system finds 3 classes in the data.\n  - Many, many more ...\n'

X = ds.data
X.shape

(150, 4)

y = ds.target
y = y == 1
y.shape

(150,)

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=101)

from sklearn.preprocessing import StandardScaler

scaler = StandardScaler()

scaler.fit(X_train)
X_train = scaler.transform(X_train)
X_test = scaler.transform(X_test)

We trainen een logistisch regressiemodel

from sklearn.linear_model import LogisticRegression

log_reg = LogisticRegression()
log_reg.fit(X_train, y_train)

LogisticRegression()

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y_pred = log_reg.predict(X_test)

from sklearn.metrics import confusion_matrix

cm = confusion_matrix(y_test, y_pred)
cm

array([[21,  4],
       [13,  7]], dtype=int64)

from sklearn.metrics import ConfusionMatrixDisplay

disp = ConfusionMatrixDisplay(confusion_matrix=cm)
disp.plot()

<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x21929cf8770>

from sklearn.metrics import precision_score, recall_score, f1_score

print(precision_score(y_test, y_pred))
print(recall_score(y_test, y_pred))
print(f1_score(y_test, y_pred))

0.6363636363636364
0.35
0.45161290322580644

En nog een tweede model, een SVC

from sklearn.svm import SVC

svc = SVC()
svc.fit(X_train, y_train)
y_pred_svc = svc.predict(X_test)
cm_svc = confusion_matrix(y_test, y_pred_svc)
cm_svc

array([[24,  1],
       [ 1, 19]], dtype=int64)

Tot slot plotten we de beide ROC-curves

from sklearn.metrics import roc_curve, auc, RocCurveDisplay

fig,ax = plt.subplots()

plt.plot([0,1], [0,1], color='red', linestyle='--')

fpr, tpr, thresholds = roc_curve(y_test, y_pred)
roc_auc = auc(fpr, tpr)
disp = RocCurveDisplay(fpr=fpr, tpr=tpr, roc_auc=roc_auc, estimator_name='Logistic Regression')
disp.plot(ax=ax)

fpr, tpr, thresholds = roc_curve(y_test, y_pred_svc)
roc_auc = auc(fpr, tpr)
disp = RocCurveDisplay(fpr=fpr, tpr=tpr, roc_auc=roc_auc, estimator_name='Support Vector Classifier')
disp.plot(ax=ax)

<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x21929f38830>