139 lines
3.7 KiB
Python
139 lines
3.7 KiB
Python
# -*- coding: utf-8 -*-
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"""
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Exercice 1 du cours 2 de machine learning avec F.Baradel
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"""
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import matplotlib
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matplotlib.use("TkAgg")
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import matplotlib.pyplot as plt
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import numpy as np
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from sklearn.datasets import load_boston
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from sklearn.linear_model import LinearRegression
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boston = load_boston()
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boston
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X = boston['data']
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Y = boston['target']
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X.shape
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n_train = 300
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crim = X[:, 0].copy()
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""" 4. Right now we will be using only the first features called CRIM for
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modelling the target variable. Plot CRIM vs target with and without normalizing your data.
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What do you observe?
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"""
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# plot target VS CRIM
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plt.scatter(crim, Y)
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plt.show()
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# c'est inexploitable, on log
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plt.scatter(np.log(crim), Y)
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plt.show()
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# C'est mieux
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#on écrit la fonction de normalisation
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def normalize (y):
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return (y - np.min(y)) / (np.max(y) - np.min(y))
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"""5. Use LinearRegression() for modelling target with CRIM on the training set and compute the predicted
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values for the validation set."""
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model = LinearRegression().fit(crim[:n_train].reshape(-1, 1), Y[:n_train])
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w, b = model.coef_, model.intercept_
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print(w, b)
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""" 6. Plot the predictions and the actual ground-truth for the training and the validation set."""
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print("entrainement")
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preds = model.predict(crim[:n_train].reshape(-1, 1))
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plt.scatter(crim[:n_train], preds, color="green")
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plt.scatter(crim[:n_train], Y[:n_train], color="red")
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plt.show()
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n_valid = 400
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print("validation")
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valids = model.predict(crim[n_train:n_valid].reshape(-1,1))
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plt.scatter(crim[n_train:n_valid], valids, color="green")
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plt.scatter(crim[n_train:n_valid], Y[n_train:n_valid], color="orange")
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plt.show()
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# On utilise maintenant le log . Je m'a planté et ait cru que c'était la normalisation d'ou les noms fallacieux.
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# Ne pas oublier de caler l'exponentielle (réciproque de log) pour remettre valeur dans le bon champ
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print(" avec log")
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normY = np.log(Y)
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model = LinearRegression().fit(crim[:n_train].reshape(-1, 1), normY[:n_train])
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print("entrainement")
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normPreds = np.exp(model.predict(crim[:n_train].reshape(-1, 1)))
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plt.scatter(crim[:n_train], normPreds, color="green")
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plt.scatter(crim[:n_train], np.exp(normY[:n_train]), color="red")
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plt.show()
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print("validation")
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normValids = np.exp(model.predict(crim[n_train:n_valid].reshape(-1,1)))
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plt.scatter(crim[n_train:n_valid], normValids, color="green")
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plt.scatter(crim[n_train:n_valid], np.exp(normY[n_train:n_valid]), color="orange")
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plt.show
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"""
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7. Implement a function which is computing the Root-Mean-Square-Error (RMSE). What is the RMSE on
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the training set and on the validation set?
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On veut calculer un score, pour savoir si le modèle est bon ou non
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on définit une fonction qui prend mes prédiciton,s mes valeurs, et retourne le mean square error
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(ŷi-yi)²
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à calculer sur le trainig set et le validation set
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Ensuite modéliser log(target) = w +b.CRIM et MSE train/val
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"""
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def mse(preds, vals):
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return np.mean((preds - vals)**2)
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mse(preds, Y[:n_train])
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mse(valids, Y[n_train:n_valid])
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# meme chose avec log
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mse(normPreds, Y[:n_train])
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mse(normValids, Y[n_train:n_valid])
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# Ensuite, faire la meme chose avec une deuxièm variabl du tableau X : ZN
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crimZn = X[:, :2]
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model = LinearRegression().fit(crimZn[:n_train].reshape(-1, 2), Y[:n_train])
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w, b = model.coef_, model.intercept_
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print(w, b)
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print("entrainement")
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preds = model.predict(crim[:n_train].reshape(-1, 2))
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plt.scatter(crimZn[:n_train], preds, color="green")
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plt.scatter(crimZn[:n_train], Y[:n_train], color="red")
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plt.show()
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n_valid = 400
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print("validation")
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valids = model.predict(crim[n_train:n_valid].reshape(-1,1))
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plt.scatter(crim[n_train:n_valid], valids, color="green")
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plt.scatter(crim[n_train:n_valid], Y[n_train:n_valid], color="orange")
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plt.show()
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