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Commit 471bfd28 authored by Léo Bouscarrat's avatar Léo Bouscarrat
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notebooks/Réduction de fôrets aléatoires.ipynb 0 → 100644
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%% Cell type:markdown id: tags:
# Groupe de travail
Le but de ce notebook est de tester l'idée de réduction des random forest
%% Cell type:markdown id: tags:
## Import scikit-learn
%% Cell type:code id: tags:
``` python
from statistics import mean
from sklearn.datasets import load_boston, load_breast_cancer
from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor
from sklearn.linear_model import OrthogonalMatchingPursuit
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split
```
%% Cell type:markdown id: tags:
## Variables globales
%% Cell type:code id: tags:
``` python
RANDOM_SEED = 566876
NB_TREES = 1000
NB_TREES_EXTRACTED = 10
```
%% Cell type:markdown id: tags:
## Load jeu de donnée
%% Cell type:code id: tags:
``` python
X, y = load_boston(return_X_y=True)
```
%% Cell type:code id: tags:
``` python
# Séparation train_test avec random_state
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = RANDOM_SEED)
```
%% Cell type:markdown id: tags:
## Entraînement de la forêt aléatoire
%% Cell type:code id: tags:
``` python
regressor = RandomForestRegressor(n_estimators=NB_TREES, random_state = RANDOM_SEED)
regressor.fit(X_train, y_train)
```
%% Output
RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,
max_features='auto', max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
min_samples_leaf=1, min_samples_split=2,
min_weight_fraction_leaf=0.0, n_estimators=1000,
n_jobs=None, oob_score=False, random_state=566876,
verbose=0, warm_start=False)
%% Cell type:code id: tags:
``` python
# Accès à la la liste des arbres
tree_list = regressor.estimators_
```
%% Cell type:markdown id: tags:
## Création de la matrice des prédictions de chaque arbre
%% Cell type:code id: tags:
``` python
# L'implémentation de scikit-learn est un peu différente que celle vue en réunion, D est de même taille que X
# et chaque élément est composé de d signaux, d'où la création suivante de D où on créé une liste pour chaque
# élément comprenant les valeurs prédites par chaque arbre
D = [[tree.predict([elem])[0] for tree in tree_list] for elem in X_train]
```
%% Cell type:code id: tags:
``` python
omp = OrthogonalMatchingPursuit(n_nonzero_coefs=NB_TREES_EXTRACTED)
omp.fit(D, y_train)
```
%% Output
OrthogonalMatchingPursuit(fit_intercept=True, n_nonzero_coefs=10,
normalize=True, precompute='auto', tol=None)
%% Cell type:code id: tags:
``` python
# Matrice avec poids de chaque arbre
omp.coef_
```
%% Output
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%% Cell type:markdown id: tags:
## Calcul des résultats des différentes méthodes
%% Cell type:markdown id: tags:
### Résultat de la forêt de base
%% Cell type:code id: tags:
``` python
mean_squared_error(regressor.predict(X_test), y_test)
```
%% Output
6.079654025784307
%% Cell type:markdown id: tags:
### Résultat de la forêt extraite avec l'OMP, où chaque arbre est multiplié par son poids
%% Cell type:code id: tags:
``` python
y_pred = [sum([tree_list[i].predict([elem])[0] * omp.coef_[i] for i in range(NB_TREES)]) for elem in X_test]
```
%% Cell type:code id: tags:
``` python
mean_squared_error(y_pred, y_test)
```
%% Output
6.420683680052282
%% Cell type:markdown id: tags:
### Résultat de la forêt extraite avec l'OMP, où on prends la moyenne des arbres extraits
%% Cell type:code id: tags:
``` python
y_pred = [mean([tree_list[i].predict([elem])[0] for i in range(NB_TREES) if omp.coef_[i] != 0])for elem in X_test]
mean_squared_error(y_pred, y_test)
```
%% Output
6.728623529411763
%% Cell type:markdown id: tags:
### Résultat d'une forêt avec le même nombre d'arbre que le nombre d'arbre extrait
%% Cell type:code id: tags:
``` python
regressor_small = RandomForestRegressor(n_estimators=NB_TREES_EXTRACTED, random_state=RANDOM_SEED)
regressor_small.fit(X_train, y_train)
```
%% Output
RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,
max_features='auto', max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
min_samples_leaf=1, min_samples_split=2,
min_weight_fraction_leaf=0.0, n_estimators=10,
n_jobs=None, oob_score=False, random_state=566876,
verbose=0, warm_start=False)
%% Cell type:code id: tags:
``` python
mean_squared_error(regressor_small.predict(X_test), y_test)
```
%% Output
6.794841176470589
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