Classification multi-classe#
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On cherche à prédire la note d’un vin avec un classifieur multi-classe.
%matplotlib inline
from papierstat.datasets import load_wines_dataset
df = load_wines_dataset()
X = df.drop(['quality', 'color'], axis=1)
y = df['quality']
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y)
from sklearn.linear_model import LogisticRegression
clr = LogisticRegression()
clr.fit(X_train, y_train)
LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,
penalty='l2', random_state=None, solver='liblinear', tol=0.0001,
verbose=0, warm_start=False)
import numpy
numpy.mean(clr.predict(X_test).ravel() == y_test.ravel()) * 100
53.84615384615385
On regarde la matrice de confusion.
from sklearn.metrics import confusion_matrix
import pandas
pandas.DataFrame(confusion_matrix(y_test, clr.predict(X_test)))
| 0 | 1 | 2 | 3 | 4 | 5 | 6 | |
|---|---|---|---|---|---|---|---|
| 0 | 0 | 0 | 6 | 0 | 0 | 0 | 0 |
| 1 | 0 | 0 | 39 | 14 | 1 | 0 | 0 |
| 2 | 0 | 0 | 338 | 208 | 2 | 0 | 0 |
| 3 | 0 | 0 | 195 | 517 | 17 | 0 | 0 |
| 4 | 0 | 0 | 19 | 200 | 20 | 0 | 0 |
| 5 | 0 | 0 | 2 | 38 | 8 | 0 | 0 |
| 6 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
On l’affiche différemment avec le nom des classes.
conf = confusion_matrix(y_test, clr.predict(X_test))
dfconf = pandas.DataFrame(conf)
labels = list(clr.classes_)
if len(labels) < dfconf.shape[1]:
labels += [9] # La classe 9 est très représentée, elle est parfois absente en train.
elif len(labels) > dfconf.shape[1]:
labels = labels[:dfconf.shape[1]] # ou l'inverse
dfconf.columns = labels
dfconf.index = labels
dfconf
| 3 | 4 | 5 | 6 | 7 | 8 | 9 | |
|---|---|---|---|---|---|---|---|
| 3 | 0 | 0 | 6 | 0 | 0 | 0 | 0 |
| 4 | 0 | 0 | 39 | 14 | 1 | 0 | 0 |
| 5 | 0 | 0 | 338 | 208 | 2 | 0 | 0 |
| 6 | 0 | 0 | 195 | 517 | 17 | 0 | 0 |
| 7 | 0 | 0 | 19 | 200 | 20 | 0 | 0 |
| 8 | 0 | 0 | 2 | 38 | 8 | 0 | 0 |
| 9 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
Pas extraordinaire. On applique la stratégie OneVsRestClassifier.
from sklearn.multiclass import OneVsRestClassifier
clr = OneVsRestClassifier(LogisticRegression())
clr.fit(X_train, y_train)
OneVsRestClassifier(estimator=LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,
penalty='l2', random_state=None, solver='liblinear', tol=0.0001,
verbose=0, warm_start=False),
n_jobs=1)
numpy.mean(clr.predict(X_test).ravel() == y_test.ravel()) * 100
53.784615384615385
Le modèle logistique régression multi-classe est équivalent à la stratégie OneVsRest. Voyons l’autre.
from sklearn.multiclass import OneVsOneClassifier
clr = OneVsOneClassifier(LogisticRegression())
clr.fit(X_train, y_train)
OneVsOneClassifier(estimator=LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,
penalty='l2', random_state=None, solver='liblinear', tol=0.0001,
verbose=0, warm_start=False),
n_jobs=1)
numpy.mean(clr.predict(X_test).ravel() == y_test.ravel()) * 100
53.47692307692308
conf = confusion_matrix(y_test, clr.predict(X_test))
dfconf = pandas.DataFrame(conf)
labels = list(clr.classes_)
if len(labels) < dfconf.shape[1]:
labels += [9] # La classe 9 est très représentée, elle est parfois absente en train.
elif len(labels) > dfconf.shape[1]:
labels = labels[:dfconf.shape[1]] # ou l'inverse
dfconf.columns = labels
dfconf.index = labels
dfconf
| 3 | 4 | 5 | 6 | 7 | 8 | 9 | |
|---|---|---|---|---|---|---|---|
| 3 | 0 | 0 | 6 | 0 | 0 | 0 | 0 |
| 4 | 0 | 0 | 38 | 15 | 1 | 0 | 0 |
| 5 | 0 | 0 | 335 | 208 | 5 | 0 | 0 |
| 6 | 0 | 0 | 197 | 491 | 41 | 0 | 0 |
| 7 | 0 | 0 | 20 | 176 | 43 | 0 | 0 |
| 8 | 0 | 0 | 1 | 34 | 13 | 0 | 0 |
| 9 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
A peu près pareil mais sans doute pas de manière significative. Voyons avec un arbre de décision.
from sklearn.tree import DecisionTreeClassifier
clr = DecisionTreeClassifier()
clr.fit(X_train, y_train)
DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
max_features=None, 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, presort=False, random_state=None,
splitter='best')
numpy.mean(clr.predict(X_test).ravel() == y_test.ravel()) * 100
59.50769230769231
Et avec OneVsRestClassifier :
clr = OneVsRestClassifier(DecisionTreeClassifier())
clr.fit(X_train, y_train)
OneVsRestClassifier(estimator=DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
max_features=None, 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, presort=False, random_state=None,
splitter='best'),
n_jobs=1)
numpy.mean(clr.predict(X_test).ravel() == y_test.ravel()) * 100
52.92307692307693
Et avec OneVsOneClassifier
clr = OneVsOneClassifier(DecisionTreeClassifier())
clr.fit(X_train, y_train)
OneVsOneClassifier(estimator=DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
max_features=None, 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, presort=False, random_state=None,
splitter='best'),
n_jobs=1)
numpy.mean(clr.predict(X_test).ravel() == y_test.ravel()) * 100
60.12307692307692
Mieux.
from sklearn.ensemble import RandomForestClassifier
clr = RandomForestClassifier()
clr.fit(X_train, y_train)
RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
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=1,
oob_score=False, random_state=None, verbose=0,
warm_start=False)
numpy.mean(clr.predict(X_test).ravel() == y_test.ravel()) * 100
66.46153846153847
clr = OneVsRestClassifier(RandomForestClassifier())
clr.fit(X_train, y_train)
OneVsRestClassifier(estimator=RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
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=1,
oob_score=False, random_state=None, verbose=0,
warm_start=False),
n_jobs=1)
numpy.mean(clr.predict(X_test).ravel() == y_test.ravel()) * 100
65.90769230769232
Proche, il faut affiner avec une validation croisée.
from sklearn.neural_network import MLPClassifier
clr = MLPClassifier(hidden_layer_sizes=30, max_iter=600)
clr.fit(X_train, y_train)
MLPClassifier(activation='relu', alpha=0.0001, batch_size='auto', beta_1=0.9,
beta_2=0.999, early_stopping=False, epsilon=1e-08,
hidden_layer_sizes=30, learning_rate='constant',
learning_rate_init=0.001, max_iter=600, momentum=0.9,
nesterovs_momentum=True, power_t=0.5, random_state=None,
shuffle=True, solver='adam', tol=0.0001, validation_fraction=0.1,
verbose=False, warm_start=False)
numpy.mean(clr.predict(X_test).ravel() == y_test.ravel()) * 100
51.323076923076925
clr = OneVsRestClassifier(MLPClassifier(hidden_layer_sizes=30, max_iter=600))
clr.fit(X_train, y_train)
OneVsRestClassifier(estimator=MLPClassifier(activation='relu', alpha=0.0001, batch_size='auto', beta_1=0.9,
beta_2=0.999, early_stopping=False, epsilon=1e-08,
hidden_layer_sizes=30, learning_rate='constant',
learning_rate_init=0.001, max_iter=600, momentum=0.9,
nesterovs_momentum=True, power_t=0.5, random_state=None,
shuffle=True, solver='adam', tol=0.0001, validation_fraction=0.1,
verbose=False, warm_start=False),
n_jobs=1)
numpy.mean(clr.predict(X_test).ravel() == y_test.ravel()) * 100
47.56923076923077
Pas foudroyant.