import matplotlib.pyplot as plt
import math
from sklearn import metrics
from sklearn.model_selection import ParameterGrid, train_test_split
from sklearn.pipeline import Pipeline
from sklearn.decomposition import PCA
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.datasets import make_classification
X, y = make_classification(n_samples=10000,
n_features=25,
n_redundant=10,
n_repeated=5,
weights=[0.2,0.8],
class_sep=0.2,
flip_y=0.1)
y.mean()
0.76829999999999998
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.35)
param_grid = [
{
'pca__n_components':[5,10,20],
'clf__n_estimators':[5,20,50,100,200],
'clf__max_depth':[1,2,3,4]
}
]
print('total number of configurations: ',len(ParameterGrid(param_grid)))
total number of configurations: 60
pipeline = Pipeline([
('pca',PCA()),
('clf',GradientBoostingClassifier())
])
num_cols = 3
num_rows = math.ceil(len(ParameterGrid(param_grid)) / num_cols)
plt.clf()
fig,axes = plt.subplots(num_rows,num_cols,sharey=True)
fig.set_size_inches(num_cols*5,num_rows*5)
for i,g in enumerate(ParameterGrid(param_grid)):
pipeline.set_params(**g)
pipeline.fit(X_train,y_train)
y_preds = pipeline.predict_proba(X_test)
# take the second column because the classifier outputs scores for
# the 0 class as well
preds = y_preds[:,1]
# fpr means false-positive-rate
# tpr means true-positive-rate
fpr, tpr, _ = metrics.roc_curve(y_test, preds)
auc_score = metrics.auc(fpr, tpr)
ax = axes[i // num_cols, i % num_cols]
# don't print the whole name or it won't fit
ax.set_title(str([r"{}:{}".format(k.split('__')[1:],v) for k,v in g.items()]),fontsize=9)
ax.plot(fpr, tpr, label='AUC = {:.3f}'.format(auc_score))
ax.legend(loc='lower right')
# it's helpful to add a diagonal to indicate where chance
# scores lie (i.e. just flipping a coin)
ax.plot([0,1],[0,1],'r--')
ax.set_xlim([-0.1,1.1])
ax.set_ylim([-0.1,1.1])
ax.set_ylabel('True Positive Rate')
ax.set_xlabel('False Positive Rate')
plt.gcf().tight_layout()
plt.show()
<matplotlib.figure.Figure at 0x7f6e4b86b240>