def main():
from sklearn.linear_model import LogisticRegression
# Load generated data
X_train, X_test, y_train, y_test = bench.load_data(params)
params.n_classes = len(np.unique(y_train))
if params.multiclass == 'auto':
params.multiclass = 'ovr' if params.n_classes == 2 else 'multinomial'
if not params.tol:
params.tol = 1e-3 if params.solver == 'newton-cg' else 1e-10
# Create our classifier object
clf = LogisticRegression(penalty='l2',
C=params.C,
n_jobs=params.n_jobs,
fit_intercept=params.fit_intercept,
verbose=params.verbose,
tol=params.tol,
max_iter=params.maxiter,
solver=params.solver,
multi_class=params.multiclass)
# Time fit and predict
fit_time, _ = bench.measure_function_time(clf.fit,
X_train,
y_train,
params=params)
y_pred = clf.predict(X_train)
y_proba = clf.predict_proba(X_train)
train_acc = bench.accuracy_score(y_train, y_pred)
train_log_loss = bench.log_loss(y_train, y_proba)
train_roc_auc = bench.roc_auc_score(y_train, y_proba)
predict_time, y_pred = bench.measure_function_time(clf.predict,
X_test,
params=params)
y_proba = clf.predict_proba(X_test)
test_acc = bench.accuracy_score(y_test, y_pred)
test_log_loss = bench.log_loss(y_test, y_proba)
test_roc_auc = bench.roc_auc_score(y_test, y_proba)
bench.print_output(
library='sklearn',
algorithm='logistic_regression',
stages=['training', 'prediction'],
params=params,
functions=['LogReg.fit', 'LogReg.predict'],
times=[fit_time, predict_time],
metric_type=['accuracy', 'log_loss', 'roc_auc'],
metrics=[
[train_acc, test_acc],
[train_log_loss, test_log_loss],
[train_roc_auc, test_roc_auc],
],
data=[X_train, X_test],
alg_instance=clf,
)
def main():
from sklearn.ensemble import RandomForestClassifier
# Load and convert data
X_train, X_test, y_train, y_test = bench.load_data(params)
# Create our random forest classifier
clf = RandomForestClassifier(
criterion=params.criterion,
n_estimators=params.num_trees,
max_depth=params.max_depth,
max_features=params.max_features,
min_samples_split=params.min_samples_split,
max_leaf_nodes=params.max_leaf_nodes,
min_impurity_decrease=params.min_impurity_decrease,
bootstrap=params.bootstrap,
random_state=params.seed,
n_jobs=params.n_jobs)
params.n_classes = len(np.unique(y_train))
fit_time, _ = bench.measure_function_time(clf.fit,
X_train,
y_train,
params=params)
y_pred = clf.predict(X_train)
y_proba = clf.predict_proba(X_train)
train_acc = bench.accuracy_score(y_train, y_pred)
train_log_loss = bench.log_loss(y_train, y_proba)
train_roc_auc = bench.roc_auc_score(y_train, y_proba)
predict_time, y_pred = bench.measure_function_time(clf.predict,
X_test,
params=params)
y_proba = clf.predict_proba(X_test)
test_acc = bench.accuracy_score(y_test, y_pred)
test_log_loss = bench.log_loss(y_test, y_proba)
test_roc_auc = bench.roc_auc_score(y_test, y_proba)
bench.print_output(
library='sklearn',
algorithm='df_clsf',
stages=['training', 'prediction'],
params=params,
functions=['df_clsf.fit', 'df_clsf.predict'],
times=[fit_time, predict_time],
metric_type=['accuracy', 'log_loss', 'roc_auc'],
metrics=[
[train_acc, test_acc],
[train_log_loss, test_log_loss],
[train_roc_auc, test_roc_auc],
],
data=[X_train, X_test],
alg_instance=clf,
)
def main():
from sklearn.neighbors import KNeighborsClassifier
# Load generated data
X_train, X_test, y_train, y_test = bench.load_data(params)
params.n_classes = len(np.unique(y_train))
# Create classification object
knn_clsf = KNeighborsClassifier(n_neighbors=params.n_neighbors,
weights=params.weights,
algorithm=params.method,
metric=params.metric,
n_jobs=params.n_jobs)
# Measure time and accuracy on fitting
train_time, _ = bench.measure_function_time(knn_clsf.fit,
X_train,
y_train,
params=params)
if params.task == 'classification':
y_pred = knn_clsf.predict(X_train)
y_proba = knn_clsf.predict_proba(X_train)
train_acc = bench.accuracy_score(y_train, y_pred)
train_log_loss = bench.log_loss(y_train, y_proba)
train_roc_auc = bench.roc_auc_score(y_train, y_proba)
# Measure time and accuracy on prediction
if params.task == 'classification':
predict_time, yp = bench.measure_function_time(knn_clsf.predict,
X_test,
params=params)
y_proba = knn_clsf.predict_proba(X_test)
test_acc = bench.accuracy_score(y_test, yp)
test_log_loss = bench.log_loss(y_test, y_proba)
test_roc_auc = bench.roc_auc_score(y_test, y_proba)
else:
predict_time, _ = bench.measure_function_time(knn_clsf.kneighbors,
X_test,
params=params)
if params.task == 'classification':
bench.print_output(
library='sklearn',
algorithm=knn_clsf._fit_method + '_knn_classification',
stages=['training', 'prediction'],
params=params,
functions=['knn_clsf.fit', 'knn_clsf.predict'],
times=[train_time, predict_time],
metric_type=['accuracy', 'log_loss', 'roc_auc'],
metrics=[
[train_acc, test_acc],
[train_log_loss, test_log_loss],
[train_roc_auc, test_roc_auc],
],
data=[X_train, X_test],
alg_instance=knn_clsf,
)
else:
bench.print_output(
library='sklearn',
algorithm=knn_clsf._fit_method + '_knn_search',
stages=['training', 'search'],
params=params,
functions=['knn_clsf.fit', 'knn_clsf.kneighbors'],
times=[train_time, predict_time],
metric_type=None,
metrics=[],
data=[X_train, X_test],
alg_instance=knn_clsf,
)
def metric_call(x, y):
return bench.log_loss(x, y)
def main():
from sklearn.svm import SVC
X_train, X_test, y_train, y_test = bench.load_data(params)
y_train = np.asfortranarray(y_train).ravel()
if params.gamma is None:
params.gamma = 1.0 / X_train.shape[1]
cache_size_bytes = bench.get_optimal_cache_size(
X_train.shape[0], max_cache=params.max_cache_size)
params.cache_size_mb = cache_size_bytes / 1024**2
params.n_classes = len(np.unique(y_train))
clf = SVC(C=params.C,
kernel=params.kernel,
cache_size=params.cache_size_mb,
tol=params.tol,
gamma=params.gamma,
probability=params.probability,
random_state=43,
degree=params.degree)
fit_time, _ = bench.measure_function_time(clf.fit,
X_train,
y_train,
params=params)
params.sv_len = clf.support_.shape[0]
if params.probability:
state_predict = 'predict_proba'
clf_predict = clf.predict_proba
train_acc = None
test_acc = None
predict_train_time, y_pred = bench.measure_function_time(clf_predict,
X_train,
params=params)
train_log_loss = bench.log_loss(y_train, y_pred)
train_roc_auc = bench.roc_auc_score(y_train, y_pred)
_, y_pred = bench.measure_function_time(clf_predict,
X_test,
params=params)
test_log_loss = bench.log_loss(y_test, y_pred)
test_roc_auc = bench.roc_auc_score(y_test, y_pred)
else:
state_predict = 'prediction'
clf_predict = clf.predict
train_log_loss = None
test_log_loss = None
train_roc_auc = None
test_roc_auc = None
predict_train_time, y_pred = bench.measure_function_time(clf_predict,
X_train,
params=params)
train_acc = bench.accuracy_score(y_train, y_pred)
_, y_pred = bench.measure_function_time(clf_predict,
X_test,
params=params)
test_acc = bench.accuracy_score(y_test, y_pred)
bench.print_output(
library='sklearn',
algorithm='SVC',
stages=['training', state_predict],
params=params,
functions=['SVM.fit', f'SVM.{state_predict}'],
times=[fit_time, predict_train_time],
metric_type=['accuracy', 'log_loss', 'roc_auc', 'n_sv'],
metrics=[
[train_acc, test_acc],
[train_log_loss, test_log_loss],
[train_roc_auc, test_roc_auc],
[int(clf.n_support_.sum()),
int(clf.n_support_.sum())],
],
data=[X_train, X_train],
alg_instance=clf,
)