def fit_forest(X_train, y_train, n_estimators=10, dirichlet=0.5, step=1.0):
clf_kwargs = {
"n_estimators": n_estimators,
"min_samples_split": 2,
"random_state": random_state,
"n_jobs": 1,
"step": step,
"dirichlet": dirichlet,
}
clf = ForestClassifier(**clf_kwargs)
clf.fit(X_train, y_train)
return clf
def set_classifier(clf_name, fit_seed, n_jobs=-1):
classifier_setting = {
"RandomForestClassifier":
RandomForestClassifier(n_estimators=100,
n_jobs=n_jobs,
random_state=fit_seed),
"HistGradientBoostingClassifier":
HistGradientBoostingClassifier(random_state=fit_seed),
"XGBClassifier":
xgb.XGBClassifier(
use_label_encoder=False,
n_jobs=n_jobs,
tree_method="hist",
random_state=fit_seed,
),
"LGBMClassifier":
lgb.LGBMClassifier(n_jobs=n_jobs, random_state=fit_seed),
"CatBoostClassifier":
CatBoostClassifier(
thread_count=n_jobs,
random_state=fit_seed,
logging_level="Silent",
allow_writing_files=False,
),
"WildWood":
ForestClassifier(n_estimators=10, n_jobs=n_jobs,
random_state=fit_seed),
}
return classifier_setting[clf_name]
def fit(
self,
params,
X_train,
y_train,
Xy_val,
sample_weight,
n_estimators=None,
seed=None,
):
if seed is not None:
params.update({"random_state": seed})
if n_estimators is not None:
params.update({"n_estimators": n_estimators})
clf = ForestClassifier(**params, n_jobs=-1)
clf.fit(
X_train,
y_train,
sample_weight=sample_weight,
categorical_features=self.categorical_features,
)
return clf, None
def fit_forest(
X_train,
y_train,
aggregation=True,
n_estimators=10,
dirichlet=0.5,
step=1.0,
min_samples_split=2,
n_jobs=1,
):
clf_kwargs = {
"n_estimators": n_estimators,
"aggregation": aggregation,
"min_samples_split": min_samples_split,
"random_state": random_state,
"n_jobs": n_jobs,
"step": step,
"dirichlet": dirichlet,
"max_features": 2,
}
clf = ForestClassifier(**clf_kwargs)
clf.fit(X_train, y_train)
return clf
def launch_run(*, run_config, experiment_id):
"""
Parameters
----------
run_config : dict
The configuration of the run
experiment_id : str
Id of the experiment that groups runs in mlflow
Returns
-------
output : dict
Metrics computed during this run
"""
wildwood_kwargs = {
key.replace("wildwood_", ""): val
for key, val in run_config.items() if key.startswith("wildwood")
}
dataset_name = run_config["dataset"]
dataset_random_state = run_config["dataset_random_state"]
loader = loader_from_name[dataset_name]
# Just get the task from the dataset
dataset = loader()
learning_task = dataset.task
# But we use the raw data in wildwood
X, y = loader(raw=True)
X_train, X_test, y_train, y_test = train_test_split(
X, y, random_state=dataset_random_state, shuffle=True, stratify=y)
label_encoder = LabelEncoder()
y_train = label_encoder.fit_transform(y_train)
y_test = label_encoder.transform(y_test)
kwargs_one_tree = wildwood_kwargs.copy()
kwargs_one_tree["n_estimators"] = 1
# Fit a single tree on the full dataset to force pre-compilation (doing so on a
# subset often fails).
# TODO: debug such cases
clf = ForestClassifier(**kwargs_one_tree)
clf.fit(X_train, y_train)
# Instantiate again just to be sure
clf = ForestClassifier(**wildwood_kwargs)
with mlflow.start_run(experiment_id=experiment_id):
# Fit and timing
tic = time()
# clf.fit(X_train, y_train, **fit_kwargs_generator(clf_name, dataset_name))
# TODO: include computations with an without categorical features ?
clf.fit(X_train, y_train)
toc = time()
fit_time = toc - tic
logging.info(f"Fitted for experiment {filename} in {fit_time}s")
# Predict and timing
tic = time()
y_scores_train = clf.predict_proba(X_train)
toc = time()
predict_train_time = toc - tic
tic = time()
y_scores_test = clf.predict_proba(X_test)
toc = time()
predict_test_time = toc - tic
# col_predict_time.append(predict_time)
logging.info(
f"Predicted for experiment {filename} on train in {predict_train_time}s and test in {predict_test_time}s"
)
y_pred_train = clf.predict(X_train)
y_pred_test = clf.predict(X_test)
metrics = compute_metrics(
learning_task=learning_task,
y_train=y_train,
y_test=y_test,
y_scores_train=y_scores_train,
y_scores_test=y_scores_test,
y_pred_train=y_pred_train,
y_pred_test=y_pred_test,
)
mlflow_metrics = dict(
**metrics,
fit_time=fit_time,
predict_train_time=predict_train_time,
predict_test_time=predict_test_time,
)
mlflow_params = dict(
**wildwood_kwargs,
dataset=dataset_name,
dataset_random_state=dataset_random_state,
)
mlflow.log_params(mlflow_params)
mlflow.log_metrics(mlflow_metrics)
# "max_bins": 8308,
"n_jobs": -1,
"dirichlet": 1e-8,
"step": 1.0,
"aggregation": False,
"verbose": True
}
# classifiers = [
# ("tree", DecisionTreeClassifier),
# ("sk_tree", SkDecisionTreeClassifier)
# ]
classifiers = [
# ("forest", ForestBinaryClassifier(n_estimators=1, **clf_kwargs)),
("forest", ForestClassifier(**clf_kwargs)),
# ("sk_forest", RandomForestClassifier(**clf_kwargs))
# ("tree", DecisionTreeClassifier(**clf_kwargs)),
# ("sk_tree", SkDecisionTreeClassifier(**clf_kwargs)),
]
n_classifiers = len(classifiers)
n_datasets = len(datasets)
h = 0.2
i = 1
# iterate over datasets
# for ds_cnt, ds in enumerate(datasets):
# # preprocess datasets, split into training and test part
# ds_name, (X, y) = ds
# X_train = rcv1_train.data
# y_train = rcv1_train.target
# X_test = rcv1_test.data
# y_test = rcv1_test.target
clf_kwargs = {
"n_estimators": 5,
"min_samples_split": 2,
"random_state": random_state,
"n_jobs": -1,
"dirichlet": 1e-5,
"step": 2.0,
"aggregation": True
}
clf = ForestClassifier(**clf_kwargs)
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.3,
random_state=42)
tic = time()
clf.fit(X_train, y_train)
toc = time()
print("time to fit: ", toc - tic)
tic = time()
y_scores = clf.predict_proba(X_test)
toc = time()
print("time to predict_proba: ", toc - tic)
noise=0.2,
random_state=data_random_state)
clf_kwargs = {
"n_estimators": 1,
"max_features": 2,
"min_samples_split": 2,
"random_state": random_state,
"n_jobs": -1,
"dirichlet": 1e-8,
"step": 1.0,
"aggregation": False,
"verbose": True,
}
clf = ForestClassifier(**clf_kwargs)
h = 0.2
i = 1
h = 0.2
fig = plt.figure(figsize=(4, 2))
i = 1
# iterate over datasets
# preprocess datasets, split into training and test part
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.4,
random_state=42)
data_random_state = 42
dataset = load_bank()
dataset.one_hot_encode = False
dataset.standardize = False
X_train, X_test, y_train, y_test = dataset.extract(
random_state=data_random_state)
n_estimators = 100
clf = ForestClassifier(
n_estimators=n_estimators,
random_state=42,
aggregation=False,
max_features=None,
categorical_features=dataset.categorical_features_,
n_jobs=1,
class_weight="balanced",
criterion="entropy",
)
clf.fit(X_train, y_train)
y_scores_train = clf.predict_proba(X_train)
y_scores_test = clf.predict_proba(X_test)
avg_prec_train = average_precision_score(y_train, y_scores_train[:, 1])
avg_prec_test = average_precision_score(y_test, y_scores_test[:, 1])
print("Categorical")
print("AP(train):", avg_prec_train, "AP(test):", avg_prec_test)
clf = ForestClassifier(
n_estimators=n_estimators,
random_state=42,
random_state = 42
classifiers = [
lambda n: (
"RFW",
RandomForestClassifier(
n_estimators=n,
n_jobs=-1,
random_state=random_state,
),
),
lambda n: (
"WildWood",
ForestClassifier(
n_estimators=n,
multiclass="ovr",
n_jobs=-1,
random_state=random_state,
),
),
lambda n: (
"ET",
ExtraTreesClassifier(
n_estimators=n,
n_jobs=-1,
random_state=random_state,
),
),
]
logging.basicConfig(level=logging.INFO,
format="%(asctime)s %(message)s",