import neptune
from sklearn.datasets import load_wine
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import f1_score
from sklearn.model_selection import train_test_split

run = neptune.init_run(project="common/quickstarts", api_token=neptune.ANONYMOUS_API_TOKEN)

data = load_wine()
X_train, X_test, y_train, y_test = train_test_split(
    data.data, data.target, test_size=0.4, random_state=1234
)

# add tags to organize
run["sys/tags"].add(["run-organization", "me"])

params = {
    "n_estimators": 10,
    "max_depth": 3,
    "min_samples_leaf": 1,
    "min_samples_split": 2,
    "max_features": 3,
}

# log parameters
run["parameters"] = params

clf = RandomForestClassifier(**params)

clf.fit(X_train, y_train)
y_train_pred = clf.predict_proba(X_train)
y_test_pred = clf.predict_proba(X_test)

# log metrics
train_f1 = f1_score(y_train, y_train_pred.argmax(axis=1), average="macro")
test_f1 = f1_score(y_test, y_test_pred.argmax(axis=1), average="macro")
print(f"Train f1:{train_f1} | Test f1:{test_f1}")

run["train/f1"] = train_f1
run["test/f1"] = test_f1