dnd 5e – Can a tree step be used on a transformed stick of the forest?

If I turn a stick of the forest into a tree, can I use it with the tree step spell?

Forest transformation staff

Tree shape: With one action you can plant one end of the stick in fertile soil and spend 1 load to transform the stick into a healthy tree. The tree is 60 feet tall and has a trunk 5 feet in diameter. The branches above are spread out within a 20 foot radius. The tree usually appears, but exudes a faint aura of transmutation magic when selected by Detect Magic. While you touch the tree and use another action to speak its command word, bring the wand back to its normal shape. Every creature in the tree falls when it returns to a staff.

Extract from Tree Stride

You will be given the opportunity to enter a tree and move inside another tree of the same species within 500 feet.

If so, how would I determine which tree species the spell is? Is it random? Would it only be possible to teleport between other transformed forest workers?

Python pipeline with OneHotEncoder & Random Forest Classifier

Can you please check my code to make sure OneHotEncoder is actually being applied in the pipeline? The initial data frame has 7 categorical characteristics and I would like to treat them with OneHotEncoder.

x=df.drop("Severity_Level",1)
y=df("Severity_Level")
x2=df.drop("Severity_Level",1)
y2=df("Severity_Level")

OneHotEncoder

onehotencoder = OneHotEncoder()

x2 = onehotencoder.fit_transform(x2).toarray()
x2.shape

RandomOverSampler

import warnings
warnings.filterwarnings('ignore')
import imblearn
warnings.simplefilter(action='ignore', category=FutureWarning)
from imblearn.over_sampling import RandomOverSampler

ros = RandomOverSampler()
ros_x, ros_y = ros.fit_sample(x2,y2)

RF build a model

train_x, test_x, train_y, test_y = train_test_split(ros_x, ros_y, test_size=.2)

RFC=RandomForestClassifier(n_estimators=1000,criterion="entropy",max_features=None)
RFC.fit(train_x, train_y)
RFC_ypred = RFC.predict(test_x)
print(classification_report(test_y, RFC_ypred))

TEST THE PIPELINE:

train_x1, test_x1, train_y1, test_y1 = train_test_split(x, y, test_size=.2)
clf = Pipeline(steps=(('ohe', OneHotEncoder()),
                  ('RFC', RandomForestClassifier(n_estimators=1000,criterion="entropy",max_features=None))))  

clf.fit(train_x1,train_y1)

pickle.dump(clf,open('model.pkl','wb'))

# load model
from flask import jsonify
model = pickle.load(open('model.pkl','rb'))

def predict(Project_Sector, IncidentType,
       Parent_Child_Type, Project_Lifecycle_Phase, LL_BU,
       Project_Size, HeadContractType):

    result = model.predict(x)

    # send back to browser
    output = {'results': (result(0))}


    # return data
    return jsonify(results=output)

python – Random Forest Method – ValueError: The input contains NaN, infinite or a value that is too large for dtype (& # 39; float32 & # 39;).

I try to use the Random Forest method on a dataset and I get the following error message: ValueError: The input contains NaN, infinite or a value that is too large for dtype (& # 39; float32 & # 39;). Could someone tell me what I can change about the function to make the code work:

"""Get ranks from Random Forest"""

    print("nMĂ©todo_Random_Forest")

    random_forest = RandomForestRegressor(n_estimators=10)
    np.nan_to_num(x_train)
    np.nan_to_num(y_train)
    random_forest.fit(x_train, y_train)

    # Get rank by doing two times a sort.
    imp_array = np.array(random_forest.feature_importances_)
    imp_order = imp_array.argsort()
    ranks = imp_order.argsort()

    # Plot Random Forest
    imp = pd.Series(random_forest.feature_importances_, index=x_train.columns)
    imp = imp.sort_values()

    imp.plot(kind="barh")
    plt.xlabel("Importance")
    plt.ylabel("Features")
    plt.title("Feature importance using Random Forest")
    # plt.show()
    plt.savefig(RESULT_PATH + '/ranks_RF.png', bbox_inches='tight')

    return ranks

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