Open RodrigoJoaquimAlmeida opened 2 years ago
I found many notebooks with same error, but no one gave me back a solution to this issue:
X_train = data_train.date[:, np.newaxis]
"Support for multi-dimensional indexing (e.g. obj[:, None]) is deprecated and will be removed in a future version."
obj[:, None]
So, after looking for some answers i found this:
instead using ndarray, use just an array and them reshape it from an 1D array to 2D array with array.reshape(-1, 1) and it will be done
from sklearn.tree import DecisionTreeRegressor
data_train = ram_prices[ram_prices.date < 2000] data_test = ram_prices[ram_prices.date >= 2000]
X_train = np.array(data_train.date) X_train = X_train.reshape(-1, 1) y_train = np.log(data_train.price)
tree = DecisionTreeRegressor().fit(X_train, y_train) linear_reg = LinearRegression().fit(X_train, y_train)
X_all = np.array(ram_prices.date) X_all = X_all.reshape(-1, 1)
pred_tree = tree.predict(X_all) pred_lr = linear_reg.predict(X_all)
price_tree = np.exp(pred_tree) price_lr = np.exp(pred_lr)
I found many notebooks with same error, but no one gave me back a solution to this issue:
X_train = data_train.date[:, np.newaxis]
"Support for multi-dimensional indexing (e.g.
obj[:, None]) is deprecated and will be removed in a future version."So, after looking for some answers i found this:
instead using ndarray, use just an array and them reshape it from an 1D array to 2D array with array.reshape(-1, 1) and it will be done
from sklearn.tree import DecisionTreeRegressor
data_train = ram_prices[ram_prices.date < 2000] data_test = ram_prices[ram_prices.date >= 2000]
X_train = np.array(data_train.date) X_train = X_train.reshape(-1, 1) y_train = np.log(data_train.price)
tree = DecisionTreeRegressor().fit(X_train, y_train) linear_reg = LinearRegression().fit(X_train, y_train)
X_all = np.array(ram_prices.date) X_all = X_all.reshape(-1, 1)
pred_tree = tree.predict(X_all) pred_lr = linear_reg.predict(X_all)
price_tree = np.exp(pred_tree) price_lr = np.exp(pred_lr)