feature-selection

The transformer should create computations over windows of past values of the features, and populate them at time t, t being the time of the forecast.

It uses pandas rolling, outputs several comptutations, mean, max, std, etc, and pandas shift to move the computations to the right row.

tmp = (data[variables]
       .rolling(window='3H').mean()  # Average the last 3 hr values.
       .

In #3324 , we had to mark some tests as expected to fail since XGBoost was throwing a FutureWarning. The warning has been addressed in XGBoost, so we're just waiting for the PR merged to be released. This issue is discussed in the #3275 issue.

evalml/tests/component_tests/test_xgboost_classifier.py needs to have the @pytest.mark.xfail removed f

Hello, when I ran your code got "TypeError: unhashable type: 'slice' ".Can you help me analyze the problem?thanks

`
import pandas as pd
from sklearn.linear_model import LogisticRegression
from feature_selection_ga import FeatureSelectionGA
data = pd.read_excel("D:\Project_CAD\实验6\data\train_data_1\train_1.xlsx")
x, y = data.iloc[:, :53], data.iloc[:, 56]
model = LogisticRegression()