from exceptions import DataNotMatchError
from sklearn.metrics import mean_squared_error
from math import sqrt
+import pandas as pd
import joblib
import warnings
warnings.filterwarnings("ignore")
def constant(self):
val = True
df = self.data.copy()
- df = df.drop_duplicates().dropna()
- df = df.loc[:, (df != 0).any(axis=0)]
- if len(df) >= 10:
- val = False
+ df = df[db.thptparam]
+ df = df.drop_duplicates()
+ df = df.loc[:, df.apply(pd.Series.nunique) != 1]
+ if df is not None:
+ df = df.dropna()
+ df = df.loc[:, (df != 0).any(axis=0)]
+ if len(df) >= 10:
+ val = False
return val
def evaluate_var(self, X, lag):