AutoViML
commented
5 years ago Thanks Prateek. Sorry there was a typo that had caused the error. It has now been fixed. Please do:
pip install autoviml --upgrade
that should fix the bug. Please try it and let me know. Ram Ram
On Mon, Dec 2, 2019 at 7:06 AM Prateek Gupta notifications@github.com wrote:
Hi,
Thanks for sharing your work! I just tested the titanic dataset downloaded from https://www.kaggle.com/c/titanic/data with XGBoost as below- m, feats, trainm, testm = Auto_ViML(train, target, test, sample_submission, scoring_parameter=scoring_parameter, hyper_param='GS',feature_reduction=True, Boosting_Flag=True,Binning_Flag=False, Add_Poly=0, Stacking_Flag=False, Imbalanced_Flag=False, verbose=1)
Once I ran the above code then found below error- ValueError: DataFrame.dtypes for data must be int, float or bool. Did not expect the data types in fields Name
Logs of the console just prior to error is as below-
Train (Size: 891,12) has Single_Label with target: ['Survived'] " ################### Binary-Class ##################### " Shuffling the data set before training Class -> Counts -> Percent 1: 342 -> 38.4% 0: 549 -> 61.6% Selecting 2-Class Classifier... Using GridSearchCV for Hyper Parameter tuning... Target Survived is already numeric. No transformation done. Top columns in Train with missing values: ['Cabin', 'Age', 'Embarked'] and their missing value totals: [687, 177, 2] Classifying variables in data set... Number of Numeric Columns = 2 Number of Integer-Categorical Columns = 3 Number of String-Categorical Columns = 1 Number of Factor-Categorical Columns = 0 Number of String-Boolean Columns = 1 Number of Numeric-Boolean Columns = 0 Number of Discrete String Columns = 2 Number of NLP String Columns = 0 Number of Date Time Columns = 0 Number of ID Columns = 2 Number of Columns to Delete = 0 11 Predictors classified... This does not include the Target column(s) 2 variables removed since they were some ID or low-information variables Completed Label Encoding, Missing Value Imputing and Scaling of data without errors. No Missing values in Train Test data has no missing values Number of numeric variables = 5 No variables were removed since no highly correlated variables found in data
Data Ready for Modeling with Target variable = Survived Starting Selection among 11 predictors... Number of numeric variables = 5 No variables were removed since no highly correlated variables found in data Adding 6 categorical variables to reduced numeric variables of 5 Selected No. of variables = 11 Finding Important Features... in 11 variables
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dsbyprateekg
deneshkumar
Naseer5543
rsesha
Hi,
Thanks for sharing your work! I just tested the titanic dataset downloaded from https://www.kaggle.com/c/titanic/data with XGBoost as below-
m, feats, trainm, testm = Auto_ViML(train, target, test, sample_submission, scoring_parameter=scoring_parameter, hyper_param='GS',feature_reduction=True, Boosting_Flag=True,Binning_Flag=False, Add_Poly=0, Stacking_Flag=False, Imbalanced_Flag=False, verbose=1)Once I ran the above code then found below error-
ValueError: DataFrame.dtypes for data must be int, float or bool. Did not expect the data types in fields NameIt seems same error occurs in case of Boosting_Flag=None. Logs of the console just prior to error is as below-
Data Ready for Modeling with Target variable = Survived Starting Selection among 11 predictors... Number of numeric variables = 5 No variables were removed since no highly correlated variables found in data Adding 6 categorical variables to reduced numeric variables of 5 Selected No. of variables = 11 Finding Important Features... in 11 variables