from sklearn import metrics
from sklearn.neighbors import KNeighborsClassifier
import pandas as pd
import numpy as np
from sklearn.pipeline import make_pipeline
from sklearn import preprocessing
import matplotlib.pyplot as plt
from datetime import datetime
from joblib import dump, load
from sklearn.metrics import make_scorer
from sklearn.metrics import roc_auc_score
# from sklearn.metrics import roc_auc_ovr
from sklearn.model_selection import GridSearchCV
#TODO move into preprocess
pkr_data = prp.pkr_data()
pkr_data.clean()
pkr_data.target = 'hand'
pkr_data.features = pkr_data.all.columns[pkr_data.all.columns != pkr_data.target]
pkr_data.init_model_data(target =['hand'],features = ['suit1','card1','suit2','card2','suit3','card3','suit4','card4'])
#ab data
ab_data = prp.ab_data()
# ab_data.encode = ['room_type']
ab_data.clean()
ab_data.target = 'room_type'
ab_data.features = ab_data.all.columns[ab_data.all.columns != ab_data.target]
ab_data.init_model_data(target=ab_data.target,features=ab_data.features)
print("Models Initiated")
from sklearn.model_selection import learning_curve
import pandas as pd
from sklearn.pipeline import make_pipeline
from sklearn import preprocessing
from sklearn.dummy import DummyClassifier
import matplotlib.pyplot as plt
from datetime import datetime
import seaborn as sns
from seaborn import distplot
from sklearn.multiclass import OneVsRestClassifier
from sklearn.model_selection import GridSearchCV
#pkr_data
#TODO move into preprocess
pkr_data = prp.pkr_data()
pkr_data.clean()
pkr_data.init_model_data(target=['hand'],
features=[
'suit1', 'card1', 'suit2', 'card2', 'suit3',
'card3', 'suit4', 'card4'
]) #
#ab data
ab_data = prp.ab_data()
ab_data.clean()
ab_data.target = 'room_type'
ab_data.features = ab_data.all.columns[ab_data.all.columns != ab_data.target]
ab_data.init_model_data(target=ab_data.target, features=ab_data.features)
# x_vars=list(ab_data.features)