def load_model_data(pickle_filepath):
'''loads data'''
data = pd.read_pickle(pickle_filepath)
train_df, test_df = data_for_model(data, odds=False)
X_train, y_train, X_test, y_test = set_up_data(train_df, test_df)
X_train = X_train.astype(theano.config.floatX)
X_test = X_test.astype(theano.config.floatX)
y_train_ohe = np_utils.to_categorical(y_train) # all ready OHE
# pdb.set_trace()
return X_train, y_train, X_test, y_test #, y_train_ohe
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from sklearn.metrics import f1_score, accuracy_score
import seaborn as sns
import pickle
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import GradientBoostingClassifier
from model import games_up_to_2018_season_filter, season2018_filter, data_for_model, set_up_data
'''Read in model data.'''
data_df = pd.read_pickle('model_data/gamelog_5_exp_clust.pkl')
train_df, test_df = data_for_model(data_df, odds=False)
X_train, y_train, X_test, y_test = set_up_data(train_df, test_df)
data = (X_train, y_train, X_test, y_test)
gb_model = GradientBoostingClassifier(learning_rate=0.1,
loss='exponential',
max_depth=2,
max_features=None,
min_samples_leaf=2,
min_samples_split=2,
n_estimators=100,
subsample=0.5)
gb_model.fit(X_train, y_train)
feat_imports = gb_model.feature_importances_
features = train_df.columns.tolist()[1:]