__author__ = 'Trevor "Autogen" Grant'
"""
This is soooooo lazy.
"""
import pandas as pd
print "loading test data"
data = pd.DataFrame.from_csv("input_data/testing_soups.csv", index_col=None)
print "testing dates_one_hot"
from m6_local.functions import dates_one_hot
date_series = pd.to_datetime(data['DATE'])
one_hot_dates = dates_one_hot(date_series)
if one_hot_dates.shape == (100,16):
print "looks good"
else:
print "DOOD, this is broken!!!!"
from sklearn.metrics import explained_variance_score, mean_absolute_error, mean_squared_error, median_absolute_error
from sklearn.cross_validation import train_test_split
from sklearn.preprocessing import OneHotEncoder
from m6_local.functions import dates_one_hot
data = pd.DataFrame.from_csv("C:/Users/tgrant/Documents/oos_pred_poc/soups.csv", index_col=None)
data = data[data['ACT_SALES_QTY'] > 0]
print "data loaded and filtered"
#data['DATE'] = pd.to_datetime(data['DATE'])
# need to handle dates teh honest way
data['fixed_dates'] = pd.to_datetime(data['DATE'])
print 'updated dates'
one_hot_store_ids = pd.get_dummies(data['STORE_ID'], sparse= True)
one_hot_item_ids = pd.get_dummies(data['ITEM_ID'], sparse=True)
one_hot_date = dates_one_hot(data['fixed_dates'])
print 'created one-hots'
#one_hot_dates = pd.get_dummies(data['DATE'], sparse=True)
# still need some other stuff - eg promo, act price/reg price
# need to normalize act_sales
# split training set
discount = data['PLAN_PRICE'] / data['REG_PRICE']
from sklearn.preprocessing import MinMaxScaler
from scipy.sparse import hstack
# need discount and data['ON_PROMOTION']
X = hstack([ one_hot_date, one_hot_item_ids, one_hot_store_ids])
