def buildInput():
#Only DATE and ASSIGNEMENT
submission = pp.loadCSV("submission.txt", sep ="\t")
submission.drop('prediction', axis=1, inplace=True)
X = submission
X['DAY_OFF']= 0
X['DATE'] = pd.to_datetime(X['DATE'])
# Setting the dates 3 days earlier.
X["DATE"] = X["DATE"].apply(lambda x: x+pd.DateOffset(days=-3))
X['WEEK_END'] = X['DATE'].apply(lambda x: x.dayofweek>=5)
X = pp.parseDate(submission,drop = False)
X = pp.oneHot(X, "ASS_ASSIGNMENT", delCol = True)
#X = pp.oneHot(X, "YEAR", delCol = True, prefix="YEAR")
X.drop('YEAR', axis=1, inplace=True)
X['MONTH_1']= 0
X = pp.oneHot(X, "MONTH", delCol = True, prefix="MONTH")
X.drop('DAY', axis=1, inplace=True)
#X = pp.oneHot(X, "DAY", delCol = True, prefix="DAY")
X = pp.oneHot(X, "CRENEAU", delCol = True, prefix="CRENEAU")
X.drop('DATE', axis=1, inplace=True)
return X
def buildInputDeep(X):
X['DATE'] = pd.to_datetime(X['DATE'])
X["DAY_OFF"] = X['DATE'].apply(lambda x : pp.isFerie(x)).astype(int)
X['WEEK_END'] = X['DATE'].apply(lambda x: x.dayofweek>=5)
X = pp.parseDate(X,drop = False)
print("DateTime data parsed.")
#transform categorical data in one-hots
X = pp.oneHot(X, "ASS_ASSIGNMENT", delCol = True)
X.drop('YEAR', axis=1, inplace=True)
X = pp.oneHot(X, "MONTH", delCol = True, prefix="MONTH")
X.drop('DAY', axis=1, inplace=True)
#X_train = oneHot(X_train, "DAY", delCol = True, prefix="DAY")
X = pp.oneHot(X, "CRENEAU", delCol = True, prefix="CRENEAU")
print("Categorical data made into one-hot")
X.drop('DATE', axis=1, inplace=True)
l = list(X)
X = X.as_matrix()
nInputDim = X.shape[1]
nInputNumber = X.shape[0]
XZ = np.zeros((nInputNumber,1, nInputDim))
for i in range(nInputNumber):
for k in range(nInputDim):
XZ[i,0,k]=X[i,k]
print("Data converted to numpy array")
print("PREPROCESSING OVER")
return XZ
