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trainingtestingdatarange.py
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"""
Name: trainingtestingdata
Extract training and testing datasets
Requirement:
numpy
Inputs:
list of local climate data for a particular variable
List of interpolated global climate data for a particular variable
Output:
X_train, y_train, X_test, y_test
"""
import numpy as np
def noised(pds_local, interpolatedlist, days):
# noise
interpolatedlist_noised = []
for i in range(0, days):
noise = pds_local[i] - interpolatedlist[i]
noise = noise*0.7 + interpolatedlist[i]
interpolatedlist_noised.append(noise.ravel())
return interpolatedlist_noised
def trainingdata(interpolatedlist_SST, interpolatedlist_Salt, pds_local, pds_local_salt, days, days_testS, days_testE):
#%% Training data
"""
Extract training dataset
new features are also created based on the SST, and salt datasets
All combined features are stored in the X_train variable
"""
interpolatedlist_SST_noised = noised(pds_local, interpolatedlist_SST, days)
interpolatedlist_Salt_noised = noised(pds_local_salt, interpolatedlist_Salt, days)
interpolatedlist_SST_train = interpolatedlist_SST_noised[:days_testS] + interpolatedlist_SST_noised[days_testE+1 :days]
interpolatedlist_Salt_train = interpolatedlist_Salt_noised[:days_testS] + interpolatedlist_Salt_noised[days_testE+1 :days]
pds_local_train = pds_local[:days_testS] + pds_local[days_testE+1 :days]
X_sst_global = np.concatenate(interpolatedlist_SST_train)
X_salt_global = np.concatenate(interpolatedlist_Salt_train)
# Lets create some new features
X_feature_1 = X_sst_global + X_salt_global
X_feature_2 = X_sst_global * X_salt_global
X_feature_3 = np.divide(X_salt_global, X_sst_global, out=np.zeros_like(X_salt_global), where=X_sst_global!=0)
daysrangeS = np.arange(0,(days_testS))
daysrangeE = np.arange(days_testE+1,days)
daysrange = np.r_[daysrangeS, daysrangeE]
X_feature_4 = np.repeat(daysrange, np.size(interpolatedlist_SST[0]))
X_train = np.concatenate((X_sst_global.reshape(-1,1), X_salt_global.reshape(-1,1), X_feature_1.reshape(-1,1)
, X_feature_2.reshape(-1,1), X_feature_3.reshape(-1,1), X_feature_4.reshape(-1,1)), axis =1)
y_train1 = np.concatenate(pds_local_train)
return X_train, y_train1
def testingdata(interpolatedlist_SST, interpolatedlist_Salt, pds_local, pds_local_salt, days, days_testS, days_testE):
interpolatedlist_SST_noised = noised(pds_local, interpolatedlist_SST, days)
interpolatedlist_Salt_noised = noised(pds_local_salt, interpolatedlist_Salt, days)
X_sst_global_test = np.concatenate(interpolatedlist_SST_noised[days_testS:days_testE+1])
X_salt_global_test = np.concatenate(interpolatedlist_Salt_noised[days_testS:days_testE+1])
X_feature_1_test = X_sst_global_test + X_salt_global_test
X_feature_2_test = X_sst_global_test * X_salt_global_test
X_feature_3_test = np.divide(X_salt_global_test, X_sst_global_test, out=np.zeros_like(X_salt_global_test), where=X_sst_global_test!=0)
daysrange = np.arange(days_testS,(days_testE+1))
X_feature_4_test = np.repeat(daysrange, np.size(interpolatedlist_SST[0]))
X_test = np.concatenate((X_sst_global_test.reshape(-1,1), X_salt_global_test.reshape(-1,1), X_feature_1_test.reshape(-1,1)
, X_feature_2_test.reshape(-1,1), X_feature_3_test.reshape(-1,1), X_feature_4_test.reshape(-1,1)), axis =1)
y_test1 = np.concatenate(pds_local[days_testS:days_testE+1])
return X_test, y_test1