Make X not baD!

Load X and Y from getting satellite channels 1 and 6 data, see satellite-to-sensor-model.ipynb for more info.



In [31]:

    
import numpy as np
import matplotlib.pyplot as plt
from data_helper_functions import *
from IPython.display import display
pd.options.display.max_columns = 999
%matplotlib inline



In [ ]:

    
with np.load('data/X.npz') as data: #old X, don't use, start at "Now with all channels..."
    X = data['X'] 

with np.load('data/Y.npz') as data: #old Y, don't use
    Y = data['Y']



In [3]:

    
print X.shape
print Y.shape









    



(2544, 2476)
(2544, 14)

Random Forest seems to be giving the best results, so we'll stick with that for now



In [4]:

    
from sklearn.ensemble import RandomForestRegressor
from sklearn.cross_validation import train_test_split
rfr = RandomForestRegressor(oob_score=True)
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.2, random_state = 14)



In [5]:

    
rfr.fit(X_train,Y_train)









    



/home/ubuntu/anaconda/lib/python2.7/site-packages/sklearn/ensemble/forest.py:659: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.
  warn("Some inputs do not have OOB scores. "






    Out[5]:





RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,
           max_features='auto', max_leaf_nodes=None, min_samples_leaf=1,
           min_samples_split=2, min_weight_fraction_leaf=0.0,
           n_estimators=10, n_jobs=1, oob_score=True, random_state=None,
           verbose=0, warm_start=False)



In [6]:

    
rfr.score(X_test,Y_test)









    Out[6]:





0.030842791854099993



In [7]:

    
Y_pred = rfr.predict(X_test)
from random import randint
val = randint(0,508)
print Y_pred[val]
print Y_test[val]









    



[-1898.04073451 -1895.44739133 -1874.99465429 -1862.62083899 -1872.08635517
 -1888.94023789 -1878.36506936 -1899.1076668  -1898.16786753 -1898.20189851
 -1898.21882193 -1898.17638099 -1897.81655789 -1898.10105721]
[-1049.38756217 -1049.66777813 -1049.1949189  -1049.17124527 -1049.44469013
 -1049.60373762 -1049.56861818 -1046.84618117 -1045.290315   -1045.314015
 -1045.0869825  -1044.91115667 -1044.6794105  -1044.81481833]

Maybe I should only use the AOD values since the sensor values are sporadic and don't add as much to the data anyway. Also, I could remove the rows were the sensor readings are zero. I could look through the Y rows and make a mask to apply to both X and Y rows.



In [8]:

    
mask = []
for i,row in enumerate(Y):
    if row[0] == 0:
        mask.append(False)
    else:
        mask.append(True)



In [9]:

    
mask = np.array(mask)



In [10]:

    
X_reduced = X[mask]
Y_reduced = Y[mask][:,-7::]



In [11]:

    
print X_reduced.shape
print Y_reduced.shape









    



(2302, 2476)
(2302, 7)



In [12]:

    
rfr = RandomForestRegressor(oob_score=True)
X_train, X_test, Y_train, Y_test = train_test_split(X_reduced, Y_reduced, test_size = 0.3, random_state = 14)



In [197]:

    
rfr.fit(X_train,Y_train)









    Out[197]:





RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,
           max_features='auto', max_leaf_nodes=None, min_samples_leaf=1,
           min_samples_split=2, min_weight_fraction_leaf=0.0,
           n_estimators=10, n_jobs=1, oob_score=True, random_state=None,
           verbose=0, warm_start=False)



In [198]:

    
print rfr.score(X_test,Y_test)
print rfr.oob_score_









    



0.0800125607355
-0.0936330642013



In [199]:

    
Y_pred = rfr.predict(X_test)



In [200]:

    
from random import randint
val = randint(0,Y_pred.shape[0])
print Y_pred[val]
print Y_test[val]









    



[-2962.90243532 -2962.88878597 -2962.89901777 -2962.90393853 -2962.9060391
 -2962.87717588 -2962.90578052]
[-3732.59078983 -3732.40831117 -3732.3851785  -3732.37497417 -3732.37765883
 -3732.33242967 -3732.37806767]

Let's try Ridge again.



In [17]:

    
from sklearn.linear_model import Ridge
ridge = Ridge(solver = 'auto')
X_train, X_test, Y_train, Y_test = train_test_split(X_reduced, Y_reduced, test_size = 0.5, random_state = 14)



In [18]:

    
ridge.fit(X_train,Y_train)









    Out[18]:





Ridge(alpha=1.0, copy_X=True, fit_intercept=True, max_iter=None,
   normalize=False, solver='auto', tol=0.001)



In [19]:

    
ridge.score(X_test,Y_test)









    Out[19]:





-2.0626980586035355



In [20]:

    
Y_pred = ridge.predict(X_test)



In [196]:

    
from random import randint
val = randint(0,Y_pred.shape[0]-1)
print Y_pred[val]
print Y_test[val]









    



[-4278.18991    -4277.98500289 -4277.94563314 -4278.02877961 -4278.02623493
 -4277.81172583 -4278.03564841]
[-4432.7487905  -4432.65517167 -4432.671593   -4432.682409   -4432.68769133
 -4432.644714   -4432.68905467]

Not too good. Or at least not an improvement over itself even.

I may have to do some feature engineering for the images. Averaging, filtering or both. I wonder if I do some ratios of channels? Sort of like the "greenness" parameter that farmers use. Also, maybe +/- 3 hours is too long for searching for a satellite image. (Reduce to +/- 1 hour?)



In [189]:

    
plt.plot(Y_pred,'go');

So, from the plot above, there are values above 0, even though my Y_train and Y_test are all below zero.



In [383]:

    
val = randint(0,2301)
_ = plt.hist(X_reduced[:,0:1972][val], alpha = 0.5, normed=True, bins=25, label='visible',range=(0,25000)) #visible light histogram
_ = plt.hist(X_reduced[:,1973:2476][val], alpha = 0.5, normed=True, bins=25, label='IR',range=(0,25000))
plt.legend(loc='upper right');



In [428]:

    
X_hist = []
for i in xrange(X_reduced.shape[0]):
    hist1, _ = np.histogram(X_reduced[:,0:1972][i], density=True, bins=25, range=(0,25000)) #vis
    hist2, _ = np.histogram(X_reduced[:,1973:2476][i], density=True, bins=25, range=(0,25000)) #IR
    X_hist.append(np.hstack((hist1,hist2)))
X_hist = np.array(X_hist)

Try random forest with new X_hist:



In [429]:

    
rfr = RandomForestRegressor(oob_score=True)
X_train, X_test, Y_train, Y_test = train_test_split(X_hist, Y_reduced, test_size = 0.3, random_state = 14)



In [430]:

    
rfr.fit(X_train,Y_train)









    Out[430]:





RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,
           max_features='auto', max_leaf_nodes=None, min_samples_leaf=1,
           min_samples_split=2, min_weight_fraction_leaf=0.0,
           n_estimators=10, n_jobs=1, oob_score=True, random_state=None,
           verbose=0, warm_start=False)



In [431]:

    
print rfr.score(X_test,Y_test)
print rfr.oob_score_









    



0.174518399271
-0.00409222016671



In [432]:

    
from random import randint
val = randint(0,Y_test.shape[0])
print Y_pred[val]
print Y_test[val]









    



[-1736.63037127 -1735.83002907 -1735.87247932 -1735.86205807 -1735.87630777
 -1735.59226777 -1735.86062898]
[-4082.73808083 -4082.59797133 -4082.65399817 -4082.671105   -4082.67535783
 -4082.6016735  -4082.6695155 ]



In [433]:

    
plt.plot(Y_pred,'go');

The score has improved (0.2) but the most remarkable is that all the predicted values are now below 0, as they should be.

Let's try something Extra Trees!



In [627]:

    
X_hist = []
for i in xrange(X_reduced.shape[0]):
    hist1, _ = np.histogram(X_reduced[:,0:1972][i], density=True, bins=30, range=(0,25000)) #vis
    hist2, _ = np.histogram(X_reduced[:,1973:2476][i], density=True, bins=30, range=(0,25000)) #IR
    X_hist.append(np.hstack((hist1,hist2)))
X_hist = np.array(X_hist)



In [628]:

    
from sklearn.ensemble import ExtraTreesRegressor
etr = ExtraTreesRegressor(oob_score=True, bootstrap=True, n_jobs=-1, n_estimators=100) #njobs uses all cores!
X_train, X_test, Y_train, Y_test = train_test_split(X_hist, Y_reduced, test_size = 0.3, random_state = 14)



In [629]:

    
etr.fit(X_train,Y_train)









    Out[629]:





ExtraTreesRegressor(bootstrap=True, criterion='mse', max_depth=None,
          max_features='auto', max_leaf_nodes=None, min_samples_leaf=1,
          min_samples_split=2, min_weight_fraction_leaf=0.0,
          n_estimators=100, n_jobs=-1, oob_score=True, random_state=None,
          verbose=0, warm_start=False)



In [630]:

    
print etr.score(X_test,Y_test)
print etr.oob_score_









    



0.237025258926
0.253519316664



In [633]:

    
from random import randint
val = randint(0,Y_test.shape[0])
print Y_pred[val]
print Y_test[val]









    



[-2085.4927687  -2084.31641815 -2084.36274315 -2084.35427948 -2084.396237
 -2083.90427887 -2084.35021117]
[-2099.20588867 -2098.90826783 -2098.97825067 -2098.99554717 -2099.00107567
 -2098.7010995  -2098.99597117]



In [634]:

    
plt.plot(Y_pred,'go');

Not much better than random forest regressor, but does offer multicore support which speeds things up significantly. Edit: actually, this is better when increasing the number of n_estimators.



In [33]:

    
with np.load('data/X_all_channels.npz') as data:
    X = data['X']
    
with np.load('data/Y_all_channels.npz') as data:
    Y = data['Y']



In [34]:

    
print X.shape
print Y.shape









    



(2544, 3988)
(2544, 14)



In [639]:

    
mask = []
for i,row in enumerate(Y):
    if row[0] == 0:
        mask.append(False)
    else:
        mask.append(True)    
mask = np.array(mask)
X_reduced = X[mask]
Y_reduced = Y[mask][:,-7::]



In [640]:

    
print X_reduced.shape
print Y_reduced.shape









    



(2302, 3988)
(2302, 7)



In [656]:

    
val = randint(0,2301)
_ = plt.hist(X_reduced[:,0:1972][val], alpha = 0.2, normed=True, bins=30, label='CH1',range=(0,25000)) #visible light histogram
_ = plt.hist(X_reduced[:,1973:2476][val], alpha = 0.2, normed=True, bins=30, label='CH2',range=(0,25000))
_ = plt.hist(X_reduced[:,2477:2980][val], alpha = 0.2, normed=True, bins=30, label='CH3',range=(0,25000))
_ = plt.hist(X_reduced[:,2981:3484][val], alpha = 0.2, normed=True, bins=30, label='CH4',range=(0,25000))
_ = plt.hist(X_reduced[:,3484:3988][val], alpha = 0.2, normed=True, bins=30, label='CH6',range=(0,25000))
plt.legend(loc='upper right');



In [737]:

    
X_hist = []
bins = 20
for i in xrange(X_reduced.shape[0]):
    hist1, _ = np.histogram(X_reduced[:,0:1972][i], density=True, bins=bins, range=(0,25000))
    hist2, _ = np.histogram(X_reduced[:,1973:2476][i], density=True, bins=bins, range=(0,25000))
    hist3, _ = np.histogram(X_reduced[:,2477:2980][i], density=True, bins=bins, range=(0,25000))
    hist4, _ = np.histogram(X_reduced[:,2981:3484][i], density=True, bins=bins, range=(0,25000))
    hist5, _ = np.histogram(X_reduced[:,3484:3988][i], density=True, bins=bins, range=(0,25000))
    X_hist.append(np.hstack((hist1,hist2,hist3,hist4,hist5)))
X_hist = np.array(X_hist)



In [738]:

    
from sklearn.ensemble import ExtraTreesRegressor
etr = ExtraTreesRegressor(
    oob_score=True, bootstrap=True, n_jobs=-1, n_estimators=1000
                         ) #nj_obs uses all cores!
X_train, X_test, Y_train, Y_test = train_test_split(X_hist, Y_reduced, test_size = 0.25, random_state = 12)



In [739]:

    
etr.fit(X_train,Y_train)









    Out[739]:





ExtraTreesRegressor(bootstrap=True, criterion='mse', max_depth=None,
          max_features='auto', max_leaf_nodes=None, min_samples_leaf=1,
          min_samples_split=2, min_weight_fraction_leaf=0.0,
          n_estimators=1000, n_jobs=-1, oob_score=True, random_state=None,
          verbose=0, warm_start=False)



In [787]:

    
print etr.score(X_test,Y_test)
print etr.oob_score_









    



0.333571847511
0.324312054499



In [788]:

    
from random import randint
val = randint(0,Y_test.shape[0])
print Y_pred[val]
print Y_test[val]









    



[-3546.09800318 -3545.97880823 -3546.01557603 -3546.02564373 -3546.02913815
 -3545.91567287 -3546.02901358]
[-2216.3428045  -2216.22484817 -2216.2996965  -2216.32683533 -2216.336912
 -2216.22664033 -2216.33727767]



In [789]:

    
plt.plot(Y_pred,'go');

Wow, even better, but is it good enough? If I include ratios between channels, spatial and other information will be implicitly collected.

Downsample CH1 and divide by CH6:



In [1091]:

    
from scipy.ndimage import zoom
from __future__ import division

X_reduced_ratio_1_2 = []
for i in xrange(X_reduced.shape[0]):
    CH1 = zoom(X_reduced[:,0:1972][i].reshape((29,68)),zoom=(0.48, 0.53), order=5)
    CH2 = X_reduced[:,1972:2476][i].reshape((14,36))
    X_reduced_ratio_1_2.append(25000* CH2  / (CH1 + CH2) )
X_reduced_ratio_1_2 = np.array(X_reduced_ratio_1_2)

X_reduced_ratio_1_6 = []
for i in xrange(X_reduced.shape[0]):
    CH1 = zoom(X_reduced[:,0:1972][i].reshape((29,68)),zoom=(0.48, 0.53), order=5)
    CH6 = X_reduced[:,3484:3988][i].reshape((14,36))
    X_reduced_ratio_1_6.append(25000* CH6 / (CH1 + CH6) )
X_reduced_ratio_1_6 = np.array(X_reduced_ratio_1_6)

X_reduced_ratio_2_6 = []
for i in xrange(X_reduced.shape[0]):
    CH2 = X_reduced[:,1972:2476][i].reshape((14,36))
    CH6 = X_reduced[:,3484:3988][i].reshape((14,36))
    X_reduced_ratio_2_6.append(25000* CH6 / (CH2 + CH6) )
X_reduced_ratio_2_6 = np.array(X_reduced_ratio_2_6)



In [1093]:

    
val = randint(0,2301)
_ = plt.hist(X_reduced[:,0:1972][val], alpha = 0.2, normed=True, bins=30, label='CH1',range=(0,25000)) #visible light histogram
_ = plt.hist(X_reduced[:,1973:2476][val], alpha = 0.2, normed=True, bins=30, label='CH2',range=(0,25000))
_ = plt.hist(X_reduced[:,2477:2980][val], alpha = 0.2, normed=True, bins=30, label='CH3',range=(0,25000))
_ = plt.hist(X_reduced[:,2981:3484][val], alpha = 0.2, normed=True, bins=30, label='CH4',range=(0,25000))
_ = plt.hist(X_reduced[:,3484:3988][val], alpha = 0.2, normed=True, bins=30, label='CH6',range=(0,25000))
_ = plt.hist(np.ravel(X_reduced_ratio_1_2[val]), alpha = 0.2, normed=True, bins=30, label='CH2/CH1',range=(0,25000))
_ = plt.hist(np.ravel(X_reduced_ratio_1_6[val]), alpha = 0.2, normed=True, bins=30, label='CH6/CH1',range=(0,25000))
_ = plt.hist(np.ravel(X_reduced_ratio_2_6[val]), alpha = 0.2, normed=True, bins=30, label='CH6/CH2',range=(0,25000))
plt.legend(loc='upper right');



In [1101]:

    
X_hist = []
bins = 30
for i in xrange(X_reduced.shape[0]):
    hist1, _ = np.histogram(X_reduced[:,0:1972][i], density=True, bins=bins, range=(0,25000))
    hist2, _ = np.histogram(X_reduced[:,1972:2476][i], density=True, bins=bins, range=(0,25000))
    hist3, _ = np.histogram(X_reduced[:,2476:2980][i], density=True, bins=bins, range=(0,25000))
    hist4, _ = np.histogram(X_reduced[:,2980:3484][i], density=True, bins=bins, range=(0,25000))
    hist5, _ = np.histogram(X_reduced[:,3484:3988][i], density=True, bins=bins, range=(0,25000))
    hist6, _ = np.histogram(np.ravel(X_reduced_ratio_1_2[i]), density=True, bins=bins, range=(0,25000))
    hist7, _ = np.histogram(np.ravel(X_reduced_ratio_1_6[i]), density=True, bins=bins, range=(0,25000))
    hist8, _ = np.histogram(np.ravel(X_reduced_ratio_2_6[i]), density=True, bins=bins, range=(0,25000))
    X_hist.append(np.hstack((hist1,hist2,hist3,hist4,hist5,hist6,hist7,hist8)))
X_hist = np.array(X_hist)



In [1105]:

    
from sklearn.ensemble import ExtraTreesRegressor
etr = ExtraTreesRegressor(oob_score=True, bootstrap=True,
                          n_jobs=-1, n_estimators=1000) #nj_obs uses all cores!
X_train, X_test, Y_train, Y_test = train_test_split(X_hist, Y_reduced, test_size = 0.2, random_state = 12)



In [1106]:

    
etr.fit(X_train,Y_train)









    Out[1106]:





ExtraTreesRegressor(bootstrap=True, criterion='mse', max_depth=None,
          max_features='auto', max_leaf_nodes=None, min_samples_leaf=1,
          min_samples_split=2, min_weight_fraction_leaf=0.0,
          n_estimators=1000, n_jobs=-1, oob_score=True, random_state=None,
          verbose=0, warm_start=False)



In [1107]:

    
print etr.score(X_test,Y_test)
print etr.oob_score_









    



0.400992066327
0.302861512548



In [1114]:

    
from random import randint
val = randint(0,Y_test.shape[0])
print Y_pred[val]
print Y_test[val]









    



[-2111.35264935 -2111.33037278 -2111.33640548 -2111.33989697 -2111.34116932
 -2111.31844367 -2111.34136852]
[-1513.74919033 -1512.226391   -1512.269637   -1512.23218683 -1512.30237917
 -1511.73857267 -1512.27493883]



In [ ]:



In [ ]:

Try it all again with just X, not X_reduced?



In [37]:

    
from scipy.ndimage import zoom
from __future__ import division
from random import randint

X_ratio_1_2 = []
for i in xrange(X.shape[0]):
    CH1 = zoom(X[:,0:1972][i].reshape((29,68)),zoom=(0.48, 0.53), order=5)
    CH2 = X[:,1972:2476][i].reshape((14,36))
    X_ratio_1_2.append(25000* (CH2)  / (CH1 + CH2+1.0) )
X_ratio_1_2 = np.array(X_ratio_1_2)

X_ratio_1_6 = []
for i in xrange(X.shape[0]):
    CH1 = zoom(X[:,0:1972][i].reshape((29,68)),zoom=(0.48, 0.53), order=5)
    CH6 = X[:,3484:3988][i].reshape((14,36))
    X_ratio_1_6.append(25000* CH6 / (CH1 + CH6 + 0.1) )
X_ratio_1_6 = np.array(X_ratio_1_6)

X_ratio_2_6 = []
for i in xrange(X.shape[0]):
    CH2 = X[:,1972:2476][i].reshape((14,36))
    CH6 = X[:,3484:3988][i].reshape((14,36))
    X_ratio_2_6.append(25000* CH6 / (CH2 + CH6 + 0.1) )
X_ratio_2_6 = np.array(X_ratio_2_6)









    



/Users/scott/anaconda/lib/python2.7/site-packages/scipy/ndimage/interpolation.py:549: UserWarning: From scipy 0.13.0, the output shape of zoom() is calculated with round() instead of int() - for these inputs the size of the returned array has changed.
  "the returned array has changed.", UserWarning)



In [38]:

    
val = randint(0,2301)
_ = plt.hist(X[:,0:1972][val], alpha = 0.2, normed=True, bins=30, label='CH1',range=(0,25000)) #visible light histogram
_ = plt.hist(X[:,1973:2476][val], alpha = 0.2, normed=True, bins=30, label='CH2',range=(0,25000))
_ = plt.hist(X[:,2477:2980][val], alpha = 0.2, normed=True, bins=30, label='CH3',range=(0,25000))
_ = plt.hist(X[:,2981:3484][val], alpha = 0.2, normed=True, bins=30, label='CH4',range=(0,25000))
_ = plt.hist(X[:,3484:3988][val], alpha = 0.2, normed=True, bins=30, label='CH6',range=(0,25000))
_ = plt.hist(np.ravel(X_ratio_1_2[val]), alpha = 0.2, normed=True, bins=30, label='CH2/CH1',range=(0,25000))
_ = plt.hist(np.ravel(X_ratio_1_6[val]), alpha = 0.2, normed=True, bins=30, label='CH6/CH1',range=(0,25000))
_ = plt.hist(np.ravel(X_ratio_2_6[val]), alpha = 0.2, normed=True, bins=30, label='CH6/CH2',range=(0,25000))
plt.legend(loc='upper right');



In [39]:

    
import pandas as pd

X_hist = []
bins = 25
for i in xrange(X.shape[0]):
    myval1 = pd.DataFrame(np.ravel(X_ratio_1_2[i])).fillna(np.mean).values.flatten();
    myval2 = pd.DataFrame(np.ravel(X_ratio_1_6[i])).fillna(np.mean).values.flatten();
    myval3 = pd.DataFrame(np.ravel(X_ratio_2_6[i])).fillna(np.mean).values.flatten();
    
    hist1, _ = np.histogram(X[:,0:1972][i], density=True, bins=bins, range=(0,25000))
    hist2, _ = np.histogram(X[:,1972:2476][i], density=True, bins=bins, range=(0,25000))
    hist3, _ = np.histogram(X[:,2476:2980][i], density=True, bins=bins, range=(0,25000))
    hist4, _ = np.histogram(X[:,2980:3484][i], density=True, bins=bins, range=(0,25000))
    hist5, _ = np.histogram(X[:,3484:3988][i], density=True, bins=bins, range=(0,25000))
    hist6, _ = np.histogram( myval1 , density=True, bins=bins, range=(0,25000) )
    hist7, _ = np.histogram( myval2 , density=True, bins=bins, range=(0,25000))
    hist8, _ = np.histogram( myval3, density=True, bins=bins, range=(0,25000))
    X_hist.append(np.hstack((hist1,hist2,hist3,hist4,hist5,hist6,hist7,hist8)))
X_hist = np.array(X_hist)



In [40]:

    
from sklearn.ensemble import ExtraTreesRegressor
from sklearn.cross_validation import train_test_split
etr = ExtraTreesRegressor(oob_score=True, bootstrap=True,
                          n_jobs=-1, n_estimators=500) #nj_obs uses all cores!
X_train, X_test, Y_train, Y_test = train_test_split(X_hist, Y, test_size = 0.2, random_state = 12)



In [41]:

    
etr.fit(X_train,Y_train) #pickle it!









    Out[41]:





ExtraTreesRegressor(bootstrap=True, criterion='mse', max_depth=None,
          max_features='auto', max_leaf_nodes=None, min_samples_leaf=1,
          min_samples_split=2, min_weight_fraction_leaf=0.0,
          n_estimators=500, n_jobs=-1, oob_score=True, random_state=None,
          verbose=0, warm_start=False)



In [45]:

    
from sklearn.externals import joblib
joblib.dump(etr, 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl')









    Out[45]:





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 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_885.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_886.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_887.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_888.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_889.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_890.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_891.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_892.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_893.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_894.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_895.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_896.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_897.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_898.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_899.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_900.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_901.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_902.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_903.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_904.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_905.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_906.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_907.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_908.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_909.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_910.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_911.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_912.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_913.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_914.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_915.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_916.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_917.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_918.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_919.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_920.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_921.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_922.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_923.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_924.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_925.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_926.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_927.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_928.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_929.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_930.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_931.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_932.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_933.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_934.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_935.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_936.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_937.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_938.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_939.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_940.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_941.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_942.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_943.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_944.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_945.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_946.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_947.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_948.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_949.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_950.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_951.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_952.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_953.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_954.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_955.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_956.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_957.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_958.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_959.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_960.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_961.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_962.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_963.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_964.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_965.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_966.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_967.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_968.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_969.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_970.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_971.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_972.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_973.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_974.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_975.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_976.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_977.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_978.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_979.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_980.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_981.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_982.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_983.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_984.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_985.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_986.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_987.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_988.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_989.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_990.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_991.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_992.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_993.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_994.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_995.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_996.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_997.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_998.npy',
 'webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl_999.npy',
 ...]



In [49]:

    
sat_to_sensor_model = joblib.load('webapp/solarApp/models/sat-to-sensor-model/sat-to-sensor-model.pkl')



In [ ]:



In [42]:

    
print etr.score(X_test,Y_test)
print etr.oob_score_









    



0.341375179423
0.448898844836



In [22]:

    
Y_pred = etr.predict(X_test)

from random import randint
val = randint(0,Y_test.shape[0])
print Y_pred[val]
print Y_test[val]









    



[-2198.65772893 -2188.5623123  -2104.60089098 -2052.34812153 -2090.54505853
 -2156.32240028 -2116.19818674 -2211.84310991 -2211.56849777 -2211.6718011
 -2211.71221744 -2211.7269635  -2211.45021478 -2211.72584788]
[-2204.97347    -2191.76434333 -2101.25588333 -2045.47811667 -2089.01256667
 -2160.20863333 -2116.29065    -2216.31089233 -2216.08557883 -2216.19287567
 -2216.236086   -2216.24175567 -2215.97105233 -2216.24139267]

After pickling the sensor to power model, use it to predict power from satellite data!



In [47]:

    
from sklearn.externals import joblib
etr2 = joblib.load('data/sensor-to-power-model/sensor-to-power-model.pkl')



In [48]:

    
etr2.predict(Y).shape #power predictions









    Out[48]:





(2544,)



In [25]:

    
with np.load('data/y.npz') as data:
    y = data['y']



In [46]:

    
y_pred2 = etr2.predict(Y_pred) #the predicted

y_pred2.shape









    



---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-46-95b375df32b7> in <module>()
----> 1 y_pred2 = etr2.predict(Y_pred) #the predicted
      2 
      3 y_pred2.shape

NameError: name 'etr2' is not defined



In [27]:

    
y.shape









    Out[27]:





(2729,)

Seems like dimensions are working out. Probably will need to pickle the sat to sensor model too and just feed in data start to finish and see how it does.



In [28]:

    
with np.load('data/good_times.npz') as data:
    good_times = data['good_times']



In [35]:

    
print good_times.shape
print etr2.predict(Y).shape









    



(2544,)
(2544,)

Nice, now we have predictions for every datetime! Now we have to check this.



In [94]:

    
from datetime import datetime, timedelta, time
pvoutput_filefolder = 'data/pvoutput/pvoutput6months/'

datetime_index = 1747
print 'Predicted power for ' + str(good_times[datetime_index]) + \
      ' is ' + str(etr2.predict(Y[datetime_index])[0]) + ' W'

#pvoutput data
desired_datetime = good_times[datetime_index]
desired_date = (desired_datetime - timedelta(hours=6)).date() #make sure correct date
desired_date = datetime.combine(desired_date, time.min) #get into datetime format
pvoutput_filename = find_file_from_date(desired_date, pvoutput_filefolder)
df_pvoutput = return_pvoutput_data(pvoutput_filename, pvoutput_filefolder)
try:
    print "True power: " + df_pvoutput[df_pvoutput.index == desired_datetime].values[0][0].astype(str)
except:
    print "0"









    



Predicted power for 2014-08-02 03:00:00 is 0.0 W
0



In [95]:

    
from datetime import datetime, timedelta, time
pvoutput_filefolder = 'data/pvoutput/pvoutput6months/'

predicted_powers = []
true_powers = []
for datetime_index in range(len(good_times)):
    predicted_powers.append(str(etr2.predict(Y[datetime_index])[0]) )
    #pvoutput data
    desired_datetime = good_times[datetime_index]
    desired_date = (desired_datetime - timedelta(hours=6)).date() #make sure correct date
    desired_date = datetime.combine(desired_date, time.min) #get into datetime format
    pvoutput_filename = find_file_from_date(desired_date, pvoutput_filefolder)
    df_pvoutput = return_pvoutput_data(pvoutput_filename, pvoutput_filefolder)
    try:
        true_powers.append(df_pvoutput[df_pvoutput.index == desired_datetime].values[0][0].astype(str))
    except:
        true_powers.append(0)



In [111]:

    
true_powers = np.array(true_powers).astype(float)



In [107]:

    
predicted_powers = np.array(predicted_powers).astype(float)



In [144]:

    
error_add = []
for datetime_index in range(len(good_times)):

        error_add.append(
            np.abs(true_powers[datetime_index]-predicted_powers[datetime_index]) \
            / true_powers[datetime_index]
            )

error_sum = 0
for error in error_add:
    if (np.isnan(error) or np.isinf(error)):
        pass
    else:
        error_sum += error
        
error_sum/len(good_times)









    Out[144]:





14.248572714879463



In [30]:

    
x = np.linspace(0,13000,100)
y = 1.0 * x
plt.figure(figsize=(8,8))
plt.plot(true_powers,predicted_powers,'go', alpha=0.25)
plt.plot(x,y,'r',linewidth = 5, alpha = 0.5)
plt.xlabel('True Power (W)', fontsize = 16)
plt.xticks(size=16)
plt.ylabel('Predicted Power (W)', fontsize = 16)
plt.yticks(size=16)









    Out[30]:





(array([     0.,   2000.,   4000.,   6000.,   8000.,  10000.,  12000.,
         14000.]), <a list of 8 Text yticklabel objects>)

Tres beau, call it a day! That's a pretty graph!



In [160]:

    
np.savez_compressed('data/true_powers.npz',true_powers=true_powers) #save
np.savez_compressed('data/predicted_powers.npz',predicted_powers=predicted_powers) #save

I shut down the nb, so restart!



In [1]:

    
import numpy as np
with np.load('data/true_powers.npz') as data:
    true_powers = data['true_powers']
with np.load('data/predicted_powers.npz') as data:
    predicted_powers = data['predicted_powers']



In [2]:

    
import matplotlib.pyplot as plt
%matplotlib inline



In [5]:

    
import statsmodels.api as sm



In [6]:

    
# Fit and summarize OLS model
mod = sm.OLS(predicted_powers,true_powers)
res = mod.fit()
print res.summary()









    



                            OLS Regression Results                            
==============================================================================
Dep. Variable:                      y   R-squared:                       0.963
Model:                            OLS   Adj. R-squared:                  0.963
Method:                 Least Squares   F-statistic:                 6.572e+04
Date:                Thu, 13 Aug 2015   Prob (F-statistic):               0.00
Time:                        10:13:25   Log-Likelihood:                -21418.
No. Observations:                2544   AIC:                         4.284e+04
Df Residuals:                    2543   BIC:                         4.284e+04
Df Model:                           1                                         
Covariance Type:            nonrobust                                         
==============================================================================
                 coef    std err          t      P>|t|      [95.0% Conf. Int.]
------------------------------------------------------------------------------
x1             0.9423      0.004    256.360      0.000         0.935     0.949
==============================================================================
Omnibus:                     1294.023   Durbin-Watson:                   1.163
Prob(Omnibus):                  0.000   Jarque-Bera (JB):            28454.726
Skew:                          -1.912   Prob(JB):                         0.00
Kurtosis:                      18.932   Cond. No.                         1.00
==============================================================================

Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.



In [4]:



In [ ]:



In [ ]:



In [ ]:



In [ ]:



In [ ]:



In [ ]:

Make X not baD!

Now with all channels! (Skip to "try it again..." for model used)

Try it all again with just X, not X_reduced?

After pickling the sensor to power model, use it to predict power from satellite data!

Seems like dimensions are working out. Probably will need to pickle the sat to sensor model too and just feed in data start to finish and see how it does.

I shut down the nb, so restart!