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LineSortedBandGapPredictor.py
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LineSortedBandGapPredictor.py
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from pymatgen import *
from numpy import zeros, mean
from sklearn import *
import matplotlib.pyplot as plt
trainFile = open("bandgapDFT.csv", "r").readlines()
def naiveVectorize(composition):
vector = zeros((MAX_Z))
for element in composition:
fraction = composition.get_atomic_fraction(element)
vector[element.Z - 1] = fraction
return(vector)
materials = []
bandgaps = []
naiveFeatures = []
MAX_Z = 100
for line in trainFile:
split = str.split(line, ',')
if(float(split[1]) == 0):
x = 1
material = Composition(split[0])
materials.append(material)
naiveFeatures.append(naiveVectorize(material))
bandgaps.append(float(split[1]))
baselineError = mean(abs(mean(bandgaps) - bandgaps))
print("The MAE of always guessing the average band gap is: " +
str(round(baselineError, 3)) + " eV")
linear = linear_model.Ridge(alpha=0.5)
cv = cross_validation.ShuffleSplit(len(bandgaps),
n_iter=10, test_size=0.1, random_state=0)
scores = cross_validation.cross_val_score(
linear,
naiveFeatures,
bandgaps,
cv=cv,
scoring='mean_absolute_error')
print("The MAE of the linear ridge using the naive features: " +
str(round(abs(mean(scores)), 3)) + " eV")
physicalFeatures = []
atmno = []
plotter = {}
plotter2 = {}
plotter3 = {}
it = 0
for material in materials:
theseFeatures = []
fraction = []
atomicNo = []
eneg = []
group = []
for element in material:
fraction.append(material.get_atomic_fraction(element))
atomicNo.append(float(element.Z))
eneg.append(element.X)
group.append(float(element.group))
mustReverse = False
if fraction[1] > fraction[0]:
mustReverse = True
for features in [fraction, atomicNo, eneg, group]:
if mustReverse:
features.reverse()
theseFeatures.append(fraction[0] / fraction[1])
theseFeatures.append(eneg[0] - eneg[1])
theseFeatures.append(group[0])
theseFeatures.append(group[1])
theseFeatures.append(atomicNo[0] + atomicNo[1])
physicalFeatures.append(theseFeatures)
ZZ = 0
for z in atomicNo:
ZZ += z
atmno.append(ZZ)
plotter[bandgaps[it]] = ZZ
plotter2[bandgaps[it]] = eneg[0] - eneg[1]
plotter3[bandgaps[it]] = fraction[0] / fraction[1]
it += 1
linear = linear_model.Ridge(alpha=0.5)
c=sorted(plotter3.iteritems(),key=lambda (x,y): float(x))
key1=[]
val1=[]
for j in c:
key1.append(j[0])
val1.append(j[1])
plt.plot(val1, key1)
plt.xlabel('Atomic Fraction')
plt.ylabel('Band Gap')
plt.show()
d=sorted(plotter2.iteritems(),key=lambda (x,y):float(x))
key2=[]
val2=[]
for k in d:
key2.append(k[0])
val2.append(k[1])
plt.plot(val2,key2)
plt.xlabel('Electro negativity difference')
plt.ylabel('Band Gap')
plt.show()
b = sorted(plotter.iteritems(), key=lambda (x, y): float(x))
key = []
val = []
for i in b:
key.append(i[0])
val.append(i[1])
plt.xlabel('Molecular weight')
plt.ylabel('Band Gap')
plt.plot(val, key)
plt.show()
cv = cross_validation.ShuffleSplit(len(bandgaps),
n_iter=10, test_size=0.1, random_state=0)
scores = cross_validation.cross_val_score(
linear,
physicalFeatures,
bandgaps,
cv=cv,
scoring='mean_absolute_error')
print("The MAE of the linear ridge using the physicalFeatures: " +
str(round(abs(mean(scores)), 3)) + " eV")
rfr = ensemble.RandomForestRegressor(n_estimators=10)
scores = cross_validation.cross_val_score(
rfr,
physicalFeatures,
bandgaps,
cv=cv,
scoring='mean_absolute_error')
print("The MAE of random forrest using physicalFeatures feature set is: " +
str(round(abs(mean(scores)), 3)) + " eV")