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%matplotlib inline
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import numpy as np
import pandas as pd
# 統計用ツール
import statsmodels.api as sm
import statsmodels.tsa.api as tsa
from patsy import dmatrices
# 自作の空間統計用ツール
from spatialstat import *
#描画
import matplotlib.pyplot as plt
from pandas.tools.plotting import autocorrelation_plot
import seaborn as sns
sns.set(font=['IPAmincho'])
#深層学習
import chainer
from chainer import cuda, Function, gradient_check, Variable, optimizers, serializers, utils
from chainer import Link, Chain, ChainList
import chainer.functions as F
import chainer.links as L
from makedata import makedata
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data = pd.read_csv("TokyoSingle.csv")
data = data.dropna()
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datas = makedata(data)
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datas = datas[datas['12世田谷区'] == 1][0:5000]
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class CAR(Chain):
def __init__(self, unit1, unit2, unit3, col_num):
self.unit1 = unit1
self.unit2 = unit2
self.unit3 = unit3
super(CAR, self).__init__(
l1 = L.Linear(col_num, unit1),
l2 = L.Linear(self.unit1, self.unit1),
l3 = L.Linear(self.unit1, self.unit2),
l4 = L.Linear(self.unit2, self.unit3),
l5 = L.Linear(self.unit3, self.unit3),
l6 = L.Linear(self.unit3, 1),
)
def __call__(self, x, y):
fv = self.fwd(x, y)
loss = F.mean_squared_error(fv, y)
return loss
def fwd(self, x, y):
h1 = F.sigmoid(self.l1(x))
h2 = F.sigmoid(self.l2(h1))
h3 = F.sigmoid(self.l3(h2))
h4 = F.sigmoid(self.l4(h3))
h5 = F.sigmoid(self.l5(h4))
h6 = self.l6(h5)
return h6
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class OLS_DLmodel(object):
def __init__(self, data, vars, bs=200, n=1000):
self.vars = vars
eq = fml_build(vars)
y, X = dmatrices(eq, data=datas, return_type='dataframe')
self.y_in = y[:-n]
self.X_in = X[:-n]
self.y_ex = y[-n:]
self.X_ex = X[-n:]
self.logy_in = np.log(self.y_in)
self.logy_ex = np.log(self.y_ex)
self.bs = bs
self.ido_in = np.array([datas['X'][:-n]])
self.keido_in = np.array([datas['Y'][:-n]])
self.W_in = ((self.ido_in.T - self.ido_in)**2 +
(self.keido_in.T - self.keido_in)**2)
N = np.array(np.matrix(self.W_in)*np.matrix(np.ones(len(self.ido_in[0]))).T)
self.W_in = self.W_in/N
self.ido_ex = np.array([datas['X'][-n:]])
self.keido_ex = np.array([datas['Y'][-n:]])
def OLS(self):
X_in = self.X_in
X_in = X_in.drop(['X', 'Y'], axis=1)
model = sm.OLS(self.logy_in, X_in, intercept=False)
self.reg = model.fit()
print(self.reg.summary())
df = (pd.DataFrame(self.reg.params)).T
df['X'] = 0
df['Y'] = 0
self.reg.params = pd.Series((df.T)[0])
def directDL(self, ite=100, bs=200, add=False):
logy_in = np.array(self.logy_in, dtype='float32')
X_in = np.array(self.X_in, dtype='float32')
y = Variable(logy_in)
x = Variable(X_in)
num, col_num = X_in.shape
if add is False:
self.model1 = CAR(15, 15, 5, col_num)
optimizer = optimizers.SGD()
optimizer.setup(self.model1)
for j in range(ite):
sffindx = np.random.permutation(num)
for i in range(0, num, bs):
x = Variable(X_in[sffindx[i:(i+bs) if (i+bs) < num else num]])
y = Variable(logy_in[sffindx[i:(i+bs) if (i+bs) < num else num]])
self.model1.zerograds()
loss = self.model1(x, y)
loss.backward()
optimizer.update()
if j % 1000 == 0:
loss_val = loss.data
print('epoch:', j)
print('train mean loss={}'.format(loss_val))
print(' - - - - - - - - - ')
y_ex = np.array(self.y_ex, dtype='float32').reshape(len(self.y_ex))
X_ex = np.array(self.X_ex, dtype='float32')
X_ex = Variable(X_ex)
logy_pred = self.model1.fwd(X_ex, X_ex).data
y_pred = np.exp(logy_pred)
error = y_ex - y_pred.reshape(len(y_pred),)
plt.hist(error[:])
def DL(self, ite=100, bs=200, add=False):
y_in = np.array(self.y_in, dtype='float32').reshape(len(self.y_in))
resid = y_in - np.exp(self.reg.predict())
resid = np.array(resid, dtype='float32').reshape(len(resid),1)
X_in = np.array(self.X_in, dtype='float32')
y = Variable(resid)
x = Variable(X_in)
num, col_num = X_in.shape
if add is False:
self.model1 = CAR(10, 10, 3, col_num)
optimizer = optimizers.Adam()
optimizer.setup(self.model1)
for j in range(ite):
sffindx = np.random.permutation(num)
for i in range(0, num, bs):
x = Variable(X_in[sffindx[i:(i+bs) if (i+bs) < num else num]])
y = Variable(resid[sffindx[i:(i+bs) if (i+bs) < num else num]])
self.model1.zerograds()
loss = self.model1(x, y)
loss.backward()
optimizer.update()
if j % 1000 == 0:
loss_val = loss.data
print('epoch:', j)
print('train mean loss={}'.format(loss_val))
print(' - - - - - - - - - ')
def predict(self):
y_ex = np.array(self.y_ex, dtype='float32').reshape(len(self.y_ex))
X_ex = np.array(self.X_ex, dtype='float32')
X_ex = Variable(X_ex)
resid_pred = self.model1.fwd(X_ex, X_ex).data
print(resid_pred[:10])
self.logy_pred = np.matrix(self.X_ex)*np.matrix(self.reg.params).T
self.error1 = np.array(y_ex - np.exp(self.logy_pred.reshape(len(self.logy_pred),)))[0]
self.pred = np.exp(self.logy_pred) + resid_pred
self.error2 = np.array(y_ex - self.pred.reshape(len(self.pred),))[0]
def compare(self):
plt.hist(self.error1)
plt.hist(self.error2)
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vars = ['P', 'S', 'L', 'R', 'RW', 'A', 'TS', 'TT', 'WOOD', 'SOUTH', 'CMD', 'IDD', 'FAR', 'X', 'Y']
#vars += vars + list(TDQ.columns)
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model = OLS_DLmodel(datas, vars)
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model.OLS()
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model.DL(ite=10, bs=200)
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model.predict()
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from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import numpy as np
X = model.X_ex['X'].values
Y = model.X_ex['Y'].values
Xs = np.linspace(min(X),max(X),10)
Ys = np.linspace(min(Y),max(Y),10)
error = model.error1
Xgrid, Ygrid = np.meshgrid(Xs, Ys)
Z = LL(X, Y, Xs, Ys, error)
fig = plt.figure()
ax = Axes3D(fig)
ax.plot_wireframe(Xgrid,Ygrid,Z) #<---ここでplot
plt.savefig("Main11.jpg")
plt.show()
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