inter = lin.intercept_
# y_pre = lin.predict(XX)
# score = lin.score(XX, Y)
y_pre = cross_val_predict(lin, XX, Y, cv=5)
score = cross_val_score(lin,
XX,
Y,
cv=5,
scoring='neg_mean_absolute_error').mean()
#
label = "Space Group: %s\n" % i + "MAE(CV): %.2f" % abs(score)
import matplotlib.pyplot as plt
p = BasePlot(font=None)
def scatter(y_true,
y_predict,
strx='y_true',
stry='y_predicted',
label=""):
x, y = y_true, y_predict
fig = plt.figure()
ax = fig.add_subplot(111)
ax.scatter(x,
y,
marker='o',
s=70,
alpha=0.8,
c='orange',
re_Tree=0,
random_state=0,
verbose=True,
add_coef=True,
inter_add=True,
cal_dim=False,
inner_add=True,
personal_map=False)
# sl.fit()
print(sl.expr)
y_pre = sl.predict(X)
score_all = sl.score(X, y, "r2")
p = BasePlot(font=None)
p.scatter(y,
y_pre,
strx='Experimental $E_{gap}$',
stry='Calculated $E_{gap}$')
import matplotlib.pyplot as plt
plt.show()
y_pre = sl.predict(X_test)
score_test = sl.score(X_test, y_test, "r2")
p = BasePlot(font=None)
p.scatter(y_test,
y_pre,
strx='Experimental $E_{gap}$',
stats=None,
verbose=True,
migrate_prob=0,
tq=True,
store=True,
personal_map=False,
stop_condition=None,
details=False,
classification=False,
score_object="y",
)
est_gp.fit(gd,
datav2,
categories=("Mul", "Div", "Add", "exp"),
power_categories=(0.5, 2))
e = est_gp.loop.top_n(100, ascending=True)
#
x0 = data2[:, 0]
x1 = data2[:, 1]
y = data2[:, 2]
pre_y = 16.18 * np.exp(-0.46792396 * x1 - 1.5177372 * x1 /
(-0.1052 * x0 - 9.002 * x1)) - 2.694
from mgetool.show import BasePlot
bp = BasePlot()
plt = bp.scatter_45_line(y, pre_y)
plt.show()
out_add=True,
cal_dim=True,
vector_add=True,
personal_map=False)
sl.fit()
score = sl.score(x, y, "r2")
print(i, sl.expr)
y_pre = sl.predict(x)
# break
y_pre = si_transformer.scale_y * y_pre
ssc = Dim.inverse_convert(y_dim, target_units=eV)[0]
y_pre = y_pre * ssc
p = BasePlot(font=None)
p.scatter(Y,
y_pre,
strx='Experimental $E_{gap}$',
stry='Calculated $E_{gap}$')
import matplotlib.pyplot as plt
plt.show()
from sklearn.linear_model import LinearRegression
lin = LinearRegression()
XX = np.vstack((
X[:, 1]**0.333,
X[:, 24] / (X[:, 22]**0.333 + X[:, 23]**0.333),
tq=True,
store=True,
personal_map=False,
stop_condition=None,
details=False,
classification=False,
score_object="y",
)
# est_gp.fit(gd, datav2,categories=("Mul", "Div", "Add", "exp"),)
# e = est_gp.loop.top_n(100, ascending=True)
x0 = data2[:, 0]
x1 = data2[:, 1]
y = data2[:, 2]
pre_y = 6.589 * np.exp(-0.10110812 * x1 + 0.11520935 * x1 /
(0.001157 * x0 + 0.3287 * x1)) + 2.031
from mgetool.show import BasePlot
bp = BasePlot()
plt = bp.scatter_45_line(y, pre_y, strx='Real Target', stry='Predict Target')
# plt.show()
plt.savefig("total.pdf")
data2 = np.concatenate((data2, pre_y.reshape(-1, 1)), axis=1)
data2 = pd.DataFrame(data2, columns=["x0", "x1", "real", "predict"])
r2_s = r2_score(y, pre_y)
MSE2_s = mean_squared_error(y, pre_y)
clf = Exhaustion(estimator,
n_select=n_select,
muti_grade=2,
muti_index=[2, X.shape[1]],
must_index=None,
n_jobs=1,
refit=True).fit(X, y)
name_ = name_to_name(X_frame.columns.values,
search=[i[0] for i in clf.score_ex[:10]],
search_which=0,
return_which=(1, ),
two_layer=True)
sc = np.array(clf.scatter)
for i in clf.score_ex[:]:
print(i[1])
for i in name_:
print(i)
t = clf.predict(X)
p = BasePlot()
p.scatter(y, t, strx='True $E_{gap}$', stry='Calculated $E_{gap}$')
plt.show()
p.scatter(sc[:, 0], sc[:, 1], strx='Number', stry='Score')
plt.show()
store.to_csv(sc, method_name + "".join([str(i) for i in n_select]))
store.to_pkl_pd(clf.score_ex,
method_name + "".join([str(i) for i in n_select]))