def test_stress():
from pymks.datasets import make_elastic_stress_random
from pymks import MKSHomogenizationModel, DiscreteIndicatorBasis
sample_size = 200
grain_size = [(5, 5), (6, 4), (4, 6), (2, 2)]
n_samples = [sample_size] * len(grain_size)
elastic_modulus = (410, 200)
poissons_ratio = (0.28, 0.3)
macro_strain = 0.001
size = (21, 21)
X, y = make_elastic_stress_random(n_samples=n_samples,
size=size,
grain_size=grain_size,
elastic_modulus=elastic_modulus,
poissons_ratio=poissons_ratio,
macro_strain=macro_strain,
seed=0)
dbasis = DiscreteIndicatorBasis(n_states=2, domain=[0, 1])
model = MKSHomogenizationModel(basis=dbasis, n_components=3, degree=3)
model.fit(X, y)
test_sample_size = 1
n_samples = [test_sample_size] * len(grain_size)
X_new, y_new = make_elastic_stress_random(n_samples=n_samples,
size=size,
grain_size=grain_size,
elastic_modulus=elastic_modulus,
poissons_ratio=poissons_ratio,
macro_strain=macro_strain,
seed=3)
y_result = model.predict(X_new)
assert np.allclose(np.round(y_new, decimals=2),
np.round(y_result, decimals=2))
def plot_component_variance(x, y): prim_basis = PrimitiveBasis(n_states=3, domain=[0, 2]) model = MKSHomogenizationModel(basis=prim_basis) model.n_components = 20 model.fit(x, y, periodic_axes=[0, 1]) # Draw the plot containing the PCA variance accumulation draw_component_variance(model.dimension_reducer.explained_variance_ratio_)
def test_stress():
from pymks.datasets import make_elastic_stress_random
from pymks import MKSHomogenizationModel, DiscreteIndicatorBasis
sample_size = 200
grain_size = [(5, 5), (6, 4), (4, 6), (2, 2)]
n_samples = [sample_size] * len(grain_size)
elastic_modulus = (410, 200)
poissons_ratio = (0.28, 0.3)
macro_strain = 0.001
size = (21, 21)
X, y = make_elastic_stress_random(n_samples=n_samples, size=size,
grain_size=grain_size,
elastic_modulus=elastic_modulus,
poissons_ratio=poissons_ratio,
macro_strain=macro_strain, seed=0)
dbasis = DiscreteIndicatorBasis(n_states=2, domain=[0, 1])
model = MKSHomogenizationModel(basis=dbasis, n_components=3, degree=3)
model.fit(X, y)
test_sample_size = 1
n_samples = [test_sample_size] * len(grain_size)
X_new, y_new = make_elastic_stress_random(
n_samples=n_samples, size=size, grain_size=grain_size,
elastic_modulus=elastic_modulus, poissons_ratio=poissons_ratio,
macro_strain=macro_strain, seed=3)
y_result = model.predict(X_new)
assert np.allclose(np.round(y_new, decimals=2),
np.round(y_result, decimals=2))
def test_intercept_setter():
from pymks import MKSHomogenizationModel
from pymks import PrimitiveBasis
p_basis = PrimitiveBasis(2)
model = MKSHomogenizationModel(basis=p_basis)
X = np.random.randint(2, size=(50, 10, 10))
y = np.random.randint(2, size=(50, ))
model.fit(X, y)
intercept = model.intercept_
model.intercept_ = intercept * 2
assert np.allclose(model.intercept_, intercept * 2)
def test_coef_setter():
from pymks import MKSHomogenizationModel
from pymks import PrimitiveBasis
p_basis = PrimitiveBasis(2)
model = MKSHomogenizationModel(basis=p_basis)
X = np.random.randint(2, size=(50, 10, 10))
y = np.random.randint(2, size=(50, ))
model.fit(X, y)
coefs = model.coef_
model.coef_ = coefs * 2
assert np.allclose(model.coef_, coefs * 2)
def test_intercept_setter():
from pymks import MKSHomogenizationModel
from pymks import PrimitiveBasis
p_basis = PrimitiveBasis(2)
model = MKSHomogenizationModel(basis=p_basis)
X = np.random.randint(2, size=(50, 10, 10))
y = np.random.randint(2, size=(50,))
model.fit(X, y)
intercept = model.intercept_
model.intercept_ = intercept * 2
assert np.allclose(model.intercept_, intercept * 2)
def test_coef_setter():
from pymks import MKSHomogenizationModel
from pymks import PrimitiveBasis
p_basis = PrimitiveBasis(2)
model = MKSHomogenizationModel(basis=p_basis)
X = np.random.randint(2, size=(50, 10, 10))
y = np.random.randint(2, size=(50,))
model.fit(X, y)
coefs = model.coef_
model.coef_ = coefs * 2
assert np.allclose(model.coef_, coefs * 2)
def plot_components(x, y, n_comps, linker_model, verbose=2):
prim_basis = PrimitiveBasis(n_states=3, domain=[0, 2])
model = MKSHomogenizationModel(basis=prim_basis,
property_linker=linker_model)
model.n_components = 5
model.fit(x,y,periodic_axes=[0,1])
print model.property_linker.coef_
draw_components([model.reduced_fit_data[0:3, :2],
model.reduced_fit_data[3:6, :2],
model.reduced_fit_data[6:9, :2],
model.reduced_fit_data[9:11, :2],
model.reduced_fit_data[11:14, :2],
model.reduced_fit_data[14:16, :2],
model.reduced_fit_data[16:17, :2],
model.reduced_fit_data[17:18, :2]],
['Ag:0.237 Cu:0.141 v:0.0525',
'Ag:0.237 Cu:0.141 v:0.0593',
'Ag:0.237 Cu:0.141 v:0.0773',
'Ag:0.237 Cu:0.141 v:0.0844',
'Ag:0.239 Cu:0.138 v:0.0791',
'Ag:0.239 Cu:0.138 v:0.0525',
'Ag:0.237 Cu:0.141 v:0.0914',
'Ag:0.237 Cu:0.141 v:0.0512'])
# Get a representative slice from the block (or ave or whatever we decide on)
best_slice = get_best_slice(metadatum['data'])
# Get 2-pt Stats for the best slice
print "--->Getting 2pt stats"
metadatum['stats'] = get_correlations_for_slice(best_slice)
print metadata[0]['stats'].shape
# Construct X and Y for PCA and linkage
print "-->Creating X and Y"
i = 0
for metadatum in metadata:
x[i,0:6*metadatum['x']**2] = metadatum['stats'].flatten()
prim_basis = PrimitiveBasis(n_states=3, domain=[0,2])
x_ = prim_basis.discretize(metadata[0]['data'])
x_corr = correlate(x_)
draw_correlations(x_corr.real)
quit()
# Reduce all 2-pt Stats via PCA
# Try linear reg on inputs and outputs
reducer = PCA(n_components=3)
linker = LinearRegression()
model = MKSHomogenizationModel(dimension_reducer=reducer,
property_linker=linker,
compute_correlations=False)
model.n_components = 40
model.fit(metadatum['stats'], y, periodic_axes=[0, 1])
print model.reduced_fit_data
# Optimizing polynomial degreee and number of components
'''params_to_tune = {'degree': np.arange(1, 4), 'n_components': np.arange(1, 8)}
fit_params = {'size': dataset[0].shape, 'periodic_axes': [0, 1]}
gs = GridSearchCV(model, params_to_tune, cv=3, n_jobs=3,
fit_params=fit_params).fit(data_train, stress_train)'''
# model = gs.best_estimator_
model.n_components = 4
model.degree = 2
print('Components'), (model.n_components)
print('Polynomail Order'), (model.degree)
# Fit data to model
model.fit(dataset, stresses, periodic_axes=[0, 1])
shapes = (data_test.shape[0],) + (dataset.shape[1:])
print shapes
data_test = data_test.reshape(shapes)
stress_predict = model.predict(data_test, periodic_axes=[0, 1])
labels = 'Long X', 'Short X', 'Long Y', 'Short Y'
# Draw PCA plot
draw_components([model.reduced_fit_data[:, :2],
model.reduced_predict_data[:, :2]],
['Training Data', 'Testing Data'])
# # Draw goodness of fit
