def test_export_python_string():
for smooth in (True, False):
model = Earth(penalty=1, smooth=smooth, max_degree=2).fit(X, y)
export_model = export_python_string(model, 'my_test_model')
six.exec_(export_model, globals())
for exp_pred, model_pred in zip(model.predict(X), my_test_model(X)):
assert_almost_equal(exp_pred, model_pred)
def test_pathological_cases():
import pandas
directory = os.path.join(
os.path.dirname(os.path.abspath(__file__)), 'pathological_data')
cases = {'issue_44': {},
'issue_50': {'penalty': 0.5,
'minspan': 1,
'allow_linear': False,
'endspan': 1,
'check_every': 1,
'sample_weight': 'issue_50_weight.csv'}}
for case, settings in cases.iteritems():
data = pandas.read_csv(os.path.join(directory, case + '.csv'))
y = data['y']
del data['y']
X = data
if 'sample_weight' in settings:
filename = os.path.join(directory, settings['sample_weight'])
sample_weight = pandas.read_csv(filename)['sample_weight']
del settings['sample_weight']
else:
sample_weight = None
model = Earth(**settings)
model.fit(X, y, sample_weight=sample_weight)
with open(os.path.join(directory, case + '.txt'), 'r') as infile:
correct = infile.read()
assert_equal(model.summary(), correct)
def test_copy_compatibility():
model = Earth(**default_params).fit(X, y)
model_copy = copy.copy(model)
assert_true(model_copy == model)
assert_true(
numpy.all(model.predict(X) == model_copy.predict(X)))
assert_true(model.basis_[0] is model.basis_[1]._get_root())
assert_true(model_copy.basis_[0] is model_copy.basis_[1]._get_root())
def test_smooth():
model = Earth(penalty=1, smooth=True)
model.fit(X, y)
res = str(model.trace()) + '\n' + model.summary()
filename = os.path.join(os.path.dirname(__file__),
'earth_regress_smooth.txt')
with open(filename, 'r') as fl:
prev = fl.read()
assert_equal(res, prev)
def test_pickle_compatibility():
earth = Earth(**default_params)
model = earth.fit(X, y)
model_copy = pickle.loads(pickle.dumps(model))
assert_true(model_copy == model)
assert_true(
numpy.all(model.predict(X) == model_copy.predict(X)))
assert_true(model.basis_[0] is model.basis_[1]._get_root())
assert_true(model_copy.basis_[0] is model_copy.basis_[1]._get_root())
def test_fit():
earth = Earth(**default_params)
earth.fit(X, y)
res = str(earth.trace()) + '\n' + earth.summary()
filename = os.path.join(os.path.dirname(__file__),
'earth_regress.txt')
with open(filename, 'r') as fl:
prev = fl.read()
assert_equal(res, prev)
def run_pyearth(X, y, **kwargs):
'''Run with pyearth. Return prediction value, training time, and number of forward pass iterations.'''
model = Earth(**kwargs)
t0 = time.time()
model.fit(X, y)
t1 = time.time()
y_pred = model.predict(X)
forward_iterations = len(model.forward_trace()) - 1
return y_pred, t1 - t0, forward_iterations
def test_exhaustive_search():
model = Earth(max_terms=13,
enable_pruning=False,
check_every=1,
thresh=0,
minspan=1,
endspan=1)
model.fit(X, y)
assert_equal(model.basis_.plen(), model.coef_.shape[1])
assert_equal(model.transform(X).shape[1], len(model.basis_))
def test_nb_terms():
for max_terms in (1, 3, 12, 13):
model = Earth(max_terms=max_terms)
model.fit(X, y)
assert_true(len(model.basis_) <= max_terms)
assert_true(len(model.coef_) <= len(model.basis_))
assert_true(len(model.coef_) >= 1)
if max_terms == 1:
assert_list_almost_equal_value(model.predict(X), y.mean())
def test_feature_importance():
criteria = ('rss', 'gcv', 'nb_subsets')
for imp in criteria:
earth = Earth(feature_importance_type=imp, **default_params)
earth.fit(X, y)
assert len(earth.feature_importances_) == X.shape[1]
earth = Earth(feature_importance_type=criteria, **default_params)
earth.fit(X, y)
assert type(earth.feature_importances_) == dict
assert set(earth.feature_importances_.keys()) == set(criteria)
for crit, val in earth .feature_importances_.items():
assert len(val) == X.shape[1]
assert_raises(
ValueError,
Earth(feature_importance_type='bad_name', **default_params).fit,
X, y)
earth = Earth(feature_importance_type=('rss',), **default_params)
earth.fit(X, y)
assert len(earth.feature_importances_) == X.shape[1]
assert_raises(
ValueError,
Earth(feature_importance_type='rss', enable_pruning=False, **default_params).fit,
X, y)
def test_fit():
earth = Earth(**default_params)
earth.fit(X, y)
res = str(earth.rsq_)
filename = os.path.join(os.path.dirname(__file__),
'earth_regress.txt')
# with open(filename, 'w') as fl:
# fl.write(res)
with open(filename, 'r') as fl:
prev = fl.read()
assert_true(abs(float(res) - float(prev)) < .05)
def test_pandas_compatibility():
import pandas
X_df = pandas.DataFrame(X)
y_df = pandas.DataFrame(y)
colnames = ['xx' + str(i) for i in range(X.shape[1])]
X_df.columns = colnames
earth = Earth(**default_params)
model = earth.fit(X_df, y_df)
assert_list_equal(
colnames, model.forward_trace()._getstate()['xlabels'])
def test_smooth():
model = Earth(penalty=1, smooth=True)
model.fit(X, y)
res = str(model.rsq_)
filename = os.path.join(os.path.dirname(__file__),
'earth_regress_smooth.txt')
# with open(filename, 'w') as fl:
# fl.write(res)
with open(filename, 'r') as fl:
prev = fl.read()
assert_true(abs(float(res) - float(prev)) < .05)
def test_linvars():
earth = Earth(**default_params)
earth.fit(X, y, linvars=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
res = str(earth.trace()) + '\n' + earth.summary()
filename = os.path.join(os.path.dirname(__file__),
'earth_linvars_regress.txt')
# with open(filename, 'w') as fl:
# fl.write(res)
with open(filename, 'r') as fl:
prev = fl.read()
assert_equal(res, prev)
def test_untrained():
model = Earth(**default_params)
assert_raises(NotFittedError, model.predict, X)
assert_raises(NotFittedError, model.transform, X)
assert_raises(NotFittedError, model.predict_deriv, X)
assert_raises(NotFittedError, model.score, X)
# the following should be changed to raise NotFittedError
assert_equal(model.forward_trace(), None)
assert_equal(model.pruning_trace(), None)
assert_equal(model.summary(), "Untrained Earth Model")
def runModel(i,featureCombo):
mae = np.array([])
logging.warning('try alpha = %s' % i)
for ktrain,ktest in kf:
x = trainCleaned.iloc[ktrain,]
y = trainCleaned.iloc[ktest,]
model = Earth()
model.fit(x[featureCombo],x['Expected'])
pred = model.predict(y[featureCombo])
mae = np.append(mae,(getMAE(pred,y['Expected'])))
logging.warning('average 10-fold MAE for alpha %s feature %s' % (i,featureCombo))
logging.warning(mae.mean())
def test_nb_degrees():
for max_degree in (1, 2, 12, 13):
model = Earth(max_terms=10,
max_degree=max_degree,
enable_pruning=False,
check_every=1,
thresh=0,
minspan=1,
endspan=1)
model.fit(X, y)
for basis in model.basis_:
assert_true(basis.degree() >= 0)
assert_true(basis.degree() <= max_degree)
def test_missing_data():
earth = Earth(allow_missing=True, **default_params)
missing_ = numpy.random.binomial(1, .05, X.shape).astype(bool)
X_ = X.copy()
X_[missing_] = None
earth.fit(X_, y)
res = str(earth.score(X_, y))
filename = os.path.join(os.path.dirname(__file__),
'earth_regress_missing_data.txt')
# with open(filename, 'w') as fl:
# fl.write(res)
with open(filename, 'r') as fl:
prev = fl.read()
assert_true(abs(float(res) - float(prev)) < .03)
def test_eq():
model1 = Earth(**default_params)
model2 = Earth(**default_params)
assert_equal(model1, model2)
assert_not_equal(model1, 5)
params = {}
params.update(default_params)
params["penalty"] = 15
model2 = Earth(**params)
assert_not_equal(model1, model2)
model3 = Earth(**default_params)
model3.unknown_parameter = 5
assert_not_equal(model1, model3)
def test_output_weight():
x = numpy.random.uniform(-1, 1, size=(1000, 1))
y = (numpy.dot(x, numpy.random.normal(0, 1, size=(1, 10)))) ** 5 + 1
y = (y - y.mean(axis=0)) / y.std(axis=0)
group = numpy.array([1] * 5 + [0] * 5)
output_weight = numpy.array([1] * 5 + [2] * 5, dtype=float)
model = Earth().fit(x, y, output_weight=output_weight)
# Check that the model fits at least better
# the more heavily weighted group
mse = ((model.predict(x) - y)**2).mean(axis=0)
group1_mean = mse[group].mean()
group2_mean = mse[numpy.logical_not(group)].mean()
assert_true(group1_mean > group2_mean or
round(abs(group1_mean - group2_mean), 7) == 0)
def test_patsy_compatibility():
import pandas
import patsy
X_df = pandas.DataFrame(X)
y_df = pandas.DataFrame(y)
colnames = ['xx' + str(i) for i in range(X.shape[1])]
X_df.columns = colnames
X_df['y'] = y
y_df, X_df = patsy.dmatrices(
'y ~ xx0 + xx1 + xx2 + xx3 + xx4 + xx5 + xx6 + xx7 + xx8 + xx9 - 1',
data=X_df)
model = Earth(**default_params).fit(X_df, y_df)
assert_list_equal(
colnames, model.forward_trace()._getstate()['xlabels'])
def test_sparse():
X_sparse = csr_matrix(X)
model = Earth(**default_params)
assert_raises(TypeError, model.fit, X_sparse, y)
model = Earth(**default_params)
model.fit(X, y)
assert_raises(TypeError, model.predict, X_sparse)
assert_raises(TypeError, model.predict_deriv, X_sparse)
assert_raises(TypeError, model.transform, X_sparse)
assert_raises(TypeError, model.score, X_sparse)
model = Earth(**default_params)
sample_weight = csr_matrix([1.] * X.shape[0])
assert_raises(TypeError, model.fit, X, y, sample_weight)
def __init__(self, maxp=100):
self.nump = 0
self.maxp = maxp
self.x = None # pylint: disable=invalid-name
self.fx = None
self.dim = None
self.model = Earth()
self.updated = False
def getTrain(trainData, testData):
size_s = len(trainData)
size_t = len(testData)
lenY = len(testData[0])
X = numpy.zeros((size_s,lenY-1))
Y = numpy.zeros((size_s,1))
z = 0
for d in trainData:
for j in range(lenY-1):
X[z][j] = d[j]
Y[z][0] = float(d[lenY-1])
z += 1
z = 0
dX = numpy.zeros((size_t,lenY-1))
for d in testData:
for j in range(lenY-1):
dX[z][j] = d[j]
z += 1
model = Earth()
model.fit(X,Y)
y_hat = model.predict(dX)
corrent = 0
for i in range(size_t):
x1 = testData[i][lenY-1]
x2 = y_hat[i]
if x1 * x2 >= 0:
corrent += 1
return corrent
def test_export_sympy():
import pandas as pd
from sympy.utilities.lambdify import lambdify
from sympy.printing.lambdarepr import NumPyPrinter
class PyEarthNumpyPrinter(NumPyPrinter):
def _print_Max(self, expr):
return 'maximum(' + ','.join(self._print(i) for i in expr.args) + ')'
def _print_NaNProtect(self, expr):
return 'where(isnan(' + ','.join(self._print(a) for a in expr.args) + '), 0, ' \
+ ','.join(self._print(a) for a in expr.args) + ')'
def _print_Missing(self, expr):
return 'isnan(' + ','.join(self._print(a) for a in expr.args) + ').astype(float)'
for smooth, n_cols, allow_missing in product((True, False), (1, 2), (True, False)):
X_df = pd.DataFrame(X.copy(), columns=['x_%d' % i for i in range(X.shape[1])])
y_df = pd.DataFrame(Y[:, :n_cols])
if allow_missing:
# Randomly remove some values so that the fitted model contains MissingnessBasisFunctions
X_df['x_1'][numpy.random.binomial(n=1, p=.1, size=X_df.shape[0]).astype(bool)] = numpy.nan
model = Earth(allow_missing=allow_missing, smooth=smooth, max_degree=2).fit(X_df, y_df)
expressions = export_sympy(model) if n_cols > 1 else [export_sympy(model)]
module_dict = {'select': numpy.select, 'less_equal': numpy.less_equal, 'isnan': numpy.isnan,
'greater_equal':numpy.greater_equal, 'logical_and': numpy.logical_and, 'less': numpy.less,
'logical_not':numpy.logical_not, "greater": numpy.greater, 'maximum':numpy.maximum,
'Missing': lambda x: numpy.isnan(x).astype(float),
'NaNProtect': lambda x: numpy.where(numpy.isnan(x), 0, x), 'nan': numpy.nan,
'float': float, 'where': numpy.where
}
for i, expression in enumerate(expressions):
# The lambdified functions for smoothed basis functions only work with modules='numpy' and
# for regular basis functions with modules={'Max':numpy.maximum}. This is a confusing situation
func = lambdify(X_df.columns, expression, printer=PyEarthNumpyPrinter, modules=module_dict)
y_pred_sympy = func(*[X_df.loc[:,var] for var in X_df.columns])
y_pred = model.predict(X_df)[:,i] if n_cols > 1 else model.predict(X_df)
assert_array_almost_equal(y_pred, y_pred_sympy)
def test_untrained():
# NotFittedError moved from utils.validation to exceptions
# some time after 0.17.1
try:
from sklearn.exceptions import NotFittedError
except ImportError:
from sklearn.utils.validation import NotFittedError
# Make sure calling methods that require a fitted Earth object
# raises the appropriate exception when using a not yet fitted
# Earth object
model = Earth(**default_params)
assert_raises(NotFittedError, model.predict, X)
assert_raises(NotFittedError, model.transform, X)
assert_raises(NotFittedError, model.predict_deriv, X)
assert_raises(NotFittedError, model.score, X)
# the following should be changed to raise NotFittedError
assert_equal(model.forward_trace(), None)
assert_equal(model.pruning_trace(), None)
assert_equal(model.summary(), "Untrained Earth Model")
def test_xlabels():
model = Earth(**default_params)
assert_raises(ValueError, model.fit, X[:, 0:5], y, xlabels=['var1', 'var2'])
model = Earth(**default_params)
model.fit(X[:, 0:3], y, xlabels=['var1', 'var2', 'var3'])
model = Earth(**default_params)
model.fit(X[:, 0:3], y, xlabels=['var1', 'var2', 'var3'])
def marsmodelorr(self, use_smY=True, slope_trunc=0.00001, savgol_window=151, savgol_order=3, ex_order=51):
Xf, Yf = self.Xf_, self.Yf_
X, Y = self.X_, self.Y_
fom = {}
# smooth the data
smY = savgol(Y, savgol_window, savgol_order)
# perform mars
model = MARS()
if use_smY:
model.fit(X, smY)
else:
model.fit(X, Y)
Y_h = model.predict(X)
'''
calculate dydx based on mars model to get knots and intercepts as this is
complicated to extract from hinge functions
'''
diff1 = np.diff(Y_h) / np.diff(X)
tdiff1 = diff1 - np.nanmin(diff1)
tdiff1 = tdiff1 / np.nanmax(tdiff1)
#calculate slopes of linear segments
ID = [i for i in range(1, len(tdiff1)) if np.abs(tdiff1[i] - tdiff1[i - 1]) > slope_trunc]
ID.insert(0, 0)
ID.append(np.argmax(X)) # this might cause an error
slopes = [np.nanmean(diff1[ID[i - 1]:ID[i]]) for i in range(1, len(ID) - 1)]
a = [Y_h[ID[i]] - slopes[i] * X[ID[i]] for i in range(len(ID) - 2)]
IDM, IDm = np.argmax(slopes), np.argmin(np.abs(slopes))
# intercept of highest slope and zero as well as highest slope and lowest slope
fom['zinter'] = -a[IDM] / slopes[IDM]
fom['lminter'] = (a[IDM] - a[IDm]) / (slopes[IDm] - slopes[IDM])
fom['max_slope'] = slopes[IDM]
fom['curr_lminter_model'] = fom['lminter'] * slopes[IDM] + a[IDM]
fom['curr_lminter_data'] = np.mean(Y[np.where(np.abs(X - fom['lminter']) < 0.5)[0]])
# calculate how the CV curves kight look like without the 'ORR part'
srYs = smY - model.predict(X)
srYf = savgol(Yf - model.predict(Xf), savgol_window, savgol_order)
# calculate their derivative
dsrYf = savgol(np.diff(srYf) / np.diff(Xf), savgol_window, savgol_order)
# find the extrema in the derivatives for extraction of redox pots
redID_f = argrelextrema(srYf, np.less, order=ex_order)
oxID_f = argrelextrema(srYf, np.greater, order=ex_order)
# calc some more foms like position of redox waves
fom['redpot_f'], fom['redpot_f_var'] = np.nanmean(Xf[redID_f]), np.nanstd(Xf[redID_f])
fom['oxpot_f'], fom['oxpot_f_var'] = np.nanmean(Xf[oxID_f]), np.nanstd(Xf[oxID_f])
fom['X'], fom['Xf'] = X, Xf
fom['srYs'], fom['srYf'], fom['smY'] = srYs, srYf, smY
fom['Y'], fom['Yf'], fom['Y_h'] = Y, Yf, Y_h
fom['noise_lvl'] = np.sum((Y_h - Y) ** 2, axis=0)
self.fom = fom
def __init__(self, dim):
self.num_pts = 0
self.X = np.empty([0, dim])
self.fX = np.empty([0, 1])
self.dim = dim
self.updated = False
try:
from pyearth import Earth
self.model = Earth()
except ImportError as err:
print("Failed to import pyearth")
raise err
def test_fast():
earth = Earth(max_terms=10,
max_degree=5,
**default_params)
earth.fit(X, y)
normal_summary = earth.summary()
earth = Earth(use_fast=True,
max_terms=10,
max_degree=5,
fast_K=10,
fast_h=1,
**default_params)
earth.fit(X, y)
fast_summary = earth.summary()
assert_equal(normal_summary, fast_summary)
def test_deriv():
model = Earth(**default_params)
model.fit(X, y)
assert_equal(X.shape + (1, ), model.predict_deriv(X).shape)
assert_equal((X.shape[0], 1, 1), model.predict_deriv(X, variables=0).shape)
assert_equal((X.shape[0], 1, 1),
model.predict_deriv(X, variables='x0').shape)
assert_equal((X.shape[0], 3, 1),
model.predict_deriv(X, variables=[1, 5, 7]).shape)
assert_equal((X.shape[0], 0, 1),
model.predict_deriv(X, variables=[]).shape)
res_deriv = model.predict_deriv(X, variables=['x2', 'x7', 'x0', 'x1'])
assert_equal((X.shape[0], 4, 1), res_deriv.shape)
res_deriv = model.predict_deriv(X, variables=['x0'])
assert_equal((X.shape[0], 1, 1), res_deriv.shape)
assert_equal((X.shape[0], 1, 1),
model.predict_deriv(X, variables=[0]).shape)
model = Earth(max_terms=50, max_degree=3)
model.fit(X,y)
#Print the model
#print(model.trace())
print(model.summary())
print "MARS degree 5"
model = Earth(max_terms=20, max_degree=5)
model.fit(X,y)
#Print the model
#print(model.trace())
print(model.summary())
"""
print "====================================="
print "MARS degree 1"
model = Earth(max_terms=70, max_degree=1)
print "Score: {}".format ( crossValidation ( model, X, y ) )
print "MARS degree 3"
model = Earth(max_terms=50, max_degree=3)
crossValidation ( model, X, y )
print "Score: {}".format ( crossValidation ( model, X, y ) )
X = np.array(X)
y = np.sin(X) + np.random.normal(size=X.shape[0])/10.0
#Defining different knots which will be used as a parameter for MARS model
knots = [2,4,5,10]
#Helpful in creating graph
axis = [[0,0],[0,1],[1,0],[1,1]]
#Defining different max_degree parameter for MARS model parameter
for degree in range(1,5):
fig,ax = plt.subplots(2,2,figsize=(10, 10))
for num_knot in range(4):
# Defining MARS model with max_term and max_degree parameter
model = Earth(max_terms=knots[num_knot],max_degree=degree,verbose=0)
#Fitting the dataset on the dataset
model.fit(X, y)
#Prediction model output
y_hat = model.predict(X)
#Potting graphs
ax[axis[num_knot][0],axis[num_knot][1]].title.set_text(f"degree = {degree}, knots = {knots[num_knot]}")
ax[axis[num_knot][0],axis[num_knot][1]].plot(X,y,'r.')
ax[axis[num_knot][0],axis[num_knot][1]].plot(X,y_hat,'b.')
plt.show()
# Plotting dataset distribution
plt.figure()
def translation_correction(cell_mesh, cell_mesh_2, buffer_cell,\
x_pos, y_pos, z_pos, x_pos_new, y_pos_new, z_pos_new, closest_no_conflict, directory ):
x_min = np.min([np.min(cell_mesh[:,0]),np.min(cell_mesh_2[:,0])]) - buffer_cell
x_max = np.max([np.max(cell_mesh[:,0]),np.max(cell_mesh_2[:,0])]) + buffer_cell
y_min = np.min([np.min(cell_mesh[:,1]),np.min(cell_mesh_2[:,1])]) - buffer_cell
y_max = np.max([np.max(cell_mesh[:,1]),np.max(cell_mesh_2[:,1])]) + buffer_cell
z_min = np.min([np.min(cell_mesh[:,2]),np.min(cell_mesh_2[:,2])]) - buffer_cell
z_max = np.max([np.max(cell_mesh[:,2]),np.max(cell_mesh_2[:,2])]) + buffer_cell
num_pts = len(x_pos)
X = []; Y = []; Z = []; U = []; V = []; W = []
for kk in range(0,num_pts):
idx = closest_no_conflict[kk]
if idx < len(closest_no_conflict):
U.append(x_pos_new[idx] - x_pos[kk])
V.append(y_pos_new[idx] - y_pos[kk])
W.append(z_pos_new[idx] - z_pos[kk])
X.append(x_pos_new[idx]); Y.append(y_pos_new[idx]); Z.append(z_pos_new[idx])
# --> limit to points that aren't too close to the cell
X_safe = []; Y_safe = []; Z_safe = []; U_safe = []; V_safe = []; W_safe = []
num_pts = len(U)
for kk in range(0,num_pts):
x_out = X[kk] < x_min or X[kk] > x_max
y_out = Y[kk] < y_min or Y[kk] > y_max
z_out = Z[kk] < z_min or Z[kk] > z_max
if x_out or y_out or z_out:
X_safe.append(X[kk])
Y_safe.append(Y[kk])
Z_safe.append(Z[kk])
U_safe.append(U[kk])
V_safe.append(V[kk])
W_safe.append(W[kk])
X_safe = np.asarray(X_safe); Y_safe = np.asarray(Y_safe); Z_safe = np.asarray(Z_safe)
U_safe = np.asarray(U_safe); V_safe = np.asarray(V_safe); W_safe = np.asarray(W_safe)
# --> fit MARS models
model_U = Earth(max_degree=2,max_terms=10)
model_U.fit(Z_safe,U_safe)
model_V = Earth(max_degree=2,max_terms=10)
model_V.fit(Z_safe,V_safe)
model_W = Earth(max_degree=2,max_terms=10)
model_W.fit(Z_safe,W_safe)
# --> re-define Z
pred_U = model_U.predict(z_pos_new)
pred_V = model_V.predict(z_pos_new)
pred_W = model_W.predict(z_pos_new)
# --> correct new bead positions
for kk in range(0,len(x_pos_new)):
x_pos_new[kk] = x_pos_new[kk] - pred_U[kk]
y_pos_new[kk] = y_pos_new[kk] - pred_V[kk]
z_pos_new[kk] = z_pos_new[kk] - pred_W[kk]
# --> correct new cell position
pred_cell_0 = model_U.predict(cell_mesh_2[:,0])
pred_cell_1 = model_V.predict(cell_mesh_2[:,1])
pred_cell_2 = model_W.predict(cell_mesh_2[:,2])
cell_mesh_2_new = np.zeros(cell_mesh_2.shape)
cell_mesh_2_new[:,0] = cell_mesh_2[:,0] - pred_cell_0
cell_mesh_2_new[:,1] = cell_mesh_2[:,1] - pred_cell_1
cell_mesh_2_new[:,2] = cell_mesh_2[:,2] - pred_cell_2
# --> plot MARS models
Z_line = np.linspace(np.min(Z),np.max(Z),100)
pred_line_U = model_U.predict(Z_line)
pred_line_V = model_V.predict(Z_line)
pred_line_W = model_W.predict(Z_line)
plt.figure(figsize=(15,5))
plt.subplot(1,3,1)
plt.plot(Z,U,'b.',label='x raw')
plt.plot(Z_line,pred_line_U,'k--',label='fit')
plt.xlabel('z position'); plt.ylabel('displacement')
plt.tight_layout(); plt.legend(); plt.title('x displacements')
plt.subplot(1,3,2)
plt.plot(Z,V,'r.',label='y raw')
plt.plot(Z_line,pred_line_V,'k--',label='fit')
plt.xlabel('z position'); plt.ylabel('displacement')
plt.tight_layout(); plt.legend(); plt.title('y displacements')
plt.subplot(1,3,3)
plt.plot(Z,W,'g.',label='z raw')
plt.plot(Z_line,pred_line_W,'k--',label='fit')
plt.xlabel('z position'); plt.ylabel('displacement')
plt.tight_layout(); plt.legend(); plt.title('z displacements')
plt.savefig(directory + '/translation_correction.png')
return x_pos_new, y_pos_new, z_pos_new, cell_mesh_2_new
test = pd.read_csv('../data/modeltest.csv',index_col=0)
label = train['Response'].values
featextra= pd.read_csv('../feat/improve.csv',index_col=0)
train = pd.concat([train,featextra.loc[train.index]],axis=1)
test = pd.concat([test,featextra.loc[test.index]],axis=1)
featextra= pd.read_csv('../feat/duplicate.csv',index_col=0)
train = pd.concat([train,featextra.loc[train.index]],axis=1)
test = pd.concat([test,featextra.loc[test.index]],axis=1)
feat = train.columns.drop('Response',1)
#Build an Earth model with a logisticregression pipeline
earth_pipe = Pipeline([('earth',Earth(use_fast=True,allow_missing=True,penalty=0.5,max_degree=3)),('log',LogisticRegression())])
earth_pipe.fit(train[feat],label)
#Parameter tuning
#param_grid = {'earth__penalty': np.arange(1,11,2),'earth__max_degree': range(1,4)}
#
#gs1 = GridSearchCV(earth_pipe,param_grid,n_jobs=1,pre_dispatch=1,cv=StratifiedKFold(label, n_folds=5, shuffle=True),scoring='log_loss',verbose=2)
#
#
#gs1.fit(train[feat],label)
#
#print gs1.best_params_
#print gs1.best_score_
#
##----------------------------------------------------------
def fit_with_subdata(scaledown, offset):
# retrieve training data and official reco hadronic energy for comparison
X = root2array('../training_data.root',
branches='calehad',
selection='mustopz<1275&&isnumucc==1',
step=scaledown, start=offset).reshape(-1,1)
recoemu_official = root2array('../training_data.root', branches='recoemu',
selection='mustopz<1275&&isnumucc==1',
step=scaledown, start=offset)
trueenu = root2array('../training_data.root', branches='trueenu',
selection='mustopz<1275&&isnumucc==1',
step=scaledown, start=offset)
y = trueenu - recoemu_official
yoff = root2array('../training_data.root', branches='recoehad',
selection='mustopz<1275&&isnumucc==1',
step=scaledown, start=offset)
# train svm with standardized regressors
mars = Earth()
mars.fit(X, y)
# save the model
os.system('mkdir -p models/1d')
modelpn = 'models/1d/hadronic_1d_energy_estimator_step{}offset{}.pkl'.format(scaledown, offset)
joblib.dump(mars, modelpn)
# estimate reco value
yest = mars.predict(X)
rest = (yest-y)/y
roff = (yoff-y)/y
# save root file
os.system('mkdir -p output_root_files/1d')
toutf = TFile('output_root_files/1d/resolution_1d_step{}offset{}.root'.format(scaledown, offset), 'recreate')
tr = TTree( 'tr', 'resolution tree' )
r1 = array( 'f', [ 0. ] )
r2 = array( 'f', [ 0. ] )
marsehad = array( 'f', [ 0. ] )
offehad = array( 'f', [ 0. ] )
trueehad = array( 'f', [ 0. ] )
tr.Branch( 'rest', r1, 'rest/F' )
tr.Branch( 'roff', r2, 'roff/F' )
tr.Branch('marsehad', marsehad, 'marsehad/F')
tr.Branch('offehad', offehad, 'offehad/F')
tr.Branch('trueehad', trueehad, 'trueehad/F')
for i in range(len(rest)):
r1[0] = rest[i]
r2[0] = roff[i]
marsehad[0] = yest[i]
offehad[0] = yoff[i]
trueehad[0] = y[i]
tr.Fill()
tr.Write()
toutf.Close()
# print out the statistics
os.system('mkdir -p performance_figures/1d')
with open('performance_figures/1d/1d_step{}offset{}.txt'.format(scaledown, offset), 'w') as outf:
outf.write(str(np.mean(rest))+'\n')
outf.write(str(tstd(rest))+'\n')
outf.write(str(skew(rest))+'\n')
outf.write(str(kurtosis(rest))+'\n')
clf.fit(X_train, y_train)
y_eval = clf.predict(X)
prediction = pd.DataFrame(y_eval,
columns=['predictions']).to_csv('outElasticNet.csv')
regr_2.fit(X_train, y_train)
y_eval = regr_2.predict(X)
prediction = pd.DataFrame(y_eval,
columns=['predictions'
]).to_csv('outAdaBoostRegressor.csv')
clf = linear_model.Lars(n_nonzero_coefs=1)
clf.fit(X_train, y_train)
y_eval = clf.predict(X)
prediction = pd.DataFrame(y_eval,
columns=['predictions']).to_csv('outLARS.csv')
"""
clf = AdaBoostRegressor(DecisionTreeRegressor(max_depth=4),n_estimators=300, random_state=rng)
clf.fit(X_train, y_train)
y_eval = clf.predict(X)
prediction = pd.DataFrame(y_eval, columns=['predictions']).to_csv('outAdaBoostRegressor.csv')
"""
from pyearth import Earth
clf = Earth()
clf.fit(X_train, y_train)
y_eval = clf.predict(X)
prediction = pd.DataFrame(y_eval,
columns=['predictions']).to_csv('outMARS.csv')
class MARSInterpolant(Surrogate):
"""Compute and evaluate a MARS interpolant
MARS builds a model of the form
.. math::
\\hat{f}(x) = \\sum_{i=1}^{k} c_i B_i(x).
The model is a weighted sum of basis functions :math:`B_i(x)`. Each basis
function :math:`B_i(x)` takes one of the following three forms:
1. a constant 1.
2. a hinge function of the form :math:`\\max(0, x - const)` or \
:math:`\\max(0, const - x)`. MARS automatically selects variables \
and values of those variables for knots of the hinge functions.
3. a product of two or more hinge functions. These basis functions c \
an model interaction between two or more variables.
:param dim: Number of dimensions
:type dim: int
:param lb: Lower variable bounds
:type lb: numpy.array
:param ub: Upper variable bounds
:type ub: numpy.array
:param output_transformation: Transformation applied to values before fitting
:type output_transformation: Callable
:ivar dim: Number of dimensions
:ivar lb: Lower variable bounds
:ivar ub: Upper variable bounds
:ivar output_transformation: Transformation to apply to function values before fitting
:ivar num_pts: Number of points in surrogate model
:ivar X: Point incorporated in surrogate model (num_pts x dim)
:ivar fX: Function values in surrogate model (num_pts x 1)
:ivar updated: True if model is up-to-date (no refit needed)
:ivar model: Earth object
"""
def __init__(self, dim, lb, ub, output_transformation=None):
super().__init__(dim=dim, lb=lb, ub=ub, output_transformation=output_transformation)
try:
from pyearth import Earth
self.model = Earth()
except ImportError as err:
print("Failed to import pyearth")
raise err
def _fit(self):
"""Compute new coefficients if the MARS interpolant is not updated."""
with warnings.catch_warnings():
warnings.simplefilter("ignore") # Surpress deprecation warnings
if self.updated is False:
fX = self.output_transformation(self.fX.copy())
self.model.fit(self._X, fX)
self.updated = True
def predict(self, xx):
"""Evaluate the MARS interpolant at the points xx
:param xx: Prediction points, must be of size num_pts x dim or (dim, )
:type xx: numpy.ndarray
:return: Prediction of size num_pts x 1
:rtype: numpy.ndarray
"""
self._fit()
xx = to_unit_box(np.atleast_2d(xx), self.lb, self.ub)
return np.expand_dims(self.model.predict(xx), axis=1)
def predict_deriv(self, xx):
"""Evaluate the derivative of the MARS interpolant at points xx
:param xx: Prediction points, must be of size num_pts x dim or (dim, )
:type xx: numpy.array
:return: Derivative of the RBF interpolant at xx
:rtype: numpy.array
"""
self._fit()
xx = to_unit_box(np.atleast_2d(xx), self.lb, self.ub)
dfx = self.model.predict_deriv(xx, variables=None)
return dfx[0] / (self.ub - self.lb)
def fit(self):
self.classifier = Earth()
self.classifier.fit(self.x_train, self.y_train)
olsMSE = kFoldValidation(5, ols, array_x_train, array_y_train)
olsMSE
# array([2.7911842 , 2.76834881, 2.84893447, 2.78335565, 2.73966849])
# OLS using only the 14 top correlated variables
sub_x_train = x_train[top[1:]]
array_x_sub = np.array(sub_x_train)
olsMSE = kFoldValidation(5, ols, array_x_sub, array_y_train)
olsMSE
# array([2.82931072, 2.80517518, 2.88589756, 2.82362708, 2.78061219])
# It seems that the model using all variables performs better
# 2. Spline
# Since it is too slow to do the k cross validation for spline,
# just use validation set to test the performance.
spline = Earth()
sub_cols = list(top[1:]) # Uses highly-correlated variables to build the model
sub_x_train = x_train[sub_cols]
array_sub_x = np.array(sub_x_train)
spline.fit(sub_x_train, y_train)
preds_val = spline.predict(x_val[sub_cols])
splineMSE = np.mean(
(preds_val - array_y_val.ravel())**2) # Calculates the mean squared error
splineMSE
# 2.457458862928802
# 3. Random Forest
# Since it is too slow to do the k cross validation for random forest,
# just use validation set to test the performance.
# Builds the model with 50 trees
rf = RandomForestRegressor(max_depth=20, random_state=42, n_estimators=50)
xArray = []
yArray = []
#seperating X and Y from the dataset
for eachXYPAir in dataset:
x = eachXYPAir[0]
y = eachXYPAir[1]
xArray.append(x)
yArray.append(y)
# print len(xArray)
xArray = numpy.asarray(xArray, "float32") # converting to numpy array
# print len(yArray)
yArray = numpy.asarray(yArray, "float32") # converting to numpy array
# Fit an Earth model
model = Earth(max_degree=1, verbose=True) # initializing py- earth package
# making model for the data
model.fit(xArray, yArray)
# Print the model
print(model.trace())
print(model.summary())
# Plot the model
y_hat = model.predict(xArray)
# print y_hat
plt.figure()
plt.plot(xArray, yArray, 'r.')
plt.plot(xArray, y_hat, 'b.')
plt.show()
import numpy
from pyearth import Earth
from matplotlib import pyplot
#Create some fake data
numpy.random.seed(0)
m = 1000
n = 10
X = 80 * numpy.random.uniform(size=(m, n)) - 40
y = numpy.abs(X[:, 6] - 4.0) + 1 * numpy.random.normal(size=m)
#Fit an Earth model
model = Earth()
model.fit(X, y)
#Print the model
print(model.trace())
print(model.summary())
#Plot the model
y_hat = model.predict(X)
pyplot.figure()
pyplot.plot(X[:, 6], y, 'r.')
pyplot.plot(X[:, 6], y_hat, 'b.')
pyplot.xlabel('x_6')
pyplot.ylabel('y')
pyplot.title('Simple Earth Example')
pyplot.show()
def test_linear_fit():
from statsmodels.regression.linear_model import GLS, OLS
earth = Earth(**default_params)
earth.fit(X, y)
earth.linear_fit(X, y)
soln = OLS(y, earth.transform(X)).fit().params
assert_almost_equal(numpy.mean((earth.coef_ - soln)**2), 0.0)
sample_weight = 1.0 / (numpy.random.normal(size=y.shape)**2)
earth.fit(X, y)
earth.linear_fit(X, y, sample_weight)
soln = GLS(y, earth.transform(X), 1.0 / sample_weight).fit().params
assert_almost_equal(numpy.mean((earth.coef_ - soln)**2), 0.0)
def test_feature_importance():
criteria = ('rss', 'gcv', 'nb_subsets')
for imp in criteria:
earth = Earth(feature_importance_type=imp, **default_params)
earth.fit(X, y)
assert len(earth.feature_importances_) == X.shape[1]
earth = Earth(feature_importance_type=criteria, **default_params)
earth.fit(X, y)
assert type(earth.feature_importances_) == dict
assert set(earth.feature_importances_.keys()) == set(criteria)
for crit, val in earth.feature_importances_.items():
assert len(val) == X.shape[1]
assert_raises(
ValueError,
Earth(feature_importance_type='bad_name', **default_params).fit, X, y)
earth = Earth(feature_importance_type=('rss', ), **default_params)
earth.fit(X, y)
assert len(earth.feature_importances_) == X.shape[1]
assert_raises(
ValueError,
Earth(feature_importance_type='rss',
enable_pruning=False,
**default_params).fit, X, y)
# Create some fake data
numpy.random.seed(2)
m = 10000
n = 10
X = numpy.random.uniform(size=(m, n))
y = (10 * numpy.sin(numpy.pi * X[:, 0] * X[:, 1]) +
20 * (X[:, 2] - 0.5) ** 2 +
10 * X[:, 3] +
5 * X[:, 4] + numpy.random.uniform(size=m))
# Fit an Earth model
criteria = ('rss', 'gcv', 'nb_subsets')
model = Earth(max_degree=3,
max_terms=10,
minspan_alpha=.5,
feature_importance_type=criteria,
verbose=True)
model.fit(X, y)
rf = RandomForestRegressor()
rf.fit(X, y)
# Print the model
print(model.trace())
print(model.summary())
print(model.summary_feature_importances(sort_by='gcv'))
# Plot the feature importances
importances = model.feature_importances_
importances['random_forest'] = rf.feature_importances_
criteria = criteria + ('random_forest',)
idx = 1
def test_export_python_function():
for smooth in (True, False):
model = Earth(penalty=1, smooth=smooth, max_degree=2).fit(X, y)
export_model = export_python_function(model)
for exp_pred, model_pred in zip(model.predict(X), export_model(X)):
assert_almost_equal(exp_pred, model_pred)
# train = pd.read_csv('boston_data.csv')
# X = np.array(train.iloc[:, 0:13])
# y = np.array(train.iloc[:, 13])
#
# test = pd.read_csv('boston_test_data.csv')
# X_test = np.array(test.iloc[:, 0:13])
# X_test_id = test.iloc[:, 0]
np.random.seed(0)
m = 1000
n = 10
X = 80 * np.random.uniform(size=(m, n)) - 40
y = np.abs(X[:, 6] - 4.0) + 1 * np.random.normal(size=m)
#Fit an Earth model
model = Earth()
model.fit(X, y)
#Print the model
print(model.trace())
print(model.summary())
X, y = load_boston(return_X_y=True)
model_rsq_dic = {}
# # % lower status of the population
lstat_x = []
[lstat_x.append(row[12]) for row in X]
lstat_x = np.array(lstat_x).reshape(-1, 1)
def csc(df, hamming_string_dict, outdir, filename):
"""CRISPR Specificity Correction
:param df: pandas dataframe with first column as gRNA and second column as logFC/metric
:param hamming_string_dict: CSC onboard dictionary object with key as gRNA and value as Hamming metrics
:param outdir: absolute filepath to output directory
:param filename: name of input file to be used as part of output filename
:return: CSC adjustment
"""
# MARS compatible file
df_mars_lst = []
df_v = np.asarray(df)
for i in range(len(df_v)):
row_lst = []
grna, metric = df_v[i][0], df_v[i][1]
try:
metric = float(metric)
except ValueError:
sys.stdout.write(
'WARNING: encountered %s which is not float compatible, skipping\n'
% metric)
continue
row_lst.append(grna)
try:
for jj in hamming_string_dict[grna]:
row_lst.append(jj)
row_lst.append(metric)
df_mars_lst.append(row_lst)
except KeyError:
sys.stdout.write('\n%s not found in selected library: passing\n' %
grna)
continue
df = pd.DataFrame(df_mars_lst,
columns=[
'gRNA', 'specificity', 'h0', 'h1', 'h2', 'h3',
'original_value'
])
# exclude infinte specificity non-target gRNAs
df = df[df['h0'] != 0]
# isolate pertinent confounder variables
df_confounders = df[['specificity', 'h0', 'h1', 'h2', 'h3']]
# knots
knots = df['original_value'].quantile([0.25, 0.5, 0.75, 1])
# training and testing data
train_x, test_x, train_y, test_y = train_test_split(df_confounders,
df['original_value'],
test_size=0.10,
random_state=1)
# Fit an Earth model
model = Earth(feature_importance_type='gcv')
try:
model.fit(train_x, train_y)
except ValueError:
sys.stdout.write(
'\nValue Error encountered. Model unable to be trained. Exiting CSC Novo\n'
)
model_processed = 'F'
sys.stdout.write(
'training input x data\n %s\ntraining input y data\n %s\n' %
(train_x, train_y))
return model_processed
# Print the model
print(model.trace())
print(model.summary())
print(model.summary_feature_importances())
# Plot the model
y_hat = model.predict(test_x)
# calculating RMSE values
rms1 = sqrt(mean_squared_error(test_y, y_hat))
print('\n\nRMSE on Predictions\n\n')
print(rms1)
# calculating R^2 for training
print('\n\nR^2 on Training Data\n\n')
print(model.score(train_x, train_y))
# calculating R^2 for testing
print('\n\nR^2 on Testing Data\n\n')
print(model.score(test_x, test_y))
# write out model metrics
with open('%s/csc_model_metrics_%s.txt' % (outdir, filename),
'w') as outfile:
outfile.write('%s\n%s\n%s\nRMSE on Predictions\n%s' %
(model.trace(), model.summary(),
model.summary_feature_importances(), rms1))
if rms1 <= 1.0:
#model processed
model_processed = 'T'
# full data prediction
df['earth_adjustment'] = model.predict(df_confounders)
# CSC correction
df['earth_corrected'] = df['original_value'] - df['earth_adjustment']
# main write out
df.to_csv('%s/csc_output_%s_earth_patched.csv' % (outdir, filename))
# pickle write out
model_file = open(
'%s/csc_output_%s_earth_model.pl' % (outdir, filename), 'wb')
pl.dump(model, model_file)
model_file.close()
sys.stdout.write('\nCSC adjustment complete\n')
sys.stdout.write('\nCSC output files written to %s\n' % outdir)
return model_processed
else:
sys.stdout.write(
'\nCSC adjustment not computed as model residual mean squared error exceeds 1.0\n'
)
model_processed = 'F'
return model_processed
)
# Select target and feature dataset(s) --> [target, feature1, feature2, ... ]
datasets = [
Dataset('runoff', database),
Dataset('runoff', database).normalized(),
Dataset('temp', database).normalized(),
Dataset('precip', database).normalized(),
Dataset('season', database).normalized()
]
# Select leadtimes for target and feature. negative:past/positive:future
leadtimes = [[1, 3], [-4, -1], [-4, -1], [-4, -1], [1, 1]]
# Select Model
model_type = Earth(max_degree=10, smooth=True)
#model_type= Lasso(alpha=0.05,normalize=True, max_iter=3000)
#model_type = Regressor(
# layers=[
# Layer("Sigmoid",units=5),
# Layer("Linear", units=1)],
# learning_rate=0.1,
# n_iter=1000)
# Set training interval
startyear = DateFormat(1900, 1)
endyear = DateFormat(2005, 36)
training_daterange = DateFormat.decadal_daterange(startyear, endyear)
# Set testing interval
startyear = DateFormat(2006, 1)
import numpy
from pyearth import Earth
from matplotlib import pyplot
# Create some fake data
numpy.random.seed(2)
m = 10000
n = 10
X = 80 * numpy.random.uniform(size=(m, n)) - 40
y = 100 * \
numpy.abs(numpy.sin((X[:, 6]) / 10) - 4.0) + \
10 * numpy.random.normal(size=m)
# Fit an Earth model
model = Earth(max_degree=3, minspan_alpha=.5)
model.fit(X, y)
# Print the model
print model.trace()
print model.summary()
# Plot the model
y_hat = model.predict(X)
pyplot.figure()
pyplot.plot(X[:, 6], y, 'r.')
pyplot.plot(X[:, 6], y_hat, 'b.')
pyplot.show()
df = pd.read_csv(dataset, sep='\t')
df = pd.read_table(dataset)
gt_mapping = {'0/0': 0, '0/1': 1, '1/1': 2}
df['GT_GATK'] = df['GT_GATK'].map(gt_mapping)
df['GT_Varscan'] = df['GT_Varscan'].map(gt_mapping)
df['GT_Freebayes'] = df['GT_Freebayes'].map(gt_mapping)
X = df.values[:100, 5:]
X = set_missing_values(X)
#print df.columns[12]
y = np.random.randint(2, size=(int(np.shape(X)[0]), ))
#print X
#print y
earth_classifier = Pipeline([('earth', Earth(allow_missing=True)),
('logistic', LogisticRegression())])
#earth_classifier = Pipeline([('earth', Earth(allow_missing=True)),
# ('logistic', RandomForestClassifier())])
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.4,
random_state=0)
ec = earth_classifier.fit(X_train, y_train)
y_hat = earth_classifier.predict(X_test)
A simple example plotting a fit of the sine function.
"""
import numpy
import matplotlib.pyplot as plt
from pyearth import Earth
# Create some fake data
numpy.random.seed(2)
m = 10000
n = 10
X = 80 * numpy.random.uniform(size=(m, n)) - 40
y = 100 * \
(numpy.sin((X[:, 6])) - 4.0) + \
10 * numpy.random.normal(size=m)
# Fit an Earth model
model = Earth(max_degree=3, minspan_alpha=.5, verbose=True)
model.fit(X, y)
# Print the model
print(model.trace())
print(model.summary())
# Plot the model
y_hat = model.predict(X)
plt.plot(X[:, 6], y, 'r.')
plt.plot(X[:, 6], y_hat, 'b.')
plt.show()
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from pyearth import Earth
## Load Data
df = pd.read_csv('hw2_data_2.txt', sep='\t')
X_train, y_train = np.array(df.iloc[:700, :-1]), np.array(df.iloc[:700, -1])
X_test, y_test = np.array(df.iloc[700:, :-1]), np.array(df.iloc[700:, -1])
## Using py-earth package, please install it follow the instructions in README file
clf = Earth()
clf.fit(X_train, y_train)
## Predict the value and calculate the testing error rate
pred_vals = clf.predict(X_test)
## Dichotomize the predicted outcome at the median
median = np.median(pred_vals)
res = np.where(pred_vals >= median, 1, -1)
# print(res)
error_rate = 1 - sum(res == y_test) / y_test.shape[0]
print("The testing error rate for MARS classifier is: %.4f" % error_rate)
import matplotlib.pyplot as plt from pyearth import Earth # Create some fake data numpy.random.seed(2) m = 10000 n = 10 X = 20 * numpy.random.uniform(size=(m, n)) - 10 y = 10 * numpy.sin(X[:, 6]) + 0.25 * numpy.random.normal(size=m) # Compute the known true derivative with respect to the predictive variable y_prime = 10 * numpy.cos(X[:, 6]) # Fit an Earth model model = Earth(max_degree=2, minspan_alpha=.5, smooth=True) model.fit(X, y) # Print the model print(model.trace()) print(model.summary()) # Get the predicted values and derivatives y_hat = model.predict(X) y_prime_hat = model.predict_deriv(X, 'x6') # Plot true and predicted function values and derivatives # for the predictive variable plt.subplot(211) plt.plot(X[:, 6], y, 'r.') plt.plot(X[:, 6], y_hat, 'b.')
st = 'CPY012'
target,start_p,stop_p,host_path=station_sel(st,mode)
if mode =='hour': n_past,n_future = 24*7,72
elif mode =='day': n_past,n_future = 60,30
data = df[start_p:stop_p]
data['Day'] = data.index.dayofyear #add day
data = data.interpolate(limit=300000000,limit_direction='both').astype('float32') #interpolate neighbor first, for rest NA fill with mean()
conclude_df=pd.DataFrame()
for n_out in range(1,n_future+1):
X,y,xlabels = to_supervise(data,target,n_out)
criteria = ('rss', 'gcv', 'nb_subsets')
model = Earth(enable_pruning = True,
# max_degree=3,
# max_terms=20,
minspan_alpha=.5,
feature_importance_type=criteria,
verbose=True)
model.fit(X,y,xlabels=xlabels)
nbsub = model.summary_feature_importances(sort_by='nb_subsets')[:2000].split()[3:83]
gcv = model.summary_feature_importances(sort_by='gcv')[:2000].split()[3:83]
rss = model.summary_feature_importances(sort_by='rss')[:2000].split()[3:83]
rss,gcv,nbsub = toDF(rss),toDF(gcv),toDF(nbsub)
top20=pd.concat([rss,gcv,nbsub],ignore_index=True)
top20 = pd.concat([rss,gcv,nbsub],ignore_index=True).drop_duplicates('feature')
top20['timestep'] = n_out
#ADDED combine all result
conclude_df = pd.concat([conclude_df,top20],ignore_index=True)
if mode=='day':
'''
Created on Feb 15, 2016
@author: jason
'''
from .sklearntools import MultipleResponseEstimator, BackwardEliminationEstimatorCV, \
QuantileRegressor, ResponseTransformingEstimator
from pyearth import Earth
from sklearn.pipeline import Pipeline
from sklearn.calibration import CalibratedClassifierCV
outcomes = ['admission_rate', 'prescription_cost_rate', '']
[('earth', Earth(max_degree=2)), ('elim', BackwardEliminationEstimatorCV())]
normalized_X = preprocessing.normalize(X)
# standardize the data attributes
standardized_X = preprocessing.scale(X)
# feature selection
from sklearn.ensemble import ExtraTreesClassifier
model = ExtraTreesClassifier()
model.fit(X, y)
# display the relative importance of each attribute
print(model.feature_importances_)
# naive bayes implementation
from matplotlib import pyplot
# create model
from pyearth import Earth
model = Earth()
# fit the earth model
model.fit(X, y)
print(" Model:")
print(model)
# make predictions
expected = y
predicted = model.predict(X)
# since the quality can only be a number, round all the outputs off
for i in range(len(predicted)):
predicted[i] = int(round(predicted[i]))
# check how far the predictions are from actual values
test = context.catalog.load('test_maskedv2')
= context.catalog.load('variable_descriptions_v2')
sample_submission = context.catalog.load('samplesubmissionv2')
#%%
train.target_pct_vunerable.hvplot.kde()
# %%
transformer = Pipeline([('poly', PolynomialFeatures()),
('scale', StandardScaler()),
('pca', PCA(15)),
('rescale', StandardScaler())])
glm = TweedieGLM(power=0, max_iter=1000)
mars = Earth()
model = Pipeline([('transformer', transformer),
('model', mars)])
offset = 1e-9
def add(y):
return (y/100 + offset)
def subtract(y):
return ((y) - offset)*100
link = Pipeline([('function', FunctionTransformer(add, subtract, validate=True))])
scorer = get_scorer('neg_root_mean_squared_error')
pipeline = TransformedTargetRegressor(regressor=model, transformer=link)
def test_score():
earth = Earth(**default_params)
model = earth.fit(X, y)
record = model.pruning_trace()
rsq = record.rsq(record.get_selected())
assert_almost_equal(rsq, model.score(X, y))
total_data = pd.concat([
total_category_data,
total_numeric_data.clip(total_numeric_data.quantile(0.01).to_dict(),
total_numeric_data.quantile(0.99).to_dict(),
axis=1)
],
axis=1)
print(total_data.shape)
total_data = total_data.fillna(total_data.mean())
print(total_data.head(5))
train_data = total_data[total_data.index < 1460]
test_data = total_data[total_data.index >= 1460]
rfe = RFE(Earth(), step=15, verbose=2).fit(train_data, train_Y)
validKeys = list(train_data.columns[rfe.support_])
train_data = train_data[validKeys]
test_data = test_data[validKeys]
model = Earth().fit(train_data, train_Y)
predict = model.predict(test_data)
predict = np.exp(predict)
submission = pd.DataFrame()
submission['Id'] = test_index
submission['SalePrice'] = predict
submission.to_csv(
"C:\\Users\\hongj\\Desktop\\kaggle\\house_price\\submission.csv",
index=False)
