def __init__(self, m=1):
if m == 0:
self.model = self.baseline()
self.type = 0
elif m == 1:
self.model = self.returnSequential2()
self.type = 2
elif m == 2:
self.model = self.returnSequential6()
self.type = 2
elif m == 3:
self.model = self.RNN()
self.type = 1
elif m == 4:
self.model = self.multi_RNN()
self.type = 1
elif m == 5:
self.model = self.lstm()
self.type = 1
elif m == 6:
self.model = self.multi_lstm()
self.type = 1
elif m == 7:
self.model = LogisticGAM()
self.type = 3
elif m == 8:
self.model = self.returnSequential9()
self.type = 2
class AdaptiveLogisticGAM(BaseEstimator, RegressorMixin):
def __init__(self, param_grid=None, gam_params=None):
# create GAM
if gam_params is None:
gam_params = {}
self.model = LogisticGAM(**gam_params)
# set grid search parameters
if param_grid is None:
param_grid = GAM_GRID_BASE
self.param_grid = param_grid
def fit(self, X, y):
if isinstance(X, pd.DataFrame):
X = X.values
# fit using grid-search
self.model.gridsearch(X, y, progress=False, **self.param_grid)
def predict(self, X):
if isinstance(X, pd.DataFrame):
X = X.values
return self.model.predict(X)
def predict_proba(self, X):
if isinstance(X, pd.DataFrame):
X = X.values
return self.model.predict_proba(X)
def test_exceptions():
# get data
X, y, w, ite, p, bs = make_te_data(n=200)
train = [i for i in range(100)]
test = [i for i in range(100, 200)]
with pytest.raises(ValueError):
# pass incorrect type of estimator
fit_and_score_te_oracle(LinearGAM(),
X,
y,
w,
p,
ite,
train=train,
test=test,
scorer='neg_mean_squared_error',
return_test_score_only=True)
with pytest.raises(ValueError):
# fit should throw an error
fit_and_score_te_oracle(IFLearnerTE(LogisticGAM()),
X,
y,
w,
p,
ite,
train=train,
test=test,
scorer='neg_mean_squared_error',
return_test_score_only=True,
error_score='raise')
with pytest.raises(ValueError):
# fit should throw an error because error score is incorrect
fit_and_score_te_oracle(IFLearnerTE(LogisticGAM()),
X,
y,
w,
p,
ite,
train=train,
test=test,
scorer='neg_mean_squared_error',
return_test_score_only=False,
error_score='asdfad')
# assert we get error score otherwise
score = fit_and_score_te_oracle(IFLearnerTE(LogisticGAM()),
X,
y,
w,
p,
ite,
train=train,
test=test,
scorer='neg_mean_squared_error',
return_test_score_only=True,
error_score=np.nan)
assert math.isnan(score)
def _plot_logodd(self):
# Gérer les manquants dans le GAM
lignes_completes = np.invert(
np.isnan(self.predictors_cont).sum(axis=1).astype(bool))
# Fit du GAM sur tout le monde
gam = LogisticGAM(dtype=['numerical' for _ in range(self.d_cont)] + ['categorical' for _ in range(
self.d_qual)]).fit(
pd.concat([pd.DataFrame(self.predictors_cont[lignes_completes, :]).apply(
lambda x: x.astype('float')),
pd.DataFrame(self.predictors_qual[lignes_completes, :]).apply(
lambda x: x.astype('category'))], axis=1), self.labels[lignes_completes])
# Quelles que soient les valeurs de predictors_cont_number et
# predictors_qual_number, on plot tout pour l'instant
plt.figure()
fig, axs = plt.subplots(1, self.d_cont + self.d_qual)
plt.rcParams['figure.figsize'] = (28, 8)
for i, ax in enumerate(axs):
try:
XX = gam.generate_X_grid(term=i)
ax.plot(XX[:, i], gam.partial_dependence(term=i, X=XX))
ax.plot(XX[:, i], gam.partial_dependence(term=i, X=XX, width=.95)[1], c='r', ls='--')
except ValueError: # pragma: no cover
continue
plt.show(block=False)
def GAM2(self):
"""GAM of splines, where we perform variable selection
to find the best model."""
from pygam import LogisticGAM, s, l, f
terms = s(0) + s(1) + s(2) + s(3) + s(4) + s(5) + s(6) + s(7)
gam = LogisticGAM(terms=terms, fit_intercept=False)
mod = gam.gridsearch(self.Xtrain.values, self.ytrain, \
lam=np.logspace(-3, 3, 11)) # Generate the model
mod.summary() # Pseudo-R2: 0.6449
ypred = mod.predict(self.Xtest)
MSE1 = np.mean((self.ytest - ypred.reshape(-1, 1))**2).values
if self.plot:
plt.plot(range(len(ypred.reshape(-1,1))),\
ypred.reshape(-1,1)-0.5,"r.", label='GAM model')
plt.plot(range(len(self.ytest)),
self.ytest,
"b.",
label='Testing Data')
plt.legend()
plt.title("GAM model with linear terms. Prediction data is\n"\
+ "scaled downwards by 0.5 for visual purposes.")
plt.ylabel("FFVC score")
plt.xlabel("Sample no.")
plt.show()
def spline_calibration(X, y):
gam = LogisticGAM(s(0, constraints='monotonic_inc')).gridsearch(
X, y) # add a linear term
# documentation of LogisticGAM: https://pygam.readthedocs.io/en/latest/api/logisticgam.html
# gam = LogisticGAM(s(0, constraints='monotonic_inc')).gridsearch(X, y) # add a linear term
# compute ece and acc after calibration
y_ = gam.predict_proba(X)
return y_
def __init__(self, param_grid=None, gam_params=None):
# create GAM
if gam_params is None:
gam_params = {}
self.model = LogisticGAM(**gam_params)
# set grid search parameters
if param_grid is None:
param_grid = GAM_GRID_BASE
self.param_grid = param_grid
def spline_classification_plot(ax, X, y, X_eval, y_eval, gam_ref):
# gam = LogisticGAM(s(0)).gridsearch(X, y)
# documentation of LogisticGAM: https://pygam.readthedocs.io/en/latest/api/logisticgam.html
gam = LogisticGAM(s(0, constraints='monotonic_inc',
n_splines=5)).gridsearch(X, y) # add a linear term
#XX = gam.generate_X_grid(term=0)
XX = np.linspace(0, 1, 100)
ax.plot(XX, gam.predict_proba(XX), c='g')
ax.plot(XX, gam.confidence_intervals(XX, width=0.95), c='r', ls='--')
# compute ece and acc after calibration
y_ = gam.predict_proba(X_eval)
ece = EceEval(np.array([1 - y_, y_]).T, y_eval, num_bins=100)
mce = MceEval(np.array([1 - y_, y_]).T, y_eval, num_bins=100)
brier = BrierEval(np.array([1 - y_, y_]).T, y_eval)
mse = MseEval(gam, gam_ref, num_bins=100)
acc = gam.accuracy(X_eval, y_eval)
ax.text(0.05,
0.75,
'ECE=%.4f\nMCE=%.4f\nBrier=%.4f\nACC=%.4f\nMSE=%.4f' %
(ece, mce, brier, acc, mse),
size=6,
ha='left',
va='center',
bbox={
'facecolor': 'green',
'alpha': 0.5,
'pad': 4
})
ax.set_xlim(0.0, 1.0)
ax.set_ylim(0.0, 1.0)
confi = gam.confidence_intervals(X_eval, width=0.95)
print gam.summary()
return ece, mce, brier, acc, mse, ax, confi
def calibrate_propensities(propensities, treatment):
"""Post-hoc calibration of propensity scores given the true treatments
Args:
propensities: propensity scores
treatment: treatment indicator
Returns:
p: calibrated version of the propensities given
"""
gam = LogisticGAM(s(0)).fit(propensities, treatment)
return gam.predict_proba(propensities)
def superlearnersetup(var_type, K=5):
"""Super Learner setup for binary and continuous variables"""
if var_type == 'binary':
# Binary variable
log_b = LogisticRegression(penalty='none',
solver='lbfgs',
max_iter=1000)
rdf_b = RandomForestClassifier(
n_estimators=500,
min_samples_leaf=20) # max features is sqrt(n_features)
gam1_b = LogisticGAM(n_splines=4, lam=0.6)
gam2_b = LogisticGAM(n_splines=6, lam=0.6)
nn1_b = MLPClassifier(hidden_layer_sizes=(4, ),
activation='relu',
solver='lbfgs',
max_iter=2000)
emp_b = EmpiricalMean()
lib = [log_b, gam1_b, gam2_b, rdf_b, nn1_b, emp_b]
libnames = [
"Logit", "GAM1", "GAM2", "Random Forest", "Neural-Net", "Mean"
]
sl = SuperLearner(lib,
libnames,
loss="nloglik",
K=K,
print_results=False)
elif var_type == 'continuous':
# Continuous variable
lin_c = LinearRegression()
rdf_c = RandomForestRegressor(n_estimators=500, min_samples_leaf=20)
gam1_c = GAM(link='identity', n_splines=4, lam=0.6)
gam2_c = GAM(link='identity', n_splines=6, lam=0.6)
nn1_c = MLPRegressor(hidden_layer_sizes=(4, ),
activation='relu',
solver='lbfgs',
max_iter=2000)
emp_c = EmpiricalMean()
lib = [lin_c, gam1_c, gam2_c, rdf_c, nn1_c, emp_c]
libnames = [
"Linear", "GAM1", "GAM2", "Random Forest", "Neural-Net", "Mean"
]
sl = SuperLearner(lib, libnames, K=K, print_results=False)
else:
raise ValueError("Not Supported")
return sl
def spline_classification(X, y, X_eval, y_eval, gam_ref):
# gam = LogisticGAM(s(0)).gridsearch(X, y)
# documentation of LogisticGAM: https://pygam.readthedocs.io/en/latest/api/logisticgam.html
gam = LogisticGAM(s(0, constraints='monotonic_inc',
n_splines=5)).gridsearch(X, y) # add a linear term
#XX = gam.generate_X_grid(term=0)
# compute ece and acc after calibration
y_ = gam.predict_proba(X_eval)
ece = EceEval(np.array([1 - y_, y_]).T, y_eval, num_bins=100)
mce = MceEval(np.array([1 - y_, y_]).T, y_eval, num_bins=100)
brier = BrierEval(np.array([1 - y_, y_]).T, y_eval)
mse = MseEval(gam, gam_ref, num_bins=100)
acc = gam.accuracy(X_eval, y_eval)
# compute the confidence on datapoints of X_eval
confi = gam.confidence_intervals(X_eval, width=0.95)
return ece, mce, brier, acc, mse, confi
def calibrate(ps, treatment):
"""Calibrate propensity scores with logistic GAM.
Ref: https://pygam.readthedocs.io/en/latest/api/logisticgam.html
Args:
ps (numpy.array): a propensity score vector
treatment (numpy.array): a binary treatment vector (0: control, 1: treated)
Returns:
(numpy.array): a calibrated propensity score vector
"""
gam = LogisticGAM(s(0)).fit(ps, treatment)
return gam.predict_proba(ps)
def test_if_learner():
# get data without noise
X, y, w, ite, p, bs = make_te_data(n=200, noise=False)
# get surrogate predictions to compare against po predictions
mu_0_plug, mu_1_plug = get_surrogate_predictions(X, y, w)
# get surrogate predictions for two folds as inside the iflearner
splitter = StratifiedKFold(n_splits=2, shuffle=True,
random_state=42)
idx_list = []
for train_index, test_index in splitter.split(X, w):
idx_list.append((train_index, test_index))
fold2_mask = np.zeros(200, dtype=bool)
fold2_mask[idx_list[0][1]] = 1
mu_0, mu_1 = np.zeros(200), np.zeros(200)
mu_0[~fold2_mask], mu_1[~fold2_mask] = get_surrogate_predictions(X, y, w, pred_mask=~fold2_mask)
mu_0[fold2_mask], mu_1[fold2_mask] = get_surrogate_predictions(X, y, w, pred_mask=fold2_mask)
pseudo_outcome = eif_transformation_CATE(y, w, p, mu_0, mu_1)
# make second stage model
t_model = LinearGAM()
t_model.fit(X, pseudo_outcome)
te_debiased = t_model.predict(X)
# fit if learner
if_learner = IFLearnerTE(LinearGAM(), n_folds=2, random_state=42, fit_base_model=True)
if_learner.fit(X, y, w, p)
te, mu_0, mu_1 = if_learner.predict(X, return_po=True)
# test outcomes
np.testing.assert_almost_equal(te, te_debiased)
np.testing.assert_almost_equal(mu_0, mu_0_plug)
np.testing.assert_almost_equal(mu_1, mu_1_plug)
np.testing.assert_almost_equal(if_learner.predict(X), te_debiased)
with pytest.raises(ValueError):
# predicting po when base model not fitted should not be possible
if_learner = IFLearnerTE(LinearGAM(), n_folds=2, random_state=42)
if_learner.fit(X, y, w, p)
te, mu_0, mu_1 = if_learner.predict(X, return_po=True)
with pytest.warns(UserWarning):
# warning raised if only one fold?
if_learner = IFLearnerTE(LinearGAM(), n_folds=1, random_state=42)
if_learner.fit(X, y, w, p)
# check that binary_y setting also works (smoketest)
X, y, w, ite, p, bs = make_te_data(n=200, baseline_model=binary_gyorfi_baseline,
noise=False, binary_y=True)
if_learner = IFLearnerTE(base_estimator=LogisticGAM(), te_estimator=LinearGAM(),
binary_y=True, setting=RR_NAME, fit_base_model=True)
if_learner.fit(X, y, w, p)
te, mu_0, mu_1 = if_learner.predict(X, return_po=True)
def simulation(No_T,n,p,box_plot=True):
err=[]
for i in range (No_T):
#generate the test data
X_train,Y_train=generate_data(n,p)
X_test,Y_test= generate_data(n,p)
logit_gam = LogisticGAM()
logit_gam.gridsearch(X_train,Y_train)
#calculate test error
test_err=sum(logit_gam.predict(X_test)!=Y_test)/n
err.append(test_err)
if box_plot:
plt.figure(num=None,figsize=(8,6),dpi=80)
plt.boxplot(err)
plt.text(1.1,0.15,"Mean:{:.2f}".format(np.mean(err)))
plt.text(1.1,0.14,"Var:{:.3f}".format(np.var(err)))
plt.title("logisticGAM")
plt.ylabel("Test Error")
plt.show()
def main():
X = pd.read_csv(
'./dataset/gradcafe/cs_preprocessed_X.csv',
usecols=[0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]).values
# X = pd.read_csv('./dataset/gradcafe/pnp_x.csv', header=None).values
y = pd.read_csv('./dataset/gradcafe/cs_preprocessed_Y.csv').values.reshape(
-1)
np.random.seed(0)
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.1,
random_state=0)
fit_gam(X_train, y_train, "Without normalization on X", False)
fit_gam(X_train, y_train, "With normalization on X", True)
# Normalization is better
X_train = StandardScaler().fit_transform(X_train)
X_test = StandardScaler().fit_transform(X_test)
np.random.seed(0)
clf = LogisticGAM()
clf.fit(X_train, y_train)
training_accuracy = clf.accuracy(X_train, y_train) * 100
testing_accuracy = clf.accuracy(X_test, y_test) * 100
print("------------------------------")
print("Results with normalization on testing set")
print("------------------------------")
print('Training accuracy: {:.2f}%'.format(training_accuracy))
print('Testing accuracy: {:.2f}%'.format(testing_accuracy))
print()
def fit_gam(X, y, comment, use_x_normalization):
print("------------------------------")
print(comment)
print("------------------------------")
np.random.seed(0)
if use_x_normalization:
X = StandardScaler().fit_transform(X)
train_scores = np.array([])
val_scores = np.array([])
kf = KFold(n_splits=10, shuffle=True)
for train_index, val_index in kf.split(X):
X_train, X_val = X[train_index], X[val_index]
y_train, y_val = y[train_index], y[val_index]
clf = LogisticGAM()
clf.fit(X_train, y_train)
train_scores = np.append(train_scores,
clf.accuracy(X_train, y_train) * 100)
val_scores = np.append(val_scores, clf.accuracy(X_val, y_val) * 100)
print('Training accuracy: {:.2f}%'.format(np.mean(train_scores)))
print('Validation accuracy: {:.2f}%'.format(np.mean(val_scores)))
print()
def get_gam_model(self, features: [Field], model_type=TYPE_LINEAR):
model_spec = f(0) if features[0].is_factor() else s(
0, n_splines=self.num_splines)
for i in range(1, len(features)):
model_spec += f(i) if features[i].is_factor() else s(
i, n_splines=self.num_splines)
if model_type == TYPE_LINEAR:
return LinearGAM(model_spec)
if model_type == TYPE_LOGISTIC:
return LogisticGAM(model_spec)
def logistic_GAM(x_tr, y_tr, x_tst, y_tst):
classifier = LogisticGAM()
classifier.fit(x_tr, y_tr)
tr_pred = classifier.predict(x_tr)
y_pred = classifier.predict(x_tst)
confusion_matrix = metrics.confusion_matrix(y_tst, y_pred)
print(confusion_matrix)
print('Accuracy of logistic regression classifier on test set: {:.2f}' \
.format(metrics.accuracy_score(y_pred, y_tst)))
print('Accuracy of logistic regression classifier on train set: {:.2f}' \
.format(metrics.accuracy_score(tr_pred, y_tr)))
'''
def fit(self):
S = s(0) if self.feature_names[0] in self.numerical_features else f(0)
for i in range(1, len(self.feature_names)):
if self.feature_names[i] in self.numerical_features:
S += s(i)
else:
S += f(i)
if self.mode == 'regression':
gam = LinearGAM(S)
gam.gridsearch(self.X_train, self.y_train)
self._is_fitted = True
self.explainer = gam
elif self.mode == 'classification':
gam = LogisticGAM(S)
gam.gridsearch(np.array(self.X_train), self.y_train)
self._is_fitted = True
self.explainer = gam
else:
raise NameError(
'ERROR: mode should be regression or classification')
def create_rand_gam(number_of_searches, new_values, pred_y, y, pca_splines,
pca_lam, pred_splines, pred_lam, pred_factor):
lams = np.random.rand(number_of_searches, new_values.shape[1] +
1) # random points on [0, 1], with shape (1000, 3)
lams = lams * 8 - 4 # shift values to -4, 4
lams = 10**lams # transforms values to 1e-4, 1e4
new_values = np.append(new_values, np.array(pred_y).reshape(-1, 1), axis=1)
titles = []
for i in range(new_values.shape[1] - 1):
titles.append(str(i))
if i == 0:
x = s(i, n_splines=pca_splines, lam=pca_lam)
else:
x = x + s(i, n_splines=pca_splines, lam=pca_lam)
if pred_factor:
x = x + pygam.terms.f(i + 1, lam=pred_lam)
else:
x = x + s(i + 1, n_splines=pred_splines, lam=pred_lam)
rand_gam = LogisticGAM(x).gridsearch(new_values, y, lam=lams)
return rand_gam, new_values, titles
def test_plugin_learner():
# get data without noise
X, y, w, ite, p, bs = make_te_data(n=200, noise=False)
# get surrogates
mu_0_plug, mu_1_plug = get_surrogate_predictions(X, y, w)
# use plug in learner
p_model = PlugInTELearner(LinearGAM())
p_model.fit(X, y, w, p)
te, mu_0, mu_1 = p_model.predict(X, return_po=True)
# test outcomes
np.testing.assert_almost_equal(te, mu_1_plug - mu_0_plug)
np.testing.assert_almost_equal(mu_0, mu_0_plug)
np.testing.assert_almost_equal(mu_1, mu_1_plug)
np.testing.assert_almost_equal(p_model.predict(X), mu_1_plug - mu_0_plug)
# check that binary_y setting also works (smoketest)
X, y, w, ite, p, bs = make_te_data(n=200, baseline_model=binary_gyorfi_baseline,
noise=False, binary_y=True)
p_model = PlugInTELearner(LogisticGAM(), binary_y=True, setting=RR_NAME)
p_model.fit(X, y, w, p)
te, mu_0, mu_1 = p_model.predict(X, return_po=True)
#-----------------------------------------------------
#load the breast cancer data set
ds = load_breast_cancer()
X, y = ds.data, ds.target
#select first 6 features only
X = X[:, 0:6]
selected_features = ds.feature_names[0:6]
#-----------------------------------------------------
#Fit a model with the default parameters
gam = LogisticGAM().fit(X, y)
gam.summary()
roc_auc_score(y, gam.predict_proba(X)) #0.994173140954495
gam.accuracy(X, y) #0.9560632688927944
#-----------------------------------------------------
# Explore and interpret individual features
plt.ion()
plt.rcParams['figure.figsize'] = (28, 8)
fig, axs = plt.subplots(1, X.shape[1])
for i, ax in enumerate(axs):
XX = gam.generate_X_grid(term=i, meshgrid=True)
gam_mse = np.mean((gam_predictions - d['mpg'])**2)
print('MSE:', gam_mse)
#Add binary and categorical predictors to the GAM
from sklearn.preprocessing import LabelEncoder
encoder = LabelEncoder()
d['h_bin'] = encoder.fit_transform(pd.cut(d['hp'], 5))
gam_model = LinearGAM().fit(d[['disp', 'wt', 'vs', 'h_bin']], d['mpg'])
print(gam_model.summary())
gam_predictions = gam_model.predict(d[['disp', 'wt', 'vs', 'h_bin']])
gam_mse = np.mean((gam_predictions - d['mpg'])**2)
print('MSE:', gam_mse)
#Performing classification (logistic regression) with the GAM
d['mpg_bin'] = encoder.fit_transform(pd.cut(d['mpg'], [0, 20, 100]))
gam_model = LogisticGAM().gridsearch(d[['disp', 'wt', 'vs', 'h_bin']],
d['mpg_bin'])
print(gam_model.summary())
print('Classification Accuracy:',
gam_model.accuracy(d[['disp', 'wt', 'vs', 'h_bin']], d['mpg_bin']))
#This models the conditional probabilities for mpg being < 20 and >=20
#Note the y-axis of these plots is the logit
'''
-------------------------------------------------------------------------------
-------------------Classification and Regression Trees-------------------------
-------------------------------------------------------------------------------
'''
#Regression tree
reg_tree = DecisionTreeRegressor(criterion='mse', min_samples_split=20).fit(
d.drop(['mpg', 'mpg_bin'], axis=1), d['mpg'])
ls='--')
if i == 0:
ax.set_ylim(-30, 30)
ax.set_title(titles[i])
######################################################
# classification
from pygam import LogisticGAM, s, f
from pygam.datasets import default
X, y = default(return_X_y=True)
X.shape
X
gam = LogisticGAM(f(0) + s(1) + s(2)).gridsearch(X, y)
fig, axs = plt.subplots(1, 3)
titles = ['student', 'balance', 'income']
for i, ax in enumerate(axs):
XX = gam.generate_X_grid(term=i)
pdep, confi = gam.partial_dependence(term=i, width=.95)
ax.plot(XX[:, i], pdep)
ax.plot(XX[:, i], confi, c='r', ls='--')
ax.set_title(titles[i])
gam.accuracy(X, y)
######################################################
"Maximum Expression Value", "Copy Number -1", "Copy Number 0",
"Copy Number 1"
]
trainX, testX, trainy, testy = train_test_split(X, Y, test_size=0.4)
from pygam import LogisticGAM, s, f
#print("allocated")
#print(trainX)
aa = s(0)
names1 = ["Silent"]
for i in range(1, 37):
#if(not(i==4 or i==6 or i==10 or i==11)):
if (not (i == 4 or i == 6 or i == 10 or i == 11)):
aa += s(i)
names1.append(names[i])
#print("yeh karre")
gam1 = LogisticGAM(aa)
#print(gam1.lam)
w = []
for y in trainy:
if (y == 0):
w.append(1)
else:
w.append(10)
gam1 = gam1.fit(trainX, trainy, weights=w)
import numpy as np
lams = np.random.rand(10, 33) # random points on [0, 1], with shape (100, 3)
n_splines = [5, 10, 15, 20, 25]
lams = lams * 6 # shift values to -3, 3
lams = lams - 3
lams = np.exp(lams)
cons = [
auc_log3 = cross_val_score(logreg, X_train_scaled_poly, Y_train, cv=10, scoring='roc_auc').mean()
print("\n calculate cross-validated AUC (M2. X_train_scaled_poly):", auc_log2)
acc_log3 = cross_val_score(logreg, X_train_scaled_poly, Y_train, cv=10, scoring='accuracy').mean()
print("\n calculate cross-validated accurancy (M2. X_train_scaled_poly):", acc_log2)
acc_logs3 = cross_validation.cross_val_predict(logreg, X_train_scaled_poly, Y_train, cv=10)
print(metrics.accuracy_score(Y_train, acc_logs2))
print(metrics.classification_report(Y_train, acc_logs3))
print(logreg.coef_)
print('\n ------------------------------------------------------------------')
# call predict_proba() to get the list of probabilities that the classifier assigned to each instance for each class:
###############################################################################################################################
# GAM
import pandas as pd
from pygam import LogisticGAM
# Fit a model with the default parameters
gam = LogisticGAM().fit(X_train_scaled, Y_train)
gam.summary()
print('gam.accuracy(X_train_scaled, Y_train):',gam.accuracy(X_train_scaled, Y_train))
print('gam.accuracy(X_test_scaled, Y_test):',gam.accuracy(X_test_scaled, Y_test))
acc_loggamc = cross_val_score(gam, X_train_scaled, Y_train, cv=10, scoring='accuracy').mean()
print('acc_loggam_cross-validation, train_scaled',acc_loggamc)
# make predictions for testing set
Y_scaler_pred_class = logreg.predict(X_test_scaled)
# calculate testing accuracy
from sklearn import metrics
print('\n ------------------------------------------------------------------')
print("\n calculate testing accuracy (M1. X_train_scaled):", metrics.accuracy_score(Y_test, Y_scaler_pred_class))
print('\n ------------------------------------------------------------------')
def score_notes_from_network(csv_dir, name = False, pca = False, load = False):
dataset = pd.read_csv(csv_dir)
y = dataset['truth']
del dataset['truth']
if not name:
name = str(uuid.uuid4())
if not pca:
pca = PCA(0.95)
pca.fit(dataset)
new_values = pca.transform(dataset)
new_values, y = datasets.load_iris(return_X_y=True)
if not load:
from sklearn.linear_model import LogisticRegression
X, y = datasets.load_iris(return_X_y=True)
clf = LogisticRegression(random_state=0, multi_class='multinomial', max_iter=1000).fit(X, y)
aaa = clf.predict_proba(X)
base_estimator = LogisticGAM(s(0) + s(1) + s(2) + s(3))
ensemble = OneVsRestClassifier(base_estimator, n_jobs=1).fit(new_values, y).predict(new_values)
ensemble.fit(new_values, y)
ensemble.predict_proba(new_values)
else:
with open(load, 'rb') as f:
rand_gam = pickle.load(f)
rand_gam.summary()
scores = rand_gam.predict_proba(new_values)
rand_gam.accuracy(new_values, y)
scr_order = np.argsort(scores)
filename = name + '.pkl'
with open('./GAM_model/models/' + name, 'wb') as f:
pickle.dump(rand_gam, f)
w = 480
h = 480
columns = 21
rows = 1
# ax enables access to manipulate each of subplots
dirList =['./pl_train_station/output_hed_images/', './pl_train_station/pretty_good_set_input/']
ordered_list = [[os.listdir('./pl_train_station/output_hed_images/')[i] for i in scr_order]][0]
ordered_labels = [[y[i] for i in scr_order]][0]
shutil.rmtree('./GAM_model/scored/rgb/')
shutil.rmtree('./GAM_model/scored/edge/')
os.makedirs('./GAM_model/scored/rgb/')
os.makedirs('./GAM_model/scored/edge/')
for i, img in enumerate(ordered_list):
res_img = cv2.resize(cv2.imread(dirList[0] + img), (w, h))
cv2.imwrite('./GAM_model/scored/edge/' + str(i) + '_' + str(ordered_labels[i]) + '_' + str(scores[scr_order[i]]) + '.bmp',
res_img)
for i, img in enumerate(ordered_list):
img = img[0:-10] + '.bmp'
res_img = cv2.imread(dirList[1] + img)
cv2.imwrite('./GAM_model/scored/rgb/' + str(i) + '_' + str(ordered_labels[i]) + '_' + str(scores[scr_order[i]]) + '.bmp', res_img)
tumors.head()
# In[21]:
# Se quita la columna de identificación de los datos.
tumors = tumors.drop(['id'], axis=1)
tumors.loc[tumors['diagnosis'] == 'M',
'diagnosis'] = 1 #Se cambia la variable a tipo binario.
tumors.loc[tumors['diagnosis'] == 'B', 'diagnosis'] = 0 #
tumors_X = tumors.iloc[:, :11].drop(
['diagnosis'], axis=1).values #Separamos las variables independientes
tumors_y = tumors['diagnosis'] #Separamos las variables dependientes.
# In[22]:
gam = LogisticGAM(n_splines=20).gridsearch(tumors_X, tumors_y)
gam.summary()
# In[23]:
print('La precisión del módelo es:',
round(gam.accuracy(tumors_X, tumors_y) * 100, 2), "%")
# In[24]:
titles = tumors.columns[1:11]
plt.figure()
fig, axs = plt.subplots(1, 10, figsize=(40, 8))
for i, ax in enumerate(axs):
XX = gam.generate_X_grid(term=i)
ax.plot(XX[:, i], gam.partial_dependence(term=i, X=XX))
def fit(self,
X,
y,
sample_weight=None,
eval_set=None,
sample_weight_eval_set=None,
**kwargs):
orig_cols = list(X.names)
import pandas as pd
import numpy as np
from sklearn.preprocessing import OneHotEncoder
from collections import Counter
import pygam
from pygam import LinearGAM, LogisticGAM
import matplotlib.pyplot as plt
# Get the logger if it exists
logger = None
if self.context and self.context.experiment_id:
logger = make_experiment_logger(
experiment_id=self.context.experiment_id,
tmp_dir=self.context.tmp_dir,
experiment_tmp_dir=self.context.experiment_tmp_dir)
# Set up temp folder
tmp_folder = self._create_tmp_folder(logger)
# Set up model
if self.num_classes >= 2:
lb = LabelEncoder()
lb.fit(self.labels)
y = lb.transform(y)
clf = LogisticGAM(terms="auto",
lam=self.params["lam"],
max_iter=self.params["max_iter"])
self.is_classifier = True
else:
clf = LinearGAM(terms="auto",
lam=self.params["lam"],
max_iter=self.params["max_iter"])
self.is_classifier = False
X = self.basic_impute(X)
# Find the datatypes
X = X.to_pandas()
X.columns = orig_cols
# Change continuous features to categorical
X_datatypes = [str(item) for item in list(X.dtypes)]
# Change all float32 values to float64
for ii in range(len(X_datatypes)):
if X_datatypes[ii] == 'float32':
X = X.astype({orig_cols[ii]: np.float64})
X_datatypes = [str(item) for item in list(X.dtypes)]
# List the categorical and numerical features
self.X_categorical = [
orig_cols[col_count] for col_count in range(len(orig_cols))
if (X_datatypes[col_count] == 'category') or (
X_datatypes[col_count] == 'object')
]
self.X_numeric = [
item for item in orig_cols if item not in self.X_categorical
]
# Find the levels and mode for each categorical feature
# for use in the test set
self.train_levels = {}
for item in self.X_categorical:
self.train_levels[item] = list(set(X[item]))
self.train_mode[item] = Counter(X[item]).most_common(1)[0][0]
# One hot encode the categorical features
# And replace missing values with a Missing category
if len(self.X_categorical) > 0:
X.loc[:, self.X_categorical] = X[self.X_categorical].fillna(
"Missing").copy()
self.enc = OneHotEncoder(handle_unknown='ignore')
self.enc.fit(X[self.X_categorical])
self.encoded_categories = list(
self.enc.get_feature_names(input_features=self.X_categorical))
X_enc = self.enc.transform(X[self.X_categorical]).toarray()
X = pd.concat([
X[self.X_numeric],
pd.DataFrame(X_enc, columns=self.encoded_categories)
],
axis=1)
# Replace missing values with a missing value code
self.median_train = {}
if len(self.X_numeric) > 0:
for colname in self.X_numeric:
self.median_train[colname] = X[colname].quantile(0.5)
X.loc[:, colname] = X[colname].fillna(
self.median_train[colname]).copy()
try:
clf.fit(X, y)
except np.linalg.LinAlgError as e:
raise IgnoreError("np.linalg.LinAlgError") from e
except pygam.utils.OptimizationError as e:
raise IgnoreError("pygam.utils.OptimizationError") from e
except ValueError as e:
if 'On entry to DLASCL parameter number' in str(e):
raise IgnoreError('On entry to DLASCL parameter number') from e
raise
p_values = np.array(clf.statistics_['p_values'])
# Plot the partial dependence plots for each feature
for ii in range(X.shape[1]):
XX = clf.generate_X_grid(term=ii)
plt.figure()
plt.plot(XX[:, ii], clf.partial_dependence(term=ii, X=XX))
plt.plot(XX[:, ii],
clf.partial_dependence(term=ii, X=XX, width=.95)[1],
c='r',
ls='--')
plt.title("Partial Dependence " + str(ii),
fontdict={'fontsize': 10})
plt.show()
plt.savefig(os.path.join(
tmp_folder, 'Feature_partial_dependence_' + str(ii) + '.png'),
bbox_inches="tight")
if max(p_values[0:(len(p_values) - 1)]) > 0:
importances = -np.log(p_values[0:(len(p_values) - 1)] + 10**(-16))
importances = list(importances / max(importances))
else:
importances = [1] * (len(p_values) - 1)
self.mean_target = np.array(sum(y) / len(y))
self.set_model_properties(model=clf,
features=list(X.columns),
importances=importances,
iterations=self.params['n_estimators'])
rfc_predictions_2020 = predict_2020(positions=position_encoder.inverse_transform(features_2020['All_NBA_Pos']),
player_names=current_dat['Player'],
binary_prediction=rfc__2020,
probability_predictions= rfc_probs_2020[:,1])
rfc_predictions_2020
rfc_predictions_2020.to_csv("rfc_predictions.csv")
##### ----- ##### ----- ##### ----- ##### -----# #### ----- ##### ----- ##### ----- ##### ----- #####
# Model 1.3 - Generalized Additive Models
from pygam import LogisticGAM
#Fit a GAM model with the default parameters
gam_model = LogisticGAM()
gam_model.fit(X_train, y_train)
gam_pred_prob = gam_model.predict_proba(X_test)
gam_preds, complete_gam_dat = top_15_predictions(entire_test_data, gam_pred_prob )
gam_performance = all_nba_test_report(complete_gam_dat)
players_missed(complete_gam_dat)