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)
n_splines = [5, 10, 15, 20, 25]
lams = lams * 6 # shift values to -3, 3
lams = lams - 3
lams = np.exp(lams)
cons = [
'convex', 'concave', 'monotonic_inc', 'monotonic_dec', 'circular', 'none'
]
random = LogisticGAM(aa).gridsearch(trainX,
trainy,
weights=w,
lam=lams,
n_splines=n_splines)
random = random.gridsearch(trainX, trainy, constraints=cons)
print(random.lam)
print(random.n_splines)
print(random.constraints)
print(random.accuracy(testX, testy))
from sklearn.metrics import confusion_matrix
preds = random.predict(testX)
print(confusion_matrix(testy, preds))
for i, term in enumerate(random.terms):
if term.isintercept:
continue
XX = random.generate_X_grid(term=i)
pdep, confi = random.partial_dependence(term=i, X=XX, width=0.95)
plt.figure()
plt.plot(XX[:, term.feature], pdep)
plt.plot(XX[:, term.feature], confi, c='r', ls='--')
plt.title(names1[i])
plt.show()
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'])
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)
pdep, confi = gam.partial_dependence(term=i,
X=XX,
meshgrid=True,
width=.95)
ax.plot(XX[0], pdep)
ax.plot(XX[0], confi[:, 0], c='grey', ls='--')
ax.plot(XX[0], confi[:, 1], c='grey', ls='--')
ax.set_title(selected_features[i])
plt.show()
#-----------------------------------------------------
# Tuning Smoothness and Penalties
n_splines = [25, 6, 25, 25, 6, 4]
lambda_ = 0.6
constraints = None
