def plot_validation_curve(model,
x_train,
y_train,
h_param,
h_range,
k=10,
log_scale=False,
scorer=None):
train_score, val_score = validation_curve(model,
x_train,
y_train,
h_param,
h_range,
cv=k,
scoring=scorer)
validation_curve_data = {'train': train_score, 'test': val_score}
for legend, scores in validation_curve_data.items():
if log_scale:
plt.semilogx(h_range, np.abs(scores.mean(axis=1)), label=legend)
else:
plt.plot(h_range, np.abs(scores.mean(axis=1)), label=legend)
plt.xlabel(h_param, labelpad=20)
plt.ylabel('score', labelpad=20)
plt.legend()
plt.show()
def plot_learning_curve(model,
x_train,
y_train,
train_sizes_ratio=np.linspace(0.1, 1.0, 10),
k=10,
scorer=None):
"""
:param model:
:param x_train:
:param y_train:
:param train_sizes_ratio:
:param k:
:param scorer:
:return:
"""
N, train_score, val_score = learning_curve(model,
x_train,
y_train,
train_sizes=train_sizes_ratio,
cv=k,
scoring=scorer)
learning_curve_data = {'train': train_score, 'test': val_score}
for legend, scores in learning_curve_data.items():
plt.plot(N, np.abs(scores.mean(axis=1)), label=legend)
plt.xlabel('train_sizes', labelpad=20)
plt.legend()
plt.show()
def train_gridsearch(data, model, param_grid, metric, k=10, p=3, v=True):
# Model name
model_label = model_name(model)
# Get training & testing data
x_train, y_train = data['train']
x_test, y_test = data['test']
# Define refit condition (first metric if evaluationg multiple metrics else False)
refit_cond = metric[0] if type(metric) is list else True
# Build grid search
gridsearch = GridSearchCV(model,
param_grid,
cv=k,
scoring=metric,
refit=refit_cond)
# Time the model training
start_training = datetime.datetime.now()
# Train model with grid search
gridsearch.fit(x_train, y_train)
end_training = datetime.datetime.now()
# Compute training time
training_time = end_training - start_training
# Format training time
training_time_str = format_run_time(training_time)
# Trained_model
trained_model = gridsearch.best_estimator_
# Get scores from cross validation
cv_scores = {}
if type(metric) is list:
for scorer_label in metric:
if scorer_label.startswith('neg'):
formatted_label = "".join([
w[0] for w in scorer_label.replace('neg_', '').split('_')
])
formatted_score = round(
np.abs(gridsearch.cv_results_[f'mean_test_{scorer_label}'])
[0], p)
cv_scores[formatted_label] = formatted_score
else:
cv_scores[scorer_label] = round(
gridsearch.cv_results_[f'mean_test_{scorer_label}'][0], p)
else:
cv_scores[metric] = round(
gridsearch.cv_results_[f'mean_test_score'][0], p)
# Get scores from testing set
testing_set_scores = get_model_scores(trained_model, x_test, y_test,
list(cv_scores.keys()), p, v)
# Display cross validation mean scores
if v:
print_score_results(cv_scores, set_type='train')
# Build model dictionary which contains GridSearchCV & model instances (with model name)
model_data = {
'gs': gridsearch, # GridSearchCV trained instance
'model': trained_model, # Model trained instance
'model_name': model_label
} # Model name
# Build additional evaluation data dictionary
additional_evaluation_data = {
'time': training_time_str, # Training time
'n_features': x_train.shape[1], # Selected features
'learning_potential': None
} # Learning potential
# Build results dictionary (merge dictionnaries)
results = dict(**model_data, **testing_set_scores,
**additional_evaluation_data)
return results
def plot_factorial_planes(self,
n_plan=None,
X_projected=None,
labels=None,
alpha=1,
illustrative_var=None,
illustrative_var_title=None,
save_as_img=False,
plot_size=(10, 8)):
"""
:param: axis_nb: the total number of axes to display (default is kaiser criterion divided by 2)
"""
X_projected = self.X_projected if X_projected is None else X_projected
factorial_plan_nb = self.default_factorial_plan_nb if n_plan is None else n_plan
axis_ranks = [(x, x + 1) for x in range(0, factorial_plan_nb, 2)]
for d1, d2 in axis_ranks:
if d2 < self.n_comp:
fig = plt.figure(figsize=plot_size)
# Display data points
if illustrative_var is None:
plt.scatter(X_projected[:, d1],
X_projected[:, d2],
alpha=alpha)
else:
illustrative_var = np.array(illustrative_var)
for value in np.unique(illustrative_var):
selected = np.where(illustrative_var == value)
plt.scatter(X_projected[selected, d1],
X_projected[selected, d2],
alpha=alpha,
label=value)
plt.legend(title=illustrative_var_title
if illustrative_var_title is not None else None)
# Display data points labels
if labels is not None:
for i, (x, y) in enumerate(X_projected[:, [d1, d2]]):
plt.text(x,
y,
labels[i],
fontsize='12',
ha='center',
va='bottom')
# Fix factorial plan limits
boundary = np.max(np.abs(X_projected[:, [d1, d2]])) * 1.1
plt.xlim([-boundary, boundary])
plt.ylim([-boundary, boundary])
# Display horizontal & vertical lines
plt.plot([-100, 100], [0, 0], color='grey', ls='--')
plt.plot([0, 0], [-100, 100], color='grey', ls='--')
# Axes labels with % explained variance
plt.xlabel('F{} ({}%)'.format(d1 + 1,
round(100 * self.evr[d1], 1)),
labelpad=20)
plt.ylabel('F{} ({}%)'.format(d2 + 1,
round(100 * self.evr[d2], 1)),
labelpad=20)
plt.title("Projection des individus (sur F{} et F{})".format(
d1 + 1, d2 + 1),
pad=20)
if save_as_img:
plt.tight_layout()
plt.savefig(
'factorial_plan_{}.jpg'.format(1 if d1 == 0 else d1))
plt.show(block=False)
