def analyze(self, test_size=0.05):
X_train, X_test, y_train, y_test = train_test_split(
self.X,
self.y,
test_size=test_size,
)
self.logit.fit(X_train, y_train)
#---------------------------------------------------------
# Show precision/recall
#---------------------------------------------------------
print('\n(1) Classification result for different classes')
y_pred = self.logit.predict(X_test)
print(metrics.classification_report(y_test, y_pred))
#---------------------------------------------------------
# Examine probabilities
#---------------------------------------------------------
print('\n(2) Prediction probabilities of samples')
y_prob = self.logit.predict_proba(X_test)
for c in self.logit.classes_:
print('{}'.format(c[0:6]), end='\t')
print('ClassLabel \tCorrect')
values = []
for i in range(len(y_prob)):
v = [round(p, 2) for p in y_prob[i]]
v.append(y_test.values[i])
v.append('Y' if y_test.values[i] == y_pred[i] else 'no')
v.append(max(y_prob[i]))
values.append(v)
values.sort(key=lambda v: v[-1], reverse=True)
n = len(self.logit.classes_)
for v in values:
for i in range(n):
print(v[i], end='\t')
print('{:10s}\t{}'.format(v[n], v[n + 1]))
#---------------------------------------------------------
# Visualize decisions
#---------------------------------------------------------
print('\n(3) Visualize the decision making process')
output_dt = os.path.join(OUTPUT_DIR, 'decision_tree')
self.dt.fit(X_train, y_train)
draw_tree.draw(self.dt, self.dt.classes_, X_train.columns, output_dt)
print('Decision process is saved to {}.png'.format(output_dt))
#---------------------------------------------------------
# Compare random decision trees
#---------------------------------------------------------
print('\n(4) Compare decision trees in a random forest')
self.rf.fit(X_train, y_train)
for i, m in enumerate(self.rf.estimators_):
output_rf = os.path.join(OUTPUT_DIR, 'random_tree_' + str(i))
draw_tree.draw(m, self.rf.classes_, X_train.columns, output_rf)
print('Decision process is saved to {}.png'.format(output_rf))
print()
def ajax():
if check_passwd(request.get_cookie("mdp")) == False:
return template('authentification', get_url=app.get_url)
reader = codecs.getreader("utf-8")
query = json.load(reader(request.body))
if query['type'] == "question":
if query['method'] == 'PE':
return methods.PE(float(query['min_interval']),
float(query['max_interval']),
float(query['proba']), int(query['choice']),
str(query['mode']))
elif query['method'] == 'LE':
return methods.LE(float(query['min_interval']),
float(query['max_interval']),
float(query['proba']), int(query['choice']),
str(query['mode']))
elif query['method'] == 'CE_Constant_Prob':
return methods.CE(float(query['min_interval']),
float(query['max_interval']),
float(query['gain']), int(query['choice']),
str(query['mode']))
else:
return query['method']
elif query['type'] == "calc_util":
return fit.regressions(query['points'])
elif query['type'] == "calc_util_multi":
return fit.multipoints(query['points'])
elif query['type'] == "k_calculus":
if query['number'] == 2:
return kcalc.calculk2(query['k']['k1'], query['k']['k2'])
elif query['number'] == 3:
return kcalc.calculk3(query['k']['k1'], query['k']['k2'],
query['k']['k3'])
elif query['number'] == 4:
return kcalc.calculk4(query['k']['k1'], query['k']['k2'],
query['k']['k3'], query['k']['k4'])
elif query['number'] == 5:
return kcalc.calculk5(query['k']['k1'], query['k']['k2'],
query['k']['k3'], query['k']['k4'],
query['k']['k5'])
elif query['number'] == 6:
return kcalc.calculk6(query['k']['k1'], query['k']['k2'],
query['k']['k3'], query['k']['k4'],
query['k']['k5'], query['k']['k6'])
elif query['type'] == "utility_calculus_multiplicative":
return kcalc.calculUtilityMultiplicative(query['k'], query['utility'])
elif query['type'] == "utility_calculus_multilinear":
return kcalc.calculUtilityMultilinear(query['k'], query['utility'])
elif query['type'] == "svg":
dictionary = query['data']
min = float(query['min'])
max = float(query['max'])
liste_cord = query['liste_cord']
width = query['width']
return plot.generate_svg_plot(dictionary, min, max, liste_cord, width)
elif query['type'] == "svgg":
dictionary = query['data']
min = float(query['min'])
max = float(query['max'])
liste_cord = query['liste_cord']
width = query['width']
choice = query['choice']
return plot.generate_svg_plot2(dictionary, min, max, liste_cord, width,
choice)
elif query['type'] == "svg_QUALI":
dictionary = query['data']
list_names = query['list_names']
width = query['width']
return plot.generate_svg_plot_QUALI(dictionary, list_names, width)
elif query['type'] == "pie_chart":
names = query['names']
probas = query['probas']
return plot.pie_chart(names, probas)
elif query['type'] == "export_xlsx":
return export_xlsx.generate_fichier(query['data'])
elif query['type'] == "export_xlsx_option":
return export_xlsx.generate_fichier_with_specification(query['data'])
elif query['type'] == "latex_render":
return latex_render.render(query['formula'])
elif query['type'] == "tree":
return draw_tree.draw(query['gain'], query['upper_label'],
query['bottom_label'], query['upper_proba'],
query['bottom_proba'], query['assess_type'])
def ajax():
if check_passwd(request.get_cookie("mdp")) == False:
return template('authentification', get_url=app.get_url)
reader = codecs.getreader("utf-8")
query = json.load(reader(request.body))
"""
query = {'type': 'export_xlsx',
'data':
{'attributes':
[{'type': 'Quantitative', 'name': 'pieniche', 'unit': 'euros', 'val_min': 20, 'val_med': ['40', '60', '80'], 'val_max': 100, 'method': 'PE', 'mode': 'Normal', 'completed': 'False', 'checked': True, 'questionnaire': {'number': 3, 'points': {'40': 0.43, '60': 0.86, '80': 0.82}, 'utility': {}}, 'fonction': 'exponential', 'numero': 0, 'pts': {'points': [1, 2, 3], 'coord': [[40, 0.43], [60, 0.86], [80, 0.82], [20, 0], [100, 1]], 'exp': {'a': -1.963679476149352, 'b': 0.028213793872664365, 'c': 1.116885624561104, 'r2': 0.9669098127318985}, 'quad': {'a': -0.5720588230939125, 'b': 0.00016102941154695627, 'c': 0.03182352938563475, 'r2': 0.9609977126945451}, 'pow': {'a': 2.2107646754127557, 'b': 1.3371874567609472, 'c': -4.168783582605269, 'r2': 0.9633217332780892}, 'lin': {'a': 0.0125, 'b': -0.25, 'r2': 0.7480678661997344}, 'expo-power': {'a': -1825.2176950884375, 'b': -7.508435131653395, 'c': 4.5317707067475485e-05, 'r2': 0.9498855834689701}}},
{'type': 'Quantitative', 'name': 'ccxw', 'unit': 'cxw', 'val_min': 10, 'val_med': ['32.5', '55', '77.5'], 'val_max': 100, 'method': 'PE', 'mode': 'Normal', 'completed': 'False', 'checked': True, 'questionnaire': {'number': 3, 'points': {'55': 0.76, '32.5': 0.32, '77.5': 0.96}, 'utility': {}}, 'fonction': '', 'numero': 0}],
'k_calculus':
[{'method': 'multiplicative', 'active': True, 'k': [], 'GK': None, 'GU': None},
{'method': 'multilinear', 'active': False, 'k': [], 'GK': None, 'GU': None}],
'settings':
{'decimals_equations': '2', 'decimals_dpl': '8', 'proba_ce': '0.3', 'proba_le': '0.3', 'language': 'french', 'display': 'trees'}
}
}
"""
if query['type'] == "question":
if query['method'] == 'PE':
return methods.PE(float(query['min_interval']),
float(query['max_interval']),
float(query['proba']), int(query['choice']),
str(query['mode']))
elif query['method'] == 'LE':
return methods.LE(float(query['min_interval']),
float(query['max_interval']),
float(query['proba']), int(query['choice']),
str(query['mode']))
elif query['method'] == 'CE_Constant_Prob':
return methods.CE(float(query['min_interval']),
float(query['max_interval']),
float(query['gain']), int(query['choice']),
str(query['mode']))
else:
return query['method']
elif query['type'] == "calc_util":
return fit.regressions(query['points'])
elif query['type'] == "calc_util_multi":
return fit.multipoints(query['points'])
elif query['type'] == "k_calculus":
if query['number'] == 2:
return kcalc.calculk2(query['k']['k1'], query['k']['k2'])
elif query['number'] == 3:
return kcalc.calculk3(query['k']['k1'], query['k']['k2'],
query['k']['k3'])
elif query['number'] == 4:
return kcalc.calculk4(query['k']['k1'], query['k']['k2'],
query['k']['k3'], query['k']['k4'])
elif query['number'] == 5:
return kcalc.calculk5(query['k']['k1'], query['k']['k2'],
query['k']['k3'], query['k']['k4'],
query['k']['k5'])
elif query['number'] == 6:
return kcalc.calculk6(query['k']['k1'], query['k']['k2'],
query['k']['k3'], query['k']['k4'],
query['k']['k5'], query['k']['k6'])
elif query['type'] == "utility_calculus_multiplicative":
return kcalc.calculUtilityMultiplicative(query['k'], query['utility'],
query['virgule'])
elif query['type'] == "utility_calculus_multilinear":
return kcalc.calculUtilityMultilinear(query['k'], query['utility'],
query['virgule'])
elif query['type'] == "svg":
dictionary = query['data']
min_ = float(query['min'])
max_ = float(query['max'])
liste_cord = query['liste_cord']
width = query['width']
liste = query['liste']
return plot.generate_svg_plot(dictionary, min_, max_, liste_cord,
width, liste)
elif query['type'] == "svgg":
dictionary = query['data']
min = float(query['min'])
max = float(query['max'])
liste_cord = query['liste_cord']
width = query['width']
choice = query['choice']
return plot.generate_svg_plot2(dictionary, min, max, liste_cord, width,
choice)
elif query['type'] == "svg_QUALI":
dictionary = query['data']
list_names = query['list_names']
width = query['width']
return plot.generate_svg_plot_QUALI(dictionary, list_names, width)
elif query['type'] == "pie_chart":
names = query['names']
probas = query['probas']
return plot.pie_chart(names, probas)
elif query['type'] == "demande_de_transformation":
return selecteur.selecteur1(query['data'], query['numero'])
elif query['type'] == "export_xlsx":
return export_xlsx.generate_fichier(query['data'])
elif query['type'] == "export_xlsx_option":
return export_xlsx.generate_fichier_with_specification(query['data'])
elif query['type'] == "latex_render":
return latex_render.render(query['formula'])
elif query['type'] == "tree":
return draw_tree.draw(query['gain'], query['upper_label'],
query['bottom_label'], query['upper_proba'],
query['bottom_proba'], query['assess_type'])
