tmp = data.copy()
return np.array(tmp["real_%s" % letter])
from sklearn import linear_model
from sklearn.externals import joblib
from sklearn import grid_search
l = AllStateDataLoader()
print("Extraction data_2...")
data_2 = l.get_data_2_train()
print("Extraction data_3...")
data_3 = l.get_data_3_train()
print("Extraction data_all...")
data_all = l.get_data_all_train()
def fit_and_save_log(parameters, dataset, letter, filename, verbose=2):
log = linear_model.LogisticRegression()
X = get_X_without_scaler(dataset)
y = get_y(letter, dataset)
model = grid_search.GridSearchCV(log, parameters, verbose=verbose)
model.fit(X, y)
print("sauvegarde model %s dans %s" % (letter, filename))
joblib.dump(model, filename)
return model
return np.array(tmp["real_%s" % letter])
from sklearn import svm
from sklearn.externals import joblib
from sklearn import grid_search
l = AllStateDataLoader()
print("Extraction data_2...")
data_2 = l.get_data_2_train()
print("Extraction data_3...")
data_3 = l.get_data_3_train()
print("Extraction data_4...")
data_4 = l.get_data_4_train()
print("Extraction data_all...")
data_all = l.get_data_all_train()
def fit_and_save_log(parameters, dataset, letter, filename,verbose=2):
log = svm.LinearSVC(class_weight="auto")
X = get_X_without_scaler(dataset)
y = get_y(letter, dataset)
model = grid_search.GridSearchCV(log, parameters, verbose=verbose)
model.fit(X,y)
print("sauvegarde model %s dans %s" % (letter, filename))
joblib.dump(model, filename)
return model
return np.array(tmp["real_%s" % letter])
from sklearn import svm
from sklearn.externals import joblib
from sklearn import grid_search
l = AllStateDataLoader()
print("Extraction data_2...")
data_2 = l.get_data_2_train(with_location_view=True)
print("Extraction data_3...")
data_3 = l.get_data_3_train(with_location_view=True)
print("Extraction data_4...")
data_4 = l.get_data_4_train(with_location_view=True)
print("Extraction data_all...")
data_all = l.get_data_all_train(with_location_view=True)
def fit_and_save_log(parameters, dataset, letter, filename,verbose=2):
log = svm.LinearSVC(class_weight="auto")
X = get_X_without_scaler(dataset)
y = get_y(letter, dataset)
model = grid_search.GridSearchCV(log, parameters, verbose=verbose)
model.fit(X,y)
print("sauvegarde model %s dans %s" % (letter, filename))
joblib.dump(model, filename)
return model
