tmp = data.copy() 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)
tmp = data.copy() 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)
tmp = data.copy() return np.array(tmp["real_%s" % letter]) from sklearn import ensemble 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 = ensemble.ExtraTreesClassifier() 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)