def load_from_json(self, fname): # load the model import_data = json_tricks.load(open(fname)) import_clf = ModifiedNB() import_clf.class_count_ = import_data['class_count_'] import_clf.class_log_prior_ = import_data['class_log_prior_'] import_clf.classes_ = import_data['classes_'] import_clf.feature_count_ = import_data['feature_count_'] import_clf.feature_log_prob_ = import_data['feature_log_prob_'] self.clf = import_clf # load the fps dict vectoriser v_fps = DictVectorizer() dv = import_data['fps_vectoriser'] v_fps.vocabulary_ = {int(k): v for k, v in dv['vocabulary_'].items()} v_fps.feature_names_ = dv['feature_names_'] self.v_fps = v_fps # load the continous variables binariser try: binariser = import_data['binariser'] kbd = KBinsDiscretizer(n_bins=10, encode='onehot', strategy='quantile') kbd.n_bins = binariser['n_bins'] kbd.n_bins_ = binariser['n_bins_'] kbd.bin_edges_ = np.asarray( [np.asarray(x) for x in binariser['bin_edges_']]) encoder = OneHotEncoder() encoder.categories = binariser['categories'] encoder._legacy_mode = False kbd._encoder = encoder self.kbd = kbd except Exception as e: pass # extra parameters self.trained = True self.con_desc_list = import_data['con_desc_list'] self.fp_type = import_data['fp_type'] self.fp_radius = import_data['fp_radius'] self.informative_cvb = import_data['informative_cvb']
x = dataset.iloc[:, :12].values y = dataset.iloc[:, 12:13].values print(len(x)) print(len(y)) sc = StandardScaler() x1 = sc.fit_transform(x) # # print(z) from sklearn.preprocessing import OneHotEncoder ohe = OneHotEncoder() ohe.categories = 'auto' z = ohe.fit_transform(y).toarray() print(z) from sklearn.model_selection import train_test_split x_train, x_test, y_train, y_test = train_test_split(x1, z, test_size=0.1) from model_archi import * model = model_archi.build(12, 4) model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) history = model.fit(x_train, y_train,