from analyser import Analyser from flask_cors import CORS, cross_origin import json import os DATA_SET_PATH = os.environ['DATA_SET_PATH'] TRAIN_HEADER = os.environ['TRAIN_HEADER'] if DATA_SET_PATH is None: DATA_SET_PATH = 'resources/data.csv' if TRAIN_HEADER is None: TRAIN_HEADER = 'preco' app = Flask(__name__) CORS(app) ds = DataSet.init_from_file(DATA_SET_PATH, TRAIN_HEADER) @app.route('/categories', methods=['GET']) def categories(): categories = [] # ds.print_config() # print("### DS CATEGORIES ###\n" + str(ds.categories)) # print("### categories size: " + str(len(ds.categories))) for k, v in ds.headers_map.items(): cat = {} cat["name"] = k cat["id"] = v cat["groups"] = ds.categories[v] categories.append(cat)
# filtering row for k, v in filters_dict.items(): if dataset.phones_categories[i][k] != v: should_jump = True t_value = dataset.get_train_value(i) should_jump = should_jump or t_value == None if should_jump: continue r_row = [0 for k in range(dataset.categories_count[category])] if dataset.phones_categories[i][category] != -1: r_row[dataset.phones_categories[i][category]] = 1 r_matrix.append(r_row) r_vector.append(t_value) return (r_matrix, r_vector) # a = False a = False if a: ds = DataSet.init_from_file("data.csv", "preco") ds.print_config() coef = Analyser.analyse({}, 6, ds)