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)
