import json
import ast
from pathlib import Path
from collections import namedtuple
from future import standard_library
from flask import Flask, request, render_template
from flask_socketio import SocketIO, emit
import database
import broadcast
import audio
Droid = namedtuple(
'Droid', 'droid_uid, member_uid, name, material, weight, transmitter_type')
Driver = namedtuple('Driver', 'member_uid, name, email')
api_key = database.get_config('mot_api_key')
site_base = database.get_config('mot_site_base')
current_droid = Droid(droid_uid=0,
member_uid=0,
name="none",
material="none",
weight="none",
transmitter_type="none")
current_member = Driver(member_uid=0, name="none", email="none")
current_run = 0
current_state = 0
app = Flask(__name__, template_folder='templates')
app.config['key'] = database.get_config('app_key')
socketio = SocketIO(app)
X = X[self.raw_features].copy()
X_pp = self.preprocessing_pipeline.transform(X)
X_pp = pd.DataFrame(X_pp, columns=self.get_feature_names())
return X_pp
def get_feature_names(self):
"""
Feature names after preprocessing.
Replicates the get_feature_names function in the sklearn Transformer classes.
"""
return self.raw_features
if __name__ == "__main__":
from preprocessing import PreProcessor # noqa
# Load data
db_config = db.get_config()
train = db.load(*db_config, 'raw_train')
X_train = train.drop('SalePrice', axis=1)
# Fit and transform training data
pp = PreProcessor()
X_train_pp = pp.fit_transform(X_train)
train_pp = X_train_pp.assign(SalePrice=train['SalePrice'])
# Save preprocessed data and fitted preprocessor
db.save(train_pp, *db_config, 'processed_train')
joblib.dump(pp, os.path.join(DIR, '../pickle/PreProcessor.pkl'))
import database import os import pandas as pd # Kaggle download file # Not implemented - just manually download and read_csv instead DIR = os.path.abspath(os.path.dirname(__file__)) train = pd.read_csv(os.path.join(DIR, '../data/raw/train.csv')) test = pd.read_csv(os.path.join(DIR, '../data/raw/test.csv')) # Save data to database uri, db, _ = database.get_config() database.save(train, uri, db, 'dev', 'raw_train') database.save(test, uri, db, 'dev', 'raw_test')