QuantumDamage

import urllib2
import json
import time
import pylab
import matplotlib.pyplot as plt
import matplotlib.animation as animation



def update_line(data):

QuantumDamage

#
# A fatal error has been detected by the Java Runtime Environment:
#
#  SIGSEGV (0xb) at pc=0x00007fdbb3a8e73f, pid=25807, tid=140581999658752
#
# JRE version: Java(TM) SE Runtime Environment (7.0_80-b15) (build 1.7.0_80-b15)
# Java VM: Java HotSpot(TM) 64-Bit Server VM (24.80-b11 mixed mode linux-amd64 compressed oops)
# Problematic frame:
# C  [libgdk-x11-2.0.so.0+0x5173f]  gdk_display_open+0x3f
#

QuantumDamage

#reducedDataFrame = bigDataFrame['2015-01-01 00:00:00':'2015-12-31 23:00:00'].loc[(slice(None),pollutedPlaces), :]
reducedDataFrame = bigDataFrame['2015-01-01 00:00:00':'2015-12-31 23:00:00'].loc[(slice(None), slice(None)), :]

QuantumDamage

def C6H6qual (value):
    if (value < 0.0):
        return np.NaN
    elif (value >= 0.0 and value <= 5.0):
        return "1 Very good"
    elif (value > 5.0 and value <= 10.0):
        return "2 Good"
    elif (value > 10.0 and value <= 15.0):
        return "3 Moderate"
    elif (value > 15.0 and value <= 20.0):

QuantumDamage

worstPlace = descriptiveFrame.xs('6 Very bad', level=1)["overall"].idxmax()
descriptiveFrame.xs(worstPlace, level=0)

QuantumDamage

stations = pd.read_excel("../input/Metadane_wer20160914.xlsx")
coolStation = [u'Gdańsk', u'Gdynia', u'Sopot', u'Kościerzyna']
selectedStations = stations[stations[u'Miejscowość'].isin(coolStation)]
stationCodes = set(list(selected_stations[u'Kod stacji'].values) + list(selected_stations[u'Stary Kod stacji'].values))

QuantumDamage

reducedDataFrame = bigDataFrame['2015-01-01 01:00:00':'2016-01-01 00:00:00'].loc[(slice(None),stationCodes), :]

QuantumDamage

heartData = pd.read_csv("../input/processed.cleveland.data", 
                        names=["age", "sex", "cp", "trestbps", "chol", "fbs", "restecg", "thalach", "exang", 
                               "oldpeak", "slope", "ca", "thal", "num"])
heartData["ca"] = pd.to_numeric(heartData["ca"], errors='coerce')
heartData["thal"] = pd.to_numeric(heartData["thal"], errors='coerce')
heartData = heartData[(heartData["num"] == 0) | (heartData["num"] == 1)]
heartTarget = heartData["num"]
heartData = heartData.drop("num", axis=1)

QuantumDamage

imp = Imputer(missing_values='NaN', strategy='mean', axis=0)
imp.fit(heartData)

QuantumDamage

X_train, X_test, y_train, y_test = train_test_split(imp.transform(heartData),heartTarget.values)
tpot = TPOTClassifier(generations=5, population_size=50, verbosity=2)
tpot.fit(X_train, y_train)
print(tpot.score(X_test, y_test))
tpot.export('../output/heart_pipeline.py')