Skip to content

Instantly share code, notes, and snippets.

QuantumDamage

Block or report user

Report or block QuantumDamage

Hide content and notifications from this user.

Learn more about blocking users

Contact Support about this user’s behavior.

Learn more about reporting abuse

Report abuse
View GitHub Profile
View gist:7892944
import urllib2
import json
import time
import pylab
import matplotlib.pyplot as plt
import matplotlib.animation as animation
def update_line(data):
View bibisco.log
#
# 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
#
View slice.py
#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)), :]
View aqip10-class.py
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):
View aqip10-worst.py
worstPlace = descriptiveFrame.xs('6 Very bad', level=1)["overall"].idxmax()
descriptiveFrame.xs(worstPlace, level=0)
View aqip11-slice.py
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))
View aqip11-slice2.py
reducedDataFrame = bigDataFrame['2015-01-01 01:00:00':'2016-01-01 00:00:00'].loc[(slice(None),stationCodes), :]
View tpot-loading.py
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)
View inputer.py
imp = Imputer(missing_values='NaN', strategy='mean', axis=0)
imp.fit(heartData)
View tpot.py
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')
You can’t perform that action at this time.