In [40]:
import h2o
import time
from h2o.estimators.glm import H2OGeneralizedLinearEstimator
from h2o.estimators.gbm import H2OGradientBoostingEstimator
from h2o.estimators.random_forest import H2ORandomForestEstimator
from h2o.estimators.deeplearning import H2ODeepLearningEstimator
In [41]:
# Explore a typical Data Science workflow with H2O and Python
#
# Goal: assist the manager of CitiBike of NYC to load-balance the bicycles
# across the CitiBike network of stations, by predicting the number of bike
# trips taken from the station every day. Use 10 million rows of historical
# data, and eventually add weather data.
# Connect to a cluster
h2o.init()
In [42]:
from h2o.h2o import _locate # private function. used to find files within h2o git project directory.
# Set this to True if you want to fetch the data directly from S3.
# This is useful if your cluster is running in EC2.
data_source_is_s3 = False
def mylocate(s):
if data_source_is_s3:
return "s3n://h2o-public-test-data/" + s
else:
return _locate(s)
In [43]:
# Pick either the big or the small demo.
# Big data is 10M rows
small_test = [mylocate("bigdata/laptop/citibike-nyc/2013-10.csv")]
big_test = [mylocate("bigdata/laptop/citibike-nyc/2013-07.csv"),
mylocate("bigdata/laptop/citibike-nyc/2013-08.csv"),
mylocate("bigdata/laptop/citibike-nyc/2013-09.csv"),
mylocate("bigdata/laptop/citibike-nyc/2013-10.csv"),
mylocate("bigdata/laptop/citibike-nyc/2013-11.csv"),
mylocate("bigdata/laptop/citibike-nyc/2013-12.csv"),
mylocate("bigdata/laptop/citibike-nyc/2014-01.csv"),
mylocate("bigdata/laptop/citibike-nyc/2014-02.csv"),
mylocate("bigdata/laptop/citibike-nyc/2014-03.csv"),
mylocate("bigdata/laptop/citibike-nyc/2014-04.csv"),
mylocate("bigdata/laptop/citibike-nyc/2014-05.csv"),
mylocate("bigdata/laptop/citibike-nyc/2014-06.csv"),
mylocate("bigdata/laptop/citibike-nyc/2014-07.csv"),
mylocate("bigdata/laptop/citibike-nyc/2014-08.csv")]
# ----------
# 1- Load data - 1 row per bicycle trip. Has columns showing the start and end
# station, trip duration and trip start time and day. The larger dataset
# totals about 10 million rows
print "Import and Parse bike data"
data = h2o.import_file(path=big_test)
In [44]:
# ----------
# 2- light data munging: group the bike starts per-day, converting the 10M rows
# of trips to about 140,000 station&day combos - predicting the number of trip
# starts per-station-per-day.
# Convert start time to: Day since the Epoch
startime = data["starttime"]
secsPerDay=1000*60*60*24
data["Days"] = (startime/secsPerDay).floor()
data.describe()
In [45]:
# Now do a monster Group-By. Count bike starts per-station per-day. Ends up
# with about 340 stations times 400 days (140,000 rows). This is what we want
# to predict.
grouped = data.group_by(["Days","start station name"])
bpd = grouped.count().get_frame() # Compute bikes-per-day
bpd.set_name(2,"bikes")
bpd.show()
bpd.describe()
bpd.dim
Out[45]:
In [46]:
# Quantiles: the data is fairly unbalanced; some station/day combos are wildly
# more popular than others.
print "Quantiles of bikes-per-day"
bpd["bikes"].quantile().show()
In [47]:
# A little feature engineering
# Add in month-of-year (seasonality; fewer bike rides in winter than summer)
secs = bpd["Days"]*secsPerDay
bpd["Month"] = secs.month().asfactor()
# Add in day-of-week (work-week; more bike rides on Sunday than Monday)
bpd["DayOfWeek"] = secs.dayOfWeek()
print "Bikes-Per-Day"
bpd.describe()
In [48]:
# ----------
# 3- Fit a model on train; using test as validation
# Function for doing class test/train/holdout split
def split_fit_predict(data):
global gbm0,drf0,glm0,dl0
# Classic Test/Train split
r = data['Days'].runif() # Random UNIForm numbers, one per row
train = data[ r < 0.6]
test = data[(0.6 <= r) & (r < 0.9)]
hold = data[ 0.9 <= r ]
print "Training data has",train.ncol,"columns and",train.nrow,"rows, test has",test.nrow,"rows, holdout has",hold.nrow
bike_names_x = data.names
bike_names_x.remove("bikes")
# Run GBM
s = time.time()
gbm0 = H2OGradientBoostingEstimator(ntrees=500, # 500 works well
max_depth=6,
learn_rate=0.1)
gbm0.train(x =bike_names_x,
y ="bikes",
training_frame =train,
validation_frame=test)
gbm_elapsed = time.time() - s
# Run DRF
s = time.time()
drf0 = H2ORandomForestEstimator(ntrees=250, max_depth=30)
drf0.train(x =bike_names_x,
y ="bikes",
training_frame =train,
validation_frame=test)
drf_elapsed = time.time() - s
# Run GLM
if "WC1" in bike_names_x: bike_names_x.remove("WC1")
s = time.time()
glm0 = H2OGeneralizedLinearEstimator(Lambda=[1e-5], family="poisson")
glm0.train(x =bike_names_x,
y ="bikes",
training_frame =train,
validation_frame=test)
glm_elapsed = time.time() - s
# Run DL
s = time.time()
dl0 = H2ODeepLearningEstimator(hidden=[50,50,50,50], epochs=50)
dl0.train(x =bike_names_x,
y ="bikes",
training_frame =train,
validation_frame=test)
dl_elapsed = time.time() - s
# ----------
# 4- Score on holdout set & report
train_r2_gbm = gbm0.model_performance(train).r2()
test_r2_gbm = gbm0.model_performance(test ).r2()
hold_r2_gbm = gbm0.model_performance(hold ).r2()
# print "GBM R2 TRAIN=",train_r2_gbm,", R2 TEST=",test_r2_gbm,", R2 HOLDOUT=",hold_r2_gbm
train_r2_drf = drf0.model_performance(train).r2()
test_r2_drf = drf0.model_performance(test ).r2()
hold_r2_drf = drf0.model_performance(hold ).r2()
# print "DRF R2 TRAIN=",train_r2_drf,", R2 TEST=",test_r2_drf,", R2 HOLDOUT=",hold_r2_drf
train_r2_glm = glm0.model_performance(train).r2()
test_r2_glm = glm0.model_performance(test ).r2()
hold_r2_glm = glm0.model_performance(hold ).r2()
# print "GLM R2 TRAIN=",train_r2_glm,", R2 TEST=",test_r2_glm,", R2 HOLDOUT=",hold_r2_glm
train_r2_dl = dl0.model_performance(train).r2()
test_r2_dl = dl0.model_performance(test ).r2()
hold_r2_dl = dl0.model_performance(hold ).r2()
# print " DL R2 TRAIN=",train_r2_dl,", R2 TEST=",test_r2_dl,", R2 HOLDOUT=",hold_r2_dl
# make a pretty HTML table printout of the results
header = ["Model", "R2 TRAIN", "R2 TEST", "R2 HOLDOUT", "Model Training Time (s)"]
table = [
["GBM", train_r2_gbm, test_r2_gbm, hold_r2_gbm, round(gbm_elapsed,3)],
["DRF", train_r2_drf, test_r2_drf, hold_r2_drf, round(drf_elapsed,3)],
["GLM", train_r2_glm, test_r2_glm, hold_r2_glm, round(glm_elapsed,3)],
["DL ", train_r2_dl, test_r2_dl, hold_r2_dl , round(dl_elapsed,3) ],
]
h2o.H2ODisplay(table,header)
# --------------
In [49]:
# Split the data (into test & train), fit some models and predict on the holdout data
split_fit_predict(bpd)
# Here we see an r^2 of 0.91 for GBM, and 0.71 for GLM. This means given just
# the station, the month, and the day-of-week we can predict 90% of the
# variance of the bike-trip-starts.
In [50]:
# ----------
# 5- Now lets add some weather
# Load weather data
wthr1 = h2o.import_file(path=[mylocate("bigdata/laptop/citibike-nyc/31081_New_York_City__Hourly_2013.csv"),
mylocate("bigdata/laptop/citibike-nyc/31081_New_York_City__Hourly_2014.csv")])
# Peek at the data
wthr1.describe()
In [51]:
# Lots of columns in there! Lets plan on converting to time-since-epoch to do
# a 'join' with the bike data, plus gather weather info that might affect
# cyclists - rain, snow, temperature. Alas, drop the "snow" column since it's
# all NA's. Also add in dew point and humidity just in case. Slice out just
# the columns of interest and drop the rest.
wthr2 = wthr1[["Year Local","Month Local","Day Local","Hour Local","Dew Point (C)","Humidity Fraction","Precipitation One Hour (mm)","Temperature (C)","Weather Code 1/ Description"]]
wthr2.set_name(wthr2.names.index("Precipitation One Hour (mm)"), "Rain (mm)")
wthr2.set_name(wthr2.names.index("Weather Code 1/ Description"), "WC1")
wthr2.describe()
# Much better!
In [52]:
# Filter down to the weather at Noon
wthr3 = wthr2[ wthr2["Hour Local"]==12 ]
In [53]:
# Lets now get Days since the epoch... we'll convert year/month/day into Epoch
# time, and then back to Epoch days. Need zero-based month and days, but have
# 1-based.
wthr3["msec"] = h2o.H2OFrame.mktime(year=wthr3["Year Local"], month=wthr3["Month Local"]-1, day=wthr3["Day Local"]-1, hour=wthr3["Hour Local"])
secsPerDay=1000*60*60*24
wthr3["Days"] = (wthr3["msec"]/secsPerDay).floor()
wthr3.describe()
# msec looks sane (numbers like 1.3e12 are in the correct range for msec since
# 1970). Epoch Days matches closely with the epoch day numbers from the
# CitiBike dataset.
In [54]:
# Lets drop off the extra time columns to make a easy-to-handle dataset.
wthr4 = wthr3.drop("Year Local").drop("Month Local").drop("Day Local").drop("Hour Local").drop("msec")
In [55]:
# Also, most rain numbers are missing - lets assume those are zero rain days
rain = wthr4["Rain (mm)"]
rain[ rain.isna() ] = 0
wthr4["Rain (mm)"] = rain
In [56]:
# ----------
# 6 - Join the weather data-per-day to the bike-starts-per-day
print "Merge Daily Weather with Bikes-Per-Day"
bpd_with_weather = bpd.merge(wthr4,allLeft=True,allRite=False)
bpd_with_weather.describe()
bpd_with_weather.show()
In [57]:
# 7 - Test/Train split again, model build again, this time with weather
split_fit_predict(bpd_with_weather)
In [ ]: