In [7]:

    

import h2o
from h2o.estimators.gbm import H2OGradientBoostingEstimator
from h2o.estimators.deeplearning import H2ODeepLearningEstimator


    

In [8]:

    

# Connect to a cluster
h2o.init()


    

    





H2O cluster uptime: 
4 minutes 13 seconds 228 milliseconds 
H2O cluster version: 
3.5.0.99999
H2O cluster name: 
ece
H2O cluster total nodes: 
1
H2O cluster total memory: 
3.56 GB
H2O cluster total cores: 
8
H2O cluster allowed cores: 
8
H2O cluster healthy: 
True
H2O Connection ip: 
127.0.0.1
H2O Connection port: 
54321

In [9]:

    

from h2o.h2o import _locate # private function. used to find files within h2o git project directory.
weather_path = _locate("smalldata/chicago/chicagoAllWeather.csv")
census_path = _locate("smalldata/chicago/chicagoCensus.csv")
crimes_path = _locate("smalldata/chicago/chicagoCrimes10k.csv.zip")

print "Import and Parse weather data"
weather = h2o.import_file(path=weather_path, col_types = ["time"] + ["numeric"]*6)
weather.drop("date")
weather.describe()

print "Import and Parse census data"
census = h2o.import_file(path=census_path, col_types = ["numeric", "enum"] + ["numeric"]*7)
census.describe()

print "Import and Parse crimes data"
crimes = h2o.import_file(path=crimes_path)
crimes.describe()


    

    




Import and Parse weather data

Parse Progress: [##################################################] 100%
Imported /Users/ece/0xdata/h2o-dev/smalldata/chicago/chicagoAllWeather.csv. Parsed 5,162 rows and 7 cols
Rows: 5,162 Cols: 7

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year
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int
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82.0
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missing_count
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13
13
13






    




Import and Parse census data

Parse Progress: [##################################################] 100%
Imported /Users/ece/0xdata/h2o-dev/smalldata/chicago/chicagoCensus.csv. Parsed 79 rows and 9 cols
Rows: 79 Cols: 9

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COMMUNITY AREA NAME
PERCENT OF HOUSING CROWDED
PERCENT HOUSEHOLDS BELOW POVERTY
PERCENT AGED 16  UNEMPLOYED
PERCENT AGED 25  WITHOUT HIGH SCHOOL DIPLOMA
PERCENT AGED UNDER 18 OR OVER 64
PER CAPITA INCOME 
HARDSHIP INDEX
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Import and Parse crimes data

Parse Progress: [##################################################] 100%
Imported /Users/ece/0xdata/h2o-dev/smalldata/chicago/chicagoCrimes10k.csv.zip. Parsed 9,999 rows and 22 cols
Rows: 9,999 Cols: 22

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In [10]:

    

def refine_date_col(data, col, pattern):
    data[col]         = data[col].as_date(pattern)
    data["Day"]       = data[col].day()
    data["Month"]     = data[col].month() + 1     # Since H2O indexes from 0
    data["Year"]      = data[col].year() + 1900   # Start of epoch is 1900
    data["WeekNum"]   = data[col].week()
    data["WeekDay"]   = data[col].dayOfWeek()
    data["HourOfDay"] = data[col].hour()
    
    data.describe()  # HACK: Force evaluation before ifelse and cut. See PUBDEV-1425.
    
    # Create weekend and season cols
    # Spring = Mar, Apr, May. Summer = Jun, Jul, Aug. Autumn = Sep, Oct. Winter = Nov, Dec, Jan, Feb.
    # data["Weekend"]   = [1 if x in ("Sun", "Sat") else 0 for x in data["WeekDay"]]
    data["Weekend"] = h2o.ifelse(data["WeekDay"] == "Sun" or data["WeekDay"] == "Sat", 1, 0)[0]
    data["Season"] = data["Month"].cut([0, 2, 5, 7, 10, 12], ["Winter", "Spring", "Summer", "Autumn", "Winter"])
    
refine_date_col(crimes, "Date", "%m/%d/%Y %I:%M:%S %p")
crimes = crimes.drop("Date")
crimes.describe()


    

    




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In [11]:

    

# Merge crimes data with weather and census
census.set_name(0,"Community Area")
weather.set_name(1,"Month")
weather.set_name(2,"Day")
weather.set_name(3,"Year")
crimes.merge(census, allLeft=True, allRite=False)
crimes.merge(weather, allLeft=True, allRite=False)
crimes.describe()


    

    




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In [12]:

    

# Create test/train split
r = crimes["Arrest"].runif()
train = crimes[r < 0.8]
test = crimes[r >= 0.8]

# Simple GBM - Predict Arrest
crimes_names_x = crimes.names
crimes_names_x.remove("Arrest")
data_gbm = H2OGradientBoostingEstimator(ntrees         =10,
                                        max_depth      =6,
                                        distribution   ="bernoulli")

data_gbm.train(x               =crimes_names_x,
               y               ="Arrest",
               training_frame  =train,
               validation_frame=test)

# Simple Deep Learning - Predict Arrest
data_dl = H2ODeepLearningEstimator(variable_importances=True,
                                   loss                ="Automatic")

data_dl.train(x                =crimes_names_x,
              y                ="Arrest",
              training_frame  =train,
              validation_frame=test)


    

    




gbm Model Build Progress: [##################################################] 100%

deeplearning Model Build Progress: [##################################################] 100%

In [13]:

    

# GBM performance on train/test data
train_auc_gbm = data_gbm.model_performance(train).auc()
test_auc_gbm  = data_gbm.model_performance(test) .auc()

# Deep Learning performance on train/test data
train_auc_dl = data_dl.model_performance(train).auc()
test_auc_dl  = data_dl.model_performance(test) .auc()

# Make a pretty HTML table printout of the results
header = ["Model", "AUC Train", "AUC Test"]
table  = [
           ["GBM", train_auc_gbm, test_auc_gbm],
           ["DL ", train_auc_dl,  test_auc_dl]
         ]
h2o.H2ODisplay(table, header)


    

    





Model
AUC Train
AUC Test
GBM
0.957115520619
0.914403005062
DL 
0.98411333776
0.910417417547






    
Out[13]:

In [14]:

    

# Create new H2OFrame of crime observations
examples = {
            "Date":                 ["02/08/2015 11:43:58 PM", "02/08/2015 11:00:39 PM"],
            "IUCR":                 [1811, 1150],
            "Primary.Type":         ["NARCOTICS", "DECEPTIVE PRACTICE"],
            "Location.Description": ["STREET", "RESIDENCE"],
            "Domestic":             ["false", "false"],
            "Beat":                 [422, 923],
            "District":             [4, 9],
            "Ward":                 [7, 14],
            "Community.Area":       [46, 63],
            "FBI.Code":             [18, 11]
            }

crime_examples = h2o.H2OFrame(python_obj = examples)

# Refine date column and merge with census data
refine_date_col(crime_examples, "Date", "%m/%d/%Y %I:%M:%S %p")
crime_examples.drop("Date")
census.set_name(0,"Community.Area")
crime_examples.merge(census, allLeft=True, allRite=False)
crime_examples.describe()


    

    




Parse Progress: [##################################################] 100%
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Location.Description
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Beat
Primary.Type
Community.Area
District
Date
Ward
Domestic
IUCR
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Month
Year
WeekNum
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In [15]:

    

# Predict probability of arrest from new observations
gbm_pred = data_gbm.predict(crime_examples)
dl_pred  = data_dl .predict(crime_examples)

# Make a pretty HTML table printout of the results
header = ["FBI Code", "GBM Arrest Prob", "DL Arrest Prob"]
table  = [
           [examples["FBI.Code"][0], gbm_pred[0,"true"], dl_pred[0,"true"]],
           [examples["FBI.Code"][1], gbm_pred[1,"true"], dl_pred[1,"true"]]
         ]
h2o.H2ODisplay(table, header)


    

    





FBI Code
GBM Arrest Prob
DL Arrest Prob
18
0.113645062475
0.00228440147384
11
0.113645062475
0.00461659407729






    
Out[15]: