H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.
172.16.2.179:56795 56.7 MB 1.03771e+06 48 768
mean 56.7 MB 1.03771e+06 48 768
min 56.7 MB 1.03771e+06 48 768
max 56.7 MB 1.03771e+06 48 768
stddev 0 B 0 0 0
total 56.7 MB 1.03771e+06 48 768
Column-by-Column Summary:
tripduration starttime stoptime start station id start station name start station latitude start station longitude end station id end station name end station latitude end station longitude bikeid usertype birth year gender Days
type int time time int enum real real int enum real real int enum int int int
mins 60.0 1.380610868e+12 1.380611083e+12 72.0 0.0 40.680342423 -74.01713445 72.0 0.0 40.680342423 -74.01713445 14529.0 0.0 1899.0 0.0 15979.0
mean 825.614754383 1.38191371692e+12 1.38191454253e+12 443.714212614 164.928709507 40.7345188586 -73.9911328848 443.207421712 165.569528925 40.7342847885 -73.9912702982 17644.0716451 0.906095332809 1975.77839486 1.12375591686 15993.8523906
maxs 1259480.0 1.383289197e+12 1.38341851e+12 3002.0 329.0 40.770513 -73.9500479759 3002.0 329.0 40.770513 -73.9500479759 20757.0 1.0 1997.0 2.0 16010.0
sigma 2000.3732323 778871729.131 778847387.504 354.434325075 100.299091797 0.0195734073053 0.0123161234106 357.398217058 100.415965049 0.0195578458116 0.0123855811965 1717.68112134 0.291696182123 11.1314906238 0.544380593291 9.02215033588
zero_count 0 0 0 0 5239 0 0 0 5449 0 0 0 97446 0 97498 0
missing_count 0 0 0 0 0 0 0 0 0 0 0 0 0 97445 0 0
Traceback (most recent call last):
File "pyunit_citi_bike_small.py", line 13, in
h2o.run_test(sys.argv, demo_citibike_small)
File "../../h2o/h2o.py", line 272, in run_test
test_to_run(ip, port)
File "pyunit_citi_bike_small.py", line 10, in demo_citibike_small
h2o.ipy_notebook_exec(h2o.locate("h2o-py/demos/citi_bike_small.ipynb"),save_and_norun=False)
File "../../h2o/h2o.py", line 287, in ipy_notebook_exec
exec(program, globals())
File "", line 53, in
File "../../h2o/h2o.py", line 530, in group_by
return frame.group_by(cols,aggregates)
File "../../h2o/frame.py", line 814, in group_by
h2o.rapids(expr) # group by
File "../../h2o/h2o.py", line 345, in rapids
result = H2OConnection.post_json("Rapids", ast=urllib.quote(expr), _rest_version=99)
File "../../h2o/connection.py", line 358, in post_json
return H2OCONN._rest_json(url_suffix, "POST", file_upload_info, kwargs)
File "../../h2o/connection.py", line 361, in _rest_json
raw_txt = self._do_raw_rest(url_suffix, method, file_upload_info, kwargs)
File "../../h2o/connection.py", line 419, in _do_raw_rest
.format(http_result.status_code,http_result.reason,method,url,detailed_error_msgs))
EnvironmentError: h2o-py got an unexpected HTTP status code:
412 Precondition Failed (method = POST; url = http://127.0.0.1:56795/99/Rapids).
detailed error messages: Unimplemented failed lookup on token: ``. Contact support@h2o.ai for more information.
Link to job :
http://172.16.2.161:8080/view/Regression/job/h2o_regression_pyunit_small/
================================================
Warning: Version mismatch. H2O is version 3.0.0.22, but the python package is version 3.0.0.99999.
H2O cluster uptime: 1 minutes 48 seconds 235 milliseconds H2O cluster version: 3.0.0.22 H2O cluster name: H2O_runit_jenkins_6065884 H2O cluster total nodes: 1 H2O cluster total memory: 2.67 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: 56795
Warning: Version mismatch. H2O is version 3.0.0.22, but the python package is version 3.0.0.99999.
H2O cluster uptime: 1 minutes 48 seconds 292 milliseconds H2O cluster version: 3.0.0.22 H2O cluster name: H2O_runit_jenkins_6065884 H2O cluster total nodes: 1 H2O cluster total memory: 2.67 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: 56795
Import and Parse bike data
Parse Progress: [ ] 00% Parse Progress: [##################################################] 100%
Parsed 1,037,712 rows and 15 cols:
File1 /home3/jenkins/slave_dir_from_mr-0xb1/workspace/h2o_regression_pyunit_small/bigdata/laptop/citibike-nyc/2013-10.csv
Rows: 1,037,712 Cols: 16
Chunk compression summary:
chunk_type chunk_name count count_percentage size size_percentage
C0L Constant Integers 17 2.21354 1.3 KB 0.00228718 C1 1-Byte Integers 48 6.25 1016.6 KB 1.75066 C1N 1-Byte Integers (w/o NAs) 48 6.25 1016.6 KB 1.75066 C1S 1-Byte Fractions 79 10.2865 1.6 MB 2.88787 C2 2-Byte Integers 243 31.6406 10.0 MB 17.6963 C2S 2-Byte Fractions 49 6.38021 2.0 MB 3.5702 C4 4-Byte Integers 32 4.16667 2.6 MB 4.67269 C4S 4-Byte Fractions 39 5.07812 3.2 MB 5.63731 C8 64-bit Integers 60 7.8125 9.9 MB 17.4327 C8D 64-bit Reals 153 19.9219 25.3 MB 44.5994
Frame distribution summary:
172.16.2.179:56795 56.7 MB 1.03771e+06 48 768 mean 56.7 MB 1.03771e+06 48 768 min 56.7 MB 1.03771e+06 48 768 max 56.7 MB 1.03771e+06 48 768 stddev 0 B 0 0 0 total 56.7 MB 1.03771e+06 48 768
Column-by-Column Summary:
type int time time int enum real real int enum real real int enum int int int mins 60.0 1.380610868e+12 1.380611083e+12 72.0 0.0 40.680342423 -74.01713445 72.0 0.0 40.680342423 -74.01713445 14529.0 0.0 1899.0 0.0 15979.0 mean 825.614754383 1.38191371692e+12 1.38191454253e+12 443.714212614 164.928709507 40.7345188586 -73.9911328848 443.207421712 165.569528925 40.7342847885 -73.9912702982 17644.0716451 0.906095332809 1975.77839486 1.12375591686 15993.8523906 maxs 1259480.0 1.383289197e+12 1.38341851e+12 3002.0 329.0 40.770513 -73.9500479759 3002.0 329.0 40.770513 -73.9500479759 20757.0 1.0 1997.0 2.0 16010.0 sigma 2000.3732323 778871729.131 778847387.504 354.434325075 100.299091797 0.0195734073053 0.0123161234106 357.398217058 100.415965049 0.0195578458116 0.0123855811965 1717.68112134 0.291696182123 11.1314906238 0.544380593291 9.02215033588 zero_count 0 0 0 0 5239 0 0 0 5449 0 0 0 97446 0 97498 0 missing_count 0 0 0 0 0 0 0 0 0 0 0 0 0 97445 0 0 Traceback (most recent call last): File "pyunit_citi_bike_small.py", line 13, in
h2o.run_test(sys.argv, demo_citibike_small)
File "../../h2o/h2o.py", line 272, in run_test
test_to_run(ip, port)
File "pyunit_citi_bike_small.py", line 10, in demo_citibike_small
h2o.ipy_notebook_exec(h2o.locate("h2o-py/demos/citi_bike_small.ipynb"),save_and_norun=False)
File "../../h2o/h2o.py", line 287, in ipy_notebook_exec
exec(program, globals())
File "", line 53, in
File "../../h2o/h2o.py", line 530, in group_by
return frame.group_by(cols,aggregates)
File "../../h2o/frame.py", line 814, in group_by
h2o.rapids(expr) # group by
File "../../h2o/h2o.py", line 345, in rapids
result = H2OConnection.post_json("Rapids", ast=urllib.quote(expr), _rest_version=99)
File "../../h2o/connection.py", line 358, in post_json
return H2OCONN._rest_json(url_suffix, "POST", file_upload_info, kwargs)
File "../../h2o/connection.py", line 361, in _rest_json
raw_txt = self._do_raw_rest(url_suffix, method, file_upload_info, kwargs)
File "../../h2o/connection.py", line 419, in _do_raw_rest
.format(http_result.status_code,http_result.reason,method,url,detailed_error_msgs))
EnvironmentError: h2o-py got an unexpected HTTP status code:
412 Precondition Failed (method = POST; url = http://127.0.0.1:56795/99/Rapids).
detailed error messages: Unimplemented failed lookup on token: ``. Contact support@h2o.ai for more information.