notebook.community

Edit and run

Setup the Environment

Load the shared core, methods, and environment before starting processing





In [1]:



    


from __future__ import print_function
import sys, os, requests, json, datetime

# Load the environment and login the user
from src.common.load_redten_ipython_env import user_token, user_login, csv_file, run_job, core, api_urls, ppj, rt_url, rt_user, rt_pass, rt_email, lg, good, boom, anmt, mark, ppj, uni_key, rest_login_as_user, rest_full_login, wait_for_job_to_finish, wait_on_job, get_job_analysis, get_job_results, get_analysis_manifest, get_job_cache_manifest, build_prediction_results, build_forecast_results, get_job_cache_manifest, search_ml_jobs, show_logs, show_errors, ipyImage, ipyHTML, ipyDisplay, pd, np

anmt("Environment ready")



    


















    






Environment ready

Analyze the IRIS dataset with an XGB Regressor





In [2]:



    


job_id = None # on success, this will store the actively running job's id

resource_url = rt_url + "/ml/run/"
query_params = {}
uni = uni_key()

lg("Building Post Data", 6)

title = "Hello World - " + str(datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S"))
desc = "This is a Description for - " + str(title)

# initial stubs
feature_column_names = []
target_column_values = []
ignore_features = []
csv_file = "/opt/work/data/src/iris.csv"
rloc = ""
sloc = ""
units_ahead = 0

ds_name = "iris_regressor"
algo_name = "xgb-regressor"

# What column has the labeled targets as integers? (added-manually to the dataset)
target_column_name = "ResultLabel"                 
# possible values in the Target Column
target_column_values = [ "Iris-setosa", "Iris-versicolor", "Iris-virginica" ] 

# What columns can the algorithms use for training and learning?
feature_column_names = [ "SepalLength", "SepalWidth", "PetalLength", "PetalWidth", "ResultTargetValue" ] 

# What column holds string labels for the Target Column?
label_column_name = "ResultLabel"
ignore_features = [ # Prune non-int/float columns as needed: 
                    target_column_name
                ]
if target_column_name != label_column_name:
    ignore_features.append(label_column_name)

predict_row = {
                "SepalLength"       : 5.4,
                "SepalWidth"        : 3.4,
                "PetalLength"       : 1.7,
                "PetalWidth"        : 0.2,
                "ResultTargetValue" : 0
            }

post_data = {
            "title" : title,
            "desc" : desc,
            "predict_this_data" : predict_row,
            "ds_name" : ds_name,
            "feature_column_names" : feature_column_names,
            "ignore_features" : ignore_features,
            "csv_file" : csv_file,
            "rloc" : rloc,
            "sloc" : sloc,
            "algo_name" : algo_name,
            "target_column_values" : target_column_values,
            "target_column_name" : target_column_name,
            "label_column_name" : label_column_name,
            "prediction_type" : "Predict",
            "ml_type" : "Predict with Filter",
            "train" : {
                    "base_score" : 0.5,
                    "col_sample_by_level" : 1.0,
                    "col_sample_by_tree" : 0.8,
                    "gamma" : 0,
                    "learning_rate" : 0.1,
                    "max_delta_step" : 0,
                    "max_depth" : 6,
                    "min_child_weight" : 1,
                    "num_estimators" : 1000,
                    "objective" : "reg:linear",
                    "reg_alpha" : 0.0,
                    "reg_lambda" : 1.0,
                    "scaled_position_weight" : 1.0,
                    "seed" : 27,
                    "sub_sample" : 0.8,
                    "debug" : True
            },
            "user_id" : 2
        }
post_headers = {
                "Content-type" : "application/json",
                "Authorization" : "JWT " + str(user_token)
            }

lg("Running ML Job url=" + str(resource_url), 6)
post_response = requests.post(resource_url, params=query_params, data=json.dumps(post_data), headers=post_headers)

if post_response.status_code != 201 and post_response.status_code != 200:
    lg("Failed with Post Response Status=" + str(post_response.status_code) + " Reason=" + str(post_response.reason), 0)
    lg("Details:\n" + str(post_response.text) + "\n", 0)
else:
    lg("SUCCESS - Post Response Status=" + str(post_response.status_code) + " Reason=" + str(post_response.reason), 5)
    record = post_response.json()
    lg(ppj(record))
    job_id = int(record["id"])
# end of post for running an ML Job



    


















    






Building Post Data
Running ML Job url=https://redten.io/ml/run/
SUCCESS - Post Response Status=201 Reason=Created
{
    "algo_name": "xgb-regressor",
    "control_state": "active",
    "created": "2017-05-26 17-49-23",
    "csv_file": "/opt/work/data/src/iris.csv",
    "desc": "This is a Description for - Hello World - 2017-05-26-17-49-22",
    "ds_name": "iris_regressor",
    "feature_column_names": [
        "SepalLength",
        "SepalWidth",
        "PetalLength",
        "PetalWidth",
        "ResultTargetValue"
    ],
    "id": 560,
    "ignore_features": [
        "ResultLabel"
    ],
    "manifest_secret_key": null,
    "manifest_sloc": null,
    "max_features": 10,
    "ml_type": "Predict with Filter",
    "prediction_type": "predict",
    "rloc": "",
    "sloc": "",
    "status": "requested",
    "target_column_name": "ResultLabel",
    "target_column_values": [
        "Iris-setosa",
        "Iris-versicolor",
        "Iris-virginica"
    ],
    "title": "Hello World - 2017-05-26-17-49-22",
    "units_ahead_set": [],
    "units_ahead_type": "",
    "updated": "2017-05-26 17-49-23",
    "version": 1
}

Wait for the job to finish





In [3]:



    


job_data = {}
job_report = {}

job_res = {}
if job_id == None:
    boom("Failed to start a new job")
else:
    job_res = wait_on_job(job_id)

    if job_res["status"] != "SUCCESS":
        boom("Job=" + str(job_id) + " failed with status=" + str(job_res["status"]) + " err=" + str(job_res["error"]))
    else:
        job_data = job_res["record"]
        anmt("Job Report:")
        lg(ppj(job_data), 5)
# end of waiting



    


















    






Waiting on job=560 url=https://redten.io/ml/560/
Job=560 is training - Step 3/10
Job=560 is plotting - Step 7/10
Job=560 is uploading - Step 9/10
Job=560 completed
Job Report:
{
    "job": {
        "algo_name": "xgb-regressor",
        "control_state": "active",
        "created": "2017-05-26 17-49-23",
        "csv_file": "/opt/work/data/src/iris.csv",
        "desc": "This is a Description for - Hello World - 2017-05-26-17-49-22",
        "ds_name": "iris_regressor",
        "feature_column_names": [
            "SepalLength",
            "SepalWidth",
            "PetalLength",
            "PetalWidth",
            "ResultTargetValue"
        ],
        "id": 560,
        "ignore_features": [
            "ResultLabel"
        ],
        "images": [
            {
                "author_name": null,
                "desc": null,
                "id": 12158,
                "image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_560_12158_68fe500cd7564213.png",
                "json_data": null,
                "label": null,
                "shareable_link": null,
                "sloc": null,
                "status": "initial",
                "title": "Joint Plot - ResultTargetValue vs SepalLength - /opt/work/data/src/jointplot_IRIS_REGRESSOR-2-560_4",
                "version": 1
            },
            {
                "author_name": null,
                "desc": null,
                "id": 12157,
                "image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_560_12157_0180f59977c44413.png",
                "json_data": null,
                "label": null,
                "shareable_link": null,
                "sloc": null,
                "status": "initial",
                "title": "Joint Plot - PetalWidth vs SepalLength - /opt/work/data/src/jointplot_IRIS_REGRESSOR-2-560_3",
                "version": 1
            },
            {
                "author_name": null,
                "desc": null,
                "id": 12156,
                "image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_560_12156_4a32649040c14ab6.png",
                "json_data": null,
                "label": null,
                "shareable_link": null,
                "sloc": null,
                "status": "initial",
                "title": "Joint Plot - PetalLength vs SepalLength - /opt/work/data/src/jointplot_IRIS_REGRESSOR-2-560_2",
                "version": 1
            },
            {
                "author_name": null,
                "desc": null,
                "id": 12155,
                "image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_560_12155_e1d895dcd1714932.png",
                "json_data": null,
                "label": null,
                "shareable_link": null,
                "sloc": null,
                "status": "initial",
                "title": "Joint Plot - SepalWidth vs SepalLength - /opt/work/data/src/jointplot_IRIS_REGRESSOR-2-560_1",
                "version": 1
            },
            {
                "author_name": null,
                "desc": null,
                "id": 12154,
                "image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_560_12154_9b0be64a57fd484b.png",
                "json_data": null,
                "label": null,
                "shareable_link": null,
                "sloc": null,
                "status": "initial",
                "title": "Joint Plot - SepalLength vs SepalWidth - /opt/work/data/src/jointplot_IRIS_REGRESSOR-2-560_0",
                "version": 1
            },
            {
                "author_name": null,
                "desc": null,
                "id": 12153,
                "image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_560_12153_80e4812b82a64e4b.png",
                "json_data": null,
                "label": null,
                "shareable_link": null,
                "sloc": null,
                "status": "initial",
                "title": "Scatter - ResultTargetValue vs SepalLength - /opt/work/data/src/scatter_IRIS_REGRESSOR-2-560_4",
                "version": 1
            },
            {
                "author_name": null,
                "desc": null,
                "id": 12152,
                "image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_560_12152_0c8343a342bb40fd.png",
                "json_data": null,
                "label": null,
                "shareable_link": null,
                "sloc": null,
                "status": "initial",
                "title": "Scatter - PetalWidth vs SepalLength - /opt/work/data/src/scatter_IRIS_REGRESSOR-2-560_3",
                "version": 1
            },
            {
                "author_name": null,
                "desc": null,
                "id": 12151,
                "image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_560_12151_17ef598b56e14f68.png",
                "json_data": null,
                "label": null,
                "shareable_link": null,
                "sloc": null,
                "status": "initial",
                "title": "Scatter - PetalLength vs SepalLength - /opt/work/data/src/scatter_IRIS_REGRESSOR-2-560_2",
                "version": 1
            },
            {
                "author_name": null,
                "desc": null,
                "id": 12150,
                "image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_560_12150_8166153a2f87499d.png",
                "json_data": null,
                "label": null,
                "shareable_link": null,
                "sloc": null,
                "status": "initial",
                "title": "Scatter - SepalWidth vs SepalLength - /opt/work/data/src/scatter_IRIS_REGRESSOR-2-560_1",
                "version": 1
            },
            {
                "author_name": null,
                "desc": null,
                "id": 12149,
                "image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_560_12149_bd798d8ff84f46f6.png",
                "json_data": null,
                "label": null,
                "shareable_link": null,
                "sloc": null,
                "status": "initial",
                "title": "Scatter - SepalLength vs SepalWidth - /opt/work/data/src/scatter_IRIS_REGRESSOR-2-560_0",
                "version": 1
            },
            {
                "author_name": null,
                "desc": null,
                "id": 12148,
                "image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_560_12148_07ed6b645dac47a2.png",
                "json_data": null,
                "label": null,
                "shareable_link": null,
                "sloc": null,
                "status": "initial",
                "title": "Pair Plot - ResultLabel - /opt/work/data/src/pairplot_IRIS_REGRESSOR-2-560",
                "version": 1
            },
            {
                "author_name": null,
                "desc": null,
                "id": 12147,
                "image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_560_12147_5247822cd02242fb.png",
                "json_data": null,
                "label": null,
                "shareable_link": null,
                "sloc": null,
                "status": "initial",
                "title": "ResultTargetValue Importance Analysis",
                "version": 1
            }
        ],
        "manifest_secret_key": "live_2_2e975a62dd094d5bbdea7bbac0ad33b1bdb540f746f429d995d76acfee6af1",
        "manifest_sloc": "redten-models-west:IRIS_REGRESSOR-2-560_manifest.json",
        "max_features": 10,
        "ml_type": "Predict with Filter",
        "prediction_type": "predict",
        "rloc": "",
        "sloc": "",
        "status": "completed",
        "target_column_name": "ResultLabel",
        "target_column_values": [
            "Iris-setosa",
            "Iris-versicolor",
            "Iris-virginica"
        ],
        "title": "Hello World - 2017-05-26-17-49-22",
        "units_ahead_set": [],
        "units_ahead_type": "",
        "updated": "2017-05-26 17-49-42",
        "version": 1
    }
}

Get Predictive Accuracies





In [4]:



    


job_report = {}
if job_id == None:
    boom("Failed to start a new job")
else:
    # Get the analysis, but do not auto-show the plots
    job_report = get_job_analysis(job_id, show_plots=False)
    if len(job_report) == 0:
        boom("Job=" + str(job_id) + " failed")
    else:
        lg("")
    # if the job failed
# end of get job analysis

# Build the predictive accuracy dictionary from the analysis
# and show the prediction dataframes
acc_results = build_prediction_results(job_report)

# for all columns in the accuracy dictionary:
for col in acc_results:
    col_node = acc_results[col]
    
    # Sensitivity vs Specificity
    # https://en.wikipedia.org/wiki/Sensitivity_and_specificity
    # 
    # successful predictions above 90%...how's that error rate though?
    if col_node["accuracy"] > 0.90: 
        good("Column=" + str(col) + " accuracy=" + str(col_node["accuracy"]) + " mse=" + str(col_node["mse"]) + " num_predictions=" + str(len(col_node["predictions_df"].index)))
    # successful predictions between 90% and 80%...how's that error rate though?
    elif 0.90 > col_node["accuracy"] > 0.80:
        lg("Column=" + str(col) + " accuracy=" + str(col_node["accuracy"]) + " mse=" + str(col_node["mse"]) + " num_predictions=" + str(len(col_node["predictions_df"].index)))
    else:
        boom("Column=" + str(col) + " is not very accurate: accuracy=" + str(col_node["accuracy"]) + " mse=" + str(col_node["mse"]) + " num_predictions=" + str(len(col_node["predictions_df"].index)))
    # end of header line

    # show the predictions
    ipyDisplay(col_node["predictions_df"])
    
    lg("")
# end of showing prediction results



    


















    






Getting analysis for job=560 url=https://redten.io/ml/analysis/560/
SUCCESS - GET Analysis Response Status=200 Reason=OK
Found Job=560 analysis

Column=SepalLength is not very accurate: accuracy=0.783988472083 mse=0.145626284054 num_predictions=66










    











  
    

      
      0
    

  
  
    

      0
      6.200572
    

    

      1
      5.619819
    

    

      2
      7.236766
    

    

      3
      5.094768
    

    

      4
      6.138592
    

    

      5
      5.744506
    

    

      6
      7.033538
    

    

      7
      6.200797
    

    

      8
      5.400625
    

    

      9
      5.872986
    

    

      10
      6.407093
    

    

      11
      4.899189
    

    

      12
      4.900123
    

    

      13
      5.298025
    

    

      14
      5.138183
    

    

      15
      5.106776
    

    

      16
      6.886637
    

    

      17
      6.744435
    

    

      18
      6.698655
    

    

      19
      6.407560
    

    

      20
      4.736238
    

    

      21
      7.020784
    

    

      22
      5.311563
    

    

      23
      6.573887
    

    

      24
      6.508574
    

    

      25
      6.374033
    

    

      26
      6.177010
    

    

      27
      6.839397
    

    

      28
      5.732828
    

    

      29
      4.802983
    

    

      ...
      ...
    

    

      36
      6.724392
    

    

      37
      4.896573
    

    

      38
      5.637219
    

    

      39
      5.643638
    

    

      40
      6.244489
    

    

      41
      6.639732
    

    

      42
      5.591598
    

    

      43
      4.998384
    

    

      44
      5.143886
    

    

      45
      5.297545
    

    

      46
      6.067437
    

    

      47
      6.670979
    

    

      48
      6.193013
    

    

      49
      7.130998
    

    

      50
      5.879887
    

    

      51
      7.722800
    

    

      52
      5.561193
    

    

      53
      4.369848
    

    

      54
      5.800178
    

    

      55
      5.614164
    

    

      56
      4.926728
    

    

      57
      4.480546
    

    

      58
      4.317115
    

    

      59
      5.821849
    

    

      60
      6.048419
    

    

      61
      4.703830
    

    

      62
      4.903234
    

    

      63
      4.994620
    

    

      64
      5.349566
    

    

      65
      5.250289
    

  



66 rows × 1 columns












    






Column=PetalWidth accuracy=0.928719140212 mse=0.0427658976593 num_predictions=66










    











  
    

      
      0
    

  
  
    

      0
      1.331062
    

    

      1
      0.345133
    

    

      2
      1.719044
    

    

      3
      1.145035
    

    

      4
      1.600555
    

    

      5
      0.220241
    

    

      6
      1.483402
    

    

      7
      2.085606
    

    

      8
      1.308045
    

    

      9
      1.417230
    

    

      10
      2.197729
    

    

      11
      0.201280
    

    

      12
      0.100199
    

    

      13
      0.237928
    

    

      14
      0.222329
    

    

      15
      1.018767
    

    

      16
      2.146031
    

    

      17
      1.539388
    

    

      18
      1.527329
    

    

      19
      1.888611
    

    

      20
      0.124799
    

    

      21
      2.500944
    

    

      22
      0.155918
    

    

      23
      1.888611
    

    

      24
      1.949726
    

    

      25
      2.068715
    

    

      26
      1.891891
    

    

      27
      1.945576
    

    

      28
      0.362699
    

    

      29
      0.186127
    

    

      ...
      ...
    

    

      36
      1.558793
    

    

      37
      0.181759
    

    

      38
      0.268458
    

    

      39
      0.373551
    

    

      40
      2.083598
    

    

      41
      1.435777
    

    

      42
      1.241935
    

    

      43
      0.216305
    

    

      44
      0.186051
    

    

      45
      1.269337
    

    

      46
      2.126385
    

    

      47
      2.298707
    

    

      48
      1.494271
    

    

      49
      1.943346
    

    

      50
      1.276093
    

    

      51
      2.233422
    

    

      52
      1.250620
    

    

      53
      0.122953
    

    

      54
      1.900949
    

    

      55
      1.077144
    

    

      56
      0.197384
    

    

      57
      0.408073
    

    

      58
      0.122953
    

    

      59
      1.248546
    

    

      60
      1.711822
    

    

      61
      0.178438
    

    

      62
      0.220872
    

    

      63
      0.237846
    

    

      64
      1.258338
    

    

      65
      0.207573
    

  



66 rows × 1 columns












    






Column=PetalLength accuracy=0.982132813713 mse=0.0572320923595 num_predictions=66










    











  
    

      
      0
    

  
  
    

      0
      4.513339
    

    

      1
      1.782143
    

    

      2
      6.363951
    

    

      3
      3.937796
    

    

      4
      4.900630
    

    

      5
      1.486664
    

    

      6
      4.613516
    

    

      7
      5.629632
    

    

      8
      3.513844
    

    

      9
      4.433239
    

    

      10
      5.102907
    

    

      11
      1.307768
    

    

      12
      1.499826
    

    

      13
      1.368648
    

    

      14
      1.407212
    

    

      15
      3.242986
    

    

      16
      5.311521
    

    

      17
      4.575236
    

    

      18
      4.797418
    

    

      19
      5.507880
    

    

      20
      1.517955
    

    

      21
      5.535549
    

    

      22
      1.423991
    

    

      23
      5.301728
    

    

      24
      5.862045
    

    

      25
      5.567652
    

    

      26
      5.150166
    

    

      27
      5.840205
    

    

      28
      1.677446
    

    

      29
      1.582241
    

    

      ...
      ...
    

    

      36
      4.724446
    

    

      37
      1.506858
    

    

      38
      1.700494
    

    

      39
      1.333011
    

    

      40
      5.045736
    

    

      41
      4.703067
    

    

      42
      4.508770
    

    

      43
      1.453211
    

    

      44
      1.502607
    

    

      45
      4.206714
    

    

      46
      5.376419
    

    

      47
      5.675442
    

    

      48
      4.786600
    

    

      49
      6.149049
    

    

      50
      3.998515
    

    

      51
      6.405871
    

    

      52
      3.793942
    

    

      53
      1.421860
    

    

      54
      5.100172
    

    

      55
      4.669762
    

    

      56
      1.300184
    

    

      57
      1.571228
    

    

      58
      1.536713
    

    

      59
      4.593300
    

    

      60
      5.356967
    

    

      61
      1.338591
    

    

      62
      1.651200
    

    

      63
      1.502607
    

    

      64
      3.821841
    

    

      65
      1.516675
    

  



66 rows × 1 columns












    






Column=SepalWidth is not very accurate: accuracy=0.368379570605 mse=0.114673327959 num_predictions=66










    











  
    

      
      0
    

  
  
    

      0
      3.021737
    

    

      1
      3.531068
    

    

      2
      3.532478
    

    

      3
      2.388528
    

    

      4
      2.615827
    

    

      5
      3.540309
    

    

      6
      2.931330
    

    

      7
      3.020647
    

    

      8
      2.673050
    

    

      9
      2.764662
    

    

      10
      2.926087
    

    

      11
      2.984484
    

    

      12
      3.099823
    

    

      13
      3.647080
    

    

      14
      3.579297
    

    

      15
      2.242815
    

    

      16
      3.042657
    

    

      17
      2.986485
    

    

      18
      2.913861
    

    

      19
      3.197633
    

    

      20
      3.155970
    

    

      21
      2.850365
    

    

      22
      3.620166
    

    

      23
      3.041274
    

    

      24
      3.035612
    

    

      25
      2.981340
    

    

      26
      2.897098
    

    

      27
      3.345549
    

    

      28
      3.375871
    

    

      29
      3.961208
    

    

      ...
      ...
    

    

      36
      2.673100
    

    

      37
      3.669731
    

    

      38
      3.890962
    

    

      39
      3.584914
    

    

      40
      2.740776
    

    

      41
      2.728949
    

    

      42
      2.927752
    

    

      43
      3.629493
    

    

      44
      3.155970
    

    

      45
      2.677202
    

    

      46
      2.573313
    

    

      47
      2.993517
    

    

      48
      2.802930
    

    

      49
      3.218131
    

    

      50
      2.912671
    

    

      51
      3.237063
    

    

      52
      2.472500
    

    

      53
      3.287420
    

    

      54
      2.699653
    

    

      55
      3.042508
    

    

      56
      3.050663
    

    

      57
      3.418985
    

    

      58
      3.437337
    

    

      59
      2.980584
    

    

      60
      2.724026
    

    

      61
      3.379375
    

    

      62
      3.575385
    

    

      63
      3.220135
    

    

      64
      3.091811
    

    

      65
      3.443473
    

  



66 rows × 1 columns












    






Column=ResultTargetValue accuracy=0.920473871675 mse=0.0542588735957 num_predictions=66










    











  
    

      
      0
    

  
  
    

      0
      1.020111
    

    

      1
      0.000028
    

    

      2
      2.032431
    

    

      3
      0.996819
    

    

      4
      2.006838
    

    

      5
      0.000028
    

    

      6
      1.000703
    

    

      7
      1.999662
    

    

      8
      0.998231
    

    

      9
      1.712724
    

    

      10
      1.999990
    

    

      11
      0.000061
    

    

      12
      0.000020
    

    

      13
      0.000028
    

    

      14
      0.000028
    

    

      15
      1.000468
    

    

      16
      2.001127
    

    

      17
      1.971385
    

    

      18
      0.848727
    

    

      19
      2.000851
    

    

      20
      0.000020
    

    

      21
      2.000139
    

    

      22
      0.000028
    

    

      23
      2.000851
    

    

      24
      1.643128
    

    

      25
      1.999664
    

    

      26
      2.003803
    

    

      27
      2.000009
    

    

      28
      0.000028
    

    

      29
      0.000028
    

    

      ...
      ...
    

    

      36
      1.308210
    

    

      37
      0.000028
    

    

      38
      0.000028
    

    

      39
      0.000028
    

    

      40
      1.910848
    

    

      41
      1.002680
    

    

      42
      0.837692
    

    

      43
      0.000028
    

    

      44
      0.000028
    

    

      45
      0.999657
    

    

      46
      1.504312
    

    

      47
      2.000062
    

    

      48
      1.369408
    

    

      49
      1.999627
    

    

      50
      1.000577
    

    

      51
      2.000009
    

    

      52
      0.990860
    

    

      53
      0.000061
    

    

      54
      2.000018
    

    

      55
      1.125566
    

    

      56
      0.000032
    

    

      57
      0.003069
    

    

      58
      0.000061
    

    

      59
      0.992903
    

    

      60
      1.840597
    

    

      61
      0.000061
    

    

      62
      0.000028
    

    

      63
      0.000028
    

    

      64
      0.999685
    

    

      65
      0.000028
    

  



66 rows × 1 columns












    






























Get the Analysis Images
















In [5]:



    


# Show the images built during the analysis
if "images" in job_report:
    for img in job_report["images"]:
        anmt(img["title"])
        lg("URL: " + str(img["image"]))
        ipyDisplay(ipyImage(url=img["image"]))
        lg("---------------------------------------------------------------------------------------")
else:
    boom("Job=" + str(job_id) + " does not have any images yet. Please wait a few seconds and retry.")
# end of if images exist



    


















    






Joint Plot - ResultTargetValue vs SepalLength - /opt/work/data/src/jointplot_IRIS_REGRESSOR-2-560_4
URL: https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_560_12158_68fe500cd7564213.png










    
















    






---------------------------------------------------------------------------------------
Joint Plot - PetalWidth vs SepalLength - /opt/work/data/src/jointplot_IRIS_REGRESSOR-2-560_3
URL: https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_560_12157_0180f59977c44413.png










    
















    






---------------------------------------------------------------------------------------
Joint Plot - PetalLength vs SepalLength - /opt/work/data/src/jointplot_IRIS_REGRESSOR-2-560_2
URL: https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_560_12156_4a32649040c14ab6.png










    
















    






---------------------------------------------------------------------------------------
Joint Plot - SepalWidth vs SepalLength - /opt/work/data/src/jointplot_IRIS_REGRESSOR-2-560_1
URL: https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_560_12155_e1d895dcd1714932.png










    
















    






---------------------------------------------------------------------------------------
Joint Plot - SepalLength vs SepalWidth - /opt/work/data/src/jointplot_IRIS_REGRESSOR-2-560_0
URL: https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_560_12154_9b0be64a57fd484b.png










    
















    






---------------------------------------------------------------------------------------
Scatter - ResultTargetValue vs SepalLength - /opt/work/data/src/scatter_IRIS_REGRESSOR-2-560_4
URL: https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_560_12153_80e4812b82a64e4b.png










    
















    






---------------------------------------------------------------------------------------
Scatter - PetalWidth vs SepalLength - /opt/work/data/src/scatter_IRIS_REGRESSOR-2-560_3
URL: https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_560_12152_0c8343a342bb40fd.png










    
















    






---------------------------------------------------------------------------------------
Scatter - PetalLength vs SepalLength - /opt/work/data/src/scatter_IRIS_REGRESSOR-2-560_2
URL: https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_560_12151_17ef598b56e14f68.png










    
















    






---------------------------------------------------------------------------------------
Scatter - SepalWidth vs SepalLength - /opt/work/data/src/scatter_IRIS_REGRESSOR-2-560_1
URL: https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_560_12150_8166153a2f87499d.png










    
















    






---------------------------------------------------------------------------------------
Scatter - SepalLength vs SepalWidth - /opt/work/data/src/scatter_IRIS_REGRESSOR-2-560_0
URL: https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_560_12149_bd798d8ff84f46f6.png










    
















    






---------------------------------------------------------------------------------------
Pair Plot - ResultLabel - /opt/work/data/src/pairplot_IRIS_REGRESSOR-2-560
URL: https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_560_12148_07ed6b645dac47a2.png










    
















    






---------------------------------------------------------------------------------------
ResultTargetValue Importance Analysis
URL: https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_560_12147_5247822cd02242fb.png










    
















    






---------------------------------------------------------------------------------------

























Get the Job Manifest
Jobs use a manifest to prevent concurrent job and model collisions


A manifest contains:
    

  1. A dictionary of model nodes
    2. 

  2. Redis model locations
    3. 

  3. S3 archival locations
    4. 

  4. Tracking data for import and export across environments
    5. 

















In [6]:



    


job_manifest = get_job_cache_manifest(job_report)
lg(ppj(job_manifest))



    


















    






{
    "11851": {
        "rloc": "MODELS:_MD_IRIS_REGRESSOR-2-560_26fbf8_0",
        "sloc": "redten-models-west:rt_models_userid_2_job_560_train_453_jobresults_416_modelid_11851_modelkey_MD_IRIS_REGRESSOR-2-560_26fbf8_0.cache.pickle.zlib",
        "target": "SepalLength",
        "tracking_id": "ML_IRIS_REGRESSOR-2-560_0253cc1aba9b4c71b1f69d5d2ae1952",
        "tracking_name": "IRIS_REGRESSOR-2-560",
        "tracking_type": "UseTargetColAndUnits"
    },
    "11852": {
        "rloc": "MODELS:_MD_IRIS_REGRESSOR-2-560_26fbf8_1",
        "sloc": "redten-models-west:rt_models_userid_2_job_560_train_453_jobresults_416_modelid_11852_modelkey_MD_IRIS_REGRESSOR-2-560_26fbf8_1.cache.pickle.zlib",
        "target": "PetalLength",
        "tracking_id": "ML_IRIS_REGRESSOR-2-560_0253cc1aba9b4c71b1f69d5d2ae1952",
        "tracking_name": "IRIS_REGRESSOR-2-560",
        "tracking_type": "UseTargetColAndUnits"
    },
    "11853": {
        "rloc": "MODELS:_MD_IRIS_REGRESSOR-2-560_26fbf8_2",
        "sloc": "redten-models-west:rt_models_userid_2_job_560_train_453_jobresults_416_modelid_11853_modelkey_MD_IRIS_REGRESSOR-2-560_26fbf8_2.cache.pickle.zlib",
        "target": "PetalWidth",
        "tracking_id": "ML_IRIS_REGRESSOR-2-560_0253cc1aba9b4c71b1f69d5d2ae1952",
        "tracking_name": "IRIS_REGRESSOR-2-560",
        "tracking_type": "UseTargetColAndUnits"
    },
    "11854": {
        "rloc": "MODELS:_MD_IRIS_REGRESSOR-2-560_26fbf8_3",
        "sloc": "redten-models-west:rt_models_userid_2_job_560_train_453_jobresults_416_modelid_11854_modelkey_MD_IRIS_REGRESSOR-2-560_26fbf8_3.cache.pickle.zlib",
        "target": "SepalWidth",
        "tracking_id": "ML_IRIS_REGRESSOR-2-560_0253cc1aba9b4c71b1f69d5d2ae1952",
        "tracking_name": "IRIS_REGRESSOR-2-560",
        "tracking_type": "UseTargetColAndUnits"
    },
    "11855": {
        "rloc": "MODELS:_MD_IRIS_REGRESSOR-2-560_26fbf8_4",
        "sloc": "redten-models-west:rt_models_userid_2_job_560_train_453_jobresults_416_modelid_11855_modelkey_MD_IRIS_REGRESSOR-2-560_26fbf8_4.cache.pickle.zlib",
        "target": "ResultTargetValue",
        "tracking_id": "ML_IRIS_REGRESSOR-2-560_0253cc1aba9b4c71b1f69d5d2ae1952",
        "tracking_name": "IRIS_REGRESSOR-2-560",
        "tracking_type": "UseTargetColAndUnits"
    }
}

























Multiple Models stored in Redis
Here's how models are stored in the Redis machine learning data store


















Search Jobs in Elasticsearch
Red10 is running an ELK stack for searching by user, dataset identifiers, and jobs.




Once a job completes it is automatically published to Elasticsearch
















In [7]:



    


search_req = {
        "title" : "Hello", # job title with completion
        "dsname" : "", # dataset name with completion
        "desc" : "", # description with completion
        "features" : "", # feature search
        "target_column" : "" # name of target column for this analysis
    }
job_search = {}
job_res = {}
if len(search_req) == 0 :
    boom("Please create a valid search request")
else:
    job_res = search_ml_jobs(search_req)

    if job_res["status"] != "SUCCESS":
        boom("Job=" + str(job_id) + " failed with status=" + str(job_res["status"]) + " err=" + str(job_res["error"]))
    else:
        job_search = job_res["record"]
        anmt("Job Matches=" + str(len(job_search)))
        lg(ppj(job_search), 5)



    


















    






Searching ML Jobs url=https://redten.io/ml/search/
SUCCESS - Job Search Response Status=200 Reason=OK
Found Job={'target_column': '', 'desc': '', 'dsname': '', 'features': '', 'title': 'Hello'} results
Job Matches=1
{
    "jobs": [
        {
            "algo_name": "xgb-regressor",
            "desc": "This is a Description for - Hello World - 2017-05-26-08-11-22",
            "ds_name": "iris_regressor",
            "feature_column_names": [
                "SepalLength",
                "SepalWidth",
                "PetalLength",
                "PetalWidth",
                "ResultTargetValue"
            ],
            "ignore_features": [
                "ResultLabel"
            ],
            "images": [
                {
                    "id": 12135,
                    "image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_559_12135_6c01fa6f6cba42dd.png",
                    "title": "ResultTargetValue Importance Analysis"
                },
                {
                    "id": 12136,
                    "image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_559_12136_dd8628872bed4cb1.png",
                    "title": "Pair Plot - ResultLabel - /opt/work/data/src/pairplot_IRIS_REGRESSOR-2-559"
                },
                {
                    "id": 12137,
                    "image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_559_12137_e7b21aeada3f4056.png",
                    "title": "Scatter - SepalLength vs SepalWidth - /opt/work/data/src/scatter_IRIS_REGRESSOR-2-559_0"
                },
                {
                    "id": 12138,
                    "image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_559_12138_d3fda8e628a0465c.png",
                    "title": "Scatter - SepalWidth vs SepalLength - /opt/work/data/src/scatter_IRIS_REGRESSOR-2-559_1"
                },
                {
                    "id": 12139,
                    "image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_559_12139_a107fe2096db4918.png",
                    "title": "Scatter - PetalLength vs SepalLength - /opt/work/data/src/scatter_IRIS_REGRESSOR-2-559_2"
                },
                {
                    "id": 12140,
                    "image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_559_12140_f8ee9a15e4554230.png",
                    "title": "Scatter - PetalWidth vs SepalLength - /opt/work/data/src/scatter_IRIS_REGRESSOR-2-559_3"
                },
                {
                    "id": 12141,
                    "image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_559_12141_bdd6cafc07684bee.png",
                    "title": "Scatter - ResultTargetValue vs SepalLength - /opt/work/data/src/scatter_IRIS_REGRESSOR-2-559_4"
                },
                {
                    "id": 12142,
                    "image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_559_12142_5f90b61973934f62.png",
                    "title": "Joint Plot - SepalLength vs SepalWidth - /opt/work/data/src/jointplot_IRIS_REGRESSOR-2-559_0"
                },
                {
                    "id": 12143,
                    "image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_559_12143_384b0e17ac724e6c.png",
                    "title": "Joint Plot - SepalWidth vs SepalLength - /opt/work/data/src/jointplot_IRIS_REGRESSOR-2-559_1"
                },
                {
                    "id": 12144,
                    "image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_559_12144_8f23906138f94274.png",
                    "title": "Joint Plot - PetalLength vs SepalLength - /opt/work/data/src/jointplot_IRIS_REGRESSOR-2-559_2"
                },
                {
                    "id": 12145,
                    "image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_559_12145_acc7e0137ba8401e.png",
                    "title": "Joint Plot - PetalWidth vs SepalLength - /opt/work/data/src/jointplot_IRIS_REGRESSOR-2-559_3"
                },
                {
                    "id": 12146,
                    "image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_559_12146_4cc07105dfd4413f.png",
                    "title": "Joint Plot - ResultTargetValue vs SepalLength - /opt/work/data/src/jointplot_IRIS_REGRESSOR-2-559_4"
                }
            ],
            "job_id": 559,
            "ml_type": "Predict with Filter",
            "prediction_type": "predict",
            "result_id": "",
            "target_column_name": "ResultLabel",
            "target_column_values": [
                "Iris-setosa",
                "Iris-versicolor",
                "Iris-virginica"
            ],
            "title": "Hello World - 2017-05-26-08-11-22",
            "train_id": 452,
            "user_id": 2,
            "username": "jay"
        },
        {
            "algo_name": "xgb-regressor",
            "desc": "This is a Description for - Hello World - 2017-05-23-22-48-19",
            "ds_name": "iris_regressor",
            "feature_column_names": [
                "SepalLength",
                "SepalWidth",
                "PetalLength",
                "PetalWidth",
                "ResultTargetValue"
            ],
            "ignore_features": [
                "ResultLabel"
            ],
            "images": [
                {
                    "id": 12080,
                    "image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170523/2_557_12080_bacd0a3063e643b1.png",
                    "title": "ResultTargetValue Importance Analysis"
                },
                {
                    "id": 12081,
                    "image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170523/2_557_12081_994b29e1e64b4c48.png",
                    "title": "Pair Plot - ResultLabel - /opt/work/data/src/pairplot_IRIS_REGRESSOR-2-557"
                },
                {
                    "id": 12082,
                    "image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170523/2_557_12082_160492c43ac5415d.png",
                    "title": "Scatter - SepalLength vs SepalWidth - /opt/work/data/src/scatter_IRIS_REGRESSOR-2-557_0"
                },
                {
                    "id": 12083,
                    "image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170523/2_557_12083_87091b7a48f64bf8.png",
                    "title": "Scatter - SepalWidth vs SepalLength - /opt/work/data/src/scatter_IRIS_REGRESSOR-2-557_1"
                },
                {
                    "id": 12084,
                    "image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170523/2_557_12084_2b85954122e4452d.png",
                    "title": "Scatter - PetalLength vs SepalLength - /opt/work/data/src/scatter_IRIS_REGRESSOR-2-557_2"
                },
                {
                    "id": 12085,
                    "image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170523/2_557_12085_87d25f7a41ba41d5.png",
                    "title": "Scatter - PetalWidth vs SepalLength - /opt/work/data/src/scatter_IRIS_REGRESSOR-2-557_3"
                },
                {
                    "id": 12086,
                    "image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170523/2_557_12086_b3ef117e966d45a4.png",
                    "title": "Scatter - ResultTargetValue vs SepalLength - /opt/work/data/src/scatter_IRIS_REGRESSOR-2-557_4"
                },
                {
                    "id": 12087,
                    "image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170523/2_557_12087_f475c7062acc4e03.png",
                    "title": "Joint Plot - SepalLength vs SepalWidth - /opt/work/data/src/jointplot_IRIS_REGRESSOR-2-557_0"
                },
                {
                    "id": 12088,
                    "image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170523/2_557_12088_7316b1cc3a86413d.png",
                    "title": "Joint Plot - SepalWidth vs SepalLength - /opt/work/data/src/jointplot_IRIS_REGRESSOR-2-557_1"
                },
                {
                    "id": 12089,
                    "image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170523/2_557_12089_db2ec909b1f84946.png",
                    "title": "Joint Plot - PetalLength vs SepalLength - /opt/work/data/src/jointplot_IRIS_REGRESSOR-2-557_2"
                },
                {
                    "id": 12090,
                    "image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170523/2_557_12090_82e81ab1f9d54d09.png",
                    "title": "Joint Plot - PetalWidth vs SepalLength - /opt/work/data/src/jointplot_IRIS_REGRESSOR-2-557_3"
                },
                {
                    "id": 12091,
                    "image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170523/2_557_12091_4fdb0c2731074b5c.png",
                    "title": "Joint Plot - ResultTargetValue vs SepalLength - /opt/work/data/src/jointplot_IRIS_REGRESSOR-2-557_4"
                }
            ],
            "job_id": 557,
            "ml_type": "Predict with Filter",
            "prediction_type": "predict",
            "result_id": "",
            "target_column_name": "ResultLabel",
            "target_column_values": [
                "Iris-setosa",
                "Iris-versicolor",
                "Iris-virginica"
            ],
            "title": "Hello World - 2017-05-23-22-48-19",
            "train_id": 449,
            "user_id": 2,
            "username": "jay"
        },
        {
            "algo_name": "xgb-regressor",
            "desc": "This is a Description for - Hello World - 2017-05-26-17-49-22",
            "ds_name": "iris_regressor",
            "feature_column_names": [
                "SepalLength",
                "SepalWidth",
                "PetalLength",
                "PetalWidth",
                "ResultTargetValue"
            ],
            "ignore_features": [
                "ResultLabel"
            ],
            "images": [
                {
                    "id": 12147,
                    "image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_560_12147_5247822cd02242fb.png",
                    "title": "ResultTargetValue Importance Analysis"
                },
                {
                    "id": 12148,
                    "image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_560_12148_07ed6b645dac47a2.png",
                    "title": "Pair Plot - ResultLabel - /opt/work/data/src/pairplot_IRIS_REGRESSOR-2-560"
                },
                {
                    "id": 12149,
                    "image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_560_12149_bd798d8ff84f46f6.png",
                    "title": "Scatter - SepalLength vs SepalWidth - /opt/work/data/src/scatter_IRIS_REGRESSOR-2-560_0"
                },
                {
                    "id": 12150,
                    "image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_560_12150_8166153a2f87499d.png",
                    "title": "Scatter - SepalWidth vs SepalLength - /opt/work/data/src/scatter_IRIS_REGRESSOR-2-560_1"
                },
                {
                    "id": 12151,
                    "image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_560_12151_17ef598b56e14f68.png",
                    "title": "Scatter - PetalLength vs SepalLength - /opt/work/data/src/scatter_IRIS_REGRESSOR-2-560_2"
                },
                {
                    "id": 12152,
                    "image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_560_12152_0c8343a342bb40fd.png",
                    "title": "Scatter - PetalWidth vs SepalLength - /opt/work/data/src/scatter_IRIS_REGRESSOR-2-560_3"
                },
                {
                    "id": 12153,
                    "image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_560_12153_80e4812b82a64e4b.png",
                    "title": "Scatter - ResultTargetValue vs SepalLength - /opt/work/data/src/scatter_IRIS_REGRESSOR-2-560_4"
                },
                {
                    "id": 12154,
                    "image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_560_12154_9b0be64a57fd484b.png",
                    "title": "Joint Plot - SepalLength vs SepalWidth - /opt/work/data/src/jointplot_IRIS_REGRESSOR-2-560_0"
                },
                {
                    "id": 12155,
                    "image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_560_12155_e1d895dcd1714932.png",
                    "title": "Joint Plot - SepalWidth vs SepalLength - /opt/work/data/src/jointplot_IRIS_REGRESSOR-2-560_1"
                },
                {
                    "id": 12156,
                    "image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_560_12156_4a32649040c14ab6.png",
                    "title": "Joint Plot - PetalLength vs SepalLength - /opt/work/data/src/jointplot_IRIS_REGRESSOR-2-560_2"
                },
                {
                    "id": 12157,
                    "image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_560_12157_0180f59977c44413.png",
                    "title": "Joint Plot - PetalWidth vs SepalLength - /opt/work/data/src/jointplot_IRIS_REGRESSOR-2-560_3"
                },
                {
                    "id": 12158,
                    "image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_560_12158_68fe500cd7564213.png",
                    "title": "Joint Plot - ResultTargetValue vs SepalLength - /opt/work/data/src/jointplot_IRIS_REGRESSOR-2-560_4"
                }
            ],
            "job_id": 560,
            "ml_type": "Predict with Filter",
            "prediction_type": "predict",
            "result_id": "",
            "target_column_name": "ResultLabel",
            "target_column_values": [
                "Iris-setosa",
                "Iris-versicolor",
                "Iris-virginica"
            ],
            "title": "Hello World - 2017-05-26-17-49-22",
            "train_id": 453,
            "user_id": 2,
            "username": "jay"
        }
    ]
}

























Get the Recent Machine Learning Jobs
















In [8]:



    


debug = False
user_token = user_login(rt_user, rt_pass, rt_url)
auth_headers = {
                "Authorization" : "JWT " + str(user_token)
            }
resource_url = rt_url + "/ml/run/"
query_params = {}
post_data = {}
uni = uni_key()

# Get the ML Job
resource_url = rt_url + "/ml/jobs/"

lg("Running Get ML Job url=" + str(resource_url), 6)
get_response = requests.get(resource_url, params=query_params, data=post_data, headers=auth_headers)

if get_response.status_code != 201 and get_response.status_code != 200:
    lg("Failed with GET Response Status=" + str(get_response.status_code) + " Reason=" + str(get_response.reason), 0)
    lg("Details:\n" + str(get_response.text) + "\n", 0)        
else:
    lg("SUCCESS - GET Response Status=" + str(get_response.status_code) + " Reason=" + str(get_response.reason)[0:10], 5)

    as_json = True
    record = {}
    if as_json:
        record = json.loads(get_response.text)
        lg(ppj(record))
# end of post for running an ML Job



    


















    






Running Get ML Job url=https://redten.io/ml/jobs/
SUCCESS - GET Response Status=200 Reason=OK
{
    "jobs": [
        {
            "algo_name": "xgb-regressor",
            "control_state": "active",
            "created": "2017-05-26 17-49-23",
            "csv_file": "/opt/work/data/src/iris.csv",
            "desc": "This is a Description for - Hello World - 2017-05-26-17-49-22",
            "ds_name": "iris_regressor",
            "feature_column_names": [
                "SepalLength",
                "SepalWidth",
                "PetalLength",
                "PetalWidth",
                "ResultTargetValue"
            ],
            "id": 560,
            "ignore_features": [
                "ResultLabel"
            ],
            "images": [
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_560_12158_68fe500cd7564213.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_560_12157_0180f59977c44413.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_560_12156_4a32649040c14ab6.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_560_12155_e1d895dcd1714932.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_560_12154_9b0be64a57fd484b.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_560_12153_80e4812b82a64e4b.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_560_12152_0c8343a342bb40fd.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_560_12151_17ef598b56e14f68.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_560_12150_8166153a2f87499d.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_560_12149_bd798d8ff84f46f6.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_560_12148_07ed6b645dac47a2.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_560_12147_5247822cd02242fb.png"
            ],
            "manifest_secret_key": "live_2_2e975a62dd094d5bbdea7bbac0ad33b1bdb540f746f429d995d76acfee6af1",
            "manifest_sloc": "redten-models-west:IRIS_REGRESSOR-2-560_manifest.json",
            "max_features": 10,
            "ml_type": "Predict with Filter",
            "prediction_type": "predict",
            "rloc": "",
            "sloc": "",
            "status": "completed",
            "target_column_name": "ResultLabel",
            "target_column_values": [
                "Iris-setosa",
                "Iris-versicolor",
                "Iris-virginica"
            ],
            "title": "Hello World - 2017-05-26-17-49-22",
            "units_ahead_set": [],
            "units_ahead_type": "",
            "updated": "2017-05-26 17-49-42",
            "version": 1
        },
        {
            "algo_name": "xgb-regressor",
            "control_state": "active",
            "created": "2017-05-26 08-11-23",
            "csv_file": "/opt/work/data/src/iris.csv",
            "desc": "This is a Description for - Hello World - 2017-05-26-08-11-22",
            "ds_name": "iris_regressor",
            "feature_column_names": [
                "SepalLength",
                "SepalWidth",
                "PetalLength",
                "PetalWidth",
                "ResultTargetValue"
            ],
            "id": 559,
            "ignore_features": [
                "ResultLabel"
            ],
            "images": [
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_559_12146_4cc07105dfd4413f.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_559_12145_acc7e0137ba8401e.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_559_12144_8f23906138f94274.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_559_12143_384b0e17ac724e6c.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_559_12142_5f90b61973934f62.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_559_12141_bdd6cafc07684bee.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_559_12140_f8ee9a15e4554230.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_559_12139_a107fe2096db4918.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_559_12138_d3fda8e628a0465c.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_559_12137_e7b21aeada3f4056.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_559_12136_dd8628872bed4cb1.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_559_12135_6c01fa6f6cba42dd.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_559_12134_2f0ad89e1bc64da7.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_559_12133_2f543645ce494629.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_559_12132_2a7bbad440484dd3.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_559_12131_b299a0098b71444f.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_559_12130_c16191ff6c8d44af.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_559_12129_371450a1ef1e47e4.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_559_12128_7c4b9e66bd3441e0.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_559_12127_87635cf922604b93.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_559_12126_6a78159747674e0f.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_559_12125_6a75a51713984e45.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_559_12124_f25d9790b4174026.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_559_12123_e8ef0af9bff14cca.png"
            ],
            "manifest_secret_key": "live_2_1060ffc68dac4497873d2548d1cfab6270625574ccb4b9994227d65ee7451c",
            "manifest_sloc": "redten-models-west:IRIS_REGRESSOR-2-559_manifest.json",
            "max_features": 10,
            "ml_type": "Predict with Filter",
            "prediction_type": "predict",
            "rloc": "",
            "sloc": "",
            "status": "completed",
            "target_column_name": "ResultLabel",
            "target_column_values": [
                "Iris-setosa",
                "Iris-versicolor",
                "Iris-virginica"
            ],
            "title": "Hello World - 2017-05-26-08-11-22",
            "units_ahead_set": [],
            "units_ahead_type": "",
            "updated": "2017-05-26 08-12-14",
            "version": 1
        },
        {
            "algo_name": "xgb-regressor",
            "control_state": "active",
            "created": "2017-05-26 08-02-13",
            "csv_file": "",
            "desc": "Forecast simulation - 2017-05-26 08:02:13",
            "ds_name": "SPY",
            "feature_column_names": [
                "FHigh",
                "FLow",
                "FOpen",
                "FClose",
                "FVolume"
            ],
            "id": 558,
            "ignore_features": [
                "Ticker",
                "Date",
                "FDate",
                "FPrice",
                "DcsnDate",
                "Decision"
            ],
            "images": [
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_558_12122_7c49dccd964548fc.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_558_12121_5ae84fdafa034174.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_558_12120_2d87f32772344921.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_558_12119_c54a1003037d4eb3.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_558_12118_192e7a3c4ba840fc.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_558_12117_6b74cc14075f4f20.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_558_12116_1fed112e3a4e42ff.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_558_12115_795d9657c2534f76.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_558_12114_c19203024ec84944.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_558_12113_1b000088cf154451.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_558_12112_c72e079979774d17.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_558_12111_b0c21c4c4d0d4e7a.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_558_12110_2a27883839464a6c.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_558_12109_325e5fefa07a444d.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_558_12108_7dfe5cad6dd341c2.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_558_12107_2fcaa612a10346d9.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_558_12106_29c5e186270348f4.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_558_12105_7139a2dd4df5447b.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_558_12104_74d4b72e2bd342d6.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_558_12103_286431b3eabc4af5.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_558_12102_b8f6174000f5455c.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_558_12101_bfe9888ad15648f9.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_558_12100_0761ea1b0f684ce9.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_558_12099_8f73f41290794595.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_558_12098_5fd3e28826754f24.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_558_12097_214858c79e704f4c.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_558_12096_168bcf41ff5142ad.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_558_12095_de06fd5548504b41.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_558_12094_354bb5267c694ba5.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_558_12093_e2ecdc03bc8a4f30.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_558_12092_954ad32f51ef47f4.png"
            ],
            "manifest_secret_key": "live_2_f3f31f711f1b4081862a1927be0758880c3b3f00e2848aeb2a7f68997b4e17",
            "manifest_sloc": "redten-models-west:SPY-2-558_manifest.json",
            "max_features": 10,
            "ml_type": "Playbook-UnitsAhead",
            "prediction_type": "forecast",
            "rloc": "",
            "sloc": "",
            "status": "completed",
            "target_column_name": "FClose",
            "target_column_values": [
                "GoodBuys",
                "BadBuys",
                "Not Finished"
            ],
            "title": "SPY Forecast v5 - 34d6bf175cad4aaf9234aa06e95f4e4",
            "units_ahead_set": [
                5,
                10,
                15,
                20,
                25,
                30
            ],
            "units_ahead_type": "Days",
            "updated": "2017-05-26 08-03-53",
            "version": 1
        },
        {
            "algo_name": "xgb-regressor",
            "control_state": "active",
            "created": "2017-05-23 22-48-19",
            "csv_file": "/opt/work/data/src/iris.csv",
            "desc": "This is a Description for - Hello World - 2017-05-23-22-48-19",
            "ds_name": "iris_regressor",
            "feature_column_names": [
                "SepalLength",
                "SepalWidth",
                "PetalLength",
                "PetalWidth",
                "ResultTargetValue"
            ],
            "id": 557,
            "ignore_features": [
                "ResultLabel"
            ],
            "images": [
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170523/2_557_12091_4fdb0c2731074b5c.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170523/2_557_12090_82e81ab1f9d54d09.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170523/2_557_12089_db2ec909b1f84946.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170523/2_557_12088_7316b1cc3a86413d.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170523/2_557_12087_f475c7062acc4e03.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170523/2_557_12086_b3ef117e966d45a4.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170523/2_557_12085_87d25f7a41ba41d5.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170523/2_557_12084_2b85954122e4452d.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170523/2_557_12083_87091b7a48f64bf8.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170523/2_557_12082_160492c43ac5415d.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170523/2_557_12081_994b29e1e64b4c48.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170523/2_557_12080_bacd0a3063e643b1.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170523/2_557_12079_61d46d9599f6484d.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170523/2_557_12078_8b49ca61b2984b6f.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170523/2_557_12077_7c6bbe897e684ea3.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170523/2_557_12076_ca83a370b4114a88.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170523/2_557_12075_a0d64fc564fb4a3b.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170523/2_557_12074_4591faca0ca54ecd.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170523/2_557_12073_2a41ad8f67804a32.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170523/2_557_12072_a4e071ac32fc4177.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170523/2_557_12071_bc9e7fd426c444bf.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170523/2_557_12070_7f5af55e914444aa.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170523/2_557_12069_01ba1d04ca204f1a.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170523/2_557_12068_11ef9edc123542be.png"
            ],
            "manifest_secret_key": "live_2_9a5a34e96cca479e98ac8cc47d6fb53448557fa02d447c0bdcf167b8e4a645",
            "manifest_sloc": "redten-models-west:IRIS_REGRESSOR-2-557_manifest.json",
            "max_features": 10,
            "ml_type": "Predict with Filter",
            "prediction_type": "predict",
            "rloc": "",
            "sloc": "",
            "status": "completed",
            "target_column_name": "ResultLabel",
            "target_column_values": [
                "Iris-setosa",
                "Iris-versicolor",
                "Iris-virginica"
            ],
            "title": "Hello World - 2017-05-23-22-48-19",
            "units_ahead_set": [],
            "units_ahead_type": "",
            "updated": "2017-05-23 22-49-13",
            "version": 1
        },
        {
            "algo_name": "xgb-regressor",
            "control_state": "active",
            "created": "2017-05-23 22-24-09",
            "csv_file": "",
            "desc": "Forecast simulation - 2017-05-23 22:24:08",
            "ds_name": "SPY",
            "feature_column_names": [
                "FHigh",
                "FLow",
                "FOpen",
                "FClose",
                "FVolume"
            ],
            "id": 556,
            "ignore_features": [
                "Ticker",
                "Date",
                "FDate",
                "FPrice",
                "DcsnDate",
                "Decision"
            ],
            "images": [
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170523/2_556_12067_a1d9483443844be5.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170523/2_556_12066_bd242932692b48f1.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170523/2_556_12065_c42e1ef3ecda4a50.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170523/2_556_12064_4929471da7b848dc.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170523/2_556_12063_3f0f9b7a91e6477d.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170523/2_556_12062_50476dc42b95485a.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170523/2_556_12061_087ef25764154835.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170523/2_556_12060_c3c6ab2f04ae4669.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170523/2_556_12059_974874fc3c0c4c4b.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170523/2_556_12058_660025f112e04a51.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170523/2_556_12057_ceced8e2215749e5.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170523/2_556_12056_53ca82cd037c4ebb.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170523/2_556_12055_76b743196398499f.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170523/2_556_12054_72baf6fe1cff49dd.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170523/2_556_12053_de8af6d2ab474eb4.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170523/2_556_12052_26fbe6058c114619.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170523/2_556_12051_184561aa8af3489a.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170523/2_556_12050_430a5186b3d945e4.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170523/2_556_12049_49d5e69d8c104eb2.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170523/2_556_12048_7f90d455f7df4e2f.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170523/2_556_12047_eba8de31f57e466a.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170523/2_556_12046_8c585f82cd0948c5.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170523/2_556_12045_db89689ffd49445c.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170523/2_556_12044_f64ef76a00cb4bb9.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170523/2_556_12043_ddeb69b9254b4595.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170523/2_556_12042_152cb94cf47f4557.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170523/2_556_12041_cf802c6530714ae7.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170523/2_556_12040_a3e6aafa5e6241c4.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170523/2_556_12039_dba3efb9b56c4bb5.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170523/2_556_12038_c8fc6f8d4e294881.png",
                "https://rt-media.s3.amazonaws.com/media/imagesml/20170523/2_556_12037_0523b881d6a64baa.png"
            ],
            "manifest_secret_key": "live_2_ab288f9631d848b2a11cc4537728dc3d844c041bf934a6abdae5d6bbce3513",
            "manifest_sloc": "redten-models-west:SPY-2-556_manifest.json",
            "max_features": 10,
            "ml_type": "Playbook-UnitsAhead",
            "prediction_type": "forecast",
            "rloc": "",
            "sloc": "",
            "status": "completed",
            "target_column_name": "FClose",
            "target_column_values": [
                "GoodBuys",
                "BadBuys",
                "Not Finished"
            ],
            "title": "SPY Forecast v5 - 1394ae9d74c24d5886f9608d3dd6328",
            "units_ahead_set": [
                5,
                10,
                15,
                20,
                25,
                30
            ],
            "units_ahead_type": "Days",
            "updated": "2017-05-23 22-25-51",
            "version": 1
        }
    ]
}

























Make a new Prediction using the pre-trained XGB Regressor Models
















In [9]:



    


predict_row = {
                "SepalLength"       : 5.4,
                "SepalWidth"        : 3.4,
                "PetalLength"       : 1.7,
                "PetalWidth"        : 0.2,
                "ResultTargetValue" : 0
            }

post_data = {
            "use_cached_job_id" : job_id,
            "predict_this_data" : predict_row,
            "title" : title,
            "desc" : desc,
            "ds_name" : ds_name,
            "feature_column_names" : feature_column_names,
            "ignore_features" : ignore_features,
            "csv_file" : csv_file,
            "rloc" : rloc,
            "sloc" : sloc,
            "units_ahead" : units_ahead,
            "algo_name" : algo_name,
            "target_column_values" : target_column_values,
            "target_column_name" : target_column_name,
            "label_column_name" : label_column_name,
            "prediction_type" : "Predict",
            "ml_type" : "Predict with Filter",
            "user_id" : 2,
            "train" : {
                    "base_score" : 0.5,
                    "col_sample_by_level" : 1.0,
                    "col_sample_by_tree" : 0.8,
                    "gamma" : 0,
                    "learning_rate" : 0.1,
                    "max_delta_step" : 0,
                    "max_depth" : 6,
                    "min_child_weight" : 1,
                    "num_estimators" : 1000,
                    "objective" : "reg:linear",
                    "reg_alpha" : 0.0,
                    "reg_lambda" : 1.0,
                    "scaled_position_weight" : 1.0,
                    "seed" : 27,
                    "sub_sample" : 0.8,
                    "debug" : True
            },
            "max_features" : 10,
            "tracking_type" : "",
            "units_ahead_set" : [],
            "units_ahead_type" : "",
            "version" : 1
    }

#
"""
Login again in case the token expired waiting on the analysis which will show in the logs as:

Failed with Post Response Status=401 Reason=Unauthorized
Details:
{"detail":"Signature has expired."}
"""
user_token = rest_login_as_user(rt_user, rt_pass, rt_url)
post_headers = {
                "Content-type" : "application/json",
                "Authorization" : "JWT " + str(user_token)
            }
resource_url = rt_url + "/ml/run/"

lg("Utilizing Cached Models for new Prediction using ML Job url=" + str(resource_url), 6)
post_response = requests.post(resource_url, params=query_params, data=json.dumps(post_data), headers=post_headers)

if post_response.status_code != 201 and post_response.status_code != 200:
    lg("Failed with Post Response Status=" + str(post_response.status_code) + " Reason=" + str(post_response.reason), 0)
    lg("Details:\n" + str(post_response.text) + "\n", 0)
else:
    lg("SUCCESS - Post Response Status=" + str(post_response.status_code) + " Reason=" + str(post_response.reason), 5)

    record = post_response.json()
    lg(ppj(record))
    job_id = int(record["id"])
# end of post for running an ML Job



    


















    






Utilizing Cached Models for new Prediction using ML Job url=https://redten.io/ml/run/
SUCCESS - Post Response Status=201 Reason=Created
{
    "algo_name": "xgb-regressor",
    "control_state": "active",
    "created": "2017-05-26 17-49-23",
    "csv_file": "/opt/work/data/src/iris.csv",
    "desc": "This is a Description for - Hello World - 2017-05-26-17-49-22",
    "ds_name": "iris_regressor",
    "feature_column_names": [
        "SepalLength",
        "SepalWidth",
        "PetalLength",
        "PetalWidth",
        "ResultTargetValue"
    ],
    "id": 560,
    "ignore_features": [
        "ResultLabel"
    ],
    "manifest_secret_key": "live_2_2e975a62dd094d5bbdea7bbac0ad33b1bdb540f746f429d995d76acfee6af1",
    "manifest_sloc": "redten-models-west:IRIS_REGRESSOR-2-560_manifest.json",
    "max_features": 10,
    "ml_type": "Predict with Filter",
    "prediction_type": "predict",
    "rloc": "",
    "sloc": "",
    "status": "completed",
    "target_column_name": "ResultLabel",
    "target_column_values": [
        "Iris-setosa",
        "Iris-versicolor",
        "Iris-virginica"
    ],
    "title": "Hello World - 2017-05-26-17-49-22",
    "units_ahead_set": [],
    "units_ahead_type": "",
    "updated": "2017-05-26 17-49-42",
    "version": 1
}

























Wait for the Job to finish
















In [10]:



    


job_data = {}
job_report = {}

job_res = {}
if job_id == None:
    boom("Failed to start a new job")
else:
    job_res = wait_on_job(job_id)

    if job_res["status"] != "SUCCESS":
        boom("Job=" + str(job_id) + " failed with status=" + str(job_res["status"]) + " err=" + str(job_res["error"]))
    else:
        job_data = job_res["record"]
        anmt("Job Report:")
        lg(ppj(job_data), 5)
# end of waiting



    


















    






Waiting on job=560 url=https://redten.io/ml/560/
Job=560 is training - Step 3/10
Job=560 is plotting - Step 7/10
Job=560 is uploading - Step 9/10
Job=560 completed
Job Report:
{
    "job": {
        "algo_name": "xgb-regressor",
        "control_state": "active",
        "created": "2017-05-26 17-49-23",
        "csv_file": "/opt/work/data/src/iris.csv",
        "desc": "This is a Description for - Hello World - 2017-05-26-17-49-22",
        "ds_name": "iris_regressor",
        "feature_column_names": [
            "SepalLength",
            "SepalWidth",
            "PetalLength",
            "PetalWidth",
            "ResultTargetValue"
        ],
        "id": 560,
        "ignore_features": [
            "ResultLabel"
        ],
        "images": [
            {
                "author_name": null,
                "desc": null,
                "id": 12170,
                "image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_560_12170_e4c5d75535004a21.png",
                "json_data": null,
                "label": null,
                "shareable_link": null,
                "sloc": null,
                "status": "initial",
                "title": "Joint Plot - ResultTargetValue vs SepalLength - /opt/work/data/src/jointplot_IRIS_REGRESSOR-2-560_4",
                "version": 1
            },
            {
                "author_name": null,
                "desc": null,
                "id": 12169,
                "image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_560_12169_e2025b07c7e24a83.png",
                "json_data": null,
                "label": null,
                "shareable_link": null,
                "sloc": null,
                "status": "initial",
                "title": "Joint Plot - PetalWidth vs SepalLength - /opt/work/data/src/jointplot_IRIS_REGRESSOR-2-560_3",
                "version": 1
            },
            {
                "author_name": null,
                "desc": null,
                "id": 12168,
                "image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_560_12168_ff223da922584537.png",
                "json_data": null,
                "label": null,
                "shareable_link": null,
                "sloc": null,
                "status": "initial",
                "title": "Joint Plot - PetalLength vs SepalLength - /opt/work/data/src/jointplot_IRIS_REGRESSOR-2-560_2",
                "version": 1
            },
            {
                "author_name": null,
                "desc": null,
                "id": 12167,
                "image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_560_12167_9b9870cab7a04c99.png",
                "json_data": null,
                "label": null,
                "shareable_link": null,
                "sloc": null,
                "status": "initial",
                "title": "Joint Plot - SepalWidth vs SepalLength - /opt/work/data/src/jointplot_IRIS_REGRESSOR-2-560_1",
                "version": 1
            },
            {
                "author_name": null,
                "desc": null,
                "id": 12166,
                "image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_560_12166_d9dd29fe5eaa48d3.png",
                "json_data": null,
                "label": null,
                "shareable_link": null,
                "sloc": null,
                "status": "initial",
                "title": "Joint Plot - SepalLength vs SepalWidth - /opt/work/data/src/jointplot_IRIS_REGRESSOR-2-560_0",
                "version": 1
            },
            {
                "author_name": null,
                "desc": null,
                "id": 12165,
                "image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_560_12165_33d5ce390f854ffb.png",
                "json_data": null,
                "label": null,
                "shareable_link": null,
                "sloc": null,
                "status": "initial",
                "title": "Scatter - ResultTargetValue vs SepalLength - /opt/work/data/src/scatter_IRIS_REGRESSOR-2-560_4",
                "version": 1
            },
            {
                "author_name": null,
                "desc": null,
                "id": 12164,
                "image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_560_12164_c1367c948b134554.png",
                "json_data": null,
                "label": null,
                "shareable_link": null,
                "sloc": null,
                "status": "initial",
                "title": "Scatter - PetalWidth vs SepalLength - /opt/work/data/src/scatter_IRIS_REGRESSOR-2-560_3",
                "version": 1
            },
            {
                "author_name": null,
                "desc": null,
                "id": 12163,
                "image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_560_12163_8dfe71c076824165.png",
                "json_data": null,
                "label": null,
                "shareable_link": null,
                "sloc": null,
                "status": "initial",
                "title": "Scatter - PetalLength vs SepalLength - /opt/work/data/src/scatter_IRIS_REGRESSOR-2-560_2",
                "version": 1
            },
            {
                "author_name": null,
                "desc": null,
                "id": 12162,
                "image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_560_12162_52608b634fff4050.png",
                "json_data": null,
                "label": null,
                "shareable_link": null,
                "sloc": null,
                "status": "initial",
                "title": "Scatter - SepalWidth vs SepalLength - /opt/work/data/src/scatter_IRIS_REGRESSOR-2-560_1",
                "version": 1
            },
            {
                "author_name": null,
                "desc": null,
                "id": 12161,
                "image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_560_12161_c89cc9f7a653453a.png",
                "json_data": null,
                "label": null,
                "shareable_link": null,
                "sloc": null,
                "status": "initial",
                "title": "Scatter - SepalLength vs SepalWidth - /opt/work/data/src/scatter_IRIS_REGRESSOR-2-560_0",
                "version": 1
            },
            {
                "author_name": null,
                "desc": null,
                "id": 12160,
                "image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_560_12160_3da4e4350ed842c9.png",
                "json_data": null,
                "label": null,
                "shareable_link": null,
                "sloc": null,
                "status": "initial",
                "title": "Pair Plot - ResultLabel - /opt/work/data/src/pairplot_IRIS_REGRESSOR-2-560",
                "version": 1
            },
            {
                "author_name": null,
                "desc": null,
                "id": 12159,
                "image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_560_12159_81f43ec15ea94279.png",
                "json_data": null,
                "label": null,
                "shareable_link": null,
                "sloc": null,
                "status": "initial",
                "title": "ResultTargetValue Importance Analysis",
                "version": 1
            },
            {
                "author_name": null,
                "desc": null,
                "id": 12158,
                "image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_560_12158_68fe500cd7564213.png",
                "json_data": null,
                "label": null,
                "shareable_link": null,
                "sloc": null,
                "status": "initial",
                "title": "Joint Plot - ResultTargetValue vs SepalLength - /opt/work/data/src/jointplot_IRIS_REGRESSOR-2-560_4",
                "version": 1
            },
            {
                "author_name": null,
                "desc": null,
                "id": 12157,
                "image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_560_12157_0180f59977c44413.png",
                "json_data": null,
                "label": null,
                "shareable_link": null,
                "sloc": null,
                "status": "initial",
                "title": "Joint Plot - PetalWidth vs SepalLength - /opt/work/data/src/jointplot_IRIS_REGRESSOR-2-560_3",
                "version": 1
            },
            {
                "author_name": null,
                "desc": null,
                "id": 12156,
                "image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_560_12156_4a32649040c14ab6.png",
                "json_data": null,
                "label": null,
                "shareable_link": null,
                "sloc": null,
                "status": "initial",
                "title": "Joint Plot - PetalLength vs SepalLength - /opt/work/data/src/jointplot_IRIS_REGRESSOR-2-560_2",
                "version": 1
            },
            {
                "author_name": null,
                "desc": null,
                "id": 12155,
                "image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_560_12155_e1d895dcd1714932.png",
                "json_data": null,
                "label": null,
                "shareable_link": null,
                "sloc": null,
                "status": "initial",
                "title": "Joint Plot - SepalWidth vs SepalLength - /opt/work/data/src/jointplot_IRIS_REGRESSOR-2-560_1",
                "version": 1
            },
            {
                "author_name": null,
                "desc": null,
                "id": 12154,
                "image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_560_12154_9b0be64a57fd484b.png",
                "json_data": null,
                "label": null,
                "shareable_link": null,
                "sloc": null,
                "status": "initial",
                "title": "Joint Plot - SepalLength vs SepalWidth - /opt/work/data/src/jointplot_IRIS_REGRESSOR-2-560_0",
                "version": 1
            },
            {
                "author_name": null,
                "desc": null,
                "id": 12153,
                "image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_560_12153_80e4812b82a64e4b.png",
                "json_data": null,
                "label": null,
                "shareable_link": null,
                "sloc": null,
                "status": "initial",
                "title": "Scatter - ResultTargetValue vs SepalLength - /opt/work/data/src/scatter_IRIS_REGRESSOR-2-560_4",
                "version": 1
            },
            {
                "author_name": null,
                "desc": null,
                "id": 12152,
                "image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_560_12152_0c8343a342bb40fd.png",
                "json_data": null,
                "label": null,
                "shareable_link": null,
                "sloc": null,
                "status": "initial",
                "title": "Scatter - PetalWidth vs SepalLength - /opt/work/data/src/scatter_IRIS_REGRESSOR-2-560_3",
                "version": 1
            },
            {
                "author_name": null,
                "desc": null,
                "id": 12151,
                "image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_560_12151_17ef598b56e14f68.png",
                "json_data": null,
                "label": null,
                "shareable_link": null,
                "sloc": null,
                "status": "initial",
                "title": "Scatter - PetalLength vs SepalLength - /opt/work/data/src/scatter_IRIS_REGRESSOR-2-560_2",
                "version": 1
            },
            {
                "author_name": null,
                "desc": null,
                "id": 12150,
                "image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_560_12150_8166153a2f87499d.png",
                "json_data": null,
                "label": null,
                "shareable_link": null,
                "sloc": null,
                "status": "initial",
                "title": "Scatter - SepalWidth vs SepalLength - /opt/work/data/src/scatter_IRIS_REGRESSOR-2-560_1",
                "version": 1
            },
            {
                "author_name": null,
                "desc": null,
                "id": 12149,
                "image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_560_12149_bd798d8ff84f46f6.png",
                "json_data": null,
                "label": null,
                "shareable_link": null,
                "sloc": null,
                "status": "initial",
                "title": "Scatter - SepalLength vs SepalWidth - /opt/work/data/src/scatter_IRIS_REGRESSOR-2-560_0",
                "version": 1
            },
            {
                "author_name": null,
                "desc": null,
                "id": 12148,
                "image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_560_12148_07ed6b645dac47a2.png",
                "json_data": null,
                "label": null,
                "shareable_link": null,
                "sloc": null,
                "status": "initial",
                "title": "Pair Plot - ResultLabel - /opt/work/data/src/pairplot_IRIS_REGRESSOR-2-560",
                "version": 1
            },
            {
                "author_name": null,
                "desc": null,
                "id": 12147,
                "image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_560_12147_5247822cd02242fb.png",
                "json_data": null,
                "label": null,
                "shareable_link": null,
                "sloc": null,
                "status": "initial",
                "title": "ResultTargetValue Importance Analysis",
                "version": 1
            }
        ],
        "manifest_secret_key": "live_2_87400592a6084c15b5df19be69196707430acf8edc440be8976af12199bb1b",
        "manifest_sloc": "redten-models-west:IRIS_REGRESSOR-2-560_manifest.json",
        "max_features": 10,
        "ml_type": "Predict with Filter",
        "prediction_type": "predict",
        "rloc": "",
        "sloc": "",
        "status": "completed",
        "target_column_name": "ResultLabel",
        "target_column_values": [
            "Iris-setosa",
            "Iris-versicolor",
            "Iris-virginica"
        ],
        "title": "Hello World - 2017-05-26-17-49-22",
        "units_ahead_set": [],
        "units_ahead_type": "",
        "updated": "2017-05-26 17-50-12",
        "version": 1
    }
}

























Get the new Prediction results
















In [11]:



    


job_report = {}
if job_id == None:
    boom("Failed to start a new prediction job")
else:
    # show the plots by default
    job_report = get_job_analysis(job_id)

    if len(job_report) == 0:
        boom("Job=" + str(job_id) + " failed making new prediction")
    # end of checking the report had data
# end of get job analysis



    


















    






Getting analysis for job=560 url=https://redten.io/ml/analysis/560/
SUCCESS - GET Analysis Response Status=200 Reason=OK
Found Job=560 analysis
Joint Plot - ResultTargetValue vs SepalLength - /opt/work/data/src/jointplot_IRIS_REGRESSOR-2-560_4
URL: https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_560_12170_e4c5d75535004a21.png










    
















    






---------------------------------------------------------------------------------------
Joint Plot - PetalWidth vs SepalLength - /opt/work/data/src/jointplot_IRIS_REGRESSOR-2-560_3
URL: https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_560_12169_e2025b07c7e24a83.png










    
















    






---------------------------------------------------------------------------------------
Joint Plot - PetalLength vs SepalLength - /opt/work/data/src/jointplot_IRIS_REGRESSOR-2-560_2
URL: https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_560_12168_ff223da922584537.png










    
















    






---------------------------------------------------------------------------------------
Joint Plot - SepalWidth vs SepalLength - /opt/work/data/src/jointplot_IRIS_REGRESSOR-2-560_1
URL: https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_560_12167_9b9870cab7a04c99.png










    
















    






---------------------------------------------------------------------------------------
Joint Plot - SepalLength vs SepalWidth - /opt/work/data/src/jointplot_IRIS_REGRESSOR-2-560_0
URL: https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_560_12166_d9dd29fe5eaa48d3.png










    
















    






---------------------------------------------------------------------------------------
Scatter - ResultTargetValue vs SepalLength - /opt/work/data/src/scatter_IRIS_REGRESSOR-2-560_4
URL: https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_560_12165_33d5ce390f854ffb.png










    
















    






---------------------------------------------------------------------------------------
Scatter - PetalWidth vs SepalLength - /opt/work/data/src/scatter_IRIS_REGRESSOR-2-560_3
URL: https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_560_12164_c1367c948b134554.png










    
















    






---------------------------------------------------------------------------------------
Scatter - PetalLength vs SepalLength - /opt/work/data/src/scatter_IRIS_REGRESSOR-2-560_2
URL: https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_560_12163_8dfe71c076824165.png










    
















    






---------------------------------------------------------------------------------------
Scatter - SepalWidth vs SepalLength - /opt/work/data/src/scatter_IRIS_REGRESSOR-2-560_1
URL: https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_560_12162_52608b634fff4050.png










    
















    






---------------------------------------------------------------------------------------
Scatter - SepalLength vs SepalWidth - /opt/work/data/src/scatter_IRIS_REGRESSOR-2-560_0
URL: https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_560_12161_c89cc9f7a653453a.png










    
















    






---------------------------------------------------------------------------------------
Pair Plot - ResultLabel - /opt/work/data/src/pairplot_IRIS_REGRESSOR-2-560
URL: https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_560_12160_3da4e4350ed842c9.png










    
















    






---------------------------------------------------------------------------------------
ResultTargetValue Importance Analysis
URL: https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_560_12159_81f43ec15ea94279.png










    
















    






---------------------------------------------------------------------------------------
Joint Plot - ResultTargetValue vs SepalLength - /opt/work/data/src/jointplot_IRIS_REGRESSOR-2-560_4
URL: https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_560_12158_68fe500cd7564213.png










    
















    






---------------------------------------------------------------------------------------
Joint Plot - PetalWidth vs SepalLength - /opt/work/data/src/jointplot_IRIS_REGRESSOR-2-560_3
URL: https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_560_12157_0180f59977c44413.png










    
















    






---------------------------------------------------------------------------------------
Joint Plot - PetalLength vs SepalLength - /opt/work/data/src/jointplot_IRIS_REGRESSOR-2-560_2
URL: https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_560_12156_4a32649040c14ab6.png










    
















    






---------------------------------------------------------------------------------------
Joint Plot - SepalWidth vs SepalLength - /opt/work/data/src/jointplot_IRIS_REGRESSOR-2-560_1
URL: https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_560_12155_e1d895dcd1714932.png










    
















    






---------------------------------------------------------------------------------------
Joint Plot - SepalLength vs SepalWidth - /opt/work/data/src/jointplot_IRIS_REGRESSOR-2-560_0
URL: https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_560_12154_9b0be64a57fd484b.png










    
















    






---------------------------------------------------------------------------------------
Scatter - ResultTargetValue vs SepalLength - /opt/work/data/src/scatter_IRIS_REGRESSOR-2-560_4
URL: https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_560_12153_80e4812b82a64e4b.png










    
















    






---------------------------------------------------------------------------------------
Scatter - PetalWidth vs SepalLength - /opt/work/data/src/scatter_IRIS_REGRESSOR-2-560_3
URL: https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_560_12152_0c8343a342bb40fd.png










    
















    






---------------------------------------------------------------------------------------
Scatter - PetalLength vs SepalLength - /opt/work/data/src/scatter_IRIS_REGRESSOR-2-560_2
URL: https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_560_12151_17ef598b56e14f68.png










    
















    






---------------------------------------------------------------------------------------
Scatter - SepalWidth vs SepalLength - /opt/work/data/src/scatter_IRIS_REGRESSOR-2-560_1
URL: https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_560_12150_8166153a2f87499d.png










    
















    






---------------------------------------------------------------------------------------
Scatter - SepalLength vs SepalWidth - /opt/work/data/src/scatter_IRIS_REGRESSOR-2-560_0
URL: https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_560_12149_bd798d8ff84f46f6.png










    
















    






---------------------------------------------------------------------------------------
Pair Plot - ResultLabel - /opt/work/data/src/pairplot_IRIS_REGRESSOR-2-560
URL: https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_560_12148_07ed6b645dac47a2.png










    
















    






---------------------------------------------------------------------------------------
ResultTargetValue Importance Analysis
URL: https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_560_12147_5247822cd02242fb.png










    
















    






---------------------------------------------------------------------------------------






















    


    

        

            

            

                Content source: jay-johnson/sci-pype
            

            
Similar notebooks:

            

            
                notebook.community |
                gallery |
                about