DataRobot API resources > API user guide > Common use cases > Python v2.x use cases > Large-scale demand forecasting > Make predictions

Make predictions¶

When the modeling process is complete and you have retrieved the best model, you can then test its prediction capabilities and deploy the model to a production environment to generate predictions.

Test predictions¶

For testing purposes, test prediction generation with the top-performing model before deploying the model to a prediction server.

Prediction Explanations¶

To include Prediction Explanations, run the cells below to first calculate Feature Impact and then initialize Prediction Explanations.

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SEGMENT_NAME = "segment_name"

combined_models = project.get_combined_models()
current_combined_model = combined_models[0]
segments_info = current_combined_model.get_segments_info()
segment_metadata = {x.segment: x for x in segments_info}

model = dr.Model.get(segment_metadata["SEGMENT_NAME"].project_id, segment_metadata["SEGMENT_NAME"].model_id)
child_project = dr.Project.get(project_id = segment_metadata["SEGMENT_NAME"].project_id)
feature_impacts = model.get_or_request_feature_impact()
SEGMENT_NAME = "segment_name"

combined_models = project.get_combined_models()
current_combined_model = combined_models[0]
segments_info = current_combined_model.get_segments_info()
segment_metadata = {x.segment: x for x in segments_info}

model = dr.Model.get(segment_metadata["SEGMENT_NAME"].project_id, segment_metadata["SEGMENT_NAME"].model_id)
child_project = dr.Project.get(project_id = segment_metadata["SEGMENT_NAME"].project_id)
feature_impacts = model.get_or_request_feature_impact()

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# Upload prediction dataset 
# This will be used for establishing prediction explanation
dataset = project.upload_dataset('Acme_Test_Cluster_0.csv')
# Upload prediction dataset 
# This will be used for establishing prediction explanation
dataset = project.upload_dataset('Acme_Test_Cluster_0.csv')

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# Run Feature Impact
# Need to do this to get Prediction Explanations
model.get_or_request_feature_impact()
# Run Feature Impact
# Need to do this to get Prediction Explanations
model.get_or_request_feature_impact()

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# Compute predictions
predict_job = model.request_predictions(dataset.id)
# Compute predictions
predict_job = model.request_predictions(dataset.id)

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# Initialize Prediction Explanations
pei_job = dr.PredictionExplanationsInitialization.create(PID, MID)
# Initialize Prediction Explanations
pei_job = dr.PredictionExplanationsInitialization.create(PID, MID)

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# Compute Prediction Explanations
pe_job = dr.PredictionExplanations.create(project.id, model.id, dataset.id)
pe = pe_job.get_result_when_complete()
# Compute Prediction Explanations
pe_job = dr.PredictionExplanations.create(project.id, model.id, dataset.id)
pe = pe_job.get_result_when_complete()

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# Get Prediction Explanations as a Dataframe
pred_exp = pe.get_all_as_dataframe()
pred_exp
# Get Prediction Explanations as a Dataframe
pred_exp = pe.get_all_as_dataframe()
pred_exp

Out[125]:

	row_id	prediction	explanation_0_feature	explanation_0_feature_value	explanation_0_label	explanation_0_qualitative_strength	explanation_0_strength	explanation_1_feature	explanation_1_feature_value	explanation_1_label	explanation_1_qualitative_strength	explanation_1_strength	explanation_2_feature	explanation_2_feature_value	explanation_2_label	explanation_2_qualitative_strength	explanation_2_strength
0	51	3.69103	SalesQty (7 day mean)	1.28571	SalesQty (actual)	---	-17.49522	SalesQty (7 day median)	0.00000	SalesQty (actual)	---	-15.28814	SalesQty (naive latest value)	3.00000	SalesQty (actual)	--	-6.23734
1	52	3.83318	SalesQty (7 day median)	0.00000	SalesQty (actual)	---	-24.76728	SalesQty (7 day mean)	1.28571	SalesQty (actual)	---	-18.77639	SalesQty (naive latest value)	3.00000	SalesQty (actual)	--	-7.17425
2	53	3.45923	SalesQty (7 day mean)	1.28571	SalesQty (actual)	---	-16.67129	SalesQty (7 day median)	0.00000	SalesQty (actual)	--	-5.25844	SalesQty (14 day median)	1.00000	SalesQty (actual)	--	-4.65901
3	54	2.57798	SalesQty (7 day mean)	1.28571	SalesQty (actual)	---	-17.32528	SalesQty (7 day median)	0.00000	SalesQty (actual)	--	-4.84710	SalesQty (14 day median)	1.00000	SalesQty (actual)	--	-4.55620
4	55	2.57798	SalesQty (7 day mean)	1.28571	SalesQty (actual)	---	-17.32528	SalesQty (7 day median)	0.00000	SalesQty (actual)	--	-4.84710	SalesQty (14 day median)	1.00000	SalesQty (actual)	--	-4.55620
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
832	7730	10.20752	SalesQty (7 day mean)	11.14286	SalesQty (actual)	---	-8.22766	SalesQty (7 day max)	25.00000	SalesQty (actual)	--	-4.19796	SalesQty (7 day median)	8.00000	SalesQty (actual)	--	-4.08395
833	7731	9.44667	SalesQty (7 day mean)	11.14286	SalesQty (actual)	---	-8.75699	SalesQty (7 day median)	8.00000	SalesQty (actual)	--	-3.66404	SalesQty (7 day max)	25.00000	SalesQty (actual)	--	-2.99822
834	7732	9.44667	SalesQty (7 day mean)	11.14286	SalesQty (actual)	---	-8.75699	SalesQty (7 day median)	8.00000	SalesQty (actual)	--	-3.66404	SalesQty (14 day min)	3.00000	SalesQty (actual)	--	-3.56161
835	7733	7.89606	SalesQty (7 day mean)	11.14286	SalesQty (actual)	---	-8.23909	SalesQty (7 day max)	25.00000	SalesQty (actual)	--	-3.11896	SalesQty (14 day median)	6.50000	SalesQty (actual)	--	-2.58671
836	7734	9.10800	SalesQty (7 day mean)	11.14286	SalesQty (actual)	---	-7.83997	SalesQty (7 day median)	8.00000	SalesQty (actual)	--	-6.35970	SalesQty (14 day median)	6.50000	SalesQty (actual)	--	-3.53018

837 rows × 17 columns

Retrieve forecasts¶

Use the following code for validation purposes prior to deploying the model. Before proceeding, review the guidelines for configuring a prediction dataset to make time series series predictions.

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RECORD = 0

# Verify correct project
PID = best_models_features['Project_ID'].values[RECORD]
MID = best_models_features['Model_ID'].values[RECORD]

project = dr.Project.get(PID)
model   = dr.Model.get(PID, MID)
print(project, model)
RECORD = 0

# Verify correct project
PID = best_models_features['Project_ID'].values[RECORD]
MID = best_models_features['Model_ID'].values[RECORD]

project = dr.Project.get(PID)
model   = dr.Model.get(PID, MID)
print(project, model)

You can then upload the predictions test dataset to get predictions with 85% prediction intervals and three Prediction Explanations.

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%%time

dataset = project.upload_dataset('Acme_Test_Cluster_0.csv')

pred_job = model.request_predictions(dataset_id = dataset.id,
                                     include_prediction_intervals = True,
                                     prediction_intervals_size = 85
                                    )

preds = pred_job.get_result_when_complete()

preds.head(5)
%%time

dataset = project.upload_dataset('Acme_Test_Cluster_0.csv')

pred_job = model.request_predictions(dataset_id = dataset.id,
                                     include_prediction_intervals = True,
                                     prediction_intervals_size = 85
                                    )

preds = pred_job.get_result_when_complete()

preds.head(5)

Use the code below to join prediction results, prediction intervals, and Prediction Explanations to one dataframe.

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fin_df = preds.merge(pred_exp, how='left', on='row_id')
fin_df.drop(labels=['prediction_y'], axis=1, inplace=True)
fin_df.rename(columns={'prediction_x':'prediction'}, inplace=True)
fin_df
fin_df = preds.merge(pred_exp, how='left', on='row_id')
fin_df.drop(labels=['prediction_y'], axis=1, inplace=True)
fin_df.rename(columns={'prediction_x':'prediction'}, inplace=True)
fin_df

Out[128]:

	series_id	forecast_distance	forecast_point	prediction	prediction_interval_lower_bound	prediction_interval_upper_bound	row_id	timestamp	explanation_0_feature	explanation_0_feature_value	explanation_0_label	explanation_0_qualitative_strength	explanation_0_strength	explanation_1_feature	explanation_1_feature_value	explanation_1_label	explanation_1_qualitative_strength	explanation_1_strength	explanation_2_feature	explanation_2_feature_value	explanation_2_label	explanation_2_qualitative_strength	explanation_2_strength
0	01053_Alcohol_3633522291	1	2018-11-16T00:00:00.000000Z	3.69103	0.47841	6.14793	51	2018-11-17T00:00:00.000000Z	SalesQty (7 day mean)	1.28571	SalesQty (actual)	---	-17.49522	SalesQty (7 day median)	0.00000	SalesQty (actual)	---	-15.28814	SalesQty (naive latest value)	3.00000	SalesQty (actual)	--	-6.23734
1	01053_Alcohol_3633522291	2	2018-11-16T00:00:00.000000Z	3.83318	0.59759	6.31103	52	2018-11-18T00:00:00.000000Z	SalesQty (7 day median)	0.00000	SalesQty (actual)	---	-24.76728	SalesQty (7 day mean)	1.28571	SalesQty (actual)	---	-18.77639	SalesQty (naive latest value)	3.00000	SalesQty (actual)	--	-7.17425
2	01053_Alcohol_3633522291	3	2018-11-16T00:00:00.000000Z	3.45923	0.21665	5.92187	53	2018-11-19T00:00:00.000000Z	SalesQty (7 day mean)	1.28571	SalesQty (actual)	---	-16.67129	SalesQty (7 day median)	0.00000	SalesQty (actual)	--	-5.25844	SalesQty (14 day median)	1.00000	SalesQty (actual)	--	-4.65901
3	01053_Alcohol_3633522291	4	2018-11-16T00:00:00.000000Z	2.57798	-0.55875	5.00040	54	2018-11-20T00:00:00.000000Z	SalesQty (7 day mean)	1.28571	SalesQty (actual)	---	-17.32528	SalesQty (7 day median)	0.00000	SalesQty (actual)	--	-4.84710	SalesQty (14 day median)	1.00000	SalesQty (actual)	--	-4.55620
4	01053_Alcohol_3633522291	5	2018-11-16T00:00:00.000000Z	2.57798	-0.60271	4.96715	55	2018-11-21T00:00:00.000000Z	SalesQty (7 day mean)	1.28571	SalesQty (actual)	---	-17.32528	SalesQty (7 day median)	0.00000	SalesQty (actual)	--	-4.84710	SalesQty (14 day median)	1.00000	SalesQty (actual)	--	-4.55620
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
832	01115_Pizzas_3633541599	3	2018-11-16T00:00:00.000000Z	10.20752	3.66792	18.94339	7730	2018-11-19T00:00:00.000000Z	SalesQty (7 day mean)	11.14286	SalesQty (actual)	---	-8.22766	SalesQty (7 day max)	25.00000	SalesQty (actual)	--	-4.19796	SalesQty (7 day median)	8.00000	SalesQty (actual)	--	-4.08395
833	01115_Pizzas_3633541599	4	2018-11-16T00:00:00.000000Z	9.44667	2.95212	18.08329	7731	2018-11-20T00:00:00.000000Z	SalesQty (7 day mean)	11.14286	SalesQty (actual)	---	-8.75699	SalesQty (7 day median)	8.00000	SalesQty (actual)	--	-3.66404	SalesQty (7 day max)	25.00000	SalesQty (actual)	--	-2.99822
834	01115_Pizzas_3633541599	5	2018-11-16T00:00:00.000000Z	9.44667	2.69762	17.61851	7732	2018-11-21T00:00:00.000000Z	SalesQty (7 day mean)	11.14286	SalesQty (actual)	---	-8.75699	SalesQty (7 day median)	8.00000	SalesQty (actual)	--	-3.66404	SalesQty (14 day min)	3.00000	SalesQty (actual)	--	-3.56161
835	01115_Pizzas_3633541599	6	2018-11-16T00:00:00.000000Z	7.89606	1.34399	16.73757	7733	2018-11-22T00:00:00.000000Z	SalesQty (7 day mean)	11.14286	SalesQty (actual)	---	-8.23909	SalesQty (7 day max)	25.00000	SalesQty (actual)	--	-3.11896	SalesQty (14 day median)	6.50000	SalesQty (actual)	--	-2.58671
836	01115_Pizzas_3633541599	7	2018-11-16T00:00:00.000000Z	9.10800	2.68591	17.80487	7734	2018-11-23T00:00:00.000000Z	SalesQty (7 day mean)	11.14286	SalesQty (actual)	---	-7.83997	SalesQty (7 day median)	8.00000	SalesQty (actual)	--	-6.35970	SalesQty (14 day median)	6.50000	SalesQty (actual)	--	-3.53018

837 rows × 23 columns

Deploy a model¶

After testing predictions with the top-performing model, use the code below to deploy the model to a dedicated prediction server.

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now = datetime.now()
current_time = now.strftime("%H:%M:%S")

# DataRobot project
project = dr.Project.get(project_id=PID)
combined_models = project.get_combined_models()
current_combined_model = combined_models[0]

# Model ID
model_id = current_combined_model.id

# Deploy the model
prediction_server = dr.PredictionServer.list()[0]
DATAROBOT_KEY = prediction_server.datarobot_key
PREDICTIONSENDPOINT = prediction_server.url
PREDICTIONSHEADERS = {'Content-Type': 'application/json', 'datarobot-key': '%s' % DATAROBOT_KEY}

deployment = dr.Deployment.create_from_learning_model(
    model_id = model_id,
    label = 'Acme Store Sales Multi Series Deployment ' + str(date.today()) + " " + str(current_time),
    description = 'Acme Sales - Example Deployment ' + str(date.today()),
    default_prediction_server_id = prediction_server.id)

DEPLOYMENT_ID = deployment.id
# Write the Deployment ID to a text file in the current working directory to reference
# later if needed
os.system("echo " + str(deployment.id) + "> ./deployment_id.txt")

print('Deployment Name is: ', deployment.label)
print('Deployment ID is:    ' + str(DEPLOYMENT_ID))
now = datetime.now()
current_time = now.strftime("%H:%M:%S")

# DataRobot project
project = dr.Project.get(project_id=PID)
combined_models = project.get_combined_models()
current_combined_model = combined_models[0]

# Model ID
model_id = current_combined_model.id

# Deploy the model
prediction_server = dr.PredictionServer.list()[0]
DATAROBOT_KEY = prediction_server.datarobot_key
PREDICTIONSENDPOINT = prediction_server.url
PREDICTIONSHEADERS = {'Content-Type': 'application/json', 'datarobot-key': '%s' % DATAROBOT_KEY}

deployment = dr.Deployment.create_from_learning_model(
    model_id = model_id,
    label = 'Acme Store Sales Multi Series Deployment ' + str(date.today()) + " " + str(current_time),
    description = 'Acme Sales - Example Deployment ' + str(date.today()),
    default_prediction_server_id = prediction_server.id)

DEPLOYMENT_ID = deployment.id
# Write the Deployment ID to a text file in the current working directory to reference
# later if needed
os.system("echo " + str(deployment.id) + "> ./deployment_id.txt")

print('Deployment Name is: ', deployment.label)
print('Deployment ID is:    ' + str(DEPLOYMENT_ID))

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deployment
deployment

Set prediction intervals for a deployment¶

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PREDICTION_INTERVAL = 85
dr.Deployment.update_prediction_intervals_settings(deployment, [PREDICTION_INTERVAL])
PREDICTION_INTERVAL = 85
dr.Deployment.update_prediction_intervals_settings(deployment, [PREDICTION_INTERVAL])

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# Verify settings
dr.Deployment.get_prediction_intervals_settings(deployment)
# Verify settings
dr.Deployment.get_prediction_intervals_settings(deployment)

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dr.Deployment.get_prediction_warning_settings(deployment)
dr.Deployment.get_prediction_warning_settings(deployment)

Use the code below to list all deployments that you can access.

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dr.Deployment.list(order_by=None, search=['Acme'])
dr.Deployment.list(order_by=None, search=['Acme'])

Make predictions with a deployment¶

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# Command outline that will need to be run
# python datarobot-predict.py --forecast_point <date> <input-file.csv> <output-file.csv>
# Command outline that will need to be run
# python datarobot-predict.py --forecast_point

To get predictions, open the DataRobot UI and navigate to Predictions > Prediction API. Copy the script to and paste it into a .py file saved as datarobot-predict.py.

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from IPython import display
display.Image("./Prediction_Script.png")
from IPython import display
display.Image("./Prediction_Script.png")

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