def main(): c = dltk_ai.DltkAiClient('xxx') response = c.face_detection_image( '../img/fd-actual-img.jpg') # it will return image data in bytes. print(response) save_img(response, "../img/fd-response.png") response = c.face_detection_json( '../img/fd-actual-img.jpg' ) # it will return the co-ordinates of detected faces print(response) response = c.eye_detection_image( '../img/fd-actual-img.jpg') # it will return image data in bytes. print(response) save_img(response, "../img/fd-response.png") response = c.eye_detection_json( '../img/fd-actual-img.jpg' ) # it will return the co-ordinates of detected faces print(response) response = c.face_detection_image_core( '../img/fd-actual-img.jpg') # it will return image data in bytes. print(response) save_img(response, "../img/fd-response.png") response = c.face_detection_json_core( '../img/fd-actual-img.jpg' ) # it will return the co-ordinates of detected licence plates. print(response) response = c.license_plate_detection_image( '../img/lp-actual-img.jpg') # it will return image data in bytes. print(response) save_img(response, "../img/lp-response.png") response = c.object_detection_image( '../img/lp-actual-img.jpg') # it will return image data in bytes. print(response) save_img(response, "../img/lp-response.png") response = c.object_detection_json( '../img/lp-actual-img.jpg' ) # it will return the co-ordinates of detected licence plates. print(response) response = c.image_classification('../img/lp-actual-img.jpg') print(response)
def main(): c = dltk_ai.DltkAiClient('xxx') cluster_data_store_response = c.store('../csv/airoplane_data.csv', Dataset.TRAIN_DATA) print(cluster_data_store_response) cluster_data = cluster_data_store_response['fileUrl'] cluster_response = c.cluster("clustering", "KMeansClustering", cluster_data, ["Activity Period", "Operating Airline"], "spotflock", 2, "Clustering Model", True) print(cluster_response) cluster_job_status_response = c.job_status(cluster_response['data']['jobId']) print(cluster_job_status_response) cluster_job_output_response = c.job_output(cluster_response['data']['jobId']) print(cluster_job_output_response) pred_file = cluster_job_output_response['output']['clusterFileUrl'] response = c.download(pred_file) print(response.text)
def main(): c = dltk_ai.DltkAiClient('xxx') cluster_data_store_response = c.store('../csv/moon_data.csv', Dataset.TRAIN_DATA) print(cluster_data_store_response) cluster_data = cluster_data_store_response['fileUrl'] cluster_response = c.cluster("clustering", "KMeansClustering", cluster_data, ["X", "Y"], "scikit", 2, "Clustering_Model", True, None) print(cluster_response) cluster_job_status_response = c.job_status( cluster_response['data']['jobId']) print(cluster_job_status_response) cluster_job_output_response = c.job_output( cluster_response['data']['jobId']) print(cluster_job_output_response) pred_file = cluster_job_output_response['output']['clusterFileUrl'] response = c.download(pred_file) print(response.text)
#Loan Default Prediction import dltk_ai from dltk_ai.dataset_types import Dataset #DLTK SDK requires Python 3.5 + . Go to https://dev.dltk.ai/ and create an app. On creation of an app, you will get an API Key. c = dltk_ai.DltkAiClient('API Key') #It stores Train and Test Files remotely. File upload API will return file storage locations from Cloud Storage in response. train_file_store_response = c.store("path/to/train/file", Dataset.TRAIN_DATA) train_data = train_file_store_response["fileUrl"] #train_data file url #'/spotflock-studio-prod/[email protected]/1551936734455-loan_train.csv' #Upload Test File test_file_store_response = c.store("path/to/test/file", Dataset.TEST_DATA) test_data = test_file_store_response["fileUrl"] #test_data file url #'/spotflock-studio-prod/[email protected]/1551936725437-loan_test.csv' #build model using NaiveBayesMultinomial algorithm. train_response_NBM = c.train( "classification", "NaiveBayesMultinomial", train_data, "Loan_Status", ["CurrentLoanAmount", "CreditScore", "MonthlyDebt", "Yr_Credit_His"], "Loan Model - NaiveBayesMultinomial", "weka", 80, True) #The train/predict jobs take some amount of time to be completed and so their status can be checked with this API. train_job_status_response_NBM = c.job_status( train_response_NBM["data"]["jobId"])
def main(): c = dltk_ai.DltkAiClient('xxx') # train model test_file_store_response = c.store('../csv/player_test.csv', Dataset.TEST_DATA) print(test_file_store_response) test_data = test_file_store_response['fileUrl'] train_data_store_response = c.store('../csv/player_train.csv', Dataset.TRAIN_DATA) print(train_data_store_response) train_data = train_data_store_response['fileUrl'] train_response = c.train("classification", "NaiveBayesMultinomial", train_data, "player_activity", ["stamina", "challenges", "achievements"]) print(train_response) train_job_status_response = c.job_status(train_response['data']['jobId']) print(train_job_status_response) train_job_output_response = c.job_output(train_response['data']['jobId']) print(train_job_output_response) model = train_job_output_response['output']['modelUrl'] predict_response = c.predict("classification", test_data, model, "weka") print(predict_response) predict_job_status_response = c.job_status( predict_response['data']['jobId']) print(predict_job_status_response) predict_job_output_response = c.job_output( predict_response['data']['jobId']) print(predict_job_output_response) pred_file = predict_job_output_response['output']['predFileUrl'] response = c.download(pred_file) print(response.text) # feedback model # Feedback config should be same as train config except training percentage. # Job id, model url, dataset url used in training a model is required to feedback any model. job_id = train_response['data']['jobId'] # IMP: Ensure the dataset has all features and label used for training the model. feedback_data_store_response = c.store('../csv/player_feedback.csv', Dataset.TRAIN_DATA) print(feedback_data_store_response) feedback_data = feedback_data_store_response['fileUrl'] feedback_response = c.feedback("classification", "NaiveBayesMultinomial", train_data, feedback_data, job_id, model, "player_activity", ["stamina", "challenges", "achievements"]) print(feedback_response) feedback_job_status_response = c.job_status(job_id) print(feedback_job_status_response) feedback_job_output_response = c.job_output(job_id) print(feedback_job_output_response) model = feedback_job_output_response['output']['modelUrl'] feedback_predict_response = c.predict("classification", test_data, model, "weka") print(feedback_predict_response) feedback_predict_job_status_response = c.job_status( predict_response['data']['jobId']) print(feedback_predict_job_status_response) feedback_predict_job_output_response = c.job_output( predict_response['data']['jobId']) print(feedback_predict_job_output_response) pred_file = feedback_predict_job_output_response['output']['predFileUrl'] response = c.download(pred_file) print(response.text)
def main(): c = dltk_ai.DltkAiClient('xxx') # put your app key here. # REGRESSION Training test_data = "" train_data = "" test_file_store_response = c.store('../csv/housing_test.csv', Dataset.TEST_DATA) print(test_file_store_response) test_data = test_file_store_response['fileUrl'] train_data_store_response = c.store('../csv/housing_train.csv', Dataset.TRAIN_DATA) print(train_data_store_response) train_data = train_data_store_response['fileUrl'] train_response = c.train("regression", "LinearRegression", train_data, "SalePrice", ["LotShape", "Street"], "Housing Price Model", "weka", 80, True) # this is the configuration. print(train_response) train_job_status_response = c.job_status(train_response['data']['jobId']) print(train_job_status_response) train_job_output_response = c.job_output(train_response['data']['jobId']) print(train_job_output_response) model = train_job_output_response['output']['modelUrl'] predict_response = c.predict("regression", test_data, model, "weka") print(predict_response) predict_job_status_response = c.job_status( predict_response['data']['jobId']) print(predict_job_status_response) predict_job_output_response = c.job_output( predict_response['data']['jobId']) print(predict_job_output_response) pred_file = predict_job_output_response['output']['predFileUrl'] prediction_response = c.download(pred_file) print(prediction_response.text) # REGRESSION Feedback job_id = train_response['data']['jobId'] # IMP: Ensure the dataset has all features and label used for training the model. feedback_data_store_response = c.store('../csv/housing_feedback.csv', Dataset.TRAIN_DATA) print(feedback_data_store_response) feedback_data = feedback_data_store_response['fileUrl'] feedback_response = c.feedback("regression", "LinearRegression", train_data, feedback_data, job_id, model, "SalePrice", ["LotShape", "Street"], "weka", "Housing Price Model", 80, True) print(feedback_response) feedback_job_status_response = c.job_status(job_id) print(feedback_job_status_response) feedback_job_output_response = c.job_output(job_id) print(feedback_job_output_response) model = train_job_output_response['output']['modelUrl'] feedback_predict_response = c.predict("classification", test_data, model, "weka") print(feedback_predict_response) feedback_predict_job_status_response = c.job_status( predict_response['data']['jobId']) print(feedback_predict_job_status_response) feedback_predict_job_output_response = c.job_output( predict_response['data']['jobId']) print(feedback_predict_job_output_response) pred_file = feedback_predict_job_output_response['output']['predFileUrl'] response = c.download(pred_file) print(response.text)
def main(): c = dltk_ai.DltkAiClient('xxx') response = c.sentiment_analysis('I am feeling good.') print(response) response = c.pos_tagger('I am Aniket.') print(response)
import dltk_ai client = dltk_ai.DltkAiClient( 'bb93840d-4578-4b2c-9bf4-9d056c44af65') # put your app key here. # REGRESSION Training test_file_store_response = client.store('../csv/rg_test.csv') print(test_file_store_response) test_data = test_file_store_response['fileUrl'] train_data_store_response = client.store('../csv/rg_train.csv') print(train_data_store_response) train_data = train_data_store_response['fileUrl'] train_response = client.train( "classification", "LogisticRegression", train_data, 'Revenue.Grid', ['children', 'year_last_moved', 'Average.Credit.Card.Transaction'], "Revenue_Grid_Model", "scikit", 80, True) # this is the configuration. print(train_response) train_job_status_response = client.job_status(train_response['data']['jobId']) print(train_job_status_response) train_job_output_response = client.job_output(train_response['data']['jobId']) print(train_job_output_response) model = train_job_output_response['output']['modelUrl'] predict_response = client.predict("classification", test_data, model, "scikit", features=[ 'children', 'year_last_moved', 'Average.Credit.Card.Transaction' ]) print(predict_response) predict_job_status_response = client.job_status(