def local_prediction(logistic_regressions, test_reader, output, args, exclude=None): """Get local logistic_regression and issue prediction """ # Only one logistic_regression at present local_logistic = LogisticRegression(logistic_regressions[0]) test_set_header = test_reader.has_headers() for input_data in test_reader: input_data_dict = test_reader.dict(input_data, filtering=False) prediction_info = local_logistic.predict( input_data_dict, by_name=test_set_header) write_prediction(prediction_info, output, args.prediction_info, input_data, exclude)
def local_prediction(logistic_regressions, test_reader, output, args, exclude=None): """Get local logistic_regression and issue prediction """ # Only one logistic_regression at present local_logistic = LogisticRegression(logistic_regressions[0], api=args.retrieve_api_) kwargs = {"full": True} if args.operating_point_: kwargs.update({"operating_point": args.operating_point_}) for input_data in test_reader: input_data_dict = test_reader.dict(input_data, filtering=False) prediction_info = local_logistic.predict( input_data_dict, **kwargs) write_prediction(prediction_info, output, args.prediction_info, input_data, exclude)
def local_prediction(logistic_regressions, test_reader, output, args, exclude=None): """Get local logistic_regression and issue prediction """ # Only one logistic_regression at present local_logistic = LogisticRegression(logistic_regressions[0]) test_set_header = test_reader.has_headers() for input_data in test_reader: input_data_dict = test_reader.dict(input_data, filtering=False) prediction_info = local_logistic.predict(input_data_dict, by_name=test_set_header) write_prediction(prediction_info, output, args.prediction_info, input_data, exclude)
def local_prediction(logistic_regressions, test_reader, output, args, exclude=None): """Get local logistic_regression and issue prediction """ # Only one logistic_regression at present local_logistic = LogisticRegression(logistic_regressions[0], api=args.retrieve_api_) test_set_header = test_reader.has_headers() kwargs = {"by_name": test_set_header} if args.operating_point_: kwargs.update({"operating_point": args.operating_point_}) for input_data in test_reader: input_data_dict = test_reader.dict(input_data, filtering=False) prediction_info = local_logistic.predict(input_data_dict, **kwargs) write_prediction(prediction_info, output, args.prediction_info, input_data, exclude)
def i_create_local_logistic_regression_from_file(step, export_file): world.local_logistic = LogisticRegression(res_filename(export_file))
def i_create_local_logistic_regression_from_file(step, export_file): world.local_logistic = LogisticRegression( \ res_filename(export_file), api=BigML("wrong-user", "wrong-api-key"))
import pandas as pd import streamlit as st from bigml.logistic import LogisticRegression from bigml.api import BigML from PIL import Image import altair as alt logisticregression = LogisticRegression( 'logisticregression/607e21b3f7af1513af005db6', api=BigML("vlv7", "5bfb457d1ad360008520230eee0c229084b85f04", domain="bigml.io")) # To make predictions fill the desired input_data in next line. @st.cache(suppress_st_warning=True) def setup_fxn(): #st.header("SetupFXN Run") input_data = { "director": "Woody Allen", "writer": "Woody Allen", "genre": 'Drama', "duration": 1, "Month": 'October', "production_company": 'twentieth century fox', "actors": 'Maggie Smith', "OscarWinner": 'FALSE', "budget": 10000 } # setup for initial data seeding main_df = pd.read_csv('data/IMDB Data v3.csv')
def i_create_a_local_logistic_model(step): world.local_model = LogisticRegression(world.logistic_regression)
def i_create_a_local_logistic_model(step): world.local_model = LogisticRegression(world.logistic_regression) if hasattr(world, "local_ensemble"): world.local_ensemble = None
# Requires BigML Python bindings # # Install via: pip install bigml # # or clone it: # git clone https://github.com/bigmlcom/python.git from bigml.logistic import LogisticRegression from bigml.api import BigML # Downloads and generates a local version of the logistic regression, # if it hasn't been downloaded previously. logisticregression = LogisticRegression('logisticregression/5c9cf98ede2d4d09b60001b7', api=BigML("rshelton", "adabd734dd2a2af5cb4e49176f0eb472cfa8ce5a", domain="bigml.io")) # To predict probabilities fill the desired input_data # in next line. Numeric fields are compulsory if the model was not # trained with missing numerics. input_data = { "EMERG_VEH": yes, "Division": d42, "DISABILITY": yes, "ACCLASS": Non-Fatal Injury, "RDSFCOND": Dry, "INVTYPE": Driver, "IMPACTYPE": Turning Movement, "TRAFFCTL": No Control, "ACCLOC": At Intersection, "VISIBILITY": Clear, "Ward_Name": scarborough, "INJURY": None, "INVAGE": unknown,
# model = api.get_model('model/563a1c7a3cd25747430023ce') # prediction = api.create_prediction(model, {'petal length': 4.07, 'sepal width': 3.15, 'petal width': 1.51}) # local_model = Model('model/56430eb8636e1c79b0001f90', api=api) # prediction = local_model.predict({'petal length': 0.96, 'sepal width': 4.1, 'petal width': 2.52}, 2, add_confidence=True, multiple=3) #local_model = Ensemble('ensemble/563219b8636e1c5eca006d38', api=api) # local_model = Ensemble('ensemble/564a081bc6c19b6cf3011c60', api=api) #prediction = local_model.predict({'petal length': 0.96, 'sepal width': 2.25, 'petal width': 1.51, 'sepal length': 6.02}, method=2, add_confidence=True) #local_model = Ensemble('ensemble/5666fb621d55051209009f0f', api=api) #prediction = local_model.predict({'Salary': 18000000, 'Team' : 'Atlanta Braves'}, method=0, add_confidence=True) #local_model = Ensemble('ensemble/566954af1d5505120900bf69', api=api) #prediction = local_model.predict({'Price' : 5.8, 'Grape' : 'Pinot Grigio', 'Rating' : 89, 'Country' : 'Italy'}, method=1, add_confidence=True, add_distribution=True) # local_ensemble = Ensemble('ensemble/564623d4636e1c79b00051f7', api=api) # prediction = local_ensemble.predict({'Price' : 5.8, 'Grape' : 'Pinot Grigio', 'Country' : 'Italy', 'Rating' : 92}, True) # local_anomaly = Anomaly('anomaly/564c5a76636e1c3d52000007', api=api) # prediction = local_anomaly.anomaly_score({'petal length': 4.07, 'sepal width': 3.15, 'petal width': 1.51, 'sepal length': 6.02, 'species': 'Iris-setosa'}, True) # prediction = local_anomaly.anomaly_score({'petal length': 0.96, 'sepal width': 4.1, 'petal width': 2.51, 'sepal length': 6.02, 'species': 'Iris-setosa'}, True) # prediction = local_anomaly.anomaly_score({'petal length': 0.96, 'sepal width': 4.1, 'petal width': 2.51}, True) logistic_regression = LogisticRegression( 'logisticregression/5697c1179ed2334090003217') prediction = logistic_regression.predict({"petal length": 4.07, "petal width": 14.07, "sepal length": 6.02, "sepal width": 3.15}) api.pprint(prediction)
# local_model = Model('model/56430eb8636e1c79b0001f90', api=api) # prediction = local_model.predict({'petal length': 0.96, 'sepal width': 4.1, 'petal width': 2.52}, 2, add_confidence=True, multiple=3) #local_model = Ensemble('ensemble/563219b8636e1c5eca006d38', api=api) # local_model = Ensemble('ensemble/564a081bc6c19b6cf3011c60', api=api) #prediction = local_model.predict({'petal length': 0.96, 'sepal width': 2.25, 'petal width': 1.51, 'sepal length': 6.02}, method=2, add_confidence=True) #local_model = Ensemble('ensemble/5666fb621d55051209009f0f', api=api) #prediction = local_model.predict({'Salary': 18000000, 'Team' : 'Atlanta Braves'}, method=0, add_confidence=True) #local_model = Ensemble('ensemble/566954af1d5505120900bf69', api=api) #prediction = local_model.predict({'Price' : 5.8, 'Grape' : 'Pinot Grigio', 'Rating' : 89, 'Country' : 'Italy'}, method=1, add_confidence=True, add_distribution=True) # local_ensemble = Ensemble('ensemble/564623d4636e1c79b00051f7', api=api) # prediction = local_ensemble.predict({'Price' : 5.8, 'Grape' : 'Pinot Grigio', 'Country' : 'Italy', 'Rating' : 92}, True) # local_anomaly = Anomaly('anomaly/564c5a76636e1c3d52000007', api=api) # prediction = local_anomaly.anomaly_score({'petal length': 4.07, 'sepal width': 3.15, 'petal width': 1.51, 'sepal length': 6.02, 'species': 'Iris-setosa'}, True) # prediction = local_anomaly.anomaly_score({'petal length': 0.96, 'sepal width': 4.1, 'petal width': 2.51, 'sepal length': 6.02, 'species': 'Iris-setosa'}, True) # prediction = local_anomaly.anomaly_score({'petal length': 0.96, 'sepal width': 4.1, 'petal width': 2.51}, True) logistic_regression = LogisticRegression( 'logisticregression/5697c1179ed2334090003217') prediction = logistic_regression.predict({ "petal length": 4.07, "petal width": 14.07, "sepal length": 6.02, "sepal width": 3.15 }) api.pprint(prediction)