def handles_predicting_cases_it_has_not_seen_before(self):
                user_tz = ['Eastern (US & Canada)', 'Pacific (US & Canada)']
                l1_time = [9, 9]
                l1_day = [0, 1]
                l2_time = [9, 9]
                l2_day = [3, 4]
                l3_time = [None, 9]
                l3_day = [None, 5]
                schedule_type = [2, 3]

                training_data = pd.DataFrame({ 'user_tz': user_tz,
                    'l1_time': l1_time,
                    'l1_day': l1_day,
                    'l2_time': l2_time,
                    'l2_day': l2_day,
                    'l3_time': l3_time,
                    'l3_day': l3_day,
                    'schedule_type': schedule_type
                    })

                business_forecast = [{'schedule_type': 1,
                         'user_tz': 'Eastern (US & Canada)',
                         'frequency': 1 },
                         {'schedule_type': 4,
                         'user_tz': 'Pacific (US & Canada)',
                         'frequency': 2 }]

                model = GeneralProbModel()

                model.fit(training_data)
                p = model.predict(business_forecast)

                expect(True).to.equal(True)
            def should_take_into_account_conditional_probability(self):
                user_tz = ['Eastern (US & Canada)', 'Pacific (US & Canada)']
                l1_time = [9, 9]
                l1_day = [0, 1]
                l2_time = [9, 9]
                l2_day = [3, 4]
                l3_time = [None, 9]
                l3_day = [None, 5]
                schedule_type = [2, 3]

                training_data = pd.DataFrame({ 'user_tz': user_tz,
                    'l1_time': l1_time,
                    'l1_day': l1_day,
                    'l2_time': l2_time,
                    'l2_day': l2_day,
                    'l3_time': l3_time,
                    'l3_day': l3_day,
                    'schedule_type': schedule_type
                    })

                business_forecast = [{'schedule_type': 2,
                         'user_tz': 'Eastern (US & Canada)',
                         'frequency': 1 },
                         {'schedule_type': 3,
                         'user_tz': 'Pacific (US & Canada)',
                         'frequency': 2 }]

                model = GeneralProbModel()

                model.fit(training_data)
                p = model.predict(business_forecast)
                expect(True).to.equal(True)
Ejemplo n.º 3
0
            def handles_predicting_cases_it_has_not_seen_before(self):
                user_tz = ['Eastern (US & Canada)', 'Pacific (US & Canada)']
                l1_time = [9, 9]
                l1_day = [0, 1]
                l2_time = [9, 9]
                l2_day = [3, 4]
                l3_time = [None, 9]
                l3_day = [None, 5]
                schedule_type = [2, 3]

                training_data = pd.DataFrame({
                    'user_tz': user_tz,
                    'l1_time': l1_time,
                    'l1_day': l1_day,
                    'l2_time': l2_time,
                    'l2_day': l2_day,
                    'l3_time': l3_time,
                    'l3_day': l3_day,
                    'schedule_type': schedule_type
                })

                business_forecast = [{
                    'schedule_type': 1,
                    'user_tz': 'Eastern (US & Canada)',
                    'frequency': 1
                }, {
                    'schedule_type': 4,
                    'user_tz': 'Pacific (US & Canada)',
                    'frequency': 2
                }]

                model = GeneralProbModel()

                model.fit(training_data)
                p = model.predict(business_forecast)

                expect(True).to.equal(True)
Ejemplo n.º 4
0
            def should_take_into_account_conditional_probability(self):
                user_tz = ['Eastern (US & Canada)', 'Pacific (US & Canada)']
                l1_time = [9, 9]
                l1_day = [0, 1]
                l2_time = [9, 9]
                l2_day = [3, 4]
                l3_time = [None, 9]
                l3_day = [None, 5]
                schedule_type = [2, 3]

                training_data = pd.DataFrame({
                    'user_tz': user_tz,
                    'l1_time': l1_time,
                    'l1_day': l1_day,
                    'l2_time': l2_time,
                    'l2_day': l2_day,
                    'l3_time': l3_time,
                    'l3_day': l3_day,
                    'schedule_type': schedule_type
                })

                business_forecast = [{
                    'schedule_type': 2,
                    'user_tz': 'Eastern (US & Canada)',
                    'frequency': 1
                }, {
                    'schedule_type': 3,
                    'user_tz': 'Pacific (US & Canada)',
                    'frequency': 2
                }]

                model = GeneralProbModel()

                model.fit(training_data)
                p = model.predict(business_forecast)
                expect(True).to.equal(True)