def should_create_a_sample_that_meets_business_forecast_criteria( self): user_tz = ['Eastern (US & Canada)', 'Pacific (US & Canada)'] l1_time = [9, 9] l2_time = [9, 9] l3_time = [None, 9] training_data = pd.DataFrame({ 'user_tz': user_tz, 'l1_time': l1_time, 'l2_time': l2_time }) business_forecast = [{ 'schedule_type': 2, 'timezone': 'Eastern (US & Canada)', 'frequency': 1 }, { 'schedule_type': 3, 'timezone': 'Pacific (US & Canada)', 'frequency': 2 }] dumb_model = DumbModel() dumb_model.fit(training_data) schedule = dumb_model.generate_sample_schedule( business_forecast) _sum = schedule._table_df.sum().sum() expect(_sum).to.equal(8)
def should_predict_with_bins(self): dumb_model = DumbModel() business_forecast = [{'schedule_type': 2, 'timezone': 'Eastern (US & Canada)', 'frequency': 1 }, {'schedule_type': 3, 'timezone': 'Pacific (US & Canada)', 'frequency': 2 }] p = dumb_model.predict(business_forecast) expect(round(p[0] + p[1] + p[2] + p[3] + p[4] + p[5])).to.equal(8)
def should_predict_with_bins(self): dumb_model = DumbModel() business_forecast = [{ 'schedule_type': 2, 'timezone': 'Eastern (US & Canada)', 'frequency': 1 }, { 'schedule_type': 3, 'timezone': 'Pacific (US & Canada)', 'frequency': 2 }] p = dumb_model.predict(business_forecast) expect(round(p[0] + p[1] + p[2] + p[3] + p[4] + p[5])).to.equal(8)
def should_create_a_sample_that_meets_business_forecast_criteria(self): user_tz = ['Eastern (US & Canada)', 'Pacific (US & Canada)'] l1_time = [9, 9] l2_time = [9, 9] l3_time = [None, 9] training_data = pd.DataFrame({ 'user_tz': user_tz, 'l1_time': l1_time, 'l2_time': l2_time }) business_forecast = [{'schedule_type': 2, 'timezone': 'Eastern (US & Canada)', 'frequency': 1 }, {'schedule_type': 3, 'timezone': 'Pacific (US & Canada)', 'frequency': 2 }] dumb_model = DumbModel() dumb_model.fit(training_data) schedule = dumb_model.generate_sample_schedule(business_forecast) _sum = schedule._table_df.sum().sum() expect(_sum).to.equal(8)