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)
