def test_get_all_pricing_one_instance(self):
price_df = aws_pricing.get_instance_pricing(['t3.large'])
self.assertIsInstance(price_df, pd.DataFrame)
columns = list(price_df.columns)
expected_columns = [
'instance_type', 'region', 'spot_price', 'on_demand_price'
]
self.assertEquals(columns, expected_columns)
def test_spot_less_than_on_demand(self):
instance_type_list = ['c5.18xlarge', 'm5a.large', 'c5.xlarge']
price_df = aws_pricing.get_instance_pricing(instance_type_list)
spot_prices = price_df[aws_pricing.DF_COL_SPOT_PRICE]
on_demand_prices = price_df[aws_pricing.DF_COL_ON_DEMAND_PRICE]
compare_results = spot_prices <= on_demand_prices
self.assertTrue(np.all(compare_results))
def test_get_all_pricing_multiple_instance(self):
instance_type_list = ['c5.18xlarge', 'm5a.large', 'c5.xlarge']
price_df = aws_pricing.get_instance_pricing(instance_type_list)
self.assertIsInstance(price_df, pd.DataFrame)
columns = list(price_df.columns)
expected_columns = [
'instance_type', 'region', 'spot_price', 'on_demand_price'
]
instance_types_df = price_df[aws_pricing.DF_COL_INSTANCE_TYPE].unique()
self.assertEquals(columns, expected_columns)
self.assertCountEqual(instance_type_list, instance_types_df)
def add_estimated_price(df):
"""
This function adds the spot and on-demand pricing to the dataframe
"""
instance_types = df["instance_type"].tolist()
price = ap.get_instance_pricing(instance_types)
complete_df = pd.merge(df, price, on="instance_type")
complete_df["est_cost_spot_price"] = \
complete_df["estimated_time_aws"]*complete_df["spot_price"]/3600
complete_df["est_cost_on_demand_price"] = \
complete_df["estimated_time_aws"]*complete_df["on_demand_price"]/3600
return complete_df
def test_get_all_pricing_multiple_instance(self):
instance_type_list = ['c5.18xlarge', 'm5a.large', 'c5.xlarge']
price_df = aws_pricing.get_instance_pricing(instance_type_list)
self.assertIsInstance(price_df, pd.DataFrame)
columns = list(price_df.columns)
expected_columns = [
aws_pricing.DF_COL_INSTANCE_TYPE, aws_pricing.DF_COL_REGION,
aws_pricing.DF_COL_SPOT_PRICE, aws_pricing.DF_COL_ON_DEMAND_PRICE
]
instance_types_df = price_df[aws_pricing.DF_COL_INSTANCE_TYPE].unique()
self.assertEquals(columns, expected_columns)
self.assertCountEqual(instance_type_list, instance_types_df)
def test_add_estimated_price(self):
"""
This function tests adding the spot and on-demand pricing
to the dataframe
"""
benchmark_df = rc.get_benchmark_data()
times, percents = [2, 4, 6], [1, 5, 10]
est_time_user = tt.find_total_time(times, percents)
user_benchmark = br.run_benchmark()
est_time_aws = benchmark_df[['runtime']] \
/ user_benchmark * est_time_user[0]
benchmark_df["estimated_time_aws"] = est_time_aws
instance_types = benchmark_df["instance_type"].tolist()
price = ap.get_instance_pricing(instance_types)
complete_df = pd.merge(benchmark_df, price, on="instance_type")
complete_df["est_cost_spot_price"] = \
complete_df["estimated_time_aws"] \
* complete_df["spot_price"] / 3600
complete_df["est_cost_on_demand_price"] = \
complete_df["estimated_time_aws"] \
* complete_df["on_demand_price"] / 3600
self.assertGreater(complete_df.shape[0], 0)