def test_for_complete_df_empty(self):
"""
test to see if added times dataframe is empty
"""
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
self.assertGreater(benchmark_df.shape[0], 0)
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)
def test_for_benchmark_df_empty(self):
"""
test to see if benchmark dataframe is empty
"""
benchmark_df = rc.get_benchmark_data()
self.assertGreater(benchmark_df.shape[0], 0)