def evaluate_cores(model, data, n_samples, stride=1000, printer=False):
"""
evaluate multicore model training time
"""
N = [n for n in range(stride, (n_samples+stride), stride)] # the number of samples we're using, stride of 1000
cores = os.cpu_count() # get the number of cores
model_dict = {f"model_{n}": model(n_jobs=n) for n in range(1, cores+1)} # contruct model:instance pairs
data_dict = {model: [] for model in model_dict.keys()} # prep data for storage
if printer: print("Starting evaluation...")
for n in N:
print(f"\n n={n}")
sample = bootstrap(data.copy(), n=n, to_df=True) # get bootstrap sample data
for k in data_dict.keys():
if printer: print(f"\tTraining model {k}")
start = time.time()
data_dict[k].append(test_model(model_dict[k], sample)) # add times to dict
if printer: print(f"\t - mean fit time: {(time.time() - start):.2f} s")
data_dict['N'] = N
df = pd.DataFrame.from_dict(data_dict)
return df