def analyze_models_from_cache(file_paths):
models = []
for f in file_paths:
models.append(pickle.load(open(f, 'rb')))
print('Available Models: ', len(models))
models = [m for m in models if m.vm30 is not None]
print('use-able Available Models: ', len(models))
#import pdb
#ipdb.set_trace()
models_bag = db.from_sequence(models, npartitions=8)
list(models_bag.map(model_analysis).compute())
def run_on_allen(number_d_sets=2):
try:
with open('allen_data.pkl', 'rb') as f:
data_sets = pickle.load(f)
except:
data_sets = get_data_sets(number_d_sets=number_d_sets)
with open('allen_data.pkl', 'wb') as f:
pickle.dump(data_sets, f)
models = []
for data_set in data_sets:
models.append(allen_to_model_and_features(data_set))
models = [mod for mod in models if mod is not None]
models = [mod[0] for mod in models]
three_feature_sets = []
for model in models:
#if model is not None:
model_analysis(model)
def run_on_allen(number_d_sets=2):
try:
with open('allen_data.pkl', 'rb') as f:
data_sets = pickle.load(f)
except:
data_sets = get_data_sets(number_d_sets=number_d_sets)
with open('allen_data.pkl', 'wb') as f:
pickle.dump(data_sets, f)
models = []
for data_set in data_sets:
models.append(allen_to_model_and_features(data_set))
models = [mod for mod in models if mod is not None]
models = [mod[0] for mod in models]
three_feature_sets = []
for model in models:
#if model is not None:
temp_path = str('allen_three_feature_folder') + str('/') + str(
model.name) + str('.p')
if not os.path.exists(temp_path):
model_analysis(model)
def analyze_models_from_cache(file_paths):
models = []
for f in file_paths:
models.append(pickle.load(open(f, 'rb')))
models_bag = db.from_sequence(models, npartitions=8)
list(models_bag.map(model_analysis).compute())