def load_models():
"""
Aim : To load the encoder, the pipeline, the model of our IA
====================================================
Entrie : None
====================================================
Return : the encoder, the pipeline, the model
"""
try:
file = open('my_models/Label_Encoder.pickle', 'rb') # to open the file
Label_encod = pickle.load(file) # to load it in a variable
file.close() # to close it once we've loaded it
file = open('my_models/PipeLine.pickle', 'rb')
pipe = pickle.load(file)
file.close()
file = open('my_models/My_Model_test.pickle', 'rb')
model = pickle.load(file)
file.close()
except:
print("problème dans le chargement des outils de machine learning")
# returning the 3 variables that contain our sklearn features
return Label_encod, pipe, model
def get_datasets_from_path(testing_flag, hchs_or_mesa):
working_directory = get_working_directory(testing_flag, hchs_or_mesa)
dataset_save_path = os.path.join(os.path.dirname(os.getcwd()), "PickledData", hchs_or_mesa)
path_to_embeddings = os.path.join(os.path.dirname(os.getcwd()), "embeddings", hchs_or_mesa)
if testing_flag:
path_to_test_train_split_dict = os.path.join(dataset_save_path, 'reduced_test_train_split_dict.pickle')
# path_to_test_train_split_dict = os.path.join(dataset_save_path, '100_users_reduced_test_train_users.pickle')
else:
path_to_test_train_split_dict = os.path.join(dataset_save_path, 'test_train_split_dict.pickle')
with open(path_to_test_train_split_dict, 'rb') as f:
test_train_split_dict = pickle.load(f)
disease_user_datasets = {}
diseases = dataset_diseases[hchs_or_mesa]
for disease in diseases:
disease_user_dataset_path = os.path.join(dataset_save_path, f'{disease}_user_datasets.pickle')
with open(disease_user_dataset_path, 'rb') as f:
user_dataset = pickle.load(f)
if testing_flag:
reduced_user_dataset = {}
for user, data in user_dataset.items():
if user in test_train_split_dict['train']:
reduced_user_dataset[user] = data
if user in test_train_split_dict['test']:
reduced_user_dataset[user] = data
user_dataset = reduced_user_dataset
disease_user_datasets[disease] = user_dataset
return disease_user_datasets, test_train_split_dict, working_directory, path_to_embeddings
def main1(test_point):
df = feature_engineering_on_app_train_test(test_point)
df_past = get_past_data(int(test_point['SK_ID_CURR'].values))
df = df.join(df_past, how='left', on='SK_ID_CURR')
del df_past
gc.collect()
with open("lgbm_clf_list_7.pkl", "rb") as f:
lgbm_list = pkl.load(f)
with open("train_column7.pkl", "rb") as f:
train_column = pkl.load(f)
gc.collect()
df = fill_the_missing_values(df)
df = calculate_cibil_score(df)
scaler = pkl.load(open('scaler_7.sav', 'rb'))
X = df[train_column]
top5_feat = X[[
'EXT_SOURCE_1', 'PAYMENT_RATE', 'DAYS_BIRTH', 'EXT_SOURCE_3',
'AMT_ANNUITY'
]]
top5_feat['DAYS_BIRTH'] = -1 * top5_feat['DAYS_BIRTH'] / 365
top5_feat.rename(columns={"DAYS_BIRTH": "AGE"}, inplace=True)
st.write('These are the values of top 5 features used in prediction')
st.dataframe(top5_feat)
X = scaler.transform(X)
test_pred_proba = 0
for j in range(0, len(lgbm_list)):
test_pred_proba += lgbm_list[j].predict_proba(
X, num_iteration=lgbm_list[j].best_iteration_)[:, 1] / 10
st.write('Probablility of being a Defaulter: ',
str(round(test_pred_proba[0], 6)))
st.write('Percentage of being a Defaulter: ',
str(round(test_pred_proba[0] * 100, 2)) + '%')
def __init__(self):
with open('binaries/lr_model', 'rb') as input_file:
self.lr_model = pickle.load(input_file)
with open('binaries/count_vectorizer', 'rb') as input_file:
self.count_vectorizer = pickle.load(input_file)
with open('binaries/tfidf_transformer', 'rb') as input_file:
self.tfidf_transformer = pickle.load(input_file)
def load_analize_policies(N, N_sim, verbose, starts):
path = f'examples/data/policies_lambdas{N}.pickle'
with open(path, 'rb') as file:
pols = pickle.load(file)
path = f'examples/data/values_lambdas{N}.pickle'
with open(path, 'rb') as file:
vals = pickle.load(file)
p_space, policies, values = read_pol(N)
distances = {s:{} for s in starts}
choques = {s:{} for s in starts}
fig_d, ax_d = plt.subplots()
fig_c, ax_c = plt.subplots()
for s in starts:
for i in pols.keys():
pol = DMSPolicy(p_space, pols[i], from_matrix=True)
ship = simulate_and_plot(p_space, pol, start=(0, 0), n=N_sim, verbose=verbose, plot=False)
ship.name = i
distances[s][i] = ship.average_obj_fun(mean=True)
choques[s][i] = ship.average_crashes(mean=True)
ax_d.plot(list(distances[s].keys()), list(distances[s].values()), label=f'start:{s}')
ax_c.plot(list(choques[s].keys()), list(choques[s].values()), label=f'start:{s}')
ax_d.set_xlabel(r'$\lambda$')
ax_d.legend()
ax_c.set_xlabel(r'$\lambda$')
ax_c.legend()
plt.show()
def update_model(self, paths):
with open(paths['lr_model'], 'rb') as input_file:
self.lr_model = pickle.load(input_file)
with open(paths['count_vectorizer'], 'rb') as input_file:
self.count_vectorizer = pickle.load(input_file)
with open(paths['tfidf_transformer'], 'rb') as input_file:
self.tfidf_transformer = pickle.load(input_file)
def get_datasets_from_paths(testing_flag):
if testing_flag:
working_directory = 'chapman_testing/'
else:
working_directory = 'chapman/'
if not os.path.exists(working_directory):
os.makedirs(working_directory)
dataset_save_path = os.path.join(os.getcwd(), "PickledData", "chapman")
path_to_patient_to_rhythm_dict = os.path.join(dataset_save_path, 'patient_to_rhythm_dict.pickle')
# paths to user datasets with no nan values
if testing_flag:
path_to_user_datasets = os.path.join(dataset_save_path, 'reduced_four_lead_user_datasets_no_nan.pickle')
path_to_test_train_split_dict = os.path.join(dataset_save_path, 'reduced_test_train_split_dict_no_nan.pickle')
else:
path_to_user_datasets = os.path.join(dataset_save_path, 'four_lead_user_datasets_no_nan.pickle')
path_to_test_train_split_dict = os.path.join(dataset_save_path, "test_train_split_dict_no_nan.pickle")
with open(path_to_user_datasets, 'rb') as f:
user_datasets = pickle.load(f)
print(f'number of patients: {len(user_datasets)}')
with open(path_to_patient_to_rhythm_dict, 'rb') as f:
patient_to_rhythm_dict = pickle.load(f)
with open(path_to_test_train_split_dict, 'rb') as f:
test_train_split_dict = pickle.load(f)
return user_datasets, patient_to_rhythm_dict, test_train_split_dict, working_directory
def load_struct_authors(name_authors_collaborators, name_authors_info):
with open(name_authors_collaborators + '.pickle', 'rb') as fp:
authors_collaborators = pickle.load(fp)
with open(name_authors_info + '.pickle', 'rb') as fp:
authors_info = pickle.load(fp)
return authors_collaborators, authors_info
def get_data(train=True):
# feats = cPickle.load(open(coco_inception_features_path, "rb"), encoding="latin1")
feats = cPickle.load(open('../data/coco_train_v3.pik', "rb"),
encoding="latin1")
feats.update(
cPickle.load(open('../data/coco_val_ins.pik', "rb"),
encoding="latin1"))
sents = []
final_feats = []
filenames = []
js = json.load(open(coco_dataset_path, "r"))
for i, img in enumerate(js["images"]):
if train and img["extrasplit"] == "val":
continue
if (not train) and img["extrasplit"] != "val":
continue
if img["filename"] not in feats:
continue
if train:
for sen in img["sentences"]:
sents.append(sen["rm_style_tokens"])
final_feats.append(feats[img["filename"]])
filenames.append(img["filename"])
else:
sents.append(img["sentences"][0]["rm_style_tokens"])
final_feats.append(feats[img["filename"]])
filenames.append(img["filename"])
final_feats = np.array(final_feats)
data_file = 'cleaned_sents_train.pkl' if train is True else 'cleaned_test_train.pkl'
if os.path.exists(data_file):
with open(data_file, 'rb') as f:
sents = cPickle.load(f)
else:
m = []
sym_spell = SymSpell(max_dictionary_edit_distance=2, prefix_length=3)
dictionary_path = pkg_resources.resource_filename(
"symspellpy", "frequency_dictionary_en_82_765.txt")
# term_index is the column of the term and count_index is the
# column of the term frequency
sym_spell.load_dictionary(dictionary_path, term_index=0, count_index=1)
for i in tqdm(sents, position=0):
l = []
for j in range(len(i)):
t = correct_spell(
i[j].replace('NOUNNOUNNOUN', '').replace(
"PARTPARTPART", "").replace("FRAMENET", "").replace(
"ADJADJADJ", "").replace('INTJINTJINTJ',
'').lower(), sym_spell)
l.append(t)
m.append(l)
sents = m
with open(data_file, 'wb') as f:
cPickle.dump(sents, f)
return final_feats, filenames, sents
def unpickle_dataset():
with open(STATIC_QUALITY_SCORE_PATH, 'rb') as f:
Static_Quality_Score = pickle.load(f)
with open(LEADERS_PATH, 'rb') as f:
Leaders_List = pickle.load(f)
return Static_Quality_Score, Leaders_List
def import_data():
global pentaData
global data
with open('data.p', 'rb') as fp:
data = pickle.load(fp)
with open('pentagram81.p', 'rb') as fp:
pentaData = pickle.load(fp)
json = {
"name": "imported the predictions",
"error": "No error",
}
return jsonify(json)
def load(self, word2index_dic = 'word2index_dic', index2word_dic = 'index2word_dic',
word2count_dic = 'word2count_dic'):
with open('Save/'+word2index_dic+'.p', 'rb') as fp:
self.word2index = pickle.load(fp)
with open('Save/'+index2word_dic+'.p', 'rb') as fp:
self.index2word = pickle.load(fp)
with open('Save/'+word2count_dic+'.p', 'rb') as fp:
self.word2count = pickle.load(fp)
self.num_words = len(self.word2index)
def load_dataset_file(filename):
print(f"Loading {filename}")
with open(filename, "rb") as f:
try:
return pickle.load(f)
except ValueError:
return pickle5.load(f)
def assemble_scores_no_order(hyperparameter_set):
"""
Assumes order of the the model vs age loop doesn't matter.
"""
# hyperparameter_set: wfst or levdist
this_load_args = load_models.gen_all_model_args()
score_store = []
for split, dataset, tags, context, model_type in this_load_args:
this_hyperparameter_folder = hyperparameter_utils.load_hyperparameter_folder(
split, dataset, tags, context, model_type)
search_string = join(
this_hyperparameter_folder,
hyperparameter_set + '_run_models_across_time_*.pkl')
age_paths = glob.glob(search_string)
for this_data_path in age_paths:
#data_df = pd.read_pickle(this_data_path)
with open(this_data_path, "rb") as fh:
data_df = pickle.load(fh)
score_store.append(data_df)
return score_store
def portfolioLabel(self) -> DataInfo:
func_name = sys._getframe().f_code.co_name
result_path = os.path.join(self.local_path, func_name + '.pkl')
if os.path.exists(result_path):
with open(result_path, 'rb') as f:
category_label = pickle.load(f)
else:
# read data
print(
f"{dt.datetime.now().strftime('%X')}: Construction the label pool"
)
data_dict = self.read_data()
price_data = data_dict['AStockData.pkl'][self.Mapping["price"]
['columns']]
ind_exp = data_dict['AStockData.pkl'][self.Mapping["industry"]
['columns']]
stock_mv_data = data_dict['AStockData.pkl'][
self.Mapping["mv"]['columns']] * 10000 # wan yuan->yuan
stock_w_data = data_dict['StockPool.pkl'][self.Mapping['stock_w1']
['columns']]
price_data = price_data.rename(
columns={
PVN.CLOSE_ADJ.value: PVN.CLOSE.value,
PVN.OPEN_ADJ.value: PVN.OPEN.value,
PVN.HIGH_ADJ.value: PVN.HIGH.value,
PVN.LOW_ADJ.value: PVN.LOW.value
})
print(
f"{dt.datetime.now().strftime('%X')}: Calculate stock daily return label"
)
stock_ret_o = self.api.stock_ret(price_data,
return_type=PVN.OPEN.value)
print(
f"{dt.datetime.now().strftime('%X')}: Calculate industry daily weight label"
)
ind_w = self.api.industry_w(stock_w_data,
industry_exposure=ind_exp)
############################################################################################################
# merge labels
print(f"{dt.datetime.now().strftime('%X')}: Merge labels")
category_label = self.merge_labels(
data_ret_open=stock_ret_o,
ind_exp=ind_exp,
mv=stock_mv_data[PVN.LIQ_MV.value],
ind_w=ind_w,
)
# sort
category_label = category_label.sort_index()
category_label.to_pickle(result_path)
dataClass = DataInfo(data=category_label,
data_category=self.__class__.__name__,
data_name=func_name)
return dataClass
def load_data(self, split, bsz):
with open(os.path.join(self.data_dir, f"{split}.bin"), "rb") as fin:
data = pickle.load(fin)['data']
nstep = data.size(0) // bsz
return data[:nstep * bsz].view(bsz, -1)
def from_bytes_gz(bytes_graph: bytes) -> BELGraph:
"""Read a graph from gzipped bytes (the result of pickling the graph).
:param bytes_graph: File or filename to write
"""
with gzip.GzipFile(fileobj=BytesIO(bytes_graph), mode='rb') as file:
return pickle.load(file)
def calculate_cibil_score(df):
#cibil_train=train_data[['3365_LATE_PAYMENT_FLAG_MEAN','CRED_FLAG_LESS_30_MEAN','ABS_YEAR_CREDIT_MAX','UNSEC_LOAN_COUNT_SUM','SEC_LOAN_COUNT_SUM','AMT_REQ_CREDIT_BUREAU_WEEK']].copy()
cibil_test = df[[
'3365_LATE_PAYMENT_FLAG_MEAN', 'CRED_FLAG_LESS_30_MEAN',
'ABS_YEAR_CREDIT_MAX', 'UNSEC_LOAN_COUNT_SUM', 'SEC_LOAN_COUNT_SUM',
'AMT_REQ_CREDIT_BUREAU_WEEK'
]].copy()
scaler_cibil = pkl.load(open('scaler_cibil_7.sav', 'rb'))
cibil_test_std = scaler_cibil.transform(cibil_test)
cibil_test = pd.DataFrame(data=cibil_test_std,
columns=[
'3365_LATE_PAYMENT_FLAG_MEAN',
'CRED_FLAG_LESS_30_MEAN',
'ABS_YEAR_CREDIT_MAX',
'UNSEC_LOAN_COUNT_SUM', 'SEC_LOAN_COUNT_SUM',
'AMT_REQ_CREDIT_BUREAU_WEEK'
])
num_test = (0.1 * cibil_test['UNSEC_LOAN_COUNT_SUM'].copy() +
0.1 * cibil_test['SEC_LOAN_COUNT_SUM'].copy() +
0.05 * cibil_test['ABS_YEAR_CREDIT_MAX'].copy() +
0.25 * cibil_test['CRED_FLAG_LESS_30_MEAN'].copy())
den_test = (0.30 * cibil_test['3365_LATE_PAYMENT_FLAG_MEAN'].copy() +
0.20 * cibil_test['AMT_REQ_CREDIT_BUREAU_WEEK'].copy()) + 1
df.loc[:, 'CIBIL_SCORE'] = (num_test.copy() / den_test.copy())
df.loc[:, 'CIBIL_SCORE'] = df['CIBIL_SCORE'].fillna(0)
return df
def load_pickle(self, name):
"""
Loads the data collection from a pickle file
Parameters
----------
name : string
name of the pickle file that is used to load the data collection
Returns
----------
DataCollection object
data collection that was saved in the pickle file
"""
out_file_dir = os.path.join(DIR_PATH, '..', 'out',
os.path.basename(self.data_folder),
'pickle')
os.makedirs(out_file_dir, exist_ok=True)
out_file_name = os.path.join(out_file_dir, name + '.pkl')
with open(out_file_name, 'rb') as output:
try:
if self.verbose:
print("loading from pickle file ", out_file_name)
self = pickle.load(output)
self.populated = True
output.close()
except EOFError:
print("not found")
pass
return self
def test_bspline_pickle_file(self):
"""Test the custom pickling and un-pickling interface"""
import copy
M = [0, 1, 1, 0]
img1 = sitk.Image([10, 10], sitk.sitkFloat64)
img1.SetOrigin((.01, 5.2))
img1.SetDirection(M)
img1 = sitk.AdditiveGaussianNoise(img1)
img2 = sitk.Image([10, 10], sitk.sitkFloat64)
img2.SetOrigin((.01, 5.2))
img2.SetDirection(M)
img2 = sitk.AdditiveGaussianNoise(img2)
tx = sitk.BSplineTransform([img1, img2], 3)
fname = os.path.join(self.test_dir, "bspline_protocol_default.pickle")
with open(fname, 'wb') as fp:
p = pickle.dump(copy.deepcopy(tx), fp)
with open(fname, 'rb') as fp:
ret = pickle.load(fp)
self.assertEqual(ret, ret, msg="pickle file with default protocol")
def gcs_load_obj(uri):
uri = urlparse(uri)
storage_client = storage.Client()
bucket = storage_client.get_bucket(uri.netloc)
b = bucket.blob(uri.path[1:], chunk_size=None)
obj = pickle.load(io.BytesIO(b.download_as_string()))
return obj
def loadModel(is_display=False, number_words=1):
print("Loading...")
dictionary = corpora.Dictionary.load('dictorionary.gensim')
corpus = pickle.load(open('corpus.pkl', 'rb'))
lda = models.LdaModel.load('model5.gensim')
topics = lda.print_topics(num_words=number_words)
dic_topics = {}
for topic in topics:
split_topic = topic[1].split("*\"")
#split_topic[1] = split_topic.replace("\"", "")
print("-" + str(split_topic))
dic_topics[str(topic[0])] = {
"topic": split_topic[1][:len(split_topic[1]) - 1],
"frecuency": split_topic[0]
}
print(topic)
print(str(dic_topics))
if is_display is True:
# pyLDAvis.enable_notebook()
lda_display = pyLDAvis.gensim.prepare(lda,
corpus,
dictionary,
sort=False)
print("Load")
pyLDAvis.save_html(lda_display, 'display.html')
return dic_topics
def load_mnist(normalize=True, flatten=True, one_hot_label=False):
"""MNISTデータセットの読み込み
Parameters
----------
normalize : 画像のピクセル値を0.0~1.0に正規化する
one_hot_label :
one_hot_labelがTrueの場合、ラベルはone-hot配列として返す
one-hot配列とは、たとえば[0,0,1,0,0,0,0,0,0,0]のような配列
flatten : 画像を一次元配列に平にするかどうか
Returns
-------
(訓練画像, 訓練ラベル), (テスト画像, テストラベル)
"""
if not os.path.exists(save_file):
init_mnist()
with open(save_file, 'rb') as f:
dataset = pickle.load(f)
if normalize:
for key in ('train_img', 'test_img'):
dataset[key] = dataset[key].astype(np.float32)
dataset[key] /= 255.0
if one_hot_label:
dataset['train_label'] = _change_one_hot_label(dataset['train_label'])
dataset['test_label'] = _change_one_hot_label(dataset['test_label'])
if not flatten:
for key in ('train_img', 'test_img'):
dataset[key] = dataset[key].reshape(-1, 1, 28, 28)
return (dataset['train_img'], dataset['train_label']), (dataset['test_img'], dataset['test_label'])
def get_cross_augmented_scores(data_child, prior_child):
'''
Load individual score from using a specific child's fine-tuned prior on the data associated with another child
Args:
data_child: name of the child whose data will be tested
prior_child: named of the child whose prior will be used
Return:
A pandas dataframe of scores
'''
score_path = utils_child.get_cross_path(data_child, prior_child)
try:
raw_scores = pd.read_pickle(score_path)
except:
with open(score_path, "rb") as fh:
data = pickle.load(fh)
path_to_protocol4 = score_path.replace('.pkl','.pkl4')
data.to_pickle(path_to_protocol4)
raw_scores = pd.read_pickle(path_to_protocol4)
raw_scores['cross_type'] = get_cross_type(data_child, prior_child)
raw_scores['data_child'] = data_child
raw_scores['prior_child'] = prior_child
return raw_scores
def train_classifier(features, y, clf):
with open('vectorizer.pkl', 'rb') as f:
vectorizer = cPickle.load(f)
vectors = vectorizer.transform(features)
clf.partial_fit(vectors.toarray(), y, classes=[0, 1])
def load_past():
# with open("df_test.pkl", "rb") as f:
# df_past = pkl.load(f)
zf = zipfile.ZipFile('df_test.zip', 'r')
df_past = pkl.load(zf.open('df_test.pkl'))
df_past['SK_ID_CURR'] = df_past.index
return df_past
def __init__(self, standard=False, feature_subset="all"):
#use if already converted to cartesian
#with open('data/pi0_cartesian_train.pkl', 'rb') as f:
#x = np.array(pickle.load(f), dtype=np.float32)
#Use if not already converted
with open('data/pi0.pkl', 'rb') as f:
xz = np.array(pickle.load(f), dtype=np.float32)
x = cartesian_converter(xz, type='x')
z = cartesian_converter(xz, type='z')
if feature_subset != "all":
x = x[:, feature_subset]
z = z[:, feature_subset]
xwithoutPid = x
self.qt = self.quant_tran(x)
#Commented out because currently ton using Quant trans.
# df_x = pd.DataFrame(self.qt.transform(x)) #Don't know how to do this without first making it a DF
# x_np = df_x.to_numpy() #And then converting back to numpy
# self.x = torch.from_numpy(np.array(x_np))
self.xz = xz
self.x = torch.from_numpy(np.array(x))
self.xwithoutPid = torch.from_numpy(np.array(xwithoutPid))
self.z = torch.from_numpy(np.array(z))
if standard:
self.standardize()
def get_credentials(logger: lg.Logger = None) -> pickle:
"""Get the proper credentials needed to write to the Google spreadsheet."""
creds = None
if osp.exists(GGL_SHEETS_TOKEN):
if logger: logger.info(F"osp.exists({GGL_SHEETS_TOKEN})")
with open(GGL_SHEETS_TOKEN, "rb") as token:
creds = pickle.load(token)
# if there are no (valid) credentials available, let the user log in.
if not creds or not creds.valid:
if logger: logger.info("creds is None or not creds.valid")
if creds and creds.expired and creds.refresh_token:
creds.refresh(Request())
if logger: logger.debug("creds.refresh(Request())")
else:
flow = InstalledAppFlow.from_client_secrets_file(
CREDENTIALS_FILE, SHEETS_RW_SCOPE)
creds = flow.run_local_server()
if logger: logger.debug("creds = flow.run_local_server()")
# save the credentials for the next run
with open(GGL_SHEETS_TOKEN, "wb") as token:
if logger: logger.debug("pickle.dump()")
pickle.dump(creds, token, pickle.HIGHEST_PROTOCOL)
return creds
def launch_jobs(temp_dir: str) -> None:
runs = []
with open(os.path.join(temp_dir, JOB_SPEC_PICKLE), "rb") as f:
job_spec = pickle.load(f) # nosec
singleton_state = job_spec["singleton_state"]
sweep_configs = job_spec["sweep_configs"]
task_function = job_spec["task_function"]
instance_id = _get_instance_id()
sweep_dir = None
for sweep_config in sweep_configs:
with open_dict(sweep_config):
sweep_config.hydra.job.id = (
f"{instance_id}_{sweep_config.hydra.job.num}"
)
setup_globals()
Singleton.set_state(singleton_state)
HydraConfig.instance().set_config(sweep_config)
ray_init_cfg = sweep_config.hydra.launcher.ray_init_cfg
ray_remote_cfg = sweep_config.hydra.launcher.ray_remote_cfg
if not sweep_dir:
sweep_dir = Path(str(HydraConfig.get().sweep.dir))
sweep_dir.mkdir(parents=True, exist_ok=True)
start_ray(ray_init_cfg)
ray_obj = launch_job_on_ray(
ray_remote_cfg, sweep_config, task_function, singleton_state
)
runs.append(ray_obj)
result = [ray.get(run) for run in runs]
_dump_job_return(result, temp_dir)
def load_steam_cache_from_disk():
try:
with open('data.pkl', 'rb') as handle:
global from_word_to_steam_cache
from_word_to_steam_cache = pickle.load(handle)
except EOFError:
from_word_to_steam_cache = {}
