code stringlengths 3 6.57k |
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get_deploy_order(contracts_to_deploy, compiled_contracts) |
compute_direct_dependency_graph(compiled_contracts.values() |
compute_deploy_order(dependency_graph) |
compute_recursive_contract_dependencies(contract_name, dependency_graph) |
all_deploy_dependencies.union(contracts_to_deploy) |
common_processing(df) |
apply(lambda x: x / 100) |
apply(lambda x: x / 100) |
apply(lambda x: x / 100) |
df.apply(smf.create_cols_family_hist, axis=1) |
get_common_cols(col1, col2) |
set(col1) |
intersection(col2) |
initial_processing() |
pd.read_pickle(properties.data_location + "/input_pckl/" + "3_q.pckl") |
filter_age(x) |
isinstance(x, int) |
value_counts() |
head(1) |
apply(filter_age) |
tschq.drop(["questionnaire_id", "created_at"], axis=1, inplace=True) |
pd.read_pickle("data/input_pckl/4_q.pckl") |
hq.isna() |
sum(axis=0) |
hq.drop(["hq05", "hq06"], axis=1, inplace=True) |
hq.set_index("user_id") |
tschq.join(hq_df.iloc[:, 2:], on="user_id") |
apply(lambda x: x / 100) |
apply(lambda x: x / 100) |
apply(lambda x: x / 100) |
df.apply(smf.create_cols_family_hist, axis=1) |
df.drop(drop_cols, axis=1, inplace=True) |
infer_objects() |
infer_objects() |
tolist() |
tolist() |
columns.get_loc(val) |
columns.get_loc(val) |
save_data_objs(df, quest_cmbs="all") |
os.path.isdir(properties.model_location + quest_cmbs) |
os.makedirs(properties.model_location + quest_cmbs) |
utility.save_model("".join(quest_cmbs + "/" + quest_cmbs + "_stat_q_data") |
print(traceback.print_exc() |
weighted_average(distress_list) |
np.asarray(distress_list, dtype=float) |
mean() |
compute_weighted_avg(n_idx, encoded_data, pred_at_list, method="mean", dist_nn=None, wt_flag=False) |
list() |
list() |
tsg_data.get_usr_mday_ts_predict(int(u_id) |
print("{}, {} Values ".format(int(pval) |
int(u_id) |
len(user_ts) |
int(pval) |
int(pval) |
len(user_ts) |
int(pval) |
len(user_ts) |
distress_list.append(value) |
print("Calling by weighted distance prediction for distress") |
preds.append(weighted_distance_prediction(distress_list, dist_nn) |
print("Calling weighted average to predict distress") |
preds.append(weighted_average(distress_list) |
weighted_distance_prediction(p_preds, distance) |
np.divide(1, distance) |
np.sum(np.multiply(p_preds, inv_dist) |
np.sum(inv_dist) |
list() |
list() |
list() |
list() |
tsg_data.get_usr_mday_ts_predict(int(u_id) |
np.abs(np.subtract(point, user_ts[:, 1]) |
np.where(diff_arr == diff_arr.min() |
print("minimum to the time point is at -- ", diff_near_idx) |
LinearRegression(normalize=True) |
reg_fit.fit(X, y) |
reg_fit.predict(np.asarray(point) |
reshape(1, -1) |
nn_preds.append(nn_pred[0][0]) |
intercepts_list.append(reg_fit.intercept_) |
coeff_list.append(reg_fit.coef_) |
print("Predicting the value of s03 for the user by a weighted average weighted by distance") |
preds.append(weighted_distance_prediction(nn_preds, dist_nn) |
print("The equation to estimate s03 for the user is {}".format("".join(str(np.asarray(coeff_list) |
mean() |
str(np.asarray(intercepts_list) |
mean() |
np.multiply(np.asarray(coeff_list) |
mean() |
np.asarray(intercepts_list) |
mean() |
preds.append(y) |
compute_linear_regression(test_nn, encoded_data, pred_list, method="mean") |
list() |
list() |
list() |
list() |
tsg_data.get_m_day_ts_enumerate(int(11) |
np.abs(np.subtract(point, user_ts[:, 1]) |
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