code stringlengths 3 6.57k |
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str("lr") |
str(y_labels[0]) |
str(y_labels[1]) |
str(y_labels[2]) |
str(pred_lr[0]) |
str(pred_lr[1]) |
TimeSeriesGroupProcessing(method=eval_method) |
properties.quest_comb.items() |
print("Building NN for the question combination -- " + str(key) |
smf.initial_processing(key, val, append_synthethic=False) |
save_data_objs(combined_df, key) |
open("".join("output/output_" + str(key) |
str(eval_method) |
open("".join("output/output_" + str(key) |
str(eval_method) |
filter_train_ids(x) |
print(x) |
filter_test_ids(x) |
print(x) |
to_list() |
combined_df.apply(filter_train_ids, axis=1, result_type="broadcast") |
dropna() |
apply(int) |
utility.save_model("".join(key + "/" + key + "_train_stat_q_data") |
to_list() |
combined_df.apply(filter_test_ids, axis=1, result_type="broadcast") |
dropna() |
apply(int) |
X_test_data_ui.to_dict("r") |
utility.save_data("".join("test_data_ui_" + key) |
HEOM(X.to_numpy() |
pdist(X.to_numpy() |
sim_matrix.mean() |
NearestNeighbors(n_neighbors=5, metric=heom.heom_distance, radius=mean_heom_distance) |
knn.fit(X.iloc[:, 1:]) |
knn.kneighbors(test.to_numpy() |
utility.save_model("".join(key + "/" + "knn_static") |
utility.save_model("".join(key + "/" + "train_sim_data.pckl") |
out_writer.close() |
csv_out_writer.close() |
initial_processing() |
print("Building KNN for the question combination -- " + str("overall") |
save_data_objs(combined_df, "overall") |
filter_train_ids(x) |
print(x) |
filter_test_ids(x) |
print(x) |
to_list() |
combined_df.apply(filter_train_ids, axis=1, result_type="broadcast") |
dropna() |
apply(int) |
utility.save_model("".join("overall" + "/" + "overall" + "_train_stat_q_data") |
to_list() |
combined_df.apply(filter_test_ids, axis=1, result_type="broadcast") |
dropna() |
apply(int) |
X_test_data_ui.to_dict("r") |
utility.save_data("test_data_ui_x_test", test_data) |
open("output/overall_output_folds_" + str(eval_method) |
open("output/overall_output_folds_" + str(eval_method) |
HEOM(X.to_numpy() |
pdist(X.to_numpy() |
sim_matrix.mean() |
NearestNeighbors(n_neighbors=5, metric=heom.heom_distance, radius=mean_heom_distance) |
knn.fit(X.to_numpy() |
knn.kneighbors(test.to_numpy() |
out_writer.close() |
csv_out_writer.close() |
utility.save_model("".join("overall/" + "knn_static") |
utility.save_model("".join("overall" + "/" + "train_sim_data.pckl") |
splitData(dataset, test_user_ids) |
isin(test_user_ids) |
isin(test_user_ids) |
save_ts_objs(train, test, location_name) |
os.path.isdir(properties.model_location + location_name) |
os.makedirs(properties.model_location + location_name) |
utility.save_model("".join(location_name + "/" + location_name + "_train_data") |
utility.save_model("".join(location_name + "/" + location_name + "_test_data") |
print(traceback.print_exc() |
ml_ts.process_data(grouping="day") |
np.zeros((X.shape[0], X.shape[0]) |
range(0, len(X) |
range(0, len(X) |
ml_ts.compute_dist(X[:, 1][i], X[:, 1][j]) |
pd.DataFrame(C) |
C_df.to_csv("sim_ema.csv") |
sum(C_df.mean() |
len(C_df) |
user_ids.append(val[0]) |
splitData(C_df, test_user_ids) |
save_ts_objs(train_data, test_data, "dynamic_ts") |
open("".join("output/output_ema_" + str(eval_method) |
open("".join("output/output_ema_" + str(eval_method) |
NearestNeighbors(n_neighbors=5, metric="precomputed", radius=threshold_distance) |
knn_ema.fit(train_data[train_data.index]) |
knn_ema.kneighbors(test_data[train_data.index], n_neighbors=5) |
out_writer.close() |
csv_out_writer.close() |
utility.save_model("".join("dynamic_ts" + "/" + "dynamic_ts" + "_knn") |
Copyright (c) |
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