code stringlengths 3 6.57k |
|---|
np.where(diff_arr == diff_arr.min() |
print(diff_near_idx) |
np.array([user_ts[n_id] for n_id in diff_near_idx[0]]) |
len(usr_vals) |
usr_vals.mean(axis=0) |
print("vavg" + str(value) |
print("v" + str(value) |
attr_list.append(value) |
pd.DataFrame(user_ts) |
LinearRegression(normalize=True) |
to_numpy() |
to_numpy() |
ravel() |
reg_model.fit(user_x, user_s03) |
intercepts_list.append(reg_model.intercept_) |
coeff_list.append(reg_model.coef_) |
np.array(attr_list) |
print(numpy_attr_list) |
mean(axis=0) |
print(avg_np_attr_list) |
np.array(coeff_list) |
print(numpy_coeff_list) |
print(numpy_coeff_list.mean(axis=0) |
np.multiply(numpy_coeff_list[:, 0].mean() |
np.multiply(numpy_coeff_list[:, 1].mean() |
np.multiply(numpy_coeff_list[:, 2].mean() |
np.multiply(numpy_coeff_list[:, 3].mean() |
np.asarray(intercepts_list) |
mean() |
preds.append(y) |
print(preds) |
create_y_labels(test_data, prediction_at, method="mean") |
list() |
range(0, len(test_data) |
tsg_data.get_usr_mday_ts_predict(int(test_data.iloc[i]["user_id"]) |
print(len(test_ts_test1) |
len(test_ts_test1) |
y_test.append(test_ts_test1[prediction_at - 1][2]) |
len(test_ts_test1) |
y_test.append(test_ts_test1[len(test_ts_test1) |
get_pred_ref_points(user_id, ndays, method="mean") |
tsg_data.get_usr_mday_ts_predict(user_id) |
round(len(user_ts_idx) |
tolist() |
tolist() |
range(0, len(test_data) |
int(test_data.iloc[i]["user_id"]) |
print("User- Id ", user_id) |
get_pred_ref_points(user_id, ndays, method=method) |
create_y_labels(X_test, preds, method="mean") |
print("Evaluating for the fold-" + str(fold_count) |
str(prediction_at_list) |
str(prediction_at_list) |
str(method) |
str(prediction_at_list) |
str(prediction_at_list) |
str(method) |
print("Computing RMSE for weighted average based predictions on the User -- " + str(user_id) |
print("---------------------------------------------------------------") |
out_writer.write("---------------------------------------------------------------\n") |
print("RMSE -- ", np.sqrt(mean_squared_error(y_labels, pred_weighted_average) |
out_writer.write("RMSE -- " + str(np.sqrt(mean_squared_error(y_labels, pred_weighted_average) |
csv_out_writer.write("".join(str(user_id) |
str(np.sqrt(mean_squared_error(y_labels, pred_weighted_average) |
str(y_labels[0]) |
str(y_labels[1]) |
str(y_labels[2]) |
str(pred_weighted_average[0]) |
str(pred_weighted_average[1]) |
str(pred_weighted_average[2]) |
csv_out_writer.write("".join(str(user_id) |
str(np.sqrt(mean_squared_error(y_labels, pred_weighted_average) |
str(y_labels[0]) |
str(y_labels[1]) |
str(y_labels[2]) |
str(pred_weighted_average[0]) |
str(pred_weighted_average[1]) |
str(pred_weighted_average[2]) |
print("-----------------------------------------------------------------------------") |
out_writer.write("---------------------------------------------------------------\n") |
format(str("weighted_distance" + str(wt_dist_flag) |
str("linear_regression") |
str(user_id) |
format(str("weighted_distance" + str(wt_dist_flag) |
str("linear_regression") |
str(user_id) |
print("RMSE -- ", np.sqrt(mean_squared_error(y_labels, pred_lr) |
out_writer.write("RMSE -- " + str(np.sqrt(mean_squared_error(y_labels, pred_lr) |
print("---------------------------------------------------------------") |
out_writer.write("---------------------------------------------------------------\n") |
csv_out_writer.write("".join(str(user_id) |
str(np.sqrt(mean_squared_error(y_labels, pred_lr) |
str("lr") |
str(y_labels[0]) |
str(y_labels[1]) |
str(y_labels[2]) |
str(pred_lr[0]) |
str(pred_lr[1]) |
csv_out_writer.write("".join(str(user_id) |
str(np.sqrt(mean_squared_error(y_labels, pred_lr) |
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