def plot_graph(start_percentage, end_percentage, step_size, data, phi, iters,
delta):
raw_X, y = helpers.get_X_Y(data)
X = phi(raw_X)
plot_data_x = []
plot_data_y = []
while start_percentage <= end_percentage:
plot_data_x.append(start_percentage)
ratio = int(len(data) * (start_percentage) / 100.0)
theta = linear_regression.linear_regression(data[:ratio], phi, iters,
delta)
plot_data_y.append(helpers.MSE(y, X * theta, theta, delta))
start_percentage += step_size
return plot_data_x, plot_data_y
def k_fold_cross_validation(data, phi, k, iters, delta):
raw_X, y = helpers.get_X_Y(data)
X = phi(raw_X)
errors = []
start = 0
eff_k = int((k * len(X)) / 100.0)
for i in range(k):
left_data = data[:start]
right_data = data[start + eff_k:]
modified_data = left_data
modified_data.extend(right_data)
validation_data = X[start:start + eff_k]
validation_labels = y[start:start + eff_k]
theta = linear_regression.linear_regression(modified_data, phi, iters,
delta)
errors.append(
helpers.MSE(validation_labels, validation_data * theta, theta,
delta))
start += eff_k
return [np.mean(errors), np.var(errors)]
# 'label_title':label_title,
# 'label_url':label_url,
# 'label_source':label_source
}
dataframe_out = pandas.DataFrame(data, index=timestamp)
dataframe_out.index.name = "timestamp"
dataframe_out = dataframe_out[[
'value', 'prediction_training', 'prediction', 'label'
# 'label_title',
# 'label_url',
# 'label_source'
]]
out_file = helpers.get_result_file_name(f, output_directory, m)
out_file_name = out_file[:-4] + ".metric-" + metric + ".csv"
dataframe_out.to_csv(out_file_name)
new_jsonf_name = out_file_name[:-3] + "json"
shutil.copyfile(jsonf_name, new_jsonf_name)
testing_value = value[testing_start:]
mse = helpers.MSE(testing_value, testing_prediction)
output_files.append(
helpers.get_result_dump_name(out_file_name) + "," + str(mse) +
"," + params)
helpers.dump_results(output_files, output_directory, m)
print("##### [" + m + "]" + str(count) +
" CSV input File processed #####")
count += 1
print("##### " + m + " done ! #####")
print("All Done !")
