def print_stat(data_frame, real_column, prediction_column):
data_frame = data_frame.sort_values(real_column)
pearson = pearson_corr(data_frame[real_column].tolist(),
data_frame[prediction_column].tolist())
spearman = spearman_corr(data_frame[real_column].tolist(),
data_frame[prediction_column].tolist())
rmse_value = rmse(data_frame[real_column].tolist(),
data_frame[prediction_column].tolist())
mae = mean_absolute_error(data_frame[real_column].tolist(),
data_frame[prediction_column].tolist())
textstr = 'RMSE=%.4f\nMAE=%.4f\nPearson Correlation=%.4f\nSpearman Correlation=%.4f' % (
rmse_value, mae, pearson, spearman)
print(textstr)
def draw_scatterplot(data_frame, real_column, prediction_column, path, topic):
data_frame = data_frame.sort_values(real_column)
sort_id = list(range(0, len(data_frame.index)))
data_frame['id'] = pd.Series(sort_id).values
data_frame = fit(data_frame, real_column)
data_frame = fit(data_frame, prediction_column)
pearson = pearson_corr(data_frame[real_column].tolist(),
data_frame[prediction_column].tolist())
spearman = spearman_corr(data_frame[real_column].tolist(),
data_frame[prediction_column].tolist())
rmse_value = rmse(data_frame[real_column].tolist(),
data_frame[prediction_column].tolist())
mae = mean_absolute_error(data_frame[real_column].tolist(),
data_frame[prediction_column].tolist())
textstr = 'RMSE=%.4f\nMAE=%.4f\nPearson Correlation=%.4f\nSpearman Correlation=%.4f' % (
rmse_value, mae, pearson, spearman)
plt.figure()
ax = data_frame.plot(kind='scatter',
x='id',
y=real_column,
color='DarkBlue',
label='z_mean',
title=topic)
ax = data_frame.plot(kind='scatter',
x='id',
y=prediction_column,
color='DarkGreen',
label='predicted z_mean',
ax=ax)
ax.text(0.5 * data_frame.shape[0],
min(min(data_frame[real_column].tolist()),
min(data_frame[prediction_column].tolist())),
textstr,
fontsize=10)
fig = ax.get_figure()
fig.savefig(path)