[[2022__CCGrid__Generic and Robust Performance Diagnosis via Causal Inference for OLTP Database Systems|CauseRank]]における、リソースメトリクスに対するインフラメトリクスの左時間シフトによる[[ピアソン相関係数|相関係数]]の計算方法。 # me Can you code the below description in Python? When it comes to the node representing the infrastructure metric group, we calculate u through the correlation between each infrastructure metric in this node and each key metric in the key metric group. In one failure, the trends of abnormal metrics are similar so that the correlation can be utilized as one of the foundations for root cause judgment. The data length used for Correlation(x,k) is lcorr + ltest where lcorr denotes the historical data length for correlation coefficient calculation. Considering that when a failure occurs, it will take a certain period of time for the failure to propagate from the root cause to the key metrics. Therefore, when calculating Correlation(x,k), we actively keep the time window of key metrics stationary and shift that of infrastructure metrics to the left step by step and then calculate the correlation. That is, for 0 ≤ i ≤ lp, we calculate the Pearson correlation coefficient of x with the time period [talert − lcorr − i, talert + ltest − i] and k with the time period [talert − lcorr , talert + ltest ] in a loop, where lp is the maximum failure propagation time and talert is the start time of the alert as stated in Sec. III-A. The maximum value obtained is regarded as the final Correlation(x, k). # ai Based on your description, I will assume that we have the data for the infrastructure metrics and the key metrics stored in two pandas dataframes, `df_infra` and `df_key`, both indexed by time. The columns of these dataframes represent different metrics. The `t_alert` is the start time of the alert, `l_corr` is the historical data length for correlation coefficient calculation, `l_test` is the length of test data, and `l_p` is the maximum failure propagation time. In order to calculate the correlation, we can use the `corr` method available in pandas, which calculates the Pearson correlation coefficient by default. Here's how you can implement it: ```python import pandas as pd from scipy.stats import pearsonr def calculate_correlation(df_infra, df_key, t_alert, l_corr, l_test, l_p): max_correlation = {} for infra_metric in df_infra.columns: for key_metric in df_key.columns: max_corr = -1 # Initialize max correlation for i in range(l_p + 1): infra_data = df_infra.loc[(t_alert - pd.Timedelta(l_corr + i, 'D')):(t_alert + pd.Timedelta(l_test - i, 'D')), infra_metric] key_data = df_key.loc[(t_alert - pd.Timedelta(l_corr, 'D')):(t_alert + pd.Timedelta(l_test, 'D')), key_metric] if not infra_data.empty and not key_data.empty: corr = pearsonr(infra_data, key_data)[0] if corr > max_corr: max_corr = corr max_correlation[(infra_metric, key_metric)] = max_corr return max_correlation ``` In this code, `calculate_correlation` function takes as input the two dataframes and the parameters `t_alert`, `l_corr`, `l_test`, and `l_p`. It iterates over all pairs of infrastructure and key metrics, and for each pair, it calculates the Pearson correlation coefficient for different time windows. The time window for the key metric is kept constant, while the time window for the infrastructure metric is shifted to the left step by step. The maximum correlation coefficient for each pair is stored in the dictionary `max_correlation`. Note that the time windows are calculated using the `pd.Timedelta` function, which creates a time delta object representing a duration. The `'D'` unit is used to represent days. Please adapt the code as necessary to fit your specific data and requirements.