baf8c13aedeb135b38cce9831b1be7034a95d8e9,numerox/report.py,Report,out_of_five,#Report#,157

Before Change

        cols = ["N", "0/5", "1/5", "2/5", "3/5", "4/5", "5/5", "mean/5"]
        df = pd.DataFrame(columns=cols)
        lb = self.lb[round1:round2]
        rounds = lb["round"].unique()
        nan = np.nan
        for r in rounds:
            d = lb[lb["round"] == r]
            if not d["resolved"].any():
                fraction = [0, nan, nan, nan, nan, nan, nan, nan]
            else:
                idx = (d.groupby("user").count()["round"] == 5)
                idx = idx[idx]
                idx = d.user.isin(idx.index)
                d = d[idx]
                d["pass"] = d["live"] < LOGLOSS_BENCHMARK
                s = d.groupby("user").sum()
                rep = s.groupby("pass").count()
                rep = rep["round"].to_frame("count")
                count = rep["count"].sum()
                fraction = 1.0 * rep["count"] / count
                mean = np.dot(fraction, np.array([0, 1, 2, 3, 4, 5]))
                fraction = fraction.tolist()
                fraction.insert(0, count)
                fraction.insert(7, mean)
            df.loc[r] = fraction
        df.loc["mean"] = df.mean()
        df["N"] = df["N"].astype(int)
        return df

After Change

        cols = ["N", "0/5", "1/5", "2/5", "3/5", "4/5", "5/5", "mean/5"]
        df = pd.DataFrame(columns=cols)
        lb = self.lb[round1:round2]
        rounds = np.sort(lb["round"].unique())
        nan = np.nan
        for r in rounds:
            d = lb[lb["round"] == r]
            if not d["resolved"].any():
                fraction = [0, nan, nan, nan, nan, nan, nan, nan]
            else:
                idx = (d.groupby("user").count()["round"] == 5)
                idx = idx[idx]
                idx = d.user.isin(idx.index)
                d = d[idx]
                d["pass"] = d["live"] < LOGLOSS_BENCHMARK
                s = d.groupby("user").sum()
                rep = s.groupby("pass").count()
                rep = rep["round"].to_frame("count")
                count = rep["count"].sum()
                fraction = 1.0 * rep["count"] / count
                mean = np.dot(fraction, np.array([0, 1, 2, 3, 4, 5]))
                fraction = fraction.tolist()
                fraction.insert(0, count)
                fraction.insert(7, mean)
            df.loc[r] = fraction
        df.loc["mean"] = df.mean()
        df["N"] = df["N"].astype(int)
        return df

Instances