DS20-WeakAL/seaborn_barcharts.py at master · jgonsior/DS20-WeakAL · GitHub

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import numpy as np
import matplotlib

#  matplotlib.use("Agg")
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd

sns.set(font_scale=1, rc={"text.usetex": False}, style="ticks", font="STIXGeneral")
sns.set_palette("tab10")


def show_values_on_bars(axs, h_v="v", space_x=5.9, space_y=-0.25):
    def _show_on_single_plot(ax):
        if h_v == "v":
            for p in ax.patches:
                _x = p.get_x() + p.get_width() / 2
                _y = p.get_y() + p.get_height()
                value = "{.2f}".format(p.get_height())
                ax.text(_x, _y, value, ha="center")
        elif h_v == "h":
            for p in ax.patches:
                _x = p.get_x() + float(space_x)
                _y = p.get_y() + p.get_height() + float(space_y)
                value = "{0:.2f}".format(float(p.get_width()))
                ax.text(_x, _y, value, ha="left", color="white")

    if isinstance(axs, np.ndarray):
        for idx, ax in np.ndenumerate(axs):
            _show_on_single_plot(ax)
    else:
        _show_on_single_plot(axs)


df_200 = pd.DataFrame(
    {
        "labels": ["saved human effort", "test accuracy", "combined score"],
        "percentage": [93.63, 69.68, 79.90],
    }
)

df_ibn_sina = pd.DataFrame(
    {
        "labels": ["saved human effort", "test accuracy", "combined score"],
        "percentage": [97.83, 96.66, 97.24],
    }
)

df_1500 = pd.DataFrame(
    {
        "labels": ["saved human effort", "test accuracy", "combined score"],
        "percentage": [55.30, 86.40, 67.43],
    }
)


df_comparison_weak = pd.DataFrame(
    {
        "labels": ["saved human effort", "test accuracy", "combined score"],
        "No Weak": [0, 86.71, 0],
        "WeakClust": [65.03, 86.32, 73.81],
        "WeakCert": [51.14, 86.26, 64.21],
        "Both": [55.3, 86.4, 67.43],
    }
)

df_comparison_all = pd.DataFrame(
    {
        "labels": [
            "saved human effort",
            "test accuracy",
            "combined score",
            "global score",
        ],
        "DWTC": [52.8, 85.7, 65.35, 78.3],
        "IBN_SINA": [85.76, 91.32, 88.46, 92.35],
        "HIVA": [97.83, 96.66, 97.24, 92.29],
        "ORANGE": [99.31, 98.11, 98.7, 91.72],
        "SYLVA": [98.28, 98.81, 98.54, 96.38],
        "ZEBRA": [96.73, 95.90, 96.32, 92.41],
    }
)


def create_barplot(df, title, aggregated=False, value_vars=[], figsize=None):
    if aggregated:
        fig = plt.figure(figsize=figsize)

        df = df.set_index("labels")
        df = df.T
        df = df.reset_index()
        print(df)
        df = pd.melt(df, value_vars=value_vars, id_vars=["index"],)
        print(df)
        print(df["labels"].unique().tolist())
        for index, label in enumerate(df["labels"].unique()):
            print(100 + 10 * len(df["labels"].unique()) + 1 * (index + 1))
            ax = fig.add_subplot(
                100 + 10 * len(df["labels"].unique()) + 1 * (index + 1)
            )
            filtered_df = df.loc[df["labels"] == label]
            print(filtered_df)
            b = sns.barplot(x="value", y="index", data=filtered_df)
            b.spines["top"].set_visible(False)
            b.spines["right"].set_visible(False)
            b.set(xlabel=label)
            b.set(ylabel=None)
            b.set(xticks=[0, 20, 40, 60, 80, 100])
            if index != 0:
                b.set(yticks=[])
            show_values_on_bars(b, "h", space_x=2, space_y=-0.19)
            #  plt.clf()
    else:
        fig = plt.figure(figsize=(2.8, 2.0))
        b = sns.barplot(x="percentage", y="labels", data=df)
        b.set(ylabel=None)
        b.set(xticks=[0, 50, 100])

    if not aggregated:
        b.spines["top"].set_visible(False)
        b.spines["right"].set_visible(False)
        #  b.spines["bottom"].set_visible(False)
        #  b.spines["left"].set_visible(False)

        #  b.set_yticklabels(b.get_yticklabels(), weight="bold")

        show_values_on_bars(b, "h")
    plt.subplots_adjust(hspace=0.05)
    plt.tight_layout()
    #  plt.show()
    plt.savefig(
        "/home/julius/win_transfer/ds-active_learning/fig/" + title + ".pdf",
        verbose=True,
    )
    plt.clf()


#  create_barplot(df_1500, "dwtc_1500")
#  create_barplot(df_200, "dwtc_200")
#  create_barplot(df_ibn_sina, "dwtc_ibn_sina")
#  create_barplot(
#  df_comparison_all,
#  "compare_all",
#  True,
#  ["saved human effort", "test accuracy", "combined score", "global score",],
#  (11, 2.4),
#  )
create_barplot(
    df_comparison_weak,
    "compare_weak",
    True,
    ["saved human effort", "test accuracy", "combined score",],
    (6, 2),
)