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| """Plot pandas.DataFrame with DBSCAN clustering.""" | |
| # pylint: disable=invalid-name, too-many-arguments | |
| import numpy as np | |
| import pandas as pd | |
| import matplotlib | |
| import matplotlib.pyplot as plt | |
| import seaborn as sns | |
| from sklearn.cluster import DBSCAN | |
| from fastlid import fastlid | |
| import logzero | |
| from logzero import logger | |
| from radiobee.cmat2tset import cmat2tset | |
| # turn interactive when in ipython session | |
| _ = """ | |
| if "get_ipython" in globals(): | |
| plt.ion() | |
| else: | |
| plt.switch_backend("Agg") | |
| # """ | |
| logzero.loglevel(20) # 10: debug on | |
| fastlid.set_languages = ["en", "zh"] | |
| # fmt: off | |
| def plot_cmat( | |
| # df_: pd.DataFrame, | |
| cmat: np.ndarray, | |
| eps: float = 10, | |
| min_samples: int = 6, | |
| # ylim: int = None, | |
| xlabel: str = "zh", | |
| ylabel: str = "en", | |
| backend: str = "Agg", | |
| showfig: bool = False, | |
| ): | |
| # ) -> plt: | |
| # fmt: on | |
| """Plot df with DBSCAN clustering. | |
| Args: | |
| df_: pandas.DataFrame, with three columns columns=["x", "y", "cos"] | |
| Returns: | |
| matplotlib.pyplot: for possible use in gradio | |
| plot_df(pd.DataFrame(cmat2tset(smat), columns=['x', 'y', 'cos'])) | |
| df_ = pd.DataFrame(cmat2tset(smat), columns=['x', 'y', 'cos']) | |
| # sort 'x', axis 0 changes, index regenerated | |
| df_s = df_.sort_values('x', axis=0, ignore_index=True) | |
| # sorintg does not seem to impact clustering | |
| DBSCAN(1.5, min_samples=3).fit(df_).labels_ | |
| DBSCAN(1.5, min_samples=3).fit(df_s).labels_ | |
| """ | |
| logger.debug( | |
| '"get_ipython" in globals(): %s', "get_ipython" in globals() | |
| ) | |
| len1, len2 = cmat.shape | |
| df_ = pd.DataFrame(cmat2tset(cmat)) | |
| df_.columns = ["x", "y", "cos"] | |
| backend_saved = matplotlib.get_backend() | |
| # switch backend if necessary | |
| if backend_saved != backend: | |
| plt.switch_backend(backend) | |
| # len1 = len(lst1) # noqa | |
| # len2 = len(lst2) # noqa | |
| # lang1, _ = fastlid(" ".join(lst1)) | |
| # lang2, _ = fastlid(" ".join(lst2)) | |
| # xlabel: str = lang1 | |
| # ylabel: str = lang2 | |
| sns.set() | |
| sns.set_style("darkgrid") | |
| # close all existing figures, necesssary for hf spaces | |
| plt.close("all") | |
| # if sys.platform not in ["win32", "linux"]: | |
| # plt.switch_backend('Agg') # to cater for Mac, thanks to WhiteFox | |
| fig = plt.figure() | |
| gs = fig.add_gridspec(2, 2, wspace=0.4, hspace=0.58) | |
| ax2 = fig.add_subplot(gs[0, 0]) | |
| ax0 = fig.add_subplot(gs[0, 1]) | |
| ax1 = fig.add_subplot(gs[1, 0]) | |
| cmap = "viridis_r" | |
| sns.heatmap(cmat, cmap=cmap, ax=ax2).invert_yaxis() | |
| ax2.set_xlabel(xlabel) | |
| ax2.set_ylabel(ylabel) | |
| ax2.set_title("cos similarity heatmap") | |
| fig.suptitle("alignment projection") | |
| _ = DBSCAN(min_samples=min_samples, eps=eps).fit(df_).labels_ > -1 | |
| # _x = DBSCAN(min_samples=min_samples, eps=eps).fit(df_).labels_ < 0 | |
| _x = ~_ | |
| df_.plot.scatter("x", "y", c="cos", cmap=cmap, ax=ax0) | |
| # clustered | |
| df_[_].plot.scatter("x", "y", c="cos", cmap=cmap, ax=ax1) | |
| # outliers | |
| df_[_x].plot.scatter("x", "y", c="r", marker="x", alpha=0.6, ax=ax0) | |
| ax0.set_xlabel(xlabel) | |
| ax0.set_ylabel(ylabel) | |
| ax0.set_xlim(0, len1) | |
| ax0.set_ylim(0, len2) | |
| ax0.set_title("max along columns ('x': outliers)") | |
| # ax1.set_xlabel("en") | |
| # ax1.set_ylabel("zh") | |
| ax1.set_xlabel(xlabel) | |
| ax1.set_ylabel(ylabel) | |
| ax1.set_xlim(0, len1) | |
| ax1.set_ylim(0, len2) | |
| ax1.set_title(f"potential aligned pairs ({round(sum(_) / len1, 2):.0%})") | |
| logger.debug(" matplotlib.get_backend(): %s", matplotlib.get_backend()) | |
| # if matplotlib.get_backend() not in ["Agg"]: | |
| if showfig: | |
| # plt.ioff() # or we'll just see the plot show and disappear | |
| # plt.show() | |
| plt.show(block=True) | |
| # restore if necessary | |
| if backend_saved != backend: | |
| plt.switch_backend(backend_saved) | |
| # return plt | |