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| """Plot pandas.DataFrame with DBSCAN clustering.""" | |
| # pylint: disable=invalid-name, too-many-arguments, unused-import | |
| import numpy as np # noqa | |
| import pandas as pd | |
| import matplotlib | |
| import matplotlib.pyplot as plt | |
| import seaborn as sns | |
| from sklearn.cluster import DBSCAN | |
| from logzero import logger # noqa | |
| # from radiobee.cmat2tset import cmat2tset | |
| # turn interactive when in ipython session | |
| _ = """ | |
| if "get_ipython" in globals(): | |
| plt.ion() | |
| else: | |
| plt.switch_backend('Agg') | |
| # """ | |
| # fastlid.set_languages = ["en", "zh"] | |
| # fmt: off | |
| def plot_df( | |
| df_: pd.DataFrame, | |
| # cmat: np.ndarray, | |
| eps: float = 10, | |
| min_samples: int = 6, | |
| xlabel: str = "", | |
| ylabel: str = "", | |
| xlim: int = 0, | |
| ylim: int = 0, | |
| backend: str = "TkAgg", | |
| ) -> 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_ | |
| """ | |
| # df_ = pd.DataFrame(cmat2tset(cmat)) | |
| if df_.shape[1] == 3: | |
| df_.columns = ["x", "y", "cos"] | |
| else: | |
| logger.error(" shape mismatch: %s, expected (x, 3)", df_.shape) | |
| # return None | |
| raise Exception(" df_.shape[1] not equal to 3 ") | |
| if not xlim: | |
| xlim = len(df_) | |
| if not ylim: | |
| ylim = df_.y.max() | |
| if not xlabel: | |
| xlabel = str(xlim) | |
| if not ylabel: | |
| ylabel = str(ylim) | |
| backend_saved = matplotlib.get_backend() | |
| # switch if necessary | |
| if backend_saved != backend: | |
| plt.switch_backend(backend) | |
| sns.set() | |
| sns.set_style("darkgrid") | |
| fig = plt.figure(figsize=(13, 8)) | |
| # 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]) | |
| gs = fig.add_gridspec(1, 1, wspace=0.4, hspace=0.58) | |
| ax0 = fig.add_subplot(gs[0, 0]) | |
| cmap = "viridis_r" | |
| _ = DBSCAN(min_samples=min_samples, eps=eps).fit(df_).labels_ > -1 | |
| _x = ~_ | |
| # clustered | |
| df_[_].plot.scatter("x", "y", c="cos", cmap=cmap, ax=ax0) | |
| # outliers | |
| df_[_x].plot.scatter("x", "y", c="r", marker="x", alpha=0.6, ax=ax0) | |
| # ax1.set_xlabel("en") | |
| # ax1.set_ylabel("zh") | |
| ax0.set_xlabel(xlabel) | |
| ax0.set_ylabel(ylabel) | |
| # ax0.set_xlim(0, xlim) | |
| # ax0.set_ylim(0, ylim) | |
| ax0.set_title("max cos ('x': outliers)") | |
| # ax1.set_title(f"potential aligned pairs ({round(sum(_) / xlim, 2):.0%})") | |
| # restore if necessary | |
| if backend_saved != backend: | |
| plt.switch_backend(backend_saved) | |
| return plt | |
| _ = """ | |
| eps: float = 10 | |
| min_samples: int = 6 | |
| xlabel: str = "" | |
| ylabel: str = "" | |
| xlim: int = 0 | |
| ylim: int = 0 | |
| """ | |