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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.pyplot as plt | |
| import seaborn as sns | |
| from sklearn.cluster import DBSCAN | |
| from logzero import logger | |
| # turn interactive when in ipython session | |
| if "get_ipython" in globals(): | |
| plt.ion() | |
| # fmt: off | |
| def plot_df( | |
| df_: pd.DataFrame, | |
| min_samples: int = 6, | |
| eps: float = 10, | |
| ylim: int = None, | |
| xlabel: str = "en", | |
| ylabel: str = "zh", | |
| ) -> 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(df_) | |
| if df_.columns.__len__() < 3: | |
| logger.error( | |
| "expected 3 columns DataFram, got: %s, cant proceed, returninng None", | |
| df_.columns.tolist(), | |
| ) | |
| return None | |
| # take first three columns | |
| columns = df_.columns[:3] | |
| df_ = df_[columns] | |
| # rename columns to "x", "y", "cos" | |
| df_.columns = ["x", "y", "cos"] | |
| sns.set() | |
| sns.set_style("darkgrid") | |
| # fig, (ax0, ax1) = plt.subplots(2, figsize=(11.69, 8.27)) | |
| fig, (ax0, ax1) = plt.subplots(1, 2, figsize=(11.69, 8.27)) | |
| 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 | |
| # ax0.scatter(df_[_].x, df_[_].y, marker='o', c='g', alpha=0.5) | |
| # ax0.grid() | |
| # print("ratio: %.2f%%" % (100 * sum(_)/len(df_))) | |
| df_.plot.scatter("x", "y", c="cos", cmap="viridis_r", ax=ax0) | |
| # clustered | |
| df_[_].plot.scatter("x", "y", c="cos", cmap="viridis_r", ax=ax1) | |
| # outliers | |
| df_[_x].plot.scatter("x", "y", c="r", marker="x", alpha=0.6, ax=ax0) | |
| # ax0.set_xlabel("") | |
| # ax0.set_ylabel("zh") | |
| ax0.set_xlabel("") | |
| ax0.set_ylabel(ylabel) | |
| xlim = len(df_) | |
| ax0.set_xlim(0, xlim) | |
| if ylim: | |
| ax0.set_ylim(0, ylim) | |
| ax0.set_title("max similarity along columns (outliers denoted by 'x')") | |
| # ax1.set_xlabel("en") | |
| # ax1.set_ylabel("zh") | |
| ax1.set_xlabel(xlabel) | |
| ax1.set_ylabel(ylabel) | |
| ax1.set_xlim(0, xlim) | |
| if ylim: | |
| ax1.set_ylim(0, ylim) | |
| ax1.set_title(f"potential aligned pairs ({round(sum(_) / len(df_), 2):.0%})") | |
| return plt | |