Spaces:
Build error
Build error
File size: 2,838 Bytes
dab2de2 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 |
"""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
|