clusterfun 0.4.0a7

Clusterfun - a plotting library to inspect data

Clusterfun

Clusterfun is a python plotting library to explore image data. Play around with a live demo on https://clusterfun.app.

• Getting started
• A simple example
• Main features
• Default parameters
• Plot types
  • Bar chart
  • Confusion matrix
  • Grid
  • Histogram
  • Pie chart
  • Scatterplot
  • Violin plot
• Data loading

Getting started

Clusterfun can be installed with pip:

pip install clusterfun

Clusterfun requires Python 3.8 or higher.

Plots accept data in the form of a pandas DataFrame, which will be installed automatically if not already present. No account, payment, or internet connection is required to use clusterfun. Clusterfun is open source and free to use.

A simple example

import pandas as pd
import clusterfun as clt

df = pd.read_csv("https://raw.githubusercontent.com/gietema/clusterfun-data/main/wiki-art.csv")
clt.scatter(df, x="x", y="y", media="img_path", color="painter")

Data can be hosted locally or on AWS S3.

As you can see, a clusterfun plot takes as input a pandas dataframe and column names indicating which columns to use for the visualisation. In this way, it is similar to the seaborn or the plotly library. But in clusterfun, you can:

• Click and drag to select data to visualise it in a grid
• Hover over data points to see them on the right side of the page
• Click on data points to view zoomed in versions of the image related to the data point

This makes clusterfun ideal for quickly visualising image data, which can be useful in the context of building datasets, exploring edge cases and debugging model performance.

Main features

Default parameters

The default parameters for the plot types are as follows:

• df: pd.DataFrame (required)

  The dataframe used for the data to plot. Most other parameters are column names in this dataframe (e.g. media, color, etc.).

• media: str (required)

  The column name of the media to display in the plot. See data loading for more information about the type of media that can be displayed.

• show: bool = True

  Whether to show the plot or not. If show is set to True, clusterfun will start a local server to display the plot in a web browser. More specifically, we start a FastAPI server where we mount the webpage as a static file. The application itself does not require an internet connection. All data is loaded locally and does not leave your machine/browser. If show is set to False, clusterfun only saves the required data to serve the plot later on and return the path to where the data is stored. If you want to serve the plot yourself later on, you can run clusterfun {path - to - data}|{uuid} in the command line to start a local server for the plot you are interested in.

• color: Optional[str] = None

  If given, points will be colored based on the values in the given column. Powerful for visualising clusters or classes of data.

• title: Optional[str] = None

  The title to use for the plot.

• bounding_box: Optional[str] = None

  You can visualise bounding boxes on top of your images by with the bounding_box parameter. For this to work, you need to have a bounding box column in the dataframe used to plot the data. Each cell in the dataframe needs to contain a dictionary or a list of dictionaries with bounding box values: xmin, ymin, xmax, ymax, label (optional), color (optional). The keys of the expected dictionary are:

  • xmin: float | int
  • ymin: float | int
  • xmax: float | int
  • ymax: float | int
  • label: Optional[str] = None
  • color: Optional[str] = None

  If no color is provided, a default color scheme will be used. The color value can be a color name or hex value. The label will be displayed in the top left of the bounding box. Example:

  single_bounding_box = {
    "xmin": 12,
    "ymin": 10,
    "xmax": 100,
    "ymax": 110,
    "color": "green",
    "label": "ground truth"
  }
  

Plot types

The following plot types are available:

• Bar chart
• Confusion matrix
• Grid
• Histogram
• Pie chart
• Scatterplot
• Violin plot

Bar chart

def bar_chart(
    df: pd.DataFrame,
    x: str,
    media: str,
    color: Optional[str] = None,
    ...
) -> Path:

Parameters

• df: pd.DataFrame The dataframe with the data to plot
• x: str The column name of the data for the bar chart. One bar per unique value will be plotted.
• media: str The column name of the media to display
• color: Optional[str] = None If added, the color will be used to create a stacked bar chart.

Example

import pandas as pd
import clusterfun as clt

df = pd.read_csv("https://raw.githubusercontent.com/gietema/clusterfun-data/main/wiki-art.csv")
clt.bar_chart(df, x="painter", media="img_path", color="style")

Confusion matrix

def confusion_matrix(
    df: pd.DataFrame,
    y_true: str,
    y_pred: str,
    media: str,
    ...
) -> Path:

Parameters

• df: pd.DataFrame

  The dataframe with the data to plot

• y_true: str

  The ground truth label. Values can be integers or strings.

• y_pred: str

  The column name of the predicted label. Values can be integers or strings.

• media: str

  The column name of the media to display

Example

import pandas as pd
import clusterfun as clt

df = pd.read_csv("https://raw.githubusercontent.com/gietema/clusterfun-data/main/cifar10.csv")
clt.confusion_matrix(df, y_true="label", y_pred="pred", media="img_path")

Grid

def grid(
    df: pd.DataFrame,
    media: str,
    ...
) -> Path:

Parameters

• df: pd.DataFrame

  The dataframe with the data to plot

• media: str

  The column name of the media to display

Example

import pandas as pd
import clusterfun as clt

df = pd.read_csv("https://raw.githubusercontent.com/gietema/clusterfun-data/main/wiki-art.csv")
clt.grid(df, media="img_path")

Histogram

def histogram(
    df: pd.DataFrame,
    x: str,
    media: str,
    bins: int = 20,
    ...
) -> Path:

Parameters

• df: pd.DataFrame

  The dataframe with the data to plot

• x: str

  The column name of the data for the histogram

• media: str

  The column name of the media to display

• bins: int = 20

  The number of bins to use for the histogram

Example

import pandas as pd
import clusterfun as clt

df = pd.read_csv("https://raw.githubusercontent.com/gietema/clusterfun-data/main/wiki-art.csv")
clt.histogram(df, x="brightness", media="img_path")

Pie chart

def pie(
    df: pd.DataFrame,
    color: str,
) -> Path:

Parameters

• df: pd.DataFrame

  The dataframe with the data to plot

• color

  Column for the pies of the pie chart

Example

import pandas as pd
import clusterfun as clt

df = pd.read_csv("https://raw.githubusercontent.com/gietema/clusterfun-data/main/wiki-art.csv")
clt.pie_chart(df, color="painter", media="img_path")

Scatterplot

    df: pd.DataFrame,
    x: str,
    y: str,
    ...
) -> Path:

Parameters

• df: pd.DataFrame

  The dataframe with the data to plot

• x: str

  The column name of the data for the x-axis

• y: str

  The column name of the data for the y-axis

Example

import pandas as pd
import clusterfun as clt

df = pd.read_csv("https://raw.githubusercontent.com/gietema/clusterfun-data/main/wiki-art.csv")
clt.scatter(df, x="x", y="y", media="img_path")

Violin plot

def violin(
    df: pd.DataFrame,
    y: str,
    ...
) -> Path:

Parameters

• df: pd.DataFrame

  The dataframe with the data to plot

• y: str

  The column name of the data for the y-axis

Example

import pandas as pd
import clusterfun as clt

df = pd.read_csv("https://raw.githubusercontent.com/gietema/clusterfun-data/main/wiki-art.csv")
df = df[df.painter.isin(["Pablo Picasso", "Juan Gris", "Georges Braque", "Fernand Leger"])]
clt.violin(df, y="brightness", media="img_path")

Data loading

Clusterfun supports AWS S3 and local data storage and loading. The dataframe column corresponding to the media value in the plot will be used to determine where to load the media from.

import clusterfun as clt

df = pd.read_csv("https://raw.githubusercontent.com/gietema/clusterfun-data/main/wiki-art.csv")
clt.grid(df, media="img_column")

AWS S3 media should start with s3://. Make sure to set a AWS_REGION environment variable to the region where your data is stored.

Support for Google Cloud Storage is coming soon.