> ## Documentation Index
> Fetch the complete documentation index at: https://docs.wherobots.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Local Outlier Factor

<Tip>
  The following content is a read-only preview of an executable Jupyter notebook.

  To run this notebook interactively:

  1. Go to [**Wherobots Cloud**](https://cloud.wherobots.com).
  2. Start a runtime.
  3. Open the notebook.
  4. In the Jupyter Launcher:
     1. Click **File > Open Path**.
     2. Paste the following path to access this notebook: `examples/Analyzing_Data/Local_Outlier_Factor.ipynb`
     3. Click **Enter**.
</Tip>

Local Outlier Factor (LOF) is a common algorithm for identifying data points that are inliers/outliers relative to their neighbors. The algorithm works by comparing how close an element is to its neighbors vs how close they are to their neighbors. The number of neighbors to use, k, is set by the user.
Scores much less than one are inliers, scores much greater are outliers, and those near one are neither.
This demo is derived from the [scikit-learn Local Outlier Detection demo](https://scikit-learn.org/stable/auto_examples/neighbors/plot_lof_outlier_detection.html).

# Define Sedona Context

```python theme={"system"}
from sedona.spark import *

config = SedonaContext.builder().getOrCreate()
sedona = SedonaContext.create(config)
```

# Data Generation

We generate some data. Most of it is random, but some data is explicitly designed to be outliers

```python theme={"system"}
import numpy as np
import pyspark.sql.functions as f

from sedona.spark import *

np.random.seed(42)

X_inliers = 0.3 * np.random.randn(100, 2)
X_inliers = np.r_[X_inliers + 2, X_inliers - 2]
X_outliers = np.random.uniform(low=-4, high=4, size=(20, 2))
X = np.r_[X_inliers, X_outliers]

```

## Generation LOF

We use the LOF implementation in Wherobots to generate this statistic on the data. We set k to 20.

```python theme={"system"}
df = sedona.createDataFrame(X).select(ST_MakePoint(f.col("_1"), f.col("_2")).alias("geometry"))
outliers_df = local_outlier_factor(df, 20)

```

```python theme={"system"}
outliers_df.show()
```

## Visualization

We visualize the results using geopandas. Some manipulations are made to the data to improve the clarity of the visualization.

```python theme={"system"}
import geopandas as gpd

pdf = (outliers_df
       .withColumn("lof", f.col("lof") * 50)
       .toPandas()
      )
gdf = gpd.GeoDataFrame(pdf, geometry="geometry")

ax = gdf.plot(
    figsize=(10, 8),
    markersize=gdf['lof'],
    edgecolor='r',
    facecolors="none",
)

gdf.plot(ax=ax, figsize=(10, 8), color="k", markersize=1, legend=True)

ax.set_title('LOF Scores')
ax.legend(['Outlier Scores', 'Data points'])
```