Unsupervised Machine Learning

In this example, we will demonstrate how to fit and score an unsupervised learning model with a sample of Landsat 8 data.

Imports and Data Preparation

We import various Spark components needed to construct our Pipeline.

import pandas as pd
from pyrasterframes import TileExploder
from pyrasterframes.rasterfunctions import rf_assemble_tile, rf_crs, rf_extent, rf_tile, rf_dimensions

from pyspark.ml.feature import VectorAssembler
from pyspark.ml.clustering import KMeans
from pyspark.ml import Pipeline

The first step is to create a Spark DataFrame of our imagery data. To achieve that we will create a catalog DataFrame using the pattern from the I/O page. In the catalog, each row represents a distinct area and time, and each column is the URI to a band’s image product. The function resource_dir_uri gives a local file system path to the sample Landsat data. The resulting Spark DataFrame may have many rows per URI, with a column corresponding to each band.

filenamePattern = "L8-B{}-Elkton-VA.tiff"
catalog_df = pd.DataFrame([
    {'b' + str(b): os.path.join(resource_dir_uri(), filenamePattern.format(b)) for b in range(1, 8)}
df = spark.read.raster(catalog=catalog_df, catalog_col_names=catalog_df.columns)
df = df.select(
 |-- crs: struct (nullable = true)
 |    |-- crsProj4: string (nullable = false)
 |-- extent: struct (nullable = true)
 |    |-- xmin: double (nullable = false)
 |    |-- ymin: double (nullable = false)
 |    |-- xmax: double (nullable = false)
 |    |-- ymax: double (nullable = false)
 |-- b1: tile (nullable = true)
 |-- b2: tile (nullable = true)
 |-- b3: tile (nullable = true)
 |-- b4: tile (nullable = true)
 |-- b5: tile (nullable = true)
 |-- b6: tile (nullable = true)
 |-- b7: tile (nullable = true)

Create ML Pipeline

SparkML requires that each observation be in its own row, and features for each observation be packed into a single Vector. For this unsupervised learning problem, we will treat each pixel as an observation and each band as a feature. The first step is to “explode” the tiles into a single row per pixel. In RasterFrames, generally a pixel is called a cell.

exploder = TileExploder()

To “vectorize” the the band columns, we use the SparkML VectorAssembler. Each of the seven bands is a different feature.

assembler = VectorAssembler() \
    .setInputCols(list(catalog_df.columns)) \

For this problem, we will use the K-means clustering algorithm and configure our model to have 5 clusters.

kmeans = KMeans().setK(5).setFeaturesCol('features')

We can combine the above stages into a single Pipeline.

pipeline = Pipeline().setStages([exploder, assembler, kmeans])

Fit the Model and Score

Fitting the pipeline actually executes exploding the tiles, assembling the features vectors, and fitting the K-means clustering model.

model = pipeline.fit(df)

We can use the transform function to score the training data in the fitted pipeline model. This will add a column called prediction with the closest cluster identifier.

clustered = model.transform(df)

Now let’s take a look at some sample output.

clustered.select('prediction', 'extent', 'column_index', 'row_index', 'features')

Showing only top 5 rows.

prediction extent column_index row_index features
0 [703986.502389, 4249549.463864264, 709547.135536023, 4254601.8671] 0 0 [9470.0,8491.0,7805.0,6697.0,17507.0,10338.0,7235.0]
0 [703986.502389, 4249549.463864264, 709547.135536023, 4254601.8671] 1 0 [9566.0,8607.0,8046.0,6898.0,18504.0,11545.0,7877.0]
4 [703986.502389, 4249549.463864264, 709547.135536023, 4254601.8671] 2 0 [9703.0,8808.0,8377.0,7222.0,20556.0,13207.0,8686.0]
4 [703986.502389, 4249549.463864264, 709547.135536023, 4254601.8671] 3 0 [9856.0,8983.0,8565.0,7557.0,19479.0,13203.0,9065.0]
4 [703986.502389, 4249549.463864264, 709547.135536023, 4254601.8671] 4 0 [10105.0,9270.0,8851.0,7912.0,19074.0,12737.0,8947.0]

If we want to inspect the model statistics, the SparkML API requires us to go through this unfortunate contortion to access the clustering results:

cluster_stage = model.stages[2]

We can then compute the sum of squared distances of points to their nearest center, which is elemental to most cluster quality metrics.

metric = cluster_stage.computeCost(clustered)
print("Within set sum of squared errors: %s" % metric)
Within set sum of squared errors: 224761830143.71

Visualize Prediction

We can recreate the tiled data structure using the metadata added by the TileExploder pipeline stage.

from pyrasterframes.rf_types import CellType

tile_dims = df.select(rf_dimensions(df.b1).alias('dims')).first()['dims']
retiled = clustered.groupBy('extent', 'crs') \
        rf_assemble_tile('column_index', 'row_index', 'prediction',
            tile_dims['cols'], tile_dims['rows'], CellType.int8()).alias('prediction')

 |-- extent: struct (nullable = true)
 |    |-- xmin: double (nullable = false)
 |    |-- ymin: double (nullable = false)
 |    |-- xmax: double (nullable = false)
 |    |-- ymax: double (nullable = false)
 |-- crs: struct (nullable = true)
 |    |-- crsProj4: string (nullable = false)
 |-- prediction: tile (nullable = true)
extent crs prediction
[703986.502389, 4249549.463864264, 709547.135536023, 4254601.8671] [+proj=utm +zone=17 +datum=WGS84 +units=m +no_defs ]

The resulting output is shown below.