From 568bb8ca4dd6541c58dfdedc1eae4f8876e1a952 Mon Sep 17 00:00:00 2001
From: Amir MOHAMMADI <amir.mohammadi@idiap.ch>
Date: Thu, 3 Mar 2022 14:47:38 +0100
Subject: [PATCH] Drop bob.db.iris
---
conda/meta.yaml | 1 -
doc/plot/plot_ML.py | 20 ++++++--------------
doc/plot/plot_kmeans.py | 20 ++++++--------------
test-requirements.txt | 1 -
4 files changed, 12 insertions(+), 30 deletions(-)
delete mode 100644 test-requirements.txt
diff --git a/conda/meta.yaml b/conda/meta.yaml
index 319c7e6..159eebb 100644
--- a/conda/meta.yaml
+++ b/conda/meta.yaml
@@ -56,7 +56,6 @@ test:
- sphinx {{ sphinx }}
- sphinx_rtd_theme {{ sphinx_rtd_theme }}
- matplotlib {{ matplotlib }}
- - bob.db.iris
about:
home: https://www.idiap.ch/software/bob/
diff --git a/doc/plot/plot_ML.py b/doc/plot/plot_ML.py
index 4717249..32f6643 100644
--- a/doc/plot/plot_ML.py
+++ b/doc/plot/plot_ML.py
@@ -5,26 +5,18 @@ import numpy
from matplotlib.lines import Line2D
from matplotlib.patches import Ellipse
-
-import bob.db.iris
+from sklearn.datasets import load_iris
from bob.learn.em import GMMMachine
logger = logging.getLogger("bob.learn.em")
logger.setLevel("DEBUG")
-data_per_class = bob.db.iris.data()
-setosa = numpy.column_stack(
- (data_per_class["setosa"][:, 0], data_per_class["setosa"][:, 3])
-)
-versicolor = numpy.column_stack(
- (data_per_class["versicolor"][:, 0], data_per_class["versicolor"][:, 3])
-)
-virginica = numpy.column_stack(
- (data_per_class["virginica"][:, 0], data_per_class["virginica"][:, 3])
-)
-
-data = numpy.vstack((setosa, versicolor, virginica))
+iris_data = load_iris()
+data = iris_data.data
+setosa = data[iris_data.target == 0]
+versicolor = data[iris_data.target == 1]
+virginica = data[iris_data.target == 2]
# Two clusters with a feature dimensionality of 3
machine = GMMMachine(
diff --git a/doc/plot/plot_kmeans.py b/doc/plot/plot_kmeans.py
index af565c1..1deeb0b 100644
--- a/doc/plot/plot_kmeans.py
+++ b/doc/plot/plot_kmeans.py
@@ -1,22 +1,14 @@
import matplotlib.pyplot as plt
-import numpy
-import bob.db.iris
+from sklearn.datasets import load_iris
from bob.learn.em import KMeansMachine
-data_per_class = bob.db.iris.data()
-setosa = numpy.column_stack(
- (data_per_class["setosa"][:, 0], data_per_class["setosa"][:, 3])
-)
-versicolor = numpy.column_stack(
- (data_per_class["versicolor"][:, 0], data_per_class["versicolor"][:, 3])
-)
-virginica = numpy.column_stack(
- (data_per_class["virginica"][:, 0], data_per_class["virginica"][:, 3])
-)
-
-data = numpy.vstack((setosa, versicolor, virginica))
+iris_data = load_iris()
+data = iris_data.data
+setosa = data[iris_data.target == 0]
+versicolor = data[iris_data.target == 1]
+virginica = data[iris_data.target == 2]
# Training KMeans
# 3 clusters with a feature dimensionality of 2
diff --git a/test-requirements.txt b/test-requirements.txt
deleted file mode 100644
index 6f36688..0000000
--- a/test-requirements.txt
+++ /dev/null
@@ -1 +0,0 @@
-bob.db.iris
--
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