cd1e8e8158
### What changes were proposed in this pull request? Now that we merged the Koalas main code into the PySpark code base (#32036), we should port the Koalas plot unit tests to PySpark. ### Why are the changes needed? Currently, the pandas-on-Spark modules are not tested fully. We should enable the plot unit tests. ### Does this PR introduce _any_ user-facing change? No. ### How was this patch tested? Enable plot unit tests. Closes #32151 from xinrong-databricks/port.plot_tests. Authored-by: Xinrong Meng <xinrong.meng@databricks.com> Signed-off-by: HyukjinKwon <gurwls223@apache.org>
403 lines
13 KiB
Python
403 lines
13 KiB
Python
#
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# Licensed to the Apache Software Foundation (ASF) under one or more
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# contributor license agreements. See the NOTICE file distributed with
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# this work for additional information regarding copyright ownership.
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# The ASF licenses this file to You under the Apache License, Version 2.0
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# (the "License"); you may not use this file except in compliance with
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# the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import base64
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from distutils.version import LooseVersion
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from io import BytesIO
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import unittest
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import numpy as np
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import pandas as pd
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from pyspark import pandas as ps
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from pyspark.pandas.config import set_option, reset_option
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from pyspark.pandas.testing.utils import have_matplotlib, ReusedSQLTestCase, TestUtils
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if have_matplotlib:
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import matplotlib
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from matplotlib import pyplot as plt
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matplotlib.use("agg")
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@unittest.skipIf(not have_matplotlib, "matplotlib is not installed.")
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class SeriesPlotMatplotlibTest(ReusedSQLTestCase, TestUtils):
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@classmethod
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def setUpClass(cls):
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super().setUpClass()
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if LooseVersion(pd.__version__) >= LooseVersion("0.25"):
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pd.set_option("plotting.backend", "matplotlib")
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set_option("plotting.backend", "matplotlib")
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set_option("plotting.max_rows", 1000)
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@classmethod
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def tearDownClass(cls):
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if LooseVersion(pd.__version__) >= LooseVersion("0.25"):
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pd.reset_option("plotting.backend")
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reset_option("plotting.backend")
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reset_option("plotting.max_rows")
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super().tearDownClass()
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@property
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def pdf1(self):
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return pd.DataFrame(
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{"a": [1, 2, 3, 4, 5, 6, 7, 8, 9, 15, 50]}, index=[0, 1, 3, 5, 6, 8, 9, 9, 9, 10, 10]
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)
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@property
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def kdf1(self):
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return ps.from_pandas(self.pdf1)
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@property
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def kdf2(self):
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return ps.range(1002)
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@property
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def pdf2(self):
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return self.kdf2.to_pandas()
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@staticmethod
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def plot_to_base64(ax):
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bytes_data = BytesIO()
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ax.figure.savefig(bytes_data, format="png")
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bytes_data.seek(0)
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b64_data = base64.b64encode(bytes_data.read())
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plt.close(ax.figure)
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return b64_data
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def test_bar_plot(self):
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pdf = self.pdf1
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kdf = self.kdf1
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ax1 = pdf["a"].plot(kind="bar", colormap="Paired")
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bin1 = self.plot_to_base64(ax1)
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ax2 = kdf["a"].plot(kind="bar", colormap="Paired")
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bin2 = self.plot_to_base64(ax2)
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self.assertEqual(bin1, bin2)
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ax1 = pdf["a"].plot(kind="bar", colormap="Paired")
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bin1 = self.plot_to_base64(ax1)
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ax2 = kdf["a"].plot(kind="bar", colormap="Paired")
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bin2 = self.plot_to_base64(ax2)
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self.assertEqual(bin1, bin2)
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def test_bar_plot_limited(self):
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pdf = self.pdf2
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kdf = self.kdf2
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_, ax1 = plt.subplots(1, 1)
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ax1 = pdf["id"][:1000].plot.bar(colormap="Paired")
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ax1.text(
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1,
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1,
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"showing top 1000 elements only",
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size=6,
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ha="right",
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va="bottom",
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transform=ax1.transAxes,
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)
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bin1 = self.plot_to_base64(ax1)
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_, ax2 = plt.subplots(1, 1)
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ax2 = kdf["id"].plot.bar(colormap="Paired")
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bin2 = self.plot_to_base64(ax2)
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self.assertEqual(bin1, bin2)
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def test_pie_plot(self):
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pdf = self.pdf1
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kdf = self.kdf1
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ax1 = pdf["a"].plot.pie(colormap="Paired")
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bin1 = self.plot_to_base64(ax1)
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ax2 = kdf["a"].plot.pie(colormap="Paired")
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bin2 = self.plot_to_base64(ax2)
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self.assertEqual(bin1, bin2)
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ax1 = pdf["a"].plot(kind="pie", colormap="Paired")
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bin1 = self.plot_to_base64(ax1)
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ax2 = kdf["a"].plot(kind="pie", colormap="Paired")
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bin2 = self.plot_to_base64(ax2)
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self.assertEqual(bin1, bin2)
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def test_pie_plot_limited(self):
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pdf = self.pdf2
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kdf = self.kdf2
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_, ax1 = plt.subplots(1, 1)
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ax1 = pdf["id"][:1000].plot.pie(colormap="Paired")
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ax1.text(
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1,
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1,
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"showing top 1000 elements only",
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size=6,
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ha="right",
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va="bottom",
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transform=ax1.transAxes,
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)
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bin1 = self.plot_to_base64(ax1)
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_, ax2 = plt.subplots(1, 1)
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ax2 = kdf["id"].plot.pie(colormap="Paired")
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bin2 = self.plot_to_base64(ax2)
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self.assertEqual(bin1, bin2)
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def test_line_plot(self):
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pdf = self.pdf1
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kdf = self.kdf1
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ax1 = pdf["a"].plot(kind="line", colormap="Paired")
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bin1 = self.plot_to_base64(ax1)
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ax2 = kdf["a"].plot(kind="line", colormap="Paired")
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bin2 = self.plot_to_base64(ax2)
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self.assertEqual(bin1, bin2)
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ax1 = pdf["a"].plot.line(colormap="Paired")
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bin1 = self.plot_to_base64(ax1)
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ax2 = kdf["a"].plot.line(colormap="Paired")
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bin2 = self.plot_to_base64(ax2)
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self.assertEqual(bin1, bin2)
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def test_barh_plot(self):
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pdf = self.pdf1
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kdf = self.kdf1
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ax1 = pdf["a"].plot(kind="barh", colormap="Paired")
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bin1 = self.plot_to_base64(ax1)
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ax2 = kdf["a"].plot(kind="barh", colormap="Paired")
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bin2 = self.plot_to_base64(ax2)
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self.assertEqual(bin1, bin2)
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def test_barh_plot_limited(self):
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pdf = self.pdf2
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kdf = self.kdf2
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_, ax1 = plt.subplots(1, 1)
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ax1 = pdf["id"][:1000].plot.barh(colormap="Paired")
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ax1.text(
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1,
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1,
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"showing top 1000 elements only",
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size=6,
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ha="right",
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va="bottom",
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transform=ax1.transAxes,
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)
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bin1 = self.plot_to_base64(ax1)
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_, ax2 = plt.subplots(1, 1)
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ax2 = kdf["id"].plot.barh(colormap="Paired")
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bin2 = self.plot_to_base64(ax2)
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self.assertEqual(bin1, bin2)
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def test_hist(self):
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pdf = pd.DataFrame(
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{"a": [1, 2, 3, 4, 5, 6, 7, 8, 9, 15, 50]}, index=[0, 1, 3, 5, 6, 8, 9, 9, 9, 10, 10]
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)
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kdf = ps.from_pandas(pdf)
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def plot_to_base64(ax):
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bytes_data = BytesIO()
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ax.figure.savefig(bytes_data, format="png")
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bytes_data.seek(0)
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b64_data = base64.b64encode(bytes_data.read())
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plt.close(ax.figure)
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return b64_data
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_, ax1 = plt.subplots(1, 1)
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# Using plot.hist() because pandas changes ticks props when called hist()
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ax1 = pdf["a"].plot.hist()
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_, ax2 = plt.subplots(1, 1)
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ax2 = kdf["a"].hist()
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self.assert_eq(plot_to_base64(ax1), plot_to_base64(ax2))
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def test_hist_plot(self):
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pdf = self.pdf1
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kdf = self.kdf1
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_, ax1 = plt.subplots(1, 1)
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ax1 = pdf["a"].plot.hist()
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bin1 = self.plot_to_base64(ax1)
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_, ax2 = plt.subplots(1, 1)
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ax2 = kdf["a"].plot.hist()
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bin2 = self.plot_to_base64(ax2)
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self.assertEqual(bin1, bin2)
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ax1 = pdf["a"].plot.hist(bins=15)
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bin1 = self.plot_to_base64(ax1)
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ax2 = kdf["a"].plot.hist(bins=15)
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bin2 = self.plot_to_base64(ax2)
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self.assertEqual(bin1, bin2)
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ax1 = pdf["a"].plot(kind="hist", bins=15)
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bin1 = self.plot_to_base64(ax1)
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ax2 = kdf["a"].plot(kind="hist", bins=15)
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bin2 = self.plot_to_base64(ax2)
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self.assertEqual(bin1, bin2)
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ax1 = pdf["a"].plot.hist(bins=3, bottom=[2, 1, 3])
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bin1 = self.plot_to_base64(ax1)
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ax2 = kdf["a"].plot.hist(bins=3, bottom=[2, 1, 3])
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bin2 = self.plot_to_base64(ax2)
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self.assertEqual(bin1, bin2)
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def test_area_plot(self):
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pdf = pd.DataFrame(
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{
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"sales": [3, 2, 3, 9, 10, 6],
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"signups": [5, 5, 6, 12, 14, 13],
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"visits": [20, 42, 28, 62, 81, 50],
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},
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index=pd.date_range(start="2018/01/01", end="2018/07/01", freq="M"),
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)
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kdf = ps.from_pandas(pdf)
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ax1 = pdf["sales"].plot(kind="area", colormap="Paired")
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bin1 = self.plot_to_base64(ax1)
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ax2 = kdf["sales"].plot(kind="area", colormap="Paired")
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bin2 = self.plot_to_base64(ax2)
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self.assertEqual(bin1, bin2)
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ax1 = pdf["sales"].plot.area(colormap="Paired")
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bin1 = self.plot_to_base64(ax1)
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ax2 = kdf["sales"].plot.area(colormap="Paired")
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bin2 = self.plot_to_base64(ax2)
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self.assertEqual(bin1, bin2)
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# just a sanity check for df.col type
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ax1 = pdf.sales.plot(kind="area", colormap="Paired")
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bin1 = self.plot_to_base64(ax1)
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ax2 = kdf.sales.plot(kind="area", colormap="Paired")
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bin2 = self.plot_to_base64(ax2)
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self.assertEqual(bin1, bin2)
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def test_box_plot(self):
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def check_box_plot(pser, kser, *args, **kwargs):
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_, ax1 = plt.subplots(1, 1)
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ax1 = pser.plot.box(*args, **kwargs)
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_, ax2 = plt.subplots(1, 1)
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ax2 = kser.plot.box(*args, **kwargs)
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diffs = [
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np.array([0, 0.5, 0, 0.5, 0, -0.5, 0, -0.5, 0, 0.5]),
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np.array([0, 0.5, 0, 0]),
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np.array([0, -0.5, 0, 0]),
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]
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try:
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for i, (line1, line2) in enumerate(zip(ax1.get_lines(), ax2.get_lines())):
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expected = line1.get_xydata().ravel()
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actual = line2.get_xydata().ravel()
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if i < 3:
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actual += diffs[i]
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self.assert_eq(pd.Series(expected), pd.Series(actual))
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finally:
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ax1.cla()
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ax2.cla()
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# Non-named Series
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pser = pd.Series([1, 2, 3, 4, 5, 6, 7, 8, 9, 15, 50], [0, 1, 3, 5, 6, 8, 9, 9, 9, 10, 10])
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kser = ps.from_pandas(pser)
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spec = [(self.pdf1.a, self.kdf1.a), (pser, kser)]
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for p, k in spec:
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check_box_plot(p, k)
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check_box_plot(p, k, showfliers=True)
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check_box_plot(p, k, sym="")
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check_box_plot(p, k, sym=".", color="r")
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check_box_plot(p, k, use_index=False, labels=["Test"])
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check_box_plot(p, k, usermedians=[2.0])
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check_box_plot(p, k, conf_intervals=[(1.0, 3.0)])
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val = (1, 3)
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self.assertRaises(
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ValueError, lambda: check_box_plot(self.pdf1, self.kdf1, usermedians=[2.0, 3.0])
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)
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self.assertRaises(
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ValueError, lambda: check_box_plot(self.pdf1, self.kdf1, conf_intervals=[val, val])
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)
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self.assertRaises(
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ValueError, lambda: check_box_plot(self.pdf1, self.kdf1, conf_intervals=[(1,)])
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)
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def test_kde_plot(self):
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def moving_average(a, n=10):
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ret = np.cumsum(a, dtype=float)
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ret[n:] = ret[n:] - ret[:-n]
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return ret[n - 1:] / n
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def check_kde_plot(pdf, kdf, *args, **kwargs):
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_, ax1 = plt.subplots(1, 1)
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ax1 = pdf["a"].plot.kde(*args, **kwargs)
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_, ax2 = plt.subplots(1, 1)
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ax2 = kdf["a"].plot.kde(*args, **kwargs)
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try:
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for i, (line1, line2) in enumerate(zip(ax1.get_lines(), ax2.get_lines())):
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expected = line1.get_xydata().ravel()
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actual = line2.get_xydata().ravel()
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# TODO: Due to implementation difference, the output is different comparing
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# to pandas'. We should identify the root cause of difference, and reduce
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# the diff.
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# Note: Data is from 1 to 50. So, it smooths them by moving average and compares
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# both.
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self.assertTrue(
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np.allclose(moving_average(actual), moving_average(expected), rtol=3)
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)
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finally:
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ax1.cla()
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ax2.cla()
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check_kde_plot(self.pdf1, self.kdf1, bw_method=0.3)
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check_kde_plot(self.pdf1, self.kdf1, ind=[1, 2, 3, 4, 5], bw_method=3.0)
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def test_empty_hist(self):
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pdf = self.pdf1.assign(categorical="A")
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kdf = ps.from_pandas(pdf)
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kser = kdf["categorical"]
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with self.assertRaisesRegex(TypeError, "Empty 'DataFrame': no numeric data to plot"):
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kser.plot.hist()
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def test_single_value_hist(self):
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pdf = self.pdf1.assign(single=2)
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kdf = ps.from_pandas(pdf)
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_, ax1 = plt.subplots(1, 1)
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ax1 = pdf["single"].plot.hist()
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bin1 = self.plot_to_base64(ax1)
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_, ax2 = plt.subplots(1, 1)
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ax2 = kdf["single"].plot.hist()
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bin2 = self.plot_to_base64(ax2)
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self.assertEqual(bin1, bin2)
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if __name__ == "__main__":
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from pyspark.pandas.tests.plot.test_series_plot_matplotlib import * # noqa: F401
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try:
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import xmlrunner # type: ignore[import]
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testRunner = xmlrunner.XMLTestRunner(output='target/test-reports', verbosity=2)
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except ImportError:
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testRunner = None
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unittest.main(testRunner=testRunner, verbosity=2)
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