190 lines
7.8 KiB
Python
190 lines
7.8 KiB
Python
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# This file is part of Patsy
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# Copyright (C) 2012-2013 Nathaniel Smith <njs@pobox.com>
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# See file LICENSE.txt for license information.
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from __future__ import print_function
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import numpy as np
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from patsy.state import Center, Standardize, center
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from patsy.util import atleast_2d_column_default
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def check_stateful(cls, accepts_multicolumn, input, output, *args, **kwargs):
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input = np.asarray(input)
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output = np.asarray(output)
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test_cases = [
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# List input, one chunk
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([input], output),
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# Scalar input, many chunks
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(input, output),
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# List input, many chunks:
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([[n] for n in input], output),
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# 0-d array input, many chunks:
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([np.array(n) for n in input], output),
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# 1-d array input, one chunk:
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([np.array(input)], output),
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# 1-d array input, many chunks:
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([np.array([n]) for n in input], output),
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# 2-d but 1 column input, one chunk:
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([np.array(input)[:, None]], atleast_2d_column_default(output)),
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# 2-d but 1 column input, many chunks:
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([np.array([[n]]) for n in input], atleast_2d_column_default(output)),
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]
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if accepts_multicolumn:
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# 2-d array input, one chunk:
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test_cases += [
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([np.column_stack((input, input[::-1]))],
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np.column_stack((output, output[::-1]))),
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# 2-d array input, many chunks:
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([np.array([[input[i], input[-i-1]]]) for i in range(len(input))],
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np.column_stack((output, output[::-1]))),
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]
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from patsy.util import have_pandas
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if have_pandas:
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import pandas
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pandas_type = (pandas.Series, pandas.DataFrame)
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pandas_index = np.linspace(0, 1, num=len(input))
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# 1d and 2d here refer to the dimensionality of the input
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if output.ndim == 1:
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output_1d = pandas.Series(output, index=pandas_index)
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else:
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output_1d = pandas.DataFrame(output, index=pandas_index)
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test_cases += [
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# Series input, one chunk
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([pandas.Series(input, index=pandas_index)], output_1d),
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# Series input, many chunks
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([pandas.Series([x], index=[idx])
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for (x, idx) in zip(input, pandas_index)],
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output_1d),
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]
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if accepts_multicolumn:
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input_2d_2col = np.column_stack((input, input[::-1]))
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output_2d_2col = np.column_stack((output, output[::-1]))
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output_2col_dataframe = pandas.DataFrame(output_2d_2col,
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index=pandas_index)
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test_cases += [
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# DataFrame input, one chunk
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([pandas.DataFrame(input_2d_2col, index=pandas_index)],
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output_2col_dataframe),
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# DataFrame input, many chunks
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([pandas.DataFrame([input_2d_2col[i, :]],
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index=[pandas_index[i]])
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for i in range(len(input))],
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output_2col_dataframe),
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]
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for input_obj, output_obj in test_cases:
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print(input_obj)
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t = cls()
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for input_chunk in input_obj:
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t.memorize_chunk(input_chunk, *args, **kwargs)
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t.memorize_finish()
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all_outputs = []
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for input_chunk in input_obj:
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output_chunk = t.transform(input_chunk, *args, **kwargs)
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if input.ndim == output.ndim:
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assert output_chunk.ndim == np.asarray(input_chunk).ndim
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all_outputs.append(output_chunk)
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if have_pandas and isinstance(all_outputs[0], pandas_type):
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all_output1 = pandas.concat(all_outputs)
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assert np.array_equal(all_output1.index, pandas_index)
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elif all_outputs[0].ndim == 0:
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all_output1 = np.array(all_outputs)
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elif all_outputs[0].ndim == 1:
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all_output1 = np.concatenate(all_outputs)
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else:
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all_output1 = np.vstack(all_outputs)
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assert all_output1.shape[0] == len(input)
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# output_obj_reshaped = np.asarray(output_obj).reshape(all_output1.shape)
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# assert np.allclose(all_output1, output_obj_reshaped)
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assert np.allclose(all_output1, output_obj)
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if np.asarray(input_obj[0]).ndim == 0:
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all_input = np.array(input_obj)
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elif have_pandas and isinstance(input_obj[0], pandas_type):
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# handles both Series and DataFrames
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all_input = pandas.concat(input_obj)
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elif np.asarray(input_obj[0]).ndim == 1:
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# Don't use vstack, because that would turn this into a 1xn
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# matrix:
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all_input = np.concatenate(input_obj)
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else:
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all_input = np.vstack(input_obj)
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all_output2 = t.transform(all_input, *args, **kwargs)
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if have_pandas and isinstance(input_obj[0], pandas_type):
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assert np.array_equal(all_output2.index, pandas_index)
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if input.ndim == output.ndim:
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assert all_output2.ndim == all_input.ndim
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assert np.allclose(all_output2, output_obj)
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def test_Center():
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check_stateful(Center, True, [1, 2, 3], [-1, 0, 1])
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check_stateful(Center, True, [1, 2, 1, 2], [-0.5, 0.5, -0.5, 0.5])
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check_stateful(Center, True,
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[1.3, -10.1, 7.0, 12.0],
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[-1.25, -12.65, 4.45, 9.45])
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def test_stateful_transform_wrapper():
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assert np.allclose(center([1, 2, 3]), [-1, 0, 1])
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assert np.allclose(center([1, 2, 1, 2]), [-0.5, 0.5, -0.5, 0.5])
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assert center([1.0, 2.0, 3.0]).dtype == np.dtype(float)
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assert (center(np.array([1.0, 2.0, 3.0], dtype=np.float32)).dtype
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== np.dtype(np.float32))
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assert center([1, 2, 3]).dtype == np.dtype(float)
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from patsy.util import have_pandas
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if have_pandas:
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import pandas
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s = pandas.Series([1, 2, 3], index=["a", "b", "c"])
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df = pandas.DataFrame([[1, 2], [2, 4], [3, 6]],
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columns=["x1", "x2"],
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index=[10, 20, 30])
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s_c = center(s)
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assert isinstance(s_c, pandas.Series)
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assert np.array_equal(s_c.index, ["a", "b", "c"])
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assert np.allclose(s_c, [-1, 0, 1])
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df_c = center(df)
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assert isinstance(df_c, pandas.DataFrame)
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assert np.array_equal(df_c.index, [10, 20, 30])
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assert np.array_equal(df_c.columns, ["x1", "x2"])
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assert np.allclose(df_c, [[-1, -2], [0, 0], [1, 2]])
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def test_Standardize():
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check_stateful(Standardize, True, [1, -1], [1, -1])
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check_stateful(Standardize, True, [12, 10], [1, -1])
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check_stateful(Standardize, True,
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[12, 11, 10],
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[np.sqrt(3./2), 0, -np.sqrt(3./2)])
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check_stateful(Standardize, True,
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[12.0, 11.0, 10.0],
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[np.sqrt(3./2), 0, -np.sqrt(3./2)])
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# XX: see the comment in Standardize.transform about why this doesn't
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# work:
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# check_stateful(Standardize,
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# [12.0+0j, 11.0+0j, 10.0],
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# [np.sqrt(3./2)+0j, 0, -np.sqrt(3./2)])
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r20 = list(range(20))
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check_stateful(Standardize, True, [1, -1], [np.sqrt(2)/2, -np.sqrt(2)/2],
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ddof=1)
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check_stateful(Standardize, True,
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r20,
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list((np.arange(20) - 9.5) / 5.7662812973353983),
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ddof=0)
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check_stateful(Standardize, True,
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r20,
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list((np.arange(20) - 9.5) / 5.9160797830996161),
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ddof=1)
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check_stateful(Standardize, True,
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r20,
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list((np.arange(20) - 9.5)),
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rescale=False, ddof=1)
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check_stateful(Standardize, True,
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r20,
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list(np.arange(20) / 5.9160797830996161),
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center=False, ddof=1)
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check_stateful(Standardize, True,
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r20,
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r20,
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center=False, rescale=False, ddof=1)
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