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