From 076b636b8aae09e56d997e5beb81b1b645cfa060 Mon Sep 17 00:00:00 2001 From: Morgan Williams Date: Tue, 26 Mar 2019 13:14:24 +0800 Subject: [PATCH 1/6] Update spider density Update util.plot --- pyrolite/util/math.py | 2 +- pyrolite/util/plot.py | 11 ++++------- 2 files changed, 5 insertions(+), 8 deletions(-) diff --git a/pyrolite/util/math.py b/pyrolite/util/math.py index eb2e5486..9683f78d 100644 --- a/pyrolite/util/math.py +++ b/pyrolite/util/math.py @@ -143,7 +143,7 @@ def flattengrid(grid): -------- :class:`numpy.ndarray` """ - return np.vstack([g.flatten() for g in grid]) + return np.vstack([g.flatten() for g in grid]).T def linspc_(_min, _max, step=0.0, bins=20): diff --git a/pyrolite/util/plot.py b/pyrolite/util/plot.py index 4a0c07c1..0fd86cd3 100644 --- a/pyrolite/util/plot.py +++ b/pyrolite/util/plot.py @@ -411,7 +411,6 @@ def conditional_prob_density( xy = interpolate_line(xy, n=resolution, logy=logy) x, y = np.swapaxes(xy, 0, 1) - xx = np.sort(x[0]) ymin, ymax = np.nanmin(y), np.nanmax(y) ystep = [(ymax - ymin) / ybins, (ymax / ymin) / ybins][logy] @@ -431,7 +430,7 @@ def conditional_prob_density( else: raise ImportError("Requires statsmodels.") # statsmodels pdf takes values in reverse order - zi = dens_c.pdf(*flattengrid([yi, xi])).reshape(xi.shape) + zi = dens_c.pdf(yi.flatten(), xi.flatten()).reshape(xi.shape) elif mode == "kde": # kde of dataset try: kde = gaussian_kde(np.vstack([x.flatten(), y.flatten()])) @@ -441,7 +440,7 @@ def conditional_prob_density( kde = gaussian_kde(flattengrid([x, y]).T) xkde = gaussian_kde(x[0])(x[0]) # marginal density along x - zi = kde(flattengrid([xi, yi])).T.reshape(xi.shape) / xkde[np.newaxis, :] + zi = kde(flattengrid([xi, yi]).T).reshape(xi.shape) / xkde[np.newaxis, :] elif mode == "binkde": # calclate a kde per bin zi = np.zeros(xi.shape) for bin in range(x.shape[1]): @@ -449,10 +448,8 @@ def conditional_prob_density( zi[:, bin] = kde(yi[:, bin]) elif "hist" in mode.lower(): # simply compute the histogram # histogram monotonically increasing bins - H, hedges = np.histogramdd( - flattengrid([x, y]).T, - bins=[bin_centres_to_edges(xx), bin_centres_to_edges(yy)], - ) + bins = [bin_centres_to_edges(xx), bin_centres_to_edges(yy)] + H, edges = np.histogramdd(flattengrid([x, y]), bins=bins) zi = H.T.reshape(xi.shape) else: raise NotImplementedError From 3302de7e1ff16829e0a435c2a2fefa35df712c1d Mon Sep 17 00:00:00 2001 From: Morgan Williams Date: Tue, 26 Mar 2019 13:29:06 +0800 Subject: [PATCH 2/6] Update Histogram to hist2d --- pyrolite/util/plot.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/pyrolite/util/plot.py b/pyrolite/util/plot.py index 0fd86cd3..4204c007 100644 --- a/pyrolite/util/plot.py +++ b/pyrolite/util/plot.py @@ -449,7 +449,8 @@ def conditional_prob_density( elif "hist" in mode.lower(): # simply compute the histogram # histogram monotonically increasing bins bins = [bin_centres_to_edges(xx), bin_centres_to_edges(yy)] - H, edges = np.histogramdd(flattengrid([x, y]), bins=bins) + assert len(bins) == 2 + H, xedges, yedges = np.histogram2d(x.flatten(), y.flatten(), bins=bins) zi = H.T.reshape(xi.shape) else: raise NotImplementedError From e448d74a4f5a33cfe1a6a6addbbc50b459518c1b Mon Sep 17 00:00:00 2001 From: Morgan Williams Date: Tue, 26 Mar 2019 14:50:06 +0800 Subject: [PATCH 3/6] Update histogram to use log grid --- pyrolite/util/math.py | 4 ++-- pyrolite/util/plot.py | 15 ++++++++++----- 2 files changed, 12 insertions(+), 7 deletions(-) diff --git a/pyrolite/util/math.py b/pyrolite/util/math.py index 9683f78d..1e7c32fa 100644 --- a/pyrolite/util/math.py +++ b/pyrolite/util/math.py @@ -191,8 +191,8 @@ def logspc_(_min, _max, step=1.0, bins=20): :class:`numpy.ndarray` Log-spaced array. """ - if step < 1: - step = 1/step + if step < 1.: + step = 1./step return np.logspace(np.log(_min / step), np.log(_max * step), bins, base=np.e) diff --git a/pyrolite/util/plot.py b/pyrolite/util/plot.py index 4204c007..44a2350a 100644 --- a/pyrolite/util/plot.py +++ b/pyrolite/util/plot.py @@ -139,8 +139,9 @@ def bin_centres_to_edges(centres): ------ * This can be updated to unevenly spaced bins, just need to calculate outer bins. """ - step = (centres[1] - centres[0]) / 2 - return np.append(centres - step, centres[-1] + step) + sortcentres = np.sort(centres.flatten()) + step = (sortcentres[1] - sortcentres[0]) / 2. + return np.append(sortcentres - step, [sortcentres[-1] + step]) def bin_edges_to_centres(edges): @@ -447,10 +448,14 @@ def conditional_prob_density( kde = gaussian_kde(y[:, bin]) zi[:, bin] = kde(yi[:, bin]) elif "hist" in mode.lower(): # simply compute the histogram - # histogram monotonically increasing bins + # histogram monotonically increasing bins, requires logbins be transformed + # calculate histogram in logy if needed + if logy: + y, yy = np.log(y), np.log(yy) bins = [bin_centres_to_edges(xx), bin_centres_to_edges(yy)] - assert len(bins) == 2 - H, xedges, yedges = np.histogram2d(x.flatten(), y.flatten(), bins=bins) + H, xe, ye = np.histogram2d(x.flatten(), y.flatten(), bins=bins) + if logy: + y, yy, yedges = np.exp(y), np.exp(yy), np.exp(ye) zi = H.T.reshape(xi.shape) else: raise 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zo3o05`PF0Qz!RtAL~Irjq~~B2oUtkTirg#KLBEQnX`E3+H2qu_3=gb<{eND&y4T&* zjcDUR8cO9H1sYQ7S|AtW;h(DnnxAwmt5%e@u>6YEK<3X|S=Jm6V&X2%N&bqvpzI=} zZW{#?yPx-#b6Fc_{3-KL~x28K-0XHYR|{c*9hEJ!8BZ5$JEv~u49CG z!$7+1M3=xDq2LN>vBYnA+z^ekoA_hMrhUQd7#xH;X mD*Ww-zy0u^r1<|258_z+(r2y@?H+{B`2n(WJV4*?8~ Date: Tue, 26 Mar 2019 15:06:28 +0800 Subject: [PATCH 5/6] Reinstate util.skl to coverage --- .coveragerc | 1 - 1 file changed, 1 deletion(-) diff --git a/.coveragerc b/.coveragerc index 82bdc670..b98a171f 100644 --- a/.coveragerc +++ b/.coveragerc @@ -10,7 +10,6 @@ omit = # Omit extensions with extra dependencies pyrolite/util/wfs.py pyrolite/util/database.py - pyrolite/util/skl.py # Ignore _version.py */_version.py From d203897e00d96c2e21399938ae7120e0687eaf34 Mon Sep 17 00:00:00 2001 From: Morgan Williams Date: Tue, 26 Mar 2019 16:54:32 +0800 Subject: [PATCH 6/6] Update Util, Tests --- docs/examples/comp/log_transformations.py | 44 +++++++++++++++++++++-- pyrolite/plot/__init__.py | 42 ++++++++++++++++++++-- pyrolite/util/missing.py | 34 ++++++++++++++++++ pyrolite/util/plot.py | 37 ++++++++++++++++++- pyrolite/util/skl.py | 31 ---------------- pyrolite/util/synthetic.py | 5 +++ test/plot/plot_plot.py | 22 ++++++++++++ test/util/util_missing.py | 36 +++++++++++++++++-- test/util/util_plot.py | 30 ++++++++++++++++ test/util/util_skl.py | 27 +------------- 10 files changed, 244 insertions(+), 64 deletions(-) diff --git a/docs/examples/comp/log_transformations.py b/docs/examples/comp/log_transformations.py index 17b2b1cd..623ca0ff 100644 --- a/docs/examples/comp/log_transformations.py +++ b/docs/examples/comp/log_transformations.py @@ -1,6 +1,46 @@ import numpy as np import pandas as pd +from pyrolite.comp.codata import ilr, inverse_ilr, close -from pyrolite.compositions import * +np.random.seed(82) -np.random.seed(110) + +def random_compositional_trend(m1, m2, c1, c2, resolution=20, size=1000): + # generate means intermediate between m1 and m2 + mv = np.vstack([ilr(close(m1)).reshape(1, -1), ilr(close(m2)).reshape(1, -1)]) + ms = np.apply_along_axis(lambda x: np.linspace(*x, resolution), 0, mv) + # generate covariance matricies intermediate between c1 and c2 + cv = np.vstack([c1.reshape(1, -1), c2.reshape(1, -1)]) + cs = np.apply_along_axis(lambda x: np.linspace(*x, resolution), 0, cv) + cs = cs.reshape(cs.shape[0], *c1.shape) + # generate samples from each + samples = np.vstack( + [ + np.random.multivariate_normal(m.flatten(), cs[ix], size=size // resolution) + for ix, m in enumerate(ms) + ] + ) + # combine together. + return inverse_ilr(samples) + + +m1, m2 = np.array([[0.3, 0.1, 2.1]]), np.array([[0.5, 2.5, 0.05]]) +c1, c2 = np.eye(2) / 100, np.eye(2) / 100 + +trend = pd.DataFrame( + random_compositional_trend(m1, m2, c1, c2, resolution=100, size=5000) +) +ax = trend.pyroplot.density(mode="density", bins=100) +ax.tax.scatter( + inverse_ilr(np.nanmean(ilr(trend.values), axis=0)[np.newaxis, :]) * 100, + marker="D", + color="k", + label="LogMean", +) + +ax.tax.scatter( + close(np.nanmean(trend.values, axis=0))[np.newaxis, :] * 100, + marker="o", + color="r", + label="LogMean", +) diff --git a/pyrolite/plot/__init__.py b/pyrolite/plot/__init__.py index 58c460c9..5f590d0d 100644 --- a/pyrolite/plot/__init__.py +++ b/pyrolite/plot/__init__.py @@ -6,6 +6,7 @@ logging.getLogger(__name__).addHandler(logging.NullHandler()) logger = logging.getLogger(__name__) +from ..util.plot import plot_cooccurence from ..util.pd import to_frame from ..util.meta import get_additional_params from ..geochem import common_elements, REE @@ -19,6 +20,7 @@ import pandas as pd +# note that only some of these methods will be valid for series @pd.api.extensions.register_series_accessor("pyroplot") @pd.api.extensions.register_dataframe_accessor("pyroplot") class pyroplot(object): @@ -224,11 +226,47 @@ def REE(self, index="radii", ax=None, mode="plot", **kwargs): obj = to_frame(self._obj) ree = REE() - reedata = obj.loc[:, ree].values - ax = spider.REE_v_radii(reedata, ree=ree, mode=mode, ax=ax, **kwargs) + ax = spider.REE_v_radii( + obj.loc[:, ree].values, ree=ree, mode=mode, ax=ax, **kwargs + ) ax.set_ylabel(" $\mathrm{X / X_{Reference}}$") return ax + def cooccurence( + self, + ax=None, + normalize=True, + log=False, + colorbar=False, + **kwargs + ): + """ + Plot the co-occurence frequency matrix for a given input. + + Parameters + ----------- + ax : :class:`matplotlib.axes.Axes`, :code:`None` + The subplot to draw on. + normalize : :class:`bool` + Whether to normalize the cooccurence to compare disparate variables. + log : :class:`bool` + Whether to take the log of the cooccurence. + colorbar : :class:`bool` + Whether to append a colorbar. + + Returns + -------- + :class:`matplotlib.axes.Axes` + Axes on which the cooccurence plot is added. + """ + obj = to_frame(self._obj) + ax = plot_cooccurence( + obj.values, ax=ax, normalize=normalize, log=log, colorbar=colorbar, **kwargs + ) + ax.set_xticklabels(obj.columns, minor=False, rotation=90) + ax.set_yticklabels(obj.columns, minor=False) + return ax + # ideally we would i) check for the same params and ii) aggregate all others across # inherited or chained functions. This simply imports the params from another docstring diff --git a/pyrolite/util/missing.py b/pyrolite/util/missing.py index 0efe83b7..c65004e0 100644 --- a/pyrolite/util/missing.py +++ b/pyrolite/util/missing.py @@ -46,3 +46,37 @@ def md_pattern(Y): for ID in np.unique(pID).astype(int): pD[ID]["freq"] = np.sum(pID == ID) return pID, pD + + +def cooccurence_pattern(Y, normalize=False, log=False): + """ + Get the co-occurence patterns from an array. + + Parameters + ------------ + Y : :class:`numpy.ndarray` + Input dataset. + normalize : :class:`bool` + Whether to normalize the cooccurence to compare disparate variables. + log : :class:`bool` + Whether to take the log of the cooccurence. + + Returns + --------- + co_occur : :class:`numpy.ndarray` + Cooccurence frequency array. + """ + _Y = Y.copy() + _Y[~np.isfinite(_Y)] = 0 + _Y[_Y > 0] = 1 + _Y = _Y.astype(int) + co_occur = _Y.T @ _Y + d = co_occur.shape[0] + if normalize: + diags = np.diagonal(co_occur) + for i in range(d): + for j in range(d): + co_occur[i, j] = co_occur[i, j] / np.max([diags[i], diags[j]]) + if log: + co_occur = np.log(co_occur) + return co_occur diff --git a/pyrolite/util/plot.py b/pyrolite/util/plot.py index 44a2350a..491757ae 100644 --- a/pyrolite/util/plot.py +++ b/pyrolite/util/plot.py @@ -36,6 +36,7 @@ linspc_, logspc_, ) +from ..util.missing import cooccurence_pattern from ..comp.codata import close, alr, ilr, clr, inverse_alr, inverse_clr, inverse_ilr logging.getLogger(__name__).addHandler(logging.NullHandler()) @@ -140,7 +141,7 @@ def bin_centres_to_edges(centres): * This can be updated to unevenly spaced bins, just need to calculate outer bins. """ sortcentres = np.sort(centres.flatten()) - step = (sortcentres[1] - sortcentres[0]) / 2. + step = (sortcentres[1] - sortcentres[0]) / 2.0 return np.append(sortcentres - step, [sortcentres[-1] + step]) @@ -746,6 +747,40 @@ def plot_Z_percentiles( return cs +def plot_cooccurence(arr, ax=None, normalize=True, log=False, colorbar=False, **kwargs): + """ + Plot the co-occurence frequency matrix for a given input. + + Parameters + ----------- + ax : :class:`matplotlib.axes.Axes`, :code:`None` + The subplot to draw on. + normalize : :class:`bool` + Whether to normalize the cooccurence to compare disparate variables. + log : :class:`bool` + Whether to take the log of the cooccurence. + colorbar : :class:`bool` + Whether to append a colorbar. + + Returns + -------- + :class:`matplotlib.axes.Axes` + Axes on which the cooccurence plot is added. + """ + arr = np.array(arr) + if ax is None: + fig, ax = plt.subplots(1, figsize=(4 + [0.0, 0.2][colorbar], 4)) + co_occur = cooccurence_pattern(arr, normalize=normalize, log=log) + heatmap = ax.pcolor(co_occur, **kwargs) + ax.set_yticks(np.arange(co_occur.shape[0]) + 0.5, minor=False) + ax.set_xticks(np.arange(co_occur.shape[1]) + 0.5, minor=False) + ax.invert_yaxis() + ax.xaxis.tick_top() + if colorbar: + add_colorbar(heatmap, **kwargs) + return ax + + def nan_scatter(xdata, ydata, ax=None, axes_width=0.2, **kwargs): """ Scatter plot with additional marginal axes to plot data for which data is partially diff --git a/pyrolite/util/skl.py b/pyrolite/util/skl.py index 5c265633..76ed2ef3 100644 --- a/pyrolite/util/skl.py +++ b/pyrolite/util/skl.py @@ -188,37 +188,6 @@ def plot_gs_results(gs, xvar=None, yvar=None): return ax -def plot_cooccurence( - df, - ax=None, - normalize=True, - log=False, - norm=mplc.Normalize(vmin=0, vmax=1.0), - **kwargs -): - if ax is None: - fig, ax = plt.subplots(1, figsize=(4.2, 4)) - co_occur = df.fillna(0) - co_occur[co_occur > 0] = 1 - co_occur = co_occur.T.dot(co_occur).astype(int) - if normalize: - diags = np.diagonal(co_occur) - for i in range(diags.shape[0]): - for j in range(diags.shape[0]): - co_occur.iloc[i, j] = co_occur.iloc[i, j] / np.max([diags[i], diags[j]]) - if log: - co_occur = co_occur.applymap(np.log) - heatmap = ax.pcolor(co_occur, norm=norm, **kwargs) - ax.set_yticks(np.arange(co_occur.shape[0]) + 0.5, minor=False) - ax.set_xticks(np.arange(co_occur.shape[1]) + 0.5, minor=False) - ax.invert_yaxis() - ax.xaxis.tick_top() - ax.set_xticklabels(df.columns, minor=False, rotation=90) - ax.set_yticklabels(df.columns, minor=False) - add_colorbar(heatmap) - return ax - - class DropBelowZero(BaseEstimator, TransformerMixin): """ Transformer for scikit-learn like use. diff --git a/pyrolite/util/synthetic.py b/pyrolite/util/synthetic.py index 281e8518..ec3fbaa3 100644 --- a/pyrolite/util/synthetic.py +++ b/pyrolite/util/synthetic.py @@ -18,9 +18,14 @@ def random_cov_matrix(dim, validate=False): -------- :class:`numpy.ndarray` Covariance matrix of shape :code:`(dim, dim)`. + + Todo + ----- + * Implement a characteristic scale for the covariance matrix. """ cov = np.random.randn(dim, dim) cov = np.dot(cov, cov.T) + if validate: try: assert (cov == cov.T).all() diff --git a/test/plot/plot_plot.py b/test/plot/plot_plot.py index 47db2a55..f689302c 100644 --- a/test/plot/plot_plot.py +++ b/test/plot/plot_plot.py @@ -75,6 +75,28 @@ def test_density_with_more_components_specified(self): def test_density_with_more_components_specified_ternary(self): self.multidf.pyroplot.density(components=self.multidf.columns[:3]) + def test_cooccurence_default(self): + self.multidf.pyroplot.cooccurence() + + def test_cooccurence_normalize(self): + for normalize in [True, False]: + with self.subTest(normalize=normalize): + self.multidf.pyroplot.cooccurence(normalize=normalize) + + def test_cooccurence_log(self): + for log in [True, False]: + with self.subTest(log=log): + self.multidf.pyroplot.cooccurence(log=log) + + def test_cooccurence_colorbar(self): + for colorbar in [True, False]: + with self.subTest(colorbar=colorbar): + self.multidf.pyroplot.cooccurence(colorbar=colorbar) + + def test_cooccurencet_external_ax(self): + fig, ax = plt.subplots(1) + self.multidf.pyroplot.cooccurence(ax=ax) + def tearDown(self): plt.close("all") diff --git a/test/util/util_missing.py b/test/util/util_missing.py index 31dce606..1135fc05 100644 --- a/test/util/util_missing.py +++ b/test/util/util_missing.py @@ -1,7 +1,7 @@ import unittest import numpy as np from pyrolite.util.synthetic import random_composition -from pyrolite.util.missing import md_pattern +from pyrolite.util.missing import md_pattern, cooccurence_pattern class TestMDPattern(unittest.TestCase): @@ -23,7 +23,7 @@ def test_md_pattern_random(self): self.assertTrue(pattern_ids.size == self.rdata.shape[0]) for i, d in PD.items(): - rows = (pattern_ids == i) + rows = pattern_ids == i where_present = np.arange(self.rdata.shape[1])[~d["pattern"]] where_not_present = np.arange(self.rdata.shape[1])[d["pattern"]] self.assertTrue([i not in where_not_present for i in where_present]) @@ -33,5 +33,37 @@ def test_md_pattern_random(self): ) +class TestCooccurencePattern(unittest.TestCase): + def setUp(self): + self.static = np.array( + [[0, 1, 1], [0, np.nan, 1], [np.nan, 0, 1], [0, 1, np.nan], [1, np.nan, 0]] + ) + self.rdata = random_composition(size=200, missing="MCAR") + + def test_default(self): + co_occur = cooccurence_pattern(self.static) + self.assertTrue(co_occur.shape == (self.static.shape[1], self.static.shape[1])) + + def test_normalize(self): + for normalize in [True, False]: + with self.subTest(normalize=normalize): + co_occur = cooccurence_pattern(self.static, normalize=normalize) + self.assertTrue( + co_occur.shape == (self.static.shape[1], self.static.shape[1]) + ) + + def test_log(self): + for log in [True, False]: + with self.subTest(log=log): + co_occur = cooccurence_pattern(self.static, log=log) + self.assertTrue( + co_occur.shape == (self.static.shape[1], self.static.shape[1]) + ) + + def test_cooccurence_pattern_random(self): + co_occur = cooccurence_pattern(self.rdata) + self.assertTrue(co_occur.shape == (self.rdata.shape[1], self.rdata.shape[1])) + + if __name__ == "__main__": unittest.main() diff --git a/test/util/util_plot.py b/test/util/util_plot.py index 42133c45..1ec5fb74 100644 --- a/test/util/util_plot.py +++ b/test/util/util_plot.py @@ -206,6 +206,36 @@ def tearDown(self): plt.close("all") +class TestPlotCooccurence(unittest.TestCase): + def setUp(self): + self.rdata = pd.DataFrame( + random_composition(size=200, D=4, missing="MCAR"), + columns=["MgO", "SiO2", "CaO", "TiO2"], + ) + + def test_default(self): + ax = plot_cooccurence(self.rdata) + + def test_normalize(self): + for normalize in [True, False]: + with self.subTest(normalize=normalize): + ax = plot_cooccurence(self.rdata, normalize=normalize) + + def test_log(self): + for log in [True, False]: + with self.subTest(log=log): + ax = plot_cooccurence(self.rdata, log=log) + + def test_colorbar(self): + for colorbar in [True, False]: + with self.subTest(colorbar=colorbar): + ax = plot_cooccurence(self.rdata, colorbar=colorbar) + + def test_external_ax(self): + fig, ax = plt.subplots(1) + ax = plot_cooccurence(self.rdata, ax=ax) + + class TestPlotStDevEllipses(unittest.TestCase): def setUp(self): diff --git a/test/util/util_skl.py b/test/util/util_skl.py index 1cd66f55..c424b6c0 100644 --- a/test/util/util_skl.py +++ b/test/util/util_skl.py @@ -21,32 +21,7 @@ def test_classifier(): HAVE_SKLEARN = False if HAVE_SKLEARN: - from pyrolite.util.skl import ( - LinearTransform, - ExpTransform, - LogTransform, - ALRTransform, - CLRTransform, - ILRTransform, - BoxCoxTransform, - BoxCoxTransform, - DropBelowZero, - ColumnSelector, - TypeSelector, - CompositionalSelector, - MajorsSelector, - ElementSelector, - REESelector, - Devolatilizer, - RedoxAggregator, - ElementAggregator, - PdUnion, - LambdaTransformer, - MultipleImputer, - PdSoftImputer, - plot_confusion_matrix, - plot_gs_results, - ) + from pyrolite.util.skl import * try: import imblearn

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