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Joint kNN-CDF calculation (#36)
* Add joint kNN CDF * add jointKNN calculation * change sub script * Update readme * update sub * Small changes * comments * update nb * Update submisison script
This commit is contained in:
Richard Stiskalek
GitHub
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5 changed files with 4569 additions and 57 deletions
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@ -7,9 +7,10 @@
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## Project Clustering
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- [ ] Add uncertainty to the kNN-CDF autocorrelation.
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- [ ] Add the joint kNN-CDF calculation.
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- [ ] Make kNN-CDF more memory friendly if generating many randoms.
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- [ ] Add uncertainty to the kNN-CDF autocorrelation?
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- [ ] Add kNN-CDF differences.
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- [x] Add the joint kNN-CDF calculation.
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- [x] Make kNN-CDF more memory friendly if generating many randoms.
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## Project Environmental Dependence
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@ -124,6 +124,62 @@ class kNN_CDF:
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cdf[cdf > 0.5] = 1 - cdf[cdf > 0.5]
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return cdf
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@staticmethod
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def clipped_cdf(cdf):
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"""
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Clip the CDF, setting values where the CDF is either 0 or after the
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first occurence of 1 to `numpy.nan`.
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Parameters
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----------
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cdf : 2- or 3-dimensional array
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CDF to be clipped.
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Returns
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-------
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clipped_cdf : 2- or 3-dimensional array
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The clipped CDF.
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"""
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cdf = numpy.copy(cdf)
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if cdf.ndim == 2:
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cdf = cdf.reshape(1, *cdf.shape)
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nknns, nneighbours, __ = cdf.shape
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for i in range(nknns):
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for k in range(nneighbours):
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ns = numpy.where(cdf[i, k, :] == 1.)[0]
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if ns.size > 1:
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cdf[i, k, ns[1]:] = numpy.nan
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cdf[cdf == 0] = numpy.nan
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cdf = cdf[0, ...] if nknns == 1 else cdf # Reshape if necessary
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return cdf
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@staticmethod
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def joint_to_corr(cdf0, cdf1, joint_cdf):
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"""
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Calculate the correlation function from the joint kNN-CDFs.
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Parameters
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----------
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cdf0 : 2-dimensional array
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CDF evaluated at `rs` of the first kNN.
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cdf1 : 2-dimensional array
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CDF evaluated at `rs` of the second kNN.
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joint_cdf : 2-dimensional array
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Joint CDF evaluated at `rs`.
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Returns
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-------
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corr : 2-dimensional array
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Correlation function evaluated at `rs`.
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"""
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assert cdf0.ndim == cdf1.ndim == joint_cdf.ndim == 2
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corr = numpy.zeros_like(joint_cdf)
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for k in range(joint_cdf.shape[0]):
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corr[k, :] = joint_cdf[k, :] - cdf0[k, :] * cdf1[k, :]
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return corr
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def brute_cdf(self, knn, nneighbours, Rmax, nsamples, rmin, rmax, neval,
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random_state=42, dtype=numpy.float32):
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"""
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@ -173,9 +229,102 @@ class kNN_CDF:
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cdf = numpy.asanyarray(cdf)
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return rs, cdf
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def joint(self, knn0, knn1, nneighbours, Rmax, nsamples, rmin, rmax,
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neval, batch_size=None, random_state=42,
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dtype=numpy.float32):
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"""
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Calculate the joint CDF for two kNNs of CSiBORG halo catalogues.
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Parameters
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----------
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knn0 : `sklearn.neighbors.NearestNeighbors` instance
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kNN of the first CSiBORG halo catalogue.
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knn1 : `sklearn.neighbors.NearestNeighbors` instance
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kNN of the second CSiBORG halo catalogue.
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neighbours : int
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Maximum number of neighbours to use for the kNN-CDF calculation.
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Rmax : float
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Maximum radius of the sphere in which to sample random points for
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the knn-CDF calculation. This should match the CSiBORG catalogues.
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nsamples : int
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Number of random points to sample for the knn-CDF calculation.
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rmin : float
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Minimum distance to evaluate the CDF.
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rmax : float
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Maximum distance to evaluate the CDF.
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neval : int
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Number of points to evaluate the CDF.
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batch_size : int, optional
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Number of random points to sample in each batch. By default equal
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to `nsamples`, however recommeded to be smaller to avoid requesting
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too much memory,
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random_state : int, optional
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Random state for the random number generator.
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dtype : numpy dtype, optional
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Calculation data type. By default `numpy.float32`.
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Returns
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-------
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rs : 1-dimensional array
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Distances at which the CDF is evaluated.
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cdf0 : 2-dimensional array
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CDF evaluated at `rs` of the first kNN.
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cdf1 : 2-dimensional array
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CDF evaluated at `rs` of the second kNN.
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joint_cdf : 2-dimensional array
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Joint CDF evaluated at `rs`.
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"""
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batch_size = nsamples if batch_size is None else batch_size
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assert nsamples >= batch_size
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nbatches = nsamples // batch_size
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bins = numpy.logspace(numpy.log10(rmin), numpy.log10(rmax), neval)
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joint_cdf = numpy.zeros((nneighbours, neval - 1), dtype=dtype)
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cdf0 = numpy.zeros_like(joint_cdf)
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cdf1 = numpy.zeros_like(joint_cdf)
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jointdist = numpy.zeros((batch_size, 2), dtype=dtype)
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for j in range(nbatches):
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rand = self.rvs_in_sphere(batch_size, Rmax,
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random_state=random_state + j)
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dist0, __ = knn0.kneighbors(rand, nneighbours)
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dist1, __ = knn1.kneighbors(rand, nneighbours)
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for k in range(nneighbours):
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jointdist[:, 0] = dist0[:, k]
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jointdist[:, 1] = dist1[:, k]
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maxdist = numpy.max(jointdist, axis=1)
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# Joint CDF
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_counts, __, __ = binned_statistic(
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maxdist, maxdist, bins=bins, statistic="count",
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range=(rmin, rmax))
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joint_cdf[k, :] += _counts
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# First CDF
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_counts, __, __ = binned_statistic(
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dist0[:, k], dist0[:, k], bins=bins, statistic="count",
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range=(rmin, rmax))
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cdf0[k, :] += _counts
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# Second CDF
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_counts, __, __ = binned_statistic(
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dist1[:, k], dist1[:, k], bins=bins, statistic="count",
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range=(rmin, rmax))
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cdf1[k, :] += _counts
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joint_cdf = numpy.cumsum(joint_cdf, axis=-1)
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cdf0 = numpy.cumsum(cdf0, axis=-1)
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cdf1 = numpy.cumsum(cdf1, axis=-1)
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for k in range(nneighbours):
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joint_cdf[k, :] /= joint_cdf[k, -1]
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cdf0[k, :] /= cdf0[k, -1]
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cdf1[k, :] /= cdf1[k, -1]
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rs = (bins[1:] + bins[:-1]) / 2 # Bin centers
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return rs, cdf0, cdf1, joint_cdf
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def __call__(self, *knns, nneighbours, Rmax, nsamples, rmin, rmax, neval,
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batch_size=None, verbose=True, random_state=42,
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left_nan=True, right_nan=True, dtype=numpy.float32):
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dtype=numpy.float32):
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"""
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Calculate the CDF for a set of kNNs of CSiBORG halo catalogues.
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@ -204,12 +353,6 @@ class kNN_CDF:
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Verbosity flag.
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random_state : int, optional
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Random state for the random number generator.
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left_nan : bool, optional
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Whether to set values where the CDF is 0 to `numpy.nan`. By
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default `True`.
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right_nan : bool, optional
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Whether to set values where the CDF is 1 to `numpy.nan` after its
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first occurence to 1. By default `True`.
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dtype : numpy dtype, optional
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Calculation data type. By default `numpy.float32`.
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@ -222,38 +365,28 @@ class kNN_CDF:
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"""
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batch_size = nsamples if batch_size is None else batch_size
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assert nsamples >= batch_size
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nbatches = nsamples // batch_size # Number of batches
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nbatches = nsamples // batch_size
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# Preallocate the bins and the CDF array
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bins = numpy.logspace(numpy.log10(rmin), numpy.log10(rmax), neval)
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cdfs = numpy.zeros((len(knns), nneighbours, neval - 1), dtype=dtype)
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for i, knn in enumerate(tqdm(knns) if verbose else knns):
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# Loop over batches. This is to avoid generating large mocks
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# requiring a lot of memory. Add counts to the CDF array
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for j in range(nbatches):
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rand = self.rvs_in_sphere(batch_size, Rmax,
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random_state=random_state + j)
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dist, __ = knn.kneighbors(rand, nneighbours)
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for k in range(nneighbours): # Count for each neighbour
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_counts, __, __ = binned_statistic(
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dist[:, k], dist[:, k], bins=bins, statistic="count",
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range=(rmin, rmax))
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cdfs[i, k, :] += _counts
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rs = (bins[1:] + bins[:-1]) / 2 # Bin centers
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cdfs = numpy.cumsum(cdfs, axis=-1) # Cumulative sum, i.e. the CDF
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for i in range(len(knns)):
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for k in range(nneighbours):
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cdfs[i, k, :] /= cdfs[i, k, -1]
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# Set to NaN values after the first point where the CDF is 1
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if right_nan:
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ns = numpy.where(cdfs[i, k, :] == 1.)[0]
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if ns.size > 1:
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cdfs[i, k, ns[1]:] = numpy.nan
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# Set to NaN values where the CDF is 0
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if left_nan:
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cdfs[cdfs == 0] = numpy.nan
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rs = (bins[1:] + bins[:-1]) / 2 # Bin centers
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cdfs = cdfs[0, ...] if len(knns) == 1 else cdfs
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return rs, cdfs
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4359
notebooks/knn.ipynb
4359
notebooks/knn.ipynb
File diff suppressed because one or more lines are too long
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@ -17,6 +17,7 @@ from os.path import join
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from argparse import ArgumentParser
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from copy import deepcopy
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from datetime import datetime
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from itertools import combinations
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from mpi4py import MPI
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from TaskmasterMPI import master_process, worker_process
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from sklearn.neighbors import NearestNeighbors
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@ -59,7 +60,8 @@ ics = [7444, 7468, 7492, 7516, 7540, 7564, 7588, 7612, 7636, 7660, 7684,
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9556, 9580, 9604, 9628, 9652, 9676, 9700, 9724, 9748, 9772, 9796,
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9820, 9844]
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dumpdir = "/mnt/extraspace/rstiskalek/csiborg/knn"
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fout = join(dumpdir, "knncdf_{}.p")
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fout_auto = join(dumpdir, "auto", "knncdf_{}.p")
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fout_cross = join(dumpdir, "cross", "knncdf_{}_{}.p")
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###############################################################################
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@ -68,7 +70,7 @@ fout = join(dumpdir, "knncdf_{}.p")
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knncdf = csiborgtools.match.kNN_CDF()
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def do_task(ic):
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def do_auto(ic):
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out = {}
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cat = csiborgtools.read.HaloCatalogue(ic, max_dist=Rmax)
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@ -83,7 +85,39 @@ def do_task(ic):
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out.update({"cdf_{}".format(i): cdf})
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out.update({"rs": rs, "mass_threshold": mass_threshold})
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joblib.dump(out, fout.format(ic))
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joblib.dump(out, fout_auto.format(ic))
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def do_cross(ics):
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out = {}
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cat1 = csiborgtools.read.HaloCatalogue(ics[0], max_dist=Rmax)
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cat2 = csiborgtools.read.HaloCatalogue(ics[1], max_dist=Rmax)
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for i, mmin in enumerate(mass_threshold):
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knn1 = NearestNeighbors()
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knn1.fit(cat1.positions[cat1["totpartmass"] > mmin, ...])
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knn2 = NearestNeighbors()
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knn2.fit(cat2.positions[cat2["totpartmass"] > mmin, ...])
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rs, cdf0, cdf1, joint_cdf = knncdf.joint(
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knn1, knn2, nneighbours=args.nneighbours, Rmax=Rmax,
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rmin=args.rmin, rmax=args.rmax, nsamples=args.nsamples,
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neval=args.neval, batch_size=args.batch_size,
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random_state=args.seed)
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corr = knncdf.joint_to_corr(cdf0, cdf1, joint_cdf)
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out.update({"corr_{}".format(i): corr})
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out.update({"rs": rs, "mass_threshold": mass_threshold})
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joblib.dump(out, fout_cross.format(*ics))
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###############################################################################
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# Autocorrelation calculation #
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###############################################################################
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if nproc > 1:
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@ -91,15 +125,34 @@ if nproc > 1:
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tasks = deepcopy(ics)
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master_process(tasks, comm, verbose=True)
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else:
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worker_process(do_task, comm, verbose=False)
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worker_process(do_auto, comm, verbose=False)
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else:
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tasks = deepcopy(ics)
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for task in tasks:
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print("{}: completing task `{}`.".format(datetime.now(), task))
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do_task(task)
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do_auto(task)
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comm.Barrier()
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###############################################################################
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# Crosscorrelation calculation #
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###############################################################################
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if nproc > 1:
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if rank == 0:
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tasks = list(combinations(ics, 2))
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master_process(tasks, comm, verbose=True)
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else:
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worker_process(do_cross, comm, verbose=False)
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else:
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tasks = deepcopy(ics)
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for task in tasks:
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print("{}: completing task `{}`.".format(datetime.now(), task))
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do_cross(task)
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comm.Barrier()
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if rank == 0:
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print("{}: all finished.".format(datetime.now()))
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quit() # Force quit the script
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quit() # Force quit the script
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@ -1,21 +1,17 @@
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nthreads=30
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memory=7
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queue="berg"
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nthreads=151
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memory=4
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queue="cmb"
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env="/mnt/zfsusers/rstiskalek/csiborgtools/venv_galomatch/bin/python"
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file="run_knn.py"
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rmin=0.01
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rmax=100
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nneighbours=16
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nsamples=1000000000
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batch_size=10000000
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nneighbours=8
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nsamples=100000000
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batch_size=1000000
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neval=10000
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# 1000,000,0
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# 10000000 # 1e7
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# 1000000000
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pythoncm="$env $file --rmin $rmin --rmax $rmax --nneighbours $nneighbours --nsamples $nsamples --neval $neval"
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pythoncm="$env $file --rmin $rmin --rmax $rmax --nneighbours $nneighbours --nsamples $nsamples --batch_size $batch_size --neval $neval"
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# echo $pythoncm
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# $pythoncm
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