From 92e72c3bf72c4740a9dbadc949b7849b03167f1d Mon Sep 17 00:00:00 2001 From: Tatiana Dembelova Date: Thu, 25 May 2017 22:31:15 +0200 Subject: [PATCH] uds - init commit (always returns zero for now... :( ) --- correlation_measures/__init__.py | 0 correlation_measures/binning.py | 18 + data/crime_small.csv | 1994 ++++++++++++++++++++++++++++++ data/testdata.csv | 72 ++ main.py | 18 +- test_uds.py | 13 + uds.py | 142 +++ 7 files changed, 2247 insertions(+), 10 deletions(-) create mode 100644 correlation_measures/__init__.py create mode 100644 correlation_measures/binning.py create mode 100644 data/crime_small.csv create mode 100644 data/testdata.csv create mode 100644 test_uds.py create mode 100644 uds.py diff --git a/correlation_measures/__init__.py b/correlation_measures/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/correlation_measures/binning.py b/correlation_measures/binning.py new file mode 100644 index 0000000..b919368 --- /dev/null +++ b/correlation_measures/binning.py @@ -0,0 +1,18 @@ +import pandas as pd + + +class Binning: + def __init__(self, data): + self.rank_data = data.rank(method='first') + + # todo (small reminder) in the original ipd it is NOT equal binning + # Series of binned points (with dropDuplicates produces not equally frequent bins) + def equal_frequency_binning(self, dim, bins_count): + return pd.qcut(self.rank_data.sort_values(by=dim)[dim], bins_count) + + def equal_frequency_binning2(self, dim, bins_count): + qcut = pd.qcut(self.rank_data.sort_values(by=dim)[dim], bins_count) + return qcut.cat.rename_categories([i for i in range(bins_count)]).reindex(qcut.index) + + def get_rank_data(self): + return self.rank_data diff --git a/data/crime_small.csv b/data/crime_small.csv new file mode 100644 index 0000000..be131ee --- /dev/null +++ b/data/crime_small.csv @@ -0,0 +1,1994 @@ +0.19,0.33,0.02,0.9,0.12,0.17,0.34,0.47,0.29,0.32,0.2,0 +0,0.16,0.12,0.74,0.45,0.07,0.26,0.59,0.35,0.27,0.02,0 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+0.2,0.2,0.2,0.2,1 +0.2,0.2,0.2,0.2,1 +0.2,0.2,0.2,0.2,1 +0.2,0.2,0.2,0.2,1 +0.2,0.2,0.2,0.2,1 +0.2,0.2,0.2,0.2,1 +0.2,0.2,0.2,0.2,1 +0.2,0.2,0.2,0.2,1 +0.2,0.2,0.2,0.2,1 \ No newline at end of file diff --git a/main.py b/main.py index 20d41b1..11ae771 100644 --- a/main.py +++ b/main.py @@ -2,6 +2,7 @@ import pandas as pd import numpy as np +from correlation_measures.binning import Binning from interaction_distance import computeIDs, compute_ID_threshold from merging import dynamic_merging @@ -59,8 +60,8 @@ def compute_optimal_discretization(data): # number of initial dist_bins # initBinsCount = int(math.ceil(math.sqrt(row_count))) # ceil in original ipd... # todo remove later - initBinsCount = 20 # ceil in original ipd... - print('initBinsCount: ', initBinsCount) + init_bins_count = 20 # ceil in original ipd... + print('initBinsCount: ', init_bins_count) # normalization step todo(optional) @@ -69,15 +70,12 @@ def compute_optimal_discretization(data): disc_macro_intervals = [] disc_points = [] - rank_data = data.rank(method='first') + orig_binning = Binning(data) + rank_data = orig_binning.get_rank_data() # iterate over all the dimensions for curr in range(dim_count): - # original ids -> ranked_ids in the current dimension - rank_data = rank_data.sort_values(by=curr) - # todo (small reminder) in the original ipd it is NOT equal binning - # Series of binned points - bin_map = pd.qcut(rank_data[curr], initBinsCount) + bin_map = orig_binning.equal_frequency_binning(curr, init_bins_count) # distinct bins dist_bins = bin_map.drop_duplicates().values @@ -93,7 +91,7 @@ def compute_optimal_discretization(data): # todo replace by empty method later # table with costs, the value in i-th row and j-th column means cost of (i+1) micro bins merged into (j+1) macro bins - F, discretizations = dynamic_merging(ID_threshold, IDs, initBinsCount) + F, discretizations = dynamic_merging(ID_threshold, IDs, init_bins_count) print('dimension ' + str(curr)) min_id = np.argmin(F[-1]) @@ -131,7 +129,7 @@ def get_discretized_points(curr, data, discretizations, dist_bins, min_id, rank_ for point in data.iterrows(): macro_points.append(find_disc_macro_id(disc_macro_intervals, point[1][curr])) - return (disc_macro_intervals, macro_points) + return disc_macro_intervals, macro_points data = pd.read_csv('example/simple.csv', delimiter=';', header=None) diff --git a/test_uds.py b/test_uds.py new file mode 100644 index 0000000..5a0c8dc --- /dev/null +++ b/test_uds.py @@ -0,0 +1,13 @@ +from unittest import TestCase +import uds as u +import pandas as pd + + +class TestUds(TestCase): + + def setUp(self): + self.data = pd.read_csv('data/testdata.csv', delimiter=',', header=None) + + def test_compute_CEs(self): + c_es = u.compute_CEs(self.data) + print(c_es) diff --git a/uds.py b/uds.py new file mode 100644 index 0000000..0775981 --- /dev/null +++ b/uds.py @@ -0,0 +1,142 @@ +import math +import pandas as pd +import numpy as np +from correlation_measures.binning import Binning + +# bins count +BETA = 20 + + +def compute_cond_CE(data, dim, I, point_ids): + totalPointsCount = data.shape[0] + return sum([len(c) / totalPointsCount * compute_CE(data.loc[point_ids.intersection(c), dim]) for c in I]) + + +# discretization of the next dimension +def dim_optimal_disc(prev, curr, binning, I, data): + binned_points = binning.equal_frequency_binning2(prev, BETA) + + # Series with bins support + support = binned_points.value_counts().sort_index().cumsum() + + b = [] + val = [] + + merged_bins = [] + # compute cost of single merged bins (transposed unlike in the paper) i - row, upper bound; j - column, lower bound + # todo worth considering implementation of the unfunc in C (slow) + f = [] + # upper bound + for i in range(BETA): + f_row = [] + merged_bins_row = [] + # lower bound + for j in range(i + 1): + merged_bin = binned_points[np.logical_and(binned_points <= i, binned_points >= j)].index + merged_bins_row.append(merged_bin) + + f_row.append(compute_cond_CE(data, curr, I, merged_bin)) + f.append(f_row) + merged_bins.append(merged_bins_row) + + b.append([[merged_bins_row[0]]]) + val.append([f_row[0]]) + + for l in range(1, BETA): + for i in range(l, BETA): + min_cost = None + arg_min = None + for j in range(l - 1, i): + temp_cost = (support[i] - support[j]) / support[i] * f[i][j + 1] + support[j] / support[i] * val[j][ + l - 1] + if not min_cost or temp_cost < min_cost: + min_cost = temp_cost + arg_min = j + + # val[i][l] + val[i].append(min_cost) + disc = b[arg_min][l - 1].copy() + disc.append(merged_bins[i][arg_min + 1]) + b[i].append(disc) + + return val[-1], b[-1] + + +def compute_CEs(data): + dim_count = data.shape[1] + CEs = [] + for curr in range(dim_count): + CE = compute_CE(data[curr]) + CEs.append(CE) + return CEs + + +def compute_CE(data): + m = data.shape[0] + if m <= 1: + return 0 + curr_data = data.sort_values() + data_diff = (curr_data[1:] - curr_data.shift(1)[1:]).reset_index(drop=True) + CE = -math.log(pd.Series([((i + 1) / m) ** ((i + 1) * data_diff[i] / m) for i in range(len(data_diff))]).prod(), 2) + return CE + + +def entropy(I, N): + return - sum([len(i) / N * math.log(len(i) / N, 2) for i in I]) + + +# compute permutation +def compute_permutation(CEs): + argsort = np.argsort(CEs).tolist() + argsort.reverse() + return argsort + + +def extend_I(I, disc): + disc_ = [i.intersection(j) for i in I for j in disc] + + return [d for d in disc_ if not d.empty] + + +if __name__ == "__main__": + data = pd.read_csv('data/testdata.csv', delimiter=',', header=None) + classLabels = data.pop(len(data.columns) - 1) + dim_count = data.shape[1] + + binning = Binning(data) + + # compute CE for all the dimensions + CEs = compute_CEs(data) + + perm = compute_permutation(CEs) + + # discretized dimensions array of arrays of point ids + I = [data.index] + es = [] + + uds = 0 + prev = perm[0] + for dim in perm[1:]: + # todo should I pass binning? + costs, discs = dim_optimal_disc(prev, dim, binning, I, data) + + # regularization step + opt_cost = None + opt_l = None + opt_I = None + for l, cost in enumerate(costs): + temp_I = extend_I(I, discs[l]) + temp_cost = cost / CEs[dim] + entropy(temp_I, len(data)) / ( + math.log(BETA, 2) + sum([math.log(e + 1, 2) for e in es])) + if not opt_cost or temp_cost < opt_cost: + opt_cost = cost + opt_l = l + opt_I = temp_I + + I = opt_I + es.append(opt_l) + uds += CEs[dim] - opt_cost + prev = dim + uds /= sum(CEs[1:]) + + print(uds)