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Makefile.old
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#
# Old bits of the Makefile that are not used at the moment; to be
# converted to Stu code if needed. Most of these are used for link
# prediction, which was used in Jérôme Kunegis' PhD thesis and papers of
# that era.
#
#
# Spectral extrapolation (visualization)
#
define TEMPLATE_spectral_extrapolation_all
$(foreach NETWORK, $(NETWORKS), spectral_extrapolation.$(1).$(NETWORK)): \
spectral_extrapolation.$(1).%: plot/spectral_extrapolation.$(1).%.eps
plot/spectral_extrapolation.$(1).%.eps: m/spectral_extrapolation.m dat/decomposition_time.split.$(1).%.mat dat/steps.%.mat
NETWORK=$$* DECOMPOSITION=$(1) $(OCTAVE) m/spectral_extrapolation
endef
$(foreach DECOMPOSITION, $(DECOMPOSITIONS_ANY), $(eval $(call TEMPLATE_spectral_extrapolation_all,$(DECOMPOSITION))))
define TEMPLATE_spectral_extrapolation_square
$(foreach NETWORK, $(NETWORKS_SQUARE), spectral_extrapolation.$(1).$(NETWORK)): \
spectral_extrapolation.$(1).%: plot/spectral_extrapolation.$(1).%.eps
plot/spectral_extrapolation.$(1).%.eps: m/spectral_extrapolation.m dat/decomposition_time.split.$(1).%.mat dat/steps.%.mat
NETWORK=$$* DECOMPOSITION=$(1) $(OCTAVE) m/spectral_extrapolation
endef
$(foreach DECOMPOSITION, $(DECOMPOSITIONS_SQUARE), $(eval $(call TEMPLATE_spectral_extrapolation_square,$(DECOMPOSITION))))
define TEMPLATE_spectral_extrapolation_asym
$(foreach NETWORK, $(NETWORKS_ASYM), spectral_extrapolation.$(1).$(NETWORK)): \
spectral_extrapolation.$(1).%: plot/spectral_extrapolation.$(1).%.eps
plot/spectral_extrapolation.$(1).%.eps: m/spectral_extrapolation.m dat/decomposition_time.split.$(1).%.mat dat/steps.%.mat
NETWORK=$$* DECOMPOSITION=$(1) $(OCTAVE) m/spectral_extrapolation
endef
$(foreach DECOMPOSITION, $(DECOMPOSITIONS_ASYM), $(eval $(call TEMPLATE_spectral_extrapolation_asym,$(DECOMPOSITION))))
$(foreach NETWORK, $(NETWORKS), spectral_extrapolation.all.$(NETWORK)): spectral_extrapolation.all.%: $(foreach DECOMPOSITION, $(DECOMPOSITIONS_ANY), spectral_extrapolation.$(DECOMPOSITION).%)
$(foreach NETWORK, $(NETWORKS_SQUARE), spectral_extrapolation.all.$(NETWORK)): spectral_extrapolation.all.%: $(foreach DECOMPOSITION, $(DECOMPOSITIONS_SQUARE), spectral_extrapolation.$(DECOMPOSITION).%)
$(foreach NETWORK, $(NETWORKS_ASYM), spectral_extrapolation.all.$(NETWORK)): spectral_extrapolation.all.%: $(foreach DECOMPOSITION, $(DECOMPOSITIONS_ASYM), spectral_extrapolation.$(DECOMPOSITION).%)
#
# Comparison of link prediction precision
#
comparison.all.all: $(foreach CLASS, $(CLASSES), comparison.any.$(CLASS) comparison.asym.$(CLASS) comparison.bip.$(CLASS) comparison.any-fast.$(CLASS) comparison.asym-fast.$(CLASS))
comparison.any.all: $(foreach CLASS, $(CLASSES), comparison.any.$(CLASS))
comparison.asym.all: $(foreach CLASS, $(CLASSES), comparison.asym.$(CLASS))
comparison.bip.all: $(foreach CLASS, $(CLASSES), comparison.bip.$(CLASS))
comparison.any-fast.all: $(foreach CLASS, $(CLASSES), comparison.any-fast.$(CLASS))
comparison.asym-fast.all: $(foreach CLASS, $(CLASSES), comparison.asym-fast.$(CLASS))
define TEMPLATE_comparison # $(1) = $(CLASS)
comparison.all.$(1): comparison.any.$(1) comparison.asym.$(1) comparison.bip.$(1)
comparison.any.$(1): plot/comparison.a.auc.any.$(1).eps
comparison.asym.$(1): plot/comparison.a.auc.asym.$(1).eps
comparison.bip.$(1): plot/comparison.a.auc.bip.$(1).eps
comparison.any-fast.$(1): plot/comparison.a.auc.any-fast.$(1).eps
comparison.asym-fast.$(1): plot/comparison.a.auc.asym-fast.$(1).eps
plot/comparison.a.auc.any.$(1).eps: m/comparison.m $$(foreach NETWORK, $$(NETWORKS_PREDICTION_$(1)), $$(foreach METHOD, $$(METHODS_LOCAL) $$(DECOMPOSITIONS_ANY), dat/precision.$$(METHOD).$$(NETWORK).mat))
NETWORKS="$$(NETWORKS_PREDICTION_$(1))" METHODS="$(METHODS_LOCAL) $(DECOMPOSITIONS_ANY)" NAME=any.$(1) $(OCTAVE) m/comparison
plot/comparison.a.auc.asym.$(1).eps: m/comparison.m $$(foreach NETWORK, $$(filter $$(NETWORKS_PREDICTION_$(1)), $$(NETWORKS_ASYM)), \
$$(foreach METHOD, $$(METHODS_LOCAL) $$(DECOMPOSITIONS), dat/precision.$$(METHOD).$$(NETWORK).mat))
NETWORKS="$$(filter $$(NETWORKS_PREDICTION_$(1)), $$(NETWORKS_ASYM))" METHODS="$(METHODS_LOCAL) $(DECOMPOSITIONS)" NAME=asym.$(1) $(OCTAVE) m/comparison
plot/comparison.a.auc.bip.$(1).eps: m/comparison.m $$(foreach NETWORK, $$(filter $$(NETWORKS_PREDICTION_$(1)), $$(NETWORKS_BIP)), \
$$(foreach METHOD, $$(METHODS_LOCAL) $$(DECOMPOSITIONS_ANY), dat/precision.$$(METHOD).$$(NETWORK).mat))
NETWORKS="$$(filter $$(NETWORKS_PREDICTION_$(1)), $$(NETWORKS_BIP))" METHODS="$(METHODS_LOCAL) $(DECOMPOSITIONS_ANY)" NAME=bip.$(1) $(OCTAVE) m/comparison
plot/comparison.a.auc.any-fast.$(1).eps: m/comparison.m $$(foreach NETWORK, $$(NETWORKS_PREDICTION_$(1)), $$(foreach METHOD, $$(METHODS_LOCAL) $$(DECOMPOSITIONS_ANY_FAST), \
dat/precision.$$(METHOD).$$(NETWORK).mat))
NETWORKS="$$(NETWORKS_PREDICTION_$(1))" METHODS="$(METHODS_LOCAL) $(DECOMPOSITIONS_ANY_FAST)" NAME=any-fast.$(1) $(OCTAVE) m/comparison
plot/comparison.a.auc.asym-fast.$(1).eps: m/comparison.m $$(foreach NETWORK, $$(filter $$(NETWORKS_PREDICTION_$(1)), $$(NETWORKS_ASYM)), \
$$(foreach METHOD, $$(METHODS_LOCAL) $$(DECOMPOSITIONS_FAST), dat/precision.$$(METHOD).$$(NETWORK).mat))
NETWORKS="$$(filter $$(NETWORKS_PREDICTION_$(1)), $$(NETWORKS_ASYM))" METHODS="$(METHODS_LOCAL) $(DECOMPOSITIONS_FAST)" NAME=asym-fast.$(1) $(OCTAVE) m/comparison
endef
$(foreach CLASS, $(CLASSES), $(eval $(call TEMPLATE_comparison,$(CLASS))))
#
# Curve fitting
#
define TEMPLATE_fit
$(foreach NETWORK, $(NETWORKS), fit.$(1).$(NETWORK)): \
fit.$(1).%: plot/fit.a.$(1).%.eps
dat/fit.$(1).%.mat: \
dat/info.% dat/decomposition_split.source.$(1).%.mat \
dat/split.%.mat dat/means.%.mat m/fit.m
NETWORK=$$* DECOMPOSITION=$(1) $(OCTAVE) m/fit
plot/fit.a.$(1).%.eps: dat/fit.$(1).%.mat m/fit_plot.m
NETWORK=$$* DECOMPOSITION=$(1) $(OCTAVE) m/fit_plot
endef
$(foreach DECOMPOSITION, $(DECOMPOSITIONS), $(eval $(call TEMPLATE_fit,$(DECOMPOSITION))))
define TEMPLATE_fit_asym
fit.$(1).all: $(foreach NETWORK, $(NETWORKS_ASYM), fit.$(1).$(NETWORK))
endef
$(foreach DECOMPOSITION, $(DECOMPOSITIONS_ASYM), $(eval $(call TEMPLATE_fit_asym,$(DECOMPOSITION))))
define TEMPLATE_fit_any
fit.$(1).all: $(foreach NETWORK, $(NETWORKS), fit.$(1).$(NETWORK))
endef
$(foreach DECOMPOSITION, $(DECOMPOSITIONS_ANY), $(eval $(call TEMPLATE_fit_any,$(DECOMPOSITION))))
$(foreach NETWORK, $(NETWORKS), fit.all.$(NETWORK)): \
fit.all.%: $(foreach DECOMPOSITION, $(DECOMPOSITIONS_ANY), fit.$(DECOMPOSITION).%)
$(foreach NETWORK, $(NETWORKS_ASYM), fit.all.$(NETWORK)): \
fit.all.%: $(foreach DECOMPOSITION, $(DECOMPOSITIONS_ASYM), fit.$(DECOMPOSITION).%)
#
# Local prediction
#
define TEMPLATE_prediction_local
$(foreach NETWORK, $(NETWORKS), prediction.$(1).$(NETWORK)): \
prediction.$(1).%: dat/prediction.$(1).%.mat
dat/prediction.$(1).%.mat: m/prediction_local.m dat/split.%.mat dat/meanst.%.mat dat/info.%
NETWORK=$$* METHOD=$(1) $(OCTAVE) m/prediction_local
endef
$(foreach METHOD, $(METHODS_LOCAL), $(eval $(call TEMPLATE_prediction_local,$(METHOD))))
#
# Prediction by decomposition
#
define TEMPLATE_prediction_decomposition
$(foreach NETWORK, $(NETWORKS), prediction.$(1).$(NETWORK)): \
prediction.$(1).%: dat/prediction.$(1).%.mat
dat/prediction.$(1).%.mat: m/prediction_decomposition.m dat/info.% dat/split.%.mat \
dat/decomposition_split.training.$(1).%.mat dat/decomposition_split.source.$(1).%.mat \
dat/fit.$(1).%.mat
NETWORK=$$* DECOMPOSITION=$(1) $(OCTAVE) m/prediction_decomposition
endef
$(foreach DECOMPOSITION, $(DECOMPOSITIONS), $(eval $(call TEMPLATE_prediction_decomposition,$(DECOMPOSITION))))
#
# Precision of link prediction (ONE)
#
define TEMPLATE_precision # $(1) = $(METHOD)
$(foreach NETWORK, $(NETWORKS), precision_one.$(1).$(NETWORK)): \
precision_one.$(1).%: plot/precision.one.a.auc.$(1).%.eps
dat/precision.$(1).%.mat: dat/prediction.$(1).%.mat m/precision_comp.m dat/split.%.mat dat/meanst.%.mat
NETWORK=$$* METHOD=$(1) $(OCTAVE) m/precision_comp
plot/precision.one.a.auc.$(1).%.eps: dat/precision.$(1).%.mat m/precision_one.m
NETWORK=$$* METHOD=$(1) $(OCTAVE) m/precision_one
endef
$(foreach METHOD, $(METHODS_LOCAL) $(DECOMPOSITIONS), $(eval $(call TEMPLATE_precision,$(METHOD))))
precision_one.all.all: $(foreach NETWORK, $(NETWORKS), precision_one.all.$(NETWORK))
$(foreach NETWORK, $(NETWORKS), precision_one.all.$(NETWORK)): \
precision_one.all.%: $(foreach METHOD, $(METHODS_LOCAL) $(DECOMPOSITIONS_ANY), precision_one.$(METHOD).%)
$(foreach NETWORK, $(NETWORKS_ASYM), precision_one.all.$(NETWORK)): \
precision_one.all.%: $(foreach METHOD, $(DECOMPOSITIONS_ASYM), precision_one.$(METHOD).%)
$(foreach DECOMPOSITION, $(DECOMPOSITIONS_ANY), precision_one.$(DECOMPOSITION).all): \
precision_one.%.all: $(foreach NETWORK, $(NETWORKS), precision_one.%.$(NETWORK))
$(foreach DECOMPOSITION, $(DECOMPOSITIONS_ASYM), precision_one.$(DECOMPOSITION).all): \
precision_one.%.all: $(foreach NETWORK, $(NETWORKS_ASYM), precision_one.%.$(NETWORK))
#
# Precision of link prediction (ALL)
#
precision_all.all: $(foreach NETWORK, $(NETWORKS), precision_all.$(NETWORK))
precision_all.asym: $(foreach NETWORK, $(NETWORKS_ASYM), precision_all.$(NETWORK))
$(foreach NETWORK, $(NETWORKS), precision_all.$(NETWORK)): \
precision_all.%: plot/precision.all.a.auc.%.eps
$(foreach NETWORK, $(NETWORKS_BIP) $(NETWORKS_SYM), plot/precision.all.a.auc.$(NETWORK).eps): \
plot/precision.all.a.auc.%.eps: m/precision_all.m \
$(foreach METHOD, $(METHODS_LOCAL) $(DECOMPOSITIONS_ANY), dat/precision.$(METHOD).%.mat)
NETWORK=$* METHODS="$(METHODS_LOCAL) $(DECOMPOSITIONS_ANY)" $(OCTAVE) m/precision_all
$(foreach NETWORK, $(NETWORKS_ASYM), plot/precision.all.a.auc.$(NETWORK).eps): \
plot/precision.all.a.auc.%.eps: m/precision_all.m \
$(foreach METHOD, $(METHODS_LOCAL) $(DECOMPOSITIONS_ANY) $(DECOMPOSITIONS_ASYM), dat/precision.$(METHOD).%.mat)
NETWORK=$* METHODS="$(METHODS_LOCAL) $(DECOMPOSITIONS_ANY) $(DECOMPOSITIONS_ASYM)" $(OCTAVE) m/precision_all
#
# Check how well a matrix decomposition is an approximation
#
approximation.all: $(foreach NETWORK, $(NETWORKS_ASYM), approximation.$(NETWORK))
$(foreach NETWORK, $(NETWORKS_ASYM), approximation.$(NETWORK)): \
approximation.%: plot/approximation.a.%.eps
$(foreach NETWORK, $(NETWORKS_ASYM), dat/approximation.$(NETWORK).mat): \
dat/approximation.%.mat: m/approximation.m dat/data.%.mat dat/info.% dat/meansi.%.mat
NETWORK=$* $(OCTAVE) m/approximation
$(foreach NETWORK, $(NETWORKS_ASYM), plot/approximation.a.$(NETWORK).eps): \
plot/approximation.a.%.eps: m/approximation_plot.m dat/approximation.%.mat
NETWORK=$* $(OCTAVE) m/approximation_plot
#
# Trend estimation
#
define TEMPLATE_trend # $(1) = $(STATISTIC), $(2) = $(TYPE)
$$(foreach NETWORK, $$(NETWORKS), trend.$(2).$(1).$$(NETWORK)): \
trend.$(2).$(1).%: plot/trend.$(2).$(1).%.eps
dat/trend.$(2).$(1).%.mat: statistic_time.$(2).$(1).% m/trend.m
NETWORK=$$* STATISTIC=$(1) TYPE=$(2) $(OCTAVE) m/trend
plot/trend.$(2).$(1).%.eps: dat/trend.$(2).$(1).%.mat m/trend_plot.m
NETWORK=$$* STATISTIC=$(1) TYPE=$(2) $(OCTAVE) m/trend_plot
endef
$(foreach STATISTIC, $(STATISTICS), $(foreach TYPE, full split, $(eval $(call TEMPLATE_trend,$(STATISTIC),$(TYPE)))))