From d3e512b3e8664d0ccb670c6195dfcf97423fa0bd Mon Sep 17 00:00:00 2001 From: Matthew Taylor Date: Mon, 24 Apr 2017 14:02:31 -0700 Subject: [PATCH] Removed base_category base_scalar experiments deleted: examples/opf/experiments/classification/base_category/UNDER_DEVELOPMENT deleted: examples/opf/experiments/classification/base_scalar/UNDER_DEVELOPMENT --- .../base_category/UNDER_DEVELOPMENT | 423 ------------------ .../base_scalar/UNDER_DEVELOPMENT | 423 ------------------ 2 files changed, 846 deletions(-) delete mode 100644 examples/opf/experiments/classification/base_category/UNDER_DEVELOPMENT delete mode 100644 examples/opf/experiments/classification/base_scalar/UNDER_DEVELOPMENT diff --git a/examples/opf/experiments/classification/base_category/UNDER_DEVELOPMENT b/examples/opf/experiments/classification/base_category/UNDER_DEVELOPMENT deleted file mode 100644 index 95a2f9c2f1..0000000000 --- a/examples/opf/experiments/classification/base_category/UNDER_DEVELOPMENT +++ /dev/null @@ -1,423 +0,0 @@ -# ---------------------------------------------------------------------- -# Numenta Platform for Intelligent Computing (NuPIC) -# Copyright (C) 2013, Numenta, Inc. Unless you have an agreement -# with Numenta, Inc., for a separate license for this software code, the -# following terms and conditions apply: -# -# This program is free software: you can redistribute it and/or modify -# it under the terms of the GNU Affero Public License version 3 as -# published by the Free Software Foundation. -# -# This program is distributed in the hope that it will be useful, -# but WITHOUT ANY WARRANTY; without even the implied warranty of -# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. -# See the GNU Affero Public License for more details. -# -# You should have received a copy of the GNU Affero Public License -# along with this program. If not, see http://www.gnu.org/licenses. -# -# http://numenta.org/licenses/ -# ---------------------------------------------------------------------- - -""" -Template file used by the OPF Experiment Generator to generate the actual -description.py file by replacing $XXXXXXXX tokens with desired values. - -This description.py file was generated by: -'~/nta/eng/lib/python2.6/site-packages/nupic/frameworks/opf/expGenerator/ExpGenerator.py' -""" - -from nupic.frameworks.opf.expdescriptionapi import ExperimentDescriptionAPI - -from nupic.frameworks.opf.expdescriptionhelpers import ( - updateConfigFromSubConfig, - applyValueGettersToContainer, - DeferredDictLookup) - -from nupic.frameworks.opf.htmpredictionmodelcallbacks import * -from nupic.frameworks.opf.metrics import MetricSpec -from nupic.frameworks.opf.opfutils import (InferenceType, - InferenceElement) -from nupic.support import aggregationDivide - -from nupic.frameworks.opf.opftaskdriver import ( - IterationPhaseSpecLearnOnly, - IterationPhaseSpecInferOnly, - IterationPhaseSpecLearnAndInfer) - - -# Model Configuration Dictionary: -# -# Define the model parameters and adjust for any modifications if imported -# from a sub-experiment. -# -# These fields might be modified by a sub-experiment; this dict is passed -# between the sub-experiment and base experiment -# -# -# NOTE: Use of DEFERRED VALUE-GETTERs: dictionary fields and list elements -# within the config dictionary may be assigned futures derived from the -# ValueGetterBase class, such as DeferredDictLookup. -# This facility is particularly handy for enabling substitution of values in -# the config dictionary from other values in the config dictionary, which is -# needed by permutation.py-based experiments. These values will be resolved -# during the call to applyValueGettersToContainer(), -# which we call after the base experiment's config dictionary is updated from -# the sub-experiment. See ValueGetterBase and -# DeferredDictLookup for more details about value-getters. -# -# For each custom encoder parameter to be exposed to the sub-experiment/ -# permutation overrides, define a variable in this section, using key names -# beginning with a single underscore character to avoid collisions with -# pre-defined keys (e.g., _dsEncoderFieldName2_N). -# -# Example: -# config = dict( -# _dsEncoderFieldName2_N = 70, -# _dsEncoderFieldName2_W = 5, -# dsEncoderSchema = [ -# base=dict( -# fieldname='Name2', type='ScalarEncoder', -# name='Name2', minval=0, maxval=270, clipInput=True, -# n=DeferredDictLookup('_dsEncoderFieldName2_N'), -# w=DeferredDictLookup('_dsEncoderFieldName2_W')), -# ], -# ) -# updateConfigFromSubConfig(config) -# applyValueGettersToContainer(config) -config = { - # Type of model that the rest of these parameters apply to. - 'model': "HTMPrediction", - - # Version that specifies the format of the config. - 'version': 1, - - # Intermediate variables used to compute fields in modelParams and also - # referenced from the control section. - 'aggregationInfo': { 'fields': [ ('numericFieldNameA', 'mean'), - ('numericFieldNameB', 'sum'), - ('categoryFieldNameC', 'first')], - 'hours': 0}, - - 'predictAheadTime': None, - - # Model parameter dictionary. - 'modelParams': { - # The type of inference that this model will perform - 'inferenceType': 'NontemporalAnomaly', - - 'sensorParams': { - # Sensor diagnostic output verbosity control; - # if > 0: sensor region will print out on screen what it's sensing - # at each step 0: silent; >=1: some info; >=2: more info; - # >=3: even more info (see compute() in py/regions/RecordSensor.py) - 'verbosity' : 0, - - # Example: - # dsEncoderSchema = [ - # DeferredDictLookup('__field_name_encoder'), - # ], - # - # (value generated from DS_ENCODER_SCHEMA) - 'encoders': { - 'f0': dict(fieldname='f0', n=100, name='f0', type='SDRCategoryEncoder', w=21), - 'f1': dict(fieldname='f1', n=100, name='f1', type='SDRCategoryEncoder', w=21), - 'f2': dict(fieldname='f2', n=100, name='f2', type='SDRCategoryEncoder', w=21), - 'f3': dict(fieldname='f3', n=100, name='f3', type='SDRCategoryEncoder', w=21), - 'f4': dict(fieldname='f4', n=100, name='f4', type='SDRCategoryEncoder', w=21), - 'f5': dict(fieldname='f5', n=100, name='f5', type='SDRCategoryEncoder', w=21), - 'f6': dict(fieldname='f6', n=100, name='f6', type='SDRCategoryEncoder', w=21), - 'f7': dict(fieldname='f7', n=100, name='f7', type='SDRCategoryEncoder', w=21), - 'f8': dict(fieldname='f8', n=100, name='f8', type='SDRCategoryEncoder', w=21), - 'f9': dict(fieldname='f9', n=100, name='f9', type='SDRCategoryEncoder', w=21), - }, - - # A dictionary specifying the period for automatically-generated - # resets from a RecordSensor; - # - # None = disable automatically-generated resets (also disabled if - # all of the specified values evaluate to 0). - # Valid keys is the desired combination of the following: - # days, hours, minutes, seconds, milliseconds, microseconds, weeks - # - # Example for 1.5 days: sensorAutoReset = dict(days=1,hours=12), - # - # (value generated from SENSOR_AUTO_RESET) - 'sensorAutoReset' : None, - }, - - 'spEnable': True, - - 'spParams': { - # SP diagnostic output verbosity control; - # 0: silent; >=1: some info; >=2: more info; - 'spVerbosity' : 0, - - 'globalInhibition': 1, - - # Number of cell columns in the cortical region (same number for - # SP and TP) - # (see also tpNCellsPerCol) - 'columnCount': 2048, - - 'inputWidth': 0, - - # SP inhibition control (absolute value); - # Maximum number of active columns in the SP region's output (when - # there are more, the weaker ones are suppressed) - 'numActiveColumnsPerInhArea': 40, - - 'seed': 1956, - - # potentialPct - # What percent of the columns's receptive field is available - # for potential synapses. At initialization time, we will - # choose potentialPct * (2*potentialRadius+1)^2 - 'potentialPct': 0.5, - - # The default connected threshold. Any synapse whose - # permanence value is above the connected threshold is - # a "connected synapse", meaning it can contribute to the - # cell's firing. Typical value is 0.10. Cells whose activity - # level before inhibition falls below minDutyCycleBeforeInh - # will have their own internal synPermConnectedCell - # threshold set below this default value. - # (This concept applies to both SP and TP and so 'cells' - # is correct here as opposed to 'columns') - 'synPermConnected': 0.1, - - 'synPermActiveInc': 0.1, - - 'synPermInactiveDec': 0.01, - }, - - # Controls whether TP is enabled or disabled; - # TP is necessary for making temporal predictions, such as predicting - # the next inputs. Without TP, the model is only capable of - # reconstructing missing sensor inputs (via SP). - 'tmEnable' : True, - - 'tmParams': { - # TP diagnostic output verbosity control; - # 0: silent; [1..6]: increasing levels of verbosity - # (see verbosity in nta/trunk/py/nupic/research/TP.py and TP10X*.py) - 'verbosity': 0, - - # Number of cell columns in the cortical region (same number for - # SP and TP) - # (see also tpNCellsPerCol) - 'columnCount': 2048, - - # The number of cells (i.e., states), allocated per column. - 'cellsPerColumn': 32, - - 'inputWidth': 2048, - - 'seed': 1960, - - # Temporal Pooler implementation selector (see _getTPClass in - # CLARegion.py). - 'temporalImp': 'cpp', - - # New Synapse formation count - # NOTE: If None, use spNumActivePerInhArea - # - # TODO: need better explanation - 'newSynapseCount': 20, - - # Maximum number of synapses per segment - # > 0 for fixed-size CLA - # -1 for non-fixed-size CLA - # - # TODO: for Ron: once the appropriate value is placed in TP - # constructor, see if we should eliminate this parameter from - # description.py. - 'maxSynapsesPerSegment': 32, - - # Maximum number of segments per cell - # > 0 for fixed-size CLA - # -1 for non-fixed-size CLA - # - # TODO: for Ron: once the appropriate value is placed in TP - # constructor, see if we should eliminate this parameter from - # description.py. - 'maxSegmentsPerCell': 128, - - # Initial Permanence - # TODO: need better explanation - 'initialPerm': 0.21, - - # Permanence Increment - 'permanenceInc': 0.1, - - # Permanence Decrement - # If set to None, will automatically default to tpPermanenceInc - # value. - 'permanenceDec' : 0.1, - - 'globalDecay': 0.0, - - 'maxAge': 0, - - # Minimum number of active synapses for a segment to be considered - # during search for the best-matching segments. - # None=use default - # Replaces: tpMinThreshold - 'minThreshold': 12, - - # Segment activation threshold. - # A segment is active if it has >= tpSegmentActivationThreshold - # connected synapses that are active due to infActiveState - # None=use default - # Replaces: tpActivationThreshold - 'activationThreshold': 16, - - 'outputType': 'normal', - - # "Pay Attention Mode" length. This tells the TP how many new - # elements to append to the end of a learned sequence at a time. - # Smaller values are better for datasets with short sequences, - # higher values are better for datasets with long sequences. - 'pamLength': 1, - }, - - 'clParams': { - 'regionName' : 'SDRClassifierRegion', - - # Classifier diagnostic output verbosity control; - # 0: silent; [1..6]: increasing levels of verbosity - 'verbosity' : 0, - - # This controls how fast the classifier learns/forgets. Higher values - # make it adapt faster and forget older patterns faster. - 'alpha': 0.001, - - # This is set after the call to updateConfigFromSubConfig and is - # computed from the aggregationInfo and predictAheadTime. - 'steps': '1', - - - }, - - 'trainSPNetOnlyIfRequested': False, - }, - - -} -# end of config dictionary - - -# Adjust base config dictionary for any modifications if imported from a -# sub-experiment -updateConfigFromSubConfig(config) - - -# Compute predictionSteps based on the predictAheadTime and the aggregation -# period, which may be permuted over. -if config['predictAheadTime'] is not None: - predictionSteps = int(round(aggregationDivide( - config['predictAheadTime'], config['aggregationInfo']))) - assert (predictionSteps >= 1) - config['modelParams']['clParams']['steps'] = str(predictionSteps) - - -# Adjust config by applying ValueGetterBase-derived -# futures. NOTE: this MUST be called after updateConfigFromSubConfig() in order -# to support value-getter-based substitutions from the sub-experiment (if any) -applyValueGettersToContainer(config) - - - -# [optional] A sequence of one or more tasks that describe what to do with the -# model. Each task consists of a task label, an input spec., iteration count, -# and a task-control spec per opfTaskSchema.json -# -# NOTE: The tasks are intended for OPF clients that make use of OPFTaskDriver. -# Clients that interact with OPFExperiment directly do not make use of -# the tasks specification. -# -control = dict( - environment='opfExperiment', - -tasks = [ - { - # Task label; this label string may be used for diagnostic logging and for - # constructing filenames or directory pathnames for task-specific files, etc. - 'taskLabel' : "Anomaly", - - # Input stream specification per py/nupic/cluster/database/StreamDef.json. - # - 'dataset' : { - 'info': 'test_NoProviders', - 'version': 1, - - 'streams': [ - { - 'columns': ['*'], - 'info': 'my simple dataset', - 'source': 'file://'+os.path.join(os.path.dirname(__file__), 'data.csv'), - } - ], - - # TODO: Aggregation is not supported yet by run_opf_experiment.py - #'aggregation' : config['aggregationInfo'] - }, - - # Iteration count: maximum number of iterations. Each iteration corresponds - # to one record from the (possibly aggregated) dataset. The task is - # terminated when either number of iterations reaches iterationCount or - # all records in the (possibly aggregated) database have been processed, - # whichever occurs first. - # - # iterationCount of -1 = iterate over the entire dataset - 'iterationCount' : -1, - - - # Task Control parameters for OPFTaskDriver (per opfTaskControlSchema.json) - 'taskControl' : { - - # Iteration cycle list consisting of opftaskdriver.IterationPhaseSpecXXXXX - # instances. - 'iterationCycle' : [ - #IterationPhaseSpecLearnOnly(1000), - IterationPhaseSpecLearnAndInfer(1000, inferenceArgs=None), - #IterationPhaseSpecInferOnly(10, inferenceArgs=None), - ], - - 'metrics' : [ - ], - - # Logged Metrics: A sequence of regular expressions that specify which of - # the metrics from the Inference Specifications section MUST be logged for - # every prediction. The regex's correspond to the automatically generated - # metric labels. This is similar to the way the optimization metric is - # specified in permutations.py. - 'loggedMetrics': ['.*nupicScore.*'], - - - # Callbacks for experimentation/research (optional) - 'callbacks' : { - # Callbacks to be called at the beginning of a task, before model iterations. - # Signature: callback(); returns nothing -# 'setup' : [htmPredictionModelControlEnableSPLearningCb, htmPredictionModelControlEnableTPLearningCb], -# 'setup' : [htmPredictionModelControlDisableTPLearningCb], - 'setup' : [], - - # Callbacks to be called after every learning/inference iteration - # Signature: callback(); returns nothing - 'postIter' : [], - - # Callbacks to be called when the experiment task is finished - # Signature: callback(); returns nothing - 'finish' : [] - } - } # End of taskControl - }, # End of task -] - -) - - - -descriptionInterface = ExperimentDescriptionAPI(modelConfig=config, - control=control) diff --git a/examples/opf/experiments/classification/base_scalar/UNDER_DEVELOPMENT b/examples/opf/experiments/classification/base_scalar/UNDER_DEVELOPMENT deleted file mode 100644 index 95a2f9c2f1..0000000000 --- a/examples/opf/experiments/classification/base_scalar/UNDER_DEVELOPMENT +++ /dev/null @@ -1,423 +0,0 @@ -# ---------------------------------------------------------------------- -# Numenta Platform for Intelligent Computing (NuPIC) -# Copyright (C) 2013, Numenta, Inc. Unless you have an agreement -# with Numenta, Inc., for a separate license for this software code, the -# following terms and conditions apply: -# -# This program is free software: you can redistribute it and/or modify -# it under the terms of the GNU Affero Public License version 3 as -# published by the Free Software Foundation. -# -# This program is distributed in the hope that it will be useful, -# but WITHOUT ANY WARRANTY; without even the implied warranty of -# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. -# See the GNU Affero Public License for more details. -# -# You should have received a copy of the GNU Affero Public License -# along with this program. If not, see http://www.gnu.org/licenses. -# -# http://numenta.org/licenses/ -# ---------------------------------------------------------------------- - -""" -Template file used by the OPF Experiment Generator to generate the actual -description.py file by replacing $XXXXXXXX tokens with desired values. - -This description.py file was generated by: -'~/nta/eng/lib/python2.6/site-packages/nupic/frameworks/opf/expGenerator/ExpGenerator.py' -""" - -from nupic.frameworks.opf.expdescriptionapi import ExperimentDescriptionAPI - -from nupic.frameworks.opf.expdescriptionhelpers import ( - updateConfigFromSubConfig, - applyValueGettersToContainer, - DeferredDictLookup) - -from nupic.frameworks.opf.htmpredictionmodelcallbacks import * -from nupic.frameworks.opf.metrics import MetricSpec -from nupic.frameworks.opf.opfutils import (InferenceType, - InferenceElement) -from nupic.support import aggregationDivide - -from nupic.frameworks.opf.opftaskdriver import ( - IterationPhaseSpecLearnOnly, - IterationPhaseSpecInferOnly, - IterationPhaseSpecLearnAndInfer) - - -# Model Configuration Dictionary: -# -# Define the model parameters and adjust for any modifications if imported -# from a sub-experiment. -# -# These fields might be modified by a sub-experiment; this dict is passed -# between the sub-experiment and base experiment -# -# -# NOTE: Use of DEFERRED VALUE-GETTERs: dictionary fields and list elements -# within the config dictionary may be assigned futures derived from the -# ValueGetterBase class, such as DeferredDictLookup. -# This facility is particularly handy for enabling substitution of values in -# the config dictionary from other values in the config dictionary, which is -# needed by permutation.py-based experiments. These values will be resolved -# during the call to applyValueGettersToContainer(), -# which we call after the base experiment's config dictionary is updated from -# the sub-experiment. See ValueGetterBase and -# DeferredDictLookup for more details about value-getters. -# -# For each custom encoder parameter to be exposed to the sub-experiment/ -# permutation overrides, define a variable in this section, using key names -# beginning with a single underscore character to avoid collisions with -# pre-defined keys (e.g., _dsEncoderFieldName2_N). -# -# Example: -# config = dict( -# _dsEncoderFieldName2_N = 70, -# _dsEncoderFieldName2_W = 5, -# dsEncoderSchema = [ -# base=dict( -# fieldname='Name2', type='ScalarEncoder', -# name='Name2', minval=0, maxval=270, clipInput=True, -# n=DeferredDictLookup('_dsEncoderFieldName2_N'), -# w=DeferredDictLookup('_dsEncoderFieldName2_W')), -# ], -# ) -# updateConfigFromSubConfig(config) -# applyValueGettersToContainer(config) -config = { - # Type of model that the rest of these parameters apply to. - 'model': "HTMPrediction", - - # Version that specifies the format of the config. - 'version': 1, - - # Intermediate variables used to compute fields in modelParams and also - # referenced from the control section. - 'aggregationInfo': { 'fields': [ ('numericFieldNameA', 'mean'), - ('numericFieldNameB', 'sum'), - ('categoryFieldNameC', 'first')], - 'hours': 0}, - - 'predictAheadTime': None, - - # Model parameter dictionary. - 'modelParams': { - # The type of inference that this model will perform - 'inferenceType': 'NontemporalAnomaly', - - 'sensorParams': { - # Sensor diagnostic output verbosity control; - # if > 0: sensor region will print out on screen what it's sensing - # at each step 0: silent; >=1: some info; >=2: more info; - # >=3: even more info (see compute() in py/regions/RecordSensor.py) - 'verbosity' : 0, - - # Example: - # dsEncoderSchema = [ - # DeferredDictLookup('__field_name_encoder'), - # ], - # - # (value generated from DS_ENCODER_SCHEMA) - 'encoders': { - 'f0': dict(fieldname='f0', n=100, name='f0', type='SDRCategoryEncoder', w=21), - 'f1': dict(fieldname='f1', n=100, name='f1', type='SDRCategoryEncoder', w=21), - 'f2': dict(fieldname='f2', n=100, name='f2', type='SDRCategoryEncoder', w=21), - 'f3': dict(fieldname='f3', n=100, name='f3', type='SDRCategoryEncoder', w=21), - 'f4': dict(fieldname='f4', n=100, name='f4', type='SDRCategoryEncoder', w=21), - 'f5': dict(fieldname='f5', n=100, name='f5', type='SDRCategoryEncoder', w=21), - 'f6': dict(fieldname='f6', n=100, name='f6', type='SDRCategoryEncoder', w=21), - 'f7': dict(fieldname='f7', n=100, name='f7', type='SDRCategoryEncoder', w=21), - 'f8': dict(fieldname='f8', n=100, name='f8', type='SDRCategoryEncoder', w=21), - 'f9': dict(fieldname='f9', n=100, name='f9', type='SDRCategoryEncoder', w=21), - }, - - # A dictionary specifying the period for automatically-generated - # resets from a RecordSensor; - # - # None = disable automatically-generated resets (also disabled if - # all of the specified values evaluate to 0). - # Valid keys is the desired combination of the following: - # days, hours, minutes, seconds, milliseconds, microseconds, weeks - # - # Example for 1.5 days: sensorAutoReset = dict(days=1,hours=12), - # - # (value generated from SENSOR_AUTO_RESET) - 'sensorAutoReset' : None, - }, - - 'spEnable': True, - - 'spParams': { - # SP diagnostic output verbosity control; - # 0: silent; >=1: some info; >=2: more info; - 'spVerbosity' : 0, - - 'globalInhibition': 1, - - # Number of cell columns in the cortical region (same number for - # SP and TP) - # (see also tpNCellsPerCol) - 'columnCount': 2048, - - 'inputWidth': 0, - - # SP inhibition control (absolute value); - # Maximum number of active columns in the SP region's output (when - # there are more, the weaker ones are suppressed) - 'numActiveColumnsPerInhArea': 40, - - 'seed': 1956, - - # potentialPct - # What percent of the columns's receptive field is available - # for potential synapses. At initialization time, we will - # choose potentialPct * (2*potentialRadius+1)^2 - 'potentialPct': 0.5, - - # The default connected threshold. Any synapse whose - # permanence value is above the connected threshold is - # a "connected synapse", meaning it can contribute to the - # cell's firing. Typical value is 0.10. Cells whose activity - # level before inhibition falls below minDutyCycleBeforeInh - # will have their own internal synPermConnectedCell - # threshold set below this default value. - # (This concept applies to both SP and TP and so 'cells' - # is correct here as opposed to 'columns') - 'synPermConnected': 0.1, - - 'synPermActiveInc': 0.1, - - 'synPermInactiveDec': 0.01, - }, - - # Controls whether TP is enabled or disabled; - # TP is necessary for making temporal predictions, such as predicting - # the next inputs. Without TP, the model is only capable of - # reconstructing missing sensor inputs (via SP). - 'tmEnable' : True, - - 'tmParams': { - # TP diagnostic output verbosity control; - # 0: silent; [1..6]: increasing levels of verbosity - # (see verbosity in nta/trunk/py/nupic/research/TP.py and TP10X*.py) - 'verbosity': 0, - - # Number of cell columns in the cortical region (same number for - # SP and TP) - # (see also tpNCellsPerCol) - 'columnCount': 2048, - - # The number of cells (i.e., states), allocated per column. - 'cellsPerColumn': 32, - - 'inputWidth': 2048, - - 'seed': 1960, - - # Temporal Pooler implementation selector (see _getTPClass in - # CLARegion.py). - 'temporalImp': 'cpp', - - # New Synapse formation count - # NOTE: If None, use spNumActivePerInhArea - # - # TODO: need better explanation - 'newSynapseCount': 20, - - # Maximum number of synapses per segment - # > 0 for fixed-size CLA - # -1 for non-fixed-size CLA - # - # TODO: for Ron: once the appropriate value is placed in TP - # constructor, see if we should eliminate this parameter from - # description.py. - 'maxSynapsesPerSegment': 32, - - # Maximum number of segments per cell - # > 0 for fixed-size CLA - # -1 for non-fixed-size CLA - # - # TODO: for Ron: once the appropriate value is placed in TP - # constructor, see if we should eliminate this parameter from - # description.py. - 'maxSegmentsPerCell': 128, - - # Initial Permanence - # TODO: need better explanation - 'initialPerm': 0.21, - - # Permanence Increment - 'permanenceInc': 0.1, - - # Permanence Decrement - # If set to None, will automatically default to tpPermanenceInc - # value. - 'permanenceDec' : 0.1, - - 'globalDecay': 0.0, - - 'maxAge': 0, - - # Minimum number of active synapses for a segment to be considered - # during search for the best-matching segments. - # None=use default - # Replaces: tpMinThreshold - 'minThreshold': 12, - - # Segment activation threshold. - # A segment is active if it has >= tpSegmentActivationThreshold - # connected synapses that are active due to infActiveState - # None=use default - # Replaces: tpActivationThreshold - 'activationThreshold': 16, - - 'outputType': 'normal', - - # "Pay Attention Mode" length. This tells the TP how many new - # elements to append to the end of a learned sequence at a time. - # Smaller values are better for datasets with short sequences, - # higher values are better for datasets with long sequences. - 'pamLength': 1, - }, - - 'clParams': { - 'regionName' : 'SDRClassifierRegion', - - # Classifier diagnostic output verbosity control; - # 0: silent; [1..6]: increasing levels of verbosity - 'verbosity' : 0, - - # This controls how fast the classifier learns/forgets. Higher values - # make it adapt faster and forget older patterns faster. - 'alpha': 0.001, - - # This is set after the call to updateConfigFromSubConfig and is - # computed from the aggregationInfo and predictAheadTime. - 'steps': '1', - - - }, - - 'trainSPNetOnlyIfRequested': False, - }, - - -} -# end of config dictionary - - -# Adjust base config dictionary for any modifications if imported from a -# sub-experiment -updateConfigFromSubConfig(config) - - -# Compute predictionSteps based on the predictAheadTime and the aggregation -# period, which may be permuted over. -if config['predictAheadTime'] is not None: - predictionSteps = int(round(aggregationDivide( - config['predictAheadTime'], config['aggregationInfo']))) - assert (predictionSteps >= 1) - config['modelParams']['clParams']['steps'] = str(predictionSteps) - - -# Adjust config by applying ValueGetterBase-derived -# futures. NOTE: this MUST be called after updateConfigFromSubConfig() in order -# to support value-getter-based substitutions from the sub-experiment (if any) -applyValueGettersToContainer(config) - - - -# [optional] A sequence of one or more tasks that describe what to do with the -# model. Each task consists of a task label, an input spec., iteration count, -# and a task-control spec per opfTaskSchema.json -# -# NOTE: The tasks are intended for OPF clients that make use of OPFTaskDriver. -# Clients that interact with OPFExperiment directly do not make use of -# the tasks specification. -# -control = dict( - environment='opfExperiment', - -tasks = [ - { - # Task label; this label string may be used for diagnostic logging and for - # constructing filenames or directory pathnames for task-specific files, etc. - 'taskLabel' : "Anomaly", - - # Input stream specification per py/nupic/cluster/database/StreamDef.json. - # - 'dataset' : { - 'info': 'test_NoProviders', - 'version': 1, - - 'streams': [ - { - 'columns': ['*'], - 'info': 'my simple dataset', - 'source': 'file://'+os.path.join(os.path.dirname(__file__), 'data.csv'), - } - ], - - # TODO: Aggregation is not supported yet by run_opf_experiment.py - #'aggregation' : config['aggregationInfo'] - }, - - # Iteration count: maximum number of iterations. Each iteration corresponds - # to one record from the (possibly aggregated) dataset. The task is - # terminated when either number of iterations reaches iterationCount or - # all records in the (possibly aggregated) database have been processed, - # whichever occurs first. - # - # iterationCount of -1 = iterate over the entire dataset - 'iterationCount' : -1, - - - # Task Control parameters for OPFTaskDriver (per opfTaskControlSchema.json) - 'taskControl' : { - - # Iteration cycle list consisting of opftaskdriver.IterationPhaseSpecXXXXX - # instances. - 'iterationCycle' : [ - #IterationPhaseSpecLearnOnly(1000), - IterationPhaseSpecLearnAndInfer(1000, inferenceArgs=None), - #IterationPhaseSpecInferOnly(10, inferenceArgs=None), - ], - - 'metrics' : [ - ], - - # Logged Metrics: A sequence of regular expressions that specify which of - # the metrics from the Inference Specifications section MUST be logged for - # every prediction. The regex's correspond to the automatically generated - # metric labels. This is similar to the way the optimization metric is - # specified in permutations.py. - 'loggedMetrics': ['.*nupicScore.*'], - - - # Callbacks for experimentation/research (optional) - 'callbacks' : { - # Callbacks to be called at the beginning of a task, before model iterations. - # Signature: callback(); returns nothing -# 'setup' : [htmPredictionModelControlEnableSPLearningCb, htmPredictionModelControlEnableTPLearningCb], -# 'setup' : [htmPredictionModelControlDisableTPLearningCb], - 'setup' : [], - - # Callbacks to be called after every learning/inference iteration - # Signature: callback(); returns nothing - 'postIter' : [], - - # Callbacks to be called when the experiment task is finished - # Signature: callback(); returns nothing - 'finish' : [] - } - } # End of taskControl - }, # End of task -] - -) - - - -descriptionInterface = ExperimentDescriptionAPI(modelConfig=config, - control=control)