Include evolutionary-keras

doc/sphinx/source/n3fit/backends.md

@@ -0,0 +1,36 @@
+Backend selection
+=================
+
+One of the advantages of ``n3fit`` is the possibility to select different backends
+for training the neural network.
+
+Currently implemented in ``n3fit`` we can find:
+
+- [Keras-Tensorflow](#keras-tensorflow)
+- [Evolutionary Algorithms](#evolutionary-algorithms)
+
+Keras-Tensorflow
+----------------
+The main and default backend for ``n3fit`` is the [TensorFlow](https://www.tensorflow.org/)
+library developed by Google.
+
+
+Evolutionary Algorithms
+-----------------------
+As part of the validation process followed during the development of ``n3fit`` we developed the
+[evolutionary-keras](https://evolutionary-keras.readthedocs.io) [[1](https://arxiv.org/abs/2002.06587)] library.
+
+This library extends [Tensorflow](#keras-tensorflow) for its use with evolutionary algorithms such as the [NGA](https://evolutionary-keras.readthedocs.io/en/latest/optimizers.html#nodal-genetic-algorithm-nga)
+used in previous versions of [NNPDF](http://arxiv.org/abs/1410.8849) or the [CMA-ES](https://evolutionary-keras.readthedocs.io/en/latest/optimizers.html#covariance-matrix-adaptation-evolution-strategy-cma-es) used in some [spin-off studies](https://arxiv.org/abs/1706.07049).
+
+The evolutionary strategies can be used by adding the keyword ``backend: evolutionary_keras`` to the ``fitting`` section in the runcard.
+
+```yaml
+fitting:
+  backend: evolutionary_keras
+  optimizer:
+    optimizer_name: 'NGA'
+    sigma_init: 15
+    population_size: 80
+    mutation_rate: 0.05
+```

doc/sphinx/source/n3fit/index.rst

@@ -6,5 +6,6 @@ n3fit
 
    introduction
    methodology
+   backends
    hyperopt
    runcard_detailed

n3fit/runcards/Basic_nga.yml

@@ -0,0 +1,142 @@
+#
+# Configuration file for n3fit
+#
+
+############################################################
+description: Basic runcard
+
+############################################################
+# frac: training fraction
+# ewk: apply ewk k-factors
+# sys: systematics treatment (see systypes)
+experiments:
+  - experiment: ALL
+    datasets:
+      - { dataset: SLACP, frac: 0.5}
+      - { dataset: NMCPD, frac: 0.5 }
+      - { dataset: CMSJETS11,     frac: 0.5, sys: 10 }
+
+############################################################
+datacuts:
+  t0pdfset     : NNPDF31_nlo_as_0118 # PDF set to generate t0 covmat
+  q2min        : 3.49                # Q2 minimum
+  w2min        : 12.5                # W2 minimum
+  combocuts    : NNPDF31             # NNPDF3.0 final kin. cuts
+  jetptcut_tev : 0                   # jet pt cut for tevatron
+  jetptcut_lhc : 0                   # jet pt cut for lhc
+  wptcut_lhc   : 30.0                # Minimum pT for W pT diff distributions
+  jetycut_tev  : 1e30                # jet rap. cut for tevatron
+  jetycut_lhc  : 1e30                # jet rap. cut for lhc
+  dymasscut_min: 0                   # dy inv.mass. min cut
+  dymasscut_max: 1e30                # dy inv.mass. max cut
+  jetcfactcut  : 1e30                # jet cfact. cut
+
+############################################################
+theory:
+  theoryid: 53        # database id
+
+############################################################
+fitting:
+  trvlseed: 1
+  nnseed: 2
+  mcseed: 3
+  epochs: 900
+  save: False
+  savefile: 'weights.hd5'
+  load: False
+  loadfile: 'weights.hd5'
+  plot: False
+
+  seed     : 9453862133528      # set the seed for the random generator
+  genrep   : True     # on = generate MC replicas, off = use real data
+  rngalgo  : 0      # 0 = ranlux, 1 = cmrg, see randomgenerator.cc
+
+  backend: 'evolutionary_keras'
+
+  parameters: # This defines the parameter dictionary that is passed to the Model Trainer
+    nodes_per_layer: [15, 10, 8]
+    activation_per_layer: ['sigmoid', 'sigmoid', 'linear']
+    initializer: 'glorot_normal'
+    optimizer:
+      optimizer_name: 'NGA'
+      sigma_init: 15
+      population_size: 80
+      mutation_rate: 0.05
+    epochs: 900
+    positivity:
+      multiplier: 1.05 # When any of the multiplier and/or the initial is not set
+      initial: # the poslambda will be used instead to compute these values per dataset
+    stopping_patience: 0.30 # percentage of the number of epochs
+    layer_type: 'dense'
+    dropout: 0.0
+    threshold_chi2: 5.0
+
+  # NN23(QED) = sng=0,g=1,v=2,t3=3,ds=4,sp=5,sm=6,(pht=7)
+  # EVOL(QED) = sng=0,g=1,v=2,v3=3,v8=4,t3=5,t8=6,(pht=7)
+  # EVOLS(QED)= sng=0,g=1,v=2,v8=4,t3=4,t8=5,ds=6,(pht=7)
+  # FLVR(QED) = g=0, u=1, ubar=2, d=3, dbar=4, s=5, sbar=6, (pht=7)
+  fitbasis: NN31IC # EVOL (7), EVOLQED (8), etc.
+  basis:
+      # remeber to change the name of PDF accordingly with fitbasis
+      # pos: True for NN squared
+      # mutsize: mutation size
+      # mutprob: mutation probability
+      # smallx, largex: preprocessing ranges
+      - { fl: sng, pos: False, mutsize: [15], mutprob: [0.05], smallx: [1.05,1.19], largex: [1.47,2.70], trainable: False }
+      - { fl: g,   pos: False, mutsize: [15], mutprob: [0.05], smallx: [0.94,1.25], largex: [0.11,5.87], trainable: False }
+      - { fl: v,   pos: False, mutsize: [15], mutprob: [0.05], smallx: [0.54,0.75], largex: [1.15,2.76], trainable: False }
+      - { fl: v3,  pos: False, mutsize: [15], mutprob: [0.05], smallx: [0.21,0.57], largex: [1.35,3.08], trainable: False }
+      - { fl: v8,  pos: False, mutsize: [15], mutprob: [0.05], smallx: [0.52,0.76], largex: [0.77,3.56], trainable: False }
+      - { fl: t3,  pos: False, mutsize: [15], mutprob: [0.05], smallx: [-0.37,1.52], largex: [1.74,3.39], trainable: False }
+      - { fl: t8,  pos: False, mutsize: [15], mutprob: [0.05], smallx: [0.56,1.29], largex: [1.45,3.03], trainable: False }
+      - { fl: cp,  pos: False, mutsize: [15], mutprob: [0.05], smallx: [0.12,1.19], largex: [1.83,6.70], trainable: False }
+
+############################################################
+stopping:
+  stopmethod: LOOKBACK  # Stopping method
+  lbdelta   : 0         # Delta for look-back stopping
+  mingen    : 0         # Minimum number of generations
+  window    : 500       # Window for moving average
+  minchi2   : 3.5       # Minimum chi2
+  minchi2exp: 6.0       # Minimum chi2 for experiments
+  nsmear    : 200       # Smear for stopping
+  deltasm   : 200       # Delta smear for stopping
+  rv        : 2         # Ratio for validation stopping
+  rt        : 0.5       # Ratio for training stopping
+  epsilon   : 1e-6      # Gradient epsilon
+
+############################################################
+positivity:
+  posdatasets:
+    - { dataset: POSF2U,   poslambda: 1e6 }  # Positivity Lagrange Multiplier
+    - { dataset: POSFLL,   poslambda: 1e4 }
+
+############################################################
+integrability:
+  integdatasets:
+    - {dataset: INTEGXT3,   poslambda: 1e2}
+
+############################################################
+closuretest:
+  filterseed  : 0   # Random seed to be used in filtering data partitions
+  fakedata    : False # on = to use FAKEPDF to generate pseudo-data
+  fakepdf     : MSTW2008nlo68cl # Theory input for pseudo-data
+  errorsize   : 1.0 # uncertainties rescaling
+  fakenoise   : False # on = to add random fluctuations to pseudo-data
+  rancutprob  : 1.0 # Fraction of data to be included in the fit
+  rancutmethod: 0   # Method to select rancutprob data fraction
+  rancuttrnval: False # 0(1) to output training(valiation) chi2 in report
+  printpdf4gen: False # To print info on PDFs during minimization
+
+############################################################
+lhagrid:
+  nx  : 150
+  xmin: 1e-9
+  xmed: 0.1
+  xmax: 1.0
+  nq  : 50
+  qmax: 1e5
+
+############################################################
+debug: true
+maxcores: 8

n3fit/setup.py

@@ -10,6 +10,11 @@
             '':['*.fitinfo', '*.yml'],
             'tests/regressions': ['*'],
         },
+        extras_require={
+          'ga' : [
+            'evolutionary-keras',
+            ],
+          },
 
         entry_points = {'console_scripts':
             ['n3fit = n3fit.scripts.n3fit_exec:main',

n3fit/src/n3fit/backends/__init__.py

@@ -1,20 +1,36 @@
-from n3fit.backends.keras_backend.internal_state import (
-    set_initial_state,
-    clear_backend_state,
-)
-from n3fit.backends.keras_backend.MetaLayer import MetaLayer
-from n3fit.backends.keras_backend.MetaModel import MetaModel
-from n3fit.backends.keras_backend.base_layers import (
-    Input,
-    concatenate,
-    Lambda,
-    base_layer_selector,
-    regularizer_selector,
-    Concatenate,
-)
-from n3fit.backends.keras_backend import losses
-from n3fit.backends.keras_backend import operations
-from n3fit.backends.keras_backend import constraints
-from n3fit.backends.keras_backend import callbacks
+# Make the chosen backend available from n3fit.backends
+# These includes:
+#   set_initial_state, clear_backend_state
+#   meta classes: MetaLayer, MetaModel
+#   modules: backend_layers, losses, operations, constraints, callbacks
+#
 
-print("Using Keras backend")
+# If no backend has been set the default is the tensorflow backend
+from sys import modules as _modules
+
+_backends_module = _modules[__name__]
+
+from n3fit.backends.keras_backend.internal_state import set_backend as set_tf
+
+set_tf(_backends_module)
+
+
+def select_backend(backend_name):
+    """Select the appropiate backend by overriding this module
+    Default is understood as TensorFlow
+    """
+    if backend_name is None:
+        backend_name = "tf"
+    try:
+        backend_name = backend_name.lower()
+    except AttributeError:
+        raise ValueError(f"select_backend accepts only strings, received: {backend_name}")
+    # Now depenidng on the backend, we need to load the backend importer function
+    if backend_name == "evolutionary_keras":
+        from n3fit.backends.ga_backend.internal_state import set_backend
+
+        set_backend(_backends_module)
+    elif backend_name in ["tf", "tensorflow", "keras"]:
+        set_tf(_backends_module)
+    else:
+        raise ValueError(f"Backend {backend_name} not recognized")

n3fit/src/n3fit/backends/ga_backend/MetaModel.py

@@ -0,0 +1,26 @@
+"""
+    MetaModel class for evolutionary_keras
+
+    Extension of the backend MetaModel class to use evolutionary_keras.
+    The MetaModel class provided by this module is a multiple inheritance
+    of the keras_backend.MetaModel and the evolutionary_keras.EvolModel
+"""
+
+import n3fit.backends.keras_backend.MetaModel as tf_MetaModel
+import evolutionary_keras.optimizers as Evolutionary_optimizers
+from evolutionary_keras.models import EvolModel
+
+# Add the evolutionary algorithms to the list of accepted optimizers
+tf_MetaModel.optimizers["NGA"] = (
+    Evolutionary_optimizers.NGA,
+    {"sigma_init": 15, "population_size": 80, "mutation_rate": 0.05},
+)
+tf_MetaModel.optimizers["CMA"] = (
+    Evolutionary_optimizers.CMA,
+    {"sigma_init": 0.3, "population_size": None, "max_evaluations": None},
+)
+
+# Mix inheritance
+class MetaModel(tf_MetaModel.MetaModel, EvolModel):
+
+    accepted_optimizers = tf_MetaModel.optimizers

n3fit/src/n3fit/backends/ga_backend/internal_state.py

@@ -0,0 +1,17 @@
+"""
+    Modify the internal state of the backend
+"""
+from n3fit.backends.ga_backend.MetaModel import MetaModel
+from n3fit.backends import select_backend
+
+
+def set_backend(module):
+    """Overwrites the necessary modules and imports from the
+    backends module.
+    Receives a reference to the module to overwrite.
+    """
+    # First ensure the backend is tensorflow
+    select_backend("Tensorflow")
+
+    # Now set the MetaModel and leave all the rest as the Tensorflow Standard
+    setattr(module, "MetaModel", MetaModel)

n3fit/src/n3fit/backends/keras_backend/MetaModel.py

@@ -11,6 +11,7 @@
 from tensorflow.keras import optimizers as Kopt
 from n3fit.backends.keras_backend.operations import numpy_to_tensor
 
+
 # Check the TF version to check if legacy-mode is needed (TF < 2.2)
 tf_version = tf.__version__.split('.')
 if int(tf_version[0]) == 2 and int(tf_version[1]) < 2:

n3fit/src/n3fit/backends/keras_backend/internal_state.py

@@ -13,11 +13,44 @@
 import random as rn
 import numpy as np
 import tensorflow as tf
+
 # for (very slow but fine grained) debugging turn eager mode on
 # tf.config.run_functions_eagerly(True)
 from tensorflow.keras import backend as K
 
 
+def set_backend(module):
+    """Overwrites the necessary modules and imports from the
+    backends module.
+    Receives a reference to the module to overwrite.
+    """
+    from n3fit.backends.keras_backend.internal_state import (
+        set_initial_state,
+        clear_backend_state,
+    )
+
+    setattr(module, "set_initial_state", set_initial_state)
+    setattr(module, "clear_backend_state", clear_backend_state)
+
+    from n3fit.backends.keras_backend.MetaLayer import MetaLayer
+    from n3fit.backends.keras_backend.MetaModel import MetaModel
+
+    setattr(module, "MetaLayer", MetaLayer)
+    setattr(module, "MetaModel", MetaModel)
+
+    import n3fit.backends.keras_backend.base_layers as backend_layers
+    from n3fit.backends.keras_backend import losses
+    from n3fit.backends.keras_backend import operations
+    from n3fit.backends.keras_backend import constraints
+    from n3fit.backends.keras_backend import callbacks
+
+    setattr(module, "backend_layers", backend_layers)
+    setattr(module, "losses", losses)
+    setattr(module, "operations", operations)
+    setattr(module, "constraints", constraints)
+    setattr(module, "callbacks", callbacks)
+
+
 def set_initial_state(seed=13):
     """
     Sets the initial state of the backend
@@ -44,10 +77,10 @@ def set_initial_state(seed=13):
 
 def clear_backend_state(max_cores=None):
     """
-        Clears the state of the backend and opens a new session.
+    Clears the state of the backend and opens a new session.
 
-        Note that this function needs to set the TF session, including threads and processes
-        i.e., this function must NEVER be called after setting the initial state.
+    Note that this function needs to set the TF session, including threads and processes
+    i.e., this function must NEVER be called after setting the initial state.
     """
     print("Clearing session")