Merge pull request #3 from psaegert/dev

Analysis Notebooks

README.md

@@ -188,7 +188,7 @@ flash_ansr evaluate -c {{ROOT}}/configs/${CONFIG}/evaluation.yaml -m "{{ROOT}}/m
 flash_ansr evaluate -c {{ROOT}}/configs/${CONFIG}/evaluation.yaml -m "{{ROOT}}/models/ansr-models/${MODEL}" -d "{{ROOT}}/data/ansr-data/test_set/feynman/dataset.yaml" -n 5000 -o {{ROOT}}/results/evaluation/${CONFIG}/feynman.pickle -v
 flash_ansr evaluate -c {{ROOT}}/configs/${CONFIG}/evaluation.yaml -m "{{ROOT}}/models/ansr-models/${MODEL}" -d "{{ROOT}}/data/ansr-data/test_set/nguyen/dataset.yaml" -n 5000 -o {{ROOT}}/results/evaluation/${CONFIG}/nguyen.pickle -v
 flash_ansr evaluate -c {{ROOT}}/configs/${CONFIG}/evaluation.yaml -m "{{ROOT}}/models/ansr-models/${MODEL}" -d "{{ROOT}}/configs/${CONFIG}/dataset_val.yaml" -n 5000 -o {{ROOT}}/results/evaluation/${CONFIG}/val.pickle -v
-flash_ansr evaluate -c {{ROOT}}/configs/${CONFIG}/evaluation.yaml -m "{{ROOT}}/models/ansr-models/${MODEL}" -d "{{ROOT}}/data/ansr-data/test_set/hard_set/dataset.yaml" -n 5000 -o {{ROOT}}/results/evaluation/${CONFIG}/hard_set.pickle -v
+flash_ansr evaluate -c {{ROOT}}/configs/${CONFIG}/evaluation.yaml -m "{{ROOT}}/models/ansr-models/${MODEL}" -d "{{ROOT}}/data/ansr-data/test_set/pool_15/dataset.yaml" -n 5000 -o {{ROOT}}/results/evaluation/${CONFIG}/pool_15.pickle -v
 flash_ansr evaluate -c {{ROOT}}/configs/${CONFIG}/evaluation.yaml -m "{{ROOT}}/models/ansr-models/${MODEL}" -d "{{ROOT}}/configs/${CONFIG}/dataset_train.yaml" -n 5000 -o {{ROOT}}/results/evaluation/${CONFIG}/train.pickle -v
 ```
 
@@ -204,15 +204,16 @@ with
 #### 4.1 Evaluate NeSymRes
 1. Clone [NeuralSymbolicRegressionThatScales](https://github.com/SymposiumOrganization/NeuralSymbolicRegressionThatScales) to a directory of your choice.
 2. Download the `100M` model as described [here](https://github.com/SymposiumOrganization/NeuralSymbolicRegressionThatScales?tab=readme-ov-file#pretrained-models)
-3. Create a Python 3.10 (!) environment and install flash-ansr as in the previous steps.
-4. Install NeSymRes in the same environment:
+3. Move the `100M` model into `flash-ansr/models/nesymres/`
+4. Create a Python 3.10 (!) environment and install flash-ansr as in the previous steps.
+5. Install NeSymRes in the same environment:
 ```sh
 cd NeuralSymbolicRegressionThatScales
 pip install -e src/
 ```
-5. Navigate back to this repository and run the evaluation
+1. Navigate back to this repository and run the evaluation
 ```sh
-cd ansr
+cd flash-ansr
 ./scripts/evaluate_nesymres <test_set>
 ```
 

configs/analysis_beams/v6.0_1-beams/dataset_train.yaml

@@ -0,0 +1,2 @@
+skeleton_pool: './skeleton_pool_train.yaml'
+padding: 'zero'

configs/analysis_beams/v6.0_1-beams/dataset_val.yaml

@@ -0,0 +1,2 @@
+skeleton_pool: './skeleton_pool_val.yaml'
+padding: 'zero'

configs/analysis_beams/v6.0_1-beams/evaluation.yaml

@@ -0,0 +1,13 @@
+n_support: 512
+beam_width: 1
+n_restarts: 8
+max_len: 32
+numeric_head: False
+equivalence_pruning: True
+pointwise_close_criterion: 0.95
+pointwise_close_accuracy_rtol: 0.05
+pointwise_close_accuracy_atol: 0.001
+r2_close_criterion: 0.95
+refiner_p0_noise: 'uniform'
+refiner_p0_noise_kwargs: {'low': -5, 'high': 5}
+device: cuda

configs/analysis_beams/v6.0_1-beams/expression_space.yaml

@@ -0,0 +1,267 @@
+operators:
+  "+":
+    realization: "+"
+    alias: ["add", "plus"]
+    inverse: "-"
+    arity: 2
+    weight: 10
+    precedence: 1
+    commutative: true
+    symmetry: 0
+    positive: false
+    monotonicity: 0
+  "-":
+    realization: "-"
+    alias: ["sub", "minus"]
+    inverse: "+"
+    arity: 2
+    weight: 5
+    precedence: 1
+    commutative: false
+    symmetry: 0
+    positive: false
+    monotonicity: 0
+  neg:
+    realization: "nsrops.neg"
+    wolfram: "Negative"
+    alias: ["negative"]
+    inverse: "neg"
+    arity: 1
+    weight: 5
+    precedence: 2.5
+    commutative: false
+    symmetry: -1 # anti-symmetric
+    positive: false
+    monotonicity: -1
+  "*":
+    realization: "*"
+    alias: ["mul", "times"]
+    inverse: "/"
+    arity: 2
+    weight: 10
+    precedence: 2
+    commutative: true
+    symmetry: 0
+    positive: false
+    monotonicity: 0
+  "/":
+    realization: "/"
+    alias: ["div", "divide"]
+    inverse: "*"
+    arity: 2
+    weight: 5
+    precedence: 2
+    commutative: false
+    symmetry: 0
+    positive: false
+    monotonicity: 0
+  abs:
+    realization: "abs"
+    wolfram: "Abs"
+    alias: ["absolute"]
+    inverse: null
+    arity: 1
+    weight: 4
+    precedence: 3
+    commutative: false
+    symmetry: 1 # symmetric
+    positive: true
+    monotonicity: 0
+  inv:
+    realization: "nsrops.inv"
+    wolfram: "Inverse"
+    alias: ["inverse"]
+    inverse: "inv"
+    arity: 1
+    weight: 4
+    precedence: 4
+    commutative: false
+    symmetry: -1 # anti-symmetric
+    positive: false
+    monotonicity: -1
+  pow2:
+    realization: "nsrops.pow2"
+    alias: ["square"]
+    inverse: null
+    arity: 1
+    weight: 4
+    precedence: 3
+    commutative: false
+    symmetry: 1 # symmetric
+    positive: true
+    monotonicity: 0
+  pow3:
+    realization: "nsrops.pow3"
+    alias: ["cube"]
+    inverse: "pow1_3"
+    arity: 1
+    weight: 2
+    precedence: 3
+    commutative: false
+    symmetry: -1 # anti-symmetric
+    positive: false
+    monotonicity: 1
+  pow4:
+    realization: "nsrops.pow4"
+    alias: []
+    inverse: null
+    arity: 1
+    weight: 1
+    precedence: 3
+    commutative: false
+    symmetry: 1
+    positive: true
+    monotonicity: 0
+  pow5:
+    realization: "nsrops.pow5"
+    alias: []
+    inverse: "pow1_5"
+    arity: 1
+    weight: 1
+    precedence: 3
+    commutative: false
+    symmetry: -1 # anti-symmetric
+    positive: false
+    monotonicity: 1
+  pow1_2:
+    realization: "nsrops.pow1_2"
+    alias: ["sqrt"]
+    inverse: null
+    arity: 1
+    weight: 4
+    precedence: 3
+    commutative: false
+    symmetry: 0
+    positive: true
+    monotonicity: 1
+  pow1_3:
+    realization: "nsrops.pow1_3"
+    alias: []
+    inverse: null
+    arity: 1
+    weight: 2
+    precedence: 3
+    commutative: false
+    symmetry: -1 # anti-symmetric
+    positive: false
+    monotonicity: 1
+  pow1_4:
+    realization: "nsrops.pow1_4"
+    alias: []
+    inverse: null
+    arity: 1
+    weight: 1
+    precedence: 3
+    commutative: false
+    symmetry: 0
+    positive: true
+    monotonicity: 1
+  pow1_5:
+    realization: "nsrops.pow1_5"
+    alias: []
+    inverse: null
+    arity: 1
+    weight: 1
+    precedence: 3
+    commutative: false
+    symmetry: -1 # anti-symmetric
+    positive: false
+    monotonicity: 1
+  sin:
+    realization: "numpy.sin"
+    wolfram: "Sin"
+    alias: []
+    inverse: "asin"
+    arity: 1
+    weight: 4
+    precedence: 2
+    commutative: false
+    symmetry: -1 # anti-symmetric
+    positive: false
+    monotonicity: 0
+  cos:
+    realization: "numpy.cos"
+    wolfram: "Cos"
+    alias: []
+    inverse: "acos"
+    arity: 1
+    weight: 4
+    precedence: 2
+    commutative: false
+    symmetry: 1 # symmetric
+    positive: false
+    monotonicity: 0
+  tan:
+    realization: "numpy.tan"
+    wolfram: "Tan"
+    alias: []
+    inverse: "atan"
+    arity: 1
+    weight: 4
+    precedence: 2
+    commutative: false
+    symmetry: -1 # anti-symmetric
+    positive: false
+    monotonicity: 0
+  asin:
+    realization: "numpy.arcsin"
+    wolfram: "ArcSin"
+    alias: ["arcsin"]
+    inverse: "sin"
+    arity: 1
+    weight: 2
+    precedence: 2
+    commutative: false
+    symmetry: -1 # anti-symmetric
+    positive: false
+    monotonicity: 1
+  acos:
+    realization: "numpy.arccos"
+    wolfram: "ArcCos"
+    alias: ["arccos"]
+    inverse: "cos"
+    arity: 1
+    weight: 2
+    precedence: 2
+    commutative: false
+    symmetry: 0
+    positive: true
+    monotonicity: 1
+  atan:
+    realization: "numpy.arctan"
+    wolfram: "ArcTan"
+    alias: ["arctan"]
+    inverse: "tan"
+    arity: 1
+    weight: 2
+    precedence: 2
+    commutative: false
+    symmetry: -1 # anti-symmetric
+    positive: false
+    monotonicity: 1
+  exp:
+    realization: "numpy.exp"
+    wolfram: "Exp"
+    alias: []
+    inverse: "log"
+    arity: 1
+    weight: 4
+    precedence: 3
+    commutative: false
+    symmetry: 0
+    positive: true
+    monotonicity: 1
+  log:
+    realization: "numpy.log"
+    wolfram: "Log"
+    alias: ["ln"]
+    inverse: "exp"
+    arity: 1
+    weight: 4
+    precedence: 2
+    commutative: false
+    symmetry: 0
+    positive: false
+    monotonicity: 1
+
+variables: 3

configs/analysis_beams/v6.0_1-beams/nsr.yaml

@@ -0,0 +1,25 @@
+encoder_max_n_variables: 4  # includes the dependent variable
+size: 512
+
+pre_encoder_input_type: "ieee-754"
+pre_encoder_support_nan: False
+
+encoder: "SetTransformer"
+encoder_kwargs:
+  hidden_size: 512
+  n_enc_isab: 5
+  n_dec_sab: 2
+  n_induce: 64
+  n_heads: 8
+  layer_norm: False
+  n_seeds: 64
+
+decoder_n_heads: 8
+decoder_ff_size: 512
+decoder_dropout: 0.1
+decoder_n_layers: 5
+
+learnable_positional_embeddings: false
+max_input_length: null
+
+expression_space: './expression_space.yaml'

configs/analysis_beams/v6.0_1-beams/skeleton_pool_train.yaml

@@ -0,0 +1,37 @@
+expression_space: './expression_space.yaml'
+holdout_pools: [
+  "{{ROOT}}/data/ansr-data/v6.0/skeleton_pool_val/",
+  "{{ROOT}}/data/ansr-data/test_set/soose_nc/skeleton_pool/",
+  "{{ROOT}}/data/ansr-data/test_set/feynman/skeleton_pool/",
+  "{{ROOT}}/data/ansr-data/test_set/nguyen/skeleton_pool/",
+  "{{ROOT}}/data/ansr-data/test_set/pool_15/skeleton_pool/"
+]
+
+sample_strategy:
+  n_operator_distribution: "length_proportional"
+  min_operators: 0
+  max_operators: 10
+  power: 1
+  max_length: 21
+  max_tries: 1
+  independent_dimensions: True
+
+allow_nan: False
+simplify: True
+
+literal_prior: 'uniform'
+literal_prior_kwargs:
+  low: -5
+  high: 5
+
+support_prior: "uniform_intervals"
+support_prior_kwargs:
+  low: -10
+  high: 10
+
+n_support_prior: "uniform"
+n_support_prior_kwargs:
+  low: 16
+  high: 512
+  min_value: 16
+  max_value: 512