# @authors lucasmiranda42
# encoding: utf-8
# deepof_experiments

"""

Snakefile for data and imputation.
Execution: sbatch snakemake
Plot DAG: snakemake --snakefile deepof_experiments.smk --forceall --dag | dot -Tpdf > deepof_experiments_DAG.pdf
Plot rule graph: snakemake --snakefile deepof_experiments.smk --forceall --rulegraph | dot -Tpdf > deepof_experiments_RULEGRAPH.pdf

"""

import os

outpath = "/psycl/g/mpsstatgen/lucas/DLC/DLC_autoencoders/DeepOF/deepof/logs/"
losses = ["ELBO"]  # , "MMD", "ELBO+MMD"]
encodings = [6]  # [2, 4, 6, 8, 10, 12, 14, 16]
cluster_numbers = [25]  # [1, 5, 10, 15, 20, 25]
latent_reg = ["none", "categorical", "variance", "categorical+variance"]
pheno_weights = [0.01, 0.1, 0.25, 0.5, 1.0, 2.0, 4.0, 10.0, 100.0]


rule deepof_experiments:
    input:
        expand(
            os.path.join(
                outpath,
                "hyperparameter_tuning/trained_weights/GMVAE_loss={loss}_k={k}_encoding={enc}_final_weights.h5",
            ),
            loss=losses,
            k=cluster_numbers,
            enc=encodings,
        ), # expand(
         #     "/psycl/g/mpsstatgen/lucas/DLC/DLC_autoencoders/DeepOF/deepof/logs/latent_regularization_experiments/trained_weights/"
         #     "GMVAE_loss={loss}_encoding={encs}_k={k}_latreg={latreg}_final_weights.h5",
         #     loss=losses,
         #     encs=encodings,
         #     k=cluster_numbers,
         #     latreg=latent_reg,
         # )
         # expand(
         #     "/psycl/g/mpsstatgen/lucas/DLC/DLC_autoencoders/DeepOF/deepof/logs/pheno_classification_experiments/trained_weights/"
         #     "GMVAE_loss={loss}_encoding={encs}_k={k}_pheno={phenos}_run_1_final_weights.h5",
         #     loss=losses,
         #     encs=encodings,
         #     k=cluster_numbers,
         #     phenos=pheno_weights,
         # ),


rule coarse_hyperparameter_tuning:
    input:
        data_path="/psycl/g/mpsstatgen/lucas/DLC/DLC_models/deepof_single_topview/",
    output:
        trained_models=os.path.join(
            outpath,
            "hyperparameter_tuning/trained_weights/GMVAE_loss={loss}_k={k}_encoding={enc}_final_weights.h5",
        ),
    shell:
        "pipenv run python -m deepof.train_model "
        "--train-path {input.data_path} "
        "--val-num 25 "
        "--components {wildcards.k} "
        "--input-type coords "
        "--predictor 0 "
        "--phenotype-classifier 0 "
        "--variational True "
        "--loss {wildcards.loss} "
        "--kl-warmup 20 "
        "--mmd-warmup 0 "
        "--encoding-size {wildcards.enc} "
        "--batch-size 256 "
        "--window-size 24 "
        "--window-step 12 "
        "--output-path {outpath}coarse_hyperparameter_tuning "
        "--hyperparameter-tuning hyperband "
        "--hpt-trials 1"


# rule latent_regularization_experiments:
#     input:
#         data_path=ancient("/psycl/g/mpsstatgen/lucas/DLC/DLC_models/deepof_single_topview/"),
#     output:
#         trained_models=os.path.join(
#             outpath,
#             "latent_regularization_experiments/trained_weights/GMVAE_loss={loss}_encoding={encs}_k={k}_latreg={latreg}_final_weights.h5",
#         ),
#     shell:
#         "pipenv run python -m deepof.train_model "
#         "--train-path {input.data_path} "
#         "--val-num 5 "
#         "--components {wildcards.k} "
#         "--input-type coords "
#         "--predictor 0 "
#         "--phenotype-classifier 0 "
#         "--variational True "
#         "--latent-reg {wildcards.latreg} "
#         "--loss {wildcards.loss} "
#         "--kl-warmup 20 "
#         "--mmd-warmup 20 "
#         "--montecarlo-kl 10 "
#         "--encoding-size {wildcards.encs} "
#         "--batch-size 256 "
#         "--window-size 24 "
#         "--window-step 12 "
#         "--exclude-bodyparts Tail_base,Tail_1,Tail_2,Tail_tip "
#         "--output-path {outpath}latent_regularization_experiments"
#
#
# rule explore_phenotype_classification:
#     input:
#         data_path="/psycl/g/mpsstatgen/lucas/DLC/DLC_models/deepof_single_topview/",
#     output:
#         trained_models=os.path.join(
#             outpath,
#             "pheno_classification_experiments/trained_weights/GMVAE_loss={loss}_encoding={encs}_k={k}_pheno={phenos}_run_1_final_weights.h5",
#         ),
#     shell:
#         "pipenv run python -m deepof.train_model "
#         "--train-path {input.data_path} "
#         "--val-num 15 "
#         "--components {wildcards.k} "
#         "--input-type coords "
#         "--predictor 0 "
#         "--phenotype-classifier {wildcards.phenos} "
#         "--variational True "
#         "--loss {wildcards.loss} "
#         "--kl-warmup 20 "
#         "--mmd-warmup 20 "
#         "--montecarlo-kl 10 "
#         "--encoding-size {wildcards.encs} "
#         "--batch-size 256 "
#         "--window-size 11 "
#         "--window-step 11 "
#         "--stability-check 3  "
#         "--output-path {outpath}pheno_classification_experiments"