Feature Extraction
Transformers
PyTorch
Safetensors
fimhawkes
time-series
temporal-point-processes
hawkes-processes
scientific-ml
custom_code
Instructions to use FIM4Science/FIM-PP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FIM4Science/FIM-PP with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="FIM4Science/FIM-PP", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("FIM4Science/FIM-PP", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
| dataset: | |
| dataset_kwargs: | |
| field_name_for_dimension_grouping: base_intensity_functions | |
| files_to_load: | |
| base_intensity_functions: base_intensity_functions.pt | |
| event_times: event_times.pt | |
| event_types: event_types.pt | |
| kernel_functions: kernel_functions.pt | |
| time_offsets: time_offsets.pt | |
| shuffle: true | |
| loader_kwargs: | |
| batch_size: 6 | |
| full_len_ratio: 0.1 | |
| max_number_of_minibatch_sizes: 8 | |
| max_path_count: 2000 | |
| max_sequence_len: 100 | |
| min_path_count: 400 | |
| min_sequence_len: 15 | |
| num_inference_paths: 1 | |
| num_inference_times: 2000 | |
| num_workers: 16 | |
| test_batch_size: 2 | |
| variable_num_of_paths: true | |
| variable_sequence_lens: | |
| train: true | |
| validation: false | |
| name: HawkesDataLoader | |
| path: | |
| train: !!python/tuple | |
| - data/synthetic_data/hawkes/1k_1D_2k_paths_const_base_exp_kernel/train | |
| - data/synthetic_data/hawkes/1k_1D_2k_paths_const_base_exp_kernel_no_interactions/train | |
| - data/synthetic_data/hawkes/1k_1D_2k_paths_Gamma_base_exp_kernel/train | |
| - data/synthetic_data/hawkes/1k_5D_2k_paths_const_base_exp_kernel/train | |
| - data/synthetic_data/hawkes/1k_5D_2k_paths_const_base_exp_kernel_no_interactions/train | |
| - data/synthetic_data/hawkes/1k_5D_2k_paths_Gamma_base_exp_kernel/train | |
| - data/synthetic_data/hawkes/1k_10D_2k_paths_const_base_exp_kernel/train | |
| - data/synthetic_data/hawkes/1k_10D_2k_paths_const_base_exp_kernel_no_interactions/train | |
| - data/synthetic_data/hawkes/1k_10D_2k_paths_poisson/train | |
| - data/synthetic_data/hawkes/1k_10D_2k_paths_Gamma_base_exp_kernel/train | |
| - data/synthetic_data/hawkes/1k_10D_2k_paths_sin_base_exp_kernel/train | |
| - data/synthetic_data/hawkes/1k_15D_2k_paths_const_base_exp_kernel/train | |
| - data/synthetic_data/hawkes/1k_15D_2k_paths_const_base_exp_kernel_no_interactions/train | |
| - data/synthetic_data/hawkes/1k_15D_2k_paths_poisson/train | |
| - data/synthetic_data/hawkes/1k_15D_2k_paths_Gamma_base_exp_kernel/train | |
| - data/synthetic_data/hawkes/1k_15D_2k_paths_sin_base_exp_kernel/train | |
| - data/synthetic_data/hawkes/5k_22D_2k_paths_const_base_exp_kernel/train | |
| - data/synthetic_data/hawkes/5k_22D_2k_paths_const_base_exp_kernel_no_interactions/train | |
| - data/synthetic_data/hawkes/5k_22D_2k_paths_poisson/train | |
| - data/synthetic_data/hawkes/5k_22D_2k_paths_Gamma_base_exp_kernel/train | |
| - data/synthetic_data/hawkes/5k_22D_2k_paths_sin_base_exp_kernel/train | |
| - data/synthetic_data/hawkes/1k_10D_2k_paths_const_base_exp_kernel_sparse/train | |
| - data/synthetic_data/hawkes/1k_10D_2k_paths_Gamma_base_exp_kernel_sparse/train | |
| - data/synthetic_data/hawkes/1k_10D_2k_paths_sin_base_exp_kernel_sparse/train | |
| - data/synthetic_data/hawkes/1k_15D_2k_paths_const_base_exp_kernel_sparse/train | |
| - data/synthetic_data/hawkes/1k_15D_2k_paths_Gamma_base_exp_kernel_sparse/train | |
| - data/synthetic_data/hawkes/1k_15D_2k_paths_sin_base_exp_kernel_sparse/train | |
| - data/synthetic_data/hawkes/5k_22D_2k_paths_const_base_exp_kernel_sparse/train | |
| - data/synthetic_data/hawkes/1k_1D_2k_paths_const_base_rayleigh_kernel/train | |
| - data/synthetic_data/hawkes/1k_5D_2k_paths_const_base_rayleigh_kernel/train | |
| - data/synthetic_data/hawkes/1k_15D_2k_paths_const_base_rayleigh_kernel/train | |
| - data/synthetic_data/hawkes/5k_22D_2k_paths_const_base_rayleigh_kernel/train | |
| - data/synthetic_data/hawkes/1k_1D_2k_paths_const_base_rayleigh_kernel_sparse/train | |
| - data/synthetic_data/hawkes/1k_5D_2k_paths_const_base_rayleigh_kernel_sparse/train | |
| - data/synthetic_data/hawkes/1k_10D_2k_paths_const_base_rayleigh_kernel_sparse/train | |
| - data/synthetic_data/hawkes/5k_22D_2k_paths_const_base_rayleigh_kernel_sparse/train | |
| validation: !!python/tuple | |
| - data/synthetic_data/hawkes/5k_22D_2k_paths_const_base_exp_kernel/val | |
| - data/synthetic_data/hawkes/5k_22D_2k_paths_const_base_exp_kernel_no_interactions/val | |
| - data/synthetic_data/hawkes/5k_22D_2k_paths_poisson/val | |
| - data/synthetic_data/hawkes/5k_22D_2k_paths_Gamma_base_exp_kernel/val | |
| - data/synthetic_data/hawkes/5k_22D_2k_paths_sin_base_exp_kernel/val | |
| - data/synthetic_data/hawkes/5k_22D_2k_paths_const_base_rayleigh_kernel/val | |
| - data/synthetic_data/hawkes/5k_22D_2k_paths_const_base_rayleigh_kernel_sparse/val | |
| distributed: | |
| activation_chekpoint: false | |
| checkpoint_type: full_state | |
| enabled: false | |
| min_num_params: 1e5 | |
| sharding_strategy: NO_SHARD | |
| wrap_policy: SIZE_BAZED | |
| experiment: | |
| device_map: auto | |
| name: FIM_Hawkes_10-22st_nll_mc_only_2000_paths_mixed_100_events_mixed-experiment-seed-10-dataset-dataset_kwargs-field_name_for_dimension_grouping-base_intensity_functions | |
| name_add_date: true | |
| seed: 10 | |
| model: | |
| alpha_decoder: | |
| hidden_act: | |
| name: torch.nn.GELU | |
| hidden_layers: !!python/tuple | |
| - 256 | |
| - 256 | |
| name: fim.models.blocks.base.MLP | |
| beta_decoder: | |
| hidden_act: | |
| name: torch.nn.GELU | |
| hidden_layers: !!python/tuple | |
| - 256 | |
| - 256 | |
| name: fim.models.blocks.base.MLP | |
| context_summary_encoder: | |
| encoder_layer: | |
| batch_first: true | |
| dropout: 0.0 | |
| name: torch.nn.TransformerEncoderLayer | |
| nhead: 4 | |
| name: torch.nn.TransformerEncoder | |
| num_layers: 2 | |
| context_summary_pooling: | |
| attention: | |
| nhead: 4 | |
| name: fim.models.blocks.neural_operators.AttentionOperator | |
| num_res_layers: 1 | |
| paths_block_attention: false | |
| context_ts_encoder: | |
| encoder_layer: | |
| batch_first: true | |
| dropout: 0.0 | |
| name: torch.nn.TransformerEncoderLayer | |
| nhead: 4 | |
| name: torch.nn.TransformerEncoder | |
| num_layers: 4 | |
| decoder_ts: | |
| decoder_layer: | |
| batch_first: true | |
| dropout: 0.0 | |
| name: torch.nn.TransformerDecoderLayer | |
| nhead: 4 | |
| name: torch.nn.TransformerDecoder | |
| num_layers: 4 | |
| delta_time_encoder: | |
| name: fim.models.blocks.positional_encodings.SineTimeEncoding | |
| out_features: 256 | |
| evaluation_mark_encoder: | |
| name: torch.nn.Linear | |
| hidden_act: | |
| name: torch.nn.GELU | |
| hidden_dim: 256 | |
| loss_weights: | |
| alpha: 0.0 | |
| mu: 0.0 | |
| nll: 1.0 | |
| relative_spike: 0.0 | |
| smape: 0.0 | |
| mark_encoder: | |
| name: torch.nn.Linear | |
| out_features: 256 | |
| mark_fusion_attention: null | |
| max_num_marks: 22 | |
| model_type: fimhawkes | |
| mu_decoder: | |
| hidden_act: | |
| name: torch.nn.GELU | |
| hidden_layers: !!python/tuple | |
| - 256 | |
| - 256 | |
| name: fim.models.blocks.base.MLP | |
| nll: | |
| method: monte_carlo | |
| num_integration_points: 200 | |
| normalize_by_max_time: false | |
| normalize_times: true | |
| thinning: null | |
| time_encoder: | |
| name: fim.models.blocks.positional_encodings.SineTimeEncoding | |
| out_features: 256 | |
| optimizers: !!python/tuple | |
| - optimizer_d: | |
| lr: 5.0e-05 | |
| name: torch.optim.AdamW | |
| weight_decay: 0.0001 | |
| trainer: | |
| best_metric: loss | |
| debug_iterations: null | |
| detect_anomaly: false | |
| epochs: 100000 | |
| evaluation_epoch: | |
| enable_plotting: false | |
| inference_path_idx: 0 | |
| iterator_name: validation | |
| path: fim.trainers.evaluation_epochs.HawkesEvaluationPlots | |
| plot_frequency: 10 | |
| experiment_dir: ./results/ | |
| gradient_accumulation_steps: 6 | |
| logging_format: RANK_%(rank)s - %(asctime)s - %(name)s - %(levelname)s - %(message)s | |
| name: Trainer | |
| precision: bf16_mixed | |
| save_every: 1 | |
| schedulers: !!python/tuple | |
| - beta: 1.0 | |
| label: gauss_nll | |
| name: fim.utils.param_scheduler.ConstantScheduler | |
| - beta: 1.0 | |
| label: init_cross_entropy | |
| name: fim.utils.param_scheduler.ConstantScheduler | |
| - beta: 1.0 | |
| label: missing_link | |
| name: fim.utils.param_scheduler.ConstantScheduler | |