Skip to main content

Forecast ML library

Project description

A library to easily build & train Transformer models for forecasting.

This library uses the Tensorflow & Tensorflow-Probability deep learning libraries to implement & train the models.

Supported versions:

Tensorflow [2.4.0+ ]
Tensorflow-Probability [0.10.0+ ]

Note: If upgrading Tensorflow, skip v2.6.0 (buggy) & go to 2.7.0 or higher

A typical workflow will look like this:

Import basic libraries
import tfr
import pandas as pd
import numpy as np
import pprint
Build the Dataset Object - a uniform interface for creating training, testing & inference datasets
# Ensure the dataset meets the following criteria:
a) No NaNs or infs
b) No mixed datatypes in any column
b) No column names may contain spaces

df = pd.read_csv(...)

Create a dictionary with following column groups based on the dataframe
'id_col': Unique identifier for time-series' in the dataset. Mandatory.  
'target_col': Target Column. Mandatory.  
'time_index_col': Any Date or Integer index column that can be used to sort the time-series in ascending order. Mandatory.  
'static_num_col_list': A list of numeric columns which are static features i.e. don't change with time. If N/A specify an empty list: []  
'static_cat_col_list': A list of string/categorical columns which are static features. If N/A specify empty list: []  
'temporal_known_num_col_list': A list of time varying numeric columns which are known at the time of inference for the required Forecast horizon. If N/A spcify empty list [].  
'temporal_unknown_num_col_list': A list of time varying numeric columns for which only historical values are known. If N/A spcify empty list [].  
'temporal_known_cat_col_list': A list of time varying categorical columns which are known at the time of inference for the required Forecast horizon. If N/A spcify empty list [].  
'temporal_unknown_cat_col_list': A list of time varying categorical columns for which only historical values are known. If N/A spcify empty list [].  
'strata_col_list': A list of categorical columns to use for stratified sampling. If N/A specify empty list [].  
'sort_col_list': A list of columns to be used for sorting the dataframe. Typically ['id_col','time_index_col']. Mandatory.  
'wt_col': A numeric column to be used for weighted sampling of time-series'. If N/A specify: None.  

columns_dict = {'id_col':'id',  
                'target_col':'Sales',  
                'time_index_col':'date',  
                'static_num_col_list':[],  
                'static_cat_col_list':['item_id','cat_id','store_id','state_id'],  
                'temporal_known_num_col_list':['abs_age'],  
                'temporal_unknown_num_col_list':['sell_price'],  
                'temporal_known_cat_col_list':['month','wday','Week','event_name_1','event_type_1'],  
                'temporal_unknown_cat_col_list':['snap_CA','snap_TX','snap_WI'],  
                'strata_col_list':['state_id','store_id'],  
                'sort_col_list':['id','date'],  
                'wt_col':'Weight'}  
Create the dataset object using the dictionary defined above.
col_dict: Columns grouping dictionary defined above.  
window_len: int(maximum look back history + forecast horizon )    
fh: int(forecast horizon)    
batch: Specifies training & testing batch size. If using stratified sampling, this is the batch size per strata.  
min_nz: Min. no. of non zero values in the Target series within the window_len for it to qualify as a training sample.  
PARALLEL_DATA_JOBS: Option to use parallel processing for training batches generation.  
PARALLEL_DATA_JOBS_BATCHSIZE: Batch size to process within each of the parallel jobs.    
 
data_obj = tfr.tfr_dataset(col_dict=columns_dict,   
                           window_len=26,   
                           fh=13,   
                           batch=16,   
                           min_nz=1,   
                           PARALLEL_DATA_JOBS=1,   
                           PARALLEL_DATA_JOBS_BATCHSIZE=64)                    
Create train & test datasets to be passed to the model (to be built soon).
df = Processed Pandas Dataframe read earlier.  
train_till = Date/time_index_col cut-off for training data.   
test_till = Date/time_index_col cut-off for testing data. Typically this will be 'train_till + forecast_horizon'  

trainset, testset = data_obj.train_test_dataset(df,   
                                                train_till=pd.to_datetime('2015-12-31', format='%Y-%M-%d'),   
                                                test_till=pd.to_datetime('2016-01-31', format='%Y-%M-%d'))  
Obtain Column info dictionary & Vocab dictionary (required arguments for model)
col_index_dict = data_obj.col_index_dict  
vocab = data_obj.vocab_list(df)  
Create Inference dataset for final predctions. This can be done separately from above.
infer_dataset, actuals_df = data_obj.infer_dataset(df,   
                                                   history_till=pd.to_datetime('2015-12-31', format='%Y-%M-%d'),   
                                                   future_till=pd.to_datetime('2016-01-31', format='%Y-%M-%d'))  

where, actuals_df is a dataframe of ground_truths (to be used for evaluation)

Build Model
num_layers: Int. Specify no. of attention layers in the Transformer model. Typical range [1-4]    
num_heads: Int. No. of heads to be used for self attention computation. Typical range [1-4]  
d_model: Int. Model Dimension. Typical range [32,64,128]. Multiple of num_heads.  
forecast_horizon: same as 'fh' defined above.  
max_inp_len: = int(window_len - fh)  
loss_type: One of ['Point','Quantile'] for Point forecasts or ['Normal','Poisson','Negbin'] for distribution based forecasts  
dropout_rate: % Dropout for regularization  
trainset, testset: tf.data.Dataset datasources obtained above  
Returns the model object  

Select a loss_type & loss_function from the following:
   
pprint.pprint(tfr.supported_losses) 

{'Huber': ['loss_type: Point', 'Usage: Huber(delta=1.0, sample_weights=False)'],
 'Negbin': ['loss_type: Negbin', 'Usage: Negbin_NLL_Loss(sample_weights=False)'],
 'Normal': ['loss_type: Normal', 'Usage: Normal_NLL_Loss(sample_weights=False)'],
 'Poisson': ['loss_type: Poisson', 'Usage: Poisson_NLL_Loss(sample_weights=False)'],
 'Quantile': ['loss_type: Quantile', 'Usage: QuantileLoss_v2(quantiles=[0.5], sample_weights=False)'],
 'RMSE': ['loss_type: Point', 'Usage: RMSE(sample_weights=False)']
 }

e.g.
loss_type = 'Quantile' 
loss_fn = QuantileLoss_Weighted(quantiles=[0.6])
  
try:
    del model
except:
    pass
    
model = Simple_Transformer(col_index_dict = col_index_dict,
                           vocab_dict = vocab,
                           num_layers = 2,
                           num_heads = 4,
                           d_model = 64,
                           forecast_horizon = 13,
                           max_inp_len = 13,
                           loss_type = 'Quantile,
                           dropout_rate=0.1)

model.build() 
Train model
train_dataset, test_dataset: tf.data.Dataset objects  
loss_function: One of the supported loss functions. See the output of pprint.pprint(supported_losses) for usage.  
metric: 'MAE' or 'MSE'  
learning_Rate: Typical range [0.001 - 0.00001]  
max_epochs, min_epochs: Max & min training epochs  
steps_per_epoch: no. of training batches/gradient descent steps per epoch  
patience: how many epochs to wait before terminating in case of non-decreasing loss  
weighted_training: True/False.   
model_prefix: Path where to save models  
logdir: Training logs location. Can be viewed with Tensorboard.  

best_model = model.train(train_dataset=trainset,   
                         test_dataset=testset,
                         loss_function=loss_fn,              
                         metric='MSE',
                         learning_rate=0.0001,
                         max_epochs=2,
                         min_epochs=1,
                         train_steps_per_epoch=10,
                         test_steps_per_epoch=5,
                         patience=2,
                         weighted_training=True,
                         model_prefix='test_models\tfr_model',
                         logdir='test_logs')                         
                         
Load Model & Predict

Skip 'model.build()' if doing only inference using a saved model.

model.load(model_path='test_models\tfr_model_1')
forecast_df = model.infer(infer_dataset)
                     
Additionally, you may use feature weighted transformer
model = Feature_Weighted_Transformer(col_index_dict = col_index_dict,
                                     vocab_dict = vocab,
                                     num_layers = 2,
                                     num_heads = 4,
                                     d_model = 64,
                                     forecast_horizon = 13,
                                     max_inp_len = 13,
                                     loss_type = 'Quantile,
                                     dropout_rate=0.1)
model.build()

model.train(...) -- usage identical to Simple_Transformer

# Inference returns two outputs:

forecast_df, feature_imp = model.infer(...)

where, 
    forecast_df - forecasts dataframe
    feature_imp - a list of variable importance dataframes in the following order: static_vars_imp_df, historical_vars_imp_df, future_vars_imp_df 

Baseline Forecasts
Prepare the baseline dataset:

baseline_infer_dataset = data_obj.baseline_infer_dataset(df, 
                                                         history_till=pd.to_datetime('2016-01-18', format='%Y-%M-%d'), 
                                                         future_till=pd.to_datetime('2016-01-31', format='%Y-%M-%d'),
                                                         ignore_cols=['event_name_1','event_type_1'])

where, ignore_cols is a list of features to zero out while forecasting so as to eliminate their contribution to total forecast.

Call infer as usual:

baseline_forecast_df, _ = model.infer(baseline_infer_dataset)

Evaluate Forecasts
Evaluation produces two metrics: Forecast_Accuracy & Forecast_Bias expressed as percentages

eval_df = model.evaluate(forecasts=forecast_df, actuals=actuals_df, aggregate_on=['item_id','state_id'])

where, aggregate_on is a list of static categorical columns which provides the level at which to summarize forecast accuracy & bias.
  

New in v0.1.10 - Sparse Attention Transformers

Build Model: 

model = Sparse_Simple_Transformer(col_index_dict = col_index_dict,
                                  vocab_dict = vocab,
                                  num_layers = 2,
                                  num_heads = 4,
                                  num_blocks = 2,
                                  kernel_size = 5,  
                                  d_model = 64,
                                  forecast_horizon = 13,
                                  max_inp_len = 14,
                                  loss_type = 'Point',
                                  dropout_rate=0.1)

or 

model = Sparse_Feature_Weighted_Transformer(col_index_dict = col_index_dict,
                                            vocab_dict = vocab,
                                            num_layers = 2,
                                            num_heads = 4,
                                            num_blocks = 2,
                                            kernel_size = 5,
                                            d_model = 64,
                                            forecast_horizon = 13,
                                            max_inp_len = 14,
                                            loss_type = 'Point',
                                            dropout_rate=0.1)

model.build()

Where,
    num_blocks - local attention window size. max_inp_len should be a multiple of num_blocks. 
                 Specify num_blocks > 1 only if working with long sequences. 
    kernel_size - Conv1D causal convolution layer's kernel size. Basically, the look_back_window at each timestep.
                  Typical values: [3,5,7,9]

Train: Same as Feature_Weighted_Transformer

New in v0.1.15

Added switch 'low_memory' & 'use_memmap' to the tfr_dataset.train_test_dataset method.
Default: low_memory = True (uses tf.data.Dataset.from_generator API for generating train/test batches). Uses less memory at the expense of speed.
         low_memory = False, uses numpy arrays in tf.data.Dataset.from_tensor_slices(). Initial trainset/testset creation takes time but the training speed improves by 4x.
Default: use_memmap = True (uses numpy.memmap files to reduce memory usage). If False, builds train/test arrays in memory (high mem usage) 

trainset, testset = data_obj.train_test_dataset(df, 
                                               train_till=pd.to_datetime('2015-12-31', format='%Y-%M-%d'), 
                                               test_till=pd.to_datetime('2016-01-31', format='%Y-%M-%d'),
                                               low_memory=False,
                                               use_memmap=False)

Added TS Visualization & fixed charset handling to 'utf-8'

Plot sample raw time-series:

data_obj.show_ts_samples(data=df, sample_ids=[], n_samples=10, n_col=2, plot_size=(300,600), save=True, filename='ts_samples.html')

Plot sample processed time-series:

data_obj.show_processed_ts_samples(data=df, n_samples=10, n_col=2, plot_size=(300,400), save=True, filename='ts_processed_samples.html')

New in 0.1.18 - EDA package

Create Interactive EDA Report

import eda

eda_object = eda.eda(col_dict=columns_dict, PARALLEL_DATA_JOBS=4, PARALLEL_DATA_JOBS_BATCHSIZE=128)  # 'columns_dict' -- similar to the one used in 'tfr_dataset'
eda_object.create_report(data=df, filename='eda_report.html') # df is the pandas dataframe, filename is the full path of the to-be generated report

The create_report method takes a few more arguments:

n_col (default (int): 2) # Configures the grid layout 
plot_size (default (tuple of ints): (400,800)) # (Height,Width) of the plot in pixels
time_lags (default (list of ints): [-1,0,1]) # Used for non-linear correlation density plots between target_col & various numeric & categorical columns for specified lags.
max_static_col_levels (default (int): 100) # If there are too many levels to a static feature, the report can get crowded with redundant plots. This parameter helps skip crowded plots with little utility.

New in 0.1.24 - TFT & Decoder Lags

TFT sample usage:

import tft

... tft.tft_dataset
... tft.supported_losses

model = tft.Temporal_Fusion_Transformer(col_index_dict = col_index_dict,
                                    vocab_dict = vocab,
                                    num_layers = 1,
                                    num_heads = 1,
                                    d_model = 32,
                                    forecast_horizon = 13,
                                    max_inp_len = 13,
                                    loss_type = 'Quantile',
                                    num_quantiles=2,
                                    decoder_start_tokens=4,
                                    dropout_rate=0.1)

model.build()

Train & Infer methods are identical to other transformers.

For other transformers, one can supply optional parameter decoder_lags (int) during model creation to customize no. of previous target values 
to be used for decoding purpose. Minimum decoder_lags = 1, maximum decoder_lags = "length of the encoder series". 
Default: max(int(len(encoder_timesteps)/4),2) 

New in 0.1.28 - STCTN, ConvTFR

STCTN sample usage:

import stctn

... stctn.stctn_dataset
... stctn.supported_losses

model = stctn.Spatial_Temporal_Transformer(col_index_dict = col_index_dict,
                                    vocab_dict = vocab,
                                    num_layers = 4,
                                    num_heads = 1,
                                    d_model = 16,
                                    temporal_kernel_size_list = [1,2,3,4],
                                    spatial_kernel_size = 3,
                                    num_shuffle = 20,
                                    forecast_horizon = 13,
                                    max_inp_len = 13,
                                    loss_type = 'Point',
                                    num_quantiles=1,
                                    dropout_rate=0.1)

model.build()

Train & Infer methods are identical to other transformers.


ConvTFR usage:

import ctfr

... ctfr.ctfr_dataset
... ctfr.supported_losses

var_model = Feature_Weighted_ConvTransformer(col_index_dict = col_index_dict,
                               vocab_dict = vocab,
                               num_layers = 2,
                               num_heads = 4,
                               kernel_sizes = [1,3,5],
                               d_model = 32,
                               forecast_horizon = 13,
                               max_inp_len = 13,
                               loss_type = 'Quantile',
                               num_quantiles = 1,             
                               decoder_lags = 2,          
                               dropout_rate=0.1)

var_model.build()

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

fmldk-0.1.31.tar.gz (273.2 kB view hashes)

Uploaded Source

Built Distribution

fmldk-0.1.31-py3-none-any.whl (300.9 kB view hashes)

Uploaded Python 3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page