Add TFT benchmark

examples/benchmarks/TFT/data_formatters/qlib_Alpha158.py

@@ -0,0 +1,220 @@
+# coding=utf-8
+# Copyright 2020 The Google Research Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# Lint as: python3
+"""Custom formatting functions for Alpha158 dataset.
+
+Defines dataset specific column definitions and data transformations.
+"""
+
+import data_formatters.base
+import libs.utils as utils
+import sklearn.preprocessing
+
+GenericDataFormatter = data_formatters.base.GenericDataFormatter
+DataTypes = data_formatters.base.DataTypes
+InputTypes = data_formatters.base.InputTypes
+
+class Alpha158Formatter(GenericDataFormatter):
+  """Defines and formats data for the Alpha158 dataset.
+
+  Attributes:
+    column_definition: Defines input and data type of column used in the
+      experiment.
+    identifiers: Entity identifiers used in experiments.
+  """
+
+  _column_definition = [
+      ('instrument', DataTypes.CATEGORICAL, InputTypes.ID),
+      ('LABEL0', DataTypes.REAL_VALUED, InputTypes.TARGET),
+      ('date', DataTypes.DATE, InputTypes.TIME),
+      ('month', DataTypes.CATEGORICAL, InputTypes.KNOWN_INPUT),
+      ('day_of_week', DataTypes.CATEGORICAL, InputTypes.KNOWN_INPUT),
+      # Selected 10 features
+      ('RESI5', DataTypes.REAL_VALUED, InputTypes.OBSERVED_INPUT),
+      ('WVMA5', DataTypes.REAL_VALUED, InputTypes.OBSERVED_INPUT),
+      ('RSQR5', DataTypes.REAL_VALUED, InputTypes.OBSERVED_INPUT),
+      ('KLEN', DataTypes.REAL_VALUED, InputTypes.OBSERVED_INPUT),
+      ('RSQR10', DataTypes.REAL_VALUED, InputTypes.OBSERVED_INPUT),
+      ('CORR5', DataTypes.REAL_VALUED, InputTypes.OBSERVED_INPUT),
+      ('CORD5', DataTypes.REAL_VALUED, InputTypes.OBSERVED_INPUT),
+      ('CORR10', DataTypes.REAL_VALUED, InputTypes.OBSERVED_INPUT),
+      ('ROC60', DataTypes.REAL_VALUED, InputTypes.OBSERVED_INPUT),
+      ('RESI10', DataTypes.REAL_VALUED, InputTypes.OBSERVED_INPUT),
+      ('const', DataTypes.CATEGORICAL, InputTypes.STATIC_INPUT),
+    ]
+
+  def __init__(self):
+    """Initialises formatter."""
+
+    self.identifiers = None
+    self._real_scalers = None
+    self._cat_scalers = None
+    self._target_scaler = None
+    self._num_classes_per_cat_input = None
+
+  def split_data(self, df, valid_boundary=2016, test_boundary=2018):
+    """Splits data frame into training-validation-test data frames.
+
+    This also calibrates scaling object, and transforms data for each split.
+
+    Args:
+      df: Source data frame to split.
+      valid_boundary: Starting year for validation data
+      test_boundary: Starting year for test data
+
+    Returns:
+      Tuple of transformed (train, valid, test) data.
+    """
+
+    print('Formatting train-valid-test splits.')
+
+    index = df['year']
+    train = df.loc[index < valid_boundary]
+    valid = df.loc[(index >= valid_boundary) & (index < test_boundary)]
+    test = df.loc[index >= test_boundary]
+
+    self.set_scalers(train)
+
+    return (self.transform_inputs(data) for data in [train, valid, test])
+
+  def set_scalers(self, df):
+    """Calibrates scalers using the data supplied.
+
+    Args:
+      df: Data to use to calibrate scalers.
+    """
+    print('Setting scalers with training data...')
+
+    column_definitions = self.get_column_definition()
+    id_column = utils.get_single_col_by_input_type(InputTypes.ID,
+                                                   column_definitions)
+    target_column = utils.get_single_col_by_input_type(InputTypes.TARGET,
+                                                       column_definitions)
+
+    # Extract identifiers in case required
+    self.identifiers = list(df[id_column].unique())
+
+    # Format real scalers
+    real_inputs = utils.extract_cols_from_data_type(
+        DataTypes.REAL_VALUED, column_definitions,
+        {InputTypes.ID, InputTypes.TIME})
+
+    data = df[real_inputs].values
+    self._real_scalers = sklearn.preprocessing.StandardScaler().fit(data)
+    self._target_scaler = sklearn.preprocessing.StandardScaler().fit(
+        df[[target_column]].values)  # used for predictions
+
+    # Format categorical scalers
+    categorical_inputs = utils.extract_cols_from_data_type(
+        DataTypes.CATEGORICAL, column_definitions,
+        {InputTypes.ID, InputTypes.TIME})
+
+    categorical_scalers = {}
+    num_classes = []
+    for col in categorical_inputs:
+      # Set all to str so that we don't have mixed integer/string columns
+      srs = df[col].apply(str)
+      categorical_scalers[col] = sklearn.preprocessing.LabelEncoder().fit(
+          srs.values)
+      num_classes.append(srs.nunique())
+
+    # Set categorical scaler outputs
+    self._cat_scalers = categorical_scalers
+    self._num_classes_per_cat_input = num_classes
+
+  def transform_inputs(self, df):
+    """Performs feature transformations.
+
+    This includes both feature engineering, preprocessing and normalisation.
+
+    Args:
+      df: Data frame to transform.
+
+    Returns:
+      Transformed data frame.
+
+    """
+    output = df.copy()
+
+    if self._real_scalers is None and self._cat_scalers is None:
+      raise ValueError('Scalers have not been set!')
+
+    column_definitions = self.get_column_definition()
+
+    real_inputs = utils.extract_cols_from_data_type(
+        DataTypes.REAL_VALUED, column_definitions,
+        {InputTypes.ID, InputTypes.TIME})
+    categorical_inputs = utils.extract_cols_from_data_type(
+        DataTypes.CATEGORICAL, column_definitions,
+        {InputTypes.ID, InputTypes.TIME})
+
+    # Format real inputs
+    output[real_inputs] = self._real_scalers.transform(df[real_inputs].values)
+
+    # Format categorical inputs
+    for col in categorical_inputs:
+      string_df = df[col].apply(str)
+      output[col] = self._cat_scalers[col].transform(string_df)
+
+    return output
+
+  def format_predictions(self, predictions):
+    """Reverts any normalisation to give predictions in original scale.
+
+    Args:
+      predictions: Dataframe of model predictions.
+
+    Returns:
+      Data frame of unnormalised predictions.
+    """
+    output = predictions.copy()
+
+    column_names = predictions.columns
+
+    for col in column_names:
+      if col not in {'forecast_time', 'identifier'}:
+        output[col] = self._target_scaler.inverse_transform(predictions[col])
+
+    return output
+
+  # Default params
+  def get_fixed_params(self):
+    """Returns fixed model parameters for experiments."""
+
+    fixed_params = {
+        'total_time_steps': 16 + 6,
+        'num_encoder_steps': 16,
+        'num_epochs': 100,
+        'early_stopping_patience': 5,
+        'multiprocessing_workers': 5,
+    }
+
+    return fixed_params
+
+  def get_default_model_params(self):
+    """Returns default optimised model parameters."""
+
+    model_params = {
+        'dropout_rate': 0.3,
+        'hidden_layer_size': 160,
+        'learning_rate': 0.01,
+        'minibatch_size': 64,
+        'max_gradient_norm': 0.01,
+        'num_heads': 1,
+        'stack_size': 1
+    }
+
+    return model_params