[Enhancement] Refactor Dataset (#184)

# Summary
I have introduced a new base `class Dataset` in `ml.py` which all datasets should subclass. It stores the dataset as a polars DataFrame with the column names and number of columns determined by the subclass. It implements generic methods such as `add_row`, `at`, `limit` and dataset saving. It also details abstract methods required by the subclasses. These include `X`, `y` and `generate_dataset`.

There are two subclasses that currently exist. `RuleBasedDataset` for the MLRR models and `EnviFormerDataset` for the enviFormer models.

# Old Dataset to New RuleBasedDataset Functionality Translation

- [x] \_\_init\_\_
    - self.columns and self.num_labels moved to base Dataset class
    - self.data moved to base class with name self.df along with initialising from list or from another DataFrame
    - struct_features, triggered and observed remain the same
- [x] \_block\_indices
    - function moved to base Dataset class
- [x] structure_id
    - stays in RuleBasedDataset, now requires an index for the row of interest
- [x] add_row
    - moved to base Dataset class, now calls add_rows so one or more rows can be added at a time
- [x] times_triggered
    - stays in RuleBasedDataset, now does a look up using polars df.filter
- [x] struct_features (see init)
- [x] triggered (see init)
- [x] observed (see init)
- [x] at
    - removed in favour of indexing with getitem
- [x] limit
    - removed in favour of indexing with getitem
- [x] classification_dataset
    - stays in RuleBasedDataset, largely the same just with new dataset construction using add_rows
- [x] generate_dataset
    - stays in RuleBasedDataset, largely the same just with new dataset construction using add_rows
- [x] X
    - moved to base Dataset as @abstract_method, RuleBasedDataset implementation functionally the same but uses polars
- [x] trig
    - stays in RuleBasedDataset, functionally the same but uses polars
- [x] y
    - moved to base Dataset as @abstract_method, RuleBasedDataset implementation functionally the same but uses polars
- [x] \_\_get_item\_\_
    - moved to base dataset, now passes item to the dataframe for polars to handle
- [x] to_arff
    - stays in RuleBasedDataset, functionally the same but uses polars
- [x] \_\_repr\_\_
    - moved to base dataset
- [x] \_\_iter\_\_
    - moved to base Dataset, now uses polars iter_rows

# Base Dataset class Features
The following functions are available in the base Dataset class

- init - Create the dataset from a list of columns and data in format list of list. Or can create a dataset from a polars Dataframe, this is essential for recreating itself during indexing. Can create an empty dataset by just passing column names.
- add_rows - Add rows to the Dataset, we check that the new data length is the same but it is presumed that the column order matches the existing dataframe
- add_row - Add one row, see add_rows
- block_indices - Returns the column indices that start with the given prefix
- columns - Property, returns dataframe.columns
- shape - Property, returns dataframe.shape
- X - Abstract method to be implemented by the subclasses, it should represent the input to a ML model
- y - Abstract method to be implemented by the subclasses, it should represent the target for a ML model
- generate_dataset - Abstract and static method to be implemented by the subclasses, should return an initialised subclass of Dataset
- iter - returns the iterable from dataframe.iter_rows()
- getitem - passes the item argument to the dataframe. If the result of indexing the dataframe is another dataframe, the new dataframe is  packaged into a new Dataset of the same subclass. If the result of indexing is something else (int, float, polar Series) return the result.
- save - Pickle and save the dataframe to the given path
- load - Static method to load the dataset from the given path
- to_numpy - returns the dataframe as a numpy array. Required for compatibility with training of the ECC model
- repr - return a representation of the dataset
- len - return the length of the dataframe
- iter_rows - Return dataframe.iterrows with arguments passed through. Mainly used to get the named iterable which returns rows of the dataframe as dict of column names: column values instead of tuple of column values.
- filter - pass to dataframe.filter and recreates self with the result
- select - pass to dataframe.select and recreates self with the result
- with_columns - pass to dataframe.with_columns and recreates self with the result
- sort - pass to dataframe.sort and recreates self with the result
- item - pass to dataframe.item
- fill_nan - fill the dataframe nan's with value
- height - Property, returns the height (number of rows) of the dataframe

- [x] App domain
- [x] MACCS alternatives

Co-authored-by: Liam Brydon <62733830+MyCreativityOutlet@users.noreply.github.com>
Reviewed-on: enviPath/enviPy#184
Reviewed-by: jebus <lorsbach@envipath.com>
Co-authored-by: liambrydon <lbry121@aucklanduni.ac.nz>
Co-committed-by: liambrydon <lbry121@aucklanduni.ac.nz>

tests/test_enviformer.py

@@ -42,13 +42,11 @@ class EnviFormerTest(TestCase):
                 threshold = float(0.5)
                 data_package_objs = [self.BBD_SUBSET]
                 eval_packages_objs = [self.BBD_SUBSET]
-                mod = EnviFormer.create(
-                    self.package, data_package_objs, eval_packages_objs, threshold=threshold
-                )
+                mod = EnviFormer.create(self.package, data_package_objs, threshold=threshold)
 
                 mod.build_dataset()
                 mod.build_model()
-                mod.evaluate_model(True, eval_packages_objs)
+                mod.evaluate_model(True, eval_packages_objs, n_splits=2)
 
                 mod.predict("CCN(CC)C(=O)C1=CC(=CC=C1)C")
 
@@ -57,12 +55,9 @@ class EnviFormerTest(TestCase):
             with self.settings(MODEL_DIR=tmpdir):
                 threshold = float(0.5)
                 data_package_objs = [self.BBD_SUBSET]
-                eval_packages_objs = [self.BBD_SUBSET]
                 mods = []
                 for _ in range(4):
-                    mod = EnviFormer.create(
-                        self.package, data_package_objs, eval_packages_objs, threshold=threshold
-                    )
+                    mod = EnviFormer.create(self.package, data_package_objs, threshold=threshold)
                     mod.build_dataset()
                     mod.build_model()
                     mods.append(mod)
@@ -73,15 +68,11 @@ class EnviFormerTest(TestCase):
 
                 # Test pathway prediction
                 times = [measure_predict(mods[1], self.BBD_SUBSET.pathways[0].pk) for _ in range(5)]
-                print(
-                    f"First pathway prediction took {times[0]} seconds, subsequent ones took {times[1:]}"
-                )
+                print(f"First pathway prediction took {times[0]} seconds, subsequent ones took {times[1:]}")
 
                 # Test eviction by performing three prediction with every model, twice.
                 times = defaultdict(list)
-                for _ in range(
-                    2
-                ):  # Eviction should cause the second iteration here to have to reload the models
+                for _ in range(2):  # Eviction should cause the second iteration here to have to reload the models
                     for mod in mods:
                         for _ in range(3):
                             times[mod.pk].append(measure_predict(mod))