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a/src/safeds/data/tabular/containers/_timeseries_table.py b/src/safeds/data/tabular/containers/_timeseries_table.py new file mode 100644 index 000000000..79d594291 --- /dev/null +++ b/src/safeds/data/tabular/containers/_timeseries_table.py @@ -0,0 +1,487 @@ +from __future__ import annotations + +import copy +import numpy as np +import pandas as pd +import torch +import torch.nn as nn +from torch.utils.data import DataLoader +from torch.utils.data import Dataset +from typing import TYPE_CHECKING + +from safeds.data.tabular.containers import Column, Row, Table, TaggedTable +from safeds.exceptions import ( + ColumnIsTargetError, + IllegalSchemaModificationError, + UnknownColumnNameError, +) + +if TYPE_CHECKING: + from collections.abc import Callable, Mapping, Sequence + from typing import Any + + +class TimeSeries(Table): + """ + A TimeSeries is a TaggedTable, which has also a time column + + Parameters + ---------- + data : Mapping[str, Sequence[Any]] + The data. + target_name : str + Name of the target column. + feature_names : list[str] | None + Names of the feature columns. If None, all columns except the target column are used. + + Raises + ------ + ColumnLengthMismatchError + If columns have different lengths. + ValueError + If the target column is also a feature column. + ValueError + If no feature columns are specified. + + + + """ + + # ------------------------------------------------------------------------------------------------------------------ + # Creation + # ------------------------------------------------------------------------------------------------------------------ + + @staticmethod + def _from_table( + table: Table, + target_name: str, + date_name: str, + window_size: int, + forecast_horizon: int, + feature_names: list[str] | None = None, + ) -> TimeSeries: + """Create a TimeSeries from a table + + Parameters + ---------- + table : Table + The table. + target_name : str + Name of the target column. + date_name: str + Name of the date column. + window_size: int + Size of the windows that will be created + forecast_horizon: int + Size of the forecast horizon + feature_names : list[str] | None + Names of the feature columns. If None, all columns except the target column are used. + + Retruns + ------- + time_series : TimeSeries + the created time series + + Raises + ------ + UnknownColumnError + If target_name matches none of the column names. + Value Error + If no feature columns are specified + + Examples + -------- + >>> from safeds.data.tabular.containers import Table, TimeSeries + >>> table = Table({"date": [0, 1, 2, 4]}, "f1": [5, 6, 7, 8])}) + >>> timeseries = TimeSeries._from_table(table, "f1", "date", 2, 1, ["f1"]) + + + """ + table = table._as_table() + # check if target column exists + if target_name not in table.column_names: + raise UnknownColumnNameError([target_name]) + + # If no feature names are specified, use all columns except the target column + if feature_names is None: + feature_names = table.column_names + # still needs to be thought about, if a target can be a feature normally yes + # feature_names.remove(target_name) + if len(feature_names) == 0: + raise ValueError("At least one feature column must be specified.") + + # Create Time Series Object + result = object.__new__(TimeSeries) + result._feature_names = feature_names + result._data = table._data + result._schema = table._schema + result._features = table.keep_only_columns(feature_names) + result._target = table.get_column(target_name) + result._date = table.get_column(date_name) + result._window_size = window_size + result._forecast_horizon = forecast_horizon + return result + + # ------------------------------------------------------------------------------------------------------------------ + # Dunder methods + # ------------------------------------------------------------------------------------------------------------------ + + def __init__( + self, + data: Mapping[str, Sequence[Any]], + date_name: str, + target_name: str, + window_size: int, + forecast_horizon: int, + feature_names: list[str] | None = None, + ): + """ + Create a timeseries from a mapping of column names to their values + A Timeseries is a tagged table + + Parameters + ---------- + data : Mapping[str, Sequence[Any]] + The data. + date_name : str + Name of the date column. + target_name : str + Name of the target column. + window_size : int + Size of the created windows + feature_names : list[str] | None + Names of the feature columns. If None, all columns except the target column are used. + + + Raises + ------ + ColumnLengthMismatchError + If columns have different lengths. + ValueError + If the target column is also a feature column. + ValueError + If no feature columns are specified. + + Examples + ------ + >>> from safeds.data.tabular.containers import TimeSeries + >>> timeseries = TimeSeries({"date":[0, 1, 2, 3, 4], "f1":[1, 2, 3, 4, 5]}, "f1", "date", "f1", 2, 1, ["f1"] ) + """ + super().__init__(data) + _data = Table(data) + + # If no feature names are specified, use all columns except the target column + if feature_names is None: + feature_names = _data.column_names + if target_name in feature_names: + feature_names.remove(target_name) + + # Validate inputs + # if target_name in feature_names: + # raise ValueError(f"Column '{target_name}' cannot be both feature and target.") + if len(feature_names) == 0: + raise ValueError("At least one feature column must be specified.") + + self._features: Table = _data.keep_only_columns(feature_names) + self._target: Column = _data.get_column(target_name) + self._date: Column = _data.get_column(date_name) + self._window_size: int = window_size + self._forecast_horizon: int = forecast_horizon + self._feature_names = feature_names + self._target_name = target_name + + + + for col in self._create_all_windows_for_column(): + print(col) + + #print(self._create_all_labels_for_target_column()) + + + # ------------------------------------------------------------------------------------------------------------------ + # Properties + # ------------------------------------------------------------------------------------------------------------------ + + @property + def features(self) -> Table: + """ + Get the feature columns of the tagged table. + + Returns + ------- + Table + The table containing the feature columns. + """ + return self._features + + @property + def target(self) -> Column: + """ + Get the target column of the tagged table. + + Returns + ------- + Column + The target column. + """ + return self._target + + + + # ------------------------------------------------------------------------------------------------------------------ + # Helpers + # ------------------------------------------------------------------------------------------------------------------ + def _copy(self) -> TimeSeries: + """ + Return a copy of this time series + + Returns + ------- + table: TimeSeries + """ + return copy.deepcopy(self) + + + # ------------------------------------------------------------------------------------------------------------------ + # Specific methods from TimeSeries class: + # ------------------------------------------------------------------------------------------------------------------ + + def add_column_as_feature(self, column: Column) -> TaggedTable: + """ + Return a new time series with the provoded column attached at the end, as a feature column. + + the original table is not modified + + Parameters + ---------- + column : Column + The column to be added + + Returns + ------- + result : TaggedTable + The table with the attached feature column. + + Raises + ------ + DuplicateColumnNameError + If the new column already exists. + ColumnSizeError + If the size of the column does not match the number of rows. + """ + return TimeSeries._from_table( + super().add_column(column), + target_name=self.target.name, + date_name= self._date.name, + window_size=self._window_size, + forecast_horizon=self.forecast_horizon, + feature_names=[*self.features.column_names, column.name], + ) + def add_columns_as_features(self, columns: list[Column] | Table) -> TaggedTable: + """ + Return a new `TaggedTable` with the provided columns attached at the end, as feature columns. + + The original table is not modified. + + Parameters + ---------- + columns : list[Column] | Table + The columns to be added as features. + + Returns + ------- + result : TaggedTable + The table with the attached feature columns. + + Raises + ------ + DuplicateColumnNameError + If any of the new feature columns already exist. + ColumnSizeError + If the size of any feature column does not match the number of rows. + """ + return TimeSeries._from_table( + super().add_columns(columns), + target_name= self.target.name, + date_name = self._date.name, + window_size= self._window_size, + forecast_horizon=self._forecast_horizon, + feature_names=self.features.column_names + + [col.name for col in (columns.to_columns() if isinstance(columns, Table) else columns)], + ) + + def _as_table(self: TaggedTable) -> Table: + """ + Return a new `Table` with the tagging removed. + + The original TaggedTable is not modified. + + Parameters + ---------- + self: TimeSeries + The TimeSeries. + + Returns + ------- + table: Table + The table as an untagged Table, i.e. without the information about which columns are features or target. + + """ + return Table.from_columns(super().to_columns()) + + def add_row(self, row: Row) -> TimeSeries: + """ + Return a new `TimeSeries` with an added Row attached. + + The original table is not modified. + + Parameters + ---------- + row : Row + The row to be added. + + Returns + ------- + table : TaggedTable + A new table with the added row at the end. + + Raises + ------ + UnknownColumnNameError + If the row has different column names than the table. + """ + return TimeSeries._from_table(super().add_row(row), + target_name=self.target.name, + date_name= self._date.name, + window_size=self._window_size, + forecast_horizon=self._forecast_horizon, + feature_names=self._feature_names) + def add_rows(self, rows: list[Row] | Table) -> TimeSeries: + """ + Return a new `TimeSeries` with multiple added Rows attached. + + The original table is not modified. + + Parameters + ---------- + rows : list[Row] or Table + The rows to be added. + + Returns + ------- + result : TimeSeries + A new table which combines the original table and the given rows. + + Raises + ------ + UnknownColumnNameError + If at least one of the rows have different column names than the table. + """ + + return TimeSeries._from_table(super().add_row(rows), + target_name=self.target.name, + date_name= self._date.name, + window_size=self._window_size, + forecast_horizon=self._forecast_horizon, + feature_names=self._feature_names) + + def filter_rows(self, query: Callable[[Row], bool]) -> TimeSeries: + """ + Return a new `TimeSeries` containing only rows that match the given Callable (e.g. lambda function). + + The original table is not modified. + + Parameters + ---------- + query : lambda function + A Callable that is applied to all rows. + + Returns + ------- + table : TaggedTable + A table containing only the rows to match the query. + """ + return TimeSeries._from_table( + super().filter_rows(query), + target_name=self.target.name, + date_name=self._date.name, + window_size= self._window_size, + forecast_horizon=self._forecast_horizon, + feature_names=self.features.column_names, + ) + def _as_table(self: TimeSeries) -> Table: + """ + Return a new `Table` with the tagging removed. + + The original TimeSeries is not modified. + + Parameters + ---------- + self: TimeSeries + The TimeSeries. + + Returns + ------- + table: Table + The table as an untagged Table, i.e. without the information about which columns are features or target. + + """ + return Table.from_columns(super().to_columns()) + + + + + # ------------------------------------------------------------------------------------------------------------------ + # Window Functions and needed class: + # for testing purposes they are here + # ------------------------------------------------------------------------------------------------------------------ + + def _create_all_windows_for_column(self, train_size: float): + #this generator generates all windows for all feature columns + def in_yield(col: Column): + testsplit_index = int(col.__len__()*train_size) + #get only the training data of a column and normalize it + ser = (col._data[:testsplit_index]-col.mean())/col.standard_deviation() + for i in range(len(ser) - self._window_size): + yield list(ser.iloc[i : i + self._window_size]) + for col_name in self._feature_names: + col = self._features.get_column(col_name) + yield list(in_yield(col)) + + + def _create_all_labels_for_target_column(self, train_size: float): + #this generator generates all forecast horizons for the target column + def _generate_label_windows( ): + testsplit_index = int(self.target.__len__()*train_size) + #get only the training data of a column and normalize it + ser = (self._target._data[:testsplit_index]-self._target.mean())/self._target.standard_deviation() + for i in range(len(ser) - self._window_size): + yield list(ser.iloc[i + self._window_size : i + self._window_size + self._forecast_horizon]) + return list((_generate_label_windows())) + + + + def into_train_DataLoader(self, train_size: float): + #code below concatenate the column like the following + #f1:[w1, w2, w3] f2[w1, w2, w3] -> [w1+w1, w2+w2, w3+w3] + x_train = np.concatenate(list(self._create_all_windows_for_column(train_size)), axis=1) + #for target this will be created: [ t1, t2, t3] + y_train = np.array(self._create_all_labels_for_target_column(train_size)) + #load them into PyTorch + dataset = TimeSeriesDataset(x_train,y_train) + return DataLoader(dataset, batch_size=1) + + +class TimeSeriesDataset(Dataset): + def __init__(self, data, labels): + self.data = data + self.labels = labels + + def __len__(self): + return len(self.data) + + def __getitem__(self, idx): + timeseries_sample = self.data[idx] + label = self.labels[idx] + return timeseries_sample, label + diff --git a/src/safeds/ml/nn/__init__.py b/src/safeds/ml/nn/__init__.py new file mode 100644 index 000000000..f4798315b --- /dev/null +++ b/src/safeds/ml/nn/__init__.py @@ -0,0 +1,5 @@ +"""Layers for neural networks tasks.""" + +from ._rnn_layer import RNN_Layer +from ._model import Model +from ._fnn_layer import FNN_Layer \ No newline at end of file diff --git a/src/safeds/ml/nn/_model.py b/src/safeds/ml/nn/_model.py new file mode 100644 index 000000000..0d3958c3a --- /dev/null +++ b/src/safeds/ml/nn/_model.py @@ -0,0 +1,58 @@ +import pandas as pd +import numpy as np +import torch +import time +import torch.nn as nn +from torch.utils.data import DataLoader +from safeds.ml.nn import RNN_Layer +from safeds.data.tabular.containers import Column, Table, TaggedTable, TimeSeries +from safeds.exceptions import ColumnSizeError, DuplicateColumnNameError +class Model(): + def __init__(self, layers : list): + self._model = PyTorchModel(layers) + + + def from_layers(self, layers: list): + pass + + #this is just a demo function + def model_forward(self, data : DataLoader): + for batch in iter(data): + inputs, labels = batch + inputs = inputs.to(torch.float32) + self._model(inputs) + + + def train(self, train_loader: DataLoader, epochs: int, learningrate : float): + start_time = time.time() + criterion = nn.MSELoss() + optimizer = torch.optim.Adam(self._model.parameters(), lr = learningrate) + + for epoch in range(epochs): + for batch in iter(train_loader): + inputs, labels = batch + optimizer.zero_grad() + + labels = labels.to(torch.float32) + inputs = inputs.to(torch.float32) + outputs = self._model(inputs) + loss = criterion(outputs, labels) + loss.backward() + optimizer.step() + print(f'Epoch: {epoch+1:2} Loss {loss.item():10.8f}') + print(f'\nDuration: {time.time()-start_time:.0f} seconds') + +class PyTorchModel(nn.Module): + def __init__(self, LayerListe :list[RNN_Layer]): + super(PyTorchModel, self).__init__() + layers = [] + for layer in LayerListe: + layers.append(layer._create_pytorch_layer()) + + self._layerliste = nn.ModuleList(layers) + + def forward(self, x): + out = x + for layer in self._layerliste: + out = layer(out) + return out diff --git a/src/safeds/ml/nn/_rnn_layer.py b/src/safeds/ml/nn/_rnn_layer.py new file mode 100644 index 000000000..5b0cabeef --- /dev/null +++ b/src/safeds/ml/nn/_rnn_layer.py @@ -0,0 +1,29 @@ +import pandas as pd +import numpy as np +import torch +import torch.nn as nn +from torch.utils.data import DataLoader +from safeds.data.tabular.containers import Column, Table, TaggedTable, TimeSeries +from safeds.exceptions import ColumnSizeError, DuplicateColumnNameError + + +class RNN_Layer(): + def __init__(self, input_dim, output_dim)-> None: + self._input_dim = input_dim + self._output_dim = output_dim + + + def _create_pytorch_layer(self): + return LSTMLayer(self._input_dim, self._output_dim) + + + +#definiere LSTM Layer in PyTorch +class LSTMLayer(nn.Module): + def __init__(self, input_dim, output_dim): + super(LSTMLayer, self).__init__() + self.lstm = nn.LSTM(input_dim, output_dim, batch_first = True) + + def forward(self, x): + lstm_out, _ = self.lstm(x) + return lstm_out \ No newline at end of file diff --git a/tests/safeds/data/tabular/containers/_table/_timeseries_table/__init__.py b/tests/safeds/data/tabular/containers/_table/_timeseries_table/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tests/safeds/data/tabular/containers/_table/_timeseries_table/test_timeseries_creation.py b/tests/safeds/data/tabular/containers/_table/_timeseries_table/test_timeseries_creation.py new file mode 100644 index 000000000..43cfd3f79 --- /dev/null +++ b/tests/safeds/data/tabular/containers/_table/_timeseries_table/test_timeseries_creation.py @@ -0,0 +1,39 @@ +import pytest +import pandas as pd +import numpy as np +import torch +import torch.nn as nn +import time +from torch.utils.data import DataLoader +from safeds.data.tabular.containers import Column, Table, TaggedTable, TimeSeries +from safeds.exceptions import ColumnSizeError, DuplicateColumnNameError +from safeds.ml.nn import FNN_Layer,RNN_Layer, Model + + + + +def test_create_timeseries() -> None: + table = Table.from_csv_file(r"tests\resources\Alcohol_Sales (1).csv") + ts = TimeSeries._from_table(table,target_name="S4248SM144NCEN", date_name="DATE", window_size=12, forecast_horizon=1, feature_names=["S4248SM144NCEN"]) + + + + # ein Modell erstellen ist in safeDS noch nicht definiert darum low level in PyTorch + # 2 ist hier die number der feature Columns + input_dim = ts._window_size * len(ts._feature_names) + hidden_dim = 256 + output_dim = ts._forecast_horizon + layer1 = RNN_Layer(input_dim, hidden_dim) + layer2 = FNN_Layer(hidden_dim, output_dim) + model = Model([layer1, layer2]) + model.train(ts.into_train_DataLoader(0.75), 50, 0.001) + + + + + + + #wenn durchläuft wurde korrekt Table in Dataloader geladen + assert False + +