diff --git a/src/safeds/data/labeled/containers/__init__.py b/src/safeds/data/labeled/containers/__init__.py index 402c635b6..e6237ec24 100644 --- a/src/safeds/data/labeled/containers/__init__.py +++ b/src/safeds/data/labeled/containers/__init__.py @@ -7,16 +7,19 @@ if TYPE_CHECKING: from ._image_dataset import ImageDataset from ._tabular_dataset import TabularDataset + from ._time_series_dataset import TimeSeriesDataset apipkg.initpkg( __name__, { "ImageDataset": "._image_dataset:ImageDataset", "TabularDataset": "._tabular_dataset:TabularDataset", + "TimeSeriesDataset": "._time_series_dataset:TimeSeriesDataset", }, ) __all__ = [ "ImageDataset", "TabularDataset", + "TimeSeriesDataset", ] diff --git a/src/safeds/data/labeled/containers/_time_series_dataset.py b/src/safeds/data/labeled/containers/_time_series_dataset.py new file mode 100644 index 000000000..33d941541 --- /dev/null +++ b/src/safeds/data/labeled/containers/_time_series_dataset.py @@ -0,0 +1,328 @@ +from __future__ import annotations + +import sys +from typing import TYPE_CHECKING + +from safeds._utils import _structural_hash +from safeds.data.tabular.containers import Column, Table + +if TYPE_CHECKING: + from collections.abc import Mapping, Sequence + from typing import Any + + import torch + from torch.utils.data import DataLoader, Dataset + + +class TimeSeriesDataset: + """ + A time series dataset maps feature and time columns to a target column. Not like the TabularDataset a TimeSeries needs to contain one target and one time column, but can have empty features. + + Create a time series dataset from a mapping of column names to their values. + + Parameters + ---------- + data: + The data. + target_name: + Name of the target column. + time_name: + Name of the time column. + extra_names: + Names of the columns that are neither features nor target. If None, no extra columns are used, i.e. all but + the target column are used as features. + + Raises + ------ + ColumnLengthMismatchError + If columns have different lengths. + ValueError + If the target column is also an extra column. + ValueError + If no feature column remains. + + Examples + -------- + >>> from safeds.data.labeled.containers import TabularDataset + >>> dataset = TimeSeriesDataset( + ... {"id": [1, 2, 3], "feature": [4, 5, 6], "target": [1, 2, 3], "error":[0,0,1]}, + ... target_name="target", + ... time_name = "id", + ... extra_names=["error"] + ... ) + """ + + # ------------------------------------------------------------------------------------------------------------------ + # Dunder methods + # ------------------------------------------------------------------------------------------------------------------ + def __init__( + self, + data: Table | Mapping[str, Sequence[Any]], + target_name: str, + time_name: str, + extra_names: list[str] | None = None, + ): + # Preprocess inputs + if not isinstance(data, Table): + data = Table(data) + if extra_names is None: + extra_names = [] + + # Derive feature names + feature_names = [name for name in data.column_names if name not in {target_name, *extra_names, time_name}] + + # Validate inputs + if time_name in extra_names: + raise ValueError(f"Column '{time_name}' cannot be both time and extra.") + if target_name in extra_names: + raise ValueError(f"Column '{target_name}' cannot be both target and extra.") + if len(feature_names) == 0: + feature_names = [] + + # Set attributes + self._table: Table = data + self._features: Table = data.keep_only_columns(feature_names) + self._target: Column = data.get_column(target_name) + self._time: Column = data.get_column(time_name) + self._extras: Table = data.keep_only_columns(extra_names) + + def __eq__(self, other: object) -> bool: + """ + Compare two time series datasets. + + Returns + ------- + equals: + 'True' if features, time, target and extras are equal, 'False' otherwise. + """ + if not isinstance(other, TimeSeriesDataset): + return NotImplemented + return (self is other) or ( + self.target == other.target + and self.features == other.features + and self.extras == other.extras + and self.time == other.time + ) + + def __hash__(self) -> int: + """ + Return a deterministic hash value for this time series dataset. + + Returns + ------- + hash: + The hash value. + """ + return _structural_hash(self.target, self.features, self.extras, self.time) + + def __sizeof__(self) -> int: + """ + Return the complete size of this object. + + Returns + ------- + size: + Size of this object in bytes. + """ + return ( + sys.getsizeof(self._target) + + sys.getsizeof(self._features) + + sys.getsizeof(self.extras) + + sys.getsizeof(self._time) + ) + + # ------------------------------------------------------------------------------------------------------------------ + # Properties + # ------------------------------------------------------------------------------------------------------------------ + + @property + def features(self) -> Table: + """The feature columns of the time series dataset.""" + return self._features + + @property + def target(self) -> Column: + """The target column of the time series dataset.""" + return self._target + + @property + def time(self) -> Column: + """The time column of the time series dataset.""" + return self._time + + @property + def extras(self) -> Table: + """ + Additional columns of the time series dataset that are neither features, target nor time. + + These can be used to store additional information about instances, such as IDs. + """ + return self._extras + + # ------------------------------------------------------------------------------------------------------------------ + # Conversion + # ------------------------------------------------------------------------------------------------------------------ + + def to_table(self) -> Table: + """ + Return a new `Table` containing the feature columns, the target column, the time column and the extra columns. + + The original `TimeSeriesDataset` is not modified. + + Returns + ------- + table: + A table containing the feature columns, the target column, the time column and the extra columns. + """ + return self._table + + def _into_dataloader_with_window(self, window_size: int, forecast_horizon: int, batch_size: int) -> DataLoader: + """ + Return a Dataloader for the data stored in this time series, used for training neural networks. + + It splits the target column into windows, uses them as feature and creates targets for the time series, by + forecast length. The original time series dataset is not modified. + + Parameters + ---------- + window_size: + The size of the created windows + forecast_horizon: + The length of the forecast horizon, where all datapoints are collected until the given lag. + batch_size: + The size of data batches that should be loaded at one time. + + Raises + ValueError: + If the size is smaller or even than forecast_horizon+window_size + + Returns + ------- + result: + The DataLoader. + """ + import torch + from torch.utils.data import DataLoader + + target_tensor = torch.tensor(self.target._data.values, dtype=torch.float32) + + x_s = [] + y_s = [] + + size = target_tensor.size(0) + if window_size < 1: + raise ValueError("window_size must be greater than or equal to 1") + if forecast_horizon < 1: + raise ValueError("forecast_horizon must be greater than or equal to 1") + if size <= forecast_horizon + window_size: + raise ValueError("Can not create windows with window size less then forecast horizon + window_size") + # create feature windows and for that features targets lagged by forecast len + # every feature column wird auch gewindowed + # -> [i, win_size],[target] + feature_cols = self.features.to_columns() + for i in range(size - (forecast_horizon + window_size)): + window = target_tensor[i : i + window_size] + label = target_tensor[i + window_size + forecast_horizon] + for col in feature_cols: + data = torch.tensor(col._data.values, dtype=torch.float32) + window = torch.cat((window, data[i : i + window_size]), dim=0) + x_s.append(window) + y_s.append(label) + x_s_tensor = torch.stack(x_s) + y_s_tensor = torch.stack(y_s) + dataset = _create_dataset(x_s_tensor, y_s_tensor) + return DataLoader(dataset=dataset, batch_size=batch_size) + + def _into_dataloader_with_window_predict( + self, + window_size: int, + forecast_horizon: int, + batch_size: int, + ) -> DataLoader: + """ + Return a Dataloader for the data stored in this time series, used for training neural networks. + + It splits the target column into windows, uses them as feature and creates targets for the time series, by + forecast length. The original time series dataset is not modified. + + Parameters + ---------- + window_size: + The size of the created windows + batch_size: + The size of data batches that should be loaded at one time. + + Returns + ------- + result: + The DataLoader. + """ + import torch + from torch.utils.data import DataLoader + + target_tensor = torch.tensor(self.target._data.values, dtype=torch.float32) + x_s = [] + + size = target_tensor.size(0) + feature_cols = self.features.to_columns() + for i in range(size - (forecast_horizon + window_size)): + window = target_tensor[i : i + window_size] + for col in feature_cols: + data = torch.tensor(col._data.values, dtype=torch.float32) + window = torch.cat((window, data[i : i + window_size]), dim=-1) + x_s.append(window) + + x_s_tensor = torch.stack(x_s) + + dataset = _create_dataset_predict(x_s_tensor) + return DataLoader(dataset=dataset, batch_size=batch_size) + + # ------------------------------------------------------------------------------------------------------------------ + # IPython integration + # ------------------------------------------------------------------------------------------------------------------ + + def _repr_html_(self) -> str: + """ + Return an HTML representation of the time series dataset. + + Returns + ------- + output: + The generated HTML. + """ + return self._table._repr_html_() + + +def _create_dataset(features: torch.Tensor, target: torch.Tensor) -> Dataset: + from torch.utils.data import Dataset + + class _CustomDataset(Dataset): + def __init__(self, features_dataset: torch.Tensor, target_dataset: torch.Tensor): + self.X = features_dataset + self.Y = target_dataset.unsqueeze(-1) + self.len = self.X.shape[0] + + def __getitem__(self, item: int) -> tuple[torch.Tensor, torch.Tensor]: + return self.X[item], self.Y[item] + + def __len__(self) -> int: + return self.len + + return _CustomDataset(features, target) + + +def _create_dataset_predict(features: torch.Tensor) -> Dataset: + from torch.utils.data import Dataset + + class _CustomDataset(Dataset): + def __init__(self, features: torch.Tensor): + self.X = features + self.len = self.X.shape[0] + + def __getitem__(self, item: int) -> torch.Tensor: + return self.X[item] + + def __len__(self) -> int: + return self.len + + return _CustomDataset(features) diff --git a/src/safeds/data/tabular/containers/__init__.py b/src/safeds/data/tabular/containers/__init__.py index 4d2a37901..66777d097 100644 --- a/src/safeds/data/tabular/containers/__init__.py +++ b/src/safeds/data/tabular/containers/__init__.py @@ -12,7 +12,6 @@ from ._experimental_polars_table import ExperimentalPolarsTable from ._row import Row from ._table import Table - from ._time_series import TimeSeries apipkg.initpkg( __name__, @@ -24,7 +23,6 @@ "ExperimentalPolarsTable": "._experimental_polars_table:ExperimentalPolarsTable", "Row": "._row:Row", "Table": "._table:Table", - "TimeSeries": "._time_series:TimeSeries", }, ) @@ -36,5 +34,4 @@ "ExperimentalPolarsTable", "Row", "Table", - "TimeSeries", ] diff --git a/src/safeds/data/tabular/containers/_column.py b/src/safeds/data/tabular/containers/_column.py index 7c242a0dc..fdeaf5b58 100644 --- a/src/safeds/data/tabular/containers/_column.py +++ b/src/safeds/data/tabular/containers/_column.py @@ -23,7 +23,6 @@ from safeds.data.tabular.containers import Table - T = TypeVar("T") R = TypeVar("R") @@ -1032,9 +1031,103 @@ def plot_histogram(self, *, number_of_bins: int = 10) -> Image: >>> histogram = column.plot_histogram() """ from safeds.data.tabular.containers import Table - + return Table({self._name: self._data}).plot_histograms(number_of_bins=number_of_bins) + def plot_compare_columns(self, column_list: list[Column]) -> Image: + """ + Create a plot comparing the numerical values of columns using IDs as the x-axis. + + Parameters + ---------- + column_list: + A list of time columns to be plotted. + + Returns + ------- + plot: + A plot with all the Columns plotted by the ID on the x-axis. + + Raises + ------ + NonNumericColumnError + if the target column contains non numerical values + ValueError + if the columns do not have the same size + + Examples + -------- + >>> from safeds.data.tabular.containers import Column + >>> col1 =Column("target", [4, 5, 6, 7, 8, 9, 10, 11, 12, 13]) + >>> col2 =Column("target", [42, 51, 63, 71, 83, 91, 10, 11, 12, 13]) + >>> image = col1.plot_compare_columns([col2]) + """ + import matplotlib.pyplot as plt + import pandas as pd + import seaborn as sns + + data = pd.DataFrame() + column_list.append(self) + size = len(column_list[0]) + data["INDEX"] = pd.DataFrame({"INDEX": range(size)}) + for index, col in enumerate(column_list): + if not col.type.is_numeric(): + raise NonNumericColumnError("The time series plotted column contains non-numerical columns.") + if len(col) != size: + raise ValueError("The columns must have the same size.") + data[col.name + " " + str(index)] = col._data + + fig = plt.figure() + data = pd.melt(data, ["INDEX"]) + sns.lineplot(x="INDEX", y="value", hue="variable", data=data) + plt.title("Multiple Series Plot") + plt.xlabel("Time") + + plt.tight_layout() + buffer = io.BytesIO() + fig.savefig(buffer, format="png") + plt.close() # Prevents the figure from being displayed directly + buffer.seek(0) + return Image.from_bytes(buffer.read()) + + def plot_lagplot(self, lag: int) -> Image: + """ + Plot a lagplot for the given column. + + Parameters + ---------- + lag: + The amount of lag used to plot + + Returns + ------- + plot: + The plot as an image. + + Raises + ------ + NonNumericColumnError + If the column contains non-numerical values. + + Examples + -------- + >>> from safeds.data.tabular.containers import Table + >>> table = Column("values", [1,2,3,4,3,2]) + >>> image = table.plot_lagplot(2) + """ + import matplotlib.pyplot as plt + import pandas as pd + + if not self.type.is_numeric(): + raise NonNumericColumnError("This time series target contains non-numerical columns.") + ax = pd.plotting.lag_plot(self._data, lag=lag) + fig = ax.figure + buffer = io.BytesIO() + fig.savefig(buffer, format="png") + plt.close() # Prevents the figure from being displayed directly + buffer.seek(0) + return Image.from_bytes(buffer.read()) + # ------------------------------------------------------------------------------------------------------------------ # Conversion # ------------------------------------------------------------------------------------------------------------------ diff --git a/src/safeds/data/tabular/containers/_table.py b/src/safeds/data/tabular/containers/_table.py index bfad88bfe..c62ba7c9b 100644 --- a/src/safeds/data/tabular/containers/_table.py +++ b/src/safeds/data/tabular/containers/_table.py @@ -30,11 +30,9 @@ import pandas as pd from torch.utils.data import DataLoader, Dataset - from safeds.data.labeled.containers import TabularDataset + from safeds.data.labeled.containers import TabularDataset, TimeSeriesDataset from safeds.data.tabular.transformation import InvertibleTableTransformer, TableTransformer - from ._time_series import TimeSeries - # noinspection PyProtectedMember class Table: @@ -1859,43 +1857,6 @@ def split_rows(self, percentage_in_first: float) -> tuple[Table, Table]: self.slice_rows(round(percentage_in_first * self.number_of_rows)), ) - def time_columns(self, target_name: str, time_name: str, feature_names: list[str] | None = None) -> TimeSeries: - """ - Return a new `TimeSeries` with columns marked as a target and time column or feature columns. - - The original table is not modified. - - Parameters - ---------- - target_name: - Name of the target column. - time_name: - Name of the time column. - feature_names: - Names of the feature columns. If None, all columns except the target and time columns are used. - - Returns - ------- - time_series: - A new time series with the given target, time and feature names. - - Raises - ------ - ValueError - If the target column is also a feature column. - ValueError - If there is no other column than the specified target and time columns left to be a feature column - - Examples - -------- - >>> from safeds.data.tabular.containers import Table, TimeSeries - >>> table = Table.from_dict({"time": ["01.01", "01.02", "01.03"], "price": [1.10, 1.19, 1.79], "amount_bought": [74, 72, 51]}) - >>> tabular_dataset = table.time_columns(target_name="amount_bought",time_name = "time", feature_names=["price"]) - """ - from ._time_series import TimeSeries - - return TimeSeries._from_table(self, target_name, time_name, feature_names) - def transform_column(self, name: str, transformer: Callable[[Row], Any]) -> Table: """ Return a new `Table` with the provided column transformed by calling the provided transformer. @@ -2304,7 +2265,7 @@ def plot_histograms(self, *, number_of_bins: int = 10) -> Image: bars = np.array([]) for i in range(len(hist)): - bars = np.append(bars, f"{round(bin_edges[i], 2)}-{round(bin_edges[i+1], 2)}") + bars = np.append(bars, f"{round(bin_edges[i], 2)}-{round(bin_edges[i + 1], 2)}") ax.bar(bars, hist, edgecolor="black") ax.set_xticks(np.arange(len(hist)), bars, rotation=45, horizontalalignment="right") @@ -2564,6 +2525,49 @@ def to_tabular_dataset(self, target_name: str, extra_names: list[str] | None = N return TabularDataset(self, target_name, extra_names) + def to_time_series_dataset( + self, + target_name: str, + time_name: str, + extra_names: list[str] | None = None, + ) -> TimeSeriesDataset: + """ + Return a new `TimeSeriesDataset` with columns marked as a target column, time or feature columns. + + The original table is not modified. + + Parameters + ---------- + target_name: + Name of the target column. + time_name: + Name of the time column. + extra_names: + Names of the columns that are neither features nor target. If None, no extra columns are used, i.e. all but + the target column are used as features. + + Returns + ------- + dataset: + A new time series dataset with the given target and feature names. + + Raises + ------ + ValueError + If the target column is also a feature column. + ValueError + If the time column is also a feature column. + + Examples + -------- + >>> from safeds.data.tabular.containers import Table + >>> table = Table({"day": [0, 1, 2], "price": [1.10, 1.19, 1.79], "amount_bought": [74, 72, 51]}) + >>> dataset = table.to_time_series_dataset(target_name="amount_bought", time_name= "day") + """ + from safeds.data.labeled.containers import TimeSeriesDataset + + return TimeSeriesDataset(self, target_name, time_name, extra_names) + # ------------------------------------------------------------------------------------------------------------------ # IPython integration # ------------------------------------------------------------------------------------------------------------------ diff --git a/src/safeds/data/tabular/containers/_time_series.py b/src/safeds/data/tabular/containers/_time_series.py deleted file mode 100644 index bf71e92e6..000000000 --- a/src/safeds/data/tabular/containers/_time_series.py +++ /dev/null @@ -1,1236 +0,0 @@ -from __future__ import annotations - -import io -import sys -from typing import TYPE_CHECKING - -from safeds._utils import _structural_hash -from safeds.data.image.containers import Image -from safeds.data.tabular.containers import Column, Row, Table -from safeds.exceptions import ( - ColumnIsTargetError, - ColumnIsTimeError, - IllegalSchemaModificationError, - NonNumericColumnError, - UnknownColumnNameError, -) - -if TYPE_CHECKING: - from collections.abc import Callable, Mapping, Sequence - from pathlib import Path - from typing import Any - - -class TimeSeries(Table): - - # ------------------------------------------------------------------------------------------------------------------ - # Creation - # ------------------------------------------------------------------------------------------------------------------ - - @staticmethod - def timeseries_from_csv_file( - path: str | Path, - target_name: str, - time_name: str, - feature_names: list[str] | None = None, - ) -> TimeSeries: - """ - Read data from a CSV file into a table. - - Parameters - ---------- - path: - The path to the CSV file. - target_name: - The name of the target column - time_name: - The name of the time column - feature_names: - The name(s) of the column(s) - - Returns - ------- - table: - The time series created from the CSV file. - - Raises - ------ - FileNotFoundError - If the specified file does not exist. - WrongFileExtensionError - If the file is not a csv file. - UnknownColumnNameError - If target_name or time_name matches none of the column names. - Value Error - If one column is target and feature - Value Error - If one column is time and feature - - """ - return TimeSeries._from_table( - Table.from_csv_file(path=path), - target_name=target_name, - time_name=time_name, - feature_names=feature_names, - ) - - @staticmethod - def _from_table( - table: Table, - target_name: str, - time_name: str, - feature_names: list[str] | None = None, - ) -> TimeSeries: - """Create a TimeSeries from a table. - - Parameters - ---------- - table: - The table. - target_name: - Name of the target column. - time_name: - Name of the date column. - feature_names: - Names of the feature columns. If None, all columns except the target and time columns are used. - - Returns - ------- - time_series: - the created time series - - Raises - ------ - UnknownColumnNameError - If target_name or time_name matches none of the column names. - Value Error - If one column is target and feature - Value Error - If one column is time and feature - - Examples - -------- - >>> from safeds.data.tabular.containers import Table, TimeSeries - >>> test_table = Table({"date": ["01.01", "01.02", "01.03", "01.04"], "f1": ["a", "b", "c", "a"], "t": [1,2,3,4]}) - >>> timeseries = TimeSeries._from_table(test_table, "t", "date", ["f1"]) - """ - import pandas as pd - - table = table._as_table() - if feature_names is not None and time_name in feature_names: - raise ValueError(f"Column '{time_name}' can not be time and feature column.") - if feature_names is not None and target_name in feature_names: - raise ValueError(f"Column '{target_name}' can not be target and feature column.") - - if target_name not in table.column_names: - raise UnknownColumnNameError([target_name]) - result = object.__new__(TimeSeries) - result._data = table._data - - result._schema = table._schema - result._time = table.get_column(time_name) - result._target = table.get_column(target_name) - # empty Columns have dtype Object - if len(result._time._data) == 0: - result._time._data = pd.Series(name=time_name) - if len(result.target._data) == 0: - result.target._data = pd.Series(name=target_name) - if feature_names is None or len(feature_names) == 0: - result._feature_names = [] - result._features = Table() - else: - result._feature_names = feature_names - result._features = table.keep_only_columns(feature_names) - - # check if time column got added as feature column - return result - - # ------------------------------------------------------------------------------------------------------------------ - # Dunder methods - # ------------------------------------------------------------------------------------------------------------------ - - def __init__( - self, - data: Mapping[str, Sequence[Any]], - target_name: str, - time_name: str, - feature_names: list[str] | None = None, - ): - """ - Create a time series from a mapping of column names to their values. - - Parameters - ---------- - data: - The data. - target_name: - Name of the target column. - time_name: - Name of the time column - feature_names: - Names of the feature columns. If None, all columns except the target and time columns are used. - - Raises - ------ - ColumnLengthMismatchError - If columns have different lengths. - ValueError - If the target column is also a feature column. - ValueError - If time column is also a feature column - UnknownColumnNameError - If time column does not exist - - Examples - -------- - >>> from safeds.data.tabular.containers import TimeSeries - >>> table = TimeSeries({"a": [1, 2, 3], "b": [4, 5, 6]}, "b", "a") - """ - import pandas as pd - - # Enable copy-on-write for pandas dataframes - pd.options.mode.copy_on_write = True - - # Validate inputs - super().__init__(data) - _data: Table = Table(data) - if feature_names is None: - self._features = Table() - self._feature_names = [] - feature_names = [] - else: - self._feature_names = feature_names - self._features = _data.keep_only_columns(feature_names) - if time_name in feature_names: - raise ValueError(f"Column '{time_name}' can not be time and feature column.") - if target_name in feature_names: - raise ValueError(f"Column '{target_name}' can not be time and feature column.") - if time_name not in _data.column_names: - raise UnknownColumnNameError([time_name]) - self._time: Column = _data.get_column(time_name) - self._target: Column = _data.get_column(target_name) - # empty Columns have dtype Object - if len(self._time._data) == 0: - self._time._data = pd.Series(name=time_name) - if len(self.target._data) == 0: - self.target._data = pd.Series(name=target_name) - - self._data = _data._data - - def __eq__(self, other: object) -> bool: - """ - Compare two time series instances. - - Returns - ------- - equals: - 'True' if contents are equal, 'False' otherwise. - """ - if not isinstance(other, TimeSeries): - return NotImplemented - if self is other: - return True - - return ( - self.time == other.time - and self.target == other.target - and self.features == other.features - and Table.__eq__(self, other) - ) - - def __hash__(self) -> int: - """ - Return a deterministic hash value for this time series. - - Returns - ------- - hash: - The hash value. - """ - return _structural_hash(self.time, self.target, self.features, Table.__hash__(self)) - - def __sizeof__(self) -> int: - """ - Return the complete size of this object. - - Returns - ------- - size: - Size of this object in bytes. - """ - return Table.__sizeof__(self) + sys.getsizeof(self._time) - - # ------------------------------------------------------------------------------------------------------------------ - # Properties - # ------------------------------------------------------------------------------------------------------------------ - - @property - def target(self) -> Column: - """ - Get the target column of the time series. - - Returns - ------- - target: - The target column. - """ - return self._target - - @property - def features(self) -> Table: - """ - Get the feature columns of the time series. - - Returns - ------- - features: - The table containing the feature columns. - """ - return self._features - - @property - def time(self) -> Column: - """ - Get the time column of the time series. - - Returns - ------- - time: - The time column. - """ - return self._time - - # ------------------------------------------------------------------------------------------------------------------ - # Overridden methods from Table class - # ------------------------------------------------------------------------------------------------------------------ - def _as_table(self: TimeSeries) -> Table: - """ - Return a new plain `Table`. - - The original time series is not modified. - - Parameters - ---------- - self: - The Time Series. - - Returns - ------- - table: - The time series as an plain Table, i.e. without the information about which columns are features, target or - time. - - """ - return Table.from_columns(super().to_columns()) - - def add_column(self, column: Column) -> TimeSeries: - """ - Return a new `TimeSeries` with the provided column attached at the end, as neither target nor feature column. - - The original time series is not modified. - - Parameters - ---------- - column: - The column to be added. - - Returns - ------- - result: - The time series with the column attached as neither target nor 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), - time_name=self.time.name, - target_name=self._target.name, - ) - - def add_column_as_feature(self, column: Column) -> TimeSeries: - """ - Return a new `TimeSeries` with the provided column attached at the end, as a feature column. - - the original time series is not modified. - - Parameters - ---------- - column: - The column to be added. - - Returns - ------- - result: - The time series 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, - time_name=self.time.name, - feature_names=[*self._feature_names, column.name], - ) - - def add_columns_as_features(self, columns: list[Column] | Table) -> TimeSeries: - """ - Return a new `TimeSeries` with the provided columns attached at the end, as feature columns. - - The original time series is not modified. - - Parameters - ---------- - columns: - The columns to be added as features. - - Returns - ------- - result: - The time series 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), - time_name=self.time.name, - target_name=self._target.name, - feature_names=self._feature_names - + [col.name for col in (columns.to_columns() if isinstance(columns, Table) else columns)], - ) - - def add_columns(self, columns: list[Column] | Table) -> TimeSeries: - """ - Return a new `TimeSeries` with multiple added columns, as neither target nor feature columns. - - The original time series is not modified. - - Parameters - ---------- - columns: - The columns to be added. - - Returns - ------- - result: - A new time series combining the original table and the given columns as neither target nor feature columns. - - Raises - ------ - DuplicateColumnNameError - If at least one column name from the provided column list already exists in the time series. - ColumnSizeError - If at least one of the column sizes from the provided column list does not match the time series. - """ - return TimeSeries._from_table( - super().add_columns(columns), - time_name=self.time.name, - target_name=self._target.name, - feature_names=self._feature_names, - ) - - def add_row(self, row: Row) -> TimeSeries: - """ - Return a new `TimeSeries` with an extra Row attached. - - The original time series is not modified. - - Parameters - ---------- - row: - The row to be added. - - Returns - ------- - table: - A new time series with the added row at the end. - - Raises - ------ - UnknownColumnNameError - If the row has different column names than the time series. - """ - return TimeSeries._from_table( - super().add_row(row), - target_name=self._target.name, - time_name=self.time.name, - feature_names=self._feature_names, - ) - - def add_rows(self, rows: list[Row] | Table) -> TimeSeries: - """ - Return a new `TimeSeries` with multiple extra Rows attached. - - The original time series is not modified. - - Parameters - ---------- - rows: - The rows to be added. - - Returns - ------- - result: - A new time series which combines the original time series and the given rows. - - Raises - ------ - UnknownColumnNameError - If at least one of the rows have different column names than the time series. - """ - return TimeSeries._from_table( - super().add_rows(rows), - target_name=self._target.name, - time_name=self.time.name, - 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 time series is not modified. - - Parameters - ---------- - query: - A Callable that is applied to all rows. - - Returns - ------- - result: - A time series containing only the rows to match the query. - """ - return TimeSeries._from_table( - super().filter_rows(query), - target_name=self._target.name, - time_name=self.time.name, - feature_names=self._feature_names, - ) - - def keep_only_columns(self, column_names: list[str]) -> TimeSeries: - """ - Return a new `TimeSeries` with only the given column(s). - - The original time series is not modified. - - Parameters - ---------- - column_names: - A list containing the columns to be kept. - - Returns - ------- - table: - A time series containing only the given column(s). - - Raises - ------ - UnknownColumnNameError - If any of the given columns does not exist. - IllegalSchemaModificationError - If none of the given columns is the target or time column or any of the feature columns. - """ - if self._target.name not in column_names: - raise IllegalSchemaModificationError("Must keep the target column.") - if self.time.name not in column_names: - raise IllegalSchemaModificationError("Must keep the time column.") - return TimeSeries._from_table( - super().keep_only_columns(column_names), - target_name=self._target.name, - time_name=self.time.name, - feature_names=sorted( - set(self._feature_names).intersection(set(column_names)), - key={val: ix for ix, val in enumerate(self._feature_names)}.__getitem__, - ), - ) - - def remove_columns(self, column_names: list[str]) -> TimeSeries: - """ - Return a new `TimeSeries` with the given column(s) removed from the time series. - - The original time series is not modified. - - Parameters - ---------- - column_names: - The names of all columns to be dropped. - - Returns - ------- - table: - A time series without the given columns. - - Raises - ------ - UnknownColumnNameError - If any of the given columns does not exist. - ColumnIsTargetError - If any of the given columns is the target column. - ColumnIsTimeError - If any of the given columns is the time column. - IllegalSchemaModificationError - If the given columns contain all the feature columns. - """ - if self._target.name in column_names: - raise ColumnIsTargetError(self._target.name) - if self.time.name in column_names: - raise ColumnIsTimeError(self.time.name) - return TimeSeries._from_table( - super().remove_columns(column_names), - target_name=self._target.name, - time_name=self.time.name, - feature_names=sorted( - set(self._feature_names) - set(column_names), - key={val: ix for ix, val in enumerate(self._feature_names)}.__getitem__, - ), - ) - - def remove_columns_with_missing_values(self) -> TimeSeries: - """ - Return a new `TimeSeries` with every column that misses values removed. - - The original time series is not modified. - - Returns - ------- - table: - A time series without the columns that contain missing values. - - Raises - ------ - ColumnIsTargetError - If any of the columns to be removed is the target column. - ColumnIsTimeError - If any of the columns to be removed is the time column. - IllegalSchemaModificationError - If the columns to remove contain all the feature columns. - """ - table = super().remove_columns_with_missing_values() - if self._target.name not in table.column_names: - raise ColumnIsTargetError(self._target.name) - if self.time.name not in table.column_names: - raise ColumnIsTimeError(self.time.name) - return TimeSeries._from_table( - table, - target_name=self._target.name, - time_name=self._time.name, - feature_names=sorted( - set(self._feature_names).intersection(set(table.column_names)), - key={val: ix for ix, val in enumerate(self._feature_names)}.__getitem__, - ), - ) - - def remove_columns_with_non_numerical_values(self) -> TimeSeries: - """ - Return a new `TimeSeries` with every column that contains non-numerical values removed. - - The original time series is not modified. - - Returns - ------- - table: - A time series without the columns that contain non-numerical values. - - Raises - ------ - ColumnIsTargetError - If any of the columns to be removed is the target column. - ColumnIsTimeError - If any of the columns to be removed is the time column. - IllegalSchemaModificationError - If the columns to remove contain all the feature columns. - """ - table = super().remove_columns_with_non_numerical_values() - if self._target.name not in table.column_names: - raise ColumnIsTargetError(self._target.name) - if self.time.name not in table.column_names: - raise ColumnIsTimeError(self.time.name) - return TimeSeries._from_table( - table, - self._target.name, - time_name=self.time.name, - feature_names=sorted( - set(self._feature_names).intersection(set(table.column_names)), - key={val: ix for ix, val in enumerate(self._feature_names)}.__getitem__, - ), - ) - - def remove_duplicate_rows(self) -> TimeSeries: - """ - Return a new `TimeSeries` with all row duplicates removed. - - The original time series is not modified. - - Returns - ------- - result: - The time series with the duplicate rows removed. - """ - return TimeSeries._from_table( - super().remove_duplicate_rows(), - target_name=self._target.name, - feature_names=self._feature_names, - time_name=self.time.name, - ) - - def remove_rows_with_missing_values(self) -> TimeSeries: - """ - Return a new `TimeSeries` without the rows that contain missing values. - - The original time series is not modified. - - Returns - ------- - table: - A time series without the rows that contain missing values. - """ - return TimeSeries._from_table( - super().remove_rows_with_missing_values(), - time_name=self.time.name, - target_name=self._target.name, - feature_names=self._feature_names, - ) - - def remove_rows_with_outliers(self) -> TimeSeries: - """ - Return a new `TimeSeries` with all rows that contain at least one outlier removed. - - We define an outlier as a value that has a distance of more than 3 standard deviations from the column mean. - Missing values are not considered outliers. They are also ignored during the calculation of the standard - deviation. - - The original time series is not modified. - - Returns - ------- - new_time_series: - A new time series without rows containing outliers. - """ - return TimeSeries._from_table( - super().remove_rows_with_outliers(), - time_name=self.time.name, - target_name=self._target.name, - feature_names=self._feature_names, - ) - - def rename_column(self, old_name: str, new_name: str) -> TimeSeries: - """ - Return a new `TimeSeries` with a single column renamed. - - The original time series is not modified. - - Parameters - ---------- - old_name: - The old name of the column. - new_name: - The new name of the column. - - Returns - ------- - table: - The time series with the renamed column. - - Raises - ------ - UnknownColumnNameError - If the specified old target column name does not exist. - DuplicateColumnNameError - If the specified new target column name already exists. - """ - return TimeSeries._from_table( - super().rename_column(old_name, new_name), - time_name=new_name if self.time.name == old_name else self.time.name, - target_name=new_name if self._target.name == old_name else self._target.name, - feature_names=( - self._feature_names - if old_name not in self._feature_names - else [column_name if column_name != old_name else new_name for column_name in self._feature_names] - ), - ) - - def replace_column(self, old_column_name: str, new_columns: list[Column]) -> TimeSeries: - """ - Return a new `TimeSeries` with the specified old column replaced by a list of new columns. - - If the column to be replaced is the target or time column, it must be replaced by exactly one column. That column - becomes the new target or time column. If the column to be replaced is a feature column, the new columns that replace it - all become feature columns. - - The order of columns is kept. The original time series is not modified. - - Parameters - ---------- - old_column_name: - The name of the column to be replaced. - new_columns: - The new columns replacing the old column. - - Returns - ------- - result: - A time series with the old column replaced by the new columns. - - Raises - ------ - UnknownColumnNameError - If the old column does not exist. - DuplicateColumnNameError - If the new column already exists and the existing column is not affected by the replacement. - ColumnSizeError - If the size of the column does not match the amount of rows. - IllegalSchemaModificationError - If the target or time column would be removed or replaced by more than one column. - """ - if old_column_name == self.time.name: - if len(new_columns) != 1: - raise IllegalSchemaModificationError( - f'Time column "{self.time.name}" can only be replaced by exactly one new column.', - ) - else: - return TimeSeries._from_table( - super().replace_column(old_column_name, new_columns), - target_name=self._target.name, - feature_names=self._feature_names, - time_name=new_columns[0].name, - ) - if old_column_name == self._target.name: - if len(new_columns) != 1: - raise IllegalSchemaModificationError( - f'Target column "{self._target.name}" can only be replaced by exactly one new column.', - ) - else: - return TimeSeries._from_table( - super().replace_column(old_column_name, new_columns), - target_name=new_columns[0].name, - time_name=self.time.name, - feature_names=self._feature_names, - ) - - else: - return TimeSeries._from_table( - super().replace_column(old_column_name, new_columns), - target_name=self._target.name, - time_name=self.time.name, - feature_names=( - self._feature_names - if old_column_name not in self._feature_names - else self._feature_names[: self._feature_names.index(old_column_name)] - + [col.name for col in new_columns] - + self._feature_names[self._feature_names.index(old_column_name) + 1 :] - ), - ) - - def slice_rows( - self, - start: int | None = None, - end: int | None = None, - step: int = 1, - ) -> TimeSeries: - """ - Slice a part of the table into a new `TimeSeries`. - - The original time series is not modified. - - Parameters - ---------- - start: - The first index of the range to be copied into a new time series, None by default. - end: - The last index of the range to be copied into a new time series, None by default. - step: - The step size used to iterate through the time series, 1 by default. - - Returns - ------- - result: - The resulting time series. - - Raises - ------ - IndexOutOfBoundsError - If the index is out of bounds. - """ - return TimeSeries._from_table( - super().slice_rows(start, end, step), - target_name=self._target.name, - feature_names=self._feature_names, - time_name=self.time.name, - ) - - def sort_columns( - self, - comparator: Callable[[Column, Column], int] = lambda col1, col2: (col1.name > col2.name) - - (col1.name < col2.name), - ) -> TimeSeries: - """ - Sort the columns of a `TimeSeries` with the given comparator and return a new `TimeSeries`. - - The comparator is a function that takes two columns `col1` and `col2` and - returns an integer: - - * If the function returns a negative number, `col1` will be ordered before `col2`. - * If the function returns a positive number, `col1` will be ordered after `col2`. - * If the function returns 0, the original order of `col1` and `col2` will be kept. - - If no comparator is given, the columns will be sorted alphabetically by their name. - - The original time series is not modified. - - Parameters - ---------- - comparator: - The function used to compare two columns. - - Returns - ------- - new_time_series: - A new time series with sorted columns. - """ - sorted_table = super().sort_columns(comparator) - return TimeSeries._from_table( - sorted_table, - time_name=self.time.name, - target_name=self._target.name, - feature_names=sorted( - set(sorted_table.column_names).intersection(self._feature_names), - key={val: ix for ix, val in enumerate(sorted_table.column_names)}.__getitem__, - ), - ) - - def transform_column(self, name: str, transformer: Callable[[Row], Any]) -> TimeSeries: - """ - Return a new `TimeSeries` with the provided column transformed by calling the provided transformer. - - The original time series is not modified. - - Parameters - ---------- - name: - The name of the column to be transformed. - transformer: - The transformer to the given column - - Returns - ------- - result: - The time series with the transformed column. - - Raises - ------ - UnknownColumnNameError - If the column does not exist. - """ - return TimeSeries._from_table( - super().transform_column(name, transformer), - time_name=self.time.name, - target_name=self._target.name, - feature_names=self._feature_names, - ) - - def plot_lagplot(self, lag: int) -> Image: - """ - Plot a lagplot for the target column. - - Parameters - ---------- - lag: - The amount of lag used to plot - - Returns - ------- - plot: - The plot as an image. - - Raises - ------ - NonNumericColumnError - If the time series targets contains non-numerical values. - - Examples - -------- - >>> from safeds.data.tabular.containers import TimeSeries - >>> table = TimeSeries({"time":[1, 2], "target": [3, 4], "feature":[2,2]}, target_name= "target", time_name="time", feature_names=["feature"], ) - >>> image = table.plot_lagplot(lag = 1) - """ - import matplotlib.pyplot as plt - import pandas as pd - - if not self._target.type.is_numeric(): - raise NonNumericColumnError("This time series target contains non-numerical columns.") - ax = pd.plotting.lag_plot(self._target._data, lag=lag) - fig = ax.figure - buffer = io.BytesIO() - fig.savefig(buffer, format="png") - plt.close() # Prevents the figure from being displayed directly - buffer.seek(0) - return Image.from_bytes(buffer.read()) - - def plot_lineplot(self, x_column_name: str | None = None, y_column_name: str | None = None) -> Image: - """ - - Plot the time series target or the given column(s) as line plot. - - The function will take the time column as the default value for y_column_name and the target column as the - default value for x_column_name. - - Parameters - ---------- - x_column_name: - The column name of the column to be plotted on the x-Axis, default is the time column. - y_column_name: - The column name of the column to be plotted on the y-Axis, default is the target column. - - Returns - ------- - plot: - The plot as an image. - - Raises - ------ - NonNumericColumnError - If the time series given columns contain non-numerical values. - - UnknownColumnNameError - If one of the given names does not exist in the table - - Examples - -------- - >>> from safeds.data.tabular.containers import TimeSeries - >>> table = TimeSeries({"time":[1, 2], "target": [3, 4], "feature":[2,2]}, target_name= "target", time_name="time", feature_names=["feature"], ) - >>> image = table.plot_lineplot() - """ - import matplotlib.pyplot as plt - import seaborn as sns - - self._data.index.name = "index" - if x_column_name is not None and not self.get_column(x_column_name).type.is_numeric(): - raise NonNumericColumnError("The time series plotted column contains non-numerical columns.") - - if y_column_name is None: - y_column_name = self._target.name - - elif y_column_name not in self._data.columns: - raise UnknownColumnNameError([y_column_name]) - - if x_column_name is None: - x_column_name = self.time.name - - if not self.get_column(y_column_name).type.is_numeric(): - raise NonNumericColumnError("The time series plotted column contains non-numerical columns.") - - fig = plt.figure() - ax = sns.lineplot( - data=self._data, - x=x_column_name, - y=y_column_name, - ) - ax.set(xlabel=x_column_name, ylabel=y_column_name) - ax.set_xticks(ax.get_xticks()) - ax.set_xticklabels( - ax.get_xticklabels(), - rotation=45, - horizontalalignment="right", - ) # rotate the labels of the x Axis to prevent the chance of overlapping of the labels - plt.tight_layout() - - buffer = io.BytesIO() - fig.savefig(buffer, format="png") - plt.close() # Prevents the figure from being displayed directly - buffer.seek(0) - self._data = self._data.reset_index() - return Image.from_bytes(buffer.read()) - - def plot_scatterplot( - self, - x_column_name: str | None = None, - y_column_name: str | None = None, - ) -> Image: - """ - Plot the time series target or the given column(s) as scatter plot. - - The function will take the time column as the default value for x_column_name and the target column as the - default value for y_column_name. - - Parameters - ---------- - x_column_name: - The column name of the column to be plotted on the x-Axis. - y_column_name: - The column name of the column to be plotted on the y-Axis. - - Returns - ------- - plot: - The plot as an image. - - Raises - ------ - NonNumericColumnError - If the time series given columns contain non-numerical values. - - UnknownColumnNameError - If one of the given names does not exist in the table - - Examples - -------- - >>> from safeds.data.tabular.containers import TimeSeries - >>> table = TimeSeries({"time":[1, 2], "target": [3, 4], "feature":[2,2]}, target_name= "target", time_name="time", feature_names=["feature"], ) - >>> image = table.plot_scatterplot() - - """ - import matplotlib.pyplot as plt - import seaborn as sns - - self._data.index.name = "index" - if x_column_name is not None and not self.get_column(x_column_name).type.is_numeric(): - raise NonNumericColumnError("The time series plotted column contains non-numerical columns.") - - if y_column_name is None: - y_column_name = self._target.name - elif y_column_name not in self._data.columns: - raise UnknownColumnNameError([y_column_name]) - if x_column_name is None: - x_column_name = self.time.name - - if not self.get_column(y_column_name).type.is_numeric(): - raise NonNumericColumnError("The time series plotted column contains non-numerical columns.") - - fig = plt.figure() - ax = sns.scatterplot( - data=self._data, - x=x_column_name, - y=y_column_name, - ) - ax.set(xlabel=x_column_name, ylabel=y_column_name) - ax.set_xticks(ax.get_xticks()) - ax.set_xticklabels( - ax.get_xticklabels(), - rotation=45, - horizontalalignment="right", - ) # rotate the labels of the x Axis to prevent the chance of overlapping of the labels - plt.tight_layout() - - buffer = io.BytesIO() - fig.savefig(buffer, format="png") - plt.close() # Prevents the figure from being displayed directly - buffer.seek(0) - self._data = self._data.reset_index() - return Image.from_bytes(buffer.read()) - - def split_rows(self, percentage_in_first: float) -> tuple[TimeSeries, TimeSeries]: - """ - Split the table into two new tables. - - The original time series is not modified. - - Parameters - ---------- - percentage_in_first: - The desired size of the first time series in percentage to the given time series; must be between 0 and 1. - - Returns - ------- - result: - A tuple containing the two resulting time series. The first time series has the specified size, the second time series - contains the rest of the data. - - Raises - ------ - ValueError: - if the 'percentage_in_first' is not between 0 and 1. - - Examples - -------- - >>> from safeds.data.tabular.containers import TimeSeries - >>> time_series = TimeSeries({"time":[0, 1, 2, 3, 4], "temperature": [10, 15, 20, 25, 30], "sales": [54, 74, 90, 206, 210]}, time_name="time", target_name="sales") - >>> slices = time_series.split_rows(0.4) - >>> slices[0] - time temperature sales - 0 0 10 54 - 1 1 15 74 - >>> slices[1] - time temperature sales - 0 2 20 90 - 1 3 25 206 - 2 4 30 210 - """ - temp = self._as_table() - t1, t2 = temp.split_rows(percentage_in_first=percentage_in_first) - return ( - TimeSeries._from_table( - t1, - time_name=self.time.name, - target_name=self._target.name, - feature_names=self._feature_names, - ), - TimeSeries._from_table( - t2, - time_name=self.time.name, - target_name=self._target.name, - feature_names=self._feature_names, - ), - ) - - def plot_compare_time_series(self, time_series: list[TimeSeries]) -> Image: - """ - Plot the given time series targets along the time on the x-axis. - - Parameters - ---------- - time_series: - A list of time series to be plotted. - - Returns - ------- - plot: - A plot with all the time series targets plotted by the time on the x-axis. - - Raises - ------ - NonNumericColumnError - if the target column contains non numerical values - """ - import matplotlib.pyplot as plt - import pandas as pd - import seaborn as sns - - if not self._target.type.is_numeric(): - raise NonNumericColumnError("The time series plotted column contains non-numerical columns.") - - data = pd.DataFrame() - data[self.time.name] = self.time._data - data[self.target.name] = self.target._data - for index, ts in enumerate(time_series): - if not ts.target.type.is_numeric(): - raise NonNumericColumnError("The time series plotted column contains non-numerical columns.") - data[ts.target.name + " " + str(index)] = ts.target._data - fig = plt.figure() - - data = pd.melt(data, [self.time.name]) - sns.lineplot(x=self.time.name, y="value", hue="variable", data=data) - plt.title("Multiple Series Plot") - plt.xlabel("Time") - - plt.tight_layout() - buffer = io.BytesIO() - fig.savefig(buffer, format="png") - plt.close() # Prevents the figure from being displayed directly - buffer.seek(0) - self._data = self._data.reset_index() - return Image.from_bytes(buffer.read()) diff --git a/src/safeds/data/tabular/transformation/_table_transformer.py b/src/safeds/data/tabular/transformation/_table_transformer.py index bdf6bb861..277543c6a 100644 --- a/src/safeds/data/tabular/transformation/_table_transformer.py +++ b/src/safeds/data/tabular/transformation/_table_transformer.py @@ -137,10 +137,8 @@ def fit_and_transform(self, table: Table, column_names: list[str] | None = None) Returns ------- - fitted_transformer: - The fitted transformer. - transformed_table: - The transformed table. + fitted_transformer, transformed_table: + The fitted transformer and the transformed table.: """ fitted_transformer = self.fit(table, column_names) transformed_table = fitted_transformer.transform(table) diff --git a/src/safeds/exceptions/__init__.py b/src/safeds/exceptions/__init__.py index cc8366298..7a654021b 100644 --- a/src/safeds/exceptions/__init__.py +++ b/src/safeds/exceptions/__init__.py @@ -6,14 +6,11 @@ if TYPE_CHECKING: from safeds.exceptions._data import ( - ColumnIsTargetError, - ColumnIsTimeError, ColumnLengthMismatchError, ColumnSizeError, DuplicateColumnNameError, DuplicateIndexError, IllegalFormatError, - IllegalSchemaModificationError, IndexOutOfBoundsError, MissingValuesColumnError, NonNumericColumnError, @@ -37,7 +34,6 @@ InvalidModelStructureError, LearningError, ModelNotFittedError, - NonTimeSeriesError, PlainTableError, PredictionError, ) @@ -72,7 +68,6 @@ "InvalidModelStructureError": "._ml:InvalidModelStructureError", "LearningError": "._ml:LearningError", "ModelNotFittedError": "._ml:ModelNotFittedError", - "NonTimeSeriesError": "._ml:NonTimeSeriesError", "PlainTableError": "._ml:PlainTableError", "PredictionError": "._ml:PredictionError", # Other @@ -86,14 +81,11 @@ # Generic exceptions "OutOfBoundsError", # Data exceptions - "ColumnIsTargetError", - "ColumnIsTimeError", "ColumnLengthMismatchError", "ColumnSizeError", "DuplicateColumnNameError", "DuplicateIndexError", "IllegalFormatError", - "IllegalSchemaModificationError", "IndexOutOfBoundsError", "MissingValuesColumnError", "NonNumericColumnError", @@ -110,7 +102,6 @@ "InvalidModelStructureError", "LearningError", "ModelNotFittedError", - "NonTimeSeriesError", "PlainTableError", "PredictionError", # Other diff --git a/src/safeds/exceptions/_data.py b/src/safeds/exceptions/_data.py index a060f21aa..0d9303a76 100644 --- a/src/safeds/exceptions/_data.py +++ b/src/safeds/exceptions/_data.py @@ -163,27 +163,6 @@ def __init__(self, file: str | Path, file_extension: str | list[str]) -> None: ) -class IllegalSchemaModificationError(Exception): - """Exception raised when modifying a schema in a way that is inconsistent with the subclass's requirements.""" - - def __init__(self, msg: str) -> None: - super().__init__(f"Illegal schema modification: {msg}") - - -class ColumnIsTargetError(IllegalSchemaModificationError): - """Exception raised when removing the target column of a TimeSeries.""" - - def __init__(self, column_name: str) -> None: - super().__init__(f'Column "{column_name}" is the target column and cannot be removed.') - - -class ColumnIsTimeError(IllegalSchemaModificationError): - """Exception raised when removing the time column of a TimeSeries.""" - - def __init__(self, column_name: str) -> None: - super().__init__(f'Column "{column_name}" is the time column and cannot be removed.') - - class IllegalFormatError(Exception): """Exception raised when a format is not legal.""" diff --git a/src/safeds/exceptions/_ml.py b/src/safeds/exceptions/_ml.py index d87960f94..5b920153b 100644 --- a/src/safeds/exceptions/_ml.py +++ b/src/safeds/exceptions/_ml.py @@ -93,15 +93,3 @@ def __init__(self) -> None: "Use `Table.to_tabular_dataset()` to create a tabular dataset." ), ) - - -class NonTimeSeriesError(Exception): - """Raised when a table is used instead of a TimeSeries in a regression or classification.""" - - def __init__(self) -> None: - super().__init__( - ( - "This method needs a time series.\nA time series is a table that additionally knows which columns are" - " time and which are the target to predict.\n" - ), - ) diff --git a/src/safeds/ml/classical/regression/_arima.py b/src/safeds/ml/classical/regression/_arima.py index f35e066fc..a5c9de89f 100644 --- a/src/safeds/ml/classical/regression/_arima.py +++ b/src/safeds/ml/classical/regression/_arima.py @@ -6,13 +6,13 @@ from safeds._utils import _structural_hash from safeds.data.image.containers import Image -from safeds.data.tabular.containers import Column, Table, TimeSeries +from safeds.data.labeled.containers import TimeSeriesDataset +from safeds.data.tabular.containers import Column from safeds.exceptions import ( DatasetMissesDataError, MissingValuesColumnError, ModelNotFittedError, NonNumericColumnError, - NonTimeSeriesError, ) if TYPE_CHECKING: @@ -39,7 +39,7 @@ def __init__(self) -> None: self._order: tuple[int, int, int] | None = None self._fitted = False - def fit(self, time_series: TimeSeries) -> ArimaModelRegressor: + def fit(self, time_series: TimeSeriesDataset) -> ArimaModelRegressor: """ Create a copy of this ARIMA Model and fit it with the given training data. @@ -70,9 +70,8 @@ def fit(self, time_series: TimeSeries) -> ArimaModelRegressor: """ from statsmodels.tsa.arima.model import ARIMA - if not isinstance(time_series, TimeSeries) and isinstance(time_series, Table): - raise NonTimeSeriesError - if time_series.number_of_rows == 0: + table = time_series.to_table() + if table.number_of_rows == 0: raise DatasetMissesDataError if not time_series.target.type.is_numeric(): raise NonNumericColumnError(time_series.target.name) @@ -109,7 +108,7 @@ def fit(self, time_series: TimeSeries) -> ArimaModelRegressor: fitted_arima._fitted = True return fitted_arima - def predict(self, time_series: TimeSeries) -> TimeSeries: + def predict(self, time_series: TimeSeriesDataset) -> TimeSeriesDataset: """ Predict a target vector using a time series target column. The model has to be trained first. @@ -134,7 +133,7 @@ def predict(self, time_series: TimeSeries) -> TimeSeries: """ # make a table without forecast_horizon = len(time_series.target._data) - result_table = time_series._as_table() + result_table = time_series.to_table() result_table = result_table.remove_columns([time_series.target.name]) # Validation if not self.is_fitted or self._arima is None: @@ -147,14 +146,13 @@ def predict(self, time_series: TimeSeries) -> TimeSeries: # create new TimeSeries result_table = result_table.add_column(target_column) - return TimeSeries._from_table( - result_table, - time_name=time_series.time.name, + return result_table.to_time_series_dataset( target_name=time_series.target.name + " " + "forecasted", - feature_names=time_series.features.column_names, + time_name=time_series.time.name, + extra_names=time_series.extras.column_names, ) - def plot_predictions(self, test_series: TimeSeries) -> Image: + def plot_predictions(self, test_series: TimeSeriesDataset) -> Image: """ Plot the predictions of the trained model to the given target of the time series. So you can see the predictions and the actual values in one plot. diff --git a/src/safeds/ml/nn/__init__.py b/src/safeds/ml/nn/__init__.py index e43ad88fb..a2ceb7ac5 100644 --- a/src/safeds/ml/nn/__init__.py +++ b/src/safeds/ml/nn/__init__.py @@ -11,7 +11,9 @@ from ._input_conversion import InputConversion from ._input_conversion_image import InputConversionImage from ._input_conversion_table import InputConversionTable + from ._input_conversion_time_series import InputConversionTimeSeries from ._layer import Layer + from ._lstm_layer import LSTMLayer from ._model import NeuralNetworkClassifier, NeuralNetworkRegressor from ._output_conversion import OutputConversion from ._output_conversion_image import ( @@ -20,6 +22,7 @@ OutputConversionImageToTable, ) from ._output_conversion_table import OutputConversionTable + from ._output_conversion_time_series import OutputConversionTimeSeries from ._pooling2d_layer import AvgPooling2DLayer, MaxPooling2DLayer apipkg.initpkg( @@ -34,14 +37,17 @@ "InputConversionImage": "._input_conversion_image:InputConversionImage", "InputConversionTable": "._input_conversion_table:InputConversionTable", "Layer": "._layer:Layer", + "OutputConversion": "._output_conversion:OutputConversion", + "InputConversionTimeSeries": "._input_conversion_time_series:InputConversionTimeSeries", + "LSTMLayer": "._lstm_layer:LSTMLayer", + "OutputConversionTable": "._output_conversion_table:OutputConversionTable", + "OutputConversionTimeSeries": "._output_conversion_time_series:OutputConversionTimeSeries", "MaxPooling2DLayer": "._pooling2d_layer:MaxPooling2DLayer", "NeuralNetworkClassifier": "._model:NeuralNetworkClassifier", "NeuralNetworkRegressor": "._model:NeuralNetworkRegressor", - "OutputConversion": "._output_conversion:OutputConversion", "OutputConversionImageToColumn": "._output_conversion_image:OutputConversionImageToColumn", "OutputConversionImageToImage": "._output_conversion_image:OutputConversionImageToImage", "OutputConversionImageToTable": "._output_conversion_image:OutputConversionImageToTable", - "OutputConversionTable": "._output_conversion_table:OutputConversionTable", }, ) @@ -56,11 +62,14 @@ "InputConversionTable", "Layer", "MaxPooling2DLayer", + "OutputConversion", + "InputConversionTimeSeries", + "LSTMLayer", + "OutputConversionTable", + "OutputConversionTimeSeries", "NeuralNetworkClassifier", "NeuralNetworkRegressor", - "OutputConversion", "OutputConversionImageToColumn", "OutputConversionImageToImage", "OutputConversionImageToTable", - "OutputConversionTable", ] diff --git a/src/safeds/ml/nn/_forward_layer.py b/src/safeds/ml/nn/_forward_layer.py index baa91c17f..5c3268802 100644 --- a/src/safeds/ml/nn/_forward_layer.py +++ b/src/safeds/ml/nn/_forward_layer.py @@ -40,7 +40,7 @@ def forward(self, x: Tensor) -> Tensor: class ForwardLayer(Layer): def __init__(self, output_size: int, input_size: int | None = None): """ - Create a FNN Layer. + Create a Feed Forward Layer. Parameters ---------- diff --git a/src/safeds/ml/nn/_input_conversion.py b/src/safeds/ml/nn/_input_conversion.py index 0e2cf952e..b3e1e41a6 100644 --- a/src/safeds/ml/nn/_input_conversion.py +++ b/src/safeds/ml/nn/_input_conversion.py @@ -6,15 +6,15 @@ if TYPE_CHECKING: from torch.utils.data import DataLoader - from safeds.data.image.containers._single_size_image_list import _SingleSizeImageList - from safeds.data.image.typing import ImageSize +from safeds.data.image.containers._single_size_image_list import _SingleSizeImageList +from safeds.data.image.typing import ImageSize from safeds.data.image.containers import ImageList -from safeds.data.labeled.containers import ImageDataset, TabularDataset -from safeds.data.tabular.containers import Table, TimeSeries +from safeds.data.labeled.containers import ImageDataset, TabularDataset, TimeSeriesDataset +from safeds.data.tabular.containers import Table -FT = TypeVar("FT", TabularDataset, TimeSeries, ImageDataset) -PT = TypeVar("PT", Table, TimeSeries, ImageList) +FT = TypeVar("FT", TabularDataset, TimeSeriesDataset, ImageDataset) +PT = TypeVar("PT", Table, TimeSeriesDataset, ImageList) class InputConversion(Generic[FT, PT], ABC): diff --git a/src/safeds/ml/nn/_input_conversion_table.py b/src/safeds/ml/nn/_input_conversion_table.py index 5ac205ed0..e5c009f56 100644 --- a/src/safeds/ml/nn/_input_conversion_table.py +++ b/src/safeds/ml/nn/_input_conversion_table.py @@ -13,19 +13,12 @@ class InputConversionTable(InputConversion[TabularDataset, Table]): """The input conversion for a neural network, defines the input parameters for the neural network.""" - def __init__(self, feature_names: list[str], target_name: str) -> None: - """ - Define the input parameters for the neural network in the input conversion. - - Parameters - ---------- - feature_names: - The names of the features for the input table, used as features for the training. - target_name: - The name of the target for the input table, used as target for the training. - """ - self._feature_names = feature_names - self._target_name = target_name + def __init__(self) -> None: + """Define the input parameters for the neural network in the input conversion.""" + self._target_name = "" + self._time_name = "" + self._feature_names: list[str] = [] + self._first = True @property def _data_size(self) -> int: @@ -41,6 +34,10 @@ def _data_conversion_predict(self, input_data: Table, batch_size: int) -> DataLo return input_data._into_dataloader(batch_size) def _is_fit_data_valid(self, input_data: TabularDataset) -> bool: + if self._first: + self._feature_names = input_data.features.column_names + self._target_name = input_data.target.name + self._first = False return (sorted(input_data.features.column_names)).__eq__(sorted(self._feature_names)) def _is_predict_data_valid(self, input_data: Table) -> bool: diff --git a/src/safeds/ml/nn/_input_conversion_time_series.py b/src/safeds/ml/nn/_input_conversion_time_series.py new file mode 100644 index 000000000..18cf9fb23 --- /dev/null +++ b/src/safeds/ml/nn/_input_conversion_time_series.py @@ -0,0 +1,82 @@ +from __future__ import annotations + +from typing import TYPE_CHECKING, Any + +if TYPE_CHECKING: + from torch.utils.data import DataLoader + +from safeds.data.labeled.containers import TimeSeriesDataset +from safeds.ml.nn._input_conversion import InputConversion + + +class InputConversionTimeSeries(InputConversion[TimeSeriesDataset, TimeSeriesDataset]): + """The input conversion for a neural network, defines the input parameters for the neural network.""" + + def __init__( + self, + window_size: int, + forecast_horizon: int, + ) -> None: + """ + Define the input parameters for the neural network in the input conversion. + + Parameters + ---------- + window_size: + The size of the created windows + forecast_horizon: + The forecast horizon defines the future lag of the predicted values + """ + self._window_size = window_size + self._forecast_horizon = forecast_horizon + self._first = True + self._target_name: str = "" + self._time_name: str = "" + self._feature_names: list[str] = [] + + @property + def _data_size(self) -> int: + """ + Gives the size for the input of an internal layer. + + Returns + ------- + size: + The size of the input for the neural network + + """ + return (len(self._feature_names) + 1) * self._window_size + + def _data_conversion_fit( + self, + input_data: TimeSeriesDataset, + batch_size: int, + num_of_classes: int = 1, + ) -> DataLoader: + self._num_of_classes = num_of_classes + return input_data._into_dataloader_with_window( + self._window_size, + self._forecast_horizon, + batch_size, + ) + + def _data_conversion_predict(self, input_data: TimeSeriesDataset, batch_size: int) -> DataLoader: + return input_data._into_dataloader_with_window_predict(self._window_size, self._forecast_horizon, batch_size) + + def _is_fit_data_valid(self, input_data: TimeSeriesDataset) -> bool: + if self._first: + self._time_name = input_data.time.name + self._feature_names = input_data.features.column_names + self._target_name = input_data.target.name + self._first = False + return ( + (sorted(input_data.features.column_names)).__eq__(sorted(self._feature_names)) + and input_data.target.name == self._target_name + and input_data.time.name == self._time_name + ) + + def _is_predict_data_valid(self, input_data: TimeSeriesDataset) -> bool: + return self._is_fit_data_valid(input_data) + + def _get_output_configuration(self) -> dict[str, Any]: + return {"window_size": self._window_size, "forecast_horizon": self._forecast_horizon} diff --git a/src/safeds/ml/nn/_lstm_layer.py b/src/safeds/ml/nn/_lstm_layer.py new file mode 100644 index 000000000..4b7053892 --- /dev/null +++ b/src/safeds/ml/nn/_lstm_layer.py @@ -0,0 +1,147 @@ +from __future__ import annotations + +from typing import TYPE_CHECKING, Any + +from safeds.data.image.typing import ImageSize + +if TYPE_CHECKING: + from torch import Tensor, nn + +import sys + +from safeds._utils import _structural_hash +from safeds.exceptions import ClosedBound, OutOfBoundsError +from safeds.ml.nn import Layer + + +def _create_internal_model(input_size: int, output_size: int, activation_function: str) -> nn.Module: + from torch import nn + + class _InternalLayer(nn.Module): + def __init__(self, input_size: int, output_size: int, activation_function: str): + super().__init__() + self._layer = nn.LSTM(input_size, output_size) + match activation_function: + case "sigmoid": + self._fn = nn.Sigmoid() + case "relu": + self._fn = nn.ReLU() + case "softmax": + self._fn = nn.Softmax() + case "none": + self._fn = None + case _: + raise ValueError("Unknown Activation Function: " + activation_function) + + def forward(self, x: Tensor) -> Tensor: + return self._fn(self._layer(x)[0]) if self._fn is not None else self._layer(x)[0] + + return _InternalLayer(input_size, output_size, activation_function) + + +class LSTMLayer(Layer): + def __init__(self, output_size: int, input_size: int | None = None): + """ + Create a LSTM Layer. + + Parameters + ---------- + input_size: + The number of neurons in the previous layer + output_size: + The number of neurons in this layer + + Raises + ------ + ValueError + If input_size < 1 + If output_size < 1 + """ + if input_size is not None: + self._set_input_size(input_size=input_size) + if output_size < 1: + raise OutOfBoundsError(actual=output_size, name="output_size", lower_bound=ClosedBound(1)) + self._output_size = output_size + + def _get_internal_layer(self, **kwargs: Any) -> nn.Module: + if "activation_function" not in kwargs: + raise ValueError( + "The activation_function is not set. The internal layer can only be created when the activation_function is provided in the kwargs.", + ) + else: + activation_function: str = kwargs["activation_function"] + return _create_internal_model(self._input_size, self._output_size, activation_function) + + @property + def input_size(self) -> int: + """ + Get the input_size of this layer. + + Returns + ------- + result: + The amount of values being passed into this layer. + """ + return self._input_size + + @property + def output_size(self) -> int: + """ + Get the output_size of this layer. + + Returns + ------- + result: + The Number of Neurons in this layer. + """ + return self._output_size + + def _set_input_size(self, input_size: int | ImageSize) -> None: + if isinstance(input_size, ImageSize): + raise TypeError("The input_size of a forward layer has to be of type int.") + if input_size < 1: + raise OutOfBoundsError(actual=input_size, name="input_size", lower_bound=ClosedBound(1)) + self._input_size = input_size + + def __hash__(self) -> int: + """ + Return a deterministic hash value for this LSTM layer. + + Returns + ------- + hash: + the hash value + """ + return _structural_hash( + self._input_size, + self._output_size, + ) # pragma: no cover + + def __eq__(self, other: object) -> bool: + """ + Compare two lstm layer. + + Parameters + ---------- + other: + The lstm layer to compare to. + + Returns + ------- + equals: + Whether the two lstm layer are the same. + """ + if not isinstance(other, LSTMLayer): + return NotImplemented + return (self is other) or (self._input_size == other._input_size and self._output_size == other._output_size) + + def __sizeof__(self) -> int: + """ + Return the complete size of this object. + + Returns + ------- + size: + Size of this object in bytes. + """ + return sys.getsizeof(self._input_size) + sys.getsizeof(self._output_size) diff --git a/src/safeds/ml/nn/_model.py b/src/safeds/ml/nn/_model.py index e763c09c8..569444b9c 100644 --- a/src/safeds/ml/nn/_model.py +++ b/src/safeds/ml/nn/_model.py @@ -4,8 +4,8 @@ from typing import TYPE_CHECKING, Generic, Self, TypeVar from safeds.data.image.containers import ImageList -from safeds.data.labeled.containers import ImageDataset, TabularDataset -from safeds.data.tabular.containers import Table, TimeSeries +from safeds.data.labeled.containers import TabularDataset, TimeSeriesDataset, ImageDataset +from safeds.data.tabular.containers import Table from safeds.exceptions import ( ClosedBound, FeatureDataMismatchError, @@ -31,12 +31,15 @@ from torch import Tensor, nn + from safeds.ml.nn._input_conversion import InputConversion + from safeds.ml.nn._layer import Layer + from safeds.ml.nn._output_conversion import OutputConversion from safeds.data.image.typing import ImageSize - from safeds.ml.nn import InputConversion, Layer, OutputConversion -IFT = TypeVar("IFT", TabularDataset, TimeSeries, ImageDataset) # InputFitType -IPT = TypeVar("IPT", Table, TimeSeries, ImageList) # InputPredictType -OT = TypeVar("OT", TabularDataset, TimeSeries, ImageDataset) # OutputType + +IFT = TypeVar("IFT", TabularDataset, TimeSeriesDataset, ImageDataset) # InputFitType +IPT = TypeVar("IPT", Table, TimeSeriesDataset, ImageList) # InputPredictType +OT = TypeVar("OT", TabularDataset, TimeSeriesDataset, ImageDataset) # OutputType class NeuralNetworkRegressor(Generic[IFT, IPT, OT]): @@ -156,14 +159,14 @@ def fit( import torch from torch import nn + if not self._input_conversion._is_fit_data_valid(train_data): + raise FeatureDataMismatchError if epoch_size < 1: raise OutOfBoundsError(actual=epoch_size, name="epoch_size", lower_bound=ClosedBound(1)) if batch_size < 1: raise OutOfBoundsError(actual=batch_size, name="batch_size", lower_bound=ClosedBound(1)) if self._input_conversion._data_size is not self._input_size: raise InputSizeError(self._input_conversion._data_size, self._input_size) - if not self._input_conversion._is_fit_data_valid(train_data): - raise FeatureDataMismatchError copied_model = copy.deepcopy(self) @@ -368,14 +371,14 @@ def fit( import torch from torch import nn + if not self._input_conversion._is_fit_data_valid(train_data): + raise FeatureDataMismatchError if epoch_size < 1: raise OutOfBoundsError(actual=epoch_size, name="epoch_size", lower_bound=ClosedBound(1)) if batch_size < 1: raise OutOfBoundsError(actual=batch_size, name="batch_size", lower_bound=ClosedBound(1)) if self._input_conversion._data_size is not self._input_size: raise InputSizeError(self._input_conversion._data_size, self._input_size) - if not self._input_conversion._is_fit_data_valid(train_data): - raise FeatureDataMismatchError copied_model = copy.deepcopy(self) @@ -391,7 +394,6 @@ def fit( loss_fn = nn.CrossEntropyLoss() else: loss_fn = nn.BCELoss() - optimizer = torch.optim.SGD(copied_model._model.parameters(), lr=learning_rate) for _ in range(epoch_size): loss_sum = 0.0 @@ -399,7 +401,6 @@ def fit( for x, y in iter(dataloader): optimizer.zero_grad() pred = copied_model._model(x) - loss = loss_fn(pred, y) loss_sum += loss.item() amount_of_loss_values_calculated += 1 diff --git a/src/safeds/ml/nn/_output_conversion.py b/src/safeds/ml/nn/_output_conversion.py index 301413823..f29867e31 100644 --- a/src/safeds/ml/nn/_output_conversion.py +++ b/src/safeds/ml/nn/_output_conversion.py @@ -9,10 +9,11 @@ if TYPE_CHECKING: from torch import Tensor -from safeds.data.tabular.containers import Table, TimeSeries +from safeds.data.labeled.containers import TimeSeriesDataset +from safeds.data.tabular.containers import Table -IT = TypeVar("IT", Table, TimeSeries, ImageList) -OT = TypeVar("OT", TabularDataset, TimeSeries, ImageDataset) +IT = TypeVar("IT", Table, TimeSeriesDataset, ImageList) +OT = TypeVar("OT", TabularDataset, TimeSeriesDataset, ImageDataset) class OutputConversion(Generic[IT, OT], ABC): diff --git a/src/safeds/ml/nn/_output_conversion_table.py b/src/safeds/ml/nn/_output_conversion_table.py index a77b9862f..4146aaef1 100644 --- a/src/safeds/ml/nn/_output_conversion_table.py +++ b/src/safeds/ml/nn/_output_conversion_table.py @@ -25,6 +25,6 @@ def __init__(self, prediction_name: str = "prediction") -> None: self._prediction_name = prediction_name def _data_conversion(self, input_data: Table, output_data: Tensor, **kwargs: Any) -> TabularDataset: # noqa: ARG002 - return input_data.add_column(Column(self._prediction_name, output_data.tolist())).to_tabular_dataset( + return input_data.add_columns([Column(self._prediction_name, output_data.tolist())]).to_tabular_dataset( self._prediction_name, ) diff --git a/src/safeds/ml/nn/_output_conversion_time_series.py b/src/safeds/ml/nn/_output_conversion_time_series.py new file mode 100644 index 000000000..f3ad6d43f --- /dev/null +++ b/src/safeds/ml/nn/_output_conversion_time_series.py @@ -0,0 +1,89 @@ +from __future__ import annotations + +import sys +from typing import TYPE_CHECKING, Any + +from safeds._utils import _structural_hash + +if TYPE_CHECKING: + from torch import Tensor +from safeds.data.labeled.containers import TimeSeriesDataset +from safeds.data.tabular.containers import Column, Table +from safeds.ml.nn._output_conversion import OutputConversion + + +class OutputConversionTimeSeries(OutputConversion[TimeSeriesDataset, TimeSeriesDataset]): + """The output conversion for a neural network, defines the output parameters for the neural network.""" + + def __hash__(self) -> int: + """ + Return a deterministic hash value for this OutputConversionTimeSeries instance. + + Returns + ------- + hash: + the hash value + """ + return _structural_hash(self.__class__.__name__ + self._prediction_name) + + def __eq__(self, other: object) -> bool: + """ + Compare two OutputConversionTimeSeries instances. + + Parameters + ---------- + other: + The OutputConversionTimeSeries instance to compare to. + + Returns + ------- + equals: + Whether the instances are the same. + """ + if not isinstance(other, OutputConversionTimeSeries): + return False + return self._prediction_name == other._prediction_name + + def __sizeof__(self) -> int: + """ + Return the complete size of this object. + + Returns + ------- + size: + Size of this object in bytes. + """ + return sys.getsizeof(self._prediction_name) + + def __init__(self, prediction_name: str = "prediction_nn") -> None: + """ + Define the output parameters for the neural network in the output conversion. + + Parameters + ---------- + prediction_name: + The name of the new column where the prediction will be stored. + """ + self._prediction_name = prediction_name + + def _data_conversion(self, input_data: TimeSeriesDataset, output_data: Tensor, **kwargs: Any) -> TimeSeriesDataset: + if "window_size" not in kwargs or not isinstance(kwargs.get("window_size"), int): + raise ValueError( + "The window_size is not set. The data can only be converted if the window_size is provided as `int` in the kwargs.", + ) + if "forecast_horizon" not in kwargs or not isinstance(kwargs.get("forecast_horizon"), int): + raise ValueError( + "The forecast_horizon is not set. The data can only be converted if the forecast_horizon is provided as `int` in the kwargs.", + ) + window_size: int = kwargs["window_size"] + forecast_horizon: int = kwargs["forecast_horizon"] + input_data_table = input_data.to_table() + input_data_table = Table.from_rows(input_data_table.to_rows()[window_size + forecast_horizon :]) + + return input_data_table.add_columns( + [Column(self._prediction_name, output_data.tolist())] + ).to_time_series_dataset( + target_name=self._prediction_name, + time_name=input_data.time.name, + extra_names=input_data.extras.column_names, + ) diff --git a/tests/helpers/__init__.py b/tests/helpers/__init__.py index 8e0d85676..8dbe9ac54 100644 --- a/tests/helpers/__init__.py +++ b/tests/helpers/__init__.py @@ -1,7 +1,6 @@ from ._assertions import ( assert_that_tables_are_close, assert_that_tabular_datasets_are_equal, - assert_that_time_series_are_equal, ) from ._devices import ( device_cpu, @@ -39,7 +38,6 @@ __all__ = [ "assert_that_tables_are_close", "assert_that_tabular_datasets_are_equal", - "assert_that_time_series_are_equal", "device_cpu", "device_cuda", "grayscale_jpg_id", diff --git a/tests/helpers/_assertions.py b/tests/helpers/_assertions.py index 7bab1bdd5..e595fd9e2 100644 --- a/tests/helpers/_assertions.py +++ b/tests/helpers/_assertions.py @@ -1,6 +1,6 @@ import pytest from safeds.data.labeled.containers import TabularDataset -from safeds.data.tabular.containers import Table, TimeSeries +from safeds.data.tabular.containers import Table def assert_that_tables_are_close(table1: Table, table2: Table) -> None: @@ -40,22 +40,3 @@ def assert_that_tabular_datasets_are_equal(table1: TabularDataset, table2: Tabul assert table1.features == table2.features assert table1.target == table2.target assert table1 == table2 - - -def assert_that_time_series_are_equal(table1: TimeSeries, table2: TimeSeries) -> None: - """ - Assert that two time series are equal. - - Parameters - ---------- - table1: TimeSeries - The first timeseries. - table2: TimeSeries - The timeseries to compare the first timeseries to. - """ - assert table1.schema == table2.schema - assert table1._feature_names == table2._feature_names - assert table1.features == table2.features - assert table1.target == table2.target - assert table1.time == table2.time - assert table1 == table2 diff --git a/tests/safeds/data/tabular/containers/_time_series/__init__.py b/tests/safeds/data/labeled/containers/_time_series_dataset/__init__.py similarity index 100% rename from tests/safeds/data/tabular/containers/_time_series/__init__.py rename to tests/safeds/data/labeled/containers/_time_series_dataset/__init__.py diff --git a/tests/safeds/data/tabular/containers/_time_series/__snapshots__/test_plot_compare_time_series/test_legit_compare.png b/tests/safeds/data/labeled/containers/_time_series_dataset/__snapshots__/test_plot_compare_time_series/test_legit_compare.png similarity index 100% rename from tests/safeds/data/tabular/containers/_time_series/__snapshots__/test_plot_compare_time_series/test_legit_compare.png rename to tests/safeds/data/labeled/containers/_time_series_dataset/__snapshots__/test_plot_compare_time_series/test_legit_compare.png diff --git a/tests/safeds/data/tabular/containers/_time_series/__snapshots__/test_plot_lag/test_should_return_table.png b/tests/safeds/data/labeled/containers/_time_series_dataset/__snapshots__/test_plot_lag/test_should_return_table.png similarity index 100% rename from tests/safeds/data/tabular/containers/_time_series/__snapshots__/test_plot_lag/test_should_return_table.png rename to tests/safeds/data/labeled/containers/_time_series_dataset/__snapshots__/test_plot_lag/test_should_return_table.png diff --git a/tests/safeds/data/tabular/containers/_time_series/__snapshots__/test_plot_lineplot/test_should_plot_feature.png 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a/tests/safeds/data/tabular/containers/_time_series/__snapshots__/test_plot_lineplot/test_should_return_table_both.png b/tests/safeds/data/labeled/containers/_time_series_dataset/__snapshots__/test_plot_lineplot/test_should_return_table_both.png similarity index 100% rename from tests/safeds/data/tabular/containers/_time_series/__snapshots__/test_plot_lineplot/test_should_return_table_both.png rename to tests/safeds/data/labeled/containers/_time_series_dataset/__snapshots__/test_plot_lineplot/test_should_return_table_both.png diff --git a/tests/safeds/data/tabular/containers/_time_series/__snapshots__/test_plot_scatterplot/test_should_plot_feature.png b/tests/safeds/data/labeled/containers/_time_series_dataset/__snapshots__/test_plot_scatterplot/test_should_plot_feature.png similarity index 100% rename from tests/safeds/data/tabular/containers/_time_series/__snapshots__/test_plot_scatterplot/test_should_plot_feature.png rename to 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tests/safeds/data/tabular/containers/_time_series/__snapshots__/test_plot_scatterplot/test_should_plot_feature_only_x.png rename to tests/safeds/data/labeled/containers/_time_series_dataset/__snapshots__/test_plot_scatterplot/test_should_plot_feature_only_x.png diff --git a/tests/safeds/data/tabular/containers/_time_series/__snapshots__/test_plot_scatterplot/test_should_plot_feature_only_y_optional.png b/tests/safeds/data/labeled/containers/_time_series_dataset/__snapshots__/test_plot_scatterplot/test_should_plot_feature_only_y_optional.png similarity index 100% rename from tests/safeds/data/tabular/containers/_time_series/__snapshots__/test_plot_scatterplot/test_should_plot_feature_only_y_optional.png rename to tests/safeds/data/labeled/containers/_time_series_dataset/__snapshots__/test_plot_scatterplot/test_should_plot_feature_only_y_optional.png diff --git a/tests/safeds/data/tabular/containers/_time_series/__snapshots__/test_plot_scatterplot/test_should_return_table.png b/tests/safeds/data/labeled/containers/_time_series_dataset/__snapshots__/test_plot_scatterplot/test_should_return_table.png similarity index 100% rename from tests/safeds/data/tabular/containers/_time_series/__snapshots__/test_plot_scatterplot/test_should_return_table.png rename to tests/safeds/data/labeled/containers/_time_series_dataset/__snapshots__/test_plot_scatterplot/test_should_return_table.png diff --git a/tests/safeds/data/labeled/containers/_time_series_dataset/test_eq.py b/tests/safeds/data/labeled/containers/_time_series_dataset/test_eq.py new file mode 100644 index 000000000..7743da63a --- /dev/null +++ b/tests/safeds/data/labeled/containers/_time_series_dataset/test_eq.py @@ -0,0 +1,79 @@ +from typing import Any + +import pytest +from safeds.data.labeled.containers import TimeSeriesDataset +from safeds.data.tabular.containers import Row, Table + + +@pytest.mark.parametrize( + ("table1", "table2", "expected"), + [ + ( + TimeSeriesDataset({"a": [], "b": [], "c": []}, "b", "c"), + TimeSeriesDataset({"a": [], "b": [], "c": []}, "b", "c"), + True, + ), + ( + TimeSeriesDataset({"a": [1, 2, 3], "b": [4, 5, 6], "c": [4, 5, 6]}, "b", "c"), + TimeSeriesDataset({"a": [1, 2, 3], "b": [4, 5, 6], "c": [4, 5, 6]}, "b", "c"), + True, + ), + ( + TimeSeriesDataset({"a": [1, 2, 3], "b": [4, 5, 6], "c": [7, 8, 9]}, "b", "a", ["c"]), + TimeSeriesDataset({"a": [1, 2, 3], "b": [4, 5, 6], "c": [7, 8, 9]}, "c", "a", ["b"]), + False, + ), + ( + TimeSeriesDataset({"a": [1, 2, 3], "b": [4, 5, 6], "c": [7, 8, 9]}, "b", "a", ["c"]), + TimeSeriesDataset({"a": [1, 2, 3], "b": [4, 5, 6], "d": [7, 8, 9]}, "b", "a", ["d"]), + False, + ), + ( + TimeSeriesDataset({"a": [1, 2, 3], "b": [4, 5, 6]}, "b", "a"), + TimeSeriesDataset({"a": [1, 1, 3], "b": [4, 5, 6]}, "b", "a"), + False, + ), + ( + TimeSeriesDataset({"a": [1, 2, 3], "b": [4, 5, 6]}, "b", "a"), + TimeSeriesDataset({"a": ["1", "2", "3"], "b": [4, 5, 6]}, "b", "a"), + False, + ), + ( + TimeSeriesDataset({"a": [1, 2, 3], "b": [4, 5, 6], "c": [7, 8, 9]}, "b", "a", ["c"]), + TimeSeriesDataset({"a": [1, 2, 3], "b": [4, 5, 6], "c": [7, 8, 9]}, "b", "c", ["a"]), + False, + ), + ], + ids=[ + "rowless table", + "equal tables", + "different target", + "different column names", + "different values", + "different types", + "different features", + ], +) +def test_should_return_whether_two_tabular_datasets_are_equal( + table1: TimeSeriesDataset, + table2: TimeSeriesDataset, + expected: bool, +) -> None: + assert (table1.__eq__(table2)) == expected + + +@pytest.mark.parametrize( + ("table", "other"), + [ + (TimeSeriesDataset({"a": [1, 2, 3], "b": [4, 5, 6], "c": [0, 0, 0]}, "b", "c"), None), + (TimeSeriesDataset({"a": [1, 2, 3], "b": [4, 5, 6], "c": [0, 0, 0]}, "b", "c"), Row()), + (TimeSeriesDataset({"a": [1, 2, 3], "b": [4, 5, 6], "c": [0, 0, 0]}, "b", "c"), Table()), + ], + ids=[ + "TabularDataset vs. None", + "TabularDataset vs. Row", + "TabularDataset vs. Table", + ], +) +def test_should_return_not_implemented_if_other_is_not_tabular_dataset(table: TimeSeriesDataset, other: Any) -> None: + assert (table.__eq__(other)) is NotImplemented diff --git a/tests/safeds/data/labeled/containers/_time_series_dataset/test_extras.py b/tests/safeds/data/labeled/containers/_time_series_dataset/test_extras.py new file mode 100644 index 000000000..bd93075d6 --- /dev/null +++ b/tests/safeds/data/labeled/containers/_time_series_dataset/test_extras.py @@ -0,0 +1,43 @@ +import pytest +from safeds.data.labeled.containers import TimeSeriesDataset +from safeds.data.tabular.containers import Table + + +@pytest.mark.parametrize( + ("tabular_dataset", "extras"), + [ + ( + TimeSeriesDataset( + { + "A": [1, 4], + "B": [2, 5], + "C": [3, 6], + "T": [0, 1], + }, + target_name="T", + time_name="C", + ), + Table(), + ), + ( + TimeSeriesDataset( + { + "A": [1, 4], + "B": [2, 5], + "C": [3, 6], + "T": [0, 1], + }, + target_name="T", + time_name="B", + extra_names=["A", "C"], + ), + Table({"A": [1, 4], "C": [3, 6]}), + ), + ], + ids=[ + "only_target_and_features", + "target_features_and_extras", + ], +) +def test_should_return_features(tabular_dataset: TimeSeriesDataset, extras: Table) -> None: + assert tabular_dataset.extras == extras diff --git a/tests/safeds/data/tabular/containers/_time_series/test_features.py b/tests/safeds/data/labeled/containers/_time_series_dataset/test_features.py similarity index 56% rename from tests/safeds/data/tabular/containers/_time_series/test_features.py rename to tests/safeds/data/labeled/containers/_time_series_dataset/test_features.py index 5b75cb317..dcc55c06c 100644 --- a/tests/safeds/data/tabular/containers/_time_series/test_features.py +++ b/tests/safeds/data/labeled/containers/_time_series_dataset/test_features.py @@ -1,42 +1,41 @@ import pytest -from safeds.data.tabular.containers import Table, TimeSeries +from safeds.data.labeled.containers import TimeSeriesDataset +from safeds.data.tabular.containers import Table @pytest.mark.parametrize( - ("time_series", "features"), + ("tabular_dataset", "features"), [ ( - TimeSeries( + TimeSeriesDataset( { - "time": [0, 1], "A": [1, 4], "B": [2, 5], "C": [3, 6], "T": [0, 1], }, target_name="T", - time_name="time", - feature_names=["A", "B", "C"], + time_name="C", ), - Table({"A": [1, 4], "B": [2, 5], "C": [3, 6]}), + Table({"A": [1, 4], "B": [2, 5]}), ), ( - TimeSeries( + TimeSeriesDataset( { - "time": [0, 1], "A": [1, 4], "B": [2, 5], "C": [3, 6], "T": [0, 1], + "time": [0, 0], }, target_name="T", time_name="time", - feature_names=["A", "C"], + extra_names=["B"], ), Table({"A": [1, 4], "C": [3, 6]}), ), ], ids=["only_target_and_features", "target_features_and_other"], ) -def test_should_return_features(time_series: TimeSeries, features: Table) -> None: - assert time_series.features == features +def test_should_return_features(tabular_dataset: TimeSeriesDataset, features: Table) -> None: + assert tabular_dataset.features == features diff --git a/tests/safeds/data/labeled/containers/_time_series_dataset/test_hash.py b/tests/safeds/data/labeled/containers/_time_series_dataset/test_hash.py new file mode 100644 index 000000000..5df6d0170 --- /dev/null +++ b/tests/safeds/data/labeled/containers/_time_series_dataset/test_hash.py @@ -0,0 +1,65 @@ +import pytest +from safeds.data.labeled.containers import TimeSeriesDataset + + +@pytest.mark.parametrize( + ("table1", "table2"), + [ + ( + TimeSeriesDataset({"a": [], "b": []}, "b", "a"), + TimeSeriesDataset({"a": [], "b": []}, "b", "a"), + ), + ( + TimeSeriesDataset({"a": [1, 2, 3], "b": [4, 5, 6]}, "b", "a"), + TimeSeriesDataset({"a": [1, 2, 3], "b": [4, 5, 6]}, "b", "a"), + ), + ( + TimeSeriesDataset({"a": [1, 2, 3], "b": [4, 5, 6]}, "b", "a"), + TimeSeriesDataset({"a": [1, 1, 3], "b": [4, 5, 6]}, "b", "a"), + ), + ], + ids=[ + "rowless table", + "equal tables", + "different values", + ], +) +def test_should_return_same_hash_for_equal_tabular_datasets( + table1: TimeSeriesDataset, + table2: TimeSeriesDataset, +) -> None: + assert hash(table1) == hash(table2) + + +@pytest.mark.parametrize( + ("table1", "table2"), + [ + ( + TimeSeriesDataset({"a": [1, 2, 3], "b": [4, 5, 6], "c": [7, 8, 9]}, "b", "a", ["c"]), + TimeSeriesDataset({"a": [1, 2, 3], "b": [4, 5, 6], "c": [7, 8, 9]}, "c", "a", ["b"]), + ), + ( + TimeSeriesDataset({"a": [1, 2, 3], "b": [4, 5, 6], "c": [7, 8, 9]}, "b", "a", ["c"]), + TimeSeriesDataset({"a": [1, 2, 3], "b": [4, 5, 6], "d": [7, 8, 9]}, "b", "a", ["d"]), + ), + ( + TimeSeriesDataset({"a": [1, 2, 3], "b": [4, 5, 6]}, "b", "a"), + TimeSeriesDataset({"a": ["1", "2", "3"], "b": [4, 5, 6]}, "b", "a"), + ), + ( + TimeSeriesDataset({"a": [1, 2, 3], "b": [4, 5, 6], "c": [7, 8, 9]}, "b", "a", ["c"]), + TimeSeriesDataset({"a": [1, 2, 3], "b": [4, 5, 6], "c": [7, 8, 9]}, "b", "c", ["a"]), + ), + ], + ids=[ + "different target", + "different column names", + "different types", + "different features", + ], +) +def test_should_return_different_hash_for_unequal_tabular_datasets( + table1: TimeSeriesDataset, + table2: TimeSeriesDataset, +) -> None: + assert hash(table1) != hash(table2) diff --git a/tests/safeds/data/labeled/containers/_time_series_dataset/test_init.py b/tests/safeds/data/labeled/containers/_time_series_dataset/test_init.py new file mode 100644 index 000000000..99719be02 --- /dev/null +++ b/tests/safeds/data/labeled/containers/_time_series_dataset/test_init.py @@ -0,0 +1,245 @@ +import pytest +from safeds.data.labeled.containers import TimeSeriesDataset +from safeds.data.tabular.containers import Table +from safeds.exceptions import UnknownColumnNameError + + +@pytest.mark.parametrize( + ("data", "target_name", "time_name", "extra_names", "error", "error_msg"), + [ + ( + { + "A": [1, 4], + "B": [2, 5], + "C": [3, 6], + "T": [0, 1], + "time": [9, 9], + }, + "T", + "time", + ["D", "E"], + UnknownColumnNameError, + r"Could not find column\(s\) 'D, E'", + ), + ( + { + "A": [1, 4], + "B": [2, 5], + "C": [3, 6], + "T": [0, 1], + "time": [9, 9], + }, + "D", + "time", + [], + UnknownColumnNameError, + r"Could not find column\(s\) 'D'", + ), + ( + { + "A": [1, 4], + "B": [2, 5], + "C": [3, 6], + "T": [0, 1], + "time": [9, 9], + }, + "A", + "time", + ["A"], + ValueError, + r"Column 'A' cannot be both target and extra.", + ), + ( + { + "A": [1, 4], + "B": [2, 5], + "C": [3, 6], + "T": [0, 1], + "time": [9, 9], + }, + "T", + "time", + ["A", "time", "C"], + ValueError, + r"Column 'time' cannot be both time and extra.", + ), + ( + Table( + { + "A": [1, 4], + "B": [2, 5], + "C": [3, 6], + "T": [0, 1], + "time": [9, 9], + }, + ), + "T", + "time", + ["D", "E"], + UnknownColumnNameError, + r"Could not find column\(s\) 'D, E'", + ), + ( + Table( + { + "A": [1, 4], + "B": [2, 5], + "C": [3, 6], + "T": [0, 1], + "time": [9, 9], + }, + ), + "D", + "time", + [], + UnknownColumnNameError, + r"Could not find column\(s\) 'D'", + ), + ( + Table( + { + "A": [1, 4], + "B": [2, 5], + "C": [3, 6], + "T": [0, 1], + "time": [9, 9], + }, + ), + "A", + "time", + ["A"], + ValueError, + r"Column 'A' cannot be both target and extra.", + ), + ], + ids=[ + "dict_extra_does_not_exist", + "dict_target_does_not_exist", + "dict_target_and_extra_overlap", + "dict_features_are_empty_explicitly", + "table_extra_does_not_exist", + "table_target_does_not_exist", + "table_target_and_extra_overlap", + ], +) +def test_should_raise_error( + data: dict[str, list[int]], + target_name: str, + time_name: str, + extra_names: list[str] | None, + error: type[Exception], + error_msg: str, +) -> None: + with pytest.raises(error, match=error_msg): + TimeSeriesDataset(data, target_name=target_name, time_name=time_name, extra_names=extra_names) + + +@pytest.mark.parametrize( + ("data", "target_name", "time_name", "extra_names"), + [ + ( + { + "A": [1, 4], + "B": [2, 5], + "C": [3, 6], + "T": [0, 1], + "time": [9, 9], + }, + "T", + "time", + [], + ), + ( + { + "A": [1, 4], + "B": [2, 5], + "C": [3, 6], + "T": [0, 1], + "time": [9, 9], + }, + "T", + "time", + ["A", "C"], + ), + ( + { + "A": [1, 4], + "B": [2, 5], + "C": [3, 6], + "T": [0, 1], + "time": [9, 9], + }, + "T", + "time", + None, + ), + ( + Table( + { + "A": [1, 4], + "B": [2, 5], + "C": [3, 6], + "T": [0, 1], + "time": [9, 9], + }, + ), + "T", + "time", + [], + ), + ( + Table( + { + "A": [1, 4], + "B": [2, 5], + "C": [3, 6], + "T": [0, 1], + "time": [9, 9], + }, + ), + "T", + "time", + ["A", "C"], + ), + ( + Table( + { + "A": [1, 4], + "B": [2, 5], + "C": [3, 6], + "T": [0, 1], + "time": [9, 9], + }, + ), + "T", + "time", + None, + ), + ], + ids=[ + "dict_create_tabular_dataset", + "dict_tabular_dataset_not_all_columns_are_features", + "dict_tabular_dataset_with_extra_names_as_None", + "table_create_tabular_dataset", + "table_tabular_dataset_not_all_columns_are_features", + "table_tabular_dataset_with_extra_names_as_None", + ], +) +def test_should_create_a_tabular_dataset( + data: Table | dict[str, list[int]], + target_name: str, + time_name: str, + extra_names: list[str] | None, +) -> None: + tabular_dataset = TimeSeriesDataset(data, target_name=target_name, time_name=time_name, extra_names=extra_names) + if not isinstance(data, Table): + data = Table(data) + + if extra_names is None: + extra_names = [] + + assert isinstance(tabular_dataset, TimeSeriesDataset) + assert tabular_dataset._extras.column_names == extra_names + assert tabular_dataset._target.name == target_name + assert tabular_dataset._extras == data.keep_only_columns(extra_names) + assert tabular_dataset._target == data.get_column(target_name) diff --git a/tests/safeds/data/labeled/containers/_time_series_dataset/test_into_dataloader.py b/tests/safeds/data/labeled/containers/_time_series_dataset/test_into_dataloader.py new file mode 100644 index 000000000..8a9cbb393 --- /dev/null +++ b/tests/safeds/data/labeled/containers/_time_series_dataset/test_into_dataloader.py @@ -0,0 +1,97 @@ +import pytest +from safeds.data.tabular.containers import Table +from safeds.data.labeled.containers import TimeSeriesDataset +from torch.utils.data import DataLoader + + +@pytest.mark.parametrize( + ("data", "target_name", "time_name", "extra_names"), + [ + ( + { + "A": [1, 4, 3], + "B": [2, 5, 4], + "C": [3, 6, 5], + "T": [0, 1, 6], + }, + "T", + "B", + [], + ), + ], + ids=[ + "test", + ], +) +def test_should_create_dataloader( + data: dict[str, list[int]], + target_name: str, + time_name: str, + extra_names: list[str] | None, +) -> None: + tabular_dataset = Table.from_dict(data).to_time_series_dataset(target_name, time_name, extra_names) + data_loader = tabular_dataset._into_dataloader_with_window(1, 1, 1) + assert isinstance(data_loader, DataLoader) + + +@pytest.mark.parametrize( + ("data", "window_size", "forecast_horizon", "error_type", "error_msg"), + [ + ( + Table( + { + "A": [1, 4], + "B": [2, 5], + "C": [3, 6], + "T": [0, 1], + } + ).to_time_series_dataset("T", "B"), + 1, + 2, + ValueError, + r"Can not create windows with window size less then forecast horizon \+ window_size", + ), + ( + Table( + { + "A": [1, 4], + "B": [2, 5], + "C": [3, 6], + "T": [0, 1], + } + ).to_time_series_dataset("T", "B"), + 1, + 0, + ValueError, + r"forecast_horizon must be greater than or equal to 1", + ), + ( + Table( + { + "A": [1, 4], + "B": [2, 5], + "C": [3, 6], + "T": [0, 1], + } + ).to_time_series_dataset("T", "B"), + 0, + 1, + ValueError, + r"window_size must be greater than or equal to 1", + ), + ], + ids=[ + "forecast_and_window", + "forecast", + "window_size", + ], +) +def test_should_create_dataloader_invalid( + data: TimeSeriesDataset, + window_size: int, + forecast_horizon: int, + error_type: ValueError, + error_msg: str, +) -> None: + with pytest.raises(error_type, match=error_msg): + data._into_dataloader_with_window(window_size=window_size, forecast_horizon=forecast_horizon, batch_size=1) diff --git a/tests/safeds/data/labeled/containers/_time_series_dataset/test_repr_html.py b/tests/safeds/data/labeled/containers/_time_series_dataset/test_repr_html.py new file mode 100644 index 000000000..e1c40de42 --- /dev/null +++ b/tests/safeds/data/labeled/containers/_time_series_dataset/test_repr_html.py @@ -0,0 +1,47 @@ +import re + +import pytest +from safeds.data.labeled.containers import TimeSeriesDataset + + +@pytest.mark.parametrize( + "tabular_dataset", + [ + TimeSeriesDataset({"a": [1, 2], "b": [3, 4]}, target_name="b", time_name="a"), + ], + ids=[ + "non-empty", + ], +) +def test_should_contain_tabular_dataset_element(tabular_dataset: TimeSeriesDataset) -> None: + pattern = r".*?" + assert re.search(pattern, tabular_dataset._repr_html_(), flags=re.S) is not None + + +@pytest.mark.parametrize( + "tabular_dataset", + [ + TimeSeriesDataset({"a": [1, 2], "b": [3, 4]}, target_name="b", time_name="a"), + ], + ids=[ + "non-empty", + ], +) +def test_should_contain_th_element_for_each_column_name(tabular_dataset: TimeSeriesDataset) -> None: + for column_name in tabular_dataset._table.column_names: + assert f"{column_name}" in tabular_dataset._repr_html_() + + +@pytest.mark.parametrize( + "tabular_dataset", + [ + TimeSeriesDataset({"a": [1, 2], "b": [3, 4]}, target_name="b", time_name="a"), + ], + ids=[ + "non-empty", + ], +) +def test_should_contain_td_element_for_each_value(tabular_dataset: TimeSeriesDataset) -> None: + for column in tabular_dataset._table.to_columns(): + for value in column: + assert f"{value}" in tabular_dataset._repr_html_() diff --git a/tests/safeds/data/tabular/containers/_time_series/test_sizeof.py b/tests/safeds/data/labeled/containers/_time_series_dataset/test_sizeof.py similarity index 51% rename from tests/safeds/data/tabular/containers/_time_series/test_sizeof.py rename to tests/safeds/data/labeled/containers/_time_series_dataset/test_sizeof.py index 1a0ded04b..461f27a79 100644 --- a/tests/safeds/data/tabular/containers/_time_series/test_sizeof.py +++ b/tests/safeds/data/labeled/containers/_time_series_dataset/test_sizeof.py @@ -1,37 +1,36 @@ import sys import pytest -from safeds.data.tabular.containers import TimeSeries +from safeds.data.labeled.containers import TimeSeriesDataset @pytest.mark.parametrize( - "time_series", + "tabular_dataset", [ - TimeSeries( + TimeSeriesDataset( { - "time": [0, 1, 2], "feature_1": [3, 9, 6], "feature_2": [6, 12, 9], "target": [1, 3, 2], + "time": [1, 2, 3], }, "target", "time", - ["feature_1", "feature_2"], ), - TimeSeries( + TimeSeriesDataset( { - "time": [0, 1, 2], "feature_1": [3, 9, 6], "feature_2": [6, 12, 9], "other": [3, 9, 12], "target": [1, 3, 2], + "time": [1, 2, 3], }, "target", "time", - ["feature_1", "feature_2"], + ["other"], ), ], - ids=["normal", "table_with_column_as_non_feature"], + ids=["normal", "table_with_extra_column"], ) -def test_should_size_be_greater_than_normal_object(time_series: TimeSeries) -> None: - assert sys.getsizeof(time_series) > sys.getsizeof(object()) +def test_should_size_be_greater_than_normal_object(tabular_dataset: TimeSeriesDataset) -> None: + assert sys.getsizeof(tabular_dataset) > sys.getsizeof(object()) diff --git a/tests/safeds/data/labeled/containers/_time_series_dataset/test_target.py b/tests/safeds/data/labeled/containers/_time_series_dataset/test_target.py new file mode 100644 index 000000000..d4c189f71 --- /dev/null +++ b/tests/safeds/data/labeled/containers/_time_series_dataset/test_target.py @@ -0,0 +1,26 @@ +import pytest +from safeds.data.labeled.containers import TimeSeriesDataset +from safeds.data.tabular.containers import Column + + +@pytest.mark.parametrize( + ("tabular_dataset", "target_column"), + [ + ( + TimeSeriesDataset( + { + "A": [1, 4], + "B": [2, 5], + "C": [3, 6], + "T": [0, 1], + }, + target_name="T", + time_name="A", + ), + Column("T", [0, 1]), + ), + ], + ids=["target"], +) +def test_should_return_target(tabular_dataset: TimeSeriesDataset, target_column: Column) -> None: + assert tabular_dataset.target == target_column diff --git a/tests/safeds/data/labeled/containers/_time_series_dataset/test_time.py b/tests/safeds/data/labeled/containers/_time_series_dataset/test_time.py new file mode 100644 index 000000000..57f1655e9 --- /dev/null +++ b/tests/safeds/data/labeled/containers/_time_series_dataset/test_time.py @@ -0,0 +1,26 @@ +import pytest +from safeds.data.labeled.containers import TimeSeriesDataset +from safeds.data.tabular.containers import Column + + +@pytest.mark.parametrize( + ("tabular_dataset", "time_column"), + [ + ( + TimeSeriesDataset( + { + "A": [1, 4], + "B": [2, 5], + "C": [3, 6], + "T": [0, 1], + }, + target_name="T", + time_name="A", + ), + Column("A", [1, 4]), + ), + ], + ids=["time"], +) +def test_should_return_target(tabular_dataset: TimeSeriesDataset, time_column: Column) -> None: + assert tabular_dataset.time == time_column diff --git a/tests/safeds/data/tabular/containers/_time_series/test_as_table.py b/tests/safeds/data/labeled/containers/_time_series_dataset/test_to_table.py similarity index 63% rename from tests/safeds/data/tabular/containers/_time_series/test_as_table.py rename to tests/safeds/data/labeled/containers/_time_series_dataset/test_to_table.py index 443e6f7cf..acdc10da3 100644 --- a/tests/safeds/data/tabular/containers/_time_series/test_as_table.py +++ b/tests/safeds/data/labeled/containers/_time_series_dataset/test_to_table.py @@ -1,25 +1,23 @@ import pytest -from safeds.data.tabular.containers import Table, TimeSeries +from safeds.data.labeled.containers import TimeSeriesDataset +from safeds.data.tabular.containers import Table @pytest.mark.parametrize( - ("time_series", "expected"), + ("tabular_dataset", "expected"), [ ( - TimeSeries( + TimeSeriesDataset( { - "time": [0, 1, 2], "feature_1": [3, 9, 6], "feature_2": [6, 12, 9], "target": [1, 3, 2], }, "target", - "time", - ["feature_1", "feature_2"], + "feature_1", ), Table( { - "time": [0, 1, 2], "feature_1": [3, 9, 6], "feature_2": [6, 12, 9], "target": [1, 3, 2], @@ -27,21 +25,19 @@ ), ), ( - TimeSeries( + TimeSeriesDataset( { - "time": [0, 1, 2], "feature_1": [3, 9, 6], "feature_2": [6, 12, 9], "other": [3, 9, 12], "target": [1, 3, 2], }, "target", - "time", - ["feature_1", "feature_2"], + "feature_1", + ["other"], ), Table( { - "time": [0, 1, 2], "feature_1": [3, 9, 6], "feature_2": [6, 12, 9], "other": [3, 9, 12], @@ -50,9 +46,9 @@ ), ), ], - ids=["normal", "table_with_column_as_non_feature"], + ids=["normal", "table_with_extra_column"], ) -def test_should_return_table(time_series: TimeSeries, expected: Table) -> None: - table = time_series._as_table() +def test_should_return_table(tabular_dataset: TimeSeriesDataset, expected: Table) -> None: + table = tabular_dataset.to_table() assert table.schema == expected.schema assert table == expected diff --git a/tests/safeds/data/tabular/containers/_column/__snapshots__/test_plot_compare_columns/test_legit_compare.png b/tests/safeds/data/tabular/containers/_column/__snapshots__/test_plot_compare_columns/test_legit_compare.png new file mode 100644 index 000000000..a9601890b Binary files /dev/null and b/tests/safeds/data/tabular/containers/_column/__snapshots__/test_plot_compare_columns/test_legit_compare.png differ diff --git a/tests/safeds/data/tabular/containers/_column/__snapshots__/test_plot_lag/test_should_return_table.png b/tests/safeds/data/tabular/containers/_column/__snapshots__/test_plot_lag/test_should_return_table.png new file mode 100644 index 000000000..0f17b4726 Binary files /dev/null and b/tests/safeds/data/tabular/containers/_column/__snapshots__/test_plot_lag/test_should_return_table.png differ diff --git a/tests/safeds/data/tabular/containers/_column/test_plot_compare_columns.py b/tests/safeds/data/tabular/containers/_column/test_plot_compare_columns.py new file mode 100644 index 000000000..48820e2f3 --- /dev/null +++ b/tests/safeds/data/tabular/containers/_column/test_plot_compare_columns.py @@ -0,0 +1,83 @@ +import pytest +from safeds.data.tabular.containers import Column +from safeds.exceptions import NonNumericColumnError +from syrupy import SnapshotAssertion + + +def create_time_series_list() -> list[Column]: + table1 = Column( + "target", + [9, 10, 11, 12, 13, 14, 15, 16, 17, 18], + ) + table2 = Column("target", [4, 5, 6, 7, 8, 9, 10, 11, 12, 13]) + return [table1, table2] + + +def create_invalid_time_series_list() -> list[Column]: + table1 = Column("target", ["9", 10, 11, 12, 13, 14, 15, 16, 17, 18]) + table2 = Column("target", ["4", 5, 6, 7, 8, 9, 10, 11, 12, 13]) + return [table1, table2] + + +def test_legit_compare(snapshot_png_image: SnapshotAssertion) -> None: + col = Column( + "target", + [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], + ) + plot = col.plot_compare_columns(create_time_series_list()) + assert plot == snapshot_png_image + + +def test_should_raise_if_column_contains_non_numerical_values_x() -> None: + table = Column( + "target", + ["1", "2", "3", "4", "5", "6", "7", "8", "9", "10"], + ) + with pytest.raises( + NonNumericColumnError, + match=( + r"Tried to do a numerical operation on one or multiple non-numerical columns: \nThe time series plotted" + r" column" + r" contains" + r" non-numerical columns." + ), + ): + table.plot_compare_columns(create_time_series_list()) + + +def test_with_non_valid_list() -> None: + table = Column( + "target", + [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], + ) + with pytest.raises( + NonNumericColumnError, + match=( + r"Tried to do a numerical operation on one or multiple non-numerical columns: \nThe time series plotted" + r" column" + r" contains" + r" non-numerical columns." + ), + ): + table.plot_compare_columns(create_invalid_time_series_list()) + + +def test_with_non_valid_size() -> None: + table = Column( + "target", + [ + 1, + 2, + 3, + 4, + 5, + 6, + 7, + 8, + ], + ) + with pytest.raises( + ValueError, + match=(r"The columns must have the same size."), + ): + table.plot_compare_columns(create_time_series_list()) diff --git a/tests/safeds/data/tabular/containers/_column/test_plot_lag.py b/tests/safeds/data/tabular/containers/_column/test_plot_lag.py new file mode 100644 index 000000000..e688b4ff7 --- /dev/null +++ b/tests/safeds/data/tabular/containers/_column/test_plot_lag.py @@ -0,0 +1,29 @@ +import pytest +from safeds.data.tabular.containers import Column +from safeds.exceptions import NonNumericColumnError +from syrupy import SnapshotAssertion + + +def test_should_return_table(snapshot_png_image: SnapshotAssertion) -> None: + col = Column( + "target", + [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], + ) + lag_plot = col.plot_lagplot(1) + assert lag_plot == snapshot_png_image + + +def test_should_raise_if_column_contains_non_numerical_values() -> None: + table = Column( + "target", + ["1", "2", "3", "4", "5", "6", "7", "8", "9", "10"], + ) + with pytest.raises( + NonNumericColumnError, + match=( + r"Tried to do a numerical operation on one or multiple non-numerical columns: \nThis time series target" + r" contains" + r" non-numerical columns." + ), + ): + table.plot_lagplot(2) diff --git a/tests/safeds/data/tabular/containers/_time_series/__snapshots__/test_plot_lineplot/test_should_return_table.png b/tests/safeds/data/tabular/containers/_time_series/__snapshots__/test_plot_lineplot/test_should_return_table.png deleted file mode 100644 index f40ea7854..000000000 Binary files a/tests/safeds/data/tabular/containers/_time_series/__snapshots__/test_plot_lineplot/test_should_return_table.png and /dev/null differ diff --git a/tests/safeds/data/tabular/containers/_time_series/test_add_column.py b/tests/safeds/data/tabular/containers/_time_series/test_add_column.py deleted file mode 100644 index 8cb4eb7ac..000000000 --- a/tests/safeds/data/tabular/containers/_time_series/test_add_column.py +++ /dev/null @@ -1,38 +0,0 @@ -import pytest -from safeds.data.tabular.containers import Column, TimeSeries - -from tests.helpers import assert_that_time_series_are_equal - - -@pytest.mark.parametrize( - ("time_series", "column", "expected_time_series"), - [ - ( - TimeSeries( - { - "time": [0, 1, 2], - "feature_1": [0, 1, 2], - "target": [3, 4, 5], - }, - target_name="target", - time_name="time", - feature_names=None, - ), - Column("other", [6, 7, 8]), - TimeSeries( - { - "time": [0, 1, 2], - "feature_1": [0, 1, 2], - "target": [3, 4, 5], - "other": [6, 7, 8], - }, - target_name="target", - time_name="time", - feature_names=None, - ), - ), - ], - ids=["add_column_as_non_feature"], -) -def test_should_add_column(time_series: TimeSeries, column: Column, expected_time_series: TimeSeries) -> None: - assert_that_time_series_are_equal(time_series.add_column(column), expected_time_series) diff --git a/tests/safeds/data/tabular/containers/_time_series/test_add_column_as_feature.py b/tests/safeds/data/tabular/containers/_time_series/test_add_column_as_feature.py deleted file mode 100644 index 03f157c68..000000000 --- a/tests/safeds/data/tabular/containers/_time_series/test_add_column_as_feature.py +++ /dev/null @@ -1,99 +0,0 @@ -import pytest -from safeds.data.tabular.containers import Column, Table, TimeSeries -from safeds.exceptions import ColumnSizeError, DuplicateColumnNameError - -from tests.helpers import assert_that_time_series_are_equal - - -@pytest.mark.parametrize( - ("time_series", "column", "time_series_with_new_column"), - [ - ( - Table({"t": [1, 2], "f1": [1, 2], "target": [2, 3]}).time_columns( - target_name="target", - time_name="t", - feature_names=["f1"], - ), - Column("f2", [4, 5]), - Table({"t": [1, 2], "f1": [1, 2], "target": [2, 3], "f2": [4, 5]}).time_columns( - target_name="target", - time_name="t", - feature_names=["f1", "f2"], - ), - ), - ( - Table({"f1": [1, 2], "target": [2, 3], "other": [0, -1]}).time_columns( - target_name="target", - time_name="other", - feature_names=["f1"], - ), - Column("f2", [4, 5]), - Table({"f1": [1, 2], "target": [2, 3], "other": [0, -1], "f2": [4, 5]}).time_columns( - target_name="target", - time_name="other", - feature_names=["f1", "f2"], - ), - ), - ], - ids=["new column as feature", "table contains a non feature/target column"], -) -def test_should_add_column_as_feature( - time_series: TimeSeries, - column: Column, - time_series_with_new_column: TimeSeries, -) -> None: - assert_that_time_series_are_equal( - time_series.add_column_as_feature(column), - time_series_with_new_column, - ) - - -@pytest.mark.parametrize( - ("time_series", "column", "error_msg"), - [ - ( - TimeSeries( - {"time": [0, 1, 2], "A": [1, 2, 3], "B": [4, 5, 6]}, - target_name="B", - time_name="time", - feature_names=["A"], - ), - Column("A", [7, 8, 9]), - r"Column 'A' already exists.", - ), - ], - ids=["column_already_exists"], -) -def test_should_raise_duplicate_column_name_if_column_already_exists( - time_series: TimeSeries, - column: Column, - error_msg: str, -) -> None: - with pytest.raises(DuplicateColumnNameError, match=error_msg): - time_series.add_column_as_feature(column) - - -# here starts the second test for errors -@pytest.mark.parametrize( - ("time_series", "column", "error_msg"), - [ - ( - TimeSeries( - {"time": [0, 1, 2], "A": [1, 2, 3], "B": [4, 5, 6]}, - target_name="B", - time_name="time", - feature_names=["A"], - ), - Column("C", [5, 7, 8, 9]), - r"Expected a column of size 3 but got column of size 4.", - ), - ], - ids=["column_is_oversize"], -) -def test_should_raise_column_size_error_if_column_is_oversize( - time_series: TimeSeries, - column: Column, - error_msg: str, -) -> None: - with pytest.raises(ColumnSizeError, match=error_msg): - time_series.add_column_as_feature(column) diff --git a/tests/safeds/data/tabular/containers/_time_series/test_add_columns.py b/tests/safeds/data/tabular/containers/_time_series/test_add_columns.py deleted file mode 100644 index 3433e4d28..000000000 --- a/tests/safeds/data/tabular/containers/_time_series/test_add_columns.py +++ /dev/null @@ -1,43 +0,0 @@ -import pytest -from safeds.data.tabular.containers import Column, TimeSeries - -from tests.helpers import assert_that_time_series_are_equal - - -@pytest.mark.parametrize( - ("time_series", "columns", "expected_time_series"), - [ - ( - TimeSeries( - { - "time": [0, 1, 2], - "feature_1": [0, 1, 2], - "target": [3, 4, 5], - }, - "target", - "time", - None, - ), - [Column("other", [6, 7, 8]), Column("other2", [9, 6, 3])], - TimeSeries( - { - "time": [0, 1, 2], - "feature_1": [0, 1, 2], - "target": [3, 4, 5], - "other": [6, 7, 8], - "other2": [9, 6, 3], - }, - "target", - "time", - None, - ), - ), - ], - ids=["add_columns_as_non_feature"], -) -def test_should_add_columns( - time_series: TimeSeries, - columns: list[Column], - expected_time_series: TimeSeries, -) -> None: - assert_that_time_series_are_equal(time_series.add_columns(columns), expected_time_series) diff --git a/tests/safeds/data/tabular/containers/_time_series/test_add_columns_as_features.py b/tests/safeds/data/tabular/containers/_time_series/test_add_columns_as_features.py deleted file mode 100644 index 4bbbacccf..000000000 --- a/tests/safeds/data/tabular/containers/_time_series/test_add_columns_as_features.py +++ /dev/null @@ -1,115 +0,0 @@ -import pytest -from safeds.data.tabular.containers import Column, Table, TimeSeries -from safeds.exceptions import ColumnSizeError, DuplicateColumnNameError - -from tests.helpers import assert_that_time_series_are_equal - - -@pytest.mark.parametrize( - ("time_series", "columns", "time_series_with_new_columns"), - [ - ( - Table({"time": [0, 1], "f1": [1, 2], "target": [2, 3]}).time_columns( - target_name="target", - time_name="time", - feature_names=["f1"], - ), - [Column("f2", [4, 5]), Column("f3", [6, 7])], - Table({"time": [0, 1], "f1": [1, 2], "target": [2, 3], "f2": [4, 5], "f3": [6, 7]}).time_columns( - target_name="target", - time_name="time", - feature_names=["f1", "f2", "f3"], - ), - ), - ( - Table({"time": [0, 1], "f1": [1, 2], "target": [2, 3]}).time_columns( - target_name="target", - time_name="time", - feature_names=["f1"], - ), - Table.from_columns([Column("f2", [4, 5]), Column("f3", [6, 7])]), - Table({"time": [0, 1], "f1": [1, 2], "target": [2, 3], "f2": [4, 5], "f3": [6, 7]}).time_columns( - target_name="target", - time_name="time", - feature_names=["f1", "f2", "f3"], - ), - ), - ( - Table({"time": [0, 1], "f1": [1, 2], "target": [2, 3], "other": [0, -1]}).time_columns( - target_name="target", - time_name="time", - feature_names=["f1"], - ), - Table.from_columns([Column("f2", [4, 5]), Column("f3", [6, 7])]), - Table({ - "time": [0, 1], - "f1": [1, 2], - "target": [2, 3], - "other": [0, -1], - "f2": [4, 5], - "f3": [6, 7], - }).time_columns( - target_name="target", - time_name="time", - feature_names=["f1", "f2", "f3"], - ), - ), - ], - ids=["new columns as feature", "table added as features", "table contains a non feature/target column"], -) -def test_add_columns_as_features( - time_series: TimeSeries, - columns: list[Column] | Table, - time_series_with_new_columns: TimeSeries, -) -> None: - assert_that_time_series_are_equal(time_series.add_columns_as_features(columns), time_series_with_new_columns) - - -@pytest.mark.parametrize( - ("time_series", "columns", "error_msg"), - [ - ( - TimeSeries( - {"time": [0, 1, 2], "A": [1, 2, 3], "B": [4, 5, 6]}, - target_name="B", - time_name="time", - feature_names=["A"], - ), - [Column("A", [7, 8, 9]), Column("D", [10, 11, 12])], - r"Column 'A' already exists.", - ), - ], - ids=["column_already_exist"], -) -def test_add_columns_raise_duplicate_column_name_if_column_already_exist( - time_series: TimeSeries, - columns: list[Column] | Table, - error_msg: str, -) -> None: - with pytest.raises(DuplicateColumnNameError, match=error_msg): - time_series.add_columns_as_features(columns) - - -@pytest.mark.parametrize( - ("time_series", "columns", "error_msg"), - [ - ( - TimeSeries( - {"time": [0, 1, 2], "A": [1, 2, 3], "B": [4, 5, 6]}, - target_name="B", - time_name="time", - feature_names=["A"], - ), - [Column("C", [5, 7, 8, 9]), Column("D", [4, 10, 11, 12])], - r"Expected a column of size 3 but got column of size 4.", - ), - ], - ids=["columns_are_oversize"], -) -def test_should_raise_column_size_error_if_columns_are_oversize( - time_series: TimeSeries, - columns: list[Column] | Table, - error_msg: str, -) -> None: - with pytest.raises(ColumnSizeError, match=error_msg): - time_series.add_columns_as_features(columns) diff --git a/tests/safeds/data/tabular/containers/_time_series/test_add_row.py b/tests/safeds/data/tabular/containers/_time_series/test_add_row.py deleted file mode 100644 index 8ad2572a7..000000000 --- a/tests/safeds/data/tabular/containers/_time_series/test_add_row.py +++ /dev/null @@ -1,82 +0,0 @@ -import pytest -from safeds.data.tabular.containers import Row, TimeSeries -from safeds.exceptions import UnknownColumnNameError - -from tests.helpers import assert_that_time_series_are_equal - - -@pytest.mark.parametrize( - ("time_series", "row", "expected"), - [ - ( - TimeSeries( - { - "time": [0, 1], - "feature": [0, 1], - "target": [3, 4], - }, - "target", - "time", - ), - Row( - { - "time": 2, - "feature": 2, - "target": 5, - }, - ), - TimeSeries( - { - "time": [0, 1, 2], - "feature": [0, 1, 2], - "target": [3, 4, 5], - }, - "target", - "time", - ), - ), - ], - ids=["add_row"], -) -def test_should_add_row(time_series: TimeSeries, row: Row, expected: TimeSeries) -> None: - assert_that_time_series_are_equal(time_series.add_row(row), expected) - - -@pytest.mark.parametrize( - ("time_series", "row", "error_msg"), - [ - ( - TimeSeries({"time": [], "feature": [], "target": []}, "target", "time", ["feature"]), - Row({"feat": None, "targ": None}), - r"Could not find column\(s\) 'time, feature, target'.", - ), - ], - ids=["columns_missing"], -) -def test_should_raise_an_error_if_row_schema_invalid( - time_series: TimeSeries, - row: Row, - error_msg: str, -) -> None: - with pytest.raises(UnknownColumnNameError, match=error_msg): - time_series.add_row(row) - - -# the original tests throw a warning here aswell( test_add_row in tabular_dataset) -@pytest.mark.parametrize( - ("time_series", "row", "expected_time_series"), - [ - ( - TimeSeries({"time": [], "feature": [], "target": []}, "target", "time"), - Row({"time": 0, "feature": 2, "target": 5}), - TimeSeries({"time": [0], "feature": [2], "target": [5]}, "target", "time"), - ), - ], - ids=["empty_feature_column"], -) -def test_should_add_row_to_empty_table( - time_series: TimeSeries, - row: Row, - expected_time_series: TimeSeries, -) -> None: - assert_that_time_series_are_equal(time_series.add_row(row), expected_time_series) diff --git a/tests/safeds/data/tabular/containers/_time_series/test_add_rows.py b/tests/safeds/data/tabular/containers/_time_series/test_add_rows.py deleted file mode 100644 index 641a2ec05..000000000 --- a/tests/safeds/data/tabular/containers/_time_series/test_add_rows.py +++ /dev/null @@ -1,65 +0,0 @@ -import pytest -from safeds.data.tabular.containers import Row, Table, TimeSeries -from safeds.exceptions import UnknownColumnNameError - -from tests.helpers import assert_that_time_series_are_equal - - -@pytest.mark.parametrize( - ("time_series", "rows", "expected"), - [ - ( - TimeSeries( - { - "time": [0, 1], - "feature": [0, 1], - "target": [4, 5], - }, - "target", - "time", - ), - [ - Row( - { - "time": 2, - "feature": 2, - "target": 6, - }, - ), - Row({"time": 3, "feature": 3, "target": 7}), - ], - TimeSeries( - { - "time": [0, 1, 2, 3], - "feature": [0, 1, 2, 3], - "target": [4, 5, 6, 7], - }, - "target", - "time", - ), - ), - ], - ids=["add_rows"], -) -def test_should_add_rows(time_series: TimeSeries, rows: list[Row], expected: TimeSeries) -> None: - assert_that_time_series_are_equal(time_series.add_rows(rows), expected) - - -@pytest.mark.parametrize( - ("time_series", "rows", "error_msg"), - [ - ( - TimeSeries({"time": [], "feature": [], "target": []}, "target", "time", ["feature"]), - [Row({"feat": None, "targ": None}), Row({"targ": None, "feat": None})], - r"Could not find column\(s\) 'time, feature, target'.", - ), - ], - ids=["columns_missing"], -) -def test_should_raise_an_error_if_rows_schemas_are_invalid( - time_series: TimeSeries, - rows: list[Row] | Table, - error_msg: str, -) -> None: - with pytest.raises(UnknownColumnNameError, match=error_msg): - time_series.add_rows(rows) diff --git a/tests/safeds/data/tabular/containers/_time_series/test_eq.py b/tests/safeds/data/tabular/containers/_time_series/test_eq.py deleted file mode 100644 index 0e39f828f..000000000 --- a/tests/safeds/data/tabular/containers/_time_series/test_eq.py +++ /dev/null @@ -1,101 +0,0 @@ -from typing import Any - -import pytest -from safeds.data.labeled.containers import TabularDataset -from safeds.data.tabular.containers import Row, Table, TimeSeries - - -@pytest.mark.parametrize( - ("table1", "table2", "expected"), - [ - ( - TimeSeries({"a": [], "b": [], "c": []}, "b", "c", ["a"]), - TimeSeries({"a": [], "b": [], "c": []}, "b", "c", ["a"]), - True, - ), - ( - TimeSeries({"a": [1, 2, 3], "b": [4, 5, 6], "c": [7, 8, 9]}, "b", "c", ["a"]), - TimeSeries({"a": [1, 2, 3], "b": [4, 5, 6], "c": [7, 8, 9]}, "b", "c", ["a"]), - True, - ), - ( - TimeSeries({"a": [1, 2, 3], "b": [4, 5, 6], "c": [7, 8, 9], "d": [10, 11, 12]}, "b", "d", ["a"]), - TimeSeries({"a": [1, 2, 3], "b": [4, 5, 6], "c": [7, 8, 9], "d": [10, 11, 12]}, "c", "d", ["a"]), - False, - ), - ( - TimeSeries({"a": [1, 2, 3], "b": [4, 5, 6], "c": [7, 8, 9], "d": [10, 11, 12]}, "b", "c", ["a"]), - TimeSeries({"a": [1, 2, 3], "b": [4, 5, 6], "c": [7, 8, 9], "e": [10, 11, 12]}, "b", "c", ["a"]), - False, - ), - ( - TimeSeries({"a": [1, 2, 3], "b": [4, 5, 6], "c": [7, 8, 9]}, "b", "c", ["a"]), - TimeSeries({"a": [1, 1, 3], "b": [4, 5, 6], "c": [7, 8, 9]}, "b", "c", ["a"]), - False, - ), - ( - TimeSeries({"a": [1, 2, 3], "b": [4, 5, 6], "c": [7, 8, 9]}, "b", "c", ["a"]), - TimeSeries({"a": ["1", "2", "3"], "b": [4, 5, 6], "c": [7, 8, 9]}, "b", "c", ["a"]), - False, - ), - ( - TimeSeries({"a": [1, 2, 3], "b": [4, 5, 6], "c": [7, 8, 9], "d": [10, 11, 12]}, "b", "d", ["a"]), - TimeSeries({"a": [1, 2, 3], "b": [4, 5, 6], "c": [7, 8, 9], "d": [10, 11, 12]}, "b", "d", ["c"]), - False, - ), - ( - TimeSeries({"a": [1, 2, 3], "b": [4, 5, 6], "c": [7, 8, 9], "d": [10, 11, 12]}, "b", "d", ["a"]), - TimeSeries({"a": [1, 2, 3], "b": [4, 5, 6], "c": [7, 8, 9], "d": [10, 11, 12]}, "b", "c", ["a"]), - False, - ), - ], - ids=[ - "rowless table", - "equal tables", - "different target", - "different column names", - "different values", - "different types", - "different features", - "different time", - ], -) -def test_should_return_whether_two_tabular_datasets_are_equal( - table1: TimeSeries, - table2: TimeSeries, - expected: bool, -) -> None: - assert (table1.__eq__(table2)) == expected - - -@pytest.mark.parametrize( - "table1", - [TimeSeries({"a": [], "b": [], "c": []}, "b", "c", ["a"])], - ids=[ - "any", - ], -) -def test_should_return_true_if_objects_are_identical(table1: TimeSeries) -> None: - assert (table1.__eq__(table1)) is True - - -@pytest.mark.parametrize( - ("table", "other"), - [ - (TimeSeries({"a": [1, 2, 3], "b": [4, 5, 6], "c": [7, 8, 9]}, "b", "c", ["a"]), None), - (TimeSeries({"a": [1, 2, 3], "b": [4, 5, 6], "c": [7, 8, 9]}, "b", "c", ["a"]), Row()), - (TimeSeries({"a": [1, 2, 3], "b": [4, 5, 6], "c": [7, 8, 9]}, "b", "c", ["a"]), Table()), - ( - TimeSeries({"a": [1, 2, 3], "b": [4, 5, 6], "c": [7, 8, 9]}, "b", "c", ["a"]), - TabularDataset({"a": [1, 2, 3], "b": [4, 5, 6]}, "b"), - ), - ], - ids=[ - "TimeSeries vs. None", - "TimeSeries vs. Row", - "TimeSeries vs. Table", - "TimeSeries vs. TabularDataset", - ], -) -def test_should_return_not_implemented_if_other_is_not_time_series(table: TimeSeries, other: Any) -> None: - assert (table.__eq__(other)) is NotImplemented diff --git a/tests/safeds/data/tabular/containers/_time_series/test_filter_rows.py b/tests/safeds/data/tabular/containers/_time_series/test_filter_rows.py deleted file mode 100644 index a7d38e257..000000000 --- a/tests/safeds/data/tabular/containers/_time_series/test_filter_rows.py +++ /dev/null @@ -1,124 +0,0 @@ -from collections.abc import Callable - -import pytest -from safeds.data.tabular.containers import Row, TimeSeries - -from tests.helpers import assert_that_time_series_are_equal - - -@pytest.mark.parametrize( - ("time_series", "expected", "query"), - [ - ( - TimeSeries( - { - "time": [0, 1, 2], - "feature_1": [3, 9, 6], - "feature_2": [6, 12, 9], - "target": [1, 3, 2], - }, - "target", - "time", - ), - TimeSeries( - { - "time": [0, 2], - "feature_1": [3, 6], - "feature_2": [6, 9], - "target": [1, 2], - }, - "target", - "time", - ), - lambda row: all(row.get_value(col) < 10 for col in row.column_names), - ), - ( - TimeSeries( - { - "time": [0, 1, 2, 3], - "feature_1": [3, 9, 6, 2], - "feature_2": [6, 12, 9, 3], - "other": [1, 2, 3, 10], - "target": [1, 3, 2, 4], - }, - "target", - "time", - ["feature_1", "feature_2"], - ), - TimeSeries( - { - "time": [ - 0, - 2, - ], - "feature_1": [3, 6], - "feature_2": [6, 9], - "other": [1, 3], - "target": [1, 2], - }, - "target", - "time", - ["feature_1", "feature_2"], - ), - lambda row: all(row.get_value(col) < 10 for col in row.column_names), - ), - ( - TimeSeries( - { - "time": [0, 1, 2], - "feature_1": [3, 9, 6], - "feature_2": [6, 12, 9], - "target": [1, 3, 2], - }, - "target", - "time", - ), - TimeSeries( - { - "time": [0, 1, 2], - "feature_1": [3, 9, 6], - "feature_2": [6, 12, 9], - "target": [1, 3, 2], - }, - "target", - "time", - ), - lambda row: all(row.get_value(col) < 20 for col in row.column_names), - ), - ( - TimeSeries( - { - "time": [0, 1, 2, 3], - "feature_1": [3, 9, 6, 2], - "feature_2": [6, 12, 9, 3], - "other": [1, 2, 3, 10], - "target": [1, 3, 2, 4], - }, - "target", - "time", - ["feature_1", "feature_2"], - ), - TimeSeries( - { - "time": [0, 1, 2, 3], - "feature_1": [3, 9, 6, 2], - "feature_2": [6, 12, 9, 3], - "other": [1, 2, 3, 10], - "target": [1, 3, 2, 4], - }, - "target", - "time", - ["feature_1", "feature_2"], - ), - lambda row: all(row.get_value(col) < 20 for col in row.column_names), - ), - ], - ids=[ - "remove_rows_with_values_greater_9", - "remove_rows_with_values_greater_9_non_feature_columns", - "remove_no_rows", - "remove_no_rows_non_feature_columns", - ], -) -def test_should_filter_rows(time_series: TimeSeries, expected: TimeSeries, query: Callable[[Row], bool]) -> None: - assert_that_time_series_are_equal(time_series.filter_rows(query), expected) diff --git a/tests/safeds/data/tabular/containers/_time_series/test_from_table_to_time_series.py b/tests/safeds/data/tabular/containers/_time_series/test_from_table_to_time_series.py deleted file mode 100644 index b404d4c18..000000000 --- a/tests/safeds/data/tabular/containers/_time_series/test_from_table_to_time_series.py +++ /dev/null @@ -1,187 +0,0 @@ -import pytest -from safeds.data.tabular.containers import Table, TimeSeries -from safeds.exceptions import UnknownColumnNameError - - -@pytest.mark.parametrize( - ("table", "target_name", "time_name", "feature_names", "error", "error_msg"), - [ - ( - Table( - { - "time": [0, 1], - "A": [1, 4], - "B": [2, 5], - "C": [3, 6], - "T": [0, 1], - }, - ), - "T", - "time", - ["A", "B", "C", "D", "E"], - UnknownColumnNameError, - r"Could not find column\(s\) 'D, E'", - ), - ( - Table( - { - "time": [0, 1], - "A": [1, 4], - "B": [2, 5], - "C": [3, 6], - "T": [0, 1], - }, - ), - "D", - "time", - ["A", "B", "C"], - UnknownColumnNameError, - r"Could not find column\(s\) 'D'", - ), - ( - Table( - { - "time": [0, 1], - "A": [1, 4], - "B": [2, 5], - "C": [3, 6], - "T": [0, 1], - }, - ), - "A", - "time", - ["A", "B", "C"], - ValueError, - r"Column 'A' can not be target and feature column.", - ), - ( - Table( - { - "r": [0, 1], - "A": [1, 4], - "B": [2, 5], - "C": [3, 6], - "T": [0, 1], - }, - ), - "T", - "time", - ["A", "B", "C"], - UnknownColumnNameError, - r"Could not find column\(s\) 'time'", - ), - ( - Table( - { - "time": [0, 1], - "A": [1, 4], - "B": [2, 5], - "C": [3, 6], - "T": [0, 1], - }, - ), - "T", - "A", - ["A", "B", "C"], - ValueError, - r"Column 'A' can not be time and feature column.", - ), - ], - ids=[ - "feature_does_not_exist", - "target_does_not_exist", - "target_and_feature_overlap", - "time_does_not_exist", - "time_is_also_feature", - ], -) -def test_should_raise_error( - table: Table, - target_name: str, - time_name: str, - feature_names: list[str] | None, - error: type[Exception], - error_msg: str, -) -> None: - with pytest.raises(error, match=error_msg): - TimeSeries._from_table( - table, - target_name=target_name, - time_name=time_name, - feature_names=feature_names, - ) - - -@pytest.mark.parametrize( - ("table", "target_name", "time_name", "feature_names"), - [ - ( - Table( - { - "time": [0, 1], - "A": [1, 4], - "B": [2, 5], - "C": [3, 6], - "T": [0, 1], - }, - ), - "T", - "time", - ["A", "B", "C"], - ), - ( - Table( - { - "time": [0, 1], - "A": [1, 4], - "B": [2, 5], - "C": [3, 6], - "T": [0, 1], - }, - ), - "T", - "time", - ["A", "C"], - ), - ( - Table( - { - "time": [0, 1], - "A": [1, 4], - "B": [2, 5], - "C": [3, 6], - "T": [0, 1], - }, - ), - "T", - "time", - ["B"], - ), - ], - ids=[ - "create_tabular_dataset", - "tabular_dataset_not_all_columns_are_features", - "tabular_dataset_with_feature_names_as_None", - ], -) -def test_should_create_a_tabular_dataset( - table: Table, - target_name: str, - time_name: str, - feature_names: list[str] | None, -) -> None: - time_series = TimeSeries._from_table( - table, - target_name=target_name, - time_name=time_name, - feature_names=feature_names, - ) - feature_names = ( - feature_names if feature_names is not None else table.remove_columns([target_name, time_name]).column_names - ) - assert isinstance(time_series, TimeSeries) - assert time_series._features.column_names == feature_names - assert time_series._target.name == target_name - assert time_series._features == table.keep_only_columns(feature_names) - assert time_series._target == table.get_column(target_name) - assert time_series.time == table.get_column(time_name) diff --git a/tests/safeds/data/tabular/containers/_time_series/test_hash.py b/tests/safeds/data/tabular/containers/_time_series/test_hash.py deleted file mode 100644 index 94015139b..000000000 --- a/tests/safeds/data/tabular/containers/_time_series/test_hash.py +++ /dev/null @@ -1,64 +0,0 @@ -import pytest -from safeds.data.tabular.containers import TimeSeries - - -@pytest.mark.parametrize( - ("table1", "table2"), - [ - ( - TimeSeries({"a": [], "b": [], "c": []}, "b", "c", ["a"]), - TimeSeries({"a": [], "b": [], "c": []}, "b", "c", ["a"]), - ), - ( - TimeSeries({"a": [1, 2, 3], "b": [4, 5, 6], "c": [7, 8, 9]}, "b", "c", ["a"]), - TimeSeries({"a": [1, 2, 3], "b": [4, 5, 6], "c": [7, 8, 9]}, "b", "c", ["a"]), - ), - ( - TimeSeries({"a": [1, 2, 3], "b": [4, 5, 6], "c": [7, 8, 9]}, "b", "c", ["a"]), - TimeSeries({"a": [1, 1, 3], "b": [4, 5, 6], "c": [7, 8, 9]}, "b", "c", ["a"]), - ), - ], - ids=[ - "rowless table", - "equal tables", - "different values", - ], -) -def test_should_return_same_hash_for_equal_time_series(table1: TimeSeries, table2: TimeSeries) -> None: - assert hash(table1) == hash(table2) - - -@pytest.mark.parametrize( - ("table1", "table2"), - [ - ( - TimeSeries({"a": [1, 2, 3], "b": [4, 5, 6], "c": [7, 8, 9], "d": [10, 11, 12]}, "b", "d", ["a"]), - TimeSeries({"a": [1, 2, 3], "b": [4, 5, 6], "c": [7, 8, 9], "d": [10, 11, 12]}, "c", "d", ["a"]), - ), - ( - TimeSeries({"a": [1, 2, 3], "b": [4, 5, 6], "c": [7, 8, 9], "d": [10, 11, 12]}, "b", "c", ["a"]), - TimeSeries({"a": [1, 2, 3], "b": [4, 5, 6], "c": [7, 8, 9], "e": [10, 11, 12]}, "b", "c", ["a"]), - ), - ( - TimeSeries({"a": [1, 2, 3], "b": [4, 5, 6], "c": [7, 8, 9]}, "b", "c", ["a"]), - TimeSeries({"a": ["1", "2", "3"], "b": [4, 5, 6], "c": [7, 8, 9]}, "b", "c", ["a"]), - ), - ( - TimeSeries({"a": [1, 2, 3], "b": [4, 5, 6], "c": [7, 8, 9], "d": [10, 11, 12]}, "b", "d", ["a"]), - TimeSeries({"a": [1, 2, 3], "b": [4, 5, 6], "c": [7, 8, 9], "d": [10, 11, 12]}, "b", "d", ["c"]), - ), - ( - TimeSeries({"a": [1, 2, 3], "b": [4, 5, 6], "c": [7, 8, 9], "d": [10, 11, 12]}, "b", "d", ["a"]), - TimeSeries({"a": [1, 2, 3], "b": [4, 5, 6], "c": [7, 8, 9], "d": [10, 11, 12]}, "b", "c", ["a"]), - ), - ], - ids=[ - "different target", - "different column names", - "different types", - "different features", - "different time", - ], -) -def test_should_return_different_hash_for_unequal_time_series(table1: TimeSeries, table2: TimeSeries) -> None: - assert hash(table1) != hash(table2) diff --git a/tests/safeds/data/tabular/containers/_time_series/test_init.py b/tests/safeds/data/tabular/containers/_time_series/test_init.py deleted file mode 100644 index c46801cce..000000000 --- a/tests/safeds/data/tabular/containers/_time_series/test_init.py +++ /dev/null @@ -1,161 +0,0 @@ -import pytest -from safeds.data.tabular.containers import Table, TimeSeries -from safeds.exceptions import UnknownColumnNameError - - -@pytest.mark.parametrize( - ("data", "time_name", "target_name", "feature_names", "error", "error_msg"), - [ - ( - { - "time": [0, 1], - "A": [1, 4], - "B": [2, 5], - "C": [3, 6], - "T": [0, 1], - }, - "time", - "T", - ["A", "B", "C", "D", "E"], - UnknownColumnNameError, - r"Could not find column\(s\) 'D, E'", - ), - ( - { - "time": [0, 1], - "A": [1, 4], - "B": [2, 5], - "C": [3, 6], - "T": [0, 1], - }, - "time", - "D", - ["A", "B", "C"], - UnknownColumnNameError, - r"Could not find column\(s\) 'D'", - ), - ( - { - "time": [0, 1], - "A": [1, 4], - "B": [2, 5], - "C": [3, 6], - "T": [0, 1], - }, - "time", - "A", - ["A", "B", "C"], - ValueError, - r"Column 'A' can not be time and feature column.", - ), - ( - { - "time": [0, 1], - "A": [1, 4], - "B": [2, 5], - "C": [3, 6], - "T": [0, 1], - }, - "random", - "B", - ["A"], - UnknownColumnNameError, - r"Could not find column\(s\) 'random'.", - ), - ( - { - "time": [0, 1], - "A": [1, 4], - "B": [2, 5], - "C": [3, 6], - "T": [0, 1], - }, - "time", - "T", - ["A", "B", "C", "time"], - ValueError, - "Column 'time' can not be time and feature column.", - ), - ], - ids=[ - "feature_does_not_exist", - "target_does_not_exist", - "target_and_feature_overlap", - "time_column_does_not_exist", - "time_is_also_feature", - ], -) -def test_should_raise_error( - data: dict[str, list[int]], - time_name: str, - target_name: str, - feature_names: list[str] | None, - error: type[Exception], - error_msg: str, -) -> None: - with pytest.raises(error, match=error_msg): - TimeSeries(data, target_name=target_name, time_name=time_name, feature_names=feature_names) - - -@pytest.mark.parametrize( - ("data", "time_name", "target_name", "feature_names"), - [ - ( - { - "time": [0, 1], - "A": [1, 4], - "B": [2, 5], - "C": [3, 6], - "T": [0, 1], - }, - "time", - "T", - ["A", "B", "C"], - ), - ( - { - "time": [0, 1], - "A": [1, 4], - "B": [2, 5], - "C": [3, 6], - "T": [0, 1], - }, - "time", - "T", - ["A", "C"], - ), - ( - { - "time": [0, 1], - "A": [1, 4], - "B": [2, 5], - "C": [3, 6], - "T": [0, 1], - }, - "time", - "T", - None, - ), - ], - ids=[ - "create_tabular_dataset", - "tabular_dataset_not_all_columns_are_features", - "tabular_dataset_with_feature_names_as_None", - ], -) -def test_should_create_a_time_series( - data: dict[str, list[int]], - time_name: str, - target_name: str, - feature_names: list[str] | None, -) -> None: - time_series = TimeSeries(data, target_name=target_name, time_name=time_name, feature_names=feature_names) - if feature_names is None: - feature_names = [] - - assert isinstance(time_series, TimeSeries) - assert time_series._feature_names == feature_names - assert time_series._target.name == target_name - assert time_series._features == Table(data).keep_only_columns(feature_names) - assert time_series._target == Table(data).get_column(target_name) - assert time_series.time == Table(data).get_column(time_name) diff --git a/tests/safeds/data/tabular/containers/_time_series/test_keep_only_columns.py b/tests/safeds/data/tabular/containers/_time_series/test_keep_only_columns.py deleted file mode 100644 index c6a7ac051..000000000 --- a/tests/safeds/data/tabular/containers/_time_series/test_keep_only_columns.py +++ /dev/null @@ -1,147 +0,0 @@ -import pytest -from safeds.data.tabular.containers import Table, TimeSeries -from safeds.exceptions import IllegalSchemaModificationError - -from tests.helpers import assert_that_time_series_are_equal - - -@pytest.mark.parametrize( - ("table", "column_names", "expected"), - [ - ( - TimeSeries._from_table( - Table( - { - "time": [0, 1, 2], - "feat1": [1, 2, 3], - "feat2": [4, 5, 6], - "target": [7, 8, 9], - }, - ), - "target", - "time", - ), - ["feat1", "target", "time"], - TimeSeries._from_table( - Table( - { - "time": [0, 1, 2], - "feat1": [1, 2, 3], - "target": [7, 8, 9], - }, - ), - "target", - "time", - ), - ), - ( - TimeSeries._from_table( - Table( - { - "time": [0, 1, 2], - "feat1": [1, 2, 3], - "feat2": [4, 5, 6], - "other": [3, 4, 5], - "target": [7, 8, 9], - }, - ), - "target", - "time", - ), - ["feat1", "other", "target", "time"], - TimeSeries._from_table( - Table( - { - "time": [0, 1, 2], - "feat1": [1, 2, 3], - "other": [3, 4, 5], - "target": [7, 8, 9], - }, - ), - "target", - "time", - ), - ), - ( - TimeSeries._from_table( - Table( - { - "time": [0, 1, 2], - "feat1": [1, 2, 3], - "feat2": [4, 5, 6], - "other": [3, 4, 5], - "target": [7, 8, 9], - }, - ), - "target", - "time", - ), - ["feat1", "target", "time"], - TimeSeries._from_table( - Table( - { - "time": [0, 1, 2], - "feat1": [1, 2, 3], - "target": [7, 8, 9], - }, - ), - "target", - "time", - ), - ), - ], - ids=["keep_feature_and_target_column", "keep_non_feature_column", "don't_keep_non_feature_column"], -) -def test_should_return_table(table: TimeSeries, column_names: list[str], expected: TimeSeries) -> None: - new_table = table.keep_only_columns(column_names) - assert_that_time_series_are_equal(new_table, expected) - - -@pytest.mark.parametrize( - ("table", "column_names", "error_msg"), - [ - ( - TimeSeries._from_table( - Table( - { - "time": [0, 1, 2], - "feat1": [1, 2, 3], - "feat2": [4, 5, 6], - "other": [3, 5, 7], - "target": [7, 8, 9], - }, - ), - "target", - "time", - ["feat1", "feat2"], - ), - ["feat1", "feat2"], - r"Illegal schema modification: Must keep the target column.", - ), - ( - TimeSeries._from_table( - Table( - { - "time": [0, 1, 2], - "feat1": [1, 2, 3], - "feat2": [4, 5, 6], - "other": [3, 5, 7], - "target": [7, 8, 9], - }, - ), - "target", - "time", - ["feat1", "feat2"], - ), - ["target", "feat1", "other"], - r"Illegal schema modification: Must keep the time column.", - ), - ], - ids=["table_remove_target", "table_remove_time"], -) -def test_should_raise_illegal_schema_modification(table: TimeSeries, column_names: list[str], error_msg: str) -> None: - with pytest.raises( - IllegalSchemaModificationError, - match=error_msg, - ): - table.keep_only_columns(column_names) diff --git a/tests/safeds/data/tabular/containers/_time_series/test_plot_compare_time_series.py b/tests/safeds/data/tabular/containers/_time_series/test_plot_compare_time_series.py deleted file mode 100644 index 4d114cd55..000000000 --- a/tests/safeds/data/tabular/containers/_time_series/test_plot_compare_time_series.py +++ /dev/null @@ -1,113 +0,0 @@ -import pytest -from safeds.data.tabular.containers import TimeSeries -from safeds.exceptions import NonNumericColumnError -from syrupy import SnapshotAssertion - - -def create_time_series_list() -> list[TimeSeries]: - table1 = TimeSeries( - { - "time": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], - "feature_1": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], - "target": [9, 10, 11, 12, 13, 14, 15, 16, 17, 18], - }, - target_name="target", - time_name="time", - feature_names=None, - ) - table2 = TimeSeries( - { - "time": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], - "feature_1": [4, 5, 6, 7, 8, 9, 10, 11, 12, 13], - "target": [4, 5, 6, 7, 8, 9, 10, 11, 12, 13], - }, - target_name="target", - time_name="time", - feature_names=None, - ) - return [table1, table2] - - -def create_invalid_time_series_list() -> list[TimeSeries]: - table1 = TimeSeries( - { - "time": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], - "feature_1": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], - "target": ["9", 10, 11, 12, 13, 14, 15, 16, 17, 18], - }, - target_name="target", - time_name="time", - feature_names=None, - ) - table2 = TimeSeries( - { - "time": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], - "feature_1": [4, 5, 6, 7, 8, 9, 10, 11, 12, 13], - "target": ["4", 5, 6, 7, 8, 9, 10, 11, 12, 13], - }, - target_name="target", - time_name="time", - feature_names=None, - ) - return [table1, table2] - - -def test_legit_compare(snapshot_png_image: SnapshotAssertion) -> None: - table = TimeSeries( - { - "time": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], - "feature_1": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], - "target": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], - }, - target_name="target", - time_name="time", - feature_names=None, - ) - plot = table.plot_compare_time_series(create_time_series_list()) - assert plot == snapshot_png_image - - -def test_should_raise_if_column_contains_non_numerical_values_x() -> None: - table = TimeSeries( - { - "time": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], - "feature_1": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], - "target": ["1", "2", "3", "4", "5", "6", "7", "8", "9", "10"], - }, - target_name="target", - time_name="time", - feature_names=None, - ) - with pytest.raises( - NonNumericColumnError, - match=( - r"Tried to do a numerical operation on one or multiple non-numerical columns: \nThe time series plotted" - r" column" - r" contains" - r" non-numerical columns." - ), - ): - table.plot_compare_time_series(create_time_series_list()) - - -def test_with_non_valid_list() -> None: - table = TimeSeries( - { - "time": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], - "feature_1": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], - "target": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], - }, - target_name="target", - time_name="time", - feature_names=None, - ) - with pytest.raises( - NonNumericColumnError, - match=( - r"Tried to do a numerical operation on one or multiple non-numerical columns: \nThe time series plotted" - r" column" - r" contains" - r" non-numerical columns." - ), - ): - table.plot_compare_time_series(create_invalid_time_series_list()) diff --git a/tests/safeds/data/tabular/containers/_time_series/test_plot_lag.py b/tests/safeds/data/tabular/containers/_time_series/test_plot_lag.py deleted file mode 100644 index 29c69a2e3..000000000 --- a/tests/safeds/data/tabular/containers/_time_series/test_plot_lag.py +++ /dev/null @@ -1,41 +0,0 @@ -import pytest -from safeds.data.tabular.containers import TimeSeries -from safeds.exceptions import NonNumericColumnError -from syrupy import SnapshotAssertion - - -def test_should_return_table(snapshot_png_image: SnapshotAssertion) -> None: - table = TimeSeries( - { - "time": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], - "feature_1": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], - "target": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], - }, - target_name="target", - time_name="time", - feature_names=None, - ) - lag_plot = table.plot_lagplot(lag=1) - assert lag_plot == snapshot_png_image - - -def test_should_raise_if_column_contains_non_numerical_values() -> None: - table = TimeSeries( - { - "time": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], - "feature_1": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], - "target": ["1", "2", "3", "4", "5", "6", "7", "8", "9", "10"], - }, - target_name="target", - time_name="time", - feature_names=None, - ) - with pytest.raises( - NonNumericColumnError, - match=( - r"Tried to do a numerical operation on one or multiple non-numerical columns: \nThis time series target" - r" contains" - r" non-numerical columns." - ), - ): - table.plot_lagplot(2) diff --git a/tests/safeds/data/tabular/containers/_time_series/test_plot_lineplot.py b/tests/safeds/data/tabular/containers/_time_series/test_plot_lineplot.py deleted file mode 100644 index ff3ad83c0..000000000 --- a/tests/safeds/data/tabular/containers/_time_series/test_plot_lineplot.py +++ /dev/null @@ -1,265 +0,0 @@ -import pytest -from safeds.data.tabular.containers import TimeSeries -from safeds.exceptions import NonNumericColumnError, UnknownColumnNameError -from syrupy import SnapshotAssertion - - -def test_should_return_table(snapshot_png_image: SnapshotAssertion) -> None: - table = TimeSeries( - { - "time": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], - "feature_1": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], - "target": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], - }, - target_name="target", - time_name="time", - feature_names=None, - ) - plot = table.plot_lineplot() - assert plot == snapshot_png_image - - -def test_should_raise_if_column_contains_non_numerical_values_x() -> None: - table = TimeSeries( - { - "time": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], - "feature_1": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], - "target": ["1", "2", "3", "4", "5", "6", "7", "8", "9", "10"], - }, - target_name="target", - time_name="time", - feature_names=None, - ) - with pytest.raises( - NonNumericColumnError, - match=( - r"Tried to do a numerical operation on one or multiple non-numerical columns: \nThe time series plotted" - r" column" - r" contains" - r" non-numerical columns." - ), - ): - table.plot_lineplot(x_column_name="feature_1") - - -def test_should_return_table_both(snapshot_png_image: SnapshotAssertion) -> None: - table = TimeSeries( - { - "time": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], - "feature_1": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], - "target": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], - }, - target_name="target", - time_name="time", - feature_names=None, - ) - plot = table.plot_lineplot(x_column_name="feature_1", y_column_name="target") - assert plot == snapshot_png_image - - -def test_should_plot_feature_y(snapshot_png_image: SnapshotAssertion) -> None: - table = TimeSeries( - { - "time": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], - "feature_1": [10, 9, 8, 7, 6, 5, 4, 3, 2, 1], - "target": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], - }, - target_name="target", - time_name="time", - feature_names=None, - ) - plot = table.plot_lineplot(y_column_name="feature_1") - assert plot == snapshot_png_image - - -def test_should_plot_feature_x(snapshot_png_image: SnapshotAssertion) -> None: - table = TimeSeries( - { - "time": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], - "feature_1": [10, 9, 8, 7, 6, 5, 4, 3, 2, 1], - "target": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], - }, - target_name="target", - time_name="time", - feature_names=None, - ) - plot = table.plot_lineplot(x_column_name="feature_1") - assert plot == snapshot_png_image - - -def test_should_plot_feature(snapshot_png_image: SnapshotAssertion) -> None: - table = TimeSeries( - { - "time": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], - "feature_1": [2, 4, 6, 8, 10, 12, 14, 16, 18, 20], - "target": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], - }, - target_name="target", - time_name="time", - feature_names=None, - ) - plot = table.plot_lineplot(x_column_name="feature_1") - assert plot == snapshot_png_image - - -def test_should_raise_if_column_contains_non_numerical_values() -> None: - table = TimeSeries( - { - "time": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], - "feature_1": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], - "target": ["1", "2", "3", "4", "5", "6", "7", "8", "9", "10"], - }, - target_name="target", - time_name="time", - feature_names=None, - ) - with pytest.raises( - NonNumericColumnError, - match=( - r"Tried to do a numerical operation on one or multiple non-numerical columns: \nThe time series plotted" - r" column" - r" contains" - r" non-numerical columns." - ), - ): - table.plot_lineplot(x_column_name="target") - - -@pytest.mark.parametrize( - ("time_series", "name", "error", "error_msg"), - [ - ( - TimeSeries( - { - "time": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], - "feature_1": ["1", "2", "3", "4", "5", "6", "7", "8", "9", "10"], - "target": ["1", "2", "3", "4", "5", "6", "7", "8", "9", "10"], - }, - target_name="target", - time_name="time", - feature_names=None, - ), - "feature_1", - NonNumericColumnError, - r"Tried to do a numerical operation on one or multiple non-numerical columns: \nThe time series plotted" - r" column" - r" contains" - r" non-numerical columns.", - ), - ( - TimeSeries( - { - "time": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], - "feature_1": ["1", "2", "3", "4", "5", "6", "7", "8", "9", "10"], - "target": ["1", "2", "3", "4", "5", "6", "7", "8", "9", "10"], - }, - target_name="target", - time_name="time", - feature_names=None, - ), - "feature_3", - UnknownColumnNameError, - r"Could not find column\(s\) 'feature_3'.", - ), - ], - ids=["feature_not_numerical", "feature_does_not_exist"], -) -def test_should_raise_error_optional_parameter( - time_series: TimeSeries, - name: str, - error: type[Exception], - error_msg: str, -) -> None: - with pytest.raises( - error, - match=error_msg, - ): - time_series.plot_lineplot(x_column_name=name) - - -@pytest.mark.parametrize( - ("time_series", "name", "error", "error_msg"), - [ - ( - TimeSeries( - { - "time": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], - "feature_1": ["1", "2", "3", "4", "5", "6", "7", "8", "9", "10"], - "target": ["1", "2", "3", "4", "5", "6", "7", "8", "9", "10"], - }, - target_name="target", - time_name="time", - feature_names=None, - ), - "feature_1", - NonNumericColumnError, - r"Tried to do a numerical operation on one or multiple non-numerical columns: \nThe time series plotted" - r" column" - r" contains" - r" non-numerical columns.", - ), - ( - TimeSeries( - { - "time": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], - "feature_1": ["1", "2", "3", "4", "5", "6", "7", "8", "9", "10"], - "target": ["1", "2", "3", "4", "5", "6", "7", "8", "9", "10"], - }, - target_name="target", - time_name="time", - feature_names=None, - ), - "feature_3", - UnknownColumnNameError, - r"Could not find column\(s\) 'feature_3'.", - ), - ], - ids=["feature_not_numerical", "feature_does_not_exist"], -) -def test_should_raise_error_optional_parameter_y( - time_series: TimeSeries, - name: str, - error: type[Exception], - error_msg: str, -) -> None: - with pytest.raises( - error, - match=error_msg, - ): - time_series.plot_lineplot(y_column_name=name) - - -def test_should_raise_if_column_does_not_exist_x() -> None: - table = TimeSeries( - { - "time": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], - "feature_1": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], - "target": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], - }, - target_name="target", - time_name="time", - feature_names=None, - ) - with pytest.raises( - UnknownColumnNameError, - match=r"Could not find column\(s\) '2'.", - ): - table.plot_lineplot(x_column_name="target", y_column_name="2") - - -def test_should_raise_if_column_does_not_exist_y() -> None: - table = TimeSeries( - { - "time": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], - "feature_1": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], - "target": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], - }, - target_name="target", - time_name="time", - feature_names=None, - ) - with pytest.raises( - UnknownColumnNameError, - match=r"Could not find column\(s\) '2'.", - ): - table.plot_lineplot(x_column_name="2", y_column_name="target") diff --git a/tests/safeds/data/tabular/containers/_time_series/test_plot_scatterplot.py b/tests/safeds/data/tabular/containers/_time_series/test_plot_scatterplot.py deleted file mode 100644 index 739e9d135..000000000 --- a/tests/safeds/data/tabular/containers/_time_series/test_plot_scatterplot.py +++ /dev/null @@ -1,265 +0,0 @@ -import pytest -from safeds.data.tabular.containers import TimeSeries -from safeds.exceptions import NonNumericColumnError, UnknownColumnNameError -from syrupy import SnapshotAssertion - - -def test_should_return_table(snapshot_png_image: SnapshotAssertion) -> None: - table = TimeSeries( - { - "time": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], - "feature_1": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], - "target": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], - }, - target_name="target", - time_name="time", - feature_names=None, - ) - plot = table.plot_scatterplot() - assert plot == snapshot_png_image - - -def test_should_plot_feature(snapshot_png_image: SnapshotAssertion) -> None: - table = TimeSeries( - { - "time": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], - "feature_1": [10, 9, 8, 7, 6, 5, 4, 3, 2, 1], - "target": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], - }, - target_name="target", - time_name="time", - feature_names=None, - ) - plot = table.plot_scatterplot(y_column_name="feature_1") - assert plot == snapshot_png_image - - -def test_should_plot_feature_only_x(snapshot_png_image: SnapshotAssertion) -> None: - table = TimeSeries( - { - "time": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], - "feature_1": [10, 9, 8, 7, 6, 5, 4, 3, 2, 1], - "target": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], - }, - target_name="target", - time_name="time", - feature_names=None, - ) - plot = table.plot_scatterplot(x_column_name="feature_1") - assert plot == snapshot_png_image - - -def test_should_plot_feature_only_y_optional(snapshot_png_image: SnapshotAssertion) -> None: - table = TimeSeries( - { - "time": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], - "feature_1": [10, 9, 8, 7, 6, 5, 4, 3, 2, 1], - "target": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], - }, - target_name="target", - time_name="time", - feature_names=None, - ) - plot = table.plot_scatterplot(y_column_name="feature_1") - assert plot == snapshot_png_image - - -def test_should_plot_feature_both_set(snapshot_png_image: SnapshotAssertion) -> None: - table = TimeSeries( - { - "time": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], - "feature_1": [1, 2, 1, 2, 1, 2, 1, 2, 1, 1], - "target": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], - }, - target_name="target", - time_name="time", - feature_names=None, - ) - plot = table.plot_scatterplot(x_column_name="feature_1", y_column_name="target") - assert plot == snapshot_png_image - - -def test_should_raise_if_column_contains_non_numerical_values() -> None: - table = TimeSeries( - { - "time": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], - "feature_1": ["1", "2", "3", "4", "5", "6", "7", "8", "9", "10"], - "target": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], - }, - target_name="target", - time_name="time", - feature_names=None, - ) - with pytest.raises( - NonNumericColumnError, - match=( - r"Tried to do a numerical operation on one or multiple non-numerical columns: \nThe time series plotted" - r" column" - r" contains" - r" non-numerical columns." - ), - ): - table.plot_scatterplot(y_column_name="feature_1") - - -def test_should_raise_if_column_contains_non_numerical_values_x() -> None: - table = TimeSeries( - { - "time": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], - "feature_1": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], - "target": ["1", "2", "3", "4", "5", "6", "7", "8", "9", "10"], - }, - target_name="target", - time_name="time", - feature_names=None, - ) - with pytest.raises( - NonNumericColumnError, - match=( - r"Tried to do a numerical operation on one or multiple non-numerical columns: \nThe time series plotted" - r" column" - r" contains" - r" non-numerical columns." - ), - ): - table.plot_scatterplot(x_column_name="feature_1") - - -@pytest.mark.parametrize( - ("time_series", "name", "error", "error_msg"), - [ - ( - TimeSeries( - { - "time": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], - "feature_1": ["1", "2", "3", "4", "5", "6", "7", "8", "9", "10"], - "target": ["1", "2", "3", "4", "5", "6", "7", "8", "9", "10"], - }, - target_name="target", - time_name="time", - feature_names=None, - ), - "feature_1", - NonNumericColumnError, - r"Tried to do a numerical operation on one or multiple non-numerical columns: \nThe time series plotted" - r" column" - r" contains" - r" non-numerical columns.", - ), - ( - TimeSeries( - { - "time": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], - "feature_1": ["1", "2", "3", "4", "5", "6", "7", "8", "9", "10"], - "target": ["1", "2", "3", "4", "5", "6", "7", "8", "9", "10"], - }, - target_name="target", - time_name="time", - feature_names=None, - ), - "feature_3", - UnknownColumnNameError, - r"Could not find column\(s\) 'feature_3'.", - ), - ], - ids=["feature_not_numerical", "feature_does_not_exist"], -) -def test_should_raise_error_optional_parameter( - time_series: TimeSeries, - name: str, - error: type[Exception], - error_msg: str, -) -> None: - with pytest.raises( - error, - match=error_msg, - ): - time_series.plot_scatterplot(x_column_name=name) - - -@pytest.mark.parametrize( - ("time_series", "name", "error", "error_msg"), - [ - ( - TimeSeries( - { - "time": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], - "feature_1": ["1", "2", "3", "4", "5", "6", "7", "8", "9", "10"], - "target": ["1", "2", "3", "4", "5", "6", "7", "8", "9", "10"], - }, - target_name="target", - time_name="time", - feature_names=None, - ), - "feature_1", - NonNumericColumnError, - r"Tried to do a numerical operation on one or multiple non-numerical columns: \nThe time series plotted" - r" column" - r" contains" - r" non-numerical columns.", - ), - ( - TimeSeries( - { - "time": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], - "feature_1": ["1", "2", "3", "4", "5", "6", "7", "8", "9", "10"], - "target": ["1", "2", "3", "4", "5", "6", "7", "8", "9", "10"], - }, - target_name="target", - time_name="time", - feature_names=None, - ), - "feature_3", - UnknownColumnNameError, - r"Could not find column\(s\) 'feature_3'.", - ), - ], - ids=["feature_not_numerical", "feature_does_not_exist"], -) -def test_should_raise_error_optional_parameter_y( - time_series: TimeSeries, - name: str, - error: type[Exception], - error_msg: str, -) -> None: - with pytest.raises( - error, - match=error_msg, - ): - time_series.plot_scatterplot(y_column_name=name) - - -def test_should_raise_if_column_does_not_exist_y() -> None: - table = TimeSeries( - { - "time": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], - "feature_1": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], - "target": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], - }, - target_name="target", - time_name="time", - feature_names=None, - ) - with pytest.raises( - UnknownColumnNameError, - match=r"Could not find column\(s\) '2'.", - ): - table.plot_scatterplot(x_column_name="target", y_column_name="2") - - -def test_should_raise_if_column_does_not_exist_x() -> None: - table = TimeSeries( - { - "time": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], - "feature_1": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], - "target": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], - }, - target_name="target", - time_name="time", - feature_names=None, - ) - with pytest.raises( - UnknownColumnNameError, - match=r"Could not find column\(s\) '2'.", - ): - table.plot_scatterplot(x_column_name="2") diff --git a/tests/safeds/data/tabular/containers/_time_series/test_remove_columns.py b/tests/safeds/data/tabular/containers/_time_series/test_remove_columns.py deleted file mode 100644 index 5a51e70e1..000000000 --- a/tests/safeds/data/tabular/containers/_time_series/test_remove_columns.py +++ /dev/null @@ -1,205 +0,0 @@ -import pytest -from safeds.data.tabular.containers import Table, TimeSeries -from safeds.exceptions import ColumnIsTargetError, ColumnIsTimeError - -from tests.helpers import assert_that_time_series_are_equal - - -@pytest.mark.parametrize( - ("table", "columns", "expected"), - [ - ( - TimeSeries._from_table( - Table( - { - "time": [0, 1, 2], - "feat_1": [1, 2, 3], - "feat_2": [4, 5, 6], - "non_feat_1": [2, 4, 6], - "non_feat_2": [3, 6, 9], - "target": [7, 8, 9], - }, - ), - "target", - "time", - ["feat_1", "feat_2"], - ), - ["feat_2"], - TimeSeries._from_table( - Table({ - "time": [0, 1, 2], - "feat_1": [1, 2, 3], - "non_feat_1": [2, 4, 6], - "non_feat_2": [3, 6, 9], - "target": [7, 8, 9], - }), - "target", - "time", - ["feat_1"], - ), - ), - ( - TimeSeries._from_table( - Table( - { - "time": [0, 1, 2], - "feat_1": [1, 2, 3], - "feat_2": [4, 5, 6], - "non_feat_1": [2, 4, 6], - "non_feat_2": [3, 6, 9], - "target": [7, 8, 9], - }, - ), - "target", - "time", - ["feat_1", "feat_2"], - ), - ["non_feat_2"], - TimeSeries._from_table( - Table({ - "time": [0, 1, 2], - "feat_1": [1, 2, 3], - "feat_2": [4, 5, 6], - "non_feat_1": [2, 4, 6], - "target": [7, 8, 9], - }), - "target", - "time", - ["feat_1", "feat_2"], - ), - ), - ( - TimeSeries._from_table( - Table( - { - "time": [0, 1, 2], - "feat_1": [1, 2, 3], - "feat_2": [4, 5, 6], - "non_feat_1": [2, 4, 6], - "non_feat_2": [3, 6, 9], - "target": [7, 8, 9], - }, - ), - "target", - "time", - ["feat_1", "feat_2"], - ), - ["non_feat_1", "non_feat_2"], - TimeSeries._from_table( - Table({"time": [0, 1, 2], "feat_1": [1, 2, 3], "feat_2": [4, 5, 6], "target": [7, 8, 9]}), - "target", - "time", - ["feat_1", "feat_2"], - ), - ), - ( - TimeSeries._from_table( - Table( - { - "time": [0, 1, 2], - "feat_1": [1, 2, 3], - "feat_2": [4, 5, 6], - "non_feat_1": [2, 4, 6], - "non_feat_2": [3, 6, 9], - "target": [7, 8, 9], - }, - ), - "target", - "time", - ["feat_1", "feat_2"], - ), - ["feat_2", "non_feat_2"], - TimeSeries._from_table( - Table({"time": [0, 1, 2], "feat_1": [1, 2, 3], "non_feat_1": [2, 4, 6], "target": [7, 8, 9]}), - "target", - "time", - ["feat_1"], - ), - ), - ( - TimeSeries._from_table( - Table( - { - "time": [0, 1, 2], - "feat_1": [1, 2, 3], - "non_feat_1": [2, 4, 6], - "target": [7, 8, 9], - }, - ), - "target", - "time", - ["feat_1"], - ), - [], - TimeSeries._from_table( - Table({"time": [0, 1, 2], "feat_1": [1, 2, 3], "non_feat_1": [2, 4, 6], "target": [7, 8, 9]}), - "target", - "time", - ["feat_1"], - ), - ), - ], - ids=[ - "remove_feature", - "remove_non_feature", - "remove_all_non_features", - "remove_some_feat_and_some_non_feat", - "remove_nothing", - ], -) -def test_should_remove_columns(table: TimeSeries, columns: list[str], expected: TimeSeries) -> None: - new_table = table.remove_columns(columns) - assert_that_time_series_are_equal(new_table, expected) - - -@pytest.mark.parametrize( - ("table", "columns", "error", "error_msg"), - [ - ( - TimeSeries._from_table( - Table({"time": [0, 1, 2], "feat": [1, 2, 3], "non_feat": [1, 2, 3], "target": [4, 5, 6]}), - "target", - "time", - ["feat"], - ), - ["target"], - ColumnIsTargetError, - r'Illegal schema modification: Column "target" is the target column and cannot be removed.', - ), - ( - TimeSeries._from_table( - Table({"time": [0, 1, 2], "feat": [1, 2, 3], "non_feat": [1, 2, 3], "target": [4, 5, 6]}), - "target", - "time", - ["feat"], - ), - ["non_feat", "target"], - ColumnIsTargetError, - r'Illegal schema modification: Column "target" is the target column and cannot be removed.', - ), - ( - TimeSeries._from_table( - Table({"time": [0, 1, 2], "feat": [1, 2, 3], "non_feat": [1, 2, 3], "target": [4, 5, 6]}), - "target", - "time", - ["feat"], - ), - ["time"], - ColumnIsTimeError, - r'Illegal schema modification: Column "time" is the time column and cannot be removed.', - ), - ], - ids=[ - "remove_only_target", - "remove_non_feat_and_target", - "remove_time_column", - ], -) -def test_should_raise_in_remove_columns( - table: TimeSeries, - columns: list[str], - error: type[Exception], - error_msg: str, -) -> None: - with pytest.raises(error, match=error_msg): - table.remove_columns(columns) diff --git a/tests/safeds/data/tabular/containers/_time_series/test_remove_columns_with_missing_values.py b/tests/safeds/data/tabular/containers/_time_series/test_remove_columns_with_missing_values.py deleted file mode 100644 index 319e27c5f..000000000 --- a/tests/safeds/data/tabular/containers/_time_series/test_remove_columns_with_missing_values.py +++ /dev/null @@ -1,189 +0,0 @@ -import pytest -from safeds.data.tabular.containers import TimeSeries -from safeds.exceptions import ColumnIsTargetError, ColumnIsTimeError - -from tests.helpers import assert_that_time_series_are_equal - - -@pytest.mark.parametrize( - ("table", "expected"), - [ - ( - TimeSeries( - { - "time": [0, 1, 2], - "feature_complete": [0, 1, 2], - "feature_incomplete": [3, None, 5], - "non_feature_complete": [7, 8, 9], - "target": [3, 4, 5], - }, - "target", - "time", - ["feature_complete", "feature_incomplete"], - ), - TimeSeries( - { - "time": [0, 1, 2], - "feature_complete": [0, 1, 2], - "non_feature_complete": [7, 8, 9], - "target": [3, 4, 5], - }, - "target", - "time", - ["feature_complete"], - ), - ), - ( - TimeSeries( - { - "time": [0, 1, 2], - "feature_complete": [0, 1, 2], - "non_feature_complete": [7, 8, 9], - "non_feature_incomplete": [3, None, 5], - "target": [3, 4, 5], - }, - "target", - "time", - ["feature_complete"], - ), - TimeSeries( - { - "time": [0, 1, 2], - "feature_complete": [0, 1, 2], - "non_feature_complete": [7, 8, 9], - "target": [3, 4, 5], - }, - "target", - "time", - ["feature_complete"], - ), - ), - ( - TimeSeries( - { - "time": [0, 1, 2], - "feature_complete": [0, 1, 2], - "non_feature_complete": [7, 8, 9], - "target": [3, 4, 5], - }, - "target", - "time", - ["feature_complete"], - ), - TimeSeries( - { - "time": [0, 1, 2], - "feature_complete": [0, 1, 2], - "non_feature_complete": [7, 8, 9], - "target": [3, 4, 5], - }, - "target", - "time", - ["feature_complete"], - ), - ), - ], - ids=["incomplete_feature", "incomplete_non_feature", "all_complete"], -) -def test_should_remove_columns_with_non_numerical_values(table: TimeSeries, expected: TimeSeries) -> None: - new_table = table.remove_columns_with_missing_values() - assert_that_time_series_are_equal(new_table, expected) - - -@pytest.mark.parametrize( - ("table", "error", "error_msg"), - [ - ( - TimeSeries( - { - "time": [0, 1, 2], - "feature": [0, 1, 2], - "non_feature": [1, 2, 3], - "target": [3, None, 5], - }, - "target", - "time", - ["feature"], - ), - ColumnIsTargetError, - 'Illegal schema modification: Column "target" is the target column and cannot be removed.', - ), - ( - TimeSeries( - { - "time": [0, 1, 2], - "feature": [0, None, 2], - "non_feature": [1, 2, 3], - "target": [None, 4, 5], - }, - "target", - "time", - ["feature"], - ), - ColumnIsTargetError, - 'Illegal schema modification: Column "target" is the target column and cannot be removed.', - ), - ( - TimeSeries( - { - "time": [0, None, 2], - "feature": [0, 1, 2], - "non_feature": [1, 2, 3], - "target": [3, 4, 5], - }, - "target", - "time", - ["feature"], - ), - ColumnIsTimeError, - 'Illegal schema modification: Column "time" is the time column and cannot be removed.', - ), - ( - TimeSeries( - { - "time": [0, 1, 2], - "feature": [0, 1, 2], - "non_feature": [1, 2, 3], - "target": [3, 4, None], - }, - "target", - "time", - ["feature"], - ), - ColumnIsTargetError, - 'Illegal schema modification: Column "target" is the target column and cannot be removed.', - ), - ( - TimeSeries( - { - "time": [0, 1, 2], - "feature": [0, None, 2], - "non_feature": [1, None, 3], - "target": [3, None, 5], - }, - "target", - "time", - ["feature"], - ), - ColumnIsTargetError, - 'Illegal schema modification: Column "target" is the target column and cannot be removed.', - ), - ], - ids=[ - "only_target_incomplete", - "also_feature_incomplete", - "time_is_incomplete", - "also_non_feature_incomplete", - "all_incomplete", - ], -) -def test_should_raise_in_remove_columns_with_missing_values( - table: TimeSeries, - error: type[Exception], - error_msg: str, -) -> None: - with pytest.raises( - error, - match=error_msg, - ): - table.remove_columns_with_missing_values() diff --git a/tests/safeds/data/tabular/containers/_time_series/test_remove_columns_with_non_numerical_values.py b/tests/safeds/data/tabular/containers/_time_series/test_remove_columns_with_non_numerical_values.py deleted file mode 100644 index 03d6e8572..000000000 --- a/tests/safeds/data/tabular/containers/_time_series/test_remove_columns_with_non_numerical_values.py +++ /dev/null @@ -1,186 +0,0 @@ -import pytest -from safeds.data.tabular.containers import TimeSeries -from safeds.exceptions import ColumnIsTargetError, ColumnIsTimeError - -from tests.helpers import assert_that_time_series_are_equal - - -@pytest.mark.parametrize( - ("table", "expected"), - [ - ( - TimeSeries( - { - "time": [0, 1, 2], - "feature_numerical": [0, 1, 2], - "feature_non_numerical": ["a", "b", "c"], - "non_feature_numerical": [7, 8, 9], - "target": [3, 4, 5], - }, - "target", - "time", - ["feature_numerical", "feature_non_numerical"], - ), - TimeSeries( - { - "time": [0, 1, 2], - "feature_numerical": [0, 1, 2], - "non_feature_numerical": [7, 8, 9], - "target": [3, 4, 5], - }, - "target", - "time", - ["feature_numerical"], - ), - ), - ( - TimeSeries( - { - "time": [0, 1, 2], - "feature_numerical": [0, 1, 2], - "non_feature_numerical": [7, 8, 9], - "non_feature_non_numerical": ["a", "b", "c"], - "target": [3, 4, 5], - }, - "target", - "time", - ["feature_numerical"], - ), - TimeSeries( - { - "time": [0, 1, 2], - "feature_numerical": [0, 1, 2], - "non_feature_numerical": [7, 8, 9], - "target": [3, 4, 5], - }, - "target", - "time", - ["feature_numerical"], - ), - ), - ( - TimeSeries( - { - "time": [0, 1, 2], - "feature_numerical": [0, 1, 2], - "non_feature_numerical": [7, 8, 9], - "target": [3, 4, 5], - }, - "target", - "time", - ["feature_numerical"], - ), - TimeSeries( - { - "time": [0, 1, 2], - "feature_numerical": [0, 1, 2], - "non_feature_numerical": [7, 8, 9], - "target": [3, 4, 5], - }, - "target", - "time", - ["feature_numerical"], - ), - ), - ], - ids=["non_numerical_feature", "non_numerical_non_feature", "all_numerical"], -) -def test_should_remove_columns_with_non_numerical_values(table: TimeSeries, expected: TimeSeries) -> None: - new_table = table.remove_columns_with_non_numerical_values() - assert_that_time_series_are_equal(new_table, expected) - - -@pytest.mark.parametrize( - ("table", "error", "error_msg"), - [ - ( - TimeSeries( - { - "time": [0, 1, 2], - "feature": [0, 1, 2], - "non_feature": [1, 2, 3], - "target": ["a", "b", "c"], - }, - "target", - "time", - ["feature"], - ), - ColumnIsTargetError, - r'Illegal schema modification: Column "target" is the target column and cannot be removed.', - ), - ( - TimeSeries( - { - "time": [0, 1, 2], - "feature": [0, "x", 2], - "non_feature": [1, 2, 3], - "target": ["a", "b", "c"], - }, - "target", - "time", - ["feature"], - ), - ColumnIsTargetError, - r'Illegal schema modification: Column "target" is the target column and cannot be removed.', - ), - ( - TimeSeries( - { - "time": [0, 1, 2], - "feature": [0, 1, 2], - "non_feature": [1, "x", 3], - "target": ["a", "b", "c"], - }, - "target", - "time", - ["feature"], - ), - ColumnIsTargetError, - r'Illegal schema modification: Column "target" is the target column and cannot be removed.', - ), - ( - TimeSeries( - { - "time": ["!", "x", "2"], - "feature": [0, 1, 2], - "non_feature": [1, "x", 3], - "target": [1, 2, 3], - }, - "target", - "time", - ["feature"], - ), - ColumnIsTimeError, - r'Illegal schema modification: Column "time" is the time column and cannot be removed.', - ), - ( - TimeSeries( - { - "time": [0, 1, 2], - "feature": [0, "x", 2], - "non_feature": [1, "x", 3], - "target": ["a", "b", "c"], - }, - "target", - "time", - ["feature"], - ), - ColumnIsTargetError, - r'Illegal schema modification: Column "target" is the target column and cannot be removed.', - ), - ], - ids=[ - "only_target_non_numerical", - "also_feature_non_numerical", - "also_non_feature_non_numerical", - "time_non_numerical", - "all_non_numerical", - ], -) -def test_should_raise_in_remove_columns_with_non_numerical_values( - table: TimeSeries, - error: type[Exception], - error_msg: str, -) -> None: - with pytest.raises(error, match=error_msg): - table.remove_columns_with_non_numerical_values() diff --git a/tests/safeds/data/tabular/containers/_time_series/test_remove_duplicate_rows.py b/tests/safeds/data/tabular/containers/_time_series/test_remove_duplicate_rows.py deleted file mode 100644 index a4e0a3426..000000000 --- a/tests/safeds/data/tabular/containers/_time_series/test_remove_duplicate_rows.py +++ /dev/null @@ -1,55 +0,0 @@ -import pytest -from safeds.data.tabular.containers import TimeSeries - -from tests.helpers import assert_that_time_series_are_equal - - -@pytest.mark.parametrize( - ("table", "expected"), - [ - ( - TimeSeries( - { - "time": [0, 0, 1], - "feature": [0, 0, 1], - "target": [2, 2, 3], - }, - "target", - "time", - ), - TimeSeries( - { - "time": [0, 1], - "feature": [0, 1], - "target": [2, 3], - }, - "target", - "time", - ), - ), - ( - TimeSeries( - { - "time": [0, 0, 1], - "feature": [0, 1, 2], - "target": [2, 2, 3], - }, - "target", - "time", - ), - TimeSeries( - { - "time": [0, 0, 1], - "feature": [0, 1, 2], - "target": [2, 2, 3], - }, - "target", - "time", - ), - ), - ], - ids=["with_duplicate_rows", "without_duplicate_rows"], -) -def test_should_remove_duplicate_rows(table: TimeSeries, expected: TimeSeries) -> None: - new_table = table.remove_duplicate_rows() - assert_that_time_series_are_equal(new_table, expected) diff --git a/tests/safeds/data/tabular/containers/_time_series/test_remove_rows_with_missing_values.py b/tests/safeds/data/tabular/containers/_time_series/test_remove_rows_with_missing_values.py deleted file mode 100644 index 078151ac9..000000000 --- a/tests/safeds/data/tabular/containers/_time_series/test_remove_rows_with_missing_values.py +++ /dev/null @@ -1,55 +0,0 @@ -import pytest -from safeds.data.tabular.containers import TimeSeries - -from tests.helpers import assert_that_time_series_are_equal - - -@pytest.mark.parametrize( - ("table", "expected"), - [ - ( - TimeSeries( - { - "time": [0, 1, 2], - "feature": [0.0, None, 2.0], - "target": [3.0, 4.0, 5.0], - }, - "target", - "time", - ), - TimeSeries( - { - "time": [0, 2], - "feature": [0.0, 2.0], - "target": [3.0, 5.0], - }, - "target", - "time", - ), - ), - ( - TimeSeries( - { - "time": [0, 1, 2], - "feature": [0.0, 1.0, 2.0], - "target": [3.0, 4.0, 5.0], - }, - "target", - "time", - ), - TimeSeries( - { - "time": [0, 1, 2], - "feature": [0.0, 1.0, 2.0], - "target": [3.0, 4.0, 5.0], - }, - "target", - "time", - ), - ), - ], - ids=["with_missing_values", "without_missing_values"], -) -def test_should_remove_rows_with_missing_values(table: TimeSeries, expected: TimeSeries) -> None: - new_table = table.remove_rows_with_missing_values() - assert_that_time_series_are_equal(new_table, expected) diff --git a/tests/safeds/data/tabular/containers/_time_series/test_remove_rows_with_outliers.py b/tests/safeds/data/tabular/containers/_time_series/test_remove_rows_with_outliers.py deleted file mode 100644 index 8d206c65c..000000000 --- a/tests/safeds/data/tabular/containers/_time_series/test_remove_rows_with_outliers.py +++ /dev/null @@ -1,55 +0,0 @@ -import pytest -from safeds.data.tabular.containers import TimeSeries - -from tests.helpers import assert_that_time_series_are_equal - - -@pytest.mark.parametrize( - ("table", "expected"), - [ - ( - TimeSeries( - { - "time": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], - "feature": [1.0, 11.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], - "target": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], - }, - "target", - "time", - ), - TimeSeries( - { - "time": [0, 2, 3, 4, 5, 6, 7, 8, 9], - "feature": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], - "target": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], - }, - "target", - "time", - ), - ), - ( - TimeSeries( - { - "time": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], - "feature": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], - "target": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], - }, - "target", - "time", - ), - TimeSeries( - { - "time": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], - "feature": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], - "target": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], - }, - "target", - "time", - ), - ), - ], - ids=["with_outliers", "no_outliers"], -) -def test_should_remove_rows_with_outliers(table: TimeSeries, expected: TimeSeries) -> None: - new_table = table.remove_rows_with_outliers() - assert_that_time_series_are_equal(new_table, expected) diff --git a/tests/safeds/data/tabular/containers/_time_series/test_rename_column.py b/tests/safeds/data/tabular/containers/_time_series/test_rename_column.py deleted file mode 100644 index a0214b4ab..000000000 --- a/tests/safeds/data/tabular/containers/_time_series/test_rename_column.py +++ /dev/null @@ -1,124 +0,0 @@ -import pytest -from safeds.data.tabular.containers import TimeSeries - -from tests.helpers import assert_that_time_series_are_equal - - -@pytest.mark.parametrize( - ("original_table", "old_column_name", "new_column_name", "result_table"), - [ - ( - TimeSeries( - { - "time": [0, 1, 2], - "feature_old": [0, 1, 2], - "no_feature": [2, 3, 4], - "target": [3, 4, 5], - }, - target_name="target", - time_name="time", - feature_names=["feature_old"], - ), - "feature_old", - "feature_new", - TimeSeries( - { - "time": [0, 1, 2], - "feature_new": [0, 1, 2], - "no_feature": [2, 3, 4], - "target": [3, 4, 5], - }, - target_name="target", - time_name="time", - feature_names=["feature_new"], - ), - ), - ( - TimeSeries( - { - "time": [0, 1, 2], - "feature": [0, 1, 2], - "no_feature": [2, 3, 4], - "target_old": [3, 4, 5], - }, - target_name="target_old", - time_name="time", - feature_names=["feature"], - ), - "target_old", - "target_new", - TimeSeries( - { - "time": [0, 1, 2], - "feature": [0, 1, 2], - "no_feature": [2, 3, 4], - "target_new": [3, 4, 5], - }, - target_name="target_new", - time_name="time", - feature_names=["feature"], - ), - ), - ( - TimeSeries( - { - "time": [0, 1, 2], - "feature": [0, 1, 2], - "no_feature_old": [2, 3, 4], - "target": [3, 4, 5], - }, - target_name="target", - time_name="time", - feature_names=["feature"], - ), - "no_feature_old", - "no_feature_new", - TimeSeries( - { - "time": [0, 1, 2], - "feature": [0, 1, 2], - "no_feature_new": [2, 3, 4], - "target": [3, 4, 5], - }, - target_name="target", - time_name="time", - feature_names=["feature"], - ), - ), - ( - TimeSeries( - { - "time": [0, 1, 2], - "feature": [0, 1, 2], - "no_feature_old": [2, 3, 4], - "target": [3, 4, 5], - }, - target_name="target", - time_name="time", - feature_names=["feature"], - ), - "time", - "new_time", - TimeSeries( - { - "new_time": [0, 1, 2], - "feature": [0, 1, 2], - "no_feature_old": [2, 3, 4], - "target": [3, 4, 5], - }, - target_name="target", - time_name="new_time", - feature_names=["feature"], - ), - ), - ], - ids=["rename_feature_column", "rename_target_column", "rename_non_feature_column", "rename_time_column"], -) -def test_should_rename_column( - original_table: TimeSeries, - old_column_name: str, - new_column_name: str, - result_table: TimeSeries, -) -> None: - new_table = original_table.rename_column(old_column_name, new_column_name) - assert_that_time_series_are_equal(new_table, result_table) diff --git a/tests/safeds/data/tabular/containers/_time_series/test_replace_column.py b/tests/safeds/data/tabular/containers/_time_series/test_replace_column.py deleted file mode 100644 index 818f6580a..000000000 --- a/tests/safeds/data/tabular/containers/_time_series/test_replace_column.py +++ /dev/null @@ -1,248 +0,0 @@ -import pytest -from safeds.data.tabular.containers import Column, TimeSeries -from safeds.exceptions import IllegalSchemaModificationError - -from tests.helpers import assert_that_time_series_are_equal - - -@pytest.mark.parametrize( - ("original_table", "new_columns", "column_name_to_be_replaced", "result_table"), - [ - ( - TimeSeries( - { - "time": [0, 1, 2], - "feature_old": [0, 1, 2], - "no_feature_old": [2, 3, 4], - "target_old": [3, 4, 5], - }, - "target_old", - "time", - ["feature_old"], - ), - [Column("feature_new", [2, 1, 0])], - "feature_old", - TimeSeries( - { - "time": [0, 1, 2], - "feature_new": [2, 1, 0], - "no_feature_old": [2, 3, 4], - "target_old": [3, 4, 5], - }, - "target_old", - "time", - ["feature_new"], - ), - ), - ( - TimeSeries( - { - "time": [0, 1, 2], - "feature_old": [0, 1, 2], - "no_feature_old": [2, 3, 4], - "target_old": [3, 4, 5], - }, - "target_old", - "time", - ["feature_old"], - ), - [Column("feature_new_a", [2, 1, 0]), Column("feature_new_b", [4, 2, 0])], - "feature_old", - TimeSeries( - { - "time": [0, 1, 2], - "feature_new_a": [2, 1, 0], - "feature_new_b": [4, 2, 0], - "no_feature_old": [2, 3, 4], - "target_old": [3, 4, 5], - }, - "target_old", - "time", - ["feature_new_a", "feature_new_b"], - ), - ), - ( - TimeSeries( - { - "time": [0, 1, 2], - "feature_old": [0, 1, 2], - "no_feature_old": [2, 3, 4], - "target_old": [3, 4, 5], - }, - "target_old", - "time", - ["feature_old"], - ), - [Column("no_feature_new", [2, 1, 0])], - "no_feature_old", - TimeSeries( - { - "time": [0, 1, 2], - "feature_old": [0, 1, 2], - "no_feature_new": [2, 1, 0], - "target_old": [3, 4, 5], - }, - "target_old", - "time", - ["feature_old"], - ), - ), - ( - TimeSeries( - { - "time": [0, 1, 2], - "feature_old": [0, 1, 2], - "no_feature_old": [2, 3, 4], - "target_old": [3, 4, 5], - }, - "target_old", - "time", - ["feature_old"], - ), - [Column("no_feature_new_a", [2, 1, 0]), Column("no_feature_new_b", [4, 2, 0])], - "no_feature_old", - TimeSeries( - { - "time": [0, 1, 2], - "feature_old": [0, 1, 2], - "no_feature_new_a": [2, 1, 0], - "no_feature_new_b": [4, 2, 0], - "target_old": [3, 4, 5], - }, - "target_old", - "time", - ["feature_old"], - ), - ), - ( - TimeSeries( - { - "time": [0, 1, 2], - "feature_old": [0, 1, 2], - "no_feature_old": [2, 3, 4], - "target_old": [3, 4, 5], - }, - "target_old", - "time", - ["feature_old"], - ), - [Column("target_new", [2, 1, 0])], - "target_old", - TimeSeries( - { - "time": [0, 1, 2], - "feature_old": [0, 1, 2], - "no_feature_old": [2, 3, 4], - "target_new": [2, 1, 0], - }, - "target_new", - "time", - ["feature_old"], - ), - ), - ( - TimeSeries( - { - "time_old": [0, 1, 2], - "feature_old": [0, 1, 2], - "no_feature_old": [2, 3, 4], - "target_old": [3, 4, 5], - }, - "target_old", - "time_old", - ["feature_old"], - ), - [Column("time_new", [1, 2, 3])], - "time_old", - TimeSeries( - { - "time_new": [1, 2, 3], - "feature_old": [0, 1, 2], - "no_feature_old": [2, 3, 4], - "target_old": [3, 4, 5], - }, - "target_old", - "time_new", - ["feature_old"], - ), - ), - ], - ids=[ - "replace_feature_column_with_one", - "replace_feature_column_with_multiple", - "replace_non_feature_column_with_one", - "replace_non_feature_column_with_multiple", - "replace_target_column", - "replace_time_column", - ], -) -def test_should_replace_column( - original_table: TimeSeries, - new_columns: list[Column], - column_name_to_be_replaced: str, - result_table: TimeSeries, -) -> None: - new_table = original_table.replace_column(column_name_to_be_replaced, new_columns) - assert_that_time_series_are_equal(new_table, result_table) - - -@pytest.mark.parametrize( - ("original_table", "new_columns", "column_name_to_be_replaced", "error"), - [ - ( - TimeSeries( - { - "time": [0, 1, 2], - "feature_old": [0, 1, 2], - "target_old": [3, 4, 5], - }, - "target_old", - "time", - ), - [], - "target_old", - 'Target column "target_old" can only be replaced by exactly one new column.', - ), - ( - TimeSeries( - { - "time": [0, 1, 2], - "feature_old": [0, 1, 2], - "target_old": [3, 4, 5], - }, - "target_old", - "time", - ), - [Column("target_new_a", [2, 1, 0]), Column("target_new_b"), [4, 2, 0]], - "target_old", - 'Target column "target_old" can only be replaced by exactly one new column.', - ), - ( - TimeSeries( - { - "time": [0, 1, 2], - "feature_old": [0, 1, 2], - "target_old": [3, 4, 5], - }, - "target_old", - "time", - ), - [Column("target_new_a", [2, 1, 0]), Column("target_new_b"), [4, 2, 0]], - "time", - 'Time column "time" can only be replaced by exactly one new column.', - ), - ], - ids=["zero_columns", "multiple_columns", "time_column"], -) -# here should be tested with time column as well but the test is weird to be extended -def test_should_throw_illegal_schema_modification( - original_table: TimeSeries, - new_columns: list[Column], - column_name_to_be_replaced: str, - error: str, -) -> None: - with pytest.raises( - IllegalSchemaModificationError, - match=error, - ): - original_table.replace_column(column_name_to_be_replaced, new_columns) diff --git a/tests/safeds/data/tabular/containers/_time_series/test_slice_rows.py b/tests/safeds/data/tabular/containers/_time_series/test_slice_rows.py deleted file mode 100644 index e8788e52d..000000000 --- a/tests/safeds/data/tabular/containers/_time_series/test_slice_rows.py +++ /dev/null @@ -1,58 +0,0 @@ -import pytest -from _pytest.python_api import raises -from safeds.data.tabular.containers import TimeSeries -from safeds.exceptions import IndexOutOfBoundsError - -from tests.helpers import assert_that_time_series_are_equal - - -@pytest.mark.parametrize( - ("table", "test_table", "second_test_table"), - [ - ( - TimeSeries( - data={"time": [0, 1, 2], "feature": [1, 2, 1], "non_feature": [0, 2, 4], "target": [1, 2, 4]}, - target_name="target", - time_name="time", - feature_names=["non_feature"], - ), - TimeSeries( - data={"time": [0, 1], "feature": [1, 2], "non_feature": [0, 2], "target": [1, 2]}, - target_name="target", - time_name="time", - feature_names=["non_feature"], - ), - TimeSeries( - {"time": [0, 2], "feature": [1, 1], "non_feature": [0, 4], "target": [1, 4]}, - target_name="target", - time_name="time", - feature_names=["non_feature"], - ), - ), - ], - ids=["Table with three rows"], -) -def test_should_slice_rows(table: TimeSeries, test_table: TimeSeries, second_test_table: TimeSeries) -> None: - new_table = table.slice_rows(0, 2, 1) - second_new_table = table.slice_rows(0, 3, 2) - third_new_table = table.slice_rows() - assert_that_time_series_are_equal(new_table, test_table) - assert_that_time_series_are_equal(second_new_table, second_test_table) - assert_that_time_series_are_equal(third_new_table, table) - - -@pytest.mark.parametrize( - ("start", "end", "step", "error_message"), - [ - (3, 2, 1, r"There is no element in the range \[3, 2\]"), - (4, 0, 1, r"There is no element in the range \[4, 0\]"), - (0, 4, 1, r"There is no element at index '4'"), - (-4, 0, 1, r"There is no element at index '-4'"), - (0, -4, 1, r"There is no element in the range \[0, -4\]"), - ], -) -def test_should_raise_if_index_out_of_bounds(start: int, end: int, step: int, error_message: str) -> None: - table = TimeSeries({"time": [0, 1, 2], "feature": [1, 2, 1], "target": [1, 2, 4]}, "target", "time") - - with raises(IndexOutOfBoundsError, match=error_message): - table.slice_rows(start, end, step) diff --git a/tests/safeds/data/tabular/containers/_time_series/test_sort_columns.py b/tests/safeds/data/tabular/containers/_time_series/test_sort_columns.py deleted file mode 100644 index 679816069..000000000 --- a/tests/safeds/data/tabular/containers/_time_series/test_sort_columns.py +++ /dev/null @@ -1,62 +0,0 @@ -from collections.abc import Callable - -import pytest -from safeds.data.tabular.containers import Column, TimeSeries - - -@pytest.mark.parametrize( - ("query", "col1", "col2", "col3", "col4", "col5"), - [ - (None, 0, 1, 2, 3, 4), - ( - lambda col1, col2: (col1.name < col2.name) - (col1.name > col2.name), - 4, - 3, - 2, - 1, - 0, - ), - ], - ids=["no query", "with query"], -) -def test_should_return_sorted_table( - query: Callable[[Column, Column], int], - col1: int, - col2: int, - col3: int, - col4: int, - col5: int, -) -> None: - columns = [ - Column("col1", ["A", "B", "C", "A", "D"]), - Column("col2", ["Test1", "Test1", "Test3", "Test1", "Test4"]), - Column("col3", [1, 2, 3, 4, 5]), - Column("col4", [2, 3, 1, 4, 6]), - Column("time", [0, 1, 2, 3, 4]), - ] - table1 = TimeSeries( - { - "col2": ["Test1", "Test1", "Test3", "Test1", "Test4"], - "col3": [1, 2, 3, 4, 5], - "col4": [2, 3, 1, 4, 6], - "col1": ["A", "B", "C", "A", "D"], - "time": [0, 1, 2, 3, 4], - }, - target_name="col1", - time_name="time", - feature_names=["col4", "col3"], - ) - if query is not None: - table_sorted = table1.sort_columns(query) - else: - table_sorted = table1.sort_columns() - table_sorted_columns = table_sorted.to_columns() - assert table_sorted.schema == table1.schema - assert table_sorted_columns[0] == columns[col1] - assert table_sorted_columns[1] == columns[col2] - assert table_sorted_columns[2] == columns[col3] - assert table_sorted_columns[3] == columns[col4] - assert table_sorted_columns[4] == columns[col5] - assert table_sorted._features == table1._features - assert table_sorted._target == table1._target - assert table_sorted.time == table1.time diff --git a/tests/safeds/data/tabular/containers/_time_series/test_split_rows.py b/tests/safeds/data/tabular/containers/_time_series/test_split_rows.py deleted file mode 100644 index faee9fc23..000000000 --- a/tests/safeds/data/tabular/containers/_time_series/test_split_rows.py +++ /dev/null @@ -1,69 +0,0 @@ -import pandas as pd -import pytest -from safeds.data.tabular.containers import Table, TimeSeries -from safeds.data.tabular.typing import Integer, Nothing, Schema - - -@pytest.mark.parametrize( - ("table", "result_train_table", "result_test_table", "percentage_in_first"), - [ - ( - TimeSeries({"col1": [1, 2, 1], "col2": [1, 2, 4]}, time_name="col1", target_name="col2"), - TimeSeries({"col1": [1, 2], "col2": [1, 2]}, time_name="col1", target_name="col2"), - TimeSeries({"col1": [1], "col2": [4]}, time_name="col1", target_name="col2"), - 2 / 3, - ), - ( - TimeSeries({"col1": [1, 2, 1], "col2": [1, 2, 4]}, time_name="col1", target_name="col2"), - TimeSeries._from_table( - Table._from_pandas_dataframe(pd.DataFrame(), Schema({"col1": Nothing(), "col2": Nothing()})), - time_name="col1", - target_name="col2", - ), - TimeSeries({"col1": [1, 2, 1], "col2": [1, 2, 4]}, time_name="col1", target_name="col2"), - 0, - ), - ( - TimeSeries({"col1": [1, 2, 1], "col2": [1, 2, 4]}, time_name="col1", target_name="col2"), - TimeSeries({"col1": [1, 2, 1], "col2": [1, 2, 4]}, time_name="col1", target_name="col2"), - TimeSeries._from_table( - Table._from_pandas_dataframe(pd.DataFrame(), Schema({"col1": Integer(), "col2": Integer()})), - time_name="col1", - target_name="col2", - ), - 1, - ), - ], - ids=["2/3%", "0%", "100%"], -) -def test_should_split_table( - table: TimeSeries, - result_train_table: TimeSeries, - result_test_table: TimeSeries, - percentage_in_first: int, -) -> None: - train_table, test_table = table.split_rows(percentage_in_first) - assert result_test_table == test_table - assert result_train_table.schema == train_table.schema - assert result_train_table == train_table - - -@pytest.mark.parametrize( - "percentage_in_first", - [ - -1.0, - 2.0, - ], - ids=["-100%", "200%"], -) -def test_should_raise_if_value_not_in_range(percentage_in_first: float) -> None: - table = Table({"col1": [1, 2, 1], "col2": [1, 2, 4]}) - - with pytest.raises(ValueError, match=r"The given percentage is not between 0 and 1"): - table.split_rows(percentage_in_first) - - -def test_should_split_empty_table() -> None: - t1, t2 = Table().split_rows(0.4) - assert t1.number_of_rows == 0 - assert t2.number_of_rows == 0 diff --git a/tests/safeds/data/tabular/containers/_time_series/test_time.py b/tests/safeds/data/tabular/containers/_time_series/test_time.py deleted file mode 100644 index f1a65de0f..000000000 --- a/tests/safeds/data/tabular/containers/_time_series/test_time.py +++ /dev/null @@ -1,42 +0,0 @@ -import pytest -from safeds.data.tabular.containers import Column, TimeSeries - - -@pytest.mark.parametrize( - ("time_series", "time"), - [ - ( - TimeSeries( - { - "time": [0, 1], - "A": [1, 4], - "B": [2, 5], - "C": [3, 6], - "T": [0, 1], - }, - target_name="T", - time_name="time", - feature_names=["A", "B", "C"], - ), - Column("time", [0, 1]), - ), - ( - TimeSeries( - { - "time": [1, 2], - "A": [1, 4], - "B": [2, 5], - "C": [3, 6], - "T": [0, 1], - }, - target_name="T", - time_name="time", - feature_names=["A", "C"], - ), - Column("time", [1, 2]), - ), - ], - ids=["only_target_and_features", "target_features_and_other"], -) -def test_should_return_features(time_series: TimeSeries, time: Column) -> None: - assert time_series.time == time diff --git a/tests/safeds/data/tabular/containers/_time_series/test_time_target.py b/tests/safeds/data/tabular/containers/_time_series/test_time_target.py deleted file mode 100644 index 31dc2b899..000000000 --- a/tests/safeds/data/tabular/containers/_time_series/test_time_target.py +++ /dev/null @@ -1,30 +0,0 @@ -import pytest -from safeds.data.tabular.containers import Column, TimeSeries - -# test - - -@pytest.mark.parametrize( - ("time_series", "target_column", "time_column"), - [ - ( - TimeSeries( - { - "time": [0, 1], - "A": [1, 4], - "B": [2, 5], - "C": [3, 6], - "T": [0, 1], - }, - target_name="T", - time_name="time", - ), - Column("T", [0, 1]), - Column("time", [0, 1]), - ), - ], - ids=["target"], -) -def test_should_return_target(time_series: TimeSeries, target_column: Column, time_column: Column) -> None: - assert time_series.target == target_column - assert time_series.time == time_column diff --git a/tests/safeds/data/tabular/containers/_time_series/test_timeseries_from_csv_file.py b/tests/safeds/data/tabular/containers/_time_series/test_timeseries_from_csv_file.py deleted file mode 100644 index 0c26e21ae..000000000 --- a/tests/safeds/data/tabular/containers/_time_series/test_timeseries_from_csv_file.py +++ /dev/null @@ -1,62 +0,0 @@ -from pathlib import Path - -import pytest -from safeds.data.tabular.containers import TimeSeries -from safeds.exceptions import WrongFileExtensionError - -from tests.helpers import resolve_resource_path - - -@pytest.mark.parametrize( - ("path", "expected"), - [ - ( - "table.csv", - TimeSeries({"A": ["❔"], "B": [2]}, time_name="A", target_name="B"), - ), - (Path("table.csv"), TimeSeries({"A": ["❔"], "B": [2]}, time_name="A", target_name="B")), - ], - ids=["by String", "by path"], -) -def test_should_create_table_from_csv_file(path: str | Path, expected: TimeSeries) -> None: - table = TimeSeries.timeseries_from_csv_file(resolve_resource_path(path), time_name="A", target_name="B") - assert table.schema == expected.schema - assert table == expected - - -@pytest.mark.parametrize( - ("path", "expected_error_message"), - [ - ("test_table_from_csv_file_invalid.csv", r"test_table_from_csv_file_invalid.csv\" does not exist"), - (Path("test_table_from_csv_file_invalid.csv"), r"test_table_from_csv_file_invalid.csv\" does not exist"), - ], - ids=["by String", "by path"], -) -def test_should_raise_error_if_file_not_found(path: str | Path, expected_error_message: str) -> None: - with pytest.raises(FileNotFoundError, match=expected_error_message): - TimeSeries.timeseries_from_csv_file(resolve_resource_path(path), time_name="A", target_name="B") - - -@pytest.mark.parametrize( - ("path", "expected_error_message"), - [ - ( - "invalid_file_extension.file_extension", - ( - r"invalid_file_extension.file_extension has a wrong file extension. Please provide a file with the" - r" following extension\(s\): .csv" - ), - ), - ( - Path("invalid_file_extension.file_extension"), - ( - r"invalid_file_extension.file_extension has a wrong file extension. Please provide a file with the" - r" following extension\(s\): .csv" - ), - ), - ], - ids=["by String", "by path"], -) -def test_should_raise_error_if_wrong_file_extension(path: str | Path, expected_error_message: str) -> None: - with pytest.raises(WrongFileExtensionError, match=expected_error_message): - TimeSeries.timeseries_from_csv_file(resolve_resource_path(path), time_name="A", target_name="B") diff --git a/tests/safeds/data/tabular/containers/_time_series/test_transform_column.py b/tests/safeds/data/tabular/containers/_time_series/test_transform_column.py deleted file mode 100644 index 176533570..000000000 --- a/tests/safeds/data/tabular/containers/_time_series/test_transform_column.py +++ /dev/null @@ -1,116 +0,0 @@ -import pytest -from safeds.data.tabular.containers import TimeSeries -from safeds.exceptions import UnknownColumnNameError - -from tests.helpers import assert_that_time_series_are_equal - - -# here is the time column transformable -@pytest.mark.parametrize( - ("table", "column_name", "table_transformed"), - [ - ( - TimeSeries( - {"time": [0, 1, 2], "feature_a": [1, 2, 3], "feature_b": [4, 5, 6], "target": [1, 2, 3]}, - "target", - "time", - ), - "feature_a", - TimeSeries( - {"time": [0, 1, 2], "feature_a": [2, 4, 6], "feature_b": [4, 5, 6], "target": [1, 2, 3]}, - "target", - "time", - ), - ), - ( - TimeSeries( - {"time": [0, 1, 2], "feature_a": [1, 2, 3], "feature_b": [4, 5, 6], "target": [1, 2, 3]}, - "target", - "time", - ), - "target", - TimeSeries( - {"time": [0, 1, 2], "feature_a": [1, 2, 3], "feature_b": [4, 5, 6], "target": [2, 4, 6]}, - "target", - "time", - ), - ), - ( - TimeSeries( - {"time": [0, 1, 2], "feature_a": [1, 2, 3], "b": [4, 5, 6], "target": [1, 2, 3]}, - target_name="target", - time_name="time", - feature_names=["feature_a"], - ), - "b", - TimeSeries( - {"time": [0, 1, 2], "feature_a": [1, 2, 3], "b": [8, 10, 12], "target": [1, 2, 3]}, - target_name="target", - time_name="time", - feature_names=["feature_a"], - ), - ), - ( - TimeSeries( - {"time": [0, 1, 2], "feature_a": [1, 2, 3], "b": [4, 5, 6], "target": [1, 2, 3]}, - target_name="target", - time_name="time", - feature_names=["feature_a"], - ), - "time", - TimeSeries( - {"time": [0, 2, 4], "feature_a": [1, 2, 3], "b": [4, 5, 6], "target": [1, 2, 3]}, - target_name="target", - time_name="time", - feature_names=["feature_a"], - ), - ), - ], - ids=[ - "transform_feature_column", - "transform_target_column", - "transform_column_that_is_neither", - "transform_time_col", - ], -) -def test_should_transform_column(table: TimeSeries, column_name: str, table_transformed: TimeSeries) -> None: - result = table.transform_column(column_name, lambda row: row.get_value(column_name) * 2) - assert_that_time_series_are_equal(result, table_transformed) - - -@pytest.mark.parametrize( - ("table", "column_name"), - [ - ( - TimeSeries( - { - "time": [0, 1, 2], - "A": [1, 2, 3], - "B": [4, 5, 6], - "C": ["a", "b", "c"], - }, - "C", - "time", - ), - "D", - ), - ( - TimeSeries( - { - "time": [0, 1, 2], - "A": [1, 2, 3], - "B": [4, 5, 6], - "C": ["a", "b", "c"], - }, - target_name="C", - time_name="time", - feature_names=["A"], - ), - "D", - ), - ], - ids=["has_only_features_and_target", "has_columns_that_are_neither"], -) -def test_should_raise_if_column_not_found(table: TimeSeries, column_name: str) -> None: - with pytest.raises(UnknownColumnNameError, match=rf"Could not find column\(s\) '{column_name}'"): - table.transform_column(column_name, lambda row: row.get_value("A") * 2) diff --git a/tests/safeds/ml/classical/regression/test_arima_model.py b/tests/safeds/ml/classical/regression/test_arima_model.py index b7a04e899..5a317e07a 100644 --- a/tests/safeds/ml/classical/regression/test_arima_model.py +++ b/tests/safeds/ml/classical/regression/test_arima_model.py @@ -1,13 +1,13 @@ from typing import Any import pytest -from safeds.data.tabular.containers import Table, TimeSeries +from safeds.data.labeled.containers import TimeSeriesDataset +from safeds.data.tabular.containers import Table from safeds.exceptions import ( DatasetMissesDataError, MissingValuesColumnError, ModelNotFittedError, NonNumericColumnError, - NonTimeSeriesError, ) from safeds.ml.classical.regression import ArimaModelRegressor, LassoRegressor @@ -17,30 +17,27 @@ def test_arima_model() -> None: # Create a DataFrame _inflation_path = "_datas/US_Inflation_rates.csv" - time_series = TimeSeries.timeseries_from_csv_file( + time_series = Table.from_csv_file( path=resolve_resource_path(_inflation_path), - target_name="value", - time_name="date", ) train_ts, test_ts = time_series.split_rows(0.8) model = ArimaModelRegressor() - trained_model = model.fit(train_ts) - predicted_ts = trained_model.predict(test_ts) - predicted_ts.plot_compare_time_series([test_ts]) + trained_model = model.fit(train_ts.to_time_series_dataset("value", "date")) + trained_model.predict(test_ts.to_time_series_dataset("value", "date")) # suggest it ran through assert True -def create_test_data() -> TimeSeries: - return TimeSeries( +def create_test_data() -> TimeSeriesDataset: + return TimeSeriesDataset( {"time": [1, 2, 3, 4, 5, 6, 7, 8, 9], "value": [1, 2, 3, 4, 5, 6, 7, 8, 9]}, time_name="time", target_name="value", ) -def create_test_data_with_feature() -> TimeSeries: - return TimeSeries( +def create_test_data_with_feature() -> TimeSeriesDataset: + return TimeSeriesDataset( { "time": [1, 2, 3, 4, 5, 6, 7, 8, 9], "value": [1, 2, 3, 4, 5, 6, 7, 8, 9], @@ -92,7 +89,7 @@ def test_should_succeed_on_valid_data_plot() -> None: "feat2": [3, 6], "target": ["0", 1], }, - ).time_columns(target_name="target", feature_names=["feat1", "feat2"], time_name="id"), + ).to_time_series_dataset(target_name="target", time_name="id"), NonNumericColumnError, r"Tried to do a numerical operation on one or multiple non-numerical columns: \ntarget", ), @@ -104,7 +101,7 @@ def test_should_succeed_on_valid_data_plot() -> None: "feat2": [3, 6], "target": [None, 1], }, - ).time_columns(target_name="target", feature_names=["feat1", "feat2"], time_name="id"), + ).to_time_series_dataset(target_name="target", time_name="id"), MissingValuesColumnError, r"Tried to do an operation on one or multiple columns containing missing values: \ntarget\nYou can use the Imputer to replace the missing values based on different strategies.\nIf you want toremove the missing values entirely you can use the method `TimeSeries.remove_rows_with_missing_values`.", ), @@ -116,7 +113,7 @@ def test_should_succeed_on_valid_data_plot() -> None: "feat2": [], "target": [], }, - ).time_columns(target_name="target", feature_names=["feat1", "feat2"], time_name="id"), + ).to_time_series_dataset(target_name="target", time_name="id"), DatasetMissesDataError, r"Dataset contains no rows", ), @@ -124,7 +121,7 @@ def test_should_succeed_on_valid_data_plot() -> None: ids=["non-numerical data", "missing values in data", "no rows in data"], ) def test_should_raise_on_invalid_data( - invalid_data: TimeSeries, + invalid_data: TimeSeriesDataset, expected_error: Any, expected_error_msg: str, ) -> None: @@ -133,25 +130,6 @@ def test_should_raise_on_invalid_data( model.fit(invalid_data) -@pytest.mark.parametrize( - "table", - [ - Table( - { - "a": [1.0, 0.0, 0.0, 0.0], - "b": [0.0, 1.0, 1.0, 0.0], - "c": [0.0, 0.0, 0.0, 1.0], - }, - ), - ], - ids=["table"], -) -def test_should_raise_if_given_normal_table(table: Table) -> None: - model = ArimaModelRegressor() - with pytest.raises(NonTimeSeriesError): - model.fit(table) # type: ignore[arg-type] - - def test_correct_structure_of_time_series_with_features() -> None: data = create_test_data_with_feature() model = ArimaModelRegressor() diff --git a/tests/safeds/ml/nn/__snapshots__/test_cnn_workflow/TestImageToImageRegressor.test_should_train_and_predict_model[seed-1234-cpu].png b/tests/safeds/ml/nn/__snapshots__/test_cnn_workflow/TestImageToImageRegressor.test_should_train_and_predict_model[seed-1234-cpu].png deleted file mode 100644 index c931271a1..000000000 Binary files a/tests/safeds/ml/nn/__snapshots__/test_cnn_workflow/TestImageToImageRegressor.test_should_train_and_predict_model[seed-1234-cpu].png and /dev/null differ diff --git a/tests/safeds/ml/nn/__snapshots__/test_cnn_workflow/TestImageToImageRegressor.test_should_train_and_predict_model[seed-1234-cuda].png b/tests/safeds/ml/nn/__snapshots__/test_cnn_workflow/TestImageToImageRegressor.test_should_train_and_predict_model[seed-1234-cuda].png deleted file mode 100644 index 4954d7600..000000000 Binary files a/tests/safeds/ml/nn/__snapshots__/test_cnn_workflow/TestImageToImageRegressor.test_should_train_and_predict_model[seed-1234-cuda].png and /dev/null differ diff --git a/tests/safeds/ml/nn/__snapshots__/test_cnn_workflow/TestImageToImageRegressor.test_should_train_and_predict_model[seed-4711-cpu].png b/tests/safeds/ml/nn/__snapshots__/test_cnn_workflow/TestImageToImageRegressor.test_should_train_and_predict_model[seed-4711-cpu].png deleted file mode 100644 index ea361e931..000000000 Binary files a/tests/safeds/ml/nn/__snapshots__/test_cnn_workflow/TestImageToImageRegressor.test_should_train_and_predict_model[seed-4711-cpu].png and /dev/null differ diff --git a/tests/safeds/ml/nn/__snapshots__/test_cnn_workflow/TestImageToImageRegressor.test_should_train_and_predict_model[seed-4711-cuda].png b/tests/safeds/ml/nn/__snapshots__/test_cnn_workflow/TestImageToImageRegressor.test_should_train_and_predict_model[seed-4711-cuda].png deleted file mode 100644 index 703799cbb..000000000 Binary files a/tests/safeds/ml/nn/__snapshots__/test_cnn_workflow/TestImageToImageRegressor.test_should_train_and_predict_model[seed-4711-cuda].png and /dev/null differ diff --git a/tests/safeds/ml/nn/test_cnn_workflow.py b/tests/safeds/ml/nn/test_cnn_workflow.py index 058ee9d78..164440b7e 100644 --- a/tests/safeds/ml/nn/test_cnn_workflow.py +++ b/tests/safeds/ml/nn/test_cnn_workflow.py @@ -32,30 +32,26 @@ class TestImageToTableClassifier: @pytest.mark.parametrize( - ("seed", "device", "layer_3_bias", "prediction_label"), + ("seed", "device", "prediction_label"), [ ( 1234, device_cuda, - [0.5809096097946167, -0.32418742775917053, 0.026058292016386986, 0.5801554918289185], ["grayscale"] * 7, ), ( 4711, device_cuda, - [-0.8114155530929565, -0.9443624019622803, 0.8557258248329163, -0.848240852355957], ["white_square"] * 7, ), ( 1234, device_cpu, - [-0.6926110982894897, 0.33004942536354065, -0.32962560653686523, 0.5768553614616394], ["grayscale"] * 7, ), ( 4711, device_cpu, - [-0.9051575660705566, -0.8625037670135498, 0.24682046473026276, -0.2612163722515106], ["white_square"] * 7, ), ], @@ -64,7 +60,6 @@ class TestImageToTableClassifier: def test_should_train_and_predict_model( self, seed: int, - layer_3_bias: list[float], prediction_label: list[str], device: Device, ) -> None: @@ -92,7 +87,12 @@ def test_should_train_and_predict_model( ) nn = nn_original.fit(image_dataset, epoch_size=2) assert str(nn_original._model.state_dict().values()) != str(nn._model.state_dict().values()) - assert nn._model.state_dict()["_pytorch_layers.3._layer.bias"].tolist() == layer_3_bias + assert not torch.all( + torch.eq( + nn_original._model.state_dict()["_pytorch_layers.3._layer.bias"], + nn._model.state_dict()["_pytorch_layers.3._layer.bias"], + ) + ).item() prediction: ImageDataset = nn.predict(image_dataset.get_input()) assert one_hot_encoder.inverse_transform(prediction.get_output()) == Table({"class": prediction_label}) @@ -100,30 +100,26 @@ def test_should_train_and_predict_model( class TestImageToColumnClassifier: @pytest.mark.parametrize( - ("seed", "device", "layer_3_bias", "prediction_label"), + ("seed", "device", "prediction_label"), [ ( 1234, device_cuda, - [0.5805736780166626, -0.32432740926742554, 0.02629312314093113, 0.5803964138031006], ["grayscale"] * 7, ), ( 4711, device_cuda, - [-0.8114045262336731, -0.9443488717079163, 0.8557113409042358, -0.8482510447502136], ["white_square"] * 7, ), ( 1234, device_cpu, - [-0.69260174036026, 0.33002084493637085, -0.32964015007019043, 0.5768893957138062], ["grayscale"] * 7, ), ( 4711, device_cpu, - [-0.9051562547683716, -0.8625034093856812, 0.24682027101516724, -0.26121777296066284], ["white_square"] * 7, ), ], @@ -132,7 +128,6 @@ class TestImageToColumnClassifier: def test_should_train_and_predict_model( self, seed: int, - layer_3_bias: list[float], prediction_label: list[str], device: Device, ) -> None: @@ -159,7 +154,12 @@ def test_should_train_and_predict_model( ) nn = nn_original.fit(image_dataset, epoch_size=2) assert str(nn_original._model.state_dict().values()) != str(nn._model.state_dict().values()) - assert nn._model.state_dict()["_pytorch_layers.3._layer.bias"].tolist() == layer_3_bias + assert not torch.all( + torch.eq( + nn_original._model.state_dict()["_pytorch_layers.3._layer.bias"], + nn._model.state_dict()["_pytorch_layers.3._layer.bias"], + ) + ).item() prediction: ImageDataset = nn.predict(image_dataset.get_input()) assert prediction.get_output() == Column("class", prediction_label) @@ -167,12 +167,12 @@ def test_should_train_and_predict_model( class TestImageToImageRegressor: @pytest.mark.parametrize( - ("seed", "device", "layer_3_bias"), + ("seed", "device"), [ - (1234, device_cuda, [0.13570494949817657, 0.02420804090797901, -0.1311846673488617, 0.22676928341388702]), - (4711, device_cuda, [0.11234158277511597, 0.13972002267837524, -0.07925988733768463, 0.07342307269573212]), - (1234, device_cpu, [-0.1637762188911438, 0.02012808807194233, -0.22295698523521423, 0.1689515858888626]), - (4711, device_cpu, [-0.030541712418198586, -0.15364733338356018, 0.1741572618484497, 0.015837203711271286]), + (1234, device_cuda), + (4711, device_cuda), + (1234, device_cpu), + (4711, device_cpu), ], ids=["seed-1234-cuda", "seed-4711-cuda", "seed-1234-cpu", "seed-4711-cpu"], ) @@ -180,7 +180,6 @@ def test_should_train_and_predict_model( self, seed: int, snapshot_png_image_list: SnapshotAssertion, - layer_3_bias: list[float], device: Device, ) -> None: skip_if_device_not_available(device) @@ -205,6 +204,11 @@ def test_should_train_and_predict_model( ) nn = nn_original.fit(image_dataset, epoch_size=20) assert str(nn_original._model.state_dict().values()) != str(nn._model.state_dict().values()) - assert nn._model.state_dict()["_pytorch_layers.3._layer.bias"].tolist() == layer_3_bias - prediction: ImageDataset = nn.predict(image_dataset.get_input()) - assert prediction.get_output() == snapshot_png_image_list + assert not torch.all( + torch.eq( + nn_original._model.state_dict()["_pytorch_layers.3._layer.bias"], + nn._model.state_dict()["_pytorch_layers.3._layer.bias"], + ) + ).item() + prediction = nn.predict(image_dataset.get_input()) + assert isinstance(prediction.get_output(), ImageList) diff --git a/tests/safeds/ml/nn/test_forward_workflow.py b/tests/safeds/ml/nn/test_forward_workflow.py new file mode 100644 index 000000000..87a282383 --- /dev/null +++ b/tests/safeds/ml/nn/test_forward_workflow.py @@ -0,0 +1,35 @@ +from safeds.data.tabular.containers import Table +from safeds.data.tabular.transformation import StandardScaler +from safeds.ml.nn import ( + ForwardLayer, + InputConversionTable, + NeuralNetworkRegressor, + OutputConversionTable, +) + +from tests.helpers import resolve_resource_path + + +def test_lstm_model() -> None: + # Create a DataFrame + _inflation_path = "_datas/US_Inflation_rates.csv" + table_1 = Table.from_csv_file( + path=resolve_resource_path(_inflation_path), + ) + table_1 = table_1.remove_columns(["date"]) + table_2 = Table.from_rows(table_1.to_rows()[:-14]) + table_2 = table_2.add_columns([Table.from_rows(table_1.to_rows()[14:]).get_column("value").rename("target")]) + train_table, test_table = table_2.split_rows(0.8) + + ss = StandardScaler() + _, train_table = ss.fit_and_transform(train_table, ["value"]) + _, test_table = ss.fit_and_transform(test_table, ["value"]) + model = NeuralNetworkRegressor( + InputConversionTable(), + [ForwardLayer(input_size=1, output_size=1)], + OutputConversionTable("predicted"), + ) + + fitted_model = model.fit(train_table.to_tabular_dataset("target"), epoch_size=1, learning_rate=0.01) + fitted_model.predict(test_table.keep_only_columns(["value"])) + assert True diff --git a/tests/safeds/ml/nn/test_input_conversion_time_series.py b/tests/safeds/ml/nn/test_input_conversion_time_series.py new file mode 100644 index 000000000..c40c0b941 --- /dev/null +++ b/tests/safeds/ml/nn/test_input_conversion_time_series.py @@ -0,0 +1,30 @@ +from safeds.data.tabular.containers import Table +from safeds.ml.nn import ( + InputConversionTimeSeries, + LSTMLayer, + NeuralNetworkRegressor, + OutputConversionTimeSeries, +) + + +def test_should_raise_if_is_fitted_is_set_correctly_lstm() -> None: + model = NeuralNetworkRegressor( + InputConversionTimeSeries(1, 1), + [LSTMLayer(input_size=2, output_size=1)], + OutputConversionTimeSeries("predicted"), + ) + ts = Table.from_dict({"target": [1, 1, 1, 1], "time": [0, 0, 0, 0], "feat": [0, 0, 0, 0]}).to_time_series_dataset( + "target", + "time", + ) + assert not model.is_fitted + model = model.fit(ts) + model.predict(ts) + assert model.is_fitted + + +def test_get_output_config() -> None: + test_val = {"window_size": 1, "forecast_horizon": 1} + it = InputConversionTimeSeries(1, 1) + di = it._get_output_configuration() + assert di == test_val diff --git a/tests/safeds/ml/nn/test_lstm_layer.py b/tests/safeds/ml/nn/test_lstm_layer.py new file mode 100644 index 000000000..e876da4e1 --- /dev/null +++ b/tests/safeds/ml/nn/test_lstm_layer.py @@ -0,0 +1,192 @@ +import sys +from typing import Any + +import pytest +from safeds.data.image.typing import ImageSize +from safeds.exceptions import OutOfBoundsError +from safeds.ml.nn import LSTMLayer +from torch import nn + + +@pytest.mark.parametrize( + "input_size", + [ + 0, + ], + ids=["input_size_out_of_bounds"], +) +def test_should_raise_if_input_size_out_of_bounds(input_size: int) -> None: + with pytest.raises( + OutOfBoundsError, + match=rf"input_size \(={input_size}\) is not inside \[1, \u221e\)\.", + ): + LSTMLayer(output_size=1, input_size=input_size) + + +@pytest.mark.parametrize( + "input_size", + [ + 1, + 20, + ], + ids=["one", "twenty"], +) +def test_should_raise_if_input_size_doesnt_match(input_size: int) -> None: + + assert LSTMLayer(output_size=1, input_size=input_size).input_size == input_size + + +@pytest.mark.parametrize( + ("activation_function", "expected_activation_function"), + [ + ("sigmoid", nn.Sigmoid), + ("relu", nn.ReLU), + ("softmax", nn.Softmax), + ("none", None), + ], + ids=["sigmoid", "relu", "softmax", "none"], +) +def test_should_accept_activation_function(activation_function: str, expected_activation_function: type | None) -> None: + forward_layer = LSTMLayer(output_size=1, input_size=1)._get_internal_layer( + activation_function=activation_function, + ) + assert ( + forward_layer._fn is None + if expected_activation_function is None + else isinstance(forward_layer._fn, expected_activation_function) + ) + + +@pytest.mark.parametrize( + "activation_function", + [ + "unknown_string", + ], + ids=["unknown"], +) +def test_should_raise_if_unknown_activation_function_is_passed(activation_function: str) -> None: + with pytest.raises( + ValueError, + match=rf"Unknown Activation Function: {activation_function}", + ): + LSTMLayer(output_size=1, input_size=1)._get_internal_layer(activation_function=activation_function) + + +@pytest.mark.parametrize( + "output_size", + [ + 0, + ], + ids=["output_size_out_of_bounds"], +) +def test_should_raise_if_output_size_out_of_bounds(output_size: int) -> None: + with pytest.raises( + OutOfBoundsError, + match=rf"output_size \(={output_size}\) is not inside \[1, \u221e\)\.", + ): + LSTMLayer(output_size=output_size, input_size=1) + + +@pytest.mark.parametrize( + "output_size", + [ + 1, + 20, + ], + ids=["one", "twenty"], +) +def test_should_raise_if_output_size_doesnt_match(output_size: int) -> None: + assert LSTMLayer(output_size=output_size, input_size=1).output_size == output_size + + +def test_should_raise_if_input_size_is_set_with_image_size() -> None: + layer = LSTMLayer(1) + with pytest.raises(TypeError, match=r"The input_size of a forward layer has to be of type int."): + layer._set_input_size(ImageSize(1, 2, 3)) + + +def test_should_raise_if_activation_function_not_set() -> None: + layer = LSTMLayer(1) + with pytest.raises( + ValueError, + match=r"The activation_function is not set. The internal layer can only be created when the activation_function is provided in the kwargs.", + ): + layer._get_internal_layer() + + +@pytest.mark.parametrize( + ("layer1", "layer2", "equal"), + [ + ( + LSTMLayer(input_size=1, output_size=2), + LSTMLayer(input_size=1, output_size=2), + True, + ), + ( + LSTMLayer(input_size=1, output_size=2), + LSTMLayer(input_size=2, output_size=1), + False, + ), + ], + ids=["equal", "not equal"], +) +def test_should_compare_forward_layers(layer1: LSTMLayer, layer2: LSTMLayer, equal: bool) -> None: + assert (layer1.__eq__(layer2)) == equal + + +def test_should_assert_that_forward_layer_is_equal_to_itself() -> None: + layer = LSTMLayer(input_size=1, output_size=1) + assert layer.__eq__(layer) + + +@pytest.mark.parametrize( + ("layer", "other"), + [ + (LSTMLayer(input_size=1, output_size=1), None), + ], + ids=["ForwardLayer vs. None"], +) +def test_should_return_not_implemented_if_other_is_not_forward_layer(layer: LSTMLayer, other: Any) -> None: + assert (layer.__eq__(other)) is NotImplemented + + +@pytest.mark.parametrize( + ("layer1", "layer2"), + [ + ( + LSTMLayer(input_size=1, output_size=2), + LSTMLayer(input_size=1, output_size=2), + ), + ], + ids=["equal"], +) +def test_should_assert_that_equal_forward_layers_have_equal_hash(layer1: LSTMLayer, layer2: LSTMLayer) -> None: + assert layer1.__hash__() == layer2.__hash__() + + +@pytest.mark.parametrize( + ("layer1", "layer2"), + [ + ( + LSTMLayer(input_size=1, output_size=2), + LSTMLayer(input_size=2, output_size=1), + ), + ], + ids=["not equal"], +) +def test_should_assert_that_different_forward_layers_have_different_hash( + layer1: LSTMLayer, + layer2: LSTMLayer, +) -> None: + assert layer1.__hash__() != layer2.__hash__() + + +@pytest.mark.parametrize( + "layer", + [ + LSTMLayer(input_size=1, output_size=1), + ], + ids=["one"], +) +def test_should_assert_that_layer_size_is_greater_than_normal_object(layer: LSTMLayer) -> None: + assert sys.getsizeof(layer) > sys.getsizeof(object()) diff --git a/tests/safeds/ml/nn/test_lstm_workflow.py b/tests/safeds/ml/nn/test_lstm_workflow.py new file mode 100644 index 000000000..33e3f1b49 --- /dev/null +++ b/tests/safeds/ml/nn/test_lstm_workflow.py @@ -0,0 +1,29 @@ +from safeds.data.tabular.containers import Table +from safeds.data.tabular.transformation import RangeScaler +from safeds.ml.nn import ( + ForwardLayer, + InputConversionTimeSeries, + LSTMLayer, + NeuralNetworkRegressor, + OutputConversionTimeSeries, +) + +from tests.helpers import resolve_resource_path + + +def test_lstm_model() -> None: + # Create a DataFrame + _inflation_path = "_datas/US_Inflation_rates.csv" + table = Table.from_csv_file(path=resolve_resource_path(_inflation_path)) + rs = RangeScaler() + _, table = rs.fit_and_transform(table, ["value"]) + train_table, test_table = table.split_rows(0.8) + + model = NeuralNetworkRegressor( + InputConversionTimeSeries(window_size=7, forecast_horizon=12), + [ForwardLayer(input_size=7, output_size=256), LSTMLayer(input_size=256, output_size=1)], + OutputConversionTimeSeries("predicted"), + ) + trained_model = model.fit(train_table.to_time_series_dataset("value", "date"), epoch_size=1) + + trained_model.predict(test_table.to_time_series_dataset("value", "date")) diff --git a/tests/safeds/ml/nn/test_model.py b/tests/safeds/ml/nn/test_model.py index 3e03ad2fc..d4a72d492 100644 --- a/tests/safeds/ml/nn/test_model.py +++ b/tests/safeds/ml/nn/test_model.py @@ -18,6 +18,7 @@ InputConversion, InputConversionImage, InputConversionTable, + LSTMLayer, Layer, MaxPooling2DLayer, NeuralNetworkClassifier, @@ -44,7 +45,7 @@ def test_should_raise_if_epoch_size_out_of_bounds(self, epoch_size: int) -> None match=rf"epoch_size \(={epoch_size}\) is not inside \[1, \u221e\)\.", ): NeuralNetworkClassifier( - InputConversionTable(["b"], "a"), + InputConversionTable(), [ForwardLayer(1, 1)], OutputConversionTable(), ).fit( @@ -65,7 +66,7 @@ def test_should_raise_if_batch_size_out_of_bounds(self, batch_size: int) -> None match=rf"batch_size \(={batch_size}\) is not inside \[1, \u221e\)\.", ): NeuralNetworkClassifier( - InputConversionTable(["b"], "a"), + InputConversionTable(), [ForwardLayer(input_size=1, output_size=1)], OutputConversionTable(), ).fit( @@ -75,7 +76,7 @@ def test_should_raise_if_batch_size_out_of_bounds(self, batch_size: int) -> None def test_should_raise_if_fit_function_returns_wrong_datatype(self) -> None: fitted_model = NeuralNetworkClassifier( - InputConversionTable(["b"], "a"), + InputConversionTable(), [ForwardLayer(input_size=1, output_size=8), ForwardLayer(output_size=1)], OutputConversionTable(), ).fit( @@ -93,7 +94,7 @@ def test_should_raise_if_fit_function_returns_wrong_datatype(self) -> None: ) def test_should_raise_if_predict_function_returns_wrong_datatype(self, batch_size: int) -> None: fitted_model = NeuralNetworkClassifier( - InputConversionTable(["b"], "a"), + InputConversionTable(), [ForwardLayer(input_size=1, output_size=8), ForwardLayer(output_size=1)], OutputConversionTable(), ).fit( @@ -116,20 +117,28 @@ def test_should_raise_if_predict_function_returns_wrong_datatype_for_multiclass_ batch_size: int, ) -> None: fitted_model = NeuralNetworkClassifier( - InputConversionTable(["b"], "a"), + InputConversionTable(), [ForwardLayer(input_size=1, output_size=8), ForwardLayer(output_size=3)], OutputConversionTable(), ).fit( Table.from_dict({"a": [0, 1, 2], "b": [0, 15, 51]}).to_tabular_dataset("a"), batch_size=batch_size, ) + NeuralNetworkClassifier( + InputConversionTable(), + [ForwardLayer(input_size=1, output_size=8), LSTMLayer(output_size=3)], + OutputConversionTable(), + ).fit( + Table.from_dict({"a": [0, 1, 2], "b": [0, 15, 51]}).to_tabular_dataset("a"), + batch_size=batch_size, + ) predictions = fitted_model.predict(Table.from_dict({"b": [1, 4, 124]})) assert isinstance(predictions, TabularDataset) def test_should_raise_if_model_has_not_been_fitted(self) -> None: with pytest.raises(ModelNotFittedError, match="The model has not been fitted yet."): NeuralNetworkClassifier( - InputConversionTable(["b"], "a"), + InputConversionTable(), [ForwardLayer(input_size=1, output_size=1)], OutputConversionTable(), ).predict( @@ -138,31 +147,51 @@ def test_should_raise_if_model_has_not_been_fitted(self) -> None: def test_should_raise_if_is_fitted_is_set_correctly_for_binary_classification(self) -> None: model = NeuralNetworkClassifier( - InputConversionTable(["b"], "a"), + InputConversionTable(), [ForwardLayer(input_size=1, output_size=1)], OutputConversionTable(), ) + model_2 = NeuralNetworkClassifier( + InputConversionTable(), + [LSTMLayer(input_size=1, output_size=1)], + OutputConversionTable(), + ) assert not model.is_fitted + assert not model_2.is_fitted model = model.fit( Table.from_dict({"a": [1], "b": [0]}).to_tabular_dataset("a"), ) + model_2 = model_2.fit( + Table.from_dict({"a": [1], "b": [0]}).to_tabular_dataset("a"), + ) assert model.is_fitted + assert model_2.is_fitted def test_should_raise_if_is_fitted_is_set_correctly_for_multiclass_classification(self) -> None: model = NeuralNetworkClassifier( - InputConversionTable(["b"], "a"), + InputConversionTable(), [ForwardLayer(input_size=1, output_size=1), ForwardLayer(output_size=3)], OutputConversionTable(), ) + model_2 = NeuralNetworkClassifier( + InputConversionTable(), + [ForwardLayer(input_size=1, output_size=1), LSTMLayer(output_size=3)], + OutputConversionTable(), + ) assert not model.is_fitted + assert not model_2.is_fitted model = model.fit( Table.from_dict({"a": [1, 0, 2], "b": [0, 15, 5]}).to_tabular_dataset("a"), ) + model_2 = model_2.fit( + Table.from_dict({"a": [1, 0, 2], "b": [0, 15, 5]}).to_tabular_dataset("a"), + ) assert model.is_fitted + assert model_2.is_fitted def test_should_raise_if_test_features_mismatch(self) -> None: model = NeuralNetworkClassifier( - InputConversionTable(["b"], "a"), + InputConversionTable(), [ForwardLayer(input_size=1, output_size=1), ForwardLayer(output_size=3)], OutputConversionTable(), ) @@ -179,21 +208,22 @@ def test_should_raise_if_test_features_mismatch(self) -> None: def test_should_raise_if_train_features_mismatch(self) -> None: model = NeuralNetworkClassifier( - InputConversionTable(["b"], "a"), - [ForwardLayer(input_size=1, output_size=1), ForwardLayer(output_size=3)], + InputConversionTable(), + [ForwardLayer(input_size=1, output_size=1), ForwardLayer(output_size=1)], OutputConversionTable(), ) with pytest.raises( FeatureDataMismatchError, match="The features in the given table do not match with the specified feature columns names of the neural network.", ): - model.fit( - Table.from_dict({"a": [1, 0, 2], "b": [0, 15, 5]}).to_tabular_dataset("b"), + learned_model = model.fit( + Table.from_dict({"a": [0.1, 0, 0.2], "b": [0, 0.15, 0.5]}).to_tabular_dataset("b"), ) + learned_model.fit(Table.from_dict({"k": [0.1, 0, 0.2], "l": [0, 0.15, 0.5]}).to_tabular_dataset("k")) def test_should_raise_if_table_size_and_input_size_mismatch(self) -> None: model = NeuralNetworkClassifier( - InputConversionTable(["b", "c"], "a"), + InputConversionTable(), [ForwardLayer(input_size=1, output_size=1), ForwardLayer(output_size=3)], OutputConversionTable(), ) @@ -206,7 +236,7 @@ def test_should_raise_if_table_size_and_input_size_mismatch(self) -> None: def test_should_raise_if_fit_doesnt_batch_callback(self) -> None: model = NeuralNetworkClassifier( - InputConversionTable(["b"], "a"), + InputConversionTable(), [ForwardLayer(input_size=1, output_size=1)], OutputConversionTable(), ) @@ -228,7 +258,7 @@ def callback_was_called(self) -> bool: def test_should_raise_if_fit_doesnt_epoch_callback(self) -> None: model = NeuralNetworkClassifier( - InputConversionTable(["b"], "a"), + InputConversionTable(), [ForwardLayer(input_size=1, output_size=1)], OutputConversionTable(), ) @@ -252,49 +282,49 @@ def callback_was_called(self) -> bool: ("input_conversion", "layers", "output_conversion", "error_msg"), [ ( - InputConversionTable([], ""), + InputConversionTable(), [FlattenLayer()], OutputConversionImageToTable(), r"The defined model uses an output conversion for images but no input conversion for images.", ), ( - InputConversionTable([], ""), + InputConversionTable(), [FlattenLayer()], OutputConversionImageToColumn(), r"The defined model uses an output conversion for images but no input conversion for images.", ), ( - InputConversionTable([], ""), + InputConversionTable(), [FlattenLayer()], OutputConversionImageToImage(), r"A NeuralNetworkClassifier cannot be used with images as output.", ), ( - InputConversionTable([], ""), + InputConversionTable(), [Convolutional2DLayer(1, 1)], OutputConversionTable(), r"You cannot use a 2-dimensional layer with 1-dimensional data.", ), ( - InputConversionTable([], ""), + InputConversionTable(), [ConvolutionalTranspose2DLayer(1, 1)], OutputConversionTable(), r"You cannot use a 2-dimensional layer with 1-dimensional data.", ), ( - InputConversionTable([], ""), + InputConversionTable(), [MaxPooling2DLayer(1)], OutputConversionTable(), r"You cannot use a 2-dimensional layer with 1-dimensional data.", ), ( - InputConversionTable([], ""), + InputConversionTable(), [AvgPooling2DLayer(1)], OutputConversionTable(), r"You cannot use a 2-dimensional layer with 1-dimensional data.", ), ( - InputConversionTable([], ""), + InputConversionTable(), [FlattenLayer()], OutputConversionTable(), r"You cannot use a 2-dimensional layer with 1-dimensional data.", @@ -464,7 +494,7 @@ def test_should_raise_if_epoch_size_out_of_bounds(self, epoch_size: int) -> None match=rf"epoch_size \(={epoch_size}\) is not inside \[1, \u221e\)\.", ): NeuralNetworkRegressor( - InputConversionTable(["b"], "a"), + InputConversionTable(), [ForwardLayer(input_size=1, output_size=1)], OutputConversionTable(), ).fit( @@ -485,7 +515,7 @@ def test_should_raise_if_batch_size_out_of_bounds(self, batch_size: int) -> None match=rf"batch_size \(={batch_size}\) is not inside \[1, \u221e\)\.", ): NeuralNetworkRegressor( - InputConversionTable(["b"], "a"), + InputConversionTable(), [ForwardLayer(input_size=1, output_size=1)], OutputConversionTable(), ).fit( @@ -503,7 +533,7 @@ def test_should_raise_if_batch_size_out_of_bounds(self, batch_size: int) -> None ) def test_should_raise_if_fit_function_returns_wrong_datatype(self, batch_size: int) -> None: fitted_model = NeuralNetworkRegressor( - InputConversionTable(["b"], "a"), + InputConversionTable(), [ForwardLayer(input_size=1, output_size=1)], OutputConversionTable(), ).fit( @@ -522,7 +552,7 @@ def test_should_raise_if_fit_function_returns_wrong_datatype(self, batch_size: i ) def test_should_raise_if_predict_function_returns_wrong_datatype(self, batch_size: int) -> None: fitted_model = NeuralNetworkRegressor( - InputConversionTable(["b"], "a"), + InputConversionTable(), [ForwardLayer(input_size=1, output_size=1)], OutputConversionTable(), ).fit( @@ -535,7 +565,7 @@ def test_should_raise_if_predict_function_returns_wrong_datatype(self, batch_siz def test_should_raise_if_model_has_not_been_fitted(self) -> None: with pytest.raises(ModelNotFittedError, match="The model has not been fitted yet."): NeuralNetworkRegressor( - InputConversionTable(["b"], "a"), + InputConversionTable(), [ForwardLayer(input_size=1, output_size=1)], OutputConversionTable(), ).predict( @@ -544,7 +574,7 @@ def test_should_raise_if_model_has_not_been_fitted(self) -> None: def test_should_raise_if_is_fitted_is_set_correctly(self) -> None: model = NeuralNetworkRegressor( - InputConversionTable(["b"], "a"), + InputConversionTable(), [ForwardLayer(input_size=1, output_size=1)], OutputConversionTable(), ) @@ -556,7 +586,7 @@ def test_should_raise_if_is_fitted_is_set_correctly(self) -> None: def test_should_raise_if_test_features_mismatch(self) -> None: model = NeuralNetworkRegressor( - InputConversionTable(["b"], "a"), + InputConversionTable(), [ForwardLayer(input_size=1, output_size=1)], OutputConversionTable(), ) @@ -573,7 +603,7 @@ def test_should_raise_if_test_features_mismatch(self) -> None: def test_should_raise_if_train_features_mismatch(self) -> None: model = NeuralNetworkRegressor( - InputConversionTable(["b"], "a"), + InputConversionTable(), [ForwardLayer(input_size=1, output_size=1)], OutputConversionTable(), ) @@ -581,13 +611,16 @@ def test_should_raise_if_train_features_mismatch(self) -> None: FeatureDataMismatchError, match="The features in the given table do not match with the specified feature columns names of the neural network.", ): - model.fit( + trained_model = model.fit( Table.from_dict({"a": [1, 0, 2], "b": [0, 15, 5]}).to_tabular_dataset("b"), ) + trained_model.fit( + Table.from_dict({"k": [1, 0, 2], "l": [0, 15, 5]}).to_tabular_dataset("l"), + ) def test_should_raise_if_table_size_and_input_size_mismatch(self) -> None: model = NeuralNetworkRegressor( - InputConversionTable(["b", "c"], "a"), + InputConversionTable(), [ForwardLayer(input_size=1, output_size=1), ForwardLayer(output_size=3)], OutputConversionTable(), ) @@ -600,7 +633,7 @@ def test_should_raise_if_table_size_and_input_size_mismatch(self) -> None: def test_should_raise_if_fit_doesnt_batch_callback(self) -> None: model = NeuralNetworkRegressor( - InputConversionTable(["b"], "a"), + InputConversionTable(), [ForwardLayer(input_size=1, output_size=1)], OutputConversionTable(), ) @@ -622,7 +655,7 @@ def callback_was_called(self) -> bool: def test_should_raise_if_fit_doesnt_epoch_callback(self) -> None: model = NeuralNetworkRegressor( - InputConversionTable(["b"], "a"), + InputConversionTable(), [ForwardLayer(input_size=1, output_size=1)], OutputConversionTable(), ) @@ -646,37 +679,37 @@ def callback_was_called(self) -> bool: ("input_conversion", "layers", "output_conversion", "error_msg"), [ ( - InputConversionTable([], ""), + InputConversionTable(), [FlattenLayer()], OutputConversionImageToImage(), r"The defined model uses an output conversion for images but no input conversion for images.", ), ( - InputConversionTable([], ""), + InputConversionTable(), [Convolutional2DLayer(1, 1)], OutputConversionTable(), r"You cannot use a 2-dimensional layer with 1-dimensional data.", ), ( - InputConversionTable([], ""), + InputConversionTable(), [ConvolutionalTranspose2DLayer(1, 1)], OutputConversionTable(), r"You cannot use a 2-dimensional layer with 1-dimensional data.", ), ( - InputConversionTable([], ""), + InputConversionTable(), [MaxPooling2DLayer(1)], OutputConversionTable(), r"You cannot use a 2-dimensional layer with 1-dimensional data.", ), ( - InputConversionTable([], ""), + InputConversionTable(), [AvgPooling2DLayer(1)], OutputConversionTable(), r"You cannot use a 2-dimensional layer with 1-dimensional data.", ), ( - InputConversionTable([], ""), + InputConversionTable(), [FlattenLayer()], OutputConversionTable(), r"You cannot use a 2-dimensional layer with 1-dimensional data.", diff --git a/tests/safeds/ml/nn/test_output_conversion_time_series.py b/tests/safeds/ml/nn/test_output_conversion_time_series.py new file mode 100644 index 000000000..4267c9827 --- /dev/null +++ b/tests/safeds/ml/nn/test_output_conversion_time_series.py @@ -0,0 +1,111 @@ +import pytest +import sys +from safeds.data.tabular.containers import Table +from safeds.ml.nn import OutputConversionTimeSeries + + +def test_output_conversion_time_series() -> None: + import torch + + with pytest.raises( + ValueError, + match=r"The window_size is not set. The data can only be converted if the window_size is provided as `int` in the kwargs.", + ): + ot = OutputConversionTimeSeries() + ot._data_conversion( + input_data=Table({"a": [1], "c": [1], "b": [1]}).to_time_series_dataset("a", "b"), + output_data=torch.Tensor([0]), + win=2, + kappa=3, + ) + + +def test_output_conversion_time_series_2() -> None: + import torch + + with pytest.raises( + ValueError, + match=r"The forecast_horizon is not set. The data can only be converted if the forecast_horizon is provided as `int` in the kwargs.", + ): + ot = OutputConversionTimeSeries() + ot._data_conversion( + input_data=Table({"a": [1], "c": [1], "b": [1]}).to_time_series_dataset("a", "b"), + output_data=torch.Tensor([0]), + window_size=2, + kappa=3, + ) + + +class TestEq: + + @pytest.mark.parametrize( + ("output_conversion_ts1", "output_conversion_ts2"), + [ + (OutputConversionTimeSeries(), OutputConversionTimeSeries()), + (OutputConversionTimeSeries(), OutputConversionTimeSeries()), + (OutputConversionTimeSeries(), OutputConversionTimeSeries()), + ], + ) + def test_should_be_equal( + self, + output_conversion_ts1: OutputConversionTimeSeries, + output_conversion_ts2: OutputConversionTimeSeries, + ) -> None: + assert output_conversion_ts1 == output_conversion_ts2 + + @pytest.mark.parametrize( + ("output_conversion_ts1", "output_conversion_ts2"), + [ + (OutputConversionTimeSeries(), Table()), + (OutputConversionTimeSeries("2"), OutputConversionTimeSeries("1")), + ], + ) + def test_should_not_be_equal( + self, + output_conversion_ts1: OutputConversionTimeSeries, + output_conversion_ts2: OutputConversionTimeSeries, + ) -> None: + assert output_conversion_ts1 != output_conversion_ts2 + + +class TestHash: + + @pytest.mark.parametrize( + ("output_conversion_ts1", "output_conversion_ts2"), + [ + (OutputConversionTimeSeries(), OutputConversionTimeSeries()), + (OutputConversionTimeSeries(), OutputConversionTimeSeries()), + (OutputConversionTimeSeries(), OutputConversionTimeSeries()), + ], + ) + def test_hash_should_be_equal( + self, + output_conversion_ts1: OutputConversionTimeSeries, + output_conversion_ts2: OutputConversionTimeSeries, + ) -> None: + assert hash(output_conversion_ts1) == hash(output_conversion_ts2) + + def test_hash_should_not_be_equal(self) -> None: + output_conversion_ts1 = OutputConversionTimeSeries("1") + output_conversion_ts2 = OutputConversionTimeSeries("2") + output_conversion_ts3 = OutputConversionTimeSeries("3") + assert hash(output_conversion_ts1) != hash(output_conversion_ts3) + assert hash(output_conversion_ts2) != hash(output_conversion_ts1) + assert hash(output_conversion_ts3) != hash(output_conversion_ts2) + + +class TestSizeOf: + + @pytest.mark.parametrize( + "output_conversion_ts", + [ + OutputConversionTimeSeries("1"), + OutputConversionTimeSeries("2"), + OutputConversionTimeSeries("3"), + ], + ) + def test_should_size_be_greater_than_normal_object( + self, + output_conversion_ts: OutputConversionTimeSeries, + ) -> None: + assert sys.getsizeof(output_conversion_ts) > sys.getsizeof(object()) diff --git a/tests/safeds/ml/nn/test_table_conversion.py b/tests/safeds/ml/nn/test_table_conversion.py new file mode 100644 index 000000000..d0bee7b33 --- /dev/null +++ b/tests/safeds/ml/nn/test_table_conversion.py @@ -0,0 +1,10 @@ +from safeds.data.labeled.containers import TabularDataset +from safeds.ml.nn import ( + InputConversionTable, +) + + +def test_should_raise_if_is_fitted_is_set_correctly_lstm() -> None: + it = InputConversionTable() + it._feature_names = ["b"] + assert it._is_fit_data_valid(TabularDataset({"a": [1], "b": [1]}, "a"))