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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,127 @@ | ||
| from typing import Optional, Tuple, Union | ||
|
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| import numpy as np | ||
| import pandas as pd | ||
| import plotly.graph_objects as go | ||
| from numpy.typing import NDArray | ||
| from plotly.basedatatypes import BaseFigure | ||
|
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| from blitzly.etc.utils import check_data, save_show_return, update_figure_layout | ||
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|
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| def simple_slope( | ||
| data: Union[pd.DataFrame, NDArray], | ||
| title: str = "Slope plot", | ||
| marker_size: int = 16, | ||
| marker_line_width: int = 4, | ||
| margin_size: int = 250, | ||
| plotly_kwargs: Optional[dict] = None, | ||
| size: Optional[Tuple[int, int]] = None, | ||
| show: bool = True, | ||
| write_html_path: Optional[str] = None, | ||
| ) -> BaseFigure: | ||
|
|
||
| """ | ||
| Creates a slope plot. These are useful to show the difference between | ||
| two sets of data which have the same categories. For instance, it can be | ||
| used to compare two binary classifiers by plotting the various classification | ||
| metrics. | ||
| Example: | ||
| ```python | ||
| from blitzly.plots.slope import simple_slope | ||
| import numpy as np | ||
| import pandas as pd | ||
| data = { | ||
| "foo": np.random.randn(10), | ||
| "bar": np.random.randn(10), | ||
| } | ||
| index = [f"category_{i+1}" for i in range(10)] | ||
| df = pd.DataFrame(data, index=index) | ||
| simple_slope(df) | ||
| ``` | ||
| Args: | ||
| data (Union[pd.DataFrame, NDArray]): Data to plot. | ||
| title (str): Title of the plot. | ||
| marker_size (int): Size of the circular marker. | ||
| marker_line_width (int): Thickness of the line joining the markers. | ||
| margin_size (int): Margin for displaying text labels in pixels. | ||
| plotly_kwargs (Optional[dict]): Additional keyword arguments to pass to Plotly `go.Scatter`. | ||
| size (Optional[Tuple[int, int]): Size of the plot. | ||
| show (bool): Whether to show the figure. | ||
| write_html_path (Optional[str]): The path to which the histogram should be written as an HTML file. | ||
| If None, the histogram will not be saved. | ||
| Returns: | ||
| BaseFigure: The slope plot. | ||
| """ | ||
|
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| data = check_data( | ||
| data, min_rows=1, min_columns=2, max_columns=2, keep_as_pandas=True | ||
| ) | ||
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| if isinstance(data, np.ndarray): | ||
| data = pd.DataFrame(data) | ||
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| data_max = data.to_numpy().max() | ||
| data_min = data.to_numpy().min() | ||
| data_range = data_max - data_min | ||
|
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| y_range_max = data_max + 0.05 * data_range | ||
| y_range_min = data_min - 0.05 * data_range | ||
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| fig = go.Figure() | ||
|
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| for column_idx in range(2): | ||
| fig.add_trace( | ||
| go.Scatter( | ||
| x=[column_idx, column_idx], | ||
| y=[y_range_max, y_range_min], | ||
| mode="lines", | ||
| line={ | ||
| "color": "black", | ||
| "width": 2, | ||
| }, | ||
| showlegend=False, | ||
| ) | ||
| ) | ||
|
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||
| for index, row in data.iterrows(): | ||
| if row.iloc[0] > row.iloc[1]: | ||
| line_color = "red" | ||
| else: | ||
| line_color = "green" | ||
|
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||
| fig.add_trace( | ||
| go.Scatter( | ||
| x=[0, 1], | ||
| y=[row.iloc[0], row.iloc[1]], | ||
| mode="markers+lines+text", | ||
| marker={ | ||
| "size": marker_size, | ||
| }, | ||
| line={ | ||
| "width": marker_line_width, | ||
| "color": line_color, | ||
| }, | ||
| text=index, | ||
| textposition=["middle left", "middle right"], | ||
| showlegend=False, | ||
| **plotly_kwargs if plotly_kwargs else {}, | ||
| ) | ||
| ) | ||
|
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| xaxis_offset = margin_size / size[0] if size is not None else 1 | ||
| fig.update_layout( | ||
| xaxis={ | ||
| "tickvals": [0, 1], | ||
| "ticktext": data.columns, | ||
| "range": [-xaxis_offset, 1 + xaxis_offset], | ||
| } | ||
| ) | ||
|
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| fig = update_figure_layout(fig, title, size) | ||
| return save_show_return(fig, write_html_path, show) |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,16 @@ | ||
| import numpy as np | ||
| import pandas as pd | ||
| import pytest | ||
|
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| # pylint: disable=missing-function-docstring | ||
|
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| @pytest.fixture() | ||
| def X_numbers_two_column() -> pd.DataFrame: | ||
| np.random.seed(42) | ||
| data = { | ||
| "foo": np.random.rand(10), | ||
| "bar": np.random.rand(10), | ||
| } | ||
| index = [f"category_{i+1}" for i in range(10)] | ||
| return pd.DataFrame(data, index=index) |
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,33 @@ | ||
| import joblib | ||
| import numpy as np | ||
| import pytest | ||
|
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| from blitzly.plots.slope import simple_slope | ||
| from tests.helper import fig_to_array | ||
|
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| # pylint: disable=missing-function-docstring, missing-class-docstring, redefined-outer-name | ||
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| @pytest.fixture(scope="session") | ||
| def expected_pandas(): | ||
| return joblib.load("tests/expected_figs/slope/simple_slope/expected_pandas.joblib") | ||
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| @pytest.fixture(scope="session") | ||
| def expected_2d_numpy(): | ||
| return joblib.load( | ||
| "tests/expected_figs/slope/simple_slope/expected_2d_numpy.joblib" | ||
| ) | ||
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| class TestSimpleSlope: | ||
| @staticmethod | ||
| def test_simple_slope_with_pandas(X_numbers_two_column, expected_pandas): | ||
| fig = simple_slope(X_numbers_two_column, size=(500, 500), show=False) | ||
| np.testing.assert_equal(fig_to_array(fig), fig_to_array(expected_pandas)) | ||
|
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| @staticmethod | ||
| def test_simple_slope_with_2d_numpy(expected_2d_numpy): | ||
| np.random.seed(42) | ||
| fig = simple_slope(np.random.randn(10, 2), size=(500, 500), show=False) | ||
| np.testing.assert_equal(fig_to_array(fig), fig_to_array(expected_2d_numpy)) |