diff --git a/.github/workflows/python-package.yml b/.github/workflows/python-package.yml index deda670..78265b5 100644 --- a/.github/workflows/python-package.yml +++ b/.github/workflows/python-package.yml @@ -32,7 +32,7 @@ jobs: run: | uv run --frozen pytest - - name: Lint with black + - name: Lint with ruff run: | - uv run --frozen black --check src/ - uv run --frozen black --check tests/ + uv run --frozen ruff format --check src/ + uv run --frozen ruff format --check tests/ diff --git a/jupyter/fidelity-plot.ipynb b/jupyter/fidelity-plot.ipynb index 4c58935..793d9b2 100644 --- a/jupyter/fidelity-plot.ipynb +++ b/jupyter/fidelity-plot.ipynb @@ -20,12 +20,38 @@ "outputs": [], "source": [ "# Make up some fake fidelity data.\n", - "fidelity_data = pl.DataFrame([\n", - " {\"column-1\":\"total_score\",\"column-2\":\"sports_flg\",\"tvd\":0.001757401,\"model\":\"LPM\",\"index\":0},\n", - " {\"column-1\":\"terrace_flg\",\"column-2\":\"darts_flg\",\"tvd\":0.0018333333,\"model\":\"LPM\",\"index\":1},\n", - " {\"column-1\":\"total_score\",\"column-2\":\"live_flg\",\"tvd\":0.0020490196,\"model\":\"LPM\",\"index\":2},\n", - " {\"column-1\":\"total_score\",\"column-2\":\"closed\",\"tvd\":0.056201461,\"model\":\"LPM\",\"index\":35}\n", - "])" + "fidelity_data = pl.DataFrame(\n", + " [\n", + " {\n", + " \"column-1\": \"total_score\",\n", + " \"column-2\": \"sports_flg\",\n", + " \"tvd\": 0.001757401,\n", + " \"model\": \"LPM\",\n", + " \"index\": 0,\n", + " },\n", + " {\n", + " \"column-1\": \"terrace_flg\",\n", + " \"column-2\": \"darts_flg\",\n", + " \"tvd\": 0.0018333333,\n", + " \"model\": \"LPM\",\n", + " \"index\": 1,\n", + " },\n", + " {\n", + " \"column-1\": \"total_score\",\n", + " \"column-2\": \"live_flg\",\n", + " \"tvd\": 0.0020490196,\n", + " \"model\": \"LPM\",\n", + " \"index\": 2,\n", + " },\n", + " {\n", + " \"column-1\": \"total_score\",\n", + " \"column-2\": \"closed\",\n", + " \"tvd\": 0.056201461,\n", + " \"model\": \"LPM\",\n", + " \"index\": 35,\n", + " },\n", + " ]\n", + ")" ] }, { @@ -39,24 +65,25 @@ "text/html": [ "\n", "\n", - "
\n", + "
\n", "" ], "text/plain": [ @@ -120,9 +148,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "LPM Plot (Python 3.12)", "language": "python", - "name": "python3" + "name": "lpm-plot" }, "language_info": { "codemirror_mode": { @@ -134,7 +162,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.3" + "version": "3.12.11" } }, "nbformat": 4, diff --git a/jupyter/heatmap-interaction-demo.ipynb b/jupyter/heatmap-interaction-demo.ipynb new file mode 100644 index 0000000..7bcc5fe --- /dev/null +++ b/jupyter/heatmap-interaction-demo.ipynb @@ -0,0 +1,141 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "b3d6cb32", + "metadata": {}, + "outputs": [], + "source": [ + "import polars as pl\n", + "\n", + "from lpm_plot import plot_heatmap" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "37116e45", + "metadata": {}, + "outputs": [], + "source": [ + "heatmap_df = pl.read_parquet(\"../tests/resources/demo-heatmap-df.parquet\")\n", + "all_data = pl.read_parquet(\n", + " \"../tests/resources/demo-detail-df.parquet\"\n", + ") # 0.1% of data used" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "2aa1df0f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "" + ], + "text/plain": [ + "alt.VConcatChart(...)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "plot_heatmap(heatmap_df, all_data, interactive=True)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "LPM Plot (Python 3.12)", + "language": "python", + "name": "lpm-plot" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/jupyter/heatmap-plot.ipynb b/jupyter/heatmap-plot.ipynb new file mode 100644 index 0000000..38e9aec --- /dev/null +++ b/jupyter/heatmap-plot.ipynb @@ -0,0 +1,148 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "7ad9bbf0", + "metadata": {}, + "outputs": [], + "source": [ + "import polars as pl\n", + "\n", + "from lpm_plot import plot_heatmap" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "16a52d65", + "metadata": {}, + "outputs": [], + "source": [ + "df = pl.read_csv(\"../tests/resources/heatmap-test-data.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "0cfd1e52", + "metadata": {}, + "outputs": [], + "source": [ + "detailed_df = pl.read_csv(\"../tests/resources/heatmap-detailed-test-data.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "e8876c85", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "" + ], + "text/plain": [ + "alt.VConcatChart(...)" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "plot_heatmap(df, detailed_df=detailed_df, interactive=True)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "LPM Plot (Python 3.12)", + "language": "python", + "name": "lpm-plot" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/jupyter/marginal-1d-plot.ipynb b/jupyter/marginal-1d-plot.ipynb index 58fa0ce..1b97e3c 100644 --- a/jupyter/marginal-1d-plot.ipynb +++ b/jupyter/marginal-1d-plot.ipynb @@ -34,24 +34,25 @@ "text/html": [ "\n", "\n", - "
\n", + "
\n", "" ], "text/plain": [ @@ -115,9 +117,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "LPM Plot (Python 3.12)", "language": "python", - "name": "python3" + "name": "lpm-plot" }, "language_info": { "codemirror_mode": { @@ -129,7 +131,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.3" + "version": "3.12.11" } }, "nbformat": 4, diff --git a/jupyter/marginal-2d-plot.ipynb b/jupyter/marginal-2d-plot.ipynb new file mode 100644 index 0000000..bdcca67 --- /dev/null +++ b/jupyter/marginal-2d-plot.ipynb @@ -0,0 +1,183 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "667db898", + "metadata": {}, + "outputs": [], + "source": [ + "import polars as pl\n", + "from lpm_plot.plot_marginal import plot_marginal_2d, prepare_2d_marginal_data" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "125c7dd1", + "metadata": {}, + "outputs": [], + "source": [ + "observed_df = pl.read_csv(\"../tests/resources/hand-written-observed.csv\")\n", + "synthetic_df = pl.read_csv(\"../tests/resources/hand-written-synthetic.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "d45fadcf", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "shape: (5, 5)
SourcefoobarcountNormalized frequency
strstrstri64f64
"Observed""a""x"120.4
"Synthetic""c""z"30.1
"Synthetic""b""x"10.033333
"Synthetic""c""x"10.033333
"Observed""c""z"70.233333
" + ], + "text/plain": [ + "shape: (5, 5)\n", + "┌───────────┬─────┬─────┬───────┬──────────────────────┐\n", + "│ Source ┆ foo ┆ bar ┆ count ┆ Normalized frequency │\n", + "│ --- ┆ --- ┆ --- ┆ --- ┆ --- │\n", + "│ str ┆ str ┆ str ┆ i64 ┆ f64 │\n", + "╞═══════════╪═════╪═════╪═══════╪══════════════════════╡\n", + "│ Observed ┆ a ┆ x ┆ 12 ┆ 0.4 │\n", + "│ Synthetic ┆ c ┆ z ┆ 3 ┆ 0.1 │\n", + "│ Synthetic ┆ b ┆ x ┆ 1 ┆ 0.033333 │\n", + "│ Synthetic ┆ c ┆ x ┆ 1 ┆ 0.033333 │\n", + "│ Observed ┆ c ┆ z ┆ 7 ┆ 0.233333 │\n", + "└───────────┴─────┴─────┴───────┴──────────────────────┘" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Prepare the data for 2D marginal plotting\n", + "combined_df = prepare_2d_marginal_data(observed_df, synthetic_df, \"foo\", \"bar\")\n", + "combined_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "c89b6030", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "" + ], + "text/plain": [ + "alt.HConcatChart(...)" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Create the 2D marginal plot\n", + "plot_marginal_2d(combined_df, \"foo\", \"bar\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "LPM Plot (Python 3.12)", + "language": "python", + "name": "lpm-plot" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/jupyter/marginal-num-cat-plot.ipynb b/jupyter/marginal-num-cat-plot.ipynb new file mode 100644 index 0000000..5f99bb3 --- /dev/null +++ b/jupyter/marginal-num-cat-plot.ipynb @@ -0,0 +1,139 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "87122caa", + "metadata": {}, + "outputs": [], + "source": [ + "import polars as pl\n", + "\n", + "from lpm_plot import plot_marginal_numerical_categorical" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "4ddb8731", + "metadata": {}, + "outputs": [], + "source": [ + "observed_df = pl.read_csv(\"../tests/resources/num-cat-observed.csv\")\n", + "synthetic_df = pl.read_csv(\"../tests/resources/num-cat-synthetic.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "5205cef4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "" + ], + "text/plain": [ + "alt.Chart(...)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "plot_marginal_numerical_categorical(observed_df, synthetic_df, \"class\", \"data\", 30)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "LPM Plot (Python 3.12)", + "language": "python", + "name": "lpm-plot" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/jupyter/marginal-num-num-plot.ipynb b/jupyter/marginal-num-num-plot.ipynb new file mode 100644 index 0000000..3cdd546 --- /dev/null +++ b/jupyter/marginal-num-num-plot.ipynb @@ -0,0 +1,162 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "59e8b48c", + "metadata": {}, + "outputs": [], + "source": [ + "import polars as pl\n", + "\n", + "from lpm_plot import plot_marginal_numerical_numerical" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "5bc1247a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "shape: (5, 2)\n", + "┌─────┬─────┐\n", + "│ foo ┆ bar │\n", + "│ --- ┆ --- │\n", + "│ i64 ┆ i64 │\n", + "╞═════╪═════╡\n", + "│ 1 ┆ 2 │\n", + "│ 2 ┆ 5 │\n", + "│ 3 ┆ 3 │\n", + "│ 4 ┆ 8 │\n", + "│ 5 ┆ 7 │\n", + "└─────┴─────┘\n" + ] + } + ], + "source": [ + "x = \"foo\"\n", + "y = \"bar\"\n", + "\n", + "observed_df = pl.DataFrame({x: [1, 2, 3, 4, 5], y: [2, 5, 3, 8, 7]})\n", + "synthetic_df = pl.DataFrame({x: [3, 1, 8, 3, 1], y: [1, 5, 4, 7, 6]})\n", + "print(observed_df)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "83add9d6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "" + ], + "text/plain": [ + "alt.Chart(...)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "plot_marginal_numerical_numerical(observed_df, synthetic_df, x, y)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "LPM Plot (Python 3.12)", + "language": "python", + "name": "lpm-plot" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/pyproject.toml b/pyproject.toml index ea552a2..8ac7a4f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -13,7 +13,6 @@ requires-python = ">=3.12" dependencies = [ "polars>=1.7.1", "altair>=5.4.1", - "pandas>=2.2.3", # Need pandas because of altair "pyarrow>=18.1.0", "vegafusion>=1.6.9", "vegafusion-python-embed>=1.6.9", @@ -23,9 +22,9 @@ dependencies = [ [dependency-groups] dev = [ - "black>=24.8.0", "jupyter>=1.1.1", "pytest>=8.3.3", + "ruff>=0.13.2", ] [build-system] diff --git a/src/lpm_plot/__init__.py b/src/lpm_plot/__init__.py index db61d90..145ee3b 100644 --- a/src/lpm_plot/__init__.py +++ b/src/lpm_plot/__init__.py @@ -1,6 +1,10 @@ # src/lpm_plot/__init__.py from .plot_fidelity import plot_fidelity -from .plot_marginal import plot_marginal_1d -from .plot_marginal import plot_marginal_2d -from .plot_heatmap import plot_heatmap +from .plot_heatmap import plot_heatmap, reformat_data +from .plot_marginal import ( + plot_marginal_1d, + plot_marginal_2d, + plot_marginal_numerical_categorical, + plot_marginal_numerical_numerical, +) diff --git a/src/lpm_plot/plot_fidelity.py b/src/lpm_plot/plot_fidelity.py index df7ca78..cba3ff6 100644 --- a/src/lpm_plot/plot_fidelity.py +++ b/src/lpm_plot/plot_fidelity.py @@ -1,5 +1,5 @@ -import polars as pl import altair as alt +import polars as pl alt.data_transformers.enable("vegafusion") @@ -11,13 +11,10 @@ STROKEDASH = 5 -def plot_fidelity(fidelity_df, metric="tvd"): - # Convert Polars DataFrame to Pandas for compatibility with Altair - fidelity_df_pandas = fidelity_df.to_pandas() - - # Altair visualization +def plot_fidelity(fidelity_df: pl.DataFrame, metric="tvd"): + "Plot the fidelity of the synthetic data generated by a given model based on a metric (tvd, kl, js)." line_chart = ( - alt.Chart(fidelity_df_pandas) + alt.Chart(fidelity_df) .mark_line(strokeDash=[STROKEDASH, STROKEDASH]) .encode( x=alt.X("index:O"), # ordinal scale, no axis labels @@ -27,7 +24,7 @@ def plot_fidelity(fidelity_df, metric="tvd"): ) point_chart = ( - alt.Chart(fidelity_df_pandas) + alt.Chart(fidelity_df) .mark_point() .encode( x=alt.X( diff --git a/src/lpm_plot/plot_heatmap.py b/src/lpm_plot/plot_heatmap.py index 47f8ad0..ff02a21 100644 --- a/src/lpm_plot/plot_heatmap.py +++ b/src/lpm_plot/plot_heatmap.py @@ -1,22 +1,43 @@ import altair as alt import polars as pl -from scipy.cluster.hierarchy import linkage, leaves_list +from scipy.cluster.hierarchy import leaves_list, linkage from scipy.spatial.distance import squareform -import numpy as np -def plot_heatmap(df: pl.DataFrame, cmap="greens"): +def plot_heatmap( + df: pl.DataFrame, + detailed_df: pl.DataFrame = None, + cmap_main: str = "greens", + cmap_detail: str = "greys", + detail_color: str = "black", + interactive: bool = True, +): """ Generates a clustered heatmap using hierarchical clustering on a Polars DataFrame and visualizes it with Altair. Parameters ---------- df : pl.DataFrame - A Polars DataFrame containing the data to be plotted, with columns "Column 1", "Column 2", and "Score". + A Polars DataFrame with columns named "Column 1", "Column 2", and "Score." "Column 1" and "Column 2" represent categorical labels along the x- and y-axes, respectively, - while "Score" provides quantitative values for coloring the heatmap. - cmap : str, optional - Color scheme name for the heatmap. Defaults to "greens". + while "Score" provides numerical values for coloring the heatmap. + detailed_df : pl.DataFrame, optional + A Polars DataFrame containing more detailed information about the additional graph displayed after clicking specific heatmap cells. + The detailed_df has the following columns: + - "Column 1": Data class 1 + - "Column 2": Data class 2 + - "comparison_type": Specifies the graph type used to compare the two classes and can take the values "num-num", "num-cat", "cat-num", "cat-cat", and "same-same" + - "x_data": Data for the x-axis + - "y_data": Data for the y-axis + cmap_main : str, optional + Color scheme name for the mutual information heatmap. Defaults to "greens". + cmap_detail : str, optional + Color scheme name for the detail heatmap. Defaults to "greys". + detail_color : str, optional + Color for detail scatter and box plots. Defaults to "black". + interactive : bool, optional + Whether to make the plot interactive. When True, enables zooming, panning, and a detailed 2D subplot of the selected heatmap cell. + Defaults to True. Returns ------- @@ -25,25 +46,67 @@ def plot_heatmap(df: pl.DataFrame, cmap="greens"): Notes ----- - - The function first converts the Polars DataFrame to a pandas DataFrame to support operations required - for hierarchical clustering and plotting with Altair. - - A square matrix (pivot table) is created with "Column 1" and "Column 2" as indices, containing "Score" + 1) Creates a square matrix (pivot table) with "Column 1" and "Column 2" as indices, containing "Score" as values. - - Hierarchical clustering is performed on the data to define the optimal ordering of rows and columns - for visualization, providing clearer patterns in the heatmap. - - The generated Altair Chart includes tooltips for "Column 1", "Column 2", and "Score" for interactive exploration. + 2) Performs hierarchical clustering on the data to define the optimal ordering of rows and columns for visualization, providing clearer patterns in the heatmap. + 3) The generated Altair Chart includes tooltips for "Column 1", "Column 2", and "Score" for interactive exploration. + + + - Current limitations: For numerical vs categorical comparisons in the detail graph, the number ticks appear on the opposite sides to avoid conflicting with + other graph labels. These number ticks show as a single 0 on other graphs which is why they are moved to the opposite + side to avoid overlaps. This is an unavoidable consequence of making an interactive vega-lite graphs like this. + - The detail graph has a frequency bar that is unable to be hidden while non-heatmap graphs are displayed due to the limitations of vega-lite """ assert "Column 1" in df.columns assert "Column 2" in df.columns assert "Score" in df.columns - # Convert the polars dataframe to pandas for clustering and plotting - df_pandas = df.to_pandas() + + # Get all unique values from both columns to ensure square matrix + all_unique_values = sorted( + set(df["Column 1"].unique().to_list() + df["Column 2"].unique().to_list()) + ) # Pivot the data to create a square matrix for clustering - df_pivot = df_pandas.pivot(index="Column 1", columns="Column 2", values="Score") + df_pivot = df.pivot( + values="Score", index="Column 1", on="Column 2", aggregate_function="first" + ) + + # Get the row labels (Column 1 values) - ensure all unique values are present + row_labels = df_pivot["Column 1"].to_list() + # Ensure all rows are present (add missing rows with nulls) + missing_rows = set(all_unique_values) - set(row_labels) + if missing_rows: + missing_df = pl.DataFrame( + { + "Column 1": list(missing_rows), + **{ + col: [None] * len(missing_rows) + for col in df_pivot.columns + if col != "Column 1" + }, + } + ) + df_pivot = pl.concat([df_pivot, missing_df]) + # Re-sort to match all_unique_values order + df_pivot = df_pivot.sort("Column 1") + row_labels = df_pivot["Column 1"].to_list() + + # Get data columns (Column 2 values) - ensure all unique values are present as columns + data_columns = [col for col in df_pivot.columns if col != "Column 1"] + missing_cols = set(all_unique_values) - set(data_columns) + if missing_cols: + for col in missing_cols: + df_pivot = df_pivot.with_columns(pl.lit(None).alias(col)) + # Reorder columns to match all_unique_values + df_pivot = df_pivot.select(["Column 1"] + all_unique_values) + data_columns = all_unique_values + + # Extract the data matrix (excluding the "Column 1" column) and convert to numpy + data_matrix = df_pivot.select(data_columns).fill_null(0).to_numpy() + # Create a condensed distance matrix from the pivoted matrix - distance_matrix = 1 - df_pivot - condensed_matrix = distance_matrix.fillna(0).to_numpy() # Handle NaNs + distance_matrix = 1 - data_matrix + condensed_matrix = distance_matrix # Already a numpy array # Perform hierarchical clustering linkage_matrix = linkage( @@ -51,28 +114,398 @@ def plot_heatmap(df: pl.DataFrame, cmap="greens"): ) # Get the order of the rows/columns after clustering - order = [df_pivot.index[i] for i in leaves_list(linkage_matrix)] + order = [row_labels[i] for i in leaves_list(linkage_matrix)] + + # Define filter fields for selected cells (only if interactive) + if interactive: + click = alt.selection_point( + fields=["Column 1", "Column 2"], + on="click", + value=[{"Column 1": None}, {"Column 2": None}], + clear=False, + ) + else: + click = None + # Create the heatmap using Altair - base = ( - alt.Chart(df_pandas) - .mark_rect() - .encode( - x=alt.X( - "Column 1:N", - title="Column 1", - sort=order, # Replace with your desired order + base = alt.Chart(df).mark_rect() + + # Add click parameter only if interactive + if click is not None: + base = base.add_params(click) + + base = base.encode( + x=alt.X( + "Column 1:N", + title="Column 1", + sort=order, # Replace with your desired order + ), + y=alt.Y( + "Column 2:N", + title="Column 2", + sort=order, # Replace with your desired order + ), + color=alt.condition( + alt.datum.Score == 0, + alt.value("white"), + alt.Color( + "Score:Q", scale=alt.Scale(scheme=cmap_main), legend=alt.Legend() ), + ), + tooltip=["Column 1", "Column 2", "Score:Q"], + ).properties(width=400, height=400) # Default scale + + # Return heatmap if no detailed data is provided + if detailed_df is None: + if interactive: + return base.interactive() + else: + return base.properties(title="Mutual Information Heatmap") + + # If not interactive, return just the base heatmap even if detailed_df is provided + if not interactive: + return base.properties(title="Mutual Information Heatmap") + + detailed_df = detailed_df.vstack( + pl.DataFrame( + { + "Column 1": None, + "Column 2": None, + "comparison_type": "none", + "x_data": None, + "y_data": None, + } + ) + ) + + cat_cat_df = detailed_df.filter(pl.col("comparison_type") == "cat-cat") + + # Get frequency counts for all categorical-categorical data + counted_df = ( + cat_cat_df.group_by( + ["Column 1", "Column 2", "comparison_type", "x_data", "y_data"] + ) + .len() + .rename({"len": "Frequency"}) + ) + + # Get all present unique category combo for each cat-cat comparison + column_combos = cat_cat_df["Column 1", "Column 2"].unique() + combo_filtered_dfs: list[pl.DataFrame] = [] + for combo in column_combos.iter_rows(): + combo_filtered_dfs.append( + cat_cat_df.filter( + (pl.col("Column 1") == combo[0]) & (pl.col("Column 2") == combo[1]) + ).unique(subset=["x_data", "y_data"]) + ) + + # Add missing category combos with a frequency of 0 + for combo_filered_df in combo_filtered_dfs: + combos = combo_filered_df["x_data", "y_data"].rows() + x_vals = combo_filered_df["x_data"].unique() + y_vals = combo_filered_df["y_data"].unique() + for x in x_vals: + for y in y_vals: + if not x == y and (x, y) not in combos: + counted_df = counted_df.vstack( + pl.DataFrame( + [combo_filered_df.row(0)[:3] + (x, y, 0)], + schema=counted_df.schema, + orient="row", + ) + ) + + # Replace detail_df cat-cat comparisons with counted versions + detailed_df = ( + detailed_df.filter(pl.col("comparison_type") != "cat-cat") + .with_columns(pl.lit(0, dtype=pl.UInt32).alias("Frequency")) + .vstack(counted_df) + ) + + # Empty graph that is displayed when data is compared to itself + empty = alt.Chart(detailed_df).mark_text( + text="No Data: self comparison", strokeWidth=0.5 + ) + if click is not None: + empty = empty.transform_filter( + click & (alt.datum.comparison_type == "same-same") + ) + + # Empty graph that is displayed when nothing is selected at the start + empty2 = alt.Chart(detailed_df).mark_text(text="Nothing selected", strokeWidth=0.5) + if click is not None: + empty2 = empty2.transform_filter(click & (alt.datum.comparison_type == "none")) + + # Scatter plot when the data compared are both numerical + scatter_plot = ( + alt.Chart(detailed_df) + .mark_circle(size=60, color=detail_color) + .encode( + x=alt.X("x_data:Q", title=None, axis=alt.Axis(orient="bottom")), + y=alt.Y("y_data:Q", title=None, axis=alt.Axis(orient="left")), + tooltip=["x_data:Q", "y_data:Q"], + ) + ) + if click is not None: + scatter_plot = scatter_plot.transform_filter( + click & (alt.datum.comparison_type == "num-num") + ) + + # Box plot when the data compared is numerical and categorical + box_plot_horizontal = ( + alt.Chart(detailed_df) + .mark_boxplot(size=60, color=detail_color) + .encode( + x=alt.X("x_data:Q", title=None, axis=alt.Axis(orient="top")), y=alt.Y( - "Column 2:N", - title="Column 2", - sort=order, # Replace with your desired order + "y_data:N", + scale=alt.Scale(padding=0.5), + title=None, + axis=alt.Axis(orient="left"), + ), + tooltip=["x_data:N", "y_data:Q"], + ) + ) + if click is not None: + box_plot_horizontal = box_plot_horizontal.transform_filter( + click & (alt.datum.comparison_type == "num-cat") + ) + + # Box plot when the data compared is categorical and numerical + box_plot_vertical = ( + alt.Chart(detailed_df) + .mark_boxplot(size=60, color=detail_color) + .encode( + x=alt.X( + "x_data:N", + scale=alt.Scale(padding=0.5), + title=None, + axis=alt.Axis(orient="bottom"), ), - color=alt.Color( - "Score:Q", - scale=alt.Scale(scheme=cmap), + y=alt.Y("y_data:Q", title=None, axis=alt.Axis(orient="right")), + tooltip=["x_data:N", "y_data:Q"], + ) + ) + if click is not None: + box_plot_vertical = box_plot_vertical.transform_filter( + click & (alt.datum.comparison_type == "cat-num") + ) + + # Heat map when the data compared are both categorical + heatmap = ( + alt.Chart(detailed_df) + .mark_rect(stroke="white") + .encode( + x=alt.X("x_data:N", title=None, axis=alt.Axis(orient="bottom")), + y=alt.Y("y_data:N", title=None, axis=alt.Axis(orient="left")), + color=alt.condition( + alt.datum.Frequency == 0, + alt.value("white"), + alt.Color( + "Frequency:Q", + scale=alt.Scale(scheme=cmap_detail, domainMin=0), + legend=alt.Legend(orient="right", offset=10), + ), ), - tooltip=["Column 1", "Column 2", "Score:Q"], + tooltip=["x_data:N", "y_data:N", "Frequency:Q"], ) ) + if click is not None: + heatmap = heatmap.transform_filter( + click & (alt.datum.comparison_type == "cat-cat") + ) + + # Y axis label - positioned to align with the detail chart + labels_y = ( + alt.Chart(detailed_df) + .mark_text(angle=270, strokeWidth=0.5, fontSize=12) + .encode( + text="Column 2:N", + x=alt.value(15), # Left of the detail chart + y=alt.value(150), # Center of the 300px high detail chart + ) + ) + if click is not None: + labels_y = labels_y.transform_filter(click) + + # Layer charts together + detail_charts = alt.hconcat( + labels_y.properties(width=30), # Narrow space for Y labels + alt.layer( + empty, + empty2, + scatter_plot, + box_plot_horizontal, + box_plot_vertical, + heatmap, + ) + .resolve_scale(x="shared", y="shared", color="independent") + .resolve_legend(color="independent") + .properties( + width=400, height=300 + ), # Increased width to accommodate legend and prevent cutoff + spacing=5, + ) + + # X axis labels - positioned to align with the detail chart + # Create a chart that matches the main detail chart width for proper alignment + labels_x_chart = ( + alt.Chart(detailed_df) + .mark_text(align="center", strokeWidth=0.5, fontSize=12) + .encode( + text="Column 1:N", + x=alt.value(200), # Center of the 400px wide detail chart (400/2) + y=alt.value(15), # Below the detail chart + ) + .properties(width=400, height=30) # Match the main detail chart width + ) + if click is not None: + labels_x_chart = labels_x_chart.transform_filter(click) + + # Create the detail section with proper horizontal alignment + # Use hconcat to include labels_x in the same horizontal layout as detail_charts + # Create an empty spacer chart to match the labels_y width + # Use detailed_df with a filter that never matches to avoid Arrow buffer alignment issues + spacer = ( + alt.Chart(detailed_df) + .mark_text(opacity=0) + .transform_filter(alt.datum.comparison_type == "never-matches") + .properties(width=30, height=30) + ) + detail_charts_with_labels = alt.hconcat( + spacer, # Spacer to match labels_y width (30px) + labels_x_chart, + spacing=5, + ) + + # Create the main layout with proper spacing + # Wrap detail_charts and labels_x in a vconcat to add title + detail_section = alt.vconcat( + detail_charts, + detail_charts_with_labels, + ).properties(title="2D Detailed View (click on heatmap cells)") + + chart = alt.vconcat( + base.properties(title="Mutual Information Heatmap"), + detail_section, + ).properties( + padding={"left": 60, "right": 130, "top": 40, "bottom": 80}, spacing=20 + ) - return base + # Apply interactivity if requested + if interactive: + return chart.interactive() + else: + return chart + + +def reformat_data( + heatmap_df: pl.DataFrame, all_data: pl.DataFrame, data_margin: float = 1.0 +): + """ + Reformats the provided polars data frame so they can be used by the plot_heatmap function + + Parameters + ---------- + heatmap_df : pl.DataFrame + A Polars DataFrame containing the data to be plotted, with columns "Column 1", "Column 2", and "Score". + "Column 1" and "Column 2" represent categorical labels along the x- and y-axes, respectively, + while "Score" provides quantitative values for coloring the heatmap. + all_data : pl.DataFrame + A Polars DataFrame containing more detailed data about the data used to make the heatmap. Columns are specific data classes + and each row should represent all the data about specific sample. + data_margin : float, optional + Ratio all_data that should be reformatted from 0-1 + + Returns + ------- + tuple[pl.DataFrame, pl.DataFrame] + Returns a tuple containing the reformatted heatmap and all_data data frames + + Notes + ----- + - For the heatmap dataframe the only change is replace the missing value for score in the case of matching column values + - all_data is reformatted to have "Column 1", "Column 2", "comparison_type", "x_data", and "y_data". "Column 1" and "Column 2" + represent which 2 data classes are being compared and "comparison_tpye" determines which graph is used to show + the comparison and can be "num-num", "num-cat", "cat-num", "cat-cat", and "same-same". "x_data" and "y_data" are the + data being shown in the comparison referring to "Column 1" and "Column 2" respectively. + """ + # Create df that will become the return resilt for all_data + detail_df = pl.DataFrame( + { + "Column 1": pl.Series("Column 1", [], dtype=pl.String), + "Column 2": pl.Series("Column 2", [], dtype=pl.String), + "comparison_type": pl.Series("comparison_type", [], dtype=pl.String), + "x_data": pl.Series("x_data", [], dtype=pl.Object), + "y_data": pl.Series("y_data", [], dtype=pl.Object), + } + ) + + # Get margin of all_data to be used + data_slice = int(len(all_data) * data_margin) + all_data = all_data[:data_slice] + + for row in heatmap_df.iter_rows(): + # Iterate through every column combo and get data pertaining to it + cat1_data = all_data.get_column(row[0]) + cat2_data = all_data.get_column(row[1]) + + # Handle if the combo is a self comparison + if row[0] == row[1]: + detail_df = pl.concat( + [ + detail_df, + pl.DataFrame( + { + "Column 1": [row[0]], + "Column 2": [row[1]], + "comparison_type": ["same-same"], + "x_data": [None], + "y_data": [None], + } + ), + ], + how="vertical_relaxed", + ) + else: + for i in range(len(all_data)): + if cat1_data[i] is not None and cat2_data[i] is not None: + # Determine the comparison type + comparison_type = "" + if cat1_data.dtype == pl.String: + comparison_type = "cat-" + comparison_type + elif cat1_data.dtype == pl.Int64 or cat1_data.dtype == pl.Float64: + comparison_type = "num-" + comparison_type + + if cat2_data.dtype == pl.String: + comparison_type += "cat" + elif cat2_data.dtype == pl.Int64 or cat1_data.dtype == pl.Float64: + comparison_type += "num" + + # Add the data to the resulting data frame + detail_df = pl.concat( + [ + detail_df, + pl.DataFrame( + { + "Column 1": [row[0]], + "Column 2": [row[1]], + "comparison_type": [comparison_type], + "x_data": [cat1_data[i]], + "y_data": [cat2_data[i]], + } + ), + ], + how="vertical_relaxed", + ) + + return ( + # Make all self-comparisons have a score of 0 in heatmap_df + heatmap_df.with_columns( + pl.when(pl.col("Column 1") == pl.col("Column 2")) + .then(pl.lit(0.0)) + .otherwise(pl.col("Score")) + .alias("Score") + ), + detail_df, + ) diff --git a/src/lpm_plot/plot_marginal.py b/src/lpm_plot/plot_marginal.py index 425bd1b..22feee0 100644 --- a/src/lpm_plot/plot_marginal.py +++ b/src/lpm_plot/plot_marginal.py @@ -1,5 +1,5 @@ -import polars as pl import altair as alt +import polars as pl OBSERVED_COLOR = "#000000" SYNTHETIC_COLOR = "#f28e2b" @@ -8,18 +8,18 @@ def get_max_frequency(column, data): - # Group by `data_source` and the given `column`, then count occurrences + "Calculate the maximum frequency value for a given column. This is used to align the axes of the comparison plots." result = ( data.group_by(["data_source", column]) - .agg(pl.count(column).alias("count")) # Count occurrences of each value + .agg(pl.count(column).alias("count")) .group_by("data_source") - .agg(pl.max("count").alias("max_count")) # Get max count for each data_source + .agg(pl.max("count").alias("max_count")) ) - # Return the max of the max counts return result.select(pl.max("max_count")).item() def plot_marginal_1d(observed_df, synthetic_df, columns): + "Plot 1D marginal plots for a given list of columns." assert len(columns) > 0.0 for c in columns: assert c in observed_df.columns, "column not in observed data" @@ -34,13 +34,13 @@ def plot_marginal_1d(observed_df, synthetic_df, columns): data = pl.concat([observed_df, synthetic_df]) - # XXX: horrible hack; but Altair doesn't allow me to add a custom legend. + # Issue: Altair doesn't allow me to add a custom legend, using this dummy data workaround. dummy_data_for_legend = pl.DataFrame( { "category": ["Observed", "Synthetic"], - "dummy": [0, 0], # Dummy column to avoid visible marks + "dummy": [0, 0], } - ).to_pandas() + ) # Create an empty plot with a legend and no visible marks legend = ( @@ -58,43 +58,78 @@ def plot_marginal_1d(observed_df, synthetic_df, columns): ) ) - def create_marginal_chart(field, color, max_count): - return ( - alt.Chart(data.to_pandas()) - .mark_bar(color=color) + # Creating the charts for observed and synthetic collections + def create_comparison(column, data): + max_count = get_max_frequency(column, data) + + # Pre-aggregate data in Polars to avoid VegaFusion type casting issues + # Filter and aggregate observed data + observed_agg = ( + data.filter(pl.col("data_source") == "observed") + .filter(pl.col(column).is_not_null()) + .group_by(column) + .agg(pl.len().alias("count")) + .with_columns(pl.lit("observed").alias("data_source")) + ) + + # Filter and aggregate synthetic data + synthetic_agg = ( + data.filter(pl.col("data_source") == "synthetic") + .filter(pl.col(column).is_not_null()) + .group_by(column) + .agg(pl.len().alias("count")) + .with_columns(pl.lit("synthetic").alias("data_source")) + ) + + # Create charts with pre-aggregated data + chart_observed = ( + alt.Chart(observed_agg) + .mark_bar(color=OBSERVED_COLOR) .encode( x=alt.X( - "count():Q", + "count:Q", scale=alt.Scale(domain=[0, max_count]), axis=alt.Axis(orient="top"), ), y=alt.Y( - f"{field}:N", + f"{column}:N", axis=alt.Axis( titleAnchor="start", titleAlign="right", titlePadding=1, - titleAngle=0, # Rotate the y-axis label by 90 degrees (change as needed) + titleAngle=0, ), ), - color=alt.value(color), + color=alt.value(OBSERVED_COLOR), ) - .transform_filter(f"datum.{field} != null") # Filter out null values + .properties(width=300, height=200) ) - # Creating the charts for observed and synthetic collections - def create_comparison(column, data): - max_count = get_max_frequency(column, data) - chart_observed = create_marginal_chart( - column, OBSERVED_COLOR, max_count - ).transform_filter(alt.datum.data_source == "observed") - chart_synthetic = create_marginal_chart( - column, SYNTHETIC_COLOR, max_count - ).transform_filter(alt.datum.data_source == "synthetic") + chart_synthetic = ( + alt.Chart(synthetic_agg) + .mark_bar(color=SYNTHETIC_COLOR) + .encode( + x=alt.X( + "count:Q", + scale=alt.Scale(domain=[0, max_count]), + axis=alt.Axis(orient="top"), + ), + y=alt.Y( + f"{column}:N", + axis=alt.Axis( + titleAnchor="start", + titleAlign="right", + titlePadding=1, + titleAngle=0, + ), + ), + color=alt.value(SYNTHETIC_COLOR), + ) + .properties(width=300, height=200) + ) return alt.hconcat(chart_observed, chart_synthetic) one_d_plots = [create_comparison(column, data) for column in columns] - # Layer the charts for observed and synthetic data points combined_chart = ( alt.vconcat(*one_d_plots, legend) .resolve_scale(color="independent") @@ -103,9 +138,56 @@ def create_comparison(column, data): return combined_chart +def prepare_2d_marginal_data(observed_df, synthetic_df, x, y): + """ + Prepare data for 2D marginal plotting by calculating normalized frequencies. + + Args: + observed_df (pl.DataFrame): Observed data + synthetic_df (pl.DataFrame): Synthetic data + x (str): First categorical column name + y (str): Second categorical column name + + Returns: + pl.DataFrame: Combined dataframe with Source and Normalized frequency columns + """ + # Ensure both dataframes have the same column order + columns = [x, y] + observed_subset = observed_df.select(columns) + synthetic_subset = synthetic_df.select(columns) + + observed_labeled = observed_subset.with_columns(pl.lit("Observed").alias("Source")) + synthetic_labeled = synthetic_subset.with_columns( + pl.lit("Synthetic").alias("Source") + ) + + combined = pl.concat([observed_labeled, synthetic_labeled]) + + freq_data = combined.group_by(["Source", x, y]).agg( + pl.count().cast(pl.Int64).alias("count") + ) + + total_counts = freq_data.group_by("Source").agg( + pl.sum("count").cast(pl.Int64).alias("total_count") + ) + + result = ( + freq_data.join(total_counts, on="Source") + .with_columns( + ( + pl.col("count").cast(pl.Float64) + / pl.col("total_count").cast(pl.Float64) + ).alias("Normalized frequency") + ) + .drop("total_count") + ) + + return result + + def plot_marginal_2d(combined_df, x, y, hm_order=None, cmap="oranges"): """ - Plots 2D marginal heatmaps of normalized frequencies for categorical variables across multiple sources. + Plots 2D marginal heatmaps of normalized frequencies to compare relationships between categorical variables in different dataset sources (e.g. observed and synthetic). This function generates a series of 2D heatmaps (one per data source) that visualize the bivariate frequencies of two categorical variables (`x` and `y`). The heatmaps are displayed side by side, sharing @@ -115,7 +197,7 @@ def plot_marginal_2d(combined_df, x, y, hm_order=None, cmap="oranges"): Args: combined_df (pl.DataFrame): A Polars DataFrame containing the combined data from different sources. It must contain the columns specified by `x`, `y`, and a "Source" column, as well as a - "Normalized frequency" column with the frequencies. + "Normalized frequency" column with the normalized frequencies pre-calculated (can use prepare_2d_marginal_data for this). x (str): The name of the first categorical column (horizontal axis of the heatmap). y (str): The name of the second categorical column (vertical axis of the heatmap). hm_order (list of str, optional): A custom order for the sources for the plot. @@ -125,31 +207,189 @@ def plot_marginal_2d(combined_df, x, y, hm_order=None, cmap="oranges"): Returns: alt.Chart: An Altair chart object containing the concatenated heatmaps, one for each source. """ - base = ( - alt.Chart(combined_df.to_pandas()) - .mark_rect() + # Check if users wanted to order the datasources. + if hm_order is None: + order = sorted(combined_df["Source"].unique().to_list()) + else: + order = hm_order + + # Pre-filter data in Polars for each source to avoid VegaFusion type casting issues + heatmaps = [] + for source in order: + source_df = combined_df.filter(pl.col("Source") == source) + heatmap = ( + alt.Chart(source_df) + .mark_rect() + .encode( + x=alt.X(f"{x}:N", title=f"{x}"), + y=alt.Y(f"{y}:N", title=f"{y}"), + color=alt.Color( + "Normalized frequency:Q", + scale=alt.Scale(scheme=cmap), + title="Normalized Count", + ), + tooltip=[x, y, "Normalized frequency:Q"], + ) + .properties(width=400, height=400, title=source) + ) + heatmaps.append(heatmap) + + combined_heatmap = alt.hconcat(*heatmaps).resolve_scale(color="shared") + return combined_heatmap + + +def plot_marginal_numerical_numerical( + observed_df: pl.DataFrame, + synthetic_df: pl.DataFrame, + x: str, + y: str, + x_domain: tuple[float | None, float | None] = [None, None], + y_domain: tuple[float | None, float | None] = [None, None], +): + """ + Plots 2D marginal scatter plot comparing numerical observed and synthetic data + which are displayed black and orange respectively. + + Args: + observed_df : pl.DataFrame + A Polars DataFrame containing the observed data and it must contain the columns specified by `x`, `y` + synthetic_df : pl.DataFrame + A Polars DataFrame containing the synthetic data and it must contain the columns specified by `x`, `y` + x : str + The name of the first numerical column (horizontal axis of the plot). + y : str + The name of the second numerical column (vertical axis of the plot). + x_domain : tuple[float | None, float | None], optional + The domain of the x-axis and defaults to min and max of data. If None is provided instead of a min + or a max, then the program will default to using the min or max of the data respectively. + y_domain : tuple[float | None, float | None], optional + The domain of the y-axis and defaults to min and max of data. If None is provided instead of a min + or a max, then the program will default to using the min or max of the data respectively. + + Returns: + alt.Chart: An Altair chart object containing the scatter plot. + """ + x_domain = [ + ( + min(observed_df[x].min(), synthetic_df[x].min()) + if x_domain[0] is None + else x_domain[0] + ), + ( + max(observed_df[x].max(), synthetic_df[x].max()) + if x_domain[1] is None + else x_domain[1] + ), + ] + y_domain = [ + ( + min(observed_df[y].min(), synthetic_df[y].min()) + if y_domain[0] is None + else y_domain[0] + ), + ( + max(observed_df[y].max(), synthetic_df[y].max()) + if y_domain[1] is None + else y_domain[1] + ), + ] + + # Make a combined data frame with a new dataset column specifying if the data is observed or synthetic + observed_df_labeled = observed_df.with_columns(pl.lit("Observed").alias("dataset")) + synthetic_df_labeled = synthetic_df.with_columns( + pl.lit("Synthetic").alias("dataset") + ) + combined_df = pl.concat([observed_df_labeled, synthetic_df_labeled], how="vertical") + + chart = ( + alt.Chart(combined_df) + .mark_circle(color=OBSERVED_COLOR) .encode( - x=alt.X(f"{x}:N", title=f"{x}"), - y=alt.Y(f"{y}:N", title=f"{y}"), + x=alt.X(x, scale=alt.Scale(domain=x_domain)), + y=alt.Y(y, scale=alt.Scale(domain=y_domain)), color=alt.Color( - "Normalized frequency:Q", - scale=alt.Scale(scheme=cmap), - title="Normalized Count", + "dataset:N", + scale=alt.Scale( + domain=["Observed", "Synthetic"], + range=[OBSERVED_COLOR, SYNTHETIC_COLOR], + ), + legend=alt.Legend(title="Legend", symbolStrokeWidth=4), ), - tooltip=[x, y, "Normalized frequency:Q"], ) + .properties(width=500, height=500) ) - # Check if users wanted to order the datasources. - if hm_order is None: - order = sorted(combined_df["Source"].unique()) - else: - order = hm_order - heatmaps = [ - base.transform_filter(alt.datum.Source == source).properties(title=source) - for source in order + + return chart + + +def plot_marginal_numerical_categorical( + observed_df: pl.DataFrame, + synthetic_df: pl.DataFrame, + x: str, + y: str, + size: float = 30.0, + y_domain: tuple[float, float] = [None, None], +): + """ + Plots 2D marginal box plot comparing numerical vs categorical observed and synthetic data + which are displayed black and orange respectively. + + Args: + observed_df : pl.DataFrame + A Polars DataFrame containing the observed data. The columns of the dataframe should be the names + of each category of the categorical data and each column contains the numerical data pertaining to that + category. + synthetic_df : pl.DataFrame + A Polars DataFrame containing the synthetix data. The columns of the dataframe should be the names + of each category of the categorical data and each column contains the numerical data pertaining to that + category. Must have the + same columns as the observed dataframe + x : str + The name of the categorical data (horizontal axis of the plot). + y : str + The name of the numerical data (vertical axis of the plot). + size : float, optional + The width of the box plot boxes. + y_domain : tuple[float, float], optional + The domain of the y-axis and defaults to min and max of data. If None is provided instead of a min + or a max, then the program will default to using the min or max of the data respectively. + + Returns: + alt.Chart: An Altair chart object containing the box plot. + """ + # Add a new column to distinguish which data set the data came from + observed_df_labeled = observed_df.with_columns(pl.lit("Observed").alias("dataset")) + synthetic_df_labeled = synthetic_df.with_columns( + pl.lit("Synthetic").alias("dataset") + ) + + combined_df = pl.concat([observed_df_labeled, synthetic_df_labeled]) + + y_domain = [ + combined_df[y].min() if y_domain[0] is None else y_domain[0], + combined_df[y].max() if y_domain[1] is None else y_domain[1], ] - # Concatenate the heatmaps horizontally and ensure they share the color scale - combined_heatmap = alt.hconcat(*heatmaps).resolve_scale( - color="shared" # Share the color scale between the heatmaps + + return ( + alt.Chart(combined_df) + .mark_boxplot(size=size, outliers=True) + .encode( + x=alt.X(f"{x}:N", scale=alt.Scale(padding=0.5)), + y=alt.Y(f"{y}:Q", scale=alt.Scale(domain=y_domain)), + color=alt.Color( + "dataset:N", + scale=alt.Scale( + domain=["Observed", "Synthetic"], + range=[OBSERVED_COLOR, SYNTHETIC_COLOR], + ), + ), + xOffset=alt.XOffset( + "dataset:N", + scale=alt.Scale( + domain=["Observed", "Synthetic"], + range=[-size, size], + ), + ), + ) + .properties(width=(size * 2 + 50) * combined_df[x].n_unique(), height=400) ) - return combined_heatmap diff --git a/tests/resources/Pairwise_mutual_information.parquet b/tests/resources/Pairwise_mutual_information.parquet new file mode 100644 index 0000000..58c249f Binary files /dev/null and b/tests/resources/Pairwise_mutual_information.parquet differ diff --git a/tests/resources/demo-detail-df.parquet b/tests/resources/demo-detail-df.parquet new file mode 100644 index 0000000..ee7c0be Binary files /dev/null and b/tests/resources/demo-detail-df.parquet differ diff --git a/tests/resources/demo-heatmap-df.parquet b/tests/resources/demo-heatmap-df.parquet new file mode 100644 index 0000000..d1e1753 Binary files /dev/null and b/tests/resources/demo-heatmap-df.parquet differ diff --git a/tests/resources/heatmap-detailed-test-data.csv b/tests/resources/heatmap-detailed-test-data.csv new file mode 100644 index 0000000..97401b5 --- /dev/null +++ b/tests/resources/heatmap-detailed-test-data.csv @@ -0,0 +1,119 @@ +Column 1,Column 2,comparison_type,x_data,y_data +num1,num1,same-same +num2,num2,same-same +cat1,cat1,same-same +cat2,cat2,same-same + +num1,num2,num-num,1.2,3.5 +num1,num2,num-num,4.0,2.4 +num1,num2,num-num,2.2,6.0 +num1,num2,num-num,1.3,4.6 +num1,num2,num-num,4.5,0.5 + +num1,cat1,num-cat,1.2,foo +num1,cat1,num-cat,3.8,foo +num1,cat1,num-cat,4.3,foo +num1,cat1,num-cat,8.3,bar +num1,cat1,num-cat,1.9,bar +num1,cat1,num-cat,3.4,bar +num1,cat1,num-cat,6.2,quagga +num1,cat1,num-cat,3.2,quagga +num1,cat1,num-cat,2.5,quagga + +num1,cat2,num-cat,7.1,yes +num1,cat2,num-cat,6.7,yes +num1,cat2,num-cat,3.2,yes +num1,cat2,num-cat,8.1,no +num1,cat2,num-cat,1.9,no +num1,cat2,num-cat,2.8,no + +num2,num1,num-num,3.5,1.2 +num2,num1,num-num,2.4,4.0 +num2,num1,num-num,6.0,2.2 +num2,num1,num-num,4.6,1.3 +num2,num1,num-num,0.5,4.5 + +num2,cat1,num-cat,4.4,foo +num2,cat1,num-cat,3.2,foo +num2,cat1,num-cat,2.0,foo +num2,cat1,num-cat,3.6,bar +num2,cat1,num-cat,1.4,bar +num2,cat1,num-cat,9.1,bar +num2,cat1,num-cat,7.6,quagga +num2,cat1,num-cat,3.2,quagga +num2,cat1,num-cat,1.0,quagga + +num2,cat2,num-cat,4.1,yes +num2,cat2,num-cat,9.3,yes +num2,cat2,num-cat,3.9,yes +num2,cat2,num-cat,7.3,no +num2,cat2,num-cat,4.5,no +num2,cat2,num-cat,6.7,no + +cat1,num1,cat-num,foo,1.2 +cat1,num1,cat-num,foo,3.8 +cat1,num1,cat-num,foo,4.3 +cat1,num1,cat-num,bar,8.3 +cat1,num1,cat-num,bar,1.9 +cat1,num1,cat-num,bar,3.4 +cat1,num1,cat-num,quagga,6.2 +cat1,num1,cat-num,quagga,3.2 +cat1,num1,cat-num,quagga,2.5 + +cat1,num2,cat-num,foo,4.4 +cat1,num2,cat-num,foo,3.2 +cat1,num2,cat-num,foo,2.0 +cat1,num2,cat-num,bar,3.6 +cat1,num2,cat-num,bar,1.4 +cat1,num2,cat-num,bar,9.1 +cat1,num2,cat-num,quagga,7.6 +cat1,num2,cat-num,quagga,3.2 +cat1,num2,cat-num,quagga,1.0 + +cat1,cat2,cat-cat,foo,yes +cat1,cat2,cat-cat,foo,yes +cat1,cat2,cat-cat,foo,no +cat1,cat2,cat-cat,foo,no +cat1,cat2,cat-cat,foo,no +cat1,cat2,cat-cat,bar,yes +cat1,cat2,cat-cat,bar,yes +cat1,cat2,cat-cat,bar,yes +cat1,cat2,cat-cat,bar,yes +cat1,cat2,cat-cat,bar,yes +cat1,cat2,cat-cat,bar,yes +cat1,cat2,cat-cat,quagga,yes +cat1,cat2,cat-cat,quagga,yes +cat1,cat2,cat-cat,quagga,no +cat1,cat2,cat-cat,quagga,no +cat1,cat2,cat-cat,quagga,no + +cat2,num1,cat-num,yes,7.1 +cat2,num1,cat-num,yes,6.7 +cat2,num1,cat-num,yes,3.2 +cat2,num1,cat-num,no,8.1 +cat2,num1,cat-num,no,1.9 +cat2,num1,cat-num,no,2.8 + +cat2,num2,cat-num,yes,4.1 +cat2,num2,cat-num,yes,9.3 +cat2,num2,cat-num,yes,3.9 +cat2,num2,cat-num,no,7.3 +cat2,num2,cat-num,no,4.5 +cat2,num2,cat-num,no,6.7 + +cat2,cat1,cat-cat,yes,foo +cat2,cat1,cat-cat,yes,foo +cat2,cat1,cat-cat,no,foo +cat2,cat1,cat-cat,no,foo +cat2,cat1,cat-cat,no,foo +cat2,cat1,cat-cat,yes,bar +cat2,cat1,cat-cat,yes,bar +cat2,cat1,cat-cat,yes,bar +cat2,cat1,cat-cat,yes,bar +cat2,cat1,cat-cat,yes,bar +cat2,cat1,cat-cat,yes,bar +cat2,cat1,cat-cat,yes,quagga +cat2,cat1,cat-cat,yes,quagga +cat2,cat1,cat-cat,no,quagga +cat2,cat1,cat-cat,no,quagga +cat2,cat1,cat-cat,no,quagga \ No newline at end of file diff --git a/tests/resources/heatmap-test-data.csv b/tests/resources/heatmap-test-data.csv new file mode 100644 index 0000000..113fecd --- /dev/null +++ b/tests/resources/heatmap-test-data.csv @@ -0,0 +1,17 @@ +Column 1,Column 2,Score +num1,num1,0 +num1,num2,0.75 +num1,cat1,0.42 +num1,cat2,0.38 +num2,num1,0.75 +num2,num2,0 +num2,cat1,0.68 +num2,cat2,0.33 +cat1,num1,0.42 +cat1,num2,0.68 +cat1,cat1,0 +cat1,cat2,0.59 +cat2,num1,0.38 +cat2,num2,0.33 +cat2,cat1,0.59 +cat2,cat2,0 \ No newline at end of file diff --git a/tests/resources/num-cat-observed.csv b/tests/resources/num-cat-observed.csv new file mode 100644 index 0000000..1c5ee62 --- /dev/null +++ b/tests/resources/num-cat-observed.csv @@ -0,0 +1,121 @@ +class,data +foo,0.8 +foo,3.5 +foo,1.2 +foo,2.9 +foo,0.5 +foo,4.1 +foo,1.8 +foo,3.2 +foo,0.9 +foo,2.6 +foo,1.5 +foo,3.8 +foo,0.7 +foo,4.2 +foo,2.1 +foo,3.7 +foo,1.3 +foo,2.8 +foo,0.6 +foo,3.9 +bar,3.2 +bar,6.8 +bar,4.1 +bar,5.5 +bar,3.8 +bar,7.2 +bar,4.7 +bar,6.3 +bar,3.5 +bar,5.9 +bar,4.3 +bar,6.7 +bar,3.9 +bar,7.1 +bar,5.2 +bar,6.1 +bar,4.5 +bar,5.7 +bar,3.6 +bar,6.9 +baz,5.9 +baz,9.2 +baz,6.7 +baz,8.3 +baz,6.1 +baz,9.8 +baz,7.4 +baz,8.7 +baz,6.3 +baz,8.1 +baz,7.1 +baz,9.4 +baz,6.8 +baz,9.1 +baz,7.8 +baz,8.5 +baz,7.2 +baz,8.9 +baz,6.5 +baz,9.6 +qux,8.1 +qux,11.7 +qux,9.4 +qux,10.2 +qux,8.9 +qux,12.1 +qux,9.8 +qux,11.3 +qux,8.5 +qux,10.7 +qux,9.2 +qux,11.9 +qux,8.7 +qux,12.3 +qux,10.1 +qux,11.6 +qux,9.5 +qux,10.8 +qux,8.8 +qux,12.2 +alpha,2.3 +alpha,6.1 +alpha,3.8 +alpha,4.7 +alpha,2.9 +alpha,5.4 +alpha,3.2 +alpha,5.9 +alpha,2.1 +alpha,4.5 +alpha,3.6 +alpha,6.2 +alpha,2.8 +alpha,5.7 +alpha,4.1 +alpha,5.3 +alpha,3.4 +alpha,4.9 +alpha,2.6 +alpha,6.0 +beta,10.5 +beta,14.2 +beta,11.9 +beta,13.1 +beta,10.8 +beta,14.7 +beta,12.3 +beta,13.8 +beta,11.2 +beta,12.9 +beta,11.7 +beta,14.1 +beta,10.9 +beta,14.5 +beta,12.6 +beta,13.4 +beta,11.8 +beta,13.7 +beta,11.1 +beta,14.3 diff --git a/tests/resources/num-cat-synthetic.csv b/tests/resources/num-cat-synthetic.csv new file mode 100644 index 0000000..12b54f0 --- /dev/null +++ b/tests/resources/num-cat-synthetic.csv @@ -0,0 +1,115 @@ +class,data +foo,1.2 +foo,2.8 +foo,0.5 +foo,1.9 +foo,2.1 +foo,3.2 +foo,1.8 +foo,2.5 +foo,0.9 +foo,2.3 +foo,0.7 +foo,3.1 +foo,1.6 +foo,2.9 +foo,1.3 +foo,2.7 +foo,0.8 +foo,3.4 +foo,2.2 +bar,3.8 +bar,4.5 +bar,2.9 +bar,3.2 +bar,4.1 +bar,5.2 +bar,3.6 +bar,4.8 +bar,2.7 +bar,3.9 +bar,2.5 +bar,4.7 +bar,3.4 +bar,4.3 +bar,3.1 +bar,4.6 +bar,2.8 +bar,5.1 +bar,3.7 +baz,5.1 +baz,6.7 +baz,4.3 +baz,5.9 +baz,6.2 +baz,7.3 +baz,5.8 +baz,6.5 +baz,4.7 +baz,6.1 +baz,4.9 +baz,7.1 +baz,5.4 +baz,6.8 +baz,5.2 +baz,6.4 +baz,4.8 +baz,7.2 +baz,5.7 +qux,7.2 +qux,8.9 +qux,6.8 +qux,7.5 +qux,8.1 +qux,9.4 +qux,7.8 +qux,8.6 +qux,6.9 +qux,8.3 +qux,7.1 +qux,9.1 +qux,7.6 +qux,8.7 +qux,7.3 +qux,8.5 +qux,6.7 +qux,9.3 +qux,7.9 +alpha,0.3 +alpha,1.9 +alpha,0.8 +alpha,1.2 +alpha,0.6 +alpha,2.1 +alpha,1.5 +alpha,0.9 +alpha,0.4 +alpha,1.7 +alpha,0.2 +alpha,2.3 +alpha,1.1 +alpha,1.8 +alpha,0.7 +alpha,1.4 +alpha,0.5 +alpha,2.2 +alpha,1.3 +beta,9.8 +beta,11.2 +beta,10.5 +beta,9.3 +beta,10.8 +beta,11.7 +beta,10.1 +beta,11.3 +beta,9.9 +beta,10.6 +beta,9.7 +beta,11.5 +beta,10.2 +beta,11.1 +beta,9.8 +beta,10.9 +beta,9.6 +beta,11.4 +beta,10.3 diff --git a/tests/resources/training-data.parquet b/tests/resources/training-data.parquet new file mode 100644 index 0000000..c98cc3a Binary files /dev/null and b/tests/resources/training-data.parquet differ diff --git a/uv.lock b/uv.lock index f4666fc..c45e4f8 100644 --- a/uv.lock +++ b/uv.lock @@ -1,5 +1,5 @@ version = 1 -revision 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