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testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p [metadata] lock-version = "2.0" python-versions = ">=3.9,<3.9.7 || >3.9.7,<3.12" -content-hash = "3bebf3701a01c8adff08f8273e615aa505a039baf3dc9e29eabb33c3cf7454d1" +content-hash = "a74c1b4096680535ddf4df1379845428cd1a38f3c4c69e01cb6e3a1365e9df2c" diff --git a/pyproject.toml b/pyproject.toml index 774b30e2..d89b1f0a 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -33,6 +33,7 @@ sdmetrics = "^0.11.0" tornado = "^6.3.3" streamlit = "^1.25.0" plotly = "^5.16.1" +umap-learn = "^0.5.3" [tool.poetry.scripts] nhssynth = "nhssynth.cli.__main__:__main__" diff --git a/src/nhssynth/modules/dashboard/Upload.py b/src/nhssynth/modules/dashboard/Upload.py index ae0491ff..73ee68f1 100644 --- a/src/nhssynth/modules/dashboard/Upload.py +++ b/src/nhssynth/modules/dashboard/Upload.py @@ -1,21 +1,37 @@ import argparse import os import pickle +from typing import Any import pandas as pd import streamlit as st -from nhssynth.modules.dataloader.metatransformer import TypedDataset -from nhssynth.modules.evaluation.utils import EvalBundle +from nhssynth.modules.dataloader.io import TypedDataset +from nhssynth.modules.evaluation.io import Evaluations +from nhssynth.modules.model.io import Experiments, SyntheticDatasets def parse_args() -> argparse.Namespace: parser = argparse.ArgumentParser(description="NHSSynth Evaluation Dashboard") - parser.add_argument("--evaluation-bundle", type=str, help="Path to an evaluation bundle pickle file.") + parser.add_argument("--evaluations", type=str, help="Path to a set of evaluations.") parser.add_argument("--experiments", type=str, help="Path to a set of experiments.") + parser.add_argument("--synthetic-datasets", type=str, help="Path to a set of synthetic datasets.") parser.add_argument("--typed", type=str, help="Path to a typed real dataset.") return parser.parse_args() +def get_component(name: str, component_type: Any, text: str): + uploaded = st.file_uploader(f"Upload a pickle file containing a {text}", type="pkl") + if getattr(args, name): + with open(os.getcwd() + "/" + getattr(args, name), "rb") as f: + loaded = pickle.load(f) + if uploaded is not None: + loaded = pickle.load(uploaded) + if loaded is not None: + assert isinstance(loaded, component_type), f"Uploaded file does not contain a {text}!" + st.session_state[name] = loaded.contents + st.success(f"Loaded {text}!") + + if __name__ == "__main__": args = parse_args() @@ -25,43 +41,15 @@ def parse_args() -> argparse.Namespace: "Welcome! Upload an evaluation bundle below to get started (optionally also the typed real dataset and bundle of experiments containing the synthetic datasets).\n\nUse the menu on the left to navigate the dashboard." ) - uploaded_eval_bundle = st.file_uploader("Upload a pickle file containing an evaluation bundle", type="pkl") - if args.evaluation_bundle: - with open(os.getcwd() + "/" + args.evaluation_bundle, "rb") as f: - eval_bundle = pickle.load(f) - if uploaded_eval_bundle is not None: - eval_bundle = pickle.load(uploaded_eval_bundle) - if eval_bundle is not None: - assert isinstance(eval_bundle, EvalBundle), "Uploaded file does not contain an evaluation bundle!" - st.session_state["evaluations"], st.session_state["experiments"] = ( - eval_bundle.evaluations, - eval_bundle.experiments, - ) - st.success(f"Loaded evaluation bundle!") - - uploaded_experiments = st.file_uploader("Upload a pickle file containing a set of experiments", type="pkl") - if args.experiments: - with open(os.getcwd() + "/" + args.experiments, "rb") as f: - experiments = pickle.load(f) - if uploaded_experiments is not None: - experiments = pickle.load(uploaded_experiments) - if experiments is not None: - experiments = pd.DataFrame(experiments) - assert ( - "dataset" in experiments.columns and "id" in experiments.columns - ), "Uploaded file does not contain a set of experiments!" - st.session_state["synthetic_data"] = experiments[["id", "dataset"]] - st.success(f"Loaded synthetic datasets from experiments!") - - uploaded_typed = st.file_uploader( - "Upload a pickle file containing the typed (by the dataloader module) real dataset", type="pkl" - ) - if args.typed: - with open(os.getcwd() + "/" + args.typed, "rb") as f: - typed = pickle.load(f) - if uploaded_typed is not None: - typed = pickle.load(uploaded_typed) - if typed is not None: - assert isinstance(typed, TypedDataset), "Uploaded file does not contain a typed real dataset!" - st.session_state["real_data"] = typed - st.success(f"Loaded real dataset!") + hide_streamlit_style = """ + + """ + st.markdown(hide_streamlit_style, unsafe_allow_html=True) + + get_component("evaluations", Evaluations, "bundle of evaluations") + get_component("experiments", Experiments, "bundle of experiments") + get_component("synthetic_datasets", SyntheticDatasets, "bundle of synthetic datasets") + get_component("typed", TypedDataset, "typed real dataset") diff --git a/src/nhssynth/modules/dashboard/io.py b/src/nhssynth/modules/dashboard/io.py index ad07242d..a2cfe65e 100644 --- a/src/nhssynth/modules/dashboard/io.py +++ b/src/nhssynth/modules/dashboard/io.py @@ -2,7 +2,12 @@ def check_input_paths( - dir_experiment: str, fn_dataset: str, fn_typed: str, fn_experiments: str, fn_evaluation_bundle: str + dir_experiment: str, + fn_dataset: str, + fn_typed: str, + fn_experiments: str, + fn_synthetic_datasets: str, + fn_evaluations: str, ) -> str: """ Sets up the input and output paths for the model files. @@ -10,19 +15,25 @@ def check_input_paths( Args: dir_experiment: The path to the experiment directory. fn_dataset: The base name of the dataset. - fn_experiments: The name of the set of experiments. - fn_evaluation_bundle: The name of the evaluation bundle file. + fn_experiments: The filename of the collection of experiments. + fn_synthetic_datasets: The filename of the collection of synthetic datasets. + fn_evaluations: The filename of the collection of evaluations. Returns: - The path to output the model. + The paths """ fn_dataset = Path(fn_dataset).stem - fn_typed, fn_experiments, fn_evaluation_bundle = consistent_endings( - [fn_typed, fn_experiments, fn_evaluation_bundle] + fn_typed, fn_experiments, fn_synthetic_datasets, fn_evaluations = consistent_endings( + [fn_typed, fn_experiments, fn_synthetic_datasets, fn_evaluations] ) - fn_typed, fn_experiments, fn_evaluation_bundle = potential_suffixes( - [fn_typed, fn_experiments, fn_evaluation_bundle], fn_dataset + fn_typed, fn_experiments, fn_synthetic_datasets, fn_evaluations = potential_suffixes( + [fn_typed, fn_experiments, fn_synthetic_datasets, fn_evaluations], fn_dataset + ) + warn_if_path_supplied([fn_typed, fn_experiments, fn_synthetic_datasets, fn_evaluations], dir_experiment) + check_exists([fn_typed, fn_experiments, fn_synthetic_datasets, fn_evaluations], dir_experiment) + return ( + dir_experiment / fn_typed, + dir_experiment / fn_experiments, + dir_experiment / fn_synthetic_datasets, + dir_experiment / fn_evaluations, ) - warn_if_path_supplied([fn_typed, fn_experiments, fn_evaluation_bundle], dir_experiment) - check_exists([fn_typed, fn_experiments, fn_evaluation_bundle], dir_experiment) - return dir_experiment / fn_typed, dir_experiment / fn_experiments, dir_experiment / fn_evaluation_bundle diff --git a/src/nhssynth/modules/dashboard/pages/1_Tables.py b/src/nhssynth/modules/dashboard/pages/1_Tables.py index d3ecf226..dd914dc2 100644 --- a/src/nhssynth/modules/dashboard/pages/1_Tables.py +++ b/src/nhssynth/modules/dashboard/pages/1_Tables.py @@ -1,152 +1,154 @@ import pandas as pd import streamlit as st +from nhssynth.modules.dashboard.utils import id_selector -@st.cache_data def convert_df(df): - return df.to_csv(index=False).encode("utf-8") + return df.to_csv().encode("utf-8") -def prepare_evals(evaluations: pd.DataFrame, experiments: pd.DataFrame, metrics: list, metadata: list) -> pd.DataFrame: - # Replace the ID column with the configuration components - evaluations_joined = evaluations.join(experiments.set_index("id"), on="id").drop(columns=["id"]) +def prepare_evals(evaluations: pd.DataFrame, experiments: pd.DataFrame) -> pd.DataFrame: # Ensure that we can only select from metrics that have been computed for this set of evaluations - valid_metrics = [m for m in metrics if not evaluations_joined[m].isna().all()] + valid_metrics = [m for m in evaluations.columns if not evaluations[m].isna().all()] metrics_to_show = st.sidebar.multiselect("Select metric(s) to display", valid_metrics, default=valid_metrics) # Filter to only the columns we want to display - return evaluations_joined[metadata + metrics_to_show] + return evaluations[metrics_to_show].join(experiments)[experiments.columns.tolist() + metrics_to_show] def group_by_display( evaluations_filtered: pd.DataFrame, - metadata: list, - selected_metric_group: str, experiments: pd.DataFrame, + title: str, ): group_by_cols = st.sidebar.multiselect( - "Select column(s) to group by", metadata, default=["architecture", "config_idx"] + "Select column(s) to group by", + experiments.columns.tolist() + experiments.index.names, + default=["architecture", "config"], ) if group_by_cols: evaluations_shown = ( - evaluations_filtered[[c for c in evaluations_filtered.columns if c not in metadata or c in group_by_cols]] - .replace(float("nan"), "N/A") - .groupby(group_by_cols) + evaluations_filtered[ + [ + c + for c in evaluations_filtered.columns + if c not in experiments.columns.tolist() + experiments.index.names or c in group_by_cols + ] + ] + .groupby(group_by_cols, dropna=False) .mean() ) - st.write(f"## `{selected_metric_group}` metrics grouped by `{group_by_cols}`") + st.write(f"## {title} grouped by `{group_by_cols}`") + download = convert_df(evaluations_shown.copy()) + # No idea why this fixes the bug with highlight_max but it does + evaluations_shown.index = evaluations_shown.index.set_levels(evaluations_shown.index.levels[0], level=0) st.dataframe(evaluations_shown.style.highlight_max(axis=0)) - st.download_button( - "Press to Download", convert_df(evaluations_shown), "evaluations.csv", "text/csv", key="download-csv" - ) - if "config_idx" in group_by_cols: + st.download_button("Press to Download", download, "evaluations.csv", "text/csv") + if "config" in group_by_cols: st.write("### Configurations") - st.dataframe( - experiments[metadata].drop(["seed", "repeat"], axis=1).groupby(["architecture", "config_idx"]).first() - ) + st.dataframe(experiments.groupby(["architecture", "config"]).first()) else: evaluations_shown = evaluations_filtered - st.write(f"## `{selected_metric_group}` metrics") + st.write(f"## {title}") st.dataframe(evaluations_shown.style.highlight_max(axis=0)) - st.download_button( - "Press to Download", convert_df(evaluations_shown), "evaluations.csv", "text/csv", key="download-csv" - ) - - -def id_selector(experiments): - architecture = st.sidebar.selectbox( - "Select architecture to display", [a for a in experiments["architecture"].unique() if a != "Real"] - ) - id = architecture - config_ids = experiments[experiments["architecture"] == architecture]["config_idx"].dropna().unique() - repeats = experiments[experiments["architecture"] == architecture]["repeat"].dropna().unique() - if len(config_ids) > 1: - config = st.sidebar.selectbox("Select configuration to display", config_ids, index=0) - id += f"_config_{config}" - if len(experiments[experiments["architecture"] == architecture]["repeat"].dropna().unique()) > 1: - repeat = st.sidebar.selectbox("Select repeat to display", repeats, index=0) - id += f"_repeat_{repeat}" - return id + st.download_button("Press to Download", convert_df(evaluations_shown), "evaluations.csv", "text/csv") def table_metrics(evaluations, experiments, selected_metric_group): - metrics = [c for c in evaluations.columns if c != "id"] - metadata = [c for c in experiments.columns if c != "id"] - prepared_evals = prepare_evals(evaluations, experiments, metrics, metadata) - group_by_display(prepared_evals, metadata, selected_metric_group, experiments) + prepared_evals = prepare_evals(evaluations, experiments) + group_by_display(prepared_evals, experiments, f"{selected_metric_group.capitalize()} metrics") def columnwise_metrics(evaluations, experiments): - evaluation_df = pd.DataFrame(evaluations) - metrics = [c for c in evaluation_df.columns if c != "id"] - metadata = [c for c in experiments.columns if c not in ["id"]] - display = st.sidebar.selectbox("Display", ["By column", "By metric", "By configuration"], index=0) if display == "By column": - columns = evaluation_df[metrics[0]][1].keys() + columns = evaluations.iloc[0, 0].keys() column = st.sidebar.selectbox("Select column to display", columns) - column_evaluations = evaluation_df.copy() - for metric in metrics: - column_evaluations[metric] = column_evaluations[metric].apply(lambda x: x[column]["score"]) - prepared_evals = prepare_evals(column_evaluations, experiments, metrics, metadata) - group_by_display(prepared_evals, metadata, "columnwise", experiments) + evaluations = evaluations.apply(lambda x: x.apply(lambda y: y[column]["score"])) + prepared_evals = prepare_evals(evaluations, experiments) + group_by_display(prepared_evals, experiments, f"Columnwise metrics for `{column}`") elif display == "By metric": - metric = st.sidebar.selectbox("Select metric to display", metrics) - metric_evaluations = pd.concat( - [ - evaluation_df["id"], - evaluation_df[metric].apply(lambda x: pd.Series({k: v["score"] for k, v in x.items()})), - ], - axis=1, + metric = st.sidebar.selectbox("Select metric to display", evaluations.columns) + metric_evaluations = ( + evaluations[metric].apply(lambda x: pd.Series(x).apply(lambda y: y["score"])).dropna(axis=1, how="all") ) - prepared_evals = metric_evaluations.join(experiments.set_index("id"), on="id").drop( - columns=["id"] + metric_evaluations.columns[metric_evaluations.isnull().all()].tolist() - ) - group_by_display(prepared_evals, metadata, "columnwise", experiments) + joined_evals = metric_evaluations.join(experiments) + group_by_display(joined_evals, experiments, f"Columnwise `{metric}`") elif display == "By configuration": - id = id_selector(experiments) - config_evaluations = evaluation_df[evaluation_df["id"] == id] - exploded_evaluations = pd.concat( - [ - pd.DataFrame.from_dict(config_evaluations[col].values[0], orient="index", columns=["score"]).rename( - columns={"score": col} - ) - for col in config_evaluations.columns - if col != "id" - ], - axis=1, + config_evaluations = id_selector(evaluations) + exploded_evaluations = config_evaluations.apply(lambda x: pd.Series(x).apply(lambda y: y["score"])).T + metrics_to_show = st.sidebar.multiselect( + "Select metric(s) to display", + exploded_evaluations.columns.tolist(), + default=exploded_evaluations.columns.tolist(), + ) + exploded_evaluations = exploded_evaluations[metrics_to_show] + st.write( + f"## Columnwise metrics for {config_evaluations.name[0]} repeat {int(config_evaluations.name[1])} configuration {int(config_evaluations.name[2])}" ) - print(exploded_evaluations) - st.write(f"## `columnwise` metrics") st.dataframe(exploded_evaluations.style.highlight_max(axis=0)) st.download_button( "Press to Download", convert_df(exploded_evaluations), "evaluations.csv", "text/csv", key="download-csv" ) st.write("### Configurations") - st.dataframe( - experiments[metadata].drop(["seed", "repeat"], axis=1).groupby(["architecture", "config_idx"]).first() - ) + st.dataframe(experiments.groupby(["architecture", "config"]).first()) def pairwise_metrics(evaluations, experiments): - st.write("## `pairwise` metrics") - evaluation_df = pd.DataFrame(evaluations) - metrics = [c for c in evaluation_df.columns if c != "id"] - metrics_to_show = st.sidebar.multiselect("Select metric(s) to display", metrics, default=metrics) - id = id_selector(experiments) - for metric in metrics_to_show: - st.write(f"## `{metric}`") - grid = evaluation_df[evaluation_df["id"] == id][metric].values[0] - st.table( - pd.DataFrame( - [(row, col, value["score"]) for (row, col), value in grid.items()], columns=["row", "col", "value"] - ) - .set_index("row") - .pivot(columns="col", values="value") - .fillna(" ") + display = st.sidebar.selectbox( + "Display", + ["By column pair", "By reference column and metric", "By configuration and metric"], + index=0, + ) + metric_column_map = { + metric: set([item for pair in evaluations[metric][0].keys() for item in pair]) for metric in evaluations.columns + } + columns = set([item for sublist in metric_column_map.values() for item in sublist]) + column_column_map = {column: set() for column in columns} + for column in columns: + for metric_columns in metric_column_map.values(): + if column in metric_columns: + column_column_map[column] = column_column_map[column].union(metric_columns).difference({column}) + if display == "By column pair": + column1 = st.sidebar.selectbox("Select first column to display", columns, index=0) + column2 = st.sidebar.selectbox("Select second column to display", column_column_map[column1], index=0) + column_pair_evaluations = evaluations.apply( + lambda x: x.apply(lambda y: (y.get((column1, column2)) or y.get((column2, column1)) or {}).get("score")) ) + prepared_evals = prepare_evals(column_pair_evaluations, experiments) + group_by_display(prepared_evals, experiments, f"Pairwise metrics for `{column1}` and `{column2}`") + elif display == "By reference column and metric": + reference_column = st.sidebar.selectbox("Select reference column", columns, index=0) + metric = st.sidebar.selectbox( + "Select metric to display", [k for k, v in metric_column_map.items() if reference_column in v] + ) + metric_evaluations = ( + evaluations[metric] + .apply(pd.Series) + .filter(like=reference_column) + .apply(lambda x: x.apply(lambda y: y["score"])) + ) + metric_evaluations.columns = [ + (col[0] + col[1]).replace(reference_column, "") for col in metric_evaluations.columns + ] + joined_evals = metric_evaluations.join(experiments) + group_by_display(joined_evals, experiments, f"Pairwise `{metric}` relative to `{reference_column}`") + elif display == "By configuration and metric": + config_evaluations = id_selector(evaluations) + metric = st.sidebar.selectbox("Select metric to display", evaluations.columns) + metric_evaluations = config_evaluations.loc[metric] + grid = pd.DataFrame(columns=list(metric_column_map[metric]), index=list(metric_column_map[metric])) + for (col1, col2), score in metric_evaluations.items(): + grid.loc[col1, col2], grid.loc[col2, col1] = score["score"], score["score"] + st.write( + f"## Pairwise `{metric}` values for {config_evaluations.name[0]} repeat {int(config_evaluations.name[1])} configuration {int(config_evaluations.name[2])}" + ) + st.table(grid) + st.download_button("Press to Download", convert_df(grid), "evaluations.csv", "text/csv", key="download-csv") + st.write("### Configurations") + st.dataframe(experiments.groupby(["architecture", "config"]).first()) def page(): @@ -156,7 +158,7 @@ def page(): "Select a metric group", [mg.capitalize() for mg in list(metric_groups)] ).lower() - evaluations = st.session_state["evaluations"][selected_metric_group] + evaluations = st.session_state["evaluations"][selected_metric_group].copy() experiments = st.session_state["experiments"] if selected_metric_group in ["table", "task", "privacy", "efficacy", "aequitas"]: table_metrics(evaluations, experiments, selected_metric_group) @@ -165,7 +167,7 @@ def page(): elif selected_metric_group == "pairwise": pairwise_metrics(evaluations, experiments) else: - st.write("## Not yet implemented.") + st.error("Not yet implemented.") if __name__ == "__main__": diff --git a/src/nhssynth/modules/dashboard/pages/2_Plots.py b/src/nhssynth/modules/dashboard/pages/2_Plots.py index 923428cc..dcbf1e41 100644 --- a/src/nhssynth/modules/dashboard/pages/2_Plots.py +++ b/src/nhssynth/modules/dashboard/pages/2_Plots.py @@ -1,137 +1,163 @@ +from typing import Any + +import numpy as np import pandas as pd +import plotly.express as px +import plotly.graph_objs as go import streamlit as st +import umap +from nhssynth.modules.dashboard.utils import id_selector, subset_selector +from sklearn.manifold import TSNE -@st.cache_data -def convert_df(df): - return df.to_csv(index=False).encode("utf-8") - - -def prepare_evals(evaluations: pd.DataFrame, experiments: pd.DataFrame, metrics: list, metadata: list) -> pd.DataFrame: - # Replace the ID column with the configuration components - evaluations_joined = evaluations.join(experiments.set_index("id"), on="id").drop(columns=["id"]) - # Ensure that we can only select from metrics that have been computed for this set of evaluations - valid_metrics = [m for m in metrics if not evaluations_joined[m].isna().all()] - metrics_to_show = st.sidebar.multiselect("Select metric(s) to display", valid_metrics, default=valid_metrics) - # Filter to only the columns we want to display - return evaluations_joined[metadata + metrics_to_show] +def triangle(matrix): + return matrix.where(np.tril(np.ones(matrix.shape), k=-1).astype(bool)) -def group_by_display( - evaluations_filtered: pd.DataFrame, - metadata: list, - selected_metric_group: str, - experiments: pd.DataFrame, -): - group_by_cols = st.sidebar.multiselect( - "Select column(s) to group by", metadata, default=["architecture", "config_idx"] +def distribution_plots(real_dataset, synthetic_datasets) -> None: + column = st.sidebar.selectbox("Select column to display", real_dataset.columns) + st.write(f"## Distribution Plot for `{column}`") + synthetic_datasets = subset_selector(synthetic_datasets) + fig = px.histogram( + pd.concat( + [ + pd.DataFrame({"Data": real_dataset[column], "Type": len(real_dataset) * ["Real"]}), + pd.concat( + [ + pd.DataFrame( + { + "Data": sd.iloc[0][column], + "Type": len(sd.iloc[0]) * [f"Synthetic: {idx[0]} (Repeat {idx[1]}, Config {idx[2]})"], + } + ) + for idx, sd in synthetic_datasets.iterrows() + ] + ), + ] + ), + x="Data", + color="Type", + barmode="overlay", ) - if group_by_cols: - evaluations_shown = ( - evaluations_filtered[[c for c in evaluations_filtered.columns if c not in metadata or c in group_by_cols]] - .replace(float("nan"), "N/A") - .groupby(group_by_cols) - .mean() - ) - st.dataframe(evaluations_shown.style.highlight_max(axis=0)) - st.download_button( - "Press to Download", convert_df(evaluations_shown), "evaluations.csv", "text/csv", key="download-csv" - ) - if "config_idx" in group_by_cols: - st.write("### Configurations") - st.dataframe( - experiments[metadata].drop(["seed", "repeat"], axis=1).groupby(["architecture", "config_idx"]).first() - ) + log_scale = st.sidebar.checkbox("Log scale", value=False) + if log_scale: + fig.update_layout(yaxis_type="log") + if real_dataset[column].dtype == "object": + sort_xaxis = st.sidebar.checkbox("Sort x-axis", value=True) + if sort_xaxis: + fig.update_layout(xaxis={"categoryorder": "total descending"}) + st.plotly_chart(fig, use_container_width=True) + + +def correlation_plots(real_dataset, synthetic_datasets) -> None: + show_continuous = st.sidebar.checkbox("Only show continuous columns", value=True) + selection = real_dataset.select_dtypes(exclude=["object"]).columns + if not show_continuous: + non_continuous_columns = [c for c in real_dataset.columns if c not in selection] + selection = st.sidebar.selectbox("Select categorical column to display", non_continuous_columns) + if not non_continuous_columns: + st.error("No categorical columns found!") + return go.Figure() + + synthetic_dataset = id_selector(synthetic_datasets).iloc[0][selection] + real_dataset = real_dataset[selection] + if not show_continuous: + synthetic_dataset = pd.get_dummies(synthetic_dataset) + real_dataset = pd.get_dummies(real_dataset) + + correlation_type = st.sidebar.selectbox("Select correlation type", ["Pearson", "Spearman", "Kendall"]).lower() + correlation_to_show = st.sidebar.selectbox("Select correlation to display", ["Real", "Synthetic", "Difference"]) + + real_corr_matrix = real_dataset.corr(method=correlation_type) + synthetic_corr_matrix = synthetic_dataset.corr(method=correlation_type) + zmin = min(real_corr_matrix.min().min(), synthetic_corr_matrix.min().min()) + zmax = max(real_corr_matrix.max().max(), synthetic_corr_matrix.max().max()) + if correlation_to_show == "Difference": + corr_matrix = triangle(abs(synthetic_corr_matrix - real_corr_matrix)) + zmin = 0 + zmax = zmax - zmin + elif correlation_to_show == "Real": + corr_matrix = triangle(real_corr_matrix) else: - evaluations_shown = evaluations_filtered - st.dataframe(evaluations_shown.style.highlight_max(axis=0)) - st.download_button( - "Press to Download", convert_df(evaluations_shown), "evaluations.csv", "text/csv", key="download-csv" - ) - - -def table_metrics(evaluations, experiments, selected_metric_group): - st.write(f"## `{selected_metric_group}` metrics") - metrics = [c for c in evaluations.columns if c != "id"] - metadata = [c for c in experiments.columns if c != "id"] - prepared_evals = prepare_evals(evaluations, experiments, metrics, metadata) - group_by_display(prepared_evals, metadata, selected_metric_group, experiments) - - -def columnwise_metrics(evaluations, experiments): - evaluation_df = pd.DataFrame(evaluations) - metrics = [c for c in evaluation_df.columns if c != "id"] - metadata = [c for c in experiments.columns if c not in ["id"]] - - display = st.sidebar.selectbox("Display", ["By column", "By metric"], index=0) - - if display == "By column": - columns = evaluation_df[metrics[0]][1].keys() - column = st.sidebar.selectbox("Select column to display", columns) - column_evaluations = evaluation_df.copy() - for metric in metrics: - column_evaluations[metric] = column_evaluations[metric].apply(lambda x: x[column]["score"]) - prepared_evals = prepare_evals(column_evaluations, experiments, metrics, metadata) - group_by_display(prepared_evals, metadata, "columnwise", experiments) - - elif display == "By metric": - metric = st.sidebar.selectbox("Select metric to display", metrics) - metric_evaluations = pd.concat( - [ - evaluation_df["id"], - evaluation_df[metric].apply(lambda x: pd.Series({k: v["score"] for k, v in x.items()})), - ], - axis=1, + corr_matrix = triangle(synthetic_corr_matrix) + fig = go.Figure( + data=go.Heatmap( + z=corr_matrix, + x=corr_matrix.columns, + y=corr_matrix.columns, + colorscale=px.colors.diverging.RdYlGn, + reversescale=True, + zmin=zmin, + zmax=zmax, + ), + layout=go.Layout(xaxis_showgrid=False, yaxis_showgrid=False, yaxis_autorange="reversed"), + ) + st.plotly_chart(fig, use_container_width=True) + + +def prepare_for_dimensionality(df: pd.DataFrame) -> pd.DataFrame: + """Factorize all categorical columns in a dataframe.""" + for col in df.columns: + if df[col].dtype == "object": + df[col] = pd.factorize(df[col])[0] + elif df[col].dtype == "datetime64[ns]": + df[col] = pd.to_numeric(df[col]) + min_val = df[col].min() + max_val = df[col].max() + df[col] = (df[col] - min_val) / (max_val - min_val) + return df + + +def plot_reducer( + real_dataset: pd.DataFrame, synthetic_dataset: pd.DataFrame, reducer: Any, reducer_name: str +) -> go.Figure: + with st.spinner(f"Running {reducer_name}..."): + fig = go.Figure(layout=go.Layout(xaxis_title="UMAP 1", yaxis_title="UMAP 2")) + proj_real = reducer.fit_transform(real_dataset) + fig.add_scatter( + x=proj_real[:, 0], y=proj_real[:, 1], mode="markers", marker=dict(size=5), opacity=0.75, name="Real data" ) - prepared_evals = metric_evaluations.join(experiments.set_index("id"), on="id").drop( - columns=["id"] + metric_evaluations.columns[metric_evaluations.isnull().all()].tolist() + proj_synth = reducer.fit_transform(synthetic_dataset) + fig.add_scatter( + x=proj_synth[:, 0], + y=proj_synth[:, 1], + mode="markers", + marker=dict(size=5), + opacity=0.75, + name="Synthetic data", ) - group_by_display(prepared_evals, metadata, "columnwise", experiments) + return fig -def pairwise_metrics(evaluations, experiments): - st.write("## `pairwise` metrics") - evaluation_df = pd.DataFrame(evaluations) - metrics = [c for c in evaluation_df.columns if c != "id"] - metrics_to_show = st.sidebar.multiselect("Select metric(s) to display", metrics, default=metrics) - architecture = st.sidebar.selectbox( - "Select architecture to display", [a for a in experiments["architecture"].unique() if a != "Real"] - ) - config = st.sidebar.selectbox( - "Select configuration to display", experiments["config_idx"].dropna().unique(), index=0 - ) - repeat = st.sidebar.selectbox("Select repeat to display", experiments["repeat"].dropna().unique(), index=0) - - for metric in metrics_to_show: - st.write(f"## `{metric}`") - grid = evaluation_df[evaluation_df["id"] == f"{architecture}_config_{config}_repeat_{repeat}"][metric].values[0] - st.table( - pd.DataFrame( - [(row, col, value["score"]) for (row, col), value in grid.items()], columns=["row", "col", "value"] - ) - .set_index("row") - .pivot(columns="col", values="value") - .fillna(" ") - ) +def dimensionality_plots(real_dataset: pd.DataFrame, synthetic_datasets: pd.DataFrame) -> None: + synthetic_dataset = prepare_for_dimensionality(id_selector(synthetic_datasets).iloc[0].copy()) + real_dataset = prepare_for_dimensionality(real_dataset.copy()) + dimensionality_method = st.sidebar.selectbox("Select dimensionality reduction method", ["UMAP", "t-SNE"]) + run = st.sidebar.button("Run dimensionality reduction") + if run: + if dimensionality_method == "UMAP": + reducer = umap.UMAP() + if dimensionality_method == "t-SNE": + reducer = TSNE(n_components=2, init="pca") + fig = plot_reducer(real_dataset, synthetic_dataset, reducer, dimensionality_method) + st.plotly_chart(fig, use_container_width=True) def page(): st.set_page_config(layout="wide") - metric_groups = st.session_state["evaluations"].keys() - selected_metric_group = st.sidebar.selectbox( - "Select a metric group", [mg.capitalize() for mg in list(metric_groups)] - ).lower() - - evaluations = st.session_state["evaluations"][selected_metric_group] - experiments = st.session_state["experiments"] - if selected_metric_group in ["table", "task", "privacy", "efficacy", "aequitas"]: - table_metrics(evaluations, experiments, selected_metric_group) - elif selected_metric_group == "columnwise": - columnwise_metrics(evaluations, experiments) - elif selected_metric_group == "pairwise": - pairwise_metrics(evaluations, experiments) - else: - st.write("## Not yet implemented.") + # metric_groups = st.session_state["evaluations"].keys() + plot_types = ["Distribution", "Correlation", "Dimensionality"] + selected_plot_type = st.sidebar.selectbox("Select a plot type", plot_types) + + real_data = st.session_state["typed"] + synthetic_datasets = st.session_state["synthetic_datasets"] + + if selected_plot_type == "Distribution": + distribution_plots(real_data, synthetic_datasets) + elif selected_plot_type == "Correlation": + correlation_plots(real_data, synthetic_datasets) + elif selected_plot_type == "Dimensionality": + dimensionality_plots(real_data, synthetic_datasets) if __name__ == "__main__": diff --git a/src/nhssynth/modules/dashboard/run.py b/src/nhssynth/modules/dashboard/run.py index 3dfe2055..fbd02f52 100644 --- a/src/nhssynth/modules/dashboard/run.py +++ b/src/nhssynth/modules/dashboard/run.py @@ -27,9 +27,22 @@ def run(args: argparse.Namespace) -> argparse.Namespace: args.module_handover.get("fn_dataset") or args.dataset, args.typed, args.experiments, - args.evaluation_bundle, + args.synthetic_datasets, + args.evaluations, + ) + command.extend( + [ + "--", + "--typed", + paths[0], + "--experiments", + paths[1], + "--synthetic-datasets", + paths[2], + "--evaluations", + paths[3], + ] ) - command.extend(["--", "--typed", paths[0], "--experiments", paths[1], "--evaluation-bundle", paths[2]]) process = subprocess.Popen(command) diff --git a/src/nhssynth/modules/dashboard/utils.py b/src/nhssynth/modules/dashboard/utils.py new file mode 100644 index 00000000..0c0c8846 --- /dev/null +++ b/src/nhssynth/modules/dashboard/utils.py @@ -0,0 +1,31 @@ +import pandas as pd +import streamlit as st + + +def id_selector(df: pd.DataFrame) -> pd.Series: + architecture = st.sidebar.selectbox( + "Select architecture to display", df.index.get_level_values("architecture").unique() + ) + repeats = df.loc[architecture].index.get_level_values("repeat").astype(int).unique() + configs = df.loc[architecture].index.get_level_values("config").astype(int).unique() + if len(repeats) > 1: + repeat = st.sidebar.selectbox("Select repeat to display", repeats) + else: + repeat = repeats[0] + if len(configs) > 1: + config = st.sidebar.selectbox("Select configuration to display", configs) + else: + config = configs[0] + return df.loc[(architecture, repeat, config)] + + +def subset_selector(df: pd.DataFrame) -> pd.DataFrame: + architectures = df.index.get_level_values("architecture").unique().tolist() + repeats = df.index.get_level_values("repeat").astype(int).unique().tolist() + configs = df.index.get_level_values("config").astype(int).unique().tolist() + selected_architectures = st.sidebar.multiselect( + "Select architectures to display", architectures, default=architectures + ) + selected_repeats = st.sidebar.multiselect("Select repeats to display", repeats, default=repeats[0]) + selected_configs = st.sidebar.multiselect("Select configurations to display", configs, default=configs) + return df.loc[(selected_architectures, selected_repeats, selected_configs)]