diff --git a/.github/workflows/commit-ci.yml b/.github/workflows/commit-ci.yml index 25430627..b221ca33 100644 --- a/.github/workflows/commit-ci.yml +++ b/.github/workflows/commit-ci.yml @@ -14,18 +14,32 @@ on: jobs: test: - uses: fredshone/actions/.github/workflows/python-install-lint-test.yml@main - with: - os: ubuntu-latest - py3version: "11" - notebook_kernel: caveat - lint: false + runs-on: ubuntu-latest + defaults: + run: + shell: bash -l {0} + steps: + - uses: actions/checkout@v4 - # aws-upload: - # needs: test - # if: needs.test.result == 'success' - # uses: fredshone/actions/.github/workflows/aws-upload.yml@main - # secrets: - # AWS_ACCESS_KEY_ID: ${{ secrets.AWS_ACCESS_KEY_ID }} - # AWS_SECRET_ACCESS_KEY: ${{ secrets.AWS_SECRET_ACCESS_KEY }} - # AWS_S3_CODE_BUCKET: ${{ secrets.AWS_S3_CODE_BUCKET }} \ No newline at end of file + - uses: mamba-org/setup-micromamba@v2 + with: + environment-name: caveat + create-args: >- + python=3.11 + -c conda-forge -c pytorch -c nvidia -c city-modelling-lab + --file=requirements/base.txt + --file=requirements/dev.txt + cache-environment: true + post-cleanup: none + + - name: Install PyPI dependencies + run: pip install -r requirements/pip.txt + + - name: Install package + run: pip install --no-deps -e . + + - name: Install kernel + run: python -m ipykernel install --user --name caveat + + - name: Run tests + run: pytest diff --git a/.github/workflows/daily-scheduled-ci.yml b/.github/workflows/daily-scheduled-ci.yml deleted file mode 100644 index 1ab2c195..00000000 --- a/.github/workflows/daily-scheduled-ci.yml +++ /dev/null @@ -1,35 +0,0 @@ -name: Daily CI - -on: - schedule: - - cron: '23 14 * * 1-5' - -jobs: - get-date: - runs-on: ubuntu-latest - steps: - - name: Add date to github output env - run: echo "DATE=$(date +'%Y-%m-%d')" >> $GITHUB_OUTPUT - - test: - needs: get-date - uses: fredshone/actions/.github/workflows/python-install-lint-test.yml@main - with: - os: ubuntu-latest - py3version: "11" - notebook_kernel: caveat - pytest_args: '--no-cov' # ignore coverage - cache_mamba_env: false - lint: false - mamba_env_name: daily-ci - - # slack-notify-ci: - # needs: test - # if: always() - # uses: fredshone/actions/.github/workflows/slack-notify.yml@main - # secrets: - # SLACK_WEBHOOK: ${{ secrets.SLACK_WEBHOOK }} - # with: - # result: needs.test.result - # channel: caveat-feed - # message: Daily CI action \ No newline at end of file diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml index c21461de..a4342aa0 100644 --- a/.github/workflows/docs.yml +++ b/.github/workflows/docs.yml @@ -10,16 +10,73 @@ on: jobs: docs-test: if: github.ref != 'refs/heads/main' - uses: fredshone/actions/.github/workflows/docs-deploy.yml@main - with: - deploy_type: test - notebook_kernel: caveat + runs-on: ubuntu-latest + defaults: + run: + shell: bash -l {0} + steps: + - uses: actions/checkout@v4 + + - uses: mamba-org/setup-micromamba@v2 + with: + environment-name: caveat + create-args: >- + python=3.11 + -c conda-forge -c pytorch -c nvidia -c city-modelling-lab + --file=requirements/base.txt + --file=requirements/dev.txt + cache-environment: true + post-cleanup: none + + - name: Install PyPI dependencies + run: pip install -r requirements/pip.txt + + - name: Install package + run: pip install --no-deps -e . + + - name: Install kernel + run: python -m ipykernel install --user --name caveat + + - name: Test docs build + run: mkdocs build --strict docs-update-latest: + if: github.ref == 'refs/heads/main' permissions: contents: write - if: github.ref == 'refs/heads/main' - uses: fredshone/actions/.github/workflows/docs-deploy.yml@main - with: - deploy_type: update_latest - notebook_kernel: caveat \ No newline at end of file + runs-on: ubuntu-latest + defaults: + run: + shell: bash -l {0} + steps: + - uses: actions/checkout@v4 + with: + fetch-depth: 0 + + - uses: mamba-org/setup-micromamba@v2 + with: + environment-name: caveat + create-args: >- + python=3.11 + -c conda-forge -c pytorch -c nvidia -c city-modelling-lab + --file=requirements/base.txt + --file=requirements/dev.txt + cache-environment: true + post-cleanup: none + + - name: Install PyPI dependencies + run: pip install -r requirements/pip.txt + + - name: Install package + run: pip install --no-deps -e . + + - name: Install kernel + run: python -m ipykernel install --user --name caveat + + - name: Configure git + run: | + git config --global user.name "github-actions[bot]" + git config --global user.email "github-actions[bot]@users.noreply.github.com" + + - name: Deploy latest docs + run: mike deploy --push --update-aliases develop latest diff --git a/.github/workflows/pr-ci.yml b/.github/workflows/pr-ci.yml index 448b5f38..b1e5fb17 100644 --- a/.github/workflows/pr-ci.yml +++ b/.github/workflows/pr-ci.yml @@ -17,35 +17,69 @@ jobs: strategy: matrix: os: [windows-latest, ubuntu-latest, macos-latest] - py3version: ["9", "11"] + py3version: ["11"] fail-fast: false - uses: fredshone/actions/.github/workflows/python-install-lint-test.yml@main - with: - os: ${{ matrix.os }} - py3version: ${{ matrix.py3version }} - notebook_kernel: caveat - lint: false - pytest_args: '--no-cov' # ignore coverage - upload_to_codecov: false - additional_mamba_args: '-c conda-forge -c pytorch -c nvidia' + runs-on: ${{ matrix.os }} + defaults: + run: + shell: bash -l {0} + steps: + - uses: actions/checkout@v4 + + - uses: mamba-org/setup-micromamba@v2 + with: + environment-name: caveat + create-args: >- + python=3.${{ matrix.py3version }} + -c conda-forge -c pytorch -c nvidia -c city-modelling-lab + --file=requirements/base.txt + --file=requirements/dev.txt + cache-environment: true + post-cleanup: none + + - name: Install PyPI dependencies + run: pip install -r requirements/pip.txt + + - name: Install package + run: pip install --no-deps -e . + + - name: Install kernel + run: python -m ipykernel install --user --name caveat + + - name: Run tests + run: pytest --no-cov test-coverage: - uses: fredshone/actions/.github/workflows/python-install-lint-test.yml@main - with: - os: ubuntu-latest - py3version: "11" - notebook_kernel: caveat - lint: false - pytest_args: 'tests/' # ignore example notebooks - upload_to_codecov: true - additional_mamba_args: '-c conda-forge -c pytorch -c nvidia' - - # memory-profile: - # uses: fredshone/actions/.github/workflows/python-memory-profile.yml@main - # with: - # py3version: "11" - # upload_flamegraph: true - # additional_mamba_args: '-c conda-forge -c pytorch -c nvidia' - - # cruft-check: - # uses: fredshone/actions/.github/workflows/template-check.yml@main + runs-on: ubuntu-latest + defaults: + run: + shell: bash -l {0} + steps: + - uses: actions/checkout@v4 + + - uses: mamba-org/setup-micromamba@v2 + with: + environment-name: caveat + create-args: >- + python=3.11 + -c conda-forge -c pytorch -c nvidia -c city-modelling-lab + --file=requirements/base.txt + --file=requirements/dev.txt + cache-environment: true + post-cleanup: none + + - name: Install PyPI dependencies + run: pip install -r requirements/pip.txt + + - name: Install package + run: pip install --no-deps -e . + + - name: Install kernel + run: python -m ipykernel install --user --name caveat + + - name: Run tests with coverage + run: pytest tests/ + + - uses: codecov/codecov-action@v4 + with: + file: reports/coverage/coverage.xml diff --git a/.github/workflows/pre-release.yml b/.github/workflows/pre-release.yml index 894a0bbd..37033a4f 100644 --- a/.github/workflows/pre-release.yml +++ b/.github/workflows/pre-release.yml @@ -7,8 +7,33 @@ on: jobs: conda-build: - uses: fredshone/actions/.github/workflows/conda-build.yml@main - secrets: - ANACONDA_TOKEN: ${{ secrets.ANACONDA_TOKEN }} - with: - package_name: caveat \ No newline at end of file + runs-on: ubuntu-latest + defaults: + run: + shell: bash -l {0} + steps: + - uses: actions/checkout@v4 + + - uses: mamba-org/setup-micromamba@v2 + with: + environment-name: build + create-args: >- + python=3.11 + boa + conda-verify + -c conda-forge -c city-modelling-lab + cache-environment: true + post-cleanup: none + + - name: Build conda package + run: | + VERSION=${GITHUB_REF_NAME#v} + conda mambabuild conda.recipe/ --no-anaconda-upload \ + -c conda-forge -c city-modelling-lab \ + --output-folder ./conda-bld + + - uses: actions/upload-artifact@v4 + with: + name: conda-package-${{ github.ref_name }} + path: conda-bld/**/*.tar.bz2 + retention-days: 3 diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index e7fd1f94..502a1076 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -6,17 +6,77 @@ on: jobs: conda-upload: - uses: fredshone/actions/.github/workflows/conda-upload.yml@main - secrets: - ANACONDA_TOKEN: ${{ secrets.ANACONDA_TOKEN }} - with: - package_name: caveat - build_workflow: pre-release.yml + runs-on: ubuntu-latest + environment: release + defaults: + run: + shell: bash -l {0} + steps: + - uses: actions/checkout@v4 + + - uses: mamba-org/setup-micromamba@v2 + with: + environment-name: publish + create-args: >- + python=3.11 + anaconda-client + -c conda-forge + cache-environment: true + post-cleanup: none + + - name: Download build artifact + uses: dawidd6/action-download-artifact@v6 + with: + workflow: pre-release.yml + name: conda-package-${{ github.ref_name }} + path: conda-bld/ + + - name: Upload to Anaconda + run: | + VERSION=${GITHUB_REF_NAME#v} + anaconda --token ${{ secrets.ANACONDA_TOKEN }} upload \ + --user city-modelling-lab \ + conda-bld/**/*.tar.bz2 docs-stable: permissions: contents: write - uses: fredshone/actions/.github/workflows/docs-deploy.yml@main - with: - deploy_type: update_stable - notebook_kernel: caveat \ No newline at end of file + runs-on: ubuntu-latest + defaults: + run: + shell: bash -l {0} + steps: + - uses: actions/checkout@v4 + with: + fetch-depth: 0 + + - uses: mamba-org/setup-micromamba@v2 + with: + environment-name: caveat + create-args: >- + python=3.11 + -c conda-forge -c pytorch -c nvidia -c city-modelling-lab + --file=requirements/base.txt + --file=requirements/dev.txt + cache-environment: true + post-cleanup: none + + - name: Install PyPI dependencies + run: pip install -r requirements/pip.txt + + - name: Install package + run: pip install --no-deps -e . + + - name: Install kernel + run: python -m ipykernel install --user --name caveat + + - name: Configure git + run: | + git config --global user.name "github-actions[bot]" + git config --global user.email "github-actions[bot]@users.noreply.github.com" + + - name: Deploy stable docs + run: | + VERSION=${GITHUB_REF_NAME#v} + mike deploy --push --update-aliases $VERSION stable + mike set-default --push stable diff --git a/.github/workflows/weekly-scheduled-ci.yml b/.github/workflows/weekly-scheduled-ci.yml new file mode 100644 index 00000000..e6abb1e9 --- /dev/null +++ b/.github/workflows/weekly-scheduled-ci.yml @@ -0,0 +1,37 @@ +name: Weekly Scheduled CI + +on: + schedule: + - cron: "0 8 * * 1" + +jobs: + test: + runs-on: ubuntu-latest + defaults: + run: + shell: bash -l {0} + steps: + - uses: actions/checkout@v4 + + - uses: mamba-org/setup-micromamba@v2 + with: + environment-name: caveat + create-args: >- + python=3.11 + -c conda-forge -c pytorch -c nvidia -c city-modelling-lab + --file=requirements/base.txt + --file=requirements/dev.txt + cache-environment: false + post-cleanup: none + + - name: Install PyPI dependencies + run: pip install -r requirements/pip.txt + + - name: Install package + run: pip install --no-deps -e . + + - name: Install kernel + run: python -m ipykernel install --user --name caveat + + - name: Run tests + run: pytest --no-cov diff --git a/README.md b/README.md index 1eda1f94..93d750d9 100644 --- a/README.md +++ b/README.md @@ -134,6 +134,7 @@ git clone git@github.com:big-ucl/caveat.git cd caveat mamba create -n caveat -c conda-forge -c city-modelling-lab -c pytorch --file requirements/base.txt --file requirements/dev.txt mamba activate caveat +pip install -r requirements/pip.txt pip install --no-deps -e . ``` diff --git a/caveat/data/samplers.py b/caveat/data/samplers.py index 4fc85856..0e56d38f 100644 --- a/caveat/data/samplers.py +++ b/caveat/data/samplers.py @@ -6,7 +6,7 @@ def sample_data( sequences: DataFrame, attributes: Optional[DataFrame], config: dict -): +) -> tuple: """Sample a proportion of the data based on sampler config. Args: @@ -15,7 +15,7 @@ def sample_data( config (dict): configuration. Returns: - DataFrame: sampled sequences. + Tuple[DataFrame, Optional[DataFrame]]: sampled sequences and attributes. """ sequences = sample_sequences(sequences, config) if attributes is not None: @@ -66,7 +66,7 @@ def random_sample(data: DataFrame, p: float) -> DataFrame: return sampled -def biased_sample(data: DataFrame, p: float, threshold: int = 20): +def biased_sample(data: DataFrame, p: float, threshold: int = 20) -> DataFrame: """ Sample sequences that contain short activities according to the threshold. diff --git a/caveat/encoding/discrete.py b/caveat/encoding/discrete.py index 667cfb83..73b6e136 100644 --- a/caveat/encoding/discrete.py +++ b/caveat/encoding/discrete.py @@ -129,19 +129,18 @@ def _encode( ) def decode(self, schedules: Tensor, argmax=True) -> pd.DataFrame: - """Decode decretised a sequences ([B, C, T, A]) into DataFrame of 'traces', eg: + """Decode discretised sequences ([B, C, T, A]) into DataFrame of 'traces', eg: pid | act | start | end pid is taken as sample enumeration. Args: - encoded (Tensor): _description_ - mapping (dict): _description_ - length (int): Length of plan in minutes. + schedules (Tensor): Encoded schedule tensor. + argmax (bool): Whether to apply argmax to get activity indices. Defaults to True. Returns: - pd.DataFrame: _description_ + pd.DataFrame: Decoded schedule DataFrame. """ if argmax: schedules = torch.argmax(schedules, dim=-1) @@ -231,19 +230,17 @@ def encode( ) def decode(self, schedules: Tensor) -> pd.DataFrame: - """Decode disretised a sequences ([B, C, T, A]) into DataFrame of 'traces', eg: + """Decode discretised sequences ([B, C, T, A]) into DataFrame of 'traces', eg: pid | act | start | end pid is taken as sample enumeration. Args: - encoded (Tensor): _description_ - mapping (dict): _description_ - length (int): Length of plan in minutes. + schedules (Tensor): Encoded schedule tensor. Returns: - pd.DataFrame: _description_ + pd.DataFrame: Decoded schedule DataFrame. """ schedules = torch.argmax(schedules, dim=-1) decoded = [] diff --git a/caveat/evaluate/__init__.py b/caveat/evaluate/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/caveat/evaluate/describe/__init__.py b/caveat/evaluate/describe/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/caveat/evaluate/describe/frequency.py b/caveat/evaluate/describe/frequency.py deleted file mode 100644 index d9d84145..00000000 --- a/caveat/evaluate/describe/frequency.py +++ /dev/null @@ -1,107 +0,0 @@ -from datetime import datetime, timedelta -from typing import Optional - -from matplotlib import pyplot as plt -from matplotlib.patches import Patch -from pandas import DataFrame - -from caveat.evaluate.features.frequency import binned_activity_density - - -def frequency_plots( - observed: DataFrame, ys: Optional[dict[DataFrame]], **kwargs -): - if ys is None: - ys = dict() - acts = list(observed.act.value_counts(ascending=False).index) - class_map = {n: i for i, n in enumerate(acts)} - - n_plots = len(ys) + 2 - ratios = [1 for _ in range(n_plots)] - ratios[-1] = 0.3 - - cmap = kwargs.pop("cmap", None) - if cmap is None: - cmap = plt.cm.Set3 - colors = cmap.colors - factor = (len(acts) // len(colors)) + 1 - cmap = dict(zip(acts, colors * factor)) - - fig, axs = plt.subplots( - sharex=True, - sharey=True, - nrows=1, - ncols=n_plots, - constrained_layout=True, - figsize=kwargs.pop("figsize", (15, 4)), - gridspec_kw={"width_ratios": ratios}, - ) - - name = kwargs.pop("observed_title", "Observed") - - plot_agg_acts(name, observed, class_map, ax=axs[0], legend=False, **kwargs) - - # now deal with ys - for i, (name, y) in enumerate(ys.items()): - ax = axs[i + 1] - plot_agg_acts(name, y, class_map, ax=ax, legend=False, **kwargs) - - # legend - elements = [Patch(facecolor=cmap[act], label=act.title()) for act in acts] - axs[-1].axis("off") - axs[-1].legend(handles=elements, loc="center left", frameon=False) - - return fig - - -def plot_agg_acts( - name: str, - population: DataFrame, - class_map: dict, - duration: int = 1440, - step: int = 10, - ax=None, - legend=True, - **kwargs, -): - interval = kwargs.pop("interval", 240) - bins = binned_activity_density( - population, duration=duration, step=step, class_map=class_map - ) - columns = list(class_map.keys()) - totals = bins.sum(0) - sorted_cols = [x for _, x in sorted(zip(totals, columns))] - df = DataFrame(bins, columns=columns)[sorted_cols] - df.index = [ - datetime(2021, 11, 1, 0) + timedelta(minutes=i * step) - for i in range(len(df.index)) - ] - fig = df.plot( - kind="bar", stacked=True, width=1, ax=ax, legend=legend, **kwargs - ) - if legend: - ax.legend(loc="upper right") - ax = fig.axes - labels = ["" for _ in range(len(df.index))] - - labels[:: int(interval / step)] = [x.strftime("%H:%M") for x in df.index][ - :: int(interval / step) - ] - # ax.set_xticklabels(labels) - # ax.set_xticks(list(ax.get_xticks())[:: int(interval / step)]) - ax.set_xticks( - [i / step for i in [0, 240, 480, 720, 960, 1200, 1440]], - labels=["00:00", "04:00", "08:00", "12:00", "16:00", "20:00", "24:00"], - rotation=90, - fontsize=8, - ) - - ax.spines["top"].set_visible(False) - ax.spines["right"].set_visible(False) - ax.spines["bottom"].set_visible(False) - ax.spines["left"].set_visible(False) - - ax.set_xlabel("Time of day") - ax.set_ylabel("Activity Proportion") - ax.set_title(name) - return ax diff --git a/caveat/evaluate/describe/times.py b/caveat/evaluate/describe/times.py deleted file mode 100644 index 9df50939..00000000 --- a/caveat/evaluate/describe/times.py +++ /dev/null @@ -1,269 +0,0 @@ -from typing import Optional, Tuple - -from matplotlib import colormaps, patches -from matplotlib import pyplot as plt -from matplotlib.figure import Axes, Figure -from pandas import DataFrame - - -def times_distributions_plot( - observed: DataFrame, ys: Optional[dict[str, DataFrame]], **kwargs -) -> Figure: - ratios = [1 for _ in range(4)] - ratios[0] = 0.2 - fig, axs = plt.subplots( - 4, - observed.act.nunique(), - figsize=kwargs.pop("figsize", (12, 5)), - sharex=True, - sharey=False, - # tight_layout=True, - constrained_layout=True, - gridspec_kw={"height_ratios": ratios}, - ) - acts = list(observed.act.value_counts(ascending=False).index) - name = kwargs.pop("observed_title", "Observed") - _times_plot(name, observed, acts, axs=axs) - if ys is None: - return fig - for name, y in ys.items(): - _times_plot(name, y, acts, axs=axs) - for ax in axs[0]: - ax.tick_params(axis="x", which="both", length=0.0) - handles, labels = axs[1][0].get_legend_handles_labels() - fig.legend( - handles, - labels, - loc="upper center", - fontsize=9, - ncol=len(ys) + 1, - frameon=False, - ) - return fig - - -def _times_plot( - name: str, - population: DataFrame, - acts: list[str], - axs: Axes, - xmin: int = 0, - xmax: int = 1440, - step: int = 30, - **kwargs, -) -> Tuple[Figure, Axes]: - starts = population.groupby("act", observed=False).start - ends = population.groupby("act", observed=False).end - durations = population.groupby("act", observed=False).duration - bins = list(range(xmin, xmax, step)) - - for i, act in enumerate(acts): - if act not in population.act.unique(): - continue - axs[0][i].spines["top"].set_visible(False) - axs[0][i].spines["right"].set_visible(False) - axs[0][i].spines["bottom"].set_visible(False) - axs[0][i].spines["left"].set_visible(False) - axs[0][i].set_xticks([]) - axs[0][i].set_yticks([]) - - axs[1][i].set_title(act.title(), fontsize="small") - start_group = starts.get_group(act) - if len(start_group) < 5: - continue - axs[1][i].hist( - starts.get_group(act), - bins=bins, - density=True, - histtype="step", - label=name, - linewidth=1.4, - **kwargs, - ) - axs[1][i].set_xlim(xmin, xmax) - axs[1][i].set_yticklabels([]) - axs[1][i].set(ylabel=None) - axs[1][i].set_xticks([]) - axs[1][i].set_yticks([]) - - axs[2][i].hist( - ends.get_group(act), - bins=bins, - density=True, - histtype="step", - label=name, - linewidth=1.4, - **kwargs, - ) - axs[2][i].set_xlim(xmin, xmax) - axs[2][i].set_yticklabels([]) - axs[2][i].set(ylabel=None) - axs[2][i].set_xticks([]) - axs[2][i].set_yticks([]) - - axs[3][i].hist( - durations.get_group(act), - bins=bins, - density=True, - histtype="step", - label=name, - linewidth=1.4, - **kwargs, - ) - axs[3][i].set_xlim(xmin, xmax) - axs[3][i].set_yticklabels([]) - axs[3][i].set(ylabel=None) - axs[3][i].set_xticks( - [0, 240, 480, 720, 960, 1200, 1440], - labels=[ - "00:00", - "04:00", - "08:00", - "12:00", - "16:00", - "20:00", - "24:00", - ], - rotation=90, - fontsize=8, - ) - axs[3][i].set_yticks([]) - axs[3][i].set_xlabel("Time/Duration", fontsize=8) - - axs[1][0].set_ylabel("Start Time\nDensities", fontsize=10) - axs[2][0].set_ylabel("End Time\nDensities", fontsize=10) - axs[3][0].set_ylabel("Duration\nDensities", fontsize=10) - - -def joint_time_distributions_plot( - observed: DataFrame, ys: Optional[dict[DataFrame]], **kwargs -) -> Figure: - if ys is None: - ys = dict() - acts = list(observed.act.value_counts(ascending=False).index) - n_acts = len(acts) - rows = len(ys) + 2 - ratios = [1 for _ in range(rows)] - ratios[0] = 0.2 - - cmaps = kwargs.pop("cmaps", {}) - legend = [] - legend_colours = [] - - fig, axs = plt.subplots( - rows, - observed.act.nunique(), - figsize=kwargs.pop("figsize", (12, 5)), - sharex=False, - sharey=False, - constrained_layout=True, - gridspec_kw={"height_ratios": ratios}, - ) - - # deal with observed first - name = kwargs.pop("observed_title", "Observed") - - legend.append(name) - cmap = cmaps.get(0, "Blues") - lcolours = colormaps[cmap]([0, 0.5, 1]) - legend_colours.append(lcolours[int(len(lcolours) / 2)]) - - _joint_time_plot(observed, axs[1], acts, cmap=cmap) - - # now deal with ys - for i, (name, y) in enumerate(ys.items()): - - legend.append(name) - cmap = cmaps.get(i + 1, "Reds") - lcolours = colormaps[cmap]([0, 0.5, 1]) - legend_colours.append(lcolours[int(len(lcolours) / 2)]) - - _joint_time_plot(y, axs[i + 2], acts, cmap=cmap) - - # xlabel on bottom row - for ax in axs[-1]: - ax.set_xlabel("Start times", fontsize=8) - ax.set_xticks( - [240, 480, 720, 960, 1200, 1440], - labels=["04:00", "08:00", "12:00", "16:00", "20:00", "24:00"], - rotation=90, - fontsize=8, - ) - - # acts - for ax, act in zip(axs[1], acts): - ax.set_title(act.title(), fontsize=9) - - # deal with legend - for i in range(n_acts): - axs[0][i].spines["top"].set_visible(False) - axs[0][i].spines["right"].set_visible(False) - axs[0][i].spines["bottom"].set_visible(False) - axs[0][i].spines["left"].set_visible(False) - axs[0][i].set_xticks([]) - axs[0][i].set_yticks([]) - for ax in axs[0]: - ax.tick_params(axis="x", which="both", length=0.0) - - handles = [ - patches.Patch(color=c, label=l) for c, l in zip(legend_colours, legend) - ] - fig.legend( - handles=handles, - loc="upper center", - fontsize=9, - ncol=len(ys) + 1, - frameon=False, - ) - - return fig - - -def _joint_time_plot( - population: DataFrame, - axs: Axes, - acts: list[str], - cmap: str, - xmin: int = 240, - xmax: int = 1441, - ymin: int = 0, - ymax: int = 960, - xstep: int = 30, - ystep: int = 30, -): - starts = population.groupby("act", observed=False).start - durations = population.groupby("act", observed=False).duration - - start_bins = list(range(xmin, xmax, xstep)) - duration_bins = list(range(ymin, ymax, ystep)) - - for i, act in enumerate(acts): - axs[i].set_xticks([]) - axs[i].set_yticks([]) - if act not in population.act.unique(): - continue - act_starts = starts.get_group(act) - act_durations = durations.get_group(act) - axs[i].hist2d( - x=act_starts, - y=act_durations, - bins=(start_bins, duration_bins), - cmap=cmap, - ) - ylabel = "Durations" - axs[0].set_ylabel(ylabel, fontsize=8) - axs[0].set_yticks( - [0, 120, 240, 360, 480, 600, 720, 840, 960], - labels=[ - "00:00", - "02:00", - "04:00", - "06:00", - "08:00", - "10:00", - "12:00", - "14:00", - "16:00", - ], - fontsize=8, - ) diff --git a/caveat/evaluate/describe/transitions.py b/caveat/evaluate/describe/transitions.py deleted file mode 100644 index 922dbfa9..00000000 --- a/caveat/evaluate/describe/transitions.py +++ /dev/null @@ -1,95 +0,0 @@ -from typing import Optional, Tuple - -from matplotlib import pyplot as plt -from matplotlib.axes import Axes -from matplotlib.colors import ListedColormap as CMap -from matplotlib.figure import Figure -from matplotlib.patches import Patch -from pandas import DataFrame - -from caveat.evaluate.features.transitions import sequence_probs - - -def sequence_prob_plot( - observed: DataFrame, ys: Optional[dict[DataFrame]], **kwargs -) -> Figure: - acts = list(observed.act.value_counts(ascending=False).index) - cmap = kwargs.pop("cmap", None) - if cmap is None: - cmap = plt.cm.Set3 - colors = cmap.colors - factor = (len(acts) // len(colors)) + 1 - cmap = dict(zip(acts, colors * factor)) - - n_plots = len(ys) + 2 - ratios = [1 for _ in range(n_plots)] - ratios[-1] = 0.3 - - fig, axs = plt.subplots( - 1, - n_plots, - figsize=kwargs.pop("figsize", (12, 5)), - sharex=True, - sharey=True, - # tight_layout=True, - constrained_layout=True, - gridspec_kw={"width_ratios": ratios}, - ) - acts = list(observed.act.value_counts(ascending=False).index) - name = kwargs.pop("observed_title", "Observed") - _probs_plot(name, observed, ax=axs[0], cmap=cmap, ylabel=True) - - if ys is None: - return fig - for i, (name, y) in enumerate(ys.items()): - _probs_plot(name, y, ax=axs[i + 1], cmap=cmap) - axs[i + 1].set_title(name) - - elements = [Patch(facecolor=cmap[act], label=act.title()) for act in acts] - axs[-1].axis("off") - axs[-1].legend(handles=elements, loc="center left", frameon=False) - - return fig - - -def _probs_plot( - name: str, - population: DataFrame, - cmap: Optional[CMap], - ax=Axes, - ylabel=False, -) -> Tuple[Figure, Axes]: - probs = sequence_probs(population) - accumulated = probs[::-1].cumsum()[::-1] - - ys = [] - widths = [] - heights = [] - lefts = [] - cols = [] - for idx, p, ap in zip(probs.index, probs, accumulated): - seq = idx[1].split(">") - width = 1 / len(seq) - for i, act in enumerate(seq): - ys.append(ap - p) - widths.append(width) - heights.append(p) - lefts.append(i * width) - cols.append(cmap[act]) - - ax.barh( - y=ys, width=widths, height=heights, left=lefts, color=cols, align="edge" - ) - ax.hlines(ys, xmin=0, xmax=1, color="white", linewidth=0.1) - - ax.spines["top"].set_visible(False) - ax.spines["right"].set_visible(False) - ax.spines["bottom"].set_visible(False) - ax.spines["left"].set_visible(False) - ax.set_xlabel("Activity\nSequence") - if ylabel: - ax.set_ylabel("Sequence Proportion") - ax.set_xticklabels([]) - ax.set_xticks([]) - ax.set_title(name) - return ax diff --git a/caveat/evaluate/distance/__init__.py b/caveat/evaluate/distance/__init__.py deleted file mode 100644 index 95683332..00000000 --- a/caveat/evaluate/distance/__init__.py +++ /dev/null @@ -1,8 +0,0 @@ -from caveat.evaluate.distance.scalar import ( - abs_av_diff, - mae, - mape, - mape_scalar, - mse, -) -from caveat.evaluate.distance.wasserstein import emd diff --git a/caveat/evaluate/distance/scalar.py b/caveat/evaluate/distance/scalar.py deleted file mode 100644 index 2c7c4daa..00000000 --- a/caveat/evaluate/distance/scalar.py +++ /dev/null @@ -1,74 +0,0 @@ -import numpy as np - - -def mape( - a: tuple[np.ndarray, np.ndarray], b: tuple[np.ndarray, np.ndarray] -) -> float: - """Calculate mean average percentage error between distributions a and b. - - Clipped at 1.0. - - Args: - a (tuple[np.ndarray, np.ndarray]): Distribution a. - b (tuple[np.ndarray, np.ndarray]): Distribution b. - - Returns: - float: MAPE. - """ - # TODO test this - # unpack - ak, aw = a - bk, bw = b - # calc weighted average - akw = (ak * aw).sum() / aw.sum() - bkw = (bk * bw).sum() / bw.sum() - diff = np.abs(akw - bkw) - if diff == 0: - return 0.0 - if bkw == 0: - return clamp(diff / akw) - return clamp(diff / bkw) - - -def clamp(x): - if x > 1.0: - return 1.0 - return x - - -def mape_scalar(a, b): - return np.abs((a - b) / a).mean() - - -def mse( - a: tuple[np.ndarray, np.ndarray], b: tuple[np.ndarray, np.ndarray] -) -> float: - # requires and b have same support. - # unpack - _, aw = a - _, bw = b - return ((aw - bw) ** 2).mean() - - -def mae( - a: tuple[np.ndarray, np.ndarray], b: tuple[np.ndarray, np.ndarray] -) -> float: - # TODO test this - # requires and b have same support. - # unpack - _, aw = a - _, bw = b - return (np.abs(aw - bw)).mean() - - -def abs_av_diff( - a: tuple[np.ndarray, np.ndarray], b: tuple[np.ndarray, np.ndarray] -) -> float: - # TODO test this - # unpack - ak, aw = a - bk, bw = b - a_average = (ak * aw).sum() / aw.sum() - b_average = (bk * bw).sum() / bw.sum() - - return np.abs(a_average - b_average) diff --git a/caveat/evaluate/distance/wasserstein.py b/caveat/evaluate/distance/wasserstein.py deleted file mode 100644 index 9d97f43e..00000000 --- a/caveat/evaluate/distance/wasserstein.py +++ /dev/null @@ -1,197 +0,0 @@ -import numpy as np -import torch -import torch.nn as nn -from ot import dist, emd2, emd2_1d -from scipy.stats import wasserstein_distance - - -def emd(a: tuple[np.array, np.array], b: tuple[np.array, np.array]) -> float: - if a[0].ndim == 1: - return emd1d(a, b) - elif a[0].ndim == 2: - return emd2d(a, b) - else: - raise ValueError("Only 1d and 2d features are supported") - - -def emd2d(a: tuple[np.array, np.array], b: tuple[np.array, np.array]) -> float: - ak, aw = a - bk, bw = b - aw = aw / aw.sum() - bw = bw / bw.sum() - d = dist(ak, bk, metric="cityblock") - return emd2(aw, bw, d, check_marginals=False) - - -def emd1d(a: tuple[np.array, np.array], b: tuple[np.array, np.array]) -> float: - ak, aw = a - bk, bw = b - if ( - aw.sum() == 0 - ): # avoid division by zero but also has to assume distribution of just [0] - ak = np.array([0.0]) - aw = np.array([1.0]) - if bw.sum() == 0: - bk = np.array([0.0]) - bw = np.array([1.0]) - aw = aw / aw.sum() - bw = bw / bw.sum() - return emd2_1d(ak, bk, aw, bw, metric="cityblock") - - -def wasserstein(x: list[list], y: list[list]): - return wasserstein_distance(x, y) - - -def sliced_wasserstein(x: list[list], y: list[list], num_proj=100): - """ - https://stats.stackexchange.com/questions/404775/calculate-earth-movers-distance-for-two-grayscale-images - """ - x = np.array(x) - y = np.array(y) - dim = x.shape[1] - ests = [] - for _ in range(num_proj): - # sample uniformly from the unit sphere - dir = np.random.randn(dim) - dir /= np.linalg.norm(dir) - - # project the data - x_proj = x @ dir - y_proj = y @ dir - - # compute 1d wasserstein - ests.append(wasserstein_distance(x_proj, y_proj)) - return np.mean(ests) - - -def sinkhorn( - x: list[list], y: list[list], eps=0.01, max_iter=10, reduction=None -): - x = torch.tensor(x, dtype=torch.float) - y = torch.tensor(y, dtype=torch.float) - model = SinkhornDistance(eps=eps, max_iter=max_iter, reduction=reduction) - return model(x, y)[0].item() - - -# Adapted from https://github.com/gpeyre/SinkhornAutoDiff -class SinkhornDistance(nn.Module): - r""" - https://dfdazac.github.io/sinkhorn.html - Given two empirical measures each with :math:`P_1` locations - :math:`x\in\mathbb{R}^{D_1}` and :math:`P_2` locations :math:`y\in\mathbb{R}^{D_2}`, - outputs an approximation of the regularized OT cost for point clouds. - - Args: - eps (float): regularization coefficient - max_iter (int): maximum number of Sinkhorn iterations - reduction (string, optional): Specifies the reduction to apply to the output: - 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, - 'mean': the sum of the output will be divided by the number of - elements in the output, 'sum': the output will be summed. Default: 'none' - - Shape: - - Input: :math:`(N, P_1, D_1)`, :math:`(N, P_2, D_2)` - - Output: :math:`(N)` or :math:`()`, depending on `reduction` - """ - - def __init__(self, eps, max_iter, reduction="none"): - super(SinkhornDistance, self).__init__() - self.eps = eps - self.max_iter = max_iter - self.reduction = reduction - - def forward(self, x, y): - # The Sinkhorn algorithm takes as input three variables : - print("calc cost matrix") - C = self._cost_matrix(x, y) # Wasserstein cost function - x_points = x.shape[-2] - y_points = y.shape[-2] - if x.dim() == 2: - batch_size = 1 - else: - batch_size = x.shape[0] - - # both marginals are fixed with equal weights - print("mu") - mu = ( - torch.empty( - batch_size, x_points, dtype=torch.float, requires_grad=False - ) - .fill_(1.0 / x_points) - .squeeze() - ) - print("nu") - nu = ( - torch.empty( - batch_size, y_points, dtype=torch.float, requires_grad=False - ) - .fill_(1.0 / y_points) - .squeeze() - ) - - u = torch.zeros_like(mu) - v = torch.zeros_like(nu) - # To check if algorithm terminates because of threshold - # or max iterations reached - actual_nits = 0 - # Stopping criterion - thresh = 1e-1 - - # Sinkhorn iterations - for i in range(self.max_iter): - print(i, "start") - u1 = u # useful to check the update - u = ( - self.eps - * ( - torch.log(mu + 1e-8) - - torch.logsumexp(self.M(C, u, v), dim=-1) - ) - + u - ) - v = ( - self.eps - * ( - torch.log(nu + 1e-8) - - torch.logsumexp(self.M(C, u, v).transpose(-2, -1), dim=-1) - ) - + v - ) - err = (u - u1).abs().sum(-1).mean() - print(i, err.item()) - - actual_nits += 1 - if err.item() < thresh: - print(f"Sinkhorn converged at iteration {i}") - break - - U, V = u, v - # Transport plan pi = diag(a)*K*diag(b) - pi = torch.exp(self.M(C, U, V)) - # Sinkhorn distance - cost = torch.sum(pi * C, dim=(-2, -1)) - - if self.reduction == "mean": - cost = cost.mean() - elif self.reduction == "sum": - cost = cost.sum() - - return cost, pi, C - - def M(self, C, u, v): - "Modified cost for logarithmic updates" - return (-C + u.unsqueeze(-1) + v.unsqueeze(-2)) / self.eps - - @staticmethod - def _cost_matrix(x, y, p=1): - "Returns the matrix of $|x_i-y_j|^p$." - x_col = x.unsqueeze(-2) - y_lin = y.unsqueeze(-3) - C = torch.sum((torch.abs(x_col - y_lin)) ** p, -1) - return C - - @staticmethod - def ave(u, u1, tau): - "Barycenter subroutine, used by kinetic acceleration through extrapolation." - return tau * u + (1 - tau) * u1 diff --git a/caveat/evaluate/evaluate.py b/caveat/evaluate/evaluate.py deleted file mode 100644 index 439053e7..00000000 --- a/caveat/evaluate/evaluate.py +++ /dev/null @@ -1,780 +0,0 @@ -from pathlib import Path -from typing import Callable, List, Optional, Tuple - -import numpy as np -from pandas import DataFrame, MultiIndex, Series, concat - -from caveat.evaluate.distance import emd -from caveat.evaluate.features import ( - creativity, - frequency, - participation, - structural, - times, - transitions, -) -from caveat.evaluate.filters import filter_novel -from caveat.evaluate.ops import ( - average, - average2d, - average_density, - feature_value, - feature_weight, -) - -count_jobs = [ - ( - ("total schedules", frequency.count_schedules), - (feature_value), - ("count", feature_value), - ("EMD", emd), - ) -] -aggregate_jobs = [ - ( - ("agg. frequency", frequency.activity_frequencies), - (feature_weight), - ("average freq.", average_density), - ("EMD", emd), - ) -] -participation_rate_jobs = [ - ( - ("lengths", structural.sequence_lengths), - (feature_weight), - ("length.", average), - ("EMD", emd), - ), - ( - ("participation rate", participation.participation_rates_by_act), - (feature_weight), - ("av. rate", average), - ("EMD", emd), - ), - ( - ("pair participation rate", participation.joint_participation_rate), - (feature_weight), - ("av rate.", average), - ("EMD", emd), - ), -] -transition_jobs = [ - ( - ("2-gram", transitions.transitions_by_act), - (feature_weight), - ("av. rate", average), - ("EMD", emd), - ), - ( - ("3-gram", transitions.transition_3s_by_act), - (feature_weight), - ("av. rate", average), - ("EMD", emd), - ), - ( - ("4-gram", transitions.transition_4s_by_act), - (feature_weight), - ("av. rate", average), - ("EMD", emd), - ), - # ( - # ("sequences", transitions.full_sequences), - # ("mean", average), - # ("EMD", emd), - # ), -] -time_jobs = [ - ( - ("start times", times.start_times_by_act_plan_enum), - (feature_weight), - ("average", average), - ("EMD", emd), - ), - # ( - # ("end times", times.end_times_by_act_plan_enum), - # (feature_weight), - # ("average", average), - # ("EMD", emd), - # ), - ( - ("durations", times.durations_by_act_plan_enum), - (feature_weight), - ("average", average), - ("EMD", emd), - ), - ( - ("start-durations", times.start_and_duration_by_act_bins), - (feature_weight), - ("average", average2d), - ("EMD", emd), - ), - ( - ("joint-durations", times.joint_durations_by_act_bins), - (feature_weight), - ("average", average2d), - ("EMD", emd), - ), -] - - -def subsample_and_evaluate( - synthetic_schedules: dict[str, DataFrame], - synthetic_attributes: dict[str, DataFrame], - target_schedules: DataFrame, - target_attributes: DataFrame, - split_on: List[str], - report_stats: bool = True, - verbose: bool = False, -): - descriptions = [] - distances = [] - for split in split_on: - target_cats = target_attributes[split].unique() - for cat in target_cats: - target_pids = target_attributes[target_attributes[split] == cat].pid - sub_target = target_schedules[ - target_schedules.pid.isin(target_pids) - ] - sub_schedules = {} - for model, attributes in synthetic_attributes.items(): - sample_pids = attributes[attributes[split] == cat].pid - if verbose: - print( - f">>> Subsampled {model} {split}={cat} with {len(sample_pids)}" - ) - sample_schedules = synthetic_schedules[model] - sub_schedules[model] = sample_schedules[ - sample_schedules.pid.isin(sample_pids) - ] - - sub_reports = process_metrics( - synthetic_schedules=sub_schedules, - target_schedules=sub_target, - verbose=verbose, - ) - for r in sub_reports: # add sub pop to index - names = list(r.index.names) + ["label", "cat"] - r.index = MultiIndex.from_tuples( - [(*i, split, cat) for i in r.index], names=names - ) - descriptions.append(sub_reports[0]) - distances.append(sub_reports[1]) - - descriptions = concat(descriptions, axis=0) - distances = concat(distances, axis=0) - - frames = describe(descriptions, distances) - frames.update(describe_labels(descriptions, distances)) - - if report_stats: - columns = list(synthetic_schedules.keys()) - for frame in frames.values(): - add_stats(data=frame, columns=columns) - - return frames - - -def evaluate( - synthetic_schedules: dict[str, DataFrame], - target_schedules: DataFrame, - report_stats: bool = True, - verbose: bool = False, -): - descriptions, distances = process_metrics( - synthetic_schedules, target_schedules, verbose=verbose - ) - frames = describe(descriptions, distances) - - if report_stats: - columns = list(synthetic_schedules.keys()) - for frame in frames.values(): - add_stats(data=frame, columns=columns) - - return frames - - -def process_metrics( - synthetic_schedules: dict[str, DataFrame], - target_schedules: DataFrame, - verbose: bool = False, -) -> Tuple[DataFrame, DataFrame]: - # evaluate creativity - descriptions, distances = [], [] - - if verbose: - print(">>> Evaluating creativity") - creativity_descriptions, creativity_distances = eval_creativity( - synthetic_schedules=synthetic_schedules, - target_schedules=target_schedules, - ) - descriptions.append(creativity_descriptions) - distances.append(creativity_distances) - - if verbose: - print(">>> Evaluating sample quality") - sample_quality = eval_sample_quality( - synthetic_schedules=synthetic_schedules, - target_schedules=target_schedules, - ) - descriptions.append(sample_quality) - distances.append(sample_quality) - - for domain, jobs in [ - # ("count", count_jobs), - # ("aggregate", aggregate_jobs), - ("participations", participation_rate_jobs), - ("transitions", transition_jobs), - ("timing", time_jobs), - ]: - for feature, size, description_job, distance_job in jobs: - if verbose: - print(f">>> Evaluating {domain} {feature[0]}") - feature_descriptions, feature_distances = eval_jobs( - synthetic_schedules=synthetic_schedules, - target_schedules=target_schedules, - domain=domain, - feature=feature, - size=size, - description_job=description_job, - distance_job=distance_job, - ) - descriptions.append(feature_descriptions) - distances.append(feature_distances) - - descriptions = concat(descriptions, axis=0) - distances = concat(distances, axis=0) - - # remove nans - descriptions = descriptions.fillna(0.0) - distances = distances.fillna(0.0) - return descriptions, distances - - -def describe( - descriptions: DataFrame, distances: DataFrame -) -> dict[str, DataFrame]: - # features - feature_descriptions = descriptions.drop("unit", axis=1) - feature_descriptions = feature_descriptions.groupby( - ["domain", "feature", "segment"] - ).apply(weighted_av) - feature_descriptions["unit"] = ( - descriptions["unit"].groupby(["domain", "feature", "segment"]).first() - ) - - feature_distances = distances.drop("unit", axis=1) - feature_distances = feature_distances.groupby( - ["domain", "feature", "segment"] - ).apply(distance_weighted_av) - feature_distances["unit"] = ( - descriptions["unit"].groupby(["domain", "feature", "segment"]).first() - ) - - # groups - remove_features = [ - ("feasibility", "not home based", "starts"), - ("feasibility", "not home based", "ends"), - ("feasibility", "consecutive", "home"), - ("feasibility", "consecutive", "work"), - ("feasibility", "consecutive", "education"), - ] - - group_descriptions = descriptions.drop("unit", axis=1) - for f in remove_features: - group_descriptions = group_descriptions.drop(f, axis=0) - group_descriptions = group_descriptions.groupby( - ["domain", "feature"] - ).apply(weighted_av) - - group_descriptions["unit"] = ( - descriptions["unit"].groupby(["domain", "feature"]).first() - ) - - group_distances = distances.drop("unit", axis=1) - for f in remove_features: - group_distances = group_distances.drop(f, axis=0) - group_distances = group_distances.groupby(["domain", "feature"]).apply( - distance_weighted_av - ) - group_distances["unit"] = ( - descriptions["unit"].groupby(["domain", "feature"]).first() - ) - - # themes - domain_descriptions = group_descriptions.drop("unit", axis=1) - domain_descriptions = domain_descriptions.drop( - ("feasibility", "not home based"), axis=0 - ) - domain_descriptions = domain_descriptions.drop( - ("feasibility", "consecutive"), axis=0 - ) - domain_descriptions = domain_descriptions.groupby("domain").mean() - - domain_distances = group_distances.drop("unit", axis=1) - domain_distances = domain_distances.drop( - ("feasibility", "not home based"), axis=0 - ) - domain_distances = domain_distances.drop( - ("feasibility", "consecutive"), axis=0 - ) - domain_distances = domain_distances.groupby("domain").mean() - frames = { - "descriptions": feature_descriptions, - "group_descriptions": group_descriptions, - "domain_descriptions": domain_descriptions, - "distances": feature_distances, - "group_distances": group_distances, - "domain_distances": domain_distances, - } - return frames - - -def describe_labels( - descriptions: DataFrame, distances: DataFrame -) -> dict[str, DataFrame]: - # features - remove_features = [ - ("feasibility", "not home based", "starts"), - ("feasibility", "not home based", "ends"), - ("feasibility", "consecutive", "home"), - ("feasibility", "consecutive", "work"), - ("feasibility", "consecutive", "education"), - ] - grouper = ["domain", "feature", "label"] - - features_descriptions = descriptions.drop("unit", axis=1) - for f in remove_features: - features_descriptions = features_descriptions.drop(f, axis=0) - features_descriptions = features_descriptions.groupby(grouper).apply( - weighted_av - ) - - features_descriptions["unit"] = ( - descriptions["unit"].groupby(["domain", "feature"]).first() - ) - - features_distances = distances.drop("unit", axis=1) - for f in remove_features: - features_distances = features_distances.drop(f, axis=0) - features_distances = features_distances.groupby(grouper).apply( - distance_weighted_av - ) - features_distances["unit"] = descriptions["unit"].groupby(grouper).first() - - # themes - grouper = ["domain", "label"] - domain_descriptions = features_descriptions.drop("unit", axis=1) - domain_descriptions = domain_descriptions.drop( - ("feasibility", "not home based"), axis=0 - ) - domain_descriptions = domain_descriptions.drop( - ("feasibility", "consecutive"), axis=0 - ) - domain_descriptions = domain_descriptions.groupby(grouper).mean() - - domain_distances = features_distances.drop("unit", axis=1) - domain_distances = domain_distances.drop( - ("feasibility", "not home based"), axis=0 - ) - domain_distances = domain_distances.drop( - ("feasibility", "consecutive"), axis=0 - ) - domain_distances = domain_distances.groupby(grouper).mean() - - frames = { - "label_descriptions": descriptions, - "label_group_descriptions": features_descriptions, - "label_domain_descriptions": domain_descriptions, - "label_distances": distances, - "label_group_distances": features_distances, - "label_domain_distances": domain_distances, - } - return frames - - -def eval_creativity( - synthetic_schedules: dict[str, DataFrame], target_schedules: DataFrame -) -> Tuple[DataFrame, DataFrame]: - # Evaluate Creativity - observed_hash = creativity.hash_population(target_schedules) - observed_diversity = creativity.diversity(target_schedules, observed_hash) - feature_count = target_schedules.pid.nunique() - creativity_descriptions = DataFrame( - { - "observed__weight": [feature_count] * 2, - "observed": [observed_diversity, 1], - } - ) - creativity_distance = DataFrame( - { - "observed__weight": [feature_count] * 2, - "observed": [1 - observed_diversity, 0], - } - ) - - creativity_descs = [] - creativity_dists = [] - for model, y in synthetic_schedules.items(): - y_hash = creativity.hash_population(y) - y_diversity = creativity.diversity(y, y_hash) - y_count = y.pid.nunique() - creativity_descs.append( - Series( - [y_diversity, creativity.novelty(observed_hash, y_hash)], - name=model, - ) - ) - creativity_descs.append( # add feature count - Series([y_count, y_count], name=f"{model}__weight") - ) - creativity_dists.append( - Series( - [ - 1 - y_diversity, - creativity.conservatism(observed_hash, y_hash), - ], - name=model, - ) - ) - creativity_dists.append( # add feature count - Series([y_count, y_count], name=f"{model}__weight") - ) - - creativity_descs.append( - Series(["prob. unique", "prob. novel"], name="unit") - ) - creativity_dists.append( - Series(["prob. not unique", "prob. conservative"], name="unit") - ) - # combine - descriptions = concat( - [creativity_descriptions, concat(creativity_descs, axis=1)], axis=1 - ) - distances = concat( - [creativity_distance, concat(creativity_dists, axis=1)], axis=1 - ) - descriptions.index = MultiIndex.from_tuples( - [("creativity", "diversity", "all"), ("creativity", "novelty", "all")], - names=["domain", "feature", "segment"], - ) - distances.index = MultiIndex.from_tuples( - [ - ("creativity", "homogeneity", "all"), - ("creativity", "conservatism", "all"), - ], - names=["domain", "feature", "segment"], - ) - return descriptions, distances - - -def eval_sample_quality( - synthetic_schedules: dict[str, DataFrame], target_schedules: DataFrame -) -> Tuple[DataFrame, DataFrame]: - observed_weights, observed_metrics = structural.feasibility_eval( - target_schedules, name="observed" - ) - results = [observed_weights, observed_metrics] - for model, y in synthetic_schedules.items(): - y = filter_novel(y, target_schedules) - weights, metrics = structural.feasibility_eval(y, name=model) - results.append(weights) - results.append(metrics) - results = concat(results, axis=1) - results["unit"] = "prob. infeasible" - return results - - -def eval_jobs( - synthetic_schedules: dict[str, DataFrame], - target_schedules: DataFrame, - domain: str, - feature: Tuple[str, Callable], - size: Callable, - description_job: Tuple[str, Callable], - distance_job: Tuple[str, Callable], -) -> Tuple[DataFrame, DataFrame]: - # unpack tuples - feature_name, feature = feature - description_name, describe = description_job - distance_name, distance_metric = distance_job - - # build observed features - observed_features = feature(target_schedules) - - # need to create a default feature for missing sampled features - default = extract_default(observed_features) - - # create an observed feature count and description - feature_weight = size(observed_features) - feature_weight.name = "observed__weight" - description = describe(observed_features) - feature_descriptions = DataFrame( - {"observed__weight": feature_weight, "observed": description} - ) - - # sort by count and description, drop description and add distance description - feature_descriptions = feature_descriptions.sort_values( - ascending=False, by=["observed__weight", "observed"] - ) - - feature_distances = feature_descriptions.copy() - - # iterate through samples - for model, y in synthetic_schedules.items(): - synth_features = feature(y) - synth_weight = size(synth_features) - synth_weight.name = f"{model}__weight" - feature_descriptions = concat( - [ - synth_weight, - feature_descriptions, - describe_feature(model, synth_features, describe), - ], - axis=1, - ) - # report sampled distances - feature_distances = concat( - [ - synth_weight, - feature_distances, - score_features( - model, - observed_features, - synth_features, - distance_metric, - default, - ), - ], - axis=1, - ) - - # add domain and feature name to index - feature_descriptions["unit"] = description_name - feature_distances["unit"] = distance_name - feature_descriptions.index = MultiIndex.from_tuples( - [(domain, feature_name, f) for f in feature_descriptions.index], - name=["domain", "feature", "segment"], - ) - feature_distances.index = MultiIndex.from_tuples( - [(domain, feature_name, f) for f in feature_distances.index], - name=["domain", "feature", "segment"], - ) - - return feature_descriptions, feature_distances - - -def rank(data: DataFrame) -> DataFrame: - # feature rank - rank = data.drop(["observed", "unit"], axis=1, errors="ignore").rank( - axis=1, method="min" - ) - col_ranks = rank.sum(axis=0) - ranked = [i for _, i in sorted(zip(col_ranks, col_ranks.index))] - return rank[ranked] - - -def report( - frames: dict[str, DataFrame], - log_dir: Optional[Path] = None, - head: Optional[int] = None, - verbose: bool = True, - suffix: str = "", - ranking: bool = False, -): - if head is not None: - frames["descriptions_short"] = ( - frames["descriptions"].groupby(["domain", "feature"]).head(head) - ) - frames["distances_short"] = ( - frames["distances"].groupby(["domain", "feature"]).head(head) - ) - else: - # default to full - frames["descriptions_short"] = frames["descriptions"] - frames["distances_short"] = frames["distances"] - - if log_dir is not None: - for name, frame in frames.items(): - frame.to_csv(Path(log_dir, f"{name}{suffix}.csv")) - - if verbose: - print("\nDescriptions:") - print_markdown(frames["descriptions_short"]) - print("\nEvalutions (Distance):") - print_markdown(frames["distances_short"]) - - print("\nGroup Descriptions:") - print_markdown(frames["group_descriptions"]) - print("\nGroup Evaluations (Distance):") - print_markdown(frames["group_distances"]) - if ranking: - print("\nGroup Evaluations (Ranked):") - print_markdown(rank(frames["group_distances"])) - - print("\nDomain Descriptions:") - print_markdown(frames["domain_descriptions"]) - print("\nDomain Evaluations (Distance):") - print_markdown(frames["domain_distances"]) - if ranking: - print("\nDomain Evaluations (Ranked):") - print_markdown(rank(frames["domain_distances"])) - - -def report_splits( - frames: dict[str, DataFrame], - log_dir: Optional[Path] = None, - head: Optional[int] = None, - verbose: bool = True, - suffix: str = "", - ranking: bool = False, -): - if head is not None: - frames["label_descriptions_short"] = ( - frames["label_descriptions"] - .groupby(["domain", "feature", "label"]) - .head(head) - ) - frames["label_distances_short"] = ( - frames["label_distances"] - .groupby(["domain", "feature", "label"]) - .head(head) - ) - else: - # default to full - frames["label_descriptions_short"] = frames["label_descriptions"] - frames["label_distances_short"] = frames["label_distances"] - - if log_dir is not None: - for name, frame in frames.items(): - frame.to_csv(Path(log_dir, f"{name}{suffix}.csv")) - - if verbose: - print("\nDescriptions:") - print_markdown(frames["label_descriptions_short"]) - print("\nEvalutions (Distance):") - print_markdown(frames["label_distances_short"]) - - print("\nGroup Descriptions:") - print_markdown(frames["label_group_descriptions"]) - print("\nGroup Evaluations (Distance):") - print_markdown(frames["label_group_distances"]) - if ranking: - print("\nGroup Evaluations (Ranked):") - print_markdown(rank(frames["label_group_distances"])) - - print("\nDomain Descriptions:") - print_markdown(frames["label_domain_descriptions"]) - print("\nDomain Evaluations (Distance):") - print_markdown(frames["label_domain_distances"]) - if ranking: - print("\nDomain Evaluations (Ranked):") - print_markdown(rank(frames["label_domain_distances"])) - - -def add_stats(data: DataFrame, columns: dict[str, DataFrame]): - data["mean"] = data[columns].mean(axis=1) - data["std"] = data[columns].std(axis=1) - - -def print_markdown(data: DataFrame): - print(data.to_markdown(tablefmt="fancy_grid", floatfmt=".3f")) - - -def describe_feature( - model: str, - features: dict[str, tuple[np.array, np.array]], - describe: Callable, -): - feature_description = describe(features) - feature_description.name = model - return feature_description - - -def score_features( - model: str, - a: dict[str, tuple[np.array, np.array]], - b: dict[str, tuple[np.array, np.array]], - distance: Callable, - default: tuple[np.array, np.array], -): - index = set(a.keys()) | set(b.keys()) - metrics = Series( - { - k: distance( - defaulting_get(a, k, default), defaulting_get(b, k, default) - ) - for k in index - }, - name=model, - ) - metrics = metrics.fillna(0) - # metrics = metrics[np.isfinite(metrics)] - return metrics - - -def defaulting_get( - features: dict[str, tuple[np.array, np.array]], - key: str, - default: tuple[np.array, np.array], -): - feature = features.get(key) - if feature is None: - return default - support, _ = feature - if len(support) == 0: - return default - return feature - - -def extract_default(features: dict[str, tuple[np.array, np.array]]): - # we use a single feature of zeros as required - # look for a size - default_shape = extract_default_shape(features) - default_support = np.zeros(default_shape) - return (default_support, np.array([1])) - - -def extract_default_shape( - features: dict[str, tuple[np.array, np.array]] -) -> np.array: - for k, _ in iter(features.values()): - if len(k) > 0: - default_shape = list(k.shape) - default_shape[0] = 1 - return default_shape - print( - f"Warning, no features found in the given dictionary: {features}, return [1]." - ) - return np.array([1]) - - -def weighted_av(report: DataFrame, suffix: str = "__weight") -> Series: - """Weighted average of dataframe using weights in the weight column.""" - cols = list(report.columns) - cols = [c for c in cols if not c.endswith(suffix)] - scores = DataFrame() - for c in cols: - weights = report[f"{c}{suffix}"] - total = weights.sum() - scores[c] = report[c] * weights / total - return scores.sum() - - -def distance_weighted_av( - report: DataFrame, - base_col: str = "observed__weight", - suffix: str = "__weight", -) -> Series: - """Weighted average of dataframe using weights in the weight column and a base column. - This deals with cases where models have different features. - """ - cols = list(report.columns) - cols = [c for c in cols if not c.endswith(suffix)] - base_weights = report[base_col] - scores = DataFrame() - for c in cols: - weights = report[f"{c}{suffix}"] - weights = (weights + base_weights) / 2 - total = weights.sum() - scores[c] = report[c] * weights / total - return scores.sum() diff --git a/caveat/evaluate/features/__init__.py b/caveat/evaluate/features/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/caveat/evaluate/features/creativity.py b/caveat/evaluate/features/creativity.py deleted file mode 100644 index 4283d320..00000000 --- a/caveat/evaluate/features/creativity.py +++ /dev/null @@ -1,90 +0,0 @@ -from pandas import DataFrame - - -def hash_schedule(schedule: DataFrame) -> list[str]: - """Hash a schedule. We first create strings of combined activities and durations. - - Args: - schedule (DataFrame): Input schedule. - - Returns: - str: hashed schedule. - """ - act_hash = schedule.act.astype(str) + schedule.duration.astype(str) - return "".join(act_hash) - - -def hash_population(population: DataFrame) -> set[str]: - """Hash a population of sequences. We first create strings of combined activities and durations. - Then create a python set of these strings. This will remove duplicates. - - Args: - population (DataFrame): Input population of sequences. - - Returns: - set[str]: set of hashed sequences. - """ - act_hash = population.act.astype(str) + population.duration.astype(str) - return set(act_hash.groupby(population.pid).apply("".join)) - - -def diversity(population: DataFrame, hashed: set[str]) -> float: - """Measure the internal diversity of a population of sequences. This is the ratio of unique - sequences to the total number of sequences. - - Args: - population (DataFrame): Input population of sequences. - hashed (set[str]): Hashed population of sequences. - - Returns: - float: Diversity of the population. - """ - n = population.pid.nunique() - if n == 0: - return 0 - unique = len(hashed) - return unique / n - - -def homogeneity(population: DataFrame, hashed: set[str]) -> float: - """Measure the internal homogeneity of a population of sequences. This is 1-diversity. - - Args: - population (DataFrame): Input population of sequences. - hashed (set[str]): Hashed population of sequences. - - Returns: - float: Homogeneity of the synthetic sample. - """ - return 1 - diversity(population, hashed) - - -def novelty(observed_hashed: set[str], synthetic_hashed: set[str]) -> float: - """Measure the novelty of a population by comparing it to an observed population. - - Args: - observed_hashed (set[str]): Hashed observed population. - synthetic_hashed (set[str]): Hashed synthetic population. - - Returns: - float: Novelty of the synthetic population. - """ - n = len(synthetic_hashed) - if n == 0: - return 0 - return len(synthetic_hashed - observed_hashed) / n - - -def conservatism( - observed_hashed: set[str], synthetic_hashed: set[str] -) -> float: - """Measure the conservatism of a population as 1-novelty. - - Args: - observed_hashed (set[str]): Hashed observed population. - synthetic_hashed (set[str]): Hashed synthetic population. - - Returns: - float: Conservatism of the synthetic population. - """ - return 1 - novelty(observed_hashed, synthetic_hashed) diff --git a/caveat/evaluate/features/frequency.py b/caveat/evaluate/features/frequency.py deleted file mode 100644 index 8dd42319..00000000 --- a/caveat/evaluate/features/frequency.py +++ /dev/null @@ -1,59 +0,0 @@ -import numpy as np -from numpy import arange, ndarray -from pandas import DataFrame - -from caveat.encoding.one_hot import descretise_population - - -def count_schedules(population: DataFrame) -> int: - count = population.pid.nunique() - return {"all": (np.array([count]), np.array([1]))} - - -def binned_activity_count( - population: DataFrame, class_map: dict, duration: int = 1440, step: int = 15 -) -> ndarray: - return ( - descretise_population( - population, duration=duration, step_size=step, class_map=class_map - ) - .sum(0) - .numpy() - )[0, :, :] - - -def binned_activity_density( - population: DataFrame, class_map: dict, duration: int = 1440, step: int = 15 -) -> ndarray: - return ( - descretise_population( - population, duration=duration, step_size=step, class_map=class_map - ) - .mean(0) - .numpy() - )[0, :, :] - - -def activity_frequencies( - population: DataFrame, duration: int = 1440, step: int = 15 -) -> dict[str, tuple[ndarray, ndarray]]: - index_to_acts = {i: a for i, a in enumerate(population.act.unique())} - class_map = {a: i for i, a in index_to_acts.items()} - bins = binned_activity_count( - population=population, class_map=class_map, duration=duration, step=step - ) - support = arange(0, 1, step / duration) - freqs = {act: (support, bins[:, i]) for act, i in class_map.items()} - return freqs - - -def activity_densities( - population: DataFrame, duration: int = 1440, step: int = 15 -) -> dict[str, tuple[ndarray, ndarray]]: - index_to_acts = {i: a for i, a in enumerate(population.act.unique())} - class_map = {a: i for i, a in index_to_acts.items()} - bins = binned_activity_density( - population=population, class_map=class_map, duration=duration, step=step - ) - support = arange(0, 1, step / duration) - return {act: (support, bins[:, i]) for act, i in class_map.items()} diff --git a/caveat/evaluate/features/participation.py b/caveat/evaluate/features/participation.py deleted file mode 100644 index 2314aad2..00000000 --- a/caveat/evaluate/features/participation.py +++ /dev/null @@ -1,157 +0,0 @@ -from numpy import array, ndarray -from pandas import DataFrame - -from caveat.evaluate.features.utils import weighted_features - - -def participation_prob_by_act( - population: DataFrame, -) -> dict[str, tuple[ndarray, ndarray]]: - """ - Calculate the participations by activity for a given population. - - Args: - population (DataFrame): The population data. - - Returns: - dict[str, tuple[array, array]]: A dictionary containing the participation for each activity. - """ - metrics = population.groupby(["pid", "act"], observed=False).size() > 0 - metrics = metrics.groupby("act", observed=False).sum().to_dict() - n = population.pid.nunique() - compressed = {} - for k, v in metrics.items(): - compressed[k] = (array([0, 1]), array([(n - v), v])) - return compressed - - -def participation_rates( - population: DataFrame, -) -> dict[str, tuple[ndarray, ndarray]]: - rates = population.groupby("pid").act.count() - return weighted_features({"all": rates.to_list()}) - - -def participation_rates_by_act( - population: DataFrame, -) -> dict[str, tuple[ndarray, ndarray]]: - rates = population.groupby("pid").act.value_counts().unstack().fillna(0) - return weighted_features(rates.to_dict(orient="list")) - - -def participation_rates_by_seq_act( - population: DataFrame, -) -> dict[str, tuple[ndarray, ndarray]]: - actseq = population.groupby("pid", as_index=False).cumcount().astype( - str - ) + population.act.astype(str) - rates = actseq.groupby(population.pid).value_counts().unstack().fillna(0) - return weighted_features(rates.to_dict(orient="list")) - - -def participation_rates_by_act_enum( - population: DataFrame, -) -> dict[str, tuple[ndarray, ndarray]]: - act_enum = population.act.astype(str) + population.groupby( - ["pid", "act"], as_index=False, observed=False - ).cumcount().astype(str) - rates = act_enum.groupby(population.pid).value_counts().unstack().fillna(0) - return weighted_features(rates.to_dict(orient="list")) - - -def combinations_with_replacement( - targets: list, length: int, prev_array=[] -) -> list[list]: - """ - Returns all possible combinations of elements in the input array with replacement, - where each combination has a length of tuple_length. - - Args: - targets (list): The input array to generate combinations from. - length (int): The length of each combination. - prev_array (list, optional): The previous array generated in the recursion. Defaults to []. - - Returns: - list: A list of all possible combinations of elements in the input array with replacement. - """ - if len(prev_array) == length: - return [prev_array] - combs = [] - for i, val in enumerate(targets): - prev_array_extended = prev_array.copy() - prev_array_extended.append(val) - combs += combinations_with_replacement( - targets[i:], length, prev_array_extended - ) - return combs - - -def calc_pair_prob(act_counts, pair): - a, b = pair - if a == b: - return (act_counts[a] > 1).sum() - return ((act_counts[a] > 0) & (act_counts[b] > 0)).sum() - - -def joint_participation_prob( - population: DataFrame, -) -> dict[str, tuple[ndarray, ndarray]]: - """ - Calculate the participation prob for all pairs of activities in the given population. - - Args: - population (pandas.DataFrame): A DataFrame containing the population data. - - Returns: - pandas.Series: A Series containing the participation rate for all pairs of activities. - """ - act_counts = ( - population.groupby("pid").act.value_counts().unstack(fill_value=0) - ) - acts = list(population.act.unique()) - pairs = combinations_with_replacement(acts, 2) - n = population.pid.nunique() - metric = {} - for pair in pairs: - p = calc_pair_prob(act_counts, pair) - metric["+".join(pair)] = (array([0, 1]), array([n - p, p])) - - return metric - - -def calc_pair_rate(act_counts, pair): - a, b = pair - if a == b: - return ((act_counts[a] / 2).astype(int)).value_counts().to_dict() - return ( - ((act_counts[[a, b]].min(axis=1) / 2).astype(int)) - .value_counts() - .to_dict() - ) - - -def joint_participation_rate( - population: DataFrame, -) -> dict[str, tuple[ndarray, ndarray]]: - """ - Calculate the participation rate for all pairs of activities in the given population. - - Args: - population (pandas.DataFrame): A DataFrame containing the population data. - - Returns: - pandas.Series: A Series containing the participation rate for all pairs of activities. - """ - act_counts = ( - population.groupby("pid").act.value_counts().unstack(fill_value=0) - ) - acts = list(population.act.unique()) - pairs = combinations_with_replacement(acts, 2) - metric = {} - for pair in pairs: - counts = calc_pair_rate(act_counts, pair) - keys = array(list(counts.keys())) - values = array(list(counts.values())) - metric["+".join(pair)] = (keys, values) - - return metric diff --git a/caveat/evaluate/features/structural.py b/caveat/evaluate/features/structural.py deleted file mode 100644 index abd1d337..00000000 --- a/caveat/evaluate/features/structural.py +++ /dev/null @@ -1,152 +0,0 @@ -from typing import List - -from numpy import array, ndarray -from pandas import DataFrame, MultiIndex, Series - -from caveat.evaluate.features.utils import weighted_features - - -def feasibility_eval(population: DataFrame, name: str) -> tuple[Series, Series]: - index = MultiIndex.from_tuples( - [ - ("feasibility", "invalid", "all"), - ("feasibility", "not home based", "all"), - ("feasibility", "not home based", "starts"), - ("feasibility", "not home based", "ends"), - ("feasibility", "consecutive", "all"), - ("feasibility", "consecutive", "home"), - ("feasibility", "consecutive", "work"), - ("feasibility", "consecutive", "education"), - ], - names=["domain", "feature", "segment"], - ) - - if population.empty: - print(f"Warning: {name} has no novel schedules for quality evaluation.") - weights = Series([0] * len(index), index=index, name=f"{name}__weight") - metrics = Series([0] * len(index), index=index, name=name) - return weights, metrics - - # home based feasibility - first_acts = population.groupby("pid").first().act - last_acts = population.groupby("pid").last().act - - not_start_at_home = first_acts != "home" - not_end_at_home = last_acts != "home" - not_home_based = not_start_at_home | not_end_at_home - - # consecutive feasibility - consecutive_home = get_consecutives(population, "home") - consecutive_work = get_consecutives(population, "work") - consecutive_education = get_consecutives(population, "education") - - consecutive = consecutive_home | consecutive_work | consecutive_education - - # combined - invalid = not_home_based | consecutive - - n = population.pid.nunique() - - metrics = Series( - [ - invalid.sum() / n, - not_home_based.sum() / n, - not_start_at_home.sum() / n, - not_end_at_home.sum() / n, - consecutive.sum() / n, - consecutive_home.sum() / n, - consecutive_work.sum() / n, - consecutive_education.sum() / n, - ], - index=index, - name=name, - dtype=float, - ) - weights = Series( - [n] * len(index), index=index, name=f"{name}__weight", dtype=int - ) - return weights, metrics - - -def get_consecutives(population: DataFrame, act: str) -> Series: - mask = population.act == act - mask = mask & mask.shift(1) - include = mask.groupby(population.pid).cumcount(ascending=True) > 0 - mask = mask & include - mask = mask.groupby(population.pid).sum() - return mask > 0 - - -def start_and_end_acts( - population: DataFrame, target: str = "home" -) -> dict[str, tuple[ndarray, ndarray]]: - n = population.pid.nunique() - first = (population.groupby("pid").first().act == target).sum() - last = (population.groupby("pid").last().act == target).sum() - return { - f"first act {target}": (array([0, 1]), array([(n - first), first])), - f"last act {target}": (array([0, 1]), array([(n - last), last])), - } - - -def act_consecutive( - population: DataFrame, targets: List[str] = ["home", "work", "education"] -) -> dict[str, tuple[ndarray, ndarray]]: - result = {} - n = population.pid.nunique() - for target in targets: - consecutive = ( - population.groupby("pid") - .apply(lambda x: contains_consecutive(x, target)) - .sum() - ) - result[f"consecutive {target}"] = ( - array([0, 1]), - array([n - consecutive, consecutive]), - ) - return result - - -def contains_consecutive(schedule: DataFrame, act: str) -> int: - mask = schedule.act.eq(act) - consecutive = mask == mask.shift(1) - return (mask & consecutive).sum() > 0 - - -def time_consistency( - population: DataFrame, target: int = 1440 -) -> dict[str, tuple[ndarray, ndarray]]: - n = population.pid.nunique() - starts = (population.groupby("pid").first().start == 0).sum() - ends = (population.groupby("pid").last().end == target).sum() - duration = (population.groupby("pid").duration.sum() == target).sum() - return { - "starts at 0": (array([0, 1]), array([(n - starts), starts])), - f"ends at {target}": (array([0, 1]), array([(n - ends), ends])), - f"duration is {target}": ( - array([0, 1]), - array([(n - duration), duration]), - ), - } - - -def duration_consistency( - population: DataFrame, factor: int = 1440 -) -> dict[str, tuple[ndarray, ndarray]]: - durations = population.groupby("pid").duration.sum() / factor - return weighted_features({"total duration": durations.array}) - - -def sequence_lengths( - population: DataFrame, -) -> dict[str, tuple[ndarray, ndarray]]: - lengths = population.groupby("pid").size().value_counts().sort_index() - keys = array(lengths.index) - values = array(lengths.values) - return {"sequence lengths": (keys, values)} - - -def trip_consistency( - population: DataFrame, -) -> dict[str, tuple[ndarray, ndarray]]: - raise NotImplementedError diff --git a/caveat/evaluate/features/times.py b/caveat/evaluate/features/times.py deleted file mode 100644 index 92599202..00000000 --- a/caveat/evaluate/features/times.py +++ /dev/null @@ -1,138 +0,0 @@ -from numpy import array, ndarray -from pandas import DataFrame - -from caveat.evaluate.features.utils import weighted_features - - -def start_times_by_act( - population: DataFrame, bin_size: int = 15, factor: int = 1440 -) -> dict[str, tuple[ndarray, ndarray]]: - return weighted_features( - population.groupby("act", observed=False).start.apply(list).to_dict(), - bin_size=bin_size, - factor=factor, - ) - - -def end_times_by_act( - population: DataFrame, bin_size: int = 15, factor: int = 1440 -) -> dict[str, tuple[ndarray, ndarray]]: - return weighted_features( - population.groupby("act", observed=False).end.apply(list).to_dict(), - bin_size=bin_size, - factor=factor, - ) - - -def durations_by_act( - population: DataFrame, bin_size: int = 15, factor: int = 1440 -) -> dict[str, tuple[ndarray, ndarray]]: - return weighted_features( - population.groupby("act", observed=False) - .duration.apply(list) - .to_dict(), - bin_size=bin_size, - factor=factor, - ) - - -def zip_columns(group, a: str = "start", b: str = "duration") -> ndarray: - return array([(s, d) for s, d in zip(group[a], group[b])]) - - -def start_durations_by_act(population: DataFrame) -> dict[str, ndarray]: - if len(population) == 0: - return {a: array([]) for a in population.act.unique()} - sds = population.groupby("act", observed=False).apply(zip_columns).to_dict() - return sds - - -def start_and_duration_by_act_bins( - population: DataFrame, bin_size: int = 15, factor: int = 1440 -) -> dict[str, tuple[ndarray, ndarray]]: - features = start_durations_by_act(population) - return weighted_features(features, bin_size=bin_size, factor=factor) - - -def joint_durations_by_act_bins( - population: DataFrame, bin_size: int = 15, factor: int = 1440 -) -> dict[str, tuple[ndarray, ndarray]]: - if len(population) == 0: - return {a: array([]) for a in population.act.unique()} - transitions = population.reset_index() - transitions = transitions.set_index(["index", "pid"]) - transitions.act = transitions.act.astype(str) - transitions["shifted"] = transitions.duration.shift(-1) - transitions = transitions.drop(transitions.groupby("pid").tail(1).index) - transitions = ( - transitions.groupby("act", observed=False) - .apply(zip_columns, a="duration", b="shifted") - .to_dict() - ) - return weighted_features(transitions, bin_size=bin_size, factor=factor) - - -def start_times_by_act_plan_seq( - population: DataFrame, -) -> dict[str, tuple[ndarray, ndarray]]: - actseq = population.groupby("pid", as_index=False).cumcount().astype( - str - ) + population.act.astype(str) - return weighted_features( - population.groupby(actseq).start.apply(list).to_dict(), factor=1440 - ) - - -def start_times_by_act_plan_enum( - population: DataFrame, -) -> dict[str, tuple[ndarray, ndarray]]: - actseq = population.act.astype(str) + population.groupby( - ["pid", "act"], as_index=False, observed=False - ).cumcount().astype(str) - return weighted_features( - population.groupby(actseq).start.apply(list).to_dict(), factor=1440 - ) - - -def end_times_by_act_plan_seq( - population: DataFrame, -) -> dict[str, tuple[ndarray, ndarray]]: - actseq = population.groupby("pid", as_index=False).cumcount().astype( - str - ) + population.act.astype(str) - return weighted_features( - population.groupby(actseq).end.apply(list).to_dict(), factor=1440 - ) - - -def end_times_by_act_plan_enum( - population: DataFrame, -) -> dict[str, tuple[ndarray, ndarray]]: - actseq = population.act.astype(str) + population.groupby( - ["pid", "act"], as_index=False, observed=False - ).cumcount().astype(str) - return weighted_features( - population.groupby(actseq).end.apply(list).to_dict(), factor=1440 - ) - - -def durations_by_act_plan_seq( - population: DataFrame, -) -> dict[str, tuple[ndarray, ndarray]]: - actseq = population.groupby("pid", as_index=False).cumcount().astype( - str - ) + population.act.astype(str) - return weighted_features( - population.groupby(actseq).duration.apply(list).to_dict(), factor=1440 - ) - - -def durations_by_act_plan_enum( - population: DataFrame, -) -> dict[str, tuple[ndarray, ndarray]]: - actseq = population.act.astype(str) + population.groupby( - ["pid", "act"], as_index=False, observed=False - ).cumcount().astype(str) - return weighted_features( - population.groupby(actseq).duration.apply(list).to_dict(), factor=1440 - ) diff --git a/caveat/evaluate/features/transitions.py b/caveat/evaluate/features/transitions.py deleted file mode 100644 index 278ec3c8..00000000 --- a/caveat/evaluate/features/transitions.py +++ /dev/null @@ -1,130 +0,0 @@ -from numpy import ndarray -from pandas import DataFrame, MultiIndex, Series - -from caveat.evaluate.features.utils import weighted_features - - -def transitions_by_act( - population: DataFrame, -) -> dict[str, tuple[ndarray, ndarray]]: - transitions = population.reset_index() - transitions = transitions.set_index(["index", "pid"]) - transitions.act = transitions.act.astype(str) - transitions = transitions.act + ">" + transitions.act.shift(-1) - transitions = transitions.drop(transitions.groupby("pid").tail(1).index) - transitions = ( - transitions.groupby("pid") - .value_counts() - .unstack() - .fillna(0) - .astype(int) - .to_dict(orient="list") - ) - return weighted_features(transitions) - - -def transition_3s_by_act( - population: DataFrame, -) -> dict[str, tuple[ndarray, ndarray]]: - transitions = population.reset_index() - transitions = transitions.set_index(["index", "pid"]) - transitions.act = transitions.act.astype(str) - transitions = ( - transitions.act - + ">" - + transitions.act.shift(-1) - + ">" - + transitions.act.shift(-2) - ) - transitions = transitions.drop(transitions.groupby("pid").tail(2).index) - transitions = ( - transitions.groupby("pid") - .value_counts() - .unstack() - .fillna(0) - .astype(int) - .to_dict(orient="list") - ) - return weighted_features(transitions) - - -def transition_4s_by_act( - population: DataFrame, -) -> dict[str, tuple[ndarray, ndarray]]: - transitions = population.reset_index() - transitions = transitions.set_index(["index", "pid"]) - transitions.act = transitions.act.astype(str) - transitions = ( - transitions.act - + ">" - + transitions.act.shift(-1) - + ">" - + transitions.act.shift(-2) - + ">" - + transitions.act.shift(-3) - ) - transitions = transitions.drop(transitions.groupby("pid").tail(3).index) - transitions = ( - transitions.groupby("pid") - .value_counts() - .unstack() - .fillna(0) - .astype(int) - .to_dict(orient="list") - ) - return weighted_features(transitions) - - -def tour(acts: Series) -> str: - """ - Extracts the tour from the given Series of activities. - - Args: - acts (Series): A Series containing the activities. - - Returns: - str: A string representation of the tour. - """ - return ">".join(acts.str[0]) - - -def full_sequences(population: DataFrame) -> dict[str, tuple[ndarray, ndarray]]: - transitions = population.reset_index() - transitions = transitions.set_index(["index", "pid"]) - transitions.act = transitions.act.astype(str) - transitions = transitions.groupby("pid").act.apply(tour) - transitions = ( - transitions.groupby("pid") - .value_counts() - .unstack() - .fillna(0) - .astype(int) - .to_dict(orient="list") - ) - return weighted_features(transitions) - - -def collect_sequence(acts: Series) -> str: - return ">".join(acts) - - -def sequence_probs(population: DataFrame) -> DataFrame: - """ - Calculates the sequence probabilities in the given population DataFrame. - - Args: - population (DataFrame): A DataFrame containing the population data. - - Returns: - DataFrame: A DataFrame containing the probability of each sequence. - """ - metrics = ( - population.groupby("pid") - .act.apply(collect_sequence) - .value_counts(normalize=True) - ) - metrics = metrics.sort_values(ascending=False) - metrics.index = MultiIndex.from_tuples( - [("sequence rate", acts) for acts in metrics.index] - ) - return metrics diff --git a/caveat/evaluate/features/utils.py b/caveat/evaluate/features/utils.py deleted file mode 100644 index c626d736..00000000 --- a/caveat/evaluate/features/utils.py +++ /dev/null @@ -1,79 +0,0 @@ -from typing import Optional, Union - -from numpy import array, ndarray, unique - - -def equals( - a: dict[str, tuple[ndarray, ndarray]], b: dict[str, tuple[ndarray, ndarray]] -) -> bool: - if set(a.keys()) != set(b.keys()): - return False - for k in a.keys(): - if not len(a[k][0]) == len(b[k][0]): - return False - if not len(a[k][1]) == len(b[k][1]): - return False - if not (a[k][0] == b[k][0]).all(): - return False - if not (a[k][1] == b[k][1]).all(): - return False - return True - - -def bin_values(values: array, bin_size: Union[int, float]) -> ndarray: - """ - Bins the input values based on the given bin size. - - Args: - values (array): Input values to be binned. - bin_size (int, float): Size of each bin. - - Returns: - array: Binned values. - """ - return (values // bin_size * bin_size) + (bin_size / 2) - - -def compress_feature( - feature: list, bin_size: Optional[int] = None, factor: int = 1440 -) -> tuple[ndarray, ndarray]: - """ - Compresses a feature by optionally binning its values and returning unique values with counts. - - Args: - feature (list): The feature to compress. - bin_size (int, optional): The size of each bin. If None, no binning is performed. - factor (int): Factor to apply to convert output values. - - Returns: - tuple: A tuple containing two arrays and the total weight. The first array contains the unique - values, and the second array contains the counts of each value. - """ - s = array(feature) - if bin_size is not None: - s = bin_values(s, bin_size) - ks, ws = unique(s, axis=0, return_counts=True) - ks = ks / factor - return ks, ws - - -def weighted_features( - features: dict[str, ndarray], - bin_size: Optional[int] = None, - factor: int = 1, -) -> dict[str, tuple[ndarray, ndarray]]: - """ - Apply optional binning and value counting to dictionary of features. - - Args: - features (dict[array): A dictionary of features to compress. - bin_size (Optional[int]): The size of the bin to use for compression. Defaults to None. - factor (int): Factor to apply to convert output values. - - Returns: - dict[str, tuple[array, array[int]]]: A dictionary of features and weights. - """ - return { - k: compress_feature(values, bin_size, factor) - for k, values in features.items() - } diff --git a/caveat/evaluate/filters.py b/caveat/evaluate/filters.py deleted file mode 100644 index c00b398c..00000000 --- a/caveat/evaluate/filters.py +++ /dev/null @@ -1,15 +0,0 @@ -from pandas import DataFrame - -from caveat.evaluate.features import creativity - - -def no_filter(scenario: DataFrame, base: DataFrame) -> DataFrame: - return scenario - - -def filter_novel(scenario: DataFrame, base: DataFrame) -> DataFrame: - base_hashed = creativity.hash_population(base) - filtered = scenario.groupby("pid", group_keys=False).apply( - lambda x: x if creativity.hash_schedule(x) not in base_hashed else None - ) - return filtered.reset_index(drop=True) diff --git a/caveat/evaluate/ops.py b/caveat/evaluate/ops.py deleted file mode 100644 index 5c48db1a..00000000 --- a/caveat/evaluate/ops.py +++ /dev/null @@ -1,51 +0,0 @@ -import numpy as np -from numpy import ndarray -from pandas import Series - - -def actual(features: dict[str, float]) -> Series: - return Series(features) - - -def feature_value(features: dict[str, tuple[ndarray, ndarray]]) -> Series: - return Series({k: v[0] for k, (v, w) in features.items()}, dtype=int) - - -def feature_length(features: dict[str, tuple[ndarray, ndarray]]) -> Series: - return Series({k: len(v) for k, (v, w) in features.items()}, dtype=int) - - -def feature_weight(features: dict[str, tuple[ndarray, ndarray]]) -> Series: - return Series({k: w.sum() for k, (v, w) in features.items()}, dtype=int) - - -def average_weight(features: dict[str, tuple[ndarray, ndarray]]) -> Series: - return Series({k: w.mean() for k, (v, w) in features.items()}, dtype=float) - - -def average_density(features: dict[str, tuple[ndarray, ndarray]]) -> Series: - total = sum(w.sum() for _, w in features.values()) - return Series( - {k: w.sum() / total for k, (v, w) in features.items()}, dtype=float - ) - - -def average(features: dict[str, tuple[ndarray, ndarray]]) -> Series: - weighted_average = {} - for k, (v, w) in features.items(): - if w.sum() > 0: - weighted_average[k] = np.average(v, axis=0, weights=w).sum() - else: - weighted_average[k] = 0 - return Series(weighted_average, dtype=float) - - -def average2d(features: dict[str, tuple[ndarray, ndarray]]) -> Series: - return Series( - { - k: np.average(v, axis=0, weights=w).sum().sum() - for k, (v, w) in features.items() - if w.sum() > 0 - }, - dtype=float, - ) diff --git a/caveat/jrunners.py b/caveat/jrunners.py index 4c993cbe..0daa1e6b 100644 --- a/caveat/jrunners.py +++ b/caveat/jrunners.py @@ -227,7 +227,7 @@ def jbatch_command( verbose: bool = False, gen: bool = True, test: bool = False, - infer=True, + infer: bool = True, sample: bool = True, patience: int = 8, ) -> None: @@ -235,7 +235,14 @@ def jbatch_command( Batch runs the training for joint-model variation. Args: - config (dict): A dictionary containing the configuration parameters. + batch_config (dict): A dictionary containing the batch configuration parameters. + stats (bool): Whether to print stats. Defaults to False. + verbose (bool): Whether to print verbose output. Defaults to False. + gen (bool): Whether to generate samples. Defaults to True. + test (bool): Whether to run test evaluation. Defaults to False. + infer: Whether to run inference. Defaults to True. + sample (bool): Whether to sample. Defaults to True. + patience (int): Training patience. Defaults to 8. Returns: None diff --git a/caveat/models/base.py b/caveat/models/base.py index a143650c..33bfff76 100644 --- a/caveat/models/base.py +++ b/caveat/models/base.py @@ -182,7 +182,7 @@ def predict(self, z: Tensor, device: int, **kwargs) -> Tensor: Args: z (tensor): [N, latent_dims]. - current_device (int): Device to run the model. + device (int): Device to run the model. Returns: tensor: [N, steps, acts]. diff --git a/caveat/models/cat_base.py b/caveat/models/cat_base.py index a5ed1fc5..16a148a6 100644 --- a/caveat/models/cat_base.py +++ b/caveat/models/cat_base.py @@ -185,7 +185,7 @@ def predict(self, z: Tensor, device: int, **kwargs) -> Tensor: Args: z (tensor): [N, latent_dims]. - current_device (int): Device to run the model. + device (int): Device to run the model. Returns: tensor: [N, steps, acts]. diff --git a/caveat/models/continuous/auto_attention.py b/caveat/models/continuous/auto_attention.py index 63158c24..9fab3106 100644 --- a/caveat/models/continuous/auto_attention.py +++ b/caveat/models/continuous/auto_attention.py @@ -79,7 +79,8 @@ def decode( """Decode latent sample to batch of output sequences. Args: - z (tensor): Latent space batch [N, latent_dims]. + context (Tensor): Latent space batch [N, latent_dims]. + mask (Optional[Tensor]): Attention mask. Returns: tensor: Output sequence batch [N, steps, acts]. @@ -93,7 +94,7 @@ def predict(self, z, device: int, **kwargs) -> Tensor: Args: z (tensor): [N, latent_dims]. - current_device (int): Device to run the model. + device (int): Device to run the model. Returns: tensor: [N, steps, acts]. @@ -107,7 +108,6 @@ def predict_sequences( """Given samples from the latent space, return the corresponding decoder space map. Args: - z (tensor): [N, latent_dims]. current_device (int): Device to run the model. Returns: diff --git a/caveat/models/continuous/cvae_lstm.py b/caveat/models/continuous/cvae_lstm.py index 85cadf1b..7c461a87 100644 --- a/caveat/models/continuous/cvae_lstm.py +++ b/caveat/models/continuous/cvae_lstm.py @@ -237,8 +237,8 @@ def decode( """Decode latent sample to batch of output sequences. Args: - hidden (tensor): Latent space batch [N, latent_dims]. - labels_size (tensor): Conditional labels [N, labels_size_size]. + z (tensor): Latent space batch [N, latent_dims]. + labels (tensor): Conditional labels [N, labels_size]. target (tensor): Target sequence batch [N, steps, acts]. Returns: @@ -273,7 +273,9 @@ def predict( """Given samples from the latent space, return the corresponding decoder space map. Args: - current_device (int): Device to run the model. + z (tensor): [N, latent_dims]. + labels (tensor): Conditional labels. + device (int): Device to run the model. Returns: tensor: [N, steps, acts]. diff --git a/caveat/models/continuous/cvae_lstm_double_nudger.py b/caveat/models/continuous/cvae_lstm_double_nudger.py index 5c18e854..c78f2f92 100644 --- a/caveat/models/continuous/cvae_lstm_double_nudger.py +++ b/caveat/models/continuous/cvae_lstm_double_nudger.py @@ -147,7 +147,9 @@ def predict( """Given samples from the latent space, return the corresponding decoder space map. Args: - current_device (int): Device to run the model. + z (tensor): [N, latent_dims]. + conditionals (tensor): Conditional labels. + device (int): Device to run the model. Returns: tensor: [N, steps, acts]. diff --git a/caveat/models/continuous/cvae_lstm_nudge_feed.py b/caveat/models/continuous/cvae_lstm_nudge_feed.py index 0798eb03..698d82d6 100644 --- a/caveat/models/continuous/cvae_lstm_nudge_feed.py +++ b/caveat/models/continuous/cvae_lstm_nudge_feed.py @@ -246,7 +246,9 @@ def predict( """Given samples from the latent space, return the corresponding decoder space map. Args: - current_device (int): Device to run the model. + z (tensor): [N, latent_dims]. + conditionals (tensor): Conditional labels. + device (int): Device to run the model. Returns: tensor: [N, steps, acts]. diff --git a/caveat/models/continuous/cvae_lstm_nudger.py b/caveat/models/continuous/cvae_lstm_nudger.py index b3c44d32..c26840ad 100644 --- a/caveat/models/continuous/cvae_lstm_nudger.py +++ b/caveat/models/continuous/cvae_lstm_nudger.py @@ -139,7 +139,9 @@ def predict( """Given samples from the latent space, return the corresponding decoder space map. Args: - current_device (int): Device to run the model. + z (tensor): [N, latent_dims]. + labels (tensor): Conditional labels. + device (int): Device to run the model. Returns: tensor: [N, steps, acts]. diff --git a/caveat/models/continuous/cvae_lstm_nudger_adversarial.py b/caveat/models/continuous/cvae_lstm_nudger_adversarial.py index 6e62911d..6a45a7e3 100644 --- a/caveat/models/continuous/cvae_lstm_nudger_adversarial.py +++ b/caveat/models/continuous/cvae_lstm_nudger_adversarial.py @@ -287,7 +287,9 @@ def predict( """Given samples from the latent space, return the corresponding decoder space map. Args: - current_device (int): Device to run the model. + z (tensor): [N, latent_dims]. + labels (tensor): Conditional labels. + device (int): Device to run the model. Returns: tensor: [N, steps, acts]. diff --git a/caveat/models/continuous/cvqvae_lstm.py b/caveat/models/continuous/cvqvae_lstm.py index d468973d..d6f6c8f6 100644 --- a/caveat/models/continuous/cvqvae_lstm.py +++ b/caveat/models/continuous/cvqvae_lstm.py @@ -284,7 +284,8 @@ def predict( Args: z (tensor): [N, latent_dims]. - current_device (int): Device to run the model. + device (int): Device to run the model. + labels (Optional[Tensor]): Optional conditional labels. Returns: tensor: [N, steps, acts]. diff --git a/caveat/models/continuous/vae_attention.py b/caveat/models/continuous/vae_attention.py index 55bba2b7..aa5fd6e2 100644 --- a/caveat/models/continuous/vae_attention.py +++ b/caveat/models/continuous/vae_attention.py @@ -148,7 +148,7 @@ def predict(self, z: Tensor, device: int, **kwargs) -> Tensor: Args: z (tensor): [N, latent_dims]. - current_device (int): Device to run the model. + device (int): Device to run the model. Returns: tensor: [N, steps, acts]. diff --git a/caveat/models/continuous/vqvae_lstm.py b/caveat/models/continuous/vqvae_lstm.py index cf9f8214..8d44b08d 100644 --- a/caveat/models/continuous/vqvae_lstm.py +++ b/caveat/models/continuous/vqvae_lstm.py @@ -224,7 +224,7 @@ def predict(self, z: Tensor, device: int, **kwargs) -> Tensor: Args: z (tensor): [N, latent_dims]. - current_device (int): Device to run the model. + device (int): Device to run the model. Returns: tensor: [N, steps, acts]. diff --git a/caveat/models/discrete/auto_discrete_lstm.py b/caveat/models/discrete/auto_discrete_lstm.py index 71822cc7..6bc38833 100644 --- a/caveat/models/discrete/auto_discrete_lstm.py +++ b/caveat/models/discrete/auto_discrete_lstm.py @@ -136,7 +136,7 @@ def __init__( max_length (int): max length of sequences. dropout (float): dropout probability. Defaults to 0. sos (int): start of sequence token. Defaults to 0. - top (bool): top1 sampling. Defaults to False. + top_sampler (bool): top1 sampling. Defaults to False. bidirectional (bool): bidirectional lstm. Defaults to False. """ super(Decoder, self).__init__() diff --git a/caveat/models/discrete/vae_discrete_xattention.py b/caveat/models/discrete/vae_discrete_xattention.py index ed46a118..e60c301a 100644 --- a/caveat/models/discrete/vae_discrete_xattention.py +++ b/caveat/models/discrete/vae_discrete_xattention.py @@ -116,7 +116,7 @@ def predict(self, z: Tensor, device: int, **kwargs) -> Tensor: Args: z (tensor): [N, latent_dims]. - current_device (int): Device to run the model. + device (int): Device to run the model. Returns: tensor: [N, steps, acts]. diff --git a/caveat/models/joint_vaes/jvae_continuous.py b/caveat/models/joint_vaes/jvae_continuous.py index 7d185afb..038ee10b 100644 --- a/caveat/models/joint_vaes/jvae_continuous.py +++ b/caveat/models/joint_vaes/jvae_continuous.py @@ -232,7 +232,7 @@ def predict(self, z: Tensor, device: int, **kwargs) -> Tensor: Args: z (tensor): [N, latent_dims]. - current_device (int): Device to run the model. + device (int): Device to run the model. Returns: tensor: [N, steps, acts]. diff --git a/caveat/models/joint_vaes/jvae_continuous_rerouted.py b/caveat/models/joint_vaes/jvae_continuous_rerouted.py index 04f621f8..8cee3525 100644 --- a/caveat/models/joint_vaes/jvae_continuous_rerouted.py +++ b/caveat/models/joint_vaes/jvae_continuous_rerouted.py @@ -204,7 +204,7 @@ def predict(self, z: Tensor, device: int, **kwargs) -> Tensor: Args: z (tensor): [N, latent_dims]. - current_device (int): Device to run the model. + device (int): Device to run the model. Returns: tensor: [N, steps, acts]. diff --git a/caveat/models/schedule2label/feedforward.py b/caveat/models/schedule2label/feedforward.py index 5fe3e45f..d923925c 100644 --- a/caveat/models/schedule2label/feedforward.py +++ b/caveat/models/schedule2label/feedforward.py @@ -52,9 +52,9 @@ def loss_function( """Calculate the loss function for the model. Args: - log_probs ((tensor, tensor)): Log probabilities for the output sequence. - mu (tensor): Mean of the latent space. - log_var (tensor): Log variance of the latent space. + probs (tensor): Predicted probabilities for each label. + target (tensor): Target label values. + mask (tensor): Mask tensor for valid labels. Returns: dict: Loss dictionary. diff --git a/caveat/models/seq2score/lstm.py b/caveat/models/seq2score/lstm.py index 73707264..3a15552c 100644 --- a/caveat/models/seq2score/lstm.py +++ b/caveat/models/seq2score/lstm.py @@ -95,12 +95,12 @@ def loss_function( return {"loss": loss} - def predict_step(self, batch, device: int, **kwargs) -> Tensor: - """Given samples from the latent space, return the corresponding decoder space map. + def predict_step(self, batch: tuple, device: int, **kwargs) -> Tensor: + """Run a prediction step on a batch. Args: - batch - current_device (int): Device to run the model. + batch: Input batch tuple of (sequences, targets, labels). + device (int): Device to run the model. Returns: tensor: [N, steps, acts]. diff --git a/caveat/models/seq2seq/lstm.py b/caveat/models/seq2seq/lstm.py index 42c40705..a5a499bf 100644 --- a/caveat/models/seq2seq/lstm.py +++ b/caveat/models/seq2seq/lstm.py @@ -174,12 +174,12 @@ def loss_function( "recon_distance_loss": recon_dist_mse.detach(), } - def predict_step(self, batch, device: int, **kwargs) -> Tensor: - """Given samples from the latent space, return the corresponding decoder space map. + def predict_step(self, batch: tuple, device: int, **kwargs) -> Tensor: + """Run a prediction step on a batch. Args: - batch - current_device (int): Device to run the model. + batch: Input batch tuple of (sequences, targets, labels). + device (int): Device to run the model. Returns: tensor: [N, steps, acts]. diff --git a/caveat/runners.py b/caveat/runners.py index 741be53d..a764ffd2 100644 --- a/caveat/runners.py +++ b/caveat/runners.py @@ -20,7 +20,7 @@ ) from caveat.data.module import DataModule from caveat.encoding import BaseDataset, BaseEncoder -from caveat.evaluate import evaluate +from acteval import evaluate from caveat.label_encoding.base import BaseLabelEncoder @@ -735,10 +735,15 @@ def train( Args: name (str): The name of the experiment. - schedules (pandas.DataFrame): The "observed" population data to train the model on. - conditionals (pandas.DataFrame): The "conditionals" data to train the model on. + data_loader (DataModule): The data module wrapping encoded training data. + encoded_schedules (BaseDataset): The encoded schedule dataset. config (dict): A dictionary containing the configuration parameters for the experiment. + test (bool): Whether to run test evaluation after training. + gen (bool): Whether to generate samples after training. logger (TensorBoardLogger): Logger. + seed (Optional[int]): Random seed. + ckpt_path (Optional[Path]): Path to checkpoint to resume from. + label_encoder (Optional[BaseLabelEncoder]): Optional label encoder. Returns: Tuple(pytorch.Trainer, BaseEncoder). @@ -1020,10 +1025,10 @@ def evaluate_synthetics( else: eval_attributes = default_eval_attributes - sub_reports = evaluate.subsample_and_evaluate( + sub_reports = evaluate.compare_splits( + observed=eval_schedules, synthetic_schedules=synthetic_schedules, synthetic_attributes=synthetic_labels, - target_schedules=eval_schedules, target_attributes=eval_attributes, split_on=split_on, report_stats=stats, diff --git a/demos/imposter_game.ipynb b/demos/imposter_game.ipynb index 39afdeae..b1a71ce5 100644 --- a/demos/imposter_game.ipynb +++ b/demos/imposter_game.ipynb @@ -31,8 +31,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Using default `ModelCheckpoint`. Consider installing `litmodels` package to enable `LitModelCheckpoint` for automatic upload to the Lightning model registry.\n", - "GPU available: True (cuda), used: True\n", + "GPU available: False, used: False\n", "TPU available: False, using: 0 TPU cores\n", "HPU available: False, using: 0 HPUs\n" ] @@ -51,14 +50,14 @@ "source": [ "with io.capture_output():\n", "\n", - " root = Path(\"../logs/TRB/cvae_foundation/conditioned/version_0\")\n", - " ckpt_path = root / \"checkpoints/epoch=91-step=14444.ckpt\"\n", + " root = Path(\"/home/fred/Data/imposter_demo\")\n", + " ckpt_path = root / \"epoch=91-step=14444.ckpt\"\n", " schedule_encoder_path = root / \"schedule_encoder.pkl\"\n", " attributes_encoder_path = root / \"attribute_encoder.pkl\"\n", "\n", - " real_schedules_path = Path(\"../tmp/nts_schedules_2019_2023_200.csv\")\n", + " real_schedules_path = root / \"nts_schedules_2019_2023_200.csv\"\n", " real_schedules = pd.read_csv(real_schedules_path)\n", - " real_labels_path = Path(\"../tmp/nts_attributes_2019_2023_200.csv\")\n", + " real_labels_path = root / \"nts_attributes_2019_2023_200.csv\"\n", " real_labels = pd.read_csv(real_labels_path)\n", "\n", " generator = imposter_utils.Generator(\n", @@ -95,12 +94,12 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 23, "metadata": {}, "outputs": [ { "data": { - "image/png": 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"text/plain": [ "
" ] @@ -115,20 +114,36 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Location must be between 1 and 4\n" + "4 is wrong! Correct answer is: 1\n", + "Score: 15.38% over 13 rounds.\n", + "None\n" ] } ], "source": [ - "imposter.guess(None) # insert your guess here, e.g., 1, 2, 3, or 4" + "imposter.guess(4) # insert your guess here, e.g., 1, 2, 3, or 4" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -147,7 +162,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.10" + "version": "3.12.8" } }, "nbformat": 4, diff --git a/examples/1_synthetic_schedules_gen.ipynb b/examples/1_synthetic_schedules_gen.ipynb index 7697f390..c82690a6 100644 --- a/examples/1_synthetic_schedules_gen.ipynb +++ b/examples/1_synthetic_schedules_gen.ipynb @@ -19,16 +19,16 @@ "\n", "from caveat.data.synth import ActivityGen\n", "from caveat.data.utils import generate_population, trace_to_pam\n", - "from caveat.evaluate.describe.times import (\n", + "from acteval.describe.times import (\n", " joint_time_distributions_plot,\n", " times_distributions_plot,\n", ")\n", - "from caveat.evaluate.describe.transitions import sequence_prob_plot" + "from acteval.describe.transitions import sequence_prob_plot" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -37,12 +37,12 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 5, "metadata": {}, "outputs": [ { "data": { - "image/png": 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496 rows × 5 columns

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" + ], + "text/plain": [ + " pid act start end duration\n", + "0 0 home 0 375 375\n", + "1 0 work 375 795 420\n", + "2 0 education 795 1005 210\n", + "3 0 home 1005 1440 435\n", + "4 1 home 0 375 375\n", + ".. ... ... ... ... ...\n", + "491 99 home 0 375 375\n", + "492 99 shop 375 405 30\n", + "493 99 work 405 825 420\n", + "494 99 shop 825 960 135\n", + "495 99 home 960 1440 480\n", + "\n", + "[496 rows x 5 columns]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "population = generate_population(gen=generator, size=100)\n", "population.act = population.act.map(generator.map)\n", @@ -75,7 +219,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -85,9 +229,200 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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countmeanstdminmax
attributeact
starteducation32.0908.087.0795.0990.0
home216.0555.0521.00.01260.0
leisure56.0829.0302.0375.01155.0
shop92.0569.0258.0375.01020.0
work100.0412.027.0375.0480.0
endeducation32.01039.042.0990.01080.0
home216.0928.0512.0375.01440.0
leisure56.0911.0321.0405.01260.0
shop92.0608.0265.0390.01035.0
work100.0922.075.0795.01020.0
durationeducation32.0131.050.090.0210.0
home216.0373.071.0180.0480.0
leisure56.081.035.030.0150.0
shop92.039.033.015.0135.0
work100.0511.069.0420.0615.0
\n", + "
" + ], + "text/plain": [ + " count mean std min max\n", + "attribute act \n", + "start education 32.0 908.0 87.0 795.0 990.0\n", + " home 216.0 555.0 521.0 0.0 1260.0\n", + " leisure 56.0 829.0 302.0 375.0 1155.0\n", + " shop 92.0 569.0 258.0 375.0 1020.0\n", + " work 100.0 412.0 27.0 375.0 480.0\n", + "end education 32.0 1039.0 42.0 990.0 1080.0\n", + " home 216.0 928.0 512.0 375.0 1440.0\n", + " leisure 56.0 911.0 321.0 405.0 1260.0\n", + " shop 92.0 608.0 265.0 390.0 1035.0\n", + " work 100.0 922.0 75.0 795.0 1020.0\n", + "duration education 32.0 131.0 50.0 90.0 210.0\n", + " home 216.0 373.0 71.0 180.0 480.0\n", + " leisure 56.0 81.0 35.0 30.0 150.0\n", + " shop 92.0 39.0 33.0 15.0 135.0\n", + " work 100.0 511.0 69.0 420.0 615.0" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "def describe_col(population, col: str) -> pd.DataFrame:\n", " description = population.groupby(\"act\")[col].describe()[\n", @@ -110,7 +445,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -127,7 +462,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -136,27 +471,60 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "_ = times_distributions_plot(population, ys={})" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "_ = joint_time_distributions_plot(population, ys={})" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "_ = sequence_prob_plot(population, ys={}, figsize=(8, 6))" ] @@ -173,7 +541,7 @@ "kernelspec": { "display_name": "caveat", "language": "python", - "name": "caveat" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -185,7 +553,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.8" + "version": "3.12.8" }, "orig_nbformat": 4 }, diff --git a/examples/1_toy_schedules_gen.ipynb b/examples/1_toy_schedules_gen.ipynb index 94e0ed52..8092d23c 100644 --- a/examples/1_toy_schedules_gen.ipynb +++ b/examples/1_toy_schedules_gen.ipynb @@ -11,7 +11,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -22,28 +22,28 @@ "\n", "from caveat.data.synth import ActivityGen\n", "from caveat.data.utils import generate_population, trace_to_pam\n", - "from caveat.evaluate.describe.times import (\n", + "from acteval.describe.times import (\n", " joint_time_distributions_plot,\n", " times_distributions_plot,\n", ")\n", - "from caveat.evaluate.describe.transitions import sequence_prob_plot" + "from acteval.describe.transitions import sequence_prob_plot" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "n = 10000\n", "\n", "schedules_write_path = Path(\"tmp/toy_schedules.csv\")\n", - "attributes_write_path = Path(\"tmp/toy_attributes.csv\")\n" + "attributes_write_path = Path(\"tmp/toy_attributes.csv\")" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -80,40 +80,40 @@ " 0\n", " home\n", " 0\n", - " 571\n", - " 571\n", + " 422\n", + " 422\n", " \n", " \n", " 1\n", " 0\n", " work\n", - " 571\n", - " 842\n", - " 271\n", + " 422\n", + " 555\n", + " 133\n", " \n", " \n", " 2\n", " 0\n", " home\n", - " 842\n", + " 555\n", " 1440\n", - " 598\n", + " 885\n", " \n", " \n", " 3\n", " 1\n", " home\n", " 0\n", - " 492\n", - " 492\n", + " 514\n", + " 514\n", " \n", " \n", " 4\n", " 1\n", " work\n", - " 492\n", - " 1028\n", - " 536\n", + " 514\n", + " 975\n", + " 461\n", " \n", " \n", "\n", @@ -121,14 +121,14 @@ ], "text/plain": [ " pid act start end duration\n", - "0 0 home 0 571 571\n", - "1 0 work 571 842 271\n", - "2 0 home 842 1440 598\n", - "3 1 home 0 492 492\n", - "4 1 work 492 1028 536" + "0 0 home 0 422 422\n", + "1 0 work 422 555 133\n", + "2 0 home 555 1440 885\n", + "3 1 home 0 514 514\n", + "4 1 work 514 975 461" ] }, - "execution_count": 14, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -163,12 +163,12 @@ " \"duration\": durations,\n", " }\n", ")\n", - "schedules.head()\n" + "schedules.head()" ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -205,10 +205,10 @@ " \n", " 0\n", " 0\n", - " m\n", - " 54\n", - " employed\n", - " high\n", + " f\n", + " 82\n", + " unemployed\n", + " medium\n", " no\n", " no\n", " \n", @@ -216,40 +216,40 @@ " 1\n", " 1\n", " f\n", - " 32\n", + " 48\n", " employed\n", " medium\n", - " no\n", + " yes\n", " no\n", " \n", " \n", " 2\n", " 2\n", " f\n", - " 42\n", + " 81\n", " unemployed\n", - " low\n", - " yes\n", + " high\n", + " no\n", " no\n", " \n", " \n", " 3\n", " 3\n", - " f\n", - " 20\n", - " unemployed\n", - " high\n", - " no\n", + " m\n", + " 79\n", + " employed\n", + " low\n", " no\n", + " yes\n", " \n", " \n", " 4\n", " 4\n", " f\n", - " 58\n", + " 36\n", " employed\n", - " medium\n", - " yes\n", + " high\n", + " no\n", " yes\n", " \n", " \n", @@ -258,14 +258,14 @@ ], "text/plain": [ " pid gender age work_status education license car_access\n", - "0 0 m 54 employed high no no\n", - "1 1 f 32 employed medium no no\n", - "2 2 f 42 unemployed low yes no\n", - "3 3 f 20 unemployed high no no\n", - "4 4 f 58 employed medium yes yes" + "0 0 f 82 unemployed medium no no\n", + "1 1 f 48 employed medium yes no\n", + "2 2 f 81 unemployed high no no\n", + "3 3 m 79 employed low no yes\n", + "4 4 f 36 employed high no yes" ] }, - "execution_count": 22, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -299,24 +299,24 @@ " \"car_access\": car_accesses,\n", " }\n", ")\n", - "attributes.head()\n" + "attributes.head()" ] }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "schedules_write_path.parent.mkdir(exist_ok=True)\n", "attributes_write_path.parent.mkdir(exist_ok=True)\n", "schedules.to_csv(schedules_write_path, index=False)\n", - "attributes.to_csv(attributes_write_path, index=False)\n" + "attributes.to_csv(attributes_write_path, index=False)" ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -362,10 +362,10 @@ " start\n", " home\n", " 20000.0\n", - " 405.0\n", + " 406.0\n", " 422.0\n", " 0.0\n", - " 1192.0\n", + " 1196.0\n", " \n", " \n", " work\n", @@ -387,19 +387,19 @@ " \n", " work\n", " 10000.0\n", - " 811.0\n", - " 164.0\n", - " 421.0\n", - " 1192.0\n", + " 812.0\n", + " 163.0\n", + " 425.0\n", + " 1196.0\n", " \n", " \n", " duration\n", " home\n", " 20000.0\n", " 539.0\n", - " 159.0\n", - " 248.0\n", - " 1019.0\n", + " 158.0\n", + " 244.0\n", + " 1015.0\n", " \n", " \n", " work\n", @@ -416,15 +416,15 @@ "text/plain": [ " count mean std min max\n", "attribute act \n", - "start home 20000.0 405.0 422.0 0.0 1192.0\n", + "start home 20000.0 406.0 422.0 0.0 1196.0\n", " work 10000.0 450.0 87.0 300.0 600.0\n", "end home 20000.0 945.0 499.0 300.0 1440.0\n", - " work 10000.0 811.0 164.0 421.0 1192.0\n", - "duration home 20000.0 539.0 159.0 248.0 1019.0\n", + " work 10000.0 812.0 163.0 425.0 1196.0\n", + "duration home 20000.0 539.0 158.0 244.0 1015.0\n", " work 10000.0 361.0 139.0 120.0 600.0" ] }, - "execution_count": 24, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -451,7 +451,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -468,14 +468,14 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { - "image/png": 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" ] }, "metadata": {}, @@ -488,14 +488,14 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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" + "
" ] }, "metadata": {}, @@ -508,12 +508,12 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -538,7 +538,7 @@ "kernelspec": { "display_name": "caveat", "language": "python", - "name": "caveat" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -550,7 +550,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.8" + "version": "3.12.8" }, "orig_nbformat": 4 }, diff --git a/examples/1_toy_work_status_schedules.ipynb b/examples/1_toy_work_status_schedules.ipynb index fcf7cd87..fb1d1b01 100644 --- a/examples/1_toy_work_status_schedules.ipynb +++ b/examples/1_toy_work_status_schedules.ipynb @@ -11,7 +11,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -23,16 +23,16 @@ "\n", "from caveat.data.synth import ActivityGen\n", "from caveat.data.utils import generate_population, trace_to_pam\n", - "from caveat.evaluate.describe.times import (\n", + "from acteval.describe.times import (\n", " joint_time_distributions_plot,\n", " times_distributions_plot,\n", ")\n", - "from caveat.evaluate.describe.transitions import sequence_prob_plot" + "from acteval.describe.transitions import sequence_prob_plot" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -49,7 +49,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -57,32 +57,32 @@ "output_type": "stream", "text": [ " pid act start end duration\n", - "0 0 home 0 669 669\n", - "1 0 other 669 803 134\n", - "2 0 home 803 1009 206\n", - "3 0 other 1009 1101 92\n", - "4 0 home 1101 1440 339\n", - "5 1 home 0 464 464\n", - "6 1 education 464 869 405\n", - "7 1 home 869 1440 571\n", - "8 2 home 0 581 581\n", - "9 2 other 581 645 64\n", - "10 2 home 645 786 141\n", - "11 2 other 786 942 156\n", - "12 2 home 942 1440 498\n", - "13 3 home 0 737 737\n", - "14 3 other 737 909 172\n", - "15 3 home 909 1440 531\n", - "16 4 home 0 459 459\n", - "17 4 work 459 927 468\n", - "18 4 home 927 1440 513\n", - "19 5 home 0 460 460\n", + "0 0 home 0 415 415\n", + "1 0 work 415 863 448\n", + "2 0 home 863 1440 577\n", + "3 1 home 0 727 727\n", + "4 1 other 727 819 92\n", + "5 1 home 819 968 149\n", + "6 1 other 968 1146 178\n", + "7 1 home 1146 1440 294\n", + "8 2 home 0 428 428\n", + "9 2 education 428 913 485\n", + "10 2 home 913 1440 527\n", + "11 3 home 0 621 621\n", + "12 3 other 621 697 76\n", + "13 3 home 697 783 86\n", + "14 3 other 783 949 166\n", + "15 3 home 949 1440 491\n", + "16 4 home 0 642 642\n", + "17 4 other 642 783 141\n", + "18 4 home 783 975 192\n", + "19 4 other 975 1081 106\n", " pid work_status\n", - "0 0 student\n", - "1 1 student\n", - "2 2 unemployed\n", - "3 3 unemployed\n", - "4 4 unemployed\n" + "0 0 unemployed\n", + "1 1 unemployed\n", + "2 2 student\n", + "3 3 student\n", + "4 4 student\n" ] } ], @@ -138,10 +138,7 @@ " home_duration = budget\n", "\n", " t = 0\n", - " for act, duration in zip(\n", - " sequence,\n", - " seq_durations,\n", - " ):\n", + " for act, duration in zip(sequence, seq_durations):\n", " pids.append(i)\n", " acts.append(act)\n", " starts.append(t)\n", @@ -159,12 +156,7 @@ " }\n", ")\n", "\n", - "attributes = pd.DataFrame(\n", - " {\n", - " \"pid\": pid,\n", - " \"work_status\": work_statuses,\n", - " }\n", - ")\n", + "attributes = pd.DataFrame({\"pid\": pid, \"work_status\": work_statuses})\n", "\n", "print(schedules.head(20))\n", "print(attributes.head())" @@ -172,7 +164,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -184,7 +176,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -229,31 +221,31 @@ " \n", " start\n", " education\n", - " 2336.0\n", - " 450.0\n", + " 2330.0\n", + " 451.0\n", " 29.0\n", " 400.0\n", " 500.0\n", " \n", " \n", " home\n", - " 24118.0\n", + " 24103.0\n", " 527.0\n", - " 454.0\n", + " 455.0\n", " 0.0\n", - " 1242.0\n", + " 1238.0\n", " \n", " \n", " other\n", - " 8124.0\n", - " 773.0\n", + " 8099.0\n", + " 774.0\n", " 151.0\n", " 500.0\n", " 1078.0\n", " \n", " \n", " work\n", - " 3658.0\n", + " 3674.0\n", " 450.0\n", " 29.0\n", " 400.0\n", @@ -262,65 +254,65 @@ " \n", " end\n", " education\n", - " 2336.0\n", + " 2330.0\n", " 850.0\n", " 64.0\n", - " 705.0\n", - " 993.0\n", + " 702.0\n", + " 999.0\n", " \n", " \n", " home\n", - " 24118.0\n", - " 969.0\n", + " 24103.0\n", + " 970.0\n", " 424.0\n", " 400.0\n", " 1440.0\n", " \n", " \n", " other\n", - " 8124.0\n", - " 893.0\n", - " 155.0\n", - " 560.0\n", - " 1242.0\n", + " 8099.0\n", + " 894.0\n", + " 154.0\n", + " 563.0\n", + " 1238.0\n", " \n", " \n", " work\n", - " 3658.0\n", - " 949.0\n", - " 64.0\n", - " 801.0\n", - " 1099.0\n", + " 3674.0\n", + " 950.0\n", + " 66.0\n", + " 803.0\n", + " 1097.0\n", " \n", " \n", " duration\n", " education\n", - " 2336.0\n", - " 400.0\n", - " 59.0\n", + " 2330.0\n", + " 399.0\n", + " 58.0\n", " 300.0\n", " 500.0\n", " \n", " \n", " home\n", - " 24118.0\n", - " 442.0\n", + " 24103.0\n", + " 443.0\n", " 165.0\n", " 60.0\n", " 800.0\n", " \n", " \n", " other\n", - " 8124.0\n", - " 119.0\n", + " 8099.0\n", + " 120.0\n", " 35.0\n", " 60.0\n", " 180.0\n", " \n", " \n", " work\n", - " 3658.0\n", - " 499.0\n", + " 3674.0\n", + " 500.0\n", " 58.0\n", " 400.0\n", " 600.0\n", @@ -332,21 +324,21 @@ "text/plain": [ " count mean std min max\n", "attribute act \n", - "start education 2336.0 450.0 29.0 400.0 500.0\n", - " home 24118.0 527.0 454.0 0.0 1242.0\n", - " other 8124.0 773.0 151.0 500.0 1078.0\n", - " work 3658.0 450.0 29.0 400.0 500.0\n", - "end education 2336.0 850.0 64.0 705.0 993.0\n", - " home 24118.0 969.0 424.0 400.0 1440.0\n", - " other 8124.0 893.0 155.0 560.0 1242.0\n", - " work 3658.0 949.0 64.0 801.0 1099.0\n", - "duration education 2336.0 400.0 59.0 300.0 500.0\n", - " home 24118.0 442.0 165.0 60.0 800.0\n", - " other 8124.0 119.0 35.0 60.0 180.0\n", - " work 3658.0 499.0 58.0 400.0 600.0" + "start education 2330.0 451.0 29.0 400.0 500.0\n", + " home 24103.0 527.0 455.0 0.0 1238.0\n", + " other 8099.0 774.0 151.0 500.0 1078.0\n", + " work 3674.0 450.0 29.0 400.0 500.0\n", + "end education 2330.0 850.0 64.0 702.0 999.0\n", + " home 24103.0 970.0 424.0 400.0 1440.0\n", + " other 8099.0 894.0 154.0 563.0 1238.0\n", + " work 3674.0 950.0 66.0 803.0 1097.0\n", + "duration education 2330.0 399.0 58.0 300.0 500.0\n", + " home 24103.0 443.0 165.0 60.0 800.0\n", + " other 8099.0 120.0 35.0 60.0 180.0\n", + " work 3674.0 500.0 58.0 400.0 600.0" ] }, - "execution_count": 22, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -373,7 +365,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -390,14 +382,14 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { - "image/png": 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" ] }, "metadata": {}, @@ -410,14 +402,14 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { - "image/png": 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" + "
" ] }, "metadata": {}, @@ -430,12 +422,12 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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"text/plain": [ "
" ] @@ -460,7 +452,7 @@ "kernelspec": { "display_name": "caveat", "language": "python", - "name": "caveat" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -472,7 +464,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.8" + "version": "3.12.8" }, "orig_nbformat": 4 }, diff --git a/examples/2_synthetic_population_gen.ipynb b/examples/2_synthetic_population_gen.ipynb index 64fad309..e5af2afc 100644 --- a/examples/2_synthetic_population_gen.ipynb +++ b/examples/2_synthetic_population_gen.ipynb @@ -11,7 +11,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -21,11 +21,11 @@ "\n", "from caveat.data.synth import ActivityGen\n", "from caveat.data.utils import generate_population_conditional, trace_to_pam\n", - "from caveat.evaluate.describe.times import (\n", + "from acteval.describe.times import (\n", " joint_time_distributions_plot,\n", " times_distributions_plot,\n", ")\n", - "from caveat.evaluate.describe.transitions import sequence_prob_plot" + "from acteval.describe.transitions import sequence_prob_plot" ] }, { diff --git a/examples/3_NTS_population_demo.ipynb b/examples/3_NTS_population_demo.ipynb index 17ca7e88..7dab3282 100644 --- a/examples/3_NTS_population_demo.ipynb +++ b/examples/3_NTS_population_demo.ipynb @@ -13,7 +13,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -24,11 +24,11 @@ "from pam.core import Population\n", "from pam.utils import datetime_to_matsim_time\n", "\n", - "from caveat.evaluate.describe.times import (\n", + "from acteval.describe.times import (\n", " joint_time_distributions_plot,\n", " times_distributions_plot,\n", ")\n", - "from caveat.evaluate.describe.transitions import sequence_prob_plot" + "from acteval.describe.transitions import sequence_prob_plot" ] }, { diff --git a/examples/4_creativity.ipynb b/examples/4_creativity.ipynb index 6908c76f..30c3e3d0 100644 --- a/examples/4_creativity.ipynb +++ b/examples/4_creativity.ipynb @@ -15,7 +15,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -23,7 +23,7 @@ "\n", "import pandas as pd\n", "\n", - "from caveat.evaluate.features import creativity" + "from acteval.features import creativity" ] }, { @@ -79,9 +79,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "Observed population of size 838 has diversity of 0.735\n", - "Synthetic population A of size 419 has diversity of 0.78\n", - "Synthetic population B of size 865 has diversity of 0.695\n" + "Observed population of size 853 has diversity of 0.675\n", + "Synthetic population A of size 430 has diversity of 0.82\n", + "Synthetic population B of size 851 has diversity of 0.76\n" ] } ], @@ -117,8 +117,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Synthetic population A of size 419 has novelty of 0.0\n", - "Synthetic population B of size 865 has novelty of 0.5611510791366906\n" + "Synthetic population A of size 430 has novelty of 0.0\n", + "Synthetic population B of size 851 has novelty of 0.5986842105263158\n" ] } ], @@ -143,7 +143,7 @@ "kernelspec": { "display_name": "caveat", "language": "python", - "name": "caveat" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -155,7 +155,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.8" + "version": "3.12.8" } }, "nbformat": 4, diff --git a/examples/5_features_and_correctness.ipynb b/examples/5_features_and_correctness.ipynb index ab894a0c..45f4f285 100644 --- a/examples/5_features_and_correctness.ipynb +++ b/examples/5_features_and_correctness.ipynb @@ -36,14 +36,16 @@ "\n", "import pandas as pd\n", "\n", - "from caveat.evaluate import ops\n", - "from caveat.evaluate.describe.times import (\n", + "# from acteval import ops, Population\n", + "from acteval import Population\n", + "from acteval._aggregation import average, average2d\n", + "from acteval.describe.times import (\n", " joint_time_distributions_plot,\n", " times_distributions_plot,\n", ")\n", - "from caveat.evaluate.describe.transitions import sequence_prob_plot\n", - "from caveat.evaluate.distance import emd, mape\n", - "from caveat.evaluate.features import participation, times" + "from acteval.describe.transitions import sequence_prob_plot\n", + "from acteval.distance import emd, mape\n", + "from acteval.features import participation, times" ] }, { @@ -67,6 +69,7 @@ "\n", "a = down_sample(observed, 0.2)\n", "b = down_sample(raw, 0.2)\n", + "\n", "synthetic = {\"a\": a, \"b\": b}" ] }, @@ -87,11 +90,11 @@ { "data": { "text/plain": [ - "education 0.655208\n", - "home 0.345277\n", - "leisure 0.368498\n", - "shop 0.366820\n", - "work 0.365031\n", + "education 0.666667\n", + "home 0.352598\n", + "leisure 0.412798\n", + "shop 0.380985\n", + "work 0.389101\n", "dtype: float64" ] }, @@ -101,8 +104,9 @@ } ], "source": [ - "starts = times.start_times_by_act(observed)\n", - "ops.average(starts)" + "observed_population = Population(observed)\n", + "starts = times.start_times_by_act(observed_population)\n", + "average(starts.aggregate())" ] }, { @@ -121,7 +125,7 @@ "outputs": [ { "data": { - "image/png": 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" ] @@ -176,18 +180,20 @@ "name": "stdout", "output_type": "stream", "text": [ - "education 0.050\n", - "home 2.080\n", - "leisure 0.745\n", - "shop 0.885\n", - "work 0.465\n", + "education 0.060\n", + "home 2.075\n", + "leisure 0.700\n", + "shop 0.805\n", + "work 0.615\n", "dtype: float64\n" ] } ], "source": [ - "participation_rates = participation.participation_rates_by_act(observed)\n", - "print(ops.average(participation_rates))" + "participation_rates = participation.participation_rates_by_act(\n", + " observed_population\n", + ")\n", + "print(average(participation_rates.aggregate()))" ] }, { @@ -197,7 +203,7 @@ "outputs": [ { "data": { - "image/png": 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"text/plain": [ "
" ] @@ -229,22 +235,24 @@ "output_type": "stream", "text": [ "0home 1.000\n", - "10leisure 0.005\n", + "10leisure 0.020\n", + "11home 0.015\n", "11work 0.005\n", "12home 0.005\n", - "1leisure 0.155\n", - "1shop 0.505\n", - "1work 0.340\n", - "2education 0.035\n", - "2home 0.210\n", - "2leisure 0.465\n", + "1leisure 0.120\n", + "1shop 0.440\n", + "1work 0.440\n", + "2education 0.050\n", + "2home 0.260\n", "dtype: float64\n" ] } ], "source": [ - "participation_rates = participation.participation_rates_by_seq_act(observed)\n", - "print(ops.average(participation_rates).head(10))" + "participation_rates = participation.participation_rates_by_seq_act(\n", + " observed_population\n", + ")\n", + "print(average(participation_rates.aggregate()).head(10))" ] }, { @@ -263,23 +271,25 @@ "name": "stdout", "output_type": "stream", "text": [ - "education0 0.050\n", + "education0 0.060\n", "home0 1.000\n", "home1 1.000\n", - "home2 0.065\n", - "home3 0.010\n", - "home4 0.005\n", - "leisure0 0.710\n", - "leisure1 0.025\n", - "leisure2 0.005\n", - "leisure3 0.005\n", + "home2 0.070\n", + "home3 0.005\n", + "leisure0 0.630\n", + "leisure1 0.030\n", + "leisure2 0.020\n", + "leisure3 0.020\n", + "shop0 0.735\n", "dtype: float64\n" ] } ], "source": [ - "participation_rates = participation.participation_rates_by_act_enum(observed)\n", - "print(ops.average(participation_rates).head(10))" + "participation_rates = participation.participation_rates_by_act_enum(\n", + " observed_population\n", + ")\n", + "print(average(participation_rates.aggregate()).head(10))" ] }, { @@ -297,7 +307,7 @@ "source": [ "# Dimensions\n", "\n", - "Start times are a one dimensional feature, but we can also consider multi-demnsional features:" + "Start times are a one dimensional feature, but we can also consider multi-dimensional features:" ] }, { @@ -308,11 +318,11 @@ { "data": { "text/plain": [ - "education 0.721528\n", - "home 0.633998\n", - "leisure 0.625886\n", - "shop 0.435891\n", - "work 0.706467\n", + "education 0.722801\n", + "home 0.634823\n", + "leisure 0.639732\n", + "shop 0.439743\n", + "work 0.711664\n", "dtype: float64" ] }, @@ -322,9 +332,11 @@ } ], "source": [ - "start_durations = times.start_and_duration_by_act_bins(observed, bin_size=10)\n", + "start_durations = times.start_and_duration_by_act_bins(\n", + " observed_population, bin_size=10\n", + ")\n", "# average 2d averages each dimension and then sums so that we can return an float\n", - "ops.average2d(start_durations)" + "average2d(start_durations.aggregate())" ] }, { @@ -334,7 +346,7 @@ "outputs": [ { "data": { - "image/png": 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" ] @@ -363,7 +375,7 @@ "outputs": [ { "data": { - "image/png": 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" ] @@ -385,24 +397,32 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "synthetic population A: 0.006147486580939153\n", - "synthetic population B: 0.004812311348643272\n" + "synthetic population A: 0.009084851310001899\n", + "synthetic population B: 0.005697791164658632\n" ] } ], "source": [ - "x = times.start_times_by_act(observed)\n", - "ya = times.start_times_by_act(synthetic[\"a\"])\n", - "yb = times.start_times_by_act(synthetic[\"b\"])\n", - "print(\"synthetic population A: \", emd(x[\"home\"], ya[\"home\"]))\n", - "print(\"synthetic population B: \", emd(x[\"home\"], yb[\"home\"]))" + "x = times.start_times_by_act(observed_population)\n", + "a, b = Population(a), Population(b)\n", + "\n", + "ya = times.start_times_by_act(a)\n", + "yb = times.start_times_by_act(b)\n", + "print(\n", + " \"synthetic population A: \",\n", + " emd(x.aggregate()[\"home\"], ya.aggregate()[\"home\"]),\n", + ")\n", + "print(\n", + " \"synthetic population B: \",\n", + " emd(x.aggregate()[\"home\"], yb.aggregate()[\"home\"]),\n", + ")" ] }, { @@ -416,24 +436,30 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "synthetic population A: 0.020689655172413814\n", - "synthetic population B: 0.10126582278481021\n" + "synthetic population A: 0.24324324324324334\n", + "synthetic population B: 0.2222222222222223\n" ] } ], "source": [ - "x = participation.participation_prob_by_act(observed)\n", - "ya = participation.participation_prob_by_act(synthetic[\"a\"])\n", - "yb = participation.participation_prob_by_act(synthetic[\"b\"])\n", - "print(\"synthetic population A: \", mape(x[\"leisure\"], ya[\"shop\"]))\n", - "print(\"synthetic population B: \", mape(x[\"leisure\"], yb[\"shop\"]))" + "x = participation.participation_rates_by_act(observed_population)\n", + "ya = participation.participation_rates_by_act(a)\n", + "yb = participation.participation_rates_by_act(b)\n", + "print(\n", + " \"synthetic population A: \",\n", + " mape(x.aggregate()[\"leisure\"], ya.aggregate()[\"shop\"]),\n", + ")\n", + "print(\n", + " \"synthetic population B: \",\n", + " mape(x.aggregate()[\"leisure\"], yb.aggregate()[\"shop\"]),\n", + ")" ] }, { diff --git a/mkdocs.yml b/mkdocs.yml index 2b166f2d..6402023e 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -21,7 +21,7 @@ markdown_extensions: - mkdocs-click - pymdownx.emoji: emoji_index: !!python/name:material.extensions.emoji.twemoji - emoji_generator: !!python/name:materialx.emoji.to_svg + emoji_generator: !!python/name:material.extensions.emoji.to_svg - pymdownx.highlight: anchor_linenums: true line_spans: __span diff --git a/pyproject.toml b/pyproject.toml index 6c9cc86d..4337356a 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,4 +1,23 @@ +[project] +classifiers = [ + "Development Status :: 2 - Pre-Alpha", + "Intended Audience :: Developers", + "License :: OSI Approved :: MIT License", + "Natural Language :: English", + "Programming Language :: Python :: 3", + ] +name = "caveat" +authors = [ + { name = "Fred Shone", email = "26383933+fredshone@users.noreply.github.com" }, +] +maintainers = [] +description = "activity sequence generation using variation auto-encoders" +readme = "README.md" +requires-python = ">=3.8" +keywords = ["caveat", "arup"] +license = { text = "MIT" } +dynamic = ["dependencies", "optional-dependencies", "version"] [tool.pytest.ini_options] minversion = "6.0" @@ -81,29 +100,8 @@ caveat = [] requires = ["setuptools"] build-backend = "setuptools.build_meta" -[project] -classifiers = [ - "Development Status :: 2 - Pre-Alpha", - "Intended Audience :: Developers", - "License :: OSI Approved :: MIT License", - "Natural Language :: English", - "Programming Language :: Python :: 3", - ] - -name = "caveat" -authors = [ - { name = "Fred Shone", email = "26383933+fredshone@users.noreply.github.com" }, -] -maintainers = [] -description = "activity sequence generation using variation auto-encoders" -readme = "README.md" -requires-python = ">=3.8" -keywords = ["caveat", "arup"] -license = { text = "MIT" } -dynamic = ["dependencies", "optional-dependencies", "version"] - [tool.setuptools.dynamic] -dependencies = { file = ["requirements/base.txt"] } +dependencies = { file = ["requirements/base.txt", "requirements/pip.txt"] } version = { attr = "caveat.__version__" } [project.scripts] diff --git a/requirements/pip.txt b/requirements/pip.txt new file mode 100644 index 00000000..c7ea22d0 --- /dev/null +++ b/requirements/pip.txt @@ -0,0 +1 @@ +acteval diff --git a/tests/test_eval/test_creativity.py b/tests/test_eval/test_creativity.py deleted file mode 100644 index b5f80883..00000000 --- a/tests/test_eval/test_creativity.py +++ /dev/null @@ -1,136 +0,0 @@ -from pandas import DataFrame - -from caveat.evaluate.features.creativity import ( - conservatism, - diversity, - hash_population, - hash_schedule, - homogeneity, - novelty, -) - - -def test_hash_schedule(): - schedule = DataFrame( - [ - {"pid": 0, "act": "home", "duration": 10}, - {"pid": 0, "act": "work", "duration": 10}, - {"pid": 0, "act": "home", "duration": 10}, - ] - ) - assert hash_schedule(schedule) == "home10work10home10" - - -def test_hash_population(): - population = DataFrame( - [ - {"pid": 0, "act": "home", "duration": 10}, - {"pid": 0, "act": "work", "duration": 10}, - {"pid": 0, "act": "home", "duration": 10}, - {"pid": 1, "act": "home", "duration": 10}, - {"pid": 1, "act": "work", "duration": 10}, - ] - ) - assert hash_population(population) == {"home10work10", "home10work10home10"} - - -def test_internal_uniqueness_full(): - population = DataFrame( - [ - {"pid": 0, "act": "home", "duration": 10}, - {"pid": 0, "act": "work", "duration": 10}, - {"pid": 0, "act": "home", "duration": 10}, - {"pid": 1, "act": "home", "duration": 10}, - {"pid": 1, "act": "work", "duration": 10}, - ] - ) - hashed = hash_population(population) - assert diversity(population, hashed) == 1 - assert homogeneity(population, hashed) == 0 - - -def test_internal_uniqueness_half(): - population = DataFrame( - [ - {"pid": 0, "act": "home", "duration": 10}, - {"pid": 0, "act": "work", "duration": 10}, - {"pid": 0, "act": "home", "duration": 10}, - {"pid": 1, "act": "home", "duration": 10}, - {"pid": 1, "act": "work", "duration": 10}, - {"pid": 1, "act": "home", "duration": 10}, - ] - ) - hashed = hash_population(population) - assert diversity(population, hashed) == 0.5 - assert homogeneity(population, hashed) == 0.5 - - -def test_novelty_none(): - a = DataFrame( - [ - {"pid": 0, "act": "home", "duration": 10}, - {"pid": 0, "act": "work", "duration": 10}, - {"pid": 0, "act": "home", "duration": 10}, - {"pid": 1, "act": "home", "duration": 10}, - {"pid": 1, "act": "work", "duration": 10}, - ] - ) - b = DataFrame( - [ - {"pid": 0, "act": "home", "duration": 10}, - {"pid": 0, "act": "work", "duration": 10}, - {"pid": 0, "act": "home", "duration": 10}, - {"pid": 1, "act": "home", "duration": 10}, - {"pid": 1, "act": "work", "duration": 10}, - ] - ) - assert novelty(hash_population(a), hash_population(b)) == 0 - assert conservatism(hash_population(a), hash_population(b)) == 1 - - -def test_novelty_full(): - a = DataFrame( - [ - {"pid": 0, "act": "home", "duration": 10}, - {"pid": 0, "act": "work", "duration": 10}, - {"pid": 0, "act": "home", "duration": 10}, - {"pid": 1, "act": "home", "duration": 10}, - {"pid": 1, "act": "work", "duration": 10}, - ] - ) - b = DataFrame( - [ - {"pid": 2, "act": "home", "duration": 10}, - {"pid": 2, "act": "work", "duration": 15}, - {"pid": 2, "act": "home", "duration": 5}, - {"pid": 3, "act": "home", "duration": 10}, - {"pid": 3, "act": "work", "duration": 10}, - {"pid": 3, "act": "shop", "duration": 10}, - ] - ) - assert novelty(hash_population(a), hash_population(b)) == 1 - assert conservatism(hash_population(a), hash_population(b)) == 0 - - -def test_novelty_partial(): - a = DataFrame( - [ - {"pid": 0, "act": "home", "duration": 10}, - {"pid": 0, "act": "work", "duration": 10}, - {"pid": 0, "act": "home", "duration": 10}, - {"pid": 1, "act": "home", "duration": 10}, - {"pid": 1, "act": "work", "duration": 10}, - ] - ) - b = DataFrame( - [ - {"pid": 2, "act": "home", "duration": 10}, - {"pid": 2, "act": "work", "duration": 10}, - {"pid": 2, "act": "home", "duration": 10}, - {"pid": 3, "act": "home", "duration": 10}, - {"pid": 3, "act": "work", "duration": 10}, - {"pid": 3, "act": "shop", "duration": 10}, - ] - ) - assert novelty(hash_population(a), hash_population(b)) == 0.5 - assert conservatism(hash_population(a), hash_population(b)) == 0.5 diff --git a/tests/test_eval/test_describe_features.py b/tests/test_eval/test_describe_features.py deleted file mode 100644 index fac49c94..00000000 --- a/tests/test_eval/test_describe_features.py +++ /dev/null @@ -1,56 +0,0 @@ -from numpy import array -from pandas import Series -from pandas.testing import assert_series_equal - -from caveat.evaluate import ops - - -def test_describe_actual(): - d = {"a": 0, "b": 1} - expected = Series(d) - assert_series_equal(ops.actual(d), expected, check_dtype=False) - - -def test_feature_length(): - d = { - "a": (array([0, 1]), array([10, 10])), - "b": (array([0, 1, 2]), array([10, 5, 3])), - } - expected = Series({"a": 2, "b": 3}) - assert_series_equal(ops.feature_length(d), expected, check_dtype=False) - - -def test_feature_weight(): - d = { - "a": (array([0, 1]), array([10, 10])), - "b": (array([0, 1, 2]), array([10, 5, 3])), - } - expected = Series({"a": 20, "b": 18}) - assert_series_equal(ops.feature_weight(d), expected, check_dtype=False) - - -def test_average_weight(): - d = { - "a": (array([0, 1]), array([10, 10])), - "b": (array([0, 1, 2]), array([10, 5, 3])), - } - expected = Series({"a": 10, "b": 6}) - assert_series_equal(ops.average_weight(d), expected, check_dtype=False) - - -def test_average(): - d = { - "a": (array([0, 1]), array([10, 10])), - "b": (array([0, 1, 2]), array([2, 2, 2])), - } - expected = Series({"a": 0.5, "b": 1}) - assert_series_equal(ops.average(d), expected, check_dtype=False) - - -def test_average2d(): - d = { - "a": (array([[0, 0], [1, 1]]), array([10, 10])), - "b": (array([[0, 0], [0, 1], [0, 2]]), array([2, 2, 2])), - } - expected = Series({"a": 1, "b": 1}) - assert_series_equal(ops.average2d(d), expected, check_dtype=False) diff --git a/tests/test_eval/test_distance_edit_distance.py b/tests/test_eval/test_distance_edit_distance.py deleted file mode 100644 index 37a58d97..00000000 --- a/tests/test_eval/test_distance_edit_distance.py +++ /dev/null @@ -1,26 +0,0 @@ -import pytest -from torch import equal, tensor -from torchmetrics.text import EditDistance - - -@pytest.mark.parametrize( - "target,preds,expected", - [ - ( - tensor([[[1, 0, 2, 2]], [[1, 0, 2, 2]]]), - tensor([[[1, 0, 2, 2]], [[1, 0, 2, 2]]]), - tensor(0.0), - ) - ], -) -def test_edit_distances(target, preds, expected): - target = target.permute(0, 2, 1).squeeze() # [B, T, C] - preds = preds.permute(0, 2, 1).squeeze() # [B, T, C] - - def to_string(xs): - return " ".join([str(x) for x in xs]) - - target = [to_string(x) for x in target.tolist()] - preds = [to_string(x) for x in preds.tolist()] - metric = EditDistance() - assert equal(metric(preds, target), expected) diff --git a/tests/test_eval/test_distance_emd.py b/tests/test_eval/test_distance_emd.py deleted file mode 100644 index 239a417a..00000000 --- a/tests/test_eval/test_distance_emd.py +++ /dev/null @@ -1,94 +0,0 @@ -import pytest -from scipy.stats import wasserstein_distance -from torch import tensor - -from caveat.evaluate.distance.wasserstein import SinkhornDistance - - -@pytest.mark.parametrize( - "x,y,expected", - [ - (tensor([1.0, 1.0, 1.0]), tensor([0.0, 0.0, 0.0]), 1.0), - (tensor([1.0, 1.0, 1.0, 1.0]), tensor([0.0, 0.0, 0.0, 1.0]), 0.75), - (tensor([1.0, 1.0, 1.0]), tensor([0.0, 0.0]), 1.0), - ], -) -def test_wasserstein_1d(x, y, expected): - dist = wasserstein_distance(x, y) - assert dist == pytest.approx(expected, rel=1e-3) - - -@pytest.mark.parametrize( - "x,y,expected", - [ - ( - tensor([[0.0, 1.0], [1.0, 1.0], [2.0, 1.0]]), - tensor([[0.0, 0.0], [1.0, 0.0], [2.0, 0.0]]), - 1.0, - ), - ( - tensor([[0.0, 1.0], [1.0, 1.0], [2.0, 1.0], [3.0, 1.0]]), - tensor([[0.0, 0.0], [1.0, 0.0], [2.0, 1.0], [3.0, 1.0]]), - 0.5, - ), - ], -) -def test_custom_sinkhorn_2d(x, y, expected): - sinkhorn = SinkhornDistance(eps=0.01, max_iter=100, reduction=None) - dist, P, C = sinkhorn(x, y) - assert dist.item() == pytest.approx(expected, rel=1e-1 * 2) - - -# @pytest.mark.parametrize( -# "x,y,expected", -# [ -# ( -# tensor([[0.0, 1.0], [1.0, 1.0], [2.0, 1.0]]), -# tensor([[0.0, 0.0], [1.0, 0.0], [2.0, 0.0]]), -# 1.0, -# ), -# ( -# tensor([[0.0, 1.0], [1.0, 1.0], [2.0, 1.0], [3.0, 1.0]]), -# tensor([[0.0, 0.0], [1.0, 0.0], [2.0, 1.0], [3.0, 1.0]]), -# 0.5, -# ), -# ], -# ) -# def test_custom_wasserstein_2d_slicer(x, y, expected): -# dist = sliced_wasserstein(x, y, 1000) -# assert dist == pytest.approx(expected, rel=1e-1 * 2) - - -@pytest.mark.parametrize( - "x,y,expected", - [ - ( - tensor([[0.0, 1.0, 0.0], [1.0, 1.0, 0.0], [2.0, 1.0, 0.0]]), - tensor([[0.0, 0.0, 0.0], [1.0, 0.0, 0.0], [2.0, 0.0, 0.0]]), - 1.0, - ), - ( - tensor( - [ - [0.0, 1.0, 0.0], - [1.0, 1.0, 0.0], - [2.0, 1.0, 0.0], - [3.0, 1.0, 0.0], - ] - ), - tensor( - [ - [0.0, 0.0, 0.0], - [1.0, 0.0, 0.0], - [2.0, 1.0, 0.0], - [3.0, 1.0, 0.0], - ] - ), - 0.5, - ), - ], -) -def test_custom_sinkhorn_3d(x, y, expected): - sinkhorn = SinkhornDistance(eps=0.1, max_iter=100, reduction=None) - dist, P, C = sinkhorn(x, y) - assert dist.item() == pytest.approx(expected, rel=1e-1 * 2) diff --git a/tests/test_eval/test_distance_hamming.py b/tests/test_eval/test_distance_hamming.py deleted file mode 100644 index e913cb10..00000000 --- a/tests/test_eval/test_distance_hamming.py +++ /dev/null @@ -1,81 +0,0 @@ -import pytest -from torch import equal, tensor -from torchmetrics.classification import MulticlassHammingDistance - - -@pytest.mark.parametrize( - "target,preds,average,expected", - [ - ( - tensor([[[1, 0, 2, 2]], [[1, 0, 2, 2]]]), - tensor([[[1, 0, 2, 2]], [[1, 0, 2, 2]]]), - "none", - tensor([0.0, 0.0, 0.0]), - ), - ( - tensor([[[1, 0, 2, 2]], [[1, 0, 2, 2]]]), - tensor([[[1, 0, 2, 2]], [[1, 0, 2, 2]]]), - "macro", - tensor(0.0), - ), - ( - tensor([[[1, 0, 2, 2]], [[1, 0, 2, 2]]]), - tensor([[[1, 0, 2, 2]], [[1, 0, 2, 2]]]), - "micro", - tensor(0.0), - ), - ( - tensor([[[1, 0, 2, 2]], [[1, 0, 2, 2]]]), - tensor([[[1, 0, 2, 1]], [[1, 0, 2, 2]]]), - "none", - tensor([0.0, 0.0, 0.25]), - ), - ( - tensor([[[1, 0, 2, 2]], [[1, 0, 2, 2]]]), - tensor([[[1, 0, 2, 1]], [[1, 0, 2, 2]]]), - "macro", - tensor(0.25 / 3), - ), - ( - tensor([[[1, 0, 2, 2]], [[1, 0, 2, 2]]]), - tensor([[[1, 0, 2, 1]], [[1, 0, 2, 2]]]), - "micro", - tensor(0.125), - ), - ( - tensor([[[1, 0, 2, 2]], [[1, 0, 2, 2]]]), - tensor([[[1, 0, 2, 1]], [[1, 0, 2, 2]]]), - "weighted", - tensor(0.125), - ), - ( - tensor([[[1, 0, 2, 2]], [[1, 0, 2, 2]]]), - tensor([[[1, 1, 2, 1]], [[1, 0, 2, 2]]]), - "none", - tensor([0.5, 0.0, 0.25]), - ), - ( - tensor([[[1, 0, 2, 2]], [[1, 0, 2, 2]]]), - tensor([[[1, 1, 2, 1]], [[1, 0, 2, 2]]]), - "macro", - tensor(0.25), - ), - ( - tensor([[[1, 0, 2, 2]], [[1, 0, 2, 2]]]), - tensor([[[1, 1, 2, 1]], [[1, 0, 2, 2]]]), - "micro", - tensor(0.25), - ), - ( - tensor([[[1, 0, 2, 2]], [[1, 0, 2, 2]]]), - tensor([[[1, 1, 2, 1]], [[1, 0, 2, 2]]]), - "weighted", - tensor(0.25), - ), - ], -) -def test_hamming_distance(target, preds, average, expected): - target = target.permute(0, 2, 1) # [B, T, C] - preds = preds.permute(0, 2, 1) # [B, T, C] - metric = MulticlassHammingDistance(num_classes=3, average=average) - assert equal(metric(preds, target), expected) diff --git a/tests/test_eval/test_distance_scalar.py b/tests/test_eval/test_distance_scalar.py deleted file mode 100644 index 80088b46..00000000 --- a/tests/test_eval/test_distance_scalar.py +++ /dev/null @@ -1,33 +0,0 @@ -from numpy import array - -from caveat.evaluate.distance import scalar - - -def test_ape_binary(): - a = (array([0, 1]), array([0, 1])) - assert scalar.mape(a, a) == 0 - b = (array([0, 1]), array([0, 100])) - assert scalar.mape(a, b) == 0 - b = (array([0, 1]), array([1, 0])) - assert scalar.mape(a, b) == 1 - b = (array([0, 1]), array([0.5, 0.5])) - assert scalar.mape(a, b) == 1 - b = (array([0, 1]), array([5, 5])) - assert scalar.mape(a, b) == 1 - # todo - - -def test_mse(): - a = (array([0, 1]), array([0, 1])) - assert scalar.mse(a, a) == 0 - b = (array([0, 1]), array([1, 0])) - assert scalar.mse(a, b) == 1 - - -def test_abs_diff(): - a = (array([10]), array([10])) - assert scalar.abs_av_diff(a, a) == 0 - b = (array([8, 10]), array([5, 5])) - assert scalar.abs_av_diff(a, b) == 1 - c = (array([8, 12]), array([5, 5])) - assert scalar.abs_av_diff(a, c) == 0 diff --git a/tests/test_eval/test_features_participation.py b/tests/test_eval/test_features_participation.py deleted file mode 100644 index d81b2701..00000000 --- a/tests/test_eval/test_features_participation.py +++ /dev/null @@ -1,142 +0,0 @@ -from numpy import array -from pandas import DataFrame - -from caveat.evaluate.features import participation -from caveat.evaluate.features.utils import equals - - -def test_participation_prob_by_act(): - population = DataFrame( - [ - {"pid": 0, "act": "home"}, - {"pid": 0, "act": "work"}, - {"pid": 1, "act": "home"}, - {"pid": 1, "act": "work"}, - ] - ) - expected = { - "home": (array([0, 1]), array([0, 2])), - "work": (array([0, 1]), array([0, 2])), - } - result = participation.participation_prob_by_act(population) - assert equals(result, expected) - - -def test_participation_rates(): - population = DataFrame( - [ - {"pid": 0, "act": "home"}, - {"pid": 0, "act": "work"}, - {"pid": 0, "act": "home"}, - {"pid": 1, "act": "home"}, - {"pid": 1, "act": "work"}, - ] - ) - expected = {"all": (array([2, 3]), array([1, 1]))} - result = participation.participation_rates(population) - print(result) - assert equals(result, expected) - - -def test_participation_rates_by_act(): - population = DataFrame( - [ - {"pid": 0, "act": "home"}, - {"pid": 0, "act": "work"}, - {"pid": 0, "act": "home"}, - {"pid": 1, "act": "home"}, - {"pid": 1, "act": "work"}, - ] - ) - expected = { - "home": (array([1, 2]), array([1, 1])), - "work": (array([1]), array([2])), - } - assert equals( - participation.participation_rates_by_act(population), expected - ) - - -def test_act_plan_seq_participation_rates(): - population = DataFrame( - [ - {"pid": 0, "act": "home"}, - {"pid": 0, "act": "work"}, - {"pid": 0, "act": "home"}, - {"pid": 1, "act": "home"}, - {"pid": 1, "act": "work"}, - ] - ) - expected = { - "0home": (array([1]), array([2])), - "1work": (array([1]), array([2])), - "2home": (array([0, 1]), array([1, 1])), - } - assert equals( - participation.participation_rates_by_seq_act(population), expected - ) - - -def test_act_seq_participation_rates(): - population = DataFrame( - [ - {"pid": 0, "act": "home"}, - {"pid": 0, "act": "work"}, - {"pid": 0, "act": "home"}, - {"pid": 1, "act": "home"}, - {"pid": 1, "act": "work"}, - ] - ) - expected = { - "home0": (array([1]), array([2])), - "work0": (array([1]), array([2])), - "home1": (array([0, 1]), array([1, 1])), - } - assert equals( - participation.participation_rates_by_act_enum(population), expected - ) - - -def test_combinations_with_replacement(): - array = ["a", "b", "c"] - tuple_length = 2 - expected = [ - ["a", "a"], - ["a", "b"], - ["a", "c"], - ["b", "b"], - ["b", "c"], - ["c", "c"], - ] - result = participation.combinations_with_replacement(array, tuple_length) - assert result == expected - - -def test_calc_pair_rate(): - act_counts = DataFrame( - { - "home": [1, 2, 2, 2, 3], - "work": [1, 1, 1, 1, 0], - "school": [1, 0, 0, 0, 0], - } - ) - pair_rate = participation.calc_pair_rate(act_counts, ("home", "home")) - assert pair_rate == {0: 1, 1: 4} - - -def test_participation_pairs(): - population = DataFrame( - [ - {"pid": 0, "act": "home"}, - {"pid": 0, "act": "work"}, - {"pid": 0, "act": "home"}, - {"pid": 1, "act": "home"}, - {"pid": 1, "act": "work"}, - ] - ) - expected = { - "home+work": (array([0, 1]), array([0, 2])), - "home+home": (array([0, 1]), array([1, 1])), - "work+work": (array([0, 1]), array([2, 0])), - } - assert equals(participation.joint_participation_prob(population), expected) diff --git a/tests/test_eval/test_features_structural.py b/tests/test_eval/test_features_structural.py deleted file mode 100644 index dd41f418..00000000 --- a/tests/test_eval/test_features_structural.py +++ /dev/null @@ -1,205 +0,0 @@ -from numpy import array -from pandas import DataFrame, MultiIndex, Series, concat - -from caveat.evaluate import evaluate -from caveat.evaluate.features.structural import ( - contains_consecutive, - duration_consistency, - feasibility_eval, - start_and_end_acts, - time_consistency, -) -from caveat.evaluate.features.utils import equals - - -def test_start_and_end_acts(): - population = DataFrame( - [ - {"pid": 0, "act": "home"}, - {"pid": 0, "act": "work"}, - {"pid": 0, "act": "home"}, - {"pid": 1, "act": "home"}, - {"pid": 1, "act": "work"}, - ] - ) - expected = { - "first act home": (array([0, 1]), array([0, 2])), - "last act home": (array([0, 1]), array([1, 1])), - } - assert equals(start_and_end_acts(population, target="home"), expected) - - -def test_time_consistency(): - population = DataFrame( - [ - {"pid": 0, "start": 0, "end": 10, "duration": 10}, - {"pid": 0, "start": 10, "end": 20, "duration": 10}, - {"pid": 0, "start": 20, "end": 30, "duration": 10}, - {"pid": 1, "start": 0, "end": 10, "duration": 10}, - {"pid": 1, "start": 10, "end": 20, "duration": 10}, - ] - ) - expected = { - "starts at 0": (array([0, 1]), array([0, 2])), - "ends at 30": (array([0, 1]), array([1, 1])), - "duration is 30": (array([0, 1]), array([1, 1])), - } - assert equals(time_consistency(population, target=30), expected) - - -def test_duration_consistency(): - population = DataFrame( - [ - {"pid": 0, "start": 0, "end": 10, "duration": 10}, - {"pid": 0, "start": 10, "end": 20, "duration": 10}, - {"pid": 0, "start": 20, "end": 30, "duration": 10}, - {"pid": 1, "start": 0, "end": 10, "duration": 10}, - {"pid": 1, "start": 10, "end": 20, "duration": 10}, - ] - ) - expected = {"total duration": (array([20, 30]), array([1, 1]))} - assert equals(duration_consistency(population, factor=1), expected) - - -def test_does_not_contains_consecutive(): - schedule = DataFrame( - [ - {"act": "home"}, - {"act": "work"}, - {"act": "home"}, - {"act": "work"}, - {"act": "home"}, - ] - ) - assert not contains_consecutive(schedule, act="home") - assert not contains_consecutive(schedule, act="work") - - -def test_contains_consecutive(): - schedule = DataFrame( - [ - {"act": "home"}, - {"act": "home"}, - {"act": "work"}, - {"act": "home"}, - {"act": "work"}, - ] - ) - assert contains_consecutive(schedule, act="home") - assert not contains_consecutive(schedule, act="work") - - -def test_feasibility_eval(): - schedule = DataFrame( - [ - {"pid": 0, "act": "home"}, - {"pid": 0, "act": "work"}, - {"pid": 0, "act": "home"}, - {"pid": 1, "act": "home"}, - {"pid": 1, "act": "home"}, - {"pid": 2, "act": "home"}, - {"pid": 2, "act": "work"}, - {"pid": 2, "act": "shop"}, - ] - ) - weights, metrics = feasibility_eval(schedule, "observed") - assert ( - weights.reset_index(drop=True) - .astype("int32") - .equals(Series([3, 3, 3, 3, 3, 3, 3, 3], dtype="int32")) - ) - assert metrics.reset_index(drop=True).equals( - Series([2 / 3, 1 / 3, 0, 1 / 3, 1 / 3, 1 / 3, 0, 0]) - ) - - -def test_describe_structural(): - index = MultiIndex.from_tuples( - [ - ("feasibility", "invalid", "all"), - ("feasibility", "not home based", "all"), - ("feasibility", "not home based", "starts"), - ("feasibility", "not home based", "ends"), - ("feasibility", "consecutive", "all"), - ("feasibility", "consecutive", "home"), - ("feasibility", "consecutive", "work"), - ("feasibility", "consecutive", "education"), - ], - names=["domain", "feature", "segment"], - ) - - observed_weights = Series( - [3, 3, 3, 3, 3, 3, 3, 3], index=index, name="observed__weight" - ) - observed_metrics = Series( - [2 / 3, 1 / 3, 0, 1 / 3, 1 / 3, 1 / 3, 0, 0], - index=index, - name="observed", - ) - weights = Series([3, 3, 3, 3, 3, 3, 3, 3], index=index, name="y__weight") - metrics = Series( - [2 / 3, 1 / 3, 0, 1 / 3, 1 / 3, 1 / 3, 0, 0], index=index, name="y" - ) - metrics = concat( - [observed_weights, observed_metrics, weights, metrics], axis=1 - ) - metrics["unit"] = "prob. invalid" - frames = evaluate.describe(metrics, metrics) - assert len(frames["descriptions"]) == 8 - assert len(frames["group_descriptions"]) == 3 - assert len(frames["domain_descriptions"]) == 1 - - assert len(frames["distances"]) == 8 - assert len(frames["group_distances"]) == 3 - assert len(frames["domain_distances"]) == 1 - - -def test_describe_splits_structural(): - index = MultiIndex.from_tuples( - [ - ("feasibility", "invalid", "all", "a"), - ("feasibility", "not home based", "all", "a"), - ("feasibility", "not home based", "starts", "a"), - ("feasibility", "not home based", "ends", "a"), - ("feasibility", "consecutive", "all", "a"), - ("feasibility", "consecutive", "home", "a"), - ("feasibility", "consecutive", "work", "a"), - ("feasibility", "consecutive", "education", "a"), - ("feasibility", "invalid", "all", "b"), - ("feasibility", "not home based", "all", "b"), - ("feasibility", "not home based", "starts", "b"), - ("feasibility", "not home based", "ends", "b"), - ("feasibility", "consecutive", "all", "b"), - ("feasibility", "consecutive", "home", "b"), - ("feasibility", "consecutive", "work", "b"), - ("feasibility", "consecutive", "education", "b"), - ], - names=["domain", "feature", "segment", "label"], - ) - - observed_weights = Series([3] * 16, index=index, name="observed__weight") - observed_metrics = Series([1 / 3] * 16, index=index, name="observed") - weights = Series([3] * 16, index=index, name="y__weight") - metrics = Series([1 / 3, 0] * 8, index=index, name="y") - metrics = concat( - [observed_weights, observed_metrics, weights, metrics], axis=1 - ) - metrics["unit"] = "prob. invalid" - frames = evaluate.describe(metrics, metrics) - print(frames["descriptions"]) - assert len(frames["descriptions"]) == 8 - assert len(frames["group_descriptions"]) == 3 - assert len(frames["domain_descriptions"]) == 1 - - assert len(frames["distances"]) == 8 - assert len(frames["group_distances"]) == 3 - assert len(frames["domain_distances"]) == 1 - - frames = evaluate.describe_labels(metrics, metrics) - assert len(frames["label_descriptions"]) == 16 - assert len(frames["label_group_descriptions"]) == 6 - assert len(frames["label_domain_descriptions"]) == 2 - - assert len(frames["label_distances"]) == 16 - assert len(frames["label_group_distances"]) == 6 - assert len(frames["label_domain_distances"]) == 2 diff --git a/tests/test_eval/test_features_times.py b/tests/test_eval/test_features_times.py deleted file mode 100644 index 04786f65..00000000 --- a/tests/test_eval/test_features_times.py +++ /dev/null @@ -1,214 +0,0 @@ -from numpy import array -from pandas import DataFrame - -from caveat.evaluate.features import times -from caveat.evaluate.features.utils import equals - - -def test_times_by_act(): - # todo; add test for start_times_by_act_bins and factors - population = DataFrame( - [ - {"pid": 0, "act": "home", "start": 0, "end": 2, "duration": 2}, - {"pid": 0, "act": "work", "start": 2, "end": 6, "duration": 4}, - {"pid": 0, "act": "home", "start": 6, "end": 8, "duration": 2}, - {"pid": 1, "act": "home", "start": 0, "end": 1, "duration": 1}, - {"pid": 1, "act": "work", "start": 1, "end": 8, "duration": 7}, - ] - ) - expected_starts = { - "home": (array([0.5, 6.5]), array([2, 1])), - "work": (array([1.5, 2.5]), array([1, 1])), - } - expected_ends = { - "home": (array([1.5, 2.5, 8.5]), array([1, 1, 1])), - "work": (array([6.5, 8.5]), array([1, 1])), - } - expected_durations = { - "home": (array([1.5, 2.5]), array([1, 2])), - "work": (array([4.5, 7.5]), array([1, 1])), - } - assert equals( - times.start_times_by_act(population, bin_size=1, factor=1), - expected_starts, - ) - assert equals( - times.end_times_by_act(population, bin_size=1, factor=1), expected_ends - ) - assert equals( - times.durations_by_act(population, bin_size=1, factor=1), - expected_durations, - ) - - -def test_start_and_duration_by_act_bins(): - population = DataFrame( - [ - {"pid": 0, "act": "home", "start": 0, "end": 2, "duration": 2}, - {"pid": 0, "act": "work", "start": 2, "end": 6, "duration": 4}, - {"pid": 0, "act": "home", "start": 6, "end": 8, "duration": 2}, - {"pid": 1, "act": "home", "start": 0, "end": 1, "duration": 1}, - {"pid": 1, "act": "work", "start": 1, "end": 8, "duration": 7}, - ] - ) - expected = { - "home": ( - array([[0.5, 1.5], [0.5, 2.5], [6.5, 2.5]]) / 1440, - array([1, 1, 1]), - ), - "work": (array([[1.5, 7.5], [2.5, 4.5]]) / 1440, array([1, 1])), - } - assert equals(times.start_and_duration_by_act_bins(population, 1), expected) - expected = { - "home": (array([[2, 2], [6, 2]]) / 1440, array([2, 1])), - "work": (array([[2, 6]]) / 1440, array([2])), - } - assert equals(times.start_and_duration_by_act_bins(population, 4), expected) - - -def test_start_times_by_act_plan_seq(): - population = DataFrame( - [ - {"pid": 0, "act": "home", "start": 0}, - {"pid": 0, "act": "work", "start": 2}, - {"pid": 0, "act": "home", "start": 6}, - {"pid": 1, "act": "home", "start": 0}, - {"pid": 1, "act": "work", "start": 1}, - ] - ) - expected = { - "0home": (array([0]) / 1440, array([2])), - "1work": (array([1, 2]) / 1440, array([1, 1])), - "2home": (array([6]) / 1440, array([1])), - } - assert equals(times.start_times_by_act_plan_seq(population), expected) - - -def test_start_times_by_act_plan_enum(): - population = DataFrame( - [ - {"pid": 0, "act": "home", "start": 0}, - {"pid": 0, "act": "work", "start": 2}, - {"pid": 0, "act": "home", "start": 6}, - {"pid": 1, "act": "home", "start": 0}, - {"pid": 1, "act": "work", "start": 1}, - ] - ) - expected = { - "home0": (array([0]) / 1440, array([2])), - "work0": (array([1, 2]) / 1440, array([1, 1])), - "home1": (array([6]) / 1440, array([1])), - } - assert equals(times.start_times_by_act_plan_enum(population), expected) - - -def test_end_times_by_act_plan_seq(): - population = DataFrame( - [ - {"pid": 0, "act": "home", "end": 0}, - {"pid": 0, "act": "work", "end": 2}, - {"pid": 0, "act": "home", "end": 6}, - {"pid": 1, "act": "home", "end": 0}, - {"pid": 1, "act": "work", "end": 1}, - ] - ) - expected = { - "0home": (array([0]) / 1440, array([2])), - "1work": (array([1, 2]) / 1440, array([1, 1])), - "2home": (array([6]) / 1440, array([1])), - } - assert equals(times.end_times_by_act_plan_seq(population), expected) - - -def test_end_times_by_act_plan_enum(): - population = DataFrame( - [ - {"pid": 0, "act": "home", "end": 0}, - {"pid": 0, "act": "work", "end": 2}, - {"pid": 0, "act": "home", "end": 6}, - {"pid": 1, "act": "home", "end": 0}, - {"pid": 1, "act": "work", "end": 1}, - ] - ) - expected = { - "home0": (array([0]) / 1440, array([2])), - "work0": (array([1, 2]) / 1440, array([1, 1])), - "home1": (array([6]) / 1440, array([1])), - } - assert equals(times.end_times_by_act_plan_enum(population), expected) - - -def test_durations_by_act_plan_seq(): - population = DataFrame( - [ - {"pid": 0, "act": "home", "duration": 0}, - {"pid": 0, "act": "work", "duration": 2}, - {"pid": 0, "act": "home", "duration": 6}, - {"pid": 1, "act": "home", "duration": 0}, - {"pid": 1, "act": "work", "duration": 1}, - ] - ) - expected = { - "0home": (array([0]) / 1440, array([2])), - "1work": (array([1, 2]) / 1440, array([1, 1])), - "2home": (array([6]) / 1440, array([1])), - } - assert equals(times.durations_by_act_plan_seq(population), expected) - - -def test_durations_by_act_plan_enum(): - population = DataFrame( - [ - {"pid": 0, "act": "home", "duration": 0}, - {"pid": 0, "act": "work", "duration": 2}, - {"pid": 0, "act": "home", "duration": 6}, - {"pid": 1, "act": "home", "duration": 0}, - {"pid": 1, "act": "work", "duration": 1}, - ] - ) - expected = { - "home0": (array([0]) / 1440, array([2])), - "work0": (array([1, 2]) / 1440, array([1, 1])), - "home1": (array([6]) / 1440, array([1])), - } - assert equals(times.durations_by_act_plan_enum(population), expected) - - -def test_joint_durations_by_act(): - population = DataFrame( - [ - {"pid": 0, "act": "home", "duration": 0}, - {"pid": 0, "act": "work", "duration": 2}, - {"pid": 0, "act": "home", "duration": 6}, - {"pid": 1, "act": "home", "duration": 0}, - {"pid": 1, "act": "work", "duration": 1}, - ] - ) - expected = { - "home": (array([[0.5, 1.5], [0.5, 2.5]]), array([1, 1])), - "work": (array([[2.5, 6.5]]), array([1])), - } - assert equals( - times.joint_durations_by_act_bins(population, bin_size=1, factor=1), - expected, - ) - - -def test_joint_durations_by_act_binned(): - population = DataFrame( - [ - {"pid": 0, "act": "home", "duration": 0}, - {"pid": 0, "act": "work", "duration": 2}, - {"pid": 0, "act": "home", "duration": 6}, - {"pid": 1, "act": "home", "duration": 0}, - {"pid": 1, "act": "work", "duration": 1}, - ] - ) - expected = { - "home": (array([[2, 2]]), array([2])), - "work": (array([[2, 6]]), array([1])), - } - assert equals( - times.joint_durations_by_act_bins(population, bin_size=4, factor=1), - expected, - ) diff --git a/tests/test_eval/test_features_transitions.py b/tests/test_eval/test_features_transitions.py deleted file mode 100644 index e116d778..00000000 --- a/tests/test_eval/test_features_transitions.py +++ /dev/null @@ -1,78 +0,0 @@ -from numpy import array -from pandas import DataFrame, Series - -from caveat.evaluate.features import transitions -from caveat.evaluate.features.utils import equals - - -def test_transitions(): - population = DataFrame( - [ - {"pid": 0, "act": "home"}, - {"pid": 0, "act": "work"}, - {"pid": 0, "act": "home"}, - {"pid": 1, "act": "home"}, - {"pid": 1, "act": "work"}, - ] - ) - expected = { - "home>work": (array([1]), array([2])), - "work>home": (array([0, 1]), array([1, 1])), - } - result = transitions.transitions_by_act(population) - assert equals(result, expected) - - -def test_transition_3s(): - population = DataFrame( - [ - {"pid": 0, "act": "home"}, - {"pid": 0, "act": "work"}, - {"pid": 0, "act": "home"}, - {"pid": 1, "act": "home"}, - {"pid": 1, "act": "work"}, - ] - ) - expected = {"home>work>home": (array([1]), array([1]))} - result = transitions.transition_3s_by_act(population) - assert equals(result, expected) - - -def test_transition_4s(): - population = DataFrame( - [ - {"pid": 0, "act": "home"}, - {"pid": 0, "act": "work"}, - {"pid": 0, "act": "home"}, - {"pid": 0, "act": "home"}, - {"pid": 1, "act": "home"}, - {"pid": 1, "act": "work"}, - {"pid": 1, "act": "work"}, - ] - ) - expected = {"home>work>home>home": (array([1]), array([1]))} - result = transitions.transition_4s_by_act(population) - assert equals(result, expected) - - -def test_tour(): - acts = Series(["home", "work", "home"]) - assert transitions.tour(acts) == "h>w>h" - - -def test_full_sequence(): - population = DataFrame( - [ - {"pid": 0, "act": "home"}, - {"pid": 0, "act": "work"}, - {"pid": 0, "act": "home"}, - {"pid": 1, "act": "home"}, - {"pid": 1, "act": "work"}, - ] - ) - expected = { - "h>w": (array([0, 1]), array([1, 1])), - "h>w>h": (array([0, 1]), array([1, 1])), - } - result = transitions.full_sequences(population) - assert equals(result, expected) diff --git a/tests/test_eval/test_features_utils.py b/tests/test_eval/test_features_utils.py deleted file mode 100644 index aa05abde..00000000 --- a/tests/test_eval/test_features_utils.py +++ /dev/null @@ -1,21 +0,0 @@ -from numpy import array - -from caveat.evaluate.features import utils - - -def test_equals(): - a = {"a": (array([1, 2, 3]), array([1, 2, 3]))} - b = {"a": (array([1, 2, 3]), array([1, 2, 3]))} - assert utils.equals(a, b) - - a = {"a": (array([1, 2, 3]), array([1, 2, 3]))} - b = {"a": (array([1, 2, 3]), array([1, 2, 4]))} - assert not utils.equals(a, b) - - a = {"a": (array([1, 2, 3]), array([1, 2, 3]))} - b = {"a": (array([1, 2]), array([1, 2]))} - assert not utils.equals(a, b) - - a = {"a": (array([1, 2, 3]), array([1, 2, 3]))} - b = {"b": (array([1, 2, 3]), array([1, 2, 3]))} - assert not utils.equals(a, b) diff --git a/tests/test_eval/test_filter.py b/tests/test_eval/test_filter.py deleted file mode 100644 index 47842187..00000000 --- a/tests/test_eval/test_filter.py +++ /dev/null @@ -1,76 +0,0 @@ -from pandas import DataFrame - -from caveat.evaluate.filters import filter_novel, no_filter - - -def test_filter_noop(): - scenario = DataFrame( - [ - {"pid": 0, "act": "home", "duration": 10}, - {"pid": 0, "act": "work", "duration": 10}, - {"pid": 0, "act": "home", "duration": 10}, - {"pid": 1, "act": "home", "duration": 5}, - {"pid": 1, "act": "work", "duration": 10}, - {"pid": 1, "act": "home", "duration": 15}, - ] - ) - base = DataFrame( - [ - {"pid": 0, "act": "home", "duration": 10}, - {"pid": 0, "act": "work", "duration": 10}, - {"pid": 0, "act": "home", "duration": 10}, - {"pid": 1, "act": "home", "duration": 5}, - {"pid": 1, "act": "work", "duration": 10}, - {"pid": 1, "act": "home", "duration": 15}, - ] - ) - assert no_filter(scenario, base).equals(scenario) - - -def test_filter_novel(): - scenario = DataFrame( - [ - {"pid": 0, "act": "home", "duration": 10}, - {"pid": 0, "act": "work", "duration": 10}, - {"pid": 0, "act": "home", "duration": 10}, - {"pid": 1, "act": "home", "duration": 5}, - {"pid": 1, "act": "visit", "duration": 10}, - {"pid": 1, "act": "home", "duration": 15}, - {"pid": 2, "act": "home", "duration": 10}, - {"pid": 2, "act": "work", "duration": 5}, - {"pid": 2, "act": "home", "duration": 15}, - {"pid": 3, "act": "home", "duration": 10}, - {"pid": 3, "act": "work", "duration": 5}, - {"pid": 3, "act": "home", "duration": 15}, - ] - ) - base = DataFrame( - [ - {"pid": 0, "act": "home", "duration": 10}, - {"pid": 0, "act": "work", "duration": 10}, - {"pid": 0, "act": "home", "duration": 10}, - {"pid": 1, "act": "home", "duration": 5}, - {"pid": 1, "act": "other", "duration": 10}, - {"pid": 1, "act": "home", "duration": 15}, - {"pid": 2, "act": "home", "duration": 10}, - {"pid": 2, "act": "work", "duration": 10}, - {"pid": 2, "act": "home", "duration": 10}, - ] - ) - filtered = filter_novel(scenario, base) - print(filtered) - assert filtered.equals( - DataFrame( - [ - {"pid": 1, "act": "home", "duration": 5}, - {"pid": 1, "act": "visit", "duration": 10}, - {"pid": 1, "act": "home", "duration": 15}, - {"pid": 2, "act": "home", "duration": 10}, - {"pid": 2, "act": "work", "duration": 5}, - {"pid": 2, "act": "home", "duration": 15}, - {"pid": 3, "act": "home", "duration": 10}, - {"pid": 3, "act": "work", "duration": 5}, - {"pid": 3, "act": "home", "duration": 15}, - ] - ) - ) diff --git a/tests/test_eval/test_frequency.py b/tests/test_eval/test_frequency.py deleted file mode 100644 index d27ca405..00000000 --- a/tests/test_eval/test_frequency.py +++ /dev/null @@ -1,107 +0,0 @@ -from numpy import array, array_equal -from pandas import DataFrame - -from caveat.evaluate.features.frequency import ( - activity_densities, - activity_frequencies, - binned_activity_count, - binned_activity_density, -) -from caveat.evaluate.features.utils import equals - - -def test_activity_count_bins(): - population = DataFrame( - [ - {"pid": 0, "act": "home", "start": 0, "end": 1, "duration": 1}, - {"pid": 0, "act": "work", "start": 1, "end": 2, "duration": 1}, - {"pid": 0, "act": "home", "start": 2, "end": 3, "duration": 1}, - {"pid": 1, "act": "home", "start": 0, "end": 1, "duration": 1}, - {"pid": 1, "act": "work", "start": 1, "end": 3, "duration": 2}, - ] - ) - class_map = {"home": 0, "work": 1} - binned = binned_activity_count( - population, class_map=class_map, duration=3, step=1 - ) - expected = array([[2, 0], [0, 2], [1, 1]]) - assert array_equal(binned, expected) - - -def test_activity_density_bins(): - population = DataFrame( - [ - {"pid": 0, "act": "home", "start": 0, "end": 1, "duration": 1}, - {"pid": 0, "act": "work", "start": 1, "end": 2, "duration": 1}, - {"pid": 0, "act": "home", "start": 2, "end": 3, "duration": 1}, - {"pid": 1, "act": "home", "start": 0, "end": 1, "duration": 1}, - {"pid": 1, "act": "work", "start": 1, "end": 3, "duration": 2}, - ] - ) - class_map = {"home": 0, "work": 1} - binned = binned_activity_density( - population, class_map=class_map, duration=3, step=1 - ) - expected = array([[1.0, 0.0], [0.0, 1.0], [0.5, 0.5]]) - assert array_equal(binned, expected) - - -def test_activity_frequencies(): - population = DataFrame( - [ - {"pid": 0, "act": "home", "start": 0, "end": 1, "duration": 1}, - {"pid": 0, "act": "work", "start": 1, "end": 2, "duration": 1}, - {"pid": 0, "act": "home", "start": 2, "end": 3, "duration": 1}, - {"pid": 1, "act": "home", "start": 0, "end": 1, "duration": 1}, - {"pid": 1, "act": "work", "start": 1, "end": 3, "duration": 2}, - ] - ) - binned = activity_frequencies(population, 3, 1) - print(binned) - expected = { - "home": (array([0, 1, 2]) / 3, array([2, 0, 1])), - "work": (array([0, 1, 2]) / 3, array([0, 2, 1])), - } - assert equals(binned, expected) - - -def test_activity_frequencies_single_act(): - population = DataFrame( - [ - {"pid": 0, "act": "home", "start": 0, "end": 3, "duration": 3}, - {"pid": 1, "act": "home", "start": 0, "end": 3, "duration": 3}, - ] - ) - binned = activity_frequencies(population, 3, 1) - expected = {"home": (array([0, 1, 2]) / 3, array([2, 2, 2]))} - assert equals(binned, expected) - - -def test_activity_densities(): - population = DataFrame( - [ - {"pid": 0, "act": "home", "start": 0, "end": 1, "duration": 1}, - {"pid": 0, "act": "work", "start": 1, "end": 2, "duration": 1}, - {"pid": 0, "act": "home", "start": 2, "end": 3, "duration": 1}, - {"pid": 1, "act": "home", "start": 0, "end": 1, "duration": 1}, - {"pid": 1, "act": "work", "start": 1, "end": 3, "duration": 2}, - ] - ) - binned = activity_densities(population, 3, 1) - expected = { - "home": (array([0, 1, 2]) / 3, array([1, 0, 0.5])), - "work": (array([0, 1, 2]) / 3, array([0, 1, 0.5])), - } - assert equals(binned, expected) - - -def test_activity_densities_single_act(): - population = DataFrame( - [ - {"pid": 0, "act": "home", "start": 0, "end": 3, "duration": 3}, - {"pid": 1, "act": "home", "start": 0, "end": 3, "duration": 3}, - ] - ) - binned = activity_densities(population, 3, 1) - expected = {"home": (array([0, 1, 2]) / 3, array([1, 1, 1]))} - assert equals(binned, expected) diff --git a/tests/test_eval/test_report.py b/tests/test_eval/test_report.py deleted file mode 100644 index 85205dc9..00000000 --- a/tests/test_eval/test_report.py +++ /dev/null @@ -1,127 +0,0 @@ -from numpy import array -from pandas import DataFrame, Series - -from caveat.evaluate.distance.scalar import mae -from caveat.evaluate.evaluate import evaluate, extract_default, score_features -from caveat.evaluate.features.times import start_times_by_act - - -def test_describe_feature(): - DataFrame( - [ - {"pid": 0, "act": "home", "start": 0}, - {"pid": 0, "act": "work", "start": 2}, - {"pid": 0, "act": "home", "start": 6}, - {"pid": 1, "act": "home", "start": 0}, - {"pid": 1, "act": "work", "start": 1}, - ] - ) - - -def test_create_default(): - feature = {"home": (array([0, 1, 2, 3]), array([10, 0, 2, 3]))} - default = extract_default(feature) - assert (default[0] == array([0])).all() - assert (default[1] == array([1])).all() - feature = {"home": (array([[0, 0], [10, 10]]), array([10, 3]))} - default = extract_default(feature) - assert (default[0] == array([[0, 0]])).all() - assert (default[1] == array([1])).all() - - -def test_score_features(): - observed = DataFrame( - [ - {"pid": 0, "act": "home", "start": 0}, - {"pid": 0, "act": "work", "start": 2}, - {"pid": 0, "act": "home", "start": 6}, - {"pid": 1, "act": "home", "start": 0}, - {"pid": 1, "act": "work", "start": 1}, - ] - ) - y = DataFrame( - [ - {"pid": 0, "act": "home", "start": 0}, - {"pid": 0, "act": "work", "start": 2}, - {"pid": 0, "act": "home", "start": 6}, - {"pid": 1, "act": "home", "start": 0}, - {"pid": 1, "act": "work", "start": 1}, - ] - ) - expected = Series({"home": 0.0, "work": 0.0}, name="test").sort_index() - x = start_times_by_act(observed) - y = start_times_by_act(y) - result = score_features( - "test", x, y, mae, (array([0]), array([1])) - ).sort_index() - assert result.equals(expected) - - -def test_score_features_with_default(): - observed = DataFrame( - [ - {"pid": 0, "act": "home", "start": 0}, - {"pid": 0, "act": "work", "start": 1}, - {"pid": 0, "act": "home", "start": 6}, - {"pid": 1, "act": "home", "start": 0}, - {"pid": 1, "act": "work", "start": 1}, - ] - ) - y = DataFrame( - [ - {"pid": 0, "act": "home", "start": 0}, - {"pid": 0, "act": "home", "start": 6}, - {"pid": 1, "act": "home", "start": 0}, - ] - ) - expected = Series({"home": 0.0, "work": 1.0}, name="test").sort_index() - x = start_times_by_act(observed) - y = start_times_by_act(y) - result = score_features( - "test", x, y, mae, (array([0]), array([1])) - ).sort_index() - assert result.equals(expected) - - -def test_report_same(): - observed = DataFrame( - [ - {"pid": 0, "act": "home", "start": 0, "end": 6, "duration": 6}, - {"pid": 0, "act": "work", "start": 6, "end": 14, "duration": 8}, - {"pid": 0, "act": "home", "start": 14, "end": 24, "duration": 10}, - {"pid": 1, "act": "home", "start": 0, "end": 10, "duration": 10}, - {"pid": 1, "act": "work", "start": 10, "end": 24, "duration": 14}, - ] - ) - y = DataFrame( - [ - {"pid": 0, "act": "home", "start": 0, "end": 6, "duration": 6}, - {"pid": 0, "act": "work", "start": 6, "end": 14, "duration": 8}, - {"pid": 0, "act": "home", "start": 14, "end": 24, "duration": 10}, - {"pid": 1, "act": "home", "start": 0, "end": 10, "duration": 10}, - {"pid": 1, "act": "work", "start": 10, "end": 24, "duration": 14}, - ] - ) - evaluate({"y": y}, observed, None) - - -def test_report(): - observed = DataFrame( - [ - {"pid": 0, "act": "home", "start": 0, "end": 6, "duration": 6}, - {"pid": 0, "act": "work", "start": 6, "end": 14, "duration": 8}, - {"pid": 0, "act": "home", "start": 14, "end": 24, "duration": 10}, - {"pid": 1, "act": "home", "start": 0, "end": 10, "duration": 10}, - {"pid": 1, "act": "work", "start": 10, "end": 24, "duration": 14}, - ] - ) - y = DataFrame( - [ - {"pid": 0, "act": "home", "start": 0, "end": 6, "duration": 6}, - {"pid": 0, "act": "shop", "start": 6, "end": 14, "duration": 8}, - {"pid": 0, "act": "home", "start": 14, "end": 24, "duration": 10}, - {"pid": 1, "act": "home", "start": 0, "end": 12, "duration": 12}, - {"pid": 1, "act": "work", "start": 12, "end": 24, "duration": 12}, - ] - ) - evaluate({"y": y}, observed, None) diff --git a/tests/test_runners/test_runners.py b/tests/test_runners/test_runners.py new file mode 100644 index 00000000..05840ca4 --- /dev/null +++ b/tests/test_runners/test_runners.py @@ -0,0 +1,150 @@ +from pathlib import Path +from unittest.mock import MagicMock, patch + +import pandas as pd +import pytest + +FIXTURES = Path(__file__).parent.parent / "fixtures" +SCHEDULES_PATH = FIXTURES / "test_schedules.csv" +ATTRIBUTES_PATH = FIXTURES / "test_attributes.csv" + + +# --------------------------------------------------------------------------- +# evaluate_synthetics +# --------------------------------------------------------------------------- + + +@patch("caveat.runners.evaluate") +@patch("caveat.runners.data") +def test_evaluate_synthetics_basic(mock_data, mock_evaluate, tmp_path): + """No split_on: calls evaluate.evaluate() and evaluate.report().""" + from caveat.runners import evaluate_synthetics + + schedules = pd.read_csv(SCHEDULES_PATH) + synthetic_schedules = {"model_a": schedules.copy()} + synthetic_labels = {"model_a": None} + eval_params = {} + + mock_evaluate.evaluate.return_value = {"model_a": MagicMock()} + + evaluate_synthetics( + synthetic_schedules=synthetic_schedules, + synthetic_labels=synthetic_labels, + default_eval_schedules=schedules, + default_eval_attributes=None, + write_path=tmp_path, + eval_params=eval_params, + ) + + mock_evaluate.evaluate.assert_called_once() + mock_evaluate.report.assert_called_once() + mock_evaluate.compare_splits.assert_not_called() + mock_evaluate.report_splits.assert_not_called() + + +@patch("caveat.runners.evaluate") +@patch("caveat.runners.data") +def test_evaluate_synthetics_with_split_on(mock_data, mock_evaluate, tmp_path): + """split_on set: calls compare_splits() and report_splits().""" + from caveat.runners import evaluate_synthetics + + schedules = pd.read_csv(SCHEDULES_PATH) + synthetic_schedules = {"model_a": schedules.copy()} + synthetic_labels = {"model_a": None} + eval_params = {"split_on": ["gender"]} + + mock_evaluate.compare_splits.return_value = {"gender": MagicMock()} + mock_evaluate.evaluate.return_value = {"model_a": MagicMock()} + + evaluate_synthetics( + synthetic_schedules=synthetic_schedules, + synthetic_labels=synthetic_labels, + default_eval_schedules=schedules, + default_eval_attributes=None, + write_path=tmp_path, + eval_params=eval_params, + ) + + mock_evaluate.compare_splits.assert_called_once() + call_kwargs = mock_evaluate.compare_splits.call_args.kwargs + assert call_kwargs["observed"] is schedules + assert call_kwargs["split_on"] == ["gender"] + + mock_evaluate.report_splits.assert_called_once() + mock_evaluate.evaluate.assert_called_once() + mock_evaluate.report.assert_called_once() + + +@patch("caveat.runners.evaluate") +@patch("caveat.runners.data") +def test_evaluate_synthetics_custom_schedules_path(mock_data, mock_evaluate, tmp_path): + """eval_params has schedules_path: custom schedules are loaded instead of default.""" + from caveat.runners import evaluate_synthetics + + default_schedules = pd.read_csv(SCHEDULES_PATH) + custom_schedules = default_schedules.copy() + mock_data.load_and_validate_schedules.return_value = custom_schedules + + synthetic_schedules = {"model_a": default_schedules.copy()} + synthetic_labels = {"model_a": None} + eval_params = {"schedules_path": str(SCHEDULES_PATH)} + + mock_evaluate.evaluate.return_value = {"model_a": MagicMock()} + + evaluate_synthetics( + synthetic_schedules=synthetic_schedules, + synthetic_labels=synthetic_labels, + default_eval_schedules=default_schedules, + default_eval_attributes=None, + write_path=tmp_path, + eval_params=eval_params, + ) + + mock_data.load_and_validate_schedules.assert_called_once_with(str(SCHEDULES_PATH)) + + call_kwargs = mock_evaluate.evaluate.call_args.kwargs + assert call_kwargs["target_schedules"] is custom_schedules + + +# --------------------------------------------------------------------------- +# load_data +# --------------------------------------------------------------------------- + + +@patch("caveat.runners.data") +def test_load_data_no_attributes(mock_data): + """Config without attributes_path returns schedules and None attributes.""" + from caveat.runners import load_data + + schedules = pd.read_csv(SCHEDULES_PATH) + mock_data.load_and_validate_schedules.return_value = schedules + mock_data.load_and_validate_attributes.return_value = (None, None) + + config = {"schedules_path": str(SCHEDULES_PATH)} + result_schedules, attrs, synth_attrs = load_data(config) + + mock_data.load_and_validate_schedules.assert_called_once() + assert attrs is None + assert synth_attrs is None + + +@patch("caveat.runners.data") +def test_load_data_with_attributes(mock_data): + """Config with attributes_path returns both schedules and attributes.""" + from caveat.runners import load_data + + schedules = pd.read_csv(SCHEDULES_PATH) + attributes = pd.read_csv(ATTRIBUTES_PATH) + mock_data.load_and_validate_schedules.return_value = schedules + mock_data.load_and_validate_attributes.return_value = (attributes, attributes) + + config = { + "schedules_path": str(SCHEDULES_PATH), + "attributes_path": str(ATTRIBUTES_PATH), + } + result_schedules, attrs, synth_attrs = load_data(config) + + mock_data.load_and_validate_schedules.assert_called_once() + mock_data.load_and_validate_attributes.assert_called_once() + assert attrs is attributes + assert synth_attrs is attributes