From 6d914f6baa55c0cfb9cbd1d53257def4340a2284 Mon Sep 17 00:00:00 2001 From: Adam Li Date: Thu, 2 Nov 2023 10:44:52 -0400 Subject: [PATCH] Adding honest forest checks Signed-off-by: Adam Li --- benchmarks_nonasv/bench_multiview_hyppo.ipynb | 743 ++++- ...mv_vs_rf_correlated_latentfactor_model.csv | 2201 +++++++++++++++ ...uc_mv_vs_rf_ind_views_gaussian_mixture.csv | 2401 +++++++++++++++++ ...mv_vs_rf_ind_views_gaussian_mixture_v2.csv | 2401 +++++++++++++++++ ..._partial_auc_mv_vs_rf_linear_transform.csv | 2201 +++++++++++++++ doc/api.rst | 13 + doc/references.bib | 7 + sktree/datasets/__init__.py | 2 + sktree/datasets/hyppo.py | 9 +- sktree/datasets/multiview.py | 4 +- sktree/experimental/mutual_info.py | 2 +- sktree/stats/forestht.py | 12 +- sktree/stats/permutationforest.py | 5 +- sktree/stats/utils.py | 6 +- sktree/tests/test_honest_forest.py | 4 +- 15 files changed, 9894 insertions(+), 117 deletions(-) create mode 100644 benchmarks_nonasv/cv_partial_auc_mv_vs_rf_correlated_latentfactor_model.csv create mode 100644 benchmarks_nonasv/cv_partial_auc_mv_vs_rf_ind_views_gaussian_mixture.csv create mode 100644 benchmarks_nonasv/cv_partial_auc_mv_vs_rf_ind_views_gaussian_mixture_v2.csv create mode 100644 benchmarks_nonasv/cv_partial_auc_mv_vs_rf_linear_transform.csv diff --git a/benchmarks_nonasv/bench_multiview_hyppo.ipynb b/benchmarks_nonasv/bench_multiview_hyppo.ipynb index f5c45a9f0..adaea8452 100644 --- a/benchmarks_nonasv/bench_multiview_hyppo.ipynb +++ b/benchmarks_nonasv/bench_multiview_hyppo.ipynb @@ -24,7 +24,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "id": "68cf4cc8-098d-4810-ad70-0e4234fef660", "metadata": {}, "outputs": [], @@ -44,7 +44,7 @@ ")\n", "\n", "from sktree import HonestForestClassifier, RandomForestClassifier, RandomForestRegressor\n", - "from sktree.datasets.multiview import make_gaussian_mixture\n", + "from sktree.datasets.multiview import make_gaussian_mixture, make_joint_factor_model\n", "from sktree.stats import (\n", " FeatureImportanceForestClassifier,\n", " FeatureImportanceForestRegressor,\n", @@ -58,7 +58,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 12, "id": "1b798cf7-b23e-4cc6-9aa5-4acd05f03785", "metadata": {}, "outputs": [ @@ -74,6 +74,20 @@ "print(\"done\")" ] }, + { + "cell_type": "code", + "execution_count": 13, + "id": "0854eca6-5b3b-450e-975c-b57ccc2bbe5d", + "metadata": {}, + "outputs": [], + "source": [ + "max_features = \"sqrt\"\n", + "n_estimators = 500\n", + "n_jobs = -1\n", + "test_size = 0.2\n", + "max_fpr = 0.1" + ] + }, { "cell_type": "markdown", "id": "2f20fdbc-8a2e-48b8-a52d-1c4696562e5c", @@ -121,14 +135,7 @@ "n_features = 100 - noise_dims\n", "n_features_2 = 10000 - noise_dims\n", "\n", - "max_features = \"sqrt\"\n", - "n_estimators = 500\n", - "n_jobs = -1\n", - "test_size = 0.2\n", - "\n", - "n_repeats = 5\n", - "\n", - "max_fpr = 0.1" + "n_repeats = 5" ] }, { @@ -1126,9 +1133,17 @@ "Instead, what we will do is first construct the signal dataset, and keep that fixed, and then add varying degrees of noise." ] }, + { + "cell_type": "markdown", + "id": "210f573c-f4bc-4a42-9685-03de2ea52c15", + "metadata": {}, + "source": [ + "## Direct and indirect effects on outcome" + ] + }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 93, "id": "9dc5d8c0-e484-4e48-a746-e74ef3ed8de4", "metadata": {}, "outputs": [], @@ -1170,7 +1185,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 86, "id": "c13bcdb7-f8e9-449c-85ef-6ae07fa98381", "metadata": {}, "outputs": [], @@ -1272,7 +1287,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 87, "id": "7a9e94d8-d69b-41ba-ae55-7fd3582cd9c7", "metadata": {}, "outputs": [], @@ -1282,7 +1297,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 88, "id": "077c45b6-18e4-457e-adb1-405b8a804fa2", "metadata": {}, "outputs": [], @@ -1298,20 +1313,117 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 89, "id": "ed6b4fec-0fbf-4b4c-97d5-63b62a7e00a5", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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n_features_2noise_dimsn_samplesmodelscore
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" + ], + "text/plain": [ + " n_features_2 noise_dims n_samples model score\n", + "0 0 80 500 rf 0.786667\n", + "1 800 80 500 rf 0.671579\n", + "2 1800 80 500 rf 0.640702\n", + "3 2800 80 500 rf 0.653333\n", + "4 3800 80 500 rf 0.625263" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "display(df.head())" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 90, "id": "8093092b-fb64-49cb-909b-1f7bb5424768", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig, ax = plt.subplots()\n", "sns.lineplot(data=df, x=\"n_features_2\", y=\"score\", marker=\"o\", hue=\"model\", ax=ax)\n", @@ -1326,125 +1438,556 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 92, "id": "7891fc3f-50af-4601-a656-c22b288c4c74", "metadata": {}, "outputs": [], "source": [ "# save the results\n", - "df.save('./cv_partial_auc_mv_vs_rf_linear_transform.csv')" + "df.to_csv(\"./cv_partial_auc_mv_vs_rf_linear_transform.csv\")" + ] + }, + { + "cell_type": "markdown", + "id": "9629999f-5b60-407e-88cd-383ff481656e", + "metadata": {}, + "source": [ + "## Independent views" ] }, { "cell_type": "code", - "execution_count": null, - "id": "2929e5ae-368c-454c-a63e-f7fc4108ce2e", + "execution_count": 7, + "id": "80a976f0-49d6-4e9d-a57e-a041ce7f00cb", "metadata": {}, "outputs": [], - "source": [] + "source": [ + "n_features_1 = 20\n", + "n_features_2 = 20\n", + "noise_dims = 80\n", + "n_estimators = 500\n", + "n_repeats = 100" + ] }, { "cell_type": "code", - "execution_count": 51, - "id": "80a976f0-49d6-4e9d-a57e-a041ce7f00cb", + "execution_count": 8, + "id": "486555c5-f98a-4a54-a369-188c61c10617", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "(500, 100) (500, 100)\n" + "[ 100 900 1800 2700 3600 4500 5400 6300 7200 8100 9000 9900]\n", + "100\n" ] } ], "source": [ - "cluster_std_first = 2.0\n", - "cluster_std_second = 5.0\n", - "n_features_1 = 20\n", - "n_features_2 = 20\n", - "noise_dims = 80\n", - "\n", - "# first construct the signal arrays\n", - "X0_first, y0 = make_blobs(\n", - " n_samples=n_samples,\n", - " cluster_std=cluster_std_first,\n", - " n_features=n_features_1,\n", - " center_box=(0, 0),\n", - " random_state=seed,\n", - " centers=1,\n", - ")\n", - "X1_first, y1 = make_blobs(\n", - " n_samples=n_samples,\n", - " cluster_std=cluster_std_second,\n", - " n_features=n_features_2,\n", - " center_box=(-5, 5),\n", - " random_state=seed + 1,\n", - " centers=1,\n", - ")\n", - "X0 = np.concatenate(\n", - " [X0_first, rng.standard_normal(size=(n_samples, noise_dims))], axis=1\n", - ")\n", - "X1 = np.concatenate(\n", - " [X1_first, rng.standard_normal(size=(n_samples, noise_dims))], axis=1\n", - ")\n", - "\n", - "print(X0.shape, X1.shape)" + "n_features_2_list = np.linspace(900, 10_000 - 100, 11, dtype=int)\n", + "n_features_2_list = np.insert(n_features_2_list, 0, 100)\n", + "print(n_features_2_list)\n", + "print(n_repeats)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "8b4556c1-7030-4cc3-ae7b-f2f684e074a9", - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [], "source": [ - "def make_multiviews(\n", - " n_samples=100,\n", - " n_features_1=100,\n", - " n_features_2=1000,\n", - " noise_dims=20,\n", - " cluster_std_first=2.0,\n", - " cluster_std_second=5.0,\n", - " X0_first=None,\n", - " y0=None,\n", - " X1_first=None,\n", - " y1=None,\n", - " seed=None,\n", - "):\n", - " rng = np.random.default_rng(seed=seed)\n", + "results = defaultdict(list)\n", "\n", - " if X0_first is None and y0 is None:\n", - " # Create a high-dimensional multiview dataset with a low-dimensional informative\n", - " # subspace in one view of the dataset.\n", - " X0_first, y0 = make_blobs(\n", - " n_samples=n_samples,\n", - " cluster_std=cluster_std_first,\n", - " n_features=n_features_1 - noise_dims,\n", - " center_box=(0, 0),\n", + "for idx in range(n_repeats):\n", + " signal_X, y = make_classification(\n", + " n_samples=n_samples,\n", + " n_features=n_features_1 + noise_dims,\n", + " n_informative=n_features_1,\n", + " n_redundant=10,\n", + " n_repeated=0,\n", + " n_classes=2,\n", + " class_sep=2.0,\n", + " flip_y=0.01,\n", + " shuffle=False,\n", + " random_state=seed + idx,\n", + " )\n", + " n_features_ends = [n_features_1 + noise_dims, signal_X.shape[1]]\n", + "\n", + " for n_features_2_ in n_features_2_list:\n", + " _X = np.hstack((signal_X, rng.standard_normal(size=(n_samples, n_features_2_))))\n", + " X = _X.copy()\n", + " n_features_ends[1] = X.shape[1]\n", + "\n", + " est = FeatureImportanceForestClassifier(\n", + " estimator=HonestForestClassifier(\n", + " n_estimators=n_estimators,\n", + " max_features=max_features,\n", + " tree_estimator=MultiViewDecisionTreeClassifier(\n", + " feature_set_ends=n_features_ends,\n", + " apply_max_features_per_feature_set=True,\n", + " ),\n", + " random_state=seed,\n", + " honest_fraction=0.5,\n", + " n_jobs=n_jobs,\n", + " ),\n", " random_state=seed,\n", - " centers=1,\n", + " test_size=test_size,\n", + " permute_per_tree=False,\n", + " sample_dataset_per_tree=False,\n", " )\n", "\n", - " X1_first, y1 = make_blobs(\n", - " n_samples=n_samples,\n", - " cluster_std=cluster_std_second,\n", - " n_features=n_features_2 - noise_dims,\n", - " center_box=(-5, 5),\n", - " random_state=seed + 1,\n", - " centers=1,\n", + " est_rf = FeatureImportanceForestClassifier(\n", + " estimator=HonestForestClassifier(\n", + " n_estimators=n_estimators,\n", + " max_features=max_features,\n", + " tree_estimator=DecisionTreeClassifier(),\n", + " random_state=seed,\n", + " honest_fraction=0.5,\n", + " n_jobs=n_jobs,\n", + " ),\n", + " random_state=seed,\n", + " test_size=test_size,\n", + " permute_per_tree=False,\n", + " sample_dataset_per_tree=False,\n", " )\n", - " # X0_first = X0_first + rng.standard_normal(size=X0_first.shape) // 10\n", - " # X1_first = X1_first + rng.standard_normal(size=X1_first.shape) // 10\n", - " X0 = np.concatenate(\n", - " [X0_first, rng.standard_normal(size=(n_samples, noise_dims))], axis=1\n", - " )\n", - " X1 = np.concatenate(\n", - " [X1_first, rng.standard_normal(size=(n_samples, noise_dims))], axis=1\n", - " )\n", "\n", - " X = np.hstack((X0, X1))\n", - " return X" + " # compute the statistic\n", + " stat = est.statistic(X, y, metric=\"auc\", max_fpr=max_fpr)\n", + " stat_rf = est_rf.statistic(X, y, metric=\"auc\", max_fpr=max_fpr)\n", + "\n", + " results[\"rf\"].append(stat_rf)\n", + " results[\"mvrf\"].append(stat)\n", + " results[\"n_samples\"].append(n_samples)\n", + " results[\"n_features_2\"].append(n_features_2_)\n", + " results[\"noise_dims\"].append(noise_dims)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "8af3ecdb-dfc1-4083-b87a-aab27265f3fe", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[100, 10000]\n" + ] + } + ], + "source": [ + "print(n_features_ends)" ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "32e86097-9215-4ec9-a355-b6ea09f021c3", + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.DataFrame(results)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "74be463b-e5a5-4d37-b2b6-96325ea55361", + "metadata": {}, + "outputs": [], + "source": [ + "# melt the dataframe, to make it easier to plot\n", + "df = pd.melt(\n", + " df,\n", + " id_vars=[\"n_features_2\", \"noise_dims\", \"n_samples\"],\n", + " var_name=\"model\",\n", + " value_name=\"score\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "67f85ab3-f0e5-4d07-b074-56fa1d0830a8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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n_features_2noise_dimsn_samplesmodelscore
010080500rf0.966316
190080500rf0.955789
2180080500rf0.962105
3270080500rf0.960000
4360080500rf0.953684
\n", + "
" + ], + "text/plain": [ + " n_features_2 noise_dims n_samples model score\n", + "0 100 80 500 rf 0.966316\n", + "1 900 80 500 rf 0.955789\n", + "2 1800 80 500 rf 0.962105\n", + "3 2700 80 500 rf 0.960000\n", + "4 3600 80 500 rf 0.953684" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(df.head())" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "22940aac-f2a9-4a95-9587-00f4f230ae91", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots()\n", + "sns.lineplot(data=df, x=\"n_features_2\", y=\"score\", marker=\"o\", hue=\"model\", ax=ax)\n", + "ax.set_ylabel(\"Cross-validated Partial-AUC\\n(at 90% specificity)\")\n", + "ax.set_xlabel(\"Number of features in second view\")\n", + "ax.set_title(\"Random Forest vs Multi-view Random Forest - Independent 2-View Model\")\n", + "ax.set(\n", + " xlim=[0, 10_000],\n", + ")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 129, + "id": "41069f12-d7b9-455a-84ef-a401d46bf47b", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots()\n", + "sns.lineplot(data=df, x=\"n_features_2\", y=\"score\", marker=\"o\", hue=\"model\", ax=ax)\n", + "ax.set_ylabel(\"Cross-validated Partial-AUC\\n(at 90% specificity)\")\n", + "ax.set_xlabel(\"Number of features in second view\")\n", + "ax.set_title(\"Random Forest vs Multi-view Random Forest - Independent 2-View Model\")\n", + "ax.set(\n", + " xlim=[0, 10_000],\n", + ")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "c3118a50-afb0-4c9b-8385-7cdb06b151a0", + "metadata": {}, + "outputs": [], + "source": [ + "# save the results\n", + "df.to_csv(\"./cv_partial_auc_mv_vs_rf_ind_views_gaussian_mixture_v2.csv\")" + ] + }, + { + "cell_type": "markdown", + "id": "47ec31a4-7dbd-4f6d-b313-844abd12eeff", + "metadata": {}, + "source": [ + "## Correlated views via a latent factor" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "2c99d69b-06bd-4564-98ed-1359eda920d5", + "metadata": {}, + "outputs": [], + "source": [ + "n_views = 2\n", + "n_features = 100\n", + "n_samples = 500\n", + "joint_rank = 2" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "db91aab8-7d87-48ee-893c-339a2d4f4579", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 0 800 1800 2800 3800 4800 5800 6800 7800 8800 9800]\n", + "100\n" + ] + } + ], + "source": [ + "n_features_2_list = np.linspace(800, 10_000 - 200, 10, dtype=int)\n", + "n_features_2_list = np.insert(n_features_2_list, 0, 0)\n", + "print(n_features_2_list)\n", + "print(n_repeats)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ff7801b4-b8bd-44a0-bba6-f1e3fb920d4f", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(500, 200) 100 100 [(500, 100), (500, 100)]\n", + "(500, 200)\n", + "(500, 1000)\n", + "(500, 2000)\n", + "(500, 3000)\n", + "(500, 4000)\n", + "(500, 5000)\n", + "(500, 6000)\n" + ] + } + ], + "source": [ + "results = defaultdict(list)\n", + "\n", + "for idx in range(n_repeats):\n", + " Xs = make_joint_factor_model(\n", + " n_views,\n", + " n_features,\n", + " n_samples=n_samples,\n", + " joint_rank=joint_rank,\n", + " noise_std=1,\n", + " m=1.5,\n", + " random_state=seed + idx,\n", + " return_decomp=False,\n", + " )\n", + " n_features_1 = Xs[0].shape[1]\n", + " n_features_2 = Xs[1].shape[1]\n", + " signal_X = np.hstack(Xs)\n", + "\n", + " print(signal_X.shape, n_features_1, n_features_2, [x.shape for x in Xs])\n", + " y = rng.binomial(\n", + " n=1, p=expit(Xs[0][:, :10].sum(axis=1)), size=n_samples\n", + " ) * rng.binomial(n=1, p=expit(Xs[1][:, :10].sum(axis=1)), size=n_samples)\n", + "\n", + " n_features_ends = [n_features_1 + noise_dims, signal_X.shape[1]]\n", + "\n", + " for n_features_2_ in n_features_2_list:\n", + " _X = np.hstack((signal_X, rng.standard_normal(size=(n_samples, n_features_2_))))\n", + " X = _X.copy()\n", + " n_features_ends[1] = X.shape[1]\n", + "\n", + " print(X.shape)\n", + " est = FeatureImportanceForestClassifier(\n", + " estimator=HonestForestClassifier(\n", + " n_estimators=n_estimators,\n", + " max_features=max_features,\n", + " tree_estimator=MultiViewDecisionTreeClassifier(\n", + " feature_set_ends=n_features_ends,\n", + " apply_max_features_per_feature_set=True,\n", + " ),\n", + " random_state=seed,\n", + " honest_fraction=0.5,\n", + " n_jobs=n_jobs,\n", + " ),\n", + " random_state=seed,\n", + " test_size=test_size,\n", + " permute_per_tree=False,\n", + " sample_dataset_per_tree=False,\n", + " )\n", + "\n", + " est_rf = FeatureImportanceForestClassifier(\n", + " estimator=HonestForestClassifier(\n", + " n_estimators=n_estimators,\n", + " max_features=max_features,\n", + " tree_estimator=DecisionTreeClassifier(),\n", + " random_state=seed,\n", + " honest_fraction=0.5,\n", + " n_jobs=n_jobs,\n", + " ),\n", + " random_state=seed,\n", + " test_size=test_size,\n", + " permute_per_tree=False,\n", + " sample_dataset_per_tree=False,\n", + " )\n", + "\n", + " # compute the statistic\n", + " stat = est.statistic(X, y, metric=\"auc\", max_fpr=max_fpr)\n", + " stat_rf = est_rf.statistic(X, y, metric=\"auc\", max_fpr=max_fpr)\n", + "\n", + " results[\"rf\"].append(stat_rf)\n", + " results[\"mvrf\"].append(stat)\n", + " results[\"n_samples\"].append(n_samples)\n", + " results[\"n_features_2\"].append(n_features_2_)\n", + " results[\"noise_dims\"].append(noise_dims)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5bcf3875-13d1-4abb-b894-ee25e04d790f", + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.DataFrame(results)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ec10c1a6-5fc6-4c57-b73d-6c1dc5233851", + "metadata": {}, + "outputs": [], + "source": [ + "# melt the dataframe, to make it easier to plot\n", + "df = pd.melt(\n", + " df,\n", + " id_vars=[\"n_features_2\", \"noise_dims\", \"n_samples\"],\n", + " var_name=\"model\",\n", + " value_name=\"score\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "84a48b30-5df6-4c08-b15e-8a6d915fb7ee", + "metadata": {}, + "outputs": [], + "source": [ + "display(df.head())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5e9e5ee1-4ca4-4a2e-9a35-1117a7d87e48", + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots()\n", + "sns.lineplot(data=df, x=\"n_features_2\", y=\"score\", marker=\"o\", hue=\"model\", ax=ax)\n", + "ax.set_ylabel(\"Cross-validated Partial-AUC\\n(at 90% specificity)\")\n", + "ax.set_xlabel(\"Number of features in second view\")\n", + "ax.set_title(\"Random Forest vs Multi-view Random Forest - Correlated Latent Factor 2-View Model\")\n", + "ax.set(\n", + " xlim=[0, 10_000],\n", + ")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "af4d3584-43c0-4cb6-b6f5-997691f6573b", + "metadata": {}, + "outputs": [], + "source": [ + "# save the results\n", + "df.to_csv(\"./cv_partial_auc_mv_vs_rf_correlated_latentfactor_model.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "69dcd394-f86e-469d-9fca-6840964fa4ca", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/benchmarks_nonasv/cv_partial_auc_mv_vs_rf_correlated_latentfactor_model.csv b/benchmarks_nonasv/cv_partial_auc_mv_vs_rf_correlated_latentfactor_model.csv new file mode 100644 index 000000000..aa9534063 --- /dev/null +++ b/benchmarks_nonasv/cv_partial_auc_mv_vs_rf_correlated_latentfactor_model.csv @@ -0,0 +1,2201 @@ +,n_features_2,noise_dims,n_samples,model,score +0,0,80,500,rf,0.7019947128094208 +1,800,80,500,rf,0.5716841571203504 +2,1800,80,500,rf,0.5901092151993378 +3,2800,80,500,rf,0.5724852466020455 +4,3800,80,500,rf,0.5035915511762664 +5,4800,80,500,rf,0.5169430425378515 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PermutationForestClassifier PermutationForestRegressor +Datasets +------------------------------ +We provide some convenience functions for simulating datasets beyond +those offered in scikit-learn. + +.. currentmodule:: sktree.datasets +.. autosummary:: + :toctree: generated/ + + make_gaussian_mixture + make_joint_factor_model + make_quadratic_classification + Experimental Functionality -------------------------- diff --git a/doc/references.bib b/doc/references.bib index e7831e030..0653d7a39 100644 --- a/doc/references.bib +++ b/doc/references.bib @@ -146,4 +146,11 @@ @misc{perry2009crossvalidation eprint = {0909.3052}, archiveprefix = {arXiv}, primaryclass = {stat.ME} +} + +@article{panda2018learning, + title = {Learning Interpretable Characteristic Kernels via Decision Forests}, + author = {Panda, Sambit and Shen, Cencheng and Vogelstein, Joshua T}, + journal = {arXiv preprint arXiv:1812.00029}, + year = {2018} } \ No newline at end of file diff --git a/sktree/datasets/__init__.py b/sktree/datasets/__init__.py index e69de29bb..74c770d6a 100644 --- a/sktree/datasets/__init__.py +++ b/sktree/datasets/__init__.py @@ -0,0 +1,2 @@ +from .hyppo import make_quadratic_classification +from .multiview import make_gaussian_mixture, make_joint_factor_model diff --git a/sktree/datasets/hyppo.py b/sktree/datasets/hyppo.py index f41d219fc..2c1cbcbdb 100644 --- a/sktree/datasets/hyppo.py +++ b/sktree/datasets/hyppo.py @@ -1,9 +1,11 @@ import numpy as np -def quadratic(n_samples: int, n_features: int, noise=False, seed=None): +def make_quadratic_classification(n_samples: int, n_features: int, noise=False, seed=None): """Simulate classification data from a quadratic model. + This is a form of the simulation used in :footcite:`panda2018learning`. + Parameters ---------- n_samples : int @@ -21,6 +23,10 @@ def quadratic(n_samples: int, n_features: int, noise=False, seed=None): Data array. v : array-like, shape (n_samples,) Target array of 1's and 0's. + + References + ---------- + .. footbibliography:: """ rng = np.random.default_rng(seed) @@ -31,6 +37,7 @@ def quadratic(n_samples: int, n_features: int, noise=False, seed=None): x_coeffs = x * coeffs y = x_coeffs**2 + noise * eps + # generate the classification labels n1 = x.shape[0] n2 = y.shape[0] v = np.vstack([np.zeros((n1, 1)), np.ones((n2, 1))]) diff --git a/sktree/datasets/multiview.py b/sktree/datasets/multiview.py index 81cdcca59..03b62aa2a 100644 --- a/sktree/datasets/multiview.py +++ b/sktree/datasets/multiview.py @@ -1,5 +1,5 @@ +# Original source: https://github.com/mvlearn/mvlearn # License: MIT -# Author: Ronan Perry import numpy as np from scipy.stats import ortho_group @@ -339,7 +339,7 @@ def make_joint_factor_model( U = np.linalg.qr(U)[0] # random noise for each view - Es = [noise_std * rng.standard_normal(size=(n_samples, d)) for d in zip(n_features)] + Es = [noise_std * rng.standard_normal(size=(n_samples, d)) for d in n_features] Xs = [(U * svals) @ view_loadings[b].T + Es[b] for b in range(n_views)] if return_decomp: diff --git a/sktree/experimental/mutual_info.py b/sktree/experimental/mutual_info.py index a1820e715..85c8f0a83 100644 --- a/sktree/experimental/mutual_info.py +++ b/sktree/experimental/mutual_info.py @@ -147,7 +147,7 @@ def mutual_info_ksg( algorithm="kd_tree", n_jobs: int = -1, transform: str = "rank", - random_seed: int = None, + random_seed: Optional[int] = None, ): """Compute the generalized (conditional) mutual information KSG estimate. diff --git a/sktree/stats/forestht.py b/sktree/stats/forestht.py index 56a044c5c..42e7f1946 100644 --- a/sktree/stats/forestht.py +++ b/sktree/stats/forestht.py @@ -1,4 +1,4 @@ -from typing import Callable, Tuple, Union +from typing import Optional, Callable, Tuple, Union import numpy as np from joblib import Parallel, delayed @@ -43,7 +43,7 @@ def _parallel_build_trees_and_compute_posteriors( predict_posteriors: bool, permute_per_tree: bool, type_of_target, - sample_weight: ArrayLike = None, + sample_weight: Optional[ArrayLike] = None, class_weight=None, missing_values_in_feature_mask=None, classes=None, @@ -255,7 +255,7 @@ def _statistic( ): raise NotImplementedError("Subclasses should implement this!") - def _check_input(self, X: ArrayLike, y: ArrayLike, covariate_index: ArrayLike = None): + def _check_input(self, X: ArrayLike, y: ArrayLike, covariate_index: Optional[ArrayLike] = None): X, y = check_X_y(X, y, ensure_2d=True, copy=True, multi_output=True) if y.ndim != 2: y = y.reshape(-1, 1) @@ -295,7 +295,7 @@ def statistic( self, X: ArrayLike, y: ArrayLike, - covariate_index: ArrayLike = None, + covariate_index: Optional[ArrayLike] = None, metric="mi", return_posteriors: bool = False, check_input: bool = True, @@ -414,7 +414,7 @@ def test( self, X, y, - covariate_index: ArrayLike = None, + covariate_index: Optional[ArrayLike] = None, metric: str = "mi", n_repeats: int = 1000, return_posteriors: bool = True, @@ -660,7 +660,7 @@ def statistic( self, X: ArrayLike, y: ArrayLike, - covariate_index: ArrayLike = None, + covariate_index: Optional[ArrayLike] = None, metric="mse", return_posteriors: bool = False, check_input: bool = True, diff --git a/sktree/stats/permutationforest.py b/sktree/stats/permutationforest.py index 4a27f539f..78a2437a4 100644 --- a/sktree/stats/permutationforest.py +++ b/sktree/stats/permutationforest.py @@ -10,6 +10,7 @@ from sktree._lib.sklearn.ensemble._forest import BaseForest, ForestClassifier, ForestRegressor from .utils import METRIC_FUNCTIONS, REGRESSOR_METRICS, _compute_null_distribution_perm +from typing import Optional class BasePermutationForest(MetaEstimatorMixin): @@ -62,7 +63,7 @@ def _statistic( estimator: BaseForest, X: ArrayLike, y: ArrayLike, - covariate_index: ArrayLike = None, + covariate_index: Optional[ArrayLike] = None, metric="mse", return_posteriors: bool = False, seed=None, @@ -117,7 +118,7 @@ def statistic( self, X: ArrayLike, y: ArrayLike, - covariate_index: ArrayLike = None, + covariate_index: Optional[ArrayLike] = None, metric="mse", return_posteriors: bool = False, check_input: bool = True, diff --git a/sktree/stats/utils.py b/sktree/stats/utils.py index f21dd00a8..7ff7a9f05 100644 --- a/sktree/stats/utils.py +++ b/sktree/stats/utils.py @@ -1,4 +1,4 @@ -from typing import Tuple +from typing import Optional, Tuple import numpy as np from numpy.typing import ArrayLike @@ -112,7 +112,7 @@ def _compute_null_distribution_perm( est: ForestClassifier, metric: str = "mse", n_repeats: int = 1000, - seed: int = None, + seed: Optional[int] = None, ) -> ArrayLike: """Compute null distribution using permutation method. @@ -173,7 +173,7 @@ def _compute_null_distribution_coleman( y_pred_proba_perm: ArrayLike, metric: str = "mse", n_repeats: int = 1000, - seed: int = None, + seed: Optional[int] = None, ) -> Tuple[ArrayLike, ArrayLike]: """Compute null distribution using Coleman method. diff --git a/sktree/tests/test_honest_forest.py b/sktree/tests/test_honest_forest.py index 14b57800c..062a90de2 100644 --- a/sktree/tests/test_honest_forest.py +++ b/sktree/tests/test_honest_forest.py @@ -9,7 +9,7 @@ from sklearn.utils.estimator_checks import parametrize_with_checks from sktree._lib.sklearn.tree import DecisionTreeClassifier -from sktree.datasets.hyppo import quadratic +from sktree.datasets.hyppo import make_quadratic_classification from sktree.ensemble import HonestForestClassifier from sktree.tree import ObliqueDecisionTreeClassifier, PatchObliqueDecisionTreeClassifier @@ -262,7 +262,7 @@ def test_honest_forest_with_sklearn_trees(): https://github.com/neurodata/scikit-tree/pull/157.""" # generate the high-dimensional quadratic data - X, y = quadratic(1024, 4096, noise=True, seed=0) + X, y = make_quadratic_classification(1024, 4096, noise=True, seed=0) y = y.squeeze() print(X.shape, y.shape) print(np.sum(y) / len(y))