From e41c9f0c04cad18ad49d664e706d75354f1ce90e Mon Sep 17 00:00:00 2001 From: Noam Bressler Date: Tue, 15 Feb 2022 14:27:54 +0200 Subject: [PATCH 1/4] Move feature name to title above both plots --- deepchecks/utils/distribution/drift.py | 8 +- .../train_test_feature_drift.ipynb | 693 +++++++++--------- .../distribution/train_test_label_drift.ipynb | 320 ++++---- 3 files changed, 519 insertions(+), 502 deletions(-) diff --git a/deepchecks/utils/distribution/drift.py b/deepchecks/utils/distribution/drift.py index 8efd22518e..8bc969118f 100644 --- a/deepchecks/utils/distribution/drift.py +++ b/deepchecks/utils/distribution/drift.py @@ -15,6 +15,7 @@ from scipy.stats import wasserstein_distance import numpy as np import pandas as pd +import re import plotly.graph_objects as go from plotly.subplots import make_subplots @@ -147,9 +148,9 @@ def calc_drift_and_plot(train_column: pd.Series, test_column: pd.Series, plot_ti # Should never reach here raise DeepchecksValueError(f'Unsupported column type for drift: {column_type}') - fig = make_subplots(rows=2, cols=1, vertical_spacing=0.4, shared_yaxes=False, shared_xaxes=False, + fig = make_subplots(rows=2, cols=1, vertical_spacing=0.2, shared_yaxes=False, shared_xaxes=False, row_heights=[0.1, 0.9], - subplot_titles=['Drift Score - ' + scorer_name, plot_title]) + subplot_titles=['Drift Score - ' + scorer_name, 'Distribution Plot']) fig.add_traces(bar_traces, rows=[1] * len(bar_traces), cols=[1] * len(bar_traces)) fig.add_traces(dist_traces, rows=[2] * len(dist_traces), cols=[1] * len(dist_traces)) @@ -164,7 +165,8 @@ def calc_drift_and_plot(train_column: pd.Series, test_column: pd.Series, plot_ti yanchor='top', y=0.6), width=700, - height=400 + height=400, + title=plot_title ) fig.update_layout(shared_layout) diff --git a/docs/source/examples/tabular/checks/distribution/train_test_feature_drift.ipynb b/docs/source/examples/tabular/checks/distribution/train_test_feature_drift.ipynb index 7e5bb29d21..dbb10ee055 100644 --- a/docs/source/examples/tabular/checks/distribution/train_test_feature_drift.ipynb +++ b/docs/source/examples/tabular/checks/distribution/train_test_feature_drift.ipynb @@ -72,14 +72,7 @@ "cell_type": "code", "execution_count": 1, "id": "3693b3f5-b3e5-4960-9373-ff5279328aea", - "metadata": { - "execution": { - "iopub.execute_input": "2022-02-14T16:54:27.848793Z", - "iopub.status.busy": "2022-02-14T16:54:27.847881Z", - "iopub.status.idle": "2022-02-14T16:54:28.086633Z", - "shell.execute_reply": "2022-02-14T16:54:28.087046Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", @@ -101,14 +94,7 @@ "cell_type": "code", "execution_count": 2, "id": "fa391638-4c7c-48b8-b1ca-a92a029d96e2", - "metadata": { - "execution": { - "iopub.execute_input": "2022-02-14T16:54:28.095488Z", - "iopub.status.busy": "2022-02-14T16:54:28.094927Z", - "iopub.status.idle": "2022-02-14T16:54:28.102854Z", - "shell.execute_reply": "2022-02-14T16:54:28.103380Z" - } - }, + "metadata": {}, "outputs": [ { "data": { @@ -216,14 +202,7 @@ "cell_type": "code", "execution_count": 3, "id": "80fccd9b-8281-48ec-9170-8933843c50ea", - "metadata": { - "execution": { - "iopub.execute_input": "2022-02-14T16:54:28.108640Z", - "iopub.status.busy": "2022-02-14T16:54:28.108092Z", - "iopub.status.idle": "2022-02-14T16:54:28.110230Z", - "shell.execute_reply": "2022-02-14T16:54:28.110761Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "df_test['numeric_with_drift'] = df_test['numeric_with_drift'].astype('float') + abs(np.random.randn(1000)) + np.arange(0, 1, 0.001) * 4\n", @@ -244,14 +223,7 @@ "cell_type": "code", "execution_count": 4, "id": "e3b2e30a-4fdf-461e-90ba-a57fa313b237", - "metadata": { - "execution": { - "iopub.execute_input": "2022-02-14T16:54:28.114458Z", - "iopub.status.busy": "2022-02-14T16:54:28.113951Z", - "iopub.status.idle": "2022-02-14T16:54:29.219060Z", - "shell.execute_reply": "2022-02-14T16:54:29.218507Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "from sklearn.compose import ColumnTransformer\n", @@ -266,14 +238,7 @@ "cell_type": "code", "execution_count": 5, "id": "fc48571d-208e-42c5-b86e-a9166e2df10c", - "metadata": { - "execution": { - "iopub.execute_input": "2022-02-14T16:54:29.224583Z", - "iopub.status.busy": "2022-02-14T16:54:29.223934Z", - "iopub.status.idle": "2022-02-14T16:54:29.225532Z", - "shell.execute_reply": "2022-02-14T16:54:29.226035Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "model = Pipeline([\n", @@ -296,14 +261,7 @@ "cell_type": "code", "execution_count": 6, "id": "654f8610-87af-4dc3-b20c-7f02d637a82e", - "metadata": { - "execution": { - "iopub.execute_input": "2022-02-14T16:54:29.231762Z", - "iopub.status.busy": "2022-02-14T16:54:29.231155Z", - "iopub.status.idle": "2022-02-14T16:54:29.241062Z", - "shell.execute_reply": "2022-02-14T16:54:29.241482Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "label = np.random.randint(0, 2, size=(df_train.shape[0],))\n", @@ -332,14 +290,7 @@ "cell_type": "code", "execution_count": 7, "id": "a7f6888e-7027-4f50-bf5c-617d9997fe2c", - "metadata": { - "execution": { - "iopub.execute_input": "2022-02-14T16:54:29.245705Z", - "iopub.status.busy": "2022-02-14T16:54:29.245118Z", - "iopub.status.idle": "2022-02-14T16:54:29.795740Z", - "shell.execute_reply": "2022-02-14T16:54:29.785251Z" - } - }, + "metadata": {}, "outputs": [ { "data": { @@ -663,152 +614,152 @@ "y": [ 0.008643564510182207, 0.0097313599036168, - 0.012291114835525434, - 0.023802879825654547, - 0.04617434556472055, - 0.059565294736461286, - 0.07752413435831333, - 0.10616641406486316, + 0.012291114835525429, + 0.02380287982565454, + 0.04617434556472058, + 0.05956529473646129, + 0.07752413435831335, + 0.10616641406486314, 0.11507599727139897, - 0.13387869832746113, - 0.1534413917055736, + 0.1338786983274612, + 0.15344139170557364, 0.1549326095304217, - 0.17829661792680426, - 0.194003123889042, - 0.20202105554567631, - 0.22537718870300008, - 0.2453264531662909, - 0.24724344900520018, - 0.2674648118198182, + 0.17829661792680415, + 0.19400312388904195, + 0.20202105554567626, + 0.22537718870300014, + 0.24532645316629087, + 0.2472434490052002, + 0.26746481181981824, 0.28209777873861897, - 0.29412304997328903, - 0.29941230083601605, + 0.294123049973289, + 0.299412300836016, 0.3095464763489191, - 0.32443290678227066, - 0.3335277217565046, - 0.34039405691542163, - 0.34168528431198636, + 0.3244329067822707, + 0.3335277217565047, + 0.3403940569154216, + 0.3416852843119863, 0.34941705963446384, 0.3547849796870528, 0.3583231713864199, - 0.35939476845706503, + 0.359394768457065, 0.3603562042276419, - 0.3604621383648967, - 0.3622804989089966, - 0.3630874535472984, + 0.36046213836489666, + 0.36228049890899666, + 0.3630874535472983, 0.3636108608395067, 0.3639627398816688, 0.36396827293793077, - 0.3644123770466785, - 0.36448850076134814, - 0.36542218254809605, + 0.36441237704667845, + 0.3644885007613481, + 0.36542218254809616, 0.3672765865505002, - 0.36877984878391473, - 0.3694941531583814, + 0.3687798487839149, + 0.36949415315838147, 0.3705503793865524, 0.37181885948267196, 0.371907712261106, - 0.37179774133782373, + 0.37179774133782384, 0.3702181925231392, - 0.3700335671941468, - 0.36747830288659716, - 0.3674775505187848, + 0.3700335671941469, + 0.3674783028865972, + 0.36747755051878467, 0.3630997580925576, - 0.35831831599080094, + 0.3583183159908009, 0.34921974876639744, - 0.34900387871780175, - 0.3414946773967452, - 0.33658609004098033, - 0.32419468419557607, + 0.3490038787178017, + 0.3414946773967453, + 0.3365860900409804, + 0.3241946841955761, 0.30713047621783895, 0.3069111492908705, - 0.2889187604218177, + 0.28891876042181763, 0.2878608081652905, 0.27067960684868714, 0.2538685334622671, - 0.2517404700430906, - 0.23398818533528418, - 0.23000324949865247, + 0.2517404700430905, + 0.2339881853352842, + 0.23000324949865253, 0.22070696808563284, 0.21333851886251348, - 0.20082009276839294, + 0.20082009276839302, 0.19207235945158094, - 0.18553541416855004, - 0.169779786216548, - 0.16515140174847648, - 0.14599574778227561, + 0.1855354141685502, + 0.16977978621654796, + 0.16515140174847653, + 0.14599574778227564, 0.14412898522359985, - 0.14255744681017873, + 0.14255744681017876, 0.12892974520978115, 0.11555329686844219, - 0.11302460137951365, + 0.11302460137951369, 0.11035784610493403, 0.10178138922764243, 0.09159737093435315, 0.08817866480530666, - 0.07943664879531603, - 0.07853752165619546, + 0.07943664879531606, + 0.07853752165619547, 0.0674406565792129, - 0.057670483055018956, - 0.052309119516128495, - 0.051650467734899244, - 0.050006980402568284, + 0.05767048305501899, + 0.05230911951612851, + 0.05165046773489929, + 0.05000698040256827, 0.0416435731110521, - 0.03372467811099224, - 0.03191830159827876, + 0.03372467811099226, + 0.03191830159827877, 0.025868439137504758, - 0.021538527179602706, - 0.021280458427895013, - 0.017428859009940994, - 0.013651160506040523, - 0.011295800447853364, - 0.010544167384582163, - 0.007817835132377637, - 0.005730833293491814, - 0.005684167296088494, - 0.004913184321687076, + 0.021538527179602692, + 0.021280458427895027, + 0.01742885900994099, + 0.01365116050604052, + 0.011295800447853378, + 0.010544167384582179, + 0.007817835132377635, + 0.005730833293491813, + 0.0056841672960885, + 0.0049131843216870755, 0.00395340158027361, - 0.003448725923613315, - 0.003344332793455088, - 0.0028889400788104243, - 0.002282303087459186, - 0.0020062140631202322, - 0.001805958869112873, - 0.0016592878214707436, - 0.001664122554701852, - 0.001674783855632724, + 0.0034487259236133175, + 0.0033443327934550896, + 0.0028889400788104265, + 0.0022823030874591847, + 0.002006214063120231, + 0.0018059588691128716, + 0.0016592878214707427, + 0.0016641225547018514, + 0.0016747838556327237, 0.0016601032441792493, 0.0015363432295141667, 0.00151360152636744, - 0.0012193646852390288, - 0.0007875981060122955, - 0.000784272948585929, - 0.00026330096248731956, - 0.0001918686269271346, - 8.590966033759907e-05, - 2.1837028820746467e-05, - 1.4075228304934558e-05, - 1.3926325391664822e-06, - 1.159171411492843e-06, - 9.647843634959636e-08, - 2.8694281371005734e-08, - 1.5013032647092797e-09, - 3.312299811861638e-10, - 8.134790895764945e-12, - 1.7829937979563843e-12, - 4.4756473675025805e-15, - 4.379803369045627e-15, - 5.238991816127292e-18, - 2.8597324760808093e-21, - 9.479744053430697e-22, - 7.279288356069833e-25, - 8.640487940915884e-29, - 4.782703324915297e-33, - 1.2345095231000215e-37, - 1.4859400068292412e-42, - 8.340535506630727e-48, - 2.183093940632519e-53, - 2.664628947904535e-59 + 0.001219364685239029, + 0.0007875981060122975, + 0.0007842729485859275, + 0.00026330096248731874, + 0.00019186862692713395, + 8.59096603375987e-05, + 2.1837028820746585e-05, + 1.4075228304934636e-05, + 1.3926325391664727e-06, + 1.1591714114928353e-06, + 9.647843634959707e-08, + 2.8694281371005995e-08, + 1.5013032647092666e-09, + 3.312299811861603e-10, + 8.134790895765032e-12, + 1.7829937979564035e-12, + 4.475647367502534e-15, + 4.379803369045579e-15, + 5.2389918161273664e-18, + 2.8597324760807687e-21, + 9.479744053430833e-22, + 7.2792883560697305e-25, + 8.6404879409157e-29, + 4.78270332491523e-33, + 1.2345095231000043e-37, + 1.4859400068292205e-42, + 8.340535506630492e-48, + 2.183093940632488e-53, + 2.6646289479045355e-59 ], "yaxis": "y2" }, @@ -1134,7 +1085,7 @@ "x": 0.5, "xanchor": "center", "xref": "paper", - "y": 1.0, + "y": 1.0000000000000002, "yanchor": "bottom", "yref": "paper" }, @@ -1143,11 +1094,11 @@ "size": 16 }, "showarrow": false, - "text": "numeric_with_drift (#1 in FI)", + "text": "Distribution Plot", "x": 0.5, "xanchor": "center", "xref": "paper", - "y": 0.54, + "y": 0.7200000000000001, "yanchor": "bottom", "yref": "paper" } @@ -1157,6 +1108,7 @@ "title": { "text": "Dataset" }, + "x": 1.02, "y": 0.6, "yanchor": "top" }, @@ -1236,7 +1188,7 @@ }, "colorscale": [ [ - 0.0, + 0, "#0d0887" ], [ @@ -1272,7 +1224,7 @@ "#fdca26" ], [ - 1.0, + 1, "#f0f921" ] ], @@ -1296,7 +1248,7 @@ }, "colorscale": [ [ - 0.0, + 0, "#0d0887" ], [ @@ -1332,7 +1284,7 @@ "#fdca26" ], [ - 1.0, + 1, "#f0f921" ] ], @@ -1347,7 +1299,7 @@ }, "colorscale": [ [ - 0.0, + 0, "#0d0887" ], [ @@ -1383,7 +1335,7 @@ "#fdca26" ], [ - 1.0, + 1, "#f0f921" ] ], @@ -1410,7 +1362,7 @@ }, "colorscale": [ [ - 0.0, + 0, "#0d0887" ], [ @@ -1446,7 +1398,7 @@ "#fdca26" ], [ - 1.0, + 1, "#f0f921" ] ], @@ -1461,7 +1413,7 @@ }, "colorscale": [ [ - 0.0, + 0, "#0d0887" ], [ @@ -1497,7 +1449,7 @@ "#fdca26" ], [ - 1.0, + 1, "#f0f921" ] ], @@ -1643,7 +1595,7 @@ }, "colorscale": [ [ - 0.0, + 0, "#0d0887" ], [ @@ -1679,7 +1631,7 @@ "#fdca26" ], [ - 1.0, + 1, "#f0f921" ] ], @@ -1770,7 +1722,7 @@ ], "sequential": [ [ - 0.0, + 0, "#0d0887" ], [ @@ -1806,13 +1758,13 @@ "#fdca26" ], [ - 1.0, + 1, "#f0f921" ] ], "sequentialminus": [ [ - 0.0, + 0, "#0d0887" ], [ @@ -1848,7 +1800,7 @@ "#fdca26" ], [ - 1.0, + 1, "#f0f921" ] ] @@ -1977,12 +1929,15 @@ } } }, + "title": { + "text": "numeric_with_drift (#1 in FI)" + }, "width": 700, "xaxis": { "anchor": "y", "domain": [ - 0.0, - 1.0 + 0, + 1 ], "dtick": 0.05, "fixedrange": true, @@ -1992,13 +1947,14 @@ 0, 0.4 ], - "showgrid": false + "showgrid": false, + "type": "linear" }, "xaxis2": { "anchor": "y2", "domain": [ - 0.0, - 1.0 + 0, + 1 ], "fixedrange": false, "range": [ @@ -2007,38 +1963,52 @@ ], "title": { "text": "Distribution" - } + }, + "type": "linear" }, "yaxis": { "anchor": "x", + "autorange": true, "color": "black", "domain": [ - 0.9400000000000001, - 1.0 + 0.9200000000000002, + 1 ], "fixedrange": true, + "range": [ + -0.5, + 0.5 + ], "showgrid": false, "showline": false, "showticklabels": false, + "type": "category", "zeroline": false }, "yaxis2": { "anchor": "x2", + "autorange": true, "domain": [ - 0.0, - 0.54 + 0, + 0.7200000000000001 ], "fixedrange": true, + "range": [ + 0, + 0.3914818023801116 + ], "title": { "text": "Probability Density" - } + }, + "type": "linear" } } }, + "image/png": "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", "text/html": [ - "