{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Analyzing replicability of functional connectivity-based multivariate BWAS on the Human Connectome Project dataset\n", "\n", "Comprehensive analysis with 52 HCP phenotypes\n", "\n", "## Imports" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2021-08-03T20:04:15.431840Z", "start_time": "2021-08-03T20:04:14.753565Z" } }, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "from sklearn.linear_model import Ridge\n", "from sklearn.svm import SVR\n", "from sklearn.model_selection import KFold, train_test_split\n", "from sklearn.pipeline import Pipeline\n", "from sklearn.decomposition import PCA\n", "from joblib import Parallel, delayed\n", "from mlxtend.evaluate import permutation_test\n", "sns.set(rc={\"figure.figsize\":(4, 2)})\n", "sns.set_style(\"whitegrid\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load HCP data\n", "\n", "We load functional network matrices (netmats) from the HCP1200-release, as published on connectomeDB: https://db.humanconnectome.org/\n", "Due to licensing issues, data is not supplied with the repository, but can be downloaded from the ConnectomeDB or via `get_data.ipynb` (requires credentials).\n", "See [readme.md](readme.md) for more details." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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T1_CountT2_Count3T_RS-fMRI_Count3T_RS-fMRI_PctCompl3T_tMRI_PctComplfMRI_WM_PctComplfMRI_Gamb_PctComplfMRI_Mot_PctComplfMRI_Lang_PctComplfMRI_Soc_PctCompl...Odor_UnadjOdor_AgeAdjPainIntens_RawScorePainInterf_TscoreTaste_UnadjTaste_AgeAdjMars_Log_ScoreMars_ErrsMars_Finalage
count1206.0000001206.0000001206.0000001206.0000001206.0000001206.0000001206.0000001206.0000001206.0000001206.000000...1204.0000001204.0000001201.0000001205.0000001200.0000001200.0000001198.0000001195.0000001195.0000001206.000000
mean1.4784411.4004983.50995087.21326788.21956989.71807689.85273689.63167587.02711487.039801...110.42132197.7275001.44962545.84771895.16698393.9985331.8454670.5815901.82225128.904229
std0.6356880.6282161.21518131.02788629.94216130.38486430.16355930.49209233.56604333.570248...9.10796311.2732511.7830697.67928814.58341214.8378510.5413930.9731720.5428933.570475
min0.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000...82.74000059.8600000.00000038.60000056.35000059.5000001.5600000.0000001.08000023.500000
25%1.0000001.0000004.000000100.000000100.000000100.000000100.000000100.000000100.000000100.000000...101.12000087.1100000.00000038.60000084.07000083.2200001.8000000.0000001.76000028.000000
50%2.0000001.0000004.000000100.000000100.000000100.000000100.000000100.000000100.000000100.000000...108.79000098.0400001.00000045.90000095.36000094.9700001.8000000.0000001.80000028.000000
75%2.0000002.0000004.000000100.000000100.000000100.000000100.000000100.000000100.000000100.000000...122.250000110.4500002.00000052.200000105.570000102.9200001.8800001.0000001.84000033.000000
max2.0000002.0000004.000000100.000000100.000000100.000000100.000000100.000000100.000000100.000000...122.250000111.41000010.00000075.300000134.650000131.38000015.00000017.00000015.00000036.000000
\n", "

8 rows × 456 columns

\n", "
" ], "text/plain": [ " T1_Count T2_Count 3T_RS-fMRI_Count 3T_RS-fMRI_PctCompl \\\n", "count 1206.000000 1206.000000 1206.000000 1206.000000 \n", "mean 1.478441 1.400498 3.509950 87.213267 \n", "std 0.635688 0.628216 1.215181 31.027886 \n", "min 0.000000 0.000000 0.000000 0.000000 \n", "25% 1.000000 1.000000 4.000000 100.000000 \n", "50% 2.000000 1.000000 4.000000 100.000000 \n", "75% 2.000000 2.000000 4.000000 100.000000 \n", "max 2.000000 2.000000 4.000000 100.000000 \n", "\n", " 3T_tMRI_PctCompl fMRI_WM_PctCompl fMRI_Gamb_PctCompl \\\n", "count 1206.000000 1206.000000 1206.000000 \n", "mean 88.219569 89.718076 89.852736 \n", "std 29.942161 30.384864 30.163559 \n", "min 0.000000 0.000000 0.000000 \n", "25% 100.000000 100.000000 100.000000 \n", "50% 100.000000 100.000000 100.000000 \n", "75% 100.000000 100.000000 100.000000 \n", "max 100.000000 100.000000 100.000000 \n", "\n", " fMRI_Mot_PctCompl fMRI_Lang_PctCompl fMRI_Soc_PctCompl ... \\\n", "count 1206.000000 1206.000000 1206.000000 ... \n", "mean 89.631675 87.027114 87.039801 ... \n", "std 30.492092 33.566043 33.570248 ... \n", "min 0.000000 0.000000 0.000000 ... \n", "25% 100.000000 100.000000 100.000000 ... \n", "50% 100.000000 100.000000 100.000000 ... \n", "75% 100.000000 100.000000 100.000000 ... \n", "max 100.000000 100.000000 100.000000 ... \n", "\n", " Odor_Unadj Odor_AgeAdj PainIntens_RawScore PainInterf_Tscore \\\n", "count 1204.000000 1204.000000 1201.000000 1205.000000 \n", "mean 110.421321 97.727500 1.449625 45.847718 \n", "std 9.107963 11.273251 1.783069 7.679288 \n", "min 82.740000 59.860000 0.000000 38.600000 \n", "25% 101.120000 87.110000 0.000000 38.600000 \n", "50% 108.790000 98.040000 1.000000 45.900000 \n", "75% 122.250000 110.450000 2.000000 52.200000 \n", "max 122.250000 111.410000 10.000000 75.300000 \n", "\n", " Taste_Unadj Taste_AgeAdj Mars_Log_Score Mars_Errs Mars_Final \\\n", "count 1200.000000 1200.000000 1198.000000 1195.000000 1195.000000 \n", "mean 95.166983 93.998533 1.845467 0.581590 1.822251 \n", "std 14.583412 14.837851 0.541393 0.973172 0.542893 \n", "min 56.350000 59.500000 1.560000 0.000000 1.080000 \n", "25% 84.070000 83.220000 1.800000 0.000000 1.760000 \n", "50% 95.360000 94.970000 1.800000 0.000000 1.800000 \n", "75% 105.570000 102.920000 1.880000 1.000000 1.840000 \n", "max 134.650000 131.380000 15.000000 17.000000 15.000000 \n", "\n", " age \n", "count 1206.000000 \n", "mean 28.904229 \n", "std 3.570475 \n", "min 23.500000 \n", "25% 28.000000 \n", "50% 28.000000 \n", "75% 33.000000 \n", "max 36.000000 \n", "\n", "[8 rows x 456 columns]" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# HCP data can be obtained from the connectomeDB\n", "# data is not part of this repository\n", "subjectIDs = pd.read_csv('hcp_data/subjectIDs.txt', header=None)\n", "\n", "netmats_pearson = pd.read_csv('hcp_data/netmats1_correlationZ.txt',\n", " sep=' ',\n", " header=None)\n", "netmats_pearson['ID'] = subjectIDs[0]\n", "netmats_pearson.set_index('ID', drop=True, inplace=True)\n", "\n", "\n", "netmats_parcor = pd.read_csv('hcp_data/netmats2_partial-correlation.txt',\n", " sep=' ',\n", " header=None)\n", "netmats_parcor['ID'] = subjectIDs[0]\n", "netmats_parcor.set_index('ID', drop=True, inplace=True)\n", "\n", "behavior = pd.read_csv('hcp_data/hcp1200_behavioral_data.csv')\n", "behavior = behavior.set_index('Subject', drop=True)\n", "\n", "# convert age to numeric\n", "age = []\n", "for s in behavior['Age']:\n", " if s == '36+':\n", " age.append(36)\n", " else:\n", " split = s.split(sep='-')\n", " age.append(np.mean((float(split[0]), float(split[1]))))\n", "\n", "behavior['age'] = age\n", "behavior.describe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Helper function to prepare target variable\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "def create_data(target='CogTotalComp_AgeAdj', feature_data=netmats_parcor):\n", " # it's a good practice to use pandas for merging, messing up subject order can be painful\n", " features = feature_data.columns\n", " df = behavior\n", " df = df.merge(feature_data, left_index=True, right_index=True, how='left')\n", "\n", " df = df.dropna(subset = [target] + features.values.tolist())\n", " y = df[target].values\n", " X = df[features].values\n", " return X, y" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Helper function implementing a single bootstrap iteration\n", "\n", "We define a workhorse function which:\n", "- randomly samples the discovery and the replication datasets,\n", "- creates cross-validated estimates of predictive performance within the discovery sample\n", "- finalizes the model by fitting it to the whole discovery sample (overfits the discovery but not the replication sample)\n", "- use it to predict the replication sample" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "def corr(X, Y):\n", " # just a small wrapper function (pandas correlation is silent in \"unlucky\" bootstraps with constant values)\n", " return pd.Series(X).corr( pd.Series(Y))\n", "\n", "def bootstrap_workhorse(X, y, sample_size, model, random_state, shuffle_y=False):\n", "\n", " #create discovery and replication samples by random sampling from the whole dataset (without replacement)\n", "\n", " # if shuffle_y is true, a null model is created bz permuting y\n", " if shuffle_y:\n", " rng = np.random.default_rng(random_state)\n", " y = rng.permutation(y)\n", "\n", " # sample the discovery and replication sets *without replacement* (with replacement introduces spurious dependencies)\n", " X_discovery, X_replication, y_discovery, y_replication = train_test_split(X, y, train_size=sample_size, test_size=sample_size, shuffle=True, random_state=random_state)\n", "\n", " # standard 10-fold cross-validation\n", " cv = KFold(10)\n", "\n", " # below we obtain cross-validated predictions in the discovery sample\n", " predicted_discovery_cv = np.zeros_like(y_discovery) # here we collect the predictions for each fold\n", " cor_per_fold = np.zeros(cv.n_splits) # here we collect the predictive performance in each fold\n", " i = 0 # just a counter\n", " for train, test in cv.split(X=X_discovery, y=y_discovery): # loop to leave one fold out\n", " model.fit(X=X_discovery[train], y=y_discovery[train]) # fit model to the training set\n", " predicted_discovery_cv[test] = model.predict(X=X_discovery[test]) # use fitted model to predict teh test set\n", " cor_per_fold[i] = corr(y_discovery[test], predicted_discovery_cv[test]) # calculate performance on tne test set\n", " i += 1\n", " # calculate mean test performance across all folds\n", " r_disc_cv = np.mean(cor_per_fold)\n", " # 'finalize' model by training it on the full discovery sample (without cross-validation)\n", " final_model = model.fit(X=X_discovery, y=y_discovery)\n", " # obtain predictions with the final model on the discovery sample, note that this model actually overfits this sample.\n", " # we do this only to demonstrate biased estimates\n", " predicted_discovery_overfit = final_model.predict(X=X_discovery)\n", " # here we obtain the biased effect size (r) estimates for demonstrational purposes\n", " r_disc_overfit = corr(predicted_discovery_overfit, y_discovery)\n", "\n", " # We use the final model to predict the replication sample\n", " # This is correct (no overfitting here), the final model did not see this data during training\n", " predicted_replication = final_model.predict(X=X_replication)\n", " # we obtain the out-of-sample prediction performance estimates\n", " r_rep = corr(predicted_replication, y_replication)\n", "\n", " # below we calculate permutation-based p-values for all three effect size estimates (in-sample unbiased, in-sample biased, out-of-sample)\n", " # (one sided tests, testing for positive correlation)\n", " p_disc_cv = permutation_test(predicted_discovery_cv, y_discovery, method='approximate', num_rounds=1000, func=lambda x, y: corr(x, y),seed=random_state)\n", " p_disc_overfit = permutation_test(predicted_discovery_overfit, y_discovery, method='approximate', num_rounds=1000, func=lambda x, y: corr(x, y),seed=random_state)\n", " p_rep = permutation_test(predicted_replication, y_replication, method='approximate', num_rounds=1000, func=lambda x, y: corr(x, y),seed=random_state)\n", " # return results\n", " return r_disc_cv, r_disc_overfit, r_rep, p_disc_cv, p_disc_overfit, p_rep" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "All set, now we start the analysis." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Replicability with sample sizes n=50, 100, 200, 300 and max\n", "Here we train a few different models on 100 bootstrap samples.\n", "\n", "We aggregate the results of our workhorse function in `n_bootstrap`=100 bootstrap cases (run in parallel).\n", "\n", "The whole process is repeated for all sample sizes, fetaure_sets and target variables.\n", "\n", "## Here we test all 33 variables, proposed by Marek et al. in their response." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "targets = [\n", "#demographics\n", " 'age',\n", "# cognition\n", " 'MMSE_Score',\n", " 'PSQI_Score',\n", " 'PicSeq_AgeAdj',\n", " 'CardSort_AgeAdj',\n", " 'Flanker_AgeAdj',\n", " 'PMAT24_A_CR',\n", " 'CogFluidComp_AgeAdj',\n", " 'CogCrystalComp_AgeAdj',\n", " 'CogEarlyComp_AgeAdj',\n", " 'CogTotalComp_AgeAdj',\n", " 'ReadEng_AgeAdj',\n", " 'PicVocab_AgeAdj',\n", " 'ProcSpeed_AgeAdj',\n", " 'DDisc_AUC_200',\n", " 'VSPLOT_TC',\n", " 'SCPT_SEN',\n", " 'SCPT_SPEC',\n", "# memory\n", " 'IWRD_TOT',\n", " 'ListSort_AgeAdj',\n", "# emotion\n", " 'ER40ANG',\n", " 'ER40FEAR',\n", " 'ER40HAP',\n", " 'ER40NOE',\n", " 'ER40SAD',\n", " 'AngAffect_Unadj',\n", " 'AngHostil_Unadj',\n", " 'AngAggr_Unadj',\n", " 'FearAffect_Unadj',\n", " 'FearSomat_Unadj',\n", " 'FearSomat_Unadj',\n", " 'Sadness_Unadj',\n", " 'LifeSatisf_Unadj',\n", " 'MeanPurp_Unadj',\n", " 'PosAffect_Unadj',\n", " 'Friendship_Unadj',\n", " 'Loneliness_Unadj',\n", " 'PercHostil_Unadj',\n", " 'PercReject_Unadj',\n", " 'EmotSupp_Unadj',\n", " 'InstruSupp_Unadj',\n", " 'PercStress_Unadj',\n", " 'SelfEff_Unadj',\n", " 'Dexterity_AgeAdj',\n", " # personality\n", " 'NEOFAC_A',\n", " 'NEOFAC_O',\n", " 'NEOFAC_C',\n", " 'NEOFAC_N',\n", " 'NEOFAC_E',\n", "# sensory\n", " 'Noise_Comp',\n", " 'Odor_AgeAdj',\n", " 'PainInterf_Tscore',\n", " 'Taste_AgeAdj',\n", " 'Mars_Final'\n", "]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Now we fit a simple Ridge regression\n", "(no feature selection, no hyperparameter optimization)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "*****************************************************************\n", "netmats_parcor ridge age max\n", "r discovery (with cv) : 0.48020846359551633 r replication: 0.48967065593640496\n", "Replicability at alpha = 0.05 : 100.0 %\n", "Replicability at alpha = 0.01 : 100.0 %\n", "Replicability at alpha = 0.005 : 100.0 %\n", "Replicability at alpha = 0.001 : 100.0 %\n", "*****************************************************************\n", "netmats_parcor ridge MMSE_Score max\n", "r discovery (with cv) : 0.11235242553787785 r replication: 0.12970357972616348\n", "Replicability at alpha = 0.05 : 89.87341772151899 %\n", "Replicability at alpha = 0.01 : 40.50632911392405 %\n", "Replicability at alpha = 0.005 : 25.31645569620253 %\n", "Replicability at alpha = 0.001 : 2.5316455696202533 %\n", "*****************************************************************\n", "netmats_parcor ridge PSQI_Score max\n", "r discovery (with cv) : 0.14291306988028807 r replication: 0.14519995923911966\n", "Replicability at alpha = 0.05 : 98.85057471264368 %\n", "Replicability at alpha = 0.01 : 72.41379310344827 %\n", "Replicability at alpha = 0.005 : 47.12643678160919 %\n", "Replicability at alpha = 0.001 : 16.091954022988507 %\n", "*****************************************************************\n", "netmats_parcor ridge PicSeq_AgeAdj max\n", "r discovery (with cv) : 0.18233069324505247 r replication: 0.18593093519593903\n", "Replicability at alpha = 0.05 : 100.0 %\n", "Replicability at alpha = 0.01 : 93.87755102040816 %\n", "Replicability at alpha = 0.005 : 90.81632653061224 %\n", "Replicability at alpha = 0.001 : 66.3265306122449 %\n", "*****************************************************************\n", "netmats_parcor ridge CardSort_AgeAdj max\n", "r discovery (with cv) : 0.1789658621130238 r replication: 0.18957693758366514\n", "Replicability at alpha = 0.05 : 100.0 %\n", "Replicability at alpha = 0.01 : 96.90721649484536 %\n", "Replicability at alpha = 0.005 : 91.75257731958763 %\n", "Replicability at alpha = 0.001 : 65.97938144329896 %\n", "*****************************************************************\n", "netmats_parcor ridge Flanker_AgeAdj max\n", "r discovery (with cv) : 0.1322612349491906 r replication: 0.14022631939127772\n", "Replicability at alpha = 0.05 : 98.87640449438202 %\n", "Replicability at alpha = 0.01 : 61.79775280898876 %\n", "Replicability at alpha = 0.005 : 42.69662921348314 %\n", "Replicability at alpha = 0.001 : 12.359550561797752 %\n", "*****************************************************************\n", "netmats_parcor ridge PMAT24_A_CR max\n", "r discovery (with cv) : 0.2922426473940083 r replication: 0.301492004778787\n", "Replicability at alpha = 0.05 : 100.0 %\n", "Replicability at alpha = 0.01 : 100.0 %\n", "Replicability at alpha = 0.005 : 100.0 %\n", "Replicability at alpha = 0.001 : 100.0 %\n", "*****************************************************************\n", "netmats_parcor ridge CogFluidComp_AgeAdj max\n", "r discovery (with cv) : 0.3049801718197025 r replication: 0.2996751646332102\n", "Replicability at alpha = 0.05 : 100.0 %\n", "Replicability at alpha = 0.01 : 100.0 %\n", "Replicability at alpha = 0.005 : 100.0 %\n", "Replicability at alpha = 0.001 : 100.0 %\n", "*****************************************************************\n", "netmats_parcor ridge CogCrystalComp_AgeAdj max\n", "r discovery (with cv) : 0.509356447724508 r replication: 0.5252483106948896\n", "Replicability at alpha = 0.05 : 100.0 %\n", "Replicability at alpha = 0.01 : 100.0 %\n", "Replicability at alpha = 0.005 : 100.0 %\n", "Replicability at alpha = 0.001 : 100.0 %\n", "*****************************************************************\n", "netmats_parcor ridge CogEarlyComp_AgeAdj max\n", "r discovery (with cv) : 0.35010656810589025 r replication: 0.346212766761039\n", "Replicability at alpha = 0.05 : 100.0 %\n", "Replicability at alpha = 0.01 : 100.0 %\n", "Replicability at alpha = 0.005 : 100.0 %\n", "Replicability at alpha = 0.001 : 100.0 %\n", "*****************************************************************\n", "netmats_parcor ridge CogTotalComp_AgeAdj max\n", "r discovery (with cv) : 0.4726518883300038 r replication: 0.47903946590450225\n", "Replicability at alpha = 0.05 : 100.0 %\n", "Replicability at alpha = 0.01 : 100.0 %\n", "Replicability at alpha = 0.005 : 100.0 %\n", "Replicability at alpha = 0.001 : 100.0 %\n", "*****************************************************************\n", "netmats_parcor ridge ReadEng_AgeAdj max\n", "r discovery (with cv) : 0.450867003576911 r replication: 0.46022188861700536\n", "Replicability at alpha = 0.05 : 100.0 %\n", "Replicability at alpha = 0.01 : 100.0 %\n", "Replicability at alpha = 0.005 : 100.0 %\n", "Replicability at alpha = 0.001 : 100.0 %\n", "*****************************************************************\n", "netmats_parcor ridge PicVocab_AgeAdj max\n", "r discovery (with cv) : 0.4748017798587243 r replication: 0.4784423901738578\n", "Replicability at alpha = 0.05 : 100.0 %\n", "Replicability at alpha = 0.01 : 100.0 %\n", "Replicability at alpha = 0.005 : 100.0 %\n", "Replicability at alpha = 0.001 : 100.0 %\n", "*****************************************************************\n", "netmats_parcor ridge ProcSpeed_AgeAdj max\n", "r discovery (with cv) : 0.11764068520351804 r replication: 0.12249903623335612\n", "Replicability at alpha = 0.05 : 84.61538461538461 %\n", "Replicability at alpha = 0.01 : 38.46153846153847 %\n", "Replicability at alpha = 0.005 : 21.794871794871796 %\n", "Replicability at alpha = 0.001 : 2.564102564102564 %\n", "*****************************************************************\n", "netmats_parcor ridge DDisc_AUC_200 max\n", "r discovery (with cv) : 0.20884491911824618 r replication: 0.20706209788397087\n", "Replicability at alpha = 0.05 : 100.0 %\n", "Replicability at alpha = 0.01 : 99.0 %\n", "Replicability at alpha = 0.005 : 95.0 %\n", "Replicability at alpha = 0.001 : 83.0 %\n", "*****************************************************************\n", "netmats_parcor ridge VSPLOT_TC max\n", "r discovery (with cv) : 0.33041854229644096 r replication: 0.3361466183063388\n", "Replicability at alpha = 0.05 : 100.0 %\n", "Replicability at alpha = 0.01 : 100.0 %\n", "Replicability at alpha = 0.005 : 100.0 %\n", "Replicability at alpha = 0.001 : 100.0 %\n", "*****************************************************************\n", "netmats_parcor ridge SCPT_SEN max\n", "r discovery (with cv) : 0.004953219542766921 r replication: 0.005954925720074722\n", "Replicability at alpha = 0.05 : 0.0 %\n", "Replicability at alpha = 0.01 : 0.0 %\n", "Replicability at alpha = 0.005 : 0.0 %\n", "Replicability at alpha = 0.001 : 0.0 %\n", "*****************************************************************\n", "netmats_parcor ridge SCPT_SPEC max\n", "r discovery (with cv) : 0.1483580336589026 r replication: 0.15543127763406267\n", "Replicability at alpha = 0.05 : 100.0 %\n", "Replicability at alpha = 0.01 : 78.02197802197803 %\n", "Replicability at alpha = 0.005 : 57.14285714285714 %\n", "Replicability at alpha = 0.001 : 26.373626373626376 %\n", "*****************************************************************\n", "netmats_parcor ridge IWRD_TOT max\n", "r discovery (with cv) : 0.0810183743864671 r replication: 0.0739403812147235\n", "Replicability at alpha = 0.05 : 36.53846153846153 %\n", "Replicability at alpha = 0.01 : 3.8461538461538463 %\n", "Replicability at alpha = 0.005 : 0.0 %\n", "Replicability at alpha = 0.001 : 0.0 %\n", "*****************************************************************\n", "netmats_parcor ridge ListSort_AgeAdj max\n", "r discovery (with cv) : 0.27262179944410553 r replication: 0.27629338784789925\n", "Replicability at alpha = 0.05 : 100.0 %\n", "Replicability at alpha = 0.01 : 100.0 %\n", "Replicability at alpha = 0.005 : 100.0 %\n", "Replicability at alpha = 0.001 : 100.0 %\n", "*****************************************************************\n", "netmats_parcor ridge ER40ANG max\n", "r discovery (with cv) : 0.07290919946421724 r replication: 0.07930980325318832\n", "Replicability at alpha = 0.05 : 44.680851063829785 %\n", "Replicability at alpha = 0.01 : 2.127659574468085 %\n", "Replicability at alpha = 0.005 : 2.127659574468085 %\n", "Replicability at alpha = 0.001 : 0.0 %\n", "*****************************************************************\n", "netmats_parcor ridge ER40FEAR max\n", "r discovery (with cv) : 0.033788344197422604 r replication: 0.03217012035635588\n", "Replicability at alpha = 0.05 : 0.0 %\n", "Replicability at alpha = 0.01 : 0.0 %\n", "Replicability at alpha = 0.005 : 0.0 %\n", "Replicability at alpha = 0.001 : 0.0 %\n", "*****************************************************************\n", "netmats_parcor ridge ER40HAP max\n", "r discovery (with cv) : -0.05910616789376827 r replication: -0.040972411502727554\n", "Replicability at alpha = 0.05 : nan %\n", "Replicability at alpha = 0.01 : nan %\n", "Replicability at alpha = 0.005 : nan %\n", "Replicability at alpha = 0.001 : nan %\n", "*****************************************************************\n", "netmats_parcor ridge ER40NOE max\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ ":58: RuntimeWarning: invalid value encountered in long_scalars\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "r discovery (with cv) : 0.05015028148085295 r replication: 0.05477097859212915\n", "Replicability at alpha = 0.05 : 16.666666666666664 %\n", "Replicability at alpha = 0.01 : 0.0 %\n", "Replicability at alpha = 0.005 : 0.0 %\n", "Replicability at alpha = 0.001 : 0.0 %\n", "*****************************************************************\n", "netmats_parcor ridge ER40SAD max\n", "r discovery (with cv) : 0.03355879047628217 r replication: 0.043958511763540556\n", "Replicability at alpha = 0.05 : 10.0 %\n", "Replicability at alpha = 0.01 : 0.0 %\n", "Replicability at alpha = 0.005 : 0.0 %\n", "Replicability at alpha = 0.001 : 0.0 %\n", "*****************************************************************\n", "netmats_parcor ridge AngAffect_Unadj max\n", "r discovery (with cv) : 0.09675041204325666 r replication: 0.097384559509148\n", "Replicability at alpha = 0.05 : 69.23076923076923 %\n", "Replicability at alpha = 0.01 : 12.307692307692308 %\n", "Replicability at alpha = 0.005 : 1.5384615384615385 %\n", "Replicability at alpha = 0.001 : 0.0 %\n", "*****************************************************************\n", "netmats_parcor ridge AngHostil_Unadj max\n", "r discovery (with cv) : 0.06336555348912118 r replication: 0.07505183534895289\n", "Replicability at alpha = 0.05 : 41.02564102564102 %\n", "Replicability at alpha = 0.01 : 2.564102564102564 %\n", "Replicability at alpha = 0.005 : 2.564102564102564 %\n", "Replicability at alpha = 0.001 : 0.0 %\n", "*****************************************************************\n", "netmats_parcor ridge AngAggr_Unadj max\n", "r discovery (with cv) : 0.19405555852511736 r replication: 0.20052370789491036\n", "Replicability at alpha = 0.05 : 100.0 %\n", "Replicability at alpha = 0.01 : 95.0 %\n", "Replicability at alpha = 0.005 : 93.0 %\n", "Replicability at alpha = 0.001 : 77.0 %\n", "*****************************************************************\n", "netmats_parcor ridge FearAffect_Unadj max\n", "r discovery (with cv) : 0.15563217224852452 r replication: 0.15122577808620358\n", "Replicability at alpha = 0.05 : 100.0 %\n", "Replicability at alpha = 0.01 : 77.01149425287356 %\n", "Replicability at alpha = 0.005 : 66.66666666666666 %\n", "Replicability at alpha = 0.001 : 29.88505747126437 %\n", "*****************************************************************\n", "netmats_parcor ridge FearSomat_Unadj max\n", "r discovery (with cv) : 0.061101136887113376 r replication: 0.054246596855310567\n", "Replicability at alpha = 0.05 : 14.285714285714285 %\n", "Replicability at alpha = 0.01 : 0.0 %\n", "Replicability at alpha = 0.005 : 0.0 %\n", "Replicability at alpha = 0.001 : 0.0 %\n", "*****************************************************************\n", "netmats_parcor ridge FearSomat_Unadj max\n", "r discovery (with cv) : 0.061101136887113376 r replication: 0.054246596855310567\n", "Replicability at alpha = 0.05 : 14.285714285714285 %\n", "Replicability at alpha = 0.01 : 0.0 %\n", "Replicability at alpha = 0.005 : 0.0 %\n", "Replicability at alpha = 0.001 : 0.0 %\n", "*****************************************************************\n", "netmats_parcor ridge Sadness_Unadj max\n", "r discovery (with cv) : 0.1327824232969155 r replication: 0.1244556654760563\n", "Replicability at alpha = 0.05 : 94.04761904761905 %\n", "Replicability at alpha = 0.01 : 54.761904761904766 %\n", "Replicability at alpha = 0.005 : 35.714285714285715 %\n", "Replicability at alpha = 0.001 : 7.142857142857142 %\n", "*****************************************************************\n", "netmats_parcor ridge LifeSatisf_Unadj max\n", "r discovery (with cv) : 0.19873341230892952 r replication: 0.18440610179827896\n", "Replicability at alpha = 0.05 : 100.0 %\n", "Replicability at alpha = 0.01 : 95.87628865979381 %\n", "Replicability at alpha = 0.005 : 92.78350515463917 %\n", "Replicability at alpha = 0.001 : 67.0103092783505 %\n", "*****************************************************************\n", "netmats_parcor ridge MeanPurp_Unadj max\n", "r discovery (with cv) : 0.09984748458857128 r replication: 0.08905980179359992\n", "Replicability at alpha = 0.05 : 50.76923076923077 %\n", "Replicability at alpha = 0.01 : 15.384615384615385 %\n", "Replicability at alpha = 0.005 : 4.615384615384616 %\n", "Replicability at alpha = 0.001 : 0.0 %\n", "*****************************************************************\n", "netmats_parcor ridge PosAffect_Unadj max\n", "r discovery (with cv) : 0.09883821052746254 r replication: 0.08771575790081709\n", "Replicability at alpha = 0.05 : 55.223880597014926 %\n", "Replicability at alpha = 0.01 : 10.44776119402985 %\n", "Replicability at alpha = 0.005 : 5.970149253731343 %\n", "Replicability at alpha = 0.001 : 0.0 %\n", "*****************************************************************\n", "netmats_parcor ridge Friendship_Unadj max\n", "r discovery (with cv) : 0.08531254415798344 r replication: 0.08428001338641648\n", "Replicability at alpha = 0.05 : 44.0 %\n", "Replicability at alpha = 0.01 : 10.0 %\n", "Replicability at alpha = 0.005 : 4.0 %\n", "Replicability at alpha = 0.001 : 2.0 %\n", "*****************************************************************\n", "netmats_parcor ridge Loneliness_Unadj max\n", "r discovery (with cv) : 0.10181181257724871 r replication: 0.10338556615021999\n", "Replicability at alpha = 0.05 : 71.66666666666667 %\n", "Replicability at alpha = 0.01 : 26.666666666666668 %\n", "Replicability at alpha = 0.005 : 6.666666666666667 %\n", "Replicability at alpha = 0.001 : 0.0 %\n", "*****************************************************************\n", "netmats_parcor ridge PercHostil_Unadj max\n", "r discovery (with cv) : 0.037622230965088856 r replication: 0.04500642736764501\n", "Replicability at alpha = 0.05 : 0.0 %\n", "Replicability at alpha = 0.01 : 0.0 %\n", "Replicability at alpha = 0.005 : 0.0 %\n", "Replicability at alpha = 0.001 : 0.0 %\n", "*****************************************************************\n", "netmats_parcor ridge PercReject_Unadj max\n", "r discovery (with cv) : 0.03688725729680184 r replication: 0.03543635186966549\n", "Replicability at alpha = 0.05 : 4.3478260869565215 %\n", "Replicability at alpha = 0.01 : 0.0 %\n", "Replicability at alpha = 0.005 : 0.0 %\n", "Replicability at alpha = 0.001 : 0.0 %\n", "*****************************************************************\n", "netmats_parcor ridge EmotSupp_Unadj max\n", "r discovery (with cv) : 0.1260287186553436 r replication: 0.12226413039802284\n", "Replicability at alpha = 0.05 : 96.55172413793103 %\n", "Replicability at alpha = 0.01 : 43.67816091954023 %\n", "Replicability at alpha = 0.005 : 28.735632183908045 %\n", "Replicability at alpha = 0.001 : 9.195402298850574 %\n", "*****************************************************************\n", "netmats_parcor ridge InstruSupp_Unadj max\n", "r discovery (with cv) : 0.04191974313059748 r replication: 0.03262089935367679\n", "Replicability at alpha = 0.05 : 4.166666666666666 %\n", "Replicability at alpha = 0.01 : 0.0 %\n", "Replicability at alpha = 0.005 : 0.0 %\n", "Replicability at alpha = 0.001 : 0.0 %\n", "*****************************************************************\n", "netmats_parcor ridge PercStress_Unadj max\n", "r discovery (with cv) : 0.10021102140816969 r replication: 0.09269067112373008\n", "Replicability at alpha = 0.05 : 62.5 %\n", "Replicability at alpha = 0.01 : 14.0625 %\n", "Replicability at alpha = 0.005 : 6.25 %\n", "Replicability at alpha = 0.001 : 0.0 %\n", "*****************************************************************\n", "netmats_parcor ridge SelfEff_Unadj max\n", "r discovery (with cv) : 0.09990355623342788 r replication: 0.10774220880935355\n", "Replicability at alpha = 0.05 : 81.81818181818183 %\n", "Replicability at alpha = 0.01 : 28.78787878787879 %\n", "Replicability at alpha = 0.005 : 12.121212121212121 %\n", "Replicability at alpha = 0.001 : 1.5151515151515151 %\n", "*****************************************************************\n", "netmats_parcor ridge Dexterity_AgeAdj max\n", "r discovery (with cv) : 0.1698377324671013 r replication: 0.16739913235673143\n", "Replicability at alpha = 0.05 : 98.9247311827957 %\n", "Replicability at alpha = 0.01 : 88.17204301075269 %\n", "Replicability at alpha = 0.005 : 81.72043010752688 %\n", "Replicability at alpha = 0.001 : 51.61290322580645 %\n", "*****************************************************************\n", "netmats_parcor ridge NEOFAC_A max\n", "r discovery (with cv) : 0.19226360180293334 r replication: 0.2105816660368088\n", "Replicability at alpha = 0.05 : 100.0 %\n", "Replicability at alpha = 0.01 : 97.95918367346938 %\n", "Replicability at alpha = 0.005 : 94.89795918367348 %\n", "Replicability at alpha = 0.001 : 82.6530612244898 %\n", "*****************************************************************\n", "netmats_parcor ridge NEOFAC_O max\n", "r discovery (with cv) : 0.26505077367461527 r replication: 0.28578623242498713\n", "Replicability at alpha = 0.05 : 100.0 %\n", "Replicability at alpha = 0.01 : 100.0 %\n", "Replicability at alpha = 0.005 : 100.0 %\n", "Replicability at alpha = 0.001 : 98.0 %\n", "*****************************************************************\n", "netmats_parcor ridge NEOFAC_C max\n", "r discovery (with cv) : 0.17973714484091477 r replication: 0.19354830382121097\n", "Replicability at alpha = 0.05 : 100.0 %\n", "Replicability at alpha = 0.01 : 92.78350515463917 %\n", "Replicability at alpha = 0.005 : 90.72164948453609 %\n", "Replicability at alpha = 0.001 : 64.94845360824742 %\n", "*****************************************************************\n", "netmats_parcor ridge NEOFAC_N max\n", "r discovery (with cv) : 0.07314281912604746 r replication: 0.06696149072259926\n", "Replicability at alpha = 0.05 : 40.0 %\n", "Replicability at alpha = 0.01 : 2.0 %\n", "Replicability at alpha = 0.005 : 2.0 %\n", "Replicability at alpha = 0.001 : 0.0 %\n", "*****************************************************************\n", "netmats_parcor ridge NEOFAC_E max\n", "r discovery (with cv) : 0.15791961819822467 r replication: 0.16447031944134946\n", "Replicability at alpha = 0.05 : 98.93617021276596 %\n", "Replicability at alpha = 0.01 : 84.04255319148936 %\n", "Replicability at alpha = 0.005 : 70.2127659574468 %\n", "Replicability at alpha = 0.001 : 35.1063829787234 %\n", "*****************************************************************\n", "netmats_parcor ridge Noise_Comp max\n", "r discovery (with cv) : 0.13754389395248953 r replication: 0.13277944361089927\n", "Replicability at alpha = 0.05 : 97.67441860465115 %\n", "Replicability at alpha = 0.01 : 62.7906976744186 %\n", "Replicability at alpha = 0.005 : 46.51162790697674 %\n", "Replicability at alpha = 0.001 : 10.465116279069768 %\n", "*****************************************************************\n", "netmats_parcor ridge Odor_AgeAdj max\n", "r discovery (with cv) : 0.03422255970866912 r replication: 0.019033276660184433\n", "Replicability at alpha = 0.05 : 0.0 %\n", "Replicability at alpha = 0.01 : 0.0 %\n", "Replicability at alpha = 0.005 : 0.0 %\n", "Replicability at alpha = 0.001 : 0.0 %\n", "*****************************************************************\n", "netmats_parcor ridge PainInterf_Tscore max\n", "r discovery (with cv) : 0.03274152032524882 r replication: 0.03329128578051491\n", "Replicability at alpha = 0.05 : 0.0 %\n", "Replicability at alpha = 0.01 : 0.0 %\n", "Replicability at alpha = 0.005 : 0.0 %\n", "Replicability at alpha = 0.001 : 0.0 %\n", "*****************************************************************\n", "netmats_parcor ridge Taste_AgeAdj max\n", "r discovery (with cv) : 0.0944194746807131 r replication: 0.08063900075841376\n", "Replicability at alpha = 0.05 : 43.103448275862064 %\n", "Replicability at alpha = 0.01 : 5.172413793103448 %\n", "Replicability at alpha = 0.005 : 0.0 %\n", "Replicability at alpha = 0.001 : 0.0 %\n", "*****************************************************************\n", "netmats_parcor ridge Mars_Final max\n", "r discovery (with cv) : 0.0815295729161019 r replication: 0.02266341772170401\n", "Replicability at alpha = 0.05 : 6.666666666666667 %\n", "Replicability at alpha = 0.01 : 0.0 %\n", "Replicability at alpha = 0.005 : 0.0 %\n", "Replicability at alpha = 0.001 : 0.0 %\n", "CPU times: user 38 s, sys: 5.9 s, total: 43.9 s\n", "Wall time: 44min 5s\n" ] }, { "data": { "text/html": [ "
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connectivitymodeltargetnr_discovery_cvr_discovery_overfitr_replicationp_discovery_cvp_discovery_overfitp_replication
0netmats_parcorridgeage5010.4806791.00.5147600.0009990.0009990.000999
1netmats_parcorridgeage5010.4772721.00.4550210.0009990.0009990.000999
2netmats_parcorridgeage5010.5286411.00.4826120.0009990.0009990.000999
3netmats_parcorridgeage5010.4888561.00.5028990.0009990.0009990.000999
4netmats_parcorridgeage5010.4291661.00.5302740.0009990.0009990.000999
.................................
5395netmats_parcorridgeMars_Final4990.0906191.00.0316580.0699300.0009990.115884
5396netmats_parcorridgeMars_Final4990.0056791.00.0381460.5654350.0009990.147852
5397netmats_parcorridgeMars_Final4990.0172331.00.0574840.4495500.0009990.106893
5398netmats_parcorridgeMars_Final4990.0097011.00.0052760.5264740.0009990.400599
5399netmats_parcorridgeMars_Final499-0.0322891.00.0177250.7232770.0009990.254745
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5400 rows × 10 columns

\n", "
" ], "text/plain": [ " connectivity model target n r_discovery_cv \\\n", "0 netmats_parcor ridge age 501 0.480679 \n", "1 netmats_parcor ridge age 501 0.477272 \n", "2 netmats_parcor ridge age 501 0.528641 \n", "3 netmats_parcor ridge age 501 0.488856 \n", "4 netmats_parcor ridge age 501 0.429166 \n", "... ... ... ... ... ... \n", "5395 netmats_parcor ridge Mars_Final 499 0.090619 \n", "5396 netmats_parcor ridge Mars_Final 499 0.005679 \n", "5397 netmats_parcor ridge Mars_Final 499 0.017233 \n", "5398 netmats_parcor ridge Mars_Final 499 0.009701 \n", "5399 netmats_parcor ridge Mars_Final 499 -0.032289 \n", "\n", " r_discovery_overfit r_replication p_discovery_cv p_discovery_overfit \\\n", "0 1.0 0.514760 0.000999 0.000999 \n", "1 1.0 0.455021 0.000999 0.000999 \n", "2 1.0 0.482612 0.000999 0.000999 \n", "3 1.0 0.502899 0.000999 0.000999 \n", "4 1.0 0.530274 0.000999 0.000999 \n", "... ... ... ... ... \n", "5395 1.0 0.031658 0.069930 0.000999 \n", "5396 1.0 0.038146 0.565435 0.000999 \n", "5397 1.0 0.057484 0.449550 0.000999 \n", "5398 1.0 0.005276 0.526474 0.000999 \n", "5399 1.0 0.017725 0.723277 0.000999 \n", "\n", " p_replication \n", "0 0.000999 \n", "1 0.000999 \n", "2 0.000999 \n", "3 0.000999 \n", "4 0.000999 \n", "... ... \n", "5395 0.115884 \n", "5396 0.147852 \n", "5397 0.106893 \n", "5398 0.400599 \n", "5399 0.254745 \n", "\n", "[5400 rows x 10 columns]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%time\n", "\n", "random_state = 42\n", "n_bootstrap = 100\n", "\n", "features = {\n", " 'netmats_parcor': netmats_parcor#,\n", " #'netmats_pearson': netmats_pearson\n", "}\n", "\n", "models = {\n", " 'ridge': Ridge()\n", "}\n", "\n", "# We aggregate all results here:\n", "df = pd.DataFrame(columns=['connectivity','model','target','n','r_discovery_cv','r_discovery_overfit','r_replication','p_discovery_cv','p_discovery_overfit','p_replication'])\n", "\n", "for feature_set in features:\n", " for model in models:\n", " for target_var in targets:\n", " for sample_size in ['max']:\n", "\n", " print('*****************************************************************')\n", " print(feature_set, model, target_var, sample_size)\n", "\n", " X, y = create_data(target=target_var, feature_data=features[feature_set])\n", "\n", " if sample_size=='max':\n", " sample_size = int(len(y)/2)\n", "\n", " # create random seeds for each bootstrap iteration for reproducibility\n", " rng = np.random.default_rng(random_state)\n", " random_sates = rng.integers(np.iinfo(np.int32).max, size=n_bootstrap)\n", "\n", " # run bootstrap iterations in parallel\n", " r_discovery_cv, r_discovery_overfit, r_replication, p_discovery_cv, p_discovery_overfit, p_replication = zip(\n", " *Parallel(n_jobs=-1)(\n", " delayed(bootstrap_workhorse)(X, y, sample_size, models[model], seed) for seed in random_sates))\n", "\n", " tmp_data_frame = pd.DataFrame({\n", " 'connectivity' : feature_set,\n", " 'model' : model,\n", " 'target' : target_var,\n", " 'n' : sample_size,\n", " 'r_discovery_cv': r_discovery_cv,\n", " 'r_discovery_overfit': r_discovery_overfit,\n", " 'r_replication': r_replication,\n", " 'p_discovery_cv': p_discovery_cv,\n", " 'p_discovery_overfit': p_discovery_overfit,\n", " 'p_replication': p_replication\n", " })\n", " #sns.scatterplot(x='r_replication', y='r_discovery_cv', data=tmp_data_frame)\n", " #plt.ylabel('in-sample (r)')\n", " #plt.xlabel('out-of-sample (r_pred)')\n", " #plt.show()\n", " print('r discovery (with cv) :', tmp_data_frame.r_discovery_cv.mean(), 'r replication:', tmp_data_frame.r_replication.mean())\n", "\n", " for alpha in [0.05, 0.01, 0.005, 0.001]:\n", " print('Replicability at alpha =', alpha, ':',\n", " (tmp_data_frame.loc[tmp_data_frame['p_discovery_cv']\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
r_discovery_cvr_replication
connectivitymodeltargetn
netmats_parcorridgeAngAffect_Unadj5010.0967500.097385
AngAggr_Unadj5010.1940560.200524
AngHostil_Unadj5010.0633660.075052
CardSort_AgeAdj5000.1789660.189577
CogCrystalComp_AgeAdj4990.5093560.525248
CogEarlyComp_AgeAdj4970.3501070.346213
CogFluidComp_AgeAdj4960.3049800.299675
CogTotalComp_AgeAdj4950.4726520.479039
DDisc_AUC_2005000.2088450.207062
Dexterity_AgeAdj5010.1698380.167399
ER40ANG5000.0729090.079310
ER40FEAR5000.0337880.032170
ER40HAP500-0.059106-0.040972
ER40NOE5000.0501500.054771
ER40SAD5000.0335590.043959
EmotSupp_Unadj5010.1260290.122264
FearAffect_Unadj5010.1556320.151226
FearSomat_Unadj5010.0611010.054247
Flanker_AgeAdj5010.1322610.140226
Friendship_Unadj5010.0853130.084280
IWRD_TOT5000.0810180.073940
InstruSupp_Unadj5010.0419200.032621
LifeSatisf_Unadj5010.1987330.184406
ListSort_AgeAdj5010.2726220.276293
Loneliness_Unadj5010.1018120.103386
MMSE_Score5010.1123520.129704
Mars_Final4990.0815300.022663
MeanPurp_Unadj5010.0998470.089060
NEOFAC_A5000.1922640.210582
NEOFAC_C5000.1797370.193548
NEOFAC_E5000.1579200.164470
NEOFAC_N5000.0731430.066961
NEOFAC_O5000.2650510.285786
Noise_Comp4980.1375440.132779
Odor_AgeAdj5000.0342230.019033
PMAT24_A_CR4990.2922430.301492
PSQI_Score5010.1429130.145200
PainInterf_Tscore5010.0327420.033291
PercHostil_Unadj5010.0376220.045006
PercReject_Unadj5010.0368870.035436
PercStress_Unadj5010.1002110.092691
PicSeq_AgeAdj5010.1823310.185931
PicVocab_AgeAdj5010.4748020.478442
PosAffect_Unadj5010.0988380.087716
ProcSpeed_AgeAdj5010.1176410.122499
ReadEng_AgeAdj5010.4508670.460222
SCPT_SEN5000.0049530.005955
SCPT_SPEC5000.1483580.155431
Sadness_Unadj5010.1327820.124456
SelfEff_Unadj5010.0999040.107742
Taste_AgeAdj4990.0944190.080639
VSPLOT_TC5000.3304190.336147
age5010.4802080.489671
\n", "" ], "text/plain": [ " r_discovery_cv r_replication\n", "connectivity model target n \n", "netmats_parcor ridge AngAffect_Unadj 501 0.096750 0.097385\n", " AngAggr_Unadj 501 0.194056 0.200524\n", " AngHostil_Unadj 501 0.063366 0.075052\n", " CardSort_AgeAdj 500 0.178966 0.189577\n", " CogCrystalComp_AgeAdj 499 0.509356 0.525248\n", " CogEarlyComp_AgeAdj 497 0.350107 0.346213\n", " CogFluidComp_AgeAdj 496 0.304980 0.299675\n", " CogTotalComp_AgeAdj 495 0.472652 0.479039\n", " DDisc_AUC_200 500 0.208845 0.207062\n", " Dexterity_AgeAdj 501 0.169838 0.167399\n", " ER40ANG 500 0.072909 0.079310\n", " ER40FEAR 500 0.033788 0.032170\n", " ER40HAP 500 -0.059106 -0.040972\n", " ER40NOE 500 0.050150 0.054771\n", " ER40SAD 500 0.033559 0.043959\n", " EmotSupp_Unadj 501 0.126029 0.122264\n", " FearAffect_Unadj 501 0.155632 0.151226\n", " FearSomat_Unadj 501 0.061101 0.054247\n", " Flanker_AgeAdj 501 0.132261 0.140226\n", " Friendship_Unadj 501 0.085313 0.084280\n", " IWRD_TOT 500 0.081018 0.073940\n", " InstruSupp_Unadj 501 0.041920 0.032621\n", " LifeSatisf_Unadj 501 0.198733 0.184406\n", " ListSort_AgeAdj 501 0.272622 0.276293\n", " Loneliness_Unadj 501 0.101812 0.103386\n", " MMSE_Score 501 0.112352 0.129704\n", " Mars_Final 499 0.081530 0.022663\n", " MeanPurp_Unadj 501 0.099847 0.089060\n", " NEOFAC_A 500 0.192264 0.210582\n", " NEOFAC_C 500 0.179737 0.193548\n", " NEOFAC_E 500 0.157920 0.164470\n", " NEOFAC_N 500 0.073143 0.066961\n", " NEOFAC_O 500 0.265051 0.285786\n", " Noise_Comp 498 0.137544 0.132779\n", " Odor_AgeAdj 500 0.034223 0.019033\n", " PMAT24_A_CR 499 0.292243 0.301492\n", " PSQI_Score 501 0.142913 0.145200\n", " PainInterf_Tscore 501 0.032742 0.033291\n", " PercHostil_Unadj 501 0.037622 0.045006\n", " PercReject_Unadj 501 0.036887 0.035436\n", " PercStress_Unadj 501 0.100211 0.092691\n", " PicSeq_AgeAdj 501 0.182331 0.185931\n", " PicVocab_AgeAdj 501 0.474802 0.478442\n", " PosAffect_Unadj 501 0.098838 0.087716\n", " ProcSpeed_AgeAdj 501 0.117641 0.122499\n", " ReadEng_AgeAdj 501 0.450867 0.460222\n", " SCPT_SEN 500 0.004953 0.005955\n", " SCPT_SPEC 500 0.148358 0.155431\n", " Sadness_Unadj 501 0.132782 0.124456\n", " SelfEff_Unadj 501 0.099904 0.107742\n", " Taste_AgeAdj 499 0.094419 0.080639\n", " VSPLOT_TC 500 0.330419 0.336147\n", " age 501 0.480208 0.489671" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.groupby(['connectivity', 'model', 'target', 'n']).mean()[['r_discovery_cv', 'r_replication']]" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_19024/3478190779.py:9: RuntimeWarning: invalid value encountered in long_scalars\n", " return (x.loc[x['p_discovery_cv']", "image/png": 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\n" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df=pd.read_csv('res/revised_results_Ridge.csv')\n", "sns.set(rc={\"figure.figsize\":(14, 2)})\n", "sns.set_style(\"white\")\n", "\n", "phenotypes = pd.read_csv('hcp_data/phenotypes-of-interest.csv').iloc[:, 1:]\n", "phenotypes = phenotypes.drop([2,3]) # Age duplicates\n", "\n", "def prep(x, alpha=0.05):\n", " return (x.loc[x['p_discovery_cv']", "image/png": 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\n" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax=sns.barplot(data=df_plot, x='phenotype', y='r_discovery', palette=colors)\n", "plt.xticks(rotation=90)\n", "ax.axhline(0.074, color='black', linestyle=':') # parametric one-sided significance threshold\n", "ax.axhline(0, color='black')\n", "plt.savefig('fig/r_discovery_all_phenotypes.pdf')" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "ename": "ValueError", "evalue": "Value of 'hover_data_1' is not the name of a column in 'data_frame'. Expected one of [0, 'r_discovery', 'category', 'phenotype'] but received: phenotype_long", "output_type": "error", "traceback": [ "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m", "\u001B[0;31mValueError\u001B[0m Traceback (most recent call last)", "Cell \u001B[0;32mIn[5], line 3\u001B[0m\n\u001B[1;32m 1\u001B[0m \u001B[38;5;28;01mimport\u001B[39;00m \u001B[38;5;21;01mplotly\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mexpress\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m \u001B[38;5;21;01mpx\u001B[39;00m\n\u001B[0;32m----> 3\u001B[0m fig \u001B[38;5;241m=\u001B[39m \u001B[43mpx\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mbar\u001B[49m\u001B[43m(\u001B[49m\u001B[43mdf_plot\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43my\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;241;43m0\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mx\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43mphenotype\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mtext_auto\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43m.2s\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[1;32m 4\u001B[0m \u001B[43m \u001B[49m\u001B[43mtitle\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mReplication probability with n=500\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[1;32m 5\u001B[0m \u001B[43m \u001B[49m\u001B[43mlabels\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43m{\u001B[49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43m0\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m:\u001B[49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43m$P_\u001B[39;49m\u001B[38;5;132;43;01m{rep}\u001B[39;49;00m\u001B[38;5;124;43m$\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m}\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 6\u001B[0m \u001B[43m \u001B[49m\u001B[43mhover_data\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43m[\u001B[49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43mcategory\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43mphenotype_long\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43mr_discovery\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m]\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcolor\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43mcategory\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m)\u001B[49m\n\u001B[1;32m 7\u001B[0m fig\u001B[38;5;241m.\u001B[39mupdate_traces(textfont_size\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m12\u001B[39m, textangle\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m0\u001B[39m, textposition\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124moutside\u001B[39m\u001B[38;5;124m\"\u001B[39m, cliponaxis\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mFalse\u001B[39;00m)\n\u001B[1;32m 8\u001B[0m fig\u001B[38;5;241m.\u001B[39mshow()\n", "File \u001B[0;32m~/src/BWAS_comment/venv/lib/python3.10/site-packages/plotly/express/_chart_types.py:373\u001B[0m, in \u001B[0;36mbar\u001B[0;34m(data_frame, x, y, color, pattern_shape, facet_row, facet_col, facet_col_wrap, facet_row_spacing, facet_col_spacing, hover_name, hover_data, custom_data, text, base, error_x, error_x_minus, error_y, error_y_minus, animation_frame, animation_group, category_orders, labels, color_discrete_sequence, color_discrete_map, color_continuous_scale, pattern_shape_sequence, pattern_shape_map, range_color, color_continuous_midpoint, opacity, orientation, barmode, log_x, log_y, range_x, range_y, text_auto, title, template, width, height)\u001B[0m\n\u001B[1;32m 325\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mbar\u001B[39m(\n\u001B[1;32m 326\u001B[0m data_frame\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m,\n\u001B[1;32m 327\u001B[0m x\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m,\n\u001B[0;32m (...)\u001B[0m\n\u001B[1;32m 367\u001B[0m height\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m,\n\u001B[1;32m 368\u001B[0m ) \u001B[38;5;241m-\u001B[39m\u001B[38;5;241m>\u001B[39m go\u001B[38;5;241m.\u001B[39mFigure:\n\u001B[1;32m 369\u001B[0m \u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[1;32m 370\u001B[0m \u001B[38;5;124;03m In a bar plot, each row of `data_frame` is represented as a rectangular\u001B[39;00m\n\u001B[1;32m 371\u001B[0m \u001B[38;5;124;03m mark.\u001B[39;00m\n\u001B[1;32m 372\u001B[0m \u001B[38;5;124;03m \"\"\"\u001B[39;00m\n\u001B[0;32m--> 373\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mmake_figure\u001B[49m\u001B[43m(\u001B[49m\n\u001B[1;32m 374\u001B[0m \u001B[43m \u001B[49m\u001B[43margs\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43mlocals\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 375\u001B[0m \u001B[43m \u001B[49m\u001B[43mconstructor\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mgo\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mBar\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 376\u001B[0m \u001B[43m \u001B[49m\u001B[43mtrace_patch\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43mdict\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43mtextposition\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mauto\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 377\u001B[0m \u001B[43m \u001B[49m\u001B[43mlayout_patch\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43mdict\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43mbarmode\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mbarmode\u001B[49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 378\u001B[0m \u001B[43m \u001B[49m\u001B[43m)\u001B[49m\n", "File \u001B[0;32m~/src/BWAS_comment/venv/lib/python3.10/site-packages/plotly/express/_core.py:1990\u001B[0m, in \u001B[0;36mmake_figure\u001B[0;34m(args, constructor, trace_patch, layout_patch)\u001B[0m\n\u001B[1;32m 1987\u001B[0m layout_patch 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\u001B[0;32m~/src/BWAS_comment/venv/lib/python3.10/site-packages/plotly/express/_core.py:1405\u001B[0m, in \u001B[0;36mbuild_dataframe\u001B[0;34m(args, constructor)\u001B[0m\n\u001B[1;32m 1402\u001B[0m args[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mcolor\u001B[39m\u001B[38;5;124m\"\u001B[39m] \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[1;32m 1403\u001B[0m \u001B[38;5;66;03m# now that things have been prepped, we do the systematic rewriting of `args`\u001B[39;00m\n\u001B[0;32m-> 1405\u001B[0m df_output, wide_id_vars \u001B[38;5;241m=\u001B[39m \u001B[43mprocess_args_into_dataframe\u001B[49m\u001B[43m(\u001B[49m\n\u001B[1;32m 1406\u001B[0m \u001B[43m \u001B[49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mwide_mode\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mvar_name\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mvalue_name\u001B[49m\n\u001B[1;32m 1407\u001B[0m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 1409\u001B[0m \u001B[38;5;66;03m# now that `df_output` exists and `args` contains only references, we complete\u001B[39;00m\n\u001B[1;32m 1410\u001B[0m \u001B[38;5;66;03m# the special-case and wide-mode handling by further rewriting args and/or mutating\u001B[39;00m\n\u001B[1;32m 1411\u001B[0m \u001B[38;5;66;03m# df_output\u001B[39;00m\n\u001B[1;32m 1413\u001B[0m count_name \u001B[38;5;241m=\u001B[39m _escape_col_name(df_output, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mcount\u001B[39m\u001B[38;5;124m\"\u001B[39m, [var_name, value_name])\n", "File \u001B[0;32m~/src/BWAS_comment/venv/lib/python3.10/site-packages/plotly/express/_core.py:1207\u001B[0m, in \u001B[0;36mprocess_args_into_dataframe\u001B[0;34m(args, wide_mode, var_name, value_name)\u001B[0m\n\u001B[1;32m 1205\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m argument \u001B[38;5;241m==\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mindex\u001B[39m\u001B[38;5;124m\"\u001B[39m:\n\u001B[1;32m 1206\u001B[0m err_msg \u001B[38;5;241m+\u001B[39m\u001B[38;5;241m=\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;130;01m\\n\u001B[39;00m\u001B[38;5;124m To use the index, pass it in directly as `df.index`.\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m-> 1207\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(err_msg)\n\u001B[1;32m 1208\u001B[0m \u001B[38;5;28;01melif\u001B[39;00m length \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;28mlen\u001B[39m(df_input[argument]) \u001B[38;5;241m!=\u001B[39m length:\n\u001B[1;32m 1209\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\n\u001B[1;32m 1210\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mAll arguments should have the same length. \u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[1;32m 1211\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mThe length of column argument `df[\u001B[39m\u001B[38;5;132;01m%s\u001B[39;00m\u001B[38;5;124m]` is \u001B[39m\u001B[38;5;132;01m%d\u001B[39;00m\u001B[38;5;124m, whereas the \u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m (...)\u001B[0m\n\u001B[1;32m 1218\u001B[0m )\n\u001B[1;32m 1219\u001B[0m )\n", "\u001B[0;31mValueError\u001B[0m: Value of 'hover_data_1' is not the name of a column in 'data_frame'. Expected one of [0, 'r_discovery', 'category', 'phenotype'] but received: phenotype_long" ] } ], "source": [ "import plotly.express as px\n", "\n", "fig = px.bar(df_plot, y=0, x='phenotype', text_auto='.2s',\n", " title=\"Replication probability with n=500\",\n", " labels={'0':'$P_{rep}$'},\n", " hover_data=['category', 'phenotype_long', 'r_discovery'], color='category')\n", "fig.update_traces(textfont_size=12, textangle=0, textposition=\"outside\", cliponaxis=False)\n", "fig.show()\n", "\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_19024/1079767499.py:9: FutureWarning:\n", "\n", "The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.\n", "\n" ] } ], "source": [ "import plotly\n", "import plotly.graph_objs as go\n", "\n", "colors = [\"#de324c\",\"#f4895f\",\"#f8e16f\",\"#95cf92\",\"#9656a2\",\"#369acc\"]\n", "\n", "cols = [ colors[c] for c in pd.Categorical(df_plot['category']).codes]\n", "\n", "df_plot['category'] = pd.Categorical(df_plot['category'])\n", "df_plot['category'].cat.reorder_categories(['Emotion', 'Sensory', 'Motor', 'Alertness', 'Personality', 'Cognition'], inplace= True)\n", "df_plot = df_plot.sort_values(by=['category', 'r_discovery'], ascending=False)\n", "\n", "trace1l = go.Scatter( name='',\n", " x=df_plot['phenotype'].values,\n", " y=[80] * len(df_plot['phenotype'].values),\n", " mode='lines',\n", " line = dict(color='gray', width=1, dash='dash'),\n", " showlegend=False,\n", " hovertemplate=\"Replication threshold\"\n", " )\n", "\n", "trace1 = go.Bar(x=df_plot['phenotype'].values,y=df_plot[0].values, name='',\n", " marker_color=cols, showlegend=False,\n", " customdata=df_plot.values,\n", " #texttemplate=\"%{y:0.0f}%\",\n", " textposition=\"outside\",\n", " textangle=0,\n", " textfont_color=\"gray\",\n", " hovertemplate=\"
\".join([\n", " \"Domain: %{customdata[2]}\",\n", " \"Effect Size: r=%{customdata[1]:.2f}\",\n", " \"Replication Probability: %{customdata[0]:.0f}%\",\n", " \"Description: %{customdata[4]}\"\n", " ])\n", " #hovertext=pd.Categorical(df_plot['category'])\n", " )\n", "\n", "trace2l = go.Scatter( name='',\n", " x=df_plot['phenotype'].values,\n", " y=[0.074] * len(df_plot['phenotype'].values),\n", " mode='lines',\n", " line = dict(color='gray', width=1, dash='dash'),\n", " showlegend=False,\n", " hovertemplate=\"Significance threshold\",\n", " visible=False\n", " )\n", "\n", "\n", "trace2 = go.Bar(x=df_plot['phenotype'].values,y=df_plot['r_discovery'], visible=False, name='',\n", " marker_color=cols, showlegend=False,\n", " customdata=df_plot.values,\n", " #texttemplate=\"%{y:0.2f}\",\n", " textposition=\"outside\",\n", " textangle=0,\n", " textfont_color=\"gray\",\n", " hovertemplate=\"
\".join([\n", " \"Domain: %{customdata[2]}\",\n", " \"Effect Size: r=%{customdata[1]:.2f}\",\n", " \"Replication Probability: %{customdata[0]:.0f}%\",\n", " \"Description: %{customdata[4]}\"\n", " ])\n", " )\n", "data = [trace1l, trace1, trace2l, trace2]\n", "\n", "updatemenus = list([\n", " dict(active=0,\n", " showactive = True,\n", " buttons=list([\n", " dict(label = \"Replication Probability\",\n", " method = \"update\",\n", " args = [{\"visible\": [True, True, False, False]}]), # hide trace2\n", " dict(label = \"Predictive Effect Size\",\n", " method = \"update\",\n", " args = [{\"visible\": [False, False, True, True]}]) # hide trace1\n", " ]),\n", " x = 0,\n", " xanchor = 'left',\n", " y = 1.5,\n", " yanchor = 'top',\n", " )])\n", "\n", "layout = dict(title=\"\",\n", " showlegend=True,\n", " xaxis=dict(title=\"Phenotype\",\n", " showgrid=False, # thin lines in the background\n", " #showticklabels=False, # numbers below),\n", " zeroline=True # thick line at x=0\n", " ),\n", " updatemenus=updatemenus,\n", " template='simple_white',\n", " font=dict(\n", " size=10,\n", " color=\"gray\"),\n", " autosize=True,\n", " height=260,\n", " margin=dict(\n", " l=5,\n", " r=5,\n", " b=10,\n", " t=10,\n", " pad=4\n", " ),\n", " )\n", "\n", "fig=dict(data=data, layout=layout)\n", "\n", "#plotly.offline.plot(fig)\n", "#go.Figure(fig).show()\n", "import plotly.io as pio\n", "pio.write_html(fig, file='docs/_includes/figure.html', auto_open=True)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": " 0 r_discovery category \\\ntarget \nCogCrystalComp_AgeAdj 100.000000 0.509356 Cognition \nPicVocab_AgeAdj 100.000000 0.474802 Cognition \nCogTotalComp_AgeAdj 100.000000 0.472652 Cognition \nReadEng_AgeAdj 100.000000 0.450867 Cognition \nCogEarlyComp_AgeAdj 100.000000 0.350107 Cognition \nVSPLOT_TC 100.000000 0.330419 Cognition \nCogFluidComp_AgeAdj 100.000000 0.304980 Cognition \nPMAT24_A_CR 100.000000 0.292243 Cognition \nListSort_AgeAdj 100.000000 0.272622 Cognition \nDDisc_AUC_200 100.000000 0.208845 Cognition \nPicSeq_AgeAdj 100.000000 0.182331 Cognition \nCardSort_AgeAdj 100.000000 0.178966 Cognition \nSCPT_SPEC 100.000000 0.148358 Cognition \nFlanker_AgeAdj 98.876404 0.132261 Cognition \nProcSpeed_AgeAdj 84.615385 0.117641 Cognition \nIWRD_TOT 36.538462 0.081018 Cognition \nSCPT_SEN 0.000000 0.004953 Cognition \nNEOFAC_O 100.000000 0.265051 Personality \nNEOFAC_A 100.000000 0.192264 Personality \nNEOFAC_C 100.000000 0.179737 Personality \nNEOFAC_E 98.936170 0.157920 Personality \nNEOFAC_N 40.000000 0.073143 Personality \nPSQI_Score 98.850575 0.142913 Alertness \nMMSE_Score 89.873418 0.112352 Alertness \nDexterity_AgeAdj 98.924731 0.169838 Motor \nNoise_Comp 97.674419 0.137544 Sensory \nTaste_AgeAdj 43.103448 0.094419 Sensory \nMars_Final 6.666667 0.081530 Sensory \nOdor_AgeAdj 0.000000 0.034223 Sensory \nPainInterf_Tscore 0.000000 0.032742 Sensory \nLifeSatisf_Unadj 100.000000 0.198733 Emotion \nAngAggr_Unadj 100.000000 0.194056 Emotion \nFearAffect_Unadj 100.000000 0.155632 Emotion \nSadness_Unadj 94.047619 0.132782 Emotion \nEmotSupp_Unadj 96.551724 0.126029 Emotion \nLoneliness_Unadj 71.666667 0.101812 Emotion \nPercStress_Unadj 62.500000 0.100211 Emotion \nSelfEff_Unadj 81.818182 0.099904 Emotion \nMeanPurp_Unadj 50.769231 0.099847 Emotion \nPosAffect_Unadj 55.223881 0.098838 Emotion \nAngAffect_Unadj 69.230769 0.096750 Emotion \nFriendship_Unadj 44.000000 0.085313 Emotion \nER40ANG 44.680851 0.072909 Emotion \nAngHostil_Unadj 41.025641 0.063366 Emotion \nFearSomat_Unadj 14.285714 0.061101 Emotion \nER40NOE 16.666667 0.050150 Emotion \nInstruSupp_Unadj 4.166667 0.041920 Emotion \nPercHostil_Unadj 0.000000 0.037622 Emotion \nPercReject_Unadj 4.347826 0.036887 Emotion \nER40FEAR 0.000000 0.033788 Emotion \nER40SAD 10.000000 0.033559 Emotion \nER40HAP NaN -0.059106 Emotion \n\n phenotype \\\ntarget \nCogCrystalComp_AgeAdj CogCrystalComp_AgeAdj \nPicVocab_AgeAdj PicVocab_AgeAdj \nCogTotalComp_AgeAdj CogTotalComp_AgeAdj \nReadEng_AgeAdj ReadEng_AgeAdj \nCogEarlyComp_AgeAdj CogEarlyComp_AgeAdj \nVSPLOT_TC VSPLOT_TC \nCogFluidComp_AgeAdj CogFluidComp_AgeAdj \nPMAT24_A_CR PMAT24_A_CR \nListSort_AgeAdj ListSort_AgeAdj \nDDisc_AUC_200 DDisc_AUC_200 \nPicSeq_AgeAdj PicSeq_AgeAdj \nCardSort_AgeAdj CardSort_AgeAdj \nSCPT_SPEC SCPT_SPEC \nFlanker_AgeAdj Flanker_AgeAdj \nProcSpeed_AgeAdj ProcSpeed_AgeAdj \nIWRD_TOT IWRD_TOT \nSCPT_SEN SCPT_SEN \nNEOFAC_O NEOFAC_O \nNEOFAC_A NEOFAC_A \nNEOFAC_C NEOFAC_C \nNEOFAC_E NEOFAC_E \nNEOFAC_N NEOFAC_N \nPSQI_Score PSQI_Score \nMMSE_Score MMSE_Score \nDexterity_AgeAdj Dexterity_AgeAdj \nNoise_Comp Noise_Comp \nTaste_AgeAdj Taste_AgeAdj \nMars_Final Mars_Final \nOdor_AgeAdj Odor_AgeAdj \nPainInterf_Tscore PainInterf_Tscore \nLifeSatisf_Unadj LifeSatisf_Unadj \nAngAggr_Unadj AngAggr_Unadj \nFearAffect_Unadj FearAffect_Unadj \nSadness_Unadj Sadness_Unadj \nEmotSupp_Unadj EmotSupp_Unadj \nLoneliness_Unadj Loneliness_Unadj \nPercStress_Unadj PercStress_Unadj \nSelfEff_Unadj SelfEff_Unadj \nMeanPurp_Unadj MeanPurp_Unadj \nPosAffect_Unadj PosAffect_Unadj \nAngAffect_Unadj AngAffect_Unadj \nFriendship_Unadj Friendship_Unadj \nER40ANG ER40ANG \nAngHostil_Unadj AngHostil_Unadj \nFearSomat_Unadj FearSomat_Unadj \nER40NOE ER40NOE \nInstruSupp_Unadj InstruSupp_Unadj \nPercHostil_Unadj PercHostil_Unadj \nPercReject_Unadj PercReject_Unadj \nER40FEAR ER40FEAR \nER40SAD ER40SAD \nER40HAP ER40HAP \n\n phenotype_long \ntarget \nCogCrystalComp_AgeAdj NIH Toolbox Cognition Crystallized Composite: ... \nPicVocab_AgeAdj NIH Toolbox Picture Vocabulary Test: Age-Adjus... \nCogTotalComp_AgeAdj NIH Toolbox Cognition Total Composite Score: A... \nReadEng_AgeAdj NIH Toolbox Oral Reading Recognition Test: Age... \nCogEarlyComp_AgeAdj NIH Toolbox Cognition Early Childhood Composit... \nVSPLOT_TC Variable Short Penn Line Orientation: Total Nu... \nCogFluidComp_AgeAdj NIH Toolbox Cognition Fluid Composite: Age Adj... \nPMAT24_A_CR Penn Progressive Matrices: Number of Correct R... \nListSort_AgeAdj NIH Toolbox List Sorting Working Memory Test: ... \nDDisc_AUC_200 Delay Discounting: Area Under the Curve for Di... \nPicSeq_AgeAdj NIH Toolbox Picture Sequence Memory Test: Age-... \nCardSort_AgeAdj NIH Toolbox Dimensional Change Card Sort Test:... \nSCPT_SPEC Short Penn Continuous Performance Test: Specif... \nFlanker_AgeAdj NIH Toolbox Flanker Inhibitory Control and Att... \nProcSpeed_AgeAdj NIH Toolbox Pattern Comparison Processing Spee... \nIWRD_TOT Penn Word Memory Test: Total Number of Correct... \nSCPT_SEN Short Penn Continuous Performance Test: Sensit... \nNEOFAC_O NEO-FFI Openness to Experience (NEOFAC_O) \nNEOFAC_A NEO-FFI Agreeableness (NEOFAC_A) \nNEOFAC_C NEO-FFI Conscientiousness (NEOFAC_C) \nNEOFAC_E NEO-FFI Extraversion (NEOFAC_E) \nNEOFAC_N NEO-FFI Neuroticism (NEOFAC_N) \nPSQI_Score Sleep (Pittsburgh Sleep Questionnaire) Total S... \nMMSE_Score Mini Mental Status Exam Total Score \nDexterity_AgeAdj NIH Toolbox 9-hole Pegboard Dexterity Test : A... \nNoise_Comp NIH Toolbox Words-In-Noise Age 6+: Computed Score \nTaste_AgeAdj NIH Toolbox Regional Taste Intensity Age 12+ A... \nMars_Final Mars Final Contrast Sensitivity Score \nOdor_AgeAdj NIH Toolbox Odor Identification Age 3+ Age-Adj... \nPainInterf_Tscore NIH Toolbox Pain Interference Survey Age 18+: ... \nLifeSatisf_Unadj NIH Toolbox General Life Satisfaction Survey: ... \nAngAggr_Unadj NIH Toolbox Anger-Physical Aggression Survey: ... \nFearAffect_Unadj NIH Toolbox Fear-Affect Survey: Unadjusted Sca... \nSadness_Unadj NIH Toolbox Sadness Survey: Unadjusted Scale S... \nEmotSupp_Unadj NIH Toolbox Emotional Support Survey: Unadjust... \nLoneliness_Unadj NIH Toolbox Loneliness Survey: Unadjusted Scal... \nPercStress_Unadj NIH Toolbox Perceived Stress Survey: Unadjuste... \nSelfEff_Unadj NIH Toolbox Self-Efficacy Survey: Unadjusted S... \nMeanPurp_Unadj NIH Toolbox Meaning and Purpose Survey: Unadju... \nPosAffect_Unadj NIH Toolbox Positive Affect Survey: Unadjusted... \nAngAffect_Unadj NIH Toolbox Anger-Affect Survey: Unadjusted Sc... \nFriendship_Unadj NIH Toolbox Friendship Survey: Unadjusted Scal... \nER40ANG Penn Emotion Recognition Test: Number of Corre... \nAngHostil_Unadj NIH Toolbox Anger-Hostility Survey: Unadjusted... \nFearSomat_Unadj NIH Toolbox Fear-Somatic Arousal Survey: Unadj... \nER40NOE Penn Emotion Recognition Test: Number of Corre... \nInstruSupp_Unadj NIH Toolbox Instrumental Support Survey: Unadj... \nPercHostil_Unadj NIH Toolbox Perceived Hostility Survey: Unadju... \nPercReject_Unadj NIH Toolbox Perceived Rejection Survey: Unadju... \nER40FEAR Penn Emotion Recognition Test: Number of Corre... \nER40SAD Penn Emotion Recognition Test: Number of Corre... \nER40HAP Penn Emotion Recognition Test: Number of Corre... ", "text/html": "
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0r_discoverycategoryphenotypephenotype_long
target
CogCrystalComp_AgeAdj100.0000000.509356CognitionCogCrystalComp_AgeAdjNIH Toolbox Cognition Crystallized Composite: ...
PicVocab_AgeAdj100.0000000.474802CognitionPicVocab_AgeAdjNIH Toolbox Picture Vocabulary Test: Age-Adjus...
CogTotalComp_AgeAdj100.0000000.472652CognitionCogTotalComp_AgeAdjNIH Toolbox Cognition Total Composite Score: A...
ReadEng_AgeAdj100.0000000.450867CognitionReadEng_AgeAdjNIH Toolbox Oral Reading Recognition Test: Age...
CogEarlyComp_AgeAdj100.0000000.350107CognitionCogEarlyComp_AgeAdjNIH Toolbox Cognition Early Childhood Composit...
VSPLOT_TC100.0000000.330419CognitionVSPLOT_TCVariable Short Penn Line Orientation: Total Nu...
CogFluidComp_AgeAdj100.0000000.304980CognitionCogFluidComp_AgeAdjNIH Toolbox Cognition Fluid Composite: Age Adj...
PMAT24_A_CR100.0000000.292243CognitionPMAT24_A_CRPenn Progressive Matrices: Number of Correct R...
ListSort_AgeAdj100.0000000.272622CognitionListSort_AgeAdjNIH Toolbox List Sorting Working Memory Test: ...
DDisc_AUC_200100.0000000.208845CognitionDDisc_AUC_200Delay Discounting: Area Under the Curve for Di...
PicSeq_AgeAdj100.0000000.182331CognitionPicSeq_AgeAdjNIH Toolbox Picture Sequence Memory Test: Age-...
CardSort_AgeAdj100.0000000.178966CognitionCardSort_AgeAdjNIH Toolbox Dimensional Change Card Sort Test:...
SCPT_SPEC100.0000000.148358CognitionSCPT_SPECShort Penn Continuous Performance Test: Specif...
Flanker_AgeAdj98.8764040.132261CognitionFlanker_AgeAdjNIH Toolbox Flanker Inhibitory Control and Att...
ProcSpeed_AgeAdj84.6153850.117641CognitionProcSpeed_AgeAdjNIH Toolbox Pattern Comparison Processing Spee...
IWRD_TOT36.5384620.081018CognitionIWRD_TOTPenn Word Memory Test: Total Number of Correct...
SCPT_SEN0.0000000.004953CognitionSCPT_SENShort Penn Continuous Performance Test: Sensit...
NEOFAC_O100.0000000.265051PersonalityNEOFAC_ONEO-FFI Openness to Experience (NEOFAC_O)
NEOFAC_A100.0000000.192264PersonalityNEOFAC_ANEO-FFI Agreeableness (NEOFAC_A)
NEOFAC_C100.0000000.179737PersonalityNEOFAC_CNEO-FFI Conscientiousness (NEOFAC_C)
NEOFAC_E98.9361700.157920PersonalityNEOFAC_ENEO-FFI Extraversion (NEOFAC_E)
NEOFAC_N40.0000000.073143PersonalityNEOFAC_NNEO-FFI Neuroticism (NEOFAC_N)
PSQI_Score98.8505750.142913AlertnessPSQI_ScoreSleep (Pittsburgh Sleep Questionnaire) Total S...
MMSE_Score89.8734180.112352AlertnessMMSE_ScoreMini Mental Status Exam Total Score
Dexterity_AgeAdj98.9247310.169838MotorDexterity_AgeAdjNIH Toolbox 9-hole Pegboard Dexterity Test : A...
Noise_Comp97.6744190.137544SensoryNoise_CompNIH Toolbox Words-In-Noise Age 6+: Computed Score
Taste_AgeAdj43.1034480.094419SensoryTaste_AgeAdjNIH Toolbox Regional Taste Intensity Age 12+ A...
Mars_Final6.6666670.081530SensoryMars_FinalMars Final Contrast Sensitivity Score
Odor_AgeAdj0.0000000.034223SensoryOdor_AgeAdjNIH Toolbox Odor Identification Age 3+ Age-Adj...
PainInterf_Tscore0.0000000.032742SensoryPainInterf_TscoreNIH Toolbox Pain Interference Survey Age 18+: ...
LifeSatisf_Unadj100.0000000.198733EmotionLifeSatisf_UnadjNIH Toolbox General Life Satisfaction Survey: ...
AngAggr_Unadj100.0000000.194056EmotionAngAggr_UnadjNIH Toolbox Anger-Physical Aggression Survey: ...
FearAffect_Unadj100.0000000.155632EmotionFearAffect_UnadjNIH Toolbox Fear-Affect Survey: Unadjusted Sca...
Sadness_Unadj94.0476190.132782EmotionSadness_UnadjNIH Toolbox Sadness Survey: Unadjusted Scale S...
EmotSupp_Unadj96.5517240.126029EmotionEmotSupp_UnadjNIH Toolbox Emotional Support Survey: Unadjust...
Loneliness_Unadj71.6666670.101812EmotionLoneliness_UnadjNIH Toolbox Loneliness Survey: Unadjusted Scal...
PercStress_Unadj62.5000000.100211EmotionPercStress_UnadjNIH Toolbox Perceived Stress Survey: Unadjuste...
SelfEff_Unadj81.8181820.099904EmotionSelfEff_UnadjNIH Toolbox Self-Efficacy Survey: Unadjusted S...
MeanPurp_Unadj50.7692310.099847EmotionMeanPurp_UnadjNIH Toolbox Meaning and Purpose Survey: Unadju...
PosAffect_Unadj55.2238810.098838EmotionPosAffect_UnadjNIH Toolbox Positive Affect Survey: Unadjusted...
AngAffect_Unadj69.2307690.096750EmotionAngAffect_UnadjNIH Toolbox Anger-Affect Survey: Unadjusted Sc...
Friendship_Unadj44.0000000.085313EmotionFriendship_UnadjNIH Toolbox Friendship Survey: Unadjusted Scal...
ER40ANG44.6808510.072909EmotionER40ANGPenn Emotion Recognition Test: Number of Corre...
AngHostil_Unadj41.0256410.063366EmotionAngHostil_UnadjNIH Toolbox Anger-Hostility Survey: Unadjusted...
FearSomat_Unadj14.2857140.061101EmotionFearSomat_UnadjNIH Toolbox Fear-Somatic Arousal Survey: Unadj...
ER40NOE16.6666670.050150EmotionER40NOEPenn Emotion Recognition Test: Number of Corre...
InstruSupp_Unadj4.1666670.041920EmotionInstruSupp_UnadjNIH Toolbox Instrumental Support Survey: Unadj...
PercHostil_Unadj0.0000000.037622EmotionPercHostil_UnadjNIH Toolbox Perceived Hostility Survey: Unadju...
PercReject_Unadj4.3478260.036887EmotionPercReject_UnadjNIH Toolbox Perceived Rejection Survey: Unadju...
ER40FEAR0.0000000.033788EmotionER40FEARPenn Emotion Recognition Test: Number of Corre...
ER40SAD10.0000000.033559EmotionER40SADPenn Emotion Recognition Test: Number of Corre...
ER40HAPNaN-0.059106EmotionER40HAPPenn Emotion Recognition Test: Number of Corre...
\n
" }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "for i in df_plot.index:\n", " df_plot.loc[i, 'phenotype_long'] = phenotypes.loc[phenotypes.columnHeader==i, 'fullDisplayName'].values[0]\n", "df_plot" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "ename": "NameError", "evalue": "name 'behavior' is not defined", "output_type": "error", "traceback": [ "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m", "\u001B[0;31mNameError\u001B[0m Traceback (most recent call last)", "Cell \u001B[0;32mIn[9], line 1\u001B[0m\n\u001B[0;32m----> 1\u001B[0m tmp \u001B[38;5;241m=\u001B[39m \u001B[43mbehavior\u001B[49m[[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mCogTotalComp_AgeAdj\u001B[39m\u001B[38;5;124m'\u001B[39m, \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mCogFluidComp_AgeAdj\u001B[39m\u001B[38;5;124m'\u001B[39m, \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mPMAT24_A_CR\u001B[39m\u001B[38;5;124m'\u001B[39m, \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mFlanker_AgeAdj\u001B[39m\u001B[38;5;124m'\u001B[39m, \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mCardSort_AgeAdj\u001B[39m\u001B[38;5;124m'\u001B[39m, \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mPicSeq_AgeAdj\u001B[39m\u001B[38;5;124m'\u001B[39m]]\n\u001B[1;32m 2\u001B[0m tmp\u001B[38;5;241m.\u001B[39mcorr()\n", "\u001B[0;31mNameError\u001B[0m: name 'behavior' is not defined" ] } ], "source": [ "tmp = behavior[['CogTotalComp_AgeAdj', 'CogFluidComp_AgeAdj', 'PMAT24_A_CR', 'Flanker_AgeAdj', 'CardSort_AgeAdj', 'PicSeq_AgeAdj']]\n", "tmp.corr()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": " \n " }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.plotly.v1+json": { "data": [ { "labels": [ "Replicable", "Not Replicable" ], "marker": { "colors": [ "green", "red" ] }, 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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import plotly.graph_objects as go\n", "from plotly.subplots import make_subplots\n", "\n", "labels = [\"Replicable\", \"Not Replicable\"]\n", "\n", "# Create subplots: use 'domain' type for Pie subplot\n", "fig = make_subplots(rows=1, cols=2, specs=[[{'type':'domain'}, {'type':'domain'}]])\n", "fig.add_trace(go.Pie(labels=labels, values=[15+4+2+1+1, 2+1+0+0+4], name=\"Trait-like\", marker_colors=['green', 'red'], rotation=-90),\n", " 1, 1)\n", "fig.add_trace(go.Pie(labels=labels, values=[6, 17], name=\"State-like\", rotation=90),\n", " 1, 2)\n", "\n", "# Use `hole` to create a donut-like pie chart\n", "fig.update_traces(hole=.5, hoverinfo=\"label+percent+name\")\n", "\n", "fig.update_layout(\n", " template='simple_white',\n", " title_text=\"\",\n", " font=dict(\n", " size=10,\n", " color=\"gray\"),\n", " autosize=True,\n", " height=70,\n", " width=500,\n", " margin=dict(\n", " l=5,\n", " r=5,\n", " b=10,\n", " t=10,\n", " pad=4),\n", " legend=dict(\n", " yanchor=\"top\",\n", " y=0.99,\n", " xanchor=\"left\",\n", " x=-0.4,\n", " font_color='black'\n", " ),\n", " # Add annotations in the center of the donut pies.\n", " annotations=[dict(text='Trait-like', x=0.0, y=1.1, font_size=12, font_color='black', showarrow=False),\n", " dict(text='State-like', x=0.72, y=1.1, font_size=12, font_color='black', showarrow=False)])\n", "fig.show()\n", "import plotly.io as pio\n", "pio.write_html(fig, file='docs/_includes/pie.html', auto_open=False)" ] }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [], "metadata": { "collapsed": false } } ], "metadata": { "hide_input": false, "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.6" } }, "nbformat": 4, "nbformat_minor": 2 }