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jGaboardijGaboardi
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clean and rerun notebooks
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3 files changed

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‎notebooks/Binomial_GLM.ipynb

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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-10-14T01:22:52.996793Z",
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}
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},
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"outputs": [],
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"source": [
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"from spglm.glm import GLM\n",
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"from spglm.family import Binomial\n",
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"import libpysal.api as ps\n",
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"import numpy as np"
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"import numpy"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-10-14T01:22:54.296350Z",
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}
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},
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"outputs": [],
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"source": [
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"# Load sample dataset - Subset of london house price dataset\n",
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"db = ps.open(ps.get_path(\"columbus.dbf\"), \"r\")\n",
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"#db = ps.open(ps.get_path(\"columbus.dbf\"), \"r\")\n",
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"\n",
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"#Set dependent variable\n",
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"y = np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,\n",
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"y = numpy.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,\n",
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" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0,\n",
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" 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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" 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
@@ -39,7 +54,7 @@
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"y = y.reshape((316,1))\n",
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"\n",
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"#Set independent variable (FLOORSZ)\n",
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"X = np.array([ 77, 75, 64, 95, 107, 100, 81, 151, 98, 260, 171, 161, 91,\n",
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"X = numpy.array([ 77, 75, 64, 95, 107, 100, 81, 151, 98, 260, 171, 161, 91,\n",
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" 80, 50, 85, 52, 69, 60, 84, 155, 97, 69, 126, 90, 43,\n",
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" 51, 41, 140, 80, 52, 86, 66, 60, 40, 155, 138, 97, 115,\n",
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" 148, 206, 60, 53, 96, 88, 160, 31, 43, 154, 60, 131, 60,\n",
@@ -70,15 +85,22 @@
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-10-14T01:22:54.317859Z",
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"iopub.status.busy": "2023-10-14T01:22:54.317442Z",
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}
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},
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"outputs": [],
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"source": [
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"# Estimate Binomial GLM\n",
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"\n",
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"# First instantiate a GLM model object\n",
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"model = GLM(\n",
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" y, X, family=Binomial()\n",
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") # Set family to Binomial family object for Binomial GLM\n",
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"# -- Set family to Binomial family object for Binomial GLM\n",
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"model = GLM(y, X, family=Binomial()) \n",
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"\n",
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"# Then use the fit method to estimate coefficients and compute diagnostics\n",
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"results = model.fit()"
@@ -87,7 +109,15 @@
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"execution": {
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}
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},
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"outputs": [
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{
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"name": "stdout",
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"metadata": {
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"execution": {
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}
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"outputs": [
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{
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"cell_type": "code",
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"execution_count": 6,
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"execution": {
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},
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"outputs": [
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{
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"name": "stdout",
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"metadata": {
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}
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"155.19347530342466\n"
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"155.1934753034247\n"
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]
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}
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],
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"metadata": {
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"anaconda-cloud": {},
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"kernelspec": {
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"display_name": "Python [Root]",
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"display_name": "Python [conda env:py311_spglm]",
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"language": "python",
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"name": "Python [Root]"
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"name": "conda-env-py311_spglm-py"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 2
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"version": 3
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython2",
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"version": "2.7.12"
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"pygments_lexer": "ipython3",
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"version": "3.11.6"
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}
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},
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"nbformat": 4,

‎notebooks/Gaussian_GLM.ipynb

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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-10-14T01:23:49.094651Z",
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"iopub.status.busy": "2023-10-14T01:23:49.094291Z",
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"iopub.status.idle": "2023-10-14T01:23:50.283296Z",
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"shell.execute_reply": "2023-10-14T01:23:50.282506Z",
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"shell.execute_reply.started": "2023-10-14T01:23:49.094628Z"
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}
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},
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"outputs": [],
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"source": [
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"from spglm.glm import GLM\n",
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"import libpysal.api as ps\n",
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"import numpy as np"
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"import libpysal\n",
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"import numpy"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-10-14T01:23:50.947534Z",
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"iopub.status.busy": "2023-10-14T01:23:50.946850Z",
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"iopub.status.idle": "2023-10-14T01:23:50.956114Z",
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"shell.execute_reply": "2023-10-14T01:23:50.954841Z",
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"shell.execute_reply.started": "2023-10-14T01:23:50.947502Z"
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}
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},
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"outputs": [],
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"source": [
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"# Load sample dataset - columbus dataset\n",
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"db = ps.open(ps.get_path(\"columbus.dbf\"), \"r\")\n",
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"db = libpysal.io.open(libpysal.examples.get_path(\"columbus.dbf\"))\n",
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"\n",
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"# Set dependent variable\n",
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"y = np.array(db.by_col(\"HOVAL\"))\n",
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"y = np.reshape(y, (49, 1))\n",
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"y = numpy.array(db.by_col(\"HOVAL\"))\n",
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"y = numpy.reshape(y, (49, 1))\n",
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"\n",
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"# Set indepdent varibLES\n",
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"X = []\n",
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"X.append(db.by_col(\"INC\"))\n",
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"X.append(db.by_col(\"CRIME\"))\n",
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"X = np.array(X).T"
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"X = numpy.array(X).T"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-10-14T01:23:52.355767Z",
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"iopub.status.busy": "2023-10-14T01:23:52.355010Z",
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"iopub.status.idle": "2023-10-14T01:23:52.367917Z",
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"shell.execute_reply": "2023-10-14T01:23:52.366305Z",
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"shell.execute_reply.started": "2023-10-14T01:23:52.355719Z"
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}
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},
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"outputs": [],
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"source": [
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"# Estimate Gaussian GLM\n",
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"\n",
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"# First instantiate a GLM model object\n",
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"model = GLM(\n",
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" y, X\n",
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") # Gaussian is the default family parameter so it doesn't need to be set\n",
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"# -- Gaussian is the default family parameter so it doesn't need to be set\n",
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"model = GLM(y, X) \n",
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"\n",
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"# Then use the fit method to estimate coefficients and compute diagnostics\n",
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"results = model.fit()"
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{
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"cell_type": "code",
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"execution_count": 4,
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"execution": {
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"iopub.execute_input": "2023-10-14T01:23:53.261041Z",
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"shell.execute_reply.started": "2023-10-14T01:23:53.261004Z"
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{
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"execution": {
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{
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"metadata": {
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"kernelspec": {
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"display_name": "Python [Root]",
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"display_name": "Python [conda env:py311_spglm]",
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"language": "python",
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"name": "Python [Root]"
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"name": "conda-env-py311_spglm-py"
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"pygments_lexer": "ipython2",
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"version": "2.7.12"
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"pygments_lexer": "ipython3",
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"version": "3.11.6"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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