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MultiLineFitterTest

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frankknoll
2023-11-17 18:16:09 +01:00
parent c8849301fe
commit 2d43c31f95
3 changed files with 251 additions and 9 deletions
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src/HowBadIsMyBatch.ipynb
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@@ -651,6 +651,214 @@
" htmlFile = os.path.normpath(webAppBaseDir + '/index.html'))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# https://scikit-learn.org/stable/auto_examples/linear_model/plot_ransac.html\n",
"import numpy as np\n",
"from matplotlib import pyplot as plt\n",
"from sklearn import linear_model\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"symptomX = 'Immunosuppression'\n",
"symptomY = 'Infection' # 'Immunoglobulin therapy'"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df = prrByLotAndSymptom[[symptomX, symptomY]]\n",
"df = df[(df[symptomX] != 0) & (df[symptomY] != 0)]\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"points = [(x, y) for [x, y] in df.values]\n",
"points"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from SymptomsCausedByVaccines.MultiLineFitting.MultiLineFitter import MultiLineFitter\n",
"\n",
"clusters, lines = MultiLineFitter.fitPointsByLines(points, consensusThreshold = 0.001)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"clusters"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"from skspatial.objects import Line\n",
"\n",
"_, ax = plt.subplots()\n",
"for line in lines:\n",
" line.plot_2d(ax, label = \"line\")\n",
"for cluster in clusters:\n",
" plt.scatter(\n",
" [x for (x, _) in cluster],\n",
" [y for (_, y) in cluster],\n",
" marker = \".\",\n",
" s = 100,\n",
" label = \"Cluster\")\n",
"# plt.scatter(\n",
"# [x for (x, _) in points],\n",
"# [y for (_, y) in points],\n",
"# color = \"blue\",\n",
"# marker = \".\",\n",
"# s = 100,\n",
"# label = \"Dots\")\n",
"plt.xlabel(symptomX)\n",
"plt.ylabel(symptomY)\n",
"plt.legend(loc=\"lower right\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Fit line using all data\n",
"lr = linear_model.LinearRegression()\n",
"lr.fit(X, y)\n",
"\n",
"# Robustly fit linear model with RANSAC algorithm\n",
"ransac = linear_model.RANSACRegressor(random_state = 0)\n",
"ransac.fit(X, y)\n",
"inlier_mask = ransac.inlier_mask_\n",
"outlier_mask = np.logical_not(inlier_mask)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"X2 = X[outlier_mask]\n",
"y2 = y[outlier_mask]\n",
"ransac2 = linear_model.RANSACRegressor(random_state = 0)\n",
"ransac2.fit(X2, y2)\n",
"inlier_mask2 = ransac2.inlier_mask_\n",
"outlier_mask2 = np.logical_not(inlier_mask2)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"X3 = X2[outlier_mask2]\n",
"y3 = y2[outlier_mask2]\n",
"ransac3 = linear_model.RANSACRegressor(random_state = 0)\n",
"ransac3.fit(X3, y3)\n",
"inlier_mask3 = ransac3.inlier_mask_\n",
"outlier_mask3 = np.logical_not(inlier_mask3)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"X_ransac_list = [(X, ransac), (X2, ransac2), (X3, ransac3)]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from matplotlib.pyplot import figure\n",
"figure(figsize=(8, 6), dpi=80)\n",
"\n",
"def plotRANSACResult(X, y, X_ransac_list):\n",
" figure(figsize=(8, 6), dpi=80)\n",
" plt.scatter(X, y, color=\"yellowgreen\", marker=\".\", label=\"Dots\")\n",
" for (X, ransac) in X_ransac_list:\n",
" line_X = np.arange(X.min(), X.max())[:, np.newaxis]\n",
" line_y_ransac = ransac.predict(line_X)\n",
" plt.plot(\n",
" line_X,\n",
" line_y_ransac,\n",
" color = \"cornflowerblue\",\n",
" linewidth = 2,\n",
" label = \"RANSAC regressor\")\n",
" plt.legend(loc=\"lower right\")\n",
" plt.xlabel(symptomX)\n",
" plt.ylabel(symptomY)\n",
" plt.show()\n",
"\n",
"# Predict data of estimated models\n",
"line_X = np.arange(X.min(), X.max())[:, np.newaxis]\n",
"line_y = lr.predict(line_X)\n",
"line_y_ransac = ransac.predict(line_X)\n",
"\n",
"# Compare estimated coefficients\n",
"print(\"Estimated coefficients (true, linear regression, RANSAC):\")\n",
"print(lr.coef_, ransac.estimator_.coef_)\n",
"\n",
"lw = 2\n",
"plt.scatter(X, y, color=\"blue\", marker=\".\", label=\"Inliers\")\n",
"#plt.plot(line_X, line_y, color=\"navy\", linewidth=lw, label=\"Linear regressor\")\n",
"#plt.plot(\n",
"# line_X,\n",
"# line_y_ransac,\n",
"# color=\"cornflowerblue\",\n",
"# linewidth=lw,\n",
"# label=\"RANSAC regressor\")\n",
"plt.legend(loc=\"lower right\")\n",
"plt.xlabel(symptomX)\n",
"plt.ylabel(symptomY)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plotRANSACResult(X, y, X_ransac_list)"
]
},
{
"cell_type": "markdown",
"metadata": {},
@@ -659,9 +867,9 @@
],
"metadata": {
"kernelspec": {
"display_name": "howbadismybatch-venv",
"display_name": "howbadismybatch-venv-kernel",
"language": "python",
"name": "python3"
"name": "howbadismybatch-venv-kernel"
},
"language_info": {
"codemirror_mode": {