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Classification and Scores

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Florent Guiotte 2018-07-09 18:02:40 +02:00
parent 4c04b1227f
commit c90ee468b2
3 changed files with 374 additions and 4 deletions
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78
Notebooks/Attribute Profiles Classifier.ipynb
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@ -17,7 +17,9 @@
"source": [
"## Setup\n",
"\n",
"### Packages"
"### Packages\n",
"\n",
"#### Attributes Profile"
]
},
{
@ -38,6 +40,26 @@
"import triskele"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Classifier"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from sklearn import metrics\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"import pandas as pd\n",
"import pickle\n",
"from CrossValidationGenerator import APsCVG"
]
},
{
"cell_type": "markdown",
"metadata": {},
@ -148,7 +170,7 @@
"outputs": [],
"source": [
"areas = [10., 100.]\n",
"areas.extend([x * 1e3 for x in range(1,100,2)])\n",
"areas.extend([x * 1e3 for x in range(1,100,1)])\n",
"plt.plot(areas, '.')\n",
"plt.show()"
]
@ -206,7 +228,8 @@
"metadata": {},
"outputs": [],
"source": [
"out_vectors.data.shape"
"att = out_vectors.data\n",
"att.shape, att.dtype"
]
},
{
@ -229,6 +252,55 @@
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Cross Valid"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"prediction = np.zeros_like(gt)\n",
"\n",
"for xt, xv, yt, yv, ti in APsCVG(gt, att, 5):\n",
" plt.imshow(ti * 1.)\n",
" plt.show()\n",
" \n",
" rfc = RandomForestClassifier(n_jobs=-1, random_state=0, n_estimators=100, verbose=True)\n",
" rfc.fit(xt, yt)\n",
" \n",
" ypred = rfc.predict(xv)\n",
" \n",
" prediction[ti] = ypred"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plt.figure(figsize=figsize)\n",
"plt.imshow(prediction)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plt.imsave('../Res/tmppred.png', prediction)\n",
"plt.imsave('../Res/gt.png', gt)\n",
"triskele.write('../Res/tmppred_8.tif', prediction)"
]
},
{
"cell_type": "code",
"execution_count": null,

298
Notebooks/Classification Scores.ipynb Normal file
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@ -0,0 +1,298 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Generic Classification Scores for DFC 2018"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"from sklearn import metrics\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"\n",
"# Triskele\n",
"import sys\n",
"from pathlib import Path\n",
"triskele_path = Path('../triskele/python')\n",
"sys.path.append(str(triskele_path.resolve()))\n",
"import triskele\n",
"\n",
"figsize = np.array((16, 9))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load Classes Metadata"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df_dfc_lbl = pd.read_csv('../labels.csv')\n",
"df_meta_idx = pd.read_csv('../metaclass_indexes.csv')\n",
"df_meta_lbl = pd.read_csv('../metaclass_labels.csv')\n",
"\n",
"df_dfc_lbl.merge(df_meta_idx).merge(df_meta_lbl)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"meta_idx = np.array(df_meta_idx['metaclass_index'], dtype=np.uint8)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load Ground Truth and Prediction"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"gt = triskele.read('../Data/ground_truth/2018_IEEE_GRSS_DFC_GT_TR.tif')\n",
"pred = triskele.read('../Res/tmppred.tif')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Display Classes"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fig, (ax_gt, ax_pred) = plt.subplots(2, figsize=figsize * 2)\n",
"ax_gt.imshow(gt)\n",
"ax_gt.set_title('Ground Truth')\n",
"ax_pred.imshow(pred)\n",
"ax_pred.set_title('Prediction')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Display Meta Classes"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fig, (ax_gt, ax_pred) = plt.subplots(2, figsize=figsize * 2)\n",
"ax_gt.imshow(meta_idx[gt])\n",
"ax_gt.set_title('Ground Truth')\n",
"ax_pred.imshow(meta_idx[pred])\n",
"ax_pred.set_title('Prediction')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Metrics\n",
"\n",
"### Classes\n",
"\n",
"#### Confusion"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"f = np.nonzero(pred)\n",
"pred_s = pred[f].flatten()\n",
"gt_s = gt[f].flatten()\n",
"\n",
"ct = pd.crosstab(gt_s, pred_s,\n",
" rownames=['Prediction'], colnames=['Reference'],\n",
" margins=True, margins_name='Total',\n",
" normalize=False # all, index, columns\n",
" )\n",
"ct"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Scores\n",
"\n",
"##### Accuracy"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"metrics.accuracy_score(gt_s, pred_s)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Kappa"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"metrics.cohen_kappa_score(gt_s, pred_s)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Precision, Recall, f1"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"metrics.precision_recall_fscore_support(gt_s, pred_s)\n",
"print(metrics.classification_report(gt_s, pred_s))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Meta Classes\n",
"\n",
"#### Confusion"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"f = np.nonzero(pred)\n",
"m_pred_s = meta_idx[pred_s]\n",
"m_gt_s = meta_idx[gt_s]\n",
"\n",
"ct = pd.crosstab(m_gt_s, m_pred_s,\n",
" rownames=['Prediction'], colnames=['Reference'],\n",
" margins=True, margins_name='Total',\n",
" normalize=False # all, index, columns\n",
" )\n",
"ct"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Scores\n",
"\n",
"##### Accuracy"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"metrics.accuracy_score(m_gt_s, m_pred_s)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Kappa"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"metrics.cohen_kappa_score(m_gt_s, m_pred_s)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Precision, Recall, f1"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"metrics.precision_recall_fscore_support(m_gt_s, m_pred_s)\n",
"print(metrics.classification_report(m_gt_s, m_pred_s))"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

2
Notebooks/Cross Validation Generator.ipynb
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@ -103,7 +103,7 @@
"metadata": {},
"outputs": [],
"source": [
"for xt, xv, yt, yv, ti in APsCVG(gt, att, 2):\n",
"for xt, xv, yt, yv, ti in APsCVG(gt, att, 1):\n",
" print(xt.shape, yt.shape, xv.shape, yv.shape)\n",
" plt.imshow(ti * 1.)\n",
" plt.show()"