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Last active August 29, 2015 14:14
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iPython notebook - Cancer classifier
Raw
Ed
{
"metadata": {
"name": "",
"signature": "sha256:85272ac1663064bca34b844ba7fc160187b15dcadf58efacf0ca4b4f043e5d58"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Analyse mortalit\u00e9 cancer\n",
"\n",
"## Importation des donn\u00e9es\n",
"\n",
"Donn\u00e9es dans tableau 'recueil ASCO.xls'.\n",
"\n",
"Conversion \u00e0 l'aide de csv2mat.py\n",
"\n",
"Il y a pas mal de dossiers incomplets. On m\u00e8nera l'\u00e9tude sur plusieurs datasets de format $n \\times p$ diff\u00e9rents (rajouter une variable fait perdre les patients pour lesquels cette variable est inconnue, c'est \u00e0 dire quand $p$ augmente, $n$ diminue).\n",
"\n",
"Pour pouvoir utiliser des m\u00e9thodes lin\u00e9aires on va rajouter des variables pour remplacer les variables descriptives dont le num\u00e9ro n'a pas de valeur s\u00e9mantique. Par exemple le type de cancer est donn\u00e9 par un chiffre, mais on va transformer \u00e7a en autant de variables binaires qu'il y a de type de cancer."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import csv\n",
"import seaborn as sns\n",
"import sys\n",
"\n",
"\n",
"with open('edouard_mat.csv', 'r', encoding='utf-8') as f:\n",
" reader = csv.reader(f)\n",
" all_headers = next(reader)\n",
" remove_index = all_headers.index(' remove')\n",
" data = []\n",
" for row in reader:\n",
" if not float(row[remove_index]):\n",
" if float(row[all_headers.index(' LATA + s\u00e9jour < 24h')]):\n",
" print(\"Patient \"+row[all_headers.index('NDA')]+\" should have been excluded\")\n",
" continue\n",
" data.append([float(v) for v in row])\n",
" data = array(data)\n",
"\n",
"#Separating X and Y\n",
"Y_headers = [' dead_90d', ' dead_hospit', ' dead_rea', ' ttt_bad', ' ttt_nan', ' ttt_ok']\n",
"X_headers = set(all_headers) - set(Y_headers)\n",
"\n",
"#NaN-ing what should be NaN-ed but wasn't because csv2mat is not perfect.\n",
"def nan_empty_headers(dataset, headers, prefix, nan_header=None):\n",
" \"\"\"Set to NaN the entire vector of headers beginning whith prefix if there is a 1 in the column prefix\n",
" \n",
" This is useful when csv2nan creates a column e.g. some_header_ which will be 1 for rows where the some_header value was\n",
" empty. An empty value should in most case be transcribed as NaN. \n",
" \"\"\"\n",
" if nan_header == None:\n",
" nan_header = prefix\n",
" cols_to_nan = [headers.index(h) for h in headers if h.startswith(prefix)]\n",
" nan_col = headers.index(nan_header)\n",
" for row_index in range(0, len(dataset)):\n",
" if dataset[row_index,nan_col]:\n",
" for c in cols_to_nan:\n",
" dataset[row_index,c] = float('nan')\n",
" try:\n",
" X_headers.remove(nan_header)\n",
" except KeyError:\n",
" Y_headers.remove(nan_header)\n",
"\n",
"\n",
"nan_empty_headers(data, all_headers, ' cancer_type_')\n",
"nan_empty_headers(data, all_headers, ' motif_hospit_')\n",
"nan_empty_headers(data, all_headers, ' ttt_', nan_header=' ttt_nan')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Patient 750727271 should have been excluded\n",
"Patient 750723478 should have been excluded\n",
"Patient 750715807 should have been excluded\n",
"Patient 750712234 should have been excluded\n",
"Patient 750706856 should have been excluded\n",
"Patient 751281222 should have been excluded\n",
"Patient 751279695 should have been excluded\n",
"Patient 751277819 should have been excluded\n",
"Patient 751260872 should have been excluded\n",
"Patient 751248490 should have been excluded\n",
"Patient 751177525 should have been excluded\n",
"Patient 751210491 should have been excluded\n",
"Patient 750735028 should have been excluded\n",
"Patient 750722038 should have been excluded\n",
"Patient 750722474 should have been excluded\n",
"Patient 750719163 should have been excluded\n",
"Patient 750705643 should have been excluded\n",
"Patient 750702173 should have been excluded\n"
]
}
],
"prompt_number": 1
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"On trie les lignes et les colonnes par le nombre de donn\u00e9es manquantes qu'elle contiennent, et on graphe le tout (voir fichier missing_data.pdf, car \u00e7a appara\u00eet mal dans le notebook)."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"sorted_headers = list(range(0,len(data[0])))\n",
"sorted_headers.sort(key = lambda x : sum(isnan(data[:,x])))\n",
"sorted_samples = list(range(0,len(data)))\n",
"sorted_samples.sort(key = lambda x : sum(isnan(data[x])))\n",
"sorted_data = data[:,sorted_headers]\n",
"sorted_data = sorted_data[sorted_samples]\n",
"sns.heatmap(isnan(sorted_data))\n",
"savefig('missing_data.pdf')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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XNZt0ATABeAXVJ51aVAPNzKy8mnkCTVeZ4RTgKuBJNJt0VlENNDOz8irLTvdFZYZPo9mk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\nlP8NvAcYC1wGfA4t5r8qX+M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"text": [
"<matplotlib.figure.Figure at 0x1075267f0>"
]
}
],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def useable_data(nb_headers):\n",
" removed_headers = [all_headers[i] for i in sorted_headers[-nb_headers:]]+['NDA', # Useless\n",
" ' remove', # Useless \n",
" ' Dur\u00e9e VM (jours)', # Not known at first\n",
" ' lata', # Not known at first\n",
" ' Dur\u00e9e s\u00e9jour r\u00e9a (jours)', # Not known at first\n",
" ' Dur\u00e9e inotropes', # Not known at first\n",
" ' LATA + s\u00e9jour < 24h', # Not part of the study\n",
" ' Dur\u00e9e s\u00e9jour hospitalisation', # Not known at first\n",
" ' IGS 2', # Too much information\n",
" ' complication_thromboembo', # Too few patients\n",
" ' Dur\u00e9e VM (jours)', # Not known at first\n",
" ]\n",
" X_j_indices = [all_headers.index(h) for h in X_headers if not h in removed_headers]\n",
" X = data[:,X_j_indices]\n",
" X_i_indices = [i for i in range(0, len(data)) if not any(isnan(X[i]))]\n",
" X = X[X_i_indices]\n",
" Y_index = all_headers.index(' dead_rea')\n",
" Y = data[X_i_indices,Y_index]\n",
" print('Removing : '+str(removed_headers))\n",
" print('Headers used for X : '+str(sorted([all_headers[i] for i in X_j_indices])))\n",
" print('Number of samples : '+str(len(X_i_indices))+'/'+str(len(data)))\n",
" return X,Y,X_j_indices\n",
"X,Y,header_order = useable_data(9)\n",
"X.shape,Y.shape"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Removing : [' Lymphocytes', ' CRP', ' vni', ' Poids kg', ' Taille m', ' chimiotherapie', ' ciblee', ' IMC kg/m\u00b2', \" Albumine \u00e0 l'admission\", 'NDA', ' remove', ' Dur\u00e9e VM (jours)', ' lata', ' Dur\u00e9e s\u00e9jour r\u00e9a (jours)', ' Dur\u00e9e inotropes', ' LATA + s\u00e9jour < 24h', ' Dur\u00e9e s\u00e9jour hospitalisation', ' IGS 2', ' complication_thromboembo', ' Dur\u00e9e VM (jours)']\n",
"Headers used for X : [' AGE', ' ALAT', ' ASAT', ' Leucocytes', ' NOMBRE LIGNE CHIMIO', ' PS', ' Plaquettes', ' bilirubine (\u00b5mol/L)', ' cancer_type_1', ' cancer_type_2', ' cancer_type_3', ' cancer_type_4', ' cancer_type_5', ' cancer_type_6', ' cancer_type_7', ' cr\u00e9atinin\u00e9mie (\u00b5mol/L)', ' diabete', ' dialyse', ' glyc\u00e9mie mmol/L', ' inotropes', ' irenal_chro', ' lactates', ' meta_autre', ' meta_cerveau', ' meta_foie', ' meta_gg', ' meta_lymph', ' meta_os', ' meta_poumon', ' metastatique', ' motif_hospit_1', ' motif_hospit_2', ' motif_hospit_3', ' neutropenie', ' pavm', ' ph', ' respi_chro', ' tabac', ' ur\u00e9e', ' vm']\n",
"Number of samples : 122/157\n"
]
},
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 3,
"text": [
"((122, 40), (122,))"
]
}
],
"prompt_number": 3
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Pr\u00e9diction de la cat\u00e9gorie\n",
"### Evaluation des m\u00e9thodes de classification\n",
"On regarde le score (avec p-valeur si \u00e7a en vaut la peine), la matrice de confusion, et l'importance des variables si la m\u00e9thode le permet."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from sklearn import cross_validation\n",
"from sklearn import metrics\n",
"\n",
"def clf_eval(clf, p_value=False, confusion=False):\n",
" #Accuracy\n",
" if not p_value:\n",
" scores = cross_validation.cross_val_score(clf, X, Y, cv=10)\n",
" print(\"Accuracy: %0.2f (+/- %0.2f)\" % (scores.mean(), scores.std() * 2))\n",
" else:\n",
" score, foo, p = cross_validation.permutation_test_score(clf, X, Y, cv=10, n_permutations=100)\n",
" print(\"Accuracy: %0.2f (p-value: %0.2f)\" % (score, p))\n",
" #Relevance info\n",
" clf.fit(X,Y)\n",
" for relevance_attribute in ['feature_importances_', 'coef_']:\n",
" try:\n",
" relevance_vector = eval('clf.'+relevance_attribute)\n",
" d = {i:r for i,r in enumerate(list([abs(x) for x in relevance_vector.flatten()]))}\n",
" for i in sorted(d.keys(), key=lambda x:d[x], reverse=True):\n",
" index_all_headers = header_order[i]\n",
" print(\"{:<25} {:<7.2}\".format(all_headers[index_all_headers], d[i]))\n",
" break\n",
" except (AttributeError, ValueError):\n",
" pass\n",
" #Confusion Matrix\n",
" if not confusion:\n",
" return\n",
" loo = cross_validation.LeaveOneOut(len(Y))\n",
" Y_true = []\n",
" Y_pred = []\n",
" for train_index, test_index in loo:\n",
" X_train, X_test = X[train_index], X[test_index]\n",
" Y_train, Y_test = Y[train_index], Y[test_index]\n",
" clf.fit(X_train, Y_train)\n",
" Y_pred.append(clf.predict(X_test))\n",
" Y_true.append(Y_test)\n",
" cm = metrics.confusion_matrix(Y_true, Y_pred)\n",
" plt.figure()\n",
" sns.heatmap(cm, annot=True, fmt='d')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 53
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### LinearSVC\n",
"\n",
"En suivant le flowchart (http://scikit-learn.org/stable/tutorial/machine_learning_map/index.html) on tombe sur un SVC lin\u00e9aire.\n",
"http://scikit-learn.org/stable/modules/svm.html#classification"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from sklearn.svm import LinearSVC\n",
"clf = LinearSVC()\n",
"clf_eval(clf)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Accuracy: 0.66 (+/- 0.19)\n",
" vm 0.35 \n",
" inotropes 0.23 \n",
" pavm 0.19 \n",
" tabac 0.12 \n",
" diabete 0.11 \n",
" dialyse 0.07 \n",
" NOMBRE LIGNE CHIMIO 0.065 \n",
" meta_poumon 0.052 \n",
" meta_autre 0.052 \n",
" meta_os 0.049 \n",
" PS 0.048 \n",
" cancer_type_6 0.045 \n",
" meta_foie 0.041 \n",
" lactates 0.037 \n",
" cancer_type_2 0.034 \n",
" cancer_type_4 0.032 \n",
" neutropenie 0.032 \n",
" motif_hospit_2 0.027 \n",
" cancer_type_1 0.024 \n",
" irenal_chro 0.02 \n",
" motif_hospit_3 0.018 \n",
" ph 0.016 \n",
" meta_lymph 0.015 \n",
" glyc\u00e9mie mmol/L 0.0097 \n",
" AGE 0.0092 \n",
" motif_hospit_1 0.0084 \n",
" meta_cerveau 0.0077 \n",
" Leucocytes 0.0068 \n",
" cancer_type_3 0.0056 \n",
" respi_chro 0.005 \n",
" meta_gg 0.0034 \n",
" cancer_type_7 0.0022 \n",
" ur\u00e9e 0.0021 \n",
" metastatique 0.002 \n",
" Plaquettes 0.002 \n",
" bilirubine (\u00b5mol/L) 0.0018 \n",
" ALAT 0.0012 \n",
" ASAT 0.0011 \n",
" cancer_type_5 0.00066\n",
" cr\u00e9atinin\u00e9mie (\u00b5mol/L) 0.00035\n"
]
}
],
"prompt_number": 54
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Pour l'instant, le r\u00e9sultat est vraiment pas bon (59% de bonne r\u00e9ponses, soit \u00e0 peine mieux que le hasard).\n",
"\n",
"Du coup si on suit le flowchart, on tombe sur les K-neighboors\n",
"### K nearest neighboors\n",
"\n",
"http://scikit-learn.org/stable/modules/neighbors.html#nearest-neighbors-classification\n",
"\n",
"La recherche manuelle du param\u00e8tre $k$ peut \u00eatre p\u00e9nible, donc, vu que la quantit\u00e9 de donn\u00e9es est faible, on va se permettre de tester toutes les valeurs raisonnables de mani\u00e8re exhaustive :\n",
"\n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from sklearn import neighbors\n",
"for k in range(3,40):\n",
" clf = neighbors.KNeighborsClassifier(k)\n",
" clf_eval(clf)\n",
"clf_eval(neighbors.KNeighborsClassifier(30), confusion=True)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Accuracy: 0.63 (+/- 0.19)\n",
"Accuracy: 0.65 (+/- 0.14)\n",
"Accuracy: 0.61 (+/- 0.21)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Accuracy: 0.62 (+/- 0.16)\n",
"Accuracy: 0.64 (+/- 0.18)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Accuracy: 0.63 (+/- 0.17)\n",
"Accuracy: 0.61 (+/- 0.21)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Accuracy: 0.61 (+/- 0.20)\n",
"Accuracy: 0.62 (+/- 0.20)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Accuracy: 0.65 (+/- 0.10)\n",
"Accuracy: 0.66 (+/- 0.12)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Accuracy: 0.63 (+/- 0.13)\n",
"Accuracy: 0.64 (+/- 0.11)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Accuracy: 0.65 (+/- 0.10)\n",
"Accuracy: 0.65 (+/- 0.10)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Accuracy: 0.66 (+/- 0.12)\n",
"Accuracy: 0.66 (+/- 0.12)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Accuracy: 0.66 (+/- 0.11)\n",
"Accuracy: 0.66 (+/- 0.11)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Accuracy: 0.66 (+/- 0.09)\n",
"Accuracy: 0.66 (+/- 0.09)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Accuracy: 0.66 (+/- 0.05)\n",
"Accuracy: 0.66 (+/- 0.09)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Accuracy: 0.66 (+/- 0.01)\n",
"Accuracy: 0.66 (+/- 0.01)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Accuracy: 0.66 (+/- 0.01)\n",
"Accuracy: 0.66 (+/- 0.01)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Accuracy: 0.66 (+/- 0.01)\n",
"Accuracy: 0.66 (+/- 0.01)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Accuracy: 0.66 (+/- 0.01)\n",
"Accuracy: 0.66 (+/- 0.01)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Accuracy: 0.66 (+/- 0.01)\n",
"Accuracy: 0.66 (+/- 0.01)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Accuracy: 0.66 (+/- 0.01)\n",
"Accuracy: 0.66 (+/- 0.01)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Accuracy: 0.66 (+/- 0.01)\n",
"Accuracy: 0.66 (+/- 0.01)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Accuracy: 0.66 (+/- 0.01)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Loo accuracy : 0.663934"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n"
]
},
{
"metadata": {},
"output_type": "display_data",
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3XV5ba/3F3NfXJXnP9t6g0QHQbjRa+GPn3FBKOXTu6+OS3La9N2h0ADTbBY1udu7fpyV5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\nNEEHwKD9H6AvoIwxt6GLAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x10b502f28>"
]
}
],
"prompt_number": 58
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"La matrice de confusion permet de voir qu'avec trop de voisins, on pr\u00e9dit toujours 0 ! Pour faire marcher un $k$-NN, il faut plus de donn\u00e9es\n",
"\n",
"## SVM"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from sklearn.svm import SVC\n",
"clf = SVC()\n",
"clf_eval(clf, confusion=True)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Accuracy: 0.66 (+/- 0.01)\n",
"Loo accuracy : 0.663934"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x10b376eb8>"
]
}
],
"prompt_number": 59
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"C'est pas mieux (on pr\u00e9dit toujours 0)\n",
"\n",
"On fait une derni\u00e8re tentative avec les\n",
"## Ensemble Methods\n",
"### Bagging"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from sklearn.ensemble import BaggingClassifier\n",
"clf = BaggingClassifier(neighbors.KNeighborsClassifier(),max_samples=0.5, max_features=0.5)\n",
"clf_eval(clf, confusion=True)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Accuracy: 0.64 (+/- 0.09)\n",
"Loo accuracy : 0.614754"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x10b5a9940>"
]
}
],
"prompt_number": 60
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Bof.\n",
"\n",
"### Forests of randomized trees"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from sklearn.ensemble import RandomForestClassifier\n",
"clf = RandomForestClassifier(n_estimators=100)\n",
"clf_eval(clf, confusion=True)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Accuracy: 0.79 (+/- 0.23)\n",
" vm 0.14 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" inotropes 0.078 \n",
" ur\u00e9e 0.07 \n",
" lactates 0.06 \n",
" ph 0.052 \n",
" Plaquettes 0.051 \n",
" pavm 0.049 \n",
" glyc\u00e9mie mmol/L 0.046 \n",
" ASAT 0.046 \n",
" bilirubine (\u00b5mol/L) 0.045 \n",
" AGE 0.045 \n",
" cr\u00e9atinin\u00e9mie (\u00b5mol/L) 0.042 \n",
" ALAT 0.036 \n",
" Leucocytes 0.036 \n",
" PS 0.024 \n",
" NOMBRE LIGNE CHIMIO 0.024 \n",
" cancer_type_6 0.015 \n",
" motif_hospit_1 0.011 \n",
" meta_autre 0.0091 \n",
" dialyse 0.0089 \n",
" metastatique 0.0088 \n",
" tabac 0.0082 \n",
" cancer_type_5 0.0081 \n",
" meta_foie 0.0078 \n",
" cancer_type_2 0.0077 \n",
" meta_poumon 0.0077 \n",
" meta_gg 0.0074 \n",
" meta_os 0.0073 \n",
" motif_hospit_2 0.007 \n",
" motif_hospit_3 0.007 \n",
" diabete 0.0054 \n",
" cancer_type_1 0.0051 \n",
" meta_lymph 0.0046 \n",
" cancer_type_4 0.0028 \n",
" cancer_type_3 0.0027 \n",
" meta_cerveau 0.0023 \n",
" irenal_chro 0.002 \n",
" neutropenie 0.0018 \n",
" cancer_type_7 0.0017 \n",
" respi_chro 0.0015 \n",
"Loo accuracy : 0.827869"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x10b37f048>"
]
}
],
"prompt_number": 61
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"C'est 'achement mieux. On tourne \u00e0 plus de 70%, la variance est pas excellente, c'est sensible au param\u00e8tre n_estimators. A creuser."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from sklearn.ensemble import ExtraTreesClassifier\n",
"clf = ExtraTreesClassifier(n_estimators=100, max_depth=None, min_samples_split=1, random_state=0)\n",
"clf_eval(clf, confusion=True)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Accuracy: 0.84 (+/- 0.17)\n",
" vm 0.22 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" inotropes 0.089 \n",
" pavm 0.065 \n",
" AGE 0.035 \n",
" glyc\u00e9mie mmol/L 0.034 \n",
" lactates 0.033 \n",
" bilirubine (\u00b5mol/L) 0.029 \n",
" ur\u00e9e 0.029 \n",
" cr\u00e9atinin\u00e9mie (\u00b5mol/L) 0.027 \n",
" metastatique 0.025 \n",
" Plaquettes 0.024 \n",
" ph 0.023 \n",
" dialyse 0.023 \n",
" PS 0.022 \n",
" tabac 0.021 \n",
" ALAT 0.021 \n",
" NOMBRE LIGNE CHIMIO 0.021 \n",
" ASAT 0.02 \n",
" Leucocytes 0.02 \n",
" cancer_type_5 0.018 \n",
" cancer_type_6 0.018 \n",
" meta_autre 0.015 \n",
" meta_foie 0.014 \n",
" motif_hospit_1 0.014 \n",
" meta_gg 0.013 \n",
" meta_os 0.012 \n",
" motif_hospit_3 0.012 \n",
" meta_poumon 0.011 \n",
" cancer_type_1 0.011 \n",
" meta_lymph 0.011 \n",
" diabete 0.01 \n",
" motif_hospit_2 0.0097 \n",
" irenal_chro 0.0095 \n",
" cancer_type_2 0.0091 \n",
" neutropenie 0.0082 \n",
" cancer_type_4 0.0066 \n",
" cancer_type_3 0.0059 \n",
" respi_chro 0.0046 \n",
" meta_cerveau 0.0045 \n",
" cancer_type_7 0.0041 \n",
"Loo accuracy : 0.852459"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x10b5a95f8>"
]
}
],
"prompt_number": 62
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"OK, m\u00eame type de r\u00e9sultats. Egalement sensible au param\u00e8tres"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"L'ordre des param\u00e8tres est pas compl\u00e8tement incoh\u00e9rent avec celui trouv\u00e9 par le classifieur lin\u00e9aire. C'est bon signe..\n",
"\n",
"Maintenant on essaye \n",
"## AdaBoost"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from sklearn.ensemble import AdaBoostClassifier\n",
"clf = AdaBoostClassifier(n_estimators=100)\n",
"clf_eval(clf, confusion=True, p_value=True)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Accuracy: 0.80 (p-value: 0.01)\n",
" vm 0.11 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" Plaquettes 0.11 \n",
" cr\u00e9atinin\u00e9mie (\u00b5mol/L) 0.1 \n",
" ASAT 0.07 \n",
" ur\u00e9e 0.06 \n",
" bilirubine (\u00b5mol/L) 0.06 \n",
" lactates 0.06 \n",
" metastatique 0.05 \n",
" pavm 0.05 \n",
" glyc\u00e9mie mmol/L 0.04 \n",
" Leucocytes 0.04 \n",
" PS 0.04 \n",
" cancer_type_6 0.04 \n",
" ph 0.03 \n",
" NOMBRE LIGNE CHIMIO 0.03 \n",
" ALAT 0.03 \n",
" cancer_type_4 0.02 \n",
" inotropes 0.02 \n",
" AGE 0.01 \n",
" meta_lymph 0.01 \n",
" cancer_type_5 0.01 \n",
" meta_poumon 0.01 \n",
" cancer_type_7 0.0 \n",
" meta_autre 0.0 \n",
" meta_cerveau 0.0 \n",
" neutropenie 0.0 \n",
" respi_chro 0.0 \n",
" cancer_type_3 0.0 \n",
" meta_os 0.0 \n",
" meta_foie 0.0 \n",
" cancer_type_1 0.0 \n",
" meta_gg 0.0 \n",
" tabac 0.0 \n",
" diabete 0.0 \n",
" motif_hospit_2 0.0 \n",
" irenal_chro 0.0 \n",
" cancer_type_2 0.0 \n",
" motif_hospit_3 0.0 \n",
" dialyse 0.0 \n",
" motif_hospit_1 0.0 \n",
"Loo accuracy : 0.754098"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x10b73d438>"
]
}
],
"prompt_number": 63
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Performances similaires, variance am\u00e9lior\u00e9e.\n",
"\n",
"Histoire d'\u00eatre complet, on termine avec\n",
"\n",
"## Gradient Tree Boosting"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from sklearn.ensemble import GradientBoostingClassifier\n",
"clf = GradientBoostingClassifier(n_estimators=150, learning_rate=1.0, max_depth=1, random_state=0)\n",
"clf_eval(clf)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Accuracy: 0.78 (+/- 0.20)\n",
" bilirubine (\u00b5mol/L) 0.12 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" AGE 0.08 \n",
" cr\u00e9atinin\u00e9mie (\u00b5mol/L) 0.08 \n",
" Plaquettes 0.08 \n",
" glyc\u00e9mie mmol/L 0.06 \n",
" ur\u00e9e 0.06 \n",
" ASAT 0.053 \n",
" vm 0.053 \n",
" pavm 0.053 \n",
" ph 0.047 \n",
" lactates 0.047 \n",
" PS 0.04 \n",
" ALAT 0.04 \n",
" Leucocytes 0.033 \n",
" meta_lymph 0.027 \n",
" inotropes 0.027 \n",
" NOMBRE LIGNE CHIMIO 0.027 \n",
" metastatique 0.02 \n",
" cancer_type_5 0.02 \n",
" cancer_type_6 0.013 \n",
" cancer_type_7 0.0067 \n",
" cancer_type_4 0.0067 \n",
" motif_hospit_3 0.0067 \n",
" meta_autre 0.0 \n",
" meta_cerveau 0.0 \n",
" neutropenie 0.0 \n",
" respi_chro 0.0 \n",
" cancer_type_3 0.0 \n",
" meta_os 0.0 \n",
" meta_foie 0.0 \n",
" cancer_type_1 0.0 \n",
" meta_gg 0.0 \n",
" tabac 0.0 \n",
" diabete 0.0 \n",
" motif_hospit_2 0.0 \n",
" irenal_chro 0.0 \n",
" meta_poumon 0.0 \n",
" cancer_type_2 0.0 \n",
" dialyse 0.0 \n",
" motif_hospit_1 0.0 \n"
]
}
],
"prompt_number": 19
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"L'importance des features est parfois en l\u00e9ger d\u00e9saccord avec le reste. A creuser."
]
}
],
"metadata": {}
}
]
}
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