Move to sklearn 0.18
Aurélien Geron
parent
9e414b6d64
commit
a40b278df5
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@ -264,7 +264,7 @@
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},
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"outputs": [],
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"source": [
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"from sklearn.cross_validation import cross_val_score\n",
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"from sklearn.model_selection import cross_val_score\n",
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"cross_val_score(sgd_clf, X_train, y_train_5, cv=3, scoring=\"accuracy\")"
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]
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},
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@ -276,18 +276,18 @@
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},
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"outputs": [],
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"source": [
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"from sklearn.cross_validation import StratifiedKFold\n",
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"from sklearn.model_selection import StratifiedKFold\n",
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"from sklearn.base import clone\n",
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"\n",
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"skfolds = StratifiedKFold(y_train_5, n_folds=3, random_state=42)\n",
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"skfolds = StratifiedKFold(n_splits=3, random_state=42)\n",
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"\n",
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"for train_index, test_index in skfolds:\n",
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"for train_index, test_index in skfolds.split(X_train, y_train_5):\n",
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" clone_clf = clone(sgd_clf)\n",
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" X_train_folds = X_train[train_index]\n",
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" y_train_folds = (y_train_5[train_index])\n",
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" X_test_fold = X_train[test_index]\n",
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" y_test_fold = (y_train_5[test_index])\n",
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" \n",
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"\n",
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" clone_clf.fit(X_train_folds, y_train_folds)\n",
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" y_pred = clone_clf.predict(X_test_fold)\n",
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" n_correct = sum(y_pred == y_test_fold)\n",
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@ -330,7 +330,7 @@
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},
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"outputs": [],
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"source": [
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"from sklearn.cross_validation import cross_val_predict\n",
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"from sklearn.model_selection import cross_val_predict\n",
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"\n",
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"y_train_pred = cross_val_predict(sgd_clf, X_train, y_train_5, cv=3)"
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]
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@ -459,32 +459,11 @@
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"cell_type": "code",
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"execution_count": 30,
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"metadata": {
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"collapsed": false
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"# Implemented in https://github.com/scikit-learn/scikit-learn/pull/6671\n",
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"# Pushed to master but not yet in pip module.\n",
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"from sklearn.cross_validation import StratifiedKFold\n",
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"from sklearn.base import clone\n",
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"\n",
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"def cross_val_predict_future(clf, X, y, cv, method=None):\n",
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" clf_clone = clone(clf) # keep original intact\n",
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" if method is None:\n",
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" return cross_val_predict(clf, X, y, cv=cv)\n",
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" else:\n",
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" method_f = getattr(clf_clone, method)\n",
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" scores = []\n",
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" skfolds = StratifiedKFold(y, n_folds=cv)\n",
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" for train_indices, test_indices in skfolds:\n",
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" clf_clone.fit(X[train_indices], y[train_indices])\n",
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" scores.append((method_f(X[test_indices]), test_indices))\n",
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" res_shape = list(scores[0][0].shape)\n",
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" res_shape[0] = len(X)\n",
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" res = np.empty(tuple(res_shape))\n",
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" for sc, test_indices in scores:\n",
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" res[test_indices] = sc\n",
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" return res"
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"y_scores = cross_val_predict(sgd_clf, X_train, y_train_5, cv=3, method=\"decision_function\")"
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]
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},
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{
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@ -494,17 +473,6 @@
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"y_scores = cross_val_predict_future(sgd_clf, X_train, y_train_5, cv=3, method=\"decision_function\")"
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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": 32,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"from sklearn.metrics import precision_recall_curve\n",
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"\n",
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@ -513,7 +481,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 33,
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"execution_count": 32,
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"metadata": {
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"collapsed": false
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@ -535,7 +503,7 @@
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{
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"cell_type": "code",
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"execution_count": 34,
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"execution_count": 33,
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"metadata": {
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"collapsed": false
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"cell_type": "code",
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"execution_count": 35,
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{
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"cell_type": "code",
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"execution_count": 36,
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"execution_count": 35,
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@ -568,7 +536,7 @@
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{
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"cell_type": "code",
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"execution_count": 37,
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"execution_count": 36,
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"metadata": {
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"collapsed": false
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@ -579,7 +547,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 38,
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"execution_count": 37,
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"metadata": {
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"collapsed": false
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@ -606,7 +574,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 39,
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"execution_count": 38,
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"metadata": {
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"collapsed": false
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},
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@ -619,7 +587,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 40,
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"execution_count": 39,
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"metadata": {
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"collapsed": false
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@ -640,7 +608,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 41,
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"execution_count": 40,
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"metadata": {
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"collapsed": false
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},
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{
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"cell_type": "code",
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"execution_count": 42,
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"execution_count": 41,
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"metadata": {
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"collapsed": false
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},
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@ -661,7 +629,7 @@
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"source": [
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"from sklearn.ensemble import RandomForestClassifier\n",
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"forest_clf = RandomForestClassifier(random_state=42)\n",
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"y_probas_forest = cross_val_predict_future(forest_clf, X_train, y_train_5, cv=3, method=\"predict_proba\")\n",
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"y_probas_forest = cross_val_predict(forest_clf, X_train, y_train_5, cv=3, method=\"predict_proba\")\n",
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"y_scores_forest = y_probas_forest[:, 1] # score = proba of positive class\n",
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"fpr_forest, tpr_forest, thresholds_forest = roc_curve(y_train_5, y_scores_forest)\n",
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"\n",
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},
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"metadata": {
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"source": [
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"from sklearn.dummy import DummyClassifier\n",
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"dmy_clf = DummyClassifier()\n",
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"y_probas_dmy = cross_val_predict_future(dmy_clf, X_train, y_train_5, cv=3, method=\"predict_proba\")\n",
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"y_probas_dmy = cross_val_predict(dmy_clf, X_train, y_train_5, cv=3, method=\"predict_proba\")\n",
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"y_scores_dmy = y_probas_dmy[:, 1]"
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]
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{
|
||||
"cell_type": "code",
|
||||
"execution_count": 76,
|
||||
"execution_count": 75,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
|
|
@ -1173,7 +1141,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 77,
|
||||
"execution_count": 76,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
|
|
@ -1184,7 +1152,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 78,
|
||||
"execution_count": 77,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
|
|
@ -1196,7 +1164,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 79,
|
||||
"execution_count": 78,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
|
|
@ -1247,7 +1215,7 @@
|
|||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.5.1"
|
||||
"version": "3.5.2"
|
||||
},
|
||||
"nav_menu": {},
|
||||
"toc": {
|
||||
|
|
|
|||
Loading…
Reference in New Issue