mhjensen
198 lines
26 KiB
Plaintext
198 lines
26 KiB
Plaintext
{
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"cells": [
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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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"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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"[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
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" 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
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" 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2\n",
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" 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
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" 2 2]\n",
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"KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
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" metric_params=None, n_jobs=1, n_neighbors=1, p=2,\n",
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" weights='uniform')\n",
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"Iris 1 is a ['setosa']\n",
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"Iris 2 is a ['setosa']\n",
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"-----------------LASSO---------------------\n",
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"training set score: 0.464937\n",
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"test set score: 0.430838\n",
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"number of features used: 4\n",
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"---------------SVC AND LOGISTIC REGRESSION-----------\n"
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]
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},
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{
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"data": {
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"image/png": 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Tbq963Jol8Maqm6DU/NdkMqFNmzb1bq95fqDbgn1vMBhqld3/+c9/hvPHTRT1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\nMRkiIiKihMZkiIiIiBLa/wMlyDMtFrFmoAAAAABJRU5ErkJggg==\n",
|
|
"text/plain": [
|
|
"<matplotlib.figure.Figure at 0x1c1ef4f940>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"----------Multi-Class Uncertainty-------\n",
|
|
"(38, 3)\n",
|
|
"[[-0.50692424 -0.53347791 1.53022838]\n",
|
|
" [-0.51151707 1.50574741 -0.48234855]\n",
|
|
" [ 1.51897822 -0.53079866 -0.48146442]\n",
|
|
" [-0.50756256 -0.53347791 1.53022838]\n",
|
|
" [ 1.51897822 -0.53079866 -0.48146442]\n",
|
|
" [-0.50985911 -0.53347791 1.53022838]]\n",
|
|
"[2 1 0 2 0 2 0 1 1 1 2 1 1 1 1 0 1 1 0 0 2 1 0 0 2 0 0 1 1 0 2 1 0 2 2 1 0\n",
|
|
" 2]\n",
|
|
"[2 1 0 2 0 2 0 1 1 1 2 1 1 1 1 0 1 1 0 0 2 1 0 0 2 0 0 1 1 0 2 1 0 2 2 1 0\n",
|
|
" 2]\n",
|
|
"[[ 0.10370714 0.10098957 0.79530329]\n",
|
|
" [ 0.10474133 0.7874172 0.10784147]\n",
|
|
" [ 0.79111482 0.10186682 0.10701837]\n",
|
|
" [ 0.10364782 0.10099625 0.79535593]\n",
|
|
" [ 0.79111482 0.10186682 0.10701837]\n",
|
|
" [ 0.10343465 0.10102027 0.79554508]]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"import mglearn\n",
|
|
"import pandas as pd\n",
|
|
"import matplotlib.pyplot as plt\n",
|
|
"import numpy as np\n",
|
|
"import IPython\n",
|
|
"import sklearn\n",
|
|
"from sklearn.datasets import load_iris\n",
|
|
"iris=load_iris()\n",
|
|
"\n",
|
|
"print (iris['target'])\n",
|
|
"\n",
|
|
"from sklearn.model_selection import train_test_split\n",
|
|
"X_train, X_test, y_train, y_test=train_test_split(iris['data'], iris['target'], random_state=0)\n",
|
|
"\n",
|
|
"from sklearn.neighbors import KNeighborsClassifier\n",
|
|
"knn=KNeighborsClassifier(n_neighbors=1)\n",
|
|
"\n",
|
|
"print (knn.fit(X_train, y_train))\n",
|
|
"X_new=np.array([[4.9,2.9,1,0.2]])\n",
|
|
"X_new.shape\n",
|
|
"\n",
|
|
"X_new2=np.array([[5,3,2,0.5]])\n",
|
|
"X_new2.shape\n",
|
|
"prediction=knn.predict(X_new2)\n",
|
|
"prediction2=knn.predict(X_new)\n",
|
|
"\n",
|
|
"print (\"Iris 1 is a \" +str(iris['target_names'][prediction]))\n",
|
|
"print (\"Iris 2 is a \" +str(iris['target_names'][prediction2]))\n",
|
|
"\n",
|
|
"y_pred=knn.predict(X_test)\n",
|
|
"np.mean(y_pred==y_test)\n",
|
|
"print(\"-----------------LASSO---------------------\")\n",
|
|
"from sklearn.linear_model import Lasso\n",
|
|
"lasso = Lasso().fit(X_train, y_train)\n",
|
|
"print(\"training set score: %f\" % lasso.score(X_train, y_train))\n",
|
|
"print(\"test set score: %f\" % lasso.score(X_test, y_test))\n",
|
|
"print(\"number of features used: %d\" % np.sum(lasso.coef_ != 3))\n",
|
|
"\n",
|
|
"\n",
|
|
"print (\"---------------SVC AND LOGISTIC REGRESSION-----------\")\n",
|
|
"\n",
|
|
"from sklearn.linear_model import LogisticRegression\n",
|
|
"from sklearn.svm import LinearSVC\n",
|
|
"\n",
|
|
"X, y=mglearn.datasets.make_forge()\n",
|
|
"\n",
|
|
"fig, axes= plt.subplots(1,2,figsize=(10,3))\n",
|
|
"\n",
|
|
"for model, ax in zip([LinearSVC(), LogisticRegression()], axes):\n",
|
|
" clf = model.fit(X, y)\n",
|
|
" mglearn.plots.plot_2d_separator(clf, X, fill=False, eps=0.5, ax=ax, alpha=.7)\n",
|
|
" ax.scatter(X[:, 0], X[:, 1], c=y, s=60, cmap=mglearn.cm2)\n",
|
|
" ax.set_title(\"%s\" % clf.__class__.__name__)\n",
|
|
"plt.show()\n",
|
|
"\n",
|
|
"print (\"----------Multi-Class Uncertainty-------\")\n",
|
|
"from sklearn.ensemble import GradientBoostingClassifier\n",
|
|
"gbrt = GradientBoostingClassifier(learning_rate=0.01, random_state=0)\n",
|
|
"gbrt.fit(X_train, y_train)\n",
|
|
"GradientBoostingClassifier(init=None, learning_rate=0.01, loss='deviance',\n",
|
|
" max_depth=3, max_features=None, max_leaf_nodes=None,\n",
|
|
" min_samples_leaf=1, min_samples_split=2,\n",
|
|
" min_weight_fraction_leaf=0.0, n_estimators=100,\n",
|
|
" presort='auto', random_state=0, subsample=1.0, verbose=0,\n",
|
|
" warm_start=False)\n",
|
|
"print(gbrt.decision_function(X_test).shape)\n",
|
|
"# plot the first few entries of the decision function\n",
|
|
"print(gbrt.decision_function(X_test)[:6, :])\n",
|
|
"print(np.argmax(gbrt.decision_function(X_test), axis=1))\n",
|
|
"print(gbrt.predict(X_test))\n",
|
|
"# show the first few entries of predict_proba\n",
|
|
"print(gbrt.predict_proba(X_test)[:6])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"collapsed": true
|
|
},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"collapsed": true
|
|
},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"collapsed": true
|
|
},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"collapsed": true
|
|
},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"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",
|
|
"version": "3.6.3"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|