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logistic-regressors/logistic-regressor.ipynb

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2 years ago
{
"cells": [
{
"cell_type": "code",
softmax regressor
2 years ago
"execution_count": 2,
Loading data
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"id": "03f8827e-7e96-494f-8f87-4eb8a2a03ff9",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['data',\n",
" 'target',\n",
" 'frame',\n",
" 'target_names',\n",
" 'DESCR',\n",
" 'feature_names',\n",
" 'filename',\n",
" 'data_module']"
]
},
softmax regressor
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"execution_count": 2,
Loading data
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn import datasets\n",
"iris = datasets.load_iris()\n",
"list(iris.keys())"
]
},
{
"cell_type": "code",
softmax regressor
2 years ago
"execution_count": 3,
Loading data
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"id": "df29211b-b8ae-4d64-a903-de8ba22daa5f",
"metadata": {},
"outputs": [
{
"data": {
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" [7.3, 2.9, 6.3, 1.8],\n",
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" [6.3, 2.7, 4.9, 1.8],\n",
" [6.7, 3.3, 5.7, 2.1],\n",
" [7.2, 3.2, 6. , 1.8],\n",
" [6.2, 2.8, 4.8, 1.8],\n",
" [6.1, 3. , 4.9, 1.8],\n",
" [6.4, 2.8, 5.6, 2.1],\n",
" [7.2, 3. , 5.8, 1.6],\n",
" [7.4, 2.8, 6.1, 1.9],\n",
" [7.9, 3.8, 6.4, 2. ],\n",
" [6.4, 2.8, 5.6, 2.2],\n",
" [6.3, 2.8, 5.1, 1.5],\n",
" [6.1, 2.6, 5.6, 1.4],\n",
" [7.7, 3. , 6.1, 2.3],\n",
" [6.3, 3.4, 5.6, 2.4],\n",
" [6.4, 3.1, 5.5, 1.8],\n",
" [6. , 3. , 4.8, 1.8],\n",
" [6.9, 3.1, 5.4, 2.1],\n",
" [6.7, 3.1, 5.6, 2.4],\n",
" [6.9, 3.1, 5.1, 2.3],\n",
" [5.8, 2.7, 5.1, 1.9],\n",
" [6.8, 3.2, 5.9, 2.3],\n",
" [6.7, 3.3, 5.7, 2.5],\n",
" [6.7, 3. , 5.2, 2.3],\n",
" [6.3, 2.5, 5. , 1.9],\n",
" [6.5, 3. , 5.2, 2. ],\n",
" [6.2, 3.4, 5.4, 2.3],\n",
" [5.9, 3. , 5.1, 1.8]]),\n",
" 'target': array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
" 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
" 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]),\n",
" 'frame': None,\n",
" 'target_names': array(['setosa', 'versicolor', 'virginica'], dtype='<U10'),\n",
" 'DESCR': '.. _iris_dataset:\\n\\nIris plants dataset\\n--------------------\\n\\n**Data Set Characteristics:**\\n\\n :Number of Instances: 150 (50 in each of three classes)\\n :Number of Attributes: 4 numeric, predictive attributes and the class\\n :Attribute Information:\\n - sepal length in cm\\n - sepal width in cm\\n - petal length in cm\\n - petal width in cm\\n - class:\\n - Iris-Setosa\\n - Iris-Versicolour\\n - Iris-Virginica\\n \\n :Summary Statistics:\\n\\n ============== ==== ==== ======= ===== ====================\\n Min Max Mean SD Class Correlation\\n ============== ==== ==== ======= ===== ====================\\n sepal length: 4.3 7.9 5.84 0.83 0.7826\\n sepal width: 2.0 4.4 3.05 0.43 -0.4194\\n petal length: 1.0 6.9 3.76 1.76 0.9490 (high!)\\n petal width: 0.1 2.5 1.20 0.76 0.9565 (high!)\\n ============== ==== ==== ======= ===== ====================\\n\\n :Missing Attribute Values: None\\n :Class Distribution: 33.3% for each of 3 classes.\\n :Creator: R.A. Fisher\\n :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\\n :Date: July, 1988\\n\\nThe famous Iris database, first used by Sir R.A. Fisher. The dataset is taken\\nfrom Fisher\\'s paper. Note that it\\'s the same as in R, but not as in the UCI\\nMachine Learning Repository, which has two wrong data points.\\n\\nThis is perhaps the best known database to be found in the\\npattern recognition literature. Fisher\\'s paper is a classic in the field and\\nis referenced frequently to this day. (See Duda & Hart, for example.) The\\ndata set contains 3 classes of 50 instances each, where each class refers to a\\ntype of iris plant. One class is linearly separable from the other 2; the\\nlatter are NOT linearly separable from each other.\\n\\n.. topic:: References\\n\\n - Fisher, R.A. \"The use of multiple measurements in taxonomic problems\"\\n Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\\n Mathematical Statistics\" (John Wiley, NY, 1950).\\n - Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis.\\n (Q327.D83) John Wiley & Sons. ISBN 0-471-22361-1. See page 218.\\n - Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\\n Structure and Classification Rule for Recognition in Partially Exposed\\n Environments\". IEEE Transactions on Pattern Analysis and Machine\\n Intelligence, Vol. PAMI-2, No. 1, 67-71.\\n - Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\". IEEE Transactions\\n on Information Theory, May 1972, 431-433.\\n - See also: 1988 MLC Proceedings, 54-64. Cheeseman et al\"s AUTOCLASS II\\n conceptual clustering system finds 3 classes in the data.\\n - Many, many more ...',\n",
" 'feature_names': ['sepal length (cm)',\n",
" 'sepal width (cm)',\n",
" 'petal length (cm)',\n",
" 'petal width (cm)'],\n",
" 'filename': 'iris.csv',\n",
" 'data_module': 'sklearn.datasets.data'}"
]
},
softmax regressor
2 years ago
"execution_count": 3,
Loading data
2 years ago
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"iris"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "b8d7e32c-5ed5-411e-b411-e4304f47c087",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
".. _iris_dataset:\n",
"\n",
"Iris plants dataset\n",
"--------------------\n",
"\n",
"**Data Set Characteristics:**\n",
"\n",
" :Number of Instances: 150 (50 in each of three classes)\n",
" :Number of Attributes: 4 numeric, predictive attributes and the class\n",
" :Attribute Information:\n",
" - sepal length in cm\n",
" - sepal width in cm\n",
" - petal length in cm\n",
" - petal width in cm\n",
" - class:\n",
" - Iris-Setosa\n",
" - Iris-Versicolour\n",
" - Iris-Virginica\n",
" \n",
" :Summary Statistics:\n",
"\n",
" ============== ==== ==== ======= ===== ====================\n",
" Min Max Mean SD Class Correlation\n",
" ============== ==== ==== ======= ===== ====================\n",
" sepal length: 4.3 7.9 5.84 0.83 0.7826\n",
" sepal width: 2.0 4.4 3.05 0.43 -0.4194\n",
" petal length: 1.0 6.9 3.76 1.76 0.9490 (high!)\n",
" petal width: 0.1 2.5 1.20 0.76 0.9565 (high!)\n",
" ============== ==== ==== ======= ===== ====================\n",
"\n",
" :Missing Attribute Values: None\n",
" :Class Distribution: 33.3% for each of 3 classes.\n",
" :Creator: R.A. Fisher\n",
" :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\n",
" :Date: July, 1988\n",
"\n",
"The famous Iris database, first used by Sir R.A. Fisher. The dataset is taken\n",
"from Fisher's paper. Note that it's the same as in R, but not as in the UCI\n",
"Machine Learning Repository, which has two wrong data points.\n",
"\n",
"This is perhaps the best known database to be found in the\n",
"pattern recognition literature. Fisher's paper is a classic in the field and\n",
"is referenced frequently to this day. (See Duda & Hart, for example.) The\n",
"data set contains 3 classes of 50 instances each, where each class refers to a\n",
"type of iris plant. One class is linearly separable from the other 2; the\n",
"latter are NOT linearly separable from each other.\n",
"\n",
".. topic:: References\n",
"\n",
" - Fisher, R.A. \"The use of multiple measurements in taxonomic problems\"\n",
" Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\n",
" Mathematical Statistics\" (John Wiley, NY, 1950).\n",
" - Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis.\n",
" (Q327.D83) John Wiley & Sons. ISBN 0-471-22361-1. See page 218.\n",
" - Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\n",
" Structure and Classification Rule for Recognition in Partially Exposed\n",
" Environments\". IEEE Transactions on Pattern Analysis and Machine\n",
" Intelligence, Vol. PAMI-2, No. 1, 67-71.\n",
" - Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\". IEEE Transactions\n",
" on Information Theory, May 1972, 431-433.\n",
" - See also: 1988 MLC Proceedings, 54-64. Cheeseman et al\"s AUTOCLASS II\n",
" conceptual clustering system finds 3 classes in the data.\n",
" - Many, many more ...\n"
]
}
],
"source": [
"print(iris.DESCR)"
]
},
{
"cell_type": "code",
softmax regressor
2 years ago
"execution_count": 4,
Loading data
2 years ago
"id": "1a602ea1-efee-4b93-aa9d-1bff49564e2f",
"metadata": {},
"outputs": [],
Last version
2 years ago
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np"
]
},
{
"cell_type": "code",
softmax regressor
2 years ago
"execution_count": 5,
Last version
2 years ago
"id": "29679ab5",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
softmax regressor
2 years ago
"[<matplotlib.lines.Line2D at 0xffff6e024f10>]"
Last version
2 years ago
]
},
softmax regressor
2 years ago
"execution_count": 5,
Last version
2 years ago
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
softmax regressor
2 years ago
"image/png": "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
Last version
2 years ago
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"lp=iris.data[:,0:1]\n",
"wp=iris.data[:,1:2]\n",
"plt.plot(lp,wp,'.k')"
]
},
{
"cell_type": "code",
softmax regressor
2 years ago
"execution_count": 6,
Last version
2 years ago
"id": "2874984a",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
softmax regressor
2 years ago
"[<matplotlib.lines.Line2D at 0xffff6df321f0>]"
Last version
2 years ago
]
},
softmax regressor
2 years ago
"execution_count": 6,
Last version
2 years ago
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
softmax regressor
2 years ago
"image/png": "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
Last version
2 years ago
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(lp,'.k')"
]
},
{
"cell_type": "code",
softmax regressor
2 years ago
"execution_count": 7,
Last version
2 years ago
"id": "68274907",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
" 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
" 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
]
},
softmax regressor
2 years ago
"execution_count": 7,
Last version
2 years ago
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"iris.target"
]
},
{
"cell_type": "markdown",
"id": "6120b70a",
"metadata": {},
"source": [
"# Logistic regressor\n",
"## Decision boudaries"
]
},
{
"cell_type": "code",
softmax regressor
2 years ago
"execution_count": 8,
Last version
2 years ago
"id": "a75ea97f",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
softmax regressor
2 years ago
"[<matplotlib.lines.Line2D at 0xffff6de45b20>]"
Last version
2 years ago
]
},
softmax regressor
2 years ago
"execution_count": 8,
Last version
2 years ago
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
softmax regressor
2 years ago
"image/png": "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
Last version
2 years ago
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"t=np.linspace(-100, 100, 1000)\n",
"sig=1/(1+np.exp(-t+3))\n",
"plt.plot(t,sig, '.b', label=r\"$\\sigma=\\frac{1}{1+e^{-t}}$\")\n",
"plt.legend(loc='upper left', fontsize=20)\n",
"plt.axis([-5, 5, -0.1, 1])\n",
"plt.plot([0,0], [0,1], 'r--')"
]
},
{
"cell_type": "markdown",
"id": "2be6077b",
"metadata": {},
"source": [
"# Iris-Setosa Classifier based on petal width"
]
},
{
"cell_type": "code",
softmax regressor
2 years ago
"execution_count": 9,
Last version
2 years ago
"id": "f59aa9ba",
"metadata": {},
"outputs": [],
"source": [
"X=iris.data[:,1:2]\n",
"y=(iris.target==0).astype(int)"
]
},
{
"cell_type": "code",
softmax regressor
2 years ago
"execution_count": 10,
Last version
2 years ago
"id": "25839b5d",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\
],
"text/plain": [
"LogisticRegression(random_state=42)"
]
},
softmax regressor
2 years ago
"execution_count": 10,
Last version
2 years ago
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.linear_model import LogisticRegression\n",
"mylr=LogisticRegression(solver='lbfgs', random_state=42)\n",
"mylr.fit(X,y)"
]
},
{
"cell_type": "code",
softmax regressor
2 years ago
"execution_count": 11,
Last version
2 years ago
"id": "4539a843",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
softmax regressor
2 years ago
"[<matplotlib.lines.Line2D at 0xffff6d2c3190>]"
Last version
2 years ago
]
},
softmax regressor
2 years ago
"execution_count": 11,
Last version
2 years ago
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
softmax regressor
2 years ago
"image/png": "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
Last version
2 years ago
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"Xnew=np.linspace(2,5,70).reshape(-1,1)\n",
"yPred=mylr.predict_proba(Xnew)\n",
"plt.plot(Xnew,yPred[:,0],'.r', label='No Iris-Set')\n",
"plt.plot(Xnew,yPred[:,1],'.b', label='Iris-Set')\n",
"plt.legend()\n",
"plt.plot(X,y,'*g')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7474195c",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
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"execution_count": 12,
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"id": "0196eda3",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
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"[<matplotlib.lines.Line2D at 0xffff6d24a070>]"
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]
},
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"execution_count": 12,
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"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"image/png": "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"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Iris-Setosa Classifier based on petal width\n",
"\n",
"X=iris.data[:,0:1]\n",
"y=(iris.target==0).astype(int)\n",
"\n",
"from sklearn.linear_model import LogisticRegression\n",
"mylr=LogisticRegression(solver='lbfgs', random_state=42)\n",
"mylr.fit(X,y)\n",
"\n",
"Xnew=np.linspace(2,8,70).reshape(-1,1)\n",
"yPred=mylr.predict_proba(Xnew)\n",
"#plt.plot(Xnew,yPred[:,0],'.r', label='No Iris-Set')\n",
"plt.plot(Xnew,yPred[:,1],'.b', label='Iris-Set')\n",
"plt.legend()\n",
"plt.plot(X,y,'*g')"
]
},
{
"cell_type": "markdown",
"id": "4d56c901",
"metadata": {},
"source": [
"# Session 2: Softmax regression\n",
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"\n",
"## Multiple features binary classifier (Virginica)"
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]
},
{
"cell_type": "code",
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"execution_count": 4,
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"id": "00ba053b",
"metadata": {},
"outputs": [
{
"data": {
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"image/png": "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"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"pl=iris.data[:,2:3]\n",
"pw=iris.data[:,3:]\n",
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"tg = iris.target\n",
"plt.plot(pl[tg==0,0],pw[tg==0,0],'.r',label='Set')\n",
"plt.plot(pl[tg==1,0],pw[tg==1,0],'.b',label='Ver')\n",
"plt.plot(pl[tg==2,0],pw[tg==2,0],'.g',label='Vir')\n",
"plt.legend()\n",
"plt.show()"
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]
},
{
"cell_type": "code",
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"execution_count": 53,
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"id": "b3849a04",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
softmax regressor
2 years ago
"<style>#sk-container-id-17 {color: black;background-color: white;}#sk-container-id-17 pre{padding: 0;}#sk-container-id-17 div.sk-toggleable {background-color: white;}#sk-container-id-17 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-17 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-17 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-17 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-17 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-17 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-17 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-17 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-17 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-17 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-17 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-17 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-17 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-17 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-17 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-17 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-17 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-17 div.sk-item {position: relative;z-index: 1;}#sk-container-id-17 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-17 div.sk-item::before, #sk-container-id-17 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-17 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-17 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-17 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-17 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-17 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-17 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-17 div.sk-label-container {text-align: center;}#sk-container-id-17 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-17 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container
Last version
2 years ago
],
"text/plain": [
softmax regressor
2 years ago
"LogisticRegression(C=10, random_state=42, solver='newton-cg', tol=1e-05)"
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2 years ago
]
},
softmax regressor
2 years ago
"execution_count": 53,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.linear_model import LogisticRegression\n",
"X=iris.data[:,2:]\n",
"y=(iris.target==2).astype(int)\n",
"\n",
softmax regressor
2 years ago
"lrvir=LogisticRegression(random_state=42, tol=1e-5, C=10, max_iter=100, solver='newton-cg')\n",
"lrvir.fit(X,y)"
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]
},
{
"cell_type": "code",
softmax regressor
2 years ago
"execution_count": 54,
Last version
2 years ago
"id": "3fa13847",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"x0, x1=np.meshgrid(\n",
" np.linspace(1,6.9,500).reshape(-1,1),\n",
" np.linspace(0.1,2.5,200).reshape(-1,1))\n",
"Xnew=np.c_[x0.ravel(), x1.ravel()]\n",
softmax regressor
2 years ago
"yProb=lrvir.predict_proba(Xnew)"
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]
},
{
"cell_type": "code",
softmax regressor
2 years ago
"execution_count": 55,
Last version
2 years ago
"id": "f34d82b2",
"metadata": {},
"outputs": [
{
"data": {
softmax regressor
2 years ago
"image/png": "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
Last version
2 years ago
"text/plain": [
"<Figure size 1000x400 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(10,4))\n",
"plt.plot(X[y==0,0], X[y==0,1],'bs',label='No Virg')\n",
"plt.plot(X[y==1,0], X[y==1,1],'g^',label='Virginica')\n",
"zz=yProb[:,1].reshape(x0.shape)\n",
"contour=plt.contour(x0,x1,zz)\n",
"plt.clabel(contour, inline=1,fontsize=15)\n",
"plt.xlabel(\"Petal Length\")\n",
"plt.ylabel(\"Petal Width\")\n",
softmax regressor
2 years ago
"plt.legend()\n",
"plt.show()"
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]
},
{
"cell_type": "markdown",
"id": "b566781b",
"metadata": {},
"source": [
softmax regressor
2 years ago
"## Multiple features and multiclass classifier (Virginica)"
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]
},
{
"cell_type": "code",
softmax regressor
2 years ago
"execution_count": 81,
Last version
2 years ago
"id": "090c8abc",
"metadata": {},
"outputs": [],
"source": [
"X=iris.data[:,2:]\n",
"y=iris.target"
]
},
{
"cell_type": "code",
softmax regressor
2 years ago
"execution_count": 82,
Last version
2 years ago
"id": "aa59ee97",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
softmax regressor
2 years ago
"<a list of 6 text.Text objects>"
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]
},
softmax regressor
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"execution_count": 82,
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"metadata": {},
"output_type": "execute_result"
},
{
"data": {
softmax regressor
2 years ago
"image/png": "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
Last version
2 years ago
"text/plain": [
"<Figure size 1000x400 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
softmax regressor
2 years ago
"lrmfmc=LogisticRegression(multi_class='multinomial',\n",
Last version
2 years ago
" solver='lbfgs',\n",
softmax regressor
2 years ago
" C=100,\n",
Last version
2 years ago
" random_state=42)\n",
softmax regressor
2 years ago
"lrmfmc.fit(X,y)\n",
Last version
2 years ago
"x0,x1=np.meshgrid(\n",
softmax regressor
2 years ago
" np.linspace(0,8,500).reshape(-1,1),\n",
Last version
2 years ago
" np.linspace(0,3.5,200).reshape(-1,1))\n",
"\n",
softmax regressor
2 years ago
"Xnew=np.c_[x0.ravel(), x1.ravel()]\n",
"yProba=lrmfmc.predict_proba(Xnew)\n",
"#yPred=lrmfmc.predict(Xnew)\n",
Last version
2 years ago
"\n",
"from matplotlib.colors import ListedColormap\n",
softmax regressor
2 years ago
"#customc=ListedColormap(['#fafab0', '#9898ff', '#a0faa0'])\n",
"zz=yProba[:,1].reshape(x0.shape)\n",
"#zz=yPred.reshape(x0.shape)\n",
Last version
2 years ago
"plt.figure(figsize=(10,4))\n",
"plt.plot(X[y==2,0], X[y==2,1],'g^',label='Virg')\n",
"plt.plot(X[y==1,0], X[y==1,1],'bs',label='Versic')\n",
"plt.plot(X[y==0,0], X[y==0,1],'yo',label='Setos')\n",
softmax regressor
2 years ago
"contour=plt.contour(x0,x1,zz,cmap=plt.cm.brg)\n",
Last version
2 years ago
"plt.clabel(contour,inline=1,fontsize=12)\n",
softmax regressor
2 years ago
"#plt.contourf(x0,x1,zz,cmap=customc)"
Last version
2 years ago
]
},
{
"cell_type": "code",
softmax regressor
2 years ago
"execution_count": 83,
"id": "ef386db5",
Last version
2 years ago
"metadata": {},
"outputs": [
{
"data": {
softmax regressor
2 years ago
"image/png": "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
Last version
2 years ago
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
softmax regressor
2 years ago
"yPred=lrmfmc.predict(Xnew)\n",
"zPred=yPred.reshape(x0.shape)\n",
"plt.contourf(x0,x1,zPred,alpha=0.4)\n",
"plt.plot(X[y==2,0], X[y==2,1],'g^',label='Virg')\n",
"plt.plot(X[y==1,0], X[y==1,1],'bs',label='Versic')\n",
"plt.plot(X[y==0,0], X[y==0,1],'yo',label='Setos')\n",
"plt.show()"
Last version
2 years ago
]
},
{
"cell_type": "markdown",
"id": "a9531c07",
"metadata": {},
"source": [
softmax regressor
2 years ago
"## Homework: Sepal multi-features and multi-class"
Last version
2 years ago
]
},
{
"cell_type": "code",
softmax regressor
2 years ago
"execution_count": 67,
Last version
2 years ago
"id": "d99ec4da",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
softmax regressor
2 years ago
"<style>#sk-container-id-19 {color: black;background-color: white;}#sk-container-id-19 pre{padding: 0;}#sk-container-id-19 div.sk-toggleable {background-color: white;}#sk-container-id-19 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-19 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-19 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-19 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-19 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-19 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-19 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-19 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-19 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-19 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-19 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-19 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-19 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-19 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-19 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-19 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-19 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-19 div.sk-item {position: relative;z-index: 1;}#sk-container-id-19 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-19 div.sk-item::before, #sk-container-id-19 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-19 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-19 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-19 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-19 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-19 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-19 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-19 div.sk-label-container {text-align: center;}#sk-container-id-19 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-19 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container
Last version
2 years ago
],
"text/plain": [
"LogisticRegression(C=10, multi_class='multinomial', random_state=42)"
]
},
softmax regressor
2 years ago
"execution_count": 67,
Last version
2 years ago
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X=iris.data[:,0:2]\n",
"y=iris.target\n",
"mlr3=LogisticRegression(\n",
" multi_class='multinomial',\n",
" solver='lbfgs',\n",
" C=10,\n",
" random_state=42)\n",
"mlr3.fit(X,y)"
]
},
{
"cell_type": "code",
softmax regressor
2 years ago
"execution_count": 68,
Last version
2 years ago
"id": "7c28a9a5",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"x0,x1=np.meshgrid(\n",
" np.linspace(0,9,500).reshape(-1,1),\n",
" np.linspace(0,7,200).reshape(-1,1))"
]
},
{
"cell_type": "code",
softmax regressor
2 years ago
"execution_count": 69,
Last version
2 years ago
"id": "258b2dd7",
"metadata": {},
"outputs": [],
"source": [
"Xnew=np.c_[x0.ravel(), x1.ravel()]\n",
"yPred=mlr3.predict_proba(Xnew)\n",
"yProba=mlr3.predict(Xnew)\n",
"zz=yProba.reshape(x0.shape)\n",
"zz1=yPred[:,1].reshape(x0.shape)"
]
},
{
"cell_type": "code",
softmax regressor
2 years ago
"execution_count": 75,
Last version
2 years ago
"id": "2d1cc5a0",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
softmax regressor
2 years ago
"<matplotlib.legend.Legend at 0xffff3d5ed7f0>"
Last version
2 years ago
]
},
softmax regressor
2 years ago
"execution_count": 75,
Last version
2 years ago
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
softmax regressor
2 years ago
"image/png": "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
Last version
2 years ago
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from matplotlib.colors import ListedColormap\n",
"plt.plot(X[y==2,0], X[y==2,1],'y*', label='Virg')\n",
"plt.plot(X[y==1,0], X[y==1,1],'g^', label='Vers')\n",
"plt.plot(X[y==0,0], X[y==0,1],'bs', label='Set')\n",
"contour=plt.contour(x0,x1,zz1, cmap=plt.cm.brg)\n",
"ccmap=ListedColormap(['#fafab0', '#9898ff', '#a0faa0'])\n",
"plt.contourf(x0,x1,zz,cmap=ccmap)\n",
"plt.clabel(contour, inline=1,fontsize=12)\n",
"plt.xlabel('Sepal Width', fontsize=12)\n",
"plt.ylabel('Sepal Length', fontsize=12)\n",
softmax regressor
2 years ago
"plt.axis([4,8.5,1,5])\n",
Last version
2 years ago
"plt.legend()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "37b5eb33",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "840fcc9a",
"metadata": {},
"outputs": [],
Loading data
2 years ago
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.8.10"
}
},
"nbformat": 4,
"nbformat_minor": 5
}