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# Header 1
## Header 2
### Header 3
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Homework 3"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Part 1"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"plt.rcParams[\"figure.dpi\"] = 300\n",
"plt.rcParams['savefig.bbox'] = 'tight'"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.svm import SVC\n",
"from sklearn.datasets import load_breast_cancer\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.preprocessing import MinMaxScaler"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# load and split the data\n",
"cancer = load_breast_cancer()\n",
"\n",
"X_train, X_test, y_train, y_test = train_test_split(\n",
"cancer.data, cancer.target, random_state=0)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Test score: 0.95\n"
]
}
],
"source": [
"# compute minimum and maximum on the training data\n",
"scaler = MinMaxScaler().fit(X_train)\n",
"\n",
"# rescale the training data\n",
"X_train_scaled = scaler.transform(X_train)\n",
"\n",
"svm = SVC(gamma = 'auto' )\n",
"\n",
"# learn an SVM on the scaled training data\n",
"svm.fit(X_train_scaled, y_train)\n",
"\n",
"# scale the test data and score the scaled data\n",
"X_test_scaled = scaler.transform(X_test)\n",
"print(\"Test score: {:.2f}\".format(svm.score(X_test_scaled, y_test)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"2- Building Pipelines"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.pipeline import Pipeline\n",
"pipe = Pipeline([(\"scaler\", MinMaxScaler()), (\"svm\", SVC(gamma = 'auto'))])"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Pipeline(memory=None,\n",
" steps=[('scaler', MinMaxScaler(copy=True, feature_range=(0, 1))), ('svm', SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
" decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',\n",
" max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
" tol=0.001, verbose=False))])"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pipe.fit(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Test score: 0.95\n"
]
}
],
"source": [
"print(\"Test score: {:.2f}\".format(pipe.score(X_test, y_test)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"3- Using Pipelines in Grid Searches (SVM)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.model_selection import GridSearchCV\n",
"\n",
"param_grid = {'svm__C': [0.001, 0.01, 0.1, 1, 10, 100],\n",
"'svm__gamma': [0.001, 0.01, 0.1, 1, 10, 100]}"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Best cross-validation accuracy: 0.98\n",
"Test set score: 0.97\n",
"Best parameters: {'svm__C': 1, 'svm__gamma': 1}\n"
]
}
],
"source": [
"grid = GridSearchCV(pipe, param_grid=param_grid, cv=5)\n",
"grid.fit(X_train, y_train)\n",
"print(\"Best cross-validation accuracy: {:.2f}\".format(grid.best_score_))\n",
"print(\"Test set score: {:.2f}\".format(grid.score(X_test, y_test)))\n",
"print(\"Best parameters: {}\".format(grid.best_params_))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"4- Using Pipelines in Grid Searches (Logistic Regression)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.preprocessing import StandardScaler\n",
"from sklearn.pipeline import make_pipeline"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"pipe = make_pipeline(StandardScaler(), LogisticRegression(solver ='lbfgs', max_iter=300))\n",
"param_grid = {'logisticregression__C': [0.01, 0.1, 1, 10, 100]}"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"GridSearchCV(cv=5, error_score='raise-deprecating',\n",
" estimator=Pipeline(memory=None,\n",
" steps=[('standardscaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('logisticregression', LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
" intercept_scaling=1, max_iter=300, multi_class='warn',\n",
" n_jobs=None, penalty='l2', random_state=None, solver='lbfgs',\n",
" tol=0.0001, verbose=0, warm_start=False))]),\n",
" fit_params=None, iid='warn', n_jobs=None,\n",
" param_grid={'logisticregression__C': [0.01, 0.1, 1, 10, 100]},\n",
" pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
" scoring=None, verbose=0)"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_train, X_test, y_train, y_test = train_test_split(\n",
"cancer.data, cancer.target, random_state=4)\n",
"grid = GridSearchCV(pipe, param_grid, cv=5)\n",
"grid.fit(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Best estimator:\n",
"Pipeline(memory=None,\n",
" steps=[('standardscaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('logisticregression', LogisticRegression(C=0.1, class_weight=None, dual=False, fit_intercept=True,\n",
" intercept_scaling=1, max_iter=300, multi_class='warn',\n",
" n_jobs=None, penalty='l2', random_state=None, solver='lbfgs',\n",
" tol=0.0001, verbose=0, warm_start=False))])\n"
]
}
],
"source": [
"print(\"Best estimator:\\n{}\".format(grid.best_estimator_))"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Logistic regression step:\n",
"LogisticRegression(C=0.1, class_weight=None, dual=False, fit_intercept=True,\n",
" intercept_scaling=1, max_iter=300, multi_class='warn',\n",
" n_jobs=None, penalty='l2', random_state=None, solver='lbfgs',\n",
" tol=0.0001, verbose=0, warm_start=False)\n"
]
}
],
"source": [
"print(\"Logistic regression step:\\n{}\".format(\n",
"grid.best_estimator_.named_steps[\"logisticregression\"]))"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Logistic regression coefficients:\n",
"[[-0.3914328 -0.38953715 -0.37739194 -0.38182177 -0.12563881 0.01490745\n",
" -0.33765699 -0.3786831 -0.05131844 0.22879421 -0.47627084 0.00635852\n",
" -0.35037191 -0.35459504 -0.0298673 0.19405433 -0.0047329 -0.07769192\n",
" 0.23537054 0.23109142 -0.54040153 -0.53565333 -0.49172645 -0.48874024\n",
" -0.39106171 -0.12979023 -0.39291414 -0.42404076 -0.33022024 -0.13611277]]\n"
]
}
],
"source": [
"print(\"Logistic regression coefficients:\\n{}\".format(\n",
"grid.best_estimator_.named_steps[\"logisticregression\"].coef_))"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Best cross-validation accuracy: 0.97\n",
"Test set score: 0.97\n",
"Best parameters: {'logisticregression__C': 0.1}\n"
]
}
],
"source": [
"grid.fit(X_train, y_train)\n",
"print(\"Best cross-validation accuracy: {:.2f}\".format(grid.best_score_))\n",
"print(\"Test set score: {:.2f}\".format(grid.score(X_test, y_test)))\n",
"print(\"Best parameters: {}\".format(grid.best_params_))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
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{
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{
"cell_type": "code",
"execution_count": null,
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}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
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"name": "ipython",
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"file_extension": ".py",
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"name": "python",
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