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online_vs_batch_digits.ipynb
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online_vs_batch_digits.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"from matplotlib.ticker import FormatStrFormatter\n",
"\n",
"from sklearn.base import BaseEstimator, TransformerMixin\n",
"from sklearn.pipeline import Pipeline\n",
"from sklearn.preprocessing import StandardScaler\n",
"from sklearn.linear_model import SGDClassifier\n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.model_selection import GridSearchCV\n",
"from sklearn import datasets\n",
"\n",
"%matplotlib inline\n",
"%config InlineBackend.figure_format = 'retina'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Online data vs batch for limited initial training data\n",
"\n",
"We will investigate a scenario where we train an initial batch model and online model to a small data set, and then observe accuracy over time as we score new points. The online model will be updated with the new samples, whereas the batch model will not.\n",
"\n",
"We will use the digits dataset for this exercise- we have 1797 samples, and the objective is to classify the digit."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"digits = datasets.load_digits()\n",
"X, y = digits.data, digits.target"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Define a generator to produce batches of data\n",
"\n",
"We do this to give us flexibility to produce batches of different sizes should we want to investigate this scenario in more detail. The generator produces indices for us to slice the feature and outcome arrays."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class GenIndex(object):\n",
" def __init__(self, shape, n_folds, seed=1234):\n",
" self.shape = shape\n",
" self.n_folds = n_folds\n",
" self.seed = seed\n",
" self.gen = self._gen()\n",
" \n",
" def _gen(self):\n",
" np.random.seed(seed = self.seed)\n",
" ix = np.arange(self.shape)\n",
" np.random.shuffle(ix)\n",
" ix = np.array_split(ix, self.n_folds)\n",
" for _ in ix:\n",
" yield _\n",
" \n",
" def next_batch(self):\n",
" '''\n",
" returns indices we can use to subset our data\n",
" '''\n",
" try:\n",
" return next(self.gen)\n",
" except:\n",
" return np.array([False])"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# we will split the dataset into 20 batches. The first will be used to train\n",
"# our batch model and initial online model\n",
"nbatch = 20\n",
"gen = GenIndex(X.shape[0], nbatch)\n",
"inital_batch_ix = gen.next_batch()\n",
"\n",
"X_initial, y_initial = X[inital_batch_ix, :], y[inital_batch_ix]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Train initial SGD model"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"StandardScaler(copy=True, with_mean=True, with_std=True)"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# scale the data: SGD models can become unstable\n",
"# when converging if data not scaled\n",
"scale = StandardScaler(with_mean = True, with_std = True)\n",
"scale.fit(X_initial)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Fitting 3 folds for each of 250 candidates, totalling 750 fits\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=3)]: Done 44 tasks | elapsed: 3.0s\n",
"[Parallel(n_jobs=3)]: Done 194 tasks | elapsed: 14.9s\n",
"[Parallel(n_jobs=3)]: Done 444 tasks | elapsed: 36.0s\n",
"[Parallel(n_jobs=3)]: Done 750 out of 750 | elapsed: 1.0min finished\n"
]
}
],
"source": [
"# initialise SGDCLassifier object\n",
"sgd_est = SGDClassifier(loss = 'log',\n",
" penalty = 'l1',\n",
" max_iter = 500,\n",
" random_state = 1234,\n",
" warm_start= False)\n",
"\n",
"# candidate hyperparameters\n",
"sgd_param_grid = dict(alpha = [0.001, 0.01, 0.1, 1, 10],\n",
" eta0 = [0.001, 0.01, 0.1, 1, 10],\n",
" l1_ratio = [0., 0.15 ,0.25, 0.5, 0.75],\n",
" learning_rate = ['optimal', 'constant'] \n",
" )\n",
"\n",
"# initialise GridSearchCV object\n",
"grid_search_sgd = GridSearchCV(estimator = sgd_est,\n",
" param_grid = sgd_param_grid,\n",
" scoring = 'accuracy',\n",
" n_jobs = 3,\n",
" cv = 3,\n",
" refit = True,\n",
" verbose = 1)\n",
"\n",
"# fit model\n",
"grid_search_sgd.fit(scale.transform(X_initial), y_initial);"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Chosen params: {'alpha': 0.001, 'eta0': 0.1, 'l1_ratio': 0.0, 'learning_rate': 'constant'}\n",
"\n",
"Train Accuracy: 0.900\n"
]
}
],
"source": [
"print('Chosen params: {}\\n\\nTrain Accuracy: {:0.3f}'.format(grid_search_sgd.best_params_,\n",
" grid_search_sgd.best_score_))\n",
"\n",
"# extract best model\n",
"sgd_cls = grid_search_sgd.best_estimator_"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Train logistic regression model\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Fitting 3 folds for each of 10 candidates, totalling 30 fits\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=3)]: Done 30 out of 30 | elapsed: 0.1s finished\n"
]
}
],
"source": [
"# initialise object\n",
"lr_est = LogisticRegression()\n",
"\n",
"# candidate hyperparameters\n",
"lr_param_grid = dict(penalty = ['l1', 'l2'],\n",
" C = [0.001, 0.01, 0.1, 1, 10])\n",
"\n",
"# initialise GridSearchCV object\n",
"grid_search_lr = GridSearchCV(estimator = lr_est,\n",
" param_grid = lr_param_grid,\n",
" scoring = 'accuracy',\n",
" n_jobs = 3,\n",
" cv = 3,\n",
" refit = True,\n",
" verbose = 1)\n",
"\n",
"# fit the model\n",
"grid_search_lr.fit(scale.transform(X_initial), y_initial);"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Chosen params: {'C': 10, 'penalty': 'l1'}\n",
"\n",
"Train Accuracy: 0.900\n"
]
}
],
"source": [
"print('Chosen params: {}\\n\\nTrain Accuracy: {:0.3f}'.format(grid_search_lr.best_params_,\n",
" grid_search_lr.best_score_))\n",
"\n",
"# extract model\n",
"lr_cls = grid_search_lr.best_estimator_"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Compare the models as we gain more data\n",
"\n",
"We see initial performance of the two models is comparable from our cross validation results. We are interested to see if the online model improves as more data is gathered and the decision function is updated.\n",
"\n",
"However, if the initial data contains enough information for the batch (logistic regression) model to train well, we would expect it to perform better: we would never expect a SGD model to converge as well as a standard logistic regression model"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# initialise empty array for holding results.\n",
"# remember we initialised the models on the first batch\n",
"update_result = np.empty((nbatch - 1, 3))"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"i = 0\n",
"while True: \n",
" # get next batch\n",
" batch_ix = gen.next_batch()\n",
" if any(batch_ix):\n",
" pass\n",
" else:\n",
" break\n",
" \n",
" features, outcome = X[batch_ix, :], y[batch_ix]\n",
" \n",
" # scale features\n",
" features = scale.transform(features)\n",
"\n",
" # predict\n",
" pred = sgd_cls.predict(features)\n",
" pred_lr = lr_cls.predict(features)\n",
"\n",
" # assess accuracy\n",
" acc = np.sum(pred == outcome) / len(pred)\n",
" acc_lr = np.sum(pred_lr == outcome) / len(pred_lr)\n",
" \n",
" update_result[i, 0] = i + 1\n",
" update_result[i, 1] = acc\n",
" update_result[i, 2] = acc_lr\n",
"\n",
" # update model\n",
" if i == 0:\n",
" sgd_cls.partial_fit(features, outcome, classes=np.unique(y[batch_ix]))\n",
" else:\n",
" sgd_cls.partial_fit(features, outcome)\n",
" i += 1\n"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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u5PDhw5w4cYKAgACPxOzj48PcuXPp378/gwcPZvjw4TRu3Jh9+/axfv16BgwY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\nwwFa9rHk5pvJmOXKxDsF9fesSr7Mo8Icvn4WBju15fwKqDoou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luMe+B+hn3rWxE5MRBK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\n/8p3Sc+WWJTz4nHgcmD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"text/plain": [
"<matplotlib.figure.Figure at 0x7f999da32240>"
]
},
"metadata": {
"image/png": {
"height": 372,
"width": 398
}
},
"output_type": "display_data"
}
],
"source": [
"def running_mean(x, N):\n",
" cumsum = np.cumsum(np.insert(x, 0, 0))\n",
" return (cumsum[N:] - cumsum[:-N])/ float(N)\n",
"\n",
"# look at cumulative accuracy as well\n",
"\n",
"fig, ax = plt.subplots(1, 1, figsize = (6,6))\n",
"\n",
"i = update_result[:, 0]\n",
"acc_sgd = update_result[:, 1]\n",
"acc_lr = update_result[:, 2]\n",
"ax.plot(i, acc_sgd, label='SGD')\n",
"ax.plot(i, acc_lr, label='LR')\n",
"ax.plot(i[3:], running_mean(acc_sgd, 4), label = 'rolling average SGD')\n",
"ax.plot(i[3:], running_mean(acc_lr, 4), label = 'rolling average LR')\n",
"\n",
"ax.legend()\n",
"ax.set_xlabel('batch number')\n",
"ax.set_ylabel('accuracy')\n",
"ax.xaxis.set_major_formatter(FormatStrFormatter('%.0f'))\n",
"ax.set_xticks(np.arange(2,22,2))\n",
"ax.set_xlim(0, 20);"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"## Summary\n",
"\n",
"We caveat that the data set is relatively small, and ideally we would want to examine over more batches. However, the results strongly suggest that the performance of the online model pulls away from that of the batch trained model as we see more data.\n",
"\n",
"Many areas to investigate remain. I would be interested to know what happens once the performance of the online model performance peaks? Would we then want to reduce the weights when we update? Or would we decide to stop updating altogether?"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [default]",
"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": "3.6.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}