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ProbabilisticGenerativeModel.html
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Probabilistic Generative Model - HackMD
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<div id="doc" class="markdown-body container-fluid" style="position: relative;"><h1 id="probabilistic-generative-model"><a class="anchor hidden-xs" href="#probabilistic-generative-model" title="probabilistic-generative-model"><span class="octicon octicon-link"></span></a>Probabilistic Generative Model</h1><p><strong>忽略在logistic regression同樣出現的function</strong></p><ol>
<li>IO</li>
<li>shuffle</li>
<li>normalize</li>
<li>split valid set</li>
<li>sigmoid</li>
</ol><h3 id="import-library"><a class="anchor hidden-xs" href="#import-library" title="import-library"><span class="octicon octicon-link"></span></a>import library</h3><pre><code class="python hljs"><div class="wrapper"><div class="gutter linenumber"><span data-linenumber="1"></span>
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<span data-linenumber="6"></span></div><div class="code"><span class="hljs-keyword">import</span> os, sys
<span class="hljs-keyword">import</span> pandas <span class="hljs-keyword">as</span> pd
<span class="hljs-keyword">import</span> numpy <span class="hljs-keyword">as</span> np
<span class="hljs-keyword">from</span> random <span class="hljs-keyword">import</span> shuffle
<span class="hljs-keyword">import</span> argparse
<span class="hljs-keyword">from</span> math <span class="hljs-keyword">import</span> log, floor
</div></div></code></pre><h3 id="get-valid-score"><a class="anchor hidden-xs" href="#get-valid-score" title="get-valid-score"><span class="octicon octicon-link"></span></a>get valid score</h3><pre><code class="python hljs"><div class="wrapper"><div class="gutter linenumber"><span data-linenumber="1"></span>
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<span data-linenumber="11"></span></div><div class="code"><span class="hljs-function"><span class="hljs-keyword">def</span> <span class="hljs-title">valid</span><span class="hljs-params">(X_valid, Y_valid, mu1, mu2, shared_sigma, N1, N2)</span>:</span>
sigma_inverse = np.linalg.inv(shared_sigma)
w = np.dot( (mu1-mu2), sigma_inverse)
x = X_valid.T
b = (<span class="hljs-number">-0.5</span>) * np.dot(np.dot([mu1], sigma_inverse), mu1) + (<span class="hljs-number">0.5</span>) * np.dot(np.dot([mu2], sigma_inverse), mu2) + np.log(float(N1)/N2)
a = np.dot(w, x) + b
y = sigmoid(a)
y_ = np.around(y)
result = (np.squeeze(Y_valid) == y_)
print(<span class="hljs-string">'Valid acc = %f'</span> % (float(result.sum()) / result.shape[<span class="hljs-number">0</span>]))
<span class="hljs-keyword">return</span>
</div></div></code></pre><h3 id="train-model"><a class="anchor hidden-xs" href="#train-model" title="train-model"><span class="octicon octicon-link"></span></a>train model</h3><pre><code class="python hljs"><div class="wrapper"><div class="gutter linenumber"><span data-linenumber="1"></span>
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<span data-linenumber="47"></span></div><div class="code"><span class="hljs-function"><span class="hljs-keyword">def</span> <span class="hljs-title">train</span><span class="hljs-params">(X_all, Y_all, save_dir)</span>:</span>
<span class="hljs-comment"># Split a 10%-validation set from the training set</span>
valid_set_percentage = <span class="hljs-number">0.1</span>
X_train, Y_train, X_valid, Y_valid = split_valid_set(X_all, Y_all, valid_set_percentage)
<span class="hljs-comment"># Gaussian distribution parameters</span>
train_data_size = X_train.shape[<span class="hljs-number">0</span>]
cnt1 = <span class="hljs-number">0</span>
cnt2 = <span class="hljs-number">0</span>
mu1 = np.zeros((<span class="hljs-number">106</span>,))
mu2 = np.zeros((<span class="hljs-number">106</span>,))
<span class="hljs-keyword">for</span> i <span class="hljs-keyword">in</span> range(train_data_size):
<span class="hljs-keyword">if</span> Y_train[i] == <span class="hljs-number">1</span>:
mu1 += X_train[i]
cnt1 += <span class="hljs-number">1</span>
<span class="hljs-keyword">else</span>:
mu2 += X_train[i]
cnt2 += <span class="hljs-number">1</span>
mu1 /= cnt1
mu2 /= cnt2
sigma1 = np.zeros((<span class="hljs-number">106</span>,<span class="hljs-number">106</span>))
sigma2 = np.zeros((<span class="hljs-number">106</span>,<span class="hljs-number">106</span>))
<span class="hljs-keyword">for</span> i <span class="hljs-keyword">in</span> range(train_data_size):
<span class="hljs-keyword">if</span> Y_train[i] == <span class="hljs-number">1</span>:
sigma1 += np.dot(np.transpose([X_train[i] - mu1]), [(X_train[i] - mu1)])
<span class="hljs-keyword">else</span>:
sigma2 += np.dot(np.transpose([X_train[i] - mu2]), [(X_train[i] - mu2)])
sigma1 /= cnt1
sigma2 /= cnt2
shared_sigma = (float(cnt1) / train_data_size) * sigma1 + (float(cnt2) / train_data_size) * sigma2
N1 = cnt1
N2 = cnt2
print(<span class="hljs-string">'=====Saving Param====='</span>)
<span class="hljs-keyword">if</span> <span class="hljs-keyword">not</span> os.path.exists(save_dir):
os.mkdir(save_dir)
param_dict = {<span class="hljs-string">'mu1'</span>:mu1, <span class="hljs-string">'mu2'</span>:mu2, <span class="hljs-string">'shared_sigma'</span>:shared_sigma, <span class="hljs-string">'N1'</span>:[N1], <span class="hljs-string">'N2'</span>:[N2]}
<span class="hljs-keyword">for</span> key <span class="hljs-keyword">in</span> sorted(param_dict):
print(<span class="hljs-string">'Saving %s'</span> % key)
np.savetxt(os.path.join(save_dir, (<span class="hljs-string">'%s'</span> % key)), param_dict[key])
print(<span class="hljs-string">'=====Validating====='</span>)
valid(X_valid, Y_valid, mu1, mu2, shared_sigma, N1, N2)
<span class="hljs-keyword">return</span>
</div></div></code></pre><h3 id="infer-amp-output-anscsv"><a class="anchor hidden-xs" href="#infer-amp-output-anscsv" title="infer-amp-output-anscsv"><span class="octicon octicon-link"></span></a>infer & output ans.csv</h3><pre><code class="python hljs"><div class="wrapper"><div class="gutter linenumber"><span data-linenumber="1"></span>
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<span data-linenumber="29"></span></div><div class="code"><span class="hljs-function"><span class="hljs-keyword">def</span> <span class="hljs-title">infer</span><span class="hljs-params">(X_test, save_dir, output_dir)</span>:</span>
<span class="hljs-comment"># Load parameters</span>
print(<span class="hljs-string">'=====Loading Param from %s====='</span> % save_dir)
mu1 = np.loadtxt(os.path.join(save_dir, <span class="hljs-string">'mu1'</span>))
mu2 = np.loadtxt(os.path.join(save_dir, <span class="hljs-string">'mu2'</span>))
shared_sigma = np.loadtxt(os.path.join(save_dir, <span class="hljs-string">'shared_sigma'</span>))
N1 = np.loadtxt(os.path.join(save_dir, <span class="hljs-string">'N1'</span>))
N2 = np.loadtxt(os.path.join(save_dir, <span class="hljs-string">'N2'</span>))
<span class="hljs-comment"># Predict</span>
sigma_inverse = np.linalg.inv(shared_sigma)
w = np.dot( (mu1-mu2), sigma_inverse)
x = X_test.T
b = (<span class="hljs-number">-0.5</span>) * np.dot(np.dot([mu1], sigma_inverse), mu1) + (<span class="hljs-number">0.5</span>) * np.dot(np.dot([mu2], sigma_inverse), mu2) + np.log(float(N1)/N2)
a = np.dot(w, x) + b
y = sigmoid(a)
y_ = np.around(y)
print(<span class="hljs-string">'=====Write output to %s ====='</span> % output_dir)
<span class="hljs-comment"># Write output</span>
<span class="hljs-keyword">if</span> <span class="hljs-keyword">not</span> os.path.exists(output_dir):
os.mkdir(output_dir)
output_path = os.path.join(output_dir, <span class="hljs-string">'prediction.csv'</span>)
<span class="hljs-keyword">with</span> open(output_path, <span class="hljs-string">'w'</span>) <span class="hljs-keyword">as</span> f:
f.write(<span class="hljs-string">'id,label\n'</span>)
<span class="hljs-keyword">for</span> i, v <span class="hljs-keyword">in</span> enumerate(y_):
f.write(<span class="hljs-string">'%d,%d\n'</span> %(i+<span class="hljs-number">1</span>, v))
<span class="hljs-keyword">return</span>
</div></div></code></pre><h3 id="main-block"><a class="anchor hidden-xs" href="#main-block" title="main-block"><span class="octicon octicon-link"></span></a>main block</h3><pre><code class="python hljs"><div class="wrapper"><div class="gutter linenumber"><span data-linenumber="1"></span>
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<span data-linenumber="41"></span></div><div class="code"><span class="hljs-function"><span class="hljs-keyword">def</span> <span class="hljs-title">main</span><span class="hljs-params">(opts)</span>:</span>
<span class="hljs-comment"># Load feature and label</span>
X_all, Y_all, X_test = load_data(opts.train_data_path, opts.train_label_path, opts.test_data_path)
<span class="hljs-comment"># Normalization</span>
X_all, X_test = normalize(X_all, X_test)
<span class="hljs-comment"># To train or to infer</span>
<span class="hljs-keyword">if</span> opts.train:
train(X_all, Y_all, opts.save_dir)
<span class="hljs-keyword">elif</span> opts.infer:
infer(X_test, opts.save_dir, opts.output_dir)
<span class="hljs-keyword">else</span>:
print(<span class="hljs-string">"Error: Argument --train or --infer not found"</span>)
<span class="hljs-keyword">return</span>
<span class="hljs-keyword">if</span> __name__ == <span class="hljs-string">'__main__'</span>:
parser = argparse.ArgumentParser(
description=<span class="hljs-string">'Probabilistic Generative Model for Binary Classification'</span>
)
group = parser.add_mutually_exclusive_group()
group.add_argument(<span class="hljs-string">'--train'</span>, action=<span class="hljs-string">'store_true'</span>, default=<span class="hljs-keyword">False</span>,
dest=<span class="hljs-string">'train'</span>, help=<span class="hljs-string">'Input --train to Train'</span>)
group.add_argument(<span class="hljs-string">'--infer'</span>, action=<span class="hljs-string">'store_true'</span>,default=<span class="hljs-keyword">False</span>,
dest=<span class="hljs-string">'infer'</span>, help=<span class="hljs-string">'Input --infer to Infer'</span>)
parser.add_argument(<span class="hljs-string">'--train_data_path'</span>, type=str,
default=<span class="hljs-string">'feature/X_train'</span>, dest=<span class="hljs-string">'train_data_path'</span>,
help=<span class="hljs-string">'Path to training data'</span>)
parser.add_argument(<span class="hljs-string">'--train_label_path'</span>, type=str,
default=<span class="hljs-string">'feature/Y_train'</span>, dest=<span class="hljs-string">'train_label_path'</span>,
help=<span class="hljs-string">'Path to training data\'s label'</span>)
parser.add_argument(<span class="hljs-string">'--test_data_path'</span>, type=str,
default=<span class="hljs-string">'feature/X_test'</span>, dest=<span class="hljs-string">'test_data_path'</span>,
help=<span class="hljs-string">'Path to testing data'</span>)
parser.add_argument(<span class="hljs-string">'--save_dir'</span>, type=str,
default=<span class="hljs-string">'generative_params/'</span>, dest=<span class="hljs-string">'save_dir'</span>,
help=<span class="hljs-string">'Path to save the model parameters'</span>)
parser.add_argument(<span class="hljs-string">'--output_dir'</span>, type=str,
default=<span class="hljs-string">'generative_output/'</span>, dest=<span class="hljs-string">'output_dir'</span>,
help=<span class="hljs-string">'Path to save output'</span>)
opts = parser.parse_args()
main(opts)
</div></div></code></pre></div>
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function smoothHashScroll() {
var hashElements = $("a[href^='#']").toArray();
for (var i = 0; i < hashElements.length; i++) {
var element = hashElements[i];
var $element = $(element);
var hash = element.hash;
if (hash) {
$element.on('click', function (e) {
// store hash
var hash = this.hash;
if ($(hash).length <= 0) return;
// prevent default anchor click behavior
e.preventDefault();
// animate
$('body, html').stop(true, true).animate({
scrollTop: $(hash).offset().top
}, 100, "linear", function () {
// when done, add hash to url
// (default click behaviour)
window.location.hash = hash;
});
});
}
}
}
smoothHashScroll();
var toc = $('.ui-toc');
var tocAffix = $('.ui-affix-toc');
var tocDropdown = $('.ui-toc-dropdown');
//toc
tocDropdown.click(function (e) {
e.stopPropagation();
});
var enoughForAffixToc = true;
function generateScrollspy() {
$(document.body).scrollspy({
target: ''
});
$(document.body).scrollspy('refresh');
if (enoughForAffixToc) {
toc.hide();
tocAffix.show();
} else {
tocAffix.hide();
toc.show();
}
$(document.body).scroll();
}
function windowResize() {
//toc right
var paddingRight = parseFloat(markdown.css('padding-right'));
var right = ($(window).width() - (markdown.offset().left + markdown.outerWidth() - paddingRight));
toc.css('right', right + 'px');
//affix toc left
var newbool;
var rightMargin = (markdown.parent().outerWidth() - markdown.outerWidth()) / 2;
//for ipad or wider device
if (rightMargin >= 133) {
newbool = true;
var affixLeftMargin = (tocAffix.outerWidth() - tocAffix.width()) / 2;
var left = markdown.offset().left + markdown.outerWidth() - affixLeftMargin;
tocAffix.css('left', left + 'px');
} else {
newbool = false;
}
if (newbool != enoughForAffixToc) {
enoughForAffixToc = newbool;
generateScrollspy();
}
}
$(window).resize(function () {
windowResize();
});
$(document).ready(function () {
windowResize();
generateScrollspy();
});
//remove hash
function removeHash() {
window.location.hash = '';
}
var backtotop = $('.back-to-top');
var gotobottom = $('.go-to-bottom');
backtotop.click(function (e) {
e.preventDefault();
e.stopPropagation();
if (scrollToTop)
scrollToTop();
removeHash();
});
gotobottom.click(function (e) {
e.preventDefault();
e.stopPropagation();
if (scrollToBottom)
scrollToBottom();
removeHash();
});
var toggle = $('.expand-toggle');
var tocExpand = false;
checkExpandToggle();
toggle.click(function (e) {
e.preventDefault();
e.stopPropagation();
tocExpand = !tocExpand;
checkExpandToggle();
})
function checkExpandToggle () {
var toc = $('.ui-toc-dropdown .toc');
var toggle = $('.expand-toggle');
if (!tocExpand) {
toc.removeClass('expand');
toggle.text('Expand all');
} else {
toc.addClass('expand');
toggle.text('Collapse all');
}
}
function scrollToTop() {
$('body, html').stop(true, true).animate({
scrollTop: 0
}, 100, "linear");
}
function scrollToBottom() {
$('body, html').stop(true, true).animate({
scrollTop: $(document.body)[0].scrollHeight
}, 100, "linear");
}
</script>
</body>
</html>