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models.html
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models.html
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<article id="content">
<header>
<h1 class="title">Module <code>ktrain.tabular.models</code></h1>
</header>
<section id="section-intro">
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">from .. import utils as U
from ..imports import *
from ..models import bn_drop_lin
MLP = "mlp"
TABULAR_MODELS = {
MLP: "a configurable multilayer perceptron with categorical variable embeddings [https://arxiv.org/abs/1604.06737]",
}
def print_tabular_classifiers():
for k, v in TABULAR_MODELS.items():
print("%s: %s" % (k, v))
def print_tabular_regression_models():
for k, v in TABULAR_MODELS.items():
print("%s: %s" % (k, v))
def _tabular_model(
name,
train_data,
multilabel=None,
is_regression=False,
metrics=["accuracy"],
hidden_layers=[1000, 500],
hidden_dropouts=[0.0, 0.5],
bn=False,
verbose=1,
):
"""
```
Build and return a classification or regression model for tabular data
Args:
name (string): currently accepts 'mlp' for multilayer perceptron
train_data (TabularDataset): TabularDataset instance returned from one of the tabular_from_* functions
multilabel (bool): If True, multilabel model will be returned.
If false, binary/multiclass model will be returned.
If None, multilabel will be inferred from data.
is_regression(bool): If True, will build a regression model, else classification model.
metrics(list): list of metrics to use
hidden_layers(list): number of units in each hidden layer of NN
hidden_dropouts(list): Dropout values after each hidden layer of NN
bn(bool): If True, BatchNormalization will be used before each fully-connected layer in NN
verbose (boolean): verbosity of output
Return:
model (Model): A Keras Model instance
```
"""
# check arguments
if not U.is_tabular_from_data(train_data):
err = """
Please pass training data in the form of data returned from a ktrain tabular_from* function.
"""
raise Exception(err)
if len(hidden_layers) != len(hidden_dropouts):
raise ValueError("len(hidden_layers) must equal len(hidden_dropouts)")
# reformat dropouts for each of construction
output_dropout = hidden_dropouts[1]
hidden_dropouts[1] = hidden_dropouts[0]
hidden_dropouts[0] = 0.0
# set model configuration values
if is_regression: # regression
if metrics is None or metrics == ["accuracy"]:
metrics = ["mae"]
num_classes = 1
multilabel = False
loss_func = "mse"
activation = "linear"
else: # classification
if metrics is None:
metrics = ["accuracy"]
# set number of classes and multilabel flag
num_classes = U.nclasses_from_data(train_data)
# determine multilabel
if multilabel is None:
multilabel = U.is_multilabel(train_data)
U.vprint("Is Multi-Label? %s" % (multilabel), verbose=verbose)
# set loss and activations
loss_func = "categorical_crossentropy"
activation = "softmax"
if multilabel:
loss_func = "binary_crossentropy"
activation = "sigmoid"
# construct model
ilayers = []
n_cat = len(train_data.cat_columns)
n_cont = len(train_data.cont_columns)
if n_cat == 0 and n_cont == 0:
raise ValueError("There are zero continuous and cateorical variables.")
# categorical inputs and embeddings
if n_cat > 0:
emblayers = []
num_uniques = [
max(c.cat.codes.values + 1) + 1
for n, c in train_data.df[train_data.cat_columns].items()
]
for i in range(n_cat):
inp = keras.layers.Input(shape=(1,))
ilayers.append(inp)
emb_size = min(50, (num_uniques[i] // 2) + 1)
# emb_size = min(600, round(1.6 * num_uniques[i]**0.56))
emb = keras.layers.Embedding(num_uniques[i], emb_size, input_length=1)(inp)
emblayers.append(emb)
x = keras.layers.concatenate(emblayers) if len(emblayers) > 1 else emblayers[0]
x = keras.layers.Flatten()(x)
# continuous inputs
if n_cont > 0:
x_cont = keras.layers.Input(shape=(n_cont,))
ilayers.append(x_cont)
x = keras.layers.concatenate([x, x_cont]) if n_cat > 0 else x_cont
# hidden layers
output = x
for i, n_out in enumerate(hidden_layers):
output = bn_drop_lin(output, n_out, bn=bn, p=hidden_dropouts[i], actn="relu")
# output layer
output = bn_drop_lin(output, num_classes, bn=bn, p=output_dropout, actn=activation)
# construct and compile model
model = keras.Model(inputs=ilayers, outputs=output)
model.compile(optimizer=U.DEFAULT_OPT, loss=loss_func, metrics=metrics)
U.vprint("done.", verbose=verbose)
return model
def tabular_classifier(
name,
train_data,
multilabel=None,
metrics=["accuracy"],
hidden_layers=[1000, 500],
hidden_dropouts=[0.0, 0.5],
bn=False,
verbose=1,
):
"""
```
Build and return a classification model for tabular data
Args:
name (string): currently accepts 'mlp' for multilayer perceptron
train_data (TabularDataset): TabularDataset instance returned from one of the tabular_from_* functions
multilabel (bool): If True, multilabel model will be returned.
If false, binary/multiclass model will be returned.
If None, multilabel will be inferred from data.
metrics(list): list of metrics to use
hidden_layers(list): number of units in each hidden layer of NN
hidden_dropouts(list): Dropout values after each hidden layer of NN
bn(bool): If True, BatchNormalization will be used before each fully-connected layer in NN
verbose (boolean): verbosity of output
Return:
model (Model): A Keras Model instance
```
"""
return _tabular_model(
name,
train_data,
multilabel=multilabel,
metrics=metrics,
hidden_layers=hidden_layers,
hidden_dropouts=hidden_dropouts,
bn=bn,
verbose=verbose,
is_regression=False,
)
def tabular_regression_model(
name,
train_data,
metrics=["mae"],
hidden_layers=[1000, 500],
hidden_dropouts=[0.0, 0.5],
bn=False,
verbose=1,
):
"""
```
Build and return a regression model for tabular data
Args:
name (string): currently accepts 'mlp' for multilayer perceptron
train_data (TabularDataset): TabularDataset instance returned from one of the tabular_from_* functions
metrics(list): list of metrics to use
hidden_layers(list): number of units in each hidden layer of NN
hidden_dropouts(list): Dropout values after each hidden layer of NN
bn(bool): If True, BatchNormalization will be before used each fully-connected layer in NN
verbose (boolean): verbosity of output
Return:
model (Model): A Keras Model instance
```
"""
return _tabular_model(
name,
train_data,
multilabel=None,
metrics=metrics,
hidden_layers=hidden_layers,
hidden_dropouts=hidden_dropouts,
bn=bn,
verbose=verbose,
is_regression=True,
)</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-functions">Functions</h2>
<dl>
<dt id="ktrain.tabular.models.print_tabular_classifiers"><code class="name flex">
<span>def <span class="ident">print_tabular_classifiers</span></span>(<span>)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def print_tabular_classifiers():
for k, v in TABULAR_MODELS.items():
print("%s: %s" % (k, v))</code></pre>
</details>
</dd>
<dt id="ktrain.tabular.models.print_tabular_regression_models"><code class="name flex">
<span>def <span class="ident">print_tabular_regression_models</span></span>(<span>)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def print_tabular_regression_models():
for k, v in TABULAR_MODELS.items():
print("%s: %s" % (k, v))</code></pre>
</details>
</dd>
<dt id="ktrain.tabular.models.tabular_classifier"><code class="name flex">
<span>def <span class="ident">tabular_classifier</span></span>(<span>name, train_data, multilabel=None, metrics=['accuracy'], hidden_layers=[1000, 500], hidden_dropouts=[0.0, 0.5], bn=False, verbose=1)</span>
</code></dt>
<dd>
<div class="desc"><pre><code>Build and return a classification model for tabular data
Args:
name (string): currently accepts 'mlp' for multilayer perceptron
train_data (TabularDataset): TabularDataset instance returned from one of the tabular_from_* functions
multilabel (bool): If True, multilabel model will be returned.
If false, binary/multiclass model will be returned.
If None, multilabel will be inferred from data.
metrics(list): list of metrics to use
hidden_layers(list): number of units in each hidden layer of NN
hidden_dropouts(list): Dropout values after each hidden layer of NN
bn(bool): If True, BatchNormalization will be used before each fully-connected layer in NN
verbose (boolean): verbosity of output
Return:
model (Model): A Keras Model instance
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def tabular_classifier(
name,
train_data,
multilabel=None,
metrics=["accuracy"],
hidden_layers=[1000, 500],
hidden_dropouts=[0.0, 0.5],
bn=False,
verbose=1,
):
"""
```
Build and return a classification model for tabular data
Args:
name (string): currently accepts 'mlp' for multilayer perceptron
train_data (TabularDataset): TabularDataset instance returned from one of the tabular_from_* functions
multilabel (bool): If True, multilabel model will be returned.
If false, binary/multiclass model will be returned.
If None, multilabel will be inferred from data.
metrics(list): list of metrics to use
hidden_layers(list): number of units in each hidden layer of NN
hidden_dropouts(list): Dropout values after each hidden layer of NN
bn(bool): If True, BatchNormalization will be used before each fully-connected layer in NN
verbose (boolean): verbosity of output
Return:
model (Model): A Keras Model instance
```
"""
return _tabular_model(
name,
train_data,
multilabel=multilabel,
metrics=metrics,
hidden_layers=hidden_layers,
hidden_dropouts=hidden_dropouts,
bn=bn,
verbose=verbose,
is_regression=False,
)</code></pre>
</details>
</dd>
<dt id="ktrain.tabular.models.tabular_regression_model"><code class="name flex">
<span>def <span class="ident">tabular_regression_model</span></span>(<span>name, train_data, metrics=['mae'], hidden_layers=[1000, 500], hidden_dropouts=[0.0, 0.5], bn=False, verbose=1)</span>
</code></dt>
<dd>
<div class="desc"><pre><code>Build and return a regression model for tabular data
Args:
name (string): currently accepts 'mlp' for multilayer perceptron
train_data (TabularDataset): TabularDataset instance returned from one of the tabular_from_* functions
metrics(list): list of metrics to use
hidden_layers(list): number of units in each hidden layer of NN
hidden_dropouts(list): Dropout values after each hidden layer of NN
bn(bool): If True, BatchNormalization will be before used each fully-connected layer in NN
verbose (boolean): verbosity of output
Return:
model (Model): A Keras Model instance
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def tabular_regression_model(
name,
train_data,
metrics=["mae"],
hidden_layers=[1000, 500],
hidden_dropouts=[0.0, 0.5],
bn=False,
verbose=1,
):
"""
```
Build and return a regression model for tabular data
Args:
name (string): currently accepts 'mlp' for multilayer perceptron
train_data (TabularDataset): TabularDataset instance returned from one of the tabular_from_* functions
metrics(list): list of metrics to use
hidden_layers(list): number of units in each hidden layer of NN
hidden_dropouts(list): Dropout values after each hidden layer of NN
bn(bool): If True, BatchNormalization will be before used each fully-connected layer in NN
verbose (boolean): verbosity of output
Return:
model (Model): A Keras Model instance
```
"""
return _tabular_model(
name,
train_data,
multilabel=None,
metrics=metrics,
hidden_layers=hidden_layers,
hidden_dropouts=hidden_dropouts,
bn=bn,
verbose=verbose,
is_regression=True,
)</code></pre>
</details>
</dd>
</dl>
</section>
<section>
</section>
</article>
<nav id="sidebar">
<h1>Index</h1>
<div class="toc">
<ul></ul>
</div>
<ul id="index">
<li><h3>Super-module</h3>
<ul>
<li><code><a title="ktrain.tabular" href="index.html">ktrain.tabular</a></code></li>
</ul>
</li>
<li><h3><a href="#header-functions">Functions</a></h3>
<ul class="">
<li><code><a title="ktrain.tabular.models.print_tabular_classifiers" href="#ktrain.tabular.models.print_tabular_classifiers">print_tabular_classifiers</a></code></li>
<li><code><a title="ktrain.tabular.models.print_tabular_regression_models" href="#ktrain.tabular.models.print_tabular_regression_models">print_tabular_regression_models</a></code></li>
<li><code><a title="ktrain.tabular.models.tabular_classifier" href="#ktrain.tabular.models.tabular_classifier">tabular_classifier</a></code></li>
<li><code><a title="ktrain.tabular.models.tabular_regression_model" href="#ktrain.tabular.models.tabular_regression_model">tabular_regression_model</a></code></li>
</ul>
</li>
</ul>
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