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</head>
<body>
<main>
<article id="content">
<header>
<h1 class="title">Module <code>ktrain.tabular.data</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 . import preprocessor as pp
def tabular_from_df(
train_df,
label_columns=[],
date_columns=[],
val_df=None,
val_pct=0.1,
is_regression=False,
max_card=20,
random_state=None,
verbose=1,
):
train_df = train_df.copy()
# strip space from string columns and check supplied val_df
train_type_dict = pp.clean_df(train_df, val_df=val_df, return_types=True)
# check label_columns
if label_columns is None or (
isinstance(label_columns, (list, np.ndarray)) and len(label_columns) == 0
):
raise ValueError("label_columns is required")
if isinstance(label_columns, (list, np.ndarray)) and len(label_columns) == 1:
label_columns = label_columns[0]
# define original predictor_columns
predictor_columns = [
col for col in train_df.columns.values if col not in label_columns
]
# create validation set
if val_df is None:
if val_pct:
df = train_df.copy()
prop = 1 - val_pct
if random_state is not None:
np.random.seed(42)
msk = np.random.rand(len(df)) < prop
train_df = df[msk]
val_df = df[~msk]
else:
val_df = val_df.copy()
procs = [pp.FillMissing, pp.Categorify, pp.Normalize]
preproc = pp.TabularPreprocessor(
predictor_columns,
label_columns,
date_columns=date_columns,
is_regression=is_regression,
procs=procs,
max_card=max_card,
)
trn = preproc.preprocess_train(train_df, verbose=verbose)
if verbose:
integer_cats = []
for col in preproc.cat_names:
if train_type_dict.get(col, None) == "integer":
integer_cats.append(col)
if integer_cats:
print(
f"\nThe following integer column(s) are being treated as categorical variables:\n{integer_cats}\n"
+ "To treat any of these column(s) as numerical, cast the column to float in DataFrame or CSV\n and re-run tabular_from* function.\n"
)
val = None if val_df is None else preproc.preprocess_test(val_df, verbose=verbose)
return (trn, val, preproc)
def tabular_from_csv(
train_csv,
label_columns=[],
date_columns=[],
val_csv=None,
val_pct=0.1,
index_col=None,
is_regression=False,
max_card=20,
random_state=None,
):
"""
```
Loads tabular data from CSV file
```
"""
# read in dataset
train_df = pd.read_csv(train_csv, index_col=index_col)
val_df = None
if val_csv is not None:
val_df = pd.read_csv(val_csv, index_col=index_col)
return tabular_from_df(
train_df,
label_columns=label_columns,
date_columns=date_columns,
val_df=val_df,
val_pct=val_pct,
is_regression=is_regression,
max_card=max_card,
random_state=random_state,
)</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-functions">Functions</h2>
<dl>
<dt id="ktrain.tabular.data.tabular_from_csv"><code class="name flex">
<span>def <span class="ident">tabular_from_csv</span></span>(<span>train_csv, label_columns=[], date_columns=[], val_csv=None, val_pct=0.1, index_col=None, is_regression=False, max_card=20, random_state=None)</span>
</code></dt>
<dd>
<div class="desc"><pre><code>Loads tabular data from CSV file
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def tabular_from_csv(
train_csv,
label_columns=[],
date_columns=[],
val_csv=None,
val_pct=0.1,
index_col=None,
is_regression=False,
max_card=20,
random_state=None,
):
"""
```
Loads tabular data from CSV file
```
"""
# read in dataset
train_df = pd.read_csv(train_csv, index_col=index_col)
val_df = None
if val_csv is not None:
val_df = pd.read_csv(val_csv, index_col=index_col)
return tabular_from_df(
train_df,
label_columns=label_columns,
date_columns=date_columns,
val_df=val_df,
val_pct=val_pct,
is_regression=is_regression,
max_card=max_card,
random_state=random_state,
)</code></pre>
</details>
</dd>
<dt id="ktrain.tabular.data.tabular_from_df"><code class="name flex">
<span>def <span class="ident">tabular_from_df</span></span>(<span>train_df, label_columns=[], date_columns=[], val_df=None, val_pct=0.1, is_regression=False, max_card=20, random_state=None, verbose=1)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def tabular_from_df(
train_df,
label_columns=[],
date_columns=[],
val_df=None,
val_pct=0.1,
is_regression=False,
max_card=20,
random_state=None,
verbose=1,
):
train_df = train_df.copy()
# strip space from string columns and check supplied val_df
train_type_dict = pp.clean_df(train_df, val_df=val_df, return_types=True)
# check label_columns
if label_columns is None or (
isinstance(label_columns, (list, np.ndarray)) and len(label_columns) == 0
):
raise ValueError("label_columns is required")
if isinstance(label_columns, (list, np.ndarray)) and len(label_columns) == 1:
label_columns = label_columns[0]
# define original predictor_columns
predictor_columns = [
col for col in train_df.columns.values if col not in label_columns
]
# create validation set
if val_df is None:
if val_pct:
df = train_df.copy()
prop = 1 - val_pct
if random_state is not None:
np.random.seed(42)
msk = np.random.rand(len(df)) < prop
train_df = df[msk]
val_df = df[~msk]
else:
val_df = val_df.copy()
procs = [pp.FillMissing, pp.Categorify, pp.Normalize]
preproc = pp.TabularPreprocessor(
predictor_columns,
label_columns,
date_columns=date_columns,
is_regression=is_regression,
procs=procs,
max_card=max_card,
)
trn = preproc.preprocess_train(train_df, verbose=verbose)
if verbose:
integer_cats = []
for col in preproc.cat_names:
if train_type_dict.get(col, None) == "integer":
integer_cats.append(col)
if integer_cats:
print(
f"\nThe following integer column(s) are being treated as categorical variables:\n{integer_cats}\n"
+ "To treat any of these column(s) as numerical, cast the column to float in DataFrame or CSV\n and re-run tabular_from* function.\n"
)
val = None if val_df is None else preproc.preprocess_test(val_df, verbose=verbose)
return (trn, val, preproc)</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.data.tabular_from_csv" href="#ktrain.tabular.data.tabular_from_csv">tabular_from_csv</a></code></li>
<li><code><a title="ktrain.tabular.data.tabular_from_df" href="#ktrain.tabular.data.tabular_from_df">tabular_from_df</a></code></li>
</ul>
</li>
</ul>
</nav>
</main>
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