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preprocessor.html
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preprocessor.html
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<header>
<h1 class="title">Module <code>ktrain.tabular.preprocessor</code></h1>
</header>
<section id="section-intro">
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">from ..imports import *
from .. import utils as U
from ..preprocessor import Preprocessor
from ..data import SequenceDataset
class TabularPreprocessor(Preprocessor):
"""
```
Tabular preprocessing base class
```
"""
def __init__(self, predictor_columns, label_columns, date_columns=[],
is_regression=False, procs=[], max_card=20):
self.is_regression=is_regression
self.c = None
self.pc = predictor_columns
self.lc = label_columns
self.lc = [self.lc] if isinstance(self.lc, str) else self.lc
self.dc = date_columns
self.label_columns = None
self.cat_names = []
self.cont_names = []
self.date_names = []
self.label_transform = None
self.procs = procs
self.max_card = max_card
@property
def na_names(self):
return [n for n in self.cat_names if n[-3:] == '_na']
def get_preprocessor(self):
return (self.label_transform, self.procs)
def get_classes(self):
return self.label_columns if not self.is_regression else []
def preprocess(self, df):
return self.preprocess_test(df)
def _validate_columns(self, df):
missing_columns = []
for col in df.columns.values:
if col not in self.lc and col not in self.pc:
missing_columns.append(col)
if len(missing_columns) > 0:
raise ValueError('df is missing columns: %s' % (missing_columns))
return
def denormalize(self, df):
normalizer = None
for proc in self.procs:
if type(proc).__name__ == 'Normalize':
normalizer = proc
break
if normalizer is None: return df
return normalizer.revert(df)
#def codify(self, df):
#df = df.copy()
#for lab in self.lc:
#df[lab] = df[lab].cat.codes
#return df
def preprocess_train(self, df, mode='train', verbose=1):
"""
```
preprocess training set
```
"""
df = df.copy()
clean_df(df, pc=self.pc, lc=self.lc, check_labels=mode=='train')
if not isinstance(df, pd.DataFrame):
raise ValueError('df must be a pd.DataFrame')
# validate columns
self._validate_columns(df)
# validate mode
#if mode != 'train' and self.label_transform is None:
#raise ValueError('self.label_transform is None but mode is %s: are you sure preprocess_train was invoked first?' % (mode))
# verbose
if verbose:
print('processing %s: %s rows x %s columns' % (mode, df.shape[0], df.shape[1]))
# convert date fields
for field in self.dc:
df = df.copy() # TODO: fix this
df, date_names = add_datepart(df, field)
self.date_names = date_names
# preprocess labels and data
if mode == 'train':
label_columns = self.lc[:]
#label_columns.sort() # leave label columns sorted in same order as in DataFrame
self.label_transform = U.YTransformDataFrame(label_columns, is_regression=self.is_regression)
df = self.label_transform.apply_train(df)
self.label_columns = self.label_transform.get_classes() if not self.is_regression else self.label_transform.label_columns
self.cont_names, self.cat_names = cont_cat_split(df, label_columns=self.label_columns, max_card=self.max_card)
self.procs = [proc(self.cat_names, self.cont_names) for proc in self.procs] # "objectivy"
else:
df = self.label_transform.apply_test(df)
for proc in self.procs: proc(df, test=mode!='train') # apply processors
return TabularDataset(df, self.cat_names, self.cont_names, self.label_columns)
def preprocess_valid(self, df, verbose=1):
"""
```
preprocess validation set
```
"""
return self.preprocess_train(df, mode='valid', verbose=verbose)
def preprocess_test(self, df, verbose=1):
"""
```
preprocess test set
```
"""
return self.preprocess_train(df, mode='test', verbose=verbose)
class TabularDataset(SequenceDataset):
def __init__(self, df, cat_columns, cont_columns, label_columns, batch_size=32, shuffle=False):
# error checks
if not isinstance(df, pd.DataFrame): raise ValueError('df must be pandas Dataframe')
all_columns = cat_columns + cont_columns + label_columns
missing_columns = []
for col in df.columns.values:
if col not in all_columns: missing_columns.append(col)
if len(missing_columns) > 0: raise ValueError('df is missing these columns: %s' % (missing_columns))
# set variables
super().__init__(batch_size=batch_size)
self.indices = np.arange(df.shape[0])
self.df = df
self.cat_columns = cat_columns
self.cont_columns = cont_columns
self.label_columns = label_columns
self.shuffle = shuffle
def __len__(self):
return math.ceil(self.df.shape[0] / self.batch_size)
def __getitem__(self, idx):
inds = self.indices[idx * self.batch_size:(idx + 1) * self.batch_size]
batch_x = []
df = self.df[self.cat_columns+self.cont_columns].iloc[inds]
for cat_name in self.cat_columns:
codes = np.stack([c.cat.codes.values for n,c in df[[cat_name]].items()], 1).astype(np.int64) + 1
batch_x.append(codes)
if len(self.cont_columns) > 0:
conts = np.stack([c.astype('float32').values for n,c in df[self.cont_columns].items()], 1)
batch_x.append(conts)
batch_y = self.df[self.label_columns].iloc[inds].values
batch_x = batch_x[0] if len(batch_x)==1 else tuple(batch_x)
return batch_x, batch_y
def nsamples(self):
return self.df.shape[0]
def get_y(self):
return self.df[self.label_columns].values
def on_epoch_end(self):
if self.shuffle: np.random.shuffle(self.indices)
def xshape(self):
return self.df.shape
def nclasses(self):
return self.get_y().shape[1]
def pd_data_types(df, return_df=False):
"""
```
infers data type of each column in Pandas DataFrame
Args:
df(pd.DataFrame): pandas DataFrame
return_df(bool): If True, returns columns and types in DataFrame.
Otherwise, a dictionary is returned.
```
"""
infer_type = lambda x: pd.api.types.infer_dtype(x, skipna=True)
df.apply(infer_type, axis=0)
# DataFrame with column names & new types
df_types = pd.DataFrame(df.apply(pd.api.types.infer_dtype, axis=0)).reset_index().rename(columns={'index': 'column', 0: 'type'})
if return_df: return df_types
cols = list(df_types['column'].values)
col_types = list(df_types['type'].values)
return dict(list(zip(cols, col_types)))
def clean_df(train_df, val_df=None, pc=[], lc=[], check_labels=True, return_types=False):
train_type_dict = pd_data_types(train_df)
for k,v in train_type_dict.items():
if v != 'string': continue
train_df[k] = train_df[k].str.strip()
if val_df is not None:
if k not in val_df.columns: raise ValueError('val_df is missing %s column' % (k))
val_df[k] = val_df[k].str.strip()
if (pc and not lc) or (not pc and lc): raise ValueError('pc and lc: both or neither must exist')
if pc and lc:
inp_cols = train_df.columns.values if check_labels else [col for col in train_df.columns.values if col not in lc]
original_cols = pc + lc if check_labels else pc
if set(original_cols) != set(inp_cols):
raise ValueError('DataFrame is either missing columns or includes extra columns: \n'+\
'expected: %s\nactual: %s' % (original_cols, inp_cols))
if return_types: return train_type_dict
return
#--------------------------------------------------------------------
# These are helper functions adapted from fastai:
# https://github.com/fastai/fastai
# -------------------------------------------------------------------
from numbers import Number
from typing import Any, AnyStr, Callable, Collection, Dict, Hashable, Iterator, List, Mapping, NewType, Optional
from typing import Sequence, Tuple, TypeVar, Union
from types import SimpleNamespace
from pandas.api.types import is_numeric_dtype, is_categorical_dtype
def ifnone(a,b):
"`a` if `a` is not None, otherwise `b`."
return b if a is None else a
def make_date(df, date_field):
"""
Make sure `df[field_name]` is of the right date type.
Reference: https://github.com/fastai/fastai
"""
field_dtype = df[date_field].dtype
if isinstance(field_dtype, pd.core.dtypes.dtypes.DatetimeTZDtype):
field_dtype = np.datetime64
if not np.issubdtype(field_dtype, np.datetime64):
df[date_field] = pd.to_datetime(df[date_field], infer_datetime_format=True)
return
def cont_cat_split(df, max_card=20, label_columns=[]):
"Helper function that returns column names of cont and cat variables from given df."
cont_names, cat_names = [], []
for col in df:
if col in label_columns: continue
if df[col].dtype == int and df[col].unique().shape[0] > max_card or df[col].dtype == float: cont_names.append(col)
else: cat_names.append(col)
return cont_names, cat_names
def add_datepart(df:pd.DataFrame, field_name:str, prefix:str=None, drop:bool=True, time:bool=False, return_added_columns=True):
"Helper function that adds columns relevant to a date in the column `field_name` of `df`."
make_date(df, field_name)
field = df[field_name]
prefix = ifnone(prefix, re.sub('[Dd]ate$', '', field_name))
attr = ['Year', 'Month', 'Week', 'Day', 'Dayofweek', 'Dayofyear', 'Is_month_end', 'Is_month_start',
'Is_quarter_end', 'Is_quarter_start', 'Is_year_end', 'Is_year_start']
if time: attr = attr + ['Hour', 'Minute', 'Second']
added_columns = []
for n in attr:
df[prefix + n] = getattr(field.dt, n.lower())
added_columns.append(prefix+n)
df[prefix + 'Elapsed'] = field.astype(np.int64) // 10 ** 9
if drop: df.drop(field_name, axis=1, inplace=True)
if return_added_columns:
return (df, added_columns)
else:
return df
def cyclic_dt_feat_names(time:bool=True, add_linear:bool=False)->List[str]:
"Return feature names of date/time cycles as produced by `cyclic_dt_features`."
fs = ['cos','sin']
attr = [f'{r}_{f}' for r in 'weekday day_month month_year day_year'.split() for f in fs]
if time: attr += [f'{r}_{f}' for r in 'hour clock min sec'.split() for f in fs]
if add_linear: attr.append('year_lin')
return attr
def cyclic_dt_features(d, time:bool=True, add_linear:bool=False)->List[float]:
"Calculate the cos and sin of date/time cycles."
tt,fs = d.timetuple(), [np.cos, np.sin]
day_year,days_month = tt.tm_yday, calendar.monthrange(d.year, d.month)[1]
days_year = 366 if calendar.isleap(d.year) else 365
rs = d.weekday()/7, (d.day-1)/days_month, (d.month-1)/12, (day_year-1)/days_year
feats = [f(r * 2 * np.pi) for r in rs for f in fs]
if time and isinstance(d, datetime) and type(d) != date:
rs = tt.tm_hour/24, tt.tm_hour%12/12, tt.tm_min/60, tt.tm_sec/60
feats += [f(r * 2 * np.pi) for r in rs for f in fs]
if add_linear:
if type(d) == date: feats.append(d.year + rs[-1])
else:
secs_in_year = (datetime(d.year+1, 1, 1) - datetime(d.year, 1, 1)).total_seconds()
feats.append(d.year + ((d - datetime(d.year, 1, 1)).total_seconds() / secs_in_year))
return feats
def add_cyclic_datepart(df:pd.DataFrame, field_name:str, prefix:str=None, drop:bool=True, time:bool=False, add_linear:bool=False):
"Helper function that adds trigonometric date/time features to a date in the column `field_name` of `df`."
make_date(df, field_name)
field = df[field_name]
prefix = ifnone(prefix, re.sub('[Dd]ate$', '', field_name))
series = field.apply(partial(cyclic_dt_features, time=time, add_linear=add_linear))
columns = [prefix + c for c in cyclic_dt_feat_names(time, add_linear)]
df_feats = pd.DataFrame([item for item in series], columns=columns, index=series.index)
for column in columns: df[column] = df_feats[column]
if drop: df.drop(field_name, axis=1, inplace=True)
return df
class TabularProc():
"A processor for tabular dataframes."
def __init__(self, cat_names, cont_names):
self.cat_names = cat_names
self.cont_names = cont_names
def __call__(self, df, test=False):
"Apply the correct function to `df` depending on `test`."
func = self.apply_test if test else self.apply_train
func(df)
def apply_train(self, df):
"Function applied to `df` if it's the train set."
raise NotImplementedError
def apply_test(self, df):
"Function applied to `df` if it's the test set."
self.apply_train(df)
class Categorify(TabularProc):
def __init__(self, cat_names, cont_names):
super().__init__(cat_names, cont_names)
self.categories = None
def apply_train(self, df):
self.categories = {}
for n in self.cat_names:
df.loc[:,n] = df.loc[:,n].astype('category').cat.as_ordered()
self.categories[n] = df[n].cat.categories
def apply_test(self, df):
for n in self.cat_names:
df.loc[:,n] = pd.Categorical(df[n], categories=self.categories[n], ordered=True)
FILL_MEDIAN = 'median'
FILL_CONSTANT = 'constant'
class FillMissing(TabularProc):
"Fill the missing values in continuous columns."
def __init__(self, cat_names, cont_names, fill_strategy=FILL_MEDIAN, add_col=True, fill_val=0.):
super().__init__(cat_names, cont_names)
self.fill_strategy = fill_strategy
self.add_col = add_col
self.fill_val = fill_val
self.na_dict = None
def apply_train(self, df):
self.na_dict = {}
self.filler_dict = {}
for name in self.cont_names:
if self.fill_strategy == FILL_MEDIAN: filler = df[name].median()
elif self.fill_strategy == FILL_CONSTANT: filler = self.fill_val
else: filler = df[name].dropna().value_counts().idxmax()
self.filler_dict[name] = filler
if pd.isnull(df[name]).sum():
if self.add_col:
df[name+'_na'] = pd.isnull(df[name])
if name+'_na' not in self.cat_names: self.cat_names.append(name+'_na')
df[name] = df[name].fillna(filler)
self.na_dict[name] = True
def apply_test(self, df):
"Fill missing values in `self.cont_names` like in `apply_train`."
for name in self.cont_names:
if name in self.na_dict:
if self.add_col:
df[name+'_na'] = pd.isnull(df[name])
if name+'_na' not in self.cat_names: self.cat_names.append(name+'_na')
df[name] = df[name].fillna(self.filler_dict[name])
elif pd.isnull(df[name]).sum() != 0:
warnings.warn(f"""There are nan values in field {name} but there were none in the training set.
Filled with {self.fill_strategy}.""")
df[name] = df[name].fillna(self.filler_dict[name])
#raise Exception(f"""There are nan values in field {name} but there were none in the training set.
#Please fix those manually.""")
class Normalize(TabularProc):
"Normalize the continuous variables."
def __init__(self, cat_names, cont_names):
super().__init__(cat_names, cont_names)
self.means = None
self.stds = None
def apply_train(self, df):
"Compute the means and stds of `self.cont_names` columns to normalize them."
self.means,self.stds = {},{}
for n in self.cont_names:
assert is_numeric_dtype(df[n]), (f"""Cannot normalize '{n}' column as it isn't numerical.
Are you sure it doesn't belong in the categorical set of columns?""")
self.means[n],self.stds[n] = df[n].mean(),df[n].std()
df[n] = (df[n]-self.means[n]) / (1e-7 + self.stds[n])
def apply_test(self, df):
"Normalize `self.cont_names` with the same statistics as in `apply_train`."
for n in self.cont_names:
df[n] = (df[n]-self.means[n]) / (1e-7 + self.stds[n])
def revert(self,df):
"""
Undoes normalization and returns reverted dataframe
"""
out_df = df.copy()
for n in self.cont_names:
out_df[n] = (df[n] * (1e-7 + self.stds[n])) + self.means[n]
return out_df</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-functions">Functions</h2>
<dl>
<dt id="ktrain.tabular.preprocessor.add_cyclic_datepart"><code class="name flex">
<span>def <span class="ident">add_cyclic_datepart</span></span>(<span>df: pandas.core.frame.DataFrame, field_name: str, prefix: str = None, drop: bool = True, time: bool = False, add_linear: bool = False)</span>
</code></dt>
<dd>
<div class="desc"><p>Helper function that adds trigonometric date/time features to a date in the column <code>field_name</code> of <code>df</code>.</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def add_cyclic_datepart(df:pd.DataFrame, field_name:str, prefix:str=None, drop:bool=True, time:bool=False, add_linear:bool=False):
"Helper function that adds trigonometric date/time features to a date in the column `field_name` of `df`."
make_date(df, field_name)
field = df[field_name]
prefix = ifnone(prefix, re.sub('[Dd]ate$', '', field_name))
series = field.apply(partial(cyclic_dt_features, time=time, add_linear=add_linear))
columns = [prefix + c for c in cyclic_dt_feat_names(time, add_linear)]
df_feats = pd.DataFrame([item for item in series], columns=columns, index=series.index)
for column in columns: df[column] = df_feats[column]
if drop: df.drop(field_name, axis=1, inplace=True)
return df</code></pre>
</details>
</dd>
<dt id="ktrain.tabular.preprocessor.add_datepart"><code class="name flex">
<span>def <span class="ident">add_datepart</span></span>(<span>df: pandas.core.frame.DataFrame, field_name: str, prefix: str = None, drop: bool = True, time: bool = False, return_added_columns=True)</span>
</code></dt>
<dd>
<div class="desc"><p>Helper function that adds columns relevant to a date in the column <code>field_name</code> of <code>df</code>.</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def add_datepart(df:pd.DataFrame, field_name:str, prefix:str=None, drop:bool=True, time:bool=False, return_added_columns=True):
"Helper function that adds columns relevant to a date in the column `field_name` of `df`."
make_date(df, field_name)
field = df[field_name]
prefix = ifnone(prefix, re.sub('[Dd]ate$', '', field_name))
attr = ['Year', 'Month', 'Week', 'Day', 'Dayofweek', 'Dayofyear', 'Is_month_end', 'Is_month_start',
'Is_quarter_end', 'Is_quarter_start', 'Is_year_end', 'Is_year_start']
if time: attr = attr + ['Hour', 'Minute', 'Second']
added_columns = []
for n in attr:
df[prefix + n] = getattr(field.dt, n.lower())
added_columns.append(prefix+n)
df[prefix + 'Elapsed'] = field.astype(np.int64) // 10 ** 9
if drop: df.drop(field_name, axis=1, inplace=True)
if return_added_columns:
return (df, added_columns)
else:
return df</code></pre>
</details>
</dd>
<dt id="ktrain.tabular.preprocessor.clean_df"><code class="name flex">
<span>def <span class="ident">clean_df</span></span>(<span>train_df, val_df=None, pc=[], lc=[], check_labels=True, return_types=False)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def clean_df(train_df, val_df=None, pc=[], lc=[], check_labels=True, return_types=False):
train_type_dict = pd_data_types(train_df)
for k,v in train_type_dict.items():
if v != 'string': continue
train_df[k] = train_df[k].str.strip()
if val_df is not None:
if k not in val_df.columns: raise ValueError('val_df is missing %s column' % (k))
val_df[k] = val_df[k].str.strip()
if (pc and not lc) or (not pc and lc): raise ValueError('pc and lc: both or neither must exist')
if pc and lc:
inp_cols = train_df.columns.values if check_labels else [col for col in train_df.columns.values if col not in lc]
original_cols = pc + lc if check_labels else pc
if set(original_cols) != set(inp_cols):
raise ValueError('DataFrame is either missing columns or includes extra columns: \n'+\
'expected: %s\nactual: %s' % (original_cols, inp_cols))
if return_types: return train_type_dict
return</code></pre>
</details>
</dd>
<dt id="ktrain.tabular.preprocessor.cont_cat_split"><code class="name flex">
<span>def <span class="ident">cont_cat_split</span></span>(<span>df, max_card=20, label_columns=[])</span>
</code></dt>
<dd>
<div class="desc"><p>Helper function that returns column names of cont and cat variables from given df.</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def cont_cat_split(df, max_card=20, label_columns=[]):
"Helper function that returns column names of cont and cat variables from given df."
cont_names, cat_names = [], []
for col in df:
if col in label_columns: continue
if df[col].dtype == int and df[col].unique().shape[0] > max_card or df[col].dtype == float: cont_names.append(col)
else: cat_names.append(col)
return cont_names, cat_names</code></pre>
</details>
</dd>
<dt id="ktrain.tabular.preprocessor.cyclic_dt_feat_names"><code class="name flex">
<span>def <span class="ident">cyclic_dt_feat_names</span></span>(<span>time: bool = True, add_linear: bool = False) ‑> List[str]</span>
</code></dt>
<dd>
<div class="desc"><p>Return feature names of date/time cycles as produced by <code><a title="ktrain.tabular.preprocessor.cyclic_dt_features" href="#ktrain.tabular.preprocessor.cyclic_dt_features">cyclic_dt_features()</a></code>.</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def cyclic_dt_feat_names(time:bool=True, add_linear:bool=False)->List[str]:
"Return feature names of date/time cycles as produced by `cyclic_dt_features`."
fs = ['cos','sin']
attr = [f'{r}_{f}' for r in 'weekday day_month month_year day_year'.split() for f in fs]
if time: attr += [f'{r}_{f}' for r in 'hour clock min sec'.split() for f in fs]
if add_linear: attr.append('year_lin')
return attr</code></pre>
</details>
</dd>
<dt id="ktrain.tabular.preprocessor.cyclic_dt_features"><code class="name flex">
<span>def <span class="ident">cyclic_dt_features</span></span>(<span>d, time: bool = True, add_linear: bool = False) ‑> List[float]</span>
</code></dt>
<dd>
<div class="desc"><p>Calculate the cos and sin of date/time cycles.</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def cyclic_dt_features(d, time:bool=True, add_linear:bool=False)->List[float]:
"Calculate the cos and sin of date/time cycles."
tt,fs = d.timetuple(), [np.cos, np.sin]
day_year,days_month = tt.tm_yday, calendar.monthrange(d.year, d.month)[1]
days_year = 366 if calendar.isleap(d.year) else 365
rs = d.weekday()/7, (d.day-1)/days_month, (d.month-1)/12, (day_year-1)/days_year
feats = [f(r * 2 * np.pi) for r in rs for f in fs]
if time and isinstance(d, datetime) and type(d) != date:
rs = tt.tm_hour/24, tt.tm_hour%12/12, tt.tm_min/60, tt.tm_sec/60
feats += [f(r * 2 * np.pi) for r in rs for f in fs]
if add_linear:
if type(d) == date: feats.append(d.year + rs[-1])
else:
secs_in_year = (datetime(d.year+1, 1, 1) - datetime(d.year, 1, 1)).total_seconds()
feats.append(d.year + ((d - datetime(d.year, 1, 1)).total_seconds() / secs_in_year))
return feats</code></pre>
</details>
</dd>
<dt id="ktrain.tabular.preprocessor.ifnone"><code class="name flex">
<span>def <span class="ident">ifnone</span></span>(<span>a, b)</span>
</code></dt>
<dd>
<div class="desc"><p><code>a</code> if <code>a</code> is not None, otherwise <code>b</code>.</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def ifnone(a,b):
"`a` if `a` is not None, otherwise `b`."
return b if a is None else a</code></pre>
</details>
</dd>
<dt id="ktrain.tabular.preprocessor.make_date"><code class="name flex">
<span>def <span class="ident">make_date</span></span>(<span>df, date_field)</span>
</code></dt>
<dd>
<div class="desc"><p>Make sure <code>df[field_name]</code> is of the right date type.
Reference: <a href="https://github.com/fastai/fastai">https://github.com/fastai/fastai</a></p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def make_date(df, date_field):
"""
Make sure `df[field_name]` is of the right date type.
Reference: https://github.com/fastai/fastai
"""
field_dtype = df[date_field].dtype
if isinstance(field_dtype, pd.core.dtypes.dtypes.DatetimeTZDtype):
field_dtype = np.datetime64
if not np.issubdtype(field_dtype, np.datetime64):
df[date_field] = pd.to_datetime(df[date_field], infer_datetime_format=True)
return</code></pre>
</details>
</dd>
<dt id="ktrain.tabular.preprocessor.pd_data_types"><code class="name flex">
<span>def <span class="ident">pd_data_types</span></span>(<span>df, return_df=False)</span>
</code></dt>
<dd>
<div class="desc"><pre><code>infers data type of each column in Pandas DataFrame
Args:
df(pd.DataFrame): pandas DataFrame
return_df(bool): If True, returns columns and types in DataFrame.
Otherwise, a dictionary is returned.
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def pd_data_types(df, return_df=False):
"""
```
infers data type of each column in Pandas DataFrame
Args:
df(pd.DataFrame): pandas DataFrame
return_df(bool): If True, returns columns and types in DataFrame.
Otherwise, a dictionary is returned.
```
"""
infer_type = lambda x: pd.api.types.infer_dtype(x, skipna=True)
df.apply(infer_type, axis=0)
# DataFrame with column names & new types
df_types = pd.DataFrame(df.apply(pd.api.types.infer_dtype, axis=0)).reset_index().rename(columns={'index': 'column', 0: 'type'})
if return_df: return df_types
cols = list(df_types['column'].values)
col_types = list(df_types['type'].values)
return dict(list(zip(cols, col_types)))</code></pre>
</details>
</dd>
</dl>
</section>
<section>
<h2 class="section-title" id="header-classes">Classes</h2>
<dl>
<dt id="ktrain.tabular.preprocessor.Categorify"><code class="flex name class">
<span>class <span class="ident">Categorify</span></span>
<span>(</span><span>cat_names, cont_names)</span>
</code></dt>
<dd>
<div class="desc"><p>A processor for tabular dataframes.</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class Categorify(TabularProc):
def __init__(self, cat_names, cont_names):
super().__init__(cat_names, cont_names)
self.categories = None
def apply_train(self, df):
self.categories = {}
for n in self.cat_names:
df.loc[:,n] = df.loc[:,n].astype('category').cat.as_ordered()
self.categories[n] = df[n].cat.categories
def apply_test(self, df):
for n in self.cat_names:
df.loc[:,n] = pd.Categorical(df[n], categories=self.categories[n], ordered=True)</code></pre>
</details>
<h3>Ancestors</h3>
<ul class="hlist">
<li><a title="ktrain.tabular.preprocessor.TabularProc" href="#ktrain.tabular.preprocessor.TabularProc">TabularProc</a></li>
</ul>
<h3>Inherited members</h3>
<ul class="hlist">
<li><code><b><a title="ktrain.tabular.preprocessor.TabularProc" href="#ktrain.tabular.preprocessor.TabularProc">TabularProc</a></b></code>:
<ul class="hlist">
<li><code><a title="ktrain.tabular.preprocessor.TabularProc.apply_test" href="#ktrain.tabular.preprocessor.TabularProc.apply_test">apply_test</a></code></li>
<li><code><a title="ktrain.tabular.preprocessor.TabularProc.apply_train" href="#ktrain.tabular.preprocessor.TabularProc.apply_train">apply_train</a></code></li>
</ul>
</li>
</ul>
</dd>
<dt id="ktrain.tabular.preprocessor.FillMissing"><code class="flex name class">
<span>class <span class="ident">FillMissing</span></span>
<span>(</span><span>cat_names, cont_names, fill_strategy='median', add_col=True, fill_val=0.0)</span>
</code></dt>
<dd>
<div class="desc"><p>Fill the missing values in continuous columns.</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class FillMissing(TabularProc):
"Fill the missing values in continuous columns."
def __init__(self, cat_names, cont_names, fill_strategy=FILL_MEDIAN, add_col=True, fill_val=0.):
super().__init__(cat_names, cont_names)
self.fill_strategy = fill_strategy
self.add_col = add_col
self.fill_val = fill_val
self.na_dict = None
def apply_train(self, df):
self.na_dict = {}
self.filler_dict = {}
for name in self.cont_names:
if self.fill_strategy == FILL_MEDIAN: filler = df[name].median()
elif self.fill_strategy == FILL_CONSTANT: filler = self.fill_val
else: filler = df[name].dropna().value_counts().idxmax()
self.filler_dict[name] = filler
if pd.isnull(df[name]).sum():
if self.add_col:
df[name+'_na'] = pd.isnull(df[name])
if name+'_na' not in self.cat_names: self.cat_names.append(name+'_na')
df[name] = df[name].fillna(filler)
self.na_dict[name] = True
def apply_test(self, df):
"Fill missing values in `self.cont_names` like in `apply_train`."
for name in self.cont_names:
if name in self.na_dict:
if self.add_col:
df[name+'_na'] = pd.isnull(df[name])
if name+'_na' not in self.cat_names: self.cat_names.append(name+'_na')
df[name] = df[name].fillna(self.filler_dict[name])
elif pd.isnull(df[name]).sum() != 0:
warnings.warn(f"""There are nan values in field {name} but there were none in the training set.
Filled with {self.fill_strategy}.""")
df[name] = df[name].fillna(self.filler_dict[name])</code></pre>
</details>
<h3>Ancestors</h3>
<ul class="hlist">
<li><a title="ktrain.tabular.preprocessor.TabularProc" href="#ktrain.tabular.preprocessor.TabularProc">TabularProc</a></li>
</ul>
<h3>Methods</h3>
<dl>
<dt id="ktrain.tabular.preprocessor.FillMissing.apply_test"><code class="name flex">
<span>def <span class="ident">apply_test</span></span>(<span>self, df)</span>
</code></dt>
<dd>
<div class="desc"><p>Fill missing values in <code>self.cont_names</code> like in <code>apply_train</code>.</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def apply_test(self, df):
"Fill missing values in `self.cont_names` like in `apply_train`."
for name in self.cont_names:
if name in self.na_dict:
if self.add_col:
df[name+'_na'] = pd.isnull(df[name])
if name+'_na' not in self.cat_names: self.cat_names.append(name+'_na')
df[name] = df[name].fillna(self.filler_dict[name])
elif pd.isnull(df[name]).sum() != 0:
warnings.warn(f"""There are nan values in field {name} but there were none in the training set.
Filled with {self.fill_strategy}.""")
df[name] = df[name].fillna(self.filler_dict[name])</code></pre>
</details>
</dd>
</dl>
<h3>Inherited members</h3>
<ul class="hlist">
<li><code><b><a title="ktrain.tabular.preprocessor.TabularProc" href="#ktrain.tabular.preprocessor.TabularProc">TabularProc</a></b></code>:
<ul class="hlist">
<li><code><a title="ktrain.tabular.preprocessor.TabularProc.apply_train" href="#ktrain.tabular.preprocessor.TabularProc.apply_train">apply_train</a></code></li>
</ul>
</li>
</ul>
</dd>
<dt id="ktrain.tabular.preprocessor.Normalize"><code class="flex name class">
<span>class <span class="ident">Normalize</span></span>
<span>(</span><span>cat_names, cont_names)</span>
</code></dt>
<dd>
<div class="desc"><p>Normalize the continuous variables.</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class Normalize(TabularProc):
"Normalize the continuous variables."
def __init__(self, cat_names, cont_names):
super().__init__(cat_names, cont_names)
self.means = None
self.stds = None
def apply_train(self, df):
"Compute the means and stds of `self.cont_names` columns to normalize them."
self.means,self.stds = {},{}
for n in self.cont_names:
assert is_numeric_dtype(df[n]), (f"""Cannot normalize '{n}' column as it isn't numerical.
Are you sure it doesn't belong in the categorical set of columns?""")
self.means[n],self.stds[n] = df[n].mean(),df[n].std()
df[n] = (df[n]-self.means[n]) / (1e-7 + self.stds[n])
def apply_test(self, df):
"Normalize `self.cont_names` with the same statistics as in `apply_train`."
for n in self.cont_names:
df[n] = (df[n]-self.means[n]) / (1e-7 + self.stds[n])
def revert(self,df):
"""
Undoes normalization and returns reverted dataframe
"""
out_df = df.copy()
for n in self.cont_names:
out_df[n] = (df[n] * (1e-7 + self.stds[n])) + self.means[n]
return out_df</code></pre>
</details>
<h3>Ancestors</h3>
<ul class="hlist">
<li><a title="ktrain.tabular.preprocessor.TabularProc" href="#ktrain.tabular.preprocessor.TabularProc">TabularProc</a></li>
</ul>
<h3>Methods</h3>
<dl>
<dt id="ktrain.tabular.preprocessor.Normalize.apply_test"><code class="name flex">
<span>def <span class="ident">apply_test</span></span>(<span>self, df)</span>
</code></dt>
<dd>
<div class="desc"><p>Normalize <code>self.cont_names</code> with the same statistics as in <code>apply_train</code>.</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def apply_test(self, df):
"Normalize `self.cont_names` with the same statistics as in `apply_train`."
for n in self.cont_names:
df[n] = (df[n]-self.means[n]) / (1e-7 + self.stds[n])</code></pre>
</details>
</dd>
<dt id="ktrain.tabular.preprocessor.Normalize.apply_train"><code class="name flex">
<span>def <span class="ident">apply_train</span></span>(<span>self, df)</span>
</code></dt>
<dd>
<div class="desc"><p>Compute the means and stds of <code>self.cont_names</code> columns to normalize them.</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def apply_train(self, df):
"Compute the means and stds of `self.cont_names` columns to normalize them."
self.means,self.stds = {},{}
for n in self.cont_names:
assert is_numeric_dtype(df[n]), (f"""Cannot normalize '{n}' column as it isn't numerical.
Are you sure it doesn't belong in the categorical set of columns?""")
self.means[n],self.stds[n] = df[n].mean(),df[n].std()
df[n] = (df[n]-self.means[n]) / (1e-7 + self.stds[n])</code></pre>
</details>
</dd>
<dt id="ktrain.tabular.preprocessor.Normalize.revert"><code class="name flex">
<span>def <span class="ident">revert</span></span>(<span>self, df)</span>
</code></dt>
<dd>
<div class="desc"><p>Undoes normalization and returns reverted dataframe</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def revert(self,df):
"""
Undoes normalization and returns reverted dataframe
"""
out_df = df.copy()
for n in self.cont_names:
out_df[n] = (df[n] * (1e-7 + self.stds[n])) + self.means[n]
return out_df</code></pre>
</details>
</dd>
</dl>
</dd>
<dt id="ktrain.tabular.preprocessor.TabularDataset"><code class="flex name class">
<span>class <span class="ident">TabularDataset</span></span>
<span>(</span><span>df, cat_columns, cont_columns, label_columns, batch_size=32, shuffle=False)</span>
</code></dt>
<dd>
<div class="desc"><pre><code>Base class for custom datasets in ktrain.
If subclass of Dataset implements a method to to_tfdataset
that converts the data to a tf.Dataset, then this will be
invoked by Learner instances just prior to training so
fit() will train using a tf.Dataset representation of your data.
Sequence methods such as __get_item__ and __len__
must still be implemented.
The signature of to_tfdataset is as follows:
def to_tfdataset(self, training=True)
See ktrain.text.preprocess.TransformerDataset as an example.
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class TabularDataset(SequenceDataset):
def __init__(self, df, cat_columns, cont_columns, label_columns, batch_size=32, shuffle=False):
# error checks
if not isinstance(df, pd.DataFrame): raise ValueError('df must be pandas Dataframe')
all_columns = cat_columns + cont_columns + label_columns
missing_columns = []
for col in df.columns.values:
if col not in all_columns: missing_columns.append(col)
if len(missing_columns) > 0: raise ValueError('df is missing these columns: %s' % (missing_columns))
# set variables
super().__init__(batch_size=batch_size)
self.indices = np.arange(df.shape[0])
self.df = df
self.cat_columns = cat_columns
self.cont_columns = cont_columns
self.label_columns = label_columns
self.shuffle = shuffle
def __len__(self):
return math.ceil(self.df.shape[0] / self.batch_size)
def __getitem__(self, idx):
inds = self.indices[idx * self.batch_size:(idx + 1) * self.batch_size]
batch_x = []
df = self.df[self.cat_columns+self.cont_columns].iloc[inds]
for cat_name in self.cat_columns:
codes = np.stack([c.cat.codes.values for n,c in df[[cat_name]].items()], 1).astype(np.int64) + 1
batch_x.append(codes)
if len(self.cont_columns) > 0:
conts = np.stack([c.astype('float32').values for n,c in df[self.cont_columns].items()], 1)
batch_x.append(conts)
batch_y = self.df[self.label_columns].iloc[inds].values
batch_x = batch_x[0] if len(batch_x)==1 else tuple(batch_x)
return batch_x, batch_y
def nsamples(self):
return self.df.shape[0]
def get_y(self):
return self.df[self.label_columns].values
def on_epoch_end(self):
if self.shuffle: np.random.shuffle(self.indices)
def xshape(self):
return self.df.shape
def nclasses(self):